From a25c2ee8b3d526223856da228b82495f9ae4074f Mon Sep 17 00:00:00 2001 From: Morgan Thomas Date: Sun, 6 Oct 2024 21:02:53 +0100 Subject: [PATCH 01/36] Inital implementation of callback function with some quick fixes of existing functions for compatability --- rocketpy/simulation/monte_carlo.py | 25 +++++++++++++++++++++---- 1 file changed, 21 insertions(+), 4 deletions(-) diff --git a/rocketpy/simulation/monte_carlo.py b/rocketpy/simulation/monte_carlo.py index cbe7b6734..ef4818d2b 100644 --- a/rocketpy/simulation/monte_carlo.py +++ b/rocketpy/simulation/monte_carlo.py @@ -80,7 +80,8 @@ class MonteCarlo: """ def __init__( - self, filename, environment, rocket, flight, export_list=None + self, filename, environment, rocket, flight, export_list=None, + export_function=None ): # pylint: disable=too-many-statements """ Initialize a MonteCarlo object. @@ -104,6 +105,11 @@ def __init__( `out_of_rail_stability_margin`, `out_of_rail_time`, `out_of_rail_velocity`, `max_mach_number`, `frontal_surface_wind`, `lateral_surface_wind`. Default is None. + export_function : callable, optional + A function which gets called at the end of a simulation to collect + additional data to be exported that isn't pre-defined. Takes the + Flight object as an argument and returns a dictionary. Default is None. + Returns ------- @@ -132,6 +138,7 @@ def __init__( self._last_print_len = 0 # used to print on the same line self.export_list = self.__check_export_list(export_list) + self.export_function = export_function try: self.import_inputs() @@ -359,6 +366,13 @@ def __export_flight_data( for export_item in self.export_list } + if self.export_function is not None: + additional_exports = self.export_function(flight) + for key in additional_exports.keys(): + if key in self.export_list: + raise ValueError(f"Invalid export function, returns dict which overwrites key, '{key}'") + results = results | additional_exports + input_file.write(json.dumps(inputs_dict, cls=RocketPyEncoder) + "\n") output_file.write(json.dumps(results, cls=RocketPyEncoder) + "\n") @@ -654,9 +668,12 @@ def set_processed_results(self): """ self.processed_results = {} for result, values in self.results.items(): - mean = np.mean(values) - stdev = np.std(values) - self.processed_results[result] = (mean, stdev) + try: + mean = np.mean(values) + stdev = np.std(values) + self.processed_results[result] = (mean, stdev) + except TypeError: + self.processed_results[result] = (None, None) # Import methods From 69aaece6534debdb9493efb57d40f35dee5c8e87 Mon Sep 17 00:00:00 2001 From: Niklas Freund Date: Sun, 10 Nov 2024 01:28:08 +0100 Subject: [PATCH 02/36] ENH: Implement optional plot saving (#597) * ENH: Add filename option to most plot functions * MNT: Use f-strings in plot_helpers.py Co-authored-by: Gui-FernandesBR <63590233+Gui-FernandesBR@users.noreply.github.com> * MNT: Run isort * DOC: Make docstring fit into 80 columns and reference matplotlib formats * MNT: Run black * MNT: Move get_matplotlib_supported_file_endings into tools * Apply suggestions from code review Co-authored-by: Lucas Prates <57069366+Lucas-Prates@users.noreply.github.com> * Fix function/__call__ check * Change plot_helpers warnings from UserWarning to ValueError * Update docstrings * Fix issues from rebase * MNT: post merge fixes regarding plots. * MNT: post merge fixes and linting. * TST: implement testing of show or save plots. * MNT: Correct CHANGELOG file. * MNT: Improve error handling of unsupported file endings. * DOC: add doc section for plot saving. --------- Co-authored-by: Gui-FernandesBR <63590233+Gui-FernandesBR@users.noreply.github.com> Co-authored-by: Lucas Prates <57069366+Lucas-Prates@users.noreply.github.com> Co-authored-by: Pedro Bressan --- CHANGELOG.md | 2 +- docs/user/first_simulation.rst | 39 +++++ rocketpy/mathutils/function.py | 40 ++++- rocketpy/motors/hybrid_motor.py | 29 ++-- rocketpy/motors/liquid_motor.py | 26 ++- rocketpy/motors/motor.py | 17 +- rocketpy/motors/solid_motor.py | 29 ++-- rocketpy/motors/tank.py | 19 ++- rocketpy/plots/aero_surface_plots.py | 67 ++++++-- rocketpy/plots/compare/compare_flights.py | 33 ++-- rocketpy/plots/environment_analysis_plots.py | 167 +++++++++++++++---- rocketpy/plots/environment_plots.py | 50 +++++- rocketpy/plots/flight_plots.py | 142 +++++++++++++--- rocketpy/plots/hybrid_motor_plots.py | 88 +++++++--- rocketpy/plots/liquid_motor_plots.py | 13 +- rocketpy/plots/motor_plots.py | 105 ++++++++---- rocketpy/plots/plot_helpers.py | 67 ++++++++ rocketpy/plots/rocket_plots.py | 35 +++- rocketpy/plots/solid_motor_plots.py | 19 ++- rocketpy/plots/tank_plots.py | 13 +- rocketpy/rocket/aero_surface/fins/fins.py | 12 +- rocketpy/rocket/aero_surface/nose_cone.py | 16 +- rocketpy/rocket/rocket.py | 9 +- rocketpy/simulation/monte_carlo.py | 4 +- rocketpy/tools.py | 18 ++ rocketpy/utilities.py | 24 ++- tests/integration/test_plots.py | 42 ++++- tests/unit/test_plots.py | 53 +++++- 28 files changed, 953 insertions(+), 225 deletions(-) create mode 100644 rocketpy/plots/plot_helpers.py diff --git a/CHANGELOG.md b/CHANGELOG.md index 8ccef502b..271c3a2bf 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -32,7 +32,7 @@ Attention: The newest changes should be on top --> ### Added -- +- ENH: Implement optional plot saving [#597](https://github.com/RocketPy-Team/RocketPy/pull/597) ### Changed diff --git a/docs/user/first_simulation.rst b/docs/user/first_simulation.rst index e2f6b4d99..5624ed926 100644 --- a/docs/user/first_simulation.rst +++ b/docs/user/first_simulation.rst @@ -654,6 +654,45 @@ Finally, the :meth:`rocketpy.Flight.export_data` method also provides a convenie os.remove("calisto_flight_data.csv") +Saving and Storing Plots +------------------------ + +Here we show how to save the plots and drawings generated by RocketPy. +For instance, we can save our rocket drawing as a ``.png`` file: + +.. jupyter-execute:: + + calisto.draw(filename="calisto_drawing.png") + +Also, if you want to save a specific rocketpy plot, every RocketPy +attribute of type :class:`rocketpy.Function` is capable of saving its plot +as an image file. For example, we can save our rocket's speed plot and the +trajectory plot as ``.jpg`` files: + +.. jupyter-execute:: + + test_flight.speed.plot(filename="speed_plot.jpg") + test_flight.plots.trajectory_3d(filename="trajectory_plot.jpg") + +The supported file formats are ``.eps``, ``.jpg``, ``.jpeg``, ``.pdf``, +``.pgf``, ``.png``, ``.ps``, ``.raw``, ``.rgba``, ``.svg``, ``.svgz``, +``.tif``, ``.tiff`` and ``.webp``. More details on manipulating data and +results can be found in the :ref:`Function Class Usage `. + +.. note:: + + The ``filename`` argument is optional. If not provided, the plot will be + shown instead. If the provided filename is in a directory that does not + exist, the directory will be created. + +.. jupyter-execute:: + :hide-code: + :hide-output: + + os.remove("calisto_drawing.png") + os.remove("speed_plot.jpg") + os.remove("trajectory_plot.jpg") + Further Analysis ---------------- diff --git a/rocketpy/mathutils/function.py b/rocketpy/mathutils/function.py index ba05636a5..314e6415b 100644 --- a/rocketpy/mathutils/function.py +++ b/rocketpy/mathutils/function.py @@ -22,6 +22,8 @@ RBFInterpolator, ) +from ..plots.plot_helpers import show_or_save_plot + # Numpy 1.x compatibility, # TODO: remove these lines when all dependencies support numpy>=2.0.0 if np.lib.NumpyVersion(np.__version__) >= "2.0.0b1": @@ -1378,7 +1380,7 @@ def remove_outliers_iqr(self, threshold=1.5): ) # Define all presentation methods - def __call__(self, *args): + def __call__(self, *args, filename=None): """Plot the Function if no argument is given. If an argument is given, return the value of the function at the desired point. @@ -1392,13 +1394,18 @@ def __call__(self, *args): evaluated at all points in the list and a list of floats will be returned. If the function is N-D, N arguments must be given, each one being an scalar or list. + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). Returns ------- ans : None, scalar, list """ if len(args) == 0: - return self.plot() + return self.plot(filename=filename) else: return self.get_value(*args) @@ -1459,8 +1466,11 @@ def plot(self, *args, **kwargs): Function.plot_2d if Function is 2-Dimensional and forward arguments and key-word arguments.""" if isinstance(self, list): + # Extract filename from kwargs + filename = kwargs.get("filename", None) + # Compare multiple plots - Function.compare_plots(self) + Function.compare_plots(self, filename) else: if self.__dom_dim__ == 1: self.plot_1d(*args, **kwargs) @@ -1488,6 +1498,7 @@ def plot_1d( # pylint: disable=too-many-statements force_points=False, return_object=False, equal_axis=False, + filename=None, ): """Plot 1-Dimensional Function, from a lower limit to an upper limit, by sampling the Function several times in the interval. The title of @@ -1518,6 +1529,11 @@ def plot_1d( # pylint: disable=too-many-statements Setting force_points to True will plot all points, as a scatter, in which the Function was evaluated in the dataset. Default value is False. + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). Returns ------- @@ -1558,7 +1574,7 @@ def plot_1d( # pylint: disable=too-many-statements plt.title(self.title) plt.xlabel(self.__inputs__[0].title()) plt.ylabel(self.__outputs__[0].title()) - plt.show() + show_or_save_plot(filename) if return_object: return fig, ax @@ -1581,6 +1597,7 @@ def plot_2d( # pylint: disable=too-many-statements disp_type="surface", alpha=0.6, cmap="viridis", + filename=None, ): """Plot 2-Dimensional Function, from a lower limit to an upper limit, by sampling the Function several times in the interval. The title of @@ -1620,6 +1637,11 @@ def plot_2d( # pylint: disable=too-many-statements cmap : string, optional Colormap of plotted graph, which can be any of the color maps available in matplotlib. Default value is viridis. + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). Returns ------- @@ -1692,7 +1714,7 @@ def plot_2d( # pylint: disable=too-many-statements axes.set_xlabel(self.__inputs__[0].title()) axes.set_ylabel(self.__inputs__[1].title()) axes.set_zlabel(self.__outputs__[0].title()) - plt.show() + show_or_save_plot(filename) @staticmethod def compare_plots( # pylint: disable=too-many-statements @@ -1707,6 +1729,7 @@ def compare_plots( # pylint: disable=too-many-statements force_points=False, return_object=False, show=True, + filename=None, ): """Plots N 1-Dimensional Functions in the same plot, from a lower limit to an upper limit, by sampling the Functions several times in @@ -1751,6 +1774,11 @@ def compare_plots( # pylint: disable=too-many-statements False. show : bool, optional If True, shows the plot. Default value is True. + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). Returns ------- @@ -1826,7 +1854,7 @@ def compare_plots( # pylint: disable=too-many-statements plt.ylabel(ylabel) if show: - plt.show() + show_or_save_plot(filename) if return_object: return fig, ax diff --git a/rocketpy/motors/hybrid_motor.py b/rocketpy/motors/hybrid_motor.py index 2916486d0..b3527c604 100644 --- a/rocketpy/motors/hybrid_motor.py +++ b/rocketpy/motors/hybrid_motor.py @@ -601,16 +601,19 @@ def add_tank(self, tank, position): ) reset_funcified_methods(self) - def draw(self): - """Draws a representation of the HybridMotor.""" - self.plots.draw() - - def info(self): - """Prints out basic data about the Motor.""" - self.prints.all() - self.plots.thrust() - - def all_info(self): - """Prints out all data and graphs available about the Motor.""" - self.prints.all() - self.plots.all() + def draw(self, filename=None): + """Draws a representation of the HybridMotor. + + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + + Returns + ------- + None + """ + self.plots.draw(filename) diff --git a/rocketpy/motors/liquid_motor.py b/rocketpy/motors/liquid_motor.py index cde0e9d03..cc7bf5027 100644 --- a/rocketpy/motors/liquid_motor.py +++ b/rocketpy/motors/liquid_motor.py @@ -463,21 +463,19 @@ def add_tank(self, tank, position): self.positioned_tanks.append({"tank": tank, "position": position}) reset_funcified_methods(self) - def draw(self): - """Draw a representation of the LiquidMotor.""" - self.plots.draw() + def draw(self, filename=None): + """Draw a representation of the LiquidMotor. - def info(self): - """Prints out basic data about the Motor.""" - self.prints.all() - self.plots.thrust() - - def all_info(self): - """Prints out all data and graphs available about the Motor. + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). - Return - ------ + Returns + ------- None """ - self.prints.all() - self.plots.all() + self.plots.draw(filename) diff --git a/rocketpy/motors/motor.py b/rocketpy/motors/motor.py index e0e6dfc9a..558c9f9a2 100644 --- a/rocketpy/motors/motor.py +++ b/rocketpy/motors/motor.py @@ -1083,15 +1083,26 @@ def get_attr_value(obj, attr_name, multiplier=1): # Write last line file.write(f"{self.thrust.source[-1, 0]:.4f} {0:.3f}\n") - def info(self): + def info(self, filename=None): """Prints out a summary of the data and graphs available about the Motor. + + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + + Returns + ------- + None """ # Print motor details self.prints.all() - self.plots.thrust() + self.plots.thrust(filename=filename) - @abstractmethod def all_info(self): """Prints out all data and graphs available about the Motor.""" self.prints.all() diff --git a/rocketpy/motors/solid_motor.py b/rocketpy/motors/solid_motor.py index f6f09967e..09a930522 100644 --- a/rocketpy/motors/solid_motor.py +++ b/rocketpy/motors/solid_motor.py @@ -727,16 +727,19 @@ def propellant_I_13(self): def propellant_I_23(self): return 0 - def draw(self): - """Draw a representation of the SolidMotor.""" - self.plots.draw() - - def info(self): - """Prints out basic data about the SolidMotor.""" - self.prints.all() - self.plots.thrust() - - def all_info(self): - """Prints out all data and graphs available about the SolidMotor.""" - self.prints.all() - self.plots.all() + def draw(self, filename=None): + """Draw a representation of the SolidMotor. + + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + + Returns + ------- + None + """ + self.plots.draw(filename) diff --git a/rocketpy/motors/tank.py b/rocketpy/motors/tank.py index a3b21f434..418124cf0 100644 --- a/rocketpy/motors/tank.py +++ b/rocketpy/motors/tank.py @@ -476,9 +476,22 @@ def underfill_height_exception(param_name, param): elif (height < bottom_tolerance).any(): underfill_height_exception(name, height) - def draw(self): - """Draws the tank geometry.""" - self.plots.draw() + def draw(self, filename=None): + """Draws the tank geometry. + + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + + Returns + ------- + None + """ + self.plots.draw(filename) class MassFlowRateBasedTank(Tank): diff --git a/rocketpy/plots/aero_surface_plots.py b/rocketpy/plots/aero_surface_plots.py index 2a696b1fc..a0b7d4c4b 100644 --- a/rocketpy/plots/aero_surface_plots.py +++ b/rocketpy/plots/aero_surface_plots.py @@ -4,6 +4,8 @@ import numpy as np from matplotlib.patches import Ellipse +from .plot_helpers import show_or_save_plot + class _AeroSurfacePlots(ABC): """Abstract class that contains all aero surface plots.""" @@ -23,7 +25,7 @@ def __init__(self, aero_surface): self.aero_surface = aero_surface @abstractmethod - def draw(self): + def draw(self, filename=None): pass def lift(self): @@ -52,10 +54,18 @@ class _NoseConePlots(_AeroSurfacePlots): """Class that contains all nosecone plots. This class inherits from the _AeroSurfacePlots class.""" - def draw(self): + def draw(self, filename=None): """Draw the nosecone shape along with some important information, including the center line and the center of pressure position. + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + Returns ------- None @@ -122,8 +132,7 @@ def draw(self): ax.set_ylabel("Radius") ax.set_title(self.aero_surface.kind + " Nose Cone") ax.legend(bbox_to_anchor=(1, -0.2)) - - plt.show() + show_or_save_plot(filename) class _FinsPlots(_AeroSurfacePlots): @@ -131,7 +140,7 @@ class _FinsPlots(_AeroSurfacePlots): _AeroSurfacePlots class.""" @abstractmethod - def draw(self): + def draw(self, filename=None): pass def airfoil(self): @@ -192,10 +201,18 @@ class _TrapezoidalFinsPlots(_FinsPlots): """Class that contains all trapezoidal fin plots.""" # pylint: disable=too-many-statements - def draw(self): + def draw(self, filename=None): """Draw the fin shape along with some important information, including the center line, the quarter line and the center of pressure position. + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + Returns ------- None @@ -306,17 +323,25 @@ def draw(self): ax.legend(bbox_to_anchor=(1.05, 1.0), loc="upper left") plt.tight_layout() - plt.show() + show_or_save_plot(filename) class _EllipticalFinsPlots(_FinsPlots): """Class that contains all elliptical fin plots.""" # pylint: disable=too-many-statements - def draw(self): + def draw(self, filename=None): """Draw the fin shape along with some important information. These being: the center line and the center of pressure position. + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + Returns ------- None @@ -377,16 +402,24 @@ def draw(self): ax.legend(bbox_to_anchor=(1.05, 1.0), loc="upper left") plt.tight_layout() - plt.show() + show_or_save_plot(filename) class _FreeFormFinsPlots(_FinsPlots): """Class that contains all free form fin plots.""" # pylint: disable=too-many-statements - def draw(self): - """Draw the fin shape along with some important information, including - the center line, the quarter line and the center of pressure position. + def draw(self, filename=None): + """Draw the fin shape along with some important information. + These being: the center line and the center of pressure position. + + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). Returns ------- @@ -444,13 +477,13 @@ def draw(self): ax.legend(bbox_to_anchor=(1.05, 1.0), loc="upper left") plt.tight_layout() - plt.show() + show_or_save_plot(filename) class _TailPlots(_AeroSurfacePlots): """Class that contains all tail plots.""" - def draw(self): + def draw(self, filename=None): # This will de done in the future pass @@ -465,7 +498,7 @@ def drag_coefficient_curve(self): else: return self.aero_surface.drag_coefficient.plot() - def draw(self): + def draw(self, filename=None): raise NotImplementedError def all(self): @@ -481,12 +514,12 @@ def all(self): class _GenericSurfacePlots(_AeroSurfacePlots): """Class that contains all generic surface plots.""" - def draw(self): + def draw(self, filename=None): pass class _LinearGenericSurfacePlots(_AeroSurfacePlots): """Class that contains all linear generic surface plots.""" - def draw(self): + def draw(self, filename=None): pass diff --git a/rocketpy/plots/compare/compare_flights.py b/rocketpy/plots/compare/compare_flights.py index 3bf2fb8ed..2689ac482 100644 --- a/rocketpy/plots/compare/compare_flights.py +++ b/rocketpy/plots/compare/compare_flights.py @@ -3,6 +3,7 @@ import matplotlib.pyplot as plt import numpy as np +from ..plot_helpers import show_or_save_fig, show_or_save_plot from .compare import Compare @@ -89,8 +90,8 @@ def __process_savefig(self, filename, fig): ------- None """ + show_or_save_fig(fig, filename) if filename: - fig.savefig(filename) print("Plot saved to file: " + filename) else: plt.show() @@ -1153,9 +1154,11 @@ def compare_trajectories_3d( # pylint: disable=too-many-statements Tuple with the size of the figure. The default is (7,7). legend : boolean, optional Whether legend will or will not be plotted. Default is True - filename : string, optional - If a filename is passed, the figure will be saved in the current - directory. The default is None. + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). Returns ------- @@ -1221,10 +1224,7 @@ def compare_trajectories_3d( # pylint: disable=too-many-statements fig1.tight_layout() # Save figure - if filename: - plt.savefig(filename) - else: - plt.show() + show_or_save_plot(filename) def trajectories_3d(self, figsize=(7, 7), legend=None, filename=None): """Creates a trajectory plot that is the combination of the trajectories @@ -1237,8 +1237,11 @@ def trajectories_3d(self, figsize=(7, 7), legend=None, filename=None): must be in the form (width, height). legend : boolean, optional Whether legend will or will not be included. Default is True - savefig : string, optional - If a string is passed, the figure will be saved in the path passed. + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). Returns ------- @@ -1411,6 +1414,11 @@ def __plot_xz( # pylint: disable=too-many-statements .svg, .pgf, .eps figsize : tuple, optional Tuple with the size of the figure. The default is (7, 7). + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). Returns ------- @@ -1454,10 +1462,7 @@ def __plot_xz( # pylint: disable=too-many-statements fig.tight_layout() # Save figure - if filename: - plt.savefig(filename) - else: - plt.show() + show_or_save_plot(filename) def __plot_yz( # pylint: disable=too-many-statements self, flights, names_list, figsize=(7, 7), legend=None, filename=None diff --git a/rocketpy/plots/environment_analysis_plots.py b/rocketpy/plots/environment_analysis_plots.py index 0b2c28990..b5b63642c 100644 --- a/rocketpy/plots/environment_analysis_plots.py +++ b/rocketpy/plots/environment_analysis_plots.py @@ -8,6 +8,7 @@ from rocketpy.units import convert_units from ..tools import find_two_closest_integers, import_optional_dependency +from .plot_helpers import show_or_save_plot # TODO: `wind_speed_limit` and `clear_range_limits` and should be numbers, not booleans @@ -73,12 +74,20 @@ def __beaufort_wind_scale(self, units, max_wind_speed=None): # Surface level plots - def wind_gust_distribution(self): + def wind_gust_distribution(self, filename=None): """Get all values of wind gust speed (for every date and hour available) and plot a single distribution. Expected result is a Weibull distribution, however, the result is not always a perfect fit, and sometimes it may look like a normal distribution. + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + Returns ------- None @@ -113,9 +122,9 @@ def wind_gust_distribution(self): plt.title("Wind Gust Speed Distribution (at surface)") plt.xlim(0, max(self.env_analysis.wind_gust_list)) plt.legend() - plt.show() + show_or_save_plot(filename) - def surface10m_wind_speed_distribution(self, wind_speed_limit=False): + def surface10m_wind_speed_distribution(self, wind_speed_limit=False, filename=None): """Get all values of sustained surface wind speed (for every date and hour available) and plot a single distribution. Expected result is a Weibull distribution. The wind speed limit is plotted as a vertical line. @@ -125,6 +134,11 @@ def surface10m_wind_speed_distribution(self, wind_speed_limit=False): wind_speed_limit : bool, optional If True, plots the wind speed limit as a vertical line. The default is False. + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). Returns ------- @@ -172,14 +186,23 @@ def surface10m_wind_speed_distribution(self, wind_speed_limit=False): plt.title("Sustained Wind Speed Distribution (at surface+10m)") plt.xlim(0, max(self.env_analysis.surface_10m_wind_speed_list)) plt.legend() - plt.show() + show_or_save_plot(filename) def average_surface_temperature_evolution( self, + filename=None, ): # pylint: disable=too-many-statements """Plots average temperature progression throughout the day, including sigma contours. + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + Returns ------- None @@ -239,10 +262,10 @@ def average_surface_temperature_evolution( plt.title("Average Temperature Along Day") plt.grid(alpha=0.25) plt.legend() - plt.show() + show_or_save_plot(filename) def average_surface10m_wind_speed_evolution( - self, wind_speed_limit=False + self, wind_speed_limit=False, filename=None ): # pylint: disable=too-many-statements """Plots average surface wind speed progression throughout the day, including sigma contours. @@ -252,6 +275,11 @@ def average_surface10m_wind_speed_evolution( wind_speed_limit : bool, optional If True, plots the wind speed limit as a horizontal line. The default is False. + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). Returns ------- @@ -334,14 +362,23 @@ def average_surface10m_wind_speed_evolution( plt.title("Average Sustained Surface Wind Speed Along Day") plt.grid(alpha=0.25) plt.legend() - plt.show() + show_or_save_plot(filename) def average_surface100m_wind_speed_evolution( self, + filename=None, ): # pylint: disable=too-many-statements """Plots average surface wind speed progression throughout the day, including sigma contours. + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + Returns ------- None @@ -408,11 +445,11 @@ def average_surface100m_wind_speed_evolution( plt.title("Average 100m Wind Speed Along Day") plt.grid(alpha=0.25) plt.legend() - plt.show() + show_or_save_plot(filename) # Average profiles plots (pressure level data) - def average_wind_speed_profile(self, clear_range_limits=False): + def average_wind_speed_profile(self, clear_range_limits=False, filename=None): """Average wind speed for all datetimes available. The plot also includes sigma contours. @@ -420,6 +457,11 @@ def average_wind_speed_profile(self, clear_range_limits=False): ---------- clear_range_limits : bool, optional If True, clears the range limits. The default is False. + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). Returns ------- @@ -510,9 +552,9 @@ def average_wind_speed_profile(self, clear_range_limits=False): ) ), ) - plt.show() + show_or_save_plot(filename) - def average_wind_velocity_xy_profile(self, clear_range_limits=False): + def average_wind_velocity_xy_profile(self, clear_range_limits=False, filename=None): """Average wind X and wind Y for all datetimes available. The X component is the wind speed in the direction of East, and the Y component is the wind speed in the direction of North. @@ -521,6 +563,11 @@ def average_wind_velocity_xy_profile(self, clear_range_limits=False): ---------- clear_range_limits : bool, optional If True, clears the range limits. The default is False. + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). Returns ------- @@ -572,15 +619,20 @@ def average_wind_velocity_xy_profile(self, clear_range_limits=False): plt.title("Average Wind X and Y Profile") plt.legend() plt.grid() - plt.show() + show_or_save_plot(filename) - def average_wind_heading_profile(self, clear_range_limits=False): + def average_wind_heading_profile(self, clear_range_limits=False, filename=None): """Average wind heading for all datetimes available. Parameters ---------- clear_range_limits : bool, optional If True, clears the range limits. The default is False. + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). Returns ------- @@ -625,9 +677,9 @@ def average_wind_heading_profile(self, clear_range_limits=False): plt.xlim(0, 360) plt.title("Average Wind heading Profile") plt.legend() - plt.show() + show_or_save_plot(filename) - def average_pressure_profile(self, clear_range_limits=False): + def average_pressure_profile(self, clear_range_limits=False, filename=None): """Average pressure profile for all datetimes available. The plot also includes sigma contours. @@ -635,6 +687,11 @@ def average_pressure_profile(self, clear_range_limits=False): ---------- clear_range_limits : bool, optional If True, clears the range limits. The default is False. + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). Returns ------- @@ -713,9 +770,9 @@ def average_pressure_profile(self, clear_range_limits=False): 0, max(np.percentile(self.env_analysis.pressure_profiles_list, 99.85, axis=0)), ) - plt.show() + show_or_save_plot(filename) - def average_temperature_profile(self, clear_range_limits=False): + def average_temperature_profile(self, clear_range_limits=False, filename=None): """Average temperature profile for all datetimes available. The plot also includes sigma contours. @@ -723,6 +780,11 @@ def average_temperature_profile(self, clear_range_limits=False): ---------- clear_range_limits : bool, optional If True, clears the range limits. The default is False. + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). Returns ------- @@ -808,7 +870,7 @@ def average_temperature_profile(self, clear_range_limits=False): ) ), ) - plt.show() + show_or_save_plot(filename) # Wind