From 789c8e26649b99b561d68550072eae292bd8c210 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Thu, 6 Oct 2022 04:15:05 +0200 Subject: [PATCH 01/68] MAINT: Remove unused function --- rocketpy/Dispersion.py | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index 204849ff9..0dd8f2c52 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -25,6 +25,16 @@ from .Rocket import Rocket from .utilities import invertedHaversine +## Tasks from the first review: +# TODO: Move some functions from utilities to supplement.py +# TODO: Document all methods +# TODO: Create a way to choose what attributes are being saved +# TODO: Allow each parameter to be varied following an specific probability distribution +# TODO: Make it more flexible so we can work with more than 1 fin set, also with different aerodynamic surfaces as well. +# TODO: Test simulations under different scenarios (with both parachutes, with only main chute, etc) +# TODO: Add unit tests +# TODO: Adjust the notebook to the new version of the code + class Dispersion: @@ -126,10 +136,6 @@ def __init__( self.realLandingPoint = None self.parachuteTriggers = [] - def classCheck(self): - rocketAttributes = [] - rocketInputs = [] - def setDistributionFunc(self, distributionType): if distributionType == "normal" or distributionType == None: return normal From 187a0fe6e114729bc818c51c4097ae6ba1ab68c6 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Thu, 6 Oct 2022 04:28:04 +0200 Subject: [PATCH 02/68] DOC: Adding docstring formats to be filled --- rocketpy/Dispersion.py | 510 ++++++++++++++++++++++++++++++++++++++++- 1 file changed, 505 insertions(+), 5 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index 0dd8f2c52..6f2ec8ec4 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -27,6 +27,7 @@ ## Tasks from the first review: # TODO: Move some functions from utilities to supplement.py +# TODO: Save instances of the class instead of just plotting # TODO: Document all methods # TODO: Create a way to choose what attributes are being saved # TODO: Allow each parameter to be varied following an specific probability distribution @@ -35,6 +36,9 @@ # TODO: Add unit tests # TODO: Adjust the notebook to the new version of the code +# TODO: Implement MRS +# TODO: Implement functions from compareDispersions + class Dispersion: @@ -211,6 +215,18 @@ def setDistributionFunc(self, distributionType): warnings.warn("Distribution type not supported") def processDispersionDict(self, dispersionDict): + """_summary_ + + Parameters + ---------- + dispersionDict : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ # Get parachutes names if "parachuteNames" in dispersionDict: # TODO: use only dispersionDict for i, name in enumerate(dispersionDict["parachuteNames"]): @@ -422,8 +438,22 @@ def processDispersionDict(self, dispersionDict): def yield_flight_setting( self, distributionFunc, analysis_parameters, number_of_simulations ): + """Yields a flight setting for the simulation - """Yields a flight setting for the simulation""" + Parameters + ---------- + distributionFunc : _type_ + _description_ + analysis_parameters : _type_ + _description_ + number_of_simulations : _type_ + _description_ + + Yields + ------ + _type_ + _description_ + """ i = 0 while i < number_of_simulations: @@ -442,7 +472,7 @@ def yield_flight_setting( # Yield a flight setting yield flight_setting - # TODO: Rework post process Flight method making it possible (and optmized) to + # TODO: Rework post process Flight method making it possible (and optimized) to # chose what is going to be exported def export_flight_data( self, @@ -452,8 +482,26 @@ def export_flight_data( dispersion_input_file, dispersion_output_file, ): + """Saves flight results in a .txt + + Parameters + ---------- + flight_setting : _type_ + _description_ + flight_data : _type_ + _description_ + exec_time : _type_ + _description_ + dispersion_input_file : _type_ + _description_ + dispersion_output_file : _type_ + _description_ - """Saves flight results in a .txt""" + Returns + ------- + _type_ + _description_ + """ # Generate flight results flight_result = { @@ -551,8 +599,34 @@ def runDispersion( image=None, realLandingPoint=None, ): + """Runs the given number of simulations and saves the data - """Runs the given number of simulations and saves the data""" + Parameters + ---------- + number_of_simulations : _type_ + _description_ + dispersionDict : _type_ + _description_ + environment : _type_ + _description_ + flight : _type_, optional + _description_, by default None + motor : _type_, optional + _description_, by default None + rocket : _type_, optional + _description_, by default None + distributionType : str, optional + _description_, by default "normal" + image : _type_, optional + _description_, by default None + realLandingPoint : _type_, optional + _description_, by default None + + Returns + ------- + _type_ + _description_ + """ self.number_of_simulations = number_of_simulations self.dispersionDict = dispersionDict @@ -759,8 +833,18 @@ def runDispersion( return None def importResults(self, dispersion_output_file): + """Import dispersion results from .txt file - """Import dispersion results from .txt file""" + Parameters + ---------- + dispersion_output_file : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ # Initialize variable to store all results dispersion_general_results = [] @@ -814,6 +898,17 @@ def importResults(self, dispersion_output_file): return dispersion_results def meanOutOfRailTime(self, dispersion_results): + """_summary_ + + Parameters + ---------- + dispersion_results : _type_ + _description_ + + Returns + ------- + None + """ print( f'Out of Rail Time - Mean Value: {np.mean(dispersion_results["outOfRailTime"]):0.3f} s' ) @@ -824,7 +919,18 @@ def meanOutOfRailTime(self, dispersion_results): return None def plotOutOfRailTime(self, dispersion_results): + """_summary_ + Parameters + ---------- + dispersion_results : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ self.meanOutOfRailTime(dispersion_results) plt.figure() @@ -837,6 +943,18 @@ def plotOutOfRailTime(self, dispersion_results): return None def meanOutOfRailVelocity(self, dispersion_results): + """_summary_ + + Parameters + ---------- + dispersion_results : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ print( f'Out of Rail Velocity - Mean Value: {np.mean(dispersion_results["outOfRailVelocity"]):0.3f} m/s' ) @@ -847,7 +965,18 @@ def meanOutOfRailVelocity(self, dispersion_results): return None def plotOutOfRailVelocity(self, dispersion_results): + """_summary_ + Parameters + ---------- + dispersion_results : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ self.meanOutOfRailVelocity(dispersion_results) plt.figure() @@ -860,6 +989,18 @@ def plotOutOfRailVelocity(self, dispersion_results): return None def meanApogeeTime(self, dispersion_results): + """_summary_ + + Parameters + ---------- + dispersion_results : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ print( f'Impact Time - Mean Value: {np.mean(dispersion_results["impactTime"]):0.3f} s' ) @@ -870,7 +1011,18 @@ def meanApogeeTime(self, dispersion_results): return None def plotApogeeTime(self, dispersion_results): + """_summary_ + Parameters + ---------- + dispersion_results : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ self.meanApogeeTime(dispersion_results) plt.figure() @@ -883,6 +1035,18 @@ def plotApogeeTime(self, dispersion_results): return None def meanApogeeAltitude(self, dispersion_results): + """_summary_ + + Parameters + ---------- + dispersion_results : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ print( f'Apogee Altitude - Mean Value: {np.mean(dispersion_results["apogeeAltitude"]):0.3f} m' ) @@ -893,7 +1057,18 @@ def meanApogeeAltitude(self, dispersion_results): return None def plotApogeeAltitude(self, dispersion_results): + """_summary_ + Parameters + ---------- + dispersion_results : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ self.meanApogeeAltitude(dispersion_results) plt.figure() @@ -906,6 +1081,18 @@ def plotApogeeAltitude(self, dispersion_results): return None def meanApogeeXPosition(self, dispersion_results): + """_summary_ + + Parameters + ---------- + dispersion_results : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ print( f'Apogee X Position - Mean Value: {np.mean(dispersion_results["apogeeX"]):0.3f} m' ) @@ -916,7 +1103,18 @@ def meanApogeeXPosition(self, dispersion_results): return None def plotApogeeXPosition(self, dispersion_results): + """_summary_ + Parameters + ---------- + dispersion_results : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ self.meanApogeeAltitude(dispersion_results) plt.figure() @@ -929,6 +1127,18 @@ def plotApogeeXPosition(self, dispersion_results): return None def meanApogeeYPosition(self, dispersion_results): + """_summary_ + + Parameters + ---------- + dispersion_results : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ print( f'Apogee Y Position - Mean Value: {np.mean(dispersion_results["apogeeY"]):0.3f} m' ) @@ -939,7 +1149,18 @@ def meanApogeeYPosition(self, dispersion_results): return None def plotApogeeYPosition(self, dispersion_results): + """_summary_ + Parameters + ---------- + dispersion_results : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ self.meanApogeeAltitude(dispersion_results) plt.figure() @@ -952,6 +1173,18 @@ def plotApogeeYPosition(self, dispersion_results): return None def meanImpactTime(self, dispersion_results): + """_summary_ + + Parameters + ---------- + dispersion_results : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ print( f'Impact Time - Mean Value: {np.mean(dispersion_results["impactTime"]):0.3f} s' ) @@ -962,7 +1195,18 @@ def meanImpactTime(self, dispersion_results): return None def plotImpactTime(self, dispersion_results): + """_summary_ + + Parameters + ---------- + dispersion_results : _type_ + _description_ + Returns + ------- + _type_ + _description_ + """ self.meanImpactTime(dispersion_results) plt.figure() @@ -975,6 +1219,18 @@ def plotImpactTime(self, dispersion_results): return None def meanImpactXPosition(self, dispersion_results): + """_summary_ + + Parameters + ---------- + dispersion_results : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ print( f'Impact X Position - Mean Value: {np.mean(dispersion_results["impactX"]):0.3f} m' ) @@ -985,7 +1241,18 @@ def meanImpactXPosition(self, dispersion_results): return None def plotImpactXPosition(self, dispersion_results): + """_summary_ + + Parameters + ---------- + dispersion_results : _type_ + _description_ + Returns + ------- + _type_ + _description_ + """ self.meanImpactXPosition(dispersion_results) plt.figure() @@ -998,6 +1265,18 @@ def plotImpactXPosition(self, dispersion_results): return None def meanImpactYPosition(self, dispersion_results): + """_summary_ + + Parameters + ---------- + dispersion_results : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ print( f'Impact Y Position - Mean Value: {np.mean(dispersion_results["impactY"]):0.3f} m' ) @@ -1008,7 +1287,18 @@ def meanImpactYPosition(self, dispersion_results): return None def plotImpactYPosition(self, dispersion_results): + """_summary_ + Parameters + ---------- + dispersion_results : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ self.meanImpactYPosition(dispersion_results) plt.figure() @@ -1021,6 +1311,18 @@ def plotImpactYPosition(self, dispersion_results): return None def meanImpactVelocity(self, dispersion_results): + """_summary_ + + Parameters + ---------- + dispersion_results : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ print( f'Impact Velocity - Mean Value: {np.mean(dispersion_results["impactVelocity"]):0.3f} m/s' ) @@ -1031,7 +1333,18 @@ def meanImpactVelocity(self, dispersion_results): return None def plotImpactVelocity(self, dispersion_results): + """_summary_ + Parameters + ---------- + dispersion_results : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ self.meanImpactVelocity(dispersion_results) plt.figure() @@ -1045,6 +1358,18 @@ def plotImpactVelocity(self, dispersion_results): return None def meanStaticMargin(self, dispersion_results): + """_summary_ + + Parameters + ---------- + dispersion_results : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ print( f'Initial Static Margin - Mean Value: {np.mean(dispersion_results["initialStaticMargin"]):0.3f} c' ) @@ -1069,7 +1394,18 @@ def meanStaticMargin(self, dispersion_results): return None def plotStaticMargin(self, dispersion_results): + """_summary_ + Parameters + ---------- + dispersion_results : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ self.meanStaticMargin(dispersion_results) plt.figure() @@ -1097,6 +1433,18 @@ def plotStaticMargin(self, dispersion_results): return None def meanMaximumVelocity(self, dispersion_results): + """_summary_ + + Parameters + ---------- + dispersion_results : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ print( f'Maximum Velocity - Mean Value: {np.mean(dispersion_results["maxVelocity"]):0.3f} m/s' ) @@ -1107,7 +1455,18 @@ def meanMaximumVelocity(self, dispersion_results): return None def plotMaximumVelocity(self, dispersion_results): + """_summary_ + + Parameters + ---------- + dispersion_results : _type_ + _description_ + Returns + ------- + _type_ + _description_ + """ self.meanMaximumVelocity(dispersion_results) plt.figure() @@ -1120,6 +1479,18 @@ def plotMaximumVelocity(self, dispersion_results): return None def meanNumberOfParachuteEvents(self, dispersion_results): + """_summary_ + + Parameters + ---------- + dispersion_results : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ print( f'Number of Parachute Events - Mean Value: {np.mean(dispersion_results["numberOfEvents"]):0.3f} s' ) @@ -1130,7 +1501,18 @@ def meanNumberOfParachuteEvents(self, dispersion_results): return None def plotNumberOfParachuteEvents(self, dispersion_results): + """_summary_ + Parameters + ---------- + dispersion_results : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ self.meanNumberOfParachuteEvents(dispersion_results) plt.figure() @@ -1143,6 +1525,18 @@ def plotNumberOfParachuteEvents(self, dispersion_results): return None def meanDrogueTriggerTime(self, dispersion_results): + """_summary_ + + Parameters + ---------- + dispersion_results : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ print( f'Drogue Trigger Time - Mean Value: {np.mean(dispersion_results["drogueTriggerTime"]):0.3f} s' ) @@ -1153,7 +1547,18 @@ def meanDrogueTriggerTime(self, dispersion_results): return None def plotDrogueTriggerTime(self, dispersion_results): + """_summary_ + Parameters + ---------- + dispersion_results : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ self.meanDrogueTriggerTime(dispersion_results) plt.figure() @@ -1166,6 +1571,18 @@ def plotDrogueTriggerTime(self, dispersion_results): return None def meanDrogueFullyInflatedTime(self, dispersion_results): + """_summary_ + + Parameters + ---------- + dispersion_results : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ print( f'Drogue Fully Inflated Time - Mean Value: {np.mean(dispersion_results["drogueInflatedTime"]):0.3f} s' ) @@ -1176,7 +1593,18 @@ def meanDrogueFullyInflatedTime(self, dispersion_results): return None def plotDrogueFullyInflatedTime(self, dispersion_results): + """_summary_ + + Parameters + ---------- + dispersion_results : _type_ + _description_ + Returns + ------- + _type_ + _description_ + """ self.meanDrogueFullyInflatedTime(dispersion_results) plt.figure() @@ -1189,6 +1617,18 @@ def plotDrogueFullyInflatedTime(self, dispersion_results): return None def meanDrogueFullyVelocity(self, dispersion_results): + """_summary_ + + Parameters + ---------- + dispersion_results : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ print( f'Drogue Parachute Fully Inflated Velocity - Mean Value: {np.mean(dispersion_results["drogueInflatedVelocity"]):0.3f} m/s' ) @@ -1199,7 +1639,18 @@ def meanDrogueFullyVelocity(self, dispersion_results): return None def plotDrogueFullyVelocity(self, dispersion_results): + """_summary_ + + Parameters + ---------- + dispersion_results : _type_ + _description_ + Returns + ------- + _type_ + _description_ + """ self.meanDrogueFullyVelocity(dispersion_results) plt.figure() @@ -1212,6 +1663,18 @@ def plotDrogueFullyVelocity(self, dispersion_results): return None def createEllipses(self, dispersion_results): + """_summary_ + + Parameters + ---------- + dispersion_results : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ """A function to create apogee and impact ellipses from the dispersion results. @@ -1514,6 +1977,13 @@ def exportEllipsesToKML( return None def meanLateralWindSpeed(self, dispersion_results): + """_summary_ + + Parameters + ---------- + dispersion_results : _type_ + _description_ + """ print( f'Lateral Surface Wind Speed - Mean Value: {np.mean(dispersion_results["lateralWind"]):0.3f} m/s' ) @@ -1522,7 +1992,13 @@ def meanLateralWindSpeed(self, dispersion_results): ) def plotLateralWindSpeed(self, dispersion_results): + """_summary_ + Parameters + ---------- + dispersion_results : _type_ + _description_ + """ self.meanLateralWindSpeed(dispersion_results) plt.figure() @@ -1533,6 +2009,13 @@ def plotLateralWindSpeed(self, dispersion_results): plt.show() def meanFrontalWindSpeed(self, dispersion_results): + """_summary_ + + Parameters + ---------- + dispersion_results : _type_ + _description_ + """ print( f'Frontal Surface Wind Speed - Mean Value: {np.mean(dispersion_results["frontalWind"]):0.3f} m/s' ) @@ -1541,7 +2024,13 @@ def meanFrontalWindSpeed(self, dispersion_results): ) def plotFrontalWindSpeed(self, dispersion_results): + """_summary_ + Parameters + ---------- + dispersion_results : _type_ + _description_ + """ self.meanFrontalWindSpeed(dispersion_results) plt.figure() @@ -1552,6 +2041,13 @@ def plotFrontalWindSpeed(self, dispersion_results): plt.show() def info(self): + """_summary_ + + Returns + ------- + _type_ + _description_ + """ dispersion_results = self.importResults(self.filename) @@ -1591,6 +2087,8 @@ def info(self): self.meanDrogueTriggerTime(dispersion_results) + return None + def allInfo(self): dispersion_results = self.importResults(self.filename) @@ -1632,6 +2130,8 @@ def allInfo(self): self.plotDrogueTriggerTime(dispersion_results) + return None + # Variables environment_inputs = { From cbd2ec604a4409beb2f7c8c79f87246b92042758 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Thu, 6 Oct 2022 04:41:48 +0200 Subject: [PATCH 03/68] MAINT: Refresh init docstring --- rocketpy/Dispersion.py | 285 ++++++++++++++++------------------------- 1 file changed, 109 insertions(+), 176 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index 6f2ec8ec4..6cfc5c41b 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -53,10 +53,7 @@ class Dispersion: part of the export filenames (e.g. 'filename.disp_outputs.txt'). When analyzing the results of a previous simulation, this attribute shall be the filename containing the outputs of a dispersion calculation. - Dispersion.image: string - Launch site PNG file to be plotted along with the dispersion ellipses. - Attribute needed to run a new simulation. - Dispersion.realLandingPoint: tuple + Dispersion.actualLandingPoint: tuple Rocket's experimental landing point relative to launch point. Dispersion.N: integer Number of simulations in an output file. @@ -84,44 +81,9 @@ def __init__( When running a new simulation, this parameter represents the initial part of the export filenames (e.g. 'filename.disp_outputs.txt'). When analyzing the results of a previous simulation, this parameter - shall be the .txt filename containing the outputs of a dispersion calculation. - number_of_simulations: integer, needed when running a new simulation - Number of simulations desired, must be greater than zero. - Default is zero. - flight: Flight - Original rocket's flight with nominal values. - Parameter needed to run a new simulation, when environment, - motor and rocket remain unchanged. - Default is None. - image: string, needed when running a new simulation - Launch site PNG file to be plotted along with the dispersion ellipses. - dispersionDict: dictionary, optional - Contains the information of which environment, motor, rocket and flight variables - will vary according to its standard deviation. - Format {'parameter0': (nominal value, standard deviation), 'parameter1': - (nominal value, standard deviation), ...} - (e.g. {'rocketMass':(20, 0.2), - 'burnOut': (3.9, 0.3), 'railLength': (5.2, 0.05)}) - Default is {}. - environment: Environment - Launch environment. - Parameter needed to run a new simulation, when Dispersion.flight remains unchanged. - Default is None. - motor: Motor, optional - Rocket's motor. - Parameter needed to run a new simulation, when Dispersion.flight remains unchanged. - Default is None. - rocket: Rocket, optional - Rocket with nominal values. - Parameter needed to run a new simulation, when Dispersion.flight remains unchanged. - Default is None. - distributionType: string, optional - Determines which type of distribution will be applied to variable parameters and - its respective standard deviation. - Default is 'normal' - realLandingPoint: tuple, optional - Rocket's experimental landing point relative to launch point. - Format (horizontal distance, vertical distance) + shall be the .txt filename containing the outputs of a previous ran + dispersion analysis. + Returns ------- None @@ -129,18 +91,20 @@ def __init__( # Save and initialize parameters self.filename = filename - self.number_of_simulations = 0 - self.flight = None - self.dispersionDict = {} - self.environment = None - self.motor = None - self.rocket = None - self.distributionType = "normal" - self.image = None - self.realLandingPoint = None - self.parachuteTriggers = [] def setDistributionFunc(self, distributionType): + """_summary_ + + Parameters + ---------- + distributionType : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ if distributionType == "normal" or distributionType == None: return normal elif distributionType == "beta": @@ -214,12 +178,12 @@ def setDistributionFunc(self, distributionType): else: warnings.warn("Distribution type not supported") - def processDispersionDict(self, dispersionDict): + def processDispersionDict(self, d): """_summary_ Parameters ---------- - dispersionDict : _type_ + d : _type_ _description_ Returns @@ -228,52 +192,38 @@ def processDispersionDict(self, dispersionDict): _description_ """ # Get parachutes names - if "parachuteNames" in dispersionDict: # TODO: use only dispersionDict - for i, name in enumerate(dispersionDict["parachuteNames"]): - if "CdS" in dispersionDict: - dispersionDict["parachute_" + name + "_CdS"] = dispersionDict[ - "CdS" - ][i] - if "trigger" in dispersionDict: - dispersionDict["parachute_" + name + "_trigger"] = dispersionDict[ - "trigger" - ][i] - if "samplingRate" in dispersionDict: - dispersionDict[ - "parachute_" + name + "_samplingRate" - ] = dispersionDict["samplingRate"][i] - if "lag" in dispersionDict: - dispersionDict["parachute_" + name + "_lag"] = dispersionDict[ - "lag" - ][i] - if "noise_mean" in dispersionDict: - dispersionDict[ - "parachute_" + name + "_noise_mean" - ] = dispersionDict["noise_mean"][i] - if "noise_sd" in dispersionDict: - dispersionDict["parachute_" + name + "_noise_std"] = dispersionDict[ - "noise_sd" - ][i] - if "noise_corr" in dispersionDict: - dispersionDict[ - "parachute_" + name + "_noise_corr" - ] = dispersionDict["noise_corr"][i] - dispersionDict.pop("CdS", None) - dispersionDict.pop("trigger", None) - dispersionDict.pop("samplingRate", None) - dispersionDict.pop("lag", None) - dispersionDict.pop("noise_mean", None) - dispersionDict.pop("noise_sd", None) - dispersionDict.pop("noise_corr", None) - self.parachute_names = dispersionDict.pop("parachuteNames", None) - - for parameter_key, parameter_value in dispersionDict.items(): + if "parachuteNames" in d: # TODO: use only d + for i, name in enumerate(d["parachuteNames"]): + if "CdS" in d: + d["parachute_" + name + "_CdS"] = d["CdS"][i] + if "trigger" in d: + d["parachute_" + name + "_trigger"] = d["trigger"][i] + if "samplingRate" in d: + d["parachute_" + name + "_samplingRate"] = d["samplingRate"][i] + if "lag" in d: + d["parachute_" + name + "_lag"] = d["lag"][i] + if "noise_mean" in d: + d["parachute_" + name + "_noise_mean"] = d["noise_mean"][i] + if "noise_sd" in d: + d["parachute_" + name + "_noise_std"] = d["noise_sd"][i] + if "noise_corr" in d: + d["parachute_" + name + "_noise_corr"] = d["noise_corr"][i] + d.pop("CdS", None) + d.pop("trigger", None) + d.pop("samplingRate", None) + d.pop("lag", None) + d.pop("noise_mean", None) + d.pop("noise_sd", None) + d.pop("noise_corr", None) + self.parachute_names = d.pop("parachuteNames", None) + + for parameter_key, parameter_value in d.items(): if isinstance(parameter_value, (tuple, list)): continue else: # if parameter_value is only the standard deviation if "parachute" in parameter_key: _, parachute_name, parameter = parameter_key.split("_") - dispersionDict[parameter_key] = ( + d[parameter_key] = ( getattr( self.rocket.parachutes[ self.parachute_names.index(parachute_name) @@ -285,61 +235,61 @@ def processDispersionDict(self, dispersionDict): else: if parameter_key in self.environment_inputs.keys(): try: - dispersionDict[parameter_key] = ( + d[parameter_key] = ( getattr(self.environment, parameter_key), parameter_value, ) except Exception as E: print("Error:") print( - "Check if parameter was inputed correctly in dispersioDict." + "Check if parameter was inputted correctly in dispersionDict." + " Dictionary values must be either tuple or lists." - + " If single value, the correponding Class must " - + "must be inputed in Dispersion.runDispersion method.\n" + + " If single value, the corresponding Class must " + + "must be inputted in Dispersion.runDispersion method.\n" ) print(traceback.format_exc()) elif parameter_key in self.solidmotor_inputs.keys(): try: - dispersionDict[parameter_key] = ( + d[parameter_key] = ( getattr(self.motor, parameter_key), parameter_value, ) except Exception as E: print("Error:") print( - "Check if parameter was inputed correctly in dispersioDict." + "Check if parameter was inputted correctly in dispersionDict." + " Dictionary values must be either tuple or lists." - + " If single value, the correponding Class must " - + "must be inputed in Dispersion.runDispersion method.\n" + + " If single value, the corresponding Class must " + + "must be inputted in Dispersion.runDispersion method.\n" ) print(traceback.format_exc()) elif parameter_key in self.rocket_inputs.keys(): try: - dispersionDict[parameter_key] = ( + d[parameter_key] = ( getattr(self.rocket, parameter_key), parameter_value, ) except Exception as E: print("Error:") print( - "Check if parameter was inputed correctly in dispersioDict." + "Check if parameter was inputed correctly in dispersionDict." + " Dictionary values must be either tuple or lists." - + " If single value, the correponding Class must " + + " If single value, the corresponding Class must " + "must be inputed in Dispersion.runDispersion method.\n" ) print(traceback.format_exc()) elif parameter_key in self.flight_inputs.keys(): try: - dispersionDict[parameter_key] = ( + d[parameter_key] = ( getattr(self.flight, parameter_key), parameter_value, ) except Exception as E: print("Error:") print( - "Check if parameter was inputed correctly in dispersioDict." + "Check if parameter was inputed correctly in dispersionDict." + " Dictionary values must be either tuple or lists." - + " If single value, the correponding Class must " + + " If single value, the corresponding Class must " + "must be inputed in Dispersion.runDispersion method.\n" ) print(traceback.format_exc()) @@ -347,93 +297,68 @@ def processDispersionDict(self, dispersionDict): # Check remaining class inputs if not all( - environment_input in dispersionDict + environment_input in d for environment_input in self.environment_inputs.keys() ): # Iterate through missing inputs - for missing_input in ( - set(self.environment_inputs.keys()) - dispersionDict.keys() - ): + for missing_input in set(self.environment_inputs.keys()) - d.keys(): missing_input = str(missing_input) # Add to the dict try: - dispersionDict[missing_input] = [ - getattr(self.environment, missing_input) - ] + d[missing_input] = [getattr(self.environment, missing_input)] except: # class was not inputed # checks if missing parameter is required if self.environment_inputs[missing_input] == "required": - warnings.warn(f'Missing "{missing_input}" in dispersionDict') + warnings.warn(f'Missing "{missing_input}" in d') else: # if not uses default value - dispersionDict[missing_input] = [ - self.environment_inputs[missing_input] - ] - if not all( - motor_input in dispersionDict - for motor_input in self.solidmotor_inputs.keys() - ): + d[missing_input] = [self.environment_inputs[missing_input]] + if not all(motor_input in d for motor_input in self.solidmotor_inputs.keys()): # Iterate through missing inputs - for missing_input in ( - set(self.solidmotor_inputs.keys()) - dispersionDict.keys() - ): + for missing_input in set(self.solidmotor_inputs.keys()) - d.keys(): missing_input = str(missing_input) # Add to the dict try: - dispersionDict[missing_input] = [getattr(self.motor, missing_input)] + d[missing_input] = [getattr(self.motor, missing_input)] except: # class was not inputed # checks if missing parameter is required if self.solidmotor_inputs[missing_input] == "required": - warnings.warn(f'Missing "{missing_input}" in dispersionDict') + warnings.warn(f'Missing "{missing_input}" in d') else: # if not uses default value - dispersionDict[missing_input] = [ - self.solidmotor_inputs[missing_input] - ] + d[missing_input] = [self.solidmotor_inputs[missing_input]] - if not all( - rocket_input in dispersionDict for rocket_input in self.rocket_inputs.keys() - ): + if not all(rocket_input in d for rocket_input in self.rocket_inputs.keys()): # Iterate through missing inputs - for missing_input in set(self.rocket_inputs.keys()) - dispersionDict.keys(): + for missing_input in set(self.rocket_inputs.keys()) - d.keys(): missing_input = str(missing_input) # Add to the dict try: - dispersionDict[missing_input] = [ - getattr(self.rocket, missing_input) - ] + d[missing_input] = [getattr(self.rocket, missing_input)] except: # class was not inputed # checks if missing parameter is required if self.rocket_inputs[missing_input] == "required": - warnings.warn(f'Missing "{missing_input}" in dispersionDict') + warnings.warn(f'Missing "{missing_input}" in d') else: # if not uses default value - dispersionDict[missing_input] = [ - self.rocket_inputs[missing_input] - ] + d[missing_input] = [self.rocket_inputs[missing_input]] - if not all( - flight_input in dispersionDict for flight_input in self.flight_inputs.keys() - ): + if not all(flight_input in d for flight_input in self.flight_inputs.keys()): # Iterate through missing inputs - for missing_input in set(self.flight_inputs.keys()) - dispersionDict.keys(): + for missing_input in set(self.flight_inputs.keys()) - d.keys(): missing_input = str(missing_input) # Add to the dict try: - dispersionDict[missing_input] = [ - getattr(self.flight, missing_input) - ] + d[missing_input] = [getattr(self.flight, missing_input)] except: # class was not inputed # checks if missing parameter is required if self.flight_inputs[missing_input] == "required": - warnings.warn(f'Missing "{missing_input}" in dispersionDict') + warnings.warn(f'Missing "{missing_input}" in d') else: # if not uses default value - dispersionDict[missing_input] = [ - self.flight_inputs[missing_input] - ] + d[missing_input] = [self.flight_inputs[missing_input]] - return dispersionDict + return d def yield_flight_setting( self, distributionFunc, analysis_parameters, number_of_simulations @@ -446,8 +371,9 @@ def yield_flight_setting( _description_ analysis_parameters : _type_ _description_ - number_of_simulations : _type_ - _description_ + number_of_simulations : int + Number of simulations desired, must be non negative. + This is needed when running a new simulation. Default is zero. Yields ------ @@ -597,30 +523,35 @@ def runDispersion( rocket=None, distributionType="normal", image=None, - realLandingPoint=None, + actualLandingPoint=None, ): """Runs the given number of simulations and saves the data Parameters ---------- - number_of_simulations : _type_ - _description_ - dispersionDict : _type_ + number_of_simulations : int + Number of simulations desired, must be non negative. + This is needed when running a new simulation. Default is zero. + d : _type_ _description_ environment : _type_ _description_ - flight : _type_, optional - _description_, by default None + flight : Flight, optional + Original rocket's flight with nominal values. Parameter needed to run + a new flight simulation when environment, motor and rocket remain + unchanged. By default None. motor : _type_, optional _description_, by default None rocket : _type_, optional _description_, by default None distributionType : str, optional _description_, by default "normal" - image : _type_, optional - _description_, by default None - realLandingPoint : _type_, optional - _description_, by default None + image : str, optional + The path to the image to be used as the background + actualLandingPoint : tuple, optional + A tuple containing the actual landing point of the rocket, if known. + Useful when comparing the dispersion results with the actual landing. + Must be given in tuple format, such as (lat, lon). By default None. # TODO: Check the order Returns ------- @@ -639,7 +570,7 @@ def runDispersion( self.rocket = rocket if rocket else self.rocket self.distributionType = distributionType self.image = image - self.realLandingPoint = realLandingPoint + self.actualLandingPoint = actualLandingPoint # Creates copy of dispersionDict that will be altered modified_dispersion_dict = {i: j for i, j in dispersionDict.items()} @@ -1739,7 +1670,7 @@ def plotEllipses( self, dispersion_results, image=None, - realLandingPoint=None, + actualLandingPoint=None, perimeterSize=3000, xlim=(-3000, 3000), ylim=(-3000, 3000), @@ -1752,10 +1683,12 @@ def plotEllipses( ---------- dispersion_results : dict A dictionary containing the results of the dispersion analysis - image : str + image : str, optional The path to the image to be used as the background - realLandingPoint : tuple, optional - A tuple containing the real landing point of the rocket, by default None + actualLandingPoint : tuple, optional + A tuple containing the actual landing point of the rocket, if known. + Useful when comparing the dispersion results with the actual landing. + Must be given in tuple format, such as (lat, lon). By default None. # TODO: Check the order """ # Import background map if image is not None: @@ -1794,10 +1727,10 @@ def plotEllipses( label="Simulated Landing Point", ) # Draw real landing point - if realLandingPoint != None: + if actualLandingPoint != None: plt.scatter( - realLandingPoint[0], - realLandingPoint[1], + actualLandingPoint[0], + actualLandingPoint[1], s=20, marker="X", color="red", @@ -2092,7 +2025,7 @@ def info(self): def allInfo(self): dispersion_results = self.importResults(self.filename) - self.plotEllipses(dispersion_results, self.image, self.realLandingPoint) + self.plotEllipses(dispersion_results, self.image, self.actualLandingPoint) self.plotApogeeAltitude(dispersion_results) From fe710dcd404524f530fca21e0a0a2ccb68fbd649 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Thu, 6 Oct 2022 04:56:28 +0200 Subject: [PATCH 04/68] MAINT: converting some variables to snake_case --- rocketpy/Dispersion.py | 300 ++++++++++++++++++++++------------------- 1 file changed, 162 insertions(+), 138 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index 6cfc5c41b..b53de9ac7 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -53,7 +53,7 @@ class Dispersion: part of the export filenames (e.g. 'filename.disp_outputs.txt'). When analyzing the results of a previous simulation, this attribute shall be the filename containing the outputs of a dispersion calculation. - Dispersion.actualLandingPoint: tuple + Dispersion.actual_landing_point: tuple Rocket's experimental landing point relative to launch point. Dispersion.N: integer Number of simulations in an output file. @@ -92,12 +92,12 @@ def __init__( # Save and initialize parameters self.filename = filename - def setDistributionFunc(self, distributionType): + def __set_distribution_function(self, distribution_type): """_summary_ Parameters ---------- - distributionType : _type_ + distribution_type : _type_ _description_ Returns @@ -105,85 +105,85 @@ def setDistributionFunc(self, distributionType): _type_ _description_ """ - if distributionType == "normal" or distributionType == None: + if distribution_type == "normal" or distribution_type == None: return normal - elif distributionType == "beta": + elif distribution_type == "beta": return beta - elif distributionType == "binomial": + elif distribution_type == "binomial": return binomial - elif distributionType == "chisquare": + elif distribution_type == "chisquare": return chisquare - elif distributionType == "dirichlet": + elif distribution_type == "dirichlet": return dirichlet - elif distributionType == "exponential": + elif distribution_type == "exponential": return exponential - elif distributionType == "f": + elif distribution_type == "f": return f - elif distributionType == "gamma": + elif distribution_type == "gamma": return gamma - elif distributionType == "geometric": + elif distribution_type == "geometric": return geometric - elif distributionType == "gumbel": + elif distribution_type == "gumbel": return gumbel - elif distributionType == "hypergeometric": + elif distribution_type == "hypergeometric": return hypergeometric - elif distributionType == "laplace": + elif distribution_type == "laplace": return laplace - elif distributionType == "logistic": + elif distribution_type == "logistic": return logistic - elif distributionType == "lognormal": + elif distribution_type == "lognormal": return lognormal - elif distributionType == "logseries": + elif distribution_type == "logseries": return logseries - elif distributionType == "multinomial": + elif distribution_type == "multinomial": return multinomial - elif distributionType == "multivariate_normal": + elif distribution_type == "multivariate_normal": return multivariate_normal - elif distributionType == "negative_binomial": + elif distribution_type == "negative_binomial": return negative_binomial - elif distributionType == "noncentral_chisquare": + elif distribution_type == "noncentral_chisquare": return noncentral_chisquare - elif distributionType == "noncentral_f": + elif distribution_type == "noncentral_f": return noncentral_f - elif distributionType == "pareto": + elif distribution_type == "pareto": return pareto - elif distributionType == "poisson": + elif distribution_type == "poisson": return poisson - elif distributionType == "power": + elif distribution_type == "power": return power - elif distributionType == "rayleigh": + elif distribution_type == "rayleigh": return rayleigh - elif distributionType == "standard_cauchy": + elif distribution_type == "standard_cauchy": return standard_cauchy - elif distributionType == "standard_exponential": + elif distribution_type == "standard_exponential": return standard_exponential - elif distributionType == "standard_gamma": + elif distribution_type == "standard_gamma": return standard_gamma - elif distributionType == "standard_normal": + elif distribution_type == "standard_normal": return standard_normal - elif distributionType == "standard_t": + elif distribution_type == "standard_t": return standard_t - elif distributionType == "triangular": + elif distribution_type == "triangular": return triangular - elif distributionType == "uneliform": + elif distribution_type == "uneliform": return uniform - elif distributionType == "vonmises": + elif distribution_type == "vonmises": return vonmises - elif distributionType == "wald": + elif distribution_type == "wald": return wald - elif distributionType == "weibull": + elif distribution_type == "weibull": return weibull - elif distributionType == "zipf": + elif distribution_type == "zipf": return zipf else: warnings.warn("Distribution type not supported") - def processDispersionDict(self, d): + def __process_dispersion_dict(self, dictionary): """_summary_ Parameters ---------- - d : _type_ + dictionary : dict _description_ Returns @@ -192,38 +192,48 @@ def processDispersionDict(self, d): _description_ """ # Get parachutes names - if "parachuteNames" in d: # TODO: use only d - for i, name in enumerate(d["parachuteNames"]): - if "CdS" in d: - d["parachute_" + name + "_CdS"] = d["CdS"][i] - if "trigger" in d: - d["parachute_" + name + "_trigger"] = d["trigger"][i] - if "samplingRate" in d: - d["parachute_" + name + "_samplingRate"] = d["samplingRate"][i] - if "lag" in d: - d["parachute_" + name + "_lag"] = d["lag"][i] - if "noise_mean" in d: - d["parachute_" + name + "_noise_mean"] = d["noise_mean"][i] - if "noise_sd" in d: - d["parachute_" + name + "_noise_std"] = d["noise_sd"][i] - if "noise_corr" in d: - d["parachute_" + name + "_noise_corr"] = d["noise_corr"][i] - d.pop("CdS", None) - d.pop("trigger", None) - d.pop("samplingRate", None) - d.pop("lag", None) - d.pop("noise_mean", None) - d.pop("noise_sd", None) - d.pop("noise_corr", None) - self.parachute_names = d.pop("parachuteNames", None) - - for parameter_key, parameter_value in d.items(): + if "parachuteNames" in dictionary: # TODO: use only dictionary + for i, name in enumerate(dictionary["parachuteNames"]): + if "CdS" in dictionary: + dictionary["parachute_" + name + "_CdS"] = dictionary["CdS"][i] + if "trigger" in dictionary: + dictionary["parachute_" + name + "_trigger"] = dictionary[ + "trigger" + ][i] + if "samplingRate" in dictionary: + dictionary["parachute_" + name + "_samplingRate"] = dictionary[ + "samplingRate" + ][i] + if "lag" in dictionary: + dictionary["parachute_" + name + "_lag"] = dictionary["lag"][i] + if "noise_mean" in dictionary: + dictionary["parachute_" + name + "_noise_mean"] = dictionary[ + "noise_mean" + ][i] + if "noise_sd" in dictionary: + dictionary["parachute_" + name + "_noise_std"] = dictionary[ + "noise_sd" + ][i] + if "noise_corr" in dictionary: + dictionary["parachute_" + name + "_noise_corr"] = dictionary[ + "noise_corr" + ][i] + dictionary.pop("CdS", None) + dictionary.pop("trigger", None) + dictionary.pop("samplingRate", None) + dictionary.pop("lag", None) + dictionary.pop("noise_mean", None) + dictionary.pop("noise_sd", None) + dictionary.pop("noise_corr", None) + self.parachute_names = dictionary.pop("parachuteNames", None) + + for parameter_key, parameter_value in dictionary.items(): if isinstance(parameter_value, (tuple, list)): continue else: # if parameter_value is only the standard deviation if "parachute" in parameter_key: _, parachute_name, parameter = parameter_key.split("_") - d[parameter_key] = ( + dictionary[parameter_key] = ( getattr( self.rocket.parachutes[ self.parachute_names.index(parachute_name) @@ -235,87 +245,95 @@ def processDispersionDict(self, d): else: if parameter_key in self.environment_inputs.keys(): try: - d[parameter_key] = ( + dictionary[parameter_key] = ( getattr(self.environment, parameter_key), parameter_value, ) except Exception as E: print("Error:") print( - "Check if parameter was inputted correctly in dispersionDict." + "Check if parameter was inputted correctly in dispersion_dictionary." + " Dictionary values must be either tuple or lists." + " If single value, the corresponding Class must " - + "must be inputted in Dispersion.runDispersion method.\n" + + "must be inputted in Dispersion.run_dispersion method.\n" ) print(traceback.format_exc()) elif parameter_key in self.solidmotor_inputs.keys(): try: - d[parameter_key] = ( + dictionary[parameter_key] = ( getattr(self.motor, parameter_key), parameter_value, ) except Exception as E: print("Error:") print( - "Check if parameter was inputted correctly in dispersionDict." + "Check if parameter was inputted correctly in dispersion_dictionary." + " Dictionary values must be either tuple or lists." + " If single value, the corresponding Class must " - + "must be inputted in Dispersion.runDispersion method.\n" + + "must be inputted in Dispersion.run_dispersion method.\n" ) print(traceback.format_exc()) elif parameter_key in self.rocket_inputs.keys(): try: - d[parameter_key] = ( + dictionary[parameter_key] = ( getattr(self.rocket, parameter_key), parameter_value, ) except Exception as E: print("Error:") print( - "Check if parameter was inputed correctly in dispersionDict." + "Check if parameter was inputted correctly in dispersion_dictionary." + " Dictionary values must be either tuple or lists." + " If single value, the corresponding Class must " - + "must be inputed in Dispersion.runDispersion method.\n" + + "must be inputted in Dispersion.run_dispersion method.\n" ) print(traceback.format_exc()) elif parameter_key in self.flight_inputs.keys(): try: - d[parameter_key] = ( + dictionary[parameter_key] = ( getattr(self.flight, parameter_key), parameter_value, ) except Exception as E: print("Error:") print( - "Check if parameter was inputed correctly in dispersionDict." + "Check if parameter was inputted correctly in dispersion_dictionary." + " Dictionary values must be either tuple or lists." + " If single value, the corresponding Class must " - + "must be inputed in Dispersion.runDispersion method.\n" + + "must be inputted in Dispersion.run_dispersion method.\n" ) print(traceback.format_exc()) # Check remaining class inputs if not all( - environment_input in d + environment_input in dictionary for environment_input in self.environment_inputs.keys() ): # Iterate through missing inputs - for missing_input in set(self.environment_inputs.keys()) - d.keys(): + for missing_input in ( + set(self.environment_inputs.keys()) - dictionary.keys() + ): missing_input = str(missing_input) # Add to the dict try: - d[missing_input] = [getattr(self.environment, missing_input)] + dictionary[missing_input] = [ + getattr(self.environment, missing_input) + ] except: - # class was not inputed + # class was not inputted # checks if missing parameter is required if self.environment_inputs[missing_input] == "required": - warnings.warn(f'Missing "{missing_input}" in d') + warnings.warn(f'Missing "{missing_input}" in dictionary') else: # if not uses default value - d[missing_input] = [self.environment_inputs[missing_input]] - if not all(motor_input in d for motor_input in self.solidmotor_inputs.keys()): + dictionary[missing_input] = [ + self.environment_inputs[missing_input] + ] + if not all( + motor_input in dictionary for motor_input in self.solidmotor_inputs.keys() + ): # Iterate through missing inputs - for missing_input in set(self.solidmotor_inputs.keys()) - d.keys(): + for missing_input in set(self.solidmotor_inputs.keys()) - dictionary.keys(): missing_input = str(missing_input) # Add to the dict try: @@ -326,48 +344,54 @@ def processDispersionDict(self, d): if self.solidmotor_inputs[missing_input] == "required": warnings.warn(f'Missing "{missing_input}" in d') else: # if not uses default value - d[missing_input] = [self.solidmotor_inputs[missing_input]] + dictionary[missing_input] = [ + self.solidmotor_inputs[missing_input] + ] - if not all(rocket_input in d for rocket_input in self.rocket_inputs.keys()): + if not all( + rocket_input in dictionary for rocket_input in self.rocket_inputs.keys() + ): # Iterate through missing inputs - for missing_input in set(self.rocket_inputs.keys()) - d.keys(): + for missing_input in set(self.rocket_inputs.keys()) - dictionary.keys(): missing_input = str(missing_input) # Add to the dict try: - d[missing_input] = [getattr(self.rocket, missing_input)] + dictionary[missing_input] = [getattr(self.rocket, missing_input)] except: - # class was not inputed + # class was not inputted # checks if missing parameter is required if self.rocket_inputs[missing_input] == "required": - warnings.warn(f'Missing "{missing_input}" in d') + warnings.warn(f'Missing "{missing_input}" in dictionary') else: # if not uses default value - d[missing_input] = [self.rocket_inputs[missing_input]] + dictionary[missing_input] = [self.rocket_inputs[missing_input]] - if not all(flight_input in d for flight_input in self.flight_inputs.keys()): + if not all( + flight_input in dictionary for flight_input in self.flight_inputs.keys() + ): # Iterate through missing inputs - for missing_input in set(self.flight_inputs.keys()) - d.keys(): + for missing_input in set(self.flight_inputs.keys()) - dictionary.keys(): missing_input = str(missing_input) # Add to the dict try: - d[missing_input] = [getattr(self.flight, missing_input)] + dictionary[missing_input] = [getattr(self.flight, missing_input)] except: - # class was not inputed + # class was not inputted # checks if missing parameter is required if self.flight_inputs[missing_input] == "required": - warnings.warn(f'Missing "{missing_input}" in d') + warnings.warn(f'Missing "{missing_input}" in dictionary') else: # if not uses default value - d[missing_input] = [self.flight_inputs[missing_input]] + dictionary[missing_input] = [self.flight_inputs[missing_input]] - return d + return dictionary - def yield_flight_setting( - self, distributionFunc, analysis_parameters, number_of_simulations + def __yield_flight_setting( + self, distribution_func, analysis_parameters, number_of_simulations ): """Yields a flight setting for the simulation Parameters ---------- - distributionFunc : _type_ + distribution_func : _type_ _description_ analysis_parameters : _type_ _description_ @@ -387,7 +411,7 @@ def yield_flight_setting( flight_setting = {} for parameter_key, parameter_value in analysis_parameters.items(): if type(parameter_value) is tuple: - flight_setting[parameter_key] = distributionFunc(*parameter_value) + flight_setting[parameter_key] = distribution_func(*parameter_value) else: # shuffles list and gets first item shuffle(parameter_value) @@ -513,17 +537,17 @@ def export_flight_error(self, flight_setting, dispersion_error_file): return None - def runDispersion( + def run_dispersion( self, number_of_simulations, - dispersionDict, + dispersion_dictionary, environment, flight=None, motor=None, rocket=None, - distributionType="normal", + distribution_type="normal", image=None, - actualLandingPoint=None, + actual_landing_point=None, ): """Runs the given number of simulations and saves the data @@ -544,11 +568,11 @@ def runDispersion( _description_, by default None rocket : _type_, optional _description_, by default None - distributionType : str, optional + distribution_type : str, optional _description_, by default "normal" image : str, optional The path to the image to be used as the background - actualLandingPoint : tuple, optional + actual_landing_point : tuple, optional A tuple containing the actual landing point of the rocket, if known. Useful when comparing the dispersion results with the actual landing. Must be given in tuple format, such as (lat, lon). By default None. # TODO: Check the order @@ -560,7 +584,7 @@ def runDispersion( """ self.number_of_simulations = number_of_simulations - self.dispersionDict = dispersionDict + self.dispersion_dictionary = dispersion_dictionary self.environment = environment self.flight = flight if flight: @@ -568,16 +592,16 @@ def runDispersion( self.rocket = flight.rocket if not rocket else rocket self.motor = motor if motor else self.motor self.rocket = rocket if rocket else self.rocket - self.distributionType = distributionType + self.distribution_type = distribution_type self.image = image - self.actualLandingPoint = actualLandingPoint + self.actual_landing_point = actual_landing_point - # Creates copy of dispersionDict that will be altered - modified_dispersion_dict = {i: j for i, j in dispersionDict.items()} + # Creates copy of dispersion_dictionary that will be altered + modified_dispersion_dict = {i: j for i, j in dispersion_dictionary.items()} - analysis_parameters = self.processDispersionDict(modified_dispersion_dict) + analysis_parameters = self.__process_dispersion_dict(modified_dispersion_dict) - self.distribuitionFunc = self.setDistributionFunc(distributionType) + self.distributionFunc = self.__set_distribution_function(distribution_type) # Basic analysis info # Create data files for inputs, outputs and error logging @@ -591,17 +615,17 @@ def runDispersion( # self.environment = Environment( # railLength=0, # ) - # if "envAtmosphericType" in dispersionDict: - # if dispersionDict["envAtmosphericType"] == "CustomAtmosphere": + # if "envAtmosphericType" in dispersion_dictionary: + # if dispersion_dictionary["envAtmosphericType"] == "CustomAtmosphere": # customAtmosphere = True - # self.environment.setDate(datetime(*dispersionDict["date"][0])) + # self.environment.setDate(datetime(*dispersion_dictionary["date"][0])) # self.environment.setAtmosphericModel( - # type=dispersionDict["envAtmosphericType"], - # file=dispersionDict["envAtmosphericFile"] - # if "envAtmosphericFile" in dispersionDict + # type=dispersion_dictionary["envAtmosphericType"], + # file=dispersion_dictionary["envAtmosphericFile"] + # if "envAtmosphericFile" in dispersion_dictionary # else None, - # dictionary=dispersionDict["envAtmosphericDictionary"] - # if "envAtmosphericDictionary" in dispersionDict + # dictionary=dispersion_dictionary["envAtmosphericDictionary"] + # if "envAtmosphericDictionary" in dispersion_dictionary # else None, # ) @@ -613,8 +637,8 @@ def runDispersion( # Iterate over flight settings out = display("Starting", display_id=True) - for setting in self.yield_flight_setting( - self.distribuitionFunc, analysis_parameters, self.number_of_simulations + for setting in self.__yield_flight_setting( + self.distributionFunc, analysis_parameters, self.number_of_simulations ): start_time = process_time() i += 1 @@ -744,7 +768,7 @@ def runDispersion( # Register time out.update( - f"Curent iteration: {i:06d} | Average Time per Iteration: {(process_time() - initial_cpu_time)/i:2.6f} s | Estimated time left: {int((number_of_simulations - i)*((process_time() - initial_cpu_time)/i))} s" + f"Current iteration: {i:06d} | Average Time per Iteration: {(process_time() - initial_cpu_time)/i:2.6f} s | Estimated time left: {int((number_of_simulations - i)*((process_time() - initial_cpu_time)/i))} s" ) # Done @@ -763,7 +787,7 @@ def runDispersion( return None - def importResults(self, dispersion_output_file): + def import_results(self, dispersion_output_file): """Import dispersion results from .txt file Parameters @@ -1670,7 +1694,7 @@ def plotEllipses( self, dispersion_results, image=None, - actualLandingPoint=None, + actual_landing_point=None, perimeterSize=3000, xlim=(-3000, 3000), ylim=(-3000, 3000), @@ -1685,7 +1709,7 @@ def plotEllipses( A dictionary containing the results of the dispersion analysis image : str, optional The path to the image to be used as the background - actualLandingPoint : tuple, optional + actual_landing_point : tuple, optional A tuple containing the actual landing point of the rocket, if known. Useful when comparing the dispersion results with the actual landing. Must be given in tuple format, such as (lat, lon). By default None. # TODO: Check the order @@ -1727,10 +1751,10 @@ def plotEllipses( label="Simulated Landing Point", ) # Draw real landing point - if actualLandingPoint != None: + if actual_landing_point != None: plt.scatter( - actualLandingPoint[0], - actualLandingPoint[1], + actual_landing_point[0], + actual_landing_point[1], s=20, marker="X", color="red", @@ -1982,7 +2006,7 @@ def info(self): _description_ """ - dispersion_results = self.importResults(self.filename) + dispersion_results = self.import_results(self.filename) self.meanApogeeAltitude(dispersion_results) @@ -2023,9 +2047,9 @@ def info(self): return None def allInfo(self): - dispersion_results = self.importResults(self.filename) + dispersion_results = self.import_results(self.filename) - self.plotEllipses(dispersion_results, self.image, self.actualLandingPoint) + self.plotEllipses(dispersion_results, self.image, self.actual_landing_point) self.plotApogeeAltitude(dispersion_results) From 4b704b59eb89a549cd5d71c5a21e42054a4cda3e Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Sat, 8 Oct 2022 17:23:00 +0200 Subject: [PATCH 05/68] MAINT: Modify variable names, start def properties --- rocketpy/Dispersion.py | 287 +++++++++++++++++++++++++---------------- 1 file changed, 173 insertions(+), 114 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index b53de9ac7..1c71484b5 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -1,10 +1,11 @@ # -*- coding: utf-8 -*- -__author__ = "Mateus Stano Junqueira, Sofia Lopes Suesdek Rocha, Abdulklech Sorban" +__author__ = "Mateus Stano Junqueira, Sofia Lopes Suesdek Rocha, Guilherme Fernandes Alves, Bruno Abdulklech Sorban" __copyright__ = "Copyright 20XX, Projeto Jupiter" __license__ = "MIT" +from functools import cached_property import math import traceback import warnings @@ -35,6 +36,7 @@ # TODO: Test simulations under different scenarios (with both parachutes, with only main chute, etc) # TODO: Add unit tests # TODO: Adjust the notebook to the new version of the code +# TODO: Optional return of matplotlib plots or abstract function to histogram plot based on stdev and mean # TODO: Implement MRS # TODO: Implement functions from compareDispersions @@ -47,6 +49,7 @@ class Dispersion: Attributes ---------- + # TODO: Update at the end! Parameters: Dispersion.filename: string When running a new simulation, this attribute represents the initial @@ -258,7 +261,7 @@ def __process_dispersion_dict(self, dictionary): + "must be inputted in Dispersion.run_dispersion method.\n" ) print(traceback.format_exc()) - elif parameter_key in self.solidmotor_inputs.keys(): + elif parameter_key in self.solid_motor_inputs.keys(): try: dictionary[parameter_key] = ( getattr(self.motor, parameter_key), @@ -330,22 +333,24 @@ def __process_dispersion_dict(self, dictionary): self.environment_inputs[missing_input] ] if not all( - motor_input in dictionary for motor_input in self.solidmotor_inputs.keys() + motor_input in dictionary for motor_input in self.solid_motor_inputs.keys() ): # Iterate through missing inputs - for missing_input in set(self.solidmotor_inputs.keys()) - dictionary.keys(): + for missing_input in ( + set(self.solid_motor_inputs.keys()) - dictionary.keys() + ): missing_input = str(missing_input) # Add to the dict try: - d[missing_input] = [getattr(self.motor, missing_input)] + dictionary[missing_input] = [getattr(self.motor, missing_input)] except: - # class was not inputed + # class was not inputted # checks if missing parameter is required - if self.solidmotor_inputs[missing_input] == "required": + if self.solid_motor_inputs[missing_input] == "required": warnings.warn(f'Missing "{missing_input}" in d') else: # if not uses default value dictionary[missing_input] = [ - self.solidmotor_inputs[missing_input] + self.solid_motor_inputs[missing_input] ] if not all( @@ -393,7 +398,7 @@ def __yield_flight_setting( ---------- distribution_func : _type_ _description_ - analysis_parameters : _type_ + analysis_parameters : dict _description_ number_of_simulations : int Number of simulations desired, must be non negative. @@ -427,7 +432,7 @@ def __yield_flight_setting( def export_flight_data( self, flight_setting, - flight_data, + flight, exec_time, dispersion_input_file, dispersion_output_file, @@ -438,7 +443,7 @@ def export_flight_data( ---------- flight_setting : _type_ _description_ - flight_data : _type_ + flight : Flight _description_ exec_time : _type_ _description_ @@ -455,68 +460,64 @@ def export_flight_data( # Generate flight results flight_result = { - "outOfRailTime": flight_data.outOfRailTime, - "outOfRailVelocity": flight_data.outOfRailVelocity, - "apogeeTime": flight_data.apogeeTime, - "apogeeAltitude": flight_data.apogee - flight_data.env.elevation, - "apogeeX": flight_data.apogeeX, - "apogeeY": flight_data.apogeeY, - "impactTime": flight_data.tFinal, - "impactX": flight_data.xImpact, - "impactY": flight_data.yImpact, - "impactVelocity": flight_data.impactVelocity, - "initialStaticMargin": flight_data.rocket.staticMargin(0), - "outOfRailStaticMargin": flight_data.rocket.staticMargin( - flight_data.outOfRailTime + "outOfRailTime": flight.outOfRailTime, + "outOfRailVelocity": flight.outOfRailVelocity, + "apogeeTime": flight.apogeeTime, + "apogeeAltitude": flight.apogee - flight.env.elevation, + "apogeeX": flight.apogeeX, + "apogeeY": flight.apogeeY, + "impactTime": flight.tFinal, + "impactX": flight.xImpact, + "impactY": flight.yImpact, + "impactVelocity": flight.impactVelocity, + "initialStaticMargin": flight.rocket.staticMargin(0), + "outOfRailStaticMargin": flight.rocket.staticMargin(flight.outOfRailTime), + "finalStaticMargin": flight.rocket.staticMargin( + flight.rocket.motor.burnOutTime ), - "finalStaticMargin": flight_data.rocket.staticMargin( - flight_data.rocket.motor.burnOutTime - ), - "numberOfEvents": len(flight_data.parachuteEvents), + "numberOfEvents": len(flight.parachuteEvents), "drogueTriggerTime": [], "drogueInflatedTime": [], "drogueInflatedVelocity": [], "executionTime": exec_time, - "lateralWind": flight_data.lateralSurfaceWind, - "frontalWind": flight_data.frontalSurfaceWind, + "lateralWind": flight.lateralSurfaceWind, + "frontalWind": flight.frontalSurfaceWind, } # Calculate maximum reached velocity - sol = np.array(flight_data.solution) - flight_data.vx = Function( + sol = np.array(flight.solution) + flight.vx = Function( sol[:, [0, 4]], "Time (s)", "Vx (m/s)", "linear", extrapolation="natural", ) - flight_data.vy = Function( + flight.vy = Function( sol[:, [0, 5]], "Time (s)", "Vy (m/s)", "linear", extrapolation="natural", ) - flight_data.vz = Function( + flight.vz = Function( sol[:, [0, 6]], "Time (s)", "Vz (m/s)", "linear", extrapolation="natural", ) - flight_data.v = ( - flight_data.vx**2 + flight_data.vy**2 + flight_data.vz**2 - ) ** 0.5 - flight_data.maxVel = np.amax(flight_data.v.source[:, 1]) - flight_result["maxVelocity"] = flight_data.maxVel + flight.speed = (flight.vx**2 + flight.vy**2 + flight.vz**2) ** 0.5 + flight.maxVel = np.amax(flight.speed.source[:, 1]) + flight_result["maxVelocity"] = flight.maxVel # Take care of parachute results - for trigger_time, parachute in flight_data.parachuteEvents: + for trigger_time, parachute in flight.parachuteEvents: flight_result[parachute.name + "_triggerTime"] = trigger_time flight_result[parachute.name + "_inflatedTime"] = ( trigger_time + parachute.lag ) - flight_result[parachute.name + "_inflatedVelocity"] = flight_data.v( + flight_result[parachute.name + "_inflatedVelocity"] = flight.speed( trigger_time + parachute.lag ) else: @@ -587,6 +588,7 @@ def run_dispersion( self.dispersion_dictionary = dispersion_dictionary self.environment = environment self.flight = flight + # TODO: What must be prioritized, the flight or the rocket and motor? if flight: self.motor = flight.rocket.motor if not motor else motor self.rocket = flight.rocket if not rocket else rocket @@ -635,32 +637,32 @@ def run_dispersion( initial_wall_time = time() initial_cpu_time = process_time() - # Iterate over flight settings + # Iterate over flight settings, start the flight simulations out = display("Starting", display_id=True) for setting in self.__yield_flight_setting( self.distributionFunc, analysis_parameters, self.number_of_simulations ): - start_time = process_time() + self.start_time = process_time() i += 1 # Creates an of environment - envDispersion = self.environment + env_dispersion = self.environment # Apply environment parameters variations on each iteration if possible - envDispersion.railLength = setting["railLength"] - envDispersion.gravity = setting["gravity"] - envDispersion.date = setting["date"] - envDispersion.latitude = setting["latitude"] - envDispersion.longitude = setting["longitude"] - envDispersion.elevation = setting["elevation"] - envDispersion.selectEnsembleMember(setting["ensembleMember"]) + env_dispersion.railLength = setting["railLength"] + env_dispersion.gravity = setting["gravity"] + env_dispersion.date = setting["date"] + env_dispersion.latitude = setting["latitude"] + env_dispersion.longitude = setting["longitude"] + env_dispersion.elevation = setting["elevation"] + env_dispersion.selectEnsembleMember(setting["ensembleMember"]) # Creates copy of motor - motorDispersion = self.motor + motor_dispersion = self.motor # Apply motor parameters variations on each iteration if possible # TODO: add hybrid motor option - motorDispersion = SolidMotor( + motor_dispersion = SolidMotor( thrustSource=setting["thrust"], burnOut=setting["burnOutTime"], grainNumber=setting["grainNumber"], @@ -675,11 +677,11 @@ def run_dispersion( ) # Creates copy of rocket - rocketDispersion = self.rocket + rocket_dispersion = self.rocket # Apply rocket parameters variations on each iteration if possible - rocketDispersion = Rocket( - motor=motorDispersion, + rocket_dispersion = Rocket( + motor=motor_dispersion, mass=setting["mass"], inertiaI=setting["inertiaI"], inertiaZ=setting["inertiaZ"], @@ -691,12 +693,12 @@ def run_dispersion( ) # Add rocket nose, fins and tail - rocketDispersion.addNose( + rocket_dispersion.addNose( length=setting["noseLength"], kind=setting["noseKind"], distanceToCM=setting["noseDistanceToCM"], ) - rocketDispersion.addFins( + rocket_dispersion.addFins( n=setting["numberOfFins"], rootChord=setting["rootChord"], tipChord=setting["tipChord"], @@ -706,7 +708,7 @@ def run_dispersion( airfoil=setting["airfoil"], ) if not "noTail" in setting: - rocketDispersion.addTail( + rocket_dispersion.addTail( topRadius=setting["topRadius"], bottomRadius=setting["bottomRadius"], length=setting["length"], @@ -715,7 +717,7 @@ def run_dispersion( # Add parachutes for num, name in enumerate(self.parachute_names): - rocketDispersion.addParachute( + rocket_dispersion.addParachute( name=name, CdS=setting["parachute_" + name + "_CdS"], trigger=setting["parachute_" + name + "_trigger"], @@ -728,7 +730,7 @@ def run_dispersion( ), ) - rocketDispersion.setRailButtons( + rocket_dispersion.setRailButtons( distanceToCM=[ setting["positionFirstRailButton"], setting["positionSecondRailButton"], @@ -739,8 +741,8 @@ def run_dispersion( # Run trajectory simulation try: TestFlight = Flight( - rocket=rocketDispersion, - environment=envDispersion, + rocket=rocket_dispersion, + environment=env_dispersion, inclination=setting["inclination"], heading=setting["heading"], # initialSolution=setting["initialSolution"] if "initialSolution" in setting else self.flight.initialSolution, @@ -755,11 +757,11 @@ def run_dispersion( ) self.export_flight_data( - setting, - TestFlight, - process_time() - start_time, - dispersion_input_file, - dispersion_output_file, + flight_setting=setting, + flight_data=TestFlight, + exec_time=process_time() - self.start_time, + dispersion_input_file=dispersion_input_file, + dispersion_output_file=dispersion_output_file, ) except Exception as E: print(E) @@ -797,7 +799,7 @@ def import_results(self, dispersion_output_file): Returns ------- - _type_ + dispersion_results: dict _description_ """ @@ -848,11 +850,21 @@ def import_results(self, dispersion_output_file): dispersion_output_file.close() # Number of flights simulated - self.N = len(dispersion_general_results) + self.number_of_loaded_simulations = len(dispersion_general_results) return dispersion_results - def meanOutOfRailTime(self, dispersion_results): + # Start the processing analysis + + @cached_property + def mean_out_of_rail_time(dispersion_results): + return np.mean(dispersion_results["outOfRailTime"]) + + @cached_property + def std_out_of_rail_time(dispersion_results): + return np.std(dispersion_results["outOfRailTime"]) + + def printMeanOutOfRailTime(self, dispersion_results): """_summary_ Parameters @@ -865,10 +877,10 @@ def meanOutOfRailTime(self, dispersion_results): None """ print( - f'Out of Rail Time - Mean Value: {np.mean(dispersion_results["outOfRailTime"]):0.3f} s' + f"Out of Rail Time - Mean Value: {self.mean_out_of_rail_time(dispersion_results):0.3f} s" ) print( - f'Out of Rail Time - Standard Deviation: {np.std(dispersion_results["outOfRailTime"]):0.3f} s' + f"Out of Rail Time - Standard Deviation: {self.std_out_of_rail_time(dispersion_results):0.3f} s" ) return None @@ -889,10 +901,13 @@ def plotOutOfRailTime(self, dispersion_results): self.meanOutOfRailTime(dispersion_results) plt.figure() - plt.hist(dispersion_results["outOfRailTime"], bins=int(self.N**0.5)) + plt.hist( + dispersion_results["outOfRailTime"], + bins=int(self.number_of_loaded_simulations**0.5), + ) plt.title("Out of Rail Time") plt.xlabel("Time (s)") - plt.ylabel("Number of Occurences") + plt.ylabel("Number of Occurrences") plt.show() return None @@ -935,10 +950,13 @@ def plotOutOfRailVelocity(self, dispersion_results): self.meanOutOfRailVelocity(dispersion_results) plt.figure() - plt.hist(dispersion_results["outOfRailVelocity"], bins=int(self.N**0.5)) + plt.hist( + dispersion_results["outOfRailVelocity"], + bins=int(self.number_of_loaded_simulations**0.5), + ) plt.title("Out of Rail Velocity") plt.xlabel("Velocity (m/s)") - plt.ylabel("Number of Occurences") + plt.ylabel("Number of Occurrences") plt.show() return None @@ -981,10 +999,13 @@ def plotApogeeTime(self, dispersion_results): self.meanApogeeTime(dispersion_results) plt.figure() - plt.hist(dispersion_results["impactTime"], bins=int(self.N**0.5)) + plt.hist( + dispersion_results["impactTime"], + bins=int(self.number_of_loaded_simulations**0.5), + ) plt.title("Impact Time") plt.xlabel("Time (s)") - plt.ylabel("Number of Occurences") + plt.ylabel("Number of Occurrences") plt.show() return None @@ -1027,10 +1048,13 @@ def plotApogeeAltitude(self, dispersion_results): self.meanApogeeAltitude(dispersion_results) plt.figure() - plt.hist(dispersion_results["apogeeAltitude"], bins=int(self.N**0.5)) + plt.hist( + dispersion_results["apogeeAltitude"], + bins=int(self.number_of_loaded_simulations**0.5), + ) plt.title("Apogee Altitude") plt.xlabel("Altitude (m)") - plt.ylabel("Number of Occurences") + plt.ylabel("Number of Occurrences") plt.show() return None @@ -1073,10 +1097,13 @@ def plotApogeeXPosition(self, dispersion_results): self.meanApogeeAltitude(dispersion_results) plt.figure() - plt.hist(dispersion_results["apogeeX"], bins=int(self.N**0.5)) + plt.hist( + dispersion_results["apogeeX"], + bins=int(self.number_of_loaded_simulations**0.5), + ) plt.title("Apogee X Position") plt.xlabel("Apogee X Position (m)") - plt.ylabel("Number of Occurences") + plt.ylabel("Number of Occurrences") plt.show() return None @@ -1119,10 +1146,13 @@ def plotApogeeYPosition(self, dispersion_results): self.meanApogeeAltitude(dispersion_results) plt.figure() - plt.hist(dispersion_results["apogeeY"], bins=int(self.N**0.5)) + plt.hist( + dispersion_results["apogeeY"], + bins=int(self.number_of_loaded_simulations**0.5), + ) plt.title("Apogee Y Position") plt.xlabel("Apogee Y Position (m)") - plt.ylabel("Number of Occurences") + plt.ylabel("Number of Occurrences") plt.show() return None @@ -1165,10 +1195,13 @@ def plotImpactTime(self, dispersion_results): self.meanImpactTime(dispersion_results) plt.figure() - plt.hist(dispersion_results["impactTime"], bins=int(self.N**0.5)) + plt.hist( + dispersion_results["impactTime"], + bins=int(self.number_of_loaded_simulations**0.5), + ) plt.title("Impact Time") plt.xlabel("Time (s)") - plt.ylabel("Number of Occurences") + plt.ylabel("Number of Occurrences") plt.show() return None @@ -1211,10 +1244,13 @@ def plotImpactXPosition(self, dispersion_results): self.meanImpactXPosition(dispersion_results) plt.figure() - plt.hist(dispersion_results["impactX"], bins=int(self.N**0.5)) + plt.hist( + dispersion_results["impactX"], + bins=int(self.number_of_loaded_simulations**0.5), + ) plt.title("Impact X Position") plt.xlabel("Impact X Position (m)") - plt.ylabel("Number of Occurences") + plt.ylabel("Number of Occurrences") plt.show() return None @@ -1257,10 +1293,13 @@ def plotImpactYPosition(self, dispersion_results): self.meanImpactYPosition(dispersion_results) plt.figure() - plt.hist(dispersion_results["impactY"], bins=int(self.N**0.5)) + plt.hist( + dispersion_results["impactY"], + bins=int(self.number_of_loaded_simulations**0.5), + ) plt.title("Impact Y Position") plt.xlabel("Impact Y Position (m)") - plt.ylabel("Number of Occurences") + plt.ylabel("Number of Occurrences") plt.show() return None @@ -1303,11 +1342,14 @@ def plotImpactVelocity(self, dispersion_results): self.meanImpactVelocity(dispersion_results) plt.figure() - plt.hist(dispersion_results["impactVelocity"], bins=int(self.N**0.5)) + plt.hist( + dispersion_results["impactVelocity"], + bins=int(self.number_of_loaded_simulations**0.5), + ) plt.title("Impact Velocity") plt.xlim(-35, 0) plt.xlabel("Velocity (m/s)") - plt.ylabel("Number of Occurences") + plt.ylabel("Number of Occurrences") plt.show() return None @@ -1367,22 +1409,22 @@ def plotStaticMargin(self, dispersion_results): plt.hist( dispersion_results["initialStaticMargin"], label="Initial", - bins=int(self.N**0.5), + bins=int(self.number_of_loaded_simulations**0.5), ) plt.hist( dispersion_results["outOfRailStaticMargin"], label="Out of Rail", - bins=int(self.N**0.5), + bins=int(self.number_of_loaded_simulations**0.5), ) plt.hist( dispersion_results["finalStaticMargin"], label="Final", - bins=int(self.N**0.5), + bins=int(self.number_of_loaded_simulations**0.5), ) plt.legend() plt.title("Static Margin") plt.xlabel("Static Margin (c)") - plt.ylabel("Number of Occurences") + plt.ylabel("Number of Occurrences") plt.show() return None @@ -1425,10 +1467,13 @@ def plotMaximumVelocity(self, dispersion_results): self.meanMaximumVelocity(dispersion_results) plt.figure() - plt.hist(dispersion_results["maxVelocity"], bins=int(self.N**0.5)) + plt.hist( + dispersion_results["maxVelocity"], + bins=int(self.number_of_loaded_simulations**0.5), + ) plt.title("Maximum Velocity") plt.xlabel("Velocity (m/s)") - plt.ylabel("Number of Occurences") + plt.ylabel("Number of Occurrences") plt.show() return None @@ -1474,7 +1519,7 @@ def plotNumberOfParachuteEvents(self, dispersion_results): plt.hist(dispersion_results["numberOfEvents"]) plt.title("Parachute Events") plt.xlabel("Number of Parachute Events") - plt.ylabel("Number of Occurences") + plt.ylabel("Number of Occurrences") plt.show() return None @@ -1517,10 +1562,13 @@ def plotDrogueTriggerTime(self, dispersion_results): self.meanDrogueTriggerTime(dispersion_results) plt.figure() - plt.hist(dispersion_results["drogueTriggerTime"], bins=int(self.N**0.5)) + plt.hist( + dispersion_results["drogueTriggerTime"], + bins=int(self.number_of_loaded_simulations**0.5), + ) plt.title("Drogue Trigger Time") plt.xlabel("Time (s)") - plt.ylabel("Number of Occurences") + plt.ylabel("Number of Occurrences") plt.show() return None @@ -1563,10 +1611,13 @@ def plotDrogueFullyInflatedTime(self, dispersion_results): self.meanDrogueFullyInflatedTime(dispersion_results) plt.figure() - plt.hist(dispersion_results["drogueInflatedTime"], bins=int(self.N**0.5)) + plt.hist( + dispersion_results["drogueInflatedTime"], + bins=int(self.number_of_loaded_simulations**0.5), + ) plt.title("Drogue Fully Inflated Time") plt.xlabel("Time (s)") - plt.ylabel("Number of Occurences") + plt.ylabel("Number of Occurrences") plt.show() return None @@ -1609,10 +1660,13 @@ def plotDrogueFullyVelocity(self, dispersion_results): self.meanDrogueFullyVelocity(dispersion_results) plt.figure() - plt.hist(dispersion_results["drogueInflatedVelocity"], bins=int(self.N**0.5)) + plt.hist( + dispersion_results["drogueInflatedVelocity"], + bins=int(self.number_of_loaded_simulations**0.5), + ) plt.title("Drogue Parachute Fully Inflated Velocity") plt.xlabel("Velocity m/s)") - plt.ylabel("Number of Occurences") + plt.ylabel("Number of Occurrences") plt.show() return None @@ -1959,10 +2013,13 @@ def plotLateralWindSpeed(self, dispersion_results): self.meanLateralWindSpeed(dispersion_results) plt.figure() - plt.hist(dispersion_results["lateralWind"], bins=int(self.N**0.5)) + plt.hist( + dispersion_results["lateralWind"], + bins=int(self.number_of_loaded_simulations**0.5), + ) plt.title("Lateral Surface Wind Speed") plt.xlabel("Velocity (m/s)") - plt.ylabel("Number of Occurences") + plt.ylabel("Number of Occurrences") plt.show() def meanFrontalWindSpeed(self, dispersion_results): @@ -1991,10 +2048,13 @@ def plotFrontalWindSpeed(self, dispersion_results): self.meanFrontalWindSpeed(dispersion_results) plt.figure() - plt.hist(dispersion_results["frontalWind"], bins=int(self.N**0.5)) + plt.hist( + dispersion_results["frontalWind"], + bins=int(self.number_of_loaded_simulations**0.5), + ) plt.title("Frontal Surface Wind Speed") plt.xlabel("Velocity (m/s)") - plt.ylabel("Number of Occurences") + plt.ylabel("Number of Occurrences") plt.show() def info(self): @@ -2002,8 +2062,7 @@ def info(self): Returns ------- - _type_ - _description_ + None """ dispersion_results = self.import_results(self.filename) @@ -2018,7 +2077,7 @@ def info(self): self.meanFrontalWindSpeed(dispersion_results) - self.meanOutOfRailTime(dispersion_results) + self.printMeanOutOfRailTime(dispersion_results) self.meanApogeeTime(dispersion_results) @@ -2102,7 +2161,7 @@ def allInfo(self): "timeZone": "UTC", } - solidmotor_inputs = { + solid_motor_inputs = { "thrust": "required", "burnOutTime": "required", "totalImpulse": 0, From a273350f81b6c8ef22499881a92defe32be75a5e Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 10 Oct 2022 02:12:53 +0200 Subject: [PATCH 06/68] MAINT: Moving functions from utilities to suppleme --- rocketpy/Dispersion.py | 12 +- rocketpy/Environment.py | 8 +- rocketpy/Flight.py | 2 +- rocketpy/__init__.py | 1 + rocketpy/supplement.py | 426 ++++++++++++++++++++++++++++++++++++++++ rocketpy/utilities.py | 422 --------------------------------------- 6 files changed, 437 insertions(+), 434 deletions(-) create mode 100644 rocketpy/supplement.py diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index 1c71484b5..8c4c009d6 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -1,7 +1,9 @@ # -*- coding: utf-8 -*- -__author__ = "Mateus Stano Junqueira, Sofia Lopes Suesdek Rocha, Guilherme Fernandes Alves, Bruno Abdulklech Sorban" -__copyright__ = "Copyright 20XX, Projeto Jupiter" +__author__ = ( + "Mateus Stano Junqueira, Sofia Lopes Suesdek Rocha, Guilherme Fernandes Alves, Bruno Abdulklech Sorban" +) +__copyright__ = "Copyright 20XX, RocketPy Team" __license__ = "MIT" @@ -19,15 +21,13 @@ from matplotlib.patches import Ellipse from numpy.random import * -from .Environment import Environment from .Flight import Flight from .Function import Function -from .Motor import HybridMotor, SolidMotor +from .Motor import SolidMotor from .Rocket import Rocket -from .utilities import invertedHaversine +from .supplement import invertedHaversine ## Tasks from the first review: -# TODO: Move some functions from utilities to supplement.py # TODO: Save instances of the class instead of just plotting # TODO: Document all methods # TODO: Create a way to choose what attributes are being saved diff --git a/rocketpy/Environment.py b/rocketpy/Environment.py index 0103c8421..7bee7c3f8 100644 --- a/rocketpy/Environment.py +++ b/rocketpy/Environment.py @@ -1,7 +1,5 @@ # -*- coding: utf-8 -*- -from .Function import Function - __author__ = "Giovani Hidalgo Ceotto, Guilherme Fernandes Alves, Lucas Azevedo Pezente, Oscar Mauricio Prada Ramirez, Lucas Kierulff Balabram" __copyright__ = "Copyright 20XX, RocketPy Team" __license__ = "MIT" @@ -18,6 +16,9 @@ import pytz import requests +from .Function import Function +from .supplement import geodesicToUtm, calculateEarthRadius + try: import netCDF4 except ImportError: @@ -3745,6 +3746,3 @@ def printEarthDetails(self): print("Gravity acceleration at launch site: Still not implemented :(") return None - - -from .utilities import calculateEarthRadius, geodesicToUtm diff --git a/rocketpy/Flight.py b/rocketpy/Flight.py index 45b7d37ee..6e9d63bd3 100644 --- a/rocketpy/Flight.py +++ b/rocketpy/Flight.py @@ -17,7 +17,7 @@ from scipy import integrate from .Function import Function -from .utilities import * +from .supplement import * class Flight: diff --git a/rocketpy/__init__.py b/rocketpy/__init__.py index 02129af66..9c61b966c 100644 --- a/rocketpy/__init__.py +++ b/rocketpy/__init__.py @@ -30,3 +30,4 @@ from .Motor import HybridMotor, SolidMotor from .Rocket import Rocket from .utilities import * +from .supplement import * diff --git a/rocketpy/supplement.py b/rocketpy/supplement.py new file mode 100644 index 000000000..7776af01f --- /dev/null +++ b/rocketpy/supplement.py @@ -0,0 +1,426 @@ +# -*- coding: utf-8 -*- +__author__ = "Guilherme Fernandes Alves" +__copyright__ = "Copyright 20XX, RocketPy Team" +__license__ = "MIT" + + +import numpy as np +import math + +# Geodesic calculations functions + + +def calculateEarthRadius(lat, datum="WGS84"): + """Simple function to calculate the Earth Radius at a specific latitude + based on ellipsoidal reference model (datum). The earth radius here is + assumed as the distance between the ellipsoid's center of gravity and a + point on ellipsoid surface at the desired + Pay attention: The ellipsoid is an approximation for the earth model and + will obviously output an estimate of the perfect distance between earth's + relief and its center of gravity. + + Parameters + ---------- + lat : float + latitude in which the Earth radius will be calculated + datum : string, optional + The desired reference ellipsoid model, the following options are + available: "SAD69", "WGS84", "NAD83", and "SIRGAS2000". The default + is "WSG84", then this model will be used if the user make some + typing mistake. + + Returns + ------- + float: + Earth Radius at the desired latitude in meters + """ + # Select the desired datum (i.e. the ellipsoid parameters) + if datum == "SAD69": + semiMajorAxis = 6378160.0 + flattening = 1 / 298.25 + elif datum == "SIRGAS2000": + semiMajorAxis = 6378137.0 + flattening = 1 / 298.257223563 + elif datum == "NAD83": + semiMajorAxis = 6378137.0 + flattening = 1 / 298.257024899 + else: + # Use WGS84 as default + semiMajorAxis = 6378137.0 + flattening = 1 / 298.257223563 + + # Calculate the semi minor axis length + semiMinorAxis = semiMajorAxis * (1 - flattening) + + # Convert latitude to radians + lat = lat * np.pi / 180 + + # Calculate the Earth Radius in meters + eRadius = np.sqrt( + ( + (np.cos(lat) * (semiMajorAxis**2)) ** 2 + + (np.sin(lat) * (semiMinorAxis**2)) ** 2 + ) + / ((np.cos(lat) * semiMajorAxis) ** 2 + (np.sin(lat) * semiMinorAxis) ** 2) + ) + + # Convert latitude back to degrees + lat = lat * 180 / np.pi + + return eRadius + + +def Haversine(lat0, lon0, lat1, lon1, eRadius=6.3781e6): + """Returns the distance between two points in meters. + The points are defined by their latitude and longitude coordinates. + + Parameters + ---------- + lat0 : float + Latitude of the first point, in degrees. + lon0 : float + Longitude of the first point, in degrees. + lat1 : float + Latitude of the second point, in degrees. + lon1 : float + Longitude of the second point, in degrees. + eRadius : float, optional + Earth's radius in meters. Default value is 6.3781e6. + + Returns + ------- + float + Distance between the two points in meters. + + """ + lat0_rad = math.radians(lat0) + lat1_rad = math.radians(lat1) + delta_lat_rad = math.radians(lat1 - lat0) + delta_lon_rad = math.radians(lon1 - lon0) + + a = ( + math.sin(delta_lat_rad / 2) ** 2 + + math.cos(lat0_rad) * math.cos(lat1_rad) * math.sin(delta_lon_rad / 2) ** 2 + ) + c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a)) + + return eRadius * c + + +def invertedHaversine(lat0, lon0, distance, bearing, eRadius=6.3781e6): + """Returns a tuple with new latitude and longitude coordinates considering + a displacement of a given distance in a given direction (bearing compass) + starting from a point defined by (lat0, lon0). This is the opposite of + Haversine function. + + Parameters + ---------- + lat0 : float + Origin latitude coordinate, in degrees. + lon0 : float + Origin longitude coordinate, in degrees. + distance : float + Distance from the origin point, in meters. + bearing : float + Azimuth (or bearing compass) from the origin point, in degrees. + eRadius : float, optional + Earth radius, in meters. Default value is 6.3781e6. + See the utilities.calculateEarthRadius() function for more accuracy. + + Returns + ------- + lat1 : float + New latitude coordinate, in degrees. + lon1 : float + New longitude coordinate, in degrees. + + """ + + # Convert coordinates to radians + lat0_rad = np.deg2rad(lat0) + lon0_rad = np.deg2rad(lon0) + + # Apply inverted Haversine formula + lat1_rad = math.asin( + math.sin(lat0_rad) * math.cos(distance / eRadius) + + math.cos(lat0_rad) * math.sin(distance / eRadius) * math.cos(bearing) + ) + + lon1_rad = lon0_rad + math.atan2( + math.sin(bearing) * math.sin(distance / eRadius) * math.cos(lat0_rad), + math.cos(distance / eRadius) - math.sin(lat0_rad) * math.sin(lat1_rad), + ) + + # Convert back to degrees and then return + lat1_deg = np.rad2deg(lat1_rad) + lon1_deg = np.rad2deg(lon1_rad) + + return lat1_deg, lon1_deg + + +def decimalDegreesToArcSeconds(angle): + """Function to convert an angle in decimal degrees to deg/min/sec. + Converts (°) to (° ' ") + + Parameters + ---------- + angle : float + The angle that you need convert to deg/min/sec. Must be given in + decimal degrees. + + Returns + ------- + deg: float + The degrees. + min: float + The arc minutes. 1 arc-minute = (1/60)*degree + sec: float + The arc Seconds. 1 arc-second = (1/3600)*degree + """ + + if angle < 0: + signal = -1 + else: + signal = 1 + + deg = (signal * angle) // 1 + min = abs(signal * angle - deg) * 60 // 1 + sec = abs((signal * angle - deg) * 60 - min) * 60 + + return deg, min, sec + + +def geodesicToUtm(lat, lon, datum="WGS84"): + """Function which converts geodesic coordinates, i.e. lat/lon, to UTM + projection coordinates. Can be used only for latitudes between -80.00° + and 84.00° + + Parameters + ---------- + lat : float + The latitude coordinates of the point of analysis, must be contained + between -80.00° and 84.00° + lon : float + The longitude coordinates of the point of analysis, must be contained + between -180.00° and 180.00° + datum : string + The desired reference ellipsoid model, the following options are + available: "SAD69", "WGS84", "NAD83", and "SIRGAS2000". The default + is "WGS84", then this model will be used if the user make some + typing mistake + + Returns + ------- + x: float + East coordinate in meters, always positive + y: + North coordinate in meters, always positive + utmZone: int + The number of the UTM zone of the point of analysis, can vary between + 1 and 60 + utmLetter: string + The letter of the UTM zone of the point of analysis, can vary between + C and X, omitting the letters "I" and "O" + NorthSouthHemisphere: string + Returns "S" for southern hemisphere and "N" for Northern hemisphere + EastWestHemisphere: string + Returns "W" for western hemisphere and "E" for eastern hemisphere + """ + + # Calculate the central meridian of UTM zone + if lon != 0: + # signal = 1 for positive longitude and -1 for negative longitude + signal = lon / abs(lon) + if signal > 0: + aux = lon - 3 + aux = aux * signal + div = aux // 6 + lon_mc = div * 6 + 3 + EastWestHemisphere = "E" # Eastern hemisphere + else: + aux = lon + 3 + aux = aux * signal + div = aux // 6 + lon_mc = (div * 6 + 3) * signal + EastWestHemisphere = "W" # Western hemisphere + else: + # If the longitude is zero, the central meridian receives number 3 + lon_mc = 3 + EastWestHemisphere = "W|E" + + # Select the desired datum (i.e. the ellipsoid parameters) + # TODO: Create separate function that returns the ellipsoid parameters + if datum == "SAD69": + semiMajorAxis = 6378160.0 + flattening = 1 / 298.25 + elif datum == "SIRGAS2000": + semiMajorAxis = 6378137.0 + flattening = 1 / 298.257223563 + elif datum == "NAD83": + semiMajorAxis = 6378137.0 + flattening = 1 / 298.257024899 + else: + # WGS84 + semiMajorAxis = 6378137.0 + flattening = 1 / 298.257223563 + + # Evaluate the S/N hemisphere and determine the N coordinate at the Equator + if lat < 0: + N0 = 10000000 + NorthSouthHemisphere = "S" + else: + N0 = 0 + NorthSouthHemisphere = "N" + + # Convert the input lat and lon to radians + lat = lat * np.pi / 180 + lon = lon * np.pi / 180 + lon_mc = lon_mc * np.pi / 180 + + # Evaluate reference parameters + K0 = 1 - 1 / 2500 + e2 = 2 * flattening - flattening**2 + e2lin = e2 / (1 - e2) + + # Evaluate auxiliary parameters + A = e2 * e2 + B = A * e2 + C = np.sin(2 * lat) + D = np.sin(4 * lat) + E = np.sin(6 * lat) + F = (1 - e2 / 4 - 3 * A / 64 - 5 * B / 256) * lat + G = (3 * e2 / 8 + 3 * A / 32 + 45 * B / 1024) * C + H = (15 * A / 256 + 45 * B / 1024) * D + I = (35 * B / 3072) * E + + # Evaluate other reference parameters + n = semiMajorAxis / ((1 - e2 * (np.sin(lat) ** 2)) ** 0.5) + t = np.tan(lat) ** 2 + c = e2lin * (np.cos(lat) ** 2) + ag = (lon - lon_mc) * np.cos(lat) + m = semiMajorAxis * (F - G + H - I) + + # Evaluate new auxiliary parameters + J = (1 - t + c) * ag * ag * ag / 6 + K = (5 - 18 * t + t * t + 72 * c - 58 * e2lin) * (ag**5) / 120 + L = (5 - t + 9 * c + 4 * c * c) * ag * ag * ag * ag / 24 + M = (61 - 58 * t + t * t + 600 * c - 330 * e2lin) * (ag**6) / 720 + + # Evaluate the final coordinates + x = 500000 + K0 * n * (ag + J + K) + y = N0 + K0 * (m + n * np.tan(lat) * (ag * ag / 2 + L + M)) + + # Convert the output lat and lon to degrees + lat = lat * 180 / np.pi + lon = lon * 180 / np.pi + lon_mc = lon_mc * 180 / np.pi + + # Calculate the UTM zone number + utmZone = int((lon_mc + 183) / 6) + + # Calculate the UTM zone letter + letters = "CDEFGHJKLMNPQRSTUVWXX" + utmLetter = letters[int(80 + lat) >> 3] + + return x, y, utmZone, utmLetter, NorthSouthHemisphere, EastWestHemisphere + + +def utmToGeodesic(x, y, utmZone, NorthSouthHemisphere, datum): + """Function to convert UTM coordinates to geodesic coordinates + (i.e. latitude and longitude). The latitude should be between -80° + and 84° + + Parameters + ---------- + x : float + East UTM coordinate in meters + y : float + North UTM coordinate in meters + utmZone : int + The number of the UTM zone of the point of analysis, can vary between + 1 and 60 + NorthSouthHemisphere : string + Equals to "S" for southern hemisphere and "N" for Northern hemisphere + datum : string + The desired reference ellipsoid model, the following options are + available: "SAD69", "WGS84", "NAD83", and "SIRGAS2000". The default + is "WGS84", then this model will be used if the user make some + typing mistake + + Returns + ------- + lat: float + latitude of the analyzed point + lon: float + latitude of the analyzed point + """ + + if NorthSouthHemisphere == "N": + y = y + 10000000 + + # Calculate the Central Meridian from the UTM zone number + centralMeridian = utmZone * 6 - 183 # degrees + + # Select the desired datum + if datum == "SAD69": + semiMajorAxis = 6378160.0 + flattening = 1 / 298.25 + elif datum == "SIRGAS2000": + semiMajorAxis = 6378137.0 + flattening = 1 / 298.257223563 + elif datum == "NAD83": + semiMajorAxis = 6378137.0 + flattening = 1 / 298.257024899 + else: + # WGS84 + semiMajorAxis = 6378137.0 + flattening = 1 / 298.257223563 + + # Calculate reference values + K0 = 1 - 1 / 2500 + e2 = 2 * flattening - flattening**2 + e2lin = e2 / (1 - e2) + e1 = (1 - (1 - e2) ** 0.5) / (1 + (1 - e2) ** 0.5) + + # Calculate auxiliary values + A = e2 * e2 + B = A * e2 + C = e1 * e1 + D = e1 * C + E = e1 * D + + m = (y - 10000000) / K0 + mi = m / (semiMajorAxis * (1 - e2 / 4 - 3 * A / 64 - 5 * B / 256)) + + # Calculate other auxiliary values + F = (3 * e1 / 2 - 27 * D / 32) * np.sin(2 * mi) + G = (21 * C / 16 - 55 * E / 32) * np.sin(4 * mi) + H = (151 * D / 96) * np.sin(6 * mi) + + lat1 = mi + F + G + H + c1 = e2lin * (np.cos(lat1) ** 2) + t1 = np.tan(lat1) ** 2 + n1 = semiMajorAxis / ((1 - e2 * (np.sin(lat1) ** 2)) ** 0.5) + qc = (1 - e2 * np.sin(lat1) * np.sin(lat1)) ** 3 + r1 = semiMajorAxis * (1 - e2) / (qc**0.5) + d = (x - 500000) / (n1 * K0) + + # Calculate other auxiliary values + I = (5 + 3 * t1 + 10 * c1 - 4 * c1 * c1 - 9 * e2lin) * d * d * d * d / 24 + J = ( + (61 + 90 * t1 + 298 * c1 + 45 * t1 * t1 - 252 * e2lin - 3 * c1 * c1) + * (d**6) + / 720 + ) + K = d - (1 + 2 * t1 + c1) * d * d * d / 6 + L = (5 - 2 * c1 + 28 * t1 - 3 * c1 * c1 + 8 * e2lin + 24 * t1 * t1) * (d**5) / 120 + + # Finally calculate the coordinates in lat/lot + lat = lat1 - (n1 * np.tan(lat1) / r1) * (d * d / 2 - I + J) + lon = centralMeridian * np.pi / 180 + (K + L) / np.cos(lat1) + + # Convert final lat/lon to Degrees + lat = lat * 180 / np.pi + lon = lon * 180 / np.pi + + return lat, lon diff --git a/rocketpy/utilities.py b/rocketpy/utilities.py index 7de584db4..2664fd0b8 100644 --- a/rocketpy/utilities.py +++ b/rocketpy/utilities.py @@ -2,11 +2,8 @@ __author__ = "Franz Masatoshi Yuri, Lucas Kierulff Balabram, Guilherme Fernandes Alves, Bruno Abdulklech Sorban" __copyright__ = "Copyright 20XX, RocketPy Team" __license__ = "MIT" -import math -import matplotlib.pyplot as plt import numpy as np -from matplotlib.patches import Ellipse from scipy.integrate import solve_ivp from .Environment import Environment @@ -241,422 +238,3 @@ def create_dispersion_dictionary(filename): except: analysis_parameters[row[0].strip()] = "" return analysis_parameters - - -# Geodesic calculations functions - - -def calculateEarthRadius(lat, datum="WGS84"): - """Simple function to calculate the Earth Radius at a specific latitude - based on ellipsoidal reference model (datum). The earth radius here is - assumed as the distance between the ellipsoid's center of gravity and a - point on ellipsoid surface at the desired - Pay attention: The ellipsoid is an approximation for the earth model and - will obviously output an estimate of the perfect distance between earth's - relief and its center of gravity. - - Parameters - ---------- - lat : float - latitude in which the Earth radius will be calculated - datum : string, optional - The desired reference ellipsoid model, the following options are - available: "SAD69", "WGS84", "NAD83", and "SIRGAS2000". The default - is "WSG84", then this model will be used if the user make some - typing mistake. - - Returns - ------- - float: - Earth Radius at the desired latitude in meters - """ - # Select the desired datum (i.e. the ellipsoid parameters) - if datum == "SAD69": - semiMajorAxis = 6378160.0 - flattening = 1 / 298.25 - elif datum == "SIRGAS2000": - semiMajorAxis = 6378137.0 - flattening = 1 / 298.257223563 - elif datum == "NAD83": - semiMajorAxis = 6378137.0 - flattening = 1 / 298.257024899 - else: - # Use WGS84 as default - semiMajorAxis = 6378137.0 - flattening = 1 / 298.257223563 - - # Calculate the semi minor axis length - semiMinorAxis = semiMajorAxis * (1 - flattening) - - # Convert latitude to radians - lat = lat * np.pi / 180 - - # Calculate the Earth Radius in meters - eRadius = np.sqrt( - ( - (np.cos(lat) * (semiMajorAxis**2)) ** 2 - + (np.sin(lat) * (semiMinorAxis**2)) ** 2 - ) - / ((np.cos(lat) * semiMajorAxis) ** 2 + (np.sin(lat) * semiMinorAxis) ** 2) - ) - - # Convert latitude back to degrees - lat = lat * 180 / np.pi - - return eRadius - - -def Haversine(lat0, lon0, lat1, lon1, eRadius=6.3781e6): - """Returns the distance between two points in meters. - The points are defined by their latitude and longitude coordinates. - - Parameters - ---------- - lat0 : float - Latitude of the first point, in degrees. - lon0 : float - Longitude of the first point, in degrees. - lat1 : float - Latitude of the second point, in degrees. - lon1 : float - Longitude of the second point, in degrees. - eRadius : float, optional - Earth's radius in meters. Default value is 6.3781e6. - - Returns - ------- - float - Distance between the two points in meters. - - """ - lat0_rad = math.radians(lat0) - lat1_rad = math.radians(lat1) - delta_lat_rad = math.radians(lat1 - lat0) - delta_lon_rad = math.radians(lon1 - lon0) - - a = ( - math.sin(delta_lat_rad / 2) ** 2 - + math.cos(lat0_rad) * math.cos(lat1_rad) * math.sin(delta_lon_rad / 2) ** 2 - ) - c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a)) - - return eRadius * c - - -def invertedHaversine(lat0, lon0, distance, bearing, eRadius=6.3781e6): - """Returns a tuple with new latitude and longitude coordinates considering - a displacement of a given distance in a given direction (bearing compass) - starting from a point defined by (lat0, lon0). This is the opposite of - Haversine function. - - Parameters - ---------- - lat0 : float - Origin latitude coordinate, in degrees. - lon0 : float - Origin longitude coordinate, in degrees. - distance : float - Distance from the origin point, in meters. - bearing : float - Azimuth (or bearing compass) from the origin point, in degrees. - eRadius : float, optional - Earth radius, in meters. Default value is 6.3781e6. - See the utilities.calculateEarthRadius() function for more accuracy. - - Returns - ------- - lat1 : float - New latitude coordinate, in degrees. - lon1 : float - New longitude coordinate, in degrees. - - """ - - # Convert coordinates to radians - lat0_rad = np.deg2rad(lat0) - lon0_rad = np.deg2rad(lon0) - - # Apply inverted Haversine formula - lat1_rad = math.asin( - math.sin(lat0_rad) * math.cos(distance / eRadius) - + math.cos(lat0_rad) * math.sin(distance / eRadius) * math.cos(bearing) - ) - - lon1_rad = lon0_rad + math.atan2( - math.sin(bearing) * math.sin(distance / eRadius) * math.cos(lat0_rad), - math.cos(distance / eRadius) - math.sin(lat0_rad) * math.sin(lat1_rad), - ) - - # Convert back to degrees and then return - lat1_deg = np.rad2deg(lat1_rad) - lon1_deg = np.rad2deg(lon1_rad) - - return lat1_deg, lon1_deg - - -def decimalDegreesToArcSeconds(angle): - """Function to convert an angle in decimal degrees to deg/min/sec. - Converts (°) to (° ' ") - - Parameters - ---------- - angle : float - The angle that you need convert to deg/min/sec. Must be given in - decimal degrees. - - Returns - ------- - deg: float - The degrees. - min: float - The arc minutes. 1 arc-minute = (1/60)*degree - sec: float - The arc Seconds. 1 arc-second = (1/3600)*degree - """ - - if angle < 0: - signal = -1 - else: - signal = 1 - - deg = (signal * angle) // 1 - min = abs(signal * angle - deg) * 60 // 1 - sec = abs((signal * angle - deg) * 60 - min) * 60 - - return deg, min, sec - - -def geodesicToUtm(lat, lon, datum="WGS84"): - """Function which converts geodesic coordinates, i.e. lat/lon, to UTM - projection coordinates. Can be used only for latitudes between -80.00° - and 84.00° - - Parameters - ---------- - lat : float - The latitude coordinates of the point of analysis, must be contained - between -80.00° and 84.00° - lon : float - The longitude coordinates of the point of analysis, must be contained - between -180.00° and 180.00° - datum : string - The desired reference ellipsoid model, the following options are - available: "SAD69", "WGS84", "NAD83", and "SIRGAS2000". The default - is "WGS84", then this model will be used if the user make some - typing mistake - - Returns - ------- - x: float - East coordinate in meters, always positive - y: - North coordinate in meters, always positive - utmZone: int - The number of the UTM zone of the point of analysis, can vary between - 1 and 60 - utmLetter: string - The letter of the UTM zone of the point of analysis, can vary between - C and X, omitting the letters "I" and "O" - NorthSouthHemisphere: string - Returns "S" for southern hemisphere and "N" for Northern hemisphere - EastWestHemisphere: string - Returns "W" for western hemisphere and "E" for eastern hemisphere - """ - - # Calculate the central meridian of UTM zone - if lon != 0: - # signal = 1 for positive longitude and -1 for negative longitude - signal = lon / abs(lon) - if signal > 0: - aux = lon - 3 - aux = aux * signal - div = aux // 6 - lon_mc = div * 6 + 3 - EastWestHemisphere = "E" # Eastern hemisphere - else: - aux = lon + 3 - aux = aux * signal - div = aux // 6 - lon_mc = (div * 6 + 3) * signal - EastWestHemisphere = "W" # Western hemisphere - else: - # If the longitude is zero, the central meridian receives number 3 - lon_mc = 3 - EastWestHemisphere = "W|E" - - # Select the desired datum (i.e. the ellipsoid parameters) - # TODO: Create separate function that returns the ellipsoid parameters - if datum == "SAD69": - semiMajorAxis = 6378160.0 - flattening = 1 / 298.25 - elif datum == "SIRGAS2000": - semiMajorAxis = 6378137.0 - flattening = 1 / 298.257223563 - elif datum == "NAD83": - semiMajorAxis = 6378137.0 - flattening = 1 / 298.257024899 - else: - # WGS84 - semiMajorAxis = 6378137.0 - flattening = 1 / 298.257223563 - - # Evaluate the S/N hemisphere and determine the N coordinate at the Equator - if lat < 0: - N0 = 10000000 - NorthSouthHemisphere = "S" - else: - N0 = 0 - NorthSouthHemisphere = "N" - - # Convert the input lat and lon to radians - lat = lat * np.pi / 180 - lon = lon * np.pi / 180 - lon_mc = lon_mc * np.pi / 180 - - # Evaluate reference parameters - K0 = 1 - 1 / 2500 - e2 = 2 * flattening - flattening**2 - e2lin = e2 / (1 - e2) - - # Evaluate auxiliary parameters - A = e2 * e2 - B = A * e2 - C = np.sin(2 * lat) - D = np.sin(4 * lat) - E = np.sin(6 * lat) - F = (1 - e2 / 4 - 3 * A / 64 - 5 * B / 256) * lat - G = (3 * e2 / 8 + 3 * A / 32 + 45 * B / 1024) * C - H = (15 * A / 256 + 45 * B / 1024) * D - I = (35 * B / 3072) * E - - # Evaluate other reference parameters - n = semiMajorAxis / ((1 - e2 * (np.sin(lat) ** 2)) ** 0.5) - t = np.tan(lat) ** 2 - c = e2lin * (np.cos(lat) ** 2) - ag = (lon - lon_mc) * np.cos(lat) - m = semiMajorAxis * (F - G + H - I) - - # Evaluate new auxiliary parameters - J = (1 - t + c) * ag * ag * ag / 6 - K = (5 - 18 * t + t * t + 72 * c - 58 * e2lin) * (ag**5) / 120 - L = (5 - t + 9 * c + 4 * c * c) * ag * ag * ag * ag / 24 - M = (61 - 58 * t + t * t + 600 * c - 330 * e2lin) * (ag**6) / 720 - - # Evaluate the final coordinates - x = 500000 + K0 * n * (ag + J + K) - y = N0 + K0 * (m + n * np.tan(lat) * (ag * ag / 2 + L + M)) - - # Convert the output lat and lon to degrees - lat = lat * 180 / np.pi - lon = lon * 180 / np.pi - lon_mc = lon_mc * 180 / np.pi - - # Calculate the UTM zone number - utmZone = int((lon_mc + 183) / 6) - - # Calculate the UTM zone letter - letters = "CDEFGHJKLMNPQRSTUVWXX" - utmLetter = letters[int(80 + lat) >> 3] - - return x, y, utmZone, utmLetter, NorthSouthHemisphere, EastWestHemisphere - - -def utmToGeodesic(x, y, utmZone, NorthSouthHemisphere, datum): - """Function to convert UTM coordinates to geodesic coordinates - (i.e. latitude and longitude). The latitude should be between -80° - and 84° - - Parameters - ---------- - x : float - East UTM coordinate in meters - y : float - North UTM coordinate in meters - utmZone : int - The number of the UTM zone of the point of analysis, can vary between - 1 and 60 - NorthSouthHemisphere : string - Equals to "S" for southern hemisphere and "N" for Northern hemisphere - datum : string - The desired reference ellipsoid model, the following options are - available: "SAD69", "WGS84", "NAD83", and "SIRGAS2000". The default - is "WGS84", then this model will be used if the user make some - typing mistake - - Returns - ------- - lat: float - latitude of the analyzed point - lon: float - latitude of the analyzed point - """ - - if NorthSouthHemisphere == "N": - y = y + 10000000 - - # Calculate the Central Meridian from the UTM zone number - centralMeridian = utmZone * 6 - 183 # degrees - - # Select the desired datum - if datum == "SAD69": - semiMajorAxis = 6378160.0 - flattening = 1 / 298.25 - elif datum == "SIRGAS2000": - semiMajorAxis = 6378137.0 - flattening = 1 / 298.257223563 - elif datum == "NAD83": - semiMajorAxis = 6378137.0 - flattening = 1 / 298.257024899 - else: - # WGS84 - semiMajorAxis = 6378137.0 - flattening = 1 / 298.257223563 - - # Calculate reference values - K0 = 1 - 1 / 2500 - e2 = 2 * flattening - flattening**2 - e2lin = e2 / (1 - e2) - e1 = (1 - (1 - e2) ** 0.5) / (1 + (1 - e2) ** 0.5) - - # Calculate auxiliary values - A = e2 * e2 - B = A * e2 - C = e1 * e1 - D = e1 * C - E = e1 * D - - m = (y - 10000000) / K0 - mi = m / (semiMajorAxis * (1 - e2 / 4 - 3 * A / 64 - 5 * B / 256)) - - # Calculate other auxiliary values - F = (3 * e1 / 2 - 27 * D / 32) * np.sin(2 * mi) - G = (21 * C / 16 - 55 * E / 32) * np.sin(4 * mi) - H = (151 * D / 96) * np.sin(6 * mi) - - lat1 = mi + F + G + H - c1 = e2lin * (np.cos(lat1) ** 2) - t1 = np.tan(lat1) ** 2 - n1 = semiMajorAxis / ((1 - e2 * (np.sin(lat1) ** 2)) ** 0.5) - qc = (1 - e2 * np.sin(lat1) * np.sin(lat1)) ** 3 - r1 = semiMajorAxis * (1 - e2) / (qc**0.5) - d = (x - 500000) / (n1 * K0) - - # Calculate other auxiliary values - I = (5 + 3 * t1 + 10 * c1 - 4 * c1 * c1 - 9 * e2lin) * d * d * d * d / 24 - J = ( - (61 + 90 * t1 + 298 * c1 + 45 * t1 * t1 - 252 * e2lin - 3 * c1 * c1) - * (d**6) - / 720 - ) - K = d - (1 + 2 * t1 + c1) * d * d * d / 6 - L = (5 - 2 * c1 + 28 * t1 - 3 * c1 * c1 + 8 * e2lin + 24 * t1 * t1) * (d**5) / 120 - - # Finally calculate the coordinates in lat/lot - lat = lat1 - (n1 * np.tan(lat1) / r1) * (d * d / 2 - I + J) - lon = centralMeridian * np.pi / 180 + (K + L) / np.cos(lat1) - - # Convert final lat/lon to Degrees - lat = lat * 180 / np.pi - lon = lon * 180 / np.pi - - return lat, lon From 623c954736260a2d3ceb84b9b964823ee8ab8319 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 10 Oct 2022 02:17:12 +0200 Subject: [PATCH 07/68] FIX: adjust environment attributes names at Flight --- rocketpy/Flight.py | 8 ++++---- rocketpy/Rocket.py | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/rocketpy/Flight.py b/rocketpy/Flight.py index 6e9d63bd3..3b96ea6bc 100644 --- a/rocketpy/Flight.py +++ b/rocketpy/Flight.py @@ -2571,7 +2571,7 @@ def potentialEnergy(self): totalMass = self.rocket.totalMass grid = self.vx[:, 0] totalMass = Function(np.column_stack([grid, totalMass(grid)]), "Time (s)") - potentialEnergy = totalMass * self.env.g * self.z + potentialEnergy = totalMass * self.env.gravity * self.z potentialEnergy.setInputs("Time (s)") return potentialEnergy @@ -2989,7 +2989,7 @@ def latitude(self): latitude: Function Rocket latitude coordinate, in degrees, at each time step. """ - lat1 = np.deg2rad(self.env.lat) # Launch lat point converted to radians + lat1 = np.deg2rad(self.env.latitude) # Launch lat point converted to radians # Applies the haversine equation to find final lat/lon coordinates latitude = np.rad2deg( @@ -3014,8 +3014,8 @@ def longitude(self): longitude: Function Rocket longitude coordinate, in degrees, at each time step. """ - lat1 = np.deg2rad(self.env.lat) # Launch lat point converted to radians - lon1 = np.deg2rad(self.env.lon) # Launch lon point converted to radians + lat1 = np.deg2rad(self.env.latitude) # Launch lat point converted to radians + lon1 = np.deg2rad(self.env.longitude) # Launch lon point converted to radians # Applies the haversine equation to find final lat/lon coordinates longitude = np.rad2deg( diff --git a/rocketpy/Rocket.py b/rocketpy/Rocket.py index bd4e19674..30d82bbfb 100644 --- a/rocketpy/Rocket.py +++ b/rocketpy/Rocket.py @@ -1282,7 +1282,7 @@ def allInfo(self): self.thrustToWeight.plot(lower=0, upper=self.motor.burnOutTime) # ax = plt.subplot(415) - # ax.plot( , self.rocket.motor.thrust()/(self.env.g() * self.rocket.totalMass())) + # ax.plot( , self.rocket.motor.thrust()/(self.env.gravity() * self.rocket.totalMass())) # ax.set_xlim(0, self.rocket.motor.burnOutTime) # ax.set_xlabel("Time (s)") # ax.set_ylabel("Thrust/Weight") From 6ba9e31e52c339c686d9f997aafd14cfa96b1f0e Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 10 Oct 2022 03:38:02 +0200 Subject: [PATCH 08/68] MAINT: Refactor some of the functions --- rocketpy/Dispersion.py | 616 ++++++++++++++++++++++++++--------------- 1 file changed, 393 insertions(+), 223 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index 8c4c009d6..061bdc298 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -1,8 +1,6 @@ # -*- coding: utf-8 -*- -__author__ = ( - "Mateus Stano Junqueira, Sofia Lopes Suesdek Rocha, Guilherme Fernandes Alves, Bruno Abdulklech Sorban" -) +__author__ = "Mateus Stano Junqueira, Sofia Lopes Suesdek Rocha, Guilherme Fernandes Alves, Bruno Abdulklech Sorban" __copyright__ = "Copyright 20XX, RocketPy Team" __license__ = "MIT" @@ -95,6 +93,11 @@ def __init__( # Save and initialize parameters self.filename = filename + # Initialize variables so they can be accessed by MATLAB + self.dispersion_results = {} + self.mean_out_of_rail_time = 0 + self.std_out_of_rail_time = 0 + def __set_distribution_function(self, distribution_type): """_summary_ @@ -182,17 +185,228 @@ def __set_distribution_function(self, distribution_type): warnings.warn("Distribution type not supported") def __process_dispersion_dict(self, dictionary): - """_summary_ + """Read the inputted dispersion dictionary from the run_dispersion method + and return a dictionary with the processed parameters, being ready to be + used in the dispersion simulation. Parameters ---------- dictionary : dict - _description_ + Dictionary containing the parameters to be varied in the dispersion + simulation. The keys of the dictionary are the names of the parameters + to be varied, and the values can be either tuple or list. If the value + is a single value, the corresponding class of the parameter need to + be passed on the run_dispersion method. Returns ------- - _type_ - _description_ + dictionary: dict + The modified dictionary with the processed parameters. + """ + # First we need to check if the dictionary is empty + + # Now we prepare all the parachute data + dictionary = self.__process_parachute_from_dict(dictionary) + + # Check remaining class inputs + # Environment + dictionary = self.__process_environment_from_dict(dictionary) + + # Motor + dictionary = self.__process_motor_from_dict(dictionary) + + # Rocket + dictionary = self.__process_rocket_from_dict(dictionary) + + # Flight + dictionary = self.__process_flight_from_dict(dictionary) + + # Finally check the inputted data + self.__check_inputted_values_from_dict(dictionary) + + return dictionary + + def __process_flight_from_dict(self, dictionary): + """Check if all the relevant inputs for the Flight class are present in + the dispersion dictionary, input the missing ones and return the modified + dictionary. + + Parameters + ---------- + dictionary : dict + Dictionary containing the parameters to be varied in the dispersion + simulation. The keys of the dictionary are the names of the parameters + to be varied, and the values can be either tuple or list. If the value + is a single value, the corresponding class of the parameter need to + be passed on the run_dispersion method. + + Returns + ------- + dictionary: dict + Modified dictionary with the processed flight parameters. + """ + if not all( + flight_input in dictionary for flight_input in self.flight_inputs.keys() + ): + # Iterate through missing inputs + for missing_input in set(self.flight_inputs.keys()) - dictionary.keys(): + missing_input = str(missing_input) + # Add to the dict + try: + dictionary[missing_input] = [getattr(self.flight, missing_input)] + except: + # class was not inputted + # checks if missing parameter is required + if self.flight_inputs[missing_input] == "required": + warnings.warn(f'Missing "{missing_input}" in dictionary') + else: # if not, uses default value + dictionary[missing_input] = [self.flight_inputs[missing_input]] + + return dictionary + + def __process_rocket_from_dict(self, dictionary): + """Check if all the relevant inputs for the Rocket class are present in + the dispersion dictionary, input the missing ones and return the modified + dictionary. + + Parameters + ---------- + dictionary : dict + Dictionary containing the parameters to be varied in the dispersion + simulation. The keys of the dictionary are the names of the parameters + to be varied, and the values can be either tuple or list. If the value + is a single value, the corresponding class of the parameter need to + be passed on the run_dispersion method. + + Returns + ------- + dictionary: dict + Modified dictionary with the processed rocket parameters. + """ + if not all( + rocket_input in dictionary for rocket_input in self.rocket_inputs.keys() + ): + # Iterate through missing inputs + for missing_input in set(self.rocket_inputs.keys()) - dictionary.keys(): + missing_input = str(missing_input) + # Add to the dict + try: + dictionary[missing_input] = [getattr(self.rocket, missing_input)] + except: + # class was not inputted + # checks if missing parameter is required + if self.rocket_inputs[missing_input] == "required": + warnings.warn(f'Missing "{missing_input}" in dictionary') + else: # if not, uses default value + dictionary[missing_input] = [self.rocket_inputs[missing_input]] + + return dictionary + + def __process_motor_from_dict(self, dictionary): + """Check if all the relevant inputs for the Motor class are present in + the dispersion dictionary, input the missing ones and return the modified + dictionary. + + Parameters + ---------- + dictionary : dict + Dictionary containing the parameters to be varied in the dispersion + simulation. The keys of the dictionary are the names of the parameters + to be varied, and the values can be either tuple or list. If the value + is a single value, the corresponding class of the parameter need to + be passed on the run_dispersion method. + + Returns + ------- + dictionary: dict + Modified dictionary with the processed rocket parameters. + """ + # TODO: Add mor options of motor (i.e. Liquid and Hybrids) + + if not all( + motor_input in dictionary for motor_input in self.solid_motor_inputs.keys() + ): + # Iterate through missing inputs + for missing_input in ( + set(self.solid_motor_inputs.keys()) - dictionary.keys() + ): + missing_input = str(missing_input) + # Add to the dict + try: + dictionary[missing_input] = [getattr(self.motor, missing_input)] + except: + # class was not inputted + # checks if missing parameter is required + if self.solid_motor_inputs[missing_input] == "required": + warnings.warn(f'Missing "{missing_input}" in d') + else: # if not uses default value + dictionary[missing_input] = [ + self.solid_motor_inputs[missing_input] + ] + return dictionary + + def __process_environment_from_dict(self, dictionary): + """Check if all the relevant inputs for the Environment class are present in + the dispersion dictionary, input the missing ones and return the modified + dictionary. + + Parameters + ---------- + dictionary : dict + Dictionary containing the parameters to be varied in the dispersion + simulation. The keys of the dictionary are the names of the parameters + to be varied, and the values can be either tuple or list. If the value + is a single value, the corresponding class of the parameter need to + be passed on the run_dispersion method. + + Returns + ------- + dictionary: dict + Modified dictionary with the processed environment parameters. + """ + if not all( + environment_input in dictionary + for environment_input in self.environment_inputs.keys() + ): + # Iterate through missing inputs + for missing_input in ( + set(self.environment_inputs.keys()) - dictionary.keys() + ): + missing_input = str(missing_input) + # Add to the dict + try: + dictionary[missing_input] = [ + getattr(self.environment, missing_input) + ] + except: + # class was not inputted + # checks if missing parameter is required + if self.environment_inputs[missing_input] == "required": + warnings.warn("Missing {} in dictionary".format(missing_input)) + else: # if not, use default value + dictionary[missing_input] = [ + self.environment_inputs[missing_input] + ] + return dictionary + + def __process_parachute_from_dict(self, dictionary): + """Check if all the relevant inputs for the Parachute class are present in + the dispersion dictionary, input the missing ones and return the modified + dictionary. + + Parameters + ---------- + dictionary : dict + Dictionary containing the parameters to be varied in the dispersion + simulation. The keys of the dictionary are the names of the parameters + to be varied, and the values can be either tuple or list. If the value + is a single value, the corresponding class of the parameter need to + be passed on the run_dispersion method. + + Returns + ------- + dictionary: dict + Modified dictionary with the processed parachute parameters. """ # Get parachutes names if "parachuteNames" in dictionary: # TODO: use only dictionary @@ -221,6 +435,7 @@ def __process_dispersion_dict(self, dictionary): dictionary["parachute_" + name + "_noise_corr"] = dictionary[ "noise_corr" ][i] + # Remove already used keys from dictionary to avoid confusion dictionary.pop("CdS", None) dictionary.pop("trigger", None) dictionary.pop("samplingRate", None) @@ -230,10 +445,29 @@ def __process_dispersion_dict(self, dictionary): dictionary.pop("noise_corr", None) self.parachute_names = dictionary.pop("parachuteNames", None) + return dictionary + + def __check_inputted_values_from_dict(self, dictionary): + """Check if the inputted values are valid. If not, raise an error. + + Parameters + ---------- + dictionary : dict + Dictionary containing the parameters to be varied in the dispersion + simulation. The keys of the dictionary are the names of the parameters + to be varied, and the values can be either tuple or list. If the value + is a single value, the corresponding class of the parameter need to + be passed on the run_dispersion method. + + Returns + ------- + dictionary: dict + The modified dictionary with the processed parameters. + """ for parameter_key, parameter_value in dictionary.items(): if isinstance(parameter_value, (tuple, list)): continue - else: # if parameter_value is only the standard deviation + else: # if parameter_value is only the std. dev. if "parachute" in parameter_key: _, parachute_name, parameter = parameter_key.split("_") dictionary[parameter_key] = ( @@ -245,7 +479,7 @@ def __process_dispersion_dict(self, dictionary): ), parameter_value, ) - else: + else: # TODO: Check if we can remove this else if parameter_key in self.environment_inputs.keys(): try: dictionary[parameter_key] = ( @@ -258,7 +492,7 @@ def __process_dispersion_dict(self, dictionary): "Check if parameter was inputted correctly in dispersion_dictionary." + " Dictionary values must be either tuple or lists." + " If single value, the corresponding Class must " - + "must be inputted in Dispersion.run_dispersion method.\n" + + " be inputted in Dispersion.run_dispersion method.\n" ) print(traceback.format_exc()) elif parameter_key in self.solid_motor_inputs.keys(): @@ -307,86 +541,6 @@ def __process_dispersion_dict(self, dictionary): ) print(traceback.format_exc()) - # Check remaining class inputs - - if not all( - environment_input in dictionary - for environment_input in self.environment_inputs.keys() - ): - # Iterate through missing inputs - for missing_input in ( - set(self.environment_inputs.keys()) - dictionary.keys() - ): - missing_input = str(missing_input) - # Add to the dict - try: - dictionary[missing_input] = [ - getattr(self.environment, missing_input) - ] - except: - # class was not inputted - # checks if missing parameter is required - if self.environment_inputs[missing_input] == "required": - warnings.warn(f'Missing "{missing_input}" in dictionary') - else: # if not uses default value - dictionary[missing_input] = [ - self.environment_inputs[missing_input] - ] - if not all( - motor_input in dictionary for motor_input in self.solid_motor_inputs.keys() - ): - # Iterate through missing inputs - for missing_input in ( - set(self.solid_motor_inputs.keys()) - dictionary.keys() - ): - missing_input = str(missing_input) - # Add to the dict - try: - dictionary[missing_input] = [getattr(self.motor, missing_input)] - except: - # class was not inputted - # checks if missing parameter is required - if self.solid_motor_inputs[missing_input] == "required": - warnings.warn(f'Missing "{missing_input}" in d') - else: # if not uses default value - dictionary[missing_input] = [ - self.solid_motor_inputs[missing_input] - ] - - if not all( - rocket_input in dictionary for rocket_input in self.rocket_inputs.keys() - ): - # Iterate through missing inputs - for missing_input in set(self.rocket_inputs.keys()) - dictionary.keys(): - missing_input = str(missing_input) - # Add to the dict - try: - dictionary[missing_input] = [getattr(self.rocket, missing_input)] - except: - # class was not inputted - # checks if missing parameter is required - if self.rocket_inputs[missing_input] == "required": - warnings.warn(f'Missing "{missing_input}" in dictionary') - else: # if not uses default value - dictionary[missing_input] = [self.rocket_inputs[missing_input]] - - if not all( - flight_input in dictionary for flight_input in self.flight_inputs.keys() - ): - # Iterate through missing inputs - for missing_input in set(self.flight_inputs.keys()) - dictionary.keys(): - missing_input = str(missing_input) - # Add to the dict - try: - dictionary[missing_input] = [getattr(self.flight, missing_input)] - except: - # class was not inputted - # checks if missing parameter is required - if self.flight_inputs[missing_input] == "required": - warnings.warn(f'Missing "{missing_input}" in dictionary') - else: # if not uses default value - dictionary[missing_input] = [self.flight_inputs[missing_input]] - return dictionary def __yield_flight_setting( @@ -406,8 +560,8 @@ def __yield_flight_setting( Yields ------ - _type_ - _description_ + flight_setting + """ i = 0 @@ -427,9 +581,8 @@ def __yield_flight_setting( # Yield a flight setting yield flight_setting - # TODO: Rework post process Flight method making it possible (and optimized) to - # chose what is going to be exported - def export_flight_data( + # TODO: allow user to chose what is going to be exported + def __export_flight_data( self, flight_setting, flight, @@ -484,32 +637,33 @@ def export_flight_data( "frontalWind": flight.frontalSurfaceWind, } - # Calculate maximum reached velocity - sol = np.array(flight.solution) - flight.vx = Function( - sol[:, [0, 4]], - "Time (s)", - "Vx (m/s)", - "linear", - extrapolation="natural", - ) - flight.vy = Function( - sol[:, [0, 5]], - "Time (s)", - "Vy (m/s)", - "linear", - extrapolation="natural", - ) - flight.vz = Function( - sol[:, [0, 6]], - "Time (s)", - "Vz (m/s)", - "linear", - extrapolation="natural", - ) - flight.speed = (flight.vx**2 + flight.vy**2 + flight.vz**2) ** 0.5 - flight.maxVel = np.amax(flight.speed.source[:, 1]) - flight_result["maxVelocity"] = flight.maxVel + # # Calculate maximum reached velocity + # sol = np.array(flight.solution) + # flight.vx = Function( + # sol[:, [0, 4]], + # "Time (s)", + # "Vx (m/s)", + # "linear", + # extrapolation="natural", + # ) + # flight.vy = Function( + # sol[:, [0, 5]], + # "Time (s)", + # "Vy (m/s)", + # "linear", + # extrapolation="natural", + # ) + # flight.vz = Function( + # sol[:, [0, 6]], + # "Time (s)", + # "Vz (m/s)", + # "linear", + # extrapolation="natural", + # ) + # flight.speed = (flight.vx**2 + flight.vy**2 + flight.vz**2) ** 0.5 + # flight.maxVel = np.amax(flight.speed.source[:, 1]) + # flight_result["maxVelocity"] = flight.maxVel + flight_result["maxVelocity"] = flight.maxSpeed # Take care of parachute results for trigger_time, parachute in flight.parachuteEvents: @@ -530,7 +684,7 @@ def export_flight_data( return None - def export_flight_error(self, flight_setting, dispersion_error_file): + def __export_flight_data(self, flight_setting, dispersion_error_file): """Saves flight error in a .txt""" @@ -547,7 +701,7 @@ def run_dispersion( motor=None, rocket=None, distribution_type="normal", - image=None, + bg_image=None, actual_landing_point=None, ): """Runs the given number of simulations and saves the data @@ -557,7 +711,7 @@ def run_dispersion( number_of_simulations : int Number of simulations desired, must be non negative. This is needed when running a new simulation. Default is zero. - d : _type_ + dispersion_dictionary : _type_ _description_ environment : _type_ _description_ @@ -571,17 +725,17 @@ def run_dispersion( _description_, by default None distribution_type : str, optional _description_, by default "normal" - image : str, optional + bg_image : str, optional The path to the image to be used as the background actual_landing_point : tuple, optional A tuple containing the actual landing point of the rocket, if known. Useful when comparing the dispersion results with the actual landing. - Must be given in tuple format, such as (lat, lon). By default None. # TODO: Check the order + Must be given in tuple format, such as (lat, lon). By default None. + # TODO: Check the order of these coordinates Returns ------- - _type_ - _description_ + None """ self.number_of_simulations = number_of_simulations @@ -595,7 +749,7 @@ def run_dispersion( self.motor = motor if motor else self.motor self.rocket = rocket if rocket else self.rocket self.distribution_type = distribution_type - self.image = image + self.image = bg_image self.actual_landing_point = actual_landing_point # Creates copy of dispersion_dictionary that will be altered @@ -740,12 +894,12 @@ def run_dispersion( # Run trajectory simulation try: + # TODO: Add initialSolution flight option TestFlight = Flight( rocket=rocket_dispersion, environment=env_dispersion, inclination=setting["inclination"], heading=setting["heading"], - # initialSolution=setting["initialSolution"] if "initialSolution" in setting else self.flight.initialSolution, terminateOnApogee=setting["terminateOnApogee"], maxTime=setting["maxTime"], maxTimeStep=setting["maxTimeStep"], @@ -756,7 +910,7 @@ def run_dispersion( verbose=setting["verbose"], ) - self.export_flight_data( + self.__export_flight_data( flight_setting=setting, flight_data=TestFlight, exec_time=process_time() - self.start_time, @@ -766,14 +920,14 @@ def run_dispersion( except Exception as E: print(E) print(traceback.format_exc()) - self.export_flight_error(setting, dispersion_error_file) + self.__export_flight_data(setting, dispersion_error_file) # Register time out.update( f"Current iteration: {i:06d} | Average Time per Iteration: {(process_time() - initial_cpu_time)/i:2.6f} s | Estimated time left: {int((number_of_simulations - i)*((process_time() - initial_cpu_time)/i))} s" ) - # Done + # Clean the house once all the simulations were already done ## Print and save total time final_string = f"Completed {i} iterations successfully. Total CPU time: {process_time() - initial_cpu_time} s. Total wall time: {time() - initial_wall_time} s" @@ -794,18 +948,18 @@ def import_results(self, dispersion_output_file): Parameters ---------- - dispersion_output_file : _type_ - _description_ + dispersion_output_file : str + Path to the dispersion output file. This file will not be overwritten, + modified or deleted by this function. Returns ------- - dispersion_results: dict - _description_ + None """ - # Initialize variable to store all results dispersion_general_results = [] + # TODO: Add more flexible way to define dispersion_results dispersion_results = { "outOfRailTime": [], "outOfRailVelocity": [], @@ -832,61 +986,77 @@ def import_results(self, dispersion_output_file): } # Get all dispersion results - # Get file - dispersion_output_file = open(dispersion_output_file, "r+") + # Open the file + file = open(dispersion_output_file, "r+") # Read each line of the file and convert to dict - for line in dispersion_output_file: + for line in file: # Skip comments lines if line[0] != "{": continue - # Eval results and store them + # Evaluate results and store them flight_result = eval(line) dispersion_general_results.append(flight_result) for parameter_key, parameter_value in flight_result.items(): dispersion_results[parameter_key].append(parameter_value) # Close data file - dispersion_output_file.close() + file.close() + + # Calculate the number of flights simulated + self.num_of_loaded_sims = len(dispersion_general_results) + + # Print the number of flights simulated + print( + f"A total of {self.num_of_loaded_sims} simulations were loaded from the following file: {dispersion_output_file}" + ) - # Number of flights simulated - self.number_of_loaded_simulations = len(dispersion_general_results) + # Save the results as an attribute of the class + self.dispersion_results = dispersion_results - return dispersion_results + return None # Start the processing analysis - @cached_property - def mean_out_of_rail_time(dispersion_results): - return np.mean(dispersion_results["outOfRailTime"]) + def outOfRailTime(self): + """Calculate the time of the rocket's departure from the rail, in seconds. - @cached_property - def std_out_of_rail_time(dispersion_results): - return np.std(dispersion_results["outOfRailTime"]) + Returns + ------- + _type_ + _description_ + """ + self.mean_out_of_rail_time = ( + np.mean(self.dispersion_results["outOfRailTime"]) + if self.dispersion_results["outOfRailTime"] + else None + ) + self.std_out_of_rail_time = ( + np.std(self.dispersion_results["outOfRailTime"]) + if self.dispersion_results["outOfRailTime"] + else None + ) + return None - def printMeanOutOfRailTime(self, dispersion_results): - """_summary_ + def printMeanOutOfRailTime(self): + """Prints out the mean and std. dev. of the "outOfRailTime" parameter. Parameters ---------- - dispersion_results : _type_ - _description_ + None Returns ------- None """ - print( - f"Out of Rail Time - Mean Value: {self.mean_out_of_rail_time(dispersion_results):0.3f} s" - ) - print( - f"Out of Rail Time - Standard Deviation: {self.std_out_of_rail_time(dispersion_results):0.3f} s" - ) + self.outOfRailTime() + print(f"Out of Rail Time -Mean Value: {self.mean_out_of_rail_time:0.3f} s") + print(f"Out of Rail Time - Std. Dev.: {self.std_out_of_rail_time:0.3f} s") return None - def plotOutOfRailTime(self, dispersion_results): - """_summary_ + def plotOutOfRailTime(self): + """Plot the out of rail time distribution Parameters ---------- @@ -898,12 +1068,12 @@ def plotOutOfRailTime(self, dispersion_results): _type_ _description_ """ - self.meanOutOfRailTime(dispersion_results) + self.outOfRailTime() plt.figure() plt.hist( - dispersion_results["outOfRailTime"], - bins=int(self.number_of_loaded_simulations**0.5), + self.dispersion_results["outOfRailTime"], + bins=int(self.num_of_loaded_sims**0.5), ) plt.title("Out of Rail Time") plt.xlabel("Time (s)") @@ -926,10 +1096,10 @@ def meanOutOfRailVelocity(self, dispersion_results): _description_ """ print( - f'Out of Rail Velocity - Mean Value: {np.mean(dispersion_results["outOfRailVelocity"]):0.3f} m/s' + f'Out of Rail Velocity -Mean Value: {np.mean(dispersion_results["outOfRailVelocity"]):0.3f} m/s' ) print( - f'Out of Rail Velocity - Standard Deviation: {np.std(dispersion_results["outOfRailVelocity"]):0.3f} m/s' + f'Out of Rail Velocity - Std. Dev.: {np.std(dispersion_results["outOfRailVelocity"]):0.3f} m/s' ) return None @@ -952,7 +1122,7 @@ def plotOutOfRailVelocity(self, dispersion_results): plt.figure() plt.hist( dispersion_results["outOfRailVelocity"], - bins=int(self.number_of_loaded_simulations**0.5), + bins=int(self.num_of_loaded_sims**0.5), ) plt.title("Out of Rail Velocity") plt.xlabel("Velocity (m/s)") @@ -975,10 +1145,10 @@ def meanApogeeTime(self, dispersion_results): _description_ """ print( - f'Impact Time - Mean Value: {np.mean(dispersion_results["impactTime"]):0.3f} s' + f'Impact Time -Mean Value: {np.mean(dispersion_results["impactTime"]):0.3f} s' ) print( - f'Impact Time - Standard Deviation: {np.std(dispersion_results["impactTime"]):0.3f} s' + f'Impact Time - Std. Dev.: {np.std(dispersion_results["impactTime"]):0.3f} s' ) return None @@ -1001,7 +1171,7 @@ def plotApogeeTime(self, dispersion_results): plt.figure() plt.hist( dispersion_results["impactTime"], - bins=int(self.number_of_loaded_simulations**0.5), + bins=int(self.num_of_loaded_sims**0.5), ) plt.title("Impact Time") plt.xlabel("Time (s)") @@ -1024,10 +1194,10 @@ def meanApogeeAltitude(self, dispersion_results): _description_ """ print( - f'Apogee Altitude - Mean Value: {np.mean(dispersion_results["apogeeAltitude"]):0.3f} m' + f'Apogee Altitude -Mean Value: {np.mean(dispersion_results["apogeeAltitude"]):0.3f} m' ) print( - f'Apogee Altitude - Standard Deviation: {np.std(dispersion_results["apogeeAltitude"]):0.3f} m' + f'Apogee Altitude - Std. Dev.: {np.std(dispersion_results["apogeeAltitude"]):0.3f} m' ) return None @@ -1050,7 +1220,7 @@ def plotApogeeAltitude(self, dispersion_results): plt.figure() plt.hist( dispersion_results["apogeeAltitude"], - bins=int(self.number_of_loaded_simulations**0.5), + bins=int(self.num_of_loaded_sims**0.5), ) plt.title("Apogee Altitude") plt.xlabel("Altitude (m)") @@ -1073,10 +1243,10 @@ def meanApogeeXPosition(self, dispersion_results): _description_ """ print( - f'Apogee X Position - Mean Value: {np.mean(dispersion_results["apogeeX"]):0.3f} m' + f'Apogee X Position -Mean Value: {np.mean(dispersion_results["apogeeX"]):0.3f} m' ) print( - f'Apogee X Position - Standard Deviation: {np.std(dispersion_results["apogeeX"]):0.3f} m' + f'Apogee X Position - Std. Dev.: {np.std(dispersion_results["apogeeX"]):0.3f} m' ) return None @@ -1099,7 +1269,7 @@ def plotApogeeXPosition(self, dispersion_results): plt.figure() plt.hist( dispersion_results["apogeeX"], - bins=int(self.number_of_loaded_simulations**0.5), + bins=int(self.num_of_loaded_sims**0.5), ) plt.title("Apogee X Position") plt.xlabel("Apogee X Position (m)") @@ -1122,10 +1292,10 @@ def meanApogeeYPosition(self, dispersion_results): _description_ """ print( - f'Apogee Y Position - Mean Value: {np.mean(dispersion_results["apogeeY"]):0.3f} m' + f'Apogee Y Position -Mean Value: {np.mean(dispersion_results["apogeeY"]):0.3f} m' ) print( - f'Apogee Y Position - Standard Deviation: {np.std(dispersion_results["apogeeY"]):0.3f} m' + f'Apogee Y Position - Std. Dev.: {np.std(dispersion_results["apogeeY"]):0.3f} m' ) return None @@ -1148,7 +1318,7 @@ def plotApogeeYPosition(self, dispersion_results): plt.figure() plt.hist( dispersion_results["apogeeY"], - bins=int(self.number_of_loaded_simulations**0.5), + bins=int(self.num_of_loaded_sims**0.5), ) plt.title("Apogee Y Position") plt.xlabel("Apogee Y Position (m)") @@ -1171,10 +1341,10 @@ def meanImpactTime(self, dispersion_results): _description_ """ print( - f'Impact Time - Mean Value: {np.mean(dispersion_results["impactTime"]):0.3f} s' + f'Impact Time -Mean Value: {np.mean(dispersion_results["impactTime"]):0.3f} s' ) print( - f'Impact Time - Standard Deviation: {np.std(dispersion_results["impactTime"]):0.3f} s' + f'Impact Time - Std. Dev.: {np.std(dispersion_results["impactTime"]):0.3f} s' ) return None @@ -1197,7 +1367,7 @@ def plotImpactTime(self, dispersion_results): plt.figure() plt.hist( dispersion_results["impactTime"], - bins=int(self.number_of_loaded_simulations**0.5), + bins=int(self.num_of_loaded_sims**0.5), ) plt.title("Impact Time") plt.xlabel("Time (s)") @@ -1220,10 +1390,10 @@ def meanImpactXPosition(self, dispersion_results): _description_ """ print( - f'Impact X Position - Mean Value: {np.mean(dispersion_results["impactX"]):0.3f} m' + f'Impact X Position -Mean Value: {np.mean(dispersion_results["impactX"]):0.3f} m' ) print( - f'Impact X Position - Standard Deviation: {np.std(dispersion_results["impactX"]):0.3f} m' + f'Impact X Position - Std. Dev.: {np.std(dispersion_results["impactX"]):0.3f} m' ) return None @@ -1246,7 +1416,7 @@ def plotImpactXPosition(self, dispersion_results): plt.figure() plt.hist( dispersion_results["impactX"], - bins=int(self.number_of_loaded_simulations**0.5), + bins=int(self.num_of_loaded_sims**0.5), ) plt.title("Impact X Position") plt.xlabel("Impact X Position (m)") @@ -1269,10 +1439,10 @@ def meanImpactYPosition(self, dispersion_results): _description_ """ print( - f'Impact Y Position - Mean Value: {np.mean(dispersion_results["impactY"]):0.3f} m' + f'Impact Y Position -Mean Value: {np.mean(dispersion_results["impactY"]):0.3f} m' ) print( - f'Impact Y Position - Standard Deviation: {np.std(dispersion_results["impactY"]):0.3f} m' + f'Impact Y Position - Std. Dev.: {np.std(dispersion_results["impactY"]):0.3f} m' ) return None @@ -1295,7 +1465,7 @@ def plotImpactYPosition(self, dispersion_results): plt.figure() plt.hist( dispersion_results["impactY"], - bins=int(self.number_of_loaded_simulations**0.5), + bins=int(self.num_of_loaded_sims**0.5), ) plt.title("Impact Y Position") plt.xlabel("Impact Y Position (m)") @@ -1318,10 +1488,10 @@ def meanImpactVelocity(self, dispersion_results): _description_ """ print( - f'Impact Velocity - Mean Value: {np.mean(dispersion_results["impactVelocity"]):0.3f} m/s' + f'Impact Velocity -Mean Value: {np.mean(dispersion_results["impactVelocity"]):0.3f} m/s' ) print( - f'Impact Velocity - Standard Deviation: {np.std(dispersion_results["impactVelocity"]):0.3f} m/s' + f'Impact Velocity - Std. Dev.: {np.std(dispersion_results["impactVelocity"]):0.3f} m/s' ) return None @@ -1344,7 +1514,7 @@ def plotImpactVelocity(self, dispersion_results): plt.figure() plt.hist( dispersion_results["impactVelocity"], - bins=int(self.number_of_loaded_simulations**0.5), + bins=int(self.num_of_loaded_sims**0.5), ) plt.title("Impact Velocity") plt.xlim(-35, 0) @@ -1368,24 +1538,24 @@ def meanStaticMargin(self, dispersion_results): _description_ """ print( - f'Initial Static Margin - Mean Value: {np.mean(dispersion_results["initialStaticMargin"]):0.3f} c' + f'Initial Static Margin - Mean Value: {np.mean(dispersion_results["initialStaticMargin"]):0.3f} c' ) print( - f'Initial Static Margin - Standard Deviation: {np.std(dispersion_results["initialStaticMargin"]):0.3f} c' + f'Initial Static Margin - Std. Dev.: {np.std(dispersion_results["initialStaticMargin"]):0.3f} c' ) print( - f'Out of Rail Static Margin - Mean Value: {np.mean(dispersion_results["outOfRailStaticMargin"]):0.3f} c' + f'Out of Rail Static Margin -Mean Value: {np.mean(dispersion_results["outOfRailStaticMargin"]):0.3f} c' ) print( - f'Out of Rail Static Margin - Standard Deviation: {np.std(dispersion_results["outOfRailStaticMargin"]):0.3f} c' + f'Out of Rail Static Margin - Std. Dev.: {np.std(dispersion_results["outOfRailStaticMargin"]):0.3f} c' ) print( - f'Final Static Margin - Mean Value: {np.mean(dispersion_results["finalStaticMargin"]):0.3f} c' + f'Final Static Margin - Mean Value: {np.mean(dispersion_results["finalStaticMargin"]):0.3f} c' ) print( - f'Final Static Margin - Standard Deviation: {np.std(dispersion_results["finalStaticMargin"]):0.3f} c' + f'Final Static Margin - Std. Dev.: {np.std(dispersion_results["finalStaticMargin"]):0.3f} c' ) return None @@ -1409,17 +1579,17 @@ def plotStaticMargin(self, dispersion_results): plt.hist( dispersion_results["initialStaticMargin"], label="Initial", - bins=int(self.number_of_loaded_simulations**0.5), + bins=int(self.num_of_loaded_sims**0.5), ) plt.hist( dispersion_results["outOfRailStaticMargin"], label="Out of Rail", - bins=int(self.number_of_loaded_simulations**0.5), + bins=int(self.num_of_loaded_sims**0.5), ) plt.hist( dispersion_results["finalStaticMargin"], label="Final", - bins=int(self.number_of_loaded_simulations**0.5), + bins=int(self.num_of_loaded_sims**0.5), ) plt.legend() plt.title("Static Margin") @@ -1443,10 +1613,10 @@ def meanMaximumVelocity(self, dispersion_results): _description_ """ print( - f'Maximum Velocity - Mean Value: {np.mean(dispersion_results["maxVelocity"]):0.3f} m/s' + f'Maximum Velocity -Mean Value: {np.mean(dispersion_results["maxVelocity"]):0.3f} m/s' ) print( - f'Maximum Velocity - Standard Deviation: {np.std(dispersion_results["maxVelocity"]):0.3f} m/s' + f'Maximum Velocity - Std. Dev.: {np.std(dispersion_results["maxVelocity"]):0.3f} m/s' ) return None @@ -1469,7 +1639,7 @@ def plotMaximumVelocity(self, dispersion_results): plt.figure() plt.hist( dispersion_results["maxVelocity"], - bins=int(self.number_of_loaded_simulations**0.5), + bins=int(self.num_of_loaded_sims**0.5), ) plt.title("Maximum Velocity") plt.xlabel("Velocity (m/s)") @@ -1492,10 +1662,10 @@ def meanNumberOfParachuteEvents(self, dispersion_results): _description_ """ print( - f'Number of Parachute Events - Mean Value: {np.mean(dispersion_results["numberOfEvents"]):0.3f} s' + f'Number of Parachute Events -Mean Value: {np.mean(dispersion_results["numberOfEvents"]):0.3f} s' ) print( - f'Number of Parachute Events - Standard Deviation: {np.std(dispersion_results["numberOfEvents"]):0.3f} s' + f'Number of Parachute Events - Std. Dev.: {np.std(dispersion_results["numberOfEvents"]):0.3f} s' ) return None @@ -1538,10 +1708,10 @@ def meanDrogueTriggerTime(self, dispersion_results): _description_ """ print( - f'Drogue Trigger Time - Mean Value: {np.mean(dispersion_results["drogueTriggerTime"]):0.3f} s' + f'Drogue Trigger Time -Mean Value: {np.mean(dispersion_results["drogueTriggerTime"]):0.3f} s' ) print( - f'Drogue Trigger Time - Standard Deviation: {np.std(dispersion_results["drogueTriggerTime"]):0.3f} s' + f'Drogue Trigger Time - Std. Dev.: {np.std(dispersion_results["drogueTriggerTime"]):0.3f} s' ) return None @@ -1564,7 +1734,7 @@ def plotDrogueTriggerTime(self, dispersion_results): plt.figure() plt.hist( dispersion_results["drogueTriggerTime"], - bins=int(self.number_of_loaded_simulations**0.5), + bins=int(self.num_of_loaded_sims**0.5), ) plt.title("Drogue Trigger Time") plt.xlabel("Time (s)") @@ -1587,10 +1757,10 @@ def meanDrogueFullyInflatedTime(self, dispersion_results): _description_ """ print( - f'Drogue Fully Inflated Time - Mean Value: {np.mean(dispersion_results["drogueInflatedTime"]):0.3f} s' + f'Drogue Fully Inflated Time -Mean Value: {np.mean(dispersion_results["drogueInflatedTime"]):0.3f} s' ) print( - f'Drogue Fully Inflated Time - Standard Deviation: {np.std(dispersion_results["drogueInflatedTime"]):0.3f} s' + f'Drogue Fully Inflated Time - Std. Dev.: {np.std(dispersion_results["drogueInflatedTime"]):0.3f} s' ) return None @@ -1613,7 +1783,7 @@ def plotDrogueFullyInflatedTime(self, dispersion_results): plt.figure() plt.hist( dispersion_results["drogueInflatedTime"], - bins=int(self.number_of_loaded_simulations**0.5), + bins=int(self.num_of_loaded_sims**0.5), ) plt.title("Drogue Fully Inflated Time") plt.xlabel("Time (s)") @@ -1636,10 +1806,10 @@ def meanDrogueFullyVelocity(self, dispersion_results): _description_ """ print( - f'Drogue Parachute Fully Inflated Velocity - Mean Value: {np.mean(dispersion_results["drogueInflatedVelocity"]):0.3f} m/s' + f'Drogue Parachute Fully Inflated Velocity -Mean Value: {np.mean(dispersion_results["drogueInflatedVelocity"]):0.3f} m/s' ) print( - f'Drogue Parachute Fully Inflated Velocity - Standard Deviation: {np.std(dispersion_results["drogueInflatedVelocity"]):0.3f} m/s' + f'Drogue Parachute Fully Inflated Velocity - Std. Dev.: {np.std(dispersion_results["drogueInflatedVelocity"]):0.3f} m/s' ) return None @@ -1662,7 +1832,7 @@ def plotDrogueFullyVelocity(self, dispersion_results): plt.figure() plt.hist( dispersion_results["drogueInflatedVelocity"], - bins=int(self.number_of_loaded_simulations**0.5), + bins=int(self.num_of_loaded_sims**0.5), ) plt.title("Drogue Parachute Fully Inflated Velocity") plt.xlabel("Velocity m/s)") @@ -1996,10 +2166,10 @@ def meanLateralWindSpeed(self, dispersion_results): _description_ """ print( - f'Lateral Surface Wind Speed - Mean Value: {np.mean(dispersion_results["lateralWind"]):0.3f} m/s' + f'Lateral Surface Wind Speed -Mean Value: {np.mean(dispersion_results["lateralWind"]):0.3f} m/s' ) print( - f'Lateral Surface Wind Speed - Standard Deviation: {np.std(dispersion_results["lateralWind"]):0.3f} m/s' + f'Lateral Surface Wind Speed - Std. Dev.: {np.std(dispersion_results["lateralWind"]):0.3f} m/s' ) def plotLateralWindSpeed(self, dispersion_results): @@ -2015,7 +2185,7 @@ def plotLateralWindSpeed(self, dispersion_results): plt.figure() plt.hist( dispersion_results["lateralWind"], - bins=int(self.number_of_loaded_simulations**0.5), + bins=int(self.num_of_loaded_sims**0.5), ) plt.title("Lateral Surface Wind Speed") plt.xlabel("Velocity (m/s)") @@ -2031,10 +2201,10 @@ def meanFrontalWindSpeed(self, dispersion_results): _description_ """ print( - f'Frontal Surface Wind Speed - Mean Value: {np.mean(dispersion_results["frontalWind"]):0.3f} m/s' + f'Frontal Surface Wind Speed -Mean Value: {np.mean(dispersion_results["frontalWind"]):0.3f} m/s' ) print( - f'Frontal Surface Wind Speed - Standard Deviation: {np.std(dispersion_results["frontalWind"]):0.3f} m/s' + f'Frontal Surface Wind Speed - Std. Dev.: {np.std(dispersion_results["frontalWind"]):0.3f} m/s' ) def plotFrontalWindSpeed(self, dispersion_results): @@ -2050,7 +2220,7 @@ def plotFrontalWindSpeed(self, dispersion_results): plt.figure() plt.hist( dispersion_results["frontalWind"], - bins=int(self.number_of_loaded_simulations**0.5), + bins=int(self.num_of_loaded_sims**0.5), ) plt.title("Frontal Surface Wind Speed") plt.xlabel("Velocity (m/s)") From d1ce648b5134a97e45effbd155743c0cde52d8e8 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 10 Oct 2022 03:42:09 +0200 Subject: [PATCH 09/68] MAINT: moving inputs dictionaries inside the class --- rocketpy/Dispersion.py | 103 ++++++++++++++++++++--------------------- 1 file changed, 51 insertions(+), 52 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index 061bdc298..8a6e35bb1 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -93,6 +93,57 @@ def __init__( # Save and initialize parameters self.filename = filename + # Initialize variables to be used in the analysis in case of missing inputs + self.environment_inputs = { + "railLength": "required", + "gravity": 9.80665, + "date": None, + "latitude": 0, + "longitude": 0, + "elevation": 0, + "datum": "WGS84", + "timeZone": "UTC", + } + + self.solid_motor_inputs = { + "thrust": "required", + "burnOutTime": "required", + "totalImpulse": 0, + "grainNumber": "required", + "grainDensity": "required", + "grainOuterRadius": "required", + "grainInitialInnerRadius": "required", + "grainInitialHeight": "required", + "grainSeparation": 0, + "nozzleRadius": 0.0335, + "throatRadius": 0.0114, + } + + self.rocket_inputs = { + "mass": "required", + "inertiaI": "required", + "inertiaZ": "required", + "radius": "required", + "distanceRocketNozzle": "required", + "distanceRocketPropellant": "required", + "powerOffDrag": "required", + "powerOnDrag": "required", + } + + self.flight_inputs = { + "inclination": 80, + "heading": 90, + "initialSolution": None, + "terminateOnApogee": False, + "maxTime": 600, + "maxTimeStep": np.inf, + "minTimeStep": 0, + "rtol": 1e-6, + "atol": 6 * [1e-3] + 4 * [1e-6] + 3 * [1e-3], + "timeOvershoot": True, + "verbose": False, + } + # Initialize variables so they can be accessed by MATLAB self.dispersion_results = {} self.mean_out_of_rail_time = 0 @@ -2317,55 +2368,3 @@ def allInfo(self): self.plotDrogueTriggerTime(dispersion_results) return None - - # Variables - - environment_inputs = { - "railLength": "required", - "gravity": 9.80665, - "date": None, - "latitude": 0, - "longitude": 0, - "elevation": 0, - "datum": "SIRGAS2000", - "timeZone": "UTC", - } - - solid_motor_inputs = { - "thrust": "required", - "burnOutTime": "required", - "totalImpulse": 0, - "grainNumber": "required", - "grainDensity": "required", - "grainOuterRadius": "required", - "grainInitialInnerRadius": "required", - "grainInitialHeight": "required", - "grainSeparation": 0, - "nozzleRadius": 0.0335, - "throatRadius": 0.0114, - } - - rocket_inputs = { - "mass": "required", - "inertiaI": "required", - "inertiaZ": "required", - "radius": "required", - "distanceRocketNozzle": "required", - "distanceRocketPropellant": "required", - "powerOffDrag": "required", - "powerOnDrag": "required", - } - - flight_inputs = { - "inclination": 80, - "heading": 90, - "initialSolution": None, - "terminateOnApogee": False, - "maxTime": 600, - "maxTimeStep": np.inf, - "minTimeStep": 0, - "rtol": 1e-6, - "atol": 6 * [1e-3] + 4 * [1e-6] + 3 * [1e-3], - "timeOvershoot": True, - "verbose": False, - } From bb2548ebf13cc4c460a241fb2515bedb6097f748 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 10 Oct 2022 03:48:11 +0200 Subject: [PATCH 10/68] MAINT: minor comments on dispersion.py --- rocketpy/Dispersion.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index 8a6e35bb1..b51aa3b7a 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -5,7 +5,6 @@ __license__ = "MIT" -from functools import cached_property import math import traceback import warnings @@ -20,7 +19,6 @@ from numpy.random import * from .Flight import Flight -from .Function import Function from .Motor import SolidMotor from .Rocket import Rocket from .supplement import invertedHaversine @@ -37,7 +35,9 @@ # TODO: Optional return of matplotlib plots or abstract function to histogram plot based on stdev and mean # TODO: Implement MRS -# TODO: Implement functions from compareDispersions +# TODO: Implement functions from compareDispersions notebook + +# TODO: Convert the dictionary to a class attributes class Dispersion: @@ -2286,7 +2286,7 @@ def info(self): None """ - dispersion_results = self.import_results(self.filename) + dispersion_results = self.dispersion_results self.meanApogeeAltitude(dispersion_results) @@ -2327,7 +2327,7 @@ def info(self): return None def allInfo(self): - dispersion_results = self.import_results(self.filename) + dispersion_results = self.dispersion_results self.plotEllipses(dispersion_results, self.image, self.actual_landing_point) From f5e134ef3a51645e5b16734bb44f085cf453f565 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 10 Oct 2022 04:37:53 +0200 Subject: [PATCH 11/68] MAINT: Moving the new notebook to docs --- .../dispersion_class_usage.ipynb | 701 ++++++++++++++++++ docs/user/index.rst | 1 + getting_started Dispersion.ipynb | 672 ----------------- 3 files changed, 702 insertions(+), 672 deletions(-) create mode 100644 docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb delete mode 100644 getting_started Dispersion.ipynb diff --git a/docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb b/docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb new file mode 100644 index 000000000..d637dc290 --- /dev/null +++ b/docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb @@ -0,0 +1,701 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Monte Carlo Dispersion Analysis with the Dispersion Class" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally the Monte Carlo Simulations can be performed using a dedicated class called Dispersion. This class is a wrapper for the Monte Carlo Simulations, and it is the recommended way to perform the simulations. Say goodbye to the long and tedious process of creating the Monte Carlo Simulations throughout jupyter notebooks!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let's import the necessary libraries, including the newest Dispersion class!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from rocketpy import Environment, Rocket, SolidMotor, Flight, Dispersion\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you are using Jupyter Notebooks, it is recommended to run the following line to make matplotlib plots which will be shown later interactive and higher quality." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Dispersion class allows us to perform Monte Carlo Simulations in a very simple way.\n", + "We just need to create an instance of the class, and then call the method run() to perform the simulations.\n", + "The class has a lot of capabilities, but we will only use a few of them in this example.\n", + "We encourage you to check the documentation of the class to learn more about the Dispersion.\n", + "\n", + "Also, you can check RocketPy's main reference for a better conceptual understanding \n", + "of the Monte Carlo Simulations: [RocketPy: Six Degree-of-Freedom Rocket Trajectory Simulator](https://doi.org/10.1061/(ASCE)AS.1943-5525.0001331)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will describe two different options of usage:\n", + "- Using a Flight object as input, speeding up the process of creating the simulations.\n", + "- Using a dictionary as input, including mean values and uncertainties for each parameter \n", + "\n", + "You need only one of them to get started. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1st Option -> Use your Flight object as input for the Dispersion class" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating an Environment for Ponte de Sôr, Portugal" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Env = Environment(\n", + " railLength=5.2, latitude=39.389700, longitude=-8.288964, elevation=160\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get weather data from the GFS forecast, available online, we run the following lines.\n", + "\n", + "First, we set tomorrow's date." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import datetime\n", + "\n", + "tomorrow = datetime.date.today() + datetime.timedelta(days=1)\n", + "\n", + "Env.setDate((tomorrow.year, tomorrow.month, tomorrow.day, 12)) # Hour given in UTC time\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, we tell Env to use a GFS forecast to get the atmospheric conditions for flight.\n", + "\n", + "Don't mind the warning, it just means that not all variables, such as wind speed or atmospheric temperature, are available at all altitudes given by the forecast." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Env.setAtmosphericModel(type=\"Forecast\", file=\"GFS\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Env.info()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating a Motor for the Rocket" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We define a motor for the rocket, using the data from the manufacturer, and following\n", + "the [RocketPy's documentation](https://docs.rocketpy.org/en/latest/user/index.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Pro75M1670 = SolidMotor(\n", + " thrustSource=\"data/motors/Cesaroni_M1670.eng\",\n", + " burnOut=3.9,\n", + " grainNumber=5,\n", + " grainSeparation=5 / 1000,\n", + " grainDensity=1815,\n", + " grainOuterRadius=33 / 1000,\n", + " grainInitialInnerRadius=15 / 1000,\n", + " grainInitialHeight=120 / 1000,\n", + " nozzleRadius=33 / 1000,\n", + " throatRadius=11 / 1000,\n", + " interpolationMethod=\"linear\",\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Pro75M1670.info()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating a Rocket" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Calisto = Rocket(\n", + " motor=Pro75M1670,\n", + " radius=127 / 2000,\n", + " mass=19.197 - 2.956,\n", + " inertiaI=6.60,\n", + " inertiaZ=0.0351,\n", + " distanceRocketNozzle=-1.255,\n", + " distanceRocketPropellant=-0.85704,\n", + " powerOffDrag=\"data/calisto/powerOffDragCurve.csv\",\n", + " powerOnDrag=\"data/calisto/powerOnDragCurve.csv\",\n", + ")\n", + "\n", + "Calisto.setRailButtons([0.2, -0.5])\n", + "\n", + "NoseCone = Calisto.addNose(length=0.55829, kind=\"vonKarman\", distanceToCM=0.71971)\n", + "\n", + "FinSet = Calisto.addFins(\n", + " 4, span=0.100, rootChord=0.120, tipChord=0.040, distanceToCM=-1.04956\n", + ")\n", + "\n", + "Tail = Calisto.addTail(\n", + " topRadius=0.0635, bottomRadius=0.0435, length=0.060, distanceToCM=-1.194656\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Additionally, we set parachutes for our Rocket, as well as the trigger functions for the deployment of such parachutes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def drogueTrigger(p, y):\n", + " # p = pressure\n", + " # y = [x, y, z, vx, vy, vz, e0, e1, e2, e3, w1, w2, w3]\n", + " # activate drogue when vz < 0 m/s.\n", + " return True if y[5] < 0 else False\n", + "\n", + "\n", + "def mainTrigger(p, y):\n", + " # p = pressure\n", + " # y = [x, y, z, vx, vy, vz, e0, e1, e2, e3, w1, w2, w3]\n", + " # activate main when vz < 0 m/s and z < 800 + 1400 m (+1400 due to surface elevation).\n", + " return True if y[5] < 0 and y[2] < 800 + 1400 else False\n", + "\n", + "\n", + "Main = Calisto.addParachute(\n", + " \"Main\",\n", + " CdS=10.0,\n", + " trigger=mainTrigger,\n", + " samplingRate=105,\n", + " lag=1.5,\n", + " noise=(0, 8.3, 0.5),\n", + ")\n", + "\n", + "Drogue = Calisto.addParachute(\n", + " \"Drogue\",\n", + " CdS=1.0,\n", + " trigger=drogueTrigger,\n", + " samplingRate=105,\n", + " lag=1.5,\n", + " noise=(0, 8.3, 0.5),\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Calisto.info()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Simulate single flight" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "TestFlight = Flight(rocket=Calisto, environment=Env, inclination=84, heading=133)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we can visualize the flight trajectory:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "TestFlight.plot3DTrajectory()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Export Flight Trajectory to a .kml file so it can be opened on Google Earth" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Starting the Monte Carlo Simulations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let's invoke the Dispersion class, we only need a filename to initialize it.\n", + "The filename will be used either to save the results of the simulations or to load them\n", + "from a previous ran simulation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "TestDispersion = Dispersion(filename=\"data/docs/dispersion_analysis/\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, we can run the simulations using the method Dispersion.run_dispersion().\n", + "But before that, we need to set some simple parameters for the simulations.\n", + "We will set them by using a dictionary, which is one of the simplest way to do it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: Fill this with the correct values and the missed ones\n", + "dispersionDict = {\n", + " \"rocketMass\": (19.197 - 2.956, 0.2),\n", + " \"radius\": 0.1 * 127 / 2000,\n", + " \"burnOut\": (3.9, 0.5),\n", + " \"parachuteNames\": [\"Main\", \"Drogue\"],\n", + " \"CdS\": [(10, 2), (1)],\n", + " \"trigger\": [[mainTrigger], [drogueTrigger]],\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let's iterate over the simulations and export the data from each flight simulation!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "TestDispersion.runDispersion(\n", + " number_of_simulations=10, # Be careful with this number, it will take a while to run\n", + " dispersionDict=dispersionDict,\n", + " flight=TestFlight,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "RocketPy separates the running of simulations from the importing of data.\n", + "This is done to allow the user to run simulations in parallel, if desired, and\n", + "to allow the user to import data from different sources.\n", + "\n", + "Remember to specify a good name for your loaded data, so you can easily identify\n", + "and treat them later. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dispersion_results = TestDispersion.import_results()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualizing the results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we finally have the results of our Monte Carlo simulations loaded!\n", + "Let's play with them." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we can print numerical information regarding the results of the simulations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "TestDispersion.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Also, we can visualize histograms of such results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "TestDispersion.allInfo()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And finally, we can export the ellipses of the results to a .kml file so it can be opened on Google Earth" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "TestDispersion.exportEllipsesToKML(\n", + " dispersion_results,\n", + " filename,\n", + " origin_lat,\n", + " origin_lon,\n", + " type=\"all\",\n", + " resolution=100,\n", + " color=\"ff0000ff\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2nd Option -> Running by using only a dictionary of parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This second option allow us to perform the Monte Carlo Simulations without the need of a Flight object. This is useful when we want to perform the simulations for a rocket that we don't have a Flight object for, or when we want to perform the simulations for a rocket that we have a Flight object for, but we want to change some parameters of the simulations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "analysis_parameters = {\n", + " # Mass Details\n", + " \"mass\": (\n", + " 8.257,\n", + " 0.001,\n", + " ), # Rocket's dry mass (kg) and its uncertainty (standard deviation)\n", + " # Propulsion Details - run help(SolidMotor) for more information\n", + " # \"thrust\": [\"docs/notebooks/dispersion_analysis/dispersion_analysis_inputs/thrustCurve.csv\"],\n", + " # \"totalImpulse\": (1415.15, 35.3), # Motor total impulse (N*s)\n", + " # \"burnOutTime\": (5.274, 1), # Motor burn out time (s)\n", + " # \"nozzleRadius\": (21.642 / 1000, 0.5 / 1000), # Motor's nozzle radius (m)\n", + " # \"throatRadius\": (8 / 1000, 0.5 / 1000), # Motor's nozzle throat radius (m)\n", + " # \"grainNumber\":[6],\n", + " # \"grainSeparation\": (\n", + " # 6 / 1000,\n", + " # 1 / 1000,\n", + " # ), # Motor's grain separation (axial distance between two grains) (m)\n", + " # \"grainDensity\": (1707, 50), # Motor's grain density (kg/m^3)\n", + " # \"grainOuterRadius\": (21.4 / 1000, 0.375 / 1000), # Motor's grain outer radius (m)\n", + " # \"grainInitialInnerRadius\": (\n", + " # 9.65 / 1000,\n", + " # 0.375 / 1000,\n", + " # ), # Motor's grain inner radius (m)\n", + " # \"grainInitialHeight\": (120 / 1000, 1 / 1000), # Motor's grain height (m)\n", + " # \"interpolationMethod\":[\"linear\"],\n", + " # Aerodynamic Details - run help(Rocket) for more information\n", + " \"inertiaI\": (\n", + " 3.675,\n", + " 0.03675,\n", + " ), # Rocket's inertia moment perpendicular to its axis (kg*m^2)\n", + " \"inertiaZ\": (\n", + " 0.007,\n", + " 0.00007,\n", + " ), # Rocket's inertia moment relative to its axis (kg*m^2)\n", + " \"radius\": (40.45 / 1000, 0.001), # Rocket's radius (kg*m^2)\n", + " \"distanceRocketNozzle\": (\n", + " -1.024,\n", + " 0.001,\n", + " ), # Distance between rocket's center of dry mass and nozzle exit plane (m) (negative)\n", + " \"distanceRocketPropellant\": (\n", + " -0.571,\n", + " 0.001,\n", + " ), # Distance between rocket's center of dry mass and and center of propellant mass (m) (negative)\n", + " \"powerOffDrag\": (\n", + " 0.9081 / 1.05,\n", + " 0.033,\n", + " ), # Multiplier for rocket's drag curve. Usually has a mean value of 1 and a uncertainty of 5% to 10%\n", + " \"powerOnDrag\": (\n", + " 0.9081 / 1.05,\n", + " 0.033,\n", + " ), # Multiplier for rocket's drag curve. Usually has a mean value of 1 and a uncertainty of 5% to 10%\n", + " \"noseKind\": [\"Von Karman\"],\n", + " \"noseLength\": (0.274, 0.001), # Rocket's nose cone length (m)\n", + " \"noseDistanceToCM\": (\n", + " 1.134,\n", + " 0.001,\n", + " ), # Axial distance between rocket's center of dry mass and nearest point in its nose cone (m)\n", + " \"numberOfFins\": [4],\n", + " \"span\": (0.077, 0.0005), # Fin span (m)\n", + " \"rootChord\": (0.058, 0.0005), # Fin root chord (m)\n", + " \"tipChord\": (0.018, 0.0005), # Fin tip chord (m)\n", + " \"distanceToCM\": (\n", + " -0.906,\n", + " 0.001,\n", + " ), # Axial distance between rocket's center of dry mass and nearest point in its fin (m)\n", + " \"airfoil\": [None],\n", + " \"noTail\": [True],\n", + " \"positionFirstRailButton\": [0.0224],\n", + " \"positionSecondRailButton\": [-0.93],\n", + " \"railButtonAngularPosition\": [30],\n", + " # Launch and Environment Details - run help(Environment) and help(Flight) for more information\n", + " \"inclination\": (\n", + " 84.7,\n", + " 1,\n", + " ), # Launch rail inclination angle relative to the horizontal plane (degrees)\n", + " \"heading\": (53, 2), # Launch rail heading relative to north (degrees)\n", + " \"railLength\": (5.7, 0.0005), # Launch rail length (m)\n", + " \"ensembleMember\": list(range(10)), # Members of the ensemble forecast to be used\n", + " # Parachute Details - run help(Rocket) for more information\n", + " \"parachuteNames\": [\"Drogue\"],\n", + " \"CdS\": [\n", + " (\n", + " 0.349 * 1.3,\n", + " 0.07,\n", + " )\n", + " ], # Drag coefficient times reference area for the drogue chute (m^2)\n", + " \"trigger\": [[drogueTrigger]],\n", + " \"lag\": [\n", + " (\n", + " 173,\n", + " 0.5,\n", + " )\n", + " ], # Time delay between parachute ejection signal is detected and parachute is inflated (s)\n", + " \"samplingRate\": [[105]],\n", + " \"noise_mean\": [[0]],\n", + " \"noise_sd\": [[8.3]],\n", + " \"noise_corr\": [[0.5]],\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# motorDisp = SolidMotor(\n", + "# thrustSource=\"docs/notebooks/dispersion_analysis/dispersion_analysis_inputs/thrustCurve.csv\",\n", + "# burnOut=5.274,\n", + "# grainNumber=6,\n", + "# grainDensity=1707,\n", + "# grainOuterRadius=21.4 / 1000,\n", + "# grainInitialInnerRadius=9.65 / 1000,\n", + "# grainInitialHeight=120 / 1000,\n", + "# grainSeparation=6 / 1000,\n", + "# nozzleRadius=21.642 / 1000,\n", + "# throatRadius=8 / 1000,\n", + "# reshapeThrustCurve=False,\n", + "# interpolationMethod=\"linear\",\n", + "# )\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Disp = Dispersion(\"test\")\n", + "# modified_dispersion_dict = {i: j for i, j in analysis_parameters.items()}\n", + "# Disp.motor = motorDisp\n", + "# Disp.processDispersionDict(modified_dispersion_dict)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Disp = Dispersion(\"test\")\n", + "# Disp.runDispersion(100, analysis_parameters, Env, motor=motorDisp)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "hide_input": false, + "kernelspec": { + "display_name": "Python 3.10.5 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "vscode": { + "interpreter": { + "hash": "26de051ba29f2982a8de78e945f0abaf191376122a1563185a90213a26c5da77" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/user/index.rst b/docs/user/index.rst index a6e1c88d6..65a4d68da 100644 --- a/docs/user/index.rst +++ b/docs/user/index.rst @@ -12,6 +12,7 @@ Welcome to RocketPy's user documentation! ../notebooks/environment_analysis_class_usage.ipynb ../notebooks/environment_analysis_EuroC_example.ipynb ../notebooks/dispersion_analysis/dispersion_analysis.ipynb + ../notebooks/dispersion_analysis/dispersion_class_usage.ipynb ../notebooks/utilities_usage.ipynb ../matlab/matlab.rst diff --git a/getting_started Dispersion.ipynb b/getting_started Dispersion.ipynb deleted file mode 100644 index 9e329f7db..000000000 --- a/getting_started Dispersion.ipynb +++ /dev/null @@ -1,672 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Getting Started" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we go through a simplified rocket trajectory simulation to get you started. Let's start by importing the rocketpy module." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from rocketpy import *" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you are using Jupyter Notebooks, it is recommended to run the following line to make matplotlib plots which will be shown later interactive and higher quality." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib widget" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from datetime import datetime" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Running with dictionary only" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Set up parachutes. This rocket, named Valetudo, only has a drogue chute.\n", - "def drogueTrigger(p, y):\n", - " # Check if rocket is going down, i.e. if it has passed the apogee\n", - " vertical_velocity = y[5]\n", - " # Return true to activate parachute once the vertical velocity is negative\n", - " return True if vertical_velocity < 0 else False" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "analysis_parameters = {\n", - " # Mass Details\n", - " \"mass\": (\n", - " 8.257,\n", - " 0.001,\n", - " ), # Rocket's dry mass (kg) and its uncertainty (standard deviation)\n", - " # Propulsion Details - run help(SolidMotor) for more information\n", - "\n", - " # \"thrust\": [\"docs/notebooks/dispersion_analysis/dispersion_analysis_inputs/thrustCurve.csv\"],\n", - " # \"totalImpulse\": (1415.15, 35.3), # Motor total impulse (N*s)\n", - " # \"burnOutTime\": (5.274, 1), # Motor burn out time (s)\n", - " # \"nozzleRadius\": (21.642 / 1000, 0.5 / 1000), # Motor's nozzle radius (m)\n", - " # \"throatRadius\": (8 / 1000, 0.5 / 1000), # Motor's nozzle throat radius (m)\n", - " # \"grainNumber\":[6],\n", - " # \"grainSeparation\": (\n", - " # 6 / 1000,\n", - " # 1 / 1000,\n", - " # ), # Motor's grain separation (axial distance between two grains) (m)\n", - " # \"grainDensity\": (1707, 50), # Motor's grain density (kg/m^3)\n", - " # \"grainOuterRadius\": (21.4 / 1000, 0.375 / 1000), # Motor's grain outer radius (m)\n", - " # \"grainInitialInnerRadius\": (\n", - " # 9.65 / 1000,\n", - " # 0.375 / 1000,\n", - " # ), # Motor's grain inner radius (m)\n", - " # \"grainInitialHeight\": (120 / 1000, 1 / 1000), # Motor's grain height (m)\n", - " # \"interpolationMethod\":[\"linear\"],\n", - " # Aerodynamic Details - run help(Rocket) for more information\n", - " \"inertiaI\": (\n", - " 3.675,\n", - " 0.03675,\n", - " ), # Rocket's inertia moment perpendicular to its axis (kg*m^2)\n", - " \"inertiaZ\": (\n", - " 0.007,\n", - " 0.00007,\n", - " ), # Rocket's inertia moment relative to its axis (kg*m^2)\n", - " \"radius\": (40.45 / 1000, 0.001), # Rocket's radius (kg*m^2)\n", - " \"distanceRocketNozzle\": (\n", - " -1.024,\n", - " 0.001,\n", - " ), # Distance between rocket's center of dry mass and nozzle exit plane (m) (negative)\n", - " \"distanceRocketPropellant\": (\n", - " -0.571,\n", - " 0.001,\n", - " ), # Distance between rocket's center of dry mass and and center of propellant mass (m) (negative)\n", - " \"powerOffDrag\": (\n", - " 0.9081 / 1.05,\n", - " 0.033,\n", - " ), # Multiplier for rocket's drag curve. Usually has a mean value of 1 and a uncertainty of 5% to 10%\n", - " \"powerOnDrag\": (\n", - " 0.9081 / 1.05,\n", - " 0.033,\n", - " ), # Multiplier for rocket's drag curve. Usually has a mean value of 1 and a uncertainty of 5% to 10%\n", - " \"noseKind\": [\"Von Karman\"],\n", - " \"noseLength\": (0.274, 0.001), # Rocket's nose cone length (m)\n", - " \"noseDistanceToCM\": (\n", - " 1.134,\n", - " 0.001,\n", - " ), # Axial distance between rocket's center of dry mass and nearest point in its nose cone (m)\n", - " \"numberOfFins\": [4],\n", - " \"span\": (0.077, 0.0005), # Fin span (m)\n", - " \"rootChord\": (0.058, 0.0005), # Fin root chord (m)\n", - " \"tipChord\": (0.018, 0.0005), # Fin tip chord (m)\n", - " \"distanceToCM\": (\n", - " -0.906,\n", - " 0.001,\n", - " ), # Axial distance between rocket's center of dry mass and nearest point in its fin (m)\n", - " \"airfoil\": [None],\n", - "\n", - " \"noTail\": [True],\n", - "\n", - " \"positionFirstRailButton\": [0.0224],\n", - " \"positionSecondRailButton\": [-0.93],\n", - " \"railButtonAngularPosition\": [30],\n", - " \n", - " # Launch and Environment Details - run help(Environment) and help(Flight) for more information\n", - " \"inclination\": (\n", - " 84.7,\n", - " 1,\n", - " ), # Launch rail inclination angle relative to the horizontal plane (degrees)\n", - " \"heading\": (53, 2), # Launch rail heading relative to north (degrees)\n", - " \"railLength\": (5.7, 0.0005), # Launch rail length (m)\n", - " \"ensembleMember\": list(range(10)), # Members of the ensemble forecast to be used\n", - " # Parachute Details - run help(Rocket) for more information\n", - " \"parachuteNames\":[\"Drogue\"],\n", - " \"CdS\": [(\n", - " 0.349 * 1.3,\n", - " 0.07,\n", - " )], # Drag coefficient times reference area for the drogue chute (m^2)\n", - " \"trigger\":[[drogueTrigger]],\n", - " \"lag\": [(\n", - " 173,\n", - " 0.5,\n", - " )], # Time delay between parachute ejection signal is detected and parachute is inflated (s)\n", - " \"samplingRate\":[[105]],\n", - " \"noise_mean\": [[0]],\n", - " \"noise_sd\": [[8.3]],\n", - " \"noise_corr\": [[0.5]], \n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Define basic Environment object\n", - "Env = Environment(\n", - " railLength=5.7, date=(2019, 8, 10, 21), latitude=-23.363611, longitude=-48.011389\n", - ")\n", - "Env.setElevation(668)\n", - "Env.maxExpectedHeight = 1500\n", - "Env.setAtmosphericModel(\n", - " type=\"Ensemble\",\n", - " file=\"docs/notebooks/dispersion_analysis/dispersion_analysis_inputs/LASC2019_reanalysis.nc\",\n", - " dictionary=\"ECMWF\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "motorDisp = SolidMotor(\n", - " thrustSource=\"docs/notebooks/dispersion_analysis/dispersion_analysis_inputs/thrustCurve.csv\",\n", - " burnOut=5.274,\n", - " grainNumber=6,\n", - " grainDensity=1707,\n", - " grainOuterRadius=21.4 / 1000,\n", - " grainInitialInnerRadius=9.65 / 1000,\n", - " grainInitialHeight=120 / 1000,\n", - " grainSeparation=6 / 1000,\n", - " nozzleRadius=21.642 / 1000,\n", - " throatRadius=8 / 1000,\n", - " reshapeThrustCurve=False,\n", - " interpolationMethod=\"linear\",\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'NoneType' object has no attribute 'latitude'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32mc:\\Mateus\\GitHub\\RocketPy\\getting_started Dispersion.ipynb Célula: 13\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 2\u001b[0m modified_dispersion_dict \u001b[39m=\u001b[39m {i: j \u001b[39mfor\u001b[39;00m i, j \u001b[39min\u001b[39;00m analysis_parameters\u001b[39m.\u001b[39mitems()}\n\u001b[0;32m 3\u001b[0m Disp\u001b[39m.\u001b[39mmotor\u001b[39m=\u001b[39mmotorDisp\n\u001b[1;32m----> 4\u001b[0m Disp\u001b[39m.\u001b[39;49mprocessDispersionDict(modified_dispersion_dict)\n", - "File \u001b[1;32mc:\\Mateus\\GitHub\\RocketPy\\rocketpy\\Dispersion.py:294\u001b[0m, in \u001b[0;36mDispersion.processDispersionDict\u001b[1;34m(self, dispersionDict)\u001b[0m\n\u001b[0;32m 291\u001b[0m missing_input \u001b[39m=\u001b[39m \u001b[39mstr\u001b[39m(missing_input)\n\u001b[0;32m 292\u001b[0m \u001b[39m# Add to the dict\u001b[39;00m\n\u001b[0;32m 293\u001b[0m dispersionDict[missing_input] \u001b[39m=\u001b[39m [\n\u001b[1;32m--> 294\u001b[0m \u001b[39mgetattr\u001b[39;49m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49menvironment, missing_input)\n\u001b[0;32m 295\u001b[0m ]\n\u001b[0;32m 297\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mall\u001b[39m(\n\u001b[0;32m 298\u001b[0m rocket_input \u001b[39min\u001b[39;00m dispersionDict \u001b[39mfor\u001b[39;00m rocket_input \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrocket_inputs\n\u001b[0;32m 299\u001b[0m ):\n\u001b[0;32m 300\u001b[0m \u001b[39m# Iterate through missing inputs\u001b[39;00m\n\u001b[0;32m 301\u001b[0m \u001b[39mfor\u001b[39;00m missing_input \u001b[39min\u001b[39;00m \u001b[39mset\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrocket_inputs) \u001b[39m-\u001b[39m dispersionDict\u001b[39m.\u001b[39mkeys():\n", - "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'latitude'" - ] - } - ], - "source": [ - "Disp = Dispersion('test')\n", - "modified_dispersion_dict = {i: j for i, j in analysis_parameters.items()}\n", - "Disp.motor=motorDisp\n", - "Disp.processDispersionDict(modified_dispersion_dict)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'SolidMotor' object has no attribute 'thrustSource'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32mc:\\Mateus\\GitHub\\RocketPy\\getting_started Dispersion.ipynb Célula: 14\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m Disp \u001b[39m=\u001b[39m Dispersion(\u001b[39m'\u001b[39m\u001b[39mtest\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m----> 2\u001b[0m Disp\u001b[39m.\u001b[39;49mrunDispersion(\u001b[39m100\u001b[39;49m,analysis_parameters,Env,motor\u001b[39m=\u001b[39;49mmotorDisp)\n", - "File \u001b[1;32mc:\\Mateus\\GitHub\\RocketPy\\rocketpy\\Dispersion.py:474\u001b[0m, in \u001b[0;36mDispersion.runDispersion\u001b[1;34m(self, number_of_simulations, dispersionDict, environment, flight, motor, rocket, distributionType, image, realLandingPoint)\u001b[0m\n\u001b[0;32m 471\u001b[0m \u001b[39m# Creates copy of dispersionDict that will be altered\u001b[39;00m\n\u001b[0;32m 472\u001b[0m modified_dispersion_dict \u001b[39m=\u001b[39m {i: j \u001b[39mfor\u001b[39;00m i, j \u001b[39min\u001b[39;00m dispersionDict\u001b[39m.\u001b[39mitems()}\n\u001b[1;32m--> 474\u001b[0m analysis_parameters \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocessDispersionDict(modified_dispersion_dict)\n\u001b[0;32m 476\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdistribuitionFunc \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msetDistributionFunc(distributionType)\n\u001b[0;32m 477\u001b[0m \u001b[39m# Basic analysis info\u001b[39;00m\n\u001b[0;32m 478\u001b[0m \n\u001b[0;32m 479\u001b[0m \u001b[39m# Create data files for inputs, outputs and error logging\u001b[39;00m\n", - "File \u001b[1;32mc:\\Mateus\\GitHub\\RocketPy\\rocketpy\\Dispersion.py:304\u001b[0m, in \u001b[0;36mDispersion.processDispersionDict\u001b[1;34m(self, dispersionDict)\u001b[0m\n\u001b[0;32m 302\u001b[0m missing_input \u001b[39m=\u001b[39m \u001b[39mstr\u001b[39m(missing_input)\n\u001b[0;32m 303\u001b[0m \u001b[39m# Add to the dict\u001b[39;00m\n\u001b[1;32m--> 304\u001b[0m dispersionDict[missing_input] \u001b[39m=\u001b[39m [\u001b[39mgetattr\u001b[39;49m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mrocket, missing_input)]\n\u001b[0;32m 306\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mall\u001b[39m(\n\u001b[0;32m 307\u001b[0m motor_input \u001b[39min\u001b[39;00m dispersionDict \u001b[39mfor\u001b[39;00m motor_input \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msolidmotor_inputs\n\u001b[0;32m 308\u001b[0m ):\n\u001b[0;32m 309\u001b[0m \u001b[39m# Iterate through missing inputs\u001b[39;00m\n\u001b[0;32m 310\u001b[0m \u001b[39mfor\u001b[39;00m missing_input \u001b[39min\u001b[39;00m \u001b[39mset\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39msolidmotor_inputs) \u001b[39m-\u001b[39m dispersionDict\u001b[39m.\u001b[39mkeys():\n", - "\u001b[1;31mAttributeError\u001b[0m: 'SolidMotor' object has no attribute 'thrustSource'" - ] - } - ], - "source": [ - "Disp = Dispersion('test')\n", - "Disp.runDispersion(100,analysis_parameters,Env,motor=motorDisp)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting Up a Simulation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creating an Environment for Spaceport America" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Env = Environment(\n", - " railLength=5.2, latitude=32.990254, longitude=-106.974998, elevation=1400\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To get weather data from the GFS forecast, available online, we run the following lines.\n", - "\n", - "First, we set tomorrow's date." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import datetime\n", - "\n", - "tomorrow = datetime.date.today() + datetime.timedelta(days=1)\n", - "\n", - "Env.setDate((tomorrow.year, tomorrow.month, tomorrow.day, 12)) # Hour given in UTC time" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then, we tell Env to use a GFS forecast to get the atmospheric conditions for flight.\n", - "\n", - "Don't mind the warning, it just means that not all variables, such as wind speed or atmospheric temperature, are available at all altitudes given by the forecast." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Env.setAtmosphericModel(type=\"Forecast\", file=\"GFS\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see what the weather will look like by calling the info method!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Env.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creating a Motor\n", - "\n", - "A solid rocket motor is used in this case. To create a motor, the SolidMotor class is used and the required arguments are given.\n", - "\n", - "The SolidMotor class requires the user to have a thrust curve ready. This can come either from a .eng file for a commercial motor, such as below, or a .csv file from a static test measurement.\n", - "\n", - "Besides the thrust curve, other parameters such as grain properties and nozzle dimensions must also be given." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Pro75M1670 = SolidMotor(\n", - " thrustSource=\"data/motors/Cesaroni_M1670.eng\",\n", - " burnOut=3.9,\n", - " grainNumber=5,\n", - " grainSeparation=5 / 1000,\n", - " grainDensity=1815,\n", - " grainOuterRadius=33 / 1000,\n", - " grainInitialInnerRadius=15 / 1000,\n", - " grainInitialHeight=120 / 1000,\n", - " nozzleRadius=33 / 1000,\n", - " throatRadius=11 / 1000,\n", - " interpolationMethod=\"linear\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To see what our thrust curve looks like, along with other import properties, we invoke the info method yet again. You may try the allInfo method if you want more information all at once!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Pro75M1670.info()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creating a Rocket" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A rocket is composed of several components. Namely, we must have a motor (good thing we have the Pro75M1670 ready), a couple of aerodynamic surfaces (nose cone, fins and tail) and parachutes (if we are not launching a missile).\n", - "\n", - "Let's start by initializing our rocket, named Calisto, supplying it with the Pro75M1670 engine, entering its inertia properties, some dimensions and also its drag curves." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Calisto = Rocket(\n", - " motor=Pro75M1670,\n", - " radius=127 / 2000,\n", - " mass=19.197 - 2.956,\n", - " inertiaI=6.60,\n", - " inertiaZ=0.0351,\n", - " distanceRocketNozzle=-1.255,\n", - " distanceRocketPropellant=-0.85704,\n", - " powerOffDrag=\"data/calisto/powerOffDragCurve.csv\",\n", - " powerOnDrag=\"data/calisto/powerOnDragCurve.csv\",\n", - ")\n", - "\n", - "Calisto.setRailButtons([0.2, -0.5])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Adding Aerodynamic Surfaces" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we define the aerodynamic surfaces. They are really straight forward." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "NoseCone = Calisto.addNose(length=0.55829, kind=\"vonKarman\", distanceToCM=0.71971)\n", - "\n", - "FinSet = Calisto.addFins(\n", - " 4, span=0.100, rootChord=0.120, tipChord=0.040, distanceToCM=-1.04956\n", - ")\n", - "\n", - "Tail = Calisto.addTail(\n", - " topRadius=0.0635, bottomRadius=0.0435, length=0.060, distanceToCM=-1.194656\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Adding Parachutes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we have parachutes! Calisto will have two parachutes, Drogue and Main.\n", - "\n", - "Both parachutes are activated by some special algorithm, which is usually really complex and a trade secret. Most algorithms are based on pressure sampling only, while some also use acceleration info.\n", - "\n", - "RocketPy allows you to define a trigger function which will decide when to activate the ejection event for each parachute. This trigger function is supplied with pressure measurement at a predefined sampling rate. This pressure signal is usually noisy, so artificial noise parameters can be given. Call help(Rocket.addParachute) for more details. Furthermore, the trigger function also receives the complete state vector of the rocket, allowing us to use velocity, acceleration or even attitude to decide when the parachute event should be triggered.\n", - "\n", - "Here, we define our trigger functions rather simply using Python. However, you can call the exact code which will fly inside your rocket as well." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def drogueTrigger(p, y):\n", - " # p = pressure\n", - " # y = [x, y, z, vx, vy, vz, e0, e1, e2, e3, w1, w2, w3]\n", - " # activate drogue when vz < 0 m/s.\n", - " return True if y[5] < 0 else False\n", - "\n", - "\n", - "def mainTrigger(p, y):\n", - " # p = pressure\n", - " # y = [x, y, z, vx, vy, vz, e0, e1, e2, e3, w1, w2, w3]\n", - " # activate main when vz < 0 m/s and z < 800 + 1400 m (+1400 due to surface elevation).\n", - " return True if y[5] < 0 and y[2] < 800 + 1400 else False\n", - "\n", - "\n", - "Main = Calisto.addParachute(\n", - " \"Main\",\n", - " CdS=10.0,\n", - " trigger=mainTrigger,\n", - " samplingRate=105,\n", - " lag=1.5,\n", - " noise=(0, 8.3, 0.5),\n", - ")\n", - "\n", - "Drogue = Calisto.addParachute(\n", - " \"Drogue\",\n", - " CdS=1.0,\n", - " trigger=drogueTrigger,\n", - " samplingRate=105,\n", - " lag=1.5,\n", - " noise=(0, 8.3, 0.5),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Just be careful if you run this last cell multiple times! If you do so, your rocket will end up with lots of parachutes which activate together, which may cause problems during the flight simulation. We advise you to re-run all cells which define our rocket before running this, preventing unwanted old parachutes. Alternatively, you can run the following lines to remove parachutes.\n", - "\n", - "```python\n", - "Calisto.parachutes.remove(Drogue)\n", - "Calisto.parachutes.remove(Main)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulating a Flight\n", - "\n", - "Simulating a flight trajectory is as simple as initializing a Flight class object givin the rocket and environnement set up above as inputs. The launch rail inclination and heading are also given here." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "TestFlight = Flight(rocket=Calisto, environment=Env, inclination=85, heading=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyzing the Results\n", - "\n", - "RocketPy gives you many plots, thats for sure! They are divided into sections to keep them organized. Alternatively, see the Flight class documentation to see how to get plots for specific variables only, instead of all of them at once." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "TestFlight.allInfo()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Export Flight Trajectory to a .kml file so it can be opened on Google Earth" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "TestDispersion = Dispersion('teste')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dispersionDict={\n", - " 'rocketMass':(19.197 - 2.956, 0.2),\n", - " 'radius': 0.1* 127 / 2000,\n", - " 'burnOut':(3.9, 0.5), \n", - " 'parachuteNames': [\"Main\",\"Drogue\"],\n", - " 'CdS': [(10,2),(1)],\n", - " 'trigger':[[mainTrigger],[drogueTrigger]]\n", - " } " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "TestDispersion.runDispersion(number_of_simulations=10,dispersionDict=dispersionDict, \n", - " flight=TestFlight, image='dispersion_analysis_inputs/Valetudo_basemap_final.jpg')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "TestDispersion.info()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "hide_input": false, - "kernelspec": { - "display_name": "Python 3.10.5 64-bit", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.5" - }, - "vscode": { - "interpreter": { - "hash": "26de051ba29f2982a8de78e945f0abaf191376122a1563185a90213a26c5da77" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From a57b0a7ef810ec3ecbae565e3e9cc849935d41ad Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 10 Oct 2022 09:32:02 +0200 Subject: [PATCH 12/68] MAINT: improve run_dispersion initialization --- rocketpy/Dispersion.py | 29 ++++++++++++++++++----------- 1 file changed, 18 insertions(+), 11 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index b51aa3b7a..b691bdcbe 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -747,11 +747,10 @@ def run_dispersion( self, number_of_simulations, dispersion_dictionary, - environment, + environment=None, flight=None, motor=None, rocket=None, - distribution_type="normal", bg_image=None, actual_landing_point=None, ): @@ -791,24 +790,32 @@ def run_dispersion( self.number_of_simulations = number_of_simulations self.dispersion_dictionary = dispersion_dictionary - self.environment = environment - self.flight = flight - # TODO: What must be prioritized, the flight or the rocket and motor? - if flight: - self.motor = flight.rocket.motor if not motor else motor - self.rocket = flight.rocket if not rocket else rocket + self.environment = None + self.motor = None + self.rocket = None + if flight: # In case a flight object is passed + self.environment = flight.env + self.motor = flight.rocket.motor + self.rocket = flight.rocket + self.environment = environment if environment else self.environment self.motor = motor if motor else self.motor self.rocket = rocket if rocket else self.rocket - self.distribution_type = distribution_type + self.flight = flight + self.distribution_type = "normal" # TODO: Must be parametrized self.image = bg_image - self.actual_landing_point = actual_landing_point + self.actual_landing_point = actual_landing_point # + + # Obs.: The flight object is not prioritized, which is a good thing, but need to be documented + + # Check if there's enough object to start a flight: + ## Raise an error in case of any troubles # Creates copy of dispersion_dictionary that will be altered modified_dispersion_dict = {i: j for i, j in dispersion_dictionary.items()} analysis_parameters = self.__process_dispersion_dict(modified_dispersion_dict) - self.distributionFunc = self.__set_distribution_function(distribution_type) + self.distributionFunc = self.__set_distribution_function(self.distribution_type) # Basic analysis info # Create data files for inputs, outputs and error logging From a652af90420d9f543f1b62f180e03a3d65df1b2a Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 10 Oct 2022 12:52:51 +0200 Subject: [PATCH 13/68] FIX: adjust railLength attribute --- rocketpy/Environment.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/rocketpy/Environment.py b/rocketpy/Environment.py index 7bee7c3f8..79ebee81e 100644 --- a/rocketpy/Environment.py +++ b/rocketpy/Environment.py @@ -57,7 +57,7 @@ class Environment: Value of Air's Gas Constant = 287.05287 J/K/Kg Gravity and Launch Rail Length: - Environment.rl : float + Environment.railLength : float Launch rail length in meters. Environment.gravity : float Positive value of gravitational acceleration in m/s^2. @@ -102,7 +102,7 @@ class Environment: Environment.elevArray: array Two-dimensional Array containing the elevation information Environment.topographicProfileActivated: bool - True if the user already set a topographic plofile + True if the user already set a topographic profile Atmosphere Static Conditions: Environment.maxExpectedHeight : float @@ -352,7 +352,7 @@ def __init__( None """ # Save launch rail length - self.rL = railLength + self.railLength = railLength # Save gravity value self.gravity = gravity @@ -2763,10 +2763,10 @@ def calculateSpeedOfSoundProfile(self): # Retrieve gas constant R and temperature T R = self.airGasConstant T = self.temperature - G = 1.4 # Unused variable, why? + G = 1.4 # Compute speed of sound using sqrt(gamma*R*T) - a = (1.4 * R * T) ** 0.5 + a = (G * R * T) ** 0.5 # Set new output for the calculated speed of sound a.setOutputs("Speed of Sound (m/s)") @@ -2866,7 +2866,7 @@ def info(self): """ # Print launch site details print("Launch Site Details") - print("\nLaunch Rail Length:", self.rL, " m") + print("\nLaunch Rail Length:", self.railLength, " m") time_format = "%Y-%m-%d %H:%M:%S" if self.date != None and "UTC" not in self.timeZone: print( @@ -3002,7 +3002,7 @@ def allInfo(self): # Print launch site details print("\n\nLaunch Site Details") - print("\nLaunch Rail Length:", self.rL, " m") + print("\nLaunch Rail Length:", self.railLength, " m") time_format = "%Y-%m-%d %H:%M:%S" if self.date != None and "UTC" not in self.timeZone: print( @@ -3305,7 +3305,7 @@ def allInfoReturned(self): # Dictionary creation, if not commented follows the SI info = dict( grav=self.gravity, - launch_rail_length=self.rL, + launch_rail_length=self.railLength, elevation=self.elevation, modelType=self.atmosphericModelType, modelTypeMaxExpectedHeight=self.maxExpectedHeight, @@ -3352,9 +3352,9 @@ def exportEnvironment(self, filename="environment"): # TODO: in the future, allow the user to select which format will be used (json, csv, etc.). Default must be JSON. # TODO: add self.exportEnvDictionary to the documentation - # TODO: find a way to documennt the workaround I've used on ma.getdata(self... + # TODO: find a way to document the workaround I've used on ma.getdata(self... self.exportEnvDictionary = { - "railLength": self.rL, + "railLength": self.railLength, "gravity": self.g, "date": [self.date.year, self.date.month, self.date.day, self.date.hour], "latitude": self.lat, From 5fdf869d77d532b7d290c7cca5610fade5286c38 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 10 Oct 2022 12:53:21 +0200 Subject: [PATCH 14/68] MAINT: Deleting duplicated functions of supplement --- rocketpy/Environment.py | 330 ---------------------------------------- rocketpy/supplement.py | 2 +- 2 files changed, 1 insertion(+), 331 deletions(-) diff --git a/rocketpy/Environment.py b/rocketpy/Environment.py index 79ebee81e..183da064d 100644 --- a/rocketpy/Environment.py +++ b/rocketpy/Environment.py @@ -3396,336 +3396,6 @@ def exportEnvironment(self, filename="environment"): return None - # Auxiliary functions - Geodesic Coordinates - def geodesicToUtm(self, lat, lon, datum): - """Function which converts geodetic coordinates, i.e. lat/lon, to UTM - projection coordinates. Can be used only for latitudes between -80.00° - and 84.00° - - Parameters - ---------- - lat : float - The latitude coordinates of the point of analysis, must be contained - between -80.00° and 84.00° - lon : float - The longitude coordinates of the point of analysis, must be contained - between -180.00° and 180.00° - datum : string - The desired reference ellipsoide model, the following options are - available: "SAD69", "WGS84", "NAD83", and "SIRGAS2000". The default - is "SIRGAS2000", then this model will be used if the user make some - typing mistake - - Returns - ------- - x: float - East coordinate, always positive - y: - North coordinate, always positive - utmZone: int - The number of the UTM zone of the point of analysis, can vary between - 1 and 60 - utmLetter: string - The letter of the UTM zone of the point of analysis, can vary between - C and X, omitting the letters "I" and "O" - hemis: string - Returns "S" for southern hemisphere and "N" for Northern hemisphere - EW: string - Returns "W" for western hemisphere and "E" for eastern hemisphere - """ - - # Calculate the central meridian of UTM zone - if lon != 0: - signal = lon / abs(lon) - if signal > 0: - aux = lon - 3 - aux = aux * signal - div = aux // 6 - lon_mc = div * 6 + 3 - EW = "E" - else: - aux = lon + 3 - aux = aux * signal - div = aux // 6 - lon_mc = (div * 6 + 3) * signal - EW = "W" - else: - lon_mc = 3 - EW = "W|E" - - # Select the desired datum (i.e. the ellipsoid parameters) - if datum == "SAD69": - semiMajorAxis = 6378160.0 - flattening = 1 / 298.25 - elif datum == "WGS84": - semiMajorAxis = 6378137.0 - flattening = 1 / 298.257223563 - elif datum == "NAD83": - semiMajorAxis = 6378137.0 - flattening = 1 / 298.257024899 - else: - # SIRGAS2000 - semiMajorAxis = 6378137.0 - flattening = 1 / 298.257223563 - - # Evaluate the hemisphere and determine the N coordinate at the Equator - if lat < 0: - N0 = 10000000 - hemis = "S" - else: - N0 = 0 - hemis = "N" - - # Convert the input lat and lon to radians - lat = lat * np.pi / 180 - lon = lon * np.pi / 180 - lon_mc = lon_mc * np.pi / 180 - - # Evaluate reference parameters - K0 = 1 - 1 / 2500 - e2 = 2 * flattening - flattening**2 - e2lin = e2 / (1 - e2) - - # Evaluate auxiliary parameters - A = e2 * e2 - B = A * e2 - C = np.sin(2 * lat) - D = np.sin(4 * lat) - E = np.sin(6 * lat) - F = (1 - e2 / 4 - 3 * A / 64 - 5 * B / 256) * lat - G = (3 * e2 / 8 + 3 * A / 32 + 45 * B / 1024) * C - H = (15 * A / 256 + 45 * B / 1024) * D - I = (35 * B / 3072) * E - - # Evaluate other reference parameters - n = semiMajorAxis / ((1 - e2 * (np.sin(lat) ** 2)) ** 0.5) - t = np.tan(lat) ** 2 - c = e2lin * (np.cos(lat) ** 2) - ag = (lon - lon_mc) * np.cos(lat) - m = semiMajorAxis * (F - G + H - I) - - # Evaluate new auxiliary parameters - J = (1 - t + c) * ag * ag * ag / 6 - K = (5 - 18 * t + t * t + 72 * c - 58 * e2lin) * (ag**5) / 120 - L = (5 - t + 9 * c + 4 * c * c) * ag * ag * ag * ag / 24 - M = (61 - 58 * t + t * t + 600 * c - 330 * e2lin) * (ag**6) / 720 - - # Evaluate the final coordinates - x = 500000 + K0 * n * (ag + J + K) - y = N0 + K0 * (m + n * np.tan(lat) * (ag * ag / 2 + L + M)) - - # Convert the output lat and lon to degrees - lat = lat * 180 / np.pi - lon = lon * 180 / np.pi - lon_mc = lon_mc * 180 / np.pi - - # Calculate the UTM zone number - utmZone = int((lon_mc + 183) / 6) - - # Calculate the UTM zone letter - letters = "CDEFGHJKLMNPQRSTUVWXX" - utmLetter = letters[int(80 + lat) >> 3] - - return x, y, utmZone, utmLetter, hemis, EW - - def utmToGeodesic(self, x, y, utmZone, hemis, datum): - """Function to convert UTM coordinates to geodesic coordinates - (i.e. latitude and longitude). The latitude should be between -80° - and 84° - - Parameters - ---------- - x : float - East UTM coordinate in meters - y : float - North UTM coordinate in meters - utmZone : int - The number of the UTM zone of the point of analysis, can vary between - 1 and 60 - hemis : string - Equals to "S" for southern hemisphere and "N" for Northern hemisphere - datum : string - The desired reference ellipsoide model, the following options are - available: "SAD69", "WGS84", "NAD83", and "SIRGAS2000". The default - is "SIRGAS2000", then this model will be used if the user make some - typing mistake - - Returns - ------- - lat: float - latitude of the analysed point - lon: float - latitude of the analysed point - """ - - if hemis == "N": - y = y + 10000000 - - # Calculate the Central Meridian from the UTM zone number - centralMeridian = utmZone * 6 - 183 # degrees - - # Select the desired datum - if datum == "SAD69": - semiMajorAxis = 6378160.0 - flattening = 1 / 298.25 - elif datum == "WGS84": - semiMajorAxis = 6378137.0 - flattening = 1 / 298.257223563 - elif datum == "NAD83": - semiMajorAxis = 6378137.0 - flattening = 1 / 298.257024899 - else: - # SIRGAS2000 - semiMajorAxis = 6378137.0 - flattening = 1 / 298.257223563 - - # Calculate reference values - K0 = 1 - 1 / 2500 - e2 = 2 * flattening - flattening**2 - e2lin = e2 / (1 - e2) - e1 = (1 - (1 - e2) ** 0.5) / (1 + (1 - e2) ** 0.5) - - # Calculate auxiliary values - A = e2 * e2 - B = A * e2 - C = e1 * e1 - D = e1 * C - E = e1 * D - - m = (y - 10000000) / K0 - mi = m / (semiMajorAxis * (1 - e2 / 4 - 3 * A / 64 - 5 * B / 256)) - - # Calculate other auxiliary values - F = (3 * e1 / 2 - 27 * D / 32) * np.sin(2 * mi) - G = (21 * C / 16 - 55 * E / 32) * np.sin(4 * mi) - H = (151 * D / 96) * np.sin(6 * mi) - - lat1 = mi + F + G + H - c1 = e2lin * (np.cos(lat1) ** 2) - t1 = np.tan(lat1) ** 2 - n1 = semiMajorAxis / ((1 - e2 * (np.sin(lat1) ** 2)) ** 0.5) - quoc = (1 - e2 * np.sin(lat1) * np.sin(lat1)) ** 3 - r1 = semiMajorAxis * (1 - e2) / (quoc**0.5) - d = (x - 500000) / (n1 * K0) - - # Calculate other auxiliary values - I = (5 + 3 * t1 + 10 * c1 - 4 * c1 * c1 - 9 * e2lin) * d * d * d * d / 24 - J = ( - (61 + 90 * t1 + 298 * c1 + 45 * t1 * t1 - 252 * e2lin - 3 * c1 * c1) - * (d**6) - / 720 - ) - K = d - (1 + 2 * t1 + c1) * d * d * d / 6 - L = ( - (5 - 2 * c1 + 28 * t1 - 3 * c1 * c1 + 8 * e2lin + 24 * t1 * t1) - * (d**5) - / 120 - ) - - # Finally calculate the coordinates in lat/lot - lat = lat1 - (n1 * np.tan(lat1) / r1) * (d * d / 2 - I + J) - lon = centralMeridian * np.pi / 180 + (K + L) / np.cos(lat1) - - # Convert final lat/lon to Degrees - lat = lat * 180 / np.pi - lon = lon * 180 / np.pi - - return lat, lon - - def calculateEarthRadius(self, lat, datum): - """Simple function to calculate the Earth Radius at a specific latitude - based on ellipsoidal reference model (datum). The earth radius here is - assumed as the distance between the ellipsoid's center of gravity and a - point on ellipsoid surface at the desired - Pay attention: The ellipsoid is an approximation for the earth model and - will obviously output an estimate of the perfect distance between earth's - relief and its center of gravity. - - Parameters - ---------- - lat : float - latitude in which the Earth radius will be calculated - datum : string - The desired reference ellipsoide model, the following options are - available: "SAD69", "WGS84", "NAD83", and "SIRGAS2000". The default - is "SIRGAS2000", then this model will be used if the user make some - typing mistake - - Returns - ------- - float: - Earth Radius at the desired latitude in meters - """ - # Select the desired datum (i.e. the ellipsoid parameters) - if datum == "SAD69": - semiMajorAxis = 6378160.0 - flattening = 1 / 298.25 - elif datum == "WGS84": - semiMajorAxis = 6378137.0 - flattening = 1 / 298.257223563 - elif datum == "NAD83": - semiMajorAxis = 6378137.0 - flattening = 1 / 298.257024899 - else: - # SIRGAS2000 - semiMajorAxis = 6378137.0 - flattening = 1 / 298.257223563 - - # Calculate the semi minor axis length - # semiMinorAxis = semiMajorAxis - semiMajorAxis*(flattening**(-1)) - semiMinorAxis = semiMajorAxis * (1 - flattening) - - # Convert latitude to radians - lat = lat * np.pi / 180 - - # Calculate the Earth Radius in meters - eRadius = np.sqrt( - ( - (np.cos(lat) * (semiMajorAxis**2)) ** 2 - + (np.sin(lat) * (semiMinorAxis**2)) ** 2 - ) - / ((np.cos(lat) * semiMajorAxis) ** 2 + (np.sin(lat) * semiMinorAxis) ** 2) - ) - - # Convert latitude to degress - lat = lat * 180 / np.pi - - return eRadius - - def decimalDegressToArcSeconds(self, angle): - """Function to convert an angle in decimal degrees to deg/min/sec. - Converts (°) to (° ' ") - - Parameters - ---------- - angle : float - The angle that you need convert to deg/min/sec. Must be given in - decimal degrees. - - Returns - ------- - deg: float - The degrees. - min: float - The arc minutes. 1 arc-minute = (1/60)*degree - sec: float - The arc Seconds. 1 arc-second = (1/3600)*degree - """ - - if angle < 0: - signal = -1 - else: - signal = 1 - - deg = (signal * angle) // 1 - min = abs(signal * angle - deg) * 60 // 1 - sec = abs((signal * angle - deg) * 60 - min) * 60 - # print("The angle {:f} is equals to {:.0f}º {:.0f}' {:.3f}'' ".format( - # angle, signal*deg, min, sec - # )) - - return deg, min, sec - def printEarthDetails(self): """[UNDER CONSTRUCTION] Function to print information about the Earth Model used in the diff --git a/rocketpy/supplement.py b/rocketpy/supplement.py index 7776af01f..4901debca 100644 --- a/rocketpy/supplement.py +++ b/rocketpy/supplement.py @@ -125,7 +125,7 @@ def invertedHaversine(lat0, lon0, distance, bearing, eRadius=6.3781e6): Azimuth (or bearing compass) from the origin point, in degrees. eRadius : float, optional Earth radius, in meters. Default value is 6.3781e6. - See the utilities.calculateEarthRadius() function for more accuracy. + See the supplement.calculateEarthRadius() function for more accuracy. Returns ------- From 1e21f788bd7097bd42a283e6d245d56cdfd86e2f Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 10 Oct 2022 12:53:43 +0200 Subject: [PATCH 15/68] MAINT: saving instance of thrustSource --- rocketpy/Motor.py | 1 + 1 file changed, 1 insertion(+) diff --git a/rocketpy/Motor.py b/rocketpy/Motor.py index be9d62707..bf8f86ca0 100644 --- a/rocketpy/Motor.py +++ b/rocketpy/Motor.py @@ -154,6 +154,7 @@ def __init__( # Thrust parameters self.interpolate = interpolationMethod self.burnOutTime = burnOut + self.thrustSource = thrustSource # Check if thrustSource is csv, eng, function or other if isinstance(thrustSource, str): From df5afe6b56b7e265fd0d384e1b267d0bee1897ce Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 10 Oct 2022 12:54:18 +0200 Subject: [PATCH 16/68] MAINT: adjust railLength at Flight.py --- rocketpy/Flight.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/rocketpy/Flight.py b/rocketpy/Flight.py index 3b96ea6bc..73d8377c7 100644 --- a/rocketpy/Flight.py +++ b/rocketpy/Flight.py @@ -610,8 +610,8 @@ def __init__( upperRButton = max(self.rocket.railButtons[0]) lowerRButton = min(self.rocket.railButtons[0]) nozzle = self.rocket.distanceRocketNozzle - self.effective1RL = self.env.rL - abs(nozzle - upperRButton) - self.effective2RL = self.env.rL - abs(nozzle - lowerRButton) + self.effective1RL = self.env.railLength - abs(nozzle - upperRButton) + self.effective2RL = self.env.railLength - abs(nozzle - lowerRButton) # Flight initialization self.__init_post_process_variables() From 27b9b888b280827e04ec0aef79bb64049feaeed3 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 10 Oct 2022 12:54:55 +0200 Subject: [PATCH 17/68] DOC: adding TODO on EnvAnalysis --- rocketpy/EnvironmentAnalysis.py | 1 + 1 file changed, 1 insertion(+) diff --git a/rocketpy/EnvironmentAnalysis.py b/rocketpy/EnvironmentAnalysis.py index 69c7594ed..d1426397a 100644 --- a/rocketpy/EnvironmentAnalysis.py +++ b/rocketpy/EnvironmentAnalysis.py @@ -3115,6 +3115,7 @@ def exportMeanProfiles(self, filename="export_env_analysis"): Exports the mean profiles of the weather data to a file in order to it be used as inputs on Environment Class by using the CustomAtmosphere model. + OBS: Not all units are allowed as inputs of Environment Class. Parameters ---------- From f316d603f84de556c5b52d72b77ae1b2f8ec7eec Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 10 Oct 2022 12:55:20 +0200 Subject: [PATCH 18/68] DOC: Updating dispersion class notebook --- .../dispersion_class_usage.ipynb | 565 +++++++++++------- 1 file changed, 363 insertions(+), 202 deletions(-) diff --git a/docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb b/docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb index d637dc290..e2d1ffc7e 100644 --- a/docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb +++ b/docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -49,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -74,7 +74,7 @@ "metadata": {}, "source": [ "We will describe two different options of usage:\n", - "- Using a Flight object as input, speeding up the process of creating the simulations.\n", + "- Using a Flight object as input, speeding up the process of creating the simulations. (currently not completely described in this notebook)\n", "- Using a dictionary as input, including mean values and uncertainties for each parameter \n", "\n", "You need only one of them to get started. " @@ -91,18 +91,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Creating an Environment for Ponte de Sôr, Portugal" + "### Creating an Environment for 'Ponte de Sôr', Portugal" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "Env = Environment(\n", - " railLength=5.2, latitude=39.389700, longitude=-8.288964, elevation=160\n", - ")\n" + " railLength=5.2, latitude=39.389700, longitude=-8.288964, elevation=113\n", + ")" ] }, { @@ -116,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -124,7 +124,7 @@ "\n", "tomorrow = datetime.date.today() + datetime.timedelta(days=1)\n", "\n", - "Env.setDate((tomorrow.year, tomorrow.month, tomorrow.day, 12)) # Hour given in UTC time\n" + "Env.setDate((tomorrow.year, tomorrow.month, tomorrow.day, 12)) # Hour given in UTC time" ] }, { @@ -142,16 +142,123 @@ "metadata": {}, "outputs": [], "source": [ - "Env.setAtmosphericModel(type=\"Forecast\", file=\"GFS\")\n" + "# Env.setAtmosphericModel(type=\"Forecast\", file=\"GFS\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load an previous ran environment analysis as input for Environment" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "\n", + "file = open(\"export_env_analysis.json\")\n", + "env_dict = json.load(file)\n", + "file.close()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "hour = str(Env.date.hour)\n", + "\n", + "pressure_profile = np.array(env_dict[\"atmosphericModelPressureProfile\"][hour])\n", + "pressure_profile = np.column_stack(\n", + " (pressure_profile[:, 0], 100 * pressure_profile[:, 1])\n", + ")\n", + "temperature_profile = np.array(env_dict[\"atmosphericModelTemperatureProfile\"][hour])\n", + "temperature_profile = np.column_stack(\n", + " (temperature_profile[:, 0], 273 + temperature_profile[:, 1])\n", + ")\n", + "wind_u = env_dict[\"atmosphericModelWindVelocityXProfile\"][hour]\n", + "wind_v = env_dict[\"atmosphericModelWindVelocityYProfile\"][hour]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ - "Env.info()\n" + "Env.setAtmosphericModel(\n", + " type=\"CustomAtmosphere\",\n", + " pressure=pressure_profile,\n", + " temperature=temperature_profile,\n", + " wind_u=wind_u,\n", + " wind_v=wind_v,\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gravity Details\n", + "\n", + "Acceleration of Gravity: 9.80665 m/s²\n", + "\n", + "\n", + "Launch Site Details\n", + "\n", + "Launch Rail Length: 5.2 m\n", + "Launch Date: 2022-10-11 12:00:00 UTC\n", + "Launch Site Latitude: 39.38970°\n", + "Launch Site Longitude: -8.28896°\n", + "Launch Site Surface Elevation: 113.0 m\n", + "\n", + "\n", + "Atmospheric Model Details\n", + "\n", + "Atmospheric Model Type: CustomAtmosphere\n", + "CustomAtmosphere Maximum Height: 13.884 km\n", + "\n", + "\n", + "Surface Atmospheric Conditions\n", + "\n", + "Surface Wind Speed: 0.79 m/s\n", + "Surface Wind Direction: 175.73°\n", + "Surface Wind Heading: 355.73°\n", + "Surface Pressure: 992.00 hPa\n", + "Surface Temperature: 291.09 K\n", + "Surface Air Density: 1.187 kg/m³\n", + "Surface Speed of Sound: 342.02 m/s\n", + "\n", + "\n", + "Atmospheric Model Plots\n" + ] + }, + { + "data": { + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-10T12:42:06.797159\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Env.allInfo()\n" ] }, { @@ -171,12 +278,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "Pro75M1670 = SolidMotor(\n", - " thrustSource=\"data/motors/Cesaroni_M1670.eng\",\n", + " thrustSource=\"../../../data/motors/Cesaroni_M1670.eng\",\n", " burnOut=3.9,\n", " grainNumber=5,\n", " grainSeparation=5 / 1000,\n", @@ -192,9 +299,37 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Motor Details\n", + "Total Burning Time: 3.9 s\n", + "Total Propellant Mass: 2.956 kg\n", + "Propellant Exhaust Velocity: 2038.745 m/s\n", + "Average Thrust: 1545.218 N\n", + "Maximum Thrust: 2200.0 N at 0.15 s after ignition.\n", + "Total Impulse: 6026.350 Ns\n", + "\n", + "Plots\n" + ] + }, + { + "data": { + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-10T12:42:26.852881\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "Pro75M1670.info()\n" ] @@ -208,7 +343,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -220,8 +355,8 @@ " inertiaZ=0.0351,\n", " distanceRocketNozzle=-1.255,\n", " distanceRocketPropellant=-0.85704,\n", - " powerOffDrag=\"data/calisto/powerOffDragCurve.csv\",\n", - " powerOnDrag=\"data/calisto/powerOnDragCurve.csv\",\n", + " powerOffDrag=\"../../../data/calisto/powerOffDragCurve.csv\",\n", + " powerOnDrag=\"../../../data/calisto/powerOnDragCurve.csv\",\n", ")\n", "\n", "Calisto.setRailButtons([0.2, -0.5])\n", @@ -246,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -285,9 +420,45 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Inertia Details\n", + "Rocket Dry Mass: 16.241 kg (No Propellant)\n", + "Rocket Total Mass: 19.196911961392022 kg (With Propellant)\n", + "\n", + "Geometrical Parameters\n", + "Rocket Radius: 0.0635 m\n", + "\n", + "Aerodynamics Stability\n", + "Initial Static Margin: 2.051 c\n", + "Final Static Margin: 3.090 c\n", + "\n", + "Main Parachute\n", + "CdS Coefficient: 10.0 m2\n", + "\n", + "Drogue Parachute\n", + "CdS Coefficient: 1.0 m2\n", + "\n", + "Aerodynamics Plots\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkAAAAHHCAYAAABXx+fLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy89olMNAAAACXBIWXMAAA9hAAAPYQGoP6dpAACPiUlEQVR4nOzdd3hT5dvA8W+SbjqhewCljEIZhSKVJSiFMlQQFRDZiIqgIq8yHCA4wAEyRBFkq4Aioj9BhoUiowwZssssLaOlFEoXdOW8f9RGQgskJW3a9P5cVy7oWbnvnCS9+5zneY5KURQFIYQQQohKRG3uAIQQQgghypoUQEIIIYSodKQAEkIIIUSlIwWQEEIIISodKYCEEEIIUelIASSEEEKISkcKICGEEEJUOlIACSGEEKLSkQJICCGEEJWOFEBCFCMvL48xY8YQEBCAWq2mR48eAGRkZPDCCy/g7e2NSqVi1KhRxMXFoVKpWLx4sVHPsXjxYlQqFXFxcSaP31yMeS0Kt/38889LPzBRKURHR6NSqVi1apW5QzE5S87NXKQAsnCFv2QLH3Z2dvj6+hIZGcmsWbNIT083d4j39Msvv9ClSxfc3d2xsbHB19eXXr16sXnz5lJ93oULF/LZZ5/xzDPPsGTJEt544w0APv74YxYvXszw4cNZtmwZ/fv3L9U4HlRWVhbvv/8+0dHR9912z549qFQqvvjiiyLrunfvjkqlYtGiRUXWPfLII/j5+d31uOvWreP99983JmyDFP5CKHxYW1tTq1YtBgwYwNmzZ03+fOby+++/07lzZ6pVq4adnR1169blzTffJCUlxdyh6Wnfvj0qlYo6deoUu37Tpk26c1Vef4kXfl/a2dlx8eLFIuvbt29Pw4YNzRCZKA1W5g5AlI3JkycTGBhIbm4uiYmJREdHM2rUKKZPn85vv/1G48aNzR2iHkVRGDJkCIsXL6Zp06aMHj0ab29vLl++zC+//EKHDh3YsWMHrVq1KpXn37x5M35+fkWKgc2bN/Pwww8zceJEvVhv3ryJtbW1Uc/Rv39/+vTpg62trUliLk5WVhaTJk0CCr6876VZs2Y4ODiwfft2XcFXaOfOnVhZWbFjxw4GDx6sW56Tk8PevXt54oknAKhRo0aR12LdunXMmTOnVIoggNdee42HHnqI3Nxc9u/fz7x581i7di2HDx/G19e3VJ6zrLz55ptMmzaNJk2aMHbsWKpWrcr+/fv58ssvWbFiBVFRUdSrV8/cYerY2dlx+vRp9uzZQ4sWLfTWff/999jZ2XHr1i0zRWe47Oxspk6dyuzZs80diihFUgBVEl26dKF58+a6n8ePH8/mzZt5/PHHefLJJzl+/Dj29vZ33T8zM5MqVaqURagATJs2jcWLF+uKNJVKpVv3zjvvsGzZMqysSu/te+XKFVxdXYtd3qBBA71lhX8xGkuj0aDRaEoaoslZWVkRHh7Ojh079JbHxsZy9epV+vbty/bt2/XW7du3j1u3btGmTRug5K/Fg2jbti3PPPMMAIMHD6Zu3bq89tprLFmyhPHjx5dpLMZQFIVbt27d9XO3fPlypk2bRu/evfn+++/13iuDBg3i0Ucf5dlnn2X//v2l+lkwRlBQEHl5eSxfvlyvALp16xa//PIL3bp14+effzZjhIYJDQ1l/vz5jB8/vsIX0cYq6+96c5JLYJXYY489xnvvvcf58+f57rvvdMsHDRqEo6MjZ86coWvXrjg5OfH8888DsG3bNp599lmqV6+Ora0tAQEBvPHGG9y8ebPI8X/66ScaNGiAnZ0dDRs25JdffmHQoEHUrFnznnHdvHmTKVOmEBwczOeff65X/BTq37+/3hfs2bNnefbZZ6latSoODg48/PDDrF27tsh+2dnZTJw4kdq1a+viHzNmDNnZ2cB//VK2bNnC0aNHdU32hZdbzp07x9q1a3XL4+Li7trv5cSJE/Tq1QsPDw/s7e2pV68e77zzjm793foA/fHHH7Rt25YqVarg5OREt27dOHr0qN42hefo4sWL9OjRA0dHRzw8PHjzzTfJz8/X5eLh4QHApEmTdDHfqyWmTZs2JCUlcfr0ad2yHTt24OzszIsvvqgrhm5fV7jf7a9f4WsxaNAg5syZA6B3uepO8+bNIygoCFtbWx566CH27t171xjv57HHHgPg3LlzumVfffUVISEh2Nra4uvry4gRI0hNTdWtnzVrFhqNRm/ZtGnTUKlUjB49WrcsPz8fJycnxo4dq1um1WqZMWMGISEh2NnZ4eXlxUsvvcT169f14qpZsyaPP/44GzZsoHnz5tjb2/PNN9/cNY9Jkybh5ubGvHnzihTKLVq0YOzYsRw+fFjvclLhJZpjx47x6KOP4uDggJ+fH59++ul9X7dFixahUqlYuHCh3vKPP/4YlUrFunXr7nsMgOeee46VK1ei1Wp1y/73v/+RlZVFr169imx//vx5XnnlFerVq4e9vT3VqlXj2WefLbZvXGpqKm+88QY1a9bE1tYWf39/BgwYoPeehIJz8tFHH+Hv74+dnR0dOnTQe0/fz9tvv01+fj5Tp06953b36vN252ft/fffR6VScfLkSfr164eLiwseHh689957KIpCQkIC3bt3x9nZGW9vb6ZNm1bsc+bn5/P222/j7e1NlSpVePLJJ0lISCiy3e7du+ncuTMuLi44ODjQrl27In/cFMZ07Ngx+vbti5ubm+6zXBlIAVTJFfZh2bhxo97yvLw8IiMj8fT05PPPP+fpp58GCoqarKwshg8fzuzZs4mMjGT27NkMGDBAb/+1a9fSu3dvrK2tmTJlCj179mTo0KHs27fvvjFt376da9eu0bdvX4NaSJKSkmjVqhUbNmzglVde4aOPPuLWrVs8+eST/PLLL7rttFotTz75JJ9//jlPPPEEs2fPpkePHnzxxRf07t0bAA8PD5YtW0ZwcDD+/v4sW7aMZcuWUb9+fZYtW4a7uzuhoaG65YUFxp0OHTpEeHg4mzdvZtiwYcycOZMePXrwv//97565LFu2jG7duuHo6Mgnn3zCe++9x7Fjx2jTpk2RXwj5+flERkZSrVo1Pv/8c9q1a8e0adOYN2+eLpevv/4agKeeekoXc8+ePe/6/IVffre39OzYsYOHH36Y8PBwrK2t2blzp946JycnmjRpUuzxXnrpJTp27KjLrfBxux9++IHPPvuMl156iQ8//JC4uDh69uxJbm7uPV+ruzlz5gwA1apVAwq+5EeMGIGvry/Tpk3j6aef5ptvvqFTp06652jbti1arVYv723btqFWq9m2bZtu2YEDB8jIyOCRRx7Ry/Gtt96idevWzJw5k8GDB/P9998TGRlZJIfY2Fiee+45OnbsyMyZMwkNDS02h1OnThEbG6v7hVicws/c77//rrf8+vXrdO7cmSZNmjBt2jSCg4MZO3Ysf/zxxz1ft8GDB/P4448zevRo3S/Uw4cPM2nSJIYOHUrXrl3vuX+hvn37cvnyZb1+Zz/88AMdOnTA09OzyPZ79+5l586d9OnTh1mzZvHyyy8TFRVF+/btycrK0m2XkZFB27ZtmT17Np06dWLmzJm8/PLLnDhxggsXLugdc+rUqfzyyy+8+eabjB8/nl27dun+iDNEYGAgAwYMYP78+Vy6dMng/QzRu3dvtFotU6dOJTw8nA8//JAZM2bQsWNH/Pz8+OSTT6hduzZvvvkmf/31V5H9P/roI9auXcvYsWN57bXX2LRpExEREXp/hG7evJlHHnmEtLQ0Jk6cyMcff0xqaiqPPfYYe/bsKXLMZ599lqysLD7++GOGDRtm0nzLNUVYtEWLFimAsnfv3rtu4+LiojRt2lT388CBAxVAGTduXJFts7KyiiybMmWKolKplPPnz+uWNWrUSPH391fS09N1y6KjoxVAqVGjxj1jnjlzpgIov/zyyz23KzRq1CgFULZt26Zblp6ergQGBio1a9ZU8vPzFUVRlGXLlilqtVpvO0VRlLlz5yqAsmPHDt2ydu3aKSEhIUWeq0aNGkq3bt30lp07d04BlEWLFumWPfLII4qTk5Pea6IoiqLVanX/Lzw3586d08Xs6uqqDBs2TG+fxMRExcXFRW954TmaPHmy3rZNmzZVwsLCdD8nJycrgDJx4sQiuRQnLS1N0Wg0ytChQ3XL6tWrp0yaNElRFEVp0aKF8tZbb+nWeXh4KB07drznazFixAiluK+awm2rVaumXLt2Tbf8119/VQDlf//73z1j3bJliwIoCxcuVJKTk5VLly4pa9euVWrWrKmoVCpl7969ypUrVxQbGxulU6dOuveBoijKl19+qdtXURQlPz9fcXZ2VsaMGaMoSsF5qlatmvLss88qGo1G9z6ePn26olarlevXryuKoijbtm1TAOX777/Xi239+vVFlteoUUMBlPXr198zL0VRlDVr1iiA8sUXX9xzO2dnZ6VZs2a6n9u1a6cAytKlS3XLsrOzFW9vb+Xpp5++7/NevnxZqVq1qtKxY0clOztbadq0qVK9enXlxo0b99339s9M8+bNde+h69evKzY2NsqSJUt05+ynn37S7Vfcd0pMTEyRPCZMmKAAyurVq4tsX/i5Kjx+/fr1lezsbN36wu+Uw4cP3zOH278vz5w5o1hZWSmvvfZasTkqSvHv90J3fu4mTpyoAMqLL76oW5aXl6f4+/srKpVKmTp1qm759evXFXt7e2XgwIG6ZYW5+fn5KWlpabrlP/74owIoM2fO1L0WderUUSIjI/W+b7KyspTAwEC9z2thTM8999w9XxdLJS1AAkdHx2JHgw0fPrzIstv7K2RmZnL16lVatWqFoigcOHAAgEuXLnH48GEGDBiAo6Ojbvt27drRqFGj+8aTlpYGgJOTk0Hxr1u3jhYtWug13To6OvLiiy8SFxfHsWPHgILWq/r16xMcHMzVq1d1j8JLJlu2bDHo+e4nOTmZv/76iyFDhlC9enW9dcVd/im0adMmUlNTee655/Ti02g0hIeHFxvfyy+/rPdz27ZtH2gElJOTE40bN9a1hFy9epXY2FhdZ/PWrVvrmtFPnjxJcnLyAzeZ9+7dGzc3N93Pbdu2BTA4jyFDhuDh4YGvry/dunUjMzOTJUuW0Lx5c/78809ycnIYNWoUavV/X3fDhg3D2dlZd5lUrVbTqlUr3V/cx48fJyUlhXHjxqEoCjExMUBBq1DDhg11/cN++uknXFxc6Nixo945CwsLw9HRscg5CwwMJDIy8r45FX4e7/cZcHJy0n1eCjk6OtKvXz/dzzY2NrRo0cKg19Pb25s5c+awadMm2rZty8GDB1m4cOFdW6Hupm/fvqxevZqcnBxWrVqFRqPhqaeeKnbb279TcnNzSUlJoXbt2ri6urJ//37dup9//pkmTZoUe5w7P1eDBw/GxsZG97Ox7ymAWrVq0b9/f+bNm8fly5cN3u9+XnjhBd3/NRoNzZs3R1EUhg4dqlvu6upKvXr1io13wIABeu+LZ555Bh8fH90lyoMHD3Lq1Cn69u1LSkqK7j2ZmZlJhw4d+Ouvv/QuT0LR75HKQgogQUZGRpEvWisrK/z9/YtsGx8fz6BBg6hataqu30m7du0AuHHjBlBwTR+gdu3aRfYvbtmdCr9sDR2if/78+WJHwtSvX18vnlOnTnH06FE8PDz0HnXr1gUKOjibQuGXlrHDZU+dOgUU9GG5M8aNGzcWic/Ozq7IJTg3N7cifU+M1aZNG11fn507d6LRaHj44YcBaNWqFfv27SM7O7tI/5+SurNILCyGDM1jwoQJbNq0ic2bN3Po0CEuXbqku7RbeO7vfH/Y2NhQq1Yt3Xoo+CW5b98+bt68ybZt2/Dx8aFZs2Y0adJEdxls+/btul+mUHDObty4gaenZ5FzlpGRUeScBQYGGpRT4efxfp+B9PT0Ip9df3//IgWBMe+LPn360K1bN/bs2cOwYcPo0KGDQfvdeYwbN27wxx9/8P333/P444/ftZi7efMmEyZMICAgAFtbW9zd3fHw8CA1NVX3nQIFlzYN/Uw96Huq0LvvvkteXt59+wIZ487YXFxcsLOzw93dvcjy4uK9c5oBlUpF7dq1dZfIC79HBg4cWOQ9+e2335Kdna33uoLh70tLUz6GDgizuXDhAjdu3ChSmNja2ur9xQwFfU46duzItWvXGDt2LMHBwVSpUoWLFy8yaNCgIn9VlFRwcDBQ0P+gcAJCU9BqtTRq1Ijp06cXuz4gIMBkz1USha/fsmXL8Pb2LrL+zpE+pTWCrE2bNsyePZsdO3awc+dOGjVqpGvJa9WqFdnZ2ezdu5ft27djZWWlK45K6m55KIpi0P6NGjUiIiLigWKAgrxzc3OJiYlh27ZtukKnbdu2bNu2jRMnTpCcnKxXAGm1Wjw9Pfn++++LPeadBeq9RlrerrB4P3To0F23OX/+PGlpaUVGJT7o65mSksLff/8NwLFjx9BqtUW+C+7Hx8eH9u3bM23aNHbs2HHPkV+vvvoqixYtYtSoUbRs2RIXFxdUKhV9+vQp8XfKg74GhWrVqkW/fv2YN28e48aNK7L+bi26hQMRDI3NVPHCf98jn3322V37mN3eMg+Gvy8tjRRAlVxhh1RDmuUPHz7MyZMnWbJkiV6n502bNultV6NGDYBiR10YMhKjTZs2uLm5sXz5ct5+++37/qKvUaMGsbGxRZafOHFCL56goCD++ecfOnTocM9LUQ+qVq1aABw5csSo/YKCggDw9PQ0yS90uPclt7u5vSN0TEwMrVu31q3z9fWlRo0a7Nixgx07dtC0aVMcHBxMHoOpFJ772NhY3XmBgvmLzp07p/c6t2jRAhsbG7Zt28a2bdt46623gIKJHufPn09UVJTu50JBQUH8+eeftG7d2qS/ROrWrUvdunVZs2YNM2fOLLb1ZOnSpQA8/vjjJntegBEjRpCens6UKVMYP348M2bM0BsJZ6i+ffvywgsv4Orqes8O1KtWrWLgwIF6o55u3bqlNyIPCl5rYz9TpvDuu+/y3Xff8cknnxRZV9iydGest7csmlphC08hRVE4ffq0bi63wu8RZ2dnk32PWCq5BFaJbd68mQ8++IDAwECDRkgUFiK3/1WiKAozZ87U287X15eGDRuydOlSMjIydMu3bt3K4cOH7/s8Dg4OjB07luPHjzN27Nhi/wr67rvvdKMZunbtyp49e3T9NKCgf9K8efOoWbOm7i/kXr16cfHiRebPn1/keDdv3iQzM/O+sRnCw8ODRx55hIULFxIfH6+37l5/0UVGRuLs7MzHH39c7Aio5ORko2MpLE7u/IK+F19fXwIDA4mKiuLvv/8uMtlkq1atWLNmDbGxsQZd/iqcU8SYGEwlIiICGxsbZs2apffaL1iwgBs3btCtWzfdMjs7Ox566CGWL19OfHy8XgvQzZs3mTVrFkFBQfj4+Oj26dWrF/n5+XzwwQdFnjsvL++Bcp4wYQLXr1/n5ZdfLtKisG/fPj755BMaNmyoG6FpCqtWrWLlypVMnTqVcePG0adPH959911Onjxp9LGeeeYZJk6cyFdffaXXH+dOGo2myOdi9uzZRXJ++umn+eeff/RGdhYqSUuJoYKCgujXrx/ffPMNiYmJeuucnZ1xd3cvMlrrq6++KrV4li5dqndpdNWqVVy+fJkuXboAEBYWRlBQEJ9//rne92+hknyPWCppAaok/vjjD06cOEFeXh5JSUls3ryZTZs2UaNGDX777TeDJq8LDg4mKCiIN998k4sXL+Ls7MzPP/9c7HXqjz/+mO7du9O6dWsGDx7M9evX+fLLL2nYsGGxH8o7vfXWWxw9epRp06axZcsWnnnmGby9vUlMTGTNmjXs2bNHNxx73LhxLF++nC5duvDaa69RtWpVlixZwrlz5/j55591zff9+/fnxx9/5OWXX2bLli20bt2a/Px8Tpw4wY8//qibn8UUZs2aRZs2bWjWrBkvvvgigYGBxMXFsXbtWg4ePFjsPs7Oznz99df079+fZs2a0adPHzw8PIiPj2ft2rW0bt2aL7/80qg47O3tadCgAStXrqRu3bpUrVqVhg0b3rcvRZs2bXStg7e3AEFBAbR8+XLddvcTFhYGFMzYHBkZiUajoU+fPkblUVIeHh6MHz+eSZMm0blzZ5588kliY2P56quveOihh/Q6C0NBsTN16lRcXFx0HfY9PT2pV68esbGxDBo0SG/7du3a8dJLLzFlyhQOHjxIp06dsLa25tSpU/z000/MnDlTN0mjsZ5//nn27t3LzJkzOXbsGM8//zxubm7s37+fhQsXUq1aNVatWmX0DOR3c+XKFYYPH86jjz7KyJEjAfjyyy/ZsmULgwYNYvv27UZdCnNxcTFo9u/HH3+cZcuW4eLiQoMGDYiJieHPP//UTWNQ6K233mLVqlU8++yzDBkyhLCwMK5du8Zvv/3G3Llz7zoVgykUTr4aGxtLSEiI3roXXniBqVOn8sILL9C8eXP++uuvEhWMhqpatSpt2rRh8ODBJCUlMWPGDGrXrq0bvq5Wq/n222/p0qULISEhDB48GD8/Py5evMiWLVtwdna+73QclYYZRp6JMlQ4rLPwYWNjo3h7eysdO3ZUZs6cqTecstDAgQOVKlWqFHu8Y8eOKREREYqjo6Pi7u6uDBs2TPnnn3+KHQq6YsUKJTg4WLG1tVUaNmyo/Pbbb8rTTz+tBAcHGxz/qlWrlE6dOilVq1ZVrKysFB8fH6V3795KdHS03nZnzpxRnnnmGcXV1VWxs7NTWrRoofz+++9FjpeTk6N88sknSkhIiGJra6u4ubkpYWFhyqRJk/SG+j7oMHhFUZQjR44oTz31lC6mevXqKe+9955u/Z3D4Att2bJFiYyMVFxcXBQ7OzslKChIGTRokPL333/rtrnbOSoc1nq7nTt3KmFhYYqNjY3BQ+K/+eYb3ZDbO+3fv1/3fkpKSrrva5GXl6e8+uqrioeHh6JSqXTxFW772WefFXkOQ+Isbkj13Xz55ZdKcHCwYm1trXh5eSnDhw/XDWW/3dq1axVA6dKli97yF154QQGUBQsWFHv8efPmKWFhYYq9vb3i5OSkNGrUSBkzZoxy6dIl3TbFvXcMsWbNGqVjx46Km5ubYmtrq9SuXVv5v//7PyU5ObnItnd73w4cOPC+00/07NlTcXJyUuLi4vSWF05L8Mknn9xz/7s99+2KO2fXr19XBg8erLi7uyuOjo5KZGSkcuLECaVGjRp6w8AVRVFSUlKUkSNHKn5+foqNjY3i7++vDBw4ULl69epdj68o9x6ufrt7TRtSOPXEnTlmZWUpQ4cOVVxcXBQnJyelV69eypUrV+46DP7O83a3z/Kdr2dhbsuXL1fGjx+veHp6Kvb29kq3bt2KTLehKIpy4MABpWfPnkq1atUUW1tbpUaNGkqvXr2UqKio+8ZUWagUpRTbDoW4Q2hoKB4eHkX6DQkhhBBlSfoAiVKRm5tLXl6e3rLo6Gj++eef+96UUwghhCht0gIkSkVcXBwRERH069cPX19fTpw4wdy5c3FxceHIkSNFru8LIYQQZUk6QYtS4ebmRlhYGN9++y3JyclUqVKFbt26MXXqVCl+hBBCmJ20AAkhhBCi0pE+QEIIIYSodKQAEkIIIUSlI32AiqHVarl06RJOTk5mncZfCCGEEIZTFIX09HR8fX3vO3GnFEDFuHTpktlvjCmEEEKIkklISMDf3/+e20gBVIzCGw8mJCTg7Oz8wMfLzc1l48aNumnyLZHkWPFZen4gOVoCS88PLD/H0swvLS2NgICAYm8gfCcpgIpReNnL2dnZZAWQg4MDzs7OFvlmBsnRElh6fiA5WgJLzw8sP8eyyM+Q7ivSCVoIIYQQlY4UQEIIIYSodKQAEkIIIUSlIwWQEEIIISodKYCEEEIIUelIASSEEEKISkcKICGEEEJUOlIACSGEEKLSkQJICCGEEJWOFEBCCCGEqHSkABJCCCFEpSMFkBBCCCEqHSmAhBCiGIqikK9VzB2GEKKUSAEkhBDFeGPlQYLf+4PFO86ZOxQhRCmQAkgIIe6gKAr/O3SZ3HyF9/93jKjjSeYOSQhhYlIACSHEHa5n5epd/np9xUFOX0k3Y0RCCFOTAkgIIe6QcC0LgGpVbGhRsyoZ2Xm8sORvbmTlmjkyIYSpSAEkhBB3iP+3AArycOSrfs3wc7UnLiWLkcv3k5evNXN0QghTkAJICCHuUFgA+Ve1x93RlnkDwrC31rDt1FWm/nHCzNEJIUxBCiAhhLhD4SWw6lUdAAjxdWFaryYAfLv9HKv2XTBbbEII05ACSAgh7pBwXb8AAujayIfXHqsNwNurD7Pv/HWzxCaEMA0pgIQQ4g7x14oWQACjIurSqYEXOflaXlz6t66lSAhR8UgBJIQQt8nN13Ip9RYAAXcUQGq1ii96hxLi60xKZg6DFu2RkWFCVFBSAAkhxG0up94iX6tga6XGw9G2yPoqtlYsGPgQ3s52nEnO5OXv9pGTJyPDhKhopAASQojbFPb/CajqgFqtKnYbbxc7Fg56iCo2GmLOpjB+9WEURe4bJkRFIgWQEELc5m79f+7UwNeZOc83Q6NW8fP+C8zefLoswhNCmIgUQEIIcZvCAijAzf6+27av58mkJ0MAmL7pJD/+nVCqsQkhTEcKICGEuI2uALpPC1Chfg/X4OV2QQCMX31YbpwqRAUhBZAQQtzmgoGXwG43tnM9nm7mT75WYcQP+2WOICEqACmAhBDiNro+QNUML4BUKhVTn27Eo/U8uJWrZeiSvXL3eCHKOSmAhBDiX2m3crn+77w+AW6GF0AA1ho1c55vRmiAK6lZuQxYsIfLN26WRphCCBOQAkgIIf5VOLNztSo2VLG1Mnp/BxsrFg56iFoeVbh04xb9vt1NSka2qcMUQpiAFEBCCPGvBCM7QBenahUblg5pga9LwUSJ/Rfs4cZNmS1aiPKmXBRAc+bMoWbNmtjZ2REeHs6ePXvuum379u1RqVRFHt26ddNtoygKEyZMwMfHB3t7eyIiIjh16lRZpCKEqMASrhVcsjKmA3Rx/N0c+O6FcNwdbTh2OY3Bi/aQmZ1nihCFECZi9gJo5cqVjB49mokTJ7J//36aNGlCZGQkV65cKXb71atXc/nyZd3jyJEjaDQann32Wd02n376KbNmzWLu3Lns3r2bKlWqEBkZya1bt8oqLSFEBWToJIiGqOXhyLKh4TjbWbE/PpUXl/3Nrdz8Bz6uEMI0zF4ATZ8+nWHDhjF48GAaNGjA3LlzcXBwYOHChcVuX7VqVby9vXWPTZs24eDgoCuAFEVhxowZvPvuu3Tv3p3GjRuzdOlSLl26xJo1a8owMyFERfPfHED3nwTREPV9nFkypAVVbDTsOJ3CyB8OkJsv9w0TojwwawGUk5PDvn37iIiI0C1Tq9VEREQQExNj0DEWLFhAnz59qFKlCgDnzp0jMTFR75guLi6Eh4cbfEwhROVkij5Ad2pa3Y1vBz6ErZWaP48nMfrHf8jXyn3DhDA344c5mNDVq1fJz8/Hy8tLb7mXlxcnTpy47/579uzhyJEjLFiwQLcsMTFRd4w7j1m47k7Z2dlkZ/83UiMtLQ2A3NxccnMfvPNi4TFMcazySnKs+Cw9P7h3jlqtorsRqq+zjUlfh+bVnfnyuSYM//4g//vnEvZWKj7s3gCVqvibrT4ISz+Plp4fWH6OpZmfMcc0awH0oBYsWECjRo1o0aLFAx1nypQpTJo0qcjyjRs34uBgur8EN23aZLJjlVeSY8Vn6flB8TmmZkNuvhVqlcKBHVv4x/S1Cf1qq1hyUs2P+y6SdCmBp2poKYUaCLD882jp+YHl51ga+WVlZRm8rVkLIHd3dzQaDUlJ+vfOSUpKwtvb+577ZmZmsmLFCiZPnqy3vHC/pKQkfHx89I4ZGhpa7LHGjx/P6NGjdT+npaUREBBAp06dcHZ2NialYuXm5rJp0yY6duyItbX1Ax+vPJIcKz5Lzw/uneOeuGuw/2/83Rx4vFvbUnn+rkDw/ouM++UoWy+raVivNqM61Dbpc1j6ebT0/MDycyzN/Aqv4BjCrAWQjY0NYWFhREVF0aNHDwC0Wi1RUVGMHDnynvv+9NNPZGdn069fP73lgYGBeHt7ExUVpSt40tLS2L17N8OHDy/2WLa2ttja2hZZbm1tbdKTY+rjlUeSY8Vn6flB8TleupEDQI1qVUo1/z7hNcnOh4m/HWVO9FlcHGx48ZEgkz+PpZ9HS88PLD/H0sjPmOOZ/RLY6NGjGThwIM2bN6dFixbMmDGDzMxMBg8eDMCAAQPw8/NjypQpevstWLCAHj16UK1aNb3lKpWKUaNG8eGHH1KnTh0CAwN577338PX11RVZQghxp4TrBXMAmbID9N0MbFWTzJw8Pl0fy8frTuBib03vh6qX+vMKIf5j9gKod+/eJCcnM2HCBBITEwkNDWX9+vW6Tszx8fGo1fqD1WJjY9m+fTsbN24s9phjxowhMzOTF198kdTUVNq0acP69euxs7Mr9XyEEBVTggnnADLEK+1rc+NmLt9sPcv41Ydxsbemc0Of++8ohDAJsxdAACNHjrzrJa/o6Ogiy+rVq4ei3H0YqUqlYvLkyUX6BwkhxN3o5gAy8iaoD2Jc52BuZOWyYm8Cry0/yMJB1rSp415mzy9EZWb2iRCFEKI8MOUs0IZSqVR89FQjujT0Jidfy4vL/uZA/PUye34hKjMpgIQQld7NnHyS0wvmAivLAghAo1Yxo08obWq7k5WTz+DFezmZlF6mMQhRGUkBJISo9C78OwGis50VLg5lP+rG1krDN/3DCA1wJTUrl/4Lduv6JAkhSocUQEKISi++FG6BYawqtlYsHvwQdb0cSUrLpu+3u0i8ITdwFqK0SAEkhKj0zNH/pziuDjZ8NzScGtUcSLh2k34LdnMtM8esMQlhqaQAEkJUegnXCuYAMncBBODpbMd3Q8Pxdrbj9JUMBizcTdoty7wnlBDmJAWQEKLSKw+XwG4XUNWB714Ip1oVG45cTGPo4r3czMk3d1hCWBQpgIQQlV5COSuAAGp7OrJkSAuc7KzYG3edF5f9TXaeFEFCmIoUQEKISk1RlHLTB+hODf1cWDz4IeytNWw7dZXXlx8kL19r7rCEsAhSAAkhKrWrGTnczM1HpQI/V3tzh1NEWI2qzB/QHBuNmvVHExnz8yG02rvPhC+EMIwUQEKISi3h3zmAfF3ssbEqn1+Jbeq482XfpmjUKlbvv8j7/zt6z9sBCSHur3x+2oUQoowU9v/xdyt/rT+36xTizbRnm6BSwdKY83y2IdbcIQlRoUkBJISo1OJTymf/n+L0aOrHB90bAvBV9Bm+ij5t5oiEqLikABJCVGrltQP03fR7uAbjuwQD8On6WJbFxJk3ICEqKCmAhBCVWmEfoOrVKkYBBPBSuyBefaw2AO/9epSf910wc0RCVDxSAAkhKrXCWaDL0xxAhhjdsS6DWtUE4K1V/7D+yGXzBiREBSMFkBCi0srJ03Lpxr8FkFvFKoBUKhUTHm/AM2H+aBV4dfkBtp5MNndYQlQYUgAJISqti6k3URSwt9bg7mhj7nCMplarmNqzEV0beZObr/DSsr/5+/x1c4clRIUgBZAQotJKuK0DtEqlMnM0JWOlUTOjd1Pa1fXgVq6WYcsOEJdu7qiEKP+kABJCVFrl7SaoJWVjpWZuvzBaBFYlIzuPr45r2BsnLUFC3IsUQEKISuu/m6CW70kQDWFvo2HRoId4ONCN7HwVQ5fuY/upq+YOS4hySwogIUSlVdHmALqfKrZWzO/fjPquWm7mahmyZC+bTySZOywhyiUpgIQQlZZuDiALKYAA7Kw1vFBPS0SwBzl5Wl5atk+GyAtRDCmAhBCVVkW6DYYxrNQwq08THm/sQ26+wogfDvDrwYvmDkuIckUKICFEpXQjK5e0W3kA+FewOYAMYa1RM7NPU54J8ydfqzBq5UF+3Jtg7rCEKDekABJCVEqF/X88nGyxt9GYOZrSoVGr+PTpxjwfXh1FgTE/H+LHv6UIEgKkABJCVFKW1gH6btRqFR/2aMjQNoEAvPPLYXafTTFzVEKYnxRAQohKyRI7QN+NSqXi3W71dX2Chn+/XzcFgBCVlRRAQohKSTcJolvFnwPIECqVis+eaUIjPxeuZebwwpK/ycjOM3dYQpiNFEBCiEopwUJmgTaGvY2G+QOa4+lkS2xSOqNWHCBfq5g7LCHMwqokO506dYotW7Zw5coVtFqt3roJEyaYJDAhhChNlaUP0J28XeyYN6A5vb6J4c/jV/h8YyxjOwebOywhypzRBdD8+fMZPnw47u7ueHt7691AUKVSSQEkhCj38rUKF6/fBKB6tcpVAAGEBrjy2TONeX3FQb6OPkNdL0eeaupv7rCEKFNGXwL78MMP+eijj0hMTOTgwYMcOHBA99i/f7/RAcyZM4eaNWtiZ2dHeHg4e/bsuef2qampjBgxAh8fH2xtbalbty7r1q3TrX///fdRqVR6j+Bg+etGCPGfxLRb5GkVbDRqvJzszB2OWXQP9WPEo0EAjP35MPvj5eaponIxugC6fv06zz77rEmefOXKlYwePZqJEyeyf/9+mjRpQmRkJFeuXCl2+5ycHDp27EhcXByrVq0iNjaW+fPn4+fnp7ddSEgIly9f1j22b99ukniFEJYh4VpB64+/mz1qteo+W1uu/+tYj44NvMjJ0/Li0n1cSr1p7pCEKDNGF0DPPvssGzduNMmTT58+nWHDhjF48GAaNGjA3LlzcXBwYOHChcVuv3DhQq5du8aaNWto3bo1NWvWpF27djRp0kRvOysrK7y9vXUPd3d3k8QrhLAMhUPgK1MH6OKo1Spm9A4l2NuJqxnZDFv6N1k5MjJMVA5G9wGqXbs27733Hrt27aJRo0ZYW1vrrX/ttdcMOk5OTg779u1j/PjxumVqtZqIiAhiYmKK3ee3336jZcuWjBgxgl9//RUPDw/69u3L2LFj0Wj+m8n11KlT+Pr6YmdnR8uWLZkyZQrVq1e/ayzZ2dlkZ2frfk5LSwMgNzeX3Nxcg/K5l8JjmOJY5ZXkWPFZen7wX27nr2YC4O9qZ3H5GnsebdQw9/lQes7dxdFLaYxeeZCZvRqX25axyvQ+tdQcSzM/Y46pUhTFqDGQgYGBdz+YSsXZs2cNOs6lS5fw8/Nj586dtGzZUrd8zJgxbN26ld27dxfZJzg4mLi4OJ5//nleeeUVTp8+zSuvvMJrr73GxIkTAfjjjz/IyMigXr16XL58mUmTJnHx4kWOHDmCk5NTsbG8//77TJo0qcjyH374AQeHyv0XohCWaMlJNftT1HSvkc9jvjIMHOBMGsw5piFfUdHZP58uAfK6iIonKyuLvn37cuPGDZydne+5rdEtQOfOnStxYA9Kq9Xi6enJvHnz0Gg0hIWFcfHiRT777DNdAdSlSxfd9o0bNyY8PJwaNWrw448/MnTo0GKPO378eEaPHq37OS0tjYCAADp16nTfF9AQubm5bNq0iY4dOxZpMbMUkmPFZ+n5wX855tm5Aml0bNmMyBAvc4dlUg9yHr33XeTtNUdZf0FD19aN6dLQu5SiLLnK9D611BxLM7/CKziGKNE8QABXr14FKHH/Gnd3dzQaDUlJSXrLk5KS8PYu/kPn4+ODtbW13uWu+vXrk5iYSE5ODjY2NkX2cXV1pW7dupw+ffqusdja2mJra1tkubW1tUlPjqmPVx5JjhWfpecHcOHfzr6Bnk4Wm2tJzmPfh2ty9moW324/x1s/HyGgmiNNq7uVUoQPpjK8Ty09x9LIz5jjGdUJunAIuru7O15eXnh5eeHu7s7IkSNJTU01KkgbGxvCwsKIiorSLdNqtURFReldErtd69atOX36tN7kiydPnsTHx6fY4gcgIyODM2fO4OPjY1R8QgjLlJ0P1zIL+glU9k7QxRnftT6PBXuSnaflhSV/E58i9wwTlsngAujatWuEh4ezZMkSnn76aaZNm8a0adPo2bMnixcvpmXLlly/btw8EqNHj2b+/PksWbKE48ePM3z4cDIzMxk8eDAAAwYM0OskPXz4cK5du8brr7/OyZMnWbt2LR9//DEjRozQbfPmm2+ydetW4uLi2LlzJ0899RQajYbnnnvOqNiEEJYp5VbBv24O1jjbWe5f1yWlUauY/VxTQnydScnMYdDiPaRm5Zg7LCFMzuBLYJMnT8bGxoYzZ87g5eVVZF2nTp2YPHkyX3zxhcFP3rt3b5KTk5kwYQKJiYmEhoayfv163fHj4+NRq/+r0QICAtiwYQNvvPEGjRs3xs/Pj9dff52xY8fqtrlw4QLPPfccKSkpeHh40KZNG3bt2oWHh4fBcQkhLFdKdsHoJmn9ubsqtlYsHPQQT83ZwdnkTF5cuo9lL7TA1kpz/52FqCAMLoDWrFnDN998U6T4AfD29ubTTz/l5ZdfNqoAAhg5ciQjR44sdl10dHSRZS1btmTXrl13Pd6KFSuMen4hROVy9d8WICmA7s3L2Y5Fg1vwzNc72RN3jbd+OsSM3qHldni8EMYy+BLY5cuXCQkJuev6hg0bkpiYaJKghBCitBS2AFW2m6CWRD1vJ77uF4aVWsVv/1xi2qZYc4ckhMkYXAC5u7sTFxd31/Xnzp2jatWqpohJCCFKTWEfICmADNOmjjtTejYCYM6WM6zYE2/miIQwDYMLoMjISN555x1ycop2hsvOzua9996jc+fOJg1OCCFMTdcHyE0KIEM92zyA1zrUAeCdNUfYcqL4+zUKUZEY1Qm6efPm1KlThxEjRhAcHIyiKBw/fpyvvvqK7Oxsli1bVpqxCiHEA9FqFa5JC1CJvBFRhwvXs1i9/yKvfL+fH4aFl9s5goQwhMEFkL+/PzExMbzyyiuMHz+ewjtoqFQqOnbsyJdffklAQECpBSqEEA8qOSObXEWFRq3Cx9XO3OFUKCqVik+ebszVjBz+OpnMkMV7+enlVtT2dDR3aEKUiFETIQYGBvLHH39w9epVdu3axa5du0hOTmb9+vXUrl27tGIUQgiTuHC9YAZoHxc7rDVGff0JwFqj5uvnm9HE34XrWbkMXLiHxBu3zB2WECVSom8ANzc3WrRoQYsWLaTjsxCiwkj4twAKcLM3cyQVV+EcQYHuVbiYepOBC/dw46Zl3rVcWDb5E0gIUWkkXJMCyBSqOdqydEgLPJ1siU1KZ9iSv7mVm2/usIQwihRAQohKI+F6wX2tpAB6cAFVHVgypAVOtlbsibvGa8sPkK9VzB2WEAaTAkgIUWnoLoHJCDCTqO/jzPyBzbGxUrPxWBLvrjmiGyAjRHlnVAGUm5vLkCFDOHfuXGnFI4QQpaawAPKXFiCTebhWNWb2DkWlguV74pm6/oQUQaJCMKoAsra25ueffy6tWIQQotTcys0nKS0bkEtgptalkQ8fP1UwW/Q3W8/yVfQZM0ckxP0ZfQmsR48erFmzphRCEUKI0lM4BN5Wo+DmYG3maCzPcy2q807X+gB8tiGWJTvjzBuQEPdh8ESIherUqcPkyZPZsWMHYWFhVKlSRW/9a6+9ZrLghBDCVAo7QLvbFkzqJ0xv2CO1SM/OY1bUKSb+dpQqtlY8E+Zv7rCEKJbRBdCCBQtwdXVl37597Nu3T2+dSqWSAkgIUS4lXCsogKrZSf+U0vRGRB3Sb+WyaEccY1b9g6Oths4NfcwdlhBFGF0ASQdoIURFFJ9SUABVtTVzIBZOpVLxXrcGZGbn8ePfF3h1+QG+HWhFu7oe5g5NCD0lHgafk5NDbGwseXl5poxHCCFKRfy/LUDu0gJU6tRqFVN6NqZbIx9y8xVeWvY3e+OumTssIfQYXQBlZWUxdOhQHBwcCAkJIT4+HoBXX32VqVOnmjxAIYQwhcICqJq0AJUJjVrFF71DaV/Pg1u5WoYs2ss/CanmDksIHaMLoPHjx/PPP/8QHR2Nnd1/d1OOiIhg5cqVJg1OCCFMQVEU3Sgw6QNUdmys1MztF0Z4YFXSs/Pov2A3Ry7eMHdYQgAlKIDWrFnDl19+SZs2bfRGUoSEhHDmjMz9IIQof65n5ZKRXXC5XvoAlS07aw0LBj1EWA030m7l0W/Bbo5dSjN3WEIYXwAlJyfj6elZZHlmZqYMLRVClEuFl7+8nG2xlhsAlTlHWysWD36I0ABXUrNyef7bXZxIlCJImJfRXwXNmzdn7dq1up8Li55vv/2Wli1bmi4yIYQwkcICSGaANh8nO2uWDm1BE38Xrmfl8vz83ZxKSjd3WKISM3oY/Mcff0yXLl04duwYeXl5zJw5k2PHjrFz5062bt1aGjEKIcQDKZwDSG6Cal7OdtYsHRLO8wt2ceRiGs/N382KFx+mtqejuUMTlZDRLUBt2rTh4MGD5OXl0ahRIzZu3IinpycxMTGEhYWVRoxCCPFAdAWQq7QAmZuLgzXfDQ2ngY8zVzOy6Tt/F+euZpo7LFEJGd0CBBAUFMT8+fNNHYsQQpQK3SWwqvZwyczBCFwdbPjuhXD6zt/FicR0+syLYfmwh6nlIS1BouwY3QI0YMAAFi1axNmzZ0sjHiGEMDnpA1T+VK1SUATV9XIkKS2b3vN2SZ8gUaaMLoBsbGyYMmUKtWvXJiAggH79+vHtt99y6tSp0ohPCCEeSG6+lss3bgHSB6i8cXe0Zfmwhwn2diI5PZs+82R0mCg7RhdA3377LSdPniQhIYFPP/0UR0dHpk2bRnBwMP7+ctdfIUT5cjn1FvlaBVsrNR6ONuYOR9yh2r9FUIivMymZOTw3b5dMlijKRIlnxHBzc6NatWq4ubnh6uqKlZUVHh5yszshRPkSf9sIMJmrrHxyq2LDDy88TJMAV65n5dJ3/i65bYYodUYXQG+//TatWrWiWrVqjBs3jlu3bjFu3DgSExM5cOBAacQohBAlVlgAVZfLX+Wai4M1y4a2+G/G6G93s+/8dXOHJSyY0aPApk6dioeHBxMnTqRnz57UrVu3NOISQgiTSLguBVBF4WxnzZIhLRiyeC97zl1jwILdLBrcghaBVc0dmrBARrcAHThwgHfeeYc9e/bQunVr/Pz86Nu3L/PmzePkyZOlEaMQQpRYYQuQv4wAqxAKb5vRKqgamTn5DFi4m+jYK+YOS1ggowugJk2a8Nprr7F69WqSk5NZt24dNjY2jBgxgvr16xsdwJw5c6hZsyZ2dnaEh4ezZ8+ee26fmprKiBEj8PHxwdbWlrp167Ju3boHOqYQwnIlyCWwCsfBxoqFgx6ifT0PbuVqGbb0b9YeumzusISFMfoSmKIoHDhwgOjoaKKjo9m+fTtpaWk0btyYdu3aGXWslStXMnr0aObOnUt4eDgzZswgMjKS2NjYYm+4mpOTQ8eOHfH09GTVqlX4+flx/vx5XF1dS3xMIYRl0/UBqiYFUEViZ61hXv/mvPHjQdYeusyry/eTmd2Yp0K9zR2asBBGF0BVq1YlIyODJk2a0K5dO4YNG0bbtm31ihBDTZ8+nWHDhjF48GAA5s6dy9q1a1m4cCHjxo0rsv3ChQu5du0aO3fuxNraGoCaNWs+0DGFEJYr7VYuqVm5AAS4OQCKeQMSRrGxUjOrT1OcbK1YsTeBMT8fIjUrGy9zByYsgtEF0HfffUfbtm1xdnZ+oCfOyclh3759jB8/XrdMrVYTERFBTExMsfv89ttvtGzZkhEjRvDrr7/i4eFB3759GTt2LBqNpkTHBMjOziY7O1v3c1pawURcubm55ObmPlCehce5/V9LJDlWfJaY37krBZ/lqlWssVErFpnjnSwxx8lPBFPFRs2CHef5+I9YOvuricjJMXdYpcYSz+HtSjM/Y45pdAHUrVs33f8vXLgAUKIJEK9evUp+fj5eXvq1vJeXFydOnCh2n7Nnz7J582aef/551q1bx+nTp3nllVfIzc1l4sSJJTomwJQpU5g0aVKR5Rs3bsTBwXTN5ps2bTLZscorybHis6T8/klRARqcVDl6fQUtKce7sbQcGynQLUDF2gQN6y+ouTl/Mz1qalFb8NROlnYO71Qa+WVlZRm8rdEFkFar5cMPP2TatGlkZGQA4OTkxP/93//xzjvvoFaXeG5Fg57b09OTefPmodFoCAsL4+LFi3z22WdMnDixxMcdP348o0eP1v2clpZGQEAAnTp1euCWLiioSDdt2kTHjh11l+4sjeRY8Vlifpe2x8HJkzQK9KFr18YWmeOdLDnHbkDojnN8tP4UWxPVuHn58fFTIVhrSu/3jjlY8jmE0s2v8AqOIYwugN555x0WLFjA1KlTad26NQDbt2/n/fff59atW3z00UcGHcfd3R2NRkNSUpLe8qSkJLy9i+/k5uPjg7W1NRqNRresfv36JCYmkpOTU6JjAtja2mJra1tkubW1tUlPjqmPVx5JjhWfJeV38d97gNV0d9TLyZJyvBtLzXFQ60DiTp9gxVkr1vxzmWs38/j6+WZUsTX611m5Z6nnsFBp5GfM8Ywum5csWcK3337L8OHDady4MY0bN+aVV15h/vz5LF682ODj2NjYEBYWRlRUlG6ZVqslKiqKli1bFrtP69atOX36NFqtVrfs5MmT+Pj4YGNjU6JjCiEsV8K1m4AMgbc0LTwU5j4fir21hr9OJvPc/F1czci+/45C3MboAujatWsEBwcXWR4cHMy1a9eMOtbo0aOZP38+S5Ys4fjx4wwfPpzMzEzdCK4BAwbodWgePnw4165d4/XXX+fkyZOsXbuWjz/+mBEjRhh8TCFE5VE4B5B/VZkE0dK0r+vBD8PCcXOw5tCFGzzz9U7iUwzv/yGE0W2GTZo04csvv2TWrFl6y7/88kuaNGli1LF69+5NcnIyEyZMIDExkdDQUNavX6/rxBwfH6/XpyggIIANGzbwxhtv0LhxY/z8/Hj99dcZO3aswccUQlQO+VqFC9elBciSNa3uxqrhrRiwYA9xKVn0/Honiwc/REM/F3OHJioAowugTz/9lG7duvHnn3/qLivFxMSQkJBQZEZmQ4wcOZKRI0cWuy46OrrIspYtW7Jr164SH1MIUTkkpd0iJ1+LlVqFj4u0AFmqIA9HVr/SikGL9nL8chp95u1ibr8w2tRxN3doopwz+hJYu3btOHnyJD179iQ1NZXU1FR69uxJbGwsbdu2LY0YhRDCaAm33QNMY8ljpQVeznasfOlhWtaqRkZ2HoMX7+HXgxfNHZYo54xqAYqLi2PTpk3k5OTQp08fGjZsWFpxCSHEAym8BUaAXP6qFJztrFk85CFG//gPaw9d5vUVB7lw/SavtA9CpZICWBRlcAG0ZcsWHn/8cW7eLLimbmVlxcKFC+nXr1+pBSeEECWVIAVQpWNrpWF2n6b4ONvx7fZzfLYhlvMpmXz0VCOLmytIPDiD3xHvvfceHTt25OLFi6SkpDBs2DDGjBlTmrEJIUSJxctd4CsltVrFu4834IPuIahV8OPfFxi0aA83blrmbSVEyRlcAB05coSPP/4YHx8f3Nzc+Oyzz7hy5QopKSmlGZ8QQpRIgowAq9T6t6zJtwOb42CjYcfpFJ75eicXrsswefEfgwugtLQ03N3/61Xv4OCAvb09N27cKJXAhBDiQej6ALlJAVRZPRbsxY8vtcTL2ZZTVzLoMWcn/ySkmjssUU4Y1Ql6w4YNuLj8N79C4SzLR44c0S178sknTRedEEKUwM2cfJLTC2YGlhagyq2hnwtrRrRm8KK9nEhMp/e8GGb0DqVzQx9zhybMzKgCaODAgUWWvfTSS7r/q1Qq8vPzHzwqIYR4AAn/XupwtrPCxcFy76UkDOPjYs+q4a0Y8f1+tp5M5uXv9jO6Y11efay2jBCrxAy+BKbVau/7kOJHCFEeFI4Aq15NWn9EAUdbKxYMbM6gVjUBmL7pJK8uP8DNHPm9VVnJuEAhhMWR/j+iOFYaNe8/GcKUno2wUqv4/dBlen0TQ+KNW+YOTZiBFEBCCIsjQ+DFvTzXojrfvVBwI9XDF2/wxJfbORB/3dxhiTImBZAQwuLIJIjifh6uVY3fRrahnpcTyenZ9J63izUH5PYZlYkUQEIIiyMtQMIQAVUd+PmVVkTU9yQnT8uolQf5aO0x8vK15g5NlAEpgIQQFkVRFBKuFUyCKC1A4n4cba2Y1785r7QPAmD+tnMMWLiHlIxsM0cmSluJC6CcnBwuXLhAfHy83kMIIczpakYON3PzUanAz9Xe3OGICkCtVjGmczBfPd8MBxsNO8+k8OSXOzh8QSb6tWRGF0CnTp2ibdu22NvbU6NGDQIDAwkMDKRmzZoEBgaWRoxCCGGwwstfvi722FhJI7cwXNdGPqwZ0ZpA9ypcTL3J03N3smrfBXOHJUqJURMhAgwaNAgrKyt+//13fHx8ZBIpIUS58l8HaGn9Ecar6+XEmhGtGb3yIFEnrvDmT/9w6EIq73ZrIAW1hTG6ADp48CD79u0jODi4NOIRQogHkiAdoMUDcrG3Zv6A5szafIoZf55iacx5jl1K48u+zfB2sTN3eMJEjC5nGzRowNWrV0sjFiGEeGAyCaIwBbVaxaiIuiwY2BwnWyv+Pn+dbrO2se1UsrlDEyZiUAGUlpame3zyySeMGTOG6OhoUlJS9NalpaWVdrxCCHFP8XIbDGFCHep78b9X29DAx5mUzBwGLNzDF5tOkq9VzB2aeEAGXQJzdXXV6+ujKAodOnTQ20ZRFLkZqhDC7GQSRGFqNd2rsPqVVkz63zGW74lnZtQp9p2/zow+obg72po7PFFCBhVAW7ZsKe04hBDigeXkabmcVnBfJ+kDJEzJzlrDlJ6NaBHoxturj7D99FW6ztzGl32b0SKwqrnDEyVgUAHUrl073f/j4+MJCAgoMvpLURQSEhJMG50QQhjhYupNFAXsrTVUq2Jj7nCEBXqqqT8NfV0Y/v1+Tl/J4Ln5u/i/TnV5+ZEg1GoZFV2RGN0JOjAwkOTkop3Arl27JvMACSHM6vZbYMgUHaK01PFy4tcRrekR6ku+VuHT9bEMWLiHK2lyV/mKxOgCqLCvz50yMjKws5PhgUII84mX/j+ijFSxteKL3qF88nQj7K01bD99lc4zt7HlxBVzhyYMZPA8QKNHjwZApVLx3nvv4eDw3xdMfn4+u3fvJjQ01OQBCiGEoS7IHECiDKlUKno/VJ2wGlV5dfkBjl9OY/DivQxtE8iYzvWwtdKYO0RxDwYXQAcOHAAKWoAOHz6Mjc1/19dtbGxo0qQJb775pukjFEIIA8XLLNDCDGp7OvLLK634ZP0JFu2IY8H2c+w6m8Ks55oS5OFo7vDEXRhcABWOBBs8eDAzZ87E2dm51IISQoiSiJcWIGEmdtYaJj4RQpva7ry16hBHL6Xx+KztTHyiAb0fKjpwSJif0X2AFi1aJMWPEKLcURSF+BQpgIR5dajvxR+vt6VVUDVu5uYzbvVhhi3dx9WMbHOHJu5gUAtQz549Wbx4Mc7OzvTs2fOe265evdokgQkhhDFu3MwlPTsPAH+5DYYwIy9nO5YNDefbbWeZtvEkfx5PIvKL60x9ujEdG3iZOzzxL4MKIBcXF13znYuLS6kGJIQQJZFw7SYAHk622NtI51NhXhq1ipfaBfFIXQ/eWHmQE4npDFv6N72bB/DeEw1wtDX6XuTCxAw6A4sWLSr2/6YyZ84cPvvsMxITE2nSpAmzZ8+mRYsWxW67ePFiBg8erLfM1taWW7f+m39h0KBBLFmyRG+byMhI1q9fb/LYhRDlg/T/EeVRfR9nfh3ZmukbTzJv21lW/p3AzrNX+aJXKM1rygzS5mRwH6B27doxefJktm3bRm5urskCWLlyJaNHj2bixIns37+fJk2aEBkZyZUrd59LwdnZmcuXL+se58+fL7JN586d9bZZvny5yWIWQpQ/UgCJ8srWSsP4rvVZPuxh/FztSbh2k17fxPDxuuPcypX7Z5qLwQVQYGAgixYtol27dri6uhIREcFHH31ETEzMA90Adfr06QwbNozBgwfToEED5s6di4ODAwsXLrzrPiqVCm9vb93Dy6voNVVbW1u9bdzc3EocoxCi/JNJEEV593Ctaqwf1Zanm/mjVWDeX2fpOnMb+85fM3dolZLBBdDixYs5d+4cZ8+eZfbs2fj5+TFv3jxat26Nm5sbXbp04bPPPjPqyXNycti3bx8RERH/BaRWExERQUxMzF33y8jIoEaNGgQEBNC9e3eOHj1aZJvo6Gg8PT2pV68ew4cPJyUlxajYhBAVy4Xr/xZAbjIHkCi/nOysmdarCd8OaI6nky1nr2byzNwYPvj9GDdzpDWoLBndC6tmzZoMGTKEIUOGAHD27FkWLlzI7Nmz2bhxI2+99ZbBx7p69Sr5+flFWnC8vLw4ceJEsfvUq1ePhQsX0rhxY27cuMHnn39Oq1atOHr0KP7+/kDB5a+ePXsSGBjImTNnePvtt+nSpQsxMTFoNEU7R2ZnZ5Od/d8QxbS0NAByc3NNcrmv8BimvHRY3kiOFV9Fz+98SiYAvi42d82houdoCEvP0VLya1enKutebcXHf8Sy+sAlFmw/x5/HkpjyVAihfgWTJ1b0HO+mNM+hMcdUKYqiGPsE58+fJzo6Wve4cuUKDz/8MO3atWPChAkGH+fSpUv4+fmxc+dOWrZsqVs+ZswYtm7dyu7du+97jNzcXOrXr89zzz3HBx98UOw2Z8+eJSgoiD///JMOHToUWf/+++8zadKkIst/+OEHvVt+CCHKp3wF3tytQauomNQsD1dbc0ckhOGOXlex8qyaGzkqVCi09VZ4vLoWWxnMaLSsrCz69u3LjRs37jtnocEtQEuXLtUVPFevXqVVq1a0a9eOYcOG8dBDD2FtbW10oO7u7mg0GpKSkvSWJyUl4e3tbdAxrK2tadq0KadPn77rNrVq1cLd3Z3Tp08XWwCNHz9ed68zKGgBCggIoFOnTiaZ9DE3N5dNmzbRsWPHEr1OFYHkWPFV5PwuXL+Jdtc2rDUq+nTvglpd/Ky7FTlHQ1l6jpaYX1fg5Vu5TFl/kp/2XeSvRBWHr6n4qGdjOob4mDs8kyvNc1h4BccQBhdAgwYNonr16owbN46hQ4eaJGgbGxvCwsKIioqiR48eAGi1WqKiohg5cqRBx8jPz+fw4cN07dr1rttcuHCBlJQUfHyKfyPZ2tpia1v0T0Zra2uTnhxTH688khwrvoqY3+X0G0BBB2hbW5v7bF0xczSWpedoaflVtbbms2dDeaKJH2+vPsSF1Fu8suIw3RpdZeITDfB0tjN3iCZXGufQmOMZ3An6q6++4uGHH2bSpEl4enryxBNPMG3aNP7++29KcBVNZ/To0cyfP58lS5Zw/Phxhg8fTmZmpm6unwEDBjB+/Hjd9pMnT2bjxo2cPXuW/fv3069fP86fP88LL7wAFHSQfuutt9i1axdxcXFERUXRvXt3ateuTWRkZInjFEKUXwmFI8BkBmhRwT1S14O1r7biMV8tGrWKtYcv02H6Vn7YHY9WW/LftaIogwugl19+mRUrVnD58mV27NhB165d2bNnD926dcPNzY1u3brx+eefGx1A7969+fzzz5kwYQKhoaEcPHiQ9evX6zpGx8fHc/nyZd32169fZ9iwYdSvX5+uXbuSlpbGzp07adCgAQAajYZDhw7x5JNPUrduXYYOHUpYWBjbtm0rtpVHCFHxyRxAwpI42FjRvYaWn18Kp5GfC+m38nj7l8P0+iaGU0np5g7PYpRoLu4GDRrQoEEDhg8fzqVLl/jqq6+YPXs269ev58033zT6eCNHjrzrJa/o6Gi9n7/44gu++OKLux7L3t6eDRs2GB2DEKLiiv/3NhhSAAlLEuLrzJoRrVmyM47PN8by9/nrdJ21jRfa1uLVx2rjYCO303gQRr96V65cYcuWLboO0SdPnsTa2pqHH36YRx99tDRiFEKIe0qQSRCFhdKoVQxpE0hkQ28mrDlC1IkrfB19hjUHLvJOt/p0a+Sju1enMI7BBdArr7xCdHQ0sbGxWFlZ0aJFC5555hkeffRRWrVqhZ2d5XXQEkJUDP8VQDIJorBMfq72fDuwOX8ev8Kk/x3lwvWbjPzhAD8ExTPpyRDqeDmZO8QKx+AC6MCBA/To0YNHH32U1q1by/w4QohyISM7j5TMHEBagIRlU6lUdGzgRds67szdeoavo8+w80wKXWZuo9/DNRj5WG3cHaWvq6EMLoDudWsKIYQwl8LWHzcHa5ztLGdYtBB3Y2etYVREXZ5u5s/k34+x6VgSi3fG8ePfCQxpHciwR2rhYi+fhfsxeBSYEEKURwkyAkxUUgFVHZg/oDnfDQ2nib8LWTn5fLnlNG0/2cxX0afJyskzd4jlmhRAQogKrXAIvL8UQKKSalPHnTUjWvNN/zDqejmSdiuPT9fH8sin0SzecY7sPLnJanGkABJCVGjSAiREQf+gyBBv/nj9Eb7o3YTqVR24mpHN+/87xmOfb+XHvxPIy9eaO8xyRQogIUSFJpMgCvEfjVrFU039+XN0Oz7s0RAvZ1supt5kzKpDdJrxF2sPXZYZpf9ldAFUq1YtUlJSiixPTU2lVq1aJglKCCEMJQWQEEXZWKnp93ANtr71KO90rY+bgzVnkzMZ8cN+nvhyO1tirzzQbawsgdEFUFxcHPn5Ra8nZmdnc/HiRZMEJYQQhtBqFS5cL5gFWu4DJkRRdtYahj1Si7/GPMqoiDo42lpx9FIagxftpdc3Mew5d83cIZqNwcPgf/vtN93/N2zYgIuLi+7n/Px8oqKiqFmzpkmDE0KIe0nOyCY7r+CmkT6uMhmrEHfjZGfNqIi6DGhZk7lbz7BkZxx7467T65sY2tX14M1O9Wjk73L/A1kQgwugHj16AAUdrQYOHKi3ztrampo1azJt2jSTBieEEPdSePnL19UOa410aRTifqpWseHtrvUZ0jqQ2ZtPsXJvAltPJrP1ZDJdGnrzf53qUtuzcswqbXABpNUW9B4PDAxk7969uLu7l1pQQghhiPgU6f8jREl4u9jx0VONePGRWsz48xRrDl7kjyOJbDiayFNN/RkVUcfiZ1Y3+k+mc+fOSfEjhCgXEq7/ew8w6f8jRInUqFaFL3qHsv71R4gM8UKrwM/7L/DYtGgm/HqEK2m3zB1iqTH6bvAAUVFRREVFceXKFV3LUKGFCxeaJDAhhLifeLkLvBAmUc/biW/6N+dgQirTNsay7dRVlsac58e/ExjUKpCX29XC1cHG3GGalNEtQJMmTaJTp05ERUVx9epVrl+/rvcQQoiyIpMgCmFaoQGuLBsazg/DwmlW3ZVbuVrmbj1D20+2MOPPk9y4mWvuEE3G6BaguXPnsnjxYvr3718a8QghhMFkDiAhSkerIHd+Hl6NzSeu8NmGWE4kpjPjz1Ms2H6OIa0DGdImsMLfcNXoFqCcnBxatWpVGrEIIYTBbuXmk5SWDcglMCFKg0qlokN9L9a91pYv+zalrpcj6bfymBl1ijafbOaLTRW7RcjoAuiFF17ghx9+KI1YhBDCYIUTIDraWuHmULH/EhWiPFOrVTze2Jf1rz/CnL7NqOfl9F8hNHUz0zed5EZWxSuEjL4EduvWLebNm8eff/5J48aNsbbW/+KZPn26yYITQoi7SbitA7RKpTJzNEJYPrVaRbfGPnRp6M36o4nM/PMUsUnpzIo6xaLt5xjcuiZD29TCpYL8QWJ0AXTo0CFCQ0MBOHLkiN46+RISQpSV//r/2Js5EiEqF7VaRddGPnQO8WbD0URmRp3iRGI6szafZtGOOAa1rsnQNoHlftSY0QXQli1bSiMOIYQwiowAE8K81GoVXRr5EBnizcZjicz4s6AQmv1vITS4nBdCJZ47/vTp02zYsIGbNwuuw1f2u8oKIcqWzAEkRPmgVqvo3NCHda+1ZW6/MOr7OJORncfszadp88kWPttwguuZOeYOswijC6CUlBQ6dOhA3bp16dq1K5cvXwZg6NCh/N///Z/JAxRCiOJIASRE+VJQCHmz9tU2fNP/v0JozpYztP5kM1PWHSc5PdvcYeoYXQC98cYbWFtbEx8fj4PDf188vXv3Zv369SYNTgghiqMoilwCE6KcUqtVRIb8VwiF+DqTlZPPN3+dpe2nm/lw3QlSy0EdZHQfoI0bN7Jhwwb8/f31ltepU4fz58+bLDAhhLiba5k5ZObko1KBn6t0ghaiPCoshDo18GJL7BVmRZ3mYEIqS2Li0ag0JDuf5s3O9c0Wn9EFUGZmpl7LT6Fr165ha2trkqCEEOJeEv6dA8jLyQ47a42ZoxFC3ItKpeKxYC8erefJ9tNXmfnnSf4+n2r24fJGXwJr27YtS5cu1f2sUqnQarV8+umnPProoyYNTgghiiO3wBCi4lGpVLSt48HyF1rwakgevcP8779TKTK6BejTTz+lQ4cO/P333+Tk5DBmzBiOHj3KtWvX2LFjR2nEKIQQehKkA7QQFVptZ7C3MW/rrdEtQA0bNuTkyZO0adOG7t27k5mZSc+ePTlw4ABBQUGlEaMQQuiJT5EWICHEgzG6BQjAxcWFd955x9SxCCGEQRKuF7YASQdoIUTJGFQAHTp0iIYNG6JWqzl06NA9t23cuLFJAhNCiLuRPkBCiAdl0CWw0NBQrl69qvt/06ZNCQ0NLfJo2rRpiYKYM2cONWvWxM7OjvDwcPbs2XPXbRcvXoxKpdJ72NnZ6W2jKAoTJkzAx8cHe3t7IiIiOHXqVIliE0KUL7n5Wi6lFowCkwJICFFSBrUAnTt3Dg8PD93/TWnlypWMHj2auXPnEh4ezowZM4iMjCQ2NhZPT89i93F2diY2Nlb38503Yf3000+ZNWsWS5YsITAwkPfee4/IyEiOHTtWpFgSQlQsl1JvolXA1kqNh5NMvSGEKBmDCqAaNWoU+39TmD59OsOGDWPw4MEAzJ07l7Vr17Jw4ULGjRtX7D4qlQpvb+9i1ymKwowZM3j33Xfp3r07AEuXLsXLy4s1a9bQp08fk8YvhChbCdcKWn8CqjoU+eNHCCEMZXQn6ClTpuDl5cWQIUP0li9cuJDk5GTGjh1r8LFycnLYt28f48eP1y1Tq9VEREQQExNz1/0yMjKoUaMGWq2WZs2a8fHHHxMSEgIUtFAlJiYSERGh297FxYXw8HBiYmKKLYCys7PJzv5vXu60tDQAcnNzyc3NNTifuyk8himOVV5JjhVfRcnvXHI6AP6udkbHWlFyfBCWnqOl5weWn2Np5mfMMVWKkbdxr1mzJj/88AOtWrXSW75792769Olj1CWyS5cu4efnx86dO2nZsqVu+ZgxY9i6dSu7d+8usk9MTAynTp2icePG3Lhxg88//5y//vqLo0eP4u/vz86dO2ndujWXLl3Cx8dHt1+vXr1QqVSsXLmyyDHff/99Jk2aVGT5Dz/8UOys10II8/ntvJqoS2oe8dbydKDW3OEIIcqRrKws+vbty40bN3B2dr7ntka3ACUmJuoVFoU8PDx0d4YvTS1bttQrllq1akX9+vX55ptv+OCDD0p0zPHjxzN69Gjdz2lpaQQEBNCpU6f7voCGyM3NZdOmTXTs2BFra/NO/V1aJMeKr6Lkt37FP3ApiTZN69O1lXGX5CtKjg/C0nO09PzA8nMszfwKr+AYwugCKCAggB07dhAYGKi3fMeOHfj6+hp1LHd3dzQaDUlJSXrLk5KS7trH507W1tY0bdqU06dPA+j2S0pK0ivUkpKSCA0NLfYYtra2xd7HzNra2qQnx9THK48kx4qvvOd38cYtAGq6O5Y4zvKeoylYeo6Wnh9Yfo6lkZ8xxzN6Juhhw4YxatQoFi1axPnz5zl//jwLFy7kjTfeYNiwYUYdy8bGhrCwMKKionTLtFotUVFReq0895Kfn8/hw4d1xU5gYCDe3t56x0xLS2P37t0GH1MIUX7p5gCqJpenhRAlZ3QL0FtvvUVKSgqvvPIKOTk5ANjZ2TF27Fi9zsyGGj16NAMHDqR58+a0aNGCGTNmkJmZqRsVNmDAAPz8/JgyZQoAkydP5uGHH6Z27dqkpqby2Wefcf78eV544QWgYITYqFGj+PDDD6lTp45uGLyvry89evQwOj4hRPlx42YuqVkFnRwD3KQAEkKUnNEFkEql4pNPPuG9997j+PHj2NvbU6dOnWIvIRmid+/eJCcnM2HCBBITEwkNDWX9+vV4eXkBEB8fj1r9X0PV9evXGTZsGImJibi5uREWFsbOnTtp0KCBbpsxY8aQmZnJiy++SGpqKm3atGH9+vUyB5AQFVzhTVDdHW2oYluiO/kIIQRQwnuBATg6OvLQQw+ZJIiRI0cycuTIYtdFR0fr/fzFF1/wxRdf3PN4KpWKyZMnM3nyZJPEJ4QoH+Qu8EIIUzGoAOrZsyeLFy/G2dmZnj173nPb1atXmyQwIYS4U2H/H7n8JYR4UAYVQC4uLroZV52dnWX2VSGEWcSlZAJQUzpACyEekEEF0FNPPaXrP7N48eLSjEcIIe7qTHJBAVTLw9HMkQghKjqDhsE/9dRTpKamAqDRaLhy5UppxiSEEMU6d7WgAAp0r2LmSIQQFZ1BBZCHhwe7du0CCm42KpfAhBBlLf1WLsnpBffsq+UhBZAQ4sEYdAns5Zdfpnv37qhUqnveiR0KJiYUQghTK2z98XCyxcnOcmfHFUKUDYMKoPfff58+ffpw+vRpnnzySRYtWoSrq2sphyaEEP85myyXv4QQpmNQAfTbb7/RpUsXgoODmThxIs8++6zcJV0IUabO/tsCFCSXv4QQJmB0J+jJkyeTkZFRmjEJIUQRZ5MLvnekBUgIYQrSCVoIUSEUXgKr5S5D4IUQD046QQshyj1FUf4bAi+XwIQQJiCdoIUQ5V5i2i1u5uajUauoLvcBE0KYgME3Qw0ODpZO0EIIszj37+Wv6lUdsNYYdOVeCCHuyehvkokTJ2JjY8Off/7JN998Q3p6OgCXLl2SztFCiFJx5mph/x+5/CWEMA2DW4AKnT9/ns6dOxMfH092djYdO3bEycmJTz75hOzsbObOnVsacQohKrFzMgeQEMLEjG4Bev3112nevDnXr1/H3t5et/ypp54iKirKpMEJIQTA2asFrctyE1QhhKkY3QK0bds2du7ciY2Njd7ymjVrcvHiRZMFJoQQheQmqEIIUzO6BUir1RY71P3ChQs4OTmZJCghhCiUnZdPwrUsQGaBFkKYjtEFUKdOnZgxY4buZ5VKRUZGBhMnTqRr166mjE0IIYi7moVWAUdbKzycbM0djhDCQhh9CWzatGlERkbSoEEDbt26Rd++fTl16hTu7u4sX768NGIUQlRiMWeuAtDY30VmoRdCmIzRBZC/vz///PMPK1as4NChQ2RkZDB06FCef/55vU7RQghhCn+dKiiAHqnrYeZIhBCWxOgCCMDKyop+/fqZOhYhhNCTnZdPzJkUAB6pIwWQEMJ0SlQAnTlzhhkzZnD8+HEAQkJCeO211wgKCjJpcEKIym1f3HVu5ubj7mhLfR8ZZCGEMB2jO0Fv2LCBBg0asGfPHho3bkzjxo3ZtWsXISEhbNq0qTRiFEJUUrrLX3Xcpf+PEMKkjG4BGjduHG+88QZTp04tsnzs2LF07NjRZMEJISq3v04mA9L/Rwhheka3AB0/fpyhQ4cWWT5kyBCOHTtmkqCEECI5PZtjl9MAaFPH3czRCCEsjdEFkIeHBwcPHiyy/ODBg3h6epoiJiGEYOOxRAAa+jnj7ijz/wghTMvoS2DDhg3jxRdf5OzZs7Rq1QqAHTt28MknnzB69GiTByiEqHzOXc3ksw2xADzR2NfM0QghLJHRBdB7772Hk5MT06ZNY/z48QD4+vry/vvv89prr5k8QCFE5XI9M4chi/eSmpVLE38XBrWuae6QhBAWyOgCSKVS8cYbb/DGG2+Qnp4OIPcAE0KYzLtrjnDuaiZ+rvbMH9gcWyuNuUMSQlggg/sA3bx5k99++01X9EBB4ePk5ERaWhq//fYb2dnZpRKkEKJyOJWUztrDlwH4pn8Ynk52Zo5ICGGpDC6A5s2bx8yZM4tt7XF2dmbWrFl8++23Jg1OCFG5fL31DACRIV409HMxczRCCEtmcAH0/fffM2rUqLuuHzVqFEuWLClREHPmzKFmzZrY2dkRHh7Onj17DNpvxYoVqFQqevToobd80KBBqFQqvUfnzp1LFJsQomwkXMvi14OXAHilfW0zRyOEsHQGF0CnTp2iSZMmd13fuHFjTp06ZXQAK1euZPTo0UycOJH9+/fTpEkTIiMjuXLlyj33i4uL480336Rt27bFru/cuTOXL1/WPeRO9UKUb/P+Oku+VqFtHXeaBLiaOxwhhIUzuADKy8sjOTn5ruuTk5PJy8szOoDp06czbNgwBg8eTIMGDZg7dy4ODg4sXLjwrvvk5+fz/PPPM2nSJGrVqlXsNra2tnh7e+sebm5uRscmhCgbV9JvsfLvBEBaf4QQZcPgUWAhISH8+eefhIWFFbt+48aNhISEGPXkOTk57Nu3TzecHkCtVhMREUFMTMxd95s8eTKenp4MHTqUbdu2FbtNdHQ0np6euLm58dhjj/Hhhx9SrVq1YrfNzs7W68CdllYw+2xubi65ublG5VScwmOY4ljlleRY8Zkzv/lbz5CTp6VpgAthAU6lFoOln0Ow/BwtPT+w/BxLMz9jjmlwATRkyBBGjx5NSEgIjz/+uN66//3vf3z00UdMnz7d8CiBq1evkp+fj5eXl95yLy8vTpw4Uew+27dvZ8GCBcXORl2oc+fO9OzZk8DAQM6cOcPbb79Nly5diImJQaMpOqR2ypQpTJo0qcjyjRs34uDgYFRO91IZbhYrOVZ8ZZ1fVh4s3acBVDSvco0//vij1J/T0s8hWH6Olp4fWH6OpZFfVlaWwdsaXAC9+OKL/PXXXzz55JMEBwdTr149AE6cOMHJkyfp1asXL774ovHRGiE9PZ3+/fszf/583N3vfm+gPn366P7fqFEjGjduTFBQENHR0XTo0KHI9uPHj9ebxTotLY2AgAA6deqEs7PzA8edm5vLpk2b6NixI9bW1g98vPJIcqz4zJXfnOizZGtPE+zlyFt9W5bqXd8t/RyC5edo6fmB5edYmvkVXsExhFETIX733Xc8+eST/PDDD5w8eRJFUahXrx6TJk2iV69eRgfq7u6ORqMhKSlJb3lSUhLe3t5Ftj9z5gxxcXE88cQTumVarbYgESsrYmNjCQoKKrJfrVq1cHd35/Tp08UWQLa2ttjaFr3XkLW1tUlPjqmPVx5JjhVfWeaXlZPHkpjzALzyWB1sbGzK5Hkt/RyC5edo6fmB5edYGvkZczyjZ4Lu1atXiYqd4tjY2BAWFkZUVJRuKLtWqyUqKoqRI0cW2T44OJjDhw/rLXv33XdJT09n5syZBAQEFPs8Fy5cICUlBR8fH5PELYQwjeV7ErielUvNag50aySfTyFE2TG6ADK10aNHM3DgQJo3b06LFi2YMWMGmZmZDB48GIABAwbg5+fHlClTsLOzo2HDhnr7u7q6AuiWZ2RkMGnSJJ5++mm8vb05c+YMY8aMoXbt2kRGRpZpbkKIu8vOy2f+X2cBeLldEBp16V36EkKIO5m9AOrduzfJyclMmDCBxMREQkNDWb9+va5jdHx8PGq1waP10Wg0HDp0iCVLlpCamoqvry+dOnXigw8+KPYylxDCPH7Zf5HEtFt4O9vxVDM/c4cjhKhkzF4AAYwcObLYS15QMJz9XhYvXqz3s729PRs2bDBRZEKI0pCXr9Xd9mLYI7XkhqdCiDJneNOKEEKYyLojiZxPycLNwZrnWhTfd08IIUqTFEBCiDKlKApfbTkNwJDWgTjYlIuGaCFEJWP0N89TTz1V7DwdKpUKOzs7ateuTd++fXXzBAkhxO02n7jCicR0HG2tGNCyprnDEUJUUka3ALm4uLB582b279+vu9P6gQMH2Lx5M3l5eaxcuZImTZqwY8eO0ohXCFGBKYrCl/+2/vR7uAYuDpY7x4kQonwzugXI29ubvn378uWXX+pGZ2m1Wl5//XWcnJxYsWIFL7/8MmPHjmX79u0mD1gIUXHtOnuNA/Gp2FqpGdom0NzhCCEqMaNbgBYsWMCoUaP0hqar1WpeffVV5s2bh0qlYuTIkRw5csSkgQohKr6vogtaf3o/FICHk0xLIYQwH6MLoLy8vGJvVHrixAny8/MBsLOzK9X7+QghKp5/ElLZduoqVmoVLz5Sy9zhCCEqOaMvgfXv35+hQ4fy9ttv89BDDwGwd+9ePv74YwYMGADA1q1bCQkJMW2kQogKrbD1p3uoH/5uDmaORghR2RldAH3xxRd4eXnx6aef6m5i6uXlxRtvvMHYsWMB6NSpE507dzZtpEKICutUUjobjiahUsHw9tL6I4QwP6MLII1GwzvvvMM777yju+28s7Oz3jbVq1c3TXRCCIvwdXTBrM+dQ7yp7elk5miEEOIBb4VxZ+EjhBB3SriWxa//XALglfa1zRyNEEIUKFEBtGrVKn788Ufi4+PJycnRW7d//36TBCaEsAzf/HWGfK3CI3U9aOTvYu5whBACKMEosFmzZjF48GC8vLw4cOAALVq0oFq1apw9e5YuXbqURoxCiArqStotfvz7AgAj2geZORohhPiP0QXQV199xbx585g9ezY2NjaMGTOGTZs28dprr3Hjxo3SiFEIUUEt2H6OnDwtzWu40SKwqrnDEUIIHaMLoPj4eFq1agWAvb096enpQMHw+OXLl5s2OiFEhZWalcN3u84DMOLR2jI3mBCiXDG6APL29ubatWtAwWivXbt2AXDu3DkURTFtdEKICmvJzvNk5uRT38eZ9vU8zB2OEELoMboAeuyxx/jtt98AGDx4MG+88QYdO3akd+/ePPXUUyYPUAhR8WRm57Fo5zkARjwaJK0/Qohyx+hRYPPmzUOr1QIwYsQIqlWrxs6dO3nyySd56aWXTB6gEKLiWb4nntSsXALdq9CloY+5wxFCiCKMKoDy8vL4+OOPGTJkCP7+/gD06dOHPn36lEpwQoiKJzsvn3l/nQVgeLsgNGpp/RFClD9GXQKzsrLi008/JS8vr7TiEUJUcD/vu8iV9Gx8XOzo0dTP3OEIIUSxjO4D1KFDB7Zu3VoasQghKri8fC1ztxbc9uLFR2phY2X0V4wQQpQJo/sAdenShXHjxnH48GHCwsKoUqWK3vonn3zSZMEJISqWtYcvE38ti6pVbOjzkNwTUAhRfhldAL3yyisATJ8+vcg6lUpFfn7+g0clhKhwtFqFr7YUtP4MbROIvY3GzBEJIcTdGV0AFY4AE0KI20WduEJsUjqOtlb0e7iGucMRQoh7kgv0QogHpigKX245DUD/ljVwsbc2c0RCCHFvRrUAabVaFi9ezOrVq4mLi0OlUhEYGMgzzzxD//79ZbKzB3Dk4g1W7k1ArQJHOys6NfCmSYCrucMSwiAxZ1L4JyEVWys1Q1oHmjscIYS4L4MLIEVRePLJJ1m3bh1NmjShUaNGKIrC8ePHGTRoEKtXr2bNmjWlGKrlOnLxBn3m7SIj+7/pBeZsOUPbOu78X6d6hEohJMq5OdEFrT99HgrAw8nWzNEIIcT9GVwALV68mL/++ouoqCgeffRRvXWbN2+mR48eLF26lAEDBpg8SEt2KfUmQxbvJSM7j2bVXWld252zVzNZfySRbaeusuP0VYa1rcUbHetiZy2dSkX5cyD+OjtOp2ClVjHskVrmDkcIIQxicB+g5cuX8/bbbxcpfqDg/mDjxo3j+++/N2lwlcGIH/ZzJT2bul6OLB7Sgv/rVI85fZsR/WZ7nmrqh1aBb/46S7dZ2zgQf93c4QpRxFfRBSO/ejT1w9/NwczRCCGEYQwugA4dOkTnzp3vur5Lly78888/JgmqskhOz+ZAfCoqFSwY+BDOdv91HA2o6sAXvUP5dkBzPJxsOZOcydNf7+SzDSfIyZOReOL+MrPzmPnnKcauOsTcrWf481gScVczydcqJnuO2MR0Nh1LQqWCl9sFmey4QghR2gy+BHbt2jW8vLzuut7Ly4vr16WFwhiHLqQCEOThSEDV4v9yjmjgRfOabrz/21HWHLzEnC1niI5NZkbvUOp4OZVhtKIiOZGYxojv93MmObPIOgcbDcHeTjT0cyHE15kQXxfqeDmWaEho4azPXRp6U9vT8QGjFkKIsmPwd15+fj5WVnevlzQaTYnvETZnzhxq1qyJnZ0d4eHh7Nmzx6D9VqxYgUqlokePHnrLFUVhwoQJ+Pj4YG9vT0REBKdOnSpRbKXpnws3AGjs73LP7VwdbJjRpylfP98MNwdrjl5Ko9vs7SzYfg6tCf+aFxWfoiis3BtP9y93cCY5Ey9nW4a3D+KJJr4EezthY6UmKyef/fGpLI05z9ifD/P47O00nLiBJ+bE8P1pNUtizrM37ppep/zixKdk8ds/lwB4pX3tskhPCCFMxqhRYIMGDcLWtvgRHtnZ2SUKYOXKlYwePZq5c+cSHh7OjBkziIyMJDY2Fk9Pz7vuFxcXx5tvvknbtm2LrPv000+ZNWsWS5YsITAwkPfee4/IyEiOHTuGnZ1dieIsDYf/bQFq4u9q0PZdGvkQVsONsT8fYktsMh/8foyo40l89mwT/FztSy9QUSHcyMrlvV+P6IqSdnU9mN6rCdUc//vM5uVrOXc1k6OX0jh66ca//6Zx42YuJxLTATV71sUCoFJBzWpVaODrrGspCvF1xv3f433z1xnytQrt6nrQ0O/eRbwQQpQ3BhdAAwcOvO82JRkBNn36dIYNG8bgwYMBmDt3LmvXrmXhwoWMGzeu2H3y8/N5/vnnmTRpEtu2bSM1NVW3TlEUZsyYwbvvvkv37t0BWLp0KV5eXqxZs4Y+ffoYHWNpUBSFwxcLWoAa3acF6HaeznYsHPQQP+yJ58Pfj7PzTAqdZ/zF5O4h9Aj1k7mYKqm/TiYzZtUhEtNuoVGr+L9OdXn5kSDUav33g5VGTR0vJ+p4Oenu1K4oChdTb3Io/jq/bttPbhUvjiemc/nGLc5dzeTc1UzWHrqsO4aXsy213B2JOZsCwIhHpfVHCFHxGFwALVq0yORPnpOTw759+xg/frxumVqtJiIigpiYmLvuN3nyZDw9PRk6dCjbtm3TW3fu3DkSExOJiIjQLXNxcSE8PJyYmJhyUwBdvnGLqxk5WKlVNPBxNmpflUrF8+E1aB3kzhs/HuRAfCpvrPyHjUeTmPRkCJ7O5aeVS5SurJw8pqw7wbJd5wEIdK/CtF5NaFbdzeBjqFQq/N0c8HK0JjdOS9euTbG2tiYlI1vXQnT00g2OXUrjXEomSWnZJKUVtPi2CqpGi8CqpZKbEEKUJqPvBWZKV69eJT8/v0jnai8vL06cOFHsPtu3b2fBggUcPHiw2PWJiYm6Y9x5zMJ1d8rOzta7hJeWlgZAbm4uubm5BuVyL4XHuP1YB84X/PVcx9MRDVpyc40f2eXnYsMPQ5ozb1scs7ec4Y9/5w56I6I2z7cIQKMuu9ag4nK0NOUtxz1x13hnzTHiUrIA6B8ewJud6uBgY1WiGO/Mz9lWTctAV1oGuuq2yczO40RiOscup5OTr+XZZn7l5vUwRHk7h6XB0nO09PzA8nMszfyMOaZZCyBjpaen079/f+bPn4+7u7vJjjtlyhQmTZpUZPnGjRtxcDDdvCabNm3S/f9/8WpAjav2BuvWrXug49YE3mgIP57VcD4jjw/WnmBR9HF6BeZTo4wHit2eo6Uyd46ZufBbvJpdVwrGMLjYKPQN0hKsPkf0n+ce+PiG5Fft33+3bzn2wM9nDuY+h2XB0nO09PzA8nMsjfyysrIM3tasBZC7uzsajYakpCS95UlJSXh7exfZ/syZM8TFxfHEE0/olhXend7KyorY2FjdfklJSfj4+OgdMzQ0tNg4xo8fz+jRo3U/p6WlERAQQKdOnXB2Nu7yVHFyc3PZtGkTHTt2xNq6YK6fHxfvA1Lo8nAIXR8KeODnABiqVVj59wWmbTrFhcw8vjhqxdNN/Xi9QxDepXxZrLgcLY25c1QUhf8dSmTaH7GkZOYA0Lu5P2M61cHZBDcfNXd+ZUFyrPgsPT+w/BxLM7/CKziGMGsBZGNjQ1hYGFFRUbqh7FqtlqioKEaOHFlk++DgYA4fPqy37N133yU9PZ2ZM2cSEBCAtbU13t7eREVF6QqetLQ0du/ezfDhw4uNw9bWttjRbdbW1iY9OYXHUxSFI5cKTlLT6tVM9hzWwMDWteja2I8p646z+sBFVu2/yO+HLzOkdSAvtw/Sm2yxNJj6NSuPzJHj8ctpTPrfUXadvQYUXDr9uGcjHqpp+v43cg4tg6XnaOn5geXnWBr5GXM8s18CGz16NAMHDqR58+a0aNGCGTNmkJmZqRsVNmDAAPz8/JgyZQp2dnY0bNhQb39XV1cAveWjRo3iww8/pE6dOrph8L6+vkXmCzKXhGs3uXEzFxuNmnrepr9G5eFky/TeoTz/cA2m/nGcvXHX+Sr6DMv3xDPi0do8H14Dexu5r1hFcC0zh2kbY1m+Jx6tArZWakY+WpuX2gVhY1WSqQuFEEJAOSiAevfuTXJyMhMmTCAxMZHQ0FDWr1+v68QcHx+PWm3cF/2YMWPIzMzkxRdfJDU1lTZt2rB+/fpyMwdQYtotAHxd7Ur1l1hYDTd+fKklm44l8cn6E5xJzuTDtceZu/UMQ9oE0v/hGjiVcouQKJlbufksiznP7M2nSLtVMCFht0Y+jO8aLPfbEkIIEzB7AQQwcuTIYi95AURHR99z38WLFxdZplKpmDx5MpMnTzZBdKaXlVPwC83BpvRffpVKRacQbx4L9mTVvgt8ueU0F67f5NP1scyNPsOgVjUZ2Kqm3mR5wnzy8rWs3n+RL/48yeUbBYVyfR9nJj7RgIdrVbvP3kIIIQxVLgqgyuZmTj5QcE+msmKlUdOnRXWeDvPnt4OX+Cr6NGeSM5m1+TRzt56lW2MfBrSsQWiAq0ymaAaKorDhaBKfb4zl9JUMAHxc7Hgjoi5Ph/mX6ZQGQghRGUgBZAZZhQWQbdm//NYaNU+H+fNUUz82HE1k7tYz/HPhBr8cuMgvBy7SyM+F/i1r8ERjX+knVAbytQobjyYya/Npjl8u6Bjv6mDNiPa16d+yBnbWcg6EEKI0SAFkBlm5/xZAZvzlplar6NLIhy6NfDiYkMrSmDh+P3SZwxdvMGbVISb9dpTODX14qqkfLYOqSQuEiaVm5bBybwLLdp3nwvWbADjaWjGoVU2GPVILFxMMaxdCCHF3UgCZQVZ2YR+g8vHXfWiAK6EBobzbrQEr9yawfE888dey+Hn/BX7efwFPJ1u6h/rSPdSPEF9nuUT2AI5dSmPJzjjWHLxIdl7BHFauDtb0f7gGQ9sE4upgY+YIhRCicpACyAwKL4GVt0tMVavYMLx9EC+3q8X++Ov8cuAivx+6zJX0bOZvO8f8befwc7WnYwMvOjbwokVgVaw1MhT7fm7m5LPhaCI/7I5nT9w13fIGPs4MalWTJ0N95VKXEEKUMSmAzOBmbtl3gjaGSqUirEZVwmpUZcLjIWw9mcyaAxfZfOIKF1NvsnhnHIt3xuFsZ0WbOu60ru1OeA1XFMXckZcfiqKwPz6VVfsS+P2fy6T/2+qnUavo3NCbQa1q0ryGm7SmCSGEmUgBZAaFw+Dty2AY/IOysVLrWnxu5eaz/dRVNh1L4s/jSaRk5rDucCLrDhfcZLaqrYat2UdoVt2Nxv6uBPs4YWtVPou80pJwLYvfD11m1b4EziRn6pb7u9nzTJg/fR6qjrdL+ZiPSgghKrPy/xvYAmWZYRi8KdhZa4ho4EVEAy/ytQoHE1LZcfoqO05fZX/8da5lwy8HLvHLgUsAWGtU1PdxppGfC8HeTtTxcqKOp6NFzDmkKAoXrt8kNjGdE4lpHE9M55+EVF2HZgB7aw1dGnnzbFgA4YFVUUtHciGEKDekADIDc8wDZGoatYqwGm6E1XDjtQ51uJF5k69/2oTGuw6HL6Vz6EIqqVm5HLpwg0MXbujtW62KDbU9HanlUQV/Nwf83ez/fTjg4WhbbgqFvHwtl2/cIuF6FgnXsoi7msGeU2oWzdvNmSuZustat7NSq2hWw42nm/nRrbEvjmaY6kAIIcT9ybezGfzXAmQ5L7+DjRX13RS6dqitu+FrwrWb/HMhlSOXbnAqKYNTV9JJuHaTlMwcUs5dY/e5a0WOY6NR4+NqR9UqNlSrYkO1KrZUdSz4f9UqNrjYW2NvrcHORoO9tQaHf/+1s9Gg+bc/TWFXJOXfTklapeDWElk5+WTl5HEzp/D/+WRk53EtM5uUjByuZuSQ8u//UzKyuZKeTZ72zo5NaqCgoLPWqAjycKS+jzPB3k7U93EmrIYbVaToEUKIck++qc3AElqA7kelUlG9mgPVqznwRBNf3fKsnDzOXMnkZFI68deyuHD9JheuF/x7+cZNcvK1nE/J4nxKlhmj/4+1RoW/mwMBVR3wd7UlI/E8HVs1o463M0EejjIKTgghKigpgMwgU9cJ2nILoLtxsLGikb8LjfxdiqzLzdeSeOMWl2/cKmiVyczhWkZOwb+ZBa0zGbfyuJmbX/DI+feRm0+Rhpo7qFUFz21v81+rkYONhiq2Vrg72ha0MDna4F7FlmqONlRztMXTyRYvZzvdJJC5ubmsWxdH5xAvrK1lokIhhKjIpAAyA10LkMz9osdaoyagakFrizEURSEnX1vsMHyVClSosNaoZMi5EEIIHSmAzMAS+wCZk0qlqnTD7YUQQjwY6cBgBlmV+BKYEEIIUR5IAWQG5X0maCGEEMLSSQFUxnLzteTmF3RWkQJICCGEMA8pgMpYYf8fkEtgQgghhLlIAVTGCkeAWalV2MgcMkIIIYRZyG/gMnb7HEAyLFsIIYQwDymAylhlmAVaCCGEKO+kACpjMgeQEEIIYX5SAJUx3RxAMgu0EEIIYTZSAJUxuQQmhBBCmJ8UQGWs8BKYDIEXQgghzEcKoDKWJbNACyGEEGYnBVAZu/lvH6Aq0glaCCGEMBspgMqYXAITQgghzE8KoDKWJZ2ghRBCCLOTAqiM6YbByyUwIYQQwmykACpj0gIkhBBCmJ8UQGVM5gESQgghzK9cFEBz5syhZs2a2NnZER4ezp49e+667erVq2nevDmurq5UqVKF0NBQli1bprfNoEGDUKlUeo/OnTuXdhoG0XWClpmghRBCCLMxe0eUlStXMnr0aObOnUt4eDgzZswgMjKS2NhYPD09i2xftWpV3nnnHYKDg7GxseH3339n8ODBeHp6EhkZqduuc+fOLFq0SPezra1tmeRzPzflXmBCCCGE2Zm9BWj69OkMGzaMwYMH06BBA+bOnYuDgwMLFy4sdvv27dvz1FNPUb9+fYKCgnj99ddp3Lgx27dv19vO1tYWb29v3cPNza0s0rmvrNyCTtByCUwIIYQwH7M2Q+Tk5LBv3z7Gjx+vW6ZWq4mIiCAmJua++yuKwubNm4mNjeWTTz7RWxcdHY2npydubm489thjfPjhh1SrVq3Y42RnZ5Odna37OS0tDYDc3Fxyc3NLkpqewmPk5uaSlV1QANloFJMcu7y4PUdLZek5Wnp+IDlaAkvPDyw/x9LMz5hjqhRFUUwegYEuXbqEn58fO3fupGXLlrrlY8aMYevWrezevbvY/W7cuIGfnx/Z2dloNBq++uorhgwZolu/YsUKHBwcCAwM5MyZM7z99ts4OjoSExODRlO05eX9999n0qRJRZb/8MMPODg4mCDT/0zYp+FGjoo3G+UR4GjSQwshhBCVWlZWFn379uXGjRs4Ozvfc9sK2RHFycmJgwcPkpGRQVRUFKNHj6ZWrVq0b98egD59+ui2bdSoEY0bNyYoKIjo6Gg6dOhQ5Hjjx49n9OjRup/T0tIICAigU6dO930BDZGbm8umTZvo2LEj7x3YBuQR8Wg7gjyqPPCxy4vbc7S2tjZ3OKXC0nO09PxAcrQElp4fWH6OpZlf4RUcQ5i1AHJ3d0ej0ZCUlKS3PCkpCW9v77vup1arqV27NgChoaEcP36cKVOm6AqgO9WqVQt3d3dOnz5dbAFka2tbbCdpa2trk54ca2trbv57M1RnB1uLfGOb+jUrjyw9R0vPDyRHS2Dp+YHl51ga+RlzPLN2graxsSEsLIyoqCjdMq1WS1RUlN4lsfvRarV6fXjudOHCBVJSUvDx8XmgeB9Ubr6W3PyCK47SCVoIIYQwH7NfAhs9ejQDBw6kefPmtGjRghkzZpCZmcngwYMBGDBgAH5+fkyZMgWAKVOm0Lx5c4KCgsjOzmbdunUsW7aMr7/+GoCMjAwmTZrE008/jbe3N2fOnGHMmDHUrl1bb5i8ORQOgQe5GaoQQghhTmYvgHr37k1ycjITJkwgMTGR0NBQ1q9fj5eXFwDx8fGo1f81VGVmZvLKK69w4cIF7O3tCQ4O5rvvvqN3794AaDQaDh06xJIlS0hNTcXX15dOnTrxwQcfmH0uoKx/L39p1CpsNGafgUAIIYSotMxeAAGMHDmSkSNHFrsuOjpa7+cPP/yQDz/88K7Hsre3Z8OGDaYMz2R0kyBaa1CpVGaORgghhKi8pBmiDOluhGorl7+EEEIIc5ICqAxlyW0whBBCiHJBCqAyVDgEXm6EKoQQQpiXFEBl6L8WICmAhBBCCHOSAqgMFXaCliHwQgghhHlJAVSGCofBSwuQEEIIYV5SAJWhm9IJWgghhCgXpAAqQwoKdtZqaQESQgghzEyaIsrQsDaBvPJoXRRFMXcoQgghRKUmLUBmILNACyGEEOYlBZAQQgghKh0pgIQQQghR6UgBJIQQQohKRwogIYQQQlQ6UgAJIYQQotKRAkgIIYQQlY4UQEIIIYSodKQAEkIIIUSlIwWQEEIIISodKYCEEEIIUelIASSEEEKISkcKICGEEEJUOlIACSGEEKLSsTJ3AOWRoigApKWlmeR4ubm5ZGVlkZaWhrW1tUmOWd5IjhWfpecHkqMlsPT8wPJzLM38Cn9vF/4evxcpgIqRnp4OQEBAgJkjEUIIIYSx0tPTcXFxuec2KsWQMqmS0Wq1XLp0CScnJ1Qq1QMfLy0tjYCAABISEnB2djZBhOWP5FjxWXp+IDlaAkvPDyw/x9LMT1EU0tPT8fX1Ra2+dy8faQEqhlqtxt/f3+THdXZ2tsg38+0kx4rP0vMDydESWHp+YPk5llZ+92v5KSSdoIUQQghR6UgBJIQQQohKRwqgMmBra8vEiROxtbU1dyilRnKs+Cw9P5AcLYGl5weWn2N5yU86QQshhBCi0pEWICGEEEJUOlIACSGEEKLSkQJICCGEEJWOFEBCCCGEqHSkACqhOXPmULNmTezs7AgPD2fPnj333P6nn34iODgYOzs7GjVqxLp16/TWK4rChAkT8PHxwd7enoiICE6dOlWaKdyXMTnOnz+ftm3b4ubmhpubGxEREUW2HzRoECqVSu/RuXPn0k7jrozJb/HixUVit7Oz09umop/D9u3bF8lRpVLRrVs33Tbl6Rz+9ddfPPHEE/j6+qJSqVizZs1994mOjqZZs2bY2tpSu3ZtFi9eXGQbYz/bpcnYHFevXk3Hjh3x8PDA2dmZli1bsmHDBr1t3n///SLnMDg4uBSzuDtj84uOji72PZqYmKi3XUU+h8V9xlQqFSEhIbptytM5nDJlCg899BBOTk54enrSo0cPYmNj77tfefidKAVQCaxcuZLRo0czceJE9u/fT5MmTYiMjOTKlSvFbr9z506ee+45hg4dyoEDB+jRowc9evTgyJEjum0+/fRTZs2axdy5c9m9ezdVqlQhMjKSW7dulVVaeozNMTo6mueee44tW7YQExNDQEAAnTp14uLFi3rbde7cmcuXL+sey5cvL4t0ijA2PyiYtfT22M+fP6+3vqKfw9WrV+vld+TIETQaDc8++6zeduXlHGZmZtKkSRPmzJlj0Pbnzp2jW7duPProoxw8eJBRo0bxwgsv6BUIJXlflCZjc/zrr7/o2LEj69atY9++fTz66KM88cQTHDhwQG+7kJAQvXO4ffv20gj/vozNr1BsbKxe/J6enrp1Ff0czpw5Uy+3hIQEqlatWuRzWF7O4datWxkxYgS7du1i06ZN5Obm0qlTJzIzM++6T7n5nagIo7Vo0UIZMWKE7uf8/HzF19dXmTJlSrHb9+rVS+nWrZvesvDwcOWll15SFEVRtFqt4u3trXz22We69ampqYqtra2yfPnyUsjg/ozN8U55eXmKk5OTsmTJEt2ygQMHKt27dzd1qCVibH6LFi1SXFxc7no8SzyHX3zxheLk5KRkZGTolpWnc3g7QPnll1/uuc2YMWOUkJAQvWW9e/dWIiMjdT8/6GtWmgzJsTgNGjRQJk2apPt54sSJSpMmTUwXmIkYkt+WLVsUQLl+/fpdt7G0c/jLL78oKpVKiYuL0y0rr+dQURTlypUrCqBs3br1rtuUl9+J0gJkpJycHPbt20dERIRumVqtJiIigpiYmGL3iYmJ0dseIDIyUrf9uXPnSExM1NvGxcWF8PDwux6zNJUkxztlZWWRm5tL1apV9ZZHR0fj6elJvXr1GD58OCkpKSaN3RAlzS8jI4MaNWoQEBBA9+7dOXr0qG6dJZ7DBQsW0KdPH6pUqaK3vDycw5K43+fQFK9ZeaPVaklPTy/yOTx16hS+vr7UqlWL559/nvj4eDNFWDKhoaH4+PjQsWNHduzYoVtuiedwwYIFREREUKNGDb3l5fUc3rhxA6DIe+525eV3ohRARrp69Sr5+fl4eXnpLffy8ipyHbpQYmLiPbcv/NeYY5amkuR4p7Fjx+Lr66v3Bu7cuTNLly4lKiqKTz75hK1bt9KlSxfy8/NNGv/9lCS/evXqsXDhQn799Ve+++47tFotrVq14sKFC4DlncM9e/Zw5MgRXnjhBb3l5eUclsTdPodpaWncvHnTJO/78ubzzz8nIyODXr166ZaFh4ezePFi1q9fz9dff825c+do27Yt6enpZozUMD4+PsydO5eff/6Zn3/+mYCAANq3b8/+/fsB03x3lSeXLl3ijz/+KPI5LK/nUKvVMmrUKFq3bk3Dhg3vul15+Z0od4MXJjd16lRWrFhBdHS0XkfhPn366P7fqFEjGjduTFBQENHR0XTo0MEcoRqsZcuWtGzZUvdzq1atqF+/Pt988w0ffPCBGSMrHQsWLKBRo0a0aNFCb3lFPoeVzQ8//MCkSZP49ddf9frIdOnSRff/xo0bEx4eTo0aNfjxxx8ZOnSoOUI1WL169ahXr57u51atWnHmzBm++OILli1bZsbISseSJUtwdXWlR48eesvL6zkcMWIER44cMVt/JGNJC5CR3N3d0Wg0JCUl6S1PSkrC29u72H28vb3vuX3hv8YcszSVJMdCn3/+OVOnTmXjxo00btz4ntvWqlULd3d3Tp8+/cAxG+NB8itkbW1N06ZNdbFb0jnMzMxkxYoVBn2RmusclsTdPofOzs7Y29ub5H1RXqxYsYIXXniBH3/8scilhju5urpSt27dCnEOi9OiRQtd7JZ0DhVFYeHChfTv3x8bG5t7blsezuHIkSP5/fff2bJlC/7+/vfctrz8TpQCyEg2NjaEhYURFRWlW6bVaomKitJrIbhdy5Yt9bYH2LRpk277wMBAvL299bZJS0tj9+7ddz1maSpJjlDQa/+DDz5g/fr1NG/e/L7Pc+HCBVJSUvDx8TFJ3IYqaX63y8/P5/Dhw7rYLeUcQsHw1OzsbPr163ff5zHXOSyJ+30OTfG+KA+WL1/O4MGDWb58ud4UBneTkZHBmTNnKsQ5LM7Bgwd1sVvKOYSC0VWnT5826A8Rc55DRVEYOXIkv/zyC5s3byYwMPC++5Sb34km605diaxYsUKxtbVVFi9erBw7dkx58cUXFVdXVyUxMVFRFEXp37+/Mm7cON32O3bsUKysrJTPP/9cOX78uDJx4kTF2tpaOXz4sG6bqVOnKq6ursqvv/6qHDp0SOnevbsSGBio3Lx5s8zzUxTjc5w6dapiY2OjrFq1Srl8+bLukZ6eriiKoqSnpytvvvmmEhMTo5w7d075888/lWbNmil16tRRbt26Ve7zmzRpkrJhwwblzJkzyr59+5Q+ffoodnZ2ytGjR3XbVPRzWKhNmzZK7969iywvb+cwPT1dOXDggHLgwAEFUKZPn64cOHBAOX/+vKIoijJu3Dilf//+uu3Pnj2rODg4KG+99ZZy/PhxZc6cOYpGo1HWr1+v2+Z+r1lZMzbH77//XrGyslLmzJmj9zlMTU3VbfN///d/SnR0tHLu3Dllx44dSkREhOLu7q5cuXKl3Of3xRdfKGvWrFFOnTqlHD58WHn99dcVtVqt/Pnnn7ptKvo5LNSvXz8lPDy82GOWp3M4fPhwxcXFRYmOjtZ7z2VlZem2Ka+/E6UAKqHZs2cr1atXV2xsbJQWLVoou3bt0q1r166dMnDgQL3tf/zxR6Vu3bqKjY2NEhISoqxdu1ZvvVarVd577z3Fy8tLsbW1VTp06KDExsaWRSp3ZUyONWrUUIAij4kTJyqKoihZWVlKp06dFA8PD8Xa2lqpUaOGMmzYMLN9KSmKcfmNGjVKt62Xl5fStWtXZf/+/XrHq+jnUFEU5cSJEwqgbNy4scixyts5LBwSfeejMKeBAwcq7dq1K7JPaGioYmNjo9SqVUtZtGhRkePe6zUra8bm2K5du3turygFQ/99fHwUGxsbxc/PT+ndu7dy+vTpsk3sX8bm98knnyhBQUGKnZ2dUrVqVaV9+/bK5s2bixy3Ip9DRSkY8m1vb6/Mmzev2GOWp3NYXG6A3mervP5OVP2bgBBCCCFEpSF9gIQQQghR6UgBJIQQQohKRwogIYQQQlQ6UgAJIYQQotKRAkgIIYQQlY4UQEIIIYSodKQAEkIIIUSlIwWQEKLce//99wkNDTV3GEZr3749o0aNMncYQohiSAEkhDDaoEGDUKlUvPzyy0XWjRgxApVKxaBBg8o+sNtER0ejUqkICQkhPz9fb52rqyuLFy82T2BCiHJBCiAhRIkEBASwYsUKbt68qVt269YtfvjhB6pXr27GyPSdPXuWpUuXmjsMk8nPz0er1Zo7DCEqPCmAhBAl0qxZMwICAli9erVu2erVq6levTpNmzbV23b9+vW0adMGV1dXqlWrxuOPP86ZM2f0trlw4QLPPfccVatWpUqVKjRv3pzdu3frbbNs2TJq1qyJi4sLffr0IT09/b5xvvrqq0ycOJHs7Oxi18fFxaFSqTh48KBuWWpqKiqViujoaOC/1qQNGzbQtGlT7O3/v737C2nqDeMA/t3O0mY6y39ZXSRSxsoECdKuhHmhJKFdGFiwyqAuGmjrH4FRQeEkiMIEK9nsprwpQpRA+sMuStGklZTIMs2QYGKLSCPdfLqIDp5mP6v54/fnfD9wYO/7Pjvv8949e89hrxk2mw2BQAB3796F1WqFxWLBzp07MTk5qbl/KBSCw+FAYmIiUlJScPLkScw+gejLly84cuQIVq1ahSVLliAvL0+dFwCam5uxdOlStLa2Yv369YiNjcXIyMi86yaiv8YCiIj+WGVlJTwej9p2u93Yu3dvRNzExAScTieePHmC+/fvw2g0Yvv27epOxqdPn1BQUIDR0VG0trbi2bNnOHbsmGanY3BwEHfu3EFbWxva2trg9XrhcrnmzbG6uhqhUAj19fVRr/f06dO4fPkyHj9+jLdv32LHjh24ePEibty4gfb2dnR0dETMc/36dZhMJnR3d+PSpUu4cOECmpqa1HGHw4HOzk60tLTg+fPnKC8vR3FxMfx+vxozOTmJuro6NDU14cWLF0hLS4t6LUS6t6BHqxKRLuzevVtKS0slEAhIbGysDA8Py/DwsCxevFjGxsaktLQ04vTn2cbGxgSA9PX1iYjIlStXJCEhQcbHx+eMP3XqlMTFxcnHjx/VvqNHj0peXt5P5/h+CncwGJTGxkZJSkqSDx8+iIhIYmKielr10NCQAJCnT5+q3w0GgwJAHj58qLnXvXv31Jja2loBIIODg2rfgQMHpKioSG0XFBSI1WqVmZkZte/48eNitVpFROTNmzeiKIqMjo5qci8sLJQTJ06IiIjH4xEA4vP5frpWIvp93AEioj+WmpqKkpISNDc3w+PxoKSkBCkpKRFxfr8fFRUVyMzMhMViQUZGBgCoj3J8Ph9yc3ORlJT007kyMjKQkJCgtlesWIFAIPBLee7btw/Jycmoq6v7jdVFysnJUT8vX74ccXFxyMzM1PT9mFN+fj4MBoPa3rJlC/x+P8LhMPr6+hAOh5GVlYX4+Hj18nq9mkeEMTExmrmJKHqmfzoBIvpvq6yshMPhAAA0NDTMGbNt2zasXr0a165dw8qVKzEzM4Ps7GxMTU0BAMxm87zzLFq0SNM2GAy//DKwyWTCuXPnsGfPHjXX74zGb78DZdZ7OdPT0/PmYDAYosoJ+PboT1EU9Pb2QlEUzVh8fLz62Ww2a4ooIooed4CIKCrFxcWYmprC9PQ0ioqKIsbHx8cxMDCAmpoaFBYWwmq1IhgMamJycnLg8/nw/v37vy3P8vJybNiwAWfOnNH0p6amAgDevXun9s1+ITpaP77I3dXVhbVr10JRFOTm5iIcDiMQCGDNmjWaKz09fcFyIKJILICIKCqKoqC/vx8vX76M2MUAgGXLliE5ORlXr17Fq1ev8ODBAzidTk1MRUUF0tPTUVZWhkePHuH169e4desWOjs7FzRXl8sFt9uNiYkJtc9sNiM/Px8ulwv9/f3wer2oqalZsDlHRkbgdDoxMDCAmzdvor6+HlVVVQCArKws7Nq1C3a7Hbdv38bQ0BC6u7tRW1uL9vb2BcuBiCKxACKiqFksFlgsljnHjEYjWlpa0Nvbi+zsbBw6dAjnz5/XxMTExKCjowNpaWnYunUrNm7cCJfLNWdBFQ2bzQabzYZQKKTpd7vdCIVC2LRpE6qrq3H27NkFm9Nut+Pz58/YvHkzDh48iKqqKuzfv18d93g8sNvtOHz4MNatW4eysjL09PT8q/5Liej/yCCzH3wTERER6QB3gIiIiEh3WAARERGR7rAAIiIiIt1hAURERES6wwKIiIiIdIcFEBEREekOCyAiIiLSHRZAREREpDssgIiIiEh3WAARERGR7rAAIiIiIt1hAURERES68xUTIHWKgauImgAAAABJRU5ErkJggg==", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-10T12:42:36.443466\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "Calisto.info()\n" ] @@ -301,11 +472,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "TestFlight = Flight(rocket=Calisto, environment=Env, inclination=84, heading=133)\n" + "TestFlight = Flight(\n", + " rocket=Calisto,\n", + " environment=Env,\n", + " inclination=84,\n", + " heading=133,\n", + ")\n" ] }, { @@ -317,11 +493,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-10T12:42:43.763115\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "TestFlight.plot3DTrajectory()" + "TestFlight.plot3dTrajectory()\n" ] }, { @@ -336,7 +524,15 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# TestFlight.exportKML(\n", + "# fileName=\"trajectory.kml\",\n", + "# timeStep=None,\n", + "# extrude=True,\n", + "# color=\"641400F0\",\n", + "# altitudeMode=\"relativetoground\",\n", + "# )\n" + ] }, { "cell_type": "markdown", @@ -360,7 +556,7 @@ "metadata": {}, "outputs": [], "source": [ - "TestDispersion = Dispersion(filename=\"data/docs/dispersion_analysis/\")" + "# TestDispersion = Dispersion(filename=\"dispersion_analysis_outputs/disp_class_example\")" ] }, { @@ -378,15 +574,7 @@ "metadata": {}, "outputs": [], "source": [ - "# TODO: Fill this with the correct values and the missed ones\n", - "dispersionDict = {\n", - " \"rocketMass\": (19.197 - 2.956, 0.2),\n", - " \"radius\": 0.1 * 127 / 2000,\n", - " \"burnOut\": (3.9, 0.5),\n", - " \"parachuteNames\": [\"Main\", \"Drogue\"],\n", - " \"CdS\": [(10, 2), (1)],\n", - " \"trigger\": [[mainTrigger], [drogueTrigger]],\n", - "}" + "# TODO: This is work in progress, we need to understand how to setup simulations of such kind\n" ] }, { @@ -402,11 +590,15 @@ "metadata": {}, "outputs": [], "source": [ - "TestDispersion.runDispersion(\n", - " number_of_simulations=10, # Be careful with this number, it will take a while to run\n", - " dispersionDict=dispersionDict,\n", - " flight=TestFlight,\n", - ")" + "# TestDispersion.run_dispersion(\n", + "# number_of_simulations=5, # Be careful with this number, it will take a while to run\n", + "# dispersion_dictionary=dispersion_dictionary,\n", + "# flight=TestFlight,\n", + "# environment=Env,\n", + "# # motor=None,\n", + "# # rocket=None,\n", + "# bg_image=None,\n", + "# )\n" ] }, { @@ -427,7 +619,7 @@ "metadata": {}, "outputs": [], "source": [ - "dispersion_results = TestDispersion.import_results()" + "# dispersion_results = TestDispersion.import_results()\n" ] }, { @@ -458,7 +650,7 @@ "metadata": {}, "outputs": [], "source": [ - "TestDispersion.info()" + "# TestDispersion.info()\n" ] }, { @@ -474,31 +666,7 @@ "metadata": {}, "outputs": [], "source": [ - "TestDispersion.allInfo()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And finally, we can export the ellipses of the results to a .kml file so it can be opened on Google Earth" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "TestDispersion.exportEllipsesToKML(\n", - " dispersion_results,\n", - " filename,\n", - " origin_lat,\n", - " origin_lon,\n", - " type=\"all\",\n", - " resolution=100,\n", - " color=\"ff0000ff\",\n", - ")" + "# TestDispersion.allInfo()\n" ] }, { @@ -517,142 +685,133 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ - "analysis_parameters = {\n", - " # Mass Details\n", - " \"mass\": (\n", - " 8.257,\n", - " 0.001,\n", - " ), # Rocket's dry mass (kg) and its uncertainty (standard deviation)\n", - " # Propulsion Details - run help(SolidMotor) for more information\n", - " # \"thrust\": [\"docs/notebooks/dispersion_analysis/dispersion_analysis_inputs/thrustCurve.csv\"],\n", - " # \"totalImpulse\": (1415.15, 35.3), # Motor total impulse (N*s)\n", - " # \"burnOutTime\": (5.274, 1), # Motor burn out time (s)\n", - " # \"nozzleRadius\": (21.642 / 1000, 0.5 / 1000), # Motor's nozzle radius (m)\n", - " # \"throatRadius\": (8 / 1000, 0.5 / 1000), # Motor's nozzle throat radius (m)\n", - " # \"grainNumber\":[6],\n", - " # \"grainSeparation\": (\n", - " # 6 / 1000,\n", - " # 1 / 1000,\n", - " # ), # Motor's grain separation (axial distance between two grains) (m)\n", - " # \"grainDensity\": (1707, 50), # Motor's grain density (kg/m^3)\n", - " # \"grainOuterRadius\": (21.4 / 1000, 0.375 / 1000), # Motor's grain outer radius (m)\n", - " # \"grainInitialInnerRadius\": (\n", - " # 9.65 / 1000,\n", - " # 0.375 / 1000,\n", - " # ), # Motor's grain inner radius (m)\n", - " # \"grainInitialHeight\": (120 / 1000, 1 / 1000), # Motor's grain height (m)\n", - " # \"interpolationMethod\":[\"linear\"],\n", - " # Aerodynamic Details - run help(Rocket) for more information\n", - " \"inertiaI\": (\n", - " 3.675,\n", - " 0.03675,\n", - " ), # Rocket's inertia moment perpendicular to its axis (kg*m^2)\n", - " \"inertiaZ\": (\n", - " 0.007,\n", - " 0.00007,\n", - " ), # Rocket's inertia moment relative to its axis (kg*m^2)\n", - " \"radius\": (40.45 / 1000, 0.001), # Rocket's radius (kg*m^2)\n", - " \"distanceRocketNozzle\": (\n", - " -1.024,\n", - " 0.001,\n", - " ), # Distance between rocket's center of dry mass and nozzle exit plane (m) (negative)\n", - " \"distanceRocketPropellant\": (\n", - " -0.571,\n", - " 0.001,\n", - " ), # Distance between rocket's center of dry mass and and center of propellant mass (m) (negative)\n", - " \"powerOffDrag\": (\n", - " 0.9081 / 1.05,\n", - " 0.033,\n", - " ), # Multiplier for rocket's drag curve. Usually has a mean value of 1 and a uncertainty of 5% to 10%\n", - " \"powerOnDrag\": (\n", - " 0.9081 / 1.05,\n", - " 0.033,\n", - " ), # Multiplier for rocket's drag curve. Usually has a mean value of 1 and a uncertainty of 5% to 10%\n", - " \"noseKind\": [\"Von Karman\"],\n", - " \"noseLength\": (0.274, 0.001), # Rocket's nose cone length (m)\n", - " \"noseDistanceToCM\": (\n", - " 1.134,\n", - " 0.001,\n", - " ), # Axial distance between rocket's center of dry mass and nearest point in its nose cone (m)\n", - " \"numberOfFins\": [4],\n", - " \"span\": (0.077, 0.0005), # Fin span (m)\n", - " \"rootChord\": (0.058, 0.0005), # Fin root chord (m)\n", - " \"tipChord\": (0.018, 0.0005), # Fin tip chord (m)\n", - " \"distanceToCM\": (\n", - " -0.906,\n", - " 0.001,\n", - " ), # Axial distance between rocket's center of dry mass and nearest point in its fin (m)\n", - " \"airfoil\": [None],\n", - " \"noTail\": [True],\n", - " \"positionFirstRailButton\": [0.0224],\n", - " \"positionSecondRailButton\": [-0.93],\n", - " \"railButtonAngularPosition\": [30],\n", - " # Launch and Environment Details - run help(Environment) and help(Flight) for more information\n", - " \"inclination\": (\n", - " 84.7,\n", - " 1,\n", - " ), # Launch rail inclination angle relative to the horizontal plane (degrees)\n", - " \"heading\": (53, 2), # Launch rail heading relative to north (degrees)\n", - " \"railLength\": (5.7, 0.0005), # Launch rail length (m)\n", - " \"ensembleMember\": list(range(10)), # Members of the ensemble forecast to be used\n", - " # Parachute Details - run help(Rocket) for more information\n", - " \"parachuteNames\": [\"Drogue\"],\n", - " \"CdS\": [\n", - " (\n", - " 0.349 * 1.3,\n", - " 0.07,\n", - " )\n", - " ], # Drag coefficient times reference area for the drogue chute (m^2)\n", - " \"trigger\": [[drogueTrigger]],\n", - " \"lag\": [\n", - " (\n", - " 173,\n", - " 0.5,\n", - " )\n", - " ], # Time delay between parachute ejection signal is detected and parachute is inflated (s)\n", - " \"samplingRate\": [[105]],\n", - " \"noise_mean\": [[0]],\n", - " \"noise_sd\": [[8.3]],\n", - " \"noise_corr\": [[0.5]],\n", - "}\n" + "TestDispersion = Dispersion(filename=\"dispersion_analysis_outputs/disp_class_example\")" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# motorDisp = SolidMotor(\n", - "# thrustSource=\"docs/notebooks/dispersion_analysis/dispersion_analysis_inputs/thrustCurve.csv\",\n", - "# burnOut=5.274,\n", - "# grainNumber=6,\n", - "# grainDensity=1707,\n", - "# grainOuterRadius=21.4 / 1000,\n", - "# grainInitialInnerRadius=9.65 / 1000,\n", - "# grainInitialHeight=120 / 1000,\n", - "# grainSeparation=6 / 1000,\n", - "# nozzleRadius=21.642 / 1000,\n", - "# throatRadius=8 / 1000,\n", - "# reshapeThrustCurve=False,\n", - "# interpolationMethod=\"linear\",\n", - "# )\n" + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'Rocket' object has no attribute 'rootChord'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn [25], line 36\u001b[0m\n\u001b[0;32m 1\u001b[0m dispersion_dictionary \u001b[39m=\u001b[39m {\n\u001b[0;32m 2\u001b[0m \u001b[39m# Environment Parameters\u001b[39;00m\n\u001b[0;32m 3\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mrailLength\u001b[39m\u001b[39m\"\u001b[39m: (Env\u001b[39m.\u001b[39mrailLength, \u001b[39m0.001\u001b[39m),\n\u001b[0;32m 4\u001b[0m \u001b[39m'\u001b[39m\u001b[39mdate\u001b[39m\u001b[39m'\u001b[39m: [Env\u001b[39m.\u001b[39mdate],\n\u001b[0;32m 5\u001b[0m \u001b[39m'\u001b[39m\u001b[39mdatum\u001b[39m\u001b[39m'\u001b[39m: [\u001b[39m\"\u001b[39m\u001b[39mWSG84\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m 6\u001b[0m \u001b[39m'\u001b[39m\u001b[39melevation\u001b[39m\u001b[39m'\u001b[39m:(Env\u001b[39m.\u001b[39melevation, \u001b[39m10\u001b[39m),\n\u001b[0;32m 7\u001b[0m \u001b[39m'\u001b[39m\u001b[39mgravity\u001b[39m\u001b[39m'\u001b[39m:(Env\u001b[39m.\u001b[39mgravity, \u001b[39m0\u001b[39m),\n\u001b[0;32m 8\u001b[0m \u001b[39m'\u001b[39m\u001b[39mlatitude\u001b[39m\u001b[39m'\u001b[39m:(Env\u001b[39m.\u001b[39mlatitude, \u001b[39m0\u001b[39m),\n\u001b[0;32m 9\u001b[0m \u001b[39m'\u001b[39m\u001b[39mlongitude\u001b[39m\u001b[39m'\u001b[39m:(Env\u001b[39m.\u001b[39mlongitude, \u001b[39m0\u001b[39m),\n\u001b[0;32m 10\u001b[0m \u001b[39m'\u001b[39m\u001b[39mtimeZone\u001b[39m\u001b[39m'\u001b[39m:[\u001b[39mstr\u001b[39m(Env\u001b[39m.\u001b[39mtimeZone)],\n\u001b[0;32m 11\u001b[0m \u001b[39m# Solid Motor Parameters\u001b[39;00m\n\u001b[0;32m 12\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mburnOutTime\u001b[39m\u001b[39m\"\u001b[39m: (Pro75M1670\u001b[39m.\u001b[39mburnOutTime, \u001b[39m0.2\u001b[39m),\n\u001b[0;32m 13\u001b[0m \u001b[39m'\u001b[39m\u001b[39mgrainDensity\u001b[39m\u001b[39m'\u001b[39m: (Pro75M1670\u001b[39m.\u001b[39mgrainDensity, \u001b[39m0.1\u001b[39m \u001b[39m*\u001b[39m Pro75M1670\u001b[39m.\u001b[39mgrainDensity),\n\u001b[0;32m 14\u001b[0m \u001b[39m'\u001b[39m\u001b[39mgrainInitialHeight\u001b[39m\u001b[39m'\u001b[39m: (Pro75M1670\u001b[39m.\u001b[39mgrainInitialHeight, \u001b[39m0.001\u001b[39m),\n\u001b[0;32m 15\u001b[0m \u001b[39m'\u001b[39m\u001b[39mgrainInitialInnerRadius\u001b[39m\u001b[39m'\u001b[39m: (Pro75M1670\u001b[39m.\u001b[39mgrainInitialInnerRadius,\u001b[39m0.001\u001b[39m),\n\u001b[0;32m 16\u001b[0m \u001b[39m'\u001b[39m\u001b[39mgrainNumber\u001b[39m\u001b[39m'\u001b[39m: [Pro75M1670\u001b[39m.\u001b[39mgrainNumber],\n\u001b[0;32m 17\u001b[0m \u001b[39m'\u001b[39m\u001b[39mgrainOuterRadius\u001b[39m\u001b[39m'\u001b[39m: (Pro75M1670\u001b[39m.\u001b[39mgrainOuterRadius, \u001b[39m0.001\u001b[39m),\n\u001b[0;32m 18\u001b[0m \u001b[39m'\u001b[39m\u001b[39mgrainSeparation\u001b[39m\u001b[39m'\u001b[39m: (Pro75M1670\u001b[39m.\u001b[39mgrainSeparation, \u001b[39m0.001\u001b[39m),\n\u001b[0;32m 19\u001b[0m \u001b[39m'\u001b[39m\u001b[39mnozzleRadius\u001b[39m\u001b[39m'\u001b[39m: (Pro75M1670\u001b[39m.\u001b[39mnozzleRadius,\u001b[39m0.001\u001b[39m),\n\u001b[0;32m 20\u001b[0m \u001b[39m'\u001b[39m\u001b[39mthroatRadius\u001b[39m\u001b[39m'\u001b[39m: (Pro75M1670\u001b[39m.\u001b[39mthroatRadius,\u001b[39m0.001\u001b[39m),\n\u001b[0;32m 21\u001b[0m \u001b[39m'\u001b[39m\u001b[39mthrust\u001b[39m\u001b[39m'\u001b[39m: [Pro75M1670\u001b[39m.\u001b[39mthrustSource],\n\u001b[0;32m 22\u001b[0m \u001b[39m'\u001b[39m\u001b[39mtotalImpulse\u001b[39m\u001b[39m'\u001b[39m: (Pro75M1670\u001b[39m.\u001b[39mtotalImpulse, \u001b[39m0.033\u001b[39m \u001b[39m*\u001b[39m Pro75M1670\u001b[39m.\u001b[39mtotalImpulse),\n\u001b[0;32m 23\u001b[0m \u001b[39m# Rocket Parameters\u001b[39;00m\n\u001b[0;32m 24\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mmass\u001b[39m\u001b[39m\"\u001b[39m: (Calisto\u001b[39m.\u001b[39mmass, \u001b[39m0.100\u001b[39m),\n\u001b[0;32m 25\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mradius\u001b[39m\u001b[39m\"\u001b[39m: (Calisto\u001b[39m.\u001b[39mradius, \u001b[39m0.001\u001b[39m),\n\u001b[0;32m 26\u001b[0m \u001b[39m'\u001b[39m\u001b[39mdistanceRocketNozzle\u001b[39m\u001b[39m'\u001b[39m: (Calisto\u001b[39m.\u001b[39mdistanceRocketNozzle, \u001b[39m0.010\u001b[39m),\n\u001b[0;32m 27\u001b[0m \u001b[39m'\u001b[39m\u001b[39mdistanceRocketPropellant\u001b[39m\u001b[39m'\u001b[39m: (Calisto\u001b[39m.\u001b[39mdistanceRocketPropellant, \u001b[39m0.010\u001b[39m),\n\u001b[0;32m 28\u001b[0m \u001b[39m'\u001b[39m\u001b[39minertiaI\u001b[39m\u001b[39m'\u001b[39m: (Calisto\u001b[39m.\u001b[39minertiaI, Calisto\u001b[39m.\u001b[39minertiaI \u001b[39m*\u001b[39m \u001b[39m0.1\u001b[39m ),\n\u001b[0;32m 29\u001b[0m \u001b[39m'\u001b[39m\u001b[39minertiaZ\u001b[39m\u001b[39m'\u001b[39m: (Calisto\u001b[39m.\u001b[39minertiaZ, Calisto\u001b[39m.\u001b[39minertiaZ \u001b[39m*\u001b[39m \u001b[39m0.1\u001b[39m ),\n\u001b[0;32m 30\u001b[0m \u001b[39m'\u001b[39m\u001b[39mpowerOffDrag\u001b[39m\u001b[39m'\u001b[39m: (\u001b[39m1\u001b[39m, \u001b[39m0.033\u001b[39m),\n\u001b[0;32m 31\u001b[0m \u001b[39m'\u001b[39m\u001b[39mpowerOnDrag\u001b[39m\u001b[39m'\u001b[39m: (\u001b[39m1\u001b[39m, \u001b[39m0.033\u001b[39m),\n\u001b[0;32m 32\u001b[0m \u001b[39m'\u001b[39m\u001b[39mnoseKind\u001b[39m\u001b[39m'\u001b[39m: [Calisto\u001b[39m.\u001b[39mnoseKind],\n\u001b[0;32m 33\u001b[0m \u001b[39m'\u001b[39m\u001b[39mnoseLength\u001b[39m\u001b[39m'\u001b[39m: (Calisto\u001b[39m.\u001b[39mnoseLength, \u001b[39m0.001\u001b[39m),\n\u001b[0;32m 34\u001b[0m \u001b[39m'\u001b[39m\u001b[39mnoseDistanceToCM\u001b[39m\u001b[39m'\u001b[39m: (Calisto\u001b[39m.\u001b[39mnoseDistanceToCM, \u001b[39m0.010\u001b[39m),\n\u001b[0;32m 35\u001b[0m \u001b[39m'\u001b[39m\u001b[39mnumberOfFins\u001b[39m\u001b[39m'\u001b[39m: [Calisto\u001b[39m.\u001b[39mnumberOfFins],\n\u001b[1;32m---> 36\u001b[0m \u001b[39m'\u001b[39m\u001b[39mrootChord\u001b[39m\u001b[39m'\u001b[39m: (Calisto\u001b[39m.\u001b[39;49mrootChord, \u001b[39m0.001\u001b[39m),\n\u001b[0;32m 37\u001b[0m \u001b[39m'\u001b[39m\u001b[39mtailTopRadius\u001b[39m\u001b[39m'\u001b[39m: (Calisto\u001b[39m.\u001b[39mtailTopRadius, \u001b[39m0.001\u001b[39m),\n\u001b[0;32m 38\u001b[0m \u001b[39m# \"parachuteNames\": [\"Main\", \"Drogue\"],\u001b[39;00m\n\u001b[0;32m 39\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mCdS\u001b[39m\u001b[39m\"\u001b[39m: [(\u001b[39m10\u001b[39m, \u001b[39m2\u001b[39m), (\u001b[39m1\u001b[39m, \u001b[39m0.3\u001b[39m)],\n\u001b[0;32m 40\u001b[0m \u001b[39m# \"trigger\": [[mainTrigger], [drogueTrigger]],\u001b[39;00m\n\u001b[0;32m 41\u001b[0m \u001b[39m# \"noseLength\": (0.588, 1 / 1000),\u001b[39;00m\n\u001b[0;32m 42\u001b[0m \u001b[39m# Flight Parameters\u001b[39;00m\n\u001b[0;32m 43\u001b[0m \u001b[39m# 'atol': ,\u001b[39;00m\n\u001b[0;32m 44\u001b[0m \u001b[39m# 'initialSolution': \"\",\u001b[39;00m\n\u001b[0;32m 45\u001b[0m \u001b[39m# 'maxTime': \"\",\u001b[39;00m\n\u001b[0;32m 46\u001b[0m \u001b[39m# 'maxTimeStep': \"\",\u001b[39;00m\n\u001b[0;32m 47\u001b[0m \u001b[39m# 'minTimeStep': \"\",\u001b[39;00m\n\u001b[0;32m 48\u001b[0m \u001b[39m# 'rtol': \"\",\u001b[39;00m\n\u001b[0;32m 49\u001b[0m \u001b[39m# 'terminateOnApogee': \"\",\u001b[39;00m\n\u001b[0;32m 50\u001b[0m \u001b[39m# 'timeOvershoot': \"\",\u001b[39;00m\n\u001b[0;32m 51\u001b[0m \u001b[39m# 'verbose': \"\",\u001b[39;00m\n\u001b[0;32m 52\u001b[0m }\n", + "\u001b[1;31mAttributeError\u001b[0m: 'Rocket' object has no attribute 'rootChord'" + ] + } + ], + "source": [ + "dispersion_dictionary = {\n", + " # Environment Parameters\n", + " \"railLength\": (Env.railLength, 0.001),\n", + " 'date': [Env.date],\n", + " 'datum': [\"WSG84\"],\n", + " 'elevation':(Env.elevation, 10),\n", + " 'gravity':(Env.gravity, 0),\n", + " 'latitude':(Env.latitude, 0),\n", + " 'longitude':(Env.longitude, 0),\n", + " 'timeZone':[str(Env.timeZone)],\n", + " # Solid Motor Parameters\n", + " \"burnOutTime\": (Pro75M1670.burnOutTime, 0.2),\n", + " 'grainDensity': (Pro75M1670.grainDensity, 0.1 * Pro75M1670.grainDensity),\n", + " 'grainInitialHeight': (Pro75M1670.grainInitialHeight, 0.001),\n", + " 'grainInitialInnerRadius': (Pro75M1670.grainInitialInnerRadius,0.001),\n", + " 'grainNumber': [Pro75M1670.grainNumber],\n", + " 'grainOuterRadius': (Pro75M1670.grainOuterRadius, 0.001),\n", + " 'grainSeparation': (Pro75M1670.grainSeparation, 0.001),\n", + " 'nozzleRadius': (Pro75M1670.nozzleRadius,0.001),\n", + " 'throatRadius': (Pro75M1670.throatRadius,0.001),\n", + " 'thrust': [Pro75M1670.thrustSource],\n", + " 'totalImpulse': (Pro75M1670.totalImpulse, 0.033 * Pro75M1670.totalImpulse),\n", + " # Rocket Parameters\n", + " \"mass\": (Calisto.mass, 0.100),\n", + " \"radius\": (Calisto.radius, 0.001),\n", + " 'distanceRocketNozzle': (Calisto.distanceRocketNozzle, 0.010),\n", + " 'distanceRocketPropellant': (Calisto.distanceRocketPropellant, 0.010),\n", + " 'inertiaI': (Calisto.inertiaI, Calisto.inertiaI * 0.1 ),\n", + " 'inertiaZ': (Calisto.inertiaZ, Calisto.inertiaZ * 0.1 ),\n", + " 'powerOffDrag': (1, 0.033),\n", + " 'powerOnDrag': (1, 0.033),\n", + " 'noseKind': [Calisto.noseKind],\n", + " 'noseLength': (Calisto.noseLength, 0.001),\n", + " 'noseDistanceToCM': (Calisto.noseDistanceToCM, 0.010),\n", + " 'numberOfFins': [Calisto.numberOfFins],\n", + " 'rootChord': (Calisto.rootChord, 0.001),\n", + " 'tailTopRadius': (Calisto.tailTopRadius, 0.001),\n", + " # \"parachuteNames\": [\"Main\", \"Drogue\"],\n", + " \"CdS\": [(10, 2), (1, 0.3)],\n", + " # \"trigger\": [[mainTrigger], [drogueTrigger]],\n", + " # \"noseLength\": (0.588, 1 / 1000),\n", + " # Flight Parameters\n", + " # 'atol': ,\n", + " # 'initialSolution': \"\",\n", + " # 'maxTime': \"\",\n", + " # 'maxTimeStep': \"\",\n", + " # 'minTimeStep': \"\",\n", + " # 'rtol': \"\",\n", + " # 'terminateOnApogee': \"\",\n", + " # 'timeOvershoot': \"\",\n", + " # 'verbose': \"\",\n", + "}" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Starting'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "{'railLength': (5.2, 0.001), 'date': [datetime.datetime(2022, 10, 11, 12, 0, tzinfo=)], 'datum': ['WSG84'], 'elevation': (113, 10), 'gravity': (9.80665, 0), 'latitude': (39.3897, 0), 'longitude': (-8.288964, 0), 'timeZone': ['UTC'], 'burnOutTime': (3.9, 0.2), 'grainDensity': (1815, 181.5), 'grainInitialHeight': (0.12, 0.001), 'grainInitialInnerRadius': (0.015, 0.001), 'grainNumber': [5], 'grainOuterRadius': (0.033, 0.001), 'grainSeparation': (0.005, 0.001), 'nozzleRadius': (0.033, 0.001), 'throatRadius': (0.011, 0.001), 'thrust': ['../../../data/motors/Cesaroni_M1670.eng'], 'totalImpulse': (6026.35, 198.86955000000003), 'mass': (16.241, 0.1), 'radius': (0.0635, 0.001), 'distanceRocketNozzle': (-1.255, 0.01), 'distanceRocketPropellant': (-0.85704, 0.01), 'inertiaI': (6.6, 0.66), 'inertiaZ': (0.0351, 0.00351), 'powerOffDrag': (1, 0.033), 'powerOnDrag': (1, 0.033), 'noseKind': ['vonKarman'], 'noseLength': (0.55829, 0.001), 'noseDistanceToCM': (0.71971, 0.01), 'numberOfFins': [4], 'tailTopRadius': (0.0635, 0.001), 'CdS': [(10, 2), (1, 0.3)], 'initialSolution': [None], 'heading': [90], 'inclination': [80], 'maxTime': [600], 'minTimeStep': [0], 'rtol': [1e-06], 'terminateOnApogee': [False], 'timeOvershoot': [True], 'verbose': [False], 'maxTimeStep': [inf], 'atol': [[0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 1e-06, 1e-06, 1e-06, 1e-06, 0.001, 0.001, 0.001]]}\n", + "\n" + ] + }, + { + "ename": "KeyError", + "evalue": "'rootChord'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn [23], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m TestDispersion\u001b[39m.\u001b[39;49mrun_dispersion(\n\u001b[0;32m 2\u001b[0m number_of_simulations\u001b[39m=\u001b[39;49m\u001b[39m5\u001b[39;49m,\n\u001b[0;32m 3\u001b[0m dispersion_dictionary\u001b[39m=\u001b[39;49mdispersion_dictionary,\n\u001b[0;32m 4\u001b[0m )\n", + "File \u001b[1;32mc:\\users\\guiga\\documents\\github-vscode\\rocketpy\\rocketpy\\Dispersion.py:910\u001b[0m, in \u001b[0;36mDispersion.run_dispersion\u001b[1;34m(self, number_of_simulations, dispersion_dictionary, environment, flight, motor, rocket, bg_image, actual_landing_point)\u001b[0m\n\u001b[0;32m 902\u001b[0m \u001b[39m# Add rocket nose, fins and tail\u001b[39;00m\n\u001b[0;32m 903\u001b[0m rocket_dispersion\u001b[39m.\u001b[39maddNose(\n\u001b[0;32m 904\u001b[0m length\u001b[39m=\u001b[39msetting[\u001b[39m\"\u001b[39m\u001b[39mnoseLength\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m 905\u001b[0m kind\u001b[39m=\u001b[39msetting[\u001b[39m\"\u001b[39m\u001b[39mnoseKind\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m 906\u001b[0m distanceToCM\u001b[39m=\u001b[39msetting[\u001b[39m\"\u001b[39m\u001b[39mnoseDistanceToCM\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m 907\u001b[0m )\n\u001b[0;32m 908\u001b[0m rocket_dispersion\u001b[39m.\u001b[39maddFins(\n\u001b[0;32m 909\u001b[0m n\u001b[39m=\u001b[39msetting[\u001b[39m\"\u001b[39m\u001b[39mnumberOfFins\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[1;32m--> 910\u001b[0m rootChord\u001b[39m=\u001b[39msetting[\u001b[39m\"\u001b[39;49m\u001b[39mrootChord\u001b[39;49m\u001b[39m\"\u001b[39;49m],\n\u001b[0;32m 911\u001b[0m tipChord\u001b[39m=\u001b[39msetting[\u001b[39m\"\u001b[39m\u001b[39mtipChord\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m 912\u001b[0m span\u001b[39m=\u001b[39msetting[\u001b[39m\"\u001b[39m\u001b[39mspan\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m 913\u001b[0m distanceToCM\u001b[39m=\u001b[39msetting[\u001b[39m\"\u001b[39m\u001b[39mdistanceToCM\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m 914\u001b[0m radius\u001b[39m=\u001b[39msetting[\u001b[39m\"\u001b[39m\u001b[39mradius\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m 915\u001b[0m airfoil\u001b[39m=\u001b[39msetting[\u001b[39m\"\u001b[39m\u001b[39mairfoil\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m 916\u001b[0m )\n\u001b[0;32m 917\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mnoTail\u001b[39m\u001b[39m\"\u001b[39m \u001b[39min\u001b[39;00m setting:\n\u001b[0;32m 918\u001b[0m rocket_dispersion\u001b[39m.\u001b[39maddTail(\n\u001b[0;32m 919\u001b[0m topRadius\u001b[39m=\u001b[39msetting[\u001b[39m\"\u001b[39m\u001b[39mtopRadius\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m 920\u001b[0m bottomRadius\u001b[39m=\u001b[39msetting[\u001b[39m\"\u001b[39m\u001b[39mbottomRadius\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m 921\u001b[0m length\u001b[39m=\u001b[39msetting[\u001b[39m\"\u001b[39m\u001b[39mlength\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m 922\u001b[0m distanceToCM\u001b[39m=\u001b[39msetting[\u001b[39m\"\u001b[39m\u001b[39mdistanceToCM\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m 923\u001b[0m )\n", + "\u001b[1;31mKeyError\u001b[0m: 'rootChord'" + ] + } + ], + "source": [ + "TestDispersion.run_dispersion(\n", + " number_of_simulations=5,\n", + " dispersion_dictionary=dispersion_dictionary,\n", + ")" + ] + }, + { + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# Disp = Dispersion(\"test\")\n", - "# modified_dispersion_dict = {i: j for i, j in analysis_parameters.items()}\n", - "# Disp.motor = motorDisp\n", - "# Disp.processDispersionDict(modified_dispersion_dict)\n" + "And finally, we can export the ellipses of the results to a .kml file so it can be opened on Google Earth" ] }, { @@ -661,14 +820,16 @@ "metadata": {}, "outputs": [], "source": [ - "# Disp = Dispersion(\"test\")\n", - "# Disp.runDispersion(100, analysis_parameters, Env, motor=motorDisp)\n" + "# TestDispersion.exportEllipsesToKML(\n", + "# dispersion_results,\n", + "# filename,\n", + "# origin_lat,\n", + "# origin_lon,\n", + "# type=\"all\",\n", + "# resolution=100,\n", + "# color=\"ff0000ff\",\n", + "# )\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] } ], "metadata": { From 7a1f1735017379330ca896617fde171404b2ac66 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 10 Oct 2022 12:57:05 +0200 Subject: [PATCH 19/68] MAINT: Minor changes at dispersion.py --- rocketpy/Dispersion.py | 40 ++++++++++++++++------------------------ 1 file changed, 16 insertions(+), 24 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index b691bdcbe..f021401f5 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -18,6 +18,7 @@ from matplotlib.patches import Ellipse from numpy.random import * +from .Environment import Environment from .Flight import Flight from .Motor import SolidMotor from .Rocket import Rocket @@ -304,10 +305,11 @@ def __process_flight_from_dict(self, dictionary): missing_input = str(missing_input) # Add to the dict try: + # First try to catch value from the Flight object if passed dictionary[missing_input] = [getattr(self.flight, missing_input)] except: # class was not inputted - # checks if missing parameter is required + # check if missing parameter is required if self.flight_inputs[missing_input] == "required": warnings.warn(f'Missing "{missing_input}" in dictionary') else: # if not, uses default value @@ -803,7 +805,7 @@ def run_dispersion( self.flight = flight self.distribution_type = "normal" # TODO: Must be parametrized self.image = bg_image - self.actual_landing_point = actual_landing_point # + self.actual_landing_point = actual_landing_point # (lat, lon) # Obs.: The flight object is not prioritized, which is a good thing, but need to be documented @@ -813,6 +815,7 @@ def run_dispersion( # Creates copy of dispersion_dictionary that will be altered modified_dispersion_dict = {i: j for i, j in dispersion_dictionary.items()} + # TODO: Take care of next line, since analysis_parameters is not what the function is returning analysis_parameters = self.__process_dispersion_dict(modified_dispersion_dict) self.distributionFunc = self.__set_distribution_function(self.distribution_type) @@ -823,26 +826,6 @@ def run_dispersion( dispersion_input_file = open(str(self.filename) + ".disp_inputs.txt", "w") dispersion_output_file = open(str(self.filename) + ".disp_outputs.txt", "w") - # # Initialize Environment - # customAtmosphere = False - # if not self.environment: - # self.environment = Environment( - # railLength=0, - # ) - # if "envAtmosphericType" in dispersion_dictionary: - # if dispersion_dictionary["envAtmosphericType"] == "CustomAtmosphere": - # customAtmosphere = True - # self.environment.setDate(datetime(*dispersion_dictionary["date"][0])) - # self.environment.setAtmosphericModel( - # type=dispersion_dictionary["envAtmosphericType"], - # file=dispersion_dictionary["envAtmosphericFile"] - # if "envAtmosphericFile" in dispersion_dictionary - # else None, - # dictionary=dispersion_dictionary["envAtmosphericDictionary"] - # if "envAtmosphericDictionary" in dispersion_dictionary - # else None, - # ) - # Initialize counter and timer i = 0 @@ -857,8 +840,16 @@ def run_dispersion( self.start_time = process_time() i += 1 - # Creates an of environment + # Creates a copy of the environment env_dispersion = self.environment + if env_dispersion is None: + env_dispersion = Environment( + railLength=dispersion_dictionary["railLength"][0], + date=dispersion_dictionary["date"][0], + latitude=dispersion_dictionary["latitude"][0], + longitude=dispersion_dictionary["longitude"][0], + elevation=dispersion_dictionary["elevation"][0], + ) # Apply environment parameters variations on each iteration if possible env_dispersion.railLength = setting["railLength"] @@ -867,7 +858,8 @@ def run_dispersion( env_dispersion.latitude = setting["latitude"] env_dispersion.longitude = setting["longitude"] env_dispersion.elevation = setting["elevation"] - env_dispersion.selectEnsembleMember(setting["ensembleMember"]) + if env_dispersion.atmosphericModelType in ["Ensemble", "Reanalysis"]: + env_dispersion.selectEnsembleMember(setting["ensembleMember"]) # Creates copy of motor motor_dispersion = self.motor From 1df459ad6eebb4ca872083daed213c85e9513e34 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 10 Oct 2022 12:58:25 +0200 Subject: [PATCH 20/68] ADD: new json with historical data by Env Analysis --- data/weather/EuroC_export_env_analysis.json | 25760 ++++++++++++++++++ 1 file changed, 25760 insertions(+) create mode 100644 data/weather/EuroC_export_env_analysis.json diff --git a/data/weather/EuroC_export_env_analysis.json b/data/weather/EuroC_export_env_analysis.json new file mode 100644 index 000000000..0598532f5 --- /dev/null +++ b/data/weather/EuroC_export_env_analysis.json @@ -0,0 +1,25760 @@ +{ + "start_date": "2002-10-06 00:00:00+01:00", + "end_date": "2021-10-23 00:00:00+01:00", + "start_hour": 4, + "end_hour": 20, + "latitude": 39.3897, + "longitude": -8.28896388889, + "elevation": 113.00194018037647, + "timeZone": "Portugal", + "unit_system": { + "length": "m", + "velocity": "m/s", + "acceleration": "g", + "mass": "kg", + "time": "s", + "pressure": "hPa", + "temperature": "degC", + "angle": "deg", + "precipitation": "mm", + "wind_speed": "m/s" + }, + "surfaceDataFile": "../../data/weather/EuroC_single_level_reanalysis_2002_2021.nc", + "pressureLevelDataFile": "../../data/weather/EuroC_pressure_levels_reanalysis_2001-2021.nc", + "atmosphericModelPressureProfile": { + "4": [ + [ + 0.0, + 977.6714690813923 + ], + [ + 140.24302904312142, + 994.7056149268931 + ], + [ + 280.48605808624285, + 969.2277928010537 + ], + [ + 420.7290871293643, + 951.6398340522569 + ], + [ + 560.9721161724857, + 938.5818940133656 + ], + [ + 701.2151452156071, + 922.7154094321005 + ], + [ + 841.4581742587286, + 907.1535456389507 + ], + [ + 981.70120330185, + 892.3999471203616 + ], + [ + 1121.9442323449714, + 877.6643553533851 + ], + [ + 1262.1872613880928, + 862.9700765693461 + ], + [ + 1402.4302904312142, + 848.6107312664914 + ], + [ + 1542.6733194743356, + 834.7683705070606 + ], + [ + 1682.9163485174572, + 820.5263826299732 + ], + [ + 1823.1593775605786, + 805.5423622070218 + ], + [ + 1963.4024066037, + 793.7765615105203 + ], + [ + 2103.645435646821, + 784.5773206603102 + ], + [ + 2243.888464689943, + 762.7889902666342 + ], + [ + 2384.1314937330644, + 747.6734826350887 + ], + [ + 2524.3745227761856, + 742.7376067890614 + ], + [ + 2664.617551819307, + 733.4368811582627 + ], + [ + 2804.8605808624284, + 720.3022328169492 + ], + [ + 2945.10360990555, + 705.4082118549836 + ], + [ + 3085.346638948671, + 690.822785065689 + ], + [ + 3225.589667991793, + 677.8330605984202 + ], + [ + 3365.8326970349144, + 666.1292563635064 + ], + [ + 3506.0757260780356, + 655.1993400411195 + ], + [ + 3646.318755121157, + 644.5313259275695 + ], + [ + 3786.5617841642784, + 633.6987991293767 + ], + [ + 3926.8048132074, + 622.6920201887227 + ], + [ + 4067.047842250521, + 611.6331180993917 + ], + [ + 4207.290871293642, + 600.6443685099937 + ], + [ + 4347.533900336764, + 589.8468100040468 + ], + [ + 4487.776929379886, + 579.3022546911122 + ], + [ + 4628.019958423007, + 568.9857538596362 + ], + [ + 4768.262987466129, + 558.8651861026583 + ], + [ + 4908.50601650925, + 548.9084286285971 + ], + [ + 5048.749045552371, + 539.0840703952814 + ], + [ + 5188.992074595492, + 529.3777128892556 + ], + [ + 5329.235103638614, + 519.7937228302019 + ], + [ + 5469.478132681736, + 510.3374897141842 + ], + [ + 5609.721161724857, + 501.0143984382358 + ], + [ + 5749.964190767979, + 491.8298072850902 + ], + [ + 5890.2072198111, + 482.78660837920796 + ], + [ + 6030.450248854221, + 473.8820795116159 + ], + [ + 6170.693277897342, + 465.1126772473739 + ], + [ + 6310.936306940464, + 456.4748662452933 + ], + [ + 6451.179335983586, + 447.96516892594906 + ], + [ + 6591.422365026707, + 439.58026709107196 + ], + [ + 6731.665394069829, + 431.3179773567003 + ], + [ + 6871.90842311295, + 423.17720818001817 + ], + [ + 7012.151452156071, + 415.156929843785 + ], + [ + 7152.394481199192, + 407.2561033696322 + ], + [ + 7292.637510242314, + 399.4736487100893 + ], + [ + 7432.880539285436, + 391.80843346563535 + ], + [ + 7573.123568328557, + 384.25913030572497 + ], + [ + 7713.366597371678, + 376.82412443976875 + ], + [ + 7853.6096264148, + 369.5017627077973 + ], + [ + 7993.852655457921, + 362.29039215529707 + ], + [ + 8134.095684501042, + 355.1883766865999 + ], + [ + 8274.338713544164, + 348.19417123291373 + ], + [ + 8414.581742587285, + 341.30644344319904 + ], + [ + 8554.824771630407, + 334.5241065107375 + ], + [ + 8695.067800673529, + 327.8461864609543 + ], + [ + 8835.31082971665, + 321.27171822106254 + ], + [ + 8975.553858759771, + 314.799736670188 + ], + [ + 9115.796887802893, + 308.4292705935431 + ], + [ + 9256.039916846014, + 302.1593260770103 + ], + [ + 9396.282945889136, + 295.9889211101079 + ], + [ + 9536.525974932258, + 289.91715265497794 + ], + [ + 9676.769003975378, + 283.9431759045826 + ], + [ + 9817.0120330185, + 278.066171266123 + ], + [ + 9957.25506206162, + 272.2853217681947 + ], + [ + 10097.498091104742, + 266.59981043969424 + ], + [ + 10237.741120147864, + 261.00882036461684 + ], + [ + 10377.984149190985, + 255.51152715814027 + ], + [ + 10518.227178234107, + 250.10712586289605 + ], + [ + 10658.470207277229, + 244.79494489362898 + ], + [ + 10798.71323632035, + 239.574498032586 + ], + [ + 10938.956265363471, + 234.4453740034528 + ], + [ + 11079.199294406593, + 229.40716944501412 + ], + [ + 11219.442323449714, + 224.45942022144567 + ], + [ + 11359.685352492836, + 219.6016266313459 + ], + [ + 11499.928381535958, + 214.8334726895174 + ], + [ + 11640.171410579078, + 210.15475649817577 + ], + [ + 11780.4144396222, + 205.565255911517 + ], + [ + 11920.65746866532, + 201.0646650775365 + ], + [ + 12060.900497708442, + 196.65222968086078 + ], + [ + 12201.143526751564, + 192.3265186492388 + ], + [ + 12341.386555794685, + 188.0858208538366 + ], + [ + 12481.629584837807, + 183.92855624847837 + ], + [ + 12621.872613880929, + 179.85319769949484 + ], + [ + 12762.11564292405, + 175.8583807745904 + ], + [ + 12902.358671967171, + 171.9435342553073 + ], + [ + 13042.601701010293, + 168.10946531111665 + ], + [ + 13182.844730053414, + 164.35763618432358 + ], + [ + 13323.087759096536, + 160.689056659278 + ], + [ + 13463.330788139658, + 157.10462082098326 + ], + [ + 13603.573817182778, + 153.60520731140528 + ], + [ + 13743.8168462259, + 150.19118503172868 + ], + [ + 13884.059875269022, + 146.85982839567592 + ] + ], + "5": [ + [ + 0.0, + 979.4216156849128 + ], + [ + 140.24302904312142, + 993.9513446297674 + ], + [ + 280.48605808624285, + 969.0019752418823 + ], + [ + 420.7290871293643, + 951.6348389535328 + ], + [ + 560.9721161724857, + 938.3810594728633 + ], + [ + 701.2151452156071, + 922.5260643571382 + ], + [ + 841.4581742587286, + 907.002491551157 + ], + [ + 981.70120330185, + 892.2227617264967 + ], + [ + 1121.9442323449714, + 877.4675268266079 + ], + [ + 1262.1872613880928, + 862.8462265791305 + ], + [ + 1402.4302904312142, + 848.4806487411403 + ], + [ + 1542.6733194743356, + 834.3183057564123 + ], + [ + 1682.9163485174572, + 820.3413618537005 + ], + [ + 1823.1593775605786, + 806.5623730782773 + ], + [ + 1963.4024066037, + 792.9811565198454 + ], + [ + 2103.645435646821, + 779.5928618821589 + ], + [ + 2243.888464689943, + 766.3955582547427 + ], + [ + 2384.1314937330644, + 753.3872224921867 + ], + [ + 2524.3745227761856, + 740.5651328816183 + ], + [ + 2664.617551819307, + 727.9266805095664 + ], + [ + 2804.8605808624284, + 715.4693962909517 + ], + [ + 2945.10360990555, + 703.1908441558012 + ], + [ + 3085.346638948671, + 691.088538215253 + ], + [ + 3225.589667991793, + 679.1601039478766 + ], + [ + 3365.8326970349144, + 667.403301108479 + ], + [ + 3506.0757260780356, + 655.8158961435392 + ], + [ + 3646.318755121157, + 644.3956756408844 + ], + [ + 3786.5617841642784, + 633.1403326984687 + ], + [ + 3926.8048132074, + 622.0474701930558 + ], + [ + 4067.047842250521, + 611.1146951305269 + ], + [ + 4207.290871293642, + 600.3396211239005 + ], + [ + 4347.533900336764, + 589.7200859643772 + ], + [ + 4487.776929379886, + 579.2545566264544 + ], + [ + 4628.019958423007, + 568.9416250077467 + ], + [ + 4768.262987466129, + 558.7798795062223 + ], + [ + 4908.50601650925, + 548.7678848844198 + ], + [ + 5048.749045552371, + 538.9035571291828 + ], + [ + 5188.992074595492, + 529.1831389611757 + ], + [ + 5329.235103638614, + 519.6026240282351 + ], + [ + 5469.478132681736, + 510.1580088133889 + ], + [ + 5609.721161724857, + 500.8452934878616 + ], + [ + 5749.964190767979, + 491.6613994890678 + ], + [ + 5890.2072198111, + 482.6092673805659 + ], + [ + 6030.450248854221, + 473.6942693864566 + ], + [ + 6170.693277897342, + 464.92178667801033 + ], + [ + 6310.936306940464, + 456.29720809213626 + ], + [ + 6451.179335983586, + 447.8259613512883 + ], + [ + 6591.422365026707, + 439.5072703387587 + ], + [ + 6731.665394069829, + 431.3201449149735 + ], + [ + 6871.90842311295, + 423.239527457674 + ], + [ + 7012.151452156071, + 415.2403659933683 + ], + [ + 7152.394481199192, + 407.29760191175916 + ], + [ + 7292.637510242314, + 399.3861577904447 + ], + [ + 7432.880539285436, + 391.4895731993953 + ], + [ + 7573.123568328557, + 383.66387363593617 + ], + [ + 7713.366597371678, + 376.0014294695306 + ], + [ + 7853.6096264148, + 368.59488100520394 + ], + [ + 7993.852655457921, + 361.51601811928344 + ], + [ + 8134.095684501042, + 354.6974909662945 + ], + [ + 8274.338713544164, + 348.0150699980972 + ], + [ + 8414.581742587285, + 341.3572574795787 + ], + [ + 8554.824771630407, + 334.70296926885214 + ], + [ + 8695.067800673529, + 328.070186863206 + ], + [ + 8835.31082971665, + 321.4770518227949 + ], + [ + 8975.553858759771, + 314.941705642052 + ], + [ + 9115.796887802893, + 308.482282502352 + ], + [ + 9256.039916846014, + 302.1168840522836 + ], + [ + 9396.282945889136, + 295.863604246989 + ], + [ + 9536.525974932258, + 289.73570624968653 + ], + [ + 9676.769003975378, + 283.73087268206757 + ], + [ + 9817.0120330185, + 277.8437047070258 + ], + [ + 9957.25506206162, + 272.06880584403484 + ], + [ + 10097.498091104742, + 266.40077961329644 + ], + [ + 10237.741120147864, + 260.83422960176546 + ], + [ + 10377.984149190985, + 255.363753906265 + ], + [ + 10518.227178234107, + 249.98399506975326 + ], + [ + 10658.470207277229, + 244.6897671571457 + ], + [ + 10798.71323632035, + 239.47822752566552 + ], + [ + 10938.956265363471, + 234.35099295463075 + ], + [ + 11079.199294406593, + 229.3102099993381 + ], + [ + 11219.442323449714, + 224.3579409866961 + ], + [ + 11359.685352492836, + 219.49622118077852 + ], + [ + 11499.928381535958, + 214.726296145405 + ], + [ + 11640.171410579078, + 210.04763237639287 + ], + [ + 11780.4144396222, + 205.45947832171075 + ], + [ + 11920.65746866532, + 200.9610014785816 + ], + [ + 12060.900497708442, + 196.5509013292908 + ], + [ + 12201.143526751564, + 192.2272169786099 + ], + [ + 12341.386555794685, + 187.98814219658482 + ], + [ + 12481.629584837807, + 183.8321824339577 + ], + [ + 12621.872613880929, + 179.75789276505182 + ], + [ + 12762.11564292405, + 175.76399959646034 + ], + [ + 12902.358671967171, + 171.85005418219086 + ], + [ + 13042.601701010293, + 168.01699442109154 + ], + [ + 13182.844730053414, + 164.26630683964345 + ], + [ + 13323.087759096536, + 160.5989851624009 + ], + [ + 13463.330788139658, + 157.01591598362788 + ], + [ + 13603.573817182778, + 153.51796848543847 + ], + [ + 13743.8168462259, + 150.10546537486104 + ], + [ + 13884.059875269022, + 146.77552753443376 + ] + ], + "6": [ + [ + 0.0, + 979.4033301243851 + ], + [ + 140.24302904312142, + 993.8173394581016 + ], + [ + 280.48605808624285, + 969.144186829941 + ], + [ + 420.7290871293643, + 951.7184913634705 + ], + [ + 560.9721161724857, + 938.4191111055725 + ], + [ + 701.2151452156071, + 922.5791669979078 + ], + [ + 841.4581742587286, + 907.0510860141973 + ], + [ + 981.70120330185, + 892.2617556062014 + ], + [ + 1121.9442323449714, + 877.5029493100384 + ], + [ + 1262.1872613880928, + 862.8778307179738 + ], + [ + 1402.4302904312142, + 848.5074684916688 + ], + [ + 1542.6733194743356, + 834.3414485304311 + ], + [ + 1682.9163485174572, + 820.3612185373835 + ], + [ + 1823.1593775605786, + 806.5783231379272 + ], + [ + 1963.4024066037, + 792.9930744979266 + ], + [ + 2103.645435646821, + 779.601298262273 + ], + [ + 2243.888464689943, + 766.4008063222659 + ], + [ + 2384.1314937330644, + 753.3890151380622 + ], + [ + 2524.3745227761856, + 740.5633264753455 + ], + [ + 2664.617551819307, + 727.9214601134988 + ], + [ + 2804.8605808624284, + 715.4609992300202 + ], + [ + 2945.10360990555, + 703.1795122847806 + ], + [ + 3085.346638948671, + 691.0744852253018 + ], + [ + 3225.589667991793, + 679.1434698312598 + ], + [ + 3365.8326970349144, + 667.3842076513538 + ], + [ + 3506.0757260780356, + 655.7944597010302 + ], + [ + 3646.318755121157, + 644.3720245019547 + ], + [ + 3786.5617841642784, + 633.114709491541 + ], + [ + 3926.8048132074, + 622.0203151189644 + ], + [ + 4067.047842250521, + 611.0866424081288 + ], + [ + 4207.290871293642, + 600.3114857743053 + ], + [ + 4347.533900336764, + 589.6926842352341 + ], + [ + 4487.776929379886, + 579.2281288923559 + ], + [ + 4628.019958423007, + 568.9157097552558 + ], + [ + 4768.262987466129, + 558.7533147931683 + ], + [ + 4908.50601650925, + 548.7388169369859 + ], + [ + 5048.749045552371, + 538.8701665128171 + ], + [ + 5188.992074595492, + 529.145480508989 + ], + [ + 5329.235103638614, + 519.5629117952835 + ], + [ + 5469.478132681736, + 510.1206145433703 + ], + [ + 5609.721161724857, + 500.8167439461948 + ], + [ + 5749.964190767979, + 491.64944507847855 + ], + [ + 5890.2072198111, + 482.6168503740353 + ], + [ + 6030.450248854221, + 473.71712801671555 + ], + [ + 6170.693277897342, + 464.94845368209707 + ], + [ + 6310.936306940464, + 456.30900983405667 + ], + [ + 6451.179335983586, + 447.7970174878505 + ], + [ + 6591.422365026707, + 439.41078207631995 + ], + [ + 6731.665394069829, + 431.14875622442173 + ], + [ + 6871.90842311295, + 423.0094231447404 + ], + [ + 7012.151452156071, + 414.9912686362494 + ], + [ + 7152.394481199192, + 407.0927763174595 + ], + [ + 7292.637510242314, + 399.3124412317916 + ], + [ + 7432.880539285436, + 391.6487950065058 + ], + [ + 7573.123568328557, + 384.1003503833863 + ], + [ + 7713.366597371678, + 376.66563629478486 + ], + [ + 7853.6096264148, + 369.34318397547906 + ], + [ + 7993.852655457921, + 362.13152448369397 + ], + [ + 8134.095684501042, + 355.02919599639506 + ], + [ + 8274.338713544164, + 348.0347863900513 + ], + [ + 8414.581742587285, + 341.14703867228906 + ], + [ + 8554.824771630407, + 334.3648891793685 + ], + [ + 8695.067800673529, + 327.6873363156042 + ], + [ + 8835.31082971665, + 321.11338319550913 + ], + [ + 8975.553858759771, + 314.6420328765806 + ], + [ + 9115.796887802893, + 308.27228012855244 + ], + [ + 9256.039916846014, + 302.0030888928564 + ], + [ + 9396.282945889136, + 295.8334485575881 + ], + [ + 9536.525974932258, + 289.76245694069553 + ], + [ + 9676.769003975378, + 283.78927485308947 + ], + [ + 9817.0120330185, + 277.9130836193368 + ], + [ + 9957.25506206162, + 272.1330662559317 + ], + [ + 10097.498091104742, + 266.44840578013674 + ], + [ + 10237.741120147864, + 260.85828539624856 + ], + [ + 10377.984149190985, + 255.3618863117103 + ], + [ + 10518.227178234107, + 249.95842899344316 + ], + [ + 10658.470207277229, + 244.64725660355043 + ], + [ + 10798.71323632035, + 239.42784794348128 + ], + [ + 10938.956265363471, + 234.29975821021304 + ], + [ + 11079.199294406593, + 229.2625505358851 + ], + [ + 11219.442323449714, + 224.31570821574192 + ], + [ + 11359.685352492836, + 219.45872585957073 + ], + [ + 11499.928381535958, + 214.69134851784622 + ], + [ + 11640.171410579078, + 210.01342757546848 + ], + [ + 11780.4144396222, + 205.42479279470135 + ], + [ + 11920.65746866532, + 200.92519563951004 + ], + [ + 12060.900497708442, + 196.5139182626259 + ], + [ + 12201.143526751564, + 192.18952326735305 + ], + [ + 12341.386555794685, + 187.95026470467306 + ], + [ + 12481.629584837807, + 183.79452418043502 + ], + [ + 12621.872613880929, + 179.72073404081348 + ], + [ + 12762.11564292405, + 175.72749636545552 + ], + [ + 12902.358671967171, + 171.81422382804521 + ], + [ + 13042.601701010293, + 167.98171096823847 + ], + [ + 13182.844730053414, + 164.23146419779098 + ], + [ + 13323.087759096536, + 160.56457505383332 + ], + [ + 13463.330788139658, + 156.98202987981037 + ], + [ + 13603.573817182778, + 153.48479616968123 + ], + [ + 13743.8168462259, + 150.07328580666982 + ], + [ + 13884.059875269022, + 146.74469257180235 + ] + ], + "7": [ + [ + 0.0, + 978.9446646625488 + ], + [ + 140.24302904312142, + 993.7517651518842 + ], + [ + 280.48605808624285, + 969.4305121698253 + ], + [ + 420.7290871293643, + 951.8546437222352 + ], + [ + 560.9721161724857, + 938.5220481374886 + ], + [ + 701.2151452156071, + 922.7066276052191 + ], + [ + 841.4581742587286, + 907.1660728249834 + ], + [ + 981.70120330185, + 892.364651990674 + ], + [ + 1121.9442323449714, + 877.6022457205609 + ], + [ + 1262.1872613880928, + 862.9729087715363 + ], + [ + 1402.4302904312142, + 848.5973915633715 + ], + [ + 1542.6733194743356, + 834.4264529608558 + ], + [ + 1682.9163485174572, + 820.4421575981428 + ], + [ + 1823.1593775605786, + 806.655902858563 + ], + [ + 1963.4024066037, + 793.0668917706881 + ], + [ + 2103.645435646821, + 779.6706259863445 + ], + [ + 2243.888464689943, + 766.4653111610817 + ], + [ + 2384.1314937330644, + 753.448828098095 + ], + [ + 2524.3745227761856, + 740.618841816408 + ], + [ + 2664.617551819307, + 727.9730451292681 + ], + [ + 2804.8605808624284, + 715.5089383946176 + ], + [ + 2945.10360990555, + 703.2240280498389 + ], + [ + 3085.346638948671, + 691.115719482632 + ], + [ + 3225.589667991793, + 679.1815497637516 + ], + [ + 3365.8326970349144, + 667.4193051350885 + ], + [ + 3506.0757260780356, + 655.82678494715 + ], + [ + 3646.318755121157, + 644.4018344748201 + ], + [ + 3786.5617841642784, + 633.1422835535906 + ], + [ + 3926.8048132074, + 622.0459652776403 + ], + [ + 4067.047842250521, + 611.1107430776109 + ], + [ + 4207.290871293642, + 600.3344676337814 + ], + [ + 4347.533900336764, + 589.7150198881317 + ], + [ + 4487.776929379886, + 579.2502149706654 + ], + [ + 4628.019958423007, + 568.9376282562483 + ], + [ + 4768.262987466129, + 558.7748079730052 + ], + [ + 4908.50601650925, + 548.7592952268081 + ], + [ + 5048.749045552371, + 538.8887281048134 + ], + [ + 5188.992074595492, + 529.1614670024937 + ], + [ + 5329.235103638614, + 519.5766269339293 + ], + [ + 5469.478132681736, + 510.1333695338165 + ], + [ + 5609.721161724857, + 500.83086061505406 + ], + [ + 5749.964190767979, + 491.66825248988755 + ], + [ + 5890.2072198111, + 482.64364909227106 + ], + [ + 6030.450248854221, + 473.752313210666 + ], + [ + 6170.693277897342, + 464.9890317857278 + ], + [ + 6310.936306940464, + 456.3485965392243 + ], + [ + 6451.179335983586, + 447.825827808071 + ], + [ + 6591.422365026707, + 439.415787516067 + ], + [ + 6731.665394069829, + 431.11980637512005 + ], + [ + 6871.90842311295, + 422.94700381638046 + ], + [ + 7012.151452156071, + 414.9069982953618 + ], + [ + 7152.394481199192, + 407.009409955139 + ], + [ + 7292.637510242314, + 399.2638980228939 + ], + [ + 7432.880539285436, + 391.68013460327427 + ], + [ + 7573.123568328557, + 384.2547713233406 + ], + [ + 7713.366597371678, + 376.95443117175114 + ], + [ + 7853.6096264148, + 369.74151701505025 + ], + [ + 7993.852655457921, + 362.5784312030357 + ], + [ + 8134.095684501042, + 355.42757286377525 + ], + [ + 8274.338713544164, + 348.25135715507486 + ], + [ + 8414.581742587285, + 341.0133074800712 + ], + [ + 8554.824771630407, + 333.733690186229 + ], + [ + 8695.067800673529, + 326.519559596936 + ], + [ + 8835.31082971665, + 319.48532697639627 + ], + [ + 8975.553858759771, + 312.74540356183473 + ], + [ + 9115.796887802893, + 306.41419315460286 + ], + [ + 9256.039916846014, + 300.60607717208956 + ], + [ + 9396.282945889136, + 295.43454755729283 + ], + [ + 9536.525974932258, + 290.50697563647026 + ], + [ + 9676.769003975378, + 285.2273804976184 + ], + [ + 9817.0120330185, + 279.66096577151825 + ], + [ + 9957.25506206162, + 273.8863748196379 + ], + [ + 10097.498091104742, + 267.98225100437514 + ], + [ + 10237.741120147864, + 262.02723792452207 + ], + [ + 10377.984149190985, + 256.0999798259682 + ], + [ + 10518.227178234107, + 250.2791578611987 + ], + [ + 10658.470207277229, + 244.64359555495622 + ], + [ + 10798.71323632035, + 239.252609561986 + ], + [ + 10938.956265363471, + 234.08293995652252 + ], + [ + 11079.199294406593, + 229.08965836406261 + ], + [ + 11219.442323449714, + 224.2277603927234 + ], + [ + 11359.685352492836, + 219.4522303472878 + ], + [ + 11499.928381535958, + 214.7300148489557 + ], + [ + 11640.171410579078, + 210.06430389047964 + ], + [ + 11780.4144396222, + 205.4651663558539 + ], + [ + 11920.65746866532, + 200.94258933262972 + ], + [ + 12060.900497708442, + 196.50609705855894 + ], + [ + 12201.143526751564, + 192.16383333635517 + ], + [ + 12341.386555794685, + 187.9162270180998 + ], + [ + 12481.629584837807, + 183.75947571897544 + ], + [ + 12621.872613880929, + 179.68977295800212 + ], + [ + 12762.11564292405, + 175.70347980706876 + ], + [ + 12902.358671967171, + 171.79774281253378 + ], + [ + 13042.601701010293, + 167.97117497553327 + ], + [ + 13182.844730053414, + 164.22463530858585 + ], + [ + 13323.087759096536, + 160.5595320825772 + ], + [ + 13463.330788139658, + 156.97718431349338 + ], + [ + 13603.573817182778, + 153.47889120692116 + ], + [ + 13743.8168462259, + 150.06539401630423 + ], + [ + 13884.059875269022, + 146.7342305648778 + ] + ], + "8": [ + [ + 0.0, + 978.6524111140432 + ], + [ + 140.24302904312142, + 993.764933975184 + ], + [ + 280.48605808624285, + 969.7132911140936 + ], + [ + 420.7290871293643, + 952.0263829429566 + ], + [ + 560.9721161724857, + 938.6644550525448 + ], + [ + 701.2151452156071, + 922.8655828289036 + ], + [ + 841.4581742587286, + 907.3133108838434 + ], + [ + 981.70120330185, + 892.5006133772619 + ], + [ + 1121.9442323449714, + 877.7343722660335 + ], + [ + 1262.1872613880928, + 863.0998102883016 + ], + [ + 1402.4302904312142, + 848.7180185115858 + ], + [ + 1542.6733194743356, + 834.5420010629529 + ], + [ + 1682.9163485174572, + 820.5528970300932 + ], + [ + 1823.1593775605786, + 806.761420668352 + ], + [ + 1963.4024066037, + 793.1672954416158 + ], + [ + 2103.645435646821, + 779.766472231777 + ], + [ + 2243.888464689943, + 766.5573198777463 + ], + [ + 2384.1314937330644, + 753.5375666137966 + ], + [ + 2524.3745227761856, + 740.7045682620217 + ], + [ + 2664.617551819307, + 728.0558378173205 + ], + [ + 2804.8605808624284, + 715.5887355310019 + ], + [ + 2945.10360990555, + 703.3006301216371 + ], + [ + 3085.346638948671, + 691.1889322019315 + ], + [ + 3225.589667991793, + 679.2513529546073 + ], + [ + 3365.8326970349144, + 667.4859265042159 + ], + [ + 3506.0757260780356, + 655.8907045806382 + ], + [ + 3646.318755121157, + 644.4636894850931 + ], + [ + 3786.5617841642784, + 633.2026839028632 + ], + [ + 3926.8048132074, + 622.1053208638128 + ], + [ + 4067.047842250521, + 611.1692159972831 + ], + [ + 4207.290871293642, + 600.391984898254 + ], + [ + 4347.533900336764, + 589.7713277748257 + ], + [ + 4487.776929379886, + 579.3050817417694 + ], + [ + 4628.019958423007, + 568.9911155119659 + ], + [ + 4768.262987466129, + 558.8272970859113 + ], + [ + 4908.50601650925, + 548.8114974969891 + ], + [ + 5048.749045552371, + 538.9416665344162 + ], + [ + 5188.992074595492, + 529.2158758282319 + ], + [ + 5329.235103638614, + 519.6322400824382 + ], + [ + 5469.478132681736, + 510.18887572990064 + ], + [ + 5609.721161724857, + 500.8839009478121 + ], + [ + 5749.964190767979, + 491.71542602645957 + ], + [ + 5890.2072198111, + 482.6816069302274 + ], + [ + 6030.450248854221, + 473.780621100065 + ], + [ + 6170.693277897342, + 465.01064849145536 + ], + [ + 6310.936306940464, + 456.3698719261497 + ], + [ + 6451.179335983586, + 447.8564974186036 + ], + [ + 6591.422365026707, + 439.4687983171592 + ], + [ + 6731.665394069829, + 431.20517438048364 + ], + [ + 6871.90842311295, + 423.06407484594433 + ], + [ + 7012.151452156071, + 415.04395235946794 + ], + [ + 7152.394481199192, + 407.14326425783213 + ], + [ + 7292.637510242314, + 399.36052049526387 + ], + [ + 7432.880539285436, + 391.69429212045793 + ], + [ + 7573.123568328557, + 384.1431728649543 + ], + [ + 7713.366597371678, + 376.70577005669554 + ], + [ + 7853.6096264148, + 369.3806931612924 + ], + [ + 7993.852655457921, + 362.1665510525083 + ], + [ + 8134.095684501042, + 355.06193898393553 + ], + [ + 8274.338713544164, + 348.06544426160696 + ], + [ + 8414.581742587285, + 341.1757911523716 + ], + [ + 8554.824771630407, + 334.39187106151365 + ], + [ + 8695.067800673529, + 327.71264081048014 + ], + [ + 8835.31082971665, + 321.137063346564 + ], + [ + 8975.553858759771, + 314.66410160200047 + ], + [ + 9115.796887802893, + 308.2927132966691 + ], + [ + 9256.039916846014, + 302.0218554887618 + ], + [ + 9396.282945889136, + 295.85053905960734 + ], + [ + 9536.525974932258, + 289.77785495973467 + ], + [ + 9676.769003975378, + 283.8029460667485 + ], + [ + 9817.0120330185, + 277.92497000275796 + ], + [ + 9957.25506206162, + 272.14308458707325 + ], + [ + 10097.498091104742, + 266.45644763968215 + ], + [ + 10237.741120147864, + 260.864216969251 + ], + [ + 10377.984149190985, + 255.36554555475385 + ], + [ + 10518.227178234107, + 249.95960875627978 + ], + [ + 10658.470207277229, + 244.64574191458044 + ], + [ + 10798.71323632035, + 239.42353737051772 + ], + [ + 10938.956265363471, + 234.29270232060506 + ], + [ + 11079.199294406593, + 229.25295867734073 + ], + [ + 11219.442323449714, + 224.3039592187994 + ], + [ + 11359.685352492836, + 219.44529168428224 + ], + [ + 11499.928381535958, + 214.67662564743878 + ], + [ + 11640.171410579078, + 209.99769742445005 + ], + [ + 11780.4144396222, + 205.40821800416265 + ], + [ + 11920.65746866532, + 200.90781174059603 + ], + [ + 12060.900497708442, + 196.49566247830796 + ], + [ + 12201.143526751564, + 192.17036758370534 + ], + [ + 12341.386555794685, + 187.93027435631367 + ], + [ + 12481.629584837807, + 183.77384014274782 + ], + [ + 12621.872613880929, + 179.69957257277937 + ], + [ + 12762.11564292405, + 175.70614815381973 + ], + [ + 12902.358671967171, + 171.7930152435248 + ], + [ + 13042.601701010293, + 167.96090556022446 + ], + [ + 13182.844730053414, + 164.21123792390387 + ], + [ + 13323.087759096536, + 160.54506382875041 + ], + [ + 13463.330788139658, + 156.96333375518495 + ], + [ + 13603.573817182778, + 153.46697842449927 + ], + [ + 13743.8168462259, + 150.05637147392454 + ], + [ + 13884.059875269022, + 146.72867987355968 + ] + ], + "9": [ + [ + 0.0, + 977.1687070127444 + ], + [ + 140.24302904312142, + 994.0997450368653 + ], + [ + 280.48605808624285, + 970.3780703437725 + ], + [ + 420.7290871293643, + 952.3580681196794 + ], + [ + 560.9721161724857, + 939.0303967469987 + ], + [ + 701.2151452156071, + 923.2660157031577 + ], + [ + 841.4581742587286, + 907.6820360704025 + ], + [ + 981.70120330185, + 892.8590604377283 + ], + [ + 1121.9442323449714, + 878.0895628016976 + ], + [ + 1262.1872613880928, + 863.4463064068411 + ], + [ + 1402.4302904312142, + 849.0568034209485 + ], + [ + 1542.6733194743356, + 834.8742342146281 + ], + [ + 1682.9163485174572, + 820.8780964027845 + ], + [ + 1823.1593775605786, + 807.0799360280065 + ], + [ + 1963.4024066037, + 793.479979102098 + ], + [ + 2103.645435646821, + 780.0733992962456 + ], + [ + 2243.888464689943, + 766.8583556131221 + ], + [ + 2384.1314937330644, + 753.8331415876542 + ], + [ + 2524.3745227761856, + 740.9951299140707 + ], + [ + 2664.617551819307, + 728.3415171054702 + ], + [ + 2804.8605808624284, + 715.8696236524694 + ], + [ + 2945.10360990555, + 703.5768173983968 + ], + [ + 3085.346638948671, + 691.460502507653 + ], + [ + 3225.589667991793, + 679.5184664854415 + ], + [ + 3365.8326970349144, + 667.7487882190038 + ], + [ + 3506.0757260780356, + 656.14955943221 + ], + [ + 3646.318755121157, + 644.7188123785448 + ], + [ + 3786.5617841642784, + 633.4543292003289 + ], + [ + 3926.8048132074, + 622.3536805512862 + ], + [ + 4067.047842250521, + 611.4144094842027 + ], + [ + 4207.290871293642, + 600.63406537866 + ], + [ + 4347.533900336764, + 590.0103234902575 + ], + [ + 4487.776929379886, + 579.5410108053089 + ], + [ + 4628.019958423007, + 569.2239970677485 + ], + [ + 4768.262987466129, + 559.0571530869973 + ], + [ + 4908.50601650925, + 549.0383471576199 + ], + [ + 5048.749045552371, + 539.1654921613675 + ], + [ + 5188.992074595492, + 529.436645919831 + ], + [ + 5329.235103638614, + 519.8499365596606 + ], + [ + 5469.478132681736, + 510.4034956163641 + ], + [ + 5609.721161724857, + 501.09545711907225 + ], + [ + 5749.964190767979, + 491.9239635857319 + ], + [ + 5890.2072198111, + 482.8871987402912 + ], + [ + 6030.450248854221, + 473.9833276507876 + ], + [ + 6170.693277897342, + 465.2105102390128 + ], + [ + 6310.936306940464, + 456.56691016216877 + ], + [ + 6451.179335983586, + 448.05072122619373 + ], + [ + 6591.422365026707, + 439.66020280750234 + ], + [ + 6731.665394069829, + 431.3937440120479 + ], + [ + 6871.90842311295, + 423.2498068438929 + ], + [ + 7012.151452156071, + 415.2268590551952 + ], + [ + 7152.394481199192, + 407.3233724027158 + ], + [ + 7292.637510242314, + 399.53785703429963 + ], + [ + 7432.880539285436, + 391.86887560019346 + ], + [ + 7573.123568328557, + 384.3150380089312 + ], + [ + 7713.366597371678, + 376.8749711075407 + ], + [ + 7853.6096264148, + 369.5473022892899 + ], + [ + 7993.852655457921, + 362.3306582650769 + ], + [ + 8134.095684501042, + 355.2236486389313 + ], + [ + 8274.338713544164, + 348.2248678221401 + ], + [ + 8414.581742587285, + 341.3330259623778 + ], + [ + 8554.824771630407, + 334.54696300258223 + ], + [ + 8695.067800673529, + 327.8655752425735 + ], + [ + 8835.31082971665, + 321.28776582594116 + ], + [ + 8975.553858759771, + 314.8124378909457 + ], + [ + 9115.796887802893, + 308.4384905540624 + ], + [ + 9256.039916846014, + 302.1648377793834 + ], + [ + 9396.282945889136, + 295.99048276308906 + ], + [ + 9536.525974932258, + 289.9145532192177 + ], + [ + 9676.769003975378, + 283.9362634193293 + ], + [ + 9817.0120330185, + 278.0548497581527 + ], + [ + 9957.25506206162, + 272.26954889224675 + ], + [ + 10097.498091104742, + 266.5795974790146 + ], + [ + 10237.741120147864, + 260.98423213941356 + ], + [ + 10377.984149190985, + 255.482682116525 + ], + [ + 10518.227178234107, + 250.07416668260862 + ], + [ + 10658.470207277229, + 244.7579868980688 + ], + [ + 10798.71323632035, + 239.53364026515263 + ], + [ + 10938.956265363471, + 234.40072824575103 + ], + [ + 11079.199294406593, + 229.3588673869123 + ], + [ + 11219.442323449714, + 224.40763681028452 + ], + [ + 11359.685352492836, + 219.54662540932605 + ], + [ + 11499.928381535958, + 214.77554568848572 + ], + [ + 11640.171410579078, + 210.0941737172357 + ], + [ + 11780.4144396222, + 205.5022616764756 + ], + [ + 11920.65746866532, + 200.9994709352652 + ], + [ + 12060.900497708442, + 196.5850183235061 + ], + [ + 12201.143526751564, + 192.2575104256611 + ], + [ + 12341.386555794685, + 188.01531855463222 + ], + [ + 12481.629584837807, + 183.85692643220762 + ], + [ + 12621.872613880929, + 179.78086998535758 + ], + [ + 12762.11564292405, + 175.78584950701776 + ], + [ + 12902.358671967171, + 171.87131257232818 + ], + [ + 13042.601701010293, + 168.03796796525143 + ], + [ + 13182.844730053414, + 164.2871811143642 + ], + [ + 13323.087759096536, + 160.6199332214687 + ], + [ + 13463.330788139658, + 157.0370971906099 + ], + [ + 13603.573817182778, + 153.53952737283936 + ], + [ + 13743.8168462259, + 150.1275451925607 + ], + [ + 13884.059875269022, + 146.79834896278166 + ] + ], + "10": [ + [ + 0.0, + 975.0457337965578 + ], + [ + 140.24302904312142, + 994.852366938782 + ], + [ + 280.48605808624285, + 971.0470034872092 + ], + [ + 420.7290871293643, + 952.663451011989 + ], + [ + 560.9721161724857, + 939.4669826140844 + ], + [ + 701.2151452156071, + 923.7134473489581 + ], + [ + 841.4581742587286, + 908.0882579521149 + ], + [ + 981.70120330185, + 893.2642683593805 + ], + [ + 1121.9442323449714, + 878.4930887121383 + ], + [ + 1262.1872613880928, + 863.841370730345 + ], + [ + 1402.4302904312142, + 849.4461249651761 + ], + [ + 1542.6733194743356, + 835.2594392700491 + ], + [ + 1682.9163485174572, + 821.258846149429 + ], + [ + 1823.1593775605786, + 807.4558826901822 + ], + [ + 1963.4024066037, + 793.8511622537158 + ], + [ + 2103.645435646821, + 780.4399480657762 + ], + [ + 2243.888464689943, + 767.2201799874755 + ], + [ + 2384.1314937330644, + 754.190047244409 + ], + [ + 2524.3745227761856, + 741.3468658294892 + ], + [ + 2664.617551819307, + 728.6880712862812 + ], + [ + 2804.8605808624284, + 716.2114661016545 + ], + [ + 2945.10360990555, + 703.9149512183609 + ], + [ + 3085.346638948671, + 691.7962494132107 + ], + [ + 3225.589667991793, + 679.8521641073896 + ], + [ + 3365.8326970349144, + 668.0791904120131 + ], + [ + 3506.0757260780356, + 656.4738193545446 + ], + [ + 3646.318755121157, + 645.0326772560807 + ], + [ + 3786.5617841642784, + 633.7554269325495 + ], + [ + 3926.8048132074, + 622.6445960435599 + ], + [ + 4067.047842250521, + 611.702818211791 + ], + [ + 4207.290871293642, + 600.9327216447658 + ], + [ + 4347.533900336764, + 590.334129860192 + ], + [ + 4487.776929379886, + 579.8927077043104 + ], + [ + 4628.019958423007, + 569.589685012337 + ], + [ + 4768.262987466129, + 559.4062916422266 + ], + [ + 4908.50601650925, + 549.3237709941326 + ], + [ + 5048.749045552371, + 539.335157990047 + ], + [ + 5188.992074595492, + 529.4814730197376 + ], + [ + 5329.235103638614, + 519.8160226539077 + ], + [ + 5469.478132681736, + 510.3921170488078 + ], + [ + 5609.721161724857, + 501.2630664888523 + ], + [ + 5749.964190767979, + 492.4699174698587 + ], + [ + 5890.2072198111, + 483.91601865653564 + ], + [ + 6030.450248854221, + 475.4180873351401 + ], + [ + 6170.693277897342, + 466.79148728468795 + ], + [ + 6310.936306940464, + 457.85158596903796 + ], + [ + 6451.179335983586, + 448.41378274594297 + ], + [ + 6591.422365026707, + 438.63713733921657 + ], + [ + 6731.665394069829, + 429.66162210559276 + ], + [ + 6871.90842311295, + 421.4850125544256 + ], + [ + 7012.151452156071, + 413.88808764977887 + ], + [ + 7152.394481199192, + 406.6516297660713 + ], + [ + 7292.637510242314, + 399.55645077901323 + ], + [ + 7432.880539285436, + 392.39513195957124 + ], + [ + 7573.123568328557, + 385.1101353527508 + ], + [ + 7713.366597371678, + 377.74743521265583 + ], + [ + 7853.6096264148, + 370.3550639931193 + ], + [ + 7993.852655457921, + 362.9810532651758 + ], + [ + 8134.095684501042, + 355.6734157919674 + ], + [ + 8274.338713544164, + 348.48015978321513 + ], + [ + 8414.581742587285, + 341.4455151035607 + ], + [ + 8554.824771630407, + 334.57730059313513 + ], + [ + 8695.067800673529, + 327.8633867914162 + ], + [ + 8835.31082971665, + 321.2914497920434 + ], + [ + 8975.553858759771, + 314.8491657241997 + ], + [ + 9115.796887802893, + 308.52420757722257 + ], + [ + 9256.039916846014, + 302.30425758099324 + ], + [ + 9396.282945889136, + 296.1770875445304 + ], + [ + 9536.525974932258, + 290.13294371328715 + ], + [ + 9676.769003975378, + 284.1714334208955 + ], + [ + 9817.0120330185, + 278.2944828114105 + ], + [ + 9957.25506206162, + 272.5040171605597 + ], + [ + 10097.498091104742, + 266.8019617447274 + ], + [ + 10237.741120147864, + 261.1902419402465 + ], + [ + 10377.984149190985, + 255.67078043020513 + ], + [ + 10518.227178234107, + 250.24548638534432 + ], + [ + 10658.470207277229, + 244.91630516364066 + ], + [ + 10798.71323632035, + 239.68425878834046 + ], + [ + 10938.956265363471, + 234.5478189461523 + ], + [ + 11079.199294406593, + 229.50508195709432 + ], + [ + 11219.442323449714, + 224.55411715646048 + ], + [ + 11359.685352492836, + 219.69304087845882 + ], + [ + 11499.928381535958, + 214.92062400683514 + ], + [ + 11640.171410579078, + 210.23682900077492 + ], + [ + 11780.4144396222, + 205.64173766853995 + ], + [ + 11920.65746866532, + 201.13534555391442 + ], + [ + 12060.900497708442, + 196.71722021792425 + ], + [ + 12201.143526751564, + 192.3863007563918 + ], + [ + 12341.386555794685, + 188.14106469958375 + ], + [ + 12481.629584837807, + 183.98000746319588 + ], + [ + 12621.872613880929, + 179.90167991617173 + ], + [ + 12762.11564292405, + 175.90478709592395 + ], + [ + 12902.358671967171, + 171.98873678904508 + ], + [ + 13042.601701010293, + 168.15413914776826 + ], + [ + 13182.844730053414, + 164.40224829898918 + ], + [ + 13323.087759096536, + 160.73389643518922 + ], + [ + 13463.330788139658, + 157.14979371761248 + ], + [ + 13603.573817182778, + 153.65063357418308 + ], + [ + 13743.8168462259, + 150.23661599146843 + ], + [ + 13884.059875269022, + 146.90496369043825 + ] + ], + "11": [ + [ + 0.0, + 971.7146757620238 + ], + [ + 140.24302904312142, + 996.5621436407151 + ], + [ + 280.48605808624285, + 971.3494367712138 + ], + [ + 420.7290871293643, + 952.6206821854494 + ], + [ + 560.9721161724857, + 939.8163923791436 + ], + [ + 701.2151452156071, + 923.9980231742422 + ], + [ + 841.4581742587286, + 908.3085255691426 + ], + [ + 981.70120330185, + 893.5224079027645 + ], + [ + 1121.9442323449714, + 878.7736823209516 + ], + [ + 1262.1872613880928, + 864.0696594360486 + ], + [ + 1402.4302904312142, + 849.6518949344969 + ], + [ + 1542.6733194743356, + 835.7099186040464 + ], + [ + 1682.9163485174572, + 821.6121300790828 + ], + [ + 1823.1593775605786, + 806.8001626685243 + ], + [ + 1963.4024066037, + 794.2525393690313 + ], + [ + 2103.645435646821, + 784.5636599172041 + ], + [ + 2243.888464689943, + 765.5688718047534 + ], + [ + 2384.1314937330644, + 749.8259327002658 + ], + [ + 2524.3745227761856, + 742.6614132683347 + ], + [ + 2664.617551819307, + 732.7306540421868 + ], + [ + 2804.8605808624284, + 719.9792751139494 + ], + [ + 2945.10360990555, + 705.8805869139695 + ], + [ + 3085.346638948671, + 691.90725274156 + ], + [ + 3225.589667991793, + 679.0981861486991 + ], + [ + 3365.8326970349144, + 667.294873689881 + ], + [ + 3506.0757260780356, + 656.1339657168745 + ], + [ + 3646.318755121157, + 645.2521552000546 + ], + [ + 3786.5617841642784, + 634.324549141551 + ], + [ + 3926.8048132074, + 623.309440767268 + ], + [ + 4067.047842250521, + 612.2918818442089 + ], + [ + 4207.290871293642, + 601.3575050454009 + ], + [ + 4347.533900336764, + 590.5917774645386 + ], + [ + 4487.776929379886, + 580.0498454208383 + ], + [ + 4628.019958423007, + 569.7178955805566 + ], + [ + 4768.262987466129, + 559.5725900323001 + ], + [ + 4908.50601650925, + 549.5906014918006 + ], + [ + 5048.749045552371, + 539.7487580537371 + ], + [ + 5188.992074595492, + 530.0324660231126 + ], + [ + 5329.235103638614, + 520.4431548325412 + ], + [ + 5469.478132681736, + 510.98403572938247 + ], + [ + 5609.721161724857, + 501.6583195780182 + ], + [ + 5749.964190767979, + 492.46920630708837 + ], + [ + 5890.2072198111, + 483.4190574945821 + ], + [ + 6030.450248854221, + 474.50605909416646 + ], + [ + 6170.693277897342, + 465.7271362411803 + ], + [ + 6310.936306940464, + 457.07921713549 + ], + [ + 6451.179335983586, + 448.55927552915085 + ], + [ + 6591.422365026707, + 440.1643828000717 + ], + [ + 6731.665394069829, + 431.89214153444954 + ], + [ + 6871.90842311295, + 423.7412077206048 + ], + [ + 7012.151452156071, + 415.71039030194606 + ], + [ + 7152.394481199192, + 407.7985032891176 + ], + [ + 7292.637510242314, + 400.00438914934807 + ], + [ + 7432.880539285436, + 392.32692640546406 + ], + [ + 7573.123568328557, + 384.7650153873645 + ], + [ + 7713.366597371678, + 377.3173422907481 + ], + [ + 7853.6096264148, + 369.9825063711943 + ], + [ + 7993.852655457921, + 362.75910532831296 + ], + [ + 8134.095684501042, + 355.64570388087196 + ], + [ + 8274.338713544164, + 348.64085107219097 + ], + [ + 8414.581742587285, + 341.74320180450087 + ], + [ + 8554.824771630407, + 334.95152257479134 + ], + [ + 8695.067800673529, + 328.26467881802597 + ], + [ + 8835.31082971665, + 321.6815603859123 + ], + [ + 8975.553858759771, + 315.2010571357653 + ], + [ + 9115.796887802893, + 308.82206230857696 + ], + [ + 9256.039916846014, + 302.54349699332187 + ], + [ + 9396.282945889136, + 296.36434431524003 + ], + [ + 9536.525974932258, + 290.2837091872438 + ], + [ + 9676.769003975378, + 284.3007953790531 + ], + [ + 9817.0120330185, + 278.41482231016965 + ], + [ + 9957.25506206162, + 272.62500952502614 + ], + [ + 10097.498091104742, + 266.93057657693015 + ], + [ + 10237.741120147864, + 261.3307428794839 + ], + [ + 10377.984149190985, + 255.82471156903216 + ], + [ + 10518.227178234107, + 250.4116486951142 + ], + [ + 10658.470207277229, + 245.09079975081087 + ], + [ + 10798.71323632035, + 239.86160858887825 + ], + [ + 10938.956265363471, + 234.7236386149447 + ], + [ + 11079.199294406593, + 229.67649805290483 + ], + [ + 11219.442323449714, + 224.71980656653173 + ], + [ + 11359.685352492836, + 219.85319829343516 + ], + [ + 11499.928381535958, + 215.07643777402814 + ], + [ + 11640.171410579078, + 210.38934144718223 + ], + [ + 11780.4144396222, + 205.79167895656414 + ], + [ + 11920.65746866532, + 201.2831101573569 + ], + [ + 12060.900497708442, + 196.86285635923295 + ], + [ + 12201.143526751564, + 192.52954377884424 + ], + [ + 12341.386555794685, + 188.28160411903303 + ], + [ + 12481.629584837807, + 184.1176575994672 + ], + [ + 12621.872613880929, + 180.03639076727768 + ], + [ + 12762.11564292405, + 176.03664466448473 + ], + [ + 12902.358671967171, + 172.11795696711965 + ], + [ + 13042.601701010293, + 168.28104638605922 + ], + [ + 13182.844730053414, + 164.52715437597115 + ], + [ + 13323.087759096536, + 160.85696687353226 + ], + [ + 13463.330788139658, + 157.2710213238454 + ], + [ + 13603.573817182778, + 153.76984109354905 + ], + [ + 13743.8168462259, + 150.35349687882533 + ], + [ + 13884.059875269022, + 147.0191996709119 + ] + ], + "12": [ + [ + 0.0, + 971.7673369173294 + ], + [ + 140.24302904312142, + 996.8829447563757 + ], + [ + 280.48605808624285, + 971.2852658122592 + ], + [ + 420.7290871293643, + 952.5442526279285 + ], + [ + 560.9721161724857, + 939.8383418317302 + ], + [ + 701.2151452156071, + 924.0078545424233 + ], + [ + 841.4581742587286, + 908.3068672974701 + ], + [ + 981.70120330185, + 893.5287005421671 + ], + [ + 1121.9442323449714, + 878.7805871084857 + ], + [ + 1262.1872613880928, + 864.0752936804521 + ], + [ + 1402.4302904312142, + 849.6596954730916 + ], + [ + 1542.6733194743356, + 835.7169303386029 + ], + [ + 1682.9163485174572, + 821.6202344255854 + ], + [ + 1823.1593775605786, + 806.8285982903948 + ], + [ + 1963.4024066037, + 794.2795329404482 + ], + [ + 2103.645435646821, + 784.5186886407641 + ], + [ + 2243.888464689943, + 765.5794389540498 + ], + [ + 2384.1314937330644, + 749.918149860086 + ], + [ + 2524.3745227761856, + 742.6796691326396 + ], + [ + 2664.617551819307, + 732.7132840563861 + ], + [ + 2804.8605808624284, + 719.9713061867549 + ], + [ + 2945.10360990555, + 705.8962803331802 + ], + [ + 3085.346638948671, + 691.9301886788949 + ], + [ + 3225.589667991793, + 679.093345388213 + ], + [ + 3365.8326970349144, + 667.2528904515036 + ], + [ + 3506.0757260780356, + 656.0809845434513 + ], + [ + 3646.318755121157, + 645.2496795404601 + ], + [ + 3786.5617841642784, + 634.4604067698381 + ], + [ + 3926.8048132074, + 623.6141098973109 + ], + [ + 4067.047842250521, + 612.6921142812982 + ], + [ + 4207.290871293642, + 601.6759165285018 + ], + [ + 4347.533900336764, + 590.5478912714391 + ], + [ + 4487.776929379886, + 579.36891031858 + ], + [ + 4628.019958423007, + 568.4238819235287 + ], + [ + 4768.262987466129, + 558.0372891863757 + ], + [ + 4908.50601650925, + 548.5331789509368 + ], + [ + 5048.749045552371, + 540.229932903359 + ], + [ + 5188.992074595492, + 533.0120573672774 + ], + [ + 5329.235103638614, + 525.8419898076105 + ], + [ + 5469.478132681736, + 517.5637653463001 + ], + [ + 5609.721161724857, + 507.0219314427862 + ], + [ + 5749.964190767979, + 493.0698471758688 + ], + [ + 5890.2072198111, + 475.2013071605681 + ], + [ + 6030.450248854221, + 456.2021317704834 + ], + [ + 6170.693277897342, + 439.9259610936668 + ], + [ + 6310.936306940464, + 430.22700033850157 + ], + [ + 6451.179335983586, + 430.9530013281794 + ], + [ + 6591.422365026707, + 445.25670976689514 + ], + [ + 6731.665394069829, + 455.25313509373694 + ], + [ + 6871.90842311295, + 453.64983076669563 + ], + [ + 7012.151452156071, + 443.46638495680827 + ], + [ + 7152.394481199192, + 427.72239095190736 + ], + [ + 7292.637510242314, + 409.44711913893804 + ], + [ + 7432.880539285436, + 391.7752553745017 + ], + [ + 7573.123568328557, + 377.3842696141248 + ], + [ + 7713.366597371678, + 366.50284509616915 + ], + [ + 7853.6096264148, + 358.6618960766527 + ], + [ + 7993.852655457921, + 353.37105056458205 + ], + [ + 8134.095684501042, + 349.9962868853716 + ], + [ + 8274.338713544164, + 347.7055420661307 + ], + [ + 8414.581742587285, + 344.80930548415984 + ], + [ + 8554.824771630407, + 340.2464567765545 + ], + [ + 8695.067800673529, + 334.26655399292514 + ], + [ + 8835.31082971665, + 327.24211684423733 + ], + [ + 8975.553858759771, + 319.54566506077094 + ], + [ + 9115.796887802893, + 311.5497219267411 + ], + [ + 9256.039916846014, + 303.6265594537215 + ], + [ + 9396.282945889136, + 296.0834245854997 + ], + [ + 9536.525974932258, + 289.07114313341214 + ], + [ + 9676.769003975378, + 282.6022411243202 + ], + [ + 9817.0120330185, + 276.59135745380974 + ], + [ + 9957.25506206162, + 270.9499246485996 + ], + [ + 10097.498091104742, + 265.5893752421321 + ], + [ + 10237.741120147864, + 260.4211416637571 + ], + [ + 10377.984149190985, + 255.35663957831517 + ], + [ + 10518.227178234107, + 250.30863549549238 + ], + [ + 10658.470207277229, + 245.22279097144087 + ], + [ + 10798.71323632035, + 240.08680984463018 + ], + [ + 10938.956265363471, + 234.93454904744263 + ], + [ + 11079.199294406593, + 229.81515178914603 + ], + [ + 11219.442323449714, + 224.77651139232867 + ], + [ + 11359.685352492836, + 219.84941131915033 + ], + [ + 11499.928381535958, + 215.04692648376584 + ], + [ + 11640.171410579078, + 210.36190942009347 + ], + [ + 11780.4144396222, + 205.78258115419789 + ], + [ + 11920.65746866532, + 201.2970798069497 + ], + [ + 12060.900497708442, + 196.89571500063494 + ], + [ + 12201.143526751564, + 192.57275002133596 + ], + [ + 12341.386555794685, + 188.32669292571967 + ], + [ + 12481.629584837807, + 184.15874715416672 + ], + [ + 12621.872613880929, + 180.07022660354568 + ], + [ + 12762.11564292405, + 176.06258822823787 + ], + [ + 12902.358671967171, + 172.1373111035033 + ], + [ + 13042.601701010293, + 168.29611706743168 + ], + [ + 13182.844730053414, + 164.54021537619093 + ], + [ + 13323.087759096536, + 160.869718353382 + ], + [ + 13463.330788139658, + 157.28457917692398 + ], + [ + 13603.573817182778, + 153.7847368928315 + ], + [ + 13743.8168462259, + 150.36968592894308 + ], + [ + 13884.059875269022, + 147.03622429841863 + ] + ], + "13": [ + [ + 0.0, + 972.0984098563364 + ], + [ + 140.24302904312142, + 997.1162541346929 + ], + [ + 280.48605808624285, + 970.9490542359403 + ], + [ + 420.7290871293643, + 952.049292184464 + ], + [ + 560.9721161724857, + 940.1269656901716 + ], + [ + 701.2151452156071, + 924.5519036579674 + ], + [ + 841.4581742587286, + 905.1544265174759 + ], + [ + 981.70120330185, + 893.8544818325463 + ], + [ + 1121.9442323449714, + 880.2354894535075 + ], + [ + 1262.1872613880928, + 863.4791624521224 + ], + [ + 1402.4302904312142, + 849.3166539763922 + ], + [ + 1542.6733194743356, + 835.766137417246 + ], + [ + 1682.9163485174572, + 821.5106576487663 + ], + [ + 1823.1593775605786, + 806.7607719945197 + ], + [ + 1963.4024066037, + 794.2375865019213 + ], + [ + 2103.645435646821, + 784.0562081528641 + ], + [ + 2243.888464689943, + 765.5018502007651 + ], + [ + 2384.1314937330644, + 750.291991737172 + ], + [ + 2524.3745227761856, + 742.5920331610373 + ], + [ + 2664.617551819307, + 732.3323064832076 + ], + [ + 2804.8605808624284, + 719.5655286516901 + ], + [ + 2945.10360990555, + 705.6228822390663 + ], + [ + 3085.346638948671, + 691.8344043403673 + ], + [ + 3225.589667991793, + 679.107463122636 + ], + [ + 3365.8326970349144, + 667.2984759245234 + ], + [ + 3506.0757260780356, + 656.1037838364615 + ], + [ + 3646.318755121157, + 645.2196185966227 + ], + [ + 3786.5617841642784, + 634.3769206479348 + ], + [ + 3926.8048132074, + 623.4988912275564 + ], + [ + 4067.047842250521, + 612.5699941253157 + ], + [ + 4207.290871293642, + 601.5745270494668 + ], + [ + 4347.533900336764, + 590.4974497120505 + ], + [ + 4487.776929379886, + 579.3884091917257 + ], + [ + 4628.019958423007, + 568.503341429049 + ], + [ + 4768.262987466129, + 558.1371275019842 + ], + [ + 4908.50601650925, + 548.5841907601756 + ], + [ + 5048.749045552371, + 540.1338491389445 + ], + [ + 5188.992074595492, + 532.6885819597813 + ], + [ + 5329.235103638614, + 525.3074012374815 + ], + [ + 5469.478132681736, + 516.9380743274091 + ], + [ + 5609.721161724857, + 506.52888577708194 + ], + [ + 5749.964190767979, + 493.0369828898932 + ], + [ + 5890.2072198111, + 475.9846469759805 + ], + [ + 6030.450248854221, + 457.885628048553 + ], + [ + 6170.693277897342, + 442.25194721791416 + ], + [ + 6310.936306940464, + 432.5963202937225 + ], + [ + 6451.179335983586, + 432.4249130040826 + ], + [ + 6591.422365026707, + 444.6320675766538 + ], + [ + 6731.665394069829, + 452.99963503954825 + ], + [ + 6871.90842311295, + 450.7974193206148 + ], + [ + 7012.151452156071, + 440.79486672400054 + ], + [ + 7152.394481199192, + 425.76142941505753 + ], + [ + 7292.637510242314, + 408.4765967968027 + ], + [ + 7432.880539285436, + 391.82596243994925 + ], + [ + 7573.123568328557, + 378.25038152216376 + ], + [ + 7713.366597371678, + 367.8857268273228 + ], + [ + 7853.6096264148, + 360.2147501957588 + ], + [ + 7993.852655457921, + 354.7201604760186 + ], + [ + 8134.095684501042, + 350.88464021168943 + ], + [ + 8274.338713544164, + 348.04558326487125 + ], + [ + 8414.581742587285, + 344.6740800478928 + ], + [ + 8554.824771630407, + 339.81544896033313 + ], + [ + 8695.067800673529, + 333.6984025012342 + ], + [ + 8835.31082971665, + 326.6621556754297 + ], + [ + 8975.553858759771, + 319.045923513446 + ], + [ + 9115.796887802893, + 311.188922698022 + ], + [ + 9256.039916846014, + 303.4300723414692 + ], + [ + 9396.282945889136, + 296.0422503006509 + ], + [ + 9536.525974932258, + 289.14505978855345 + ], + [ + 9676.769003975378, + 282.7453868320815 + ], + [ + 9817.0120330185, + 276.7655826290178 + ], + [ + 9957.25506206162, + 271.12488763896744 + ], + [ + 10097.498091104742, + 265.7425423227289 + ], + [ + 10237.741120147864, + 260.53778703395744 + ], + [ + 10377.984149190985, + 255.42985125882618 + ], + [ + 10518.227178234107, + 250.339304622222 + ], + [ + 10658.470207277229, + 245.2197542787818 + ], + [ + 10798.71323632035, + 240.06548305490696 + ], + [ + 10938.956265363471, + 234.90885385396925 + ], + [ + 11079.199294406593, + 229.79590370513927 + ], + [ + 11219.442323449714, + 224.77137038929254 + ], + [ + 11359.685352492836, + 219.8614592291941 + ], + [ + 11499.928381535958, + 215.07260196202506 + ], + [ + 11640.171410579078, + 210.3938624915687 + ], + [ + 11780.4144396222, + 205.81020316670643 + ], + [ + 11920.65746866532, + 201.3065189271332 + ], + [ + 12060.900497708442, + 196.8714883821803 + ], + [ + 12201.143526751564, + 192.50893835134465 + ], + [ + 12341.386555794685, + 188.23132829075547 + ], + [ + 12481.629584837807, + 184.0534963873919 + ], + [ + 12621.872613880929, + 179.99039138399505 + ], + [ + 12762.11564292405, + 176.05709617387677 + ], + [ + 12902.358671967171, + 172.26035377747303 + ], + [ + 13042.601701010293, + 168.56970163730776 + ], + [ + 13182.844730053414, + 164.9437877501499 + ], + [ + 13323.087759096536, + 161.340235369637 + ], + [ + 13463.330788139658, + 157.7165083681657 + ], + [ + 13603.573817182778, + 154.03005621925666 + ], + [ + 13743.8168462259, + 150.23852720135002 + ], + [ + 13884.059875269022, + 146.35289636760064 + ] + ], + "14": [ + [ + 0.0, + 974.0381263937168 + ], + [ + 140.24302904312142, + 997.2811336071599 + ], + [ + 280.48605808624285, + 970.0265745092667 + ], + [ + 420.7290871293643, + 951.6145794295137 + ], + [ + 560.9721161724857, + 939.9017262706947 + ], + [ + 701.2151452156071, + 924.169878560734 + ], + [ + 841.4581742587286, + 904.5077906271381 + ], + [ + 981.70120330185, + 893.8484761280251 + ], + [ + 1121.9442323449714, + 880.0412610897072 + ], + [ + 1262.1872613880928, + 863.1012870620112 + ], + [ + 1402.4302904312142, + 849.0249091985431 + ], + [ + 1542.6733194743356, + 835.4651083487497 + ], + [ + 1682.9163485174572, + 821.2219782663545 + ], + [ + 1823.1593775605786, + 806.6192290551014 + ], + [ + 1963.4024066037, + 793.961836784144 + ], + [ + 2103.645435646821, + 783.2461465692045 + ], + [ + 2243.888464689943, + 765.2788649217398 + ], + [ + 2384.1314937330644, + 750.6744494543032 + ], + [ + 2524.3745227761856, + 742.3841606823262 + ], + [ + 2664.617551819307, + 731.7161365559291 + ], + [ + 2804.8605808624284, + 718.8915353135656 + ], + [ + 2945.10360990555, + 705.1089217310865 + ], + [ + 3085.346638948671, + 691.564105191131 + ], + [ + 3225.589667991793, + 679.0107124537935 + ], + [ + 3365.8326970349144, + 667.2723651139796 + ], + [ + 3506.0757260780356, + 656.0551495213248 + ], + [ + 3646.318755121157, + 645.0651358757382 + ], + [ + 3786.5617841642784, + 634.0535234234807 + ], + [ + 3926.8048132074, + 623.0101337264497 + ], + [ + 4067.047842250521, + 612.0033514246933 + ], + [ + 4207.290871293642, + 601.1016138160328 + ], + [ + 4347.533900336764, + 590.3730091388315 + ], + [ + 4487.776929379886, + 579.8553443123355 + ], + [ + 4628.019958423007, + 569.5345973531248 + ], + [ + 4768.262987466129, + 559.3916788081063 + ], + [ + 4908.50601650925, + 549.4075041142233 + ], + [ + 5048.749045552371, + 539.5631803102194 + ], + [ + 5188.992074595492, + 529.8479089182198 + ], + [ + 5329.235103638614, + 520.2628522265996 + ], + [ + 5469.478132681736, + 510.8101456285942 + ], + [ + 5609.721161724857, + 501.49192414434606 + ], + [ + 5749.964190767979, + 492.31035784553535 + ], + [ + 5890.2072198111, + 483.2667895138579 + ], + [ + 6030.450248854221, + 474.3592299794927 + ], + [ + 6170.693277897342, + 465.58489248478094 + ], + [ + 6310.936306940464, + 456.94099215616984 + ], + [ + 6451.179335983586, + 448.42476715117414 + ], + [ + 6591.422365026707, + 440.03355013842025 + ], + [ + 6731.665394069829, + 431.7652125303528 + ], + [ + 6871.90842311295, + 423.6184493770946 + ], + [ + 7012.151452156071, + 415.5920522935813 + ], + [ + 7152.394481199192, + 407.68482073044044 + ], + [ + 7292.637510242314, + 399.89558129252 + ], + [ + 7432.880539285436, + 392.2231509812858 + ], + [ + 7573.123568328557, + 384.6662976133728 + ], + [ + 7713.366597371678, + 377.22361051606566 + ], + [ + 7853.6096264148, + 369.8936245450267 + ], + [ + 7993.852655457921, + 362.67487312750063 + ], + [ + 8134.095684501042, + 355.5658695152988 + ], + [ + 8274.338713544164, + 348.56516076451095 + ], + [ + 8414.581742587285, + 341.6714599350287 + ], + [ + 8554.824771630407, + 334.88363019789506 + ], + [ + 8695.067800673529, + 328.2006155269441 + ], + [ + 8835.31082971665, + 321.62137665159156 + ], + [ + 8975.553858759771, + 315.1448743468137 + ], + [ + 9115.796887802893, + 308.77006846448484 + ], + [ + 9256.039916846014, + 302.4959059386436 + ], + [ + 9396.282945889136, + 296.3213498870733 + ], + [ + 9536.525974932258, + 290.245352221052 + ], + [ + 9676.769003975378, + 284.26691515553705 + ], + [ + 9817.0120330185, + 278.3850582483159 + ], + [ + 9957.25506206162, + 272.5988006671377 + ], + [ + 10097.498091104742, + 266.90716157917984 + ], + [ + 10237.741120147864, + 261.30916011960835 + ], + [ + 10377.984149190985, + 255.8038105857424 + ], + [ + 10518.227178234107, + 250.3901210760941 + ], + [ + 10658.470207277229, + 245.06748035087477 + ], + [ + 10798.71323632035, + 239.83645680218297 + ], + [ + 10938.956265363471, + 234.69796267622607 + ], + [ + 11079.199294406593, + 229.65295608408442 + ], + [ + 11219.442323449714, + 224.7023758097281 + ], + [ + 11359.685352492836, + 219.84567065129838 + ], + [ + 11499.928381535958, + 215.07869888035003 + ], + [ + 11640.171410579078, + 210.39677128420774 + ], + [ + 11780.4144396222, + 205.79513233875474 + ], + [ + 11920.65746866532, + 201.26894778901098 + ], + [ + 12060.900497708442, + 196.8148400213198 + ], + [ + 12201.143526751564, + 192.44110840462014 + ], + [ + 12341.386555794685, + 188.16082564508838 + ], + [ + 12481.629584837807, + 183.98728721195 + ], + [ + 12621.872613880929, + 179.93386017291908 + ], + [ + 12762.11564292405, + 176.01404988628272 + ], + [ + 12902.358671967171, + 172.23194783714794 + ], + [ + 13042.601701010293, + 168.55287271112826 + ], + [ + 13182.844730053414, + 164.93272392616285 + ], + [ + 13323.087759096536, + 161.3268107733856 + ], + [ + 13463.330788139658, + 157.69029094535802 + ], + [ + 13603.573817182778, + 153.97830622788572 + ], + [ + 13743.8168462259, + 150.14690647275924 + ], + [ + 13884.059875269022, + 146.21737128598724 + ] + ], + "15": [ + [ + 0.0, + 975.9245526292757 + ], + [ + 140.24302904312142, + 996.8707479971782 + ], + [ + 280.48605808624285, + 969.2551416896587 + ], + [ + 420.7290871293643, + 951.5217894245978 + ], + [ + 560.9721161724857, + 939.0576147233314 + ], + [ + 701.2151452156071, + 923.1359859630538 + ], + [ + 841.4581742587286, + 907.5107020637982 + ], + [ + 981.70120330185, + 892.789355274558 + ], + [ + 1121.9442323449714, + 878.0368801153706 + ], + [ + 1262.1872613880928, + 863.3895223497964 + ], + [ + 1402.4302904312142, + 849.0125844288985 + ], + [ + 1542.6733194743356, + 834.8426138727599 + ], + [ + 1682.9163485174572, + 820.8575624521013 + ], + [ + 1823.1593775605786, + 807.0733213704599 + ], + [ + 1963.4024066037, + 793.489386831652 + ], + [ + 2103.645435646821, + 780.099581628933 + ], + [ + 2243.888464689943, + 766.901150771195 + ], + [ + 2384.1314937330644, + 753.8915577253431 + ], + [ + 2524.3745227761856, + 741.0678546853259 + ], + [ + 2664.617551819307, + 728.4273817123744 + ], + [ + 2804.8605808624284, + 715.9677355217951 + ], + [ + 2945.10360990555, + 703.6865842041535 + ], + [ + 3085.346638948671, + 691.5815571041765 + ], + [ + 3225.589667991793, + 679.6503882827267 + ], + [ + 3365.8326970349144, + 667.890943215966 + ], + [ + 3506.0757260780356, + 656.301074799914 + ], + [ + 3646.318755121157, + 644.8786021491574 + ], + [ + 3786.5617841642784, + 633.6212625671228 + ], + [ + 3926.8048132074, + 622.526806490432 + ], + [ + 4067.047842250521, + 611.593021088309 + ], + [ + 4207.290871293642, + 600.8176955031246 + ], + [ + 4347.533900336764, + 590.198674382209 + ], + [ + 4487.776929379886, + 579.7338607250146 + ], + [ + 4628.019958423007, + 569.4211407188583 + ], + [ + 4768.262987466129, + 559.2583965204574 + ], + [ + 4908.50601650925, + 549.2435183651053 + ], + [ + 5048.749045552371, + 539.3744429008412 + ], + [ + 5188.992074595492, + 529.6492161581207 + ], + [ + 5329.235103638614, + 520.0659154761257 + ], + [ + 5469.478132681736, + 510.62262192715383 + ], + [ + 5609.721161724857, + 501.3174141057919 + ], + [ + 5749.964190767979, + 492.1484012607572 + ], + [ + 5890.2072198111, + 483.1137518201494 + ], + [ + 6030.450248854221, + 474.21165208189603 + ], + [ + 6170.693277897342, + 465.440281557061 + ], + [ + 6310.936306940464, + 456.79782131454823 + ], + [ + 6451.179335983586, + 448.28248098521277 + ], + [ + 6591.422365026707, + 439.89257063891193 + ], + [ + 6731.665394069829, + 431.62656290509057 + ], + [ + 6871.90842311295, + 423.48302361971776 + ], + [ + 7012.151452156071, + 415.46052799845154 + ], + [ + 7152.394481199192, + 407.55765619726924 + ], + [ + 7292.637510242314, + 399.77299692158556 + ], + [ + 7432.880539285436, + 392.105105083984 + ], + [ + 7573.123568328557, + 384.55251799289124 + ], + [ + 7713.366597371678, + 377.1137735595404 + ], + [ + 7853.6096264148, + 369.78740901072865 + ], + [ + 7993.852655457921, + 362.5719608003981 + ], + [ + 8134.095684501042, + 355.46596175527014 + ], + [ + 8274.338713544164, + 348.4680119346129 + ], + [ + 8414.581742587285, + 341.5769038936954 + ], + [ + 8554.824771630407, + 334.79158112332226 + ], + [ + 8695.067800673529, + 328.11102715661235 + ], + [ + 8835.31082971665, + 321.5342329517754 + ], + [ + 8975.553858759771, + 315.06018951400625 + ], + [ + 9115.796887802893, + 308.6878819861435 + ], + [ + 9256.039916846014, + 302.4162613828259 + ], + [ + 9396.282945889136, + 296.2442695597878 + ], + [ + 9536.525974932258, + 290.17081874598557 + ], + [ + 9676.769003975378, + 284.1948834801883 + ], + [ + 9817.0120330185, + 278.3154643147059 + ], + [ + 9957.25506206162, + 272.5315618727736 + ], + [ + 10097.498091104742, + 266.84217677682096 + ], + [ + 10237.741120147864, + 261.2463095980265 + ], + [ + 10377.984149190985, + 255.74295520641522 + ], + [ + 10518.227178234107, + 250.33110857328205 + ], + [ + 10658.470207277229, + 245.01020996664144 + ], + [ + 10798.71323632035, + 239.78084576589316 + ], + [ + 10938.956265363471, + 234.64388643830853 + ], + [ + 11079.199294406593, + 229.6002486885617 + ], + [ + 11219.442323449714, + 224.65081859642177 + ], + [ + 11359.685352492836, + 219.7948358508288 + ], + [ + 11499.928381535958, + 215.02815952418112 + ], + [ + 11640.171410579078, + 210.34624714662547 + ], + [ + 11780.4144396222, + 205.74450099822334 + ], + [ + 11920.65746866532, + 201.21825381789537 + ], + [ + 12060.900497708442, + 196.76456964688109 + ], + [ + 12201.143526751564, + 192.39211751764677 + ], + [ + 12341.386555794685, + 188.11368870049765 + ], + [ + 12481.629584837807, + 183.94225823302833 + ], + [ + 12621.872613880929, + 179.89086911841815 + ], + [ + 12762.11564292405, + 175.97269584808407 + ], + [ + 12902.358671967171, + 172.19028351453701 + ], + [ + 13042.601701010293, + 168.50814024811638 + ], + [ + 13182.844730053414, + 164.88280186695516 + ], + [ + 13323.087759096536, + 161.27026770949462 + ], + [ + 13463.330788139658, + 157.62639144349154 + ], + [ + 13603.573817182778, + 153.90700993069976 + ], + [ + 13743.8168462259, + 150.0696108868331 + ], + [ + 13884.059875269022, + 146.14265678059203 + ] + ], + "16": [ + [ + 0.0, + 977.1865378848745 + ], + [ + 140.24302904312142, + 996.6400509633053 + ], + [ + 280.48605808624285, + 968.8842022415643 + ], + [ + 420.7290871293643, + 951.385218195992 + ], + [ + 560.9721161724857, + 938.8983301009263 + ], + [ + 701.2151452156071, + 922.967450911019 + ], + [ + 841.4581742587286, + 907.3700097155165 + ], + [ + 981.70120330185, + 892.6625688116268 + ], + [ + 1121.9442323449714, + 877.9179793164783 + ], + [ + 1262.1872613880928, + 863.2783451633717 + ], + [ + 1402.4302904312142, + 848.906249710657 + ], + [ + 1542.6733194743356, + 834.7384253905072 + ], + [ + 1682.9163485174572, + 820.7545499144059 + ], + [ + 1823.1593775605786, + 806.9715614677407 + ], + [ + 1963.4024066037, + 793.389414679443 + ], + [ + 2103.645435646821, + 780.0021372507521 + ], + [ + 2243.888464689943, + 766.8068073255401 + ], + [ + 2384.1314937330644, + 753.8004401490674 + ], + [ + 2524.3745227761856, + 740.979662231409 + ], + [ + 2664.617551819307, + 728.3417980778173 + ], + [ + 2804.8605808624284, + 715.884589529038 + ], + [ + 2945.10360990555, + 703.6058437383749 + ], + [ + 3085.346638948671, + 691.503312140242 + ], + [ + 3225.589667991793, + 679.5747062045707 + ], + [ + 3365.8326970349144, + 667.817861491572 + ], + [ + 3506.0757260780356, + 656.230619430318 + ], + [ + 3646.318755121157, + 644.8107951254875 + ], + [ + 3786.5617841642784, + 633.556160319012 + ], + [ + 3926.8048132074, + 622.4642506661472 + ], + [ + 4067.047842250521, + 611.5325032224447 + ], + [ + 4207.290871293642, + 600.7583571255581 + ], + [ + 4347.533900336764, + 590.139311281051 + ], + [ + 4487.776929379886, + 579.6734985449438 + ], + [ + 4628.019958423007, + 569.3601625576173 + ], + [ + 4768.262987466129, + 559.1986716861691 + ], + [ + 4908.50601650925, + 549.1884051611091 + ], + [ + 5048.749045552371, + 539.3287038582099 + ], + [ + 5188.992074595492, + 529.6164360903356 + ], + [ + 5329.235103638614, + 520.044966898191 + ], + [ + 5469.478132681736, + 510.6073908818681 + ], + [ + 5609.721161724857, + 501.29678772291567 + ], + [ + 5749.964190767979, + 492.106305613302 + ], + [ + 5890.2072198111, + 483.0340687685987 + ], + [ + 6030.450248854221, + 474.09208669038225 + ], + [ + 6170.693277897342, + 465.2949576620094 + ], + [ + 6310.936306940464, + 456.65728508490025 + ], + [ + 6451.179335983586, + 448.1937060877084 + ], + [ + 6591.422365026707, + 439.91802331390824 + ], + [ + 6731.665394069829, + 431.8149924918548 + ], + [ + 6871.90842311295, + 423.8319635846407 + ], + [ + 7012.151452156071, + 415.9136049202689 + ], + [ + 7152.394481199192, + 408.00458901200966 + ], + [ + 7292.637510242314, + 400.0495946453246 + ], + [ + 7432.880539285436, + 391.9939150691048 + ], + [ + 7573.123568328557, + 383.86280090556915 + ], + [ + 7713.366597371678, + 375.8614626464333 + ], + [ + 7853.6096264148, + 368.221986003446 + ], + [ + 7993.852655457921, + 361.13399177525804 + ], + [ + 8134.095684501042, + 354.50005077196596 + ], + [ + 8274.338713544164, + 348.10392714332744 + ], + [ + 8414.581742587285, + 341.73163011060683 + ], + [ + 8554.824771630407, + 335.2666400903644 + ], + [ + 8695.067800673529, + 328.7327383406788 + ], + [ + 8835.31082971665, + 322.16574024507145 + ], + [ + 8975.553858759771, + 315.60146122492193 + ], + [ + 9115.796887802893, + 309.0757100986571 + ], + [ + 9256.039916846014, + 302.62425787893926 + ], + [ + 9396.282945889136, + 296.2828481776061 + ], + [ + 9536.525974932258, + 290.08420728130807 + ], + [ + 9676.769003975378, + 284.03389979429977 + ], + [ + 9817.0120330185, + 278.12229751843535 + ], + [ + 9957.25506206162, + 272.3395720852181 + ], + [ + 10097.498091104742, + 266.67589512821286 + ], + [ + 10237.741120147864, + 261.1214381723165 + ], + [ + 10377.984149190985, + 255.66636596728884 + ], + [ + 10518.227178234107, + 250.30085790590195 + ], + [ + 10658.470207277229, + 245.0155999398789 + ], + [ + 10798.71323632035, + 239.80490046898043 + ], + [ + 10938.956265363471, + 234.67242562407506 + ], + [ + 11079.199294406593, + 229.62396141808463 + ], + [ + 11219.442323449714, + 224.66523325092908 + ], + [ + 11359.685352492836, + 219.80007022559187 + ], + [ + 11499.928381535958, + 215.02780904595363 + ], + [ + 11640.171410579078, + 210.3436391006315 + ], + [ + 11780.4144396222, + 205.742075058322 + ], + [ + 11920.65746866532, + 201.21756563840768 + ], + [ + 12060.900497708442, + 196.76701712868856 + ], + [ + 12201.143526751564, + 192.3986146083676 + ], + [ + 12341.386555794685, + 188.12422947372139 + ], + [ + 12481.629584837807, + 183.95630552900238 + ], + [ + 12621.872613880929, + 179.90735542132705 + ], + [ + 12762.11564292405, + 175.98998468150057 + ], + [ + 12902.358671967171, + 172.20315160710126 + ], + [ + 13042.601701010293, + 168.51112648273977 + ], + [ + 13182.844730053414, + 164.87301771637848 + ], + [ + 13323.087759096536, + 161.24732194784008 + ], + [ + 13463.330788139658, + 157.59239199865266 + ], + [ + 13603.573817182778, + 153.86656406895233 + ], + [ + 13743.8168462259, + 150.0314313471485 + ], + [ + 13884.059875269022, + 146.12532720511467 + ] + ], + "17": [ + [ + 0.0, + 977.3681571334478 + ], + [ + 140.24302904312142, + 996.9317420211219 + ], + [ + 280.48605808624285, + 968.65365084599 + ], + [ + 420.7290871293643, + 950.6689052416824 + ], + [ + 560.9721161724857, + 940.0315336749685 + ], + [ + 701.2151452156071, + 924.1594575519041 + ], + [ + 841.4581742587286, + 899.2460890875111 + ], + [ + 981.70120330185, + 895.2067892627718 + ], + [ + 1121.9442323449714, + 881.8336416514246 + ], + [ + 1262.1872613880928, + 861.5652489004729 + ], + [ + 1402.4302904312142, + 848.1166856030064 + ], + [ + 1542.6733194743356, + 835.7188278278962 + ], + [ + 1682.9163485174572, + 821.0929372939198 + ], + [ + 1823.1593775605786, + 804.0379263356193 + ], + [ + 1963.4024066037, + 793.274670244098 + ], + [ + 2103.645435646821, + 791.6093928702151 + ], + [ + 2243.888464689943, + 762.9133049134954 + ], + [ + 2384.1314937330644, + 740.1212336847755 + ], + [ + 2524.3745227761856, + 743.2930212691788 + ], + [ + 2664.617551819307, + 738.8116218051804 + ], + [ + 2804.8605808624284, + 725.7626377174205 + ], + [ + 2945.10360990555, + 708.4073111359564 + ], + [ + 3085.346638948671, + 691.0025497870141 + ], + [ + 3225.589667991793, + 676.5815085774542 + ], + [ + 3365.8326970349144, + 664.7310383400426 + ], + [ + 3506.0757260780356, + 654.3995739379309 + ], + [ + 3646.318755121157, + 644.5355207194925 + ], + [ + 3786.5617841642784, + 634.2146359782915 + ], + [ + 3926.8048132074, + 623.3426890865549 + ], + [ + 4067.047842250521, + 612.1824762568773 + ], + [ + 4207.290871293642, + 600.998926841211 + ], + [ + 4347.533900336764, + 590.0553031788256 + ], + [ + 4487.776929379886, + 579.4935160406075 + ], + [ + 4628.019958423007, + 569.2331116011779 + ], + [ + 4768.262987466129, + 559.1679022155625 + ], + [ + 4908.50601650925, + 549.1916943615104 + ], + [ + 5048.749045552371, + 539.2021566096831 + ], + [ + 5188.992074595492, + 529.2113859552898 + ], + [ + 5329.235103638614, + 519.367208003921 + ], + [ + 5469.478132681736, + 509.8258166229351 + ], + [ + 5609.721161724857, + 500.74340724785424 + ], + [ + 5749.964190767979, + 492.27540687246955 + ], + [ + 5890.2072198111, + 484.39819854776687 + ], + [ + 6030.450248854221, + 476.68467490752477 + ], + [ + 6170.693277897342, + 468.6521620865494 + ], + [ + 6310.936306940464, + 459.81799085664676 + ], + [ + 6451.179335983586, + 449.69950917992696 + ], + [ + 6591.422365026707, + 438.11783189532093 + ], + [ + 6731.665394069829, + 427.7737166165326 + ], + [ + 6871.90842311295, + 419.1182356242174 + ], + [ + 7012.151452156071, + 411.7025303811428 + ], + [ + 7152.394481199192, + 405.07774290945446 + ], + [ + 7292.637510242314, + 398.79502315653787 + ], + [ + 7432.880539285436, + 392.40815916147494 + ], + [ + 7573.123568328557, + 385.6600058577524 + ], + [ + 7713.366597371678, + 378.6010891132921 + ], + [ + 7853.6096264148, + 371.30953457300654 + ], + [ + 7993.852655457921, + 363.8634674877366 + ], + [ + 8134.095684501042, + 356.3410179966581 + ], + [ + 8274.338713544164, + 348.8203824810924 + ], + [ + 8414.581742587285, + 341.3780448749782 + ], + [ + 8554.824771630407, + 334.06182803884235 + ], + [ + 8695.067800673529, + 326.9207226450462 + ], + [ + 8835.31082971665, + 320.00938037693214 + ], + [ + 8975.553858759771, + 313.3824529460958 + ], + [ + 9115.796887802893, + 307.0945852593206 + ], + [ + 9256.039916846014, + 301.20038105767276 + ], + [ + 9396.282945889136, + 295.75407270485 + ], + [ + 9536.525974932258, + 290.55155318071974 + ], + [ + 9676.769003975378, + 285.1411971608927 + ], + [ + 9817.0120330185, + 279.54363583144624 + ], + [ + 9957.25506206162, + 273.8134443219073 + ], + [ + 10097.498091104742, + 268.00519776472555 + ], + [ + 10237.741120147864, + 262.17347105852195 + ], + [ + 10377.984149190985, + 256.3728334010837 + ], + [ + 10518.227178234107, + 250.65787646495676 + ], + [ + 10658.470207277229, + 245.08375598429603 + ], + [ + 10798.71323632035, + 239.70132317326082 + ], + [ + 10938.956265363471, + 234.51212430707687 + ], + [ + 11079.199294406593, + 229.48614521136727 + ], + [ + 11219.442323449714, + 224.5926522005422 + ], + [ + 11359.685352492836, + 219.79873183290874 + ], + [ + 11499.928381535958, + 215.0721887144072 + ], + [ + 11640.171410579078, + 210.40383452296774 + ], + [ + 11780.4144396222, + 205.79678139575805 + ], + [ + 11920.65746866532, + 201.25423422654717 + ], + [ + 12060.900497708442, + 196.78294798250374 + ], + [ + 12201.143526751564, + 192.39985811384597 + ], + [ + 12341.386555794685, + 188.12002988097703 + ], + [ + 12481.629584837807, + 183.95380472292456 + ], + [ + 12621.872613880929, + 179.91117634554288 + ], + [ + 12762.11564292405, + 176.00206447815742 + ], + [ + 12902.358671967171, + 172.2192042230491 + ], + [ + 13042.601701010293, + 168.52501282806753 + ], + [ + 13182.844730053414, + 164.88000166563234 + ], + [ + 13323.087759096536, + 161.24502648132568 + ], + [ + 13463.330788139658, + 157.5808476435011 + ], + [ + 13603.573817182778, + 153.848208768189 + ], + [ + 13743.8168462259, + 150.01289441535846 + ], + [ + 13884.059875269022, + 146.12057926838187 + ] + ], + "18": [ + [ + 0.0, + 974.4610873677278 + ], + [ + 140.24302904312142, + 998.1941909191057 + ], + [ + 280.48605808624285, + 968.6704326291962 + ], + [ + 420.7290871293643, + 951.0353993046401 + ], + [ + 560.9721161724857, + 938.9479036263915 + ], + [ + 701.2151452156071, + 922.8954276189161 + ], + [ + 841.4581742587286, + 907.249378300469 + ], + [ + 981.70120330185, + 892.5856983063686 + ], + [ + 1121.9442323449714, + 877.8469975495132 + ], + [ + 1262.1872613880928, + 863.2051204438918 + ], + [ + 1402.4302904312142, + 848.8417045524262 + ], + [ + 1542.6733194743356, + 834.681305633764 + ], + [ + 1682.9163485174572, + 820.7011137214362 + ], + [ + 1823.1593775605786, + 806.9217386752101 + ], + [ + 1963.4024066037, + 793.3434635783877 + ], + [ + 2103.645435646821, + 779.9594674943398 + ], + [ + 2243.888464689943, + 766.7671788936033 + ], + [ + 2384.1314937330644, + 753.7637456546145 + ], + [ + 2524.3745227761856, + 740.9453595020652 + ], + [ + 2664.617551819307, + 728.309403806467 + ], + [ + 2804.8605808624284, + 715.8539147455901 + ], + [ + 2945.10360990555, + 703.5769637110901 + ], + [ + 3085.346638948671, + 691.476461018079 + ], + [ + 3225.589667991793, + 679.5500280683862 + ], + [ + 3365.8326970349144, + 667.7953098040696 + ], + [ + 3506.0757260780356, + 656.2099665407005 + ], + [ + 3646.318755121157, + 644.7916495591603 + ], + [ + 3786.5617841642784, + 633.5381282471557 + ], + [ + 3926.8048132074, + 622.447223956161 + ], + [ + 4067.047842250521, + 611.5167294484764 + ], + [ + 4207.290871293642, + 600.7444467049289 + ], + [ + 4347.533900336764, + 590.1281790281593 + ], + [ + 4487.776929379886, + 579.6658164869164 + ], + [ + 4628.019958423007, + 569.3556618470516 + ], + [ + 4768.262987466129, + 559.1960768333029 + ], + [ + 4908.50601650925, + 549.1854185974778 + ], + [ + 5048.749045552371, + 539.3221496296323 + ], + [ + 5188.992074595492, + 529.6041166643715 + ], + [ + 5329.235103638614, + 520.0277434108666 + ], + [ + 5469.478132681736, + 510.58930573208727 + ], + [ + 5609.721161724857, + 501.28508713333554 + ], + [ + 5749.964190767979, + 492.11139364377306 + ], + [ + 5890.2072198111, + 483.0657434803737 + ], + [ + 6030.450248854221, + 474.1509437937897 + ], + [ + 6170.693277897342, + 465.37120251449085 + ], + [ + 6310.936306940464, + 456.7307320417706 + ], + [ + 6451.179335983586, + 448.2337703638044 + ], + [ + 6591.422365026707, + 439.8845796351607 + ], + [ + 6731.665394069829, + 431.6795097045747 + ], + [ + 6871.90842311295, + 423.59904865920606 + ], + [ + 7012.151452156071, + 415.62189760742467 + ], + [ + 7152.394481199192, + 407.7267545425086 + ], + [ + 7292.637510242314, + 399.89231529265027 + ], + [ + 7432.880539285436, + 392.09728658410467 + ], + [ + 7573.123568328557, + 384.34230331427807 + ], + [ + 7713.366597371678, + 376.70650854977276 + ], + [ + 7853.6096264148, + 369.2863536314275 + ], + [ + 7993.852655457921, + 362.1569250329419 + ], + [ + 8134.095684501042, + 355.24860304604846 + ], + [ + 8274.338713544164, + 348.4317912490524 + ], + [ + 8414.581742587285, + 341.57723563467835 + ], + [ + 8554.824771630407, + 334.6217302233568 + ], + [ + 8695.067800673529, + 327.64403269919745 + ], + [ + 8835.31082971665, + 320.74135369870294 + ], + [ + 8975.553858759771, + 314.0109039293824 + ], + [ + 9115.796887802893, + 307.5498872376493 + ], + [ + 9256.039916846014, + 301.45546962609023 + ], + [ + 9396.282945889136, + 295.8243776746731 + ], + [ + 9536.525974932258, + 290.4832462987368 + ], + [ + 9676.769003975378, + 284.9879691956889 + ], + [ + 9817.0120330185, + 279.3517666094196 + ], + [ + 9957.25506206162, + 273.61970308498456 + ], + [ + 10097.498091104742, + 267.8368431654638 + ], + [ + 10237.741120147864, + 262.0482510913068 + ], + [ + 10377.984149190985, + 256.2989796047275 + ], + [ + 10518.227178234107, + 250.63409211412844 + ], + [ + 10658.470207277229, + 245.09934792482636 + ], + [ + 10798.71323632035, + 239.7385835420776 + ], + [ + 10938.956265363471, + 234.5556300021787 + ], + [ + 11079.199294406593, + 229.5256944588611 + ], + [ + 11219.442323449714, + 224.62316820244672 + ], + [ + 11359.685352492836, + 219.81955309308916 + ], + [ + 11499.928381535958, + 215.086382133999 + ], + [ + 11640.171410579078, + 210.41425510321722 + ], + [ + 11780.4144396222, + 205.80509578164137 + ], + [ + 11920.65746866532, + 201.26105623126256 + ], + [ + 12060.900497708442, + 196.79071825680646 + ], + [ + 12201.143526751564, + 192.4109291358297 + ], + [ + 12341.386555794685, + 188.13628321440783 + ], + [ + 12481.629584837807, + 183.9771340217716 + ], + [ + 12621.872613880929, + 179.94351450838928 + ], + [ + 12762.11564292405, + 176.04460127609346 + ], + [ + 12902.358671967171, + 172.26601043904247 + ], + [ + 13042.601701010293, + 168.56955402329174 + ], + [ + 13182.844730053414, + 164.91702497861888 + ], + [ + 13323.087759096536, + 161.27046301811197 + ], + [ + 13463.330788139658, + 157.59180326035244 + ], + [ + 13603.573817182778, + 153.84296338309923 + ], + [ + 13743.8168462259, + 149.9931148614546 + ], + [ + 13884.059875269022, + 146.09570161331914 + ] + ], + "19": [ + [ + 0.0, + 973.8264622157496 + ], + [ + 140.24302904312142, + 998.4530992011906 + ], + [ + 280.48605808624285, + 968.9281933836647 + ], + [ + 420.7290871293643, + 951.1297938740942 + ], + [ + 560.9721161724857, + 939.070517542616 + ], + [ + 701.2151452156071, + 923.0085646697701 + ], + [ + 841.4581742587286, + 907.3452971013623 + ], + [ + 981.70120330185, + 892.6799220907794 + ], + [ + 1121.9442323449714, + 877.9399608059739 + ], + [ + 1262.1872613880928, + 863.2961023013647 + ], + [ + 1402.4302904312142, + 848.9330327001643 + ], + [ + 1542.6733194743356, + 834.7742474256211 + ], + [ + 1682.9163485174572, + 820.7948050071548 + ], + [ + 1823.1593775605786, + 807.0148205810002 + ], + [ + 1963.4024066037, + 793.4356960451081 + ], + [ + 2103.645435646821, + 780.0506495765565 + ], + [ + 2243.888464689943, + 766.8566823820141 + ], + [ + 2384.1314937330644, + 753.8515132384041 + ], + [ + 2524.3745227761856, + 741.0316961570215 + ], + [ + 2664.617551819307, + 728.3944046912912 + ], + [ + 2804.8605808624284, + 715.9375348183663 + ], + [ + 2945.10360990555, + 703.6590833361217 + ], + [ + 3085.346638948671, + 691.5569711410078 + ], + [ + 3225.589667991793, + 679.6289078361494 + ], + [ + 3365.8326970349144, + 667.8726217655504 + ], + [ + 3506.0757260780356, + 656.2858556997119 + ], + [ + 3646.318755121157, + 644.8663260454462 + ], + [ + 3786.5617841642784, + 633.6117867805445 + ], + [ + 3926.8048132074, + 622.5199592006418 + ], + [ + 4067.047842250521, + 611.5885045782103 + ], + [ + 4207.290871293642, + 600.8150860512352 + ], + [ + 4347.533900336764, + 590.1973971615323 + ], + [ + 4487.776929379886, + 579.733368086177 + ], + [ + 4628.019958423007, + 569.4215087738518 + ], + [ + 4768.262987466129, + 559.2604034519893 + ], + [ + 4908.50601650925, + 549.248640355346 + ], + [ + 5048.749045552371, + 539.384905754769 + ], + [ + 5188.992074595492, + 529.6669010044174 + ], + [ + 5329.235103638614, + 520.0904004348705 + ], + [ + 5469.478132681736, + 510.6509939394888 + ], + [ + 5609.721161724857, + 501.3442778940472 + ], + [ + 5749.964190767979, + 492.16584292429235 + ], + [ + 5890.2072198111, + 483.1131154997204 + ], + [ + 6030.450248854221, + 474.1908574228306 + ], + [ + 6170.693277897342, + 465.40565484435837 + ], + [ + 6310.936306940464, + 456.76409665207257 + ], + [ + 6451.179335983586, + 448.2728083118413 + ], + [ + 6591.422365026707, + 439.93838654782286 + ], + [ + 6731.665394069829, + 431.75559220341523 + ], + [ + 6871.90842311295, + 423.6975587240613 + ], + [ + 7012.151452156071, + 415.7351382241819 + ], + [ + 7152.394481199192, + 407.8391801822409 + ], + [ + 7292.637510242314, + 399.98052285652295 + ], + [ + 7432.880539285436, + 392.1300249227161 + ], + [ + 7573.123568328557, + 384.2874975751471 + ], + [ + 7713.366597371678, + 376.5532591667933 + ], + [ + 7853.6096264148, + 369.0494372302504 + ], + [ + 7993.852655457921, + 361.8768202273707 + ], + [ + 8134.095684501042, + 354.9913116865881 + ], + [ + 8274.338713544164, + 348.28863336556634 + ], + [ + 8414.581742587285, + 341.66462849655255 + ], + [ + 8554.824771630407, + 335.0501190968139 + ], + [ + 8695.067800673529, + 328.45221367576005 + ], + [ + 8835.31082971665, + 321.8881196860127 + ], + [ + 8975.553858759771, + 315.3750446553872 + ], + [ + 9115.796887802893, + 308.93018971421924 + ], + [ + 9256.039916846014, + 302.57072150638163 + ], + [ + 9396.282945889136, + 296.3137645445249 + ], + [ + 9536.525974932258, + 290.17565121800703 + ], + [ + 9676.769003975378, + 284.16061007433626 + ], + [ + 9817.0120330185, + 278.26354656600216 + ], + [ + 9957.25506206162, + 272.47912864019355 + ], + [ + 10097.498091104742, + 266.8020242375537 + ], + [ + 10237.741120147864, + 261.2269010743838 + ], + [ + 10377.984149190985, + 255.74841267437225 + ], + [ + 10518.227178234107, + 250.36123453665522 + ], + [ + 10658.470207277229, + 245.060774669505 + ], + [ + 10798.71323632035, + 239.84401575486376 + ], + [ + 10938.956265363471, + 234.71261360187663 + ], + [ + 11079.199294406593, + 229.6695869320126 + ], + [ + 11219.442323449714, + 224.71755229244766 + ], + [ + 11359.685352492836, + 219.85615713573674 + ], + [ + 11499.928381535958, + 215.083348066388 + ], + [ + 11640.171410579078, + 210.39511561482527 + ], + [ + 11780.4144396222, + 205.78701518705378 + ], + [ + 11920.65746866532, + 201.25489960347528 + ], + [ + 12060.900497708442, + 196.80200784085224 + ], + [ + 12201.143526751564, + 192.4382991597197 + ], + [ + 12341.386555794685, + 188.1742431323793 + ], + [ + 12481.629584837807, + 184.02074248563613 + ], + [ + 12621.872613880929, + 179.9887795634578 + ], + [ + 12762.11564292405, + 176.0875680424421 + ], + [ + 12902.358671967171, + 172.3003194146568 + ], + [ + 13042.601701010293, + 168.59187024725 + ], + [ + 13182.844730053414, + 164.9269798323268 + ], + [ + 13323.087759096536, + 161.26970884046915 + ], + [ + 13463.330788139658, + 157.5839428417375 + ], + [ + 13603.573817182778, + 153.83354899670434 + ], + [ + 13743.8168462259, + 149.99204543669273 + ], + [ + 13884.059875269022, + 146.11738081059784 + ] + ] + }, + "atmosphericModelTemperatureProfile": { + "4": [ + [ + 0.0, + 14.175259311314452 + ], + [ + 140.24302904312142, + 17.295716394934587 + ], + [ + 280.48605808624285, + 16.530448964029105 + ], + [ + 420.7290871293643, + 16.037720891287307 + ], + [ + 560.9721161724857, + 15.653543977488612 + ], + [ + 701.2151452156071, + 14.848831673679008 + ], + [ + 841.4581742587286, + 14.011402600877574 + ], + [ + 981.70120330185, + 13.224359460867667 + ], + [ + 1121.9442323449714, + 12.423638547091466 + ], + [ + 1262.1872613880928, + 11.620501194477031 + ], + [ + 1402.4302904312142, + 10.851535929463582 + ], + [ + 1542.6733194743356, + 10.144078221860592 + ], + [ + 1682.9163485174572, + 9.392487846099426 + ], + [ + 1823.1593775605786, + 8.546752423531549 + ], + [ + 1963.4024066037, + 8.067031340087388 + ], + [ + 2103.645435646821, + 7.8657669799462795 + ], + [ + 2243.888464689943, + 6.113131917504724 + ], + [ + 2384.1314937330644, + 5.133037987489223 + ], + [ + 2524.3745227761856, + 5.35669670493324 + ], + [ + 2664.617551819307, + 5.0265261087819075 + ], + [ + 2804.8605808624284, + 4.2015992191246445 + ], + [ + 2945.10360990555, + 3.128138213553452 + ], + [ + 3085.346638948671, + 2.0533723560304917 + ], + [ + 3225.589667991793, + 1.1352247143159488 + ], + [ + 3365.8326970349144, + 0.34008140454190366 + ], + [ + 3506.0757260780356, + -0.39022555343092613 + ], + [ + 3646.318755121157, + -1.1144607432377014 + ], + [ + 3786.5617841642784, + -1.884380842678872 + ], + [ + 3926.8048132074, + -2.703279736814044 + ], + [ + 4067.047842250521, + -3.5585413553336718 + ], + [ + 4207.290871293642, + -4.437364664350579 + ], + [ + 4347.533900336764, + -5.325333877163843 + ], + [ + 4487.776929379886, + -6.212780049534784 + ], + [ + 4628.019958423007, + -7.100510699992798 + ], + [ + 4768.262987466129, + -7.990211619382933 + ], + [ + 4908.50601650925, + -8.884096476447688 + ], + [ + 5048.749045552371, + -9.786070283907211 + ], + [ + 5188.992074595492, + -10.698802949919434 + ], + [ + 5329.235103638614, + -11.62241613991998 + ], + [ + 5469.478132681736, + -12.55691299029333 + ], + [ + 5609.721161724857, + -13.501891751128046 + ], + [ + 5749.964190767979, + -14.455760585367294 + ], + [ + 5890.2072198111, + -15.41704773685224 + ], + [ + 6030.450248854221, + -16.3853931331675 + ], + [ + 6170.693277897342, + -17.360542088264165 + ], + [ + 6310.936306940464, + -18.34227692001798 + ], + [ + 6451.179335983586, + -19.330877445347127 + ], + [ + 6591.422365026707, + -20.327363888862628 + ], + [ + 6731.665394069829, + -21.332290344694492 + ], + [ + 6871.90842311295, + -22.345733195204403 + ], + [ + 7012.151452156071, + -23.367758909603168 + ], + [ + 7152.394481199192, + -24.398458982769995 + ], + [ + 7292.637510242314, + -25.437757778153042 + ], + [ + 7432.880539285436, + -26.484774652584765 + ], + [ + 7573.123568328557, + -27.538575189268943 + ], + [ + 7713.366597371678, + -28.598658226225737 + ], + [ + 7853.6096264148, + -29.66463687755255 + ], + [ + 7993.852655457921, + -30.736123952626112 + ], + [ + 8134.095684501042, + -31.81270048046802 + ], + [ + 8274.338713544164, + -32.893835476333486 + ], + [ + 8414.581742587285, + -33.97888756758776 + ], + [ + 8554.824771630407, + -35.06663665713244 + ], + [ + 8695.067800673529, + -36.15539412369939 + ], + [ + 8835.31082971665, + -37.243362041877624 + ], + [ + 8975.553858759771, + -38.32874166271946 + ], + [ + 9115.796887802893, + -39.40972629911297 + ], + [ + 9256.039916846014, + -40.48469353060315 + ], + [ + 9396.282945889136, + -41.55205790866637 + ], + [ + 9536.525974932258, + -42.60975942028931 + ], + [ + 9676.769003975378, + -43.65578277874309 + ], + [ + 9817.0120330185, + -44.68829336408379 + ], + [ + 9957.25506206162, + -45.70548801794614 + ], + [ + 10097.498091104742, + -46.70556358674669 + ], + [ + 10237.741120147864, + -47.68672194357082 + ], + [ + 10377.984149190985, + -48.64727570101173 + ], + [ + 10518.227178234107, + -49.585394878131595 + ], + [ + 10658.470207277229, + -50.49875780834453 + ], + [ + 10798.71323632035, + -51.38401252284466 + ], + [ + 10938.956265363471, + -52.236904671081994 + ], + [ + 11079.199294406593, + -53.05271765883682 + ], + [ + 11219.442323449714, + -53.828082028908184 + ], + [ + 11359.685352492836, + -54.56227117220025 + ], + [ + 11499.928381535958, + -55.25496746034089 + ], + [ + 11640.171410579078, + -55.90570782204889 + ], + [ + 11780.4144396222, + -56.51437315933034 + ], + [ + 11920.65746866532, + -57.080639615724955 + ], + [ + 12060.900497708442, + -57.60410088110795 + ], + [ + 12201.143526751564, + -58.08703094556553 + ], + [ + 12341.386555794685, + -58.5332752450212 + ], + [ + 12481.629584837807, + -58.946323034626126 + ], + [ + 12621.872613880929, + -59.32949239878881 + ], + [ + 12762.11564292405, + -59.68576501150805 + ], + [ + 12902.358671967171, + -60.01673498387926 + ], + [ + 13042.601701010293, + -60.32356063504205 + ], + [ + 13182.844730053414, + -60.608340605430705 + ], + [ + 13323.087759096536, + -60.873804088146386 + ], + [ + 13463.330788139658, + -61.12280594078468 + ], + [ + 13603.573817182778, + -61.35819914246126 + ], + [ + 13743.8168462259, + -61.58284989372839 + ], + [ + 13884.059875269022, + -61.79898269878744 + ] + ], + "5": [ + [ + 0.0, + 14.138125198676379 + ], + [ + 140.24302904312142, + 17.09488516818948 + ], + [ + 280.48605808624285, + 16.463511849393633 + ], + [ + 420.7290871293643, + 15.957273791195007 + ], + [ + 560.9721161724857, + 15.51497082281941 + ], + [ + 701.2151452156071, + 14.73359050836641 + ], + [ + 841.4581742587286, + 13.921414837435346 + ], + [ + 981.70120330185, + 13.137849105859644 + ], + [ + 1121.9442323449714, + 12.33832072169622 + ], + [ + 1262.1872613880928, + 11.547673930237611 + ], + [ + 1402.4302904312142, + 10.784342978037424 + ], + [ + 1542.6733194743356, + 10.043209699017883 + ], + [ + 1682.9163485174572, + 9.321130983311061 + ], + [ + 1823.1593775605786, + 8.61442390775449 + ], + [ + 1963.4024066037, + 7.912257479166359 + ], + [ + 2103.645435646821, + 7.204672328744908 + ], + [ + 2243.888464689943, + 6.490749356019002 + ], + [ + 2384.1314937330644, + 5.771408382594373 + ], + [ + 2524.3745227761856, + 5.044596459833482 + ], + [ + 2664.617551819307, + 4.3083935968074565 + ], + [ + 2804.8605808624284, + 3.5610503363950676 + ], + [ + 2945.10360990555, + 2.800859798983657 + ], + [ + 3085.346638948671, + 2.0272498098474587 + ], + [ + 3225.589667991793, + 1.2414351125876775 + ], + [ + 3365.8326970349144, + 0.445007886218908 + ], + [ + 3506.0757260780356, + -0.3603666460977387 + ], + [ + 3646.318755121157, + -1.1730558218091505 + ], + [ + 3786.5617841642784, + -1.9929386214782667 + ], + [ + 3926.8048132074, + -2.820976933244192 + ], + [ + 4067.047842250521, + -3.658329012767322 + ], + [ + 4207.290871293642, + -4.506257236820406 + ], + [ + 4347.533900336764, + -5.365455169090564 + ], + [ + 4487.776929379886, + -6.2352396326159125 + ], + [ + 4628.019958423007, + -7.114965137193785 + ], + [ + 4768.262987466129, + -8.003967698607733 + ], + [ + 4908.50601650925, + -8.901653714258996 + ], + [ + 5048.749045552371, + -9.808151978669486 + ], + [ + 5188.992074595492, + -10.72395854093643 + ], + [ + 5329.235103638614, + -11.649226399359247 + ], + [ + 5469.478132681736, + -12.584111386119046 + ], + [ + 5609.721161724857, + -13.528604670083315 + ], + [ + 5749.964190767979, + -14.482268027768827 + ], + [ + 5890.2072198111, + -15.443838529634265 + ], + [ + 6030.450248854221, + -16.412179870400937 + ], + [ + 6170.693277897342, + -17.386174255877062 + ], + [ + 6310.936306940464, + -18.364718716659382 + ], + [ + 6451.179335983586, + -19.346911915292907 + ], + [ + 6591.422365026707, + -20.333337372912393 + ], + [ + 6731.665394069829, + -21.327665897364657 + ], + [ + 6871.90842311295, + -22.333910772440944 + ], + [ + 7012.151452156071, + -23.356090225937624 + ], + [ + 7152.394481199192, + -24.39824460634607 + ], + [ + 7292.637510242314, + -25.46416906264437 + ], + [ + 7432.880539285436, + -26.555164022247112 + ], + [ + 7573.123568328557, + -27.660548982197714 + ], + [ + 7713.366597371678, + -28.764086808342668 + ], + [ + 7853.6096264148, + -29.849553528233084 + ], + [ + 7993.852655457921, + -30.904125823829194 + ], + [ + 8134.095684501042, + -31.937674202558892 + ], + [ + 8274.338713544164, + -32.96957141223876 + ], + [ + 8414.581742587285, + -34.01742360705201 + ], + [ + 8554.824771630407, + -35.08341092225727 + ], + [ + 8695.067800673529, + -36.162922374985435 + ], + [ + 8835.31082971665, + -37.25125589616692 + ], + [ + 8975.553858759771, + -38.34370838785821 + ], + [ + 9115.796887802893, + -39.435568269398814 + ], + [ + 9256.039916846014, + -40.52228425991317 + ], + [ + 9396.282945889136, + -41.59911331468485 + ], + [ + 9536.525974932258, + -42.66188234500959 + ], + [ + 9676.769003975378, + -43.70926200005772 + ], + [ + 9817.0120330185, + -44.740573848666166 + ], + [ + 9957.25506206162, + -45.755160823020326 + ], + [ + 10097.498091104742, + -46.75236585568098 + ], + [ + 10237.741120147864, + -47.731537169278184 + ], + [ + 10377.984149190985, + -48.69207661715003 + ], + [ + 10518.227178234107, + -49.63303496038241 + ], + [ + 10658.470207277229, + -50.552356117930685 + ], + [ + 10798.71323632035, + -51.445741595489174 + ], + [ + 10938.956265363471, + -52.30705634604121 + ], + [ + 11079.199294406593, + -53.12979529097771 + ], + [ + 11219.442323449714, + -53.90936324184275 + ], + [ + 11359.685352492836, + -54.64475881151337 + ], + [ + 11499.928381535958, + -55.33614614579748 + ], + [ + 11640.171410579078, + -55.98399625524354 + ], + [ + 11780.4144396222, + -56.58898909208766 + ], + [ + 11920.65746866532, + -57.15123694066944 + ], + [ + 12060.900497708442, + -57.670280754777934 + ], + [ + 12201.143526751564, + -58.14840501847161 + ], + [ + 12341.386555794685, + -58.589549989387486 + ], + [ + 12481.629584837807, + -58.99755758147348 + ], + [ + 12621.872613880929, + -59.376150607395275 + ], + [ + 12762.11564292405, + -59.7288738929266 + ], + [ + 12902.358671967171, + -60.05801090488873 + ], + [ + 13042.601701010293, + -60.36507052764016 + ], + [ + 13182.844730053414, + -60.65192092796515 + ], + [ + 13323.087759096536, + -60.920757629498645 + ], + [ + 13463.330788139658, + -61.17387280252821 + ], + [ + 13603.573817182778, + -61.413555712830906 + ], + [ + 13743.8168462259, + -61.64210316517283 + ], + [ + 13884.059875269022, + -61.861130397408616 + ] + ], + "6": [ + [ + 0.0, + 13.929301768465946 + ], + [ + 140.24302904312142, + 16.873861965551495 + ], + [ + 280.48605808624285, + 16.30389054335554 + ], + [ + 420.7290871293643, + 15.823924925397163 + ], + [ + 560.9721161724857, + 15.40076487978963 + ], + [ + 701.2151452156071, + 14.63881902720934 + ], + [ + 841.4581742587286, + 13.837747403587997 + ], + [ + 981.70120330185, + 13.05612789232172 + ], + [ + 1121.9442323449714, + 12.25692792878161 + ], + [ + 1262.1872613880928, + 11.472319793741718 + ], + [ + 1402.4302904312142, + 10.715951653705984 + ], + [ + 1542.6733194743356, + 9.97865739291744 + ], + [ + 1682.9163485174572, + 9.257306007522233 + ], + [ + 1823.1593775605786, + 8.54740073207231 + ], + [ + 1963.4024066037, + 7.839511696353455 + ], + [ + 2103.645435646821, + 7.12767921969139 + ], + [ + 2243.888464689943, + 6.41373065782864 + ], + [ + 2384.1314937330644, + 5.694872847037981 + ], + [ + 2524.3745227761856, + 4.9654801682240945 + ], + [ + 2664.617551819307, + 4.225502833133125 + ], + [ + 2804.8605808624284, + 3.4758601436758787 + ], + [ + 2945.10360990555, + 2.7173867102564033 + ], + [ + 3085.346638948671, + 1.9505526322317066 + ], + [ + 3225.589667991793, + 1.1742636716695054 + ], + [ + 3365.8326970349144, + 0.38772976783944657 + ], + [ + 3506.0757260780356, + -0.4096906375486564 + ], + [ + 3646.318755121157, + -1.2181097366824176 + ], + [ + 3786.5617841642784, + -2.0364523603654923 + ], + [ + 3926.8048132074, + -2.8639501585498643 + ], + [ + 4067.047842250521, + -3.700136543991831 + ], + [ + 4207.290871293642, + -4.544745686321959 + ], + [ + 4347.533900336764, + -5.39788256780796 + ], + [ + 4487.776929379886, + -6.259792605999382 + ], + [ + 4628.019958423007, + -7.130996822450298 + ], + [ + 4768.262987466129, + -8.011988753641898 + ], + [ + 4908.50601650925, + -8.90323751727886 + ], + [ + 5048.749045552371, + -9.805535246994092 + ], + [ + 5188.992074595492, + -10.719098261645946 + ], + [ + 5329.235103638614, + -11.643593383161724 + ], + [ + 5469.478132681736, + -12.5786891522638 + ], + [ + 5609.721161724857, + -13.52382965445508 + ], + [ + 5749.964190767979, + -14.477995323001622 + ], + [ + 5890.2072198111, + -15.44028528375247 + ], + [ + 6030.450248854221, + -16.41037527803099 + ], + [ + 6170.693277897342, + -17.38795501267005 + ], + [ + 6310.936306940464, + -18.372736428308908 + ], + [ + 6451.179335983586, + -19.364745315253884 + ], + [ + 6591.422365026707, + -20.364593080297745 + ], + [ + 6731.665394069829, + -21.372887101786073 + ], + [ + 6871.90842311295, + -22.39003649449726 + ], + [ + 7012.151452156071, + -23.416471891267246 + ], + [ + 7152.394481199192, + -24.45263140075731 + ], + [ + 7292.637510242314, + -25.498609662852218 + ], + [ + 7432.880539285436, + -26.553471753368825 + ], + [ + 7573.123568328557, + -27.616010353661224 + ], + [ + 7713.366597371678, + -28.685155604044848 + ], + [ + 7853.6096264148, + -29.75983094800438 + ], + [ + 7993.852655457921, + -30.838965486214367 + ], + [ + 8134.095684501042, + -31.921568590569578 + ], + [ + 8274.338713544164, + -33.00699262246949 + ], + [ + 8414.581742587285, + -34.09473684509071 + ], + [ + 8554.824771630407, + -35.18356093590864 + ], + [ + 8695.067800673529, + -36.27191062262328 + ], + [ + 8835.31082971665, + -37.35820187873499 + ], + [ + 8975.553858759771, + -38.440849551284266 + ], + [ + 9115.796887802893, + -39.518251712553386 + ], + [ + 9256.039916846014, + -40.58891827223128 + ], + [ + 9396.282945889136, + -41.65106272924923 + ], + [ + 9536.525974932258, + -42.7028640044571 + ], + [ + 9676.769003975378, + -43.74287878701951 + ], + [ + 9817.0120330185, + -44.76981749932741 + ], + [ + 9957.25506206162, + -45.78240851112076 + ], + [ + 10097.498091104742, + -46.77938019020501 + ], + [ + 10237.741120147864, + -47.759464074848474 + ], + [ + 10377.984149190985, + -48.72137902742221 + ], + [ + 10518.227178234107, + -49.66354206869321 + ], + [ + 10658.470207277229, + -50.58342168362875 + ], + [ + 10798.71323632035, + -51.476572852085276 + ], + [ + 10938.956265363471, + -52.33730762753689 + ], + [ + 11079.199294406593, + -53.15971464962961 + ], + [ + 11219.442323449714, + -53.93970838757587 + ], + [ + 11359.685352492836, + -54.67630356037419 + ], + [ + 11499.928381535958, + -55.36923303839756 + ], + [ + 11640.171410579078, + -56.01842903921144 + ], + [ + 11780.4144396222, + -56.62399740750897 + ], + [ + 11920.65746866532, + -57.18542679596536 + ], + [ + 12060.900497708442, + -57.70219086536978 + ], + [ + 12201.143526751564, + -58.17716213023786 + ], + [ + 12341.386555794685, + -58.61472161168456 + ], + [ + 12481.629584837807, + -59.01908183664861 + ], + [ + 12621.872613880929, + -59.39434585123191 + ], + [ + 12762.11564292405, + -59.74447793051505 + ], + [ + 12902.358671967171, + -60.0718108668695 + ], + [ + 13042.601701010293, + -60.377623105975516 + ], + [ + 13182.844730053414, + -60.66363039871289 + ], + [ + 13323.087759096536, + -60.93193634384482 + ], + [ + 13463.330788139658, + -61.18474433536178 + ], + [ + 13603.573817182778, + -61.424255346388534 + ], + [ + 13743.8168462259, + -61.6526804696957 + ], + [ + 13884.059875269022, + -61.871611678546124 + ] + ], + "7": [ + [ + 0.0, + 13.673615631342681 + ], + [ + 140.24302904312142, + 16.645655907514584 + ], + [ + 280.48605808624285, + 16.146128760651337 + ], + [ + 420.7290871293643, + 15.688051637079816 + ], + [ + 560.9721161724857, + 15.280571673575713 + ], + [ + 701.2151452156071, + 14.529101255479109 + ], + [ + 841.4581742587286, + 13.737439355256202 + ], + [ + 981.70120330185, + 12.973158051624923 + ], + [ + 1121.9442323449714, + 12.189111140150311 + ], + [ + 1262.1872613880928, + 11.408318993965608 + ], + [ + 1402.4302904312142, + 10.65142864493131 + ], + [ + 1542.6733194743356, + 9.915648265892456 + ], + [ + 1682.9163485174572, + 9.193918364012411 + ], + [ + 1823.1593775605786, + 8.479918095870744 + ], + [ + 1963.4024066037, + 7.767976419648315 + ], + [ + 2103.645435646821, + 7.053055083434536 + ], + [ + 2243.888464689943, + 6.337034757365256 + ], + [ + 2384.1314937330644, + 5.618382560004483 + ], + [ + 2524.3745227761856, + 4.8910634061388905 + ], + [ + 2664.617551819307, + 4.154134631166824 + ], + [ + 2804.8605808624284, + 3.4082065051445816 + ], + [ + 2945.10360990555, + 2.653798748806663 + ], + [ + 3085.346638948671, + 1.8913232759509828 + ], + [ + 3225.589667991793, + 1.1196203631159805 + ], + [ + 3365.8326970349144, + 0.33787190813733936 + ], + [ + 3506.0757260780356, + -0.45455294781438077 + ], + [ + 3646.318755121157, + -1.2577831668483834 + ], + [ + 3786.5617841642784, + -2.0706562021894395 + ], + [ + 3926.8048132074, + -2.892557963746351 + ], + [ + 4067.047842250521, + -3.7232685375385706 + ], + [ + 4207.290871293642, + -4.562770885167765 + ], + [ + 4347.533900336764, + -5.411383622162297 + ], + [ + 4487.776929379886, + -6.269520960062765 + ], + [ + 4628.019958423007, + -7.137690587681367 + ], + [ + 4768.262987466129, + -8.016375644996087 + ], + [ + 4908.50601650925, + -8.905978916760644 + ], + [ + 5048.749045552371, + -9.807068751539083 + ], + [ + 5188.992074595492, + -10.71951124696254 + ], + [ + 5329.235103638614, + -11.642602732403791 + ], + [ + 5469.478132681736, + -12.575636821537813 + ], + [ + 5609.721161724857, + -13.51777674406993 + ], + [ + 5749.964190767979, + -14.468198378210316 + ], + [ + 5890.2072198111, + -15.426657514204747 + ], + [ + 6030.450248854221, + -16.393843960443927 + ], + [ + 6170.693277897342, + -17.370521258835222 + ], + [ + 6310.936306940464, + -18.357462235773806 + ], + [ + 6451.179335983586, + -19.355394361439277 + ], + [ + 6591.422365026707, + -20.36473999981922 + ], + [ + 6731.665394069829, + -21.38488710652628 + ], + [ + 6871.90842311295, + -22.413926129680167 + ], + [ + 7012.151452156071, + -23.449901952155255 + ], + [ + 7152.394481199192, + -24.490849801594223 + ], + [ + 7292.637510242314, + -25.53467068211672 + ], + [ + 7432.880539285436, + -26.579028948656898 + ], + [ + 7573.123568328557, + -27.623422135314176 + ], + [ + 7713.366597371678, + -28.671970039088972 + ], + [ + 7853.6096264148, + -29.72941192087984 + ], + [ + 7993.852655457921, + -30.800496425494714 + ], + [ + 8134.095684501042, + -31.89014967672849 + ], + [ + 8274.338713544164, + -33.00354429807395 + ], + [ + 8414.581742587285, + -34.145486077587904 + ], + [ + 8554.824771630407, + -35.31139499173739 + ], + [ + 8695.067800673529, + -36.4829909832415 + ], + [ + 8835.31082971665, + -37.64083013808771 + ], + [ + 8975.553858759771, + -38.76546752906805 + ], + [ + 9115.796887802893, + -39.83741274332537 + ], + [ + 9256.039916846014, + -40.837091876733666 + ], + [ + 9396.282945889136, + -41.744796923980694 + ], + [ + 9536.525974932258, + -42.6200592030985 + ], + [ + 9676.769003975378, + -43.5543957540953 + ], + [ + 9817.0120330185, + -44.5358903970702 + ], + [ + 9957.25506206162, + -45.55054286722065 + ], + [ + 10097.498091104742, + -46.58435289599161 + ], + [ + 10237.741120147864, + -47.62332236414276 + ], + [ + 10377.984149190985, + -48.65352323003519 + ], + [ + 10518.227178234107, + -49.66104146972744 + ], + [ + 10658.470207277229, + -50.63108082683028 + ], + [ + 10798.71323632035, + -51.54990991035411 + ], + [ + 10938.956265363471, + -52.415757972076584 + ], + [ + 11079.199294406593, + -53.22994527237427 + ], + [ + 11219.442323449714, + -53.99520064056301 + ], + [ + 11359.685352492836, + -54.717193318113075 + ], + [ + 11499.928381535958, + -55.400851108300614 + ], + [ + 11640.171410579078, + -56.04545299253427 + ], + [ + 11780.4144396222, + -56.64943597204551 + ], + [ + 11920.65746866532, + -57.210804144512416 + ], + [ + 12060.900497708442, + -57.7280198312003 + ], + [ + 12201.143526751564, + -58.20264281089885 + ], + [ + 12341.386555794685, + -58.638717783529245 + ], + [ + 12481.629584837807, + -59.04067496107819 + ], + [ + 12621.872613880929, + -59.41280279860152 + ], + [ + 12762.11564292405, + -59.75923740221932 + ], + [ + 12902.358671967171, + -60.082369589161004 + ], + [ + 13042.601701010293, + -60.38382274134201 + ], + [ + 13182.844730053414, + -60.66559993453687 + ], + [ + 13323.087759096536, + -60.930065463008766 + ], + [ + 13463.330788139658, + -61.179707737015114 + ], + [ + 13603.573817182778, + -61.41701040239759 + ], + [ + 13743.8168462259, + -61.644399234641796 + ], + [ + 13884.059875269022, + -61.863543077836965 + ] + ], + "8": [ + [ + 0.0, + 13.439975090308216 + ], + [ + 140.24302904312142, + 16.42396506638434 + ], + [ + 280.48605808624285, + 15.980257899769759 + ], + [ + 420.7290871293643, + 15.550944320790878 + ], + [ + 560.9721161724857, + 15.165982684827359 + ], + [ + 701.2151452156071, + 14.42899860550639 + ], + [ + 841.4581742587286, + 13.646491746917551 + ], + [ + 981.70120330185, + 12.89217468116854 + ], + [ + 1121.9442323449714, + 12.11662858273703 + ], + [ + 1262.1872613880928, + 11.337607093806998 + ], + [ + 1402.4302904312142, + 10.57938890789212 + ], + [ + 1542.6733194743356, + 9.844751799148943 + ], + [ + 1682.9163485174572, + 9.12510604243066 + ], + [ + 1823.1593775605786, + 8.410607360238048 + ], + [ + 1963.4024066037, + 7.6992334484310145 + ], + [ + 2103.645435646821, + 6.992859306646033 + ], + [ + 2243.888464689943, + 6.292511539527964 + ], + [ + 2384.1314937330644, + 5.587564554064161 + ], + [ + 2524.3745227761856, + 4.86491461564629 + ], + [ + 2664.617551819307, + 4.124253732343681 + ], + [ + 2804.8605808624284, + 3.3701123790017746 + ], + [ + 2945.10360990555, + 2.6070970963148614 + ], + [ + 3085.346638948671, + 1.8388747924251276 + ], + [ + 3225.589667991793, + 1.0657308797288352 + ], + [ + 3365.8326970349144, + 0.28694962673260804 + ], + [ + 3506.0757260780356, + -0.4981231497528397 + ], + [ + 3646.318755121157, + -1.2900652517645128 + ], + [ + 3786.5617841642784, + -2.0895945732090957 + ], + [ + 3926.8048132074, + -2.898307388649384 + ], + [ + 4067.047842250521, + -3.718125932128851 + ], + [ + 4207.290871293642, + -4.550955890896724 + ], + [ + 4347.533900336764, + -5.397591292104299 + ], + [ + 4487.776929379886, + -6.257286104212743 + ], + [ + 4628.019958423007, + -7.128977165280812 + ], + [ + 4768.262987466129, + -8.01158200439952 + ], + [ + 4908.50601650925, + -8.904048878422604 + ], + [ + 5048.749045552371, + -9.806254662728053 + ], + [ + 5188.992074595492, + -10.718391184865778 + ], + [ + 5329.235103638614, + -11.640434151492862 + ], + [ + 5469.478132681736, + -12.572362002674057 + ], + [ + 5609.721161724857, + -13.514150406174855 + ], + [ + 5749.964190767979, + -14.465757790096 + ], + [ + 5890.2072198111, + -15.426926459439143 + ], + [ + 6030.450248854221, + -16.39766338553882 + ], + [ + 6170.693277897342, + -17.37798253379735 + ], + [ + 6310.936306940464, + -18.367890587522137 + ], + [ + 6451.179335983586, + -19.36710391240684 + ], + [ + 6591.422365026707, + -20.37523735794567 + ], + [ + 6731.665394069829, + -21.39221740753284 + ], + [ + 6871.90842311295, + -22.417866550085567 + ], + [ + 7012.151452156071, + -23.452027676250825 + ], + [ + 7152.394481199192, + -24.494527664837403 + ], + [ + 7292.637510242314, + -25.545205781568132 + ], + [ + 7432.880539285436, + -26.603695204118985 + ], + [ + 7573.123568328557, + -27.669030159816096 + ], + [ + 7713.366597371678, + -28.74012929469939 + ], + [ + 7853.6096264148, + -29.815892925427203 + ], + [ + 7993.852655457921, + -30.895231250347827 + ], + [ + 8134.095684501042, + -31.977269654785594 + ], + [ + 8274.338713544164, + -33.06128438678576 + ], + [ + 8414.581742587285, + -34.146443093604645 + ], + [ + 8554.824771630407, + -35.231920552393746 + ], + [ + 8695.067800673529, + -36.31687511370732 + ], + [ + 8835.31082971665, + -37.40044696528721 + ], + [ + 8975.553858759771, + -38.481775518158464 + ], + [ + 9115.796887802893, + -39.55994334951268 + ], + [ + 9256.039916846014, + -40.63384819838712 + ], + [ + 9396.282945889136, + -41.702115219260946 + ], + [ + 9536.525974932258, + -42.762820770849025 + ], + [ + 9676.769003975378, + -43.81364430129137 + ], + [ + 9817.0120330185, + -44.85225392972727 + ], + [ + 9957.25506206162, + -45.87633664063846 + ], + [ + 10097.498091104742, + -46.8835794152725 + ], + [ + 10237.741120147864, + -47.871670667123546 + ], + [ + 10377.984149190985, + -48.838445546756525 + ], + [ + 10518.227178234107, + -49.78191566859445 + ], + [ + 10658.470207277229, + -50.69949855810723 + ], + [ + 10798.71323632035, + -51.587344402108506 + ], + [ + 10938.956265363471, + -52.44105291006632 + ], + [ + 11079.199294406593, + -53.2559495508058 + ], + [ + 11219.442323449714, + -54.028650436818666 + ], + [ + 11359.685352492836, + -54.75848669877835 + ], + [ + 11499.928381535958, + -55.44562533840582 + ], + [ + 11640.171410579078, + -56.0899415875718 + ], + [ + 11780.4144396222, + -56.691423001513066 + ], + [ + 11920.65746866532, + -57.249631862616965 + ], + [ + 12060.900497708442, + -57.76434552151923 + ], + [ + 12201.143526751564, + -58.237776818502056 + ], + [ + 12341.386555794685, + -58.673364764986765 + ], + [ + 12481.629584837807, + -59.07448393686376 + ], + [ + 12621.872613880929, + -59.44437593777821 + ], + [ + 12762.11564292405, + -59.78618903005166 + ], + [ + 12902.358671967171, + -60.10191321787451 + ], + [ + 13042.601701010293, + -60.3937163370867 + ], + [ + 13182.844730053414, + -60.66466998062768 + ], + [ + 13323.087759096536, + -60.91821733994445 + ], + [ + 13463.330788139658, + -61.15792228380865 + ], + [ + 13603.573817182778, + -61.38734054262224 + ], + [ + 13743.8168462259, + -61.60987423310999 + ], + [ + 13884.059875269022, + -61.82766813100526 + ] + ], + "9": [ + [ + 0.0, + 13.366333636786528 + ], + [ + 140.24302904312142, + 16.37753599105018 + ], + [ + 280.48605808624285, + 15.885802489272303 + ], + [ + 420.7290871293643, + 15.45068768994212 + ], + [ + 560.9721161724857, + 15.10102651325885 + ], + [ + 701.2151452156071, + 14.377773909648257 + ], + [ + 841.4581742587286, + 13.602468842048232 + ], + [ + 981.70120330185, + 12.85202581532939 + ], + [ + 1121.9442323449714, + 12.074739070072491 + ], + [ + 1262.1872613880928, + 11.295559000432048 + ], + [ + 1402.4302904312142, + 10.543300397611102 + ], + [ + 1542.6733194743356, + 9.817057198337402 + ], + [ + 1682.9163485174572, + 9.103076842032282 + ], + [ + 1823.1593775605786, + 8.391510394604055 + ], + [ + 1963.4024066037, + 7.6830567553132605 + ], + [ + 2103.645435646821, + 6.980662577070627 + ], + [ + 2243.888464689943, + 6.2847120765763655 + ], + [ + 2384.1314937330644, + 5.5849160548899714 + ], + [ + 2524.3745227761856, + 4.866562867529805 + ], + [ + 2664.617551819307, + 4.127534767113113 + ], + [ + 2804.8605808624284, + 3.3730937010519035 + ], + [ + 2945.10360990555, + 2.6089315281960057 + ], + [ + 3085.346638948671, + 1.8400835471946542 + ], + [ + 3225.589667991793, + 1.0679954815192392 + ], + [ + 3365.8326970349144, + 0.29164877281405005 + ], + [ + 3506.0757260780356, + -0.4900973056969368 + ], + [ + 3646.318755121157, + -1.278184133432578 + ], + [ + 3786.5617841642784, + -2.0737602977035614 + ], + [ + 3926.8048132074, + -2.878617568451602 + ], + [ + 4067.047842250521, + -3.6947556116470395 + ], + [ + 4207.290871293642, + -4.524197061482213 + ], + [ + 4347.533900336764, + -5.368326651097557 + ], + [ + 4487.776929379886, + -6.226662343896609 + ], + [ + 4628.019958423007, + -7.0980742307780735 + ], + [ + 4768.262987466129, + -7.981380588055163 + ], + [ + 4908.50601650925, + -8.875294664634236 + ], + [ + 5048.749045552371, + -9.779010340641697 + ], + [ + 5188.992074595492, + -10.692319172987697 + ], + [ + 5329.235103638614, + -11.615120877083934 + ], + [ + 5469.478132681736, + -12.547341916624353 + ], + [ + 5609.721161724857, + -13.488982068589888 + ], + [ + 5749.964190767979, + -14.44023141778707 + ], + [ + 5890.2072198111, + -15.40112726322349 + ], + [ + 6030.450248854221, + -16.37187517803082 + ], + [ + 6170.693277897342, + -17.35267118924275 + ], + [ + 6310.936306940464, + -18.343687328550747 + ], + [ + 6451.179335983586, + -19.344683961448553 + ], + [ + 6591.422365026707, + -20.355150000618526 + ], + [ + 6731.665394069829, + -21.37462321474352 + ], + [ + 6871.90842311295, + -22.402381064331763 + ], + [ + 7012.151452156071, + -23.437707312410396 + ], + [ + 7152.394481199192, + -24.479899863771994 + ], + [ + 7292.637510242314, + -25.528552822858458 + ], + [ + 7432.880539285436, + -26.583408861053417 + ], + [ + 7573.123568328557, + -27.643961726722786 + ], + [ + 7713.366597371678, + -28.709858113624236 + ], + [ + 7853.6096264148, + -29.780775406399734 + ], + [ + 7993.852655457921, + -30.856398850478147 + ], + [ + 8134.095684501042, + -31.936528639234893 + ], + [ + 8274.338713544164, + -33.02068406652092 + ], + [ + 8414.581742587285, + -34.107939934429965 + ], + [ + 8554.824771630407, + -35.197206308613104 + ], + [ + 8695.067800673529, + -36.28720220471348 + ], + [ + 8835.31082971665, + -37.37660183716955 + ], + [ + 8975.553858759771, + -38.464078745820686 + ], + [ + 9115.796887802893, + -39.54826597312209 + ], + [ + 9256.039916846014, + -40.62774768877627 + ], + [ + 9396.282945889136, + -41.701069564641415 + ], + [ + 9536.525974932258, + -42.76621713507454 + ], + [ + 9676.769003975378, + -43.820732546551 + ], + [ + 9817.0120330185, + -44.86220135322402 + ], + [ + 9957.25506206162, + -45.888231478254966 + ], + [ + 10097.498091104742, + -46.89643084080876 + ], + [ + 10237.741120147864, + -47.884406881406136 + ], + [ + 10377.984149190985, + -48.84990475434932 + ], + [ + 10518.227178234107, + -49.79092065632556 + ], + [ + 10658.470207277229, + -50.705121050792876 + ], + [ + 10798.71323632035, + -51.589305648173756 + ], + [ + 10938.956265363471, + -52.43977687147158 + ], + [ + 11079.199294406593, + -53.252489707776576 + ], + [ + 11219.442323449714, + -54.024483664028956 + ], + [ + 11359.685352492836, + -54.75500610395624 + ], + [ + 11499.928381535958, + -55.443779884950594 + ], + [ + 11640.171410579078, + -56.090345722180444 + ], + [ + 11780.4144396222, + -56.69439764890102 + ], + [ + 11920.65746866532, + -57.25524457041759 + ], + [ + 12060.900497708442, + -57.77234419744657 + ], + [ + 12201.143526751564, + -58.24777187739389 + ], + [ + 12341.386555794685, + -58.684495673142244 + ], + [ + 12481.629584837807, + -59.08538042812646 + ], + [ + 12621.872613880929, + -59.453151118650695 + ], + [ + 12762.11564292405, + -59.79047299507181 + ], + [ + 12902.358671967171, + -60.09922198927275 + ], + [ + 13042.601701010293, + -60.381917978000274 + ], + [ + 13182.844730053414, + -60.64254550507044 + ], + [ + 13323.087759096536, + -60.88545291404674 + ], + [ + 13463.330788139658, + -61.115108571293234 + ], + [ + 13603.573817182778, + -61.335973559626616 + ], + [ + 13743.8168462259, + -61.55232098730251 + ], + [ + 13884.059875269022, + -61.76691831802591 + ] + ], + "10": [ + [ + 0.0, + 14.007651641143813 + ], + [ + 140.24302904312142, + 16.855972867703485 + ], + [ + 280.48605808624285, + 15.879474050533865 + ], + [ + 420.7290871293643, + 15.369456172954717 + ], + [ + 560.9721161724857, + 15.1049870476255 + ], + [ + 701.2151452156071, + 14.375985198097196 + ], + [ + 841.4581742587286, + 13.591594178665757 + ], + [ + 981.70120330185, + 12.852018317743246 + ], + [ + 1121.9442323449714, + 12.083151650005185 + ], + [ + 1262.1872613880928, + 11.314556668113578 + ], + [ + 1402.4302904312142, + 10.572828508415716 + ], + [ + 1542.6733194743356, + 9.848701516067273 + ], + [ + 1682.9163485174572, + 9.134424494790888 + ], + [ + 1823.1593775605786, + 8.425270040470963 + ], + [ + 1963.4024066037, + 7.717258762667752 + ], + [ + 2103.645435646821, + 7.012366728232801 + ], + [ + 2243.888464689943, + 6.315166589917552 + ], + [ + 2384.1314937330644, + 5.615937937226838 + ], + [ + 2524.3745227761856, + 4.8972771070135845 + ], + [ + 2664.617551819307, + 4.156128961837682 + ], + [ + 2804.8605808624284, + 3.398745174478204 + ], + [ + 2945.10360990555, + 2.6319607333057493 + ], + [ + 3085.346638948671, + 1.861991770513282 + ], + [ + 3225.589667991793, + 1.0906795697883034 + ], + [ + 3365.8326970349144, + 0.3162999439558255 + ], + [ + 3506.0757260780356, + -0.46312591795566094 + ], + [ + 3646.318755121157, + -1.2493990519460667 + ], + [ + 3786.5617841642784, + -2.043582978863867 + ], + [ + 3926.8048132074, + -2.8465316515955656 + ], + [ + 4067.047842250521, + -3.6592176690579317 + ], + [ + 4207.290871293642, + -4.4825605393231855 + ], + [ + 4347.533900336764, + -5.31773009747944 + ], + [ + 4487.776929379886, + -6.166838651699662 + ], + [ + 4628.019958423007, + -7.031843392239087 + ], + [ + 4768.262987466129, + -7.914611346976902 + ], + [ + 4908.50601650925, + -8.816980291805159 + ], + [ + 5048.749045552371, + -9.738798541643083 + ], + [ + 5188.992074595492, + -10.672430994059024 + ], + [ + 5329.235103638614, + -11.60873378787498 + ], + [ + 5469.478132681736, + -12.538622681853663 + ], + [ + 5609.721161724857, + -13.452998442364846 + ], + [ + 5749.964190767979, + -14.344999076675306 + ], + [ + 5890.2072198111, + -15.229868810233773 + ], + [ + 6030.450248854221, + -16.136556328591546 + ], + [ + 6170.693277897342, + -17.094156599714353 + ], + [ + 6310.936306940464, + -18.13176519865202 + ], + [ + 6451.179335983586, + -19.278180572530122 + ], + [ + 6591.422365026707, + -20.50773295261634 + ], + [ + 6731.665394069829, + -21.64010807552467 + ], + [ + 6871.90842311295, + -22.674446858732306 + ], + [ + 7012.151452156071, + -23.64404910763993 + ], + [ + 7152.394481199192, + -24.582221351884282 + ], + [ + 7292.637510242314, + -25.52256323369367 + ], + [ + 7432.880539285436, + -26.4970654923205 + ], + [ + 7573.123568328557, + -27.5141442695975 + ], + [ + 7713.366597371678, + -28.566298562578236 + ], + [ + 7853.6096264148, + -29.64574350859001 + ], + [ + 7993.852655457921, + -30.744701180106375 + ], + [ + 8134.095684501042, + -31.855514393517446 + ], + [ + 8274.338713544164, + -32.97012127756341 + ], + [ + 8414.581742587285, + -34.08046961374779 + ], + [ + 8554.824771630407, + -35.18393198589577 + ], + [ + 8695.067800673529, + -36.28070478756984 + ], + [ + 8835.31082971665, + -37.37097262012957 + ], + [ + 8975.553858759771, + -38.45491964619084 + ], + [ + 9115.796887802893, + -39.53267912337279 + ], + [ + 9256.039916846014, + -40.604352450636554 + ], + [ + 9396.282945889136, + -41.6703052492471 + ], + [ + 9536.525974932258, + -42.73008335906111 + ], + [ + 9676.769003975378, + -43.78129264582174 + ], + [ + 9817.0120330185, + -44.82123450433415 + ], + [ + 9957.25506206162, + -45.84723348832387 + ], + [ + 10097.498091104742, + -46.85661414829951 + ], + [ + 10237.741120147864, + -47.84670169335407 + ], + [ + 10377.984149190985, + -48.81495823486405 + ], + [ + 10518.227178234107, + -49.75897819514579 + ], + [ + 10658.470207277229, + -50.67574658515212 + ], + [ + 10798.71323632035, + -51.561776561238325 + ], + [ + 10938.956265363471, + -52.41373016184835 + ], + [ + 11079.199294406593, + -53.22796389017961 + ], + [ + 11219.442323449714, + -54.00187058312883 + ], + [ + 11359.685352492836, + -54.73508553463019 + ], + [ + 11499.928381535958, + -55.427112628925684 + ], + [ + 11640.171410579078, + -56.076928343744726 + ], + [ + 11780.4144396222, + -56.6837306387561 + ], + [ + 11920.65746866532, + -57.246380180654484 + ], + [ + 12060.900497708442, + -57.763887877704306 + ], + [ + 12201.143526751564, + -58.238348034110786 + ], + [ + 12341.386555794685, + -58.672912810848274 + ], + [ + 12481.629584837807, + -59.070449146210606 + ], + [ + 12621.872613880929, + -59.433656125703784 + ], + [ + 12762.11564292405, + -59.76517909820773 + ], + [ + 12902.358671967171, + -60.06694928888581 + ], + [ + 13042.601701010293, + -60.34155517787166 + ], + [ + 13182.844730053414, + -60.593484233337485 + ], + [ + 13323.087759096536, + -60.827801713460524 + ], + [ + 13463.330788139658, + -61.0497022130597 + ], + [ + 13603.573817182778, + -61.26437541749402 + ], + [ + 13743.8168462259, + -61.47682835710176 + ], + [ + 13884.059875269022, + -61.69045331670816 + ] + ], + "11": [ + [ + 0.0, + 14.894122922155082 + ], + [ + 140.24302904312142, + 17.81456107530076 + ], + [ + 280.48605808624285, + 16.046363525017714 + ], + [ + 420.7290871293643, + 15.295676370699573 + ], + [ + 560.9721161724857, + 15.13422650305392 + ], + [ + 701.2151452156071, + 14.450939400783756 + ], + [ + 841.4581742587286, + 13.684427299257639 + ], + [ + 981.70120330185, + 12.968241443827967 + ], + [ + 1121.9442323449714, + 12.200263738729994 + ], + [ + 1262.1872613880928, + 11.406227323500485 + ], + [ + 1402.4302904312142, + 10.643474322459602 + ], + [ + 1542.6733194743356, + 9.942191675169985 + ], + [ + 1682.9163485174572, + 9.224054991849457 + ], + [ + 1823.1593775605786, + 8.412841095006568 + ], + [ + 1963.4024066037, + 7.877298666118253 + ], + [ + 2103.645435646821, + 7.685917579342196 + ], + [ + 2243.888464689943, + 6.263507994484058 + ], + [ + 2384.1314937330644, + 5.223956078225329 + ], + [ + 2524.3745227761856, + 5.245225859302435 + ], + [ + 2664.617551819307, + 4.860024126166705 + ], + [ + 2804.8605808624284, + 4.067174099779252 + ], + [ + 2945.10360990555, + 3.0635206789252036 + ], + [ + 3085.346638948671, + 2.0458783751216383 + ], + [ + 3225.589667991793, + 1.1522409363896622 + ], + [ + 3365.8326970349144, + 0.3617669912888305 + ], + [ + 3506.0757260780356, + -0.3732733106161411 + ], + [ + 3646.318755121157, + -1.100525214490836 + ], + [ + 3786.5617841642784, + -1.8631265796892236 + ], + [ + 3926.8048132074, + -2.667430923441917 + ], + [ + 4067.047842250521, + -3.5032905247095387 + ], + [ + 4207.290871293642, + -4.360555478331689 + ], + [ + 4347.533900336764, + -5.2290127618662385 + ], + [ + 4487.776929379886, + -6.1011302141992605 + ], + [ + 4628.019958423007, + -6.977726478672097 + ], + [ + 4768.262987466129, + -7.860787473299499 + ], + [ + 4908.50601650925, + -8.752172357950867 + ], + [ + 5048.749045552371, + -9.653745233080025 + ], + [ + 5188.992074595492, + -10.56685735067322 + ], + [ + 5329.235103638614, + -11.49132022868061 + ], + [ + 5469.478132681736, + -12.42680106884964 + ], + [ + 5609.721161724857, + -13.372981521580739 + ], + [ + 5749.964190767979, + -14.329611506872716 + ], + [ + 5890.2072198111, + -15.296434931623658 + ], + [ + 6030.450248854221, + -16.273519820218336 + ], + [ + 6170.693277897342, + -17.26098407126189 + ], + [ + 6310.936306940464, + -18.258928516016653 + ], + [ + 6451.179335983586, + -19.267135906276998 + ], + [ + 6591.422365026707, + -20.285120327548817 + ], + [ + 6731.665394069829, + -21.312154970992584 + ], + [ + 6871.90842311295, + -22.34707751671252 + ], + [ + 7012.151452156071, + -23.388700928568227 + ], + [ + 7152.394481199192, + -24.435872413054767 + ], + [ + 7292.637510242314, + -25.48777868161968 + ], + [ + 7432.880539285436, + -26.543814784246187 + ], + [ + 7573.123568328557, + -27.603365104630686 + ], + [ + 7713.366597371678, + -28.66635632935968 + ], + [ + 7853.6096264148, + -29.732813080470436 + ], + [ + 7993.852655457921, + -30.802764437352113 + ], + [ + 8134.095684501042, + -31.8763392685312 + ], + [ + 8274.338713544164, + -32.953403423616784 + ], + [ + 8414.581742587285, + -34.033371758605874 + ], + [ + 8554.824771630407, + -35.11542661786044 + ], + [ + 8695.067800673529, + -36.19843255607588 + ], + [ + 8835.31082971665, + -37.281200200830355 + ], + [ + 8975.553858759771, + -38.36253952422234 + ], + [ + 9115.796887802893, + -39.44119939026685 + ], + [ + 9256.039916846014, + -40.515792024237534 + ], + [ + 9396.282945889136, + -41.58512996197721 + ], + [ + 9536.525974932258, + -42.6475621899418 + ], + [ + 9676.769003975378, + -43.70072491974454 + ], + [ + 9817.0120330185, + -44.74218106794888 + ], + [ + 9957.25506206162, + -45.76950453244601 + ], + [ + 10097.498091104742, + -46.780269194592606 + ], + [ + 10237.741120147864, + -47.77205263840363 + ], + [ + 10377.984149190985, + -48.74264077374109 + ], + [ + 10518.227178234107, + -49.6899837862604 + ], + [ + 10658.470207277229, + -50.61096100973117 + ], + [ + 10798.71323632035, + -51.50176481530681 + ], + [ + 10938.956265363471, + -52.35879675250297 + ], + [ + 11079.199294406593, + -53.17828399763296 + ], + [ + 11219.442323449714, + -53.95753760636859 + ], + [ + 11359.685352492836, + -54.696858177135454 + ], + [ + 11499.928381535958, + -55.39617176726915 + ], + [ + 11640.171410579078, + -56.05389307797794 + ], + [ + 11780.4144396222, + -56.66831054824741 + ], + [ + 11920.65746866532, + -57.237422166512474 + ], + [ + 12060.900497708442, + -57.75899189726779 + ], + [ + 12201.143526751564, + -58.23400323554154 + ], + [ + 12341.386555794685, + -58.665822049110595 + ], + [ + 12481.629584837807, + -59.058096525319826 + ], + [ + 12621.872613880929, + -59.41436865120723 + ], + [ + 12762.11564292405, + -59.73807778260826 + ], + [ + 12902.358671967171, + -60.0320259201687 + ], + [ + 13042.601701010293, + -60.299520449913416 + ], + [ + 13182.844730053414, + -60.545515049096686 + ], + [ + 13323.087759096536, + -60.775431365955676 + ], + [ + 13463.330788139658, + -60.994804617952596 + ], + [ + 13603.573817182778, + -61.209169289270434 + ], + [ + 13743.8168462259, + -61.423922488260374 + ], + [ + 13884.059875269022, + -61.642868656163955 + ] + ], + "12": [ + [ + 0.0, + 15.800937669398726 + ], + [ + 140.24302904312142, + 18.636706719669508 + ], + [ + 280.48605808624285, + 16.931941633920047 + ], + [ + 420.7290871293643, + 15.71253801874122 + ], + [ + 560.9721161724857, + 15.154891377285738 + ], + [ + 701.2151452156071, + 14.445821053542172 + ], + [ + 841.4581742587286, + 13.709598293012146 + ], + [ + 981.70120330185, + 12.97195297591722 + ], + [ + 1121.9442323449714, + 12.193704036802457 + ], + [ + 1262.1872613880928, + 11.419293912519624 + ], + [ + 1402.4302904312142, + 10.683985096722047 + ], + [ + 1542.6733194743356, + 10.002147278261406 + ], + [ + 1682.9163485174572, + 9.284400688989217 + ], + [ + 1823.1593775605786, + 8.461335482706447 + ], + [ + 1963.4024066037, + 7.913656956893875 + ], + [ + 2103.645435646821, + 7.7092225871386555 + ], + [ + 2243.888464689943, + 6.285998505797902 + ], + [ + 2384.1314937330644, + 5.253618974417907 + ], + [ + 2524.3745227761856, + 5.2696771963608855 + ], + [ + 2664.617551819307, + 4.881758846720184 + ], + [ + 2804.8605808624284, + 4.089686199946728 + ], + [ + 2945.10360990555, + 3.0875985640313535 + ], + [ + 3085.346638948671, + 2.0693641018306823 + ], + [ + 3225.589667991793, + 1.170866351553763 + ], + [ + 3365.8326970349144, + 0.3747922556272035 + ], + [ + 3506.0757260780356, + -0.3616811396925992 + ], + [ + 3646.318755121157, + -1.0813850092308843 + ], + [ + 3786.5617841642784, + -1.8236932013386935 + ], + [ + 3926.8048132074, + -2.6034826210159885 + ], + [ + 4067.047842250521, + -3.4260880247991814 + ], + [ + 4207.290871293642, + -4.296864350557723 + ], + [ + 4347.533900336764, + -5.22084334193938 + ], + [ + 4487.776929379886, + -6.189520186559445 + ], + [ + 4628.019958423007, + -7.158614524169401 + ], + [ + 4768.262987466129, + -8.077638840475863 + ], + [ + 4908.50601650925, + -8.89609054309306 + ], + [ + 5048.749045552371, + -9.564367618075273 + ], + [ + 5188.992074595492, + -10.09919582294498 + ], + [ + 5329.235103638614, + -10.657179402170893 + ], + [ + 5469.478132681736, + -11.412869458710013 + ], + [ + 5609.721161724857, + -12.540766995904155 + ], + [ + 5749.964190767979, + -14.214278640186201 + ], + [ + 5890.2072198111, + -16.51030857614407 + ], + [ + 6030.450248854221, + -19.00931126864703 + ], + [ + 6170.693277897342, + -21.13073835597987 + ], + [ + 6310.936306940464, + -22.293924415562362 + ], + [ + 6451.179335983586, + -21.91865187064078 + ], + [ + 6591.422365026707, + -19.52724223176693 + ], + [ + 6731.665394069829, + -17.809635780828152 + ], + [ + 6871.90842311295, + -17.860114336971733 + ], + [ + 7012.151452156071, + -19.21975291793482 + ], + [ + 7152.394481199192, + -21.429678469060377 + ], + [ + 7292.637510242314, + -24.0303415996994 + ], + [ + 7432.880539285436, + -26.553129346797427 + ], + [ + 7573.123568328557, + -28.60336434785637 + ], + [ + 7713.366597371678, + -30.15652034157746 + ], + [ + 7853.6096264148, + -31.293291981555683 + ], + [ + 7993.852655457921, + -32.09737760398419 + ], + [ + 8134.095684501042, + -32.67280158796342 + ], + [ + 8274.338713544164, + -33.14361982526569 + ], + [ + 8414.581742587285, + -33.70533606696553 + ], + [ + 8554.824771630407, + -34.48240033281173 + ], + [ + 8695.067800673529, + -35.44720526531393 + ], + [ + 8835.31082971665, + -36.55545171512738 + ], + [ + 8975.553858759771, + -37.76284045078233 + ], + [ + 9115.796887802893, + -39.02503520149651 + ], + [ + 9256.039916846014, + -40.2976032859834 + ], + [ + 9396.282945889136, + -41.54149673169201 + ], + [ + 9536.525974932258, + -42.73066399842981 + ], + [ + 9676.769003975378, + -43.85758730603 + ], + [ + 9817.0120330185, + -44.92921538837377 + ], + [ + 9957.25506206162, + -45.95296539202103 + ], + [ + 10097.498091104742, + -46.93625441259562 + ], + [ + 10237.741120147864, + -47.886502159899216 + ], + [ + 10377.984149190985, + -48.81125931740376 + ], + [ + 10518.227178234107, + -49.718005725756775 + ], + [ + 10658.470207277229, + -50.61087206076811 + ], + [ + 10798.71323632035, + -51.48940477836511 + ], + [ + 10938.956265363471, + -52.3466705218621 + ], + [ + 11079.199294406593, + -53.1735343831702 + ], + [ + 11219.442323449714, + -53.96228003219031 + ], + [ + 11359.685352492836, + -54.70911575525477 + ], + [ + 11499.928381535958, + -55.411887190494284 + ], + [ + 11640.171410579078, + -56.06989219634767 + ], + [ + 11780.4144396222, + -56.68292321761232 + ], + [ + 11920.65746866532, + -57.250257871369776 + ], + [ + 12060.900497708442, + -57.770899119090956 + ], + [ + 12201.143526751564, + -58.246368904392675 + ], + [ + 12341.386555794685, + -58.67992384519642 + ], + [ + 12481.629584837807, + -59.07477153438371 + ], + [ + 12621.872613880929, + -59.433989908441006 + ], + [ + 12762.11564292405, + -59.76055930908575 + ], + [ + 12902.358671967171, + -60.056792900107325 + ], + [ + 13042.601701010293, + -60.32585907630278 + ], + [ + 13182.844730053414, + -60.572685570792494 + ], + [ + 13323.087759096536, + -60.802650830421435 + ], + [ + 13463.330788139658, + -61.02125377929266 + ], + [ + 13603.573817182778, + -61.23399431809803 + ], + [ + 13743.8168462259, + -61.44628418779293 + ], + [ + 13884.059875269022, + -61.662033323847304 + ] + ], + "13": [ + [ + 0.0, + 16.099943790702604 + ], + [ + 140.24302904312142, + 19.130261741049072 + ], + [ + 280.48605808624285, + 17.551793181468046 + ], + [ + 420.7290871293643, + 16.322039411615027 + ], + [ + 560.9721161724857, + 15.633565625680184 + ], + [ + 701.2151452156071, + 14.666039605969328 + ], + [ + 841.4581742587286, + 13.410169586267232 + ], + [ + 981.70120330185, + 13.016473893436213 + ], + [ + 1121.9442323449714, + 12.369090617659168 + ], + [ + 1262.1872613880928, + 11.382508716265464 + ], + [ + 1402.4302904312142, + 10.67081441364512 + ], + [ + 1542.6733194743356, + 10.043736582931647 + ], + [ + 1682.9163485174572, + 9.332475405323004 + ], + [ + 1823.1593775605786, + 8.538064353835477 + ], + [ + 1963.4024066037, + 7.975263683857798 + ], + [ + 2103.645435646821, + 7.6586260012037 + ], + [ + 2243.888464689943, + 6.346009644439053 + ], + [ + 2384.1314937330644, + 5.390303220914266 + ], + [ + 2524.3745227761856, + 5.267175877194375 + ], + [ + 2664.617551819307, + 4.808580191486101 + ], + [ + 2804.8605808624284, + 4.022960518037667 + ], + [ + 2945.10360990555, + 3.0668911623480546 + ], + [ + 3085.346638948671, + 2.097862639648761 + ], + [ + 3225.589667991793, + 1.223616904103989 + ], + [ + 3365.8326970349144, + 0.42854416245076127 + ], + [ + 3506.0757260780356, + -0.3214163506309318 + ], + [ + 3646.318755121157, + -1.0604764525692063 + ], + [ + 3786.5617841642784, + -1.8191623877852547 + ], + [ + 3926.8048132074, + -2.608122329091277 + ], + [ + 4067.047842250521, + -3.4324272117498635 + ], + [ + 4207.290871293642, + -4.297217978355586 + ], + [ + 4347.533900336764, + -5.207893757183014 + ], + [ + 4487.776929379886, + -6.159139182117621 + ], + [ + 4628.019958423007, + -7.112340984613225 + ], + [ + 4768.262987466129, + -8.022782227483592 + ], + [ + 4908.50601650925, + -8.845704328313325 + ], + [ + 5048.749045552371, + -9.536259390074616 + ], + [ + 5188.992074595492, + -10.107807560193637 + ], + [ + 5329.235103638614, + -10.701377304762818 + ], + [ + 5469.478132681736, + -11.474825742189196 + ], + [ + 5609.721161724857, + -12.585960063368969 + ], + [ + 5749.964190767979, + -14.19203056827298 + ], + [ + 5890.2072198111, + -16.365819643345482 + ], + [ + 6030.450248854221, + -18.72916255621091 + ], + [ + 6170.693277897342, + -20.75343612423393 + ], + [ + 6310.936306940464, + -21.909893389492943 + ], + [ + 6451.179335983586, + -21.670173490281318 + ], + [ + 6591.422365026707, + -19.59542304333265 + ], + [ + 6731.665394069829, + -18.123494377684096 + ], + [ + 6871.90842311295, + -18.263697889360184 + ], + [ + 7012.151452156071, + -19.594949012871176 + ], + [ + 7152.394481199192, + -21.696183516474147 + ], + [ + 7292.637510242314, + -24.145626331103283 + ], + [ + 7432.880539285436, + -26.512859571688562 + ], + [ + 7573.123568328557, + -28.439471099103617 + ], + [ + 7713.366597371678, + -29.914501179310545 + ], + [ + 7853.6096264148, + -31.025324187178914 + ], + [ + 7993.852655457921, + -31.859326202720997 + ], + [ + 8134.095684501042, + -32.50400374642013 + ], + [ + 8274.338713544164, + -33.05869197842292 + ], + [ + 8414.581742587285, + -33.6931720212898 + ], + [ + 8554.824771630407, + -34.515995416645445 + ], + [ + 8695.067800673529, + -35.50271040649861 + ], + [ + 8835.31082971665, + -36.613915947465706 + ], + [ + 8975.553858759771, + -37.81021101293972 + ], + [ + 9115.796887802893, + -39.052149039201 + ], + [ + 9256.039916846014, + -40.300300557587875 + ], + [ + 9396.282945889136, + -41.52084984515451 + ], + [ + 9536.525974932258, + -42.692465029055846 + ], + [ + 9676.769003975378, + -43.808716985519155 + ], + [ + 9817.0120330185, + -44.875687054911644 + ], + [ + 9957.25506206162, + -45.89990848629272 + ], + [ + 10097.498091104742, + -46.887914507670885 + ], + [ + 10237.741120147864, + -47.84624123651677 + ], + [ + 10377.984149190985, + -48.78150519853881 + ], + [ + 10518.227178234107, + -49.69997940560848 + ], + [ + 10658.470207277229, + -50.60421193007404 + ], + [ + 10798.71323632035, + -51.492048565157155 + ], + [ + 10938.956265363471, + -52.355717774944736 + ], + [ + 11079.199294406593, + -53.18543158862695 + ], + [ + 11219.442323449714, + -53.972992206173735 + ], + [ + 11359.685352492836, + -54.71517466063787 + ], + [ + 11499.928381535958, + -55.41154169509171 + ], + [ + 11640.171410579078, + -56.06337790313769 + ], + [ + 11780.4144396222, + -56.67260633983283 + ], + [ + 11920.65746866532, + -57.240682387036586 + ], + [ + 12060.900497708442, + -57.768065903746596 + ], + [ + 12201.143526751564, + -58.25488205490513 + ], + [ + 12341.386555794685, + -58.70158768491656 + ], + [ + 12481.629584837807, + -59.10817004622932 + ], + [ + 12621.872613880929, + -59.47443017567392 + ], + [ + 12762.11564292405, + -59.800011220372504 + ], + [ + 12902.358671967171, + -60.0852788463287 + ], + [ + 13042.601701010293, + -60.33672542374349 + ], + [ + 13182.844730053414, + -60.564393350449926 + ], + [ + 13323.087759096536, + -60.779025493283804 + ], + [ + 13463.330788139658, + -60.99151006869622 + ], + [ + 13603.573817182778, + -61.21273677036887 + ], + [ + 13743.8168462259, + -61.453443465752756 + ], + [ + 13884.059875269022, + -61.7169732314223 + ] + ], + "14": [ + [ + 0.0, + 16.20758677566643 + ], + [ + 140.24302904312142, + 18.984103427882886 + ], + [ + 280.48605808624285, + 17.687250134235036 + ], + [ + 420.7290871293643, + 16.925355318944046 + ], + [ + 560.9721161724857, + 16.289154126001524 + ], + [ + 701.2151452156071, + 15.031698681129635 + ], + [ + 841.4581742587286, + 13.525498220665447 + ], + [ + 981.70120330185, + 13.081027593463713 + ], + [ + 1121.9442323449714, + 12.347628159517596 + ], + [ + 1262.1872613880928, + 11.333156249675008 + ], + [ + 1402.4302904312142, + 10.658832098548134 + ], + [ + 1542.6733194743356, + 10.049056166117971 + ], + [ + 1682.9163485174572, + 9.350647021547985 + ], + [ + 1823.1593775605786, + 8.58536133401866 + ], + [ + 1963.4024066037, + 8.017275320702511 + ], + [ + 2103.645435646821, + 7.642890530831114 + ], + [ + 2243.888464689943, + 6.365560370689062 + ], + [ + 2384.1314937330644, + 5.462887315197307 + ], + [ + 2524.3745227761856, + 5.283442318536645 + ], + [ + 2664.617551819307, + 4.781354441684203 + ], + [ + 2804.8605808624284, + 3.9859435065099893 + ], + [ + 2945.10360990555, + 3.044050619334136 + ], + [ + 3085.346638948671, + 2.1021053009640127 + ], + [ + 3225.589667991793, + 1.251770688789119 + ], + [ + 3365.8326970349144, + 0.47107811059825533 + ], + [ + 3506.0757260780356, + -0.2761383114110199 + ], + [ + 3646.318755121157, + -1.0257741532635234 + ], + [ + 3786.5617841642784, + -1.8075964630474988 + ], + [ + 3926.8048132074, + -2.622493060834301 + ], + [ + 4067.047842250521, + -3.4619140234756274 + ], + [ + 4207.290871293642, + -4.317327327326546 + ], + [ + 4347.533900336764, + -5.180485196316494 + ], + [ + 4487.776929379886, + -6.046854393951794 + ], + [ + 4628.019958423007, + -6.918303998909563 + ], + [ + 4768.262987466129, + -7.797342948912551 + ], + [ + 4908.50601650925, + -8.686387950519805 + ], + [ + 5048.749045552371, + -9.587358990745793 + ], + [ + 5188.992074595492, + -10.501048631999387 + ], + [ + 5329.235103638614, + -11.426710356660367 + ], + [ + 5469.478132681736, + -12.363498560825809 + ], + [ + 5609.721161724857, + -13.310652993829313 + ], + [ + 5749.964190767979, + -14.267835773327299 + ], + [ + 5890.2072198111, + -15.23495369327253 + ], + [ + 6030.450248854221, + -16.212159835117056 + ], + [ + 6170.693277897342, + -17.199646124698017 + ], + [ + 6310.936306940464, + -18.197542830578715 + ], + [ + 6451.179335983586, + -19.205622170324126 + ], + [ + 6591.422365026707, + -20.223220780053833 + ], + [ + 6731.665394069829, + -21.249199737623634 + ], + [ + 6871.90842311295, + -22.282273886278496 + ], + [ + 7012.151452156071, + -23.32113747290691 + ], + [ + 7152.394481199192, + -24.364554688304064 + ], + [ + 7292.637510242314, + -25.411535905038342 + ], + [ + 7432.880539285436, + -26.461468390454343 + ], + [ + 7573.123568328557, + -27.514450125924935 + ], + [ + 7713.366597371678, + -28.570938765064838 + ], + [ + 7853.6096264148, + -29.631420783201058 + ], + [ + 7993.852655457921, + -30.69638168843469 + ], + [ + 8134.095684501042, + -31.766331488107763 + ], + [ + 8274.338713544164, + -32.84148785148367 + ], + [ + 8414.581742587285, + -33.921179921200654 + ], + [ + 8554.824771630407, + -35.00405444051985 + ], + [ + 8695.067800673529, + -36.088612924002504 + ], + [ + 8835.31082971665, + -37.17335069644456 + ], + [ + 8975.553858759771, + -38.256762824160226 + ], + [ + 9115.796887802893, + -39.33732393105713 + ], + [ + 9256.039916846014, + -40.41366909305807 + ], + [ + 9396.282945889136, + -41.48475712667074 + ], + [ + 9536.525974932258, + -42.54934163541806 + ], + [ + 9676.769003975378, + -43.60547820920046 + ], + [ + 9817.0120330185, + -44.65105901990029 + ], + [ + 9957.25506206162, + -45.68397690348445 + ], + [ + 10097.498091104742, + -46.70212471722113 + ], + [ + 10237.741120147864, + -47.70339877132168 + ], + [ + 10377.984149190985, + -48.685760075897726 + ], + [ + 10518.227178234107, + -49.647124757093785 + ], + [ + 10658.470207277229, + -50.58406714563742 + ], + [ + 10798.71323632035, + -51.49138028896958 + ], + [ + 10938.956265363471, + -52.363464920643594 + ], + [ + 11079.199294406593, + -53.194494461901016 + ], + [ + 11219.442323449714, + -53.97944915214738 + ], + [ + 11359.685352492836, + -54.717886856581146 + ], + [ + 11499.928381535958, + -55.41184491977686 + ], + [ + 11640.171410579078, + -56.06330152194087 + ], + [ + 11780.4144396222, + -56.67450171640003 + ], + [ + 11920.65746866532, + -57.24766027486417 + ], + [ + 12060.900497708442, + -57.78372369997379 + ], + [ + 12201.143526751564, + -58.28175750456794 + ], + [ + 12341.386555794685, + -58.74078120818448 + ], + [ + 12481.629584837807, + -59.1592302794367 + ], + [ + 12621.872613880929, + -59.53531415265208 + ], + [ + 12762.11564292405, + -59.86703255612043 + ], + [ + 12902.358671967171, + -60.153964153349904 + ], + [ + 13042.601701010293, + -60.40322668784637 + ], + [ + 13182.844730053414, + -60.62598240415915 + ], + [ + 13323.087759096536, + -60.83436426189702 + ], + [ + 13463.330788139658, + -61.04067702654598 + ], + [ + 13603.573817182778, + -61.25722610629344 + ], + [ + 13743.8168462259, + -61.49601947393235 + ], + [ + 13884.059875269022, + -61.759973982993586 + ] + ], + "15": [ + [ + 0.0, + 15.651328726248193 + ], + [ + 140.24302904312142, + 18.56658184343626 + ], + [ + 280.48605808624285, + 17.80360742780815 + ], + [ + 420.7290871293643, + 17.42847218628227 + ], + [ + 560.9721161724857, + 16.735196406128203 + ], + [ + 701.2151452156071, + 15.34774790273851 + ], + [ + 841.4581742587286, + 14.123427251693649 + ], + [ + 981.70120330185, + 13.139848382961976 + ], + [ + 1121.9442323449714, + 12.218453156930774 + ], + [ + 1262.1872613880928, + 11.390178361130765 + ], + [ + 1402.4302904312142, + 10.662876817986373 + ], + [ + 1542.6733194743356, + 9.985047368697911 + ], + [ + 1682.9163485174572, + 9.333077602569526 + ], + [ + 1823.1593775605786, + 8.688679116389151 + ], + [ + 1963.4024066037, + 8.025017660395426 + ], + [ + 2103.645435646821, + 7.334490034536357 + ], + [ + 2243.888464689943, + 6.629620107624505 + ], + [ + 2384.1314937330644, + 5.91440829758481 + ], + [ + 2524.3745227761856, + 5.1824277197847985 + ], + [ + 2664.617551819307, + 4.433396220507703 + ], + [ + 2804.8605808624284, + 3.672051100492807 + ], + [ + 2945.10360990555, + 2.9031804586077223 + ], + [ + 3085.346638948671, + 2.1307297363169315 + ], + [ + 3225.589667991793, + 1.3549315045671435 + ], + [ + 3365.8326970349144, + 0.5733382598927549 + ], + [ + 3506.0757260780356, + -0.21657666147037624 + ], + [ + 3646.318755121157, + -1.0170359868078886 + ], + [ + 3786.5617841642784, + -1.8287957459140676 + ], + [ + 3926.8048132074, + -2.6514285460513185 + ], + [ + 4067.047842250521, + -3.484389109275833 + ], + [ + 4207.290871293642, + -4.327132880410598 + ], + [ + 4347.533900336764, + -5.178912622684386 + ], + [ + 4487.776929379886, + -6.03945442200769 + ], + [ + 4628.019958423007, + -6.909048582215701 + ], + [ + 4768.262987466129, + -7.788027313546975 + ], + [ + 4908.50601650925, + -8.676750105285027 + ], + [ + 5048.749045552371, + -9.576025331613376 + ], + [ + 5188.992074595492, + -10.486466437250407 + ], + [ + 5329.235103638614, + -11.408443708908546 + ], + [ + 5469.478132681736, + -12.34233729985669 + ], + [ + 5609.721161724857, + -13.288539800238842 + ], + [ + 5749.964190767979, + -14.2468432239391 + ], + [ + 5890.2072198111, + -15.216396499165604 + ], + [ + 6030.450248854221, + -16.19620981852529 + ], + [ + 6170.693277897342, + -17.185265960613847 + ], + [ + 6310.936306940464, + -18.182507462504816 + ], + [ + 6451.179335983586, + -19.186885893552518 + ], + [ + 6591.422365026707, + -20.198121582097187 + ], + [ + 6731.665394069829, + -21.216229825471014 + ], + [ + 6871.90842311295, + -22.24111238380337 + ], + [ + 7012.151452156071, + -23.272639524887943 + ], + [ + 7152.394481199192, + -24.310709403836498 + ], + [ + 7292.637510242314, + -25.35520606252353 + ], + [ + 7432.880539285436, + -26.405643969030734 + ], + [ + 7573.123568328557, + -27.461446338874346 + ], + [ + 7713.366597371678, + -28.52233841303117 + ], + [ + 7853.6096264148, + -29.588115265849893 + ], + [ + 7993.852655457921, + -30.658577505426866 + ], + [ + 8134.095684501042, + -31.733569950262485 + ], + [ + 8274.338713544164, + -32.81274701837469 + ], + [ + 8414.581742587285, + -33.895253372572604 + ], + [ + 8554.824771630407, + -34.97988365486742 + ], + [ + 8695.067800673529, + -36.065261686641094 + ], + [ + 8835.31082971665, + -37.149977564629104 + ], + [ + 8975.553858759771, + -38.23262063192289 + ], + [ + 9115.796887802893, + -39.31177117110867 + ], + [ + 9256.039916846014, + -40.38633357364947 + ], + [ + 9396.282945889136, + -41.45544984888644 + ], + [ + 9536.525974932258, + -42.51800175488742 + ], + [ + 9676.769003975378, + -43.572251419553616 + ], + [ + 9817.0120330185, + -44.616352135377305 + ], + [ + 9957.25506206162, + -45.64846536465221 + ], + [ + 10097.498091104742, + -46.666752602115544 + ], + [ + 10237.741120147864, + -47.669378555920446 + ], + [ + 10377.984149190985, + -48.6545083026831 + ], + [ + 10518.227178234107, + -49.62012605621458 + ], + [ + 10658.470207277229, + -50.563002219261115 + ], + [ + 10798.71323632035, + -51.47765829640901 + ], + [ + 10938.956265363471, + -52.35795565762929 + ], + [ + 11079.199294406593, + -53.197472936543946 + ], + [ + 11219.442323449714, + -53.990464208933616 + ], + [ + 11359.685352492836, + -54.73573880734701 + ], + [ + 11499.928381535958, + -55.43518750107509 + ], + [ + 11640.171410579078, + -56.090908902382196 + ], + [ + 11780.4144396222, + -56.70530130935859 + ], + [ + 11920.65746866532, + -57.28087655541575 + ], + [ + 12060.900497708442, + -57.81897134023298 + ], + [ + 12201.143526751564, + -58.31896830518618 + ], + [ + 12341.386555794685, + -58.78012191077224 + ], + [ + 12481.629584837807, + -59.201072210253585 + ], + [ + 12621.872613880929, + -59.580259027786546 + ], + [ + 12762.11564292405, + -59.9158920834651 + ], + [ + 12902.358671967171, + -60.207860548159985 + ], + [ + 13042.601701010293, + -60.46299236208199 + ], + [ + 13182.844730053414, + -60.69186330986707 + ], + [ + 13323.087759096536, + -60.90598740622771 + ], + [ + 13463.330788139658, + -61.117033245255925 + ], + [ + 13603.573817182778, + -61.33667273253935 + ], + [ + 13743.8168462259, + -61.57619035059235 + ], + [ + 13884.059875269022, + -61.83734925816412 + ] + ], + "16": [ + [ + 0.0, + 14.635151135323174 + ], + [ + 140.24302904312142, + 17.887923321875267 + ], + [ + 280.48605808624285, + 17.509286138542993 + ], + [ + 420.7290871293643, + 17.251640775545148 + ], + [ + 560.9721161724857, + 16.755253219253238 + ], + [ + 701.2151452156071, + 15.588816120589037 + ], + [ + 841.4581742587286, + 14.376496763942253 + ], + [ + 981.70120330185, + 13.309913210422216 + ], + [ + 1121.9442323449714, + 12.332074654877694 + ], + [ + 1262.1872613880928, + 11.449954361559312 + ], + [ + 1402.4302904312142, + 10.66815685422704 + ], + [ + 1542.6733194743356, + 9.963593636511803 + ], + [ + 1682.9163485174572, + 9.311419044952023 + ], + [ + 1823.1593775605786, + 8.677473791359088 + ], + [ + 1963.4024066037, + 8.029376252368422 + ], + [ + 2103.645435646821, + 7.357373959331198 + ], + [ + 2243.888464689943, + 6.665165509593276 + ], + [ + 2384.1314937330644, + 5.9530016897411695 + ], + [ + 2524.3745227761856, + 5.219169878534615 + ], + [ + 2664.617551819307, + 4.466721306945952 + ], + [ + 2804.8605808624284, + 3.7016426963123785 + ], + [ + 2945.10360990555, + 2.9298491791196817 + ], + [ + 3085.346638948671, + 2.1561365577621108 + ], + [ + 3225.589667991793, + 1.380548675827737 + ], + [ + 3365.8326970349144, + 0.5996564040730573 + ], + [ + 3506.0757260780356, + -0.1900699079113029 + ], + [ + 3646.318755121157, + -0.9917833592180425 + ], + [ + 3786.5617841642784, + -1.8063260184181436 + ], + [ + 3926.8048132074, + -2.6323421569195835 + ], + [ + 4067.047842250521, + -3.4682505785137017 + ], + [ + 4207.290871293642, + -4.312425303869909 + ], + [ + 4347.533900336764, + -5.1634501531164 + ], + [ + 4487.776929379886, + -6.021444671624173 + ], + [ + 4628.019958423007, + -6.887333328437558 + ], + [ + 4768.262987466129, + -7.762074792107125 + ], + [ + 4908.50601650925, + -8.646746887433427 + ], + [ + 5048.749045552371, + -9.542454837110501 + ], + [ + 5188.992074595492, + -10.449863283619681 + ], + [ + 5329.235103638614, + -11.369900000070986 + ], + [ + 5469.478132681736, + -12.303544859189353 + ], + [ + 5609.721161724857, + -13.2517633728262 + ], + [ + 5749.964190767979, + -14.214931252433615 + ], + [ + 5890.2072198111, + -15.192015383011004 + ], + [ + 6030.450248854221, + -16.179600026252103 + ], + [ + 6170.693277897342, + -17.173850832377628 + ], + [ + 6310.936306940464, + -18.170861229228713 + ], + [ + 6451.179335983586, + -19.16671486879696 + ], + [ + 6591.422365026707, + -20.15882269301732 + ], + [ + 6731.665394069829, + -21.149711411410415 + ], + [ + 6871.90842311295, + -22.14731495414589 + ], + [ + 7012.151452156071, + -23.159917217649216 + ], + [ + 7152.394481199192, + -24.19586649621808 + ], + [ + 7292.637510242314, + -25.263544289162635 + ], + [ + 7432.880539285436, + -26.370434226676842 + ], + [ + 7573.123568328557, + -27.511715810440627 + ], + [ + 7713.366597371678, + -28.656811513689743 + ], + [ + 7853.6096264148, + -29.77137134715034 + ], + [ + 7993.852655457921, + -30.827185729812285 + ], + [ + 8134.095684501042, + -31.837515530367693 + ], + [ + 8274.338713544164, + -32.83265611186143 + ], + [ + 8414.581742587285, + -33.842407599490066 + ], + [ + 8554.824771630407, + -34.88235635279271 + ], + [ + 8695.067800673529, + -35.94760494658812 + ], + [ + 8835.31082971665, + -37.03146409783499 + ], + [ + 8975.553858759771, + -38.12724370175687 + ], + [ + 9115.796887802893, + -39.22827560408631 + ], + [ + 9256.039916846014, + -40.32828875050881 + ], + [ + 9396.282945889136, + -41.42121518390643 + ], + [ + 9536.525974932258, + -42.50111022944609 + ], + [ + 9676.769003975378, + -43.56539747477148 + ], + [ + 9817.0120330185, + -44.61361256299363 + ], + [ + 9957.25506206162, + -45.6453333981741 + ], + [ + 10097.498091104742, + -46.66013792085875 + ], + [ + 10237.741120147864, + -47.65760472664695 + ], + [ + 10377.984149190985, + -48.637217386693 + ], + [ + 10518.227178234107, + -49.5979088770347 + ], + [ + 10658.470207277229, + -50.53761413285581 + ], + [ + 10798.71323632035, + -51.45184089881165 + ], + [ + 10938.956265363471, + -52.3341793508777 + ], + [ + 11079.199294406593, + -53.17785104548981 + ], + [ + 11219.442323449714, + -53.97724158089534 + ], + [ + 11359.685352492836, + -54.73036267704781 + ], + [ + 11499.928381535958, + -55.43774204956296 + ], + [ + 11640.171410579078, + -56.10037854176885 + ], + [ + 11780.4144396222, + -56.71955515726571 + ], + [ + 11920.65746866532, + -57.296484095056314 + ], + [ + 12060.900497708442, + -57.83189149514438 + ], + [ + 12201.143526751564, + -58.32630238125504 + ], + [ + 12341.386555794685, + -58.780721271427666 + ], + [ + 12481.629584837807, + -59.19559102439168 + ], + [ + 12621.872613880929, + -59.57118295112202 + ], + [ + 12762.11564292405, + -59.90746152808324 + ], + [ + 12902.358671967171, + -60.205397702217326 + ], + [ + 13042.601701010293, + -60.470759076281055 + ], + [ + 13182.844730053414, + -60.7122950073551 + ], + [ + 13323.087759096536, + -60.939701032318546 + ], + [ + 13463.330788139658, + -61.16280718393268 + ], + [ + 13603.573817182778, + -61.39145321792135 + ], + [ + 13743.8168462259, + -61.635068835310605 + ], + [ + 13884.059875269022, + -61.893950864891316 + ] + ], + "17": [ + [ + 0.0, + 14.899279759359194 + ], + [ + 140.24302904312142, + 18.139133458026656 + ], + [ + 280.48605808624285, + 17.571763920536398 + ], + [ + 420.7290871293643, + 17.19066285916898 + ], + [ + 560.9721161724857, + 16.898116150693603 + ], + [ + 701.2151452156071, + 15.765262744751428 + ], + [ + 841.4581742587286, + 13.571277673035668 + ], + [ + 981.70120330185, + 13.675983444668097 + ], + [ + 1121.9442323449714, + 12.858921700657088 + ], + [ + 1262.1872613880928, + 11.353404072729997 + ], + [ + 1402.4302904312142, + 10.637236224334774 + ], + [ + 1542.6733194743356, + 10.099353535479315 + ], + [ + 1682.9163485174572, + 9.362111241378967 + ], + [ + 1823.1593775605786, + 8.352501235033822 + ], + [ + 1963.4024066037, + 8.050864443139146 + ], + [ + 2103.645435646821, + 8.765870606010628 + ], + [ + 2243.888464689943, + 6.2480330952780045 + ], + [ + 2384.1314937330644, + 4.384849974946694 + ], + [ + 2524.3745227761856, + 5.527016734554452 + ], + [ + 2664.617551819307, + 5.7290594158357075 + ], + [ + 2804.8605808624284, + 4.890990408552576 + ], + [ + 2945.10360990555, + 3.51912032326606 + ], + [ + 3085.346638948671, + 2.11827995611808 + ], + [ + 3225.589667991793, + 1.0451448286995517 + ], + [ + 3365.8326970349144, + 0.24870417339949624 + ], + [ + 3506.0757260780356, + -0.39688719663473954 + ], + [ + 3646.318755121157, + -1.0172513825819902 + ], + [ + 3786.5617841642784, + -1.7215545656167948 + ], + [ + 3926.8048132074, + -2.5198028929021303 + ], + [ + 4067.047842250521, + -3.379975945864564 + ], + [ + 4207.290871293642, + -4.269704246101655 + ], + [ + 4347.533900336764, + -5.1565662464933295 + ], + [ + 4487.776929379886, + -6.023818042255793 + ], + [ + 4628.019958423007, + -6.882402570498523 + ], + [ + 4768.262987466129, + -7.7463780300421305 + ], + [ + 4908.50601650925, + -8.630012069056342 + ], + [ + 5048.749045552371, + -9.547178568376125 + ], + [ + 5188.992074595492, + -10.495491928402968 + ], + [ + 5329.235103638614, + -11.454062512888907 + ], + [ + 5469.478132681736, + -12.40090333698135 + ], + [ + 5609.721161724857, + -13.313923236581534 + ], + [ + 5749.964190767979, + -14.170739333829523 + ], + [ + 5890.2072198111, + -14.974054989870934 + ], + [ + 6030.450248854221, + -15.783454393611597 + ], + [ + 6170.693277897342, + -16.666364421619633 + ], + [ + 6310.936306940464, + -17.690169465492186 + ], + [ + 6451.179335983586, + -18.922287528925334 + ], + [ + 6591.422365026707, + -20.387623424688837 + ], + [ + 6731.665394069829, + -21.700884848858955 + ], + [ + 6871.90842311295, + -22.7975301722344 + ], + [ + 7012.151452156071, + -23.741321370533893 + ], + [ + 7152.394481199192, + -24.59609305684304 + ], + [ + 7292.637510242314, + -25.425719154265987 + ], + [ + 7432.880539285436, + -26.29294846046543 + ], + [ + 7573.123568328557, + -27.23336748596384 + ], + [ + 7713.366597371678, + -28.239773956618365 + ], + [ + 7853.6096264148, + -29.301287746218136 + ], + [ + 7993.852655457921, + -30.407026580075247 + ], + [ + 8134.095684501042, + -31.546032898169607 + ], + [ + 8274.338713544164, + -32.70722331141879 + ], + [ + 8414.581742587285, + -33.87967820074201 + ], + [ + 8554.824771630407, + -35.055090784526385 + ], + [ + 8695.067800673529, + -36.22215888910214 + ], + [ + 8835.31082971665, + -37.36834735023925 + ], + [ + 8975.553858759771, + -38.481119780231445 + ], + [ + 9115.796887802893, + -39.547983186744986 + ], + [ + 9256.039916846014, + -40.55685032332128 + ], + [ + 9396.282945889136, + -41.495835838673116 + ], + [ + 9536.525974932258, + -42.39967696487066 + ], + [ + 9676.769003975378, + -43.34827970151972 + ], + [ + 9817.0120330185, + -44.336587882255856 + ], + [ + 9957.25506206162, + -45.35344474095065 + ], + [ + 10097.498091104742, + -46.38769361403568 + ], + [ + 10237.741120147864, + -47.428177560356744 + ], + [ + 10377.984149190985, + -48.46363527985444 + ], + [ + 10518.227178234107, + -49.482465033051696 + ], + [ + 10658.470207277229, + -50.472053114607704 + ], + [ + 10798.71323632035, + -51.41833494191086 + ], + [ + 10938.956265363471, + -52.315257368125046 + ], + [ + 11079.199294406593, + -53.162369042144775 + ], + [ + 11219.442323449714, + -53.95997685264868 + ], + [ + 11359.685352492836, + -54.711617071230954 + ], + [ + 11499.928381535958, + -55.42209328017538 + ], + [ + 11640.171410579078, + -56.09182927619782 + ], + [ + 11780.4144396222, + -56.719118380138646 + ], + [ + 11920.65746866532, + -57.302427532266535 + ], + [ + 12060.900497708442, + -57.84033765109354 + ], + [ + 12201.143526751564, + -58.33288345441523 + ], + [ + 12341.386555794685, + -58.781933726450745 + ], + [ + 12481.629584837807, + -59.189786606189436 + ], + [ + 12621.872613880929, + -59.558656015305274 + ], + [ + 12762.11564292405, + -59.89039234920958 + ], + [ + 12902.358671967171, + -60.18759377371876 + ], + [ + 13042.601701010293, + -60.45562400466168 + ], + [ + 13182.844730053414, + -60.7021759800828 + ], + [ + 13323.087759096536, + -60.93596312507073 + ], + [ + 13463.330788139658, + -61.165838896356426 + ], + [ + 13603.573817182778, + -61.400669666037004 + ], + [ + 13743.8168462259, + -61.64877656498537 + ], + [ + 13884.059875269022, + -61.90955443110939 + ] + ], + "18": [ + [ + 0.0, + 14.842944258295795 + ], + [ + 140.24302904312142, + 18.136275460483503 + ], + [ + 280.48605808624285, + 17.18161628430644 + ], + [ + 420.7290871293643, + 17.029246603236402 + ], + [ + 560.9721161724857, + 16.734201940336952 + ], + [ + 701.2151452156071, + 15.536237241079084 + ], + [ + 841.4581742587286, + 14.368334830347578 + ], + [ + 981.70120330185, + 13.406714669843774 + ], + [ + 1121.9442323449714, + 12.475744184256275 + ], + [ + 1262.1872613880928, + 11.58986657104926 + ], + [ + 1402.4302904312142, + 10.786173911637496 + ], + [ + 1542.6733194743356, + 10.056837338637132 + ], + [ + 1682.9163485174572, + 9.380812396264409 + ], + [ + 1823.1593775605786, + 8.734453198500836 + ], + [ + 1963.4024066037, + 8.09146570130534 + ], + [ + 2103.645435646821, + 7.4274519074890515 + ], + [ + 2243.888464689943, + 6.72564013962264 + ], + [ + 2384.1314937330644, + 5.993158319203482 + ], + [ + 2524.3745227761856, + 5.246894425084119 + ], + [ + 2664.617551819307, + 4.492768863344575 + ], + [ + 2804.8605808624284, + 3.7319169451050116 + ], + [ + 2945.10360990555, + 2.965505736439416 + ], + [ + 3085.346638948671, + 2.193795626612528 + ], + [ + 3225.589667991793, + 1.4159835408832218 + ], + [ + 3365.8326970349144, + 0.6305937577188183 + ], + [ + 3506.0757260780356, + -0.16382231756601023 + ], + [ + 3646.318755121157, + -0.967937801870828 + ], + [ + 3786.5617841642784, + -1.7813642328526662 + ], + [ + 3926.8048132074, + -2.603729455562951 + ], + [ + 4067.047842250521, + -3.4348284316067357 + ], + [ + 4207.290871293642, + -4.274577076224994 + ], + [ + 4347.533900336764, + -5.123087712338094 + ], + [ + 4487.776929379886, + -5.980661629593175 + ], + [ + 4628.019958423007, + -6.847914259030389 + ], + [ + 4768.262987466129, + -7.725444104523428 + ], + [ + 4908.50601650925, + -8.613819927158916 + ], + [ + 5048.749045552371, + -9.513443132646616 + ], + [ + 5188.992074595492, + -10.424404899509664 + ], + [ + 5329.235103638614, + -11.346848546461699 + ], + [ + 5469.478132681736, + -12.280934821468755 + ], + [ + 5609.721161724857, + -13.226735503303162 + ], + [ + 5749.964190767979, + -14.184280194610903 + ], + [ + 5890.2072198111, + -15.153338477091776 + ], + [ + 6030.450248854221, + -16.13255834848186 + ], + [ + 6170.693277897342, + -17.120400600552266 + ], + [ + 6310.936306940464, + -18.115346490391577 + ], + [ + 6451.179335983586, + -19.115908145814824 + ], + [ + 6591.422365026707, + -20.12090357705897 + ], + [ + 6731.665394069829, + -21.130613810546212 + ], + [ + 6871.90842311295, + -22.14747293809199 + ], + [ + 7012.151452156071, + -23.17409591177227 + ], + [ + 7152.394481199192, + -24.21311243635837 + ], + [ + 7292.637510242314, + -25.267208557806065 + ], + [ + 7432.880539285436, + -26.338607652313488 + ], + [ + 7573.123568328557, + -27.426190915786144 + ], + [ + 7713.366597371678, + -28.518409424505986 + ], + [ + 7853.6096264148, + -29.60146517075792 + ], + [ + 7993.852655457921, + -30.664636499964956 + ], + [ + 8134.095684501042, + -31.717978478237953 + ], + [ + 8274.338713544164, + -32.77983287139436 + ], + [ + 8414.581742587285, + -33.868473951592684 + ], + [ + 8554.824771630407, + -34.99142731045147 + ], + [ + 8695.067800673529, + -36.133262144478344 + ], + [ + 8835.31082971665, + -37.27551032306865 + ], + [ + 8975.553858759771, + -38.399702221278034 + ], + [ + 9115.796887802893, + -39.48740976520774 + ], + [ + 9256.039916846014, + -40.52077415165639 + ], + [ + 9396.282945889136, + -41.482368139094454 + ], + [ + 9536.525974932258, + -42.4027062954256 + ], + [ + 9676.769003975378, + -43.36012998153794 + ], + [ + 9817.0120330185, + -44.350600815891845 + ], + [ + 9957.25506206162, + -45.36438514875123 + ], + [ + 10097.498091104742, + -46.391749464735504 + ], + [ + 10237.741120147864, + -47.42295959172839 + ], + [ + 10377.984149190985, + -48.44817732302988 + ], + [ + 10518.227178234107, + -49.457036115517255 + ], + [ + 10658.470207277229, + -50.438352545614485 + ], + [ + 10798.71323632035, + -51.37991804929093 + ], + [ + 10938.956265363471, + -52.2762017805845 + ], + [ + 11079.199294406593, + -53.12670892960998 + ], + [ + 11219.442323449714, + -53.9315266439176 + ], + [ + 11359.685352492836, + -54.692982024827636 + ], + [ + 11499.928381535958, + -55.41352588530729 + ], + [ + 11640.171410579078, + -56.09183647510721 + ], + [ + 11780.4144396222, + -56.72464477776877 + ], + [ + 11920.65746866532, + -57.309083649282925 + ], + [ + 12060.900497708442, + -57.843091168837816 + ], + [ + 12201.143526751564, + -58.32818277658929 + ], + [ + 12341.386555794685, + -58.76774058342745 + ], + [ + 12481.629584837807, + -59.165458643063666 + ], + [ + 12621.872613880929, + -59.52496041472687 + ], + [ + 12762.11564292405, + -59.84953291340837 + ], + [ + 12902.358671967171, + -60.14303901197187 + ], + [ + 13042.601701010293, + -60.4100676859472 + ], + [ + 13182.844730053414, + -60.65762683350614 + ], + [ + 13323.087759096536, + -60.893803293911525 + ], + [ + 13463.330788139658, + -61.12681222921302 + ], + [ + 13603.573817182778, + -61.36488204807404 + ], + [ + 13743.8168462259, + -61.61548480584347 + ], + [ + 13884.059875269022, + -61.87725190493766 + ] + ], + "19": [ + [ + 0.0, + 15.070696432275344 + ], + [ + 140.24302904312142, + 18.2445561814975 + ], + [ + 280.48605808624285, + 17.187775841670774 + ], + [ + 420.7290871293643, + 16.945295387186484 + ], + [ + 560.9721161724857, + 16.576577490949077 + ], + [ + 701.2151452156071, + 15.394192505719136 + ], + [ + 841.4581742587286, + 14.30866488993988 + ], + [ + 981.70120330185, + 13.409082049780094 + ], + [ + 1121.9442323449714, + 12.503211267283978 + ], + [ + 1262.1872613880928, + 11.63840411489461 + ], + [ + 1402.4302904312142, + 10.851327526547735 + ], + [ + 1542.6733194743356, + 10.123374876736335 + ], + [ + 1682.9163485174572, + 9.437369752419663 + ], + [ + 1823.1593775605786, + 8.773272678951383 + ], + [ + 1963.4024066037, + 8.109196571540984 + ], + [ + 2103.645435646821, + 7.430373766844154 + ], + [ + 2243.888464689943, + 6.7255798872386325 + ], + [ + 2384.1314937330644, + 5.996138420502127 + ], + [ + 2524.3745227761856, + 5.251722502040023 + ], + [ + 2664.617551819307, + 4.49698950637199 + ], + [ + 2804.8605808624284, + 3.7341521238503015 + ], + [ + 2945.10360990555, + 2.9656569587077115 + ], + [ + 3085.346638948671, + 2.19280306008305 + ], + [ + 3225.589667991793, + 1.414942844417008 + ], + [ + 3365.8326970349144, + 0.6304426626656668 + ], + [ + 3506.0757260780356, + -0.16236620064603896 + ], + [ + 3646.318755121157, + -0.9643590120754372 + ], + [ + 3786.5617841642784, + -1.7753705194893747 + ], + [ + 3926.8048132074, + -2.5957167976175284 + ], + [ + 4067.047842250521, + -3.4260173352633743 + ], + [ + 4207.290871293642, + -4.267022596076507 + ], + [ + 4347.533900336764, + -5.11932435947209 + ], + [ + 4487.776929379886, + -5.982491616636986 + ], + [ + 4628.019958423007, + -6.855875145180375 + ], + [ + 4768.262987466129, + -7.7387500447775155 + ], + [ + 4908.50601650925, + -8.630344765450085 + ], + [ + 5048.749045552371, + -9.530469242143786 + ], + [ + 5188.992074595492, + -10.439947577188693 + ], + [ + 5329.235103638614, + -11.359929959383127 + ], + [ + 5469.478132681736, + -12.291590527356627 + ], + [ + 5609.721161724857, + -13.23604262425213 + ], + [ + 5749.964190767979, + -14.194068399439843 + ], + [ + 5890.2072198111, + -15.165199424882926 + ], + [ + 6030.450248854221, + -16.147307024868628 + ], + [ + 6170.693277897342, + -17.138045678187755 + ], + [ + 6310.936306940464, + -18.135093779982082 + ], + [ + 6451.179335983586, + -19.136101667456177 + ], + [ + 6591.422365026707, + -20.139082563695986 + ], + [ + 6731.665394069829, + -21.14468349233339 + ], + [ + 6871.90842311295, + -22.156789485905342 + ], + [ + 7012.151452156071, + -23.179522165936593 + ], + [ + 7152.394481199192, + -24.21701219903449 + ], + [ + 7292.637510242314, + -25.273587576345225 + ], + [ + 7432.880539285436, + -26.353404479988466 + ], + [ + 7573.123568328557, + -27.455605111425324 + ], + [ + 7713.366597371678, + -28.56473862486715 + ], + [ + 7853.6096264148, + -29.662360739071225 + ], + [ + 7993.852655457921, + -30.733099540416788 + ], + [ + 8134.095684501042, + -31.782337541426973 + ], + [ + 8274.338713544164, + -32.82349658831526 + ], + [ + 8414.581742587285, + -33.8698436997206 + ], + [ + 8554.824771630407, + -34.92993085644528 + ], + [ + 8695.067800673529, + -36.00134080503615 + ], + [ + 8835.31082971665, + -37.08012724994852 + ], + [ + 8975.553858759771, + -38.16234246686392 + ], + [ + 9115.796887802893, + -39.24408135856545 + ], + [ + 9256.039916846014, + -40.32214873374614 + ], + [ + 9396.282945889136, + -41.39391991189897 + ], + [ + 9536.525974932258, + -42.456127038833806 + ], + [ + 9676.769003975378, + -43.50650435612337 + ], + [ + 9817.0120330185, + -44.54400252651067 + ], + [ + 9957.25506206162, + -45.56762498112688 + ], + [ + 10097.498091104742, + -46.57637523836368 + ], + [ + 10237.741120147864, + -47.569255217507326 + ], + [ + 10377.984149190985, + -48.54519008214819 + ], + [ + 10518.227178234107, + -49.50241943060796 + ], + [ + 10658.470207277229, + -50.43848402004503 + ], + [ + 10798.71323632035, + -51.35005444777822 + ], + [ + 10938.956265363471, + -52.23290291171749 + ], + [ + 11079.199294406593, + -53.08236192258775 + ], + [ + 11219.442323449714, + -53.89422012559097 + ], + [ + 11359.685352492836, + -54.66606820724514 + ], + [ + 11499.928381535958, + -55.39508472579795 + ], + [ + 11640.171410579078, + -56.078188073864624 + ], + [ + 11780.4144396222, + -56.7125258807314 + ], + [ + 11920.65746866532, + -57.295770412327336 + ], + [ + 12060.900497708442, + -57.826552552670364 + ], + [ + 12201.143526751564, + -58.30763022432158 + ], + [ + 12341.386555794685, + -58.7432713848857 + ], + [ + 12481.629584837807, + -59.13719815365632 + ], + [ + 12621.872613880929, + -59.492984816296946 + ], + [ + 12762.11564292405, + -59.81393748225411 + ], + [ + 12902.358671967171, + -60.104122773102134 + ], + [ + 13042.601701010293, + -60.367874630237104 + ], + [ + 13182.844730053414, + -60.612125853794026 + ], + [ + 13323.087759096536, + -60.84504304708698 + ], + [ + 13463.330788139658, + -61.07493924726211 + ], + [ + 13603.573817182778, + -61.31014075961052 + ], + [ + 13743.8168462259, + -61.557971189859146 + ], + [ + 13884.059875269022, + -61.816881813940626 + ] + ] + }, + "atmosphericModelWindVelocityXProfile": { + "4": [ + [ + 0.0, + 0.05975253641103586 + ], + [ + 140.24302904312142, + -0.12105108934146276 + ], + [ + 280.48605808624285, + 0.048393224947125085 + ], + [ + 420.7290871293643, + 0.48342913975273993 + ], + [ + 560.9721161724857, + 0.9623662433328655 + ], + [ + 701.2151452156071, + 1.385295428134752 + ], + [ + 841.4581742587286, + 1.7213838213380492 + ], + [ + 981.70120330185, + 1.9933657947668197 + ], + [ + 1121.9442323449714, + 2.227639908835345 + ], + [ + 1262.1872613880928, + 2.4429619528724316 + ], + [ + 1402.4302904312142, + 2.6504560992788098 + ], + [ + 1542.6733194743356, + 2.851869046671696 + ], + [ + 1682.9163485174572, + 3.05795179298655 + ], + [ + 1823.1593775605786, + 3.278724878013396 + ], + [ + 1963.4024066037, + 3.4523528313439074 + ], + [ + 2103.645435646821, + 3.581366658304242 + ], + [ + 2243.888464689943, + 3.8963055000003006 + ], + [ + 2384.1314937330644, + 4.113384124244404 + ], + [ + 2524.3745227761856, + 4.185434584401804 + ], + [ + 2664.617551819307, + 4.332033417629795 + ], + [ + 2804.8605808624284, + 4.540752391140537 + ], + [ + 2945.10360990555, + 4.775422163901467 + ], + [ + 3085.346638948671, + 5.000527652139862 + ], + [ + 3225.589667991793, + 5.196269698427465 + ], + [ + 3365.8326970349144, + 5.370427446853416 + ], + [ + 3506.0757260780356, + 5.534195942456559 + ], + [ + 3646.318755121157, + 5.698286848849614 + ], + [ + 3786.5617841642784, + 5.8694635620744835 + ], + [ + 3926.8048132074, + 6.045752349035022 + ], + [ + 4067.047842250521, + 6.222894173208381 + ], + [ + 4207.290871293642, + 6.396843470354744 + ], + [ + 4347.533900336764, + 6.56620796723421 + ], + [ + 4487.776929379886, + 6.733862879627378 + ], + [ + 4628.019958423007, + 6.904090434093128 + ], + [ + 4768.262987466129, + 7.081142544920256 + ], + [ + 4908.50601650925, + 7.268110692695415 + ], + [ + 5048.749045552371, + 7.463504192955759 + ], + [ + 5188.992074595492, + 7.661818326740881 + ], + [ + 5329.235103638614, + 7.85713530224667 + ], + [ + 5469.478132681736, + 8.043643845582801 + ], + [ + 5609.721161724857, + 8.2163013071135 + ], + [ + 5749.964190767979, + 8.37398314425641 + ], + [ + 5890.2072198111, + 8.520253405088447 + ], + [ + 6030.450248854221, + 8.66009847947187 + ], + [ + 6170.693277897342, + 8.798537113818027 + ], + [ + 6310.936306940464, + 8.940517181002246 + ], + [ + 6451.179335983586, + 9.089700287552835 + ], + [ + 6591.422365026707, + 9.246099877365623 + ], + [ + 6731.665394069829, + 9.406214587099512 + ], + [ + 6871.90842311295, + 9.565410393699882 + ], + [ + 7012.151452156071, + 9.718953580033748 + ], + [ + 7152.394481199192, + 9.862202635983836 + ], + [ + 7292.637510242314, + 9.991930962497136 + ], + [ + 7432.880539285436, + 10.10742879403126 + ], + [ + 7573.123568328557, + 10.21000012780444 + ], + [ + 7713.366597371678, + 10.302603969690194 + ], + [ + 7853.6096264148, + 10.38867429069832 + ], + [ + 7993.852655457921, + 10.471639398360457 + ], + [ + 8134.095684501042, + 10.554534746237778 + ], + [ + 8274.338713544164, + 10.638591358186972 + ], + [ + 8414.581742587285, + 10.72521651038363 + ], + [ + 8554.824771630407, + 10.817720198008553 + ], + [ + 8695.067800673529, + 10.918478598052673 + ], + [ + 8835.31082971665, + 11.029452765291827 + ], + [ + 8975.553858759771, + 11.152602604779144 + ], + [ + 9115.796887802893, + 11.290012959840013 + ], + [ + 9256.039916846014, + 11.44452635993918 + ], + [ + 9396.282945889136, + 11.618111974815413 + ], + [ + 9536.525974932258, + 11.806697029934345 + ], + [ + 9676.769003975378, + 12.002901378449412 + ], + [ + 9817.0120330185, + 12.199909589234291 + ], + [ + 9957.25506206162, + 12.391055266554206 + ], + [ + 10097.498091104742, + 12.56967209556223 + ], + [ + 10237.741120147864, + 12.72907930704581 + ], + [ + 10377.984149190985, + 12.862000702994198 + ], + [ + 10518.227178234107, + 12.960884174350252 + ], + [ + 10658.470207277229, + 13.024616591202848 + ], + [ + 10798.71323632035, + 13.065766247036674 + ], + [ + 10938.956265363471, + 13.09945534536415 + ], + [ + 11079.199294406593, + 13.14074216746216 + ], + [ + 11219.442323449714, + 13.203939922253854 + ], + [ + 11359.685352492836, + 13.292120599120642 + ], + [ + 11499.928381535958, + 13.391100965025581 + ], + [ + 11640.171410579078, + 13.484772704847833 + ], + [ + 11780.4144396222, + 13.558092876369846 + ], + [ + 11920.65746866532, + 13.596287419334777 + ], + [ + 12060.900497708442, + 13.590731639109778 + ], + [ + 12201.143526751564, + 13.546039445292283 + ], + [ + 12341.386555794685, + 13.472140380103246 + ], + [ + 12481.629584837807, + 13.378407540432182 + ], + [ + 12621.872613880929, + 13.273820498652295 + ], + [ + 12762.11564292405, + 13.166833634777213 + ], + [ + 12902.358671967171, + 13.063481059012863 + ], + [ + 13042.601701010293, + 12.964424749412604 + ], + [ + 13182.844730053414, + 12.866891623284102 + ], + [ + 13323.087759096536, + 12.768103771736206 + ], + [ + 13463.330788139658, + 12.665494685602512 + ], + [ + 13603.573817182778, + 12.556507935115478 + ], + [ + 13743.8168462259, + 12.438812168606297 + ], + [ + 13884.059875269022, + 12.310034577653608 + ] + ], + "5": [ + [ + 0.0, + -0.09581216793885428 + ], + [ + 140.24302904312142, + -0.2901107505328242 + ], + [ + 280.48605808624285, + -0.11753448018325317 + ], + [ + 420.7290871293643, + 0.36184680407551967 + ], + [ + 560.9721161724857, + 0.8832647111527724 + ], + [ + 701.2151452156071, + 1.3349832059582614 + ], + [ + 841.4581742587286, + 1.693634317672904 + ], + [ + 981.70120330185, + 1.981725190672378 + ], + [ + 1121.9442323449714, + 2.231442709948685 + ], + [ + 1262.1872613880928, + 2.467589986866224 + ], + [ + 1402.4302904312142, + 2.6990928834853163 + ], + [ + 1542.6733194743356, + 2.919203905396589 + ], + [ + 1682.9163485174572, + 3.1248258375001075 + ], + [ + 1823.1593775605786, + 3.3266656126272367 + ], + [ + 1963.4024066037, + 3.521661541756688 + ], + [ + 2103.645435646821, + 3.706365269180036 + ], + [ + 2243.888464689943, + 3.892114796912935 + ], + [ + 2384.1314937330644, + 4.08310153097989 + ], + [ + 2524.3745227761856, + 4.278578862543754 + ], + [ + 2664.617551819307, + 4.477909970378289 + ], + [ + 2804.8605808624284, + 4.679043678168432 + ], + [ + 2945.10360990555, + 4.879930091992764 + ], + [ + 3085.346638948671, + 5.07879633940901 + ], + [ + 3225.589667991793, + 5.274417733046291 + ], + [ + 3365.8326970349144, + 5.466678330819155 + ], + [ + 3506.0757260780356, + 5.655561878679655 + ], + [ + 3646.318755121157, + 5.84071785055161 + ], + [ + 3786.5617841642784, + 6.021783785400308 + ], + [ + 3926.8048132074, + 6.198346822362298 + ], + [ + 4067.047842250521, + 6.369935907150596 + ], + [ + 4207.290871293642, + 6.536253877491432 + ], + [ + 4347.533900336764, + 6.698595122748041 + ], + [ + 4487.776929379886, + 6.859677355553664 + ], + [ + 4628.019958423007, + 7.021930986749313 + ], + [ + 4768.262987466129, + 7.187690280403371 + ], + [ + 4908.50601650925, + 7.358505019226913 + ], + [ + 5048.749045552371, + 7.533297833366291 + ], + [ + 5188.992074595492, + 7.709312536657306 + ], + [ + 5329.235103638614, + 7.884147276159122 + ], + [ + 5469.478132681736, + 8.055520331485953 + ], + [ + 5609.721161724857, + 8.221451934042518 + ], + [ + 5749.964190767979, + 8.381708212410173 + ], + [ + 5890.2072198111, + 8.538478619730958 + ], + [ + 6030.450248854221, + 8.694035829818768 + ], + [ + 6170.693277897342, + 8.850455526704835 + ], + [ + 6310.936306940464, + 9.009827761191818 + ], + [ + 6451.179335983586, + 9.17425123495282 + ], + [ + 6591.422365026707, + 9.343077868155856 + ], + [ + 6731.665394069829, + 9.512333185761708 + ], + [ + 6871.90842311295, + 9.677646895000603 + ], + [ + 7012.151452156071, + 9.834662620251182 + ], + [ + 7152.394481199192, + 9.979068661001504 + ], + [ + 7292.637510242314, + 10.107247100109467 + ], + [ + 7432.880539285436, + 10.218021180686925 + ], + [ + 7573.123568328557, + 10.315421467570951 + ], + [ + 7713.366597371678, + 10.40623712655213 + ], + [ + 7853.6096264148, + 10.497576149484226 + ], + [ + 7993.852655457921, + 10.595656723013253 + ], + [ + 8134.095684501042, + 10.7004044894769 + ], + [ + 8274.338713544164, + 10.807698716201061 + ], + [ + 8414.581742587285, + 10.913683778033874 + ], + [ + 8554.824771630407, + 11.01909214001776 + ], + [ + 8695.067800673529, + 11.12528847502759 + ], + [ + 8835.31082971665, + 11.2332699717602 + ], + [ + 8975.553858759771, + 11.344032430325319 + ], + [ + 9115.796887802893, + 11.458667772148475 + ], + [ + 9256.039916846014, + 11.579111793327298 + ], + [ + 9396.282945889136, + 11.707037878973974 + ], + [ + 9536.525974932258, + 11.841323445961992 + ], + [ + 9676.769003975378, + 11.978507703715506 + ], + [ + 9817.0120330185, + 12.115614517392268 + ], + [ + 9957.25506206162, + 12.249812488433664 + ], + [ + 10097.498091104742, + 12.378270291394925 + ], + [ + 10237.741120147864, + 12.498143269139433 + ], + [ + 10377.984149190985, + 12.60607607011278 + ], + [ + 10518.227178234107, + 12.698385799213703 + ], + [ + 10658.470207277229, + 12.774275576905097 + ], + [ + 10798.71323632035, + 12.83938509068122 + ], + [ + 10938.956265363471, + 12.901826026852389 + ], + [ + 11079.199294406593, + 12.969837957109608 + ], + [ + 11219.442323449714, + 13.051001926336818 + ], + [ + 11359.685352492836, + 13.146643125624617 + ], + [ + 11499.928381535958, + 13.247511724401221 + ], + [ + 11640.171410579078, + 13.341859058175189 + ], + [ + 11780.4144396222, + 13.418579480642848 + ], + [ + 11920.65746866532, + 13.466934908853602 + ], + [ + 12060.900497708442, + 13.481106580556503 + ], + [ + 12201.143526751564, + 13.46420036069762 + ], + [ + 12341.386555794685, + 13.42362406251743 + ], + [ + 12481.629584837807, + 13.366051265526005 + ], + [ + 12621.872613880929, + 13.297814511538082 + ], + [ + 12762.11564292405, + 13.224616114685487 + ], + [ + 12902.358671967171, + 13.149611412206774 + ], + [ + 13042.601701010293, + 13.072151454241164 + ], + [ + 13182.844730053414, + 12.989584810096565 + ], + [ + 13323.087759096536, + 12.899999904795308 + ], + [ + 13463.330788139658, + 12.80173215048416 + ], + [ + 13603.573817182778, + 12.693125124968116 + ], + [ + 13743.8168462259, + 12.572917553484366 + ], + [ + 13884.059875269022, + 12.44058222779078 + ] + ], + "6": [ + [ + 0.0, + -0.2744567926448979 + ], + [ + 140.24302904312142, + -0.5158352322288213 + ], + [ + 280.48605808624285, + -0.37052789549127185 + ], + [ + 420.7290871293643, + 0.1341128698139211 + ], + [ + 560.9721161724857, + 0.7026695702864003 + ], + [ + 701.2151452156071, + 1.1884906864491236 + ], + [ + 841.4581742587286, + 1.5623801056753692 + ], + [ + 981.70120330185, + 1.8601564666954007 + ], + [ + 1121.9442323449714, + 2.121459424091444 + ], + [ + 1262.1872613880928, + 2.3726839609803925 + ], + [ + 1402.4302904312142, + 2.61840550270393 + ], + [ + 1542.6733194743356, + 2.851438535271949 + ], + [ + 1682.9163485174572, + 3.0718233785118048 + ], + [ + 1823.1593775605786, + 3.2866698481721963 + ], + [ + 1963.4024066037, + 3.494094540010056 + ], + [ + 2103.645435646821, + 3.6924191788496943 + ], + [ + 2243.888464689943, + 3.887334204452051 + ], + [ + 2384.1314937330644, + 4.082457074731397 + ], + [ + 2524.3745227761856, + 4.280652149499247 + ], + [ + 2664.617551819307, + 4.482084372119386 + ], + [ + 2804.8605808624284, + 4.68505732413065 + ], + [ + 2945.10360990555, + 4.888011635702934 + ], + [ + 3085.346638948671, + 5.0900769429015975 + ], + [ + 3225.589667991793, + 5.290508577981728 + ], + [ + 3365.8326970349144, + 5.487948002811035 + ], + [ + 3506.0757260780356, + 5.680869276158699 + ], + [ + 3646.318755121157, + 5.866975942273867 + ], + [ + 3786.5617841642784, + 6.044630571253565 + ], + [ + 3926.8048132074, + 6.214912286358626 + ], + [ + 4067.047842250521, + 6.379474373357724 + ], + [ + 4207.290871293642, + 6.539996459021995 + ], + [ + 4347.533900336764, + 6.69947461743143 + ], + [ + 4487.776929379886, + 6.859869306155752 + ], + [ + 4628.019958423007, + 7.021290663741153 + ], + [ + 4768.262987466129, + 7.183699410198411 + ], + [ + 4908.50601650925, + 7.346690500817404 + ], + [ + 5048.749045552371, + 7.508541012433086 + ], + [ + 5188.992074595492, + 7.668694685944979 + ], + [ + 5329.235103638614, + 7.827953203950819 + ], + [ + 5469.478132681736, + 7.987234495441049 + ], + [ + 5609.721161724857, + 8.147688368285543 + ], + [ + 5749.964190767979, + 8.310893045716714 + ], + [ + 5890.2072198111, + 8.477359471313282 + ], + [ + 6030.450248854221, + 8.645911707786304 + ], + [ + 6170.693277897342, + 8.815061931626335 + ], + [ + 6310.936306940464, + 8.983334774387346 + ], + [ + 6451.179335983586, + 9.14942503630764 + ], + [ + 6591.422365026707, + 9.3115675952656 + ], + [ + 6731.665394069829, + 9.468266753706613 + ], + [ + 6871.90842311295, + 9.618837920523312 + ], + [ + 7012.151452156071, + 9.762691859276462 + ], + [ + 7152.394481199192, + 9.899278485451692 + ], + [ + 7292.637510242314, + 10.02856787084769 + ], + [ + 7432.880539285436, + 10.15123768180039 + ], + [ + 7573.123568328557, + 10.268417700185037 + ], + [ + 7713.366597371678, + 10.381375217716752 + ], + [ + 7853.6096264148, + 10.49141858872417 + ], + [ + 7993.852655457921, + 10.599856170083486 + ], + [ + 8134.095684501042, + 10.707664629047628 + ], + [ + 8274.338713544164, + 10.81433402057536 + ], + [ + 8414.581742587285, + 10.91977819348623 + ], + [ + 8554.824771630407, + 11.024534077761032 + ], + [ + 8695.067800673529, + 11.128776079315884 + ], + [ + 8835.31082971665, + 11.232502600186361 + ], + [ + 8975.553858759771, + 11.33571007042249 + ], + [ + 9115.796887802893, + 11.438409779211055 + ], + [ + 9256.039916846014, + 11.541276148879001 + ], + [ + 9396.282945889136, + 11.645189359545489 + ], + [ + 9536.525974932258, + 11.750153773402326 + ], + [ + 9676.769003975378, + 11.855322284605277 + ], + [ + 9817.0120330185, + 11.959885290935972 + ], + [ + 9957.25506206162, + 12.063128682342322 + ], + [ + 10097.498091104742, + 12.16433839889848 + ], + [ + 10237.741120147864, + 12.262802733513707 + ], + [ + 10377.984149190985, + 12.357770277018387 + ], + [ + 10518.227178234107, + 12.44830958493666 + ], + [ + 10658.470207277229, + 12.533534696055614 + ], + [ + 10798.71323632035, + 12.61367388589628 + ], + [ + 10938.956265363471, + 12.69175672325861 + ], + [ + 11079.199294406593, + 12.770892896121785 + ], + [ + 11219.442323449714, + 12.853815126013435 + ], + [ + 11359.685352492836, + 12.941726891051223 + ], + [ + 11499.928381535958, + 13.03095976166426 + ], + [ + 11640.171410579078, + 13.114388163600893 + ], + [ + 11780.4144396222, + 13.185273877990578 + ], + [ + 11920.65746866532, + 13.2373351661554 + ], + [ + 12060.900497708442, + 13.267178046821746 + ], + [ + 12201.143526751564, + 13.27591294188623 + ], + [ + 12341.386555794685, + 13.268088815531561 + ], + [ + 12481.629584837807, + 13.246891331727983 + ], + [ + 12621.872613880929, + 13.215068845619664 + ], + [ + 12762.11564292405, + 13.17493957659699 + ], + [ + 12902.358671967171, + 13.126842514196746 + ], + [ + 13042.601701010293, + 13.068960454213594 + ], + [ + 13182.844730053414, + 12.998307228955005 + ], + [ + 13323.087759096536, + 12.913762424060634 + ], + [ + 13463.330788139658, + 12.814583170826777 + ], + [ + 13603.573817182778, + 12.700020117895578 + ], + [ + 13743.8168462259, + 12.569600904255452 + ], + [ + 13884.059875269022, + 12.42396863433451 + ] + ], + "7": [ + [ + 0.0, + -0.3826452642741105 + ], + [ + 140.24302904312142, + -0.652714209566475 + ], + [ + 280.48605808624285, + -0.5370262751507601 + ], + [ + 420.7290871293643, + -0.035273236472956375 + ], + [ + 560.9721161724857, + 0.5529686330264696 + ], + [ + 701.2151452156071, + 1.058857944534216 + ], + [ + 841.4581742587286, + 1.4415139463700999 + ], + [ + 981.70120330185, + 1.7477574710176345 + ], + [ + 1121.9442323449714, + 2.017646571430194 + ], + [ + 1262.1872613880928, + 2.272289269611772 + ], + [ + 1402.4302904312142, + 2.5198027045824123 + ], + [ + 1542.6733194743356, + 2.761217253809231 + ], + [ + 1682.9163485174572, + 2.9928872535628437 + ], + [ + 1823.1593775605786, + 3.2143075465621522 + ], + [ + 1963.4024066037, + 3.427085281412249 + ], + [ + 2103.645435646821, + 3.636091137053812 + ], + [ + 2243.888464689943, + 3.848172599004311 + ], + [ + 2384.1314937330644, + 4.063257920066181 + ], + [ + 2524.3745227761856, + 4.28031521237662 + ], + [ + 2664.617551819307, + 4.496576005155489 + ], + [ + 2804.8605808624284, + 4.708380928947301 + ], + [ + 2945.10360990555, + 4.912196509856161 + ], + [ + 3085.346638948671, + 5.106496676548107 + ], + [ + 3225.589667991793, + 5.2934004848956135 + ], + [ + 3365.8326970349144, + 5.474149769341179 + ], + [ + 3506.0757260780356, + 5.64967868177234 + ], + [ + 3646.318755121157, + 5.8202624251164785 + ], + [ + 3786.5617841642784, + 5.985028940800503 + ], + [ + 3926.8048132074, + 6.145088288891834 + ], + [ + 4067.047842250521, + 6.302301203748753 + ], + [ + 4207.290871293642, + 6.458212611791184 + ], + [ + 4347.533900336764, + 6.614679733126369 + ], + [ + 4487.776929379886, + 6.7732432968312715 + ], + [ + 4628.019958423007, + 6.933806727633609 + ], + [ + 4768.262987466129, + 7.096112091219616 + ], + [ + 4908.50601650925, + 7.26021599519358 + ], + [ + 5048.749045552371, + 7.425019651350629 + ], + [ + 5188.992074595492, + 7.589436678347052 + ], + [ + 5329.235103638614, + 7.753719064122536 + ], + [ + 5469.478132681736, + 7.9182162319453315 + ], + [ + 5609.721161724857, + 8.083158797048311 + ], + [ + 5749.964190767979, + 8.2490008843539 + ], + [ + 5890.2072198111, + 8.41602620929409 + ], + [ + 6030.450248854221, + 8.582685119003386 + ], + [ + 6170.693277897342, + 8.7470879676312 + ], + [ + 6310.936306940464, + 8.907389859739 + ], + [ + 6451.179335983586, + 9.062420453263208 + ], + [ + 6591.422365026707, + 9.211489799348016 + ], + [ + 6731.665394069829, + 9.354621666819636 + ], + [ + 6871.90842311295, + 9.492838056909292 + ], + [ + 7012.151452156071, + 9.627271033542844 + ], + [ + 7152.394481199192, + 9.759075237835903 + ], + [ + 7292.637510242314, + 9.889324319283444 + ], + [ + 7432.880539285436, + 10.018118432530246 + ], + [ + 7573.123568328557, + 10.144880097580856 + ], + [ + 7713.366597371678, + 10.26956752606431 + ], + [ + 7853.6096264148, + 10.392207162638224 + ], + [ + 7993.852655457921, + 10.512818760554405 + ], + [ + 8134.095684501042, + 10.631178158144943 + ], + [ + 8274.338713544164, + 10.746517834240553 + ], + [ + 8414.581742587285, + 10.860217181122879 + ], + [ + 8554.824771630407, + 10.973718192955975 + ], + [ + 8695.067800673529, + 11.085248612216317 + ], + [ + 8835.31082971665, + 11.192680523992808 + ], + [ + 8975.553858759771, + 11.293884825397713 + ], + [ + 9115.796887802893, + 11.38678233456867 + ], + [ + 9256.039916846014, + 11.469582265620403 + ], + [ + 9396.282945889136, + 11.539896339965413 + ], + [ + 9536.525974932258, + 11.611525786312123 + ], + [ + 9676.769003975378, + 11.704438545386719 + ], + [ + 9817.0120330185, + 11.812891184833255 + ], + [ + 9957.25506206162, + 11.930686389494214 + ], + [ + 10097.498091104742, + 12.051626889275637 + ], + [ + 10237.741120147864, + 12.169517518534319 + ], + [ + 10377.984149190985, + 12.278251710696313 + ], + [ + 10518.227178234107, + 12.371792600868993 + ], + [ + 10658.470207277229, + 12.44632984781913 + ], + [ + 10798.71323632035, + 12.503855756748852 + ], + [ + 10938.956265363471, + 12.552681977292185 + ], + [ + 11079.199294406593, + 12.601855020655936 + ], + [ + 11219.442323449714, + 12.660132646681697 + ], + [ + 11359.685352492836, + 12.732379409719165 + ], + [ + 11499.928381535958, + 12.815605193611546 + ], + [ + 11640.171410579078, + 12.902565617444038 + ], + [ + 11780.4144396222, + 12.986270105928382 + ], + [ + 11920.65746866532, + 13.060079713049365 + ], + [ + 12060.900497708442, + 13.119414922366419 + ], + [ + 12201.143526751564, + 13.16369814763368 + ], + [ + 12341.386555794685, + 13.194196199043704 + ], + [ + 12481.629584837807, + 13.210061022093903 + ], + [ + 12621.872613880929, + 13.209975069439732 + ], + [ + 12762.11564292405, + 13.192412160505208 + ], + [ + 12902.358671967171, + 13.1553599326336 + ], + [ + 13042.601701010293, + 13.096971166418964 + ], + [ + 13182.844730053414, + 13.016521710760086 + ], + [ + 13323.087759096536, + 12.915938206740776 + ], + [ + 13463.330788139658, + 12.797560574454756 + ], + [ + 13603.573817182778, + 12.663716373384089 + ], + [ + 13743.8168462259, + 12.516831766474494 + ], + [ + 13884.059875269022, + 12.35969891091312 + ] + ], + "8": [ + [ + 0.0, + -0.48143992989553125 + ], + [ + 140.24302904312142, + -0.7726463209240623 + ], + [ + 280.48605808624285, + -0.6665727555272393 + ], + [ + 420.7290871293643, + -0.1544118742850826 + ], + [ + 560.9721161724857, + 0.4421857300540506 + ], + [ + 701.2151452156071, + 0.9550834759521467 + ], + [ + 841.4581742587286, + 1.344312473184715 + ], + [ + 981.70120330185, + 1.6462740614205609 + ], + [ + 1121.9442323449714, + 1.9055608306301308 + ], + [ + 1262.1872613880928, + 2.1532873346460386 + ], + [ + 1402.4302904312142, + 2.4026540012595636 + ], + [ + 1542.6733194743356, + 2.6577664300371664 + ], + [ + 1682.9163485174572, + 2.9050734967926557 + ], + [ + 1823.1593775605786, + 3.1360527306878856 + ], + [ + 1963.4024066037, + 3.359949508453841 + ], + [ + 2103.645435646821, + 3.5848496116736306 + ], + [ + 2243.888464689943, + 3.812384649553542 + ], + [ + 2384.1314937330644, + 4.0384950367362835 + ], + [ + 2524.3745227761856, + 4.260692975590104 + ], + [ + 2664.617551819307, + 4.477267858782961 + ], + [ + 2804.8605808624284, + 4.685466370189367 + ], + [ + 2945.10360990555, + 4.882517945545015 + ], + [ + 3085.346638948671, + 5.068339095543464 + ], + [ + 3225.589667991793, + 5.246323192725561 + ], + [ + 3365.8326970349144, + 5.4189971801954275 + ], + [ + 3506.0757260780356, + 5.588530893956111 + ], + [ + 3646.318755121157, + 5.755946865403545 + ], + [ + 3786.5617841642784, + 5.920140088536452 + ], + [ + 3926.8048132074, + 6.080511719431065 + ], + [ + 4067.047842250521, + 6.237070216269466 + ], + [ + 4207.290871293642, + 6.389768549144544 + ], + [ + 4347.533900336764, + 6.539179476476786 + ], + [ + 4487.776929379886, + 6.687314920032076 + ], + [ + 4628.019958423007, + 6.835861775947603 + ], + [ + 4768.262987466129, + 6.9863650391437195 + ], + [ + 4908.50601650925, + 7.140624127561974 + ], + [ + 5048.749045552371, + 7.2991970392135785 + ], + [ + 5188.992074595492, + 7.461071009309595 + ], + [ + 5329.235103638614, + 7.625995019687556 + ], + [ + 5469.478132681736, + 7.793781467727866 + ], + [ + 5609.721161724857, + 7.963920752604281 + ], + [ + 5749.964190767979, + 8.135440747315112 + ], + [ + 5890.2072198111, + 8.307903420924132 + ], + [ + 6030.450248854221, + 8.479166194726188 + ], + [ + 6170.693277897342, + 8.646759947483513 + ], + [ + 6310.936306940464, + 8.80826159140592 + ], + [ + 6451.179335983586, + 8.962542819636033 + ], + [ + 6591.422365026707, + 9.109796273288548 + ], + [ + 6731.665394069829, + 9.250890944972147 + ], + [ + 6871.90842311295, + 9.387949220252532 + ], + [ + 7012.151452156071, + 9.523206603855828 + ], + [ + 7152.394481199192, + 9.658926992236882 + ], + [ + 7292.637510242314, + 9.796932778487466 + ], + [ + 7432.880539285436, + 9.93722912557741 + ], + [ + 7573.123568328557, + 10.078377762294547 + ], + [ + 7713.366597371678, + 10.218423099091762 + ], + [ + 7853.6096264148, + 10.355328963284274 + ], + [ + 7993.852655457921, + 10.487048424676258 + ], + [ + 8134.095684501042, + 10.6112557767321 + ], + [ + 8274.338713544164, + 10.72528913017573 + ], + [ + 8414.581742587285, + 10.829033178440966 + ], + [ + 8554.824771630407, + 10.924937307923738 + ], + [ + 8695.067800673529, + 11.01549900660872 + ], + [ + 8835.31082971665, + 11.103150916231618 + ], + [ + 8975.553858759771, + 11.190325531569458 + ], + [ + 9115.796887802893, + 11.279587262187272 + ], + [ + 9256.039916846014, + 11.374033272640055 + ], + [ + 9396.282945889136, + 11.476382123663845 + ], + [ + 9536.525974932258, + 11.5854739326158 + ], + [ + 9676.769003975378, + 11.6986060705913 + ], + [ + 9817.0120330185, + 11.813017079168933 + ], + [ + 9957.25506206162, + 11.926001769311073 + ], + [ + 10097.498091104742, + 12.034854976600236 + ], + [ + 10237.741120147864, + 12.13686205477037 + ], + [ + 10377.984149190985, + 12.229041599135508 + ], + [ + 10518.227178234107, + 12.30762359813761 + ], + [ + 10658.470207277229, + 12.371655365823754 + ], + [ + 10798.71323632035, + 12.425754584332836 + ], + [ + 10938.956265363471, + 12.476336275772493 + ], + [ + 11079.199294406593, + 12.530149122100934 + ], + [ + 11219.442323449714, + 12.594583331272414 + ], + [ + 11359.685352492836, + 12.67129398134863 + ], + [ + 11499.928381535958, + 12.755391865318197 + ], + [ + 11640.171410579078, + 12.841847296033714 + ], + [ + 11780.4144396222, + 12.926100820016732 + ], + [ + 11920.65746866532, + 13.003561736622956 + ], + [ + 12060.900497708442, + 13.071849680596205 + ], + [ + 12201.143526751564, + 13.13201140211009 + ], + [ + 12341.386555794685, + 13.182556102982112 + ], + [ + 12481.629584837807, + 13.219195670200827 + ], + [ + 12621.872613880929, + 13.237251132667906 + ], + [ + 12762.11564292405, + 13.231754278649914 + ], + [ + 12902.358671967171, + 13.197833853375828 + ], + [ + 13042.601701010293, + 13.13316210665239 + ], + [ + 13182.844730053414, + 13.039539338433123 + ], + [ + 13323.087759096536, + 12.921426625674153 + ], + [ + 13463.330788139658, + 12.783634669752509 + ], + [ + 13603.573817182778, + 12.630971123716806 + ], + [ + 13743.8168462259, + 12.468444853743593 + ], + [ + 13884.059875269022, + 12.300807079680427 + ] + ], + "9": [ + [ + 0.0, + -0.5326453672774275 + ], + [ + 140.24302904312142, + -0.9094948627577304 + ], + [ + 280.48605808624285, + -0.8668188266989217 + ], + [ + 420.7290871293643, + -0.3469349298618799 + ], + [ + 560.9721161724857, + 0.2699981503382397 + ], + [ + 701.2151452156071, + 0.7888660884942983 + ], + [ + 841.4581742587286, + 1.1808741756239969 + ], + [ + 981.70120330185, + 1.4823967756911667 + ], + [ + 1121.9442323449714, + 1.7356345423775086 + ], + [ + 1262.1872613880928, + 1.9733107896641549 + ], + [ + 1402.4302904312142, + 2.216831722282685 + ], + [ + 1542.6733194743356, + 2.4745915026743535 + ], + [ + 1682.9163485174572, + 2.7303630079332306 + ], + [ + 1823.1593775605786, + 2.972204210844605 + ], + [ + 1963.4024066037, + 3.2085080728872937 + ], + [ + 2103.645435646821, + 3.4428325365826793 + ], + [ + 2243.888464689943, + 3.6720695687690066 + ], + [ + 2384.1314937330644, + 3.8956791291713775 + ], + [ + 2524.3745227761856, + 4.115186682551433 + ], + [ + 2664.617551819307, + 4.329998869125241 + ], + [ + 2804.8605808624284, + 4.538073861315107 + ], + [ + 2945.10360990555, + 4.737207892605408 + ], + [ + 3085.346638948671, + 4.927527615640532 + ], + [ + 3225.589667991793, + 5.11119238497076 + ], + [ + 3365.8326970349144, + 5.288597075664848 + ], + [ + 3506.0757260780356, + 5.459697695983509 + ], + [ + 3646.318755121157, + 5.623861770955538 + ], + [ + 3786.5617841642784, + 5.779816523142207 + ], + [ + 3926.8048132074, + 5.927098691522974 + ], + [ + 4067.047842250521, + 6.065964311516253 + ], + [ + 4207.290871293642, + 6.196822623076902 + ], + [ + 4347.533900336764, + 6.321202500807541 + ], + [ + 4487.776929379886, + 6.443210004222702 + ], + [ + 4628.019958423007, + 6.567613743655277 + ], + [ + 4768.262987466129, + 6.6990612238055505 + ], + [ + 4908.50601650925, + 6.841846015593148 + ], + [ + 5048.749045552371, + 6.997105100581865 + ], + [ + 5188.992074595492, + 7.1623558135430185 + ], + [ + 5329.235103638614, + 7.335001385910042 + ], + [ + 5469.478132681736, + 7.512465623615457 + ], + [ + 5609.721161724857, + 7.692073346686597 + ], + [ + 5749.964190767979, + 7.871830918016359 + ], + [ + 5890.2072198111, + 8.051492459409586 + ], + [ + 6030.450248854221, + 8.229549933100378 + ], + [ + 6170.693277897342, + 8.404227774570654 + ], + [ + 6310.936306940464, + 8.573831766537097 + ], + [ + 6451.179335983586, + 8.737921564334407 + ], + [ + 6591.422365026707, + 8.896405942437335 + ], + [ + 6731.665394069829, + 9.049488534203261 + ], + [ + 6871.90842311295, + 9.198565498556857 + ], + [ + 7012.151452156071, + 9.345129047735972 + ], + [ + 7152.394481199192, + 9.49059900115812 + ], + [ + 7292.637510242314, + 9.63563203529102 + ], + [ + 7432.880539285436, + 9.780114684381358 + ], + [ + 7573.123568328557, + 9.923181348910024 + ], + [ + 7713.366597371678, + 10.06342159728898 + ], + [ + 7853.6096264148, + 10.199398601538608 + ], + [ + 7993.852655457921, + 10.329676345075642 + ], + [ + 8134.095684501042, + 10.452750768101891 + ], + [ + 8274.338713544164, + 10.566963066431025 + ], + [ + 8414.581742587285, + 10.672059816281088 + ], + [ + 8554.824771630407, + 10.770083397096329 + ], + [ + 8695.067800673529, + 10.863047298893648 + ], + [ + 8835.31082971665, + 10.952830666073408 + ], + [ + 8975.553858759771, + 11.041312160250113 + ], + [ + 9115.796887802893, + 11.130461323927939 + ], + [ + 9256.039916846014, + 11.22266484690063 + ], + [ + 9396.282945889136, + 11.320285459222465 + ], + [ + 9536.525974932258, + 11.422658483360008 + ], + [ + 9676.769003975378, + 11.527915521462528 + ], + [ + 9817.0120330185, + 11.634294930390842 + ], + [ + 9957.25506206162, + 11.740116234347116 + ], + [ + 10097.498091104742, + 11.843698984708906 + ], + [ + 10237.741120147864, + 11.943349225068632 + ], + [ + 10377.984149190985, + 12.037023016614699 + ], + [ + 10518.227178234107, + 12.121735417486928 + ], + [ + 10658.470207277229, + 12.196747409798519 + ], + [ + 10798.71323632035, + 12.265554919352356 + ], + [ + 10938.956265363471, + 12.33306836336965 + ], + [ + 11079.199294406593, + 12.404944614239334 + ], + [ + 11219.442323449714, + 12.487416640662067 + ], + [ + 11359.685352492836, + 12.580825833648083 + ], + [ + 11499.928381535958, + 12.67958275261682 + ], + [ + 11640.171410579078, + 12.777776338111627 + ], + [ + 11780.4144396222, + 12.869588165282963 + ], + [ + 11920.65746866532, + 12.949415680573559 + ], + [ + 12060.900497708442, + 13.014710734906483 + ], + [ + 12201.143526751564, + 13.067595924455176 + ], + [ + 12341.386555794685, + 13.107546118970388 + ], + [ + 12481.629584837807, + 13.131555368776903 + ], + [ + 12621.872613880929, + 13.136187276433175 + ], + [ + 12762.11564292405, + 13.117405932108671 + ], + [ + 12902.358671967171, + 13.07071876754231 + ], + [ + 13042.601701010293, + 12.99365150124757 + ], + [ + 13182.844730053414, + 12.888409719761126 + ], + [ + 13323.087759096536, + 12.759679833967523 + ], + [ + 13463.330788139658, + 12.61251376581635 + ], + [ + 13603.573817182778, + 12.451969865498306 + ], + [ + 13743.8168462259, + 12.283408878642167 + ], + [ + 13884.059875269022, + 12.111943488059918 + ] + ], + "10": [ + [ + 0.0, + -0.40678916289675876 + ], + [ + 140.24302904312142, + -0.8643680595356692 + ], + [ + 280.48605808624285, + -0.9026331112517069 + ], + [ + 420.7290871293643, + -0.431368853240063 + ], + [ + 560.9721161724857, + 0.15377357031114416 + ], + [ + 701.2151452156071, + 0.651713392510042 + ], + [ + 841.4581742587286, + 1.0274429575269592 + ], + [ + 981.70120330185, + 1.3184015396100486 + ], + [ + 1121.9442323449714, + 1.5678170335560948 + ], + [ + 1262.1872613880928, + 1.8041041528380495 + ], + [ + 1402.4302904312142, + 2.0449551473875234 + ], + [ + 1542.6733194743356, + 2.297572771437222 + ], + [ + 1682.9163485174572, + 2.548554289045103 + ], + [ + 1823.1593775605786, + 2.7872545077609443 + ], + [ + 1963.4024066037, + 3.023378899768393 + ], + [ + 2103.645435646821, + 3.262660714861363 + ], + [ + 2243.888464689943, + 3.4988566435941846 + ], + [ + 2384.1314937330644, + 3.7253885816072736 + ], + [ + 2524.3745227761856, + 3.9415307721163324 + ], + [ + 2664.617551819307, + 4.148685239607374 + ], + [ + 2804.8605808624284, + 4.347787033858549 + ], + [ + 2945.10360990555, + 4.5394832777433 + ], + [ + 3085.346638948671, + 4.725824159036117 + ], + [ + 3225.589667991793, + 4.9085173379777585 + ], + [ + 3365.8326970349144, + 5.086094700833433 + ], + [ + 3506.0757260780356, + 5.256517251087129 + ], + [ + 3646.318755121157, + 5.417081901799828 + ], + [ + 3786.5617841642784, + 5.566038330266238 + ], + [ + 3926.8048132074, + 5.704267791208874 + ], + [ + 4067.047842250521, + 5.83381623449077 + ], + [ + 4207.290871293642, + 5.957053977405349 + ], + [ + 4347.533900336764, + 6.076796097173212 + ], + [ + 4487.776929379886, + 6.196676509475218 + ], + [ + 4628.019958423007, + 6.320834675371863 + ], + [ + 4768.262987466129, + 6.453275201832599 + ], + [ + 4908.50601650925, + 6.597544385265144 + ], + [ + 5048.749045552371, + 6.755110264316003 + ], + [ + 5188.992074595492, + 6.924053840715867 + ], + [ + 5329.235103638614, + 7.101597433586302 + ], + [ + 5469.478132681736, + 7.284916233643388 + ], + [ + 5609.721161724857, + 7.4712613952048645 + ], + [ + 5749.964190767979, + 7.658155421681683 + ], + [ + 5890.2072198111, + 7.844059042999135 + ], + [ + 6030.450248854221, + 8.026454109371375 + ], + [ + 6170.693277897342, + 8.202746207896684 + ], + [ + 6310.936306940464, + 8.370354043230869 + ], + [ + 6451.179335983586, + 8.527744253476522 + ], + [ + 6591.422365026707, + 8.677773181841586 + ], + [ + 6731.665394069829, + 8.834216360448389 + ], + [ + 6871.90842311295, + 8.997199959670466 + ], + [ + 7012.151452156071, + 9.164189223306836 + ], + [ + 7152.394481199192, + 9.332619377666843 + ], + [ + 7292.637510242314, + 9.499042078209088 + ], + [ + 7432.880539285436, + 9.659491910050685 + ], + [ + 7573.123568328557, + 9.812568437357802 + ], + [ + 7713.366597371678, + 9.957907396249903 + ], + [ + 7853.6096264148, + 10.095278274373793 + ], + [ + 7993.852655457921, + 10.224450795778397 + ], + [ + 8134.095684501042, + 10.345147747564434 + ], + [ + 8274.338713544164, + 10.457391731244892 + ], + [ + 8414.581742587285, + 10.56182349993305 + ], + [ + 8554.824771630407, + 10.660511894766866 + ], + [ + 8695.067800673529, + 10.755347085600008 + ], + [ + 8835.31082971665, + 10.847987548063887 + ], + [ + 8975.553858759771, + 10.940090689372296 + ], + [ + 9115.796887802893, + 11.033372261943011 + ], + [ + 9256.039916846014, + 11.129795667492113 + ], + [ + 9396.282945889136, + 11.23157617125161 + ], + [ + 9536.525974932258, + 11.338267791808915 + ], + [ + 9676.769003975378, + 11.447879863643196 + ], + [ + 9817.0120330185, + 11.558858932539973 + ], + [ + 9957.25506206162, + 11.669771065773109 + ], + [ + 10097.498091104742, + 11.77918235653446 + ], + [ + 10237.741120147864, + 11.885648318200122 + ], + [ + 10377.984149190985, + 11.987502316938794 + ], + [ + 10518.227178234107, + 12.081698033228095 + ], + [ + 10658.470207277229, + 12.16555756100945 + ], + [ + 10798.71323632035, + 12.241399017569867 + ], + [ + 10938.956265363471, + 12.312747711011587 + ], + [ + 11079.199294406593, + 12.383517346399744 + ], + [ + 11219.442323449714, + 12.459615721456428 + ], + [ + 11359.685352492836, + 12.544227975405125 + ], + [ + 11499.928381535958, + 12.633518692361276 + ], + [ + 11640.171410579078, + 12.722872205903165 + ], + [ + 11780.4144396222, + 12.80781622286694 + ], + [ + 11920.65746866532, + 12.883648137379328 + ], + [ + 12060.900497708442, + 12.946018892624675 + ], + [ + 12201.143526751564, + 12.994372616487832 + ], + [ + 12341.386555794685, + 13.027287650904684 + ], + [ + 12481.629584837807, + 13.041373151682814 + ], + [ + 12621.872613880929, + 13.032761085303534 + ], + [ + 12762.11564292405, + 12.997128342672509 + ], + [ + 12902.358671967171, + 12.931167540767479 + ], + [ + 13042.601701010293, + 12.834623930520419 + ], + [ + 13182.844730053414, + 12.710640313819631 + ], + [ + 13323.087759096536, + 12.564490524730969 + ], + [ + 13463.330788139658, + 12.401855463880501 + ], + [ + 13603.573817182778, + 12.228418318436997 + ], + [ + 13743.8168462259, + 12.05005298415421 + ], + [ + 13884.059875269022, + 11.871550600826241 + ] + ], + "11": [ + [ + 0.0, + -0.1822376974664744 + ], + [ + 140.24302904312142, + -0.455483465339052 + ], + [ + 280.48605808624285, + -0.3723374634294809 + ], + [ + 420.7290871293643, + -0.06148892786298469 + ], + [ + 560.9721161724857, + 0.4148287396848312 + ], + [ + 701.2151452156071, + 0.9252269041212308 + ], + [ + 841.4581742587286, + 1.2754084871898907 + ], + [ + 981.70120330185, + 1.5042521250112653 + ], + [ + 1121.9442323449714, + 1.6944395307902114 + ], + [ + 1262.1872613880928, + 1.889491933920113 + ], + [ + 1402.4302904312142, + 2.101001884073827 + ], + [ + 1542.6733194743356, + 2.328268202019118 + ], + [ + 1682.9163485174572, + 2.5634847134761634 + ], + [ + 1823.1593775605786, + 2.796297580865416 + ], + [ + 1963.4024066037, + 3.0257558503384403 + ], + [ + 2103.645435646821, + 3.2526141885152153 + ], + [ + 2243.888464689943, + 3.476278578855902 + ], + [ + 2384.1314937330644, + 3.693874581439269 + ], + [ + 2524.3745227761856, + 3.9078265111937416 + ], + [ + 2664.617551819307, + 4.119071848937901 + ], + [ + 2804.8605808624284, + 4.323584033479275 + ], + [ + 2945.10360990555, + 4.516997689493501 + ], + [ + 3085.346638948671, + 4.697451131464934 + ], + [ + 3225.589667991793, + 4.8673666305886165 + ], + [ + 3365.8326970349144, + 5.0281648680634365 + ], + [ + 3506.0757260780356, + 5.180834636917604 + ], + [ + 3646.318755121157, + 5.325631049178856 + ], + [ + 3786.5617841642784, + 5.462338219209022 + ], + [ + 3926.8048132074, + 5.593012160802613 + ], + [ + 4067.047842250521, + 5.720267722951825 + ], + [ + 4207.290871293642, + 5.846815422389549 + ], + [ + 4347.533900336764, + 5.9756801645243485 + ], + [ + 4487.776929379886, + 6.108644375095692 + ], + [ + 4628.019958423007, + 6.247898934316919 + ], + [ + 4768.262987466129, + 6.3957644152294675 + ], + [ + 4908.50601650925, + 6.554435782396764 + ], + [ + 5048.749045552371, + 6.725138073024329 + ], + [ + 5188.992074595492, + 6.9064575139744555 + ], + [ + 5329.235103638614, + 7.095711204050236 + ], + [ + 5469.478132681736, + 7.290101714533273 + ], + [ + 5609.721161724857, + 7.486867185096391 + ], + [ + 5749.964190767979, + 7.682796492901109 + ], + [ + 5890.2072198111, + 7.876470509539128 + ], + [ + 6030.450248854221, + 8.067051721225003 + ], + [ + 6170.693277897342, + 8.253543346766367 + ], + [ + 6310.936306940464, + 8.434938175081239 + ], + [ + 6451.179335983586, + 8.610858621618998 + ], + [ + 6591.422365026707, + 8.7821421628586 + ], + [ + 6731.665394069829, + 8.94843008191011 + ], + [ + 6871.90842311295, + 9.109413525519624 + ], + [ + 7012.151452156071, + 9.26486082006029 + ], + [ + 7152.394481199192, + 9.414478120840753 + ], + [ + 7292.637510242314, + 9.557540028510818 + ], + [ + 7432.880539285436, + 9.692470526710219 + ], + [ + 7573.123568328557, + 9.81921612167888 + ], + [ + 7713.366597371678, + 9.938968051245293 + ], + [ + 7853.6096264148, + 10.05315456197765 + ], + [ + 7993.852655457921, + 10.16322182632624 + ], + [ + 8134.095684501042, + 10.2706797100337 + ], + [ + 8274.338713544164, + 10.376904375297203 + ], + [ + 8414.581742587285, + 10.483658514641709 + ], + [ + 8554.824771630407, + 10.59235866526108 + ], + [ + 8695.067800673529, + 10.70286815485928 + ], + [ + 8835.31082971665, + 10.814665121527263 + ], + [ + 8975.553858759771, + 10.927224881090849 + ], + [ + 9115.796887802893, + 11.03998960194792 + ], + [ + 9256.039916846014, + 11.15270592851057 + ], + [ + 9396.282945889136, + 11.2659257529581 + ], + [ + 9536.525974932258, + 11.378636788488588 + ], + [ + 9676.769003975378, + 11.490044161183537 + ], + [ + 9817.0120330185, + 11.600417154026893 + ], + [ + 9957.25506206162, + 11.710168705563365 + ], + [ + 10097.498091104742, + 11.819711744565135 + ], + [ + 10237.741120147864, + 11.929455967156121 + ], + [ + 10377.984149190985, + 12.039756856855618 + ], + [ + 10518.227178234107, + 12.14923298546189 + ], + [ + 10658.470207277229, + 12.254412634650024 + ], + [ + 10798.71323632035, + 12.355075881541195 + ], + [ + 10938.956265363471, + 12.451290386839592 + ], + [ + 11079.199294406593, + 12.543122484058564 + ], + [ + 11219.442323449714, + 12.633234178021521 + ], + [ + 11359.685352492836, + 12.724122865508633 + ], + [ + 11499.928381535958, + 12.812185084175626 + ], + [ + 11640.171410579078, + 12.893001264991776 + ], + [ + 11780.4144396222, + 12.962243453121252 + ], + [ + 11920.65746866532, + 13.015643778639475 + ], + [ + 12060.900497708442, + 13.050560196688977 + ], + [ + 12201.143526751564, + 13.068472964471619 + ], + [ + 12341.386555794685, + 13.070033214615085 + ], + [ + 12481.629584837807, + 13.054078376957742 + ], + [ + 12621.872613880929, + 13.018981245900024 + ], + [ + 12762.11564292405, + 12.96244889514888 + ], + [ + 12902.358671967171, + 12.88021216472017 + ], + [ + 13042.601701010293, + 12.769821371160736 + ], + [ + 13182.844730053414, + 12.633236043806345 + ], + [ + 13323.087759096536, + 12.47444038808784 + ], + [ + 13463.330788139658, + 12.297870468767197 + ], + [ + 13603.573817182778, + 12.107940551921613 + ], + [ + 13743.8168462259, + 11.90934363868864 + ], + [ + 13884.059875269022, + 11.707366568448526 + ] + ], + "12": [ + [ + 0.0, + 0.06070264871764198 + ], + [ + 140.24302904312142, + -0.08764365569303438 + ], + [ + 280.48605808624285, + 0.03375488569048653 + ], + [ + 420.7290871293643, + 0.18614716110713442 + ], + [ + 560.9721161724857, + 0.455703179261567 + ], + [ + 701.2151452156071, + 0.842139844447927 + ], + [ + 841.4581742587286, + 1.1817177595723654 + ], + [ + 981.70120330185, + 1.437213114370947 + ], + [ + 1121.9442323449714, + 1.6409184187504955 + ], + [ + 1262.1872613880928, + 1.8444145559580944 + ], + [ + 1402.4302904312142, + 2.073631228723875 + ], + [ + 1542.6733194743356, + 2.322255268863468 + ], + [ + 1682.9163485174572, + 2.569435083686158 + ], + [ + 1823.1593775605786, + 2.8028413070849796 + ], + [ + 1963.4024066037, + 3.0249566490546815 + ], + [ + 2103.645435646821, + 3.248429976594959 + ], + [ + 2243.888464689943, + 3.470075926368333 + ], + [ + 2384.1314937330644, + 3.698316374431081 + ], + [ + 2524.3745227761856, + 3.9301147730906107 + ], + [ + 2664.617551819307, + 4.1487309398011805 + ], + [ + 2804.8605808624284, + 4.3531501141601865 + ], + [ + 2945.10360990555, + 4.544601156483875 + ], + [ + 3085.346638948671, + 4.726148942847912 + ], + [ + 3225.589667991793, + 4.901601947806487 + ], + [ + 3365.8326970349144, + 5.070912467339333 + ], + [ + 3506.0757260780356, + 5.2333295506815 + ], + [ + 3646.318755121157, + 5.386862985252566 + ], + [ + 3786.5617841642784, + 5.528659752792856 + ], + [ + 3926.8048132074, + 5.660993758603854 + ], + [ + 4067.047842250521, + 5.788109950493729 + ], + [ + 4207.290871293642, + 5.914462476857634 + ], + [ + 4347.533900336764, + 6.046965731791873 + ], + [ + 4487.776929379886, + 6.189538324935445 + ], + [ + 4628.019958423007, + 6.3367321896390925 + ], + [ + 4768.262987466129, + 6.4815950362799555 + ], + [ + 4908.50601650925, + 6.617022727844673 + ], + [ + 5048.749045552371, + 6.733373535922543 + ], + [ + 5188.992074595492, + 6.831220184740433 + ], + [ + 5329.235103638614, + 6.940319141908297 + ], + [ + 5469.478132681736, + 7.094235347247846 + ], + [ + 5609.721161724857, + 7.326416906619712 + ], + [ + 5749.964190767979, + 7.670131812474497 + ], + [ + 5890.2072198111, + 8.141189558291654 + ], + [ + 6030.450248854221, + 8.649493605901034 + ], + [ + 6170.693277897342, + 9.07012420310707 + ], + [ + 6310.936306940464, + 9.27808567289681 + ], + [ + 6451.179335983586, + 9.149167153739596 + ], + [ + 6591.422365026707, + 8.581651867925068 + ], + [ + 6731.665394069829, + 8.154730507706162 + ], + [ + 6871.90842311295, + 8.103323559955474 + ], + [ + 7012.151452156071, + 8.327553534295983 + ], + [ + 7152.394481199192, + 8.727567865488123 + ], + [ + 7292.637510242314, + 9.203151527431238 + ], + [ + 7432.880539285436, + 9.651958569773708 + ], + [ + 7573.123568328557, + 9.990161344184784 + ], + [ + 7713.366597371678, + 10.216249954079315 + ], + [ + 7853.6096264148, + 10.35163469044472 + ], + [ + 7993.852655457921, + 10.417950417902631 + ], + [ + 8134.095684501042, + 10.438264164214745 + ], + [ + 8274.338713544164, + 10.4374679132289 + ], + [ + 8414.581742587285, + 10.451278346354162 + ], + [ + 8554.824771630407, + 10.503393963258095 + ], + [ + 8695.067800673529, + 10.589662657229717 + ], + [ + 8835.31082971665, + 10.70239206422463 + ], + [ + 8975.553858759771, + 10.833887461756413 + ], + [ + 9115.796887802893, + 10.976405951797434 + ], + [ + 9256.039916846014, + 11.122670964629986 + ], + [ + 9396.282945889136, + 11.266932021726856 + ], + [ + 9536.525974932258, + 11.404041200459233 + ], + [ + 9676.769003975378, + 11.532786821639974 + ], + [ + 9817.0120330185, + 11.6552880870816 + ], + [ + 9957.25506206162, + 11.773829633117682 + ], + [ + 10097.498091104742, + 11.890696049009971 + ], + [ + 10237.741120147864, + 12.008176405322939 + ], + [ + 10377.984149190985, + 12.128540129129956 + ], + [ + 10518.227178234107, + 12.251902596239631 + ], + [ + 10658.470207277229, + 12.375151711116333 + ], + [ + 10798.71323632035, + 12.495690975884184 + ], + [ + 10938.956265363471, + 12.610396721038677 + ], + [ + 11079.199294406593, + 12.71643787589724 + ], + [ + 11219.442323449714, + 12.813662333639588 + ], + [ + 11359.685352492836, + 12.903573434256385 + ], + [ + 11499.928381535958, + 12.984768851199068 + ], + [ + 11640.171410579078, + 13.055043347643515 + ], + [ + 11780.4144396222, + 13.111513732949689 + ], + [ + 11920.65746866532, + 13.1519556127217 + ], + [ + 12060.900497708442, + 13.176100773208919 + ], + [ + 12201.143526751564, + 13.185565131087262 + ], + [ + 12341.386555794685, + 13.179962738122079 + ], + [ + 12481.629584837807, + 13.15822209543456 + ], + [ + 12621.872613880929, + 13.119040087463151 + ], + [ + 12762.11564292405, + 13.060001847168003 + ], + [ + 12902.358671967171, + 12.975830717523328 + ], + [ + 13042.601701010293, + 12.863811513433665 + ], + [ + 13182.844730053414, + 12.726412481591932 + ], + [ + 13323.087759096536, + 12.567098970790378 + ], + [ + 13463.330788139658, + 12.389623640376948 + ], + [ + 13603.573817182778, + 12.19772527467736 + ], + [ + 13743.8168462259, + 11.995565386164124 + ], + [ + 13884.059875269022, + 11.788147784004394 + ] + ], + "13": [ + [ + 0.0, + 0.4094193505425669 + ], + [ + 140.24302904312142, + 0.3223636675050678 + ], + [ + 280.48605808624285, + 0.4551231622059286 + ], + [ + 420.7290871293643, + 0.5856818163124866 + ], + [ + 560.9721161724857, + 0.7315487984233083 + ], + [ + 701.2151452156071, + 0.9446180759404852 + ], + [ + 841.4581742587286, + 1.237533309964865 + ], + [ + 981.70120330185, + 1.4492085232125307 + ], + [ + 1121.9442323449714, + 1.6503320391976974 + ], + [ + 1262.1872613880928, + 1.8633041351612663 + ], + [ + 1402.4302904312142, + 2.0679205866599477 + ], + [ + 1542.6733194743356, + 2.306750868069865 + ], + [ + 1682.9163485174572, + 2.56778939394985 + ], + [ + 1823.1593775605786, + 2.8238862346712237 + ], + [ + 1963.4024066037, + 3.045748708858542 + ], + [ + 2103.645435646821, + 3.247474136519846 + ], + [ + 2243.888464689943, + 3.5769559712973575 + ], + [ + 2384.1314937330644, + 3.860011387986431 + ], + [ + 2524.3745227761856, + 4.0281961775965565 + ], + [ + 2664.617551819307, + 4.217116587965324 + ], + [ + 2804.8605808624284, + 4.427039966387985 + ], + [ + 2945.10360990555, + 4.641381725691799 + ], + [ + 3085.346638948671, + 4.844760110862045 + ], + [ + 3225.589667991793, + 5.027844846713926 + ], + [ + 3365.8326970349144, + 5.194292070428863 + ], + [ + 3506.0757260780356, + 5.349689002309177 + ], + [ + 3646.318755121157, + 5.498546863987108 + ], + [ + 3786.5617841642784, + 5.643818988102327 + ], + [ + 3926.8048132074, + 5.78656522668222 + ], + [ + 4067.047842250521, + 5.927407906923987 + ], + [ + 4207.290871293642, + 6.067195840171474 + ], + [ + 4347.533900336764, + 6.20830997833369 + ], + [ + 4487.776929379886, + 6.352660161782633 + ], + [ + 4628.019958423007, + 6.497632397059137 + ], + [ + 4768.262987466129, + 6.639746210817476 + ], + [ + 4908.50601650925, + 6.775744615582752 + ], + [ + 5048.749045552371, + 6.901022418518727 + ], + [ + 5188.992074595492, + 7.01612227493567 + ], + [ + 5329.235103638614, + 7.140577464426645 + ], + [ + 5469.478132681736, + 7.296515302213458 + ], + [ + 5609.721161724857, + 7.505611967795369 + ], + [ + 5749.964190767979, + 7.788935488360598 + ], + [ + 5890.2072198111, + 8.157492118368786 + ], + [ + 6030.450248854221, + 8.551554325517232 + ], + [ + 6170.693277897342, + 8.887197678795577 + ], + [ + 6310.936306940464, + 9.080441020500656 + ], + [ + 6451.179335983586, + 9.047911844119623 + ], + [ + 6591.422365026707, + 8.720544422765535 + ], + [ + 6731.665394069829, + 8.489045807438218 + ], + [ + 6871.90842311295, + 8.51399189919627 + ], + [ + 7012.151452156071, + 8.726178586772596 + ], + [ + 7152.394481199192, + 9.056522416627002 + ], + [ + 7292.637510242314, + 9.435876879804464 + ], + [ + 7432.880539285436, + 9.79307558785978 + ], + [ + 7573.123568328557, + 10.070353159689757 + ], + [ + 7713.366597371678, + 10.26696532662754 + ], + [ + 7853.6096264148, + 10.397940853534026 + ], + [ + 7993.852655457921, + 10.478298665780958 + ], + [ + 8134.095684501042, + 10.522838630539647 + ], + [ + 8274.338713544164, + 10.548454822874296 + ], + [ + 8414.581742587285, + 10.584919360479658 + ], + [ + 8554.824771630407, + 10.651139293824116 + ], + [ + 8695.067800673529, + 10.743864540331975 + ], + [ + 8835.31082971665, + 10.857408749198592 + ], + [ + 8975.553858759771, + 10.98608519622533 + ], + [ + 9115.796887802893, + 11.124219311772375 + ], + [ + 9256.039916846014, + 11.266465454592147 + ], + [ + 9396.282945889136, + 11.407793617597852 + ], + [ + 9536.525974932258, + 11.544228358592273 + ], + [ + 9676.769003975378, + 11.675094610451687 + ], + [ + 9817.0120330185, + 11.801973635159694 + ], + [ + 9957.25506206162, + 11.926521351327084 + ], + [ + 10097.498091104742, + 12.050393572808145 + ], + [ + 10237.741120147864, + 12.17525395394361 + ], + [ + 10377.984149190985, + 12.302756942464873 + ], + [ + 10518.227178234107, + 12.433105067470372 + ], + [ + 10658.470207277229, + 12.563519646162213 + ], + [ + 10798.71323632035, + 12.689770295506591 + ], + [ + 10938.956265363471, + 12.805926629995392 + ], + [ + 11079.199294406593, + 12.90607989453853 + ], + [ + 11219.442323449714, + 12.986606005365285 + ], + [ + 11359.685352492836, + 13.04887260350387 + ], + [ + 11499.928381535958, + 13.096211722849002 + ], + [ + 11640.171410579078, + 13.133332410507089 + ], + [ + 11780.4144396222, + 13.164348917710294 + ], + [ + 11920.65746866532, + 13.194037863800347 + ], + [ + 12060.900497708442, + 13.225532252994757 + ], + [ + 12201.143526751564, + 13.257257892035124 + ], + [ + 12341.386555794685, + 13.282362101559142 + ], + [ + 12481.629584837807, + 13.293319962320194 + ], + [ + 12621.872613880929, + 13.282603224387048 + ], + [ + 12762.11564292405, + 13.241858136386073 + ], + [ + 12902.358671967171, + 13.16315714554513 + ], + [ + 13042.601701010293, + 13.045894998565966 + ], + [ + 13182.844730053414, + 12.896637507962652 + ], + [ + 13323.087759096536, + 12.72252424834291 + ], + [ + 13463.330788139658, + 12.530802530944593 + ], + [ + 13603.573817182778, + 12.328711338021678 + ], + [ + 13743.8168462259, + 12.1235901549949 + ], + [ + 13884.059875269022, + 11.920770265332946 + ] + ], + "14": [ + [ + 0.0, + 0.7983011524347337 + ], + [ + 140.24302904312142, + 0.781764698013081 + ], + [ + 280.48605808624285, + 0.9561914147413487 + ], + [ + 420.7290871293643, + 1.0822810453830154 + ], + [ + 560.9721161724857, + 1.184611967199937 + ], + [ + 701.2151452156071, + 1.31850795094902 + ], + [ + 841.4581742587286, + 1.5086181251318704 + ], + [ + 981.70120330185, + 1.6255322429296593 + ], + [ + 1121.9442323449714, + 1.779708436396037 + ], + [ + 1262.1872613880928, + 1.9639108569017025 + ], + [ + 1402.4302904312142, + 2.1479022558207754 + ], + [ + 1542.6733194743356, + 2.381412565066502 + ], + [ + 1682.9163485174572, + 2.645304040385109 + ], + [ + 1823.1593775605786, + 2.9074242937510584 + ], + [ + 1963.4024066037, + 3.1528199282923492 + ], + [ + 2103.645435646821, + 3.3904976892045577 + ], + [ + 2243.888464689943, + 3.6923605603872383 + ], + [ + 2384.1314937330644, + 3.959668774102775 + ], + [ + 2524.3745227761856, + 4.160501741249103 + ], + [ + 2664.617551819307, + 4.365033625451886 + ], + [ + 2804.8605808624284, + 4.573051885301888 + ], + [ + 2945.10360990555, + 4.776747673784354 + ], + [ + 3085.346638948671, + 4.96926888550944 + ], + [ + 3225.589667991793, + 5.14734383164582 + ], + [ + 3365.8326970349144, + 5.314148283071206 + ], + [ + 3506.0757260780356, + 5.473577291603479 + ], + [ + 3646.318755121157, + 5.628731586601843 + ], + [ + 3786.5617841642784, + 5.782103778664774 + ], + [ + 3926.8048132074, + 5.933983380341722 + ], + [ + 4067.047842250521, + 6.08405864295797 + ], + [ + 4207.290871293642, + 6.232288654108256 + ], + [ + 4347.533900336764, + 6.378586677979039 + ], + [ + 4487.776929379886, + 6.523001692398103 + ], + [ + 4628.019958423007, + 6.666785726369818 + ], + [ + 4768.262987466129, + 6.8114084644318105 + ], + [ + 4908.50601650925, + 6.9587576740709265 + ], + [ + 5048.749045552371, + 7.11094916718701 + ], + [ + 5188.992074595492, + 7.267765669513184 + ], + [ + 5329.235103638614, + 7.427812687241017 + ], + [ + 5469.478132681736, + 7.589550352367519 + ], + [ + 5609.721161724857, + 7.750952337621433 + ], + [ + 5749.964190767979, + 7.909505030463382 + ], + [ + 5890.2072198111, + 8.065989787637642 + ], + [ + 6030.450248854221, + 8.222735324557412 + ], + [ + 6170.693277897342, + 8.382128182705797 + ], + [ + 6310.936306940464, + 8.546522517423952 + ], + [ + 6451.179335983586, + 8.718267408779662 + ], + [ + 6591.422365026707, + 8.897933711535032 + ], + [ + 6731.665394069829, + 9.082174124769328 + ], + [ + 6871.90842311295, + 9.266510998279056 + ], + [ + 7012.151452156071, + 9.446442567811367 + ], + [ + 7152.394481199192, + 9.61756308457741 + ], + [ + 7292.637510242314, + 9.776165988381612 + ], + [ + 7432.880539285436, + 9.91982360245396 + ], + [ + 7573.123568328557, + 10.049690343097028 + ], + [ + 7713.366597371678, + 10.16874401386329 + ], + [ + 7853.6096264148, + 10.280028590426047 + ], + [ + 7993.852655457921, + 10.386585241940184 + ], + [ + 8134.095684501042, + 10.491190994033472 + ], + [ + 8274.338713544164, + 10.596079676265008 + ], + [ + 8414.581742587285, + 10.703625346693475 + ], + [ + 8554.824771630407, + 10.814551755825926 + ], + [ + 8695.067800673529, + 10.929031110694272 + ], + [ + 8835.31082971665, + 11.047277904301417 + ], + [ + 8975.553858759771, + 11.169506940421202 + ], + [ + 9115.796887802893, + 11.295956472805369 + ], + [ + 9256.039916846014, + 11.427267044678917 + ], + [ + 9396.282945889136, + 11.563455292139363 + ], + [ + 9536.525974932258, + 11.703306743363703 + ], + [ + 9676.769003975378, + 11.846172760955227 + ], + [ + 9817.0120330185, + 11.991276735444874 + ], + [ + 9957.25506206162, + 12.13779253742144 + ], + [ + 10097.498091104742, + 12.28489386220913 + ], + [ + 10237.741120147864, + 12.431765392515706 + ], + [ + 10377.984149190985, + 12.577536136084541 + ], + [ + 10518.227178234107, + 12.72053894769247 + ], + [ + 10658.470207277229, + 12.858530252637566 + ], + [ + 10798.71323632035, + 12.987618266200927 + ], + [ + 10938.956265363471, + 13.102027381271467 + ], + [ + 11079.199294406593, + 13.19632749822666 + ], + [ + 11219.442323449714, + 13.26621354889323 + ], + [ + 11359.685352492836, + 13.31087826032815 + ], + [ + 11499.928381535958, + 13.33469511293903 + ], + [ + 11640.171410579078, + 13.345382690391965 + ], + [ + 11780.4144396222, + 13.349986778426711 + ], + [ + 11920.65746866532, + 13.35625657224763 + ], + [ + 12060.900497708442, + 13.370223835051604 + ], + [ + 12201.143526751564, + 13.391674701396942 + ], + [ + 12341.386555794685, + 13.412430146346974 + ], + [ + 12481.629584837807, + 13.423584758943298 + ], + [ + 12621.872613880929, + 13.416502008460247 + ], + [ + 12762.11564292405, + 13.38195335142159 + ], + [ + 12902.358671967171, + 13.311767596801587 + ], + [ + 13042.601701010293, + 13.204195435540463 + ], + [ + 13182.844730053414, + 13.065505768375864 + ], + [ + 13323.087759096536, + 12.902408852097965 + ], + [ + 13463.330788139658, + 12.721526762959352 + ], + [ + 13603.573817182778, + 12.529475940731999 + ], + [ + 13743.8168462259, + 12.332755027321072 + ], + [ + 13884.059875269022, + 12.13596435745739 + ] + ], + "15": [ + [ + 0.0, + 1.1618301044814903 + ], + [ + 140.24302904312142, + 1.2171821002639487 + ], + [ + 280.48605808624285, + 1.432106456692824 + ], + [ + 420.7290871293643, + 1.5481305923802942 + ], + [ + 560.9721161724857, + 1.6444186555998355 + ], + [ + 701.2151452156071, + 1.7436909726297791 + ], + [ + 841.4581742587286, + 1.812797686599896 + ], + [ + 981.70120330185, + 1.8923800308667156 + ], + [ + 1121.9442323449714, + 2.0015195270836137 + ], + [ + 1262.1872613880928, + 2.1086895693934715 + ], + [ + 1402.4302904312142, + 2.2437929601025783 + ], + [ + 1542.6733194743356, + 2.4549465075043746 + ], + [ + 1682.9163485174572, + 2.7271571211869503 + ], + [ + 1823.1593775605786, + 3.024499189759561 + ], + [ + 1963.4024066037, + 3.3151640512945635 + ], + [ + 2103.645435646821, + 3.5882535432546336 + ], + [ + 2243.888464689943, + 3.8496574096815284 + ], + [ + 2384.1314937330644, + 4.097858288497836 + ], + [ + 2524.3745227761856, + 4.325526403663505 + ], + [ + 2664.617551819307, + 4.531675412835185 + ], + [ + 2804.8605808624284, + 4.721402516268552 + ], + [ + 2945.10360990555, + 4.900498176933965 + ], + [ + 3085.346638948671, + 5.07420702525523 + ], + [ + 3225.589667991793, + 5.2456439640561205 + ], + [ + 3365.8326970349144, + 5.415781430468741 + ], + [ + 3506.0757260780356, + 5.58511955573314 + ], + [ + 3646.318755121157, + 5.753285680328805 + ], + [ + 3786.5617841642784, + 5.919076365073985 + ], + [ + 3926.8048132074, + 6.08151173268202 + ], + [ + 4067.047842250521, + 6.239988149298322 + ], + [ + 4207.290871293642, + 6.394279467891194 + ], + [ + 4347.533900336764, + 6.545394526677584 + ], + [ + 4487.776929379886, + 6.694336809577464 + ], + [ + 4628.019958423007, + 6.842167002064704 + ], + [ + 4768.262987466129, + 6.990139661758398 + ], + [ + 4908.50601650925, + 7.139577992661609 + ], + [ + 5048.749045552371, + 7.290672042172406 + ], + [ + 5188.992074595492, + 7.442767122979391 + ], + [ + 5329.235103638614, + 7.594811767361071 + ], + [ + 5469.478132681736, + 7.745592271499366 + ], + [ + 5609.721161724857, + 7.893650885090978 + ], + [ + 5749.964190767979, + 8.038712498540509 + ], + [ + 5890.2072198111, + 8.183238263771798 + ], + [ + 6030.450248854221, + 8.330337999688018 + ], + [ + 6170.693277897342, + 8.483190981060558 + ], + [ + 6310.936306940464, + 8.644921609859912 + ], + [ + 6451.179335983586, + 8.818197683740154 + ], + [ + 6591.422365026707, + 9.002297758861609 + ], + [ + 6731.665394069829, + 9.192608330732499 + ], + [ + 6871.90842311295, + 9.383429908679256 + ], + [ + 7012.151452156071, + 9.568978783514117 + ], + [ + 7152.394481199192, + 9.743557147810092 + ], + [ + 7292.637510242314, + 9.902047475381627 + ], + [ + 7432.880539285436, + 10.041886369636373 + ], + [ + 7573.123568328557, + 10.165063290924618 + ], + [ + 7713.366597371678, + 10.27563189888659 + ], + [ + 7853.6096264148, + 10.377811418159613 + ], + [ + 7993.852655457921, + 10.475824196223371 + ], + [ + 8134.095684501042, + 10.573778848114065 + ], + [ + 8274.338713544164, + 10.675556136993919 + ], + [ + 8414.581742587285, + 10.784110730940286 + ], + [ + 8554.824771630407, + 10.899910317627507 + ], + [ + 8695.067800673529, + 11.022801444032359 + ], + [ + 8835.31082971665, + 11.152686157213356 + ], + [ + 8975.553858759771, + 11.289466382677466 + ], + [ + 9115.796887802893, + 11.433051398721357 + ], + [ + 9256.039916846014, + 11.583305755160223 + ], + [ + 9396.282945889136, + 11.739249715960526 + ], + [ + 9536.525974932258, + 11.899161794139948 + ], + [ + 9676.769003975378, + 12.06183718334215 + ], + [ + 9817.0120330185, + 12.225565786358208 + ], + [ + 9957.25506206162, + 12.38854291021106 + ], + [ + 10097.498091104742, + 12.548963644959269 + ], + [ + 10237.741120147864, + 12.705031869561582 + ], + [ + 10377.984149190985, + 12.854849315724623 + ], + [ + 10518.227178234107, + 12.99631193830626 + ], + [ + 10658.470207277229, + 13.128459444221035 + ], + [ + 10798.71323632035, + 13.248867582630295 + ], + [ + 10938.956265363471, + 13.3542883604669 + ], + [ + 11079.199294406593, + 13.442598516190271 + ], + [ + 11219.442323449714, + 13.512316630507323 + ], + [ + 11359.685352492836, + 13.561417950805662 + ], + [ + 11499.928381535958, + 13.592168874211195 + ], + [ + 11640.171410579078, + 13.608492072744411 + ], + [ + 11780.4144396222, + 13.61320002247236 + ], + [ + 11920.65746866532, + 13.609369042426401 + ], + [ + 12060.900497708442, + 13.601395846966565 + ], + [ + 12201.143526751564, + 13.591747940849702 + ], + [ + 12341.386555794685, + 13.57706218818255 + ], + [ + 12481.629584837807, + 13.554101081939931 + ], + [ + 12621.872613880929, + 13.519908017438436 + ], + [ + 12762.11564292405, + 13.471128605856345 + ], + [ + 12902.358671967171, + 13.402949622256383 + ], + [ + 13042.601701010293, + 13.312043015360699 + ], + [ + 13182.844730053414, + 13.200626935390881 + ], + [ + 13323.087759096536, + 13.070831265349756 + ], + [ + 13463.330788139658, + 12.924659621113795 + ], + [ + 13603.573817182778, + 12.764121544521837 + ], + [ + 13743.8168462259, + 12.591139817871094 + ], + [ + 13884.059875269022, + 12.407838706637799 + ] + ], + "16": [ + [ + 0.0, + 1.3701921270205335 + ], + [ + 140.24302904312142, + 1.4598935932456247 + ], + [ + 280.48605808624285, + 1.7016368186195934 + ], + [ + 420.7290871293643, + 1.8223793085774695 + ], + [ + 560.9721161724857, + 1.9072411265635831 + ], + [ + 701.2151452156071, + 1.9870692240407664 + ], + [ + 841.4581742587286, + 2.0285738930793134 + ], + [ + 981.70120330185, + 2.060590822125373 + ], + [ + 1121.9442323449714, + 2.1055007487704973 + ], + [ + 1262.1872613880928, + 2.1623164787448976 + ], + [ + 1402.4302904312142, + 2.275966814686074 + ], + [ + 1542.6733194743356, + 2.480478627151215 + ], + [ + 1682.9163485174572, + 2.752988074309081 + ], + [ + 1823.1593775605786, + 3.0634067460065046 + ], + [ + 1963.4024066037, + 3.376400507654678 + ], + [ + 2103.645435646821, + 3.6692362722968657 + ], + [ + 2243.888464689943, + 3.9395551660139234 + ], + [ + 2384.1314937330644, + 4.185808285260531 + ], + [ + 2524.3745227761856, + 4.4071199716150184 + ], + [ + 2664.617551819307, + 4.608847124978089 + ], + [ + 2804.8605808624284, + 4.798287841953716 + ], + [ + 2945.10360990555, + 4.982450692733978 + ], + [ + 3085.346638948671, + 5.1659869662505615 + ], + [ + 3225.589667991793, + 5.349027554301351 + ], + [ + 3365.8326970349144, + 5.5305604030946665 + ], + [ + 3506.0757260780356, + 5.7095175653172126 + ], + [ + 3646.318755121157, + 5.884622532907381 + ], + [ + 3786.5617841642784, + 6.0555395914387535 + ], + [ + 3926.8048132074, + 6.222159174903632 + ], + [ + 4067.047842250521, + 6.384425211465592 + ], + [ + 4207.290871293642, + 6.542746076566961 + ], + [ + 4347.533900336764, + 6.697965349032161 + ], + [ + 4487.776929379886, + 6.8500687517673144 + ], + [ + 4628.019958423007, + 6.999115044937706 + ], + [ + 4768.262987466129, + 7.145399000327705 + ], + [ + 4908.50601650925, + 7.289221592864493 + ], + [ + 5048.749045552371, + 7.4310739527998475 + ], + [ + 5188.992074595492, + 7.572011991094434 + ], + [ + 5329.235103638614, + 7.712947928648593 + ], + [ + 5469.478132681736, + 7.854609120427436 + ], + [ + 5609.721161724857, + 7.997506950778466 + ], + [ + 5749.964190767979, + 8.14211718202341 + ], + [ + 5890.2072198111, + 8.289278868799986 + ], + [ + 6030.450248854221, + 8.439440207826914 + ], + [ + 6170.693277897342, + 8.593085997284673 + ], + [ + 6310.936306940464, + 8.750631409011987 + ], + [ + 6451.179335983586, + 8.912108527537349 + ], + [ + 6591.422365026707, + 9.076015094853773 + ], + [ + 6731.665394069829, + 9.239961558543813 + ], + [ + 6871.90842311295, + 9.401773035032424 + ], + [ + 7012.151452156071, + 9.55925215011733 + ], + [ + 7152.394481199192, + 9.710327953752822 + ], + [ + 7292.637510242314, + 9.853830617497911 + ], + [ + 7432.880539285436, + 9.99074939710546 + ], + [ + 7573.123568328557, + 10.122714346651705 + ], + [ + 7713.366597371678, + 10.248271622799116 + ], + [ + 7853.6096264148, + 10.365352982346124 + ], + [ + 7993.852655457921, + 10.472941440448915 + ], + [ + 8134.095684501042, + 10.576945706213428 + ], + [ + 8274.338713544164, + 10.68509957561441 + ], + [ + 8414.581742587285, + 10.803410355318917 + ], + [ + 8554.824771630407, + 10.934433699219264 + ], + [ + 8695.067800673529, + 11.076449681753708 + ], + [ + 8835.31082971665, + 11.227327431660937 + ], + [ + 8975.553858759771, + 11.384935172621452 + ], + [ + 9115.796887802893, + 11.547134254155505 + ], + [ + 9256.039916846014, + 11.711986338371172 + ], + [ + 9396.282945889136, + 11.878233502232032 + ], + [ + 9536.525974932258, + 12.04465013479923 + ], + [ + 9676.769003975378, + 12.21026875158366 + ], + [ + 9817.0120330185, + 12.374006478654008 + ], + [ + 9957.25506206162, + 12.534704026704352 + ], + [ + 10097.498091104742, + 12.691201992440527 + ], + [ + 10237.741120147864, + 12.842349389066577 + ], + [ + 10377.984149190985, + 12.986967544061534 + ], + [ + 10518.227178234107, + 13.123594997675161 + ], + [ + 10658.470207277229, + 13.250160507552751 + ], + [ + 10798.71323632035, + 13.36488373189675 + ], + [ + 10938.956265363471, + 13.466956079677363 + ], + [ + 11079.199294406593, + 13.55668870695644 + ], + [ + 11219.442323449714, + 13.63503689877705 + ], + [ + 11359.685352492836, + 13.702019842911145 + ], + [ + 11499.928381535958, + 13.757170989440098 + ], + [ + 11640.171410579078, + 13.798977835071515 + ], + [ + 11780.4144396222, + 13.824912264569164 + ], + [ + 11920.65746866532, + 13.83246287698609 + ], + [ + 12060.900497708442, + 13.82159273970853 + ], + [ + 12201.143526751564, + 13.795352384049758 + ], + [ + 12341.386555794685, + 13.755248917358934 + ], + [ + 12481.629584837807, + 13.703370969191848 + ], + [ + 12621.872613880929, + 13.641889043037553 + ], + [ + 12762.11564292405, + 13.57287460822061 + ], + [ + 12902.358671967171, + 13.495642012346464 + ], + [ + 13042.601701010293, + 13.407469036740189 + ], + [ + 13182.844730053414, + 13.307856773846577 + ], + [ + 13323.087759096536, + 13.195774538334325 + ], + [ + 13463.330788139658, + 13.070159311278669 + ], + [ + 13603.573817182778, + 12.929956090442074 + ], + [ + 13743.8168462259, + 12.773945384256562 + ], + [ + 13884.059875269022, + 12.6020742185872 + ] + ], + "17": [ + [ + 0.0, + 1.4961440368066188 + ], + [ + 140.24302904312142, + 1.6266761365089712 + ], + [ + 280.48605808624285, + 1.9057602330936838 + ], + [ + 420.7290871293643, + 2.0364432634457046 + ], + [ + 560.9721161724857, + 2.0926359158010803 + ], + [ + 701.2151452156071, + 2.1149045256455166 + ], + [ + 841.4581742587286, + 2.1293477060015054 + ], + [ + 981.70120330185, + 2.091132233045171 + ], + [ + 1121.9442323449714, + 2.094038465307731 + ], + [ + 1262.1872613880928, + 2.1707075956900375 + ], + [ + 1402.4302904312142, + 2.2860571686125715 + ], + [ + 1542.6733194743356, + 2.4649160525019993 + ], + [ + 1682.9163485174572, + 2.734327406126595 + ], + [ + 1823.1593775605786, + 3.0915018950606856 + ], + [ + 1963.4024066037, + 3.382940347853847 + ], + [ + 2103.645435646821, + 3.5328887037330796 + ], + [ + 2243.888464689943, + 3.998304413302999 + ], + [ + 2384.1314937330644, + 4.3904906502343 + ], + [ + 2524.3745227761856, + 4.419002233752132 + ], + [ + 2664.617551819307, + 4.5206467601002664 + ], + [ + 2804.8605808624284, + 4.7207770763432935 + ], + [ + 2945.10360990555, + 4.97297993159928 + ], + [ + 3085.346638948671, + 5.229319918202259 + ], + [ + 3225.589667991793, + 5.450676018662261 + ], + [ + 3365.8326970349144, + 5.640112814262023 + ], + [ + 3506.0757260780356, + 5.809714147388161 + ], + [ + 3646.318755121157, + 5.971328459492258 + ], + [ + 3786.5617841642784, + 6.136330828314975 + ], + [ + 3926.8048132074, + 6.305946979047464 + ], + [ + 4067.047842250521, + 6.4762397447077085 + ], + [ + 4207.290871293642, + 6.643621484224632 + ], + [ + 4347.533900336764, + 6.8048704380189475 + ], + [ + 4487.776929379886, + 6.957854126588439 + ], + [ + 4628.019958423007, + 7.103872072384802 + ], + [ + 4768.262987466129, + 7.244873032463522 + ], + [ + 4908.50601650925, + 7.38264638612478 + ], + [ + 5048.749045552371, + 7.519340294109697 + ], + [ + 5188.992074595492, + 7.6562851203009465 + ], + [ + 5329.235103638614, + 7.7929155574834414 + ], + [ + 5469.478132681736, + 7.928358902194173 + ], + [ + 5609.721161724857, + 8.061872501102949 + ], + [ + 5749.964190767979, + 8.192901205975346 + ], + [ + 5890.2072198111, + 8.322121983610938 + ], + [ + 6030.450248854221, + 8.452754515311087 + ], + [ + 6170.693277897342, + 8.588561617262995 + ], + [ + 6310.936306940464, + 8.733276710553708 + ], + [ + 6451.179335983586, + 8.890109611437376 + ], + [ + 6591.422365026707, + 9.059229051687424 + ], + [ + 6731.665394069829, + 9.224189151784032 + ], + [ + 6871.90842311295, + 9.379852470908528 + ], + [ + 7012.151452156071, + 9.525751621148746 + ], + [ + 7152.394481199192, + 9.661454814741347 + ], + [ + 7292.637510242314, + 9.787235532729737 + ], + [ + 7432.880539285436, + 9.905488569415194 + ], + [ + 7573.123568328557, + 10.020081518411976 + ], + [ + 7713.366597371678, + 10.134147557031316 + ], + [ + 7853.6096264148, + 10.250844039417565 + ], + [ + 7993.852655457921, + 10.373331969198793 + ], + [ + 8134.095684501042, + 10.504564951109773 + ], + [ + 8274.338713544164, + 10.646142083428895 + ], + [ + 8414.581742587285, + 10.79658420308065 + ], + [ + 8554.824771630407, + 10.952901813080487 + ], + [ + 8695.067800673529, + 11.113836755609434 + ], + [ + 8835.31082971665, + 11.278388977707499 + ], + [ + 8975.553858759771, + 11.44555742817333 + ], + [ + 9115.796887802893, + 11.61435169905649 + ], + [ + 9256.039916846014, + 11.784136332525565 + ], + [ + 9396.282945889136, + 11.956198944568664 + ], + [ + 9536.525974932258, + 12.126435911152349 + ], + [ + 9676.769003975378, + 12.282618512374254 + ], + [ + 9817.0120330185, + 12.426798482010806 + ], + [ + 9957.25506206162, + 12.562039726930252 + ], + [ + 10097.498091104742, + 12.691406112245783 + ], + [ + 10237.741120147864, + 12.817966480356455 + ], + [ + 10377.984149190985, + 12.944826403776933 + ], + [ + 10518.227178234107, + 13.074707506512855 + ], + [ + 10658.470207277229, + 13.20708904605056 + ], + [ + 10798.71323632035, + 13.338958052889641 + ], + [ + 10938.956265363471, + 13.467474310524869 + ], + [ + 11079.199294406593, + 13.589753964342066 + ], + [ + 11219.442323449714, + 13.70339122294707 + ], + [ + 11359.685352492836, + 13.806712644812881 + ], + [ + 11499.928381535958, + 13.89703905315347 + ], + [ + 11640.171410579078, + 13.969984124119849 + ], + [ + 11780.4144396222, + 14.02078412322923 + ], + [ + 11920.65746866532, + 14.044646762773146 + ], + [ + 12060.900497708442, + 14.039367188728066 + ], + [ + 12201.143526751564, + 14.007906165135225 + ], + [ + 12341.386555794685, + 13.954185261131718 + ], + [ + 12481.629584837807, + 13.88237356844765 + ], + [ + 12621.872613880929, + 13.796613752884692 + ], + [ + 12762.11564292405, + 13.701243071734767 + ], + [ + 12902.358671967171, + 13.598268413852953 + ], + [ + 13042.601701010293, + 13.486727563424436 + ], + [ + 13182.844730053414, + 13.366765164824288 + ], + [ + 13323.087759096536, + 13.238385021501488 + ], + [ + 13463.330788139658, + 13.101648750799276 + ], + [ + 13603.573817182778, + 12.956618450291941 + ], + [ + 13743.8168462259, + 12.802923189873736 + ], + [ + 13884.059875269022, + 12.640468055804902 + ] + ], + "18": [ + [ + 0.0, + 1.618202841206727 + ], + [ + 140.24302904312142, + 1.8464185341965278 + ], + [ + 280.48605808624285, + 2.159812853283336 + ], + [ + 420.7290871293643, + 2.209850834159649 + ], + [ + 560.9721161724857, + 2.1435208724812327 + ], + [ + 701.2151452156071, + 2.050880952638018 + ], + [ + 841.4581742587286, + 1.9512840720827749 + ], + [ + 981.70120330185, + 1.8985235738100592 + ], + [ + 1121.9442323449714, + 1.9234028366159408 + ], + [ + 1262.1872613880928, + 2.0224992828576895 + ], + [ + 1402.4302904312142, + 2.1898577669482653 + ], + [ + 1542.6733194743356, + 2.4243755814618027 + ], + [ + 1682.9163485174572, + 2.7236688672535734 + ], + [ + 1823.1593775605786, + 3.0653370271465845 + ], + [ + 1963.4024066037, + 3.408456481865547 + ], + [ + 2103.645435646821, + 3.7242213843007668 + ], + [ + 2243.888464689943, + 4.000705844863638 + ], + [ + 2384.1314937330644, + 4.2416916809265155 + ], + [ + 2524.3745227761856, + 4.457944501092153 + ], + [ + 2664.617551819307, + 4.6583139860601985 + ], + [ + 2804.8605808624284, + 4.849512646807497 + ], + [ + 2945.10360990555, + 5.0374981008247985 + ], + [ + 3085.346638948671, + 5.226640123863293 + ], + [ + 3225.589667991793, + 5.41601318523997 + ], + [ + 3365.8326970349144, + 5.603655402157025 + ], + [ + 3506.0757260780356, + 5.7882188033093085 + ], + [ + 3646.318755121157, + 5.968690769553029 + ], + [ + 3786.5617841642784, + 6.145117174577228 + ], + [ + 3926.8048132074, + 6.317541359455963 + ], + [ + 4067.047842250521, + 6.485183961601642 + ], + [ + 4207.290871293642, + 6.647166032871108 + ], + [ + 4347.533900336764, + 6.803146778594685 + ], + [ + 4487.776929379886, + 6.953172587248319 + ], + [ + 4628.019958423007, + 7.098560228947242 + ], + [ + 4768.262987466129, + 7.241043666087599 + ], + [ + 4908.50601650925, + 7.382299656336576 + ], + [ + 5048.749045552371, + 7.5236043255101865 + ], + [ + 5188.992074595492, + 7.6657680264569485 + ], + [ + 5329.235103638614, + 7.808018505447345 + ], + [ + 5469.478132681736, + 7.949298242165187 + ], + [ + 5609.721161724857, + 8.088634440939979 + ], + [ + 5749.964190767979, + 8.225571309639129 + ], + [ + 5890.2072198111, + 8.35958659695338 + ], + [ + 6030.450248854221, + 8.491252740575154 + ], + [ + 6170.693277897342, + 8.621557452830006 + ], + [ + 6310.936306940464, + 8.751587277951696 + ], + [ + 6451.179335983586, + 8.882664777701862 + ], + [ + 6591.422365026707, + 9.015937142291282 + ], + [ + 6731.665394069829, + 9.151974764639073 + ], + [ + 6871.90842311295, + 9.290586755524853 + ], + [ + 7012.151452156071, + 9.431446553929643 + ], + [ + 7152.394481199192, + 9.574116337041016 + ], + [ + 7292.637510242314, + 9.717607202756488 + ], + [ + 7432.880539285436, + 9.860742679447142 + ], + [ + 7573.123568328557, + 10.003209073892146 + ], + [ + 7713.366597371678, + 10.14269158119482 + ], + [ + 7853.6096264148, + 10.276427874158683 + ], + [ + 7993.852655457921, + 10.402538264915362 + ], + [ + 8134.095684501042, + 10.52513298727401 + ], + [ + 8274.338713544164, + 10.650740348863007 + ], + [ + 8414.581742587285, + 10.784666053711716 + ], + [ + 8554.824771630407, + 10.928562405951501 + ], + [ + 8695.067800673529, + 11.081337515333328 + ], + [ + 8835.31082971665, + 11.24157942453155 + ], + [ + 8975.553858759771, + 11.407875527509571 + ], + [ + 9115.796887802893, + 11.578802108772585 + ], + [ + 9256.039916846014, + 11.753003633547195 + ], + [ + 9396.282945889136, + 11.93019285189751 + ], + [ + 9536.525974932258, + 12.105156752553034 + ], + [ + 9676.769003975378, + 12.266813277253501 + ], + [ + 9817.0120330185, + 12.41686132911461 + ], + [ + 9957.25506206162, + 12.557838471946402 + ], + [ + 10097.498091104742, + 12.692282214702216 + ], + [ + 10237.741120147864, + 12.822734085331492 + ], + [ + 10377.984149190985, + 12.95194449280664 + ], + [ + 10518.227178234107, + 13.082324445129279 + ], + [ + 10658.470207277229, + 13.21312828937516 + ], + [ + 10798.71323632035, + 13.342370182674724 + ], + [ + 10938.956265363471, + 13.46893743530791 + ], + [ + 11079.199294406593, + 13.591151671660532 + ], + [ + 11219.442323449714, + 13.708295125083257 + ], + [ + 11359.685352492836, + 13.820949172534013 + ], + [ + 11499.928381535958, + 13.926092184917305 + ], + [ + 11640.171410579078, + 14.01785391812034 + ], + [ + 11780.4144396222, + 14.090507711497574 + ], + [ + 11920.65746866532, + 14.138024765035226 + ], + [ + 12060.900497708442, + 14.155375104550057 + ], + [ + 12201.143526751564, + 14.142781895992686 + ], + [ + 12341.386555794685, + 14.103398328105543 + ], + [ + 12481.629584837807, + 14.040057895221928 + ], + [ + 12621.872613880929, + 13.95545767048217 + ], + [ + 12762.11564292405, + 13.852680581631473 + ], + [ + 12902.358671967171, + 13.734346921527663 + ], + [ + 13042.601701010293, + 13.601959129410302 + ], + [ + 13182.844730053414, + 13.457844267825896 + ], + [ + 13323.087759096536, + 13.304720459246536 + ], + [ + 13463.330788139658, + 13.145423112760023 + ], + [ + 13603.573817182778, + 12.982786328431898 + ], + [ + 13743.8168462259, + 12.819088563274962 + ], + [ + 13884.059875269022, + 12.655241390186259 + ] + ], + "19": [ + [ + 0.0, + 1.6786928988762346 + ], + [ + 140.24302904312142, + 1.998880270064884 + ], + [ + 280.48605808624285, + 2.2580134262630747 + ], + [ + 420.7290871293643, + 2.1227275035634445 + ], + [ + 560.9721161724857, + 1.9162062400126305 + ], + [ + 701.2151452156071, + 1.76472677277538 + ], + [ + 841.4581742587286, + 1.6391752339129846 + ], + [ + 981.70120330185, + 1.5976697656461432 + ], + [ + 1121.9442323449714, + 1.6668569359132481 + ], + [ + 1262.1872613880928, + 1.824175572828138 + ], + [ + 1402.4302904312142, + 2.056972082352351 + ], + [ + 1542.6733194743356, + 2.3552876749699454 + ], + [ + 1682.9163485174572, + 2.7014547653545575 + ], + [ + 1823.1593775605786, + 3.0658827102959862 + ], + [ + 1963.4024066037, + 3.410568122460189 + ], + [ + 2103.645435646821, + 3.715006688922865 + ], + [ + 2243.888464689943, + 3.9762774570663826 + ], + [ + 2384.1314937330644, + 4.200557009275054 + ], + [ + 2524.3745227761856, + 4.39930245642398 + ], + [ + 2664.617551819307, + 4.582668029819638 + ], + [ + 2804.8605808624284, + 4.758509660592744 + ], + [ + 2945.10360990555, + 4.934009229298639 + ], + [ + 3085.346638948671, + 5.114380948968579 + ], + [ + 3225.589667991793, + 5.298369585374536 + ], + [ + 3365.8326970349144, + 5.483328553690483 + ], + [ + 3506.0757260780356, + 5.667290719195644 + ], + [ + 3646.318755121157, + 5.84869046790545 + ], + [ + 3786.5617841642784, + 6.027611090861402 + ], + [ + 3926.8048132074, + 6.204034542344768 + ], + [ + 4067.047842250521, + 6.376832551320898 + ], + [ + 4207.290871293642, + 6.544721463658011 + ], + [ + 4347.533900336764, + 6.706825787063422 + ], + [ + 4487.776929379886, + 6.863009911611571 + ], + [ + 4628.019958423007, + 7.0150246701832115 + ], + [ + 4768.262987466129, + 7.165108315264293 + ], + [ + 4908.50601650925, + 7.315323066550106 + ], + [ + 5048.749045552371, + 7.4676632384010855 + ], + [ + 5188.992074595492, + 7.623223186135928 + ], + [ + 5329.235103638614, + 7.7807381616684435 + ], + [ + 5469.478132681736, + 7.938606423273976 + ], + [ + 5609.721161724857, + 8.095265287287027 + ], + [ + 5749.964190767979, + 8.248694595558097 + ], + [ + 5890.2072198111, + 8.396186006557755 + ], + [ + 6030.450248854221, + 8.537030287006422 + ], + [ + 6170.693277897342, + 8.671214294992629 + ], + [ + 6310.936306940464, + 8.798797434746554 + ], + [ + 6451.179335983586, + 8.920791072964844 + ], + [ + 6591.422365026707, + 9.039714833266839 + ], + [ + 6731.665394069829, + 9.157961899204896 + ], + [ + 6871.90842311295, + 9.276442838634814 + ], + [ + 7012.151452156071, + 9.395807604498419 + ], + [ + 7152.394481199192, + 9.516733077843957 + ], + [ + 7292.637510242314, + 9.639312390846202 + ], + [ + 7432.880539285436, + 9.762289583847132 + ], + [ + 7573.123568328557, + 9.885043293657299 + ], + [ + 7713.366597371678, + 10.006270874578139 + ], + [ + 7853.6096264148, + 10.124524705780935 + ], + [ + 7993.852655457921, + 10.239174996133048 + ], + [ + 8134.095684501042, + 10.35485587901777 + ], + [ + 8274.338713544164, + 10.478327044258029 + ], + [ + 8414.581742587285, + 10.616208564475814 + ], + [ + 8554.824771630407, + 10.770610766230446 + ], + [ + 8695.067800673529, + 10.938400781895576 + ], + [ + 8835.31082971665, + 11.115745838977297 + ], + [ + 8975.553858759771, + 11.298814309598978 + ], + [ + 9115.796887802893, + 11.483861643819342 + ], + [ + 9256.039916846014, + 11.667636250889585 + ], + [ + 9396.282945889136, + 11.8468992133433 + ], + [ + 9536.525974932258, + 12.019713270809923 + ], + [ + 9676.769003975378, + 12.185945331521774 + ], + [ + 9817.0120330185, + 12.346243639349323 + ], + [ + 9957.25506206162, + 12.501266367982572 + ], + [ + 10097.498091104742, + 12.651671330952313 + ], + [ + 10237.741120147864, + 12.798114836551173 + ], + [ + 10377.984149190985, + 12.941234386529706 + ], + [ + 10518.227178234107, + 13.081376008465996 + ], + [ + 10658.470207277229, + 13.217845103042924 + ], + [ + 10798.71323632035, + 13.348672892639959 + ], + [ + 10938.956265363471, + 13.473590402550531 + ], + [ + 11079.199294406593, + 13.592603532289951 + ], + [ + 11219.442323449714, + 13.70652131086105 + ], + [ + 11359.685352492836, + 13.816707846280865 + ], + [ + 11499.928381535958, + 13.921993505994337 + ], + [ + 11640.171410579078, + 14.017461818387407 + ], + [ + 11780.4144396222, + 14.097912645254747 + ], + [ + 11920.65746866532, + 14.157686151540988 + ], + [ + 12060.900497708442, + 14.191580523598494 + ], + [ + 12201.143526751564, + 14.19697919741696 + ], + [ + 12341.386555794685, + 14.174752574345677 + ], + [ + 12481.629584837807, + 14.125870558911199 + ], + [ + 12621.872613880929, + 14.051099128466559 + ], + [ + 12762.11564292405, + 13.951573726111578 + ], + [ + 12902.358671967171, + 13.829150839448747 + ], + [ + 13042.601701010293, + 13.687707587014467 + ], + [ + 13182.844730053414, + 13.531251753298038 + ], + [ + 13323.087759096536, + 13.364077697949618 + ], + [ + 13463.330788139658, + 13.19069086055583 + ], + [ + 13603.573817182778, + 13.015599674823411 + ], + [ + 13743.8168462259, + 12.842816958550985 + ], + [ + 13884.059875269022, + 12.673933461608101 + ] + ] + }, + "atmosphericModelWindVelocityYProfile": { + "4": [ + [ + 0.0, + -0.451875189774473 + ], + [ + 140.24302904312142, + -0.2560799456281367 + ], + [ + 280.48605808624285, + -0.2128055562844784 + ], + [ + 420.7290871293643, + 0.13469614497139 + ], + [ + 560.9721161724857, + 0.5415255345488322 + ], + [ + 701.2151452156071, + 0.8225892729140477 + ], + [ + 841.4581742587286, + 1.0302399396526583 + ], + [ + 981.70120330185, + 1.180762699430602 + ], + [ + 1121.9442323449714, + 1.284239151363068 + ], + [ + 1262.1872613880928, + 1.3680616592268233 + ], + [ + 1402.4302904312142, + 1.4274784057144734 + ], + [ + 1542.6733194743356, + 1.4466635496967295 + ], + [ + 1682.9163485174572, + 1.4499932553282135 + ], + [ + 1823.1593775605786, + 1.4528530764169487 + ], + [ + 1963.4024066037, + 1.4284038067624583 + ], + [ + 2103.645435646821, + 1.3799367462698726 + ], + [ + 2243.888464689943, + 1.4003075377407521 + ], + [ + 2384.1314937330644, + 1.3967359732505553 + ], + [ + 2524.3745227761856, + 1.3461887457238058 + ], + [ + 2664.617551819307, + 1.3210644536438574 + ], + [ + 2804.8605808624284, + 1.3141605127715026 + ], + [ + 2945.10360990555, + 1.3096875308301972 + ], + [ + 3085.346638948671, + 1.2941983136958335 + ], + [ + 3225.589667991793, + 1.2644015879505677 + ], + [ + 3365.8326970349144, + 1.2277049217309262 + ], + [ + 3506.0757260780356, + 1.1928764092982582 + ], + [ + 3646.318755121157, + 1.1669669966507517 + ], + [ + 3786.5617841642784, + 1.1504094875267672 + ], + [ + 3926.8048132074, + 1.1382793179392496 + ], + [ + 4067.047842250521, + 1.1241179407274087 + ], + [ + 4207.290871293642, + 1.1017954895953472 + ], + [ + 4347.533900336764, + 1.0679497743928552 + ], + [ + 4487.776929379886, + 1.0230596453813332 + ], + [ + 4628.019958423007, + 0.9695669973711156 + ], + [ + 4768.262987466129, + 0.9102599411581666 + ], + [ + 4908.50601650925, + 0.8479037945933405 + ], + [ + 5048.749045552371, + 0.7848183202494052 + ], + [ + 5188.992074595492, + 0.7222065683932203 + ], + [ + 5329.235103638614, + 0.6609145017285134 + ], + [ + 5469.478132681736, + 0.6017126128856014 + ], + [ + 5609.721161724857, + 0.5450887487442392 + ], + [ + 5749.964190767979, + 0.48859477490621434 + ], + [ + 5890.2072198111, + 0.42914739175413014 + ], + [ + 6030.450248854221, + 0.3640365080581249 + ], + [ + 6170.693277897342, + 0.2904388674288861 + ], + [ + 6310.936306940464, + 0.2054864743456763 + ], + [ + 6451.179335983586, + 0.10899268468324941 + ], + [ + 6591.422365026707, + 0.006922558381975482 + ], + [ + 6731.665394069829, + -0.09620783645123386 + ], + [ + 6871.90842311295, + -0.19697756995200205 + ], + [ + 7012.151452156071, + -0.29186192895538593 + ], + [ + 7152.394481199192, + -0.3774850414674561 + ], + [ + 7292.637510242314, + -0.45250123621726285 + ], + [ + 7432.880539285436, + -0.5215472900343137 + ], + [ + 7573.123568328557, + -0.5883480236209266 + ], + [ + 7713.366597371678, + -0.6547597433122919 + ], + [ + 7853.6096264148, + -0.7227229179558958 + ], + [ + 7993.852655457921, + -0.7941551064148408 + ], + [ + 8134.095684501042, + -0.8704639385523129 + ], + [ + 8274.338713544164, + -0.9520578069462572 + ], + [ + 8414.581742587285, + -1.037927298463606 + ], + [ + 8554.824771630407, + -1.1278908725826862 + ], + [ + 8695.067800673529, + -1.2220733700877848 + ], + [ + 8835.31082971665, + -1.320428634806035 + ], + [ + 8975.553858759771, + -1.4229097649431133 + ], + [ + 9115.796887802893, + -1.529540931044175 + ], + [ + 9256.039916846014, + -1.6403841547373177 + ], + [ + 9396.282945889136, + -1.7528420861156329 + ], + [ + 9536.525974932258, + -1.8631832647111795 + ], + [ + 9676.769003975378, + -1.9681609502913 + ], + [ + 9817.0120330185, + -2.0650908859534196 + ], + [ + 9957.25506206162, + -2.1513831979852847 + ], + [ + 10097.498091104742, + -2.2244480593678646 + ], + [ + 10237.741120147864, + -2.2816862884890017 + ], + [ + 10377.984149190985, + -2.320254959814878 + ], + [ + 10518.227178234107, + -2.33994531472681 + ], + [ + 10658.470207277229, + -2.350883348901762 + ], + [ + 10798.71323632035, + -2.3655012410370677 + ], + [ + 10938.956265363471, + -2.394526453029017 + ], + [ + 11079.199294406593, + -2.44818286941804 + ], + [ + 11219.442323449714, + -2.5324314544477 + ], + [ + 11359.685352492836, + -2.6392695022105745 + ], + [ + 11499.928381535958, + -2.754010180358006 + ], + [ + 11640.171410579078, + -2.862163336431312 + ], + [ + 11780.4144396222, + -2.9498649926923384 + ], + [ + 11920.65746866532, + -3.0038149689383427 + ], + [ + 12060.900497708442, + -3.01655765541376 + ], + [ + 12201.143526751564, + -2.991544035900692 + ], + [ + 12341.386555794685, + -2.935942894985688 + ], + [ + 12481.629584837807, + -2.856839583759689 + ], + [ + 12621.872613880929, + -2.7612430665397087 + ], + [ + 12762.11564292405, + -2.655812007914559 + ], + [ + 12902.358671967171, + -2.5462861830898125 + ], + [ + 13042.601701010293, + -2.433946445749713 + ], + [ + 13182.844730053414, + -2.319546056529121 + ], + [ + 13323.087759096536, + -2.2044383938338132 + ], + [ + 13463.330788139658, + -2.0899187543826385 + ], + [ + 13603.573817182778, + -1.9773156026445928 + ], + [ + 13743.8168462259, + -1.8681840336951807 + ], + [ + 13884.059875269022, + -1.7635855634374604 + ] + ], + "5": [ + [ + 0.0, + -0.3835743565178399 + ], + [ + 140.24302904312142, + -0.15406643024044925 + ], + [ + 280.48605808624285, + -0.08909714258908226 + ], + [ + 420.7290871293643, + 0.2255850127274388 + ], + [ + 560.9721161724857, + 0.5912030513937192 + ], + [ + 701.2151452156071, + 0.8347166125999415 + ], + [ + 841.4581742587286, + 0.9983727568002544 + ], + [ + 981.70120330185, + 1.1109178420132135 + ], + [ + 1121.9442323449714, + 1.186511421828901 + ], + [ + 1262.1872613880928, + 1.2444900512748023 + ], + [ + 1402.4302904312142, + 1.2811196933167879 + ], + [ + 1542.6733194743356, + 1.2900529050462342 + ], + [ + 1682.9163485174572, + 1.2927137800001405 + ], + [ + 1823.1593775605786, + 1.296163978745293 + ], + [ + 1963.4024066037, + 1.2933507922213907 + ], + [ + 2103.645435646821, + 1.2886767826497367 + ], + [ + 2243.888464689943, + 1.2901656612066232 + ], + [ + 2384.1314937330644, + 1.2977439119619631 + ], + [ + 2524.3745227761856, + 1.3072125275585196 + ], + [ + 2664.617551819307, + 1.3138340816475158 + ], + [ + 2804.8605808624284, + 1.313720716012044 + ], + [ + 2945.10360990555, + 1.30283838984419 + ], + [ + 3085.346638948671, + 1.2789410445659388 + ], + [ + 3225.589667991793, + 1.2458743037171867 + ], + [ + 3365.8326970349144, + 1.2084237405560567 + ], + [ + 3506.0757260780356, + 1.171596657552996 + ], + [ + 3646.318755121157, + 1.1390567230525857 + ], + [ + 3786.5617841642784, + 1.1096130940856666 + ], + [ + 3926.8048132074, + 1.080196602482594 + ], + [ + 4067.047842250521, + 1.046933122454093 + ], + [ + 4207.290871293642, + 1.0062530898172015 + ], + [ + 4347.533900336764, + 0.9568976380323597 + ], + [ + 4487.776929379886, + 0.8991906056004271 + ], + [ + 4628.019958423007, + 0.8347686781463537 + ], + [ + 4768.262987466129, + 0.7656352838889812 + ], + [ + 4908.50601650925, + 0.6937800651445934 + ], + [ + 5048.749045552371, + 0.61987631205354 + ], + [ + 5188.992074595492, + 0.5447047265752463 + ], + [ + 5329.235103638614, + 0.46921734937428616 + ], + [ + 5469.478132681736, + 0.3942623222832797 + ], + [ + 5609.721161724857, + 0.32042681923720423 + ], + [ + 5749.964190767979, + 0.2487213682577026 + ], + [ + 5890.2072198111, + 0.18035234616722196 + ], + [ + 6030.450248854221, + 0.11516245193306124 + ], + [ + 6170.693277897342, + 0.052766219522105785 + ], + [ + 6310.936306940464, + -0.007204496110066404 + ], + [ + 6451.179335983586, + -0.06531092465940712 + ], + [ + 6591.422365026707, + -0.12325126414177943 + ], + [ + 6731.665394069829, + -0.18323713843332595 + ], + [ + 6871.90842311295, + -0.24729748547157748 + ], + [ + 7012.151452156071, + -0.31732651636069875 + ], + [ + 7152.394481199192, + -0.39539195111276754 + ], + [ + 7292.637510242314, + -0.4822401101294142 + ], + [ + 7432.880539285436, + -0.5760725860748606 + ], + [ + 7573.123568328557, + -0.6757740732738815 + ], + [ + 7713.366597371678, + -0.7804723973602161 + ], + [ + 7853.6096264148, + -0.8894186347447861 + ], + [ + 7993.852655457921, + -1.000933272851368 + ], + [ + 8134.095684501042, + -1.1070346044542658 + ], + [ + 8274.338713544164, + -1.2001371989781953 + ], + [ + 8414.581742587285, + -1.2758508751261792 + ], + [ + 8554.824771630407, + -1.3355077980322718 + ], + [ + 8695.067800673529, + -1.38262658149667 + ], + [ + 8835.31082971665, + -1.4206214659793064 + ], + [ + 8975.553858759771, + -1.4529068511546401 + ], + [ + 9115.796887802893, + -1.4829293073960546 + ], + [ + 9256.039916846014, + -1.5137875048318223 + ], + [ + 9396.282945889136, + -1.5475247916195018 + ], + [ + 9536.525974932258, + -1.5857116420860142 + ], + [ + 9676.769003975378, + -1.6291452261900883 + ], + [ + 9817.0120330185, + -1.6787931808872327 + ], + [ + 9957.25506206162, + -1.7356800235484084 + ], + [ + 10097.498091104742, + -1.8008303079665224 + ], + [ + 10237.741120147864, + -1.875250161365168 + ], + [ + 10377.984149190985, + -1.959681992754008 + ], + [ + 10518.227178234107, + -2.054521577357757 + ], + [ + 10658.470207277229, + -2.1589474151827304 + ], + [ + 10798.71323632035, + -2.271438765302103 + ], + [ + 10938.956265363471, + -2.3895400199362737 + ], + [ + 11079.199294406593, + -2.5107672649764976 + ], + [ + 11219.442323449714, + -2.632350433266692 + ], + [ + 11359.685352492836, + -2.751205504640338 + ], + [ + 11499.928381535958, + -2.8615505822692273 + ], + [ + 11640.171410579078, + -2.956726830137686 + ], + [ + 11780.4144396222, + -3.0301946909488797 + ], + [ + 11920.65746866532, + -3.0759743707967058 + ], + [ + 12060.900497708442, + -3.089306365984969 + ], + [ + 12201.143526751564, + -3.071207024332348 + ], + [ + 12341.386555794685, + -3.0261062715226292 + ], + [ + 12481.629584837807, + -2.9591101882360675 + ], + [ + 12621.872613880929, + -2.8752462413260633 + ], + [ + 12762.11564292405, + -2.7792316530733476 + ], + [ + 12902.358671967171, + -2.675561474690317 + ], + [ + 13042.601701010293, + -2.5660401596740074 + ], + [ + 13182.844730053414, + -2.452525244876707 + ], + [ + 13323.087759096536, + -2.3371870827916883 + ], + [ + 13463.330788139658, + -2.222152068986425 + ], + [ + 13603.573817182778, + -2.109587884733917 + ], + [ + 13743.8168462259, + -2.001779666944521 + ], + [ + 13884.059875269022, + -1.900446037165099 + ] + ], + "6": [ + [ + 0.0, + -0.3111986784517982 + ], + [ + 140.24302904312142, + -0.020630303469561184 + ], + [ + 280.48605808624285, + 0.07540274916321796 + ], + [ + 420.7290871293643, + 0.35879317978368985 + ], + [ + 560.9721161724857, + 0.6732016480401359 + ], + [ + 701.2151452156071, + 0.8668273004041936 + ], + [ + 841.4581742587286, + 0.9875924239975956 + ], + [ + 981.70120330185, + 1.0765907751635937 + ], + [ + 1121.9442323449714, + 1.1374605755238385 + ], + [ + 1262.1872613880928, + 1.1728239868929375 + ], + [ + 1402.4302904312142, + 1.1880495379412397 + ], + [ + 1542.6733194743356, + 1.1876982003025878 + ], + [ + 1682.9163485174572, + 1.1893347679549124 + ], + [ + 1823.1593775605786, + 1.1985620352719404 + ], + [ + 1963.4024066037, + 1.2121545312811364 + ], + [ + 2103.645435646821, + 1.2265840439940097 + ], + [ + 2243.888464689943, + 1.2388419794258094 + ], + [ + 2384.1314937330644, + 1.24766001579556 + ], + [ + 2524.3745227761856, + 1.2502439627015338 + ], + [ + 2664.617551819307, + 1.2441937580380493 + ], + [ + 2804.8605808624284, + 1.2297902224593074 + ], + [ + 2945.10360990555, + 1.2072326636586856 + ], + [ + 3085.346638948671, + 1.177447720260071 + ], + [ + 3225.589667991793, + 1.1436166795893825 + ], + [ + 3365.8326970349144, + 1.107427246370779 + ], + [ + 3506.0757260780356, + 1.0706513109146674 + ], + [ + 3646.318755121157, + 1.0343908205351544 + ], + [ + 3786.5617841642784, + 0.9975624482116457 + ], + [ + 3926.8048132074, + 0.958203756906018 + ], + [ + 4067.047842250521, + 0.9136132374531537 + ], + [ + 4207.290871293642, + 0.8614975533843267 + ], + [ + 4347.533900336764, + 0.8010557131333407 + ], + [ + 4487.776929379886, + 0.7323606268407714 + ], + [ + 4628.019958423007, + 0.6574758886356367 + ], + [ + 4768.262987466129, + 0.5788052844934659 + ], + [ + 4908.50601650925, + 0.4985507213358769 + ], + [ + 5048.749045552371, + 0.41789232530592785 + ], + [ + 5188.992074595492, + 0.3379139231406373 + ], + [ + 5329.235103638614, + 0.2595127263077715 + ], + [ + 5469.478132681736, + 0.1834981528570157 + ], + [ + 5609.721161724857, + 0.11051352565339773 + ], + [ + 5749.964190767979, + 0.041972127406348356 + ], + [ + 5890.2072198111, + -0.020190504301200733 + ], + [ + 6030.450248854221, + -0.07559770499959793 + ], + [ + 6170.693277897342, + -0.12413816929765077 + ], + [ + 6310.936306940464, + -0.16569260836408625 + ], + [ + 6451.179335983586, + -0.20048063024034615 + ], + [ + 6591.422365026707, + -0.23058370527325833 + ], + [ + 6731.665394069829, + -0.25898139502556294 + ], + [ + 6871.90842311295, + -0.2881969985202336 + ], + [ + 7012.151452156071, + -0.32064128355045884 + ], + [ + 7152.394481199192, + -0.35887020465830993 + ], + [ + 7292.637510242314, + -0.4051113081635162 + ], + [ + 7432.880539285436, + -0.4600672562879374 + ], + [ + 7573.123568328557, + -0.522883520326547 + ], + [ + 7713.366597371678, + -0.5918273465162692 + ], + [ + 7853.6096264148, + -0.6651730910902963 + ], + [ + 7993.852655457921, + -0.7411812052688356 + ], + [ + 8134.095684501042, + -0.8179218188728077 + ], + [ + 8274.338713544164, + -0.8938333303495719 + ], + [ + 8414.581742587285, + -0.9678988708031877 + ], + [ + 8554.824771630407, + -1.0402358835529713 + ], + [ + 8695.067800673529, + -1.1115643912843076 + ], + [ + 8835.31082971665, + -1.1826290695994857 + ], + [ + 8975.553858759771, + -1.2541745264979234 + ], + [ + 9115.796887802893, + -1.3268598131563403 + ], + [ + 9256.039916846014, + -1.4009428577157494 + ], + [ + 9396.282945889136, + -1.4770130981556089 + ], + [ + 9536.525974932258, + -1.5555067768483324 + ], + [ + 9676.769003975378, + -1.636302098331649 + ], + [ + 9817.0120330185, + -1.7192240375559724 + ], + [ + 9957.25506206162, + -1.8041047501534315 + ], + [ + 10097.498091104742, + -1.8907764180951068 + ], + [ + 10237.741120147864, + -1.9790558123724478 + ], + [ + 10377.984149190985, + -2.068762407364398 + ], + [ + 10518.227178234107, + -2.160733150860763 + ], + [ + 10658.470207277229, + -2.255628708582211 + ], + [ + 10798.71323632035, + -2.353915159141073 + ], + [ + 10938.956265363471, + -2.456019312471847 + ], + [ + 11079.199294406593, + -2.5621258558679174 + ], + [ + 11219.442323449714, + -2.670080232621501 + ], + [ + 11359.685352492836, + -2.7766964491074737 + ], + [ + 11499.928381535958, + -2.8771528064012406 + ], + [ + 11640.171410579078, + -2.9650533580760445 + ], + [ + 11780.4144396222, + -3.0344563592803553 + ], + [ + 11920.65746866532, + -3.080693346656197 + ], + [ + 12060.900497708442, + -3.1014470551423625 + ], + [ + 12201.143526751564, + -3.097024610586444 + ], + [ + 12341.386555794685, + -3.0707591757978645 + ], + [ + 12481.629584837807, + -3.026162510452795 + ], + [ + 12621.872613880929, + -2.9665333185985427 + ], + [ + 12762.11564292405, + -2.894566368753094 + ], + [ + 12902.358671967171, + -2.8122317484027803 + ], + [ + 13042.601701010293, + -2.7206683532028215 + ], + [ + 13182.844730053414, + -2.621679865131262 + ], + [ + 13323.087759096536, + -2.5177074402215225 + ], + [ + 13463.330788139658, + -2.4112156079665774 + ], + [ + 13603.573817182778, + -2.304722795794905 + ], + [ + 13743.8168462259, + -2.201005413058189 + ], + [ + 13884.059875269022, + -2.1025676444339747 + ] + ], + "7": [ + [ + 0.0, + -0.19304634819806202 + ], + [ + 140.24302904312142, + 0.15842759520186311 + ], + [ + 280.48605808624285, + 0.2933080763172795 + ], + [ + 420.7290871293643, + 0.5494947276839064 + ], + [ + 560.9721161724857, + 0.8136772534830864 + ], + [ + 701.2151452156071, + 0.9600086714930502 + ], + [ + 841.4581742587286, + 1.0434730379342894 + ], + [ + 981.70120330185, + 1.108271790621457 + ], + [ + 1121.9442323449714, + 1.1523321935898305 + ], + [ + 1262.1872613880928, + 1.168027053422024 + ], + [ + 1402.4302904312142, + 1.1689357220831031 + ], + [ + 1542.6733194743356, + 1.1663286924809173 + ], + [ + 1682.9163485174572, + 1.166706847151261 + ], + [ + 1823.1593775605786, + 1.1689582258063302 + ], + [ + 1963.4024066037, + 1.1744750472478809 + ], + [ + 2103.645435646821, + 1.185038878472527 + ], + [ + 2243.888464689943, + 1.1989796263080688 + ], + [ + 2384.1314937330644, + 1.209782678374117 + ], + [ + 2524.3745227761856, + 1.2081199115835184 + ], + [ + 2664.617551819307, + 1.19114285082985 + ], + [ + 2804.8605808624284, + 1.162610705004964 + ], + [ + 2945.10360990555, + 1.1263354422286869 + ], + [ + 3085.346638948671, + 1.085873132897644 + ], + [ + 3225.589667991793, + 1.0448952577761583 + ], + [ + 3365.8326970349144, + 1.0033180785341387 + ], + [ + 3506.0757260780356, + 0.9609541246437786 + ], + [ + 3646.318755121157, + 0.9184414283716523 + ], + [ + 3786.5617841642784, + 0.8749137304036839 + ], + [ + 3926.8048132074, + 0.8287089004443686 + ], + [ + 4067.047842250521, + 0.7778006457258893 + ], + [ + 4207.290871293642, + 0.7201529314754835 + ], + [ + 4347.533900336764, + 0.653785404546491 + ], + [ + 4487.776929379886, + 0.5787023433639943 + ], + [ + 4628.019958423007, + 0.4972459903803569 + ], + [ + 4768.262987466129, + 0.4120119736984176 + ], + [ + 4908.50601650925, + 0.3255501375077344 + ], + [ + 5048.749045552371, + 0.24077351277434478 + ], + [ + 5188.992074595492, + 0.15966622926333868 + ], + [ + 5329.235103638614, + 0.08373603595634407 + ], + [ + 5469.478132681736, + 0.014453429754833935 + ], + [ + 5609.721161724857, + -0.04699445965452431 + ], + [ + 5749.964190767979, + -0.1010100319617036 + ], + [ + 5890.2072198111, + -0.14814926317721525 + ], + [ + 6030.450248854221, + -0.19021470939721452 + ], + [ + 6170.693277897342, + -0.22931618122628178 + ], + [ + 6310.936306940464, + -0.26756643872989844 + ], + [ + 6451.179335983586, + -0.3064315328678049 + ], + [ + 6591.422365026707, + -0.3463527794350638 + ], + [ + 6731.665394069829, + -0.38790513648548813 + ], + [ + 6871.90842311295, + -0.43125295560189253 + ], + [ + 7012.151452156071, + -0.47646877129783155 + ], + [ + 7152.394481199192, + -0.5237541602211158 + ], + [ + 7292.637510242314, + -0.5731799315330628 + ], + [ + 7432.880539285436, + -0.6250800520582652 + ], + [ + 7573.123568328557, + -0.6800209241472053 + ], + [ + 7713.366597371678, + -0.7372598032698329 + ], + [ + 7853.6096264148, + -0.795872241714308 + ], + [ + 7993.852655457921, + -0.8549263520062065 + ], + [ + 8134.095684501042, + -0.9133753253250347 + ], + [ + 8274.338713544164, + -0.9708446347733702 + ], + [ + 8414.581742587285, + -1.0263945288241723 + ], + [ + 8554.824771630407, + -1.0799912495948085 + ], + [ + 8695.067800673529, + -1.1345070739180367 + ], + [ + 8835.31082971665, + -1.1931403920170038 + ], + [ + 8975.553858759771, + -1.2590892869072456 + ], + [ + 9115.796887802893, + -1.335414911727145 + ], + [ + 9256.039916846014, + -1.424750246271375 + ], + [ + 9396.282945889136, + -1.5298791634044604 + ], + [ + 9536.525974932258, + -1.63875847295105 + ], + [ + 9676.769003975378, + -1.733396117851107 + ], + [ + 9817.0120330185, + -1.8162125848723396 + ], + [ + 9957.25506206162, + -1.8900201054494106 + ], + [ + 10097.498091104742, + -1.9576309360452897 + ], + [ + 10237.741120147864, + -2.021851698547179 + ], + [ + 10377.984149190985, + -2.0855336331212233 + ], + [ + 10518.227178234107, + -2.152148426179585 + ], + [ + 10658.470207277229, + -2.2248671338918427 + ], + [ + 10798.71323632035, + -2.3060000118987873 + ], + [ + 10938.956265363471, + -2.394927663276205 + ], + [ + 11079.199294406593, + -2.4900345658618344 + ], + [ + 11219.442323449714, + -2.5879554526726585 + ], + [ + 11359.685352492836, + -2.6845974477881214 + ], + [ + 11499.928381535958, + -2.7753051091136385 + ], + [ + 11640.171410579078, + -2.855151956066224 + ], + [ + 11780.4144396222, + -2.9198807361972587 + ], + [ + 11920.65746866532, + -2.9663134826746513 + ], + [ + 12060.900497708442, + -2.9936287538275415 + ], + [ + 12201.143526751564, + -3.0011984682533077 + ], + [ + 12341.386555794685, + -2.991197144407302 + ], + [ + 12481.629584837807, + -2.966312813261738 + ], + [ + 12621.872613880929, + -2.9290868644352597 + ], + [ + 12762.11564292405, + -2.8812707398120456 + ], + [ + 12902.358671967171, + -2.8236925174423804 + ], + [ + 13042.601701010293, + -2.7578935008138252 + ], + [ + 13182.844730053414, + -2.685474682628436 + ], + [ + 13323.087759096536, + -2.6078815572276564 + ], + [ + 13463.330788139658, + -2.526519367660118 + ], + [ + 13603.573817182778, + -2.442855366663633 + ], + [ + 13743.8168462259, + -2.3588746409086068 + ], + [ + 13884.059875269022, + -2.2765809640610426 + ] + ], + "8": [ + [ + 0.0, + -0.09214866421909611 + ], + [ + 140.24302904312142, + 0.3263116046020117 + ], + [ + 280.48605808624285, + 0.518446034541588 + ], + [ + 420.7290871293643, + 0.7755393157899095 + ], + [ + 560.9721161724857, + 1.0068511277749297 + ], + [ + 701.2151452156071, + 1.1096218521545431 + ], + [ + 841.4581742587286, + 1.1529893906286341 + ], + [ + 981.70120330185, + 1.1849382491385474 + ], + [ + 1121.9442323449714, + 1.203908530747345 + ], + [ + 1262.1872613880928, + 1.2043319372017165 + ], + [ + 1402.4302904312142, + 1.198801883074818 + ], + [ + 1542.6733194743356, + 1.1913039129185217 + ], + [ + 1682.9163485174572, + 1.1803615904459246 + ], + [ + 1823.1593775605786, + 1.1661076406530568 + ], + [ + 1963.4024066037, + 1.1587923244318032 + ], + [ + 2103.645435646821, + 1.1615152905310977 + ], + [ + 2243.888464689943, + 1.1667667111248408 + ], + [ + 2384.1314937330644, + 1.167116006374588 + ], + [ + 2524.3745227761856, + 1.1548334549405008 + ], + [ + 2664.617551819307, + 1.127751070368713 + ], + [ + 2804.8605808624284, + 1.0913447682750304 + ], + [ + 2945.10360990555, + 1.051151449975068 + ], + [ + 3085.346638948671, + 1.0116656115822846 + ], + [ + 3225.589667991793, + 0.9749378863234479 + ], + [ + 3365.8326970349144, + 0.9369515359812043 + ], + [ + 3506.0757260780356, + 0.8933990051749728 + ], + [ + 3646.318755121157, + 0.8423389630041305 + ], + [ + 3786.5617841642784, + 0.7835170690930425 + ], + [ + 3926.8048132074, + 0.7186477651083134 + ], + [ + 4067.047842250521, + 0.6498450702344426 + ], + [ + 4207.290871293642, + 0.5786803042404279 + ], + [ + 4347.533900336764, + 0.5046310466239089 + ], + [ + 4487.776929379886, + 0.4273586606854939 + ], + [ + 4628.019958423007, + 0.34773417072163015 + ], + [ + 4768.262987466129, + 0.26675209116601994 + ], + [ + 4908.50601650925, + 0.18556746013417735 + ], + [ + 5048.749045552371, + 0.10623270486290158 + ], + [ + 5188.992074595492, + 0.030082270211727498 + ], + [ + 5329.235103638614, + -0.041983063468749006 + ], + [ + 5469.478132681736, + -0.10906443989552314 + ], + [ + 5609.721161724857, + -0.17049523788840276 + ], + [ + 5749.964190767979, + -0.22717423578175497 + ], + [ + 5890.2072198111, + -0.2797691579252127 + ], + [ + 6030.450248854221, + -0.3298246851181538 + ], + [ + 6170.693277897342, + -0.37916365525523177 + ], + [ + 6310.936306940464, + -0.4295991523168206 + ], + [ + 6451.179335983586, + -0.48195529423339895 + ], + [ + 6591.422365026707, + -0.535693306829233 + ], + [ + 6731.665394069829, + -0.590176813762216 + ], + [ + 6871.90842311295, + -0.6441119865678534 + ], + [ + 7012.151452156071, + -0.6961055808168635 + ], + [ + 7152.394481199192, + -0.744934039867361 + ], + [ + 7292.637510242314, + -0.7898665035927135 + ], + [ + 7432.880539285436, + -0.8309928831756344 + ], + [ + 7573.123568328557, + -0.8698421872203529 + ], + [ + 7713.366597371678, + -0.908214449576735 + ], + [ + 7853.6096264148, + -0.9479030576120427 + ], + [ + 7993.852655457921, + -0.9906872968193211 + ], + [ + 8134.095684501042, + -1.037980792735428 + ], + [ + 8274.338713544164, + -1.0912375501081435 + ], + [ + 8414.581742587285, + -1.151449276921896 + ], + [ + 8554.824771630407, + -1.2175183114548496 + ], + [ + 8695.067800673529, + -1.287731271164042 + ], + [ + 8835.31082971665, + -1.3604302911060897 + ], + [ + 8975.553858759771, + -1.4339579213874878 + ], + [ + 9115.796887802893, + -1.5066074247665509 + ], + [ + 9256.039916846014, + -1.576811781252794 + ], + [ + 9396.282945889136, + -1.642728055416125 + ], + [ + 9536.525974932258, + -1.7032377571632515 + ], + [ + 9676.769003975378, + -1.758888530287144 + ], + [ + 9817.0120330185, + -1.8108819932413356 + ], + [ + 9957.25506206162, + -1.8603985012514068 + ], + [ + 10097.498091104742, + -1.9086184166409652 + ], + [ + 10237.741120147864, + -1.956712519836913 + ], + [ + 10377.984149190985, + -2.0057529674318157 + ], + [ + 10518.227178234107, + -2.0572554999979107 + ], + [ + 10658.470207277229, + -2.1134635928374843 + ], + [ + 10798.71323632035, + -2.1763296016377693 + ], + [ + 10938.956265363471, + -2.2463606591820837 + ], + [ + 11079.199294406593, + -2.3232356492382324 + ], + [ + 11219.442323449714, + -2.4050171507194023 + ], + [ + 11359.685352492836, + -2.488917901523426 + ], + [ + 11499.928381535958, + -2.5715660382835464 + ], + [ + 11640.171410579078, + -2.6492372680714196 + ], + [ + 11780.4144396222, + -2.719107445402041 + ], + [ + 11920.65746866532, + -2.7792715297052504 + ], + [ + 12060.900497708442, + -2.829034609304102 + ], + [ + 12201.143526751564, + -2.866054161846788 + ], + [ + 12341.386555794685, + -2.8902116568276677 + ], + [ + 12481.629584837807, + -2.9016840980042535 + ], + [ + 12621.872613880929, + -2.9005515609146784 + ], + [ + 12762.11564292405, + -2.8861737987801175 + ], + [ + 12902.358671967171, + -2.85815515401145 + ], + [ + 13042.601701010293, + -2.8186254569820255 + ], + [ + 13182.844730053414, + -2.76964299373404 + ], + [ + 13323.087759096536, + -2.712794317111105 + ], + [ + 13463.330788139658, + -2.6495679626896136 + ], + [ + 13603.573817182778, + -2.581514772366125 + ], + [ + 13743.8168462259, + -2.5107160151052925 + ], + [ + 13884.059875269022, + -2.43879170076375 + ] + ], + "9": [ + [ + 0.0, + 0.07368322696657943 + ], + [ + 140.24302904312142, + 0.5317436444227621 + ], + [ + 280.48605808624285, + 0.7632761196996933 + ], + [ + 420.7290871293643, + 1.0427700788528538 + ], + [ + 560.9721161724857, + 1.257181145419362 + ], + [ + 701.2151452156071, + 1.311344434630688 + ], + [ + 841.4581742587286, + 1.3061516869455152 + ], + [ + 981.70120330185, + 1.3009115097181125 + ], + [ + 1121.9442323449714, + 1.2952503214140896 + ], + [ + 1262.1872613880928, + 1.287804593259075 + ], + [ + 1402.4302904312142, + 1.2826688015819427 + ], + [ + 1542.6733194743356, + 1.2725544142678635 + ], + [ + 1682.9163485174572, + 1.253618061293078 + ], + [ + 1823.1593775605786, + 1.2284139339340816 + ], + [ + 1963.4024066037, + 1.2081976877309548 + ], + [ + 2103.645435646821, + 1.1961337062482302 + ], + [ + 2243.888464689943, + 1.1848697195702789 + ], + [ + 2384.1314937330644, + 1.17067814488332 + ], + [ + 2524.3745227761856, + 1.1507496158098554 + ], + [ + 2664.617551819307, + 1.1219684728857084 + ], + [ + 2804.8605808624284, + 1.0858274732506723 + ], + [ + 2945.10360990555, + 1.0438059032043225 + ], + [ + 3085.346638948671, + 0.9988854725611573 + ], + [ + 3225.589667991793, + 0.9543767736549829 + ], + [ + 3365.8326970349144, + 0.9079525097559514 + ], + [ + 3506.0757260780356, + 0.8568906692632553 + ], + [ + 3646.318755121157, + 0.7993201463229642 + ], + [ + 3786.5617841642784, + 0.7342605806601308 + ], + [ + 3926.8048132074, + 0.6639870932138828 + ], + [ + 4067.047842250521, + 0.5917206393254469 + ], + [ + 4207.290871293642, + 0.5202944264902496 + ], + [ + 4347.533900336764, + 0.4504526815926751 + ], + [ + 4487.776929379886, + 0.3816283606732711 + ], + [ + 4628.019958423007, + 0.31335942062730415 + ], + [ + 4768.262987466129, + 0.24520762950959427 + ], + [ + 4908.50601650925, + 0.17699318741624973 + ], + [ + 5048.749045552371, + 0.10923981314034295 + ], + [ + 5188.992074595492, + 0.041771457035617826 + ], + [ + 5329.235103638614, + -0.026379565841253626 + ], + [ + 5469.478132681736, + -0.0962045766893434 + ], + [ + 5609.721161724857, + -0.16888538401061226 + ], + [ + 5749.964190767979, + -0.24529804069744757 + ], + [ + 5890.2072198111, + -0.3239170625931536 + ], + [ + 6030.450248854221, + -0.4026128283808089 + ], + [ + 6170.693277897342, + -0.4793413281313876 + ], + [ + 6310.936306940464, + -0.5520035309696396 + ], + [ + 6451.179335983586, + -0.6181640783362785 + ], + [ + 6591.422365026707, + -0.6769179386245234 + ], + [ + 6731.665394069829, + -0.7293217413178189 + ], + [ + 6871.90842311295, + -0.7763749402238898 + ], + [ + 7012.151452156071, + -0.8190081615834442 + ], + [ + 7152.394481199192, + -0.8583089147980946 + ], + [ + 7292.637510242314, + -0.895610491433579 + ], + [ + 7432.880539285436, + -0.9314815648750324 + ], + [ + 7573.123568328557, + -0.966353600257146 + ], + [ + 7713.366597371678, + -1.0009361351780404 + ], + [ + 7853.6096264148, + -1.0359192225894798 + ], + [ + 7993.852655457921, + -1.0719773555686853 + ], + [ + 8134.095684501042, + -1.1094883172784857 + ], + [ + 8274.338713544164, + -1.1490188483026915 + ], + [ + 8414.581742587285, + -1.1920715399360076 + ], + [ + 8554.824771630407, + -1.238743561988614 + ], + [ + 8695.067800673529, + -1.2884101631087166 + ], + [ + 8835.31082971665, + -1.3405417849117836 + ], + [ + 8975.553858759771, + -1.3946102989024576 + ], + [ + 9115.796887802893, + -1.4500834256920347 + ], + [ + 9256.039916846014, + -1.5066635393100152 + ], + [ + 9396.282945889136, + -1.5633599620038945 + ], + [ + 9536.525974932258, + -1.6183979822384003 + ], + [ + 9676.769003975378, + -1.6716918092367805 + ], + [ + 9817.0120330185, + -1.7237208271611622 + ], + [ + 9957.25506206162, + -1.7749178752629287 + ], + [ + 10097.498091104742, + -1.8257157845018859 + ], + [ + 10237.741120147864, + -1.876531835023932 + ], + [ + 10377.984149190985, + -1.9275674833676713 + ], + [ + 10518.227178234107, + -1.9791114914374224 + ], + [ + 10658.470207277229, + -2.0329614817871158 + ], + [ + 10798.71323632035, + -2.0913466675008006 + ], + [ + 10938.956265363471, + -2.154404734287209 + ], + [ + 11079.199294406593, + -2.2212975198004496 + ], + [ + 11219.442323449714, + -2.2897944211816736 + ], + [ + 11359.685352492836, + -2.3573160907308632 + ], + [ + 11499.928381535958, + -2.4219596169921433 + ], + [ + 11640.171410579078, + -2.483369100522211 + ], + [ + 11780.4144396222, + -2.542692721380814 + ], + [ + 11920.65746866532, + -2.60214359964843 + ], + [ + 12060.900497708442, + -2.6634792331738377 + ], + [ + 12201.143526751564, + -2.7237442380322388 + ], + [ + 12341.386555794685, + -2.779576594271895 + ], + [ + 12481.629584837807, + -2.8268038673766624 + ], + [ + 12621.872613880929, + -2.8611375903492244 + ], + [ + 12762.11564292405, + -2.87765111886134 + ], + [ + 12902.358671967171, + -2.8733900862406156 + ], + [ + 13042.601701010293, + -2.8507317329589585 + ], + [ + 13182.844730053414, + -2.8130513179892125 + ], + [ + 13323.087759096536, + -2.7634252860317723 + ], + [ + 13463.330788139658, + -2.7047689233274954 + ], + [ + 13603.573817182778, + -2.6400508847959796 + ], + [ + 13743.8168462259, + -2.572583711505525 + ], + [ + 13884.059875269022, + -2.504157805452004 + ] + ], + "10": [ + [ + 0.0, + 0.20033156411557038 + ], + [ + 140.24302904312142, + 0.7366927011001225 + ], + [ + 280.48605808624285, + 1.035696436664274 + ], + [ + 420.7290871293643, + 1.3016772262987215 + ], + [ + 560.9721161724857, + 1.4745182816328153 + ], + [ + 701.2151452156071, + 1.505262689140049 + ], + [ + 841.4581742587286, + 1.4803134652862655 + ], + [ + 981.70120330185, + 1.4485494521014473 + ], + [ + 1121.9442323449714, + 1.4190757005303247 + ], + [ + 1262.1872613880928, + 1.398783646813674 + ], + [ + 1402.4302904312142, + 1.3928921996000794 + ], + [ + 1542.6733194743356, + 1.3898913567083553 + ], + [ + 1682.9163485174572, + 1.3765172517684965 + ], + [ + 1823.1593775605786, + 1.3522394020909483 + ], + [ + 1963.4024066037, + 1.3256469338428594 + ], + [ + 2103.645435646821, + 1.298947662848724 + ], + [ + 2243.888464689943, + 1.2698146186244634 + ], + [ + 2384.1314937330644, + 1.2403724958726574 + ], + [ + 2524.3745227761856, + 1.211787160425229 + ], + [ + 2664.617551819307, + 1.1806844951966449 + ], + [ + 2804.8605808624284, + 1.144748852538736 + ], + [ + 2945.10360990555, + 1.101578022737501 + ], + [ + 3085.346638948671, + 1.0520546061803064 + ], + [ + 3225.589667991793, + 1.0007438648641758 + ], + [ + 3365.8326970349144, + 0.9491138671527657 + ], + [ + 3506.0757260780356, + 0.8983355457640179 + ], + [ + 3646.318755121157, + 0.8483337575896465 + ], + [ + 3786.5617841642784, + 0.796216150822515 + ], + [ + 3926.8048132074, + 0.741264894296307 + ], + [ + 4067.047842250521, + 0.6838479694568798 + ], + [ + 4207.290871293642, + 0.624390478758714 + ], + [ + 4347.533900336764, + 0.5633625443312587 + ], + [ + 4487.776929379886, + 0.5011983711596055 + ], + [ + 4628.019958423007, + 0.43779891163354057 + ], + [ + 4768.262987466129, + 0.3730071357476165 + ], + [ + 4908.50601650925, + 0.3070673504641571 + ], + [ + 5048.749045552371, + 0.24119620016754342 + ], + [ + 5188.992074595492, + 0.17454714827979542 + ], + [ + 5329.235103638614, + 0.10470852197829392 + ], + [ + 5469.478132681736, + 0.029206753277270098 + ], + [ + 5609.721161724857, + -0.0548958400348227 + ], + [ + 5749.964190767979, + -0.15110422394300738 + ], + [ + 5890.2072198111, + -0.25677863903369424 + ], + [ + 6030.450248854221, + -0.36449990367885166 + ], + [ + 6170.693277897342, + -0.46663238693526987 + ], + [ + 6310.936306940464, + -0.555445880368153 + ], + [ + 6451.179335983586, + -0.6222923184503801 + ], + [ + 6591.422365026707, + -0.6674618807840859 + ], + [ + 6731.665394069829, + -0.7186620739342314 + ], + [ + 6871.90842311295, + -0.7770317989004804 + ], + [ + 7012.151452156071, + -0.8384267824327902 + ], + [ + 7152.394481199192, + -0.8988447070588746 + ], + [ + 7292.637510242314, + -0.9548558646395213 + ], + [ + 7432.880539285436, + -1.0035125334849682 + ], + [ + 7573.123568328557, + -1.044303623170647 + ], + [ + 7713.366597371678, + -1.0783981918264285 + ], + [ + 7853.6096264148, + -1.1068999761512468 + ], + [ + 7993.852655457921, + -1.1308987754025113 + ], + [ + 8134.095684501042, + -1.151208877671897 + ], + [ + 8274.338713544164, + -1.168818458993671 + ], + [ + 8414.581742587285, + -1.185482410048914 + ], + [ + 8554.824771630407, + -1.2024923294339425 + ], + [ + 8695.067800673529, + -1.2210866499439053 + ], + [ + 8835.31082971665, + -1.2427103840842726 + ], + [ + 8975.553858759771, + -1.2688104557672246 + ], + [ + 9115.796887802893, + -1.300872026568912 + ], + [ + 9256.039916846014, + -1.340485408520157 + ], + [ + 9396.282945889136, + -1.388399574050832 + ], + [ + 9536.525974932258, + -1.4431314517520968 + ], + [ + 9676.769003975378, + -1.5031787594282795 + ], + [ + 9817.0120330185, + -1.5669371241322672 + ], + [ + 9957.25506206162, + -1.6327125183668307 + ], + [ + 10097.498091104742, + -1.6988108902299461 + ], + [ + 10237.741120147864, + -1.7635239336014752 + ], + [ + 10377.984149190985, + -1.8249211117576278 + ], + [ + 10518.227178234107, + -1.8811811017803564 + ], + [ + 10658.470207277229, + -1.9328083640702383 + ], + [ + 10798.71323632035, + -1.983018042476987 + ], + [ + 10938.956265363471, + -2.0338984960449973 + ], + [ + 11079.199294406593, + -2.0867957012526617 + ], + [ + 11219.442323449714, + -2.141436286591516 + ], + [ + 11359.685352492836, + -2.196288005406448 + ], + [ + 11499.928381535958, + -2.2497468887966847 + ], + [ + 11640.171410579078, + -2.302184300637646 + ], + [ + 11780.4144396222, + -2.355628600873319 + ], + [ + 11920.65746866532, + -2.413382017202728 + ], + [ + 12060.900497708442, + -2.4784414627859737 + ], + [ + 12201.143526751564, + -2.548298268419042 + ], + [ + 12341.386555794685, + -2.6185365773258797 + ], + [ + 12481.629584837807, + -2.682976089564053 + ], + [ + 12621.872613880929, + -2.7352426085389636 + ], + [ + 12762.11564292405, + -2.7684329922295428 + ], + [ + 12902.358671967171, + -2.777763456550214 + ], + [ + 13042.601701010293, + -2.7645890241763555 + ], + [ + 13182.844730053414, + -2.732255483037836 + ], + [ + 13323.087759096536, + -2.6846798782898693 + ], + [ + 13463.330788139658, + -2.6256355651225687 + ], + [ + 13603.573817182778, + -2.558927727477169 + ], + [ + 13743.8168462259, + -2.4885445656915146 + ], + [ + 13884.059875269022, + -2.416920084013453 + ] + ], + "11": [ + [ + 0.0, + 0.2855839584117207 + ], + [ + 140.24302904312142, + 0.7419620625268664 + ], + [ + 280.48605808624285, + 0.9836227945217749 + ], + [ + 420.7290871293643, + 1.2869015265327641 + ], + [ + 560.9721161724857, + 1.460368775270272 + ], + [ + 701.2151452156071, + 1.436144198340873 + ], + [ + 841.4581742587286, + 1.3738853426215691 + ], + [ + 981.70120330185, + 1.3371773338359074 + ], + [ + 1121.9442323449714, + 1.320023764732997 + ], + [ + 1262.1872613880928, + 1.3060982720310594 + ], + [ + 1402.4302904312142, + 1.292862924395942 + ], + [ + 1542.6733194743356, + 1.281398396158237 + ], + [ + 1682.9163485174572, + 1.2733516562505018 + ], + [ + 1823.1593775605786, + 1.2655800734498184 + ], + [ + 1963.4024066037, + 1.2767145830472955 + ], + [ + 2103.645435646821, + 1.3033180460117781 + ], + [ + 2243.888464689943, + 1.215398779420275 + ], + [ + 2384.1314937330644, + 1.1580417869596935 + ], + [ + 2524.3745227761856, + 1.1986672295797982 + ], + [ + 2664.617551819307, + 1.2132446308122802 + ], + [ + 2804.8605808624284, + 1.194569380290527 + ], + [ + 2945.10360990555, + 1.151874244776949 + ], + [ + 3085.346638948671, + 1.0975943888892503 + ], + [ + 3225.589667991793, + 1.0461256584523102 + ], + [ + 3365.8326970349144, + 0.9991002958452183 + ], + [ + 3506.0757260780356, + 0.9556669854000687 + ], + [ + 3646.318755121157, + 0.9131382821719918 + ], + [ + 3786.5617841642784, + 0.8653502236203185 + ], + [ + 3926.8048132074, + 0.80990055327698 + ], + [ + 4067.047842250521, + 0.7465501064564476 + ], + [ + 4207.290871293642, + 0.6755180278126978 + ], + [ + 4347.533900336764, + 0.5991758306433211 + ], + [ + 4487.776929379886, + 0.5214288238273713 + ], + [ + 4628.019958423007, + 0.44544994304795776 + ], + [ + 4768.262987466129, + 0.3744028112787465 + ], + [ + 4908.50601650925, + 0.31129509694491825 + ], + [ + 5048.749045552371, + 0.25750834465260775 + ], + [ + 5188.992074595492, + 0.21057258503320084 + ], + [ + 5329.235103638614, + 0.16616081161419693 + ], + [ + 5469.478132681736, + 0.1197327664348747 + ], + [ + 5609.721161724857, + 0.06653276659636267 + ], + [ + 5749.964190767979, + 0.0018242544757313883 + ], + [ + 5890.2072198111, + -0.07454107205816111 + ], + [ + 6030.450248854221, + -0.15933731336700918 + ], + [ + 6170.693277897342, + -0.24888065665623252 + ], + [ + 6310.936306940464, + -0.3394421805144813 + ], + [ + 6451.179335983586, + -0.4262781520888914 + ], + [ + 6591.422365026707, + -0.5048182008355208 + ], + [ + 6731.665394069829, + -0.5742918351259183 + ], + [ + 6871.90842311295, + -0.6351746017167545 + ], + [ + 7012.151452156071, + -0.6880357514486004 + ], + [ + 7152.394481199192, + -0.7335158439820855 + ], + [ + 7292.637510242314, + -0.7726088538402506 + ], + [ + 7432.880539285436, + -0.8072389406837412 + ], + [ + 7573.123568328557, + -0.8381345250302351 + ], + [ + 7713.366597371678, + -0.8654369037542416 + ], + [ + 7853.6096264148, + -0.8891491700859945 + ], + [ + 7993.852655457921, + -0.909264078721904 + ], + [ + 8134.095684501042, + -0.9259044732495778 + ], + [ + 8274.338713544164, + -0.9412167428415664 + ], + [ + 8414.581742587285, + -0.9575428326836722 + ], + [ + 8554.824771630407, + -0.9777906340924041 + ], + [ + 8695.067800673529, + -1.0050162671083587 + ], + [ + 8835.31082971665, + -1.0424404184913307 + ], + [ + 8975.553858759771, + -1.0932876239983482 + ], + [ + 9115.796887802893, + -1.160748984437931 + ], + [ + 9256.039916846014, + -1.2459636885501926 + ], + [ + 9396.282945889136, + -1.3467409466772269 + ], + [ + 9536.525974932258, + -1.4591923692081827 + ], + [ + 9676.769003975378, + -1.5789649568201558 + ], + [ + 9817.0120330185, + -1.7017356807880422 + ], + [ + 9957.25506206162, + -1.8230979209389364 + ], + [ + 10097.498091104742, + -1.938645045885036 + ], + [ + 10237.741120147864, + -2.0439611003144478 + ], + [ + 10377.984149190985, + -2.1347672139921885 + ], + [ + 10518.227178234107, + -2.208233258641477 + ], + [ + 10658.470207277229, + -2.2634701806700552 + ], + [ + 10798.71323632035, + -2.302018452881941 + ], + [ + 10938.956265363471, + -2.3241465343649343 + ], + [ + 11079.199294406593, + -2.330005015270447 + ], + [ + 11219.442323449714, + -2.323888386250065 + ], + [ + 11359.685352492836, + -2.31082724836954 + ], + [ + 11499.928381535958, + -2.295249302769344 + ], + [ + 11640.171410579078, + -2.2841546506002084 + ], + [ + 11780.4144396222, + -2.286212615509844 + ], + [ + 11920.65746866532, + -2.304903327979749 + ], + [ + 12060.900497708442, + -2.3386458880478376 + ], + [ + 12201.143526751564, + -2.3828051386434344 + ], + [ + 12341.386555794685, + -2.4303325057094787 + ], + [ + 12481.629584837807, + -2.470950815277359 + ], + [ + 12621.872613880929, + -2.494070909939749 + ], + [ + 12762.11564292405, + -2.4917731197386375 + ], + [ + 12902.358671967171, + -2.4608901310668396 + ], + [ + 13042.601701010293, + -2.403291143707507 + ], + [ + 13182.844730053414, + -2.323911565334489 + ], + [ + 13323.087759096536, + -2.229932212395546 + ], + [ + 13463.330788139658, + -2.128659359323263 + ], + [ + 13603.573817182778, + -2.0273698021929056 + ], + [ + 13743.8168462259, + -1.9326312143434257 + ], + [ + 13884.059875269022, + -1.8480338260085418 + ] + ], + "12": [ + [ + 0.0, + 0.4376729483584739 + ], + [ + 140.24302904312142, + 0.8732755525910104 + ], + [ + 280.48605808624285, + 1.0317510788892752 + ], + [ + 420.7290871293643, + 1.2003933861704859 + ], + [ + 560.9721161724857, + 1.3904011757208585 + ], + [ + 701.2151452156071, + 1.4981092628344417 + ], + [ + 841.4581742587286, + 1.5015905975046775 + ], + [ + 981.70120330185, + 1.4813611638634405 + ], + [ + 1121.9442323449714, + 1.4803660803512886 + ], + [ + 1262.1872613880928, + 1.4876007940513587 + ], + [ + 1402.4302904312142, + 1.4934949201748156 + ], + [ + 1542.6733194743356, + 1.4936432421934651 + ], + [ + 1682.9163485174572, + 1.491138447590549 + ], + [ + 1823.1593775605786, + 1.4857066133274597 + ], + [ + 1963.4024066037, + 1.4931402074750053 + ], + [ + 2103.645435646821, + 1.5136389550114437 + ], + [ + 2243.888464689943, + 1.442204861946279 + ], + [ + 2384.1314937330644, + 1.3941131169590903 + ], + [ + 2524.3745227761856, + 1.4202352103983102 + ], + [ + 2664.617551819307, + 1.4207907285306927 + ], + [ + 2804.8605808624284, + 1.3933535252179092 + ], + [ + 2945.10360990555, + 1.34887050718887 + ], + [ + 3085.346638948671, + 1.2986080236769408 + ], + [ + 3225.589667991793, + 1.2526610492000547 + ], + [ + 3365.8326970349144, + 1.2109424392562986 + ], + [ + 3506.0757260780356, + 1.171405891466613 + ], + [ + 3646.318755121157, + 1.131396840273458 + ], + [ + 3786.5617841642784, + 1.0871979039886914 + ], + [ + 3926.8048132074, + 1.0378830956461504 + ], + [ + 4067.047842250521, + 0.9842069924944964 + ], + [ + 4207.290871293642, + 0.9273546877905384 + ], + [ + 4347.533900336764, + 0.869570881209266 + ], + [ + 4487.776929379886, + 0.8132600897577447 + ], + [ + 4628.019958423007, + 0.7587274509298837 + ], + [ + 4768.262987466129, + 0.7060586898880341 + ], + [ + 4908.50601650925, + 0.655024632341709 + ], + [ + 5048.749045552371, + 0.6043516714899357 + ], + [ + 5188.992074595492, + 0.5525758550383051 + ], + [ + 5329.235103638614, + 0.5021955688754052 + ], + [ + 5469.478132681736, + 0.45616288764987206 + ], + [ + 5609.721161724857, + 0.4172271287422114 + ], + [ + 5749.964190767979, + 0.38778296792076467 + ], + [ + 5890.2072198111, + 0.3700376014275243 + ], + [ + 6030.450248854221, + 0.3500928017881947 + ], + [ + 6170.693277897342, + 0.30844491575059446 + ], + [ + 6310.936306940464, + 0.22568694902819741 + ], + [ + 6451.179335983586, + 0.08411178424196453 + ], + [ + 6591.422365026707, + -0.1289546386621294 + ], + [ + 6731.665394069829, + -0.3081561922566832 + ], + [ + 6871.90842311295, + -0.41022040244932106 + ], + [ + 7012.151452156071, + -0.4510978242511574 + ], + [ + 7152.394481199192, + -0.44686397149375306 + ], + [ + 7292.637510242314, + -0.4163209114273654 + ], + [ + 7432.880539285436, + -0.3837131264947207 + ], + [ + 7573.123568328557, + -0.37074373203910405 + ], + [ + 7713.366597371678, + -0.38241527513736634 + ], + [ + 7853.6096264148, + -0.41939510456999 + ], + [ + 7993.852655457921, + -0.4830156420808258 + ], + [ + 8134.095684501042, + -0.578472738509631 + ], + [ + 8274.338713544164, + -0.7055239322917911 + ], + [ + 8414.581742587285, + -0.8403663979429535 + ], + [ + 8554.824771630407, + -0.9656553484416706 + ], + [ + 8695.067800673529, + -1.0805775332863272 + ], + [ + 8835.31082971665, + -1.1854306366818084 + ], + [ + 8975.553858759771, + -1.280515192731538 + ], + [ + 9115.796887802893, + -1.3660921174615532 + ], + [ + 9256.039916846014, + -1.444473291439376 + ], + [ + 9396.282945889136, + -1.5210111885477124 + ], + [ + 9536.525974932258, + -1.5983331647760117 + ], + [ + 9676.769003975378, + -1.6787816014615793 + ], + [ + 9817.0120330185, + -1.7643032111490147 + ], + [ + 9957.25506206162, + -1.856753058386309 + ], + [ + 10097.498091104742, + -1.9579862152560341 + ], + [ + 10237.741120147864, + -2.0698473127787653 + ], + [ + 10377.984149190985, + -2.1927662668463643 + ], + [ + 10518.227178234107, + -2.3151761025355015 + ], + [ + 10658.470207277229, + -2.42052384989943 + ], + [ + 10798.71323632035, + -2.493819458044295 + ], + [ + 10938.956265363471, + -2.519326779466024 + ], + [ + 11079.199294406593, + -2.4871124837979495 + ], + [ + 11219.442323449714, + -2.41117260089993 + ], + [ + 11359.685352492836, + -2.310735550558518 + ], + [ + 11499.928381535958, + -2.2054094415932552 + ], + [ + 11640.171410579078, + -2.1168751834923247 + ], + [ + 11780.4144396222, + -2.0663207489070063 + ], + [ + 11920.65746866532, + -2.056878791276834 + ], + [ + 12060.900497708442, + -2.081405438849249 + ], + [ + 12201.143526751564, + -2.1299761214324455 + ], + [ + 12341.386555794685, + -2.189462674841471 + ], + [ + 12481.629584837807, + -2.24350630629858 + ], + [ + 12621.872613880929, + -2.275782573147008 + ], + [ + 12762.11564292405, + -2.277240007618092 + ], + [ + 12902.358671967171, + -2.245185575990809 + ], + [ + 13042.601701010293, + -2.1814805628513856 + ], + [ + 13182.844730053414, + -2.092094518406327 + ], + [ + 13323.087759096536, + -1.9858885237889503 + ], + [ + 13463.330788139658, + -1.87201727452702 + ], + [ + 13603.573817182778, + -1.7595223121841108 + ], + [ + 13743.8168462259, + -1.6558307645090202 + ], + [ + 13884.059875269022, + -1.5655775873307938 + ] + ], + "13": [ + [ + 0.0, + 0.5277954692463657 + ], + [ + 140.24302904312142, + 0.9601205204355695 + ], + [ + 280.48605808624285, + 1.0954862456366603 + ], + [ + 420.7290871293643, + 1.1976529544403645 + ], + [ + 560.9721161724857, + 1.3322807546098965 + ], + [ + 701.2151452156071, + 1.449243486503489 + ], + [ + 841.4581742587286, + 1.523322106036759 + ], + [ + 981.70120330185, + 1.6458547952113507 + ], + [ + 1121.9442323449714, + 1.6930817034737218 + ], + [ + 1262.1872613880928, + 1.6798134163351535 + ], + [ + 1402.4302904312142, + 1.6823768166414108 + ], + [ + 1542.6733194743356, + 1.6825965973027104 + ], + [ + 1682.9163485174572, + 1.675596411403264 + ], + [ + 1823.1593775605786, + 1.6695029570461968 + ], + [ + 1963.4024066037, + 1.6564510332456719 + ], + [ + 2103.645435646821, + 1.6311141953192547 + ], + [ + 2243.888464689943, + 1.6120888019748252 + ], + [ + 2384.1314937330644, + 1.5833738104145967 + ], + [ + 2524.3745227761856, + 1.545089559613145 + ], + [ + 2664.617551819307, + 1.5165056142494093 + ], + [ + 2804.8605808624284, + 1.494118718248609 + ], + [ + 2945.10360990555, + 1.4717718560838096 + ], + [ + 3085.346638948671, + 1.4434145070115907 + ], + [ + 3225.589667991793, + 1.407230584839932 + ], + [ + 3365.8326970349144, + 1.3655106904128431 + ], + [ + 3506.0757260780356, + 1.3209493022353902 + ], + [ + 3646.318755121157, + 1.2760447900687775 + ], + [ + 3786.5617841642784, + 1.2319114293366118 + ], + [ + 3926.8048132074, + 1.1880975015859134 + ], + [ + 4067.047842250521, + 1.144053967691683 + ], + [ + 4207.290871293642, + 1.099617008434231 + ], + [ + 4347.533900336764, + 1.0554774772913569 + ], + [ + 4487.776929379886, + 1.0126791389684102 + ], + [ + 4628.019958423007, + 0.9711400575023703 + ], + [ + 4768.262987466129, + 0.9307436000697661 + ], + [ + 4908.50601650925, + 0.8907021547031322 + ], + [ + 5048.749045552371, + 0.8484364115958392 + ], + [ + 5188.992074595492, + 0.8021251316209892 + ], + [ + 5329.235103638614, + 0.7538776203724163 + ], + [ + 5469.478132681736, + 0.7061637176060801 + ], + [ + 5609.721161724857, + 0.6616555357318068 + ], + [ + 5749.964190767979, + 0.6243519470659544 + ], + [ + 5890.2072198111, + 0.5977647252717851 + ], + [ + 6030.450248854221, + 0.5707386828129082 + ], + [ + 6170.693277897342, + 0.5273442134842958 + ], + [ + 6310.936306940464, + 0.4517638219980422 + ], + [ + 6451.179335983586, + 0.32913462813083555 + ], + [ + 6591.422365026707, + 0.14731445321477438 + ], + [ + 6731.665394069829, + -0.007648427463980546 + ], + [ + 6871.90842311295, + -0.09891421641752406 + ], + [ + 7012.151452156071, + -0.1388812041928871 + ], + [ + 7152.394481199192, + -0.14017256420170235 + ], + [ + 7292.637510242314, + -0.11801373364923907 + ], + [ + 7432.880539285436, + -0.09160044756479688 + ], + [ + 