roses (surface level data) @@ -856,7 +918,7 @@ def plot_wind_rose( ax.yaxis.set_major_formatter(mtick.PercentFormatter(decimals=0)) return ax - def average_wind_rose_specific_hour(self, hour, fig=None): + def average_wind_rose_specific_hour(self, hour, fig=None, filename=None): """Plot a specific hour of the average windrose Parameters @@ -865,6 +927,11 @@ def average_wind_rose_specific_hour(self, hour, fig=None): Hour to be plotted fig: matplotlib.pyplot.figure Figure to plot the windrose + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). Returns ------- @@ -882,11 +949,21 @@ def average_wind_rose_specific_hour(self, hour, fig=None): ), fig=fig, ) - plt.show() + show_or_save_plot(filename) - def average_wind_rose_grid(self): # pylint: disable=too-many-statements + def average_wind_rose_grid( + self, filename=None + ): # pylint: disable=too-many-statements """Plot wind roses for all hours of a day, in a grid like plot. + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + Returns ------- None @@ -950,7 +1027,7 @@ def average_wind_rose_grid(self): # pylint: disable=too-many-statements y=1, ) plt.bbox_inches = "tight" - plt.show() + show_or_save_plot(filename) def animate_average_wind_rose(self, figsize=(5, 5), filename="wind_rose.gif"): """Animates the wind_rose of an average day. The inputs of a wind_rose @@ -1010,10 +1087,20 @@ def animate_average_wind_rose(self, figsize=(5, 5), filename="wind_rose.gif"): # More plots and animations - def wind_gust_distribution_grid(self): # pylint: disable=too-many-statements + def wind_gust_distribution_grid( + self, filename=None + ): # pylint: disable=too-many-statements """Plots shown in the animation of how the wind gust distribution varies throughout the day. + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + Returns ------- None @@ -1079,7 +1166,7 @@ def wind_gust_distribution_grid(self): # pylint: disable=too-many-statements f"Wind Gust Speed ({self.env_analysis.unit_system['wind_speed']})" ) fig.supylabel("Probability") - plt.show() + show_or_save_plot(filename) def animate_wind_gust_distribution(self): # pylint: disable=too-many-statements """Animation of how the wind gust distribution varies throughout the day. @@ -1177,7 +1264,7 @@ def update(frame): return HTML(animation.to_jshtml()) def surface_wind_speed_distribution_grid( - self, wind_speed_limit=False + self, wind_speed_limit=False, filename=None ): # pylint: disable=too-many-statements """Plots shown in the animation of how the sustained surface wind speed distribution varies throughout the day. The plots are histograms of the @@ -1188,6 +1275,11 @@ def surface_wind_speed_distribution_grid( ---------- wind_speed_limit : bool, optional Whether to plot the wind speed limit as a vertical line + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). Returns ------- @@ -1275,11 +1367,10 @@ def surface_wind_speed_distribution_grid( f"Sustained Surface Wind Speed ({self.env_analysis.unit_system['wind_speed']})" ) fig.supylabel("Probability") - plt.show() + show_or_save_plot(filename) - def animate_surface_wind_speed_distribution( - self, wind_speed_limit=False - ): # pylint: disable=too-many-statements + # pylint: disable=too-many-statements + def animate_surface_wind_speed_distribution(self, wind_speed_limit=False): """Animation of how the sustained surface wind speed distribution varies throughout the day. Each frame is a histogram of the wind speed distribution for a specific hour. @@ -1402,7 +1493,7 @@ def update(frame): return HTML(animation.to_jshtml()) def wind_speed_profile_grid( - self, clear_range_limits=False + self, clear_range_limits=False, filename=None ): # pylint: disable=too-many-statements """Creates a grid of plots with the wind profile over the average day. Each subplot represents a different hour of the day. @@ -1411,6 +1502,11 @@ def wind_speed_profile_grid( ---------- clear_range_limits : bool, optional Whether to clear the sky range limits or not, by default False + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). Returns ------- @@ -1493,10 +1589,10 @@ def wind_speed_profile_grid( fig.suptitle("Average Wind Profile") fig.supxlabel(f"Wind speed ({self.env_analysis.unit_system['wind_speed']})") fig.supylabel(f"Altitude AGL ({self.env_analysis.unit_system['length']})") - plt.show() + show_or_save_plot(filename) def wind_heading_profile_grid( - self, clear_range_limits=False + self, clear_range_limits=False, filename=None ): # pylint: disable=too-many-statements """Creates a grid of plots with the wind heading profile over the average day. Each subplot represents a different hour of the day. @@ -1506,6 +1602,11 @@ def wind_heading_profile_grid( clear_range_limits : bool, optional Whether to clear the sky range limits or not, by default False. This is useful when the launch site is constrained in terms or altitude. + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). Returns ------- @@ -1582,7 +1683,7 @@ def wind_heading_profile_grid( fig.suptitle("Average Wind Heading Profile") fig.supxlabel(f"Wind heading ({self.env_analysis.unit_system['angle']})") fig.supylabel(f"Altitude AGL ({self.env_analysis.unit_system['length']})") - plt.show() + show_or_save_plot(filename) def animate_wind_speed_profile( self, clear_range_limits=False diff --git a/rocketpy/plots/environment_plots.py b/rocketpy/plots/environment_plots.py index 39fb9548e..f15d066af 100644 --- a/rocketpy/plots/environment_plots.py +++ b/rocketpy/plots/environment_plots.py @@ -1,6 +1,8 @@ import matplotlib.pyplot as plt import numpy as np +from .plot_helpers import show_or_save_plot + class _EnvironmentPlots: """Class that holds plot methods for Environment class. @@ -171,10 +173,18 @@ def __pressure_temperature(self, ax): return ax - def gravity_model(self): + def gravity_model(self, filename=None): """Plots the gravity model graph that represents the gravitational acceleration as a function of height. + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + Returns ------- None @@ -192,13 +202,21 @@ def gravity_model(self): ax.set_ylim(self.grid[0], self.grid[-1]) plt.xticks(rotation=45) - plt.show() + show_or_save_plot(filename) - def atmospheric_model(self): + def atmospheric_model(self, filename=None): """Plots all atmospheric model graphs available. This includes wind speed and wind direction, density and speed of sound, wind u and wind v, and pressure and temperature. + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + Returns ------- None @@ -224,13 +242,21 @@ def atmospheric_model(self): ax4 = self.__pressure_temperature(ax4) plt.subplots_adjust(wspace=0.5, hspace=0.3) - plt.show() + show_or_save_plot(filename) # pylint: disable=too-many-statements - def ensemble_member_comparison(self): + def ensemble_member_comparison(self, filename=None): """Plots ensemble member comparisons. It requires that the environment model has been set as Ensemble. + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + Returns ------- None @@ -321,16 +347,24 @@ def ensemble_member_comparison(self): # Display plot plt.subplots_adjust(wspace=0.5, hspace=0.3) - plt.show() + show_or_save_plot(filename) # Clean up self.environment.select_ensemble_member(current_member) - def info(self): + def info(self, filename=None): """Plots a summary of the atmospheric model, including wind speed and wind direction, density and speed of sound. This is important for the Environment.info() method. + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + Returns ------- None @@ -346,7 +380,7 @@ def info(self): ax2 = self.__density_speed_of_sound(ax2) plt.subplots_adjust(wspace=0.5) - plt.show() + show_or_save_plot(filename) def all(self): """Prints out all graphs available about the Environment. This includes diff --git a/rocketpy/plots/flight_plots.py b/rocketpy/plots/flight_plots.py index 0babaff4c..edc30b956 100644 --- a/rocketpy/plots/flight_plots.py +++ b/rocketpy/plots/flight_plots.py @@ -3,6 +3,8 @@ import matplotlib.pyplot as plt import numpy as np +from .plot_helpers import show_or_save_plot + class _FlightPlots: """Class that holds plot methods for Flight class. @@ -52,9 +54,17 @@ def first_event_time_index(self): else: return -1 - def trajectory_3d(self): # pylint: disable=too-many-statements + def trajectory_3d(self, filename=None): # pylint: disable=too-many-statements """Plot a 3D graph of the trajectory + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + Returns ------- None @@ -121,11 +131,21 @@ def trajectory_3d(self): # pylint: disable=too-many-statements ax1.set_zlim(min_z, max_z) ax1.view_init(15, 45) ax1.set_box_aspect(None, zoom=0.95) # 95% for label adjustment - plt.show() + show_or_save_plot(filename) - def linear_kinematics_data(self): # pylint: disable=too-many-statements + def linear_kinematics_data( + self, filename=None + ): # pylint: disable=too-many-statements """Prints out all Kinematics graphs available about the Flight + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + Returns ------- None @@ -193,11 +213,19 @@ def linear_kinematics_data(self): # pylint: disable=too-many-statements ax4up.tick_params("y", colors="#1f77b4") plt.subplots_adjust(hspace=0.5) - plt.show() + show_or_save_plot(filename) - def attitude_data(self): # pylint: disable=too-many-statements + def attitude_data(self, filename=None): # pylint: disable=too-many-statements """Prints out all Angular position graphs available about the Flight + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + Returns ------- None @@ -243,12 +271,20 @@ def attitude_data(self): # pylint: disable=too-many-statements ax4.grid(True) plt.subplots_adjust(hspace=0.5) - plt.show() + show_or_save_plot(filename) - def flight_path_angle_data(self): + def flight_path_angle_data(self, filename=None): """Prints out Flight path and Rocket Attitude angle graphs available about the Flight + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + Returns ------- None @@ -285,12 +321,22 @@ def flight_path_angle_data(self): ax2.grid(True) plt.subplots_adjust(hspace=0.5) - plt.show() + show_or_save_plot(filename) - def angular_kinematics_data(self): # pylint: disable=too-many-statements + def angular_kinematics_data( + self, filename=None + ): # pylint: disable=too-many-statements """Prints out all Angular velocity and acceleration graphs available about the Flight + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + Returns ------- None @@ -351,11 +397,19 @@ def angular_kinematics_data(self): # pylint: disable=too-many-statements ax3up.tick_params("y", colors="#1f77b4") plt.subplots_adjust(hspace=0.5) - plt.show() + show_or_save_plot(filename) - def rail_buttons_forces(self): # pylint: disable=too-many-statements + def rail_buttons_forces(self, filename=None): # pylint: disable=too-many-statements """Prints out all Rail Buttons Forces graphs available about the Flight. + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + Returns ------- None @@ -434,11 +488,19 @@ def rail_buttons_forces(self): # pylint: disable=too-many-statements ax2.set_title("Rail Buttons Shear Force") plt.subplots_adjust(hspace=0.5) - plt.show() + show_or_save_plot(filename) - def aerodynamic_forces(self): # pylint: disable=too-many-statements + def aerodynamic_forces(self, filename=None): # pylint: disable=too-many-statements """Prints out all Forces and Moments graphs available about the Flight + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + Returns ------- None @@ -514,11 +576,19 @@ def aerodynamic_forces(self): # pylint: disable=too-many-statements ax4.grid() plt.subplots_adjust(hspace=0.5) - plt.show() + show_or_save_plot(filename) - def energy_data(self): # pylint: disable=too-many-statements + def energy_data(self, filename=None): # pylint: disable=too-many-statements """Prints out all Energy components graphs available about the Flight + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + Returns ------- None @@ -625,12 +695,22 @@ def energy_data(self): # pylint: disable=too-many-statements ax4.grid() plt.subplots_adjust(hspace=1) - plt.show() + show_or_save_plot(filename) - def fluid_mechanics_data(self): # pylint: disable=too-many-statements + def fluid_mechanics_data( + self, filename=None + ): # pylint: disable=too-many-statements """Prints out a summary of the Fluid Mechanics graphs available about the Flight + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + Returns ------- None @@ -715,12 +795,22 @@ def fluid_mechanics_data(self): # pylint: disable=too-many-statements ax6.grid() plt.subplots_adjust(hspace=0.5) - plt.show() + show_or_save_plot(filename) - def stability_and_control_data(self): # pylint: disable=too-many-statements + def stability_and_control_data( + self, filename=None + ): # pylint: disable=too-many-statements """Prints out Rocket Stability and Control parameters graphs available about the Flight + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + Returns ------- None @@ -795,11 +885,19 @@ def stability_and_control_data(self): # pylint: disable=too-many-statements ax2.grid() plt.subplots_adjust(hspace=0.5) - plt.show() + show_or_save_plot(filename) - def pressure_rocket_altitude(self): + def pressure_rocket_altitude(self, filename=None): """Plots out pressure at rocket's altitude. + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + Returns ------- None @@ -816,7 +914,7 @@ def pressure_rocket_altitude(self): ax1.set_xlim(0, self.flight.t_final) ax1.grid() - plt.show() + show_or_save_plot(filename) def pressure_signals(self): """Plots out all Parachute Trigger Pressure Signals. diff --git a/rocketpy/plots/hybrid_motor_plots.py b/rocketpy/plots/hybrid_motor_plots.py index 21a415986..eb13c4d69 100644 --- a/rocketpy/plots/hybrid_motor_plots.py +++ b/rocketpy/plots/hybrid_motor_plots.py @@ -1,6 +1,7 @@ from matplotlib import pyplot as plt from .motor_plots import _MotorPlots +from .plot_helpers import show_or_save_plot class _HybridMotorPlots(_MotorPlots): @@ -13,7 +14,7 @@ class _HybridMotorPlots(_MotorPlots): """ - def grain_inner_radius(self, lower_limit=None, upper_limit=None): + def grain_inner_radius(self, lower_limit=None, upper_limit=None, filename=None): """Plots grain_inner_radius of the hybrid_motor as a function of time. Parameters @@ -24,15 +25,22 @@ def grain_inner_radius(self, lower_limit=None, upper_limit=None): upper_limit : float Upper limit of the plot. Default is none, which means that the plot limits will be automatically calculated. + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). - Return - ------ + Returns + ------- None """ - self.motor.solid.grain_inner_radius.plot(lower=lower_limit, upper=upper_limit) + self.motor.solid.grain_inner_radius.plot( + lower=lower_limit, upper=upper_limit, filename=filename + ) - def grain_height(self, lower_limit=None, upper_limit=None): + def grain_height(self, lower_limit=None, upper_limit=None, filename=None): """Plots grain_height of the hybrid_motor as a function of time. Parameters @@ -43,15 +51,22 @@ def grain_height(self, lower_limit=None, upper_limit=None): upper_limit : float Upper limit of the plot. Default is none, which means that the plot limits will be automatically calculated. + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). - Return - ------ + Returns + ------- None """ - self.motor.solid.grain_height.plot(lower=lower_limit, upper=upper_limit) + self.motor.solid.grain_height.plot( + lower=lower_limit, upper=upper_limit, filename=filename + ) - def burn_rate(self, lower_limit=None, upper_limit=None): + def burn_rate(self, lower_limit=None, upper_limit=None, filename=None): """Plots burn_rate of the hybrid_motor as a function of time. Parameters @@ -62,15 +77,22 @@ def burn_rate(self, lower_limit=None, upper_limit=None): upper_limit : float Upper limit of the plot. Default is none, which means that the plot limits will be automatically calculated. + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). - Return - ------ + Returns + ------- None """ - self.motor.solid.burn_rate.plot(lower=lower_limit, upper=upper_limit) + self.motor.solid.burn_rate.plot( + lower=lower_limit, upper=upper_limit, filename=filename + ) - def burn_area(self, lower_limit=None, upper_limit=None): + def burn_area(self, lower_limit=None, upper_limit=None, filename=None): """Plots burn_area of the hybrid_motor as a function of time. Parameters @@ -81,15 +103,22 @@ def burn_area(self, lower_limit=None, upper_limit=None): upper_limit : float Upper limit of the plot. Default is none, which means that the plot limits will be automatically calculated. + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). - Return - ------ + Returns + ------- None """ - self.motor.solid.burn_area.plot(lower=lower_limit, upper=upper_limit) + self.motor.solid.burn_area.plot( + lower=lower_limit, upper=upper_limit, filename=filename + ) - def Kn(self, lower_limit=None, upper_limit=None): + def Kn(self, lower_limit=None, upper_limit=None, filename=None): """Plots Kn of the hybrid_motor as a function of time. Parameters @@ -100,17 +129,32 @@ def Kn(self, lower_limit=None, upper_limit=None): upper_limit : float Upper limit of the plot. Default is none, which means that the plot limits will be automatically calculated. + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). - Return - ------ + Returns + ------- None """ - self.motor.solid.Kn.plot(lower=lower_limit, upper=upper_limit) + self.motor.solid.Kn.plot( + lower=lower_limit, upper=upper_limit, filename=filename + ) - def draw(self): + def draw(self, filename=None): """Draw a representation of the HybridMotor. + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + Returns ------- None @@ -144,7 +188,7 @@ def draw(self): ax.set_title("Hybrid Motor Representation") self._draw_center_of_mass(ax) self._set_plot_properties(ax) - plt.show() + show_or_save_plot(filename) def all(self): """Prints out all graphs available about the HybridMotor. It simply calls diff --git a/rocketpy/plots/liquid_motor_plots.py b/rocketpy/plots/liquid_motor_plots.py index 3e5e7703b..822364fc2 100644 --- a/rocketpy/plots/liquid_motor_plots.py +++ b/rocketpy/plots/liquid_motor_plots.py @@ -1,6 +1,7 @@ import matplotlib.pyplot as plt from .motor_plots import _MotorPlots +from .plot_helpers import show_or_save_plot class _LiquidMotorPlots(_MotorPlots): @@ -13,9 +14,17 @@ class _LiquidMotorPlots(_MotorPlots): """ - def draw(self): + def draw(self, filename=None): """Draw a representation of the LiquidMotor. + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + Returns ------- None @@ -41,7 +50,7 @@ def draw(self): ax.set_title("Liquid Motor Representation") self._draw_center_of_mass(ax) self._set_plot_properties(ax) - plt.show() + show_or_save_plot(filename) def all(self): """Prints out all graphs available about the LiquidMotor. It simply calls diff --git a/rocketpy/plots/motor_plots.py b/rocketpy/plots/motor_plots.py index 39cdcaeb6..e7ddb7efa 100644 --- a/rocketpy/plots/motor_plots.py +++ b/rocketpy/plots/motor_plots.py @@ -2,6 +2,8 @@ import numpy as np from matplotlib.patches import Polygon +from ..plots.plot_helpers import show_or_save_plot + class _MotorPlots: """Class that holds plot methods for Motor class. @@ -27,7 +29,7 @@ def __init__(self, motor): """ self.motor = motor - def thrust(self, lower_limit=None, upper_limit=None): + def thrust(self, lower_limit=None, upper_limit=None, filename=None): """Plots thrust of the motor as a function of time. Parameters @@ -38,14 +40,19 @@ def thrust(self, lower_limit=None, upper_limit=None): upper_limit : float Upper limit of the plot. Default is none, which means that the plot limits will be automatically calculated. + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). - Return - ------ + Returns + ------- None """ - self.motor.thrust.plot(lower=lower_limit, upper=upper_limit) + self.motor.thrust.plot(lower=lower_limit, upper=upper_limit, filename=filename) - def total_mass(self, lower_limit=None, upper_limit=None): + def total_mass(self, lower_limit=None, upper_limit=None, filename=None): """Plots total_mass of the motor as a function of time. Parameters @@ -56,14 +63,21 @@ def total_mass(self, lower_limit=None, upper_limit=None): upper_limit : float Upper limit of the plot. Default is none, which means that the plot limits will be automatically calculated. + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). - Return - ------ + Returns + ------- None """ - self.motor.total_mass.plot(lower=lower_limit, upper=upper_limit) + self.motor.total_mass.plot( + lower=lower_limit, upper=upper_limit, filename=filename + ) - def propellant_mass(self, lower_limit=None, upper_limit=None): + def propellant_mass(self, lower_limit=None, upper_limit=None, filename=None): """Plots propellant_mass of the motor as a function of time. Parameters @@ -74,14 +88,21 @@ def propellant_mass(self, lower_limit=None, upper_limit=None): upper_limit : float Upper limit of the plot. Default is None, which means that the plot limits will be automatically calculated. + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). - Return - ------ + Returns + ------- None """ - self.motor.propellant_mass.plot(lower=lower_limit, upper=upper_limit) + self.motor.propellant_mass.plot( + lower=lower_limit, upper=upper_limit, filename=filename + ) - def center_of_mass(self, lower_limit=None, upper_limit=None): + def center_of_mass(self, lower_limit=None, upper_limit=None, filename=None): """Plots center_of_mass of the motor as a function of time. Parameters @@ -92,14 +113,21 @@ def center_of_mass(self, lower_limit=None, upper_limit=None): upper_limit : float Upper limit of the plot. Default is none, which means that the plot limits will be automatically calculated. + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). - Return - ------ + Returns + ------- None """ - self.motor.center_of_mass.plot(lower=lower_limit, upper=upper_limit) + self.motor.center_of_mass.plot( + lower=lower_limit, upper=upper_limit, filename=filename + ) - def mass_flow_rate(self, lower_limit=None, upper_limit=None): + def mass_flow_rate(self, lower_limit=None, upper_limit=None, filename=None): """Plots mass_flow_rate of the motor as a function of time. Parameters @@ -110,14 +138,21 @@ def mass_flow_rate(self, lower_limit=None, upper_limit=None): upper_limit : float Upper limit of the plot. Default is none, which means that the plot limits will be automatically calculated. + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). - Return - ------ + Returns + ------- None """ - self.motor.mass_flow_rate.plot(lower=lower_limit, upper=upper_limit) + self.motor.mass_flow_rate.plot( + lower=lower_limit, upper=upper_limit, filename=filename + ) - def exhaust_velocity(self, lower_limit=None, upper_limit=None): + def exhaust_velocity(self, lower_limit=None, upper_limit=None, filename=None): """Plots exhaust_velocity of the motor as a function of time. Parameters @@ -128,14 +163,23 @@ def exhaust_velocity(self, lower_limit=None, upper_limit=None): upper_limit : float Upper limit of the plot. Default is none, which means that the plot limits will be automatically calculated. + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). - Return - ------ + Returns + ------- None """ - self.motor.exhaust_velocity.plot(lower=lower_limit, upper=upper_limit) + self.motor.exhaust_velocity.plot( + lower=lower_limit, upper=upper_limit, filename=filename + ) - def inertia_tensor(self, lower_limit=None, upper_limit=None, show_products=False): + def inertia_tensor( + self, lower_limit=None, upper_limit=None, show_products=False, filename=None + ): """Plots all inertia tensors (I_11, I_22, I_33, I_12, I_13, I_23) of the motor as a function of time in a single chart. @@ -152,9 +196,14 @@ def inertia_tensor(self, lower_limit=None, upper_limit=None, show_products=False in the plot. Default is False. These are kept as hidden by default because they are usually very small compared to the main inertia components. + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). - Return - ------ + Returns + ------- None """ lower = lower_limit or self.motor.burn_start_time @@ -181,7 +230,7 @@ def inertia_tensor(self, lower_limit=None, upper_limit=None, show_products=False ax.legend() ax.grid(True) - plt.show() + show_or_save_plot(filename) def _generate_nozzle(self, translate=(0, 0), csys=1): """Generates a patch that represents the nozzle of the motor. It is diff --git a/rocketpy/plots/plot_helpers.py b/rocketpy/plots/plot_helpers.py new file mode 100644 index 000000000..f795d46e9 --- /dev/null +++ b/rocketpy/plots/plot_helpers.py @@ -0,0 +1,67 @@ +from pathlib import Path + +import matplotlib.pyplot as plt +from matplotlib.figure import Figure + +from ..tools import get_matplotlib_supported_file_endings + +SAVEFIG_DPI = 300 + + +def show_or_save_plot(filename=None): + """Shows or saves the current matplotlib plot. If a filename is given, the + plot will be saved, otherwise it will be shown. + + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to, by default None. Supported file + endings are: eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, + tif, tiff and webp (these are the formats supported by matplotlib). + """ + if filename is None: + plt.show() + else: + file_ending = Path(filename).suffix + supported_endings = get_matplotlib_supported_file_endings() + if file_ending not in supported_endings: + raise ValueError( + f"Unsupported file ending '{file_ending}'." + f"Supported file endings are: {supported_endings}." + ) + + # Before export, ensure the folder the file should go into exists + Path(filename).parent.mkdir(parents=True, exist_ok=True) + + plt.savefig(filename, dpi=SAVEFIG_DPI) + plt.close() + + +def show_or_save_fig(fig: Figure, filename=None): + """Shows or saves the given matplotlib Figure. If a filename is given, the + figure will be saved, otherwise it will be shown. + + Parameters + ---------- + fig : matplotlib.figure.Figure + The figure to be saved or shown. + filename : str | None, optional + The path the figure should be saved to, by default None. Supported file + endings are: eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, + tif, tiff and webp (these are the formats supported by matplotlib). + """ + if filename is None: + fig.show() + else: + file_ending = Path(filename).suffix + supported_endings = get_matplotlib_supported_file_endings() + if file_ending not in supported_endings: + raise ValueError( + f"Unsupported file ending '{file_ending}'." + f"Supported file endings are: {supported_endings}." + ) + + # Before export, ensure the folder the file should go into exists + Path(filename).parent.mkdir(parents=True, exist_ok=True) + + fig.savefig(filename, dpi=SAVEFIG_DPI) diff --git a/rocketpy/plots/rocket_plots.py b/rocketpy/plots/rocket_plots.py index 4ae47cdc4..9689a257b 100644 --- a/rocketpy/plots/rocket_plots.py +++ b/rocketpy/plots/rocket_plots.py @@ -7,6 +7,8 @@ from rocketpy.rocket.aero_surface import Fins, NoseCone, Tail from rocketpy.rocket.aero_surface.generic_surface import GenericSurface +from .plot_helpers import show_or_save_plot + class _RocketPlots: """Class that holds plot methods for Rocket class. @@ -52,15 +54,23 @@ def reduced_mass(self): self.rocket.reduced_mass() - def static_margin(self): + def static_margin(self, filename=None): """Plots static margin of the rocket as a function of time. + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + Returns ------- None """ - self.rocket.static_margin() + self.rocket.static_margin(filename=filename) def stability_margin(self): """Plots static margin of the rocket as a function of time. @@ -113,9 +123,17 @@ def power_off_drag(self): self.rocket.power_off_drag() # pylint: disable=too-many-statements - def drag_curves(self): + def drag_curves(self, filename=None): """Plots power off and on drag curves of the rocket as a function of time. + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + Returns ------- None @@ -150,7 +168,7 @@ def drag_curves(self): ax.axvspan(0.8, 1.2, alpha=0.3, color="gray", label="Transonic Region") ax.legend(loc="best", shadow=True) plt.grid(True) - plt.show() + show_or_save_plot(filename) def thrust_to_weight(self): """ @@ -161,7 +179,7 @@ def thrust_to_weight(self): lower=0, upper=self.rocket.motor.burn_out_time ) - def draw(self, vis_args=None, plane="xz"): + def draw(self, vis_args=None, plane="xz", filename=None): """Draws the rocket in a matplotlib figure. Parameters @@ -188,6 +206,11 @@ def draw(self, vis_args=None, plane="xz"): plane : str, optional Plane in which the rocket will be drawn. Default is 'xz'. Other options is 'yz'. Used only for sensors representation. + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). """ if vis_args is None: vis_args = { @@ -223,7 +246,7 @@ def draw(self, vis_args=None, plane="xz"): plt.ylabel("Radius (m)") plt.legend(bbox_to_anchor=(1.05, 1), loc="upper left") plt.tight_layout() - plt.show() + show_or_save_plot(filename) def _draw_aerodynamic_surfaces(self, ax, vis_args, plane): """Draws the aerodynamic surfaces and saves the position of the points diff --git a/rocketpy/plots/solid_motor_plots.py b/rocketpy/plots/solid_motor_plots.py index 57af737b8..202220102 100644 --- a/rocketpy/plots/solid_motor_plots.py +++ b/rocketpy/plots/solid_motor_plots.py @@ -1,6 +1,7 @@ import matplotlib.pyplot as plt from .motor_plots import _MotorPlots +from .plot_helpers import show_or_save_plot class _SolidMotorPlots(_MotorPlots): @@ -100,7 +101,11 @@ def Kn(self, lower_limit=None, upper_limit=None): upper_limit : float Upper limit of the plot. Default is none, which means that the plot limits will be automatically calculated. - + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). Return ------ None @@ -108,9 +113,17 @@ def Kn(self, lower_limit=None, upper_limit=None): self.motor.Kn.plot(lower=lower_limit, upper=upper_limit) - def draw(self): + def draw(self, filename=None): """Draw a representation of the SolidMotor. + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + Returns ------- None @@ -139,7 +152,7 @@ def draw(self): ax.set_title("Solid Motor Representation") self._draw_center_of_mass(ax) self._set_plot_properties(ax) - plt.show() + show_or_save_plot(filename) def all(self): """Prints out all graphs available about the SolidMotor. It simply calls diff --git a/rocketpy/plots/tank_plots.py b/rocketpy/plots/tank_plots.py index 7c0541eb2..a4bc0dd2a 100644 --- a/rocketpy/plots/tank_plots.py +++ b/rocketpy/plots/tank_plots.py @@ -2,6 +2,8 @@ import numpy as np from matplotlib.patches import Polygon +from .plot_helpers import show_or_save_plot + class _TankPlots: """Class that holds plot methods for Tank class. @@ -67,9 +69,17 @@ def _generate_tank(self, translate=(0, 0), csys=1): # Don't set any plot config here. Use the draw methods for that return tank - def draw(self): + def draw(self, filename=None): """Draws the tank geometry. + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + Returns ------- None @@ -89,6 +99,7 @@ def draw(self): y_max = self.geometry.radius.y_array.max() ax.set_xlim(-1.2 * x_max, 1.2 * x_max) ax.set_ylim(-1.5 * y_max, 1.5 * y_max) + show_or_save_plot(filename) def all(self): """Prints out all graphs available about the Tank. It simply calls diff --git a/rocketpy/rocket/aero_surface/fins/fins.py b/rocketpy/rocket/aero_surface/fins/fins.py index 29f4e8cff..c7973bc69 100644 --- a/rocketpy/rocket/aero_surface/fins/fins.py +++ b/rocketpy/rocket/aero_surface/fins/fins.py @@ -426,12 +426,20 @@ def compute_forces_and_moments( M3 = M3_forcing - M3_damping return R1, R2, R3, M1, M2, M3 - def draw(self): + def draw(self, filename=None): """Draw the fin shape along with some important information, including the center line, the quarter line and the center of pressure position. + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + Returns ------- None """ - self.plots.draw() + self.plots.draw(filename) diff --git a/rocketpy/rocket/aero_surface/nose_cone.py b/rocketpy/rocket/aero_surface/nose_cone.py index 7d59473e3..e9fbe4324 100644 --- a/rocketpy/rocket/aero_surface/nose_cone.py +++ b/rocketpy/rocket/aero_surface/nose_cone.py @@ -486,15 +486,23 @@ def evaluate_center_of_pressure(self): self.cp = (self.cpx, self.cpy, self.cpz) return self.cp - def draw(self): - """Draw the fin shape along with some important information, including - the center line, the quarter line and the center of pressure position. + def draw(self, filename=None): + """Draw the nosecone shape along with some important information, + including the center of pressure position. + + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). Returns ------- None """ - self.plots.draw() + self.plots.draw(filename) def info(self): """Prints and plots summarized information of the nose cone. diff --git a/rocketpy/rocket/rocket.py b/rocketpy/rocket/rocket.py index ed376f582..58067a03f 100644 --- a/rocketpy/rocket/rocket.py +++ b/rocketpy/rocket/rocket.py @@ -1839,7 +1839,7 @@ def add_thrust_eccentricity(self, x, y): self.thrust_eccentricity_x = y return self - def draw(self, vis_args=None, plane="xz"): + def draw(self, vis_args=None, plane="xz", filename=None): """Draws the rocket in a matplotlib figure. Parameters @@ -1866,8 +1866,13 @@ def draw(self, vis_args=None, plane="xz"): plane : str, optional Plane in which the rocket will be drawn. Default is 'xz'. Other options is 'yz'. Used only for sensors representation. + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). """ - self.plots.draw(vis_args, plane) + self.plots.draw(vis_args, plane, filename) def info(self): """Prints out a summary of the data and graphs available about diff --git a/rocketpy/simulation/monte_carlo.py b/rocketpy/simulation/monte_carlo.py index c6cd1bed4..4ed051a4f 100644 --- a/rocketpy/simulation/monte_carlo.py +++ b/rocketpy/simulation/monte_carlo.py @@ -870,10 +870,10 @@ def export_ellipses_to_kml( # pylint: disable=too-many-statements for i, points in enumerate(kml_data): if i < len(impact_ellipses): - name = f"Impact Ellipse {i+1}" + name = f"Impact Ellipse {i + 1}" ellipse_color = colors[0] # default is blue else: - name = f"Apogee Ellipse {i +1- len(impact_ellipses)}" + name = f"Apogee Ellipse {i + 1 - len(impact_ellipses)}" ellipse_color = colors[1] # default is green mult_ell = kml.newmultigeometry(name=name) diff --git a/rocketpy/tools.py b/rocketpy/tools.py index 7668ecbc8..46660755a 100644 --- a/rocketpy/tools.py +++ b/rocketpy/tools.py @@ -15,6 +15,7 @@ import time from bisect import bisect_left +import matplotlib.pyplot as plt import numpy as np import pytz from cftime import num2pydate @@ -1149,6 +1150,23 @@ def euler313_to_quaternions(phi, theta, psi): return e0, e1, e2, e3 +def get_matplotlib_supported_file_endings(): + """Gets the file endings supported by matplotlib. + + Returns + ------- + list[str] + List of file endings prepended with a dot + """ + # Get matplotlib's supported file ending and return them (without descriptions, hence only keys) + filetypes = plt.gcf().canvas.get_supported_filetypes().keys() + + # Ensure the dot is included in the filetype endings + filetypes = ["." + filetype for filetype in filetypes] + + return filetypes + + if __name__ == "__main__": import doctest diff --git a/rocketpy/utilities.py b/rocketpy/utilities.py index adb925eee..fb728dbb6 100644 --- a/rocketpy/utilities.py +++ b/rocketpy/utilities.py @@ -9,6 +9,7 @@ from .environment.environment import Environment from .mathutils.function import Function +from .plots.plot_helpers import show_or_save_plot from .rocket.aero_surface import TrapezoidalFins from .simulation.flight import Flight @@ -202,7 +203,12 @@ def du(z, u): # pylint: disable=too-many-statements def fin_flutter_analysis( - fin_thickness, shear_modulus, flight, see_prints=True, see_graphs=True + fin_thickness, + shear_modulus, + flight, + see_prints=True, + see_graphs=True, + filename=None, ): """Calculate and plot the Fin Flutter velocity using the pressure profile provided by the selected atmospheric model. It considers the Flutter @@ -225,6 +231,11 @@ def fin_flutter_analysis( see_graphs : boolean, optional True if you want to see the graphs, False otherwise. If False, the function will return the vectors containing the data for the graphs. + filename : str | None, optional + The path the plot should be saved to. By default None, in which case the + plot will be shown instead of saved. Supported file endings are: eps, + jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff and webp + (these are the formats supported by matplotlib). Return ------ @@ -270,7 +281,7 @@ def fin_flutter_analysis( flight, ) if see_graphs: - _flutter_plots(flight, flutter_mach, safety_factor) + _flutter_plots(flight, flutter_mach, safety_factor, filename) else: return flutter_mach, safety_factor @@ -309,7 +320,7 @@ def _flutter_safety_factor(flight, flutter_mach): return safety_factor -def _flutter_plots(flight, flutter_mach, safety_factor): +def _flutter_plots(flight, flutter_mach, safety_factor, filename=None): """Plot the Fin Flutter Mach Number and the Safety Factor for the flutter. Parameters @@ -322,6 +333,11 @@ def _flutter_plots(flight, flutter_mach, safety_factor): safety_factor : rocketpy.Function Function containing the Safety Factor for the fin flutter. See fin_flutter_analysis for more details. + filename : str | None, optional + The path the plot should be saved to. By default None, in which case the + plot will be shown instead of saved. Supported file endings are: eps, + jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff and webp + (these are the formats supported by matplotlib). Returns ------- @@ -357,7 +373,7 @@ def _flutter_plots(flight, flutter_mach, safety_factor): ax2.grid() plt.subplots_adjust(hspace=0.5) - plt.show() + show_or_save_plot(filename) def _flutter_prints( diff --git a/tests/integration/test_plots.py b/tests/integration/test_plots.py index 232ef71c6..7595855e4 100644 --- a/tests/integration/test_plots.py +++ b/tests/integration/test_plots.py @@ -2,12 +2,50 @@ import os from unittest.mock import patch +import matplotlib.pyplot as plt + from rocketpy import Flight from rocketpy.plots.compare import CompareFlights @patch("matplotlib.pyplot.show") -def test_compare_flights(mock_show, calisto, example_plain_env): +def test_compare(mock_show, flight_calisto): + """Here we want to test the 'x_attributes' argument, which is the only one + that is not tested in the other tests. + + Parameters + ---------- + mock_show : + Mocks the matplotlib.pyplot.show() function to avoid showing the plots. + flight_calisto : rocketpy.Flight + Flight object to be used in the tests. See conftest.py for more details. + """ + flight = flight_calisto + + objects = [flight, flight, flight] + + comparison = CompareFlights(objects) + + fig, _ = comparison.create_comparison_figure( + y_attributes=["z"], + n_rows=1, + n_cols=1, + figsize=(10, 10), + legend=False, + title="Test", + x_labels=["Time (s)"], + y_labels=["Altitude (m)"], + x_lim=(0, 3), + y_lim=(0, 1000), + x_attributes=["time"], + ) + + assert isinstance(fig, plt.Figure) + + +@patch("matplotlib.pyplot.show") +@patch("matplotlib.figure.Figure.show") +def test_compare_flights(mock_show, mock_figure_show, calisto, example_plain_env): """Tests the CompareFlights class. It simply ensures that all the methods are being called without errors. It does not test the actual plots, which would be very difficult to do. @@ -16,6 +54,8 @@ def test_compare_flights(mock_show, calisto, example_plain_env): ---------- mock_show : Mocks the matplotlib.pyplot.show() function to avoid showing the plots. + mock_figure_show : + Mocks the matplotlib.figure.Figure.show() function to avoid showing the plots. calisto : rocketpy.Rocket Rocket object to be used in the tests. See conftest.py for more details. example_plain_env : rocketpy.Environment diff --git a/tests/unit/test_plots.py b/tests/unit/test_plots.py index cd35f8d11..6d7c0cce2 100644 --- a/tests/unit/test_plots.py +++ b/tests/unit/test_plots.py @@ -1,8 +1,11 @@ -from unittest.mock import patch +import os +from unittest.mock import MagicMock, patch import matplotlib.pyplot as plt +import pytest from rocketpy.plots.compare import Compare +from rocketpy.plots.plot_helpers import show_or_save_fig, show_or_save_plot @patch("matplotlib.pyplot.show") @@ -38,3 +41,51 @@ def test_compare(mock_show, flight_calisto): # pylint: disable=unused-argument ) assert isinstance(fig, plt.Figure) + + +@patch("matplotlib.pyplot.show") +@pytest.mark.parametrize("filename", [None, "test.png"]) +def test_show_or_save_plot(mock_show, filename): + """This test is to check if the show_or_save_plot function is + working properly. + + Parameters + ---------- + mock_show : + Mocks the matplotlib.pyplot.show() function to avoid showing + the plots. + filename : str + Name of the file to save the plot. If None, the plot will be + shown instead. + """ + plt.subplots() + show_or_save_plot(filename) + + if filename is None: + mock_show.assert_called_once() + else: + assert os.path.exists(filename) + os.remove(filename) + + +@pytest.mark.parametrize("filename", [None, "test.png"]) +def test_show_or_save_fig(filename): + """This test is to check if the show_or_save_fig function is + working properly. + + Parameters + ---------- + filename : str + Name of the file to save the plot. If None, the plot will be + shown instead. + """ + fig, _ = plt.subplots() + + fig.show = MagicMock() + show_or_save_fig(fig, filename) + + if filename is None: + fig.show.assert_called_once() + else: + assert os.path.exists(filename) + os.remove(filename) From 6fea165ab1ee3c0ad8803212ecebbdeb63ee18e2 Mon Sep 17 00:00:00 2001 From: alessio26gas Date: Sat, 9 Nov 2024 23:39:41 +0100 Subject: [PATCH 03/36] DOC: fixed documentation about spherical caps --- docs/user/motors/tanks.rst | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/docs/user/motors/tanks.rst b/docs/user/motors/tanks.rst index 5d027b3a3..49b5a0687 100644 --- a/docs/user/motors/tanks.rst +++ b/docs/user/motors/tanks.rst @@ -93,8 +93,9 @@ The predefined ``CylindricalTank`` class is easy to use and is defined as such: .. note:: The ``spherical_caps`` parameter is optional and defaults to ``False``. If set - to ``True``, the tank will be defined as a cylinder with flat caps. - If True, the tank will have spherical caps. + to ``True``, the tank will have spherical caps at the top and bottom with the + same radius as the cylindrical part. If set to ``False``, the tank will + be defined as a cylinder with flat caps. The predefined ``SphericalTank`` is defined with: From 59296567703a6d90b0601dd46189cc08eeca4893 Mon Sep 17 00:00:00 2001 From: Lucas Prates Date: Mon, 11 Nov 2024 10:42:56 -0300 Subject: [PATCH 04/36] bug: fix print error when summary contains None results --- rocketpy/prints/monte_carlo_prints.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/rocketpy/prints/monte_carlo_prints.py b/rocketpy/prints/monte_carlo_prints.py index 6249626ce..dc7cc1265 100644 --- a/rocketpy/prints/monte_carlo_prints.py +++ b/rocketpy/prints/monte_carlo_prints.py @@ -24,4 +24,7 @@ def all(self): print(f"{'Parameter':>25} {'Mean':>15} {'Std. Dev.':>15}") print("-" * 60) for key, value in self.monte_carlo.processed_results.items(): - print(f"{key:>25} {value[0]:>15.3f} {value[1]:>15.3f}") + try: + print(f"{key:>25} {value[0]:>15.3f} {value[1]:>15.3f}") + except TypeError: + print(f"{key:>25} {str(value[0]):>15} {str(value[1]):>15}") From 62b3edbd7afec61229c0d4da1f2057368457bdb3 Mon Sep 17 00:00:00 2001 From: Lucas Prates Date: Mon, 11 Nov 2024 10:50:12 -0300 Subject: [PATCH 05/36] ENH: avoid computing mean and std when it might not make sense (e.g dates/impact_state) --- rocketpy/simulation/monte_carlo.py | 22 ++++++++++++++++------ 1 file changed, 16 insertions(+), 6 deletions(-) diff --git a/rocketpy/simulation/monte_carlo.py b/rocketpy/simulation/monte_carlo.py index ea864ee8a..7c7545e8c 100644 --- a/rocketpy/simulation/monte_carlo.py +++ b/rocketpy/simulation/monte_carlo.py @@ -80,8 +80,13 @@ class MonteCarlo: """ def __init__( - self, filename, environment, rocket, flight, export_list=None, - export_function=None + self, + filename, + environment, + rocket, + flight, + export_list=None, + export_function=None, ): # pylint: disable=too-many-statements """ Initialize a MonteCarlo object. @@ -370,7 +375,9 @@ def __export_flight_data( additional_exports = self.export_function(flight) for key in additional_exports.keys(): if key in self.export_list: - raise ValueError(f"Invalid export function, returns dict which overwrites key, '{key}'") + raise ValueError( + f"Invalid export function, returns dict which overwrites key, '{key}'" + ) results = results | additional_exports input_file.write(json.dumps(inputs_dict, cls=RocketPyEncoder) + "\n") @@ -669,9 +676,12 @@ def set_processed_results(self): self.processed_results = {} for result, values in self.results.items(): try: - mean = np.mean(values) - stdev = np.std(values) - self.processed_results[result] = (mean, stdev) + if isinstance(values[0], float): + mean = np.mean(values) + stdev = np.std(values) + self.processed_results[result] = (mean, stdev) + else: + self.processed_results[result] = (None, None) except TypeError: self.processed_results[result] = (None, None) From b60294980302525025d6fc53b0c9c806e1e288fb Mon Sep 17 00:00:00 2001 From: Lucas Prates Date: Mon, 11 Nov 2024 11:12:08 -0300 Subject: [PATCH 06/36] ENH: raises an error when user 'export_function' contains errors from any kind (attribute, logical, etc) --- rocketpy/simulation/monte_carlo.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/rocketpy/simulation/monte_carlo.py b/rocketpy/simulation/monte_carlo.py index 7c7545e8c..9ad83f1bd 100644 --- a/rocketpy/simulation/monte_carlo.py +++ b/rocketpy/simulation/monte_carlo.py @@ -372,7 +372,14 @@ def __export_flight_data( } if self.export_function is not None: - additional_exports = self.export_function(flight) + try: + additional_exports = self.export_function(flight) + except Exception as e: + raise ValueError( + "An error was encountered running your custom export function. " + "Check for errors in 'export_function' definition." + ) from e + for key in additional_exports.keys(): if key in self.export_list: raise ValueError( From b0e0a08d69702ab61ade074c570d528ea0fee668 Mon Sep 17 00:00:00 2001 From: Lucas Prates Date: Mon, 11 Nov 2024 16:22:09 -0300 Subject: [PATCH 07/36] TST: providing tests to monte carlo callbacks 'export_function' argument --- tests/integration/test_monte_carlo.py | 53 +++++++++++++++++++++++++++ 1 file changed, 53 insertions(+) diff --git a/tests/integration/test_monte_carlo.py b/tests/integration/test_monte_carlo.py index b5caddbc8..764a64338 100644 --- a/tests/integration/test_monte_carlo.py +++ b/tests/integration/test_monte_carlo.py @@ -111,3 +111,56 @@ def test_monte_carlo_export_ellipses_to_kml(monte_carlo_calisto_pre_loaded): ) os.remove("monte_carlo_class_example.kml") + + +@pytest.mark.slow +def test_monte_carlo_callback(monte_carlo_calisto): + """Tests the export_function argument of the MonteCarlo class. + + Parameters + ---------- + monte_carlo_calisto : MonteCarlo + The MonteCarlo object, this is a pytest fixture. + """ + + def valid_export_function(flight): + custom_export_dict = { + "name": flight.name, + "density_t0": flight.env.density(0), + } + return custom_export_dict + + monte_carlo_calisto.export_function = valid_export_function + # NOTE: this is really slow, it runs 10 flight simulations + monte_carlo_calisto.simulate(number_of_simulations=10, append=False) + + # tests if print works when we have None in summary + monte_carlo_calisto.info() + + # tests if logical errors in export functions raise errors + def export_function_with_logical_error(flight): + custom_export_dict = { + "date": flight.env.date, + "density_t0": flight.env.density(0) / "0", + } + return custom_export_dict + + monte_carlo_calisto.export_function = export_function_with_logical_error + # NOTE: this is really slow, it runs 10 flight simulations + with pytest.raises(ValueError): + monte_carlo_calisto.simulate(number_of_simulations=10, append=False) + + # tests if overwriting default exports raises errors + def export_function_with_overwriting_error(flight): + custom_export_dict = { + "apogee": flight.apogee, + } + return custom_export_dict + + monte_carlo_calisto.export_function = export_function_with_overwriting_error + with pytest.raises(ValueError): + monte_carlo_calisto.simulate(number_of_simulations=10, append=False) + + os.remove("monte_carlo_test.errors.txt") + os.remove("monte_carlo_test.outputs.txt") + os.remove("monte_carlo_test.inputs.txt") From ed85b3edef85310f75973b85f1fde84156ae7749 Mon Sep 17 00:00:00 2001 From: MateusStano Date: Wed, 13 Nov 2024 14:07:17 +0100 Subject: [PATCH 08/36] BUG: Corrected calculation of beta angle in aerodynamic computations Co-authored-by: kevin-alcaniz --- rocketpy/rocket/aero_surface/generic_surface.py | 8 ++++---- rocketpy/simulation/flight.py | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/rocketpy/rocket/aero_surface/generic_surface.py b/rocketpy/rocket/aero_surface/generic_surface.py index 1a0e1015d..372f9fcc1 100644 --- a/rocketpy/rocket/aero_surface/generic_surface.py +++ b/rocketpy/rocket/aero_surface/generic_surface.py @@ -273,7 +273,7 @@ def compute_forces_and_moments( # Angles of attack and sideslip alpha = np.arctan2(stream_velocity[1], stream_velocity[2]) - beta = np.arctan2(-stream_velocity[0], stream_velocity[2]) + beta = np.arctan2(stream_velocity[0], stream_velocity[2]) # Compute aerodynamic forces and moments lift, side, drag, pitch, yaw, roll = self._compute_from_coefficients( @@ -283,9 +283,9 @@ def compute_forces_and_moments( beta, stream_mach, reynolds, - omega[0], - omega[1], - omega[2], + omega[0], # q + omega[1], # r + omega[2], # p ) # Conversion from aerodynamic frame to body frame diff --git a/rocketpy/simulation/flight.py b/rocketpy/simulation/flight.py index 706bcf534..a43ec15de 100644 --- a/rocketpy/simulation/flight.py +++ b/rocketpy/simulation/flight.py @@ -2699,7 +2699,7 @@ def angle_of_sideslip(self): # Stream velocity in standard aerodynamic frame stream_velocity = -self.stream_velocity_body_frame beta = np.arctan2( - -stream_velocity[:, 0], + stream_velocity[:, 0], stream_velocity[:, 2], ) # x-z plane return np.column_stack([self.time, np.rad2deg(beta)]) From 9bf6db1f5f783175d356b0b7e0fdc534da4b5152 Mon Sep 17 00:00:00 2001 From: MateusStano Date: Wed, 13 Nov 2024 14:08:14 +0100 Subject: [PATCH 09/36] BUG: fix damping coefficients sign in linear model Co-authored-by: kevin-alcaniz --- .../aero_surface/linear_generic_surface.py | 30 +++++++++---------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/rocketpy/rocket/aero_surface/linear_generic_surface.py b/rocketpy/rocket/aero_surface/linear_generic_surface.py index fba2b1db8..65ad72cb1 100644 --- a/rocketpy/rocket/aero_surface/linear_generic_surface.py +++ b/rocketpy/rocket/aero_surface/linear_generic_surface.py @@ -62,22 +62,22 @@ def __init__( Coefficient of lift derivative with respect to yaw rate. Default is 0.\n cQ_0: callable, str, optional - Coefficient of pitch moment at zero angle of attack. + Coefficient of side force at zero angle of attack. Default is 0.\n cQ_alpha: callable, str, optional - Coefficient of pitch moment derivative with respect to angle of + Coefficient of side force derivative with respect to angle of attack. Default is 0.\n cQ_beta: callable, str, optional - Coefficient of pitch moment derivative with respect to sideslip + Coefficient of side force derivative with respect to sideslip angle. Default is 0.\n cQ_p: callable, str, optional - Coefficient of pitch moment derivative with respect to roll rate. + Coefficient of side force derivative with respect to roll rate. Default is 0.\n cQ_q: callable, str, optional - Coefficient of pitch moment derivative with respect to pitch rate. + Coefficient of side force derivative with respect to pitch rate. Default is 0.\n cQ_r: callable, str, optional - Coefficient of pitch moment derivative with respect to yaw rate. + Coefficient of side force derivative with respect to yaw rate. Default is 0.\n cD_0: callable, str, optional Coefficient of drag at zero angle of attack. Default is 0.\n @@ -258,11 +258,11 @@ def total_coefficient( ): return ( c_p(alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate) - * pitch_rate + * roll_rate + c_q(alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate) - * yaw_rate + * pitch_rate + c_r(alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate) - * roll_rate + * yaw_rate ) return Function( @@ -372,38 +372,38 @@ def _compute_from_coefficients( # Compute aerodynamic forces lift = dyn_pressure_area * self.cLf( alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ) - dyn_pressure_area_damping * self.cLd( + ) + dyn_pressure_area_damping * self.cLd( alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate ) side = dyn_pressure_area * self.cQf( alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ) - dyn_pressure_area_damping * self.cQd( + ) + dyn_pressure_area_damping * self.cQd( alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate ) drag = dyn_pressure_area * self.cDf( alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ) - dyn_pressure_area_damping * self.cDd( + ) + dyn_pressure_area_damping * self.cDd( alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate ) # Compute aerodynamic moments pitch = dyn_pressure_area_length * self.cmf( alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ) - dyn_pressure_area_length_damping * self.cmd( + ) + dyn_pressure_area_length_damping * self.cmd( alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate ) yaw = dyn_pressure_area_length * self.cnf( alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ) - dyn_pressure_area_length_damping * self.cnd( + ) + dyn_pressure_area_length_damping * self.cnd( alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate ) roll = dyn_pressure_area_length * self.clf( alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate - ) - dyn_pressure_area_length_damping * self.cld( + ) + dyn_pressure_area_length_damping * self.cld( alpha, beta, mach, reynolds, pitch_rate, yaw_rate, roll_rate ) From 5d0f6435f6e1662c508180ad40ca7debc365f33d Mon Sep 17 00:00:00 2001 From: MateusStano Date: Wed, 13 Nov 2024 16:24:44 +0100 Subject: [PATCH 10/36] DEV: changelog --- CHANGELOG.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 271c3a2bf..f47eab880 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -36,7 +36,7 @@ Attention: The newest changes should be on top --> ### Changed -- +- BUG: Sideslip Angle and Damping Coefficient Calculation [#729](https://github.com/RocketPy-Team/RocketPy/pull/729) ### Fixed From d2232e0c6b85ee59e0776be344dc5200a473caea Mon Sep 17 00:00:00 2001 From: Lucas Prates Date: Wed, 13 Nov 2024 15:27:09 -0300 Subject: [PATCH 11/36] DOC: add simple example on how to use the callback export function --- .../monte_carlo_class_usage.ipynb | 88 ++++++++++++++++++- 1 file changed, 87 insertions(+), 1 deletion(-) diff --git a/docs/notebooks/monte_carlo_analysis/monte_carlo_class_usage.ipynb b/docs/notebooks/monte_carlo_analysis/monte_carlo_class_usage.ipynb index 3886f72c4..10be08133 100644 --- a/docs/notebooks/monte_carlo_analysis/monte_carlo_class_usage.ipynb +++ b/docs/notebooks/monte_carlo_analysis/monte_carlo_class_usage.ipynb @@ -1115,6 +1115,92 @@ " type=\"impact\",\n", ")" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Custom exports using callback functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have shown, so far, how to perform to use the `MonteCarlo` class and visualize its results. By default, some variables exported to the output files, such as *apogee* and *x_impact*. The `export_list` argument provides a simplified way for the user to export additional variables listed in the documentation, such as *inclination* and *heading*. \n", + "\n", + "There are applications in which you might need to extract more information in the results than the `export_list` argument can handle. To that end, the `MonteCarlo` class has a `export_function` argument which allows you customize further the output of the simulation.\n", + "\n", + "To exemplify its use, we show how to export the *date* of the environment used in the simulation together with the *average reynolds number* along with the default variables." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use the `stochastic_env`, `stochastic_rocket` and `stochastic_flight` objects previously defined, and only change the `MonteCarlo` object. First, we need to define our customized export function." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "def custom_export_function(flight):\n", + " reynold_number_list = flight.reynolds_number(flight.time)\n", + " average_reynolds_number = np.mean(reynold_number_list)\n", + " custom_exports = {\n", + " \"average_reynolds_number\": average_reynolds_number,\n", + " \"date\": flight.env.date,\n", + " }\n", + " return custom_exports" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "The `export_function` must be a function which takes a `Flight` object and outputs a dictionary whose keys are variables names to export and their values. Notice how we can compute complex expressions in this function and just export the result. For instance, the *average_reynolds_number* calls the `flight.reynolds_number` method for each value in `flight.time` list and computes the average value using numpy's `mean`. The *date* variable is straightforward.\n", + "\n", + "After we define the export function, we pass it as an argument to the `MonteCarlo` class." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test_dispersion = MonteCarlo(\n", + " filename=\"monte_carlo_analysis_outputs/monte_carlo_class_example_customized\",\n", + " environment=stochastic_env,\n", + " rocket=stochastic_rocket,\n", + " flight=stochastic_flight,\n", + " export_function=custom_export_function,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test_dispersion.simulate(number_of_simulations=10, append=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test_dispersion.prints.all()" + ] } ], "metadata": { @@ -1134,7 +1220,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.11" + "version": "3.11.2" } }, "nbformat": 4, From afb4e3fc657ec47f133e9bd16079232eaa189c6e Mon Sep 17 00:00:00 2001 From: Lucas Prates Date: Wed, 13 Nov 2024 15:28:04 -0300 Subject: [PATCH 12/36] MNT: make black to notebook example --- .../notebooks/monte_carlo_analysis/monte_carlo_class_usage.ipynb | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/notebooks/monte_carlo_analysis/monte_carlo_class_usage.ipynb b/docs/notebooks/monte_carlo_analysis/monte_carlo_class_usage.ipynb index 10be08133..42d0ae424 100644 --- a/docs/notebooks/monte_carlo_analysis/monte_carlo_class_usage.ipynb +++ b/docs/notebooks/monte_carlo_analysis/monte_carlo_class_usage.ipynb @@ -1149,6 +1149,7 @@ "source": [ "import numpy as np\n", "\n", + "\n", "def custom_export_function(flight):\n", " reynold_number_list = flight.reynolds_number(flight.time)\n", " average_reynolds_number = np.mean(reynold_number_list)\n", From 1574aeafde14ae279d833858869c09ce1b39e56f Mon Sep 17 00:00:00 2001 From: MateusStano Date: Fri, 15 Nov 2024 22:32:56 +0100 Subject: [PATCH 13/36] DEV: changelog --- CHANGELOG.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index f47eab880..d4d083a77 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -36,11 +36,11 @@ Attention: The newest changes should be on top --> ### Changed -- BUG: Sideslip Angle and Damping Coefficient Calculation [#729](https://github.com/RocketPy-Team/RocketPy/pull/729) +- ### Fixed -- +- BUG: Sideslip Angle and Damping Coefficient Calculation [#729](https://github.com/RocketPy-Team/RocketPy/pull/729) ## [v1.6.2] - 2024-11-08 From f19cb815877bf9061e7bdc2648367f91ec2f02bd Mon Sep 17 00:00:00 2001 From: Lucas de Oliveira Prates Date: Fri, 22 Nov 2024 15:53:38 -0300 Subject: [PATCH 14/36] MNT: refactoring 'export_list' to 'data_collectors' dict of callbacks --- rocketpy/simulation/monte_carlo.py | 66 +++++++++++++++++++++--------- 1 file changed, 46 insertions(+), 20 deletions(-) diff --git a/rocketpy/simulation/monte_carlo.py b/rocketpy/simulation/monte_carlo.py index 9ad83f1bd..e240f88ea 100644 --- a/rocketpy/simulation/monte_carlo.py +++ b/rocketpy/simulation/monte_carlo.py @@ -86,7 +86,7 @@ def __init__( rocket, flight, export_list=None, - export_function=None, + data_collector=None, ): # pylint: disable=too-many-statements """ Initialize a MonteCarlo object. @@ -110,11 +110,10 @@ def __init__( `out_of_rail_stability_margin`, `out_of_rail_time`, `out_of_rail_velocity`, `max_mach_number`, `frontal_surface_wind`, `lateral_surface_wind`. Default is None. - export_function : callable, optional - A function which gets called at the end of a simulation to collect - additional data to be exported that isn't pre-defined. Takes the - Flight object as an argument and returns a dictionary. Default is None. - + data_collector : dict, optional + A dictionary whose keys are the names of the exported variables + and the values are callback functions. The callback functions receive + a Flight object and returns a value of that variable. Returns ------- @@ -143,7 +142,8 @@ def __init__( self._last_print_len = 0 # used to print on the same line self.export_list = self.__check_export_list(export_list) - self.export_function = export_function + self._check_data_collector(data_collector) + self.data_collector = data_collector try: self.import_inputs() @@ -371,20 +371,15 @@ def __export_flight_data( for export_item in self.export_list } - if self.export_function is not None: - try: - additional_exports = self.export_function(flight) - except Exception as e: - raise ValueError( - "An error was encountered running your custom export function. " - "Check for errors in 'export_function' definition." - ) from e - - for key in additional_exports.keys(): - if key in self.export_list: + if self.data_collector is not None: + additional_exports = {} + for key, callback in self.data_collector.items(): + try: + additional_exports[key] = callback(flight) + except Exception as e: raise ValueError( - f"Invalid export function, returns dict which overwrites key, '{key}'" - ) + f"An error was encountered running 'data_collector' callback {key}. " + ) from e results = results | additional_exports input_file.write(json.dumps(inputs_dict, cls=RocketPyEncoder) + "\n") @@ -494,6 +489,37 @@ def __check_export_list(self, export_list): return export_list + def _check_data_collector(self, data_collector): + """Check if data collector provided is a valid + + Parameters + ---------- + data_collector : dict + A dictionary whose keys are the names of the exported variables + and the values are callback functions that receive a Flight object + and returns a value of that variable + """ + + if data_collector is not None: + + if not isinstance(data_collector, dict): + raise ValueError( + "Invalid 'data_collector' argument! " + "It must be a dict of callback functions." + ) + + for key, callback in data_collector.items(): + if key in self.export_list: + raise ValueError( + "Invalid 'data_collector' key! " + f"Variable names overwrites standard key '{key}'." + ) + if not callable(callback): + raise ValueError( + f"Invalid value in 'data_collector' for key '{key}'! " + "Values must be python callables (callback functions)." + ) + def __reprint(self, msg, end="\n", flush=False): """ Prints a message on the same line as the previous one and replaces the From f10cad0ea69de459f53fc8faeee7c15ec76f5166 Mon Sep 17 00:00:00 2001 From: Lucas de Oliveira Prates Date: Fri, 22 Nov 2024 15:54:19 -0300 Subject: [PATCH 15/36] TST: refactoring tests to new 'data_collector' input format (dict of callbacks) --- tests/integration/test_monte_carlo.py | 52 ++++++++++++++------------- 1 file changed, 27 insertions(+), 25 deletions(-) diff --git a/tests/integration/test_monte_carlo.py b/tests/integration/test_monte_carlo.py index 764a64338..51d8bfae9 100644 --- a/tests/integration/test_monte_carlo.py +++ b/tests/integration/test_monte_carlo.py @@ -115,7 +115,7 @@ def test_monte_carlo_export_ellipses_to_kml(monte_carlo_calisto_pre_loaded): @pytest.mark.slow def test_monte_carlo_callback(monte_carlo_calisto): - """Tests the export_function argument of the MonteCarlo class. + """Tests the data_collector argument of the MonteCarlo class. Parameters ---------- @@ -123,41 +123,43 @@ def test_monte_carlo_callback(monte_carlo_calisto): The MonteCarlo object, this is a pytest fixture. """ - def valid_export_function(flight): - custom_export_dict = { - "name": flight.name, - "density_t0": flight.env.density(0), - } - return custom_export_dict + # define valid data collector + valid_data_collector = { + "name": lambda flight: flight.name, + "density_t0": lambda flight: flight.env.density(0), + } - monte_carlo_calisto.export_function = valid_export_function + monte_carlo_calisto.data_collector = valid_data_collector # NOTE: this is really slow, it runs 10 flight simulations monte_carlo_calisto.simulate(number_of_simulations=10, append=False) # tests if print works when we have None in summary monte_carlo_calisto.info() - # tests if logical errors in export functions raise errors - def export_function_with_logical_error(flight): - custom_export_dict = { - "date": flight.env.date, - "density_t0": flight.env.density(0) / "0", - } - return custom_export_dict + ## tests if an error is raised for invalid data_collector definitions + # invalid type + def invalid_data_collector(flight): + return flight.name - monte_carlo_calisto.export_function = export_function_with_logical_error - # NOTE: this is really slow, it runs 10 flight simulations with pytest.raises(ValueError): - monte_carlo_calisto.simulate(number_of_simulations=10, append=False) + monte_carlo_calisto._check_data_collector(invalid_data_collector) + + # invalid key overwrite + invalid_data_collector = {"apogee": lambda flight: flight.apogee} + with pytest.raises(ValueError): + monte_carlo_calisto._check_data_collector(invalid_data_collector) - # tests if overwriting default exports raises errors - def export_function_with_overwriting_error(flight): - custom_export_dict = { - "apogee": flight.apogee, - } - return custom_export_dict + # invalid callback definition + invalid_data_collector = {"name": "Calisto"} # callbacks must be callables! + with pytest.raises(ValueError): + monte_carlo_calisto._check_data_collector(invalid_data_collector) - monte_carlo_calisto.export_function = export_function_with_overwriting_error + # invalid logic (division by zero) + invalid_data_collector = { + "density_t0": lambda flight: flight.env.density(0) / "0", + } + monte_carlo_calisto.data_collector = invalid_data_collector + # NOTE: this is really slow, it runs 10 flight simulations with pytest.raises(ValueError): monte_carlo_calisto.simulate(number_of_simulations=10, append=False) From 1a09345998b22172d88db506a154190f42cf6549 Mon Sep 17 00:00:00 2001 From: Lucas de Oliveira Prates Date: Fri, 22 Nov 2024 16:01:07 -0300 Subject: [PATCH 16/36] MNT: removing float check on 'set_processed_results' --- rocketpy/simulation/monte_carlo.py | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/rocketpy/simulation/monte_carlo.py b/rocketpy/simulation/monte_carlo.py index e240f88ea..566d4247f 100644 --- a/rocketpy/simulation/monte_carlo.py +++ b/rocketpy/simulation/monte_carlo.py @@ -512,7 +512,7 @@ def _check_data_collector(self, data_collector): if key in self.export_list: raise ValueError( "Invalid 'data_collector' key! " - f"Variable names overwrites standard key '{key}'." + f"Variable names overwrites 'export_list' key '{key}'." ) if not callable(callback): raise ValueError( @@ -709,12 +709,9 @@ def set_processed_results(self): self.processed_results = {} for result, values in self.results.items(): try: - if isinstance(values[0], float): - mean = np.mean(values) - stdev = np.std(values) - self.processed_results[result] = (mean, stdev) - else: - self.processed_results[result] = (None, None) + mean = np.mean(values) + stdev = np.std(values) + self.processed_results[result] = (mean, stdev) except TypeError: self.processed_results[result] = (None, None) From 1a51082b382ed262ac1b148193769275c85eb467 Mon Sep 17 00:00:00 2001 From: Lucas de Oliveira Prates Date: Fri, 22 Nov 2024 16:25:29 -0300 Subject: [PATCH 17/36] DOC: updating notebook example and class documentation after callback refactoring --- .../monte_carlo_class_usage.ipynb | 32 +++++++++++-------- rocketpy/simulation/monte_carlo.py | 14 ++++++-- 2 files changed, 30 insertions(+), 16 deletions(-) diff --git a/docs/notebooks/monte_carlo_analysis/monte_carlo_class_usage.ipynb b/docs/notebooks/monte_carlo_analysis/monte_carlo_class_usage.ipynb index 42d0ae424..cfd31aa66 100644 --- a/docs/notebooks/monte_carlo_analysis/monte_carlo_class_usage.ipynb +++ b/docs/notebooks/monte_carlo_analysis/monte_carlo_class_usage.ipynb @@ -1129,7 +1129,7 @@ "source": [ "We have shown, so far, how to perform to use the `MonteCarlo` class and visualize its results. By default, some variables exported to the output files, such as *apogee* and *x_impact*. The `export_list` argument provides a simplified way for the user to export additional variables listed in the documentation, such as *inclination* and *heading*. \n", "\n", - "There are applications in which you might need to extract more information in the results than the `export_list` argument can handle. To that end, the `MonteCarlo` class has a `export_function` argument which allows you customize further the output of the simulation.\n", + "There are applications in which you might need to extract more information in the results than the `export_list` argument can handle. To that end, the `MonteCarlo` class has a `data_collector` argument which allows you customize further the output of the simulation.\n", "\n", "To exemplify its use, we show how to export the *date* of the environment used in the simulation together with the *average reynolds number* along with the default variables." ] @@ -1138,36 +1138,39 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We will use the `stochastic_env`, `stochastic_rocket` and `stochastic_flight` objects previously defined, and only change the `MonteCarlo` object. First, we need to define our customized export function." + "We will use the `stochastic_env`, `stochastic_rocket` and `stochastic_flight` objects previously defined, and only change the `MonteCarlo` object. First, we need to define our customized data collector." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", - "\n", - "def custom_export_function(flight):\n", + "# Defining custom callback functions\n", + "def get_average_reynolds_number(flight):\n", " reynold_number_list = flight.reynolds_number(flight.time)\n", " average_reynolds_number = np.mean(reynold_number_list)\n", - " custom_exports = {\n", - " \"average_reynolds_number\": average_reynolds_number,\n", - " \"date\": flight.env.date,\n", - " }\n", - " return custom_exports" + " return average_reynolds_number\n", + "\n", + "def get_date(flight):\n", + " return flight.env.date\n", + "\n", + "custom_data_collector = {\n", + " \"average_reynolds_number\": get_average_reynolds_number,\n", + " \"date\": get_date,\n", + "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ + "The `data_collector` must be a dictionary whose keys are the names of the variables we want to export and the values are callback functions (python callables) that compute these variable values. Notice how we can compute complex expressions in this function and just export the result. For instance, the *get_average_reynolds_number* calls the `flight.reynolds_number` method for each value in `flight.time` list and computes the average value using numpy's `mean`. The *date* variable is straightforward.\n", "\n", - "The `export_function` must be a function which takes a `Flight` object and outputs a dictionary whose keys are variables names to export and their values. Notice how we can compute complex expressions in this function and just export the result. For instance, the *average_reynolds_number* calls the `flight.reynolds_number` method for each value in `flight.time` list and computes the average value using numpy's `mean`. The *date* variable is straightforward.\n", - "\n", - "After we define the export function, we pass it as an argument to the `MonteCarlo` class." + "After we define the data collector, we pass it as an argument to the `MonteCarlo` class." ] }, { @@ -1181,7 +1184,8 @@ " environment=stochastic_env,\n", " rocket=stochastic_rocket,\n", " flight=stochastic_flight,\n", - " export_function=custom_export_function,\n", + " export_list=[\"apogee\", \"apogee_time\", \"x_impact\"],\n", + " data_collector=custom_data_collector,\n", ")" ] }, diff --git a/rocketpy/simulation/monte_carlo.py b/rocketpy/simulation/monte_carlo.py index 566d4247f..70584838a 100644 --- a/rocketpy/simulation/monte_carlo.py +++ b/rocketpy/simulation/monte_carlo.py @@ -48,6 +48,9 @@ class MonteCarlo: The stochastic flight object to be iterated over. export_list : list The list of variables to export at each simulation. + data_collector : dict + A dictionary whose keys are the names of the additional + exported variables and the values are callback functions. inputs_log : list List of dictionaries with the inputs used in each simulation. outputs_log : list @@ -112,8 +115,15 @@ def __init__( `lateral_surface_wind`. Default is None. data_collector : dict, optional A dictionary whose keys are the names of the exported variables - and the values are callback functions. The callback functions receive - a Flight object and returns a value of that variable. + and the values are callback functions. The keys (variable names) must not + overwrite the default names on 'export_list'. The callback functions receive + a Flight object and returns a value of that variable. For instance + + .. code-block:: python + custom_data_collector = { + "max_acceleration": lambda flight: max(flight.acceleration(flight.time)), + "date": lambda flight: flight.env.date, + } Returns ------- From e4aead78b1014824f394b8679958e719967c9482 Mon Sep 17 00:00:00 2001 From: Lucas de Oliveira Prates Date: Fri, 22 Nov 2024 16:31:56 -0300 Subject: [PATCH 18/36] MNT: make black for notebook --- .../monte_carlo_analysis/monte_carlo_class_usage.ipynb | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/notebooks/monte_carlo_analysis/monte_carlo_class_usage.ipynb b/docs/notebooks/monte_carlo_analysis/monte_carlo_class_usage.ipynb index cfd31aa66..2f94a0b16 100644 --- a/docs/notebooks/monte_carlo_analysis/monte_carlo_class_usage.ipynb +++ b/docs/notebooks/monte_carlo_analysis/monte_carlo_class_usage.ipynb @@ -1149,15 +1149,18 @@ "source": [ "import numpy as np\n", "\n", + "\n", "# Defining custom callback functions\n", "def get_average_reynolds_number(flight):\n", " reynold_number_list = flight.reynolds_number(flight.time)\n", " average_reynolds_number = np.mean(reynold_number_list)\n", " return average_reynolds_number\n", "\n", + "\n", "def get_date(flight):\n", " return flight.env.date\n", "\n", + "\n", "custom_data_collector = {\n", " \"average_reynolds_number\": get_average_reynolds_number,\n", " \"date\": get_date,\n", From 6c477e3f2f3fef3f1a81dcab25a048453ebbee59 Mon Sep 17 00:00:00 2001 From: Lucas de Oliveira Prates Date: Fri, 22 Nov 2024 18:16:21 -0300 Subject: [PATCH 19/36] DOC: update CHANGELOG --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index d4d083a77..90d73549b 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -32,6 +32,7 @@ Attention: The newest changes should be on top --> ### Added +- ENH: Callback function for collecting additional data from Monte Carlo sims [#702](https://github.com/RocketPy-Team/RocketPy/pull/702) - ENH: Implement optional plot saving [#597](https://github.com/RocketPy-Team/RocketPy/pull/597) ### Changed From 041424999ea75a5ae4d6db55a328f0a105510191 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lucas=20Gon=C3=A7alves?= Date: Sat, 23 Nov 2024 00:11:48 +0000 Subject: [PATCH 20/36] DOC: RED Flight Example (#733) * DOC: Add drag for airbrakes * DOC: Add flight data * DOC : Add actuation from the aibrakes. (not needed but saved in case of corrections) * DOC: Updates/ Fixes to simulation file * DOC: Add simulation to index * DEV: changelog --- CHANGELOG.md | 1 + data/rockets/camoes/actuation.csv | 319 + data/rockets/camoes/drag_airbrakes.csv | 122 + .../camoes/drag_coefficient_power_off.csv | 11 + .../camoes/drag_coefficient_power_on.csv | 11 + data/rockets/camoes/flight_data.csv | 14347 ++++++++++++++++ data/rockets/camoes/gnss_flight.csv | 501 + data/rockets/camoes/thrust_source.csv | 125 + docs/examples/camoes_flight_sim.ipynb | 835 + docs/examples/index.rst | 2 + 10 files changed, 16274 insertions(+) create mode 100644 data/rockets/camoes/actuation.csv create mode 100644 data/rockets/camoes/drag_airbrakes.csv create mode 100644 data/rockets/camoes/drag_coefficient_power_off.csv create mode 100644 data/rockets/camoes/drag_coefficient_power_on.csv create mode 100644 data/rockets/camoes/flight_data.csv create mode 100644 data/rockets/camoes/gnss_flight.csv create mode 100644 data/rockets/camoes/thrust_source.csv create mode 100644 docs/examples/camoes_flight_sim.ipynb diff --git a/CHANGELOG.md b/CHANGELOG.md index 90d73549b..e8842a0f4 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -32,6 +32,7 @@ Attention: The newest changes should be on top --> ### Added +- DOC: Camoes Flight Example [#733](https://github.com/RocketPy-Team/RocketPy/pull/733) - ENH: Callback function for collecting additional data from Monte Carlo sims [#702](https://github.com/RocketPy-Team/RocketPy/pull/702) - ENH: Implement optional plot saving [#597](https://github.com/RocketPy-Team/RocketPy/pull/597) diff --git a/data/rockets/camoes/actuation.csv b/data/rockets/camoes/actuation.csv new file mode 100644 index 000000000..d33391687 --- /dev/null +++ b/data/rockets/camoes/actuation.csv @@ -0,0 +1,319 @@ +0,0 +3.8,1 +3.85,1 +3.9,1 +3.95,1 +4,1 +4.05,1 +4.1,1 +4.15,1 +4.2,1 +4.25,1 +4.3,1 +4.3,1 +4.4,1 +4.4,1 +4.5,1 +4.5,1 +4.6,1 +4.6,1 +4.7,1 +4.7,1 +4.8,1 +4.8,1 +4.9,1 +4.9,1 +5,1 +5,1 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results were extracted out of the flight card:\n", + "\n", + "1. Team number: `20`\n", + "2. Launch date: `October 14th, 2023. 14h local time`\n", + "3. Simulated apogee: `3003 m`\n", + "4. Official recorded apogee: `3015 m`\n", + "\n", + "The relative error of altitude apogee is only `0.39%`!" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from rocketpy import Function, Environment, Flight, Rocket, SolidMotor\n", + "from rocketpy import AirBrakes, _Controller\n", + "from rocketpy.motors import CylindricalTank, SolidMotor\n", + "from rocketpy.motors.tank import MassFlowRateBasedTank\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.style.use(\"seaborn-v0_8-colorblind\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Environment" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Gravity Details\n", + "\n", + "Acceleration of gravity at surface level: 9.8066 m/s²\n", + "Acceleration of gravity at 4.000 km (ASL): 9.8066 m/s²\n", + "\n", + "\n", + "Launch Site Details\n", + "\n", + "Launch Date: 2023-10-14 13:00:00 UTC | 2023-10-14 14:00:00 Portugal\n", + "Launch Site Latitude: 39.39000°\n", + "Launch Site Longitude: -8.28954°\n", + "Reference Datum: WGS84\n", + "Launch Site UTM coordinates: 44367.90 W 4373424.89 N\n", + "Launch Site UTM zone: 30S\n", + "Launch Site Surface Elevation: 107.0 m\n", + "\n", + "\n", + "Atmospheric Model Details\n", + "\n", + "Atmospheric Model Type: Reanalysis\n", + "Reanalysis Maximum Height: 4.000 km\n", + "Reanalysis Time Period: from 2023-10-12 08:00:00 to 2023-10-15 18:00:00 utc\n", + "Reanalysis Hour Interval: 1 hrs\n", + "Reanalysis Latitude Range: From 40.0° to 39.0°\n", + "Reanalysis Longitude Range: From -9.0° to -7.0°\n", + "\n", + "Surface Atmospheric Conditions\n", + "\n", + "Surface Wind Speed: 207.00 m/s\n", + "Surface Wind Direction: 27.00°\n", + "Surface Wind Heading: 0.90°\n", + "Surface Pressure: 1000.00 hPa\n", + "Surface Temperature: 295.59 K\n", + "Surface Air Density: 1.179 kg/m³\n", + "Surface Speed of Sound: 344.66 m/s\n", + "\n", + "\n", + "Earth Model Details\n", + "\n", + "Earth Radius at Launch site: 6369.57 km\n", + "Semi-major Axis: 6378.14 km\n", + "Semi-minor Axis: 6356.75 km\n", + "Flattening: 0.0034\n", + "\n", + "\n", + "Atmospheric Model Plots\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "env = Environment(\n", + " gravity=9.80665,\n", + " date=(2023, 10, 14, 14),\n", + " latitude=39.3900032043457,\n", + " longitude=-8.2895383834838,\n", + " elevation=107,\n", + " datum=\"WGS84\",\n", + " timezone=\"Portugal\",\n", + ")\n", + "\n", + "env.set_atmospheric_model(\n", + " type=\"Reanalysis\",\n", + " file=\"../../data/weather/euroc_2023_all_windows.nc\",\n", + " dictionary=\"ECMWF\",\n", + ")\n", + "env.max_expected_height = 4000\n", + "env.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Motor" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "Mariachi = SolidMotor(\n", + " thrust_source=\"../../data/rockets/camoes/thrust_source.csv\",\n", + " burn_time=3.72,\n", + " grain_number=6,\n", + " grain_density=1637,\n", + " grain_initial_inner_radius=0.015,\n", + " grain_outer_radius=0.045,\n", + " grain_initial_height=0.15,\n", + " nozzle_radius=0.034,\n", + " throat_radius=0.0135,\n", + " grain_separation=0.005,\n", + " grains_center_of_mass_position=-0.7566,\n", + " dry_inertia=(0, 0, 0),\n", + " center_of_dry_mass_position=0,\n", + " dry_mass=0,\n", + " nozzle_position=-1.3346,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Nozzle Details\n", + "Nozzle Radius: 0.034 m\n", + "Nozzle Throat Radius: 0.0135 m\n", + "\n", + "Grain Details\n", + "Number of Grains: 6\n", + "Grain Spacing: 0.005 m\n", + "Grain Density: 1637 kg/m3\n", + "Grain Outer Radius: 0.045 m\n", + "Grain Inner Radius: 0.015 m\n", + "Grain Height: 0.15 m\n", + "Grain Volume: 0.001 m3\n", + "Grain Mass: 1.389 kg\n", + "\n", + "Motor Details\n", + "Total Burning Time: 3.72 s\n", + "Total Propellant Mass: 8.331 kg\n", + "Structural Mass Ratio: 0.000\n", + "Average Propellant Exhaust Velocity: 1320.764 m/s\n", + "Average Thrust: 2957.984 N\n", + "Maximum Thrust: 4145.791 N at 1.65 s after ignition.\n", + "Total Impulse: 11003.702 Ns\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Mariachi.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Rocket and Aerodynamic surfaces" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For comparison purposes, no parachute will be added to the simulation" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [], + "source": [ + "CAMOES = Rocket(\n", + " radius=0.0715,\n", + " mass=22.8,\n", + " inertia=(16.2, 16.2, 0.066),\n", + " center_of_mass_without_motor=0,\n", + " power_off_drag=\"../../data/rockets/camoes/drag_coefficient_power_off.csv\",\n", + " power_on_drag=\"../../data/rockets/camoes/drag_coefficient_power_on.csv\",\n", + " coordinate_system_orientation=\"tail_to_nose\",\n", + ")\n", + "\n", + "CAMOES.set_rail_buttons(0.5, 0.2)\n", + "\n", + "CAMOES.add_motor(Mariachi, position=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [], + "source": [ + "NoseCone = CAMOES.add_nose(\n", + " length=0.455,\n", + " kind=\"vonKarman\",\n", + " position=1.1884,\n", + ")\n", + "\n", + "FinSet = CAMOES.add_trapezoidal_fins(\n", + " n=4,\n", + " span=0.155,\n", + " root_chord=0.185,\n", + " tip_chord=0.15,\n", + " position=-1.0866,\n", + ")\n", + "\n", + "Tail = CAMOES.add_tail(\n", + " top_radius=0.0715, bottom_radius=0.037, length=0.048, position=-1.2866\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [], + "source": [ + "def drogue_trigger(p, h, y):\n", + " return True if y[5] < 5 and y[2] > 300 else False\n", + "\n", + "\n", + "Drogue = CAMOES.add_parachute(\n", + " \"Drogue\", cd_s=0.33, sampling_rate=400, lag=1.5, trigger=drogue_trigger\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "CAMOES.draw()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Air Brakes - Active Control Systems" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For privacy reasons, the controller function is not the one developed and used by the team. \n", + "\n", + "The controller function just describes the actuation with time of the algorithm developed by the team. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "\n", + "def controller_function(\n", + " time, sampling_rate, state, state_history, observed_variables, air_brakes\n", + "):\n", + " new_deployment_level = 0\n", + "\n", + " if time <= 11.4:\n", + " new_deployment_level = 1\n", + " else:\n", + " new_deployment_level = (\n", + " -0.002906 * np.power(time, 3)\n", + " + 0.1497 * np.power(time, 2)\n", + " + -2.563 * time\n", + " + 14.96\n", + " )\n", + "\n", + " if time > 19.6:\n", + " new_deployment_level = 0\n", + "\n", + " if time < 3.8:\n", + " new_deployment_level = 0\n", + "\n", + " air_brakes.deployment_level = new_deployment_level\n", + "\n", + " return time, air_brakes.deployment_level" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "air_brakes = CAMOES.add_air_brakes(\n", + " drag_coefficient_curve=[\n", + " # Mach 0.1\n", + " [0, 0.1, 0.7692],\n", + " [10 / 100, 0.1, 0.7663],\n", + " [20 / 100, 0.1, 0.7762],\n", + " [30 / 100, 0.1, 0.7847],\n", + " [40 / 100, 0.1, 0.7999],\n", + " [50 / 100, 0.1, 0.8246],\n", + " [60 / 100, 0.1, 0.8310],\n", + " [70 / 100, 0.1, 0.8499],\n", + " [80 / 100, 0.1, 0.8738],\n", + " [90 / 100, 0.1, 0.8945],\n", + " [100 / 100, 0.1, 1.1018],\n", + " # Mach 0.2\n", + " [0, 0.2, 0.7508],\n", + " [10 / 100, 0.2, 0.7513],\n", + " [20 / 100, 0.2, 0.7670],\n", + " [30 / 100, 0.2, 0.7782],\n", + " [40 / 100, 0.2, 0.7943],\n", + " [50 / 100, 0.2, 0.8063],\n", + " [60 / 100, 0.2, 0.8243],\n", + " [70 / 100, 0.2, 0.8493],\n", + " [80 / 100, 0.2, 0.8781],\n", + " [90 / 100, 0.2, 0.8925],\n", + " [100 / 100, 0.2, 0.9851],\n", + " # Mach 0.3\n", + " [0, 0.3, 0.7445],\n", + " [10 / 100, 0.3, 0.7571],\n", + " [20 / 100, 0.3, 0.7699],\n", + " [30 / 100, 0.3, 0.7828],\n", + " [40 / 100, 0.3, 0.7985],\n", + " [50 / 100, 0.3, 0.8114],\n", + " [60 / 100, 0.3, 0.8379],\n", + " [70 / 100, 0.3, 0.8634],\n", + " [80 / 100, 0.3, 0.8834],\n", + " [90 / 100, 0.3, 0.8947],\n", + " [100 / 100, 0.3, 0.9782],\n", + " # Mach 0.4\n", + " [0, 0.4, 0.7492],\n", + " [10 / 100, 0.4, 0.7566],\n", + " [20 / 100, 0.4, 0.7698],\n", + " [30 / 100, 0.4, 0.7840],\n", + " [40 / 100, 0.4, 0.7994],\n", + " [50 / 100, 0.4, 0.8136],\n", + " [60 / 100, 0.4, 0.8399],\n", + " [70 / 100, 0.4, 0.8665],\n", + " [80 / 100, 0.4, 0.8834],\n", + " [90 / 100, 0.4, 0.8969],\n", + " [100 / 100, 0.4, 0.9659],\n", + " # Mach 0.5\n", + " [0, 0.5, 0.7454],\n", + " [10 / 100, 0.5, 0.7537],\n", + " [20 / 100, 0.5, 0.7651],\n", + " [30 / 100, 0.5, 0.7811],\n", + " [40 / 100, 0.5, 0.7987],\n", + " [50 / 100, 0.5, 0.8163],\n", + " [60 / 100, 0.5, 0.8404],\n", + " [70 / 100, 0.5, 0.8631],\n", + " [80 / 100, 0.5, 0.8788],\n", + " [90 / 100, 0.5, 0.8951],\n", + " [100 / 100, 0.5, 0.9416],\n", + " # Mach 0.6\n", + " [0, 0.6, 0.7036],\n", + " [10 / 100, 0.6, 0.7237],\n", + " [20 / 100, 0.6, 0.7359],\n", + " [30 / 100, 0.6, 0.7496],\n", + " [40 / 100, 0.6, 0.7684],\n", + " [50 / 100, 0.6, 0.7899],\n", + " [60 / 100, 0.6, 0.8153],\n", + " [70 / 100, 0.6, 0.8406],\n", + " [80 / 100, 0.6, 0.8539],\n", + " [90 / 100, 0.6, 0.8712],\n", + " [100 / 100, 0.6, 0.9286],\n", + " # Mach 0.7\n", + " [0, 0.7, 0.6810],\n", + " [10 / 100, 0.7, 0.6948],\n", + " [20 / 100, 0.7, 0.7076],\n", + " [30 / 100, 0.7, 0.7233],\n", + " [40 / 100, 0.7, 0.7427],\n", + " [50 / 100, 0.7, 0.7629],\n", + " [60 / 100, 0.7, 0.7876],\n", + " [70 / 100, 0.7, 0.8073],\n", + " [80 / 100, 0.7, 0.8272],\n", + " [90 / 100, 0.7, 0.8453],\n", + " [100 / 100, 0.7, 0.8960],\n", + " # Mach 0.8\n", + " [0, 0.8, 0.6578],\n", + " [10 / 100, 0.8, 0.6739],\n", + " [20 / 100, 0.8, 0.6870],\n", + " [30 / 100, 0.8, 0.7036],\n", + " [40 / 100, 0.8, 0.7240],\n", + " [50 / 100, 0.8, 0.7457],\n", + " [60 / 100, 0.8, 0.7676],\n", + " [70 / 100, 0.8, 0.7880],\n", + " [80 / 100, 0.8, 0.8098],\n", + " [90 / 100, 0.8, 0.8278],\n", + " [100 / 100, 0.8, 0.8799],\n", + " # Mach 0.9\n", + " [0, 0.9, 0.6574],\n", + " [10 / 100, 0.9, 0.6634],\n", + " [20 / 100, 0.9, 0.6794],\n", + " [30 / 100, 0.9, 0.6958],\n", + " [40 / 100, 0.9, 0.7201],\n", + " [50 / 100, 0.9, 0.7395],\n", + " [60 / 100, 0.9, 0.7618],\n", + " [70 / 100, 0.9, 0.7844],\n", + " [80 / 100, 0.9, 0.8057],\n", + " [90 / 100, 0.9, 0.8278],\n", + " [100 / 100, 0.9, 0.8628],\n", + " # Mach 1.0\n", + " [0, 1.0, 0.8350],\n", + " [10 / 100, 1.0, 0.8241],\n", + " [20 / 100, 1.0, 0.8403],\n", + " [30 / 100, 1.0, 0.8593],\n", + " [40 / 100, 1.0, 0.8832],\n", + " [50 / 100, 1.0, 0.9070],\n", + " [60 / 100, 1.0, 0.9358],\n", + " [70 / 100, 1.0, 0.9639],\n", + " [80 / 100, 1.0, 0.9881],\n", + " [90 / 100, 1.0, 1.0093],\n", + " [100 / 100, 1.0, 1.0347],\n", + " # Mach 1.1\n", + " [0, 1.1, 0.8610],\n", + " [10 / 100, 1.1, 0.8447],\n", + " [20 / 100, 1.1, 0.8617],\n", + " [30 / 100, 1.1, 0.8800],\n", + " [40 / 100, 1.1, 0.9027],\n", + " [50 / 100, 1.1, 0.9244],\n", + " [60 / 100, 1.1, 0.9515],\n", + " [70 / 100, 1.1, 0.9810],\n", + " [80 / 100, 1.1, 1.0069],\n", + " [90 / 100, 1.1, 1.0253],\n", + " [100 / 100, 1.1, 1.0560],\n", + " ],\n", + " controller_function=controller_function,\n", + " sampling_rate=10,\n", + " reference_area=None,\n", + " clamp=True,\n", + " initial_observed_variables=(0, 0),\n", + " override_rocket_drag=True,\n", + " name=\"Air Brakes\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "air_brakes.all_info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Flight Simulation Data" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "R:\\RocketPy\\rocketpy\\simulation\\flight.py:1201: UserWarning: time_overshoot has been set to False due to the presence of controllers or sensors. \n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "test_flight = Flight(\n", + " rocket=CAMOES, environment=env, inclination=84, heading=133, rail_length=12\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Apogee State\n", + "\n", + "Apogee Time: 23.034 s\n", + "Apogee Altitude: 3110.284 m (ASL) | 3003.284 m (AGL)\n", + "Apogee Freestream Speed: 16.887 m/s\n", + "Apogee X position: 399.259 m\n", + "Apogee Y position: -457.173 m\n", + "Apogee latitude: 39.3858907°\n", + "Apogee longitude: -8.2848916°\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "test_flight.prints.apogee_conditions()\n", + "test_flight.plots.trajectory_3d()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Airbrakes actuation analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "time_list, deployment_level_list = [], []\n", + "\n", + "obs_vars = test_flight.get_controller_observed_variables()\n", + "\n", + "for time, deployment_level in obs_vars:\n", + " time_list.append(time)\n", + " deployment_level_list.append(deployment_level)\n", + "\n", + "# Plot deployment level by time\n", + "plt.plot(time_list, deployment_level_list)\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(\"Deployment Level\")\n", + "plt.title(\"Deployment Level by Time\")\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "from rocketpy.simulation.flight_data_importer import FlightDataImporter" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The following attributes were create and are now available to be used: ['time', 'altitude', 'az']\n" + ] + } + ], + "source": [ + "columns_map = {\n", + " \"ts\": \"time\",\n", + " \"filtered_altitude_AGL\": \"altitude\",\n", + " \"filtered_acceleration\": \"az\",\n", + "}\n", + "\n", + "cots_altimeter_flight = FlightDataImporter(\n", + " name=\"COTS Altimeter Flight\",\n", + " paths=\"../../data/rockets/camoes/flight_data.csv\",\n", + " columns_map=columns_map,\n", + " units=None,\n", + " interpolation=\"linear\",\n", + " extrapolation=\"zero\",\n", + " delimiter=\",\",\n", + " encoding=\"utf-8\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Function.compare_plots(\n", + " [\n", + " (test_flight.altitude, \"RocketPy\"),\n", + " (cots_altimeter_flight.altitude, \"COTS Altimeter\"),\n", + " ],\n", + " title=\"Altitude Comparison\",\n", + " xlabel=\"Time (s)\",\n", + " ylabel=\"Altitude (m)\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The following attributes were create and are now available to be used: ['time', 'latitude', 'longitude']\n" + ] + } + ], + "source": [ + "columns_map = {\n", + " \"ts\": \"time\",\n", + " \"latitude\": \"latitude\",\n", + " \"longitude\": \"longitude\",\n", + "}\n", + "\n", + "cots_gnss_flight = FlightDataImporter(\n", + " name=\"COTS GNSS Flight\",\n", + " paths=\"../../data/rockets/camoes/gnss_flight.csv\",\n", + " columns_map=columns_map,\n", + " units=None,\n", + " interpolation=\"linear\",\n", + " extrapolation=\"zero\",\n", + " delimiter=\",\",\n", + " encoding=\"utf-8\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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eIzs7W5k6dari5uam2NnZKffcc49y+fLlEpcbiIuLU5566inFz89P0Wq1ire3t9K/f3/l448/LtNrlZ+fr3z66adKz549FScnJ0Wr1Sr+/v7KxIkTiy0BcPDgQSU0NFSxt7dXbG1tlb59+yq7du0q8bW/cYmFonreOH1+/Pjxip2dnelx0dT4N998U1myZIni5+en6HQ6pWfPnsqRI0fMzr1y5Ypy//33K87OzoqTk5MyYsQIJTo6utjrVNq9r3+uSFhYmHLfffcpvr6+irW1teLr66uMHj1aOX36tNl5cXFxysSJExV3d3fF2tpaad26tbJ69WqzMtfX5UYlvZdCVHcqRZGRc3ciKSnJ7D/XMWPGMHz4cLMB0AEBASUu73/lyhX8/PzYtWsXwcHBN71PYmIiVlZWODs74+3tzf/93/8xc+bMEsveeN2XXnqJTZs2ma1mW1QmPDycrl27YmNjw/z580scuN2wYUOzsQd//fUXffv2JTk52TRNWAhRfhcvXiQwMJA333yTZ5991tLhCCGuI11sd8jV1RVXV1fTYxsbGzw9PWncuPEtzy2aeVLU138z7u7uAGzdupX4+HjuvffeMl83Kyur2BTdojENRWU7dOhAREREmeIWQgghajtJkKrInj172LdvHz169MDFxYVz587x0ksv0ahRI1PrUVRUFP379+fzzz+nc+fOAKxevZrmzZvj4eFBeHg406ZN45lnnjHN+CnLdYcOHcrSpUtZsGABo0ePJj09nRdeeAF/f3/at28PwNy5c7n77rtp0KABDz74IGq1miNHjnD8+HFeffVVAGJjY4mNjTWNDzl27BgODg40aNDALEkUQgghajqZxVZFbG1tWbduHf3796dp06ZMmjSJNm3asG3bNtMsNYPBQEREBFlZWabzIiIiGDZsGM2bN2fBggW8+OKLvPXWW+W6br9+/fj6669Zv3497du3Z9CgQeh0OjZt2mRaKyU0NJQNGzawefNm7rrrLrp27WraPLPIihUraN++PZMnTwagV69etG/fnl9++aXSXz8hhBCiKskYJCGEEEKIG0gLkhBCCCHEDSRBEkIIIYS4gQzSvk1Go5Ho6GgcHBzKvT2DEEIIISxDURTS09Px9fUtNsP7epIg3abo6OgK349JCCGEEFXj8uXL1K9fv9TnJUG6TUXL61++fLnC92WCwhltmzdvZuDAgaXu1VZbSd2l7lL3ukPqLnWv6rqnpaXh5+d3y21yJEG6TUXdao6OjpWWINna2uLo6Fgnf3Ck7lL3ukTqLnWXule9Ww2PkUHaQgghhBA3kARJCCGEEOIGkiAJIYQQQtxAxiAJIYSolvLz88nLy7N0GJXGYDCg1WrJysqqk2OQKqvu1tbWWFndeXojCZIQQohqRVEUIiMjSUxMtHQolc7Ly8u0AXhdU5l1d3d3p0GDBne0TqEkSEIIIaqVouSoXr162Nvb33QxPyGuZzQaycjIICoqCsBsw/XykgRJCCFEtZGfn29Kjry9vS0djqiB7O3tAYiKiiIzM5PmzZvfVkuSpOVCCCGqjaIxR0V/5IS4HUXfPzt27ODo0aMoilLua0iCJIQQotqRbjVxJ4q+f1QqFTt27CA9Pb3816jooIQQQgghqgMHBweys7PJyMgo97kWT5Def/99AgIC0Ov1dOnShb179960/Nq1a2nWrBl6vZ7WrVuzceNGs+fXrVvHwIEDcXNzQ6VScfjw4WLXyMnJ4amnnsLNzQ17e3uGDx9OXFxcRVZLCCGEqFATJkxg2LBhlg6jRlGr1SiKgtFoLP+5lRBPmX333XfMmDGDefPmcfDgQdq2bUtoaCjx8fEllt+1axejR49m0qRJHDp0iGHDhjFs2DCOHz9uKpOZmUmPHj144403Sr3vM888w6+//sratWvZtm0b0dHRPPDAAxVePyGEEHXHhAkTUKlUqFQqtFotgYGBPPfcc+Tk5Fg6NABefvll2rVrV+x4QECAKW47Ozs6dOjA2rVrqz7AasaiCdLbb7/N5MmTmThxIi1atGDFihXY2tqyatWqEsu/8847DBo0iJkzZ9K8eXNeeeUVOnTowHvvvWcqM3bsWObOnUtISEiJ10hNTWXlypW8/fbb9OvXj44dO7J69Wp27drF7t27K6We5RGXnc6ljCR2RcUQkZ1DZi1eJE0IIWqbQYMGERMTw/nz51m6dCkfffQR8+bNs3RYt7RgwQJiYmI4dOgQd911Fw899BC7du2ydFgWZbFp/nl5eRw4cIDZs2ebjqnVakJCQggPDy/xnPDwcGbMmGF2LDQ0lPXr15f5vgcOHMBgMJglUM2aNaNBgwaEh4fTtWvXEs/Lzc0lNzfX9DgtLQ0oXA3UYDCU+f63Mm7b12yOOWN6POvb/XiqvPko+D6GNr799RxqkqLXsyJf15pC6i51r2turHtNfw10Op1peQI/Pz9CQkLYsmULb7zxBrm5ucycOZNvv/2WtLQ0OnXqxNKlS7nrrrtM5584cYJZs2axfft2FEWhXbt2fPbZZzRq1KjYvfbt28eQIUN49tlnmTVrFikpKTz77LP8/PPP5Obmmq7ftm1bPvvsM+bPnw/8u4v96tWrmTBhAlA4Vsfb2xtvb2/ef/99vvzyS3799Vfy8/Pp378/ly9fNlt2Yfr06Rw4cIC///67sl7KCmE0GjEajeTn55f7e8xiCVJiYiIFBQV4eXmZHffy8uLUqVMlnhMbG1ti+djY2DLfNzY2Fmtra5ydnct1nUWLFpm+ua63efNmbG1ty3z/W0lOv4o1agxK4WqyqBXiieH+7Z8x9s92DK9Xd5aj37Jli6VDsBipe90kdQetVlvs97yiKGTlFVR5TLbWmjtaifn48ePs2rXLtFjhc889x48//siaNWvw9/dn8eLFhIaGcvbsWVxdXYmKiqJXr1706dOHrVu34ujoyM6dO8nPzy927a1bt/LAAw+wePFiHnvsMQBGjBiBjY0Nv/32G05OTnz00Uf079+f06dP89BDD3H8+HE2bdrEH3/8AYCTk1OJcVtZWaHVasnLy6NXr140bNiQL774gpkzZwKFCcZXX33F4sWLb/u1qSqXL18mOjqabdu2mf7uZ2VllelcWSiyjGbPnm3WepWWloafnx8DBw7E0dGxwu4zhCFA4Tfgb79vJqdRYx47sJYs60y+SDtDL4+HGH+XX4XdrzoyGAxs2bKFAQMG1Mn9iaTuUve65Ma6Z2VlFdt+IiuvAPsXfqvy2DIWDsZOV74/kxs2bMDe3p78/Hxyc3NRq9W89957ZGZm8uGHH/LZZ58xePBgAD755BO2bNnCypUrmTlzJu+//z5OTk58++23pu+DJk2aFLvHTz/9xLhx4/j000956KGHgML1fvbu3Ut8fDw6nQ6At956i/Xr1/PDDz/w2GOPYW9vj5WV1U0X4MzLy2PJkiWkpqbSr18/ACZNmsTq1atNCdKvv/5KTk4OI0eOLNdrYwl+fn4YjUZ69+5NvXr1gH97gG7FYgmSu7s7Go2m2OyxuLi4Ut88b2/vcpUv7Rp5eXmkpKSYtSLd6jo6nc70TXc9rVZbab/QrNQqRjZvSnPfyXT+dTkFjld5Ytsf1PMJYXBgw0q5Z3VSma9tdSd1l7rXNUV1r+n179u3Lx9++CGZmZksXboUKysrhg8fztGjRzEYDHTv3t1UVqvV0rlzZ/755x8ADh8+TM+ePW/6GuzZs4cNGzbwww8/mM1oO3LkCBkZGbi5uZmVz87O5ty5c7eMe9asWcyZM4ecnBzs7e15/fXXGTp0KFA4+HzOnDns3r2brl278tlnnzFy5Ejs7OzK89JYhFqtRq1Wm1rFgDJ/j1ksQbK2tqZjx46EhYWZ3mSj0UhYWBhTpkwp8Zzg4GDCwsKYPn266diWLVsIDg4u8307duyIVqslLCyM4cOHAxAREUFkZGS5rlOVOrjX55FGHVhzbj/53qcZ8udpTrg8SwtnWYZfCFH72VpryFg42CL3LS87OzsaN24MwKpVq2jbti0rV640G2dUGhsbm1uWadSoEW5ubqxatYqhQ4ea/thnZGTg4+PDX3/9VeycG4eUlGTmzJlMmDABe3t7vLy8zLoWPT09ueeee1i9ejWBgYH89ttvJd6ntrFoF9uMGTMYP348nTp1onPnzixbtozMzEwmTpwIwLhx46hXrx6LFi0CYNq0afTu3ZslS5YwdOhQvv32W/bv38/HH39sumZSUhKRkZFER0cDhckPYBp85uTkxKRJk5gxYwaurq44Ojry9NNPExwcXOoA7epgXvsBnE65SnjcZbDK59PjR3i7hyRIQojaT6VSlburqzpQq9W88MILzJgxg7Nnz2Jtbc3OnTtNY5IMBgP79u0z/dPfpk0b1qxZg8FgKLWVw93dnXXr1tGnTx9GjhzJ999/j1arpUOHDsTGxmJlZUVAQECJ51pbW1NQUPJYLnd3d1NiV5L//Oc/jB49mvr169OoUSOzlrDayqLT/B966CHeeust5s6dS7t27Th8+DCbNm0yDdCLjIwkJibGVL5bt258/fXXfPzxx7Rt25YffviB9evX06pVK1OZX375hfbt25uaBkeNGkX79u1ZsWKFqczSpUu5++67GT58OL169cLb25t169ZVUa1vT6CDG7vufYr2+pYAfHfmHwtHJIQQ4lZGjBiBRqPhww8/5IknnmDmzJls2rSJkydPMnnyZLKyspg0aRIAU6ZMIS0tjVGjRrF//37OnDnDF198YfpHv4inpydbt27l1KlTjB49mvz8fEJCQggODmbYsGFs3ryZixcvsmvXLl588UX2798PFK53dOHCBQ4fPkxiYqLZzOxbCQ0NxdHRkVdffdXUiFHbWXwl7SlTpnDp0iVyc3PZs2cPXbp0MT33119/8dlnn5mVHzFiBBEREeTm5nL8+HGGDBli9vyECRNQFKXYx8svv2wqo9fref/990lKSiIzM5N169bVmF2jn+nYHoBoQzyRSWUbiS+EEMIyrKysmDJlCosXL+a1115j+PDhjB07lg4dOnD27Fl+//13XFxcAHBzc2Pr1q1kZGTQu3dvOnbsyCeffFJia5K3tzdbt27l2LFjjBkzBqPRyMaNG+nVqxcTJ06kSZMmjBo1ikuXLpkaHYYPH86gQYPo27cvHh4efPPNN2Wuh1qtZsKECRQUFDBu3LiKeXGquZrXZlnHPdikKeN3q1C0eSzbc5K3B3eydEhCCCGg2D/0RZ5//nmef/55AJYvX87y5ctLvUabNm34/fffy3R9Hx8fs9YlBweHm15fp9Pxww8/FDt+8eLFUuO5XlRUFEOGDMHHx6dM5Ws6i7cgifKxsdLSyK6wteuLf05iNCoWjkgIIURtlpqayo4dO/j66695+umnLR1OlZEEqQYa6h8EQKKSwK6LSRaORgghRG123333MXDgQB5//HEGDBhg6XCqjHSx1UB9fRryzj/bwT6ZtUei6dHQ7dYnCSGEELehLkzpL4m0INVAIb5B6NVasM7hm4hT0s0mhBBCVDBJkGogO62O+/0LlzZIsLrMvssplg1ICCGEqGUkQaqhxgddm73mlMB3R6IsG4wQQghRy0iCVEP192mMk5UtWBlYe/64pcMRQgghahVJkGooK7WGUQ3bAXBFieT81UzLBiSEEELUIpIg1WCTmlzrZnNMZN3xK5YNRgghhKhFJEGqwTq5++Fu5QRqI1+ePmrpcIQQQlRTKpWK9evXWzqMGkUSpBpMpVIxpH5zAI5lRJKZm2/hiIQQom4LDw9Ho9GYNkwvj4CAAJYtW1bxQVWgvLw83nzzTTp06ICdnR1OTk60bduWOXPmEB0dbSo3YcIEVCoVr7/+utn569evR6VSmR375JNPaNu2Lfb29jg7O9O+fXsWLVpkej4rK4vZs2fTqFEj9Ho9Hh4e9O7dm59//rlS6yoJUg03olELAIw2yYSdSbRwNEIIUbetXLmSp59+mu3bt5slDLVBbm4uAwYMYOHChUyYMIHt27dz7Ngxli9fTmJiIu+++65Zeb1ezxtvvEFycnKp11y1ahXTp09n6tSpHD58mJ07d/Lcc8+RkZFhKvP444+zbt063n33XU6dOsWmTZt48MEHuXr1aqXVFSRBqvF6eTdEhQp0OXx74pylwxFCiDorIyOD7777jieeeIKhQ4eWuHntr7/+yl133YVer8fd3Z37778fgD59+nDp0iWeeeYZVCqVqZXl5Zdfpl27dmbXWLZsGQEBAabH+/btY8CAAbi7u+Pk5ETv3r05ePBghddv6dKl7Nixg61btzJ16lQ6duxIgwYN6N27NytWrGDhwoVm5UNCQvD29jZrDbrRL7/8wsiRI5k0aRKNGzemZcuWjB49mtdee82szAsvvMCQIUMICAigY8eOPP300zz66KMVXsfrSYJUwzla62lqX7h57aYrZ1AUWVVbCFG7KIpCpiG3yj/K+/v0+++/p1mzZjRt2pRHHnmEVatWmV3jf//7H/fffz9Dhgzh0KFDhIWF0blzZwDWrVtH/fr1WbBgATExMcTExJT5vunp6YwfP54dO3awe/dugoKCGDJkCOnp6eWK/1a++eYbBgwYQPv27Ut8/sauM41Gw8KFC3n33Xe5cqXkiUTe3t7s3r2bS5culXpfb29vNm7cWOH1uRXZi60WuDegOaeOx5CsjudEbDqtfBwtHZIQQlSYrPw87L98scrvm/HIa9hpdWUuv3LlSh555BEABg0aRGpqKtu2baNPnz4AvPbaa4waNYr58+ebzmnbti0Arq6uaDQaHBwc8Pb2Llec/fr1M3v88ccf4+zszLZt27j77rvLda2bOX36tKkuRe6//362bNkCQJs2bdi1a1ex59u1a8e8efNYuXJlsWvOmzePBx54gICAAJo0aUJwcDBDhgzhwQcfRK1Wm+ozZswY3NzcaNu2LT169ODBBx+ke/fuFVa3kkgLUi1wt1+zwi+c4/ny2FnLBiOEEHVQREQEe/fuZfTo0QBYWVnx0EMPmSUFhw8fpn///hV+77i4OCZPnkxQUBBOTk44OjqSkZFBZGRkmc7/6quvsLe3N338/fffZb73Bx98wOHDh3n00UfJysoqscwbb7zBmjVr+Oeff4o95+PjQ3h4OMeOHWPatGnk5+czfvx4Bg0ahNFoBKBXr16cP3+esLAwHnzwQU6cOEHPnj155ZVXyhzn7ZAWpFog2NMfnUpLrmLg3csbeJ0Olg5JCCEqjK2VNRmPvHbrgpVw37JauXIl+fn5+Pr6mo4pioJOp+O9997DyckJGxubcsegVquLdfUZDAazx+PHj+fq1au88847+Pv7o9PpCA4OJi8vr0z3uPfee+nSpYvpcb169UosFxQUREREhNkxHx8foLAFrDS9evUiNDSU2bNnM2HChBLLtGrVilatWvHkk0/y+OOP07NnT7Zt20bfvn0B0Gq19OzZk549ezJr1ixeffVVFixYwKxZs7C2Lvv7VB6SINUCVmoNM1v059UTm8hSpRGbnoW3g62lwxJCiAqhUqnK1dVV1fLz8/n8889ZsmQJAwcONHtu2LBhfPPNNzz++OO0adOGsLAwJk6cWOJ1rK2tKSgoMDvm4eFBbGwsiqKYxvgcPnzYrMzOnTv54IMPGDJkCACXL18mMbHss5odHBxwcHC4ZbnRo0czZ84cDh06VOo4pNK8/vrrtGvXjqZNm96ybIsWhbOzMzNL3yGiRYsW5Ofnk5OTIwmSuLkFd/Vn0bEwCtQG1hyPYFZw+b55hRBC3J4NGzaQnJzMpEmTcHJyMntu+PDhrFy5kscff5x58+bRv39/GjVqxKhRo8jPz2fjxo3MmjULKFwHafv27YwaNQqdToe7uzt9+vQhISGBxYsX8+CDD7Jp0yZ+++03HB3/HWsaFBTEF198QadOnUhLS2PmzJm31Vp1K8888wz/+9//6N+/P/PmzaNnz564uLhw+vRpfvvtNzQaTanntm7dmjFjxrB8+XKz40888QS+vr7069eP+vXrExMTw6uvvoqHhwfBwcFA4Qy/0aNH06lTJ9zc3Dh58iQvvPACffv2NXsdKpqMQaolVCoVvtbuAPx6Tqb7CyFEVVm5ciUhISHFkiMoTJD279/P0aNH6dOnD2vXruWXX36hXbt29OvXj71795rKLliwgIsXL9KoUSM8PDwAaN68OR988AHvv/8+bdu2Ze/evTz77LPF7p+cnEyHDh0YO3YsU6dOxdPTs8LrqdfrCQsLY9asWaxevZoePXrQvHlzpk+fTvfu3W+5UveCBQtM44qKhISEsHv3bkaMGEGTJk0YPny46T5ubm4AhIaGsmbNGgYOHEjz5s15+umnCQ0N5fvvv6/wOl5Ppci88NuSlpaGk5MTqamplZLBGgwGNm7cyJAhQ9BqtWU658Hfv+XH6P3YpjUg/emnUatVtz6pGrqdutcWUnepe12ve1ZWFv/88w/NmzfH1laGCojbU/R9dO7cOS5dusTo0aOpX78+UPa/39KCVIsMCmgIQJY6lcPRqRaORgghhKi5JEGqRTq6X5t5oM/kfyfjLBuMEEIIUYNJglSLtHD2Qo0KrPJZH1G29S+EEEIIUZwkSLWITmOFn50LAAcTYknMyLVwREIIIUTNJAlSLdPUqXAmG9psNpyMt2wwQghxm26c7SREeRR9/9zJPDRJkGqZRo6F0yLRZfPT8bJvdiiEENVB0aJ/GRkZFo5E1GRF3z83rjpeHrJQZC3T2OFaC5J1Dr9HJJCRm4+9Tt5mIUTNYGVlhbu7O1FRUQDY29ubNi0V4laMRiMZGRlERUWRkpJyRy2R8pezlmnkUNiCpLPJJTffyKZT8TzY1vcWZwkhRPXRoEEDAFOSJER5paSkEBcXR0FBAWq1Giur8qc7kiDVMv92seUACuuOxUqCJISoUVQqFf7+/pw9e5aTJ0/i4eFRKxfQVBSFyMhIGjRoYNpnra6ozLobDAaMRiNGo5G4uDg8PDxua0FnSZBqmYbXWpBylTzQ5LPhZBy5+QXorErfI0cIIaqj7t27k56ezqlTpywdSqVQFIWYmBhyc3PrZIJU2XVXFAV3d3cGDx58W6uyS4JUy9haWeNj40hMdhrOHumkxGrZeiaRwc29LB2aEEKUi16vZ9CgQTRp0uSmO7vXVPn5+ezfv59OnTrdVhdQTVYVddfpdPj4+Jj2tSuvuvWO1BGNHN2IyU4jxf0YxPZi3bFYSZCEEDWSXq+nRYsWlg6jUhgMBuLj4+nUqVOt7EK8mZpQd5kaUAu92Kb/vw+0ufx8IpYCo+xJLIQQQpSVJEi10KD6zWjqVNik6OBgICEjj7/OJlo4KiGEEKLmkASpliqa7t8usLDp8tvD0ZYMRwghhKhRJEGqpYoWjPS9Njbtx6Mx5OXL0v1CCCFEWUiCVEs1dixMkHLVmfg46kjONrD5dIKFoxJCCCFqBkmQaqnG1xaMPJd+lZHXFor89pCsSiuEEEKUhSRItVRRF9u59EQealeYIK0/HktWXr4lwxJCCCFqBEmQail/exc0KjVZ+QaO5Z7Gz11NZl4B//sn3tKhCSGEENWeJEi1lLXGigB7FwD+u+sHVH6FS/VLN5sQQghxa5Ig1WKvdxpCN88AAHLUGQD87594UrMNFoxKCCGEqP4kQarFHgxoy5e9RgOQnp9DCy97cvON/HA0xsKRCSGEENWbJEi1nJvODoDsAgOjO/gAsGb/ZUuGJIQQQlR7kiDVcg5aHRpV4ds8qJULahX8fT6J81dr387YQgghREWRBKmWU6lUuOpsANBZGwkJKlxa+/P9VywZlhBCCFGtSYJUB7jqbAFIysti/F31gcIEyWhULBmWEEIIUW1JglQHFI1DupqTxbBW3jjorLiQlMWOC0kWjkwIIYSoniRBqgOKutiS8rKwtbYybT0ig7WFEEKIkkmCVAe4Wl/rYsvNAjB1s609EiNbjwghhBAlkASpDjCNQbqWIPUIdKWhmy3pufmsOxZrydCEEEKIakkSpDrANAbpWoKkUqkY38kPgJV7Ii0WlxBCCFFdSYJUB5jGIF1LkAAe7eyHWgV/nbvK6YQMS4UmhBBCVEuSINUBRV1siTn/Lg5Z39mGIc29APhkt7QiCSGEENeTBKkOCHRwBeBkShyK8u/aR491bQDAZ/suk5tfYJHYhBBCiOrIytIBiMrXzrUe1moNibmZ/BV7Dh8bRwACfcDDNZ+EjCw+2P8Pg5t5FjtXr7Ei4FqCJYQQQtQVkiDVATqNFe1cfdmbeJl+m1aYP1m4JBIz/tnHjH9KPv/NTnfzbOs+lRqjEEIIUZ1IglRHTGneg5n7N2AwmnelGY0KKdkGAJxttKjVKtNz2fkGsgsMHE6KrtJYhRBCCEuTBKmOGNu4I2MbdyzxucGf7GbTqQQe79eYRUObm44vO7GdZ/b+QoFirKowhRBCiGpBBmkLHuvqD8CqvZHk5f+bDGlUhd8eRkU2tRVCCFG3SIIkuLuFFz6OOuIz8vjpWIzpuFpV2N0mLUhCCCHqGkmQBFqN2tSK9N7Oi6bjGlOCJC1IQggh6hZJkARQ2M1mpVax40ISh6NSgX+72KQFSQghRF0jCZIAwNdJz4NtfAB4b8dFQBIkIYQQdZfFE6T333+fgIAA9Ho9Xbp0Ye/evTctv3btWpo1a4Zer6d169Zs3LjR7HlFUZg7dy4+Pj7Y2NgQEhLCmTNnzMqcPn2a++67D3d3dxwdHenRowd//vlnhdetppnSIxCArw5eISkrT7rYhBBC1FkWTZC+++47ZsyYwbx58zh48CBt27YlNDSU+Pj4Esvv2rWL0aNHM2nSJA4dOsSwYcMYNmwYx48fN5VZvHgxy5cvZ8WKFezZswc7OztCQ0PJyckxlbn77rvJz89n69atHDhwgLZt23L33XcTGxtb6XWuzroFuNDO15GcfCMr90SiUcssNiGEEHWTRROkt99+m8mTJzNx4kRatGjBihUrsLW1ZdWqVSWWf+eddxg0aBAzZ86kefPmvPLKK3To0IH33nsPKGw9WrZsGXPmzOG+++6jTZs2fP7550RHR7N+/XoAEhMTOXPmDM8//zxt2rQhKCiI119/naysLLNEqy5SqVQ8fa0V6YNdF1EpMotNCCFE3WSxhSLz8vI4cOAAs2fPNh1Tq9WEhIQQHh5e4jnh4eHMmDHD7FhoaKgp+blw4QKxsbGEhISYnndycqJLly6Eh4czatQo3NzcaNq0KZ9//jkdOnRAp9Px0Ucf4enpSceOJS+kCJCbm0tubq7pcVpaGgAGgwGDwVDu+t9K0TUr49o382BrT2Zu0HIxKZuDl1MAyC8oqNI4LFX36kDqLnWva6TuUndL3ftWLJYgJSYmUlBQgJeXl9lxLy8vTp06VeI5sbGxJZYv6hor+nyzMiqVij/++INhw4bh4OCAWq3G09OTTZs24eLiUmq8ixYtYv78+cWOb968GVtb21vU9vZt2bKl0q5dml6uKtZnqfjxwAVwg4Skq8XGelUFS9S9upC6101S97pJ6l61srKyylSuzm01oigKTz31FJ6envz999/Y2Njw6aefcs8997Bv3z58fHxKPG/27NlmrVdpaWn4+fkxcOBAHB0dKzxOg8HAli1bGDBgAFqttsKvfzMtkrL4+c3tXMpQgxs4OTszZNCQKru/JetuaVJ3qbvUve6Qulum7kU9QLdisQTJ3d0djUZDXFyc2fG4uDi8vb1LPMfb2/um5Ys+x8XFmSU6cXFxtGvXDoCtW7eyYcMGkpOTTYnNBx98wJYtW1izZg3PP/98iffW6XTodLpix7VabaW+uZV9/ZIEeTlxX0tv1l9KBMCIYpEfXkvUvbqQukvd6xqpu9S9Ku9ZFhYbpG1tbU3Hjh0JCwszHTMajYSFhREcHFziOcHBwWblobB5rqh8YGAg3t7eZmXS0tLYs2ePqUxR05pabV51tVqN0SiDkYv8X++GcG2Qdm5BgYWjEUIIIaqWRWexzZgxg08++YQ1a9bwzz//8MQTT5CZmcnEiRMBGDdunNkg7mnTprFp0yaWLFnCqVOnePnll9m/fz9TpkwBCscXTZ8+nVdffZVffvmFY8eOMW7cOHx9fRk2bBhQmGS5uLgwfvx4jhw5wunTp5k5cyYXLlxg6NChVf4aVFfdA11p4mEPQFxG7i1KCyGEELWLRccgPfTQQyQkJDB37lxiY2Np164dmzZtMg2yjoyMNGvp6datG19//TVz5szhhRdeICgoiPXr19OqVStTmeeee47MzEwee+wxUlJS6NGjB5s2bUKv1wOFXXubNm3ixRdfpF+/fhgMBlq2bMnPP/9M27Ztq/YFqMZUKhUPtvFl4TlIyMgl21CAjVZj6bCEEEKIKmHxQdpTpkwxtQDd6K+//ip2bMSIEYwYMaLU66lUKhYsWMCCBQtKLdOpUyd+//33csda1/Rp6M7Cc4XrIH154AqTr21oK4QQQtR2Ft9qRFRfWs2/3x5vbzuP0SgragshhKgbJEESpSrarFatVjgVn8Fvp0reAkYIIYSobSRBEqUqSpAc9IU9sUu2nbNkOEIIIUSVkQRJlEqjLpzm76DXoFGr+PPsVQ5dSbVwVEIIIUTlkwRJlKqoBUmlgpFtfQFY/OdZS4YkhBBCVAlJkESpihKkAsXIc30bAfD9kWjOJmZaMiwhhBCi0kmCJEqlURV2sRUoCu3qOTG4mSdGBd6UViQhhBC1nCRIolTqogTp2hYss/s3BuCzfVeITs2xWFxCCCFEZZMESZTq+i42gJ4N3ege4EJegZGl289bMjQhhBCiUkmCJEr1b4L07wKRs/sHAbAi/CLJWXkWiUsIIYSobJIgiVIVjUEyXpcgDWnuSWsfBzJyC3hv50ULRSaEEEJULkmQRKlu7GKDwr3unu9XOBbpne3nyczNt0hsQgghRGWSBEmUqqQECQrXRGroZsvVLAOf7om0RGhCCCFEpZIESZSqaCXt68cgAVhp1MzsU7gu0lt/nSMv31jsXCGEEKImkwRJlKq0FiSACXf54e2g40pqDl8cuFLVoQkhhBCVShIkUaqidZAAjDckSXqthv/rXdiKtDDsDPkF0ookhBCi9pAESZSqqAUJzGeyFXmimz/udtacv5rFVwejqjI0IYQQolJJgiRKpbmuBenGcUgAdjornr02Fum1P6QVSQghRO0hCZIo1fUtSCWNQwJ4qnsAbrZaziRm8t3h6KoKTQghhKhUkiCJUpUlQbLXWTHj2likV7acpsBYvKVJCCGEqGkkQRKlMutiu0niM6VHAC42WiISMll7RFqRhBBC1HySIIlSlaUFCcBRr+WZ3g2BwlYko7QiCSGEqOEkQRKlUpsN0r75AOypPQJx0ltxMi6DH4/FVHZoQgghRKWSBEmUSqVSoaL4hrUlcbLRMr2XtCIJIYSoHSRBEjdVNA6ppGn+N5rWMxBHvRXHYtJZfzy2skMTQgghKo0kSOKmNOrStxu5kYutNdN6BgKwYMtplDIkVUIIIUR1JAmSuKl/W5DKtgjk9F4NcdBZcSQ6jZ+OSSuSEEKImkkSJHFT/25YW7bWIFdba6b3KmxFmvt7hKyLJIQQokaSBEnc1L8JUtm3EZnRuxHONlpOxKbzvayuLYQQogaSBEncVNFU/1vNYrues42WZ/sUzmib93uE7NEmhBCixpEESdxUeccgFZnaoyHudtacSczkywNRlRGaEEIIUWkkQRI3Vd4xSEUc9FbM6tsYgPlbIsjLl1YkIYQQNYckSOKmTAmSsfwJzpPd/fF20HExKZvV+yIrOjQhhBCi0kiCJG7qdrvYAGytrXgxJAiAV7acIcdQUKGxCSGEEJVFEiRxU7fbxVZkctcG+DnriUrN4ePdlyoyNCGEEKLSSIIkbup2pvlfT2el4aUBTQBYGHaWrLz8CotNCCGEqCySIImb0qjLP83/RhPu8qOhmy1x6bm8v/NiBUUmhBBCVB6r8hQ2Go1s27aNv//+m0uXLpGVlYWHhwft27cnJCQEPz+/yopTWIiawgRp4dEwfM7uu2lZHxtHXmobgt5Ka3Zcq1Ezb2ATxn9zmDe2nuXx4AAc9OX61hNCCCGqVJn+SmVnZ7NkyRI+/PBDkpKSaNeuHb6+vtjY2HD27FnWr1/P5MmTGThwIHPnzqVr166VHbeoIu56O0iFTVERZSrf2d2P+/xbFTs+pkN9Fv5xhoiETJb9fd7U7SaEEEJUR2VKkJo0aUJwcDCffPIJAwYMQKvVFitz6dIlvv76a0aNGsWLL77I5MmTKzxYUfVWdh/JT5HHb9nF9tnZfUSkJpCSl13i8xq1ivmhTRn15UHe+uscT3YLwM3OujJCFkIIIe5YmRKkzZs307x585uW8ff3Z/bs2Tz77LNERsqaN7VFkJMHz7Xue8tyexMiiUhNILvAUGqZEW19eX3rWQ5Hp/H61rO8eU+LigxVCCGEqDBlGqR9q+ToelqtlkaNGt12QKJmsrk27iinoPRZamq1ikVDC7+X3t1xgSspJbc2CSGEEJZW7pGyR48eLfG4SqVCr9fToEEDdDrdHQcmaha9pjBBulkLEkBoUw96N3Jj27mrzN98mk9Gtq2K8IQQQohyKXeC1K5dO1TXVlcuiVar5aGHHuKjjz5Cr9ffUXCi5tBrCr+VbtaCBIWJ9KIhzej27k5W7Y3k/3o3pJmXQ1WEKIQQQpRZuddB+umnnwgKCuLjjz/m8OHDHD58mI8//pimTZvy9ddfs3LlSrZu3cqcOXMqI15RTdkUtSDl37wFCSA4wJV7W3phVOClTWWbHSeEEEJUpXK3IL322mu88847hIaGmo61bt2a+vXr89JLL7F3717s7Oz4v//7P956660KDVZUX2VtQSry2uBm/Hoyjh+OxrAvMoW7GjhXYnRCCCFE+ZS7BenYsWP4+/sXO+7v78+xY8eAwm64mJiYO49O1BhFg7RvNQapSCsfR8Z2rA/ACxv/qbS4hBBCiNtR7gSpWbNmvP766+Tl5ZmOGQwGXn/9dZo1awZAVFQUXl5eFRelqPb+bUEqW4IEMD+0KVqNij/OJPLH6YTKCk0IIYQot3J3sb3//vvce++91K9fnzZt2gCFrUoFBQVs2LABgPPnz/Pkk09WbKSiWivPGKQiAa62PNEtgOV/X2D2xn/oH+R+0wkAQgghRFUpd4LUrVs3Lly4wFdffcXp06cBGDFiBA8//DAODoWzkcaOHVuxUYpqr2iaf1nHIBV5sX8QK/dEsv9yKuuOxTC8jW9lhCeEEEKUy23tGOrg4MDjjz9e0bGIGqy8Y5CKeDromNG7Ia9sOcOLG09xX0vvyghPCCGEKJdyj0EC+OKLL+jRowe+vr5cunQJgKVLl/Lzzz9XaHCi5ijvLLbr/V/vRrjZaolIyOTz/VcqOjQhhBCi3MqdIH344YfMmDGDwYMHk5ycTEFBAQAuLi4sW7asouMTNcTtjEEq4mSj5YWQIADm/R5BjqGgQmMTQgghyqvcCdK7777LJ598wosvvoiV1b89dJ06dTJN8xd1z+3MYrvek90CqO+k50pqDh+Gy2bHQgghLKvcCdKFCxdo3759seM6nY7MzMwKCUrUPLc7BqmIXqthfmhTAF7/8zwZt3cZIYQQokKUO0EKDAzk8OHDxY5v2rSJ5s2bV0RMoga63Vls1xt/lx8tvR1Izjbww2WZ7i+EEMJyyj2LbcaMGTz11FPk5OSgKAp79+7lm2++YdGiRXz66aeVEaOoAUxjkG6zBQlAo1bxxtDm3L1yL/+LgsiUbBp5aCsqRCGEEKLMyp0g/ec//8HGxoY5c+aQlZXFww8/jK+vL++88w6jRo2qjBhFDXAns9iuN6S5J70burLtfBIvbz7DF2M6VkR4QgghRLnc1jT/MWPGcObMGTIyMoiNjeXKlStMmjSpomMTNUjRGKTcgnwURbnt66hUKhYNLhyL9NWhaA5HpVZIfEIIIUR53FaCVMTW1hZPT8+KikXUYEUtSHDnrUid/Jzo4aGgKDBrg2xkK4QQouqVqYutffv2Zd4j6+DBg3cUkKiZisYgQeFU/6IWpds1JkBhT5KazacT2BKRwICmHncaohBCCFFmZWpBGjZsGPfddx/33XcfoaGhnDt3Dp1OR58+fejTpw96vZ5z584RGhpa2fGKaspKrUGjKvx2upOB2kV8bODxrg0AeG7DSYzG2++2E0IIIcqrTC1I8+bNM339n//8h6lTp/LKK68UK3P58uWKjU7UKDYaLRn5uXfcxVZkdr9GrDkQxeHoNL4+FMUjHetXyHWFEEKIWyn3GKS1a9cybty4YscfeeQRfvzxxwoJStRMReOQbme7kZK421kzu19jAF787ZRsQSKEEKLKlDtBsrGxYefOncWO79y5E71eXyFBiZqpaNxRRbUgAUzr1ZB6Tnoik7N5f+fFCruuEEIIcTPlXgdp+vTpPPHEExw8eJDOnTsDsGfPHlatWsVLL71U4QGKmsPUglQBY5CK2Gg1vDKoKY9+d4RX/zjDxM5+uNpaV9j1hRBCiJKUuwXp+eefZ82aNRw4cICpU6cydepUDh48yOrVq3n++efLHcD7779PQEAAer2eLl26sHfv3puWX7t2Lc2aNUOv19O6dWs2btxo9ryiKMydOxcfHx9sbGwICQnhzJkzxa7zv//9jy5dumBjY4OLiwvDhg0rd+zCnI1pu5GK3UhtXCc/Wnk7kJJtYFHY2Qq9thBCCFGS21oHaeTIkezcuZOkpCSSkpLYuXMnI0eOLPd1vvvuO2bMmMG8efM4ePAgbdu2JTQ0lPj4+BLL79q1i9GjRzNp0iQOHTrEsGHDGDZsGMePHzeVWbx4McuXL2fFihXs2bMHOzs7QkNDycnJMZX58ccfGTt2LBMnTuTIkSPs3LmThx9+uPwvhDBT0WOQimjUKhbfXbjP3/K/L3AxKatCry+EEELcqEwJ0p2sjHwzb7/9NpMnT2bixIm0aNGCFStWYGtry6pVq0os/8477zBo0CBmzpxJ8+bNeeWVV+jQoQPvvfeeKc5ly5YxZ84c7rvvPtq0acPnn39OdHQ069evByA/P59p06bx5ptv8vjjj9OkSRNatGhxWwmeMFcZY5CKDGrmSd/GbuQVGHlp06kKv74QQghxvTKNQWrZsiVz587lgQcewNq69PEfZ86c4e2338bf3/+W3W15eXkcOHCA2bNnm46p1WpCQkIIDw8v8Zzw8HBmzJhhdiw0NNSU/Fy4cIHY2FhCQkJMzzs5OdGlSxfCw8MZNWoUBw8eJCoqCrVaTfv27YmNjaVdu3a8+eabtGrVqtR4c3Nzyc3NNT1OS0sDwGAwYDBUbItJ0XWv/1wT6FQaANLzcu4o7tLqvnBQE4LfC+erg1E83c2f9vUcbz/Yaqomvu8VReouda9rpO6WqXtZ71mmBOndd99l1qxZPPnkkwwYMIBOnTrh6+uLXq8nOTmZkydPsmPHDk6cOMGUKVN44oknbnnNxMRECgoK8PLyMjvu5eXFqVMltxDExsaWWD42Ntb0fNGx0sqcP38egJdffpm3336bgIAAlixZQp8+fTh9+jSurq4l3nvRokXMnz+/2PHNmzdja2t7q+reti1btlTatStaanoSAPsPH8LtVOwdX6+kuvf0UPF3gorHvtjJ/Da1d/HImvS+VzSpe90kda+bLFH3rKyyDdMoU4LUv39/9u/fz44dO/juu+/46quvuHTpEtnZ2bi7u9O+fXvGjRvHmDFjcHFxuaPAK5vRaATgxRdfZPjw4QCsXr2a+vXrs3btWv773/+WeN7s2bPNWq/S0tLw8/Nj4MCBODpWfEuGwWBgy5YtDBgwAK32zrbtqCpf/53KnktXadyiGUOadb/t69ys7s27ZtFqyd8cSQFNo06E1rItSGri+15RpO5Sd6l73WHJuhf1AN1Kuab59+jRgx49etxWQDdyd3dHo9EQFxdndjwuLg5vb+8Sz/H29r5p+aLPcXFx+Pj4mJVp164dgOl4ixYtTM/rdDoaNmxIZGRkqfHqdDp0Ol2x41qttlLf3Mq+fkWysy58fQyKsUJiLqnuTbycmNojkCXbzjNrYwSDmntjpbmjPZerpZr0vlc0qbvUva6Ruldt3ct6P4v9ZbG2tqZjx46EhYWZjhmNRsLCwggODi7xnODgYLPyUNg8V1Q+MDAQb29vszJpaWns2bPHVKZjx47odDoiIiJMZQwGAxcvXsTf37/C6lcXVcY6SCWZM6AJbrZaTsZl8Ome0pNaIYQQ4nZZ9F/vGTNm8Mknn7BmzRr++ecfnnjiCTIzM5k4cSIA48aNMxvEPW3aNDZt2sSSJUs4deoUL7/8Mvv372fKlCkAqFQqpk+fzquvvsovv/zCsWPHGDduHL6+vqZ1jhwdHXn88ceZN28emzdvJiIiwjRmasSIEVX7AtQy/66DVPGz2K7nbKPl5dCmAMz9PYK0nLo3wFEIIUTlKvdK2hXpoYceIiEhgblz55pmk23atMk0yDoyMhK1+t8crlu3bnz99dfMmTOHF154gaCgINavX282++y5554jMzOTxx57jJSUFHr06MGmTZvMtkF58803sbKyYuzYsWRnZ9OlSxe2bt1a7cdPVXf6awnSL5EniMn6t49Xp7HimZa9aO7sVdqp5fbfYH/e23GBiIRMFoWdZdHQ5hV2bSGEEMKiCRLAlClTTC1AN/rrr7+KHRsxYsRNW3pUKhULFixgwYIFpZbRarW89dZbvPXWW+WOV5TOx8YBgH9S4/kn1XyxzzRDLt/2eaTC7qXVqHnznhbcu2ofS7ef57/B/gS4Vt5sQiGEEHWLxRMkUXs82qQzeistKbnZpmPnM67y4alwjiZFV/j97m7hRb/G7mw9m8js//3DN2M7Vvg9hBBC1E23lSCdO3eO1atXc+7cOd555x08PT357bffaNCgAS1btqzoGEUNYWtlzX+adDE7diUzhQ9PhXM6LZHcgnx0morLyVUqFUvubUGHpdv59nA0U3sGEhxQ8jpWQgghRHmUe5D2tm3baN26NXv27GHdunVkZGQAcOTIEebNm1fhAYqarZ6tE07WegoUI6dTEyr8+u3qOTGhkx8AM345WWnb4gghhKhbyp0gPf/887z66qts2bLFbNuRfv36sXv37goNTtR8KpWKVs6F61MdT7nz1bVL8urgZthaa9h9KZm1R2Iq5R5CCCHqlnInSMeOHeP+++8vdtzT05PExMQKCUrULq1criVIyZWTIPk66ZnVtzEAs/53khxDQaXcRwghRN1R7gTJ2dmZmJji/6UfOnSIevXqVUhQonZp6Vy5CRLA//VuSD0nPReTsln+94VKu48QQoi6odwJ0qhRo5g1axaxsbGoVCqMRiM7d+7k2WefZdy4cZURo6jhTC1IldTFBmCns+K1wc0AeC3sDAkZuZV2LyGEELVfuROkhQsX0qxZM/z8/MjIyKBFixb06tWLbt26MWfOnMqIUdRwRQnShfQkMg2Vl7iM7VifDvWdSMvJZ+6miFufIIQQQpSi3AmStbU1n3zyCefOnWPDhg18+eWXnDp1ii+++AKNRlMZMYoazkNvj6feHgWl2AKSFUmtVvH2vYWbEH+0+xL7IlMq7V5CCCFqt9vei61BgwYMGTKEkSNHEhQUVJExiVqosgdqF+ndyJ0xHeqhKPDEj0cpMMq0fyGEEOVXplX7ZsyYUeYLvv3227cdjKi9Wjp7szXmbKUnSABL7m3JhpNxHLiSyoe7LjKlR2Cl31MIIUTtUqYE6dChQ2aPDx48SH5+Pk2bFu6ofvr0aTQaDR07ylYPomRVMVC7iJeDjkVDm/Pkj8d48bdTDG/jg4+j/tYnCiGEENeUKUH6888/TV+//fbbODg4sGbNGlxcXABITk5m4sSJ9OzZs3KiFDVe0WKRJ6qgBQngsa7+rN57mX2XU5jx8wnZp00IIUS5lHsM0pIlS1i0aJEpOQJwcXHh1VdfZcmSJRUanKg9Wrp4AXAlK9VsM9vKolGrWPFga9Qq+PZwNL+eqJrETAghRO1Q7gQpLS2NhITie2olJCSQnp5eIUGJ2sfJ2gY/O2cATlRBNxtAh/rO/F/vRgD894ejJGflVcl9hRBC1HzlTpDuv/9+Jk6cyLp167hy5QpXrlzhxx9/ZNKkSTzwwAOVEaOoJVpVwYraN1owqCnNPO2JSctl+s8nquy+QggharZyJ0grVqxg8ODBPPzww/j7++Pv78/DDz/MoEGD+OCDDyojRlFLFHWzVcVA7SJ6rYbVo9qhVsHn+6+w4WRcld1bCCFEzVXuBMnW1pYPPviAq1evcujQIQ4dOkRSUhIffPABdnZ2lRGjqCWqeqB2ka7+Lsy41tX22NojJElXmxBCiFu47YUi7ezsaNOmDW3atJHESJRJKxcfoGpbkIosGNSUph52xKTl8tjaoyiKLCAphBCidGWa5n+9vn37olKpSn1+69atdxSQqL2aO3uiQkVCTibx2el42jhU2b1ttBq+GtOB4Hd38OPRGD7ZHcljwf5Vdn8hhBA1S7lbkNq1a0fbtm1NHy1atCAvL4+DBw/SunXryohR1BK2VtY0cnADqnagdpGOfs4sHNwcgOk/H+dkrMy6FEIIUbJytyAtXbq0xOMvv/wyGRkZdxyQqN1aunhxNj2R4ymx9POt+j38ZvRuyJbTCWw+ncCoLw+wd1pP9FrZZFkIIYS52x6DdKNHHnmEVatWVdTlRC3170Bty8wmU6tVrBndDk97a47FpPPUumMyHkkIIUQxFZYghYeHo9fLflfi5qpyT7bSeDvq+WpMB9QqWLX3Mh+FX7JYLEIIIaqncnex3bgYpKIoxMTEsH//fl566aUKC0zUTqYEKTkWRVFuOuC/MoU08WDRkObM+t8/TF1/nDY+jnQLdLVILEIIIaqfcrcgOTo64uTkZPpwdXWlT58+bNy4kXnz5lVGjKIWaeLogZVKTZohhyuZqRaNZWbfRoxo64OhQGH4mv1Ep+ZYNB4hhBDVR7lbkD777LNKCEPUFdYaK5o4eXAyJY7jKTH42TtbLBaVSsWqh9pxMi6DE7HpDP10D9uf6o6Dvtw/FkIIIWqZcrcgNWzYkKtXrxY7npKSQsOGDSskKFG7WXqg9vXsdVb8+mhnPO2tORydxsgv9pNfYLR0WEIIISys3AnSxYsXKSgoKHY8NzeXqKioCglK1G5F45COJEdbOJJCgW62bJjUBRutmk2nEmRmmxBCiLJ3sf3yyy+mr3///XecnJxMjwsKCggLCyMgIKBCgxO1UzfPAAA2R53mYnoSN47TNhjyiS/I4VJGMlptxXd3+dk5o1aZ/29wVwNnvnmkI/d/to+Pd0dSz8mGuQObVPi9hRBC1Axl/uszbNgwoHDcxvjx482e02q1BAQEsGTJkgoNTtROvbwb4mStJz4ng8AfFpZecP2+Srn/PX4t+CXk0WLH72vlzfJhrXj6p+PM+z0CW62GZ/s2qpQYhBBCVG9lTpCMxsJxGYGBgezbtw93d/dKC0rUblq1hv9r2Zs3jv1JgVLyeB9jgRG1psKW6Sq8pqKQZyxgT0JkqWWm9AgkNcfAnN8imLnhJDZaNU/1CKzQOIQQQlR/5e6/uHDhQmXEIeqYl9oN4KV2A0p8zmAwsHHjRoYMGYJWq62we55MiaXlT2+RX0pSVuTFkCZkG4y89scZpvx0HK1GLRvbCiFEHVOmBGn58uU89thj6PV6li9fftOyU6dOrZDAhKhoVqrCPdfyjbeepfbKoKZkGwp4e9t5/vvDUdJz8/m/PtLdJoQQdUWZEqSlS5cyZswY9Hp9qZvVQuH4JEmQRHVlpS7ssiutW+96KpWKt+5pgZVaxeI/z/HsrydJyTawYFBTi63+LYQQouqUKUG6vltNuthETWV1bebarbrYiqhUKt64uwUuNlpmbzzFq3+c4WpWHsuHtcKqgsdHCSGEqF7K/Vt+wYIFZGVlFTuenZ3NggULKiQoISpDUQtSWbrYrvd8/yDef6A1AB/uusS9q/aRlmOo8PiEEEJUH+VOkObPn09GRkax41lZWcyfP79CghKiMhS1IBUoxnIvBPlk9wB+HN8JG62a307F0+3dnVxMKv6PghBCiNqh3AlSaTuwHzlyBFdX2Q1dVF+a6xaHLMs4pBs90MaHv5/qjo+jjhOx6XR+52/CTidUZIhCCCGqiTInSC4uLri6uqJSqWjSpAmurq6mDycnJwYMGMDIkSMrM1Yh7khRFxtAwW1uJdLRz5m903rSvp4jCRl5DPh4N69sOY3RKFuTCCFEbVLmdZCWLVuGoig8+uijzJ8/32yrEWtrawICAggODq6UIIWoCFbXtSDlGwvQaW5vG5P6zjbsfLoHT687zsq9kczdFMGO80l88XB7PB10FRWuEEIICyrzX4ii7UUCAwPp1q1bhS7gJ0RVsFJrTF+XdSZbaWy0Gj59qC09G7ryxI9H2Xw6gVZv/cXHD7ZhWGufOw1VCCGEhZV7DFLv3r1NyVFOTg5paWlmH0JUV+YtSHeWIBUZf5cfe6f1pJW3AwkZedz/2X7GfX2I5Ky8Crm+EEIIyyh3gpSVlcWUKVPw9PTEzs4OFxcXsw8hqiv1dZML7rQF6XqtfBzZ/0xPnu/XGLUKvjhwhVZvbuOHI9Hlni0nhBCieih3gjRz5ky2bt3Khx9+iE6n49NPP2X+/Pn4+vry+eefV0aMQlQIlUplmslWUS1IRXRWGhYNbc6OKd0JcrcjOi2HEZ8fYPAnezibmFmh9xJCCFH5yp0g/frrr3zwwQcMHz4cKysrevbsyZw5c1i4cCFfffVVZcQoRIUpz3YjtyM4wJUjz/Zm7oAmWGvU/B6RQKs3/2LuplOk5+RXyj2FEEJUvHInSElJSTRs2BAAR0dHkpKSAOjRowfbt2+v2OiEqGDl3W7kdthoNcwf1JTjM3szsIkHuflGXtlyhsaLwvhw10UMBZV3byGEEBWj3AlSw4YNTfuxNWvWjO+//x4obFlydnau0OCEqGi3u93I7QjysGfTY134YXxHGrvbEZ+Rx5M/HqPVm3/x/eFoWTtJCCGqsXInSBMnTuTIkSMAPP/887z//vvo9XqeeeYZZs6cWeEBClGR/m1BKqiS+6lUKoa38eXkc3147/5WeNhbczohk4e+OECrt/7iqwNXyJcWJSGEqHbKvVLeM888Y/o6JCSEU6dOceDAARo3bkybNm0qNDghKlrRWkhV0YJ0Pa1GzVM9AhnbqT5vbzvPsu3n+Scug0e+PsTLm0/zfL/GjOlQD82tLyWEEKIKlLsF6Ub+/v488MADuLq68thjj1VETEJUGs21qf63u9XInXLUa3k5tCmX5oSwcEgz3O2sOZuYyX++P0KDV/9g3uYzJOVaJDQhhBDXueMEqcjVq1dZuXJlRV1OiEph6mIzVk0XW2mcbLTM7h/ExRf7s+TeFvg560nIyGPR1nNM3qvikW8Os+P8VVlHSQghLKTCEiQhagJTF1slzmIrDzudFTN6N+L8C/35YXxHegS4UKCo+P5ILD3f30WzN/7kja1niUnLsXSoQghRp0iCJOoUq0paKPJOWWnUDG/jy9bHu/B2ByMTOtXDzlrD6YRMnv/fP/i98gf3rNzLj0ejyTZYtvVLCCHqgtvbzlyIGso0zb+atCCVpKE9TBnSmuX3t2HtkWhW7Y1k58VkNpyMY8PJOOx1Goa18mZUu3oMaOKBtZX8nyOEEBWtzAnSAw88cNPnU1JS7jQWISpddW1BKomD3opHuzTg0S4NiIjPYPXey3xzOIrI5Gy+PBDFlweicLHR8kBrH0a286FPI3dJloQQooKUOUFycnK65fPjxo2744CEqExFe7FV1lYjlaWppz2v392chUOasScymW8PR/P94Whi03NZuTeSlXsjcdRbMaSZJ8NaeTO4uSeOeq2lwxZCiBqrzAnS6tWrKzMOIapETehiuxm1WkVwgCvBAa68fW9Ltp+/yreHolh/PJb4jDy+PRzNt4ej0WpU9G3kzrDW3tzb0ot6TjaWDl0IIWoUGYMk6pSa1MV2Kxq1ir6N3enb2J0Ph7dhT2Qy64/H8vPxWCISMtl8OoHNpxN48sdjtK/nyJDmXgxu5kmXBs5YaaQrTgghbkYSJFGn/NuCVLtmgl3fsvTG3S2IiM/g5+OxrD8ey+7IZA5FpXEoKo3X/jiDi42WgU09GNzMk0HNPPFy0Fk6fCGEqHYkQRJ1Sm1qQbqZpp72PNevMc/1a0xcei6/R8Tz2z/x/B6RQHK2ge8OR/Pd4WgAOtZ3YkhzTwY386RzAxc0apWFoxdCCMuTBEnUKdVtociq4OWgY1wnP8Z18iO/wMjeyBR+OxXPb6fiOXAl1fTxypYzuNpqGdDEg9CmHgxs6iFjl4QQdZYkSKJOMe3FVstbkEpjpVHTLdCVboGuvDK4GbFpOfwekcDGf+LZfDqBpCzz1qWW3g4MvJYw9Wrkho1WttMVQtQNkiCJOqWmz2KraN6Oesbf5cf4uwpbl/ZEprA5IoHfI+LZdzmFE7HpnIhNZ+n28+is1PRq6EpoU08GNvWglbcDKpV0xwkhaidJkESdYqW61sVWR1uQbsZKo6Z7oCvdA12ZP6gpSVl5hJ1J5PdThQnTldQctpxOZMvpRPgVfB31DGzqwcAmHgxo4o67vQz2FkLUHpIgiTpFWpDKztXWmhFtfRnR1hdFUTgVn8HvEQlsjkjgr3OJRKfl8Nm+y3y27zIqFXSo52QauxTs7yqregsharRq8Rvs/fffJyAgAL1eT5cuXdi7d+9Ny69du5ZmzZqh1+tp3bo1GzduNHteURTmzp2Lj48PNjY2hISEcObMmRKvlZubS7t27VCpVBw+fLiiqiSqqboyi62iqVQqmns5ML1XQzZO7kLSK4P4479dmdmnEW18HFEUOHAllYVhZ+nzQThuczdx36q9vL/jAmcTM1EUxdJVEEKIcrF4gvTdd98xY8YM5s2bx8GDB2nbti2hoaHEx8eXWH7Xrl2MHj2aSZMmcejQIYYNG8awYcM4fvy4qczixYtZvnw5K1asYM+ePdjZ2REaGkpOTk6x6z333HP4+vpWWv1E9VLUglTTthqpbvRaDf2beLD4nhYcebY30fMGsGZ0O8Z0qIeHvTUZuQX8ciKOKT8dJ2jRVhot3MoTPxzlp2MxpGYbLB2+EELcksUTpLfffpvJkyczceJEWrRowYoVK7C1tWXVqlUlln/nnXcYNGgQM2fOpHnz5rzyyit06NCB9957DyhsPVq2bBlz5szhvvvuo02bNnz++edER0ezfv16s2v99ttvbN68mbfeequyqymqCVMLkiRIFcrHUc+4Tn58OaYDsfMGcvCZXiwa0ow+jdzQalRcSMpiRfglHvhsP25zf6fnezt5Zctp9kYmU2CU1iUhRPVj0TFIeXl5HDhwgNmzZ5uOqdVqQkJCCA8PL/Gc8PBwZsyYYXYsNDTUlPxcuHCB2NhYQkJCTM87OTnRpUsXwsPDGTVqFABxcXFMnjyZ9evXY2trW8E1E9WVRrrYKp1araJ9fSfa13fi+f5BZOTm89e5q/x+qnApgdMJmey4kMSOC0nM3RSBq62WkCAP+jd2RclGuuOEENWCRROkxMRECgoK8PLyMjvu5eXFqVOnSjwnNja2xPKxsbGm54uOlVZGURQmTJjA448/TqdOnbh48eItY83NzSU3N9f0OC0tDQCDwYDBUPFdBkXXrIxrV3eVWfeiJtPc/Mp53+5UbXzfdWoIDXIlNMgVaMbFpCz+OHOVzacT+fPcVZKyDHx/JJrvj0QDal6J+Ivejdzo3dCVnoEuNHKzrfXLCdTG972spO5Sd0vd+1bq5Cy2d999l/T0dLOWq1tZtGgR8+fPL3Z88+bNldoCtWXLlkq7dnVXGXWPyrwMwKnTEWy8knuL0pZT2993X2CCG4x1hTNpcChZxZEUOJsOUWm5fH0omq8PFS5W6Wat0MoZWjoVfvbRQ23Nl2r7+34zUve6yRJ1z8rKKlM5iyZI7u7uaDQa4uLizI7HxcXh7e1d4jne3t43LV/0OS4uDh8fH7My7dq1A2Dr1q2Eh4ej05mv29KpUyfGjBnDmjVrit139uzZZl17aWlp+Pn5MXDgQBwdHctY47IzGAxs2bKFAQMGoNVqK/z61Vll1j1s/69wKoaARo0Y0j60Qq9dEer6+75h0xbsgjqwMzKN7eeT2Hs5hat5sC0etsUXZkWe9tZ0aeBM12sfHes7YWtds1f4ruvvu9Rd6l6VinqAbsWiCZK1tTUdO3YkLCyMYcOGAWA0GgkLC2PKlCklnhMcHExYWBjTp083HduyZQvBwcEABAYG4u3tTVhYmCkhSktLY8+ePTzxxBMALF++nFdffdV0fnR0NKGhoXz33Xd06dKlxPvqdLpiCRWAVqut1De3sq9fnVVG3a01166nolq/rnX1fddpYGAzL4a2rg9AVl4+uy+lsO3cVf46l8juSynEZ+Tx68l4fj1ZONPVSq2iXT1Hgv1d6RbgQrC/Cw1cbGpkt1xdfd9B6i51r9p7loXFu9hmzJjB+PHj6dSpE507d2bZsmVkZmYyceJEAMaNG0e9evVYtGgRANOmTaN3794sWbKEoUOH8u2337J//34+/vhjoHC9lunTp/Pqq68SFBREYGAgL730Er6+vqYkrEGDBmYx2NvbA9CoUSPq169fRTUXliALRdYsttZW9Atyp1+QO9CUHEMBB6+kEn4pufDjYjLRaTnsv5zK/supvLvjAgAe9tZ0rO9Ex/rO1z474edcM5MmIYRlWDxBeuihh0hISGDu3LnExsbSrl07Nm3aZBpkHRkZiVr972oE3bp14+uvv2bOnDm88MILBAUFsX79elq1amUq89xzz5GZmcljjz1GSkoKPXr0YNOmTej1+iqvn6heZBZbzabXakyb7ULhhIvI5GyzhOlQVCoJGXlsOpXAplMJpnPd7azpUM+Jjn6FCVP7ek4EuNiiVkvSVBdl5uYTk56Lm60WF1trS4cjqiGLJ0gAU6ZMKbVL7a+//ip2bMSIEYwYMaLU66lUKhYsWMCCBQvKdP+AgACZWlxHSAtS7aJSqfB3tcXf1ZZR7esBkGMo4GhMGgeupHLgcioHrqRwPDadxMw8Np9OYPPpf5MmO2sNLb0daO3tSGsfB1r7ONLK2wFPB9lXriYqMCokZOQSm55LTFoOselFX+cSm55j9nVGbgEAK0e25dEuDW5xZVEXVYsESYiqIluN1H56rYbODVzo3MDFdCzHUMCxmHQOXEkpTJyupHAiNoPMvAL2RqawNzLF7Bqe9ta09nGkpbcDTT3saephRxMPe+o56aXFyQIycvNvSHiufZ2WS0x6DrFphcfjM3Ipz7qjttYasgwFlRe4qNEkQRJ1irQg1U16rYa7GjhzVwNn0zFDgZGziZkci0nneGwax2LSORaTxvmkLOIz8gg7k0jYmUSz69ho1QS529PEw44mHnY09bSnsZsdAa62eDvoJHkqhxxDAQkZeUSnZLI/CeL3XSEhq+REKDOv7EmMSgWe9jp8HHR4O+rwdtDj46jD26H41w56+RMoSiffHaJOKWpBkr3YhFajprmXA829HBjJv/sxZubmczIug+OxaZyITedMYianEzI5dzWTbIORozFpHI0pPk3YWqOmgYsN/i42BLjY4u9qQ4CLDf4uttRzKvzDbGtde3/l5uUbScrKIz4jj/iMXBKufS78uPFYHum5+dedrYbr9tMsib1Og7eDHm8H3bUk5/qv/01+3O2ssdJYfBctUQvU3p9WIUpgpS5cL0e62ERp7HRWxVqbAPILjFxMzuZ0QganEzI5nZBBRHwm55MyuZySQ961FqmziZmlXttBZ1X4x/y6P+reDjocdWouxoMmIgEPBxucbbQ422hxsdFibVX5f+zzC4xk5BWQkZtf+FH0dV4B6Tn5JGcbSM7OIynLQHKWgeRsA0lZeYXHswwkZRvIKkcrTxGtRoWnnTXWBTk0re+Bj6PNv8mPY1ErUOFrZK+TP1eiasl3nKhTNNemeUsXmygvK42axu52NHa3Y0hz8+fyC4xEpeZwKTmbi8lZhZ+T/v0cnZZDtsFIem4+6bn5nCkxiVKz5NSBYkc1ahU2WjU2Wg02Wg16q3+/1mpUqFUqVBR2LalQXfsMapWKAkUhr8CIoaDwc16+sfBzgZG8fIXc/AIy8grIza+Ynwe1CtzsrPG01+FpX/jZw84aT4d/H3va6/C49rWT3or8/Hw2btzIkCGd6uxaQKJ6kgRJ1Cn/DtKWgZmi4lhp1KbZdL1wK/a8oihk5BaYDSiOTc8hJi2XuPRcrmbmcj4qDo2tIyk5+aRkG0jNKeyCKjAWnls066pS66FW4aCzwl6nwV5nhb114dcuNlpcbKxxtdXiYlvYsuVqa1143PRYi6Nei0bGYYlaQhIkUaeYutikBUlUIZVKhYPeCge9PU087Is9bzAYrrWidDe1ohQYFdJyDGQZCsg2GMkp+pxfQPa1r/MKjChKYQKmQOHXKCgKGBUFjVqFtUZd+GFV+Fmr+feYzkptlgxVRXeeEDWFJEiiTpFB2qKm0KhVuNha43LrokKISiD/Log6xTTNXwZpCyGEuAlJkESdYhqDJC1IQgghbkISJFGnyF5sQgghykISJFGnyEraQgghykISJFGnyF5sQgghykISJFGnFLUgySw2IYQQNyMJkqhTZKsRIYQQZSEJkqhTZBabEEKIspAESdQppr3YZKsRIYQQNyEJkqhTZKsRIYQQZSEJkqhT/t1qRLFwJEIIIaozSZBEnfLvViPSxSaEEKJ0kiCJOkUGaQshhCgLSZBEnSKb1QohhCgLSZBEnaKRFiQhhBBlIAmSqFNkqxEhhBBlIQmSqFNkqxEhhBBlIQmSqFOsVLIOkhBCiFuTBEnUKTJIWwghRFlIgiTqlOu72BRZLFIIIUQpJEESdUrRLDaQcUhCCCFKJwmSqFOsrkuQpJtNCCFEaSRBEnVKURcbSAuSEEKI0kmCJOoUsxYkSZCEEEKUQhIkUadc34IkXWxCCCFKIwmSqFPUKjUqVIC0IAkhhCidJEiiztGoriVI0oIkhBCiFJIgiTpHthsRQghxK5IgiTpHthsRQghxK1aWDkCIqlbUgtR1w3JTsmRjpeWjbsMJ8W1iydCEEEJUE9KCJOqcVi7eACTkZBKTnUZMdhrn06+y4lS4hSMTQghRXUgLkqhzwkL/yz+p8abHx5JjGLv9G3bGX0RRFFTXBnELIYSouyRBEnWOtcaKtq6+psdNHD2YqPqO2Ox0LmUkE+DgasHohBBCVAfSxSbqPBsrLR3c6gEQnnDJwtEIIYSoDiRBEgII9gwAYFf8RYvGIYQQonqQBEkIoJunPyAJkhBCiEKSIAkBdLvWgnQkKYZMQ65lgxFCCGFxkiAJAdS3c6a+rRMFipFn921AURRLhySEEMKCJEES4pqe3g0BWBERzpAtnxKTlWbhiIQQQliKJEhCXPNqh0H8p0kX9BorNkVF0HPj++QV5Fs6LCGEEBYgCZIQ1zR0cOOT7iM4cO90vGwcOJd+lW8vHLZ0WEIIISxAFooU4gYtnL2Z1qIHLxz4jdePbiXfeOtNbb1tHBhcv5mswi2EELWEJEhClOC/TYN57UgY/6TGM2nn92U6Z/PAxxhQTza7FUKI2kASJCFK4KqzZWX3kXx1/iAKN5/RdiolgbPpiWyOjpAESQghaglJkIQoxUMN2/FQw3a3LPfF2QOM+/sbdsRdrPSYhBBCVA0ZpC3EHerpFQjA/sTLZOXnWTgaIYQQFUESJCHukL+9C/VtnchXjOxJiLR0OEIIISqAdLEJcYdUKhU9vAL59sJhHvrrC+7xa4mDVoe9lTXWGo1ZWW8bR8Y16ojeSmuhaIUQQpSFJEhCVIDHmwXz46VjJORksurM3puW3RwVwfd9x6JWSQOuEEJUV5IgCVEBens34tzw2fweHUF8dgYZ+blkGPIwGAtMZQoUI5+d3c+Pl47h9c18vGzssbWyxtZKywDfJrzQpr8FayCEEOJ6kiAJUUH87J35T5MuNy3Tx7sRE3d8R2JuJom5mabj22LPcyo1ngf9WpOjFNzkCkIIIaqCJEhCVKGHG3VgUP1mnE5NICs/j6wCAz9cPMqas/v58txBvjx3EGvUrApLwElnAxS2Tj3VvLuFIxdCiLpFEiQhqpirzpaunv6mx3f7teDhhu359sJh/ow+y8XMZDbHnDE9v/biUSIzkunvG0QDOxf87Jyw0+osEboQQtQZkiAJUQ0MrNeUgfWakpeXx4e//oBVswagVvHdhcP8HXeBxcf/YvHxv0zlXaxt0GsKZ8I1cXJnZfeRNHJ0t1D0QghR+0iCJEQ1olKpCLCyY0iTLmi1Wp5oFswXZw/wU+RxzqVd5VJmMumGXJLzsoFsAGKy0+iyYTnLuwzDTW9HGxcffGwdLVsRIYSo4SRBEqIaU6vUjA+6i/FBd5mOpeZlcyUzlXzFSE6BgafCf+LA1SuM2f41AD42jnzeaxRuOjvaufqiUqksFb4QQtRYkiAJUcM4WdvgZG1jerxt8BPM3L+B3fGRXMpMJiY7jQG/fwzA5z1HM7ZxR0uFKoQQNZasVCdEDWen1fFB8HAO3vcMGwdMooNbPbxsHAD4M/ashaMTQoiaSRIkIWqRLh7+HLj3GT4MfgCAw1ejLRyREELUTNUiQXr//fcJCAhAr9fTpUsX9u69+VYNa9eupVmzZuj1elq3bs3GjRvNnlcUhblz5+Lj44ONjQ0hISGcOfPvtOmLFy8yadIkAgMDsbGxoVGjRsybN4+8PNmJXdQObV19ATiREmu2mrcQQoiysXiC9N133zFjxgzmzZvHwYMHadu2LaGhocTHx5dYfteuXYwePZpJkyZx6NAhhg0bxrBhwzh+/LipzOLFi1m+fDkrVqxgz5492NnZERoaSk5ODgCnTp3CaDTy0UcfceLECZYuXcqKFSt44YUXqqTOQlS2AHsXHLV68owFnEop+WdJCCFE6SyeIL399ttMnjyZiRMn0qJFC1asWIGtrS2rVq0qsfw777zDoEGDmDlzJs2bN+eVV16hQ4cOvPfee0Bh69GyZcuYM2cO9913H23atOHzzz8nOjqa9evXAzBo0CBWr17NwIEDadiwIffeey/PPvss69atq6pqC1Gp1Co1bV19ADicJN1sQojqy6gY+SvmLDvjLpCUm0VKbjZGxWjpsCw7iy0vL48DBw4we/Zs0zG1Wk1ISAjh4eElnhMeHs6MGTPMjoWGhpqSnwsXLhAbG0tISIjpeScnJ7p06UJ4eDijRo0q8bqpqam4urqWGmtubi65ubmmx2lpaQAYDAYMBsPNK3obiq5ZGdeu7qTuFVP31s7e/B13gUOJVxjl3+aOr1fZ5H2Xutc1da3uBmMBR5Ki2ZN4mbS8bGJzYjh9YjufntvP6bQEs7I+Ng582WM0Pb0CKz6OMr7eFk2QEhMTKSgowMvLy+y4l5cXp06dKvGc2NjYEsvHxsaani86VlqZG509e5Z3332Xt956q9RYFy1axPz584sd37x5M7a2tqWed6e2bNlSadeu7qTud0aVmwhA2JljbIy748tVGXnf6yape+2hKAqJxjxOF6RxMT+TWGMOMQXZXC7IIpcbWoYOFc60tVVp0KImVSlMXmKy05m49UvecexQ4Wu5ZWVllalcnV8HKSoqikGDBjFixAgmT55carnZs2ebtVylpaXh5+fHwIEDcXSs+FWLDQYDW7ZsYcCAAWi12gq/fnUmda+YuntdvcL7v50hWmNg8ODB1X7BSHnfpe5S95ontyCfQ0lR7Iy/xMXMJI4kxXA8JZaM/JInPblY29DVvQGu1jacjorExd2NEJ8gHm18Fw5aHfmKkcScTJr//BaRBVnYdmxGX+9GFRpzUQ/QrVg0QXJ3d0ej0RAXZ/7vbVxcHN7e3iWe4+3tfdPyRZ/j4uLw8fExK9OuXTuz86Kjo+nbty/dunXj448/vmmsOp0Ona74BqFarbZSv7Er+/rVmdT9zure1r0+GpWaxNxMEgzZ1LNzqqDoKpe871L3uqYm1D0n30B0dhoHEq9wOTOF8IRL7Iy7SFxOOkZFKVZeo1LTysWbzu5+NHH0oLGjO02dPGjq5IFapcZgMLBx40aG9B9iVndroIFOz4SguziaHIOtta7CX5uyXs+iCZK1tTUdO3YkLCyMYcOGAWA0GgkLC2PKlCklnhMcHExYWBjTp083HduyZQvBwcEABAYG4u3tTVhYmCkhSktLY8+ePTzxxBOmc6Kioujbty8dO3Zk9erVqNUWH68uRIWysdLSzMmDEylxTNn9E+Mad6S7ZwCe1xaRFEKIIoqikJyXzaWMZNPHiZRYkvOySczJZF/iZTJLaRVy19nRwyuQ5s6eNHPy5C53Pxo5uGGtuf0UY1mX+9CqNbd9fkWweBfbjBkzGD9+PJ06daJz584sW7aMzMxMJk6cCMC4ceOoV68eixYtAmDatGn07t2bJUuWMHToUL799lv2799vagFSqVRMnz6dV199laCgIAIDA3nppZfw9fU1JWFRUVH06dMHf39/3nrrLRIS/h0cVlrLlRA10cMNO/Diwd9YH3mc9ZHHUaHi3gYtmNK8O329G6ORfwyEqBOMipH47AwuFiVAmf8mQhczkriUkUJGfu5Nr6FCRQe3egTYu9DJ3Y/ungE0dnTH28ahwrvwLZ0cQTVIkB566CESEhKYO3cusbGxtGvXjk2bNpkGWUdGRpq17nTr1o2vv/6aOXPm8MILLxAUFMT69etp1aqVqcxzzz1HZmYmjz32GCkpKfTo0YNNmzah1+uBwhans2fPcvbsWerXr28Wj1JCU6EQNdULbfszuH4zPjm9m79jL3A8JZafI0/wc+QJvG0cGFSvKbZW1tzt15zB9ZtbOlwhxG3IK8jnTFoi0VlpRGelEp2dRmxWOvE5GcRkpxGdlUZkZgq5Bfm3vJan3p4Aexf87V0IcvTA28YBN50tAfauBHv6V/uxjBXJ4gkSwJQpU0rtUvvrr7+KHRsxYgQjRowo9XoqlYoFCxawYMGCEp+fMGECEyZMuJ1Qhahx2rvV44Pg4QCcSonnnZN/8/3FI8Rmp/PZ2f0AfHBqFy+1DWF++9A69QtQiOoqJ99AUl4WsVnpXMlK5cDVK6Tl5ZBuyCU6K82U+CTkZKJQtn/s1SoVvjaOBDi44m9XmAT527uYEqIGdi7YWFXvsVBVqVokSEKIqtHM2ZMPuw3nnS73sTn6NAevXuFCehKfnd3PK0f+oLGjO+Mad7J0mELUWBmGXLLy88xabLLyDcRmpxObnUZyXjYABUYj59OvEpUezbItUcTnZpKSl01ybjYFipG8cm4R5KjV08DOGV9bR3xsHfGxccTZWk8Dexe8bRwIsHehvp1ztei6qikkQRKiDrLWWHG3Xwvu9msBQKCDK/MObWb839/y9flDfNnrYdz1dhaOUoiql1eQT64xn9yCAhJzMsnMzyO3IJ/LmSlczkzB1kpLdoGBmKx0orMLu7Su5mRxJSsVBYW0vNwyt+iYxF0t8bBGpcbJujDxaevqi4+NI7ZWWnxtHa99OOGpt8dKrcZNZ4taJWMKK5IkSEIIXmwTQlj0WbbHnef3qAjePrGNhR2HWDosIcpNURTyFSMGYwGx2elEZaYSnZVGVFYqUVmppObloABRWalkXZuVFZedQaohh+x8A2mGnAqJw0Nvh7O1DQB6jRZvGwe8bOxxtbZFfa0b20fvwPlTEdzVtj0Bjm64WNvgorMxJUaOWr10eVuQJEhCCDRqNf8bMIln9/3KRxG7eePYnzR19GB80F2WDk0IM+mGHM6kJnLlWsJzJfPfz1eyUojKTLvlbKyycra2wd7KGq1aQz07J+rZOpFuyMHZ2gYfG0dTd5aztR5/exesVBpcdDZ46O3LdH2DwcDGi+kMadSx2q+DVBdJgiSEAMBeq+O9rvezP/EKB65eYcKO73CytmGYf6tbnyxEBUjMySQ738DFjCRyCvI5nBRFbHY6V3OzuJyZQmRGCuczrpa4MGFJ9Bor6tkWJja+to7Us3XCVWeLSgVuOjvcdIXbRDlodXjbOKDTWOGht8dWo8VKrcZKxuvUaZIgCSFMrNQafug7jmY/LSa3IJ+Rf33B5oGT6ePT2NKhiVpAURRis9O5kJ7E+YyrnE+/yomkWC6lRzHtp2Ncykwp03U89fb42TlT386J+rZO1LNzor5t4eN6tk646+3QqtXYW+mki0rcNkmQhBBmAhxcSRw9n+Fb17A5+jSjt33F933G0tO7YbGyBUYj4/7+hi3Rp/HQ29PbuyFPNetOSxdZcLWuUhSF1LwcjiRHcykjmf2JVziUFMXVnEwuXGsZKtG1Dda1ag2+to7YW1kT5OhBkKM79lprGti5EOjgSmMH9xqzbY6o2SRBEkIUY6/V8Xmv0XT733ucT79K798+5Pk2fXml/SCz1bcPXL3C1+cPAZCQk8nJlDg+PBVOAztn+vk0ZliDVvT3DcJeW3wfQ1GzKYpCSl420VlpHE+O5e+481zKTGZX/CWSckvfLV2tUuFn50ygvSuBDq4E2btxMeIMDwT3obtPQ/leEdWGJEhCiBJ52Thw6N5nmLZnPZ+d3c+io1vJNOTxTtdhpjKn0wq36fG1deSV9oP4+vwh/oo9R2RmCp+d3c9nZ/ejVWu4r0FLQnyCGNWwHU7XZvaImqPAaORCRhI74y5wPiOJnXEX2Z1wqdS9uaDwe6KZkyctnb3p6tEATxt7Au1daWDvYrYWj8FgYOOlTPr5NJaByqJakQRJCFEqR2s9q3uOoo93Iybs+I7l/+zAXW/HkPrN6eBWjzNpiQAMqd+cR5t05tEmnckw5BIef4n/XfmHXyJPcCEjiR8uHuWHi0d5avdPdHSrx8jAtjwU2I76ds6WraAwURSFhJwMknKz2R53njNpCcRnZ7Az/iIXMpJKHRjtYm2Dk7We+/1b42/nQrCnPy2dvbC1spbxP6JGkwRJCHFL44PuIiY7ndkHNjL30O/MPfQ7n3QfwenUwhakJo7uprL2Wh0D6jVhQL0mLOtyHwcSr7Dh8km+v3iEkylx7E28zN7Ey8zc9z96eQfycMMODPdvjaPG2lLVq3MyDbnsSYjkYkYyv0Wd4lzaVWKz04nJTiv1HK1aQ2MHN7p4NKCrhz/dvQIIsHeVLjFRa0mCJIQok1mt+2IwFrDqzF4uZiTz1vG/sLMqTGqCHD1KPa+je306utdnXvuBXMlM4dfLJ/nm/CH+jrvAttjzbIs9z1Ph6xjoG4RvjoFWmSk0ci79eqLsFEXhSFI0v0dFkFVg4EDiFQ5ejSI+J4MCxVisvAoV9lprGjm40curIa46W7p6+tPK2RsvG3uZ9i7qFEmQhBBlolKpeKndAKa37Em9714h4lrrEUDQdS1IN1PfzpknmnXjiWbdiMxI5tsLh/nm/CEOJ0WzMSoCgJU/Laabpz8jAtrSwyuQtq4+8oe5DIyKkX9S4lkfeZx/UuI5mhzD5cwUUq7t/XUjPztnmjt50srFmz7ejXDV2dLBrb5sVirENZIgCSHKxUGr5+nmPVh4NAwonJXUyMGt3NdpYO/Cc6378lzrvvyTEscPF47w3fE9nMhPZWf8RXbGXwTAzsqaYE9/engG0t0rgK4e/nW+W0dRFA4nRbMr/iKnUuM5khRNePwl8ktoFdJprOjn3bgwIXL2pKuHP352zjJVXohbkARJCFFu89sPJM+Yz99xF7ivQUv0d9jq0NzZi+db9aVNZDZtenfn+8ijbI87z874i6Tm5fBH9Bn+iD4DFG7g2c7Vlx5egfTwCqC7ZyA+to4VUa1qR1EUzqYlcikzmQOJV9gRf4Hw+Euk5uWUmAxZqdSE1mtKD69AWjh7EWjvShMnD3Qa+VUvRHnJT40Qotys1BrevOueSrl2fTsnZrXpxyz6YVSMnEiOY0f8BXbEXWBn/EUuZSRz4GrhdijvnPwbgHauvtzv34oWzl50dKtP4G20aFlakjGXby8cJhcjB64trnghPYn4nIwSy+s0VvTxbkRTRw/auvrSztWXFs5ed5ysCiEKSYIkhKi21Co1rV19aO3qwxPNugFwOSOFndcSph3xFzmaFMPhpGgOJ0Wbzuvq4U8PrwAG12tGG1df3PV2lqqCSdE0+vPpSZxPv8r59CQi0uLZHR9JfE46aYZc2Lm32Hk6jRWB9q60dPaiu1cg3T0DqGfrhIvOBlsrmfknRGWRBEkIUaP42Tszyr49oxq2B+BqTibfXjjM3oRITqbGcSAxit0Jl9idcIm3jm8DoKmTB4PrNaOdqy/dvQJp5OBWKWv0ZOcbiMxM5lJGMhczij4ncSkjmTNpiaW2BhVp5exNPTsnWjp7mcYKtXerJ11kQliA/NQJIWo0N70dTzXvzlPNuwMQmZHMttjz/BZ1ir9izhGTnUZEaoLZrDsPvR2NHNzp5F4fK5WaIEd3ghw9UKnA3kpHRn5uiffKNxq5mptFal42+YqRqMzUwkQoszAhistOv2msKlTUt3OioYMrDR3caGjvRif3+tTTO3B8x24eHHqvrCYtRDUhCZIQolZpYO/C2MYdGdu4IwBJuVmERZ9h45V/OJOWyL7EyyTkZJKQk8nuhEsVfn97Kx0B9i7427tc99mVAHsXWrn4lDiN3mAwcF4lv46FqE7kJ1IIUau56mwZEdiWEYFtAcjJN3AsOYbwhEvEZqdjMBZwJCmGuOx0jIpCTHYa3jYOaFTqYtdSq1S46exwstajUanwtXX6NxGycyHAwRUXaxvZYkOIWkASJCFEnaK30nKXRwPu8mhg6VCEENVY8X+RhBBCCCHqOEmQhBBCCCFuIAmSEEIIIcQNJEESQgghhLiBJEhCCCGEEDeQBEkIIYQQ4gaSIAkhhBBC3EASJCGEEEKIG0iCJIQQQghxA0mQhBBCCCFuIAmSEEIIIcQNJEESQgghhLiBJEhCCCGEEDeQBEkIIYQQ4gZWlg6gplIUBYC0tLRKub7BYCArK4u0tDS0Wm2l3KO6krpL3aXudYfUXepe1XUv+rtd9He8NJIg3ab09HQA/Pz8LByJEEIIIcorPT0dJyenUp9XKbdKoUSJjEYj0dHRODg4oFKpKvz6aWlp+Pn5cfnyZRwdHSv8+tWZ1F3qLnWvO6TuUveqrruiKKSnp+Pr64taXfpII2lBuk1qtZr69etX+n0cHR3r3A9OEam71L2ukbpL3esaS9X9Zi1HRWSQthBCCCHEDSRBEkIIIYS4gSRI1ZROp2PevHnodDpLh1LlpO5S97pG6i51r2tqQt1lkLYQQgghxA2kBUkIIYQQ4gaSIAkhhBBC3EASJCGEEEKIG0iCJIQQQghxA0mQqqn333+fgIAA9Ho9Xbp0Ye/evZYOqcItWrSIu+66CwcHBzw9PRk2bBgRERFmZXJycnjqqadwc3PD3t6e4cOHExcXZ6GIK8frr7+OSqVi+vTppmO1ud5RUVE88sgjuLm5YWNjQ+vWrdm/f7/peUVRmDt3Lj4+PtjY2BASEsKZM2csGHHFKCgo4KWXXiIwMBAbGxsaNWrEK6+8YrYfVG2q+/bt27nnnnvw9fVFpVKxfv16s+fLUtekpCTGjBmDo6Mjzs7OTJo0iYyMjCqsxe25Wd0NBgOzZs2idevW2NnZ4evry7hx44iOjja7Rm2s+40ef/xxVCoVy5YtMzteXeouCVI19N133zFjxgzmzZvHwYMHadu2LaGhocTHx1s6tAq1bds2nnrqKXbv3s2WLVswGAwMHDiQzMxMU5lnnnmGX3/9lbVr17Jt2zaio6N54IEHLBh1xdq3bx8fffQRbdq0MTteW+udnJxM9+7d0Wq1/Pbbb5w8eZIlS5bg4uJiKrN48WKWL1/OihUr2LNnD3Z2doSGhpKTk2PByO/cG2+8wYcffsh7773HP//8wxtvvMHixYt59913TWVqU90zMzNp27Yt77//fonPl6WuY8aM4cSJE2zZsoUNGzawfft2Hnvssaqqwm27Wd2zsrI4ePAgL730EgcPHmTdunVERERw7733mpWrjXW/3k8//cTu3bvx9fUt9ly1qbsiqp3OnTsrTz31lOlxQUGB4uvrqyxatMiCUVW++Ph4BVC2bdumKIqipKSkKFqtVlm7dq2pzD///KMASnh4uKXCrDDp6elKUFCQsmXLFqV3797KtGnTFEWp3fWeNWuW0qNHj1KfNxqNire3t/Lmm2+ajqWkpCg6nU755ptvqiLESjN06FDl0UcfNTv2wAMPKGPGjFEUpXbXHVB++ukn0+Oy1PXkyZMKoOzbt89U5rffflNUKpUSFRVVZbHfqRvrXpK9e/cqgHLp0iVFUWp/3a9cuaLUq1dPOX78uOLv768sXbrU9Fx1qru0IFUzeXl5HDhwgJCQENMxtVpNSEgI4eHhFoys8qWmpgLg6uoKwIEDBzAYDGavRbNmzWjQoEGteC2eeuophg4dalY/qN31/uWXX+jUqRMjRozA09OT9u3b88knn5iev3DhArGxsWZ1d3JyokuXLjW+7t26dSMsLIzTp08DcOTIEXbs2MHgwYOB2l33G5WlruHh4Tg7O9OpUydTmZCQENRqNXv27KnymCtTamoqKpUKZ2dnoHbX3Wg0MnbsWGbOnEnLli2LPV+d6i6b1VYziYmJFBQU4OXlZXbcy8uLU6dOWSiqymc0Gpk+fTrdu3enVatWAMTGxmJtbW36pVHEy8uL2NhYC0RZcb799lsOHjzIvn37ij1Xm+t9/vx5PvzwQ2bMmMELL7zAvn37mDp1KtbW1owfP95Uv5K+/2t63Z9//nnS0tJo1qwZGo2GgoICXnvtNcaMGQNQq+t+o7LUNTY2Fk9PT7PnrayscHV1rVWvR05ODrNmzWL06NGmTVtrc93feOMNrKysmDp1aonPV6e6S4IkqoWnnnqK48ePs2PHDkuHUukuX77MtGnT2LJlC3q93tLhVCmj0UinTp1YuHAhAO3bt+f48eOsWLGC8ePHWzi6yvX999/z1Vdf8fXXX9OyZUsOHz7M9OnT8fX1rfV1FyUzGAyMHDkSRVH48MMPLR1OpTtw4ADvvPMOBw8eRKVSWTqcW5IutmrG3d0djUZTbMZSXFwc3t7eFoqqck2ZMoUNGzbw559/Ur9+fdNxb29v8vLySElJMStf01+LAwcOEB8fT4cOHbCyssLKyopt27axfPlyrKys8PLyqpX1BvDx8aFFixZmx5o3b05kZCSAqX618ft/5syZPP/884waNYrWrVszduxYnnnmGRYtWgTU7rrfqCx19fb2LjYxJT8/n6SkpFrxehQlR5cuXWLLli2m1iOovXX/+++/iY+Pp0GDBqbffZcuXeL//u//CAgIAKpX3SVBqmasra3p2LEjYWFhpmNGo5GwsDCCg4MtGFnFUxSFKVOm8NNPP7F161YCAwPNnu/YsSNardbstYiIiCAyMrJGvxb9+/fn2LFjHD582PTRqVMnxowZY/q6NtYboHv37sWWcjh9+jT+/v4ABAYG4u3tbVb3tLQ09uzZU+PrnpWVhVpt/itXo9FgNBqB2l33G5WlrsHBwaSkpHDgwAFTma1bt2I0GunSpUuVx1yRipKjM2fO8Mcff+Dm5mb2fG2t+9ixYzl69KjZ7z5fX19mzpzJ77//DlSzulfpkHBRJt9++62i0+mUzz77TDl58qTy2GOPKc7OzkpsbKylQ6tQTzzxhOLk5KT89ddfSkxMjOkjKyvLVObxxx9XGjRooGzdulXZv3+/EhwcrAQHB1sw6spx/Sw2Ram99d67d69iZWWlvPbaa8qZM2eUr776SrG1tVW+/PJLU5nXX39dcXZ2Vn7++Wfl6NGjyn333acEBgYq2dnZFoz8zo0fP16pV6+esmHDBuXChQvKunXrFHd3d+W5554zlalNdU9PT1cOHTqkHDp0SAGUt99+Wzl06JBpplZZ6jpo0CClffv2yp49e5QdO3YoQUFByujRoy1VpTK7Wd3z8vKUe++9V6lfv75y+PBhs999ubm5pmvUxrqX5MZZbIpSfeouCVI19e677yoNGjRQrK2tlc6dOyu7d++2dEgVDijxY/Xq1aYy2dnZypNPPqm4uLgotra2yv3336/ExMRYLuhKcmOCVJvr/euvvyqtWrVSdDqd0qxZM+Xjjz82e95oNCovvfSS4uXlpeh0OqV///5KRESEhaKtOGlpacq0adOUBg0aKHq9XmnYsKHy4osvmv1RrE11//PPP0v8+R4/fryiKGWr69WrV5XRo0cr9vb2iqOjozJx4kQlPT3dArUpn5vV/cKFC6X+7vvzzz9N16iNdS9JSQlSdam7SlGuW8ZVCCGEEELIGCQhhBBCiBtJgiSEEEIIcQNJkIQQQgghbiAJkhBCCCHEDSRBEkIIIYS4gSRIQgghhBA3kARJCCGEEOIGkiAJIYQQQtxAEiQhRK0xYcIEhg0bZrH7jx07loULF5ap7KhRo1iyZEklRySEuF2ykrYQokZQqVQ3fX7evHk888wzKP/fzv2ERLXGYRz/morKHHHEsTERlHDQENMRFWwhMeRA0Ow0cDGQoAsZFLdCzKKFC5caKQRCA6IDYi2CrEUuqo2J2DAFFmLTqkJFofwHY3dx6eAZ9ZZex3uT5wMHzvmd8/K+7yyGh/c9nB8/sNvtpzOoPd68eYPH4yEWi2EYxi+fj0ajNDY2srS0RE5OzimMUESOQgFJRP4Inz9/Ns/D4TDBYJCFhQWzZhjGbwWTZGlvbyctLY3h4eHfblNXV8etW7cIBAJJHJmIHIe22ETkj1BQUGAeOTk5pKSkWGqGYezbYrt69SpdXV309PSQm5uL0+nk/v37fP/+nba2NrKzsyktLeXJkyeWvqLRKNevX8cwDJxOJ36/n+Xl5UPHFo/HmZiYwOfzWer37t3D5XKRmZmJ0+mkubnZct/n8zE+Pv7vfxwROXEKSCJypj148ACHw8HMzAxdXV10dnbS0tLClStXmJubw+v14vf72djYAGBtbQ2Px4Pb7WZ2dpapqSm+fPnCzZs3D+0jEomwvr5ObW2tWZudnaW7u5s7d+6wsLDA1NQUjY2Nlnb19fXMzMywvb2dnMmLyLEpIInImVZVVcXt27dxuVz09vaSmZmJw+Ggo6MDl8tFMBhkZWWFSCQCwN27d3G73fT19VFeXo7b7WZkZITp6Wnev39/YB+xWIzU1FTOnz9v1j59+oTNZuPGjRsUFxfjdrvp7u62tCssLGRnZ8eyfSgi/w8KSCJypl2+fNk8T01NJS8vj8rKSrPmdDoB+Pr1K/D3y9bT09PmO02GYVBeXg7A4uLigX1sbm6SkZFheZG8qamJ4uJiLl68iN/vZ3R01Fyl+ikrKwtgX11E/nsKSCJypqWnp1uuU1JSLLWfoWZ3dxeAb9++4fP5mJ+ftxwfPnzYt0X2k8PhYGNjg52dHbOWnZ3N3NwcY2NjXLhwgWAwSFVVFWtra+Yzq6urAOTn55/IXEXk5CggiYjsUVNTw9u3bykpKaG0tNRy2Gy2A9tUV1cD8O7dO0s9LS2Na9eu0d/fTyQS4ePHjzx//ty8H41GKSoqwuFwJG0+InI8CkgiInsEAgFWV1dpbW3l9evXLC4u8vTpU9ra2ojH4we2yc/Pp6amhpcvX5q1x48fMzAwwPz8PLFYjFAoxO7uLmVlZeYzL168wOv1Jn1OInJ0CkgiInsUFhby6tUr4vE4Xq+XyspKenp6sNvtnDt3+F9me3s7o6Oj5rXdbmdychKPx8OlS5cYHh5mbGyMiooKALa2tnj06BEdHR1Jn5OIHJ0+FCkicgI2NzcpKysjHA7T0NDwy+eHhoZ4+PAhz549O4XRichRaQVJROQEZGVlEQqF/vGDknulp6czODiY5FGJyHFpBUlEREQkgVaQRERERBIoIImIiIgkUEASERERSaCAJCIiIpJAAUlEREQkgQKSiIiISAIFJBEREZEECkgiIiIiCRSQRERERBL8BSTijwYy+4siAAAAAElFTkSuQmCC", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Function.compare_plots(\n", + " [(test_flight.latitude, \"RocketPy\"), (cots_gnss_flight.latitude, \"Actual - GNSS\")],\n", + " title=\"Latitude Comparison\",\n", + " xlabel=\"Time (s)\",\n", + " ylabel=\"Latitude (deg)\",\n", + ")\n", + "Function.compare_plots(\n", + " [\n", + " (test_flight.longitude, \"RocketPy\"),\n", + " (cots_gnss_flight.longitude, \"Actual - GNSS\"),\n", + " ],\n", + " title=\"Longitude Comparison\",\n", + " xlabel=\"Time (s)\",\n", + " ylabel=\"Longitude (deg)\",\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "RocketPy", + "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": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/examples/index.rst b/docs/examples/index.rst index 64c672a95..02eaf51e8 100644 --- a/docs/examples/index.rst +++ b/docs/examples/index.rst @@ -22,6 +22,7 @@ apogee of some rockets. "NDRT (2020)": (1296.77, 1316.75), "Prometheus (2022)": (4190.05, 3898.37), "Cavour (2023)": (2818.90, 2789), + "Camoes (2023): (3003.28, 3015)" "Juno III (2023)": (3026.05, 3213), "Halcyon (2023)": (3212.775, 3450), } @@ -76,4 +77,5 @@ In the next sections you will find the simulations of the rockets listed above. prometheus_2022_flight_sim.ipynb halcyon_flight_sim.ipynb cavour_flight_sim.ipynb + camoes_flight_sim.ipynb From 09507ecb3747411bb2d5260a397304d581539653 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lucas=20Gon=C3=A7alves?= Date: Sat, 23 Nov 2024 01:52:42 +0000 Subject: [PATCH 21/36] DOC: Faraday 2023 flight sim (#734) * DOCS: Add drag files for faraday flight example * DOCS: add measured data * DOCS: Add Faraday flight example from 2023 * DOC: Fix and update simulation file * DOC: run black * DOC: Change data folder name * DOC: Rename simulation example file * DOC: Add Example to index * DEV: Changelog * DOC: Update drag in simulation --------- Co-authored-by: Gui-FernandesBR <63590233+Gui-FernandesBR@users.noreply.github.com> --- CHANGELOG.md | 1 + .../genesis/drag_coefficient_power_off.csv | 2 + .../genesis/drag_coefficient_power_on.csv | 2 + data/rockets/genesis/flight_data_faraday.csv | 17934 ++++++++++++++++ data/rockets/genesis/gnss_faraday.csv | 247 + docs/examples/genesis_flight_sim.ipynb | 460 + docs/examples/index.rst | 4 +- 7 files changed, 18649 insertions(+), 1 deletion(-) create mode 100644 data/rockets/genesis/drag_coefficient_power_off.csv create mode 100644 data/rockets/genesis/drag_coefficient_power_on.csv create mode 100644 data/rockets/genesis/flight_data_faraday.csv create mode 100644 data/rockets/genesis/gnss_faraday.csv create mode 100644 docs/examples/genesis_flight_sim.ipynb diff --git a/CHANGELOG.md b/CHANGELOG.md index e8842a0f4..0bbb7605e 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -32,6 +32,7 @@ Attention: The newest changes should be on top --> ### Added +- DOC: GENESIS Flight Example [#734](https://github.com/RocketPy-Team/RocketPy/pull/734) - DOC: Camoes Flight Example [#733](https://github.com/RocketPy-Team/RocketPy/pull/733) - ENH: Callback function for collecting additional data from Monte Carlo sims [#702](https://github.com/RocketPy-Team/RocketPy/pull/702) - ENH: Implement optional plot saving [#597](https://github.com/RocketPy-Team/RocketPy/pull/597) diff --git a/data/rockets/genesis/drag_coefficient_power_off.csv b/data/rockets/genesis/drag_coefficient_power_off.csv new file mode 100644 index 000000000..bd209c040 --- /dev/null +++ b/data/rockets/genesis/drag_coefficient_power_off.csv @@ -0,0 +1,2 @@ +0,0.55 +1,0.55 diff --git a/data/rockets/genesis/drag_coefficient_power_on.csv b/data/rockets/genesis/drag_coefficient_power_on.csv new file mode 100644 index 000000000..bd209c040 --- /dev/null +++ b/data/rockets/genesis/drag_coefficient_power_on.csv @@ -0,0 +1,2 @@ +0,0.55 +1,0.55 diff --git a/data/rockets/genesis/flight_data_faraday.csv 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"cell_type": "markdown", + "metadata": {}, + "source": [ + "# GENESIS - Faraday Rocketry UPV - 2023\n", + "\n", + "Launched at EuRoC'23, having a total success in their flight. The trajectory was nominal through the whole flight, which allowed the team to win the award for 3km flight. \n", + "Permission to use flight data given by Nacho Coello, 2024.\n", + "\n", + "These results were extracted out of the flight card:\n", + "\n", + "1. Team number: `11`\n", + "2. Launch date: `October 12th, 2023. Around 14hrs local time`\n", + "3. Simulated apogee: `3044 m` \n", + "4. Official recorded apogee: `2916 m`\n", + "\n", + "The relative error of altitude apogee is only `4.4%`" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from rocketpy import Function, Environment, Flight, Rocket, SolidMotor\n", + "from rocketpy.motors import CylindricalTank, SolidMotor\n", + "from rocketpy.motors.tank import MassFlowRateBasedTank\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.style.use(\"seaborn-v0_8-colorblind\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Environment" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Gravity Details\n", + "\n", + "Acceleration of gravity at surface level: 9.8066 m/s²\n", + "Acceleration of gravity at 4.000 km (ASL): 9.8066 m/s²\n", + "\n", + "\n", + "Launch Site Details\n", + "\n", + "Launch Date: 2023-10-12 13:00:00 UTC | 2023-10-12 14:00:00 Portugal\n", + "Launch Site Latitude: 39.38895°\n", + "Launch Site Longitude: -8.28837°\n", + "Reference Datum: WGS84\n", + "Launch Site UTM coordinates: 44461.73 W 4373301.99 N\n", + "Launch Site UTM zone: 30S\n", + "Launch Site Surface Elevation: 160.0 m\n", + "\n", + "\n", + "Atmospheric Model Details\n", + "\n", + "Atmospheric Model Type: Reanalysis\n", + "Reanalysis Maximum Height: 4.000 km\n", + "Reanalysis Time Period: from 2023-10-12 08:00:00 to 2023-10-15 18:00:00 utc\n", + "Reanalysis Hour Interval: 1 hrs\n", + "Reanalysis Latitude Range: From 40.0° to 39.0°\n", + "Reanalysis Longitude Range: From -9.0° to -7.0°\n", + "\n", + "Surface Atmospheric Conditions\n", + "\n", + "Surface Wind Speed: 193.47 m/s\n", + "Surface Wind Direction: 13.47°\n", + "Surface Wind Heading: 2.95°\n", + "Surface Pressure: 999.67 hPa\n", + "Surface Temperature: 303.08 K\n", + "Surface Air Density: 1.149 kg/m³\n", + "Surface Speed of Sound: 349.00 m/s\n", + "\n", + "\n", + "Earth Model Details\n", + "\n", + "Earth Radius at Launch site: 6369.57 km\n", + "Semi-major Axis: 6378.14 km\n", + "Semi-minor Axis: 6356.75 km\n", + "Flattening: 0.0034\n", + "\n", + "\n", + "Atmospheric Model Plots\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "env = Environment(\n", + " gravity=9.80665,\n", + " date=(2023, 10, 12, 14),\n", + " latitude=39.38895,\n", + " longitude=-8.28837,\n", + " elevation=160,\n", + " datum=\"WGS84\",\n", + " timezone=\"Portugal\",\n", + ")\n", + "\n", + "env.set_atmospheric_model(\n", + " type=\"Reanalysis\",\n", + " file=\"../../data/weather/euroc_2023_all_windows.nc\",\n", + " dictionary=\"ECMWF\",\n", + ")\n", + "env.max_expected_height = 4000\n", + "env.