7573.123568328557, + -0.0795988253708362 + ], + [ + 7713.366597371678, + -0.08957291911998944 + ], + [ + 7853.6096264148, + -0.12564586629574537 + ], + [ + 7993.852655457921, + -0.1919362933449338 + ], + [ + 8134.095684501042, + -0.29190779547542967 + ], + [ + 8274.338713544164, + -0.42263394534753407 + ], + [ + 8414.581742587285, + -0.5610494416327729 + ], + [ + 8554.824771630407, + -0.69147771025835 + ], + [ + 8695.067800673529, + -0.813994295608885 + ], + [ + 8835.31082971665, + -0.9297367251014251 + ], + [ + 8975.553858759771, + -1.0398455889726577 + ], + [ + 9115.796887802893, + -1.145461685311432 + ], + [ + 9256.039916846014, + -1.2488007967789827 + ], + [ + 9396.282945889136, + -1.3532622217275432 + ], + [ + 9536.525974932258, + -1.459258400003785 + ], + [ + 9676.769003975378, + -1.567031791006888 + ], + [ + 9817.0120330185, + -1.676275242881238 + ], + [ + 9957.25506206162, + -1.7865717080117258 + ], + [ + 10097.498091104742, + -1.8975041460542719 + ], + [ + 10237.741120147864, + -2.008643332509036 + ], + [ + 10377.984149190985, + -2.1184984318207065 + ], + [ + 10518.227178234107, + -2.217943567406552 + ], + [ + 10658.470207277229, + -2.2949216062385287 + ], + [ + 10798.71323632035, + -2.339392165550021 + ], + [ + 10938.956265363471, + -2.340431568337131 + ], + [ + 11079.199294406593, + -2.2925581263671146 + ], + [ + 11219.442323449714, + -2.2092775721077977 + ], + [ + 11359.685352492836, + -2.1080361329007555 + ], + [ + 11499.928381535958, + -2.0063258736870866 + ], + [ + 11640.171410579078, + -1.9235603138312076 + ], + [ + 11780.4144396222, + -1.8783379656344499 + ], + [ + 11920.65746866532, + -1.8722341061932986 + ], + [ + 12060.900497708442, + -1.8981033145029345 + ], + [ + 12201.143526751564, + -1.9460994284279653 + ], + [ + 12341.386555794685, + -2.003971865027418 + ], + [ + 12481.629584837807, + -2.056428476207535 + ], + [ + 12621.872613880929, + -2.088262387218856 + ], + [ + 12762.11564292405, + -2.091722165887782 + ], + [ + 12902.358671967171, + -2.0646557584361402 + ], + [ + 13042.601701010293, + -2.0083428799274237 + ], + [ + 13182.844730053414, + -1.927291255469334 + ], + [ + 13323.087759096536, + -1.8289360934532037 + ], + [ + 13463.330788139658, + -1.7210912945003447 + ], + [ + 13603.573817182778, + -1.611439249070559 + ], + [ + 13743.8168462259, + -1.5060405794143092 + ], + [ + 13884.059875269022, + -1.4096850475093605 + ] + ], + "14": [ + [ + 0.0, + 0.49978668925219444 + ], + [ + 140.24302904312142, + 0.9238247370606948 + ], + [ + 280.48605808624285, + 1.062674368823593 + ], + [ + 420.7290871293643, + 1.157652501122846 + ], + [ + 560.9721161724857, + 1.2526544448857362 + ], + [ + 701.2151452156071, + 1.3300713448591786 + ], + [ + 841.4581742587286, + 1.4031610999286845 + ], + [ + 981.70120330185, + 1.562331273517175 + ], + [ + 1121.9442323449714, + 1.6617850936539151 + ], + [ + 1262.1872613880928, + 1.695653849183498 + ], + [ + 1402.4302904312142, + 1.728395220004209 + ], + [ + 1542.6733194743356, + 1.744048519352651 + ], + [ + 1682.9163485174572, + 1.7410220236647134 + ], + [ + 1823.1593775605786, + 1.7274363224624083 + ], + [ + 1963.4024066037, + 1.7261673109275375 + ], + [ + 2103.645435646821, + 1.7338062486162178 + ], + [ + 2243.888464689943, + 1.6364771403463076 + ], + [ + 2384.1314937330644, + 1.5764680718851067 + ], + [ + 2524.3745227761856, + 1.5989704783540857 + ], + [ + 2664.617551819307, + 1.5965424676580966 + ], + [ + 2804.8605808624284, + 1.5694794037873157 + ], + [ + 2945.10360990555, + 1.5297761877396523 + ], + [ + 3085.346638948671, + 1.4881619605848417 + ], + [ + 3225.589667991793, + 1.4511996859108969 + ], + [ + 3365.8326970349144, + 1.4172608226348973 + ], + [ + 3506.0757260780356, + 1.3835497302610928 + ], + [ + 3646.318755121157, + 1.34787051182599 + ], + [ + 3786.5617841642784, + 1.3097047464800906 + ], + [ + 3926.8048132074, + 1.270264914953776 + ], + [ + 4067.047842250521, + 1.2315439550388851 + ], + [ + 4207.290871293642, + 1.1957326501019494 + ], + [ + 4347.533900336764, + 1.1637708242325564 + ], + [ + 4487.776929379886, + 1.134734694197921 + ], + [ + 4628.019958423007, + 1.1070259059362528 + ], + [ + 4768.262987466129, + 1.0791273698851371 + ], + [ + 4908.50601650925, + 1.0488930774275294 + ], + [ + 5048.749045552371, + 1.0146085186543894 + ], + [ + 5188.992074595492, + 0.9762043374036383 + ], + [ + 5329.235103638614, + 0.933800836645855 + ], + [ + 5469.478132681736, + 0.8873782861225915 + ], + [ + 5609.721161724857, + 0.8371666194864393 + ], + [ + 5749.964190767979, + 0.7838631969980551 + ], + [ + 5890.2072198111, + 0.7274490555855029 + ], + [ + 6030.450248854221, + 0.6676905637153596 + ], + [ + 6170.693277897342, + 0.604589121179123 + ], + [ + 6310.936306940464, + 0.5383540415398433 + ], + [ + 6451.179335983586, + 0.4706018583568054 + ], + [ + 6591.422365026707, + 0.4031509195001402 + ], + [ + 6731.665394069829, + 0.3384807330997133 + ], + [ + 6871.90842311295, + 0.2792498822108155 + ], + [ + 7012.151452156071, + 0.22804911735009795 + ], + [ + 7152.394481199192, + 0.18701059014149796 + ], + [ + 7292.637510242314, + 0.15318634619911045 + ], + [ + 7432.880539285436, + 0.12112864325448401 + ], + [ + 7573.123568328557, + 0.08460001769324127 + ], + [ + 7713.366597371678, + 0.03656472881286515 + ], + [ + 7853.6096264148, + -0.029990091337068366 + ], + [ + 7993.852655457921, + -0.12209995667451197 + ], + [ + 8134.095684501042, + -0.24449759892531098 + ], + [ + 8274.338713544164, + -0.3922111120065885 + ], + [ + 8414.581742587285, + -0.5578969106373107 + ], + [ + 8554.824771630407, + -0.7333610410309536 + ], + [ + 8695.067800673529, + -0.9100654015981802 + ], + [ + 8835.31082971665, + -1.0795517997678248 + ], + [ + 8975.553858759771, + -1.2333633682657714 + ], + [ + 9115.796887802893, + -1.3632365995902056 + ], + [ + 9256.039916846014, + -1.4676512778516082 + ], + [ + 9396.282945889136, + -1.5525798566061535 + ], + [ + 9536.525974932258, + -1.6235770397124218 + ], + [ + 9676.769003975378, + -1.6862938973315234 + ], + [ + 9817.0120330185, + -1.7460996750430389 + ], + [ + 9957.25506206162, + -1.8082949619799382 + ], + [ + 10097.498091104742, + -1.8781804116211966 + ], + [ + 10237.741120147864, + -1.9610512545311443 + ], + [ + 10377.984149190985, + -2.0592164119856693 + ], + [ + 10518.227178234107, + -2.1595767156182286 + ], + [ + 10658.470207277229, + -2.243960484326295 + ], + [ + 10798.71323632035, + -2.2961951109958934 + ], + [ + 10938.956265363471, + -2.299532105295938 + ], + [ + 11079.199294406593, + -2.2460996330582508 + ], + [ + 11219.442323449714, + -2.1521278116773837 + ], + [ + 11359.685352492836, + -2.0379959945249433 + ], + [ + 11499.928381535958, + -1.9238749149119467 + ], + [ + 11640.171410579078, + -1.8316248213099977 + ], + [ + 11780.4144396222, + -1.7812030222608002 + ], + [ + 11920.65746866532, + -1.7730031063295084 + ], + [ + 12060.900497708442, + -1.7983013668203107 + ], + [ + 12201.143526751564, + -1.8460169023146156 + ], + [ + 12341.386555794685, + -1.9029186826450668 + ], + [ + 12481.629584837807, + -1.9536045064587297 + ], + [ + 12621.872613880929, + -1.9830791786576596 + ], + [ + 12762.11564292405, + -1.9847040486402954 + ], + [ + 12902.358671967171, + -1.9578663560359797 + ], + [ + 13042.601701010293, + -1.905266545094717 + ], + [ + 13182.844730053414, + -1.831827666437673 + ], + [ + 13323.087759096536, + -1.744474570292338 + ], + [ + 13463.330788139658, + -1.650406022093753 + ], + [ + 13603.573817182778, + -1.5566610618639678 + ], + [ + 13743.8168462259, + -1.468480334362298 + ], + [ + 13884.059875269022, + -1.389402800657239 + ] + ], + "15": [ + [ + 0.0, + 0.3271541367110917 + ], + [ + 140.24302904312142, + 0.7119221628168965 + ], + [ + 280.48605808624285, + 0.8304922968010789 + ], + [ + 420.7290871293643, + 0.9243926211187932 + ], + [ + 560.9721161724857, + 1.0160144981194883 + ], + [ + 701.2151452156071, + 1.0816484977611742 + ], + [ + 841.4581742587286, + 1.1610772993191874 + ], + [ + 981.70120330185, + 1.2699643670398555 + ], + [ + 1121.9442323449714, + 1.391710065120972 + ], + [ + 1262.1872613880928, + 1.5030328468835488 + ], + [ + 1402.4302904312142, + 1.5843965120134194 + ], + [ + 1542.6733194743356, + 1.630147909420852 + ], + [ + 1682.9163485174572, + 1.6491267945803096 + ], + [ + 1823.1593775605786, + 1.6489378189983546 + ], + [ + 1963.4024066037, + 1.6358836468400078 + ], + [ + 2103.645435646821, + 1.6116797460095318 + ], + [ + 2243.888464689943, + 1.5776944816537055 + ], + [ + 2384.1314937330644, + 1.541966000278576 + ], + [ + 2524.3745227761856, + 1.5149015539645236 + ], + [ + 2664.617551819307, + 1.4996307853679973 + ], + [ + 2804.8605808624284, + 1.4908897929075344 + ], + [ + 2945.10360990555, + 1.482432556062595 + ], + [ + 3085.346638948671, + 1.468332927102466 + ], + [ + 3225.589667991793, + 1.445993402630727 + ], + [ + 3365.8326970349144, + 1.4169574095520607 + ], + [ + 3506.0757260780356, + 1.383277537507254 + ], + [ + 3646.318755121157, + 1.3471428998458355 + ], + [ + 3786.5617841642784, + 1.3106786553778482 + ], + [ + 3926.8048132074, + 1.2752221539016884 + ], + [ + 4067.047842250521, + 1.2417890812378483 + ], + [ + 4207.290871293642, + 1.2112763458240052 + ], + [ + 4347.533900336764, + 1.1837608403319801 + ], + [ + 4487.776929379886, + 1.1582128970379206 + ], + [ + 4628.019958423007, + 1.1334731193659675 + ], + [ + 4768.262987466129, + 1.1085524059054546 + ], + [ + 4908.50601650925, + 1.082280237660823 + ], + [ + 5048.749045552371, + 1.0541842180928738 + ], + [ + 5188.992074595492, + 1.025244466132009 + ], + [ + 5329.235103638614, + 0.996389727684803 + ], + [ + 5469.478132681736, + 0.9683938807296764 + ], + [ + 5609.721161724857, + 0.9418989683135544 + ], + [ + 5749.964190767979, + 0.9160453644238671 + ], + [ + 5890.2072198111, + 0.8876865401535239 + ], + [ + 6030.450248854221, + 0.8535008760594034 + ], + [ + 6170.693277897342, + 0.8104428130742893 + ], + [ + 6310.936306940464, + 0.7557587707431485 + ], + [ + 6451.179335983586, + 0.6893616906110784 + ], + [ + 6591.422365026707, + 0.6144971365959591 + ], + [ + 6731.665394069829, + 0.5364393363547241 + ], + [ + 6871.90842311295, + 0.46067593277379615 + ], + [ + 7012.151452156071, + 0.39261800674477926 + ], + [ + 7152.394481199192, + 0.3370722706779246 + ], + [ + 7292.637510242314, + 0.2924221470955854 + ], + [ + 7432.880539285436, + 0.25174141876576106 + ], + [ + 7573.123568328557, + 0.20646696206623086 + ], + [ + 7713.366597371678, + 0.1471571010232553 + ], + [ + 7853.6096264148, + 0.06436834758818528 + ], + [ + 7993.852655457921, + -0.05136247264252298 + ], + [ + 8134.095684501042, + -0.206532958490399 + ], + [ + 8274.338713544164, + -0.39535316540719395 + ], + [ + 8414.581742587285, + -0.6069154374024224 + ], + [ + 8554.824771630407, + -0.8295461583033311 + ], + [ + 8695.067800673529, + -1.0511274302091203 + ], + [ + 8835.31082971665, + -1.259562218013097 + ], + [ + 8975.553858759771, + -1.442754366248941 + ], + [ + 9115.796887802893, + -1.589019612054042 + ], + [ + 9256.039916846014, + -1.6950274146648172 + ], + [ + 9396.282945889136, + -1.7682774732104025 + ], + [ + 9536.525974932258, + -1.8170491190062912 + ], + [ + 9676.769003975378, + -1.8497575418204875 + ], + [ + 9817.0120330185, + -1.8747602609194831 + ], + [ + 9957.25506206162, + -1.9003813407451866 + ], + [ + 10097.498091104742, + -1.9349449213537382 + ], + [ + 10237.741120147864, + -1.9867723930550407 + ], + [ + 10377.984149190985, + -2.06009846084533 + ], + [ + 10518.227178234107, + -2.1415140795802623 + ], + [ + 10658.470207277229, + -2.2110137019041307 + ], + [ + 10798.71323632035, + -2.2513625062255844 + ], + [ + 10938.956265363471, + -2.245064042951864 + ], + [ + 11079.199294406593, + -2.1852003620944305 + ], + [ + 11219.442323449714, + -2.089991213397594 + ], + [ + 11359.685352492836, + -1.9810967757323892 + ], + [ + 11499.928381535958, + -1.8773529127342583 + ], + [ + 11640.171410579078, + -1.7984391933472006 + ], + [ + 11780.4144396222, + -1.7615914592702657 + ], + [ + 11920.65746866532, + -1.7635774910136466 + ], + [ + 12060.900497708442, + -1.7930746923063956 + ], + [ + 12201.143526751564, + -1.839045074387219 + ], + [ + 12341.386555794685, + -1.8898356090765256 + ], + [ + 12481.629584837807, + -1.9319340163332581 + ], + [ + 12621.872613880929, + -1.952425866283592 + ], + [ + 12762.11564292405, + -1.9471527295791395 + ], + [ + 12902.358671967171, + -1.9174442682594002 + ], + [ + 13042.601701010293, + -1.8665056551977217 + ], + [ + 13182.844730053414, + -1.798927772440699 + ], + [ + 13323.087759096536, + -1.7208402952993263 + ], + [ + 13463.330788139658, + -1.6386013422971923 + ], + [ + 13603.573817182778, + -1.5583892074423877 + ], + [ + 13743.8168462259, + -1.4844298277033852 + ], + [ + 13884.059875269022, + -1.4190598242750128 + ] + ], + "16": [ + [ + 0.0, + 0.04389679600972524 + ], + [ + 140.24302904312142, + 0.4000884442116785 + ], + [ + 280.48605808624285, + 0.487004266971293 + ], + [ + 420.7290871293643, + 0.5788157432148089 + ], + [ + 560.9721161724857, + 0.6746680047910655 + ], + [ + 701.2151452156071, + 0.7541075970800121 + ], + [ + 841.4581742587286, + 0.8563057266496301 + ], + [ + 981.70120330185, + 0.9811533881244701 + ], + [ + 1121.9442323449714, + 1.1140986901099499 + ], + [ + 1262.1872613880928, + 1.250390414879604 + ], + [ + 1402.4302904312142, + 1.3673786460263933 + ], + [ + 1542.6733194743356, + 1.4430979876898646 + ], + [ + 1682.9163485174572, + 1.472751187250523 + ], + [ + 1823.1593775605786, + 1.4649783411667876 + ], + [ + 1963.4024066037, + 1.4472872084003157 + ], + [ + 2103.645435646821, + 1.4276475380559186 + ], + [ + 2243.888464689943, + 1.4010105444358798 + ], + [ + 2384.1314937330644, + 1.3755251791174457 + ], + [ + 2524.3745227761856, + 1.3598857623915268 + ], + [ + 2664.617551819307, + 1.35432770430462 + ], + [ + 2804.8605808624284, + 1.353786342621612 + ], + [ + 2945.10360990555, + 1.3525518956507874 + ], + [ + 3085.346638948671, + 1.3460134964465444 + ], + [ + 3225.589667991793, + 1.332392312653218 + ], + [ + 3365.8326970349144, + 1.312673096679605 + ], + [ + 3506.0757260780356, + 1.288205740036118 + ], + [ + 3646.318755121157, + 1.2600221873698148 + ], + [ + 3786.5617841642784, + 1.2286996915338912 + ], + [ + 3926.8048132074, + 1.1951162672018272 + ], + [ + 4067.047842250521, + 1.160010773284083 + ], + [ + 4207.290871293642, + 1.1239122175879193 + ], + [ + 4347.533900336764, + 1.0883416961134282 + ], + [ + 4487.776929379886, + 1.0549299428015535 + ], + [ + 4628.019958423007, + 1.0247760524751917 + ], + [ + 4768.262987466129, + 0.9991178906077681 + ], + [ + 4908.50601650925, + 0.9793120213693212 + ], + [ + 5048.749045552371, + 0.9646419859112952 + ], + [ + 5188.992074595492, + 0.9535235311112072 + ], + [ + 5329.235103638614, + 0.9440979751956858 + ], + [ + 5469.478132681736, + 0.934383282466193 + ], + [ + 5609.721161724857, + 0.9217975228597745 + ], + [ + 5749.964190767979, + 0.9036956257297881 + ], + [ + 5890.2072198111, + 0.8781090986860303 + ], + [ + 6030.450248854221, + 0.8436139070337132 + ], + [ + 6170.693277897342, + 0.7990808454051547 + ], + [ + 6310.936306940464, + 0.743785570575726 + ], + [ + 6451.179335983586, + 0.6796180310975987 + ], + [ + 6591.422365026707, + 0.6097637016328714 + ], + [ + 6731.665394069829, + 0.5380102393351543 + ], + [ + 6871.90842311295, + 0.4680847798438613 + ], + [ + 7012.151452156071, + 0.40362096484275634 + ], + [ + 7152.394481199192, + 0.34756208452788206 + ], + [ + 7292.637510242314, + 0.29723198646194665 + ], + [ + 7432.880539285436, + 0.24714716936030576 + ], + [ + 7573.123568328557, + 0.1914279276056072 + ], + [ + 7713.366597371678, + 0.12361108781744419 + ], + [ + 7853.6096264148, + 0.037196665916300185 + ], + [ + 7993.852655457921, + -0.07432967253051508 + ], + [ + 8134.095684501042, + -0.21444233149509595 + ], + [ + 8274.338713544164, + -0.37745907402983986 + ], + [ + 8414.581742587285, + -0.5563858665195882 + ], + [ + 8554.824771630407, + -0.7437451930085011 + ], + [ + 8695.067800673529, + -0.9323319245236491 + ], + [ + 8835.31082971665, + -1.1151390568866335 + ], + [ + 8975.553858759771, + -1.2851595369604212 + ], + [ + 9115.796887802893, + -1.4357278119172527 + ], + [ + 9256.039916846014, + -1.5661169587800599 + ], + [ + 9396.282945889136, + -1.6796976084147957 + ], + [ + 9536.525974932258, + -1.7782259864226069 + ], + [ + 9676.769003975378, + -1.8634872919859382 + ], + [ + 9817.0120330185, + -1.9369931412169032 + ], + [ + 9957.25506206162, + -2.0002309120879915 + ], + [ + 10097.498091104742, + -2.0546881162826627 + ], + [ + 10237.741120147864, + -2.10186028202087 + ], + [ + 10377.984149190985, + -2.1419131944178758 + ], + [ + 10518.227178234107, + -2.168616763949617 + ], + [ + 10658.470207277229, + -2.176601831607372 + ], + [ + 10798.71323632035, + -2.163990201980251 + ], + [ + 10938.956265363471, + -2.128150750587631 + ], + [ + 11079.199294406593, + -2.070773383301444 + ], + [ + 11219.442323449714, + -2.0042160989566535 + ], + [ + 11359.685352492836, + -1.9387120979800665 + ], + [ + 11499.928381535958, + -1.8817788952677992 + ], + [ + 11640.171410579078, + -1.8426826442018227 + ], + [ + 11780.4144396222, + -1.8296725834305436 + ], + [ + 11920.65746866532, + -1.8393170977814144 + ], + [ + 12060.900497708442, + -1.865260965356225 + ], + [ + 12201.143526751564, + -1.9019536790804799 + ], + [ + 12341.386555794685, + -1.942011692344406 + ], + [ + 12481.629584837807, + -1.9760684454512925 + ], + [ + 12621.872613880929, + -1.995010323399296 + ], + [ + 12762.11564292405, + -1.9954450207702703 + ], + [ + 12902.358671967171, + -1.9767576436829508 + ], + [ + 13042.601701010293, + -1.9397095347978057 + ], + [ + 13182.844730053414, + -1.887268571574505 + ], + [ + 13323.087759096536, + -1.8238112683668743 + ], + [ + 13463.330788139658, + -1.7539364533151633 + ], + [ + 13603.573817182778, + -1.6821268133473843 + ], + [ + 13743.8168462259, + -1.6116449444311889 + ], + [ + 13884.059875269022, + -1.5452277171733229 + ] + ], + "17": [ + [ + 0.0, + -0.23964453826954463 + ], + [ + 140.24302904312142, + 0.09184649039762131 + ], + [ + 280.48605808624285, + 0.1544946533195857 + ], + [ + 420.7290871293643, + 0.23224223698872984 + ], + [ + 560.9721161724857, + 0.3675125456004715 + ], + [ + 701.2151452156071, + 0.4609323706222131 + ], + [ + 841.4581742587286, + 0.4133471806850514 + ], + [ + 981.70120330185, + 0.792245321423583 + ], + [ + 1121.9442323449714, + 0.972779111161921 + ], + [ + 1262.1872613880928, + 1.0153989631211928 + ], + [ + 1402.4302904312142, + 1.166662558969932 + ], + [ + 1542.6733194743356, + 1.2753080958577365 + ], + [ + 1682.9163485174572, + 1.3018571117997588 + ], + [ + 1823.1593775605786, + 1.2942973188479676 + ], + [ + 1963.4024066037, + 1.2733765257107224 + ], + [ + 2103.645435646821, + 1.2390356746478368 + ], + [ + 2243.888464689943, + 1.2377348871569844 + ], + [ + 2384.1314937330644, + 1.220979398111075 + ], + [ + 2524.3745227761856, + 1.1828362019315846 + ], + [ + 2664.617551819307, + 1.1703816575603774 + ], + [ + 2804.8605808624284, + 1.1769963939926973 + ], + [ + 2945.10360990555, + 1.1899924024545057 + ], + [ + 3085.346638948671, + 1.197629162624078 + ], + [ + 3225.589667991793, + 1.1936038523897732 + ], + [ + 3365.8326970349144, + 1.1788856148482523 + ], + [ + 3506.0757260780356, + 1.1553037735706018 + ], + [ + 3646.318755121157, + 1.1248802989218218 + ], + [ + 3786.5617841642784, + 1.089646636752473 + ], + [ + 3926.8048132074, + 1.0514359939102964 + ], + [ + 4067.047842250521, + 1.0118988755551148 + ], + [ + 4207.290871293642, + 0.972456806496447 + ], + [ + 4347.533900336764, + 0.9353085434011028 + ], + [ + 4487.776929379886, + 0.9024695175420875 + ], + [ + 4628.019958423007, + 0.8750235669945396 + ], + [ + 4768.262987466129, + 0.8541129115029694 + ], + [ + 4908.50601650925, + 0.8408831017355648 + ], + [ + 5048.749045552371, + 0.8336492518810719 + ], + [ + 5188.992074595492, + 0.8304268574759367 + ], + [ + 5329.235103638614, + 0.8298153528719214 + ], + [ + 5469.478132681736, + 0.8303538321709333 + ], + [ + 5609.721161724857, + 0.8300737334041861 + ], + [ + 5749.964190767979, + 0.8281484315588531 + ], + [ + 5890.2072198111, + 0.8233781323634733 + ], + [ + 6030.450248854221, + 0.8108038585611981 + ], + [ + 6170.693277897342, + 0.7852351571103002 + ], + [ + 6310.936306940464, + 0.7418786803568567 + ], + [ + 6451.179335983586, + 0.6776542711444826 + ], + [ + 6591.422365026707, + 0.5926594038408569 + ], + [ + 6731.665394069829, + 0.5200708862542278 + ], + [ + 6871.90842311295, + 0.46666179739518526 + ], + [ + 7012.151452156071, + 0.42863297954778556 + ], + [ + 7152.394481199192, + 0.4014188054136778 + ], + [ + 7292.637510242314, + 0.37622677472726135 + ], + [ + 7432.880539285436, + 0.3428580872269346 + ], + [ + 7573.123568328557, + 0.2932903434587206 + ], + [ + 7713.366597371678, + 0.22355985430007091 + ], + [ + 7853.6096264148, + 0.13036445805412394 + ], + [ + 7993.852655457921, + 0.010415162928548358 + ], + [ + 8134.095684501042, + -0.13707882103853333 + ], + [ + 8274.338713544164, + -0.3067601179733232 + ], + [ + 8414.581742587285, + -0.49238131521517636 + ], + [ + 8554.824771630407, + -0.6876286853103617 + ], + [ + 8695.067800673529, + -0.887177167068746 + ], + [ + 8835.31082971665, + -1.0859164760921842 + ], + [ + 8975.553858759771, + -1.2787372935112398 + ], + [ + 9115.796887802893, + -1.460551292638408 + ], + [ + 9256.039916846014, + -1.6270601686246753 + ], + [ + 9396.282945889136, + -1.7740416777465966 + ], + [ + 9536.525974932258, + -1.8990451910531332 + ], + [ + 9676.769003975378, + -2.002173933305936 + ], + [ + 9817.0120330185, + -2.078623477222419 + ], + [ + 9957.25506206162, + -2.123224359205266 + ], + [ + 10097.498091104742, + -2.1308073110525374 + ], + [ + 10237.741120147864, + -2.0962477275285996 + ], + [ + 10377.984149190985, + -2.0203144877584953 + ], + [ + 10518.227178234107, + -1.9184094741198683 + ], + [ + 10658.470207277229, + -1.8110528139328668 + ], + [ + 10798.71323632035, + -1.7205422353104383 + ], + [ + 10938.956265363471, + -1.668309956780638 + ], + [ + 11079.199294406593, + -1.6647980117498558 + ], + [ + 11219.442323449714, + -1.701644451829843 + ], + [ + 11359.685352492836, + -1.7653253553513921 + ], + [ + 11499.928381535958, + -1.8420108215193352 + ], + [ + 11640.171410579078, + -1.9199009718689029 + ], + [ + 11780.4144396222, + -1.9897821688537436 + ], + [ + 11920.65746866532, + -2.05198882411778 + ], + [ + 12060.900497708442, + -2.1105753825106732 + ], + [ + 12201.143526751564, + -2.165705708763122 + ], + [ + 12341.386555794685, + -2.21425260642817 + ], + [ + 12481.629584837807, + -2.2514148819698048 + ], + [ + 12621.872613880929, + -2.271747568998351 + ], + [ + 12762.11564292405, + -2.2683062702472787 + ], + [ + 12902.358671967171, + -2.2360366257836755 + ], + [ + 13042.601701010293, + -2.1772906722474854 + ], + [ + 13182.844730053414, + -2.0976150631424066 + ], + [ + 13323.087759096536, + -2.0037181126271517 + ], + [ + 13463.330788139658, + -1.9025830080608264 + ], + [ + 13603.573817182778, + -1.8012200585471114 + ], + [ + 13743.8168462259, + -1.7068858506536744 + ], + [ + 13884.059875269022, + -1.623952364636244 + ] + ], + "18": [ + [ + 0.0, + -0.5421096370723323 + ], + [ + 140.24302904312142, + -0.26092173252786494 + ], + [ + 280.48605808624285, + -0.28820610221772824 + ], + [ + 420.7290871293643, + -0.2092155009715138 + ], + [ + 560.9721161724857, + -0.034825875977624474 + ], + [ + 701.2151452156071, + 0.16524908987224332 + ], + [ + 841.4581742587286, + 0.38750849272067783 + ], + [ + 981.70120330185, + 0.6082107495088895 + ], + [ + 1121.9442323449714, + 0.7997883533843819 + ], + [ + 1262.1872613880928, + 0.9577603921966691 + ], + [ + 1402.4302904312142, + 1.0772548392498578 + ], + [ + 1542.6733194743356, + 1.1492378361488842 + ], + [ + 1682.9163485174572, + 1.1722492416902732 + ], + [ + 1823.1593775605786, + 1.157662353933082 + ], + [ + 1963.4024066037, + 1.1305436778734366 + ], + [ + 2103.645435646821, + 1.1012392984680586 + ], + [ + 2243.888464689943, + 1.0680529012913105 + ], + [ + 2384.1314937330644, + 1.0360811289385707 + ], + [ + 2524.3745227761856, + 1.0134261133405496 + ], + [ + 2664.617551819307, + 1.0017393175057847 + ], + [ + 2804.8605808624284, + 0.997943443670933 + ], + [ + 2945.10360990555, + 0.9986528353898105 + ], + [ + 3085.346638948671, + 1.0006183352256557 + ], + [ + 3225.589667991793, + 1.0014768024335667 + ], + [ + 3365.8326970349144, + 0.9992032015764064 + ], + [ + 3506.0757260780356, + 0.9915738308166353 + ], + [ + 3646.318755121157, + 0.9764761176306604 + ], + [ + 3786.5617841642784, + 0.952949562321593 + ], + [ + 3926.8048132074, + 0.9228046283574789 + ], + [ + 4067.047842250521, + 0.8885893879142169 + ], + [ + 4207.290871293642, + 0.8527941078337328 + ], + [ + 4347.533900336764, + 0.819166157055396 + ], + [ + 4487.776929379886, + 0.7907813549090073 + ], + [ + 4628.019958423007, + 0.7695902437733569 + ], + [ + 4768.262987466129, + 0.7575087888895178 + ], + [ + 4908.50601650925, + 0.7557919953239772 + ], + [ + 5048.749045552371, + 0.7623706336296147 + ], + [ + 5188.992074595492, + 0.7738753642273954 + ], + [ + 5329.235103638614, + 0.7862551485802569 + ], + [ + 5469.478132681736, + 0.7954004478686115 + ], + [ + 5609.721161724857, + 0.7983501183712022 + ], + [ + 5749.964190767979, + 0.7952947714464896 + ], + [ + 5890.2072198111, + 0.7877539728454 + ], + [ + 6030.450248854221, + 0.7777986542140846 + ], + [ + 6170.693277897342, + 0.767898466721782 + ], + [ + 6310.936306940464, + 0.759596617467195 + ], + [ + 6451.179335983586, + 0.7489014209834266 + ], + [ + 6591.422365026707, + 0.7284476249320575 + ], + [ + 6731.665394069829, + 0.6915812690962849 + ], + [ + 6871.90842311295, + 0.631776018426837 + ], + [ + 7012.151452156071, + 0.5423301714575703 + ], + [ + 7152.394481199192, + 0.417812568060366 + ], + [ + 7292.637510242314, + 0.2609695500640291 + ], + [ + 7432.880539285436, + 0.08025910266402057 + ], + [ + 7573.123568328557, + -0.11577449896092451 + ], + [ + 7713.366597371678, + -0.3192495225613502 + ], + [ + 7853.6096264148, + -0.5221728358651136 + ], + [ + 7993.852655457921, + -0.7162706712235853 + ], + [ + 8134.095684501042, + -0.8930999491813469 + ], + [ + 8274.338713544164, + -1.0480755394609085 + ], + [ + 8414.581742587285, + -1.1801516493992084 + ], + [ + 8554.824771630407, + -1.2894684395166447 + ], + [ + 8695.067800673529, + -1.3782440146630808 + ], + [ + 8835.31082971665, + -1.4491082570554719 + ], + [ + 8975.553858759771, + -1.5046939437461564 + ], + [ + 9115.796887802893, + -1.5477670359145541 + ], + [ + 9256.039916846014, + -1.5826682778020353 + ], + [ + 9396.282945889136, + -1.6127889338693415 + ], + [ + 9536.525974932258, + -1.6429767380342744 + ], + [ + 9676.769003975378, + -1.680465555885852 + ], + [ + 9817.0120330185, + -1.723635098132372 + ], + [ + 9957.25506206162, + -1.770243028565291 + ], + [ + 10097.498091104742, + -1.8180471595197811 + ], + [ + 10237.741120147864, + -1.864789480338616 + ], + [ + 10377.984149190985, + -1.9068050368787004 + ], + [ + 10518.227178234107, + -1.9386505362058197 + ], + [ + 10658.470207277229, + -1.9602085820782025 + ], + [ + 10798.71323632035, + -1.9766233770524353 + ], + [ + 10938.956265363471, + -1.9955340502067422 + ], + [ + 11079.199294406593, + -2.0262060631217484 + ], + [ + 11219.442323449714, + -2.076317992755582 + ], + [ + 11359.685352492836, + -2.142448801050321 + ], + [ + 11499.928381535958, + -2.214012885935303 + ], + [ + 11640.171410579078, + -2.2788220879876566 + ], + [ + 11780.4144396222, + -2.324618372726963 + ], + [ + 11920.65746866532, + -2.3401408556874594 + ], + [ + 12060.900497708442, + -2.3241287915242803 + ], + [ + 12201.143526751564, + -2.285155293183301 + ], + [ + 12341.386555794685, + -2.231818195485357 + ], + [ + 12481.629584837807, + -2.1724065493665923 + ], + [ + 12621.872613880929, + -2.1148843840739118 + ], + [ + 12762.11564292405, + -2.066738617797497 + ], + [ + 12902.358671967171, + -2.0271547813002546 + ], + [ + 13042.601701010293, + -1.990913483694822 + ], + [ + 13182.844730053414, + -1.9549926908676474 + ], + [ + 13323.087759096536, + -1.916802530273236 + ], + [ + 13463.330788139658, + -1.8739920873680889 + ], + [ + 13603.573817182778, + -1.8242198355807004 + ], + [ + 13743.8168462259, + -1.7660545647257069 + ], + [ + 13884.059875269022, + -1.703040551565444 + ] + ], + "19": [ + [ + 0.0, + -0.8537097907021887 + ], + [ + 140.24302904312142, + -0.7521992810640642 + ], + [ + 280.48605808624285, + -0.9005794318896804 + ], + [ + 420.7290871293643, + -0.7092819429949696 + ], + [ + 560.9721161724857, + -0.32332672379453753 + ], + [ + 701.2151452156071, + 0.05089076794919283 + ], + [ + 841.4581742587286, + 0.37041635205385937 + ], + [ + 981.70120330185, + 0.6292762874893809 + ], + [ + 1121.9442323449714, + 0.8282450875623109 + ], + [ + 1262.1872613880928, + 0.9765488968017004 + ], + [ + 1402.4302904312142, + 1.077159546659897 + ], + [ + 1542.6733194743356, + 1.1337419079559763 + ], + [ + 1682.9163485174572, + 1.1491057939309977 + ], + [ + 1823.1593775605786, + 1.1253809292504282 + ], + [ + 1963.4024066037, + 1.0825537651735517 + ], + [ + 2103.645435646821, + 1.0365049405941775 + ], + [ + 2243.888464689943, + 0.989053883389407 + ], + [ + 2384.1314937330644, + 0.945184729413815 + ], + [ + 2524.3745227761856, + 0.9117344896564058 + ], + [ + 2664.617551819307, + 0.8893421515015981 + ], + [ + 2804.8605808624284, + 0.8756238846794606 + ], + [ + 2945.10360990555, + 0.8680725050959799 + ], + [ + 3085.346638948671, + 0.8637408812146462 + ], + [ + 3225.589667991793, + 0.8606418413945442 + ], + [ + 3365.8326970349144, + 0.8563215180846997 + ], + [ + 3506.0757260780356, + 0.8478037013490286 + ], + [ + 3646.318755121157, + 0.8325819982573301 + ], + [ + 3786.5617841642784, + 0.809399857571813 + ], + [ + 3926.8048132074, + 0.7797289621968181 + ], + [ + 4067.047842250521, + 0.7462258261156738 + ], + [ + 4207.290871293642, + 0.7115638889031083 + ], + [ + 4347.533900336764, + 0.6791536968166647 + ], + [ + 4487.776929379886, + 0.6525284558250161 + ], + [ + 4628.019958423007, + 0.6347366894958816 + ], + [ + 4768.262987466129, + 0.6286925265400333 + ], + [ + 4908.50601650925, + 0.6362471801664872 + ], + [ + 5048.749045552371, + 0.6560443112546368 + ], + [ + 5188.992074595492, + 0.6834120807112279 + ], + [ + 5329.235103638614, + 0.7121998324684707 + ], + [ + 5469.478132681736, + 0.7362464427168691 + ], + [ + 5609.721161724857, + 0.751169250315572 + ], + [ + 5749.964190767979, + 0.7559324172822558 + ], + [ + 5890.2072198111, + 0.7525632068733242 + ], + [ + 6030.450248854221, + 0.7441118249125309 + ], + [ + 6170.693277897342, + 0.7339588845779543 + ], + [ + 6310.936306940464, + 0.7239164772491742 + ], + [ + 6451.179335983586, + 0.7096678010588173 + ], + [ + 6591.422365026707, + 0.6847571675282489 + ], + [ + 6731.665394069829, + 0.6430286103220866 + ], + [ + 6871.90842311295, + 0.5790641710756101 + ], + [ + 7012.151452156071, + 0.48733863351435974 + ], + [ + 7152.394481199192, + 0.36453972431854564 + ], + [ + 7292.637510242314, + 0.21745896662260297 + ], + [ + 7432.880539285436, + 0.0534530120812544 + ], + [ + 7573.123568328557, + -0.12079832283390148 + ], + [ + 7713.366597371678, + -0.2993659683820724 + ], + [ + 7853.6096264148, + -0.476265310328653 + ], + [ + 7993.852655457921, + -0.6453420077236086 + ], + [ + 8134.095684501042, + -0.8031062044390582 + ], + [ + 8274.338713544164, + -0.9518651775075878 + ], + [ + 8414.581742587285, + -1.089829418864067 + ], + [ + 8554.824771630407, + -1.2156350312391095 + ], + [ + 8695.067800673529, + -1.3302313119197964 + ], + [ + 8835.31082971665, + -1.4350170923832555 + ], + [ + 8975.553858759771, + -1.531396363659935 + ], + [ + 9115.796887802893, + -1.620373118107646 + ], + [ + 9256.039916846014, + -1.6998709096298346 + ], + [ + 9396.282945889136, + -1.7689949325146035 + ], + [ + 9536.525974932258, + -1.8303163826345445 + ], + [ + 9676.769003975378, + -1.8845330235529583 + ], + [ + 9817.0120330185, + -1.9307198545868045 + ], + [ + 9957.25506206162, + -1.9677914656617765 + ], + [ + 10097.498091104742, + -1.994662390941023 + ], + [ + 10237.741120147864, + -2.010273134386783 + ], + [ + 10377.984149190985, + -2.0157366987585124 + ], + [ + 10518.227178234107, + -2.016057533627526 + ], + [ + 10658.470207277229, + -2.01533961411936 + ], + [ + 10798.71323632035, + -2.017989834763668 + ], + [ + 10938.956265363471, + -2.031176133920646 + ], + [ + 11079.199294406593, + -2.0586953195793374 + ], + [ + 11219.442323449714, + -2.098915984977363 + ], + [ + 11359.685352492836, + -2.1484255724901793 + ], + [ + 11499.928381535958, + -2.2033357656408996 + ], + [ + 11640.171410579078, + -2.257713566236685 + ], + [ + 11780.4144396222, + -2.3066961556881544 + ], + [ + 11920.65746866532, + -2.349313908439445 + ], + [ + 12060.900497708442, + -2.387650181646503 + ], + [ + 12201.143526751564, + -2.4211041521710706 + ], + [ + 12341.386555794685, + -2.4468495800209453 + ], + [ + 12481.629584837807, + -2.4616251618853617 + ], + [ + 12621.872613880929, + -2.461594002528968 + ], + [ + 12762.11564292405, + -2.4418134780735348 + ], + [ + 12902.358671967171, + -2.3970713135708337 + ], + [ + 13042.601701010293, + -2.3285785393732827 + ], + [ + 13182.844730053414, + -2.2418863474497948 + ], + [ + 13323.087759096536, + -2.1430184810436255 + ], + [ + 13463.330788139658, + -2.038277940364498 + ], + [ + 13603.573817182778, + -1.9339959466090721 + ], + [ + 13743.8168462259, + -1.8370946359257168 + ], + [ + 13884.059875269022, + -1.7531166288578541 + ] + ] + } +} \ No newline at end of file From 80fc2f061846c245335356a914347dba36ac8a3c Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 10 Oct 2022 19:34:21 +0100 Subject: [PATCH 21/68] FIX: saving rocket attributes --- rocketpy/Rocket.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/rocketpy/Rocket.py b/rocketpy/Rocket.py index 30d82bbfb..8208e0950 100644 --- a/rocketpy/Rocket.py +++ b/rocketpy/Rocket.py @@ -548,15 +548,17 @@ def addTrapezoidalFins( Object of the Rocket class. """ - # Save parameters for Dispersion + # Save parameters for Dispersion # TODO: We need to be more flexible here! Not all the rockets has exactly 1 fin set self.numberOfFins = n self.finRadius = radius self.finAirfoil = airfoil + self.finDistanceToCM = distanceToCM # Retrieves and convert basic geometrical parameters - Cr, Ct = rootChord, tipChord - s = span + Cr, Ct = self.finRootChord, self.finTipChord = rootChord, tipChord + s = self.span = span radius = self.radius if radius is None else radius + self.cantAngle = cantAngle cantAngleRad = np.radians(cantAngle) # Compute auxiliary geometrical parameters From be6597cfb7585c954aa518ddc3076ecf49efe498 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 10 Oct 2022 19:34:44 +0100 Subject: [PATCH 22/68] MAINT: adding checking initial object --- rocketpy/Dispersion.py | 231 ++++++++++++++++++++++++++--------------- 1 file changed, 147 insertions(+), 84 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index f021401f5..0095b8079 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -18,6 +18,8 @@ from matplotlib.patches import Ellipse from numpy.random import * +from rocketpy.Function import Function + from .Environment import Environment from .Flight import Flight from .Motor import SolidMotor @@ -519,80 +521,92 @@ def __check_inputted_values_from_dict(self, dictionary): """ for parameter_key, parameter_value in dictionary.items(): if isinstance(parameter_value, (tuple, list)): + # Everything is right with the data, we have mean and stdev continue - else: # if parameter_value is only the std. dev. - if "parachute" in parameter_key: - _, parachute_name, parameter = parameter_key.split("_") + + # In this case the parameter_value is only the std. dev. + ## First solve the parachute values + if "parachute" in parameter_key: + _, parachute_name, parameter = parameter_key.split("_") + dictionary[parameter_key] = ( + getattr( + self.rocket.parachutes[ + self.parachute_names.index(parachute_name) + ], + parameter, + ), + parameter_value, + ) + + ## Second corrections - Environment + if parameter_key in self.environment_inputs.keys(): + try: dictionary[parameter_key] = ( - getattr( - self.rocket.parachutes[ - self.parachute_names.index(parachute_name) - ], - parameter, - ), + getattr(self.environment, parameter_key), parameter_value, ) - else: # TODO: Check if we can remove this else - if parameter_key in self.environment_inputs.keys(): - try: - dictionary[parameter_key] = ( - getattr(self.environment, parameter_key), - parameter_value, - ) - except Exception as E: - print("Error:") - print( - "Check if parameter was inputted correctly in dispersion_dictionary." - + " Dictionary values must be either tuple or lists." - + " If single value, the corresponding Class must " - + " be inputted in Dispersion.run_dispersion method.\n" - ) - print(traceback.format_exc()) - elif parameter_key in self.solid_motor_inputs.keys(): - try: - dictionary[parameter_key] = ( - getattr(self.motor, parameter_key), - parameter_value, - ) - except Exception as E: - print("Error:") - print( - "Check if parameter was inputted correctly in dispersion_dictionary." - + " Dictionary values must be either tuple or lists." - + " If single value, the corresponding Class must " - + "must be inputted in Dispersion.run_dispersion method.\n" - ) - print(traceback.format_exc()) - elif parameter_key in self.rocket_inputs.keys(): - try: - dictionary[parameter_key] = ( - getattr(self.rocket, parameter_key), - parameter_value, - ) - except Exception as E: - print("Error:") - print( - "Check if parameter was inputted correctly in dispersion_dictionary." - + " Dictionary values must be either tuple or lists." - + " If single value, the corresponding Class must " - + "must be inputted in Dispersion.run_dispersion method.\n" - ) - print(traceback.format_exc()) - elif parameter_key in self.flight_inputs.keys(): - try: - dictionary[parameter_key] = ( - getattr(self.flight, parameter_key), - parameter_value, - ) - except Exception as E: - print("Error:") - print( - "Check if parameter was inputted correctly in dispersion_dictionary." - + " Dictionary values must be either tuple or lists." - + " If single value, the corresponding Class must " - + "must be inputted in Dispersion.run_dispersion method.\n" - ) - print(traceback.format_exc()) + except Exception as E: + print("Error:") + print( + "Please check if the parameter was inputted correctly in dispersion_dictionary." + + " Dictionary values must be either tuple or lists." + + " If single value, the corresponding Class must " + + " be inputted in Dispersion.run_dispersion method.\n" + ) + print(traceback.format_exc()) + + ## Third corrections - SolidMotor + elif parameter_key in self.solid_motor_inputs.keys(): + try: + dictionary[parameter_key] = ( + getattr(self.motor, parameter_key), + parameter_value, + ) + except Exception as E: + print("Error:") + print( + "Please check if the parameter was inputted correctly in dispersion_dictionary." + + " Dictionary values must be either tuple or lists." + + " If single value, the corresponding Class must " + + "must be inputted in Dispersion.run_dispersion method.\n" + ) + print(traceback.format_exc()) + + # Fourth correction - Rocket + elif parameter_key in self.rocket_inputs.keys(): + try: + dictionary[parameter_key] = ( + getattr(self.rocket, parameter_key), + parameter_value, + ) + except Exception as E: + print("Error:") + print( + "Please check if the parameter was inputted correctly in dispersion_dictionary." + + " Dictionary values must be either tuple or lists." + + " If single value, the corresponding Class must " + + "must be inputted in Dispersion.run_dispersion method.\n" + ) + print(traceback.format_exc()) + + # Fifth correction - Flight + elif parameter_key in self.flight_inputs.keys(): + try: + dictionary[parameter_key] = ( + getattr(self.flight, parameter_key), + parameter_value, + ) + except Exception as E: + print("Error:") + print( + "Please check if the parameter was inputted correctly in dispersion_dictionary." + + " Dictionary values must be either tuple or lists." + + " If single value, the corresponding Class must " + + "must be inputted in Dispersion.run_dispersion method.\n" + ) + print(traceback.format_exc()) + + # The analysis parameter dictionary must be corrected now! return dictionary @@ -624,6 +638,8 @@ def __yield_flight_setting( for parameter_key, parameter_value in analysis_parameters.items(): if type(parameter_value) is tuple: flight_setting[parameter_key] = distribution_func(*parameter_value) + elif isinstance(parameter_value, Function): + flight_setting[parameter_key] = distribution_func(*parameter_value) else: # shuffles list and gets first item shuffle(parameter_value) @@ -811,15 +827,15 @@ def run_dispersion( # Check if there's enough object to start a flight: ## Raise an error in case of any troubles + self.__check_initial_objects() # Creates copy of dispersion_dictionary that will be altered modified_dispersion_dict = {i: j for i, j in dispersion_dictionary.items()} - # TODO: Take care of next line, since analysis_parameters is not what the function is returning analysis_parameters = self.__process_dispersion_dict(modified_dispersion_dict) + # TODO: This should be more flexible, allow different distributions for different parameters self.distributionFunc = self.__set_distribution_function(self.distribution_type) - # Basic analysis info # Create data files for inputs, outputs and error logging dispersion_error_file = open(str(self.filename) + ".disp_errors.txt", "w") @@ -842,14 +858,6 @@ def run_dispersion( # Creates a copy of the environment env_dispersion = self.environment - if env_dispersion is None: - env_dispersion = Environment( - railLength=dispersion_dictionary["railLength"][0], - date=dispersion_dictionary["date"][0], - latitude=dispersion_dictionary["latitude"][0], - longitude=dispersion_dictionary["longitude"][0], - elevation=dispersion_dictionary["elevation"][0], - ) # Apply environment parameters variations on each iteration if possible env_dispersion.railLength = setting["railLength"] @@ -904,10 +912,10 @@ def run_dispersion( ) rocket_dispersion.addFins( n=setting["numberOfFins"], - rootChord=setting["rootChord"], - tipChord=setting["tipChord"], - span=setting["span"], - distanceToCM=setting["distanceToCM"], + rootChord=setting["finRootChord"], + tipChord=setting["finTipChord"], + span=setting["finSpan"], + distanceToCM=setting["finDistanceToCM"], radius=setting["radius"], airfoil=setting["airfoil"], ) @@ -993,6 +1001,61 @@ def run_dispersion( return None + def __check_initial_objects(self): + """Create rocketpy objects (Environment, Motor, Rocket, Flight) in case + that + + Returns + ------- + _type_ + _description_ + """ + if self.environment is None: + self.environment = Environment( + railLength=self.dispersion_dictionary["railLength"][0] + ) + if self.motor is None: + self.motor = SolidMotor( + thrustSource=self.dispersion_dictionary["thrustSource"][0], + burnOut=self.dispersion_dictionary["burnOutTime"][0], + grainNumber=self.dispersion_dictionary["grainNumber"][0], + grainDensity=self.dispersion_dictionary["grainDensity"][0], + grainOuterRadius=self.dispersion_dictionary["grainOuterRadius"][0], + grainInitialInnerRadius=self.dispersion_dictionary[ + "grainInitialInnerRadius" + ][0], + grainInitialHeight=self.dispersion_dictionary["grainInitialHeight"][0], + ) + if self.rocket is None: + self.rocket = Rocket( + motor=self.motor, + mass=self.dispersion_dictionary["mass"][0], + radius=self.dispersion_dictionary["radius"][0], + inertiaI=self.dispersion_dictionary["inertiaI"][ + 0 + ], # TODO: remove hardcode + inertiaZ=self.dispersion_dictionary["inertiaZ"][ + 0 + ], # TODO: remove hardcode + distanceRocketPropellant=self.dispersion_dictionary[ + "distanceRocketPropellant" + ][0], + distanceRocketNozzle=self.dispersion_dictionary["distanceRocketNozzle"][ + 0 + ], + powerOffDrag=0.6, # TODO: Remove this hardcoded + powerOnDrag=0.6, # TODO: Remove this hardcoded + ) + self.rocket.setRailButtons(distanceToCM=[0.2, -0.5]) + if self.flight is None: + self.flight = Flight( + rocket=self.rocket, + environment=self.environment, + inclination=self.dispersion_dictionary["inclination"][0], + heading=self.dispersion_dictionary["heading"][0], + ) + return None + def import_results(self, dispersion_output_file): """Import dispersion results from .txt file From 415292dad12e87ce969fe0e942677f9a257d473f Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 10 Oct 2022 19:35:04 +0100 Subject: [PATCH 23/68] MAINT: updating notebook --- .../dispersion_class_usage.ipynb | 304 +++++++++++------- 1 file changed, 193 insertions(+), 111 deletions(-) diff --git a/docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb b/docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb index e2d1ffc7e..b91bacb06 100644 --- a/docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb +++ b/docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb @@ -16,9 +16,18 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "%load_ext autoreload\n", "%autoreload 2" @@ -33,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -49,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -96,13 +105,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "Env = Environment(\n", " railLength=5.2, latitude=39.389700, longitude=-8.288964, elevation=113\n", - ")" + ")\n" ] }, { @@ -116,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -124,7 +133,7 @@ "\n", "tomorrow = datetime.date.today() + datetime.timedelta(days=1)\n", "\n", - "Env.setDate((tomorrow.year, tomorrow.month, tomorrow.day, 12)) # Hour given in UTC time" + "Env.setDate((tomorrow.year, tomorrow.month, tomorrow.day, 12)) # Hour given in UTC time\n" ] }, { @@ -154,20 +163,20 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "import json\n", "\n", - "file = open(\"export_env_analysis.json\")\n", + "file = open(\"../../../data/weather/EuroC_export_env_analysis.json\")\n", "env_dict = json.load(file)\n", "file.close()\n" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -189,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -204,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -248,7 +257,7 @@ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAx0AAAMcCAYAAAA1xcg2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy89olMNAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3wT9RvA8U/STfeiCyh7711ZKhsUUUDZigjqT1w4EBURnIAiOFBxD5CpgOzKHmXvVTYthbbQ0j3T3O+Po4EymzZpBs/79eqL5O5y93yTcN889x2nURRFQQghhBBCCCHMRGvpAIQQQgghhBD2TZIOIYQQQgghhFlJ0iGEEEIIIYQwK0k6hBBCCCGEEGYlSYcQQgghhBDCrCTpEEIIIYQQQpiVJB1CCCGEEEIIs5KkQwghhBBCCGFWknQIIYQQQgghzEqSDmGTok4lUfmtZaRm55dqP6/N28+I33eZJCZT7suajz13ZwxDftperG0/XXGM8YsPmTkiIYS4t5jznJ+n09Nhyjp2n0sGIDY5i8pvLePwhVSzHM8U1kcn0n36JvR6xdKhiDtwtHQA4t7257ZzfLL8KPvHd8HRQc2BM3N1NJqwmmbhvsx9NsKwbdSpJAb8sI0Nb9xPs3BfdrzTES9X836FC48JoNGAh7MjFf3K0a5GAMPbVqG8l6th2/G96qKY+XwXm5xFu8nrWPZSW+qFepfpsQFy8gv4fPVxZgxqalhWoFcYv+QQqw4nUC/Ui8/6NSLAwwWAke2r0n7yOoa3rUol/3LmD1AIIczktXn7WbjnPACOWg0+5ZyoHexFr0ah9G1WAa1WU2ax3HjOf+L7KOqGejH+4Xql3ves7eeo6FuOZuF+pd7XjXLyC2gyMZIVL7ejcoC70a8/FJfKt+tPkZ6rQ1EUxj9cl+rlPbm/VnmmRh5n0b44HmtaweRxC9OQlg5hURHV/MnMK+BA3LUrKDvOJhPo6cK+2BRy8gsMy6NOJxHm40a4vzvOjlrKe7qi0ZTNSX7tax3Y/nZHFo9qw3P3V2Pzyct0mbaRY/Fphm28XJ3wdnO67T7ydHqzxXe3Y5vKikMX8XB1pHnla5XRv/svcCElh9+fbkn9UG8+Xx1tWOfn7kz7mgH8uf2c2WMTQghz61AzkB3vdGTzmAf5dVhLIqr5M+Hfwzz92050BeY7x9/IXOd8RVH4Peocj7eoaPJ9A2w6cZkwX7cSJRwA9cO8ebRJGOk5+ew4k8zuc1cM6/o2q8CvW8+aKFJhDtLSISyqWqAH5T1d2HY6iaaVfAHYdjqJznWD2Hoqib0xKURU8zcsb11VfVzYArF/fBe83ZyYvyuWiUuP8PXApkz89zAXU3NoXtmPz/o2NLRGFOgVPl5+lHm7YnHQaniieUUUitc84O/hgrebE+U9oWqgB13qBtHjy028+88hFjx/H6BeBUvLyeeHoc0B9cpTrWBPHLQaFu2No1awJ3NGRhAdn87Hy4+y82wy5ZwdaFcjkHEP1cXP3RkAvV5h5qbT/LUjhospOQR4ODOwVSVGPViDdpPXAdDzy80AtKrix9xnI246dq6ugE+WH+Pf/RdIz9XRMMybcQ/VpVFFnyLv36xnWvHpimOcSEynbogXU/o1olqgx23fh3/3X6RTnaAiy1Kz86ng60atIE9OBmew8lBmkfUdawfx2epo3u5Rp1jvtRBCWKvCC14Awd6u1A/zpklFHwb+uJ0Fu8/Tv2UlQD0vfrzsKJFHE8jT6Wlw9RxcN9QLgC8ij7P6SAIj2lXh89XHScvOp0OtQD7t0xAPF/Wn2fKDF5n+3wnOJmXi5uxAvVAvfhjanHLOjkXO+a/N28/2M8lsP5PML1vOArDpzQcY/NN2BrWqxMj21QzxH76QSs8vN7P+9ftv+cP/YFwq55IyebB2+du+BwV6hbcWHmB3zBX+GN6KMB83TiZm8NbCAxyIS6WSXznef7geg3/azvdDmtG1XrDhtZFH4g11SOF7MOy+ykz77zgp2fk81jSMCb3q88Om0/y46QyKojCsTWVGPVjDsI9OdYPoVDeIHzedJqJqgGF5xzpBvLf4MOeSMgn3L1lSI8xLWjqExUVU8yfqVJLh+bZTanLRqoofUafV5Tn5BeyLvZaA3EpOfgE/bDzNF080Zt6zEVxIyeaj5UcN63/YdJoFu88zpW9DFjwXQUpWPqsPJ5QoZlcnBwa1CmfXuStczsi97XYLd5/H2UHLgufv46NHG5Canc/AH7ZRL9SLJaPa8uuwllzOyOWFWXsMr5m06hjfrj/Fiw/WIHJ0e6YPaGLorrT4hTYAzHqmFTve6cj3Q5rd8rifLD/GikMX+ezxRix7sS3h/u4M/XkHKVl5Rbabsiqad3rW4d9RbXHUanlzwYE7lnvn2WQahHkXWda7SRh7Yq5Q890VfLTsKKMerF5kfaOKPlxMzSE2OeuO+xZCCFt0X/UA6oR4sfJwvGHZC7P2kJSZy6/DWvDvi22pH+bFoB+3FTkHxyRlsvpwAj8/1YKfnmrB9jPJfLv+JACJaTm89Nde+jWvwH+jOzBnZGu61Qu+ZTfa8b3q0rSSDwNaVmTHOx3Z8U5HQn3ceLx5RebvOl9k2/m7ztOyit9tWxp2nEmmSoC7IfG5Ua6ugP/N2s2Ri2nMfzaCMB83CvQKI//YhZuzA4v+14ZPHmvAlOtavAvp9QprjyXSue61C1cxSZmsP57Ib0+35Mv+TZi38zzDft1JfGoOc59tzZjutfls9XH2xqgtGtf3fkjOzOOXrWcMz8N83AjwcGHHmeRbxi4sT1o6hMVFVPVn4tIj6Ar05Oj0HL6QRqsqfuQX6Jm1PQaAPeeukKfT3zHpyC9Q+OjR+oYrHE9GhDN9zUnD+p83n+F/91ejW/0QAD56tD4bT1wqcdzVAtXjnL+SbUgKblQ5wJ2x113h/2rNCeqGevFmt9qGZZP7NiTik7WcvpRBeS9Xftlylom96tG3mdovNdzfnRZXuzMVtob4lHMyXG27UVaejlnbz/FZv0Y8UEu9WvVpnwa0nXSJuTtjebbDtateb3StZWg9ev7+agz7dSc5+QW4OjnctN/U7HzSc3QEeRU9rrebE0tfbEdieg7+7i443NCvOchLfW/iUrKp6CfjOoQQ9qdaoDvH4tMB9eLM/tgUdo3rhIujei59p2ddVh9JYPnBeAa2UltD9Ap89ngjww/8x5qEseVkEm90hcT0XHR6hW71g6ngq543awd73fLYXq5OODlocXVyKFIv9G1WgamRx9kXm0Ljij7kF+hZsv/CHVud41KybzrHF8rKK+DpX3eSp9Pz18jWeLmq3bs2nbhETFIWc0a2Nhz/jS61GHzDhCN7Y9XEocnVFvfC92ByX/U9qBHkSetq/py+lMGvT7VAq9VQLdCD7zacIup0Ek0q+TJ/93kW742jQFFQFJjUp2GRYwR5uRCXkn3b8gnLkqRDWFzrqv5k5RWw/3wqadn5VAlwx9/DhdZV/XljwQFy8gvYdjqJSn7lCPNxu+1+3JwcijSpBnq6kpSptkKk5eSTmJ5L4+tOdo4OWhqEeRezg9XNCl93p1ElN7YKHI1PY9vpJOq+t/Kmbc8lZ5GWoyNPp6dN9YCb1hfXuaQs8gsUmoX7GpY5OWhpVMGHk4kZRbatHexpeBzoqSYHSZl5t3yfc69eYXJxvHUD6e2SoMIEJvu6K1RCCGFPFK7VBUcvppGZp6PJxMgi2+TkF3Au+Vr30wq+bkVaFAI9XQx1Vp0QL9pU96fbtE20rxlAuxqB9Kgfgne54o/jCPJy5YFa5Zm3K5bGFX1Yc7WrV88GIbd9TU6+/rbn+Jf+2kuwtyt/jWhd5MLU6UuZhPi4FqkDGlX0vun1q48k8GDt8kUG3N/4HgR4OOOg8SiyTYCHC0kZagvRkNbhDGkdftv4XZ0cpK6xYpJ0CIurHOBOiLcr204nkZqdT6uq6lX9IC9XQr1d2XPuClGnk7jvDq0cAI4ORX/+azSYdUanU1d/wFfwvUMi5Fy0xSAzt4COtYN4q3vtm7Yt7+VCTBl3QSqcMQzU9wu47ZSDPuWc0WgweprilCx1e/+rrTRCCGFvTiVmGFpyM3MLKO/pypyRrW/azuu6wd/Xn38BNBoN+qtj0R20Gv4c3ord566w8cRlftt6ls9WRbPohTZGtRj3b1GRV+ft472H6jJ/13keahhyU710PT93J6KvmyDlevfXKs+ivXHsOXeF+0pwYey/IwmM6Va07rvpPUBzi2WgL2ZlnpKVJ3WNFZMxHcIqRFT1Z9vppCKDxQFaVvFj/fFL7I9NvWPXqrvxcnWi/NUZsQrpCvQciivZvOM5+QXM3hFDyyp++N+ma9Wt1A/z4nhiOhWuzt5x/V85Z0cq+7vj6qRly8nLt3y989UrUPo7TJIS7l8OZwdtkVk98gv0HDifSo2g2w8SvxtnRy01yntw4obWkrs5npCOk4OGmkGed99YCCFszNaTlzkWn063+uqA6fphXlzKyMVBq7npPO9nxA9ijUZD88p+jO5ck2UvtcPJQcuq68aNXM/ZUXvLC0YP1C5POWcH/tx2jg3HL9Gv+Z1npaoX6s2pS5kot/iRP7h1JcZ0q8Uzv+9i2+lr4zCrBrpzMSWHS+nXxjceOF+0bj1zOZO4lGza1Qi84/FLIye/gJjkrCLTyQvrIkmHsAqtq/mz82wyRy6k0arKteSiVRV/Zm+PIa9AT0TVkicdAMPaVOHbDadYdTiek4kZjFt8iLQcXbFem5SRS2J6DmcuZ7Jk/wX6fLuVK5l5fNS7vlExDI2oTGpWPi/N2cv+2BTOJWWy4fglXp+/nwK9gquTA891qMYnK46xcPd5ziVlsifmCnN3qmNb/N2dcXXSsuF4IpfSc0nLubnVoZyzI4NaV+Lj5UdZH53IiYR03lp4kOz8Ap5oXsmoeG/UvkYgu84aN0hvx5lkWlT2u+U4ESGEsCV5Oj2J6TnEp+ZwKC6Vb9adZMTvu+hYuzx9rt4fom31AJpW8mHkH7vZePwSsclZ7D6XzJRVxzhwPqVYx9kbc4Vv1p3kwPkU4lKyWXkonuTMPKqVv/WFowq+buyLTSE2OYvkzDxDAuKg1dC3WQUmr4ymcoB7kW63txJR1Z+sPB3HE259cempNlV4rUsthv+6k51X64J2NQKp5F+O1+bv5+jFNHadTeazqwPJC/sfRB6Jp231gDu2spTW3pgUnB20hpkwhfWR7lXCKkRU9ScnX0+1QHfD2AKAVlX9yMjVUTXQvciN+EpiRLsqJKbn8Pq8/Wg08HjzinSpF0R6MRKPBz/fgEYD7ldvDti+RgDD21W57TiG2wnycmXB8/fx6YqjDPlpO3kFesJ83OhQszyFXVhferAGjloNUyOPk5ieQ3lPV8PAQ0cHLe8/XI8v15xgauRxWlT2K3IDxUJjutVGUWD0vP1kXJ0y9/enWxrVH/hWnmhRkYe/3kxaTr5hEOHd/HvgAq90qlmq4wohhDXYcPwSLT9ag6NWg7ebE3VCvBjfqx59m167OaBGo+GXYS35bFU0byzYT3JmHoEeLrSs4nfbSUdu5OnqyPYzyfy8+QzpuToq+LjxTs86hslBbjSiXVVem7+fzl9sICdfz6Y3HzB0w3qieSW+WXeKfs3uftM8X3dnutQLZtG+uJu6QhUa3raKOpXtLzv57ekWNAv3Y+aQ5ry18ACPfL2Fin5uvN2jDsN/24XL1YtNkUcSDEmZuSzZf4FHmoSZNbERpaNRbtWGJoQQt/G/WbupF+rNCw9Uv+u266IT+WjZUVa+3O6mfrpCCCHMb8eZZAb9uI2tb3UsclHvdo5eTGPIT9vZ8MYDuN9m6ty72XU2mb7fRbHhjfvxdHWi5Uf/ETW2eMcvieTMPB78fD3/jmorsyRaMfkVIIQwytjudXAv5pWk7LwCpvRtKAmHEEKUsVxdARdTs5n233F6NAgp9g/+OiFejOlWm9grxZ/YZOWheDadULuSbT5xmbF/H6R5uC/h/u6kZOXxbs86Zks4AM5fyeKDR+pLwmHlpKVDCCGEEMLOzN8Vy5iFB6gb6sWPQ1sQ7F26Lsp3snD3eb5ed5K4lGz8yjnTpnoA7/asg6/MJCWuI0mHEEIIIYQQwqykz4MQQgghhBDCrCTpEEIIIYQQQpiVJB1CCCGEEEIIs5L7dJjI71Fn+X7DaS5l5FInxIsJverRuKKPpcOyiO2nk5i58TQH41JJTM/l+yHN6Fov2LBeURS+iDzOXztjScvOp3llXz7s3YAqAe6GbVKy8hi/5DBrjiai0UD3+sGMf7heiafvs3bfrDvJqsPxnErMwNXJgabhvrzVvTbVAq/dCConv4CPlh3l3wMXyNPpaV8jkA961y8yI0hcSjbv/nOQqNNJuDs70qdZBd7sWstuZ4/6Y9s5Zm07x/kr2QDUCPLgpY41DHPZy3sm7JUxdc5fO2L4e895ouPTAWhQwZs3uta2yjqqpHXpkv0XeOmvvXSuG8QPQ5ubP1AjGFum1Ox8PlsVzcrD8aRm5RPm68Z7D9Xlgdq3vkeHpRhbrp82n2HWtnPqYHN3Z7rXD+HNbrWs5saxd/vtcitRp5L4cNkRTiRkEOLjyqgHqt/1ru9lzdhyrTx0kT+3xXDkYhp5Oj01gjx4pVNNOtQs/d3kpVY1gX/3X+DDpUd5uVMNlr3Ylrohngz9aTuXM3ItHZpFZOUXUCfEi4mP3Ppu3d9tOM0vW8/yUe/6LHqhDW5Ojgz9eTs5+QWGbV6es4/jCRn8MbwlPz/Vgh1nkhn798GyKkKZ234mmSGtw/nnhTb8MbwVugI9Q3/aQVbetRsXfrD0CGuOJjBjYFPmjowgIT2H5/7cbVhfoFd4+ped5BcoLHz+Pj57vBELdp9nauRxSxSpTIR4uTKmW23+fbEtS0a14b5q/oz8fRfHE9QfV/KeCXtkbJ2z7XQSvRqF8tfI1vz9vzaEeLsx5KftxKfmlHHkd1bSujQ2OYuPlx2lZWW/Moq0+IwtU55Oz5CftnP+ShbfDmrKmtc68MljDQgq5c1xTc3Yci3eF8eklcd4uVMN/hvdgUl9GrL0wAWmrIou48hv726/XW4Um5zF07/uJKKqP8tfbsvTbarw1t8H2XD8kpkjNY6x5dp+Jpm2NQL45akW/PtiWyKq+vPMbzs5FJda+mAUUWq9vt6sjFt00PC8oECvtPwoUvlm3QkLRmUdwscsVVYeumh4rtfrleYfRirfbzhpWJaanafUeGe5snhfnKIoinIiIU0JH7NU2R97xbDNumMJSuW3lirxqdllFrslXU7PUcLHLFW2nbqsKIr6HlV/e5my7MAFwzYnEtKV8DFLld3nkhVFUZS1xxKUKm8tVRLTcgzb/BF1Vqn/3kolN7+gbAtgQQ3fX6XM2XFO3jNht0pb5+gK9Eq991YqC3bFmivEEilJuXQFeuWxGVuUOTvOKaPn7lOe+W1nWYRabMaW6Y+os0q7SWuVPJ11n3+MLde4RQeVATOjiiz74N/DSp8ZW8waZ0nd+NvlVj5efkTpPHV9kWUvzNqtDPlpuzlDK5XilOtWOn2+XpkWebzUx5eWjlLK0+k5FJdKm+oBhmVarYY21QPYcy7FcoFZqdjkbC6l5xZ5v7xcnWhc0Yc9564AsOdcCl6ujjSs4GPYpm31ALQaDXtjUso4YstIz1FbOHzKqXOcHzqfSn6BUuR9q17egzAfN8P7tvfcFWoFexXpOtShZiDpuTrDlX97VqBXWLL/Atl5BTSt5CvvmbBLpqhzsvMLyC/Q41POyUxRGq+k5Zq+5gT+7s480aJSGURpnJKU6b+jCTSt5MN7iw/R/MNIunyxgW/WnaRAbz13NyhJuZqF+3IwLpV9ser6mKQs1kUnWl2XMWPsPZdS5D0AaF8zkL1X6xd7odcrZObqTHK+sM8O8mXoSlYeBXqFAI+id9oM9HDh1KVMC0VlvS5lqM35gbd4vy5dbZa9lJF70/vp6KDFx83JsI090+sVJi49QvNwX2oFewLqe+LsoMXbreh/+gAP5xveN+cb1rsY1tmrY/FpPDZjK7k6PeWcHfh+SDNqBHly5GKavGfC7piizvl0xVGCvFxv+sFkSSUp186zyczbGcvyl9uVRYhGK0mZYpKz2Holm96NQ/nlqZacTcpk3OJD5BfoeaVTzbII+65KUq5HGoeRnJlHv++2oiig0ysMalWJFx6oXhYhm8WtfqsEeriQnqsjJ7/AasaqlNbMTafJzCugZ8OQUu9Lkg4hrMy4xYeIjk9nwfMRlg7FJlQN8GD5S+1Iz9Gx/NBFXpu/n7kjW1s6LCGs0oz1J/l3/0XmjGxt0z+KMnJ1vDp3H5/0aYCfHd31WlEgwN2ZTx5riINWQ4MK3iSk5fD9xtNWk3SURNSpJL5Zd4oPHqlP40o+nL2cxcR/D/PlmhO81LGGpcMTt7F4XxzT/zvBD0Ob35RglYQkHaXkW84ZB63mpsFTlzJyb7qaLyDQQx0Mdykjl/LXDYy7lJFL3RCvq9u43PR+6gr0pGTn2/17+t7iQ6w9lsi8ZyMI8XYzLA/0cCGvQE9qdn6RK/eXM/IM70mghwv7YosO9Cp8H+35fXN21FL56sxnDSp4c+B8Cj9vOcvDDUPkPRN2pzR1zsyNp/h2/SlmPdOKOlfPt9bC2HKdS8rk/JVsnvltl2GZXlG7IFV7ezlrX+tAuL/7Ta8rSyX5rAI9XXBy0OCg1RiWVSvvwaX0XPJ0epwdLd8rviTlmhoZzWNNw+jfUu0GVzvYi+x8HWP/PsioB6qjva68tuJWv1UuZeTi6eJo0wl9oSX7LzBm4QFmDGpK2xqmaRW1/LfXxjk7aqkf5s3Wk5cNy/R6ha0nk2ga7mO5wKxURT83Aj1d2HoyybAsPSeffbEpNA33BaBpuA9pOToOnr/2Y3DrqST0ikKTSj5lHXKZUBSF9xYfYtXheGaPaE1Fv3JF1tev4I2Tg6bI9+zUpQziUrIN71uTcF+i49OKnAQ3nbiMp4sjNYI8uFfo9WqfY3nPhD0qaZ3z3YZTfLXmJL893bLIeDlrYWy5qgV6sOqV9ix/qZ3hr1OdIHUmoZfaFbloYykl+ayah/ty9nIW+uvGcJy5lEl5TxerSDigZOXKzi9Ac0Neob26wHpGqxinSbhPkd8yAJtPXKbJ1frFli3eF8cb8/fzZf8mPFg7yGT7lZYOE3imbRVem7+fBhV8aFzRm582nyUrT0e/ZtY1V3NZyczVcTbpWr/O2OQsDl9IxaecM2E+bjzdpgpfrT1B5QB3Kvq58fnq4wR5udClrvrFrl7ekw41A3nr7wN89GgDdAV6xi85zMMNQ61u2kBTGbf4EIv3XeCHoc1xd3EgMV0d++Ll6oSrkwNerk483rwiHy47inc5JzxdnBi/5BBNK/nQtJJ6gmtfI5Aa5T15de4+xnavw6WMXD5fHc2QiHBcHG3/qsutTFp5jPtrBhLq40Zmno7F+y6w7UwSvz/dUt4zYbfuVueMnruPIG91OmmAb9ef4ovI40zv35gKvm6G84u7s6NV3fvImHK5OjkYxrwV8nJVWzRvXG5Jxn5Wg1uH83vUOSb8e5gn76vM2aRMZqw/yVP3VbZgKW5mbLk61g7ip81nqBfqTZOKPpxNymRq5HE61gkq0qpjSXf77TJp5TESUnOY+kRjAAa3Cuf3ref4ZPlR+jWvSNSpyyw7eJGfn2phoRLcmrHlWrwvjtfm7Wf8w3VpXMnHcL4o/C1SGtZztrFhDzcKJTkzjy8ij3MpPZc6oV789nTLIjPi3EsOnE9lwA/bDM8/XHYUgD5NK/D54414rkNVsvPUZtW0nHxaVPblt2EtizRHTu/fmPcWH2bQD9vQajR0qx/M+73qlXlZysqf22IA6D9zW5HlU/o2NNxoaNxDddFqjvL8n3vUG93VDOCD3tfm3XbQavjpqea8u+gQj327hXLOjvRpGsbozrbbD/hukjJyGT1vP5fSc/F0daR2iCe/P92SdjXUmxjJeybs0d3qnLiUbDTXXVb+c9s58gr0PD9rT5H9vNyxBq9a0Xfd2HLZAmPLFOrjxm9Pt+SDpUfoNn0TwV6uDGtThec6VLNUEW7J2HK9+GB1NBr4fHU08ak5+Ls707FOEK93rWWpItzkbr9dEtNyiUvJNqyv6FeOn59qwQdLj/DLlrMEe7vy6WMNTHITPVMytlyzt8eg0yuMW3yYcYsPG5YXbl8aGkVRbLVlSwghhBBCCGEDrKODoBBCCCGEEMJuSdIhhBBCCCGEMCtJOoQQQgghhBBmJUmHEEIIIYQQwqwk6RBCCCGEEEKYlSQdQgghhBBCCLOSpMNEcnUFfBF5nFxdgaVDsSnyvhlP3rOSkfdN3Cvs9btuj+WyxzKBlMuWlGWZJOkwkTydnulrTpCn01s6FJsi75vx5D0rGXnfxL3CXr/r9lgueywTSLlsSVmWSZIOIYQQQgghhFlJ0iGEEEIIIYQwK0dLB2AvdDodurTLnD9/Hk9XJ0uHYzMy83Toc7O4cCEOd2f5OhaHvGclY6vvm16vJyEhgSZNmuDoaDtxC8vQ6XTs2b2bgpxMm/uu342t/h++E3ssE0i5bElxy2SKukijKIpS0kDFNVu2badtRGtLhyGEsFM7duygRYsWlg5DWLmdO3fSsmVLS4chhLBTpamL7CNNswIVQ0MA9cMICQmxcDTG0el0rFmzho4dO9r8lVR7KYu9lAPspyyWKsfFixdp2bIlQUFBZXZMYbsKvyfX10Xyf9C62Es5wH7KIuW4O1PURbb7zloZrVYdHhMSEkKFChUsHI1x8vPzCQgIICwsDCcn2+4aZi9lsZdygP2UxdLlKDzHCHEnt6qLLP3dNRUph/Wxl7JIOYqvNHWR1GJCCCGEEEIIs5KkQwghhBBCCGFWknQIIYQQQgghzEqSDiGEEEIIIYRZWTTp2LhxIw8//DChoaFoNBoWLVp0222fe+45NBoN06ZNK7I8OTmZQYMG4eXlhY+PD8OHDycjI6PINgcOHKBdu3a4urpSsWJFJk+efNP+58+fT+3atXF1daVBgwYsX77cFEUUQghh5aQuEkII87No0pGZmUmjRo345ptv7rjdP//8w7Zt2wgNDb1p3aBBgzh8+DCRkZEsXbqUjRs3MnLkSMP6tLQ0unTpQnh4OLt372bKlCm8//77zJw507DN1q1bGTBgAMOHD2fv3r307t2b3r17c+jQIdMVVgghhFWSukgIIczPolPmdu/ene7du99xm7i4OF588UVWrVpFz549i6w7evQoK1euZOfOnTRv3hyAr776ih49evDZZ58RGhrKrFmzyMvL4+eff8bZ2Zl69eqxb98+pk6daqgQpk+fTrdu3XjjjTcA+OCDD4iMjOTrr7/mu+++M0PJhRBCWAupi4QQwvys+j4der2eIUOG8MYbb1CvXr2b1kdFReHj42M4yQN06tQJrVbL9u3befTRR4mKiqJ9+/Y4OzsbtunatSuTJk3iypUr+Pr6EhUVxejRo4vsu2vXrndsYs/NzSU3N9fwPD09HVBvzJKfn1/SIltEYby2Fvet2EtZ7KUcYD9lsVQ5dDpdmR5P3MzW6yL5P2hd7KUcYD9lkXLcnSnqIqtOOiZNmoSjoyMvvfTSLdfHx8dTvnz5IsscHR3x8/MjPj7esE2VKlWKbFN4N8X4+Hh8fX2Jj4+/6Q6LQUFBhn3cyieffMKECRNuWr5mzRoCAgLuXjgrFBkZaekQTMZeymIv5QArLIuigEZj9MvKuhyXL18u0+OJm9lLXWR1/wdLSMphfeylLFKO2zNFXWS1Scfu3buZPn06e/bsQVOCHwbmNnbs2CJXpOLi4qhbty4dO3YkLCzMgpEZLz8/n8jISDp37mzTd+IE+ymLvZQDSl+WGetPczwxg/91qELNIE/TBKUoOMwfjBLeFn2LkaB1uOtLLPWZxMXFldmxxM3soS663XdXURRW/zuHHw7p+X5wU/wr1Snz+I1hL+dFeykH2E9ZpBx3Z4q6yGqTjk2bNpGYmEilSpUMywoKCnjttdeYNm0aZ8+eJTg4mMTExCKv0+l0JCcnExwcDEBwcDAJCQlFtil8frdtCtffiouLCy4uLobnaWlpgHp1y1a/sE5OTjYb+43spSz2Ug4oeVk2nLjMnpgUHm4USr0KfqYJJnoFnFgFJ1bhcOQf6PUlBDco1kvL+jNxdLTa0/Q9wZ7qohu/u4qiMPOgngM5gfy07jDvPNPwzm+GlbCX86K9lAPspyxSjtszRV1ktffpGDJkCAcOHGDfvn2Gv9DQUN544w1WrVoFQEREBCkpKezevdvwurVr16LX62nVqpVhm40bNxbp3xYZGUmtWrXw9fU1bLNmzZoix4+MjCQiIsLcxRRC3IW/h/qD6nJGnul2WqMrPPwluHjDhT0w8374bwLkZ5vuGMIu2HNdpNFoeLWhGs/vp8qRmJZjluMIIQRYOOnIyMgwnMQBzpw5w759+4iJicHf35/69esX+XNyciI4OJhatWoBUKdOHbp168aIESPYsWMHW7ZsYdSoUfTv398wpeHAgQNxdnZm+PDhHD58mLlz5zJ9+vQizdEvv/wyK1eu5PPPP+fYsWO8//777Nq1i1GjRpX5eyKEKCrAkHTk3mVLI2i10OxJGLUD6vQCvQ42T4Vv28CZTaY7jrAJ93JddH+r5jTRnCBXcWTGuhNmO44QQlg06di1axdNmjShSZMmAIwePZomTZrw3nvvFXsfs2bNonbt2nTs2JEePXrQtm3bIvOee3t7s3r1as6cOUOzZs147bXXeO+994rMn37fffcxe/ZsZs6cSaNGjViwYAGLFi2ifv36piusEKJEAj3U2X5MmnQU8gyGJ/6AJ2aBZwgkn4LfHoIlL0L2FdMfT1ile7ku0oQ04jX3FQDM3h7DxVRp7RNCmIdFOwvff//9KIpS7O3Pnj170zI/Pz9mz559x9c1bNiQTZvufPWyX79+9OvXr9ixCCHKRoDn1ZaOdBN2r7pRnYegSju1i9Wun2DP7xC9EnpMgbqPlGiWK2E77um6SKulTY1gWu47yg59Hb5ee5KPHi3e+CYhhDCG1Y7pEEIIMFP3qltx9YaHpsKwlRBQEzITYf6TMG8oZCaZ99hCWJCm2v2MdpoPwLxdscQmZ1k4IiGEPZKkQwhh1cpfbelITDdz0lEoPAKe2wwdxoDWEY4ugRmt0Zy0j/nbhbhJ1ftprT1GW+0h8gsUpkYet3REQgg7JEmHEMKqBXm5AhCflmNUF5hScXSBB96GEWshsDZkJuI4dwANY36BvIyyiUGIsuJXBXzCedPxLwAW7Yvj6MU0CwclhLA3knQIIaxaYdKRp9NzJSv/LlubWEgjGLkBWr8AQJWkdTj++ADE7ijbOIQwt6r301B7hp6Bl1AUmLIq2tIRCSHsjCQdQgir5uyoJeDqDFYWmVnHyRW6fYxu0N9kOfmhuXIGfu4Kaz4AnRkHtwtRlqo9AMBrDvNw0GpYeyyRHWeSLRyUEMKeSNIhhLB6ha0dCRa8eZlSuT3ran+EvsHjoOhh02fwUye4JFeEhR2o3B7QUDVlC0808gfg0xVHy65LoxDC7knSIYSwesFXk46LqZa9Y7LO0Z2CXjOg32/g5gsX98OPneDkmru/WAhr5u4PIQ0BeLnSGVydtOyJSWHV4XgLByaEsBeSdAghrF5gWdyrwxj1esP/tkGl+yA3DWb1g10/WzoqIUqnemcAgs6vZES7qgB8uuIYeTq9JaMSQtgJSTqEEFav8F4dSZllNG1ucXgGw9BF0LA/KAWw9FVY9Q7oCywdmRAlU7Ob+u/JNTzbthIBHi6cTcpi1vZzlo1LCGEXJOkQQli9woHkZr9BoLEcXeDR7+CBd9XnUV/D3CGQl2nZuIQoibCmUM4fctPwSNjF6M41AZi+5gSpZT1znBDC7kjSIYSwegHW1r3qehoNdHgD+vwEDi4QvQx+6QFpFy0dmRDG0TpAjS7q4+Mrebx5BWqU9yAlK59v1p+0bGxCCJsnSYcQwuoVdq+yupaO6zXoC0/+q14pvrgPfuwIV6RbirAxhqRjFY4OWt7uWQeAX7ecJSYpy4KBCSFsnSQdQgir51tO7V6Vkm3lXTwqtYJn1kBATUiLg/lPyb08hG2p3hG0TpB0AhKOcH/NQNpWDyCvQM8nK45aOjohhA2TpEMIYfXcnBwAyMm3gUHaflVg8EJw9YELeyBynKUjEqL4XL2hhjqLFYcWoNFoGPdQXbQaWHEonqhTSZaNTwhhsyTpEEJYPVcn9VSVk19gGzcr86kEj36vPt7+HRxZbNl4hDBG/T7qvwcXgKJQK9iTQa3CAZi49AgFehv4PyiEsDqSdAghrJ6rs9rSoVcgv8BGfvDU6gZtXlYfLx4FyactG48QxVWrOzi5Q8o5OL8LgFc718TL1ZGjF9OYuzPWwgEKIWyRJB1CCKvn6uhgeJxtC12sCj04Diq2Vm8gOP8pyLfsHdWFKBZnd6jdQ318aAEAfu7OvHp1Ct3PVkeTau3jq4QQVkeSDiGE1XNy0KDVqI9zbSnpcHCCvj9fndFqP2z7xtIRCVE8Dfqp/x5aaJgMYXDrcKqX9yA5M48v15ywYHBCCFskSYcQwiYUdiPXFmYftsI7DDq9rz4+uNCioQhRbNUeBI9gyLwER5cA4OSgZdxDdQH4betZTiSkWzJCIYSNkaRDCGH18gr0hscujjZ42qrzMGgdIfEwXJabrAkb4OAEzYepj3fMNCzuUDOQznWD0OkV3v/3sG1M7CCEsAo2WHsLIe41ubrrkw6HO2xppdx8oUoH9fFRmclK2Ihmw9R7dsRuhwv7DIvH9ayLs6OWLSeTWHko3nLxCSFsiiQdQgirl5t/LelwcrCx7lWF6vZS/5Xpc4Wt8AyCuo+oj3f8YFhcyb8cz7WvCsCHy46SnWdD46yEEBYjSYcQwurl6tQfNS6OWjQaG006aj8EGq06oPzKWUtHI0TxtByp/ntwPmReuzHg8/dXJ8zHjbiUbL7dcMpCwQkhbIkkHUIIq1d4JdXVyQa7VhVyD4AKLdTHsTstG4sQxVWxJYQ0goJc2DbDsNjN2YF3etYB4LsNpziXlGmpCIUQNkKSDiGE1Su8J4C3m5OFIyklN1/1X122ZeMQorg0Gmj/hvp4+3dFWju61w+mbfUA8nR63l8ig8qFEHcmSYcQwuoVJh0+5Ww86XBwVv/V5Vo2DiGMUfshCG4IeRmwdbphsUaj4f1e9XBy0LAu+hL/HU20YJBCCGsnSYcQwuqlZNlJS4eji/qvJB3Clmg08MA76uMdP0DGteSienkPnmmnDip/f8lhGVQuhLgtSTqEEFYvxdDS4WzhSErJ4WrSUSBJh7AxNbtCWDPIz4LNXxRZ9eKD1Qn1dlUHla+X+9AIIW5Nkg4hhNVLzcoDwNvN0cKRlJJjYfeqPMvGIYSxNBp44G318c6fICXGsKqcs6PhTuXfbTjNmcsyqFwIcTOLJh0bN27k4YcfJjQ0FI1Gw6JFiwzr8vPzGTNmDA0aNMDd3Z3Q0FCGDh3KhQsXiuwjOTmZQYMG4eXlhY+PD8OHDycjI6PINgcOHKBdu3a4urpSsWJFJk+efFMs8+fPp3bt2ri6utKgQQOWL19uljILIYyXebXLhruLjScdmZfUf129LRuHKELqomKq1hHC26otdSveKrKqW/1g2tUIIK9Az3gZVC6EuAWLJh2ZmZk0atSIb7755qZ1WVlZ7Nmzh3HjxrFnzx7+/vtvoqOj6dWrV5HtBg0axOHDh4mMjGTp0qVs3LiRkSNHGtanpaXRpUsXwsPD2b17N1OmTOH9999n5syZhm22bt3KgAEDGD58OHv37qV379707t2bQ4cOma/wQohiy85Xk45yTjaedFyKVv8NrGXZOEQRUhcVk0YDPT8DrSNEL4PoFdet0jDxkfo4O2jZePwSK+RO5UKIGylWAlD++eefO26zY8cOBVDOnTunKIqiHDlyRAGUnTt3GrZZsWKFotFolLi4OEVRFGXGjBmKr6+vkpuba9hmzJgxSq1atQzPH3/8caVnz55FjtWqVSvl2WefLXb8sbGxCqDExsYW+zXWIi8vT1m0aJGSl5dn6VBKzV7KYi/lUBTTlOXVOXuV8DFLle83nDRhZMYpdTnycxXlfV9FGe+lKCnni/0yWz632CJ7rItMfj5ZPU79Hk+tryi5GUVWfb46Wgkfs1Rp9dF/SnpOvmmOd5W9nBftpRyKYj9lkXLcnSnqIpu6bJiamopGo8HHxweAqKgofHx8aN68uWGbTp06odVq2b59O48++ihRUVG0b98eZ+drA1C7du3KpEmTuHLlCr6+vkRFRTF69Ogix+ratWuRJvYb5ebmkpt7bTBoeno6ADqdjvz8fBOUtuwUxmtrcd+KvZTFXsoBpilLZq76Wmet5d6TUpfjUjROSgGKswc6t0Ao5n50Ol3JjifMxtbqIpOfT+4bjePBhWhSYyhYPwn9A+MMq0a0qcTfe85z/ko2X6w+xlvdTNeqZy/nRXspB9hPWaQcd2eKushmko6cnBzGjBnDgAED8PLyAiA+Pp7y5csX2c7R0RE/Pz/i4+MN21SpUqXINkFBQYZ1vr6+xMfHG5Zdv03hPm7lk08+YcKECTctX7NmDQEBAcYX0ApERkZaOgSTsZey2Es5oHRlibmgBbQcP3qY5UmW7WpS0nKEXtlBC+CKYxCbVqy46/aFLl++XKLjCfOw5brIlOeTYP8+tEqbjmbr12xKDibdLcywrkeQhplXHPhly1kCMk4RWs5khwXs57xoL+UA+ymLlOP2TFEX2UTSkZ+fz+OPP46iKHz77beWDgeAsWPHFrkiFRcXR926denYsSNhYWF3eKX1yc/PJzIyks6dO+PkZNv3QbCXsthLOcA0ZfkrfiekXKF508b0aBhi4giLp7Tl0K7dBWfBp3orevToUezXxcXFGX0sYR62WheZ53zSA/28Y2hPrOL+1HkU9F5muPllD+DM7H1EHk1kTUoAsx5rgVarKfUR7eW8aC/lAPspi5Tj7kxRF1l90lF4kj937hxr1641XFkCCA4OJjGx6B1QdTodycnJBAcHG7ZJSEgosk3h87ttU7j+VlxcXHBxcTE8T0tLA9SrW7b6hXVycrLZ2G9kL2Wxl3JA6cri5qyeqnSKxuLvR4nLcWIlANoaHdEa8XpHR6s/Td8T7KEuMvn55KEv4Nv70F7ci3bTJOg80bDq/Ufqs/nkBnadS2HxwQQeb17RZIe1l/OivZQD7KcsUo7bM0VdZNX36Sg8yZ84cYL//vsPf3//IusjIiJISUlh9+7dhmVr165Fr9fTqlUrwzYbN24s0r8tMjKSWrVq4evra9hmzZo1RfYdGRlJRESEuYomhDBCuatJR1aujY5vuHQcLh8HrRPU6GzpaISRpC66De8weORr9fGW6XBqrWFVmI8br3auAcAny4+SnCn3phHiXmfRpCMjI4N9+/axb98+AM6cOcO+ffuIiYkhPz+fvn37smvXLmbNmkVBQQHx8fHEx8eTl6eevOrUqUO3bt0YMWIEO3bsYMuWLYwaNYr+/fsTGhoKwMCBA3F2dmb48OEcPnyYuXPnMn369CLN0S+//DIrV67k888/59ixY7z//vvs2rWLUaNGlfl7IoS4WTlnB+Da/TpsTvQy9d8q7eUeHVZI6qJSqPMwNH9affzPc5B5rd/3sDZVqB3syZWsfD5dcdRCAQohrIVRbSV6vZ4NGzawadMmzp07R1ZWFoGBgTRp0oROnTpRsaJxzae7du3igQceMDwvPPk++eSTvP/++yxZsgSAxo0bF3ndunXruP/++wGYNWsWo0aNomPHjmi1Wvr06cOXX35p2Nbb25vVq1fzwgsv0KxZMwICAnjvvfeKzJ9+3333MXv2bN59913efvttatSowaJFi6hfv75R5RFCmEfhTQGzbTXpOLpU/bd2T8vGYQdMXQ+B1EWl1uUjOLcVLh2DRc/DwHmg0eDkoOXD3vXp+10U83adp1/zirSo7GfpaIUQFlKspCM7O5vPP/+cb7/9luTkZBo3bkxoaChubm6cPHmSRYsWMWLECLp06cJ7771H69ati3Xw+++//453Lb3TukJ+fn7Mnj37jts0bNiQTZs23XGbfv360a9fv7seTwhR9q61dNhg96q0CxC3S30sSUeJmaseAqmLSs25HPT9GWY+ACdWQ9TXcN+LADSv7Ef/FhWZszOWd/45yLKX2uHkYNU9u4UQZlKspKNmzZpERETwww8/3HZE/Llz55g9ezb9+/fnnXfeYcSIESYPVghxb/Ipp55zkjJssF/43j/Vfyu2Bs/bDwgWdyb1kJULqgddP4Llr0PkexBYB2p0AmBMt9qsPpLA8YQMfth0mv/dX93CwQohLKFYlxtWr17NvHnz6NGjx21Hw4eHhzN27FhOnDjBgw8+aNIghRD3toq+6kT/sVeyLByJkQryYdfP6uMWz1g2Fhsn9ZANaPEMNB4Mih4WDINL0QD4ujvzbs86AEz/7wQxSTb2/1gIYRLFSjrq1KlT7B06OTlRrVq1EgckhBA3quh3NelItrEfK0f/hfSL4F4e6j5i6WhsmtRDNkCjgYemQqX7IDcNZj8BWckAPNokjDbV/cnV6Xln0cFidVkTQtiXEk26m5OTw4EDB0hMTESv1xdZ16tXL5MEJoQQhQqTjssZeWTl6QxT6Fq9HT+o/zYfBo7Olo3Fzkg9ZKUcXeCJP+CHB+DKGZg3FAb/jcbRmQ97N6DrtI1sOnGZJfsv8Ehj27qRrhCidIyuuVeuXMnQoUNveTt0jUZDQYGNzi4jhLBa3m5OeLs5kZqdT2xyNrWCPS0d0t3FH4SYraB1hGbDLB2NXZF6yMq5B8CAufBTZzi7CVa8AQ9No0qAOy8+UJ3PI4/zwdIjdKgZiE85ScaFuFcYPYXEiy++SL9+/bh48SJ6vb7In5zohRDmUtHPDYAYW+liteXqdKl1eoFXiGVjsTNSD9mAoLrQ5ydAA7t/hU2fAfBsh2rUKO/B5Yw8Pll+zKIhCiHKltFJR0JCAqNHjyYoKMgc8QghxC0VDiY/bwuDyS/sg4Pz1MdtXrZoKPZI6iEbUasbdPtEfbz2Q9jxA86OWj55rAEAc3fFsvXkza1VQgj7ZHTS0bdvX9avX2+GUIQQ4vYq+KotHeevZFs4krtQFFj9rvq4weMQ2tii4dgjqYdsSOvnocMY9fHy12H/XJpX9mNI63AAxv5z0HZv+imEMIrRYzq+/vpr+vXrx6ZNm2jQoMFNUxe+9NJLJgtOCCEKVbCVlo4Tq9V+7A4u0HGcpaOxS1IP2Zj7x0J2Cuz4Xr1juYsnb3brzH9HEziXlMW0NccZ2734s5MJIWyT0UnHX3/9xerVq3F1dWX9+vVoNBrDOo1GIyd7IYRZ2ERLR4FOvTEaQOvnwKeSZeOxU1IP2RiNBrp9qk6ju/8vmP8UnoMX8GHv+gz/bRc/bjrDww1DqR/mbelIhRBmZHT3qnfeeYcJEyaQmprK2bNnOXPmjOHv9OnT5ohRCCEM0+ZaddKx5ze4dAzc/KDtaEtHY7ekHrJBWi30+hpqPwQFufDXADp6xPBQwxAK9ApvLjhAfoH+7vsRQtgso5OOvLw8nnjiCbRao18qhBAlFuTlCkBqdj45+VbYBzzhyLWxHB3eBDcfi4Zjz6QeslEOjuqMVlU6QF4G/PEo45vm4FPOiSMX0/hx0xlLRyiEMCOjz9hPPvkkc+fONUcsQghxW65O105XuToruyKakwpzB0N+FlS9H1qOtHREdk3qIRvm5AoD/oLK7SAvncCFfXm3ldrTe9p/xzlzOdPCAQohzMXoMR0FBQVMnjyZVatW0bBhw5sG8E2dOtVkwQkhRCFnh2tJR541JR16PfzzPCSfAu+K0Odn0DpYOiq7JvWQjXN2h4HzYM5AOL2OPjsGsjjsFzbF6Xlr4QH+GtEarVZz9/0IIWyK0UnHwYMHadKkCQCHDh0qsu76wXxCCGFKGo0GZwcteQV68qyp7/fmqRC9TJ2t6vHfwd3f0hHZPamH7IBzORgwB+YORnMyko+vvE4Xx8lsP5PMnJ2xDGwlkzAIYW+MTjrWrVtnjjiEEOKunB2vJh3W0tJxco160zOAnp9BWFPLxnOPkHrITji5Qv9ZMO9JKh5fwWva2XzIQD5ZfpQHa5cn2NvV0hEKIUxIRuEJIWyGs6N6yrKKpCP+ICx4GlCg6VD1TwhhHMerLYR1HmaYZhmNtKdIz9Xx3uJDd3+tEMKmFCvpeO655zh//nyxdjh37lxmzZpVqqCEEOJWCu9cfP2gcouIPwS/9YKcFKjQErpPsWw89wCph+yYozP0/QWHBo8xyXEmjuhYfSSByCMJlo5MCGFCxepeFRgYSL169WjTpg0PP/wwzZs3JzQ0FFdXV65cucKRI0fYvHkzc+bMITQ0lJkzZ5o7biHEPSY7r4Dsq1Pl+rk7Wy6QxKMwqzdkJ0NoUxi8QO0mIsxK6iE75+AEj/1AbedXeWb7cr4r6MX4+du4762euLsY3RNcCGGFinW58IMPPuD48eO0adOGGTNm0Lp1aypVqkT58uWpVasWQ4cO5fTp08ycOZNt27bRsGFDc8cthLjHJGXmAuosVh4W+hHimX0ex1mPQlYShDaBIf+Aq9xFuSxIPXQP0DrAw9N5uV0IFTSJXMh2ZNrPv4OiWDoyIYQJFLvmDgoK4p133uGdd97hypUrxMTEkJ2dTUBAANWqVZMZQ4QQZpWcmQeorRwWOd9ciua+k5+i0aVBSCM14ZAbAJYpqYfuARoNbt3e54PM7xm2E34+F0DveROo1+89S0cmhCilEl0u9PX1xdfX19SxCCHEbSVdTTr8PSzQterScRxnPYqTLg0lqAGaIYvATc6BliT1kH17oM+z9LywgGVxbry9z5e/HZ6Fh6ZbOiwhRCnI7FVCCJtwOV3tXlXm4zkSjsCvPdBkJpLqVgndwIVQzq9sYxDiHvTekw/h6aSwX6nOnH2XcZg/BIeCXEuHJYQoIUk6hBA24fyVbAAq+LqV3UHjD8JvD0HmJZTy9dlafYwkHEKUkSAvV17rVg+AyboBpJzczn0nP4WsZAtHJoQoCUk6hBA2IfZKFgAVfMuVzQEv7IVfH1IHjYc0Rjf4H/IcPcvm2EIIAAa3Dqd2sCepuDNZGYJf1ikcf+8JKbGWDk0IYSRJOoQQNuF8strSUdGvDJKO87vgt0fU+3CENYehi2UMhxAW4Oig5YPe9QGYm9eWbdomaJJOwE9d1K6PQgibIUmHEMImXGvpMHP3qpht8HtvyE2Fiq1lliohLKxFZT8eaxqGArzJK+j8a0H6BfilG5yLsnR4QohiKtbsVU2aNCn2VIR79uwpVUBCCHGjPJ2e+LQcACqas3tVzDb44zHIz4TwtjBwLrh4mO94otikHrq3je1eh9WHE4jJglntf+bJk6/C+R3wR2/o8yPUedjSIQoh7qJYSUfv3r3NHIYQQtxefGoOigIujloCzDVlbtxu+LOvmnBU6QAD5oBzGY0fEXcl9dC9LdDThVc7VeeDZcf4fH0cPV6aT+DKZ+H4Spg7BLp8ABGjQO7VIoTVKlbSMX78eLMcfOPGjUyZMoXdu3dz8eJF/vnnnyIVi6IojB8/nh9++IGUlBTatGnDt99+S40aNQzbJCcn8+KLL/Lvv/+i1Wrp06cP06dPx8Pj2tXJAwcO8MILL7Bz504CAwN58cUXefPNN4vEMn/+fMaNG8fZs2epUaMGkyZNokePHmYptxDCOBdS1fEcId6u5rkBXPxBtYUjL11t4ZCEw+qYqx4CqYtsxcAWFfhl/VHOZ+r45L+zTH1iFqx4E3b9BKvfheQz0H0yOJToFmRCCDMr0ZiOlJQUfvzxR8aOHUtysjp13Z49e4iLizNqP5mZmTRq1IhvvvnmlusnT57Ml19+yXfffcf27dtxd3ena9eu5OTkGLYZNGgQhw8fJjIykqVLl7Jx40ZGjhxpWJ+WlkaXLl0IDw9n9+7dTJkyhffff5+ZM2cattm6dSsDBgxg+PDh7N27l969e9O7d28OHTpkVHmEEOZx0ZB0mGE8x6VodQxHTgpUaAEDJeGwBaaqh0DqIlvh6KDl8SoFaDTw9544tp1LhZ6fQ5ePAI2afPzVH3LTLR2qEOJWFCPt379fCQwMVKpXr644Ojoqp06dUhRFUd555x1lyJAhxu7OAFD++ecfw3O9Xq8EBwcrU6ZMMSxLSUlRXFxclL/++ktRFEU5cuSIAig7d+40bLNixQpFo9EocXFxiqIoyowZMxRfX18lNzfXsM2YMWOUWrVqGZ4//vjjSs+ePYvE06pVK+XZZ58tdvyxsbEKoMTGxhb7NdYiLy9PWbRokZKXl2fpUErNXspiL+VQFNOU5eu1J5TwMUuVV+fuNV1giqIol08qypSaijLeS1G+basoWVduu6mlPhNbPreYi7nqIUWxz7rIXs4nheV4a8E+JXzMUqXj5+uV3PwCdeXhxYryQZD6f3lGG0VJOW/ZYO/AXj4PRbGfskg57s4UdZHRbZCjR4/mqaeeYvLkyXh6XpuzvkePHgwcONAkiRDAmTNniI+Pp1OnToZl3t7etGrViqioKPr3709UVBQ+Pj40b97csE2nTp3QarVs376dRx99lKioKNq3b4+z87V+4F27dmXSpElcuXIFX19foqKiGD16dJHjd+3alUWLFt02vtzcXHJzr90ZNT1dvbKi0+nIz88vbfHLVGG8thb3rdhLWeylHGCassRdyQQgyNPZdO9J6nkcf++FJiMeJbA2ugHzwdEdbrN/S30mOp2uTI9nC8qqHgL7qIvs5XxSGP9L91dm9ZFETiZmMHPDSZ5tXwVqdEczeDEO8wehSTiI8sOD6J6YDcENLRz1zezl8wD7KYuU4+5MURcZnXTs3LmT77///qblYWFhxMfHlzqgQoX7CgoKKrI8KCjIsC4+Pp7y5csXWe/o6Iifn1+RbapUqXLTPgrX+fr6Eh8ff8fj3Monn3zChAkTblq+Zs0aAgICilNEqxMZGWnpEEzGXspiL+WA0pXlwAktoCUp9iTLl58odSyu+Vdoc/wjnPISyXAJYnPQ/8hdv71Yry3rz+Ty5ctlejxbUFb1ENhXXWQv55Mdm9fTPUTDnycdmL7mOC6XjlL+as9Lt/C3aH16Kl4ZcWh+7sa+8GeI821t2YBvw14+D7Cfskg5bs8UdZHRSYeLiwtpaWk3LT9+/DiBgYGlDshWjB07tsgVqbi4OOrWrUvHjh0JCwuzYGTGy8/PJzIyks6dO+Pk5GTpcErFXspiL+UA05Tlx5htcCWNByOa0bF2+bu/4E4yL+P4Zy80eYko3pVwGfovHb3u/n/WUp9JScYo2Duph64pTl1kL+eT68vR3dGRM7/tYcupJFYkBzB7eAsctFcnmcjpjf6fZ3A8vY7mZ2fQJFiD/oFxoLWOAeb28nmA/ZRFynF3pqiLjP4f2KtXLyZOnMi8efMA0Gg0xMTEMGbMGPr06VPqgAoFBwcDkJCQQEhIiGF5QkICjRs3NmyTmJhY5HU6nY7k5GTD64ODg0lISCiyTeHzu21TuP5WXFxccHFxMTwvrAAdHR1t9gvr5ORks7HfyF7KYi/lgNKV5XJGHgAhPu6lez+yr8Bf/eDycfAMRfPUvzj5VjZqF2X9mTg6WscPJWtSVvUQ2FddZC/nk8JyTO7XiK5fbGRPTAp/bD/PiPZVr24QAIMXwpqJsGUaDtu+wSHxMPT9Bcr5WTb469jL5wH2UxYpx+2Zoi4yevaqzz//nIyMDMqXL092djYdOnSgevXqeHp68tFHH5U6oEJVqlQhODiYNWvWGJalpaWxfft2IiIiAIiIiCAlJYXdu3cbtlm7di16vZ5WrVoZttm4cWOR/m2RkZHUqlULX19fwzbXH6dwm8LjCCEsR69XuJyh9lkP9HS5y9Z3kJMGf/aBhIPgXh6e/BeMTDiEdSiregikLrJmYT5ujHuoDgBTVkdzMjHj2kqtA3SeoCYaTuXg9HqY2QEuHrBMsEII41s6vL29iYyMZPPmzRw4cICMjAyaNm1aZJBdcWVkZHDy5EnD8zNnzrBv3z78/PyoVKkSr7zyCh9++CE1atSgSpUqjBs3jtDQUMP86XXq1KFbt26MGDGC7777jvz8fEaNGkX//v0JDQ0FYODAgUyYMIHhw4czZswYDh06xPTp0/niiy8Mx3355Zfp0KEDn3/+OT179mTOnDns2rWryFSGQgjLSM3OJ79AASDAo4RJR14mzH5CvQGgmx8MXQwB1U0YpShLpqyHQOoiW/Z484osPxjPhuOXeG3+fhY+F4Gjw3XXU+s/BoG1YM4guHIGfuoCj3wNDfpaLmgh7lXGTncVExNT4qmybrRu3ToFuOnvySefVBRFnapw3LhxSlBQkOLi4qJ07NhRiY6OLrKPpKQkZcCAAYqHh4fi5eWlDBs2TElPTy+yzf79+5W2bdsqLi4uSlhYmPLpp5/eFMu8efOUmjVrKs7Ozkq9evWUZcuWGVUWW57W0l6milMU+ymLvZRDUUpfluj4NCV8zFKl8YRVJQsgN1NRfuulTqX5cUVFidtbot3IlLnWw5T1kKLYf11kL+eT25XjQkqWUn/8SiV8zFLl67Unbv3irGRF+f1R9Tww3ktRVrylKPk5ZRD1zezl81AU+ymLlOPuLDJlbuXKlWnbti2DBw+mb9++hmbhkrj//vtRFOW26zUaDRMnTmTixIm33cbPz4/Zs2ff8TgNGzZk06ZNd9ymX79+9OvX784BCyHK3KV0tWtViVo5cjPUm4Wd3QRO7jB4AYQ2Nm2AosyZsh4CqYtsXYi3G+89VJc3FhxgauRxmof70qqqf9GN3Hxh0HxY+yFsngrbZsC5LdDnZ2n1FKKMGD2mY9euXbRs2ZKJEycSEhJC7969WbBgQZF5woUQwlSSMtVB5H7uznfZ8gY5qfDnY2rC4eypDiyt2NIMEYqyJvWQuFHfZhXo3TiUAr3CqL/2kpiWc/NGWgfoNB4GzFG7WV7cD9+3h72z4A5JpxDCNIxOOpo0acKUKVOIiYlhxYoVBAYGMnLkSIKCgnj66afNEaMQ4h6WfHUQub+HEUlHVjL8/gjEbgdXb3UMR7gMxrUXUg+JG2k0Gj5+rAE1gzy4lJ7LqNl7yS/Q33rjWt3h+S1QuR3kZ8Li/8HC4eqFCiGE2RiddBTSaDQ88MAD/PDDD/z3339UqVKF3377zZSxCSEEyca2dGReht96wYW96tXMJ/+FCs3MGKGwFKmHxPXKOTvy7eBmeLg4suNsMlNWRd9+Y69Q9WLEg+NA4wCHFsJ3bSF2Z9kFLMQ9psRJx/nz55k8eTKNGzemZcuWeHh48M0335gyNiGEuK57VTHGdKTHwy89rk2LO2w5hDQyc4TCUqQeEjeqFujBlL4NAZi58TQrD128/cZaB2j/Ojy9CnwqQUoM/NwVNn4G+oIyiliIe4fRScf3339Phw4dqFy5Mr///jtPPPEEp06dYtOmTTz33HPmiFEIcQ8rbOnwv1tLR/Jp+LkbXI4Gz1AYtgLK1ymDCEVZk3pI3En3BiGMaFcFgNfnH+DoxZvvXl9ExRbw3Gao3weUAlj7AfzaE5JOlUG0Qtw7jE46PvzwQ1q1asXu3bs5dOgQY8eOJTw83ByxCSFE8QaSX9irzr9/5Qz4hKstHDIjjd2SekjczZvdatO6qh8ZuTqG/bKTi6nZd36Bqzf0+QkemQHOHhATpXa32j4T9LcZGyKEMIrRU+bGxMSg0WjMEYsQQtzE0NJxu4HkJ/+DuUPVAaHBDWHQAvAMKsMIRVmTekjcjZODlu8HN6fvd1s5kZjBUz/vZP7zEXi5Ot3+RRoNNBkEldvC4hfUme9WvAFHl8Aj34CvJLZClIbRLR0ajYZNmzYxePBgIiIiiIuLA+CPP/5g8+bNJg9QCHFvSyqcvepWYzr2z1HvNJ6fCVU6wFPLJOG4B0g9JIrDu5wTvwxrQaCnC9EJ6Tz/527ydMVotfANh6FLoMdn4FROTT6+vQ92/SxT6wpRCkYnHQsXLqRr1664ubmxd+9ew7zoqampfPzxxyYPUAhx7yrQK6Rk5wM3dK9SFNgyHf55FvQ6aNBPbeFw9bJQpKIsST0kiquCbzl+eaoF7s4ObDmZxFsLD9zxRpAGWi20HKFOrVvpPsjLgKWvwh+PQup58wcuhB0q0ZiO7777jh9++AEnp2vNlG3atGHPnj0mDU4IcW+7kpWHoqi9HnzLXT3f6PWw6m2IfE99HjEKHp0JjkbePFDYLKmHhDHqh3kzY3AzHLQa/t4bx+erjxf/xX5V1RbUrp+AoyucXgczImDvn9LqIYSRjE46oqOjad++/U3Lvb29SUlJMUVMQggBQFKGOp7Dx80JRwctFOhgySjYNkPdoMuH0PUj9aqkuGdIPSSM1aFmIJ882gCAr9ed5K8dMcV/sVYLEf+D57ZAhZaQm6aO+ZgzCDIumSliIeyP0TV1cHAwJ0+evGn55s2bqVq1qkmCEkIIwDDjTLC3G+jy1LsG75ul3szr0e/hvhctHKGwBKmHREk83qIiL3WsAcC7iw6xLjrRuB0EVIenV0KnCaB1guhlMKM1HFtmhmiFsD9GJx0jRozg5ZdfZvv27Wg0Gi5cuMCsWbN4/fXXef75580RoxDiHhWfmgNAiKcTzB0ERxaplf3jv0Gj/pYNTliM1EOipF7tVIPHmoZRoFd4YdYeDsWlGrcDrQO0fQVGroPy9SDrMswZqLZ85KabJWYh7IXRU+a+9dZb6PV6OnbsSFZWFu3bt8fFxYXXX3+dF1+Uq45CCNOJT1OTjqDEjZCzGhzdoP+fUL2ThSMTliT1kCgpjUbDp481JCEthy0nkxj2607++d99VPAtZ9yOghuoicfaD2HrV+oYjzOb4NHvIPw+8wQvhI0r0ZS577zzDsnJyRw6dIht27Zx6dIlxo8fz4ULF8wRoxDiHhWfpF6FDMk4As6eMORvSTiE1EOiVJwdtXw7uBm1gjy5lJ7LU7/sJDUr3/gdObpAlw/UgeY+lSDlHPzSQ53kQpdr+sCFsHElHn3p7OxM3bp1admyJR4eHhw+fJiKFSuaMjYhxL0sL4uLx7YBEOycA08ukSuIogiph0RJebmq9/AI8nLhZGIGL8zeg15fwtmoKrdRB5k3Hgxcnc77x06QEmvSmIWwdTLlixDC+igKLH2F2BxXAMK6vwFhTS0clBDCnoT6uPHLUy1xc3Jg88nL/LT5TMl35uoFvb+B/rOhnD/EH4AfHoTYnaYLWAgbJ0mHEML67PyR/P0LiFHKA1C1Zl0LBySEsEd1Q71472H1/DJlVTRHL6aVboe1e8LI9eog88xE+LUnHJhf+kCFsAOSdAghrEvsDlg5llglEB2OuDk5EOzlaumohBB2qn+LinSqE0RegZ5X5uwjJ7+gdDv0qQTDV0GtHlCQC38/ow441+tNE7AQNqrYs1cdOHDgjuujo6NLHYwQ4h6XkQjzhoI+n9MVHoXTUCXAHY1GY+nIhBWQekiYg0aj4dM+Deg27QrRCelMWRXNuIdK2brq4glP/AlrJqhjPDZOgUvR6uxWGmfTBC6EjSl20tG4cWM0Gg2KcvNAq8Ll8sNACFFiBTpY8DSkX4SAmpyp/iScPk3VQHdLRyashNRDwlwCPFyY3LchT/+6i582n+HB2uVpUz2gdDvVOkDniRBQC/59GY4ugStnod8sk8QshK0pdtJx5kwpBlgJIcTdHP4Hzm4CZw944k9ObVSnnKwaIEmHUEk9JMzpwdpBDGpViVnbYxi/5DCRr7Y3TRLbZBD4VVVvcBp/AMe5A9CGvFL6/QphY4qddISHh5szDiHEvS56mfpvq2chsBaHL2wGoHaIlwWDEtZE6iFhbm91r83CPec5mZjB3tgUmlbyNc2OwyPgmTXwYyc0iYdoWPAH0Ns0+xbCRshAciGE5RXkw8k16uNaPcgv0BMdnw5A/VBvCwYmhLiXeLo60aN+CADzd5037c79qkDfn1DQEJ60Ac3+2abdvxBWTpIOIYTlxURBbhqUC4DQppxMzCCvQI+nqyMV/dwsHZ0Q4h7St1kFAJbuv1D6maxuVPV+9B3eAsBh5ZsQf9C0+xfCiknSIYSwvOOr1H9rdgWtlkNxqQDUDfGSgcFCiDLVuqo/FXzdSM/VsepwvMn3r2/zKgleDdHoctTZ+nJSTX4MIayRJB1CCMs7vlL9t2ZXAA5fUG/QVT9MulYJIcqWVquhT1O1tcPkXawANFp2hz+L4hUGyadhyYumP4YQVkiSDiGEZWVfgaST6uOq9wNwLF5NOurIIHIhhAUUdrHaeuoyVzLzTL7/fEdPCh77BbSOcGQxHF1q8mMIYW2KPXvV9RYsWMC8efOIiYkhL6/of8Y9e/aYJDAhxD0i+eo0qB5B4Kq2bJxMzACgVpCnpaISVk7qIWFOFf3KUSfEi6MX01gXnchjV1s+TEkJawptXoZNn8PyN6BqB/WmgkLYKaNbOr788kuGDRtGUFAQe/fupWXLlvj7+3P69Gm6d+9u0uAKCgoYN24cVapUwc3NjWrVqvHBBx8UuTGUoii89957hISE4ObmRqdOnThx4kSR/SQnJzNo0CC8vLzw8fFh+PDhZGRkFNnmwIEDtGvXDldXVypWrMjkyZNNWhYhxG1cOav+61sZgOTMPC5nqD8iq5WXe3SIm5VlPQRSF92rOtUpD8B/RxPMd5D2b6jnvvQLsPYj8x1HCCtgdNIxY8YMZs6cyVdffYWzszNvvvkmkZGRvPTSS6SmmnYw1KRJk/j222/5+uuvOXr0KJMmTWLy5Ml89dVXhm0mT57Ml19+yXfffcf27dtxd3ena9eu5OTkGLYZNGgQhw8fJjIykqVLl7Jx40ZGjhxpWJ+WlkaXLl0IDw9n9+7dTJkyhffff5+ZM2eatDxCiFu4crWlw7cKcK2Vo4KvG+WcS9QYK+xcWdZDIHXRvapTnSAANh6/TK7OxLNYFXJyg55T1cc7vocLe81zHCGsgNE1ekxMDPfddx8Abm5upKerc+kPGTKE1q1b8/XXX5ssuK1bt/LII4/Qs2dPACpXrsxff/3Fjh07APXK0rRp03j33Xd55JFHAPj9998JCgpi0aJF9O/fn6NHj7Jy5Up27txJ8+bNAfjqq6/o0aMHn332GaGhocyaNYu8vDx+/vlnnJ2dqVevHvv27WPq1KlFKoTr5ebmkpuba3he+D7odDry8/NN9h6UhcJ4bS3uW7GXsthLOeDuZXFIOo0WKPCuiD4/n6MXUgCoFuhuVeW31Gei0+nK9Hi2oCzrIbD9ushezidlXY7a5csR6OHMpYw8tpxIpF31AJPs96ZyhLfHod5jaA//jbLkJXTDVqtjPWyAfLesiznLYYq6yOhvdXBwMMnJyYSHh1OpUiW2bdtGo0aNOHPmTJGmZlO47777mDlzJsePH6dmzZrs37+fzZs3M3WqelXgzJkzxMfH06lTJ8NrvL29adWqFVFRUfTv35+oqCh8fHwMJ3mATp06odVq2b59O48++ihRUVG0b98eZ2dnwzZdu3Zl0qRJXLlyBV/fm+9I+sknnzBhwoSblq9Zs4aAANOcmMpaZGSkpUMwGXspi72UA25dFq0+j/uj1+IJ7D+XSuzy5aw5owW0aNMTWb58eZnHeTdl/Zlcvny5TI9nC8qyHgL7qYvs5XxSluWoXk7LpQwt0/7dRVpNPaacwfv6crho7udBh5U4xx/g4vf92FdxGIqNJB4g3y1rY45ymKIuMvob/eCDD7JkyRKaNGnCsGHDePXVV1mwYAG7du3iscceK3VA13vrrbdIS0ujdu3aODg4UFBQwEcffcSgQYMAiI9X588OCgoq8rqgoCDDuvj4eMqXL19kvaOjI35+fkW2qVKlyk37KFx3qxP92LFjGT16tOF5XFwcdevWpWPHjoSFhZWm2GUuPz+fyMhIOnfujJOTk6XDKRV7KYu9lAPuUBZFwWHZK2hzLqC4+dLg0Vdo4FGe1XMPQHw89zWpQ4+IcMsFfgNLfSZxcXFldixbUZb1ENh+XWQv5xNLlKNSXBp9Z27nQLKWrOAG9GtW+vr9duXQVHdFWfwclZI3UcELCvr8aphcw1rJd8u6mLMcpqiLjE46Zs6ciV6vB+CFF17A39+frVu30qtXL5599tlSB3S9efPmMWvWLGbPnm1oZn7llVcIDQ3lySefNOmxjOXi4oKLi4vheVqaOsWno6OjzX5hnZycbDb2G9lLWeylHHCLsuz6GfbPAo0WTZ+fcPJVK/O0XLUJ18/d1SrLXtafiaOj7VztLCtlWQ+B/dRF9nI+KctyNKnsz2tdajJ5ZTQTlx2lZVV/qpc3zQxTN5Wj8RNQzhcWDEN7dhPa33rAoHmGSTasmXy3rIs5ymGKusjoPWi1WrTaa+PP+/fvT//+/UsdyK288cYbvPXWW4b9N2jQgHPnzvHJJ5/w5JNPEhwcDEBCQgIhISGG1yUkJNC4cWNAbYZPTEwssl+dTkdycrLh9cHBwSQkFJ2dovB54TZCCBOK3QHL31QfPzgOqnc0rErLVvuiervZ/olfmEdZ1kMgddG97rn21dh6MonNJy8zavZeFr3QBlcnB/McrGYXGLYCZj8Bl6Phx04wYA5UaH731wph5Up0c8BNmzYxePBgIiIiDM0tf/zxB5s3bzZpcFlZWUUqFgAHBwfDFa4qVaoQHBzMmjVrDOvT0tLYvn07ERERAERERJCSksLu3bsN26xduxa9Xk+rVq0M22zcuLHIwJvIyEhq1ap1y+ZsIUQppCfAvKGgz4e6j0DbV4usTilMOspJ0iFur6zqIZC66F6n1WqY+kQjAjycORafzgdLj5j3gCENYcQaCG4AmZfg157qDQSFsHFGJx0LFy6ka9euuLm5sXfvXsOsGampqXz88ccmDe7hhx/mo48+YtmyZZw9e5Z//vmHqVOn8uijjwKg0Wh45ZVX+PDDD1myZAkHDx5k6NChhIaG0rt3bwDq1KlDt27dGDFiBDt27GDLli2MGjWK/v37ExoaCsDAgQNxdnZm+PDhHD58mLlz5zJ9+vQi/WSFECaQfBrmDob0ixBYGx75hhtHZqZKS4e4i7Ksh0DqIgHlPV354onGAMzaHsPC3efNe0CvULXFo0YX0OXAvCdh7YeQY/opoYUoM4qRGjdurPz222+KoiiKh4eHcurUKUVRFGXPnj1KUFCQsbu7o7S0NOXll19WKlWqpLi6uipVq1ZV3nnnHSU3N9ewjV6vV8aNG6cEBQUpLi4uSseOHZXo6Ogi+0lKSlIGDBigeHh4KF5eXsqwYcOU9PT0Itvs379fadu2reLi4qKEhYUpn376qVGxxsbGKoASGxtb8gJbSF5enrJo0SIlLy/P0qGUmr2UxV7KoShqWdb+9aVSMPdJRXnfR1HGeynKxxUU5dKJW25f4+3lSviYpcr5K1llG+hdWOozseVzi7mUZT2kKLZfF9nL+cQayvHpiqNK+JilSviYpcq4RQeV7Dyd0fswqhy6fEVZ+pp63iw8d0aOV5T0BOODNwNr+ExMQcpxd6aoi4we0xEdHU379u1vWu7t7U1KSkppc6AiPD09mTZtGtOmTbvtNhqNhokTJzJx4sTbbuPn58fs2bPveKyGDRuyadOmkoYqhLiVmG04bPyMB05eN31f9c7QcRwEVL/lSxy0GigAvd70U58K+1CW9RBIXSSuea1zTXQFen7YdIbfo86x/XQyXw1sQs0g0wwuv4mDI/SYApVaw8YpcOkYbP4ComZAk8HQ5iWbGGguBJSge1VwcDAnT568afnmzZupWrWqSYISQtgwRYET/8HP3eHnrmhPRqKgQV+3Nzy7CQYvgJBGt325k4Pa3SqvQF9GAQtbI/WQsBRHBy3v9KzLb0+3JMDDmeiEdB7+ajN/bjtnlnvEAGoX1AZ94fko6D8bwppDQS7s+gm+bAoLn4GEw+Y5thAmZHTSMWLECF5++WW2b9+ORqPhwoULzJo1i9dff53nn3/eHDEKIWyBvgAO/Q3ft4dZfSBmK2id0DcezJo6kyh49Ed1gORdODuqpyVdgbR0iFuTekhYWoeagax4uT3tawaSq9Pz7qJDPPfnblKy8sx3UK0WaveEZ/6DJ5dCtQdBKYCD8+Hb+2DW4xCzzXzHF6KUjO5e9dZbb6HX6+nYsSNZWVm0b98eFxcXXn/9dV588UVzxCiEsGa6PDgwBzZPg+RT6jInd2g+DCJeoMAtkEwj7izu5KAmHfnS0iFuQ+ohYQ0CPV349akW/LzlDJNWHmPV4QQOnN/EtCca06qqv/kOrNFAlXbq34V9anerI4vhxCr1r1IEtB0NNTrfNFGHEJZkdNKh0Wh45513eOONNzh58iQZGRnUrVsXDw8Pc8QnhLBGOalw8j+IXgknIyH7irrczRdaPQctR0I5P3XZddN/Fofj1e5VuTpJOsStST0krIVWq+GZdlVpXdWfF//ay5nLmQz4YRuPN6/Iq51rEuTlat4AQhvD479B0inYMg32/QUxUTC7H/hWgfp91L+guuaNQ4hiKNF9OgCcnZ2pW7cuQUFBxMTEGOYrF0LYqeQzsO1b+K0XTK4KC56Gg/PUhMMzBLp8BK8cgvvfupZwlIC7s3otJD3HuGRF3HukHhLWon6YN0tfbEu/ZhXQKzBnZywdpqzjs1XRZXMu868Gvb6CVw5AxChw9oArZ2DTZ/BtBHzTGjZMUZMTISyk2EnHzz//zNSpU4ssGzlyJFWrVqVBgwbUr1+f2NhYkwcohLAQfQHEbIf/3lcrrC8bw8q34MwG0OsgoCbc95I6l/wrh+C+UeBS+ivNgZ4uACRlmLFvtLBJUg8Ja+bu4siUfo1Y8FwETSv5kJOv5+t1J+kwZT2/bDlDXlm03nqFQteP4PXj0PdnqP0QODjDpaOw7kP4qil83wG2fAkp8n9FlK1iJx0zZ84sckfUlStX8ssvv/D777+zc+dOfHx8mDBhglmCFEKUkdwMOLIE/nkePqsJP3dR+wtfOgoaB6jcTm3ReHEPjNoJXT6A8PvUaR1NJMBDTTouZ+SabJ/CPkg9JGxB88p+LHz+Pr4b3Iyqge4kZ+Yx4d8jdJq6gSX7L5TNdODO7mq3qv6z4PUT8MgMqNZRPY9f3AeR42BaffipK2yfCRmJ5o9J3POK/UvhxIkTNG/e3PB88eLFPPLIIwwaNAiAjz/+mGHDhpk+QiGEeaXEwvGVEL0Czm6CgutaGFy8oUYnqNld/dfN9/b7MZEAD2dAkg5xM6mHhK3QaDR0qx9Mpzrlmbsrlmn/nSAmOYuX/tpL/VAvOvho6FFWwbj5QJNB6l/mZXXQ+aG/4dwWiN2m/q0co15Uqt8H6jxcqi6yQtxOsZOO7OxsvLy8DM+3bt3K8OHDDc+rVq1KfHy8aaMTQphHSgzs+UNNNBIOFl3nWwVqdVf/KkWAg1OZhlbY0iHdq8SNpB4StsbRQcugVuE82iSMHzed4fsNpzh0IY1DFxw4+PtuxveqT7XAMpwAwT0AWgxX/9IuwOFFcGghxO1Su86e2QDLXoPqHaHRAKjTS52qVwgTKHbSER4ezu7duwkPD+fy5cscPnyYNm3aGNbHx8fj7e1tliCFECZyKVqd2vbgPHVcBoBGCxVaXks0AmpadJpFX3e1pSMpU5IOUZTUQ8JWlXN25KWONRjYqhLTI6OZtSOGjSeSeOTrLUx9vBFd6gWXfVBeoRDxP/Uv+Qwc/lttAUk4pLZ+H18JQQ2g4zio0UWm3xWlVuyk48knn+SFF17g8OHDrF27ltq1a9OsWTPD+q1bt1K/fn2zBCmEKKW4PbB5KhxdClztT1ylPTQaqFYm7macU95I2qsVm9Rv4kZSDwlbF+DhwnsP1SE89wyrUwLZcfYKI//YzaudavLig9XRai104vOrAu1eU/8Sj8GBubDzR7UlfPbjaqt3x/fUMXxClFCxk44333yTrKws/v77b4KDg5k/f36R9Vu2bGHAgAEmD1AIUUKKovbZ3fQ5nFp7bXnth9QbR1VodvvXWpDu6k0BHaVJX9xA6iFhLwLd4NfezZi8+iS/bj3LF/8d58jFVD5/vDEeLqabmKNEyteGTuPhvhfViUR2zFTv/fFLd6jeSU0+QhpZNkZhk4r9zdZqtUycOJGJEyfecv2NJ38hhIUoChxfpSYb53eoyzQO0KAftH0FytexaHh3o7s6s4ujpa74Casl9ZCwJ04OWt7vVY+6IV68u+gQqw4ncGbGFn4Y2pxwf3dLh6cOJu/yAbR+HjZMhj2/qzeFPfkf1HsUHngXvMMtHaWwIXIpUQh7oS+Agwvgu7bw1xNqwuHgAi2egZf2wGPfW33CAVBwNelwcJCkQwhh/x5vUZE5z7amvKcLxxMy6PX1FjaduGTpsK7xCoWHp6nTpDfoB2jg8D/wTUsclr2Ca16SpSMUNkKSDiHsQWYSfHsfLByuDgJ09oQ2r8ArB6Hn5+Bb2dIRFpu0dAgh7jVNK/ny74ttaVzRh9TsfJ78eQcrDl60dFhF+VeDPj/Cc5uhZjdQCtDu+5NOR95Ec3SxpaMTNkCSDiHsQVYSXD5x7Xm9R9S7hXsGWS6mEjp1KQMAv6uzWAkhxL0gyMuVOSNbUzXQHb0C+2JTLB3SrQXXh4Fzof2bADgo+Wgu7rNsTMImSNIhhD0IrAlPr4Lwq9OH7v0TpjdS++HmZlg2NiMoisK6Y+qdcdvXDLRwNEIIUbYS03I5czkTgL7NKlg4mjvYP0cdNwhc9G6Cvt2bFg5I2IISJx15eXlER0ej0+lMGY8QoqQqtoCnlsGghRDcEPLSYd1HavKx7TvQWf8dvo/Fp3MxNQdXJy0RVa1nGl9hnaQeEvbmt6izKIp60aVGkKelw7m1bd/CP8+CUoC+wRPsrPISOLlZOiphA4xOOrKyshg+fDjlypWjXr16xMTEAPDiiy/y6aefmjxAIYQRNBqo0QlGboC+P4NfVci6DCvHwFfNYd9f6oBzK7X2aitHm2oBuDo5WDgaYa2kHhL2KCNXx7ydsQA83aayZYO5FUWBtR/ByrfU563/R8HDX6Fo5FwtisfopGPs2LHs37+f9evX4+rqaljeqVMn5s6da9LghBAlpNVC/T7wwg54aBp4hkBqDCx6Dr5to94kUK+3dJQ3Kexa9UDt8haORFgzqYeEPVqwK5b0XB3VAt1pX8PKupfqC2D567Bxsvr8wXeh68egkV76oviMvgPNokWLmDt3Lq1bt0Zz3S2D69Wrx6lTp0wanBCilBycoPkwaPiEeoOnzV/ApaMwdxAE1IKI/0HD/uDkevd9mdmVzDz2xFwBJOkQdyb1kLA3uboCftx8BoBhbapY7s7kt5KeAP+MhNPrAY06I2KL4ZaOStggo1PUS5cuUb78zT8IMjMzi5z8hRBWxLmcemPAl/dDu9fUKXUvR8O/L8MX9WD9p5Bh2Xnh1x9PRK9A7WBPwnykf7C4PamHhL35I+oc569kU97Thceahlk6nGtOroHv2qgJh1M5tduuJByihIxOOpo3b86yZcsMzwtP8D/++CMRERGmi0wIYXpuPtDxPRh9RG0a966ojvlY/4mafCx5CS5FWyS0/46qXas61pFWDnFnUg8Je5KSlcdXa08C8HqXWpRzNroTiukV5MN/78Ofj0HmJShfD0auh/qPWToyYcOM/mZ//PHHdO/enSNHjqDT6Zg+fTpHjhxh69atbNiwwRwxCiFMzdULIl6Als/C0cWw9Wu4sAf2/Kb+1egCEaOgSnt1cLqZ5Rfo2RittrR0rGN79xYRZUvqIWFPvlp7ktTsfGoHe9LHGqbJTYmBBcPh/A71efPh0PUjmaFKlJrRLR1t27Zl37596HQ6GjRowOrVqylfvjxRUVE0a9bMHDEKIczFwVEdcD5iLQxbCbUfAjRwYjX83gu+b6fOx67LM2sYO88kk56rw9/dmcYVfMx6LGH7pB4S9uJcUia/R50FYGyPOjhYeizHkSXwXVs14XDxhn6/wUNTJeEQJlGiNrxq1arxww8/mDoWIYSlaDQQHqH+JZ2CbTNg7yyIP6jOx/7f++osWLW6meXwhV2rHqhd3roGUAqrJfWQsAeTV0WTX6DQrkYAHSx9Q9T/3lcnGwEIaw59fwLfypaMSNgZo1s6OnXqxK+//kpaWpo54hFCWJp/Neg0QR1wztUEIP0i7P7FbIfcdS4ZwPKVrrAJUg8Je7EvJgWAmOQs9semWC4QRYEd1yXxFVuqE44IYUJGJx316tVj7NixBAcH069fPxYvXkx+fr45YhNClLXLJ2D5mzC1Dqz7EFDAyR2aPw0PfWG2w56/kg1AtUAPsx1D2A+ph4S9mN6/MaHerpxLyqLPt1v5bsMp9Hql7APRaGDw32oLB6it3dMbwtoPITul7OMRdsnopGP69OnExcWxaNEi3N3dGTp0KEFBQYwcOdIsA/ji4uIYPHgw/v7+uLm50aBBA3bt2mVYrygK7733HiEhIbi5udGpUydOnDhRZB/JyckMGjQILy8vfHx8GD58OBkZGUW2OXDgAO3atcPV1ZWKFSsyefJkk5dFCKukL4Bjy+H33vB1c9jxPeSmgX916DYJXjuqJhxeoWY5fGaujuRMdcxImK/0GxZ3V9b1EEhdJMyjeWU/Vrzcnh4NgtHpFT5dcYzBP20nIS2n7IOp1Aqe+Q8GzoPghpCXARunqMnHhimQm172MQm7UqJbSWq1Wrp06cKvv/5KQkIC33//PTt27ODBBx80aXBXrlyhTZs2ODk5sWLFCo4cOcLnn3+Or6+vYZvJkyfz5Zdf8t1337F9+3bc3d3p2rUrOTnX/sMOGjSIw4cPExkZydKlS9m4cSMjR440rE9LS6NLly6Eh4eze/dupkyZwvvvv8/MmTNNWh4hrEpmktp/d3pjmDMATq9T7y5bqwcM+Qde2AmtnwNXb7OGEZeitnJ4uTri7eZk1mMJ+1FW9RBIXSTMy7ucE98MbMqkPg1wc3Jg66kkuk3bSOSRhLIPRqOBml1h5AZ4/A8IrAM5qWrL97SGsOVLyMsq+7iEXSjVZNDx8fHMmTOHP//8kwMHDtCyZUtTxQXApEmTqFixIr/8cq0veZUqVQyPFUVh2rRpvPvuuzzyyCMA/P777wQFBbFo0SL69+/P0aNHWblyJTt37qR5c7XZ8KuvvqJHjx589tlnhIaGMmvWLPLy8vj5559xdnamXr167Nu3j6lTpxapEISweYoCF/aqfXcPLYSCXHW5mx80Hap2o/INL9OQ4q52rQrzLVemxxX2wdz1EEhdJMxPo9HwRItKNAv34+U5ezl8IY0Rv+9iSOtw3ulZB1cnh7INSKuFur2gdk849Des/xiST0PkOIj6Wh3z1+wpSnjtWtyjjE460tLSWLhwIbNnz2b9+vVUrVqVQYMGMXfuXKpVq2bS4JYsWULXrl3p168fGzZsICwsjP/973+MGDECgDNnzhAfH0+nTp0Mr/H29qZVq1ZERUXRv39/oqKi8PHxMZzkQR2EqNVq2b59O48++ihRUVG0b98eZ2dnwzZdu3Zl0qRJXLlypcjVrEK5ubnk5uYanqenq82OOp3O5voWF8Zra3Hfir2UxaTlyM9Cc24LmpP/oT31H5qUc4ZV+pDG6Js/g1K3Nzi6Fh689Me8/vB3Kcu5y+r/nTBvF6v+3Cz13dLpdGV6PFtQlvUQ2H5dJOdF63KncoT7ujB3REu++O8EP205xx/bzrE+OpE3utSgW70gw40wy1Sd3lDrITQH5+GwaQqa1FhY8SbKhklQ51G8sirb9WdiS8xZDlPURUYnHUFBQfj6+vLEE0/wySefFDmBmtrp06f59ttvGT16NG+//TY7d+7kpZdewtnZmSeffJL4+HhDTDfGWLguPj6e8uWL3uHY0dERPz+/Ittcf9Xq+n3Gx8ff8kT/ySefMGHChJuWr1mzhoCAgBKW2LIiIyMtHYLJ2EtZSlqOcrkJBKUdIChtPwHpR3FQrp2ACjSOXPBpxenATqS4V4PzwPm1Jor49m5Xlq0xWkBL9pUEli9fbvY4Squsv1uXL18u0+PZgrKsh8B+6qJ7/bxobe5UjobA83U0zDqpJfZKNi/NPUBlD4XelQuoYrFJpXzQVJ5AeNIGaiYswS0rCafdP/IAkPLVD5z0b8d53wjyHW131qt74btVUqaoi4xOOpYsWULHjh3Ras3fpKbX62nevDkff/wxAE2aNOHQoUN89913PPnkk2Y//p2MHTuW0aNHG57HxcVRt25dOnbsSFhYmAUjM15+fj6RkZF07twZJyfb7lNvL2Uxuhy6XDQxUWhORaI9+R+a5FNFViteYeirdUKp3gmlcjuCnT0INlPsN7pbWaKWHIG48zSpU50eHauXUVTGs9R3Ky4ursyOZSvKsh4C26+L7tnzopUqbjl6AM/m6vhx81l+3HKWsxl6ph1ypEf9IF7rXINKfpbqktoL9JPQnVoL+2ahPbESn+xz+Jw/R4OL81BqdEPfaABK1QdAW6pe/GXmXvtulYQp6iKjvw2dO3cG4NKlS0RHRwNQq1YtAgNNP79+SEgIdevWLbKsTp06LFy4EIDgYPVnU0JCAiEhIYZtEhISaNy4sWGbxMTEIvvQ6XQkJycbXh8cHExCQtEBW4XPC7e5kYuLCy4uLobnhfPFOzo62uwX1snJyWZjv5G9lOWO5UiJVe8cfvI/OL0B8jOvrdM6QqUIqNEZanRBE1gbB0s0y1/ndmVJyVKbbAO93GziMyvr75ajo21U2mWpLOshsJ+66J44L9qQ4pTDx8mJ17vVYXBEFaZGRjN/93mWH0og8mgiT0ZUZtSD1fEp53zHfZiHE9TtSX6NLqxaMpcuIak4HJiDJv4AmmNL0B5bAh7B0OgJaDwYAmtaIEbj3UvfLWOZoi4y+jJRVlYWTz/9NCEhIbRv35727dsTGhrK8OHDycoy7YwGbdq0MVQohY4fP054uDrQtUqVKgQHB7NmzRrD+rS0NLZv305ERAQAERERpKSksHv3bsM2a9euRa/X06pVK8M2GzduLNIHLjIyklq1at2yOVsIiyjIhzObYPU4+KY1TKsPy0ZD9HI14fAIhiaD4fHf4c3T8NRSaPMylK+jzkhipQqny/Vzt0TFKWxRWdZDIHWRsLxgb1cm923E8pfa0a5GAPkFCj9uPkOHKev5cdNpcnUFFostz9ETfYuR8NwmeG4ztHoeyvlDRjxsmQ7ftIAfO8GuX9SZsMQ9y+ik49VXX2XDhg38+++/pKSkkJKSwuLFi9mwYQOvvfaaSYN79dVX2bZtGx9//DEnT55k9uzZzJw5kxdeeAFQZ3t45ZVX+PDDD1myZAkHDx5k6NChhIaG0rt3b0C9GtWtWzdGjBjBjh072LJlC6NGjaJ///6Ehqr3HRg4cCDOzs4MHz6cw4cPM3fuXKZPn16kyVoIi1D0cHgRzB0Ck6vCbw/B1i/h0lF1etuKreHBcfDsJnjtGDzyDdR9xOzT3JpSUqY6CNZfkg5RTGVZDxUeT+oiYQ3qhHjxx/BW/PZ0S2oFeZKanc+Hy47SeepG1hy1wBS7NwpuAN0/hdHH1Cl3a3YHjQOc3wlLX4HPasHCEZBw2NKRCktQjOTv76+sW7fupuVr165VAgICjN3dXf37779K/fr1FRcXF6V27drKzJkzi6zX6/XKuHHjlKCgIMXFxUXp2LGjEh0dXWSbpKQkZcCAAYqHh4fi5eWlDBs2TElPTy+yzf79+5W2bdsqLi4uSlhYmPLpp58aFWdsbKwCKLGxsSUrqAXl5eUpixYtUvLy8iwdSqnZS1nycnOUHb+8rei/bqko472u/U2qoigLRyrKgfmKkplk6TCL5W6fSaMJq5TwMUuVoxdTyzgy41jqu2XL5xZzKet6SFFsuy6ym/OilKMIXYFe+Wv7OaX5h5FK+JilSviYpcq0yOOKXq83UaR3V6yypMUryubpinJ9fTbBX1HWT1IUnXV8lvLdujtT1EVGd9DKysq6aYYOgPLly5ulWfuhhx7ioYceuu16jUbDxIkTmThx4m238fPzY/bs2Xc8TsOGDdm0aVOJ4xTCJPR6OPYvjus/pUXiEXWZize0GA61H4LQJur86XYiJ7+AlCy1K0mIl9yNXBRPWddDIHWRsD4OWg39W1bi4UahTFkVza9bz/LFf8c5eSmDKX0blv29PW7HMwjavAT3vQhxe2DTZ2q34HUfwdF/ofcMtYVE2D2jf71EREQwfvz4IndZzc7OZsKECYa+q0IIIymKevL9vj3MG4om8Qj5WjcK2r4OrxyATuOhQjO7SjgA4lPV84ibkwNebjJgWhSP1ENCXOPu4sj7verxyWMNcNRq+Hf/BZ74PorEtJy7v7gsaTRqPdZ/NvT5Cdx8If4AzLwf1k9Sxy0Ku2Z0LT99+nS6du1KhQoVaNSoEQD79+/H1dWVVatWmTxAIeyaoqhXfNZ/AvEH1WXOnhS0GElkWnU6d+iHgx3MpHE7F68mHSE+rpa56ZWwSVIPCXGzAS0rUdnfnedn7Wb/+VQe+WYLPwxtTv0wKxvjp9FAg75QuZ06Gcqxpeodz4/9C4/MgJCGlo5QmInRSUf9+vU5ceIEs2bN4tixYwAMGDCAQYMG4eYm3SOEKBZFgeOr1GTj4j51mbMHtHoOIl5A7+RJvg3cKK+04tOyAQjxdrVwJMKWSD0kxK1FVPNn0f/aMPy3nZy6lEm/76L44olGdKsfcvcXlzXPIHjiTzi0EJa/oV54++EBaPc6tHsNHGVyEXtTov4M5cqVY8SIEaaORYh7Q24G/NUfzl7tt+3kDq2eVfu7lvNTl+XfG83MF1LUlo5gGc8hjCT1kBC3VjnAnb//14ZRs/ew6cRlnvtzDx/0rs+Q1uGWDu1mha0eVdqrrR5H/4UNn8KJVfDUMnB2t3SEwoRK1EE8OjqaUaNG0bFjRzp27MioUaMMV5uEEHegy4N5Q9SEw6kctHkFXjmojtkoTDjuIXEpaktHmI+0dAjjSD0kxO15uznxy1MteDJCTTTeX3KYLScvWziqO/Aor06x2/dndazHhb2w8i1LRyVMzOikY+HChdSvX5/du3fTqFEjGjVqxJ49e2jQoIHh7qxCiFvQ62HxC3BqrZpwPLkUOk8Ad39LR2YxscnqTEMV/cpZOBJhS6QeEuLuHB20vN+rHo81CaNAr/DC7D3EJJlndjeT0Gigfh/1BrdoYM/vcGSxpaMSJmR096o333yTsWPH3jQt4Pjx43nzzTfp06ePyYITwq78Nx4OzlNvlPT47+osHvc4STpESUg9JETxaDQaPn6sAacuZbD/fCojft/F3/+7D3cXK54tsEp7aPMybJkGS16CsObgHWbpqIQJGN3ScfHiRYYOHXrT8sGDB3Px4kWTBCWE3Yn6Rr2TOMAjX0ONzpaNxwoU6BXOX1G7V1WSpEMYQeohIYrP1cmB74c0J9DTheiEdEbP24der1g6rDt74B31vlQ5KfDPs6AvsHREwgSMTjruv//+W964aPPmzbRr184kQQlhVw4ugFVvq487jofGAy0bj5W4mJqNTq/g7KAlyEvGdIjik3pICOMEe7vy/ZBmODtoWXU4gS/XnrB0SHfm6Kzey8PJXR0DuWW6pSMSJlCs9rUlS5YYHvfq1YsxY8awe/duWrduDcC2bduYP38+EyZMME+UQtiqC3th0fPq45bPQttXLRuPFSnsWxzm64aDVu7RIe5M6iEhSqdpJV8+fLQ+by44wLT/TlCjvCc9G1rhVLqF/KtB90mwZJR69/JqD0JoY0tHJUqhWElH7969b1o2Y8YMZsyYUWTZCy+8wHPPPWeSwISwedkpMO9JKMiDmt2h2yfqQDkBwNmrSUe4v3StEncn9ZAQpfd484pEx6fz0+YzjJ63jwq+bjSq6GPpsG6vyWA4sRqOLlG7WY3cAE7SMm6ritW9Sq/XF+uvoED63AkBqDf/W/wCpJwDn0rw6LegdbB0VFblbFImAJX9ZR52cXdSDwlhGm/3qMODtcuTq9PzzO+7uHB16nKrpNHAQ1+Ae3m4dAzWfmDpiEQplOg+HbeSkpLC119/bardCWHbtn8Hx5aC1gn6/arOOy6KOHNZTTqqBEjSIUxD6iEh7s5Bq+HLAU2oHezJpfRchv+2i8xcnaXDuj33AOh1dSKWqG/g7BbLxiNKrNRJx5o1axg4cCAhISGMHz/eFDEJYdvO74bV49THXT+GMJka91bOXk06KkvSIUpJ6iEhjOPh4siPTzYnwMOZoxfTeHnOPgqseUarWt2hyRBAgUXPQW66pSMSJVCipCM2NpaJEydSpUoVunTpgkaj4Z9//iE+Pt7U8QlhWzIuwfynQJ8PdXtDyxGWjsgq6fUK567eo6OKdK8SJSD1kBClU8G3HDOHNsfZUct/RxP4cNkRFMWKE4+uH6vdlVNiYPkbajdmYVOKnXTk5+czf/58unbtSq1atdi3bx9TpkxBq9Xyzjvv0K1bN5ycnMwZqxDWLSsZfn8EUmPAr6raHCwDx28pLiWbPJ0eJwcNoT4yKFAUj9RDQphW00q+fNavEQC/bDnLxKVWnHi4ekHv7wAN7P8LIsdJ4mFjin1LyrCwMGrXrs3gwYOZM2cOvr5qH/UBAwaYLTghbEZ2CvzRGxIPg0cQDJwPrt6WjspqnUzMAKBqgAeODiYbWibsnNRDQpher0ahpGbnM27RIX7ZcpZcnZ4PH6mP1hqnMq/cBh6aCktfha1fgaMrPPiupaMSxVTs2l6n06HRaNBoNDg4yCw8QhjkpMGffeDifigXAEOXQEB1S0dl1U4kqv1xawR5WDgSYUukHhLCPIa0Dmdyn4ZoNDB7ewxvLjxgvWM8mj8N3SerjzdOgQ1TLBuPKLZiJx0XLlxg5MiR/PXXXwQHB9OnTx/++ecfNNJ9RNzLcjNg9uMQt0udoWroYihf29JRWb0TCWpLR43ynhaORNgSqYeEMJ/HW1Rk2hONcdBqWLD7PK/M3Ud+gd7SYd1aq2eh89Xpc9d9KHcstxHFTjpcXV0ZNGgQa9eu5eDBg9SpU4eXXnoJnU7HRx99RGRkpMyPLu4teVnwV3+IiVK7Ug1ZBMH1LR2VTTh+tXuVtHQIY0g9JIR5PdI4jK8HNMHJQcO/+y8wavYe8nRWmni0eela16rI92Dbt5aNR9xViTpTV6tWjQ8//JBz586xbNkycnNzeeihhwgKCjJ1fEJYp/wcmDsIzm4CZ08Y/A+ENrZ0VDZBURROFSYd5SXpECUj9ZAQ5tG9QQjfDW6Gs4OWVYcTGP7bTjKs9T4e7d+A9m+qj1e+pd7HQ1itUo3g1Gq1dO/enQULFnD+/HnefvttU8UlhPXS5cK8IXBqLTi5w+AFUEHuxVFcscnZZOTqcHbQyj06RKlJPSSE6XWsE8RPTzXHzcmBTScu88T3USSm51g6rFt74G1o+6r6eNXbsO4TmdXKSpls2pjAwEBGjx5tqt0JYZ10eep9OE6sBkc3GDQPKrW2dFQ25fCFVABqBXviJDNXCROSekgI02lXI5A5I1vj7+7M4QtpPDZjK6cuZVg6rJtpNNBx/LWuVhs+VZMPvZV2C7uHSY0vRHEV5MPC4RC9XJ2mb+AcqNzW0lHZnCMX0wCoG+Jl4UiEEELcSaOKPvz9v/uo7F+O81ey6fPtVnafS7Z0WDfTaNSuVt2vzmS1bQYseREKrLRb2D1Kkg4hiqNAB3+PhKNLwMEZ+s+CqvdbOiqbdPiCmnTUC5OkQwghrF24vzsLn7+PxhV9SMnKZ+AP21l5KN7SYd1aq5HqDQQ1DrDvT1gwTO0SLayCJB1C3I1eD4v/B4f/Bq0TPPEnVO9k6ahs1pEL0tIhhBC2xN/Dhb9GtKZTnfLk6vQ8P2s3v0edtXRYt9Z4ADz+m3qB8OgS+GuAOtuksDijk46JEyeSlXXzh5ednc3EiRNNEpQQVmXN+3BgLmgd1RNZza6WjshmXc7IJT4tB40GakvSIUpI6iEhyp6bswPfDW7GwFaVUBR4b/Fhpqw+jlXeQ7DOwzBwHjiVg1NrYM5AddZJYVFGJx0TJkwgI+PmgURZWVlMmDDBJEEJYTV2/XLtpkO9v4XaPS0bj407eF4dRF41wB0PF0cLRyNsldRDQliGo4OWj3rX5/UuNQGYueksf57UWue9PKo9AEP+UWeZPL0O5g6WrlYWZnTSoSjKLe/+un//fvz8/EwSlBBW4eQaWPaa+vj+t6Hh45aNxw4cuJp0NKzgY9lAhE2TekgIy9FoNIx6sAaf9WuEo1bD7stanvljD2k5+ZYO7WaVWquzTDq6wclImPekOgulsIhiJx2+vr74+fmh0WioWbMmfn5+hj9vb286d+7M44+b90fZp59+ikaj4ZVXXjEsy8nJ4YUXXsDf3x8PDw/69OlDQkJCkdfFxMTQs2dPypUrR/ny5XnjjTfQ6YrOaLB+/XqaNm2Ki4sL1atX59dffzVrWYSVSziiTo2rFEDD/tDhTUtHZBcOxqUA0LCCt2UDETbJGuohkLpICIC+zSowc0gTXLQKUaeTefy7KC6mZls6rJtVbqvONunoCsdXqIPLC6wwQboHFLt/w7Rp01AUhaeffpoJEybg7X3tR4OzszOVK1cmIiLCLEEC7Ny5k++//56GDRsWWf7qq6+ybNky5s+fj7e3N6NGjeKxxx5jy5YtABQUFNCzZ0+Cg4PZunUrFy9eZOjQoTg5OfHxxx8DcObMGXr27Mlzzz3HrFmzWLNmDc888wwhISF07Sr99+856Qkw+3HITYPwNtDrS3U6PlFq11o6JOkQxrN0PQRSFwlxvXbVA3ipfgG/ni7Hsfh0HpuxlV+HtaRWsKelQyuq6v3qrJN/DYBjS+HvEfDYj+Ag3XzLlGKk9evXK3l5eca+rFTS09OVGjVqKJGRkUqHDh2Ul19+WVEURUlJSVGcnJyU+fPnG7Y9evSoAihRUVGKoijK8uXLFa1Wq8THxxu2+fbbbxUvLy8lNzdXURRFefPNN5V69eoVOeYTTzyhdO3atdgxxsbGKoASGxtb0mJaTF5enrJo0aIy/1zNodRlyc1UlO/vV5TxXoryZVNFyUwybYDFZI+fSczlNCV8zFKlyltLlaxcnaXDMpqlPhNbPreYiyXqIUWx3brIXs4nUg7rU1iWM4mpyoOfrVPCxyxV6o9fqUSdumzp0G4teqWiTPBX6/i/n1MUvV5RFPv5TMxZDlPURUaneB06dECv13P8+HESExPR33DHx/bt25sgFSrqhRdeoGfPnnTq1IkPP/zQsHz37t3k5+fTqdO16Utr165NpUqViIqKonXr1kRFRdGgQQOCgoIM23Tt2pXnn3+ew4cP06RJE6Kioorso3Cb65vOb5Sbm0tu7rUBSenp6QDodDry822r2a4wXluL+1ZKVRZ9AQ4Lh6O9sAfFzQ/d47PByRMs8L7Y42eyL0a9oVT1QA8cNXry861w4OEdWOozubH7jbBMPQS2WxfZy/lEymF9CstQ3t2ROc+05PnZe9l1LoUhP23n874N6F4/2MIR3qDKg2ge+wmHhcPQ7J9NgWco+g5v2c1nYs5ymKIuMjrp2LZtGwMHDuTcuXMoStF50jQaDQUFBaUO6npz5sxhz5497Ny586Z18fHxODs74+PjU2R5UFAQ8fHxhm2uP8kXri9cd6dt0tLSyM7Oxs3N7aZjf/LJJ7ecJWXNmjUEBAQUv4BWJDIy0tIhmExJylLv/CyqX1pFgcaJrRX+R/K2Y8Ax0wdnBHv6TP7dfADQ4quksXz5ckuHU2Jl/Zlcvny5TI9nC8q6HgL7qIvs5Xwi5bA+hWXpHwx56VoOJGt5ee5+1m/fS4cQ65tTt1KFJ2kS+zMOmz/jQEwKMf7qhQp7+UzMUQ5T1EVGJx3PPfcczZs3Z9myZYSEhNxyBhFTiY2N5eWXXyYyMhJXV1ezHackxo4dy+jRow3P4+LiqFu3Lh07diQsLMyCkRkvPz+fyMhIOnfujJOTk6XDKZWSlkW780cc9q4CQOk9g9Z1HzVXiMVij59JlmsAkEz31vXo0bKipcMymqU+k7i4uDI7lq0oy3oIbL8uspfziZTD+tyqLA/pFT5cfow/t8fy91kH/CpU5o3ONdD+n737jo6iehs4/t1NJaRCICEh9N47hKpSFQtKFxEVQVRsWBALRX+K5VVEBUXsCogooCIt0kvovXcIgSSEkN422Xn/uCQhBEhCdjO7m+dzTk52Z+/MPHfL3LkztxhtqW/kPWSv8cFp8zRanP+Rhh16suJ4ht1/Jtb8blmiLCp2peP48eP88ccf1KlTp8Q7L8zOnTuJiYmhVatWucuys7NZv349X375JStWrCAzM5P4+Ph8V5iio6MJDFS39AIDA9m2bVu+7eaMKHJtmutHGYmOjsbb2/uGV5YA3NzccHNzy32emKhmWXZ2drbbL6yLi4vdxn69YuXl6HIIe0M97j4R5+a2MzSuo3wmmgYHL6pmHy2rVbDrPJX2Z+LsLB0dr1ea5RA4TlnkKMcTyYftuTYvLsC7/ZoS5OfBR8uP8u3GM8QmZ/LRgOa4Ohd7pgbr6TkJks5j2L8At7+exKvm6w7zmVgjH5Yoi4r96bdv354TJ06UeMdF0b17d/bv38+ePXty/9q0acOwYcNyH7u4uLBq1arcdY4ePcq5c+dyRzAJDQ1l//79xMTE5KYJCwvD29ubRo0a5aa5dhs5aaw9CoqwARf2qOHzNDO0HA6dxxW6iii+uAy4kmrCxclAgyo2NqqJsDulWQ6BlEVCFJfBYOCZO+rwydW5PBbvucATP24nyZbm8jAY4IEZUL0zhowkQk9+AokX9Y7KoRWp2rJv377cx8899xwvv/wyUVFRNG3atEBN6vphBEvCy8uLJk2a5FtWvnx5KlasmLt85MiRjBs3jgoVKuDt7c1zzz1HaGgoHTp0AKBXr140atSI4cOH89FHHxEVFcVbb73Fs88+m3t1aMyYMXz55Ze89tprPPHEE6xevZrff/+df//912J5ETYo+hD82h9MqVDrTrh3mgyNayXnktX7Wj/QCzdnJ52jEfZIr3IIpCwS4nb1b10Vfy83nv51JxtPxDJ41hZ+fLwtlb1tpJmisxsM+RXt256Uu3wc8x/D4YkV4GIj8TmYIlU6WrRogcFgyNdh74knnsh9nPOatTrw3cq0adMwGo3079+fjIwMevfuzcyZM3Nfd3JyYsmSJTz99NOEhoZSvnx5RowYwTvvvJObpmbNmvz777+89NJLTJ8+napVq/Ltt9/KuOiOLOYw/HQfpMZCleYw6Cdwsv9bqrYqp9LRXGYiF7fJlsshkLJIiJvpVq8S80eH8viP2zh0MZEHZ27mpyfaUaeyp96hKeX8yBryG+avu+F2cQ/8O07dAZGLkBZXpErH6dOnrR1Hka1duzbfc3d3d2bMmMGMGTNuuk716tULHS3njjvuYPfu3ZYIUdi6mCN5FY7AZjB8MbjLZHXWdC5FKh2iZGypHAIpi4QojqZVfVj4dCce/X4rZy6nMuDrzXw3og2tq1fQOzTFtzo7aj5Lx5MfY9gzB4JaQrtRekflcIpU6ahevbq14xCidFw6piocKZcgsCk8+hd42MhBz0FlmzXOJavHzUN8dY1F2C8ph4Swb9UqevDn0x154qcd7I2I5+HZW/liaEt6NbaNuTxivRpjvmsSTqsmwfLXoXIjqNFJ77AcSrG7ov/99983XG4wGHB3d6dOnTrUrFmzxIEJYXGxx+GneyElBgKawqN/S4WjFJy6lEKm2YCHq5Pt3E4Xdk3KISHsU0VPN+aNas/YubtZfSSGMb/u5N1+TRjW3jYuKpjbP4NT9H448AcsGAGj14GPfU2DYMuKXeno169fgXa1kL89befOnVm8eDF+fn4WC1SIEok5Aj8/AMnRENBE7nCUon2RCQA0DvLGyabGaRf2SsohIeyXh6sz3wxvzZuLDjB/RwRvLjrAhfg0Xu5ZX/+5PAwGuP8LuHQUovfD/Efg8aXgcuMhq0XxFHvI3LCwMNq2bUtYWBgJCQkkJCQQFhZG+/btWbJkCevXr+fy5cu88sor1ohXiOI7txW+7w3JUVC5sbrDUb6i3lGVGbsj4gFoFuytbyDCYUg5JIR9c3Yy8kH/przQvS4AM9acZOy8XaRllv4gEAW4esCQX6GcH1zYBX89qyabEiVW7DsdL7zwAt988w0dO3bMXda9e3fc3d0ZPXo0Bw8e5LPPPss3qogQujm6HBY8BllpULUtPPy73OEoZdvPXAGgbQ254iwsQ8ohIeyfwWDgpZ71qOpXjjcW7Wfp/igi4sKZ/WgbAn10HrLWrwYM+gV+eRAO/AkV68Cdb+gbkwMo9p2OkydP4u1d8Iqlt7c3p06dAqBu3brExsaWPDohSmL3r/Dbw6rCUbeXNKnSwaWkDE7FpmJAo011qXQIy5BySAjHMbBNCHOe7ICfhwv7IxN4YMZG9p9P0DssqNkF7vtMPV73Iez7XddwHEGxKx2tW7fm1Vdf5dKlS7nLLl26xGuvvUbbtm0BOH78OCEhIZaLUoji0DSMm6dfvSWaDc0fhiFzwbW83pGVOdvPxAFQxQN8ysk8KMIypBwSwrG0q1mBv57tTN3KnkQnZjBw1maW7beB2cFbPgKdXlCP/3oWzm3RNx47V+xKx3fffcfp06epWrUqderUoU6dOlStWpUzZ87w7bffApCcnMxbb71l8WCFKJRmpknkHJzWvKued3oB+s2Uif90su20qnTU8Zb2sMJypBwSwvFUq+jBn890pFu9SqSbzDw9Zxdfrj5eYMCIUtd9MjS4F7IzVeuJONuaM8ieFLtPR/369Tl06BArV67k2LFjuct69uyJ0ajqMP369bNokEIUiSkNp0VPUfvSSvW89/sQ+qy+MZVxW05dBqC2VDqEBUk5JIRj8nZ34bsRbXhv6WF+2HSG/1t5jP2RCXzUvzk+HjpdPDQa4aFv4Ie74eJe1c9jxN/gW02feOxYsSsdAEajkT59+tCnTx9LxyPE7UmJhXlDMZ7fhtnghPn+L3Fu+bDeUZVpEXGpHIlKwmiQOx3C8qQcEsIxOTsZmXRfY+pU9mTy3wdZcTCagxc28MXQlrSsplPfQNfyMHS+Ggnzymn4/m5V8ahYW5947FSRKh2ff/45o0ePxt3dnc8///yWaZ9//nmLBCZEkV06BnMHwpUzaO4+bK76DO2bDNQ7qjLvv8PRALSu7oeny6VCUgtxa1IOCVG2DGtfnabBPoydu5tzcakM/Dqc1/rU58nOtfSZz8O7Cjy+TM35dfm4uvMxfDEENCr9WOxUkSod06ZNY9iwYbi7uzNt2rSbpjMYDHKwF6XrzEb4bRikx4NvdbIG/8blbcf1jkoAYYdUpaNHg0qQIJUOUTJSDglR9jSr6suS5zszYeF+/t13kfeXHiH85GU+GdSCCuVdSz8gn2BV8filH0QfgB/vgeGLIKhl6cdih4pU6Th9+vQNHwuhq73z1WgSZpOag2PIPHDzBaTSobeEVBNbr3Yi796wMge3HNI5ImHvpBwSomzydnfhy6Et6VTbnyn/HGTN0UvcM30Dnw9tSbuaOgyD71kJRvwDcwZA5E746X41B1j10NKPxc4Ue/SqHJmZmRw9epSsrCxLxiNE4cxmWDMVFo1WFY5GD6gDgGclvSMTV605GkO2WaN+gBfVK3joHY5wUFIOCVE2GAwGHm5fjcXPdqJWpfJEJaYz5Jtwvlx9HLNZhz6DHhXU3F/VO0FGIvz6EJxaW/px2JliVzpSU1MZOXIkHh4eNG7cmHPnzgHw3HPP8cEHH1g8QCHySY2DuYNg3dXvWqcXYMCP4FJO17BEfisORgHQs1GAzpEIRyTlkBBlU8Mq3vwztjMPtQrGrMH/rTzGYz9uJy4ls/SDcfOCYX9A7e5gSoV5D8P5HaUfhx0pdqVjwoQJ7N27l7Vr1+LunjdNfY8ePZg/f75FgxMin/M7YVZXOBEGzu7wwAzo+Y4azk7YjLTMbNYeVX04+jQJ1Dka4YikHBKi7Crv5syng1rw8YBmuLsYWX/sEn0/38DOs3GlH4yrBwydB7XuBFOKanJ16Wjpx2Enin22tnjxYr788ks6d+6MwZA3ekDjxo05efKkRYMTAgBNg22z1VB1CRFQoRY8+Z+aKVTYnHXHYkgzZVPVrxyNg7z1Dkc4ICmHhBAD24TkNre6mJDO4Flb+HbDqdKfTNDZDQb/CsGtIe2KmscjPqJ0Y7ATxa50XLp0icqVKxdYnpKSku/gL4RFZCTDnyNh6Suq/0bD+2D0Wghsqndk4iaWHVBNq+5uEijHBGEVUg4JIQAaBHrz99jO3Nc8iCyzxv/+PcyYX3eSkGYq3UDcPOHhBeBfDxIjVcUj5XLpxmAHil3paNOmDf/++2/u85wD/LfffktoqPTcFxYUcwRm3wkH/gSjs5phfNAv4O6jd2TiJjKysll9OAaAPk2q6ByNcFRSDgkhcni6OfP5kBa8+0BjXJ2MrDgYzX1fbORAZELpBlK+oho+17uqmsdjzgDISCrdGGxcsWckf//997n77rs5dOgQWVlZTJ8+nUOHDrF582bWrVtnjRhFWWPOhm3fwKp3VOcsryow8Eeo1kHvyEQhNp+4TFJGFgHebrQM8dU7HOGgpBwSQlzLYDAwPLQGzUN8eWbOLs7FpfLQV5v5fEjL0u1b6FNVVTy+7w0XdsHfz6nzFwHcxp2Ozp07s2fPHrKysmjatCkrV66kcuXKhIeH07p1a2vEKMqS6EPwXS9Y/rqqcNTsBk9tkAqHnVh3THUg79EwQJ8ZY0WZIOWQEOJGmlX15d/nutCjYWUys8yMnbuLpfsvlm4QleqpzuUAh5dAZkrp7t+GFftOB0Dt2rWZPXu2pWMRZZkpHTZ8Ahs/BXMWuHlDzynQ6jEZncqObDoRC0DnOv46RyIcnZRDQogb8fFw4etHWvPqH/tYtDuS5+btJtuscV/zoNILIqQ9+ISowW/ObYE63Utv3zasyJWOxMTEIqXz9pbRakQxnQ2Hf56H2GPqef2+0Pf/wLsUDxCixKIT0zkek4zBAKG1K+odjnBAUg4JIYrC2cnI/w1sjtFg4M9d53nht92YNY0HWgSXTgAGA9TsCnvmwOn1Uum4qsiVDl9f31uOCqJpGgaDgezsbIsEJsqA9ET4bzLs+E499wyAez6GhverH6ywK5tPqrscTYJ88PVw1Tka4YikHBJCFJWT0cDHA5rhbDQwf0cEL83fQ1a2Rv/WVUsngGsrHQIoRqVjzZo1uY81TeOee+7h22+/JTi4lGqNwrEcXQ5LXoKkC+p5y+HQ610o56dvXOK2bTqhhgfsJE2rhJVIOSSEKA6j0cDUh5piNBqYt+0cr/yxl8xsM0Pahlh/eO0aXdT/i3sgPUFG3qQYlY5u3brle+7k5ESHDh2oVauWxYMSDiwrA1a+Ddtmqed+NeG+6VCr263XEzYtIdXEf4ejAehUR5pWCeuQckgIUVxGo4H3+jXB2Wjgly1nmbBwP2GHoplyf2NCKnhYb8c+wVCxrho+d8Hj0P9b8Khgvf3ZAemhK0rP5ZPwXc+8CkeHZ+GZcKlwOIBp/x0jPtVE3cqehNaSSocQQgjbYTQaeOeBxrzUox4uTgZWH4mh57R1zFhzgowsKzbH7DMVXDzg5Co171j0Qevtyw5IpUOUjgMLYVY3uLgXylWAh3+HPu+DSzm9IxMldDQqiV+2nAVg8v2NcXaSw4oQQgjbYjAYeKFHXZa90IXQWhVJN5n5eMVR7p6+gfBTVpo9vG5PGLkSfKvDlTPwbU84uMg6+7IDJTo7sHp7OGH/TGmq78Yfj0NmElQLhTEboV5vvSMTFqBpGpP/Pki2WePuJoHSn0OUOimHhBDFUaeyF3NHteezwS3w93Tl1KUUHv1hJz8fN3IpKcPyOwxsCqPXQq07wZQCCx5Tg+iYy96AF0WudDz00EP5/tLT0xkzZkyB5ZY0depU2rZti5eXF5UrV6Zfv34cPXo0X5r09HSeffZZKlasiKenJ/379yc6OjpfmnPnztG3b188PDyoXLkyr776KllZWfnSrF27llatWuHm5kadOnX48ccfLZqXMin2OHzbA3Z8r553Hgcjlqh2jsIhLDsQRfipy7g5G3njnoZ6hyMcnB7lEEhZJISjMRgM9GsZzKqX72B4h+oYDLAz1kjvzzfxc/gZss2aZXfoUQGG/QEdn1fPN06DuYMg7Ypl92Pjilzp8PHxyff3yCOPEBQUVGC5Ja1bt45nn32WLVu2EBYWhslkolevXqSk5M3u+NJLL/HPP/+wYMEC1q1bx4ULF/IVOtnZ2fTt25fMzEw2b97MTz/9xI8//sjEiRNz05w+fZq+ffty5513smfPHl588UWefPJJVqxYYdH8lBmaBrt+Uc2pog+Ahz888if0mAROtzUfpbBB6aZs3vv3MABjutW2boc8IdCnHAIpi4RwVD7lXHi3XxP+GN2ekPIaSelZTPzrIA/N3EREXKpld+bkrEbp7P8dOJeDE//B7LsgxUpNu2yRZkdiYmI0QFu3bp2maZoWHx+vubi4aAsWLMhNc/jwYQ3QwsPDNU3TtKVLl2pGo1GLiorKTfPVV19p3t7eWkZGhqZpmvbaa69pjRs3zrevwYMHa7179y5ybBERERqgRURE3Hb+9JKZmaktXrxYy8zMLPnGEiI17dcBmjbJW/390FfTEi6UfLtFZNG86Mge8jF/2zmt+vglWof3/9NSM7Jums4e8lIUeuXDno8tjsreyiL5DdoWR8mHpjlOXjIzM7WFixZr3284oTWZtFyrPn6J1mLKCm3ziVjr7PDCXk37tLE6T/pvisU2a83PwxJlkV1ddk5ISACgQgU15NjOnTsxmUz06NEjN02DBg2oVq0a4eHhdOjQgfDwcJo2bUpAQEBumt69e/P0009z8OBBWrZsSXh4eL5t5KR58cUXbxpLRkYGGRl5bf+SkpIAyMrKwmQylTivpSkn3hLFrWkYDizAaeUEDOkJaE5umLu9jrn9M2B0glJ6TyySFxtg6/nQNI3vN50GYHiHEJwNZkwm8w3T2npeikqvfFzf/Eboz97KIvkN2hZHyQc4Tl5MJhNGAwxuVYXuDSrzzNw9HLiQyCPfbeWNu+szvL2F5/Xwb4ih53s4//Eo2vZvyWo/Fty8SrxZa34eliiL7KbSYTabefHFF+nUqRNNmjQBICoqCldXV3x9ffOlDQgIICoqKjfNtQf5nNdzXrtVmsTERNLS0ihXruAIS1OnTmXKlCkFlq9atQp/f/vsTBsWFnZb67mZ4mke8SNVEnYBcMWjFrurjSLpSjAs16dZwO3mxdbYaj5OJsKRKGdcjBq+lw+zdOnhQtex1bwUV2nnIzY2tlT3J27Nnssi+Q3aFkfJBzhOXnLyMaIqzM80siPWyLv/HmHltkMMqmXG2ZKDM2pmursF4pkexZF5b3Kqch+Lbdoan4clyiK7qXQ8++yzHDhwgI0bN+odCgATJkxg3Lhxuc8jIyNp1KgR3bt3t7vZcU0mE2FhYfTs2RMXF5eir6hpGA4twmnFJAxpV9CMLpi7voZn6HN0Merz1brtvNgYW8/Hc7/tBaJ5qFUIAx9odMu0tp6XotIrH5GRkaW2L1E4eyyL5DdoWxwlH+A4eblRPh7QNL7ffJaPVhxj6yUj6W5+zBjanABvd4vt1xAUB0vH0SRpHQ2GfwJOJXsPrfl5WKIssotKx9ixY1myZAnr16+natWqucsDAwPJzMwkPj4+3xWm6OhoAgMDc9Ns27Yt3/ZyRhS5Ns31o4xER0fj7e19wytLAG5ubri5ueU+T0xMBMDZ2dluf3guLi5Fjz0lFv4dB4f+Us8Dm2Lo9zVOgU1wsl6IRVasvNgwW8xHZHwaYYdjAHi8c80ix2eLebkdpZ0PZ2e7OEyXCfZeFslv0LY4Sj7AcfJyfT7G3FGXRkG+jJ27i73nE3jo6618Pbw1rar5WWaHLYfBug8wJEbicvQfaD7YIpu1xudhibLIpmfx0jSNsWPHsmjRIlavXk3NmjXzvd66dWtcXFxYtWpV7rKjR49y7tw5QkNDAQgNDWX//v3ExMTkpgkLC8Pb25tGjRrlprl2GzlpcrYhrnPoL5jRXv03OkO312HUGghsondkohTMWHOCbLNGaK2KNAj01jscIaxOyiIhyq6u9Srx99jO1AvwJCYpg8GzwnPLwRJzcYcOY9Tjte87/BC6Nl3pePbZZ/n111+ZO3cuXl5eREVFERUVRVpaGqCGTxw5ciTjxo1jzZo17Ny5k8cff5zQ0FA6dOgAQK9evWjUqBHDhw9n7969rFixgrfeeotnn3029+rQmDFjOHXqFK+99hpHjhxh5syZ/P7777z00ku65d0mpcbBH0/A749CaixUbgRProI7J5T4lqCwD+uOXWLu1nMAPNe9js7RCFE6pCwSomyr4V+ehc904p6mgZiyNT5ecZRBs8I5ezml8JUL0/ZJ8AlRM5b/MdKhJw206UrHV199RUJCAnfccQdVqlTJ/Zs/f35ummnTpnHvvffSv39/unbtSmBgIAsXLsx93cnJiSVLluDk5ERoaCiPPPIIjz76KO+8805umpo1a/Lvv/8SFhZG8+bN+eSTT/j222/p3Vtmzc51eIm6u3HgTzA4QZdX1AybQS30jkyUkoRUE6/9sReAxzrWoGNt+xwwQYjikrJICOHp5syMh1vxfwOb4+nmzM6zV7h7+gbmbTuHppXgroe7DwyZo+buOLlKzVbuoGy6sXBRPkR3d3dmzJjBjBkzbpqmevXqLF269JbbueOOO9i9e3exY3R4qXGwbDzs/109r9QA+n0Fwa30jUuUuol/HyA6MYNa/uUZ36eB3uEIUWqkLBJCgJrJfEDrqnSoVYFXFuxly6k4Jizcz3+HopnavymVvW6zk3mV5tBvhmpNsvlzCGwKzQZZNngbYNN3OoTOjiyFmR1UhcNghM4vweh1UuEog5bsu8Bfey5gNMAng5pTztUWhgsQQgghSl9VPw/mPtmBt/o2xNXJyKojMfSetp7lBy7e/kab9IfOV0ei+/s5iNxlmWBtiFQ6REHpibBoDPw2FJKjwb8ejAyDHpNVpydRpkQnpvPW4gMAPHtnHVpaatQOIYQQwk4ZjQae7FKLf57rTMMq3lxJNTHm1128/PtektJvc3K+u96Gen0gKx1+GwZJ0YWvY0ek0iHyi9gOX3eGvfPU3Y2Oz8NTG6BqG70jEzpIzcziyZ92EJ9qonGQN8/dVVfvkIQQQgibUT/Qi7+e7cQzd9TGaIA/d53n7ukb2HY6rvgbMxrhoW/Uxd6kC/DnSMgu+UzgtkIqHULRzBg3fgrf94b4s+BbDR5fBr3elbsbZVS2WeOF3/awPzKBCuVdmTmsFa4WnY5VCCGEsH+uzkZe69OA358KJaRCOc5fSWPwN+F8uPwImVnm4m3M3QcGzwFXTzizAda8Z52gdSBnEAISL9DxxIc4rXsftGzVrnDMRqjWQe/IhI6mLj1M2KFoXJ2NfDO8NdUrltc7JCGEEMJmtalRgaXPd2Fg66poGny19iQPztzE8eik4m2oUj24/3P1eOOncHS55YPVgVQ6yrrD/+A8uyuVkg+juZRXI1P1/07VtEWZ9euWs3y78TQA/zewOW1qVNA5IiGEEML2ebm78PHA5nz9SCv8PFw4eCGRe7/YyA+bTmMuzoSCTfpDu6fU40Wj1Twedk4qHWVVZir88yLMfwRDejxXPGqS9eRqaPEwGAx6Ryd0tPZoDJP+PgjAK73qcX/zIJ0jEkIIIexLnyZVWPFiV7rVq0RGlpkp/xxixA/biEpIL/pGev0PgttAegL8PgKyMqwXcCmQSkdZlHgRvusJO38AIDv0OTbUfRsq1NY5MKG3nWfjGDt3N9lmjf6tqvLsnTLruBBCCHE7Knu78+PjbXnngca4ORvZcDyW3p+t55+9F4q2AWdXGPgjlPODi3tg6atQkokIdSaVjrIm9jh81wuiD0D5yjB8Mea7JqEZbXqeSFEKwk9eZvh320jOyKJj7YpMfagpBrnrJYQQQtw2g8HAo6E1+Pf5LjQN9iEhzcRz83bz/LzdxKdmFr4B3xB46FvAALt+gm2zrR6ztUiloyw5v1NVOBLOqbsaT4ZB7Tv1jkrYgPXHLvHYD9tIzcymS11/vhvRVkaqEkIIISykTmVPFj7TkefvqoOT0cDfey/Q+7P1rD92qfCV6/aAnlPU4+Wvw6m1Vo3VWuSsoqw4/h/8dC+kxUFQSxi5Evxq6B2VsAH/HYrmyZ92kJFl5q4GlZn9aBuZcVwIIYSwMBcnI+N61eePMaHU8i9PdGIGj36/jYl/HSA1s5D5ODo+D82HqlFGfx8Bl0+WTtAWJJWOsmDvbzBvMJhSofZdMGIJlPfXOyphA5btv8iYX3eSmW2mT+NAvn6kNe4uUuEQQgghrKVlNT/+fb4LI0KrA/Bz+Fn6fr6R3eeu3HwlgwHu/QyqtoX0eJg3RHUwtyNS6XBkmgabpsOip8CcBU0HwdD54Oapd2RCZ9lmjblbzzF23m6yzBr3Nw/iy4dbSpMqIYQQohSUc3ViygNN+PmJdgR6u3M6NoX+X23mk5VHbz6hoIu7mjjQOxhij8EfI8GcXbqBl4CcYTiq9ARY8BiETVTPQ8fCg7PUSAiizEo3ZTNn61m6f7KWNxbtJ9usMbB1VaYNboGzkxwOhBBCiNLUtV4lVrzYlQdaBGHW4IvVJ7j/y43sP3+TuxheATBkLjiXgxNhdjVjuZxlOKKLe2FWNzi0GIzO0OcD6P0eGOXjLqsSUk18ufo4nT9czZuLDnDmcio+5Vx4pVc9PuzfDCejjFIlhBBC6MHHw4XpQ1ry5cMtqVDelSNRSfSbuYkPlx8h3XSDOxlBLeCBL9XjDZ/AkX9LNd7bJeOkOhJNgx3fw/IJkJ0BPiEw4AcIaat3ZEInF+LT+H7jaeZtO0dKpjpwBfuWY2TnmgxuG0J5NzkECCGEELbg3mZBhNaqyKS/D7Jk30W+WnuSlQej+GhAc1pX98ufuOkAOL8Dtn4Fi8bA6LXgXU2XuItKzjgcRUYS/PMCHPhTPa/XB/p9BR4V9I1L6OJYdBKz1p3irz2RZJnVREINAr14qlst7m0WhIs0pRJCCCFsTkVPN758uBX3Novi7b8OcPJSCgO+3swTnWrySq/6+UeX7PUuXNgNEVtg/iMwYpl+gReBVDocQdR+NXxa3EkwOEGPydDxOTXSgSgzNE1j+5krzFp3klVHYnKXh9aqyFPdatGtXiWZ7E8IIYSwA32aBNKhVgXeXXKYP3ed57uNp/nvcDQf9m9Gh1oVVSInFxj0E3zdBWIO4bR0HLjcr2/gtyCVDntmSoPwGbD+Y8hKV6MZDPgBqrXXOzJRiuJSMvln7wX+3HWefVc7nhkMcHeTQEZ3rU2LEF99AxRCCCFEsfl6uPLJoObc27wKbyzcz9nLqQz5ZguPdazB63c3UEPcewXCwB/hp/swHvyTmlU9gL56h35DUumwR5oGBxdC2GQ1uzhAnZ5qdKryFXUNTZSOdFM2qw7HsGj3edYevZTbhMrV2ciA1lUZ1aUWNf3L6xylEEIIIUrqzvqVWfFSV6YuPcK8bef4cfMZNhy/xPQhLWkS7AM1OkHPd2DlmzSOnIc5eiRUbal32AVIpcPenN8JKyZAxFb13DtYNadqMkBGp3JwZrPG9jNxLNodyb/7L5KUnjd7adNgHx5sGcz9LYLw93TTMUohhBBCWJq3uwtTH2pKnyaBvLpgLycvpdBvxiZe6lmPMd1q4xT6LOZT63A6sRLjolHw1Hpw9dA77Hyk0mEvEiJh1RTYN189d/GATi+qvhs29qUSlnXyUjKLdkWyaHckkfFpucuDfcvxQIsgHmoVTJ3KXjpGKIQQQojS0O3qvB5vLt7P0v1RfLziKKuPxDBtUAuq3Ps5mV92wP3ycXWB+r7peoebj1Q6bF1mippVfNPnkHX1hLP5w9D9bfAO0jc2YTWXkzNYd9HAt19vYX9kYu5yLzdn7mlahX4tg2lfswJGmV9DCCGEKFP8yrsy4+FWLNwVyaS/D7Lz7BXunr6eN+9pQLXqT9Hp5EcYdv4Ite6Exv30DjeXVDpslaapuxr/TYaki2pZtVDo/T4Et9I1NGEdWdlmlh+MYuGuSNYdu0S22QlIxNlooFu9SjzYKpgeDQNUxzEhhBBClFkGg4H+ravSrmYFXv59L9vOxPHG4oM09WvKt61fImDnp/DP8xDcGnxD9A4XkEqHbUq+BH89A8dXque+1aDnu9DoARkG10HtPHuFtxcf4NDFvLsa1cprjLijIf1aVqWi9NMQQgghxHVCKngwb3QHZm84xScrj7L/ipFeOzvwmudjDEn6CadfH4JH/lTnkjqTSoetOR4Gi5+GlEvg5AZ3vA4dngEXd70jE1ZwOTmDD5cf4fcd5wHwKefCIx2qcV/TQI5uX8c9Harh4uKic5RCCCGEsFVORgNjutWmY00/nv5xM5GpWbyZ3ov5zg35X8zXNPu2Bwz7A6o00zVOqXTYClM6/DcJtn6tnlduBP2/g4BG+sYlrCLbrPHb9nN8tPwoCWkmAAa1qcr4Pg2o6OmGyWTiqM4xCiGEEMJ+NKzixcvNsomr0Jjpq06yLyOEB/gfD19Zxavf98d3yCyofZdu8UmlwxZEH4I/R0LMIfW8/RjoMUXubjiofefjeXvxAfZencivYRVv/tevMa2rV9A5MiGEEELYMycDjAitzv0tqzJ16REW7Y5kTnYPliW14/WfvmDAg9EYWw7VJTapdOhJ02DbN7DybcjOgPKVoN9XULen3pEJK0hINfHxyiPM2XoOTVMjUY3rVY/hHarj7CRzrAghhBDCMip7uTNtcAsGtw1h4uL9HIuB1zKf5Lffj/Fu5Bc07ju21PsJy5nOdWbMmEGNGjVwd3enffv2bNu2zTo7So6BOQNh2WuqwlG3Fzy9WSocDkjTNBbsiOCuT9by6xZV4ejXIohVL3fj8U41pcIhhMin1MohIYTD61CrIv++0JU37q6Ph1M2u7R63LexBpM//5qE5LTCN2BBcrZzjfnz5zNu3DgmTZrErl27aN68Ob179yYmJsbyO4vYCifCVGfxuz+Gh38Hz8qW34/QnabB/O0RXE7JpG5lT+aN6sBnQ1pS2Vuazwkh8ivVckgIUSa4OBkZ3a0Oq17rSd/gNMwY+e1iAImRR0o1Dql0XOPTTz9l1KhRPP744zRq1Iivv/4aDw8Pvv/+e8vvrOF9cNdbMHottB8tQ+E6MKPRwDsPNGHC3Q1Y+kIXQmtX1DskIYSNKtVySAhRplTxKceM5wbwa08zkzs4EVK/ZanuX/p0XJWZmcnOnTuZMGFC7jKj0UiPHj0IDw8vkD4jI4OMjIzc50lJSQBkZWVhMpmKttPQF9X/oqa3kpx4ixy3DbPVvNStVI66laqBORuTObvQ9Laaj9vhKHnRKx9ZWVmluj+hn+KWQ1C0skh+g7bFUfIBjpOXspaP9l370L4I6a5libJIKh1XxcbGkp2dTUBAQL7lAQEBHDlS8PbT1KlTmTJlSoHlq1atwt/f32pxWlNYWJjeIViMo+TFUfIBjpOX0s5HbGxsqe5P6Ke45RAUryyS36BtcZR8gOPkRfJxc5Yoi6TScZsmTJjAuHHjcp9HRkbSqFEjunfvTnBwsI6RFZ/JZCIsLIyePXva/UR0jpIXR8kHOE5e9MpHZGRkqe1L2J+ilEXyG7QtjpIPcJy8SD4KZ4mySCodV/n7++Pk5ER0dHS+5dHR0QQGBhZI7+bmhpubW+7zxMREAJydne32C+vi4mK3sV/PUfLiKPkAx8lLaefD2VkO02VFccshKF5ZJL9B2+Io+QDHyYvk4+YsURZJR/KrXF1dad26NatWrcpdZjabWbVqFaGhoTpGJoQQoiyQckgI4cjkEto1xo0bx4gRI2jTpg3t2rXjs88+IyUlhccff1zv0IQQQpQBUg4JIRyVVDquMXjwYC5dusTEiROJioqiRYsWLF++vECnPiGEEMIapBwSQjgqqXRcZ+zYsYwdO1bvMIQQQpRRUg4JIRyR9OkQQgghhBBCWJVUOoQQQgghhBBWJZUOIYQQQgghhFVJnw4LMZvNAFy8eFHnSIovKyuL2NhYIiMj7X5OAEfJi6PkAxwnL3rlI+eYknOMEeJWblQWyW/QtjhKPsBx8iL5KJwlyiL7fWdtTM5kTu3atdM5EiGEI4qOjqZatWp6hyFsnJRFQghrKklZZNA0TbNwPGVSVlYWu3fvJiAgAKPRzlqtZSTBjHbw7DZw89I7mpJxlLw4Sj7AcfKiUz7MZjPR0dG0bNnSrq/AidJxw7JIfoO2xVHyAY6TF8lHoSxRFkkJZiHOzs60bdtW7zBuT3oieBshOBjcvfWOpmQcJS+Okg9wnLzomA+5wyGK6oZlkfwGbYuj5AMcJy+SjyIpaVlkZ5fkhRBCCCGEEPZGKh1CCCGEEEIIq5JKhwBnN+j2uvpv7xwlL46SD3CcvDhKPkTZ4yjfXcmH7XGUvEg+SoV0JBdCCCGEEEJYldzpEEIIIYQQQliVVDqEEEIIIYQQViWVDiGEEEIIIYRVSaVDCCGEEEIIYVUyOaCj2/AJHP4HYo+DszuEtIeeU8C/7s3X2T0H/nom/zInN3g7xrqxFmbNVFj3Qf5lFevCcztuvs7BRbD6PYg/BxVrQ48pUK+XdeMszLSmkHCu4PK2T0LfTwout6XP48wm2Pw5XNgDyVEweA40vDfvdU2DNe/Drp8gPUF93+6dpt77W9k2GzZ9DsnRENgE7v4YqrbWJx/ZJlj9LhwPgytnwM0bat0BPSaDd5Wbb/N2vp9C3K6iHNvjTsHKt+FcOGRlQp3ucM/H4Fk5L01qHCx7DY4uB4MRGt0HfT4EN8/Sycf2b2H79+oYDVC5AXQbD3V7quemdFj5Jhz482oe7oK+n+bPQ3wE/DsOTm8A1/LQYih0nwxOpXiKU1g+dvwA+/+Ai3shMwnGn4Vyvvm3ofdnkeNWeUmNg7VT4eRqSDgPHv7QoC/c9Sa4++Rtwx4+k39egFNrISlKxRjSXp0jVKpnX/nIoWkwZwCc+K9g2WwL+UAqHY7vzCZoOwqCW4E5C1a9A788CM9uVV+8m3HzhrHXnCwZDNaPtSgqNYRH/8p7brzFV/jcVvhjJPSYBPX6wP4F8NvD8NR6CGhk/VhvZvQaMGfnPY85BL/0g0b9br6OrXweplQIaAItH4H5jxR8fdNnsHUWPPgV+FaHNe9d/b5tAxf3G2/zwJ+w4g1VOQluA1tmwq8Pwtid4Fmp9PNhSlUnBl1fhcCmkBYPy8fDvCHw1Lpbb7c4308hSqKwY3tminoe0ARG/KPWWf0ezB0MT64C49WGDgtHQVI0PLpYVbj/ekadjA34rnTy4R2sKvQVa6uTpr1zYd5QGLMBKjeEFRPg2EoY+JOaYXnpq+o3O3KlWt+cDXMHqUrIyJXqwsWip8Dooo79paWwfJjSVKWvTndYNeXG29D7s8hxq7xoGiRdhF7/g0r11cnskpfUssG/qPXt5TOp0gKaDgKfqpB2BdZ+oH4zL+4Do5P95CPHlpnADc4NbCUfAJooW5Ivadokb007vfHmaXb9qmnvh5ReTEW1+n1Nm9mp6Ol/H6Fpvw7Mv+ybuzTt7xcsGVXJLR2vaZ811zSz+cav2+rnMclb0w79k/fcbNa0j+tq2sbpecvS4jXtnUqatm/BzbfzzZ2atuTlvOfZ2Zr2f/U1bf0nlo/5Rq7Px42c36HSXTl38zTF/X4KYUnXH9uP/6dpk301LS0hL01avKZN8tG0E6vV85gjap3zO/PSHAtTaRIulFbkBU2tpmk7f1LxTqmoaQcW5b0Wc1TFfG6ben5spcpnUnRemm3fatr7VTXNlFGqYReQk49rnVqv4k+9kn+5rX4WOW6UlxwHFmraO/6almVSz+3tM8lxcb/6DC6fVM/tKR8X9mra/zXQtMSogmWaDeVD+nSUNekJ6n85v1uny0yGaU3g00aqRh1z2PqxFUXcSfi/+vBZM/jzSXWV5WYitqtmMdeq0x3Ob7dqiMWSlQn75qsr7re6e2Grn8e1rpxRV1Cufc/dfaBqm5u/51mZqonTtesYjeq5LX1O6YmAIX/zgRspzvdTCEu6/tienQkY8k8S5uyumu2c26KeR2xT3+ngVnlpat2h0kTq0CzQnK2aIJlSoWo7dWwwm/IfHyrVA58QOL9NPY/YBpUb529uVac7ZCTCJZ2Ok9fnoyhs7bPIUZS8pCeCm1deUx17/EwyU2DPHHWH3ruqWmYv+chMVeVN3/8Dr4CC69hQPuTef1liNsPyCRDS4dbNi/zrwgMzIKCx+lJu/gK+6wXPbAGf4NKL93pV20C/maqdfHIUrP0QfrgbnglXB7zrJUfn/5EBlK+kltuKI0vUyUKLYTdPY6ufx/WSr/YxKc57nnoZtOwbrxN7zPIx3g5TOvw3CZoOUM07bqa4308hLOVGx/aqbVUzq7BJ0H0ioMF/k9XvLTlKpUmOVr+1azk5q4pLaR4now/Ctz0hKx1cPVV79MoNIGo/OLkW7Ptw7TElObpgM8zyV48nyaXc7+1m+SgKW/kschQ1LymXYf3H0PqxvGX29Jlsm61+I6YUdex+dDE4u16N1U7ysWIChLRTfWtuxIbyIZWOsmTpy+oK+RPLb50upJ36y33eHr5sCzt/gLvesm6Mt5Kv01QT1f7/s6aqs3irR3ULq0R2/6LydasOyrb6eZQF2SZY8JhqR9v301undcTvp7APNzq2l/eHgT+qzqNbv1ZXzJsOgCrN1WNbUrGuap+ekQiH/oLFY+CxpXpHVXw3y0dRKx62pCh5SU+EuQNV3447JugX660Ulo9mg6D2Xaoz+eYv1PH+iZU374Ool5vlI+4UnF4PT23QO8IikUpHWfHvK3BsBTy+tPhXx51coEoz9eW2JeV8Vceqm8XlGVCwFp9ySS23BfHn1MgZg38t3nq2+nl4XnPlxCswb3nKJdUh+0Y8KoLByTY/p5wKR0KE6oh7q7scN1LY91MIS7jVsb1Od3hhr7oabXRS38mP60KTGup1zwD1W7tWdpbqVFuavz9n17wR7oJaQuQu2PoVNH5INRNLi89/t+Pa44NngEp/rZSb3HW1tpvl477pha9rK59FjsLykpEEv/bPu+ru5JK3rj19Ju4+6q9ibXV38MPqqgVC0wH2kQ/nchB3Gj6olj/978OhWkd4/F+byoeNXe4QFqdpqlA6skSdOPnVKP42zNkQfQg8AwtPW5oyktWP7WZxhbSF09eNNnRyjTqw2ILdc9Tt9Lq9i7eerX4efjXUwe3a9zw9Ec7vuPl77uwKQS3yr2M2w6l1+n5OORWOyyfVaFQeFYq/jcK+n0KURHGO7eUrqpP2U+vUiW39e9TykHaqeeeF3XlpT68Dzazu1OlFM6v+XkEt1Ag71x4fYo+rCwE57dlD2kHMQUi+5oT95Bo14l8lne8w5OSjKGz1s8hxbV7SE9UoT06uMPS3gncF7PYz0dTvKitDPbWHfHR+CZ7eDGM25v0B9J4K/WaoxzaUD7nT4ej+fVl1Oho6V12RSLraNtTdG1zKqccLn1LNe3pMVs/Xfqjap1eopQ6Cmz9XB3m9m4iseBPq3606ESZFwdr31dW7pgPU69fno/3T8OM96pZp3d5qaNYLu4t21cnazGbVaa350ILjZNvy55GRnP/KffxZuLhPtTv2DYEOT6v2vRVqg191NUSnVyA0uGa88J/ugwb3QfvR6nnos7DoaXX1Jri1GvbPlKI61+uRD69A+P1RNWzuw/NVJS/nd1POL6+97/X5KOz7KYQlFeXYvvtX8K+vKh0R29XQz6HP5s3lUak+1OkBfz8P936mOm0vfRWa9L91k09L+m8y1Omphi3NTFZDm5/ZCMMXqivQrYar31Y5P9U3aulrqsIRcvWiRO271InTotHQ8x3Vfn31/9S8R9d2otczH6A+n+TovONOzCH1uflUVRc1bOGzKEpeciocpjQY8o2645GRpNYr76+OefbwmcSdhoMLVawe/pB4ATZOUxWoulfn8rKHfHgF3LjzuE/VvAsRtpIPpNLh+HZcHd/7x+s6GD0wE1pe7byccD5/G9/0eDU2eHI0uPuqq00jV+rfLjXxgpp3Iy1OHSSqdYAn/1MHOiiYj2rtof+36se16h11Ijxkrr5zdOQ4tUZVHFoOL/iaLX8eF3bDT9dUIFa8of43f1jNzdHpRTWSxj8vqApStQ7wyML8V8LizqgO5Dma9FfNP9a8f3VywKZqHWve9r1VPu54HY5ebU/+def8641YAjW7qMfX56Ow76cQllSUY3vscfhvimqi41sNuryiKh3Xemi2Orn9+X513Gl4P9z9ofXjz5FyCRaNUZ3b3bzVgBnDr54MgrpiazDC/OGqqVXtu/L3rzI6qYsDS8apjrauHupizp1vll4eipKPHd/nnzz0h7vV/2s/L70/ixy3ysvpDXmjaX3eMv96L+xTF5vs4TNJvAhnw2HLV6r5nmdlqN4RRobldbq2h3wUha3kAzBomqaV+l6FEEIIIYQQZYb06RBCCCGEEEJYlVQ6hBBCCCGEEFYllQ4hhBBCCCGEVUmlQwghhBBCCGFVUukQQgghhBBCWJVUOoQQQgghhBBWJZUOIYQQQgghhFVJpUMIIYQQQghhVVLpEPbr9AaY7KNmEy2JRU/DvIctEpIufugLy14vPN33d8O+BdaP51oLHofNX5TuPoUQwpZdOavKrov7Sradw0tgeguY4le0MsDWFLUMP7UWvmwL5uzSiEqJOQKfNITMlNLbZxkglQ6hv+3fwfvBkJ2VtywjGd6pqE6or5VzkIo7BSHt4eVj4O5j/Rh3/ghfdYL3gmBqNfi6M2z4xPr7tZQjSyElBpr0t8z29syF73oXnq7rq7D+/yA9wTL7FUKIokiJhSUvwaeN4d1K8HFd+OVBOLdF78gsZ8mL0OgBeOkQ3PXmjdNE7Ye5Q+Cj2vBuZZjWFBY8BsmXSjPSkgmbqMoSo5NltvdZUzi55tZpKjeAqm0gfIZl9ikAcNY7ACGo2RUyk+HCbghpq5adCwfPAIjcAaZ0cHFXy89sAJ8QqFBLPfcKsH58u36B5RPg7g+heifIzoTogxBzyPr7tpStX0OLYWC00HWGI/9C/bsLTxfQCCrUhH2/Q7tRltm3EEIUZv5wdax+8Cvwq6FOsk+vhdQ4vSOzjIxkSLkEdbqDd5Ubp0mJhZ/uh3p9YPhCdYEu/hwcXQamFKBSqYZ8W86GQ9wZaHi/ZbYXdQDSEqBG58LTtnwE/n4eOo8DJzldtgR5F4X+/OuCZ6CqUORUOs5sgPr3wOn1cH471OxydflGqHH18ekN8NO9MP4slPOF3XNU5WDg9+p/QiRU6wD9ZoJXoFrHnA0r34bdv6oT8JbDAe3W8R1dBo0fhFaP5i2r3DB/mkVPq6v5VZrBtm8gKxOaDoC7PwJn16v7NsOmaequSXIMVKyjrt407pe3nehDEPa2OtC6ekDtu6D3VChfUb2emQJLxsHhf8DNEzo+V/j7mxKr3se7P8y/fLIP3DsNji5Xr/uGwAMzwKOiOtBe2AUBTeChWXmVPFCVwJNroPsk9XzbbNgyU73f7t5QLRQG/5KXvt7dcOBPqXQIIUpHWjyc2wyP/Zt3culbDaq2zp9usg/0/UQd489sVBe6er6T/5iccB5WvKmOeQYDVO8IfT4Av+p5aXb+BOFfqmZTvtWg/VP5j3fnd8KSF+DSMVV2dH2lCHm4oppMHVumypManVR5UrF2XtkH8NN96v+IJXnlZI5zWyAjEe7/Iu+k2a+GutB3rTMbVbkYfQDK+UHzoXDX23nrTGsKHZ6G0Gfy1vmqMzToC3dOyHsv7/scjq+EE6tURajXe9Dgnrx1jq2E5a9DYiRUbav2U5gDf0LtO/IuPAKsmaoufLV/CtZ+oN6r5kPgno9Vc97wGaCZocMYVcZe6+hSVVFzclEVsKWvqouc2Sb12fV8F+r1Umlr3am2fXYj1Lqj8FhFoaR5lbANNbuoikaO0xtUYVGjU95yUxqc31HwwHotU6o66Dw4Cx5fqgqMlW/lvb75C9gzBx74Ep5YoQ4oh5fcOjbPyqriE3/u1ulOr4NLR1VBN+A7VTFY90He6xs/gb2/qRP9Z7ZAh2dg4Wh1wAdVUP50HwQ2g9Fr4ZE/VeVkwYi8bax8G85ugqFzYfgite7FvbeO61w4uHiAf/2Cr637WB2sx2wE/3rw50h1y77LSyoGNHVQvj6f3lWgUj2I3AXLxsOdb8JzO1TM1TvlTx/cGiJ3QlbGreMUQghLcPVUf0f+Lfy4s/o9dRV9zCZoNgj+eEIdx0GdiP7ykLrA88QyGLkSXMvDr/1VRQDUXdw176uT9LHboPtEWPOeaoIK6o7E3EFQqQE8tQ7umJC/TLqZxc+ou/9Df4Mnw0DTYM4AFVNIexi7U6Ub9ItqZhzSvuA2PAPAnAVH/lHr30jiBZgzEIJbqfeg76ew+xdY/3HhMV5v3YfqAt3Tm6BuL1g4Ku/OUsJ5mP+IukM+ZqO6iPff5MK3eS4cgloWXH7lNJwIU2XOgO9UzHMGqvw8vhR6ToHV/1PnDNc6ulRVlgD+fUV9Px5fBk9vhh5T1Oebw9kVApuqi4DCIqTSIWxDjS5wbqvq15GRBFH7VKWjeqe8k/KIbZCdkXen40bMJnVSH9wKglqoq02n1uW9vuUr6DIOGt0PlerDvZ+pq/O3csfr6rb0Z03hi9bqrsaBherOxbWcXNSdgsoNoV5vuPMN2DpLpcvKgA2fqtfr9FBNjloOU4Xcjh/U+ttmqzslPSapE/oqzVX6Mxsg9oQqvHb/Ar3eVVddAhpDv69UoXIr8RHgWenGTataDoMmD4F/Hej0oqpYNR2kYqxUH9qPyXv/c1zbtCrhvDpI1+utrhJVaa6uLl3LK1A1c0iOvnWcQghhCU7O6g73nrnwQTX4rhf8N0U1rble437QeoQ6Bt71ljrB3TpLvXZgobpifv+X6nhbqT48MFMd93Iuhq15H3q/p8oUvxrqf4dn847r+xfkbaNyQ6jfBzo+f+v4L59UJ8f3f6HurAQ2hf7fQuJFOLJEnQyX91dpy/mpZsY5d9SvFdIWurwMfz4JH9VUlaVN09XFrBzbvwXvYLjn/1S50/BeVTEK/7JgGVeYFg+rO/wVa6vKV2ayujAFqu9mhZrqvfKvq8q+FkUYwCU+Arxu0HxMM18tbxuo8qhGF7h8XN2F8q+rmkZVrKvu4udIvKCaRtfpoZ4nnFetIQIaq9jq91EXOq/lFQgJEcV7H8RNSfMqYRtqdFZtTC/sUlf8K9ZRB9XqndQVH1O6Ovn1q6GaAd2Mi0f+pkBegardK6jmT8lRENwm73UnZ1XI3OwqUM42nvxPNX06u0lVfhY/Dbt+hkcW5p3MBzRRTaJyhLRTB93E86pZlCkVfu6Xf9vZmaqiARC9X93heS+oYAxXTkNWmkp/bfweFVRheStZaeDsfuPXAhrnPfa82r43oNE1yypDVjqkJ6rKmabBseUw8Ef1eu07VR+b6c3VgbxOD2hwb/73waWc+m9Ku3WcQghhKY0egLq9VTOr8zvgeJg64b7/C3WxJUfVdvnXC2mnOl+DOibHnVIDnVwrK10dkzNT1P+/xqomqTnMWXkXs2KPqePstc2DQq7b5/UuHQWjs+rInCPnWH/pWNHyn6P7RAgdq+5Qn98BO75Xg6A8vkzFdemoisdgyFunWoerZVfkrcvb611bnriWBzfvvPI39lj+sgsKfx/gavnlVnC5bzVw88p77llZdTS/9uKaZ2XVvDjH0aUqb+V81fP2T8G/4+DkanUhr+H9ENgk/35cyqmyW1iEVDqEbahYW11tOb0e0uPzmuh4VwGfYIjYqq4sXd8W9XpGl+sWGCi0z0ZRBTRSf+1Gwdkn4Ic+qq1nYTFB3rB7w34veNUm54CamaKutPSYUnB9r0BV+N0Oj4o3H5Iw3/tluPky7eoVr8idqkDNuZXv5gVPrVefzcnVqlnB2qkwak3egT3tytU4/G8vfiGEuB0u7qpfXO27oNtrqnKwdmr+SsetZKaoO+YPzS74Wnn/vOP6/Z+rZqTXstRIS5bgUUE1e2r8oOqLN6vL1WbIXxdtfcMNylGzqWC6G5W/WjHvllzvZuXXjfZV2P6PLlN9RXO0HqH6dxxbocqvDZ+qOzHtn8pLk3YF/GqWLA8ilzSvErajRhd1N+PMxvwjS1TvqNpuRu6EGkU4wb8Zdx/VYT3ymjae2VlwYU/xt1Xpav+IzGuugEQfyH81//x21a7Yu6pK7+SmbudWrJ3/z6eqSl+luRob3Ld6wTSu5dWBz+iSP/60K+pW/K0ENlNNm3JO/kviyL/q6uG1BaqTs7rj0etd1S42/lz+W9oxh1SFMqczvBBC6KFSg4LzLpzfXvC5fz31uEpzdXwtX6ngMdndR11J96oCV84UfN2vhtqGfz3VpMeUfvN9Foizvrq4c21/hNQ41cw2p+y5Xc6uqizJeR8q1Vd376+9239uC7h6qeM2qApWUlTe6+mJqtN8cfjXU2X4tQp7H0CVXzl9bEoiI1m1JLi20gGq/G07EobMgY5j1aAA14o5nNcaQZSYVDqE7ajZRR3sovZfV+noDDt+VE2LbtWJvCg6jIGN01Tn8UvH1K3VwuaQWPISrPtIxRZ/DiK2w6Ix6sr9tbeHs03qSlrMETVKx5qp6q6I0ajuCHR8To2qtWeuumtxYY9qO5zT4bDtKFUx+PMJdXCOOwUn/lPNy8zZqjNjq+GwcqLqpxJ9SL1mKORnXKW5ulp0bmuJ3jrg6pWia4bKPboctnytJrmKPwd756krS/5189KcDVeVEiGEKA2pcfDjvbB3vurHceUMHFykmlc1uO6k89BiNSx67AnVPyNyJ7QbrV5rOkgdO397GM5uVts5vQGWvqZG6wPV/2HDp+o4GHtCVTB2/wqbv7y6jYHqTsE/z+eVDYVNmFqxNtTvq9Y5G67KxIWj1J3/nE7QRXF0Ofw5Sv2PPQGxx2HT1RGmcrbT9knVjGrpq6pMPPKvuhsU+mxeU6WaXWHffPUeRB9UzYuLeyenzRMQd1J1oo89riaqzSn7bqVOd9WZvKRO/KeabV876tiy19XyK2dUeXx6g+rXkuPKWdUPREaushhpXiVsR40uqv2mfz11BSl3eSfITFKdwnKGvr1doc9BUrQ6aBoMasjchveqKzc3U+sOVYhs/w7S4lQhVLUtjPhb3bbOUbObKix+uFtVkJr0VwVSjrveUleMNnyqDnLuPqpC0OVl9bp3FTU6SthENYlVVqZqT1unR17Foue76grVvCHqLkrHsbeOHVTh0HIY7P9dNd+6XXGn1F+d7nnL3H3UKF1rp6rO8hVrQ//v8oYUNqWrQuyRP29/v0IIURyu5VV/iC0z1BwPZpO6at96RN7xNscdE9SwrP++rDpk9/9OdU4G1Tft8WXw3yQ18lJGsjpO1+yW15+g9QjVl3DzdDXcuYuH6tvQ4Wn1upsnDJ2vLl7N6qLuLPSYAr8Pv3Ue+s1QJ8VzB6vypHpHGPaHGrCkqCrVV30SVr6pKknOrlChturX0nyISuMdBMMWqJERv+6kOqa3HJ5/qNnO49QJ+NzBqp/GXW8W/06Hb4gaaWvFBNj6jWqO1n0i/PXsrddrOlCVibHH81/MKq6jSwvOLaVlqxGsEi+oz7NOD+gzNe/1A3+opnm+1W5/vyIfg6bdqgetEKJIcubpGFqEKzd6SIqGme1V/4vbPYBu/hJOrYVH/ij6Otu/VXeVHl18e/sUQghrmewDg+eoC0/Cdq18S41qed/021s/Owv+rw4M+7PgXC03k5UJX7RSo4ZV63B7+xUFSPMqIcoCrwA1ZGPC+dvfhneQGm64OIwuasImIYQQ4nZ0eUWNkljcIXxzpF1RwxgHtyr6OgkRqryTCodFyZ0OISzB1u90CCGEyE/udAhRqqTSIYQQQgghhLAqaV4lhBBCCCGEsCqpdAghhBBCCCGsSiodQgghhBBCCKuSSocQQgghhBDCqqTSIYQQQgghhLAqqXQIIYQQQgghrEoqHUIIIYQQwqZ8svIoExbuK9Y6Y+fuYvb6U1aKSJSUs94BCCGEEELYohqv/3vL11/oXpeXetYrpWhKR6cPVvNE55qM7FxTtxhiktL5YdMZlr/YJXfZy7/vJTHdxOxH2+QuW7r/Ii/O38OrveozqmstnrurLoNmhTO4XQje7i56hC5uQSodQgghhBA3sO3N7rmPl+y9yLSwY6x6pVvusvKu9nEapWka2WYNZ6fSa+CSmWXG1fn29jd/WwStqvtR1c/jpml+23aOiX8d5H8PNmFQmxAA6gd6Ub2iB4t3R/JoaI3b2rewHvv4tQghhBBClLLKXu65j73cncGQf9lv284xe8MpIq6kUdWvHI93rMHwqye7EXGpdPloDV8+3JKfNp9h3/kE6gd68dngFiSlZ/HW4gOcvJRM2xoV+HRQcyp6ugF5V/QbB3nzc/hZMrPM3N8iiMn3Nc49iTebNb5ad5J5285xKSmDmv7leb57Xe5pWgWA8JOXGTp7Cz883pZPVh7laFQSPz/RniBfd95dcpg9EVdIzcymTmVPXuvdgM51/QEYPCucyPg03l1yiHeXHALgzAd9mRZ2jJWHoln2Qt6dh+82nub7jafZ9Ppd+eJuXtWHn8PP4upsZOP4u7gQn8Z7/x5m/fFLGA0G2taowKT7GhFS4eYVin/2XeCRDtVv+vrX604yLewYnw9tSZ8mgfle694ggH/2XpBKhw2SSocQQgghRDEt3h3Jp2HHeOeBxjQO8uHghQReX7ifcq7ODGhdNTfdtLBjTLyvMcG+7rz6xz5e+G0P5d2cmHRfI9xdnBg7dxefhh3jvQeb5q6z+UQsbs5GfhvdgfNXUnl1wT78PFx4tXcDAGauPcGi3ZG892BTalYsz9bTl3lx/h4qlHelQ62Kudv5cNkR3uzbkGoVPPAp58KF+HTubFCJV3vXx9XZyMJd5xn503ZWv3IHwb7lmDW8NXdP38DQdtUY0i6k2O/J5hOxeLk58+uT7QEwZZt59PtttKrmy4IxoTgbDXyx+gQjftjG8he63vBOSHxqJsdjkmka7HPDfUxddphfw8/y/WNt6VTHv8DrzUN8mLHmBBlZ2bg5OxU7D8J6pNIhhBBCCFFM0/47xpt9G9Knibq7EFLBg+PRyczdejZfpWN011p0q1cJgMc71eT5ebuZ+2R72tSoAMCgtiH8sfN8vm27OBv5eEBzyrk6US/Ai5d61mPq0sO83LM+JrOZGWtO8uuT7Wld3Q+AahU92HHmCnO3nstX6RjXsx5d6lbKfe7r4UqjIO/c5y/3qs+Kg1H8dyiaER1r4OvhitFgoLybc747OkVVztWZD/o3y61MLNp9HrOm8WH/ZhgMBgA+HtCcZlNWsOXUZbrWq1RgG5HxaWgaBHgX3P+6o5cIOxTN3Cfb0/EGFQ5Q62Vmm7mUlHHL5lmi9EmlQwghhBCiGFIzszh7OZXxf+5jwsL9ucuzzBre7vlPrRoE5p3k+3u6AqrvQd4yNy4nZ+Zbp2GgN+Vc867St6rmR0pmNhcS0kjNzCbNlM3w77bmW8eUbaZRUP67A82q+uZ7npKRxWf/HWP1kRhikjLINmukm7K5EJ9WjNzfXINAr3x3Lw5fTOLs5VQaT1qRL11Glpmzcak33Ea6yQyA2w3ugjSo4kVcSibT/jtG8xBfyrsVPI11d3G6up3s286HsA6pdAghhBBCFENKhjqh/eChZrQI8c33mpPRkO+5s1PecwOGq8uM1ywDs6YVY99ZAHz/WFsCr7sbcH1zpWsrLgDvLT3MxuOxvHFPQ2r4e+Du7MTTc3aRmW2+5T6NBgPadTFm3WCd6/eXkpFFk2Afpg9uUSBthasVsALLy6vlCWmm3H4uOQK83Zk5rBVDZ29hxPfb+PGJdnheV/GIT828up386wr9SaVDCCGEEKIYKnm5EeDtxrm4VPq1DLb49g9HJZJuys69ar874grlXZ0I8imHr4crrs5GLsSn5WtKVRQ7z1xhQOuquZ2vUzKyOH8lFaiQm8bV2YjZnL+CUcHTldjkDDRNy20mdehiYqH7axLsw5J9F6no6YpXEYewrV7BAy83Z47HJFOrkmeB16v6eTB/dGhuxeOn6yoex6KTqOLjnlt5EbZDJgcUQgghhCiml3rUY+baE/yw6TSnLiVzJCqR33dE8O2Gkk9OZ8oy89of+zgencSaIzFMCzvOox1rYDQa8HRzZnSXWry75BB/7DzP2cspHIhM4MdNpwv0DbleDX8Plh+I4uCFBA5dSOSF33Zz/U2Wqn7l2Ho6jqiEdOJS1F2D0FoVuJySydfrTnH2cgo/h59h7dFLheajX4tgKpR3ZdTPO9h2Oo6IuFTCT15m8t8HuZhw4yZdRqOBTnX82XEm7qbbDfItx2+jO3A5OYNHv9tKUrop97Vtp6/Qpe6N+3sIfUmlQwghhBCimIa0q8aH/ZuxYMd5+ny2gcGztvDHzvMW6bzcsY4/NfzLM2hWOGPn7qJno8q82KNu7usv96rHc3fVZebaE/T4dB0jvt/G6qOXCPErd8vtvtW3ET7lXOj/1Wae/Gk7XetVovE1HcsBXupZj/NXUun68RpavRsGQJ3KXrz7QBN+CT/D3dM3sCcintFdaxWaj3KuTsx/qgNBvuUY8+tOun+6jvF/7iMjK7tAs6hrDW4Xwj97Lxa443KtKj7l+G10KFdSTTz6/TaS0k2km7JZeSiKIe2qFRqbKH0G7fpGekIIIYQQQhc3mnm7rNE0jX4zNvFE55o80KLozdd+2XKWlQej+GVkeytGJ26X3OkQQgghhBA2w2Aw8P5DTcm+xZ2OG3ExGph8f2MrRSVKSjqSCyGEEEIIm9I4yIfGQTeeIPBmpFmVbZPmVUIIIYQQQgirkuZVQgghhBBCCKuSSocQQgghhBDCqqRPhxBCCCFu24w1J1hxMIqTMcm4uzjRqrofr9/dgNpXJ3aLT81kWtgxNhyPJTI+jYrlXenVOJBxverhfc2EcZHxaby1aD/hpy5T3tWZ/q2r8lrv+vlm77Y1heX9Wpqm8dgP21l37BKzhremd+PA3NfsMe9Q9PzvPHuF/1txlD0R8TgZDTSq4s3PI9vlTn4Yn5rJpL8PsupwDAYD3N0kkEn3Nab8LYbVtQVFyX9MUjpTlx5hw/FYUjKyqFWpPGPvrMPdTavkprHX/BeXY+VGCCGEEKVq6+k4hneoTvMQX7KyNT5ecYRHv9tG2LiueLg6E52YQXRiBm/c05C6AZ5EXknjzcUHiE5M56tHWgOQbdZ44oftVPJy48+nOxKTlMHLv+/F2WjgtT4NdM7hzRWW92t9t/E0Vyfzzsde8w5Fy//Os1d47PttPH1nbaY80Bgno4HDFxPzvRcv/LaHmKQMfhnZjiyzxqsL9jJh4X4+H9pSp5wVTVHy//Lve0lMM/HtiDZU8HDlrz2RPDt3F3+P7UyTYNVR3l7zX2yaEEIIIYSFxCala9XHL9G2nIy9aZoley9odd9YqpmysjVN07TVR6K1mq8v0WIS03PT/BJ+RmsycbmWYcq2esyWcrO8H4iM19q/958WnZimVR+/RFt+4GLua46Sd027cf4f+HKj9n8rjtx0nePRiVr18Uu0vRFXcpetORKt1Xh9iRaVkGbNcC3uRvlv+PYy7c+dEfnSNZ+yQpu39aymaY6V/8LY9n07IYQQQtiVpPQsAHw9XG+RxoSnu3Nu86HdZ69QP9CbSl5uuWm61atEUkYWx6KTrBuwBd0o72mZ2bzw2x7eeaAxlb3cC6zjKHmHgvmPTc5gT0Q8Fcu78tDMTbT5XxiDZoWz/Uxc7jq7zsbj7e5Ms6q+ucs61/HHaDCw+1x8aYZfYjf6/FtX92PJvovEp2ZiNmv8vfcCGSYzHWpVBBwr/4WR5lVCCCGEsAizWeOdJYdoU92P+oFeN0wTl5LJF6tPMLRdSO6yS8kZ+Hvmr6T4e7rlvmYPbpb3d5YconU1P3pd04fjWo6Qd7hx/s/FpQLw2arjvHFPQxpV8WbhrkiGzd7Kipe6UtO//NX8u+XblrOTEd9yLnaff4AvH27F2Lm7aPFOGM5GA+VcnJg1vDU1/MsDOEz+i0LudAghhBDCIt7+6wBHo5L44uEbt0VPSjfx+I/bqVPZkxd71Cvl6KzrRnkPOxRN+MlYJt7XSMfISseN8q9dnQru4XbVGNQmhCbBPky8rxG1KpXn9x0ReoVqFTf77n+68iiJ6VnMebI9f4/tzMguNXl27i6ORCXqFKl+5E6HEEIIIUps4l8HWH0kht+fCqWKT7kCrydnZDHi+214uqkrvS7XjMxUydONPREJ+dLHXr3KW+m6q8C26GZ533wylrNxqTSbsjJf+qd/3UnbGhWY/1So3ecdbp7/nOZkdQPyj2ZVu7InF+LTAJXH2Ouu6Gdlm4lPM9l9/s9eTuGn8LOsfKkr9QLU3Y9GQd5sPxPHz+Fnef/Bpg6R/6KSSocQQgghbpumaUz6+yArDkbx2+hQQip4FEiTlG7i0e+34epk5NtH2+YOlZqjZXU/vlxzgthrmppsOB6Ll5tzgRNWW1JY3p++ozZD2lbLt6z3Z+t5+95G9GgYANhv3qHw/Ff1K0eAtxunLqXkW376Ugp31K8EQKvqviSmZ7H/fAJNq6rRnDafvIxZ02hZzbdU8nG7Cst/mikbAON1o5YZDYbcu0D2nP/ikuZVQgghhLhtb/91gEW7I5k+pCXl3ZyISUonJimd9KsnXEnpJoZ/t420zGw+GtCMpAxTbppsszrx6lq3EnUre/HS/D0cupDIumOX+GTlUYaHVsfN2elWu9dVYXmv7OVO/UCvfH8AQb7lck9Q7TXvUHj+DQYDo7vW5sdNZ1i6/yJnYlP4ZOVRTl5KZnBb1aenTmUvutWrxOsL97EnIp4dZ+KY9PdB7msWRIB3wY73tqSw/Neu5EmNih68sfAAeyLiOXs5hdnrT7HxRCy9Gqk+Pvac/+IyaDlVLSGEEEKIYqrx+r83XP7xgGYMbBNC+MnLDJ295YZpNrx2Z+7J9/krqby1+ABbTl3Gw9WZ/q2CGd+ngU1PkFdY3m+2zvWTA9pj3qHo+Z+59gS/hJ8lPtVEwypeTLinIW1rVMh9PT41k4l/HWTV4WiMBgN9mgQy+X7bnxyvKPk/HZvCh8uOsONsHCkZ2VSv6MHorrV4qFXV3PT2mv/ikkqHEEIIIYQQwqpsuwothBBCCCGEsHtS6RBCCCGEEEJYlVQ6hBBCCCGEEFYllQ4hhBBCCCGEVUmlQwghhBBCCGFVUukQQgghhBBCWJVUOoQQQghRKjKyspkWdoyMrGy9Qyl1ZTnvIPkv6/kHqXQIIYQQopRkZpmZvuo4mVlmvUMpdWU57yD5L+v5B6l0CCGEEEIIIaxMKh1CCCGEEEIIq3LWOwBHYTabuXDhAl5eXhgMBr3DEUI4CE3TSEpKIigoCKNRrhOJW8vKymL37t0EBATY5PclJTMLc0YqFy5EUt61bJ2ClOW8g+Tf3vNvNpuJjo6mZcuWODvfXvwGTdM0C8dVJp0/f56QkBC9wxBCOKiIiAiqVq2qdxjCxm3fvp127drpHYYQwkFt27aNtm3b3ta69lfVslFeXl6AOjHw9vbWORrLMJlMrFy5kl69euHi4qJ3OHZJ3sOSK+vvYWJiIiEhIbnHGCFuJSAgAFAnBlWqVLH6/rKysli1ahXdu3e/7auf9kTy69gkvzd38eJF2rVrl3uMuR2O/46WkpwmVd7e3g5V6fDw8MDb27tMnuxZgryHJSfvoSLNNkVR5DSpqlKlSqncGTOZTPj7+xMcHFwmfp+SX8cm+S1cSZpt2l6DTyGEEEIIIYRDkUqHEEIIIYQQwqqk0iGEEEIIIYSwKql0CCGEEEIIIaxKKh1CCCGEEEIIq9K10rF+/Xruu+8+goKCMBgMLF68+KZpx4wZg8Fg4LPPPsu3PC4ujmHDhuHt7Y2vry8jR44kOTk5X5p9+/bRpUsX3N3dCQkJ4aOPPiqw/QULFtCgQQPc3d1p2rQpS5cutUQWhRBC2Dgpi4QQwvp0rXSkpKTQvHlzZsyYcct0ixYtYsuWLQQFBRV4bdiwYRw8eJCwsDCWLFnC+vXrGT16dO7riYmJ9OrVi+rVq7Nz504+/vhjJk+ezDfffJObZvPmzQwdOpSRI0eye/du+vXrR79+/Thw4IDlMiuEEMImSVkkhBClQLMRgLZo0aICy8+fP68FBwdrBw4c0KpXr65NmzYt97VDhw5pgLZ9+/bcZcuWLdMMBoMWGRmpaZqmzZw5U/Pz89MyMjJy04wfP16rX79+7vNBgwZpffv2zbff9u3ba0899VSR409ISNAALSEhocjr2LrMzExt8eLFWmZmpt6h2C15D0uurL+HjnhssWX2XhZFRERogBYREVHkdUqirP0+Jb+OTfJ7c5Y4ttj05IBms5nhw4fz6quv0rhx4wKvh4eH4+vrS5s2bXKX9ejRA6PRyNatW3nwwQcJDw+na9euuLq65qbp3bs3H374IVeuXMHPz4/w8HDGjRuXb9u9e/e+5S32jIwMMjIycp8nJiYCaqIVk8l0u1m2KTn5cJT86EHew5Ir6+9hWc23LbGnsigpKQlQMw2XxnenrP0+Jb+OTfJ7c1lZWSXen01XOj788EOcnZ15/vnnb/h6VFQUlStXzrfM2dmZChUqEBUVlZumZs2a+dLkTOEeFRWFn58fUVFRBaZ1DwgIyN3GjUydOpUpU6YUWL5y5Uo8PDwKz1wJuWfGke5awer7AQgLCyuV/TgyeQ9LrjTfw7gM8HUFow1MAp6amqp3CGWePZZFq1atwt/fv/DMAYbMJLKcPHFyuv0vfFk7xkl+HZvkt6DY2NgS78dmKx07d+5k+vTp7Nq1C4PBBkr+60yYMCHfFanExERCQkLo1asX3t7e1t150kWcv+qAVqML2Q98BW5eVtmNyWQiLCyMnj174uLiYpV9ODp5D0uutN/DmKQM+n+9hQaBXkwb1AxPN30Pkzl3UYU+7K0sioyMpFGjRnTv3p3g4OBC13/x0+8IuxLId/dVoEO70GLvv6wd4yS/jk3ye3ORkZEl3p/NVjo2bNhATEwM1apVy12WnZ3Nyy+/zGeffcaZM2cIDAwkJiYm33pZWVnExcURGBgIQGBgINHR0fnS5DwvLE3O6zfi5uaGm5tbgeUuLi7W/6Ju+BBMKRjS4jCW9wMrF4SlkicHJ+9hyZXWe3gyNp4rqSbWHotl6Lfb+XZEG6r6Wf/u5c3I90Zf9lYW5VRSnZ2di/TdcTIYyMSFHXv30aVT10LT30xZO8ZJfh2b5LcgZ+eSVxlsdp6O4cOHs2/fPvbs2ZP7FxQUxKuvvsqKFSsACA0NJT4+np07d+aut3r1asxmM+3bt89Ns379+nzt1cLCwqhfvz5+fn65aVatWpVv/2FhYYSGFv+qj9Vd3Ae756jHvd+3eoVDiLKma71K/P5UKJW83DgSlUS/GZvYde6K3mEJnTh6WdS+YQ0Atl4wQVam1fYjhBC6VjqSk5NzD+IAp0+fZs+ePZw7d46KFSvSpEmTfH8uLi4EBgZSv359ABo2bEifPn0YNWoU27ZtY9OmTYwdO5YhQ4bkDmn48MMP4+rqysiRIzl48CDz589n+vTp+W5Hv/DCCyxfvpxPPvmEI0eOMHnyZHbs2MHYsWNL/T25JU2DlW8CGjTpDyFt9Y5ICIfUPMSXv57tRKMq3sQmZzLkmy38tafkt5aFbSrLZVH7th0A2J1Vg4yjZasduxCidOla6dixYwctW7akZcuWAIwbN46WLVsyceLEIm9jzpw5NGjQgO7du3PPPffQuXPnfOOe+/j4sHLlSk6fPk3r1q15+eWXmThxYr7x0zt27MjcuXP55ptvaN68OX/88QeLFy+mSZMmlsusJRxbDqfXg5MbdJ+kdzRCOLQg33IsGBNKz0YBZGaZeeG3PXwadgxN0/QOTVhYWS6Lagd4U9HFRAau7Nvyn9X2I4QQuvbpuOOOO4pVgJ85c6bAsgoVKjB37txbrtesWTM2bNhwyzQDBw5k4MCBRY6l1GWbYOVb6nHoM+BXXd94hCgDyrs5M+uR1ny44giz1p3i81XHOXkpmU8GNsfdxUnv8ISFlOWyyGAw0K66N8tOpLHtTDxt065AOb9S278Qouyw2T4d4jrhM+DyCfDwh87jCk8vhLAIo9HAhLsb8lH/ZjgbDfy77yKPfreNxPSyMY67cHztG6qhfLdk14ODi3SORgjhqKTSYQ9ij8Oa99XjnlPA3cpD8gohChjUNoRfRrbHy82ZbWfieHj2Fi4nZxS+ohA2rl3NigDsNNfDtGueztEIIRyVVDpsnTkb/noWsjOg9l3QYpjeEQlRZoXWrsi80R2oWN6VA5GJDJoVzoX4NL3DEqJEGgR64ePuRCruHDh/GWKO6B2SEMIBSaXD1m2bDRFbwdUT7psuQ+QKobMmwT78PiaUIB93Tl5KYeDX4Zy6lKx3WELcNqPRQLtaavbyreaGsOdXnSMSQjgiqXTYsrjTsGqKetxzCvhWu3V6IUSpqF3JkwVPd6SWf3ki49MYNCucgxcS9A5LiNvWvmYF4GqlY+9vavASIYSwIKl02CpNg3+eB1MqVO8MrZ/QOyIhxDWCfcvx+5jQfHN57ImI1zssIW5Lh1qqX8cOrQHZybFwfKXOEQkhHI1UOmzVocVqTg5nd7j/czDKRyWErfH3dGPe6A60reFHUnoWI3/cTkRcqt5hCVFsDat44+XuTJJWjv1aLdj1s94hCSEcjJzJ2iJTOoRdnfyv0wtQsba+8QghbsqnnAs/Pt6OxkHeXE7J5LEftpGQKk1ThH1xMhroWFvd7dhobqLudCSc1zkqIYQjkUqHLdr6NcSfBa8qqtIhhLBp5d2c+W5EWwK9Vefyp+fsJDPLrHdYQhRL57qVAFjv0gU0M+z6ReeIhBCORCodtib5Eqz/P/W4+0RwLa9vPEKIIgn0cef7x9pS3tWJzScv8+ai/cWa5VoIvXWtq0aw2p1RhWTNHXb/AtlZOkclhHAUUumwNWveg8wkqNICmg3ROxohRDE0CvLmy4dbYTTAgp3nmbn2pN4hCVFk1SuWJ6RCOUxmA1ud20FiJJz4T++whBAOQiodtuTKGdj1k3rcZ6p0HhfCDt3ZoDJT7m8MwMcrjrLxeKzOEQlRdF2uNrFa5/uAWrDjex2jEUI4EjmrtSVHl6t2tDW6QPWOekcjhLhNw0Nr8HB7Na/Ou0sOkZUt/TuEfbizfmUAwhJD0DRUh/IrZ3SNSQjhGKTSYUtOrlb/6/bUNw4hRIm91rs+PuVcOBqdxPwdEXqHI0SRdKnrj4erExeTstgbNAjQ5G6HEMIipNJhK7Iy4MwG9bj2XfrGIoQoMV8PV17sUReAT1ceIzFdhtEVts/dxYk7G6i7HcvL91MLd/2ihnIXQogSkEqHrYjYqmYfL18ZKjfWOxohhAU80qE6tSqV53JKJjPWnNA7HCGK5O4mgQAsv+iB5h0CaXFwcKHOUQkh7J1UOmxFTtOq2ndJB3IhHISLk5E372kIwA8bz3DussxWLmzfnfUr4+ps5MzlVI7WH6MWbv9W36CEEHZPzm5txYU96n+1DrqGIYSwrLsaVKZzHX8ys838suWM3uEIUajybs50q6dGsforuyM4uULkTji/Q+fIhBD2TCodtqKcr/pvStM1DCGEZRkMBga3DQFg7dFLOkcjRNH0bxUMwB/74zA1GqgWbvlKx4iEEPZOKh22wquK+p8cpW8cQgiL61LXH6MBjsckExkvFxaE7burQQD+nq5cSspgTcCjauGhxZB4Qde4hBD2SyodtsJLddwjSSodQjgaXw9XWoT4ArD+mNztELbP1dlI/9ZVAfjthBNU7wTmLOnbIYS4bVLpsBWeOZWOi/rGIYSwijuuTrq29miMzpEIUTSD2+Q0C4zhYtOn1cIdP0gzYCHEbZFKh63IudORKJUOIRxRTsfcTScuY5IZyoUdqFXJk3Y1K2DW4Pcr9cC3mho+d998vUMTQtghqXTYCv966v/lE9LESggH1DTYBxcnA8kZWcQkZegdjhBFMqx9NQB+3nKOtFZXh88NnwlmqTgLIYpHKh22wrsKVG0LaHD4H72jEUJYmNFowM/DFYArKZk6RyNE0fRtWoWqfuW4nJLJ71p3cPOG2KNwIkzv0IQQdkYqHbak0QPq/6G/9I1DCGEVFcpfrXSkSqVD2AdnJyNPda0FwDfhFzC1fFy9sPkLHaMSQtgjqXTYkob3q/9nN0GydDYVwtHk3OmIkzsdwo4MbBOCv6crkfFp/O05EIzOcGYDRO7SOzQhhB2RSoct8asOQa1AM0sTKyEcUO6dDql0CDvi7uLEE51rAvDVtjjMjQeoF8K/1DEqIYS9kUqHrclpYnV0mb5xCCEszrucCwAJaVk6RyJE8TzSoTpebs6ciEnmn4pXm1gdXAzxZ3WNSwhhP3StdKxfv5777ruPoKAgDAYDixcvzn3NZDIxfvx4mjZtSvny5QkKCuLRRx/lwoX8s6HGxcUxbNgwvL298fX1ZeTIkSQnJ+dLs2/fPrp06YK7uzshISF89NFHBWJZsGABDRo0wN3dnaZNm7J06VKr5LlQNbuq/5E7QNP0iUEIYRWJaSYAvNyddY5EXEvKosJ5u7sw6mrfjg/DU0iv2Qu0bIxbZugcmRDCXuha6UhJSaF58+bMmFHwoJWamsquXbt4++232bVrFwsXLuTo0aPcf//9+dINGzaMgwcPEhYWxpIlS1i/fj2jR4/OfT0xMZFevXpRvXp1du7cyccff8zkyZP55ptvctNs3ryZoUOHMnLkSHbv3k2/fv3o168fBw4csF7mbyagCTi5QtoVuHKm9PcvhLCamKR0AAK83XWORFxLyqKiGdWlFkE+7lxISGe251MAGPfOxc2UoHNkQgi7oNkIQFu0aNEt02zbtk0DtLNnz2qapmmHDh3SAG379u25aZYtW6YZDAYtMjJS0zRNmzlzpubn56dlZGTkphk/frxWv3793OeDBg3S+vbtm29f7du315566qkix5+QkKABWkJCQpHXualZd2jaJG9N2/9HybdVApmZmdrixYu1zMxMXeOwZ/IelpwjvYddPlytVR+/RNt2+nKR17HosUUUyt7LooiICA3QIiIiirxOcSzefV6rPn6J1vDtZVrUzPs0bZK3dnTmMIf4fRaFIx2PikLy69iKk19LHFvs6h5/QkICBoMBX19fAMLDw/H19aVNmza5aXr06IHRaGTr1q08+OCDhIeH07VrV1xdXXPT9O7dmw8//JArV67g5+dHeHg448aNy7ev3r1757vFfr2MjAwyMvIm+EpMTATUrXiTyVSifBqrtMTpwi6yI7Zjrn9/4StYSU4+Spqfskzew5JzlPdQ07TcOx0VyjkVOT/2nm9HZMtlUVJSEgBZWVlW+e7c3agSP4T4sCcigY+cRvEJ66gZuwpTchx4VrD4/myNoxyPikry69iKk9+srJL3RbSbSkd6ejrjx49n6NCheHt7AxAVFUXlypXzpXN2dqZChQpERUXlpqlZs2a+NAEBAbmv+fn5ERUVlbvs2jQ527iRqVOnMmXKlALLV65ciYeHR/EzeI2Qy0ZaAVcOrGZTZmiJtmUJYWEyCVRJyXtYcvb+HqZmQbpJHXJ3bVrLAacirpeaasWoRHHZS1m0atUq/P39i5/BIrjTB/ZEOLPwlIGB5dvSIXs7xxa8zYmAvlbZny2y9+NRcUl+HVtR8hsbG1vi/dhFpcNkMjFo0CA0TeOrr77SOxwAJkyYkO+KVGJiIiEhIfTq1Su3ILpdhlMecG42Fd2zueeee0oa6m0zmUyEhYXRs2dPXFxcdIvDnsl7WHKO8h5uPR0H23fg7+lKv/t6FXm9nLuoQn/2UBZFRkbSqFEjunfvTnBwsNX2e8ppP3/tvchEp+f4J+sJGiWspt4j/wcu5ay2T1vgKMejopL8Orbi5DcyMrLE+7P5SkfOQf7s2bOsXr063wl9YGAgMTH5J9HLysoiLi6OwMDA3DTR0dH50uQ8LyxNzus34ubmhpubW4HlLi4uJf+ixh0HwFCpgU186S2SpzJO3sOSs/f3cNOpKwB0qVupWPmw5zw7Enspi3Iqqc7Ozlb97ky8rzEbT1zmWCJ84jqMN1J+wmXfXOgwxmr7tCX2fjwqLsmvYytKfp2dS15lsOl5OnIO8sePH+e///6jYsWK+V4PDQ0lPj6enTt35i5bvXo1ZrOZ9u3b56ZZv359vvZqYWFh1K9fHz8/v9w0q1atyrftsLAwQkN1atoUfXWkkoAm+uxfCGFxa49eAqBbvUo6RyKKq8yWRbdQ0dON9x5UZdS3mb3Ya64Fmz4DU7q+gQkhbJaulY7k5GT27NnDnj17ADh9+jR79uzh3LlzmEwmBgwYwI4dO5gzZw7Z2dlERUURFRVFZqaazbdhw4b06dOHUaNGsW3bNjZt2sTYsWMZMmQIQUFBADz88MO4uroycuRIDh48yPz585k+fXq+29EvvPACy5cv55NPPuHIkSNMnjyZHTt2MHbs2FJ/T4C8SkegVDqEcATRiekcvpiIwQBdpdJhc6Qsuj19mlThvmaBmDHwcvZzpCfGwp5f9Q5LCGGjinWvxGw2s27dOjZs2MDZs2dJTU2lUqVKtGzZkh49ehASElKsne/YsYM777wz93nOwXfEiBFMnjyZv//+G4AWLVrkW2/NmjXccccdAMyZM4exY8fSvXt3jEYj/fv35/PPP89N6+Pjw8qVK3n22Wdp3bo1/v7+TJw4Md/46R07dmTu3Lm89dZbvPHGG9StW5fFixfTpIkOJ/3ZWRBzRD0OaFz6+xdCWNy6q3c5mlX1pUJ510JSi1uxdDkEUhaVxMS+DVl3+CInTAF8ykDe2PgZtHwUnOV7LoTIr0iVjrS0ND755BO++uor4uLiaNGiBUFBQZQrV44TJ06wePFiRo0aRa9evZg4cSIdOnQo0s7vuOMOtFvMun2r13JUqFCBuXPn3jJNs2bN2LBhwy3TDBw4kIEDBxa6P6u7uAeyM8DVE3xr6B2NEMICwg6rdvrStOr2WascAimLSsLXw4XBtc3MPuLE7Ox76Ba3l05750HrEXqHJoSwMUWqdNSrV4/Q0FBmz5590x7uZ8+eZe7cuQwZMoQ333yTUaNGWTxYh6dpsOrq0If17wajTXe5EUIUQURcKquuVjr6Nq2iczT2S8oh29XET2NQ62B+3xnJ86axLFkzgyrNh8rdDiFEPkWqdKxcuZKGDRveMk316tWZMGECr7zyCufOnbNIcGXOsRVwej04ucFdb+sdjRDCAn7afAazBp3r+FM/0EvvcOyWlEO27e2+DTh4IYGDF+GZywOZv/NnXNs/qXdYQggbUqRL6YUd6K/l4uJC7dq1bzugMivbBCvfUo9DnwG/6vrGI4QoseSMLOZvjwBgZOeahaQWtyLlkG1zd3Hiq0fa4u1iZrdWl/8tOyUjWQkh8rmtQXfT09PZt28fMTExmM3mfK/df//9FgmszNnxPVw+DuUrQedxhacXQti837dHkJSRRa1K5aU/h4VJOWR7qlX04LPBLXni1738nN6JVot/p9/AR/UOSwhhI4pd6Vi+fDmPPvroDadDNxgMZGdnWySwMiXtCqydqh7f+Sa4l2xGcyGE/rLNGj9uPgPAE51qYjQa9A3IgUg5ZLvualKV5xtu4vPDnry+04v67WNoWK2y3mEJIWxAsXsqP/fccwwcOJCLFy9iNpvz/cmB/jZt/kJVPCo1hJbD9Y5GCGEBi3dHci4uFV8PF/q3qqp3OA5FyiHb9sLQfnRxPUY6roz8fjPRidLMSghxG5WO6Ohoxo0bR0BAgDXiKXtSLsPWWepx97fBqeTTzAsh9JVuyuaTlUcBGNOtNuVcnXSOyLFIOWTbnFzd+OLeKtQ2RHIh3YXHvgsnOSNL77CEEDordqVjwIABrF271gqhlFHhX0BmMgQ2g/r36B2NEMICftp8hgsJ6VTxceexjjX0DsfhSDlk+3zbDOTHKovwJ57D0ak8/etOTNnmwlcUQjisYl9W//LLLxk4cCAbNmygadOmBcZKf/755y0WnMNLiYWt36jHd0wAg7T5FsLexadmMmPNCQBe7lUfdxe5y2FpUg7ZAaMTIX1e5PtfxzM4cyIbjsfyxsL9fDSgGQYp64Qok4pd6Zg3bx4rV67E3d2dtWvX5jt4GAwGOdgXx+bPwZQCVVqoyQCFEHZvxpoTJKZn0SDQiwdbBusdjkOScshO1O1Fs5rT+fLU54wyvcKCneep6ufBCz3q6h2ZEEIHxW5e9eabbzJlyhQSEhI4c+YMp0+fzv07deqUNWJ0TOkJsG22eix3OYRwCGcvp/DT5rMAjL+7AU4yYpVVSDlkJwwG6DGF7k67edflBwCm/XeMedtk4kYhyqJiVzoyMzMZPHgwRmOxVxXXSjgPplQo5wf1eusdjRCihDRN441F+8nMNtO5jj93yLwcViPlkB0JaQsN7mWY0388U2kfAG8s2s+i3ed1DkwIUdqKfcQeMWIE8+fPt0YsZYt2tUOdk6vc5RDCASzYeZ5NJy7j5mzkf/2aSLt1K5JyyM70mAJGZ15N/IDhjVzQNHj5970s3X9R78iEEKWo2H06srOz+eijj1ixYgXNmjUr0IHv008/tVhwDi2n0mGQK3VC2LuYpHTe+/cwAON61qOGf3mdI3JsUg7ZGf860OYJDNu+YUrKu6S3/oIFOyN5ft5u3F2M3NVAhj4WoiwodqVj//79tGzZEoADBw7ke02u7BWDpl19IO+ZEPZuyj+HSEgz0STYm5Gda+odjsOTcsgOdRsPe3/DGL2PDzocJT2rHv/svcCYX3fx/Yi2dK7rr3eEQggrK3alY82aNdaIo+yROx1COISwQ9H8u+8iTkYDHzzUDGcn+U1bm5RDdqi8P3R+CVZNwWnNO3z6zDYyTNmsPBTNkz9v5+cn2tOuZgW9oxRCWJGUjnoxXq3vpSdARrK+sQghbktcSiZvLtoPwKgutWgS7KNzRELYsA5Pg3dVSDyPy45v+OLhlnSrV4l0k5nHftjGttNxekcohLCiIlU6xowZw/nzRRtpYv78+cyZM6dEQZUJlRtChVqQmQRbv9I7GiFEMWmaxvg/9xGTlEGdyp68KHMPWJWUQw7ApRx0f1s93vApbhnxzBremk51KpKamc1jP2xjy6nL+sYohLCaIlU6KlWqROPGjbnnnnv46quv2L59O5GRkVy+fJkTJ07w999/89prr1GtWjWmTZtG06ZNrR23/XNygTvfVI83fQGpcoVHCHvy2/YIwg5F4+JkYPqQFjLzuJVJOeQgmg6CwKaQkQjrP8bdxYnvRrSlS11/UjOzefyH7YSflIqHEI6oSJWOd999l2PHjtGpUydmzpxJhw4dqFatGpUrV6Z+/fo8+uijnDp1im+++YYtW7bQrFkza8ftGBo/BJUbQ0aCmp1cCGEXTl1K5p1/DgHwSq/6NA6SZlXWJuWQgzAaoee76vH2byHuFO4uTsx+tA1d61UizZTN4z9uY9OJWH3jFEJYXJH7dAQEBPDmm2+yf/9+YmNj2bVrF5s2beLo0aNcuXKFP/74gz59+lgzVsdjNObdat7yNSRF6xuPEKJQpmwzL83fQ5opm9BaFRnVpZbeIZUZUg45iNp3Qp0eYDbBf1MAcHdx4pvhrbmzvurj8cSP29l4XCoeQjiS2+pI7ufnR/PmzenQoQN16tSRIQpLol4fqNoWstJg3Qd6RyOEKMT0/46z93wC3u7OfDKoOUajHP/0IOWQnesxBTDAocUQsR1QFY+vh7eme4PKZGSZGfnTdtYejdE1TCGE5cjoVXozGKD7JPV4x/dwUoaCFMJWbT8Tx8y1JwB4/6GmBPmW0zkiIexUYBNoMUw9DpuYO3eVm7MTMx9pRY+GAWRkmRn18w5WHIzSMVAhhKVIpcMW1OwCbUaqx4vGSKdyIWxQYrqJF3/bg1mDh1oFc2+zIL1DEsK+3fkGOLvDuc1wbHnuYjdnJ2YOa0XfplUwZWs8M2cXf+2J1DFQIYQlSKXDVvT6H/jXg+Qo+Pu5a2YsF0LYgsl/HSQyPo2QCuWYcn9jvcMRwv75BKu5OwDCJkF2Vu5Lrs5Gpg9pwUOtgsk2a7w4fw/zt5/TKVAhhCVIpcNWuHpA/2/B6AJHlsCun/WOSAhx1T97L7BwdyRGA0wb1AIvdxe9QxLCMXR6Ecr5QexR2JN/bhVnJyP/N6A5w9pXQ9Ng/J/7+XHTaX3iFEKUmFQ6bEmV5nmjWS1/HWJP6BuPEIIL8Wm5s44/e2cd2tSooHNEQjiQcr7Q9TX1eO1UyEzJ97LRaOB//ZowqktNACb/cyi3X5UQwr44FyVRy5YtizwyyK5du0oUUJkX+hyc+A9Or4fFY+CJlWpoXSFEqTObNV5ZsJfE9Cyah/jyfHeZdVwvUg45sLYjYetXEH8OwmdAt9fyvWwwGHjjnoZ4uDozfdVxPlp+lIQ0E6/3aSCjlglhR4p0NtuvXz8eeOCBIv0Vx/r167nvvvsICgrCYDCwePHifK9rmsbEiROpUqUK5cqVo0ePHhw/fjxfmri4OIYNG4a3tze+vr6MHDmS5OTkfGn27dtHly5dcHd3JyQkhI8++qhALAsWLKBBgwa4u7vTtGlTli5dWqy8WIzRCP2+BlcvOL8ddv2kTxxCCH7ZcpbNJy9TzsWJzwa3wMVJLgDoxVrlEEhZpDtnt7xRHDdOg8SLBZIYDAZe6lmPCXc3AGDWulO8smAfpmxzaUYqhCiBIt3pmDRpklV2npKSQvPmzXniiSd46KGHCrz+0Ucf8fnnn/PTTz9Rs2ZN3n77bXr37s2hQ4dwd3cHYNiwYVy8eJGwsDBMJhOPP/44o0ePZu7cuQAkJibSq1cvevTowddff83+/ft54okn8PX1ZfTo0QBs3ryZoUOHMnXqVO69917mzp1Lv3792LVrF02aNLFK3m/JJxjuelM1sfpvMjS4FzwrlX4cQpRhp2NTmLrsMAAT7mlATf/yOkdUtlmrHAIpi2xCk/6wdRac3war34V+M2+Y7KlutalQ3pXXF+7nz13niUvJYMawVni4Ful0RgihJ+02XLlyRZs9e7b2+uuva5cvX9Y0TdN27typnT9//nY2p2mapgHaokWLcp+bzWYtMDBQ+/jjj3OXxcfHa25ubtq8efM0TdO0Q4cOaYC2ffv23DTLli3TDAaDFhkZqWmaps2cOVPz8/PTMjIyctOMHz9eq1+/fu7zQYMGaX379s0XT/v27bWnnnqqyPEnJCRogJaQkFDkdW4py6RpX3XStEnemraw6HFYUmZmprZ48WItMzNTl/07AnkPS06P9zAr26w9OGOjVn38Eu3h2eFadra51PZ9PYsfWxyENcohTbP/sigiIkIDtIiIiCKvUxIWoQhuNgAAk8ZJREFU/X1GbFdl3iQfTYvcdcukqw5HafXfWqpVH79Ee+DLjVpccsYt01tKWTumS34dW3Hya4ljS7EvDezbt48ePXrg4+PDmTNnGDVqFBUqVGDhwoWcO3eOn3+2zKhLp0+fJioqih49euQu8/HxoX379oSHhzNkyBDCw8Px9fWlTZs2uWl69OiB0Whk69atPPjgg4SHh9O1a1dcXV1z0/Tu3ZsPP/yQK1eu4OfnR3h4OOPGjcu3/969exe4xX6tjIwMMjIycp8nJiYCYDKZMJlMJc0+AIY+/4fTj30w7J1HVtPBaNU7W2S7RZWTD0vlpyyS97Dk9HgPZ60/za5z8ZR3c+L9BxqRnZ1Fdnap7T4f+e4UVFrlENhfWZSUlARAVlZWqXx3LPr7DGiOU5MBGA/8gXnZ62QP/0dNoHsDXWpX4OfH2jD6193siYin/1eb+WFEK6tP2FnWjumSX8dWnPxmZWUVmqYwxa50jBs3jscee4yPPvoILy+v3OX33HMPDz/8cIkDyhEVpWYgDQgIyLc8ICAg97WoqCgqV66c73VnZ2cqVKiQL03NmjULbCPnNT8/P6Kiom65nxuZOnUqU6ZMKbB85cqVeHh4FCWLRdLM/05qxq4mbcHTrGnwHpqx9G8hh4WFlfo+HY28hyVXWu/hhVSYts8JMHB/1Uz2bF7DnlLZ842lpqbquHfbVFrlENhvWbRq1Sr8/f2LkkWLsNTv013rRHfD3zhHbGHH3Clc9Gt3y/RP14OvDjtxKjaF+z9fz1MNswkuhZaQZe2YLvl1bEXJb2xsbIn3U+wz2O3btzNr1qwCy4ODg295YHQ0EyZMyHdFKjExkZCQEHr16oW3t7fldpTWEW1WKF4pF+lb5TLm1o9bbtuFMJlMhIWF0bNnT1xcZF6C2yHvYcmV5nuYkWVmwNdbyNaSubO+P1OGFX3EJGvJuYsq8kg5lOf6sigyMpJGjRrRvXt3goODrb5/a/w+DX6RsPH/aHt5IVkDXgY3r1umvzshnSd+2smJSynMOOLGJwOb0r1B5Vuuc7vK2jFd8uvYipPfyMjIEu+v2JUONze3GxaCx44do1Ily3V2DgwMBCA6OpoqVarkLo+OjqZFixa5aWJiYvKtl5WVRVxcXO76gYGBREdH50uT87ywNDmv34ibmxtubm4Flru4uFj2i+pSSY1hvuxVnDZ9ilPr4eBi3dvHBUKwdJ7KIHkPS6403sP/CzvMkehkKpR35cMBzfM1hdGLfG8KKq1yCOyvLMp5X5ydnUv1u2PR32fXl+HAAgzxZ3HZ8CHc/eEtk1fzd+HPpzvxzNydbDpxmafn7mF8nwY81bWW1S4alLVjuuTXsRUlv87OJW9pU+zxH++//37eeeed3PZfBoOBc+fOMX78ePr371/igHLUrFmTwMBAVq1albssMTGRrVu3EhoaCkBoaCjx8fHs3LkzN83q1asxm820b98+N8369evztVcLCwujfv36+Pn55aa5dj85aXL2o7vWI8C7KiRdhB0/6B2NEA4p/ORlvtlwCoAPHmpKZS93nSMSN1Na5RBIWaQLVw+4d5p6vHUWRO68dXrAx8OFHx9vxyMd1OzlHyw7wisL9pGRpVNnLCFEAcWudHzyySckJydTuXJl0tLS6NatG3Xq1MHLy4v33nuvWNtKTk5mz5497NmzB1Ad9vbs2cO5c+cwGAy8+OKL/O9//+Pvv/9m//79PProowQFBdGvXz8AGjZsSJ8+fRg1ahTbtm1j06ZNjB07liFDhhAUFATAww8/jKurKyNHjuTgwYPMnz+f6dOn57sd/cILL7B8+XI++eQTjhw5wuTJk9mxYwdjx44t7ttjHc5u0O1V9XjjpwVmbBVClExCmomXf9+DpsGQtiH0anzzK8tCf5Ysh0DKIptUpzs0HQho8M8LkF14J1YXJyP/69eUKfc3xmiAP3edZ9jsrcQmZxS6rhCiFNzusFcbNmzQZsyYoX344YdaWFjYbW1jzZo1GlDgb8SIEZqmqaEK3377bS0gIEBzc3PTunfvrh09ejTfNi5fvqwNHTpU8/T01Ly9vbXHH39cS0pKypdm7969WufOnTU3NzctODhY++CDDwrE8vvvv2v16tXTXF1dtcaNG2v//vtvsfJi9WEtszI17bNmajjBDZ9aZx/XKWtDx1mDvIclVxrv4QvzdmnVxy/Run60WktON1ltP7dDhsy9OUuUQ5rmWGWRXQ+Ze72kGE2bWk2VexunF2vVdUdjtCaTlmvVxy/ROk5dpR2+aJnfT1k7pkt+HVtpD5lr0DRNK04lJSIigpCQEMvVehxEYmIiPj4+JCQkWLYj+bX2zIPFY6CcH7y4v9DOdSVlMplYunQp99xzT5lq22hJ8h6WnLXfw8W7I3lx/h6MBlgwpiOtq/tZfB8lUSrHFjsj5dDNnT9/npCQECIiIqhatarV92f1Y9yuX+DvseBcDp7eBBVrF3nVEzHJPPnTds5cTqW8qxNT+zfj/uZBJQqnrB3TJb+OrTj5tcSxpdjNq2rUqEG3bt2YPXs2V65cua2ditvUbBBUrANpV9SBWAhRIkejkpiwcD8Az91V1+YqHOLGpBwqQ1o+AjW7QlYa/PE4ZBW9qVSdyp4sfrYTHWtXJCUzm+fn7eatxftJN0k/DyH0UOxKx44dO2jXrh3vvPMOVapUoV+/fvzxxx/5JicSVmJ0gtCrbXu3zITssjF5jRDWkJRu4ulfd5JmyqZzHX+e715X75BEEUk5VIYYDNDva3WH/+JeWPVOsVb39XDl5yfaMfbOOgD8uuUc/b/azNnL0jdSiNJW7EpHy5Yt+fjjjzl37hzLli2jUqVKjB49moCAAJ544glrxCiu1XwolK8ECRFwcLHe0QhhlzRN47U/9nEqNoUqPu5MH9ICJ6O+83GIopNyqIzxCYYHZqrH4V/C8eJN3ObsZOSV3vX58fG2+Hm4cPBCIvd+vpFl+y9aIVghxM0Uu9KRw2AwcOeddzJ79mz+++8/atasyU8//WTJ2MSNuLhDu9Hq8ebpULwuOUII4LuNp1l2IAoXJwMzh7WiomfBOXeE7ZNyqAxpcE9e2bdoDCQVfxLIO+pXZukLXWhT3Y+kjCyenrOLyX8fJDPLbOFghRA3ctuVjvPnz/PRRx/RokUL2rVrh6enJzNmzLBkbOJm2j4JLh4QtR9OrdU7GiHsyrbTcUxddgSAifc2omU16cdhr6QcKmN6vgsBTSE1FhY9BebiVxaq+JRj3ugOPNWtFgA/bj7DwK83c+5yqqWjFUJcp9iVjlmzZtGtWzdq1KjBzz//zODBgzl58iQbNmxgzJgx1ohRXM+jgupcB7D5C31jEcKORMSl8vSvO8k2a/RrEcQjHarrHZK4DVIOlVEu7jDge3XR7dRaWF28/h25m3EyMuHuhnz7aBt8yrmw93wCd09fz+87IijmgJ5CiGIodqXjf//7H+3bt2fnzp0cOHCACRMmUL26FNylrsMzYDDCyVUQfUjvaISweUnpJkb+tJ3LKZk0quLN+w81xWCQfhz2SMqhMqxSPbhvunq8cRrs/e22N9WjUQD/Pt+ZtjX8SMnM5rU/9jHm151clskEhbAK5+KukDNDq9BZhZrQ8D449JfqWNdvpt4RCWGzsrLNPDdvN8eik6ns5cZ3j7XBw7XYhz9hI6QcKuOaDYJLR2DDJ/D3c+BXE6q1v61NVfXz4LfRocxaf5JpYcdYcTCanWfj+WhAU+5qEGDhwIUo24p9p8NgMLBhwwYeeeQRQkNDiYyMBOCXX35h48aNFg9Q3ELH59X/fb/fVqc6IcqK95YeZu3RS7i7GPl2RBuq+JTTOyRRAlIOCe58CxrcC9mZMH8YxJ+77U05GQ08c0cdFj3TibqVPYlNzuCJH3fw5qL9pGZmWTBoIcq2Ylc6/vzzT3r37k25cuXYvXt37rjoCQkJvP/++xYPUNxC1TYQ0gHMJtg6S+9ohLBJv245yw+bzgDw6aAWNKvqq2s8ouSkHBIYjfDQNxDYFFIuwbyhkJFcok02Cfbhn+c680SnmgDM2XqOvp9vZPc5mYBSCEu4rT4dX3/9NbNnz843ZXqnTp3YtWuXRYMTRdDxOfV/x/eQkaRvLELYmM0nYpn090EAXulVj3uaVtE5ImEJUg4JAFzLw9DfoHxliD4AC0fd1ohW13J3cWLifY2Y82R7Ar3dOR2bwoCvw/nsv2NkZcvQukKURLErHUePHqVr164Flvv4+BAfH2+JmERx1L8bKtaB9HjY8pXe0QhhM85fSeXZubvINms82DKYZ6/OSCzsn5RDIpdPVRg6D5zc4OhSWDXZIpvtVMefFS925f7mQWSbNT777ziDv9lCxBUZWleI21XsSkdgYCAnTpwosHzjxo3UqlXLIkGJYjA6wR0T1OPNX0BqnL7xCGED0k3ZPP3rLq6kmmgS7M1UGanKoUg5JPKp2iZvMJVN02H3rxbZrI+HC58Pbclng1vg5ebMzrNXuH/GFnZckmOJELej2JWOUaNG8cILL7B161YMBgMXLlxgzpw5vPLKKzz99NPWiFEUpvFDql1rRiJs/FTvaITQlaZpvL34APsjE/DzcOHrR1rj7uKkd1jCgqQcEgU0HQDdxqvH/7wIZyw3oEC/lsEsfaELrav7kZyRxS8nnBi3YB+J6SaL7UOIsqDYY0a+/vrrmM1munfvTmpqKl27dsXNzY1XXnmF5557zhoxisIYjXDXRJg7ELbNVnN4eAfpHZUQupi77RwLdp7HaIDPh7akqp+H3iEJC5NySNxQt9ch9hgcXATzH4EnV0HF2hbZdEgFD+aP7sDnq47x5eoT/LMvit0RCXw2uAVtalSwyD6EcHS3NWTum2++SVxcHAcOHGDLli1cunSJSZMmceHCBWvEKIqibk+oFgpZ6bDuQ72jEUIXu85dYfLVjuOv9m5Al7qVdI5IWIOUQ+KGjEbo9xUEtYK0KzBvCKTFW2zzzk5GnruzNs83yaaqXznOX0lj0KxwpoVJJ3MhiqLYlY4crq6uNGrUiHbt2uHp6cnBgwcJCQmxZGyiOAwG6D5JPd71C0Qd0DceIUpZuimbF37bjSlb4+4mgYzpJm37HZ2UQ6IAl3KqY7l3sLrr8dezoGkW3UVNL/j7mVAeahmMWYPpq47z5M87SJLmVkLc0m1XOoQNqh6qZinXstUsreZsvSMSotT8sOkMEXFpBHq789GAZtJxXIiyyisQhswFowscWQJ7f7P8Ltyd+XRwC6YPaYG7i5G1Ry8x8OtwLsSnWXxfQjgKqXQ4mrs/BjcfuLALtn6tdzRClIrY5AxmrFGjGb3auz5e7i6FrCGEcGhBLeDOqyM7LnsN4iOsspsHWgTz+1OhVPJy40hUEv1mbGL/+QSr7EsIeyeVDkfjXYX/b+++46Ku/wCOv+7YG0UFVETEPXIPXOBEc6TZcOVMszQzM8vql2mlZUtNzcxMKzVzm5sUnIiKe4t7AE6WbO77++Mrp4QDhOPu4P18PHhw953vz/HlPve+z+f7+dB+ovp46xdw96JRwxGiIHwfdIaElHRqlXGhe90yxg5HCGEKmo0Cr8bqyI6r3szzxIGP81xZV1YNb0YVdyduxKfwys+hbD4eZZBzCWHOcjx61ZEjR564/vTp03kORuSTuv3gyFK4tBPWvgt9V6j3fAhRCJ2OiuevvZcB+KRTNbRaudYLK6mHRK5oLdQby2e3gIs7IOwn8BtukFOVcbVj2Zt+DF90kO1nbvLGn+F8/Hw1Bjf3ka6eQtyX46SjTp06aDQalEfckJW5XP6xTIRWC12nwyw/OLcVDi+GOr2NHZUQBvHl+pPoFOhQw4PGFdyMHY4wIKmHRK65+ULgF+oXcP9OgIptoWQVg5zKydaKef0bMH7NcRaGXeaLdSe5ejeJ8V2qy3UpBLlIOi5cuGDIOER+c/OFgA9hywRY/z6UbQglKhk7KiHy1Z17qWw/cxOAcc9XNXI0wtCkHhLPpP5AOLUOIv6FXdOh20yDncrSQssX3WriU8KBL9efZP7ui1TxcKJXo3IGO6cQ5iLHSYe3t7ch4xCG0HQkRGxRu1ktHQCv/6sOJyhEIXEqMg4Abzd7vN0cjByNMDSph8Qz0Wig5Vg16Ti+AjpMBltnA55Ow+stKpCuU/hqwynGrzlO7bKuVC9tuHMKYQ7kRvLCzMISXvoVHEpC9DF1BA8hCpGTUfEAVPVwMnIkQgiT5tUISlSBtEQ4tqxATjm0RQVaVSlJarqOEYsOkJCSXiDnFcJUSdJR2Dl5QI+5gAYO/G6Q8cqFMJbMlo6qHvINohDiCTQaqNdPfXzg9wI5pVar4btX6uDpYsv5W/f4aMXRR96PJERRIUlHUVAhQL2/A9Sb6W6cMmo4QuSXU/dbOqp5SkuHEOIpavdSJwy8fhAinzwSWn4p7mDNj73qYqHVsObwdf7aZ5j5QoQwB5J0FBUt31eTj7REWDYQ0lONHZEQeZKhUzgTndm9Slo6hBBP4eAG1Tqrjw/+UWCnbVC+OO8HqiNmjV9znHM3Ewrs3EKYEkk6igqtBbz4C9iXgBsnYPc0Y0ckRJ7EJqWRkq5O9lWmmAyQIITIgdr3h48/scZgkwU+ytAWFWhRqQSp6To+XX1MulmJIumZko5ly5bxyiuv0KRJE+rVq5flJz9lZGTwv//9Dx8fH+zs7PD19eXzzz/P8s+qKAqffvopnp6e2NnZ0bZtW86ePZvlOHfu3KFPnz44Ozvj6urK4MGDSUjI+k3DkSNHaNGiBba2tnh5eTFlypR8LYtJcCyljtoBsO0buH3OuPEIkQd3E9XWOicbS6ws5PuToqag6iGQuqhQqeAP1k6QEAXXwgvstFqthi+71cLGUsuuiNusOXy9wM4thKnIdU09ffp0Bg4ciLu7OwcPHqRRo0a4ublx/vx5OnbsmK/Bff311/z000/MmDGDkydP8vXXXzNlyhR+/PFH/TZTpkxh+vTpzJ49m7CwMBwcHAgMDCQ5OVm/TZ8+fTh+/DhBQUGsXbuW7du3M3ToUP36uLg42rdvj7e3N+Hh4XzzzTd89tlnzJkzJ1/LYxJqvQy+rSEjBf55B+TbFmGmYhLTAHB1sDJyJKKgFWQ9BFIXFSqWNlA5UH18ck2Bnrqcmz3DW1UE4It1J4lLTivQ8wthdEouValSRVm0aJGiKIri6OionDt3TlEURfnf//6nDB8+PLeHe6JOnTopgwYNyrLsxRdfVPr06aMoiqLodDrFw8ND+eabb/TrY2JiFBsbG2Xx4sWKoijKiRMnFEDZt2+ffpsNGzYoGo1GuXbtmqIoijJr1iylWLFiSkpKin6bDz74QKlSpUqOY42NjVUAJTY2NvcFLWi3zyvK5+6KMt5ZUQ4ufOxmqampyqpVq5TU1NQCDK5wkdcw7x73Gv57Ikrx/mCt0nn6DiNFVjDM6r2lgBRkPaQo5lUXXblyRQGUK1eu5L6gz8As3+OOrVDrv2l1FEWny9WueS1vclq60uqbYMX7g7XK+NXHnukYBcks/755IOV9vPx4b8nx5ICZLl++TNOmTQGws7MjPl69kfO1116jSZMmzJgxI7/yIZo2bcqcOXM4c+YMlStX5vDhw+zcuZPvv/8eUGenjYqKom3btvp9XFxcaNy4MaGhofTs2ZPQ0FBcXV1p0KCBfpu2bdui1WoJCwuje/fuhIaG0rJlS6ytrfXbBAYG8vXXX3P37l2KFSuWLbaUlBRSUlL0z+Pi1KE709LSSEsz8W8vnMqibfE+FsETUTZ9THr5VuBQIttmmeUw+fKYMHkN8+5xr+HtePUbZBc7y0L9+hbmsj2rgqyHwLzqoszXIj09vUCuHbN8jysfgKWFDZo750m7fgRKVc/xrnktrxb4tHNVBswP5/fQi3Sp5c5zZV2e6VgFwSz/vnkg5X289PS8zzOT66TDw8ODO3fu4O3tTbly5dizZw+1a9fmwoUL+X5j1IcffkhcXBxVq1bFwsKCjIwMvvzyS/r06QNAVFQUAO7u7ln2c3d316+LioqiVKlSWdZbWlpSvHjxLNv4+PhkO0bmuke90U+ePJkJEyZkW75582bs7e2fpbgFSqOUx9/WC5ekK0Qs+YQzHl0fu21QUFABRlY4yWuYd/99DZdGaAEtKbE3Wb9+vXGCKgCJiYnGDsHkFGQ9BOZZF23ZsoUSJbJ/mWQo5vYe18ixOp6xB7n19zvs9RmlzuORC3ktbz03LQdua3nt1z28XiUDXxMfgM/c/r55JeXN7tatW3k+T66TjtatW7NmzRrq1q3LwIEDeffdd1m2bBn79+/nxRdfzHNAD/v7779ZuHAhixYtokaNGhw6dIhRo0ZRunRp+vfvn6/nyq1x48YxevRo/fO4uDi8vLxo3749zs4m/u5xn9bzDmwYQxXLq1R8/vls69PS0ggKCqJdu3ZYWUm/+Wchr2HePeo1DLtwh32h+9Fo4P1uTahbztW4QRpQZiuqeKAg6yEwr7ro2rVrVK9enTZt2lCmTBmDn99s3+OivFDmB+IZe5DOpSLRNXw9R7vlV3n9AlJ5/Y8DHLkax+zT1nzboyYda3o88/EMxWz/vs9Iyvt4165dy/P5cp10zJkzB939YeaGDx+Om5sbu3fvpmvXrrzxxht5Duhh77//Ph9++CE9e/YEoFatWly6dInJkyfTv39/PDzUf9Do6Gg8PT31+0VHR1OnTh1A/Ubsxo0bWY6bnp7OnTt39Pt7eHgQHR2dZZvM55nb/JeNjQ02NjbZlltZWZnPhVq5PWwA7dV9aDMSwfbRTbxmVSYTJa9h3mW+hinpGXy65iQAfRqXo5FvSSNHZlhy3WRXkPUQmFddlJmkWlpaFui1Y3bvcV71oN3nsPEDLLZ8ioVPM/B8Lse757W8pVysWDK0KSP/OkjQiWhGLjnCx/FpvN7CB00uW10Kgtn9ffNIypudpWWuU4Zscj16lVarzXLinj17Mn36dN5+++0s/VDzQ2JiIlpt1hAtLCz0lY2Pjw8eHh5s2bJFvz4uLo6wsDD8/PwA8PPzIyYmhvDwB0Pjbd26FZ1OR+PGjfXbbN++PUuftqCgIKpUqfLI5uxCo5g3uFUEJQMubDd2NELkyE8h5zh/6x4lnWx4P7CqscMRRlCQ9RBIXVRoNX4DKneEjFRYNghSCnbSPjtrC2b3rU9/P28Avlx/ks/WHCdDJ6NKisLpmQa337FjB3379sXPz0/f3PLHH3+wc+fOfA2uS5cufPnll6xbt46LFy+ycuVKvv/+e7p37w6ARqNh1KhRfPHFF6xZs4ajR4/Sr18/SpcuTbdu3QCoVq0aHTp0YMiQIezdu5ddu3YxYsQIevbsSenSpQHo3bs31tbWDB48mOPHj7NkyRKmTZuWpcm60Kp4/8bHiH+NG4cQOXDuZgKzgtX5ZcZ3qY6LXdH5JkpkVVD1EEhdVGhpNPDCTHAqDbfPwoaxBR6ChVbDZ11r8EmnagAsCL3EG3+Ek5ia95t2hTA1uU46li9fTmBgIHZ2dhw8eFA/akZsbCyTJk3K1+B+/PFHXnrpJd566y2qVavGmDFjeOONN/j888/124wdO5a3336boUOH0rBhQxISEti4cSO2trb6bRYuXEjVqlVp06YNzz//PM2bN88y7rmLiwubN2/mwoUL1K9fn/fee49PP/00y/jphZZvG/V3xFbjxiHEU5yKimfEooOkZugIqFKSTrU8n76TKJQKsh4CqYsKNQc36PELaLRwaCFsmwK6jAINQaPR8HqLCszsXQ9rSy3/noym28xdrDsSKa0eolDRKLkc6qNu3bq8++679OvXDycnJw4fPkyFChU4ePAgHTt21I/CUdTExcXh4uJCbGys2dxIDkB8FHxXRX08PibLCB5paWmsX7+e559/vkj1bcxP8hrm3c3YREbP28KuG1p0CjjZWrJ+ZAu8ipv+KHH5wWzfWwxI6qHHu3r1Kl5eXly5coWyZcsa/HyF5j1u2xQI/lJ9XL4FdP8ZXLLfiG/o8u6/eIchv+/n7v3JTyuUcGCYvy/d6pbB2vKZOqfkSaH5++aQlPfx8uO9JddX8OnTp2nZsmW25S4uLsTExDxTEMKIFLVPMlrLXA8ZKIQhZegUFoVdpv20neyIVhOOTrU82TiqZZFJOMSjST0k8l3L99WuVlYOcHEHzG4GJ/8p8DAalC/O1vcCeKdNJVzsrDh/6x5jlx/B/5tg5u28IN2uhFnLddLh4eFBREREtuU7d+6kQoUK+RKUKECZzcjavI9KIER+Cb90l24zd/HRyqPcTUzDw07h94H1mdmnHmVc7YwdnjAyqYdEvtNooG5feGM7eNaBpLuwpC/8MwpSC3aunGIO1rzbrjK7PmzNR89XpZSTDZGxyUxce4LmXwczY+tZYpOKxuR1onDJddIxZMgQ3nnnHcLCwtBoNFy/fp2FCxcyZswY3nzzTUPEKAxJd/9bE0k6hAm4EZ/Me38fpsdPuzl6LRYnG0s+fr4KY5/LwK+Cm7HDEyZC6iFhMCUqwuAgaPYOoIHw32COP0QeKfBQHG0sGdrSl+1jW/Fl95qUK27PnXupfLv5DM2+2spXG05xMz7l6QcSwkTk+pPmhx9+iE6no02bNiQmJtKyZUtsbGwYM2YMb7/9tiFiFIakb+mwMG4cokhLSElncdhlpm05S0KKmgi/0qAsYztUxcVGy/r1x40coTAlUg8Jg7K0hnYTwbc1rBwGt87A3DbQZjzUH1zg4dhaWdCnsTevNvBi3dFIZgWf43R0PLO3neO3XRd4pYEX/ZuWp2IpxwKPTYjcyHXSodFo+Pjjj3n//feJiIggISGB6tWr4+goF7tZSk9Wf0tLhyhgOp3Cngu3WRZ+lQ1Ho0hKUxPg2mVd+KxrDeqWU+cleHjOAiFA6iFRQCoEwLBdsOZtOL0ONn+MZfhvlHYKBKVDgYdjaaHlhTpl6PJcabacusHM4AgOXYnhjz2X+GPPJap7OtOldmk6P+cp970Jk/TMnzStra2pXr06ly5d4vLly1StWjXb5EnCDNy+3y/a1du4cYgi4/LtRJYduMry8Ktci0nSL69QwoE3/Cvwcn0vtFoZ1EA8ndRDwuAc3KDnQgifD1smorkdQcPbEShzt0Gb/0HlwAIfhEWr1dCuujttq5Ui9Pxtft1xgW1nbnIiMo4TkXF8vfEUdcu50uU5NQEp5Wz79IMKUQBynHTMmzePmJiYLJMUDR06lF9//RWAKlWqsGnTJry8vPI/SmE4N06ov92rGzcOUaglpKSz/mgky8KvsvfCHf1yJxtLOtcuzUv1y1KvnCsaGUFNPIHUQ8IoNBpoMBBq9iBj9wx0O6dhdeMYLH4VyjaE1v+DCv5GCEtDU98SNPUtwd17qWw8HsU/h68Tev42By/HcPByDJ+vO0ETHze61C5Nx5oeFHOwLvA4hciU46+E5syZQ7FixfTPN27cyG+//cbvv//Ovn37cHV1ZcKECQYJUhhQ9P2+8qUk6RD5S6dT2H3uFqP/PkTDL/5l7LIj7L1wB40GWlQqwbSeddj3SVsmv1iL+t7FJOEQTyX1kDAqW2d0Ld4nqMZ3ZPi9DZZ2cHUf/N4VFnSBK3uNFloxB2t6NSrHoiFNCBvXhvFdqlOvnCuKAqHnb/PRyqM0/PJfBvy2l+XhV4lPlm6rouDluKXj7NmzNGjQQP989erVvPDCC/Tp0weASZMmMXDgwPyPUBjWjZPqb0k6RD55UvepHvXL8mK9Mni6yLC3IvekHhKmIM3SCV3r8Vg0fRt2fKeOcHVhO/zaDip3gFYfg+dzRouvlLMtA5v5MLCZD1fuJLLuaCT/HL7O8etxhJy+Scjpm1iv1NK6Sim61C5N66qlsLOWwWSE4eU46UhKSsoyG+7u3bsZPPjBKA4VKlQo0rPAmqXURLhzXn3sXsO4sQizd+DyXaZsPMWe89J9ShiG1EPCpDi5w/NToOkI2PY1HFoMZzaqP7Vegee/ATtXo4boVdyeYf6+DPP3JeJGAmuPXGfN4eucv3mPjcej2Hg8CntrC7o8V5oRrSvKDejCoHKcdHh7exMeHo63tze3bt3i+PHjNGvWTL8+KioKFxcXgwQpDCTpDqCAxgLsSxg7GmGmbiWk8PWGUywNvwqo3Z+bVyzBS/XLEljDA1sr+QZN5A+ph4RJci2nzmbe7F0ImQTHlsPRv+FKGLyyAErXNXaEAFQs5ciotpV5p00lTkbG88+R6/xz+DpX7yaxZP8VVhy8Su9G5RjeuiKlnOTmc5H/cpx09O/fn+HDh3P8+HG2bt1K1apVqV+/vn797t27qVmzpkGCFAbi6KEOlatLh/jr4FLW2BEJM5KeoeOPPZf4PugM8cnq3Bov1S/L6HaVKS2zhgsDkHpImLQSFeGledBkOCwbADGX4Nf20GEyNBhc4KNcPY5Go6F6aWeql3ZmbGAV9l28y/QtZ9kZcYsFoZdYsv8KA5r6MMy/Ag5WphGzKBxynHSMHTuWxMREVqxYgYeHB0uXLs2yfteuXfTq1SvfAxQGZGGpfkNz5zzcuSBJh8ixsPO3Gb/mOKei4gGoWcaZCV1rUt+72FP2FOLZST0kzELZ+vDGdlj1FpxeD+veg0u7ocs0sHEydnRZaDQaGvkU58/XG7M74hbfbD7NwcsxzN52joV7LjG4eXk8M4wdpSgscpx0aLVaJk6cyMSJEx+5/r9v/sJMFPNRk467F8CnhbGjESYuOi6ZSetPsvrQdQBc7Kx4P7AKvRqVw0Lm1hAGJvWQMBt2xaDnIgidAf9+pna5ijwMr/xusvdQNq1YghW+bmw9dYNvNp3mVFQ8U7dE4GBpQazbJfo19ZHusiJPZBaloq64j/r7zgXjxiFMWmq6jjnbz9H62xBWH7qORgO9G5cjeEwAfZt4S8IhhBD/pdFA07dhwHpwLqNOxvtLazj4p7EjeyyNRkObau6sH9mC6b3qUt7NnnvpGiZtOE2rb0NYFHaZtAydscMUZkqSjqKu2P2k49Iu0Ekbqni0KRtPMWn9Ke6lZlCrjAurhzdjUvdaFJeJpoQQ4snKNYY3dkDFtpCeDKuHw+mNxo7qibRaDV1rl2bD203pWSEDTxdbImOT+WjlUb7ddNrY4QkzJUlHUVc5ECxt1VE2Nn1s7GiEifIt5ai/BzIqLpnrMcnGDUgIIcyJgxt0nALcfyO1NY9R1iwttDQupdCqyoMRLsu5ybC64tlI0lHUlagE3X9WH4f9BHt/MW48wiT1alSOpW/4UaGkAzfjUxj2ZzhvLQznRrwkH0IIkSPBkwAFfFuDt5+xo8mR1HQdf5zVsmjvVTQamNC1Bn0aexs7LGGmnjnpSE1N5fTp06Snp+dnPMIYanSD1v9TH2/4ACL+NWo4wjQ1KF+c9SNbMLyVLxZaDeuPRtHu++0sD7+KoijGDk8UQVIPCbNx/RAcW6Y+bvuZMSPJscTUdIYtPMiB21ostRqmvlqH/k3LGzssYcZynXQkJiYyePBg7O3tqVGjBpcvXwbg7bff5quvvsr3AEUBafEe1O4NSgYsHQg3Tho7ImGCbK0seD+wKmtGNKNGaWdik9J4b+lh+v+2j6t3E40dnigipB4SZmfLBPV3rZfBs7ZxY8mBu/dS6f1LGDsibmOtVZjTty4v1Clj7LCEmct10jFu3DgOHz5MSEgItrYPZqxs27YtS5YsydfgRAHSaKDLVCjXFFLiYOErkBBt7KiEiapR2oVVw5sxtkMVrC21bD9zk8AftjNv5wUZ2UQYnNRDwqycD4FzW0FrBa1M/97JK3cSefnnUA5dicHVzorh1TNoUanE03cU4ilynXSsWrWKGTNm0Lx5czQPza5Zo0YNzp07l6/BiQJmaQM9F0LxChB7GcuFL2KdFmfsqISJsrLQ8lZARTa804KG5YtxLzWDiWtPEDh1O1tORkuXK2EwUg8Js5GWpE4OCNBg0INh6k3UuiORPD99BxE3EvB0sWXR6w0pb1rzGQozluuk4+bNm5QqVSrb8nv37mV58xdmyr449FkGTp5obp2mWcRXcO+WsaMSJsy3pCNLhvrxZfeauDlYc/7mPQYv2E+fuWEcvx5r7PBEIST1kDAbW79Q5+dw8oRW44wdzWMlp2Xw0cqjDF90gPjkdOqVc2X5m02pVMrR2KGJQiTXSUeDBg1Yt26d/nnmG/zcuXPx8zOP0RjEU7j5Qv+1KI7uOCdfxXJRD7h329hRCROm1Wro09ib4PcDGObvi7WFlt3nbtP5x528v/Qw0XEyypXIP1IPCbNwKRRCZ6qPu0xXZyk3QWei4+k6YyeLwi6j0cBbAb4secOP0q52xg5NFDKWud1h0qRJdOzYkRMnTpCens60adM4ceIEu3fvZtu2bYaIURhDiYqk91lFxq8dsL1xHP54AfqtUVtChHgMZ1srPuxYlT6NyzFl02n+OXydpeFXWXskkmH+vgxp6YO9da7fdoTIQuohYfJS78HqtwAF6vaFyu2NHVE2iqLw174rTPjnOMlpOko42jD11To0l/s3hIHkuqWjefPmHDp0iPT0dGrVqsXmzZspVaoUoaGh1K9f3xAxCmMpUYldlT5EcSgJUUfhj26QdNfYUQkz4FXcnh971WXFW02pV86VpLQMfvj3DK2+DWFZ+FV0OrnfQzw7qYeEydsyEe6cB+cyEDjJ2NFkE5ecxojFBxm34ijJaTpaVi7JhndaSMIhDOqZvnL09fXll19kErmiIMG2DOl9VmL1ZzeIPAx/9YEB60D6TYscqFeuGMvfbMq6o5F8teEUV+8mMWbpYYJP32Bm73rGDk+YMamHhMmKOgZhs9XHgZNMbvbxtAwdL/20mzPRCQB80KEqb7SsgFYr9bowrFy3dLRt25b58+cTFyejGhUZJatC/3/AwgYu7YLoY8aOSJgRnQIpaboseWp8skzmJp6d1EPCpCkZoLFQH2/6CM4FGzee/9AA91Iy9M/XHL7OvzLioCgAuU46atSowbhx4/Dw8ODll19m9erVpKWlGSI2YUrcq0OldurjYyuMG4swC4qisOFoJB2mbue9pYe5cieJEo42TOhag1/6SRcY8eykHhImzbM2DNoExX0h7praNXn9WEg1jQlULS20rH+nBSPbVMLRxpKTkXEM/SOcF2buIvj0DUk+hMHkOumYNm0a165dY9WqVTg4ONCvXz/c3d0ZOnSoQW7gu3btGn379sXNzQ07Oztq1arF/v379esVReHTTz/F09MTOzs72rZty9mzZ7Mc486dO/Tp0wdnZ2dcXV0ZPHgwCQkJWbY5cuQILVq0wNbWFi8vL6ZMmZLvZTF7NV9Ufx9fAfKmJB5DURRCTt+g64xdvLnwAGdvJOBiZ8UHHaqyfWwA/ZuWx8bSwthhCjNW0PUQSF0kcsmrIQzbAQ1fV5/v/Rl+bgFXw40b130udlaMbleZnR+04q0AX+ytLThyNZaBv+2jx0+72RVxS5IPke9ynXQAaLVa2rdvz/z584mOjubnn39m7969tG7dOl+Du3v3Ls2aNcPKyooNGzZw4sQJvvvuO4oVezDs3JQpU5g+fTqzZ88mLCwMBwcHAgMDSU5+MERnnz59OH78OEFBQaxdu5bt27czdOhQ/fq4uDjat2+Pt7c34eHhfPPNN3z22WfMmTMnX8tj9ip3ACt7uHsRrh8wdjTCBO29cIdXf97DgN/2cfRaLA7WFoxsXZHtY1vxZoCvjFwl8k1B1UMgdZF4RtYO0Ok76LtcnafjdgT82g6CJ0GGabTMudpbM7ZDVbaPbcWQFj7YWGo5cDmGPnPD6DlnD/suyuAxIh8peRAZGan88MMPSv369RWNRqM0btw4L4fL5oMPPlCaN2/+2PU6nU7x8PBQvvnmG/2ymJgYxcbGRlm8eLGiKIpy4sQJBVD27dun32bDhg2KRqNRrl27piiKosyaNUspVqyYkpKSkuXcVapUyXGssbGxCqDExsbmeB9Tl5qaqqxatUpJTU19sPDvAYoy3llRNn5kvMDMyCNfw0ImI0OnhJ2/rfT7NUzx/mCt4v3BWqXSx+uVz/85rtyKT87z8YvCa/gkhfG9JT8Zuh5SFPOqi65cuaIAypUrV3K8T14Utf/PZy5v4h1FWTZYrT/HOyvK7BaKEnnUMEHmQXRskjJ+9TGl0kfr9e/n7SetUbafilIyMnTGDs/g5Hp+vPx4b8n1145xcXEsX76cRYsWERISQoUKFejTpw9LlizB19c3XxOiNWvWEBgYyMsvv8y2bdsoU6YMb731FkOGDAHgwoULREVF0bZtW/0+Li4uNG7cmNDQUHr27EloaCiurq40aNBAv03btm3RarWEhYXRvXt3QkNDadmyJdbW1vptAgMD+frrr7l7926Wb7MypaSkkJKSkuV1AUhLSys0fYszy/FweTRVX8Dy+AqU0xtIb/2ZcQIzI496DQuLczfvsfrwdf45HMnVGPXbXEuthpfrl+GtgAp4ONsCeS97YX4Nc6KolvtJCrIeAvOqi+Lj4wFIT08vkGunqP1/PnN5LR2h609oKgZisfF9NJGHUWY3R6nejYyWY8GtkgGizb1idhZ83LEyg5qWY9a28ywLv8bpWC2v/bafssXs6F7Hk251SlOuuL2xQzUIuZ4fLz097wPA5DrpcHd3p1ixYrz66qtMnjw5yxtofjt//jw//fQTo0eP5qOPPmLfvn2MHDkSa2tr+vfvT1RUlD6m/8aYuS4qKopSpUplWW9paUnx4sWzbOPj45PtGJnrHvVGP3nyZCZMmJBt+ebNm7G3L1z/jEFBQfrHdqm3aQ/o7l5i/bq1oHmmHnpFzsOvoTmLS4UDtzXsv6nlyr0Hw1HZWCjUdVNoV0ZHCcuLHNh5Md/PXVhew9xKTDSNm09NSUHWQ2CeddGWLVsoUaLg5lwoav+fz15ea2x9P6Pm1YWUidmL5sRKNCdWcaV4U057dCPRxv3phyggfpZQuTb8e11L+C0NV+8m8WPweX4MPo+vk0KjUjrquCnYFsJb9OR6zu7WrVt5Pk+uk441a9bQpk0btFrDf9jU6XQ0aNCASZPUiXXq1q3LsWPHmD17Nv379zf4+Z9k3LhxjB49Wv88Li4OLy8v2rdvj7OzsxEjyz9paWkEBQXRrl07rKys1IUZaSjH38NCSed5/0bgWOrJByniHvkampl7KekEnbzB6sOR7D53m8x5/Sy1GlpUcuOF2qVpXaUkdtaGqXkKw2uYFzIsbHYFWQ+BedVF165do3r16rRp04YyZcoY/PxF7f8z/8rbh7Soo1hs/xrt2Y2Uu7MLr5gwlOd6ktF8DLiUzbeY8yItLQ23oCB+HNSakLN3WXHwOrvP3+ZcvIZz8RasuqwlsLo7L9YrTePyxc1+rg+5nh/v2rVreT5frpOOdu3UYVNv3rzJ6dOnAahSpQolS5bMczD/5enpSfXq1bMsq1atGsuXLwfAw8MDgOjoaDw9PfXbREdHU6dOHf02N27cyHKM9PR07ty5o9/fw8OD6OjoLNtkPs/c5r9sbGywsbHJttzKyqrQXahZymRlBU4eEB+JVWI0FDN8pVYYmNt1kZ6hY0fELVYdvMbm49EkpT0Y071eOVe61S1Dp1qeuDlm/x8wFHN7DfNLUSzz0xRkPQTmVRdlJqmWlpYFeu0Utf/PfCmvVz3oswSuhUPwJDQR/6I59Cfao39Dvf7Q4j1w9nz6cQqAs70tPRqUo0eDckTGJrHiwDWWH7jK+Zv3WHU4klWHIynjaseL9crQo15ZypdwMHbIeSLXc3aWlnkfCCbXXxMlJiYyaNAgPD09admyJS1btqR06dIMHjw437sBNGvWTF+hZDpz5gze3t4A+Pj44OHhwZYtW/Tr4+LiCAsLw8/PDwA/Pz9iYmIID38wTN3WrVvR6XQ0btxYv8327duz9GkLCgqiSpUqj2zOLvKcS6u/4/Ke9QrToSgKh6/E8Nma4zSZvIWBv+1j9aHrJKVl4FPCgXfbViZkTAAr3mpGP7/yBZpwCPGwgqyHQOoiYWBl6qsjXA3aBOVbQEYq7PsFpteBjR9Bwk1jR5iFp4sdw1tVZMtof1a81ZQ+jcvhZGvJtZgkftwaQcC3Ibz0024W771MXHLRuDdC5Eyuk453332Xbdu28c8//xATE0NMTAyrV69m27ZtvPfee/ka3LvvvsuePXuYNGkSERERLFq0iDlz5jB8+HAANBoNo0aN4osvvmDNmjUcPXqUfv36Ubp0abp16wao30Z16NCBIUOGsHfvXnbt2sWIESPo2bMnpUurH5579+6NtbU1gwcP5vjx4yxZsoRp06ZlabIWD3G+37oRK0lHYaDcn8SvzffbeGHmLubvvsithFTcHKwZ0LQ8q4Y3Y+t7/rzTtpLZf3slCoeCrIcyzyd1kTC4ck1gwFro/w94NYH0ZNgzE6Y9B7ummdz8WBqNhnrlivFl91rs+7gtM3rXJaBKSbQa2H/pLuNWHKXhF//yv1XHJPkQqtwOd+Xm5qYEBwdnW75161alRIkSzzyM1uP8888/Ss2aNRUbGxulatWqypw5c7Ks1+l0yv/+9z/F3d1dsbGxUdq0aaOcPn06yza3b99WevXqpTg6OirOzs7KwIEDlfj4+CzbHD58WGnevLliY2OjlClTRvnqq69yFWdhHNbysUOpbfpYHfJv7WjjBGZGTH34vcu37ykDf9urHxqxyifrlZGLDyhbT0UrqekZxg5PURTTfw0NrTC+t+RVQddDimI+dZEMmWtYBVZenU5RzgYpys8BD4bZXdxbUZJiDHve/3iW8kbFJimzQyKUtt+F6OuWxl/+q/x7IsqAkeYPuZ4fzyhD5iYmJmYboQOgVKlSBmnW7ty5M507d37seo1Gw8SJE5k4ceJjtylevDiLFi164nmee+45duzY8cxxFimeddTf1w8ZMwqRB2kZOubtvMDUf8+SlJaBlYWGYf6+vOHvi6ONTOAnTFtB10MgdZEoYBoNVGwLvm1g/zzY+CGcWgtzTsKrf4J79acfw0jcnW15w9+XoS0rEHruNh+tPMrF24kMXrCfF+qUZnyXGhR3sH76gUShk+vuVX5+fowfPz7LLKtJSUlMmDBB33dVFHKl66q/o45CeqpxYxG5duDyXbr8uJPJG06RlJZBI5/ibHinBe+1ryIJhzALUg+JIkOjgYaDYeBGcC4Ld87B3DZwdJmxI3sqjUZD04ol2DiqJW+0rIBWA6sPXaft99tYc/g6iol1FxOGl+tPGNOmTSMwMJCyZctSu3ZtAA4fPoytrS2bNm3K9wCFCSpeAWxdIDkWbp4Ez9rGjkjkQGxSGt9sOsXCsMsoCrjaW/HR89V4uX5ZNBrzHuZQFC1SD4kip2x9eGMbLBsEF7bB8sFwdT+0/xwsTHuUJVsrC8Y9X43na3kydtkRTkfHM3LxQdYcus6X3Wvifn8iWVH45TrpqFmzJmfPnmXhwoWcOnUKgF69etGnTx/s7OzyPUBhgjQatbXjfAhcPyhJh4lTFIW1RyKZuPYEN+PVmYt71CvLR89XlRGohFmSekgUSQ4l4LWVsPUL2Pk9hP2k1sEvzzeZoXWfpLaXK/+83ZxZIRHMDI7g35PRhF24zSedqvFKAy/58qsIeKa+FPb29gwZMiS/YxHmJDPpuBYO9QcYOxrxBPN2XeTztScAqFDCgS+616Spb8HNVCyEIUg9JIokrQW0HQ9lG8DKYXBlD8wLhCHB4OBm7OieytpSy6i2lelY05Oxyw5z+GosHyw/SlRsCu+0rWTs8ISBPdN0rqdPn2bEiBG0adOGNm3aMGLECP23TaKIKNdU/X0uxOSG8RMPHLoSw+T1JwF4w78CG0a1kIRDFApSD4kirWonGBoCrt4QcwmW9ocM8xmWtoqHEyveasaY9pUBmLblDHvO3zZyVMLQcp10LF++nJo1axIeHk7t2rWpXbs2Bw4coFatWvrZWUURUL4ZWNhA7GW4ddbY0YhHiE1KY8SiA6TrFDrV8uTDDlWxsbQwdlhC5JnUQ0IAbr7QewlYO8HFHbBhrLEjyhULrYYRrSvxUv2y6BQY9dch7t6TwWkKs1x3rxo7dizjxo3LNizg+PHjGTt2LD169Mi34IQJs3YA76ZwPhgi/oWSlY0dkXiIoih8uPwIV+8m4VXcjsk9akl/WVFoSD0kxH2lqkGPubC4pzq0bqnq0Mi8uh1O6FqDA5fvcv7mPd5fdphf+jWQ+qqQynVLR2RkJP369cu2vG/fvkRGRuZLUMJMVGyr/o7417hxiGz+3HOJDceisLLQMLN3PZxtTXt0EyFyQ+ohIR5SpYN6nwfAhg/g/DbjxpNLDjaW/NirLtYWWv49eYP5uy8aOyRhILlOOgICAh45cdHOnTtp0aJFvgQlzERm0nFpF6QlGTcWoXf8eiyfr1Xv4xjXsRrPlXU1bkBC5DOph4T4j2aj4LlXQclQ7++4fc7YEeVKjdIufNypGgCT15/i2LVYI0ckDCFH3avWrFmjf9y1a1c++OADwsPDadKkCQB79uxh6dKlTJgwwTBRCtNUsgq4eEHsFbiwHSoHGjuiIi8tQ8eYpUdIzdDRtpo7A5uVN3ZIQuQLqYeEeAKNBrpMh9sR6qiSi16F14PArpixI8uxfn7e7Iy4RdCJaEb+dZB1b7fAzlruQyxMcpR0dOvWLduyWbNmMWvWrCzLhg8fzrBhw/IlMGEGNBqo1B72/wpnNknSYQLm7rjAycg4itlb8bXcxyEKEamHhHgKK1vouQh+aQO3z8Lf/aDvCpOfPDCTRqNhSo/n6HB1O+dv3uPL9Sf4olstY4cl8lGOulfpdLoc/WRkZBg6XmFqKndQf5/ZJEPnGtnFW/eY+u8ZAD7pVF0m/hOFitRDQuSAkwf0/gusHdUeCGvfNau6uZiDNd++rE44/Oeey2w5GW3kiER+eqZ5Oh4lJiaGGTNm5NfhhLnwaQGWdhB3FaKPGzuaIktRFD5edZSUdB3NKrrxYr0yxg5JiAIn9ZAQgEcteGkeaLRw8A/YPd3YEeVKi0olGdzcB4Cxy45wMz7FyBGJ/JLnpGPLli307t0bT09Pxo8fnx8xCXNiZQcVAtTHZzYaNZSibPmBa+yKuI2NpZYvu0m3KlG0SD0kxH9UDoTAyerjoPFwYs2Ttzcx7wdWoaqHE7fvpTJ22WEUM2qtEY/3TEnHlStXmDhxIj4+PrRv3x6NRsPKlSuJiorK7/iEOci8l0OSDqO4FpPEF+tOADCqbWXKl3AwckRCGJ7UQ0I8ReM3oOEQQIEVQ+FcsLEjyjFbKwum9qyDtaWW4NM3mRkcYeyQRD7IcdKRlpbG0qVLCQwMpEqVKhw6dIhvvvkGrVbLxx9/TIcOHbCyMo+blUQ+y0w6ru6HhJvGjaWISUxNZ8iC/cQkplGzjDOvt/AxdkhCGIzUQ0LkgkYDHb6CSoGQngSLXjGrFo+qHs58cn8Y3W83n2FWiCQe5i7HSUeZMmX48ccf6dGjB9euXWPFihW89NJLhoxNmAvn0uBZG1Dg7CZjR1NkKIrC+0uPcCIyDjcHa2b3rY+VRb7dpiWEyZF6SIhcsrCEV/+Aal0hI1Wdw+PA78aOKsf6+ZVndLvKAEzZeFoSDzOX408o6enpaDQaNBoNFhYybrL4jyrPq79PbzBuHEXIzOAI1h2NxMpCw09961O2mL2xQxLCoKQeEuIZWNrAy/OhXj9QdLDmbdg1zdhR5djINpV476HEQ7pama8cJx3Xr19n6NChLF68GA8PD3r06MHKlSvlhlWhyhw691wwpCUbN5YiIOhENN9uVofHndC1Jo18ihs5IiEMT+ohIZ6R1kKdPLDZKPV50KfqDeZmcoP22w8lHt9sksTDXOU46bC1taVPnz5s3bqVo0ePUq1aNUaOHEl6ejpffvklQUFBMj56UeZZG5xKQ9o9uLjD2NEUamei4xn110EAXmviTe/G5YwckRAFQ+ohIfJAo4F2E6DdRPX5rqnwzzuQkW7UsHLq7TaVGNNeEg9z9kwdwH19ffniiy+4dOkS69atIyUlhc6dO+Pu7p7f8QlzodFAlfutHafWGTeWQuzOvVReX7Cfe6kZNKlQnE+7VDd2SEIYhdRDQjyjZu9A1x/VeTwOLIDFr0JyrLGjypERrbMmHpPXn5ThdM1Inu461Wq1dOzYkWXLlnH16lU++uij/IpLmKOqndTfp9aBTr5tzG+p6TqG/RnO5TuJeBW3Y1YfuXFcCKmHhHgG9frBK3+AlT1E/Au/toc7F4wdVY6MaF2JcR2rAvDz9vOMWXqEtAydkaMSOZFvn1hKlizJ6NGj8+twwhyVbwm2LnDvBlwJM3Y0hYqiKPxv1TH2XriDk40l8/o3pLiDtbHDEsKkSD0kRC5U6wwDN6hdo2+egrlt4NJuY0eVI2/4+zLlpeew0GpYfuAqQ3/fT2KqeXQTK8rka1KRfyytoXJH9fHJf4wbSyHz684LLNl/Ba0GpveuSyV3J2OHJIQQwtyVrgNDtkLpupB4GxZ0hYMLjR1VjrzSwIs5r9XH1kqdQLDP3DDu3ks1dljiCSTpEPmrelf198l/zGZUDFMXfOoGk9afBODjTtVpVaWUkSMSQghRaDh7woD1UL0b6NJg9Vvq6FY60++y1KaaOwtfb4yLnRUHL8fw8s+hXI9JMnZY4jEk6RD5y7c1WDlA7BW4fsDY0Zi9iBsJvL34IDoFejXyYlCz8sYOSQghRGFjbQ8v/QYtx6rPd02DJX0hNcG4ceVAfe/iLB3mh4ezLRE3Eujx027O3TT9uIuiXCcdEydOJDExMdvypKQkJk6cmC9BCTNmZQeV26uPT6w2bixmLik1g+ELD5CQkk5jn+JM6FpT5iMQAqmHhDAIrRZafwwvzgULGzi9DssFnbFNvW3syJ6qsrsTy99qim9JByJjk+k5Zw8RNyTxMDW5TjomTJhAQkL2P2RiYiITJkzIl6CEmav+gvr7xGrpYpUHn605zunoeEo62TCjdz2sLaVhUgiQekgIg3ruZRiwFhxKorlxDP/Tn6Exg54LZVztWPKGH1U9nLgZn0LPOXs4Ex1v7LDEQ3L9KUZRlEd+23r48GGKF5dZkQVQqT1Y2sHdixB52NjRmKWVB6/qbxyf1rMOJZ1sjB2SECZD6iEhDMyrEQzZilKqOrbpsVj80RWOrTB2VE9VwtGGRUOaUM3TmVsJKfSas4dTUXHGDkvcl+Oko1ixYhQvXhyNRkPlypUpXry4/sfFxYV27drxyiuvGDJWvvrqKzQaDaNGjdIvS05OZvjw4bi5ueHo6EiPHj2Ijo7Ost/ly5fp1KkT9vb2lCpVivfff5/09KxDq4WEhFCvXj1sbGyoWLEi8+fPN2hZCjVrB6jUTn18YpVRQzFHETcS+HjlMQBGtqlEU98SRo5ICNNgCvUQSF0kigjXcqT3W0eUcx006cmwbCBsm2LyPRiKO1izeEhjapZx5va9VHrN2cOJ65J4mALLnG44depUFEVh0KBBTJgwARcXF/06a2trypcvj5+fn0GCBNi3bx8///wzzz33XJbl7777LuvWrWPp0qW4uLgwYsQIXnzxRXbt2gVARkYGnTp1wsPDg927dxMZGUm/fv2wsrJi0qRJAFy4cIFOnToxbNgwFi5cyJYtW3j99dfx9PQkMDDQYGUq1Gp0g5Nr1C5WbcarM5aLp8q8jyMxNYOmvm683bqSsUMSwmQYux4CqYtEEWPjRFiFUXS22YtF2CwI/hJunoYXZoKVrbGjeyxXe2sWDm7Ca/PCOHI1lt5z9/Dn4MbULOPy9J2F4Si5FBISoqSmpuZ2tzyJj49XKlWqpAQFBSn+/v7KO++8oyiKosTExChWVlbK0qVL9duePHlSAZTQ0FBFURRl/fr1ilarVaKiovTb/PTTT4qzs7OSkpKiKIqijB07VqlRo0aWc7766qtKYGBgjmOMjY1VACU2NvZZi2lyUlNTlVWrVj3b3zs5XlE+L6Uo450VJfJI/gdnJnL7Gn6w7LDi/cFapf7nQUp0XJKBozMPeboOC4HC+N6SV8aohxTFPOqiK1euKIBy5cqVZy1mrhS1/88iXd798xVlQnG1Xv+lraLE3zB2eE8Vk5iqvDBjp+L9wVql9oRNytnouCduX6T/vk+RH+8tOW7pyOTv749Op+PMmTPcuHED3X/GcW7ZsmU+pEJZDR8+nE6dOtG2bVu++OIL/fLw8HDS0tJo27atflnVqlUpV64coaGhNGnShNDQUGrVqoW7u7t+m8DAQN58802OHz9O3bp1CQ0NzXKMzG0ebjr/r5SUFFJSUvTP4+LUpru0tDTS0tLyWmSTkFmOZyqP1gYLH3+0ZzeRcXI9Oreq+RydecjNa7jy4HX+2ncFjQa+e6kmxWwtCs21lBd5ug4LgaJa7icxRj0E5lEXxcerN86mp6cXyLVT1P4/i3R5n+uNxtkLi+UD0VzdizK3DemvLoYSlY0c5ePZW8K8fvUYsGA/R67G8dqve1kypBGeLo9upSnSf9+n+G9X0GeR66Rjz5499O7dm0uXLqH8p1+fRqMhIyMjz0E97K+//uLAgQPs27cv27qoqCisra1xdXXNstzd3Z2oqCj9Ng+/yWeuz1z3pG3i4uJISkrCzs4u27knT578yFFSNm/ejL29fc4LaAaCgoKeaT/v5NLUAWL3/82OuKKZdGR62mt4PRG+P2oBaOhQJoOY02GsP10wsZmLZ70Ozd2jhoYt6gq6HgLzq4u2bNlCiRIFdz9YUfv/LMrldfQZR+Nz3+EYcwnmtmWvz9vccqphxOie7lUPiLplQWRsMq/M3MbIGhk4WD1++6L8932cW7du5fk8uU46hg0bRoMGDVi3bh2enp4GnTfgypUrvPPOOwQFBWFra1p9B8eNG8fo0aP1z+Pi4vDy8qJ9+/Y4OzsbMbL8k5aWRlBQEO3atcPK6gn/nY8TVwd+/I1i987xvH8jcCh6N0Tn5DW8l5LOi7PDSNPdo5mvGz/0q4eFVu6ByZTn69DMZbaiigcKsh4C86qLrl27RvXq1WnTpg1lypQx+PmL2v+nlPe+xG7olvXH6soemp7/jowO36DUfc14geZAM/8kXp2zl6j4FJbfKMFv/etjZ22RZRv5+z7etWvX8ny+XCcdZ8+eZdmyZVSsWDHPJ3+a8PBwbty4Qb169fTLMjIy2L59OzNmzGDTpk2kpqYSExOT5Rum6OhoPDw8APDw8GDv3r1Zjps5osjD2/x3lJHo6GicnZ0f+c0SgI2NDTY22YcxtbKyKnQX6jOXyc0bPGqhiTqK1aVtULtn/gdnJh73GiqKwvjlxzh/6x4ezrZM71UXWxtrI0Ro+grj/1ZOFMUyP01B1kNgXnVRZpJqaWlZoNdOUfv/LPLldfGA/mtg9XA0R5diuf5diL0IbT5TJxk0QeVLWvH74Ma8PHs34ZdjGL3sKLP71sfSInu8Rf7v+wiWlrlOGbLJ9ZXRuHFjIiIi8nzinGjTpg1Hjx7l0KFD+p8GDRrQp08f/WMrKyu2bNmi3+f06dNcvnxZP4KJn58fR48e5caNG/ptgoKCcHZ2pnr16vptHj5G5jaGHgWlSKh0f8SVMxuNG4eJWhh2mdWHrmOh1fBj77q4Ocp8HEI8TUHWQyB1kRCPZGkDL/4C/h+qz3dNg6X9INV0u4RW8XBibv+G2Fhq+ffkDT5aeTRbF01hODlKW44cOaJ//Pbbb/Pee+8RFRVFrVq1smVG/x1GMC+cnJyoWbNmlmUODg64ubnplw8ePJjRo0dTvHhxnJ2defvtt/Hz86NJkyYAtG/fnurVq/Paa68xZcoUoqKi+OSTTxg+fLj+26Fhw4YxY8YMxo4dy6BBg9i6dSt///0369aty7eyFFmVA2HHt3A+BHQ6k/0GxBh2nL3JZ2uOAzA2sAoNy8ukZkI8jrHqIZC6SIjH0mig1TgoXgHWjICT/0BsJ+j1Fzi5P31/I2jkU5wfe9Vl2J/h/L3/KhVKOjLM39fYYRUJOUo66tSpg0ajyZINDho0SP84c52hbuB7kh9++AGtVkuPHj1ISUkhMDCQWbNm6ddbWFiwdu1a3nzzTfz8/HBwcKB///5MnDhRv42Pjw/r1q3j3XffZdq0aZQtW5a5c+fKuOj5oXRdsHKApLtw8xS4Vzd2RCbhdFQ8b/15gHSdwgt1SjO0ZQVjhySESTPlegikLhJFXO1XwdUL/uoN1w/A3DbQ+2+TrfPb1/BgfJcajF9znK83nsK3pCPtqptmklSY5CjpuHDhgqHjyLGQkJAsz21tbZk5cyYzZ8587D7e3t6sX7/+iccNCAjg4MGD+RGieJiFFXg1gvPBcGmXyb4BFaTouGQG/raX+JR0GpUvzpSXnjP4jbBCmDtTqodA6iIhsvFuCq9vgYUvw51zMC8QXlkAvq2NHdkj9fPz5uyNeP7cc5l3/jrI8jebUrHEo++dEvkjR0mHt7e3oeMQhZl3s/tJx25oNMTY0RjVvZR0Bi/Yx/XYZCqUdGBOv/rYWFo8fUchijiph4QwA26+8Pq/8FcfuLwb/nwJOn8P9QcYO7JsNBoN47vU4MKte+yKuM3rC/az7I1Gxg6rUMv1rehr1qx55HKNRoOtrS0VK1bEx8cnz4GJQsS7qfr70m5QFLUPaBGUoVMYufggx67FUdzBmt8GNMTVXkaqEiK3pB4SwoTZF4d+q2DN23BkCfzzDtw5b5IjW1lZaJnZux7dZu7i4u1Ehi8+TB9PY0dVeOU66ejWrVu2frWQtT9t8+bNWbVqFcWKFcu3QIUZK1MfLKwhIUp943ErejdsKYrCJ6uOsuXUDWwstfzSrwHebg7GDksIsyT1kBAmztIGuv+s3mAeMlkd2epWBHSfDbamNZeZq701c/s3pPusXRy4HIMmUUsXGdHKIHKdcgYFBdGwYUOCgoKIjY0lNjaWoKAgGjduzNq1a9m+fTu3b99mzJgxhohXmCMrWzXxALW1o4hRFPhi/WkW772CVgNTX61DfW/5ICTEs5J6SAgzoNFAwIfqsLoW1nB6nXqD+a2zxo4sm4qlHJnVR52YN/yWlu+CCm5I7qIk1y0d77zzDnPmzKFp06b6ZW3atMHW1pahQ4dy/Phxpk6dmmVUESEo5weXQ9WfeqY9a2l+UhSFNZe1bL1+GYApL9WmYy1puxUiL6QeEsKMPPcKFPeFJX3h1hn4pbXaClL1eWNHlkWLSiX58oXqfLjyOD/vuEDZ4va85lfe2GEVKrlu6Th37hzOztmbxpydnTl//jwAlSpV4tatW3mPThQeD9/XUYRM33qOrdfVf7Mvu9fkpfpljRyREOZP6iEhzEzZ+vDGNijXFFLi4K9eEPKVOn+XCelRrwzPe6lDbn+65jgbj0UZOaLCJddJR/369Xn//fe5efOmftnNmzcZO3YsDRs2BODs2bN4eXnlX5TC/Hk1AjRw9wLERRo7mgIxMziCGSHqB6CPn69Cn8Yy+o4Q+UHqISHMkGMp6L8GGg1Vn4dMhiV9IDnOuHH9R/syCq82KIOiwDt/HST80h1jh1Ro5Drp+PXXX7lw4QJly5alYsWKVKxYkbJly3Lx4kXmzp0LQEJCAp988km+ByvMmK0LeNyf0fdy4W/tmLvjPN9sOg1A13IZDPCThEOI/CL1kBBmysIKnv8GXpgFFjZwer3a3erGKWNHpqfRwGedq9GmailS0nUMXrCfiBvxxg6rUMj1PR1VqlThxIkTbN68mTNnzuiXtWvXDu39odC6deuWr0GKQqJcU4g6Chd3Qs0exo7GYGZvO8dXG9Q30JGtffFNOm3kiIQoXKQeEsLM1e0Dpaqp93ncPgs/t4S2n0HjYSYxrK6lhZYfe9el1y9hHL4Sw6s/7+G3gQ15rqyrsUMza7lOOgC0Wi0dOnSgQ4cO+R2PKMwqBMDen+HcVmNHYhCKojD137NM26KOzDGydUVGBPiwYYMkHULkN6mHhDBzZerB0G2wahhE/AubxqktH91mgWs5Y0eHvbUl8/o3oP9vezl2LY5ec/Yw+7X6tKhU0tihma0cJR3Tp09n6NCh2NraMn369CduO3LkyHwJTBRCPi1AawV3L8Ltc4Vqvg5FUZi84RRztqv3cIztUIW3AiqSlpZm5MiEKBykHhKiEHIsCX2WQfhvsOljuLgDfmoGHb+G2r2MPpmwm6MNi4c0Ydif4eyKuM2g+fv47pU6dK1d2qhxmascJR0//PADffr0wdbWlh9++OGx22k0GnmzF49n4wTlmqhvKhFbCk3SodMpjF9znD/2XAJgfJfqDGwmsyELkZ+kHhKikNJooMEg8PGHVW/ClTD196l10HmqmpgYkZOtFfMGNGT034dZdySSkYsPcjshRer5Z5CjpOPChQuPfCxErvm2VpOOc1ug8VBjR5NnGTqFD5YfYVn4VTQamNS9Fr0aGb9ZWIjCRuohIQo5N18YuEGdvTx4EpxaqyYgXaZB1U5GDc3G0oIfe9alhIM1C0IvMeGfE9xKSGFM+ypojNwaY06e+W6d1NRUTp8+TXp6en7GIwq7im3V3xd2QHqKcWPJo+S0DEYuPsiy8KtYaDV8/0ptSTiEKEBSDwlRyGgtoMVoGLIVSlWHezfhr96wejikJBg3NK2Gz7rWYEz7ygDMDD7Hx6uOoSiKUeMyJ7lOOhITExk8eDD29vbUqFGDy5fVmZbffvttvvrqq3wPUBQyHrXArhik3YMbJ4wdzTO7nZBCn7lhrDsaiZWFhhm96tK9rkz8J0RBkHpIiELO8zkYGgLN3gE0cPBP+LkFXA03algajYYRrSvx1Yu10GpgUdhlJvxzQhKPHMp10jFu3DgOHz5MSEgItra2+uVt27ZlyZIl+RqcKIQ0GvB4Tn0cddS4sTyjczcTePGn3YRfuouzrSULBjWiYy1PY4clRJEh9ZAQRYClDbSbCAPWgnNZuHMefm0H274BXYZRQ+vZqBxTXqoNwPzdF5m84ZQkHjmQ66Rj1apVzJgxg+bNm2fpx1ajRg3OnTuXr8GJQsqjlvrbDJOOsPO3eXHWbi7dTsSruB0r3mpKU98Sxg5LiCJF6iEhipDyzeHNnVDjRVAyIPgLmN8J7l4yalgv1S/LpO7q55k528/zQ9AZo8ZjDnKddNy8eZNSpUplW37v3j25mUbkjJm2dKw6eI3Xft1LbFIadbxcWflWMyqWcjJ2WEIUOVIPCVHE2BWDl+ZB95/B2gkuh8Ls5nBkqVHD6t24HOO7VAdg+tYIZgZHGDUeU5frpKNBgwasW7dO/zzzDX7u3Ln4+fnlX2Si8PLMTDqOgU5n3FhyQFEUpv17llFLDpGaoeP5Wh78NbQJJRxtjB2aEEWS1ENCFEEaDdTuqbZ6eDWGlDhY8Tosfx2SY40W1sBmPozrWBWAbzadZu6O80aLxdTlekbySZMm0bFjR06cOEF6ejrTpk3jxIkT7N69m23bthkiRlHYuFUCC2tIjYfYK1DM29gRPVZsUhpjlx1m0/FoAN7wr8AHgVXRauXbVCGMReohIYqwYuVhwHrY8R1s+xqOLoVbZ6HfarBzNUpIb/j7kpKu4/ugM3yx7iQtK5eksrv0hPivXLd0NG/enEOHDpGenk6tWrXYvHkzpUqVIjQ0lPr16xsiRlHYWFiC5v6lp3nmUZsN7ti1WLr8uJNNx6OxttAy+cVajOtYTRIOIYxM6iEhijgLSwj4AAZtBHs3iDwEf/aA5DijhfR264q0rKxOZLj11A2jxWHKct3SAeDr68svv/yS37GIoiI9FdKT1cc2jsaN5REUReGvfVcYv+Y4qek6yhazY1afejxX1tXYoQkh7pN6SAiBVyO1hWNBF7i2Hxa+DH2XG+WzhUajoU3VUmw/c5MdZ28yzN+3wGMwdTlOOuLicpY9Ojs7P3MwoohIfWiCH2vTan5MTE3nk5XHWHHwGgBtqpbi+1fq4GJvZeTIhBBSDwkhsvGoBa+tgt+7wpU9sOhV6LMUrO0LPJTmldTRLPdduEtSagZ21hYFHoMpy3HS4erq+sRRQRRFQaPRkJFh3LGThRlIuf/BwcpebSI1ERE3EnhrYThnohOw0GoY074Kb7SsIN2phDARUg8JIR6pdB3ouxJ+fwEu7YS/ekGvJWBl+9Rd81OFEg6UcbXjWkwSYRduE1Al+yh7RVmOP/EFBwfrHyuKwvPPP8/cuXMpU6aMQQIThVhKvPrbxnRaOf45fJ0Plx/hXmoGJZ1smNGrLo0ruBk7LCHEQ6QeEkI8Vtn60HcZ/PEinA+BRa9A1x8LdLAajUZD84olWLL/ChuPReFfuaQM4/2QHCcd/v7+WZ5bWFjQpEkTKlSokO9BiUIu8+bxlARIT1FnHTWS1HQdX647wYJQdZIhvwpuTOtVh1JOBfvtiBDi6aQeEkI8Ubkm0Odv+PMluLANZjSAxsOgxXsFNrJVu+ruLNl/hb/2XSE1XccX3Wtib206vTqMyXSHDhKFV8lq4OgBaffgwg6jhXEtJolXfg7VJxwjWlXkj8GNJOEQQgghzFX55vB6EPi0hIxU2D0dpteBPT+pA9kYWJtqpRjboQpaDaw4eI0XZuzibHS8wc9rDiTpEAVPq4Wqz6uPT601Sgghp2/QafoODl2JwcXOinkDGjAmsAqWFvIvIYQQQpg1j1rQbw30Xgolq0LSXdj4IcxsBMdXgaIY7NQajYa3AiqyeEgTSjnZcPZGAl1n7GLFgasGO6e5yNMnLOmnJp5Z1U7q79PrC3RW8gydwvdBZxg4fx8xiWnUKuPC2reb07qqe4HFIITIP1IPCSEeSaOByu1h2C7oMg0cSsHdC7C0P/zaHi6HGfT0jSu4sf6dFjSvWIKktAxG/32YD5cfITmt6A50keOk48UXX8zyk5yczLBhw7Itz0+TJ0+mYcOGODk5UapUKbp168bp06ezbJOcnMzw4cNxc3PD0dGRHj16EB0dnWWby5cv06lTJ+zt7SlVqhTvv/8+6enpWbYJCQmhXr162NjYULFiRebPn5+vZRH/Ub4l2DhDQjRcCy+QU95OSGHAb3uZvuUsigJ9Gpdj6TA/vIoX/LB6QojcM0Y9BFIXCWHWLCyh/gAYeRD8P1BHzry6F+a1hyWvwe1zBjt1CUcbFgxqxKi2ldBo4K99V+g2cxfnbyY8fedCKMd3tri4uGR53rdv33wP5r+2bdvG8OHDadiwIenp6Xz00Ue0b9+eEydO4ODgAMC7777LunXrWLp0KS4uLowYMYIXX3yRXbt2AZCRkUGnTp3w8PBg9+7dREZG0q9fP6ysrJg0aRIAFy5coFOnTgwbNoyFCxeyZcsWXn/9dTw9PQkMDDR4OYskS2uo1B6OLYMzG8CroUFPF37pLiMWHSAyNhk7Kwu+7F6TF+uVNeg5zYVOpyM11fD9XJ9VWloalpaWJCcnF8qhUK2srLCwkLHcc8IY9RBIXSREoWDjCK0+gvoDIfhLOLQQTq6BMxuh8w9Q1zDvJxZaDaPaVqaBd3He+esgp6LiefGn3fw72p8SjsYbSMcYNIpiwI5t+ezmzZuUKlWKbdu20bJlS2JjYylZsiSLFi3ipZdeAuDUqVNUq1aN0NBQmjRpwoYNG+jcuTPXr1/H3V3tQjN79mw++OADbt68ibW1NR988AHr1q3j2LFj+nP17NmTmJgYNm7c+MhYUlJSSElJ0T+Pi4vDy8uLW7duFZqJqdLS0ggKCqJdu3ZYWeX/5Hja4C+x2P0DGQ2GoAucnO/HB3VYzd/3XOarjWdI1yn4uNkzo1dtKrsXzHC9hn4N8yo1NZUrV66gK8AubrmlKArJycnY2toW2q40zs7OlCpV6pHli4uLo0SJEsTGxhaa9xZzZ8p10bVr16hevToXLlwokKGETf09Lr9JeQuRGyew+Pd/aC9sAyCj/mBSAsYTtDXEYOWNjktm4IJwzt64x9AW5Xm/feV8P0du5Obve+3aNXx8fLhy5Qplyz7bl7ZmNYZXbGwsAMWLFwcgPDyctLQ02rZtq9+matWqlCtXTv9GHxoaSq1atfRv8gCBgYG8+eabHD9+nLp16xIaGprlGJnbjBo16rGxTJ48mQkTJmRbvnnzZuztC1d3naCgIIMc97krh/EBzl69yen16/P9+CkZsPicloO31V6Eddx09PKNIyJ8BxH5frYnM9RrmFfFixenWLFilCwpY4kbg6IopKamcvPmTc6cOUN8fPYRThITE40QmXgSc6iLtmzZQokSJfJSzFwx1fc4Q5HyFhIuA6ns4Ua1qBVYhP9K4umdWJd/26Dl9S+m4ewNCxbsukD5pAgcTCCXy0l5b926lefzmE3SodPpGDVqFM2aNaNmzZoAREVFYW1tjaura5Zt3d3diYqK0m/z8Jt85vrMdU/aJi4ujqSkJOzs7LLFM27cOEaPHq1/ntnS0b59+0LzbaShv+GwWLkCbkGlWg3xbfR8vh777I0ERiw+zPnb97DUahgbWJkBfuUK/IO1KX9LlJ6ezoULFyhdurRJX7OKohAfH4+Tk1OhTYxsbW2xsbGhadOm2bpaxcXFGSkq8SimXhdltnS0adNGWjoMQMpbGHUm/Ux3LFa/SYmE0wSc/hRNz4VYlDNMt++OisLOWXs4FRXPNcfKjGpT0SDnyYnctnTkldkkHcOHD+fYsWPs3LnT2KEAYGNjg41N9r54VlZWhe4f02BlSlE/TFk4uGGRj8dfc3928cTUDNydbZjZux4NyhfPt+M/C1O8LjIyMtBoNNjY2KDVmu5QwZldvzQajUnHmReOjo76b5H+e52Y2nVT1Jl6XZSZpFpaWhbotWOK73GGJOUtZGp0hVJVUf7qhd3tCJRF3dB0mQZ1ehnkdO+0qcSbCw/w+57LvBFQEWdb4762Ofn7WlrmPWUwixp8xIgRrF27luDg4Cz9yDw8PEhNTSUmJibL9tHR0Xh4eOi3+e8IIpnPn7aNs7PzI79ZEvkk6a76265YvhwuOS2D8auPMXLxQRJTM2jq68a6kS2MnnCYusLaemBO5G9gHqQuEqIQK1mZ9AGbiXKugyYjBVYNg/VjIS0p308VWMODyu6OxCenM3/XxXw/vqky6aRDURRGjBjBypUr2bp1Kz4+PlnW169fHysrK7Zs2aJfdvr0aS5fvoyfnx8Afn5+HD16lBs3bui3CQoKwtnZmerVq+u3efgYmdtkHkMYSMr9/us2eb+p+2x0PN1m7tLPLj68lS9/DG5c5EaGEELkP6mLhCgibJ0JqzCKjOZj1Od7f4bZzeHS7nw9jVarYXgrtVvVjOAI9l+8k6/HN1UmnXQMHz6cP//8k0WLFuHk5ERUVBRRUVEkJalZp4uLC4MHD2b06NEEBwcTHh7OwIED8fPzo0mTJgC0b9+e6tWr89prr3H48GE2bdrEJ598wvDhw/VN0sOGDeP8+fOMHTuWU6dOMWvWLP7++2/effddo5W9SNDcv/yUZx85SVEU/gi9SOcfd3IqKp7iDtbMG9CA9wOrYqGVb4+FKiQkBI1Gk+2b6NwaMGAA3bp1y5eYhPmQukiIIkSjRef/IfT+Gxw94HYE/NYR1o6G5Py7x67Lc6VpV92d1HQdQ37fz8Vb9/Lt2KbKpJOOn376idjYWAICAvD09NT/LFmyRL/NDz/8QOfOnenRowctW7bEw8ODFStW6NdbWFiwdu1aLCws8PPzo2/fvvTr14+JEyfqt/Hx8WHdunUEBQVRu3ZtvvvuO+bOnSvjohua9n7/QV3aM+1+KyGF1xfs53+rj5OSrqNl5ZJsHNVCZhcvxGbPno2Tk1OWCdUSEhKwsrIiICAgy7aZica5c+do2rQpkZGR2eZ5yG/z58/PdjNxJo1Gw6pVqwx6fmEYUhcJUQRVDoThYVCvv/p8/68wqwmc2ZQvh9dqNUzrWYfnyrpwNzGNgfP3cfee6c6ZlR9M+kbynEwhYmtry8yZM5k5c+Zjt/H29mb9U4ZkDQgI4ODBg7mOUeSBxf2kIyP9yds9QsjpG4xZeoRbCSlYW2j5oGNVBjYtj1ZaNwq1Vq1akZCQwP79+/XfIO/YsQMPDw/CwsL083kABAcHU65cOXx9fYEH/eaFyC2pi4Qoouxcoet0qNkD/hkJdy/Coleg1svQ4StwyNuw1PbWlszt34DuM3dz4dY9hv6xnz8GN8bWqnBOGGvSLR2ikLPIfUtHcloGE/45zoDf9nErIYVKpRxZPaIZg5v7SMKRR4qikJiabpSfnM5RWqVKFTw9PQkJCdEvCwkJ4YUXXsDHx4c9e/ZkWd6qVSv944e7V2W2SGzatIlq1arh6OhIhw4diIyM1O+fkZHB6NGjcXV1xc3NjbFjx+Y4TiGEEIVIBX94MxSavq12DT+6FGY2giNLIY/1QiknW34b2BAnW0v2XbzL+8uOoNMVzrrGpFs6RCGX2b0qI2dJR8SNeEYsOsipKPUG9P5+3ox7vlqh/UagoCWlZVD90/xpNs6tExMDsbfO2dtRq1atCA4O5sMPPwTUFo2xY8eSkZFBcHAwAQEBJCUlERYWxqBBgx57nMTERL799lv++OMPtFotffv2ZcyYMSxcuBCA7777jvnz5zNv3jyqVavGd999x8qVK2ndunXeCyyEEMK8WNtD+y+gRndY/TbcOA4rXlcTkM4/gMuzz4tT2d2J2X3r03/eXv45fJ3ybva8175KPgZvGqSlQxiPfpjQJ2f0iqLw974rdPlxF6ei4nG7f7P4hBdqSsJRBLVq1Ypdu3aRnp5OfHw8Bw8exN/fn5YtW+pbQEJDQ0lJSdG3dDxKWloas2fPpkGDBtSrV48RI0ZkGTlo6tSpjBs3jhdffJFq1aoxe/Zsg98TIoQQwsSVqQ9DQ6DVJ2BhDWc3qfd67P8NdM8+ME6ziiWY9GItAH7cGsHaI9fzKWDTIS0dwngyWzgsrB+7SXxyGh+vPMaaw+o/X7OKbvzwSh1KOdsWRIRFip2VBScmGueGVbtcJI8BAQHcu3ePffv2cffuXSpXrkzJkiXx9/dn4MCBJCcnExISQoUKFShXrtxjj2Nvb6+/3wPA09NTP5xpbGwskZGRNG7cWL/e0tKSBg0aSBcrIYQo6iytwf99qNYF1oyAq/tg7Sg4tly9B6R4hWc67CsNvDgbHc8vOy4wZulhyrs5ULNM4fmyS5IOYTwZ90dp0D56FswjV2MYseggl+8kYqHVMLpdZd7095V7NwxEo9HkuIuTMVWsWJGyZcsSHBzM3bt38ff3B6B06dJ4eXmxe/dugoODn9oN6r+zr2o0mjwnFM7Ozty7dw+dTpdl9vTMe0mkpUQIIQqRUlVh0CYI+xm2fg4Xd8CsptD6E2jyJmhz3xvjw47VOB2dwPYzNxn6+37WvN280Mw5Jt2rhPHo7o9aZZH1w59OpzB3x3l6/LSby3cSKeNqx99vNGF4q4qScAhA7WIVEhJCSEhIlqFyW7ZsyYYNG9i7d+8Tu1Y9jYuLC56enoSFhemXpaenEx4e/sT9qlSpQnp6OocOHcqy/MCBAwBUrlz5mWMSQghhgrQW4PcWvLkbfFpCehJs/hh+bQ83Tub6cBZaDT/2qkuFEg5cj03mzT/DSU1/9m5bpkSSDmE8mS0dDyUdtxNSGLxgH1+sO0lahkLHmh6sH9mC+t7FjRSkMEWtWrVi586dHDp0SN/SAeDv78/PP/9MampqnpIOgHfeeYevvvqKVatWcerUKd56662nTi5Yo0YN2rdvz6BBg9iyZQsXLlxg48aNvPXWW7z66quUKfPsNxoKIYQwYcV9oN8a6DINbJzh2n6Y3QK2TYH03M2/4WJnxS/9G+Bko45oNX7N8ULRtVeSDmE8mfd03O9edTIyjq4zdhF8+ibWllq+6FaTWX3q4WL/6O5Xouhq1aoVSUlJVKxYEXf3B5NB+vv7Ex8frx9aNy/ee+89XnvtNfr374+fnx9OTk507979qfstWbIEf39/3njjDWrUqMHIkSN54YUXmDt3bp7iEUIIYeI0Gqg/AN7aA5U7qFMCBH8JP7eEy2FP3f1hviUdmd6rLhoNLN57mV93XjBMzAXI9Dtwi8JLud9cqNGy+XgUo5YcIjE1g/Ju9sx+rT5VPZyNG58wWeXLl3/ktz7e3t6PXB4QEJBl+YABAxgwYECWbbp165ZlG0tLS6ZOncrUqVNzFZurqyvTpk1j2rRpudpPCCFEIeFSBnr9BUeXwcYP4eZJmBcIDQZB2/Fgm7P7+1pVLcXHz1fji3Un+WLdSdydbelSu7SBgzccaekQRqR+wFt24Cpv/BlOYmoGzSq6sWp4M0k4hBBCCGG+NBp47mUYsQ/q9AUU2P8rzGgEJ1bneFLBwc19GNC0PADv/X2YsPO3DRezgUnSIYwmc8LNP/ZcRlGgn5838wc2wtX+8UPoCiGEEEKYDfvi0G0m9P8HivtCQhT83Q/+6g2xV5+6u0aj4X+dq9OhhgepGTqG/L6fs9HxBRB4/pOkQxjFjbhkbsYnA+pIDV90q8nEF2piZSGXpBBCCCEKGZ+W6ghXLceq97KeXg8zG8Oen0CX8cRdLbQapvasQ33vYsQlpzPgt31ExyUXUOD5Rz7hiQKlKApBJ6LpOmMXaRnqPR2fda1J3ybeRo5MCCGEEMKArGyh9ccwbCd4NYHUBPWej7lt4PrBJ+5qa2XBL/0aUKGEA9dikhj42z7upaQXUOD5Q5IOUWCOXI2h55w9DPl9P1FxyVjfv/qe8ypm3MCEEEIIIQpKqaowcAN0/gFsXNSEY04rWPceJN197G7FHayZP7ARJRytOREZxzt/HUKnM5+hdCXpEAZ3LSaJd5ccouuMXYRduIONpZa3AnwpYX//8nvMjORCCCGEEIWSVquOZjViL9R6GVBg31z4sQEcWvTYG83Ludkzp18DrC21/Hsymq83nSrYuPNAkg5hMPHJaUzZeIrW34aw8uA1ALrXLcPWMQGM7VAVrX5yQLlxXAghhBBFkJMH9Jir3mheogok3oJVb8JvHSH6+CN3qVeuGN+89BwAP287z9L9Vwoy4mcm83SIfJeeoWPxvitMDTrD7XtqYtHYpzifdKpOrbIPjU2tu98X0UIuQyGEEEIUYT4t1Xs99syCbV/D5VB1RvPGw6DVOLBxyrL5C3XKEHEjgR+3RvDRyqOUL+FAw/LFjRR8zkhLh8g3iqKw5WQ0gVO3879Vx7h9L5UKJR34pV8D/hraJGvCAdlmJBdCCCGEKLIsraH5KBi+F6p1BSUD9syEGQ3ViQb/0+Xq3baVeb6WB2kZCm/8Ec6VO4nGiTuHJOkQ+eLE9Tj6/hrG4AX7OXfzHsUdrJn4Qg02jWpJu+ruaDSarDvcOQ/SvUoUkJCQEDQaDTExMXk6zoABA+jWrVu+xCSEEEI8kqsXvPoH9FkOxXwgPhKWD4Y/e8DdS/rNtFoN371ch1plXLhzL5XXF+wnMdV0R7SSpEPkyY24ZD5YdoROP+5gV8RtrC21DPP3JeT9APr5lX/0vBsJN+GPFwEFStcDx1IFHrcwT7Nnz8bJyYn09AdvqgkJCVhZWREQEJBl28xE49y5czRt2pTIyEhcXP7T2paPli9fjoWFBdeuXXvk+kqVKjF69GiDnV8IIUQhU6ktvLUHAj4CCxs4twVmNYHQmfq5Peys1aF0SzrZcDo6no9WHEXJ4WznBU2SDvFMktMy+HHLWQK+DWHJ/isoCnR+zpMto/35sGNVnG0f02UqJQEWvQx3L4BrOei1GP7bCiLEY7Rq1YqEhAT279+vX7Zjxw48PDwICwsjOfnBZEnBwcGUK1cOX19frK2t8fDwyN7ilo+6du2Km5sbCxYsyLZu+/btREREMHjwYIOdXwghRCFkZQsBH8Cbu8C7GaQlwqaPYG5biDoKgIeLLTN718NCq2HVoev8sefSUw5qHJJ0iFzR6RRWHbxG629D+C7oDImpGdTxcmX5m02Z0bseXsXtH79zRhos7a+OR21XHPquVEdtEKZBUSD1nnF+cvitTJUqVfD09CQkJES/LCQkhBdeeAEfHx/27NmTZXmrVq30jx/uXjV//nxcXV3ZtGkT1apVw9HRkQ4dOhAZGanfPyMjg9GjR+Pq6oqbmxtjx4594rdHVlZWvPbaa8yfPz/bunnz5tG4cWNq1KiRo3IKIYQQWZSoBP3XQpdp9+f2OABzAuDfCZCWRCOf4ozrWBWAz9ee4MDlx8/3YSwybJDIsf0X7/D5upMcvhIDQBlXO8Z2qELX2qWf/g2yosCatyHiX7Cyhz5LoURFwwctci4tESaVNs65P7oO1g452rRVq1YEBwfz4YcfAmqLxtixY8nIyCA4OJiAgACSkpIICwtj0KBBjz1OYmIi3377LX/88QdarZa+ffsyZswYFi5cCMB3333H/PnzmTdvHtWqVeO7775j5cqVtG7d+rHHHDx4MN9//z3bt2+nZcuWgNr9a9myZfzwww85fTWEEEKI7LRaqD8AKneA9e/DyTWw83s4sRq6TGNw8+YcuHyX9UejeOvPA6wd2ZwSjjbGjlpPWjrEUyWkpDN80QFemh3K4SsxOFhb8H5gFba8588LdcrkLOH49zM4vBg0FvDyfCjboCBCF4VQq1at2LVrF+np6cTHx3Pw4EH8/f1p2bKlvgUkNDSUlJQUfUvHo6SlpTF79mwaNGhAvXr1GDFiBFu2bNGvnzp1KuPGjePFF1+kWrVqzJ49+6n3hFSvXp0mTZowb948/bK///4bRVHo2bNn3gouhBBCgNpL5NU/4NWF4OQJd87Bgs5o1oxgSnt3fEs6EBWXzNuLDpKclmHsaPWkpUM81debzrDuSCRaDbza0It321WmlJNtznbW6WDDWNj3i/q8yzSoHGi4YMWzs7JXWxyMde4cCggI4N69e+zbt4+7d+9SuXJlSpYsib+/PwMHDiQ5OZmQkBAqVKhAuXLlHnsce3t7fH199c89PT25ceMGALGxsURGRtK4cWP9ektLSxo0aPDUG/QGDRrEu+++y48//oiTkxPz5s3j5ZdfxsnJ6Yn7CSGEELlSrTP4tFC7WO3/FQ7+iePRZfxcYyQvxNYj9Pxtes7Zw5zX6lPKOYef2wxIWjrEE12Ih7/2XQVgwaBGTH7xuZwnHOkpsHzQ/YRDAx2/gXqvGS5YkTcajdrFyRg/ubjBu2LFipQtW5bg4GCCg4Px9/cHoHTp0nh5ebF7926Cg4Of2A0K1HswshZfky8jfmS2aPz999+cPXuWXbt2yQ3kQgghDMPWBTp/D4M2g1cTSE+m4uEp/GL5DS6W6Ry6EkOXGTv1XeONSZIO8VhpGTqWnLMA4JUGZWlRqWTOd06Jh4Uvw/GV6uR/L/0KjYcaKFJR1LRq1YqQkBBCQkKyDJXbsmVLNmzYwN69e5/YteppXFxc8PT0JCwsTL8sPT2d8PDwp+7r5OTEyy+/zLx58/jtt9+oXLkyLVq0eOZYhBBCiKcq1xgGbYQ+y8CzNk114azRjqGS9jrRcSm88nMoqw89ekj3giJJh3isebsuEZmkoZi9FeM6Vsv5jgk3YX5nuLANrBygz99Qs4fhAhVFTqtWrdi5cyeHDh3St3QA+Pv78/PPP5OampqnpAPgnXfe4auvvmLVqlWcOnWKt956K8eTCw4ePJjdu3cze/bsJ97MLoQQQuQbjQYqtYOh2+CV3/F2L84Kq//RRnuAlHQd7/x1iK/XHSNDZ5x5PCTp+I+ZM2dSvnx5bG1tady4MXv37jV2SEZx+XYiPwafA+CjjlUo5pDDWcPvXoR57SHyENi7wYC14Pvkbi5C5FarVq1ISkqiYsWKuLu765f7+/sTHx+vH1o3L9577z1ee+01+vfvj5+fH05OTnTv3j1H+zZv3pwqVaoQFxdHv3798hSHKHqkHhJC5IlGA9VfgDd34/TiNOaUWs6bFqsB+GnHJYb+uJL4e0kFHpYkHQ9ZsmQJo0ePZvz48Rw4cIDatWsTGBiov7m0qFAUhU9WHyMlXUdlFx0v1M7hh7eoY/Bre7hzHlzKqf0Ly9QzbLCiSCpfvjyKonDy5Mksy729vVEUhVOnTmVZHhAQgKIouLq6AjBgwIBsrRbdunXLck+HpaUlU6dOJTY2lrt37/Ldd9+xYMECVq1alaMYT506RXp6ep6TH1G0SD0khMg3Wguo/SoWb+/jg+5+THNaiA2pbIm0ofvkv7h6/uTTj5Gf4RTo2Uzc999/z5AhQxg4cCDVq1dn9uzZ2NvbZxn+siiISUzjRlwy1pZaXvbR5XwWZ60lZKRCqRoweLPMwyGEELkk9ZAQIt9ZWEGDgbwwdh5/t7yNuyaGNEWLY8nHj/BoCDJk7n2pqamEh4czbtw4/TKtVkvbtm0JDQ3Ntn1KSgopKSn653FxcYA69n9aWprhAzYgR2sNK4Y15tClO9w8tTfn5SnmC72Xg6u3OpqCmb8O+SHztTPFayItLQ1FUdDpdOh0OmOH81iZrQ+ZsRZGOp0ORVFIS0vDwsIiyzpTvHaEYeS2HoLsdVF8fDygDnxQENeOKb/HGYKUt3Ar/OW1oHq7/qyod4eEm5dxsFW7zuekvOnp6Xk+uyQd9926dYuMjIws/cMB3N3ds3XVAJg8eTITJkzItnzz5s3Y2+d8zgFzEBQUlMs9rhokDnOW+9fQ8CwtLfHw8CAhIYHU1FRjh/NUmR+mCqPU1FSSkpLYvn17tjf2xMREI0UlClpu6yF4fF20ZcsWSpQoYZA4H8UU3+MMScpbuBWV8p46r87NlZPy3rp1K8/nk6TjGY0bN47Ro0frn8fFxeHl5UX79u1xdnY2YmT5Jy0tjaCgINq1a5dtTgORM6b8GiYnJ3PlyhUcHR2xtTX+pEGPoygK8fHxODk55byrn5lJTk7Gzs6Oli1bZvtbZLaiCvEo/62Lrl27RvXq1WnTpg1lypQx+PlN+T3OEKS8hZuU9/GuXcv7cLuSdNxXokQJLCwsiI6OzrI8OjoaDw+PbNvb2NhgY2OTbbmVlVWhu1ALY5kKmim+hhkZGWg0GjQaDVqt6d7eldmlytTjzIvMv8OjrhNTu26E4eS2HoLsdVFmkmppaVmg144pvscZkpS3cJPyZmdpmfeUoXDW4M/A2tqa+vXrs2XLFv0ynU7Hli1b8PPzM2JkQhhG5r0D5tC1qrDL7EJVlCo5kZ3UQ0KIwkxaOh4yevRo+vfvT4MGDWjUqBFTp07l3r17DBw40NihCZHvLC0tsbe35+bNm1hZWZlsK4JOpyM1NZXk5GSTjfFZKYpCYmIiN27cwNXVNdtN5KLokXpICFFYSdLxkFdffZWbN2/y6aefEhUVRZ06ddi4cWO2m/qEKAw0Gg2enp5cuHCBS5cuGTucx1IUhaSkJOzs7ArtPR2urq6P7T4jihaph4QQhZUkHf8xYsQIRowYYewwhCgQ1tbWVKpUyaS7WKWlpbF9+3ZatmxZKLsfWVlZSQuHyELqISFEYSRJhxBFnFarNenRqywsLEhPT8fW1rZQJh1CCCFEUVC4OkgLIYQQQgghTI4kHUIIIYQQQgiDkqRDCCGEEEIIYVByT0c+URQFKFyzB6elpZGYmEhcXJz0pX9G8hrmXVF/DTPfUzLfY4R4kszJNCMjIwvkfOnp6dy6dYtr167ly+Rhpk7KW7hJeR8v8z0l8z3mWRT+V7SAxMfHA+Dl5WXkSIQQhVF8fDwuLi7GDkOYuMzZzBs1amTkSIQQhVF0dDTlypV7pn01inx9li90Oh3Xr1/Hycmp0MwlEBcXh5eXF1euXMHZ2dnY4ZgleQ3zrqi/hoqiEB8fT+nSpQvd5Igi/6Wnp3Pw4EHc3d0L5npJiYeZjWD4XrBxMvz5jE3KW7hJeR9Lp9MRHR1N3bp1n7kVSFo68olWq6Vs2bLGDsMgnJ2di+SHvfwkr2HeFeXXUFo4RE5ZWlrSsGHDgjthchw4a6FMGbAtAv+fUt7CTcr7RM/awpFJvjYTQgghhBBCGJQkHUIIIYQQQgiDkqRDPJaNjQ3jx4/HxsbG2KGYLXkN805eQyFMmKUN+H+o/i4KpLyFm5TXoORGciGEEEIIIYRBSUuHEEIIIYQQwqAk6RBCCCGEEEIYlCQdQgghhBBCCIOSpEMIIYQQQghhUJJ0iMeaOXMm5cuXx9bWlsaNG7N3715jh2Q2PvvsMzQaTZafqlWrGjssk7Z9+3a6dOlC6dKl0Wg0rFq1Kst6RVH49NNP8fT0xM7OjrZt23L27FnjBCtEYbbjO5gTAJPKwBRfWNwbbv3nfy0tGda9B1+Xhy9Lw5K+kHAj6zYxV2Dhy/CFh3qczZ9ARnpBleLZ7fgePnOBDR8+WFbYyht3HZYPUcvzhTvM8oNrBx6sVxTY+iV8W1ldv6Ar3D6X9RiJd2D56zCpLEwuB6uHQ0pCgRYjR3QZsPULmFpLLcu02rBtilrGTOZc3ou7YNGr8G0V9bo9uTbr+vwqW9QxmNcBPi8F31eHnVNzHaokHeKRlixZwujRoxk/fjwHDhygdu3aBAYGcuPGjafvLACoUaMGkZGR+p+dO3caOySTdu/ePWrXrs3MmTMfuX7KlClMnz6d2bNnExYWhoODA4GBgSQnJxdwpEIUchd3QcMh8Pq/0G8V6NLgj+6Qeu/BNpvGwemN8PICGLgO4qPUD+KZdBmw6BXISIXBm6H7bDi0CIK/LPDi5Mq1cAj/DdxrZl1emMqbdBd+DQQLK+izHIaHQfsvwM71wTa7pkLYz9D5B3h9C1g7qNdA2kPvtyuGwI1T6jXSewlc2g3/vFPAhcmBnT/Avl/h+W9h+F5oOwF2TVPLl8mcy5uWqF6vnb599Pr8KFtynLqPixe8sQ3aTYSQr2D/b7mLVRHiERo1aqQMHz5c/zwjI0MpXbq0MnnyZCNGZT7Gjx+v1K5d29hhmC1AWblypf65TqdTPDw8lG+++Ua/LCYmRrGxsVEWL15shAiFKEISbirKeGdFubBTfZ4UoygT3BTl2MoH29w4rW5zea/6/MxmRfnMVVHiox9ss3euokwqqyhpKQUWeq4kxyvKtLqKErFVUeY9ryjrP1CXF7bybv5UUX4NfPx6nU5RvqmkKDunPViWFKMoE0sqypGl6vMbp9TyXw1/sM2ZIEUZ76IosdcNEvYz+/NlRVn1VtZlf/VRlGWvq48LU3nHOyvKiX8ePM+vsu39RVEml8t6LW/+VFGm189VeNLSIbJJTU0lPDyctm3b6pdptVratm1LaGioESMzL2fPnqV06dJUqFCBPn36cPnyZWOHZLYuXLhAVFRUlmvSxcWFxo0byzUphKElx6q/7Yqpv68fUls/KgQ82KZkZfVb0Kv3u+Fe2QulaoBjqQfbVGwDKXFw82RBRJ1768dA5UDwbZV1eWEr7+kNULou/N1P7QY2uzmEz3+w/u5FSIjOWl5bFyjbAK7uU59f2asuK1PvwTYVAkCjhWv7DV+G3PBqBOe3w60I9XnUUbi8Byq1U58XtvI+LL/KdmUfeDcDS+sH21RsA7fPqi1nOWT5rOUQhdetW7fIyMjA3d09y3J3d3dOnTplpKjMS+PGjZk/fz5VqlQhMjKSCRMm0KJFC44dO4aTk5OxwzM7UVFRAI+8JjPXCSEMQKeDjePAqwm4V1eXJdwAC+us3XEAHEqqH3BA/e1Y8j/rSz3Y39QcXQaRh2FIcPZ1ha28dy+q3Y38hkOL99R7OTZ8oJaxTu8H8T6cQEH28jr8p7wWlmpimrmNqWg+GlLiYUYD0FqoXeHa/A+ee0VdX9jK+7D8KltCNBTz/s8xHrq+M7+QeApJOoQwgI4dO+ofP/fcczRu3Bhvb2/+/vtvBg8ebMTIhBAiF9a/BzdOwqCNxo7EcGKvwsYP4bVVYGVr7GgMT9GpLR1tx6vPPWurf+P989Sko7A5vgKOLoUec6FUNbWlY+OH4ORZOMtrwqR7lcimRIkSWFhYEB2dNXuPjo7Gw8PDSFGZN1dXVypXrkxERISxQzFLmdedXJNCFKB1Y+DMJhjwD7iUebDcsZR6w3RSTNbt790Ex/utkY7ukHDzP+sf862rsV0/pMb+c0uYUFz9ubQTwmarjx1LFq7yOnlAySpZl5WsrCZf8CDe/7bQ/Le89/5T3ox0tauNY9YWaaML+hSavwu1XgL3GlC7JzQZro5SBoWvvA/Lr7Ll0/UtSYfIxtramvr167Nlyxb9Mp1Ox5YtW/Dz8zNiZOYrISGBc+fO4enpaexQzJKPjw8eHh5Zrsm4uDjCwsLkmhQivymKmnCcWgv9/4Fi5bOuL10HtFZwYduDZbfOQuwVKNtIfe7VCG4cz/pB5Vww2DhDSRMbPryCP7wZCsN2PvgpXVftfpP5uDCV16sx3P7PF2C3z6n3qID693Z0z1re5Di4uh/KNrx/jEbqvT7XDz7Y5sI2tRWlTAODhp9raYmg0WRdptWqsULhK+/D8qtsXg3h0i7ISHuwzblgcKuU465VIN2rxGOMHj2a/v3706BBAxo1asTUqVO5d+8eAwcONHZoZmHMmDF06dIFb29vrl+/zvjx47GwsKBXr17GDs1kJSQkZGkJunDhAocOHaJ48eKUK1eOUaNG8cUXX1CpUiV8fHz43//+R+nSpenWrZvxghaiMFr3nnqPQ69FYO0I8fdbGG2dwcpOvem03muw6WP1A4eNE6wfq34A97r/Qca3tfphe+VQdXjNhGh1roSGr4OljfHK9ig2Tg/uV8lk5QB2xR8sL0zl9XsLfm0P27+FGt3VezrC50OXaep6jQaavAnbv4Hivmpf/q1fqi0kVTur25SsAhXbwpqR0HmqeqP9+vehZg9wNrEv1yp3hO3fqUlVyaoQdQRCZ0Ld+0Mem3t5UxLgzvkHz2MuQeQR9Vp19cqfstV6GUK+htUjoPkouHFCbQkMnJSrUDWK8vDsKEI8MGPGDL755huioqKoU6cO06dPp3HjxsYOyyz07NmT7du3c/v2bUqWLEnz5s358ssv8fX1NXZoJiskJIRWrVplW96/f3/mz5+PoiiMHz+eOXPmEBMTQ/PmzZk1axaVK1c2QrRCFGKfuTx6+QuzoG4f9XFaMmz+WE1OMlLVD92dvgenh7qaxFyGtaPh4k6wtofavdQ5EizM4PvO3zqBRy3o+JX6vLCV9/RG2DJBbeEo5q3eVF5/wIP1igLBk9RkJDkWyjVRy1ui4oNtEu+oH07PbFRHOqrWFTp+DTaOBV2aJ0uJVz9on1qrdiNy8oCaL4H/Bw9GYzLn8l7YAQs6Z19euzd0/yn/yhZ1TB3h7doBsHeDxkPVbmu5IEmHEEIIIYQQwqDkng4hhBBCCCGEQUnSIYQQQgghhDAoSTqEEEIIIYQQBiVJhxBCCCGEEMKgJOkQQgghhBBCGJQkHUIIIYQQQgiDkqRDCCGEEEIIYVCSdAghhBBCCCEMSpIOYbZCQkLQaDTExMTk6TgDBgygW7du+RKTMQQEBDBq1KinbteyZUsWLVpk+IAe0rNnT7777rsCPacQQogCsvULWDPywfPfOsGGDw17zqDx6uzZwuxI0iGMbvbs2Tg5OZGenq5flpCQgJWVFQEBAVm2zUw0zp07R9OmTYmMjMTFxcXgMf7yyy/Url0bR0dHXF1dqVu3LpMnTzb4efPLmjVriI6OpmfPnvlyvAULFtC8efOnbvfJJ5/w5ZdfEhsbmy/nFUIIk7HyTfjMRf2ZWAKm1YGQryEj/am7Fgrx0bBnNrQck7v9fqj14HX70hNmt4DjK3O+f9ORcGgx3LmQu/MKo5OkQxhdq1atSEhIYP/+/fplO3bswMPDg7CwMJKTk/XLg4ODKVeuHL6+vlhbW+Ph4YFGozFofPPmzWPUqFGMHDmSQ4cOsWvXLsaOHUtCQoJBz5ufpk+fzsCBA9Fq8+dffvXq1XTt2vWp29WsWRNfX1/+/PPPfDmvEEKYlIpt4b0zMPIANB0BIZNh97RHb5ueWrCx5UReYjrwO3g1Atdyud+31cfq6/bGDihTD5YOhMthOdvXwQ0qtob9v+b+vMKoJOkQRlelShU8PT0JCQnRLwsJCeGFF17Ax8eHPXv2ZFneqlUr/eOHu1fNnz8fV1dXNm3aRLVq1XB0dKRDhw5ERkbq98/IyGD06NG4urri5ubG2LFjURTlifGtWbOGV155hcGDB1OxYkVq1KhBr169+PLLL/XbZHbRmjBhAiVLlsTZ2Zlhw4aRmvrgDV2n0zF58mR8fHyws7Ojdu3aLFu2LMu5jh07RseOHXF0dMTd3Z3XXnuNW7du6dffu3ePfv364ejoiKenZ466Lt28eZOtW7fSpUuXLMs1Gg0///wznTt3xt7enmrVqhEaGkpERAQBAQE4ODjQtGlTzp07l2W/5ORkNm/erE86Zs2aRaVKlbC1tcXd3Z2XXnopy/ZdunThr7/+emqcQghhdixswMld/eDd8HWoEACnN6jrVr4Ji3vD9m/g2yowo766PPYq/N0fJpeDr7xhcS+4e+nBMS/sgDmt1FaAyeXg1/YQc1ldF3UU5neGSWVgUln4uSVcO6CuC54MP/2nBTp0ltqykOlZY3qUY8uhSsfsyxUdbP6fepxvKqlx/Ze1o/q6lagIz38HVnZwZgPoMmD1cJhaC75whx/rw56fsu9fuSMcW/Hk+ITJkaRDmIRWrVoRHBysfx4cHExAQAD+/v765UlJSYSFhemTjkdJTEzk22+/5Y8//mD79u1cvnyZMWMeNP1+9913zJ8/n3nz5rFz507u3LnDypVPbtb18PBgz549XLr05DfgLVu2cPLkSUJCQli8eDErVqxgwoQJ+vWTJ0/m999/Z/bs2Rw/fpx3332Xvn37sm3bNgBiYmJo3bo1devWZf/+/WzcuJHo6GheeeUV/THef/99tm3bxurVq9m8eTMhISEcOHDgiXHt3LlTn1T81+eff06/fv04dOgQVatWpXfv3rzxxhuMGzeO/fv3oygKI0aMyFbOMmXKULVqVfbv38/IkSOZOHEip0+fZuPGjbRs2TLL9o0aNWLv3r2kpKQ8MU4hhDB7VnaQ8VDrwYVtcCsC+q2C3n9DRhr88SLYOMKgDTB4M1g7wJ891FaHjHT4qw+UbwZv7oLXg6D+AOB+i/7yIeBcGoYEwxvboPm7YGGVuxhzG9OjJN6Bm6egdN3s6w4vVvcfshXaTYRtX8O5rY+Px8IStFZqHIoOnMvAywtgeBj4fwBbJmZPMMrUh7hrT0+MhGlRhDABv/zyi+Lg4KCkpaUpcXFxiqWlpXLjxg1l0aJFSsuWLRVFUZQtW7YogHLp0iVFURQlODhYAZS7d+8qiqIov/32mwIoERER+uPOnDlTcXd31z/39PRUpkyZon+elpamlC1bVnnhhRceG9v169eVJk2aKIBSuXJlpX///sqSJUuUjIwM/Tb9+/dXihcvrty7d0+/7KefflIcHR2VjIwMJTk5WbG3t1d2796d5diDBw9WevXqpSiKonz++edK+/bts6y/cuWKAiinT59W4uPjFWtra+Xvv//Wr799+7ZiZ2envPPOO4+N/4cfflAqVKiQbTmgfPLJJ/rnoaGhCqD8+uuv+mWLFy9WbG1ts+w3ZMgQZcyYMYqiKMry5csVZ2dnJS4u7rHnP3z4sAIoFy9efOw2QghhdlYMU5RF6vu3otMpSsRWRZlYUlE2ffxg/ZSKipKW8mCfQ38pyvT66vaZ0lIU5XN3RTn7r6Lcu60o450V5cKOR5/zyzKKcnDho9dtnaQos5plXbZ7pqJ8XzNrzLmN6VGuH1bjjLmSdfm85xXl18Csy34OUJTNnz54/n1NNa7M82z/Vj3W6Y2PPtfa9xTlr75ZlyXFPvl1EibJ0njpjhAPBAQEcO/ePfbt28fdu3epXLkyJUuWxN/fn4EDB5KcnExISAgVKlSgXLnH9x+1t7fH19dX/9zT05MbN24AEBsbS2RkJI0bN9avt7S0pEGDBk/sYuXp6UloaCjHjh1j+/bt7N69m/79+zN37lw2btyov0+idu3a2Nvb6/fz8/MjISGBK1eukJCQQGJiIu3atcty7NTUVOrWVb8pOnz4MMHBwTg6OmaL4dy5cyQlJZGampol/uLFi1OlSpXHxg5qC5Gtre0j1z333HP6x+7u7gDUqlUry7Lk5GTi4uJwdnZGURT++ecf/v77bwDatWuHt7c3FSpUoEOHDnTo0IHu3btneR3s7OwAtRVKCCEKlTMb4cvSoLv/LX2tlyFg3IP17tXB0vrB8+ijcOe82j3qYenJcPcCVGwDdfqoLQ++rdTuWjW6g5OHup3fcFjzNhz+6/66blC8Qu5izm1Mj5J+/15Ly0fULe41sj538oB7t7Iu+3e8OvJVerLa1artZ1A5UF239xc4+Ifa5SstWW058qiVdX8rtV4hLemxxRSmR5IOYRIqVqxI2bJlCQ4O5u7du/j7+wNQunRpvLy82L17N8HBwbRu3fqJx7GyytrMrNFonnrPRk7VrFmTmjVr8tZbbzFs2DBatGjBtm3bntjdK1PmTefr1q2jTJmsb+w2Njb6bbp06cLXX3+dbX9PT08iIiKeKe4SJUpw9+7dR657+PXKvCH/Uct0Oh0Ae/fuJT09naZNmwLg5OTEgQMHCAkJYfPmzXz66ad89tln7Nu3D1dXVwDu3LkDQMmSJZ8pfiGEMFk+LaDT92BhDU6ealehh1k5ZH2eeg9K14EXf8l+LIcS6u9us6DxGxDxr9qtaOsX8Noq8GoIrcapic3ZTXA2SL1x/aV5UK0LaLTAf+o7XVr28zxLTP9l76b+TorJvo32v929NGpC9rCmI6FObzXhcCwFmQPCHF0Gmz+B9l+oN6lbO8Lu6XA1POv+SXezxiHMgtzTIUxGq1atCAkJISQkJMtQuS1btmTDhg3s3bs3Rx/wH8fFxQVPT0/Cwh6MkJGenk54ePgT9nq06tWrA+qN3ZkOHz5MUtKDb1327NmDo6MjXl5eVK9eHRsbGy5fvkzFihWz/Hh5eQFQr149jh8/Tvny5bNt4+DggK+vL1ZWVlniv3v3LmfOnHlirHXr1iUqKuqxiUdurF69mk6dOmFhYaFfZmlpSdu2bZkyZQpHjhzh4sWLbN36oP/usWPHKFu2LCVKPKbyEkIIc2XlAG6+4OqVPeF4FM/acPscOJRU93v4x9Yl63Yt3lPv6ShVDY4ufbCuREW1xaPfKjXZOLhQXe7gBgnR8PAXbVFH8y+mhxXzARtn9b6OZ2Hvph7fyf1BwgFwJUxNNhoNUeNy83300Lg3TqjJTans9yoK0yVJhzAZrVq1YufOnRw6dEjf0gHg7+/Pzz//TGpqap6SDoB33nmHr776ilWrVnHq1Cneeuutp04u+Oabb/L555+za9cuLl26xJ49e+jXrx8lS5bEz89Pv11qaiqDBw/mxIkTrF+/nvHjxzNixAi0Wi1OTk6MGTOGd999lwULFnDu3DkOHDjAjz/+yIIFCwAYPnw4d+7coVevXuzbt49z586xadMmBg4cSEZGBo6OjgwePJj333+frVu3cuzYMQYMGPDUYXDr1q1LiRIl2LVrV55eO1BH8np4qNy1a9cyffp0Dh06xKVLl/j999/R6XRZunzt2LGD9u3b5/ncQghh9mq9on7g/qs3XNoNdy+qo1WtHwux19Tn/34GV/aqI1ZFbFETgpKV1a5E68ao28dchst71JGrSlZWj12+hdqNaddUtbvU3l/U1pC8xvQoWi1U8IfLofnysugV94Xrh9RWnlsRaivP9YPZt7sUCt5+D7pZCbMg3auEyWjVqhVJSUlUrVpVf38BqElHfHy8fmjdvHjvvfeIjIykf//+aLVaBg0aRPfu3Z84eV3btm2ZN28eP/30E7dv36ZEiRL4+fmxZcsW3NweNO22adOGSpUq0bJlS1JSUujVqxefffaZfv3nn39OyZIlmTx5MufPn8fV1ZV69erx0UcfAWpXsl27dvHBBx/Qvn17UlJS8Pb2pkOHDvrE4ptvvtF3w3JycuK999576sR7FhYWDBw4kIULF9K5c+dnfu3OnTtHREQEgYGB+mWurq6sWLGCzz77jOTkZCpVqsTixYupUUPt05ucnMyqVavYuHHjM59XCCEKDWt7GLhBvadhSV9ISQBnT/DxBxsn9R6HW2fh0GuQdAccPdRv/esPAl26umzlMLh3Q00UqnWBALUOoWQV6PQd7Pgetn0D1btC07chfEHeYnqcev3V2cjbfa4mIfmhwUCIOgJLB6kDdtV8CRoOhrP/Zt3u2HIIMPDM5yLfaZT86vAuRBE2YMAAYmJiWLVqlbFDeaSoqChq1KjBgQMH8Pb2fqZjfP/99/z777+sX78+x/v89NNPrFy5ks2bNz/TOYUQQpgoRYFfWqtdvWq99PTt88vZINj0Mby5O2dd2oTJkO5VQhQBHh4e/Prrr1y+fPmZj1G2bFnGjRv39A0fYmVlxY8//vjM5xRCCGGiNBroMk1tgSlIqffUm+0l4TA70tIhRD4w9ZYOIYQQQghjkqRDCCGEEEIIYVDSvUoIIYQQQghhUJJ0CCGEEEIIIQxKkg4hhBBCCCGEQUnSIYQQQgghhDAoSTqEEEIIIYQQBiVJhxBCCCGEEMKgJOkQQgghhBBCGJQkHUIIIYQQQgiD+j8IF57bJVUfkwAAAABJRU5ErkJggg==", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-10T12:42:06.797159\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-10T18:34:20.452796\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -278,7 +287,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -299,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -321,7 +330,7 @@ { "data": { "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-10T12:42:26.852881\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-10T18:34:28.022285\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -343,7 +352,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -381,7 +390,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -420,7 +429,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -450,7 +459,7 @@ { "data": { "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-10T12:42:36.443466\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-10T18:34:37.897592\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -472,7 +481,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -493,13 +502,13 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-10T12:42:43.763115\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-10T18:34:43.880121\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -521,17 +530,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.00000000e+00 1.33000000e+02]\n", + " [1.41076548e-03 1.33000000e+02]\n", + " [2.82153096e-03 1.33000000e+02]\n", + " ...\n", + " [4.61443985e+02 8.97087177e+01]\n", + " [4.62487459e+02 8.96739146e+01]\n", + " [4.63526604e+02 8.96399417e+01]]\n", + "File trajectory.kml saved with success!\n" + ] + } + ], "source": [ - "# TestFlight.exportKML(\n", - "# fileName=\"trajectory.kml\",\n", - "# timeStep=None,\n", - "# extrude=True,\n", - "# color=\"641400F0\",\n", - "# altitudeMode=\"relativetoground\",\n", - "# )\n" + "TestFlight.exportKML(\n", + " fileName=\"trajectory.kml\",\n", + " timeStep=None,\n", + " extrude=True,\n", + " color=\"641400F0\",\n", + " altitudeMode=\"relativetoground\",\n", + ")\n" ] }, { @@ -552,11 +576,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ - "# TestDispersion = Dispersion(filename=\"dispersion_analysis_outputs/disp_class_example\")" + "TestDispersion = Dispersion(filename=\"dispersion_analysis_outputs/disp_class_example\")\n" ] }, { @@ -570,11 +594,70 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ - "# TODO: This is work in progress, we need to understand how to setup simulations of such kind\n" + "# TODO: This is work in progress, we need to understand how to setup simulations of such kind\n", + "disp_dict_model_1 = {\n", + " # Environment Parameters\n", + " \"railLength\": 0.001,\n", + " # \"date\": ,\n", + " # \"datum\": [\"WSG84\"],\n", + " \"elevation\": 10,\n", + " \"gravity\": 0,\n", + " \"latitude\": 0,\n", + " \"longitude\": 0,\n", + " # \"timeZone\": [str(Env.timeZone)],\n", + " # Solid Motor Parameters\n", + " \"burnOutTime\": 0.2,\n", + " \"grainDensity\": 0.1 * Pro75M1670.grainDensity,\n", + " \"grainInitialHeight\": 0.001,\n", + " \"grainInitialInnerRadius\": 0.001,\n", + " \"grainNumber\": 0,\n", + " \"grainOuterRadius\": 0.001,\n", + " \"grainSeparation\": 0.001,\n", + " \"nozzleRadius\": 0.001,\n", + " \"throatRadius\": 0.001,\n", + " # \"thrustSource\": ,\n", + " \"totalImpulse\": 0.033 * Pro75M1670.totalImpulse,\n", + " # Rocket Parameters\n", + " \"mass\": 0.100,\n", + " \"radius\": 0.001,\n", + " \"distanceRocketNozzle\": 0.010,\n", + " \"distanceRocketPropellant\": 0.010,\n", + " \"inertiaI\": Calisto.inertiaI * 0.1,\n", + " \"inertiaZ\": Calisto.inertiaZ * 0.1,\n", + " \"powerOffDrag\": 0.033, # Multiplier\n", + " \"powerOnDrag\": 0.033, # Multiplier\n", + " # \"noseKind\": [Calisto.noseKind],\n", + " \"noseLength\": 0.001,\n", + " \"noseDistanceToCM\": 0.010,\n", + " \"numberOfFins\": 0,\n", + " \"finRootChord\": 0.001,\n", + " \"finTipChord\": 0.001,\n", + " \"span\": 0.001,\n", + " \"distanceToCM\": 0.010,\n", + " \"finRadius\": 0.001,\n", + " # \"finAirfoil\": Calisto.finAirfoil,\n", + " \"tailTopRadius\": 0.001,\n", + " # \"parachuteNames\": [\"Main\", \"Drogue\"],\n", + " \"CdS\": [2, 0.3],\n", + " \"inclination\": 1,\n", + " \"heading\": 2,\n", + " # \"trigger\": [[mainTrigger], [drogueTrigger]],\n", + " # \"noseLength\": (0.588, 1 / 1000),\n", + " # Flight Parameters\n", + " # 'atol': ,\n", + " # 'initialSolution': \"\",\n", + " # 'maxTime': \"\",\n", + " # 'maxTimeStep': \"\",\n", + " # 'minTimeStep': \"\",\n", + " # 'rtol': \"\",\n", + " # 'terminateOnApogee': \"\",\n", + " # 'timeOvershoot': \"\",\n", + " # 'verbose': \"\",\n", + "}" ] }, { @@ -586,19 +669,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'Starting'" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# TestDispersion.run_dispersion(\n", - "# number_of_simulations=5, # Be careful with this number, it will take a while to run\n", - "# dispersion_dictionary=dispersion_dictionary,\n", - "# flight=TestFlight,\n", - "# environment=Env,\n", - "# # motor=None,\n", - "# # rocket=None,\n", - "# bg_image=None,\n", - "# )\n" + "TestDispersion.run_dispersion(\n", + " number_of_simulations=5, # Be careful with this number, it will take a while to run\n", + " dispersion_dictionary=disp_dict_model_1,\n", + " flight=TestFlight,\n", + " environment=Env,\n", + " # motor=None,\n", + " # rocket=None,\n", + " bg_image=None,\n", + ")\n" ] }, { @@ -685,70 +778,65 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ - "TestDispersion = Dispersion(filename=\"dispersion_analysis_outputs/disp_class_example\")" + "TestDispersion = Dispersion(filename=\"dispersion_analysis_outputs/disp_class_example\")\n" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 37, "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'Rocket' object has no attribute 'rootChord'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn [25], line 36\u001b[0m\n\u001b[0;32m 1\u001b[0m dispersion_dictionary \u001b[39m=\u001b[39m {\n\u001b[0;32m 2\u001b[0m \u001b[39m# Environment Parameters\u001b[39;00m\n\u001b[0;32m 3\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mrailLength\u001b[39m\u001b[39m\"\u001b[39m: (Env\u001b[39m.\u001b[39mrailLength, \u001b[39m0.001\u001b[39m),\n\u001b[0;32m 4\u001b[0m \u001b[39m'\u001b[39m\u001b[39mdate\u001b[39m\u001b[39m'\u001b[39m: [Env\u001b[39m.\u001b[39mdate],\n\u001b[0;32m 5\u001b[0m \u001b[39m'\u001b[39m\u001b[39mdatum\u001b[39m\u001b[39m'\u001b[39m: [\u001b[39m\"\u001b[39m\u001b[39mWSG84\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m 6\u001b[0m \u001b[39m'\u001b[39m\u001b[39melevation\u001b[39m\u001b[39m'\u001b[39m:(Env\u001b[39m.\u001b[39melevation, \u001b[39m10\u001b[39m),\n\u001b[0;32m 7\u001b[0m \u001b[39m'\u001b[39m\u001b[39mgravity\u001b[39m\u001b[39m'\u001b[39m:(Env\u001b[39m.\u001b[39mgravity, \u001b[39m0\u001b[39m),\n\u001b[0;32m 8\u001b[0m \u001b[39m'\u001b[39m\u001b[39mlatitude\u001b[39m\u001b[39m'\u001b[39m:(Env\u001b[39m.\u001b[39mlatitude, \u001b[39m0\u001b[39m),\n\u001b[0;32m 9\u001b[0m \u001b[39m'\u001b[39m\u001b[39mlongitude\u001b[39m\u001b[39m'\u001b[39m:(Env\u001b[39m.\u001b[39mlongitude, \u001b[39m0\u001b[39m),\n\u001b[0;32m 10\u001b[0m \u001b[39m'\u001b[39m\u001b[39mtimeZone\u001b[39m\u001b[39m'\u001b[39m:[\u001b[39mstr\u001b[39m(Env\u001b[39m.\u001b[39mtimeZone)],\n\u001b[0;32m 11\u001b[0m \u001b[39m# Solid Motor Parameters\u001b[39;00m\n\u001b[0;32m 12\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mburnOutTime\u001b[39m\u001b[39m\"\u001b[39m: (Pro75M1670\u001b[39m.\u001b[39mburnOutTime, \u001b[39m0.2\u001b[39m),\n\u001b[0;32m 13\u001b[0m \u001b[39m'\u001b[39m\u001b[39mgrainDensity\u001b[39m\u001b[39m'\u001b[39m: (Pro75M1670\u001b[39m.\u001b[39mgrainDensity, \u001b[39m0.1\u001b[39m \u001b[39m*\u001b[39m Pro75M1670\u001b[39m.\u001b[39mgrainDensity),\n\u001b[0;32m 14\u001b[0m \u001b[39m'\u001b[39m\u001b[39mgrainInitialHeight\u001b[39m\u001b[39m'\u001b[39m: (Pro75M1670\u001b[39m.\u001b[39mgrainInitialHeight, \u001b[39m0.001\u001b[39m),\n\u001b[0;32m 15\u001b[0m \u001b[39m'\u001b[39m\u001b[39mgrainInitialInnerRadius\u001b[39m\u001b[39m'\u001b[39m: (Pro75M1670\u001b[39m.\u001b[39mgrainInitialInnerRadius,\u001b[39m0.001\u001b[39m),\n\u001b[0;32m 16\u001b[0m \u001b[39m'\u001b[39m\u001b[39mgrainNumber\u001b[39m\u001b[39m'\u001b[39m: [Pro75M1670\u001b[39m.\u001b[39mgrainNumber],\n\u001b[0;32m 17\u001b[0m \u001b[39m'\u001b[39m\u001b[39mgrainOuterRadius\u001b[39m\u001b[39m'\u001b[39m: (Pro75M1670\u001b[39m.\u001b[39mgrainOuterRadius, \u001b[39m0.001\u001b[39m),\n\u001b[0;32m 18\u001b[0m \u001b[39m'\u001b[39m\u001b[39mgrainSeparation\u001b[39m\u001b[39m'\u001b[39m: (Pro75M1670\u001b[39m.\u001b[39mgrainSeparation, \u001b[39m0.001\u001b[39m),\n\u001b[0;32m 19\u001b[0m \u001b[39m'\u001b[39m\u001b[39mnozzleRadius\u001b[39m\u001b[39m'\u001b[39m: (Pro75M1670\u001b[39m.\u001b[39mnozzleRadius,\u001b[39m0.001\u001b[39m),\n\u001b[0;32m 20\u001b[0m \u001b[39m'\u001b[39m\u001b[39mthroatRadius\u001b[39m\u001b[39m'\u001b[39m: (Pro75M1670\u001b[39m.\u001b[39mthroatRadius,\u001b[39m0.001\u001b[39m),\n\u001b[0;32m 21\u001b[0m \u001b[39m'\u001b[39m\u001b[39mthrust\u001b[39m\u001b[39m'\u001b[39m: [Pro75M1670\u001b[39m.\u001b[39mthrustSource],\n\u001b[0;32m 22\u001b[0m \u001b[39m'\u001b[39m\u001b[39mtotalImpulse\u001b[39m\u001b[39m'\u001b[39m: (Pro75M1670\u001b[39m.\u001b[39mtotalImpulse, \u001b[39m0.033\u001b[39m \u001b[39m*\u001b[39m Pro75M1670\u001b[39m.\u001b[39mtotalImpulse),\n\u001b[0;32m 23\u001b[0m \u001b[39m# Rocket Parameters\u001b[39;00m\n\u001b[0;32m 24\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mmass\u001b[39m\u001b[39m\"\u001b[39m: (Calisto\u001b[39m.\u001b[39mmass, \u001b[39m0.100\u001b[39m),\n\u001b[0;32m 25\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mradius\u001b[39m\u001b[39m\"\u001b[39m: (Calisto\u001b[39m.\u001b[39mradius, \u001b[39m0.001\u001b[39m),\n\u001b[0;32m 26\u001b[0m \u001b[39m'\u001b[39m\u001b[39mdistanceRocketNozzle\u001b[39m\u001b[39m'\u001b[39m: (Calisto\u001b[39m.\u001b[39mdistanceRocketNozzle, \u001b[39m0.010\u001b[39m),\n\u001b[0;32m 27\u001b[0m \u001b[39m'\u001b[39m\u001b[39mdistanceRocketPropellant\u001b[39m\u001b[39m'\u001b[39m: (Calisto\u001b[39m.\u001b[39mdistanceRocketPropellant, \u001b[39m0.010\u001b[39m),\n\u001b[0;32m 28\u001b[0m \u001b[39m'\u001b[39m\u001b[39minertiaI\u001b[39m\u001b[39m'\u001b[39m: (Calisto\u001b[39m.\u001b[39minertiaI, Calisto\u001b[39m.\u001b[39minertiaI \u001b[39m*\u001b[39m \u001b[39m0.1\u001b[39m ),\n\u001b[0;32m 29\u001b[0m \u001b[39m'\u001b[39m\u001b[39minertiaZ\u001b[39m\u001b[39m'\u001b[39m: (Calisto\u001b[39m.\u001b[39minertiaZ, Calisto\u001b[39m.\u001b[39minertiaZ \u001b[39m*\u001b[39m \u001b[39m0.1\u001b[39m ),\n\u001b[0;32m 30\u001b[0m \u001b[39m'\u001b[39m\u001b[39mpowerOffDrag\u001b[39m\u001b[39m'\u001b[39m: (\u001b[39m1\u001b[39m, \u001b[39m0.033\u001b[39m),\n\u001b[0;32m 31\u001b[0m \u001b[39m'\u001b[39m\u001b[39mpowerOnDrag\u001b[39m\u001b[39m'\u001b[39m: (\u001b[39m1\u001b[39m, \u001b[39m0.033\u001b[39m),\n\u001b[0;32m 32\u001b[0m \u001b[39m'\u001b[39m\u001b[39mnoseKind\u001b[39m\u001b[39m'\u001b[39m: [Calisto\u001b[39m.\u001b[39mnoseKind],\n\u001b[0;32m 33\u001b[0m \u001b[39m'\u001b[39m\u001b[39mnoseLength\u001b[39m\u001b[39m'\u001b[39m: (Calisto\u001b[39m.\u001b[39mnoseLength, \u001b[39m0.001\u001b[39m),\n\u001b[0;32m 34\u001b[0m \u001b[39m'\u001b[39m\u001b[39mnoseDistanceToCM\u001b[39m\u001b[39m'\u001b[39m: (Calisto\u001b[39m.\u001b[39mnoseDistanceToCM, \u001b[39m0.010\u001b[39m),\n\u001b[0;32m 35\u001b[0m \u001b[39m'\u001b[39m\u001b[39mnumberOfFins\u001b[39m\u001b[39m'\u001b[39m: [Calisto\u001b[39m.\u001b[39mnumberOfFins],\n\u001b[1;32m---> 36\u001b[0m \u001b[39m'\u001b[39m\u001b[39mrootChord\u001b[39m\u001b[39m'\u001b[39m: (Calisto\u001b[39m.\u001b[39;49mrootChord, \u001b[39m0.001\u001b[39m),\n\u001b[0;32m 37\u001b[0m \u001b[39m'\u001b[39m\u001b[39mtailTopRadius\u001b[39m\u001b[39m'\u001b[39m: (Calisto\u001b[39m.\u001b[39mtailTopRadius, \u001b[39m0.001\u001b[39m),\n\u001b[0;32m 38\u001b[0m \u001b[39m# \"parachuteNames\": [\"Main\", \"Drogue\"],\u001b[39;00m\n\u001b[0;32m 39\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mCdS\u001b[39m\u001b[39m\"\u001b[39m: [(\u001b[39m10\u001b[39m, \u001b[39m2\u001b[39m), (\u001b[39m1\u001b[39m, \u001b[39m0.3\u001b[39m)],\n\u001b[0;32m 40\u001b[0m \u001b[39m# \"trigger\": [[mainTrigger], [drogueTrigger]],\u001b[39;00m\n\u001b[0;32m 41\u001b[0m \u001b[39m# \"noseLength\": (0.588, 1 / 1000),\u001b[39;00m\n\u001b[0;32m 42\u001b[0m \u001b[39m# Flight Parameters\u001b[39;00m\n\u001b[0;32m 43\u001b[0m \u001b[39m# 'atol': ,\u001b[39;00m\n\u001b[0;32m 44\u001b[0m \u001b[39m# 'initialSolution': \"\",\u001b[39;00m\n\u001b[0;32m 45\u001b[0m \u001b[39m# 'maxTime': \"\",\u001b[39;00m\n\u001b[0;32m 46\u001b[0m \u001b[39m# 'maxTimeStep': \"\",\u001b[39;00m\n\u001b[0;32m 47\u001b[0m \u001b[39m# 'minTimeStep': \"\",\u001b[39;00m\n\u001b[0;32m 48\u001b[0m \u001b[39m# 'rtol': \"\",\u001b[39;00m\n\u001b[0;32m 49\u001b[0m \u001b[39m# 'terminateOnApogee': \"\",\u001b[39;00m\n\u001b[0;32m 50\u001b[0m \u001b[39m# 'timeOvershoot': \"\",\u001b[39;00m\n\u001b[0;32m 51\u001b[0m \u001b[39m# 'verbose': \"\",\u001b[39;00m\n\u001b[0;32m 52\u001b[0m }\n", - "\u001b[1;31mAttributeError\u001b[0m: 'Rocket' object has no attribute 'rootChord'" - ] - } - ], + "outputs": [], "source": [ "dispersion_dictionary = {\n", " # Environment Parameters\n", " \"railLength\": (Env.railLength, 0.001),\n", - " 'date': [Env.date],\n", - " 'datum': [\"WSG84\"],\n", - " 'elevation':(Env.elevation, 10),\n", - " 'gravity':(Env.gravity, 0),\n", - " 'latitude':(Env.latitude, 0),\n", - " 'longitude':(Env.longitude, 0),\n", - " 'timeZone':[str(Env.timeZone)],\n", + " \"date\": [Env.date],\n", + " \"datum\": [\"WSG84\"],\n", + " \"elevation\": (Env.elevation, 10),\n", + " \"gravity\": (Env.gravity, 0),\n", + " \"latitude\": (Env.latitude, 0),\n", + " \"longitude\": (Env.longitude, 0),\n", + " \"timeZone\": [str(Env.timeZone)],\n", " # Solid Motor Parameters\n", " \"burnOutTime\": (Pro75M1670.burnOutTime, 0.2),\n", - " 'grainDensity': (Pro75M1670.grainDensity, 0.1 * Pro75M1670.grainDensity),\n", - " 'grainInitialHeight': (Pro75M1670.grainInitialHeight, 0.001),\n", - " 'grainInitialInnerRadius': (Pro75M1670.grainInitialInnerRadius,0.001),\n", - " 'grainNumber': [Pro75M1670.grainNumber],\n", - " 'grainOuterRadius': (Pro75M1670.grainOuterRadius, 0.001),\n", - " 'grainSeparation': (Pro75M1670.grainSeparation, 0.001),\n", - " 'nozzleRadius': (Pro75M1670.nozzleRadius,0.001),\n", - " 'throatRadius': (Pro75M1670.throatRadius,0.001),\n", - " 'thrust': [Pro75M1670.thrustSource],\n", - " 'totalImpulse': (Pro75M1670.totalImpulse, 0.033 * Pro75M1670.totalImpulse),\n", + " \"grainDensity\": (Pro75M1670.grainDensity, 0.1 * Pro75M1670.grainDensity),\n", + " \"grainInitialHeight\": (Pro75M1670.grainInitialHeight, 0.001),\n", + " \"grainInitialInnerRadius\": (Pro75M1670.grainInitialInnerRadius, 0.001),\n", + " \"grainNumber\": [Pro75M1670.grainNumber],\n", + " \"grainOuterRadius\": (Pro75M1670.grainOuterRadius, 0.001),\n", + " \"grainSeparation\": (Pro75M1670.grainSeparation, 0.001),\n", + " \"nozzleRadius\": (Pro75M1670.nozzleRadius, 0.001),\n", + " \"throatRadius\": (Pro75M1670.throatRadius, 0.001),\n", + " \"thrustSource\": [Pro75M1670.thrustSource],\n", + " \"totalImpulse\": (Pro75M1670.totalImpulse, 0.033 * Pro75M1670.totalImpulse),\n", " # Rocket Parameters\n", " \"mass\": (Calisto.mass, 0.100),\n", " \"radius\": (Calisto.radius, 0.001),\n", - " 'distanceRocketNozzle': (Calisto.distanceRocketNozzle, 0.010),\n", - " 'distanceRocketPropellant': (Calisto.distanceRocketPropellant, 0.010),\n", - " 'inertiaI': (Calisto.inertiaI, Calisto.inertiaI * 0.1 ),\n", - " 'inertiaZ': (Calisto.inertiaZ, Calisto.inertiaZ * 0.1 ),\n", - " 'powerOffDrag': (1, 0.033),\n", - " 'powerOnDrag': (1, 0.033),\n", - " 'noseKind': [Calisto.noseKind],\n", - " 'noseLength': (Calisto.noseLength, 0.001),\n", - " 'noseDistanceToCM': (Calisto.noseDistanceToCM, 0.010),\n", - " 'numberOfFins': [Calisto.numberOfFins],\n", - " 'rootChord': (Calisto.rootChord, 0.001),\n", - " 'tailTopRadius': (Calisto.tailTopRadius, 0.001),\n", + " \"distanceRocketNozzle\": (Calisto.distanceRocketNozzle, 0.010),\n", + " \"distanceRocketPropellant\": (Calisto.distanceRocketPropellant, 0.010),\n", + " \"inertiaI\": (Calisto.inertiaI, Calisto.inertiaI * 0.1),\n", + " \"inertiaZ\": (Calisto.inertiaZ, Calisto.inertiaZ * 0.1),\n", + " \"powerOffDrag\": (1, 0.033),\n", + " \"powerOnDrag\": (1, 0.033),\n", + " \"noseKind\": [Calisto.noseKind],\n", + " \"noseLength\": (Calisto.noseLength, 0.001),\n", + " \"noseDistanceToCM\": (Calisto.noseDistanceToCM, 0.010),\n", + " \"numberOfFins\": [Calisto.numberOfFins],\n", + " \"finRootChord\": (Calisto.finRootChord, 0.001),\n", + " \"finTipChord\": (Calisto.finTipChord, 0.001),\n", + " \"span\": (Calisto.span, 0.001),\n", + " \"distanceToCM\": (Calisto.finDistanceToCM, 0.010),\n", + " \"finRadius\": (Calisto.finRadius, 0.001),\n", + " \"finAirfoil\": Calisto.finAirfoil,\n", + " \"tailTopRadius\": (Calisto.tailTopRadius, 0.001),\n", " # \"parachuteNames\": [\"Main\", \"Drogue\"],\n", " \"CdS\": [(10, 2), (1, 0.3)],\n", + " \"inclination\":[85],\n", + " \"heading\":[90],\n", " # \"trigger\": [[mainTrigger], [drogueTrigger]],\n", " # \"noseLength\": (0.588, 1 / 1000),\n", " # Flight Parameters\n", @@ -761,12 +849,12 @@ " # 'terminateOnApogee': \"\",\n", " # 'timeOvershoot': \"\",\n", " # 'verbose': \"\",\n", - "}" + "}\n" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -779,24 +867,18 @@ "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "{'railLength': (5.2, 0.001), 'date': [datetime.datetime(2022, 10, 11, 12, 0, tzinfo=)], 'datum': ['WSG84'], 'elevation': (113, 10), 'gravity': (9.80665, 0), 'latitude': (39.3897, 0), 'longitude': (-8.288964, 0), 'timeZone': ['UTC'], 'burnOutTime': (3.9, 0.2), 'grainDensity': (1815, 181.5), 'grainInitialHeight': (0.12, 0.001), 'grainInitialInnerRadius': (0.015, 0.001), 'grainNumber': [5], 'grainOuterRadius': (0.033, 0.001), 'grainSeparation': (0.005, 0.001), 'nozzleRadius': (0.033, 0.001), 'throatRadius': (0.011, 0.001), 'thrust': ['../../../data/motors/Cesaroni_M1670.eng'], 'totalImpulse': (6026.35, 198.86955000000003), 'mass': (16.241, 0.1), 'radius': (0.0635, 0.001), 'distanceRocketNozzle': (-1.255, 0.01), 'distanceRocketPropellant': (-0.85704, 0.01), 'inertiaI': (6.6, 0.66), 'inertiaZ': (0.0351, 0.00351), 'powerOffDrag': (1, 0.033), 'powerOnDrag': (1, 0.033), 'noseKind': ['vonKarman'], 'noseLength': (0.55829, 0.001), 'noseDistanceToCM': (0.71971, 0.01), 'numberOfFins': [4], 'tailTopRadius': (0.0635, 0.001), 'CdS': [(10, 2), (1, 0.3)], 'initialSolution': [None], 'heading': [90], 'inclination': [80], 'maxTime': [600], 'minTimeStep': [0], 'rtol': [1e-06], 'terminateOnApogee': [False], 'timeOvershoot': [True], 'verbose': [False], 'maxTimeStep': [inf], 'atol': [[0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 1e-06, 1e-06, 1e-06, 1e-06, 0.001, 0.001, 0.001]]}\n", - "\n" - ] - }, - { - "ename": "KeyError", - "evalue": "'rootChord'", + "ename": "TypeError", + "evalue": "must be real number, not NoneType", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn [23], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m TestDispersion\u001b[39m.\u001b[39;49mrun_dispersion(\n\u001b[0;32m 2\u001b[0m number_of_simulations\u001b[39m=\u001b[39;49m\u001b[39m5\u001b[39;49m,\n\u001b[0;32m 3\u001b[0m dispersion_dictionary\u001b[39m=\u001b[39;49mdispersion_dictionary,\n\u001b[0;32m 4\u001b[0m )\n", - "File \u001b[1;32mc:\\users\\guiga\\documents\\github-vscode\\rocketpy\\rocketpy\\Dispersion.py:910\u001b[0m, in \u001b[0;36mDispersion.run_dispersion\u001b[1;34m(self, number_of_simulations, dispersion_dictionary, environment, flight, motor, rocket, bg_image, actual_landing_point)\u001b[0m\n\u001b[0;32m 902\u001b[0m \u001b[39m# Add rocket nose, fins and tail\u001b[39;00m\n\u001b[0;32m 903\u001b[0m rocket_dispersion\u001b[39m.\u001b[39maddNose(\n\u001b[0;32m 904\u001b[0m length\u001b[39m=\u001b[39msetting[\u001b[39m\"\u001b[39m\u001b[39mnoseLength\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m 905\u001b[0m kind\u001b[39m=\u001b[39msetting[\u001b[39m\"\u001b[39m\u001b[39mnoseKind\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m 906\u001b[0m distanceToCM\u001b[39m=\u001b[39msetting[\u001b[39m\"\u001b[39m\u001b[39mnoseDistanceToCM\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m 907\u001b[0m )\n\u001b[0;32m 908\u001b[0m rocket_dispersion\u001b[39m.\u001b[39maddFins(\n\u001b[0;32m 909\u001b[0m n\u001b[39m=\u001b[39msetting[\u001b[39m\"\u001b[39m\u001b[39mnumberOfFins\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[1;32m--> 910\u001b[0m rootChord\u001b[39m=\u001b[39msetting[\u001b[39m\"\u001b[39;49m\u001b[39mrootChord\u001b[39;49m\u001b[39m\"\u001b[39;49m],\n\u001b[0;32m 911\u001b[0m tipChord\u001b[39m=\u001b[39msetting[\u001b[39m\"\u001b[39m\u001b[39mtipChord\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m 912\u001b[0m span\u001b[39m=\u001b[39msetting[\u001b[39m\"\u001b[39m\u001b[39mspan\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m 913\u001b[0m distanceToCM\u001b[39m=\u001b[39msetting[\u001b[39m\"\u001b[39m\u001b[39mdistanceToCM\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m 914\u001b[0m radius\u001b[39m=\u001b[39msetting[\u001b[39m\"\u001b[39m\u001b[39mradius\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m 915\u001b[0m airfoil\u001b[39m=\u001b[39msetting[\u001b[39m\"\u001b[39m\u001b[39mairfoil\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m 916\u001b[0m )\n\u001b[0;32m 917\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mnoTail\u001b[39m\u001b[39m\"\u001b[39m \u001b[39min\u001b[39;00m setting:\n\u001b[0;32m 918\u001b[0m rocket_dispersion\u001b[39m.\u001b[39maddTail(\n\u001b[0;32m 919\u001b[0m topRadius\u001b[39m=\u001b[39msetting[\u001b[39m\"\u001b[39m\u001b[39mtopRadius\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m 920\u001b[0m bottomRadius\u001b[39m=\u001b[39msetting[\u001b[39m\"\u001b[39m\u001b[39mbottomRadius\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m 921\u001b[0m length\u001b[39m=\u001b[39msetting[\u001b[39m\"\u001b[39m\u001b[39mlength\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m 922\u001b[0m distanceToCM\u001b[39m=\u001b[39msetting[\u001b[39m\"\u001b[39m\u001b[39mdistanceToCM\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m 923\u001b[0m )\n", - "\u001b[1;31mKeyError\u001b[0m: 'rootChord'" + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn [38], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m TestDispersion\u001b[39m.\u001b[39;49mrun_dispersion(\n\u001b[0;32m 2\u001b[0m number_of_simulations\u001b[39m=\u001b[39;49m\u001b[39m5\u001b[39;49m,\n\u001b[0;32m 3\u001b[0m dispersion_dictionary\u001b[39m=\u001b[39;49mdispersion_dictionary,\n\u001b[0;32m 4\u001b[0m )\n", + "File \u001b[1;32mc:\\users\\guiga\\documents\\github-vscode\\rocketpy\\rocketpy\\Dispersion.py:849\u001b[0m, in \u001b[0;36mDispersion.run_dispersion\u001b[1;34m(self, number_of_simulations, dispersion_dictionary, environment, flight, motor, rocket, bg_image, actual_landing_point)\u001b[0m\n\u001b[0;32m 847\u001b[0m \u001b[39m# Iterate over flight settings, start the flight simulations\u001b[39;00m\n\u001b[0;32m 848\u001b[0m out \u001b[39m=\u001b[39m display(\u001b[39m\"\u001b[39m\u001b[39mStarting\u001b[39m\u001b[39m\"\u001b[39m, display_id\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m--> 849\u001b[0m \u001b[39mfor\u001b[39;00m setting \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__yield_flight_setting(\n\u001b[0;32m 850\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdistributionFunc, analysis_parameters, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnumber_of_simulations\n\u001b[0;32m 851\u001b[0m ):\n\u001b[0;32m 852\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstart_time \u001b[39m=\u001b[39m process_time()\n\u001b[0;32m 853\u001b[0m i \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n", + "File \u001b[1;32mc:\\users\\guiga\\documents\\github-vscode\\rocketpy\\rocketpy\\Dispersion.py:638\u001b[0m, in \u001b[0;36mDispersion.__yield_flight_setting\u001b[1;34m(self, distribution_func, analysis_parameters, number_of_simulations)\u001b[0m\n\u001b[0;32m 636\u001b[0m \u001b[39mfor\u001b[39;00m parameter_key, parameter_value \u001b[39min\u001b[39;00m analysis_parameters\u001b[39m.\u001b[39mitems():\n\u001b[0;32m 637\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mtype\u001b[39m(parameter_value) \u001b[39mis\u001b[39;00m \u001b[39mtuple\u001b[39m:\n\u001b[1;32m--> 638\u001b[0m flight_setting[parameter_key] \u001b[39m=\u001b[39m distribution_func(\u001b[39m*\u001b[39;49mparameter_value)\n\u001b[0;32m 639\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 640\u001b[0m \u001b[39m# shuffles list and gets first item\u001b[39;00m\n\u001b[0;32m 641\u001b[0m shuffle(parameter_value)\n", + "File \u001b[1;32mmtrand.pyx:1510\u001b[0m, in \u001b[0;36mnumpy.random.mtrand.RandomState.normal\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32m_common.pyx:604\u001b[0m, in \u001b[0;36mnumpy.random._common.cont\u001b[1;34m()\u001b[0m\n", + "\u001b[1;31mTypeError\u001b[0m: must be real number, not NoneType" ] } ], @@ -804,7 +886,7 @@ "TestDispersion.run_dispersion(\n", " number_of_simulations=5,\n", " dispersion_dictionary=dispersion_dictionary,\n", - ")" + ")\n" ] }, { From 12ac1db95fd2d886db433e861e7986a555920a4c Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Sun, 23 Oct 2022 23:00:42 +0200 Subject: [PATCH 24/68] MAINT: Introducing AeroSurfaces classes --- rocketpy/AeroSurfaces.py | 646 +++++++++++++++++++++++++++++++++++++++ rocketpy/Flight.py | 7 +- rocketpy/Rocket.py | 436 +++----------------------- 3 files changed, 691 insertions(+), 398 deletions(-) create mode 100644 rocketpy/AeroSurfaces.py diff --git a/rocketpy/AeroSurfaces.py b/rocketpy/AeroSurfaces.py new file mode 100644 index 000000000..df632c9b6 --- /dev/null +++ b/rocketpy/AeroSurfaces.py @@ -0,0 +1,646 @@ +__author__ = "Guilherme Fernandes Alves" +__copyright__ = "Copyright 20XX, RocketPy Team" +__license__ = "MIT" + +import numpy as np +from .Function import Function + + +class NoseCone: + """Keeps nose cone information. + + Attributes + ---------- + NoseCone.length : float + Nose cone length. Has units of length and must be given in meters. + NoseCone.kind : string + Nose cone kind. Can be "conical", "ogive" or "lvhaack". + NoseCone.distanceToCM : float + Distance between nose cone tip and rocket center of mass. Has units of + length and must be given in meters. + NoseCone.name : string + Nose cone name. Has no impact in simulation, as it is only used to + display data in a more organized matter. + NoseCone.cp : tuple + Tuple with the x, y and z coordinates of the nose cone center of pressure + relative to the rocket center of mass. Has units of length and must be + given in meters. + NoseCone.cl : Function + Function which defines the lift coefficient as a function of the angle of + attack and the Mach number. It must take as input the angle of attack in + radians and the Mach number. It should return the lift coefficient. + """ + + def __init__(self, length, kind, distanceToCM, name="Nose Cone"): + """Initializes the nose cone. It is used to define the nose cone + length, kind, distance to center of mass and name. + + Parameters + ---------- + length : float + Nose cone length. Has units of length and must be given in meters. + kind : string + Nose cone kind. Can be "conical", "ogive" or "lvhaack". + distanceToCM : _type_ + Distance between nose cone tip and rocket center of mass. Has units of + length and must be given in meters. + name : str, optional + Nose cone name. Has no impact in simulation, as it is only used to + display data in a more organized matter. + + Returns + ------- + None + """ + self.length = length + self.kind = kind + self.distanceToCM = distanceToCM + self.name = name + + # Analyze type + if self.kind == "conical": + self.k = 1 - 1 / 3 + elif self.kind == "ogive": + self.k = 1 - 0.534 + elif self.kind == "lvhaack": + self.k = 1 - 0.437 + else: + self.k = 0.5 + # Calculate cp position relative to cm + self.cpz = self.distanceToCM + np.sign(self.distanceToCM) * self.k * length + self.cpy = 0 + self.cpx = 0 + self.cp = (self.cpx, self.cpy, self.cpz) + + # Calculate clalpha + self.clalpha = 2 + self.cl = Function( + lambda alpha, mach: self.clalpha * alpha, + ["Alpha (rad)", "Mach"], + "Cl", + ) + # # Store values + # nose = {"cp": (0, 0, self.cpz), "cl": self.cl, "name": name} + + return None + + +class TrapezoidalFins: + """Keeps trapezoidal fins information. + + Attributes + ---------- + + """ + + def __init__( + self, + n, + rootChord, + tipChord, + span, + distanceToCM, + cantAngle=0, + radius=None, + airfoil=None, + name="Fins", + ): + """Initializes the trapezoidal fins. It is used to define the number of + fins, root chord, tip chord, span, distance to center of mass, cant angle + and name. + + Parameters + ---------- + n : int + Number of fins, from 2 to infinity. + span : int, float + Fin span in meters. + rootChord : int, float + Fin root chord in meters. + tipChord : int, float + Fin tip chord in meters. + distanceToCM : int, float + Fin set position relative to rocket unloaded center of + mass, considering positive direction from center of mass to + nose cone. Consider the center point belonging to the top + of the fins to calculate distance. + cantAngle : int, float, optional + Fins cant angle with respect to the rocket centerline. Must + be given in degrees. + radius : int, float, optional + Reference radius to calculate lift coefficient. If None, which + is default, use rocket radius. Otherwise, enter the radius + of the rocket in the section of the fins, as this impacts + its lift coefficient. + airfoil : tuple, optional + Default is null, in which case fins will be treated as flat plates. + Otherwise, if tuple, fins will be considered as airfoils. The + tuple's first item specifies the airfoil's lift coefficient + by angle of attack and must be either a .csv, .txt, ndarray + or callable. The .csv and .txt files must contain no headers + and the first column must specify the angle of attack, while + the second column must specify the lift coefficient. The + ndarray should be as [(x0, y0), (x1, y1), (x2, y2), ...] + where x0 is the angle of attack and y0 is the lift coefficient. + If callable, it should take an angle of attack as input and + return the lift coefficient at that angle of attack. + The tuple's second item is the unit of the angle of attack, + accepting either "radians" or "degrees". + + Returns + ------- + None + """ + # Store values + self.numberOfFins = n + self.finRadius = radius + self.finAirfoil = airfoil + self.finDistanceToCM = distanceToCM + self.finCantAngle = cantAngle + self.finRootChord = rootChord + self.finTipChord = tipChord + self.finSpan = span + self.name = name + + # get some nicknames + Cr, Ct = self.finRootChord, self.finTipChord + s = self.finSpan + cantAngleRad = np.radians(cantAngle) + + # Compute auxiliary geometrical parameters + d = 2 * radius + Aref = np.pi * radius**2 + Yr = Cr + Ct + Af = Yr * s / 2 # Fin area + AR = 2 * s**2 / Af # Fin aspect ratio + gamac = np.arctan((Cr - Ct) / (2 * s)) # Mid chord angle + Yma = (s / 3) * (Cr + 2 * Ct) / Yr # Span wise coord of mean aero chord + rollGeometricalConstant = ( + (Cr + 3 * Ct) * s**3 + + 4 * (Cr + 2 * Ct) * radius * s**2 + + 6 * (Cr + Ct) * s * radius**2 + ) / 12 + + # Center of pressure position relative to CDM (center of dry mass) + cpz = distanceToCM + np.sign(distanceToCM) * ( + ((Cr - Ct) / 3) * ((Cr + 2 * Ct) / (Cr + Ct)) + + (1 / 6) * (Cr + Ct - Cr * Ct / (Cr + Ct)) + ) + + # Fin–body interference correction parameters + tau = (s + radius) / radius + liftInterferenceFactor = 1 + 1 / tau + λ = Ct / Cr + + # Defines beta parameter + def beta(mach): + """Defines a parameter that is commonly used in aerodynamic + equations. It is commonly used in the Prandtl factor which + corrects subsonic force coefficients for compressible flow. + + Parameters + ---------- + mach : int, float + Number of mach. + + Returns + ------- + beta : int, float + Value that characterizes flow speed based on the mach number. + """ + + if mach < 0.8: + return np.sqrt(1 - mach**2) + elif mach < 1.1: + return np.sqrt(1 - 0.8**2) + else: + return np.sqrt(mach**2 - 1) + + # Defines number of fins factor + def finNumCorrection(n): + """Calculates a correction factor for the lift coefficient of multiple fins. + The specifics values are documented at: + Niskanen, S. (2013). “OpenRocket technical documentation”. In: Development + of an Open Source model rocket simulation software. + + Parameters + ---------- + n : int + Number of fins. + + Returns + ------- + Corrector factor : int + Factor that accounts for the number of fins. + """ + correctorFactor = [2.37, 2.74, 2.99, 3.24] + if n >= 5 and n <= 8: + return correctorFactor[n - 5] + else: + return n / 2 + + if not airfoil: + # Defines clalpha2D as 2*pi for planar fins + clalpha2D = Function(lambda mach: 2 * np.pi / beta(mach)) + else: + # Defines clalpha2D as the derivative of the + # lift coefficient curve for a specific airfoil + airfoilCl = Function( + airfoil[0], + interpolation="linear", + ) + + # Differentiating at x = 0 to get cl_alpha + clalpha2D_Mach0 = airfoilCl.differentiate(x=1e-3, dx=1e-3) + + # Convert to radians if needed + if airfoil[1] == "degrees": + clalpha2D_Mach0 *= 180 / np.pi + + # Correcting for compressible flow + clalpha2D = Function(lambda mach: clalpha2D_Mach0 / beta(mach)) + + # Diederich's Planform Correlation Parameter + FD = 2 * np.pi * AR / (clalpha2D * np.cos(gamac)) + + # Lift coefficient derivative for a single fin + clalphaSingleFin = Function( + lambda mach: (clalpha2D(mach) * FD(mach) * (Af / Aref) * np.cos(gamac)) + / (2 + FD(mach) * np.sqrt(1 + (2 / FD(mach)) ** 2)) + ) + + # Lift coefficient derivative for a number of n fins corrected for Fin-Body interference + clalphaMultipleFins = ( + liftInterferenceFactor * finNumCorrection(n) * clalphaSingleFin + ) # Function of mach number + + # Calculates clalpha * alpha + cl = Function( + lambda alpha, mach: alpha * clalphaMultipleFins(mach), + ["Alpha (rad)", "Mach"], + "Cl", + ) + + # Parameters for Roll Moment. + # Documented at: https://github.com/RocketPy-Team/RocketPy/blob/master/docs/technical/aerodynamics/Roll_Equations.pdf + rollDampingInterferenceFactor = 1 + ( + ((tau - λ) / (tau)) - ((1 - λ) / (tau - 1)) * np.log(tau) + ) / ( + ((tau + 1) * (tau - λ)) / (2) - ((1 - λ) * (tau**3 - 1)) / (3 * (tau - 1)) + ) + rollForcingInterferenceFactor = (1 / np.pi**2) * ( + (np.pi**2 / 4) * ((tau + 1) ** 2 / tau**2) + + ((np.pi * (tau**2 + 1) ** 2) / (tau**2 * (tau - 1) ** 2)) + * np.arcsin((tau**2 - 1) / (tau**2 + 1)) + - (2 * np.pi * (tau + 1)) / (tau * (tau - 1)) + + ((tau**2 + 1) ** 2) + / (tau**2 * (tau - 1) ** 2) + * (np.arcsin((tau**2 - 1) / (tau**2 + 1))) ** 2 + - (4 * (tau + 1)) + / (tau * (tau - 1)) + * np.arcsin((tau**2 - 1) / (tau**2 + 1)) + + (8 / (tau - 1) ** 2) * np.log((tau**2 + 1) / (2 * tau)) + ) + clfDelta = ( + rollForcingInterferenceFactor * n * (Yma + radius) * clalphaSingleFin / d + ) # Function of mach number + cldOmega = ( + 2 + * rollDampingInterferenceFactor + * n + * clalphaSingleFin + * np.cos(cantAngleRad) + * rollGeometricalConstant + / (Aref * d**2) + ) # Function of mach number + rollParameters = [clfDelta, cldOmega, cantAngleRad] + + # Save and store parameters + self.rollParameters = rollParameters + self.cl = cl + self.clalphaSingleFin = clalphaSingleFin + self.clalphaMultipleFins = clalphaMultipleFins + self.cpx = 0 + self.cpy = 0 + self.cpz = cpz + self.cp = (self.cpx, self.cpy, self.cpz) + + def info(self): + "Still not implemented. Must print important information." + + return None + + return None + + +class EllipticalFins: + """Class that defines the aerodynamic model of an elliptical fin. + + Parameters + ---------- + n : int + Number of fins. + radius : int, float + Fin radius. + span : int, float + Fin span. + cantAngle : int, float + Cant angle of the fin. + + Returns + ------- + None + """ + + def __init__( + self, + n, + rootChord, + span, + distanceToCM, + cantAngle=0, + radius=None, + airfoil=None, + name="Fins", + ): + """Initializes the class, defining the parameters of the fins. + + Parameters + ---------- + n : int + Number of fins. + rootChord : _type_ + _description_ + span : _type_ + _description_ + distanceToCM : _type_ + _description_ + cantAngle : int, optional + _description_, by default 0 + radius : _type_, optional + _description_, by default None + airfoil : _type_, optional + _description_, by default None + name : str, optional + _description_, by default "Fins" + + Returns + ------- + None + + """ + + # Save attributes + self.numberOfFins = n + self.rootChord = rootChord + self.span = span + self.distanceToCM = distanceToCM + self.cantAngle = cantAngle + self.radius = radius + self.airfoil = airfoil + self.name = name + + # Get some nicknames + Cr = self.rootChord + s = self.span + cantAngleRad = np.radians(cantAngle) + + # Compute auxiliary geometrical parameters + d = 2 * radius + Aref = np.pi * radius**2 # Reference area for coefficients + Af = (np.pi * Cr / 2 * s) / 2 # Fin area + AR = 2 * s**2 / Af # Fin aspect ratio + Yma = ( + s / (3 * np.pi) * np.sqrt(9 * np.pi**2 - 16) + ) # Span wise coord of mean aero chord + rollGeometricalConstant = ( + Cr + * s + * (3 * np.pi * s**2 + 32 * radius * s + 12 * np.pi * radius**2) + / 48 + ) + + # Center of pressure position relative to CDM (center of dry mass) + cpz = distanceToCM + np.sign(distanceToCM) * (0.288 * Cr) + + # Fin–body interference correction parameters + tau = (s + radius) / radius + liftInterferenceFactor = 1 + 1 / tau + rollDampingInterferenceFactor = 1 + ( + (radius**2) + * ( + 2 + * (radius**2) + * np.sqrt(s**2 - radius**2) + * np.log((2 * s * np.sqrt(s**2 - radius**2) + 2 * s**2) / radius) + - 2 * (radius**2) * np.sqrt(s**2 - radius**2) * np.log(2 * s) + + 2 * s**3 + - np.pi * radius * s**2 + - 2 * (radius**2) * s + + np.pi * radius**3 + ) + ) / (2 * (s**2) * (s / 3 + np.pi * radius / 4) * (s**2 - radius**2)) + rollForcingInterferenceFactor = (1 / np.pi**2) * ( + (np.pi**2 / 4) * ((tau + 1) ** 2 / tau**2) + + ((np.pi * (tau**2 + 1) ** 2) / (tau**2 * (tau - 1) ** 2)) + * np.arcsin((tau**2 - 1) / (tau**2 + 1)) + - (2 * np.pi * (tau + 1)) / (tau * (tau - 1)) + + ((tau**2 + 1) ** 2) + / (tau**2 * (tau - 1) ** 2) + * (np.arcsin((tau**2 - 1) / (tau**2 + 1))) ** 2 + - (4 * (tau + 1)) + / (tau * (tau - 1)) + * np.arcsin((tau**2 - 1) / (tau**2 + 1)) + + (8 / (tau - 1) ** 2) * np.log((tau**2 + 1) / (2 * tau)) + ) + + # Auxiliary functions + # Defines beta parameter + def beta(mach): + """Defines a parameter that is commonly used in aerodynamic + equations. It is commonly used in the Prandtl factor which + corrects subsonic force coefficients for compressible flow. + + Parameters + ---------- + mach : int, float + Number of mach. + + Returns + ------- + beta : int, float + Value that characterizes flow speed based on the mach number. + """ + + if mach < 0.8: + return np.sqrt(1 - mach**2) + elif mach < 1.1: + return np.sqrt(1 - 0.8**2) + else: + return np.sqrt(mach**2 - 1) + + # Defines number of fins correction + def finNumCorrection(n): + """Calculates a corrector factor for the lift coefficient of multiple fins. + The specifics values are documented at: + Niskanen, S. (2013). “OpenRocket technical documentation”. In: Development + of an Open Source model rocket simulation software. + + Parameters + ---------- + n : int + Number of fins. + + Returns + ------- + Corrector factor : int + Factor that accounts for the number of fins. + """ + correctorFactor = [2.37, 2.74, 2.99, 3.24] + if n >= 5 and n <= 8: + return correctorFactor[n - 5] + else: + return n / 2 + + if not airfoil: + # Defines clalpha2D as 2*pi for planar fins + clalpha2D = Function(lambda mach: 2 * np.pi / beta(mach)) + else: + # Defines clalpha2D as the derivative of the + # lift coefficient curve for a specific airfoil + airfoilCl = Function( + airfoil[0], + interpolation="linear", + ) + + # Differentiating at x = 0 to get cl_alpha + clalpha2D_Mach0 = airfoilCl.differentiate(x=1e-3, dx=1e-3) + + # Convert to radians if needed + if airfoil[1] == "degrees": + clalpha2D_Mach0 *= 180 / np.pi + + # Correcting for compressible flow + clalpha2D = Function(lambda mach: clalpha2D_Mach0 / beta(mach)) + # Diederich's Planform Correlation Parameter + FD = 2 * np.pi * AR / (clalpha2D) + + # Lift coefficient derivative for a single fin + clalphaSingleFin = Function( + lambda mach: (clalpha2D(mach) * FD(mach) * (Af / Aref)) + / (2 + FD(mach) * np.sqrt(1 + (2 / FD(mach)) ** 2)) + ) + + # Lift coefficient derivative for a number of n fins corrected for Fin-Body interference + clalphaMultipleFins = ( + liftInterferenceFactor * finNumCorrection(n) * clalphaSingleFin + ) # Function of mach number + + # Calculates clalpha * alpha + cl = Function( + lambda alpha, mach: alpha * clalphaMultipleFins(mach), + ["Alpha (rad)", "Mach"], + "Cl", + ) + + # Parameters for Roll Moment. + # Documented at: https://github.com/RocketPy-Team/RocketPy/blob/develop/docs/technical/aerodynamics/Roll_Equations.pdf + clfDelta = ( + rollForcingInterferenceFactor * n * (Yma + radius) * clalphaSingleFin / d + ) # Function of mach number + cldOmega = ( + 2 + * rollDampingInterferenceFactor + * n + * clalphaSingleFin + * np.cos(cantAngleRad) + * rollGeometricalConstant + / (Aref * d**2) + ) + # Function of mach number + rollParameters = [clfDelta, cldOmega, cantAngleRad] + + # Store values + self.cpx = 0 + self.cpy = 0 + self.cpz = cpz + self.cp = (self.cpx, self.cpy, self.cpz) + self.cl = cl + self.rollParameters = rollParameters + self.clalphaMultipleFins = clalphaMultipleFins + self.clalphaSingleFin = clalphaSingleFin + + return None + + +class Tail: + """Class that defines a tail for the rocket. + + Parameters + ---------- + length : int, float + Length of the tail. + ... + + + """ + + def __init__( + self, topRadius, bottomRadius, length, distanceToCM, radius, name="Tail" + ): + """_summary_ + + Parameters + ---------- + topRadius : _type_ + _description_ + bottomRadius : _type_ + _description_ + length : _type_ + _description_ + distanceToCM : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ + + # Store arguments as attributes + self.tailTopRadius = topRadius + self.tailBottomRadius = bottomRadius + self.tailLength = length + self.tailDistanceToCM = distanceToCM + self.name = name + self.radius = radius + + # Calculate ratio between top and bottom radius + r = topRadius / bottomRadius + + # Retrieve reference radius + rref = self.radius + + # Calculate cp position relative to center of dry mass + if distanceToCM < 0: + cpz = distanceToCM - (length / 3) * (1 + (1 - r) / (1 - r**2)) + else: + cpz = distanceToCM + (length / 3) * (1 + (1 - r) / (1 - r**2)) + + # Calculate clalpha + clalpha = -2 * (1 - r ** (-2)) * (topRadius / rref) ** 2 + cl = Function( + lambda alpha, mach: clalpha * alpha, + ["Alpha (rad)", "Mach"], + "Cl", + ) + + # Store values as class attributes + self.cpx = 0 + self.cpy = 0 + self.cpz = cpz + self.cp = (self.cpx, self.cpy, self.cpz) + self.cl = cl + self.clalpha = clalpha + + return None diff --git a/rocketpy/Flight.py b/rocketpy/Flight.py index 73d8377c7..315bda53a 100644 --- a/rocketpy/Flight.py +++ b/rocketpy/Flight.py @@ -1389,7 +1389,7 @@ def uDot(self, t, u, postProcessing=False): vzB = a13 * vx + a23 * vy + a33 * vz # Calculate lift and moment for each component of the rocket for aerodynamicSurface in self.rocket.aerodynamicSurfaces: - compCp = aerodynamicSurface["cp"][2] + compCp = aerodynamicSurface.cp[2] # Component absolute velocity in body frame compVxB = vxB + compCp * omega2 compVyB = vyB - compCp * omega1 @@ -1417,6 +1417,7 @@ def uDot(self, t, u, postProcessing=False): if -1 * compStreamVzBn < 1: compAttackAngle = np.arccos(-compStreamVzBn) cLift = aerodynamicSurface["cl"](compAttackAngle, freestreamMach) + cLift = aerodynamicSurface.cl(compAttackAngle, freestreamMach) # Component lift force magnitude compLift = ( 0.5 * rho * (compStreamSpeed**2) * self.rocket.area * cLift @@ -1432,8 +1433,8 @@ def uDot(self, t, u, postProcessing=False): M1 -= (compCp + a) * compLiftYB M2 += (compCp + a) * compLiftXB # Calculates Roll Moment - if aerodynamicSurface["name"] == "Fins": - Clfdelta, Cldomega, cantAngleRad = aerodynamicSurface["roll parameters"] + if aerodynamicSurface.name == "Fins": + Clfdelta, Cldomega, cantAngleRad = aerodynamicSurface.rollParameters M3f = ( (1 / 2 * rho * freestreamSpeed**2) * self.rocket.area diff --git a/rocketpy/Rocket.py b/rocketpy/Rocket.py index 8208e0950..af926dd7a 100644 --- a/rocketpy/Rocket.py +++ b/rocketpy/Rocket.py @@ -13,6 +13,7 @@ from .Function import Function from .Parachute import Parachute +from .AeroSurfaces import NoseCone, TrapezoidalFins, EllipticalFins, Tail class Rocket: @@ -314,13 +315,13 @@ def evaluateStaticMargin(self): if len(self.aerodynamicSurfaces) > 0: for aerodynamicSurface in self.aerodynamicSurfaces: self.totalLiftCoeffDer += Function( - lambda alpha: aerodynamicSurface["cl"](alpha, 0) + lambda alpha: aerodynamicSurface.cl(alpha, 0) ).differentiate(x=1e-2, dx=1e-3) self.cpPosition += ( Function( - lambda alpha: aerodynamicSurface["cl"](alpha, 0) + lambda alpha: aerodynamicSurface.cl(alpha, 0) ).differentiate(x=1e-2, dx=1e-3) - * aerodynamicSurface["cp"][2] + * aerodynamicSurface.cp[2] ) self.cpPosition /= self.totalLiftCoeffDer @@ -335,7 +336,9 @@ def evaluateStaticMargin(self): # Return self return self - def addTail(self, topRadius, bottomRadius, length, distanceToCM): + def addTail( + self, topRadius, bottomRadius, length, distanceToCM, radius=None, name="Tail" + ): """Create a new tail or rocket diameter change, storing its parameters as part of the aerodynamicSurfaces list. Its parameters are the axial position along the rocket and its @@ -369,33 +372,14 @@ def addTail(self, topRadius, bottomRadius, length, distanceToCM): self : Rocket Object of the Rocket class. """ - # Save parameters for Dispersion - self.tailTopRadius = topRadius - self.tailBottomRadius = bottomRadius - self.tailLength = length - self.tailDistanceToCM = distanceToCM - # Calculate ratio between top and bottom radius - r = topRadius / bottomRadius - - # Retrieve reference radius - rref = self.radius - - # Calculate cp position relative to cm - if distanceToCM < 0: - cpz = distanceToCM - (length / 3) * (1 + (1 - r) / (1 - r**2)) - else: - cpz = distanceToCM + (length / 3) * (1 + (1 - r) / (1 - r**2)) - - # Calculate clalpha - clalpha = -2 * (1 - r ** (-2)) * (topRadius / rref) ** 2 - cl = Function( - lambda alpha, mach: clalpha * alpha, - ["Alpha (rad)", "Mach"], - "Cl", - ) - # Store values as new aerodynamic surface - tail = {"cp": (0, 0, cpz), "cl": cl, "name": "Tail"} + # Modify reference radius if not provided + radius = self.radius if radius is None else radius + + # Create new tail as an object of the Tail class + tail = Tail(topRadius, bottomRadius, length, distanceToCM, radius, name) + + # Add tail to aerodynamic surfaces list self.aerodynamicSurfaces.append(tail) # Refresh static margin calculation @@ -404,7 +388,7 @@ def addTail(self, topRadius, bottomRadius, length, distanceToCM): # Return self return self.aerodynamicSurfaces[-1] - def addNose(self, length, kind, distanceToCM): + def addNose(self, length, kind, distanceToCM, name="Nose Cone"): """Creates a nose cone, storing its parameters as part of the aerodynamicSurfaces list. Its parameters are the axial position along the rocket and its derivative of the coefficient of lift @@ -424,6 +408,8 @@ def addNose(self, length, kind, distanceToCM): mass, considering positive direction from center of mass to nose cone. Consider the center point belonging to the nose cone base to calculate distance. + name : string + Nose cone name. Default is "Nose Cone". Returns ------- @@ -437,34 +423,9 @@ def addNose(self, length, kind, distanceToCM): self : Rocket Object of the Rocket class. """ - - # Save parameters for Dispersion - self.noseLength = length - self.noseKind = kind - self.noseDistanceToCM = distanceToCM - - # Analyze type - if kind == "conical": - k = 1 - 1 / 3 - elif kind == "ogive": - k = 1 - 0.534 - elif kind == "lvhaack": - k = 1 - 0.437 - else: - k = 0.5 - # Calculate cp position relative to cm - cpz = distanceToCM + np.sign(distanceToCM) * k * length - - # Calculate clalpha - clalpha = 2 - cl = Function( - lambda alpha, mach: clalpha * alpha, - ["Alpha (rad)", "Mach"], - "Cl", - ) - - # Store values - nose = {"cp": (0, 0, cpz), "cl": cl, "name": "Nose Cone"} + # Create a nose as an object of NoseCone class + nose = NoseCone(length, kind, distanceToCM, name) + # Add nose to the list of aerodynamic surfaces self.aerodynamicSurfaces.append(nose) # Refresh static margin calculation @@ -495,6 +456,7 @@ def addTrapezoidalFins( cantAngle=0, radius=None, airfoil=None, + name="Fins", ): """Create a trapezoidal fin set, storing its parameters as part of the aerodynamicSurfaces list. Its parameters are the axial position @@ -548,175 +510,16 @@ def addTrapezoidalFins( Object of the Rocket class. """ - # Save parameters for Dispersion # TODO: We need to be more flexible here! Not all the rockets has exactly 1 fin set - self.numberOfFins = n - self.finRadius = radius - self.finAirfoil = airfoil - self.finDistanceToCM = distanceToCM - - # Retrieves and convert basic geometrical parameters - Cr, Ct = self.finRootChord, self.finTipChord = rootChord, tipChord - s = self.span = span - radius = self.radius if radius is None else radius - self.cantAngle = cantAngle - cantAngleRad = np.radians(cantAngle) - - # Compute auxiliary geometrical parameters - d = 2 * radius - Aref = np.pi * radius**2 - Yr = Cr + Ct - Af = Yr * s / 2 # Fin area - AR = 2 * s**2 / Af # Fin aspect ratio - gamac = np.arctan((Cr - Ct) / (2 * s)) # Mid chord angle - Yma = (s / 3) * (Cr + 2 * Ct) / Yr # Span wise coord of mean aero chord - rollGeometricalConstant = ( - (Cr + 3 * Ct) * s**3 - + 4 * (Cr + 2 * Ct) * radius * s**2 - + 6 * (Cr + Ct) * s * radius**2 - ) / 12 - - # Center of pressure position relative to CDM (center of dry mass) - cpz = distanceToCM + np.sign(distanceToCM) * ( - ((Cr - Ct) / 3) * ((Cr + 2 * Ct) / (Cr + Ct)) - + (1 / 6) * (Cr + Ct - Cr * Ct / (Cr + Ct)) - ) - - # Fin–body interference correction parameters - tau = (s + radius) / radius - liftInterferenceFactor = 1 + 1 / tau - λ = Ct / Cr - - # Defines beta parameter - def beta(mach): - """Defines a parameter that is commonly used in aerodynamic - equations. It is commonly used in the Prandtl factor which - corrects subsonic force coefficients for compressible flow. - - Parameters - ---------- - mach : int, float - Number of mach. - - Returns - ------- - beta : int, float - Value that characterizes flow speed based on the mach number. - """ - - if mach < 0.8: - return np.sqrt(1 - mach**2) - elif mach < 1.1: - return np.sqrt(1 - 0.8**2) - else: - return np.sqrt(mach**2 - 1) - - # Defines number of fins factor - def finNumCorrection(n): - """Calculates a correction factor for the lift coefficient of multiple fins. - The specifics values are documented at: - Niskanen, S. (2013). “OpenRocket technical documentation”. In: Development - of an Open Source model rocket simulation software. - - Parameters - ---------- - n : int - Number of fins. - - Returns - ------- - Corrector factor : int - Factor that accounts for the number of fins. - """ - correctorFactor = [2.37, 2.74, 2.99, 3.24] - if n >= 5 and n <= 8: - return correctorFactor[n - 5] - else: - return n / 2 - - if not airfoil: - # Defines clalpha2D as 2*pi for planar fins - clalpha2D = Function(lambda mach: 2 * np.pi / beta(mach)) - else: - # Defines clalpha2D as the derivative of the - # lift coefficient curve for a specific airfoil - airfoilCl = Function( - airfoil[0], - interpolation="linear", - ) - - # Differentiating at x = 0 to get cl_alpha - clalpha2D_Mach0 = airfoilCl.differentiate(x=1e-3, dx=1e-3) - - # Convert to radians if needed - if airfoil[1] == "degrees": - clalpha2D_Mach0 *= 180 / np.pi - - # Correcting for compressible flow - clalpha2D = Function(lambda mach: clalpha2D_Mach0 / beta(mach)) + # Modify radius if not given, use rocket radius, otherwise use given. + radius = radius if radius is not None else self.radius - # Diederich's Planform Correlation Parameter - FD = 2 * np.pi * AR / (clalpha2D * np.cos(gamac)) - - # Lift coefficient derivative for a single fin - clalphaSingleFin = Function( - lambda mach: (clalpha2D(mach) * FD(mach) * (Af / Aref) * np.cos(gamac)) - / (2 + FD(mach) * np.sqrt(1 + (2 / FD(mach)) ** 2)) - ) - - # Lift coefficient derivative for a number of n fins corrected for Fin-Body interference - clalphaMultipleFins = ( - liftInterferenceFactor * finNumCorrection(n) * clalphaSingleFin - ) # Function of mach number - - # Calculates clalpha * alpha - cl = Function( - lambda alpha, mach: alpha * clalphaMultipleFins(mach), - ["Alpha (rad)", "Mach"], - "Cl", + # Create a fin set as an object of TrapezoidalFins class + finSet = TrapezoidalFins( + n, rootChord, tipChord, span, distanceToCM, cantAngle, radius, airfoil, name ) - # Parameters for Roll Moment. - # Documented at: https://github.com/Projeto-Jupiter/RocketPy/blob/master/docs/technical/aerodynamics/Roll_Equations.pdf - rollDampingInterferenceFactor = 1 + ( - ((tau - λ) / (tau)) - ((1 - λ) / (tau - 1)) * np.log(tau) - ) / ( - ((tau + 1) * (tau - λ)) / (2) - ((1 - λ) * (tau**3 - 1)) / (3 * (tau - 1)) - ) - rollForcingInterferenceFactor = (1 / np.pi**2) * ( - (np.pi**2 / 4) * ((tau + 1) ** 2 / tau**2) - + ((np.pi * (tau**2 + 1) ** 2) / (tau**2 * (tau - 1) ** 2)) - * np.arcsin((tau**2 - 1) / (tau**2 + 1)) - - (2 * np.pi * (tau + 1)) / (tau * (tau - 1)) - + ((tau**2 + 1) ** 2) - / (tau**2 * (tau - 1) ** 2) - * (np.arcsin((tau**2 - 1) / (tau**2 + 1))) ** 2 - - (4 * (tau + 1)) - / (tau * (tau - 1)) - * np.arcsin((tau**2 - 1) / (tau**2 + 1)) - + (8 / (tau - 1) ** 2) * np.log((tau**2 + 1) / (2 * tau)) - ) - clfDelta = ( - rollForcingInterferenceFactor * n * (Yma + radius) * clalphaSingleFin / d - ) # Function of mach number - cldOmega = ( - 2 - * rollDampingInterferenceFactor - * n - * clalphaSingleFin - * np.cos(cantAngleRad) - * rollGeometricalConstant - / (Aref * d**2) - ) # Function of mach number - rollParameters = [clfDelta, cldOmega, cantAngleRad] - - # Store values - fin = { - "cp": (0, 0, cpz), - "cl": cl, - "roll parameters": rollParameters, - "name": "Fins", - } - self.aerodynamicSurfaces.append(fin) + # Add fin set to the list of aerodynamic surfaces + self.aerodynamicSurfaces.append(finSet) # Refresh static margin calculation self.evaluateStaticMargin() @@ -733,6 +536,7 @@ def addEllipticalFins( cantAngle=0, radius=None, airfoil=None, + name="Fins", ): """Create an elliptical fin set, storing its parameters as part of the aerodynamicSurfaces list. Its parameters are the axial position @@ -786,175 +590,17 @@ def addEllipticalFins( self : Rocket Object of the Rocket class. """ - # Retrieves and convert basic geometrical parameters - Cr = rootChord - s = span - radius = self.radius if radius is None else radius - cantAngleRad = np.radians(cantAngle) - - # Compute auxiliary geometrical parameters - d = 2 * radius - Aref = np.pi * radius**2 # Reference area for coefficients - Af = (np.pi * Cr / 2 * s) / 2 # Fin area - AR = 2 * s**2 / Af # Fin aspect ratio - Yma = ( - s / (3 * np.pi) * np.sqrt(9 * np.pi**2 - 16) - ) # Span wise coord of mean aero chord - rollGeometricalConstant = ( - Cr - * s - * (3 * np.pi * s**2 + 32 * radius * s + 12 * np.pi * radius**2) - / 48 - ) - # Center of pressure position relative to CDM (center of dry mass) - cpz = distanceToCM + np.sign(distanceToCM) * (0.288 * Cr) - - # Fin–body interference correction parameters - tau = (s + radius) / radius - liftInterferenceFactor = 1 + 1 / tau - rollDampingInterferenceFactor = 1 + ( - (radius**2) - * ( - 2 - * (radius**2) - * np.sqrt(s**2 - radius**2) - * np.log((2 * s * np.sqrt(s**2 - radius**2) + 2 * s**2) / radius) - - 2 * (radius**2) * np.sqrt(s**2 - radius**2) * np.log(2 * s) - + 2 * s**3 - - np.pi * radius * s**2 - - 2 * (radius**2) * s - + np.pi * radius**3 - ) - ) / (2 * (s**2) * (s / 3 + np.pi * radius / 4) * (s**2 - radius**2)) - rollForcingInterferenceFactor = (1 / np.pi**2) * ( - (np.pi**2 / 4) * ((tau + 1) ** 2 / tau**2) - + ((np.pi * (tau**2 + 1) ** 2) / (tau**2 * (tau - 1) ** 2)) - * np.arcsin((tau**2 - 1) / (tau**2 + 1)) - - (2 * np.pi * (tau + 1)) / (tau * (tau - 1)) - + ((tau**2 + 1) ** 2) - / (tau**2 * (tau - 1) ** 2) - * (np.arcsin((tau**2 - 1) / (tau**2 + 1))) ** 2 - - (4 * (tau + 1)) - / (tau * (tau - 1)) - * np.arcsin((tau**2 - 1) / (tau**2 + 1)) - + (8 / (tau - 1) ** 2) * np.log((tau**2 + 1) / (2 * tau)) - ) + # Modify radius if not given, use rocket radius, otherwise use given. + radius = radius if radius is not None else self.radius - # Auxiliary functions - # Defines beta parameter - def beta(mach): - """Defines a parameter that is commonly used in aerodynamic - equations. It is commonly used in the Prandtl factor which - corrects subsonic force coefficients for compressible flow. - - Parameters - ---------- - mach : int, float - Number of mach. - - Returns - ------- - beta : int, float - Value that characterizes flow speed based on the mach number. - """ - - if mach < 0.8: - return np.sqrt(1 - mach**2) - elif mach < 1.1: - return np.sqrt(1 - 0.8**2) - else: - return np.sqrt(mach**2 - 1) - - # Defines number of fins correction - def finNumCorrection(n): - """Calculates a corrector factor for the lift coefficient of multiple fins. - The specifics values are documented at: - Niskanen, S. (2013). “OpenRocket technical documentation”. In: Development - of an Open Source model rocket simulation software. - - Parameters - ---------- - n : int - Number of fins. - - Returns - ------- - Corrector factor : int - Factor that accounts for the number of fins. - """ - correctorFactor = [2.37, 2.74, 2.99, 3.24] - if n >= 5 and n <= 8: - return correctorFactor[n - 5] - else: - return n / 2 - - if not airfoil: - # Defines clalpha2D as 2*pi for planar fins - clalpha2D = Function(lambda mach: 2 * np.pi / beta(mach)) - else: - # Defines clalpha2D as the derivative of the - # lift coefficient curve for a specific airfoil - airfoilCl = Function( - airfoil[0], - interpolation="linear", - ) - - # Differentiating at x = 0 to get cl_alpha - clalpha2D_Mach0 = airfoilCl.differentiate(x=1e-3, dx=1e-3) - - # Convert to radians if needed - if airfoil[1] == "degrees": - clalpha2D_Mach0 *= 180 / np.pi - - # Correcting for compressible flow - clalpha2D = Function(lambda mach: clalpha2D_Mach0 / beta(mach)) - # Diederich's Planform Correlation Parameter - FD = 2 * np.pi * AR / (clalpha2D) - - # Lift coefficient derivative for a single fin - clalphaSingleFin = Function( - lambda mach: (clalpha2D(mach) * FD(mach) * (Af / Aref)) - / (2 + FD(mach) * np.sqrt(1 + (2 / FD(mach)) ** 2)) + # Create a fin set as an object of EllipticalFins class + finSet = EllipticalFins( + n, rootChord, span, distanceToCM, cantAngle, radius, airfoil, name ) - # Lift coefficient derivative for a number of n fins corrected for Fin-Body interference - clalphaMultipleFins = ( - liftInterferenceFactor * finNumCorrection(n) * clalphaSingleFin - ) # Function of mach number - - # Calculates clalpha * alpha - cl = Function( - lambda alpha, mach: alpha * clalphaMultipleFins(mach), - ["Alpha (rad)", "Mach"], - "Cl", - ) - - # Parameters for Roll Moment. - # Documented at: https://github.com/RocketPy-Team/RocketPy/blob/develop/docs/technical/aerodynamics/Roll_Equations.pdf - clfDelta = ( - rollForcingInterferenceFactor * n * (Yma + radius) * clalphaSingleFin / d - ) # Function of mach number - cldOmega = ( - 2 - * rollDampingInterferenceFactor - * n - * clalphaSingleFin - * np.cos(cantAngleRad) - * rollGeometricalConstant - / (Aref * d**2) - ) - # Function of mach number - rollParameters = [clfDelta, cldOmega, cantAngleRad] - - # Store values - fin = { - "cp": (0, 0, cpz), - "cl": cl, - "roll parameters": rollParameters, - "name": "Fins", - } - self.aerodynamicSurfaces.append(fin) + # Add fin set to the list of aerodynamic surfaces + self.aerodynamicSurfaces.append(finSet) # Refresh static margin calculation self.evaluateStaticMargin() @@ -1053,7 +699,7 @@ def setRailButtons(self, distanceToCM, angularPosition=45): # Order distance to CM if distanceToCM[0] < distanceToCM[1]: distanceToCM.reverse() - # Save + # Save important attributes self.railButtons = self.railButtonPair(distanceToCM, angularPosition) self.RBdistanceToCM = distanceToCM self.angularPosition = angularPosition @@ -1230,9 +876,9 @@ def allInfo(self): # Print rocket aerodynamics quantities print("\nAerodynamics Lift Coefficient Derivatives") for aerodynamicSurface in self.aerodynamicSurfaces: - name = aerodynamicSurface["name"] + name = aerodynamicSurface.name clalpha = Function( - lambda alpha: aerodynamicSurface["cl"](alpha, 0), + lambda alpha: aerodynamicSurface.cl(alpha, 0), ).differentiate(x=1e-2, dx=1e-3) print( name + " Lift Coefficient Derivative: {:.3f}".format(clalpha) + "/rad" @@ -1240,8 +886,8 @@ def allInfo(self): print("\nAerodynamics Center of Pressure") for aerodynamicSurface in self.aerodynamicSurfaces: - name = aerodynamicSurface["name"] - cpz = aerodynamicSurface["cp"][2] + name = aerodynamicSurface.name + cpz = aerodynamicSurface.cp[2] print(name + " Center of Pressure to CM: {:.3f}".format(cpz) + " m") print( "Distance - Center of Pressure to CM: " From ce35ddcde55adb6cdcfcff3a5faebe85522e3881 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 24 Oct 2022 01:09:21 +0200 Subject: [PATCH 25/68] MAINT: adapting attributes names at AeroSurfaces --- rocketpy/AeroSurfaces.py | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/rocketpy/AeroSurfaces.py b/rocketpy/AeroSurfaces.py index df632c9b6..142cef8f0 100644 --- a/rocketpy/AeroSurfaces.py +++ b/rocketpy/AeroSurfaces.py @@ -153,18 +153,18 @@ def __init__( """ # Store values self.numberOfFins = n - self.finRadius = radius - self.finAirfoil = airfoil - self.finDistanceToCM = distanceToCM - self.finCantAngle = cantAngle - self.finRootChord = rootChord - self.finTipChord = tipChord - self.finSpan = span + self.radius = radius + self.airfoil = airfoil + self.distanceToCM = distanceToCM + self.cantAngle = cantAngle + self.rootChord = rootChord + self.tipChord = tipChord + self.span = span self.name = name # get some nicknames - Cr, Ct = self.finRootChord, self.finTipChord - s = self.finSpan + Cr, Ct = self.rootChord, self.tipChord + s = self.span cantAngleRad = np.radians(cantAngle) # Compute auxiliary geometrical parameters @@ -608,10 +608,10 @@ def __init__( """ # Store arguments as attributes - self.tailTopRadius = topRadius - self.tailBottomRadius = bottomRadius - self.tailLength = length - self.tailDistanceToCM = distanceToCM + self.topRadius = topRadius + self.bottomRadius = bottomRadius + self.length = length + self.distanceToCM = distanceToCM self.name = name self.radius = radius From 48fb7daab2bb5f74d7b2f511453ca5191a291b7e Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 24 Oct 2022 01:09:56 +0200 Subject: [PATCH 26/68] MAINT: temporary saving rail buttons parameters --- rocketpy/Rocket.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/rocketpy/Rocket.py b/rocketpy/Rocket.py index af926dd7a..32523add6 100644 --- a/rocketpy/Rocket.py +++ b/rocketpy/Rocket.py @@ -701,8 +701,10 @@ def setRailButtons(self, distanceToCM, angularPosition=45): distanceToCM.reverse() # Save important attributes self.railButtons = self.railButtonPair(distanceToCM, angularPosition) - self.RBdistanceToCM = distanceToCM - self.angularPosition = angularPosition + # Saving in a special format just for dispersion class + self.positionFirstRailButton = distanceToCM[0] + self.positionSecondRailButton = distanceToCM[1] + self.railButtonAngularPosition = angularPosition return None def addCMEccentricity(self, x, y): From 75f6ca7713310bfc592ec765f89a0c43aa475f07 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 24 Oct 2022 01:11:05 +0200 Subject: [PATCH 27/68] ENH: adding static margin properties at flight --- rocketpy/Flight.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/rocketpy/Flight.py b/rocketpy/Flight.py index 315bda53a..5fc56ae78 100644 --- a/rocketpy/Flight.py +++ b/rocketpy/Flight.py @@ -2751,6 +2751,19 @@ def attitudeFrequencyResponse(self): def staticMargin(self): return self.rocket.staticMargin + # Save important Static Margin values + @cached_property + def initialStaticMargin(self): + return self.staticMargin(0) + + @cached_property + def outOfRailStaticMargin(self): + return self.staticMargin(self.outOfRailTime) + + @cached_property + def finalStaticMargin(self): + return self.staticMargin(self.staticMargin(0)) + # Rail Button Forces @cached_property def railButton1NormalForce(self): From 695d63f2af58b2cf3064fc1332a22ec8b292f4a2 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 24 Oct 2022 01:12:17 +0200 Subject: [PATCH 28/68] MAINT: removing cLift unused definition --- rocketpy/Flight.py | 1 - 1 file changed, 1 deletion(-) diff --git a/rocketpy/Flight.py b/rocketpy/Flight.py index 5fc56ae78..06c3e10c7 100644 --- a/rocketpy/Flight.py +++ b/rocketpy/Flight.py @@ -1416,7 +1416,6 @@ def uDot(self, t, u, postProcessing=False): compStreamVzBn = compStreamVzB / compStreamSpeed if -1 * compStreamVzBn < 1: compAttackAngle = np.arccos(-compStreamVzBn) - cLift = aerodynamicSurface["cl"](compAttackAngle, freestreamMach) cLift = aerodynamicSurface.cl(compAttackAngle, freestreamMach) # Component lift force magnitude compLift = ( From 9b5f7857f488ee19d4adde199dfa657a71c22c7c Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 24 Oct 2022 01:12:53 +0200 Subject: [PATCH 29/68] MAINT: sort imports --- rocketpy/Dispersion.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index 0095b8079..d883e5025 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -7,6 +7,7 @@ import math import traceback +import types import warnings from time import process_time, time @@ -18,13 +19,13 @@ from matplotlib.patches import Ellipse from numpy.random import * -from rocketpy.Function import Function - from .Environment import Environment from .Flight import Flight +from .Function import Function from .Motor import SolidMotor from .Rocket import Rocket from .supplement import invertedHaversine +from .AeroSurfaces import NoseCone, TrapezoidalFins, EllipticalFins, Tail ## Tasks from the first review: # TODO: Save instances of the class instead of just plotting From a2740c026762a8f5a08e09fef765e69285428b37 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 24 Oct 2022 01:13:41 +0200 Subject: [PATCH 30/68] ENH: improving self.__inputs dictionaries --- rocketpy/Dispersion.py | 41 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 41 insertions(+) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index d883e5025..0b3401302 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -134,6 +134,47 @@ def __init__( "powerOnDrag": "required", } + self.nose_inputs = { + "nose_name_length": "required", + "nose_name_kind": "Von Karman", + "nose_name_distanceToCM": "required", + "nose_name_name": "Nose Cone", + } + + self.fins_inputs = { + "finSet_name_numberOfFins": "required", + "finSet_name_rootChord": "required", + "finSet_name_tipChord": "required", + "finSet_name_span": "required", + "finSet_name_distanceToCM": "required", + "finSet_name_cantAngle": 0, + "finSet_name_radius": None, + "finSet_name_airfoil": None, + } + + self.tail_inputs = { + "tail_name_topRadius": "required", + "tail_name_bottomRadius": "required", + "tail_name_length": "required", + "tail_name_distanceToCM": "required", + } + + self.rail_buttons_inputs = { + "positionFirstRailButton": "required", + "positionSecondRailButton": "required", + "railButtonAngularPosition": 45, + } + + self.parachute_inputs = { + "parachute_name_CdS": "required", + "parachute_name_trigger": "required", + "parachute_name_samplingRate": 100, + "parachute_name_lag": 0, + "parachute_name_noise": (0, 0, 0), + # "parachute_name_noiseStd": 0, + # "parachute_name_noiseCorr": 0, + } + self.flight_inputs = { "inclination": 80, "heading": 90, From ad8cc9fcde028a092b38b150ca385e705a14e561 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 24 Oct 2022 01:15:04 +0200 Subject: [PATCH 31/68] ENH: reorganize __process_dispersion_dict --- rocketpy/Dispersion.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index 0b3401302..129f87e15 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -300,6 +300,11 @@ def __process_dispersion_dict(self, dictionary): The modified dictionary with the processed parameters. """ # First we need to check if the dictionary is empty + if not dictionary: + warnings.warn( + "The dispersion dictionary is empty, no dispersion will be performed" + ) + return dictionary # Now we prepare all the parachute data dictionary = self.__process_parachute_from_dict(dictionary) @@ -314,6 +319,12 @@ def __process_dispersion_dict(self, dictionary): # Rocket dictionary = self.__process_rocket_from_dict(dictionary) + # Rail button + dictionary = self.__process_rail_buttons_from_dict(dictionary) + + # Aerodynamic Surfaces + dictionary = self.__process_aerodynamic_surfaces_from_dict(dictionary) + # Flight dictionary = self.__process_flight_from_dict(dictionary) From f55a0fa28106832891562a32422c789ed490a513 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 24 Oct 2022 01:15:39 +0200 Subject: [PATCH 32/68] ENH: Adding process rail buttons --- rocketpy/Dispersion.py | 46 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 46 insertions(+) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index 129f87e15..57695be32 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -410,6 +410,52 @@ def __process_rocket_from_dict(self, dictionary): return dictionary + def __process_rail_buttons_from_dict(self, dictionary): + """Check if all the relevant inputs for the RailButtons class are present + in the dispersion dictionary, input the missing ones and return the + modified dictionary. + + Parameters + ---------- + dictionary : dict + Dictionary containing the parameters to be varied in the dispersion + simulation. The keys of the dictionary are the names of the parameters + to be varied, and the values can be either tuple or list. If the value + is a single value, the corresponding class of the parameter need to + be passed on the run_dispersion method. + + Returns + ------- + dictionary: dict + Modified dictionary with the processed rail buttons parameters. + """ + + if not all( + rail_buttons_input in dictionary + for rail_buttons_input in self.rail_buttons_inputs.keys() + ): + # Iterate through missing inputs + for missing_input in ( + set(self.rail_buttons_inputs.keys()) - dictionary.keys() + ): + missing_input = str(missing_input) + # Add to the dict + try: + dictionary[missing_input] = [ + getattr(self.rocket, missing_input) + ] + except: + # class was not inputted + # checks if missing parameter is required + if self.rail_buttons_inputs[missing_input] == "required": + warnings.warn(f'Missing "{missing_input}" in dictionary') + else: + # if not, uses default value + dictionary[missing_input] = [ + self.rail_buttons_inputs[missing_input] + ] + + return dictionary def __process_motor_from_dict(self, dictionary): """Check if all the relevant inputs for the Motor class are present in the dispersion dictionary, input the missing ones and return the modified From c90898fb16798f69387c15f55eeffeb1f5818835 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 24 Oct 2022 01:16:26 +0200 Subject: [PATCH 33/68] ENH: deal with n number of nose cones --- rocketpy/Dispersion.py | 66 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 66 insertions(+) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index 57695be32..de89e5c40 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -456,6 +456,72 @@ def __process_rail_buttons_from_dict(self, dictionary): ] return dictionary + + def __process_aerodynamic_surfaces_from_dict(self, dictionary): + """Still not implemented. + Must check if all the relevant inputs for the AerodynamicSurfaces class + are present in the dispersion dictionary, input the missing ones and + return the modified dictionary. + Something similar to the __process_parachute_from_dict method can be + used here, since aerodynamic surfaces are optional for the simulation. + + Parameters + ---------- + dictionary : _type_ + _description_ + """ + + # Check the number of fin sets, noses, and tails + self.nose_names = [] + self.finSet_names = [] + self.tail_names = [] + # Get names from the input dictionary + for var in dictionary.keys(): + if "nose" in var: + self.nose_names.append(var).split("_")[1] + elif "finSet" in var: + self.finSet_names.append(var).split("_")[1] + elif "tail" in var: + self.tail_names.append(var).split("_")[1] + # Get names from the rocket object + for surface in self.rocket.aerodynamicSurfaces: + if isinstance(surface, NoseCone): + self.nose_names.append(surface.name) + elif isinstance(surface, (TrapezoidalFins, EllipticalFins)): + self.finSet_names.append(surface.name) + elif isinstance(surface, Tail): + self.tail_names.append(surface.name) + # Remove duplicates + self.nose_names = list(set(self.nose_names)) + self.finSet_names = list(set(self.finSet_names)) + self.tail_names = list(set(self.tail_names)) + + # Check if there are enough arguments for each kind of aero surface + + # Iterate through nose names + for name in self.nose_names: + # Iterate through aerodynamic surface available at rocket object + for surface in self.rocket.aerodynamicSurfaces: + if surface.name == name and isinstance(surface, NoseCone): + # in case we find the corresponding nose, check if all the + # inputs are present in the dictionary + for input in self.nose_inputs.keys(): + _, _, parameter = input.split("_") + if f"nose_{name}_{parameter}" not in dictionary: + # Try to get the value from the rocket object + try: + dictionary[f"nose_{name}_{parameter}"] = [ + getattr(surface, parameter) + ] + except: + # If not possible, check if the parameter is required + if self.nose_inputs[input] == "required": + warnings.warn(f'Missing "{input}" in dictionary') + else: + # If not required, use default value + dictionary[f"nose_{name}_{parameter}"] = [ + self.nose_inputs[input] + ] def __process_motor_from_dict(self, dictionary): """Check if all the relevant inputs for the Motor class are present in the dispersion dictionary, input the missing ones and return the modified From 910e0a7a9a88ecc386846df6ec5b2a796f295054 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 24 Oct 2022 01:16:58 +0200 Subject: [PATCH 34/68] ENH: deal with n number of finsets --- rocketpy/Dispersion.py | 28 ++++++++++++++++++++++++++++ 1 file changed, 28 insertions(+) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index de89e5c40..02e320869 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -522,6 +522,34 @@ def __process_aerodynamic_surfaces_from_dict(self, dictionary): dictionary[f"nose_{name}_{parameter}"] = [ self.nose_inputs[input] ] + + # Iterate through fin sets names + for name in self.finSet_names: + # Iterate through aerodynamic surface available at rocket object + for surface in self.rocket.aerodynamicSurfaces: + if surface.name == name and isinstance( + surface, (TrapezoidalFins, EllipticalFins) + ): + # in case we find the corresponding fin set, check if all the + # inputs are present in the dictionary + for input in self.fins_inputs.keys(): + _, _, parameter = input.split("_") + if f"finSet_{name}_{parameter}" not in dictionary: + # Try to get the value from the rocket object + try: + dictionary[f"finSet_{name}_{parameter}"] = [ + getattr(surface, parameter) + ] + except: + # If not possible, check if the parameter is required + if self.fins_inputs[input] == "required": + warnings.warn(f'Missing "{input}" in dictionary') + else: + # If not required, use default value + dictionary[f"finSet_{name}_{parameter}"] = [ + self.fins_inputs[input] + ] + def __process_motor_from_dict(self, dictionary): """Check if all the relevant inputs for the Motor class are present in the dispersion dictionary, input the missing ones and return the modified From 2c85773ffd3d71fb50ccd759bb0508b57093d88a Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 24 Oct 2022 01:17:23 +0200 Subject: [PATCH 35/68] ENH: deal with n number of tails --- rocketpy/Dispersion.py | 27 +++++++++++++++++++++++++++ 1 file changed, 27 insertions(+) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index 02e320869..f6bcae95f 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -550,6 +550,33 @@ def __process_aerodynamic_surfaces_from_dict(self, dictionary): self.fins_inputs[input] ] + # Iterate through tail names + for name in self.tail_names: + # Iterate through aerodynamic surface available at rocket object + for surface in self.rocket.aerodynamicSurfaces: + if surface.name == name and isinstance(surface, Tail): + # in case we find the corresponding tail, check if all the + # inputs are present in the dictionary + for input in self.tail_inputs.keys(): + _, _, parameter = input.split("_") + if f"tail_{name}_{parameter}" not in dictionary: + # Try to get the value from the rocket object + try: + dictionary[f"tail_{name}_{parameter}"] = [ + getattr(surface, parameter) + ] + except: + # If not possible, check if the parameter is required + if self.tail_inputs[input] == "required": + warnings.warn(f'Missing "{input}" in dictionary') + else: + # If not required, use default value + dictionary[f"tail_{name}_{parameter}"] = [ + self.tail_inputs[input] + ] + + return dictionary + def __process_motor_from_dict(self, dictionary): """Check if all the relevant inputs for the Motor class are present in the dispersion dictionary, input the missing ones and return the modified From 427aa291585c54f0c74141615f8956b5d08f733f Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 24 Oct 2022 01:19:18 +0200 Subject: [PATCH 36/68] MAINT: updating comments and docs --- rocketpy/Dispersion.py | 57 +++++++++++------------------------------- 1 file changed, 15 insertions(+), 42 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index f6bcae95f..a2a2453dc 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -27,51 +27,20 @@ from .supplement import invertedHaversine from .AeroSurfaces import NoseCone, TrapezoidalFins, EllipticalFins, Tail -## Tasks from the first review: -# TODO: Save instances of the class instead of just plotting -# TODO: Document all methods -# TODO: Create a way to choose what attributes are being saved +## Tasks : # TODO: Allow each parameter to be varied following an specific probability distribution -# TODO: Make it more flexible so we can work with more than 1 fin set, also with different aerodynamic surfaces as well. # TODO: Test simulations under different scenarios (with both parachutes, with only main chute, etc) # TODO: Add unit tests # TODO: Adjust the notebook to the new version of the code -# TODO: Optional return of matplotlib plots or abstract function to histogram plot based on stdev and mean - -# TODO: Implement MRS +# TODO: Implement MRS method # TODO: Implement functions from compareDispersions notebook -# TODO: Convert the dictionary to a class attributes - class Dispersion: """Monte Carlo analysis to predict probability distributions of the rocket's landing point, apogee and other relevant information. - Attributes - ---------- - # TODO: Update at the end! - Parameters: - Dispersion.filename: string - When running a new simulation, this attribute represents the initial - part of the export filenames (e.g. 'filename.disp_outputs.txt'). - When analyzing the results of a previous simulation, this attribute - shall be the filename containing the outputs of a dispersion calculation. - Dispersion.actual_landing_point: tuple - Rocket's experimental landing point relative to launch point. - Dispersion.N: integer - Number of simulations in an output file. - Other classes: - Dispersion.environment: Environment - Launch environment. - Attribute needed to run a new simulation, when Dispersion.flight remains unchanged. - Dispersion.motor: Motor - Rocket's motor. - Attribute needed to run a new simulation, when Dispersion.flight remains unchanged. - Dispersion.rocket: Rocket - Rocket with nominal values. - Attribute needed to run a new simulation, when Dispersion.flight remains unchanged. """ def __init__( @@ -195,17 +164,18 @@ def __init__( self.std_out_of_rail_time = 0 def __set_distribution_function(self, distribution_type): - """_summary_ + """Sets the distribution function to be used in the analysis. Parameters ---------- - distribution_type : _type_ - _description_ + distribution_type : string + The type of distribution to be used in the analysis. It can be + 'uniform', 'normal', 'lognormal', etc. Returns ------- - _type_ - _description_ + np.random distribution function + The distribution function to be used in the analysis. """ if distribution_type == "normal" or distribution_type == None: return normal @@ -363,11 +333,11 @@ def __process_flight_from_dict(self, dictionary): # First try to catch value from the Flight object if passed dictionary[missing_input] = [getattr(self.flight, missing_input)] except: - # class was not inputted + # Flight class was not inputted # check if missing parameter is required if self.flight_inputs[missing_input] == "required": warnings.warn(f'Missing "{missing_input}" in dictionary') - else: # if not, uses default value + else: # if not required, uses default value dictionary[missing_input] = [self.flight_inputs[missing_input]] return dictionary @@ -596,7 +566,7 @@ def __process_motor_from_dict(self, dictionary): dictionary: dict Modified dictionary with the processed rocket parameters. """ - # TODO: Add mor options of motor (i.e. Liquid and Hybrids) + # TODO: Add more options of motor (i.e. Liquid and Hybrids) if not all( motor_input in dictionary for motor_input in self.solid_motor_inputs.keys() @@ -639,6 +609,7 @@ def __process_environment_from_dict(self, dictionary): dictionary: dict Modified dictionary with the processed environment parameters. """ + # Check if there is any missing input for the environment if not all( environment_input in dictionary for environment_input in self.environment_inputs.keys() @@ -650,6 +621,7 @@ def __process_environment_from_dict(self, dictionary): missing_input = str(missing_input) # Add to the dict try: + # First try to catch value from the Environment object if passed dictionary[missing_input] = [ getattr(self.environment, missing_input) ] @@ -826,7 +798,8 @@ def __check_inputted_values_from_dict(self, dictionary): ) print(traceback.format_exc()) - # The analysis parameter dictionary must be corrected now! + # The analysis parameter dictionary is ready! Now we have mean and stdev + # for all parameters return dictionary From fc8cc2c2d29edfd175648a9e2ce8cd4e3bb7b488 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 24 Oct 2022 01:19:44 +0200 Subject: [PATCH 37/68] MAINT: New check initial objects --- rocketpy/Dispersion.py | 50 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 50 insertions(+) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index a2a2453dc..9554e8423 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -803,6 +803,56 @@ def __check_inputted_values_from_dict(self, dictionary): return dictionary + def __check_initial_objects(self): + """Create rocketpy objects (Environment, Motor, Rocket, Flight) in case + that they were not created yet. + + Returns + ------- + None + """ + if self.environment is None: + self.environment = Environment( + railLength=self.dispersion_dictionary["railLength"][0] + ) + if self.motor is None: + self.motor = SolidMotor( + thrustSource=self.dispersion_dictionary["thrustSource"][0], + burnOut=self.dispersion_dictionary["burnOutTime"][0], + grainNumber=self.dispersion_dictionary["grainNumber"][0], + grainDensity=self.dispersion_dictionary["grainDensity"][0], + grainOuterRadius=self.dispersion_dictionary["grainOuterRadius"][0], + grainInitialInnerRadius=self.dispersion_dictionary[ + "grainInitialInnerRadius" + ][0], + grainInitialHeight=self.dispersion_dictionary["grainInitialHeight"][0], + ) + if self.rocket is None: + self.rocket = Rocket( + motor=self.motor, + mass=self.dispersion_dictionary["mass"][0], + radius=self.dispersion_dictionary["radius"][0], + inertiaI=self.dispersion_dictionary["inertiaI"][0], + inertiaZ=self.dispersion_dictionary["inertiaZ"][0], + distanceRocketPropellant=self.dispersion_dictionary[ + "distanceRocketPropellant" + ][0], + distanceRocketNozzle=self.dispersion_dictionary["distanceRocketNozzle"][ + 0 + ], + powerOffDrag=self.dispersion_dictionary["powerOffDrag"][0], + powerOnDrag=self.dispersion_dictionary["dispersion_dictionary"][0], + ) + self.rocket.setRailButtons(distanceToCM=[0.2, -0.5]) + if self.flight is None: + self.flight = Flight( + rocket=self.rocket, + environment=self.environment, + inclination=self.dispersion_dictionary["inclination"][0], + heading=self.dispersion_dictionary["heading"][0], + ) + return None + def __yield_flight_setting( self, distribution_func, analysis_parameters, number_of_simulations ): From d2ce453aa0f00ecd1e68c5df6ef066fca217258c Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 24 Oct 2022 01:20:40 +0200 Subject: [PATCH 38/68] ENH: abstract adding aero surfaces and parachutes --- rocketpy/Dispersion.py | 63 +++++++++++++++++++++++++----------------- 1 file changed, 38 insertions(+), 25 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index 9554e8423..7189b3735 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -1147,44 +1147,57 @@ def run_dispersion( powerOnDrag=setting["powerOnDrag"], ) + # Clean up aerodynamic surfaces + rocket_dispersion.aerodynamicSurfaces = [] # Remove all surfaces + # Add rocket nose, fins and tail - rocket_dispersion.addNose( - length=setting["noseLength"], - kind=setting["noseKind"], - distanceToCM=setting["noseDistanceToCM"], - ) - rocket_dispersion.addFins( - n=setting["numberOfFins"], - rootChord=setting["finRootChord"], - tipChord=setting["finTipChord"], - span=setting["finSpan"], - distanceToCM=setting["finDistanceToCM"], - radius=setting["radius"], - airfoil=setting["airfoil"], - ) - if not "noTail" in setting: + # Nose + for nose in self.nose_names: + rocket_dispersion.addNose( + length=setting[f"nose_{nose}_length"], + kind=setting[f"nose_{nose}_kind"], + distanceToCM=setting[f"nose_{nose}_distanceToCM"], + name=nose, + ) + + # Fins + for finSet in self.finSet_names: + # TODO: Allow elliptical fins as well + rocket_dispersion.addTrapezoidalFins( + n=setting[f"finSet_{finSet}_numberOfFins"], + rootChord=setting[f"finSet_{finSet}_rootChord"], + tipChord=setting[f"finSet_{finSet}_tipChord"], + span=setting[f"finSet_{finSet}_span"], + distanceToCM=setting[f"finSet_{finSet}_distanceToCM"], + radius=setting[f"finSet_{finSet}_radius"], + airfoil=setting[f"finSet_{finSet}_airfoil"], + name=finSet, + ) + + # Tail + for tail in self.tail_names: rocket_dispersion.addTail( - topRadius=setting["topRadius"], - bottomRadius=setting["bottomRadius"], - length=setting["length"], - distanceToCM=setting["distanceToCM"], + topRadius=setting[f"tail_{tail}_topRadius"], + bottomRadius=setting[f"tail_{tail}_bottomRadius"], + length=setting[f"tail_{tail}_length"], + distanceToCM=setting[f"tail_{tail}_distanceToCM"], + radius=None, + name="Tail", ) # Add parachutes - for num, name in enumerate(self.parachute_names): + rocket_dispersion.parachutes = [] # Remove existing parachutes + for name in self.parachute_names: rocket_dispersion.addParachute( name=name, CdS=setting["parachute_" + name + "_CdS"], trigger=setting["parachute_" + name + "_trigger"], samplingRate=setting["parachute_" + name + "_samplingRate"], lag=setting["parachute_" + name + "_lag"], - noise=( - setting["parachute_" + name + "_noise_mean"], - setting["parachute_" + name + "_noise_std"], - setting["parachute_" + name + "_noise_corr"], - ), + noise=setting["parachute_" + name + "_noise"], ) + # TODO: Remove hard-coded rail buttons definition rocket_dispersion.setRailButtons( distanceToCM=[ setting["positionFirstRailButton"], From 3cdfabf80f2fe3ba8bf2f405084d1c094c08214a Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 24 Oct 2022 01:21:35 +0200 Subject: [PATCH 39/68] MAINT: New import_results method, more flexible --- rocketpy/Dispersion.py | 114 ++++++++--------------------------------- 1 file changed, 22 insertions(+), 92 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index 7189b3735..7fb38add7 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -1257,106 +1257,23 @@ def run_dispersion( return None - def __check_initial_objects(self): - """Create rocketpy objects (Environment, Motor, Rocket, Flight) in case - that - - Returns - ------- - _type_ - _description_ - """ - if self.environment is None: - self.environment = Environment( - railLength=self.dispersion_dictionary["railLength"][0] - ) - if self.motor is None: - self.motor = SolidMotor( - thrustSource=self.dispersion_dictionary["thrustSource"][0], - burnOut=self.dispersion_dictionary["burnOutTime"][0], - grainNumber=self.dispersion_dictionary["grainNumber"][0], - grainDensity=self.dispersion_dictionary["grainDensity"][0], - grainOuterRadius=self.dispersion_dictionary["grainOuterRadius"][0], - grainInitialInnerRadius=self.dispersion_dictionary[ - "grainInitialInnerRadius" - ][0], - grainInitialHeight=self.dispersion_dictionary["grainInitialHeight"][0], - ) - if self.rocket is None: - self.rocket = Rocket( - motor=self.motor, - mass=self.dispersion_dictionary["mass"][0], - radius=self.dispersion_dictionary["radius"][0], - inertiaI=self.dispersion_dictionary["inertiaI"][ - 0 - ], # TODO: remove hardcode - inertiaZ=self.dispersion_dictionary["inertiaZ"][ - 0 - ], # TODO: remove hardcode - distanceRocketPropellant=self.dispersion_dictionary[ - "distanceRocketPropellant" - ][0], - distanceRocketNozzle=self.dispersion_dictionary["distanceRocketNozzle"][ - 0 - ], - powerOffDrag=0.6, # TODO: Remove this hardcoded - powerOnDrag=0.6, # TODO: Remove this hardcoded - ) - self.rocket.setRailButtons(distanceToCM=[0.2, -0.5]) - if self.flight is None: - self.flight = Flight( - rocket=self.rocket, - environment=self.environment, - inclination=self.dispersion_dictionary["inclination"][0], - heading=self.dispersion_dictionary["heading"][0], - ) - return None - - def import_results(self, dispersion_output_file): - """Import dispersion results from .txt file + def import_results(self): + """Import dispersion results from .txt file and save it into a dictionary. Parameters ---------- - dispersion_output_file : str - Path to the dispersion output file. This file will not be overwritten, - modified or deleted by this function. + None Returns ------- None """ # Initialize variable to store all results - dispersion_general_results = [] - - # TODO: Add more flexible way to define dispersion_results - dispersion_results = { - "outOfRailTime": [], - "outOfRailVelocity": [], - "apogeeTime": [], - "apogeeAltitude": [], - "apogeeX": [], - "apogeeY": [], - "impactTime": [], - "impactX": [], - "impactY": [], - "impactVelocity": [], - "initialStaticMargin": [], - "outOfRailStaticMargin": [], - "finalStaticMargin": [], - "numberOfEvents": [], - "maxVelocity": [], - "drogueTriggerTime": [], - "drogueInflatedTime": [], - "drogueInflatedVelocity": [], - "executionTime": [], - "railDepartureAngleOfAttack": [], - "lateralWind": [], - "frontalWind": [], - } + dispersion_results = {} # Get all dispersion results # Open the file - file = open(dispersion_output_file, "r+") + file = open(self.filename.split(".")[0] + ".disp_outputs.txt", "r+") # Read each line of the file and convert to dict for line in file: @@ -1365,19 +1282,32 @@ def import_results(self, dispersion_output_file): continue # Evaluate results and store them flight_result = eval(line) - dispersion_general_results.append(flight_result) + # Append to the list for parameter_key, parameter_value in flight_result.items(): - dispersion_results[parameter_key].append(parameter_value) + if parameter_key not in dispersion_results.keys(): + # Create a new list to store the parameter + dispersion_results[parameter_key] = [parameter_value] + else: + # Append the parameter value to the list + dispersion_results[parameter_key].append(parameter_value) # Close data file file.close() # Calculate the number of flights simulated - self.num_of_loaded_sims = len(dispersion_general_results) + len_dict = {key: len(value) for key, value in dispersion_results.items()} + if min(len_dict.values()) - max(len_dict.values()) > 1: + print( + "Warning: The number of simulations imported from the file is not " + "the same for all parameters. The number of simulations will be " + "set to the minimum number of simulations found." + ) + self.num_of_loaded_sims = min(len_dict.values()) # Print the number of flights simulated print( - f"A total of {self.num_of_loaded_sims} simulations were loaded from the following file: {dispersion_output_file}" + f"A total of {self.num_of_loaded_sims} simulations were loaded from" + f" the following file: {self.filename.split('.')[0] + '.disp_outputs.txt'}" ) # Save the results as an attribute of the class From 2680c8e74cd635d156591362b198e4f4bf3c13b8 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 24 Oct 2022 01:23:06 +0200 Subject: [PATCH 40/68] ENH: process, print, and plot abstract methods --- rocketpy/Dispersion.py | 845 +++-------------------------------------- 1 file changed, 61 insertions(+), 784 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index 7fb38add7..43b1d3298 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -1317,828 +1317,105 @@ def import_results(self): # Start the processing analysis - def outOfRailTime(self): - """Calculate the time of the rocket's departure from the rail, in seconds. - - Returns - ------- - _type_ - _description_ - """ - self.mean_out_of_rail_time = ( - np.mean(self.dispersion_results["outOfRailTime"]) - if self.dispersion_results["outOfRailTime"] - else None - ) - self.std_out_of_rail_time = ( - np.std(self.dispersion_results["outOfRailTime"]) - if self.dispersion_results["outOfRailTime"] - else None - ) - return None - - def printMeanOutOfRailTime(self): - """Prints out the mean and std. dev. of the "outOfRailTime" parameter. + def process_results(self, variables=None): + """Save the mean and standard deviation of each parameter in the results + dictionary. Create class attributes for each parameter. Parameters ---------- - None + variables : list, optional + List of variables to be processed. If None, all variables will be + processed. The default is None. Example: ['outOfRailTime', 'apogeeTime'] Returns ------- None """ - self.outOfRailTime() - print(f"Out of Rail Time -Mean Value: {self.mean_out_of_rail_time:0.3f} s") - print(f"Out of Rail Time - Std. Dev.: {self.std_out_of_rail_time:0.3f} s") - - return None - - def plotOutOfRailTime(self): - """Plot the out of rail time distribution - - Parameters - ---------- - dispersion_results : _type_ - _description_ - - Returns - ------- - _type_ - _description_ - """ - self.outOfRailTime() - - plt.figure() - plt.hist( - self.dispersion_results["outOfRailTime"], - bins=int(self.num_of_loaded_sims**0.5), - ) - plt.title("Out of Rail Time") - plt.xlabel("Time (s)") - plt.ylabel("Number of Occurrences") - plt.show() - - return None - - def meanOutOfRailVelocity(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - - Returns - ------- - _type_ - _description_ - """ - print( - f'Out of Rail Velocity -Mean Value: {np.mean(dispersion_results["outOfRailVelocity"]):0.3f} m/s' - ) - print( - f'Out of Rail Velocity - Std. Dev.: {np.std(dispersion_results["outOfRailVelocity"]):0.3f} m/s' - ) - - return None - - def plotOutOfRailVelocity(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - - Returns - ------- - _type_ - _description_ - """ - self.meanOutOfRailVelocity(dispersion_results) - - plt.figure() - plt.hist( - dispersion_results["outOfRailVelocity"], - bins=int(self.num_of_loaded_sims**0.5), - ) - plt.title("Out of Rail Velocity") - plt.xlabel("Velocity (m/s)") - plt.ylabel("Number of Occurrences") - plt.show() - - return None - - def meanApogeeTime(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - - Returns - ------- - _type_ - _description_ - """ - print( - f'Impact Time -Mean Value: {np.mean(dispersion_results["impactTime"]):0.3f} s' - ) - print( - f'Impact Time - Std. Dev.: {np.std(dispersion_results["impactTime"]):0.3f} s' - ) - - return None - - def plotApogeeTime(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - - Returns - ------- - _type_ - _description_ - """ - self.meanApogeeTime(dispersion_results) - - plt.figure() - plt.hist( - dispersion_results["impactTime"], - bins=int(self.num_of_loaded_sims**0.5), - ) - plt.title("Impact Time") - plt.xlabel("Time (s)") - plt.ylabel("Number of Occurrences") - plt.show() - - return None - - def meanApogeeAltitude(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - - Returns - ------- - _type_ - _description_ - """ - print( - f'Apogee Altitude -Mean Value: {np.mean(dispersion_results["apogeeAltitude"]):0.3f} m' - ) - print( - f'Apogee Altitude - Std. Dev.: {np.std(dispersion_results["apogeeAltitude"]):0.3f} m' - ) - - return None - - def plotApogeeAltitude(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - - Returns - ------- - _type_ - _description_ - """ - self.meanApogeeAltitude(dispersion_results) - - plt.figure() - plt.hist( - dispersion_results["apogeeAltitude"], - bins=int(self.num_of_loaded_sims**0.5), - ) - plt.title("Apogee Altitude") - plt.xlabel("Altitude (m)") - plt.ylabel("Number of Occurrences") - plt.show() - + if isinstance(variables, list): + for result in variables: + mean = np.mean(self.dispersion_results[result]) + stdev = np.std(self.dispersion_results[result]) + setattr(self, str(result), (mean, stdev)) + else: + for result in self.dispersion_results.keys(): + mean = np.mean(self.dispersion_results[result]) + stdev = np.std(self.dispersion_results[result]) + setattr(self, str(result), (mean, stdev)) return None - def meanApogeeXPosition(self, dispersion_results): - """_summary_ + # TODO: print as a table instead of prints + def print_results(self, variables=None): + """Print the mean and standard deviation of each parameter in the results + dictionary or of the variables passed as argument. Parameters ---------- - dispersion_results : _type_ - _description_ + variables : list, optional + List of variables to be processed. If None, all variables will be + processed. The default is None. Example: ['outOfRailTime', 'apogee'] Returns ------- - _type_ - _description_ - """ - print( - f'Apogee X Position -Mean Value: {np.mean(dispersion_results["apogeeX"]):0.3f} m' - ) - print( - f'Apogee X Position - Std. Dev.: {np.std(dispersion_results["apogeeX"]):0.3f} m' - ) - - return None - - def plotApogeeXPosition(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ + None - Returns - ------- - _type_ - _description_ + Raises + ------ + TypeError + If the variable passed as argument is not a string. """ - self.meanApogeeAltitude(dispersion_results) - - plt.figure() - plt.hist( - dispersion_results["apogeeX"], - bins=int(self.num_of_loaded_sims**0.5), - ) - plt.title("Apogee X Position") - plt.xlabel("Apogee X Position (m)") - plt.ylabel("Number of Occurrences") - plt.show() - - return None + # Check if the variables argument is a list, if not, use all variables + if not isinstance(variables, list): + variables = self.dispersion_results.keys() - def meanApogeeYPosition(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ + # Check if the variables are strings + if not all(isinstance(var, str) for var in variables): + raise TypeError("The list of variables must be a list of strings.") - Returns - ------- - _type_ - _description_ - """ - print( - f'Apogee Y Position -Mean Value: {np.mean(dispersion_results["apogeeY"]):0.3f} m' - ) - print( - f'Apogee Y Position - Std. Dev.: {np.std(dispersion_results["apogeeY"]):0.3f} m' - ) + for var in variables: + tp = getattr(self, var) # Get the tuple with the mean and stdev + print("{}: \u03BC = {:.3f}, \u03C3 = {:.3f}".format(var, tp[0], tp[1])) return None - def plotApogeeYPosition(self, dispersion_results): + def plot_results(self, variables=None): """_summary_ Parameters ---------- - dispersion_results : _type_ - _description_ + variables : _type_, optional + _description_, by default None Returns ------- _type_ _description_ - """ - self.meanApogeeAltitude(dispersion_results) - - plt.figure() - plt.hist( - dispersion_results["apogeeY"], - bins=int(self.num_of_loaded_sims**0.5), - ) - plt.title("Apogee Y Position") - plt.xlabel("Apogee Y Position (m)") - plt.ylabel("Number of Occurrences") - plt.show() - - return None - - def meanImpactTime(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - Returns - ------- - _type_ + Raises + ------ + TypeError _description_ """ - print( - f'Impact Time -Mean Value: {np.mean(dispersion_results["impactTime"]):0.3f} s' - ) - print( - f'Impact Time - Std. Dev.: {np.std(dispersion_results["impactTime"]):0.3f} s' - ) + # Check if the variables argument is a list, if not, use all variables + if not isinstance(variables, list): + variables = self.dispersion_results.keys() + + # Check if the variables are strings + if not all(isinstance(var, str) for var in variables): + raise TypeError("The list of variables must be a list of strings.") + + for var in variables: + plt.figure() + plt.hist( + self.dispersion_results[var], + ) + plt.title("Histogram of " + var) + # plt.xlabel("Time (s)") + plt.ylabel("Number of Occurrences") + plt.show() return None - def plotImpactTime(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - - Returns - ------- - _type_ - _description_ - """ - self.meanImpactTime(dispersion_results) - - plt.figure() - plt.hist( - dispersion_results["impactTime"], - bins=int(self.num_of_loaded_sims**0.5), - ) - plt.title("Impact Time") - plt.xlabel("Time (s)") - plt.ylabel("Number of Occurrences") - plt.show() - - return None - - def meanImpactXPosition(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - - Returns - ------- - _type_ - _description_ - """ - print( - f'Impact X Position -Mean Value: {np.mean(dispersion_results["impactX"]):0.3f} m' - ) - print( - f'Impact X Position - Std. Dev.: {np.std(dispersion_results["impactX"]):0.3f} m' - ) - - return None - - def plotImpactXPosition(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - - Returns - ------- - _type_ - _description_ - """ - self.meanImpactXPosition(dispersion_results) - - plt.figure() - plt.hist( - dispersion_results["impactX"], - bins=int(self.num_of_loaded_sims**0.5), - ) - plt.title("Impact X Position") - plt.xlabel("Impact X Position (m)") - plt.ylabel("Number of Occurrences") - plt.show() - - return None - - def meanImpactYPosition(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - - Returns - ------- - _type_ - _description_ - """ - print( - f'Impact Y Position -Mean Value: {np.mean(dispersion_results["impactY"]):0.3f} m' - ) - print( - f'Impact Y Position - Std. Dev.: {np.std(dispersion_results["impactY"]):0.3f} m' - ) - - return None - - def plotImpactYPosition(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - - Returns - ------- - _type_ - _description_ - """ - self.meanImpactYPosition(dispersion_results) - - plt.figure() - plt.hist( - dispersion_results["impactY"], - bins=int(self.num_of_loaded_sims**0.5), - ) - plt.title("Impact Y Position") - plt.xlabel("Impact Y Position (m)") - plt.ylabel("Number of Occurrences") - plt.show() - - return None - - def meanImpactVelocity(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - - Returns - ------- - _type_ - _description_ - """ - print( - f'Impact Velocity -Mean Value: {np.mean(dispersion_results["impactVelocity"]):0.3f} m/s' - ) - print( - f'Impact Velocity - Std. Dev.: {np.std(dispersion_results["impactVelocity"]):0.3f} m/s' - ) - - return None - - def plotImpactVelocity(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - - Returns - ------- - _type_ - _description_ - """ - self.meanImpactVelocity(dispersion_results) - - plt.figure() - plt.hist( - dispersion_results["impactVelocity"], - bins=int(self.num_of_loaded_sims**0.5), - ) - plt.title("Impact Velocity") - plt.xlim(-35, 0) - plt.xlabel("Velocity (m/s)") - plt.ylabel("Number of Occurrences") - plt.show() - - return None - - def meanStaticMargin(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - - Returns - ------- - _type_ - _description_ - """ - print( - f'Initial Static Margin - Mean Value: {np.mean(dispersion_results["initialStaticMargin"]):0.3f} c' - ) - print( - f'Initial Static Margin - Std. Dev.: {np.std(dispersion_results["initialStaticMargin"]):0.3f} c' - ) - - print( - f'Out of Rail Static Margin -Mean Value: {np.mean(dispersion_results["outOfRailStaticMargin"]):0.3f} c' - ) - print( - f'Out of Rail Static Margin - Std. Dev.: {np.std(dispersion_results["outOfRailStaticMargin"]):0.3f} c' - ) - - print( - f'Final Static Margin - Mean Value: {np.mean(dispersion_results["finalStaticMargin"]):0.3f} c' - ) - print( - f'Final Static Margin - Std. Dev.: {np.std(dispersion_results["finalStaticMargin"]):0.3f} c' - ) - - return None - - def plotStaticMargin(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - - Returns - ------- - _type_ - _description_ - """ - self.meanStaticMargin(dispersion_results) - - plt.figure() - plt.hist( - dispersion_results["initialStaticMargin"], - label="Initial", - bins=int(self.num_of_loaded_sims**0.5), - ) - plt.hist( - dispersion_results["outOfRailStaticMargin"], - label="Out of Rail", - bins=int(self.num_of_loaded_sims**0.5), - ) - plt.hist( - dispersion_results["finalStaticMargin"], - label="Final", - bins=int(self.num_of_loaded_sims**0.5), - ) - plt.legend() - plt.title("Static Margin") - plt.xlabel("Static Margin (c)") - plt.ylabel("Number of Occurrences") - plt.show() - - return None - - def meanMaximumVelocity(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - - Returns - ------- - _type_ - _description_ - """ - print( - f'Maximum Velocity -Mean Value: {np.mean(dispersion_results["maxVelocity"]):0.3f} m/s' - ) - print( - f'Maximum Velocity - Std. Dev.: {np.std(dispersion_results["maxVelocity"]):0.3f} m/s' - ) - - return None - - def plotMaximumVelocity(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - - Returns - ------- - _type_ - _description_ - """ - self.meanMaximumVelocity(dispersion_results) - - plt.figure() - plt.hist( - dispersion_results["maxVelocity"], - bins=int(self.num_of_loaded_sims**0.5), - ) - plt.title("Maximum Velocity") - plt.xlabel("Velocity (m/s)") - plt.ylabel("Number of Occurrences") - plt.show() - - return None - - def meanNumberOfParachuteEvents(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - - Returns - ------- - _type_ - _description_ - """ - print( - f'Number of Parachute Events -Mean Value: {np.mean(dispersion_results["numberOfEvents"]):0.3f} s' - ) - print( - f'Number of Parachute Events - Std. Dev.: {np.std(dispersion_results["numberOfEvents"]):0.3f} s' - ) - - return None - - def plotNumberOfParachuteEvents(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - - Returns - ------- - _type_ - _description_ - """ - self.meanNumberOfParachuteEvents(dispersion_results) - - plt.figure() - plt.hist(dispersion_results["numberOfEvents"]) - plt.title("Parachute Events") - plt.xlabel("Number of Parachute Events") - plt.ylabel("Number of Occurrences") - plt.show() - - return None - - def meanDrogueTriggerTime(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - - Returns - ------- - _type_ - _description_ - """ - print( - f'Drogue Trigger Time -Mean Value: {np.mean(dispersion_results["drogueTriggerTime"]):0.3f} s' - ) - print( - f'Drogue Trigger Time - Std. Dev.: {np.std(dispersion_results["drogueTriggerTime"]):0.3f} s' - ) - - return None - - def plotDrogueTriggerTime(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - - Returns - ------- - _type_ - _description_ - """ - self.meanDrogueTriggerTime(dispersion_results) - - plt.figure() - plt.hist( - dispersion_results["drogueTriggerTime"], - bins=int(self.num_of_loaded_sims**0.5), - ) - plt.title("Drogue Trigger Time") - plt.xlabel("Time (s)") - plt.ylabel("Number of Occurrences") - plt.show() - - return None - - def meanDrogueFullyInflatedTime(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - - Returns - ------- - _type_ - _description_ - """ - print( - f'Drogue Fully Inflated Time -Mean Value: {np.mean(dispersion_results["drogueInflatedTime"]):0.3f} s' - ) - print( - f'Drogue Fully Inflated Time - Std. Dev.: {np.std(dispersion_results["drogueInflatedTime"]):0.3f} s' - ) - - return None - - def plotDrogueFullyInflatedTime(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - - Returns - ------- - _type_ - _description_ - """ - self.meanDrogueFullyInflatedTime(dispersion_results) - - plt.figure() - plt.hist( - dispersion_results["drogueInflatedTime"], - bins=int(self.num_of_loaded_sims**0.5), - ) - plt.title("Drogue Fully Inflated Time") - plt.xlabel("Time (s)") - plt.ylabel("Number of Occurrences") - plt.show() - - return None - - def meanDrogueFullyVelocity(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - - Returns - ------- - _type_ - _description_ - """ - print( - f'Drogue Parachute Fully Inflated Velocity -Mean Value: {np.mean(dispersion_results["drogueInflatedVelocity"]):0.3f} m/s' - ) - print( - f'Drogue Parachute Fully Inflated Velocity - Std. Dev.: {np.std(dispersion_results["drogueInflatedVelocity"]):0.3f} m/s' - ) - - return None - - def plotDrogueFullyVelocity(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - - Returns - ------- - _type_ - _description_ - """ - self.meanDrogueFullyVelocity(dispersion_results) - - plt.figure() - plt.hist( - dispersion_results["drogueInflatedVelocity"], - bins=int(self.num_of_loaded_sims**0.5), - ) - plt.title("Drogue Parachute Fully Inflated Velocity") - plt.xlabel("Velocity m/s)") - plt.ylabel("Number of Occurrences") - plt.show() - - return None + # TODO: Create evolution plots to analyze convergence def createEllipses(self, dispersion_results): """_summary_ From 84e35b6652ae16802af23e202cc614a19c20d43d Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 24 Oct 2022 01:23:35 +0200 Subject: [PATCH 41/68] MAINT: refactoring process parachutes --- rocketpy/Dispersion.py | 66 +++++++++++++++++++----------------------- 1 file changed, 30 insertions(+), 36 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index 43b1d3298..2ccc695c7 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -655,42 +655,36 @@ def __process_parachute_from_dict(self, dictionary): dictionary: dict Modified dictionary with the processed parachute parameters. """ - # Get parachutes names - if "parachuteNames" in dictionary: # TODO: use only dictionary - for i, name in enumerate(dictionary["parachuteNames"]): - if "CdS" in dictionary: - dictionary["parachute_" + name + "_CdS"] = dictionary["CdS"][i] - if "trigger" in dictionary: - dictionary["parachute_" + name + "_trigger"] = dictionary[ - "trigger" - ][i] - if "samplingRate" in dictionary: - dictionary["parachute_" + name + "_samplingRate"] = dictionary[ - "samplingRate" - ][i] - if "lag" in dictionary: - dictionary["parachute_" + name + "_lag"] = dictionary["lag"][i] - if "noise_mean" in dictionary: - dictionary["parachute_" + name + "_noise_mean"] = dictionary[ - "noise_mean" - ][i] - if "noise_sd" in dictionary: - dictionary["parachute_" + name + "_noise_std"] = dictionary[ - "noise_sd" - ][i] - if "noise_corr" in dictionary: - dictionary["parachute_" + name + "_noise_corr"] = dictionary[ - "noise_corr" - ][i] - # Remove already used keys from dictionary to avoid confusion - dictionary.pop("CdS", None) - dictionary.pop("trigger", None) - dictionary.pop("samplingRate", None) - dictionary.pop("lag", None) - dictionary.pop("noise_mean", None) - dictionary.pop("noise_sd", None) - dictionary.pop("noise_corr", None) - self.parachute_names = dictionary.pop("parachuteNames", None) + # Get the number and names of parachutes + self.parachute_names = [] + for key in dictionary.keys(): + if "parachute_" in key: + self.parachute_names.append(key.split("_")[1]) + # Remove duplicates + self.parachute_names = list(set(self.parachute_names)) + + # Check if there is enough arguments for defining each parachute + for name in self.parachute_names: + for parachute_input in self.parachute_inputs.keys(): + _, _, parameter = parachute_input.split("_") + if "parachute_{}_{}".format(name, parameter) not in dictionary.keys(): + try: # Try to get the value from the Parachute object + for chute in self.rocket.parachutes: + if getattr(chute, "name") == name: + dictionary[ + "parachute_{}_{}".format(name, parameter) + ] = [getattr(chute, parameter)] + except: # Class not passed + if self.parachute_inputs[parachute_input] == "required": + warnings.warn( + "Missing {} for parachute {} in dictionary, which is required to run a simulation".format( + parachute_input.split("_")[2], name + ) + ) + else: + dictionary["parachute_{}_{}".format(name, parameter)] = [ + self.parachute_inputs[parachute_input], + ] return dictionary From 312644af5ca52c81ddf2769312efa2a009fc9d87 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 24 Oct 2022 01:24:35 +0200 Subject: [PATCH 42/68] MAINT: improving error raises --- rocketpy/Dispersion.py | 59 ++++++++++++++++++++---------------------- 1 file changed, 28 insertions(+), 31 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index 2ccc695c7..ce29c1cc3 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -731,15 +731,14 @@ def __check_inputted_values_from_dict(self, dictionary): getattr(self.environment, parameter_key), parameter_value, ) - except Exception as E: - print("Error:") - print( - "Please check if the parameter was inputted correctly in dispersion_dictionary." - + " Dictionary values must be either tuple or lists." - + " If single value, the corresponding Class must " - + " be inputted in Dispersion.run_dispersion method.\n" + except: + raise AttributeError( + f"Please check if the parameter {parameter_key} was inputted" + "correctly in dispersion_dictionary." + " Dictionary values must be either tuple or lists." + " If single value, the corresponding Class must" + " be inputted in the run_dispersion method." ) - print(traceback.format_exc()) ## Third corrections - SolidMotor elif parameter_key in self.solid_motor_inputs.keys(): @@ -748,15 +747,14 @@ def __check_inputted_values_from_dict(self, dictionary): getattr(self.motor, parameter_key), parameter_value, ) - except Exception as E: - print("Error:") - print( - "Please check if the parameter was inputted correctly in dispersion_dictionary." - + " Dictionary values must be either tuple or lists." - + " If single value, the corresponding Class must " - + "must be inputted in Dispersion.run_dispersion method.\n" + except: + raise AttributeError( + f"Please check if the parameter {parameter_key} was inputted" + "correctly in dispersion_dictionary." + " Dictionary values must be either tuple or lists." + " If single value, the corresponding Class must" + " be inputted in the run_dispersion method." ) - print(traceback.format_exc()) # Fourth correction - Rocket elif parameter_key in self.rocket_inputs.keys(): @@ -765,15 +763,14 @@ def __check_inputted_values_from_dict(self, dictionary): getattr(self.rocket, parameter_key), parameter_value, ) - except Exception as E: - print("Error:") - print( - "Please check if the parameter was inputted correctly in dispersion_dictionary." - + " Dictionary values must be either tuple or lists." - + " If single value, the corresponding Class must " - + "must be inputted in Dispersion.run_dispersion method.\n" + except: + raise AttributeError( + f"Please check if the parameter {parameter_key} was inputted" + "correctly in dispersion_dictionary." + " Dictionary values must be either tuple or lists." + " If single value, the corresponding Class must" + " be inputted in the run_dispersion method." ) - print(traceback.format_exc()) # Fifth correction - Flight elif parameter_key in self.flight_inputs.keys(): @@ -782,13 +779,13 @@ def __check_inputted_values_from_dict(self, dictionary): getattr(self.flight, parameter_key), parameter_value, ) - except Exception as E: - print("Error:") - print( - "Please check if the parameter was inputted correctly in dispersion_dictionary." - + " Dictionary values must be either tuple or lists." - + " If single value, the corresponding Class must " - + "must be inputted in Dispersion.run_dispersion method.\n" + except: + raise AttributeError( + f"Please check if the parameter {parameter_key} was inputted" + "correctly in dispersion_dictionary." + " Dictionary values must be either tuple or lists." + " If single value, the corresponding Class must" + " be inputted in the run_dispersion method." ) print(traceback.format_exc()) From 35206f5b3a90167514b0fb3c6cba3a2ad279df44 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 24 Oct 2022 01:25:02 +0200 Subject: [PATCH 43/68] ENH: flexible export data seelection --- rocketpy/Dispersion.py | 100 ++++++++++++++++++----------------------- 1 file changed, 44 insertions(+), 56 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index ce29c1cc3..a3a534629 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -884,7 +884,6 @@ def __yield_flight_setting( # Yield a flight setting yield flight_setting - # TODO: allow user to chose what is going to be exported def __export_flight_data( self, flight_setting, @@ -892,6 +891,7 @@ def __export_flight_data( exec_time, dispersion_input_file, dispersion_output_file, + variables=None, ): """Saves flight results in a .txt @@ -914,59 +914,49 @@ def __export_flight_data( _description_ """ - # Generate flight results - flight_result = { - "outOfRailTime": flight.outOfRailTime, - "outOfRailVelocity": flight.outOfRailVelocity, - "apogeeTime": flight.apogeeTime, - "apogeeAltitude": flight.apogee - flight.env.elevation, - "apogeeX": flight.apogeeX, - "apogeeY": flight.apogeeY, - "impactTime": flight.tFinal, - "impactX": flight.xImpact, - "impactY": flight.yImpact, - "impactVelocity": flight.impactVelocity, - "initialStaticMargin": flight.rocket.staticMargin(0), - "outOfRailStaticMargin": flight.rocket.staticMargin(flight.outOfRailTime), - "finalStaticMargin": flight.rocket.staticMargin( - flight.rocket.motor.burnOutTime - ), - "numberOfEvents": len(flight.parachuteEvents), - "drogueTriggerTime": [], - "drogueInflatedTime": [], - "drogueInflatedVelocity": [], - "executionTime": exec_time, - "lateralWind": flight.lateralSurfaceWind, - "frontalWind": flight.frontalSurfaceWind, - } - - # # Calculate maximum reached velocity - # sol = np.array(flight.solution) - # flight.vx = Function( - # sol[:, [0, 4]], - # "Time (s)", - # "Vx (m/s)", - # "linear", - # extrapolation="natural", - # ) - # flight.vy = Function( - # sol[:, [0, 5]], - # "Time (s)", - # "Vy (m/s)", - # "linear", - # extrapolation="natural", - # ) - # flight.vz = Function( - # sol[:, [0, 6]], - # "Time (s)", - # "Vz (m/s)", - # "linear", - # extrapolation="natural", - # ) - # flight.speed = (flight.vx**2 + flight.vy**2 + flight.vz**2) ** 0.5 - # flight.maxVel = np.amax(flight.speed.source[:, 1]) - # flight_result["maxVelocity"] = flight.maxVel - flight_result["maxVelocity"] = flight.maxSpeed + # In case not variables are passed, export default variables + if not isinstance(variables, list): + variables = [ + "apogee", + "apogeeTime", + "apogeeX", + "apogeeY", + "executionTime", + "finalStaticMargin", + "frontalSurfaceWind", + "impactVelocity", + "initialStaticMargin", + "lateralSurfaceWind", + "maxAcceleration", + "maxAccelerationTime", + "maxSpeed", + "maxSpeedTime", + "numberOfEvents", + "outOfRailStaticMargin", + "outOfRailTime", + "outOfRailVelocity", + "tFinal", + "xImpact", + "yImpact", + ] + else: # Check if variables are valid and raise error if not + if not all([isinstance(var, str) for var in variables]): + raise TypeError("Variables must be strings.") + + # First, capture the flight data that are saved in the flight object + attributes_list = list(set(dir(flight)).intersection(variables)) + flight_result = {} + for var in attributes_list: + flight_result[str(var)] = getattr(flight, var) + + # Second, capture data that needs to be calculated + for var in list(set(variables) - set(attributes_list)): + if var == "executionTime": + flight_result[str(var)] = exec_time + elif var == "numberOfEvents": + flight_result[str(var)] = len(flight.parachuteEvents) + else: + raise ValueError(f"Variable {var} could not be found.") # Take care of parachute results for trigger_time, parachute in flight.parachuteEvents: @@ -977,8 +967,6 @@ def __export_flight_data( flight_result[parachute.name + "_inflatedVelocity"] = flight.speed( trigger_time + parachute.lag ) - else: - flight_result["parachuteInfo"] = "No Parachute Events" # Write flight setting and results to file flight_setting.pop("thrust", None) From 8366a5625eeffd8f117b5b03d23a37ca2e48fcc5 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 24 Oct 2022 01:25:25 +0200 Subject: [PATCH 44/68] MAINT: new __export_flight_data_error method --- rocketpy/Dispersion.py | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index a3a534629..eb409669b 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -975,9 +975,21 @@ def __export_flight_data( return None - def __export_flight_data(self, flight_setting, dispersion_error_file): + def __export_flight_data_error(setting, flight_setting, dispersion_error_file): + """Saves flight error in a .txt - """Saves flight error in a .txt""" + Parameters + ---------- + setting : _type_ + _description_ + dispersion_error_file : _type_ + _description_ + + Returns + ------- + _type_ + _description_ + """ dispersion_error_file.write(str(flight_setting) + "\n") From 6489e2144002c82d43318afdb30d55a1b635efd7 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 24 Oct 2022 01:26:23 +0200 Subject: [PATCH 45/68] ENH: open_mode options added --- rocketpy/Dispersion.py | 19 ++++++++----------- 1 file changed, 8 insertions(+), 11 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index eb409669b..ca33e1aa9 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -1004,7 +1004,7 @@ def run_dispersion( motor=None, rocket=None, bg_image=None, - actual_landing_point=None, + append=False, ): """Runs the given number of simulations and saves the data @@ -1027,13 +1027,9 @@ def run_dispersion( _description_, by default None distribution_type : str, optional _description_, by default "normal" - bg_image : str, optional - The path to the image to be used as the background - actual_landing_point : tuple, optional - A tuple containing the actual landing point of the rocket, if known. - Useful when comparing the dispersion results with the actual landing. - Must be given in tuple format, such as (lat, lon). By default None. - # TODO: Check the order of these coordinates + append : bool, optional + If True, the results will be appended to the existing files. If False, + the files will be overwritten. By default False. Returns ------- @@ -1072,9 +1068,10 @@ def run_dispersion( self.distributionFunc = self.__set_distribution_function(self.distribution_type) # Create data files for inputs, outputs and error logging - dispersion_error_file = open(str(self.filename) + ".disp_errors.txt", "w") - dispersion_input_file = open(str(self.filename) + ".disp_inputs.txt", "w") - dispersion_output_file = open(str(self.filename) + ".disp_outputs.txt", "w") + open_mode = "a" if append else "w" + dispersion_error_file = open(f"{self.filename}.disp_errors.txt", open_mode) + dispersion_input_file = open(f"{self.filename}.disp_inputs.txt", open_mode) + dispersion_output_file = open(f"{self.filename}.disp_outputs.txt", open_mode) # Initialize counter and timer i = 0 From e8430e6e6294fe708c707a676827a51b547fa533 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 24 Oct 2022 01:27:17 +0200 Subject: [PATCH 46/68] MAINT: initializing or saving variables --- rocketpy/Dispersion.py | 23 ++++++++++++++++++----- 1 file changed, 18 insertions(+), 5 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index ca33e1aa9..e782c2be4 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -160,8 +160,21 @@ def __init__( # Initialize variables so they can be accessed by MATLAB self.dispersion_results = {} - self.mean_out_of_rail_time = 0 - self.std_out_of_rail_time = 0 + self.dispersion_dictionary = {} + self.nose_names = [] + self.finSet_names = [] + self.tail_names = [] + self.parachute_names = [] + self.distributionFunc = None + self.distribution_type = None + self.environment = None + self.flight = None + self.motor = None + self.rocket = None + self.rocket_dispersion = None + self.number_of_simulations = 0 + self.num_of_loaded_sims = 0 + self.start_time = 0 def __set_distribution_function(self, distribution_type): """Sets the distribution function to be used in the analysis. @@ -1038,9 +1051,9 @@ def run_dispersion( self.number_of_simulations = number_of_simulations self.dispersion_dictionary = dispersion_dictionary - self.environment = None - self.motor = None - self.rocket = None + self.environment = environment + self.motor = motor + self.rocket = rocket if flight: # In case a flight object is passed self.environment = flight.env self.motor = flight.rocket.motor From 835d253f1d75001968135d88df1966e9b43a8846 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 24 Oct 2022 01:27:46 +0200 Subject: [PATCH 47/68] MAINT: info and allInfo refactored --- rocketpy/Dispersion.py | 157 ++++------------------------------------- 1 file changed, 13 insertions(+), 144 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index e782c2be4..d9c0a2577 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -1734,76 +1734,6 @@ def exportEllipsesToKML( kml.save(filename) return None - def meanLateralWindSpeed(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - """ - print( - f'Lateral Surface Wind Speed -Mean Value: {np.mean(dispersion_results["lateralWind"]):0.3f} m/s' - ) - print( - f'Lateral Surface Wind Speed - Std. Dev.: {np.std(dispersion_results["lateralWind"]):0.3f} m/s' - ) - - def plotLateralWindSpeed(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - """ - self.meanLateralWindSpeed(dispersion_results) - - plt.figure() - plt.hist( - dispersion_results["lateralWind"], - bins=int(self.num_of_loaded_sims**0.5), - ) - plt.title("Lateral Surface Wind Speed") - plt.xlabel("Velocity (m/s)") - plt.ylabel("Number of Occurrences") - plt.show() - - def meanFrontalWindSpeed(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - """ - print( - f'Frontal Surface Wind Speed -Mean Value: {np.mean(dispersion_results["frontalWind"]):0.3f} m/s' - ) - print( - f'Frontal Surface Wind Speed - Std. Dev.: {np.std(dispersion_results["frontalWind"]):0.3f} m/s' - ) - - def plotFrontalWindSpeed(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - """ - self.meanFrontalWindSpeed(dispersion_results) - - plt.figure() - plt.hist( - dispersion_results["frontalWind"], - bins=int(self.num_of_loaded_sims**0.5), - ) - plt.title("Frontal Surface Wind Speed") - plt.xlabel("Velocity (m/s)") - plt.ylabel("Number of Occurrences") - plt.show() - def info(self): """_summary_ @@ -1812,85 +1742,24 @@ def info(self): None """ - dispersion_results = self.dispersion_results - - self.meanApogeeAltitude(dispersion_results) - - self.meanOutOfRailVelocity(dispersion_results) - - self.meanStaticMargin(dispersion_results) - - self.meanLateralWindSpeed(dispersion_results) - - self.meanFrontalWindSpeed(dispersion_results) - - self.printMeanOutOfRailTime(dispersion_results) - - self.meanApogeeTime(dispersion_results) - - self.meanApogeeXPosition(dispersion_results) - - self.meanApogeeYPosition(dispersion_results) - - self.meanImpactTime(dispersion_results) - - self.meanImpactVelocity(dispersion_results) - - self.meanImpactXPosition(dispersion_results) - - self.meanImpactYPosition(dispersion_results) - - self.meanMaximumVelocity(dispersion_results) - - self.meanNumberOfParachuteEvents(dispersion_results) - - self.meanDrogueFullyInflatedTime(dispersion_results) - - self.meanDrogueFullyVelocity(dispersion_results) - - self.meanDrogueTriggerTime(dispersion_results) + print("Monte Carlo Simulation by RocketPy") + print("Data Source: ", self.filename) + print("Number of simulations: ", self.num_of_loaded_sims) + print("Results: ") + self.print_results() return None def allInfo(self): dispersion_results = self.dispersion_results - self.plotEllipses(dispersion_results, self.image, self.actual_landing_point) - - self.plotApogeeAltitude(dispersion_results) - - self.plotOutOfRailVelocity(dispersion_results) - - self.plotStaticMargin(dispersion_results) - - self.plotLateralWindSpeed(dispersion_results) - - self.plotFrontalWindSpeed(dispersion_results) - - self.plotOutOfRailTime(dispersion_results) - - self.plotApogeeTime(dispersion_results) - - self.plotApogeeXPosition(dispersion_results) - - self.plotApogeeYPosition(dispersion_results) - - self.plotImpactTime(dispersion_results) - - self.plotImpactVelocity(dispersion_results) - - self.plotImpactXPosition(dispersion_results) - - self.plotImpactYPosition(dispersion_results) - - self.plotMaximumVelocity(dispersion_results) - - self.plotNumberOfParachuteEvents(dispersion_results) - - self.plotDrogueFullyInflatedTime(dispersion_results) - - self.plotDrogueFullyVelocity(dispersion_results) - - self.plotDrogueTriggerTime(dispersion_results) + print("Monte Carlo Simulation by RocketPy") + print("Data Source: ", self.filename) + print("Number of simulations: ", self.num_of_loaded_sims) + print("Results: ") + self.print_results() + print("Plotting results: ") + self.plotEllipses(dispersion_results=dispersion_results) + self.plot_results() return None From c539da0199a8f0ff7c15944d053dbd1b2645d3f1 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 24 Oct 2022 01:29:28 +0200 Subject: [PATCH 48/68] MAINT: refreshing comments and TODOs --- rocketpy/Dispersion.py | 22 ++++++++-------------- 1 file changed, 8 insertions(+), 14 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index d9c0a2577..b2d742428 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -800,7 +800,6 @@ def __check_inputted_values_from_dict(self, dictionary): " If single value, the corresponding Class must" " be inputted in the run_dispersion method." ) - print(traceback.format_exc()) # The analysis parameter dictionary is ready! Now we have mean and stdev # for all parameters @@ -1019,6 +1018,7 @@ def run_dispersion( bg_image=None, append=False, ): + # TODO: Separate into different functions to make it more readable """Runs the given number of simulations and saves the data Parameters @@ -1058,15 +1058,8 @@ def run_dispersion( self.environment = flight.env self.motor = flight.rocket.motor self.rocket = flight.rocket - self.environment = environment if environment else self.environment - self.motor = motor if motor else self.motor - self.rocket = rocket if rocket else self.rocket self.flight = flight self.distribution_type = "normal" # TODO: Must be parametrized - self.image = bg_image - self.actual_landing_point = actual_landing_point # (lat, lon) - - # Obs.: The flight object is not prioritized, which is a good thing, but need to be documented # Check if there's enough object to start a flight: ## Raise an error in case of any troubles @@ -1088,7 +1081,6 @@ def run_dispersion( # Initialize counter and timer i = 0 - initial_wall_time = time() initial_cpu_time = process_time() @@ -1117,7 +1109,7 @@ def run_dispersion( motor_dispersion = self.motor # Apply motor parameters variations on each iteration if possible - # TODO: add hybrid motor option + # TODO: add hybrid and liquid motor option motor_dispersion = SolidMotor( thrustSource=setting["thrust"], burnOut=setting["burnOutTime"], @@ -1441,10 +1433,11 @@ def createEllipses(self, dispersion_results): """ # Retrieve dispersion data por apogee and impact XY position + # TODO: Exception handling for missing data apogeeX = np.array(dispersion_results["apogeeX"]) apogeeY = np.array(dispersion_results["apogeeY"]) - impactX = np.array(dispersion_results["impactX"]) - impactY = np.array(dispersion_results["impactY"]) + impactX = np.array(dispersion_results["xImpact"]) + impactY = np.array(dispersion_results["yImpact"]) # Define function to calculate eigen values def eigsorted(cov): @@ -1520,10 +1513,11 @@ def plotEllipses( img = imread(image) # Retrieve dispersion data por apogee and impact XY position + # TODO: Exception handling for missing data apogeeX = np.array(dispersion_results["apogeeX"]) apogeeY = np.array(dispersion_results["apogeeY"]) - impactX = np.array(dispersion_results["impactX"]) - impactY = np.array(dispersion_results["impactY"]) + impactX = np.array(dispersion_results["xImpact"]) + impactY = np.array(dispersion_results["yImpact"]) impact_ellipses, apogee_ellipses = self.createEllipses(dispersion_results) From 7129cda5264072535f3be427492260559390d634 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 24 Oct 2022 01:30:39 +0200 Subject: [PATCH 49/68] MAINT: improving warnings again --- rocketpy/Dispersion.py | 38 +++++++++++++++++++++++++------------- 1 file changed, 25 insertions(+), 13 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index b2d742428..af0bd96eb 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -596,7 +596,7 @@ def __process_motor_from_dict(self, dictionary): # class was not inputted # checks if missing parameter is required if self.solid_motor_inputs[missing_input] == "required": - warnings.warn(f'Missing "{missing_input}" in d') + warnings.warn(f'Missing "{missing_input}" in dictionary') else: # if not uses default value dictionary[missing_input] = [ self.solid_motor_inputs[missing_input] @@ -642,8 +642,12 @@ def __process_environment_from_dict(self, dictionary): # class was not inputted # checks if missing parameter is required if self.environment_inputs[missing_input] == "required": - warnings.warn("Missing {} in dictionary".format(missing_input)) - else: # if not, use default value + warnings.warn( + "Missing {} in dictionary, which is required to run a simulation".format( + missing_input + ) + ) + else: # if not required, use default value dictionary[missing_input] = [ self.environment_inputs[missing_input] ] @@ -727,15 +731,20 @@ def __check_inputted_values_from_dict(self, dictionary): ## First solve the parachute values if "parachute" in parameter_key: _, parachute_name, parameter = parameter_key.split("_") - dictionary[parameter_key] = ( - getattr( - self.rocket.parachutes[ - self.parachute_names.index(parachute_name) - ], - parameter, - ), - parameter_value, - ) + # try: + if isinstance(parameter_value, types.FunctionType): + # Deal with trigger functions + dictionary[parameter_key] = parameter_value + else: + dictionary[parameter_key] = ( + getattr( + self.rocket.parachutes[ + self.parachute_names.index(parachute_name) + ], + parameter, + ), + parameter_value, + ) ## Second corrections - Environment if parameter_key in self.environment_inputs.keys(): @@ -886,6 +895,9 @@ def __yield_flight_setting( flight_setting[parameter_key] = distribution_func(*parameter_value) elif isinstance(parameter_value, Function): flight_setting[parameter_key] = distribution_func(*parameter_value) + elif isinstance(parameter_value, types.FunctionType): + # Deal with parachute triggers functions + flight_setting[parameter_key] = parameter_value else: # shuffles list and gets first item shuffle(parameter_value) @@ -1227,7 +1239,7 @@ def run_dispersion( except Exception as E: print(E) print(traceback.format_exc()) - self.__export_flight_data(setting, dispersion_error_file) + self.__export_flight_data_error(setting, dispersion_error_file) # Register time out.update( From e3bc3c9ef86074d1991a1b60e9a53c6eed1fbe70 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 24 Oct 2022 01:31:23 +0200 Subject: [PATCH 50/68] MAINT: adjusting variables names and inputs --- rocketpy/Dispersion.py | 19 ++++++++++++++----- 1 file changed, 14 insertions(+), 5 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index af0bd96eb..8093fa20a 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -1027,7 +1027,7 @@ def run_dispersion( flight=None, motor=None, rocket=None, - bg_image=None, + exported_variables=None, append=False, ): # TODO: Separate into different functions to make it more readable @@ -1052,6 +1052,8 @@ def run_dispersion( _description_, by default None distribution_type : str, optional _description_, by default "normal" + exported_variables : list, optional + A list containing the variables to be exported. By default None. append : bool, optional If True, the results will be appended to the existing files. If False, the files will be overwritten. By default False. @@ -1214,7 +1216,7 @@ def run_dispersion( # Run trajectory simulation try: # TODO: Add initialSolution flight option - TestFlight = Flight( + dispersion_flight = Flight( rocket=rocket_dispersion, environment=env_dispersion, inclination=setting["inclination"], @@ -1231,10 +1233,11 @@ def run_dispersion( self.__export_flight_data( flight_setting=setting, - flight_data=TestFlight, + flight=dispersion_flight, exec_time=process_time() - self.start_time, dispersion_input_file=dispersion_input_file, dispersion_output_file=dispersion_output_file, + variables=exported_variables, ) except Exception as E: print(E) @@ -1243,13 +1246,19 @@ def run_dispersion( # Register time out.update( - f"Current iteration: {i:06d} | Average Time per Iteration: {(process_time() - initial_cpu_time)/i:2.6f} s | Estimated time left: {int((number_of_simulations - i)*((process_time() - initial_cpu_time)/i))} s" + f"Current iteration: {i:06d} | Average Time per Iteration: " + f"{(process_time() - initial_cpu_time)/i:2.6f} s | Estimated time" + f" left: {int((number_of_simulations - i)*((process_time() - initial_cpu_time)/i))} s" ) # Clean the house once all the simulations were already done ## Print and save total time - final_string = f"Completed {i} iterations successfully. Total CPU time: {process_time() - initial_cpu_time} s. Total wall time: {time() - initial_wall_time} s" + final_string = ( + f"Completed {i} iterations successfully. Total CPU time: " + f"{process_time() - initial_cpu_time} s. Total wall time: " + f"{time() - initial_wall_time} s" + ) out.update(final_string) dispersion_input_file.write(final_string + "\n") dispersion_output_file.write(final_string + "\n") From bb7181825c606a0ef760908028b4c6e36828dcd2 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Mon, 24 Oct 2022 01:31:39 +0200 Subject: [PATCH 51/68] MAINT: updating class usage notebook --- .../dispersion_class_usage.ipynb | 809 +++++++++++------- 1 file changed, 501 insertions(+), 308 deletions(-) diff --git a/docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb b/docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb index b91bacb06..a6bfe5370 100644 --- a/docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb +++ b/docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -58,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -105,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -125,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -147,108 +147,53 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Env.setAtmosphericModel(type=\"Forecast\", file=\"GFS\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load an previous ran environment analysis as input for Environment" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "\n", - "file = open(\"../../../data/weather/EuroC_export_env_analysis.json\")\n", - "env_dict = json.load(file)\n", - "file.close()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", - "\n", - "hour = str(Env.date.hour)\n", - "\n", - "pressure_profile = np.array(env_dict[\"atmosphericModelPressureProfile\"][hour])\n", - "pressure_profile = np.column_stack(\n", - " (pressure_profile[:, 0], 100 * pressure_profile[:, 1])\n", - ")\n", - "temperature_profile = np.array(env_dict[\"atmosphericModelTemperatureProfile\"][hour])\n", - "temperature_profile = np.column_stack(\n", - " (temperature_profile[:, 0], 273 + temperature_profile[:, 1])\n", - ")\n", - "wind_u = env_dict[\"atmosphericModelWindVelocityXProfile\"][hour]\n", - "wind_v = env_dict[\"atmosphericModelWindVelocityYProfile\"][hour]\n" + "Env.setAtmosphericModel(type=\"Forecast\", file=\"GFS\")\n" ] }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "Env.setAtmosphericModel(\n", - " type=\"CustomAtmosphere\",\n", - " pressure=pressure_profile,\n", - " temperature=temperature_profile,\n", - " wind_u=wind_u,\n", - " wind_v=wind_v,\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, + "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Gravity Details\n", - "\n", - "Acceleration of Gravity: 9.80665 m/s²\n", - "\n", - "\n", "Launch Site Details\n", "\n", "Launch Rail Length: 5.2 m\n", - "Launch Date: 2022-10-11 12:00:00 UTC\n", + "Launch Date: 2022-10-25 12:00:00 UTC\n", "Launch Site Latitude: 39.38970°\n", "Launch Site Longitude: -8.28896°\n", - "Launch Site Surface Elevation: 113.0 m\n", + "Reference Datum: SIRGAS2000\n", + "Launch Site UTM coordinates: 44415.43 W 4373388.31 N\n", + "Launch Site UTM zone: 30S\n", + "Launch Site Surface Elevation: 141.8 m\n", "\n", "\n", "Atmospheric Model Details\n", "\n", - "Atmospheric Model Type: CustomAtmosphere\n", - "CustomAtmosphere Maximum Height: 13.884 km\n", + "Atmospheric Model Type: Forecast\n", + "Forecast Maximum Height: 78.440 km\n", + "Forecast Time Period: From 2022-10-23 12:00:00 to 2022-11-08 12:00:00 UTC\n", + "Forecast Hour Interval: 3 hrs\n", + "Forecast Latitude Range: From -90.0 ° To 90.0 °\n", + "Forecast Longitude Range: From 0.0 ° To 359.75 °\n", "\n", "\n", "Surface Atmospheric Conditions\n", "\n", - "Surface Wind Speed: 0.79 m/s\n", - "Surface Wind Direction: 175.73°\n", - "Surface Wind Heading: 355.73°\n", - "Surface Pressure: 992.00 hPa\n", - "Surface Temperature: 291.09 K\n", - "Surface Air Density: 1.187 kg/m³\n", - "Surface Speed of Sound: 342.02 m/s\n", + "Surface Wind Speed: 7.53 m/s\n", + "Surface Wind Direction: 218.09°\n", + "Surface Wind Heading: 38.09°\n", + "Surface Pressure: 997.18 hPa\n", + "Surface Temperature: 294.80 K\n", + "Surface Air Density: 1.178 kg/m³\n", + "Surface Speed of Sound: 344.20 m/s\n", "\n", "\n", "Atmospheric Model Plots\n" @@ -256,10 +201,10 @@ }, { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-10T18:34:20.452796\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T00:58:23.971600\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -267,7 +212,7 @@ } ], "source": [ - "Env.allInfo()\n" + "Env.info()" ] }, { @@ -287,7 +232,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -308,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -330,7 +275,7 @@ { "data": { "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-10T18:34:28.022285\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T00:58:25.216239\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -352,7 +297,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -390,7 +335,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -429,7 +374,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -459,7 +404,7 @@ { "data": { "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-10T18:34:37.897592\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T00:59:10.688315\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -481,7 +426,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ @@ -502,13 +447,13 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 55, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-10T18:34:43.880121\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T00:59:12.375246\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -521,43 +466,6 @@ "TestFlight.plot3dTrajectory()\n" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Export Flight Trajectory to a .kml file so it can be opened on Google Earth" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0.00000000e+00 1.33000000e+02]\n", - " [1.41076548e-03 1.33000000e+02]\n", - " [2.82153096e-03 1.33000000e+02]\n", - " ...\n", - " [4.61443985e+02 8.97087177e+01]\n", - " [4.62487459e+02 8.96739146e+01]\n", - " [4.63526604e+02 8.96399417e+01]]\n", - "File trajectory.kml saved with success!\n" - ] - } - ], - "source": [ - "TestFlight.exportKML(\n", - " fileName=\"trajectory.kml\",\n", - " timeStep=None,\n", - " extrude=True,\n", - " color=\"641400F0\",\n", - " altitudeMode=\"relativetoground\",\n", - ")\n" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -576,7 +484,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ @@ -594,70 +502,27 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ - "# TODO: This is work in progress, we need to understand how to setup simulations of such kind\n", - "disp_dict_model_1 = {\n", - " # Environment Parameters\n", - " \"railLength\": 0.001,\n", - " # \"date\": ,\n", - " # \"datum\": [\"WSG84\"],\n", - " \"elevation\": 10,\n", - " \"gravity\": 0,\n", - " \"latitude\": 0,\n", - " \"longitude\": 0,\n", - " # \"timeZone\": [str(Env.timeZone)],\n", + "disp_dictionary = {\n", " # Solid Motor Parameters\n", " \"burnOutTime\": 0.2,\n", - " \"grainDensity\": 0.1 * Pro75M1670.grainDensity,\n", - " \"grainInitialHeight\": 0.001,\n", - " \"grainInitialInnerRadius\": 0.001,\n", - " \"grainNumber\": 0,\n", - " \"grainOuterRadius\": 0.001,\n", - " \"grainSeparation\": 0.001,\n", - " \"nozzleRadius\": 0.001,\n", - " \"throatRadius\": 0.001,\n", - " # \"thrustSource\": ,\n", " \"totalImpulse\": 0.033 * Pro75M1670.totalImpulse,\n", " # Rocket Parameters\n", " \"mass\": 0.100,\n", " \"radius\": 0.001,\n", - " \"distanceRocketNozzle\": 0.010,\n", - " \"distanceRocketPropellant\": 0.010,\n", - " \"inertiaI\": Calisto.inertiaI * 0.1,\n", - " \"inertiaZ\": Calisto.inertiaZ * 0.1,\n", - " \"powerOffDrag\": 0.033, # Multiplier\n", - " \"powerOnDrag\": 0.033, # Multiplier\n", - " # \"noseKind\": [Calisto.noseKind],\n", - " \"noseLength\": 0.001,\n", - " \"noseDistanceToCM\": 0.010,\n", - " \"numberOfFins\": 0,\n", - " \"finRootChord\": 0.001,\n", - " \"finTipChord\": 0.001,\n", - " \"span\": 0.001,\n", - " \"distanceToCM\": 0.010,\n", - " \"finRadius\": 0.001,\n", - " # \"finAirfoil\": Calisto.finAirfoil,\n", - " \"tailTopRadius\": 0.001,\n", - " # \"parachuteNames\": [\"Main\", \"Drogue\"],\n", - " \"CdS\": [2, 0.3],\n", + " \"powerOffDrag\": 0.033, # Multiplier\n", + " \"powerOnDrag\": 0.033, # Multiplier\n", + " \"parachute_Main_CdS\": 1,\n", + " \"parachute_Drogue_CdS\": 0.1,\n", + " \"parachute_Main_lag\": 0.1,\n", + " \"parachute_Drogue_lag\": 0.1,\n", + " # Flight Parameters\n", " \"inclination\": 1,\n", " \"heading\": 2,\n", - " # \"trigger\": [[mainTrigger], [drogueTrigger]],\n", - " # \"noseLength\": (0.588, 1 / 1000),\n", - " # Flight Parameters\n", - " # 'atol': ,\n", - " # 'initialSolution': \"\",\n", - " # 'maxTime': \"\",\n", - " # 'maxTimeStep': \"\",\n", - " # 'minTimeStep': \"\",\n", - " # 'rtol': \"\",\n", - " # 'terminateOnApogee': \"\",\n", - " # 'timeOvershoot': \"\",\n", - " # 'verbose': \"\",\n", - "}" + "}\n" ] }, { @@ -669,13 +534,13 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'Starting'" + "'Completed 10 iterations successfully. Total CPU time: 22.40625 s. Total wall time: 23.7100830078125 s'" ] }, "metadata": {}, @@ -684,35 +549,20 @@ ], "source": [ "TestDispersion.run_dispersion(\n", - " number_of_simulations=5, # Be careful with this number, it will take a while to run\n", - " dispersion_dictionary=disp_dict_model_1,\n", + " number_of_simulations=10,\n", + " dispersion_dictionary=disp_dictionary,\n", " flight=TestFlight,\n", - " environment=Env,\n", - " # motor=None,\n", - " # rocket=None,\n", - " bg_image=None,\n", + " append=False,\n", ")\n" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "RocketPy separates the running of simulations from the importing of data.\n", - "This is done to allow the user to run simulations in parallel, if desired, and\n", - "to allow the user to import data from different sources.\n", - "\n", - "Remember to specify a good name for your loaded data, so you can easily identify\n", - "and treat them later. " - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 74, "metadata": {}, "outputs": [], "source": [ - "# dispersion_results = TestDispersion.import_results()\n" + "TestDispersion.process_results()" ] }, { @@ -739,11 +589,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 75, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "maxAccelerationTime: μ = 64.155, σ = 76.067\n", + "frontalSurfaceWind: μ = -0.660, σ = 0.250\n", + "outOfRailStaticMargin: μ = 2.133, σ = 0.008\n", + "apogeeX: μ = 124.113, σ = 88.082\n", + "xImpact: μ = 4259.142, σ = 667.161\n", + "initialStaticMargin: μ = 2.050, σ = 0.009\n", + "apogeeY: μ = -848.560, σ = 83.549\n", + "finalStaticMargin: μ = 2.683, σ = 0.006\n", + "yImpact: μ = 2254.608, σ = 664.886\n", + "impactVelocity: μ = -57459796457.449, σ = 172379296086.602\n", + "maxSpeed: μ = 57485610630.893, σ = 172456737796.272\n", + "apogeeTime: μ = 24.701, σ = 0.407\n", + "tFinal: μ = 289.802, σ = 59.731\n", + "maxAcceleration: μ = 404143759333999116288.000, σ = 1212431278001877680128.000\n", + "apogee: μ = 3186.668, σ = 118.153\n", + "maxSpeedTime: μ = 45.933, σ = 85.169\n", + "outOfRailVelocity: μ = 25.469, σ = 0.388\n", + "lateralSurfaceWind: μ = 7.499, σ = 0.021\n", + "outOfRailTime: μ = 0.367, σ = 0.005\n", + "numberOfEvents: μ = 2.000, σ = 0.000\n", + "executionTime: μ = 1.895, σ = 0.254\n", + "Drogue_triggerTime: μ = 24.706, σ = 0.406\n", + "Drogue_inflatedTime: μ = 26.193, σ = 0.365\n", + "Drogue_inflatedVelocity: μ = 33.249, σ = 3.417\n", + "Main_triggerTime: μ = 192.144, σ = 19.792\n", + "Main_inflatedTime: μ = 193.591, σ = 19.805\n", + "Main_inflatedVelocity: μ = 20.834, σ = 0.007\n" + ] + } + ], "source": [ - "# TestDispersion.info()\n" + "TestDispersion.print_results()\n" ] }, { @@ -755,11 +639,358 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 76, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Monte Carlo Simulation by RocketPy\n", + "Data Source: dispersion_analysis_outputs/disp_class_example\n", + "Number of simulations: 10\n", + "Results: \n", + "maxAccelerationTime: μ = 64.155, σ = 76.067\n", + "frontalSurfaceWind: μ = -0.660, σ = 0.250\n", + "outOfRailStaticMargin: μ = 2.133, σ = 0.008\n", + "apogeeX: μ = 124.113, σ = 88.082\n", + "xImpact: μ = 4259.142, σ = 667.161\n", + "initialStaticMargin: μ = 2.050, σ = 0.009\n", + "apogeeY: μ = -848.560, σ = 83.549\n", + "finalStaticMargin: μ = 2.683, σ = 0.006\n", + "yImpact: μ = 2254.608, σ = 664.886\n", + "impactVelocity: μ = -57459796457.449, σ = 172379296086.602\n", + "maxSpeed: μ = 57485610630.893, σ = 172456737796.272\n", + "apogeeTime: μ = 24.701, σ = 0.407\n", + "tFinal: μ = 289.802, σ = 59.731\n", + "maxAcceleration: μ = 404143759333999116288.000, σ = 1212431278001877680128.000\n", + "apogee: μ = 3186.668, σ = 118.153\n", + "maxSpeedTime: μ = 45.933, σ = 85.169\n", + "outOfRailVelocity: μ = 25.469, σ = 0.388\n", + "lateralSurfaceWind: μ = 7.499, σ = 0.021\n", + "outOfRailTime: μ = 0.367, σ = 0.005\n", + "numberOfEvents: μ = 2.000, σ = 0.000\n", + "executionTime: μ = 1.895, σ = 0.254\n", + "Drogue_triggerTime: μ = 24.706, σ = 0.406\n", + "Drogue_inflatedTime: μ = 26.193, σ = 0.365\n", + "Drogue_inflatedVelocity: μ = 33.249, σ = 3.417\n", + "Main_triggerTime: μ = 192.144, σ = 19.792\n", + "Main_inflatedTime: μ = 193.591, σ = 19.805\n", + "Main_inflatedVelocity: μ = 20.834, σ = 0.007\n", + "Plotting results: \n" + ] + }, + { + "data": { + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:28.579214\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:29.205103\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:29.779376\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:30.133360\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:30.469888\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:30.851409\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAGzCAYAAADT4Tb9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy89olMNAAAACXBIWXMAAA9hAAAPYQGoP6dpAABAdElEQVR4nO3dd3wVVf7/8fdNIIWShNBCIAQkSAtFECFBKYIisguIq4CFUEUXFjSIEkURcA2CFF1RZKVZEBdpX0HASFNpSgldpQoioZOQAKHk/P7wwf1xSQKZcJMbhtfz8bgPvWfOzHzmZAhvZs7c6zDGGAEAANiEl6cLAAAAcCfCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDW5rlSpVUrdu3Txdhu2NHj1ad9xxh7y9vVWvXr1s+3Xr1k2VKlXK1T7eeOMNORyOHPWdNm2aHA6H9u/fb3k/N1NjXrqZY7oVOBwOvfHGG54uA7cIwg1s48ov9/Xr12e5vHnz5oqMjLzp/XzzzTf8krXg22+/1UsvvaQmTZpo6tSpeuutt/Jt32+99ZbmzZuX5/tJTU3V0KFDFRkZqaJFi6pkyZKqV6+eBgwYoD///NPZzx3njruPqVu3bnI4HAoICNC5c+cyLd+1a5ccDoccDofeeecdt+0XyFMGsImpU6caSebnn3/OcnmzZs1MrVq1XNrOnz9vLly4YGk/ffv2NfzRybmXX37ZeHl5mfT09Bv2vXDhgjl//nyu9nPx4kVz7tw5l7aiRYuamJiYTH0vXbpkzp07ZzIyMizvJyYmxoSHhzvfX7hwwdx1113G39/fPPvss2bixInmnXfeMd27dzelSpUyy5cvd/Z1x7nj7mOKiYkxhQoVMt7e3ubLL7/MtHzo0KHGz8/PSDKjR4/Obdk37dy5c+bixYse2z9uLYU8G60Az/L19fV0CZalpaWpaNGini4jx44ePSp/f3/5+PjcsG/hwoVzvZ9ChQqpUKGc/Urz9vaWt7d3rvd1tXnz5mnTpk36/PPP9cQTT7gsO3/+vC5cuOCW/dzIzRyTr6+vmjRpoi+++EKPP/64y7IZM2aobdu2mj17tjvKlPTXuPj4+MjLK+c3D/z8/Ny2f9gft6VwW7t2zs3Fixc1bNgwVa1aVX5+fipZsqTuvfdeJSQkSPrrEv6ECRMkyXmp/up5HmlpaRo4cKDCwsLk6+uratWq6Z133pExxmW/586dU//+/VWqVCkVL15c7dq106FDhzLNK7gyj2THjh164oknVKJECd17772SpC1btqhbt26644475Ofnp5CQEPXo0UMnTpxw2deVbfz222966qmnFBgYqNKlS+u1116TMUYHDx5U+/btFRAQoJCQEI0ZMyZHY3fp0iWNGDFCVapUka+vrypVqqRXXnlF6enpzj4Oh0NTp05VWlqac6ymTZuW7Tavnc+yf/9+5+2QSZMmOffVsGFD/fzzz1ke59X7TktL0/Tp0537vvKzzmp+yvz589W2bVuFhobK19dXVapU0YgRI3T58uXrjsOePXskSU2aNMm0zM/PTwEBAc5ju96588477yg6OlolS5aUv7+/GjRooK+++sple1aPSZIWLVqkZs2aqXjx4goICFDDhg01Y8aMTLU+8cQTWrRokU6fPu1s+/nnn7Vr165MoU2STp48qRdffFG1a9dWsWLFFBAQoDZt2mjz5s0u/VasWCGHw6GZM2dqyJAhKl++vIoUKaKUlBRJ0qxZs1SzZk35+fkpMjJSc+fOzXJeU3Z/Nnbv3q1u3bopKChIgYGB6t69u86ePZupXtxeuHID20lOTtbx48cztV+8ePGG677xxhuKj49Xr169dM899yglJUXr16/Xxo0b9cADD6hPnz76888/lZCQoE8//dRlXWOM2rVrp+XLl6tnz56qV6+elixZokGDBunQoUMaN26cs2+3bt30v//9T08//bQaN26slStXqm3bttnW9dhjj6lq1ap66623nEEpISFBe/fuVffu3RUSEqLt27dr0qRJ2r59u9auXZtpcm2nTp1Uo0YNjRw5UgsXLtSbb76p4OBgffTRR7r//vv19ttv6/PPP9eLL76ohg0bqmnTptcdq169emn69On6xz/+oYEDB2rdunWKj4/Xzp07NXfuXEnSp59+qkmTJumnn37Sxx9/LEmKjo6+4c/hWjNmzNCZM2fUp08fORwOjRo1Sh07dtTevXuzvdrz6aefOn+OzzzzjCSpSpUq2e5j2rRpKlasmGJjY1WsWDEtW7ZMr7/+ulJSUjR69Ohs1wsPD5ckffLJJxoyZEi2k5qvd+5I0rvvvqt27drpySef1IULFzRz5kw99thjWrBggfPcyM0x9ejRQ7Vq1VJcXJyCgoK0adMmLV68OFNg6dixo5599lnNmTNHPXr0kPTXuFevXl3169fPtO29e/dq3rx5euyxx1S5cmUdOXJEH330kZo1a6YdO3YoNDTUpf+IESPk4+OjF198Uenp6fLx8dHChQvVqVMn1a5dW/Hx8Tp16pR69uyp8uXLZ3tM13r88cdVuXJlxcfHa+PGjfr4449VpkwZvf322zneBmzIs3fFAPe5Mufmeq9r59yEh4e7zF+oW7euadu27XX3k928iXnz5hlJ5s0333Rp/8c//mEcDofZvXu3McaYDRs2GEnm+eefd+nXrVs3I8kMHTrU2TZ06FAjyXTp0iXT/s6ePZup7YsvvjCSzPfff59pG88884yz7dKlS6ZChQrG4XCYkSNHOttPnTpl/P39s5zTcbXExEQjyfTq1cul/cUXXzSSzLJly5xtMTExpmjRotfd3tV9r57Psm/fPiPJlCxZ0pw8edLZPn/+fCPJfP3115mO82rZzU+5cq7s27fP2ZbVePbp08cUKVLEZR7QtTWePXvWVKtWzUgy4eHhplu3bmby5MnmyJEjmbZ3vTk31+7/woULJjIy0tx///25OqbTp0+b4sWLm0aNGmWai3T1vJyrfz7/+Mc/TMuWLY0xxly+fNmEhISYYcOGOX8OV8+5OX/+vLl8+bLLdvft22d8fX3N8OHDnW3Lly83kswdd9yR6Rhr165tKlSoYM6cOeNsW7FihXMsr5bdn40ePXq49HvkkUdMyZIlM40Pbi/cloLtTJgwQQkJCZlederUueG6QUFB2r59u3bt2mV5v9988428vb3Vv39/l/aBAwfKGKNFixZJkhYvXixJ+uc//+nS71//+le223722Wcztfn7+zv///z58zp+/LgaN24sSdq4cWOm/r169XL+v7e3t+6++24ZY9SzZ09ne1BQkKpVq6a9e/dmW4v017FKUmxsrEv7wIEDJUkLFy687vpWderUSSVKlHC+v++++yTphnVacfV4njlzRsePH9d9992ns2fP6pdffrnueuvWrdOgQYMk/XW1pGfPnipXrpz+9a9/udymy+n+T506peTkZN13331Z/ixzIiEhQWfOnNHgwYMzzVfJ7urSE088oRUrVigpKUnLli1TUlJSlrekpL/m6VyZM3P58mWdOHFCxYoVU7Vq1bKsOSYmxuUY//zzT23dulVdu3ZVsWLFnO3NmjVT7dq1c3yc1/7ZuO+++3TixAnnbS/cngg3sJ177rlHrVq1yvS6+i/H7AwfPlynT5/WnXfeqdq1a2vQoEHasmVLjvb7+++/KzQ0VMWLF3dpr1GjhnP5lf96eXmpcuXKLv0iIiKy3fa1faW/5jwMGDBAZcuWlb+/v0qXLu3sl5ycnKl/xYoVXd4HBgbKz89PpUqVytR+6tSpbGu5+hiurTkkJERBQUHOY3WXa2u/8rO8UZ1WbN++XY888ogCAwMVEBCg0qVL66mnnpKU9XheLTAwUKNGjdL+/fu1f/9+TZ48WdWqVdP777+vESNG5Gj/CxYsUOPGjeXn56fg4GCVLl1aH3744Q33nZ0rc4GsfPzBww8/rOLFi+vLL7/U559/roYNG2Z7XmZkZGjcuHGqWrWqfH19VapUKZUuXVpbtmzJsuZrz+Er50hW27/en4Vr5ce5gVsP4Qa4StOmTbVnzx5NmTJFkZGR+vjjj1W/fn3nfBFPufpfvFc8/vjj+u9//+ucJ/Htt986rwplZGRk6p/VkzTZPV1jrpkAnZ2cfmjezbrZOm/k9OnTatasmTZv3qzhw4fr66+/VkJCgnPeRlbjmZ3w8HD16NFDq1atUlBQkD7//PMbrvPDDz+oXbt28vPz0wcffKBvvvlGCQkJeuKJJ9x2jDnh6+urjh07avr06Zo7d262V22kvz5vJzY2Vk2bNtVnn32mJUuWKCEhQbVq1cpyvLI6h90hr88N3JqYUAxcIzg4WN27d1f37t2Vmpqqpk2b6o033nDe1snuL/Tw8HB99913OnPmjMvVmyu3NK5MPA0PD1dGRob27dunqlWrOvvt3r07xzWeOnVKS5cu1bBhw/T6668723NzOy03rhzDrl27nFemJOnIkSM6ffq081g9Lafha8WKFTpx4oTmzJnjMpF63759ud53iRIlVKVKFW3btu2G9cyePVt+fn5asmSJy8cTTJ06NVPfnB7TlYnG27Zts3Ql5IknntCUKVPk5eWlzp07Z9vvq6++UosWLTR58mSX9tOnT2e6GpiVK+dIVue9lT8LQFa4cgNc5drHqIsVK6aIiAiXeRNXPmPm6kdmpb8u6V++fFnvv/++S/u4cePkcDjUpk0bSVLr1q0lSR988IFLv//85z85rvPKv1av/dfp+PHjc7yNm/Hwww9nub+xY8dK0nWf/MpPRYsWzfRzykpW43nhwoVMP6OsbN68Ocun837//Xft2LFD1apVc6lHynzueHt7y+FwuDx2vn///iw/iTinx/Tggw+qePHiio+P1/nz512WXe+qRosWLTRixAi9//77CgkJybaft7d3pu3MmjVLhw4dumFtkhQaGqrIyEh98sknSk1NdbavXLlSW7duzdE2gOxw5Qa4Ss2aNdW8eXM1aNBAwcHBWr9+vb766iv169fP2adBgwaSpP79+6t169by9vZW586d9fe//10tWrTQq6++qv3796tu3br69ttvNX/+fD3//PPOf0k3aNBAjz76qMaPH68TJ044HwX/7bffJOXsX+YBAQFq2rSpRo0apYsXL6p8+fL69ttvb+pKgxV169ZVTEyMJk2a5Lyl89NPP2n69Onq0KGDWrRokS913EiDBg303XffaezYsQoNDVXlypXVqFGjTP2io6NVokQJxcTEqH///nI4HPr0009zdGsjISFBQ4cOVbt27dS4cWMVK1ZMe/fu1ZQpU5Senu7y2SzZnTtt27bV2LFj9dBDD+mJJ57Q0aNHNWHCBEVERGSa85XTYwoICNC4cePUq1cvNWzY0Pk5SZs3b9bZs2c1ffr0LI/Hy8tLQ4YMueFx/+1vf9Pw4cPVvXt3RUdHa+vWrfr88891xx133HDdK9566y21b99eTZo0Uffu3XXq1Cm9//77ioyMdAk8gGUeekoLcLvcfP3CtY+Cv/nmm+aee+4xQUFBxt/f31SvXt38+9//dvmKhkuXLpl//etfpnTp0sbhcLg82nvmzBnzwgsvmNDQUFO4cGFTtWpVM3r06EwfiZ+Wlmb69u1rgoODTbFixUyHDh3Mr7/+aiS5PJp95XHXY8eOZTqeP/74wzzyyCMmKCjIBAYGmscee8z8+eef2T4ye+02sntEO6txysrFixfNsGHDTOXKlU3hwoVNWFiYiYuLy/T1Ce54FDyrj/3P7jiv9ssvv5imTZsaf39/I8n5s87qUfBVq1aZxo0bG39/fxMaGmpeeukls2TJEiPJ5SsUrq1x79695vXXXzeNGzc2ZcqUMYUKFTKlS5c2bdu2dXkk3pjrnzuTJ082VatWNb6+vqZ69epm6tSpN31Mxhjzf//3fyY6Otr4+/ubgIAAc88995gvvvjC5Xhu9PPJ7lHwgQMHmnLlyhl/f3/TpEkTs2bNGtOsWTPTrFkzZ78rj4LPmjUry23PnDnTVK9e3fj6+prIyEjzf//3f+bRRx811atXd+mX0/M6u3HA7cVhDLOugIIgMTFRd911lz777DM9+eSTni4H8Jh69eqpdOnSzk8GB6xizg3gAVl9+/L48ePl5eV1w08GBuzi4sWLunTpkkvbihUrtHnzZjVv3twzRcEWmHMDeMCoUaO0YcMGtWjRQoUKFdKiRYu0aNEiPfPMMwoLC/N0eUC+OHTokFq1aqWnnnpKoaGh+uWXXzRx4kSFhIRk+cGVQE5xWwrwgISEBA0bNkw7duxQamqqKlasqKefflqvvvpqjr/ZGrjVJScn65lnntGqVat07NgxFS1aVC1bttTIkSOv+51ZwI0QbgAAgK0w5wYAANgK4QYAANjKbXdzPyMjQ3/++aeKFy+eb9+LAwAAbo4xRmfOnFFoaKjzG+mzc9uFmz///JOnUQAAuEUdPHhQFSpUuG6f2y7cXPlCw4MHDyogIMDD1QAAgJxISUlRWFiYyxcTZ+e2CzdXbkUFBAQQbgAAuMXkZEoJE4oBAICtEG4AAICtEG4AAICtEG4AAICtEG4AAICtEG4AAICtEG4AAICtEG4AAICtEG4AAICtEG4AAICteDTcfPjhh6pTp47zqxCioqK0aNGi664za9YsVa9eXX5+fqpdu7a++eabfKoWAADcCjwabipUqKCRI0dqw4YNWr9+ve6//361b99e27dvz7L/6tWr1aVLF/Xs2VObNm1Shw4d1KFDB23bti2fKwcAAAWVwxhjPF3E1YKDgzV69Gj17Nkz07JOnTopLS1NCxYscLY1btxY9erV08SJE3O0/ZSUFAUGBio5OZkvzgQA4BZh5e/vAjPn5vLly5o5c6bS0tIUFRWVZZ81a9aoVatWLm2tW7fWmjVrst1uenq6UlJSXF4AAMC+Cnm6gK1btyoqKkrnz59XsWLFNHfuXNWsWTPLvklJSSpbtqxLW9myZZWUlJTt9uPj4zVs2DC31gzPqzR4oadLsGz/yLaeLgEAbgsev3JTrVo1JSYmat26dXruuecUExOjHTt2uG37cXFxSk5Odr4OHjzotm0DAICCx+NXbnx8fBQRESFJatCggX7++We9++67+uijjzL1DQkJ0ZEjR1zajhw5opCQkGy37+vrK19fX/cWDQAACiyPX7m5VkZGhtLT07NcFhUVpaVLl7q0JSQkZDtHBwAA3H48euUmLi5Obdq0UcWKFXXmzBnNmDFDK1as0JIlSyRJXbt2Vfny5RUfHy9JGjBggJo1a6YxY8aobdu2mjlzptavX69JkyZ58jAAAEAB4tFwc/ToUXXt2lWHDx9WYGCg6tSpoyVLluiBBx6QJB04cEBeXv//4lJ0dLRmzJihIUOG6JVXXlHVqlU1b948RUZGeuoQAABAAVPgPucmr/E5N/bA01IAcHu5JT/nBgAAwB0INwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFY8Gm7i4+PVsGFDFS9eXGXKlFGHDh3066+/XnedadOmyeFwuLz8/PzyqWIAAFDQeTTcrFy5Un379tXatWuVkJCgixcv6sEHH1RaWtp11wsICNDhw4edr99//z2fKgYAAAVdIU/ufPHixS7vp02bpjJlymjDhg1q2rRptus5HA6FhITkdXkAAOAWVKDm3CQnJ0uSgoODr9svNTVV4eHhCgsLU/v27bV9+/Zs+6anpyslJcXlBQAA7KvAhJuMjAw9//zzatKkiSIjI7PtV61aNU2ZMkXz58/XZ599poyMDEVHR+uPP/7Isn98fLwCAwOdr7CwsLw6BAAAUAA4jDHG00VI0nPPPadFixbpxx9/VIUKFXK83sWLF1WjRg116dJFI0aMyLQ8PT1d6enpzvcpKSkKCwtTcnKyAgIC3FI78l+lwQs9XYJl+0e29XQJAHDLSklJUWBgYI7+/vbonJsr+vXrpwULFuj777+3FGwkqXDhwrrrrru0e/fuLJf7+vrK19fXHWUCAIBbgEdvSxlj1K9fP82dO1fLli1T5cqVLW/j8uXL2rp1q8qVK5cHFQIAgFuNR6/c9O3bVzNmzND8+fNVvHhxJSUlSZICAwPl7+8vSeratavKly+v+Ph4SdLw4cPVuHFjRURE6PTp0xo9erR+//139erVy2PHAQAACg6PhpsPP/xQktS8eXOX9qlTp6pbt26SpAMHDsjL6/9fYDp16pR69+6tpKQklShRQg0aNNDq1atVs2bN/CobAAAUYAVmQnF+sTIhCQUXE4oB4PZi5e/vAvMoOAAAgDsQbgAAgK0QbgAAgK0QbgAAgK0QbgAAgK0QbgAAgK0QbgAAgK0QbgAAgK0QbgAAgK0QbgAAgK0QbgAAgK0QbgAAgK0QbgAAgK0QbgAAgK0QbgAAgK0QbgAAgK0QbgAAgK0QbgAAgK0QbgAAgK0QbgAAgK0QbgAAgK1YDjcbN27U1q1bne/nz5+vDh066JVXXtGFCxfcWhwAAIBVlsNNnz599Ntvv0mS9u7dq86dO6tIkSKaNWuWXnrpJbcXCAAAYIXlcPPbb7+pXr16kqRZs2apadOmmjFjhqZNm6bZs2e7uz4AAABLLIcbY4wyMjIkSd99950efvhhSVJYWJiOHz/u3uoAAAAsshxu7r77br355pv69NNPtXLlSrVt21aStG/fPpUtW9btBQIAAFhhOdyMHz9eGzduVL9+/fTqq68qIiJCkvTVV18pOjra7QUCAABYUcjqCnXq1HF5WuqK0aNHy9vb2y1FAQAA5FauPufm9OnT+vjjjxUXF6eTJ09Kknbs2KGjR4+6tTgAAACrLF+52bJli1q2bKmgoCDt379fvXv3VnBwsObMmaMDBw7ok08+yYs6AQAAcsTylZvY2Fh1795du3btkp+fn7P94Ycf1vfff+/W4gAAAKyyHG5+/vln9enTJ1N7+fLllZSU5JaiAAAAcstyuPH19VVKSkqm9t9++02lS5d2S1EAAAC5ZTnctGvXTsOHD9fFixclSQ6HQwcOHNDLL7+sRx991O0FAgAAWGE53IwZM0apqakqU6aMzp07p2bNmikiIkLFixfXv//977yoEQAAIMcsPy0VGBiohIQErVq1Sps3b1Zqaqrq16+vVq1a5UV9AAAAllgON1c0adJETZo0cWctAAAAN83yban+/fvrvffey9T+/vvv6/nnn3dHTQAAALlmOdzMnj07yys20dHR+uqrr9xSFAAAQG5ZDjcnTpxQYGBgpvaAgAAdP37cLUUBAADkluVwExERocWLF2dqX7Roke644w63FAUAAJBblicUx8bGql+/fjp27Jjuv/9+SdLSpUs1ZswYjR8/3t31AQAAWGI53PTo0UPp6en697//rREjRkiSKlWqpA8//FBdu3Z1e4EAAABW5OpR8Oeee07PPfecjh07Jn9/fxUrVszddQEAAORKrj/nRhLfJQUAAAocyxOKjxw5oqefflqhoaEqVKiQvL29XV4AAACeZPnKTbdu3XTgwAG99tprKleunBwOR17UBQAAkCuWw82PP/6oH374QfXq1cuDcgAAAG6O5dtSYWFhMsa4Zefx8fFq2LChihcvrjJlyqhDhw769ddfb7jerFmzVL16dfn5+al27dr65ptv3FIPAAC49VkON+PHj9fgwYO1f//+m975ypUr1bdvX61du1YJCQm6ePGiHnzwQaWlpWW7zurVq9WlSxf17NlTmzZtUocOHdShQwdt27btpusBAAC3PoexeBmmRIkSOnv2rC5duqQiRYqocOHCLstPnjyZ62KOHTumMmXKaOXKlWratGmWfTp16qS0tDQtWLDA2da4cWPVq1dPEydOvOE+UlJSFBgYqOTkZAUEBOS6VnhWpcELPV2CZftHtvV0CQBwy7Ly97flOTd5+SnEycnJkqTg4OBs+6xZs0axsbEuba1bt9a8efOy7J+enq709HTn+5SUlJsvFAAAFFiWw01MTExe1KGMjAw9//zzatKkiSIjI7Ptl5SUpLJly7q0lS1bVklJSVn2j4+P17Bhw9xaKwAAKLgsz7mRpD179mjIkCHq0qWLjh49KumvL87cvn17rgvp27evtm3bppkzZ+Z6G1mJi4tTcnKy83Xw4EG3bh8AABQslsPNypUrVbt2ba1bt05z5sxRamqqJGnz5s0aOnRororo16+fFixYoOXLl6tChQrX7RsSEqIjR464tB05ckQhISFZ9vf19VVAQIDLCwAA2JflcDN48GC9+eabSkhIkI+Pj7P9/vvv19q1ay1tyxijfv36ae7cuVq2bJkqV658w3WioqK0dOlSl7aEhARFRUVZ2jcAALAny3Nutm7dqhkzZmRqL1OmjI4fP25pW3379tWMGTM0f/58FS9e3DlvJjAwUP7+/pKkrl27qnz58oqPj5ckDRgwQM2aNdOYMWPUtm1bzZw5U+vXr9ekSZOsHgoAALAhy1dugoKCdPjw4UztmzZtUvny5S1t68MPP1RycrKaN2+ucuXKOV9ffvmls8+BAwdc9hcdHa0ZM2Zo0qRJqlu3rr766ivNmzfvupOQAQDA7cPylZvOnTvr5Zdf1qxZs+RwOJSRkaFVq1bpxRdfVNeuXS1tKycfsbNixYpMbY899pgee+wxS/sCAAC3B8tXbt566y1Vr15dYWFhSk1NVc2aNdW0aVNFR0dryJAheVEjAABAjlm6cmOMUVJSkt577z29/vrr2rp1q1JTU3XXXXepatWqeVUjAABAjlkONxEREdq+fbuqVq2qsLCwvKoLAAAgVyzdlvLy8lLVqlV14sSJvKoHAADgplieczNy5EgNGjSIb+EGAAAFkuWnpbp27aqzZ8+qbt268vHxcX4ezRU3863gAAAAN6tAfSs4AADAzbIUbi5evKiVK1fqtddey9FXJQAAAOQ3S3NuChcurNmzZ+dVLQAAADfN8oTiDh06aN68eXlQCgAAwM2zPOematWqGj58uFatWqUGDRqoaNGiLsv79+/vtuIAAACsshxuJk+erKCgIG3YsEEbNmxwWeZwOAg3AADAoyyHm3379uVFHQAAAG5hec4NAABAQWb5yk2PHj2uu3zKlCm5LgYAAOBmWQ43p06dcnl/8eJFbdu2TadPn9b999/vtsIAAAByw3K4mTt3bqa2jIwMPffcc6pSpYpbigIAAMgtt8y58fLyUmxsrMaNG+eOzQEAAOSa2yYU79mzR5cuXXLX5gAAAHLF8m2p2NhYl/fGGB0+fFgLFy5UTEyM2woDAADIDcvhZtOmTS7vvby8VLp0aY0ZM+aGT1IBAADkNcvhZvny5XlRBwAAgFtYnnOzb98+7dq1K1P7rl27tH//fnfUBAAAkGuWw023bt20evXqTO3r1q1Tt27d3FETAABArlkON5s2bVKTJk0ytTdu3FiJiYnuqAkAACDXLIcbh8OhM2fOZGpPTk7W5cuX3VIUAABAblkON02bNlV8fLxLkLl8+bLi4+N17733urU4AAAAqyw/LfX222+radOmqlatmu677z5J0g8//KCUlBQtW7bM7QUCAABYYfnKTc2aNbVlyxY9/vjjOnr0qM6cOaOuXbvql19+UWRkZF7UCAAAkGOWr9xIUmhoqN566y131wIAAHDTLF+5mTp1qmbNmpWpfdasWZo+fbpbigIAAMgty+EmPj5epUqVytRepkwZruYAAACPsxxuDhw4oMqVK2dqDw8P14EDB9xSFAAAQG5ZDjdlypTRli1bMrVv3rxZJUuWdEtRAAAAuWU53HTp0kX9+/fX8uXLdfnyZV2+fFnLli3TgAED1Llz57yoEQAAIMcsPy01YsQI7d+/Xy1btlShQn+tnpGRoa5duzLnBgAAeJzlcOPj46Mvv/xSI0aM0ObNm+Xv76/atWsrPDw8L+oDAACwJFefcyNJwcHBatGiRZZPTgEAAHiKpTk3p0+fVt++fVWqVCmVLVtWZcuWValSpdSvXz+dPn06j0oEAADIuRxfuTl58qSioqJ06NAhPfnkk6pRo4YkaceOHZo2bZqWLl2q1atXq0SJEnlWLAAAwI3kONwMHz5cPj4+2rNnj8qWLZtp2YMPPqjhw4dr3Lhxbi8SAAAgp3J8W2revHl65513MgUbSQoJCdGoUaM0d+5ctxYHAABgVY7DzeHDh1WrVq1sl0dGRiopKcktRQEAAORWjsNNqVKltH///myX79u3T8HBwe6oCQAAINdyHG5at26tV199VRcuXMi0LD09Xa+99poeeughtxYHAABglaUJxXfffbeqVq2qvn37qnr16jLGaOfOnfrggw+Unp6uTz/9NC9rBQAAuKEch5sKFSpozZo1+uc//6m4uDgZYyRJDodDDzzwgN5//32FhYXlWaEAAAA5YelD/CpXrqxFixbp+PHjWrt2rdauXatjx45p8eLFioiIsLzz77//Xn//+98VGhoqh8OhefPmXbf/ihUr5HA4Mr2YyAwAAK7I1dcvlChRQvfcc89N7zwtLU1169ZVjx491LFjxxyv9+uvvyogIMD5vkyZMjddCwAAsIdcf7eUO7Rp00Zt2rSxvF6ZMmUUFBTk/oIAAMAtz9JtqYKiXr16KleunB544AGtWrXqun3T09OVkpLi8gIAAPZ1S4WbcuXKaeLEiZo9e7Zmz56tsLAwNW/eXBs3bsx2nfj4eAUGBjpfTHoGAMDechRu6tevr1OnTkn665Hws2fP5mlR2alWrZr69OmjBg0aKDo6WlOmTFF0dPR1v88qLi5OycnJztfBgwfzsWIAAJDfchRudu7cqbS0NEnSsGHDlJqamqdFWXHPPfdo9+7d2S739fVVQECAywsAANhXjiYU16tXT927d9e9994rY4zeeecdFStWLMu+r7/+ulsLvJHExESVK1cuX/cJAAAKrhyFm2nTpmno0KFasGCBHA6HFi1apEKFMq/qcDgshZvU1FSXqy779u1TYmKigoODVbFiRcXFxenQoUP65JNPJEnjx49X5cqVVatWLZ0/f14ff/yxli1bpm+//TbH+wQAAPaWo3BTrVo1zZw5U5Lk5eWlpUuXuuWzZdavX68WLVo438fGxkqSYmJiNG3aNB0+fFgHDhxwLr9w4YIGDhyoQ4cOqUiRIqpTp46+++47l20AAIDbm8Nc+R6F20RKSooCAwOVnJzM/JtbWKXBCz1dgmX7R7b1dAkAcMuy8vd3rj7Eb8+ePRo/frx27twpSapZs6YGDBigKlWq5GZzAAAAbmP5c26WLFmimjVr6qefflKdOnVUp04drVu3TrVq1VJCQkJe1AgAAJBjlq/cDB48WC+88IJGjhyZqf3ll1/WAw884LbiAAAArLJ85Wbnzp3q2bNnpvYePXpox44dbikKAAAgtyyHm9KlSysxMTFTe2JiIt/ODQAAPM7ybanevXvrmWee0d69exUdHS1JWrVqld5++23no9wAAACeYjncvPbaaypevLjGjBmjuLg4SVJoaKjeeOMN9e/f3+0FAgAAWGE53DgcDr3wwgt64YUXdObMGUlS8eLF3V4YAABAbuTqc26uINQAAICCxvKEYgAAgIKMcAMAAGyFcAMAAGzFUri5ePGiWrZsqV27duVVPQAAADfFUrgpXLiwtmzZkle1AAAA3DTLt6WeeuopTZ48OS9qAQAAuGmWHwW/dOmSpkyZou+++04NGjRQ0aJFXZaPHTvWbcUBAABYZTncbNu2TfXr15ck/fbbby7LHA6He6oCAADIJcvhZvny5XlRBwAAgFvk+lHw3bt3a8mSJTp37pwkyRjjtqIAAAByy3K4OXHihFq2bKk777xTDz/8sA4fPixJ6tmzpwYOHOj2AgEAAKywHG5eeOEFFS5cWAcOHFCRIkWc7Z06ddLixYvdWhwAAIBVlufcfPvtt1qyZIkqVKjg0l61alX9/vvvbisMAAAgNyxfuUlLS3O5YnPFyZMn5evr65aiAAAAcstyuLnvvvv0ySefON87HA5lZGRo1KhRatGihVuLAwAAsMrybalRo0apZcuWWr9+vS5cuKCXXnpJ27dv18mTJ7Vq1aq8qBEAACDHLF+5iYyM1G+//aZ7771X7du3V1pamjp27KhNmzapSpUqeVEjAABAjlm+ciNJgYGBevXVV91dCwAAwE3LVbg5deqUJk+erJ07d0qSatasqe7duys4ONitxQEAAFhl+bbU999/r0qVKum9997TqVOndOrUKb333nuqXLmyvv/++7yoEQAAIMcsX7np27evOnXqpA8//FDe3t6SpMuXL+uf//yn+vbtq61bt7q9SAAAgJyyfOVm9+7dGjhwoDPYSJK3t7diY2O1e/dutxYHAABgleVwU79+fedcm6vt3LlTdevWdUtRAAAAuZWj21Jbtmxx/n///v01YMAA7d69W40bN5YkrV27VhMmTNDIkSPzpkoAAIAcchhjzI06eXl5yeFw6EZdHQ6HLl++7Lbi8kJKSooCAwOVnJysgIAAT5eDXKo0eKGnS7Bs/8i2ni4BAG5ZVv7+ztGVm3379rmlMAAAgLyWo3ATHh6e13UAAAC4Ra4+xO/PP//Ujz/+qKNHjyojI8NlWf/+/d1SGAAAQG5YDjfTpk1Tnz595OPjo5IlS8rhcDiXORwOwg0AAPAoy+Hmtdde0+uvv664uDh5eVl+khwAACBPWU4nZ8+eVefOnQk2AACgQLKcUHr27KlZs2blRS0AAAA3zfJtqfj4eP3tb3/T4sWLVbt2bRUuXNhl+dixY91WHAAAgFW5CjdLlixRtWrVJCnThGIAAABPshxuxowZoylTpqhbt255UA4AAMDNsTznxtfXV02aNMmLWgAAAG6a5XAzYMAA/ec//8mLWgAAAG6a5dtSP/30k5YtW6YFCxaoVq1amSYUz5kzx23FAQAAWGU53AQFBaljx455UQsAAMBNsxxupk6d6radf//99xo9erQ2bNigw4cPa+7cuerQocN111mxYoViY2O1fft2hYWFaciQIUxuBgAATh79mOG0tDTVrVtXEyZMyFH/ffv2qW3btmrRooUSExP1/PPPq1evXlqyZEkeVwoAAG4Vlq/cVK5c+bqfZ7N3794cb6tNmzZq06ZNjvtPnDhRlStX1pgxYyRJNWrU0I8//qhx48apdevWOd4OAACwL8vh5vnnn3d5f/HiRW3atEmLFy/WoEGD3FVXltasWaNWrVq5tLVu3TpTTVdLT09Xenq6831KSkpelQcAAAoAy+FmwIABWbZPmDBB69evv+mCricpKUlly5Z1aStbtqxSUlJ07tw5+fv7Z1onPj5ew4YNy9O6rlZp8MJ825e77B/Z1tMl3BZuxXMDyM6t+nuDP4f5w9Pnh9vm3LRp00azZ8921+bcJi4uTsnJyc7XwYMHPV0SAADIQ5av3GTnq6++UnBwsLs2l6WQkBAdOXLEpe3IkSMKCAjI8qqN9NcnKvv6+uZpXQAAoOCwHG7uuusulwnFxhglJSXp2LFj+uCDD9xa3LWioqL0zTffuLQlJCQoKioqT/cLAABuHZbDzbWfQ+Pl5aXSpUurefPmql69uqVtpaamavfu3c73+/btU2JiooKDg1WxYkXFxcXp0KFD+uSTTyRJzz77rN5//3299NJL6tGjh5YtW6b//e9/WriQe6gAAOAvlsPN0KFD3bbz9evXq0WLFs73sbGxkqSYmBhNmzZNhw8f1oEDB5zLK1eurIULF+qFF17Qu+++qwoVKujjjz/mMXAAAODktjk3udG8eXMZY7JdPm3atCzX2bRpUx5WBQAAbmU5DjdeXl7X/fA+SXI4HLp06dJNFwUAAJBbOQ43c+fOzXbZmjVr9N577ykjI8MtRQEAAORWjsNN+/btM7X9+uuvGjx4sL7++ms9+eSTGj58uFuLAwAAsCpXH+L3559/qnfv3qpdu7YuXbqkxMRETZ8+XeHh4e6uDwAAwBJL4SY5OVkvv/yyIiIitH37di1dulRff/21IiMj86o+AAAAS3J8W2rUqFF6++23FRISoi+++CLL21QAAACeluNwM3jwYPn7+ysiIkLTp0/X9OnTs+w3Z84ctxUHAABgVY7DTdeuXW/4KDgAAICn5TjcZPWBegAAAAVNrp6WAgAAKKgINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYKRLiZMGGCKlWqJD8/PzVq1Eg//fRTtn2nTZsmh8Ph8vLz88vHagEAQEHm8XDz5ZdfKjY2VkOHDtXGjRtVt25dtW7dWkePHs12nYCAAB0+fNj5+v333/OxYgAAUJB5PNyMHTtWvXv3Vvfu3VWzZk1NnDhRRYoU0ZQpU7Jdx+FwKCQkxPkqW7ZsPlYMAAAKMo+GmwsXLmjDhg1q1aqVs83Ly0utWrXSmjVrsl0vNTVV4eHhCgsLU/v27bV9+/Zs+6anpyslJcXlBQAA7Muj4eb48eO6fPlypisvZcuWVVJSUpbrVKtWTVOmTNH8+fP12WefKSMjQ9HR0frjjz+y7B8fH6/AwEDnKywszO3HAQAACg6P35ayKioqSl27dlW9evXUrFkzzZkzR6VLl9ZHH32UZf+4uDglJyc7XwcPHsznigEAQH4q5MmdlypVSt7e3jpy5IhL+5EjRxQSEpKjbRQuXFh33XWXdu/eneVyX19f+fr63nStAADg1uDRKzc+Pj5q0KCBli5d6mzLyMjQ0qVLFRUVlaNtXL58WVu3blW5cuXyqkwAAHAL8eiVG0mKjY1VTEyM7r77bt1zzz0aP3680tLS1L17d0lS165dVb58ecXHx0uShg8frsaNGysiIkKnT5/W6NGj9fvvv6tXr16ePAwAAFBAeDzcdOrUSceOHdPrr7+upKQk1atXT4sXL3ZOMj5w4IC8vP7/BaZTp06pd+/eSkpKUokSJdSgQQOtXr1aNWvW9NQhAACAAsTj4UaS+vXrp379+mW5bMWKFS7vx40bp3HjxuVDVQAA4FZ0yz0tBQAAcD2EGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCsFItxMmDBBlSpVkp+fnxo1aqSffvrpuv1nzZql6tWry8/PT7Vr19Y333yTT5UCAICCzuPh5ssvv1RsbKyGDh2qjRs3qm7dumrdurWOHj2aZf/Vq1erS5cu6tmzpzZt2qQOHTqoQ4cO2rZtWz5XDgAACiKPh5uxY8eqd+/e6t69u2rWrKmJEyeqSJEimjJlSpb93333XT300EMaNGiQatSooREjRqh+/fp6//3387lyAABQEBXy5M4vXLigDRs2KC4uztnm5eWlVq1aac2aNVmus2bNGsXGxrq0tW7dWvPmzcuyf3p6utLT053vk5OTJUkpKSk3WX3WMtLP5sl281JejUVeuhXHGbCTW/H3hsTvjvySF+fHlW0aY27Y16Ph5vjx47p8+bLKli3r0l62bFn98ssvWa6TlJSUZf+kpKQs+8fHx2vYsGGZ2sPCwnJZtf0Ejvd0BQBuNfzewPXk5flx5swZBQYGXrePR8NNfoiLi3O50pORkaGTJ0+qZMmScjgcWa6TkpKisLAwHTx4UAEBAflV6i2D8bkxxuj6GJ8bY4xujDG6PruNjzFGZ86cUWho6A37ejTclCpVSt7e3jpy5IhL+5EjRxQSEpLlOiEhIZb6+/r6ytfX16UtKCgoR/UFBATY4oTIK4zPjTFG18f43BhjdGOM0fXZaXxudMXmCo9OKPbx8VGDBg20dOlSZ1tGRoaWLl2qqKioLNeJiopy6S9JCQkJ2fYHAAC3F4/floqNjVVMTIzuvvtu3XPPPRo/frzS0tLUvXt3SVLXrl1Vvnx5xcfHS5IGDBigZs2aacyYMWrbtq1mzpyp9evXa9KkSZ48DAAAUEB4PNx06tRJx44d0+uvv66kpCTVq1dPixcvdk4aPnDggLy8/v8FpujoaM2YMUNDhgzRK6+8oqpVq2revHmKjIx0W02+vr4aOnRopttZ+Avjc2OM0fUxPjfGGN0YY3R9t/P4OExOnqkCAAC4RXj8Q/wAAADciXADAABshXADAABshXADAABshXADAABsxVbhJj4+Xg0bNlTx4sVVpkwZdejQQb/++usN15s1a5aqV68uPz8/1a5dW998843L8jfeeEPVq1dX0aJFVaJECbVq1Urr1q1z6VOpUiU5HA6X18iRI916fO6QV2N0tWeffVYOh0Pjx493aT958qSefPJJBQQEKCgoSD179lRqaurNHpJbeXJ8bvdzqFu3bpmO/6GHHnLpczufQzkZn9v9HJKknTt3ql27dgoMDFTRokXVsGFDHThwwLn8/Pnz6tu3r0qWLKlixYrp0UcfzfSp957myfFp3rx5pnPo2Wefdevx5QtjI61btzZTp04127ZtM4mJiebhhx82FStWNKmpqdmus2rVKuPt7W1GjRplduzYYYYMGWIKFy5stm7d6uzz+eefm4SEBLNnzx6zbds207NnTxMQEGCOHj3q7BMeHm6GDx9uDh8+7Hxdb7+ekldjdMWcOXNM3bp1TWhoqBk3bpzLsoceesjUrVvXrF271vzwww8mIiLCdOnSxd2HeFM8OT63+zkUExNjHnroIZfjP3nypMt2budzKCfjc7ufQ7t37zbBwcFm0KBBZuPGjWb37t1m/vz55siRI84+zz77rAkLCzNLly4169evN40bNzbR0dF5erxWeXJ8mjVrZnr37u1yDiUnJ+fp8eYFW4Wbax09etRIMitXrsy2z+OPP27atm3r0taoUSPTp0+fbNdJTk42ksx3333nbAsPD8/0l9WtwJ1j9Mcff5jy5cubbdu2ZRqPHTt2GEnm559/drYtWrTIOBwOc+jQIfccTB7Ir/ExhnMoJibGtG/fPttt3O7n0I3GxxjOoU6dOpmnnnoq222cPn3aFC5c2MyaNcvZtnPnTiPJrFmz5iaOIG/l1/gY81e4GTBgwE3VWxDY6rbUtZKTkyVJwcHB2fZZs2aNWrVq5dLWunVrrVmzJsv+Fy5c0KRJkxQYGKi6deu6LBs5cqRKliypu+66S6NHj9alS5du8gjynrvGKCMjQ08//bQGDRqkWrVqZbmNoKAg3X333c62Vq1aycvLK9MtvoIkv8bnitv5HJKkFStWqEyZMqpWrZqee+45nThxwmUbt/M5JF1/fK64Xc+hjIwMLVy4UHfeeadat26tMmXKqFGjRpo3b56z/4YNG3Tx4kWX7VSvXl0VK1bM9nd+QZBf43PF559/rlKlSikyMlJxcXE6e/as+w4mn3j86xfySkZGhp5//nk1adLkul/NkJSU5PyqhyvKli2rpKQkl7YFCxaoc+fOOnv2rMqVK6eEhASVKlXKubx///6qX7++goODtXr1asXFxenw4cMaO3asew/Mjdw5Rm+//bYKFSqk/v37Z7uNMmXKuLQVKlRIwcHBmca6oMjP8ZE4hx566CF17NhRlStX1p49e/TKK6+oTZs2WrNmjby9vW/7c+hG4yPd3ufQ0aNHlZqaqpEjR+rNN9/U22+/rcWLF6tjx45avny5mjVrpqSkJPn4+CgoKCjb7RQ0+Tk+kvTEE08oPDxcoaGh2rJli15++WX9+uuvmjNnTt4dZB6wbbjp27evtm3bph9//NEt22vRooUSExN1/Phx/fe//9Xjjz+udevWOX/ZxsbGOvvWqVNHPj4+6tOnj+Lj4wvs93q4a4w2bNigd999Vxs3bpTD4XBTdZ6X3+NzO59DktS5c2fn/9euXVt16tRRlSpVtGLFCrVs2fKmt+8J+T0+t/M5lJGRIUlq3769XnjhBUlSvXr1tHr1ak2cONH5l/etJr/H55lnnnGuU7t2bZUrV04tW7bUnj17VKVKlZuqIT/Z8rZUv379tGDBAi1fvlwVKlS4bt+QkJBMM+WPHDmikJAQl7aiRYsqIiJCjRs31uTJk1WoUCFNnjw52+02atRIly5d0v79+3N9HHnJnWP0ww8/6OjRo6pYsaIKFSqkQoUK6ffff9fAgQNVqVIl5zaOHj3qso1Lly7p5MmTmca6IMjv8cnK7XQOZeWOO+5QqVKltHv3buc2btdzKCvXjk9WbqdzqFSpUipUqJBq1qzp0qdGjRrOp4FCQkJ04cIFnT59OtvtFCT5PT5ZadSokSRd9zwriGwVbowx6tevn+bOnatly5apcuXKN1wnKipKS5cudWlLSEhQVFTUddfLyMhQenp6tssTExPl5eWV6TK6p+XFGD399NPasmWLEhMTna/Q0FANGjRIS5YscW7j9OnT2rBhg3Mby5YtU0ZGhvMPT0HgqfHJyu10DmXljz/+0IkTJ1SuXDnnNm7Xcygr145PVm6nc8jHx0cNGzbM9Mj0b7/9pvDwcElSgwYNVLhwYZft/Prrrzpw4MANf+fnJ0+NT1YSExMl6brnWYHkwcnMbvfcc8+ZwMBAs2LFCpfH2M6ePevs8/TTT5vBgwc7369atcoUKlTIvPPOO2bnzp1m6NChLo/Ppaammri4OLNmzRqzf/9+s379etO9e3fj6+trtm3bZowxZvXq1WbcuHEmMTHR7Nmzx3z22WemdOnSpmvXrvk7ADmQF2OUlaye2njooYfMXXfdZdatW2d+/PFHU7Vq1QL3GK+nxud2P4fOnDljXnzxRbNmzRqzb98+891335n69eubqlWrmvPnzzu3c7ueQzkZn9v9HDLmr49aKFy4sJk0aZLZtWuX+c9//mO8vb3NDz/84Ozz7LPPmooVK5ply5aZ9evXm6ioKBMVFZU/B55Dnhqf3bt3m+HDh5v169ebffv2mfnz55s77rjDNG3aNP8O3k1sFW4kZfmaOnWqs0+zZs1MTEyMy3r/+9//zJ133ml8fHxMrVq1zMKFC53Lzp07Zx555BETGhpqfHx8TLly5Uy7du3MTz/95OyzYcMG06hRIxMYGGj8/PxMjRo1zFtvveXyS7mgyIsxykpW4ebEiROmS5cuplixYiYgIMB0797dnDlzxk1H5h6eGp/b/Rw6e/asefDBB03p0qVN4cKFTXh4uOndu7dJSkpy2cbteg7lZHxu93PoismTJ5uIiAjj5+dn6tata+bNm+ey/Ny5c+af//ynKVGihClSpIh55JFHzOHDh/PiMHPNU+Nz4MAB07RpUxMcHGx8fX1NRESEGTRo0C35OTcOY4zJq6tCAAAA+c1Wc24AAAAINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFb+H8z2S45EO3wWAAAAAElFTkSuQmCC", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:31.206223\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAGzCAYAAADT4Tb9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy89olMNAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA+jklEQVR4nO3daXgUVf728buDWdgS1iQsERCQfUcggQEUMCKjZMY/Ii4BBEQFAYMIcQEBNQgiqCDLKIvOMIygoCObAVkUgqxhlx2CmAQQSCBggOQ8L3zosU0CqdChQ/H9XFdfY586VfWr0+3ktupUtcMYYwQAAGATXp4uAAAAwJ0INwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYINwAAwFYIN0ABVblyZfXo0cPTZdjeuHHjdNddd6lQoUJq2LChp8sB4AaEG+AmmDVrlhwOhzZt2pTt8rZt26pu3bo3vJ/FixfrjTfeuOHt3C6+/fZbvfzyy2rZsqVmzpypt99+29MlFRhxcXHy8vJSdHR0tsvfeecdORwOLVq06CZXBlzfHZ4uAED29u7dKy8va//9sXjxYk2ePJmAk0vfffedvLy89Mknn8jHx8fT5RQooaGh6tu3r8aPH68nn3xSderUcS47evSoRo0apS5duqhTp04erBLIHmdugALK19dX3t7eni7DkrS0NE+XYMmJEydUuHBhgk0OxowZozJlyqhv3776428sv/DCC/L29tb777/vweqAnBFugALqz3NuLl++rJEjR6p69ery8/NT6dKl1apVK8XGxkqSevToocmTJ0uSHA6H83VVWlqaBg8erJCQEPn6+qpGjRp69913Xf5oSdLFixc1YMAAlSlTRsWLF9fDDz+s48ePy+FwuJwReuONN+RwOLR79249/vjjKlmypFq1aiVJ2r59u3r06KG77rpLfn5+Cg4O1tNPP61ff/3VZV9Xt7Fv3z49+eSTCggIUNmyZfX666/LGKNjx46pc+fO8vf3V3BwsMaPH5+rsbty5YpGjx6tqlWrytfXV5UrV9Yrr7yi9PR0Zx+Hw6GZM2cqLS3NOVazZs3KcZvff/+9unTpojvvvFO+vr4KCQnRiy++qIsXL7r069Gjh4oVK6ZDhw4pPDxcRYsWVfny5TVq1KgsY+3uz0SSjh8/rqefflpBQUHy9fVVnTp1NGPGjCzHk56erhEjRqhatWrO43n55ZddxiggIEDvv/++1q5dq48//liStGDBAv33v//VmDFjVK5cuWt+DoCncFkKuIlSUlJ06tSpLO2XL1++7rpvvPGGYmJi1Lt3bzVr1kypqanatGmTtmzZog4dOqhv37765ZdfFBsbq88++8xlXWOMHn74Ya1cuVK9evVSw4YNtWzZMg0ZMkTHjx/XhAkTnH179Oihzz//XE899ZRatGih1atXX/PSQ5cuXVS9enW9/fbbzj/KsbGxOnTokHr27Kng4GDt2rVL06dP165du7R+/XqX0CVJXbt2Va1atTRmzBgtWrRIb775pkqVKqVp06bpvvvu0zvvvKN//etfeumll3TPPfeodevW1xyr3r17a/bs2fq///s/DR48WD/++KNiYmK0Z88eLViwQJL02Wefafr06dqwYYPzD3dYWFiO25w3b54uXLig5557TqVLl9aGDRv04Ycf6ueff9a8efNc+mZkZOiBBx5QixYtNHbsWC1dulQjRozQlStXNGrUqHz7TJKTk9WiRQs5HA71799fZcuW1ZIlS9SrVy+lpqZq0KBBkqTMzEw9/PDD+uGHH/TMM8+oVq1a2rFjhyZMmKB9+/Zp4cKFLp9vp06dNHToULVr104DBw5UWFiY+vbte83PAPAoAyDfzZw500i65qtOnTou61SqVMl0797d+b5BgwamU6dO19xPv379THb/Wi9cuNBIMm+++aZL+//93/8Zh8NhDhw4YIwxZvPmzUaSGTRokEu/Hj16GElmxIgRzrYRI0YYSaZbt25Z9nfhwoUsbf/+97+NJLNmzZos23jmmWecbVeuXDEVK1Y0DofDjBkzxtl+5swZU7hwYZcxyU58fLyRZHr37u3S/tJLLxlJ5rvvvnO2de/e3RQtWvSa27vWMcXExBiHw2GOHj3qsk1J5oUXXnC2ZWZmmk6dOhkfHx9z8uRJY0z+fCa9evUy5cqVM6dOnXLp+9hjj5mAgADnMXz22WfGy8vLfP/99y79pk6daiSZtWvXurQfOXLEFC1a1JQqVcp4e3ubHTt2XHOsAE/jshRwE02ePFmxsbFZXvXr17/uuiVKlNCuXbu0f/9+y/tdvHixChUqpAEDBri0Dx48WMYYLVmyRJK0dOlSSdLzzz/v0u+FF17IcdvPPvtslrbChQs7//m3337TqVOn1KJFC0nSli1bsvTv3bu3858LFSqkpk2byhijXr16OdtLlCihGjVq6NChQznWIv1+rJIUFRXl0j548GBJyvPdPX88prS0NJ06dUphYWEyxmjr1q1Z+vfv39/5z1fPpFy6dEnLly931unOz8QYoy+++EIPPfSQjDE6deqU8xUeHq6UlBTn2M+bN0+1atVSzZo1Xfrdd999kqSVK1e6bLtSpUoaMWKETp8+raioKLfc2QfkJy5LATdRs2bN1LRp0yztJUuWzPZy1R+NGjVKnTt31t133626devqgQce0FNPPZWrYHT06FGVL19exYsXd2mvVauWc/nV//Xy8lKVKlVc+lWrVi3Hbf+5rySdPn1aI0eO1Ny5c3XixAmXZSkpKVn633nnnS7vAwIC5OfnpzJlymRp//O8nT+7egx/rjk4OFglSpRwHqtVCQkJGj58uL7++mudOXPGZdmfj8nLy0t33XWXS9vdd98tSTpy5IizTnd+JidPntTZs2c1ffp0TZ8+PdtjuPpZ7N+/X3v27FHZsmWv2e+P7rnnHknK9vsLFDSEG+AW0bp1ax08eFBfffWVvv32W3388ceaMGGCpk6d6nLm42b74xmNqx599FGtW7dOQ4YMUcOGDVWsWDFlZmbqgQceUGZmZpb+hQoVylWbpCyTbXPy53k9NyIjI0MdOnTQ6dOnNXToUNWsWVNFixbV8ePH1aNHj2yP6Wa7WsOTTz6p7t27Z9vnahDOzMxUvXr19N5772XbLyQkJH+KBG4Swg1wCylVqpR69uypnj176vz582rdurXeeOMNZ7jJ6Q96pUqVtHz5cp07d87lTMFPP/3kXH71fzMzM3X48GFVr17d2e/AgQO5rvHMmTNasWKFRo4cqeHDhzvb83I5LS+uHsP+/fudZ0Gk3yfbnj171nmsVuzYsUP79u3T7NmzFRkZ6Wy/eqfan2VmZurQoUPOszWStG/fPkm/3wV3tU53fiZly5ZV8eLFlZGRofbt21/zeKpWrapt27apXbt2bg2BQEHBnBvgFvHnyzHFihVTtWrVXG7dLVq0qCTp7NmzLn0ffPBBZWRkaNKkSS7tEyZMkMPhUMeOHSVJ4eHhkqSPPvrIpd+HH36Y6zqvnnH58xmWiRMn5nobN+LBBx/Mdn9Xz1Lk5aFz2R2TMeaaz3n541gbYzRp0iR5e3urXbt2zjrd+ZkUKlRIjzzyiL744gvt3LkzSz0nT550/vOjjz6q48eP6x//+EeWfhcvXrzlnlcE/BlnboBbRO3atdW2bVs1adJEpUqV0qZNmzR//nyXiatNmjSRJA0YMEDh4eEqVKiQHnvsMT300EO699579eqrr+rIkSNq0KCBvv32W3311VcaNGiQqlat6lz/kUce0cSJE/Xrr786bzu+etYhN/+V7+/vr9atW2vs2LG6fPmyKlSooG+//VaHDx/Oh1HJqkGDBurevbumT5+us2fPqk2bNtqwYYNmz56tiIgI3XvvvZa3WbNmTVWtWlUvvfSSjh8/Ln9/f33xxRdZ5t5c5efnp6VLl6p79+5q3ry5lixZokWLFumVV15xznPJj89kzJgxWrlypZo3b64+ffqodu3aOn36tLZs2aLly5fr9OnTkqSnnnpKn3/+uZ599lmtXLlSLVu2VEZGhn766Sd9/vnnWrZsGXNrcGvz0F1awG3l6q3gGzduzHZ5mzZtrnsr+JtvvmmaNWtmSpQoYQoXLmxq1qxp3nrrLXPp0iVnnytXrpgXXnjBlC1b1jgcDpfbws+dO2defPFFU758eePt7W2qV69uxo0bZzIzM132m5aWZvr162dKlSplihUrZiIiIszevXuNJJdbs6/exn311uY/+vnnn83f/vY3U6JECRMQEGC6dOlifvnllxxvJ//zNnK6RTu7ccrO5cuXzciRI02VKlWMt7e3CQkJMdHR0ea3337L1X6ys3v3btO+fXtTrFgxU6ZMGdOnTx+zbds2I8nMnDkzyzYPHjxo7r//flOkSBETFBRkRowYYTIyMly26e7PxBhjkpOTTb9+/UxISIjx9vY2wcHBpl27dmb69Oku/S5dumTeeecdU6dOHePr62tKlixpmjRpYkaOHGlSUlKyHP/KlSuNJDNv3rxcjRfgSQ5jcjk7D8BtKz4+Xo0aNdI///lPPfHEE54up0Dr0aOH5s+fr/Pnz+frfvhMgJwx5waAiz//nID0+/wVLy+v6z4ZGPmDzwSwhjk3AFyMHTtWmzdv1r333qs77rhDS5Ys0ZIlS/TMM89wi7CH8JkA1hBuALgICwtTbGysRo8erfPnz+vOO+/UG2+8oVdffdXTpd22+EwAa5hzAwAAbIU5NwAAwFYINwAAwFZuuzk3mZmZ+uWXX1S8eHEeOw4AwC3CGKNz586pfPny8vK69rmZ2y7c/PLLL9xdAADALerYsWOqWLHiNfvcduHm6g/UHTt2TP7+/h6uBgAA5EZqaqpCQkJcfmg2J7dduLl6Kcrf359wAwDALSY3U0qYUAwAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGylwISbMWPGyOFwaNCgQdfsN2/ePNWsWVN+fn6qV6+eFi9efHMKBAAAt4QCEW42btyoadOmqX79+tfst27dOnXr1k29evXS1q1bFRERoYiICO3cufMmVQoAAAo6j4eb8+fP64knntA//vEPlSxZ8pp933//fT3wwAMaMmSIatWqpdGjR6tx48aaNGnSTaoWAAAUdB4PN/369VOnTp3Uvn376/aNi4vL0i88PFxxcXE5rpOenq7U1FSXFwAAsK87PLnzuXPnasuWLdq4cWOu+iclJSkoKMilLSgoSElJSTmuExMTo5EjR95QnQBuHZWHLfJ0CZYdGdPJ0yUAtuKxMzfHjh3TwIED9a9//Ut+fn75tp/o6GilpKQ4X8eOHcu3fQEAAM/z2JmbzZs368SJE2rcuLGzLSMjQ2vWrNGkSZOUnp6uQoUKuawTHBys5ORkl7bk5GQFBwfnuB9fX1/5+vq6t3gAAFBgeezMTbt27bRjxw7Fx8c7X02bNtUTTzyh+Pj4LMFGkkJDQ7VixQqXttjYWIWGht6ssgEAQAHnsTM3xYsXV926dV3aihYtqtKlSzvbIyMjVaFCBcXExEiSBg4cqDZt2mj8+PHq1KmT5s6dq02bNmn69Ok3vX4AAFAwefxuqWtJSEhQYmKi831YWJjmzJmj6dOnq0GDBpo/f74WLlyYJSQBAIDbl8MYYzxdxM2UmpqqgIAApaSkyN/f39PlAHAz7pYC7MnK3+8CfeYGAADAKsINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFY+GmylTpqh+/fry9/eXv7+/QkNDtWTJkhz7z5o1Sw6Hw+Xl5+d3EysGAAAF3R2e3HnFihU1ZswYVa9eXcYYzZ49W507d9bWrVtVp06dbNfx9/fX3r17ne8dDsfNKhcAANwCPBpuHnroIZf3b731lqZMmaL169fnGG4cDoeCg4NvRnkAAOAWVGDm3GRkZGju3LlKS0tTaGhojv3Onz+vSpUqKSQkRJ07d9auXbuuud309HSlpqa6vAAAgH15PNzs2LFDxYoVk6+vr5599lktWLBAtWvXzrZvjRo1NGPGDH311Vf65z//qczMTIWFhennn3/OcfsxMTEKCAhwvkJCQvLrUAAAQAHgMMYYTxZw6dIlJSQkKCUlRfPnz9fHH3+s1atX5xhw/ujy5cuqVauWunXrptGjR2fbJz09Xenp6c73qampCgkJUUpKivz9/d12HAAKhsrDFnm6BMuOjOnk6RKAAi81NVUBAQG5+vvt0Tk3kuTj46Nq1apJkpo0aaKNGzfq/fff17Rp0667rre3txo1aqQDBw7k2MfX11e+vr5uqxcAABRsHr8s9WeZmZkuZ1quJSMjQzt27FC5cuXyuSoAAHCr8OiZm+joaHXs2FF33nmnzp07pzlz5mjVqlVatmyZJCkyMlIVKlRQTEyMJGnUqFFq0aKFqlWrprNnz2rcuHE6evSoevfu7cnDAAAABYhHw82JEycUGRmpxMREBQQEqH79+lq2bJk6dOggSUpISJCX1/9OLp05c0Z9+vRRUlKSSpYsqSZNmmjdunW5mp8DAABuDx6fUHyzWZmQBODWw4RiwJ6s/P0ucHNuAAAAbgThBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2IpHw82UKVNUv359+fv7y9/fX6GhoVqyZMk115k3b55q1qwpPz8/1atXT4sXL75J1QIAgFuBR8NNxYoVNWbMGG3evFmbNm3Sfffdp86dO2vXrl3Z9l+3bp26deumXr16aevWrYqIiFBERIR27tx5kysHAAAFlcMYYzxdxB+VKlVK48aNU69evbIs69q1q9LS0vTNN98421q0aKGGDRtq6tSpudp+amqqAgIClJKSIn9/f7fVDaBgqDxskadLsOzImE6eLgEo8Kz8/bZ85mbLli3asWOH8/1XX32liIgIvfLKK7p06ZL1av+/jIwMzZ07V2lpaQoNDc22T1xcnNq3b+/SFh4erri4uBy3m56ertTUVJcXAACwrzusrtC3b18NGzZM9erV06FDh/TYY4/pb3/7m+bNm6cLFy5o4sSJlra3Y8cOhYaG6rffflOxYsW0YMEC1a5dO9u+SUlJCgoKcmkLCgpSUlJSjtuPiYnRyJEjLdWEgo//OgcA5MTymZt9+/apYcOGkn6f3Nu6dWvNmTNHs2bN0hdffGG5gBo1aig+Pl4//vijnnvuOXXv3l27d++2vJ2cREdHKyUlxfk6duyY27YNAAAKHstnbowxyszMlCQtX75cf/3rXyVJISEhOnXqlOUCfHx8VK1aNUlSkyZNtHHjRr3//vuaNm1alr7BwcFKTk52aUtOTlZwcHCO2/f19ZWvr6/lugAAwK3J8pmbpk2b6s0339Rnn32m1atXq1On30+1Hz58OMslo7zIzMxUenp6tstCQ0O1YsUKl7bY2Ngc5+gAAIDbj+UzNxMnTtQTTzyhhQsX6tVXX3WedZk/f77CwsIsbSs6OlodO3bUnXfeqXPnzmnOnDlatWqVli1bJkmKjIxUhQoVFBMTI0kaOHCg2rRpo/Hjx6tTp06aO3euNm3apOnTp1s9DAAAYFOWw039+vVd7pa6aty4cSpUqJClbZ04cUKRkZFKTExUQECA6tevr2XLlqlDhw6SpISEBHl5/e/kUlhYmObMmaPXXntNr7zyiqpXr66FCxeqbt26Vg8DAADYlOVwI0lnz57V/PnzdfDgQQ0ZMkSlSpXS7t27FRQUpAoVKuR6O5988sk1l69atSpLW5cuXdSlSxerJQMAgNuE5XCzfft2tWvXTiVKlNCRI0fUp08flSpVSl9++aUSEhL06aef5kedAAAAuWJ5QnFUVJR69uyp/fv3y8/Pz9n+4IMPas2aNW4tDgAAwCrL4Wbjxo3q27dvlvYKFSpc82F6AAAAN4PlcOPr65vtTxjs27dPZcuWdUtRAAAAeWU53Dz88MMaNWqULl++LElyOBxKSEjQ0KFD9cgjj7i9QAAAACssh5vx48fr/PnzCgwM1MWLF9WmTRtVq1ZNxYsX11tvvZUfNQIAAOSa5bulAgICFBsbq7Vr12rbtm06f/68GjdunOXXugEAADwhT8+5kaSWLVuqZcuW7qwFAADghlm+LDVgwAB98MEHWdonTZqkQYMGuaMmAACAPLMcbr744otsz9iEhYVp/vz5bikKAAAgryyHm19//VUBAQFZ2v39/XXq1Cm3FAUAAJBXlsNNtWrVtHTp0iztS5Ys0V133eWWogAAAPLK8oTiqKgo9e/fXydPntR9990nSVqxYoXGjx+viRMnurs+AAAASyyHm6efflrp6el66623NHr0aElS5cqVNWXKFEVGRrq9QAAAACvydCv4c889p+eee04nT55U4cKFVaxYMXfXBQAAkCd5fs6NJH5LCgAAFDiWJxQnJyfrqaeeUvny5XXHHXeoUKFCLi8AAABPsnzmpkePHkpISNDrr7+ucuXKyeFw5EddAAAAeWI53Pzwww/6/vvv1bBhw3woBwAA4MZYviwVEhIiY0x+1AIAAHDDLIebiRMnatiwYTpy5Eg+lAMAAHBjLF+W6tq1qy5cuKCqVauqSJEi8vb2dll++vRptxUHAABgleVww1OIAQBAQWY53HTv3j0/6gAAAHALy3NuJOngwYN67bXX1K1bN504cULS7z+cuWvXLrcWBwAAYJXlcLN69WrVq1dPP/74o7788kudP39ekrRt2zaNGDHC7QUCAABYYTncDBs2TG+++aZiY2Pl4+PjbL/vvvu0fv16txYHAABgleVws2PHDv3tb3/L0h4YGKhTp065pSgAAIC8shxuSpQoocTExCztW7duVYUKFdxSFAAAQF5ZDjePPfaYhg4dqqSkJDkcDmVmZmrt2rV66aWXFBkZmR81AgAA5JrlcPP222+rZs2aCgkJ0fnz51W7dm21bt1aYWFheu211/KjRgAAgFyz9JwbY4ySkpL0wQcfaPjw4dqxY4fOnz+vRo0aqXr16vlVIwAAQK5ZDjfVqlXTrl27VL16dYWEhORXXQAAAHli6bKUl5eXqlevrl9//dUtO4+JidE999yj4sWLKzAwUBEREdq7d+8115k1a5YcDofLy8/Pzy31AACAW5/lOTdjxozRkCFDtHPnzhve+erVq9WvXz+tX79esbGxunz5su6//36lpaVdcz1/f38lJiY6X0ePHr3hWgAAgD1Y/m2pyMhIXbhwQQ0aNJCPj48KFy7sstzKr4IvXbrU5f2sWbMUGBiozZs3q3Xr1jmu53A4FBwcbK1wAABwWyhQvwqekpIiSSpVqtQ1+50/f16VKlVSZmamGjdurLffflt16tTJtm96errS09Od71NTU91XMAAAKHAshZvLly9r9erVev3111WlShW3FpKZmalBgwapZcuWqlu3bo79atSooRkzZqh+/fpKSUnRu+++q7CwMO3atUsVK1bM0j8mJkYjR450a60AAKDgsjTnxtvbW1988UW+FNKvXz/t3LlTc+fOvWa/0NBQRUZGqmHDhmrTpo2+/PJLlS1bVtOmTcu2f3R0tFJSUpyvY8eO5Uf5AACggLA8oTgiIkILFy50axH9+/fXN998o5UrV2Z79uVavL291ahRIx04cCDb5b6+vvL393d5AQAA+7I856Z69eoaNWqU1q5dqyZNmqho0aIuywcMGJDrbRlj9MILL2jBggVatWpVni51ZWRkaMeOHXrwwQctrwsAAOzHcrj55JNPVKJECW3evFmbN292WeZwOCyFm379+mnOnDn66quvVLx4cSUlJUmSAgICnHdhRUZGqkKFCoqJiZEkjRo1Si1atFC1atV09uxZjRs3TkePHlXv3r2tHgoAALAhy+Hm8OHDbtv5lClTJElt27Z1aZ85c6Z69OghSUpISJCX1/+unp05c0Z9+vRRUlKSSpYsqSZNmmjdunWqXbu22+oCAAC3Lsvhxp2MMdfts2rVKpf3EyZM0IQJE/KpIgAAcKuzHG6efvrpay6fMWNGnosBAAC4UZbDzZkzZ1zeX758WTt37tTZs2d13333ua0wAACAvLAcbhYsWJClLTMzU88995yqVq3qlqIAAADyyvJzbrLdiJeXoqKimAsDAAA8zi3hRpIOHjyoK1euuGtzAAAAeWL5slRUVJTLe2OMEhMTtWjRInXv3t1thQEAAOSF5XCzdetWl/deXl4qW7asxo8ff907qQAAAPKb5XCzcuXK/KgDAADALSzPuTl8+LD279+fpX3//v06cuSIO2oCAADIM8vhpkePHlq3bl2W9h9//NH5kwkAAACeYjncbN26VS1btszS3qJFC8XHx7ujJgAAgDyzHG4cDofOnTuXpT0lJUUZGRluKQoAACCvLIeb1q1bKyYmxiXIZGRkKCYmRq1atXJrcQAAAFZZvlvqnXfeUevWrVWjRg395S9/kSR9//33Sk1N1Xfffef2AgEAAKywfOamdu3a2r59ux599FGdOHFC586dU2RkpH766SfVrVs3P2oEAADINctnbiSpfPnyevvtt91dCwAAwA2zfOZm5syZmjdvXpb2efPmafbs2W4pCgAAIK8sh5uYmBiVKVMmS3tgYCBncwAAgMdZDjcJCQmqUqVKlvZKlSopISHBLUUBAADkleVwExgYqO3bt2dp37Ztm0qXLu2WogAAAPLKcrjp1q2bBgwYoJUrVyojI0MZGRn67rvvNHDgQD322GP5USMAAECuWb5bavTo0Tpy5IjatWunO+74ffXMzExFRkYy5wYAAHic5XDj4+Oj//znPxo9erS2bdumwoULq169eqpUqVJ+1AcAAGBJnp5zI0mlSpXSvffem+2dUwAAAJ5iac7N2bNn1a9fP5UpU0ZBQUEKCgpSmTJl1L9/f509ezafSgQAAMi9XJ+5OX36tEJDQ3X8+HE98cQTqlWrliRp9+7dmjVrllasWKF169apZMmS+VYsAADA9eQ63IwaNUo+Pj46ePCggoKCsiy7//77NWrUKE2YMMHtRQIAAORWri9LLVy4UO+++26WYCNJwcHBGjt2rBYsWODW4gAAAKzKdbhJTExUnTp1clxet25dJSUluaUoAACAvMp1uClTpoyOHDmS4/LDhw+rVKlS7qgJAAAgz3IdbsLDw/Xqq6/q0qVLWZalp6fr9ddf1wMPPODW4gAAAKyyNKG4adOmql69uvr166eaNWvKGKM9e/boo48+Unp6uj777LP8rBUAAOC6ch1uKlasqLi4OD3//POKjo6WMUaS5HA41KFDB02aNEkhISH5VigAAEBuWHqIX5UqVbRkyRKdOnVK69ev1/r163Xy5EktXbpU1apVs7zzmJgY3XPPPSpevLgCAwMVERGhvXv3Xne9efPmqWbNmvLz81O9evW0ePFiy/sGAAD2ZPlXwSWpZMmSatasmZo1a3ZDk4hXr16tfv36af369YqNjdXly5d1//33Ky0tLcd11q1bp27duqlXr17aunWrIiIiFBERoZ07d+a5DgAAYB8Oc/X6UgFw8uRJBQYGavXq1WrdunW2fbp27aq0tDR98803zrYWLVqoYcOGmjp16nX3kZqaqoCAAKWkpMjf399ttePmqjxskadLsOzImE6eLuG2wHcDsCcrf7/zdOYmv6SkpEjSNc8GxcXFqX379i5t4eHhiouLy7Z/enq6UlNTXV4AAMC+Cky4yczM1KBBg9SyZUvVrVs3x35JSUlZnpIcFBSU4wMEY2JiFBAQ4Hwx6RkAAHvLVbhp3Lixzpw5I+n3W8IvXLjg9kL69eunnTt3au7cuW7dbnR0tFJSUpyvY8eOuXX7AACgYMlVuNmzZ49zku/IkSN1/vx5txbRv39/ffPNN1q5cqUqVqx4zb7BwcFKTk52aUtOTlZwcHC2/X19feXv7+/yAgAA9pWr59w0bNhQPXv2VKtWrWSM0bvvvqtixYpl23f48OG53rkxRi+88IIWLFigVatWqUqVKtddJzQ0VCtWrNCgQYOcbbGxsQoNDc31fgEAgH3lKtzMmjVLI0aM0DfffCOHw6ElS5bojjuyrupwOCyFm379+mnOnDn66quvVLx4cee8mYCAABUuXFiSFBkZqQoVKigmJkaSNHDgQLVp00bjx49Xp06dNHfuXG3atEnTp0/P9X4BAIB95Src1KhRwzkXxsvLSytWrFBgYOAN73zKlCmSpLZt27q0z5w5Uz169JAkJSQkyMvrf1fPwsLCNGfOHL322mt65ZVXVL16dS1cuPCak5ABAMDtI9c/v3BVZmam23aem0fsrFq1Kktbly5d1KVLF7fVAQAA7MNyuJGkgwcPauLEidqzZ48kqXbt2ho4cKCqVq3q1uIAAACssvycm2XLlql27drasGGD6tevr/r16+vHH39UnTp1FBsbmx81AgAA5JrlMzfDhg3Tiy++qDFjxmRpHzp0qDp06OC24gAAAKyyfOZmz5496tWrV5b2p59+Wrt373ZLUQAAAHllOdyULVtW8fHxWdrj4+PdcgcVAADAjbB8WapPnz565plndOjQIYWFhUmS1q5dq3feeUdRUVFuLxAAAMAKy+Hm9ddfV/HixTV+/HhFR0dLksqXL6833nhDAwYMcHuBAAAAVlgONw6HQy+++KJefPFFnTt3TpJUvHhxtxcGAACQF3l6zs1VhBoAAFDQWJ5QDAAAUJARbgAAgK0QbgAAgK1YCjeXL19Wu3bttH///vyqBwAA4IZYCjfe3t7avn17ftUCAABwwyxflnryySf1ySef5EctAAAAN8zyreBXrlzRjBkztHz5cjVp0kRFixZ1Wf7ee++5rTgAAACrLIebnTt3qnHjxpKkffv2uSxzOBzuqQoAACCPLIeblStX5kcdAAAAbpHnW8EPHDigZcuW6eLFi5IkY4zbigIAAMgry+Hm119/Vbt27XT33XfrwQcfVGJioiSpV69eGjx4sNsLBAAAsMJyuHnxxRfl7e2thIQEFSlSxNnetWtXLV261K3FAQAAWGV5zs23336rZcuWqWLFii7t1atX19GjR91WGAAAQF5YPnOTlpbmcsbmqtOnT8vX19ctRQEAAOSV5XDzl7/8RZ9++qnzvcPhUGZmpsaOHat7773XrcUBAABYZfmy1NixY9WuXTtt2rRJly5d0ssvv6xdu3bp9OnTWrt2bX7UCAAAkGuWz9zUrVtX+/btU6tWrdS5c2elpaXp73//u7Zu3aqqVavmR40AAAC5ZvnMjSQFBATo1VdfdXctAAAANyxP4ebMmTP65JNPtGfPHklS7dq11bNnT5UqVcqtxQEAAFhl+bLUmjVrVLlyZX3wwQc6c+aMzpw5ow8++EBVqlTRmjVr8qNGAACAXLN85qZfv37q2rWrpkyZokKFCkmSMjIy9Pzzz6tfv37asWOH24sEAADILctnbg4cOKDBgwc7g40kFSpUSFFRUTpw4IBbiwMAALDKcrhp3Lixc67NH+3Zs0cNGjRwS1EAAAB5lavLUtu3b3f+84ABAzRw4EAdOHBALVq0kCStX79ekydP1pgxY/KnSgAAgFzKVbhp2LChHA6HjDHOtpdffjlLv8cff1xdu3bN9c7XrFmjcePGafPmzUpMTNSCBQsUERGRY/9Vq1Zl+xTkxMREBQcH53q/AADAvnIVbg4fPpwvO09LS1ODBg309NNP6+9//3uu19u7d6/8/f2d7wMDA/OjPAAAcAvKVbipVKlSvuy8Y8eO6tixo+X1AgMDVaJECfcXBAAAbnl5eojfL7/8oh9++EEnTpxQZmamy7IBAwa4pbBradiwodLT01W3bl298cYbatmyZY5909PTlZ6e7nyfmpqa7/UBAADPsRxuZs2apb59+8rHx0elS5eWw+FwLnM4HPkabsqVK6epU6eqadOmSk9P18cff6y2bdvqxx9/VOPGjbNdJyYmRiNHjsy3mgAAQMFiOdy8/vrrGj58uKKjo+XlZflO8htSo0YN1ahRw/k+LCxMBw8e1IQJE/TZZ59lu050dLSioqKc71NTUxUSEpLvtQIAAM+wHG4uXLigxx577KYHm5w0a9ZMP/zwQ47LfX195evrexMrAgAAnmQ5ofTq1Uvz5s3Lj1ryJD4+XuXKlfN0GQAAoICwfOYmJiZGf/3rX7V06VLVq1dP3t7eLsvfe++9XG/r/PnzLj/ZcPjwYcXHx6tUqVK68847FR0drePHj+vTTz+VJE2cOFFVqlRRnTp19Ntvv+njjz/Wd999p2+//dbqYQAAAJvKU7hZtmyZc+7LnycUW7Fp0yaXh/JdnRvTvXt3zZo1S4mJiUpISHAuv3TpkgYPHqzjx4+rSJEiql+/vpYvX57tg/0AAMDtyWH++NjhXChZsqQmTJigHj165FNJ+Ss1NVUBAQFKSUlxeRAgbi2Vhy3ydAmWHRnTydMl3Bb4bgD2ZOXvt+U5N76+vtd8rgwAAIAnWQ43AwcO1IcffpgftQAAANwwy3NuNmzYoO+++07ffPON6tSpk2VC8Zdffum24gAAAKyyHG5KlChh6UcuAQAAbibL4WbmzJn5UQcAAIBbFIzHDAMAALiJ5TM3VapUuebzbA4dOnRDBQEAANwIy+Fm0KBBLu8vX76srVu3aunSpRoyZIi76gIAAMgTy+Fm4MCB2bZPnjxZmzZtuuGCAAAAboTb5tx07NhRX3zxhbs2BwAAkCduCzfz589XqVKl3LU5AACAPLF8WapRo0YuE4qNMUpKStLJkyf10UcfubU4AAAAqyyHm4iICJf3Xl5eKlu2rNq2bauaNWu6qy4AAIA8sRxuRowYkR91AAAAuAUP8QMAALaS6zM3Xl5e13x4nyQ5HA5duXLlhosCAADIq1yHmwULFuS4LC4uTh988IEyMzPdUhQAAEBe5TrcdO7cOUvb3r17NWzYMP33v//VE088oVGjRrm1OAAAAKvyNOfml19+UZ8+fVSvXj1duXJF8fHxmj17tipVquTu+gAAACyxFG5SUlI0dOhQVatWTbt27dKKFSv03//+V3Xr1s2v+gAAACzJ9WWpsWPH6p133lFwcLD+/e9/Z3uZCgAAwNNyHW6GDRumwoULq1q1apo9e7Zmz56dbb8vv/zSbcUBAABYletwExkZed1bwQEAADwt1+Fm1qxZ+VgGAACAe/CEYgAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCuEGwAAYCseDTdr1qzRQw89pPLly8vhcGjhwoXXXWfVqlVq3LixfH19Va1aNZ6cDAAAXHg03KSlpalBgwaaPHlyrvofPnxYnTp10r333qv4+HgNGjRIvXv31rJly/K5UgAAcKvI9W9L5YeOHTuqY8eOue4/depUValSRePHj5ck1apVSz/88IMmTJig8PDw/CoTAADcQm6pOTdxcXFq3769S1t4eLji4uJyXCc9PV2pqakuLwAAYF8ePXNjVVJSkoKCglzagoKClJqaqosXL6pw4cJZ1omJidHIkSNvVomqPGzRTdsXAMD+bsW/K0fGdPLo/m+pMzd5ER0drZSUFOfr2LFjni4JAADko1vqzE1wcLCSk5Nd2pKTk+Xv75/tWRtJ8vX1la+v780oDwAAFAC31Jmb0NBQrVixwqUtNjZWoaGhHqoIAAAUNB4NN+fPn1d8fLzi4+Ml/X6rd3x8vBISEiT9fkkpMjLS2f/ZZ5/VoUOH9PLLL+unn37SRx99pM8//1wvvviiJ8oHAAAFkEfDzaZNm9SoUSM1atRIkhQVFaVGjRpp+PDhkqTExERn0JGkKlWqaNGiRYqNjVWDBg00fvx4ffzxx9wGDgAAnDw656Zt27YyxuS4PLunD7dt21Zbt27Nx6oAAMCt7JaacwMAAHA9hBsAAGArhBsAAGArhBsAAGArhBsAAGArhBsAAGArhBsAAGArhBsAAGArhBsAAGArhBsAAGArhBsAAGArhBsAAGArhBsAAGArhBsAAGArhBsAAGArhBsAAGArhBsAAGArhBsAAGArhBsAAGArhBsAAGArhBsAAGArhBsAAGArhBsAAGArhBsAAGArhBsAAGArhBsAAGArhBsAAGArhBsAAGArhBsAAGArhBsAAGArhBsAAGArhBsAAGArhBsAAGArBSLcTJ48WZUrV5afn5+aN2+uDRs25Nh31qxZcjgcLi8/P7+bWC0AACjIPB5u/vOf/ygqKkojRozQli1b1KBBA4WHh+vEiRM5ruPv76/ExETn6+jRozexYgAAUJB5PNy899576tOnj3r27KnatWtr6tSpKlKkiGbMmJHjOg6HQ8HBwc5XUFDQTawYAAAUZB4NN5cuXdLmzZvVvn17Z5uXl5fat2+vuLi4HNc7f/68KlWqpJCQEHXu3Fm7du3KsW96erpSU1NdXgAAwL48Gm5OnTqljIyMLGdegoKClJSUlO06NWrU0IwZM/TVV1/pn//8pzIzMxUWFqaff/452/4xMTEKCAhwvkJCQtx+HAAAoODw+GUpq0JDQxUZGamGDRuqTZs2+vLLL1W2bFlNmzYt2/7R0dFKSUlxvo4dO3aTKwYAADfTHZ7ceZkyZVSoUCElJye7tCcnJys4ODhX2/D29lajRo104MCBbJf7+vrK19f3hmsFAAC3Bo+eufHx8VGTJk20YsUKZ1tmZqZWrFih0NDQXG0jIyNDO3bsULly5fKrTAAAcAvx6JkbSYqKilL37t3VtGlTNWvWTBMnTlRaWpp69uwpSYqMjFSFChUUExMjSRo1apRatGihatWq6ezZsxo3bpyOHj2q3r17e/IwAABAAeHxcNO1a1edPHlSw4cPV1JSkho2bKilS5c6JxknJCTIy+t/J5jOnDmjPn36KCkpSSVLllSTJk20bt061a5d21OHAAAAChCPhxtJ6t+/v/r375/tslWrVrm8nzBhgiZMmHATqgIAALeiW+5uKQAAgGsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFspEOFm8uTJqly5svz8/NS8eXNt2LDhmv3nzZunmjVrys/PT/Xq1dPixYtvUqUAAKCg83i4+c9//qOoqCiNGDFCW7ZsUYMGDRQeHq4TJ05k23/dunXq1q2bevXqpa1btyoiIkIRERHauXPnTa4cAAAURB4PN++995769Omjnj17qnbt2po6daqKFCmiGTNmZNv//fff1wMPPKAhQ4aoVq1aGj16tBo3bqxJkybd5MoBAEBBdIcnd37p0iVt3rxZ0dHRzjYvLy+1b99ecXFx2a4TFxenqKgol7bw8HAtXLgw2/7p6elKT093vk9JSZEkpaam3mD12ctMv5Av28WtL7++c3B1K/47yHcD18J32nWbxpjr9vVouDl16pQyMjIUFBTk0h4UFKSffvop23WSkpKy7Z+UlJRt/5iYGI0cOTJLe0hISB6rBvImYKKnK0BBxXcDdpOf3+lz584pICDgmn08Gm5uhujoaJczPZmZmTp9+rRKly4th8PhwcpurtTUVIWEhOjYsWPy9/f3dDkFEmN0fYzR9TFG18cYXR9jlJUxRufOnVP58uWv29ej4aZMmTIqVKiQkpOTXdqTk5MVHByc7TrBwcGW+vv6+srX19elrUSJEnkv+hbn7+/PvyjXwRhdH2N0fYzR9TFG18cYubreGZurPDqh2MfHR02aNNGKFSucbZmZmVqxYoVCQ0OzXSc0NNSlvyTFxsbm2B8AANxePH5ZKioqSt27d1fTpk3VrFkzTZw4UWlpaerZs6ckKTIyUhUqVFBMTIwkaeDAgWrTpo3Gjx+vTp06ae7cudq0aZOmT5/uycMAAAAFhMfDTdeuXXXy5EkNHz5cSUlJatiwoZYuXeqcNJyQkCAvr/+dYAoLC9OcOXP02muv6ZVXXlH16tW1cOFC1a1b11OHcEvw9fXViBEjslyiw/8wRtfHGF0fY3R9jNH1MUY3xmFyc08VAADALcLjD/EDAABwJ8INAACwFcINAACwFcINAACwFcINAACwFcKNzWzZskUdOnRQiRIlVLp0aT3zzDM6f/68Sx+Hw5HlNXfuXJc+q1atUuPGjeXr66tq1app1qxZN/Eo8lduxighIUGdOnVSkSJFFBgYqCFDhujKlSsufew8Rvv27VPnzp1VpkwZ+fv7q1WrVlq5cqVLn9v9e5SbMbqdv0erVq3K9jvicDi0ceNGSdKRI0eyXb5+/XqXbc2bN081a9aUn5+f6tWrp8WLF3vikNwuN2MkSdu3b9df/vIX+fn5KSQkRGPHjs2yLbuOUZ4Z2Mbx48dNyZIlzbPPPmt++ukns2HDBhMWFmYeeeQRl36SzMyZM01iYqLzdfHiRefyQ4cOmSJFipioqCize/du8+GHH5pChQqZpUuX3uxDcrvcjNGVK1dM3bp1Tfv27c3WrVvN4sWLTZkyZUx0dLSzj53HyBhjqlevbh588EGzbds2s2/fPvP888+bIkWKmMTERGef2/l7ZMz1x+h2/x6lp6e7fDcSExNN7969TZUqVUxmZqYxxpjDhw8bSWb58uUu/S5duuTcztq1a02hQoXM2LFjze7du81rr71mvL29zY4dOzx1aG6TmzFKSUkxQUFB5oknnjA7d+40//73v03hwoXNtGnTnNux8xjlFeHGRqZNm2YCAwNNRkaGs2379u1Gktm/f7+zTZJZsGBBjtt5+eWXTZ06dVzaunbtasLDw91e882WmzFavHix8fLyMklJSc4+U6ZMMf7+/iY9Pd0YY+8xOnnypJFk1qxZ42xLTU01kkxsbKyz7Xb+HuVmjG7379GfXbp0yZQtW9aMGjXK2XY13GzdujXH9R599FHTqVMnl7bmzZubvn375lepHpPdGH300UemZMmSzu+MMcYMHTrU1KhRw/n+dhqj3OKylI2kp6fLx8fH5YnOhQsXliT98MMPLn379eunMmXKqFmzZpoxY4bMH57lGBcXp/bt27v0Dw8PV1xcXD5Wf3PkZozi4uJUr14951Oypd+PPzU1Vbt27XL2sesYlS5dWjVq1NCnn36qtLQ0XblyRdOmTVNgYKCaNGni0vd2/R7lZoxu9+/Rn3399df69ddfnT+t80cPP/ywAgMD1apVK3399dcuy273MYqLi1Pr1q3l4+PjbAsPD9fevXt15swZZ5/bZYxyi3BjI/fdd5+SkpI0btw4Xbp0SWfOnNGwYcMkSYmJic5+o0aN0ueff67Y2Fg98sgjev755/Xhhx86lyclJbn8H7IkBQUFKTU1VRcvXrw5B5NPcjNGOR3/1WXX6mOHMXI4HFq+fLm2bt2q4sWLy8/PT++9956WLl2qkiVLOvvdzt+j3IzR7f49+rNPPvlE4eHhqlixorOtWLFiGj9+vObNm6dFixapVatWioiIcAk4OY3R1TG0k+zG6Ea+R3Yco9wi3NwChg0bluOks6uvn376SXXq1NHs2bM1fvx4FSlSRMHBwapSpYqCgoJczlS8/vrratmypRo1aqShQ4fq5Zdf1rhx4zx4hDfO3WNkR7kdI2OM+vXrp8DAQH3//ffasGGDIiIi9NBDD7mE5Nv5e5TbMbKj3I7RH/38889atmyZevXq5dJepkwZRUVFqXnz5rrnnns0ZswYPfnkk7fN9+iPchoj5I3HfzgT1zd48GD16NHjmn3uuusuSdLjjz+uxx9/XMnJySpatKgcDofee+895/LsNG/eXKNHj1Z6erp8fX0VHBys5ORklz7Jycny9/d3XsIpaNw5RsHBwdqwYYPLulfHIzg42Pm/dh2j7777Tt98843OnDkjf39/SdJHH32k2NhYzZ4923mm689up+9Rbsbodv8e/dHMmTNVunRpPfzww9fdfvPmzRUbG+t8n9MYXR3DgsidY5TT8V9ddq0+BXmM8hvh5hZQtmxZlS1b1tI6V09RzpgxQ35+furQoUOOfePj41WyZEnnr8+GhoZmuY0wNjZWoaGhFiu/edw5RqGhoXrrrbd04sQJBQYGSvr9+P39/VW7dm1nH7uO0YULFyQpy5ksLy8vZWZm5rje7fQ9ys0Y3e7fo6uMMZo5c6YiIyPl7e193f7x8fEqV66c831oaKhWrFihQYMGOdtupzEKDQ3Vq6++qsuXLzuXxcbGqkaNGs5LoLfiGOU7T85mhvt9+OGHZvPmzWbv3r1m0qRJpnDhwub99993Lv/666/NP/7xD7Njxw6zf/9+89FHH5kiRYqY4cOHO/tcvT11yJAhZs+ePWby5Mm2uT3VmOuP0dVbeO+//34THx9vli5dasqWLZvtLbx2HKOTJ0+a0qVLm7///e8mPj7e7N2717z00kvG29vbxMfHG2P4HuVmjG7379FVy5cvN5LMnj17siybNWuWmTNnjtmzZ4/Zs2ePeeutt4yXl5eZMWOGs8/atWvNHXfcYd59912zZ88eM2LECNvd5nytMTp79qwJCgoyTz31lNm5c6eZO3euKVKkSJZbwe0+RlYRbmzmqaeeMqVKlTI+Pj6mfv365tNPP3VZvmTJEtOwYUNTrFgxU7RoUdOgQQMzdepUl1ujjTFm5cqVpmHDhsbHx8fcddddZubMmTfxKPLX9cbIGGOOHDliOnbsaAoXLmzKlCljBg8ebC5fvuzSx85jtHHjRnP//febUqVKmeLFi5sWLVqYxYsXO5fzPbr+GBnD98gYY7p162bCwsKyXTZr1ixTq1YtU6RIEePv72+aNWtm5s2bl6Xf559/bu6++27j4+Nj6tSpYxYtWpTfZd9U1xojY4zZtm2badWqlfH19TUVKlQwY8aMydLH7mNklcOYP9y7CQAAcIuz9+0hAADgtkO4AQAAtkK4AQAAtkK4AQAAtkK4AQAAtkK4AQAAtkK4AQAAtkK4AQAAtkK4AQAAtkK4AQAAtkK4AQAAtvL/ANvgbR2UvfyrAAAAAElFTkSuQmCC", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:31.585078\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:31.950037\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:32.314063\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:32.662671\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:33.122442\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:33.507413\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:33.932178\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:34.346076\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:34.712095\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:35.077119\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:35.489021\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:35.931834\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkMAAAGzCAYAAAAsQxMfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy89olMNAAAACXBIWXMAAA9hAAAPYQGoP6dpAABCjUlEQVR4nO3deVyVZf7/8fcBZXEBRDYXXBJDzQXDVNRcyqJyJp2aRp3KNbPSXHBamEpTK0wznco0J5WWcSxza8olRMtyqVzINRPTMBPcQdBw4fr90Y/z7QgYR8/hiPfr+XicR53rvu7r/lzcIG/u7diMMUYAAAAW5eXpAgAAADyJMAQAACyNMAQAACyNMAQAACyNMAQAACyNMAQAACyNMAQAACyNMAQAACyNMAQAACyNMARcgXr16qlfv36eLuOaN2nSJF133XXy9vZWTEyMp8u5IsuXL1dMTIz8/Pxks9l08uRJt2zn888/l81m0+eff25v69evn+rVq+eSsYBrCWEI+P+Sk5Nls9m0cePGYpd37txZTZs2veLtLF26VM8///wVj2MVn332mZ588km1b99ec+bM0UsvvVRm2z59+rSef/75S4aAtWvX6i9/+YvCw8Pl6+urevXqafDgwcrIyCjS99ixY/rb3/4mf39/TZs2Te+9954qV66sfv36yWaz2V++vr66/vrrNXr0aP36669umdvF2yzpRdiHFVTwdAFAebZ79255eTn3N8XSpUs1bdo0AlEprVq1Sl5eXpo1a5Z8fHzKdNunT5/W2LFjJf0Whi/2+uuva/jw4bruuuv0+OOPq0aNGtq1a5fefvttffDBB1q6dKnatWtn7//tt9/q1KlTGj9+vLp27eowlq+vr95++21JUnZ2tpYsWaLx48dr7969+s9//uN07R07dtSZM2dK/JoNHjzYoYZ9+/Zp9OjRevjhh3XzzTfb2xs0aKA2bdpcciygvCMMAVfA19fX0yU4LS8vT5UrV/Z0GaV2+PBh+fv7X3W/iNeuXasRI0aoQ4cOWr58uSpVqmRf9uijj6p9+/b661//qh07dqhatWqSfpuLJAUFBRUZr0KFCnrggQfs7x977DG1a9dO//3vf/Xqq68qPDzcqfq8vLzk5+dX4vK4uDjFxcXZ32/cuFGjR49WXFycQx2FLjUWUN5xmgy4AhdfM3Tu3DmNHTtWDRs2lJ+fn6pXr64OHTooJSVF0m+nJqZNmyZJDqciCuXl5WnUqFGKjIyUr6+voqOj9corr8gY47DdM2fOaNiwYQoJCVHVqlV199136+DBg7LZbA5HnJ5//nnZbDbt3LlTf//731WtWjV16NBBkrR161b169dP1113nfz8/BQREaEBAwbo2LFjDtsqHOOHH37QAw88oMDAQIWGhuq5556TMUYHDhxQ9+7dFRAQoIiICE2ePLlUX7vz589r/PjxatCggf300j//+U/l5+fb+9hsNs2ZM0d5eXn2r1VycvIlx50/f75iY2Pl7++vkJAQPfDAAzp48KBDn86dOxd7pOf319Ts379foaGhkqSxY8fat1/49R0/frxsNpveeecdhyAk/XY0ZeLEiTp06JDeeust+zb79u0rSbrpppv+8BSUzWZThw4dZIzRjz/+aG//6aef9Nhjjyk6Olr+/v6qXr267rvvPu3fv99hfVde51PcWIWnjbdu3apOnTqpUqVKioqK0kcffSRJ+uKLL9SmTRv5+/srOjpaK1euLDLuwYMHNWDAAPspxhtuuEGzZ8++4noBZ3FkCLhIdna2jh49WqT93Llzf7ju888/r6SkJD300ENq3bq1cnJytHHjRm3evFm33XabBg8erF9++UUpKSl67733HNY1xujuu+/W6tWrNXDgQMXExGjFihV64okndPDgQU2ZMsXet1+/fvrwww/14IMPqm3btvriiy/UrVu3Euu677771LBhQ7300kv2YJWSkqIff/xR/fv3V0REhHbs2KGZM2dqx44d2rBhg0NIk6SePXuqcePGmjBhgj799FO98MILCg4O1ltvvaVbbrlFL7/8sv7zn//oH//4h2666SZ17Njxkl+rhx56SO+8847++te/atSoUfr666+VlJSkXbt2adGiRZKk9957TzNnztQ333xjP4X0+9NOF0tOTlb//v110003KSkpSVlZWfrXv/6ltWvXasuWLcUekSlJaGiopk+frkcffVR/+ctfdM8990iSmjdvrtOnTys1NVU333yz6tevX+z6PXv21MMPP6xPPvlETz/9tJ555hlFR0dr5syZGjdunOrXr68GDRpcsobCgFN4ZEn67VTbunXr1KtXL9WuXVv79+/X9OnT1blzZ+3cubNIMHOnEydO6E9/+pN69eql++67T9OnT1evXr30n//8RyNGjNAjjzyiv//975o0aZL++te/6sCBA6pataokKSsrS23btpXNZtPQoUMVGhqqZcuWaeDAgcrJydGIESPKbB6ADABjjDFz5swxki75uuGGGxzWqVu3runbt6/9fYsWLUy3bt0uuZ0hQ4aY4n70Fi9ebCSZF154waH9r3/9q7HZbCY9Pd0YY8ymTZuMJDNixAiHfv369TOSzJgxY+xtY8aMMZJM7969i2zv9OnTRdr++9//GklmzZo1RcZ4+OGH7W3nz583tWvXNjabzUyYMMHefuLECePv7+/wNSlOWlqakWQeeughh/Z//OMfRpJZtWqVva1v376mcuXKlxzPGGPOnj1rwsLCTNOmTc2ZM2fs7Z988omRZEaPHm1v69Spk+nUqVORMfr27Wvq1q1rf3/kyJEiX9Pf1z98+PBL1tS8eXMTHBxsf1/4Pfbtt98W2W7lypXNkSNHzJEjR0x6erp55ZVXjM1mM02bNjUFBQX2vsXtt/Xr1xtJ5t1337W3rV692kgyq1evLnF+v/ftt98aSWbOnDlFlhU3VqdOnYwkM3fuXHvb999/byQZLy8vs2HDBnv7ihUriow9cOBAU6NGDXP06FGHbfXq1csEBgYWO0/AXThNBlxk2rRpSklJKfJq3rz5H64bFBSkHTt2aM+ePU5vd+nSpfL29tawYcMc2keNGiVjjJYtWybpt1uzpd+uKfm9xx9/vMSxH3nkkSJt/v7+9v//9ddfdfToUbVt21aStHnz5iL9H3roIfv/e3t7q1WrVjLGaODAgfb2oKAgRUdHO5zWKc7SpUslSQkJCQ7to0aNkiR9+umnl1y/OBs3btThw4f12GOPOVzf0q1bNzVq1OiyxizJqVOnJMl+lKMkVatWVU5OTqnGzMvLU2hoqEJDQxUVFaV//OMfat++vZYsWeJwlO73++3cuXM6duyYoqKiFBQUVOx+c6cqVaqoV69e9vfR0dEKCgpS48aN1aZNG3t74f8Xfl8YY7RgwQL9+c9/ljFGR48etb/i4+OVnZ1d5nOBtXGaDLhI69at1apVqyLt1apVK/b02e+NGzdO3bt31/XXX6+mTZvqjjvu0IMPPliqIPXTTz+pZs2aRX7BNm7c2L688L9eXl5FTs9ERUWVOHZxp3KOHz+usWPHat68efYLewtlZ2cX6V+nTh2H94GBgfLz81NISEiR9ouvO7pY4RwurjkiIkJBQUH2uTqjcJ3o6Ogiyxo1aqSvvvrK6TFLUriPCkNRSU6dOvWHgamQn5+f/ve//0mSfv75Z02cONF+8fjvnTlzRklJSZozZ44OHjzocD1ZcfvNnWrXrl3kdGpgYKAiIyOLtEm/nVaTpCNHjujkyZOaOXOmZs6cWezYF39PAu5EGAJcqGPHjtq7d6+WLFmizz77TG+//bamTJmiGTNmOBxZKWsX/0KVpL/97W9at26dnnjiCcXExKhKlSoqKCjQHXfcoYKCgiL9vb29S9UmqcgF3yW5+BdpWbHZbMXWeOHChVKtHxUVpQoVKmjr1q0l9snPz9fu3buLDdbF8fb2drjVPT4+Xo0aNdLgwYP18ccf29sff/xxzZkzRyNGjFBcXJwCAwNls9nUq1evYvebO5W0///o+6KwzgceeMB+UfnFSvMHBOAqhCHAxYKDg9W/f3/1799fubm56tixo55//nl7GCopANStW1crV64scjTh+++/ty8v/G9BQYH27dunhg0b2vulp6eXusYTJ04oNTVVY8eO1ejRo+3tl3N673IUzmHPnj32I1/SbxfVnjx50j5XZ8eUfnv20y233OKwbPfu3Q5jVqtWrdhTeRcfkSppX1WuXFldunTRqlWr9NNPPxVb74cffqj8/Hz96U9/cnouklSjRg2NHDlSY8eO1YYNG+ynMD/66CP17dvX4a69X3/91W1PsnaH0NBQVa1aVRcuXCjyvCXAE7hmCHChi08PValSRVFRUQ63ixc+4+fiX1533XWXLly4oDfeeMOhfcqUKbLZbLrzzjsl/XbEQJLefPNNh36vv/56qess/Mv94qMjU6dOLfUYV+Kuu+4qdnuvvvqqJF3yzriStGrVSmFhYZoxY4bD13vZsmXatWuXw5gNGjTQ999/ryNHjtjbvvvuO61du9ZhzMI7s4oLGs8++6yMMerXr5/OnDnjsGzfvn168sknVaNGDQ0ePNjpuRR6/PHHValSJU2YMMHe5u3tXWS/vf7666U+qnU18Pb21r333qsFCxZo+/btRZb/fr8AZYEjQ4ALNWnSRJ07d1ZsbKyCg4O1ceNGffTRRxo6dKi9T2xsrCRp2LBhio+Pl7e3t3r16qU///nP6tKli5555hnt379fLVq00GeffaYlS5ZoxIgR9tuwY2Njde+992rq1Kk6duyY/db6H374QVLpTj0FBASoY8eOmjhxos6dO6datWrps88+0759+9zwVSmqRYsW6tu3r2bOnKmTJ0+qU6dO+uabb/TOO++oR48e6tKli9NjVqxYUS+//LL69++vTp06qXfv3vZb6+vVq6eRI0fa+w4YMECvvvqq4uPjNXDgQB0+fFgzZszQDTfc4HDBs7+/v5o0aaIPPvhA119/vYKDg9W0aVM1bdpUHTt21CuvvKKEhAQ1b95c/fr1U40aNfT999/r3//+twoKCrR06VKH2+KdVb16dfXv319vvvmmdu3apcaNG+tPf/qT3nvvPQUGBqpJkyZav369Vq5cqerVq1/2djxhwoQJWr16tdq0aaNBgwapSZMmOn78uDZv3qyVK1fq+PHjni4RVuKhu9iAq05Jtz0X6tSp0x/eWv/CCy+Y1q1bm6CgIOPv728aNWpkXnzxRXP27Fl7n/Pnz5vHH3/chIaGGpvN5nCb/alTp8zIkSNNzZo1TcWKFU3Dhg3NpEmTHG6tNsaYvLw8M2TIEBMcHGyqVKlievToYXbv3m0kOdzqXnhb/JEjR4rM5+effzZ/+ctfTFBQkAkMDDT33Xef+eWXX0q8Pf/iMUq65b24r1Nxzp07Z8aOHWvq169vKlasaCIjI01iYqL59ddfS7WdknzwwQemZcuWxtfX1wQHB5v777/f/Pzzz0X6vf/+++a6664zPj4+JiYmxqxYsaLYW8/XrVtnYmNjjY+PT7G32a9Zs8Z0797dhISEmIoVK5o6deqYQYMGmf379xfZ5h/dWl+cvXv3Gm9vb/v32YkTJ0z//v1NSEiIqVKliomPjzfff/99ke/Fsri1vrj9XLdu3WIfLyHJDBkyxKEtKyvLDBkyxERGRpqKFSuaiIgIc+utt5qZM2cWWyPgLjZjSnmlI4CrWlpamlq2bKn3339f999/v6fLAYByg2uGgHLo4mtUpN+uv/Hy8vrDJz8DABxxzRBQDk2cOFGbNm1Sly5dVKFCBS1btkzLli3Tww8/XOQZLwCAS+M0GVAOpaSkaOzYsdq5c6dyc3NVp04dPfjgg3rmmWdUoQJ/4wCAMwhDAADA0rhmCAAAWBphCAAAWJrlLi4oKCjQL7/8oqpVq3rsc5EAAIBzjDE6deqUatasKS8v1x7LsVwY+uWXX7jbBgCAcurAgQOqXbu2S8e0XBgq/ADMAwcOKCAgwMPVAACA0sjJyVFkZKTDB1m7iuXCUOGpsYCAAMIQAADljDsuceECagAAYGmEIQAAYGmEIQAAYGmEIQAAYGmEIQAAYGmEIQAAYGmEIQAAYGmEIQAAYGmEIQAAYGmEIQAAYGkeDUPTp09X8+bN7R+NERcXp2XLll1ynfnz56tRo0by8/NTs2bNtHTp0jKqFgAAXIs8GoZq166tCRMmaNOmTdq4caNuueUWde/eXTt27Ci2/7p169S7d28NHDhQW7ZsUY8ePdSjRw9t3769jCsHAADXCpsxxni6iN8LDg7WpEmTNHDgwCLLevbsqby8PH3yySf2trZt2yomJkYzZswo1fg5OTkKDAxUdnY2H9QKAEA54c7f31fNNUMXLlzQvHnzlJeXp7i4uGL7rF+/Xl27dnVoi4+P1/r160scNz8/Xzk5OQ4vAACAQhU8XcC2bdsUFxenX3/9VVWqVNGiRYvUpEmTYvtmZmYqPDzcoS08PFyZmZkljp+UlKSxY8e6tGYAV696T3/q6RKctn9CN0+XAFiax48MRUdHKy0tTV9//bUeffRR9e3bVzt37nTZ+ImJicrOzra/Dhw44LKxAQBA+efxI0M+Pj6KioqSJMXGxurbb7/Vv/71L7311ltF+kZERCgrK8uhLSsrSxERESWO7+vrK19fX9cWDQAArhkePzJ0sYKCAuXn5xe7LC4uTqmpqQ5tKSkpJV5jBAAA8Ec8emQoMTFRd955p+rUqaNTp05p7ty5+vzzz7VixQpJUp8+fVSrVi0lJSVJkoYPH65OnTpp8uTJ6tatm+bNm6eNGzdq5syZnpwGAAAoxzwahg4fPqw+ffro0KFDCgwMVPPmzbVixQrddtttkqSMjAx5ef3fwat27dpp7ty5evbZZ/XPf/5TDRs21OLFi9W0aVNPTQEAAJRzV91zhtyN5wwB1zbuJgOuTZZ4zhAAAIAnEIYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAICleTQMJSUl6aabblLVqlUVFhamHj16aPfu3ZdcJzk5WTabzeHl5+dXRhUDAIBrjUfD0BdffKEhQ4Zow4YNSklJ0blz53T77bcrLy/vkusFBATo0KFD9tdPP/1URhUDAIBrTQVPbnz58uUO75OTkxUWFqZNmzapY8eOJa5ns9kUERHh7vIAAIAFXFXXDGVnZ0uSgoODL9kvNzdXdevWVWRkpLp3764dO3aU2Dc/P185OTkOLwAAgEJXTRgqKCjQiBEj1L59ezVt2rTEftHR0Zo9e7aWLFmi999/XwUFBWrXrp1+/vnnYvsnJSUpMDDQ/oqMjHTXFAAAQDlkM8YYTxchSY8++qiWLVumr776SrVr1y71eufOnVPjxo3Vu3dvjR8/vsjy/Px85efn29/n5OQoMjJS2dnZCggIcEntAK4e9Z7+1NMlOG3/hG6eLgG46uXk5CgwMNAtv789es1QoaFDh+qTTz7RmjVrnApCklSxYkW1bNlS6enpxS739fWVr6+vK8oEAADXII+eJjPGaOjQoVq0aJFWrVql+vXrOz3GhQsXtG3bNtWoUcMNFQIAgGudR48MDRkyRHPnztWSJUtUtWpVZWZmSpICAwPl7+8vSerTp49q1aqlpKQkSdK4cePUtm1bRUVF6eTJk5o0aZJ++uknPfTQQx6bBwAAKL88GoamT58uSercubND+5w5c9SvXz9JUkZGhry8/u8A1okTJzRo0CBlZmaqWrVqio2N1bp169SkSZOyKhsAAFxDrpoLqMuKOy/AAuB5XEANXJvc+fv7qrm1HgAAwBMIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNIIQwAAwNKcDkObN2/Wtm3b7O+XLFmiHj166J///KfOnj3r0uIAAADczekwNHjwYP3www+SpB9//FG9evVSpUqVNH/+fD355JMuLxAAAMCdnA5DP/zwg2JiYiRJ8+fPV8eOHTV37lwlJydrwYIFrq4PAADArZwOQ8YYFRQUSJJWrlypu+66S5IUGRmpo0ePurY6AAAAN3M6DLVq1UovvPCC3nvvPX3xxRfq1q2bJGnfvn0KDw93eYEAAADu5HQYmjp1qjZv3qyhQ4fqmWeeUVRUlCTpo48+Urt27VxeIAAAgDtVcHaF5s2bO9xNVmjSpEny9vZ2SVEAAABl5bKeM3Ty5Em9/fbbSkxM1PHjxyVJO3fu1OHDh11aHAAAgLs5fWRo69atuvXWWxUUFKT9+/dr0KBBCg4O1sKFC5WRkaF3333XHXUCAAC4hdNHhhISEtS/f3/t2bNHfn5+9va77rpLa9ascWlxAAAA7uZ0GPr22281ePDgIu21atVSZmamS4oCAAAoK06HIV9fX+Xk5BRp/+GHHxQaGuqSogAAAMqK02Ho7rvv1rhx43Tu3DlJks1mU0ZGhp566inde++9Li8QAADAnZwOQ5MnT1Zubq7CwsJ05swZderUSVFRUapatapefPFFd9QIAADgNk7fTRYYGKiUlBStXbtW3333nXJzc3XjjTeqa9eu7qgPAADArZwOQ4Xat2+v9u3bu7IWAACAMuf0abJhw4bptddeK9L+xhtvaMSIEa6oCQAAoMw4HYYWLFhQ7BGhdu3a6aOPPnJJUQAAAGXF6TB07NgxBQYGFmkPCAjQ0aNHXVIUAABAWXE6DEVFRWn58uVF2pctW6brrrvOJUUBAACUFacvoE5ISNDQoUN15MgR3XLLLZKk1NRUTZ48WVOnTnV1fQAAAG7ldBgaMGCA8vPz9eKLL2r8+PGSpHr16mn69Onq06ePywsEAABwp8u6tf7RRx/Vo48+qiNHjsjf319VqlRxdV0AAABl4rKfMySJzyIDAADlntMXUGdlZenBBx9UzZo1VaFCBXl7ezu8AAAAyhOnjwz169dPGRkZeu6551SjRg3ZbDZ31AUAAFAmnA5DX331lb788kvFxMS4oRwAAICy5fRpssjISBljXLLxpKQk3XTTTapatarCwsLUo0cP7d69+w/Xmz9/vho1aiQ/Pz81a9ZMS5cudUk9AADAepwOQ1OnTtXTTz+t/fv3X/HGv/jiCw0ZMkQbNmxQSkqKzp07p9tvv115eXklrrNu3Tr17t1bAwcO1JYtW9SjRw/16NFD27dvv+J6AACA9diMk4d5qlWrptOnT+v8+fOqVKmSKlas6LD8+PHjl13MkSNHFBYWpi+++EIdO3Ystk/Pnj2Vl5enTz75xN7Wtm1bxcTEaMaMGX+4jZycHAUGBio7O1sBAQGXXSuAq1O9pz/1dAlO2z+hm6dLAK567vz97fQ1Q+58ynR2drYkKTg4uMQ+69evV0JCgkNbfHy8Fi9eXGz//Px85efn29/n5ORceaEAAOCa4XQY6tu3rzvqUEFBgUaMGKH27duradOmJfbLzMxUeHi4Q1t4eLgyMzOL7Z+UlKSxY8e6tNZL4a9SAADKF6evGZKkvXv36tlnn1Xv3r11+PBhSb99UOuOHTsuu5AhQ4Zo+/btmjdv3mWPUZzExERlZ2fbXwcOHHDp+AAAoHxzOgx98cUXatasmb7++mstXLhQubm5kqTvvvtOY8aMuawihg4dqk8++USrV69W7dq1L9k3IiJCWVlZDm1ZWVmKiIgotr+vr68CAgIcXgAAAIWcDkNPP/20XnjhBaWkpMjHx8fefsstt2jDhg1OjWWM0dChQ7Vo0SKtWrVK9evX/8N14uLilJqa6tCWkpKiuLg4p7YNAAAgXcY1Q9u2bdPcuXOLtIeFheno0aNOjTVkyBDNnTtXS5YsUdWqVe3X/QQGBsrf31+S1KdPH9WqVUtJSUmSpOHDh6tTp06aPHmyunXrpnnz5mnjxo2aOXOms1MBAABw/shQUFCQDh06VKR9y5YtqlWrllNjTZ8+XdnZ2ercubNq1Khhf33wwQf2PhkZGQ7ba9eunebOnauZM2eqRYsW+uijj7R48eJLXnQNAABQEqePDPXq1UtPPfWU5s+fL5vNpoKCAq1du1b/+Mc/1KdPH6fGKs0jjj7//PMibffdd5/uu+8+p7YFAABQHKePDL300ktq1KiRIiMjlZubqyZNmqhjx45q166dnn32WXfUCAAA4DZOHRkyxigzM1OvvfaaRo8erW3btik3N1ctW7ZUw4YN3VUjAACA2zgdhqKiorRjxw41bNhQkZGR7qoLAACgTDh1mszLy0sNGzbUsWPH3FUPAABAmXL6mqEJEyboiSee4FPiAQDANcHpu8n69Omj06dPq0WLFvLx8bE/D6jQlXxqPQAAQFm7qj61HgAAoKw5FYbOnTunL774Qs8991ypPjoDAADgaufUNUMVK1bUggUL3FULAABAmXP6AuoePXpo8eLFbigFAACg7Dl9zVDDhg01btw4rV27VrGxsapcubLD8mHDhrmsOAAAAHdzOgzNmjVLQUFB2rRpkzZt2uSwzGazEYYAAEC54nQY2rdvnzvqAAAA8AinrxkCAAC4ljh9ZGjAgAGXXD579uzLLgYAAKCsOR2GTpw44fD+3Llz2r59u06ePKlbbrnFZYUBAACUBafD0KJFi4q0FRQU6NFHH1WDBg1cUhQAAEBZcck1Q15eXkpISNCUKVNcMRwAAECZcdkF1Hv37tX58+ddNRwAAECZcPo0WUJCgsN7Y4wOHTqkTz/9VH379nVZYQAAAGXB6TC0ZcsWh/deXl4KDQ3V5MmT//BOMwAAgKuN02Fo9erV7qgDAADAI5y+Zmjfvn3as2dPkfY9e/Zo//79rqgJAACgzDgdhvr166d169YVaf/666/Vr18/V9QEAABQZpwOQ1u2bFH79u2LtLdt21ZpaWmuqAkAAKDMOB2GbDabTp06VaQ9OztbFy5ccElRAAAAZcXpMNSxY0clJSU5BJ8LFy4oKSlJHTp0cGlxAAAA7ub03WQvv/yyOnbsqOjoaN18882SpC+//FI5OTlatWqVywsEAABwJ6ePDDVp0kRbt27V3/72Nx0+fFinTp1Snz599P3336tp06buqBEAAMBtnD4yJEk1a9bUSy+95OpaAAAAypzTR4bmzJmj+fPnF2mfP3++3nnnHZcUBQAAUFacDkNJSUkKCQkp0h4WFsbRIgAAUO44HYYyMjJUv379Iu1169ZVRkaGS4oCAAAoK06HobCwMG3durVI+3fffafq1au7pCgAAICy4nQY6t27t4YNG6bVq1frwoULunDhglatWqXhw4erV69e7qgRAADAbZy+m2z8+PHav3+/br31VlWo8NvqBQUF6tOnD9cMAQCAcsfpMOTj46MPPvhA48eP13fffSd/f381a9ZMdevWdUd9AAAAbnVZzxmSpODgYHXp0qXYO8sAAADKC6euGTp58qSGDBmikJAQhYeHKzw8XCEhIRo6dKhOnjzpphIBAADcp9RHho4fP664uDgdPHhQ999/vxo3bixJ2rlzp5KTk5Wamqp169apWrVqbisWAADA1UodhsaNGycfHx/t3btX4eHhRZbdfvvtGjdunKZMmeLyIgEAANyl1KfJFi9erFdeeaVIEJKkiIgITZw4UYsWLXJpcQAAAO5W6jB06NAh3XDDDSUub9q0qTIzM11SFAAAQFkpdRgKCQnR/v37S1y+b98+BQcHu6ImAACAMlPqMBQfH69nnnlGZ8+eLbIsPz9fzz33nO644w6XFgcAAOBuTl1A3apVKzVs2FBDhgxRo0aNZIzRrl279Oabbyo/P1/vvfeeO2sFAABwuVKHodq1a2v9+vV67LHHlJiYKGOMJMlms+m2227TG2+8ocjISLcVCgAA4A5OPXSxfv36WrZsmY4ePaoNGzZow4YNOnLkiJYvX66oqCinN75mzRr9+c9/Vs2aNWWz2bR48eJL9v/8889ls9mKvLhwGwAAXK7L+jiOatWqqXXr1le88by8PLVo0UIDBgzQPffcU+r1du/erYCAAPv7sLCwK64FAABY02V/Npkr3HnnnbrzzjudXi8sLExBQUGuLwgAAFiOU6fJrhYxMTGqUaOGbrvtNq1du/aSffPz85WTk+PwAgAAKFSuwlCNGjU0Y8YMLViwQAsWLFBkZKQ6d+6szZs3l7hOUlKSAgMD7S8u8gYAAL9XqjB044036sSJE5J+u8X+9OnTbi2qJNHR0Ro8eLBiY2PVrl07zZ49W+3atbvk56ElJiYqOzvb/jpw4EAZVgwAAK52pQpDu3btUl5eniRp7Nixys3NdWtRzmjdurXS09NLXO7r66uAgACHFwAAQKFSXUAdExOj/v37q0OHDjLG6JVXXlGVKlWK7Tt69GiXFvhH0tLSVKNGjTLdJgAAuHaUKgwlJydrzJgx+uSTT2Sz2bRs2TJVqFB0VZvN5lQYys3NdTiqs2/fPqWlpSk4OFh16tRRYmKiDh48qHfffVeSNHXqVNWvX1833HCDfv31V7399ttatWqVPvvss1JvEwAA4PdKFYaio6M1b948SZKXl5dSU1Nd8myfjRs3qkuXLvb3CQkJkqS+ffsqOTlZhw4dUkZGhn352bNnNWrUKB08eFCVKlVS8+bNtXLlSocxAAAAnOH0c4YKCgpctvHOnTvbP9ajOMnJyQ7vn3zyST355JMu2z4AAMBlPXRx7969mjp1qnbt2iVJatKkiYYPH64GDRq4tDgAAAB3c/o5QytWrFCTJk30zTffqHnz5mrevLm+/vpr3XDDDUpJSXFHjQAAAG7j9JGhp59+WiNHjtSECROKtD/11FO67bbbXFYcAACAuzl9ZGjXrl0aOHBgkfYBAwZo586dLikKAACgrDgdhkJDQ5WWllakPS0tjU+PBwAA5Y7Tp8kGDRqkhx9+WD/++KPatWsnSVq7dq1efvll+63xAAAA5YXTYei5555T1apVNXnyZCUmJkqSatasqeeff17Dhg1zeYEAAADu5HQYstlsGjlypEaOHKlTp05JkqpWrerywgAAAMrCZT1nqBAhCAAAlHdOX0ANAABwLSEMAQAASyMMAQAAS3MqDJ07d0633nqr9uzZ4656AAAAypRTYahixYraunWru2oBAAAoc06fJnvggQc0a9Ysd9QCAABQ5py+tf78+fOaPXu2Vq5cqdjYWFWuXNlh+auvvuqy4gAAANzN6TC0fft23XjjjZKkH374wWGZzWZzTVUAAABlxOkwtHr1anfUAQAA4BGXfWt9enq6VqxYoTNnzkiSjDEuKwoAAKCsOB2Gjh07pltvvVXXX3+97rrrLh06dEiSNHDgQI0aNcrlBQIAALiT02Fo5MiRqlixojIyMlSpUiV7e8+ePbV8+XKXFgcAAOBuTl8z9Nlnn2nFihWqXbu2Q3vDhg31008/uawwAACAsuD0kaG8vDyHI0KFjh8/Ll9fX5cUBQAAUFacDkM333yz3n33Xft7m82mgoICTZw4UV26dHFpcQAAAO7m9GmyiRMn6tZbb9XGjRt19uxZPfnkk9qxY4eOHz+utWvXuqNGAAAAt3H6yFDTpk31ww8/qEOHDurevbvy8vJ0zz33aMuWLWrQoIE7agQAAHAbp48MSVJgYKCeeeYZV9cCAABQ5i4rDJ04cUKzZs3Srl27JElNmjRR//79FRwc7NLiAAAA3M3p02Rr1qxRvXr19Nprr+nEiRM6ceKEXnvtNdWvX19r1qxxR40AAABu4/SRoSFDhqhnz56aPn26vL29JUkXLlzQY489piFDhmjbtm0uLxIAAMBdnD4ylJ6erlGjRtmDkCR5e3srISFB6enpLi0OAADA3ZwOQzfeeKP9WqHf27Vrl1q0aOGSogAAAMpKqU6Tbd261f7/w4YN0/Dhw5Wenq62bdtKkjZs2KBp06ZpwoQJ7qkSAADATUoVhmJiYmSz2WSMsbc9+eSTRfr9/e9/V8+ePV1XHQAAgJuVKgzt27fP3XUAAAB4RKnCUN26dd1dBwAAgEdc1kMXf/nlF3311Vc6fPiwCgoKHJYNGzbMJYUBAACUBafDUHJysgYPHiwfHx9Vr15dNpvNvsxmsxGGAABAueJ0GHruuec0evRoJSYmysvL6TvzAQAAripOp5nTp0+rV69eBCEAAHBNcDrRDBw4UPPnz3dHLQAAAGXO6dNkSUlJ+tOf/qTly5erWbNmqlixosPyV1991WXFAQAAuNtlhaEVK1YoOjpakopcQA0AAFCeOB2GJk+erNmzZ6tfv35uKAcAAKBsOX3NkK+vr9q3b++OWgAAAMqc02Fo+PDhev31191RCwAAQJlz+jTZN998o1WrVumTTz7RDTfcUOQC6oULF7qsOAAAAHdzOgwFBQXpnnvucUctAAAAZc7pMDRnzhyXbXzNmjWaNGmSNm3apEOHDmnRokXq0aPHJdf5/PPPlZCQoB07digyMlLPPvssF3MDAIDL5tHHSOfl5alFixaaNm1aqfrv27dP3bp1U5cuXZSWlqYRI0booYce0ooVK9xcKQAAuFY5fWSofv36l3ye0I8//ljqse68807deeedpe4/Y8YM1a9fX5MnT5YkNW7cWF999ZWmTJmi+Pj4Uo8DAABQyOkwNGLECIf3586d05YtW7R8+XI98cQTrqqrWOvXr1fXrl0d2uLj44vU9Hv5+fnKz8+3v8/JyXFXeQAAoBxyOgwNHz682PZp06Zp48aNV1zQpWRmZio8PNyhLTw8XDk5OTpz5oz8/f2LrJOUlKSxY8e6ta7yrt7Tn3q6BKftn9DN0yVYQnn83iiP+DqXnfL4b0d5/P4ob19nl10zdOedd2rBggWuGs5lEhMTlZ2dbX8dOHDA0yUBAICriNNHhkry0UcfKTg42FXDFSsiIkJZWVkObVlZWQoICCj2qJD02xOzfX193VoXAAAov5wOQy1btnS4gNoYo8zMTB05ckRvvvmmS4u7WFxcnJYuXerQlpKSori4OLduFwAAXLucDkMXPwfIy8tLoaGh6ty5sxo1auTUWLm5uUpPT7e/37dvn9LS0hQcHKw6deooMTFRBw8e1LvvvitJeuSRR/TGG2/oySef1IABA7Rq1Sp9+OGH+vTT8nc+FQAAXB2cDkNjxoxx2cY3btyoLl262N8nJCRIkvr27avk5GQdOnRIGRkZ9uX169fXp59+qpEjR+pf//qXateurbfffpvb6gEAwGVz2TVDl6Nz584yxpS4PDk5udh1tmzZ4saqAACAlZQ6DHl5eV3yYYuSZLPZdP78+SsuCgAAoKyUOgwtWrSoxGXr16/Xa6+9poKCApcUBQAAUFZKHYa6d+9epG337t16+umn9b///U/333+/xo0b59LiAAAA3O2yHrr4yy+/aNCgQWrWrJnOnz+vtLQ0vfPOO6pbt66r6wMAAHArp8JQdna2nnrqKUVFRWnHjh1KTU3V//73PzVt2tRd9QEAALhVqU+TTZw4US+//LIiIiL03//+t9jTZgAAAOVNqcPQ008/LX9/f0VFRemdd97RO++8U2y/hQsXuqw4AAAAdyt1GOrTp88f3loPAABQ3pQ6DBX3AEQAAIDy7rLuJgMAALhWEIYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClEYYAAIClXRVhaNq0aapXr578/PzUpk0bffPNNyX2TU5Ols1mc3j5+fmVYbUAAOBa4vEw9MEHHyghIUFjxozR5s2b1aJFC8XHx+vw4cMlrhMQEKBDhw7ZXz/99FMZVgwAAK4lHg9Dr776qgYNGqT+/furSZMmmjFjhipVqqTZs2eXuI7NZlNERIT9FR4eXoYVAwCAa4lHw9DZs2e1adMmde3a1d7m5eWlrl27av369SWul5ubq7p16yoyMlLdu3fXjh07Suybn5+vnJwchxcAAEAhj4aho0eP6sKFC0WO7ISHhyszM7PYdaKjozV79mwtWbJE77//vgoKCtSuXTv9/PPPxfZPSkpSYGCg/RUZGenyeQAAgPLL46fJnBUXF6c+ffooJiZGnTp10sKFCxUaGqq33nqr2P6JiYnKzs62vw4cOFDGFQMAgKtZBU9uPCQkRN7e3srKynJoz8rKUkRERKnGqFixolq2bKn09PRil/v6+srX1/eKawUAANcmjx4Z8vHxUWxsrFJTU+1tBQUFSk1NVVxcXKnGuHDhgrZt26YaNWq4q0wAAHAN8+iRIUlKSEhQ37591apVK7Vu3VpTp05VXl6e+vfvL0nq06ePatWqpaSkJEnSuHHj1LZtW0VFRenkyZOaNGmSfvrpJz300EOenAYAACinPB6GevbsqSNHjmj06NHKzMxUTEyMli9fbr+oOiMjQ15e/3cA68SJExo0aJAyMzNVrVo1xcbGat26dWrSpImnpgAAAMoxj4chSRo6dKiGDh1a7LLPP//c4f2UKVM0ZcqUMqgKAABYQbm7mwwAAMCVCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSCEMAAMDSroowNG3aNNWrV09+fn5q06aNvvnmm0v2nz9/vho1aiQ/Pz81a9ZMS5cuLaNKAQDAtcbjYeiDDz5QQkKCxowZo82bN6tFixaKj4/X4cOHi+2/bt069e7dWwMHDtSWLVvUo0cP9ejRQ9u3by/jygEAwLXA42Ho1Vdf1aBBg9S/f381adJEM2bMUKVKlTR79uxi+//rX//SHXfcoSeeeEKNGzfW+PHjdeONN+qNN94o48oBAMC1oIInN3727Flt2rRJiYmJ9jYvLy917dpV69evL3ad9evXKyEhwaEtPj5eixcvLrZ/fn6+8vPz7e+zs7MlSTk5OVdYffEK8k+7ZVw4ctf+gyO+n3GtKY//dpTHn0N3fJ0LxzTGuHxsj4aho0eP6sKFCwoPD3doDw8P1/fff1/sOpmZmcX2z8zMLLZ/UlKSxo4dW6Q9MjLyMqvG1SBwqqcrAFAe8W9H2XDn1/nUqVMKDAx06ZgeDUNlITEx0eFIUkFBgY4fP67q1avLZrN5sLIrl5OTo8jISB04cEABAQGeLqfMMG/mbQXMm3lbgTPzNsbo1KlTqlmzpsvr8GgYCgkJkbe3t7Kyshzas7KyFBERUew6ERERTvX39fWVr6+vQ1tQUNDlF30VCggIsNQPTyHmbS3M21qYt7WUdt6uPiJUyKMXUPv4+Cg2Nlapqan2toKCAqWmpiouLq7YdeLi4hz6S1JKSkqJ/QEAAC7F46fJEhIS1LdvX7Vq1UqtW7fW1KlTlZeXp/79+0uS+vTpo1q1aikpKUmSNHz4cHXq1EmTJ09Wt27dNG/ePG3cuFEzZ8705DQAAEA55fEw1LNnTx05ckSjR49WZmamYmJitHz5cvtF0hkZGfLy+r8DWO3atdPcuXP17LPP6p///KcaNmyoxYsXq2nTpp6agsf4+vpqzJgxRU4DXuuYN/O2AubNvK3gapm3zbjjHjUAAIBywuMPXQQAAPAkwhAAALA0whAAALA0whAAALA0whAAALA0wlAZmjZtmurVqyc/Pz+1adNG33zzTYl9Fy5cqFatWikoKEiVK1dWTEyM3nvvvSL9du3apbvvvluBgYGqXLmybrrpJmVkZNiX//rrrxoyZIiqV6+uKlWq6N577y3yBO+MjAx169ZNlSpVUlhYmJ544gmdP3++3M77+PHjevzxxxUdHS1/f3/VqVNHw4YNs39IbyGbzVbkNW/evHI7b0nq3LlzkTk98sgjDmNca/t7//79xe5Lm82m+fPn28cob/u7pDlNmjTJ3uf48eO6//77FRAQoKCgIA0cOFC5ubkO42zdulU333yz/Pz8FBkZqYkTJ7pszp6Y9/79+zVw4EDVr19f/v7+atCggcaMGaOzZ8/axyjpe2LDhg3ldt6SVK9evSLLJ0yY4DCOO/d3Wc/5888/L7HPt99+K8mF+9qgTMybN8/4+PiY2bNnmx07dphBgwaZoKAgk5WVVWz/1atXm4ULF5qdO3ea9PR0M3XqVOPt7W2WL19u75Oenm6Cg4PNE088YTZv3mzS09PNkiVLHMZ85JFHTGRkpElNTTUbN240bdu2Ne3atbMvP3/+vGnatKnp2rWr2bJli1m6dKkJCQkxiYmJ5Xbe27ZtM/fcc4/5+OOPTXp6uklNTTUNGzY09957r8O2JJk5c+aYQ4cO2V9nzpwpt/M2xphOnTqZQYMGOcwpOzvbvvxa3N/nz593mO+hQ4fM2LFjTZUqVcypU6fs45S3/X3xnGbPnm1sNpvZu3evvc8dd9xhWrRoYTZs2GC+/PJLExUVZXr37m1fnp2dbcLDw839999vtm/fbv773/8af39/89Zbb5XbeS9btsz069fPrFixwuzdu9csWbLEhIWFmVGjRtnH2Ldvn5FkVq5c6TDW2bNny+28jTGmbt26Zty4cQ79cnNz7cvdub89Mef8/PwifR566CFTv359U1BQYIxx3b4mDJWR1q1bmyFDhtjfX7hwwdSsWdMkJSWVeoyWLVuaZ5991v6+Z8+e5oEHHiix/8mTJ03FihXN/Pnz7W27du0yksz69euNMcYsXbrUeHl5mczMTHuf6dOnm4CAAJOfn1/q2kriiXkX58MPPzQ+Pj7m3Llz9jZJZtGiRU6NU1qemnenTp3M8OHDS1xulf0dExNjBgwY4NBW3vb3xbp3725uueUW+/udO3caSebbb7+1ty1btszYbDZz8OBBY4wxb775pqlWrZrDvn3qqadMdHR0qeu6FE/MuzgTJ0409evXt78v/AW5ZcuWUtfhDE/Nu27dumbKlCklruPO/X017OuzZ8+a0NBQM27cOHubq/Y1p8nKwNmzZ7Vp0yZ17drV3ubl5aWuXbtq/fr1f7i+MUapqanavXu3OnbsKOm3z3D79NNPdf311ys+Pl5hYWFq06aNFi9ebF9v06ZNOnfunMN2GzVqpDp16ti3u379ejVr1sz+xG9Jio+PV05Ojnbs2FEu512c7OxsBQQEqEIFx4euDxkyRCEhIWrdurVmz54t44JnkHp63v/5z38UEhKipk2bKjExUadPn7Yvs8L+3rRpk9LS0jRw4MAiy8rL/r5YVlaWPv30U4c5rV+/XkFBQWrVqpW9rWvXrvLy8tLXX39t79OxY0f5+PjY+8THx2v37t06ceKE03P9PU/NuzjZ2dkKDg4u0n733XcrLCxMHTp00Mcff/yHNZWGp+c9YcIEVa9eXS1bttSkSZMcTnG7a397es6FPv74Yx07dsz+cV2/d6X72uMfx2EFR48e1YULFxx+AUlSeHi4vv/++xLXy87OVq1atZSfny9vb2+9+eabuu222yRJhw8fVm5uriZMmKAXXnhBL7/8spYvX6577rlHq1evVqdOnZSZmSkfHx8FBQUV2W5mZqYkKTMzs9i6CpeVx3kXV8f48eP18MMPO7SPGzdOt9xyiypVqqTPPvtMjz32mHJzczVs2LByO++///3vqlu3rmrWrKmtW7fqqaee0u7du7Vw4UJJ1tjfs2bNUuPGjdWuXTuH9vK0vy/2zjvvqGrVqrrnnnvsbZmZmQoLC3PoV6FCBQUHBzv8fNevX79IXYXLqlWrVvqJXsRT875Yenq6Xn/9db3yyiv2tipVqmjy5Mlq3769vLy8tGDBAvXo0UOLFy/W3Xff7eRMHXly3sOGDdONN96o4OBgrVu3TomJiTp06JBeffVVSe7b31fLvp41a5bi4+NVu3Zte5ur9jVh6CpWtWpVpaWlKTc3V6mpqUpISNB1112nzp07q6CgQJLUvXt3jRw5UpIUExOjdevWacaMGcX+kigvXDnvnJwcdevWTU2aNNHzzz/vsOy5556z/3/Lli2Vl5enSZMmXfEvx8vlinn/PvA1a9ZMNWrU0K233qq9e/eqQYMGZT+pUnDl/j5z5ozmzp3rsG8Llaf9fbHZs2fr/vvvl5+fX9kX6mKunPfBgwd1xx136L777tOgQYPs7SEhIUpISLC/v+mmm/TLL79o0qRJVxyGLpcr5v37OTVv3lw+Pj4aPHiwkpKSPP7ZXsVx5b7++eeftWLFCn344YcO7a7a14ShMhASEiJvb+8id3FlZWUpIiKixPW8vLwUFRUl6bdfALt27VJSUpI6d+6skJAQVahQQU2aNHFYp3Hjxvrqq68kSRERETp79qxOnjzpcHTo99uNiIgockdAYZ2Xqq00PDXvQqdOndIdd9yhqlWratGiRapYseIl623Tpo3Gjx+v/Pz8K/qHxdPzvnhO0m9/PTdo0OCa3t+S9NFHH+n06dPq06fPH9Z7Ne/v3/vyyy+1e/duffDBBw7tEREROnz4sEPb+fPndfz4cYef7+LqKlx2JTw170K//PKLunTponbt2mnmzJl/WG+bNm2UkpLyh/3+iKfn/Xtt2rTR+fPntX//fkVHR7ttf18Nc54zZ46qV69eqoBzOfuaa4bKgI+Pj2JjY5WammpvKygoUGpqquLi4ko9TkFBgfLz8+1j3nTTTdq9e7dDnx9++EF169aVJMXGxqpixYoO2929e7cyMjLs242Li9O2bdsc/lFNSUlRQEBAkV9AzvLUvKXfjgjdfvvt8vHx0ccff1yqv6jT0tJUrVq1K/4Ly5PzvlhaWpokqUaNGpKu3f1daNasWbr77rsVGhr6h9u5mvf3782aNUuxsbFq0aKFQ3tcXJxOnjypTZs22dtWrVqlgoICewiOi4vTmjVrdO7cOXuflJQURUdHX9EpMslz85Z+OyLUuXNnxcbGas6cOfLy+uNfZWlpafafgyvhyXlfLC0tTV5eXvbTpe7a356eszFGc+bMUZ8+ff7wj1rpMvf1FV1+jVKbN2+e8fX1NcnJyWbnzp3m4YcfNkFBQfa7eh588EHz9NNP2/u/9NJL5rPPPjN79+41O3fuNK+88oqpUKGC+fe//23vs3DhQlOxYkUzc+ZMs2fPHvP6668bb29v8+WXX9r7PPLII6ZOnTpm1apVZuPGjSYuLs7ExcXZlxfean377bebtLQ0s3z5chMaGurSW63Let7Z2dmmTZs2plmzZiY9Pd3hdsvz588bY4z5+OOPzb///W+zbds2s2fPHvPmm2+aSpUqmdGjR5fbeaenp5tx48aZjRs3mn379pklS5aY6667znTs2NE+xrW4vwvt2bPH2Gw2s2zZsiJ1lcf9bcxv38uVKlUy06dPL3a7d9xxh2nZsqX5+uuvzVdffWUaNmzocGv9yZMnTXh4uHnwwQfN9u3bzbx580ylSpVcemt9Wc/7559/NlFRUebWW281P//8s8PPd6Hk5GQzd+5cs2vXLrNr1y7z4osvGi8vLzN79uxyO+9169aZKVOmmLS0NLN3717z/vvvm9DQUNOnTx97H3fub099jxtjzMqVK40ks2vXriLLXLWvCUNl6PXXXzd16tQxPj4+pnXr1mbDhg32ZZ06dTJ9+/a1v3/mmWdMVFSU8fPzM9WqVTNxcXFm3rx5RcacNWuWvV+LFi3M4sWLHZafOXPGPPbYY6ZatWqmUqVK5i9/+YvDPxrGGLN//35z5513Gn9/fxMSEmJGjRrlcAt6eZv36tWrjaRiX/v27TPG/HYLckxMjKlSpYqpXLmyadGihZkxY4a5cOFCuZ13RkaG6dixowkODja+vr4mKirKPPHEEw7PGTLm2tvfhRITE01kZGSx+7C87u+33nrL+Pv7m5MnTxa7zWPHjpnevXubKlWqmICAANO/f3+HZysZY8x3331nOnToYHx9fU2tWrXMhAkTXDPh/6+s5z1nzpwSf74LJScnm8aNG5tKlSqZgIAA07p1a4dHjJTHeW/atMm0adPGBAYGGj8/P9O4cWPz0ksvmV9//dWhnzv3tye+x40xpnfv3g7Px/s9V+1rmzEuuLcUAACgnOKaIQAAYGmEIQAAYGmEIQAAYGmEIQAAYGmEIQAAYGmEIQAAYGmEIQAAYGmEIQAAYGmEIQAAYGmEIQAAYGmEIQAAYGn/D5bmgS2S630VAAAAAElFTkSuQmCC", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:36.336750\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:36.744500\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:37.131467\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:37.631129\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:38.082921\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAGzCAYAAADT4Tb9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy89olMNAAAACXBIWXMAAA9hAAAPYQGoP6dpAABCzklEQVR4nO3deVxU9f7H8feAAm6gpIALLolXxbUsEyz3NLPSNk0r1EzN8Lp180qbWd2wzPRWZnm7Li1eC02tzC23UrFyyzV3xRRcUkHQEOH7+6MH83NikYODg8fX8/GYR833fM85n/nOCG/O+Z4zDmOMEQAAgE14eboAAAAAdyLcAAAAWyHcAAAAWyHcAAAAWyHcAAAAWyHcAAAAWyHcAAAAWyHcAAAAWyHcAAAAWyHcoFirWbOm+vTp4+kybG/cuHG68cYb5e3traZNm3q6nGvKyy+/LIfDUej1f/75Z0VGRqpMmTJyOBzavHnzFW+zKBw8eFAOh0PTp0+/ZvexcuVKORwOrVy5ski2j+KDcIOrZvr06XI4HFq/fn2uy9u0aaOGDRte8X6+/fZbvfzyy1e8nevFkiVLNHLkSLVs2VLTpk3T66+/nmffPn36yOFwOB9ly5bVjTfeqIceekhz5sxRVlbWVaz82peRkaGHH35Yp06d0oQJE/TJJ5+oRo0aV7TN999/v0gDSLbGjRurevXqyu8bfFq2bKng4GBdvHixyOsprJkzZ2rixImeLgNuVsLTBQD52bVrl7y8rGXwb7/9VpMmTSLgFNDy5cvl5eWl//73v/Lx8blsf19fX3300UeSpPPnz+vQoUP6+uuv9dBDD6lNmzaaP3++/P39i7rsYuOFF17QqFGjCrXuvn37dOjQIf3nP//Rk08+6ZZ63n//fVWsWLHIj3g++uijGjVqlH744Qe1atUqx/KDBw8qPj5egwcPVokSxeNXTatWrXT+/HmXz/nMmTO1bds2DRs2zHOFwe04coNizdfXVyVLlvR0GZakpaV5ugRLjh8/rlKlShUo2EhSiRIl9Nhjj+mxxx5T//799dprr+mXX35RbGysVq5cqf79++e7vjFG58+fd0fpxUKJEiXk5+dXqHWPHz8uSSpfvrwbK7o6evXqJYfDoZkzZ+a6/H//+5+MMXr00UevcmV58/Lykp+fn+U/mHDt4R1GsfbXOTcZGRkaM2aM6tSpIz8/P91www26/fbbtXTpUkl/njaZNGmSJLmcPsmWlpamZ555RqGhofL19VXdunX11ltv5Ti0fv78eQ0ZMkQVK1ZUuXLldN999+nIkSNyOBwuR4Sy50bs2LFDvXr1UoUKFXT77bdLkrZs2aI+ffroxhtvlJ+fn0JCQvTEE0/o999/d9lX9jZ2796txx57TAEBAapUqZJefPFFGWN0+PBhde3aVf7+/goJCdH48eMLNHYXL17Uq6++qtq1a8vX11c1a9bUc889p/T0dGcfh8OhadOmKS0tzTlWhT2lMWrUKHXs2FFxcXHavXu3s71mzZq65557tHjxYt1yyy0qVaqUPvzwQ0nS/v379fDDDyswMFClS5dWixYttGDBghzbPnTokO677z6VKVNGQUFBGj58uBYvXpxj/kRec7TatGmjNm3auLSlp6dr9OjRCgsLk6+vr0JDQzVy5EiX8SmI3ObHOBwODR48WPPmzVPDhg3l6+urBg0aaNGiRc4+ffr0UevWrSVJDz/8sBwOR44aLzVt2jS1a9dOQUFB8vX1VXh4uCZPnuzSp2bNmtq+fbtWrVrlfD8v3eaZM2c0bNgw5+c/LCxMb7zxRo7TiWfOnFGfPn0UEBCg8uXLq3fv3jpz5oxLn9DQULVq1UqzZ89WRkZGjnpnzpyp2rVr67bbbpMkHTlyRE888YSCg4Od4zF16tQ8X++lli9frjvuuENlypRR+fLl1bVrV+3cuTNHvyNHjqhfv36qUqWKfH19VatWLQ0aNEgXLlyQlHPOTZs2bbRgwQIdOnTIOV41a9ZUamqqypQpo6FDh+bYx2+//SZvb2/FxsYWqHZ4RvE4VojrSnJysk6ePJmjPbcfkH/18ssvKzY2Vk8++aSaN2+ulJQUrV+/Xhs3btSdd96pgQMH6ujRo1q6dKk++eQTl3WNMbrvvvu0YsUK9evXT02bNtXixYv17LPP6siRI5owYYKzb58+ffTFF1/o8ccfV4sWLbRq1Sp16dIlz7oefvhh1alTR6+//rozKC1dulT79+9X3759FRISou3bt2vKlCnavn271q1bl+MXYo8ePVS/fn2NHTtWCxYs0GuvvabAwEB9+OGHateund544w199tln+sc//qFbb70111MBl3ryySc1Y8YMPfTQQ3rmmWf0448/KjY2Vjt37tTcuXMlSZ988ommTJmin376yXmqKTIy8rLvQ14ef/xxLVmyREuXLtXf/vY3Z/uuXbvUs2dPDRw4UP3791fdunV17NgxRUZG6ty5cxoyZIhuuOEGzZgxQ/fdd59mz56t+++/X9KfgbRdu3ZKTEzU0KFDFRISopkzZ2rFihWFrjMrK0v33XefVq9erQEDBqh+/fraunWrJkyYoN27d2vevHmF3na21atX68svv9TTTz+tcuXK6Z133tGDDz6ohIQE3XDDDRo4cKCqVq2q119/XUOGDNGtt96q4ODgPLc3efJkNWjQQPfdd59KlCihr7/+Wk8//bSysrIUHR0tSZo4caL+/ve/q2zZsnr++eclybnNc+fOqXXr1jpy5IgGDhyo6tWra+3atYqJiVFiYqJz3okxRl27dtXq1av11FNPqX79+po7d6569+6do6ZHH31UAwYM0OLFi3XPPfc427du3apt27bppZdekiQdO3ZMLVq0cIa+SpUqaeHCherXr59SUlLyPSX03XffqXPnzrrxxhv18ssv6/z583r33XfVsmVLbdy4UTVr1pQkHT16VM2bN9eZM2c0YMAA1atXT0eOHNHs2bN17ty5XI9MPv/880pOTtZvv/3m/PdftmxZlS1bVvfff78+//xzvf322/L29nauUxyPSCEXBrhKpk2bZiTl+2jQoIHLOjVq1DC9e/d2Pm/SpInp0qVLvvuJjo42uX20582bZySZ1157zaX9oYceMg6Hw+zdu9cYY8yGDRuMJDNs2DCXfn369DGSzOjRo51to0ePNpJMz549c+zv3LlzOdr+97//GUnm+++/z7GNAQMGONsuXrxoqlWrZhwOhxk7dqyz/fTp06ZUqVIuY5KbzZs3G0nmySefdGn/xz/+YSSZ5cuXO9t69+5typQpk+/2Ctp306ZNRpIZPny4s61GjRpGklm0aJFL32HDhhlJ5ocffnC2nT171tSqVcvUrFnTZGZmGmOMGT9+vJFk5s2b5+x3/vx5U69ePSPJrFixwmVfuY1N69atTevWrZ3PP/nkE+Pl5eWyb2OM+eCDD4wks2bNmnzH4VLZ79+lJBkfHx/nZ8oYY3755Rcjybz77rvOthUrVhhJJi4u7rLbzO3z1KlTJ3PjjTe6tDVo0MDltWZ79dVXTZkyZczu3btd2keNGmW8vb1NQkKCMeb//528+eabzj4XL140d9xxh5Fkpk2b5mw/deqU8fX1zfH5HzVqlJFkdu3aZYwxpl+/fqZy5crm5MmTLv0eeeQRExAQ4HxtBw4cyLGPpk2bmqCgIPP7778723755Rfj5eVloqKinG1RUVHGy8vL/Pzzzzlee1ZWljHm/8f70s9Mly5dTI0aNXKss3jxYiPJLFy40KW9cePGuY4vihdOS+GqmzRpkpYuXZrj0bhx48uuW758eW3fvl179uyxvN9vv/1W3t7eGjJkiEv7M888I2OMFi5cKEnOUwdPP/20S7+///3veW77qaeeytFWqlQp5///8ccfOnnypFq0aCFJ2rhxY47+l04o9fb21i233CJjjPr16+dsL1++vOrWrav9+/fnWYv052uVpBEjRri0P/PMM5KU66kfdyhbtqwk6ezZsy7ttWrVUqdOnXLU2Lx5c+dpvOz1BwwYoIMHD2rHjh2S/nw/qlatqvvuu8/Zz8/P77Jze/ITFxen+vXrq169ejp58qTz0a5dO0m6oqNC2Tp06KDatWs7nzdu3Fj+/v6Xfe/ycunnKfvoZ+vWrbV//34lJydfdv24uDjdcccdqlChgstr7tChgzIzM/X9999L+vN9KVGihAYNGuRc19vbO9fPf4UKFXT33Xfrq6++cs41M8Zo1qxZuuWWW/S3v/1NxhjNmTNH9957r4wxLvvu1KmTkpOTc/33IEmJiYnavHmz+vTpo8DAQGd748aNdeeddzo/51lZWZo3b57uvfde3XLLLTm2U5jL6jt06KAqVaros88+c7Zt27ZNW7Zs0WOPPWZ5e7i6OC2Fq6558+a5/gDK/qGbn1deeUVdu3bV3/72NzVs2FB33XWXHn/88QIFo0OHDqlKlSoqV66cS3v9+vWdy7P/6+XlpVq1arn0CwsLy3Pbf+0rSadOndKYMWM0a9Ys58TRbLn9MqpevbrL84CAAPn5+alixYo52v86b+evsl/DX2sOCQlR+fLlna/V3VJTUyUpxxjnNj6HDh1yzse41KXvR8OGDXXo0CHVrl07xy+o/N6Py9mzZ4927typSpUq5br8r+9XYfz1/ZT+/IyfPn26UNtbs2aNRo8erfj4eJ07d85lWXJysgICAvJdf8+ePdqyZctlX/OhQ4dUuXJlZ1DNVrdu3VzXe/TRRzV37lzNnz9fvXr10tq1a3Xw4EHnfJUTJ07ozJkzmjJliqZMmZLvvv8q+3Oa277r16+vxYsXKy0tTampqUpJSXHLrSSyeXl56dFHH9XkyZN17tw5lS5dWp999pn8/Pz08MMPu20/KBqEG1xTWrVqpX379mn+/PlasmSJPvroI02YMEEffPCB2y6lLYxL/6rO1r17d61du1bPPvusmjZtqrJlyyorK0t33XVXrveDufS8fn5tkvK9t8ilrvaN4LZt2yYpZ/DIbXyKQl6vNzMz02Uss7Ky1KhRI7399tu59g8NDb3iWq70vbvUvn371L59e9WrV09vv/22QkND5ePjo2+//VYTJkwo0P2FsrKydOedd2rkyJG5Lr90jpQV99xzjwICAjRz5kz16tVLM2fOlLe3tx555BHnfiXpsccey3XejqQC/XHiCVFRURo3bpzmzZunnj17aubMmc7Xi+KNcINrTmBgoPr27au+ffsqNTVVrVq10ssvv+wMN3n9gqtRo4a+++47nT171uXIwq+//upcnv3frKwsHThwQHXq1HH227t3b4FrPH36tJYtW6YxY8Y4J1VKKtTptMLIfg179uxxHgmR/pzYeebMmSu+UVxePvnkEzkcDt15550FqnHXrl052nN7P3bs2CFjjMt7m9v7UaFChRxX9Uh/HgG48cYbnc9r166tX375Re3bty92dwLOzddff6309HR99dVXLkeEcjt9ltfrqV27tlJTU9WhQ4d891WjRg0tW7ZMqampLkdvcnuvpD9v1/DQQw/p448/1rFjxxQXF6d27dopJCREklSpUiWVK1dOmZmZl913brXkte9ff/1VFStWVJkyZVSqVCn5+/s7w7UV+b3/DRs21E033aTPPvtM1apVU0JCgt59913L+8DVx5wbXFP+ejqmbNmyCgsLc7l8t0yZMpKU45fc3XffrczMTL333nsu7RMmTJDD4VDnzp0lyTk35P3333fpZ+WHWvZf7X/9K/1q3Qn17rvvznV/2Ucq8rvyq7DGjh2rJUuWqEePHi6hML8af/rpJ8XHxzvb0tLSNGXKFNWsWVPh4eGS/nw/jhw5oq+++srZ748//tB//vOfHNusXbu21q1b57z0V5K++eYbHT582KVf9+7ddeTIkVy3cf78+WJ3r6LcPk/JycmaNm1ajr5lypTJNeB1795d8fHxWrx4cY5lZ86ccd5F+O6779bFixddLjPPzMzM9/P/6KOPKiMjQwMHDtSJEydcriTy9vbWgw8+qDlz5uQaPk6cOJHnditXrqymTZtqxowZLq9p27ZtWrJkifNz7uXlpW7duunrr7/O9Q7o+R0tK1OmTL5zlrKvAJw4caJuuOEG588JFG8cucE1JTw8XG3atFGzZs0UGBio9evXa/bs2Ro8eLCzT7NmzSRJQ4YMUadOnZyHyO+99161bdtWzz//vA4ePKgmTZpoyZIlmj9/voYNG+ac/NmsWTM9+OCDmjhxon7//XfnpeDZ924pyF/6/v7+atWqld58801lZGSoatWqWrJkiQ4cOFAEo5JTkyZN1Lt3b02ZMkVnzpxR69at9dNPP2nGjBnq1q2b2rZtW+htX7x4UZ9++qmkP0PGoUOH9NVXX2nLli1q27ZtnvMq/mrUqFH63//+p86dO2vIkCEKDAzUjBkzdODAAc2ZM8d5o7WBAwfqvffeU8+ePTV06FBVrlzZOfdBcn0/nnzySc2ePVt33XWXunfvrn379unTTz91mdgr/fkL64svvtBTTz2lFStWqGXLlsrMzNSvv/6qL774wnlPnuKiY8eO8vHx0b333quBAwcqNTVV//nPfxQUFKTExESXvs2aNdPkyZP12muvKSwsTEFBQWrXrp2effZZffXVV7rnnnvUp08fNWvWTGlpadq6datmz56tgwcPqmLFirr33nvVsmVLjRo1SgcPHlR4eLi+/PLLfANA69atVa1aNc2fP1+lSpXSAw884LJ87NixWrFihW677Tb1799f4eHhOnXqlDZu3KjvvvtOp06dynPb48aNU+fOnRUREaF+/fo5LwUPCAhwuefU66+/riVLlqh169bOy/sTExMVFxen1atX53mjxGbNmunzzz/XiBEjdOutt6ps2bK69957nct79eqlkSNHau7cuRo0aNA1d1PR65aHrtLCdSj7UvDcLtU05s/LdS93Kfhrr71mmjdvbsqXL29KlSpl6tWrZ/71r3+ZCxcuOPtcvHjR/P3vfzeVKlUyDofD5ZLas2fPmuHDh5sqVaqYkiVLmjp16phx48Y5LxXNlpaWZqKjo01gYKApW7as6datm9m1a5eR5HJpdvYluydOnMjxen777Tdz//33m/Lly5uAgADz8MMPm6NHj+Z5Oflft5HXZde5jVNuMjIyzJgxY0ytWrVMyZIlTWhoqImJiTF//PFHgfaTm969e7tcul+6dGlTs2ZN8+CDD5rZs2c7L9++VI0aNfK8fH/fvn3moYceMuXLlzd+fn6mefPm5ptvvsnRb//+/aZLly6mVKlSplKlSuaZZ54xc+bMMZLMunXrXPqOHz/eVK1a1fj6+pqWLVua9evX57gU3BhjLly4YN544w3ToEED4+vraypUqGCaNWtmxowZY5KTkws0HsbkfSl4dHR0rmNx6efZyqXgX331lWncuLHx8/MzNWvWNG+88YaZOnWqkWQOHDjg7JeUlGS6dOliypUrZyS5vO6zZ8+amJgYExYWZnx8fEzFihVNZGSkeeutt1z+Df3+++/m8ccfN/7+/iYgIMA8/vjjzsv8L71M+1LPPvuskWS6d++e6/Jjx46Z6OhoExoaakqWLGlCQkJM+/btzZQpU5x9crsU3BhjvvvuO9OyZUtTqlQp4+/vb+69916zY8eOHPs4dOiQiYqKMpUqVTK+vr7mxhtvNNHR0SY9Pd1lvC+9FDw1NdX06tXLlC9f3kjK9bLwu+++20gya9euzfW1ofhxGFOI2W3AdWjz5s266aab9Omnn3IDr2Jg4sSJGj58uH777TdVrVrV0+XAxu6//35t3brV0rw7eBZzboBc5PbdRxMnTpSXl9dl7wwM9/vr+/HHH3/oww8/VJ06dQg2KFKJiYlasGCBHn/8cU+XAguYcwPk4s0339SGDRvUtm1blShRQgsXLtTChQs1YMAAt1wmDGseeOABVa9eXU2bNlVycrI+/fRT/frrry43WHO35OTky37BZ/YVQbCfAwcOaM2aNfroo49UsmRJDRw40NMlwQLCDZCLyMhILV26VK+++qpSU1NVvXp1vfzyy87v68HV1alTJ3300Uf67LPPlJmZqfDwcM2aNUs9evQosn0OHTpUM2bMyLcPZ/Xta9WqVerbt6+qV6+uGTNmEGSvMcy5AYBc7NixQ0ePHs23j9X7tgC4Ogg3AADAVphQDAAAbOW6m3OTlZWlo0ePqly5ctfEbdcBAMCfc9zOnj2rKlWqOG/ymZfrLtwcPXqUq10AALhGHT58WNWqVcu3z3UXbrK/MPHw4cPy9/f3cDUAAKAgUlJSFBoa6vLFx3m57sJN9qkof39/wg0AANeYgkwpYUIxAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFY+Gm8mTJ6tx48bOr0KIiIjQwoUL810nLi5O9erVk5+fnxo1aqRvv/32KlULAACuBR4NN9WqVdPYsWO1YcMGrV+/Xu3atVPXrl21ffv2XPuvXbtWPXv2VL9+/bRp0yZ169ZN3bp107Zt265y5QAAoLhyGGOMp4u4VGBgoMaNG6d+/frlWNajRw+lpaXpm2++cba1aNFCTZs21QcffFCg7aekpCggIEDJycl8cSYAANcIK7+/i82cm8zMTM2aNUtpaWmKiIjItU98fLw6dOjg0tapUyfFx8fnud309HSlpKS4PAAAgH2V8HQBW7duVUREhP744w+VLVtWc+fOVXh4eK59k5KSFBwc7NIWHByspKSkPLcfGxurMWPGuLVmoDBqjlrg6RIsOzi2i6dLAADLPH7kpm7dutq8ebN+/PFHDRo0SL1799aOHTvctv2YmBglJyc7H4cPH3bbtgEAQPHj8SM3Pj4+CgsLkyQ1a9ZMP//8s/7973/rww8/zNE3JCREx44dc2k7duyYQkJC8ty+r6+vfH193Vs0AAAotjx+5OavsrKylJ6enuuyiIgILVu2zKVt6dKlec7RAQAA1x+PHrmJiYlR586dVb16dZ09e1YzZ87UypUrtXjxYklSVFSUqlatqtjYWEnS0KFD1bp1a40fP15dunTRrFmztH79ek2ZMsWTLwMAABQjHg03x48fV1RUlBITExUQEKDGjRtr8eLFuvPOOyVJCQkJ8vL6/4NLkZGRmjlzpl544QU999xzqlOnjubNm6eGDRt66iUAAIBiptjd56aocZ8beApXSwFA4V2T97kBAABwB8INAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFcINAACwFY+Gm9jYWN16660qV66cgoKC1K1bN+3atSvfdaZPny6Hw+Hy8PPzu0oVAwCA4s6j4WbVqlWKjo7WunXrtHTpUmVkZKhjx45KS0vLdz1/f38lJiY6H4cOHbpKFQMAgOKuhCd3vmjRIpfn06dPV1BQkDZs2KBWrVrluZ7D4VBISEhRlwcAAK5BxWrOTXJysiQpMDAw336pqamqUaOGQkND1bVrV23fvj3Pvunp6UpJSXF5AAAA+yo24SYrK0vDhg1Ty5Yt1bBhwzz71a1bV1OnTtX8+fP16aefKisrS5GRkfrtt99y7R8bG6uAgADnIzQ0tKheAgAAKAYcxhjj6SIkadCgQVq4cKFWr16tatWqFXi9jIwM1a9fXz179tSrr76aY3l6errS09Odz1NSUhQaGqrk5GT5+/u7pXagIGqOWuDpEiw7OLaLp0sAAEl//v4OCAgo0O9vj865yTZ48GB98803+v777y0FG0kqWbKkbrrpJu3duzfX5b6+vvL19XVHmQAA4Brg0dNSxhgNHjxYc+fO1fLly1WrVi3L28jMzNTWrVtVuXLlIqgQAABcazx65CY6OlozZ87U/PnzVa5cOSUlJUmSAgICVKpUKUlSVFSUqlatqtjYWEnSK6+8ohYtWigsLExnzpzRuHHjdOjQIT355JMeex0AAKD48Gi4mTx5siSpTZs2Lu3Tpk1Tnz59JEkJCQny8vr/A0ynT59W//79lZSUpAoVKqhZs2Zau3atwsPDr1bZAACgGCs2E4qvFisTkgB3YkIxABSeld/fxeZScAAAAHcg3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFsh3AAAAFuxHG42btyorVu3Op/Pnz9f3bp103PPPacLFy64tTgAAACrLIebgQMHavfu3ZKk/fv365FHHlHp0qUVFxenkSNHur1AAAAAKyyHm927d6tp06aSpLi4OLVq1UozZ87U9OnTNWfOHHfXBwAAYInlcGOMUVZWliTpu+++09133y1JCg0N1cmTJ91bHQAAgEWWw80tt9yi1157TZ988olWrVqlLl26SJIOHDig4OBgtxcIAABgheVwM3HiRG3cuFGDBw/W888/r7CwMEnS7NmzFRkZ6fYCAQAArChhdYXGjRu7XC2Vbdy4cfL29nZLUQAAAIVVqPvcnDlzRh999JFiYmJ06tQpSdKOHTt0/PhxtxYHAABgleUjN1u2bFH79u1Vvnx5HTx4UP3791dgYKC+/PJLJSQk6OOPPy6KOgEAAArE8pGbESNGqG/fvtqzZ4/8/Pyc7Xfffbe+//57txYHAABgleVw8/PPP2vgwIE52qtWraqkpCS3FAUAAFBYlsONr6+vUlJScrTv3r1blSpVcktRAAAAhWU53Nx333165ZVXlJGRIUlyOBxKSEjQP//5Tz344INuLxAAAMAKy+Fm/PjxSk1NVVBQkM6fP6/WrVsrLCxM5cqV07/+9a+iqBEAAKDALF8tFRAQoKVLl2rNmjX65ZdflJqaqptvvlkdOnQoivoAAAAssRxusrVs2VItW7Z0Zy0AAABXzPJpqSFDhuidd97J0f7ee+9p2LBh7qgJAACg0CyHmzlz5uR6xCYyMlKzZ892S1EAAACFZTnc/P777woICMjR7u/vr5MnT7qlKAAAgMKyHG7CwsK0aNGiHO0LFy7UjTfe6JaiAAAACsvyhOIRI0Zo8ODBOnHihNq1aydJWrZsmcaPH6+JEye6uz4AAABLLIebJ554Qunp6frXv/6lV199VZJUs2ZNTZ48WVFRUW4vEAAAwIpCXQo+aNAgDRo0SCdOnFCpUqVUtmxZd9cFAABQKIW+z40kvksKAAAUO5YnFB87dkyPP/64qlSpohIlSsjb29vlAQAA4EmWj9z06dNHCQkJevHFF1W5cmU5HI6iqAsAAKBQLIeb1atX64cfflDTpk2LoBwAAIArY/m0VGhoqIwxbtl5bGysbr31VpUrV05BQUHq1q2bdu3addn14uLiVK9ePfn5+alRo0b69ttv3VIPAAC49lkONxMnTtSoUaN08ODBK975qlWrFB0drXXr1mnp0qXKyMhQx44dlZaWluc6a9euVc+ePdWvXz9t2rRJ3bp1U7du3bRt27YrrgcAAFz7HMbiYZgKFSro3LlzunjxokqXLq2SJUu6LD916lShizlx4oSCgoK0atUqtWrVKtc+PXr0UFpamr755htnW4sWLdS0aVN98MEHl91HSkqKAgIClJycLH9//0LXClhVc9QCT5dg2cGxXTxdAgBIsvb72/Kcm6K8C3FycrIkKTAwMM8+8fHxGjFihEtbp06dNG/evFz7p6enKz093fk8JSXlygsFAADFluVw07t376KoQ1lZWRo2bJhatmyphg0b5tkvKSlJwcHBLm3BwcFKSkrKtX9sbKzGjBnj1loB4Hp3LR6JlDgaeb2wPOdGkvbt26cXXnhBPXv21PHjxyX9+cWZ27dvL3Qh0dHR2rZtm2bNmlXobeQmJiZGycnJzsfhw4fdun0AAFC8WA43q1atUqNGjfTjjz/qyy+/VGpqqiTpl19+0ejRowtVxODBg/XNN99oxYoVqlatWr59Q0JCdOzYMZe2Y8eOKSQkJNf+vr6+8vf3d3kAAAD7shxuRo0apddee01Lly6Vj4+Ps71du3Zat26dpW0ZYzR48GDNnTtXy5cvV61atS67TkREhJYtW+bStnTpUkVERFjaNwAAsCfLc262bt2qmTNn5mgPCgrSyZMnLW0rOjpaM2fO1Pz581WuXDnnvJmAgACVKlVKkhQVFaWqVasqNjZWkjR06FC1bt1a48ePV5cuXTRr1iytX79eU6ZMsfpSAACADVk+clO+fHklJibmaN+0aZOqVq1qaVuTJ09WcnKy2rRpo8qVKzsfn3/+ubNPQkKCy/4iIyM1c+ZMTZkyRU2aNNHs2bM1b968fCchAwCA64flIzePPPKI/vnPfyouLk4Oh0NZWVlas2aN/vGPfygqKsrStgpyi52VK1fmaHv44Yf18MMPW9oXAAC4Plg+cvP666+rXr16Cg0NVWpqqsLDw9WqVStFRkbqhRdeKIoaAQAACszSkRtjjJKSkvTOO+/opZde0tatW5WamqqbbrpJderUKaoaAQAACsxyuAkLC9P27dtVp04dhYaGFlVdAAAAhWLptJSXl5fq1Kmj33//vajqAQAAuCKW59yMHTtWzz77LN/CDQAAiiXLV0tFRUXp3LlzatKkiXx8fJz3o8l2Jd8KDgAAcKWK1beCAwAAXClL4SYjI0OrVq3Siy++WKCvSgAAALjaLM25KVmypObMmVNUtQAAAFwxyxOKu3Xrpnnz5hVBKQAAAFfO8pybOnXq6JVXXtGaNWvUrFkzlSlTxmX5kCFD3FYcAACAVZbDzX//+1+VL19eGzZs0IYNG1yWORwOwg0AAPAoy+HmwIEDRVEHAACAW1iecwMAAFCcWT5y88QTT+S7fOrUqYUuBgAA4EpZDjenT592eZ6RkaFt27bpzJkzateundsKAwAAKAzL4Wbu3Lk52rKysjRo0CDVrl3bLUUBAAAUllvm3Hh5eWnEiBGaMGGCOzYHAABQaG6bULxv3z5dvHjRXZsDAAAoFMunpUaMGOHy3BijxMRELViwQL1793ZbYQAAAIVhOdxs2rTJ5bmXl5cqVaqk8ePHX/ZKKgAAgKJmOdysWLGiKOoAAABwC8tzbg4cOKA9e/bkaN+zZ48OHjzojpoAAAAKzXK46dOnj9auXZuj/ccff1SfPn3cURMAAEChWQ43mzZtUsuWLXO0t2jRQps3b3ZHTQAAAIVmOdw4HA6dPXs2R3tycrIyMzPdUhQAAEBhWQ43rVq1UmxsrEuQyczMVGxsrG6//Xa3FgcAAGCV5aul3njjDbVq1Up169bVHXfcIUn64YcflJKSouXLl7u9QAAAACssH7kJDw/Xli1b1L17dx0/flxnz55VVFSUfv31VzVs2LAoagQAACgwy0duJKlKlSp6/fXX3V0LAADAFbN85GbatGmKi4vL0R4XF6cZM2a4pSgAAIDCshxuYmNjVbFixRztQUFBHM0BAAAeZzncJCQkqFatWjnaa9SooYSEBLcUBQAAUFiWw01QUJC2bNmSo/2XX37RDTfc4JaiAAAACstyuOnZs6eGDBmiFStWKDMzU5mZmVq+fLmGDh2qRx55pChqBAAAKDDLV0u9+uqrOnjwoNq3b68SJf5cPSsrS1FRUcy5AQAAHmc53Pj4+Ojzzz/Xq6++ql9++UWlSpVSo0aNVKNGjaKoDwAAwJJC3edGkgIDA9W2bdtcr5wCAADwFEtzbs6cOaPo6GhVrFhRwcHBCg4OVsWKFTV48GCdOXOmiEoEAAAouAIfuTl16pQiIiJ05MgRPfroo6pfv74kaceOHZo+fbqWLVumtWvXqkKFCkVWLAAAwOUUONy88sor8vHx0b59+xQcHJxjWceOHfXKK69owoQJbi8SAACgoAp8WmrevHl66623cgQbSQoJCdGbb76puXPnurU4AAAAqwocbhITE9WgQYM8lzds2FBJSUluKQoAAKCwChxuKlasqIMHD+a5/MCBAwoMDHRHTQAAAIVW4HDTqVMnPf/887pw4UKOZenp6XrxxRd11113ubU4AAAAqyxNKL7llltUp04dRUdHq169ejLGaOfOnXr//feVnp6uTz75pChrBQAAuKwCh5tq1aopPj5eTz/9tGJiYmSMkSQ5HA7deeedeu+99xQaGlpkhQIAABSEpZv41apVSwsXLtTJkye1bt06rVu3TidOnNCiRYsUFhZmeefff/+97r33XlWpUkUOh0Pz5s3Lt//KlSvlcDhyPJjIDAAAshXq6xcqVKig5s2bX/HO09LS1KRJEz3xxBN64IEHCrzerl275O/v73weFBR0xbUAAAB7KPR3S7lD586d1blzZ8vrBQUFqXz58u4vCAAAXPMsnZYqLpo2barKlSvrzjvv1Jo1a/Ltm56erpSUFJcHAACwr2sq3FSuXFkffPCB5syZozlz5ig0NFRt2rTRxo0b81wnNjZWAQEBzgeTngEAsLcChZubb75Zp0+flvTnJeHnzp0r0qLyUrduXQ0cOFDNmjVTZGSkpk6dqsjIyHy/zyomJkbJycnOx+HDh69ixQAA4GorULjZuXOn0tLSJEljxoxRampqkRZlRfPmzbV37948l/v6+srf39/lAQAA7KtAE4qbNm2qvn376vbbb5cxRm+99ZbKli2ba9+XXnrJrQVezubNm1W5cuWruk8AAFB8FSjcTJ8+XaNHj9Y333wjh8OhhQsXqkSJnKs6HA5L4SY1NdXlqMuBAwe0efNmBQYGqnr16oqJidGRI0f08ccfS5ImTpyoWrVqqUGDBvrjjz/00Ucfafny5VqyZEmB9wkAAOytQOGmbt26mjVrliTJy8tLy5Ytc8u9ZdavX6+2bds6n48YMUKS1Lt3b02fPl2JiYlKSEhwLr9w4YKeeeYZHTlyRKVLl1bjxo313XffuWwDAABc3yzf5yYrK8ttO2/Tpo3zaxxyM336dJfnI0eO1MiRI922fwAAYD+Fuonfvn37NHHiRO3cuVOSFB4erqFDh6p27dpuLQ4AAMAqy/e5Wbx4scLDw/XTTz+pcePGaty4sX788Uc1aNBAS5cuLYoaAQAACszykZtRo0Zp+PDhGjt2bI72f/7zn7rzzjvdVhwAAIBVlo/c7Ny5U/369cvR/sQTT2jHjh1uKQoAAKCwLIebSpUqafPmzTnaN2/ezLdzAwAAj7N8Wqp///4aMGCA9u/fr8jISEnSmjVr9MYbbzgv5QYAAPAUy+HmxRdfVLly5TR+/HjFxMRIkqpUqaKXX35ZQ4YMcXuBAAAAVlgONw6HQ8OHD9fw4cN19uxZSVK5cuXcXhgAAEBhFOo+N9kINQAAoLixPKEYAACgOCPcAAAAWyHcAAAAW7EUbjIyMtS+fXvt2bOnqOoBAAC4IpbCTcmSJbVly5aiqgUAAOCKWT4t9dhjj+m///1vUdQCAABwxSxfCn7x4kVNnTpV3333nZo1a6YyZcq4LH/77bfdVhwAAIBVlsPNtm3bdPPNN0uSdu/e7bLM4XC4pyoAAIBCshxuVqxYURR1AAAAuEWhLwXfu3evFi9erPPnz0uSjDFuKwoAAKCwLIeb33//Xe3bt9ff/vY33X333UpMTJQk9evXT88884zbCwQAALDCcrgZPny4SpYsqYSEBJUuXdrZ3qNHDy1atMitxQEAAFhlec7NkiVLtHjxYlWrVs2lvU6dOjp06JDbCgMAACgMy0du0tLSXI7YZDt16pR8fX3dUhQAAEBhWQ43d9xxhz7++GPnc4fDoaysLL355ptq27atW4sDAACwyvJpqTfffFPt27fX+vXrdeHCBY0cOVLbt2/XqVOntGbNmqKoEQAAoMAsH7lp2LChdu/erdtvv11du3ZVWlqaHnjgAW3atEm1a9cuihoBAAAKzPKRG0kKCAjQ888/7+5aAAAArlihws3p06f13//+Vzt37pQkhYeHq2/fvgoMDHRrcQAAAFZZPi31/fffq2bNmnrnnXd0+vRpnT59Wu+8845q1aql77//vihqBAAAKDDLR26io6PVo0cPTZ48Wd7e3pKkzMxMPf3004qOjtbWrVvdXiQAAEBBWT5ys3fvXj3zzDPOYCNJ3t7eGjFihPbu3evW4gAAAKyyHG5uvvlm51ybS+3cuVNNmjRxS1EAAACFVaDTUlu2bHH+/5AhQzR06FDt3btXLVq0kCStW7dOkyZN0tixY4umSgAAgAIqULhp2rSpHA6HjDHOtpEjR+bo16tXL/Xo0cN91QEAAFhUoHBz4MCBoq4DAADALQoUbmrUqFHUdQAAALhFoW7id/ToUa1evVrHjx9XVlaWy7IhQ4a4pTAAAIDCsBxupk+froEDB8rHx0c33HCDHA6Hc5nD4SDcAAAAj7Icbl588UW99NJLiomJkZeX5SvJAQAAipTldHLu3Dk98sgjBBsAAFAsWU4o/fr1U1xcXFHUAgAAcMUsn5aKjY3VPffco0WLFqlRo0YqWbKky/K3337bbcUBAABYVahws3jxYtWtW1eSckwoBgAA8CTL4Wb8+PGaOnWq+vTpUwTlAAAAXBnLc258fX3VsmXLoqgFAADgilkON0OHDtW7775bFLUAAABcMcunpX766SctX75c33zzjRo0aJBjQvGXX37ptuIAAACsshxuypcvrwceeKAoagEAALhilsPNtGnT3Lbz77//XuPGjdOGDRuUmJiouXPnqlu3bvmus3LlSo0YMULbt29XaGioXnjhBSY3AwAAJ4/eZjgtLU1NmjTRpEmTCtT/wIED6tKli9q2bavNmzdr2LBhevLJJ7V48eIirhQAAFwrLB+5qVWrVr73s9m/f3+Bt9W5c2d17ty5wP0/+OAD1apVS+PHj5ck1a9fX6tXr9aECRPUqVOnAm8HAADYl+VwM2zYMJfnGRkZ2rRpkxYtWqRnn33WXXXlKj4+Xh06dHBp69SpU46aLpWenq709HTn85SUlKIqDwAAFAOWw83QoUNzbZ80aZLWr19/xQXlJykpScHBwS5twcHBSklJ0fnz51WqVKkc68TGxmrMmDFFWtelao5acNX25S4Hx3bxdAkopq7Fz/O1iH+DVw+f6avD059pt8256dy5s+bMmeOuzblNTEyMkpOTnY/Dhw97uiQAAFCELB+5ycvs2bMVGBjors3lKiQkRMeOHXNpO3bsmPz9/XM9aiP9eUdlX1/fIq0LAAAUH5bDzU033eQyodgYo6SkJJ04cULvv/++W4v7q4iICH377bcubUuXLlVERESR7hcAAFw7LIebv96HxsvLS5UqVVKbNm1Ur149S9tKTU3V3r17nc8PHDigzZs3KzAwUNWrV1dMTIyOHDmijz/+WJL01FNP6b333tPIkSP1xBNPaPny5friiy+0YAHnUAEAwJ8sh5vRo0e7befr169X27Ztnc9HjBghSerdu7emT5+uxMREJSQkOJfXqlVLCxYs0PDhw/Xvf/9b1apV00cffcRl4AAAwMltc24Ko02bNjLG5Ll8+vTpua6zadOmIqwKAABcywocbry8vPK9eZ8kORwOXbx48YqLAgAAKKwCh5u5c+fmuSw+Pl7vvPOOsrKy3FIUAABAYRU43HTt2jVH265duzRq1Ch9/fXXevTRR/XKK6+4tTgAAACrCnUTv6NHj6p///5q1KiRLl68qM2bN2vGjBmqUaOGu+sDAACwxFK4SU5O1j//+U+FhYVp+/btWrZsmb7++ms1bNiwqOoDAACwpMCnpd5880298cYbCgkJ0f/+979cT1MBAAB4WoHDzahRo1SqVCmFhYVpxowZmjFjRq79vvzyS7cVBwAAYFWBw01UVNRlLwUHAADwtAKHm9xuqAcAAFDcFOpqKQAAgOKKcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGyFcAMAAGylWISbSZMmqWbNmvLz89Ntt92mn376Kc++06dPl8PhcHn4+fldxWoBAEBx5vFw8/nnn2vEiBEaPXq0Nm7cqCZNmqhTp046fvx4nuv4+/srMTHR+Th06NBVrBgAABRnHg83b7/9tvr376++ffsqPDxcH3zwgUqXLq2pU6fmuY7D4VBISIjzERwcfBUrBgAAxZlHw82FCxe0YcMGdejQwdnm5eWlDh06KD4+Ps/1UlNTVaNGDYWGhqpr167avn17nn3T09OVkpLi8gAAAPbl0XBz8uRJZWZm5jjyEhwcrKSkpFzXqVu3rqZOnar58+fr008/VVZWliIjI/Xbb7/l2j82NlYBAQHOR2hoqNtfBwAAKD48flrKqoiICEVFRalp06Zq3bq1vvzyS1WqVEkffvhhrv1jYmKUnJzsfBw+fPgqVwwAAK6mEp7cecWKFeXt7a1jx465tB87dkwhISEF2kbJkiV10003ae/evbku9/X1la+v7xXXCgAArg0ePXLj4+OjZs2aadmyZc62rKwsLVu2TBEREQXaRmZmprZu3arKlSsXVZkAAOAa4tEjN5I0YsQI9e7dW7fccouaN2+uiRMnKi0tTX379pUkRUVFqWrVqoqNjZUkvfLKK2rRooXCwsJ05swZjRs3TocOHdKTTz7pyZcBAACKCY+Hmx49eujEiRN66aWXlJSUpKZNm2rRokXOScYJCQny8vr/A0ynT59W//79lZSUpAoVKqhZs2Zau3atwsPDPfUSAABAMeLxcCNJgwcP1uDBg3NdtnLlSpfnEyZM0IQJE65CVQAA4Fp0zV0tBQAAkB/CDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsBXCDQAAsJViEW4mTZqkmjVrys/PT7fddpt++umnfPvHxcWpXr168vPzU6NGjfTtt99epUoBAEBx5/Fw8/nnn2vEiBEaPXq0Nm7cqCZNmqhTp046fvx4rv3Xrl2rnj17ql+/ftq0aZO6deumbt26adu2bVe5cgAAUBx5PNy8/fbb6t+/v/r27avw8HB98MEHKl26tKZOnZpr/3//+9+666679Oyzz6p+/fp69dVXdfPNN+u99967ypUDAIDiqIQnd37hwgVt2LBBMTExzjYvLy916NBB8fHxua4THx+vESNGuLR16tRJ8+bNy7V/enq60tPTnc+Tk5MlSSkpKVdYfe6y0s8VyXaLUlGNBVxdi58NXB3X4r9BPs/IT1F8prO3aYy5bF+PhpuTJ08qMzNTwcHBLu3BwcH69ddfc10nKSkp1/5JSUm59o+NjdWYMWNytIeGhhayavsJmOjpCoDrG/8GYTdF+Zk+e/asAgIC8u3j0XBzNcTExLgc6cnKytKpU6d0ww03yOFweLAyz0lJSVFoaKgOHz4sf39/T5dzTWDMrGPMrGPMrGG8rLuWx8wYo7Nnz6pKlSqX7evRcFOxYkV5e3vr2LFjLu3Hjh1TSEhIruuEhIRY6u/r6ytfX1+XtvLlyxe+aBvx9/e/5j7cnsaYWceYWceYWcN4WXetjtnljthk8+iEYh8fHzVr1kzLli1ztmVlZWnZsmWKiIjIdZ2IiAiX/pK0dOnSPPsDAIDri8dPS40YMUK9e/fWLbfcoubNm2vixIlKS0tT3759JUlRUVGqWrWqYmNjJUlDhw5V69atNX78eHXp0kWzZs3S+vXrNWXKFE++DAAAUEx4PNz06NFDJ06c0EsvvaSkpCQ1bdpUixYtck4aTkhIkJfX/x9gioyM1MyZM/XCCy/oueeeU506dTRv3jw1bNjQUy/hmuPr66vRo0fnOF2HvDFm1jFm1jFm1jBe1l0vY+YwBbmmCgAA4Brh8Zv4AQAAuBPhBgAA2ArhBgAA2ArhBgAA2ArhBgAA2ArhxsZiY2N16623qly5cgoKClK3bt20a9culz5JSUl6/PHHFRISojJlyujmm2/WnDlzPFSxZ02ePFmNGzd23rkzIiJCCxcudC7/448/FB0drRtuuEFly5bVgw8+mONu2deb/Mbs1KlT+vvf/666deuqVKlSql69uoYMGeL88trr1eU+Z9mMMercubMcDkeeXwx8vSjImMXHx6tdu3YqU6aM/P391apVK50/f95DFXvW5cbrevi5T7ixsVWrVik6Olrr1q3T0qVLlZGRoY4dOyotLc3ZJyoqSrt27dJXX32lrVu36oEHHlD37t21adMmD1buGdWqVdPYsWO1YcMGrV+/Xu3atVPXrl21fft2SdLw4cP19ddfKy4uTqtWrdLRo0f1wAMPeLhqz8pvzI4ePaqjR4/qrbfe0rZt2zR9+nQtWrRI/fr183TZHnW5z1m2iRMnXrfff/dXlxuz+Ph43XXXXerYsaN++ukn/fzzzxo8eLDLPdKuJ5cbr+vi577BdeP48eNGklm1apWzrUyZMubjjz926RcYGGj+85//XO3yiqUKFSqYjz76yJw5c8aULFnSxMXFOZft3LnTSDLx8fEerLD4yR6z3HzxxRfGx8fHZGRkXOWqire/jtmmTZtM1apVTWJiopFk5s6d67niiqlLx+y2224zL7zwgocrKt4uHa/r4ef+9Rlrr1PZpwMCAwOdbZGRkfr888916tQpZWVladasWfrjjz/Upk0bD1VZPGRmZmrWrFlKS0tTRESENmzYoIyMDHXo0MHZp169eqpevbri4+M9WGnx8dcxy01ycrL8/f1VooTHb45eLOQ2ZufOnVOvXr00adKkPL8Q+Hr21zE7fvy4fvzxRwUFBSkyMlLBwcFq3bq1Vq9e7elSi4XcPmPXxc99T6crXB2ZmZmmS5cupmXLli7tp0+fNh07djSSTIkSJYy/v79ZvHixh6r0vC1btpgyZcoYb29vExAQYBYsWGCMMeazzz4zPj4+OfrfeuutZuTIkVe7zGIlrzH7qxMnTpjq1aub55577ipXWPzkN2YDBgww/fr1cz4XR26MMXmPWXx8vJFkAgMDzdSpU83GjRvNsGHDjI+Pj9m9e7eHq/ac/D5j18PPff58uk5ER0dr27ZtOf6aefHFF3XmzBl99913qlixoubNm6fu3bvrhx9+UKNGjTxUrefUrVtXmzdvVnJysmbPnq3evXtr1apVni6rWMtrzMLDw519UlJS1KVLF4WHh+vll1/2XLHFRF5jtnfvXi1fvtxecx/cJK8xy8rKkiQNHDjQ+YXLN910k5YtW6apU6c6v3T5epPfv8vr4ue+p9MVil50dLSpVq2a2b9/v0v73r17jSSzbds2l/b27dubgQMHXs0Si6327dubAQMGmGXLlhlJ5vTp0y7Lq1evbt5++23PFFdMZY9ZtpSUFBMREWHat29vzp8/78HKiq/sMRs6dKhxOBzG29vb+ZBkvLy8TOvWrT1dZrGSPWb79+83kswnn3zisrx79+6mV69eHqqu+Mker+vl5z5zbmzMGKPBgwdr7ty5Wr58uWrVquWy/Ny5c5KU44oCb29v519D17usrCylp6erWbNmKlmypJYtW+ZctmvXLiUkJOQ5v+R6lT1m0p9HbDp27CgfHx999dVX8vPz83B1xVP2mI0aNUpbtmzR5s2bnQ9JmjBhgqZNm+bZIouZ7DGrWbOmqlSpkuM2F7t371aNGjU8VF3xkz1e183PfU+nKxSdQYMGmYCAALNy5UqTmJjofJw7d84YY8yFCxdMWFiYueOOO8yPP/5o9u7da9566y3jcDjynDdhZ6NGjTKrVq0yBw4cMFu2bDGjRo0yDofDLFmyxBhjzFNPPWWqV69uli9fbtavX28iIiJMRESEh6v2rPzGLDk52dx2222mUaNGZu/evS6fwYsXL3q6dI+53Ofsr8Scm8uO2YQJE4y/v7+Ji4sze/bsMS+88ILx8/Mze/fu9XDlnpHfeF0vP/cJNzYmKdfHtGnTnH12795tHnjgARMUFGRKly5tGjdunOMSwevFE088YWrUqGF8fHxMpUqVTPv27V1+4Zw/f948/fTTpkKFCqZ06dLm/vvvN4mJiR6s2PPyG7MVK1bk+Rk8cOCAZwv3oMt9zv6KcFOwMYuNjTXVqlUzpUuXNhEREeaHH37wULWed7nxuh5+7juMMcYTR4wAAACKAnNuAACArRBuAACArRBuAACArRBuAACArRBuAACArRBuAACArRBuAACArRBuAACArRBuAACArRBuAACArRBuAACArfwfNxIVS6DhznoAAAAASUVORK5CYII=", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:38.577597\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:39.000467\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:39.385439\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:39.836231\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# TestDispersion.allInfo()\n" + "TestDispersion.allInfo()\n" ] }, { @@ -778,115 +1009,77 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "TestDispersion = Dispersion(filename=\"dispersion_analysis_outputs/disp_class_example\")\n" + "# TestDispersion = Dispersion(filename=\"dispersion_analysis_outputs/disp_class_example\")\n" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "dispersion_dictionary = {\n", - " # Environment Parameters\n", - " \"railLength\": (Env.railLength, 0.001),\n", - " \"date\": [Env.date],\n", - " \"datum\": [\"WSG84\"],\n", - " \"elevation\": (Env.elevation, 10),\n", - " \"gravity\": (Env.gravity, 0),\n", - " \"latitude\": (Env.latitude, 0),\n", - " \"longitude\": (Env.longitude, 0),\n", - " \"timeZone\": [str(Env.timeZone)],\n", - " # Solid Motor Parameters\n", - " \"burnOutTime\": (Pro75M1670.burnOutTime, 0.2),\n", - " \"grainDensity\": (Pro75M1670.grainDensity, 0.1 * Pro75M1670.grainDensity),\n", - " \"grainInitialHeight\": (Pro75M1670.grainInitialHeight, 0.001),\n", - " \"grainInitialInnerRadius\": (Pro75M1670.grainInitialInnerRadius, 0.001),\n", - " \"grainNumber\": [Pro75M1670.grainNumber],\n", - " \"grainOuterRadius\": (Pro75M1670.grainOuterRadius, 0.001),\n", - " \"grainSeparation\": (Pro75M1670.grainSeparation, 0.001),\n", - " \"nozzleRadius\": (Pro75M1670.nozzleRadius, 0.001),\n", - " \"throatRadius\": (Pro75M1670.throatRadius, 0.001),\n", - " \"thrustSource\": [Pro75M1670.thrustSource],\n", - " \"totalImpulse\": (Pro75M1670.totalImpulse, 0.033 * Pro75M1670.totalImpulse),\n", - " # Rocket Parameters\n", - " \"mass\": (Calisto.mass, 0.100),\n", - " \"radius\": (Calisto.radius, 0.001),\n", - " \"distanceRocketNozzle\": (Calisto.distanceRocketNozzle, 0.010),\n", - " \"distanceRocketPropellant\": (Calisto.distanceRocketPropellant, 0.010),\n", - " \"inertiaI\": (Calisto.inertiaI, Calisto.inertiaI * 0.1),\n", - " \"inertiaZ\": (Calisto.inertiaZ, Calisto.inertiaZ * 0.1),\n", - " \"powerOffDrag\": (1, 0.033),\n", - " \"powerOnDrag\": (1, 0.033),\n", - " \"noseKind\": [Calisto.noseKind],\n", - " \"noseLength\": (Calisto.noseLength, 0.001),\n", - " \"noseDistanceToCM\": (Calisto.noseDistanceToCM, 0.010),\n", - " \"numberOfFins\": [Calisto.numberOfFins],\n", - " \"finRootChord\": (Calisto.finRootChord, 0.001),\n", - " \"finTipChord\": (Calisto.finTipChord, 0.001),\n", - " \"span\": (Calisto.span, 0.001),\n", - " \"distanceToCM\": (Calisto.finDistanceToCM, 0.010),\n", - " \"finRadius\": (Calisto.finRadius, 0.001),\n", - " \"finAirfoil\": Calisto.finAirfoil,\n", - " \"tailTopRadius\": (Calisto.tailTopRadius, 0.001),\n", - " # \"parachuteNames\": [\"Main\", \"Drogue\"],\n", - " \"CdS\": [(10, 2), (1, 0.3)],\n", - " \"inclination\":[85],\n", - " \"heading\":[90],\n", - " # \"trigger\": [[mainTrigger], [drogueTrigger]],\n", - " # \"noseLength\": (0.588, 1 / 1000),\n", - " # Flight Parameters\n", - " # 'atol': ,\n", - " # 'initialSolution': \"\",\n", - " # 'maxTime': \"\",\n", - " # 'maxTimeStep': \"\",\n", - " # 'minTimeStep': \"\",\n", - " # 'rtol': \"\",\n", - " # 'terminateOnApogee': \"\",\n", - " # 'timeOvershoot': \"\",\n", - " # 'verbose': \"\",\n", - "}\n" + "# dispersion_dictionary = {\n", + "# # Environment Parameters\n", + "# \"railLength\": (Env.railLength, 0.001),\n", + "# \"date\": [Env.date],\n", + "# \"datum\": [\"WSG84\"],\n", + "# \"elevation\": (Env.elevation, 10),\n", + "# \"gravity\": (Env.gravity, 0),\n", + "# \"latitude\": (Env.latitude, 0),\n", + "# \"longitude\": (Env.longitude, 0),\n", + "# \"timeZone\": [str(Env.timeZone)],\n", + "# # Solid Motor Parameters\n", + "# \"burnOutTime\": (Pro75M1670.burnOutTime, 0.2),\n", + "# \"grainDensity\": (Pro75M1670.grainDensity, 0.1 * Pro75M1670.grainDensity),\n", + "# \"grainInitialHeight\": (Pro75M1670.grainInitialHeight, 0.001),\n", + "# \"grainInitialInnerRadius\": (Pro75M1670.grainInitialInnerRadius, 0.001),\n", + "# \"grainNumber\": [Pro75M1670.grainNumber],\n", + "# \"grainOuterRadius\": (Pro75M1670.grainOuterRadius, 0.001),\n", + "# \"grainSeparation\": (Pro75M1670.grainSeparation, 0.001),\n", + "# \"nozzleRadius\": (Pro75M1670.nozzleRadius, 0.001),\n", + "# \"throatRadius\": (Pro75M1670.throatRadius, 0.001),\n", + "# \"thrustSource\": [Pro75M1670.thrustSource],\n", + "# \"totalImpulse\": (Pro75M1670.totalImpulse, 0.033 * Pro75M1670.totalImpulse),\n", + "# # Rocket Parameters\n", + "# \"mass\": (Calisto.mass, 0.100),\n", + "# \"radius\": (Calisto.radius, 0.001),\n", + "# \"distanceRocketNozzle\": (Calisto.distanceRocketNozzle, 0.010),\n", + "# \"distanceRocketPropellant\": (Calisto.distanceRocketPropellant, 0.010),\n", + "# \"inertiaI\": (Calisto.inertiaI, Calisto.inertiaI * 0.1),\n", + "# \"inertiaZ\": (Calisto.inertiaZ, Calisto.inertiaZ * 0.1),\n", + "# \"powerOffDrag\": (1, 0.033),\n", + "# \"powerOnDrag\": (1, 0.033),\n", + "# \"noseKind\": [Calisto.noseKind],\n", + "# \"noseLength\": (Calisto.noseLength, 0.001),\n", + "# \"noseDistanceToCM\": (Calisto.noseDistanceToCM, 0.010),\n", + "# \"numberOfFins\": [Calisto.numberOfFins],\n", + "# \"finRootChord\": (Calisto.finRootChord, 0.001),\n", + "# \"finTipChord\": (Calisto.finTipChord, 0.001),\n", + "# \"span\": (Calisto.span, 0.001),\n", + "# \"distanceToCM\": (Calisto.finDistanceToCM, 0.010),\n", + "# \"finRadius\": (Calisto.finRadius, 0.001),\n", + "# \"finAirfoil\": Calisto.finAirfoil,\n", + "# \"tailTopRadius\": (Calisto.tailTopRadius, 0.001),\n", + "# \"CdS\": [(10, 2), (1, 0.3)],\n", + "# \"inclination\": [85],\n", + "# \"heading\": [90],\n", + "# }\n" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Starting'" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "TypeError", - "evalue": "must be real number, not NoneType", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn [38], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m TestDispersion\u001b[39m.\u001b[39;49mrun_dispersion(\n\u001b[0;32m 2\u001b[0m number_of_simulations\u001b[39m=\u001b[39;49m\u001b[39m5\u001b[39;49m,\n\u001b[0;32m 3\u001b[0m dispersion_dictionary\u001b[39m=\u001b[39;49mdispersion_dictionary,\n\u001b[0;32m 4\u001b[0m )\n", - "File \u001b[1;32mc:\\users\\guiga\\documents\\github-vscode\\rocketpy\\rocketpy\\Dispersion.py:849\u001b[0m, in \u001b[0;36mDispersion.run_dispersion\u001b[1;34m(self, number_of_simulations, dispersion_dictionary, environment, flight, motor, rocket, bg_image, actual_landing_point)\u001b[0m\n\u001b[0;32m 847\u001b[0m \u001b[39m# Iterate over flight settings, start the flight simulations\u001b[39;00m\n\u001b[0;32m 848\u001b[0m out \u001b[39m=\u001b[39m display(\u001b[39m\"\u001b[39m\u001b[39mStarting\u001b[39m\u001b[39m\"\u001b[39m, display_id\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m--> 849\u001b[0m \u001b[39mfor\u001b[39;00m setting \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__yield_flight_setting(\n\u001b[0;32m 850\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdistributionFunc, analysis_parameters, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnumber_of_simulations\n\u001b[0;32m 851\u001b[0m ):\n\u001b[0;32m 852\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstart_time \u001b[39m=\u001b[39m process_time()\n\u001b[0;32m 853\u001b[0m i \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n", - "File \u001b[1;32mc:\\users\\guiga\\documents\\github-vscode\\rocketpy\\rocketpy\\Dispersion.py:638\u001b[0m, in \u001b[0;36mDispersion.__yield_flight_setting\u001b[1;34m(self, distribution_func, analysis_parameters, number_of_simulations)\u001b[0m\n\u001b[0;32m 636\u001b[0m \u001b[39mfor\u001b[39;00m parameter_key, parameter_value \u001b[39min\u001b[39;00m analysis_parameters\u001b[39m.\u001b[39mitems():\n\u001b[0;32m 637\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mtype\u001b[39m(parameter_value) \u001b[39mis\u001b[39;00m \u001b[39mtuple\u001b[39m:\n\u001b[1;32m--> 638\u001b[0m flight_setting[parameter_key] \u001b[39m=\u001b[39m distribution_func(\u001b[39m*\u001b[39;49mparameter_value)\n\u001b[0;32m 639\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 640\u001b[0m \u001b[39m# shuffles list and gets first item\u001b[39;00m\n\u001b[0;32m 641\u001b[0m shuffle(parameter_value)\n", - "File \u001b[1;32mmtrand.pyx:1510\u001b[0m, in \u001b[0;36mnumpy.random.mtrand.RandomState.normal\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32m_common.pyx:604\u001b[0m, in \u001b[0;36mnumpy.random._common.cont\u001b[1;34m()\u001b[0m\n", - "\u001b[1;31mTypeError\u001b[0m: must be real number, not NoneType" - ] - } - ], + "outputs": [], "source": [ - "TestDispersion.run_dispersion(\n", - " number_of_simulations=5,\n", - " dispersion_dictionary=dispersion_dictionary,\n", - ")\n" + "# TestDispersion.run_dispersion(\n", + "# number_of_simulations=5,\n", + "# dispersion_dictionary=dispersion_dictionary,\n", + "# )\n" ] }, { From c12eb04e377593c8647880181d9022697356a84b Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Wed, 2 Nov 2022 02:17:24 +0100 Subject: [PATCH 52/68] ENH: get_parachute_by_name --- rocketpy/Rocket.py | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/rocketpy/Rocket.py b/rocketpy/Rocket.py index 32523add6..6f1dca51c 100644 --- a/rocketpy/Rocket.py +++ b/rocketpy/Rocket.py @@ -792,6 +792,24 @@ def addThrustEccentricity(self, x, y): # Return self return self + def get_parachute_by_name(self, name): + """Returns parachute object by name. + + Parameters + ---------- + name : str + Name of the parachute. + + Returns + ------- + parachute : Parachute + Parachute object with the given name. + """ + for parachute in self.parachutes: + if parachute.name == name: + return parachute + raise ValueError("No parachute with name {} found.".format(name)) + def info(self): """Prints out a summary of the data and graphs available about the Rocket. From 79938f18d08c59e545df6566cfd44b439ddd4257 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Wed, 2 Nov 2022 02:20:18 +0100 Subject: [PATCH 53/68] BUG: insert missing inputs ar dictionary as list --- rocketpy/Dispersion.py | 10 ++-------- 1 file changed, 2 insertions(+), 8 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index 8093fa20a..e3530169c 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -424,9 +424,7 @@ def __process_rail_buttons_from_dict(self, dictionary): missing_input = str(missing_input) # Add to the dict try: - dictionary[missing_input] = [ - getattr(self.rocket, missing_input) - ] + dictionary[missing_input] = [getattr(self.rocket, missing_input)] except: # class was not inputted # checks if missing parameter is required @@ -738,10 +736,7 @@ def __check_inputted_values_from_dict(self, dictionary): else: dictionary[parameter_key] = ( getattr( - self.rocket.parachutes[ - self.parachute_names.index(parachute_name) - ], - parameter, + self.rocket.get_parachute_by_name(parachute_name), parameter ), parameter_value, ) @@ -1204,7 +1199,6 @@ def run_dispersion( noise=setting["parachute_" + name + "_noise"], ) - # TODO: Remove hard-coded rail buttons definition rocket_dispersion.setRailButtons( distanceToCM=[ setting["positionFirstRailButton"], From fb59377943ab56107275b55d21f1089b0c8f1500 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Thu, 3 Nov 2022 12:59:57 +0100 Subject: [PATCH 54/68] MAINT: update docstrings --- rocketpy/Dispersion.py | 239 +++++++++++++++++++++++++---------------- 1 file changed, 148 insertions(+), 91 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index e3530169c..1d5314213 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -41,6 +41,19 @@ class Dispersion: """Monte Carlo analysis to predict probability distributions of the rocket's landing point, apogee and other relevant information. + Parameters + ---------- + filename : string + The name of the file containing the data to be used in the analysis. + + Attributes + ---------- # TODO: add "Dispersion" at the beginning of each attribute + filename : string + The name of the file containing the data to be used in the analysis. + num_of_loaded_sims : int + The number of simulations loaded from the file. + num_of_sims : int + The number of simulations to be performed. """ def __init__( @@ -176,6 +189,8 @@ def __init__( self.num_of_loaded_sims = 0 self.start_time = 0 + return None + def __set_distribution_function(self, distribution_type): """Sets the distribution function to be used in the analysis. @@ -335,6 +350,8 @@ def __process_flight_from_dict(self, dictionary): dictionary: dict Modified dictionary with the processed flight parameters. """ + # First check if all the inputs for the flight class are present in the + # dictionary, if not, input the missing ones if not all( flight_input in dictionary for flight_input in self.flight_inputs.keys() ): @@ -439,31 +456,31 @@ def __process_rail_buttons_from_dict(self, dictionary): return dictionary def __process_aerodynamic_surfaces_from_dict(self, dictionary): - """Still not implemented. - Must check if all the relevant inputs for the AerodynamicSurfaces class + """Check if all the relevant inputs for the AerodynamicSurfaces class are present in the dispersion dictionary, input the missing ones and return the modified dictionary. - Something similar to the __process_parachute_from_dict method can be - used here, since aerodynamic surfaces are optional for the simulation. Parameters ---------- - dictionary : _type_ - _description_ + dictionary : dict + Dictionary containing the parameters to be varied in the dispersion + simulation. The keys of the dictionary are the names of the parameters + to be varied, and the values can be either tuple or list. If the value + is a single value, the corresponding class of the parameter need to + be passed on the run_dispersion method. """ # Check the number of fin sets, noses, and tails - self.nose_names = [] - self.finSet_names = [] - self.tail_names = [] + self.nose_names, self.finSet_names, self.tail_names = [], [], [] + # Get names from the input dictionary for var in dictionary.keys(): if "nose" in var: - self.nose_names.append(var).split("_")[1] + self.nose_names.append(var.split("_")[1]) elif "finSet" in var: - self.finSet_names.append(var).split("_")[1] + self.finSet_names.append(var.split("_")[1]) elif "tail" in var: - self.tail_names.append(var).split("_")[1] + self.tail_names.append(var.split("_")[1]) # Get names from the rocket object for surface in self.rocket.aerodynamicSurfaces: if isinstance(surface, NoseCone): @@ -848,7 +865,7 @@ def __check_initial_objects(self): 0 ], powerOffDrag=self.dispersion_dictionary["powerOffDrag"][0], - powerOnDrag=self.dispersion_dictionary["dispersion_dictionary"][0], + powerOnDrag=self.dispersion_dictionary["powerOnDrag"][0], ) self.rocket.setRailButtons(distanceToCM=[0.2, -0.5]) if self.flight is None: @@ -867,17 +884,19 @@ def __yield_flight_setting( Parameters ---------- - distribution_func : _type_ - _description_ + distribution_func : np.random distribution function + The function that will be used to generate the random values. analysis_parameters : dict - _description_ + The dictionary with the parameters to be analyzed. This includes the + mean and standard deviation of the parameters. number_of_simulations : int Number of simulations desired, must be non negative. This is needed when running a new simulation. Default is zero. Yields ------ - flight_setting + flight_setting: dict + A dictionary with the flight setting for one simulation. """ @@ -916,21 +935,20 @@ def __export_flight_data( Parameters ---------- - flight_setting : _type_ - _description_ + flight_setting : dict + The flight setting used in the simulation. flight : Flight - _description_ - exec_time : _type_ - _description_ - dispersion_input_file : _type_ - _description_ - dispersion_output_file : _type_ - _description_ + The flight object. + exec_time : float + The execution time of the simulation. + dispersion_input_file : str + The name of the file containing all the inputs for the simulation. + dispersion_output_file : str + The name of the file containing all the outputs for the simulation. Returns ------- - _type_ - _description_ + None """ # In case not variables are passed, export default variables @@ -999,15 +1017,14 @@ def __export_flight_data_error(setting, flight_setting, dispersion_error_file): Parameters ---------- - setting : _type_ - _description_ - dispersion_error_file : _type_ - _description_ + setting : dict + The flight setting used in the simulation. + dispersion_error_file : str + The name of the file containing all the errors for the simulation. Returns ------- - _type_ - _description_ + None """ dispersion_error_file.write(str(flight_setting) + "\n") @@ -1025,7 +1042,6 @@ def run_dispersion( exported_variables=None, append=False, ): - # TODO: Separate into different functions to make it more readable """Runs the given number of simulations and saves the data Parameters @@ -1033,20 +1049,22 @@ def run_dispersion( number_of_simulations : int Number of simulations desired, must be non negative. This is needed when running a new simulation. Default is zero. - dispersion_dictionary : _type_ - _description_ - environment : _type_ - _description_ + dispersion_dictionary : dict + The dictionary with the parameters to be analyzed. This includes the + mean and standard deviation of the parameters. + environment : Environment, optional + The environment object. Default is None. flight : Flight, optional Original rocket's flight with nominal values. Parameter needed to run a new flight simulation when environment, motor and rocket remain unchanged. By default None. - motor : _type_, optional - _description_, by default None - rocket : _type_, optional - _description_, by default None + motor : Motor, optional + The motor object of the rocket. Default is None. + rocket : Rocket, optional + The rocket object. Default is None. distribution_type : str, optional - _description_, by default "normal" + The probability distribution function to be used in the analysis, + by default "normal" exported_variables : list, optional A list containing the variables to be exported. By default None. append : bool, optional @@ -1058,6 +1076,7 @@ def run_dispersion( None """ + # Saving the arguments as attributes self.number_of_simulations = number_of_simulations self.dispersion_dictionary = dispersion_dictionary self.environment = environment @@ -1265,12 +1284,13 @@ def run_dispersion( return None - def import_results(self): + def import_results(self, variables=None): """Import dispersion results from .txt file and save it into a dictionary. Parameters ---------- - None + variables : list of str, optional + List of variables to be imported. If None, all variables will be imported. Returns ------- @@ -1321,13 +1341,16 @@ def import_results(self): # Save the results as an attribute of the class self.dispersion_results = dispersion_results + # Process the results and save them as attributes of the class + self.__process_results(variables=variables) + return None # Start the processing analysis - def process_results(self, variables=None): - """Save the mean and standard deviation of each parameter in the results - dictionary. Create class attributes for each parameter. + def __process_results(self, variables=None): + """Save the mean and standard deviation of each parameter available + in the results dictionary. Create class attributes for each parameter. Parameters ---------- @@ -1386,22 +1409,17 @@ def print_results(self, variables=None): return None def plot_results(self, variables=None): - """_summary_ + """Plot the results of the dispersion analysis. Parameters ---------- - variables : _type_, optional - _description_, by default None + variables : list, optional + List of variables to be plotted. If None, all variables will be + plotted. The default is None. Example: ['outOfRailTime', 'apogee'] Returns ------- - _type_ - _description_ - - Raises - ------ - TypeError - _description_ + None """ # Check if the variables argument is a list, if not, use all variables if not isinstance(variables, list): @@ -1425,19 +1443,7 @@ def plot_results(self, variables=None): # TODO: Create evolution plots to analyze convergence - def createEllipses(self, dispersion_results): - """_summary_ - - Parameters - ---------- - dispersion_results : _type_ - _description_ - - Returns - ------- - _type_ - _description_ - """ + def __createEllipses(self, dispersion_results): """A function to create apogee and impact ellipses from the dispersion results. @@ -1445,14 +1451,38 @@ def createEllipses(self, dispersion_results): ---------- dispersion_results : dict A dictionary containing the results of the dispersion analysis. + + Returns + ------- + apogee_ellipse : Ellipse + An ellipse object representing the apogee ellipse. + impact_ellipse : Ellipse + An ellipse object representing the impact ellipse. + apogeeX : np.array + An array containing the x coordinates of the apogee ellipse. + apogeeY : np.array + An array containing the y coordinates of the apogee ellipse. + impactX : np.array + An array containing the x coordinates of the impact ellipse. + impactY : np.array + An array containing the y coordinates of the impact ellipse. """ # Retrieve dispersion data por apogee and impact XY position - # TODO: Exception handling for missing data - apogeeX = np.array(dispersion_results["apogeeX"]) - apogeeY = np.array(dispersion_results["apogeeY"]) - impactX = np.array(dispersion_results["xImpact"]) - impactY = np.array(dispersion_results["yImpact"]) + try: + apogeeX = np.array(dispersion_results["apogeeX"]) + apogeeY = np.array(dispersion_results["apogeeY"]) + except KeyError: + print("No apogee data found.") + apogeeX = np.array([]) + apogeeY = np.array([]) + try: + impactX = np.array(dispersion_results["xImpact"]) + impactY = np.array(dispersion_results["yImpact"]) + except KeyError: + print("No impact data found.") + impactX = np.array([]) + impactY = np.array([]) # Define function to calculate eigen values def eigsorted(cov): @@ -1497,7 +1527,7 @@ def eigsorted(cov): ) apogeeEll.set_facecolor((0, 1, 0, 0.2)) apogee_ellipses.append(apogeeEll) - return impact_ellipses, apogee_ellipses + return impact_ellipses, apogee_ellipses, apogeeX, apogeeY, impactX, impactY def plotEllipses( self, @@ -1522,19 +1552,29 @@ def plotEllipses( A tuple containing the actual landing point of the rocket, if known. Useful when comparing the dispersion results with the actual landing. Must be given in tuple format, such as (lat, lon). By default None. # TODO: Check the order + perimeterSize : int, optional + The size of the perimeter to be plotted. The default is 3000. + xlim : tuple, optional + The limits of the x axis. The default is (-3000, 3000). + ylim : tuple, optional + The limits of the y axis. The default is (-3000, 3000). + + Returns + ------- + None """ # Import background map if image is not None: img = imread(image) - # Retrieve dispersion data por apogee and impact XY position - # TODO: Exception handling for missing data - apogeeX = np.array(dispersion_results["apogeeX"]) - apogeeY = np.array(dispersion_results["apogeeY"]) - impactX = np.array(dispersion_results["xImpact"]) - impactY = np.array(dispersion_results["yImpact"]) - - impact_ellipses, apogee_ellipses = self.createEllipses(dispersion_results) + ( + impact_ellipses, + apogee_ellipses, + apogeeX, + apogeeY, + impactX, + impactY, + ) = self.__createEllipses(dispersion_results) # Create plot figure plt.figure(num=None, figsize=(8, 6), dpi=150, facecolor="w", edgecolor="k") @@ -1581,6 +1621,7 @@ def plotEllipses( ax.set_xlabel("East (m)") # Add background image to plot + # TODO: In the future, integrate with other libraries to plot the map (e.g. cartopy, ee, etc.) # You can translate the basemap by changing dx and dy (in meters) dx = 0 dy = 0 @@ -1606,7 +1647,7 @@ def plotEllipses( plt.show() return None - def prepareEllipses(self, ellipses, origin_lat, origin_lon, resolution=100): + def __prepareEllipses(self, ellipses, origin_lat, origin_lon, resolution=100): """Generate a list of latitude and longitude points for each ellipse in ellipses. @@ -1620,6 +1661,12 @@ def prepareEllipses(self, ellipses, origin_lat, origin_lon, resolution=100): Longitude of the origin of the coordinate system. resolution : int, optional Number of points to generate for each ellipse, by default 100 + + Returns + ------- + list + List of lists of tuples containing the latitude and longitude of each + point in each ellipse. """ outputs = [] @@ -1690,18 +1737,22 @@ def exportEllipsesToKML( Number of points to be used to draw the ellipse. Default is 100. color : String Color of the ellipse. Default is 'ff0000ff', which is red. + + Returns + ------- + None """ - impact_ellipses, apogee_ellipses = self.createEllipses(dispersion_results) + impact_ellipses, apogee_ellipses = self.__createEllipses(dispersion_results) outputs = [] if type == "all" or type == "impact": - outputs = outputs + self.prepareEllipses( + outputs = outputs + self.__prepareEllipses( impact_ellipses, origin_lat, origin_lon, resolution=resolution ) if type == "all" or type == "apogee": - outputs = outputs + self.prepareEllipses( + outputs = outputs + self.__prepareEllipses( apogee_ellipses, origin_lat, origin_lon, resolution=resolution ) @@ -1744,7 +1795,7 @@ def exportEllipsesToKML( return None def info(self): - """_summary_ + """Print information about the dispersion model. Returns ------- @@ -1760,6 +1811,12 @@ def info(self): return None def allInfo(self): + """Print and plot information about the dispersion model and the results. + + Returns + ------- + None + """ dispersion_results = self.dispersion_results print("Monte Carlo Simulation by RocketPy") From 0b8286e5919c84950d0b8bda555188fb6ddf5657 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Thu, 3 Nov 2022 13:08:52 +0100 Subject: [PATCH 55/68] FIX: wrong argument on exportEllipsesToKML --- rocketpy/Dispersion.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index 1d5314213..a6cd796e7 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -1710,7 +1710,6 @@ def __prepareEllipses(self, ellipses, origin_lat, origin_lon, resolution=100): def exportEllipsesToKML( self, - dispersion_results, filename, origin_lat, origin_lon, @@ -1743,7 +1742,14 @@ def exportEllipsesToKML( None """ - impact_ellipses, apogee_ellipses = self.__createEllipses(dispersion_results) + ( + impact_ellipses, + apogee_ellipses, + _, + _, + _, + _, + ) = self.__createEllipses(self.dispersion_results) outputs = [] if type == "all" or type == "impact": From 09b7d60937626783f1b178a776060d2339c3e0eb Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Thu, 3 Nov 2022 13:09:03 +0100 Subject: [PATCH 56/68] MAINT: Update notebook --- .../dispersion_class_usage.ipynb | 556 ++++++++++-------- 1 file changed, 317 insertions(+), 239 deletions(-) diff --git a/docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb b/docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb index a6bfe5370..c232cf1ef 100644 --- a/docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb +++ b/docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb @@ -16,18 +16,9 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" @@ -42,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -58,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -105,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -125,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -147,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -156,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -166,34 +157,30 @@ "Launch Site Details\n", "\n", "Launch Rail Length: 5.2 m\n", - "Launch Date: 2022-10-25 12:00:00 UTC\n", + "Launch Date: 2022-11-04 12:00:00 UTC\n", "Launch Site Latitude: 39.38970°\n", "Launch Site Longitude: -8.28896°\n", "Reference Datum: SIRGAS2000\n", "Launch Site UTM coordinates: 44415.43 W 4373388.31 N\n", "Launch Site UTM zone: 30S\n", - "Launch Site Surface Elevation: 141.8 m\n", + "Launch Site Surface Elevation: 113.0 m\n", "\n", "\n", "Atmospheric Model Details\n", "\n", - "Atmospheric Model Type: Forecast\n", - "Forecast Maximum Height: 78.440 km\n", - "Forecast Time Period: From 2022-10-23 12:00:00 to 2022-11-08 12:00:00 UTC\n", - "Forecast Hour Interval: 3 hrs\n", - "Forecast Latitude Range: From -90.0 ° To 90.0 °\n", - "Forecast Longitude Range: From 0.0 ° To 359.75 °\n", + "Atmospheric Model Type: StandardAtmosphere\n", + "StandardAtmosphere Maximum Height: 80.000 km\n", "\n", "\n", "Surface Atmospheric Conditions\n", "\n", - "Surface Wind Speed: 7.53 m/s\n", - "Surface Wind Direction: 218.09°\n", - "Surface Wind Heading: 38.09°\n", - "Surface Pressure: 997.18 hPa\n", - "Surface Temperature: 294.80 K\n", - "Surface Air Density: 1.178 kg/m³\n", - "Surface Speed of Sound: 344.20 m/s\n", + "Surface Wind Speed: 0.00 m/s\n", + "Surface Wind Direction: 0.00°\n", + "Surface Wind Heading: 0.00°\n", + "Surface Pressure: 999.75 hPa\n", + "Surface Temperature: 287.42 K\n", + "Surface Air Density: 1.212 kg/m³\n", + "Surface Speed of Sound: 339.83 m/s\n", "\n", "\n", "Atmospheric Model Plots\n" @@ -201,8 +188,8 @@ }, { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T00:58:23.971600\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:00:45.307220\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -212,7 +199,7 @@ } ], "source": [ - "Env.info()" + "Env.info()\n" ] }, { @@ -232,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -253,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -275,7 +262,7 @@ { "data": { "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T00:58:25.216239\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:00:47.734733\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -297,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -335,7 +322,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -349,8 +336,8 @@ "def mainTrigger(p, y):\n", " # p = pressure\n", " # y = [x, y, z, vx, vy, vz, e0, e1, e2, e3, w1, w2, w3]\n", - " # activate main when vz < 0 m/s and z < 800 + 1400 m (+1400 due to surface elevation).\n", - " return True if y[5] < 0 and y[2] < 800 + 1400 else False\n", + " # activate main when vz < 0 m/s and z < 500 + 100 m (+100 due to surface elevation).\n", + " return True if y[5] < 0 and y[2] < 500 + 100 else False\n", "\n", "\n", "Main = Calisto.addParachute(\n", @@ -374,7 +361,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -404,7 +391,7 @@ { "data": { "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T00:59:10.688315\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:00:48.760950\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -426,7 +413,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -447,13 +434,13 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T00:59:12.375246\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:00:50.603632\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -484,7 +471,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -502,7 +489,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -534,13 +521,13 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'Completed 10 iterations successfully. Total CPU time: 22.40625 s. Total wall time: 23.7100830078125 s'" + "'Completed 50 iterations successfully. Total CPU time: 90.40625 s. Total wall time: 98.26740670204163 s'" ] }, "metadata": {}, @@ -549,22 +536,13 @@ ], "source": [ "TestDispersion.run_dispersion(\n", - " number_of_simulations=10,\n", + " number_of_simulations=50,\n", " dispersion_dictionary=disp_dictionary,\n", " flight=TestFlight,\n", " append=False,\n", ")\n" ] }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [], - "source": [ - "TestDispersion.process_results()" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -589,40 +567,57 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A total of 50 simulations were loaded from the following file: dispersion_analysis_outputs/disp_class_example.disp_outputs.txt\n" + ] + } + ], + "source": [ + "TestDispersion.import_results()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "maxAccelerationTime: μ = 64.155, σ = 76.067\n", - "frontalSurfaceWind: μ = -0.660, σ = 0.250\n", - "outOfRailStaticMargin: μ = 2.133, σ = 0.008\n", - "apogeeX: μ = 124.113, σ = 88.082\n", - "xImpact: μ = 4259.142, σ = 667.161\n", - "initialStaticMargin: μ = 2.050, σ = 0.009\n", - "apogeeY: μ = -848.560, σ = 83.549\n", - "finalStaticMargin: μ = 2.683, σ = 0.006\n", - "yImpact: μ = 2254.608, σ = 664.886\n", - "impactVelocity: μ = -57459796457.449, σ = 172379296086.602\n", - "maxSpeed: μ = 57485610630.893, σ = 172456737796.272\n", - "apogeeTime: μ = 24.701, σ = 0.407\n", - "tFinal: μ = 289.802, σ = 59.731\n", - "maxAcceleration: μ = 404143759333999116288.000, σ = 1212431278001877680128.000\n", - "apogee: μ = 3186.668, σ = 118.153\n", - "maxSpeedTime: μ = 45.933, σ = 85.169\n", - "outOfRailVelocity: μ = 25.469, σ = 0.388\n", - "lateralSurfaceWind: μ = 7.499, σ = 0.021\n", - "outOfRailTime: μ = 0.367, σ = 0.005\n", + "outOfRailStaticMargin: μ = 2.133, σ = 0.006\n", + "tFinal: μ = 262.715, σ = 12.630\n", + "finalStaticMargin: μ = 2.679, σ = 0.007\n", + "yImpact: μ = -537.050, σ = 96.246\n", + "initialStaticMargin: μ = 2.051, σ = 0.007\n", + "apogeeY: μ = -460.407, σ = 85.017\n", + "lateralSurfaceWind: μ = 0.000, σ = 0.000\n", + "apogee: μ = 3199.872, σ = 130.593\n", + "maxAccelerationTime: μ = 0.474, σ = 0.472\n", + "maxSpeed: μ = 281.146, σ = 9.272\n", + "xImpact: μ = 574.929, σ = 103.509\n", + "maxSpeedTime: μ = 3.370, σ = 0.014\n", + "apogeeTime: μ = 24.869, σ = 0.431\n", + "impactVelocity: μ = -5.152, σ = 0.248\n", + "maxAcceleration: μ = 101.210, σ = 3.712\n", + "apogeeX: μ = 492.929, σ = 91.671\n", + "outOfRailVelocity: μ = 25.537, σ = 0.441\n", + "outOfRailTime: μ = 0.366, σ = 0.006\n", + "frontalSurfaceWind: μ = 0.000, σ = 0.000\n", "numberOfEvents: μ = 2.000, σ = 0.000\n", - "executionTime: μ = 1.895, σ = 0.254\n", - "Drogue_triggerTime: μ = 24.706, σ = 0.406\n", - "Drogue_inflatedTime: μ = 26.193, σ = 0.365\n", - "Drogue_inflatedVelocity: μ = 33.249, σ = 3.417\n", - "Main_triggerTime: μ = 192.144, σ = 19.792\n", - "Main_inflatedTime: μ = 193.591, σ = 19.805\n", - "Main_inflatedVelocity: μ = 20.834, σ = 0.007\n" + "executionTime: μ = 1.547, σ = 0.258\n", + "Drogue_triggerTime: μ = 24.874, σ = 0.431\n", + "Drogue_inflatedTime: μ = 26.364, σ = 0.446\n", + "Drogue_inflatedVelocity: μ = 28.374, σ = 3.780\n", + "Main_triggerTime: μ = 173.166, σ = 10.765\n", + "Main_inflatedTime: μ = 174.681, σ = 10.744\n", + "Main_inflatedVelocity: μ = 16.680, σ = 0.773\n" ] } ], @@ -639,7 +634,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -648,42 +643,42 @@ "text": [ "Monte Carlo Simulation by RocketPy\n", "Data Source: dispersion_analysis_outputs/disp_class_example\n", - "Number of simulations: 10\n", + "Number of simulations: 50\n", "Results: \n", - "maxAccelerationTime: μ = 64.155, σ = 76.067\n", - "frontalSurfaceWind: μ = -0.660, σ = 0.250\n", - "outOfRailStaticMargin: μ = 2.133, σ = 0.008\n", - "apogeeX: μ = 124.113, σ = 88.082\n", - "xImpact: μ = 4259.142, σ = 667.161\n", - "initialStaticMargin: μ = 2.050, σ = 0.009\n", - "apogeeY: μ = -848.560, σ = 83.549\n", - "finalStaticMargin: μ = 2.683, σ = 0.006\n", - "yImpact: μ = 2254.608, σ = 664.886\n", - "impactVelocity: μ = -57459796457.449, σ = 172379296086.602\n", - "maxSpeed: μ = 57485610630.893, σ = 172456737796.272\n", - "apogeeTime: μ = 24.701, σ = 0.407\n", - "tFinal: μ = 289.802, σ = 59.731\n", - "maxAcceleration: μ = 404143759333999116288.000, σ = 1212431278001877680128.000\n", - "apogee: μ = 3186.668, σ = 118.153\n", - "maxSpeedTime: μ = 45.933, σ = 85.169\n", - "outOfRailVelocity: μ = 25.469, σ = 0.388\n", - "lateralSurfaceWind: μ = 7.499, σ = 0.021\n", - "outOfRailTime: μ = 0.367, σ = 0.005\n", + "outOfRailStaticMargin: μ = 2.133, σ = 0.006\n", + "tFinal: μ = 262.715, σ = 12.630\n", + "finalStaticMargin: μ = 2.679, σ = 0.007\n", + "yImpact: μ = -537.050, σ = 96.246\n", + "initialStaticMargin: μ = 2.051, σ = 0.007\n", + "apogeeY: μ = -460.407, σ = 85.017\n", + "lateralSurfaceWind: μ = 0.000, σ = 0.000\n", + "apogee: μ = 3199.872, σ = 130.593\n", + "maxAccelerationTime: μ = 0.474, σ = 0.472\n", + "maxSpeed: μ = 281.146, σ = 9.272\n", + "xImpact: μ = 574.929, σ = 103.509\n", + "maxSpeedTime: μ = 3.370, σ = 0.014\n", + "apogeeTime: μ = 24.869, σ = 0.431\n", + "impactVelocity: μ = -5.152, σ = 0.248\n", + "maxAcceleration: μ = 101.210, σ = 3.712\n", + "apogeeX: μ = 492.929, σ = 91.671\n", + "outOfRailVelocity: μ = 25.537, σ = 0.441\n", + "outOfRailTime: μ = 0.366, σ = 0.006\n", + "frontalSurfaceWind: μ = 0.000, σ = 0.000\n", "numberOfEvents: μ = 2.000, σ = 0.000\n", - "executionTime: μ = 1.895, σ = 0.254\n", - "Drogue_triggerTime: μ = 24.706, σ = 0.406\n", - "Drogue_inflatedTime: μ = 26.193, σ = 0.365\n", - "Drogue_inflatedVelocity: μ = 33.249, σ = 3.417\n", - "Main_triggerTime: μ = 192.144, σ = 19.792\n", - "Main_inflatedTime: μ = 193.591, σ = 19.805\n", - "Main_inflatedVelocity: μ = 20.834, σ = 0.007\n", + "executionTime: μ = 1.547, σ = 0.258\n", + "Drogue_triggerTime: μ = 24.874, σ = 0.431\n", + "Drogue_inflatedTime: μ = 26.364, σ = 0.446\n", + "Drogue_inflatedVelocity: μ = 28.374, σ = 3.780\n", + "Main_triggerTime: μ = 173.166, σ = 10.765\n", + "Main_inflatedTime: μ = 174.681, σ = 10.744\n", + "Main_inflatedVelocity: μ = 16.680, σ = 0.773\n", "Plotting results: \n" ] }, { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:28.579214\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:32.767107\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -693,8 +688,8 @@ }, { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:29.205103\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:33.918449\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -704,8 +699,8 @@ }, { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:29.779376\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:34.439390\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -715,8 +710,8 @@ }, { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:30.133360\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:35.037885\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -726,8 +721,8 @@ }, { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:30.469888\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjIAAAGzCAYAAAA1yP25AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy89olMNAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA3DUlEQVR4nO3deXgUVf7+/bsTsoEkEEiAaICwCLJviiwiCJoJuOAKDMMuoAYDBlnyRUQQCaBghkVERUDGBUFgHFYjoDDKDmGXNQgju4SEgIaEnN8fPvRjm4U0dtJd+H5dV19Sp05Vfaorbe5U1am2GWOMAAAALMjL3QUAAADcLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIM4GEqV66snj17uruMW96bb76pKlWqyNvbWw0aNHB3OQBuEkEGKERz5syRzWbT1q1bc53funVr1alT509vZ/ny5Xrttdf+9Hr+Kr766isNHTpULVq00OzZszVu3Lg/vU5XHUtPMG7cOC1ZssTdZQAFUszdBQBwdODAAXl5Ofc3xvLlyzV9+nTCTAGtWbNGXl5emjVrlnx9fd1djscZN26cnnrqKXXs2NHdpQA3xBkZwMP4+fnJx8fH3WU45fLly+4uwSlnz55VQEAAIQa4BRBkAA/zx3tkMjMzNXr0aFWvXl3+/v4qU6aMWrZsqcTERElSz549NX36dEmSzWazv667fPmyBg8erPDwcPn5+alGjRp666239Mcvvv/ll18UExOjsmXLqmTJknr00Uf1008/yWazOZzpee2112Sz2bRv3z79/e9/V+nSpdWyZUtJ0q5du9SzZ09VqVJF/v7+Kl++vHr37q2ff/7ZYVvX13Hw4EH94x//UFBQkEJCQjRy5EgZY3TixAk99thjCgwMVPny5TVp0qQCvXdZWVl6/fXXVbVqVfn5+aly5cr6v//7P2VkZNj72Gw2zZ49W5cvX7a/V3PmzMl1faNGjZKPj4/OnTuXY16/fv1UqlQp/frrr3nWY7PZNGDAAC1YsEC1atVSQECAmjVrpt27d0uSZs6cqWrVqsnf31+tW7fWsWPHHJa/frlq27Ztat68uQICAhQREaF3333Xod/Vq1f16quvqnHjxgoKClKJEiV03333ae3atTlqys7O1j//+U/VrVtX/v7+CgkJ0d/+9jf75U+bzabLly9r7ty59veHe7bgybi0BBSB1NRUnT9/Pkd7ZmbmDZd97bXXFB8fr2effVb33HOP0tLStHXrVm3fvl0PPvig+vfvr5MnTyoxMVHz5s1zWNYYo0cffVRr165Vnz591KBBA61atUpDhgzRTz/9pLffftvet2fPnvr888/VrVs33Xvvvfr222/VoUOHPOt6+umnVb16dY0bN84eihITE3X06FH16tVL5cuX1969e/Xee+9p79692rhxo0PAkqROnTrprrvu0vjx47Vs2TKNHTtWwcHBmjlzph544AFNmDBBH3/8sV5++WXdfffdatWqVb7v1bPPPqu5c+fqqaee0uDBg7Vp0ybFx8dr//79Wrx4sSRp3rx5eu+997R582Z98MEHkqTmzZvnur5u3bppzJgxmj9/vgYMGGBvv3r1qhYuXKgnn3xS/v7++da0fv16ffnll4qOjpYkxcfH6+GHH9bQoUP1zjvv6IUXXlBKSoomTpyo3r17a82aNQ7Lp6SkqH379nrmmWfUpUsXff7553r++efl6+ur3r17S5LS0tL0wQcfqEuXLurbt68uXbqkWbNmKTIyUps3b3a4mblPnz6aM2eOoqKi9OyzzyorK0vr16/Xxo0b1aRJE82bN8/+s9avXz9JUtWqVfPdR8CtDIBCM3v2bCMp31ft2rUdlqlUqZLp0aOHfbp+/fqmQ4cO+W4nOjra5PZxXrJkiZFkxo4d69D+1FNPGZvNZg4fPmyMMWbbtm1Gkhk0aJBDv549expJZtSoUfa2UaNGGUmmS5cuObZ35cqVHG2ffvqpkWTWrVuXYx39+vWzt2VlZZk77rjD2Gw2M378eHt7SkqKCQgIcHhPcpOUlGQkmWeffdah/eWXXzaSzJo1a+xtPXr0MCVKlMh3fdc1a9bMNG3a1KFt0aJFRpJZu3atve3+++/PcSwlGT8/P5OcnGxvmzlzppFkypcvb9LS0uztcXFxRpJD3/vvv99IMpMmTbK3ZWRkmAYNGpjQ0FBz9epVY8xv711GRobDtlNSUky5cuVM79697W1r1qwxkkxMTEyO/czOzrb/u0SJEjd8vwFPwaUloAhMnz5diYmJOV716tW74bKlSpXS3r17dejQIae3u3z5cnl7eysmJsahffDgwTLGaMWKFZKklStXSpJeeOEFh34vvvhinut+7rnncrQFBATY//3rr7/q/PnzuvfeeyVJ27dvz9H/2Weftf/b29tbTZo0kTFGffr0sbeXKlVKNWrU0NGjR/OsRfptXyUpNjbWoX3w4MGSpGXLluW7fF66d++uTZs26ciRI/a2jz/+WOHh4br//vtvuHzbtm1VuXJl+3TTpk0lSU8++aRKliyZo/2P+1msWDH179/fPu3r66v+/fvr7Nmz2rZtm6Tf3rvr9/tkZ2frwoULysrKUpMmTRze9y+++EI2m02jRo3KUecfz5YBVkGQAYrAPffco3bt2uV4lS5d+obLjhkzRhcvXtSdd96punXrasiQIdq1a1eBtvvjjz8qLCzM4RemJN111132+df/6+XlpYiICId+1apVy3Pdf+wrSRcuXNDAgQNVrlw5BQQEKCQkxN4vNTU1R/+KFSs6TAcFBcnf319ly5bN0Z6SkpJnLb/fhz/WXL58eZUqVcq+r87q1KmT/Pz89PHHH0v6bT+WLl2qrl27FuiXf277KEnh4eG5tv9xP8PCwlSiRAmHtjvvvFOSHO6pmTt3rurVq2e/jyokJETLli1zeN+PHDmisLAwBQcH37BuwCoIMoCHa9WqlY4cOaIPP/xQderU0QcffKBGjRrZ7+9wl9+ffbnumWee0fvvv6/nnntOixYt0ldffWU/25OdnZ2jv7e3d4HaJOW4OTkvrj6zULp0aT388MP2ILNw4UJlZGToH//4R4GWz2t//ux+/t6//vUv9ezZU1WrVtWsWbO0cuVKJSYm6oEHHsj1fQduJQQZwAKCg4PVq1cvffrppzpx4oTq1avnMJIor1/elSpV0smTJ3Xp0iWH9h9++ME+//p/s7OzlZyc7NDv8OHDBa4xJSVFq1ev1vDhwzV69Gg9/vjjevDBB1WlSpUCr+PPuL4Pf7wEd+bMGV28eNG+rzeje/fuOnjwoLZs2aKPP/5YDRs2VO3atf9syQVy8uTJHMPbDx48KEn2S1YLFy5UlSpVtGjRInXr1k2RkZFq165djhFVVatW1cmTJ3XhwoV8t8llJlgJQQbwcH8cunzbbbepWrVqDkOKr196uHjxokPf9u3b69q1a5o2bZpD+9tvvy2bzaaoqChJUmRkpCTpnXfeceg3derUAtd5/QzDH88oJCQkFHgdf0b79u1z3d7kyZMlKd8RWDcSFRWlsmXLasKECfr2228LfDbGFbKysjRz5kz79NWrVzVz5kyFhISocePGknJ/7zdt2qQNGzY4rOvJJ5+UMUajR4/OsZ3fL1uiRIkcP0uAp2L4NeDhatWqpdatW6tx48YKDg7W1q1btXDhQofhwNd/ocXExCgyMlLe3t7q3LmzHnnkEbVp00YjRozQsWPHVL9+fX311Vf697//rUGDBtmH1TZu3FhPPvmkEhIS9PPPP9uHX1//y78gf6EHBgaqVatWmjhxojIzM3X77bfrq6++ynGWp7DUr19fPXr00HvvvaeLFy/q/vvv1+bNmzV37lx17NhRbdq0uel1+/j4qHPnzpo2bZq8vb3VpUsXF1aev7CwME2YMEHHjh3TnXfeqfnz5yspKUnvvfee/cGJDz/8sBYtWqTHH39cHTp0UHJyst59913VqlVL6enp9nW1adNG3bp105QpU3To0CH97W9/U3Z2ttavX682bdrYf6YaN26sr7/+WpMnT1ZYWJgiIiLsNyMDHsd9A6aAW9/14ddbtmzJdX5uQ3b/OPx67Nix5p577jGlSpUyAQEBpmbNmuaNN96wD7015rfhty+++KIJCQkxNpvNYSj2pUuXzEsvvWTCwsKMj4+PqV69unnzzTcdhtsaY8zly5dNdHS0CQ4ONrfddpvp2LGjOXDggJHkMBz6+tDpc+fO5dif//3vf+bxxx83pUqVMkFBQebpp582J0+ezHMI9x/Xkdew6Nzep9xkZmaa0aNHm4iICOPj42PCw8NNXFyc+fXXXwu0nfxs3rzZSDIPPfRQrvPzGn4dHR3t0JacnGwkmTfffNOhfe3atUaSWbBgQY51bt261TRr1sz4+/ubSpUqmWnTpjksm52dbcaNG2cqVapk/Pz8TMOGDc3SpUtNjx49TKVKlRz6ZmVlmTfffNPUrFnT+Pr6mpCQEBMVFWW2bdtm7/PDDz+YVq1amYCAACOJodjwaDZjbuLOMgB/CUlJSWrYsKH+9a9/qWvXru4ux6127typBg0a6KOPPlK3bt2KZJutW7fW+fPntWfPniLZHmBF3CMDQNJvX1HwRwkJCfLy8rrhE3X/Ct5//33ddttteuKJJ9xdCoDf4R4ZAJKkiRMnatu2bWrTpo2KFSumFStWaMWKFerXr1+OZ578lfznP//Rvn379N5772nAgAE5nukCwL24tARA0m/fkzR69Gjt27dP6enpqlixorp166YRI0aoWLG/7t88lStX1pkzZxQZGal58+bleLhgYeLSEnBjBBkAAGBZ3CMDAAAsiyADAAAs65a/8J2dna2TJ0+qZMmSPHYbAACLMMbo0qVLCgsLk5dX3uddbvkgc/Lkyb/0iAsAAKzsxIkTuuOOO/Kcf8sHmesjDE6cOKHAwEA3VwMAAAoiLS1N4eHhNxwpeMsHmeuXkwIDAwkyAABYzI1uC+FmXwAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFluDTLr1q3TI488orCwMNlsNi1ZsiTPvs8995xsNpsSEhKKrD4AAODZ3BpkLl++rPr162v69On59lu8eLE2btyosLCwIqoMAABYgVu/NDIqKkpRUVH59vnpp5/04osvatWqVerQoUMRVQYAAKzAo7/9Ojs7W926ddOQIUNUu3btAi2TkZGhjIwM+3RaWlphlQcAANzMo4PMhAkTVKxYMcXExBR4mfj4eI0ePboQqwJuXZWHL3N3CU47Np4ztcBfmceOWtq2bZv++c9/as6cObLZbAVeLi4uTqmpqfbXiRMnCrFKAADgTh4bZNavX6+zZ8+qYsWKKlasmIoVK6Yff/xRgwcPVuXKlfNczs/PT4GBgQ4vAABwa/LYS0vdunVTu3btHNoiIyPVrVs39erVy01VAQAAT+LWIJOenq7Dhw/bp5OTk5WUlKTg4GBVrFhRZcqUcejv4+Oj8uXLq0aNGkVdKgAA8EBuDTJbt25VmzZt7NOxsbGSpB49emjOnDluqgoAAFiFW4NM69atZYwpcP9jx44VXjEAAMByPPZmXwAAgBshyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMtya5BZt26dHnnkEYWFhclms2nJkiX2eZmZmRo2bJjq1q2rEiVKKCwsTN27d9fJkyfdVzAAAPAobg0yly9fVv369TV9+vQc865cuaLt27dr5MiR2r59uxYtWqQDBw7o0UcfdUOlAADAExVz58ajoqIUFRWV67ygoCAlJiY6tE2bNk333HOPjh8/rooVKxZFiQAAwIO5Ncg4KzU1VTabTaVKlcqzT0ZGhjIyMuzTaWlpRVAZAABwB8vc7Pvrr79q2LBh6tKliwIDA/PsFx8fr6CgIPsrPDy8CKsEAABFyRJBJjMzU88884yMMZoxY0a+fePi4pSammp/nThxooiqBAAARc3jLy1dDzE//vij1qxZk+/ZGEny8/OTn59fEVUHAADcyaODzPUQc+jQIa1du1ZlypRxd0kAAMCDuDXIpKen6/Dhw/bp5ORkJSUlKTg4WBUqVNBTTz2l7du3a+nSpbp27ZpOnz4tSQoODpavr6+7ygYAAB7CrUFm69atatOmjX06NjZWktSjRw+99tpr+vLLLyVJDRo0cFhu7dq1at26dVGVCQAAPJRbg0zr1q1ljMlzfn7zAAAALDFqCQAAIDcEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFlOB5nt27dr9+7d9ul///vf6tixo/7v//5PV69edWlxAAAA+XE6yPTv318HDx6UJB09elSdO3dW8eLFtWDBAg0dOtTlBQIAAOTF6SBz8OBBNWjQQJK0YMECtWrVSp988onmzJmjL774wtX1AQAA5MnpIGOMUXZ2tiTp66+/Vvv27SVJ4eHhOn/+vGurAwAAyIfTQaZJkyYaO3as5s2bp2+//VYdOnSQJCUnJ6tcuXIuLxAAACAvTgeZhIQEbd++XQMGDNCIESNUrVo1SdLChQvVvHlzlxcIAACQl2LOLlCvXj2HUUvXvfnmm/L29nZJUQAAAAVxU8+RuXjxoj744APFxcXpwoULkqR9+/bp7NmzLi0OAAAgP06fkdm1a5fatm2rUqVK6dixY+rbt6+Cg4O1aNEiHT9+XB999FFh1AkAAJCD02dkYmNj1atXLx06dEj+/v729vbt22vdunUuLQ4AACA/TgeZLVu2qH///jnab7/9dp0+fdqpda1bt06PPPKIwsLCZLPZtGTJEof5xhi9+uqrqlChggICAtSuXTsdOnTI2ZIBAMAtyukg4+fnp7S0tBztBw8eVEhIiFPrunz5surXr6/p06fnOn/ixImaMmWK3n33XW3atEklSpRQZGSkfv31V2fLBgAAtyCn75F59NFHNWbMGH3++eeSJJvNpuPHj2vYsGF68sknnVpXVFSUoqKicp1njFFCQoJeeeUVPfbYY5Kkjz76SOXKldOSJUvUuXNnZ0sHAAC3GKfPyEyaNEnp6ekKDQ3VL7/8ovvvv1/VqlVTyZIl9cYbb7issOTkZJ0+fVrt2rWztwUFBalp06basGFDnstlZGQoLS3N4QUAAG5NTp+RCQoKUmJior777jvt3LlT6enpatSokUPgcIXr99v88WnB5cqVy/denPj4eI0ePdqltQAAAM/kdJC5rkWLFmrRooUra3GJuLg4xcbG2qfT0tIUHh7uxooAAEBhcfrSUkxMjKZMmZKjfdq0aRo0aJArapIklS9fXpJ05swZh/YzZ87Y5+XGz89PgYGBDi8AAHBrcjrIfPHFF7meiWnevLkWLlzokqIkKSIiQuXLl9fq1avtbWlpadq0aZOaNWvmsu0AAADrcvrS0s8//6ygoKAc7YGBgTp//rxT60pPT9fhw4ft08nJyUpKSlJwcLAqVqyoQYMGaezYsapevboiIiI0cuRIhYWFqWPHjs6WDQAAbkFOn5GpVq2aVq5cmaN9xYoVqlKlilPr2rp1qxo2bKiGDRtK+u2pwQ0bNtSrr74qSRo6dKhefPFF9evXT3fffbfS09O1cuVKhycKAwCAvy6nz8jExsZqwIABOnfunB544AFJ0urVqzVp0iQlJCQ4ta7WrVvLGJPnfJvNpjFjxmjMmDHOlgkAAP4CnA4yvXv3VkZGht544w29/vrrkqTKlStrxowZ6t69u8sLBAAAyMtNDb9+/vnn9fzzz+vcuXMKCAjQbbfd5uq6AAAAbuimnyMjyenvVgIAAHAlp2/2PXPmjLp166awsDAVK1ZM3t7eDi8AAICi4vQZmZ49e+r48eMaOXKkKlSoIJvNVhh1AQAA3JDTQea///2v1q9frwYNGhRCOQAAAAXn9KWl8PDwfIdMAwAAFBWng0xCQoKGDx+uY8eOFUI5AAAABef0paVOnTrpypUrqlq1qooXLy4fHx+H+RcuXHBZcQAAAPlxOsg4+/ReAACAwuJ0kOnRo0dh1AEAAOA0p++RkaQjR47olVdeUZcuXXT27FlJv31p5N69e11aHAAAQH6cDjLffvut6tatq02bNmnRokVKT0+XJO3cuVOjRo1yeYEAAAB5cTrIDB8+XGPHjlViYqJ8fX3t7Q888IA2btzo0uIAAADy43SQ2b17tx5//PEc7aGhoTp//rxLigIAACgIp4NMqVKldOrUqRztO3bs0O233+6SogAAAArC6SDTuXNnDRs2TKdPn5bNZlN2dra+++47vfzyy+revXth1AgAAJArp4PMuHHjVLNmTYWHhys9PV21atVSq1at1Lx5c73yyiuFUSMAAECunHqOjDFGp0+f1pQpU/Tqq69q9+7dSk9PV8OGDVW9evXCqhEAACBXTgeZatWqae/evapevbrCw8MLqy4AAIAbcurSkpeXl6pXr66ff/65sOoBAAAoMKfvkRk/fryGDBmiPXv2FEY9AAAABeb0dy11795dV65cUf369eXr66uAgACH+Xz7NQAAKCp8+zUAALAsp4JMZmamvv32W40cOVIRERGFVRMAAECBOHWPjI+Pj7744ovCqgUAAMApTt/s27FjRy1ZsqQQSgEAAHCO0/fIVK9eXWPGjNF3332nxo0bq0SJEg7zY2JiXFYcAABAfpwOMrNmzVKpUqW0bds2bdu2zWGezWYjyAAAgCLjdJBJTk4ujDoAAACc5vQ9MgAAAJ7C6TMyvXv3znf+hx9+eNPFAAAAOMPpIJOSkuIwnZmZqT179ujixYt64IEHXFYYAADAjTgdZBYvXpyjLTs7W88//7yqVq3qkqIAAAAKwiX3yHh5eSk2NlZvv/22K1YHAABQIC672ffIkSPKyspy1eoAAABuyOlLS7GxsQ7TxhidOnVKy5YtU48ePVxWGAAAwI04HWR27NjhMO3l5aWQkBBNmjTphiOaAAAAXMnpILN27drCqAMAAMBpTt8jk5ycrEOHDuVoP3TokI4dO+aKmuyuXbumkSNHKiIiQgEBAapatapef/11GWNcuh0AAGBNTgeZnj176vvvv8/RvmnTJvXs2dMVNdlNmDBBM2bM0LRp07R//35NmDBBEydO1NSpU126HQAAYE03dY9MixYtcrTfe++9GjBggEuKuu7777/XY489pg4dOkiSKleurE8//VSbN2926XYAAIA1OX1Gxmaz6dKlSznaU1NTde3aNZcUdV3z5s21evVqHTx4UJK0c+dO/fe//1VUVFSey2RkZCgtLc3hBQAAbk1On5Fp1aqV4uPj9emnn8rb21vSb/eyxMfHq2XLli4tbvjw4UpLS1PNmjXl7e2ta9eu6Y033lDXrl3zXCY+Pl6jR492aR0APFfl4cvcXYLTjo3v4O4S/hL42fhrcDrITJgwQa1atVKNGjV03333SZLWr1+vtLQ0rVmzxqXFff755/r444/1ySefqHbt2kpKStKgQYMUFhaW5zNr4uLiHJ51k5aWpvDwcJfWBQAAPIPTQaZWrVratWuXpk2bpp07dyogIEDdu3fXgAEDFBwc7NLihgwZouHDh6tz586SpLp16+rHH39UfHx8nkHGz89Pfn5+Lq0DAAB4JqeDjCSFhYVp3Lhxrq4lhytXrsjLy/E2Hm9vb2VnZxf6tgEAgOdzOsjMnj1bt912m55++mmH9gULFujKlSsu/ZqCRx55RG+88YYqVqyo2rVra8eOHZo8eTJPEAYAAJJuYtRSfHy8ypYtm6M9NDTU5Wdppk6dqqeeekovvPCC7rrrLr388svq37+/Xn/9dZduBwAAWJPTZ2SOHz+uiIiIHO2VKlXS8ePHXVLUdSVLllRCQoISEhJcul4AAHBrcPqMTGhoqHbt2pWjfefOnSpTpoxLigIAACgIp4NMly5dFBMTo7Vr1+ratWu6du2a1qxZo4EDB9pHFwEAABQFpy8tvf766zp27Jjatm2rYsV+Wzw7O1vdu3cvkpFMAAAA1zkdZHx9fTV//ny9/vrr9ufI1K1bV5UqVSqM+gAAAPJ0U8+RkaTg4GC1adMm1xFMAAAARcGpe2QuXryo6OholS1bVuXKlVO5cuVUtmxZDRgwQBcvXiykEgEAAHJX4DMyFy5cULNmzfTTTz+pa9euuuuuuyRJ+/bt05w5c7R69Wp9//33Kl26dKEVCwAA8HsFDjJjxoyRr6+vjhw5onLlyuWY99BDD2nMmDF6++23XV4kAABAbgp8aWnJkiV66623coQYSSpfvrwmTpyoxYsXu7Q4AACA/BQ4yJw6dUq1a9fOc36dOnV0+vRplxQFAABQEAUOMmXLltWxY8fynJ+cnKzg4GBX1AQAAFAgBQ4ykZGRGjFihK5evZpjXkZGhkaOHKm//e1vLi0OAAAgP07d7NukSRNVr15d0dHRqlmzpowx2r9/v9555x1lZGRo3rx5hVkrAACAgwIHmTvuuEMbNmzQCy+8oLi4OBljJEk2m00PPvigpk2bpvDw8EIrFAAA4I+cerJvRESEVqxYoZSUFB06dEiSVK1aNe6NAQAAbnFTX1FQunRp3XPPPa6uBQAAwClOfUUBAACAJyHIAAAAyyLIAAAAyypQkGnUqJFSUlIk/TYM+8qVK4VaFAAAQEEUKMjs379fly9fliSNHj1a6enphVoUAABAQRRo1FKDBg3Uq1cvtWzZUsYYvfXWW7rtttty7fvqq6+6tEAAAIC8FCjIzJkzR6NGjdLSpUtls9m0YsUKFSuWc1GbzUaQAQAARaZAQaZGjRr67LPPJEleXl5avXq1QkNDC7UwAACAG3H6gXjZ2dmFUQcAAIDTburJvkeOHFFCQoL2798vSapVq5YGDhyoqlWrurQ4AACA/Dj9HJlVq1apVq1a2rx5s+rVq6d69epp06ZNql27thITEwujRgAAgFw5fUZm+PDheumllzR+/Pgc7cOGDdODDz7osuIAAADy4/QZmf3796tPnz452nv37q19+/a5pCgAAICCcDrIhISEKCkpKUd7UlISI5kAAECRcvrSUt++fdWvXz8dPXpUzZs3lyR99913mjBhgmJjY11eIAAAQF6cDjIjR45UyZIlNWnSJMXFxUmSwsLC9NprrykmJsblBQIAAOTF6SBjs9n00ksv6aWXXtKlS5ckSSVLlnR5YQAAADdyU8+RuY4AAwAA3Mnpm30BAAA8BUEGAABYFkEGAABYllNBJjMzU23bttWhQ4cKqx4AAIACcyrI+Pj4aNeuXYVVCwAAgFOcvrT0j3/8Q7NmzSqMWgAAAJzi9PDrrKwsffjhh/r666/VuHFjlShRwmH+5MmTXVacJP30008aNmyYVqxYoStXrqhatWqaPXu2mjRp4tLtAAAA63E6yOzZs0eNGjWSJB08eNBhns1mc01V/5+UlBS1aNFCbdq00YoVKxQSEqJDhw6pdOnSLt0OAACwJqeDzNq1awujjlxNmDBB4eHhmj17tr0tIiIi32UyMjKUkZFhn05LSyu0+gAAgHvd9PDrw4cPa9WqVfrll18kScYYlxV13ZdffqkmTZro6aefVmhoqBo2bKj3338/32Xi4+MVFBRkf4WHh7u8LgAA4BmcDjI///yz2rZtqzvvvFPt27fXqVOnJEl9+vTR4MGDXVrc0aNHNWPGDFWvXl2rVq3S888/r5iYGM2dOzfPZeLi4pSammp/nThxwqU1AQAAz+F0kHnppZfk4+Oj48ePq3jx4vb2Tp06aeXKlS4tLjs7W40aNdK4cePUsGFD9evXT3379tW7776b5zJ+fn4KDAx0eAEAgFuT00Hmq6++0oQJE3THHXc4tFevXl0//vijywqTpAoVKqhWrVoObXfddZeOHz/u0u0AAABrcjrIXL582eFMzHUXLlyQn5+fS4q6rkWLFjpw4IBD28GDB1WpUiWXbgcAAFiT00Hmvvvu00cffWSfttlsys7O1sSJE9WmTRuXFvfSSy9p48aNGjdunA4fPqxPPvlE7733nqKjo126HQAAYE1OD7+eOHGi2rZtq61bt+rq1asaOnSo9u7dqwsXLui7775zaXF33323Fi9erLi4OI0ZM0YRERFKSEhQ165dXbodAABgTU4HmTp16ujgwYOaNm2aSpYsqfT0dD3xxBOKjo5WhQoVXF7gww8/rIcfftjl6wUAANbndJCRpKCgII0YMcLVtQAAADjlpoJMSkqKZs2apf3790uSatWqpV69eik4ONilxQEAAOTH6Zt9161bp8qVK2vKlClKSUlRSkqKpkyZooiICK1bt64wagQAAMiV02dkoqOj1alTJ82YMUPe3t6SpGvXrumFF15QdHS0du/e7fIiAQAAcuP0GZnDhw9r8ODB9hAjSd7e3oqNjdXhw4ddWhwAAEB+nA4yjRo1st8b83v79+9X/fr1XVIUAABAQRTo0tKuXbvs/46JidHAgQN1+PBh3XvvvZKkjRs3avr06Ro/fnzhVAkAAJCLAgWZBg0ayGazyRhjbxs6dGiOfn//+9/VqVMn11UHAACQjwIFmeTk5MKuAwAAwGkFCjJ8SSMAAPBEN/VAvJMnT+q///2vzp49q+zsbId5MTExLikMAADgRpwOMnPmzFH//v3l6+urMmXKyGaz2efZbDaCDAAAKDJOB5mRI0fq1VdfVVxcnLy8nB69DQAA4DJOJ5ErV66oc+fOhBgAAOB2TqeRPn36aMGCBYVRCwAAgFOcvrQUHx+vhx9+WCtXrlTdunXl4+PjMH/y5MkuKw4AACA/NxVkVq1apRo1akhSjpt9AQAAiorTQWbSpEn68MMP1bNnz0IoBwAAoOCcDjJ+fn5q0aJFYdQC3FIqD1/m7hLgoaz4s3FsfAd3lwDkyumbfQcOHKipU6cWRi0AAABOcfqMzObNm7VmzRotXbpUtWvXznGz76JFi1xWHAAAQH6cDjKlSpXSE088URi1AAAAOMXpIDN79uzCqAMAAMBpPJ4XAABYltNnZCIiIvJ9XszRo0f/VEEAAAAF5XSQGTRokMN0ZmamduzYoZUrV2rIkCGuqgsAAOCGnA4yAwcOzLV9+vTp2rp1658uCAAAoKBcdo9MVFSUvvjiC1etDgAA4IZcFmQWLlyo4OBgV60OAADghpy+tNSwYUOHm32NMTp9+rTOnTund955x6XFAQAA5MfpINOxY0eHaS8vL4WEhKh169aqWbOmq+oCAAC4IaeDzKhRowqjDgAAAKfxQDwAAGBZBT4j4+Xlle+D8CTJZrMpKyvrTxcFAABQEAUOMosXL85z3oYNGzRlyhRlZ2e7pCgAAICCKHCQeeyxx3K0HThwQMOHD9d//vMfde3aVWPGjHFpcQAAAPm5qXtkTp48qb59+6pu3brKyspSUlKS5s6dq0qVKrm6PgAAgDw5FWRSU1M1bNgwVatWTXv37tXq1av1n//8R3Xq1Cms+gAAAPJU4EtLEydO1IQJE1S+fHl9+umnuV5qAgAAKEoFDjLDhw9XQECAqlWrprlz52ru3Lm59lu0aJHLivuj8ePHKy4uTgMHDlRCQkKhbQcAAFhDgYNM9+7dbzj8ujBt2bJFM2fOVL169dxWAwAA8CwFDjJz5swpxDLyl56erq5du+r999/X2LFj3VYHAADwLJZ4sm90dLQ6dOigdu3a3bBvRkaG0tLSHF4AAODW5PR3LRW1zz77TNu3b9eWLVsK1D8+Pl6jR48u5KoAAIAn8OgzMidOnNDAgQP18ccfy9/fv0DLxMXFKTU11f46ceJEIVcJAADcxaPPyGzbtk1nz55Vo0aN7G3Xrl3TunXrNG3aNGVkZMjb29thGT8/P/n5+RV1qQAAwA08Osi0bdtWu3fvdmjr1auXatasqWHDhuUIMQAA4K/Fo4NMyZIlczw1uESJEipTpgxPEwYAAJ59jwwAAEB+PPqMTG6++eYbd5cAAAA8BGdkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRVzdwEAAM9Xefgyd5fwl2DF9/nY+A5u3T5nZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGV5dJCJj4/X3XffrZIlSyo0NFQdO3bUgQMH3F0WAADwEB4dZL799ltFR0dr48aNSkxMVGZmph566CFdvnzZ3aUBAAAPUMzdBeRn5cqVDtNz5sxRaGiotm3bplatWrmpKgAA4Ck8Osj8UWpqqiQpODg4zz4ZGRnKyMiwT6elpRV6XQAAwD0sE2Sys7M1aNAgtWjRQnXq1MmzX3x8vEaPHl0kNVUevqxItuNqx8Z3cHcJTrPqew0AKFwefY/M70VHR2vPnj367LPP8u0XFxen1NRU++vEiRNFVCEAAChqljgjM2DAAC1dulTr1q3THXfckW9fPz8/+fn5FVFlAADAnTw6yBhj9OKLL2rx4sX65ptvFBER4e6SAACAB/HoIBMdHa1PPvlE//73v1WyZEmdPn1akhQUFKSAgAA3VwcAANzNo++RmTFjhlJTU9W6dWtVqFDB/po/f767SwMAAB7Ao8/IGGPcXQIAAPBgHn1GBgAAID8EGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFmWCDLTp09X5cqV5e/vr6ZNm2rz5s3uLgkAAHgAjw8y8+fPV2xsrEaNGqXt27erfv36ioyM1NmzZ91dGgAAcDOPDzKTJ09W37591atXL9WqVUvvvvuuihcvrg8//NDdpQEAADcr5u4C8nP16lVt27ZNcXFx9jYvLy+1a9dOGzZsyHWZjIwMZWRk2KdTU1MlSWlpaS6vLzvjisvXWRQK470obFZ9rwHgVldYv1Our9cYk28/jw4y58+f17Vr11SuXDmH9nLlyumHH37IdZn4+HiNHj06R3t4eHih1GhFQQnurgAAcKso7N8ply5dUlBQUJ7zPTrI3Iy4uDjFxsbap7Ozs3XhwgWVKVNGly5dUnh4uE6cOKHAwEA3VoncpKWlcXw8HMfI83GMPBvHp+CMMbp06ZLCwsLy7efRQaZs2bLy9vbWmTNnHNrPnDmj8uXL57qMn5+f/Pz8HNpKlSolSbLZbJKkwMBAfoA8GMfH83GMPB/HyLNxfAomvzMx13n0zb6+vr5q3LixVq9ebW/Lzs7W6tWr1axZMzdWBgAAPIFHn5GRpNjYWPXo0UNNmjTRPffco4SEBF2+fFm9evVyd2kAAMDNPD7IdOrUSefOndOrr76q06dPq0GDBlq5cmWOG4ALws/PT6NGjcpx6QmegePj+ThGno9j5Nk4Pq5nMzca1wQAAOChPPoeGQAAgPwQZAAAgGURZAAAgGURZAAAgGURZAAAgGXdkkHm4MGDeuyxx1S2bFkFBgaqZcuWWrt2rUOf48ePq0OHDipevLhCQ0M1ZMgQZWVlOfT55ptv1KhRI/n5+alatWqaM2dOEe7Freubb76RzWbL9bVlyxZ7v127dum+++6Tv7+/wsPDNXHixBzrWrBggWrWrCl/f3/VrVtXy5cvL8pduaUtW7ZMTZs2VUBAgEqXLq2OHTs6zOcz5F6VK1fO8fkZP368Qx8+Q54hIyNDDRo0kM1mU1JSksM8jpELmFtQ9erVTfv27c3OnTvNwYMHzQsvvGCKFy9uTp06ZYwxJisry9SpU8e0a9fO7NixwyxfvtyULVvWxMXF2ddx9OhRU7x4cRMbG2v27dtnpk6dary9vc3KlSvdtVu3jIyMDHPq1CmH17PPPmsiIiJMdna2McaY1NRUU65cOdO1a1ezZ88e8+mnn5qAgAAzc+ZM+3q+++474+3tbSZOnGj27dtnXnnlFePj42N2797trl27ZSxcuNCULl3azJgxwxw4cMDs3bvXzJ8/3z6fz5D7VapUyYwZM8bhc5Senm6fz2fIc8TExJioqCgjyezYscPezjFyjVsuyJw7d85IMuvWrbO3paWlGUkmMTHRGGPM8uXLjZeXlzl9+rS9z4wZM0xgYKDJyMgwxhgzdOhQU7t2bYd1d+rUyURGRhbBXvy1XL161YSEhJgxY8bY29555x1TunRp+/Ewxphhw4aZGjVq2KefeeYZ06FDB4d1NW3a1PTv37/wi76FZWZmmttvv9188MEHefbhM+R+lSpVMm+//Xae8/kMeYbly5ebmjVrmr179+YIMhwj17jlLi2VKVNGNWrU0EcffaTLly8rKytLM2fOVGhoqBo3bixJ2rBhg+rWrevwdODIyEilpaVp79699j7t2rVzWHdkZKQ2bNhQdDvzF/Hll1/q559/dvjaiQ0bNqhVq1by9fW1t0VGRurAgQNKSUmx9+EYud727dv1008/ycvLSw0bNlSFChUUFRWlPXv22PvwGfIM48ePV5kyZdSwYUO9+eabDpf2+Ay535kzZ9S3b1/NmzdPxYsXzzGfY+Qat1yQsdls+vrrr7Vjxw6VLFlS/v7+mjx5slauXKnSpUtLkk6fPp3jKw6uT58+fTrfPmlpafrll1+KYE/+OmbNmqXIyEjdcccd9rY/c4yuz8fNOXr0qCTptdde0yuvvKKlS5eqdOnSat26tS5cuCCJz5AniImJ0Weffaa1a9eqf//+GjdunIYOHWqfz2fIvYwx6tmzp5577jk1adIk1z4cI9ewTJAZPnx4njeIXn/98MMPMsYoOjpaoaGhWr9+vTZv3qyOHTvqkUce0alTp9y9G7e0gh6j3/vf//6nVatWqU+fPm6q+q+joMcnOztbkjRixAg9+eSTaty4sWbPni2bzaYFCxa4eS9ubc58hmJjY9W6dWvVq1dPzz33nCZNmqSpU6cqIyPDzXtxayvoMZo6daouXbqkuLg4d5d8y/P4L428bvDgwerZs2e+fapUqaI1a9Zo6dKlSklJUWBgoCTpnXfeUWJioubOnavhw4erfPny2rx5s8OyZ86ckSSVL1/e/t/rbb/vExgYqICAABft1a2loMfo92bPnq0yZcro0UcfdWjP6/2/Pi+/Ptfnw1FBj8/1wF+rVi17u5+fn6pUqaLjx49LEp+hQnIzn6HrmjZtqqysLB07dkw1atTgM1RInPldtGHDhhxfDtmkSRN17dpVc+fO5Ri5iGWCTEhIiEJCQm7Y78qVK5IkLy/Hk01eXl72vzSbNWumN954Q2fPnlVoaKgkKTExUYGBgfb/eTdr1izHELfExEQ1a9bsT+/Lraqgx+g6Y4xmz56t7t27y8fHx2Fes2bNNGLECGVmZtrnJSYmqkaNGvZLhM2aNdPq1as1aNAg+3Ico7wV9Pg0btxYfn5+OnDggFq2bClJyszM1LFjx1SpUiVJfIYKi7Ofod9LSkqSl5eX/XjwGSocBT1GU6ZM0dixY+3TJ0+eVGRkpObPn6+mTZtK4hi5jJtvNna5c+fOmTJlypgnnnjCJCUlmQMHDpiXX37Z+Pj4mKSkJGPM/z909KGHHjJJSUlm5cqVJiQkJNeho0OGDDH79+8306dPZ+ioi3399ddGktm/f3+OeRcvXjTlypUz3bp1M3v27DGfffaZKV68eI5hicWKFTNvvfWW2b9/vxk1ahTDEl1k4MCB5vbbbzerVq0yP/zwg+nTp48JDQ01Fy5cMMbwGXK377//3rz99tsmKSnJHDlyxPzrX/8yISEhpnv37vY+fIY8S3Jyco5RSxwj17jlgowxxmzZssU89NBDJjg42JQsWdLce++9Zvny5Q59jh07ZqKiokxAQIApW7asGTx4sMnMzHTos3btWtOgQQPj6+trqlSpYmbPnl2Ee3Hr69Kli2nevHme83fu3Glatmxp/Pz8zO23327Gjx+fo8/nn39u7rzzTuPr62tq165tli1bVpgl/2VcvXrVDB482ISGhpqSJUuadu3amT179jj04TPkPtu2bTNNmzY1QUFBxt/f39x1111m3Lhx5tdff3Xox2fIc+QWZIzhGLmCzRhj3HxSCAAA4KZYZtQSAADAHxFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZf0/HchQL15mO7wAAAAASUVORK5CYII=", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:35.730770\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -737,8 +732,8 @@ }, { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:30.851409\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:36.226720\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -748,8 +743,8 @@ }, { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:31.206223\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:36.950369\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -759,8 +754,8 @@ }, { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:31.585078\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:37.522930\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -770,8 +765,8 @@ }, { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:31.950037\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:38.074991\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -781,8 +776,8 @@ }, { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:32.314063\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:38.658911\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -792,8 +787,8 @@ }, { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:32.662671\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:39.300201\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -803,8 +798,8 @@ }, { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:33.122442\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:39.942430\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -814,8 +809,8 @@ }, { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:33.507413\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:40.598752\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -825,8 +820,8 @@ }, { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:33.932178\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:41.359067\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -836,8 +831,8 @@ }, { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:34.346076\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:42.121036\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -847,8 +842,8 @@ }, { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:34.712095\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:42.844071\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -858,8 +853,8 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAioAAAGzCAYAAAABsTylAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy89olMNAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA6MElEQVR4nO3deVxU9f7H8feAMuLCEAoiiWhI7ktqmVqWaaKZWbcNs3JLW1Bzq+R2XWhDbfPaNcquqe2lZd1bqbl7XXDfM7c0yX0DXAHh+/ujB/NrHFSGBuYor+fjMQ853/Odcz4zh+Xt93zPGZsxxggAAMCC/HxdAAAAwMUQVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVFDiVK9eXT169PB1GVe9119/Xdddd538/f3VuHFjX5dTYthsNo0aNcrj5/Xo0UPVq1f3ej3AX0VQwRVtypQpstlsWr16db7rb7/9dtWvX/8v7+fHH38s1C//kuqnn37S888/r1atWmny5Ml67bXXfF2SRzZt2qQHHnhAUVFRKlOmjK699lrdeeedeuedd3xdmkdsNluBHgsXLvR1qcBFlfJ1AUBx27Ztm/z8PMvoP/74oyZMmEBYKaD58+fLz89PkyZNUkBAgK/L8ciyZcvUpk0bVatWTX369FF4eLhSU1OVkpKif/7zn+rfv7+vSyywjz/+2GX5o48+0pw5c9za69Spow8++EC5ubnFWR5QIAQVlDh2u93XJXjs9OnTKleunK/LKLDDhw8rMDDwigspkvTqq6/K4XBo1apVCg4Odll3+PBh3xRVSI8++qjLckpKiubMmePWDlgZp35Q4lw4RyU7O1uJiYmKiYlRmTJlVLFiRd1yyy2aM2eOpD/O3U+YMEGS61B6ntOnT2vIkCGKjIyU3W5XrVq19MYbb+jCDyY/e/asBgwYoEqVKqlChQq65557tG/fPrc5BaNGjZLNZtPPP/+sRx55RNdcc41uueUWSdLGjRvVo0cPXXfddSpTpozCw8PVq1cvHTt2zGVfedvYvn27Hn30UTkcDoWGhmr48OEyxig1NVVdunRRUFCQwsPD9eabbxbovTt//rxefvllRUdHy263q3r16vr73/+uzMxMZx+bzabJkyfr9OnTzvdqypQpF91m3um5jRs36rbbblPZsmVVs2ZNTZ8+XZK0aNEiNW/eXIGBgapVq5bmzp3r8vzffvtNzzzzjGrVqqXAwEBVrFhRDz74oPbs2ePsY4xRmzZtFBoa6hI2srKy1KBBA0VHR+v06dOSpF27dqlevXpuIUWSwsLCXJZtNpv69eunTz/9VLVq1VKZMmXUtGlTLV682O25+/btU69evVS5cmXZ7XbVq1dPH374oVu/zMxMjRw5UjVr1pTdbldkZKSef/55l/c4r9+gQYMUGhrq/H76/fffL/o+X86Fc1T27Nkjm82mN954QxMmTNB1112nsmXLqn379kpNTZUxRi+//LKqVq2qwMBAdenSRcePH3fb7syZM3XrrbeqXLlyqlChgjp16qQtW7YUuk6UPIyo4KqQnp6uo0ePurVnZ2df9rmjRo1SUlKSnnjiCd10003KyMjQ6tWrtXbtWt1555168skntX///nyHzI0xuueee7RgwQL17t1bjRs31uzZs/Xcc89p3759evvtt519e/Tooa+++kqPPfaYbr75Zi1atEidOnW6aF0PPvigYmJi9NprrzlDz5w5c/Trr7+qZ8+eCg8P15YtWzRx4kRt2bJFKSkpLgFKkh5++GHVqVNHo0eP1g8//KBXXnlFISEhev/993XHHXdozJgx+vTTTzV06FDdeOONat269SXfqyeeeEJTp07VAw88oCFDhmjFihVKSkrS1q1bNWPGDEl/nG6YOHGiVq5cqX//+9+SpJYtW15yuydOnNDdd9+tuLg4Pfjgg0pOTlZcXJw+/fRTDRw4UE899ZQeeeQRvf7663rggQeUmpqqChUqSJJWrVqlZcuWKS4uTlWrVtWePXuUnJys22+/XT///LPKli0rm82mDz/8UA0bNtRTTz2lb775RpI0cuRIbdmyRQsXLnSOWEVFRWn58uXavHlzgeY3LVq0SF9++aUGDBggu92ud999Vx06dNDKlSudzz906JBuvvlmZ7AJDQ3VzJkz1bt3b2VkZGjgwIGSpNzcXN1zzz1asmSJ+vbtqzp16mjTpk16++23tX37dn377bcux+KTTz7RI488opYtW2r+/PmX/H4qrE8//VRZWVnq37+/jh8/rrFjx+qhhx7SHXfcoYULF+qFF17Qzp079c4772jo0KEu4evjjz9W9+7dFRsbqzFjxujMmTNKTk7WLbfconXr1jF5FwVjgCvY5MmTjaRLPurVq+fynKioKNO9e3fncqNGjUynTp0uuZ/4+HiT34/Lt99+aySZV155xaX9gQceMDabzezcudMYY8yaNWuMJDNw4ECXfj169DCSzMiRI51tI0eONJJM165d3fZ35swZt7bPP//cSDKLFy9220bfvn2dbefPnzdVq1Y1NpvNjB492tl+4sQJExgY6PKe5Gf9+vVGknniiSdc2ocOHWokmfnz5zvbunfvbsqVK3fJ7eW57bbbjCTz2WefOdt++eUXI8n4+fmZlJQUZ/vs2bONJDN58mRnW37vyfLly40k89FHH7m0v//++0aS+eSTT0xKSorx9/d3OyY//fST8ff3N/7+/qZFixbm+eefN7NnzzZZWVlu+8n7Hlu9erWz7bfffjNlypQx9913n7Otd+/epkqVKubo0aMuz4+LizMOh8P5Gj7++GPj5+dn/ve//7n0e++994wks3TpUmPM/x+LZ555xqXfI4884vb99GcX+z425o9jFhUV5VzevXu3kWRCQ0NNWlqasz0hIcFIMo0aNTLZ2dnO9q5du5qAgABz7tw5Y4wxJ0+eNMHBwaZPnz4u+zl48KBxOBxu7cDFcOoHV4UJEyZozpw5bo+GDRte9rnBwcHasmWLduzY4fF+f/zxR/n7+2vAgAEu7UOGDJExRjNnzpQkzZo1S5L0zDPPuPS71MTMp556yq0tMDDQ+fW5c+d09OhR3XzzzZKktWvXuvV/4oknnF/7+/urWbNmMsaod+/ezvbg4GDVqlVLv/7660Vrkf54rZI0ePBgl/YhQ4ZIkn744YdLPv9Sypcvr7i4OOdyrVq1FBwcrDp16qh58+bO9ryv/1zrn9+T7OxsHTt2TDVr1lRwcLDbe9K3b1/Fxsaqf//+euyxxxQdHe12RdKdd96p5cuX65577tGGDRs0duxYxcbG6tprr9V//vMft9pbtGihpk2bOperVaumLl26aPbs2crJyZExRl9//bU6d+4sY4yOHj3qfMTGxio9Pd1Z57Rp01SnTh3Vrl3bpd8dd9whSVqwYIGk/z8WF37f5Y3MeNODDz4oh8PhXM47Bo8++qhKlSrl0p6VlaV9+/ZJ+mP0Ly0tTV27dnV5Lf7+/mrevLnztQCXw6kfXBVuuukmNWvWzK39mmuuyfeU0J+99NJL6tKli66//nrVr19fHTp00GOPPVagkPPbb78pIiLCeRoiT506dZzr8/718/NTjRo1XPrVrFnzotu+sK8kHT9+XImJifriiy/cJnamp6e79a9WrZrLssPhUJkyZVSpUiW39gvnuVwo7zVcWHN4eLiCg4Odr7Uwqlat6nbayuFwKDIy0q1N+uNUUZ6zZ88qKSlJkydP1r59+1zmBuX3nkyaNEnR0dHasWOHli1b5hJ08tx444365ptvlJWVpQ0bNmjGjBl6++239cADD2j9+vWqW7eus29MTIzb86+//nqdOXNGR44ckZ+fn9LS0jRx4kRNnDgx39efdyx37NihrVu3KjQ09JL98o5FdHS0y/patWrl+7y/Ir/vIUmXPTZ5wT8vZF0oKCjIq3Xi6kVQQYnXunVr7dq1S999951++ukn/fvf/9bbb7+t9957z2VEorjl9wf0oYce0rJly/Tcc8+pcePGKl++vHJzc9WhQ4d8Ly319/cvUJskt8m/F3NhoPCGi9VUkFr79++vyZMna+DAgWrRooUcDodsNpvi4uLyfU8WLlzonJi6adMmtWjR4qJ1BQQE6MYbb9SNN96o66+/Xj179tS0adM0cuTIAr+2vBoeffRRde/ePd8+eaE4NzdXDRo00FtvvZVvvwvDQXEo7LHJe90ff/yxwsPD3fr9eTQGuBS+UwBJISEh6tmzp3r27KlTp06pdevWGjVqlDOoXOyPc1RUlObOnauTJ0+6jKr88ssvzvV5/+bm5mr37t0u/wPfuXNngWs8ceKE5s2bp8TERI0YMcLZXphTVoWR9xp27NjhHDGS/pgompaW5nytxW369Onq3r27y5VL586dU1pamlvfAwcOqH///mrfvr0CAgI0dOhQxcbGFqj2vBG7AwcOuLTn9/5v375dZcuWdY6MVKhQQTk5OWrXrt0l9xEdHa0NGzaobdu2lwyEecdi165dLqMo27Ztu+zrKC55oz1hYWGXfd3ApTBHBSXehac8ypcvr5o1a7pcDpp3RciFf/zuuusu5eTk6F//+pdL+9tvvy2bzaaOHTtKkmJjYyVJ7777rks/T+50mvc/2AtHPsaNG1fgbfwVd911V777y/vff1FccVIQ/v7+bu/JO++8o5ycHLe+ffr0UW5uriZNmqSJEyeqVKlS6t27t8vzFyxYkO/oUt68kAtPryxfvtxlLkxqaqq+++47tW/fXv7+/vL399f999+vr7/+Wps3b3bb7pEjR5xfP/TQQ9q3b58++OADt35nz551XkKd9301fvx4lz7F9b1QELGxsQoKCtJrr72W79V3f37dwKUwooISr27durr99tvVtGlThYSEaPXq1Zo+fbr69evn7JM3WXLAgAGKjY2Vv7+/4uLi1LlzZ7Vp00Yvvvii9uzZo0aNGumnn37Sd999p4EDBzr/V9m0aVPdf//9GjdunI4dO+a8PHn79u2SCnY6JSgoSK1bt9bYsWOVnZ2ta6+9Vj/99JN2795dBO+Ku0aNGql79+6aOHGi0tLSdNttt2nlypWaOnWq7r33XrVp06ZY6rjQ3XffrY8//lgOh0N169bV8uXLNXfuXFWsWNGl3+TJk/XDDz9oypQpqlq1qqQ/As2jjz6q5ORk50Tn/v3768yZM7rvvvtUu3ZtZWVladmyZfryyy9VvXp19ezZ02W79evXV2xsrMvlyZKUmJjo7DN69GgtWLBAzZs3V58+fVS3bl0dP35ca9eu1dy5c533H3nsscf01Vdf6amnntKCBQvUqlUr5eTk6JdfftFXX32l2bNnq1mzZmrcuLG6du2qd999V+np6WrZsqXmzZvn0QhdUQsKClJycrIee+wxNWnSRHFxcQoNDdXevXv1ww8/qFWrVm4BH8iXby42Arwj7/LkVatW5bv+tttuu+zlya+88oq56aabTHBwsAkMDDS1a9c2r776qsvlqOfPnzf9+/c3oaGhxmazuVziefLkSTNo0CATERFhSpcubWJiYszrr79ucnNzXfZ7+vRpEx8fb0JCQkz58uXNvffea7Zt22YkuVwunHdp8ZEjR9xez++//27uu+8+ExwcbBwOh3nwwQfN/v37L3qJ84XbuNhlw/m9T/nJzs42iYmJpkaNGqZ06dImMjLSJCQkOC9Jvdx+8nOxfUdFReV72bgkEx8f71w+ceKE6dmzp6lUqZIpX768iY2NNb/88ovLcU5NTTUOh8N07tzZbXv33XefKVeunPn111+NMcbMnDnT9OrVy9SuXduUL1/eBAQEmJo1a5r+/fubQ4cO5VvLJ598YmJiYozdbjc33HCDWbBggdt+Dh06ZOLj401kZKQpXbq0CQ8PN23btjUTJ0506ZeVlWXGjBlj6tWrZ+x2u7nmmmtM06ZNTWJioklPT3f2O3v2rBkwYICpWLGiKVeunOncubNJTU31+uXJr7/+uku/BQsWGElm2rRpLu0X+1lcsGCBiY2NNQ6Hw5QpU8ZER0ebHj16uFzSDVyKzZgCzqAD4HXr16/XDTfcoE8++UTdunXzdTnwkM1mU3x8PCMDQBFijgpQTM6ePevWNm7cOPn5+V32jrAAUFIxRwUoJmPHjtWaNWvUpk0blSpVSjNnztTMmTPVt29fn1x2CgBXAoIKUExatmypOXPm6OWXX9apU6dUrVo1jRo1Si+++KKvSwMAy2KOCgAAsCzmqAAAAMsiqAAAAMu6oueo5Obmav/+/apQoUKRfP4IAADwPmOMTp48qYiICPn5XXrM5IoOKvv37+dqCQAArlCpqanOO0VfzBUdVPI+BC41NZWPDAcA4AqRkZGhyMhIlw9zvZgrOqjkne4JCgoiqAAAcIUpyLQNJtMCAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADL8mlQycnJ0fDhw1WjRg0FBgYqOjpaL7/8sowxviwLAABYhE8/62fMmDFKTk7W1KlTVa9ePa1evVo9e/aUw+HQgAEDfFkaAACwAJ8GlWXLlqlLly7q1KmTJKl69er6/PPPtXLlSl+WBQAALMKnp35atmypefPmafv27ZKkDRs2aMmSJerYsWO+/TMzM5WRkeHyAAAAVy+fjqgMGzZMGRkZql27tvz9/ZWTk6NXX31V3bp1y7d/UlKSEhMTi62+6sN+KLZ9ecue0Z18XQIAAF7j0xGVr776Sp9++qk+++wzrV27VlOnTtUbb7yhqVOn5ts/ISFB6enpzkdqamoxVwwAAIqTT0dUnnvuOQ0bNkxxcXGSpAYNGui3335TUlKSunfv7tbfbrfLbrcXd5kAAMBHfDqicubMGfn5uZbg7++v3NxcH1UEAACsxKcjKp07d9arr76qatWqqV69elq3bp3eeust9erVy5dlAQAAi/BpUHnnnXc0fPhwPfPMMzp8+LAiIiL05JNPasSIEb4sCwAAWIRPg0qFChU0btw4jRs3zpdlAAAAi+KzfgAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGX5NKhUr15dNpvN7REfH+/LsgAAgEWU8uXOV61apZycHOfy5s2bdeedd+rBBx/0YVUAAMAqfBpUQkNDXZZHjx6t6Oho3XbbbT6qCAAAWIlPg8qfZWVl6ZNPPtHgwYNls9ny7ZOZmanMzEznckZGRnGVBwAAfMAyk2m//fZbpaWlqUePHhftk5SUJIfD4XxERkYWX4EAAKDYWSaoTJo0SR07dlRERMRF+yQkJCg9Pd35SE1NLcYKAQBAcbPEqZ/ffvtNc+fO1TfffHPJfna7XXa7vZiqAgAAvmaJEZXJkycrLCxMnTp18nUpAADAQnweVHJzczV58mR1795dpUpZYoAHAABYhM+Dyty5c7V371716tXL16UAAACL8fkQRvv27WWM8XUZAADAgnw+ogIAAHAxBBUAAGBZBBUAAGBZBBUAAGBZBBUAAGBZBBUAAGBZBBUAAGBZBBUAAGBZBBUAAGBZBBUAAGBZBBUAAGBZBBUAAGBZBBUAAGBZBBUAAGBZBBUAAGBZBBUAAGBZBBUAAGBZBBUAAGBZBBUAAGBZBBUAAGBZBBUAAGBZBBUAAGBZBBUAAGBZBBUAAGBZBBUAAGBZBBUAAGBZBBUAAGBZBBUAAGBZBBUAAGBZBBUAAGBZBBUAAGBZBBUAAGBZBBUAAGBZPg8q+/bt06OPPqqKFSsqMDBQDRo00OrVq31dFgAAsIBSvtz5iRMn1KpVK7Vp00YzZ85UaGioduzYoWuuucaXZQEAAIvwaVAZM2aMIiMjNXnyZGdbjRo1fFgRAACwEo9P/axdu1abNm1yLn/33Xe699579fe//11ZWVkebes///mPmjVrpgcffFBhYWG64YYb9MEHH1y0f2ZmpjIyMlweAADg6uVxUHnyySe1fft2SdKvv/6quLg4lS1bVtOmTdPzzz/v0bZ+/fVXJScnKyYmRrNnz9bTTz+tAQMGaOrUqfn2T0pKksPhcD4iIyM9LR8AAFxBbMYY48kTHA6H1q5dq+joaI0ZM0bz58/X7NmztXTpUsXFxSk1NbXA2woICFCzZs20bNkyZ9uAAQO0atUqLV++3K1/ZmamMjMzncsZGRmKjIxUenq6goKCPHkZBVJ92A9e32ZR2zO6k69LAADgkjIyMuRwOAr099vjERVjjHJzcyVJc+fO1V133SVJioyM1NGjRz3aVpUqVVS3bl2Xtjp16mjv3r359rfb7QoKCnJ5AACAq5fHQaVZs2Z65ZVX9PHHH2vRokXq1OmP/8Hv3r1blStX9mhbrVq10rZt21zatm/frqioKE/LAgAAVyGPg8q4ceO0du1a9evXTy+++KJq1qwpSZo+fbpatmzp0bYGDRqklJQUvfbaa9q5c6c+++wzTZw4UfHx8Z6WBQAArkIez1G5mHPnzsnf31+lS5f26Hnff/+9EhIStGPHDtWoUUODBw9Wnz59CvRcT85xFQZzVAAA8D5P/n4X6j4qaWlpmj59unbt2qXnnntOISEh+vnnn1W5cmVde+21Hm3r7rvv1t13312YMgAAwFXO46CyceNGtW3bVsHBwdqzZ4/69OmjkJAQffPNN9q7d68++uijoqgTAACUQB7PURk8eLB69uypHTt2qEyZMs72u+66S4sXL/ZqcQAAoGTzOKisWrVKTz75pFv7tddeq4MHD3qlKAAAAKkQQcVut+d76/rt27crNDTUK0UBAABIhQgq99xzj1566SVlZ2dLkmw2m/bu3asXXnhB999/v9cLBAAAJZfHQeXNN9/UqVOnFBYWprNnz+q2225TzZo1VaFCBb366qtFUSMAACihPL7qx+FwaM6cOVq6dKk2bNigU6dOqUmTJmrXrl1R1AcAAEqwQt1HRfrj9vetWrXyZi0AAAAuPD71M2DAAI0fP96t/V//+pcGDhzojZoAAAAkFSKofP311/mOpLRs2VLTp0/3SlEAAABSIYLKsWPH5HA43NqDgoJ09OhRrxQFAAAgFSKo1KxZU7NmzXJrnzlzpq677jqvFAUAACAVYjLt4MGD1a9fPx05ckR33HGHJGnevHl68803NW7cOG/XBwAASjCPg0qvXr2UmZmpV199VS+//LIkqXr16kpOTtbjjz/u9QIBAEDJVajLk59++mk9/fTTOnLkiAIDA1W+fHlv1wUAAFD4+6hI4rN9AABAkfJ4Mu2hQ4f02GOPKSIiQqVKlZK/v7/LAwAAwFs8HlHp0aOH9u7dq+HDh6tKlSqy2WxFURcAAIDnQWXJkiX63//+p8aNGxdBOQAAAP/P41M/kZGRMsYURS0AAAAuPA4q48aN07Bhw7Rnz54iKAcAAOD/eXzq5+GHH9aZM2cUHR2tsmXLqnTp0i7rjx8/7rXiAABAyeZxUOHuswAAoLh4HFS6d+9eFHUAAAC48XiOiiTt2rVL//jHP9S1a1cdPnxY0h8fSrhlyxavFgcAAEo2j4PKokWL1KBBA61YsULffPONTp06JUnasGGDRo4c6fUCAQBAyeVxUBk2bJheeeUVzZkzRwEBAc72O+64QykpKV4tDgAAlGweB5VNmzbpvvvuc2sPCwvT0aNHvVIUAACAVIigEhwcrAMHDri1r1u3Ttdee61XigIAAJAKEVTi4uL0wgsv6ODBg7LZbMrNzdXSpUs1dOhQPf7440VRIwAAKKE8DiqvvfaaateurcjISJ06dUp169ZV69at1bJlS/3jH/8oihoBAEAJ5dF9VIwxOnjwoMaPH68RI0Zo06ZNOnXqlG644QbFxMQUVY0AAKCE8jio1KxZU1u2bFFMTIwiIyOLqi4AAADPTv34+fkpJiZGx44d88rOR40aJZvN5vKoXbu2V7YNAACufB7PURk9erSee+45bd682SsF1KtXTwcOHHA+lixZ4pXtAgCAK5/Hn/Xz+OOP68yZM2rUqJECAgIUGBjost7TT08uVaqUwsPDPS0DAACUAD7/9OQdO3YoIiJCZcqUUYsWLZSUlKRq1arl2zczM1OZmZnO5YyMDK/WAgAArMWjoJKdna1FixZp+PDhqlGjxl/eefPmzTVlyhTVqlVLBw4cUGJiom699VZt3rxZFSpUcOuflJSkxMTEv7xfAABwZbAZY4wnT3A4HFq/fr1XgsqF0tLSFBUVpbfeeku9e/d2W5/fiEpkZKTS09MVFBTk9XqqD/vB69ssantGd/J1CQAAXFJGRoYcDkeB/n57PJn23nvv1bffflvY2i4pODhY119/vXbu3JnvervdrqCgIJcHAAC4enk8RyUmJkYvvfSSli5dqqZNm6pcuXIu6wcMGFDoYk6dOqVdu3bpscceK/Q2AADA1cPjoDJp0iQFBwdrzZo1WrNmjcs6m83mUVAZOnSoOnfurKioKO3fv18jR46Uv7+/unbt6mlZAADgKuRxUNm9e7fXdv7777+ra9euOnbsmEJDQ3XLLbcoJSVFoaGhXtsHAAC4cnkcVLzpiy++8OXuAQCAxXkcVHr16nXJ9R9++GGhiwEAAPgzj4PKiRMnXJazs7O1efNmpaWl6Y477vBaYQAAAB4HlRkzZri15ebm6umnn1Z0dLRXigIAAJAKcR+VfDfi56fBgwfr7bff9sbmAAAAJHkpqEjSrl27dP78eW9tDgAAwPNTP4MHD3ZZNsbowIED+uGHH9S9e3evFQYAAOBxUFm3bp3Lsp+fn0JDQ/Xmm29e9oogAAAAT3gcVBYsWFAUdQAAALjxeI7K7t27tWPHDrf2HTt2aM+ePd6oCQAAQFIhgkqPHj20bNkyt/YVK1aoR48e3qgJAABAUiGCyrp169SqVSu39ptvvlnr16/3Rk0AAACSChFUbDabTp486daenp6unJwcrxQFAAAgFSKotG7dWklJSS6hJCcnR0lJSbrlllu8WhwAACjZPL7qZ8yYMWrdurVq1aqlW2+9VZL0v//9TxkZGZo/f77XCwQAACWXxyMqdevW1caNG/XQQw/p8OHDOnnypB5//HH98ssvql+/flHUCAAASiiPR1QkKSIiQq+99pq3awEAAHDh8YjK5MmTNW3aNLf2adOmaerUqV4pCgAAQCpEUElKSlKlSpXc2sPCwhhlAQAAXuVxUNm7d69q1Kjh1h4VFaW9e/d6pSgAAACpEEElLCxMGzdudGvfsGGDKlas6JWiAAAApEIEla5du2rAgAFasGCBcnJylJOTo/nz5+vZZ59VXFxcUdQIAABKKI+v+nn55Ze1Z88etW3bVqVK/fH03NxcPf7448xRAQAAXuVxUAkICNCXX36pl19+WRs2bFBgYKAaNGigqKiooqgPAACUYIW6j4okhYSEqE2bNvleAQQAAOANHs1RSUtLU3x8vCpVqqTKlSurcuXKqlSpkvr166e0tLQiKhEAAJRUBR5ROX78uFq0aKF9+/apW7duqlOnjiTp559/1pQpUzRv3jwtW7ZM11xzTZEVCwAASpYCB5WXXnpJAQEB2rVrlypXruy2rn379nrppZf09ttve71IAABQMhX41M+3336rN954wy2kSFJ4eLjGjh2rGTNmeLU4AABQshU4qBw4cED16tW76Pr69evr4MGDXikKAABA8iCoVKpUSXv27Lno+t27dyskJMQbNQEAAEjyIKjExsbqxRdfVFZWltu6zMxMDR8+XB06dPBqcQAAoGTzaDJts2bNFBMTo/j4eNWuXVvGGG3dulXvvvuuMjMz9fHHHxdlrQAAoIQpcFCpWrWqli9frmeeeUYJCQkyxkiSbDab7rzzTv3rX/9SZGRkkRUKAABKHo9u+FajRg3NnDlTR48eVUpKilJSUnTkyBHNmjVLNWvW/EuFjB49WjabTQMHDvxL2wEAAFePQt1C/5prrtFNN93ktSJWrVql999/Xw0bNvTaNgEAwJXPoxGVonDq1Cl169ZNH3zwAXe1BQAALnweVOLj49WpUye1a9fusn0zMzOVkZHh8gAAAFevQn96sjd88cUXWrt2rVatWlWg/klJSUpMTCziqgAAgFUUaESlSZMmOnHihKQ/LlM+c+bMX95xamqqnn32WX366acqU6ZMgZ6TkJCg9PR05yM1NfUv1wEAAKyrQEFl69atOn36tCQpMTFRp06d+ss7XrNmjQ4fPqwmTZqoVKlSKlWqlBYtWqTx48erVKlSysnJcXuO3W5XUFCQywMAAFy9CnTqp3HjxurZs6duueUWGWP0xhtvqHz58vn2HTFiRIF23LZtW23atMmlrWfPnqpdu7ZeeOEF+fv7F2g7AADg6lWgoDJlyhSNHDlS33//vWw2m2bOnKlSpdyfarPZChxUKlSooPr167u0lStXThUrVnRrBwAAJVOBgkqtWrX0xRdfSJL8/Pw0b948hYWFFWlhAAAAHl/1k5ubWxR1SJIWLlxYZNsGAABXnkJdnrxr1y6NGzdOW7dulSTVrVtXzz77rKKjo71aHAAAKNk8vuHb7NmzVbduXa1cuVINGzZUw4YNtWLFCtWrV09z5swpihoBAEAJ5fGIyrBhwzRo0CCNHj3arf2FF17QnXfe6bXiAABAyebxiMrWrVvVu3dvt/ZevXrp559/9kpRAAAAUiGCSmhoqNavX+/Wvn79eq4EAgAAXuXxqZ8+ffqob9+++vXXX9WyZUtJ0tKlSzVmzBgNHjzY6wUCAICSy+OgMnz4cFWoUEFvvvmmEhISJEkREREaNWqUBgwY4PUCAQBAyeVxULHZbBo0aJAGDRqkkydPSvrjLrMAAADeVqj7qOQhoAAAgKLk8WRaAACA4kJQAQAAlkVQAQAAluVRUMnOzlbbtm21Y8eOoqoHAADAyaOgUrp0aW3cuLGoagEAAHDh8amfRx99VJMmTSqKWgAAAFx4fHny+fPn9eGHH2ru3Llq2rSpypUr57L+rbfe8lpxAACgZPM4qGzevFlNmjSRJG3fvt1lnc1m805VAAAAKkRQWbBgQVHUAQAA4KbQlyfv3LlTs2fP1tmzZyVJxhivFQUAACAVIqgcO3ZMbdu21fXXX6+77rpLBw4ckCT17t1bQ4YM8XqBAACg5PI4qAwaNEilS5fW3r17VbZsWWf7ww8/rFmzZnm1OAAAULJ5PEflp59+0uzZs1W1alWX9piYGP32229eKwwAAMDjEZXTp0+7jKTkOX78uOx2u1eKAgAAkAoRVG699VZ99NFHzmWbzabc3FyNHTtWbdq08WpxAACgZPP41M/YsWPVtm1brV69WllZWXr++ee1ZcsWHT9+XEuXLi2KGgEAQAnl8YhK/fr1tX37dt1yyy3q0qWLTp8+rb/97W9at26doqOji6JGAABQQnk8oiJJDodDL774ordrAQAAcFGooHLixAlNmjRJW7dulSTVrVtXPXv2VEhIiFeLAwAAJZvHp34WL16s6tWra/z48Tpx4oROnDih8ePHq0aNGlq8eHFR1AgAAEooj0dU4uPj9fDDDys5OVn+/v6SpJycHD3zzDOKj4/Xpk2bvF4kAAAomTweUdm5c6eGDBniDCmS5O/vr8GDB2vnzp1eLQ4AAJRsHgeVJk2aOOem/NnWrVvVqFEjrxQFAAAgFfDUz8aNG51fDxgwQM8++6x27typm2++WZKUkpKiCRMmaPTo0UVTJQAAKJFsxhhzuU5+fn6y2Wy6XFebzaacnJwC7zw5OVnJycnas2ePJKlevXoaMWKEOnbsWKDnZ2RkyOFwKD09XUFBQQXeb0FVH/aD17dZ1PaM7uTrEgAAuCRP/n4XaERl9+7dXinsQlWrVtXo0aMVExMjY4ymTp2qLl26aN26dapXr16R7BMAAFw5ChRUoqKiimTnnTt3dll+9dVXlZycrJSUFIIKAAAo3A3f9u/fryVLlujw4cPKzc11WTdgwIBCFZKTk6Np06bp9OnTatGiRb59MjMzlZmZ6VzOyMgo1L4AAMCVweOgMmXKFD355JMKCAhQxYoVZbPZnOtsNpvHQWXTpk1q0aKFzp07p/Lly2vGjBmqW7duvn2TkpKUmJjoackAAOAKVaDJtH8WGRmpp556SgkJCfLz8/jqZjdZWVnau3ev0tPTNX36dP373//WokWL8g0r+Y2oREZGMpn2T5hMCwCwOq9Ppv2zM2fOKC4uzishRZICAgJUs2ZNSVLTpk21atUq/fOf/9T777/v1tdut8tut3tlvwAAwPo8Thu9e/fWtGnTiqIWSVJubq7LqAkAACi5PB5RSUpK0t13361Zs2apQYMGKl26tMv6t956q8DbSkhIUMeOHVWtWjWdPHlSn332mRYuXKjZs2d7WhYAALgKFSqozJ49W7Vq1ZIkt8m0njh8+LAef/xxHThwQA6HQw0bNtTs2bN15513eloWAAC4CnkcVN588019+OGH6tGjx1/e+aRJk/7yNgAAwNXL4zkqdrtdrVq1KopaAAAAXHgcVJ599lm98847RVELAACAC49P/axcuVLz58/X999/r3r16rlNpv3mm2+8VhwAACjZPA4qwcHB+tvf/lYUtQAAALjwOKhMnjy5KOoAAABw453bywIAABQBj0dUatSoccn7pfz6669/qSAAAIA8HgeVgQMHuixnZ2dr3bp1mjVrlp577jlv1QUAAOB5UHn22WfzbZ8wYYJWr179lwsCAADI47U5Kh07dtTXX3/trc0BAAB4L6hMnz5dISEh3tocAACA56d+brjhBpfJtMYYHTx4UEeOHNG7777r1eIAAEDJ5nFQuffee12W/fz8FBoaqttvv121a9f2Vl0AAACeB5WRI0cWRR0AAABuuOEbAACwrAKPqPj5+V3yRm+SZLPZdP78+b9cFAAAgORBUJkxY8ZF1y1fvlzjx49Xbm6uV4oCAACQPAgqXbp0cWvbtm2bhg0bpv/+97/q1q2bXnrpJa8WBwAASrZCzVHZv3+/+vTpowYNGuj8+fNav369pk6dqqioKG/XBwAASjCPgkp6erpeeOEF1axZU1u2bNG8efP03//+V/Xr1y+q+gAAQAlW4FM/Y8eO1ZgxYxQeHq7PP/8831NBAAAA3mQzxpiCdPTz81NgYKDatWsnf3//i/b75ptvvFbc5WRkZMjhcCg9PV1BQUFe3371YT94fZtFbc/oTr4uAQCAS/Lk73eBR1Qef/zxy16eDAAA4E0FDipTpkwpwjIAAADccWdaAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWT4NKklJSbrxxhtVoUIFhYWF6d5779W2bdt8WRIAALAQnwaVRYsWKT4+XikpKZozZ46ys7PVvn17nT592pdlAQAAiyjwZ/0UhVmzZrksT5kyRWFhYVqzZo1at27to6oAAIBV+DSoXCg9PV2SFBISku/6zMxMZWZmOpczMjKKpS4AAOAblgkqubm5GjhwoFq1aqX69evn2ycpKUmJiYnFXBkAAN5RfdgPvi7BY3tGd/Lp/i1z1U98fLw2b96sL7744qJ9EhISlJ6e7nykpqYWY4UAAKC4WWJEpV+/fvr++++1ePFiVa1a9aL97Ha77HZ7MVYGAAB8yadBxRij/v37a8aMGVq4cKFq1Kjhy3IAAIDF+DSoxMfH67PPPtN3332nChUq6ODBg5Ikh8OhwMBAX5YGAAAswKdzVJKTk5Wenq7bb79dVapUcT6+/PJLX5YFAAAswuenfgAAAC7GMlf9AAAAXIigAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALMunQWXx4sXq3LmzIiIiZLPZ9O233/qyHAAAYDE+DSqnT59Wo0aNNGHCBF+WAQAALKqUL3fesWNHdezY0ZclAAAAC/NpUPFUZmamMjMzncsZGRk+rAYAABS1K2oybVJSkhwOh/MRGRnp65IAAEARuqKCSkJCgtLT052P1NRUX5cEAACK0BV16sdut8tut/u6DAAAUEyuqBEVAABQsvh0ROXUqVPauXOnc3n37t1av369QkJCVK1aNR9WBgAArMCnQWX16tVq06aNc3nw4MGSpO7du2vKlCk+qgoAAFiFT4PK7bffLmOML0sAAAAWxhwVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWZYIKhMmTFD16tVVpkwZNW/eXCtXrvR1SQAAwAJ8HlS+/PJLDR48WCNHjtTatWvVqFEjxcbG6vDhw74uDQAA+JjPg8pbb72lPn36qGfPnqpbt67ee+89lS1bVh9++KGvSwMAAD5Wypc7z8rK0po1a5SQkOBs8/PzU7t27bR8+XK3/pmZmcrMzHQup6enS5IyMjKKpL7czDNFst2iVFTvBQDgr+Pvius2jTGX7evToHL06FHl5OSocuXKLu2VK1fWL7/84tY/KSlJiYmJbu2RkZFFVuOVxjHO1xUAAK4mRfl35eTJk3I4HJfs49Og4qmEhAQNHjzYuZybm6vjx4+rYsWKstlsHm8vIyNDkZGRSk1NVVBQkDdLxV/AcbEmjot1cWysieNyccYYnTx5UhEREZft69OgUqlSJfn7++vQoUMu7YcOHVJ4eLhbf7vdLrvd7tIWHBz8l+sICgrim8iCOC7WxHGxLo6NNXFc8ne5kZQ8Pp1MGxAQoKZNm2revHnOttzcXM2bN08tWrTwYWUAAMAKfH7qZ/DgwerevbuaNWumm266SePGjdPp06fVs2dPX5cGAAB8zOdB5eGHH9aRI0c0YsQIHTx4UI0bN9asWbPcJtgWBbvdrpEjR7qdToJvcVysieNiXRwba+K4eIfNFOTaIAAAAB/w+Q3fAAAALoagAgAALIugAgAALIugAgAALIugAgAALKvEBpUJEyaoevXqKlOmjJo3b66VK1f6uqQSZdSoUbLZbC6P2rVrO9efO3dO8fHxqlixosqXL6/777/f7Q7G8I7Fixerc+fOioiIkM1m07fffuuy3hijESNGqEqVKgoMDFS7du20Y8cOlz7Hjx9Xt27dFBQUpODgYPXu3VunTp0qxldx9bnccenRo4fbz1CHDh1c+nBcvC8pKUk33nijKlSooLCwMN17773atm2bS5+C/P7au3evOnXqpLJlyyosLEzPPfeczp8/X5wv5YpRIoPKl19+qcGDB2vkyJFau3atGjVqpNjYWB0+fNjXpZUo9erV04EDB5yPJUuWONcNGjRI//3vfzVt2jQtWrRI+/fv19/+9jcfVnv1On36tBo1aqQJEybku37s2LEaP3683nvvPa1YsULlypVTbGyszp075+zTrVs3bdmyRXPmzNH333+vxYsXq2/fvsX1Eq5KlzsuktShQweXn6HPP//cZT3HxfsWLVqk+Ph4paSkaM6cOcrOzlb79u11+vRpZ5/L/f7KyclRp06dlJWVpWXLlmnq1KmaMmWKRowY4YuXZH2mBLrppptMfHy8czknJ8dERESYpKQkH1ZVsowcOdI0atQo33VpaWmmdOnSZtq0ac62rVu3Gklm+fLlxVRhySTJzJgxw7mcm5trwsPDzeuvv+5sS0tLM3a73Xz++efGGGN+/vlnI8msWrXK2WfmzJnGZrOZffv2FVvtV7MLj4sxxnTv3t106dLlos/huBSPw4cPG0lm0aJFxpiC/f768ccfjZ+fnzl48KCzT3JysgkKCjKZmZnF+wKuACVuRCUrK0tr1qxRu3btnG1+fn5q166dli9f7sPKSp4dO3YoIiJC1113nbp166a9e/dKktasWaPs7GyXY1S7dm1Vq1aNY1TMdu/erYMHD7ocC4fDoebNmzuPxfLlyxUcHKxmzZo5+7Rr105+fn5asWJFsddckixcuFBhYWGqVauWnn76aR07dsy5juNSPNLT0yVJISEhkgr2+2v58uVq0KCByx3YY2NjlZGRoS1bthRj9VeGEhdUjh49qpycHLdb9FeuXFkHDx70UVUlT/PmzTVlyhTNmjVLycnJ2r17t2699VadPHlSBw8eVEBAgNsnY3OMil/e+32pn5eDBw8qLCzMZX2pUqUUEhLC8SpCHTp00EcffaR58+ZpzJgxWrRokTp27KicnBxJHJfikJubq4EDB6pVq1aqX7++JBXo99fBgwfz/ZnKWwdXPv+sH5RMHTt2dH7dsGFDNW/eXFFRUfrqq68UGBjow8qAK0NcXJzz6wYNGqhhw4aKjo7WwoUL1bZtWx9WVnLEx8dr8+bNLvPr4H0lbkSlUqVK8vf3d5uBfejQIYWHh/uoKgQHB+v666/Xzp07FR4erqysLKWlpbn04RgVv7z3+1I/L+Hh4W4T0c+fP6/jx49zvIrRddddp0qVKmnnzp2SOC5FrV+/fvr++++1YMECVa1a1dlekN9f4eHh+f5M5a2DqxIXVAICAtS0aVPNmzfP2Zabm6t58+apRYsWPqysZDt16pR27dqlKlWqqGnTpipdurTLMdq2bZv27t3LMSpmNWrUUHh4uMuxyMjI0IoVK5zHokWLFkpLS9OaNWucfebPn6/c3Fw1b9682GsuqX7//XcdO3ZMVapUkcRxKSrGGPXr108zZszQ/PnzVaNGDZf1Bfn91aJFC23atMklSM6ZM0dBQUGqW7du8byQK4mvZ/P6whdffGHsdruZMmWK+fnnn03fvn1NcHCwywxsFK0hQ4aYhQsXmt27d5ulS5eadu3amUqVKpnDhw8bY4x56qmnTLVq1cz8+fPN6tWrTYsWLUyLFi18XPXV6eTJk2bdunVm3bp1RpJ56623zLp168xvv/1mjDFm9OjRJjg42Hz33Xdm48aNpkuXLqZGjRrm7Nmzzm106NDB3HDDDWbFihVmyZIlJiYmxnTt2tVXL+mqcKnjcvLkSTN06FCzfPlys3v3bjN37lzTpEkTExMTY86dO+fcBsfF+55++mnjcDjMwoULzYEDB5yPM2fOOPtc7vfX+fPnTf369U379u3N+vXrzaxZs0xoaKhJSEjwxUuyvBIZVIwx5p133jHVqlUzAQEB5qabbjIpKSm+LqlEefjhh02VKlVMQECAufbaa83DDz9sdu7c6Vx/9uxZ88wzz5hrrrnGlC1b1tx3333mwIEDPqz46rVgwQIjye3RvXt3Y8wflygPHz7cVK5c2djtdtO2bVuzbds2l20cO3bMdO3a1ZQvX94EBQWZnj17mpMnT/rg1Vw9LnVczpw5Y9q3b29CQ0NN6dKlTVRUlOnTp4/bf7Y4Lt6X3zGRZCZPnuzsU5DfX3v27DEdO3Y0gYGBplKlSmbIkCEmOzu7mF/NlcFmjDHFPYoDAABQECVujgoAALhyEFQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBl/R/buHohe8DxSwAAAABJRU5ErkJggg==", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:35.077119\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:43.589078\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -869,8 +864,8 @@ }, { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:35.489021\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:44.297185\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -880,8 +875,8 @@ }, { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:35.931834\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:44.963548\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -891,8 +886,8 @@ }, { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:36.336750\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:45.397387\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -902,8 +897,8 @@ }, { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:36.744500\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjIAAAGzCAYAAAA1yP25AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy89olMNAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA6HklEQVR4nO3deVyU9eL+/2tAQVwYxAVEcUnJNfWEJyW3NM2lTI94XOokmmUnMReykjypmIVb6sfS+pwybDML0/q0qCmZHs2lSLPU3E1LwdwAMRHl/v7Rj/mdkW1uGhhuej0fj3novO/tmhuQy3uZsRmGYQgAAMCCvDwdAAAAoLgoMgAAwLIoMgAAwLIoMgAAwLIoMgAAwLIoMgAAwLIoMgAAwLIoMgAAwLIoMgAAwLIoMvjTa9iwoUaMGOHpGOXe3LlzddNNN8nb21tt27b1dBxTpk+fLpvNprNnz3o6SqGsvI+B4qLIoFxZtmyZbDabvvnmm3yn33HHHWrVqtUf3s5nn32m6dOn/+H1/Fl8/vnnevLJJ9WxY0clJCTo+eef93SkMs8wDL311lvq0qWLAgICVLlyZd1yyy2aMWOGMjMz88xf0D5u2LChbDZbvo/evXuX9svK1/PPP68PP/zQ0zFgURU8HQDwtAMHDsjLy1yn/+yzz7R48WLKjIu++OILeXl5aenSpfLx8fF0nDLv+vXruu+++/T++++rc+fOmj59uipXrqz//Oc/iouLU2JiojZs2KCgoCDHMoXt47Zt2+rxxx/Ps52QkJASfy2ueP755zVo0CANGDDA01FgQRQZ/On5+vp6OoJpmZmZqlKliqdjuOzMmTPy8/OjxBTh8uXLqly5subMmaP3339fkyZN0ty5cx3TR48ercGDB2vAgAEaMWKE1qxZ45hW2D6uW7eu/vGPf5TKawBKG6eW8Kd34zUy2dnZiouLU1hYmCpVqqQaNWqoU6dOWr9+vSRpxIgRWrx4sSQ5HabPlZmZqccff1yhoaHy9fVV06ZNNW/ePN34QfO//fabxo0bp5o1a6patWq699579csvv8hmszkd6cm9PmPfvn267777VL16dXXq1EmStGfPHo0YMUI33XSTKlWqpODgYD344IM6d+6c07Zy13Hw4EH94x//kN1uV61atfTMM8/IMAydPHlS/fv3l7+/v4KDg/XCCy+4tO+uXbumZ599Vo0bN5avr68aNmyop59+WllZWY55bDabEhISlJmZ6dhXy5YtK3Cduaf/9u3bp27duqly5cqqW7eu5syZ4zRf7mnE48ePO41/+eWXstls+vLLL/Osc8+ePeratasqV66sJk2aaOXKlZKkTZs2qX379vLz81PTpk21YcOGfLOdPXtWgwcPlr+/v2rUqKHx48frypUreeZ7++23FR4eLj8/PwUGBmro0KE6efJkvq8zOTlZXbp0UeXKlfX000/rt99+09y5c3XzzTcrPj4+z7r79eunqKgorV27Vtu3by/WPr7RvHnzZLPZ9NNPP+WZFhsbKx8fH124cMExtmPHDvXu3Vt2u12VK1dW165dtXXrVqflcr/nDh8+rBEjRiggIEB2u10jR47U5cuXHfPZbDZlZmbqjTfecGTP/XnMyMjQhAkT1LBhQ/n6+qp27drq2bOnvv32W5dfG8o/igzKpbS0NJ09ezbPIzs7u8hlp0+frri4OHXr1k0vvfSSpkyZovr16zv+8XzkkUfUs2dPSdJbb73leEi/X9dw7733asGCBerdu7fmz5+vpk2b6oknnlBMTIzTdkaMGKEXX3xRffv21ezZs+Xn56e77767wFx///vfdfnyZT3//PN6+OGHJUnr16/X0aNHNXLkSL344osaOnSoVqxYob59++YpTpI0ZMgQ5eTkaNasWWrfvr1mzpyphQsXqmfPnqpbt65mz56tJk2aaNKkSdq8eXOR++qhhx7S1KlTdeutt2rBggXq2rWr4uPjNXToUMc8b731ljp37ixfX1/HvurSpUuh671w4YJ69+6tNm3a6IUXXlCzZs301FNPOR2BMOvChQu655571L59e82ZM0e+vr4aOnSo3nvvPQ0dOlR9+/bVrFmzlJmZqUGDBikjIyPPOgYPHqwrV64oPj5effv21aJFizR69GineZ577jkNHz5cYWFhmj9/viZMmKCkpCR16dJFFy9edJr33Llz6tOnj9q2bauFCxeqW7du2rJliy5cuKD77rtPFSrkf9B8+PDhkqRPPvlEUtH7ODs7O9+fh99++83xumw2m95///0823r//fd11113qXr16pJ+P4XVpUsXpaena9q0aXr++ed18eJFde/eXTt37sx3n2VkZCg+Pl6DBw/WsmXLFBcX55j+1ltvydfXV507d3Zkf+SRRyRJ//znP/Xyyy8rMjJSS5Ys0aRJk+Tn56f9+/fnu1/wJ2UA5UhCQoIhqdBHy5YtnZZp0KCBERUV5Xjepk0b4+677y50O9HR0UZ+Pz4ffvihIcmYOXOm0/igQYMMm81mHD582DAMw0hOTjYkGRMmTHCab8SIEYYkY9q0aY6xadOmGZKMYcOG5dne5cuX84y9++67hiRj8+bNedYxevRox9i1a9eMevXqGTabzZg1a5Zj/MKFC4afn5/TPsnP7t27DUnGQw895DQ+adIkQ5LxxRdfOMaioqKMKlWqFLq+XF27djUkGW+++aZjLCsrywgODjYiIyMdY7lf62PHjjktv3HjRkOSsXHjxjzrXL58uWPsxx9/NCQZXl5exvbt2x3j69atMyQZCQkJjrHc/Xfvvfc6bWvMmDGGJOO7774zDMMwjh8/bnh7exvPPfec03zff/+9UaFCBafx3EyvvPKK07wLFy40JBmrV68ucB+dP3/ekGQMHDjQMVbQPm7QoEGBPwvx8fGO+SIiIozw8HCnZXfu3On0tcjJyTHCwsKMXr16GTk5OY75Ll++bDRq1Mjo2bNnnn324IMPOq3zb3/7m1GjRg2nsSpVquT7/Wa3243o6OgC9wNgGIbBERmUS4sXL9b69evzPFq3bl3ksgEBAdq7d68OHTpkerufffaZvL29NW7cOKfxxx9/XIZhOI4orF27VpI0ZswYp/kee+yxAtf9z3/+M8+Yn5+f4+9XrlzR2bNn1aFDB0nK9/D7Qw895Pi7t7e32rVrJ8MwNGrUKMd4QECAmjZtqqNHjxaYRfr9tUrKc6Qp96LSTz/9tNDlC1O1alWnazp8fHx02223FZmpqHX+95Gipk2bKiAgQM2bN1f79u0d47l/z29b0dHRTs9zv165+2LVqlXKycnR4MGDnY58BAcHKywsTBs3bnRa3tfXVyNHjnQayz0SVK1atQJfS+609PT0wl/0f72m/H4ehg0b5phnyJAhSk5O1pEjRxxj7733nnx9fdW/f39J0u7du3Xo0CHdd999OnfunOP1ZWZm6s4779TmzZuVk5PjtO0bv287d+6sc+fOuZQ9ICBAO3bs0KlTp1x6nfhz4mJflEu33Xab2rVrl2e8evXqRb4XyIwZM9S/f3/dfPPNatWqlXr37q0HHnjApRL0008/KSQkJM8voebNmzum5/7p5eWlRo0aOc3XpEmTAtd947ySdP78ecXFxWnFihU6c+aM07S0tLQ889evX9/pud1uV6VKlVSzZs084zdeZ3Oj3NdwY+bg4GAFBATke72Fq+rVq+d03ZH0+9duz549bl2n3W5XaGhonjFJTteE5AoLC3N63rhxY3l5eTmu0zl06JAMw8gzX66KFSs6Pa9bt26ei3Nzv3fyO7WVy5Wy899q1qypHj16FDrP3//+d8XExOi9997T008/LcMwlJiYqD59+sjf31+SHOU+KiqqwPWkpaU5TkNJeb/ncqdduHDBsd6CzJkzR1FRUQoNDVV4eLj69u2r4cOH66abbip0Ofy5UGSAG3Tp0kVHjhzRRx99pM8//1yvvfaaFixYoFdeecXpiEZp+++jL7kGDx6sr776Sk888YTatm2rqlWrKicnR717987zP2Pp96MwroxJyvcam/zcWA7cwZVMBW33+vXrptb5R17/jRlycnJks9m0Zs2afNdbtWpVp+f5fU1zS++ePXsKvB05t9C1aNGiyIyuCgkJUefOnfX+++/r6aef1vbt23XixAnNnj3bMU/u99TcuXMLfMO9G1/jH9m/gwcPVufOnbV69Wp9/vnnmjt3rmbPnq1Vq1apT58+Lr4ylHcUGSAfgYGBGjlypEaOHKlLly6pS5cumj59uqPIFPRLtEGDBtqwYYMyMjKc/rf8448/Oqbn/pmTk6Njx445/e/98OHDLme8cOGCkpKSFBcXp6lTpzrGi3NKrDhyX8OhQ4ccv3wlKTU1VRcvXnS81pKS+z/7Gy+g/SNHgopy6NAhpyNjhw8fVk5Ojho2bCjp9yM0hmGoUaNGuvnmm4u1jU6dOikgIEDLly/XlClT8i0Cb775piTpnnvuKdY2CjJkyBCNGTNGBw4c0HvvvafKlSurX79+jumNGzeWJPn7+xd5hMeMwspwnTp1NGbMGI0ZM0ZnzpzRrbfequeee44iAweukQFucOMplapVq6pJkyZOtxTnvofLjb9E+/btq+vXr+ull15yGl+wYIFsNpvjH99evXpJkpYsWeI034svvuhyztxfcDf+z3bhwoUur+OP6Nu3b77bmz9/viQVegeWO+T+Uv3vu6uuX7+uf//73yW2zdzb7nPlfr1yv64DBw6Ut7e34uLi8nxdDMMo8nSdJFWuXFmTJk3SgQMHNGXKlDzTP/30Uy1btky9evVyXA/lLpGRkfL29ta7776rxMRE3XPPPU7vVxQeHq7GjRtr3rx5unTpUp7lf/3112Jtt0qVKnl+lq5fv57n9Gjt2rUVEhLi9LMIcEQGuEGLFi10xx13KDw8XIGBgfrmm2+0cuVKjR071jFPeHi4JGncuHHq1auXvL29NXToUPXr10/dunXTlClTdPz4cbVp00aff/65PvroI02YMMHxyzc8PFyRkZFauHChzp07pw4dOmjTpk06ePCgJNdO1/j7+6tLly6aM2eOsrOzVbduXX3++ec6duxYCeyVvNq0aaOoqCj9+9//1sWLF9W1a1ft3LlTb7zxhgYMGKBu3bqV6PZbtmypDh06KDY2VufPn1dgYKBWrFiha9euldg2jx07pnvvvVe9e/fWtm3b9Pbbb+u+++5TmzZtJP1ermbOnKnY2FgdP35cAwYMULVq1XTs2DGtXr1ao0eP1qRJk4rczuTJk7Vr1y7Nnj1b27ZtU2RkpPz8/LRlyxa9/fbbat68ud544w2Xc//yyy96++2384xXrVrV6fRV7dq11a1bN82fP18ZGRkaMmSI0/xeXl567bXX1KdPH7Vs2VIjR45U3bp19csvv2jjxo3y9/fXxx9/7HKuXOHh4dqwYYPmz5+vkJAQNWrUSE2bNlW9evU0aNAgtWnTRlWrVtWGDRv09ddfu/w+R/iT8MzNUkDJyL0l9+uvv853eteuXYu8/XrmzJnGbbfdZgQEBBh+fn5Gs2bNjOeee864evWqY55r164Zjz32mFGrVi3DZrM53YqdkZFhTJw40QgJCTEqVqxohIWFGXPnznW6XdUwDCMzM9OIjo42AgMDjapVqxoDBgwwDhw4YEhyuh069zbWX3/9Nc/r+fnnn42//e1vRkBAgGG3242///3vxqlTpwq8hfvGdRR0y25++yk/2dnZRlxcnNGoUSOjYsWKRmhoqBEbG2tcuXLFpe3kp6BtR0VFGQ0aNHAaO3LkiNGjRw/D19fXCAoKMp5++mlj/fr1+d5+nd86GzRokO+t9pKcbvvN3X/79u0zBg0aZFSrVs2oXr26MXbsWOO3337Ls/wHH3xgdOrUyahSpYpRpUoVo1mzZkZ0dLRx4MCBIjPlun79upGQkGB07NjR8Pf3NypVqmS0bNnSiIuLMy5dupTv/jF7+/WN+9MwDOPVV181JBnVqlXL97UZhmHs2rXLGDhwoFGjRg3D19fXaNCggTF48GAjKSkpzz678Xsuv9vmf/zxR6NLly6Gn5+fIcmIiooysrKyjCeeeMJo06aNUa1aNaNKlSpGmzZtjCVLlhS4z/DnZDMMF6/oA1Didu/erb/85S96++23df/993s6DgCUeVwjA3hI7ruq/reFCxfKy8uryHe+BQD8jmtkAA+ZM2eOkpOT1a1bN1WoUEFr1qzRmjVrNHr06DzvbQIAyB+nlgAPWb9+veLi4rRv3z5dunRJ9evX1wMPPKApU6YU+Bk7AABnFBkAAGBZXCMDAAAsiyIDAAAsq9yfiM/JydGpU6dUrVq1EvlMGAAA4H6GYSgjI0MhISHy8ir4uEu5LzKnTp3iDhAAACzq5MmTqlevXoHTy32Ryf3gvpMnTxb5kfEAAKBsSE9PV2hoqNMH8Oan3BeZ3NNJ/v7+FBkAACymqMtCuNgXAABYFkUGAABYFkUGAABYFkUGAABYFkUGAABYFkUGAABYFkUGAABYFkUGAABYFkUGAABYFkUGAABYlkeLzPTp02Wz2ZwezZo1c0y/cuWKoqOjVaNGDVWtWlWRkZFKTU31YGIAAFCWePyITMuWLXX69GnHY8uWLY5pEydO1Mcff6zExERt2rRJp06d0sCBAz2YFgAAlCUe/9DIChUqKDg4OM94Wlqali5dquXLl6t79+6SpISEBDVv3lzbt29Xhw4dSjsqAAAoYzx+RObQoUMKCQnRTTfdpPvvv18nTpyQJCUnJys7O1s9evRwzNusWTPVr19f27ZtK3B9WVlZSk9Pd3oAAIDyyaNHZNq3b69ly5apadOmOn36tOLi4tS5c2f98MMPSklJkY+PjwICApyWCQoKUkpKSoHrjI+PV1xcXAknB1BWNJz8qacjmHZ81t2ejgCUGx4tMn369HH8vXXr1mrfvr0aNGig999/X35+fsVaZ2xsrGJiYhzP09PTFRoa+oezAgCAssfjp5b+W0BAgG6++WYdPnxYwcHBunr1qi5evOg0T2pqar7X1OTy9fWVv7+/0wMAAJRPZarIXLp0SUeOHFGdOnUUHh6uihUrKikpyTH9wIEDOnHihCIiIjyYEgAAlBUePbU0adIk9evXTw0aNNCpU6c0bdo0eXt7a9iwYbLb7Ro1apRiYmIUGBgof39/PfbYY4qIiOCOJQAAIMnDRebnn3/WsGHDdO7cOdWqVUudOnXS9u3bVatWLUnSggUL5OXlpcjISGVlZalXr15asmSJJyMDAIAyxGYYhuHpECUpPT1ddrtdaWlpXC8DlEPctQSUT67+/i5T18gAAACYQZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWVWaKzKxZs2Sz2TRhwgTH2JUrVxQdHa0aNWqoatWqioyMVGpqqudCAgCAMqVMFJmvv/5a//u//6vWrVs7jU+cOFEff/yxEhMTtWnTJp06dUoDBw70UEoAAFDWeLzIXLp0Sffff79effVVVa9e3TGelpampUuXav78+erevbvCw8OVkJCgr776Stu3b/dgYgAAUFZ4vMhER0fr7rvvVo8ePZzGk5OTlZ2d7TTerFkz1a9fX9u2bStwfVlZWUpPT3d6AACA8qmCJze+YsUKffvtt/r666/zTEtJSZGPj48CAgKcxoOCgpSSklLgOuPj4xUXF+fuqAAAoAzy2BGZkydPavz48XrnnXdUqVIlt603NjZWaWlpjsfJkyfdtm4AAFC2eKzIJCcn68yZM7r11ltVoUIFVahQQZs2bdKiRYtUoUIFBQUF6erVq7p48aLTcqmpqQoODi5wvb6+vvL393d6AACA8sljp5buvPNOff/9905jI0eOVLNmzfTUU08pNDRUFStWVFJSkiIjIyVJBw4c0IkTJxQREeGJyAAAoIzxWJGpVq2aWrVq5TRWpUoV1ahRwzE+atQoxcTEKDAwUP7+/nrssccUERGhDh06eCIyAAAoYzx6sW9RFixYIC8vL0VGRiorK0u9evXSkiVLPB0LAACUETbDMAxPhyhJ6enpstvtSktL43oZoBxqOPlTT0cw7fisuz0dASjzXP397fH3kQEAACguigwAALAsigwAALAsigwAALAsigwAALAsigwAALAsigwAALAsigwAALAsigwAALAs00Xm22+/dfqwx48++kgDBgzQ008/ratXr7o1HAAAQGFMF5lHHnlEBw8elCQdPXpUQ4cOVeXKlZWYmKgnn3zS7QEBAAAKYrrIHDx4UG3btpUkJSYmqkuXLlq+fLmWLVumDz74wN35AAAACmS6yBiGoZycHEnShg0b1LdvX0lSaGiozp496950AAAAhTBdZNq1a6eZM2fqrbfe0qZNm3T33b9/iuuxY8cUFBTk9oAAAAAFMV1kFi5cqG+//VZjx47VlClT1KRJE0nSypUrdfvtt7s9IAAAQEEqmF2gdevWTnct5Zo7d668vb3dEgoAAMAVxXofmYsXL+q1115TbGyszp8/L0nat2+fzpw549ZwAAAAhTF9RGbPnj268847FRAQoOPHj+vhhx9WYGCgVq1apRMnTujNN98siZwAAAB5mD4iExMTo5EjR+rQoUOqVKmSY7xv377avHmzW8MBAAAUxnSR+frrr/XII4/kGa9bt65SUlLcEgoAAMAVpouMr6+v0tPT84wfPHhQtWrVcksoAAAAV5guMvfee69mzJih7OxsSZLNZtOJEyf01FNPKTIy0u0BAQAACmK6yLzwwgu6dOmSateurd9++01du3ZVkyZNVK1aNT333HMlkREAACBfpu9astvtWr9+vbZu3arvvvtOly5d0q233qoePXqURD4AAIACmS4yuTp27KiOHTu6MwsAAIAppk8tjRs3TosWLcoz/tJLL2nChAnuyAQAAOAS00Xmgw8+yPdIzO23366VK1e6JRQAAIArTBeZc+fOyW635xn39/fX2bNn3RIKAADAFaaLTJMmTbR27do842vWrNFNN93kllAAAACuMH2xb0xMjMaOHatff/1V3bt3lyQlJSXphRde0MKFC92dDwAAoECmi8yDDz6orKwsPffcc3r22WclSQ0bNtTLL7+s4cOHuz0gAABAQYp1+/Wjjz6qRx99VL/++qv8/PxUtWpVd+cCAAAoUrHfR0YSn60EAAA8yvTFvqmpqXrggQcUEhKiChUqyNvb2+kBAABQWkwfkRkxYoROnDihZ555RnXq1JHNZiuJXAAAAEUyXWS2bNmi//znP2rbtm0JxAEAAHCd6VNLoaGhMgyjJLIAAACYYrrILFy4UJMnT9bx48dLIA4AAIDrTJ9aGjJkiC5fvqzGjRurcuXKqlixotP08+fPuy0cAABAYUwXGd69FwAAlBWmi0xUVFRJ5AAAADDN9DUyknTkyBH961//0rBhw3TmzBlJv39o5N69e90aDgAAoDCmi8ymTZt0yy23aMeOHVq1apUuXbokSfruu+80bdo0twcEAAAoiOkiM3nyZM2cOVPr16+Xj4+PY7x79+7avn27W8MBAAAUxnSR+f777/W3v/0tz3jt2rV19uxZt4QCAABwhekiExAQoNOnT+cZ37Vrl+rWreuWUAAAAK4wXWSGDh2qp556SikpKbLZbMrJydHWrVs1adIkDR8+vCQyAgAA5Mt0kXn++efVrFkzhYaG6tKlS2rRooW6dOmi22+/Xf/6179KIiMAAEC+TL2PjGEYSklJ0aJFizR16lR9//33unTpkv7yl78oLCyspDICAADky3SRadKkifbu3auwsDCFhoaWVC4AAIAimTq15OXlpbCwMJ07d66k8gAAALjM9DUys2bN0hNPPKEffvihJPIAAAC4zPRnLQ0fPlyXL19WmzZt5OPjIz8/P6fpfPo1AAAoLXz6NQAAsCxTRSY7O1ubNm3SM888o0aNGpVUJgAAAJeYukamYsWK+uCDD0oqCwAAgCmmL/YdMGCAPvzwwxKIAgAAYI7pa2TCwsI0Y8YMbd26VeHh4apSpYrT9HHjxrktHAAAQGFMF5mlS5cqICBAycnJSk5Odppms9koMgAAoNSYPrV07NixAh9Hjx41ta6XX35ZrVu3lr+/v/z9/RUREaE1a9Y4pl+5ckXR0dGqUaOGqlatqsjISKWmppqNDAAAyinTRcad6tWrp1mzZik5OVnffPONunfvrv79+2vv3r2SpIkTJ+rjjz9WYmKiNm3apFOnTmngwIGejAwAAMoQm2EYhpkFHnzwwUKnv/76638oUGBgoObOnatBgwapVq1aWr58uQYNGiRJ+vHHH9W8eXNt27ZNHTp0cGl96enpstvtSktLk7+//x/KBqDsaTj5U09HMO34rLs9HQEo81z9/W36GpkLFy44Pc/OztYPP/ygixcvqnv37uaT/n+uX7+uxMREZWZmKiIiQsnJycrOzlaPHj0c8zRr1kz169cvtMhkZWUpKyvL8Tw9Pb3YmQAAQNlmusisXr06z1hOTo4effRRNW7c2HSA77//XhEREbpy5YqqVq2q1atXq0WLFtq9e7d8fHwUEBDgNH9QUJBSUlIKXF98fLzi4uJM5wAAANbjlmtkvLy8FBMTowULFphetmnTptq9e7d27NihRx99VFFRUdq3b1+xs8TGxiotLc3xOHnyZLHXBQAAyjbTR2QKcuTIEV27ds30cj4+PmrSpIkkKTw8XF9//bX+53/+R0OGDNHVq1d18eJFp6MyqampCg4OLnB9vr6+8vX1NZ0DAABYj+kiExMT4/TcMAydPn1an376qaKiov5woJycHGVlZSk8PFwVK1ZUUlKSIiMjJUkHDhzQiRMnFBER8Ye3AwAArM90kdm1a5fTcy8vL9WqVUsvvPBCkXc03Sg2NlZ9+vRR/fr1lZGRoeXLl+vLL7/UunXrZLfbNWrUKMXExCgwMFD+/v567LHHFBER4fIdSwAAoHwzXWQ2btzoto2fOXNGw4cP1+nTp2W329W6dWutW7dOPXv2lCQtWLBAXl5eioyMVFZWlnr16qUlS5a4bfsAAMDaTL+PzLFjx3Tt2jWFhYU5jR86dEgVK1ZUw4YN3ZnvD+N9ZIDyjfeRAconV39/m75racSIEfrqq6/yjO/YsUMjRowwuzoAAIBiM11kdu3apY4dO+YZ79Chg3bv3u2OTAAAAC4xXWRsNpsyMjLyjKelpen69etuCQUAAOAK00WmS5cuio+Pdyot169fV3x8vDp16uTWcAAAAIUxfdfS7Nmz1aVLFzVt2lSdO3eWJP3nP/9Renq6vvjiC7cHBAAAKIjpIzItWrTQnj17NHjwYJ05c0YZGRkaPny4fvzxR7Vq1aokMgIAAOSrWB9REBISoueff97dWQAAAEwxfUQmISFBiYmJecYTExP1xhtvuCUUAACAK0wXmfj4eNWsWTPPeO3atTlKAwAASpXpInPixAk1atQoz3iDBg104sQJt4QCAABwhekiU7t2be3ZsyfP+HfffacaNWq4JRQAAIArTBeZYcOGady4cdq4caOuX7+u69ev64svvtD48eM1dOjQksgIAACQL9N3LT377LM6fvy47rzzTlWo8PviOTk5Gj58ONfIAACAUmW6yPj4+Oi9997Ts88+q++++05+fn665ZZb1KBBg5LIBwAAUKBivY+MJAUGBqpbt2753sEEAABQGkxdI3Px4kVFR0erZs2aCgoKUlBQkGrWrKmxY8fq4sWLJRQRAAAgfy4fkTl//rwiIiL0yy+/6P7771fz5s0lSfv27dOyZcuUlJSkr776StWrVy+xsAAAAP/N5SIzY8YM+fj46MiRIwoKCsoz7a677tKMGTO0YMECt4cEAADIj8unlj788EPNmzcvT4mRpODgYM2ZM0erV692azgAAIDCuFxkTp8+rZYtWxY4vVWrVkpJSXFLKAAAAFe4XGRq1qyp48ePFzj92LFjCgwMdEcmAAAAl7hcZHr16qUpU6bo6tWreaZlZWXpmWeeUe/evd0aDgAAoDCmLvZt166dwsLCFB0drWbNmskwDO3fv19LlixRVlaW3nrrrZLMCgAA4MTlIlOvXj1t27ZNY8aMUWxsrAzDkCTZbDb17NlTL730kkJDQ0ssKAAAwI1MvbNvo0aNtGbNGl24cEGHDh2SJDVp0oRrYwAAgEcU6yMKqlevrttuu83dWQAAAEwx9REFAAAAZQlFBgAAWBZFBgAAWJZLRebWW2/VhQsXJP1+G/bly5dLNBQAAIArXCoy+/fvV2ZmpiQpLi5Oly5dKtFQAAAArnDprqW2bdtq5MiR6tSpkwzD0Lx581S1atV85506dapbAwIAABTEpSKzbNkyTZs2TZ988olsNpvWrFmjChXyLmqz2SgyAACg1LhUZJo2baoVK1ZIkry8vJSUlKTatWuXaDAAAICimH5DvJycnJLIAQAAYFqx3tn3yJEjWrhwofbv3y9JatGihcaPH6/GjRu7NRwAAEBhTL+PzLp169SiRQvt3LlTrVu3VuvWrbVjxw61bNlS69evL4mMAAAA+TJ9RGby5MmaOHGiZs2alWf8qaeeUs+ePd0WDgAAoDCmj8js379fo0aNyjP+4IMPat++fW4JBQAA4ArTRaZWrVravXt3nvHdu3dzJxMAAChVpk8tPfzwwxo9erSOHj2q22+/XZK0detWzZ49WzExMW4PCAAAUBDTReaZZ55RtWrV9MILLyg2NlaSFBISounTp2vcuHFuDwgAAFAQ00XGZrNp4sSJmjhxojIyMiRJ1apVc3swAACAohTrfWRyUWAAAIAnmb7YFwAAoKygyAAAAMuiyAAAAMsyVWSys7N155136tChQyWVBwAAwGWmikzFihW1Z8+eksoCAABgiulTS//4xz+0dOnSksgCAABgiunbr69du6bXX39dGzZsUHh4uKpUqeI0ff78+W4LBwAAUBjTReaHH37QrbfeKkk6ePCg0zSbzeaeVAAAAC4wXWQ2btxYEjkAAABMK/bt14cPH9a6dev022+/SZIMw3BbKAAAAFeYLjLnzp3TnXfeqZtvvll9+/bV6dOnJUmjRo3S448/7vaAAAAABTFdZCZOnKiKFSvqxIkTqly5smN8yJAhWrt2rVvDAQAAFMb0NTKff/651q1bp3r16jmNh4WF6aeffnJbMAAAgKKYPiKTmZnpdCQm1/nz5+Xr6+uWUAAAAK4wXWQ6d+6sN9980/HcZrMpJydHc+bMUbdu3dwaDgAAoDCmTy3NmTNHd955p7755htdvXpVTz75pPbu3avz589r69atJZERAAAgX6aPyLRq1UoHDx5Up06d1L9/f2VmZmrgwIHatWuXGjdubGpd8fHx+utf/6pq1aqpdu3aGjBggA4cOOA0z5UrVxQdHa0aNWqoatWqioyMVGpqqtnYAACgHDJ9REaS7Ha7pkyZ8oc3vmnTJkVHR+uvf/2rrl27pqefflp33XWX9u3b5/jog4kTJ+rTTz9VYmKi7Ha7xo4dq4EDB3L0BwAAFK/IXLhwQUuXLtX+/fslSS1atNDIkSMVGBhoaj033q69bNky1a5dW8nJyerSpYvS0tK0dOlSLV++XN27d5ckJSQkqHnz5tq+fbs6dOiQZ51ZWVnKyspyPE9PTzf78gAAgEWYPrW0efNmNWzYUIsWLdKFCxd04cIFLVq0SI0aNdLmzZv/UJi0tDRJchSi5ORkZWdnq0ePHo55mjVrpvr162vbtm35riM+Pl52u93xCA0N/UOZAABA2WW6yERHR2vIkCE6duyYVq1apVWrVuno0aMaOnSooqOjix0kJydHEyZMUMeOHdWqVStJUkpKinx8fBQQEOA0b1BQkFJSUvJdT2xsrNLS0hyPkydPFjsTAAAo20yfWjp8+LBWrlwpb29vx5i3t7diYmKcbss2Kzo6Wj/88IO2bNlS7HVIkq+vL+9nAwDAn4TpIzK33nqr49qY/7Z//361adOmWCHGjh2rTz75RBs3bnR6x+Dg4GBdvXpVFy9edJo/NTVVwcHBxdoWAAAoP1w6IrNnzx7H38eNG6fx48fr8OHDjottt2/frsWLF2vWrFmmNm4Yhh577DGtXr1aX375pRo1auQ0PTw8XBUrVlRSUpIiIyMlSQcOHNCJEycUERFhalsAAKD8sRmGYRQ1k5eXl2w2m4qa1Waz6fr16y5vfMyYMVq+fLk++ugjNW3a1DFut9vl5+cnSXr00Uf12WefadmyZfL399djjz0mSfrqq69c2kZ6errsdrvS0tLk7+/vcjYA1tBw8qeejmDa8Vl3ezoCUOa5+vvbpSMyx44dc1uw//byyy9Lku644w6n8YSEBI0YMUKStGDBAnl5eSkyMlJZWVnq1auXlixZUiJ5AACAtbhUZBo0aFAiG3fhYJAqVaqkxYsXa/HixSWSAQAAWFex3hDv1KlT2rJli86cOaOcnBynaePGjXNLMAAAgKKYLjLLli3TI488Ih8fH9WoUUM2m80xzWazUWQAAECpMV1knnnmGU2dOlWxsbHy8jJ99zYAAIDbmG4ily9f1tChQykxAADA40y3kVGjRikxMbEksgAAAJhi+tRSfHy87rnnHq1du1a33HKLKlas6DR9/vz5bgsHAABQmGIVmXXr1jnewO7Gi30BAABKi+ki88ILL+j11193vGEdAACAp5i+RsbX11cdO3YsiSwAAACmmC4y48eP14svvlgSWQAAAEwxfWpp586d+uKLL/TJJ5+oZcuWeS72XbVqldvCAQAAFMZ0kQkICNDAgQNLIgsAAIAppotMQkJCSeQAAAAwjbfnBQAAlmX6iEyjRo0Kfb+Yo0eP/qFAAAAArjJdZCZMmOD0PDs7W7t27dLatWv1xBNPuCsXAABAkUwXmfHjx+c7vnjxYn3zzTd/OBAAAICr3HaNTJ8+ffTBBx+4a3UAAABFcluRWblypQIDA921OgAAgCKZPrX0l7/8xeliX8MwlJKSol9//VVLlixxazgAAIDCmC4yAwYMcHru5eWlWrVq6Y477lCzZs3clQsAAKBIpovMtGnTSiIHAACAabwhHgAAsCyXj8h4eXkV+kZ4kmSz2XTt2rU/HAoAAMAVLheZ1atXFzht27ZtWrRokXJyctwSCgAAwBUuF5n+/fvnGTtw4IAmT56sjz/+WPfff79mzJjh1nAAAACFKdY1MqdOndLDDz+sW265RdeuXdPu3bv1xhtvqEGDBu7OBwAAUCBTRSYtLU1PPfWUmjRpor179yopKUkff/yxWrVqVVL5AAAACuTyqaU5c+Zo9uzZCg4O1rvvvpvvqSYAAIDSZDMMw3BlRi8vL/n5+alHjx7y9vYucL5Vq1a5LZw7pKeny263Ky0tTf7+/p6OA8DNGk7+1NMRTDs+625PRwDKPFd/f7t8RGb48OFF3n4NAABQmlwuMsuWLSvBGAAAAObxzr4AAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyPFpkNm/erH79+ikkJEQ2m00ffvih03TDMDR16lTVqVNHfn5+6tGjhw4dOuSZsAAAoMzxaJHJzMxUmzZttHjx4nynz5kzR4sWLdIrr7yiHTt2qEqVKurVq5euXLlSykkBAEBZVMGTG+/Tp4/69OmT7zTDMLRw4UL961//Uv/+/SVJb775poKCgvThhx9q6NChpRkVAACUQWX2Gpljx44pJSVFPXr0cIzZ7Xa1b99e27ZtK3C5rKwspaenOz0AAED5VGaLTEpKiiQpKCjIaTwoKMgxLT/x8fGy2+2OR2hoaInmBAAAnlNmi0xxxcbGKi0tzfE4efKkpyMBAIASUmaLTHBwsCQpNTXVaTw1NdUxLT++vr7y9/d3egAAgPKpzBaZRo0aKTg4WElJSY6x9PR07dixQxERER5MBgAAygqP3rV06dIlHT582PH82LFj2r17twIDA1W/fn1NmDBBM2fOVFhYmBo1aqRnnnlGISEhGjBggOdCAwCAMsOjReabb75Rt27dHM9jYmIkSVFRUVq2bJmefPJJZWZmavTo0bp48aI6deqktWvXqlKlSp6KDAAAyhCbYRiGp0OUpPT0dNntdqWlpXG9DFAONZz8qacjmHZ81t2ejgCUea7+/i6z18gAAAAUhSIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsyxJFZvHixWrYsKEqVaqk9u3ba+fOnZ6OBAAAyoAyX2Tee+89xcTEaNq0afr222/Vpk0b9erVS2fOnPF0NAAA4GFlvsjMnz9fDz/8sEaOHKkWLVrolVdeUeXKlfX66697OhoAAPCwCp4OUJirV68qOTlZsbGxjjEvLy/16NFD27Zty3eZrKwsZWVlOZ6npaVJktLT00s2LACPyMm67OkIpvHvEVC03J8TwzAKna9MF5mzZ8/q+vXrCgoKchoPCgrSjz/+mO8y8fHxiouLyzMeGhpaIhkBwCz7Qk8nAKwjIyNDdru9wOllusgUR2xsrGJiYhzPc3JydP78edWoUUM2m82DycqG9PR0hYaG6uTJk/L39/d0nHKL/Vw62M+lg/1cOtjPzgzDUEZGhkJCQgqdr0wXmZo1a8rb21upqalO46mpqQoODs53GV9fX/n6+jqNBQQElFREy/L39+cHpRSwn0sH+7l0sJ9LB/v5/1fYkZhcZfpiXx8fH4WHhyspKckxlpOTo6SkJEVERHgwGQAAKAvK9BEZSYqJiVFUVJTatWun2267TQsXLlRmZqZGjhzp6WgAAMDDynyRGTJkiH799VdNnTpVKSkpatu2rdauXZvnAmC4xtfXV9OmTctz+g3uxX4uHezn0sF+Lh3s5+KxGUXd1wQAAFBGlelrZAAAAApDkQEAAJZFkQEAAJZFkQEAAJZFkQEAAJZFkSlHNm/erH79+ikkJEQ2m00ffvhhkctkZWVpypQpatCggXx9fdWwYUM+WbwIxdnP77zzjtq0aaPKlSurTp06evDBB3Xu3LmSD2th8fHx+utf/6pq1aqpdu3aGjBggA4cOFDkcomJiWrWrJkqVaqkW265RZ999lkppLWu4uznV199VZ07d1b16tVVvXp19ejRQzt37iylxNZU3O/nXCtWrJDNZtOAAQNKLqRFUWTKkczMTLVp00aLFy92eZnBgwcrKSlJS5cu1YEDB/Tuu++qadOmJZjS+szu561bt2r48OEaNWqU9u7dq8TERO3cuVMPP/xwCSe1tk2bNik6Olrbt2/X+vXrlZ2drbvuukuZmZkFLvPVV19p2LBhGjVqlHbt2qUBAwZowIAB+uGHH0oxubUUZz9/+eWXGjZsmDZu3Kht27YpNDRUd911l3755ZdSTG4txdnPuY4fP65Jkyapc+fOpZDUggyUS5KM1atXFzrPmjVrDLvdbpw7d650QpVDruznuXPnGjfddJPT2KJFi4y6deuWYLLy58yZM4YkY9OmTQXOM3jwYOPuu+92Gmvfvr3xyCOPlHS8csOV/Xyja9euGdWqVTPeeOONEkxWvri6n69du2bcfvvtxmuvvWZERUUZ/fv3L52AFsIRmT+x//u//1O7du00Z84c1a1bVzfffLMmTZqk3377zdPRypWIiAidPHlSn332mQzDUGpqqlauXKm+fft6OpqlpKWlSZICAwMLnGfbtm3q0aOH01ivXr20bdu2Es1Wnriyn290+fJlZWdnm1rmz87V/TxjxgzVrl1bo0aNKo1YllTmP6IAJefo0aPasmWLKlWqpNWrV+vs2bMaM2aMzp07p4SEBE/HKzc6duyod955R0OGDNGVK1d07do19evXz9QpwD+7nJwcTZgwQR07dlSrVq0KnC8lJSXPx5cEBQUpJSWlpCOWC67u5xs99dRTCgkJyVMikT9X9/OWLVu0dOlS7d69u/TCWRBHZP7EcnJyZLPZ9M477+i2225T3759NX/+fL3xxhsclXGjffv2afz48Zo6daqSk5O1du1aHT9+XP/85z89Hc0yoqOj9cMPP2jFihWejlKuFWc/z5o1SytWrNDq1atVqVKlEkxXfriynzMyMvTAAw/o1VdfVc2aNUsxnfVwROZPrE6dOqpbt67sdrtjrHnz5jIMQz///LPCwsI8mK78iI+PV8eOHfXEE09Iklq3bq0qVaqoc+fOmjlzpurUqePhhGXb2LFj9cknn2jz5s2qV69eofMGBwcrNTXVaSw1NVXBwcElGbFcMLOfc82bN0+zZs3Shg0b1Lp16xJOWD64up+PHDmi48ePq1+/fo6xnJwcSVKFChV04MABNW7cuMTzWgFHZP7EOnbsqFOnTunSpUuOsYMHD8rLy8vlf8hQtMuXL8vLy/lHzdvbW5Jk8JmtBTIMQ2PHjtXq1av1xRdfqFGjRkUuExERoaSkJKex9evXKyIioqRiWl5x9rMkzZkzR88++6zWrl2rdu3alXBK6zO7n5s1a6bvv/9eu3fvdjzuvfdedevWTbt371ZoaGgpJbcAT15pDPfKyMgwdu3aZezatcuQZMyfP9/YtWuX8dNPPxmGYRiTJ082HnjgAaf569WrZwwaNMjYu3evsWnTJiMsLMx46KGHPPUSLMHsfk5ISDAqVKhgLFmyxDhy5IixZcsWo127dsZtt93mqZdgCY8++qhht9uNL7/80jh9+rTjcfnyZcc8DzzwgDF58mTH861btxoVKlQw5s2bZ+zfv9+YNm2aUbFiReP777/3xEuwhOLs51mzZhk+Pj7GypUrnZbJyMjwxEuwhOLs5xtx11L+KDLlyMaNGw1JeR5RUVGGYfz+Q9C1a1enZfbv32/06NHD8PPzM+rVq2fExMQ4/WAhr+Ls50WLFhktWrQw/Pz8jDp16hj333+/8fPPP5d+eAvJbx9LMhISEhzzdO3a1bHfc73//vvGzTffbPj4+BgtW7Y0Pv3009INbjHF2c8NGjTId5lp06aVen6rKO7383+jyOTPZhgc2wYAANbENTIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCy/h8irRGySfEO6AAAAABJRU5ErkJggg==", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:45.806292\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -913,8 +908,8 @@ }, { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:37.131467\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:46.154362\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -924,8 +919,8 @@ }, { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:37.631129\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:46.530359\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -935,8 +930,8 @@ }, { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:38.082921\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:46.933281\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -946,8 +941,8 @@ }, { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:38.577597\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:47.465858\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -957,8 +952,8 @@ }, { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:39.000467\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:47.816920\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -968,8 +963,8 @@ }, { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:39.385439\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:48.168977\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -979,8 +974,8 @@ }, { "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-24T01:07:39.836231\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "image/png": "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", + "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:48.541983\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "text/plain": [ "
" ] @@ -993,6 +988,27 @@ "TestDispersion.allInfo()\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Export to kml so it can be visualized in Google Earth" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "TestDispersion.exportEllipsesToKML(\n", + " filename=\"dispersion_analysis_outputs/disp_class_example.kml\",\n", + " origin_lat=Env.latitude,\n", + " origin_lon=Env.longitude,\n", + " type=\"impact\",\n", + ")\n" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1009,77 +1025,119 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ - "# TestDispersion = Dispersion(filename=\"dispersion_analysis_outputs/disp_class_example\")\n" + "TestDispersion2 = Dispersion(filename=\"dispersion_analysis_outputs/disp_class_example2\")\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ - "# dispersion_dictionary = {\n", - "# # Environment Parameters\n", - "# \"railLength\": (Env.railLength, 0.001),\n", - "# \"date\": [Env.date],\n", - "# \"datum\": [\"WSG84\"],\n", - "# \"elevation\": (Env.elevation, 10),\n", - "# \"gravity\": (Env.gravity, 0),\n", - "# \"latitude\": (Env.latitude, 0),\n", - "# \"longitude\": (Env.longitude, 0),\n", - "# \"timeZone\": [str(Env.timeZone)],\n", - "# # Solid Motor Parameters\n", - "# \"burnOutTime\": (Pro75M1670.burnOutTime, 0.2),\n", - "# \"grainDensity\": (Pro75M1670.grainDensity, 0.1 * Pro75M1670.grainDensity),\n", - "# \"grainInitialHeight\": (Pro75M1670.grainInitialHeight, 0.001),\n", - "# \"grainInitialInnerRadius\": (Pro75M1670.grainInitialInnerRadius, 0.001),\n", - "# \"grainNumber\": [Pro75M1670.grainNumber],\n", - "# \"grainOuterRadius\": (Pro75M1670.grainOuterRadius, 0.001),\n", - "# \"grainSeparation\": (Pro75M1670.grainSeparation, 0.001),\n", - "# \"nozzleRadius\": (Pro75M1670.nozzleRadius, 0.001),\n", - "# \"throatRadius\": (Pro75M1670.throatRadius, 0.001),\n", - "# \"thrustSource\": [Pro75M1670.thrustSource],\n", - "# \"totalImpulse\": (Pro75M1670.totalImpulse, 0.033 * Pro75M1670.totalImpulse),\n", - "# # Rocket Parameters\n", - "# \"mass\": (Calisto.mass, 0.100),\n", - "# \"radius\": (Calisto.radius, 0.001),\n", - "# \"distanceRocketNozzle\": (Calisto.distanceRocketNozzle, 0.010),\n", - "# \"distanceRocketPropellant\": (Calisto.distanceRocketPropellant, 0.010),\n", - "# \"inertiaI\": (Calisto.inertiaI, Calisto.inertiaI * 0.1),\n", - "# \"inertiaZ\": (Calisto.inertiaZ, Calisto.inertiaZ * 0.1),\n", - "# \"powerOffDrag\": (1, 0.033),\n", - "# \"powerOnDrag\": (1, 0.033),\n", - "# \"noseKind\": [Calisto.noseKind],\n", - "# \"noseLength\": (Calisto.noseLength, 0.001),\n", - "# \"noseDistanceToCM\": (Calisto.noseDistanceToCM, 0.010),\n", - "# \"numberOfFins\": [Calisto.numberOfFins],\n", - "# \"finRootChord\": (Calisto.finRootChord, 0.001),\n", - "# \"finTipChord\": (Calisto.finTipChord, 0.001),\n", - "# \"span\": (Calisto.span, 0.001),\n", - "# \"distanceToCM\": (Calisto.finDistanceToCM, 0.010),\n", - "# \"finRadius\": (Calisto.finRadius, 0.001),\n", - "# \"finAirfoil\": Calisto.finAirfoil,\n", - "# \"tailTopRadius\": (Calisto.tailTopRadius, 0.001),\n", - "# \"CdS\": [(10, 2), (1, 0.3)],\n", - "# \"inclination\": [85],\n", - "# \"heading\": [90],\n", - "# }\n" + "aerodynamic_surfaces = Calisto.aerodynamicSurfaces\n", + "nose, fins, tail = aerodynamic_surfaces\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ - "# TestDispersion.run_dispersion(\n", - "# number_of_simulations=5,\n", - "# dispersion_dictionary=dispersion_dictionary,\n", - "# )\n" + "dispersion_dictionary2 = {\n", + " # Environment Parameters\n", + " \"railLength\": (Env.railLength, 0.001),\n", + " \"date\": [Env.date],\n", + " \"datum\": [\"WSG84\"],\n", + " \"elevation\": (Env.elevation, 10),\n", + " \"gravity\": (Env.gravity, 0),\n", + " \"latitude\": (Env.latitude, 0),\n", + " \"longitude\": (Env.longitude, 0),\n", + " \"timeZone\": [str(Env.timeZone)],\n", + " # Solid Motor Parameters\n", + " \"burnOutTime\": (Pro75M1670.burnOutTime, 0.2),\n", + " \"grainDensity\": (Pro75M1670.grainDensity, 0.1 * Pro75M1670.grainDensity),\n", + " \"grainInitialHeight\": (Pro75M1670.grainInitialHeight, 0.001),\n", + " \"grainInitialInnerRadius\": (Pro75M1670.grainInitialInnerRadius, 0.001),\n", + " \"grainNumber\": [Pro75M1670.grainNumber],\n", + " \"grainOuterRadius\": (Pro75M1670.grainOuterRadius, 0.001),\n", + " \"grainSeparation\": (Pro75M1670.grainSeparation, 0.001),\n", + " \"nozzleRadius\": (Pro75M1670.nozzleRadius, 0.001),\n", + " \"throatRadius\": (Pro75M1670.throatRadius, 0.001),\n", + " \"thrustSource\": [Pro75M1670.thrustSource],\n", + " \"totalImpulse\": (Pro75M1670.totalImpulse, 0.033 * Pro75M1670.totalImpulse),\n", + " # Rocket Parameters\n", + " \"mass\": (Calisto.mass, 0.100),\n", + " \"radius\": (Calisto.radius, 0.001),\n", + " \"distanceRocketNozzle\": (Calisto.distanceRocketNozzle, 0.010),\n", + " \"distanceRocketPropellant\": (Calisto.distanceRocketPropellant, 0.010),\n", + " \"inertiaI\": (Calisto.inertiaI, Calisto.inertiaI * 0.1),\n", + " \"inertiaZ\": (Calisto.inertiaZ, Calisto.inertiaZ * 0.1),\n", + " \"powerOffDrag\": (1, 0.033),\n", + " \"powerOnDrag\": (1, 0.033),\n", + " \"nose_name_kind\": [nose.kind],\n", + " \"nose_name_length\": (nose.length, 0.001),\n", + " \"nose_name_distanceToCM\": (nose.distanceToCM, 0.010),\n", + " \"finSet_name_numberOfFins\": [fins.numberOfFins],\n", + " \"finSet_name_rootChord\": (fins.rootChord, 0.001),\n", + " \"finSet_name_tipChord\": (fins.tipChord, 0.001),\n", + " \"finSet_name_span\": (fins.span, 0.001),\n", + " \"finSet_name_distanceToCM\": (fins.distanceToCM, 0.010),\n", + " \"finSet_name_radius\": (fins.radius, 0.001),\n", + " \"finSet_name_airfoil\": [fins.airfoil],\n", + " \"tail_name_topRadius\": (tail.topRadius, 0.001),\n", + " \"tail_name_bottomRadius\": (tail.bottomRadius, 0.001),\n", + " \"tail_name_length\": (tail.length, 0.001),\n", + " \"tail_name_distanceToCM\": (tail.distanceToCM, 0.010),\n", + " \"CdS\": [(10, 2), (1, 0.3)],\n", + " # Flight Parameters\n", + " \"inclination\": [85],\n", + " \"heading\": [90],\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Completed 50 iterations successfully. Total CPU time: 27.390625 s. Total wall time: 32.03746747970581 s'" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "TestDispersion2.run_dispersion(\n", + " number_of_simulations=50,\n", + " dispersion_dictionary=dispersion_dictionary2,\n", + ")\n", + "# TODO: NEeds to be tested, apparently is not capturing the parachutes correctly\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A total of 50 simulations were loaded from the following file: dispersion_analysis_outputs/disp_class_example2.disp_outputs.txt\n" + ] + } + ], + "source": [ + "TestDispersion2.import_results()\n" ] }, { @@ -1091,19 +1149,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "outOfRailStaticMargin: μ = 2.137, σ = 0.243\n", + "tFinal: μ = 43.816, σ = 0.789\n", + "finalStaticMargin: μ = 2.678, σ = 0.297\n", + "yImpact: μ = 0.002, σ = 0.001\n", + "initialStaticMargin: μ = 2.055, σ = 0.248\n", + "apogeeY: μ = 0.000, σ = 0.000\n", + "lateralSurfaceWind: μ = 0.000, σ = 0.000\n", + "apogee: μ = 2234.290, σ = 81.435\n", + "maxAccelerationTime: μ = 0.241, σ = 0.256\n", + "maxSpeed: μ = 263.080, σ = 6.998\n", + "xImpact: μ = 571.125, σ = 21.041\n", + "maxSpeedTime: μ = 3.266, σ = 0.008\n", + "apogeeTime: μ = 19.651, σ = 0.329\n", + "impactVelocity: μ = -135.936, σ = 2.657\n", + "maxAcceleration: μ = 102.448, σ = 3.946\n", + "apogeeX: μ = 337.368, σ = 11.944\n", + "outOfRailVelocity: μ = 25.742, σ = 0.428\n", + "outOfRailTime: μ = 0.364, σ = 0.006\n", + "frontalSurfaceWind: μ = 0.000, σ = 0.000\n", + "numberOfEvents: μ = 0.000, σ = 0.000\n", + "executionTime: μ = 0.373, σ = 0.080\n" + ] + } + ], "source": [ - "# TestDispersion.exportEllipsesToKML(\n", - "# dispersion_results,\n", - "# filename,\n", - "# origin_lat,\n", - "# origin_lon,\n", - "# type=\"all\",\n", - "# resolution=100,\n", - "# color=\"ff0000ff\",\n", - "# )\n" + "TestDispersion2.print_results()\n" ] } ], @@ -1124,7 +1202,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.10.0" }, "vscode": { "interpreter": { From a45ab4835081a5cf41e98287124dcf54fd861b13 Mon Sep 17 00:00:00 2001 From: Lint Action Date: Thu, 3 Nov 2022 12:36:40 +0000 Subject: [PATCH 57/68] Fix code style issues with Black --- rocketpy/AeroSurfaces.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/rocketpy/AeroSurfaces.py b/rocketpy/AeroSurfaces.py index e186843e4..dc4bf98de 100644 --- a/rocketpy/AeroSurfaces.py +++ b/rocketpy/AeroSurfaces.py @@ -820,7 +820,6 @@ def __init__( + (8 / (tau - 1) ** 2) * np.log((tau**2 + 1) / (2 * tau)) ) - self.tipChord = tipChord self.sweepLength = sweepLength self.sweepAngle = sweepAngle @@ -977,11 +976,9 @@ def draw(self): plt.show() - return None - class EllipticalFins(Fins): """Class that defines and holds information for an elliptical fin set. @@ -1047,7 +1044,6 @@ def __init__( rootChord, span, distanceToCM, - rocketRadius, cantAngle=0, airfoil=None, @@ -1295,7 +1291,6 @@ def draw(self): plt.show() - return None From 0618afd6aa7215220e7468efe88b62a43d3dea67 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Sat, 12 Nov 2022 21:35:18 +0100 Subject: [PATCH 58/68] MAINT: cleaning TODOs and fixing warnings --- rocketpy/Dispersion.py | 97 +++++++++++++++++++++++++----------------- 1 file changed, 59 insertions(+), 38 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index a6cd796e7..1cb2cc64f 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -8,7 +8,6 @@ import math import traceback import types -import warnings from time import process_time, time import matplotlib.pyplot as plt @@ -27,14 +26,6 @@ from .supplement import invertedHaversine from .AeroSurfaces import NoseCone, TrapezoidalFins, EllipticalFins, Tail -## Tasks : -# TODO: Allow each parameter to be varied following an specific probability distribution -# TODO: Test simulations under different scenarios (with both parachutes, with only main chute, etc) -# TODO: Add unit tests -# TODO: Adjust the notebook to the new version of the code -# TODO: Implement MRS method -# TODO: Implement functions from compareDispersions notebook - class Dispersion: @@ -276,7 +267,9 @@ def __set_distribution_function(self, distribution_type): elif distribution_type == "zipf": return zipf else: - warnings.warn("Distribution type not supported") + raise ValueError( + "Distribution type not recognized. Please use a valid distribution type." + ) def __process_dispersion_dict(self, dictionary): """Read the inputted dispersion dictionary from the run_dispersion method @@ -299,10 +292,9 @@ def __process_dispersion_dict(self, dictionary): """ # First we need to check if the dictionary is empty if not dictionary: - warnings.warn( - "The dispersion dictionary is empty, no dispersion will be performed" + raise ValueError( + "The dispersion dictionary is empty. no dispersion can be performed" ) - return dictionary # Now we prepare all the parachute data dictionary = self.__process_parachute_from_dict(dictionary) @@ -362,11 +354,15 @@ def __process_flight_from_dict(self, dictionary): try: # First try to catch value from the Flight object if passed dictionary[missing_input] = [getattr(self.flight, missing_input)] - except: + except AttributeError: # Flight class was not inputted # check if missing parameter is required if self.flight_inputs[missing_input] == "required": - warnings.warn(f'Missing "{missing_input}" in dictionary') + raise ValueError( + "The input {} is required for the Flight class.".format( + missing_input + ) + ) else: # if not required, uses default value dictionary[missing_input] = [self.flight_inputs[missing_input]] @@ -400,11 +396,15 @@ def __process_rocket_from_dict(self, dictionary): # Add to the dict try: dictionary[missing_input] = [getattr(self.rocket, missing_input)] - except: + except AttributeError: # class was not inputted # checks if missing parameter is required if self.rocket_inputs[missing_input] == "required": - warnings.warn(f'Missing "{missing_input}" in dictionary') + raise ValueError( + "The input {} is required for the Rocket class.".format( + missing_input + ) + ) else: # if not, uses default value dictionary[missing_input] = [self.rocket_inputs[missing_input]] @@ -442,11 +442,15 @@ def __process_rail_buttons_from_dict(self, dictionary): # Add to the dict try: dictionary[missing_input] = [getattr(self.rocket, missing_input)] - except: + except AttributeError: # class was not inputted # checks if missing parameter is required if self.rail_buttons_inputs[missing_input] == "required": - warnings.warn(f'Missing "{missing_input}" in dictionary') + raise ValueError( + "The input {} is required for the RailButtons class.".format( + missing_input + ) + ) else: # if not, uses default value dictionary[missing_input] = [ @@ -511,10 +515,15 @@ def __process_aerodynamic_surfaces_from_dict(self, dictionary): dictionary[f"nose_{name}_{parameter}"] = [ getattr(surface, parameter) ] - except: + except AttributeError: # If not possible, check if the parameter is required if self.nose_inputs[input] == "required": - warnings.warn(f'Missing "{input}" in dictionary') + raise ValueError( + "The input {} is required for the NoseCone class.".format( + input + ) + ) + else: # If not required, use default value dictionary[f"nose_{name}_{parameter}"] = [ @@ -538,10 +547,14 @@ def __process_aerodynamic_surfaces_from_dict(self, dictionary): dictionary[f"finSet_{name}_{parameter}"] = [ getattr(surface, parameter) ] - except: + except AttributeError: # If not possible, check if the parameter is required if self.fins_inputs[input] == "required": - warnings.warn(f'Missing "{input}" in dictionary') + raise ValueError( + "The input {} is required for the Fins class.".format( + input + ) + ) else: # If not required, use default value dictionary[f"finSet_{name}_{parameter}"] = [ @@ -563,10 +576,14 @@ def __process_aerodynamic_surfaces_from_dict(self, dictionary): dictionary[f"tail_{name}_{parameter}"] = [ getattr(surface, parameter) ] - except: + except AttributeError: # If not possible, check if the parameter is required if self.tail_inputs[input] == "required": - warnings.warn(f'Missing "{input}" in dictionary') + raise ValueError( + "The input {} is required for the Tail class.".format( + input + ) + ) else: # If not required, use default value dictionary[f"tail_{name}_{parameter}"] = [ @@ -607,11 +624,15 @@ def __process_motor_from_dict(self, dictionary): # Add to the dict try: dictionary[missing_input] = [getattr(self.motor, missing_input)] - except: + except AttributeError: # class was not inputted # checks if missing parameter is required if self.solid_motor_inputs[missing_input] == "required": - warnings.warn(f'Missing "{missing_input}" in dictionary') + raise ValueError( + "The input {} is required for the SolidMotor class.".format( + missing_input + ) + ) else: # if not uses default value dictionary[missing_input] = [ self.solid_motor_inputs[missing_input] @@ -653,12 +674,12 @@ def __process_environment_from_dict(self, dictionary): dictionary[missing_input] = [ getattr(self.environment, missing_input) ] - except: + except AttributeError: # class was not inputted # checks if missing parameter is required if self.environment_inputs[missing_input] == "required": - warnings.warn( - "Missing {} in dictionary, which is required to run a simulation".format( + raise ValueError( + "The input {} is required for the Environment class.".format( missing_input ) ) @@ -706,11 +727,11 @@ def __process_parachute_from_dict(self, dictionary): dictionary[ "parachute_{}_{}".format(name, parameter) ] = [getattr(chute, parameter)] - except: # Class not passed + except AttributeError: # Class not passed if self.parachute_inputs[parachute_input] == "required": - warnings.warn( - "Missing {} for parachute {} in dictionary, which is required to run a simulation".format( - parachute_input.split("_")[2], name + raise ValueError( + "The input {} is required for the Parachute class.".format( + parachute_input ) ) else: @@ -765,7 +786,7 @@ def __check_inputted_values_from_dict(self, dictionary): getattr(self.environment, parameter_key), parameter_value, ) - except: + except AttributeError: raise AttributeError( f"Please check if the parameter {parameter_key} was inputted" "correctly in dispersion_dictionary." @@ -781,7 +802,7 @@ def __check_inputted_values_from_dict(self, dictionary): getattr(self.motor, parameter_key), parameter_value, ) - except: + except AttributeError: raise AttributeError( f"Please check if the parameter {parameter_key} was inputted" "correctly in dispersion_dictionary." @@ -797,7 +818,7 @@ def __check_inputted_values_from_dict(self, dictionary): getattr(self.rocket, parameter_key), parameter_value, ) - except: + except AttributeError: raise AttributeError( f"Please check if the parameter {parameter_key} was inputted" "correctly in dispersion_dictionary." @@ -813,7 +834,7 @@ def __check_inputted_values_from_dict(self, dictionary): getattr(self.flight, parameter_key), parameter_value, ) - except: + except AttributeError: raise AttributeError( f"Please check if the parameter {parameter_key} was inputted" "correctly in dispersion_dictionary." From 7c8f5c0096035ae587804c0ecfd6fe30a306c435 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Sat, 12 Nov 2022 21:35:36 +0100 Subject: [PATCH 59/68] MAINT: removed unused .json file --- data/weather/EuroC_export_env_analysis.json | 25760 ------------------ 1 file changed, 25760 deletions(-) delete mode 100644 data/weather/EuroC_export_env_analysis.json diff --git a/data/weather/EuroC_export_env_analysis.json b/data/weather/EuroC_export_env_analysis.json deleted file mode 100644 index 0598532f5..000000000 --- a/data/weather/EuroC_export_env_analysis.json +++ /dev/null @@ -1,25760 +0,0 @@ -{ - "start_date": "2002-10-06 00:00:00+01:00", - "end_date": "2021-10-23 00:00:00+01:00", - "start_hour": 4, - "end_hour": 20, - "latitude": 39.3897, - "longitude": -8.28896388889, - "elevation": 113.00194018037647, - "timeZone": "Portugal", - "unit_system": { - "length": "m", - "velocity": "m/s", - "acceleration": "g", - "mass": "kg", - "time": "s", - "pressure": "hPa", - "temperature": "degC", - "angle": "deg", - "precipitation": "mm", - "wind_speed": "m/s" - }, - "surfaceDataFile": "../../data/weather/EuroC_single_level_reanalysis_2002_2021.nc", - "pressureLevelDataFile": "../../data/weather/EuroC_pressure_levels_reanalysis_2001-2021.nc", - "atmosphericModelPressureProfile": { - "4": [ - [ - 0.0, - 977.6714690813923 - ], - [ - 140.24302904312142, - 994.7056149268931 - ], - [ - 280.48605808624285, - 969.2277928010537 - ], - [ - 420.7290871293643, - 951.6398340522569 - ], - [ - 560.9721161724857, - 938.5818940133656 - ], - [ - 701.2151452156071, - 922.7154094321005 - ], - [ - 841.4581742587286, - 907.1535456389507 - ], - [ - 981.70120330185, - 892.3999471203616 - ], - [ - 1121.9442323449714, - 877.6643553533851 - ], - [ - 1262.1872613880928, - 862.9700765693461 - ], - [ - 1402.4302904312142, - 848.6107312664914 - ], - [ - 1542.6733194743356, - 834.7683705070606 - ], - [ - 1682.9163485174572, - 820.5263826299732 - ], - [ - 1823.1593775605786, - 805.5423622070218 - ], - [ - 1963.4024066037, - 793.7765615105203 - ], - [ - 2103.645435646821, - 784.5773206603102 - ], - [ - 2243.888464689943, - 762.7889902666342 - ], - [ - 2384.1314937330644, - 747.6734826350887 - ], - [ - 2524.3745227761856, - 742.7376067890614 - ], - [ - 2664.617551819307, - 733.4368811582627 - ], - [ - 2804.8605808624284, - 720.3022328169492 - ], - [ - 2945.10360990555, - 705.4082118549836 - ], - [ - 3085.346638948671, - 690.822785065689 - ], - [ - 3225.589667991793, - 677.8330605984202 - ], - [ - 3365.8326970349144, - 666.1292563635064 - ], - [ - 3506.0757260780356, - 655.1993400411195 - ], - [ - 3646.318755121157, - 644.5313259275695 - ], - [ - 3786.5617841642784, - 633.6987991293767 - ], - [ - 3926.8048132074, - 622.6920201887227 - ], - [ - 4067.047842250521, - 611.6331180993917 - ], - [ - 4207.290871293642, - 600.6443685099937 - ], - [ - 4347.533900336764, - 589.8468100040468 - ], - [ - 4487.776929379886, - 579.3022546911122 - ], - [ - 4628.019958423007, - 568.9857538596362 - ], - [ - 4768.262987466129, - 558.8651861026583 - ], - [ - 4908.50601650925, - 548.9084286285971 - ], - [ - 5048.749045552371, - 539.0840703952814 - ], - [ - 5188.992074595492, - 529.3777128892556 - ], - [ - 5329.235103638614, - 519.7937228302019 - ], - [ - 5469.478132681736, - 510.3374897141842 - ], - [ - 5609.721161724857, - 501.0143984382358 - ], - [ - 5749.964190767979, - 491.8298072850902 - ], - [ - 5890.2072198111, - 482.78660837920796 - ], - [ - 6030.450248854221, - 473.8820795116159 - ], - [ - 6170.693277897342, - 465.1126772473739 - ], - [ - 6310.936306940464, - 456.4748662452933 - ], - [ - 6451.179335983586, - 447.96516892594906 - ], - [ - 6591.422365026707, - 439.58026709107196 - ], - [ - 6731.665394069829, - 431.3179773567003 - ], - [ - 6871.90842311295, - 423.17720818001817 - ], - [ - 7012.151452156071, - 415.156929843785 - ], - [ - 7152.394481199192, - 407.2561033696322 - ], - [ - 7292.637510242314, - 399.4736487100893 - ], - [ - 7432.880539285436, - 391.80843346563535 - ], - [ - 7573.123568328557, - 384.25913030572497 - ], - [ - 7713.366597371678, - 376.82412443976875 - ], - [ - 7853.6096264148, - 369.5017627077973 - ], - [ - 7993.852655457921, - 362.29039215529707 - ], - [ - 8134.095684501042, - 355.1883766865999 - ], - [ - 8274.338713544164, - 348.19417123291373 - ], - [ - 8414.581742587285, - 341.30644344319904 - ], - [ - 8554.824771630407, - 334.5241065107375 - ], - [ - 8695.067800673529, - 327.8461864609543 - ], - [ - 8835.31082971665, - 321.27171822106254 - ], - [ - 8975.553858759771, - 314.799736670188 - ], - [ - 9115.796887802893, - 308.4292705935431 - ], - [ - 9256.039916846014, - 302.1593260770103 - ], - [ - 9396.282945889136, - 295.9889211101079 - ], - [ - 9536.525974932258, - 289.91715265497794 - ], - [ - 9676.769003975378, - 283.9431759045826 - ], - [ - 9817.0120330185, - 278.066171266123 - ], - [ - 9957.25506206162, - 272.2853217681947 - ], - [ - 10097.498091104742, - 266.59981043969424 - ], - [ - 10237.741120147864, - 261.00882036461684 - ], - [ - 10377.984149190985, - 255.51152715814027 - ], - [ - 10518.227178234107, - 250.10712586289605 - ], - [ - 10658.470207277229, - 244.79494489362898 - ], - [ - 10798.71323632035, - 239.574498032586 - ], - [ - 10938.956265363471, - 234.4453740034528 - ], - [ - 11079.199294406593, - 229.40716944501412 - ], - [ - 11219.442323449714, - 224.45942022144567 - ], - [ - 11359.685352492836, - 219.6016266313459 - ], - [ - 11499.928381535958, - 214.8334726895174 - ], - [ - 11640.171410579078, - 210.15475649817577 - ], - [ - 11780.4144396222, - 205.565255911517 - ], - [ - 11920.65746866532, - 201.0646650775365 - ], - [ - 12060.900497708442, - 196.65222968086078 - ], - [ - 12201.143526751564, - 192.3265186492388 - ], - [ - 12341.386555794685, - 188.0858208538366 - ], - [ - 12481.629584837807, - 183.92855624847837 - ], - [ - 12621.872613880929, - 179.85319769949484 - ], - [ - 12762.11564292405, - 175.8583807745904 - ], - [ - 12902.358671967171, - 171.9435342553073 - ], - [ - 13042.601701010293, - 168.10946531111665 - ], - [ - 13182.844730053414, - 164.35763618432358 - ], - [ - 13323.087759096536, - 160.689056659278 - ], - [ - 13463.330788139658, - 157.10462082098326 - ], - [ - 13603.573817182778, - 153.60520731140528 - ], - [ - 13743.8168462259, - 150.19118503172868 - ], - [ - 13884.059875269022, - 146.85982839567592 - ] - ], - "5": [ - [ - 0.0, - 979.4216156849128 - ], - [ - 140.24302904312142, - 993.9513446297674 - ], - [ - 280.48605808624285, - 969.0019752418823 - ], - [ - 420.7290871293643, - 951.6348389535328 - ], - [ - 560.9721161724857, - 938.3810594728633 - ], - [ - 701.2151452156071, - 922.5260643571382 - ], - [ - 841.4581742587286, - 907.002491551157 - ], - [ - 981.70120330185, - 892.2227617264967 - ], - [ - 1121.9442323449714, - 877.4675268266079 - ], - [ - 1262.1872613880928, - 862.8462265791305 - ], - [ - 1402.4302904312142, - 848.4806487411403 - ], - [ - 1542.6733194743356, - 834.3183057564123 - ], - [ - 1682.9163485174572, - 820.3413618537005 - ], - [ - 1823.1593775605786, - 806.5623730782773 - ], - [ - 1963.4024066037, - 792.9811565198454 - ], - [ - 2103.645435646821, - 779.5928618821589 - ], - [ - 2243.888464689943, - 766.3955582547427 - ], - [ - 2384.1314937330644, - 753.3872224921867 - ], - [ - 2524.3745227761856, - 740.5651328816183 - ], - [ - 2664.617551819307, - 727.9266805095664 - ], - [ - 2804.8605808624284, - 715.4693962909517 - ], - [ - 2945.10360990555, - 703.1908441558012 - ], - [ - 3085.346638948671, - 691.088538215253 - ], - [ - 3225.589667991793, - 679.1601039478766 - ], - [ - 3365.8326970349144, - 667.403301108479 - ], - [ - 3506.0757260780356, - 655.8158961435392 - ], - [ - 3646.318755121157, - 644.3956756408844 - ], - [ - 3786.5617841642784, - 633.1403326984687 - ], - [ - 3926.8048132074, - 622.0474701930558 - ], - [ - 4067.047842250521, - 611.1146951305269 - ], - [ - 4207.290871293642, - 600.3396211239005 - ], - [ - 4347.533900336764, - 589.7200859643772 - ], - [ - 4487.776929379886, - 579.2545566264544 - ], - [ - 4628.019958423007, - 568.9416250077467 - ], - [ - 4768.262987466129, - 558.7798795062223 - ], - [ - 4908.50601650925, - 548.7678848844198 - ], - [ - 5048.749045552371, - 538.9035571291828 - ], - [ - 5188.992074595492, - 529.1831389611757 - ], - [ - 5329.235103638614, - 519.6026240282351 - ], - [ - 5469.478132681736, - 510.1580088133889 - ], - [ - 5609.721161724857, - 500.8452934878616 - ], - [ - 5749.964190767979, - 491.6613994890678 - ], - [ - 5890.2072198111, - 482.6092673805659 - ], - [ - 6030.450248854221, - 473.6942693864566 - ], - [ - 6170.693277897342, - 464.92178667801033 - ], - [ - 6310.936306940464, - 456.29720809213626 - ], - [ - 6451.179335983586, - 447.8259613512883 - ], - [ - 6591.422365026707, - 439.5072703387587 - ], - [ - 6731.665394069829, - 431.3201449149735 - ], - [ - 6871.90842311295, - 423.239527457674 - ], - [ - 7012.151452156071, - 415.2403659933683 - ], - [ - 7152.394481199192, - 407.29760191175916 - ], - [ - 7292.637510242314, - 399.3861577904447 - ], - [ - 7432.880539285436, - 391.4895731993953 - ], - [ - 7573.123568328557, - 383.66387363593617 - ], - [ - 7713.366597371678, - 376.0014294695306 - ], - [ - 7853.6096264148, - 368.59488100520394 - ], - [ - 7993.852655457921, - 361.51601811928344 - ], - [ - 8134.095684501042, - 354.6974909662945 - ], - [ - 8274.338713544164, - 348.0150699980972 - ], - [ - 8414.581742587285, - 341.3572574795787 - ], - [ - 8554.824771630407, - 334.70296926885214 - ], - [ - 8695.067800673529, - 328.070186863206 - ], - [ - 8835.31082971665, - 321.4770518227949 - ], - [ - 8975.553858759771, - 314.941705642052 - ], - [ - 9115.796887802893, - 308.482282502352 - ], - [ - 9256.039916846014, - 302.1168840522836 - ], - [ - 9396.282945889136, - 295.863604246989 - ], - [ - 9536.525974932258, - 289.73570624968653 - ], - [ - 9676.769003975378, - 283.73087268206757 - ], - [ - 9817.0120330185, - 277.8437047070258 - ], - [ - 9957.25506206162, - 272.06880584403484 - ], - [ - 10097.498091104742, - 266.40077961329644 - ], - [ - 10237.741120147864, - 260.83422960176546 - ], - [ - 10377.984149190985, - 255.363753906265 - ], - [ - 10518.227178234107, - 249.98399506975326 - ], - [ - 10658.470207277229, - 244.6897671571457 - ], - [ - 10798.71323632035, - 239.47822752566552 - ], - [ - 10938.956265363471, - 234.35099295463075 - ], - [ - 11079.199294406593, - 229.3102099993381 - ], - [ - 11219.442323449714, - 224.3579409866961 - ], - [ - 11359.685352492836, - 219.49622118077852 - ], - [ - 11499.928381535958, - 214.726296145405 - ], - [ - 11640.171410579078, - 210.04763237639287 - ], - [ - 11780.4144396222, - 205.45947832171075 - ], - [ - 11920.65746866532, - 200.9610014785816 - ], - [ - 12060.900497708442, - 196.5509013292908 - ], - [ - 12201.143526751564, - 192.2272169786099 - ], - [ - 12341.386555794685, - 187.98814219658482 - ], - [ - 12481.629584837807, - 183.8321824339577 - ], - [ - 12621.872613880929, - 179.75789276505182 - ], - [ - 12762.11564292405, - 175.76399959646034 - ], - [ - 12902.358671967171, - 171.85005418219086 - ], - [ - 13042.601701010293, - 168.01699442109154 - ], - [ - 13182.844730053414, - 164.26630683964345 - ], - [ - 13323.087759096536, - 160.5989851624009 - ], - [ - 13463.330788139658, - 157.01591598362788 - ], - [ - 13603.573817182778, - 153.51796848543847 - ], - [ - 13743.8168462259, - 150.10546537486104 - ], - [ - 13884.059875269022, - 146.77552753443376 - ] - ], - "6": [ - [ - 0.0, - 979.4033301243851 - ], - [ - 140.24302904312142, - 993.8173394581016 - ], - [ - 280.48605808624285, - 969.144186829941 - ], - [ - 420.7290871293643, - 951.7184913634705 - ], - [ - 560.9721161724857, - 938.4191111055725 - ], - [ - 701.2151452156071, - 922.5791669979078 - ], - [ - 841.4581742587286, - 907.0510860141973 - ], - [ - 981.70120330185, - 892.2617556062014 - ], - [ - 1121.9442323449714, - 877.5029493100384 - ], - [ - 1262.1872613880928, - 862.8778307179738 - ], - [ - 1402.4302904312142, - 848.5074684916688 - ], - [ - 1542.6733194743356, - 834.3414485304311 - ], - [ - 1682.9163485174572, - 820.3612185373835 - ], - [ - 1823.1593775605786, - 806.5783231379272 - ], - [ - 1963.4024066037, - 792.9930744979266 - ], - [ - 2103.645435646821, - 779.601298262273 - ], - [ - 2243.888464689943, - 766.4008063222659 - ], - [ - 2384.1314937330644, - 753.3890151380622 - ], - [ - 2524.3745227761856, - 740.5633264753455 - ], - [ - 2664.617551819307, - 727.9214601134988 - ], - [ - 2804.8605808624284, - 715.4609992300202 - ], - [ - 2945.10360990555, - 703.1795122847806 - ], - [ - 3085.346638948671, - 691.0744852253018 - ], - [ - 3225.589667991793, - 679.1434698312598 - ], - [ - 3365.8326970349144, - 667.3842076513538 - ], - [ - 3506.0757260780356, - 655.7944597010302 - ], - [ - 3646.318755121157, - 644.3720245019547 - ], - [ - 3786.5617841642784, - 633.114709491541 - ], - [ - 3926.8048132074, - 622.0203151189644 - ], - [ - 4067.047842250521, - 611.0866424081288 - ], - [ - 4207.290871293642, - 600.3114857743053 - ], - [ - 4347.533900336764, - 589.6926842352341 - ], - [ - 4487.776929379886, - 579.2281288923559 - ], - [ - 4628.019958423007, - 568.9157097552558 - ], - [ - 4768.262987466129, - 558.7533147931683 - ], - [ - 4908.50601650925, - 548.7388169369859 - ], - [ - 5048.749045552371, - 538.8701665128171 - ], - [ - 5188.992074595492, - 529.145480508989 - ], - [ - 5329.235103638614, - 519.5629117952835 - ], - [ - 5469.478132681736, - 510.1206145433703 - ], - [ - 5609.721161724857, - 500.8167439461948 - ], - [ - 5749.964190767979, - 491.64944507847855 - ], - [ - 5890.2072198111, - 482.6168503740353 - ], - [ - 6030.450248854221, - 473.71712801671555 - ], - [ - 6170.693277897342, - 464.94845368209707 - ], - [ - 6310.936306940464, - 456.30900983405667 - ], - [ - 6451.179335983586, - 447.7970174878505 - ], - [ - 6591.422365026707, - 439.41078207631995 - ], - [ - 6731.665394069829, - 431.14875622442173 - ], - [ - 6871.90842311295, - 423.0094231447404 - ], - [ - 7012.151452156071, - 414.9912686362494 - ], - [ - 7152.394481199192, - 407.0927763174595 - ], - [ - 7292.637510242314, - 399.3124412317916 - ], - [ - 7432.880539285436, - 391.6487950065058 - ], - [ - 7573.123568328557, - 384.1003503833863 - ], - [ - 7713.366597371678, - 376.66563629478486 - ], - [ - 7853.6096264148, - 369.34318397547906 - ], - [ - 7993.852655457921, - 362.13152448369397 - ], - [ - 8134.095684501042, - 355.02919599639506 - ], - [ - 8274.338713544164, - 348.0347863900513 - ], - [ - 8414.581742587285, - 341.14703867228906 - ], - [ - 8554.824771630407, - 334.3648891793685 - ], - [ - 8695.067800673529, - 327.6873363156042 - ], - [ - 8835.31082971665, - 321.11338319550913 - ], - [ - 8975.553858759771, - 314.6420328765806 - ], - [ - 9115.796887802893, - 308.27228012855244 - ], - [ - 9256.039916846014, - 302.0030888928564 - ], - [ - 9396.282945889136, - 295.8334485575881 - ], - [ - 9536.525974932258, - 289.76245694069553 - ], - [ - 9676.769003975378, - 283.78927485308947 - ], - [ - 9817.0120330185, - 277.9130836193368 - ], - [ - 9957.25506206162, - 272.1330662559317 - ], - [ - 10097.498091104742, - 266.44840578013674 - ], - [ - 10237.741120147864, - 260.85828539624856 - ], - [ - 10377.984149190985, - 255.3618863117103 - ], - [ - 10518.227178234107, - 249.95842899344316 - ], - [ - 10658.470207277229, - 244.64725660355043 - ], - [ - 10798.71323632035, - 239.42784794348128 - ], - [ - 10938.956265363471, - 234.29975821021304 - ], - [ - 11079.199294406593, - 229.2625505358851 - ], - [ - 11219.442323449714, - 224.31570821574192 - ], - [ - 11359.685352492836, - 219.45872585957073 - ], - [ - 11499.928381535958, - 214.69134851784622 - ], - [ - 11640.171410579078, - 210.01342757546848 - ], - [ - 11780.4144396222, - 205.42479279470135 - ], - [ - 11920.65746866532, - 200.92519563951004 - ], - [ - 12060.900497708442, - 196.5139182626259 - ], - [ - 12201.143526751564, - 192.18952326735305 - ], - [ - 12341.386555794685, - 187.95026470467306 - ], - [ - 12481.629584837807, - 183.79452418043502 - ], - [ - 12621.872613880929, - 179.72073404081348 - ], - [ - 12762.11564292405, - 175.72749636545552 - ], - [ - 12902.358671967171, - 171.81422382804521 - ], - [ - 13042.601701010293, - 167.98171096823847 - ], - [ - 13182.844730053414, - 164.23146419779098 - ], - [ - 13323.087759096536, - 160.56457505383332 - ], - [ - 13463.330788139658, - 156.98202987981037 - ], - [ - 13603.573817182778, - 153.48479616968123 - ], - [ - 13743.8168462259, - 150.07328580666982 - ], - [ - 13884.059875269022, - 146.74469257180235 - ] - ], - "7": [ - [ - 0.0, - 978.9446646625488 - ], - [ - 140.24302904312142, - 993.7517651518842 - ], - [ - 280.48605808624285, - 969.4305121698253 - ], - [ - 420.7290871293643, - 951.8546437222352 - ], - [ - 560.9721161724857, - 938.5220481374886 - ], - [ - 701.2151452156071, - 922.7066276052191 - ], - [ - 841.4581742587286, - 907.1660728249834 - ], - [ - 981.70120330185, - 892.364651990674 - ], - [ - 1121.9442323449714, - 877.6022457205609 - ], - [ - 1262.1872613880928, - 862.9729087715363 - ], - [ - 1402.4302904312142, - 848.5973915633715 - ], - [ - 1542.6733194743356, - 834.4264529608558 - ], - [ - 1682.9163485174572, - 820.4421575981428 - ], - [ - 1823.1593775605786, - 806.655902858563 - ], - [ - 1963.4024066037, - 793.0668917706881 - ], - [ - 2103.645435646821, - 779.6706259863445 - ], - [ - 2243.888464689943, - 766.4653111610817 - ], - [ - 2384.1314937330644, - 753.448828098095 - ], - [ - 2524.3745227761856, - 740.618841816408 - ], - [ - 2664.617551819307, - 727.9730451292681 - ], - [ - 2804.8605808624284, - 715.5089383946176 - ], - [ - 2945.10360990555, - 703.2240280498389 - ], - [ - 3085.346638948671, - 691.115719482632 - ], - [ - 3225.589667991793, - 679.1815497637516 - ], - [ - 3365.8326970349144, - 667.4193051350885 - ], - [ - 3506.0757260780356, - 655.82678494715 - ], - [ - 3646.318755121157, - 644.4018344748201 - ], - [ - 3786.5617841642784, - 633.1422835535906 - ], - [ - 3926.8048132074, - 622.0459652776403 - ], - [ - 4067.047842250521, - 611.1107430776109 - ], - [ - 4207.290871293642, - 600.3344676337814 - ], - [ - 4347.533900336764, - 589.7150198881317 - ], - [ - 4487.776929379886, - 579.2502149706654 - ], - [ - 4628.019958423007, - 568.9376282562483 - ], - [ - 4768.262987466129, - 558.7748079730052 - ], - [ - 4908.50601650925, - 548.7592952268081 - ], - [ - 5048.749045552371, - 538.8887281048134 - ], - [ - 5188.992074595492, - 529.1614670024937 - ], - [ - 5329.235103638614, - 519.5766269339293 - ], - [ - 5469.478132681736, - 510.1333695338165 - ], - [ - 5609.721161724857, - 500.83086061505406 - ], - [ - 5749.964190767979, - 491.66825248988755 - ], - [ - 5890.2072198111, - 482.64364909227106 - ], - [ - 6030.450248854221, - 473.752313210666 - ], - [ - 6170.693277897342, - 464.9890317857278 - ], - [ - 6310.936306940464, - 456.3485965392243 - ], - [ - 6451.179335983586, - 447.825827808071 - ], - [ - 6591.422365026707, - 439.415787516067 - ], - [ - 6731.665394069829, - 431.11980637512005 - ], - [ - 6871.90842311295, - 422.94700381638046 - ], - [ - 7012.151452156071, - 414.9069982953618 - ], - [ - 7152.394481199192, - 407.009409955139 - ], - [ - 7292.637510242314, - 399.2638980228939 - ], - [ - 7432.880539285436, - 391.68013460327427 - ], - [ - 7573.123568328557, - 384.2547713233406 - ], - [ - 7713.366597371678, - 376.95443117175114 - ], - [ - 7853.6096264148, - 369.74151701505025 - ], - [ - 7993.852655457921, - 362.5784312030357 - ], - [ - 8134.095684501042, - 355.42757286377525 - ], - [ - 8274.338713544164, - 348.25135715507486 - ], - [ - 8414.581742587285, - 341.0133074800712 - ], - [ - 8554.824771630407, - 333.733690186229 - ], - [ - 8695.067800673529, - 326.519559596936 - ], - [ - 8835.31082971665, - 319.48532697639627 - ], - [ - 8975.553858759771, - 312.74540356183473 - ], - [ - 9115.796887802893, - 306.41419315460286 - ], - [ - 9256.039916846014, - 300.60607717208956 - ], - [ - 9396.282945889136, - 295.43454755729283 - ], - [ - 9536.525974932258, - 290.50697563647026 - ], - [ - 9676.769003975378, - 285.2273804976184 - ], - [ - 9817.0120330185, - 279.66096577151825 - ], - [ - 9957.25506206162, - 273.8863748196379 - ], - [ - 10097.498091104742, - 267.98225100437514 - ], - [ - 10237.741120147864, - 262.02723792452207 - ], - [ - 10377.984149190985, - 256.0999798259682 - ], - [ - 10518.227178234107, - 250.2791578611987 - ], - [ - 10658.470207277229, - 244.64359555495622 - ], - [ - 10798.71323632035, - 239.252609561986 - ], - [ - 10938.956265363471, - 234.08293995652252 - ], - [ - 11079.199294406593, - 229.08965836406261 - ], - [ - 11219.442323449714, - 224.2277603927234 - ], - [ - 11359.685352492836, - 219.4522303472878 - ], - [ - 11499.928381535958, - 214.7300148489557 - ], - [ - 11640.171410579078, - 210.06430389047964 - ], - [ - 11780.4144396222, - 205.4651663558539 - ], - [ - 11920.65746866532, - 200.94258933262972 - ], - [ - 12060.900497708442, - 196.50609705855894 - ], - [ - 12201.143526751564, - 192.16383333635517 - ], - [ - 12341.386555794685, - 187.9162270180998 - ], - [ - 12481.629584837807, - 183.75947571897544 - ], - [ - 12621.872613880929, - 179.68977295800212 - ], - [ - 12762.11564292405, - 175.70347980706876 - ], - [ - 12902.358671967171, - 171.79774281253378 - ], - [ - 13042.601701010293, - 167.97117497553327 - ], - [ - 13182.844730053414, - 164.22463530858585 - ], - [ - 13323.087759096536, - 160.5595320825772 - ], - [ - 13463.330788139658, - 156.97718431349338 - ], - [ - 13603.573817182778, - 153.47889120692116 - ], - [ - 13743.8168462259, - 150.06539401630423 - ], - [ - 13884.059875269022, - 146.7342305648778 - ] - ], - "8": [ - [ - 0.0, - 978.6524111140432 - ], - [ - 140.24302904312142, - 993.764933975184 - ], - [ - 280.48605808624285, - 969.7132911140936 - ], - [ - 420.7290871293643, - 952.0263829429566 - ], - [ - 560.9721161724857, - 938.6644550525448 - ], - [ - 701.2151452156071, - 922.8655828289036 - ], - [ - 841.4581742587286, - 907.3133108838434 - ], - [ - 981.70120330185, - 892.5006133772619 - ], - [ - 1121.9442323449714, - 877.7343722660335 - ], - [ - 1262.1872613880928, - 863.0998102883016 - ], - [ - 1402.4302904312142, - 848.7180185115858 - ], - [ - 1542.6733194743356, - 834.5420010629529 - ], - [ - 1682.9163485174572, - 820.5528970300932 - ], - [ - 1823.1593775605786, - 806.761420668352 - ], - [ - 1963.4024066037, - 793.1672954416158 - ], - [ - 2103.645435646821, - 779.766472231777 - ], - [ - 2243.888464689943, - 766.5573198777463 - ], - [ - 2384.1314937330644, - 753.5375666137966 - ], - [ - 2524.3745227761856, - 740.7045682620217 - ], - [ - 2664.617551819307, - 728.0558378173205 - ], - [ - 2804.8605808624284, - 715.5887355310019 - ], - [ - 2945.10360990555, - 703.3006301216371 - ], - [ - 3085.346638948671, - 691.1889322019315 - ], - [ - 3225.589667991793, - 679.2513529546073 - ], - [ - 3365.8326970349144, - 667.4859265042159 - ], - [ - 3506.0757260780356, - 655.8907045806382 - ], - [ - 3646.318755121157, - 644.4636894850931 - ], - [ - 3786.5617841642784, - 633.2026839028632 - ], - [ - 3926.8048132074, - 622.1053208638128 - ], - [ - 4067.047842250521, - 611.1692159972831 - ], - [ - 4207.290871293642, - 600.391984898254 - ], - [ - 4347.533900336764, - 589.7713277748257 - ], - [ - 4487.776929379886, - 579.3050817417694 - ], - [ - 4628.019958423007, - 568.9911155119659 - ], - [ - 4768.262987466129, - 558.8272970859113 - ], - [ - 4908.50601650925, - 548.8114974969891 - ], - [ - 5048.749045552371, - 538.9416665344162 - ], - [ - 5188.992074595492, - 529.2158758282319 - ], - [ - 5329.235103638614, - 519.6322400824382 - ], - [ - 5469.478132681736, - 510.18887572990064 - ], - [ - 5609.721161724857, - 500.8839009478121 - ], - [ - 5749.964190767979, - 491.71542602645957 - ], - [ - 5890.2072198111, - 482.6816069302274 - ], - [ - 6030.450248854221, - 473.780621100065 - ], - [ - 6170.693277897342, - 465.01064849145536 - ], - [ - 6310.936306940464, - 456.3698719261497 - ], - [ - 6451.179335983586, - 447.8564974186036 - ], - [ - 6591.422365026707, - 439.4687983171592 - ], - [ - 6731.665394069829, - 431.20517438048364 - ], - [ - 6871.90842311295, - 423.06407484594433 - ], - [ - 7012.151452156071, - 415.04395235946794 - ], - [ - 7152.394481199192, - 407.14326425783213 - ], - [ - 7292.637510242314, - 399.36052049526387 - ], - [ - 7432.880539285436, - 391.69429212045793 - ], - [ - 7573.123568328557, - 384.1431728649543 - ], - [ - 7713.366597371678, - 376.70577005669554 - ], - [ - 7853.6096264148, - 369.3806931612924 - ], - [ - 7993.852655457921, - 362.1665510525083 - ], - [ - 8134.095684501042, - 355.06193898393553 - ], - [ - 8274.338713544164, - 348.06544426160696 - ], - [ - 8414.581742587285, - 341.1757911523716 - ], - [ - 8554.824771630407, - 334.39187106151365 - ], - [ - 8695.067800673529, - 327.71264081048014 - ], - [ - 8835.31082971665, - 321.137063346564 - ], - [ - 8975.553858759771, - 314.66410160200047 - ], - [ - 9115.796887802893, - 308.2927132966691 - ], - [ - 9256.039916846014, - 302.0218554887618 - ], - [ - 9396.282945889136, - 295.85053905960734 - ], - [ - 9536.525974932258, - 289.77785495973467 - ], - [ - 9676.769003975378, - 283.8029460667485 - ], - [ - 9817.0120330185, - 277.92497000275796 - ], - [ - 9957.25506206162, - 272.14308458707325 - ], - [ - 10097.498091104742, - 266.45644763968215 - ], - [ - 10237.741120147864, - 260.864216969251 - ], - [ - 10377.984149190985, - 255.36554555475385 - ], - [ - 10518.227178234107, - 249.95960875627978 - ], - [ - 10658.470207277229, - 244.64574191458044 - ], - [ - 10798.71323632035, - 239.42353737051772 - ], - [ - 10938.956265363471, - 234.29270232060506 - ], - [ - 11079.199294406593, - 229.25295867734073 - ], - [ - 11219.442323449714, - 224.3039592187994 - ], - [ - 11359.685352492836, - 219.44529168428224 - ], - [ - 11499.928381535958, - 214.67662564743878 - ], - [ - 11640.171410579078, - 209.99769742445005 - ], - [ - 11780.4144396222, - 205.40821800416265 - ], - [ - 11920.65746866532, - 200.90781174059603 - ], - [ - 12060.900497708442, - 196.49566247830796 - ], - [ - 12201.143526751564, - 192.17036758370534 - ], - [ - 12341.386555794685, - 187.93027435631367 - ], - [ - 12481.629584837807, - 183.77384014274782 - ], - [ - 12621.872613880929, - 179.69957257277937 - ], - [ - 12762.11564292405, - 175.70614815381973 - ], - [ - 12902.358671967171, - 171.7930152435248 - ], - [ - 13042.601701010293, - 167.96090556022446 - ], - [ - 13182.844730053414, - 164.21123792390387 - ], - [ - 13323.087759096536, - 160.54506382875041 - ], - [ - 13463.330788139658, - 156.96333375518495 - ], - [ - 13603.573817182778, - 153.46697842449927 - ], - [ - 13743.8168462259, - 150.05637147392454 - ], - [ - 13884.059875269022, - 146.72867987355968 - ] - ], - "9": [ - [ - 0.0, - 977.1687070127444 - ], - [ - 140.24302904312142, - 994.0997450368653 - ], - [ - 280.48605808624285, - 970.3780703437725 - ], - [ - 420.7290871293643, - 952.3580681196794 - ], - [ - 560.9721161724857, - 939.0303967469987 - ], - [ - 701.2151452156071, - 923.2660157031577 - ], - [ - 841.4581742587286, - 907.6820360704025 - ], - [ - 981.70120330185, - 892.8590604377283 - ], - [ - 1121.9442323449714, - 878.0895628016976 - ], - [ - 1262.1872613880928, - 863.4463064068411 - ], - [ - 1402.4302904312142, - 849.0568034209485 - ], - [ - 1542.6733194743356, - 834.8742342146281 - ], - [ - 1682.9163485174572, - 820.8780964027845 - ], - [ - 1823.1593775605786, - 807.0799360280065 - ], - [ - 1963.4024066037, - 793.479979102098 - ], - [ - 2103.645435646821, - 780.0733992962456 - ], - [ - 2243.888464689943, - 766.8583556131221 - ], - [ - 2384.1314937330644, - 753.8331415876542 - ], - [ - 2524.3745227761856, - 740.9951299140707 - ], - [ - 2664.617551819307, - 728.3415171054702 - ], - [ - 2804.8605808624284, - 715.8696236524694 - ], - [ - 2945.10360990555, - 703.5768173983968 - ], - [ - 3085.346638948671, - 691.460502507653 - ], - [ - 3225.589667991793, - 679.5184664854415 - ], - [ - 3365.8326970349144, - 667.7487882190038 - ], - [ - 3506.0757260780356, - 656.14955943221 - ], - [ - 3646.318755121157, - 644.7188123785448 - ], - [ - 3786.5617841642784, - 633.4543292003289 - ], - [ - 3926.8048132074, - 622.3536805512862 - ], - [ - 4067.047842250521, - 611.4144094842027 - ], - [ - 4207.290871293642, - 600.63406537866 - ], - [ - 4347.533900336764, - 590.0103234902575 - ], - [ - 4487.776929379886, - 579.5410108053089 - ], - [ - 4628.019958423007, - 569.2239970677485 - ], - [ - 4768.262987466129, - 559.0571530869973 - ], - [ - 4908.50601650925, - 549.0383471576199 - ], - [ - 5048.749045552371, - 539.1654921613675 - ], - [ - 5188.992074595492, - 529.436645919831 - ], - [ - 5329.235103638614, - 519.8499365596606 - ], - [ - 5469.478132681736, - 510.4034956163641 - ], - [ - 5609.721161724857, - 501.09545711907225 - ], - [ - 5749.964190767979, - 491.9239635857319 - ], - [ - 5890.2072198111, - 482.8871987402912 - ], - [ - 6030.450248854221, - 473.9833276507876 - ], - [ - 6170.693277897342, - 465.2105102390128 - ], - [ - 6310.936306940464, - 456.56691016216877 - ], - [ - 6451.179335983586, - 448.05072122619373 - ], - [ - 6591.422365026707, - 439.66020280750234 - ], - [ - 6731.665394069829, - 431.3937440120479 - ], - [ - 6871.90842311295, - 423.2498068438929 - ], - [ - 7012.151452156071, - 415.2268590551952 - ], - [ - 7152.394481199192, - 407.3233724027158 - ], - [ - 7292.637510242314, - 399.53785703429963 - ], - [ - 7432.880539285436, - 391.86887560019346 - ], - [ - 7573.123568328557, - 384.3150380089312 - ], - [ - 7713.366597371678, - 376.8749711075407 - ], - [ - 7853.6096264148, - 369.5473022892899 - ], - [ - 7993.852655457921, - 362.3306582650769 - ], - [ - 8134.095684501042, - 355.2236486389313 - ], - [ - 8274.338713544164, - 348.2248678221401 - ], - [ - 8414.581742587285, - 341.3330259623778 - ], - [ - 8554.824771630407, - 334.54696300258223 - ], - [ - 8695.067800673529, - 327.8655752425735 - ], - [ - 8835.31082971665, - 321.28776582594116 - ], - [ - 8975.553858759771, - 314.8124378909457 - ], - [ - 9115.796887802893, - 308.4384905540624 - ], - [ - 9256.039916846014, - 302.1648377793834 - ], - [ - 9396.282945889136, - 295.99048276308906 - ], - [ - 9536.525974932258, - 289.9145532192177 - ], - [ - 9676.769003975378, - 283.9362634193293 - ], - [ - 9817.0120330185, - 278.0548497581527 - ], - [ - 9957.25506206162, - 272.26954889224675 - ], - [ - 10097.498091104742, - 266.5795974790146 - ], - [ - 10237.741120147864, - 260.98423213941356 - ], - [ - 10377.984149190985, - 255.482682116525 - ], - [ - 10518.227178234107, - 250.07416668260862 - ], - [ - 10658.470207277229, - 244.7579868980688 - ], - [ - 10798.71323632035, - 239.53364026515263 - ], - [ - 10938.956265363471, - 234.40072824575103 - ], - [ - 11079.199294406593, - 229.3588673869123 - ], - [ - 11219.442323449714, - 224.40763681028452 - ], - [ - 11359.685352492836, - 219.54662540932605 - ], - [ - 11499.928381535958, - 214.77554568848572 - ], - [ - 11640.171410579078, - 210.0941737172357 - ], - [ - 11780.4144396222, - 205.5022616764756 - ], - [ - 11920.65746866532, - 200.9994709352652 - ], - [ - 12060.900497708442, - 196.5850183235061 - ], - [ - 12201.143526751564, - 192.2575104256611 - ], - [ - 12341.386555794685, - 188.01531855463222 - ], - [ - 12481.629584837807, - 183.85692643220762 - ], - [ - 12621.872613880929, - 179.78086998535758 - ], - [ - 12762.11564292405, - 175.78584950701776 - ], - [ - 12902.358671967171, - 171.87131257232818 - ], - [ - 13042.601701010293, - 168.03796796525143 - ], - [ - 13182.844730053414, - 164.2871811143642 - ], - [ - 13323.087759096536, - 160.6199332214687 - ], - [ - 13463.330788139658, - 157.0370971906099 - ], - [ - 13603.573817182778, - 153.53952737283936 - ], - [ - 13743.8168462259, - 150.1275451925607 - ], - [ - 13884.059875269022, - 146.79834896278166 - ] - ], - "10": [ - [ - 0.0, - 975.0457337965578 - ], - [ - 140.24302904312142, - 994.852366938782 - ], - [ - 280.48605808624285, - 971.0470034872092 - ], - [ - 420.7290871293643, - 952.663451011989 - ], - [ - 560.9721161724857, - 939.4669826140844 - ], - [ - 701.2151452156071, - 923.7134473489581 - ], - [ - 841.4581742587286, - 908.0882579521149 - ], - [ - 981.70120330185, - 893.2642683593805 - ], - [ - 1121.9442323449714, - 878.4930887121383 - ], - [ - 1262.1872613880928, - 863.841370730345 - ], - [ - 1402.4302904312142, - 849.4461249651761 - ], - [ - 1542.6733194743356, - 835.2594392700491 - ], - [ - 1682.9163485174572, - 821.258846149429 - ], - [ - 1823.1593775605786, - 807.4558826901822 - ], - [ - 1963.4024066037, - 793.8511622537158 - ], - [ - 2103.645435646821, - 780.4399480657762 - ], - [ - 2243.888464689943, - 767.2201799874755 - ], - [ - 2384.1314937330644, - 754.190047244409 - ], - [ - 2524.3745227761856, - 741.3468658294892 - ], - [ - 2664.617551819307, - 728.6880712862812 - ], - [ - 2804.8605808624284, - 716.2114661016545 - ], - [ - 2945.10360990555, - 703.9149512183609 - ], - [ - 3085.346638948671, - 691.7962494132107 - ], - [ - 3225.589667991793, - 679.8521641073896 - ], - [ - 3365.8326970349144, - 668.0791904120131 - ], - [ - 3506.0757260780356, - 656.4738193545446 - ], - [ - 3646.318755121157, - 645.0326772560807 - ], - [ - 3786.5617841642784, - 633.7554269325495 - ], - [ - 3926.8048132074, - 622.6445960435599 - ], - [ - 4067.047842250521, - 611.702818211791 - ], - [ - 4207.290871293642, - 600.9327216447658 - ], - [ - 4347.533900336764, - 590.334129860192 - ], - [ - 4487.776929379886, - 579.8927077043104 - ], - [ - 4628.019958423007, - 569.589685012337 - ], - [ - 4768.262987466129, - 559.4062916422266 - ], - [ - 4908.50601650925, - 549.3237709941326 - ], - [ - 5048.749045552371, - 539.335157990047 - ], - [ - 5188.992074595492, - 529.4814730197376 - ], - [ - 5329.235103638614, - 519.8160226539077 - ], - [ - 5469.478132681736, - 510.3921170488078 - ], - [ - 5609.721161724857, - 501.2630664888523 - ], - [ - 5749.964190767979, - 492.4699174698587 - ], - [ - 5890.2072198111, - 483.91601865653564 - ], - [ - 6030.450248854221, - 475.4180873351401 - ], - [ - 6170.693277897342, - 466.79148728468795 - ], - [ - 6310.936306940464, - 457.85158596903796 - ], - [ - 6451.179335983586, - 448.41378274594297 - ], - [ - 6591.422365026707, - 438.63713733921657 - ], - [ - 6731.665394069829, - 429.66162210559276 - ], - [ - 6871.90842311295, - 421.4850125544256 - ], - [ - 7012.151452156071, - 413.88808764977887 - ], - [ - 7152.394481199192, - 406.6516297660713 - ], - [ - 7292.637510242314, - 399.55645077901323 - ], - [ - 7432.880539285436, - 392.39513195957124 - ], - [ - 7573.123568328557, - 385.1101353527508 - ], - [ - 7713.366597371678, - 377.74743521265583 - ], - [ - 7853.6096264148, - 370.3550639931193 - ], - [ - 7993.852655457921, - 362.9810532651758 - ], - [ - 8134.095684501042, - 355.6734157919674 - ], - [ - 8274.338713544164, - 348.48015978321513 - ], - [ - 8414.581742587285, - 341.4455151035607 - ], - [ - 8554.824771630407, - 334.57730059313513 - ], - [ - 8695.067800673529, - 327.8633867914162 - ], - [ - 8835.31082971665, - 321.2914497920434 - ], - [ - 8975.553858759771, - 314.8491657241997 - ], - [ - 9115.796887802893, - 308.52420757722257 - ], - [ - 9256.039916846014, - 302.30425758099324 - ], - [ - 9396.282945889136, - 296.1770875445304 - ], - [ - 9536.525974932258, - 290.13294371328715 - ], - [ - 9676.769003975378, - 284.1714334208955 - ], - [ - 9817.0120330185, - 278.2944828114105 - ], - [ - 9957.25506206162, - 272.5040171605597 - ], - [ - 10097.498091104742, - 266.8019617447274 - ], - [ - 10237.741120147864, - 261.1902419402465 - ], - [ - 10377.984149190985, - 255.67078043020513 - ], - [ - 10518.227178234107, - 250.24548638534432 - ], - [ - 10658.470207277229, - 244.91630516364066 - ], - [ - 10798.71323632035, - 239.68425878834046 - ], - [ - 10938.956265363471, - 234.5478189461523 - ], - [ - 11079.199294406593, - 229.50508195709432 - ], - [ - 11219.442323449714, - 224.55411715646048 - ], - [ - 11359.685352492836, - 219.69304087845882 - ], - [ - 11499.928381535958, - 214.92062400683514 - ], - [ - 11640.171410579078, - 210.23682900077492 - ], - [ - 11780.4144396222, - 205.64173766853995 - ], - [ - 11920.65746866532, - 201.13534555391442 - ], - [ - 12060.900497708442, - 196.71722021792425 - ], - [ - 12201.143526751564, - 192.3863007563918 - ], - [ - 12341.386555794685, - 188.14106469958375 - ], - [ - 12481.629584837807, - 183.98000746319588 - ], - [ - 12621.872613880929, - 179.90167991617173 - ], - [ - 12762.11564292405, - 175.90478709592395 - ], - [ - 12902.358671967171, - 171.98873678904508 - ], - [ - 13042.601701010293, - 168.15413914776826 - ], - [ - 13182.844730053414, - 164.40224829898918 - ], - [ - 13323.087759096536, - 160.73389643518922 - ], - [ - 13463.330788139658, - 157.14979371761248 - ], - [ - 13603.573817182778, - 153.65063357418308 - ], - [ - 13743.8168462259, - 150.23661599146843 - ], - [ - 13884.059875269022, - 146.90496369043825 - ] - ], - "11": [ - [ - 0.0, - 971.7146757620238 - ], - [ - 140.24302904312142, - 996.5621436407151 - ], - [ - 280.48605808624285, - 971.3494367712138 - ], - [ - 420.7290871293643, - 952.6206821854494 - ], - [ - 560.9721161724857, - 939.8163923791436 - ], - [ - 701.2151452156071, - 923.9980231742422 - ], - [ - 841.4581742587286, - 908.3085255691426 - ], - [ - 981.70120330185, - 893.5224079027645 - ], - [ - 1121.9442323449714, - 878.7736823209516 - ], - [ - 1262.1872613880928, - 864.0696594360486 - ], - [ - 1402.4302904312142, - 849.6518949344969 - ], - [ - 1542.6733194743356, - 835.7099186040464 - ], - [ - 1682.9163485174572, - 821.6121300790828 - ], - [ - 1823.1593775605786, - 806.8001626685243 - ], - [ - 1963.4024066037, - 794.2525393690313 - ], - [ - 2103.645435646821, - 784.5636599172041 - ], - [ - 2243.888464689943, - 765.5688718047534 - ], - [ - 2384.1314937330644, - 749.8259327002658 - ], - [ - 2524.3745227761856, - 742.6614132683347 - ], - [ - 2664.617551819307, - 732.7306540421868 - ], - [ - 2804.8605808624284, - 719.9792751139494 - ], - [ - 2945.10360990555, - 705.8805869139695 - ], - [ - 3085.346638948671, - 691.90725274156 - ], - [ - 3225.589667991793, - 679.0981861486991 - ], - [ - 3365.8326970349144, - 667.294873689881 - ], - [ - 3506.0757260780356, - 656.1339657168745 - ], - [ - 3646.318755121157, - 645.2521552000546 - ], - [ - 3786.5617841642784, - 634.324549141551 - ], - [ - 3926.8048132074, - 623.309440767268 - ], - [ - 4067.047842250521, - 612.2918818442089 - ], - [ - 4207.290871293642, - 601.3575050454009 - ], - [ - 4347.533900336764, - 590.5917774645386 - ], - [ - 4487.776929379886, - 580.0498454208383 - ], - [ - 4628.019958423007, - 569.7178955805566 - ], - [ - 4768.262987466129, - 559.5725900323001 - ], - [ - 4908.50601650925, - 549.5906014918006 - ], - [ - 5048.749045552371, - 539.7487580537371 - ], - [ - 5188.992074595492, - 530.0324660231126 - ], - [ - 5329.235103638614, - 520.4431548325412 - ], - [ - 5469.478132681736, - 510.98403572938247 - ], - [ - 5609.721161724857, - 501.6583195780182 - ], - [ - 5749.964190767979, - 492.46920630708837 - ], - [ - 5890.2072198111, - 483.4190574945821 - ], - [ - 6030.450248854221, - 474.50605909416646 - ], - [ - 6170.693277897342, - 465.7271362411803 - ], - [ - 6310.936306940464, - 457.07921713549 - ], - [ - 6451.179335983586, - 448.55927552915085 - ], - [ - 6591.422365026707, - 440.1643828000717 - ], - [ - 6731.665394069829, - 431.89214153444954 - ], - [ - 6871.90842311295, - 423.7412077206048 - ], - [ - 7012.151452156071, - 415.71039030194606 - ], - [ - 7152.394481199192, - 407.7985032891176 - ], - [ - 7292.637510242314, - 400.00438914934807 - ], - [ - 7432.880539285436, - 392.32692640546406 - ], - [ - 7573.123568328557, - 384.7650153873645 - ], - [ - 7713.366597371678, - 377.3173422907481 - ], - [ - 7853.6096264148, - 369.9825063711943 - ], - [ - 7993.852655457921, - 362.75910532831296 - ], - [ - 8134.095684501042, - 355.64570388087196 - ], - [ - 8274.338713544164, - 348.64085107219097 - ], - [ - 8414.581742587285, - 341.74320180450087 - ], - [ - 8554.824771630407, - 334.95152257479134 - ], - [ - 8695.067800673529, - 328.26467881802597 - ], - [ - 8835.31082971665, - 321.6815603859123 - ], - [ - 8975.553858759771, - 315.2010571357653 - ], - [ - 9115.796887802893, - 308.82206230857696 - ], - [ - 9256.039916846014, - 302.54349699332187 - ], - [ - 9396.282945889136, - 296.36434431524003 - ], - [ - 9536.525974932258, - 290.2837091872438 - ], - [ - 9676.769003975378, - 284.3007953790531 - ], - [ - 9817.0120330185, - 278.41482231016965 - ], - [ - 9957.25506206162, - 272.62500952502614 - ], - [ - 10097.498091104742, - 266.93057657693015 - ], - [ - 10237.741120147864, - 261.3307428794839 - ], - [ - 10377.984149190985, - 255.82471156903216 - ], - [ - 10518.227178234107, - 250.4116486951142 - ], - [ - 10658.470207277229, - 245.09079975081087 - ], - [ - 10798.71323632035, - 239.86160858887825 - ], - [ - 10938.956265363471, - 234.7236386149447 - ], - [ - 11079.199294406593, - 229.67649805290483 - ], - [ - 11219.442323449714, - 224.71980656653173 - ], - [ - 11359.685352492836, - 219.85319829343516 - ], - [ - 11499.928381535958, - 215.07643777402814 - ], - [ - 11640.171410579078, - 210.38934144718223 - ], - [ - 11780.4144396222, - 205.79167895656414 - ], - [ - 11920.65746866532, - 201.2831101573569 - ], - [ - 12060.900497708442, - 196.86285635923295 - ], - [ - 12201.143526751564, - 192.52954377884424 - ], - [ - 12341.386555794685, - 188.28160411903303 - ], - [ - 12481.629584837807, - 184.1176575994672 - ], - [ - 12621.872613880929, - 180.03639076727768 - ], - [ - 12762.11564292405, - 176.03664466448473 - ], - [ - 12902.358671967171, - 172.11795696711965 - ], - [ - 13042.601701010293, - 168.28104638605922 - ], - [ - 13182.844730053414, - 164.52715437597115 - ], - [ - 13323.087759096536, - 160.85696687353226 - ], - [ - 13463.330788139658, - 157.2710213238454 - ], - [ - 13603.573817182778, - 153.76984109354905 - ], - [ - 13743.8168462259, - 150.35349687882533 - ], - [ - 13884.059875269022, - 147.0191996709119 - ] - ], - "12": [ - [ - 0.0, - 971.7673369173294 - ], - [ - 140.24302904312142, - 996.8829447563757 - ], - [ - 280.48605808624285, - 971.2852658122592 - ], - [ - 420.7290871293643, - 952.5442526279285 - ], - [ - 560.9721161724857, - 939.8383418317302 - ], - [ - 701.2151452156071, - 924.0078545424233 - ], - [ - 841.4581742587286, - 908.3068672974701 - ], - [ - 981.70120330185, - 893.5287005421671 - ], - [ - 1121.9442323449714, - 878.7805871084857 - ], - [ - 1262.1872613880928, - 864.0752936804521 - ], - [ - 1402.4302904312142, - 849.6596954730916 - ], - [ - 1542.6733194743356, - 835.7169303386029 - ], - [ - 1682.9163485174572, - 821.6202344255854 - ], - [ - 1823.1593775605786, - 806.8285982903948 - ], - [ - 1963.4024066037, - 794.2795329404482 - ], - [ - 2103.645435646821, - 784.5186886407641 - ], - [ - 2243.888464689943, - 765.5794389540498 - ], - [ - 2384.1314937330644, - 749.918149860086 - ], - [ - 2524.3745227761856, - 742.6796691326396 - ], - [ - 2664.617551819307, - 732.7132840563861 - ], - [ - 2804.8605808624284, - 719.9713061867549 - ], - [ - 2945.10360990555, - 705.8962803331802 - ], - [ - 3085.346638948671, - 691.9301886788949 - ], - [ - 3225.589667991793, - 679.093345388213 - ], - [ - 3365.8326970349144, - 667.2528904515036 - ], - [ - 3506.0757260780356, - 656.0809845434513 - ], - [ - 3646.318755121157, - 645.2496795404601 - ], - [ - 3786.5617841642784, - 634.4604067698381 - ], - [ - 3926.8048132074, - 623.6141098973109 - ], - [ - 4067.047842250521, - 612.6921142812982 - ], - [ - 4207.290871293642, - 601.6759165285018 - ], - [ - 4347.533900336764, - 590.5478912714391 - ], - [ - 4487.776929379886, - 579.36891031858 - ], - [ - 4628.019958423007, - 568.4238819235287 - ], - [ - 4768.262987466129, - 558.0372891863757 - ], - [ - 4908.50601650925, - 548.5331789509368 - ], - [ - 5048.749045552371, - 540.229932903359 - ], - [ - 5188.992074595492, - 533.0120573672774 - ], - [ - 5329.235103638614, - 525.8419898076105 - ], - [ - 5469.478132681736, - 517.5637653463001 - ], - [ - 