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Motor" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "ProL995 = SolidMotor(\n", + " thrust_source=\"../../data/motors/cesaroni/Cesaroni_3618L995-P.eng\",\n", + " burn_time=3.8,\n", + " grain_number=3,\n", + " grain_density=1815,\n", + " grain_initial_inner_radius=0.015,\n", + " grain_outer_radius=0.033,\n", + " grain_initial_height=0.12,\n", + " nozzle_radius=0.042354,\n", + " throat_radius=0.00925,\n", + " grain_separation=0.005,\n", + " grains_center_of_mass_position=0.243,\n", + " dry_inertia=(0, 0, 0),\n", + " center_of_dry_mass_position=0.895,\n", + " dry_mass=0,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Nozzle Details\n", + "Nozzle Radius: 0.042354 m\n", + "Nozzle Throat Radius: 0.00925 m\n", + "\n", + "Grain Details\n", + "Number of Grains: 3\n", + "Grain Spacing: 0.005 m\n", + "Grain Density: 1815 kg/m3\n", + "Grain Outer Radius: 0.033 m\n", + "Grain Inner Radius: 0.015 m\n", + "Grain Height: 0.12 m\n", + "Grain Volume: 0.000 m3\n", + "Grain Mass: 0.591 kg\n", + "\n", + "Motor Details\n", + "Total Burning Time: 3.8 s\n", + "Total Propellant Mass: 1.774 kg\n", + "Structural Mass Ratio: 0.000\n", + "Average Propellant Exhaust Velocity: 2039.968 m/s\n", + "Average Thrust: 952.100 N\n", + "Maximum Thrust: 1280.0 N at 1.2 s after ignition.\n", + "Total Impulse: 3617.980 Ns\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ProL995.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Rocket and Aerodynamic surfaces" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "GENESIS = Rocket(\n", + " radius=0.047,\n", + " mass=9.214,\n", + " inertia=(7.5046, 7.5046, 0.042241),\n", + " center_of_mass_without_motor=0.895,\n", + " power_off_drag=\"../../data/rockets/genesis/drag_coefficient_power_off.csv\",\n", + " power_on_drag=\"../../data/rockets/genesis/drag_coefficient_power_on.csv\",\n", + " coordinate_system_orientation=\"tail_to_nose\",\n", + ")\n", + "\n", + "GENESIS.set_rail_buttons(0.98, 0.095)\n", + "\n", + "GENESIS.add_motor(ProL995, position=0.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "NoseCone = GENESIS.add_nose(length=0.27, kind=\"vonKarman\", position=2.372)\n", + "\n", + "FinSet = GENESIS.add_trapezoidal_fins(\n", + " n=4,\n", + " span=0.105,\n", + " root_chord=0.2,\n", + " tip_chord=0.11,\n", + " position=0.25,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "Drogue = GENESIS.add_parachute(\n", + " name=\"Drogue\",\n", + " cd_s=0.285005285533666,\n", + " trigger=\"apogee\",\n", + " sampling_rate=105,\n", + " lag=1,\n", + " noise=(0, 8.3, 0.5),\n", + ")\n", + "\n", + "Main = GENESIS.add_parachute(\n", + " name=\"Main\",\n", + " cd_s=1.1,\n", + " trigger=870,\n", + " sampling_rate=105,\n", + " lag=1,\n", + " noise=(0, 8.3, 0.5),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "GENESIS.draw()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Flight Simulation Data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "test_flight = Flight(\n", + " rocket=GENESIS, environment=env, inclination=84, heading=133, rail_length=12\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Apogee State\n", + "\n", + "Apogee Time: 24.165 s\n", + "Apogee Altitude: 3204.100 m (ASL) | 3044.100 m (AGL)\n", + "Apogee Freestream Speed: 27.443 m/s\n", + "Apogee X position: 401.587 m\n", + "Apogee Y position: -590.700 m\n", + "Apogee latitude: 39.3836364°\n", + "Apogee longitude: -8.2836963°\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "test_flight.prints.apogee_conditions()\n", + "test_flight.plots.trajectory_3d()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The following attributes were create and are now available to be used: ['time', 'altitude', 'az']\n" + ] + } + ], + "source": [ + "from rocketpy.simulation.flight_data_importer import FlightDataImporter\n", + "\n", + "columns_map = {\n", + " \"ts\": \"time\",\n", + " \"filtered_altitude_AGL\": \"altitude\",\n", + " \"filtered_acceleration\": \"az\",\n", + "}\n", + "\n", + "cots_altimeter_flight = FlightDataImporter(\n", + " name=\"COTS Altimeter Flight\",\n", + " paths=\"../../data/rockets/genesis/flight_data_faraday.csv\",\n", + " columns_map=columns_map,\n", + " units=None,\n", + " interpolation=\"linear\",\n", + " extrapolation=\"zero\",\n", + " delimiter=\",\",\n", + " encoding=\"utf-8\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Function.compare_plots(\n", + " [\n", + " (test_flight.altitude, \"RocketPy\"),\n", + " (cots_altimeter_flight.altitude, \"COTS Altimeter\"),\n", + " ],\n", + " title=\"Altitude Comparison\",\n", + " xlabel=\"Time (s)\",\n", + " ylabel=\"Altitude (m)\",\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "RocketPy", + "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": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/examples/index.rst b/docs/examples/index.rst index 02eaf51e8..df4b24935 100644 --- a/docs/examples/index.rst +++ b/docs/examples/index.rst @@ -22,7 +22,8 @@ apogee of some rockets. "NDRT (2020)": (1296.77, 1316.75), "Prometheus (2022)": (4190.05, 3898.37), "Cavour (2023)": (2818.90, 2789), - "Camoes (2023): (3003.28, 3015)" + "Genesis (2023)": (3076.45, 2916), + "Camoes (2023): (3003.28, 3015)", "Juno III (2023)": (3026.05, 3213), "Halcyon (2023)": (3212.775, 3450), } @@ -77,5 +78,6 @@ In the next sections you will find the simulations of the rockets listed above. prometheus_2022_flight_sim.ipynb halcyon_flight_sim.ipynb cavour_flight_sim.ipynb + genesis_flight_sim.ipynb camoes_flight_sim.ipynb From a4d886a4f49a52399346c88ce8104df1fc447557 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Sat, 23 Nov 2024 02:01:50 -0300 Subject: [PATCH 22/36] DOC: fix formatting issues in examples index --- docs/examples/index.rst | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/examples/index.rst b/docs/examples/index.rst index df4b24935..0dba59ac2 100644 --- a/docs/examples/index.rst +++ b/docs/examples/index.rst @@ -3,7 +3,7 @@ Flights simulated with RocketPy RocketPy has been used to simulate many flights, from small amateur rockets to large professional ones. -This section contains some of the most interesting +This section contains some of the most interesting results obtained with RocketPy. The following plot shows the comparison between the simulated and measured apogee of some rockets. @@ -23,7 +23,7 @@ apogee of some rockets. "Prometheus (2022)": (4190.05, 3898.37), "Cavour (2023)": (2818.90, 2789), "Genesis (2023)": (3076.45, 2916), - "Camoes (2023): (3003.28, 3015)", + "Camoes (2023)": (3003.28, 3015)", "Juno III (2023)": (3026.05, 3213), "Halcyon (2023)": (3212.775, 3450), } @@ -62,14 +62,14 @@ apogee of some rockets. In the next sections you will find the simulations of the rockets listed above. .. note:: - + If you want to see your rocket here, please contact the maintainers! \ We would love to include your rocket in the examples. .. toctree:: :maxdepth: 1 :caption: Contents: - + bella_lui_flight_sim.ipynb ndrt_2020_flight_sim.ipynb valetudo_flight_sim.ipynb From c861c0116d463b1e173ee9e605343fa1fe66c04f Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Sat, 23 Nov 2024 02:18:15 -0300 Subject: [PATCH 23/36] DOC: fix syntax error in examples index --- docs/examples/index.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/examples/index.rst b/docs/examples/index.rst index 0dba59ac2..bf183ed6e 100644 --- a/docs/examples/index.rst +++ b/docs/examples/index.rst @@ -23,7 +23,7 @@ apogee of some rockets. "Prometheus (2022)": (4190.05, 3898.37), "Cavour (2023)": (2818.90, 2789), "Genesis (2023)": (3076.45, 2916), - "Camoes (2023)": (3003.28, 3015)", + "Camoes (2023)": (3003.28, 3015), "Juno III (2023)": (3026.05, 3213), "Halcyon (2023)": (3212.775, 3450), } From ddf71f94ec6a75245eb6bc9ab7ad35c54fee439d Mon Sep 17 00:00:00 2001 From: William Armstrong Date: Sat, 23 Nov 2024 03:49:39 -0700 Subject: [PATCH 24/36] BUG: forecast and reanalysis models - move wind_speed to correct position (#735) * forecast_reanalysis - move wind_speed to correct position * forecast_reanalysis - move wind_speed to correct position * DEV: adds #735 to CHANGELOG --------- Co-authored-by: Gui-FernandesBR Co-authored-by: Gui-FernandesBR <63590233+Gui-FernandesBR@users.noreply.github.com> --- CHANGELOG.md | 1 + rocketpy/environment/environment.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 0bbb7605e..47b37198a 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -43,6 +43,7 @@ Attention: The newest changes should be on top --> ### Fixed +- BUG: forecast and reanalysis models - move wind_speed to correct position [#735](https://github.com/RocketPy-Team/RocketPy/pull/735) - BUG: Sideslip Angle and Damping Coefficient Calculation [#729](https://github.com/RocketPy-Team/RocketPy/pull/729) ## [v1.6.2] - 2024-11-08 diff --git a/rocketpy/environment/environment.py b/rocketpy/environment/environment.py index 5cef60b21..c7a85c002 100644 --- a/rocketpy/environment/environment.py +++ b/rocketpy/environment/environment.py @@ -1884,9 +1884,9 @@ def process_forecast_reanalysis( temper, wind_u, wind_v, - wind_speed, wind_heading, wind_direction, + wind_speed, ) # Save atmospheric data self.__set_pressure_function(data_array[:, (1, 0)]) From 1f3eefc2dde3ec7eb68f75ed38d9aee6f5e34dee Mon Sep 17 00:00:00 2001 From: William Armstrong Date: Sat, 23 Nov 2024 16:24:16 -0700 Subject: [PATCH 25/36] BUG: forecast and reanalysis models - update ECMWF weather model variables dictionary (#736) * forecast_reanalysis - move wind_speed to correct position * forecast_reanalysis - move wind_speed to correct position * DEV: adds #735 to CHANGELOG * update ECMWF dictionary values * allow depreciated .nc files * allow .nc files with only one time variable * TST? parameterize acceptance test for NDRT 2020 rocket data with multiple environment files * STY: apply black --------- Co-authored-by: Gui-FernandesBR Co-authored-by: Gui-FernandesBR <63590233+Gui-FernandesBR@users.noreply.github.com> --- CHANGELOG.md | 1 + .../ndrt_2020_weather_data_ERA5_new.nc | Bin 0 -> 2468487 bytes rocketpy/environment/environment.py | 9 ++++++++- rocketpy/environment/weather_model_mapping.py | 15 ++++++++++++++- tests/acceptance/test_ndrt_2020_rocket.py | 12 ++++++++++-- 5 files changed, 33 insertions(+), 4 deletions(-) create mode 100644 data/weather/ndrt_2020_weather_data_ERA5_new.nc diff --git a/CHANGELOG.md b/CHANGELOG.md index 47b37198a..8df36b8df 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -43,6 +43,7 @@ Attention: The newest changes should be on top --> ### Fixed +- BUG: forecast and reanalysis models - Update ECMWF dictionary values [#736](https://github.com/RocketPy-Team/RocketPy/pull/736) - BUG: forecast and reanalysis models - move wind_speed to correct position [#735](https://github.com/RocketPy-Team/RocketPy/pull/735) - BUG: Sideslip Angle and Damping Coefficient Calculation [#729](https://github.com/RocketPy-Team/RocketPy/pull/729) diff --git a/data/weather/ndrt_2020_weather_data_ERA5_new.nc b/data/weather/ndrt_2020_weather_data_ERA5_new.nc new file mode 100644 index 0000000000000000000000000000000000000000..7fea7c5e236baff62d4d415e3138505238fc6545 GIT binary patch literal 2468487 zcmeEv2_RNW|NpV?vL}(pzONx#?6NPRO~QjGlr0`vO7?6eYg$ycNDJzgQb-}B#gg`H zQBes|O7cH*o^$l*R(E;t{k`wK|7Y&a=b1S(b7sCX-!lpQbj`<1qJx|6Y#z`10+V=CcgljCBfY*$Vb5z@9ROpttH@zo`eA0O1}Vt z8$OU2$f^t8M+41+6U2iiCossB6dvM$B}5XEE<{ZQ8~8oIU`Vn-5UUgf$1$5J13%ww 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zjLAqz(vjqAQ8}$w$~lK9^Aui&C8Q(8YlIZ`P{_S-k(FM8^C8~;#8{VPYi8!pNwhWKQa_W^s-ufSVg8ZZwuB?fsxnynnh7eM^uGB)x zO>6HWxfbXJc9+|%UMpl(97^~CVl(9HxbT@O1#1wTicXO39>P_wPbR^sY!j|kA0<6n zli*ZF1211xG|Se~U%zO1e>__Fd}6=LQ3Z!wcjR#RRe$tgluNHcI2A37(?MP+wsc)i zfbn22Qu~apU00ugM#kNE*-9^-Yz`QjR`lOZFnnp$DY@@^t>J<9|8u>8m>W+r{tr#9 zH@>7Np*=$WBhzzkkW)@b)2Ji?A3{~*agl?@#sjcWD`&#u#)W9FJ`or(oTa+PW&YrW zxPeKPy|`2y3`+cKhC|g(wu8crqN-bR7>=^C-#;)KcpolhfWp-HZW7I^JW3ARUEyqJatgBSexDoPP4Cs_n!CEiZkF1Y zmd1B8d%3yhu5PiLrM9J|?d{A~ZmzhkTMTEdU1@20KeLmY%kS$J%UNtwT3SAk*~m@B z52!ZNNo_7`4WG--(pvhQ Date: Sat, 23 Nov 2024 21:09:40 -0700 Subject: [PATCH 26/36] allow stochastic fins with multiple fin sets --- CHANGELOG.md | 1 + rocketpy/stochastic/stochastic_rocket.py | 3 +-- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 8df36b8df..57974eb04 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -43,6 +43,7 @@ Attention: The newest changes should be on top --> ### Fixed +- BUG: Allow multiple sets of stochastic fins [#737](https://github.com/RocketPy-Team/RocketPy/pull/737) - BUG: forecast and reanalysis models - Update ECMWF dictionary values [#736](https://github.com/RocketPy-Team/RocketPy/pull/736) - BUG: forecast and reanalysis models - move wind_speed to correct position [#735](https://github.com/RocketPy-Team/RocketPy/pull/735) - BUG: Sideslip Angle and Damping Coefficient Calculation [#729](https://github.com/RocketPy-Team/RocketPy/pull/729) diff --git a/rocketpy/stochastic/stochastic_rocket.py b/rocketpy/stochastic/stochastic_rocket.py index d9333dfc7..01e6c66a5 100644 --- a/rocketpy/stochastic/stochastic_rocket.py +++ b/rocketpy/stochastic/stochastic_rocket.py @@ -442,8 +442,7 @@ def get_surface_position(self_object, _): for surface in surfaces: if surface.component == validated_object.obj: return surface.position - else: - raise AssertionError(error_msg) + raise AssertionError(error_msg) return get_surface_position From a1709f977d22167e2531514e50110cc4b717a593 Mon Sep 17 00:00:00 2001 From: Pedro Henrique Marinho Bressan <87212571+phmbressan@users.noreply.github.com> Date: Sat, 30 Nov 2024 03:44:35 +0100 Subject: [PATCH 27/36] MNT: Place filename save parameter to the end. (#739) * MNT: Place filename save parameter to the end. * DEV: Add TODO for refactoring CompareFlights class to use show_or_save_plot --------- Co-authored-by: Gui-FernandesBR --- CHANGELOG.md | 2 +- rocketpy/mathutils/function.py | 3 ++ rocketpy/motors/hybrid_motor.py | 2 +- rocketpy/motors/liquid_motor.py | 2 +- rocketpy/motors/motor.py | 2 +- rocketpy/motors/solid_motor.py | 2 +- rocketpy/motors/tank.py | 2 +- rocketpy/plots/aero_surface_plots.py | 20 ++++++------ rocketpy/plots/compare/compare_flights.py | 2 ++ rocketpy/plots/environment_analysis_plots.py | 34 ++++++++++++-------- rocketpy/plots/environment_plots.py | 8 ++--- rocketpy/plots/flight_plots.py | 26 ++++++++------- rocketpy/plots/hybrid_motor_plots.py | 12 +++---- rocketpy/plots/liquid_motor_plots.py | 2 +- rocketpy/plots/motor_plots.py | 14 ++++---- rocketpy/plots/rocket_plots.py | 6 ++-- rocketpy/plots/solid_motor_plots.py | 2 +- rocketpy/plots/tank_plots.py | 2 +- rocketpy/rocket/aero_surface/fins/fins.py | 2 +- rocketpy/rocket/aero_surface/nose_cone.py | 2 +- rocketpy/rocket/rocket.py | 4 +-- rocketpy/utilities.py | 3 +- 22 files changed, 85 insertions(+), 69 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 57974eb04..b1197708a 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -39,7 +39,7 @@ Attention: The newest changes should be on top --> ### Changed -- +- MNT: Place filename save parameter to the end. [#739](https://github.com/RocketPy-Team/RocketPy/pull/739) ### Fixed diff --git a/rocketpy/mathutils/function.py b/rocketpy/mathutils/function.py index 314e6415b..ca6005cf3 100644 --- a/rocketpy/mathutils/function.py +++ b/rocketpy/mathutils/function.py @@ -1498,6 +1498,7 @@ def plot_1d( # pylint: disable=too-many-statements force_points=False, return_object=False, equal_axis=False, + *, filename=None, ): """Plot 1-Dimensional Function, from a lower limit to an upper limit, @@ -1597,6 +1598,7 @@ def plot_2d( # pylint: disable=too-many-statements disp_type="surface", alpha=0.6, cmap="viridis", + *, filename=None, ): """Plot 2-Dimensional Function, from a lower limit to an upper limit, @@ -1729,6 +1731,7 @@ def compare_plots( # pylint: disable=too-many-statements force_points=False, return_object=False, show=True, + *, filename=None, ): """Plots N 1-Dimensional Functions in the same plot, from a lower diff --git a/rocketpy/motors/hybrid_motor.py b/rocketpy/motors/hybrid_motor.py index b3527c604..58c2fab8e 100644 --- a/rocketpy/motors/hybrid_motor.py +++ b/rocketpy/motors/hybrid_motor.py @@ -601,7 +601,7 @@ def add_tank(self, tank, position): ) reset_funcified_methods(self) - def draw(self, filename=None): + def draw(self, *, filename=None): """Draws a representation of the HybridMotor. Parameters diff --git a/rocketpy/motors/liquid_motor.py b/rocketpy/motors/liquid_motor.py index cc7bf5027..d99d0165b 100644 --- a/rocketpy/motors/liquid_motor.py +++ b/rocketpy/motors/liquid_motor.py @@ -463,7 +463,7 @@ def add_tank(self, tank, position): self.positioned_tanks.append({"tank": tank, "position": position}) reset_funcified_methods(self) - def draw(self, filename=None): + def draw(self, *, filename=None): """Draw a representation of the LiquidMotor. Parameters diff --git a/rocketpy/motors/motor.py b/rocketpy/motors/motor.py index 558c9f9a2..93571b879 100644 --- a/rocketpy/motors/motor.py +++ b/rocketpy/motors/motor.py @@ -1083,7 +1083,7 @@ def get_attr_value(obj, attr_name, multiplier=1): # Write last line file.write(f"{self.thrust.source[-1, 0]:.4f} {0:.3f}\n") - def info(self, filename=None): + def info(self, *, filename=None): """Prints out a summary of the data and graphs available about the Motor. diff --git a/rocketpy/motors/solid_motor.py b/rocketpy/motors/solid_motor.py index 09a930522..9f8622cea 100644 --- a/rocketpy/motors/solid_motor.py +++ b/rocketpy/motors/solid_motor.py @@ -727,7 +727,7 @@ def propellant_I_13(self): def propellant_I_23(self): return 0 - def draw(self, filename=None): + def draw(self, *, filename=None): """Draw a representation of the SolidMotor. Parameters diff --git a/rocketpy/motors/tank.py b/rocketpy/motors/tank.py index 418124cf0..14511eab0 100644 --- a/rocketpy/motors/tank.py +++ b/rocketpy/motors/tank.py @@ -476,7 +476,7 @@ def underfill_height_exception(param_name, param): elif (height < bottom_tolerance).any(): underfill_height_exception(name, height) - def draw(self, filename=None): + def draw(self, *, filename=None): """Draws the tank geometry. Parameters diff --git a/rocketpy/plots/aero_surface_plots.py b/rocketpy/plots/aero_surface_plots.py index a0b7d4c4b..d501d1352 100644 --- a/rocketpy/plots/aero_surface_plots.py +++ b/rocketpy/plots/aero_surface_plots.py @@ -25,7 +25,7 @@ def __init__(self, aero_surface): self.aero_surface = aero_surface @abstractmethod - def draw(self, filename=None): + def draw(self, *, filename=None): pass def lift(self): @@ -54,7 +54,7 @@ class _NoseConePlots(_AeroSurfacePlots): """Class that contains all nosecone plots. This class inherits from the _AeroSurfacePlots class.""" - def draw(self, filename=None): + def draw(self, *, filename=None): """Draw the nosecone shape along with some important information, including the center line and the center of pressure position. @@ -140,7 +140,7 @@ class _FinsPlots(_AeroSurfacePlots): _AeroSurfacePlots class.""" @abstractmethod - def draw(self, filename=None): + def draw(self, *, filename=None): pass def airfoil(self): @@ -201,7 +201,7 @@ class _TrapezoidalFinsPlots(_FinsPlots): """Class that contains all trapezoidal fin plots.""" # pylint: disable=too-many-statements - def draw(self, filename=None): + def draw(self, *, filename=None): """Draw the fin shape along with some important information, including the center line, the quarter line and the center of pressure position. @@ -330,7 +330,7 @@ class _EllipticalFinsPlots(_FinsPlots): """Class that contains all elliptical fin plots.""" # pylint: disable=too-many-statements - def draw(self, filename=None): + def draw(self, *, filename=None): """Draw the fin shape along with some important information. These being: the center line and the center of pressure position. @@ -409,7 +409,7 @@ class _FreeFormFinsPlots(_FinsPlots): """Class that contains all free form fin plots.""" # pylint: disable=too-many-statements - def draw(self, filename=None): + def draw(self, *, filename=None): """Draw the fin shape along with some important information. These being: the center line and the center of pressure position. @@ -483,7 +483,7 @@ def draw(self, filename=None): class _TailPlots(_AeroSurfacePlots): """Class that contains all tail plots.""" - def draw(self, filename=None): + def draw(self, *, filename=None): # This will de done in the future pass @@ -498,7 +498,7 @@ def drag_coefficient_curve(self): else: return self.aero_surface.drag_coefficient.plot() - def draw(self, filename=None): + def draw(self, *, filename=None): raise NotImplementedError def all(self): @@ -514,12 +514,12 @@ def all(self): class _GenericSurfacePlots(_AeroSurfacePlots): """Class that contains all generic surface plots.""" - def draw(self, filename=None): + def draw(self, *, filename=None): pass class _LinearGenericSurfacePlots(_AeroSurfacePlots): """Class that contains all linear generic surface plots.""" - def draw(self, filename=None): + def draw(self, *, filename=None): pass diff --git a/rocketpy/plots/compare/compare_flights.py b/rocketpy/plots/compare/compare_flights.py index 2689ac482..521b2cf6b 100644 --- a/rocketpy/plots/compare/compare_flights.py +++ b/rocketpy/plots/compare/compare_flights.py @@ -6,6 +6,8 @@ from ..plot_helpers import show_or_save_fig, show_or_save_plot from .compare import Compare +# TODO: needs to refactor this class to use the show_or_save_plot + class CompareFlights(Compare): # pylint: disable=too-many-public-methods """A class to compare the results of multiple flights. diff --git a/rocketpy/plots/environment_analysis_plots.py b/rocketpy/plots/environment_analysis_plots.py index b5b63642c..010ae5c06 100644 --- a/rocketpy/plots/environment_analysis_plots.py +++ b/rocketpy/plots/environment_analysis_plots.py @@ -74,7 +74,7 @@ def __beaufort_wind_scale(self, units, max_wind_speed=None): # Surface level plots - def wind_gust_distribution(self, filename=None): + def wind_gust_distribution(self, *, filename=None): """Get all values of wind gust speed (for every date and hour available) and plot a single distribution. Expected result is a Weibull distribution, however, the result is not always a perfect fit, and sometimes it may @@ -124,7 +124,9 @@ def wind_gust_distribution(self, filename=None): plt.legend() show_or_save_plot(filename) - def surface10m_wind_speed_distribution(self, wind_speed_limit=False, filename=None): + def surface10m_wind_speed_distribution( + self, wind_speed_limit=False, *, filename=None + ): """Get all values of sustained surface wind speed (for every date and hour available) and plot a single distribution. Expected result is a Weibull distribution. The wind speed limit is plotted as a vertical line. @@ -190,6 +192,7 @@ def surface10m_wind_speed_distribution(self, wind_speed_limit=False, filename=No def average_surface_temperature_evolution( self, + *, filename=None, ): # pylint: disable=too-many-statements """Plots average temperature progression throughout the day, including @@ -265,7 +268,7 @@ def average_surface_temperature_evolution( show_or_save_plot(filename) def average_surface10m_wind_speed_evolution( - self, wind_speed_limit=False, filename=None + self, wind_speed_limit=False, *, filename=None ): # pylint: disable=too-many-statements """Plots average surface wind speed progression throughout the day, including sigma contours. @@ -366,6 +369,7 @@ def average_surface10m_wind_speed_evolution( def average_surface100m_wind_speed_evolution( self, + *, filename=None, ): # pylint: disable=too-many-statements """Plots average surface wind speed progression throughout the day, including @@ -449,7 +453,7 @@ def average_surface100m_wind_speed_evolution( # Average profiles plots (pressure level data) - def average_wind_speed_profile(self, clear_range_limits=False, filename=None): + def average_wind_speed_profile(self, clear_range_limits=False, *, filename=None): """Average wind speed for all datetimes available. The plot also includes sigma contours. @@ -554,7 +558,9 @@ def average_wind_speed_profile(self, clear_range_limits=False, filename=None): ) show_or_save_plot(filename) - def average_wind_velocity_xy_profile(self, clear_range_limits=False, filename=None): + def average_wind_velocity_xy_profile( + self, clear_range_limits=False, *, filename=None + ): """Average wind X and wind Y for all datetimes available. The X component is the wind speed in the direction of East, and the Y component is the wind speed in the direction of North. @@ -621,7 +627,7 @@ def average_wind_velocity_xy_profile(self, clear_range_limits=False, filename=No plt.grid() show_or_save_plot(filename) - def average_wind_heading_profile(self, clear_range_limits=False, filename=None): + def average_wind_heading_profile(self, clear_range_limits=False, *, filename=None): """Average wind heading for all datetimes available. Parameters @@ -679,7 +685,7 @@ def average_wind_heading_profile(self, clear_range_limits=False, filename=None): plt.legend() show_or_save_plot(filename) - def average_pressure_profile(self, clear_range_limits=False, filename=None): + def average_pressure_profile(self, clear_range_limits=False, *, filename=None): """Average pressure profile for all datetimes available. The plot also includes sigma contours. @@ -772,7 +778,7 @@ def average_pressure_profile(self, clear_range_limits=False, filename=None): ) show_or_save_plot(filename) - def average_temperature_profile(self, clear_range_limits=False, filename=None): + def average_temperature_profile(self, clear_range_limits=False, *, filename=None): """Average temperature profile for all datetimes available. The plot also includes sigma contours. @@ -918,7 +924,7 @@ def plot_wind_rose( ax.yaxis.set_major_formatter(mtick.PercentFormatter(decimals=0)) return ax - def average_wind_rose_specific_hour(self, hour, fig=None, filename=None): + def average_wind_rose_specific_hour(self, hour, fig=None, *, filename=None): """Plot a specific hour of the average windrose Parameters @@ -952,7 +958,7 @@ def average_wind_rose_specific_hour(self, hour, fig=None, filename=None): show_or_save_plot(filename) def average_wind_rose_grid( - self, filename=None + self, *, filename=None ): # pylint: disable=too-many-statements """Plot wind roses for all hours of a day, in a grid like plot. @@ -1088,7 +1094,7 @@ def animate_average_wind_rose(self, figsize=(5, 5), filename="wind_rose.gif"): # More plots and animations def wind_gust_distribution_grid( - self, filename=None + self, *, filename=None ): # pylint: disable=too-many-statements """Plots shown in the animation of how the wind gust distribution varies throughout the day. @@ -1264,7 +1270,7 @@ def update(frame): return HTML(animation.to_jshtml()) def surface_wind_speed_distribution_grid( - self, wind_speed_limit=False, filename=None + self, wind_speed_limit=False, *, filename=None ): # pylint: disable=too-many-statements """Plots shown in the animation of how the sustained surface wind speed distribution varies throughout the day. The plots are histograms of the @@ -1493,7 +1499,7 @@ def update(frame): return HTML(animation.to_jshtml()) def wind_speed_profile_grid( - self, clear_range_limits=False, filename=None + self, clear_range_limits=False, *, filename=None ): # pylint: disable=too-many-statements """Creates a grid of plots with the wind profile over the average day. Each subplot represents a different hour of the day. @@ -1592,7 +1598,7 @@ def wind_speed_profile_grid( show_or_save_plot(filename) def wind_heading_profile_grid( - self, clear_range_limits=False, filename=None + self, clear_range_limits=False, *, filename=None ): # pylint: disable=too-many-statements """Creates a grid of plots with the wind heading profile over the average day. Each subplot represents a different hour of the day. diff --git a/rocketpy/plots/environment_plots.py b/rocketpy/plots/environment_plots.py index f15d066af..4b8a91e15 100644 --- a/rocketpy/plots/environment_plots.py +++ b/rocketpy/plots/environment_plots.py @@ -173,7 +173,7 @@ def __pressure_temperature(self, ax): return ax - def gravity_model(self, filename=None): + def gravity_model(self, *, filename=None): """Plots the gravity model graph