5609.721161724857, - 507.0219314427862 - ], - [ - 5749.964190767979, - 493.0698471758688 - ], - [ - 5890.2072198111, - 475.2013071605681 - ], - [ - 6030.450248854221, - 456.2021317704834 - ], - [ - 6170.693277897342, - 439.9259610936668 - ], - [ - 6310.936306940464, - 430.22700033850157 - ], - [ - 6451.179335983586, - 430.9530013281794 - ], - [ - 6591.422365026707, - 445.25670976689514 - ], - [ - 6731.665394069829, - 455.25313509373694 - ], - [ - 6871.90842311295, - 453.64983076669563 - ], - [ - 7012.151452156071, - 443.46638495680827 - ], - [ - 7152.394481199192, - 427.72239095190736 - ], - [ - 7292.637510242314, - 409.44711913893804 - ], - [ - 7432.880539285436, - 391.7752553745017 - ], - [ - 7573.123568328557, - 377.3842696141248 - ], - [ - 7713.366597371678, - 366.50284509616915 - ], - [ - 7853.6096264148, - 358.6618960766527 - ], - [ - 7993.852655457921, - 353.37105056458205 - ], - [ - 8134.095684501042, - 349.9962868853716 - ], - [ - 8274.338713544164, - 347.7055420661307 - ], - [ - 8414.581742587285, - 344.80930548415984 - ], - [ - 8554.824771630407, - 340.2464567765545 - ], - [ - 8695.067800673529, - 334.26655399292514 - ], - [ - 8835.31082971665, - 327.24211684423733 - ], - [ - 8975.553858759771, - 319.54566506077094 - ], - [ - 9115.796887802893, - 311.5497219267411 - ], - [ - 9256.039916846014, - 303.6265594537215 - ], - [ - 9396.282945889136, - 296.0834245854997 - ], - [ - 9536.525974932258, - 289.07114313341214 - ], - [ - 9676.769003975378, - 282.6022411243202 - ], - [ - 9817.0120330185, - 276.59135745380974 - ], - [ - 9957.25506206162, - 270.9499246485996 - ], - [ - 10097.498091104742, - 265.5893752421321 - ], - [ - 10237.741120147864, - 260.4211416637571 - ], - [ - 10377.984149190985, - 255.35663957831517 - ], - [ - 10518.227178234107, - 250.30863549549238 - ], - [ - 10658.470207277229, - 245.22279097144087 - ], - [ - 10798.71323632035, - 240.08680984463018 - ], - [ - 10938.956265363471, - 234.93454904744263 - ], - [ - 11079.199294406593, - 229.81515178914603 - ], - [ - 11219.442323449714, - 224.77651139232867 - ], - [ - 11359.685352492836, - 219.84941131915033 - ], - [ - 11499.928381535958, - 215.04692648376584 - ], - [ - 11640.171410579078, - 210.36190942009347 - ], - [ - 11780.4144396222, - 205.78258115419789 - ], - [ - 11920.65746866532, - 201.2970798069497 - ], - [ - 12060.900497708442, - 196.89571500063494 - ], - [ - 12201.143526751564, - 192.57275002133596 - ], - [ - 12341.386555794685, - 188.32669292571967 - ], - [ - 12481.629584837807, - 184.15874715416672 - ], - [ - 12621.872613880929, - 180.07022660354568 - ], - [ - 12762.11564292405, - 176.06258822823787 - ], - [ - 12902.358671967171, - 172.1373111035033 - ], - [ - 13042.601701010293, - 168.29611706743168 - ], - [ - 13182.844730053414, - 164.54021537619093 - ], - [ - 13323.087759096536, - 160.869718353382 - ], - [ - 13463.330788139658, - 157.28457917692398 - ], - [ - 13603.573817182778, - 153.7847368928315 - ], - [ - 13743.8168462259, - 150.36968592894308 - ], - [ - 13884.059875269022, - 147.03622429841863 - ] - ], - "13": [ - [ - 0.0, - 972.0984098563364 - ], - [ - 140.24302904312142, - 997.1162541346929 - ], - [ - 280.48605808624285, - 970.9490542359403 - ], - [ - 420.7290871293643, - 952.049292184464 - ], - [ - 560.9721161724857, - 940.1269656901716 - ], - [ - 701.2151452156071, - 924.5519036579674 - ], - [ - 841.4581742587286, - 905.1544265174759 - ], - [ - 981.70120330185, - 893.8544818325463 - ], - [ - 1121.9442323449714, - 880.2354894535075 - ], - [ - 1262.1872613880928, - 863.4791624521224 - ], - [ - 1402.4302904312142, - 849.3166539763922 - ], - [ - 1542.6733194743356, - 835.766137417246 - ], - [ - 1682.9163485174572, - 821.5106576487663 - ], - [ - 1823.1593775605786, - 806.7607719945197 - ], - [ - 1963.4024066037, - 794.2375865019213 - ], - [ - 2103.645435646821, - 784.0562081528641 - ], - [ - 2243.888464689943, - 765.5018502007651 - ], - [ - 2384.1314937330644, - 750.291991737172 - ], - [ - 2524.3745227761856, - 742.5920331610373 - ], - [ - 2664.617551819307, - 732.3323064832076 - ], - [ - 2804.8605808624284, - 719.5655286516901 - ], - [ - 2945.10360990555, - 705.6228822390663 - ], - [ - 3085.346638948671, - 691.8344043403673 - ], - [ - 3225.589667991793, - 679.107463122636 - ], - [ - 3365.8326970349144, - 667.2984759245234 - ], - [ - 3506.0757260780356, - 656.1037838364615 - ], - [ - 3646.318755121157, - 645.2196185966227 - ], - [ - 3786.5617841642784, - 634.3769206479348 - ], - [ - 3926.8048132074, - 623.4988912275564 - ], - [ - 4067.047842250521, - 612.5699941253157 - ], - [ - 4207.290871293642, - 601.5745270494668 - ], - [ - 4347.533900336764, - 590.4974497120505 - ], - [ - 4487.776929379886, - 579.3884091917257 - ], - [ - 4628.019958423007, - 568.503341429049 - ], - [ - 4768.262987466129, - 558.1371275019842 - ], - [ - 4908.50601650925, - 548.5841907601756 - ], - [ - 5048.749045552371, - 540.1338491389445 - ], - [ - 5188.992074595492, - 532.6885819597813 - ], - [ - 5329.235103638614, - 525.3074012374815 - ], - [ - 5469.478132681736, - 516.9380743274091 - ], - [ - 5609.721161724857, - 506.52888577708194 - ], - [ - 5749.964190767979, - 493.0369828898932 - ], - [ - 5890.2072198111, - 475.9846469759805 - ], - [ - 6030.450248854221, - 457.885628048553 - ], - [ - 6170.693277897342, - 442.25194721791416 - ], - [ - 6310.936306940464, - 432.5963202937225 - ], - [ - 6451.179335983586, - 432.4249130040826 - ], - [ - 6591.422365026707, - 444.6320675766538 - ], - [ - 6731.665394069829, - 452.99963503954825 - ], - [ - 6871.90842311295, - 450.7974193206148 - ], - [ - 7012.151452156071, - 440.79486672400054 - ], - [ - 7152.394481199192, - 425.76142941505753 - ], - [ - 7292.637510242314, - 408.4765967968027 - ], - [ - 7432.880539285436, - 391.82596243994925 - ], - [ - 7573.123568328557, - 378.25038152216376 - ], - [ - 7713.366597371678, - 367.8857268273228 - ], - [ - 7853.6096264148, - 360.2147501957588 - ], - [ - 7993.852655457921, - 354.7201604760186 - ], - [ - 8134.095684501042, - 350.88464021168943 - ], - [ - 8274.338713544164, - 348.04558326487125 - ], - [ - 8414.581742587285, - 344.6740800478928 - ], - [ - 8554.824771630407, - 339.81544896033313 - ], - [ - 8695.067800673529, - 333.6984025012342 - ], - [ - 8835.31082971665, - 326.6621556754297 - ], - [ - 8975.553858759771, - 319.045923513446 - ], - [ - 9115.796887802893, - 311.188922698022 - ], - [ - 9256.039916846014, - 303.4300723414692 - ], - [ - 9396.282945889136, - 296.0422503006509 - ], - [ - 9536.525974932258, - 289.14505978855345 - ], - [ - 9676.769003975378, - 282.7453868320815 - ], - [ - 9817.0120330185, - 276.7655826290178 - ], - [ - 9957.25506206162, - 271.12488763896744 - ], - [ - 10097.498091104742, - 265.7425423227289 - ], - [ - 10237.741120147864, - 260.53778703395744 - ], - [ - 10377.984149190985, - 255.42985125882618 - ], - [ - 10518.227178234107, - 250.339304622222 - ], - [ - 10658.470207277229, - 245.2197542787818 - ], - [ - 10798.71323632035, - 240.06548305490696 - ], - [ - 10938.956265363471, - 234.90885385396925 - ], - [ - 11079.199294406593, - 229.79590370513927 - ], - [ - 11219.442323449714, - 224.77137038929254 - ], - [ - 11359.685352492836, - 219.8614592291941 - ], - [ - 11499.928381535958, - 215.07260196202506 - ], - [ - 11640.171410579078, - 210.3938624915687 - ], - [ - 11780.4144396222, - 205.81020316670643 - ], - [ - 11920.65746866532, - 201.3065189271332 - ], - [ - 12060.900497708442, - 196.8714883821803 - ], - [ - 12201.143526751564, - 192.50893835134465 - ], - [ - 12341.386555794685, - 188.23132829075547 - ], - [ - 12481.629584837807, - 184.0534963873919 - ], - [ - 12621.872613880929, - 179.99039138399505 - ], - [ - 12762.11564292405, - 176.05709617387677 - ], - [ - 12902.358671967171, - 172.26035377747303 - ], - [ - 13042.601701010293, - 168.56970163730776 - ], - [ - 13182.844730053414, - 164.9437877501499 - ], - [ - 13323.087759096536, - 161.340235369637 - ], - [ - 13463.330788139658, - 157.7165083681657 - ], - [ - 13603.573817182778, - 154.03005621925666 - ], - [ - 13743.8168462259, - 150.23852720135002 - ], - [ - 13884.059875269022, - 146.35289636760064 - ] - ], - "14": [ - [ - 0.0, - 974.0381263937168 - ], - [ - 140.24302904312142, - 997.2811336071599 - ], - [ - 280.48605808624285, - 970.0265745092667 - ], - [ - 420.7290871293643, - 951.6145794295137 - ], - [ - 560.9721161724857, - 939.9017262706947 - ], - [ - 701.2151452156071, - 924.169878560734 - ], - [ - 841.4581742587286, - 904.5077906271381 - ], - [ - 981.70120330185, - 893.8484761280251 - ], - [ - 1121.9442323449714, - 880.0412610897072 - ], - [ - 1262.1872613880928, - 863.1012870620112 - ], - [ - 1402.4302904312142, - 849.0249091985431 - ], - [ - 1542.6733194743356, - 835.4651083487497 - ], - [ - 1682.9163485174572, - 821.2219782663545 - ], - [ - 1823.1593775605786, - 806.6192290551014 - ], - [ - 1963.4024066037, - 793.961836784144 - ], - [ - 2103.645435646821, - 783.2461465692045 - ], - [ - 2243.888464689943, - 765.2788649217398 - ], - [ - 2384.1314937330644, - 750.6744494543032 - ], - [ - 2524.3745227761856, - 742.3841606823262 - ], - [ - 2664.617551819307, - 731.7161365559291 - ], - [ - 2804.8605808624284, - 718.8915353135656 - ], - [ - 2945.10360990555, - 705.1089217310865 - ], - [ - 3085.346638948671, - 691.564105191131 - ], - [ - 3225.589667991793, - 679.0107124537935 - ], - [ - 3365.8326970349144, - 667.2723651139796 - ], - [ - 3506.0757260780356, - 656.0551495213248 - ], - [ - 3646.318755121157, - 645.0651358757382 - ], - [ - 3786.5617841642784, - 634.0535234234807 - ], - [ - 3926.8048132074, - 623.0101337264497 - ], - [ - 4067.047842250521, - 612.0033514246933 - ], - [ - 4207.290871293642, - 601.1016138160328 - ], - [ - 4347.533900336764, - 590.3730091388315 - ], - [ - 4487.776929379886, - 579.8553443123355 - ], - [ - 4628.019958423007, - 569.5345973531248 - ], - [ - 4768.262987466129, - 559.3916788081063 - ], - [ - 4908.50601650925, - 549.4075041142233 - ], - [ - 5048.749045552371, - 539.5631803102194 - ], - [ - 5188.992074595492, - 529.8479089182198 - ], - [ - 5329.235103638614, - 520.2628522265996 - ], - [ - 5469.478132681736, - 510.8101456285942 - ], - [ - 5609.721161724857, - 501.49192414434606 - ], - [ - 5749.964190767979, - 492.31035784553535 - ], - [ - 5890.2072198111, - 483.2667895138579 - ], - [ - 6030.450248854221, - 474.3592299794927 - ], - [ - 6170.693277897342, - 465.58489248478094 - ], - [ - 6310.936306940464, - 456.94099215616984 - ], - [ - 6451.179335983586, - 448.42476715117414 - ], - [ - 6591.422365026707, - 440.03355013842025 - ], - [ - 6731.665394069829, - 431.7652125303528 - ], - [ - 6871.90842311295, - 423.6184493770946 - ], - [ - 7012.151452156071, - 415.5920522935813 - ], - [ - 7152.394481199192, - 407.68482073044044 - ], - [ - 7292.637510242314, - 399.89558129252 - ], - [ - 7432.880539285436, - 392.2231509812858 - ], - [ - 7573.123568328557, - 384.6662976133728 - ], - [ - 7713.366597371678, - 377.22361051606566 - ], - [ - 7853.6096264148, - 369.8936245450267 - ], - [ - 7993.852655457921, - 362.67487312750063 - ], - [ - 8134.095684501042, - 355.5658695152988 - ], - [ - 8274.338713544164, - 348.56516076451095 - ], - [ - 8414.581742587285, - 341.6714599350287 - ], - [ - 8554.824771630407, - 334.88363019789506 - ], - [ - 8695.067800673529, - 328.2006155269441 - ], - [ - 8835.31082971665, - 321.62137665159156 - ], - [ - 8975.553858759771, - 315.1448743468137 - ], - [ - 9115.796887802893, - 308.77006846448484 - ], - [ - 9256.039916846014, - 302.4959059386436 - ], - [ - 9396.282945889136, - 296.3213498870733 - ], - [ - 9536.525974932258, - 290.245352221052 - ], - [ - 9676.769003975378, - 284.26691515553705 - ], - [ - 9817.0120330185, - 278.3850582483159 - ], - [ - 9957.25506206162, - 272.5988006671377 - ], - [ - 10097.498091104742, - 266.90716157917984 - ], - [ - 10237.741120147864, - 261.30916011960835 - ], - [ - 10377.984149190985, - 255.8038105857424 - ], - [ - 10518.227178234107, - 250.3901210760941 - ], - [ - 10658.470207277229, - 245.06748035087477 - ], - [ - 10798.71323632035, - 239.83645680218297 - ], - [ - 10938.956265363471, - 234.69796267622607 - ], - [ - 11079.199294406593, - 229.65295608408442 - ], - [ - 11219.442323449714, - 224.7023758097281 - ], - [ - 11359.685352492836, - 219.84567065129838 - ], - [ - 11499.928381535958, - 215.07869888035003 - ], - [ - 11640.171410579078, - 210.39677128420774 - ], - [ - 11780.4144396222, - 205.79513233875474 - ], - [ - 11920.65746866532, - 201.26894778901098 - ], - [ - 12060.900497708442, - 196.8148400213198 - ], - [ - 12201.143526751564, - 192.44110840462014 - ], - [ - 12341.386555794685, - 188.16082564508838 - ], - [ - 12481.629584837807, - 183.98728721195 - ], - [ - 12621.872613880929, - 179.93386017291908 - ], - [ - 12762.11564292405, - 176.01404988628272 - ], - [ - 12902.358671967171, - 172.23194783714794 - ], - [ - 13042.601701010293, - 168.55287271112826 - ], - [ - 13182.844730053414, - 164.93272392616285 - ], - [ - 13323.087759096536, - 161.3268107733856 - ], - [ - 13463.330788139658, - 157.69029094535802 - ], - [ - 13603.573817182778, - 153.97830622788572 - ], - [ - 13743.8168462259, - 150.14690647275924 - ], - [ - 13884.059875269022, - 146.21737128598724 - ] - ], - "15": [ - [ - 0.0, - 975.9245526292757 - ], - [ - 140.24302904312142, - 996.8707479971782 - ], - [ - 280.48605808624285, - 969.2551416896587 - ], - [ - 420.7290871293643, - 951.5217894245978 - ], - [ - 560.9721161724857, - 939.0576147233314 - ], - [ - 701.2151452156071, - 923.1359859630538 - ], - [ - 841.4581742587286, - 907.5107020637982 - ], - [ - 981.70120330185, - 892.789355274558 - ], - [ - 1121.9442323449714, - 878.0368801153706 - ], - [ - 1262.1872613880928, - 863.3895223497964 - ], - [ - 1402.4302904312142, - 849.0125844288985 - ], - [ - 1542.6733194743356, - 834.8426138727599 - ], - [ - 1682.9163485174572, - 820.8575624521013 - ], - [ - 1823.1593775605786, - 807.0733213704599 - ], - [ - 1963.4024066037, - 793.489386831652 - ], - [ - 2103.645435646821, - 780.099581628933 - ], - [ - 2243.888464689943, - 766.901150771195 - ], - [ - 2384.1314937330644, - 753.8915577253431 - ], - [ - 2524.3745227761856, - 741.0678546853259 - ], - [ - 2664.617551819307, - 728.4273817123744 - ], - [ - 2804.8605808624284, - 715.9677355217951 - ], - [ - 2945.10360990555, - 703.6865842041535 - ], - [ - 3085.346638948671, - 691.5815571041765 - ], - [ - 3225.589667991793, - 679.6503882827267 - ], - [ - 3365.8326970349144, - 667.890943215966 - ], - [ - 3506.0757260780356, - 656.301074799914 - ], - [ - 3646.318755121157, - 644.8786021491574 - ], - [ - 3786.5617841642784, - 633.6212625671228 - ], - [ - 3926.8048132074, - 622.526806490432 - ], - [ - 4067.047842250521, - 611.593021088309 - ], - [ - 4207.290871293642, - 600.8176955031246 - ], - [ - 4347.533900336764, - 590.198674382209 - ], - [ - 4487.776929379886, - 579.7338607250146 - ], - [ - 4628.019958423007, - 569.4211407188583 - ], - [ - 4768.262987466129, - 559.2583965204574 - ], - [ - 4908.50601650925, - 549.2435183651053 - ], - [ - 5048.749045552371, - 539.3744429008412 - ], - [ - 5188.992074595492, - 529.6492161581207 - ], - [ - 5329.235103638614, - 520.0659154761257 - ], - [ - 5469.478132681736, - 510.62262192715383 - ], - [ - 5609.721161724857, - 501.3174141057919 - ], - [ - 5749.964190767979, - 492.1484012607572 - ], - [ - 5890.2072198111, - 483.1137518201494 - ], - [ - 6030.450248854221, - 474.21165208189603 - ], - [ - 6170.693277897342, - 465.440281557061 - ], - [ - 6310.936306940464, - 456.79782131454823 - ], - [ - 6451.179335983586, - 448.28248098521277 - ], - [ - 6591.422365026707, - 439.89257063891193 - ], - [ - 6731.665394069829, - 431.62656290509057 - ], - [ - 6871.90842311295, - 423.48302361971776 - ], - [ - 7012.151452156071, - 415.46052799845154 - ], - [ - 7152.394481199192, - 407.55765619726924 - ], - [ - 7292.637510242314, - 399.77299692158556 - ], - [ - 7432.880539285436, - 392.105105083984 - ], - [ - 7573.123568328557, - 384.55251799289124 - ], - [ - 7713.366597371678, - 377.1137735595404 - ], - [ - 7853.6096264148, - 369.78740901072865 - ], - [ - 7993.852655457921, - 362.5719608003981 - ], - [ - 8134.095684501042, - 355.46596175527014 - ], - [ - 8274.338713544164, - 348.4680119346129 - ], - [ - 8414.581742587285, - 341.5769038936954 - ], - [ - 8554.824771630407, - 334.79158112332226 - ], - [ - 8695.067800673529, - 328.11102715661235 - ], - [ - 8835.31082971665, - 321.5342329517754 - ], - [ - 8975.553858759771, - 315.06018951400625 - ], - [ - 9115.796887802893, - 308.6878819861435 - ], - [ - 9256.039916846014, - 302.4162613828259 - ], - [ - 9396.282945889136, - 296.2442695597878 - ], - [ - 9536.525974932258, - 290.17081874598557 - ], - [ - 9676.769003975378, - 284.1948834801883 - ], - [ - 9817.0120330185, - 278.3154643147059 - ], - [ - 9957.25506206162, - 272.5315618727736 - ], - [ - 10097.498091104742, - 266.84217677682096 - ], - [ - 10237.741120147864, - 261.2463095980265 - ], - [ - 10377.984149190985, - 255.74295520641522 - ], - [ - 10518.227178234107, - 250.33110857328205 - ], - [ - 10658.470207277229, - 245.01020996664144 - ], - [ - 10798.71323632035, - 239.78084576589316 - ], - [ - 10938.956265363471, - 234.64388643830853 - ], - [ - 11079.199294406593, - 229.6002486885617 - ], - [ - 11219.442323449714, - 224.65081859642177 - ], - [ - 11359.685352492836, - 219.7948358508288 - ], - [ - 11499.928381535958, - 215.02815952418112 - ], - [ - 11640.171410579078, - 210.34624714662547 - ], - [ - 11780.4144396222, - 205.74450099822334 - ], - [ - 11920.65746866532, - 201.21825381789537 - ], - [ - 12060.900497708442, - 196.76456964688109 - ], - [ - 12201.143526751564, - 192.39211751764677 - ], - [ - 12341.386555794685, - 188.11368870049765 - ], - [ - 12481.629584837807, - 183.94225823302833 - ], - [ - 12621.872613880929, - 179.89086911841815 - ], - [ - 12762.11564292405, - 175.97269584808407 - ], - [ - 12902.358671967171, - 172.19028351453701 - ], - [ - 13042.601701010293, - 168.50814024811638 - ], - [ - 13182.844730053414, - 164.88280186695516 - ], - [ - 13323.087759096536, - 161.27026770949462 - ], - [ - 13463.330788139658, - 157.62639144349154 - ], - [ - 13603.573817182778, - 153.90700993069976 - ], - [ - 13743.8168462259, - 150.0696108868331 - ], - [ - 13884.059875269022, - 146.14265678059203 - ] - ], - "16": [ - [ - 0.0, - 977.1865378848745 - ], - [ - 140.24302904312142, - 996.6400509633053 - ], - [ - 280.48605808624285, - 968.8842022415643 - ], - [ - 420.7290871293643, - 951.385218195992 - ], - [ - 560.9721161724857, - 938.8983301009263 - ], - [ - 701.2151452156071, - 922.967450911019 - ], - [ - 841.4581742587286, - 907.3700097155165 - ], - [ - 981.70120330185, - 892.6625688116268 - ], - [ - 1121.9442323449714, - 877.9179793164783 - ], - [ - 1262.1872613880928, - 863.2783451633717 - ], - [ - 1402.4302904312142, - 848.906249710657 - ], - [ - 1542.6733194743356, - 834.7384253905072 - ], - [ - 1682.9163485174572, - 820.7545499144059 - ], - [ - 1823.1593775605786, - 806.9715614677407 - ], - [ - 1963.4024066037, - 793.389414679443 - ], - [ - 2103.645435646821, - 780.0021372507521 - ], - [ - 2243.888464689943, - 766.8068073255401 - ], - [ - 2384.1314937330644, - 753.8004401490674 - ], - [ - 2524.3745227761856, - 740.979662231409 - ], - [ - 2664.617551819307, - 728.3417980778173 - ], - [ - 2804.8605808624284, - 715.884589529038 - ], - [ - 2945.10360990555, - 703.6058437383749 - ], - [ - 3085.346638948671, - 691.503312140242 - ], - [ - 3225.589667991793, - 679.5747062045707 - ], - [ - 3365.8326970349144, - 667.817861491572 - ], - [ - 3506.0757260780356, - 656.230619430318 - ], - [ - 3646.318755121157, - 644.8107951254875 - ], - [ - 3786.5617841642784, - 633.556160319012 - ], - [ - 3926.8048132074, - 622.4642506661472 - ], - [ - 4067.047842250521, - 611.5325032224447 - ], - [ - 4207.290871293642, - 600.7583571255581 - ], - [ - 4347.533900336764, - 590.139311281051 - ], - [ - 4487.776929379886, - 579.6734985449438 - ], - [ - 4628.019958423007, - 569.3601625576173 - ], - [ - 4768.262987466129, - 559.1986716861691 - ], - [ - 4908.50601650925, - 549.1884051611091 - ], - [ - 5048.749045552371, - 539.3287038582099 - ], - [ - 5188.992074595492, - 529.6164360903356 - ], - [ - 5329.235103638614, - 520.044966898191 - ], - [ - 5469.478132681736, - 510.6073908818681 - ], - [ - 5609.721161724857, - 501.29678772291567 - ], - [ - 5749.964190767979, - 492.106305613302 - ], - [ - 5890.2072198111, - 483.0340687685987 - ], - [ - 6030.450248854221, - 474.09208669038225 - ], - [ - 6170.693277897342, - 465.2949576620094 - ], - [ - 6310.936306940464, - 456.65728508490025 - ], - [ - 6451.179335983586, - 448.1937060877084 - ], - [ - 6591.422365026707, - 439.91802331390824 - ], - [ - 6731.665394069829, - 431.8149924918548 - ], - [ - 6871.90842311295, - 423.8319635846407 - ], - [ - 7012.151452156071, - 415.9136049202689 - ], - [ - 7152.394481199192, - 408.00458901200966 - ], - [ - 7292.637510242314, - 400.0495946453246 - ], - [ - 7432.880539285436, - 391.9939150691048 - ], - [ - 7573.123568328557, - 383.86280090556915 - ], - [ - 7713.366597371678, - 375.8614626464333 - ], - [ - 7853.6096264148, - 368.221986003446 - ], - [ - 7993.852655457921, - 361.13399177525804 - ], - [ - 8134.095684501042, - 354.50005077196596 - ], - [ - 8274.338713544164, - 348.10392714332744 - ], - [ - 8414.581742587285, - 341.73163011060683 - ], - [ - 8554.824771630407, - 335.2666400903644 - ], - [ - 8695.067800673529, - 328.7327383406788 - ], - [ - 8835.31082971665, - 322.16574024507145 - ], - [ - 8975.553858759771, - 315.60146122492193 - ], - [ - 9115.796887802893, - 309.0757100986571 - ], - [ - 9256.039916846014, - 302.62425787893926 - ], - [ - 9396.282945889136, - 296.2828481776061 - ], - [ - 9536.525974932258, - 290.08420728130807 - ], - [ - 9676.769003975378, - 284.03389979429977 - ], - [ - 9817.0120330185, - 278.12229751843535 - ], - [ - 9957.25506206162, - 272.3395720852181 - ], - [ - 10097.498091104742, - 266.67589512821286 - ], - [ - 10237.741120147864, - 261.1214381723165 - ], - [ - 10377.984149190985, - 255.66636596728884 - ], - [ - 10518.227178234107, - 250.30085790590195 - ], - [ - 10658.470207277229, - 245.0155999398789 - ], - [ - 10798.71323632035, - 239.80490046898043 - ], - [ - 10938.956265363471, - 234.67242562407506 - ], - [ - 11079.199294406593, - 229.62396141808463 - ], - [ - 11219.442323449714, - 224.66523325092908 - ], - [ - 11359.685352492836, - 219.80007022559187 - ], - [ - 11499.928381535958, - 215.02780904595363 - ], - [ - 11640.171410579078, - 210.3436391006315 - ], - [ - 11780.4144396222, - 205.742075058322 - ], - [ - 11920.65746866532, - 201.21756563840768 - ], - [ - 12060.900497708442, - 196.76701712868856 - ], - [ - 12201.143526751564, - 192.3986146083676 - ], - [ - 12341.386555794685, - 188.12422947372139 - ], - [ - 12481.629584837807, - 183.95630552900238 - ], - [ - 12621.872613880929, - 179.90735542132705 - ], - [ - 12762.11564292405, - 175.98998468150057 - ], - [ - 12902.358671967171, - 172.20315160710126 - ], - [ - 13042.601701010293, - 168.51112648273977 - ], - [ - 13182.844730053414, - 164.87301771637848 - ], - [ - 13323.087759096536, - 161.24732194784008 - ], - [ - 13463.330788139658, - 157.59239199865266 - ], - [ - 13603.573817182778, - 153.86656406895233 - ], - [ - 13743.8168462259, - 150.0314313471485 - ], - [ - 13884.059875269022, - 146.12532720511467 - ] - ], - "17": [ - [ - 0.0, - 977.3681571334478 - ], - [ - 140.24302904312142, - 996.9317420211219 - ], - [ - 280.48605808624285, - 968.65365084599 - ], - [ - 420.7290871293643, - 950.6689052416824 - ], - [ - 560.9721161724857, - 940.0315336749685 - ], - [ - 701.2151452156071, - 924.1594575519041 - ], - [ - 841.4581742587286, - 899.2460890875111 - ], - [ - 981.70120330185, - 895.2067892627718 - ], - [ - 1121.9442323449714, - 881.8336416514246 - ], - [ - 1262.1872613880928, - 861.5652489004729 - ], - [ - 1402.4302904312142, - 848.1166856030064 - ], - [ - 1542.6733194743356, - 835.7188278278962 - ], - [ - 1682.9163485174572, - 821.0929372939198 - ], - [ - 1823.1593775605786, - 804.0379263356193 - ], - [ - 1963.4024066037, - 793.274670244098 - ], - [ - 2103.645435646821, - 791.6093928702151 - ], - [ - 2243.888464689943, - 762.9133049134954 - ], - [ - 2384.1314937330644, - 740.1212336847755 - ], - [ - 2524.3745227761856, - 743.2930212691788 - ], - [ - 2664.617551819307, - 738.8116218051804 - ], - [ - 2804.8605808624284, - 725.7626377174205 - ], - [ - 2945.10360990555, - 708.4073111359564 - ], - [ - 3085.346638948671, - 691.0025497870141 - ], - [ - 3225.589667991793, - 676.5815085774542 - ], - [ - 3365.8326970349144, - 664.7310383400426 - ], - [ - 3506.0757260780356, - 654.3995739379309 - ], - [ - 3646.318755121157, - 644.5355207194925 - ], - [ - 3786.5617841642784, - 634.2146359782915 - ], - [ - 3926.8048132074, - 623.3426890865549 - ], - [ - 4067.047842250521, - 612.1824762568773 - ], - [ - 4207.290871293642, - 600.998926841211 - ], - [ - 4347.533900336764, - 590.0553031788256 - ], - [ - 4487.776929379886, - 579.4935160406075 - ], - [ - 4628.019958423007, - 569.2331116011779 - ], - [ - 4768.262987466129, - 559.1679022155625 - ], - [ - 4908.50601650925, - 549.1916943615104 - ], - [ - 5048.749045552371, - 539.2021566096831 - ], - [ - 5188.992074595492, - 529.2113859552898 - ], - [ - 5329.235103638614, - 519.367208003921 - ], - [ - 5469.478132681736, - 509.8258166229351 - ], - [ - 5609.721161724857, - 500.74340724785424 - ], - [ - 5749.964190767979, - 492.27540687246955 - ], - [ - 5890.2072198111, - 484.39819854776687 - ], - [ - 6030.450248854221, - 476.68467490752477 - ], - [ - 6170.693277897342, - 468.6521620865494 - ], - [ - 6310.936306940464, - 459.81799085664676 - ], - [ - 6451.179335983586, - 449.69950917992696 - ], - [ - 6591.422365026707, - 438.11783189532093 - ], - [ - 6731.665394069829, - 427.7737166165326 - ], - [ - 6871.90842311295, - 419.1182356242174 - ], - [ - 7012.151452156071, - 411.7025303811428 - ], - [ - 7152.394481199192, - 405.07774290945446 - ], - [ - 7292.637510242314, - 398.79502315653787 - ], - [ - 7432.880539285436, - 392.40815916147494 - ], - [ - 7573.123568328557, - 385.6600058577524 - ], - [ - 7713.366597371678, - 378.6010891132921 - ], - [ - 7853.6096264148, - 371.30953457300654 - ], - [ - 7993.852655457921, - 363.8634674877366 - ], - [ - 8134.095684501042, - 356.3410179966581 - ], - [ - 8274.338713544164, - 348.8203824810924 - ], - [ - 8414.581742587285, - 341.3780448749782 - ], - [ - 8554.824771630407, - 334.06182803884235 - ], - [ - 8695.067800673529, - 326.9207226450462 - ], - [ - 8835.31082971665, - 320.00938037693214 - ], - [ - 8975.553858759771, - 313.3824529460958 - ], - [ - 9115.796887802893, - 307.0945852593206 - ], - [ - 9256.039916846014, - 301.20038105767276 - ], - [ - 9396.282945889136, - 295.75407270485 - ], - [ - 9536.525974932258, - 290.55155318071974 - ], - [ - 9676.769003975378, - 285.1411971608927 - ], - [ - 9817.0120330185, - 279.54363583144624 - ], - [ - 9957.25506206162, - 273.8134443219073 - ], - [ - 10097.498091104742, - 268.00519776472555 - ], - [ - 10237.741120147864, - 262.17347105852195 - ], - [ - 10377.984149190985, - 256.3728334010837 - ], - [ - 10518.227178234107, - 250.65787646495676 - ], - [ - 10658.470207277229, - 245.08375598429603 - ], - [ - 10798.71323632035, - 239.70132317326082 - ], - [ - 10938.956265363471, - 234.51212430707687 - ], - [ - 11079.199294406593, - 229.48614521136727 - ], - [ - 11219.442323449714, - 224.5926522005422 - ], - [ - 11359.685352492836, - 219.79873183290874 - ], - [ - 11499.928381535958, - 215.0721887144072 - ], - [ - 11640.171410579078, - 210.40383452296774 - ], - [ - 11780.4144396222, - 205.79678139575805 - ], - [ - 11920.65746866532, - 201.25423422654717 - ], - [ - 12060.900497708442, - 196.78294798250374 - ], - [ - 12201.143526751564, - 192.39985811384597 - ], - [ - 12341.386555794685, - 188.12002988097703 - ], - [ - 12481.629584837807, - 183.95380472292456 - ], - [ - 12621.872613880929, - 179.91117634554288 - ], - [ - 12762.11564292405, - 176.00206447815742 - ], - [ - 12902.358671967171, - 172.2192042230491 - ], - [ - 13042.601701010293, - 168.52501282806753 - ], - [ - 13182.844730053414, - 164.88000166563234 - ], - [ - 13323.087759096536, - 161.24502648132568 - ], - [ - 13463.330788139658, - 157.5808476435011 - ], - [ - 13603.573817182778, - 153.848208768189 - ], - [ - 13743.8168462259, - 150.01289441535846 - ], - [ - 13884.059875269022, - 146.12057926838187 - ] - ], - "18": [ - [ - 0.0, - 974.4610873677278 - ], - [ - 140.24302904312142, - 998.1941909191057 - ], - [ - 280.48605808624285, - 968.6704326291962 - ], - [ - 420.7290871293643, - 951.0353993046401 - ], - [ - 560.9721161724857, - 938.9479036263915 - ], - [ - 701.2151452156071, - 922.8954276189161 - ], - [ - 841.4581742587286, - 907.249378300469 - ], - [ - 981.70120330185, - 892.5856983063686 - ], - [ - 1121.9442323449714, - 877.8469975495132 - ], - [ - 1262.1872613880928, - 863.2051204438918 - ], - [ - 1402.4302904312142, - 848.8417045524262 - ], - [ - 1542.6733194743356, - 834.681305633764 - ], - [ - 1682.9163485174572, - 820.7011137214362 - ], - [ - 1823.1593775605786, - 806.9217386752101 - ], - [ - 1963.4024066037, - 793.3434635783877 - ], - [ - 2103.645435646821, - 779.9594674943398 - ], - [ - 2243.888464689943, - 766.7671788936033 - ], - [ - 2384.1314937330644, - 753.7637456546145 - ], - [ - 2524.3745227761856, - 740.9453595020652 - ], - [ - 2664.617551819307, - 728.309403806467 - ], - [ - 2804.8605808624284, - 715.8539147455901 - ], - [ - 2945.10360990555, - 703.5769637110901 - ], - [ - 3085.346638948671, - 691.476461018079 - ], - [ - 3225.589667991793, - 679.5500280683862 - ], - [ - 3365.8326970349144, - 667.7953098040696 - ], - [ - 3506.0757260780356, - 656.2099665407005 - ], - [ - 3646.318755121157, - 644.7916495591603 - ], - [ - 3786.5617841642784, - 633.5381282471557 - ], - [ - 3926.8048132074, - 622.447223956161 - ], - [ - 4067.047842250521, - 611.5167294484764 - ], - [ - 4207.290871293642, - 600.7444467049289 - ], - [ - 4347.533900336764, - 590.1281790281593 - ], - [ - 4487.776929379886, - 579.6658164869164 - ], - [ - 4628.019958423007, - 569.3556618470516 - ], - [ - 4768.262987466129, - 559.1960768333029 - ], - [ - 4908.50601650925, - 549.1854185974778 - ], - [ - 5048.749045552371, - 539.3221496296323 - ], - [ - 5188.992074595492, - 529.6041166643715 - ], - [ - 5329.235103638614, - 520.0277434108666 - ], - [ - 5469.478132681736, - 510.58930573208727 - ], - [ - 5609.721161724857, - 501.28508713333554 - ], - [ - 5749.964190767979, - 492.11139364377306 - ], - [ - 5890.2072198111, - 483.0657434803737 - ], - [ - 6030.450248854221, - 474.1509437937897 - ], - [ - 6170.693277897342, - 465.37120251449085 - ], - [ - 6310.936306940464, - 456.7307320417706 - ], - [ - 6451.179335983586, - 448.2337703638044 - ], - [ - 6591.422365026707, - 439.8845796351607 - ], - [ - 6731.665394069829, - 431.6795097045747 - ], - [ - 6871.90842311295, - 423.59904865920606 - ], - [ - 7012.151452156071, - 415.62189760742467 - ], - [ - 7152.394481199192, - 407.7267545425086 - ], - [ - 7292.637510242314, - 399.89231529265027 - ], - [ - 7432.880539285436, - 392.09728658410467 - ], - [ - 7573.123568328557, - 384.34230331427807 - ], - [ - 7713.366597371678, - 376.70650854977276 - ], - [ - 7853.6096264148, - 369.2863536314275 - ], - [ - 7993.852655457921, - 362.1569250329419 - ], - [ - 8134.095684501042, - 355.24860304604846 - ], - [ - 8274.338713544164, - 348.4317912490524 - ], - [ - 8414.581742587285, - 341.57723563467835 - ], - [ - 8554.824771630407, - 334.6217302233568 - ], - [ - 8695.067800673529, - 327.64403269919745 - ], - [ - 8835.31082971665, - 320.74135369870294 - ], - [ - 8975.553858759771, - 314.0109039293824 - ], - [ - 9115.796887802893, - 307.5498872376493 - ], - [ - 9256.039916846014, - 301.45546962609023 - ], - [ - 9396.282945889136, - 295.8243776746731 - ], - [ - 9536.525974932258, - 290.4832462987368 - ], - [ - 9676.769003975378, - 284.9879691956889 - ], - [ - 9817.0120330185, - 279.3517666094196 - ], - [ - 9957.25506206162, - 273.61970308498456 - ], - [ - 10097.498091104742, - 267.8368431654638 - ], - [ - 10237.741120147864, - 262.0482510913068 - ], - [ - 10377.984149190985, - 256.2989796047275 - ], - [ - 10518.227178234107, - 250.63409211412844 - ], - [ - 10658.470207277229, - 245.09934792482636 - ], - [ - 10798.71323632035, - 239.7385835420776 - ], - [ - 10938.956265363471, - 234.5556300021787 - ], - [ - 11079.199294406593, - 229.5256944588611 - ], - [ - 11219.442323449714, - 224.62316820244672 - ], - [ - 11359.685352492836, - 219.81955309308916 - ], - [ - 11499.928381535958, - 215.086382133999 - ], - [ - 11640.171410579078, - 210.41425510321722 - ], - [ - 11780.4144396222, - 205.80509578164137 - ], - [ - 11920.65746866532, - 201.26105623126256 - ], - [ - 12060.900497708442, - 196.79071825680646 - ], - [ - 12201.143526751564, - 192.4109291358297 - ], - [ - 12341.386555794685, - 188.13628321440783 - ], - [ - 12481.629584837807, - 183.9771340217716 - ], - [ - 12621.872613880929, - 179.94351450838928 - ], - [ - 12762.11564292405, - 176.04460127609346 - ], - [ - 12902.358671967171, - 172.26601043904247 - ], - [ - 13042.601701010293, - 168.56955402329174 - ], - [ - 13182.844730053414, - 164.91702497861888 - ], - [ - 13323.087759096536, - 161.27046301811197 - ], - [ - 13463.330788139658, - 157.59180326035244 - ], - [ - 13603.573817182778, - 153.84296338309923 - ], - [ - 13743.8168462259, - 149.9931148614546 - ], - [ - 13884.059875269022, - 146.09570161331914 - ] - ], - "19": [ - [ - 0.0, - 973.8264622157496 - ], - [ - 140.24302904312142, - 998.4530992011906 - ], - [ - 280.48605808624285, - 968.9281933836647 - ], - [ - 420.7290871293643, - 951.1297938740942 - ], - [ - 560.9721161724857, - 939.070517542616 - ], - [ - 701.2151452156071, - 923.0085646697701 - ], - [ - 841.4581742587286, - 907.3452971013623 - ], - [ - 981.70120330185, - 892.6799220907794 - ], - [ - 1121.9442323449714, - 877.9399608059739 - ], - [ - 1262.1872613880928, - 863.2961023013647 - ], - [ - 1402.4302904312142, - 848.9330327001643 - ], - [ - 1542.6733194743356, - 834.7742474256211 - ], - [ - 1682.9163485174572, - 820.7948050071548 - ], - [ - 1823.1593775605786, - 807.0148205810002 - ], - [ - 1963.4024066037, - 793.4356960451081 - ], - [ - 2103.645435646821, - 780.0506495765565 - ], - [ - 2243.888464689943, - 766.8566823820141 - ], - [ - 2384.1314937330644, - 753.8515132384041 - ], - [ - 2524.3745227761856, - 741.0316961570215 - ], - [ - 2664.617551819307, - 728.3944046912912 - ], - [ - 2804.8605808624284, - 715.9375348183663 - ], - [ - 2945.10360990555, - 703.6590833361217 - ], - [ - 3085.346638948671, - 691.5569711410078 - ], - [ - 3225.589667991793, - 679.6289078361494 - ], - [ - 3365.8326970349144, - 667.8726217655504 - ], - [ - 3506.0757260780356, - 656.2858556997119 - ], - [ - 3646.318755121157, - 644.8663260454462 - ], - [ - 3786.5617841642784, - 633.6117867805445 - ], - [ - 3926.8048132074, - 622.5199592006418 - ], - [ - 4067.047842250521, - 611.5885045782103 - ], - [ - 4207.290871293642, - 600.8150860512352 - ], - [ - 4347.533900336764, - 590.1973971615323 - ], - [ - 4487.776929379886, - 579.733368086177 - ], - [ - 4628.019958423007, - 569.4215087738518 - ], - [ - 4768.262987466129, - 559.2604034519893 - ], - [ - 4908.50601650925, - 549.248640355346 - ], - [ - 5048.749045552371, - 539.384905754769 - ], - [ - 5188.992074595492, - 529.6669010044174 - ], - [ - 5329.235103638614, - 520.0904004348705 - ], - [ - 5469.478132681736, - 510.6509939394888 - ], - [ - 5609.721161724857, - 501.3442778940472 - ], - [ - 5749.964190767979, - 492.16584292429235 - ], - [ - 5890.2072198111, - 483.1131154997204 - ], - [ - 6030.450248854221, - 474.1908574228306 - ], - [ - 6170.693277897342, - 465.40565484435837 - ], - [ - 6310.936306940464, - 456.76409665207257 - ], - [ - 6451.179335983586, - 448.2728083118413 - ], - [ - 6591.422365026707, - 439.93838654782286 - ], - [ - 6731.665394069829, - 431.75559220341523 - ], - [ - 6871.90842311295, - 423.6975587240613 - ], - [ - 7012.151452156071, - 415.7351382241819 - ], - [ - 7152.394481199192, - 407.8391801822409 - ], - [ - 7292.637510242314, - 399.98052285652295 - ], - [ - 7432.880539285436, - 392.1300249227161 - ], - [ - 7573.123568328557, - 384.2874975751471 - ], - [ - 7713.366597371678, - 376.5532591667933 - ], - [ - 7853.6096264148, - 369.0494372302504 - ], - [ - 7993.852655457921, - 361.8768202273707 - ], - [ - 8134.095684501042, - 354.9913116865881 - ], - [ - 8274.338713544164, - 348.28863336556634 - ], - [ - 8414.581742587285, - 341.66462849655255 - ], - [ - 8554.824771630407, - 335.0501190968139 - ], - [ - 8695.067800673529, - 328.45221367576005 - ], - [ - 8835.31082971665, - 321.8881196860127 - ], - [ - 8975.553858759771, - 315.3750446553872 - ], - [ - 9115.796887802893, - 308.93018971421924 - ], - [ - 9256.039916846014, - 302.57072150638163 - ], - [ - 9396.282945889136, - 296.3137645445249 - ], - [ - 9536.525974932258, - 290.17565121800703 - ], - [ - 9676.769003975378, - 284.16061007433626 - ], - [ - 9817.0120330185, - 278.26354656600216 - ], - [ - 9957.25506206162, - 272.47912864019355 - ], - [ - 10097.498091104742, - 266.8020242375537 - ], - [ - 10237.741120147864, - 261.2269010743838 - ], - [ - 10377.984149190985, - 255.74841267437225 - ], - [ - 10518.227178234107, - 250.36123453665522 - ], - [ - 10658.470207277229, - 245.060774669505 - ], - [ - 10798.71323632035, - 239.84401575486376 - ], - [ - 10938.956265363471, - 234.71261360187663 - ], - [ - 11079.199294406593, - 229.6695869320126 - ], - [ - 11219.442323449714, - 224.71755229244766 - ], - [ - 11359.685352492836, - 219.85615713573674 - ], - [ - 11499.928381535958, - 215.083348066388 - ], - [ - 11640.171410579078, - 210.39511561482527 - ], - [ - 11780.4144396222, - 205.78701518705378 - ], - [ - 11920.65746866532, - 201.25489960347528 - ], - [ - 12060.900497708442, - 196.80200784085224 - ], - [ - 12201.143526751564, - 192.4382991597197 - ], - [ - 12341.386555794685, - 188.1742431323793 - ], - [ - 12481.629584837807, - 184.02074248563613 - ], - [ - 12621.872613880929, - 179.9887795634578 - ], - [ - 12762.11564292405, - 176.0875680424421 - ], - [ - 12902.358671967171, - 172.3003194146568 - ], - [ - 13042.601701010293, - 168.59187024725 - ], - [ - 13182.844730053414, - 164.9269798323268 - ], - [ - 13323.087759096536, - 161.26970884046915 - ], - [ - 13463.330788139658, - 157.5839428417375 - ], - [ - 13603.573817182778, - 153.83354899670434 - ], - [ - 13743.8168462259, - 149.99204543669273 - ], - [ - 13884.059875269022, - 146.11738081059784 - ] - ] - }, - "atmosphericModelTemperatureProfile": { - "4": [ - [ - 0.0, - 14.175259311314452 - ], - [ - 140.24302904312142, - 17.295716394934587 - ], - [ - 280.48605808624285, - 16.530448964029105 - ], - [ - 420.7290871293643, - 16.037720891287307 - ], - [ - 560.9721161724857, - 15.653543977488612 - ], - [ - 701.2151452156071, - 14.848831673679008 - ], - [ - 841.4581742587286, - 14.011402600877574 - ], - [ - 981.70120330185, - 13.224359460867667 - ], - [ - 1121.9442323449714, - 12.423638547091466 - ], - [ - 1262.1872613880928, - 11.620501194477031 - ], - [ - 1402.4302904312142, - 10.851535929463582 - ], - [ - 1542.6733194743356, - 10.144078221860592 - ], - [ - 1682.9163485174572, - 9.392487846099426 - ], - [ - 1823.1593775605786, - 8.546752423531549 - ], - [ - 1963.4024066037, - 8.067031340087388 - ], - [ - 2103.645435646821, - 7.8657669799462795 - ], - [ - 2243.888464689943, - 6.113131917504724 - ], - [ - 2384.1314937330644, - 5.133037987489223 - ], - [ - 2524.3745227761856, - 5.35669670493324 - ], - [ - 2664.617551819307, - 5.0265261087819075 - ], - [ - 2804.8605808624284, - 4.2015992191246445 - ], - [ - 2945.10360990555, - 3.128138213553452 - ], - [ - 3085.346638948671, - 2.0533723560304917 - ], - [ - 3225.589667991793, - 1.1352247143159488 - ], - [ - 3365.8326970349144, - 0.34008140454190366 - ], - [ - 3506.0757260780356, - -0.39022555343092613 - ], - [ - 3646.318755121157, - -1.1144607432377014 - ], - [ - 3786.5617841642784, - -1.884380842678872 - ], - [ - 3926.8048132074, - -2.703279736814044 - ], - [ - 4067.047842250521, - -3.5585413553336718 - ], - [ - 4207.290871293642, - -4.437364664350579 - ], - [ - 4347.533900336764, - -5.325333877163843 - ], - [ - 4487.776929379886, - -6.212780049534784 - ], - [ - 4628.019958423007, - -7.100510699992798 - ], - [ - 4768.262987466129, - -7.990211619382933 - ], - [ - 4908.50601650925, - -8.884096476447688 - ], - [ - 5048.749045552371, - -9.786070283907211 - ], - [ - 5188.992074595492, - -10.698802949919434 - ], - [ - 5329.235103638614, - -11.62241613991998 - ], - [ - 5469.478132681736, - -12.55691299029333 - ], - [ - 5609.721161724857, - -13.501891751128046 - ], - [ - 5749.964190767979, - -14.455760585367294 - ], - [ - 5890.2072198111, - -15.41704773685224 - ], - [ - 6030.450248854221, - -16.3853931331675 - ], - [ - 6170.693277897342, - -17.360542088264165 - ], - [ - 6310.936306940464, - -18.34227692001798 - ], - [ - 6451.179335983586, - -19.330877445347127 - ], - [ - 6591.422365026707, - -20.327363888862628 - ], - [ - 6731.665394069829, - -21.332290344694492 - ], - [ - 6871.90842311295, - -22.345733195204403 - ], - [ - 7012.151452156071, - -23.367758909603168 - ], - [ - 7152.394481199192, - -24.398458982769995 - ], - [ - 7292.637510242314, - -25.437757778153042 - ], - [ - 7432.880539285436, - -26.484774652584765 - ], - [ - 7573.123568328557, - -27.538575189268943 - ], - [ - 7713.366597371678, - -28.598658226225737 - ], - [ - 7853.6096264148, - -29.66463687755255 - ], - [ - 7993.852655457921, - -30.736123952626112 - ], - [ - 8134.095684501042, - -31.81270048046802 - ], - [ - 8274.338713544164, - -32.893835476333486 - ], - [ - 8414.581742587285, - -33.97888756758776 - ], - [ - 8554.824771630407, - -35.06663665713244 - ], - [ - 8695.067800673529, - -36.15539412369939 - ], - [ - 8835.31082971665, - -37.243362041877624 - ], - [ - 8975.553858759771, - -38.32874166271946 - ], - [ - 9115.796887802893, - -39.40972629911297 - ], - [ - 9256.039916846014, - -40.48469353060315 - ], - [ - 9396.282945889136, - -41.55205790866637 - ], - [ - 9536.525974932258, - -42.60975942028931 - ], - [ - 9676.769003975378, - -43.65578277874309 - ], - [ - 9817.0120330185, - -44.68829336408379 - ], - [ - 9957.25506206162, - -45.70548801794614 - ], - [ - 10097.498091104742, - -46.70556358674669 - ], - [ - 10237.741120147864, - -47.68672194357082 - ], - [ - 10377.984149190985, - -48.64727570101173 - ], - [ - 10518.227178234107, - -49.585394878131595 - ], - [ - 10658.470207277229, - -50.49875780834453 - ], - [ - 10798.71323632035, - -51.38401252284466 - ], - [ - 10938.956265363471, - -52.236904671081994 - ], - [ - 11079.199294406593, - -53.05271765883682 - ], - [ - 11219.442323449714, - -53.828082028908184 - ], - [ - 11359.685352492836, - -54.56227117220025 - ], - [ - 11499.928381535958, - -55.25496746034089 - ], - [ - 11640.171410579078, - -55.90570782204889 - ], - [ - 11780.4144396222, - -56.51437315933034 - ], - [ - 11920.65746866532, - -57.080639615724955 - ], - [ - 12060.900497708442, - -57.60410088110795 - ], - [ - 12201.143526751564, - -58.08703094556553 - ], - [ - 12341.386555794685, - -58.5332752450212 - ], - [ - 12481.629584837807, - -58.946323034626126 - ], - [ - 12621.872613880929, - -59.32949239878881 - ], - [ - 12762.11564292405, - -59.68576501150805 - ], - [ - 12902.358671967171, - -60.01673498387926 - ], - [ - 13042.601701010293, - -60.32356063504205 - ], - [ - 13182.844730053414, - -60.608340605430705 - ], - [ - 13323.087759096536, - -60.873804088146386 - ], - [ - 13463.330788139658, - -61.12280594078468 - ], - [ - 13603.573817182778, - -61.35819914246126 - ], - [ - 13743.8168462259, - -61.58284989372839 - ], - [ - 13884.059875269022, - -61.79898269878744 - ] - ], - "5": [ - [ - 0.0, - 14.138125198676379 - ], - [ - 140.24302904312142, - 17.09488516818948 - ], - [ - 280.48605808624285, - 16.463511849393633 - ], - [ - 420.7290871293643, - 15.957273791195007 - ], - [ - 560.9721161724857, - 15.51497082281941 - ], - [ - 701.2151452156071, - 14.73359050836641 - ], - [ - 841.4581742587286, - 13.921414837435346 - ], - [ - 981.70120330185, - 13.137849105859644 - ], - [ - 1121.9442323449714, - 12.33832072169622 - ], - [ - 1262.1872613880928, - 11.547673930237611 - ], - [ - 1402.4302904312142, - 10.784342978037424 - ], - [ - 1542.6733194743356, - 10.043209699017883 - ], - [ - 1682.9163485174572, - 9.321130983311061 - ], - [ - 1823.1593775605786, - 8.61442390775449 - ], - [ - 1963.4024066037, - 7.912257479166359 - ], - [ - 2103.645435646821, - 7.204672328744908 - ], - [ - 2243.888464689943, - 6.490749356019002 - ], - [ - 2384.1314937330644, - 5.771408382594373 - ], - [ - 2524.3745227761856, - 5.044596459833482 - ], - [ - 2664.617551819307, - 4.3083935968074565 - ], - [ - 2804.8605808624284, - 3.5610503363950676 - ], - [ - 2945.10360990555, - 2.800859798983657 - ], - [ - 3085.346638948671, - 2.0272498098474587 - ], - [ - 3225.589667991793, - 1.2414351125876775 - ], - [ - 3365.8326970349144, - 0.445007886218908 - ], - [ - 3506.0757260780356, - -0.3603666460977387 - ], - [ - 3646.318755121157, - -1.1730558218091505 - ], - [ - 3786.5617841642784, - -1.9929386214782667 - ], - [ - 3926.8048132074, - -2.820976933244192 - ], - [ - 4067.047842250521, - -3.658329012767322 - ], - [ - 4207.290871293642, - -4.506257236820406 - ], - [ - 4347.533900336764, - -5.365455169090564 - ], - [ - 4487.776929379886, - -6.2352396326159125 - ], - [ - 4628.019958423007, - -7.114965137193785 - ], - [ - 4768.262987466129, - -8.003967698607733 - ], - [ - 4908.50601650925, - -8.901653714258996 - ], - [ - 5048.749045552371, - -9.808151978669486 - ], - [ - 5188.992074595492, - -10.72395854093643 - ], - [ - 5329.235103638614, - -11.649226399359247 - ], - [ - 5469.478132681736, - -12.584111386119046 - ], - [ - 5609.721161724857, - -13.528604670083315 - ], - [ - 5749.964190767979, - -14.482268027768827 - ], - [ - 5890.2072198111, - -15.443838529634265 - ], - [ - 6030.450248854221, - -16.412179870400937 - ], - [ - 6170.693277897342, - -17.386174255877062 - ], - [ - 6310.936306940464, - -18.364718716659382 - ], - [ - 6451.179335983586, - -19.346911915292907 - ], - [ - 6591.422365026707, - -20.333337372912393 - ], - [ - 6731.665394069829, - -21.327665897364657 - ], - [ - 6871.90842311295, - -22.333910772440944 - ], - [ - 7012.151452156071, - -23.356090225937624 - ], - [ - 7152.394481199192, - -24.39824460634607 - ], - [ - 7292.637510242314, - -25.46416906264437 - ], - [ - 7432.880539285436, - -26.555164022247112 - ], - [ - 7573.123568328557, - -27.660548982197714 - ], - [ - 7713.366597371678, - -28.764086808342668 - ], - [ - 7853.6096264148, - -29.849553528233084 - ], - [ - 7993.852655457921, - -30.904125823829194 - ], - [ - 8134.095684501042, - -31.937674202558892 - ], - [ - 8274.338713544164, - -32.96957141223876 - ], - [ - 8414.581742587285, - -34.01742360705201 - ], - [ - 8554.824771630407, - -35.08341092225727 - ], - [ - 8695.067800673529, - -36.162922374985435 - ], - [ - 8835.31082971665, - -37.25125589616692 - ], - [ - 8975.553858759771, - -38.34370838785821 - ], - [ - 9115.796887802893, - -39.435568269398814 - ], - [ - 9256.039916846014, - -40.52228425991317 - ], - [ - 9396.282945889136, - -41.59911331468485 - ], - [ - 9536.525974932258, - -42.66188234500959 - ], - [ - 9676.769003975378, - -43.70926200005772 - ], - [ - 9817.0120330185, - -44.740573848666166 - ], - [ - 9957.25506206162, - -45.755160823020326 - ], - [ - 10097.498091104742, - -46.75236585568098 - ], - [ - 10237.741120147864, - -47.731537169278184 - ], - [ - 10377.984149190985, - -48.69207661715003 - ], - [ - 10518.227178234107, - -49.63303496038241 - ], - [ - 10658.470207277229, - -50.552356117930685 - ], - [ - 10798.71323632035, - -51.445741595489174 - ], - [ - 10938.956265363471, - -52.30705634604121 - ], - [ - 11079.199294406593, - -53.12979529097771 - ], - [ - 11219.442323449714, - -53.90936324184275 - ], - [ - 11359.685352492836, - -54.64475881151337 - ], - [ - 11499.928381535958, - -55.33614614579748 - ], - [ - 11640.171410579078, - -55.98399625524354 - ], - [ - 11780.4144396222, - -56.58898909208766 - ], - [ - 11920.65746866532, - -57.15123694066944 - ], - [ - 12060.900497708442, - -57.670280754777934 - ], - [ - 12201.143526751564, - -58.14840501847161 - ], - [ - 12341.386555794685, - -58.589549989387486 - ], - [ - 12481.629584837807, - -58.99755758147348 - ], - [ - 12621.872613880929, - -59.376150607395275 - ], - [ - 12762.11564292405, - -59.7288738929266 - ], - [ - 12902.358671967171, - -60.05801090488873 - ], - [ - 13042.601701010293, - -60.36507052764016 - ], - [ - 13182.844730053414, - -60.65192092796515 - ], - [ - 13323.087759096536, - -60.920757629498645 - ], - [ - 13463.330788139658, - -61.17387280252821 - ], - [ - 13603.573817182778, - -61.413555712830906 - ], - [ - 13743.8168462259, - -61.64210316517283 - ], - [ - 13884.059875269022, - -61.861130397408616 - ] - ], - "6": [ - [ - 0.0, - 13.929301768465946 - ], - [ - 140.24302904312142, - 16.873861965551495 - ], - [ - 280.48605808624285, - 16.30389054335554 - ], - [ - 420.7290871293643, - 15.823924925397163 - ], - [ - 560.9721161724857, - 15.40076487978963 - ], - [ - 701.2151452156071, - 14.63881902720934 - ], - [ - 841.4581742587286, - 13.837747403587997 - ], - [ - 981.70120330185, - 13.05612789232172 - ], - [ - 1121.9442323449714, - 12.25692792878161 - ], - [ - 1262.1872613880928, - 11.472319793741718 - ], - [ - 1402.4302904312142, - 10.715951653705984 - ], - [ - 1542.6733194743356, - 9.97865739291744 - ], - [ - 1682.9163485174572, - 9.257306007522233 - ], - [ - 1823.1593775605786, - 8.54740073207231 - ], - [ - 1963.4024066037, - 7.839511696353455 - ], - [ - 2103.645435646821, - 7.12767921969139 - ], - [ - 2243.888464689943, - 6.41373065782864 - ], - [ - 2384.1314937330644, - 5.694872847037981 - ], - [ - 2524.3745227761856, - 4.9654801682240945 - ], - [ - 2664.617551819307, - 4.225502833133125 - ], - [ - 2804.8605808624284, - 3.4758601436758787 - ], - [ - 2945.10360990555, - 2.7173867102564033 - ], - [ - 3085.346638948671, - 1.9505526322317066 - ], - [ - 3225.589667991793, - 1.1742636716695054 - ], - [ - 3365.8326970349144, - 0.38772976783944657 - ], - [ - 3506.0757260780356, - -0.4096906375486564 - ], - [ - 3646.318755121157, - -1.2181097366824176 - ], - [ - 3786.5617841642784, - -2.0364523603654923 - ], - [ - 3926.8048132074, - -2.8639501585498643 - ], - [ - 4067.047842250521, - -3.700136543991831 - ], - [ - 4207.290871293642, - -4.544745686321959 - ], - [ - 4347.533900336764, - -5.39788256780796 - ], - [ - 4487.776929379886, - -6.259792605999382 - ], - [ - 4628.019958423007, - -7.130996822450298 - ], - [ - 4768.262987466129, - -8.011988753641898 - ], - [ - 4908.50601650925, - -8.90323751727886 - ], - [ - 5048.749045552371, - -9.805535246994092 - ], - [ - 5188.992074595492, - -10.719098261645946 - ], - [ - 5329.235103638614, - -11.643593383161724 - ], - [ - 5469.478132681736, - -12.5786891522638 - ], - [ - 5609.721161724857, - -13.52382965445508 - ], - [ - 5749.964190767979, - -14.477995323001622 - ], - [ - 5890.2072198111, - -15.44028528375247 - ], - [ - 6030.450248854221, - -16.41037527803099 - ], - [ - 6170.693277897342, - -17.38795501267005 - ], - [ - 6310.936306940464, - -18.372736428308908 - ], - [ - 6451.179335983586, - -19.364745315253884 - ], - [ - 6591.422365026707, - -20.364593080297745 - ], - [ - 6731.665394069829, - -21.372887101786073 - ], - [ - 6871.90842311295, - -22.39003649449726 - ], - [ - 7012.151452156071, - -23.416471891267246 - ], - [ - 7152.394481199192, - -24.45263140075731 - ], - [ - 7292.637510242314, - -25.498609662852218 - ], - [ - 7432.880539285436, - -26.553471753368825 - ], - [ - 7573.123568328557, - -27.616010353661224 - ], - [ - 7713.366597371678, - -28.685155604044848 - ], - [ - 7853.6096264148, - -29.75983094800438 - ], - [ - 7993.852655457921, - -30.838965486214367 - ], - [ - 8134.095684501042, - -31.921568590569578 - ], - [ - 8274.338713544164, - -33.00699262246949 - ], - [ - 8414.581742587285, - -34.09473684509071 - ], - [ - 8554.824771630407, - -35.18356093590864 - ], - [ - 8695.067800673529, - -36.27191062262328 - ], - [ - 8835.31082971665, - -37.35820187873499 - ], - [ - 8975.553858759771, - -38.440849551284266 - ], - [ - 9115.796887802893, - -39.518251712553386 - ], - [ - 9256.039916846014, - -40.58891827223128 - ], - [ - 9396.282945889136, - -41.65106272924923 - ], - [ - 9536.525974932258, - -42.7028640044571 - ], - [ - 9676.769003975378, - -43.74287878701951 - ], - [ - 9817.0120330185, - -44.76981749932741 - ], - [ - 9957.25506206162, - -45.78240851112076 - ], - [ - 10097.498091104742, - -46.77938019020501 - ], - [ - 10237.741120147864, - -47.759464074848474 - ], - [ - 10377.984149190985, - -48.72137902742221 - ], - [ - 10518.227178234107, - -49.66354206869321 - ], - [ - 10658.470207277229, - -50.58342168362875 - ], - [ - 10798.71323632035, - -51.476572852085276 - ], - [ - 10938.956265363471, - -52.33730762753689 - ], - [ - 11079.199294406593, - -53.15971464962961 - ], - [ - 11219.442323449714, - -53.93970838757587 - ], - [ - 11359.685352492836, - -54.67630356037419 - ], - [ - 11499.928381535958, - -55.36923303839756 - ], - [ - 11640.171410579078, - -56.01842903921144 - ], - [ - 11780.4144396222, - -56.62399740750897 - ], - [ - 11920.65746866532, - -57.18542679596536 - ], - [ - 12060.900497708442, - -57.70219086536978 - ], - [ - 12201.143526751564, - -58.17716213023786 - ], - [ - 12341.386555794685, - -58.61472161168456 - ], - [ - 12481.629584837807, - -59.01908183664861 - ], - [ - 12621.872613880929, - -59.39434585123191 - ], - [ - 12762.11564292405, - -59.74447793051505 - ], - [ - 12902.358671967171, - -60.0718108668695 - ], - [ - 13042.601701010293, - -60.377623105975516 - ], - [ - 13182.844730053414, - -60.66363039871289 - ], - [ - 13323.087759096536, - -60.93193634384482 - ], - [ - 13463.330788139658, - -61.18474433536178 - ], - [ - 13603.573817182778, - -61.424255346388534 - ], - [ - 13743.8168462259, - -61.6526804696957 - ], - [ - 13884.059875269022, - -61.871611678546124 - ] - ], - "7": [ - [ - 0.0, - 13.673615631342681 - ], - [ - 140.24302904312142, - 16.645655907514584 - ], - [ - 280.48605808624285, - 16.146128760651337 - ], - [ - 420.7290871293643, - 15.688051637079816 - ], - [ - 560.9721161724857, - 15.280571673575713 - ], - [ - 701.2151452156071, - 14.529101255479109 - ], - [ - 841.4581742587286, - 13.737439355256202 - ], - [ - 981.70120330185, - 12.973158051624923 - ], - [ - 1121.9442323449714, - 12.189111140150311 - ], - [ - 1262.1872613880928, - 11.408318993965608 - ], - [ - 1402.4302904312142, - 10.65142864493131 - ], - [ - 1542.6733194743356, - 9.915648265892456 - ], - [ - 1682.9163485174572, - 9.193918364012411 - ], - [ - 1823.1593775605786, - 8.479918095870744 - ], - [ - 1963.4024066037, - 7.767976419648315 - ], - [ - 2103.645435646821, - 7.053055083434536 - ], - [ - 2243.888464689943, - 6.337034757365256 - ], - [ - 2384.1314937330644, - 5.618382560004483 - ], - [ - 2524.3745227761856, - 4.8910634061388905 - ], - [ - 2664.617551819307, - 4.154134631166824 - ], - [ - 2804.8605808624284, - 3.4082065051445816 - ], - [ - 2945.10360990555, - 2.653798748806663 - ], - [ - 3085.346638948671, - 1.8913232759509828 - ], - [ - 3225.589667991793, - 1.1196203631159805 - ], - [ - 3365.8326970349144, - 0.33787190813733936 - ], - [ - 3506.0757260780356, - -0.45455294781438077 - ], - [ - 3646.318755121157, - -1.2577831668483834 - ], - [ - 3786.5617841642784, - -2.0706562021894395 - ], - [ - 3926.8048132074, - -2.892557963746351 - ], - [ - 4067.047842250521, - -3.7232685375385706 - ], - [ - 4207.290871293642, - -4.562770885167765 - ], - [ - 4347.533900336764, - -5.411383622162297 - ], - [ - 4487.776929379886, - -6.269520960062765 - ], - [ - 4628.019958423007, - -7.137690587681367 - ], - [ - 4768.262987466129, - -8.016375644996087 - ], - [ - 4908.50601650925, - -8.905978916760644 - ], - [ - 5048.749045552371, - -9.807068751539083 - ], - [ - 5188.992074595492, - -10.71951124696254 - ], - [ - 5329.235103638614, - -11.642602732403791 - ], - [ - 5469.478132681736, - -12.575636821537813 - ], - [ - 5609.721161724857, - -13.51777674406993 - ], - [ - 5749.964190767979, - -14.468198378210316 - ], - [ - 5890.2072198111, - -15.426657514204747 - ], - [ - 6030.450248854221, - -16.393843960443927 - ], - [ - 6170.693277897342, - -17.370521258835222 - ], - [ - 6310.936306940464, - -18.357462235773806 - ], - [ - 6451.179335983586, - -19.355394361439277 - ], - [ - 6591.422365026707, - -20.36473999981922 - ], - [ - 6731.665394069829, - -21.38488710652628 - ], - [ - 6871.90842311295, - -22.413926129680167 - ], - [ - 7012.151452156071, - -23.449901952155255 - ], - [ - 7152.394481199192, - -24.490849801594223 - ], - [ - 7292.637510242314, - -25.53467068211672 - ], - [ - 7432.880539285436, - -26.579028948656898 - ], - [ - 7573.123568328557, - -27.623422135314176 - ], - [ - 7713.366597371678, - -28.671970039088972 - ], - [ - 7853.6096264148, - -29.72941192087984 - ], - [ - 7993.852655457921, - -30.800496425494714 - ], - [ - 8134.095684501042, - -31.89014967672849 - ], - [ - 8274.338713544164, - -33.00354429807395 - ], - [ - 8414.581742587285, - -34.145486077587904 - ], - [ - 8554.824771630407, - -35.31139499173739 - ], - [ - 8695.067800673529, - -36.4829909832415 - ], - [ - 8835.31082971665, - -37.64083013808771 - ], - [ - 8975.553858759771, - -38.76546752906805 - ], - [ - 9115.796887802893, - -39.83741274332537 - ], - [ - 9256.039916846014, - -40.837091876733666 - ], - [ - 9396.282945889136, - -41.744796923980694 - ], - [ - 9536.525974932258, - -42.6200592030985 - ], - [ - 9676.769003975378, - -43.5543957540953 - ], - [ - 9817.0120330185, - -44.5358903970702 - ], - [ - 9957.25506206162, - -45.55054286722065 - ], - [ - 10097.498091104742, - -46.58435289599161 - ], - [ - 10237.741120147864, - -47.62332236414276 - ], - [ - 10377.984149190985, - -48.65352323003519 - ], - [ - 10518.227178234107, - -49.66104146972744 - ], - [ - 10658.470207277229, - -50.63108082683028 - ], - [ - 10798.71323632035, - -51.54990991035411 - ], - [ - 10938.956265363471, - -52.415757972076584 - ], - [ - 11079.199294406593, - -53.22994527237427 - ], - [ - 11219.442323449714, - -53.99520064056301 - ], - [ - 11359.685352492836, - -54.717193318113075 - ], - [ - 11499.928381535958, - -55.400851108300614 - ], - [ - 11640.171410579078, - -56.04545299253427 - ], - [ - 11780.4144396222, - -56.64943597204551 - ], - [ - 11920.65746866532, - -57.210804144512416 - ], - [ - 12060.900497708442, - -57.7280198312003 - ], - [ - 12201.143526751564, - -58.20264281089885 - ], - [ - 12341.386555794685, - -58.638717783529245 - ], - [ - 12481.629584837807, - -59.04067496107819 - ], - [ - 12621.872613880929, - -59.41280279860152 - ], - [ - 12762.11564292405, - -59.75923740221932 - ], - [ - 12902.358671967171, - -60.082369589161004 - ], - [ - 13042.601701010293, - -60.38382274134201 - ], - [ - 13182.844730053414, - -60.66559993453687 - ], - [ - 13323.087759096536, - -60.930065463008766 - ], - [ - 13463.330788139658, - -61.179707737015114 - ], - [ - 13603.573817182778, - -61.41701040239759 - ], - [ - 13743.8168462259, - -61.644399234641796 - ], - [ - 13884.059875269022, - -61.863543077836965 - ] - ], - "8": [ - [ - 0.0, - 13.439975090308216 - ], - [ - 140.24302904312142, - 16.42396506638434 - ], - [ - 280.48605808624285, - 15.980257899769759 - ], - [ - 420.7290871293643, - 15.550944320790878 - ], - [ - 560.9721161724857, - 15.165982684827359 - ], - [ - 701.2151452156071, - 14.42899860550639 - ], - [ - 841.4581742587286, - 13.646491746917551 - ], - [ - 981.70120330185, - 12.89217468116854 - ], - [ - 1121.9442323449714, - 12.11662858273703 - ], - [ - 1262.1872613880928, - 11.337607093806998 - ], - [ - 1402.4302904312142, - 10.57938890789212 - ], - [ - 1542.6733194743356, - 9.844751799148943 - ], - [ - 1682.9163485174572, - 9.12510604243066 - ], - [ - 1823.1593775605786, - 8.410607360238048 - ], - [ - 1963.4024066037, - 7.6992334484310145 - ], - [ - 2103.645435646821, - 6.992859306646033 - ], - [ - 2243.888464689943, - 6.292511539527964 - ], - [ - 2384.1314937330644, - 5.587564554064161 - ], - [ - 2524.3745227761856, - 4.86491461564629 - ], - [ - 2664.617551819307, - 4.124253732343681 - ], - [ - 2804.8605808624284, - 3.3701123790017746 - ], - [ - 2945.10360990555, - 2.6070970963148614 - ], - [ - 3085.346638948671, - 1.8388747924251276 - ], - [ - 3225.589667991793, - 1.0657308797288352 - ], - [ - 3365.8326970349144, - 0.28694962673260804 - ], - [ - 3506.0757260780356, - -0.4981231497528397 - ], - [ - 3646.318755121157, - -1.2900652517645128 - ], - [ - 3786.5617841642784, - -2.0895945732090957 - ], - [ - 3926.8048132074, - -2.898307388649384 - ], - [ - 4067.047842250521, - -3.718125932128851 - ], - [ - 4207.290871293642, - -4.550955890896724 - ], - [ - 4347.533900336764, - -5.397591292104299 - ], - [ - 4487.776929379886, - -6.257286104212743 - ], - [ - 4628.019958423007, - -7.128977165280812 - ], - [ - 4768.262987466129, - -8.01158200439952 - ], - [ - 4908.50601650925, - -8.904048878422604 - ], - [ - 5048.749045552371, - -9.806254662728053 - ], - [ - 5188.992074595492, - -10.718391184865778 - ], - [ - 5329.235103638614, - -11.640434151492862 - ], - [ - 5469.478132681736, - -12.572362002674057 - ], - [ - 5609.721161724857, - -13.514150406174855 - ], - [ - 5749.964190767979, - -14.465757790096 - ], - [ - 5890.2072198111, - -15.426926459439143 - ], - [ - 6030.450248854221, - -16.39766338553882 - ], - [ - 6170.693277897342, - -17.37798253379735 - ], - [ - 6310.936306940464, - -18.367890587522137 - ], - [ - 6451.179335983586, - -19.36710391240684 - ], - [ - 6591.422365026707, - -20.37523735794567 - ], - [ - 6731.665394069829, - -21.39221740753284 - ], - [ - 6871.90842311295, - -22.417866550085567 - ], - [ - 7012.151452156071, - -23.452027676250825 - ], - [ - 7152.394481199192, - -24.494527664837403 - ], - [ - 7292.637510242314, - -25.545205781568132 - ], - [ - 7432.880539285436, - -26.603695204118985 - ], - [ - 7573.123568328557, - -27.669030159816096 - ], - [ - 7713.366597371678, - -28.74012929469939 - ], - [ - 7853.6096264148, - -29.815892925427203 - ], - [ - 7993.852655457921, - -30.895231250347827 - ], - [ - 8134.095684501042, - -31.977269654785594 - ], - [ - 8274.338713544164, - -33.06128438678576 - ], - [ - 8414.581742587285, - -34.146443093604645 - ], - [ - 8554.824771630407, - -35.231920552393746 - ], - [ - 8695.067800673529, - -36.31687511370732 - ], - [ - 8835.31082971665, - -37.40044696528721 - ], - [ - 8975.553858759771, - -38.481775518158464 - ], - [ - 9115.796887802893, - -39.55994334951268 - ], - [ - 9256.039916846014, - -40.63384819838712 - ], - [ - 9396.282945889136, - -41.702115219260946 - ], - [ - 9536.525974932258, - -42.762820770849025 - ], - [ - 9676.769003975378, - -43.81364430129137 - ], - [ - 9817.0120330185, - -44.85225392972727 - ], - [ - 9957.25506206162, - -45.87633664063846 - ], - [ - 10097.498091104742, - -46.8835794152725 - ], - [ - 10237.741120147864, - -47.871670667123546 - ], - [ - 10377.984149190985, - -48.838445546756525 - ], - [ - 10518.227178234107, - -49.78191566859445 - ], - [ - 10658.470207277229, - -50.69949855810723 - ], - [ - 10798.71323632035, - -51.587344402108506 - ], - [ - 10938.956265363471, - -52.44105291006632 - ], - [ - 11079.199294406593, - -53.2559495508058 - ], - [ - 11219.442323449714, - -54.028650436818666 - ], - [ - 11359.685352492836, - -54.75848669877835 - ], - [ - 11499.928381535958, - -55.44562533840582 - ], - [ - 11640.171410579078, - -56.0899415875718 - ], - [ - 11780.4144396222, - -56.691423001513066 - ], - [ - 11920.65746866532, - -57.249631862616965 - ], - [ - 12060.900497708442, - -57.76434552151923 - ], - [ - 12201.143526751564, - -58.237776818502056 - ], - [ - 12341.386555794685, - -58.673364764986765 - ], - [ - 12481.629584837807, - -59.07448393686376 - ], - [ - 12621.872613880929, - -59.44437593777821 - ], - [ - 12762.11564292405, - -59.78618903005166 - ], - [ - 12902.358671967171, - -60.10191321787451 - ], - [ - 13042.601701010293, - -60.3937163370867 - ], - [ - 13182.844730053414, - -60.66466998062768 - ], - [ - 13323.087759096536, - -60.91821733994445 - ], - [ - 13463.330788139658, - -61.15792228380865 - ], - [ - 13603.573817182778, - -61.38734054262224 - ], - [ - 13743.8168462259, - -61.60987423310999 - ], - [ - 13884.059875269022, - -61.82766813100526 - ] - ], - "9": [ - [ - 0.0, - 13.366333636786528 - ], - [ - 140.24302904312142, - 16.37753599105018 - ], - [ - 280.48605808624285, - 15.885802489272303 - ], - [ - 420.7290871293643, - 15.45068768994212 - ], - [ - 560.9721161724857, - 15.10102651325885 - ], - [ - 701.2151452156071, - 14.377773909648257 - ], - [ - 841.4581742587286, - 13.602468842048232 - ], - [ - 981.70120330185, - 12.85202581532939 - ], - [ - 1121.9442323449714, - 12.074739070072491 - ], - [ - 1262.1872613880928, - 11.295559000432048 - ], - [ - 1402.4302904312142, - 10.543300397611102 - ], - [ - 1542.6733194743356, - 9.817057198337402 - ], - [ - 1682.9163485174572, - 9.103076842032282 - ], - [ - 1823.1593775605786, - 8.391510394604055 - ], - [ - 1963.4024066037, - 7.6830567553132605 - ], - [ - 2103.645435646821, - 6.980662577070627 - ], - [ - 2243.888464689943, - 6.2847120765763655 - ], - [ - 2384.1314937330644, - 5.5849160548899714 - ], - [ - 2524.3745227761856, - 4.866562867529805 - ], - [ - 2664.617551819307, - 4.127534767113113 - ], - [ - 2804.8605808624284, - 3.3730937010519035 - ], - [ - 2945.10360990555, - 2.6089315281960057 - ], - [ - 3085.346638948671, - 1.8400835471946542 - ], - [ - 3225.589667991793, - 1.0679954815192392 - ], - [ - 3365.8326970349144, - 0.29164877281405005 - ], - [ - 3506.0757260780356, - -0.4900973056969368 - ], - [ - 3646.318755121157, - -1.278184133432578 - ], - [ - 3786.5617841642784, - -2.0737602977035614 - ], - [ - 3926.8048132074, - -2.878617568451602 - ], - [ - 4067.047842250521, - -3.6947556116470395 - ], - [ - 4207.290871293642, - -4.524197061482213 - ], - [ - 4347.533900336764, - -5.368326651097557 - ], - [ - 4487.776929379886, - -6.226662343896609 - ], - [ - 4628.019958423007, - -7.0980742307780735 - ], - [ - 4768.262987466129, - -7.981380588055163 - ], - [ - 4908.50601650925, - -8.875294664634236 - ], - [ - 5048.749045552371, - -9.779010340641697 - ], - [ - 5188.992074595492, - -10.692319172987697 - ], - [ - 5329.235103638614, - -11.615120877083934 - ], - [ - 5469.478132681736, - -12.547341916624353 - ], - [ - 5609.721161724857, - -13.488982068589888 - ], - [ - 5749.964190767979, - -14.44023141778707 - ], - [ - 5890.2072198111, - -15.40112726322349 - ], - [ - 6030.450248854221, - -16.37187517803082 - ], - [ - 6170.693277897342, - -17.35267118924275 - ], - [ - 6310.936306940464, - -18.343687328550747 - ], - [ - 6451.179335983586, - -19.344683961448553 - ], - [ - 6591.422365026707, - -20.355150000618526 - ], - [ - 6731.665394069829, - -21.37462321474352 - ], - [ - 6871.90842311295, - -22.402381064331763 - ], - [ - 7012.151452156071, - -23.437707312410396 - ], - [ - 7152.394481199192, - -24.479899863771994 - ], - [ - 7292.637510242314, - -25.528552822858458 - ], - [ - 7432.880539285436, - -26.583408861053417 - ], - [ - 7573.123568328557, - -27.643961726722786 - ], - [ - 7713.366597371678, - -28.709858113624236 - ], - [ - 7853.6096264148, - -29.780775406399734 - ], - [ - 7993.852655457921, - -30.856398850478147 - ], - [ - 8134.095684501042, - -31.936528639234893 - ], - [ - 8274.338713544164, - -33.02068406652092 - ], - [ - 8414.581742587285, - -34.107939934429965 - ], - [ - 8554.824771630407, - -35.197206308613104 - ], - [ - 8695.067800673529, - -36.28720220471348 - ], - [ - 8835.31082971665, - -37.37660183716955 - ], - [ - 8975.553858759771, - -38.464078745820686 - ], - [ - 9115.796887802893, - -39.54826597312209 - ], - [ - 9256.039916846014, - -40.62774768877627 - ], - [ - 9396.282945889136, - -41.701069564641415 - ], - [ - 9536.525974932258, - -42.76621713507454 - ], - [ - 9676.769003975378, - -43.820732546551 - ], - [ - 9817.0120330185, - -44.86220135322402 - ], - [ - 9957.25506206162, - -45.888231478254966 - ], - [ - 10097.498091104742, - -46.89643084080876 - ], - [ - 10237.741120147864, - -47.884406881406136 - ], - [ - 10377.984149190985, - -48.84990475434932 - ], - [ - 10518.227178234107, - -49.79092065632556 - ], - [ - 10658.470207277229, - -50.705121050792876 - ], - [ - 10798.71323632035, - -51.589305648173756 - ], - [ - 10938.956265363471, - -52.43977687147158 - ], - [ - 11079.199294406593, - -53.252489707776576 - ], - [ - 11219.442323449714, - -54.024483664028956 - ], - [ - 11359.685352492836, - -54.75500610395624 - ], - [ - 11499.928381535958, - -55.443779884950594 - ], - [ - 11640.171410579078, - -56.090345722180444 - ], - [ - 11780.4144396222, - -56.69439764890102 - ], - [ - 11920.65746866532, - -57.25524457041759 - ], - [ - 12060.900497708442, - -57.77234419744657 - ], - [ - 12201.143526751564, - -58.24777187739389 - ], - [ - 12341.386555794685, - -58.684495673142244 - ], - [ - 12481.629584837807, - -59.08538042812646 - ], - [ - 12621.872613880929, - -59.453151118650695 - ], - [ - 12762.11564292405, - -59.79047299507181 - ], - [ - 12902.358671967171, - -60.09922198927275 - ], - [ - 13042.601701010293, - -60.381917978000274 - ], - [ - 13182.844730053414, - -60.64254550507044 - ], - [ - 13323.087759096536, - -60.88545291404674 - ], - [ - 13463.330788139658, - -61.115108571293234 - ], - [ - 13603.573817182778, - -61.335973559626616 - ], - [ - 13743.8168462259, - -61.55232098730251 - ], - [ - 13884.059875269022, - -61.76691831802591 - ] - ], - "10": [ - [ - 0.0, - 14.007651641143813 - ], - [ - 140.24302904312142, - 16.855972867703485 - ], - [ - 280.48605808624285, - 15.879474050533865 - ], - [ - 420.7290871293643, - 15.369456172954717 - ], - [ - 560.9721161724857, - 15.1049870476255 - ], - [ - 701.2151452156071, - 14.375985198097196 - ], - [ - 841.4581742587286, - 13.591594178665757 - ], - [ - 981.70120330185, - 12.852018317743246 - ], - [ - 1121.9442323449714, - 12.083151650005185 - ], - [ - 1262.1872613880928, - 11.314556668113578 - ], - [ - 1402.4302904312142, - 10.572828508415716 - ], - [ - 1542.6733194743356, - 9.848701516067273 - ], - [ - 1682.9163485174572, - 9.134424494790888 - ], - [ - 1823.1593775605786, - 8.425270040470963 - ], - [ - 1963.4024066037, - 7.717258762667752 - ], - [ - 2103.645435646821, - 7.012366728232801 - ], - [ - 2243.888464689943, - 6.315166589917552 - ], - [ - 2384.1314937330644, - 5.615937937226838 - ], - [ - 2524.3745227761856, - 4.8972771070135845 - ], - [ - 2664.617551819307, - 4.156128961837682 - ], - [ - 2804.8605808624284, - 3.398745174478204 - ], - [ - 2945.10360990555, - 2.6319607333057493 - ], - [ - 3085.346638948671, - 1.861991770513282 - ], - [ - 3225.589667991793, - 1.0906795697883034 - ], - [ - 3365.8326970349144, - 0.3162999439558255 - ], - [ - 3506.0757260780356, - -0.46312591795566094 - ], - [ - 3646.318755121157, - -1.2493990519460667 - ], - [ - 3786.5617841642784, - -2.043582978863867 - ], - [ - 3926.8048132074, - -2.8465316515955656 - ], - [ - 4067.047842250521, - -3.6592176690579317 - ], - [ - 4207.290871293642, - -4.4825605393231855 - ], - [ - 4347.533900336764, - -5.31773009747944 - ], - [ - 4487.776929379886, - -6.166838651699662 - ], - [ - 4628.019958423007, - -7.031843392239087 - ], - [ - 4768.262987466129, - -7.914611346976902 - ], - [ - 4908.50601650925, - -8.816980291805159 - ], - [ - 5048.749045552371, - -9.738798541643083 - ], - [ - 5188.992074595492, - -10.672430994059024 - ], - [ - 5329.235103638614, - -11.60873378787498 - ], - [ - 5469.478132681736, - -12.538622681853663 - ], - [ - 5609.721161724857, - -13.452998442364846 - ], - [ - 5749.964190767979, - -14.344999076675306 - ], - [ - 5890.2072198111, - -15.229868810233773 - ], - [ - 6030.450248854221, - -16.136556328591546 - ], - [ - 6170.693277897342, - -17.094156599714353 - ], - [ - 6310.936306940464, - -18.13176519865202 - ], - [ - 6451.179335983586, - -19.278180572530122 - ], - [ - 6591.422365026707, - -20.50773295261634 - ], - [ - 6731.665394069829, - -21.64010807552467 - ], - [ - 6871.90842311295, - -22.674446858732306 - ], - [ - 7012.151452156071, - -23.64404910763993 - ], - [ - 7152.394481199192, - -24.582221351884282 - ], - [ - 7292.637510242314, - -25.52256323369367 - ], - [ - 7432.880539285436, - -26.4970654923205 - ], - [ - 7573.123568328557, - -27.5141442695975 - ], - [ - 7713.366597371678, - -28.566298562578236 - ], - [ - 7853.6096264148, - -29.64574350859001 - ], - [ - 7993.852655457921, - -30.744701180106375 - ], - [ - 8134.095684501042, - -31.855514393517446 - ], - [ - 8274.338713544164, - -32.97012127756341 - ], - [ - 8414.581742587285, - -34.08046961374779 - ], - [ - 8554.824771630407, - -35.18393198589577 - ], - [ - 8695.067800673529, - -36.28070478756984 - ], - [ - 8835.31082971665, - -37.37097262012957 - ], - [ - 8975.553858759771, - -38.45491964619084 - ], - [ - 9115.796887802893, - -39.53267912337279 - ], - [ - 9256.039916846014, - -40.604352450636554 - ], - [ - 9396.282945889136, - -41.6703052492471 - ], - [ - 9536.525974932258, - -42.73008335906111 - ], - [ - 9676.769003975378, - -43.78129264582174 - ], - [ - 9817.0120330185, - -44.82123450433415 - ], - [ - 9957.25506206162, - -45.84723348832387 - ], - [ - 10097.498091104742, - -46.85661414829951 - ], - [ - 10237.741120147864, - -47.84670169335407 - ], - [ - 10377.984149190985, - -48.81495823486405 - ], - [ - 10518.227178234107, - -49.75897819514579 - ], - [ - 10658.470207277229, - -50.67574658515212 - ], - [ - 10798.71323632035, - -51.561776561238325 - ], - [ - 10938.956265363471, - -52.41373016184835 - ], - [ - 11079.199294406593, - -53.22796389017961 - ], - [ - 11219.442323449714, - -54.00187058312883 - ], - [ - 11359.685352492836, - -54.73508553463019 - ], - [ - 11499.928381535958, - -55.427112628925684 - ], - [ - 11640.171410579078, - -56.076928343744726 - ], - [ - 11780.4144396222, - -56.6837306387561 - ], - [ - 11920.65746866532, - -57.246380180654484 - ], - [ - 12060.900497708442, - -57.763887877704306 - ], - [ - 12201.143526751564, - -58.238348034110786 - ], - [ - 12341.386555794685, - -58.672912810848274 - ], - [ - 12481.629584837807, - -59.070449146210606 - ], - [ - 12621.872613880929, - -59.433656125703784 - ], - [ - 12762.11564292405, - -59.76517909820773 - ], - [ - 12902.358671967171, - -60.06694928888581 - ], - [ - 13042.601701010293, - -60.34155517787166 - ], - [ - 13182.844730053414, - -60.593484233337485 - ], - [ - 13323.087759096536, - -60.827801713460524 - ], - [ - 13463.330788139658, - -61.0497022130597 - ], - [ - 13603.573817182778, - -61.26437541749402 - ], - [ - 13743.8168462259, - -61.47682835710176 - ], - [ - 13884.059875269022, - -61.69045331670816 - ] - ], - "11": [ - [ - 0.0, - 14.894122922155082 - ], - [ - 140.24302904312142, - 17.81456107530076 - ], - [ - 280.48605808624285, - 16.046363525017714 - ], - [ - 420.7290871293643, - 15.295676370699573 - ], - [ - 560.9721161724857, - 15.13422650305392 - ], - [ - 701.2151452156071, - 14.450939400783756 - ], - [ - 841.4581742587286, - 13.684427299257639 - ], - [ - 981.70120330185, - 12.968241443827967 - ], - [ - 1121.9442323449714, - 12.200263738729994 - ], - [ - 1262.1872613880928, - 11.406227323500485 - ], - [ - 1402.4302904312142, - 10.643474322459602 - ], - [ - 1542.6733194743356, - 9.942191675169985 - ], - [ - 1682.9163485174572, - 9.224054991849457 - ], - [ - 1823.1593775605786, - 8.412841095006568 - ], - [ - 1963.4024066037, - 7.877298666118253 - ], - [ - 2103.645435646821, - 7.685917579342196 - ], - [ - 2243.888464689943, - 6.263507994484058 - ], - [ - 2384.1314937330644, - 5.223956078225329 - ], - [ - 2524.3745227761856, - 5.245225859302435 - ], - [ - 2664.617551819307, - 4.860024126166705 - ], - [ - 2804.8605808624284, - 4.067174099779252 - ], - [ - 2945.10360990555, - 3.0635206789252036 - ], - [ - 3085.346638948671, - 2.0458783751216383 - ], - [ - 3225.589667991793, - 1.1522409363896622 - ], - [ - 3365.8326970349144, - 0.3617669912888305 - ], - [ - 3506.0757260780356, - -0.3732733106161411 - ], - [ - 3646.318755121157, - -1.100525214490836 - ], - [ - 3786.5617841642784, - -1.8631265796892236 - ], - [ - 3926.8048132074, - -2.667430923441917 - ], - [ - 4067.047842250521, - -3.5032905247095387 - ], - [ - 4207.290871293642, - -4.360555478331689 - ], - [ - 4347.533900336764, - -5.2290127618662385 - ], - [ - 4487.776929379886, - -6.1011302141992605 - ], - [ - 4628.019958423007, - -6.977726478672097 - ], - [ - 4768.262987466129, - -7.860787473299499 - ], - [ - 4908.50601650925, - -8.752172357950867 - ], - [ - 5048.749045552371, - -9.653745233080025 - ], - [ - 5188.992074595492, - -10.56685735067322 - ], - [ - 5329.235103638614, - -11.49132022868061 - ], - [ - 5469.478132681736, - -12.42680106884964 - ], - [ - 5609.721161724857, - -13.372981521580739 - ], - [ - 5749.964190767979, - -14.329611506872716 - ], - [ - 5890.2072198111, - -15.296434931623658 - ], - [ - 6030.450248854221, - -16.273519820218336 - ], - [ - 6170.693277897342, - -17.26098407126189 - ], - [ - 6310.936306940464, - -18.258928516016653 - ], - [ - 6451.179335983586, - -19.267135906276998 - ], - [ - 6591.422365026707, - -20.285120327548817 - ], - [ - 6731.665394069829, - -21.312154970992584 - ], - [ - 6871.90842311295, - -22.34707751671252 - ], - [ - 7012.151452156071, - -23.388700928568227 - ], - [ - 7152.394481199192, - -24.435872413054767 - ], - [ - 7292.637510242314, - -25.48777868161968 - ], - [ - 7432.880539285436, - -26.543814784246187 - ], - [ - 7573.123568328557, - -27.603365104630686 - ], - [ - 7713.366597371678, - -28.66635632935968 - ], - [ - 7853.6096264148, - -29.732813080470436 - ], - [ - 7993.852655457921, - -30.802764437352113 - ], - [ - 8134.095684501042, - -31.8763392685312 - ], - [ - 8274.338713544164, - -32.953403423616784 - ], - [ - 8414.581742587285, - -34.033371758605874 - ], - [ - 8554.824771630407, - -35.11542661786044 - ], - [ - 8695.067800673529, - -36.19843255607588 - ], - [ - 8835.31082971665, - -37.281200200830355 - ], - [ - 8975.553858759771, - -38.36253952422234 - ], - [ - 9115.796887802893, - -39.44119939026685 - ], - [ - 9256.039916846014, - -40.515792024237534 - ], - [ - 9396.282945889136, - -41.58512996197721 - ], - [ - 9536.525974932258, - -42.6475621899418 - ], - [ - 9676.769003975378, - -43.70072491974454 - ], - [ - 9817.0120330185, - -44.74218106794888 - ], - [ - 9957.25506206162, - -45.76950453244601 - ], - [ - 10097.498091104742, - -46.780269194592606 - ], - [ - 10237.741120147864, - -47.77205263840363 - ], - [ - 10377.984149190985, - -48.74264077374109 - ], - [ - 10518.227178234107, - -49.6899837862604 - ], - [ - 10658.470207277229, - -50.61096100973117 - ], - [ - 10798.71323632035, - -51.50176481530681 - ], - [ - 10938.956265363471, - -52.35879675250297 - ], - [ - 11079.199294406593, - -53.17828399763296 - ], - [ - 11219.442323449714, - -53.95753760636859 - ], - [ - 11359.685352492836, - -54.696858177135454 - ], - [ - 11499.928381535958, - -55.39617176726915 - ], - [ - 11640.171410579078, - -56.05389307797794 - ], - [ - 11780.4144396222, - -56.66831054824741 - ], - [ - 11920.65746866532, - -57.237422166512474 - ], - [ - 12060.900497708442, - -57.75899189726779 - ], - [ - 12201.143526751564, - -58.23400323554154 - ], - [ - 12341.386555794685, - -58.665822049110595 - ], - [ - 12481.629584837807, - -59.058096525319826 - ], - [ - 12621.872613880929, - -59.41436865120723 - ], - [ - 12762.11564292405, - -59.73807778260826 - ], - [ - 12902.358671967171, - -60.0320259201687 - ], - [ - 13042.601701010293, - -60.299520449913416 - ], - [ - 13182.844730053414, - -60.545515049096686 - ], - [ - 13323.087759096536, - -60.775431365955676 - ], - [ - 13463.330788139658, - -60.994804617952596 - ], - [ - 13603.573817182778, - -61.209169289270434 - ], - [ - 13743.8168462259, - -61.423922488260374 - ], - [ - 13884.059875269022, - -61.642868656163955 - ] - ], - "12": [ - [ - 0.0, - 15.800937669398726 - ], - [ - 140.24302904312142, - 18.636706719669508 - ], - [ - 280.48605808624285, - 16.931941633920047 - ], - [ - 420.7290871293643, - 15.71253801874122 - ], - [ - 560.9721161724857, - 15.154891377285738 - ], - [ - 701.2151452156071, - 14.445821053542172 - ], - [ - 841.4581742587286, - 13.709598293012146 - ], - [ - 981.70120330185, - 12.97195297591722 - ], - [ - 1121.9442323449714, - 12.193704036802457 - ], - [ - 1262.1872613880928, - 11.419293912519624 - ], - [ - 1402.4302904312142, - 10.683985096722047 - ], - [ - 1542.6733194743356, - 10.002147278261406 - ], - [ - 1682.9163485174572, - 9.284400688989217 - ], - [ - 1823.1593775605786, - 8.461335482706447 - ], - [ - 1963.4024066037, - 7.913656956893875 - ], - [ - 2103.645435646821, - 7.7092225871386555 - ], - [ - 2243.888464689943, - 6.285998505797902 - ], - [ - 2384.1314937330644, - 5.253618974417907 - ], - [ - 2524.3745227761856, - 5.2696771963608855 - ], - [ - 2664.617551819307, - 4.881758846720184 - ], - [ - 2804.8605808624284, - 4.089686199946728 - ], - [ - 2945.10360990555, - 3.0875985640313535 - ], - [ - 3085.346638948671, - 2.0693641018306823 - ], - [ - 3225.589667991793, - 1.170866351553763 - ], - [ - 3365.8326970349144, - 0.3747922556272035 - ], - [ - 3506.0757260780356, - -0.3616811396925992 - ], - [ - 3646.318755121157, - -1.0813850092308843 - ], - [ - 3786.5617841642784, - -1.8236932013386935 - ], - [ - 3926.8048132074, - -2.6034826210159885 - ], - [ - 4067.047842250521, - -3.4260880247991814 - ], - [ - 4207.290871293642, - -4.296864350557723 - ], - [ - 4347.533900336764, - -5.22084334193938 - ], - [ - 4487.776929379886, - -6.189520186559445 - ], - [ - 4628.019958423007, - -7.158614524169401 - ], - [ - 4768.262987466129, - -8.077638840475863 - ], - [ - 4908.50601650925, - -8.89609054309306 - ], - [ - 5048.749045552371, - -9.564367618075273 - ], - [ - 5188.992074595492, - -10.09919582294498 - ], - [ - 5329.235103638614, - -10.657179402170893 - ], - [ - 5469.478132681736, - -11.412869458710013 - ], - [ - 5609.721161724857, - -12.540766995904155 - ], - [ - 5749.964190767979, - -14.214278640186201 - ], - [ - 5890.2072198111, - -16.51030857614407 - ], - [ - 6030.450248854221, - -19.00931126864703 - ], - [ - 6170.693277897342, - -21.13073835597987 - ], - [ - 6310.936306940464, - -22.293924415562362 - ], - [ - 6451.179335983586, - -21.91865187064078 - ], - [ - 6591.422365026707, - -19.52724223176693 - ], - [ - 6731.665394069829, - -17.809635780828152 - ], - [ - 6871.90842311295, - -17.860114336971733 - ], - [ - 7012.151452156071, - -19.21975291793482 - ], - [ - 7152.394481199192, - -21.429678469060377 - ], - [ - 7292.637510242314, - -24.0303415996994 - ], - [ - 7432.880539285436, - -26.553129346797427 - ], - [ - 7573.123568328557, - -28.60336434785637 - ], - [ - 7713.366597371678, - -30.15652034157746 - ], - [ - 7853.6096264148, - -31.293291981555683 - ], - [ - 7993.852655457921, - -32.09737760398419 - ], - [ - 8134.095684501042, - -32.67280158796342 - ], - [ - 8274.338713544164, - -33.14361982526569 - ], - [ - 8414.581742587285, - -33.70533606696553 - ], - [ - 8554.824771630407, - -34.48240033281173 - ], - [ - 8695.067800673529, - -35.44720526531393 - ], - [ - 8835.31082971665, - -36.55545171512738 - ], - [ - 8975.553858759771, - -37.76284045078233 - ], - [ - 9115.796887802893, - -39.02503520149651 - ], - [ - 9256.039916846014, - -40.2976032859834 - ], - [ - 9396.282945889136, - -41.54149673169201 - ], - [ - 9536.525974932258, - -42.73066399842981 - ], - [ - 9676.769003975378, - -43.85758730603 - ], - [ - 9817.0120330185, - -44.92921538837377 - ], - [ - 9957.25506206162, - -45.95296539202103 - ], - [ - 10097.498091104742, - -46.93625441259562 - ], - [ - 10237.741120147864, - -47.886502159899216 - ], - [ - 10377.984149190985, - -48.81125931740376 - ], - [ - 10518.227178234107, - -49.718005725756775 - ], - [ - 10658.470207277229, - -50.61087206076811 - ], - [ - 10798.71323632035, - -51.48940477836511 - ], - [ - 10938.956265363471, - -52.3466705218621 - ], - [ - 11079.199294406593, - -53.1735343831702 - ], - [ - 11219.442323449714, - -53.96228003219031 - ], - [ - 11359.685352492836, - -54.70911575525477 - ], - [ - 11499.928381535958, - -55.411887190494284 - ], - [ - 11640.171410579078, - -56.06989219634767 - ], - [ - 11780.4144396222, - -56.68292321761232 - ], - [ - 11920.65746866532, - -57.250257871369776 - ], - [ - 12060.900497708442, - -57.770899119090956 - ], - [ - 12201.143526751564, - -58.246368904392675 - ], - [ - 12341.386555794685, - -58.67992384519642 - ], - [ - 12481.629584837807, - -59.07477153438371 - ], - [ - 12621.872613880929, - -59.433989908441006 - ], - [ - 12762.11564292405, - -59.76055930908575 - ], - [ - 12902.358671967171, - -60.056792900107325 - ], - [ - 13042.601701010293, - -60.32585907630278 - ], - [ - 13182.844730053414, - -60.572685570792494 - ], - [ - 13323.087759096536, - -60.802650830421435 - ], - [ - 13463.330788139658, - -61.02125377929266 - ], - [ - 13603.573817182778, - -61.23399431809803 - ], - [ - 13743.8168462259, - -61.44628418779293 - ], - [ - 13884.059875269022, - -61.662033323847304 - ] - ], - "13": [ - [ - 0.0, - 16.099943790702604 - ], - [ - 140.24302904312142, - 19.130261741049072 - ], - [ - 280.48605808624285, - 17.551793181468046 - ], - [ - 420.7290871293643, - 16.322039411615027 - ], - [ - 560.9721161724857, - 15.633565625680184 - ], - [ - 701.2151452156071, - 14.666039605969328 - ], - [ - 841.4581742587286, - 13.410169586267232 - ], - [ - 981.70120330185, - 13.016473893436213 - ], - [ - 1121.9442323449714, - 12.369090617659168 - ], - [ - 1262.1872613880928, - 11.382508716265464 - ], - [ - 1402.4302904312142, - 10.67081441364512 - ], - [ - 1542.6733194743356, - 10.043736582931647 - ], - [ - 1682.9163485174572, - 9.332475405323004 - ], - [ - 1823.1593775605786, - 8.538064353835477 - ], - [ - 1963.4024066037, - 7.975263683857798 - ], - [ - 2103.645435646821, - 7.6586260012037 - ], - [ - 2243.888464689943, - 6.346009644439053 - ], - [ - 2384.1314937330644, - 5.390303220914266 - ], - [ - 2524.3745227761856, - 5.267175877194375 - ], - [ - 2664.617551819307, - 4.808580191486101 - ], - [ - 2804.8605808624284, - 4.022960518037667 - ], - [ - 2945.10360990555, - 3.0668911623480546 - ], - [ - 3085.346638948671, - 2.097862639648761 - ], - [ - 3225.589667991793, - 1.223616904103989 - ], - [ - 3365.8326970349144, - 0.42854416245076127 - ], - [ - 3506.0757260780356, - -0.3214163506309318 - ], - [ - 3646.318755121157, - -1.0604764525692063 - ], - [ - 3786.5617841642784, - -1.8191623877852547 - ], - [ - 3926.8048132074, - -2.608122329091277 - ], - [ - 4067.047842250521, - -3.4324272117498635 - ], - [ - 4207.290871293642, - -4.297217978355586 - ], - [ - 4347.533900336764, - -5.207893757183014 - ], - [ - 4487.776929379886, - -6.159139182117621 - ], - [ - 4628.019958423007, - -7.112340984613225 - ], - [ - 4768.262987466129, - -8.022782227483592 - ], - [ - 4908.50601650925, - -8.845704328313325 - ], - [ - 5048.749045552371, - -9.536259390074616 - ], - [ - 5188.992074595492, - -10.107807560193637 - ], - [ - 5329.235103638614, - -10.701377304762818 - ], - [ - 5469.478132681736, - -11.474825742189196 - ], - [ - 5609.721161724857, - -12.585960063368969 - ], - [ - 5749.964190767979, - -14.19203056827298 - ], - [ - 5890.2072198111, - -16.365819643345482 - ], - [ - 6030.450248854221, - -18.72916255621091 - ], - [ - 6170.693277897342, - -20.75343612423393 - ], - [ - 6310.936306940464, - -21.909893389492943 - ], - [ - 6451.179335983586, - -21.670173490281318 - ], - [ - 6591.422365026707, - -19.59542304333265 - ], - [ - 6731.665394069829, - -18.123494377684096 - ], - [ - 6871.90842311295, - -18.263697889360184 - ], - [ - 7012.151452156071, - -19.594949012871176 - ], - [ - 7152.394481199192, - -21.696183516474147 - ], - [ - 7292.637510242314, - -24.145626331103283 - ], - [ - 7432.880539285436, - -26.512859571688562 - ], - [ - 7573.123568328557, - -28.439471099103617 - ], - [ - 7713.366597371678, - -29.914501179310545 - ], - [ - 7853.6096264148, - -31.025324187178914 - ], - [ - 7993.852655457921, - -31.859326202720997 - ], - [ - 8134.095684501042, - -32.50400374642013 - ], - [ - 8274.338713544164, - -33.05869197842292 - ], - [ - 8414.581742587285, - -33.6931720212898 - ], - [ - 8554.824771630407, - -34.515995416645445 - ], - [ - 8695.067800673529, - -35.50271040649861 - ], - [ - 8835.31082971665, - -36.613915947465706 - ], - [ - 8975.553858759771, - -37.81021101293972 - ], - [ - 9115.796887802893, - -39.052149039201 - ], - [ - 9256.039916846014, - -40.300300557587875 - ], - [ - 9396.282945889136, - -41.52084984515451 - ], - [ - 9536.525974932258, - -42.692465029055846 - ], - [ - 9676.769003975378, - -43.808716985519155 - ], - [ - 9817.0120330185, - -44.875687054911644 - ], - [ - 9957.25506206162, - -45.89990848629272 - ], - [ - 10097.498091104742, - -46.887914507670885 - ], - [ - 10237.741120147864, - -47.84624123651677 - ], - [ - 10377.984149190985, - -48.78150519853881 - ], - [ - 10518.227178234107, - -49.69997940560848 - ], - [ - 10658.470207277229, - -50.60421193007404 - ], - [ - 10798.71323632035, - -51.492048565157155 - ], - [ - 10938.956265363471, - -52.355717774944736 - ], - [ - 11079.199294406593, - -53.18543158862695 - ], - [ - 11219.442323449714, - -53.972992206173735 - ], - [ - 11359.685352492836, - -54.71517466063787 - ], - [ - 11499.928381535958, - -55.41154169509171 - ], - [ - 11640.171410579078, - -56.06337790313769 - ], - [ - 11780.4144396222, - -56.67260633983283 - ], - [ - 11920.65746866532, - -57.240682387036586 - ], - [ - 12060.900497708442, - -57.768065903746596 - ], - [ - 12201.143526751564, - -58.25488205490513 - ], - [ - 12341.386555794685, - -58.70158768491656 - ], - [ - 12481.629584837807, - -59.10817004622932 - ], - [ - 12621.872613880929, - -59.47443017567392 - ], - [ - 12762.11564292405, - -59.800011220372504 - ], - [ - 12902.358671967171, - -60.0852788463287 - ], - [ - 13042.601701010293, - -60.33672542374349 - ], - [ - 13182.844730053414, - -60.564393350449926 - ], - [ - 13323.087759096536, - -60.779025493283804 - ], - [ - 13463.330788139658, - -60.99151006869622 - ], - [ - 13603.573817182778, - -61.21273677036887 - ], - [ - 13743.8168462259, - -61.453443465752756 - ], - [ - 13884.059875269022, - -61.7169732314223 - ] - ], - "14": [ - [ - 0.0, - 16.20758677566643 - ], - [ - 140.24302904312142, - 18.984103427882886 - ], - [ - 280.48605808624285, - 17.687250134235036 - ], - [ - 420.7290871293643, - 16.925355318944046 - ], - [ - 560.9721161724857, - 16.289154126001524 - ], - [ - 701.2151452156071, - 15.031698681129635 - ], - [ - 841.4581742587286, - 13.525498220665447 - ], - [ - 981.70120330185, - 13.081027593463713 - ], - [ - 1121.9442323449714, - 12.347628159517596 - ], - [ - 1262.1872613880928, - 11.333156249675008 - ], - [ - 1402.4302904312142, - 10.658832098548134 - ], - [ - 1542.6733194743356, - 10.049056166117971 - ], - [ - 1682.9163485174572, - 9.350647021547985 - ], - [ - 1823.1593775605786, - 8.58536133401866 - ], - [ - 1963.4024066037, - 8.017275320702511 - ], - [ - 2103.645435646821, - 7.642890530831114 - ], - [ - 2243.888464689943, - 6.365560370689062 - ], - [ - 2384.1314937330644, - 5.462887315197307 - ], - [ - 2524.3745227761856, - 5.283442318536645 - ], - [ - 2664.617551819307, - 4.781354441684203 - ], - [ - 2804.8605808624284, - 3.9859435065099893 - ], - [ - 2945.10360990555, - 3.044050619334136 - ], - [ - 3085.346638948671, - 2.1021053009640127 - ], - [ - 3225.589667991793, - 1.251770688789119 - ], - [ - 3365.8326970349144, - 0.47107811059825533 - ], - [ - 3506.0757260780356, - -0.2761383114110199 - ], - [ - 3646.318755121157, - -1.0257741532635234 - ], - [ - 3786.5617841642784, - -1.8075964630474988 - ], - [ - 3926.8048132074, - -2.622493060834301 - ], - [ - 4067.047842250521, - -3.4619140234756274 - ], - [ - 4207.290871293642, - -4.317327327326546 - ], - [ - 4347.533900336764, - -5.180485196316494 - ], - [ - 4487.776929379886, - -6.046854393951794 - ], - [ - 4628.019958423007, - -6.918303998909563 - ], - [ - 4768.262987466129, - -7.797342948912551 - ], - [ - 4908.50601650925, - -8.686387950519805 - ], - [ - 5048.749045552371, - -9.587358990745793 - ], - [ - 5188.992074595492, - -10.501048631999387 - ], - [ - 5329.235103638614, - -11.426710356660367 - ], - [ - 5469.478132681736, - -12.363498560825809 - ], - [ - 5609.721161724857, - -13.310652993829313 - ], - [ - 5749.964190767979, - -14.267835773327299 - ], - [ - 5890.2072198111, - -15.23495369327253 - ], - [ - 6030.450248854221, - -16.212159835117056 - ], - [ - 6170.693277897342, - -17.199646124698017 - ], - [ - 6310.936306940464, - -18.197542830578715 - ], - [ - 6451.179335983586, - -19.205622170324126 - ], - [ - 6591.422365026707, - -20.223220780053833 - ], - [ - 6731.665394069829, - -21.249199737623634 - ], - [ - 6871.90842311295, - -22.282273886278496 - ], - [ - 7012.151452156071, - -23.32113747290691 - ], - [ - 7152.394481199192, - -24.364554688304064 - ], - [ - 7292.637510242314, - -25.411535905038342 - ], - [ - 7432.880539285436, - -26.461468390454343 - ], - [ - 7573.123568328557, - -27.514450125924935 - ], - [ - 7713.366597371678, - -28.570938765064838 - ], - [ - 7853.6096264148, - -29.631420783201058 - ], - [ - 7993.852655457921, - -30.69638168843469 - ], - [ - 8134.095684501042, - -31.766331488107763 - ], - [ - 8274.338713544164, - -32.84148785148367 - ], - [ - 8414.581742587285, - -33.921179921200654 - ], - [ - 8554.824771630407, - -35.00405444051985 - ], - [ - 8695.067800673529, - -36.088612924002504 - ], - [ - 8835.31082971665, - -37.17335069644456 - ], - [ - 8975.553858759771, - -38.256762824160226 - ], - [ - 9115.796887802893, - -39.33732393105713 - ], - [ - 9256.039916846014, - -40.41366909305807 - ], - [ - 9396.282945889136, - -41.48475712667074 - ], - [ - 9536.525974932258, - -42.54934163541806 - ], - [ - 9676.769003975378, - -43.60547820920046 - ], - [ - 9817.0120330185, - -44.65105901990029 - ], - [ - 9957.25506206162, - -45.68397690348445 - ], - [ - 10097.498091104742, - -46.70212471722113 - ], - [ - 10237.741120147864, - -47.70339877132168 - ], - [ - 10377.984149190985, - -48.685760075897726 - ], - [ - 10518.227178234107, - -49.647124757093785 - ], - [ - 10658.470207277229, - -50.58406714563742 - ], - [ - 10798.71323632035, - -51.49138028896958 - ], - [ - 10938.956265363471, - -52.363464920643594 - ], - [ - 11079.199294406593, - -53.194494461901016 - ], - [ - 11219.442323449714, - -53.97944915214738 - ], - [ - 11359.685352492836, - -54.717886856581146 - ], - [ - 11499.928381535958, - -55.41184491977686 - ], - [ - 11640.171410579078, - -56.06330152194087 - ], - [ - 11780.4144396222, - -56.67450171640003 - ], - [ - 11920.65746866532, - -57.24766027486417 - ], - [ - 12060.900497708442, - -57.78372369997379 - ], - [ - 12201.143526751564, - -58.28175750456794 - ], - [ - 12341.386555794685, - -58.74078120818448 - ], - [ - 12481.629584837807, - -59.1592302794367 - ], - [ - 12621.872613880929, - -59.53531415265208 - ], - [ - 12762.11564292405, - -59.86703255612043 - ], - [ - 12902.358671967171, - -60.153964153349904 - ], - [ - 13042.601701010293, - -60.40322668784637 - ], - [ - 13182.844730053414, - -60.62598240415915 - ], - [ - 13323.087759096536, - -60.83436426189702 - ], - [ - 13463.330788139658, - -61.04067702654598 - ], - [ - 13603.573817182778, - -61.25722610629344 - ], - [ - 13743.8168462259, - -61.49601947393235 - ], - [ - 13884.059875269022, - -61.759973982993586 - ] - ], - "15": [ - [ - 0.0, - 15.651328726248193 - ], - [ - 140.24302904312142, - 18.56658184343626 - ], - [ - 280.48605808624285, - 17.80360742780815 - ], - [ - 420.7290871293643, - 17.42847218628227 - ], - [ - 560.9721161724857, - 16.735196406128203 - ], - [ - 701.2151452156071, - 15.34774790273851 - ], - [ - 841.4581742587286, - 14.123427251693649 - ], - [ - 981.70120330185, - 13.139848382961976 - ], - [ - 1121.9442323449714, - 12.218453156930774 - ], - [ - 1262.1872613880928, - 11.390178361130765 - ], - [ - 1402.4302904312142, - 10.662876817986373 - ], - [ - 1542.6733194743356, - 9.985047368697911 - ], - [ - 1682.9163485174572, - 9.333077602569526 - ], - [ - 1823.1593775605786, - 8.688679116389151 - ], - [ - 1963.4024066037, - 8.025017660395426 - ], - [ - 2103.645435646821, - 7.334490034536357 - ], - [ - 2243.888464689943, - 6.629620107624505 - ], - [ - 2384.1314937330644, - 5.91440829758481 - ], - [ - 2524.3745227761856, - 5.1824277197847985 - ], - [ - 2664.617551819307, - 4.433396220507703 - ], - [ - 2804.8605808624284, - 3.672051100492807 - ], - [ - 2945.10360990555, - 2.9031804586077223 - ], - [ - 3085.346638948671, - 2.1307297363169315 - ], - [ - 3225.589667991793, - 1.3549315045671435 - ], - [ - 3365.8326970349144, - 0.5733382598927549 - ], - [ - 3506.0757260780356, - -0.21657666147037624 - ], - [ - 3646.318755121157, - -1.0170359868078886 - ], - [ - 3786.5617841642784, - -1.8287957459140676 - ], - [ - 3926.8048132074, - -2.6514285460513185 - ], - [ - 4067.047842250521, - -3.484389109275833 - ], - [ - 4207.290871293642, - -4.327132880410598 - ], - [ - 4347.533900336764, - -5.178912622684386 - ], - [ - 4487.776929379886, - -6.03945442200769 - ], - [ - 4628.019958423007, - -6.909048582215701 - ], - [ - 4768.262987466129, - -7.788027313546975 - ], - [ - 4908.50601650925, - -8.676750105285027 - ], - [ - 5048.749045552371, - -9.576025331613376 - ], - [ - 5188.992074595492, - -10.486466437250407 - ], - [ - 5329.235103638614, - -11.408443708908546 - ], - [ - 5469.478132681736, - -12.34233729985669 - ], - [ - 5609.721161724857, - -13.288539800238842 - ], - [ - 5749.964190767979, - -14.2468432239391 - ], - [ - 5890.2072198111, - -15.216396499165604 - ], - [ - 6030.450248854221, - -16.19620981852529 - ], - [ - 6170.693277897342, - -17.185265960613847 - ], - [ - 6310.936306940464, - -18.182507462504816 - ], - [ - 6451.179335983586, - -19.186885893552518 - ], - [ - 6591.422365026707, - -20.198121582097187 - ], - [ - 6731.665394069829, - -21.216229825471014 - ], - [ - 6871.90842311295, - -22.24111238380337 - ], - [ - 7012.151452156071, - -23.272639524887943 - ], - [ - 7152.394481199192, - -24.310709403836498 - ], - [ - 7292.637510242314, - -25.35520606252353 - ], - [ - 7432.880539285436, - -26.405643969030734 - ], - [ - 7573.123568328557, - -27.461446338874346 - ], - [ - 7713.366597371678, - -28.52233841303117 - ], - [ - 7853.6096264148, - -29.588115265849893 - ], - [ - 7993.852655457921, - -30.658577505426866 - ], - [ - 8134.095684501042, - -31.733569950262485 - ], - [ - 8274.338713544164, - -32.81274701837469 - ], - [ - 8414.581742587285, - -33.895253372572604 - ], - [ - 8554.824771630407, - -34.97988365486742 - ], - [ - 8695.067800673529, - -36.065261686641094 - ], - [ - 8835.31082971665, - -37.149977564629104 - ], - [ - 8975.553858759771, - -38.23262063192289 - ], - [ - 9115.796887802893, - -39.31177117110867 - ], - [ - 9256.039916846014, - -40.38633357364947 - ], - [ - 9396.282945889136, - -41.45544984888644 - ], - [ - 9536.525974932258, - -42.51800175488742 - ], - [ - 9676.769003975378, - -43.572251419553616 - ], - [ - 9817.0120330185, - -44.616352135377305 - ], - [ - 9957.25506206162, - -45.64846536465221 - ], - [ - 10097.498091104742, - -46.666752602115544 - ], - [ - 10237.741120147864, - -47.669378555920446 - ], - [ - 10377.984149190985, - -48.6545083026831 - ], - [ - 10518.227178234107, - -49.62012605621458 - ], - [ - 10658.470207277229, - -50.563002219261115 - ], - [ - 10798.71323632035, - -51.47765829640901 - ], - [ - 10938.956265363471, - -52.35795565762929 - ], - [ - 11079.199294406593, - -53.197472936543946 - ], - [ - 11219.442323449714, - -53.990464208933616 - ], - [ - 11359.685352492836, - -54.73573880734701 - ], - [ - 11499.928381535958, - -55.43518750107509 - ], - [ - 11640.171410579078, - -56.090908902382196 - ], - [ - 11780.4144396222, - -56.70530130935859 - ], - [ - 11920.65746866532, - -57.28087655541575 - ], - [ - 12060.900497708442, - -57.81897134023298 - ], - [ - 12201.143526751564, - -58.31896830518618 - ], - [ - 12341.386555794685, - -58.78012191077224 - ], - [ - 12481.629584837807, - -59.201072210253585 - ], - [ - 12621.872613880929, - -59.580259027786546 - ], - [ - 12762.11564292405, - -59.9158920834651 - ], - [ - 12902.358671967171, - -60.207860548159985 - ], - [ - 13042.601701010293, - -60.46299236208199 - ], - [ - 13182.844730053414, - -60.69186330986707 - ], - [ - 13323.087759096536, - -60.90598740622771 - ], - [ - 13463.330788139658, - -61.117033245255925 - ], - [ - 13603.573817182778, - -61.33667273253935 - ], - [ - 13743.8168462259, - -61.57619035059235 - ], - [ - 13884.059875269022, - -61.83734925816412 - ] - ], - "16": [ - [ - 0.0, - 14.635151135323174 - ], - [ - 140.24302904312142, - 17.887923321875267 - ], - [ - 280.48605808624285, - 17.509286138542993 - ], - [ - 420.7290871293643, - 17.251640775545148 - ], - [ - 560.9721161724857, - 16.755253219253238 - ], - [ - 701.2151452156071, - 15.588816120589037 - ], - [ - 841.4581742587286, - 14.376496763942253 - ], - [ - 981.70120330185, - 13.309913210422216 - ], - [ - 1121.9442323449714, - 12.332074654877694 - ], - [ - 1262.1872613880928, - 11.449954361559312 - ], - [ - 1402.4302904312142, - 10.66815685422704 - ], - [ - 1542.6733194743356, - 9.963593636511803 - ], - [ - 1682.9163485174572, - 9.311419044952023 - ], - [ - 1823.1593775605786, - 8.677473791359088 - ], - [ - 1963.4024066037, - 8.029376252368422 - ], - [ - 2103.645435646821, - 7.357373959331198 - ], - [ - 2243.888464689943, - 6.665165509593276 - ], - [ - 2384.1314937330644, - 5.9530016897411695 - ], - [ - 2524.3745227761856, - 5.219169878534615 - ], - [ - 2664.617551819307, - 4.466721306945952 - ], - [ - 2804.8605808624284, - 3.7016426963123785 - ], - [ - 2945.10360990555, - 2.9298491791196817 - ], - [ - 3085.346638948671, - 2.1561365577621108 - ], - [ - 3225.589667991793, - 1.380548675827737 - ], - [ - 3365.8326970349144, - 0.5996564040730573 - ], - [ - 3506.0757260780356, - -0.1900699079113029 - ], - [ - 3646.318755121157, - -0.9917833592180425 - ], - [ - 3786.5617841642784, - -1.8063260184181436 - ], - [ - 3926.8048132074, - -2.6323421569195835 - ], - [ - 4067.047842250521, - -3.4682505785137017 - ], - [ - 4207.290871293642, - -4.312425303869909 - ], - [ - 4347.533900336764, - -5.1634501531164 - ], - [ - 4487.776929379886, - -6.021444671624173 - ], - [ - 4628.019958423007, - -6.887333328437558 - ], - [ - 4768.262987466129, - -7.762074792107125 - ], - [ - 4908.50601650925, - -8.646746887433427 - ], - [ - 5048.749045552371, - -9.542454837110501 - ], - [ - 5188.992074595492, - -10.449863283619681 - ], - [ - 5329.235103638614, - -11.369900000070986 - ], - [ - 5469.478132681736, - -12.303544859189353 - ], - [ - 5609.721161724857, - -13.2517633728262 - ], - [ - 5749.964190767979, - -14.214931252433615 - ], - [ - 5890.2072198111, - -15.192015383011004 - ], - [ - 6030.450248854221, - -16.179600026252103 - ], - [ - 6170.693277897342, - -17.173850832377628 - ], - [ - 6310.936306940464, - -18.170861229228713 - ], - [ - 6451.179335983586, - -19.16671486879696 - ], - [ - 6591.422365026707, - -20.15882269301732 - ], - [ - 6731.665394069829, - -21.149711411410415 - ], - [ - 6871.90842311295, - -22.14731495414589 - ], - [ - 7012.151452156071, - -23.159917217649216 - ], - [ - 7152.394481199192, - -24.19586649621808 - ], - [ - 7292.637510242314, - -25.263544289162635 - ], - [ - 7432.880539285436, - -26.370434226676842 - ], - [ - 7573.123568328557, - -27.511715810440627 - ], - [ - 7713.366597371678, - -28.656811513689743 - ], - [ - 7853.6096264148, - -29.77137134715034 - ], - [ - 7993.852655457921, - -30.827185729812285 - ], - [ - 8134.095684501042, - -31.837515530367693 - ], - [ - 8274.338713544164, - -32.83265611186143 - ], - [ - 8414.581742587285, - -33.842407599490066 - ], - [ - 8554.824771630407, - -34.88235635279271 - ], - [ - 8695.067800673529, - -35.94760494658812 - ], - [ - 8835.31082971665, - -37.03146409783499 - ], - [ - 8975.553858759771, - -38.12724370175687 - ], - [ - 9115.796887802893, - -39.22827560408631 - ], - [ - 9256.039916846014, - -40.32828875050881 - ], - [ - 9396.282945889136, - -41.42121518390643 - ], - [ - 9536.525974932258, - -42.50111022944609 - ], - [ - 9676.769003975378, - -43.56539747477148 - ], - [ - 9817.0120330185, - -44.61361256299363 - ], - [ - 9957.25506206162, - -45.6453333981741 - ], - [ - 10097.498091104742, - -46.66013792085875 - ], - [ - 10237.741120147864, - -47.65760472664695 - ], - [ - 10377.984149190985, - -48.637217386693 - ], - [ - 10518.227178234107, - -49.5979088770347 - ], - [ - 10658.470207277229, - -50.53761413285581 - ], - [ - 10798.71323632035, - -51.45184089881165 - ], - [ - 10938.956265363471, - -52.3341793508777 - ], - [ - 11079.199294406593, - -53.17785104548981 - ], - [ - 11219.442323449714, - -53.97724158089534 - ], - [ - 11359.685352492836, - -54.73036267704781 - ], - [ - 11499.928381535958, - -55.43774204956296 - ], - [ - 11640.171410579078, - -56.10037854176885 - ], - [ - 11780.4144396222, - -56.71955515726571 - ], - [ - 11920.65746866532, - -57.296484095056314 - ], - [ - 12060.900497708442, - -57.83189149514438 - ], - [ - 12201.143526751564, - -58.32630238125504 - ], - [ - 12341.386555794685, - -58.780721271427666 - ], - [ - 12481.629584837807, - -59.19559102439168 - ], - [ - 12621.872613880929, - -59.57118295112202 - ], - [ - 12762.11564292405, - -59.90746152808324 - ], - [ - 12902.358671967171, - -60.205397702217326 - ], - [ - 13042.601701010293, - -60.470759076281055 - ], - [ - 13182.844730053414, - -60.7122950073551 - ], - [ - 13323.087759096536, - -60.939701032318546 - ], - [ - 13463.330788139658, - -61.16280718393268 - ], - [ - 13603.573817182778, - -61.39145321792135 - ], - [ - 13743.8168462259, - -61.635068835310605 - ], - [ - 13884.059875269022, - -61.893950864891316 - ] - ], - "17": [ - [ - 0.0, - 14.899279759359194 - ], - [ - 140.24302904312142, - 18.139133458026656 - ], - [ - 280.48605808624285, - 17.571763920536398 - ], - [ - 420.7290871293643, - 17.19066285916898 - ], - [ - 560.9721161724857, - 16.898116150693603 - ], - [ - 701.2151452156071, - 15.765262744751428 - ], - [ - 841.4581742587286, - 13.571277673035668 - ], - [ - 981.70120330185, - 13.675983444668097 - ], - [ - 1121.9442323449714, - 12.858921700657088 - ], - [ - 1262.1872613880928, - 11.353404072729997 - ], - [ - 1402.4302904312142, - 10.637236224334774 - ], - [ - 1542.6733194743356, - 10.099353535479315 - ], - [ - 1682.9163485174572, - 9.362111241378967 - ], - [ - 1823.1593775605786, - 8.352501235033822 - ], - [ - 1963.4024066037, - 8.050864443139146 - ], - [ - 2103.645435646821, - 8.765870606010628 - ], - [ - 2243.888464689943, - 6.2480330952780045 - ], - [ - 2384.1314937330644, - 4.384849974946694 - ], - [ - 2524.3745227761856, - 5.527016734554452 - ], - [ - 2664.617551819307, - 5.7290594158357075 - ], - [ - 2804.8605808624284, - 4.890990408552576 - ], - [ - 2945.10360990555, - 3.51912032326606 - ], - [ - 3085.346638948671, - 2.11827995611808 - ], - [ - 3225.589667991793, - 1.0451448286995517 - ], - [ - 3365.8326970349144, - 0.24870417339949624 - ], - [ - 3506.0757260780356, - -0.39688719663473954 - ], - [ - 3646.318755121157, - -1.0172513825819902 - ], - [ - 3786.5617841642784, - -1.7215545656167948 - ], - [ - 3926.8048132074, - -2.5198028929021303 - ], - [ - 4067.047842250521, - -3.379975945864564 - ], - [ - 4207.290871293642, - -4.269704246101655 - ], - [ - 4347.533900336764, - -5.1565662464933295 - ], - [ - 4487.776929379886, - -6.023818042255793 - ], - [ - 4628.019958423007, - -6.882402570498523 - ], - [ - 4768.262987466129, - -7.7463780300421305 - ], - [ - 4908.50601650925, - -8.630012069056342 - ], - [ - 5048.749045552371, - -9.547178568376125 - ], - [ - 5188.992074595492, - -10.495491928402968 - ], - [ - 5329.235103638614, - -11.454062512888907 - ], - [ - 5469.478132681736, - -12.40090333698135 - ], - [ - 5609.721161724857, - -13.313923236581534 - ], - [ - 5749.964190767979, - -14.170739333829523 - ], - [ - 5890.2072198111, - -14.974054989870934 - ], - [ - 6030.450248854221, - -15.783454393611597 - ], - [ - 6170.693277897342, - -16.666364421619633 - ], - [ - 6310.936306940464, - -17.690169465492186 - ], - [ - 6451.179335983586, - -18.922287528925334 - ], - [ - 6591.422365026707, - -20.387623424688837 - ], - [ - 6731.665394069829, - -21.700884848858955 - ], - [ - 6871.90842311295, - -22.7975301722344 - ], - [ - 7012.151452156071, - -23.741321370533893 - ], - [ - 7152.394481199192, - -24.59609305684304 - ], - [ - 7292.637510242314, - -25.425719154265987 - ], - [ - 7432.880539285436, - -26.29294846046543 - ], - [ - 7573.123568328557, - -27.23336748596384 - ], - [ - 7713.366597371678, - -28.239773956618365 - ], - [ - 7853.6096264148, - -29.301287746218136 - ], - [ - 7993.852655457921, - -30.407026580075247 - ], - [ - 8134.095684501042, - -31.546032898169607 - ], - [ - 8274.338713544164, - -32.70722331141879 - ], - [ - 8414.581742587285, - -33.87967820074201 - ], - [ - 8554.824771630407, - -35.055090784526385 - ], - [ - 8695.067800673529, - -36.22215888910214 - ], - [ - 8835.31082971665, - -37.36834735023925 - ], - [ - 8975.553858759771, - -38.481119780231445 - ], - [ - 9115.796887802893, - -39.547983186744986 - ], - [ - 9256.039916846014, - -40.55685032332128 - ], - [ - 9396.282945889136, - -41.495835838673116 - ], - [ - 9536.525974932258, - -42.39967696487066 - ], - [ - 9676.769003975378, - -43.34827970151972 - ], - [ - 9817.0120330185, - -44.336587882255856 - ], - [ - 9957.25506206162, - -45.35344474095065 - ], - [ - 10097.498091104742, - -46.38769361403568 - ], - [ - 10237.741120147864, - -47.428177560356744 - ], - [ - 10377.984149190985, - -48.46363527985444 - ], - [ - 10518.227178234107, - -49.482465033051696 - ], - [ - 10658.470207277229, - -50.472053114607704 - ], - [ - 10798.71323632035, - -51.41833494191086 - ], - [ - 10938.956265363471, - -52.315257368125046 - ], - [ - 11079.199294406593, - -53.162369042144775 - ], - [ - 11219.442323449714, - -53.95997685264868 - ], - [ - 11359.685352492836, - -54.711617071230954 - ], - [ - 11499.928381535958, - -55.42209328017538 - ], - [ - 11640.171410579078, - -56.09182927619782 - ], - [ - 11780.4144396222, - -56.719118380138646 - ], - [ - 11920.65746866532, - -57.302427532266535 - ], - [ - 12060.900497708442, - -57.84033765109354 - ], - [ - 12201.143526751564, - -58.33288345441523 - ], - [ - 12341.386555794685, - -58.781933726450745 - ], - [ - 12481.629584837807, - -59.189786606189436 - ], - [ - 12621.872613880929, - -59.558656015305274 - ], - [ - 12762.11564292405, - -59.89039234920958 - ], - [ - 12902.358671967171, - -60.18759377371876 - ], - [ - 13042.601701010293, - -60.45562400466168 - ], - [ - 13182.844730053414, - -60.7021759800828 - ], - [ - 13323.087759096536, - -60.93596312507073 - ], - [ - 13463.330788139658, - -61.165838896356426 - ], - [ - 13603.573817182778, - -61.400669666037004 - ], - [ - 13743.8168462259, - -61.64877656498537 - ], - [ - 13884.059875269022, - -61.90955443110939 - ] - ], - "18": [ - [ - 0.0, - 14.842944258295795 - ], - [ - 140.24302904312142, - 18.136275460483503 - ], - [ - 280.48605808624285, - 17.18161628430644 - ], - [ - 420.7290871293643, - 17.029246603236402 - ], - [ - 560.9721161724857, - 16.734201940336952 - ], - [ - 701.2151452156071, - 15.536237241079084 - ], - [ - 841.4581742587286, - 14.368334830347578 - ], - [ - 981.70120330185, - 13.406714669843774 - ], - [ - 1121.9442323449714, - 12.475744184256275 - ], - [ - 1262.1872613880928, - 11.58986657104926 - ], - [ - 1402.4302904312142, - 10.786173911637496 - ], - [ - 1542.6733194743356, - 10.056837338637132 - ], - [ - 1682.9163485174572, - 9.380812396264409 - ], - [ - 1823.1593775605786, - 8.734453198500836 - ], - [ - 1963.4024066037, - 8.09146570130534 - ], - [ - 2103.645435646821, - 7.4274519074890515 - ], - [ - 2243.888464689943, - 6.72564013962264 - ], - [ - 2384.1314937330644, - 5.993158319203482 - ], - [ - 2524.3745227761856, - 5.246894425084119 - ], - [ - 2664.617551819307, - 4.492768863344575 - ], - [ - 2804.8605808624284, - 3.7319169451050116 - ], - [ - 2945.10360990555, - 2.965505736439416 - ], - [ - 3085.346638948671, - 2.193795626612528 - ], - [ - 3225.589667991793, - 1.4159835408832218 - ], - [ - 3365.8326970349144, - 0.6305937577188183 - ], - [ - 3506.0757260780356, - -0.16382231756601023 - ], - [ - 3646.318755121157, - -0.967937801870828 - ], - [ - 3786.5617841642784, - -1.7813642328526662 - ], - [ - 3926.8048132074, - -2.603729455562951 - ], - [ - 4067.047842250521, - -3.4348284316067357 - ], - [ - 4207.290871293642, - -4.274577076224994 - ], - [ - 4347.533900336764, - -5.123087712338094 - ], - [ - 4487.776929379886, - -5.980661629593175 - ], - [ - 4628.019958423007, - -6.847914259030389 - ], - [ - 4768.262987466129, - -7.725444104523428 - ], - [ - 4908.50601650925, - -8.613819927158916 - ], - [ - 5048.749045552371, - -9.513443132646616 - ], - [ - 5188.992074595492, - -10.424404899509664 - ], - [ - 5329.235103638614, - -11.346848546461699 - ], - [ - 5469.478132681736, - -12.280934821468755 - ], - [ - 5609.721161724857, - -13.226735503303162 - ], - [ - 5749.964190767979, - -14.184280194610903 - ], - [ - 5890.2072198111, - -15.153338477091776 - ], - [ - 6030.450248854221, - -16.13255834848186 - ], - [ - 6170.693277897342, - -17.120400600552266 - ], - [ - 6310.936306940464, - -18.115346490391577 - ], - [ - 6451.179335983586, - -19.115908145814824 - ], - [ - 6591.422365026707, - -20.12090357705897 - ], - [ - 6731.665394069829, - -21.130613810546212 - ], - [ - 6871.90842311295, - -22.14747293809199 - ], - [ - 7012.151452156071, - -23.17409591177227 - ], - [ - 7152.394481199192, - -24.21311243635837 - ], - [ - 7292.637510242314, - -25.267208557806065 - ], - [ - 7432.880539285436, - -26.338607652313488 - ], - [ - 7573.123568328557, - -27.426190915786144 - ], - [ - 7713.366597371678, - -28.518409424505986 - ], - [ - 7853.6096264148, - -29.60146517075792 - ], - [ - 7993.852655457921, - -30.664636499964956 - ], - [ - 8134.095684501042, - -31.717978478237953 - ], - [ - 8274.338713544164, - -32.77983287139436 - ], - [ - 8414.581742587285, - -33.868473951592684 - ], - [ - 8554.824771630407, - -34.99142731045147 - ], - [ - 8695.067800673529, - -36.133262144478344 - ], - [ - 8835.31082971665, - -37.27551032306865 - ], - [ - 8975.553858759771, - -38.399702221278034 - ], - [ - 9115.796887802893, - -39.48740976520774 - ], - [ - 9256.039916846014, - -40.52077415165639 - ], - [ - 9396.282945889136, - -41.482368139094454 - ], - [ - 9536.525974932258, - -42.4027062954256 - ], - [ - 9676.769003975378, - -43.36012998153794 - ], - [ - 9817.0120330185, - -44.350600815891845 - ], - [ - 9957.25506206162, - -45.36438514875123 - ], - [ - 10097.498091104742, - -46.391749464735504 - ], - [ - 10237.741120147864, - -47.42295959172839 - ], - [ - 10377.984149190985, - -48.44817732302988 - ], - [ - 10518.227178234107, - -49.457036115517255 - ], - [ - 10658.470207277229, - -50.438352545614485 - ], - [ - 10798.71323632035, - -51.37991804929093 - ], - [ - 10938.956265363471, - -52.2762017805845 - ], - [ - 11079.199294406593, - -53.12670892960998 - ], - [ - 11219.442323449714, - -53.9315266439176 - ], - [ - 11359.685352492836, - -54.692982024827636 - ], - [ - 11499.928381535958, - -55.41352588530729 - ], - [ - 11640.171410579078, - -56.09183647510721 - ], - [ - 11780.4144396222, - -56.72464477776877 - ], - [ - 11920.65746866532, - -57.309083649282925 - ], - [ - 12060.900497708442, - -57.843091168837816 - ], - [ - 12201.143526751564, - -58.32818277658929 - ], - [ - 12341.386555794685, - -58.76774058342745 - ], - [ - 12481.629584837807, - -59.165458643063666 - ], - [ - 12621.872613880929, - -59.52496041472687 - ], - [ - 12762.11564292405, - -59.84953291340837 - ], - [ - 12902.358671967171, - -60.14303901197187 - ], - [ - 13042.601701010293, - -60.4100676859472 - ], - [ - 13182.844730053414, - -60.65762683350614 - ], - [ - 13323.087759096536, - -60.893803293911525 - ], - [ - 13463.330788139658, - -61.12681222921302 - ], - [ - 13603.573817182778, - -61.36488204807404 - ], - [ - 13743.8168462259, - -61.61548480584347 - ], - [ - 13884.059875269022, - -61.87725190493766 - ] - ], - "19": [ - [ - 0.0, - 15.070696432275344 - ], - [ - 140.24302904312142, - 18.2445561814975 - ], - [ - 280.48605808624285, - 17.187775841670774 - ], - [ - 420.7290871293643, - 16.945295387186484 - ], - [ - 560.9721161724857, - 16.576577490949077 - ], - [ - 701.2151452156071, - 15.394192505719136 - ], - [ - 841.4581742587286, - 14.30866488993988 - ], - [ - 981.70120330185, - 13.409082049780094 - ], - [ - 1121.9442323449714, - 12.503211267283978 - ], - [ - 1262.1872613880928, - 11.63840411489461 - ], - [ - 1402.4302904312142, - 10.851327526547735 - ], - [ - 1542.6733194743356, - 10.123374876736335 - ], - [ - 1682.9163485174572, - 9.437369752419663 - ], - [ - 1823.1593775605786, - 8.773272678951383 - ], - [ - 1963.4024066037, - 8.109196571540984 - ], - [ - 2103.645435646821, - 7.430373766844154 - ], - [ - 2243.888464689943, - 6.7255798872386325 - ], - [ - 2384.1314937330644, - 5.996138420502127 - ], - [ - 2524.3745227761856, - 5.251722502040023 - ], - [ - 2664.617551819307, - 4.49698950637199 - ], - [ - 2804.8605808624284, - 3.7341521238503015 - ], - [ - 2945.10360990555, - 2.9656569587077115 - ], - [ - 3085.346638948671, - 2.19280306008305 - ], - [ - 3225.589667991793, - 1.414942844417008 - ], - [ - 3365.8326970349144, - 0.6304426626656668 - ], - [ - 3506.0757260780356, - -0.16236620064603896 - ], - [ - 3646.318755121157, - -0.9643590120754372 - ], - [ - 3786.5617841642784, - -1.7753705194893747 - ], - [ - 3926.8048132074, - -2.5957167976175284 - ], - [ - 4067.047842250521, - -3.4260173352633743 - ], - [ - 4207.290871293642, - -4.267022596076507 - ], - [ - 4347.533900336764, - -5.11932435947209 - ], - [ - 4487.776929379886, - -5.982491616636986 - ], - [ - 4628.019958423007, - -6.855875145180375 - ], - [ - 4768.262987466129, - -7.7387500447775155 - ], - [ - 4908.50601650925, - -8.630344765450085 - ], - [ - 5048.749045552371, - -9.530469242143786 - ], - [ - 5188.992074595492, - -10.439947577188693 - ], - [ - 5329.235103638614, - -11.359929959383127 - ], - [ - 5469.478132681736, - -12.291590527356627 - ], - [ - 5609.721161724857, - -13.23604262425213 - ], - [ - 5749.964190767979, - -14.194068399439843 - ], - [ - 5890.2072198111, - -15.165199424882926 - ], - [ - 6030.450248854221, - -16.147307024868628 - ], - [ - 6170.693277897342, - -17.138045678187755 - ], - [ - 6310.936306940464, - -18.135093779982082 - ], - [ - 6451.179335983586, - -19.136101667456177 - ], - [ - 6591.422365026707, - -20.139082563695986 - ], - [ - 6731.665394069829, - -21.14468349233339 - ], - [ - 6871.90842311295, - -22.156789485905342 - ], - [ - 7012.151452156071, - -23.179522165936593 - ], - [ - 7152.394481199192, - -24.21701219903449 - ], - [ - 7292.637510242314, - -25.273587576345225 - ], - [ - 7432.880539285436, - -26.353404479988466 - ], - [ - 7573.123568328557, - -27.455605111425324 - ], - [ - 7713.366597371678, - -28.56473862486715 - ], - [ - 7853.6096264148, - -29.662360739071225 - ], - [ - 7993.852655457921, - -30.733099540416788 - ], - [ - 8134.095684501042, - -31.782337541426973 - ], - [ - 8274.338713544164, - -32.82349658831526 - ], - [ - 8414.581742587285, - -33.8698436997206 - ], - [ - 8554.824771630407, - -34.92993085644528 - ], - [ - 8695.067800673529, - -36.00134080503615 - ], - [ - 8835.31082971665, - -37.08012724994852 - ], - [ - 8975.553858759771, - -38.16234246686392 - ], - [ - 9115.796887802893, - -39.24408135856545 - ], - [ - 9256.039916846014, - -40.32214873374614 - ], - [ - 9396.282945889136, - -41.39391991189897 - ], - [ - 9536.525974932258, - -42.456127038833806 - ], - [ - 9676.769003975378, - -43.50650435612337 - ], - [ - 9817.0120330185, - -44.54400252651067 - ], - [ - 9957.25506206162, - -45.56762498112688 - ], - [ - 10097.498091104742, - -46.57637523836368 - ], - [ - 10237.741120147864, - -47.569255217507326 - ], - [ - 10377.984149190985, - -48.54519008214819 - ], - [ - 10518.227178234107, - -49.50241943060796 - ], - [ - 10658.470207277229, - -50.43848402004503 - ], - [ - 10798.71323632035, - -51.35005444777822 - ], - [ - 10938.956265363471, - -52.23290291171749 - ], - [ - 11079.199294406593, - -53.08236192258775 - ], - [ - 11219.442323449714, - -53.89422012559097 - ], - [ - 11359.685352492836, - -54.66606820724514 - ], - [ - 11499.928381535958, - -55.39508472579795 - ], - [ - 11640.171410579078, - -56.078188073864624 - ], - [ - 11780.4144396222, - -56.7125258807314 - ], - [ - 11920.65746866532, - -57.295770412327336 - ], - [ - 12060.900497708442, - -57.826552552670364 - ], - [ - 12201.143526751564, - -58.30763022432158 - ], - [ - 12341.386555794685, - -58.7432713848857 - ], - [ - 12481.629584837807, - -59.13719815365632 - ], - [ - 12621.872613880929, - -59.492984816296946 - ], - [ - 12762.11564292405, - -59.81393748225411 - ], - [ - 12902.358671967171, - -60.104122773102134 - ], - [ - 13042.601701010293, - -60.367874630237104 - ], - [ - 13182.844730053414, - -60.612125853794026 - ], - [ - 13323.087759096536, - -60.84504304708698 - ], - [ - 13463.330788139658, - -61.07493924726211 - ], - [ - 13603.573817182778, - -61.31014075961052 - ], - [ - 13743.8168462259, - -61.557971189859146 - ], - [ - 13884.059875269022, - -61.816881813940626 - ] - ] - }, - "atmosphericModelWindVelocityXProfile": { - "4": [ - [ - 0.0, - 0.05975253641103586 - ], - [ - 140.24302904312142, - -0.12105108934146276 - ], - [ - 280.48605808624285, - 0.048393224947125085 - ], - [ - 420.7290871293643, - 0.48342913975273993 - ], - [ - 560.9721161724857, - 0.9623662433328655 - ], - [ - 701.2151452156071, - 1.385295428134752 - ], - [ - 841.4581742587286, - 1.7213838213380492 - ], - [ - 981.70120330185, - 1.9933657947668197 - ], - [ - 1121.9442323449714, - 2.227639908835345 - ], - [ - 1262.1872613880928, - 2.4429619528724316 - ], - [ - 1402.4302904312142, - 2.6504560992788098 - ], - [ - 1542.6733194743356, - 2.851869046671696 - ], - [ - 1682.9163485174572, - 3.05795179298655 - ], - [ - 1823.1593775605786, - 3.278724878013396 - ], - [ - 1963.4024066037, - 3.4523528313439074 - ], - [ - 2103.645435646821, - 3.581366658304242 - ], - [ - 2243.888464689943, - 3.8963055000003006 - ], - [ - 2384.1314937330644, - 4.113384124244404 - ], - [ - 2524.3745227761856, - 4.185434584401804 - ], - [ - 2664.617551819307, - 4.332033417629795 - ], - [ - 2804.8605808624284, - 4.540752391140537 - ], - [ - 2945.10360990555, - 4.775422163901467 - ], - [ - 3085.346638948671, - 5.000527652139862 - ], - [ - 3225.589667991793, - 5.196269698427465 - ], - [ - 3365.8326970349144, - 5.370427446853416 - ], - [ - 3506.0757260780356, - 5.534195942456559 - ], - [ - 3646.318755121157, - 5.698286848849614 - ], - [ - 3786.5617841642784, - 5.8694635620744835 - ], - [ - 3926.8048132074, - 6.045752349035022 - ], - [ - 4067.047842250521, - 6.222894173208381 - ], - [ - 4207.290871293642, - 6.396843470354744 - ], - [ - 4347.533900336764, - 6.56620796723421 - ], - [ - 4487.776929379886, - 6.733862879627378 - ], - [ - 4628.019958423007, - 6.904090434093128 - ], - [ - 4768.262987466129, - 7.081142544920256 - ], - [ - 4908.50601650925, - 7.268110692695415 - ], - [ - 5048.749045552371, - 7.463504192955759 - ], - [ - 5188.992074595492, - 7.661818326740881 - ], - [ - 5329.235103638614, - 7.85713530224667 - ], - [ - 5469.478132681736, - 8.043643845582801 - ], - [ - 5609.721161724857, - 8.2163013071135 - ], - [ - 5749.964190767979, - 8.37398314425641 - ], - [ - 5890.2072198111, - 8.520253405088447 - ], - [ - 6030.450248854221, - 8.66009847947187 - ], - [ - 6170.693277897342, - 8.798537113818027 - ], - [ - 6310.936306940464, - 8.940517181002246 - ], - [ - 6451.179335983586, - 9.089700287552835 - ], - [ - 6591.422365026707, - 9.246099877365623 - ], - [ - 6731.665394069829, - 9.406214587099512 - ], - [ - 6871.90842311295, - 9.565410393699882 - ], - [ - 7012.151452156071, - 9.718953580033748 - ], - [ - 7152.394481199192, - 9.862202635983836 - ], - [ - 7292.637510242314, - 9.991930962497136 - ], - [ - 7432.880539285436, - 10.10742879403126 - ], - [ - 7573.123568328557, - 10.21000012780444 - ], - [ - 7713.366597371678, - 10.302603969690194 - ], - [ - 7853.6096264148, - 10.38867429069832 - ], - [ - 7993.852655457921, - 10.471639398360457 - ], - [ - 8134.095684501042, - 10.554534746237778 - ], - [ - 8274.338713544164, - 10.638591358186972 - ], - [ - 8414.581742587285, - 10.72521651038363 - ], - [ - 8554.824771630407, - 10.817720198008553 - ], - [ - 8695.067800673529, - 10.918478598052673 - ], - [ - 8835.31082971665, - 11.029452765291827 - ], - [ - 8975.553858759771, - 11.152602604779144 - ], - [ - 9115.796887802893, - 11.290012959840013 - ], - [ - 9256.039916846014, - 11.44452635993918 - ], - [ - 9396.282945889136, - 11.618111974815413 - ], - [ - 9536.525974932258, - 11.806697029934345 - ], - [ - 9676.769003975378, - 12.002901378449412 - ], - [ - 9817.0120330185, - 12.199909589234291 - ], - [ - 9957.25506206162, - 12.391055266554206 - ], - [ - 10097.498091104742, - 12.56967209556223 - ], - [ - 10237.741120147864, - 12.72907930704581 - ], - [ - 10377.984149190985, - 12.862000702994198 - ], - [ - 10518.227178234107, - 12.960884174350252 - ], - [ - 10658.470207277229, - 13.024616591202848 - ], - [ - 10798.71323632035, - 13.065766247036674 - ], - [ - 10938.956265363471, - 13.09945534536415 - ], - [ - 11079.199294406593, - 13.14074216746216 - ], - [ - 11219.442323449714, - 13.203939922253854 - ], - [ - 11359.685352492836, - 13.292120599120642 - ], - [ - 11499.928381535958, - 13.391100965025581 - ], - [ - 11640.171410579078, - 13.484772704847833 - ], - [ - 11780.4144396222, - 13.558092876369846 - ], - [ - 11920.65746866532, - 13.596287419334777 - ], - [ - 12060.900497708442, - 13.590731639109778 - ], - [ - 12201.143526751564, - 13.546039445292283 - ], - [ - 12341.386555794685, - 13.472140380103246 - ], - [ - 12481.629584837807, - 13.378407540432182 - ], - [ - 12621.872613880929, - 13.273820498652295 - ], - [ - 12762.11564292405, - 13.166833634777213 - ], - [ - 12902.358671967171, - 13.063481059012863 - ], - [ - 13042.601701010293, - 12.964424749412604 - ], - [ - 13182.844730053414, - 12.866891623284102 - ], - [ - 13323.087759096536, - 12.768103771736206 - ], - [ - 13463.330788139658, - 12.665494685602512 - ], - [ - 13603.573817182778, - 12.556507935115478 - ], - [ - 13743.8168462259, - 12.438812168606297 - ], - [ - 13884.059875269022, - 12.310034577653608 - ] - ], - "5": [ - [ - 0.0, - -0.09581216793885428 - ], - [ - 140.24302904312142, - -0.2901107505328242 - ], - [ - 280.48605808624285, - -0.11753448018325317 - ], - [ - 420.7290871293643, - 0.36184680407551967 - ], - [ - 560.9721161724857, - 0.8832647111527724 - ], - [ - 701.2151452156071, - 1.3349832059582614 - ], - [ - 841.4581742587286, - 1.693634317672904 - ], - [ - 981.70120330185, - 1.981725190672378 - ], - [ - 1121.9442323449714, - 2.231442709948685 - ], - [ - 1262.1872613880928, - 2.467589986866224 - ], - [ - 1402.4302904312142, - 2.6990928834853163 - ], - [ - 1542.6733194743356, - 2.919203905396589 - ], - [ - 1682.9163485174572, - 3.1248258375001075 - ], - [ - 1823.1593775605786, - 3.3266656126272367 - ], - [ - 1963.4024066037, - 3.521661541756688 - ], - [ - 2103.645435646821, - 3.706365269180036 - ], - [ - 2243.888464689943, - 3.892114796912935 - ], - [ - 2384.1314937330644, - 4.08310153097989 - ], - [ - 2524.3745227761856, - 4.278578862543754 - ], - [ - 2664.617551819307, - 4.477909970378289 - ], - [ - 2804.8605808624284, - 4.679043678168432 - ], - [ - 2945.10360990555, - 4.879930091992764 - ], - [ - 3085.346638948671, - 5.07879633940901 - ], - [ - 3225.589667991793, - 5.274417733046291 - ], - [ - 3365.8326970349144, - 5.466678330819155 - ], - [ - 3506.0757260780356, - 5.655561878679655 - ], - [ - 3646.318755121157, - 5.84071785055161 - ], - [ - 3786.5617841642784, - 6.021783785400308 - ], - [ - 3926.8048132074, - 6.198346822362298 - ], - [ - 4067.047842250521, - 6.369935907150596 - ], - [ - 4207.290871293642, - 6.536253877491432 - ], - [ - 4347.533900336764, - 6.698595122748041 - ], - [ - 4487.776929379886, - 6.859677355553664 - ], - [ - 4628.019958423007, - 7.021930986749313 - ], - [ - 4768.262987466129, - 7.187690280403371 - ], - [ - 4908.50601650925, - 7.358505019226913 - ], - [ - 5048.749045552371, - 7.533297833366291 - ], - [ - 5188.992074595492, - 7.709312536657306 - ], - [ - 5329.235103638614, - 7.884147276159122 - ], - [ - 5469.478132681736, - 8.055520331485953 - ], - [ - 5609.721161724857, - 8.221451934042518 - ], - [ - 5749.964190767979, - 8.381708212410173 - ], - [ - 5890.2072198111, - 8.538478619730958 - ], - [ - 6030.450248854221, - 8.694035829818768 - ], - [ - 6170.693277897342, - 8.850455526704835 - ], - [ - 6310.936306940464, - 9.009827761191818 - ], - [ - 6451.179335983586, - 9.17425123495282 - ], - [ - 6591.422365026707, - 9.343077868155856 - ], - [ - 6731.665394069829, - 9.512333185761708 - ], - [ - 6871.90842311295, - 9.677646895000603 - ], - [ - 7012.151452156071, - 9.834662620251182 - ], - [ - 7152.394481199192, - 9.979068661001504 - ], - [ - 7292.637510242314, - 10.107247100109467 - ], - [ - 7432.880539285436, - 10.218021180686925 - ], - [ - 7573.123568328557, - 10.315421467570951 - ], - [ - 7713.366597371678, - 10.40623712655213 - ], - [ - 7853.6096264148, - 10.497576149484226 - ], - [ - 7993.852655457921, - 10.595656723013253 - ], - [ - 8134.095684501042, - 10.7004044894769 - ], - [ - 8274.338713544164, - 10.807698716201061 - ], - [ - 8414.581742587285, - 10.913683778033874 - ], - [ - 8554.824771630407, - 11.01909214001776 - ], - [ - 8695.067800673529, - 11.12528847502759 - ], - [ - 8835.31082971665, - 11.2332699717602 - ], - [ - 8975.553858759771, - 11.344032430325319 - ], - [ - 9115.796887802893, - 11.458667772148475 - ], - [ - 9256.039916846014, - 11.579111793327298 - ], - [ - 9396.282945889136, - 11.707037878973974 - ], - [ - 9536.525974932258, - 11.841323445961992 - ], - [ - 9676.769003975378, - 11.978507703715506 - ], - [ - 9817.0120330185, - 12.115614517392268 - ], - [ - 9957.25506206162, - 12.249812488433664 - ], - [ - 10097.498091104742, - 12.378270291394925 - ], - [ - 10237.741120147864, - 12.498143269139433 - ], - [ - 10377.984149190985, - 12.60607607011278 - ], - [ - 10518.227178234107, - 12.698385799213703 - ], - [ - 10658.470207277229, - 12.774275576905097 - ], - [ - 10798.71323632035, - 12.83938509068122 - ], - [ - 10938.956265363471, - 12.901826026852389 - ], - [ - 11079.199294406593, - 12.969837957109608 - ], - [ - 11219.442323449714, - 13.051001926336818 - ], - [ - 11359.685352492836, - 13.146643125624617 - ], - [ - 11499.928381535958, - 13.247511724401221 - ], - [ - 11640.171410579078, - 13.341859058175189 - ], - [ - 11780.4144396222, - 13.418579480642848 - ], - [ - 11920.65746866532, - 13.466934908853602 - ], - [ - 12060.900497708442, - 13.481106580556503 - ], - [ - 12201.143526751564, - 13.46420036069762 - ], - [ - 12341.386555794685, - 13.42362406251743 - ], - [ - 12481.629584837807, - 13.366051265526005 - ], - [ - 12621.872613880929, - 13.297814511538082 - ], - [ - 12762.11564292405, - 13.224616114685487 - ], - [ - 12902.358671967171, - 13.149611412206774 - ], - [ - 13042.601701010293, - 13.072151454241164 - ], - [ - 13182.844730053414, - 12.989584810096565 - ], - [ - 13323.087759096536, - 12.899999904795308 - ], - [ - 13463.330788139658, - 12.80173215048416 - ], - [ - 13603.573817182778, - 12.693125124968116 - ], - [ - 13743.8168462259, - 12.572917553484366 - ], - [ - 13884.059875269022, - 12.44058222779078 - ] - ], - "6": [ - [ - 0.0, - -0.2744567926448979 - ], - [ - 140.24302904312142, - -0.5158352322288213 - ], - [ - 280.48605808624285, - -0.37052789549127185 - ], - [ - 420.7290871293643, - 0.1341128698139211 - ], - [ - 560.9721161724857, - 0.7026695702864003 - ], - [ - 701.2151452156071, - 1.1884906864491236 - ], - [ - 841.4581742587286, - 1.5623801056753692 - ], - [ - 981.70120330185, - 1.8601564666954007 - ], - [ - 1121.9442323449714, - 2.121459424091444 - ], - [ - 1262.1872613880928, - 2.3726839609803925 - ], - [ - 1402.4302904312142, - 2.61840550270393 - ], - [ - 1542.6733194743356, - 2.851438535271949 - ], - [ - 1682.9163485174572, - 3.0718233785118048 - ], - [ - 1823.1593775605786, - 3.2866698481721963 - ], - [ - 1963.4024066037, - 3.494094540010056 - ], - [ - 2103.645435646821, - 3.6924191788496943 - ], - [ - 2243.888464689943, - 3.887334204452051 - ], - [ - 2384.1314937330644, - 4.082457074731397 - ], - [ - 2524.3745227761856, - 4.280652149499247 - ], - [ - 2664.617551819307, - 4.482084372119386 - ], - [ - 2804.8605808624284, - 4.68505732413065 - ], - [ - 2945.10360990555, - 4.888011635702934 - ], - [ - 3085.346638948671, - 5.0900769429015975 - ], - [ - 3225.589667991793, - 5.290508577981728 - ], - [ - 3365.8326970349144, - 5.487948002811035 - ], - [ - 3506.0757260780356, - 5.680869276158699 - ], - [ - 3646.318755121157, - 5.866975942273867 - ], - [ - 3786.5617841642784, - 6.044630571253565 - ], - [ - 3926.8048132074, - 6.214912286358626 - ], - [ - 4067.047842250521, - 6.379474373357724 - ], - [ - 4207.290871293642, - 6.539996459021995 - ], - [ - 4347.533900336764, - 6.69947461743143 - ], - [ - 4487.776929379886, - 6.859869306155752 - ], - [ - 4628.019958423007, - 7.021290663741153 - ], - [ - 4768.262987466129, - 7.183699410198411 - ], - [ - 4908.50601650925, - 7.346690500817404 - ], - [ - 5048.749045552371, - 7.508541012433086 - ], - [ - 5188.992074595492, - 7.668694685944979 - ], - [ - 5329.235103638614, - 7.827953203950819 - ], - [ - 5469.478132681736, - 7.987234495441049 - ], - [ - 5609.721161724857, - 8.147688368285543 - ], - [ - 5749.964190767979, - 8.310893045716714 - ], - [ - 5890.2072198111, - 8.477359471313282 - ], - [ - 6030.450248854221, - 8.645911707786304 - ], - [ - 6170.693277897342, - 8.815061931626335 - ], - [ - 6310.936306940464, - 8.983334774387346 - ], - [ - 6451.179335983586, - 9.14942503630764 - ], - [ - 6591.422365026707, - 9.3115675952656 - ], - [ - 6731.665394069829, - 9.468266753706613 - ], - [ - 6871.90842311295, - 9.618837920523312 - ], - [ - 7012.151452156071, - 9.762691859276462 - ], - [ - 7152.394481199192, - 9.899278485451692 - ], - [ - 7292.637510242314, - 10.02856787084769 - ], - [ - 7432.880539285436, - 10.15123768180039 - ], - [ - 7573.123568328557, - 10.268417700185037 - ], - [ - 7713.366597371678, - 10.381375217716752 - ], - [ - 7853.6096264148, - 10.49141858872417 - ], - [ - 7993.852655457921, - 10.599856170083486 - ], - [ - 8134.095684501042, - 10.707664629047628 - ], - [ - 8274.338713544164, - 10.81433402057536 - ], - [ - 8414.581742587285, - 10.91977819348623 - ], - [ - 8554.824771630407, - 11.024534077761032 - ], - [ - 8695.067800673529, - 11.128776079315884 - ], - [ - 8835.31082971665, - 11.232502600186361 - ], - [ - 8975.553858759771, - 11.33571007042249 - ], - [ - 9115.796887802893, - 11.438409779211055 - ], - [ - 9256.039916846014, - 11.541276148879001 - ], - [ - 9396.282945889136, - 11.645189359545489 - ], - [ - 9536.525974932258, - 11.750153773402326 - ], - [ - 9676.769003975378, - 11.855322284605277 - ], - [ - 9817.0120330185, - 11.959885290935972 - ], - [ - 9957.25506206162, - 12.063128682342322 - ], - [ - 10097.498091104742, - 12.16433839889848 - ], - [ - 10237.741120147864, - 12.262802733513707 - ], - [ - 10377.984149190985, - 12.357770277018387 - ], - [ - 10518.227178234107, - 12.44830958493666 - ], - [ - 10658.470207277229, - 12.533534696055614 - ], - [ - 10798.71323632035, - 12.61367388589628 - ], - [ - 10938.956265363471, - 12.69175672325861 - ], - [ - 11079.199294406593, - 12.770892896121785 - ], - [ - 11219.442323449714, - 12.853815126013435 - ], - [ - 11359.685352492836, - 12.941726891051223 - ], - [ - 11499.928381535958, - 13.03095976166426 - ], - [ - 11640.171410579078, - 13.114388163600893 - ], - [ - 11780.4144396222, - 13.185273877990578 - ], - [ - 11920.65746866532, - 13.2373351661554 - ], - [ - 12060.900497708442, - 13.267178046821746 - ], - [ - 12201.143526751564, - 13.27591294188623 - ], - [ - 12341.386555794685, - 13.268088815531561 - ], - [ - 12481.629584837807, - 13.246891331727983 - ], - [ - 12621.872613880929, - 13.215068845619664 - ], - [ - 12762.11564292405, - 13.17493957659699 - ], - [ - 12902.358671967171, - 13.126842514196746 - ], - [ - 13042.601701010293, - 13.068960454213594 - ], - [ - 13182.844730053414, - 12.998307228955005 - ], - [ - 13323.087759096536, - 12.913762424060634 - ], - [ - 13463.330788139658, - 12.814583170826777 - ], - [ - 13603.573817182778, - 12.700020117895578 - ], - [ - 13743.8168462259, - 12.569600904255452 - ], - [ - 13884.059875269022, - 12.42396863433451 - ] - ], - "7": [ - [ - 0.0, - -0.3826452642741105 - ], - [ - 140.24302904312142, - -0.652714209566475 - ], - [ - 280.48605808624285, - -0.5370262751507601 - ], - [ - 420.7290871293643, - -0.035273236472956375 - ], - [ - 560.9721161724857, - 0.5529686330264696 - ], - [ - 701.2151452156071, - 1.058857944534216 - ], - [ - 841.4581742587286, - 1.4415139463700999 - ], - [ - 981.70120330185, - 1.7477574710176345 - ], - [ - 1121.9442323449714, - 2.017646571430194 - ], - [ - 1262.1872613880928, - 2.272289269611772 - ], - [ - 1402.4302904312142, - 2.5198027045824123 - ], - [ - 1542.6733194743356, - 2.761217253809231 - ], - [ - 1682.9163485174572, - 2.9928872535628437 - ], - [ - 1823.1593775605786, - 3.2143075465621522 - ], - [ - 1963.4024066037, - 3.427085281412249 - ], - [ - 2103.645435646821, - 3.636091137053812 - ], - [ - 2243.888464689943, - 3.848172599004311 - ], - [ - 2384.1314937330644, - 4.063257920066181 - ], - [ - 2524.3745227761856, - 4.28031521237662 - ], - [ - 2664.617551819307, - 4.496576005155489 - ], - [ - 2804.8605808624284, - 4.708380928947301 - ], - [ - 2945.10360990555, - 4.912196509856161 - ], - [ - 3085.346638948671, - 5.106496676548107 - ], - [ - 3225.589667991793, - 5.2934004848956135 - ], - [ - 3365.8326970349144, - 5.474149769341179 - ], - [ - 3506.0757260780356, - 5.64967868177234 - ], - [ - 3646.318755121157, - 5.8202624251164785 - ], - [ - 3786.5617841642784, - 5.985028940800503 - ], - [ - 3926.8048132074, - 6.145088288891834 - ], - [ - 4067.047842250521, - 6.302301203748753 - ], - [ - 4207.290871293642, - 6.458212611791184 - ], - [ - 4347.533900336764, - 6.614679733126369 - ], - [ - 4487.776929379886, - 6.7732432968312715 - ], - [ - 4628.019958423007, - 6.933806727633609 - ], - [ - 4768.262987466129, - 7.096112091219616 - ], - [ - 4908.50601650925, - 7.26021599519358 - ], - [ - 5048.749045552371, - 7.425019651350629 - ], - [ - 5188.992074595492, - 7.589436678347052 - ], - [ - 5329.235103638614, - 7.753719064122536 - ], - [ - 5469.478132681736, - 7.9182162319453315 - ], - [ - 5609.721161724857, - 8.083158797048311 - ], - [ - 5749.964190767979, - 8.2490008843539 - ], - [ - 5890.2072198111, - 8.41602620929409 - ], - [ - 6030.450248854221, - 8.582685119003386 - ], - [ - 6170.693277897342, - 8.7470879676312 - ], - [ - 6310.936306940464, - 8.907389859739 - ], - [ - 6451.179335983586, - 9.062420453263208 - ], - [ - 6591.422365026707, - 9.211489799348016 - ], - [ - 6731.665394069829, - 9.354621666819636 - ], - [ - 6871.90842311295, - 9.492838056909292 - ], - [ - 7012.151452156071, - 9.627271033542844 - ], - [ - 7152.394481199192, - 9.759075237835903 - ], - [ - 7292.637510242314, - 9.889324319283444 - ], - [ - 7432.880539285436, - 10.018118432530246 - ], - [ - 7573.123568328557, - 10.144880097580856 - ], - [ - 7713.366597371678, - 10.26956752606431 - ], - [ - 7853.6096264148, - 10.392207162638224 - ], - [ - 7993.852655457921, - 10.512818760554405 - ], - [ - 8134.095684501042, - 10.631178158144943 - ], - [ - 8274.338713544164, - 10.746517834240553 - ], - [ - 8414.581742587285, - 10.860217181122879 - ], - [ - 8554.824771630407, - 10.973718192955975 - ], - [ - 8695.067800673529, - 11.085248612216317 - ], - [ - 8835.31082971665, - 11.192680523992808 - ], - [ - 8975.553858759771, - 11.293884825397713 - ], - [ - 9115.796887802893, - 11.38678233456867 - ], - [ - 9256.039916846014, - 11.469582265620403 - ], - [ - 9396.282945889136, - 11.539896339965413 - ], - [ - 9536.525974932258, - 11.611525786312123 - ], - [ - 9676.769003975378, - 11.704438545386719 - ], - [ - 9817.0120330185, - 11.812891184833255 - ], - [ - 9957.25506206162, - 11.930686389494214 - ], - [ - 10097.498091104742, - 12.051626889275637 - ], - [ - 10237.741120147864, - 12.169517518534319 - ], - [ - 10377.984149190985, - 12.278251710696313 - ], - [ - 10518.227178234107, - 12.371792600868993 - ], - [ - 10658.470207277229, - 12.44632984781913 - ], - [ - 10798.71323632035, - 12.503855756748852 - ], - [ - 10938.956265363471, - 12.552681977292185 - ], - [ - 11079.199294406593, - 12.601855020655936 - ], - [ - 11219.442323449714, - 12.660132646681697 - ], - [ - 11359.685352492836, - 12.732379409719165 - ], - [ - 11499.928381535958, - 12.815605193611546 - ], - [ - 11640.171410579078, - 12.902565617444038 - ], - [ - 11780.4144396222, - 12.986270105928382 - ], - [ - 11920.65746866532, - 13.060079713049365 - ], - [ - 12060.900497708442, - 13.119414922366419 - ], - [ - 12201.143526751564, - 13.16369814763368 - ], - [ - 12341.386555794685, - 13.194196199043704 - ], - [ - 12481.629584837807, - 13.210061022093903 - ], - [ - 12621.872613880929, - 13.209975069439732 - ], - [ - 12762.11564292405, - 13.192412160505208 - ], - [ - 12902.358671967171, - 13.1553599326336 - ], - [ - 13042.601701010293, - 13.096971166418964 - ], - [ - 13182.844730053414, - 13.016521710760086 - ], - [ - 13323.087759096536, - 12.915938206740776 - ], - [ - 13463.330788139658, - 12.797560574454756 - ], - [ - 13603.573817182778, - 12.663716373384089 - ], - [ - 13743.8168462259, - 12.516831766474494 - ], - [ - 13884.059875269022, - 12.35969891091312 - ] - ], - "8": [ - [ - 0.0, - -0.48143992989553125 - ], - [ - 140.24302904312142, - -0.7726463209240623 - ], - [ - 280.48605808624285, - -0.6665727555272393 - ], - [ - 420.7290871293643, - -0.1544118742850826 - ], - [ - 560.9721161724857, - 0.4421857300540506 - ], - [ - 701.2151452156071, - 0.9550834759521467 - ], - [ - 841.4581742587286, - 1.344312473184715 - ], - [ - 981.70120330185, - 1.6462740614205609 - ], - [ - 1121.9442323449714, - 1.9055608306301308 - ], - [ - 1262.1872613880928, - 2.1532873346460386 - ], - [ - 1402.4302904312142, - 2.4026540012595636 - ], - [ - 1542.6733194743356, - 2.6577664300371664 - ], - [ - 1682.9163485174572, - 2.9050734967926557 - ], - [ - 1823.1593775605786, - 3.1360527306878856 - ], - [ - 1963.4024066037, - 3.359949508453841 - ], - [ - 2103.645435646821, - 3.5848496116736306 - ], - [ - 2243.888464689943, - 3.812384649553542 - ], - [ - 2384.1314937330644, - 4.0384950367362835 - ], - [ - 2524.3745227761856, - 4.260692975590104 - ], - [ - 2664.617551819307, - 4.477267858782961 - ], - [ - 2804.8605808624284, - 4.685466370189367 - ], - [ - 2945.10360990555, - 4.882517945545015 - ], - [ - 3085.346638948671, - 5.068339095543464 - ], - [ - 3225.589667991793, - 5.246323192725561 - ], - [ - 3365.8326970349144, - 5.4189971801954275 - ], - [ - 3506.0757260780356, - 5.588530893956111 - ], - [ - 3646.318755121157, - 5.755946865403545 - ], - [ - 3786.5617841642784, - 5.920140088536452 - ], - [ - 3926.8048132074, - 6.080511719431065 - ], - [ - 4067.047842250521, - 6.237070216269466 - ], - [ - 4207.290871293642, - 6.389768549144544 - ], - [ - 4347.533900336764, - 6.539179476476786 - ], - [ - 4487.776929379886, - 6.687314920032076 - ], - [ - 4628.019958423007, - 6.835861775947603 - ], - [ - 4768.262987466129, - 6.9863650391437195 - ], - [ - 4908.50601650925, - 7.140624127561974 - ], - [ - 5048.749045552371, - 7.2991970392135785 - ], - [ - 5188.992074595492, - 7.461071009309595 - ], - [ - 5329.235103638614, - 7.625995019687556 - ], - [ - 5469.478132681736, - 7.793781467727866 - ], - [ - 5609.721161724857, - 7.963920752604281 - ], - [ - 5749.964190767979, - 8.135440747315112 - ], - [ - 5890.2072198111, - 8.307903420924132 - ], - [ - 6030.450248854221, - 8.479166194726188 - ], - [ - 6170.693277897342, - 8.646759947483513 - ], - [ - 6310.936306940464, - 8.80826159140592 - ], - [ - 6451.179335983586, - 8.962542819636033 - ], - [ - 6591.422365026707, - 9.109796273288548 - ], - [ - 6731.665394069829, - 9.250890944972147 - ], - [ - 6871.90842311295, - 9.387949220252532 - ], - [ - 7012.151452156071, - 9.523206603855828 - ], - [ - 7152.394481199192, - 9.658926992236882 - ], - [ - 7292.637510242314, - 9.796932778487466 - ], - [ - 7432.880539285436, - 9.93722912557741 - ], - [ - 7573.123568328557, - 10.078377762294547 - ], - [ - 7713.366597371678, - 10.218423099091762 - ], - [ - 7853.6096264148, - 10.355328963284274 - ], - [ - 7993.852655457921, - 10.487048424676258 - ], - [ - 8134.095684501042, - 10.6112557767321 - ], - [ - 8274.338713544164, - 10.72528913017573 - ], - [ - 8414.581742587285, - 10.829033178440966 - ], - [ - 8554.824771630407, - 10.924937307923738 - ], - [ - 8695.067800673529, - 11.01549900660872 - ], - [ - 8835.31082971665, - 11.103150916231618 - ], - [ - 8975.553858759771, - 11.190325531569458 - ], - [ - 9115.796887802893, - 11.279587262187272 - ], - [ - 9256.039916846014, - 11.374033272640055 - ], - [ - 9396.282945889136, - 11.476382123663845 - ], - [ - 9536.525974932258, - 11.5854739326158 - ], - [ - 9676.769003975378, - 11.6986060705913 - ], - [ - 9817.0120330185, - 11.813017079168933 - ], - [ - 9957.25506206162, - 11.926001769311073 - ], - [ - 10097.498091104742, - 12.034854976600236 - ], - [ - 10237.741120147864, - 12.13686205477037 - ], - [ - 10377.984149190985, - 12.229041599135508 - ], - [ - 10518.227178234107, - 12.30762359813761 - ], - [ - 10658.470207277229, - 12.371655365823754 - ], - [ - 10798.71323632035, - 12.425754584332836 - ], - [ - 10938.956265363471, - 12.476336275772493 - ], - [ - 11079.199294406593, - 12.530149122100934 - ], - [ - 11219.442323449714, - 12.594583331272414 - ], - [ - 11359.685352492836, - 12.67129398134863 - ], - [ - 11499.928381535958, - 12.755391865318197 - ], - [ - 11640.171410579078, - 12.841847296033714 - ], - [ - 11780.4144396222, - 12.926100820016732 - ], - [ - 11920.65746866532, - 13.003561736622956 - ], - [ - 12060.900497708442, - 13.071849680596205 - ], - [ - 12201.143526751564, - 13.13201140211009 - ], - [ - 12341.386555794685, - 13.182556102982112 - ], - [ - 12481.629584837807, - 13.219195670200827 - ], - [ - 12621.872613880929, - 13.237251132667906 - ], - [ - 12762.11564292405, - 13.231754278649914 - ], - [ - 12902.358671967171, - 13.197833853375828 - ], - [ - 13042.601701010293, - 13.13316210665239 - ], - [ - 13182.844730053414, - 13.039539338433123 - ], - [ - 13323.087759096536, - 12.921426625674153 - ], - [ - 13463.330788139658, - 12.783634669752509 - ], - [ - 13603.573817182778, - 12.630971123716806 - ], - [ - 13743.8168462259, - 12.468444853743593 - ], - [ - 13884.059875269022, - 12.300807079680427 - ] - ], - "9": [ - [ - 0.0, - -0.5326453672774275 - ], - [ - 140.24302904312142, - -0.9094948627577304 - ], - [ - 280.48605808624285, - -0.8668188266989217 - ], - [ - 420.7290871293643, - -0.3469349298618799 - ], - [ - 560.9721161724857, - 0.2699981503382397 - ], - [ - 701.2151452156071, - 0.7888660884942983 - ], - [ - 841.4581742587286, - 1.1808741756239969 - ], - [ - 981.70120330185, - 1.4823967756911667 - ], - [ - 1121.9442323449714, - 1.7356345423775086 - ], - [ - 1262.1872613880928, - 1.9733107896641549 - ], - [ - 1402.4302904312142, - 2.216831722282685 - ], - [ - 1542.6733194743356, - 2.4745915026743535 - ], - [ - 1682.9163485174572, - 2.7303630079332306 - ], - [ - 1823.1593775605786, - 2.972204210844605 - ], - [ - 1963.4024066037, - 3.2085080728872937 - ], - [ - 2103.645435646821, - 3.4428325365826793 - ], - [ - 2243.888464689943, - 3.6720695687690066 - ], - [ - 2384.1314937330644, - 3.8956791291713775 - ], - [ - 2524.3745227761856, - 4.115186682551433 - ], - [ - 2664.617551819307, - 4.329998869125241 - ], - [ - 2804.8605808624284, - 4.538073861315107 - ], - [ - 2945.10360990555, - 4.737207892605408 - ], - [ - 3085.346638948671, - 4.927527615640532 - ], - [ - 3225.589667991793, - 5.11119238497076 - ], - [ - 3365.8326970349144, - 5.288597075664848 - ], - [ - 3506.0757260780356, - 5.459697695983509 - ], - [ - 3646.318755121157, - 5.623861770955538 - ], - [ - 3786.5617841642784, - 5.779816523142207 - ], - [ - 3926.8048132074, - 5.927098691522974 - ], - [ - 4067.047842250521, - 6.065964311516253 - ], - [ - 4207.290871293642, - 6.196822623076902 - ], - [ - 4347.533900336764, - 6.321202500807541 - ], - [ - 4487.776929379886, - 6.443210004222702 - ], - [ - 4628.019958423007, - 6.567613743655277 - ], - [ - 4768.262987466129, - 6.6990612238055505 - ], - [ - 4908.50601650925, - 6.841846015593148 - ], - [ - 5048.749045552371, - 6.997105100581865 - ], - [ - 5188.992074595492, - 7.1623558135430185 - ], - [ - 5329.235103638614, - 7.335001385910042 - ], - [ - 5469.478132681736, - 7.512465623615457 - ], - [ - 5609.721161724857, - 7.692073346686597 - ], - [ - 5749.964190767979, - 7.871830918016359 - ], - [ - 5890.2072198111, - 8.051492459409586 - ], - [ - 6030.450248854221, - 8.229549933100378 - ], - [ - 6170.693277897342, - 8.404227774570654 - ], - [ - 6310.936306940464, - 8.573831766537097 - ], - [ - 6451.179335983586, - 8.737921564334407 - ], - [ - 6591.422365026707, - 8.896405942437335 - ], - [ - 6731.665394069829, - 9.049488534203261 - ], - [ - 6871.90842311295, - 9.198565498556857 - ], - [ - 7012.151452156071, - 9.345129047735972 - ], - [ - 7152.394481199192, - 9.49059900115812 - ], - [ - 7292.637510242314, - 9.63563203529102 - ], - [ - 7432.880539285436, - 9.780114684381358 - ], - [ - 7573.123568328557, - 9.923181348910024 - ], - [ - 7713.366597371678, - 10.06342159728898 - ], - [ - 7853.6096264148, - 10.199398601538608 - ], - [ - 7993.852655457921, - 10.329676345075642 - ], - [ - 8134.095684501042, - 10.452750768101891 - ], - [ - 8274.338713544164, - 10.566963066431025 - ], - [ - 8414.581742587285, - 10.672059816281088 - ], - [ - 8554.824771630407, - 10.770083397096329 - ], - [ - 8695.067800673529, - 10.863047298893648 - ], - [ - 8835.31082971665, - 10.952830666073408 - ], - [ - 8975.553858759771, - 11.041312160250113 - ], - [ - 9115.796887802893, - 11.130461323927939 - ], - [ - 9256.039916846014, - 11.22266484690063 - ], - [ - 9396.282945889136, - 11.320285459222465 - ], - [ - 9536.525974932258, - 11.422658483360008 - ], - [ - 9676.769003975378, - 11.527915521462528 - ], - [ - 9817.0120330185, - 11.634294930390842 - ], - [ - 9957.25506206162, - 11.740116234347116 - ], - [ - 10097.498091104742, - 11.843698984708906 - ], - [ - 10237.741120147864, - 11.943349225068632 - ], - [ - 10377.984149190985, - 12.037023016614699 - ], - [ - 10518.227178234107, - 12.121735417486928 - ], - [ - 10658.470207277229, - 12.196747409798519 - ], - [ - 10798.71323632035, - 12.265554919352356 - ], - [ - 10938.956265363471, - 12.33306836336965 - ], - [ - 11079.199294406593, - 12.404944614239334 - ], - [ - 11219.442323449714, - 12.487416640662067 - ], - [ - 11359.685352492836, - 12.580825833648083 - ], - [ - 11499.928381535958, - 12.67958275261682 - ], - [ - 11640.171410579078, - 12.777776338111627 - ], - [ - 11780.4144396222, - 12.869588165282963 - ], - [ - 11920.65746866532, - 12.949415680573559 - ], - [ - 12060.900497708442, - 13.014710734906483 - ], - [ - 12201.143526751564, - 13.067595924455176 - ], - [ - 12341.386555794685, - 13.107546118970388 - ], - [ - 12481.629584837807, - 13.131555368776903 - ], - [ - 12621.872613880929, - 13.136187276433175 - ], - [ - 12762.11564292405, - 13.117405932108671 - ], - [ - 12902.358671967171, - 13.07071876754231 - ], - [ - 13042.601701010293, - 12.99365150124757 - ], - [ - 13182.844730053414, - 12.888409719761126 - ], - [ - 13323.087759096536, - 12.759679833967523 - ], - [ - 13463.330788139658, - 12.61251376581635 - ], - [ - 13603.573817182778, - 12.451969865498306 - ], - [ - 13743.8168462259, - 12.283408878642167 - ], - [ - 13884.059875269022, - 12.111943488059918 - ] - ], - "10": [ - [ - 0.0, - -0.40678916289675876 - ], - [ - 140.24302904312142, - -0.8643680595356692 - ], - [ - 280.48605808624285, - -0.9026331112517069 - ], - [ - 420.7290871293643, - -0.431368853240063 - ], - [ - 560.9721161724857, - 0.15377357031114416 - ], - [ - 701.2151452156071, - 0.651713392510042 - ], - [ - 841.4581742587286, - 1.0274429575269592 - ], - [ - 981.70120330185, - 1.3184015396100486 - ], - [ - 1121.9442323449714, - 1.5678170335560948 - ], - [ - 1262.1872613880928, - 1.8041041528380495 - ], - [ - 1402.4302904312142, - 2.0449551473875234 - ], - [ - 1542.6733194743356, - 2.297572771437222 - ], - [ - 1682.9163485174572, - 2.548554289045103 - ], - [ - 1823.1593775605786, - 2.7872545077609443 - ], - [ - 1963.4024066037, - 3.023378899768393 - ], - [ - 2103.645435646821, - 3.262660714861363 - ], - [ - 2243.888464689943, - 3.4988566435941846 - ], - [ - 2384.1314937330644, - 3.7253885816072736 - ], - [ - 2524.3745227761856, - 3.9415307721163324 - ], - [ - 2664.617551819307, - 4.148685239607374 - ], - [ - 2804.8605808624284, - 4.347787033858549 - ], - [ - 2945.10360990555, - 4.5394832777433 - ], - [ - 3085.346638948671, - 4.725824159036117 - ], - [ - 3225.589667991793, - 4.9085173379777585 - ], - [ - 3365.8326970349144, - 5.086094700833433 - ], - [ - 3506.0757260780356, - 5.256517251087129 - ], - [ - 3646.318755121157, - 5.417081901799828 - ], - [ - 3786.5617841642784, - 5.566038330266238 - ], - [ - 3926.8048132074, - 5.704267791208874 - ], - [ - 4067.047842250521, - 5.83381623449077 - ], - [ - 4207.290871293642, - 5.957053977405349 - ], - [ - 4347.533900336764, - 6.076796097173212 - ], - [ - 4487.776929379886, - 6.196676509475218 - ], - [ - 4628.019958423007, - 6.320834675371863 - ], - [ - 4768.262987466129, - 6.453275201832599 - ], - [ - 4908.50601650925, - 6.597544385265144 - ], - [ - 5048.749045552371, - 6.755110264316003 - ], - [ - 5188.992074595492, - 6.924053840715867 - ], - [ - 5329.235103638614, - 7.101597433586302 - ], - [ - 5469.478132681736, - 7.284916233643388 - ], - [ - 5609.721161724857, - 7.4712613952048645 - ], - [ - 5749.964190767979, - 7.658155421681683 - ], - [ - 5890.2072198111, - 7.844059042999135 - ], - [ - 6030.450248854221, - 8.026454109371375 - ], - [ - 6170.693277897342, - 8.202746207896684 - ], - [ - 6310.936306940464, - 8.370354043230869 - ], - [ - 6451.179335983586, - 8.527744253476522 - ], - [ - 6591.422365026707, - 8.677773181841586 - ], - [ - 6731.665394069829, - 8.834216360448389 - ], - [ - 6871.90842311295, - 8.997199959670466 - ], - [ - 7012.151452156071, - 9.164189223306836 - ], - [ - 7152.394481199192, - 9.332619377666843 - ], - [ - 7292.637510242314, - 9.499042078209088 - ], - [ - 7432.880539285436, - 9.659491910050685 - ], - [ - 7573.123568328557, - 9.812568437357802 - ], - [ - 7713.366597371678, - 9.957907396249903 - ], - [ - 7853.6096264148, - 10.095278274373793 - ], - [ - 7993.852655457921, - 10.224450795778397 - ], - [ - 8134.095684501042, - 10.345147747564434 - ], - [ - 8274.338713544164, - 10.457391731244892 - ], - [ - 8414.581742587285, - 10.56182349993305 - ], - [ - 8554.824771630407, - 10.660511894766866 - ], - [ - 8695.067800673529, - 10.755347085600008 - ], - [ - 8835.31082971665, - 10.847987548063887 - ], - [ - 8975.553858759771, - 10.940090689372296 - ], - [ - 9115.796887802893, - 11.033372261943011 - ], - [ - 9256.039916846014, - 11.129795667492113 - ], - [ - 9396.282945889136, - 11.23157617125161 - ], - [ - 9536.525974932258, - 11.338267791808915 - ], - [ - 9676.769003975378, - 11.447879863643196 - ], - [ - 9817.0120330185, - 11.558858932539973 - ], - [ - 9957.25506206162, - 11.669771065773109 - ], - [ - 10097.498091104742, - 11.77918235653446 - ], - [ - 10237.741120147864, - 11.885648318200122 - ], - [ - 10377.984149190985, - 11.987502316938794 - ], - [ - 10518.227178234107, - 12.081698033228095 - ], - [ - 10658.470207277229, - 12.16555756100945 - ], - [ - 10798.71323632035, - 12.241399017569867 - ], - [ - 10938.956265363471, - 12.312747711011587 - ], - [ - 11079.199294406593, - 12.383517346399744 - ], - [ - 11219.442323449714, - 12.459615721456428 - ], - [ - 11359.685352492836, - 12.544227975405125 - ], - [ - 11499.928381535958, - 12.633518692361276 - ], - [ - 11640.171410579078, - 12.722872205903165 - ], - [ - 11780.4144396222, - 12.80781622286694 - ], - [ - 11920.65746866532, - 12.883648137379328 - ], - [ - 12060.900497708442, - 12.946018892624675 - ], - [ - 12201.143526751564, - 12.994372616487832 - ], - [ - 12341.386555794685, - 13.027287650904684 - ], - [ - 12481.629584837807, - 13.041373151682814 - ], - [ - 12621.872613880929, - 13.032761085303534 - ], - [ - 12762.11564292405, - 12.997128342672509 - ], - [ - 12902.358671967171, - 12.931167540767479 - ], - [ - 13042.601701010293, - 12.834623930520419 - ], - [ - 13182.844730053414, - 12.710640313819631 - ], - [ - 13323.087759096536, - 12.564490524730969 - ], - [ - 13463.330788139658, - 12.401855463880501 - ], - [ - 13603.573817182778, - 12.228418318436997 - ], - [ - 13743.8168462259, - 12.05005298415421 - ], - [ - 13884.059875269022, - 11.871550600826241 - ] - ], - "11": [ - [ - 0.0, - -0.1822376974664744 - ], - [ - 140.24302904312142, - -0.455483465339052 - ], - [ - 280.48605808624285, - -0.3723374634294809 - ], - [ - 420.7290871293643, - -0.06148892786298469 - ], - [ - 560.9721161724857, - 0.4148287396848312 - ], - [ - 701.2151452156071, - 0.9252269041212308 - ], - [ - 841.4581742587286, - 1.2754084871898907 - ], - [ - 981.70120330185, - 1.5042521250112653 - ], - [ - 1121.9442323449714, - 1.6944395307902114 - ], - [ - 1262.1872613880928, - 1.889491933920113 - ], - [ - 1402.4302904312142, - 2.101001884073827 - ], - [ - 1542.6733194743356, - 2.328268202019118 - ], - [ - 1682.9163485174572, - 2.5634847134761634 - ], - [ - 1823.1593775605786, - 2.796297580865416 - ], - [ - 1963.4024066037, - 3.0257558503384403 - ], - [ - 2103.645435646821, - 3.2526141885152153 - ], - [ - 2243.888464689943, - 3.476278578855902 - ], - [ - 2384.1314937330644, - 3.693874581439269 - ], - [ - 2524.3745227761856, - 3.9078265111937416 - ], - [ - 2664.617551819307, - 4.119071848937901 - ], - [ - 2804.8605808624284, - 4.323584033479275 - ], - [ - 2945.10360990555, - 4.516997689493501 - ], - [ - 3085.346638948671, - 4.697451131464934 - ], - [ - 3225.589667991793, - 4.8673666305886165 - ], - [ - 3365.8326970349144, - 5.0281648680634365 - ], - [ - 3506.0757260780356, - 5.180834636917604 - ], - [ - 3646.318755121157, - 5.325631049178856 - ], - [ - 3786.5617841642784, - 5.462338219209022 - ], - [ - 3926.8048132074, - 5.593012160802613 - ], - [ - 4067.047842250521, - 5.720267722951825 - ], - [ - 4207.290871293642, - 5.846815422389549 - ], - [ - 4347.533900336764, - 5.9756801645243485 - ], - [ - 4487.776929379886, - 6.108644375095692 - ], - [ - 4628.019958423007, - 6.247898934316919 - ], - [ - 4768.262987466129, - 6.3957644152294675 - ], - [ - 4908.50601650925, - 6.554435782396764 - ], - [ - 5048.749045552371, - 6.725138073024329 - ], - [ - 5188.992074595492, - 6.9064575139744555 - ], - [ - 5329.235103638614, - 7.095711204050236 - ], - [ - 5469.478132681736, - 7.290101714533273 - ], - [ - 5609.721161724857, - 7.486867185096391 - ], - [ - 5749.964190767979, - 7.682796492901109 - ], - [ - 5890.2072198111, - 7.876470509539128 - ], - [ - 6030.450248854221, - 8.067051721225003 - ], - [ - 6170.693277897342, - 8.253543346766367 - ], - [ - 6310.936306940464, - 8.434938175081239 - ], - [ - 6451.179335983586, - 8.610858621618998 - ], - [ - 6591.422365026707, - 8.7821421628586 - ], - [ - 6731.665394069829, - 8.94843008191011 - ], - [ - 6871.90842311295, - 9.109413525519624 - ], - [ - 7012.151452156071, - 9.26486082006029 - ], - [ - 7152.394481199192, - 9.414478120840753 - ], - [ - 7292.637510242314, - 9.557540028510818 - ], - [ - 7432.880539285436, - 9.692470526710219 - ], - [ - 7573.123568328557, - 9.81921612167888 - ], - [ - 7713.366597371678, - 9.938968051245293 - ], - [ - 7853.6096264148, - 10.05315456197765 - ], - [ - 7993.852655457921, - 10.16322182632624 - ], - [ - 8134.095684501042, - 10.2706797100337 - ], - [ - 8274.338713544164, - 10.376904375297203 - ], - [ - 8414.581742587285, - 10.483658514641709 - ], - [ - 8554.824771630407, - 10.59235866526108 - ], - [ - 8695.067800673529, - 10.70286815485928 - ], - [ - 8835.31082971665, - 10.814665121527263 - ], - [ - 8975.553858759771, - 10.927224881090849 - ], - [ - 9115.796887802893, - 11.03998960194792 - ], - [ - 9256.039916846014, - 11.15270592851057 - ], - [ - 9396.282945889136, - 11.2659257529581 - ], - [ - 9536.525974932258, - 11.378636788488588 - ], - [ - 9676.769003975378, - 11.490044161183537 - ], - [ - 9817.0120330185, - 11.600417154026893 - ], - [ - 9957.25506206162, - 11.710168705563365 - ], - [ - 10097.498091104742, - 11.819711744565135 - ], - [ - 10237.741120147864, - 11.929455967156121 - ], - [ - 10377.984149190985, - 12.039756856855618 - ], - [ - 10518.227178234107, - 12.14923298546189 - ], - [ - 10658.470207277229, - 12.254412634650024 - ], - [ - 10798.71323632035, - 12.355075881541195 - ], - [ - 10938.956265363471, - 12.451290386839592 - ], - [ - 11079.199294406593, - 12.543122484058564 - ], - [ - 11219.442323449714, - 12.633234178021521 - ], - [ - 11359.685352492836, - 12.724122865508633 - ], - [ - 11499.928381535958, - 12.812185084175626 - ], - [ - 11640.171410579078, - 12.893001264991776 - ], - [ - 11780.4144396222, - 12.962243453121252 - ], - [ - 11920.65746866532, - 13.015643778639475 - ], - [ - 12060.900497708442, - 13.050560196688977 - ], - [ - 12201.143526751564, - 13.068472964471619 - ], - [ - 12341.386555794685, - 13.070033214615085 - ], - [ - 12481.629584837807, - 13.054078376957742 - ], - [ - 12621.872613880929, - 13.018981245900024 - ], - [ - 12762.11564292405, - 12.96244889514888 - ], - [ - 12902.358671967171, - 12.88021216472017 - ], - [ - 13042.601701010293, - 12.769821371160736 - ], - [ - 13182.844730053414, - 12.633236043806345 - ], - [ - 13323.087759096536, - 12.47444038808784 - ], - [ - 13463.330788139658, - 12.297870468767197 - ], - [ - 13603.573817182778, - 12.107940551921613 - ], - [ - 13743.8168462259, - 11.90934363868864 - ], - [ - 13884.059875269022, - 11.707366568448526 - ] - ], - "12": [ - [ - 0.0, - 0.06070264871764198 - ], - [ - 140.24302904312142, - -0.08764365569303438 - ], - [ - 280.48605808624285, - 0.03375488569048653 - ], - [ - 420.7290871293643, - 0.18614716110713442 - ], - [ - 560.9721161724857, - 0.455703179261567 - ], - [ - 701.2151452156071, - 0.842139844447927 - ], - [ - 841.4581742587286, - 1.1817177595723654 - ], - [ - 981.70120330185, - 1.437213114370947 - ], - [ - 1121.9442323449714, - 1.6409184187504955 - ], - [ - 1262.1872613880928, - 1.8444145559580944 - ], - [ - 1402.4302904312142, - 2.073631228723875 - ], - [ - 1542.6733194743356, - 2.322255268863468 - ], - [ - 1682.9163485174572, - 2.569435083686158 - ], - [ - 1823.1593775605786, - 2.8028413070849796 - ], - [ - 1963.4024066037, - 3.0249566490546815 - ], - [ - 2103.645435646821, - 3.248429976594959 - ], - [ - 2243.888464689943, - 3.470075926368333 - ], - [ - 2384.1314937330644, - 3.698316374431081 - ], - [ - 2524.3745227761856, - 3.9301147730906107 - ], - [ - 2664.617551819307, - 4.1487309398011805 - ], - [ - 2804.8605808624284, - 4.3531501141601865 - ], - [ - 2945.10360990555, - 4.544601156483875 - ], - [ - 3085.346638948671, - 4.726148942847912 - ], - [ - 3225.589667991793, - 4.901601947806487 - ], - [ - 3365.8326970349144, - 5.070912467339333 - ], - [ - 3506.0757260780356, - 5.2333295506815 - ], - [ - 3646.318755121157, - 5.386862985252566 - ], - [ - 3786.5617841642784, - 5.528659752792856 - ], - [ - 3926.8048132074, - 5.660993758603854 - ], - [ - 4067.047842250521, - 5.788109950493729 - ], - [ - 4207.290871293642, - 5.914462476857634 - ], - [ - 4347.533900336764, - 6.046965731791873 - ], - [ - 4487.776929379886, - 6.189538324935445 - ], - [ - 4628.019958423007, - 6.3367321896390925 - ], - [ - 4768.262987466129, - 6.4815950362799555 - ], - [ - 4908.50601650925, - 6.617022727844673 - ], - [ - 5048.749045552371, - 6.733373535922543 - ], - [ - 5188.992074595492, - 6.831220184740433 - ], - [ - 5329.235103638614, - 6.940319141908297 - ], - [ - 5469.478132681736, - 7.094235347247846 - ], - [ - 5609.721161724857, - 7.326416906619712 - ], - [ - 5749.964190767979, - 7.670131812474497 - ], - [ - 5890.2072198111, - 8.141189558291654 - ], - [ - 6030.450248854221, - 8.649493605901034 - ], - [ - 6170.693277897342, - 9.07012420310707 - ], - [ - 6310.936306940464, - 9.27808567289681 - ], - [ - 6451.179335983586, - 9.149167153739596 - ], - [ - 6591.422365026707, - 8.581651867925068 - ], - [ - 6731.665394069829, - 8.154730507706162 - ], - [ - 6871.90842311295, - 8.103323559955474 - ], - [ - 7012.151452156071, - 8.327553534295983 - ], - [ - 7152.394481199192, - 8.727567865488123 - ], - [ - 7292.637510242314, - 9.203151527431238 - ], - [ - 7432.880539285436, - 9.651958569773708 - ], - [ - 7573.123568328557, - 9.990161344184784 - ], - [ - 7713.366597371678, - 10.216249954079315 - ], - [ - 7853.6096264148, - 10.35163469044472 - ], - [ - 7993.852655457921, - 10.417950417902631 - ], - [ - 8134.095684501042, - 10.438264164214745 - ], - [ - 8274.338713544164, - 10.4374679132289 - ], - [ - 8414.581742587285, - 10.451278346354162 - ], - [ - 8554.824771630407, - 10.503393963258095 - ], - [ - 8695.067800673529, - 10.589662657229717 - ], - [ - 8835.31082971665, - 10.70239206422463 - ], - [ - 8975.553858759771, - 10.833887461756413 - ], - [ - 9115.796887802893, - 10.976405951797434 - ], - [ - 9256.039916846014, - 11.122670964629986 - ], - [ - 9396.282945889136, - 11.266932021726856 - ], - [ - 9536.525974932258, - 11.404041200459233 - ], - [ - 9676.769003975378, - 11.532786821639974 - ], - [ - 9817.0120330185, - 11.6552880870816 - ], - [ - 9957.25506206162, - 11.773829633117682 - ], - [ - 10097.498091104742, - 11.890696049009971 - ], - [ - 10237.741120147864, - 12.008176405322939 - ], - [ - 10377.984149190985, - 12.128540129129956 - ], - [ - 10518.227178234107, - 12.251902596239631 - ], - [ - 10658.470207277229, - 12.375151711116333 - ], - [ - 10798.71323632035, - 12.495690975884184 - ], - [ - 10938.956265363471, - 12.610396721038677 - ], - [ - 11079.199294406593, - 12.71643787589724 - ], - [ - 11219.442323449714, - 12.813662333639588 - ], - [ - 11359.685352492836, - 12.903573434256385 - ], - [ - 11499.928381535958, - 12.984768851199068 - ], - [ - 11640.171410579078, - 13.055043347643515 - ], - [ - 11780.4144396222, - 13.111513732949689 - ], - [ - 11920.65746866532, - 13.1519556127217 - ], - [ - 12060.900497708442, - 13.176100773208919 - ], - [ - 12201.143526751564, - 13.185565131087262 - ], - [ - 12341.386555794685, - 13.179962738122079 - ], - [ - 12481.629584837807, - 13.15822209543456 - ], - [ - 12621.872613880929, - 13.119040087463151 - ], - [ - 12762.11564292405, - 13.060001847168003 - ], - [ - 12902.358671967171, - 12.975830717523328 - ], - [ - 13042.601701010293, - 12.863811513433665 - ], - [ - 13182.844730053414, - 12.726412481591932 - ], - [ - 13323.087759096536, - 12.567098970790378 - ], - [ - 13463.330788139658, - 12.389623640376948 - ], - [ - 13603.573817182778, - 12.19772527467736 - ], - [ - 13743.8168462259, - 11.995565386164124 - ], - [ - 13884.059875269022, - 11.788147784004394 - ] - ], - "13": [ - [ - 0.0, - 0.4094193505425669 - ], - [ - 140.24302904312142, - 0.3223636675050678 - ], - [ - 280.48605808624285, - 0.4551231622059286 - ], - [ - 420.7290871293643, - 0.5856818163124866 - ], - [ - 560.9721161724857, - 0.7315487984233083 - ], - [ - 701.2151452156071, - 0.9446180759404852 - ], - [ - 841.4581742587286, - 1.237533309964865 - ], - [ - 981.70120330185, - 1.4492085232125307 - ], - [ - 1121.9442323449714, - 1.6503320391976974 - ], - [ - 1262.1872613880928, - 1.8633041351612663 - ], - [ - 1402.4302904312142, - 2.0679205866599477 - ], - [ - 1542.6733194743356, - 2.306750868069865 - ], - [ - 1682.9163485174572, - 2.56778939394985 - ], - [ - 1823.1593775605786, - 2.8238862346712237 - ], - [ - 1963.4024066037, - 3.045748708858542 - ], - [ - 2103.645435646821, - 3.247474136519846 - ], - [ - 2243.888464689943, - 3.5769559712973575 - ], - [ - 2384.1314937330644, - 3.860011387986431 - ], - [ - 2524.3745227761856, - 4.0281961775965565 - ], - [ - 2664.617551819307, - 4.217116587965324 - ], - [ - 2804.8605808624284, - 4.427039966387985 - ], - [ - 2945.10360990555, - 4.641381725691799 - ], - [ - 3085.346638948671, - 4.844760110862045 - ], - [ - 3225.589667991793, - 5.027844846713926 - ], - [ - 3365.8326970349144, - 5.194292070428863 - ], - [ - 3506.0757260780356, - 5.349689002309177 - ], - [ - 3646.318755121157, - 5.498546863987108 - ], - [ - 3786.5617841642784, - 5.643818988102327 - ], - [ - 3926.8048132074, - 5.78656522668222 - ], - [ - 4067.047842250521, - 5.927407906923987 - ], - [ - 4207.290871293642, - 6.067195840171474 - ], - [ - 4347.533900336764, - 6.20830997833369 - ], - [ - 4487.776929379886, - 6.352660161782633 - ], - [ - 4628.019958423007, - 6.497632397059137 - ], - [ - 4768.262987466129, - 6.639746210817476 - ], - [ - 4908.50601650925, - 6.775744615582752 - ], - [ - 5048.749045552371, - 6.901022418518727 - ], - [ - 5188.992074595492, - 7.01612227493567 - ], - [ - 5329.235103638614, - 7.140577464426645 - ], - [ - 5469.478132681736, - 7.296515302213458 - ], - [ - 5609.721161724857, - 7.505611967795369 - ], - [ - 5749.964190767979, - 7.788935488360598 - ], - [ - 5890.2072198111, - 8.157492118368786 - ], - [ - 6030.450248854221, - 8.551554325517232 - ], - [ - 6170.693277897342, - 8.887197678795577 - ], - [ - 6310.936306940464, - 9.080441020500656 - ], - [ - 6451.179335983586, - 9.047911844119623 - ], - [ - 6591.422365026707, - 8.720544422765535 - ], - [ - 6731.665394069829, - 8.489045807438218 - ], - [ - 6871.90842311295, - 8.51399189919627 - ], - [ - 7012.151452156071, - 8.726178586772596 - ], - [ - 7152.394481199192, - 9.056522416627002 - ], - [ - 7292.637510242314, - 9.435876879804464 - ], - [ - 7432.880539285436, - 9.79307558785978 - ], - [ - 7573.123568328557, - 10.070353159689757 - ], - [ - 7713.366597371678, - 10.26696532662754 - ], - [ - 7853.6096264148, - 10.397940853534026 - ], - [ - 7993.852655457921, - 10.478298665780958 - ], - [ - 8134.095684501042, - 10.522838630539647 - ], - [ - 8274.338713544164, - 10.548454822874296 - ], - [ - 8414.581742587285, - 10.584919360479658 - ], - [ - 8554.824771630407, - 10.651139293824116 - ], - [ - 8695.067800673529, - 10.743864540331975 - ], - [ - 8835.31082971665, - 10.857408749198592 - ], - [ - 8975.553858759771, - 10.98608519622533 - ], - [ - 9115.796887802893, - 11.124219311772375 - ], - [ - 9256.039916846014, - 11.266465454592147 - ], - [ - 9396.282945889136, - 11.407793617597852 - ], - [ - 9536.525974932258, - 11.544228358592273 - ], - [ - 9676.769003975378, - 11.675094610451687 - ], - [ - 9817.0120330185, - 11.801973635159694 - ], - [ - 9957.25506206162, - 11.926521351327084 - ], - [ - 10097.498091104742, - 12.050393572808145 - ], - [ - 10237.741120147864, - 12.17525395394361 - ], - [ - 10377.984149190985, - 12.302756942464873 - ], - [ - 10518.227178234107, - 12.433105067470372 - ], - [ - 10658.470207277229, - 12.563519646162213 - ], - [ - 10798.71323632035, - 12.689770295506591 - ], - [ - 10938.956265363471, - 12.805926629995392 - ], - [ - 11079.199294406593, - 12.90607989453853 - ], - [ - 11219.442323449714, - 12.986606005365285 - ], - [ - 11359.685352492836, - 13.04887260350387 - ], - [ - 11499.928381535958, - 13.096211722849002 - ], - [ - 11640.171410579078, - 13.133332410507089 - ], - [ - 11780.4144396222, - 13.164348917710294 - ], - [ - 11920.65746866532, - 13.194037863800347 - ], - [ - 12060.900497708442, - 13.225532252994757 - ], - [ - 12201.143526751564, - 13.257257892035124 - ], - [ - 12341.386555794685, - 13.282362101559142 - ], - [ - 12481.629584837807, - 13.293319962320194 - ], - [ - 12621.872613880929, - 13.282603224387048 - ], - [ - 12762.11564292405, - 13.241858136386073 - ], - [ - 12902.358671967171, - 13.16315714554513 - ], - [ - 13042.601701010293, - 13.045894998565966 - ], - [ - 13182.844730053414, - 12.896637507962652 - ], - [ - 13323.087759096536, - 12.72252424834291 - ], - [ - 13463.330788139658, - 12.530802530944593 - ], - [ - 13603.573817182778, - 12.328711338021678 - ], - [ - 13743.8168462259, - 12.1235901549949 - ], - [ - 13884.059875269022, - 11.920770265332946 - ] - ], - "14": [ - [ - 0.0, - 0.7983011524347337 - ], - [ - 140.24302904312142, - 0.781764698013081 - ], - [ - 280.48605808624285, - 0.9561914147413487 - ], - [ - 420.7290871293643, - 1.0822810453830154 - ], - [ - 560.9721161724857, - 1.184611967199937 - ], - [ - 701.2151452156071, - 1.31850795094902 - ], - [ - 841.4581742587286, - 1.5086181251318704 - ], - [ - 981.70120330185, - 1.6255322429296593 - ], - [ - 1121.9442323449714, - 1.779708436396037 - ], - [ - 1262.1872613880928, - 1.9639108569017025 - ], - [ - 1402.4302904312142, - 2.1479022558207754 - ], - [ - 1542.6733194743356, - 2.381412565066502 - ], - [ - 1682.9163485174572, - 2.645304040385109 - ], - [ - 1823.1593775605786, - 2.9074242937510584 - ], - [ - 1963.4024066037, - 3.1528199282923492 - ], - [ - 2103.645435646821, - 3.3904976892045577 - ], - [ - 2243.888464689943, - 3.6923605603872383 - ], - [ - 2384.1314937330644, - 3.959668774102775 - ], - [ - 2524.3745227761856, - 4.160501741249103 - ], - [ - 2664.617551819307, - 4.365033625451886 - ], - [ - 2804.8605808624284, - 4.573051885301888 - ], - [ - 2945.10360990555, - 4.776747673784354 - ], - [ - 3085.346638948671, - 4.96926888550944 - ], - [ - 3225.589667991793, - 5.14734383164582 - ], - [ - 3365.8326970349144, - 5.314148283071206 - ], - [ - 3506.0757260780356, - 5.473577291603479 - ], - [ - 3646.318755121157, - 5.628731586601843 - ], - [ - 3786.5617841642784, - 5.782103778664774 - ], - [ - 3926.8048132074, - 5.933983380341722 - ], - [ - 4067.047842250521, - 6.08405864295797 - ], - [ - 4207.290871293642, - 6.232288654108256 - ], - [ - 4347.533900336764, - 6.378586677979039 - ], - [ - 4487.776929379886, - 6.523001692398103 - ], - [ - 4628.019958423007, - 6.666785726369818 - ], - [ - 4768.262987466129, - 6.8114084644318105 - ], - [ - 4908.50601650925, - 6.9587576740709265 - ], - [ - 5048.749045552371, - 7.11094916718701 - ], - [ - 5188.992074595492, - 7.267765669513184 - ], - [ - 5329.235103638614, - 7.427812687241017 - ], - [ - 5469.478132681736, - 7.589550352367519 - ], - [ - 5609.721161724857, - 7.750952337621433 - ], - [ - 5749.964190767979, - 7.909505030463382 - ], - [ - 5890.2072198111, - 8.065989787637642 - ], - [ - 6030.450248854221, - 8.222735324557412 - ], - [ - 6170.693277897342, - 8.382128182705797 - ], - [ - 6310.936306940464, - 8.546522517423952 - ], - [ - 6451.179335983586, - 8.718267408779662 - ], - [ - 6591.422365026707, - 8.897933711535032 - ], - [ - 6731.665394069829, - 9.082174124769328 - ], - [ - 6871.90842311295, - 9.266510998279056 - ], - [ - 7012.151452156071, - 9.446442567811367 - ], - [ - 7152.394481199192, - 9.61756308457741 - ], - [ - 7292.637510242314, - 9.776165988381612 - ], - [ - 7432.880539285436, - 9.91982360245396 - ], - [ - 7573.123568328557, - 10.049690343097028 - ], - [ - 7713.366597371678, - 10.16874401386329 - ], - [ - 7853.6096264148, - 10.280028590426047 - ], - [ - 7993.852655457921, - 10.386585241940184 - ], - [ - 8134.095684501042, - 10.491190994033472 - ], - [ - 8274.338713544164, - 10.596079676265008 - ], - [ - 8414.581742587285, - 10.703625346693475 - ], - [ - 8554.824771630407, - 10.814551755825926 - ], - [ - 8695.067800673529, - 10.929031110694272 - ], - [ - 8835.31082971665, - 11.047277904301417 - ], - [ - 8975.553858759771, - 11.169506940421202 - ], - [ - 9115.796887802893, - 11.295956472805369 - ], - [ - 9256.039916846014, - 11.427267044678917 - ], - [ - 9396.282945889136, - 11.563455292139363 - ], - [ - 9536.525974932258, - 11.703306743363703 - ], - [ - 9676.769003975378, - 11.846172760955227 - ], - [ - 9817.0120330185, - 11.991276735444874 - ], - [ - 9957.25506206162, - 12.13779253742144 - ], - [ - 10097.498091104742, - 12.28489386220913 - ], - [ - 10237.741120147864, - 12.431765392515706 - ], - [ - 10377.984149190985, - 12.577536136084541 - ], - [ - 10518.227178234107, - 12.72053894769247 - ], - [ - 10658.470207277229, - 12.858530252637566 - ], - [ - 10798.71323632035, - 12.987618266200927 - ], - [ - 10938.956265363471, - 13.102027381271467 - ], - [ - 11079.199294406593, - 13.19632749822666 - ], - [ - 11219.442323449714, - 13.26621354889323 - ], - [ - 11359.685352492836, - 13.31087826032815 - ], - [ - 11499.928381535958, - 13.33469511293903 - ], - [ - 11640.171410579078, - 13.345382690391965 - ], - [ - 11780.4144396222, - 13.349986778426711 - ], - [ - 11920.65746866532, - 13.35625657224763 - ], - [ - 12060.900497708442, - 13.370223835051604 - ], - [ - 12201.143526751564, - 13.391674701396942 - ], - [ - 12341.386555794685, - 13.412430146346974 - ], - [ - 12481.629584837807, - 13.423584758943298 - ], - [ - 12621.872613880929, - 13.416502008460247 - ], - [ - 12762.11564292405, - 13.38195335142159 - ], - [ - 12902.358671967171, - 13.311767596801587 - ], - [ - 13042.601701010293, - 13.204195435540463 - ], - [ - 13182.844730053414, - 13.065505768375864 - ], - [ - 13323.087759096536, - 12.902408852097965 - ], - [ - 13463.330788139658, - 12.721526762959352 - ], - [ - 13603.573817182778, - 12.529475940731999 - ], - [ - 13743.8168462259, - 12.332755027321072 - ], - [ - 13884.059875269022, - 12.13596435745739 - ] - ], - "15": [ - [ - 0.0, - 1.1618301044814903 - ], - [ - 140.24302904312142, - 1.2171821002639487 - ], - [ - 280.48605808624285, - 1.432106456692824 - ], - [ - 420.7290871293643, - 1.5481305923802942 - ], - [ - 560.9721161724857, - 1.6444186555998355 - ], - [ - 701.2151452156071, - 1.7436909726297791 - ], - [ - 841.4581742587286, - 1.812797686599896 - ], - [ - 981.70120330185, - 1.8923800308667156 - ], - [ - 1121.9442323449714, - 2.0015195270836137 - ], - [ - 1262.1872613880928, - 2.1086895693934715 - ], - [ - 1402.4302904312142, - 2.2437929601025783 - ], - [ - 1542.6733194743356, - 2.4549465075043746 - ], - [ - 1682.9163485174572, - 2.7271571211869503 - ], - [ - 1823.1593775605786, - 3.024499189759561 - ], - [ - 1963.4024066037, - 3.3151640512945635 - ], - [ - 2103.645435646821, - 3.5882535432546336 - ], - [ - 2243.888464689943, - 3.8496574096815284 - ], - [ - 2384.1314937330644, - 4.097858288497836 - ], - [ - 2524.3745227761856, - 4.325526403663505 - ], - [ - 2664.617551819307, - 4.531675412835185 - ], - [ - 2804.8605808624284, - 4.721402516268552 - ], - [ - 2945.10360990555, - 4.900498176933965 - ], - [ - 3085.346638948671, - 5.07420702525523 - ], - [ - 3225.589667991793, - 5.2456439640561205 - ], - [ - 3365.8326970349144, - 5.415781430468741 - ], - [ - 3506.0757260780356, - 5.58511955573314 - ], - [ - 3646.318755121157, - 5.753285680328805 - ], - [ - 3786.5617841642784, - 5.919076365073985 - ], - [ - 3926.8048132074, - 6.08151173268202 - ], - [ - 4067.047842250521, - 6.239988149298322 - ], - [ - 4207.290871293642, - 6.394279467891194 - ], - [ - 4347.533900336764, - 6.545394526677584 - ], - [ - 4487.776929379886, - 6.694336809577464 - ], - [ - 4628.019958423007, - 6.842167002064704 - ], - [ - 4768.262987466129, - 6.990139661758398 - ], - [ - 4908.50601650925, - 7.139577992661609 - ], - [ - 5048.749045552371, - 7.290672042172406 - ], - [ - 5188.992074595492, - 7.442767122979391 - ], - [ - 5329.235103638614, - 7.594811767361071 - ], - [ - 5469.478132681736, - 7.745592271499366 - ], - [ - 5609.721161724857, - 7.893650885090978 - ], - [ - 5749.964190767979, - 8.038712498540509 - ], - [ - 5890.2072198111, - 8.183238263771798 - ], - [ - 6030.450248854221, - 8.330337999688018 - ], - [ - 6170.693277897342, - 8.483190981060558 - ], - [ - 6310.936306940464, - 8.644921609859912 - ], - [ - 6451.179335983586, - 8.818197683740154 - ], - [ - 6591.422365026707, - 9.002297758861609 - ], - [ - 6731.665394069829, - 9.192608330732499 - ], - [ - 6871.90842311295, - 9.383429908679256 - ], - [ - 7012.151452156071, - 9.568978783514117 - ], - [ - 7152.394481199192, - 9.743557147810092 - ], - [ - 7292.637510242314, - 9.902047475381627 - ], - [ - 7432.880539285436, - 10.041886369636373 - ], - [ - 7573.123568328557, - 10.165063290924618 - ], - [ - 7713.366597371678, - 10.27563189888659 - ], - [ - 7853.6096264148, - 10.377811418159613 - ], - [ - 7993.852655457921, - 10.475824196223371 - ], - [ - 8134.095684501042, - 10.573778848114065 - ], - [ - 8274.338713544164, - 10.675556136993919 - ], - [ - 8414.581742587285, - 10.784110730940286 - ], - [ - 8554.824771630407, - 10.899910317627507 - ], - [ - 8695.067800673529, - 11.022801444032359 - ], - [ - 8835.31082971665, - 11.152686157213356 - ], - [ - 8975.553858759771, - 11.289466382677466 - ], - [ - 9115.796887802893, - 11.433051398721357 - ], - [ - 9256.039916846014, - 11.583305755160223 - ], - [ - 9396.282945889136, - 11.739249715960526 - ], - [ - 9536.525974932258, - 11.899161794139948 - ], - [ - 9676.769003975378, - 12.06183718334215 - ], - [ - 9817.0120330185, - 12.225565786358208 - ], - [ - 9957.25506206162, - 12.38854291021106 - ], - [ - 10097.498091104742, - 12.548963644959269 - ], - [ - 10237.741120147864, - 12.705031869561582 - ], - [ - 10377.984149190985, - 12.854849315724623 - ], - [ - 10518.227178234107, - 12.99631193830626 - ], - [ - 10658.470207277229, - 13.128459444221035 - ], - [ - 10798.71323632035, - 13.248867582630295 - ], - [ - 10938.956265363471, - 13.3542883604669 - ], - [ - 11079.199294406593, - 13.442598516190271 - ], - [ - 11219.442323449714, - 13.512316630507323 - ], - [ - 11359.685352492836, - 13.561417950805662 - ], - [ - 11499.928381535958, - 13.592168874211195 - ], - [ - 11640.171410579078, - 13.608492072744411 - ], - [ - 11780.4144396222, - 13.61320002247236 - ], - [ - 11920.65746866532, - 13.609369042426401 - ], - [ - 12060.900497708442, - 13.601395846966565 - ], - [ - 12201.143526751564, - 13.591747940849702 - ], - [ - 12341.386555794685, - 13.57706218818255 - ], - [ - 12481.629584837807, - 13.554101081939931 - ], - [ - 12621.872613880929, - 13.519908017438436 - ], - [ - 12762.11564292405, - 13.471128605856345 - ], - [ - 12902.358671967171, - 13.402949622256383 - ], - [ - 13042.601701010293, - 13.312043015360699 - ], - [ - 13182.844730053414, - 13.200626935390881 - ], - [ - 13323.087759096536, - 13.070831265349756 - ], - [ - 13463.330788139658, - 12.924659621113795 - ], - [ - 13603.573817182778, - 12.764121544521837 - ], - [ - 13743.8168462259, - 12.591139817871094 - ], - [ - 13884.059875269022, - 12.407838706637799 - ] - ], - "16": [ - [ - 0.0, - 1.3701921270205335 - ], - [ - 140.24302904312142, - 1.4598935932456247 - ], - [ - 280.48605808624285, - 1.7016368186195934 - ], - [ - 420.7290871293643, - 1.8223793085774695 - ], - [ - 560.9721161724857, - 1.9072411265635831 - ], - [ - 701.2151452156071, - 1.9870692240407664 - ], - [ - 841.4581742587286, - 2.0285738930793134 - ], - [ - 981.70120330185, - 2.060590822125373 - ], - [ - 1121.9442323449714, - 2.1055007487704973 - ], - [ - 1262.1872613880928, - 2.1623164787448976 - ], - [ - 1402.4302904312142, - 2.275966814686074 - ], - [ - 1542.6733194743356, - 2.480478627151215 - ], - [ - 1682.9163485174572, - 2.752988074309081 - ], - [ - 1823.1593775605786, - 3.0634067460065046 - ], - [ - 1963.4024066037, - 3.376400507654678 - ], - [ - 2103.645435646821, - 3.6692362722968657 - ], - [ - 2243.888464689943, - 3.9395551660139234 - ], - [ - 2384.1314937330644, - 4.185808285260531 - ], - [ - 2524.3745227761856, - 4.4071199716150184 - ], - [ - 2664.617551819307, - 4.608847124978089 - ], - [ - 2804.8605808624284, - 4.798287841953716 - ], - [ - 2945.10360990555, - 4.982450692733978 - ], - [ - 3085.346638948671, - 5.1659869662505615 - ], - [ - 3225.589667991793, - 5.349027554301351 - ], - [ - 3365.8326970349144, - 5.5305604030946665 - ], - [ - 3506.0757260780356, - 5.7095175653172126 - ], - [ - 3646.318755121157, - 5.884622532907381 - ], - [ - 3786.5617841642784, - 6.0555395914387535 - ], - [ - 3926.8048132074, - 6.222159174903632 - ], - [ - 4067.047842250521, - 6.384425211465592 - ], - [ - 4207.290871293642, - 6.542746076566961 - ], - [ - 4347.533900336764, - 6.697965349032161 - ], - [ - 4487.776929379886, - 6.8500687517673144 - ], - [ - 4628.019958423007, - 6.999115044937706 - ], - [ - 4768.262987466129, - 7.145399000327705 - ], - [ - 4908.50601650925, - 7.289221592864493 - ], - [ - 5048.749045552371, - 7.4310739527998475 - ], - [ - 5188.992074595492, - 7.572011991094434 - ], - [ - 5329.235103638614, - 7.712947928648593 - ], - [ - 5469.478132681736, - 7.854609120427436 - ], - [ - 5609.721161724857, - 7.997506950778466 - ], - [ - 5749.964190767979, - 8.14211718202341 - ], - [ - 5890.2072198111, - 8.289278868799986 - ], - [ - 6030.450248854221, - 8.439440207826914 - ], - [ - 6170.693277897342, - 8.593085997284673 - ], - [ - 6310.936306940464, - 8.750631409011987 - ], - [ - 6451.179335983586, - 8.912108527537349 - ], - [ - 6591.422365026707, - 9.076015094853773 - ], - [ - 6731.665394069829, - 9.239961558543813 - ], - [ - 6871.90842311295, - 9.401773035032424 - ], - [ - 7012.151452156071, - 9.55925215011733 - ], - [ - 7152.394481199192, - 9.710327953752822 - ], - [ - 7292.637510242314, - 9.853830617497911 - ], - [ - 7432.880539285436, - 9.99074939710546 - ], - [ - 7573.123568328557, - 10.122714346651705 - ], - [ - 7713.366597371678, - 10.248271622799116 - ], - [ - 7853.6096264148, - 10.365352982346124 - ], - [ - 7993.852655457921, - 10.472941440448915 - ], - [ - 8134.095684501042, - 10.576945706213428 - ], - [ - 8274.338713544164, - 10.68509957561441 - ], - [ - 8414.581742587285, - 10.803410355318917 - ], - [ - 8554.824771630407, - 10.934433699219264 - ], - [ - 8695.067800673529, - 11.076449681753708 - ], - [ - 8835.31082971665, - 11.227327431660937 - ], - [ - 8975.553858759771, - 11.384935172621452 - ], - [ - 9115.796887802893, - 11.547134254155505 - ], - [ - 9256.039916846014, - 11.711986338371172 - ], - [ - 9396.282945889136, - 11.878233502232032 - ], - [ - 9536.525974932258, - 12.04465013479923 - ], - [ - 9676.769003975378, - 12.21026875158366 - ], - [ - 9817.0120330185, - 12.374006478654008 - ], - [ - 9957.25506206162, - 12.534704026704352 - ], - [ - 10097.498091104742, - 12.691201992440527 - ], - [ - 10237.741120147864, - 12.842349389066577 - ], - [ - 10377.984149190985, - 12.986967544061534 - ], - [ - 10518.227178234107, - 13.123594997675161 - ], - [ - 10658.470207277229, - 13.250160507552751 - ], - [ - 10798.71323632035, - 13.36488373189675 - ], - [ - 10938.956265363471, - 13.466956079677363 - ], - [ - 11079.199294406593, - 13.55668870695644 - ], - [ - 11219.442323449714, - 13.63503689877705 - ], - [ - 11359.685352492836, - 13.702019842911145 - ], - [ - 11499.928381535958, - 13.757170989440098 - ], - [ - 11640.171410579078, - 13.798977835071515 - ], - [ - 11780.4144396222, - 13.824912264569164 - ], - [ - 11920.65746866532, - 13.83246287698609 - ], - [ - 12060.900497708442, - 13.82159273970853 - ], - [ - 12201.143526751564, - 13.795352384049758 - ], - [ - 12341.386555794685, - 13.755248917358934 - ], - [ - 12481.629584837807, - 13.703370969191848 - ], - [ - 12621.872613880929, - 13.641889043037553 - ], - [ - 12762.11564292405, - 13.57287460822061 - ], - [ - 12902.358671967171, - 13.495642012346464 - ], - [ - 13042.601701010293, - 13.407469036740189 - ], - [ - 13182.844730053414, - 13.307856773846577 - ], - [ - 13323.087759096536, - 13.195774538334325 - ], - [ - 13463.330788139658, - 13.070159311278669 - ], - [ - 13603.573817182778, - 12.929956090442074 - ], - [ - 13743.8168462259, - 12.773945384256562 - ], - [ - 13884.059875269022, - 12.6020742185872 - ] - ], - "17": [ - [ - 0.0, - 1.4961440368066188 - ], - [ - 140.24302904312142, - 1.6266761365089712 - ], - [ - 280.48605808624285, - 1.9057602330936838 - ], - [ - 420.7290871293643, - 2.0364432634457046 - ], - [ - 560.9721161724857, - 2.0926359158010803 - ], - [ - 701.2151452156071, - 2.1149045256455166 - ], - [ - 841.4581742587286, - 2.1293477060015054 - ], - [ - 981.70120330185, - 2.091132233045171 - ], - [ - 1121.9442323449714, - 2.094038465307731 - ], - [ - 1262.1872613880928, - 2.1707075956900375 - ], - [ - 1402.4302904312142, - 2.2860571686125715 - ], - [ - 1542.6733194743356, - 2.4649160525019993 - ], - [ - 1682.9163485174572, - 2.734327406126595 - ], - [ - 1823.1593775605786, - 3.0915018950606856 - ], - [ - 1963.4024066037, - 3.382940347853847 - ], - [ - 2103.645435646821, - 3.5328887037330796 - ], - [ - 2243.888464689943, - 3.998304413302999 - ], - [ - 2384.1314937330644, - 4.3904906502343 - ], - [ - 2524.3745227761856, - 4.419002233752132 - ], - [ - 2664.617551819307, - 4.5206467601002664 - ], - [ - 2804.8605808624284, - 4.7207770763432935 - ], - [ - 2945.10360990555, - 4.97297993159928 - ], - [ - 3085.346638948671, - 5.229319918202259 - ], - [ - 3225.589667991793, - 5.450676018662261 - ], - [ - 3365.8326970349144, - 5.640112814262023 - ], - [ - 3506.0757260780356, - 5.809714147388161 - ], - [ - 3646.318755121157, - 5.971328459492258 - ], - [ - 3786.5617841642784, - 6.136330828314975 - ], - [ - 3926.8048132074, - 6.305946979047464 - ], - [ - 4067.047842250521, - 6.4762397447077085 - ], - [ - 4207.290871293642, - 6.643621484224632 - ], - [ - 4347.533900336764, - 6.8048704380189475 - ], - [ - 4487.776929379886, - 6.957854126588439 - ], - [ - 4628.019958423007, - 7.103872072384802 - ], - [ - 4768.262987466129, - 7.244873032463522 - ], - [ - 4908.50601650925, - 7.38264638612478 - ], - [ - 5048.749045552371, - 7.519340294109697 - ], - [ - 5188.992074595492, - 7.6562851203009465 - ], - [ - 5329.235103638614, - 7.7929155574834414 - ], - [ - 5469.478132681736, - 7.928358902194173 - ], - [ - 5609.721161724857, - 8.061872501102949 - ], - [ - 5749.964190767979, - 8.192901205975346 - ], - [ - 5890.2072198111, - 8.322121983610938 - ], - [ - 6030.450248854221, - 8.452754515311087 - ], - [ - 6170.693277897342, - 8.588561617262995 - ], - [ - 6310.936306940464, - 8.733276710553708 - ], - [ - 6451.179335983586, - 8.890109611437376 - ], - [ - 6591.422365026707, - 9.059229051687424 - ], - [ - 6731.665394069829, - 9.224189151784032 - ], - [ - 6871.90842311295, - 9.379852470908528 - ], - [ - 7012.151452156071, - 9.525751621148746 - ], - [ - 7152.394481199192, - 9.661454814741347 - ], - [ - 7292.637510242314, - 9.787235532729737 - ], - [ - 7432.880539285436, - 9.905488569415194 - ], - [ - 7573.123568328557, - 10.020081518411976 - ], - [ - 7713.366597371678, - 10.134147557031316 - ], - [ - 7853.6096264148, - 10.250844039417565 - ], - [ - 7993.852655457921, - 10.373331969198793 - ], - [ - 8134.095684501042, - 10.504564951109773 - ], - [ - 8274.338713544164, - 10.646142083428895 - ], - [ - 8414.581742587285, - 10.79658420308065 - ], - [ - 8554.824771630407, - 10.952901813080487 - ], - [ - 8695.067800673529, - 11.113836755609434 - ], - [ - 8835.31082971665, - 11.278388977707499 - ], - [ - 8975.553858759771, - 11.44555742817333 - ], - [ - 9115.796887802893, - 11.61435169905649 - ], - [ - 9256.039916846014, - 11.784136332525565 - ], - [ - 9396.282945889136, - 11.956198944568664 - ], - [ - 9536.525974932258, - 12.126435911152349 - ], - [ - 9676.769003975378, - 12.282618512374254 - ], - [ - 9817.0120330185, - 12.426798482010806 - ], - [ - 9957.25506206162, - 12.562039726930252 - ], - [ - 10097.498091104742, - 12.691406112245783 - ], - [ - 10237.741120147864, - 12.817966480356455 - ], - [ - 10377.984149190985, - 12.944826403776933 - ], - [ - 10518.227178234107, - 13.074707506512855 - ], - [ - 10658.470207277229, - 13.20708904605056 - ], - [ - 10798.71323632035, - 13.338958052889641 - ], - [ - 10938.956265363471, - 13.467474310524869 - ], - [ - 11079.199294406593, - 13.589753964342066 - ], - [ - 11219.442323449714, - 13.70339122294707 - ], - [ - 11359.685352492836, - 13.806712644812881 - ], - [ - 11499.928381535958, - 13.89703905315347 - ], - [ - 11640.171410579078, - 13.969984124119849 - ], - [ - 11780.4144396222, - 14.02078412322923 - ], - [ - 11920.65746866532, - 14.044646762773146 - ], - [ - 12060.900497708442, - 14.039367188728066 - ], - [ - 12201.143526751564, - 14.007906165135225 - ], - [ - 12341.386555794685, - 13.954185261131718 - ], - [ - 12481.629584837807, - 13.88237356844765 - ], - [ - 12621.872613880929, - 13.796613752884692 - ], - [ - 12762.11564292405, - 13.701243071734767 - ], - [ - 12902.358671967171, - 13.598268413852953 - ], - [ - 13042.601701010293, - 13.486727563424436 - ], - [ - 13182.844730053414, - 13.366765164824288 - ], - [ - 13323.087759096536, - 13.238385021501488 - ], - [ - 13463.330788139658, - 13.101648750799276 - ], - [ - 13603.573817182778, - 12.956618450291941 - ], - [ - 13743.8168462259, - 12.802923189873736 - ], - [ - 13884.059875269022, - 12.640468055804902 - ] - ], - "18": [ - [ - 0.0, - 1.618202841206727 - ], - [ - 140.24302904312142, - 1.8464185341965278 - ], - [ - 280.48605808624285, - 2.159812853283336 - ], - [ - 420.7290871293643, - 2.209850834159649 - ], - [ - 560.9721161724857, - 2.1435208724812327 - ], - [ - 701.2151452156071, - 2.050880952638018 - ], - [ - 841.4581742587286, - 1.9512840720827749 - ], - [ - 981.70120330185, - 1.8985235738100592 - ], - [ - 1121.9442323449714, - 1.9234028366159408 - ], - [ - 1262.1872613880928, - 2.0224992828576895 - ], - [ - 1402.4302904312142, - 2.1898577669482653 - ], - [ - 1542.6733194743356, - 2.4243755814618027 - ], - [ - 1682.9163485174572, - 2.7236688672535734 - ], - [ - 1823.1593775605786, - 3.0653370271465845 - ], - [ - 1963.4024066037, - 3.408456481865547 - ], - [ - 2103.645435646821, - 3.7242213843007668 - ], - [ - 2243.888464689943, - 4.000705844863638 - ], - [ - 2384.1314937330644, - 4.2416916809265155 - ], - [ - 2524.3745227761856, - 4.457944501092153 - ], - [ - 2664.617551819307, - 4.6583139860601985 - ], - [ - 2804.8605808624284, - 4.849512646807497 - ], - [ - 2945.10360990555, - 5.0374981008247985 - ], - [ - 3085.346638948671, - 5.226640123863293 - ], - [ - 3225.589667991793, - 5.41601318523997 - ], - [ - 3365.8326970349144, - 5.603655402157025 - ], - [ - 3506.0757260780356, - 5.7882188033093085 - ], - [ - 3646.318755121157, - 5.968690769553029 - ], - [ - 3786.5617841642784, - 6.145117174577228 - ], - [ - 3926.8048132074, - 6.317541359455963 - ], - [ - 4067.047842250521, - 6.485183961601642 - ], - [ - 4207.290871293642, - 6.647166032871108 - ], - [ - 4347.533900336764, - 6.803146778594685 - ], - [ - 4487.776929379886, - 6.953172587248319 - ], - [ - 4628.019958423007, - 7.098560228947242 - ], - [ - 4768.262987466129, - 7.241043666087599 - ], - [ - 4908.50601650925, - 7.382299656336576 - ], - [ - 5048.749045552371, - 7.5236043255101865 - ], - [ - 5188.992074595492, - 7.6657680264569485 - ], - [ - 5329.235103638614, - 7.808018505447345 - ], - [ - 5469.478132681736, - 7.949298242165187 - ], - [ - 5609.721161724857, - 8.088634440939979 - ], - [ - 5749.964190767979, - 8.225571309639129 - ], - [ - 5890.2072198111, - 8.35958659695338 - ], - [ - 6030.450248854221, - 8.491252740575154 - ], - [ - 6170.693277897342, - 8.621557452830006 - ], - [ - 6310.936306940464, - 8.751587277951696 - ], - [ - 6451.179335983586, - 8.882664777701862 - ], - [ - 6591.422365026707, - 9.015937142291282 - ], - [ - 6731.665394069829, - 9.151974764639073 - ], - [ - 6871.90842311295, - 9.290586755524853 - ], - [ - 7012.151452156071, - 9.431446553929643 - ], - [ - 7152.394481199192, - 9.574116337041016 - ], - [ - 7292.637510242314, - 9.717607202756488 - ], - [ - 7432.880539285436, - 9.860742679447142 - ], - [ - 7573.123568328557, - 10.003209073892146 - ], - [ - 7713.366597371678, - 10.14269158119482 - ], - [ - 7853.6096264148, - 10.276427874158683 - ], - [ - 7993.852655457921, - 10.402538264915362 - ], - [ - 8134.095684501042, - 10.52513298727401 - ], - [ - 8274.338713544164, - 10.650740348863007 - ], - [ - 8414.581742587285, - 10.784666053711716 - ], - [ - 8554.824771630407, - 10.928562405951501 - ], - [ - 8695.067800673529, - 11.081337515333328 - ], - [ - 8835.31082971665, - 11.24157942453155 - ], - [ - 8975.553858759771, - 11.407875527509571 - ], - [ - 9115.796887802893, - 11.578802108772585 - ], - [ - 9256.039916846014, - 11.753003633547195 - ], - [ - 9396.282945889136, - 11.93019285189751 - ], - [ - 9536.525974932258, - 12.105156752553034 - ], - [ - 9676.769003975378, - 12.266813277253501 - ], - [ - 9817.0120330185, - 12.41686132911461 - ], - [ - 9957.25506206162, - 12.557838471946402 - ], - [ - 10097.498091104742, - 12.692282214702216 - ], - [ - 10237.741120147864, - 12.822734085331492 - ], - [ - 10377.984149190985, - 12.95194449280664 - ], - [ - 10518.227178234107, - 13.082324445129279 - ], - [ - 10658.470207277229, - 13.21312828937516 - ], - [ - 10798.71323632035, - 13.342370182674724 - ], - [ - 10938.956265363471, - 13.46893743530791 - ], - [ - 11079.199294406593, - 13.591151671660532 - ], - [ - 11219.442323449714, - 13.708295125083257 - ], - [ - 11359.685352492836, - 13.820949172534013 - ], - [ - 11499.928381535958, - 13.926092184917305 - ], - [ - 11640.171410579078, - 14.01785391812034 - ], - [ - 11780.4144396222, - 14.090507711497574 - ], - [ - 11920.65746866532, - 14.138024765035226 - ], - [ - 12060.900497708442, - 14.155375104550057 - ], - [ - 12201.143526751564, - 14.142781895992686 - ], - [ - 12341.386555794685, - 14.103398328105543 - ], - [ - 12481.629584837807, - 14.040057895221928 - ], - [ - 12621.872613880929, - 13.95545767048217 - ], - [ - 12762.11564292405, - 13.852680581631473 - ], - [ - 12902.358671967171, - 13.734346921527663 - ], - [ - 13042.601701010293, - 13.601959129410302 - ], - [ - 13182.844730053414, - 13.457844267825896 - ], - [ - 13323.087759096536, - 13.304720459246536 - ], - [ - 13463.330788139658, - 13.145423112760023 - ], - [ - 13603.573817182778, - 12.982786328431898 - ], - [ - 13743.8168462259, - 12.819088563274962 - ], - [ - 13884.059875269022, - 12.655241390186259 - ] - ], - "19": [ - [ - 0.0, - 1.6786928988762346 - ], - [ - 140.24302904312142, - 1.998880270064884 - ], - [ - 280.48605808624285, - 2.2580134262630747 - ], - [ - 420.7290871293643, - 2.1227275035634445 - ], - [ - 560.9721161724857, - 1.9162062400126305 - ], - [ - 701.2151452156071, - 1.76472677277538 - ], - [ - 841.4581742587286, - 1.6391752339129846 - ], - [ - 981.70120330185, - 1.5976697656461432 - ], - [ - 1121.9442323449714, - 1.6668569359132481 - ], - [ - 1262.1872613880928, - 1.824175572828138 - ], - [ - 1402.4302904312142, - 2.056972082352351 - ], - [ - 1542.6733194743356, - 2.3552876749699454 - ], - [ - 1682.9163485174572, - 2.7014547653545575 - ], - [ - 1823.1593775605786, - 3.0658827102959862 - ], - [ - 1963.4024066037, - 3.410568122460189 - ], - [ - 2103.645435646821, - 3.715006688922865 - ], - [ - 2243.888464689943, - 3.9762774570663826 - ], - [ - 2384.1314937330644, - 4.200557009275054 - ], - [ - 2524.3745227761856, - 4.39930245642398 - ], - [ - 2664.617551819307, - 4.582668029819638 - ], - [ - 2804.8605808624284, - 4.758509660592744 - ], - [ - 2945.10360990555, - 4.934009229298639 - ], - [ - 3085.346638948671, - 5.114380948968579 - ], - [ - 3225.589667991793, - 5.298369585374536 - ], - [ - 3365.8326970349144, - 5.483328553690483 - ], - [ - 3506.0757260780356, - 5.667290719195644 - ], - [ - 3646.318755121157, - 5.84869046790545 - ], - [ - 3786.5617841642784, - 6.027611090861402 - ], - [ - 3926.8048132074, - 6.204034542344768 - ], - [ - 4067.047842250521, - 6.376832551320898 - ], - [ - 4207.290871293642, - 6.544721463658011 - ], - [ - 4347.533900336764, - 6.706825787063422 - ], - [ - 4487.776929379886, - 6.863009911611571 - ], - [ - 4628.019958423007, - 7.0150246701832115 - ], - [ - 4768.262987466129, - 7.165108315264293 - ], - [ - 4908.50601650925, - 7.315323066550106 - ], - [ - 5048.749045552371, - 7.4676632384010855 - ], - [ - 5188.992074595492, - 7.623223186135928 - ], - [ - 5329.235103638614, - 7.7807381616684435 - ], - [ - 5469.478132681736, - 7.938606423273976 - ], - [ - 5609.721161724857, - 8.095265287287027 - ], - [ - 5749.964190767979, - 8.248694595558097 - ], - [ - 5890.2072198111, - 8.396186006557755 - ], - [ - 6030.450248854221, - 8.537030287006422 - ], - [ - 6170.693277897342, - 8.671214294992629 - ], - [ - 6310.936306940464, - 8.798797434746554 - ], - [ - 6451.179335983586, - 8.920791072964844 - ], - [ - 6591.422365026707, - 9.039714833266839 - ], - [ - 6731.665394069829, - 9.157961899204896 - ], - [ - 6871.90842311295, - 9.276442838634814 - ], - [ - 7012.151452156071, - 9.395807604498419 - ], - [ - 7152.394481199192, - 9.516733077843957 - ], - [ - 7292.637510242314, - 9.639312390846202 - ], - [ - 7432.880539285436, - 9.762289583847132 - ], - [ - 7573.123568328557, - 9.885043293657299 - ], - [ - 7713.366597371678, - 10.006270874578139 - ], - [ - 7853.6096264148, - 10.124524705780935 - ], - [ - 7993.852655457921, - 10.239174996133048 - ], - [ - 8134.095684501042, - 10.35485587901777 - ], - [ - 8274.338713544164, - 10.478327044258029 - ], - [ - 8414.581742587285, - 10.616208564475814 - ], - [ - 8554.824771630407, - 10.770610766230446 - ], - [ - 8695.067800673529, - 10.938400781895576 - ], - [ - 8835.31082971665, - 11.115745838977297 - ], - [ - 8975.553858759771, - 11.298814309598978 - ], - [ - 9115.796887802893, - 11.483861643819342 - ], - [ - 9256.039916846014, - 11.667636250889585 - ], - [ - 9396.282945889136, - 11.8468992133433 - ], - [ - 9536.525974932258, - 12.019713270809923 - ], - [ - 9676.769003975378, - 12.185945331521774 - ], - [ - 9817.0120330185, - 12.346243639349323 - ], - [ - 9957.25506206162, - 12.501266367982572 - ], - [ - 10097.498091104742, - 12.651671330952313 - ], - [ - 10237.741120147864, - 12.798114836551173 - ], - [ - 10377.984149190985, - 12.941234386529706 - ], - [ - 10518.227178234107, - 13.081376008465996 - ], - [ - 10658.470207277229, - 13.217845103042924 - ], - [ - 10798.71323632035, - 13.348672892639959 - ], - [ - 10938.956265363471, - 13.473590402550531 - ], - [ - 11079.199294406593, - 13.592603532289951 - ], - [ - 11219.442323449714, - 13.70652131086105 - ], - [ - 11359.685352492836, - 13.816707846280865 - ], - [ - 11499.928381535958, - 13.921993505994337 - ], - [ - 11640.171410579078, - 14.017461818387407 - ], - [ - 11780.4144396222, - 14.097912645254747 - ], - [ - 11920.65746866532, - 14.157686151540988 - ], - [ - 12060.900497708442, - 14.191580523598494 - ], - [ - 12201.143526751564, - 14.19697919741696 - ], - [ - 12341.386555794685, - 14.174752574345677 - ], - [ - 12481.629584837807, - 14.125870558911199 - ], - [ - 12621.872613880929, - 14.051099128466559 - ], - [ - 12762.11564292405, - 13.951573726111578 - ], - [ - 12902.358671967171, - 13.829150839448747 - ], - [ - 13042.601701010293, - 13.687707587014467 - ], - [ - 13182.844730053414, - 13.531251753298038 - ], - [ - 13323.087759096536, - 13.364077697949618 - ], - [ - 13463.330788139658, - 13.19069086055583 - ], - [ - 13603.573817182778, - 13.015599674823411 - ], - [ - 13743.8168462259, - 12.842816958550985 - ], - [ - 13884.059875269022, - 12.673933461608101 - ] - ] - }, - "atmosphericModelWindVelocityYProfile": { - "4": [ - [ - 0.0, - -0.451875189774473 - ], - [ - 140.24302904312142, - -0.2560799456281367 - ], - [ - 280.48605808624285, - -0.2128055562844784 - ], - [ - 420.7290871293643, - 0.13469614497139 - ], - [ - 560.9721161724857, - 0.5415255345488322 - ], - [ - 701.2151452156071, - 0.8225892729140477 - ], - [ - 841.4581742587286, - 1.0302399396526583 - ], - [ - 981.70120330185, - 1.180762699430602 - ], - [ - 1121.9442323449714, - 1.284239151363068 - ], - [ - 1262.1872613880928, - 1.3680616592268233 - ], - [ - 1402.4302904312142, - 1.4274784057144734 - ], - [ - 1542.6733194743356, - 1.4466635496967295 - ], - [ - 1682.9163485174572, - 1.4499932553282135 - ], - [ - 1823.1593775605786, - 1.4528530764169487 - ], - [ - 1963.4024066037, - 1.4284038067624583 - ], - [ - 2103.645435646821, - 1.3799367462698726 - ], - [ - 2243.888464689943, - 1.4003075377407521 - ], - [ - 2384.1314937330644, - 1.3967359732505553 - ], - [ - 2524.3745227761856, - 1.3461887457238058 - ], - [ - 2664.617551819307, - 1.3210644536438574 - ], - [ - 2804.8605808624284, - 1.3141605127715026 - ], - [ - 2945.10360990555, - 1.3096875308301972 - ], - [ - 3085.346638948671, - 1.2941983136958335 - ], - [ - 3225.589667991793, - 1.2644015879505677 - ], - [ - 3365.8326970349144, - 1.2277049217309262 - ], - [ - 3506.0757260780356, - 1.1928764092982582 - ], - [ - 3646.318755121157, - 1.1669669966507517 - ], - [ - 3786.5617841642784, - 1.1504094875267672 - ], - [ - 3926.8048132074, - 1.1382793179392496 - ], - [ - 4067.047842250521, - 1.1241179407274087 - ], - [ - 4207.290871293642, - 1.1017954895953472 - ], - [ - 4347.533900336764, - 1.0679497743928552 - ], - [ - 4487.776929379886, - 1.0230596453813332 - ], - [ - 4628.019958423007, - 0.9695669973711156 - ], - [ - 4768.262987466129, - 0.9102599411581666 - ], - [ - 4908.50601650925, - 0.8479037945933405 - ], - [ - 5048.749045552371, - 0.7848183202494052 - ], - [ - 5188.992074595492, - 0.7222065683932203 - ], - [ - 5329.235103638614, - 0.6609145017285134 - ], - [ - 5469.478132681736, - 0.6017126128856014 - ], - [ - 5609.721161724857, - 0.5450887487442392 - ], - [ - 5749.964190767979, - 0.48859477490621434 - ], - [ - 5890.2072198111, - 0.42914739175413014 - ], - [ - 6030.450248854221, - 0.3640365080581249 - ], - [ - 6170.693277897342, - 0.2904388674288861 - ], - [ - 6310.936306940464, - 0.2054864743456763 - ], - [ - 6451.179335983586, - 0.10899268468324941 - ], - [ - 6591.422365026707, - 0.006922558381975482 - ], - [ - 6731.665394069829, - -0.09620783645123386 - ], - [ - 6871.90842311295, - -0.19697756995200205 - ], - [ - 7012.151452156071, - -0.29186192895538593 - ], - [ - 7152.394481199192, - -0.3774850414674561 - ], - [ - 7292.637510242314, - -0.45250123621726285 - ], - [ - 7432.880539285436, - -0.5215472900343137 - ], - [ - 7573.123568328557, - -0.5883480236209266 - ], - [ - 7713.366597371678, - -0.6547597433122919 - ], - [ - 7853.6096264148, - -0.7227229179558958 - ], - [ - 7993.852655457921, - -0.7941551064148408 - ], - [ - 8134.095684501042, - -0.8704639385523129 - ], - [ - 8274.338713544164, - -0.9520578069462572 - ], - [ - 8414.581742587285, - -1.037927298463606 - ], - [ - 8554.824771630407, - -1.1278908725826862 - ], - [ - 8695.067800673529, - -1.2220733700877848 - ], - [ - 8835.31082971665, - -1.320428634806035 - ], - [ - 8975.553858759771, - -1.4229097649431133 - ], - [ - 9115.796887802893, - -1.529540931044175 - ], - [ - 9256.039916846014, - -1.6403841547373177 - ], - [ - 9396.282945889136, - -1.7528420861156329 - ], - [ - 9536.525974932258, - -1.8631832647111795 - ], - [ - 9676.769003975378, - -1.9681609502913 - ], - [ - 9817.0120330185, - -2.0650908859534196 - ], - [ - 9957.25506206162, - -2.1513831979852847 - ], - [ - 10097.498091104742, - -2.2244480593678646 - ], - [ - 10237.741120147864, - -2.2816862884890017 - ], - [ - 10377.984149190985, - -2.320254959814878 - ], - [ - 10518.227178234107, - -2.33994531472681 - ], - [ - 10658.470207277229, - -2.350883348901762 - ], - [ - 10798.71323632035, - -2.3655012410370677 - ], - [ - 10938.956265363471, - -2.394526453029017 - ], - [ - 11079.199294406593, - -2.44818286941804 - ], - [ - 11219.442323449714, - -2.5324314544477 - ], - [ - 11359.685352492836, - -2.6392695022105745 - ], - [ - 11499.928381535958, - -2.754010180358006 - ], - [ - 11640.171410579078, - -2.862163336431312 - ], - [ - 11780.4144396222, - -2.9498649926923384 - ], - [ - 11920.65746866532, - -3.0038149689383427 - ], - [ - 12060.900497708442, - -3.01655765541376 - ], - [ - 12201.143526751564, - -2.991544035900692 - ], - [ - 12341.386555794685, - -2.935942894985688 - ], - [ - 12481.629584837807, - -2.856839583759689 - ], - [ - 12621.872613880929, - -2.7612430665397087 - ], - [ - 12762.11564292405, - -2.655812007914559 - ], - [ - 12902.358671967171, - -2.5462861830898125 - ], - [ - 13042.601701010293, - -2.433946445749713 - ], - [ - 13182.844730053414, - -2.319546056529121 - ], - [ - 13323.087759096536, - -2.2044383938338132 - ], - [ - 13463.330788139658, - -2.0899187543826385 - ], - [ - 13603.573817182778, - -1.9773156026445928 - ], - [ - 13743.8168462259, - -1.8681840336951807 - ], - [ - 13884.059875269022, - -1.7635855634374604 - ] - ], - "5": [ - [ - 0.0, - -0.3835743565178399 - ], - [ - 140.24302904312142, - -0.15406643024044925 - ], - [ - 280.48605808624285, - -0.08909714258908226 - ], - [ - 420.7290871293643, - 0.2255850127274388 - ], - [ - 560.9721161724857, - 0.5912030513937192 - ], - [ - 701.2151452156071, - 0.8347166125999415 - ], - [ - 841.4581742587286, - 0.9983727568002544 - ], - [ - 981.70120330185, - 1.1109178420132135 - ], - [ - 1121.9442323449714, - 1.186511421828901 - ], - [ - 1262.1872613880928, - 1.2444900512748023 - ], - [ - 1402.4302904312142, - 1.2811196933167879 - ], - [ - 1542.6733194743356, - 1.2900529050462342 - ], - [ - 1682.9163485174572, - 1.2927137800001405 - ], - [ - 1823.1593775605786, - 1.296163978745293 - ], - [ - 1963.4024066037, - 1.2933507922213907 - ], - [ - 2103.645435646821, - 1.2886767826497367 - ], - [ - 2243.888464689943, - 1.2901656612066232 - ], - [ - 2384.1314937330644, - 1.2977439119619631 - ], - [ - 2524.3745227761856, - 1.3072125275585196 - ], - [ - 2664.617551819307, - 1.3138340816475158 - ], - [ - 2804.8605808624284, - 1.313720716012044 - ], - [ - 2945.10360990555, - 1.30283838984419 - ], - [ - 3085.346638948671, - 1.2789410445659388 - ], - [ - 3225.589667991793, - 1.2458743037171867 - ], - [ - 3365.8326970349144, - 1.2084237405560567 - ], - [ - 3506.0757260780356, - 1.171596657552996 - ], - [ - 3646.318755121157, - 1.1390567230525857 - ], - [ - 3786.5617841642784, - 1.1096130940856666 - ], - [ - 3926.8048132074, - 1.080196602482594 - ], - [ - 4067.047842250521, - 1.046933122454093 - ], - [ - 4207.290871293642, - 1.0062530898172015 - ], - [ - 4347.533900336764, - 0.9568976380323597 - ], - [ - 4487.776929379886, - 0.8991906056004271 - ], - [ - 4628.019958423007, - 0.8347686781463537 - ], - [ - 4768.262987466129, - 0.7656352838889812 - ], - [ - 4908.50601650925, - 0.6937800651445934 - ], - [ - 5048.749045552371, - 0.61987631205354 - ], - [ - 5188.992074595492, - 0.5447047265752463 - ], - [ - 5329.235103638614, - 0.46921734937428616 - ], - [ - 5469.478132681736, - 0.3942623222832797 - ], - [ - 5609.721161724857, - 0.32042681923720423 - ], - [ - 5749.964190767979, - 0.2487213682577026 - ], - [ - 5890.2072198111, - 0.18035234616722196 - ], - [ - 6030.450248854221, - 0.11516245193306124 - ], - [ - 6170.693277897342, - 0.052766219522105785 - ], - [ - 6310.936306940464, - -0.007204496110066404 - ], - [ - 6451.179335983586, - -0.06531092465940712 - ], - [ - 6591.422365026707, - -0.12325126414177943 - ], - [ - 6731.665394069829, - -0.18323713843332595 - ], - [ - 6871.90842311295, - -0.24729748547157748 - ], - [ - 7012.151452156071, - -0.31732651636069875 - ], - [ - 7152.394481199192, - -0.39539195111276754 - ], - [ - 7292.637510242314, - -0.4822401101294142 - ], - [ - 7432.880539285436, - -0.5760725860748606 - ], - [ - 7573.123568328557, - -0.6757740732738815 - ], - [ - 7713.366597371678, - -0.7804723973602161 - ], - [ - 7853.6096264148, - -0.8894186347447861 - ], - [ - 7993.852655457921, - -1.000933272851368 - ], - [ - 8134.095684501042, - -1.1070346044542658 - ], - [ - 8274.338713544164, - -1.2001371989781953 - ], - [ - 8414.581742587285, - -1.2758508751261792 - ], - [ - 8554.824771630407, - -1.3355077980322718 - ], - [ - 8695.067800673529, - -1.38262658149667 - ], - [ - 8835.31082971665, - -1.4206214659793064 - ], - [ - 8975.553858759771, - -1.4529068511546401 - ], - [ - 9115.796887802893, - -1.4829293073960546 - ], - [ - 9256.039916846014, - -1.5137875048318223 - ], - [ - 9396.282945889136, - -1.5475247916195018 - ], - [ - 9536.525974932258, - -1.5857116420860142 - ], - [ - 9676.769003975378, - -1.6291452261900883 - ], - [ - 9817.0120330185, - -1.6787931808872327 - ], - [ - 9957.25506206162, - -1.7356800235484084 - ], - [ - 10097.498091104742, - -1.8008303079665224 - ], - [ - 10237.741120147864, - -1.875250161365168 - ], - [ - 10377.984149190985, - -1.959681992754008 - ], - [ - 10518.227178234107, - -2.054521577357757 - ], - [ - 10658.470207277229, - -2.1589474151827304 - ], - [ - 10798.71323632035, - -2.271438765302103 - ], - [ - 10938.956265363471, - -2.3895400199362737 - ], - [ - 11079.199294406593, - -2.5107672649764976 - ], - [ - 11219.442323449714, - -2.632350433266692 - ], - [ - 11359.685352492836, - -2.751205504640338 - ], - [ - 11499.928381535958, - -2.8615505822692273 - ], - [ - 11640.171410579078, - -2.956726830137686 - ], - [ - 11780.4144396222, - -3.0301946909488797 - ], - [ - 11920.65746866532, - -3.0759743707967058 - ], - [ - 12060.900497708442, - -3.089306365984969 - ], - [ - 12201.143526751564, - -3.071207024332348 - ], - [ - 12341.386555794685, - -3.0261062715226292 - ], - [ - 12481.629584837807, - -2.9591101882360675 - ], - [ - 12621.872613880929, - -2.8752462413260633 - ], - [ - 12762.11564292405, - -2.7792316530733476 - ], - [ - 12902.358671967171, - -2.675561474690317 - ], - [ - 13042.601701010293, - -2.5660401596740074 - ], - [ - 13182.844730053414, - -2.452525244876707 - ], - [ - 13323.087759096536, - -2.3371870827916883 - ], - [ - 13463.330788139658, - -2.222152068986425 - ], - [ - 13603.573817182778, - -2.109587884733917 - ], - [ - 13743.8168462259, - -2.001779666944521 - ], - [ - 13884.059875269022, - -1.900446037165099 - ] - ], - "6": [ - [ - 0.0, - -0.3111986784517982 - ], - [ - 140.24302904312142, - -0.020630303469561184 - ], - [ - 280.48605808624285, - 0.07540274916321796 - ], - [ - 420.7290871293643, - 0.35879317978368985 - ], - [ - 560.9721161724857, - 0.6732016480401359 - ], - [ - 701.2151452156071, - 0.8668273004041936 - ], - [ - 841.4581742587286, - 0.9875924239975956 - ], - [ - 981.70120330185, - 1.0765907751635937 - ], - [ - 1121.9442323449714, - 1.1374605755238385 - ], - [ - 1262.1872613880928, - 1.1728239868929375 - ], - [ - 1402.4302904312142, - 1.1880495379412397 - ], - [ - 1542.6733194743356, - 1.1876982003025878 - ], - [ - 1682.9163485174572, - 1.1893347679549124 - ], - [ - 1823.1593775605786, - 1.1985620352719404 - ], - [ - 1963.4024066037, - 1.2121545312811364 - ], - [ - 2103.645435646821, - 1.2265840439940097 - ], - [ - 2243.888464689943, - 1.2388419794258094 - ], - [ - 2384.1314937330644, - 1.24766001579556 - ], - [ - 2524.3745227761856, - 1.2502439627015338 - ], - [ - 2664.617551819307, - 1.2441937580380493 - ], - [ - 2804.8605808624284, - 1.2297902224593074 - ], - [ - 2945.10360990555, - 1.2072326636586856 - ], - [ - 3085.346638948671, - 1.177447720260071 - ], - [ - 3225.589667991793, - 1.1436166795893825 - ], - [ - 3365.8326970349144, - 1.107427246370779 - ], - [ - 3506.0757260780356, - 1.0706513109146674 - ], - [ - 3646.318755121157, - 1.0343908205351544 - ], - [ - 3786.5617841642784, - 0.9975624482116457 - ], - [ - 3926.8048132074, - 0.958203756906018 - ], - [ - 4067.047842250521, - 0.9136132374531537 - ], - [ - 4207.290871293642, - 0.8614975533843267 - ], - [ - 4347.533900336764, - 0.8010557131333407 - ], - [ - 4487.776929379886, - 0.7323606268407714 - ], - [ - 4628.019958423007, - 0.6574758886356367 - ], - [ - 4768.262987466129, - 0.5788052844934659 - ], - [ - 4908.50601650925, - 0.4985507213358769 - ], - [ - 5048.749045552371, - 0.41789232530592785 - ], - [ - 5188.992074595492, - 0.3379139231406373 - ], - [ - 5329.235103638614, - 0.2595127263077715 - ], - [ - 5469.478132681736, - 0.1834981528570157 - ], - [ - 5609.721161724857, - 0.11051352565339773 - ], - [ - 5749.964190767979, - 0.041972127406348356 - ], - [ - 5890.2072198111, - -0.020190504301200733 - ], - [ - 6030.450248854221, - -0.07559770499959793 - ], - [ - 6170.693277897342, - -0.12413816929765077 - ], - [ - 6310.936306940464, - -0.16569260836408625 - ], - [ - 6451.179335983586, - -0.20048063024034615 - ], - [ - 6591.422365026707, - -0.23058370527325833 - ], - [ - 6731.665394069829, - -0.25898139502556294 - ], - [ - 6871.90842311295, - -0.2881969985202336 - ], - [ - 7012.151452156071, - -0.32064128355045884 - ], - [ - 7152.394481199192, - -0.35887020465830993 - ], - [ - 7292.637510242314, - -0.4051113081635162 - ], - [ - 7432.880539285436, - -0.4600672562879374 - ], - [ - 7573.123568328557, - -0.522883520326547 - ], - [ - 7713.366597371678, - -0.5918273465162692 - ], - [ - 7853.6096264148, - -0.6651730910902963 - ], - [ - 7993.852655457921, - -0.7411812052688356 - ], - [ - 8134.095684501042, - -0.8179218188728077 - ], - [ - 8274.338713544164, - -0.8938333303495719 - ], - [ - 8414.581742587285, - -0.9678988708031877 - ], - [ - 8554.824771630407, - -1.0402358835529713 - ], - [ - 8695.067800673529, - -1.1115643912843076 - ], - [ - 8835.31082971665, - -1.1826290695994857 - ], - [ - 8975.553858759771, - -1.2541745264979234 - ], - [ - 9115.796887802893, - -1.3268598131563403 - ], - [ - 9256.039916846014, - -1.4009428577157494 - ], - [ - 9396.282945889136, - -1.4770130981556089 - ], - [ - 9536.525974932258, - -1.5555067768483324 - ], - [ - 9676.769003975378, - -1.636302098331649 - ], - [ - 9817.0120330185, - -1.7192240375559724 - ], - [ - 9957.25506206162, - -1.8041047501534315 - ], - [ - 10097.498091104742, - -1.8907764180951068 - ], - [ - 10237.741120147864, - -1.9790558123724478 - ], - [ - 10377.984149190985, - -2.068762407364398 - ], - [ - 10518.227178234107, - -2.160733150860763 - ], - [ - 10658.470207277229, - -2.255628708582211 - ], - [ - 10798.71323632035, - -2.353915159141073 - ], - [ - 10938.956265363471, - -2.456019312471847 - ], - [ - 11079.199294406593, - -2.5621258558679174 - ], - [ - 11219.442323449714, - -2.670080232621501 - ], - [ - 11359.685352492836, - -2.7766964491074737 - ], - [ - 11499.928381535958, - -2.8771528064012406 - ], - [ - 11640.171410579078, - -2.9650533580760445 - ], - [ - 11780.4144396222, - -3.0344563592803553 - ], - [ - 11920.65746866532, - -3.080693346656197 - ], - [ - 12060.900497708442, - -3.1014470551423625 - ], - [ - 12201.143526751564, - -3.097024610586444 - ], - [ - 12341.386555794685, - -3.0707591757978645 - ], - [ - 12481.629584837807, - -3.026162510452795 - ], - [ - 12621.872613880929, - -2.9665333185985427 - ], - [ - 12762.11564292405, - -2.894566368753094 - ], - [ - 12902.358671967171, - -2.8122317484027803 - ], - [ - 13042.601701010293, - -2.7206683532028215 - ], - [ - 13182.844730053414, - -2.621679865131262 - ], - [ - 13323.087759096536, - -2.5177074402215225 - ], - [ - 13463.330788139658, - -2.4112156079665774 - ], - [ - 13603.573817182778, - -2.304722795794905 - ], - [ - 13743.8168462259, - -2.201005413058189 - ], - [ - 13884.059875269022, - -2.1025676444339747 - ] - ], - "7": [ - [ - 0.0, - -0.19304634819806202 - ], - [ - 140.24302904312142, - 0.15842759520186311 - ], - [ - 280.48605808624285, - 0.2933080763172795 - ], - [ - 420.7290871293643, - 0.5494947276839064 - ], - [ - 560.9721161724857, - 0.8136772534830864 - ], - [ - 701.2151452156071, - 0.9600086714930502 - ], - [ - 841.4581742587286, - 1.0434730379342894 - ], - [ - 981.70120330185, - 1.108271790621457 - ], - [ - 1121.9442323449714, - 1.1523321935898305 - ], - [ - 1262.1872613880928, - 1.168027053422024 - ], - [ - 1402.4302904312142, - 1.1689357220831031 - ], - [ - 1542.6733194743356, - 1.1663286924809173 - ], - [ - 1682.9163485174572, - 1.166706847151261 - ], - [ - 1823.1593775605786, - 1.1689582258063302 - ], - [ - 1963.4024066037, - 1.1744750472478809 - ], - [ - 2103.645435646821, - 1.185038878472527 - ], - [ - 2243.888464689943, - 1.1989796263080688 - ], - [ - 2384.1314937330644, - 1.209782678374117 - ], - [ - 2524.3745227761856, - 1.2081199115835184 - ], - [ - 2664.617551819307, - 1.19114285082985 - ], - [ - 2804.8605808624284, - 1.162610705004964 - ], - [ - 2945.10360990555, - 1.1263354422286869 - ], - [ - 3085.346638948671, - 1.085873132897644 - ], - [ - 3225.589667991793, - 1.0448952577761583 - ], - [ - 3365.8326970349144, - 1.0033180785341387 - ], - [ - 3506.0757260780356, - 0.9609541246437786 - ], - [ - 3646.318755121157, - 0.9184414283716523 - ], - [ - 3786.5617841642784, - 0.8749137304036839 - ], - [ - 3926.8048132074, - 0.8287089004443686 - ], - [ - 4067.047842250521, - 0.7778006457258893 - ], - [ - 4207.290871293642, - 0.7201529314754835 - ], - [ - 4347.533900336764, - 0.653785404546491 - ], - [ - 4487.776929379886, - 0.5787023433639943 - ], - [ - 4628.019958423007, - 0.4972459903803569 - ], - [ - 4768.262987466129, - 0.4120119736984176 - ], - [ - 4908.50601650925, - 0.3255501375077344 - ], - [ - 5048.749045552371, - 0.24077351277434478 - ], - [ - 5188.992074595492, - 0.15966622926333868 - ], - [ - 5329.235103638614, - 0.08373603595634407 - ], - [ - 5469.478132681736, - 0.014453429754833935 - ], - [ - 5609.721161724857, - -0.04699445965452431 - ], - [ - 5749.964190767979, - -0.1010100319617036 - ], - [ - 5890.2072198111, - -0.14814926317721525 - ], - [ - 6030.450248854221, - -0.19021470939721452 - ], - [ - 6170.693277897342, - -0.22931618122628178 - ], - [ - 6310.936306940464, - -0.26756643872989844 - ], - [ - 6451.179335983586, - -0.3064315328678049 - ], - [ - 6591.422365026707, - -0.3463527794350638 - ], - [ - 6731.665394069829, - -0.38790513648548813 - ], - [ - 6871.90842311295, - -0.43125295560189253 - ], - [ - 7012.151452156071, - -0.47646877129783155 - ], - [ - 7152.394481199192, - -0.5237541602211158 - ], - [ - 7292.637510242314, - -0.5731799315330628 - ], - [ - 7432.880539285436, - -0.6250800520582652 - ], - [ - 7573.123568328557, - -0.6800209241472053 - ], - [ - 7713.366597371678, - -0.7372598032698329 - ], - [ - 7853.6096264148, - -0.795872241714308 - ], - [ - 7993.852655457921, - -0.8549263520062065 - ], - [ - 8134.095684501042, - -0.9133753253250347 - ], - [ - 8274.338713544164, - -0.9708446347733702 - ], - [ - 8414.581742587285, - -1.0263945288241723 - ], - [ - 8554.824771630407, - -1.0799912495948085 - ], - [ - 8695.067800673529, - -1.1345070739180367 - ], - [ - 8835.31082971665, - -1.1931403920170038 - ], - [ - 8975.553858759771, - -1.2590892869072456 - ], - [ - 9115.796887802893, - -1.335414911727145 - ], - [ - 9256.039916846014, - -1.424750246271375 - ], - [ - 9396.282945889136, - -1.5298791634044604 - ], - [ - 9536.525974932258, - -1.63875847295105 - ], - [ - 9676.769003975378, - -1.733396117851107 - ], - [ - 9817.0120330185, - -1.8162125848723396 - ], - [ - 9957.25506206162, - -1.8900201054494106 - ], - [ - 10097.498091104742, - -1.9576309360452897 - ], - [ - 10237.741120147864, - -2.021851698547179 - ], - [ - 10377.984149190985, - -2.0855336331212233 - ], - [ - 10518.227178234107, - -2.152148426179585 - ], - [ - 10658.470207277229, - -2.2248671338918427 - ], - [ - 10798.71323632035, - -2.3060000118987873 - ], - [ - 10938.956265363471, - -2.394927663276205 - ], - [ - 11079.199294406593, - -2.4900345658618344 - ], - [ - 11219.442323449714, - -2.5879554526726585 - ], - [ - 11359.685352492836, - -2.6845974477881214 - ], - [ - 11499.928381535958, - -2.7753051091136385 - ], - [ - 11640.171410579078, - -2.855151956066224 - ], - [ - 11780.4144396222, - -2.9198807361972587 - ], - [ - 11920.65746866532, - -2.9663134826746513 - ], - [ - 12060.900497708442, - -2.9936287538275415 - ], - [ - 12201.143526751564, - -3.0011984682533077 - ], - [ - 12341.386555794685, - -2.991197144407302 - ], - [ - 12481.629584837807, - -2.966312813261738 - ], - [ - 12621.872613880929, - -2.9290868644352597 - ], - [ - 12762.11564292405, - -2.8812707398120456 - ], - [ - 12902.358671967171, - -2.8236925174423804 - ], - [ - 13042.601701010293, - -2.7578935008138252 - ], - [ - 13182.844730053414, - -2.685474682628436 - ], - [ - 13323.087759096536, - -2.6078815572276564 - ], - [ - 13463.330788139658, - -2.526519367660118 - ], - [ - 13603.573817182778, - -2.442855366663633 - ], - [ - 13743.8168462259, - -2.3588746409086068 - ], - [ - 13884.059875269022, - -2.2765809640610426 - ] - ], - "8": [ - [ - 0.0, - -0.09214866421909611 - ], - [ - 140.24302904312142, - 0.3263116046020117 - ], - [ - 280.48605808624285, - 0.518446034541588 - ], - [ - 420.7290871293643, - 0.7755393157899095 - ], - [ - 560.9721161724857, - 1.0068511277749297 - ], - [ - 701.2151452156071, - 1.1096218521545431 - ], - [ - 841.4581742587286, - 1.1529893906286341 - ], - [ - 981.70120330185, - 1.1849382491385474 - ], - [ - 1121.9442323449714, - 1.203908530747345 - ], - [ - 1262.1872613880928, - 1.2043319372017165 - ], - [ - 1402.4302904312142, - 1.198801883074818 - ], - [ - 1542.6733194743356, - 1.1913039129185217 - ], - [ - 1682.9163485174572, - 1.1803615904459246 - ], - [ - 1823.1593775605786, - 1.1661076406530568 - ], - [ - 1963.4024066037, - 1.1587923244318032 - ], - [ - 2103.645435646821, - 1.1615152905310977 - ], - [ - 2243.888464689943, - 1.1667667111248408 - ], - [ - 2384.1314937330644, - 1.167116006374588 - ], - [ - 2524.3745227761856, - 1.1548334549405008 - ], - [ - 2664.617551819307, - 1.127751070368713 - ], - [ - 2804.8605808624284, - 1.0913447682750304 - ], - [ - 2945.10360990555, - 1.051151449975068 - ], - [ - 3085.346638948671, - 1.0116656115822846 - ], - [ - 3225.589667991793, - 0.9749378863234479 - ], - [ - 3365.8326970349144, - 0.9369515359812043 - ], - [ - 3506.0757260780356, - 0.8933990051749728 - ], - [ - 3646.318755121157, - 0.8423389630041305 - ], - [ - 3786.5617841642784, - 0.7835170690930425 - ], - [ - 3926.8048132074, - 0.7186477651083134 - ], - [ - 4067.047842250521, - 0.6498450702344426 - ], - [ - 4207.290871293642, - 0.5786803042404279 - ], - [ - 4347.533900336764, - 0.5046310466239089 - ], - [ - 4487.776929379886, - 0.4273586606854939 - ], - [ - 4628.019958423007, - 0.34773417072163015 - ], - [ - 4768.262987466129, - 0.26675209116601994 - ], - [ - 4908.50601650925, - 0.18556746013417735 - ], - [ - 5048.749045552371, - 0.10623270486290158 - ], - [ - 5188.992074595492, - 0.030082270211727498 - ], - [ - 5329.235103638614, - -0.041983063468749006 - ], - [ - 5469.478132681736, - -0.10906443989552314 - ], - [ - 5609.721161724857, - -0.17049523788840276 - ], - [ - 5749.964190767979, - -0.22717423578175497 - ], - [ - 5890.2072198111, - -0.2797691579252127 - ], - [ - 6030.450248854221, - -0.3298246851181538 - ], - [ - 6170.693277897342, - -0.37916365525523177 - ], - [ - 6310.936306940464, - -0.4295991523168206 - ], - [ - 6451.179335983586, - -0.48195529423339895 - ], - [ - 6591.422365026707, - -0.535693306829233 - ], - [ - 6731.665394069829, - -0.590176813762216 - ], - [ - 6871.90842311295, - -0.6441119865678534 - ], - [ - 7012.151452156071, - -0.6961055808168635 - ], - [ - 7152.394481199192, - -0.744934039867361 - ], - [ - 7292.637510242314, - -0.7898665035927135 - ], - [ - 7432.880539285436, - -0.8309928831756344 - ], - [ - 7573.123568328557, - -0.8698421872203529 - ], - [ - 7713.366597371678, - -0.908214449576735 - ], - [ - 7853.6096264148, - -0.9479030576120427 - ], - [ - 7993.852655457921, - -0.9906872968193211 - ], - [ - 8134.095684501042, - -1.037980792735428 - ], - [ - 8274.338713544164, - -1.0912375501081435 - ], - [ - 8414.581742587285, - -1.151449276921896 - ], - [ - 8554.824771630407, - -1.2175183114548496 - ], - [ - 8695.067800673529, - -1.287731271164042 - ], - [ - 8835.31082971665, - -1.3604302911060897 - ], - [ - 8975.553858759771, - -1.4339579213874878 - ], - [ - 9115.796887802893, - -1.5066074247665509 - ], - [ - 9256.039916846014, - -1.576811781252794 - ], - [ - 9396.282945889136, - -1.642728055416125 - ], - [ - 9536.525974932258, - -1.7032377571632515 - ], - [ - 9676.769003975378, - -1.758888530287144 - ], - [ - 9817.0120330185, - -1.8108819932413356 - ], - [ - 9957.25506206162, - -1.8603985012514068 - ], - [ - 10097.498091104742, - -1.9086184166409652 - ], - [ - 10237.741120147864, - -1.956712519836913 - ], - [ - 10377.984149190985, - -2.0057529674318157 - ], - [ - 10518.227178234107, - -2.0572554999979107 - ], - [ - 10658.470207277229, - -2.1134635928374843 - ], - [ - 10798.71323632035, - -2.1763296016377693 - ], - [ - 10938.956265363471, - -2.2463606591820837 - ], - [ - 11079.199294406593, - -2.3232356492382324 - ], - [ - 11219.442323449714, - -2.4050171507194023 - ], - [ - 11359.685352492836, - -2.488917901523426 - ], - [ - 11499.928381535958, - -2.5715660382835464 - ], - [ - 11640.171410579078, - -2.6492372680714196 - ], - [ - 11780.4144396222, - -2.719107445402041 - ], - [ - 11920.65746866532, - -2.7792715297052504 - ], - [ - 12060.900497708442, - -2.829034609304102 - ], - [ - 12201.143526751564, - -2.866054161846788 - ], - [ - 12341.386555794685, - -2.8902116568276677 - ], - [ - 12481.629584837807, - -2.9016840980042535 - ], - [ - 12621.872613880929, - -2.9005515609146784 - ], - [ - 12762.11564292405, - -2.8861737987801175 - ], - [ - 12902.358671967171, - -2.85815515401145 - ], - [ - 13042.601701010293, - -2.8186254569820255 - ], - [ - 13182.844730053414, - -2.76964299373404 - ], - [ - 13323.087759096536, - -2.712794317111105 - ], - [ - 13463.330788139658, - -2.6495679626896136 - ], - [ - 13603.573817182778, - -2.581514772366125 - ], - [ - 13743.8168462259, - -2.5107160151052925 - ], - [ - 13884.059875269022, - -2.43879170076375 - ] - ], - "9": [ - [ - 0.0, - 0.07368322696657943 - ], - [ - 140.24302904312142, - 0.5317436444227621 - ], - [ - 280.48605808624285, - 0.7632761196996933 - ], - [ - 420.7290871293643, - 1.0427700788528538 - ], - [ - 560.9721161724857, - 1.257181145419362 - ], - [ - 701.2151452156071, - 1.311344434630688 - ], - [ - 841.4581742587286, - 1.3061516869455152 - ], - [ - 981.70120330185, - 1.3009115097181125 - ], - [ - 1121.9442323449714, - 1.2952503214140896 - ], - [ - 1262.1872613880928, - 1.287804593259075 - ], - [ - 1402.4302904312142, - 1.2826688015819427 - ], - [ - 1542.6733194743356, - 1.2725544142678635 - ], - [ - 1682.9163485174572, - 1.253618061293078 - ], - [ - 1823.1593775605786, - 1.2284139339340816 - ], - [ - 1963.4024066037, - 1.2081976877309548 - ], - [ - 2103.645435646821, - 1.1961337062482302 - ], - [ - 2243.888464689943, - 1.1848697195702789 - ], - [ - 2384.1314937330644, - 1.17067814488332 - ], - [ - 2524.3745227761856, - 1.1507496158098554 - ], - [ - 2664.617551819307, - 1.1219684728857084 - ], - [ - 2804.8605808624284, - 1.0858274732506723 - ], - [ - 2945.10360990555, - 1.0438059032043225 - ], - [ - 3085.346638948671, - 0.9988854725611573 - ], - [ - 3225.589667991793, - 0.9543767736549829 - ], - [ - 3365.8326970349144, - 0.9079525097559514 - ], - [ - 3506.0757260780356, - 0.8568906692632553 - ], - [ - 3646.318755121157, - 0.7993201463229642 - ], - [ - 3786.5617841642784, - 0.7342605806601308 - ], - [ - 3926.8048132074, - 0.6639870932138828 - ], - [ - 4067.047842250521, - 0.5917206393254469 - ], - [ - 4207.290871293642, - 0.5202944264902496 - ], - [ - 4347.533900336764, - 0.4504526815926751 - ], - [ - 4487.776929379886, - 0.3816283606732711 - ], - [ - 4628.019958423007, - 0.31335942062730415 - ], - [ - 4768.262987466129, - 0.24520762950959427 - ], - [ - 4908.50601650925, - 0.17699318741624973 - ], - [ - 5048.749045552371, - 0.10923981314034295 - ], - [ - 5188.992074595492, - 0.041771457035617826 - ], - [ - 5329.235103638614, - -0.026379565841253626 - ], - [ - 5469.478132681736, - -0.0962045766893434 - ], - [ - 5609.721161724857, - -0.16888538401061226 - ], - [ - 5749.964190767979, - -0.24529804069744757 - ], - [ - 5890.2072198111, - -0.3239170625931536 - ], - [ - 6030.450248854221, - -0.4026128283808089 - ], - [ - 6170.693277897342, - -0.4793413281313876 - ], - [ - 6310.936306940464, - -0.5520035309696396 - ], - [ - 6451.179335983586, - -0.6181640783362785 - ], - [ - 6591.422365026707, - -0.6769179386245234 - ], - [ - 6731.665394069829, - -0.7293217413178189 - ], - [ - 6871.90842311295, - -0.7763749402238898 - ], - [ - 7012.151452156071, - -0.8190081615834442 - ], - [ - 7152.394481199192, - -0.8583089147980946 - ], - [ - 7292.637510242314, - -0.895610491433579 - ], - [ - 7432.880539285436, - -0.9314815648750324 - ], - [ - 7573.123568328557, - -0.966353600257146 - ], - [ - 7713.366597371678, - -1.0009361351780404 - ], - [ - 7853.6096264148, - -1.0359192225894798 - ], - [ - 7993.852655457921, - -1.0719773555686853 - ], - [ - 8134.095684501042, - -1.1094883172784857 - ], - [ - 8274.338713544164, - -1.1490188483026915 - ], - [ - 8414.581742587285, - -1.1920715399360076 - ], - [ - 8554.824771630407, - -1.238743561988614 - ], - [ - 8695.067800673529, - -1.2884101631087166 - ], - [ - 8835.31082971665, - -1.3405417849117836 - ], - [ - 8975.553858759771, - -1.3946102989024576 - ], - [ - 9115.796887802893, - -1.4500834256920347 - ], - [ - 9256.039916846014, - -1.5066635393100152 - ], - [ - 9396.282945889136, - -1.5633599620038945 - ], - [ - 9536.525974932258, - -1.6183979822384003 - ], - [ - 9676.769003975378, - -1.6716918092367805 - ], - [ - 9817.0120330185, - -1.7237208271611622 - ], - [ - 9957.25506206162, - -1.7749178752629287 - ], - [ - 10097.498091104742, - -1.8257157845018859 - ], - [ - 10237.741120147864, - -1.876531835023932 - ], - [ - 10377.984149190985, - -1.9275674833676713 - ], - [ - 10518.227178234107, - -1.9791114914374224 - ], - [ - 10658.470207277229, - -2.0329614817871158 - ], - [ - 10798.71323632035, - -2.0913466675008006 - ], - [ - 10938.956265363471, - -2.154404734287209 - ], - [ - 11079.199294406593, - -2.2212975198004496 - ], - [ - 11219.442323449714, - -2.2897944211816736 - ], - [ - 11359.685352492836, - -2.3573160907308632 - ], - [ - 11499.928381535958, - -2.4219596169921433 - ], - [ - 11640.171410579078, - -2.483369100522211 - ], - [ - 11780.4144396222, - -2.542692721380814 - ], - [ - 11920.65746866532, - -2.60214359964843 - ], - [ - 12060.900497708442, - -2.6634792331738377 - ], - [ - 12201.143526751564, - -2.7237442380322388 - ], - [ - 12341.386555794685, - -2.779576594271895 - ], - [ - 12481.629584837807, - -2.8268038673766624 - ], - [ - 12621.872613880929, - -2.8611375903492244 - ], - [ - 12762.11564292405, - -2.87765111886134 - ], - [ - 12902.358671967171, - -2.8733900862406156 - ], - [ - 13042.601701010293, - -2.8507317329589585 - ], - [ - 13182.844730053414, - -2.8130513179892125 - ], - [ - 13323.087759096536, - -2.7634252860317723 - ], - [ - 13463.330788139658, - -2.7047689233274954 - ], - [ - 13603.573817182778, - -2.6400508847959796 - ], - [ - 13743.8168462259, - -2.572583711505525 - ], - [ - 13884.059875269022, - -2.504157805452004 - ] - ], - "10": [ - [ - 0.0, - 0.20033156411557038 - ], - [ - 140.24302904312142, - 0.7366927011001225 - ], - [ - 280.48605808624285, - 1.035696436664274 - ], - [ - 420.7290871293643, - 1.3016772262987215 - ], - [ - 560.9721161724857, - 1.4745182816328153 - ], - [ - 701.2151452156071, - 1.505262689140049 - ], - [ - 841.4581742587286, - 1.4803134652862655 - ], - [ - 981.70120330185, - 1.4485494521014473 - ], - [ - 1121.9442323449714, - 1.4190757005303247 - ], - [ - 1262.1872613880928, - 1.398783646813674 - ], - [ - 1402.4302904312142, - 1.3928921996000794 - ], - [ - 1542.6733194743356, - 1.3898913567083553 - ], - [ - 1682.9163485174572, - 1.3765172517684965 - ], - [ - 1823.1593775605786, - 1.3522394020909483 - ], - [ - 1963.4024066037, - 1.3256469338428594 - ], - [ - 2103.645435646821, - 1.298947662848724 - ], - [ - 2243.888464689943, - 1.2698146186244634 - ], - [ - 2384.1314937330644, - 1.2403724958726574 - ], - [ - 2524.3745227761856, - 1.211787160425229 - ], - [ - 2664.617551819307, - 1.1806844951966449 - ], - [ - 2804.8605808624284, - 1.144748852538736 - ], - [ - 2945.10360990555, - 1.101578022737501 - ], - [ - 3085.346638948671, - 1.0520546061803064 - ], - [ - 3225.589667991793, - 1.0007438648641758 - ], - [ - 3365.8326970349144, - 0.9491138671527657 - ], - [ - 3506.0757260780356, - 0.8983355457640179 - ], - [ - 3646.318755121157, - 0.8483337575896465 - ], - [ - 3786.5617841642784, - 0.796216150822515 - ], - [ - 3926.8048132074, - 0.741264894296307 - ], - [ - 4067.047842250521, - 0.6838479694568798 - ], - [ - 4207.290871293642, - 0.624390478758714 - ], - [ - 4347.533900336764, - 0.5633625443312587 - ], - [ - 4487.776929379886, - 0.5011983711596055 - ], - [ - 4628.019958423007, - 0.43779891163354057 - ], - [ - 4768.262987466129, - 0.3730071357476165 - ], - [ - 4908.50601650925, - 0.3070673504641571 - ], - [ - 5048.749045552371, - 0.24119620016754342 - ], - [ - 5188.992074595492, - 0.17454714827979542 - ], - [ - 5329.235103638614, - 0.10470852197829392 - ], - [ - 5469.478132681736, - 0.029206753277270098 - ], - [ - 5609.721161724857, - -0.0548958400348227 - ], - [ - 5749.964190767979, - -0.15110422394300738 - ], - [ - 5890.2072198111, - -0.25677863903369424 - ], - [ - 6030.450248854221, - -0.36449990367885166 - ], - [ - 6170.693277897342, - -0.46663238693526987 - ], - [ - 6310.936306940464, - -0.555445880368153 - ], - [ - 6451.179335983586, - -0.6222923184503801 - ], - [ - 6591.422365026707, - -0.6674618807840859 - ], - [ - 6731.665394069829, - -0.7186620739342314 - ], - [ - 6871.90842311295, - -0.7770317989004804 - ], - [ - 7012.151452156071, - -0.8384267824327902 - ], - [ - 7152.394481199192, - -0.8988447070588746 - ], - [ - 7292.637510242314, - -0.9548558646395213 - ], - [ - 7432.880539285436, - -1.0035125334849682 - ], - [ - 7573.123568328557, - -1.044303623170647 - ], - [ - 7713.366597371678, - -1.0783981918264285 - ], - [ - 7853.6096264148, - -1.1068999761512468 - ], - [ - 7993.852655457921, - -1.1308987754025113 - ], - [ - 8134.095684501042, - -1.151208877671897 - ], - [ - 8274.338713544164, - -1.168818458993671 - ], - [ - 8414.581742587285, - -1.185482410048914 - ], - [ - 8554.824771630407, - -1.2024923294339425 - ], - [ - 8695.067800673529, - -1.2210866499439053 - ], - [ - 8835.31082971665, - -1.2427103840842726 - ], - [ - 8975.553858759771, - -1.2688104557672246 - ], - [ - 9115.796887802893, - -1.300872026568912 - ], - [ - 9256.039916846014, - -1.340485408520157 - ], - [ - 9396.282945889136, - -1.388399574050832 - ], - [ - 9536.525974932258, - -1.4431314517520968 - ], - [ - 9676.769003975378, - -1.5031787594282795 - ], - [ - 9817.0120330185, - -1.5669371241322672 - ], - [ - 9957.25506206162, - -1.6327125183668307 - ], - [ - 10097.498091104742, - -1.6988108902299461 - ], - [ - 10237.741120147864, - -1.7635239336014752 - ], - [ - 10377.984149190985, - -1.8249211117576278 - ], - [ - 10518.227178234107, - -1.8811811017803564 - ], - [ - 10658.470207277229, - -1.9328083640702383 - ], - [ - 10798.71323632035, - -1.983018042476987 - ], - [ - 10938.956265363471, - -2.0338984960449973 - ], - [ - 11079.199294406593, - -2.0867957012526617 - ], - [ - 11219.442323449714, - -2.141436286591516 - ], - [ - 11359.685352492836, - -2.196288005406448 - ], - [ - 11499.928381535958, - -2.2497468887966847 - ], - [ - 11640.171410579078, - -2.302184300637646 - ], - [ - 11780.4144396222, - -2.355628600873319 - ], - [ - 11920.65746866532, - -2.413382017202728 - ], - [ - 12060.900497708442, - -2.4784414627859737 - ], - [ - 12201.143526751564, - -2.548298268419042 - ], - [ - 12341.386555794685, - -2.6185365773258797 - ], - [ - 12481.629584837807, - -2.682976089564053 - ], - [ - 12621.872613880929, - -2.7352426085389636 - ], - [ - 12762.11564292405, - -2.7684329922295428 - ], - [ - 12902.358671967171, - -2.777763456550214 - ], - [ - 13042.601701010293, - -2.7645890241763555 - ], - [ - 13182.844730053414, - -2.732255483037836 - ], - [ - 13323.087759096536, - -2.6846798782898693 - ], - [ - 13463.330788139658, - -2.6256355651225687 - ], - [ - 13603.573817182778, - -2.558927727477169 - ], - [ - 13743.8168462259, - -2.4885445656915146 - ], - [ - 13884.059875269022, - -2.416920084013453 - ] - ], - "11": [ - [ - 0.0, - 0.2855839584117207 - ], - [ - 140.24302904312142, - 0.7419620625268664 - ], - [ - 280.48605808624285, - 0.9836227945217749 - ], - [ - 420.7290871293643, - 1.2869015265327641 - ], - [ - 560.9721161724857, - 1.460368775270272 - ], - [ - 701.2151452156071, - 1.436144198340873 - ], - [ - 841.4581742587286, - 1.3738853426215691 - ], - [ - 981.70120330185, - 1.3371773338359074 - ], - [ - 1121.9442323449714, - 1.320023764732997 - ], - [ - 1262.1872613880928, - 1.3060982720310594 - ], - [ - 1402.4302904312142, - 1.292862924395942 - ], - [ - 1542.6733194743356, - 1.281398396158237 - ], - [ - 1682.9163485174572, - 1.2733516562505018 - ], - [ - 1823.1593775605786, - 1.2655800734498184 - ], - [ - 1963.4024066037, - 1.2767145830472955 - ], - [ - 2103.645435646821, - 1.3033180460117781 - ], - [ - 2243.888464689943, - 1.215398779420275 - ], - [ - 2384.1314937330644, - 1.1580417869596935 - ], - [ - 2524.3745227761856, - 1.1986672295797982 - ], - [ - 2664.617551819307, - 1.2132446308122802 - ], - [ - 2804.8605808624284, - 1.194569380290527 - ], - [ - 2945.10360990555, - 1.151874244776949 - ], - [ - 3085.346638948671, - 1.0975943888892503 - ], - [ - 3225.589667991793, - 1.0461256584523102 - ], - [ - 3365.8326970349144, - 0.9991002958452183 - ], - [ - 3506.0757260780356, - 0.9556669854000687 - ], - [ - 3646.318755121157, - 0.9131382821719918 - ], - [ - 3786.5617841642784, - 0.8653502236203185 - ], - [ - 3926.8048132074, - 0.80990055327698 - ], - [ - 4067.047842250521, - 0.7465501064564476 - ], - [ - 4207.290871293642, - 0.6755180278126978 - ], - [ - 4347.533900336764, - 0.5991758306433211 - ], - [ - 4487.776929379886, - 0.5214288238273713 - ], - [ - 4628.019958423007, - 0.44544994304795776 - ], - [ - 4768.262987466129, - 0.3744028112787465 - ], - [ - 4908.50601650925, - 0.31129509694491825 - ], - [ - 5048.749045552371, - 0.25750834465260775 - ], - [ - 5188.992074595492, - 0.21057258503320084 - ], - [ - 5329.235103638614, - 0.16616081161419693 - ], - [ - 5469.478132681736, - 0.1197327664348747 - ], - [ - 5609.721161724857, - 0.06653276659636267 - ], - [ - 5749.964190767979, - 0.0018242544757313883 - ], - [ - 5890.2072198111, - -0.07454107205816111 - ], - [ - 6030.450248854221, - -0.15933731336700918 - ], - [ - 6170.693277897342, - -0.24888065665623252 - ], - [ - 6310.936306940464, - -0.3394421805144813 - ], - [ - 6451.179335983586, - -0.4262781520888914 - ], - [ - 6591.422365026707, - -0.5048182008355208 - ], - [ - 6731.665394069829, - -0.5742918351259183 - ], - [ - 6871.90842311295, - -0.6351746017167545 - ], - [ - 7012.151452156071, - -0.6880357514486004 - ], - [ - 7152.394481199192, - -0.7335158439820855 - ], - [ - 7292.637510242314, - -0.7726088538402506 - ], - [ - 7432.880539285436, - -0.8072389406837412 - ], - [ - 7573.123568328557, - -0.8381345250302351 - ], - [ - 7713.366597371678, - -0.8654369037542416 - ], - [ - 7853.6096264148, - -0.8891491700859945 - ], - [ - 7993.852655457921, - -0.909264078721904 - ], - [ - 8134.095684501042, - -0.9259044732495778 - ], - [ - 8274.338713544164, - -0.9412167428415664 - ], - [ - 8414.581742587285, - -0.9575428326836722 - ], - [ - 8554.824771630407, - -0.9777906340924041 - ], - [ - 8695.067800673529, - -1.0050162671083587 - ], - [ - 8835.31082971665, - -1.0424404184913307 - ], - [ - 8975.553858759771, - -1.0932876239983482 - ], - [ - 9115.796887802893, - -1.160748984437931 - ], - [ - 9256.039916846014, - -1.2459636885501926 - ], - [ - 9396.282945889136, - -1.3467409466772269 - ], - [ - 9536.525974932258, - -1.4591923692081827 - ], - [ - 9676.769003975378, - -1.5789649568201558 - ], - [ - 9817.0120330185, - -1.7017356807880422 - ], - [ - 9957.25506206162, - -1.8230979209389364 - ], - [ - 10097.498091104742, - -1.938645045885036 - ], - [ - 10237.741120147864, - -2.0439611003144478 - ], - [ - 10377.984149190985, - -2.1347672139921885 - ], - [ - 10518.227178234107, - -2.208233258641477 - ], - [ - 10658.470207277229, - -2.2634701806700552 - ], - [ - 10798.71323632035, - -2.302018452881941 - ], - [ - 10938.956265363471, - -2.3241465343649343 - ], - [ - 11079.199294406593, - -2.330005015270447 - ], - [ - 11219.442323449714, - -2.323888386250065 - ], - [ - 11359.685352492836, - -2.31082724836954 - ], - [ - 11499.928381535958, - -2.295249302769344 - ], - [ - 11640.171410579078, - -2.2841546506002084 - ], - [ - 11780.4144396222, - -2.286212615509844 - ], - [ - 11920.65746866532, - -2.304903327979749 - ], - [ - 12060.900497708442, - -2.3386458880478376 - ], - [ - 12201.143526751564, - -2.3828051386434344 - ], - [ - 12341.386555794685, - -2.4303325057094787 - ], - [ - 12481.629584837807, - -2.470950815277359 - ], - [ - 12621.872613880929, - -2.494070909939749 - ], - [ - 12762.11564292405, - -2.4917731197386375 - ], - [ - 12902.358671967171, - -2.4608901310668396 - ], - [ - 13042.601701010293, - -2.403291143707507 - ], - [ - 13182.844730053414, - -2.323911565334489 - ], - [ - 13323.087759096536, - -2.229932212395546 - ], - [ - 13463.330788139658, - -2.128659359323263 - ], - [ - 13603.573817182778, - -2.0273698021929056 - ], - [ - 13743.8168462259, - -1.9326312143434257 - ], - [ - 13884.059875269022, - -1.8480338260085418 - ] - ], - "12": [ - [ - 0.0, - 0.4376729483584739 - ], - [ - 140.24302904312142, - 0.8732755525910104 - ], - [ - 280.48605808624285, - 1.0317510788892752 - ], - [ - 420.7290871293643, - 1.2003933861704859 - ], - [ - 560.9721161724857, - 1.3904011757208585 - ], - [ - 701.2151452156071, - 1.4981092628344417 - ], - [ - 841.4581742587286, - 1.5015905975046775 - ], - [ - 981.70120330185, - 1.4813611638634405 - ], - [ - 1121.9442323449714, - 1.4803660803512886 - ], - [ - 1262.1872613880928, - 1.4876007940513587 - ], - [ - 1402.4302904312142, - 1.4934949201748156 - ], - [ - 1542.6733194743356, - 1.4936432421934651 - ], - [ - 1682.9163485174572, - 1.491138447590549 - ], - [ - 1823.1593775605786, - 1.4857066133274597 - ], - [ - 1963.4024066037, - 1.4931402074750053 - ], - [ - 2103.645435646821, - 1.5136389550114437 - ], - [ - 2243.888464689943, - 1.442204861946279 - ], - [ - 2384.1314937330644, - 1.3941131169590903 - ], - [ - 2524.3745227761856, - 1.4202352103983102 - ], - [ - 2664.617551819307, - 1.4207907285306927 - ], - [ - 2804.8605808624284, - 1.3933535252179092 - ], - [ - 2945.10360990555, - 1.34887050718887 - ], - [ - 3085.346638948671, - 1.2986080236769408 - ], - [ - 3225.589667991793, - 1.2526610492000547 - ], - [ - 3365.8326970349144, - 1.2109424392562986 - ], - [ - 3506.0757260780356, - 1.171405891466613 - ], - [ - 3646.318755121157, - 1.131396840273458 - ], - [ - 3786.5617841642784, - 1.0871979039886914 - ], - [ - 3926.8048132074, - 1.0378830956461504 - ], - [ - 4067.047842250521, - 0.9842069924944964 - ], - [ - 4207.290871293642, - 0.9273546877905384 - ], - [ - 4347.533900336764, - 0.869570881209266 - ], - [ - 4487.776929379886, - 0.8132600897577447 - ], - [ - 4628.019958423007, - 0.7587274509298837 - ], - [ - 4768.262987466129, - 0.7060586898880341 - ], - [ - 4908.50601650925, - 0.655024632341709 - ], - [ - 5048.749045552371, - 0.6043516714899357 - ], - [ - 5188.992074595492, - 0.5525758550383051 - ], - [ - 5329.235103638614, - 0.5021955688754052 - ], - [ - 5469.478132681736, - 0.45616288764987206 - ], - [ - 5609.721161724857, - 0.4172271287422114 - ], - [ - 5749.964190767979, - 0.38778296792076467 - ], - [ - 5890.2072198111, - 0.3700376014275243 - ], - [ - 6030.450248854221, - 0.3500928017881947 - ], - [ - 6170.693277897342, - 0.30844491575059446 - ], - [ - 6310.936306940464, - 0.22568694902819741 - ], - [ - 6451.179335983586, - 0.08411178424196453 - ], - [ - 6591.422365026707, - -0.1289546386621294 - ], - [ - 6731.665394069829, - -0.3081561922566832 - ], - [ - 6871.90842311295, - -0.41022040244932106 - ], - [ - 7012.151452156071, - -0.4510978242511574 - ], - [ - 7152.394481199192, - -0.44686397149375306 - ], - [ - 7292.637510242314, - -0.4163209114273654 - ], - [ - 7432.880539285436, - -0.3837131264947207 - ], - [ - 7573.123568328557, - -0.37074373203910405 - ], - [ - 7713.366597371678, - -0.38241527513736634 - ], - [ - 7853.6096264148, - -0.41939510456999 - ], - [ - 7993.852655457921, - -0.4830156420808258 - ], - [ - 8134.095684501042, - -0.578472738509631 - ], - [ - 8274.338713544164, - -0.7055239322917911 - ], - [ - 8414.581742587285, - -0.8403663979429535 - ], - [ - 8554.824771630407, - -0.9656553484416706 - ], - [ - 8695.067800673529, - -1.0805775332863272 - ], - [ - 8835.31082971665, - -1.1854306366818084 - ], - [ - 8975.553858759771, - -1.280515192731538 - ], - [ - 9115.796887802893, - -1.3660921174615532 - ], - [ - 9256.039916846014, - -1.444473291439376 - ], - [ - 9396.282945889136, - -1.5210111885477124 - ], - [ - 9536.525974932258, - -1.5983331647760117 - ], - [ - 9676.769003975378, - -1.6787816014615793 - ], - [ - 9817.0120330185, - -1.7643032111490147 - ], - [ - 9957.25506206162, - -1.856753058386309 - ], - [ - 10097.498091104742, - -1.9579862152560341 - ], - [ - 10237.741120147864, - -2.0698473127787653 - ], - [ - 10377.984149190985, - -2.1927662668463643 - ], - [ - 10518.227178234107, - -2.3151761025355015 - ], - [ - 10658.470207277229, - -2.42052384989943 - ], - [ - 10798.71323632035, - -2.493819458044295 - ], - [ - 10938.956265363471, - -2.519326779466024 - ], - [ - 11079.199294406593, - -2.4871124837979495 - ], - [ - 11219.442323449714, - -2.41117260089993 - ], - [ - 11359.685352492836, - -2.310735550558518 - ], - [ - 11499.928381535958, - -2.2054094415932552 - ], - [ - 11640.171410579078, - -2.1168751834923247 - ], - [ - 11780.4144396222, - -2.0663207489070063 - ], - [ - 11920.65746866532, - -2.056878791276834 - ], - [ - 12060.900497708442, - -2.081405438849249 - ], - [ - 12201.143526751564, - -2.1299761214324455 - ], - [ - 12341.386555794685, - -2.189462674841471 - ], - [ - 12481.629584837807, - -2.24350630629858 - ], - [ - 12621.872613880929, - -2.275782573147008 - ], - [ - 12762.11564292405, - -2.277240007618092 - ], - [ - 12902.358671967171, - -2.245185575990809 - ], - [ - 13042.601701010293, - -2.1814805628513856 - ], - [ - 13182.844730053414, - -2.092094518406327 - ], - [ - 13323.087759096536, - -1.9858885237889503 - ], - [ - 13463.330788139658, - -1.87201727452702 - ], - [ - 13603.573817182778, - -1.7595223121841108 - ], - [ - 13743.8168462259, - -1.6558307645090202 - ], - [ - 13884.059875269022, - -1.5655775873307938 - ] - ], - "13": [ - [ - 0.0, - 0.5277954692463657 - ], - [ - 140.24302904312142, - 0.9601205204355695 - ], - [ - 280.48605808624285, - 1.0954862456366603 - ], - [ - 420.7290871293643, - 1.1976529544403645 - ], - [ - 560.9721161724857, - 1.3322807546098965 - ], - [ - 701.2151452156071, - 1.449243486503489 - ], - [ - 841.4581742587286, - 1.523322106036759 - ], - [ - 981.70120330185, - 1.6458547952113507 - ], - [ - 1121.9442323449714, - 1.6930817034737218 - ], - [ - 1262.1872613880928, - 1.6798134163351535 - ], - [ - 1402.4302904312142, - 1.6823768166414108 - ], - [ - 1542.6733194743356, - 1.6825965973027104 - ], - [ - 1682.9163485174572, - 1.675596411403264 - ], - [ - 1823.1593775605786, - 1.6695029570461968 - ], - [ - 1963.4024066037, - 1.6564510332456719 - ], - [ - 2103.645435646821, - 1.6311141953192547 - ], - [ - 2243.888464689943, - 1.6120888019748252 - ], - [ - 2384.1314937330644, - 1.5833738104145967 - ], - [ - 2524.3745227761856, - 1.545089559613145 - ], - [ - 2664.617551819307, - 1.5165056142494093 - ], - [ - 2804.8605808624284, - 1.494118718248609 - ], - [ - 2945.10360990555, - 1.4717718560838096 - ], - [ - 3085.346638948671, - 1.4434145070115907 - ], - [ - 3225.589667991793, - 1.407230584839932 - ], - [ - 3365.8326970349144, - 1.3655106904128431 - ], - [ - 3506.0757260780356, - 1.3209493022353902 - ], - [ - 3646.318755121157, - 1.2760447900687775 - ], - [ - 3786.5617841642784, - 1.2319114293366118 - ], - [ - 3926.8048132074, - 1.1880975015859134 - ], - [ - 4067.047842250521, - 1.144053967691683 - ], - [ - 4207.290871293642, - 1.099617008434231 - ], - [ - 4347.533900336764, - 1.0554774772913569 - ], - [ - 4487.776929379886, - 1.0126791389684102 - ], - [ - 4628.019958423007, - 0.9711400575023703 - ], - [ - 4768.262987466129, - 0.9307436000697661 - ], - [ - 4908.50601650925, - 0.8907021547031322 - ], - [ - 5048.749045552371, - 0.8484364115958392 - ], - [ - 5188.992074595492, - 0.8021251316209892 - ], - [ - 5329.235103638614, - 0.7538776203724163 - ], - [ - 5469.478132681736, - 0.7061637176060801 - ], - [ - 5609.721161724857, - 0.6616555357318068 - ], - [ - 5749.964190767979, - 0.6243519470659544 - ], - [ - 5890.2072198111, - 0.5977647252717851 - ], - [ - 6030.450248854221, - 0.5707386828129082 - ], - [ - 6170.693277897342, - 0.5273442134842958 - ], - [ - 6310.936306940464, - 0.4517638219980422 - ], - [ - 6451.179335983586, - 0.32913462813083555 - ], - [ - 6591.422365026707, - 0.14731445321477438 - ], - [ - 6731.665394069829, - -0.007648427463980546 - ], - [ - 6871.90842311295, - -0.09891421641752406 - ], - [ - 7012.151452156071, - -0.1388812041928871 - ], - [ - 7152.394481199192, - -0.14017256420170235 - ], - [ - 7292.637510242314, - -0.11801373364923907 - ], - [ - 7432.880539285436, - -0.09160044756479688 - ], - [ - 7573.123568328557, - -0.0795988253708362 - ], - [ - 7713.366597371678, - -0.08957291911998944 - ], - [ - 7853.6096264148, - -0.12564586629574537 - ], - [ - 7993.852655457921, - -0.1919362933449338 - ], - [ - 8134.095684501042, - -0.29190779547542967 - ], - [ - 8274.338713544164, - -0.42263394534753407 - ], - [ - 8414.581742587285, - -0.5610494416327729 - ], - [ - 8554.824771630407, - -0.69147771025835 - ], - [ - 8695.067800673529, - -0.813994295608885 - ], - [ - 8835.31082971665, - -0.9297367251014251 - ], - [ - 8975.553858759771, - -1.0398455889726577 - ], - [ - 9115.796887802893, - -1.145461685311432 - ], - [ - 9256.039916846014, - -1.2488007967789827 - ], - [ - 9396.282945889136, - -1.3532622217275432 - ], - [ - 9536.525974932258, - -1.459258400003785 - ], - [ - 9676.769003975378, - -1.567031791006888 - ], - [ - 9817.0120330185, - -1.676275242881238 - ], - [ - 9957.25506206162, - -1.7865717080117258 - ], - [ - 10097.498091104742, - -1.8975041460542719 - ], - [ - 10237.741120147864, - -2.008643332509036 - ], - [ - 10377.984149190985, - -2.1184984318207065 - ], - [ - 10518.227178234107, - -2.217943567406552 - ], - [ - 10658.470207277229, - -2.2949216062385287 - ], - [ - 10798.71323632035, - -2.339392165550021 - ], - [ - 10938.956265363471, - -2.340431568337131 - ], - [ - 11079.199294406593, - -2.2925581263671146 - ], - [ - 11219.442323449714, - -2.2092775721077977 - ], - [ - 11359.685352492836, - -2.1080361329007555 - ], - [ - 11499.928381535958, - -2.0063258736870866 - ], - [ - 11640.171410579078, - -1.9235603138312076 - ], - [ - 11780.4144396222, - -1.8783379656344499 - ], - [ - 11920.65746866532, - -1.8722341061932986 - ], - [ - 12060.900497708442, - -1.8981033145029345 - ], - [ - 12201.143526751564, - -1.9460994284279653 - ], - [ - 12341.386555794685, - -2.003971865027418 - ], - [ - 12481.629584837807, - -2.056428476207535 - ], - [ - 12621.872613880929, - -2.088262387218856 - ], - [ - 12762.11564292405, - -2.091722165887782 - ], - [ - 12902.358671967171, - -2.0646557584361402 - ], - [ - 13042.601701010293, - -2.0083428799274237 - ], - [ - 13182.844730053414, - -1.927291255469334 - ], - [ - 13323.087759096536, - -1.8289360934532037 - ], - [ - 13463.330788139658, - -1.7210912945003447 - ], - [ - 13603.573817182778, - -1.611439249070559 - ], - [ - 13743.8168462259, - -1.5060405794143092 - ], - [ - 13884.059875269022, - -1.4096850475093605 - ] - ], - "14": [ - [ - 0.0, - 0.49978668925219444 - ], - [ - 140.24302904312142, - 0.9238247370606948 - ], - [ - 280.48605808624285, - 1.062674368823593 - ], - [ - 420.7290871293643, - 1.157652501122846 - ], - [ - 560.9721161724857, - 1.2526544448857362 - ], - [ - 701.2151452156071, - 1.3300713448591786 - ], - [ - 841.4581742587286, - 1.4031610999286845 - ], - [ - 981.70120330185, - 1.562331273517175 - ], - [ - 1121.9442323449714, - 1.6617850936539151 - ], - [ - 1262.1872613880928, - 1.695653849183498 - ], - [ - 1402.4302904312142, - 1.728395220004209 - ], - [ - 1542.6733194743356, - 1.744048519352651 - ], - [ - 1682.9163485174572, - 1.7410220236647134 - ], - [ - 1823.1593775605786, - 1.7274363224624083 - ], - [ - 1963.4024066037, - 1.7261673109275375 - ], - [ - 2103.645435646821, - 1.7338062486162178 - ], - [ - 2243.888464689943, - 1.6364771403463076 - ], - [ - 2384.1314937330644, - 1.5764680718851067 - ], - [ - 2524.3745227761856, - 1.5989704783540857 - ], - [ - 2664.617551819307, - 1.5965424676580966 - ], - [ - 2804.8605808624284, - 1.5694794037873157 - ], - [ - 2945.10360990555, - 1.5297761877396523 - ], - [ - 3085.346638948671, - 1.4881619605848417 - ], - [ - 3225.589667991793, - 1.4511996859108969 - ], - [ - 3365.8326970349144, - 1.4172608226348973 - ], - [ - 3506.0757260780356, - 1.3835497302610928 - ], - [ - 3646.318755121157, - 1.34787051182599 - ], - [ - 3786.5617841642784, - 1.3097047464800906 - ], - [ - 3926.8048132074, - 1.270264914953776 - ], - [ - 4067.047842250521, - 1.2315439550388851 - ], - [ - 4207.290871293642, - 1.1957326501019494 - ], - [ - 4347.533900336764, - 1.1637708242325564 - ], - [ - 4487.776929379886, - 1.134734694197921 - ], - [ - 4628.019958423007, - 1.1070259059362528 - ], - [ - 4768.262987466129, - 1.0791273698851371 - ], - [ - 4908.50601650925, - 1.0488930774275294 - ], - [ - 5048.749045552371, - 1.0146085186543894 - ], - [ - 5188.992074595492, - 0.9762043374036383 - ], - [ - 5329.235103638614, - 0.933800836645855 - ], - [ - 5469.478132681736, - 0.8873782861225915 - ], - [ - 5609.721161724857, - 0.8371666194864393 - ], - [ - 5749.964190767979, - 0.7838631969980551 - ], - [ - 5890.2072198111, - 0.7274490555855029 - ], - [ - 6030.450248854221, - 0.6676905637153596 - ], - [ - 6170.693277897342, - 0.604589121179123 - ], - [ - 6310.936306940464, - 0.5383540415398433 - ], - [ - 6451.179335983586, - 0.4706018583568054 - ], - [ - 6591.422365026707, - 0.4031509195001402 - ], - [ - 6731.665394069829, - 0.3384807330997133 - ], - [ - 6871.90842311295, - 0.2792498822108155 - ], - [ - 7012.151452156071, - 0.22804911735009795 - ], - [ - 7152.394481199192, - 0.18701059014149796 - ], - [ - 7292.637510242314, - 0.15318634619911045 - ], - [ - 7432.880539285436, - 0.12112864325448401 - ], - [ - 7573.123568328557, - 0.08460001769324127 - ], - [ - 7713.366597371678, - 0.03656472881286515 - ], - [ - 7853.6096264148, - -0.029990091337068366 - ], - [ - 7993.852655457921, - -0.12209995667451197 - ], - [ - 8134.095684501042, - -0.24449759892531098 - ], - [ - 8274.338713544164, - -0.3922111120065885 - ], - [ - 8414.581742587285, - -0.5578969106373107 - ], - [ - 8554.824771630407, - -0.7333610410309536 - ], - [ - 8695.067800673529, - -0.9100654015981802 - ], - [ - 8835.31082971665, - -1.0795517997678248 - ], - [ - 8975.553858759771, - -1.2333633682657714 - ], - [ - 9115.796887802893, - -1.3632365995902056 - ], - [ - 9256.039916846014, - -1.4676512778516082 - ], - [ - 9396.282945889136, - -1.5525798566061535 - ], - [ - 9536.525974932258, - -1.6235770397124218 - ], - [ - 9676.769003975378, - -1.6862938973315234 - ], - [ - 9817.0120330185, - -1.7460996750430389 - ], - [ - 9957.25506206162, - -1.8082949619799382 - ], - [ - 10097.498091104742, - -1.8781804116211966 - ], - [ - 10237.741120147864, - -1.9610512545311443 - ], - [ - 10377.984149190985, - -2.0592164119856693 - ], - [ - 10518.227178234107, - -2.1595767156182286 - ], - [ - 10658.470207277229, - -2.243960484326295 - ], - [ - 10798.71323632035, - -2.2961951109958934 - ], - [ - 10938.956265363471, - -2.299532105295938 - ], - [ - 11079.199294406593, - -2.2460996330582508 - ], - [ - 11219.442323449714, - -2.1521278116773837 - ], - [ - 11359.685352492836, - -2.0379959945249433 - ], - [ - 11499.928381535958, - -1.9238749149119467 - ], - [ - 11640.171410579078, - -1.8316248213099977 - ], - [ - 11780.4144396222, - -1.7812030222608002 - ], - [ - 11920.65746866532, - -1.7730031063295084 - ], - [ - 12060.900497708442, - -1.7983013668203107 - ], - [ - 12201.143526751564, - -1.8460169023146156 - ], - [ - 12341.386555794685, - -1.9029186826450668 - ], - [ - 12481.629584837807, - -1.9536045064587297 - ], - [ - 12621.872613880929, - -1.9830791786576596 - ], - [ - 12762.11564292405, - -1.9847040486402954 - ], - [ - 12902.358671967171, - -1.9578663560359797 - ], - [ - 13042.601701010293, - -1.905266545094717 - ], - [ - 13182.844730053414, - -1.831827666437673 - ], - [ - 13323.087759096536, - -1.744474570292338 - ], - [ - 13463.330788139658, - -1.650406022093753 - ], - [ - 13603.573817182778, - -1.5566610618639678 - ], - [ - 13743.8168462259, - -1.468480334362298 - ], - [ - 13884.059875269022, - -1.389402800657239 - ] - ], - "15": [ - [ - 0.0, - 0.3271541367110917 - ], - [ - 140.24302904312142, - 0.7119221628168965 - ], - [ - 280.48605808624285, - 0.8304922968010789 - ], - [ - 420.7290871293643, - 0.9243926211187932 - ], - [ - 560.9721161724857, - 1.0160144981194883 - ], - [ - 701.2151452156071, - 1.0816484977611742 - ], - [ - 841.4581742587286, - 1.1610772993191874 - ], - [ - 981.70120330185, - 1.2699643670398555 - ], - [ - 1121.9442323449714, - 1.391710065120972 - ], - [ - 1262.1872613880928, - 1.5030328468835488 - ], - [ - 1402.4302904312142, - 1.5843965120134194 - ], - [ - 1542.6733194743356, - 1.630147909420852 - ], - [ - 1682.9163485174572, - 1.6491267945803096 - ], - [ - 1823.1593775605786, - 1.6489378189983546 - ], - [ - 1963.4024066037, - 1.6358836468400078 - ], - [ - 2103.645435646821, - 1.6116797460095318 - ], - [ - 2243.888464689943, - 1.5776944816537055 - ], - [ - 2384.1314937330644, - 1.541966000278576 - ], - [ - 2524.3745227761856, - 1.5149015539645236 - ], - [ - 2664.617551819307, - 1.4996307853679973 - ], - [ - 2804.8605808624284, - 1.4908897929075344 - ], - [ - 2945.10360990555, - 1.482432556062595 - ], - [ - 3085.346638948671, - 1.468332927102466 - ], - [ - 3225.589667991793, - 1.445993402630727 - ], - [ - 3365.8326970349144, - 1.4169574095520607 - ], - [ - 3506.0757260780356, - 1.383277537507254 - ], - [ - 3646.318755121157, - 1.3471428998458355 - ], - [ - 3786.5617841642784, - 1.3106786553778482 - ], - [ - 3926.8048132074, - 1.2752221539016884 - ], - [ - 4067.047842250521, - 1.2417890812378483 - ], - [ - 4207.290871293642, - 1.2112763458240052 - ], - [ - 4347.533900336764, - 1.1837608403319801 - ], - [ - 4487.776929379886, - 1.1582128970379206 - ], - [ - 4628.019958423007, - 1.1334731193659675 - ], - [ - 4768.262987466129, - 1.1085524059054546 - ], - [ - 4908.50601650925, - 1.082280237660823 - ], - [ - 5048.749045552371, - 1.0541842180928738 - ], - [ - 5188.992074595492, - 1.025244466132009 - ], - [ - 5329.235103638614, - 0.996389727684803 - ], - [ - 5469.478132681736, - 0.9683938807296764 - ], - [ - 5609.721161724857, - 0.9418989683135544 - ], - [ - 5749.964190767979, - 0.9160453644238671 - ], - [ - 5890.2072198111, - 0.8876865401535239 - ], - [ - 6030.450248854221, - 0.8535008760594034 - ], - [ - 6170.693277897342, - 0.8104428130742893 - ], - [ - 6310.936306940464, - 0.7557587707431485 - ], - [ - 6451.179335983586, - 0.6893616906110784 - ], - [ - 6591.422365026707, - 0.6144971365959591 - ], - [ - 6731.665394069829, - 0.5364393363547241 - ], - [ - 6871.90842311295, - 0.46067593277379615 - ], - [ - 7012.151452156071, - 0.39261800674477926 - ], - [ - 7152.394481199192, - 0.3370722706779246 - ], - [ - 7292.637510242314, - 0.2924221470955854 - ], - [ - 7432.880539285436, - 0.25174141876576106 - ], - [ - 7573.123568328557, - 0.20646696206623086 - ], - [ - 7713.366597371678, - 0.1471571010232553 - ], - [ - 7853.6096264148, - 0.06436834758818528 - ], - [ - 7993.852655457921, - -0.05136247264252298 - ], - [ - 8134.095684501042, - -0.206532958490399 - ], - [ - 8274.338713544164, - -0.39535316540719395 - ], - [ - 8414.581742587285, - -0.6069154374024224 - ], - [ - 8554.824771630407, - -0.8295461583033311 - ], - [ - 8695.067800673529, - -1.0511274302091203 - ], - [ - 8835.31082971665, - -1.259562218013097 - ], - [ - 8975.553858759771, - -1.442754366248941 - ], - [ - 9115.796887802893, - -1.589019612054042 - ], - [ - 9256.039916846014, - -1.6950274146648172 - ], - [ - 9396.282945889136, - -1.7682774732104025 - ], - [ - 9536.525974932258, - -1.8170491190062912 - ], - [ - 9676.769003975378, - -1.8497575418204875 - ], - [ - 9817.0120330185, - -1.8747602609194831 - ], - [ - 9957.25506206162, - -1.9003813407451866 - ], - [ - 10097.498091104742, - -1.9349449213537382 - ], - [ - 10237.741120147864, - -1.9867723930550407 - ], - [ - 10377.984149190985, - -2.06009846084533 - ], - [ - 10518.227178234107, - -2.1415140795802623 - ], - [ - 10658.470207277229, - -2.2110137019041307 - ], - [ - 10798.71323632035, - -2.2513625062255844 - ], - [ - 10938.956265363471, - -2.245064042951864 - ], - [ - 11079.199294406593, - -2.1852003620944305 - ], - [ - 11219.442323449714, - -2.089991213397594 - ], - [ - 11359.685352492836, - -1.9810967757323892 - ], - [ - 11499.928381535958, - -1.8773529127342583 - ], - [ - 11640.171410579078, - -1.7984391933472006 - ], - [ - 11780.4144396222, - -1.7615914592702657 - ], - [ - 11920.65746866532, - -1.7635774910136466 - ], - [ - 12060.900497708442, - -1.7930746923063956 - ], - [ - 12201.143526751564, - -1.839045074387219 - ], - [ - 12341.386555794685, - -1.8898356090765256 - ], - [ - 12481.629584837807, - -1.9319340163332581 - ], - [ - 12621.872613880929, - -1.952425866283592 - ], - [ - 12762.11564292405, - -1.9471527295791395 - ], - [ - 12902.358671967171, - -1.9174442682594002 - ], - [ - 13042.601701010293, - -1.8665056551977217 - ], - [ - 13182.844730053414, - -1.798927772440699 - ], - [ - 13323.087759096536, - -1.7208402952993263 - ], - [ - 13463.330788139658, - -1.6386013422971923 - ], - [ - 13603.573817182778, - -1.5583892074423877 - ], - [ - 13743.8168462259, - -1.4844298277033852 - ], - [ - 13884.059875269022, - -1.4190598242750128 - ] - ], - "16": [ - [ - 0.0, - 0.04389679600972524 - ], - [ - 140.24302904312142, - 0.4000884442116785 - ], - [ - 280.48605808624285, - 0.487004266971293 - ], - [ - 420.7290871293643, - 0.5788157432148089 - ], - [ - 560.9721161724857, - 0.6746680047910655 - ], - [ - 701.2151452156071, - 0.7541075970800121 - ], - [ - 841.4581742587286, - 0.8563057266496301 - ], - [ - 981.70120330185, - 0.9811533881244701 - ], - [ - 1121.9442323449714, - 1.1140986901099499 - ], - [ - 1262.1872613880928, - 1.250390414879604 - ], - [ - 1402.4302904312142, - 1.3673786460263933 - ], - [ - 1542.6733194743356, - 1.4430979876898646 - ], - [ - 1682.9163485174572, - 1.472751187250523 - ], - [ - 1823.1593775605786, - 1.4649783411667876 - ], - [ - 1963.4024066037, - 1.4472872084003157 - ], - [ - 2103.645435646821, - 1.4276475380559186 - ], - [ - 2243.888464689943, - 1.4010105444358798 - ], - [ - 2384.1314937330644, - 1.3755251791174457 - ], - [ - 2524.3745227761856, - 1.3598857623915268 - ], - [ - 2664.617551819307, - 1.35432770430462 - ], - [ - 2804.8605808624284, - 1.353786342621612 - ], - [ - 2945.10360990555, - 1.3525518956507874 - ], - [ - 3085.346638948671, - 1.3460134964465444 - ], - [ - 3225.589667991793, - 1.332392312653218 - ], - [ - 3365.8326970349144, - 1.312673096679605 - ], - [ - 3506.0757260780356, - 1.288205740036118 - ], - [ - 3646.318755121157, - 1.2600221873698148 - ], - [ - 3786.5617841642784, - 1.2286996915338912 - ], - [ - 3926.8048132074, - 1.1951162672018272 - ], - [ - 4067.047842250521, - 1.160010773284083 - ], - [ - 4207.290871293642, - 1.1239122175879193 - ], - [ - 4347.533900336764, - 1.0883416961134282 - ], - [ - 4487.776929379886, - 1.0549299428015535 - ], - [ - 4628.019958423007, - 1.0247760524751917 - ], - [ - 4768.262987466129, - 0.9991178906077681 - ], - [ - 4908.50601650925, - 0.9793120213693212 - ], - [ - 5048.749045552371, - 0.9646419859112952 - ], - [ - 5188.992074595492, - 0.9535235311112072 - ], - [ - 5329.235103638614, - 0.9440979751956858 - ], - [ - 5469.478132681736, - 0.934383282466193 - ], - [ - 5609.721161724857, - 0.9217975228597745 - ], - [ - 5749.964190767979, - 0.9036956257297881 - ], - [ - 5890.2072198111, - 0.8781090986860303 - ], - [ - 6030.450248854221, - 0.8436139070337132 - ], - [ - 6170.693277897342, - 0.7990808454051547 - ], - [ - 6310.936306940464, - 0.743785570575726 - ], - [ - 6451.179335983586, - 0.6796180310975987 - ], - [ - 6591.422365026707, - 0.6097637016328714 - ], - [ - 6731.665394069829, - 0.5380102393351543 - ], - [ - 6871.90842311295, - 0.4680847798438613 - ], - [ - 7012.151452156071, - 0.40362096484275634 - ], - [ - 7152.394481199192, - 0.34756208452788206 - ], - [ - 7292.637510242314, - 0.29723198646194665 - ], - [ - 7432.880539285436, - 0.24714716936030576 - ], - [ - 7573.123568328557, - 0.1914279276056072 - ], - [ - 7713.366597371678, - 0.12361108781744419 - ], - [ - 7853.6096264148, - 0.037196665916300185 - ], - [ - 7993.852655457921, - -0.07432967253051508 - ], - [ - 8134.095684501042, - -0.21444233149509595 - ], - [ - 8274.338713544164, - -0.37745907402983986 - ], - [ - 8414.581742587285, - -0.5563858665195882 - ], - [ - 8554.824771630407, - -0.7437451930085011 - ], - [ - 8695.067800673529, - -0.9323319245236491 - ], - [ - 8835.31082971665, - -1.1151390568866335 - ], - [ - 8975.553858759771, - -1.2851595369604212 - ], - [ - 9115.796887802893, - -1.4357278119172527 - ], - [ - 9256.039916846014, - -1.5661169587800599 - ], - [ - 9396.282945889136, - -1.6796976084147957 - ], - [ - 9536.525974932258, - -1.7782259864226069 - ], - [ - 9676.769003975378, - -1.8634872919859382 - ], - [ - 9817.0120330185, - -1.9369931412169032 - ], - [ - 9957.25506206162, - -2.0002309120879915 - ], - [ - 10097.498091104742, - -2.0546881162826627 - ], - [ - 10237.741120147864, - -2.10186028202087 - ], - [ - 10377.984149190985, - -2.1419131944178758 - ], - [ - 10518.227178234107, - -2.168616763949617 - ], - [ - 10658.470207277229, - -2.176601831607372 - ], - [ - 10798.71323632035, - -2.163990201980251 - ], - [ - 10938.956265363471, - -2.128150750587631 - ], - [ - 11079.199294406593, - -2.070773383301444 - ], - [ - 11219.442323449714, - -2.0042160989566535 - ], - [ - 11359.685352492836, - -1.9387120979800665 - ], - [ - 11499.928381535958, - -1.8817788952677992 - ], - [ - 11640.171410579078, - -1.8426826442018227 - ], - [ - 11780.4144396222, - -1.8296725834305436 - ], - [ - 11920.65746866532, - -1.8393170977814144 - ], - [ - 12060.900497708442, - -1.865260965356225 - ], - [ - 12201.143526751564, - -1.9019536790804799 - ], - [ - 12341.386555794685, - -1.942011692344406 - ], - [ - 12481.629584837807, - -1.9760684454512925 - ], - [ - 12621.872613880929, - -1.995010323399296 - ], - [ - 12762.11564292405, - -1.9954450207702703 - ], - [ - 12902.358671967171, - -1.9767576436829508 - ], - [ - 13042.601701010293, - -1.9397095347978057 - ], - [ - 13182.844730053414, - -1.887268571574505 - ], - [ - 13323.087759096536, - -1.8238112683668743 - ], - [ - 13463.330788139658, - -1.7539364533151633 - ], - [ - 13603.573817182778, - -1.6821268133473843 - ], - [ - 13743.8168462259, - -1.6116449444311889 - ], - [ - 13884.059875269022, - -1.5452277171733229 - ] - ], - "17": [ - [ - 0.0, - -0.23964453826954463 - ], - [ - 140.24302904312142, - 0.09184649039762131 - ], - [ - 280.48605808624285, - 0.1544946533195857 - ], - [ - 420.7290871293643, - 0.23224223698872984 - ], - [ - 560.9721161724857, - 0.3675125456004715 - ], - [ - 701.2151452156071, - 0.4609323706222131 - ], - [ - 841.4581742587286, - 0.4133471806850514 - ], - [ - 981.70120330185, - 0.792245321423583 - ], - [ - 1121.9442323449714, - 0.972779111161921 - ], - [ - 1262.1872613880928, - 1.0153989631211928 - ], - [ - 1402.4302904312142, - 1.166662558969932 - ], - [ - 1542.6733194743356, - 1.2753080958577365 - ], - [ - 1682.9163485174572, - 1.3018571117997588 - ], - [ - 1823.1593775605786, - 1.2942973188479676 - ], - [ - 1963.4024066037, - 1.2733765257107224 - ], - [ - 2103.645435646821, - 1.2390356746478368 - ], - [ - 2243.888464689943, - 1.2377348871569844 - ], - [ - 2384.1314937330644, - 1.220979398111075 - ], - [ - 2524.3745227761856, - 1.1828362019315846 - ], - [ - 2664.617551819307, - 1.1703816575603774 - ], - [ - 2804.8605808624284, - 1.1769963939926973 - ], - [ - 2945.10360990555, - 1.1899924024545057 - ], - [ - 3085.346638948671, - 1.197629162624078 - ], - [ - 3225.589667991793, - 1.1936038523897732 - ], - [ - 3365.8326970349144, - 1.1788856148482523 - ], - [ - 3506.0757260780356, - 1.1553037735706018 - ], - [ - 3646.318755121157, - 1.1248802989218218 - ], - [ - 3786.5617841642784, - 1.089646636752473 - ], - [ - 3926.8048132074, - 1.0514359939102964 - ], - [ - 4067.047842250521, - 1.0118988755551148 - ], - [ - 4207.290871293642, - 0.972456806496447 - ], - [ - 4347.533900336764, - 0.9353085434011028 - ], - [ - 4487.776929379886, - 0.9024695175420875 - ], - [ - 4628.019958423007, - 0.8750235669945396 - ], - [ - 4768.262987466129, - 0.8541129115029694 - ], - [ - 4908.50601650925, - 0.8408831017355648 - ], - [ - 5048.749045552371, - 0.8336492518810719 - ], - [ - 5188.992074595492, - 0.8304268574759367 - ], - [ - 5329.235103638614, - 0.8298153528719214 - ], - [ - 5469.478132681736, - 0.8303538321709333 - ], - [ - 5609.721161724857, - 0.8300737334041861 - ], - [ - 5749.964190767979, - 0.8281484315588531 - ], - [ - 5890.2072198111, - 0.8233781323634733 - ], - [ - 6030.450248854221, - 0.8108038585611981 - ], - [ - 6170.693277897342, - 0.7852351571103002 - ], - [ - 6310.936306940464, - 0.7418786803568567 - ], - [ - 6451.179335983586, - 0.6776542711444826 - ], - [ - 6591.422365026707, - 0.5926594038408569 - ], - [ - 6731.665394069829, - 0.5200708862542278 - ], - [ - 6871.90842311295, - 0.46666179739518526 - ], - [ - 7012.151452156071, - 0.42863297954778556 - ], - [ - 7152.394481199192, - 0.4014188054136778 - ], - [ - 7292.637510242314, - 0.37622677472726135 - ], - [ - 7432.880539285436, - 0.3428580872269346 - ], - [ - 7573.123568328557, - 0.2932903434587206 - ], - [ - 7713.366597371678, - 0.22355985430007091 - ], - [ - 7853.6096264148, - 0.13036445805412394 - ], - [ - 7993.852655457921, - 0.010415162928548358 - ], - [ - 8134.095684501042, - -0.13707882103853333 - ], - [ - 8274.338713544164, - -0.3067601179733232 - ], - [ - 8414.581742587285, - -0.49238131521517636 - ], - [ - 8554.824771630407, - -0.6876286853103617 - ], - [ - 8695.067800673529, - -0.887177167068746 - ], - [ - 8835.31082971665, - -1.0859164760921842 - ], - [ - 8975.553858759771, - -1.2787372935112398 - ], - [ - 9115.796887802893, - -1.460551292638408 - ], - [ - 9256.039916846014, - -1.6270601686246753 - ], - [ - 9396.282945889136, - -1.7740416777465966 - ], - [ - 9536.525974932258, - -1.8990451910531332 - ], - [ - 9676.769003975378, - -2.002173933305936 - ], - [ - 9817.0120330185, - -2.078623477222419 - ], - [ - 9957.25506206162, - -2.123224359205266 - ], - [ - 10097.498091104742, - -2.1308073110525374 - ], - [ - 10237.741120147864, - -2.0962477275285996 - ], - [ - 10377.984149190985, - -2.0203144877584953 - ], - [ - 10518.227178234107, - -1.9184094741198683 - ], - [ - 10658.470207277229, - -1.8110528139328668 - ], - [ - 10798.71323632035, - -1.7205422353104383 - ], - [ - 10938.956265363471, - -1.668309956780638 - ], - [ - 11079.199294406593, - -1.6647980117498558 - ], - [ - 11219.442323449714, - -1.701644451829843 - ], - [ - 11359.685352492836, - -1.7653253553513921 - ], - [ - 11499.928381535958, - -1.8420108215193352 - ], - [ - 11640.171410579078, - -1.9199009718689029 - ], - [ - 11780.4144396222, - -1.9897821688537436 - ], - [ - 11920.65746866532, - -2.05198882411778 - ], - [ - 12060.900497708442, - -2.1105753825106732 - ], - [ - 12201.143526751564, - -2.165705708763122 - ], - [ - 12341.386555794685, - -2.21425260642817 - ], - [ - 12481.629584837807, - -2.2514148819698048 - ], - [ - 12621.872613880929, - -2.271747568998351 - ], - [ - 12762.11564292405, - -2.2683062702472787 - ], - [ - 12902.358671967171, - -2.2360366257836755 - ], - [ - 13042.601701010293, - -2.1772906722474854 - ], - [ - 13182.844730053414, - -2.0976150631424066 - ], - [ - 13323.087759096536, - -2.0037181126271517 - ], - [ - 13463.330788139658, - -1.9025830080608264 - ], - [ - 13603.573817182778, - -1.8012200585471114 - ], - [ - 13743.8168462259, - -1.7068858506536744 - ], - [ - 13884.059875269022, - -1.623952364636244 - ] - ], - "18": [ - [ - 0.0, - -0.5421096370723323 - ], - [ - 140.24302904312142, - -0.26092173252786494 - ], - [ - 280.48605808624285, - -0.28820610221772824 - ], - [ - 420.7290871293643, - -0.2092155009715138 - ], - [ - 560.9721161724857, - -0.034825875977624474 - ], - [ - 701.2151452156071, - 0.16524908987224332 - ], - [ - 841.4581742587286, - 0.38750849272067783 - ], - [ - 981.70120330185, - 0.6082107495088895 - ], - [ - 1121.9442323449714, - 0.7997883533843819 - ], - [ - 1262.1872613880928, - 0.9577603921966691 - ], - [ - 1402.4302904312142, - 1.0772548392498578 - ], - [ - 1542.6733194743356, - 1.1492378361488842 - ], - [ - 1682.9163485174572, - 1.1722492416902732 - ], - [ - 1823.1593775605786, - 1.157662353933082 - ], - [ - 1963.4024066037, - 1.1305436778734366 - ], - [ - 2103.645435646821, - 1.1012392984680586 - ], - [ - 2243.888464689943, - 1.0680529012913105 - ], - [ - 2384.1314937330644, - 1.0360811289385707 - ], - [ - 2524.3745227761856, - 1.0134261133405496 - ], - [ - 2664.617551819307, - 1.0017393175057847 - ], - [ - 2804.8605808624284, - 0.997943443670933 - ], - [ - 2945.10360990555, - 0.9986528353898105 - ], - [ - 3085.346638948671, - 1.0006183352256557 - ], - [ - 3225.589667991793, - 1.0014768024335667 - ], - [ - 3365.8326970349144, - 0.9992032015764064 - ], - [ - 3506.0757260780356, - 0.9915738308166353 - ], - [ - 3646.318755121157, - 0.9764761176306604 - ], - [ - 3786.5617841642784, - 0.952949562321593 - ], - [ - 3926.8048132074, - 0.9228046283574789 - ], - [ - 4067.047842250521, - 0.8885893879142169 - ], - [ - 4207.290871293642, - 0.8527941078337328 - ], - [ - 4347.533900336764, - 0.819166157055396 - ], - [ - 4487.776929379886, - 0.7907813549090073 - ], - [ - 4628.019958423007, - 0.7695902437733569 - ], - [ - 4768.262987466129, - 0.7575087888895178 - ], - [ - 4908.50601650925, - 0.7557919953239772 - ], - [ - 5048.749045552371, - 0.7623706336296147 - ], - [ - 5188.992074595492, - 0.7738753642273954 - ], - [ - 5329.235103638614, - 0.7862551485802569 - ], - [ - 5469.478132681736, - 0.7954004478686115 - ], - [ - 5609.721161724857, - 0.7983501183712022 - ], - [ - 5749.964190767979, - 0.7952947714464896 - ], - [ - 5890.2072198111, - 0.7877539728454 - ], - [ - 6030.450248854221, - 0.7777986542140846 - ], - [ - 6170.693277897342, - 0.767898466721782 - ], - [ - 6310.936306940464, - 0.759596617467195 - ], - [ - 6451.179335983586, - 0.7489014209834266 - ], - [ - 6591.422365026707, - 0.7284476249320575 - ], - [ - 6731.665394069829, - 0.6915812690962849 - ], - [ - 6871.90842311295, - 0.631776018426837 - ], - [ - 7012.151452156071, - 0.5423301714575703 - ], - [ - 7152.394481199192, - 0.417812568060366 - ], - [ - 7292.637510242314, - 0.2609695500640291 - ], - [ - 7432.880539285436, - 0.08025910266402057 - ], - [ - 7573.123568328557, - -0.11577449896092451 - ], - [ - 7713.366597371678, - -0.3192495225613502 - ], - [ - 7853.6096264148, - -0.5221728358651136 - ], - [ - 7993.852655457921, - -0.7162706712235853 - ], - [ - 8134.095684501042, - -0.8930999491813469 - ], - [ - 8274.338713544164, - -1.0480755394609085 - ], - [ - 8414.581742587285, - -1.1801516493992084 - ], - [ - 8554.824771630407, - -1.2894684395166447 - ], - [ - 8695.067800673529, - -1.3782440146630808 - ], - [ - 8835.31082971665, - -1.4491082570554719 - ], - [ - 8975.553858759771, - -1.5046939437461564 - ], - [ - 9115.796887802893, - -1.5477670359145541 - ], - [ - 9256.039916846014, - -1.5826682778020353 - ], - [ - 9396.282945889136, - -1.6127889338693415 - ], - [ - 9536.525974932258, - -1.6429767380342744 - ], - [ - 9676.769003975378, - -1.680465555885852 - ], - [ - 9817.0120330185, - -1.723635098132372 - ], - [ - 9957.25506206162, - -1.770243028565291 - ], - [ - 10097.498091104742, - -1.8180471595197811 - ], - [ - 10237.741120147864, - -1.864789480338616 - ], - [ - 10377.984149190985, - -1.9068050368787004 - ], - [ - 10518.227178234107, - -1.9386505362058197 - ], - [ - 10658.470207277229, - -1.9602085820782025 - ], - [ - 10798.71323632035, - -1.9766233770524353 - ], - [ - 10938.956265363471, - -1.9955340502067422 - ], - [ - 11079.199294406593, - -2.0262060631217484 - ], - [ - 11219.442323449714, - -2.076317992755582 - ], - [ - 11359.685352492836, - -2.142448801050321 - ], - [ - 11499.928381535958, - -2.214012885935303 - ], - [ - 11640.171410579078, - -2.2788220879876566 - ], - [ - 11780.4144396222, - -2.324618372726963 - ], - [ - 11920.65746866532, - -2.3401408556874594 - ], - [ - 12060.900497708442, - -2.3241287915242803 - ], - [ - 12201.143526751564, - -2.285155293183301 - ], - [ - 12341.386555794685, - -2.231818195485357 - ], - [ - 12481.629584837807, - -2.1724065493665923 - ], - [ - 12621.872613880929, - -2.1148843840739118 - ], - [ - 12762.11564292405, - -2.066738617797497 - ], - [ - 12902.358671967171, - -2.0271547813002546 - ], - [ - 13042.601701010293, - -1.990913483694822 - ], - [ - 13182.844730053414, - -1.9549926908676474 - ], - [ - 13323.087759096536, - -1.916802530273236 - ], - [ - 13463.330788139658, - -1.8739920873680889 - ], - [ - 13603.573817182778, - -1.8242198355807004 - ], - [ - 13743.8168462259, - -1.7660545647257069 - ], - [ - 13884.059875269022, - -1.703040551565444 - ] - ], - "19": [ - [ - 0.0, - -0.8537097907021887 - ], - [ - 140.24302904312142, - -0.7521992810640642 - ], - [ - 280.48605808624285, - -0.9005794318896804 - ], - [ - 420.7290871293643, - -0.7092819429949696 - ], - [ - 560.9721161724857, - -0.32332672379453753 - ], - [ - 701.2151452156071, - 0.05089076794919283 - ], - [ - 841.4581742587286, - 0.37041635205385937 - ], - [ - 981.70120330185, - 0.6292762874893809 - ], - [ - 1121.9442323449714, - 0.8282450875623109 - ], - [ - 1262.1872613880928, - 0.9765488968017004 - ], - [ - 1402.4302904312142, - 1.077159546659897 - ], - [ - 1542.6733194743356, - 1.1337419079559763 - ], - [ - 1682.9163485174572, - 1.1491057939309977 - ], - [ - 1823.1593775605786, - 1.1253809292504282 - ], - [ - 1963.4024066037, - 1.0825537651735517 - ], - [ - 2103.645435646821, - 1.0365049405941775 - ], - [ - 2243.888464689943, - 0.989053883389407 - ], - [ - 2384.1314937330644, - 0.945184729413815 - ], - [ - 2524.3745227761856, - 0.9117344896564058 - ], - [ - 2664.617551819307, - 0.8893421515015981 - ], - [ - 2804.8605808624284, - 0.8756238846794606 - ], - [ - 2945.10360990555, - 0.8680725050959799 - ], - [ - 3085.346638948671, - 0.8637408812146462 - ], - [ - 3225.589667991793, - 0.8606418413945442 - ], - [ - 3365.8326970349144, - 0.8563215180846997 - ], - [ - 3506.0757260780356, - 0.8478037013490286 - ], - [ - 3646.318755121157, - 0.8325819982573301 - ], - [ - 3786.5617841642784, - 0.809399857571813 - ], - [ - 3926.8048132074, - 0.7797289621968181 - ], - [ - 4067.047842250521, - 0.7462258261156738 - ], - [ - 4207.290871293642, - 0.7115638889031083 - ], - [ - 4347.533900336764, - 0.6791536968166647 - ], - [ - 4487.776929379886, - 0.6525284558250161 - ], - [ - 4628.019958423007, - 0.6347366894958816 - ], - [ - 4768.262987466129, - 0.6286925265400333 - ], - [ - 4908.50601650925, - 0.6362471801664872 - ], - [ - 5048.749045552371, - 0.6560443112546368 - ], - [ - 5188.992074595492, - 0.6834120807112279 - ], - [ - 5329.235103638614, - 0.7121998324684707 - ], - [ - 5469.478132681736, - 0.7362464427168691 - ], - [ - 5609.721161724857, - 0.751169250315572 - ], - [ - 5749.964190767979, - 0.7559324172822558 - ], - [ - 5890.2072198111, - 0.7525632068733242 - ], - [ - 6030.450248854221, - 0.7441118249125309 - ], - [ - 6170.693277897342, - 0.7339588845779543 - ], - [ - 6310.936306940464, - 0.7239164772491742 - ], - [ - 6451.179335983586, - 0.7096678010588173 - ], - [ - 6591.422365026707, - 0.6847571675282489 - ], - [ - 6731.665394069829, - 0.6430286103220866 - ], - [ - 6871.90842311295, - 0.5790641710756101 - ], - [ - 7012.151452156071, - 0.48733863351435974 - ], - [ - 7152.394481199192, - 0.36453972431854564 - ], - [ - 7292.637510242314, - 0.21745896662260297 - ], - [ - 7432.880539285436, - 0.0534530120812544 - ], - [ - 7573.123568328557, - -0.12079832283390148 - ], - [ - 7713.366597371678, - -0.2993659683820724 - ], - [ - 7853.6096264148, - -0.476265310328653 - ], - [ - 7993.852655457921, - -0.6453420077236086 - ], - [ - 8134.095684501042, - -0.8031062044390582 - ], - [ - 8274.338713544164, - -0.9518651775075878 - ], - [ - 8414.581742587285, - -1.089829418864067 - ], - [ - 8554.824771630407, - -1.2156350312391095 - ], - [ - 8695.067800673529, - -1.3302313119197964 - ], - [ - 8835.31082971665, - -1.4350170923832555 - ], - [ - 8975.553858759771, - -1.531396363659935 - ], - [ - 9115.796887802893, - -1.620373118107646 - ], - [ - 9256.039916846014, - -1.6998709096298346 - ], - [ - 9396.282945889136, - -1.7689949325146035 - ], - [ - 9536.525974932258, - -1.8303163826345445 - ], - [ - 9676.769003975378, - -1.8845330235529583 - ], - [ - 9817.0120330185, - -1.9307198545868045 - ], - [ - 9957.25506206162, - -1.9677914656617765 - ], - [ - 10097.498091104742, - -1.994662390941023 - ], - [ - 10237.741120147864, - -2.010273134386783 - ], - [ - 10377.984149190985, - -2.0157366987585124 - ], - [ - 10518.227178234107, - -2.016057533627526 - ], - [ - 10658.470207277229, - -2.01533961411936 - ], - [ - 10798.71323632035, - -2.017989834763668 - ], - [ - 10938.956265363471, - -2.031176133920646 - ], - [ - 11079.199294406593, - -2.0586953195793374 - ], - [ - 11219.442323449714, - -2.098915984977363 - ], - [ - 11359.685352492836, - -2.1484255724901793 - ], - [ - 11499.928381535958, - -2.2033357656408996 - ], - [ - 11640.171410579078, - -2.257713566236685 - ], - [ - 11780.4144396222, - -2.3066961556881544 - ], - [ - 11920.65746866532, - -2.349313908439445 - ], - [ - 12060.900497708442, - -2.387650181646503 - ], - [ - 12201.143526751564, - -2.4211041521710706 - ], - [ - 12341.386555794685, - -2.4468495800209453 - ], - [ - 12481.629584837807, - -2.4616251618853617 - ], - [ - 12621.872613880929, - -2.461594002528968 - ], - [ - 12762.11564292405, - -2.4418134780735348 - ], - [ - 12902.358671967171, - -2.3970713135708337 - ], - [ - 13042.601701010293, - -2.3285785393732827 - ], - [ - 13182.844730053414, - -2.2418863474497948 - ], - [ - 13323.087759096536, - -2.1430184810436255 - ], - [ - 13463.330788139658, - -2.038277940364498 - ], - [ - 13603.573817182778, - -1.9339959466090721 - ], - [ - 13743.8168462259, - -1.8370946359257168 - ], - [ - 13884.059875269022, - -1.7531166288578541 - ] - ] - } -} \ No newline at end of file From 469e281aa092479a9fe4c9f14a06e875758137f5 Mon Sep 17 00:00:00 2001 From: MateusStano Date: Sun, 15 Jan 2023 13:43:33 -0300 Subject: [PATCH 60/68] ENH: class_inputs dict to one inputs_dict, fixes to __process methods, and extra fixes to parachute __process method --- rocketpy/Dispersion.py | 305 +++++++++++++++++++++-------------------- 1 file changed, 158 insertions(+), 147 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index 1cb2cc64f..0e97edf30 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -18,13 +18,13 @@ from matplotlib.patches import Ellipse from numpy.random import * +from .AeroSurfaces import EllipticalFins, NoseCone, Tail, TrapezoidalFins from .Environment import Environment from .Flight import Flight from .Function import Function from .Motor import SolidMotor from .Rocket import Rocket from .supplement import invertedHaversine -from .AeroSurfaces import NoseCone, TrapezoidalFins, EllipticalFins, Tail class Dispersion: @@ -71,95 +71,89 @@ def __init__( self.filename = filename # Initialize variables to be used in the analysis in case of missing inputs - self.environment_inputs = { - "railLength": "required", - "gravity": 9.80665, - "date": None, - "latitude": 0, - "longitude": 0, - "elevation": 0, - "datum": "WGS84", - "timeZone": "UTC", - } - - self.solid_motor_inputs = { - "thrust": "required", - "burnOutTime": "required", - "totalImpulse": 0, - "grainNumber": "required", - "grainDensity": "required", - "grainOuterRadius": "required", - "grainInitialInnerRadius": "required", - "grainInitialHeight": "required", - "grainSeparation": 0, - "nozzleRadius": 0.0335, - "throatRadius": 0.0114, - } - - self.rocket_inputs = { - "mass": "required", - "inertiaI": "required", - "inertiaZ": "required", - "radius": "required", - "distanceRocketNozzle": "required", - "distanceRocketPropellant": "required", - "powerOffDrag": "required", - "powerOnDrag": "required", - } - - self.nose_inputs = { - "nose_name_length": "required", - "nose_name_kind": "Von Karman", - "nose_name_distanceToCM": "required", - "nose_name_name": "Nose Cone", - } - - self.fins_inputs = { - "finSet_name_numberOfFins": "required", - "finSet_name_rootChord": "required", - "finSet_name_tipChord": "required", - "finSet_name_span": "required", - "finSet_name_distanceToCM": "required", - "finSet_name_cantAngle": 0, - "finSet_name_radius": None, - "finSet_name_airfoil": None, - } - - self.tail_inputs = { - "tail_name_topRadius": "required", - "tail_name_bottomRadius": "required", - "tail_name_length": "required", - "tail_name_distanceToCM": "required", - } - - self.rail_buttons_inputs = { - "positionFirstRailButton": "required", - "positionSecondRailButton": "required", - "railButtonAngularPosition": 45, - } - - self.parachute_inputs = { - "parachute_name_CdS": "required", - "parachute_name_trigger": "required", - "parachute_name_samplingRate": 100, - "parachute_name_lag": 0, - "parachute_name_noise": (0, 0, 0), - # "parachute_name_noiseStd": 0, - # "parachute_name_noiseCorr": 0, - } - - self.flight_inputs = { - "inclination": 80, - "heading": 90, - "initialSolution": None, - "terminateOnApogee": False, - "maxTime": 600, - "maxTimeStep": np.inf, - "minTimeStep": 0, - "rtol": 1e-6, - "atol": 6 * [1e-3] + 4 * [1e-6] + 3 * [1e-3], - "timeOvershoot": True, - "verbose": False, + self.inputs_dict = { + "environment": { + "railLength": "required", + "gravity": 9.80665, + "date": None, + "latitude": 0, + "longitude": 0, + "elevation": 0, + "datum": "WGS84", + "timeZone": "UTC", + }, + "solidmotor": { + "thrust": "required", + "burnOutTime": "required", + "totalImpulse": 0, + "grainNumber": "required", + "grainDensity": "required", + "grainOuterRadius": "required", + "grainInitialInnerRadius": "required", + "grainInitialHeight": "required", + "grainSeparation": 0, + "nozzleRadius": 0.0335, + "throatRadius": 0.0114, + }, + "rocket": { + "mass": "required", + "inertiaI": "required", + "inertiaZ": "required", + "radius": "required", + "distanceRocketNozzle": "required", + "distanceRocketPropellant": "required", + "powerOffDrag": "required", + "powerOnDrag": "required", + }, + "nose": { + "length": "required", + "kind": "Von Karman", + "distanceToCM": "required", + "name": "Nose Cone", + }, + "fins": { + "n": "required", + "rootChord": "required", + "tipChord": "required", + "span": "required", + "distanceToCM": "required", + "cantAngle": 0, + "radius": None, + "airfoil": None, + }, + "tail": { + "topRadius": "required", + "bottomRadius": "required", + "length": "required", + "distanceToCM": "required", + }, + "railbuttons": { + "positionFirstRailButton": "required", + "positionSecondRailButton": "required", + "railButtonAngularPosition": 45, + }, + "parachute": { + "CdS": "required", + "trigger": "required", + "samplingRate": 100, + "lag": 0, + "noise": (0, 0, 0), + # "noiseStd": 0, + # "noiseCorr": 0, + }, + "flight": { + "inclination": 80, + "heading": 90, + "initialSolution": None, + "terminateOnApogee": False, + "maxTime": 600, + "maxTimeStep": np.inf, + "minTimeStep": 0, + "rtol": 1e-6, + "atol": 6 * [1e-3] + 4 * [1e-6] + 3 * [1e-3], + "timeOvershoot": True, + "verbose": False, + }, } # Initialize variables so they can be accessed by MATLAB @@ -345,10 +339,13 @@ def __process_flight_from_dict(self, dictionary): # First check if all the inputs for the flight class are present in the # dictionary, if not, input the missing ones if not all( - flight_input in dictionary for flight_input in self.flight_inputs.keys() + flight_input in dictionary + for flight_input in self.inputs_dict["flight"].keys() ): # Iterate through missing inputs - for missing_input in set(self.flight_inputs.keys()) - dictionary.keys(): + for missing_input in ( + set(self.inputs_dict["flight"].keys()) - dictionary.keys() + ): missing_input = str(missing_input) # Add to the dict try: @@ -357,14 +354,16 @@ def __process_flight_from_dict(self, dictionary): except AttributeError: # Flight class was not inputted # check if missing parameter is required - if self.flight_inputs[missing_input] == "required": + if self.inputs_dict["flight"][missing_input] == "required": raise ValueError( "The input {} is required for the Flight class.".format( missing_input ) ) else: # if not required, uses default value - dictionary[missing_input] = [self.flight_inputs[missing_input]] + dictionary[missing_input] = [ + self.inputs_dict["flight"][missing_input] + ] return dictionary @@ -387,11 +386,16 @@ def __process_rocket_from_dict(self, dictionary): dictionary: dict Modified dictionary with the processed rocket parameters. """ + + # Checks if there are any missing rocket inputs in dictionary if not all( - rocket_input in dictionary for rocket_input in self.rocket_inputs.keys() + rocket_input in dictionary + for rocket_input in self.inputs_dict["rocket"].keys() ): # Iterate through missing inputs - for missing_input in set(self.rocket_inputs.keys()) - dictionary.keys(): + for missing_input in ( + set(self.inputs_dict["rocket"].keys()) - dictionary.keys() + ): missing_input = str(missing_input) # Add to the dict try: @@ -399,14 +403,16 @@ def __process_rocket_from_dict(self, dictionary): except AttributeError: # class was not inputted # checks if missing parameter is required - if self.rocket_inputs[missing_input] == "required": + if self.inputs_dict["rocket"][missing_input] == "required": raise ValueError( "The input {} is required for the Rocket class.".format( missing_input ) ) else: # if not, uses default value - dictionary[missing_input] = [self.rocket_inputs[missing_input]] + dictionary[missing_input] = [ + self.inputs_dict["rocket"][missing_input] + ] return dictionary @@ -432,11 +438,11 @@ def __process_rail_buttons_from_dict(self, dictionary): if not all( rail_buttons_input in dictionary - for rail_buttons_input in self.rail_buttons_inputs.keys() + for rail_buttons_input in self.inputs_dict["railbuttons"].keys() ): # Iterate through missing inputs for missing_input in ( - set(self.rail_buttons_inputs.keys()) - dictionary.keys() + set(self.inputs_dict["railbuttons"].keys()) - dictionary.keys() ): missing_input = str(missing_input) # Add to the dict @@ -445,7 +451,7 @@ def __process_rail_buttons_from_dict(self, dictionary): except AttributeError: # class was not inputted # checks if missing parameter is required - if self.rail_buttons_inputs[missing_input] == "required": + if self.inputs_dict["railbuttons"][missing_input] == "required": raise ValueError( "The input {} is required for the RailButtons class.".format( missing_input @@ -454,7 +460,7 @@ def __process_rail_buttons_from_dict(self, dictionary): else: # if not, uses default value dictionary[missing_input] = [ - self.rail_buttons_inputs[missing_input] + self.inputs_dict["railbuttons"][missing_input] ] return dictionary @@ -507,17 +513,16 @@ def __process_aerodynamic_surfaces_from_dict(self, dictionary): if surface.name == name and isinstance(surface, NoseCone): # in case we find the corresponding nose, check if all the # inputs are present in the dictionary - for input in self.nose_inputs.keys(): - _, _, parameter = input.split("_") - if f"nose_{name}_{parameter}" not in dictionary: + for input in self.inputs_dict["nose"].keys(): + if f"nose_{name}_{input}" not in dictionary: # Try to get the value from the rocket object try: dictionary[f"nose_{name}_{parameter}"] = [ getattr(surface, parameter) ] except AttributeError: - # If not possible, check if the parameter is required - if self.nose_inputs[input] == "required": + # If not possible, check if the input is required + if self.inputs_dict["nose"][input] == "required": raise ValueError( "The input {} is required for the NoseCone class.".format( input @@ -526,8 +531,8 @@ def __process_aerodynamic_surfaces_from_dict(self, dictionary): else: # If not required, use default value - dictionary[f"nose_{name}_{parameter}"] = [ - self.nose_inputs[input] + dictionary[f"nose_{name}_{input}"] = [ + self.inputs_dict["nose"][input] ] # Iterate through fin sets names @@ -539,17 +544,16 @@ def __process_aerodynamic_surfaces_from_dict(self, dictionary): ): # in case we find the corresponding fin set, check if all the # inputs are present in the dictionary - for input in self.fins_inputs.keys(): - _, _, parameter = input.split("_") - if f"finSet_{name}_{parameter}" not in dictionary: + for input in self.inputs_dict["fins"].keys(): + if f"finSet_{name}_{input}" not in dictionary: # Try to get the value from the rocket object try: dictionary[f"finSet_{name}_{parameter}"] = [ getattr(surface, parameter) ] except AttributeError: - # If not possible, check if the parameter is required - if self.fins_inputs[input] == "required": + # If not possible, check if the input is required + if self.inputs_dict["fins"][input] == "required": raise ValueError( "The input {} is required for the Fins class.".format( input @@ -557,8 +561,8 @@ def __process_aerodynamic_surfaces_from_dict(self, dictionary): ) else: # If not required, use default value - dictionary[f"finSet_{name}_{parameter}"] = [ - self.fins_inputs[input] + dictionary[f"finSet_{name}_{input}"] = [ + self.inputs_dict["fins"][input] ] # Iterate through tail names @@ -568,17 +572,16 @@ def __process_aerodynamic_surfaces_from_dict(self, dictionary): if surface.name == name and isinstance(surface, Tail): # in case we find the corresponding tail, check if all the # inputs are present in the dictionary - for input in self.tail_inputs.keys(): - _, _, parameter = input.split("_") - if f"tail_{name}_{parameter}" not in dictionary: + for input in self.inputs_dict["tail"].keys(): + if f"tail_{name}_{input}" not in dictionary: # Try to get the value from the rocket object try: dictionary[f"tail_{name}_{parameter}"] = [ getattr(surface, parameter) ] except AttributeError: - # If not possible, check if the parameter is required - if self.tail_inputs[input] == "required": + # If not possible, check if the input is required + if self.inputs_dict["tail"][input] == "required": raise ValueError( "The input {} is required for the Tail class.".format( input @@ -586,8 +589,8 @@ def __process_aerodynamic_surfaces_from_dict(self, dictionary): ) else: # If not required, use default value - dictionary[f"tail_{name}_{parameter}"] = [ - self.tail_inputs[input] + dictionary[f"tail_{name}_{input}"] = [ + self.inputs_dict["tail"][input] ] return dictionary @@ -614,11 +617,12 @@ def __process_motor_from_dict(self, dictionary): # TODO: Add more options of motor (i.e. Liquid and Hybrids) if not all( - motor_input in dictionary for motor_input in self.solid_motor_inputs.keys() + motor_input in dictionary + for motor_input in self.inputs_dict["solidmotor"].keys() ): # Iterate through missing inputs for missing_input in ( - set(self.solid_motor_inputs.keys()) - dictionary.keys() + set(self.inputs_dict["solidmotor"].keys()) - dictionary.keys() ): missing_input = str(missing_input) # Add to the dict @@ -627,7 +631,7 @@ def __process_motor_from_dict(self, dictionary): except AttributeError: # class was not inputted # checks if missing parameter is required - if self.solid_motor_inputs[missing_input] == "required": + if self.inputs_dict["solidmotor"][missing_input] == "required": raise ValueError( "The input {} is required for the SolidMotor class.".format( missing_input @@ -635,7 +639,7 @@ def __process_motor_from_dict(self, dictionary): ) else: # if not uses default value dictionary[missing_input] = [ - self.solid_motor_inputs[missing_input] + self.inputs_dict["solidmotor"][missing_input] ] return dictionary @@ -661,11 +665,11 @@ def __process_environment_from_dict(self, dictionary): # Check if there is any missing input for the environment if not all( environment_input in dictionary - for environment_input in self.environment_inputs.keys() + for environment_input in self.inputs_dict["environment"].keys() ): # Iterate through missing inputs for missing_input in ( - set(self.environment_inputs.keys()) - dictionary.keys() + set(self.inputs_dict["environment"].keys()) - dictionary.keys() ): missing_input = str(missing_input) # Add to the dict @@ -677,7 +681,7 @@ def __process_environment_from_dict(self, dictionary): except AttributeError: # class was not inputted # checks if missing parameter is required - if self.environment_inputs[missing_input] == "required": + if self.inputs_dict["environment"][missing_input] == "required": raise ValueError( "The input {} is required for the Environment class.".format( missing_input @@ -685,7 +689,7 @@ def __process_environment_from_dict(self, dictionary): ) else: # if not required, use default value dictionary[missing_input] = [ - self.environment_inputs[missing_input] + self.inputs_dict["environment"][missing_input] ] return dictionary @@ -718,25 +722,32 @@ def __process_parachute_from_dict(self, dictionary): # Check if there is enough arguments for defining each parachute for name in self.parachute_names: - for parachute_input in self.parachute_inputs.keys(): - _, _, parameter = parachute_input.split("_") - if "parachute_{}_{}".format(name, parameter) not in dictionary.keys(): + for parachute_input in self.inputs_dict["parachute"].keys(): + if ( + "parachute_{}_{}".format(name, parachute_input) + not in dictionary.keys() + ): try: # Try to get the value from the Parachute object - for chute in self.rocket.parachutes: - if getattr(chute, "name") == name: - dictionary[ - "parachute_{}_{}".format(name, parameter) - ] = [getattr(chute, parameter)] - except AttributeError: # Class not passed - if self.parachute_inputs[parachute_input] == "required": + if len(self.rocket.parachutes) > 0: + for chute in self.rocket.parachutes: + if getattr(chute, "name") == name: + dictionary[ + "parachute_{}_{}".format(name, parachute_input) + ] = [getattr(chute, parachute_input)] + else: + raise Exception + except Exception: # Class not passed + if self.inputs_dict["parachute"][parachute_input] == "required": raise ValueError( "The input {} is required for the Parachute class.".format( parachute_input ) ) else: - dictionary["parachute_{}_{}".format(name, parameter)] = [ - self.parachute_inputs[parachute_input], + dictionary[ + "parachute_{}_{}".format(name, parachute_input) + ] = [ + self.inputs_dict["parachute"][parachute_input], ] return dictionary @@ -780,7 +791,7 @@ def __check_inputted_values_from_dict(self, dictionary): ) ## Second corrections - Environment - if parameter_key in self.environment_inputs.keys(): + elif parameter_key in self.inputs_dict["environment"].keys(): try: dictionary[parameter_key] = ( getattr(self.environment, parameter_key), @@ -796,7 +807,7 @@ def __check_inputted_values_from_dict(self, dictionary): ) ## Third corrections - SolidMotor - elif parameter_key in self.solid_motor_inputs.keys(): + elif parameter_key in self.inputs_dict["solidmotor"].keys(): try: dictionary[parameter_key] = ( getattr(self.motor, parameter_key), @@ -812,7 +823,7 @@ def __check_inputted_values_from_dict(self, dictionary): ) # Fourth correction - Rocket - elif parameter_key in self.rocket_inputs.keys(): + elif parameter_key in self.inputs_dict["rocket"].keys(): try: dictionary[parameter_key] = ( getattr(self.rocket, parameter_key), @@ -828,7 +839,7 @@ def __check_inputted_values_from_dict(self, dictionary): ) # Fifth correction - Flight - elif parameter_key in self.flight_inputs.keys(): + elif parameter_key in self.inputs_dict["flight"].keys(): try: dictionary[parameter_key] = ( getattr(self.flight, parameter_key), From 304de074fd7ee0897b719dc6be5883612b4c645c Mon Sep 17 00:00:00 2001 From: MateusStano Date: Sun, 15 Jan 2023 14:08:45 -0300 Subject: [PATCH 61/68] ENH: change __export_flight_data add exportable_list, create __check_export_list --- rocketpy/Dispersion.py | 58 +++++++++++++++++++++++++++++++++++++++--- 1 file changed, 54 insertions(+), 4 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index 0e97edf30..fad9fb421 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -155,7 +155,29 @@ def __init__( "verbose": False, }, } - + self.exportable_list = [ + "apogee", + "apogeeTime", + "apogeeX", + "apogeeY", + "executionTime", + "finalStaticMargin", + "frontalSurfaceWind", + "impactVelocity", + "initialStaticMargin", + "lateralSurfaceWind", + "maxAcceleration", + "maxAccelerationTime", + "maxSpeed", + "maxSpeedTime", + "numberOfEvents", + "outOfRailStaticMargin", + "outOfRailTime", + "outOfRailVelocity", + "tFinal", + "xImpact", + "yImpact", + ] # Initialize variables so they can be accessed by MATLAB self.dispersion_results = {} self.dispersion_dictionary = {} @@ -954,6 +976,35 @@ def __yield_flight_setting( # Yield a flight setting yield flight_setting + def __check_export_list(self, export_list): + """Check if export list is valid or if it is None. In case it is + None, export all possible attributes. + + Parameters + ---------- + export_list : list + List of strings with the names of the attributes to be exported + + Returns + ------- + export_list + """ + + if export_list: + for attr in export_list: + if not isinstance(attr, str): + raise TypeError("Variables must be strings.") + + # Checks if attribute is not valid + if attr not in self.export_list: + raise ValueError( + "Attribute can not be exported. Check export_list." + ) + else: + export_list = self.exportable_list + + return export_list + def __export_flight_data( self, flight_setting, @@ -961,7 +1012,6 @@ def __export_flight_data( exec_time, dispersion_input_file, dispersion_output_file, - variables=None, ): """Saves flight results in a .txt @@ -1013,13 +1063,13 @@ def __export_flight_data( raise TypeError("Variables must be strings.") # First, capture the flight data that are saved in the flight object - attributes_list = list(set(dir(flight)).intersection(variables)) + attributes_list = list(set(dir(flight)).intersection(self.export_list)) flight_result = {} for var in attributes_list: flight_result[str(var)] = getattr(flight, var) # Second, capture data that needs to be calculated - for var in list(set(variables) - set(attributes_list)): + for var in list(set(self.export_list) - set(attributes_list)): if var == "executionTime": flight_result[str(var)] = exec_time elif var == "numberOfEvents": From 398c4539438fe493b886ca5faa489822eeb5eaa9 Mon Sep 17 00:00:00 2001 From: MateusStano Date: Sun, 15 Jan 2023 14:13:18 -0300 Subject: [PATCH 62/68] BUG: Aerosurfaces dictionary check --- rocketpy/Dispersion.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index fad9fb421..6b02ce846 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -539,8 +539,8 @@ def __process_aerodynamic_surfaces_from_dict(self, dictionary): if f"nose_{name}_{input}" not in dictionary: # Try to get the value from the rocket object try: - dictionary[f"nose_{name}_{parameter}"] = [ - getattr(surface, parameter) + dictionary[f"nose_{name}_{input}"] = [ + getattr(surface, input) ] except AttributeError: # If not possible, check if the input is required @@ -570,8 +570,8 @@ def __process_aerodynamic_surfaces_from_dict(self, dictionary): if f"finSet_{name}_{input}" not in dictionary: # Try to get the value from the rocket object try: - dictionary[f"finSet_{name}_{parameter}"] = [ - getattr(surface, parameter) + dictionary[f"finSet_{name}_{input}"] = [ + getattr(surface, input) ] except AttributeError: # If not possible, check if the input is required @@ -598,8 +598,8 @@ def __process_aerodynamic_surfaces_from_dict(self, dictionary): if f"tail_{name}_{input}" not in dictionary: # Try to get the value from the rocket object try: - dictionary[f"tail_{name}_{parameter}"] = [ - getattr(surface, parameter) + dictionary[f"tail_{name}_{input}"] = [ + getattr(surface, input) ] except AttributeError: # If not possible, check if the input is required From b0086b25c263d920015327fdaae4e126bd1a18de Mon Sep 17 00:00:00 2001 From: MateusStano Date: Sun, 15 Jan 2023 14:19:38 -0300 Subject: [PATCH 63/68] ENH: add try except in __create_initial_objects --- rocketpy/Dispersion.py | 98 ++++++++++++++++++++++++++---------------- 1 file changed, 60 insertions(+), 38 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index 6b02ce846..54b5b5cd0 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -881,7 +881,7 @@ def __check_inputted_values_from_dict(self, dictionary): return dictionary - def __check_initial_objects(self): + def __create_initial_objects(self): """Create rocketpy objects (Environment, Motor, Rocket, Flight) in case that they were not created yet. @@ -890,45 +890,67 @@ def __check_initial_objects(self): None """ if self.environment is None: - self.environment = Environment( - railLength=self.dispersion_dictionary["railLength"][0] - ) + try: + self.environment = Environment( + railLength=self.dispersion_dictionary["railLength"][0] + ) + except: + raise TypeError( + "Cannot define basic Environment. Missing railLength value in dictionary" + ) if self.motor is None: - self.motor = SolidMotor( - thrustSource=self.dispersion_dictionary["thrustSource"][0], - burnOut=self.dispersion_dictionary["burnOutTime"][0], - grainNumber=self.dispersion_dictionary["grainNumber"][0], - grainDensity=self.dispersion_dictionary["grainDensity"][0], - grainOuterRadius=self.dispersion_dictionary["grainOuterRadius"][0], - grainInitialInnerRadius=self.dispersion_dictionary[ - "grainInitialInnerRadius" - ][0], - grainInitialHeight=self.dispersion_dictionary["grainInitialHeight"][0], - ) + try: + self.motor = SolidMotor( + thrustSource=self.dispersion_dictionary["thrustSource"][0], + burnOut=self.dispersion_dictionary["burnOutTime"][0], + grainNumber=self.dispersion_dictionary["grainNumber"][0], + grainDensity=self.dispersion_dictionary["grainDensity"][0], + grainOuterRadius=self.dispersion_dictionary["grainOuterRadius"][0], + grainInitialInnerRadius=self.dispersion_dictionary[ + "grainInitialInnerRadius" + ][0], + grainInitialHeight=self.dispersion_dictionary["grainInitialHeight"][ + 0 + ], + ) + except: + raise TypeError( + "Cannot define basic SolidMotor. Missing required parameters in dictionary" + ) if self.rocket is None: - self.rocket = Rocket( - motor=self.motor, - mass=self.dispersion_dictionary["mass"][0], - radius=self.dispersion_dictionary["radius"][0], - inertiaI=self.dispersion_dictionary["inertiaI"][0], - inertiaZ=self.dispersion_dictionary["inertiaZ"][0], - distanceRocketPropellant=self.dispersion_dictionary[ - "distanceRocketPropellant" - ][0], - distanceRocketNozzle=self.dispersion_dictionary["distanceRocketNozzle"][ - 0 - ], - powerOffDrag=self.dispersion_dictionary["powerOffDrag"][0], - powerOnDrag=self.dispersion_dictionary["powerOnDrag"][0], - ) - self.rocket.setRailButtons(distanceToCM=[0.2, -0.5]) + try: + self.rocket = Rocket( + motor=self.motor, + mass=self.dispersion_dictionary["mass"][0], + radius=self.dispersion_dictionary["radius"][0], + inertiaI=self.dispersion_dictionary["inertiaI"][0], + inertiaZ=self.dispersion_dictionary["inertiaZ"][0], + distanceRocketPropellant=self.dispersion_dictionary[ + "distanceRocketPropellant" + ][0], + distanceRocketNozzle=self.dispersion_dictionary[ + "distanceRocketNozzle" + ][0], + powerOffDrag=self.dispersion_dictionary["powerOffDrag"][0], + powerOnDrag=self.dispersion_dictionary["powerOnDrag"][0], + ) + self.rocket.setRailButtons(distanceToCM=[0.2, -0.5]) + except: + raise TypeError( + "Cannot define basic Rocket and add rail buttons. Missing required parameters in dictionary" + ) if self.flight is None: - self.flight = Flight( - rocket=self.rocket, - environment=self.environment, - inclination=self.dispersion_dictionary["inclination"][0], - heading=self.dispersion_dictionary["heading"][0], - ) + try: + self.flight = Flight( + rocket=self.rocket, + environment=self.environment, + inclination=self.dispersion_dictionary["inclination"][0], + heading=self.dispersion_dictionary["heading"][0], + ) + except: + raise TypeError( + "Cannot define basic Flight. Missing required parameters in dictionary" + ) return None def __yield_flight_setting( @@ -1173,7 +1195,7 @@ def run_dispersion( # Check if there's enough object to start a flight: ## Raise an error in case of any troubles - self.__check_initial_objects() + self.__create_initial_objects() # Creates copy of dispersion_dictionary that will be altered modified_dispersion_dict = {i: j for i, j in dispersion_dictionary.items()} From f2eb098b73d67a2befdeb29e5574d184ad803867 Mon Sep 17 00:00:00 2001 From: MateusStano Date: Sun, 15 Jan 2023 14:48:13 -0300 Subject: [PATCH 64/68] ENH: adapted run_dispersion and deleted few unecessary and slow lines --- rocketpy/Dispersion.py | 46 +++++++++++++++++++++++------------------- 1 file changed, 25 insertions(+), 21 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index 54b5b5cd0..cae9602cd 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -1143,7 +1143,8 @@ def run_dispersion( flight=None, motor=None, rocket=None, - exported_variables=None, + distribution_type="normal", + export_list=None, append=False, ): """Runs the given number of simulations and saves the data @@ -1183,15 +1184,20 @@ def run_dispersion( # Saving the arguments as attributes self.number_of_simulations = number_of_simulations self.dispersion_dictionary = dispersion_dictionary - self.environment = environment - self.motor = motor - self.rocket = rocket if flight: # In case a flight object is passed self.environment = flight.env self.motor = flight.rocket.motor self.rocket = flight.rocket - self.flight = flight - self.distribution_type = "normal" # TODO: Must be parametrized + self.flight = flight + if rocket: + self.rocket = rocket + self.motor = rocket.motor + if motor: + self.motor = motor + if environment: + self.environment = environment + + self.distribution_type = distribution_type # Check if there's enough object to start a flight: ## Raise an error in case of any troubles @@ -1211,6 +1217,18 @@ def run_dispersion( dispersion_input_file = open(f"{self.filename}.disp_inputs.txt", open_mode) dispersion_output_file = open(f"{self.filename}.disp_outputs.txt", open_mode) + # Checks export_list + self.export_list = self.__check_export_list(export_list) + + # Creates a copy of the environment + env_dispersion = self.environment + + # Creates copy of motor + motor_dispersion = self.motor + + # Creates copy of rocket + rocket_dispersion = self.rocket + # Initialize counter and timer i = 0 initial_wall_time = time() @@ -1224,9 +1242,6 @@ def run_dispersion( self.start_time = process_time() i += 1 - # Creates a copy of the environment - env_dispersion = self.environment - # Apply environment parameters variations on each iteration if possible env_dispersion.railLength = setting["railLength"] env_dispersion.gravity = setting["gravity"] @@ -1237,9 +1252,6 @@ def run_dispersion( if env_dispersion.atmosphericModelType in ["Ensemble", "Reanalysis"]: env_dispersion.selectEnsembleMember(setting["ensembleMember"]) - # Creates copy of motor - motor_dispersion = self.motor - # Apply motor parameters variations on each iteration if possible # TODO: add hybrid and liquid motor option motor_dispersion = SolidMotor( @@ -1256,9 +1268,6 @@ def run_dispersion( reshapeThrustCurve=(setting["burnOutTime"], setting["totalImpulse"]), ) - # Creates copy of rocket - rocket_dispersion = self.rocket - # Apply rocket parameters variations on each iteration if possible rocket_dispersion = Rocket( motor=motor_dispersion, @@ -1272,9 +1281,6 @@ def run_dispersion( powerOnDrag=setting["powerOnDrag"], ) - # Clean up aerodynamic surfaces - rocket_dispersion.aerodynamicSurfaces = [] # Remove all surfaces - # Add rocket nose, fins and tail # Nose for nose in self.nose_names: @@ -1289,12 +1295,11 @@ def run_dispersion( for finSet in self.finSet_names: # TODO: Allow elliptical fins as well rocket_dispersion.addTrapezoidalFins( - n=setting[f"finSet_{finSet}_numberOfFins"], + n=setting[f"finSet_{finSet}_n"], rootChord=setting[f"finSet_{finSet}_rootChord"], tipChord=setting[f"finSet_{finSet}_tipChord"], span=setting[f"finSet_{finSet}_span"], distanceToCM=setting[f"finSet_{finSet}_distanceToCM"], - radius=setting[f"finSet_{finSet}_radius"], airfoil=setting[f"finSet_{finSet}_airfoil"], name=finSet, ) @@ -1354,7 +1359,6 @@ def run_dispersion( exec_time=process_time() - self.start_time, dispersion_input_file=dispersion_input_file, dispersion_output_file=dispersion_output_file, - variables=exported_variables, ) except Exception as E: print(E) From 88aeadb75ba3e706fafd2a2683b0d2cb0b24f9ab Mon Sep 17 00:00:00 2001 From: MateusStano Date: Sun, 15 Jan 2023 14:49:22 -0300 Subject: [PATCH 65/68] ENH: remove uncessary check --- rocketpy/Dispersion.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index cae9602cd..a9980f3b8 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -306,11 +306,6 @@ def __process_dispersion_dict(self, dictionary): dictionary: dict The modified dictionary with the processed parameters. """ - # First we need to check if the dictionary is empty - if not dictionary: - raise ValueError( - "The dispersion dictionary is empty. no dispersion can be performed" - ) # Now we prepare all the parachute data dictionary = self.__process_parachute_from_dict(dictionary) From 3aab22cc96d3d0ea799c64111d56914fc7f14028 Mon Sep 17 00:00:00 2001 From: MateusStano Date: Sun, 15 Jan 2023 14:51:45 -0300 Subject: [PATCH 66/68] MAINT: improved/added docs --- rocketpy/Dispersion.py | 101 ++++++++++++++++++++++++++++++++--------- 1 file changed, 79 insertions(+), 22 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index a9980f3b8..994f950a9 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -38,13 +38,43 @@ class Dispersion: The name of the file containing the data to be used in the analysis. Attributes - ---------- # TODO: add "Dispersion" at the beginning of each attribute - filename : string - The name of the file containing the data to be used in the analysis. - num_of_loaded_sims : int - The number of simulations loaded from the file. - num_of_sims : int - The number of simulations to be performed. + ---------- # TODO: finish documentation + Dispersion.filename : string + Directory and name of dispersion files. When running a new simulation, + this parameter represents the initial part of the export filenames + (e.g. 'filename.disp_outputs.txt'). When analyzing the results of a + previous simulation, this parameter shall be the .txt filename containing + the outputs of a previous ran dispersion analysis. + Dispersion.inputs_dict : dict + Contains information regarding the input arguments of the + classes. Its keys refers to each of the classes that must be defined during + the simulation. Its values are dictionaries where the keys are the input + arguments of each class and the values are either the string "required" + (meaning it is not an optional argument) or the default value if that argument + is optional. + Dispersion.dispersion_results : dict + Holds dispersion results. + Dispersion.dispersion_dictionary : dict + Contains inputs to run dispersion + Dispersion.nose_names = [] + Dispersion.finSet_names = [] + Dispersion.tail_names = [] + Dispersion.parachute_names = [] + Dispersion.distributionFunc = None + Dispersion.distribution_type = None + Dispersion.environment = None + Dispersion.flight = None + Dispersion.motor = None + Dispersion.rocket = None + Dispersion.rocket_dispersion = None + Dispersion.number_of_simulations = 0 + Dispersion.num_of_loaded_sims = 0 + Dispersion.start_time = 0 + + Dispersion.num_of_loaded_sims : int + The number of simulations loaded from the file. + Dispersion.num_of_sims : int + The number of simulations to be performed. """ def __init__( @@ -522,7 +552,6 @@ def __process_aerodynamic_surfaces_from_dict(self, dictionary): self.tail_names = list(set(self.tail_names)) # Check if there are enough arguments for each kind of aero surface - # Iterate through nose names for name in self.nose_names: # Iterate through aerodynamic surface available at rocket object @@ -1142,31 +1171,52 @@ def run_dispersion( export_list=None, append=False, ): - """Runs the given number of simulations and saves the data + """Runs the dispersion simulation and saves all data. For the simulation to be run + all classes must be defined. This can happen either trough the dispersion_dictionary + or by inputing objects Parameters ---------- number_of_simulations : int - Number of simulations desired, must be non negative. - This is needed when running a new simulation. Default is zero. + Number of simulations to be run, must be non negative. dispersion_dictionary : dict - The dictionary with the parameters to be analyzed. This includes the - mean and standard deviation of the parameters. + The dictionary with the parameters to be analyzed. The keys must be the + names of the attributes that will be used in the dispersion simulation. + The values can either be a tuple, containing the nominal values of that + parameter and its standard deviation, a list, containing the possible + values to be randomly chosen in each simulation, or a single value (int + or float), being the standard deviation of that parameter. See example + for further explanations. environment : Environment, optional - The environment object. Default is None. + Environment object that will be used in the simulations. Default is None. + If none, environment must be defined via passing its attributes in the + dispersion_dictionary. Arguments related to environment will only vary + according to the distribution method if the standard deviation for the + desired attributes are on the dispersion_dictionary. flight : Flight, optional - Original rocket's flight with nominal values. Parameter needed to run - a new flight simulation when environment, motor and rocket remain - unchanged. By default None. + Flight object that will be used in the simulations. Default is None. + If none, Flight must be defined via passing its attributes in the + dispersion_dictionary. Arguments related to Flight will only vary + according to the distribution method if the standard deviation for the + desired attributes are on the dispersion_dictionary. motor : Motor, optional - The motor object of the rocket. Default is None. + Motor object that will be used in the simulations. Default is None. + If none, Motor must be defined via passing its attributes in the + dispersion_dictionary. Arguments related to Motor will only vary + according to the distribution method if the standard deviation for the + desired attributes are on the dispersion_dictionary. rocket : Rocket, optional - The rocket object. Default is None. + Rocket object that will be used in the simulations. Default is None. + If none, Rocket must be defined via passing its attributes in the + dispersion_dictionary. Arguments related to Rocket will only vary + according to the distribution method if the standard deviation for the + desired attributes are on the dispersion_dictionary. distribution_type : str, optional The probability distribution function to be used in the analysis, - by default "normal" - exported_variables : list, optional - A list containing the variables to be exported. By default None. + by default "normal". Options are any numpy.ramdom distributions + export_list : list, optional + A list containing the name of the attributes to be saved on the dispersion + outputs file. See Examples for all possible attribues append : bool, optional If True, the results will be appended to the existing files. If False, the files will be overwritten. By default False. @@ -1174,6 +1224,13 @@ def run_dispersion( Returns ------- None + + Examples + -------- + + TODO: add list of all possible attributes in dispersion_dictionaries and + all possible attributes in export_list + """ # Saving the arguments as attributes From 5de9043e86cc1fd6d31356f99f8eb8c04833196d Mon Sep 17 00:00:00 2001 From: MateusStano Date: Sun, 15 Jan 2023 14:53:21 -0300 Subject: [PATCH 67/68] MAINT: add TODOs --- rocketpy/Dispersion.py | 35 +++++------------------------------ 1 file changed, 5 insertions(+), 30 deletions(-) diff --git a/rocketpy/Dispersion.py b/rocketpy/Dispersion.py index 994f950a9..c94efcfdf 100644 --- a/rocketpy/Dispersion.py +++ b/rocketpy/Dispersion.py @@ -185,6 +185,7 @@ def __init__( "verbose": False, }, } + # TODO: add all exportable attributes to this list self.exportable_list = [ "apogee", "apogeeTime", @@ -1078,35 +1079,9 @@ def __export_flight_data( ------- None """ - - # In case not variables are passed, export default variables - if not isinstance(variables, list): - variables = [ - "apogee", - "apogeeTime", - "apogeeX", - "apogeeY", - "executionTime", - "finalStaticMargin", - "frontalSurfaceWind", - "impactVelocity", - "initialStaticMargin", - "lateralSurfaceWind", - "maxAcceleration", - "maxAccelerationTime", - "maxSpeed", - "maxSpeedTime", - "numberOfEvents", - "outOfRailStaticMargin", - "outOfRailTime", - "outOfRailVelocity", - "tFinal", - "xImpact", - "yImpact", - ] - else: # Check if variables are valid and raise error if not - if not all([isinstance(var, str) for var in variables]): - raise TypeError("Variables must be strings.") + # TODO: This method is called at every loop of the dispersion + # so all the for loops are slowing down de dispersion + # find a more efficient way to save attributes # First, capture the flight data that are saved in the flight object attributes_list = list(set(dir(flight)).intersection(self.export_list)) @@ -1345,7 +1320,7 @@ def run_dispersion( # Fins for finSet in self.finSet_names: - # TODO: Allow elliptical fins as well + # TODO: Allow elliptical fins rocket_dispersion.addTrapezoidalFins( n=setting[f"finSet_{finSet}_n"], rootChord=setting[f"finSet_{finSet}_rootChord"], From eba00606c7b07b128ed3c1abdef20911d4e0c3d2 Mon Sep 17 00:00:00 2001 From: MateusStano Date: Sun, 15 Jan 2023 14:56:47 -0300 Subject: [PATCH 68/68] ENH: necessary notebook changes --- .../dispersion_class_usage.ipynb | 656 ++---------------- 1 file changed, 45 insertions(+), 611 deletions(-) diff --git a/docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb b/docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb index c232cf1ef..41278e098 100644 --- a/docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb +++ b/docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -49,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -96,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -116,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -138,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -147,57 +147,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Launch Site Details\n", - "\n", - "Launch Rail Length: 5.2 m\n", - "Launch Date: 2022-11-04 12:00:00 UTC\n", - "Launch Site Latitude: 39.38970°\n", - "Launch Site Longitude: -8.28896°\n", - "Reference Datum: SIRGAS2000\n", - "Launch Site UTM coordinates: 44415.43 W 4373388.31 N\n", - "Launch Site UTM zone: 30S\n", - "Launch Site Surface Elevation: 113.0 m\n", - "\n", - "\n", - "Atmospheric Model Details\n", - "\n", - "Atmospheric Model Type: StandardAtmosphere\n", - "StandardAtmosphere Maximum Height: 80.000 km\n", - "\n", - "\n", - "Surface Atmospheric Conditions\n", - "\n", - "Surface Wind Speed: 0.00 m/s\n", - "Surface Wind Direction: 0.00°\n", - "Surface Wind Heading: 0.00°\n", - "Surface Pressure: 999.75 hPa\n", - "Surface Temperature: 287.42 K\n", - "Surface Air Density: 1.212 kg/m³\n", - "Surface Speed of Sound: 339.83 m/s\n", - "\n", - "\n", - "Atmospheric Model Plots\n" - ] - }, - { - "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:00:45.307220\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "Env.info()\n" ] @@ -219,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -240,37 +192,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Motor Details\n", - "Total Burning Time: 3.9 s\n", - "Total Propellant Mass: 2.956 kg\n", - "Propellant Exhaust Velocity: 2038.745 m/s\n", - "Average Thrust: 1545.218 N\n", - "Maximum Thrust: 2200.0 N at 0.15 s after ignition.\n", - "Total Impulse: 6026.350 Ns\n", - "\n", - "Plots\n" - ] - }, - { - "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:00:47.734733\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "Pro75M1670.info()\n" ] @@ -284,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -322,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -361,45 +285,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Inertia Details\n", - "Rocket Dry Mass: 16.241 kg (No Propellant)\n", - "Rocket Total Mass: 19.196911961392022 kg (With Propellant)\n", - "\n", - "Geometrical Parameters\n", - "Rocket Radius: 0.0635 m\n", - "\n", - "Aerodynamics Stability\n", - "Initial Static Margin: 2.051 c\n", - "Final Static Margin: 3.090 c\n", - "\n", - "Main Parachute\n", - "CdS Coefficient: 10.0 m2\n", - "\n", - "Drogue Parachute\n", - "CdS Coefficient: 1.0 m2\n", - "\n", - "Aerodynamics Plots\n" - ] - }, - { - "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:00:48.760950\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "Calisto.info()\n" ] @@ -413,7 +301,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -434,21 +322,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:00:50.603632\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "TestFlight.plot3dTrajectory()\n" ] @@ -471,7 +347,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -489,10 +365,11 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "#TODO: explain that this is std only\n", "disp_dictionary = {\n", " # Solid Motor Parameters\n", " \"burnOutTime\": 0.2,\n", @@ -521,19 +398,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Completed 50 iterations successfully. Total CPU time: 90.40625 s. Total wall time: 98.26740670204163 s'" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "TestDispersion.run_dispersion(\n", " number_of_simulations=50,\n", @@ -567,60 +434,18 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "A total of 50 simulations were loaded from the following file: dispersion_analysis_outputs/disp_class_example.disp_outputs.txt\n" - ] - } - ], + "outputs": [], "source": [ "TestDispersion.import_results()\n" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "outOfRailStaticMargin: μ = 2.133, σ = 0.006\n", - "tFinal: μ = 262.715, σ = 12.630\n", - "finalStaticMargin: μ = 2.679, σ = 0.007\n", - "yImpact: μ = -537.050, σ = 96.246\n", - "initialStaticMargin: μ = 2.051, σ = 0.007\n", - "apogeeY: μ = -460.407, σ = 85.017\n", - "lateralSurfaceWind: μ = 0.000, σ = 0.000\n", - "apogee: μ = 3199.872, σ = 130.593\n", - "maxAccelerationTime: μ = 0.474, σ = 0.472\n", - "maxSpeed: μ = 281.146, σ = 9.272\n", - "xImpact: μ = 574.929, σ = 103.509\n", - "maxSpeedTime: μ = 3.370, σ = 0.014\n", - "apogeeTime: μ = 24.869, σ = 0.431\n", - "impactVelocity: μ = -5.152, σ = 0.248\n", - "maxAcceleration: μ = 101.210, σ = 3.712\n", - "apogeeX: μ = 492.929, σ = 91.671\n", - "outOfRailVelocity: μ = 25.537, σ = 0.441\n", - "outOfRailTime: μ = 0.366, σ = 0.006\n", - "frontalSurfaceWind: μ = 0.000, σ = 0.000\n", - "numberOfEvents: μ = 2.000, σ = 0.000\n", - "executionTime: μ = 1.547, σ = 0.258\n", - "Drogue_triggerTime: μ = 24.874, σ = 0.431\n", - "Drogue_inflatedTime: μ = 26.364, σ = 0.446\n", - "Drogue_inflatedVelocity: μ = 28.374, σ = 3.780\n", - "Main_triggerTime: μ = 173.166, σ = 10.765\n", - "Main_inflatedTime: μ = 174.681, σ = 10.744\n", - "Main_inflatedVelocity: μ = 16.680, σ = 0.773\n" - ] - } - ], + "outputs": [], "source": [ "TestDispersion.print_results()\n" ] @@ -634,356 +459,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Monte Carlo Simulation by RocketPy\n", - "Data Source: dispersion_analysis_outputs/disp_class_example\n", - "Number of simulations: 50\n", - "Results: \n", - "outOfRailStaticMargin: μ = 2.133, σ = 0.006\n", - "tFinal: μ = 262.715, σ = 12.630\n", - "finalStaticMargin: μ = 2.679, σ = 0.007\n", - "yImpact: μ = -537.050, σ = 96.246\n", - "initialStaticMargin: μ = 2.051, σ = 0.007\n", - "apogeeY: μ = -460.407, σ = 85.017\n", - "lateralSurfaceWind: μ = 0.000, σ = 0.000\n", - "apogee: μ = 3199.872, σ = 130.593\n", - "maxAccelerationTime: μ = 0.474, σ = 0.472\n", - "maxSpeed: μ = 281.146, σ = 9.272\n", - "xImpact: μ = 574.929, σ = 103.509\n", - "maxSpeedTime: μ = 3.370, σ = 0.014\n", - "apogeeTime: μ = 24.869, σ = 0.431\n", - "impactVelocity: μ = -5.152, σ = 0.248\n", - "maxAcceleration: μ = 101.210, σ = 3.712\n", - "apogeeX: μ = 492.929, σ = 91.671\n", - "outOfRailVelocity: μ = 25.537, σ = 0.441\n", - "outOfRailTime: μ = 0.366, σ = 0.006\n", - "frontalSurfaceWind: μ = 0.000, σ = 0.000\n", - "numberOfEvents: μ = 2.000, σ = 0.000\n", - "executionTime: μ = 1.547, σ = 0.258\n", - "Drogue_triggerTime: μ = 24.874, σ = 0.431\n", - "Drogue_inflatedTime: μ = 26.364, σ = 0.446\n", - "Drogue_inflatedVelocity: μ = 28.374, σ = 3.780\n", - "Main_triggerTime: μ = 173.166, σ = 10.765\n", - "Main_inflatedTime: μ = 174.681, σ = 10.744\n", - "Main_inflatedVelocity: μ = 16.680, σ = 0.773\n", - "Plotting results: \n" - ] - }, - { - "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:32.767107\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:33.918449\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:34.439390\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:35.037885\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:35.730770\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:36.226720\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjIAAAGzCAYAAAA1yP25AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy89olMNAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA350lEQVR4nO3dd3hUVf7H8c8khBBKAiGhBCO9g1RBiiwIGpEV2bWAi0IQERUFDFLyQ3pJQClLEZdVyroqigi60qQpiiBSQpceQTAUAwnNEJLz+8Mn8zCkMIMzzNzwfj3PPDLnnnvne+ZMko+3jc0YYwQAAGBBft4uAAAA4FYRZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAfUaFCBUVHR3u7jHzvzTffVKVKleTv76/69et7uxwAfxJBBvCAefPmyWazacuWLTkub926terUqfOnX2fZsmUaOXLkn97OneKrr77SoEGD1KJFC82dO1fjx4/3dkk+Y+PGjfLz81NsbGyOyydMmCCbzaalS5fe5sqAvBXwdgEA/rB//375+bn2/xbLli3TzJkzCTNOWrt2rfz8/PTee++pYMGC3i7HpzRr1ky9e/fWpEmT9Mwzz6h27dr2ZT///LNGjx6tJ598Uh06dPBilUB27JEBfERgYKACAgK8XYZLLl265O0SXHL69GkFBQURYnIRHx+vsLAw9e7dW9d/n/Crr76qgIAA/fOf//RidUDOCDKAj7jxHJn09HSNGjVKVatWVaFChVSyZEm1bNlSq1atkiRFR0dr5syZkiSbzWZ/ZLl06ZIGDBigyMhIBQYGqnr16nrrrbd04xfeX7lyRX379lVYWJiKFSumjh076sSJE7LZbA57ekaOHCmbzaa9e/fqH//4h0qUKKGWLVtKknbu3Kno6GhVqlRJhQoVUpkyZfTcc8/pt99+c3itrG0cOHBAzzzzjEJCQhQeHq5hw4bJGKPjx4/rscceU3BwsMqUKaNJkyY59d5du3ZNY8aMUeXKlRUYGKgKFSro//7v/5SWlmbvY7PZNHfuXF26dMn+Xs2bNy/XbX777bd68skndffddyswMFCRkZF67bXXdOXKFYd+0dHRKlq0qI4cOaKoqCgVKVJEERERGj16dLb32t1zIkknTpzQc889p9KlSyswMFC1a9fWnDlzso0nLS1NI0aMUJUqVezjGTRokMN7FBISon/+85/asGGD3n33XUnS4sWL9b///U/x8fEqW7ZsnvMAeAOHlgAPSklJ0dmzZ7O1p6en33TdkSNHKi4uTs8//7yaNGmi1NRUbdmyRdu2bdODDz6o3r176+TJk1q1apXef/99h3WNMerYsaPWrVunnj17qn79+lq5cqUGDhyoEydOaMqUKfa+0dHR+uSTT/Tss8/qvvvu0zfffJPn4YMnn3xSVatW1fjx4+1/gFetWqUjR46oR48eKlOmjPbs2aPZs2drz5492rRpk0PAkqTOnTurZs2aio+P19KlSzV27FiFhobqX//6lx544AFNmDBBH3zwgV5//XXde++9atWqVZ7v1fPPP6/58+friSee0IABA/TDDz8oLi5O+/bt0+LFiyVJ77//vmbPnq3Nmzfb/0g3b948120uXLhQly9f1ksvvaSSJUtq8+bNmj59un755RctXLjQoW9GRoYefvhh3XfffZo4caJWrFihESNG6Nq1axo9erTH5uTUqVO67777ZLPZ9Morryg8PFzLly9Xz549lZqaqv79+0uSMjMz1bFjR3333Xd64YUXVLNmTe3atUtTpkzRgQMHtGTJEof57dChgwYPHqy2bduqX79+at68uXr37p3nHABeYwC43dy5c42kPB+1a9d2WKd8+fKme/fu9uf16tUzHTp0yPN1+vTpY3L6MV6yZImRZMaOHevQ/sQTTxibzWYOHTpkjDFm69atRpLp37+/Q7/o6GgjyYwYMcLeNmLECCPJPP3009le7/Lly9naPvroIyPJrF+/Pts2XnjhBXvbtWvXzF133WVsNpuJj4+3t587d84EBQU5vCc5SUhIMJLM888/79D++uuvG0lm7dq19rbu3bubIkWK5Lm9vMYUFxdnbDab+fnnnx22Kcm8+uqr9rbMzEzToUMHU7BgQXPmzBljjGfmpGfPnqZs2bLm7NmzDn27dOliQkJC7GN4//33jZ+fn/n2228d+r3zzjtGktmwYYNDe2JioilSpIgJDQ01AQEBZteuXXm+V4A3cWgJ8KCZM2dq1apV2R733HPPTdctXry49uzZo4MHD7r8usuWLZO/v7/69u3r0D5gwAAZY7R8+XJJ0ooVKyRJL7/8skO/V199Nddtv/jii9nagoKC7P/+/fffdfbsWd13332SpG3btmXr//zzz9v/7e/vr8aNG8sYo549e9rbixcvrurVq+vIkSO51iL9MVZJiomJcWgfMGCAJN3yVTbXj+nSpUs6e/asmjdvLmOMtm/fnq3/K6+8Yv931h6Sq1evavXq1fY63TknxhgtWrRIjz76qIwxOnv2rP0RFRWllJQU+3u/cOFC1axZUzVq1HDo98ADD0iS1q1b57Dt8uXLa8SIEUpOTlZMTIxbrrADPIVDS4AHNWnSRI0bN87WXqJEiRwPOV1v9OjReuyxx1StWjXVqVNHDz/8sJ599lmnQtDPP/+siIgIFStWzKG9Zs2a9uVZ//Xz81PFihUd+lWpUiXXbd/YV5KSk5M1atQoLViwQKdPn3ZYlpKSkq3/3Xff7fA8JCREhQoVUlhYWLb2G8+zuVHWGG6suUyZMipevLh9rK46duyYhg8fri+++ELnzp1zWHbjmPz8/FSpUiWHtmrVqkmSEhMT7XW6c07OnDmj8+fPa/bs2Zo9e3aOY8iai4MHD2rfvn0KDw/Ps9/17r33XknK8fML+BKCDOCjWrVqpcOHD+vzzz/XV199pXfffVdTpkzRO++847BH43a7fk9Flqeeekrff/+9Bg4cqPr166to0aLKzMzUww8/rMzMzGz9/f39nWqTlO1E2NzceB7On5GRkaEHH3xQycnJGjx4sGrUqKEiRYroxIkTio6OznFMt1tWDc8884y6d++eY5+s0JuZmam6detq8uTJOfaLjIz0TJHAbUCQAXxYaGioevTooR49eujixYtq1aqVRo4caQ8yuf3xLl++vFavXq0LFy447AH46aef7Muz/puZmamjR4+qatWq9n6HDh1yusZz585pzZo1GjVqlIYPH25vv5VDYrciawwHDx60792Q/jgR9vz58/axumLXrl06cOCA5s+fr27dutnbs64Yu1FmZqaOHDli3wsjSQcOHJD0x9VoWXW6c07Cw8NVrFgxZWRkqF27dnmOp3LlytqxY4fatm3r1sAH+ALOkQF81I2HVIoWLaoqVao4XC5bpEgRSdL58+cd+j7yyCPKyMjQjBkzHNqnTJkim82m9u3bS5KioqIkSW+//bZDv+nTpztdZ9aelBv3nEydOtXpbfwZjzzySI6vl7X34VZu4JbTmIwxed5H5fr32hijGTNmKCAgQG3btrXX6c458ff31+OPP65FixZp9+7d2eo5c+aM/d9PPfWUTpw4oX//+9/Z+l25csVy9wMCrsceGcBH1apVS61bt1ajRo0UGhqqLVu26NNPP3U4qbRRo0aSpL59+yoqKkr+/v7q0qWLHn30UbVp00ZDhw5VYmKi6tWrp6+++kqff/65+vfvr8qVK9vXf/zxxzV16lT99ttv9kt9s/YmOPN/78HBwWrVqpUmTpyo9PR0lStXTl999ZWOHj3qgXclu3r16ql79+6aPXu2zp8/r7/85S/avHmz5s+fr06dOqlNmzYub7NGjRqqXLmyXn/9dZ04cULBwcFatGhRtnNlshQqVEgrVqxQ9+7d1bRpUy1fvlxLly7V//3f/9nPS/HEnMTHx2vdunVq2rSpevXqpVq1aik5OVnbtm3T6tWrlZycLEl69tln9cknn+jFF1/UunXr1KJFC2VkZOinn37SJ598opUrV3IuDKzLS1dLAfla1uXXP/74Y47L//KXv9z08uuxY8eaJk2amOLFi5ugoCBTo0YNM27cOHP16lV7n2vXrplXX33VhIeHG5vN5nAp9oULF8xrr71mIiIiTEBAgKlatap58803TWZmpsPrXrp0yfTp08eEhoaaokWLmk6dOpn9+/cbSQ6XQ2ddOp11OfH1fvnlF/O3v/3NFC9e3ISEhJgnn3zSnDx5MtdLuG/cRm6XRef0PuUkPT3djBo1ylSsWNEEBASYyMhIExsba37//XenXicne/fuNe3atTNFixY1YWFhplevXmbHjh1Gkpk7d262bR4+fNg89NBDpnDhwqZ06dJmxIgRJiMjw2Gb7p4TY4w5deqU6dOnj4mMjDQBAQGmTJkypm3btmb27NkO/a5evWomTJhgateubQIDA02JEiVMo0aNzKhRo0xKSkq28a9bt85IMgsXLnTq/QK8xWaMk2fSAbhjJCQkqEGDBvrvf/+rrl27erscnxYdHa1PP/1UFy9e9OjrMCdAzjhHBrjD3XjLfemP8038/PxuekddeAZzAjiPc2SAO9zEiRO1detWtWnTRgUKFNDy5cu1fPlyvfDCC1yW6yXMCeA8ggxwh2vevLlWrVqlMWPG6OLFi7r77rs1cuRIDR061Nul3bGYE8B5nCMDAAAsi3NkAACAZRFkAACAZeX7c2QyMzN18uRJFStWjFtzAwBgEcYYXbhwQREREfLzy32/S74PMidPnuQsfwAALOr48eO66667cl2e74NM1pezHT9+XMHBwV6uBgAAOCM1NVWRkZEOX7Kak3wfZLIOJwUHBxNkAACwmJudFsLJvgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIKeLsAAL6jwpCl3i7BZYnxHbxdAgAvYo8MAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLK8GmfXr1+vRRx9VRESEbDablixZYl+Wnp6uwYMHq27duipSpIgiIiLUrVs3nTx50nsFAwAAn+LVIHPp0iXVq1dPM2fOzLbs8uXL2rZtm4YNG6Zt27bps88+0/79+9WxY0cvVAoAAHxRAW++ePv27dW+ffscl4WEhGjVqlUObTNmzFCTJk107Ngx3X333Tmul5aWprS0NPvz1NRU9xUMAAB8iqXOkUlJSZHNZlPx4sVz7RMXF6eQkBD7IzIy8vYVCAAAbivLBJnff/9dgwcP1tNPP63g4OBc+8XGxiolJcX+OH78+G2sEgAA3E5ePbTkrPT0dD311FMyxmjWrFl59g0MDFRgYOBtqgwAAHiTzweZrBDz888/a+3atXnujQEAAHcWnw4yWSHm4MGDWrdunUqWLOntkgAAgA/xapC5ePGiDh06ZH9+9OhRJSQkKDQ0VGXLltUTTzyhbdu26csvv1RGRoaSkpIkSaGhoSpYsKC3ygYAAD7Cq0Fmy5YtatOmjf15TEyMJKl79+4aOXKkvvjiC0lS/fr1HdZbt26dWrdufbvKBAAAPsqrQaZ169YyxuS6PK9lAAAAlrn8GgAA4EYEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFkEGQAAYFleDTLr16/Xo48+qoiICNlsNi1ZssRhuTFGw4cPV9myZRUUFKR27drp4MGD3ikWAAD4HK8GmUuXLqlevXqaOXNmjssnTpyoadOm6Z133tEPP/ygIkWKKCoqSr///vttrhQAAPiiAt588fbt26t9+/Y5LjPGaOrUqXrjjTf02GOPSZL+85//qHTp0lqyZIm6dOlyO0sFAAA+yGfPkTl69KiSkpLUrl07e1tISIiaNm2qjRs35rpeWlqaUlNTHR4AACB/8uoembwkJSVJkkqXLu3QXrp0afuynMTFxWnUqFEerQ2A76gwZKm3S3BZYnwHb5cA5Bs+u0fmVsXGxiolJcX+OH78uLdLAgAAHuKzQaZMmTKSpFOnTjm0nzp1yr4sJ4GBgQoODnZ4AACA/Mlng0zFihVVpkwZrVmzxt6WmpqqH374Qc2aNfNiZQAAwFd49RyZixcv6tChQ/bnR48eVUJCgkJDQ3X33Xerf//+Gjt2rKpWraqKFStq2LBhioiIUKdOnbxXNAAA8BkuB5lt27YpICBAdevWlSR9/vnnmjt3rmrVqqWRI0eqYMGCTm9ry5YtatOmjf15TEyMJKl79+6aN2+eBg0apEuXLumFF17Q+fPn1bJlS61YsUKFChVytWwAAJAPuXxoqXfv3jpw4IAk6ciRI+rSpYsKFy6shQsXatCgQS5tq3Xr1jLGZHvMmzdPkmSz2TR69GglJSXp999/1+rVq1WtWjVXSwYAAPmUy0HmwIEDql+/viRp4cKFatWqlT788EPNmzdPixYtcnd9AAAAuXI5yBhjlJmZKUlavXq1HnnkEUlSZGSkzp49697qAAAA8uBykGncuLHGjh2r999/X9988406dPjjxk5Hjx7NdvM6AAAAT3I5yEydOlXbtm3TK6+8oqFDh6pKlSqSpE8//VTNmzd3e4EAAAC5cfmqpXvuuUe7du3K1v7mm2/K39/fLUUBAAA445ZuiHf+/Hm9++67io2NVXJysiRp7969On36tFuLAwAAyIvLe2R27typtm3bqnjx4kpMTFSvXr0UGhqqzz77TMeOHdN//vMfT9QJAACQjct7ZGJiYtSjRw8dPHjQ4cZ0jzzyiNavX+/W4gAAAPLicpD58ccf1bt372zt5cqVU1JSkluKAgAAcIbLQSYwMFCpqanZ2g8cOKDw8HC3FAUAAOAMl4NMx44dNXr0aKWnp0v642sEjh07psGDB+vxxx93e4EAAAC5cTnITJo0SRcvXlSpUqV05coV/eUvf1GVKlVUrFgxjRs3zhM1AgAA5Mjlq5ZCQkK0atUqbdiwQTt27NDFixfVsGFDtWvXzhP1AQAA5MrlIJOlRYsWatGihTtrAQAAcInLh5b69u2radOmZWufMWOG+vfv746aAAAAnOJykFm0aFGOe2KaN2+uTz/91C1FAQAAOMPlIPPbb78pJCQkW3twcLDOnj3rlqIAAACc4XKQqVKlilasWJGtffny5apUqZJbigIAAHCGyyf7xsTE6JVXXtGZM2f0wAMPSJLWrFmjSZMmaerUqe6uDwAAIFcuB5nnnntOaWlpGjdunMaMGSNJqlChgmbNmqVu3bq5vUAAAIDc3NLl1y+99JJeeuklnTlzRkFBQSpatKi76wIAALipW76PjCS+WwkAAHiVyyf7njp1Ss8++6wiIiJUoEAB+fv7OzwAAABuF5f3yERHR+vYsWMaNmyYypYtK5vN5om6AAAAbsrlIPPdd9/p22+/Vf369T1QDgAAgPNcPrQUGRkpY4wnagEAAHCJy0Fm6tSpGjJkiBITEz1QDgAAgPNcPrTUuXNnXb58WZUrV1bhwoUVEBDgsDw5OdltxQEAAOTF5SDD3XsBAICvcDnIdO/e3RN1AAAAuMzlc2Qk6fDhw3rjjTf09NNP6/Tp05L++NLIPXv2uLU4AACAvLgcZL755hvVrVtXP/zwgz777DNdvHhRkrRjxw6NGDHC7QUCAADkxuUgM2TIEI0dO1arVq1SwYIF7e0PPPCANm3a5NbiAAAA8uJykNm1a5f+9re/ZWsvVaqUzp4965aiAAAAnOFykClevLh+/fXXbO3bt29XuXLl3FIUAACAM1wOMl26dNHgwYOVlJQkm82mzMxMbdiwQa+//rq6devmiRoBAABy5HKQGT9+vGrUqKHIyEhdvHhRtWrVUqtWrdS8eXO98cYbnqgRAAAgRy7dR8YYo6SkJE2bNk3Dhw/Xrl27dPHiRTVo0EBVq1b1VI0AAAA5cjnIVKlSRXv27FHVqlUVGRnpqboAAABuyqVDS35+fqpatap+++03T9UDAADgNJfPkYmPj9fAgQO1e/duT9QDAADgNJe/a6lbt266fPmy6tWrp4IFCyooKMhhOd9+DQAAbhe+/RoAAFiWS0EmPT1d33zzjYYNG6aKFSt6qia7jIwMjRw5Uv/973+VlJSkiIgIRUdH64033pDNZvP46wMAAN/m0jkyAQEBWrRokadqyWbChAmaNWuWZsyYoX379mnChAmaOHGipk+ffttqAAAAvsvlk307deqkJUuWeKCU7L7//ns99thj6tChgypUqKAnnnhCDz30kDZv3nxbXh8AAPg2l8+RqVq1qkaPHq0NGzaoUaNGKlKkiMPyvn37uq245s2ba/bs2Tpw4ICqVaumHTt26LvvvtPkyZNzXSctLU1paWn256mpqW6rBwAA+BaXg8x7772n4sWLa+vWrdq6davDMpvN5tYgM2TIEKWmpqpGjRry9/dXRkaGxo0bp65du+a6TlxcnEaNGuW2GgAA1lRhyFJvl+CyxPgO3i7BclwOMkePHvVEHTn65JNP9MEHH+jDDz9U7dq1lZCQoP79+ysiIkLdu3fPcZ3Y2FjFxMTYn6empnIHYgAA8imXg8ztNHDgQA0ZMkRdunSRJNWtW1c///yz4uLicg0ygYGBCgwMvJ1lAgAAL3E5yDz33HN5Lp8zZ84tF3Ojy5cvy8/P8Xxkf39/ZWZmuu01AACAdbkcZM6dO+fwPD09Xbt379b58+f1wAMPuK0wSXr00Uc1btw43X333apdu7a2b9+uyZMn3zRMAQCAO4PLQWbx4sXZ2jIzM/XSSy+pcuXKbikqy/Tp0zVs2DC9/PLLOn36tCIiItS7d28NHz7cra8DAACsyS3nyPj5+SkmJkatW7fWoEGD3LFJSVKxYsU0depUvhYBAADkyOUb4uXm8OHDunbtmrs2BwAAcFMu75G5/tJmSTLG6Ndff9XSpUtzvZIIAADAE1wOMtu3b3d47ufnp/DwcE2aNImTcAEAwG3lcpBZt26dJ+oAAABwmcvnyBw9elQHDx7M1n7w4EElJia6oyYAAACnuBxkoqOj9f3332dr/+GHHxQdHe2OmgAAAJzicpDZvn27WrRoka39vvvuU0JCgjtqAgAAcIrLQcZms+nChQvZ2lNSUpSRkeGWogAAAJzhcpBp1aqV4uLiHEJLRkaG4uLi1LJlS7cWBwAAkBeXr1qaMGGCWrVqperVq+v++++XJH377bdKTU3V2rVr3V4gAABAblzeI1OrVi3t3LlTTz31lE6fPq0LFy6oW7du+umnn1SnTh1P1AgAAJCjW/qupYiICI0fP97dtQAAALjE5T0yc+fO1cKFC7O1L1y4UPPnz3dLUQAAAM5wOcjExcUpLCwsW3upUqXYSwMAAG4rl4PMsWPHVLFixWzt5cuX17Fjx9xSFAAAgDNcDjKlSpXSzp07s7Xv2LFDJUuWdEtRAAAAznA5yDz99NPq27ev1q1bp4yMDGVkZGjt2rXq16+funTp4okaAQAAcuTyVUtjxoxRYmKi2rZtqwIF/lg9MzNT3bp14xwZAABwW7kcZAoWLKiPP/5YY8aM0Y4dOxQUFKS6deuqfPnynqgPAAAgV7d0HxlJCg0NVZs2bXK8ggkAAOB2cOkcmfPnz6tPnz4KCwtT6dKlVbp0aYWFhemVV17R+fPnPVQiAABAzpzeI5OcnKxmzZrpxIkT6tq1q2rWrClJ2rt3r+bNm6c1a9bo+++/V4kSJTxWLAAAwPWcDjKjR49WwYIFdfjwYZUuXTrbsoceekijR4/WlClT3F4kAABATpw+tLRkyRK99dZb2UKMJJUpU0YTJ07U4sWL3VocAABAXpwOMr/++qtq166d6/I6deooKSnJLUUBAAA4w+kgExYWpsTExFyXHz16VKGhoe6oCQAAwClOB5moqCgNHTpUV69ezbYsLS1Nw4YN08MPP+zW4gAAAPLi0sm+jRs3VtWqVdWnTx/VqFFDxhjt27dPb7/9ttLS0vT+++97slYAAAAHTgeZu+66Sxs3btTLL7+s2NhYGWMkSTabTQ8++KBmzJihyMhIjxUKAABwI5fu7FuxYkUtX75c586d08GDByVJVapU4dwYAADgFbf0FQUlSpRQkyZN3F0LAACAS1z6igIAAABfQpABAACWRZABAACW5VSQadiwoc6dOyfpj8uwL1++7NGiAAAAnOFUkNm3b58uXbokSRo1apQuXrzo0aIAAACc4dRVS/Xr11ePHj3UsmVLGWP01ltvqWjRojn2HT58uFsLBAAAyI1TQWbevHkaMWKEvvzyS9lsNi1fvlwFCmRf1WazEWQAAMBt41SQqV69uhYsWCBJ8vPz05o1a1SqVCmPFgYAAHAzLt8QLzMz0xN1AAAAuOyW7ux7+PBhTZ06Vfv27ZMk1apVS/369VPlypXdWhwAAEBeXL6PzMqVK1WrVi1t3rxZ99xzj+655x798MMPql27tlatWuWJGgEAAHLk8h6ZIUOG6LXXXlN8fHy29sGDB+vBBx90W3EAAAB5cXmPzL59+9SzZ89s7c8995z27t3rlqKud+LECT3zzDMqWbKkgoKCVLduXW3ZssXtrwMAAKzH5T0y4eHhSkhIUNWqVR3aExIS3H4l07lz59SiRQu1adNGy5cvV3h4uA4ePKgSJUq49XUAAIA1uRxkevXqpRdeeEFHjhxR8+bNJUkbNmzQhAkTFBMT49biJkyYoMjISM2dO9feVrFiRbe+BgAAsC6Xg8ywYcNUrFgxTZo0SbGxsZKkiIgIjRw5Un379nVrcV988YWioqL05JNP6ptvvlG5cuX08ssvq1evXrmuk5aWprS0NPvz1NRUt9YEAAB8h8vnyNhsNr322mv65ZdflJKSopSUFP3yyy/q16+fbDabW4s7cuSIZs2apapVq2rlypV66aWX1LdvX82fPz/XdeLi4hQSEmJ/REZGurUmAADgO1wOMtcrVqyYihUr5q5assnMzFTDhg01fvx4NWjQQC+88IJ69eqld955J9d1YmNj7QErJSVFx48f91h9AADAu/5UkPG0smXLqlatWg5tNWvW1LFjx3JdJzAwUMHBwQ4PAACQP/l0kGnRooX279/v0HbgwAGVL1/eSxUBAABf4tNB5rXXXtOmTZs0fvx4HTp0SB9++KFmz56tPn36eLs0AADgA1wKMunp6Wrbtq0OHjzoqXoc3HvvvVq8eLE++ugj1alTR2PGjNHUqVPVtWvX2/L6AADAt7l0+XVAQIB27tzpqVpy9Ne//lV//etfb+trAgAAa3D50NIzzzyj9957zxO1AAAAuMTlG+Jdu3ZNc+bM0erVq9WoUSMVKVLEYfnkyZPdVhwAAEBeXA4yu3fvVsOGDSX9cQXR9dx9QzwAAIC8uBxk1q1b54k6AAAAXHbLl18fOnRIK1eu1JUrVyRJxhi3FQUAAOAMl4PMb7/9prZt26patWp65JFH9Ouvv0qSevbsqQEDBri9QAAAgNy4HGRee+01BQQE6NixYypcuLC9vXPnzlqxYoVbiwMAAMiLy+fIfPXVV1q5cqXuuusuh/aqVavq559/dlthAAAAN+PyHplLly457InJkpycrMDAQLcUBQAA4AyXg8z999+v//znP/bnNptNmZmZmjhxotq0aePW4gAAAPLi8qGliRMnqm3bttqyZYuuXr2qQYMGac+ePUpOTtaGDRs8USMAAECOXN4jU6dOHR04cEAtW7bUY489pkuXLunvf/+7tm/frsqVK3uiRgAAgBy5vEdGkkJCQjR06FB31wIAAOCSWwoy586d03vvvad9+/ZJkmrVqqUePXooNDTUrcUBAADkxeVDS+vXr1eFChU0bdo0nTt3TufOndO0adNUsWJFrV+/3hM1AgAA5MjlPTJ9+vRR586dNWvWLPn7+0uSMjIy9PLLL6tPnz7atWuX24sEAADIict7ZA4dOqQBAwbYQ4wk+fv7KyYmRocOHXJrcQAAAHlxOcg0bNjQfm7M9fbt26d69eq5pSgAAABnOHVoaefOnfZ/9+3bV/369dOhQ4d03333SZI2bdqkmTNnKj4+3jNVAgAA5MCpIFO/fn3ZbDYZY+xtgwYNytbvH//4hzp37uy+6gAAAPLgVJA5evSop+sAgDtGhSFLvV2CyxLjO3i7BCBHTgWZ8uXLe7oOAAAAl93SDfFOnjyp7777TqdPn1ZmZqbDsr59+7qlMAAAgJtxOcjMmzdPvXv3VsGCBVWyZEnZbDb7MpvNRpABAAC3jctBZtiwYRo+fLhiY2Pl5+fy1dsAAABu43ISuXz5srp06UKIAQAAXudyGunZs6cWLlzoiVoAAABc4vKhpbi4OP31r3/VihUrVLduXQUEBDgsnzx5stuKAwAAyMstBZmVK1eqevXqkpTtZF8AAIDbxeUgM2nSJM2ZM0fR0dEeKAcAAMB5Lp8jExgYqBYtWniiFgAAAJe4HGT69eun6dOne6IWAAAAl7h8aGnz5s1au3atvvzyS9WuXTvbyb6fffaZ24oDAADIi8tBpnjx4vr73//uiVoAAABc4nKQmTt3rifqAAAAcBm35wUAAJbl8h6ZihUr5nm/mCNHjvypggAAAJzlcpDp37+/w/P09HRt375dK1as0MCBA91VFwAAwE25HGT69euXY/vMmTO1ZcuWP10QAACAs9x2jkz79u21aNEid20OAADgptwWZD799FOFhoa6a3MAAAA35fKhpQYNGjic7GuMUVJSks6cOaO3337brcUBAADkxeUg06lTJ4fnfn5+Cg8PV+vWrVWjRg131QUAAHBTLgeZESNGeKIOp8THxys2Nlb9+vXT1KlTvVYHAADwDZa5Id6PP/6of/3rX7rnnnu8XQoAAPARTgcZPz8/+fv75/koUMDlHTxOuXjxorp27ap///vfKlGihEdeAwAAWI/TyWPx4sW5Ltu4caOmTZumzMxMtxR1oz59+qhDhw5q166dxo4dm2fftLQ0paWl2Z+npqZ6pCYAAOB9TgeZxx57LFvb/v37NWTIEP3vf/9T165dNXr0aLcWJ0kLFizQtm3b9OOPPzrVPy4uTqNGjXJ7HYCrKgxZ6u0SACDfu6VzZE6ePKlevXqpbt26unbtmhISEjR//nyVL1/ercUdP35c/fr10wcffKBChQo5tU5sbKxSUlLsj+PHj7u1JgAA4DtcOqklJSVF48eP1/Tp01W/fn2tWbNG999/v6dq09atW3X69Gk1bNjQ3paRkaH169drxowZSktLk7+/v8M6gYGBCgwM9FhNAADAdzgdZCZOnKgJEyaoTJky+uijj3I81ORubdu21a5duxzaevTooRo1amjw4MHZQgwAALizOB1khgwZoqCgIFWpUkXz58/X/Pnzc+z32Wefua24YsWKqU6dOg5tRYoUUcmSJbO1AwCAO4/TQaZbt24OX00AAADgbU4HmXnz5nmwDOd9/fXX3i4BAAD4CMvc2RcAAOBGBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBbxdAADA91UYstTbJdwRrPg+J8Z38Orrs0cGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYFkEGAABYlk8Hmbi4ON17770qVqyYSpUqpU6dOmn//v3eLgsAAPgInw4y33zzjfr06aNNmzZp1apVSk9P10MPPaRLly55uzQAAOADCni7gLysWLHC4fm8efNUqlQpbd26Va1atfJSVQAAwFf4dJC5UUpKiiQpNDQ01z5paWlKS0uzP09NTfV4XQAAwDssE2QyMzPVv39/tWjRQnXq1Mm1X1xcnEaNGnVbaqowZOlteR13S4zv4O0SXGbV9xoA4Fk+fY7M9fr06aPdu3drwYIFefaLjY1VSkqK/XH8+PHbVCEAALjdLLFH5pVXXtGXX36p9evX66677sqzb2BgoAIDA29TZQAAwJt8OsgYY/Tqq69q8eLF+vrrr1WxYkVvlwQAAHyITweZPn366MMPP9Tnn3+uYsWKKSkpSZIUEhKioKAgL1cHAAC8zafPkZk1a5ZSUlLUunVrlS1b1v74+OOPvV0aAADwAT69R8YY4+0SAACAD/PpPTIAAAB5IcgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLskSQmTlzpipUqKBChQqpadOm2rx5s7dLAgAAPsDng8zHH3+smJgYjRgxQtu2bVO9evUUFRWl06dPe7s0AADgZT4fZCZPnqxevXqpR48eqlWrlt555x0VLlxYc+bM8XZpAADAywp4u4C8XL16VVu3blVsbKy9zc/PT+3atdPGjRtzXCctLU1paWn25ykpKZKk1NRUt9eXmXbZ7du8HTzxXniaVd9rAMjvPPU3JWu7xpg8+/l0kDl79qwyMjJUunRph/bSpUvrp59+ynGduLg4jRo1Klt7ZGSkR2q0opCp3q4AAJBfePpvyoULFxQSEpLrcp8OMrciNjZWMTEx9ueZmZlKTk5WyZIlZbPZJP2R8iIjI3X8+HEFBwd7q1SPuhPGKN0Z47wTxijdGeO8E8YoMc78xJtjNMbowoULioiIyLOfTweZsLAw+fv769SpUw7tp06dUpkyZXJcJzAwUIGBgQ5txYsXz7FvcHBwvv3wZbkTxijdGeO8E8Yo3RnjvBPGKDHO/MRbY8xrT0wWnz7Zt2DBgmrUqJHWrFljb8vMzNSaNWvUrFkzL1YGAAB8gU/vkZGkmJgYde/eXY0bN1aTJk00depUXbp0ST169PB2aQAAwMt8Psh07txZZ86c0fDhw5WUlKT69etrxYoV2U4AdkVgYKBGjBiR7RBUfnInjFG6M8Z5J4xRujPGeSeMUWKc+YkVxmgzN7uuCQAAwEf59DkyAAAAeSHIAAAAyyLIAAAAyyLIAAAAyyLIAAAAy8q3Qebrr7+WzWbL8fHjjz/a++3cuVP333+/ChUqpMjISE2cODHbthYuXKgaNWqoUKFCqlu3rpYtW3Y7h+KUpUuXqmnTpgoKClKJEiXUqVMnh+U5vQ8LFixw6PP111+rYcOGCgwMVJUqVTRv3rzbNwAn3GyMx44dU4cOHVS4cGGVKlVKAwcO1LVr1xz6+PoYK1SokG2e4uPj7csTExNznMtNmzY5bMeXP7M3G6OUf34upT++yLZ+/fqy2WxKSEiwt+eHubxebuOUrD+fHTt21N13361ChQqpbNmyevbZZ3Xy5En78vwylzcbp+Sjc2nyqbS0NPPrr786PJ5//nlTsWJFk5mZaYwxJiUlxZQuXdp07drV7N6923z00UcmKCjI/Otf/7JvZ8OGDcbf399MnDjR7N2717zxxhsmICDA7Nq1y1tDy+bTTz81JUqUMLNmzTL79+83e/bsMR9//LFDH0lm7ty5Du/HlStX7MuPHDliChcubGJiYszevXvN9OnTjb+/v1mxYsXtHk6ObjbGa9eumTp16ph27dqZ7du3m2XLlpmwsDATGxtr7+PrYzTGmPLly5vRo0c7zNPFixfty48ePWokmdWrVzv0uXr1qr2Pr39mbzbG/PJzmaVv376mffv2RpLZvn27vT0/zOX1chtnfpjPyZMnm40bN5rExESzYcMG06xZM9OsWTP78vwylzcbp6/OZb4NMje6evWqCQ8PN6NHj7a3vf3226ZEiRImLS3N3jZ48GBTvXp1+/OnnnrKdOjQwWFbTZs2Nb179/Z80U5IT0835cqVM++++26e/SSZxYsX57p80KBBpnbt2g5tnTt3NlFRUe4o809xZozLli0zfn5+Jikpyd42a9YsExwcbJ9fXx5jlvLly5spU6bkujzrF+b1fyhu5Ouf2ZuNMT/8XGZZtmyZqVGjhtmzZ0+uQcbKc5klr3Hmp/nM8vnnnxubzWYPKvlpLq934zh9dS7z7aGlG33xxRf67bffHL7aYOPGjWrVqpUKFixob4uKitL+/ft17tw5e5927do5bCsqKkobN268PYXfxLZt23TixAn5+fmpQYMGKlu2rNq3b6/du3dn69unTx+FhYWpSZMmmjNnjsx190L05XE6M8aNGzeqbt26Dnd8joqKUmpqqvbs2WPv46tjvF58fLxKliypBg0a6M0338x2eEz6YxdwqVKl1LJlS33xxRcOy6wwzrzGmB9+LqU/vty2V69eev/991W4cOFc+1l9Lm82zvwyn1mSk5P1wQcfqHnz5goICHBYZvW5vF5O4/TVubxjgsx7772nqKgo3XXXXfa2pKSkbF91kPU8KSkpzz5Zy73tyJEjkqSRI0fqjTfe0JdffqkSJUqodevWSk5OtvcbPXq0PvnkE61atUqPP/64Xn75ZU2fPt2+PLdxpqam6sqVK7dnMLlwZox/Zi59YYxZ+vbtqwULFmjdunXq3bu3xo8fr0GDBtmXFy1aVJMmTdLChQu1dOlStWzZUp06dXL4penrn9mbjTE//FwaYxQdHa0XX3xRjRs3zrFPfphLZ8aZH+ZTkgYPHqwiRYqoZMmSOnbsmD7//HP7svwwl1nyGqevzqXlgsyQIUNyPYk36/HTTz85rPPLL79o5cqV6tmzp5eqdp2z48zMzJQkDR06VI8//rgaNWqkuXPnymazaeHChfbtDRs2TC1atFCDBg00ePBgDRo0SG+++aa3hifJ/WP0Va58ZmNiYtS6dWvdc889evHFFzVp0iRNnz5daWlpkqSwsDDFxMSoadOmuvfeexUfH69nnnnGMnMp3XyMvszZcU6fPl0XLlxQbGxsrtvy1bmU3DtOX+Xq35KBAwdq+/bt+uqrr+Tv769u3brZ92rnh7nMktc4fZXPf2nkjQYMGKDo6Og8+1SqVMnh+dy5c1WyZEl17NjRob1MmTI6deqUQ1vW8zJlyuTZJ2u5pzg7zl9//VWSVKtWLXt7YGCgKlWqpGPHjuW6btOmTTVmzBilpaUpMDAw13EGBwcrKCjo1geSB3eOsUyZMtq8ebPDus7OpSfHKN3aZzZL06ZNde3aNSUmJqp69eq59lm1apX9uTc+s+4cY374uVy7dq02btyY7Yv2GjdurK5du2r+/Pk5rusLcym5d5y+Op+ufmbDwsIUFhamatWqqWbNmoqMjNSmTZvUrFmzHNe12lxmyWucvjqXlgsy4eHhCg8Pd7q/MUZz585Vt27dsh3PbNasmYYOHar09HT7slWrVql69eoqUaKEvc+aNWvUv39/+3qrVq3K9cPrLs6Os1GjRgoMDNT+/fvVsmVLSVJ6eroSExNVvnz5XNdLSEhQiRIl7L+AmjVrlu0SOU+P051jbNasmcaNG6fTp0+rVKlS9vqDg4PtAcgbY5Rc/8xeLyEhQX5+fvYx5danbNmy9ufe+My6c4z54edy2rRpGjt2rP35yZMnFRUVpY8//lhNmzbNdT1fmEvJveP01fn8M5/ZrL3Eee1FtNpc5uTGcfrqXOb7q5ZWr15tJJl9+/ZlW3b+/HlTunRp8+yzz5rdu3ebBQsWmMKFC2e7lKxAgQLmrbfeMvv27TMjRozwuUvm+vXrZ8qVK2dWrlxpfvrpJ9OzZ09TqlQpk5ycbIwx5osvvjD//ve/za5du8zBgwfN22+/bQoXLmyGDx9u30bWpckDBw40+/btMzNnzvSpS5NvNsasy68feughk5CQYFasWGHCw8NzvPzaV8f4/fffmylTppiEhARz+PBh89///teEh4ebbt262fvMmzfPfPjhh2bfvn1m3759Zty4ccbPz8/MmTPH3seXP7POjDG//FxeL6erWqw+lznJaZxWn89NmzaZ6dOnm+3bt5vExESzZs0a07x5c1O5cmXz+++/G2Pyx1w6M05fnct8H2Sefvpp07x581yX79ixw7Rs2dIEBgaacuXKmfj4+Gx9PvnkE1OtWjVTsGBBU7t2bbN06VJPluyyq1evmgEDBphSpUqZYsWKmXbt2pndu3fbly9fvtzUr1/fFC1a1BQpUsTUq1fPvPPOOyYjI8NhO+vWrTP169c3BQsWNJUqVTJz5869zSPJ3c3GaIwxiYmJpn379iYoKMiEhYWZAQMGmPT0dIc+vjzGrVu3mqZNm5qQkBBTqFAhU7NmTTN+/Hj7LxFj/viFWbNmTVO4cGETHBxsmjRpYhYuXJhtW776mXVmjMbkj5/L6+UWZKw8lznJ7TJkK8/nzp07TZs2bUxoaKgJDAw0FSpUMC+++KL55Zdf7H3yw1w6M05jfHMubcb4+Fk8AAAAubDcVUsAAABZCDIAAMCyCDIAAMCyCDIAAMCyCDIAAMCyCDIAAMCyCDIAAMCyCDIAAMCyCDIAAMCyCDIAAMCyCDIAAMCy/h90a9U4QdAYwQAAAABJRU5ErkJggg==", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:36.950369\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:37.522930\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:38.074991\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:38.658911\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:39.300201\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjIAAAGzCAYAAAA1yP25AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy89olMNAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA0UUlEQVR4nO3deXRTdeL+8SdtaSlLWwtdqBYoUEX2pSOyiCJVBBzFcQG+KC2gMlqnQBmE6rALBRRlGBHFQWBcR2TREQErMKIOshRZRdYi/ISCSBdatND28/vDQ46hCwmmTS68X+fkSD733uRJLjEPd4vNGGMEAABgQT6eDgAAAHC5KDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDKAl2nYsKESExM9HeOK9/zzz6tRo0by9fVVmzZtPB0HwGWiyACVaOHChbLZbNqyZUuZ02+77Ta1aNHidz/PJ598ogkTJvzux7lafPrpp3r66afVuXNnLViwQFOnTv3dj+mudekNpk6dquXLl3s6BuAUP08HAOBo79698vFx7d8Yn3zyiebMmUOZcdLatWvl4+Oj+fPny9/f39NxvM7UqVP1wAMPqE+fPp6OAlwSW2QALxMQEKBq1ap5OoZLCgoKPB3BJSdPnlRgYCAlBrgCUGQAL3PxMTLnz5/XxIkTFRsbq+rVq6tOnTrq0qWL0tPTJUmJiYmaM2eOJMlms9lvFxQUFGjkyJGKjo5WQECAbrjhBr3wwgu6+Ifvf/75ZyUnJ6tu3bqqXbu27rnnHv3www+y2WwOW3omTJggm82mb7/9Vv/3f/+na665Rl26dJEk7dixQ4mJiWrUqJGqV6+uyMhIDR48WD/99JPDc114jH379unhhx9WcHCwwsLCNHbsWBljdPToUd17770KCgpSZGSkZs6c6dR7V1RUpMmTJ6tx48YKCAhQw4YN9cwzz6iwsNA+j81m04IFC1RQUGB/rxYuXFjm4+3Zs0eBgYEaOHCgw/iXX34pX19fjR49usI8NptNTz31lBYvXqxmzZopMDBQHTt21M6dOyVJr732mpo0aaLq1avrtttu0+HDhx2Wv7C7KiMjQ506dVJgYKBiYmL06quvOsx37tw5jRs3Tu3bt1dwcLBq1qypW265RevWrSuVqaSkRH//+9/VsmVLVa9eXWFhYbrrrrvsuz9tNpsKCgq0aNEi+/vDMVvwZuxaAqpAbm6uTp06VWr8/Pnzl1x2woQJSktL06OPPqqbbrpJeXl52rJli7Zu3ao77rhDQ4cO1bFjx5Senq4333zTYVljjO655x6tW7dOQ4YMUZs2bbR69WqNGjVKP/zwg1566SX7vImJiXr//ff1yCOP6Oabb9bnn3+u3r17l5vrwQcfVGxsrKZOnWovRenp6Tp06JAGDRqkyMhI7d69W/PmzdPu3bv19ddfOxQsSerbt69uvPFGTZs2TStWrNBzzz2n0NBQvfbaa7r99ts1ffp0vf322/rrX/+qP/zhD+ratWuF79Wjjz6qRYsW6YEHHtDIkSO1ceNGpaWlac+ePVq2bJkk6c0339S8efO0adMm/fOf/5QkderUqczHu/HGGzV58mSNGjVKDzzwgO655x4VFBQoMTFRTZs21aRJkyrMI0lffPGFPvroIyUlJUmS0tLSdPfdd+vpp5/WK6+8oieffFLZ2dmaMWOGBg8erLVr1zosn52drV69eumhhx5S//799f777+uJJ56Qv7+/Bg8eLEnKy8vTP//5T/Xv31+PPfaYzpw5o/nz56tHjx7atGmTw8HMQ4YM0cKFC9WzZ089+uijKioq0hdffKGvv/5acXFxevPNN+1/1x5//HFJUuPGjS/5OgGPMQAqzYIFC4ykCm/Nmzd3WKZBgwYmISHBfr9169amd+/eFT5PUlKSKevjvHz5ciPJPPfccw7jDzzwgLHZbObAgQPGGGMyMjKMJDN8+HCH+RITE40kM378ePvY+PHjjSTTv3//Us939uzZUmPvvvuukWTWr19f6jEef/xx+1hRUZG57rrrjM1mM9OmTbOPZ2dnm8DAQIf3pCzbtm0zksyjjz7qMP7Xv/7VSDJr1661jyUkJJiaNWtW+HgXFBcXmy5dupiIiAhz6tQpk5SUZPz8/MzmzZsd5rv11ltLrUtJJiAgwGRmZtrHXnvtNSPJREZGmry8PPt4amqqkeQw76233mokmZkzZ9rHCgsLTZs2bUx4eLg5d+6cMebX966wsNDhubOzs01ERIQZPHiwfWzt2rVGkklOTi71OktKSux/rlmz5iXfb8BbsGsJqAJz5sxRenp6qVurVq0uuWxISIh2796t/fv3u/y8n3zyiXx9fZWcnOwwPnLkSBljtHLlSknSqlWrJElPPvmkw3x/+ctfyn3sP//5z6XGAgMD7X/+5ZdfdOrUKd18882SpK1bt5aa/9FHH7X/2dfXV3FxcTLGaMiQIfbxkJAQ3XDDDTp06FC5WaRfX6skpaSkOIyPHDlSkrRixYoKly+Pj4+PFi5cqPz8fPXs2VOvvPKKUlNTFRcX59Ty3bt3V8OGDe33O3ToIEm6//77Vbt27VLjF79OPz8/DR061H7f399fQ4cO1cmTJ5WRkSHp1/fuwvE+JSUlOn36tIqKihQXF+fwvi9ZskQ2m03jx48vlfPirWWAVVBkgCpw0003KT4+vtTtmmuuueSykyZNUk5Ojq6//nq1bNlSo0aN0o4dO5x63u+//15RUVEOX5jSr7tMLky/8F8fHx/FxMQ4zNekSZNyH/vieSXp9OnTGjZsmCIiIhQYGKiwsDD7fLm5uaXmr1+/vsP94OBgVa9eXXXr1i01np2dXW6W376GizNHRkYqJCTE/lovR+PGjTVhwgRt3rxZzZs319ixY51etqzXKEnR0dFljl/8OqOiolSzZk2Hseuvv16SHI6pWbRokVq1amU/jiosLEwrVqxweN8PHjyoqKgohYaGOp0f8HYUGcDLde3aVQcPHtQbb7yhFi1a6J///KfatWtnP77DU3679eWChx56SK+//rr+/Oc/a+nSpfr000/tW3tKSkpKze/r6+vUmKRSByeXp7K2LHz66aeSpGPHjpU6eLki5b2e3/s6f+utt95SYmKiGjdurPnz52vVqlVKT0/X7bffXub7DlxJKDKABYSGhmrQoEF69913dfToUbVq1crhTKLyvrwbNGigY8eO6cyZMw7j3333nX36hf+WlJQoMzPTYb4DBw44nTE7O1tr1qzRmDFjNHHiRN13332644471KhRI6cf4/e48Bou3gV34sQJ5eTk2F/r5Xj11VeVnp6uKVOm6Ny5cw67eirbsWPHSp3evm/fPkmy77L64IMP1KhRIy1dulSPPPKIevToofj4eP3yyy8OyzVu3FjHjh3T6dOnK3xOdjPBSigygJe7+F//tWrVUpMmTRxOKb6w6yEnJ8dh3l69eqm4uFgvv/yyw/hLL70km82mnj17SpJ69OghSXrllVcc5vvHP/7hdM4LWxgu3qIwa9Yspx/j9+jVq1eZz/fiiy9KUoVnYFUkMzNTo0aN0v33369nnnlGL7zwgj766CP961//+l15nVVUVKTXXnvNfv/cuXN67bXXFBYWpvbt20sq+73fuHGjNmzY4PBY999/v4wxmjhxYqnn+e2yNWvWLPV3CfBWnH4NeLlmzZrptttuU/v27RUaGqotW7bogw8+0FNPPWWf58IXWnJysnr06CFfX1/169dPf/zjH9WtWzc9++yzOnz4sFq3bq1PP/1UH374oYYPH24/rbZ9+/a6//77NWvWLP3000/2068v/MvfmX+hBwUFqWvXrpoxY4bOnz+va6+9Vp9++mmprTyVpXXr1kpISNC8efOUk5OjW2+9VZs2bdKiRYvUp08fdevWzeXHNMZo8ODBCgwM1Ny5cyVJQ4cO1ZIlSzRs2DDFx8crKirK3S/FQVRUlKZPn67Dhw/r+uuv17///W9t27ZN8+bNs1848e6779bSpUt13333qXfv3srMzNSrr76qZs2aKT8/3/5Y3bp10yOPPKLZs2dr//79uuuuu1RSUqIvvvhC3bp1s/+dat++vT777DO9+OKLioqKUkxMjP1gZMDreOx8KeAqcOH064tP1b2grFN2Lz79+rnnnjM33XSTCQkJMYGBgaZp06ZmypQp9lNvjfn19Nu//OUvJiwszNhsNodTsc+cOWNGjBhhoqKiTLVq1UxsbKx5/vnnHU63NcaYgoICk5SUZEJDQ02tWrVMnz59zN69e40kh9OhL5w6/eOPP5Z6Pf/v//0/c99995mQkBATHBxsHnzwQXPs2LFyT+G++DHKOy26rPepLOfPnzcTJ040MTExplq1aiY6OtqkpqaaX375xannudjf//53I8ksWbLEYfzIkSMmKCjI9OrVq8KMkkxSUpLDWGZmppFknn/+eYfxdevWGUlm8eLFpR5zy5YtpmPHjqZ69eqmQYMG5uWXX3ZYtqSkxEydOtU0aNDABAQEmLZt25qPP/7YJCQkmAYNGjjMW1RUZJ5//nnTtGlT4+/vb8LCwkzPnj1NRkaGfZ7vvvvOdO3a1QQGBhpJnIoNr2Yz5jKOLANwVdi2bZvatm2rt956SwMGDPB0nKvObbfdplOnTmnXrl2ejgJ4LY6RASDp158ouNisWbPk4+NzySvqAoCncIwMAEnSjBkzlJGRoW7dusnPz08rV67UypUr9fjjj5e65gkAeAuKDABJv/7eUHp6uiZPnqz8/HzVr19fEyZM0LPPPuvpaABQLo6RAQAAlsUxMgAAwLIoMgAAwLKu+GNkSkpKdOzYMdWuXZvLbgMAYBHGGJ05c0ZRUVHy8Sl/u8sVX2SOHTvGGRcAAFjU0aNHdd1115U7/YovMrVr15b06xsRFBTk4TQAAMAZeXl5io6Otn+Pl+eKLzIXdicFBQVRZAAAsJhLHRbCwb4AAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCyKDIAAMCy/DwdAID3aDhmhacjuOzwtN6ejgDAg9giAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALIsiAwAALMujRWb9+vX64x//qKioKNlsNi1fvtxhujFG48aNU7169RQYGKj4+Hjt37/fM2EBAIDX8WiRKSgoUOvWrTVnzpwyp8+YMUOzZ8/Wq6++qo0bN6pmzZrq0aOHfvnllypOCgAAvJGfJ5+8Z8+e6tmzZ5nTjDGaNWuW/va3v+nee++VJP3rX/9SRESEli9frn79+lVlVAAA4IW89hiZzMxMZWVlKT4+3j4WHBysDh06aMOGDeUuV1hYqLy8PIcbAAC4Mnl0i0xFsrKyJEkREREO4xEREfZpZUlLS9PEiRMrNRsA79FwzApPR3DZ4Wm9PR0BuGJ47RaZy5Wamqrc3Fz77ejRo56OBAAAKonXFpnIyEhJ0okTJxzGT5w4YZ9WloCAAAUFBTncAADAlclri0xMTIwiIyO1Zs0a+1heXp42btyojh07ejAZAADwFh49RiY/P18HDhyw38/MzNS2bdsUGhqq+vXra/jw4XruuecUGxurmJgYjR07VlFRUerTp4/nQgMAAK/h0SKzZcsWdevWzX4/JSVFkpSQkKCFCxfq6aefVkFBgR5//HHl5OSoS5cuWrVqlapXr+6pyAAAwIvYjDHG0yEqU15enoKDg5Wbm8vxMsAlWPEMICvirCXg0pz9/vbaY2QAAAAuhSIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsiyIDAAAsy+Uis3XrVu3cudN+/8MPP1SfPn30zDPP6Ny5c24NBwAAUBGXi8zQoUO1b98+SdKhQ4fUr18/1ahRQ4sXL9bTTz/t9oAAAADlcbnI7Nu3T23atJEkLV68WF27dtU777yjhQsXasmSJe7OBwAAUC6Xi4wxRiUlJZKkzz77TL169ZIkRUdH69SpU+5NBwAAUAGXi0xcXJyee+45vfnmm/r888/Vu3dvSVJmZqYiIiLcHhAAAKA8LheZWbNmaevWrXrqqaf07LPPqkmTJpKkDz74QJ06dXJruOLiYo0dO1YxMTEKDAxU48aNNXnyZBlj3Po8AADAmvxcXaBVq1YOZy1d8Pzzz8vX19ctoS6YPn265s6dq0WLFql58+basmWLBg0apODgYCUnJ7v1uQAAgPW4XGQkKScnRx988IEOHjyoUaNGKTQ0VN9++60iIiJ07bXXui3c//73P91777323VcNGzbUu+++q02bNrntOQAAgHW5vGtpx44dio2N1fTp0/XCCy8oJydHkrR06VKlpqa6NVynTp20Zs0a++ne27dv15dffqmePXuWu0xhYaHy8vIcbgAA4MrkcpFJSUnRoEGDtH//flWvXt0+3qtXL61fv96t4caMGaN+/fqpadOmqlatmtq2bavhw4drwIAB5S6Tlpam4OBg+y06OtqtmQAAgPdwuchs3rxZQ4cOLTV+7bXXKisryy2hLnj//ff19ttv65133tHWrVu1aNEivfDCC1q0aFG5y6Smpio3N9d+O3r0qFszAQAA7+HyMTIBAQFl7q7Zt2+fwsLC3BLqglGjRtm3ykhSy5Yt9f333ystLU0JCQnl5gsICHBrDgAA4J1c3iJzzz33aNKkSTp//rwkyWaz6ciRIxo9erTuv/9+t4Y7e/asfHwcI/r6+tovyAcAAK5uLheZmTNnKj8/X+Hh4fr555916623qkmTJqpdu7amTJni1nB//OMfNWXKFK1YsUKHDx/WsmXL9OKLL+q+++5z6/MAAABrcnnXUnBwsNLT0/XVV19p+/btys/PV7t27RQfH+/2cP/4xz80duxYPfnkkzp58qSioqI0dOhQjRs3zu3PBQAArMdmrvDL5Obl5Sk4OFi5ubkKCgrydBzAqzUcs8LTEa4Kh6f19nQEwOs5+/3t8q6l5ORkzZ49u9T4yy+/rOHDh7v6cAAAAJfN5SKzZMkSde7cudR4p06d9MEHH7glFAAAgDNcLjI//fSTgoODS40HBQXp1KlTbgkFAADgDJeLTJMmTbRq1apS4ytXrlSjRo3cEgoAAMAZLp+1lJKSoqeeeko//vijbr/9dknSmjVrNHPmTM2aNcvd+QAAAMrlcpEZPHiwCgsLNWXKFE2ePFnSr79KPXfuXA0cONDtAQEAAMrjcpGRpCeeeEJPPPGEfvzxRwUGBqpWrVruzgUAAHBJl1VkLnD3bysBAAC4wuWDfU+cOKFHHnlEUVFR8vPzk6+vr8MNAACgqri8RSYxMVFHjhzR2LFjVa9ePdlstsrIBQAAcEkuF5kvv/xSX3zxhdq0aVMJcQAAAJzn8q6l6OhoXeE/zwQAACzC5SIza9YsjRkzRocPH66EOAAAAM5zeddS3759dfbsWTVu3Fg1atRQtWrVHKafPn3abeEAAAAq4nKR4eq9AADAW7hcZBISEiojBwAAgMtcPkZGkg4ePKi//e1v6t+/v06ePCnp1x+N3L17t1vDAQAAVMTlIvP555+rZcuW2rhxo5YuXar8/HxJ0vbt2zV+/Hi3BwQAACiPy0VmzJgxeu6555Seni5/f3/7+O23366vv/7areEAAAAq4nKR2blzp+67775S4+Hh4Tp16pRbQgEAADjD5SITEhKi48ePlxr/5ptvdO2117olFAAAgDNcLjL9+vXT6NGjlZWVJZvNppKSEn311Vf661//qoEDB1ZGRgAAgDK5XGSmTp2qpk2bKjo6Wvn5+WrWrJm6du2qTp066W9/+1tlZAQAACiTS9eRMcYoKytLs2fP1rhx47Rz507l5+erbdu2io2NrayMAAAAZXK5yDRp0kS7d+9WbGysoqOjKysXAADAJbm0a8nHx0exsbH66aefKisPAACA01w+RmbatGkaNWqUdu3aVRl5AAAAnObyby0NHDhQZ8+eVevWreXv76/AwECH6fz6NQAAqCr8+jUAALAsl4rM+fPn9fnnn2vs2LGKiYmprEwAAABOcekYmWrVqmnJkiWVlQUAAMAlLh/s26dPHy1fvrwSogAAALjG5WNkYmNjNWnSJH311Vdq3769atas6TA9OTnZbeEAAAAq4nKRmT9/vkJCQpSRkaGMjAyHaTabjSIDAACqjMtFJjMzszJyAAAAuMzlY2QAAAC8hctbZAYPHlzh9DfeeOOywwAAALjC5SKTnZ3tcP/8+fPatWuXcnJydPvtt7stGAAAwKW4XGSWLVtWaqykpERPPPGEGjdu7JZQAAAAznDLMTI+Pj5KSUnRSy+95I6HAwAAcIrbDvY9ePCgioqK3PVwAAAAl+TyrqWUlBSH+8YYHT9+XCtWrFBCQoLbggEAAFyKy0Xmm2++cbjv4+OjsLAwzZw585JnNAEAALiTy0Vm3bp1lZEDAADAZS4fI5OZman9+/eXGt+/f78OHz7sjkwAAABOcbnIJCYm6n//+1+p8Y0bNyoxMdEdmQAAAJzicpH55ptv1Llz51LjN998s7Zt2+aOTAAAAE5xucjYbDadOXOm1Hhubq6Ki4vdEgoAAMAZLheZrl27Ki0tzaG0FBcXKy0tTV26dHFrOAAAgIq4fNbS9OnT1bVrV91www265ZZbJElffPGF8vLytHbtWrcHBAAAKI/LW2SaNWumHTt26KGHHtLJkyd15swZDRw4UN99951atGhRGRkBAADK5PIWGUmKiorS1KlT3Z0FAADAJS5vkVmwYIEWL15canzx4sVatGiRW0IBAAA4w+Uik5aWprp165YaDw8Pr5StND/88IMefvhh1alTR4GBgWrZsqW2bNni9ucBAADW4/KupSNHjigmJqbUeIMGDXTkyBG3hLogOztbnTt3Vrdu3bRy5UqFhYVp//79uuaaa9z6PAAAwJpcLjLh4eHasWOHGjZs6DC+fft21alTx125JP16hlR0dLQWLFhgHyurRAEAgKuTy7uW+vfvr+TkZK1bt07FxcUqLi7W2rVrNWzYMPXr18+t4T766CPFxcXpwQcfVHh4uNq2bavXX3+9wmUKCwuVl5fncAMAAFcml7fITJ48WYcPH1b37t3l5/fr4iUlJRo4cKDbj5E5dOiQ5s6dq5SUFD3zzDPavHmzkpOT5e/vr4SEhDKXSUtL08SJE92aA57XcMwKT0dw2eFpvT0dAQCueDZjjLmcBfft26ft27fbD8Bt0KCBu7PJ399fcXFxDj9SmZycrM2bN2vDhg1lLlNYWKjCwkL7/by8PEVHRys3N1dBQUFuz4iqQZGpGlZ8n63Iin83gKqWl5en4ODgS35/X9Z1ZCQpNDRU3bp1K/MMJnepV6+emjVr5jB24403asmSJeUuExAQoICAgErLBAAAvIdLx8jk5OQoKSlJdevWVUREhCIiIlS3bl099dRTysnJcXu4zp07a+/evQ5j+/btq5StPwAAwHqc3iJz+vRpdezYUT/88IMGDBigG2+8UZL07bffauHChVqzZo3+97//ufXU6BEjRqhTp06aOnWqHnroIW3atEnz5s3TvHnz3PYcAADAupwuMpMmTZK/v78OHjyoiIiIUtPuvPNOTZo0SS+99JLbwv3hD3/QsmXLlJqaqkmTJikmJkazZs3SgAED3PYcAADAupzetbR8+XK98MILpUqMJEVGRmrGjBlatmyZW8NJ0t13362dO3fql19+0Z49e/TYY4+5/TkAAIA1OV1kjh8/rubNm5c7vUWLFsrKynJLKAAAAGc4XWTq1q2rw4cPlzs9MzNToaGh7sgEAADgFKeLTI8ePfTss8/q3LlzpaYVFhZq7Nixuuuuu9waDgAAoCIuHewbFxen2NhYJSUlqWnTpjLGaM+ePXrllVdUWFioN998szKzAgAAOHC6yFx33XXasGGDnnzySaWmpurCBYFtNpvuuOMOvfzyy4qOjq60oAAAABdz6cq+MTExWrlypbKzs7V//35JUpMmTTg2BgAAeMRl/UTBNddco5tuusndWQAAAFzi0k8UAAAAeBOKDAAAsCyKDAAAsCyniky7du2UnZ0t6dfTsM+ePVupoQAAAJzhVJHZs2ePCgoKJEkTJ05Ufn5+pYYCAABwhlNnLbVp00aDBg1Sly5dZIzRCy+8oFq1apU577hx49waEAAAoDxOFZmFCxdq/Pjx+vjjj2Wz2bRy5Ur5+ZVe1GazUWQAAECVcarI3HDDDXrvvfckST4+PlqzZo3Cw8MrNRgAAMCluHxBvJKSksrIAQAA4LLLurLvwYMHNWvWLO3Zs0eS1KxZMw0bNkyNGzd2azgAAICKuHwdmdWrV6tZs2batGmTWrVqpVatWmnjxo1q3ry50tPTKyMjAABAmVzeIjNmzBiNGDFC06ZNKzU+evRo3XHHHW4LBwAAUBGXt8js2bNHQ4YMKTU+ePBgffvtt24JBQAA4AyXi0xYWJi2bdtWanzbtm2cyQQAAKqUy7uWHnvsMT3++OM6dOiQOnXqJEn66quvNH36dKWkpLg9IAAAQHlcLjJjx45V7dq1NXPmTKWmpkqSoqKiNGHCBCUnJ7s9IAAAQHlcLjI2m00jRozQiBEjdObMGUlS7dq13R4MAADgUi7rOjIXUGAAAIAn/a4iA6B8Dces8HQEeCkr/t04PK23pyMAZXL5rCUAAABvQZEBAACW5VKROX/+vLp37679+/dXVh4AAACnuVRkqlWrph07dlRWFgAAAJe4vGvp4Ycf1vz58ysjCwAAgEtcPmupqKhIb7zxhj777DO1b99eNWvWdJj+4osvui0cAABARVwuMrt27VK7du0kSfv27XOYZrPZ3JMKAADACS4XmXXr1lVGDgAAAJdd9unXBw4c0OrVq/Xzzz9LkowxbgsFAADgDJeLzE8//aTu3bvr+uuvV69evXT8+HFJ0pAhQzRy5Ei3BwQAACiPy0VmxIgRqlatmo4cOaIaNWrYx/v27atVq1a5NRwAAEBFXD5G5tNPP9Xq1at13XXXOYzHxsbq+++/d1swAACAS3F5i0xBQYHDlpgLTp8+rYCAALeEAgAAcIbLReaWW27Rv/71L/t9m82mkpISzZgxQ926dXNrOAAAgIq4vGtpxowZ6t69u7Zs2aJz587p6aef1u7du3X69Gl99dVXlZERAACgTC5vkWnRooX27dunLl266N5771VBQYH+9Kc/6ZtvvlHjxo0rIyMAAECZXN4iI0nBwcF69tln3Z0FAADAJZdVZLKzszV//nzt2bNHktSsWTMNGjRIoaGhbg0HAABQEZd3La1fv14NGzbU7NmzlZ2drezsbM2ePVsxMTFav359ZWQEAAAok8tbZJKSktS3b1/NnTtXvr6+kqTi4mI9+eSTSkpK0s6dO90eEgAAoCwub5E5cOCARo4caS8xkuTr66uUlBQdOHDAreEAAAAq4nKRadeunf3YmN/as2ePWrdu7ZZQAAAAznBq19KOHTvsf05OTtawYcN04MAB3XzzzZKkr7/+WnPmzNG0adMqJyUAAEAZbMYYc6mZfHx8ZLPZdKlZbTabiouL3RbOHfLy8hQcHKzc3FwFBQV5Og4uU8MxKzwdAbiqHZ7W29MRcJVx9vvbqS0ymZmZbgsGAADgLk4VmQYNGlR2DgAAAJdd1gXxjh07pi+//FInT55USUmJw7Tk5GS3BAMAALgUl4vMwoULNXToUPn7+6tOnTqy2Wz2aTabrVKLzLRp05Samqphw4Zp1qxZlfY8AADAGlwuMmPHjtW4ceOUmpoqHx+Xz96+bJs3b9Zrr72mVq1aVdlzAgAA7+ZyEzl79qz69etXpSUmPz9fAwYM0Ouvv65rrrmmyp4XAAB4N5fbyJAhQ7R48eLKyFKupKQk9e7dW/Hx8Zect7CwUHl5eQ43AABwZXJ511JaWpruvvturVq1Si1btlS1atUcpr/44otuCydJ7733nrZu3arNmzc7nW/ixIluzQAAVzsrXsuJa99cHS6ryKxevVo33HCDJJU62Nedjh49qmHDhik9PV3Vq1d3apnU1FSlpKTY7+fl5Sk6OtqtuQAAgHdwucjMnDlTb7zxhhITEyshjqOMjAydPHlS7dq1s48VFxdr/fr1evnll1VYWOjw45WSFBAQoICAgErPBgAAPM/lIhMQEKDOnTtXRpZSunfvrp07dzqMDRo0SE2bNtXo0aNLlRgAAHB1cbnIDBs2TP/4xz80e/bsysjjoHbt2mrRooXDWM2aNVWnTp1S4wAA4OrjcpHZtGmT1q5dq48//ljNmzcvdbDv0qVL3RYOAACgIi4XmZCQEP3pT3+qjCxO+e9//+ux5wYAAN7F5SKzYMGCysgBAADgsqq7PC8AAICbubxFJiYmpsLrxRw6dOh3BQIAAHCWy0Vm+PDhDvfPnz+vb775RqtWrdKoUaPclQsAAOCSLuv067LMmTNHW7Zs+d2BAAAAnOW2Y2R69uypJUuWuOvhAAAALsltReaDDz5QaGioux4OAADgklzetdS2bVuHg32NMcrKytKPP/6oV155xa3hAAAAKuJykenTp4/DfR8fH4WFhem2225T06ZN3ZULAADgklwuMuPHj6+MHAAAAC7jgngAAMCynN4i4+PjU+GF8CTJZrOpqKjod4cCAABwhtNFZtmyZeVO27Bhg2bPnq2SkhK3hAIAAHCG00Xm3nvvLTW2d+9ejRkzRv/5z380YMAATZo0ya3hAAAAKnJZx8gcO3ZMjz32mFq2bKmioiJt27ZNixYtUoMGDdydDwAAoFwuFZnc3FyNHj1aTZo00e7du7VmzRr95z//UYsWLSorHwAAQLmc3rU0Y8YMTZ8+XZGRkXr33XfL3NUEAABQlWzGGOPMjD4+PgoMDFR8fLx8fX3LnW/p0qVuC+cOeXl5Cg4OVm5uroKCgjwdB5ep4ZgVno4AwGIOT+vt6Qj4HZz9/nZ6i8zAgQMvefo1AABAVXK6yCxcuLASYwAAALiOK/sCAADLosgAAADLosgAAADLosgAAADLosgAAADLcvqsJQAAULmseM0sT1+vhy0yAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsigyAADAsry6yKSlpekPf/iDateurfDwcPXp00d79+71dCwAAOAlvLrIfP7550pKStLXX3+t9PR0nT9/XnfeeacKCgo8HQ0AAHgBP08HqMiqVasc7i9cuFDh4eHKyMhQ165dPZQKAAB4C68uMhfLzc2VJIWGhpY7T2FhoQoLC+338/LyKj0XAADwDMsUmZKSEg0fPlydO3dWixYtyp0vLS1NEydOrJJMDcesqJLnAQC4jv9HXx28+hiZ30pKStKuXbv03nvvVThfamqqcnNz7bejR49WUUIAAFDVLLFF5qmnntLHH3+s9evX67rrrqtw3oCAAAUEBFRRMgAA4EleXWSMMfrLX/6iZcuW6b///a9iYmI8HQkAAHgRry4ySUlJeuedd/Thhx+qdu3aysrKkiQFBwcrMDDQw+kAAICnefUxMnPnzlVubq5uu+021atXz37797//7eloAADAC3j1FhljjKcjAAAAL+bVW2QAAAAqQpEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWRZEBAACWZYkiM2fOHDVs2FDVq1dXhw4dtGnTJk9HAgAAXsDri8y///1vpaSkaPz48dq6datat26tHj166OTJk56OBgAAPMzri8yLL76oxx57TIMGDVKzZs306quvqkaNGnrjjTc8HQ0AAHiYn6cDVOTcuXPKyMhQamqqfczHx0fx8fHasGFDmcsUFhaqsLDQfj83N1eSlJeX5/Z8JYVn3f6YAABYSWV8v/72cY0xFc7n1UXm1KlTKi4uVkREhMN4RESEvvvuuzKXSUtL08SJE0uNR0dHV0pGAACuZsGzKvfxz5w5o+Dg4HKne3WRuRypqalKSUmx3y8pKdHp06dVp04d2Ww2Dybzbnl5eYqOjtbRo0cVFBTk6ThXLdaDd2A9eAfWg3fw1HowxujMmTOKioqqcD6vLjJ169aVr6+vTpw44TB+4sQJRUZGlrlMQECAAgICHMZCQkIqK+IVJygoiP9heAHWg3dgPXgH1oN38MR6qGhLzAVefbCvv7+/2rdvrzVr1tjHSkpKtGbNGnXs2NGDyQAAgDfw6i0ykpSSkqKEhATFxcXppptu0qxZs1RQUKBBgwZ5OhoAAPAwry8yffv21Y8//qhx48YpKytLbdq00apVq0odAIzfJyAgQOPHjy+1Ww5Vi/XgHVgP3oH14B28fT3YzKXOawIAAPBSXn2MDAAAQEUoMgAAwLIoMgAAwLIoMgAAwLIoMgAAwLIoMleJadOmyWazafjw4faxX375RUlJSapTp45q1aql+++/v9RVlI8cOaLevXurRo0aCg8P16hRo1RUVFTF6a1twoQJstlsDremTZvap7Meqs4PP/yghx9+WHXq1FFgYKBatmypLVu22KcbYzRu3DjVq1dPgYGBio+P1/79+x0e4/Tp0xowYICCgoIUEhKiIUOGKD8/v6pfimU1bNiw1OfBZrMpKSlJEp+HqlBcXKyxY8cqJiZGgYGBaty4sSZPnuzw44yW+iwYXPE2bdpkGjZsaFq1amWGDRtmH//zn/9soqOjzZo1a8yWLVvMzTffbDp16mSfXlRUZFq0aGHi4+PNN998Yz755BNTt25dk5qa6oFXYV3jx483zZs3N8ePH7fffvzxR/t01kPVOH36tGnQoIFJTEw0GzduNIcOHTKrV682Bw4csM8zbdo0ExwcbJYvX262b99u7rnnHhMTE2N+/vln+zx33XWXad26tfn666/NF198YZo0aWL69+/viZdkSSdPnnT4LKSnpxtJZt26dcYYPg9VYcqUKaZOnTrm448/NpmZmWbx4sWmVq1a5u9//7t9Hit9FigyV7gzZ86Y2NhYk56ebm699VZ7kcnJyTHVqlUzixcvts+7Z88eI8ls2LDBGGPMJ598Ynx8fExWVpZ9nrlz55qgoCBTWFhYpa/DysaPH29at25d5jTWQ9UZPXq06dKlS7nTS0pKTGRkpHn++eftYzk5OSYgIMC8++67xhhjvv32WyPJbN682T7PypUrjc1mMz/88EPlhb+CDRs2zDRu3NiUlJTweagivXv3NoMHD3YY+9Of/mQGDBhgjLHeZ4FdS1e4pKQk9e7dW/Hx8Q7jGRkZOn/+vMN406ZNVb9+fW3YsEGStGHDBrVs2dLhKso9evRQXl6edu/eXTUv4Aqxf/9+RUVFqVGjRhowYICOHDkiifVQlT766CPFxcXpwQcfVHh4uNq2bavXX3/dPj0zM1NZWVkO6yI4OFgdOnRwWBchISGKi4uzzxMfHy8fHx9t3Lix6l7MFeLcuXN66623NHjwYNlsNj4PVaRTp05as2aN9u3bJ0navn27vvzyS/Xs2VOS9T4LXv8TBbh87733nrZu3arNmzeXmpaVlSV/f/9SvwweERGhrKws+zwX/xTEhfsX5sGldejQQQsXLtQNN9yg48ePa+LEibrlllu0a9cu1kMVOnTokObOnauUlBQ988wz2rx5s5KTk+Xv76+EhAT7e1nWe/3bdREeHu4w3c/PT6GhoayLy7B8+XLl5OQoMTFREv9fqipjxoxRXl6emjZtKl9fXxUXF2vKlCkaMGCAJFnus0CRuUIdPXpUw4YNU3p6uqpXr+7pOFe1C//KkaRWrVqpQ4cOatCggd5//30FBgZ6MNnVpaSkRHFxcZo6daokqW3bttq1a5deffVVJSQkeDjd1Wn+/Pnq2bOnoqKiPB3lqvL+++/r7bff1jvvvKPmzZtr27ZtGj58uKKioiz5WWDX0hUqIyNDJ0+eVLt27eTn5yc/Pz99/vnnmj17tvz8/BQREaFz584pJyfHYbkTJ04oMjJSkhQZGVnqbIEL9y/MA9eFhITo+uuv14EDBxQZGcl6qCL16tVTs2bNHMZuvPFG+26+C+9lWe/1b9fFyZMnHaYXFRXp9OnTrAsXff/99/rss8/06KOP2sf4PFSNUaNGacyYMerXr59atmypRx55RCNGjFBaWpok630WKDJXqO7du2vnzp3atm2b/RYXF6cBAwbY/1ytWjWtWbPGvszevXt15MgRdezYUZLUsWNH7dy50+Eva3p6uoKCgkp9IcB5+fn5OnjwoOrVq6f27duzHqpI586dtXfvXoexffv2qUGDBpKkmJgYRUZGOqyLvLw8bdy40WFd5OTkKCMjwz7P2rVrVVJSog4dOlTBq7hyLFiwQOHh4erdu7d9jM9D1Th79qx8fBy//n19fVVSUiLJgp+FKj20GB7127OWjPn1NMf69eubtWvXmi1btpiOHTuajh072qdfOM3xzjvvNNu2bTOrVq0yYWFhnOboopEjR5r//ve/JjMz03z11VcmPj7e1K1b15w8edIYw3qoKps2bTJ+fn5mypQpZv/+/ebtt982NWrUMG+99ZZ9nmnTppmQkBDz4Ycfmh07dph77723zFNO27ZtazZu3Gi+/PJLExsby+nXLiouLjb169c3o0ePLjWNz0PlS0hIMNdee6399OulS5eaunXrmqeffto+j5U+CxSZq8jFRebnn382Tz75pLnmmmtMjRo1zH333WeOHz/usMzhw4dNz549TWBgoKlbt64ZOXKkOX/+fBUnt7a+ffuaevXqGX9/f3Pttdeavn37Oly7hPVQdf7zn/+YFi1amICAANO0aVMzb948h+klJSVm7NixJiIiwgQEBJju3bubvXv3Oszz008/mf79+5tatWqZoKAgM2jQIHPmzJmqfBmWt3r1aiOp1HtrDJ+HqpCXl2eGDRtm6tevb6pXr24aNWpknn32WYfT1630WbAZ85tL+QEAAFgIx8gAAADLosgAAADLosgAAADLosgAAADLosgAAADLosgAAADLosgAAADLosgAAADLosgAAADLosgAAADLosgAAADL+v/bqJXlnWvQKwAAAABJRU5ErkJggg==", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:39.942430\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:40.598752\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:41.359067\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:42.121036\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:42.844071\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:43.589078\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:44.297185\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAioAAAGzCAYAAAABsTylAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy89olMNAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA2OUlEQVR4nO3dd3xUVf7/8fckpAEhISShSAglSAtNmhQDCgorrtiJgkBAQInSFIVlKQElgFJWRJRVyrIWFkXZFQEhIChFuig9EQSRXhKaAZLz+4Nv5ucwCWbiDHMhr+fjMQ+Yc8/c+7lzQubNuWVsxhgjAAAAC/LxdgEAAAB5IagAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgA/6dixYrq1q2bt8u45b3++uuqXLmyfH19Va9ePW+X86csXrxY9erVU2BgoGw2m86cOeOR7Xz99dey2Wz6+uuv7W3dunVTxYoV3bIuwMoIKrglzZo1SzabTRs3bsx1eatWrRQbG/unt/Pll19q5MiRf3o9hcVXX32ll19+Wc2bN9fMmTM1ZsyYG7btCxcuaOTIkdf9gF69erUefvhhlS5dWgEBAapYsaJ69+6tAwcOOPU9efKknnjiCQUFBWnq1KmaM2eOihUrpm7duslms9kfAQEBuv322zV8+HD99ttvHtm3a7eZ14MgjptREW8XAFjF7t275ePjWnb/8ssvNXXqVMJKPi1fvlw+Pj56//335e/vf0O3feHCBSUlJUm6GlSvNWXKFPXr10+VK1fWCy+8oLJly2rnzp167733NHfuXH355Zdq1qyZvf+GDRt09uxZjR49Wm3atHFYV0BAgN577z1JUnp6uhYsWKDRo0crLS1NH3zwgcu1x8XF6eLFi3m+Z71793aoYd++fRo+fLh69eqlu+66y95epUoVNWnS5LrrAqyGoAL8n4CAAG+X4LLz58+rWLFi3i4j344dO6agoCDLfUiuXr1a/fv3V4sWLbR48WIVLVrUvuy5555T8+bN9dhjj2n79u0qWbKkpKv7IkmhoaFO6ytSpIg6d+5sf96nTx81a9ZMH330kSZOnKjSpUu7VJ+Pj48CAwPzXN60aVM1bdrU/nzjxo0aPny4mjZt6lBHjuutC7AaDv0A/+fac1QuX76spKQkVa1aVYGBgSpVqpRatGihpUuXSro63T516lRJcphez3H+/Hm9+OKLioqKUkBAgKpVq6Y33nhD135h+cWLF9W3b1+Fh4crODhYDz74oA4dOiSbzeYwUzNy5EjZbDbt2LFDTz31lEqWLKkWLVpIkrZt26Zu3bqpcuXKCgwMVJkyZdS9e3edPHnSYVs569izZ486d+6skJAQRUREaNiwYTLG6ODBg+rQoYNKlCihMmXKaMKECfl6765cuaLRo0erSpUq9kMmf/vb35SZmWnvY7PZNHPmTJ0/f97+Xs2aNeu66503b54aNGigoKAghYeHq3Pnzjp06JBDn1atWuU6Q/L7czj279+viIgISVJSUpJ9+znv7+jRo2Wz2TR79myHkCJdnYUYP368Dh8+rHfffde+za5du0qSGjVq9IeHVWw2m1q0aCFjjH766Sd7+88//6w+ffqoWrVqCgoKUqlSpfT4449r//79Dq9353klua0r51Dotm3b1LJlSxUtWlQxMTH65JNPJEkrV65UkyZNFBQUpGrVqmnZsmVO6z106JC6d+9uP2xWq1YtzZgx40/XCzCjgltaenq6Tpw44dR++fLlP3ztyJEjlZycrGeeeUaNGzdWRkaGNm7cqM2bN+vee+9V79699euvv2rp0qWaM2eOw2uNMXrwwQe1YsUK9ejRQ/Xq1dOSJUs0aNAgHTp0SJMmTbL37datm/7zn//o6aef1p133qmVK1eqffv2edb1+OOPq2rVqhozZow99CxdulQ//fSTEhISVKZMGW3fvl3Tp0/X9u3btW7dOocAJUkdO3ZUjRo1NHbsWC1cuFCvvvqqwsLC9O677+qee+7RuHHj9MEHH+ill15So0aNFBcXd9336plnntHs2bP12GOP6cUXX9R3332n5ORk7dy5U5999pkkac6cOZo+fbrWr19vPyzy+0Mp15o1a5YSEhLUqFEjJScn6+jRo/rHP/6h1atXa8uWLbnOZOQlIiJC06ZN03PPPaeHH35YjzzyiCSpTp06unDhglJSUnTXXXepUqVKub6+Y8eO6tWrl7744gsNHjxYQ4cOVbVq1TR9+nSNGjVKlSpVUpUqVa5bQ074yJmRka4ePlqzZo3i4+NVvnx57d+/X9OmTVOrVq20Y8cOp9DkSadPn9YDDzyg+Ph4Pf7445o2bZri4+P1wQcfqH///nr22Wf11FNP6fXXX9djjz2mgwcPKjg4WJJ09OhR3XnnnbLZbHr++ecVERGhRYsWqUePHsrIyFD//v1v2H7gFmSAW9DMmTONpOs+atWq5fCa6Oho07VrV/vzunXrmvbt2193O4mJiSa3f0aff/65kWReffVVh/bHHnvM2Gw2k5qaaowxZtOmTUaS6d+/v0O/bt26GUlmxIgR9rYRI0YYSebJJ5902t6FCxec2j766CMjyaxatcppHb169bK3XblyxZQvX97YbDYzduxYe/vp06dNUFCQw3uSm61btxpJ5plnnnFof+mll4wks3z5cntb165dTbFixa67PmOMuXTpkomMjDSxsbHm4sWL9vYvvvjCSDLDhw+3t7Vs2dK0bNnSaR1du3Y10dHR9ufHjx93ek9/X3+/fv2uW1OdOnVMWFiY/XnOz9iGDRuctlusWDFz/Phxc/z4cZOammreeOMNY7PZTGxsrMnOzrb3zW3c1q5daySZf/3rX/a2FStWGElmxYoVee7f723YsMFIMjNnznRaltu6WrZsaSSZDz/80N62a9cuI8n4+PiYdevW2duXLFnitO4ePXqYsmXLmhMnTjhsKz4+3oSEhOS6n0B+cegHt7SpU6dq6dKlTo86der84WtDQ0O1fft27d271+Xtfvnll/L19VXfvn0d2l988UUZY7Ro0SJJVy9vla6ew/B7L7zwQp7rfvbZZ53agoKC7H//7bffdOLECd15552SpM2bNzv1f+aZZ+x/9/X1VcOGDWWMUY8ePeztoaGhqlatmsOhitx8+eWXkqSBAwc6tL/44ouSpIULF1739bnZuHGjjh07pj59+jicT9G+fXtVr169QOvMy9mzZyXJPjuQl+DgYGVkZORrnefPn1dERIQiIiIUExOjl156Sc2bN9eCBQscZrd+P26XL1/WyZMnFRMTo9DQ0FzHzZOKFy+u+Ph4+/Nq1aopNDRUNWrUUJMmTeztOX/P+bkwxujTTz/VX//6VxljdOLECfujbdu2Sk9Pv+H7glsLh35wS2vcuLEaNmzo1F6yZMlcDwn93qhRo9ShQwfdfvvtio2NVbt27fT000/nK+T8/PPPKleunNOHX40aNezLc/708fFxOuQQExOT57pzOzxx6tQpJSUl6eOPP7af5JkjPT3dqX+FChUcnoeEhCgwMFDh4eFO7dee53KtnH24tuYyZcooNDTUvq+uyHlNtWrVnJZVr15d3377rcvrzEvOGOUElrycPXv2D8NMjsDAQP3vf/+TJP3yyy8aP368/UTi37t48aKSk5M1c+ZMHTp0yOH8pdzGzZPKly/vdIgwJCREUVFRTm3S1UNFknT8+HGdOXNG06dP1/Tp03Nd97U/k4ArCCpAHuLi4pSWlqYFCxboq6++0nvvvadJkybpnXfecZiRuNGu/bCTpCeeeEJr1qzRoEGDVK9ePRUvXlzZ2dlq166dsrOznfr7+vrmq02S08m/ebn2Q+5GsdlsudaYlZWVr9fHxMSoSJEi2rZtW559MjMztXv37lxDb258fX0dLhdu27atqlevrt69e+u///2vvf2FF17QzJkz1b9/fzVt2lQhISGy2WyKj4/Pddw8Ka/x/6Ofi5w6O3fubD/B+Fr5CfdAXggqwHWEhYUpISFBCQkJOnfunOLi4jRy5Eh7UMnrwzk6OlrLli1z+l/4rl277Mtz/szOzta+fftUtWpVe7/U1NR813j69GmlpKQoKSlJw4cPt7cX5JBVQeTsw969e+0zRtLVEyzPnDlj31dX1yldvbfNPffc47Bs9+7dDussWbJkroenrp3JyWusihUrprvvvlvLly/Xzz//nGu9//nPf5SZmakHHnjA5X2RpLJly2rAgAFKSkrSunXr7IflPvnkE3Xt2tXh6qrffvvNY3e49YSIiAgFBwcrKyvL6X4ygDtwjgqQh2sPeRQvXlwxMTEOl9zm3MPk2g+W+++/X1lZWXrrrbcc2idNmiSbzaa//OUvkq7+T1uS3n77bYd+U6ZMyXedOf/jvXZWYfLkyflex59x//3357q9iRMnStJ1r2DKS8OGDRUZGal33nnH4f1etGiRdu7c6bDOKlWqaNeuXTp+/Li97fvvv9fq1asd1plzBU1uIeDvf/+7jDHq1q2bLl686LBs3759evnll1W2bFn17t3b5X3J8cILL6ho0aIaO3asvc3X19dp3KZMmZLv2SAr8PX11aOPPqpPP/1UP/74o9Py348LUBDMqAB5qFmzplq1aqUGDRooLCxMGzdu1CeffKLnn3/e3qdBgwaSpL59+6pt27by9fVVfHy8/vrXv+ruu+/W0KFDtX//ftWtW1dfffWVFixYoP79+9svZW3QoIEeffRRTZ48WSdPnrRfnrxnzx5J+TucUqJECcXFxWn8+PG6fPmybrvtNn311Vfat2+fB94VZ3Xr1lXXrl01ffp0nTlzRi1bttT69es1e/ZsPfTQQ7r77rtdXqefn5/GjRunhIQEtWzZUk8++aT98uSKFStqwIAB9r7du3fXxIkT1bZtW/Xo0UPHjh3TO++8o1q1ajmc/BoUFKSaNWtq7ty5uv322xUWFqbY2FjFxsYqLi5Ob7zxhgYOHKg6deqoW7duKlu2rHbt2qV//vOfys7O1pdffulwabGrSpUqpYSEBL399tvauXOnatSooQceeEBz5sxRSEiIatasqbVr12rZsmUqVapUgbfjDWPHjtWKFSvUpEkT9ezZUzVr1tSpU6e0efNmLVu2TKdOnfJ2ibiZeelqI8Cj8rp0NEfLli3/8PLkV1991TRu3NiEhoaaoKAgU716dfPaa6+ZS5cu2ftcuXLFvPDCCyYiIsLYbDaHS5XPnj1rBgwYYMqVK2f8/PxM1apVzeuvv+5weaoxxpw/f94kJiaasLAwU7x4cfPQQw+Z3bt3G0kOlwvnXFp8/Phxp/355ZdfzMMPP2xCQ0NNSEiIefzxx82vv/6a5yXO164jr8uGc3ufcnP58mWTlJRkKlWqZPz8/ExUVJQZMmSI+e233/K1nbzMnTvX1K9f3wQEBJiwsDDTqVMn88svvzj1+/e//20qV65s/P39Tb169cySJUtyvXx3zZo1pkGDBsbf3z/XS5VXrVplOnToYMLDw42fn5+pUKGC6dmzp9m/f7/TNv/o8uTcpKWlGV9fX/vP2enTp01CQoIJDw83xYsXN23btjW7du1y+lm8EZcn5zbO0dHRuV6iL8kkJiY6tB09etQkJiaaqKgo4+fnZ8qUKWNat25tpk+fnmuNQH7ZjMnnmXIAbpitW7eqfv36+ve//61OnTp5uxwA8BrOUQG87NpzIqSr53v4+Pj84R1hAeBWxzkqgJeNHz9emzZt0t13360iRYpo0aJFWrRokXr16uV0DwsAKGw49AN42dKlS5WUlKQdO3bo3LlzqlChgp5++mkNHTpURYrwfwkAhRtBBQAAWBbnqAAAAMsiqAAAAMu6qQ+AZ2dn69dff1VwcLDXvmcEAAC4xhijs2fPqly5cvLxuf6cyU0dVH799VeuigAA4CZ18OBBlS9f/rp9buqgkvNlbwcPHlSJEiW8XA0AAMiPjIwMRUVFOXxpa15u6qCSc7inRIkSBBUAAG4y+Tltg5NpAQCAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRFUAACAZRXxdgEAboyKgxd6uwSX7R/b3tslAPAyZlQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBlEVQAAIBleTWoZGVladiwYapUqZKCgoJUpUoVjR49WsYYb5YFAAAsoog3Nz5u3DhNmzZNs2fPVq1atbRx40YlJCQoJCREffv29WZpAADAArwaVNasWaMOHTqoffv2kqSKFSvqo48+0vr163Ptn5mZqczMTPvzjIyMG1InAADwDq8GlWbNmmn69Onas2ePbr/9dn3//ff69ttvNXHixFz7JycnKykp6QZXCU+rOHiht0tw2f6x7b1dAgAUCl4NKoMHD1ZGRoaqV68uX19fZWVl6bXXXlOnTp1y7T9kyBANHDjQ/jwjI0NRUVE3qlwAAHCDeTWo/Oc//9EHH3ygDz/8ULVq1dLWrVvVv39/lStXTl27dnXqHxAQoICAAC9UCgAAvMGrQWXQoEEaPHiw4uPjJUm1a9fWzz//rOTk5FyDCgAAKFy8ennyhQsX5OPjWIKvr6+ys7O9VBEAALASr86o/PWvf9Vrr72mChUqqFatWtqyZYsmTpyo7t27e7MsAABgEV4NKlOmTNGwYcPUp08fHTt2TOXKlVPv3r01fPhwb5YFAAAswqtBJTg4WJMnT9bkyZO9WQYAALAovusHAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYFkEFAABYlstBZfPmzfrhhx/szxcsWKCHHnpIf/vb33Tp0iW3FgcAAAo3l4NK7969tWfPHknSTz/9pPj4eBUtWlTz5s3Tyy+/7PYCAQBA4eVyUNmzZ4/q1asnSZo3b57i4uL04YcfatasWfr000/dXR8AACjEXA4qxhhlZ2dLkpYtW6b7779fkhQVFaUTJ064tzoAAFCouRxUGjZsqFdffVVz5szRypUr1b59e0nSvn37VLp0abcXCAAACi+Xg8rkyZO1efNmPf/88xo6dKhiYmIkSZ988omaNWvm9gIBAEDhVcTVF9SpU8fhqp8cr7/+unx9fd1SFAAAgFTA+6icOXNG7733noYMGaJTp05Jknbs2KFjx465tTgAAFC4uTyjsm3bNrVu3VqhoaHav3+/evbsqbCwMM2fP18HDhzQv/71L0/UCQAACiGXZ1QGDhyohIQE7d27V4GBgfb2+++/X6tWrXJrcQAAoHBzOahs2LBBvXv3dmq/7bbbdOTIEbcUBQAAIBUgqAQEBCgjI8Opfc+ePYqIiHBLUQAAAFIBgsqDDz6oUaNG6fLly5Ikm82mAwcO6JVXXtGjjz7q9gIBAEDh5XJQmTBhgs6dO6fIyEhdvHhRLVu2VExMjIKDg/Xaa695okYAAFBIuXzVT0hIiJYuXarVq1fr+++/17lz53THHXeoTZs2nqgPAAAUYi4HlRzNmzdX8+bN3VkLAACAA5cP/fTt21dvvvmmU/tbb72l/v37u6MmAAAASQUIKp9++mmuMynNmjXTJ5984paiAAAApAIElZMnTyokJMSpvUSJEjpx4oRbigIAAJAKEFRiYmK0ePFip/ZFixapcuXKbikKAABAKsDJtAMHDtTzzz+v48eP65577pEkpaSkaMKECZo8ebK76wMAAIWYy0Gle/fuyszM1GuvvabRo0dLkipWrKhp06apS5cubi8QAAAUXgW6PPm5557Tc889p+PHjysoKEjFixd3d10AAAAFv4+KJL7bBwAAeJTLJ9MePXpUTz/9tMqVK6ciRYrI19fX4QEAAOAuLs+odOvWTQcOHNCwYcNUtmxZ2Ww2T9QFAADgelD59ttv9c0336hevXoeKAcAAOD/c/nQT1RUlIwxbivg0KFD6ty5s0qVKqWgoCDVrl1bGzdudNv6AQDAzcvloDJ58mQNHjxY+/fv/9MbP336tJo3by4/Pz8tWrRIO3bs0IQJE1SyZMk/vW4AAHDzc/nQT8eOHXXhwgVVqVJFRYsWlZ+fn8PyU6dO5Xtd48aNU1RUlGbOnGlvq1SpkqslAQCAW5TLQcWdd5/973//q7Zt2+rxxx/XypUrddttt6lPnz7q2bNnrv0zMzOVmZlpf56RkeG2WgAAgPXYjDtPOHFRYGCgpKu35X/88ce1YcMG9evXT++88466du3q1H/kyJFKSkpyak9PT1eJEiU8Xi88o+Lghd4uAXCb/WPbe7sEwPIyMjIUEhKSr8/vAgWVtLQ0zZw5U2lpafrHP/6hyMhILVq0SBUqVFCtWrXyvR5/f381bNhQa9assbf17dtXGzZs0Nq1a5365zajEhUVRVC5yRFUcCshqAB/zJWg4vLJtCtXrlTt2rX13Xffaf78+Tp37pwk6fvvv9eIESNcWlfZsmVVs2ZNh7YaNWrowIEDufYPCAhQiRIlHB4AAODW5XJQGTx4sF599VUtXbpU/v7+9vZ77rlH69atc2ldzZs31+7dux3a9uzZo+joaFfLAgAAtyCXg8oPP/yghx9+2Kk9MjJSJ06ccGldAwYM0Lp16zRmzBilpqbqww8/1PTp05WYmOhqWQAA4BbkclAJDQ3V4cOHndq3bNmi2267zaV1NWrUSJ999pk++ugjxcbGavTo0Zo8ebI6derkalkAAOAW5PLlyfHx8XrllVc0b9482Ww2ZWdna/Xq1XrppZfUpUsXlwt44IEH9MADD7j8OgAAcOtzeUZlzJgxql69uqKionTu3DnVrFlTcXFxatasmf7+9797okYAAFBIuTSjYozRkSNH9Oabb2r48OH64YcfdO7cOdWvX19Vq1b1VI0AAKCQcjmoxMTEaPv27apataqioqI8VRcAAIBrh358fHxUtWpVnTx50lP1AAAA2Ll8jsrYsWM1aNAg/fjjj56oBwAAwM7lq366dOmiCxcuqG7duvL391dQUJDDcle+PRkAAOB6vPrtyQAAANfjUlC5fPmyVq5cqWHDhqlSpUqeqgkAAECSi+eo+Pn56dNPP/VULQAAAA5cPpn2oYce0ueff+6BUgAAABy5fI5K1apVNWrUKK1evVoNGjRQsWLFHJb37dvXbcUBAIDCzeWg8v777ys0NFSbNm3Spk2bHJbZbDaCCgAAcBuXg8q+ffs8UQcAAIATl89RAQAAuFFcnlHp3r37dZfPmDGjwMUAAAD8nstB5fTp0w7PL1++rB9//FFnzpzRPffc47bCAAAAXA4qn332mVNbdna2nnvuOVWpUsUtRQEAAEhuOkfFx8dHAwcO1KRJk9yxOgAAAEluPJk2LS1NV65ccdfqAAAAXD/0M3DgQIfnxhgdPnxYCxcuVNeuXd1WGAAAgMtBZcuWLQ7PfXx8FBERoQkTJvzhFUEAAACucDmorFixwhN1AAAAOHH5HJV9+/Zp7969Tu179+7V/v373VETAACApAIElW7dumnNmjVO7d999526devmjpoAAAAkFSCobNmyRc2bN3dqv/POO7V161Z31AQAACCpAEHFZrPp7NmzTu3p6enKyspyS1EAAABSAYJKXFyckpOTHUJJVlaWkpOT1aJFC7cWBwAACjeXr/oZN26c4uLiVK1aNd11112SpG+++UYZGRlavny52wsEAACFl8szKjVr1tS2bdv0xBNP6NixYzp79qy6dOmiXbt2KTY21hM1AgCAQsrlGRVJKleunMaMGePuWgAAABy4PKMyc+ZMzZs3z6l93rx5mj17tluKAgAAkAoQVJKTkxUeHu7UHhkZySwLAABwK5cP/Rw4cECVKlVyao+OjtaBAwfcUhQA3KwqDl7o7RJctn9se2+XUGjw8+E6l2dUIiMjtW3bNqf277//XqVKlXJLUQAAAFIBgsqTTz6pvn37asWKFcrKylJWVpaWL1+ufv36KT4+3hM1AgCAQsrlQz+jR4/W/v371bp1axUpcvXl2dnZ6tKlC+eoAAAAt3I5qPj7+2vu3LkaPXq0vv/+ewUFBal27dqKjo72RH0AAKAQK9B9VCQpLCxMd999d65XAAEAALiDS+eonDlzRomJiQoPD1fp0qVVunRphYeH6/nnn9eZM2c8VCIAACis8j2jcurUKTVt2lSHDh1Sp06dVKNGDUnSjh07NGvWLKWkpGjNmjUqWbKkx4oFAACFS76DyqhRo+Tv76+0tDSVLl3aadl9992nUaNGadKkSW4vEgAAFE75PvTz+eef64033nAKKZJUpkwZjR8/Xp999plbiwMAAIVbvoPK4cOHVatWrTyXx8bG6siRI24pCgAAQHIhqISHh2v//v15Lt+3b5/CwsLcURMAAIAkF4JK27ZtNXToUF26dMlpWWZmpoYNG6Z27dq5tTgAAFC4uXQybcOGDVW1alUlJiaqevXqMsZo586devvtt5WZmak5c+Z4slYAAFDI5DuolC9fXmvXrlWfPn00ZMgQGWMkSTabTffee6/eeustRUVFeaxQAABQ+Lh0Z9pKlSpp0aJFOn36tPbu3StJiomJ4dwUAADgEQW6hX7JkiXVuHFjd9cCAADgwKVb6AMAANxIBBUAAGBZBBUAAGBZ+Qoqd9xxh06fPi3p6mXKFy5c8GhRAAAAUj6Dys6dO3X+/HlJUlJSks6dO+fRogAAAKR8XvVTr149JSQkqEWLFjLG6I033lDx4sVz7Tt8+HC3FggAAAqvfAWVWbNmacSIEfriiy9ks9m0aNEiFSni/FKbzUZQAQAAbpOvoFKtWjV9/PHHkiQfHx+lpKQoMjLSo4UBAAC4fMO37OxsT9QBAADgpEB3pk1LS9PkyZO1c+dOSVLNmjXVr18/ValSxa3FAQCAws3l+6gsWbJENWvW1Pr161WnTh3VqVNH3333nWrVqqWlS5d6okYAAFBIuTyjMnjwYA0YMEBjx451an/llVd07733uq04AABQuLk8o7Jz50716NHDqb179+7asWOHW4oCAACQChBUIiIitHXrVqf2rVu3ciUQAABwK5cP/fTs2VO9evXSTz/9pGbNmkmSVq9erXHjxmngwIFuLxAAABReLgeVYcOGKTg4WBMmTNCQIUMkSeXKldPIkSPVt29ftxcIAAAKL5eDis1m04ABAzRgwACdPXtWkhQcHOz2wgAAAAp0H5UcBBQAAOBJLp9M6yljx46VzWZT//79vV0KAACwCEsElQ0bNujdd99VnTp1vF0KAACwEK8HlXPnzqlTp0765z//qZIlS3q7HAAAYCEuBZXLly+rdevW2rt3r9sKSExMVPv27dWmTZs/7JuZmamMjAyHBwAAuHW5dDKtn5+ftm3b5raNf/zxx9q8ebM2bNiQr/7JyclKSkpy2/ZvRRUHL/R2CQBuMjfj7439Y9t7uwTcIC4f+uncubPef//9P73hgwcPql+/fvrggw8UGBiYr9cMGTJE6enp9sfBgwf/dB0AAMC6XL48+cqVK5oxY4aWLVumBg0aqFixYg7LJ06cmK/1bNq0SceOHdMdd9xhb8vKytKqVav01ltvKTMzU76+vg6vCQgIUEBAgKslAwCAm5TLQeXHH3+0h4s9e/Y4LLPZbPleT+vWrfXDDz84tCUkJKh69ep65ZVXnEIKAAAofFwOKitWrHDLhoODgxUbG+vQVqxYMZUqVcqpHQAAFE4Fvjw5NTVVS5Ys0cWLFyVJxhi3FQUAACAVYEbl5MmTeuKJJ7RixQrZbDbt3btXlStXVo8ePVSyZElNmDChwMV8/fXXBX4tAAC49bg8ozJgwAD5+fnpwIEDKlq0qL29Y8eOWrx4sVuLAwAAhZvLMypfffWVlixZovLlyzu0V61aVT///LPbCgMAAHB5RuX8+fMOMyk5Tp06xaXDAADArVwOKnfddZf+9a9/2Z/bbDZlZ2dr/Pjxuvvuu91aHAAAKNxcPvQzfvx4tW7dWhs3btSlS5f08ssva/v27Tp16pRWr17tiRoBAEAh5fKMSmxsrPbs2aMWLVqoQ4cOOn/+vB555BFt2bJFVapU8USNAACgkHJ5RkWSQkJCNHToUHfXAgAA4KBAQeX06dN6//33tXPnTklSzZo1lZCQoLCwMLcWBwAACjeXD/2sWrVKFStW1JtvvqnTp0/r9OnTevPNN1WpUiWtWrXKEzUCAIBCyuUZlcTERHXs2FHTpk2zf3FgVlaW+vTpo8TERKcvGgQAACgol2dUUlNT9eKLLzp8u7Gvr68GDhyo1NRUtxYHAAAKN5eDyh133GE/N+X3du7cqbp167qlKAAAACmfh362bdtm/3vfvn3Vr18/paam6s4775QkrVu3TlOnTtXYsWM9UyUAACiU8hVU6tWrJ5vNJmOMve3ll1926vfUU0+pY8eO7qsOAAAUavkKKvv27fN0HQAAAE7yFVSio6M9XQcAAICTAt3w7ddff9W3336rY8eOKTs722FZ37593VIYAACAy0Fl1qxZ6t27t/z9/VWqVCnZbDb7MpvNRlABAABu43JQGTZsmIYPH64hQ4bIx8flq5sBAADyzeWkceHCBcXHxxNSAACAx7mcNnr06KF58+Z5ohYAAAAHLh/6SU5O1gMPPKDFixerdu3a8vPzc1g+ceJEtxUHAAAKtwIFlSVLlqhatWqS5HQyLQAAgLu4HFQmTJigGTNmqFu3bh4oBwAA4P9z+RyVgIAANW/e3BO1AAAAOHA5qPTr109TpkzxRC0AAAAOXD70s379ei1fvlxffPGFatWq5XQy7fz5891WHAAAKNxcDiqhoaF65JFHPFELAACAA5eDysyZMz1RBwAAgBNuLwsAACzL5RmVSpUqXfd+KT/99NOfKggAACCHy0Glf//+Ds8vX76sLVu2aPHixRo0aJC76gIAAHA9qPTr1y/X9qlTp2rjxo1/uiAAAIAcbjtH5S9/+Ys+/fRTd60OAADAfUHlk08+UVhYmLtWBwAA4Pqhn/r16zucTGuM0ZEjR3T8+HG9/fbbbi0OAAAUbi4HlYceesjhuY+PjyIiItSqVStVr17dXXUBAAC4HlRGjBjhiToAAACccMM3AABgWfmeUfHx8bnujd4kyWaz6cqVK3+6KAAAAMmFoPLZZ5/luWzt2rV68803lZ2d7ZaiAAAAJBeCSocOHZzadu/ercGDB+t///ufOnXqpFGjRrm1OAAAULgV6ByVX3/9VT179lTt2rV15coVbd26VbNnz1Z0dLS76wMAAIWYS0ElPT1dr7zyimJiYrR9+3alpKTof//7n2JjYz1VHwAAKMTyfehn/PjxGjdunMqUKaOPPvoo10NBAAAA7pTvoDJ48GAFBQUpJiZGs2fP1uzZs3PtN3/+fLcVBwAACrd8B5UuXbr84eXJAAAA7pTvoDJr1iwPlgEAAOCMO9MCAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADLIqgAAADL8mpQSU5OVqNGjRQcHKzIyEg99NBD2r17tzdLAgAAFuLVoLJy5UolJiZq3bp1Wrp0qS5fvqz77rtP58+f92ZZAADAIop4c+OLFy92eD5r1ixFRkZq06ZNiouL81JVAADAKrwaVK6Vnp4uSQoLC8t1eWZmpjIzM+3PMzIybkhdAADAOywTVLKzs9W/f381b95csbGxufZJTk5WUlLSDaup4uCFN2xbAADAmWWu+klMTNSPP/6ojz/+OM8+Q4YMUXp6uv1x8ODBG1ghAAC40Swxo/L888/riy++0KpVq1S+fPk8+wUEBCggIOAGVgYAALzJq0HFGKMXXnhBn332mb7++mtVqlTJm+UAAACL8WpQSUxM1IcffqgFCxYoODhYR44ckSSFhIQoKCjIm6UBAAAL8Oo5KtOmTVN6erpatWqlsmXL2h9z5871ZlkAAMAivH7oBwAAIC+WueoHAADgWgQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWUW8XQAAAK6qOHiht0vADcKMCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCxLBJWpU6eqYsWKCgwMVJMmTbR+/XpvlwQAACzA60Fl7ty5GjhwoEaMGKHNmzerbt26atu2rY4dO+bt0gAAgJd5PahMnDhRPXv2VEJCgmrWrKl33nlHRYsW1YwZM7xdGgAA8LIi3tz4pUuXtGnTJg0ZMsTe5uPjozZt2mjt2rVO/TMzM5WZmWl/np6eLknKyMjwSH3ZmRc8sl4AAG4WnviMzVmnMeYP+3o1qJw4cUJZWVkqXbq0Q3vp0qW1a9cup/7JyclKSkpyao+KivJYjQAAFGYhkz237rNnzyokJOS6fbwaVFw1ZMgQDRw40P48Oztbp06dUqlSpWSz2Qq83oyMDEVFRengwYMqUaKEO0qFGzAu1sOYWBPjYj2MyfUZY3T27FmVK1fuD/t6NaiEh4fL19dXR48edWg/evSoypQp49Q/ICBAAQEBDm2hoaFuq6dEiRL8QFkQ42I9jIk1MS7Ww5jk7Y9mUnJ49WRaf39/NWjQQCkpKfa27OxspaSkqGnTpl6sDAAAWIHXD/0MHDhQXbt2VcOGDdW4cWNNnjxZ58+fV0JCgrdLAwAAXub1oNKxY0cdP35cw4cP15EjR1SvXj0tXrzY6QRbTwoICNCIESOcDivBuxgX62FMrIlxsR7GxH1sJj/XBgEAAHiB12/4BgAAkBeCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsKxbIqhMnTpVFStWVGBgoJo0aaL169fn2Xf+/Plq2LChQkNDVaxYMdWrV09z5sxx6NOtWzfZbDaHR7t27Rz6VKxY0anP2LFjPbJ/Nyt3j4sk7dy5Uw8++KBCQkJUrFgxNWrUSAcOHLAv/+2335SYmKhSpUqpePHievTRR53ufFyYeWNMWrVq5fRv5dlnn/XI/t2s3D0u177fOY/XX3/d3ufUqVPq1KmTSpQoodDQUPXo0UPnzp3z2D7ejLwxLny25MLc5D7++GPj7+9vZsyYYbZv32569uxpQkNDzdGjR3Ptv2LFCjN//nyzY8cOk5qaaiZPnmx8fX3N4sWL7X26du1q2rVrZw4fPmx/nDp1ymE90dHRZtSoUQ59zp0759F9vZl4YlxSU1NNWFiYGTRokNm8ebNJTU01CxYscFjns88+a6KiokxKSorZuHGjufPOO02zZs08vr83A2+NScuWLU3Pnj0d/q2kp6d7fH9vFp4Yl9+/14cPHzYzZswwNpvNpKWl2fu0a9fO1K1b16xbt8588803JiYmxjz55JMe39+bhbfGhc8WZzd9UGncuLFJTEy0P8/KyjLlypUzycnJ+V5H/fr1zd///nf7865du5oOHTpc9zXR0dFm0qRJrpZbaHhiXDp27Gg6d+6cZ/8zZ84YPz8/M2/ePHvbzp07jSSzdu1aF/fg1uONMTHmalDp16+fy/UWFp4Yl2t16NDB3HPPPfbnO3bsMJLMhg0b7G2LFi0yNpvNHDp0yMU9uDV5Y1yM4bMlNzf1oZ9Lly5p06ZNatOmjb3Nx8dHbdq00dq1a//w9cYYpaSkaPfu3YqLi3NY9vXXXysyMlLVqlXTc889p5MnTzq9fuzYsSpVqpTq16+v119/XVeuXPnzO3UL8MS4ZGdna+HChbr99tvVtm1bRUZGqkmTJvr888/tr9u0aZMuX77ssN3q1aurQoUK+drurcxbY5Ljgw8+UHh4uGJjYzVkyBBduHDBbft2M/Pk77AcR48e1cKFC9WjRw9729q1axUaGqqGDRva29q0aSMfHx999913f2KPbg3eGpccfLY48vot9P+MEydOKCsry+l2+6VLl9auXbvyfF16erpuu+02ZWZmytfXV2+//bbuvfde+/J27drpkUceUaVKlZSWlqa//e1v+stf/qK1a9fK19dXktS3b1/dcccdCgsL05o1azRkyBAdPnxYEydO9MzO3kQ8MS7Hjh3TuXPnNHbsWL366qsaN26cFi9erEceeUQrVqxQy5YtdeTIEfn7+zt9o3bp0qV15MgRt+/nzcRbYyJJTz31lKKjo1WuXDlt27ZNr7zyinbv3q358+d7bodvEp76HfZ7s2fPVnBwsB555BF725EjRxQZGenQr0iRIgoLCyv0/1Yk742LxGdLbm7qoFJQwcHB2rp1q86dO6eUlBQNHDhQlStXVqtWrSRJ8fHx9r61a9dWnTp1VKVKFX399ddq3bq1pKtfppijTp068vf3V+/evZWcnMx3OxTQ9cYlOztbktShQwcNGDBAklSvXj2tWbNG77zzjv1DEe7ljjHp1auXfX21a9dW2bJl1bp1a6WlpalKlSo3fqduAX/0O+z3ZsyYoU6dOikwMPDGF1rIuGNc+GxxdlMHlfDwcPn6+jpd1XH06FGVKVMmz9f5+PgoJiZG0tVfrDt37lRycnKuP0ySVLlyZYWHhys1NdUeVK7VpEkTXblyRfv371e1atUKtkO3CE+MS3h4uIoUKaKaNWs6vKZGjRr69ttvJUllypTRpUuXdObMGYdZlT/abmHgrTHJTZMmTSRJqamphT6oePp32DfffKPdu3dr7ty5Du1lypTRsWPHHNquXLmiU6dOFfp/K5L3xiU3fLbc5Jcn+/v7q0GDBkpJSbG3ZWdnKyUlRU2bNs33erKzs5WZmZnn8l9++UUnT55U2bJl8+yzdetW+fj4OE2nFkaeGBd/f381atRIu3fvduizZ88eRUdHS5IaNGggPz8/h+3u3r1bBw4ccGm7tyJvjUlutm7dKknX/fdUWHj6d9j777+vBg0aqG7dug7tTZs21ZkzZ7Rp0yZ72/Lly5WdnW0PkoWZt8YlN3y26Na4PDkgIMDMmjXL7Nixw/Tq1cuEhoaaI0eOGGOMefrpp83gwYPt/ceMGWO++uork5aWZnbs2GHeeOMNU6RIEfPPf/7TGGPM2bNnzUsvvWTWrl1r9u3bZ5YtW2buuOMOU7VqVfPbb78ZY4xZs2aNmTRpktm6datJS0sz//73v01ERITp0qXLjX8DLMrd42KMMfPnzzd+fn5m+vTpZu/evWbKlCnG19fXfPPNN/Y+zz77rKlQoYJZvny52bhxo2natKlp2rTpjdtxC/PGmKSmpppRo0aZjRs3mn379pkFCxaYypUrm7i4uBu78xbmiXExxpj09HRTtGhRM23atFy3265dO1O/fn3z3XffmW+//dZUrVqVy5N/xxvjwmdL7m76oGKMMVOmTDEVKlQw/v7+pnHjxmbdunX2ZS1btjRdu3a1Px86dKiJiYkxgYGBpmTJkqZp06bm448/ti+/cOGCue+++0xERITx8/Mz0dHRpmfPnvYfTmOM2bRpk2nSpIkJCQkxgYGBpkaNGmbMmDH2IIOr3DkuOd5//317v7p165rPP//cYfnFixdNnz59TMmSJU3RokXNww8/bA4fPuyxfbzZ3OgxOXDggImLizNhYWEmICDAxMTEmEGDBnEflWt4YlzeffddExQUZM6cOZPrNk+ePGmefPJJU7x4cVOiRAmTkJBgzp496/Z9u5nd6HHhsyV3NmOM8fasDgAAQG5u6nNUAADArY2gAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALIugAgAALOv/ASzux5opKOAqAAAAAElFTkSuQmCC", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:44.963548\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:45.397387\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:45.806292\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:46.154362\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:46.530359\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:46.933281\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:47.465858\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:47.816920\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:48.168977\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "image/svg+xml": "\n\n\n \n \n \n \n 2022-11-03T13:02:48.541983\n image/svg+xml\n \n \n Matplotlib v3.6.0, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "TestDispersion.allInfo()\n" ] @@ -997,7 +475,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1025,7 +503,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1034,7 +512,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1044,7 +522,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1082,18 +560,20 @@ " \"nose_name_kind\": [nose.kind],\n", " \"nose_name_length\": (nose.length, 0.001),\n", " \"nose_name_distanceToCM\": (nose.distanceToCM, 0.010),\n", - " \"finSet_name_numberOfFins\": [fins.numberOfFins],\n", + " \"finSet_name_n\": [fins.n],\n", " \"finSet_name_rootChord\": (fins.rootChord, 0.001),\n", " \"finSet_name_tipChord\": (fins.tipChord, 0.001),\n", " \"finSet_name_span\": (fins.span, 0.001),\n", " \"finSet_name_distanceToCM\": (fins.distanceToCM, 0.010),\n", - " \"finSet_name_radius\": (fins.radius, 0.001),\n", " \"finSet_name_airfoil\": [fins.airfoil],\n", " \"tail_name_topRadius\": (tail.topRadius, 0.001),\n", " \"tail_name_bottomRadius\": (tail.bottomRadius, 0.001),\n", " \"tail_name_length\": (tail.length, 0.001),\n", " \"tail_name_distanceToCM\": (tail.distanceToCM, 0.010),\n", - " \"CdS\": [(10, 2), (1, 0.3)],\n", + " \"parachute_Main_CdS\": (10,2),\n", + " \"parachute_Main_trigger\": mainTrigger,\n", + " \"parachute_Drogue_CdS\": (1,0.3),\n", + " \"parachute_Drogue_trigger\": drogueTrigger,\n", " # Flight Parameters\n", " \"inclination\": [85],\n", " \"heading\": [90],\n", @@ -1102,19 +582,9 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Completed 50 iterations successfully. Total CPU time: 27.390625 s. Total wall time: 32.03746747970581 s'" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "TestDispersion2.run_dispersion(\n", " number_of_simulations=50,\n", @@ -1125,17 +595,9 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "A total of 50 simulations were loaded from the following file: dispersion_analysis_outputs/disp_class_example2.disp_outputs.txt\n" - ] - } - ], + "outputs": [], "source": [ "TestDispersion2.import_results()\n" ] @@ -1149,37 +611,9 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "outOfRailStaticMargin: μ = 2.137, σ = 0.243\n", - "tFinal: μ = 43.816, σ = 0.789\n", - "finalStaticMargin: μ = 2.678, σ = 0.297\n", - "yImpact: μ = 0.002, σ = 0.001\n", - "initialStaticMargin: μ = 2.055, σ = 0.248\n", - "apogeeY: μ = 0.000, σ = 0.000\n", - "lateralSurfaceWind: μ = 0.000, σ = 0.000\n", - "apogee: μ = 2234.290, σ = 81.435\n", - "maxAccelerationTime: μ = 0.241, σ = 0.256\n", - "maxSpeed: μ = 263.080, σ = 6.998\n", - "xImpact: μ = 571.125, σ = 21.041\n", - "maxSpeedTime: μ = 3.266, σ = 0.008\n", - "apogeeTime: μ = 19.651, σ = 0.329\n", - "impactVelocity: μ = -135.936, σ = 2.657\n", - "maxAcceleration: μ = 102.448, σ = 3.946\n", - "apogeeX: μ = 337.368, σ = 11.944\n", - "outOfRailVelocity: μ = 25.742, σ = 0.428\n", - "outOfRailTime: μ = 0.364, σ = 0.006\n", - "frontalSurfaceWind: μ = 0.000, σ = 0.000\n", - "numberOfEvents: μ = 0.000, σ = 0.000\n", - "executionTime: μ = 0.373, σ = 0.080\n" - ] - } - ], + "outputs": [], "source": [ "TestDispersion2.print_results()\n" ] @@ -1202,7 +636,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.0" + "version": "3.10.5 (tags/v3.10.5:f377153, Jun 6 2022, 16:14:13) [MSC v.1929 64 bit (AMD64)]" }, "vscode": { "interpreter": {