that represents the gravitational acceleration as a function of height. @@ -204,7 +204,7 @@ def gravity_model(self, filename=None): show_or_save_plot(filename) - def atmospheric_model(self, filename=None): + def atmospheric_model(self, *, filename=None): """Plots all atmospheric model graphs available. This includes wind speed and wind direction, density and speed of sound, wind u and wind v, and pressure and temperature. @@ -245,7 +245,7 @@ def atmospheric_model(self, filename=None): show_or_save_plot(filename) # pylint: disable=too-many-statements - def ensemble_member_comparison(self, filename=None): + def ensemble_member_comparison(self, *, filename=None): """Plots ensemble member comparisons. It requires that the environment model has been set as Ensemble. @@ -352,7 +352,7 @@ def ensemble_member_comparison(self, filename=None): # Clean up self.environment.select_ensemble_member(current_member) - def info(self, filename=None): + def info(self, *, filename=None): """Plots a summary of the atmospheric model, including wind speed and wind direction, density and speed of sound. This is important for the Environment.info() method. diff --git a/rocketpy/plots/flight_plots.py b/rocketpy/plots/flight_plots.py index edc30b956..9ece84cdd 100644 --- a/rocketpy/plots/flight_plots.py +++ b/rocketpy/plots/flight_plots.py @@ -54,7 +54,7 @@ def first_event_time_index(self): else: return -1 - def trajectory_3d(self, filename=None): # pylint: disable=too-many-statements + def trajectory_3d(self, *, filename=None): # pylint: disable=too-many-statements """Plot a 3D graph of the trajectory Parameters @@ -134,7 +134,7 @@ def trajectory_3d(self, filename=None): # pylint: disable=too-many-statements show_or_save_plot(filename) def linear_kinematics_data( - self, filename=None + self, *, filename=None ): # pylint: disable=too-many-statements """Prints out all Kinematics graphs available about the Flight @@ -215,7 +215,7 @@ def linear_kinematics_data( plt.subplots_adjust(hspace=0.5) show_or_save_plot(filename) - def attitude_data(self, filename=None): # pylint: disable=too-many-statements + def attitude_data(self, *, filename=None): # pylint: disable=too-many-statements """Prints out all Angular position graphs available about the Flight Parameters @@ -273,7 +273,7 @@ def attitude_data(self, filename=None): # pylint: disable=too-many-statements plt.subplots_adjust(hspace=0.5) show_or_save_plot(filename) - def flight_path_angle_data(self, filename=None): + def flight_path_angle_data(self, *, filename=None): """Prints out Flight path and Rocket Attitude angle graphs available about the Flight @@ -324,7 +324,7 @@ def flight_path_angle_data(self, filename=None): show_or_save_plot(filename) def angular_kinematics_data( - self, filename=None + self, *, filename=None ): # pylint: disable=too-many-statements """Prints out all Angular velocity and acceleration graphs available about the Flight @@ -399,7 +399,9 @@ def angular_kinematics_data( plt.subplots_adjust(hspace=0.5) show_or_save_plot(filename) - def rail_buttons_forces(self, filename=None): # pylint: disable=too-many-statements + def rail_buttons_forces( + self, *, filename=None + ): # pylint: disable=too-many-statements """Prints out all Rail Buttons Forces graphs available about the Flight. Parameters @@ -490,7 +492,9 @@ def rail_buttons_forces(self, filename=None): # pylint: disable=too-many-statem plt.subplots_adjust(hspace=0.5) show_or_save_plot(filename) - def aerodynamic_forces(self, filename=None): # pylint: disable=too-many-statements + def aerodynamic_forces( + self, *, filename=None + ): # pylint: disable=too-many-statements """Prints out all Forces and Moments graphs available about the Flight Parameters @@ -578,7 +582,7 @@ def aerodynamic_forces(self, filename=None): # pylint: disable=too-many-stateme plt.subplots_adjust(hspace=0.5) show_or_save_plot(filename) - def energy_data(self, filename=None): # pylint: disable=too-many-statements + def energy_data(self, *, filename=None): # pylint: disable=too-many-statements """Prints out all Energy components graphs available about the Flight Parameters @@ -698,7 +702,7 @@ def energy_data(self, filename=None): # pylint: disable=too-many-statements show_or_save_plot(filename) def fluid_mechanics_data( - self, filename=None + self, *, filename=None ): # pylint: disable=too-many-statements """Prints out a summary of the Fluid Mechanics graphs available about the Flight @@ -798,7 +802,7 @@ def fluid_mechanics_data( show_or_save_plot(filename) def stability_and_control_data( - self, filename=None + self, *, filename=None ): # pylint: disable=too-many-statements """Prints out Rocket Stability and Control parameters graphs available about the Flight @@ -887,7 +891,7 @@ def stability_and_control_data( plt.subplots_adjust(hspace=0.5) show_or_save_plot(filename) - def pressure_rocket_altitude(self, filename=None): + def pressure_rocket_altitude(self, *, filename=None): """Plots out pressure at rocket's altitude. Parameters diff --git a/rocketpy/plots/hybrid_motor_plots.py b/rocketpy/plots/hybrid_motor_plots.py index eb13c4d69..61dc0940e 100644 --- a/rocketpy/plots/hybrid_motor_plots.py +++ b/rocketpy/plots/hybrid_motor_plots.py @@ -14,7 +14,7 @@ class _HybridMotorPlots(_MotorPlots): """ - def grain_inner_radius(self, lower_limit=None, upper_limit=None, filename=None): + def grain_inner_radius(self, lower_limit=None, upper_limit=None, *, filename=None): """Plots grain_inner_radius of the hybrid_motor as a function of time. Parameters @@ -40,7 +40,7 @@ def grain_inner_radius(self, lower_limit=None, upper_limit=None, filename=None): lower=lower_limit, upper=upper_limit, filename=filename ) - def grain_height(self, lower_limit=None, upper_limit=None, filename=None): + def grain_height(self, lower_limit=None, upper_limit=None, *, filename=None): """Plots grain_height of the hybrid_motor as a function of time. Parameters @@ -66,7 +66,7 @@ def grain_height(self, lower_limit=None, upper_limit=None, filename=None): lower=lower_limit, upper=upper_limit, filename=filename ) - def burn_rate(self, lower_limit=None, upper_limit=None, filename=None): + def burn_rate(self, lower_limit=None, upper_limit=None, *, filename=None): """Plots burn_rate of the hybrid_motor as a function of time. Parameters @@ -92,7 +92,7 @@ def burn_rate(self, lower_limit=None, upper_limit=None, filename=None): lower=lower_limit, upper=upper_limit, filename=filename ) - def burn_area(self, lower_limit=None, upper_limit=None, filename=None): + def burn_area(self, lower_limit=None, upper_limit=None, *, filename=None): """Plots burn_area of the hybrid_motor as a function of time. Parameters @@ -118,7 +118,7 @@ def burn_area(self, lower_limit=None, upper_limit=None, filename=None): lower=lower_limit, upper=upper_limit, filename=filename ) - def Kn(self, lower_limit=None, upper_limit=None, filename=None): + def Kn(self, lower_limit=None, upper_limit=None, *, filename=None): """Plots Kn of the hybrid_motor as a function of time. Parameters @@ -144,7 +144,7 @@ def Kn(self, lower_limit=None, upper_limit=None, filename=None): lower=lower_limit, upper=upper_limit, filename=filename ) - def draw(self, filename=None): + def draw(self, *, filename=None): """Draw a representation of the HybridMotor. Parameters diff --git a/rocketpy/plots/liquid_motor_plots.py b/rocketpy/plots/liquid_motor_plots.py index 822364fc2..08a385802 100644 --- a/rocketpy/plots/liquid_motor_plots.py +++ b/rocketpy/plots/liquid_motor_plots.py @@ -14,7 +14,7 @@ class _LiquidMotorPlots(_MotorPlots): """ - def draw(self, filename=None): + def draw(self, *, filename=None): """Draw a representation of the LiquidMotor. Parameters diff --git a/rocketpy/plots/motor_plots.py b/rocketpy/plots/motor_plots.py index e7ddb7efa..8ed7e3fd6 100644 --- a/rocketpy/plots/motor_plots.py +++ b/rocketpy/plots/motor_plots.py @@ -29,7 +29,7 @@ def __init__(self, motor): """ self.motor = motor - def thrust(self, lower_limit=None, upper_limit=None, filename=None): + def thrust(self, lower_limit=None, upper_limit=None, *, filename=None): """Plots thrust of the motor as a function of time. Parameters @@ -52,7 +52,7 @@ def thrust(self, lower_limit=None, upper_limit=None, filename=None): """ self.motor.thrust.plot(lower=lower_limit, upper=upper_limit, filename=filename) - def total_mass(self, lower_limit=None, upper_limit=None, filename=None): + def total_mass(self, lower_limit=None, upper_limit=None, *, filename=None): """Plots total_mass of the motor as a function of time. Parameters @@ -77,7 +77,7 @@ def total_mass(self, lower_limit=None, upper_limit=None, filename=None): lower=lower_limit, upper=upper_limit, filename=filename ) - def propellant_mass(self, lower_limit=None, upper_limit=None, filename=None): + def propellant_mass(self, lower_limit=None, upper_limit=None, *, filename=None): """Plots propellant_mass of the motor as a function of time. Parameters @@ -102,7 +102,7 @@ def propellant_mass(self, lower_limit=None, upper_limit=None, filename=None): lower=lower_limit, upper=upper_limit, filename=filename ) - def center_of_mass(self, lower_limit=None, upper_limit=None, filename=None): + def center_of_mass(self, lower_limit=None, upper_limit=None, *, filename=None): """Plots center_of_mass of the motor as a function of time. Parameters @@ -127,7 +127,7 @@ def center_of_mass(self, lower_limit=None, upper_limit=None, filename=None): lower=lower_limit, upper=upper_limit, filename=filename ) - def mass_flow_rate(self, lower_limit=None, upper_limit=None, filename=None): + def mass_flow_rate(self, lower_limit=None, upper_limit=None, *, filename=None): """Plots mass_flow_rate of the motor as a function of time. Parameters @@ -152,7 +152,7 @@ def mass_flow_rate(self, lower_limit=None, upper_limit=None, filename=None): lower=lower_limit, upper=upper_limit, filename=filename ) - def exhaust_velocity(self, lower_limit=None, upper_limit=None, filename=None): + def exhaust_velocity(self, lower_limit=None, upper_limit=None, *, filename=None): """Plots exhaust_velocity of the motor as a function of time. Parameters @@ -178,7 +178,7 @@ def exhaust_velocity(self, lower_limit=None, upper_limit=None, filename=None): ) def inertia_tensor( - self, lower_limit=None, upper_limit=None, show_products=False, filename=None + self, lower_limit=None, upper_limit=None, show_products=False, *, filename=None ): """Plots all inertia tensors (I_11, I_22, I_33, I_12, I_13, I_23) of the motor as a function of time in a single chart. diff --git a/rocketpy/plots/rocket_plots.py b/rocketpy/plots/rocket_plots.py index 9689a257b..f305fcb99 100644 --- a/rocketpy/plots/rocket_plots.py +++ b/rocketpy/plots/rocket_plots.py @@ -54,7 +54,7 @@ def reduced_mass(self): self.rocket.reduced_mass() - def static_margin(self, filename=None): + def static_margin(self, *, filename=None): """Plots static margin of the rocket as a function of time. Parameters @@ -123,7 +123,7 @@ def power_off_drag(self): self.rocket.power_off_drag() # pylint: disable=too-many-statements - def drag_curves(self, filename=None): + def drag_curves(self, *, filename=None): """Plots power off and on drag curves of the rocket as a function of time. Parameters @@ -179,7 +179,7 @@ def thrust_to_weight(self): lower=0, upper=self.rocket.motor.burn_out_time ) - def draw(self, vis_args=None, plane="xz", filename=None): + def draw(self, vis_args=None, plane="xz", *, filename=None): """Draws the rocket in a matplotlib figure. Parameters diff --git a/rocketpy/plots/solid_motor_plots.py b/rocketpy/plots/solid_motor_plots.py index 202220102..cda7bb718 100644 --- a/rocketpy/plots/solid_motor_plots.py +++ b/rocketpy/plots/solid_motor_plots.py @@ -113,7 +113,7 @@ def Kn(self, lower_limit=None, upper_limit=None): self.motor.Kn.plot(lower=lower_limit, upper=upper_limit) - def draw(self, filename=None): + def draw(self, *, filename=None): """Draw a representation of the SolidMotor. Parameters diff --git a/rocketpy/plots/tank_plots.py b/rocketpy/plots/tank_plots.py index a4bc0dd2a..6f1c0e5f2 100644 --- a/rocketpy/plots/tank_plots.py +++ b/rocketpy/plots/tank_plots.py @@ -69,7 +69,7 @@ def _generate_tank(self, translate=(0, 0), csys=1): # Don't set any plot config here. Use the draw methods for that return tank - def draw(self, filename=None): + def draw(self, *, filename=None): """Draws the tank geometry. Parameters diff --git a/rocketpy/rocket/aero_surface/fins/fins.py b/rocketpy/rocket/aero_surface/fins/fins.py index c7973bc69..3c98855d0 100644 --- a/rocketpy/rocket/aero_surface/fins/fins.py +++ b/rocketpy/rocket/aero_surface/fins/fins.py @@ -426,7 +426,7 @@ def compute_forces_and_moments( M3 = M3_forcing - M3_damping return R1, R2, R3, M1, M2, M3 - def draw(self, filename=None): + def draw(self, *, filename=None): """Draw the fin shape along with some important information, including the center line, the quarter line and the center of pressure position. diff --git a/rocketpy/rocket/aero_surface/nose_cone.py b/rocketpy/rocket/aero_surface/nose_cone.py index e9fbe4324..6c295d1c5 100644 --- a/rocketpy/rocket/aero_surface/nose_cone.py +++ b/rocketpy/rocket/aero_surface/nose_cone.py @@ -486,7 +486,7 @@ def evaluate_center_of_pressure(self): self.cp = (self.cpx, self.cpy, self.cpz) return self.cp - def draw(self, filename=None): + def draw(self, *, filename=None): """Draw the nosecone shape along with some important information, including the center of pressure position. diff --git a/rocketpy/rocket/rocket.py b/rocketpy/rocket/rocket.py index 58067a03f..aacf5b4a6 100644 --- a/rocketpy/rocket/rocket.py +++ b/rocketpy/rocket/rocket.py @@ -1839,7 +1839,7 @@ def add_thrust_eccentricity(self, x, y): self.thrust_eccentricity_x = y return self - def draw(self, vis_args=None, plane="xz", filename=None): + def draw(self, vis_args=None, plane="xz", *, filename=None): """Draws the rocket in a matplotlib figure. Parameters @@ -1872,7 +1872,7 @@ def draw(self, vis_args=None, plane="xz", filename=None): eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff and webp (these are the formats supported by matplotlib). """ - self.plots.draw(vis_args, plane, filename) + self.plots.draw(vis_args, plane, filename=filename) def info(self): """Prints out a summary of the data and graphs available about diff --git a/rocketpy/utilities.py b/rocketpy/utilities.py index fb728dbb6..f6e68d162 100644 --- a/rocketpy/utilities.py +++ b/rocketpy/utilities.py @@ -208,6 +208,7 @@ def fin_flutter_analysis( flight, see_prints=True, see_graphs=True, + *, filename=None, ): """Calculate and plot the Fin Flutter velocity using the pressure profile @@ -320,7 +321,7 @@ def _flutter_safety_factor(flight, flutter_mach): return safety_factor -def _flutter_plots(flight, flutter_mach, safety_factor, filename=None): +def _flutter_plots(flight, flutter_mach, safety_factor, *, filename=None): """Plot the Fin Flutter Mach Number and the Safety Factor for the flutter. Parameters From e50d646123fb3b21b90cef46d6b3e758aa86a122 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR <63590233+Gui-FernandesBR@users.noreply.github.com> Date: Sat, 30 Nov 2024 00:46:52 -0300 Subject: [PATCH 28/36] DOC: improvements to developers documentation (#732) * DOC: improvements to developers documentation * MNT: git rename a few files from the root directory * DOC: more upgrades to development docs * DEV: create script to delete local branches that no longer exist on remote * DOC: I followed the step by step, looked at other parts of the documentation, and didn't find anything to correct or improve. I think it's fine as it is. I added a model to requirements.txt, which was missing when I ran the make html command. * DOC: Update developer docs on sphinx building. * DOC: Clarify testing structure on testing docs. * DOC: Re-run docs requirements freeze. * DOC: Introduce Conflicts Guideline and update First PR. * DOC: fixing typos on development docs. * DOC: Solve review comments of dev docs. * DOC: final touches to developers documentation * DOC: update cSpell configuration to enable additional file types * DEV: move docker ignore file --------- Co-authored-by: Julio Machado Co-authored-by: Pedro Bressan Co-authored-by: Lucas de Oliveira Prates --- CITATION.cff => .github/CITATION.cff | 0 CODEOWNERS => .github/CODEOWNERS | 0 .../CODE_OF_CONDUCT.md | 0 CONTRIBUTING.md => .github/CONTRIBUTING.md | 0 .github/clear-local-branches.ps1 | 2 + .vscode/settings.json | 14 +- .dockerignore => docker/.dockerignore | 0 Dockerfile => docker/Dockerfile | 0 .../docker-compose.yml | 0 docs/conf.py | 1 + docs/development/build_docs.rst | 61 ++-- docs/development/conflicts.rst | 123 ++++++++ docs/development/docker.rst | 42 +-- docs/development/first_pr.rst | 139 +++++++++ docs/development/index.rst | 19 +- docs/development/pro_tips.rst | 99 +++++++ docs/development/rocketpy_as_developer.rst | 277 ------------------ docs/development/setting_up.rst | 122 ++++++++ docs/development/style_guide.rst | 95 ++++-- docs/development/testing.rst | 52 +++- docs/requirements.in | 1 + docs/requirements.txt | 5 + docs/user/motors/motors.rst | 8 +- 23 files changed, 699 insertions(+), 361 deletions(-) rename CITATION.cff => .github/CITATION.cff (100%) rename CODEOWNERS => .github/CODEOWNERS (100%) rename CODE_OF_CONDUCT.md => .github/CODE_OF_CONDUCT.md (100%) rename CONTRIBUTING.md => .github/CONTRIBUTING.md (100%) create mode 100644 .github/clear-local-branches.ps1 rename .dockerignore => docker/.dockerignore (100%) rename Dockerfile => docker/Dockerfile (100%) rename docker-compose.yml => docker/docker-compose.yml (100%) create mode 100644 docs/development/conflicts.rst create mode 100644 docs/development/first_pr.rst create mode 100644 docs/development/pro_tips.rst delete mode 100644 docs/development/rocketpy_as_developer.rst create mode 100644 docs/development/setting_up.rst diff --git a/CITATION.cff b/.github/CITATION.cff similarity index 100% rename from CITATION.cff rename to .github/CITATION.cff diff --git a/CODEOWNERS b/.github/CODEOWNERS similarity index 100% rename from CODEOWNERS rename to .github/CODEOWNERS diff --git a/CODE_OF_CONDUCT.md b/.github/CODE_OF_CONDUCT.md similarity index 100% rename from CODE_OF_CONDUCT.md rename to .github/CODE_OF_CONDUCT.md diff --git a/CONTRIBUTING.md b/.github/CONTRIBUTING.md similarity index 100% rename from CONTRIBUTING.md rename to .github/CONTRIBUTING.md diff --git a/.github/clear-local-branches.ps1 b/.github/clear-local-branches.ps1 new file mode 100644 index 000000000..f06055c08 --- /dev/null +++ b/.github/clear-local-branches.ps1 @@ -0,0 +1,2 @@ +# Delete local branches that no longer exists on remote (windows command line) +git branch -vv | Where-Object { $_ -match 'gone\]' } | ForEach-Object { $_.Trim().Split()[0] } | ForEach-Object { git branch -D $_ } diff --git a/.vscode/settings.json b/.vscode/settings.json index 62481957c..98010d539 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -5,12 +5,14 @@ "python.testing.unittestEnabled": false, "python.testing.pytestEnabled": true, "autoDocstring.docstringFormat": "numpy", - "cSpell.enableFiletypes": [ - "python", - "jupyter", - "markdown", - "restructuredtext" - ], + "cSpell.enabledFileTypes": { + "plaintext": true, + "python": true, + "jupyter": true, + "markdown": true, + "tex": true, + "rst": true + }, "cSpell.language": "en-US", "cSpell.words": [ "Abdulklech", diff --git a/.dockerignore b/docker/.dockerignore similarity index 100% rename from .dockerignore rename to docker/.dockerignore diff --git a/Dockerfile b/docker/Dockerfile similarity index 100% rename from Dockerfile rename to docker/Dockerfile diff --git a/docker-compose.yml b/docker/docker-compose.yml similarity index 100% rename from docker-compose.yml rename to docker/docker-compose.yml diff --git a/docs/conf.py b/docs/conf.py index e834d180e..9324ad9a6 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -40,6 +40,7 @@ "sphinx.ext.napoleon", "sphinx.ext.viewcode", "sphinx.ext.mathjax", + "sphinx_tabs.tabs", "sphinx_copybutton", "sphinx_design", "jupyter_sphinx", diff --git a/docs/development/build_docs.rst b/docs/development/build_docs.rst index f94c4f0fb..398781399 100644 --- a/docs/development/build_docs.rst +++ b/docs/development/build_docs.rst @@ -31,64 +31,67 @@ requirements that are needed to run the sphinx generator. All these requirements are listed in the ``requirements.txt`` file inside the ``docs`` folder. -To install the requirements, navigate the terminal to the ``docs`` folder and -run the following command: +The easiest way to install the requirements and build the documentation is by +RocketPy's ``Makefile`` command: .. code-block:: bash - - pip install -r requirements.txt -After installing the requirements, you can build the documentation by running -the following command in your terminal: + make build-docs -.. code-block:: bash - - make html +This command will install the requirements and generate the documentation in the +``docs/_build/html`` folder. + +.. note:: -The file named ``Makefile`` contains the commands to build the documentation. -The ``make html`` command will generate the documentation in the ``docs/_build/html`` -folder. + This requires the ``make`` command to be available in your system. Check out + GNU Make documentation on how to install it. To see the documentation, open the ``docs/_build/html/index.html`` file in your browser. -.. note:: Watch out for any warnings or errors that may appear in the terminal - when building the documentation. If you find any, fix them before - pushing the changes to the repository or at least warn the team about - them. +.. important:: + + Watch out for any introduced warnings or errors that may appear in the terminal + when building the documentation. If you find any, fix them before + pushing the changes to the repository or at least warn the team about + them. Sometimes you may face problems when building the documentation after several -times of building it. -This may happen because sphinx does not clean the ``docs/_build`` folder before -building the documentation again. -To clean the ``docs/_build`` folder, run the following command in your terminal: +times of building it. This may happen because sphinx does not clean the ``docs/_build`` +folder before building the documentation again. + +One can use the internal Makefile inside the ``docs`` folder generated by sphinx +to have specific commands to build and clean the documentation. + +To clean the ``docs/_build`` folder, run the following commands in your terminal: .. code-block:: bash + cd docs/ make clean After cleaning the ``docs/_build`` folder, you can build the documentation again -by running the ``make html`` command. +by going back to rocketpy root directory and using the ``make build-docs`` command +described above. If the error persists, it may be related to other files, such as the ``.rst`` files or the ``conf.py`` file. .. danger:: - Do not modify the Makefile or the ``make.bat`` files. These files are \ - automatically generated by sphinx and any changes will be lost. + Do not modify sphinx internal Makefile or the ``docs/make.bat`` files. + These files are automatically generated by sphinx and any changes will be lost. How to integrate the documentation with ReadTheDocs --------------------------------------------------- The documentation is automatically built and hosted by `ReadTheDocs`. -Every time a commit is pushed to the repository, `ReadTheDocs` will build the -documentation and host it automatically. -This includes other branches besides the master branch. -However, the documentation will only be available for the master branch, and you -need to configure the `ReadTheDocs` project to build the documentation for other -branches. +Every time a commit is pushed to selected branches of the repository (such as `master` and `develop`), +`ReadTheDocs` will build the documentation and host it automatically. + +However, custom branches need to be manually added. If you need to have your branch +available on `ReadTheDocs`, ask to a maintainer for help. The connection between the GitHub repository and the `ReadTheDocs` project is already configured and defined in the ``readthedocs.yml`` file, available at the diff --git a/docs/development/conflicts.rst b/docs/development/conflicts.rst new file mode 100644 index 000000000..dbdb09e61 --- /dev/null +++ b/docs/development/conflicts.rst @@ -0,0 +1,123 @@ +Solving Git Conflicts +===================== + +What Are Git Conflicts? +----------------------- + +Git conflicts occur when Git is unable to automatically merge changes made to the \ +same lines of code or when changes overlap in a file across branches. +This often occurs in RocketPy repository when two developers make changes to the same \ +file and try to merge them. +In this scenario, Git pauses the operation and marks the conflicting files, \ +requiring manual intervention. + +Merge vs Rebase +--------------- + +**Merge** + +The ``merge`` operation combines the changes from one branch into another. \ +It creates a new commit that marks the merging of the two branches together. + +* Retains the full history of both branches. +* Non-linear history shows how changes were combined. +* The history can become cluttered if there are many branches, due to the number of merge commits. + +**Rebase** + +The ``rebase`` operation integrates changes from one branch into another by reapplying \ +the commits on top of the target branch. It results in a more linear history. + +* Produces a cleaner, linear commit history. +* Easier to follow the sequence of changes. +* Can rewrite history, leading to potential issues when working on shared branches. + +Example Commands +---------------- + +Let's consider a common scenario from which conflicts can arise: +updating a feature branch with the latest changes from the branch it was created. +Both ``merge`` and ``rebase`` can be used to update the feature branch. +However, the ``rebase`` option is highly recommended to keep a more linear history. + +Merge +~~~~~ + +Suppose you have two branches, ``enh/feature`` that was branched off ``develop``. +It is likely that ``develop`` has been updated since you branched off ``enh/feature``, \ +therefore before merging ``enh/feature`` into ``develop``, you should update ``enh/feature`` \ +with the latest changes from ``develop``. + +One way to do this is by merging ``develop`` into ``enh/feature`` as follows: + +.. code-block:: console + + git checkout develop + git pull + git checkout enh/feature + git merge develop + +If there are conflicts, Git will pause the operation and mark the conflicting files. \ +VS Code provides a visual interface to resolve these conflicts in the **Merge Editor**. +If you want to abort the merge, you can use ``git merge --abort``. + +Rebase +~~~~~~ + +Similarly, another way to update ``enh/feature`` with the latest changes from ``develop`` \ +is by rebasing ``enh/feature`` onto ``develop`` as follows: + +.. code-block:: console + + git checkout develop + git pull + git checkout enh/feature + git rebase develop + +Differently from merge, if there are conflicts, they must be resolved commit by commit. \ +After resolving each step conflicts, you can continue the rebase with ``git rebase --continue``. \ +If you want to abort the rebase, you can use ``git rebase --abort``. + +When to Use Merge or Rebase +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Generally, the maintainers will inform you which operation to use when merging your PR. \ +Notice that there is no wrong way to merge branches, but ``rebase`` is usually preferred \ +to keep a more linear history. + +Furthermore, it is important to avoid letting conflicts accumulate, since they can become \ +harder to resolve. It is recommended to update your feature branch frequently with the latest \ +changes from the branch it was branched off. + +Solving Conflicts +----------------- + +When conflicts arise, Git marks the conflicting files. The file will contain markers like: + +.. code-block:: console + + <<<<<<< HEAD + Current branch changes + ======= + Incoming branch changes + >>>>>>> branch-name + +The ``HEAD`` section contains the changes from the current branch, while the ``branch-name`` section \ +contains the changes from the incoming branch. +The ``=======`` line separates the two sections. +One can manually edit the file to resolve the conflict, removing the markers and keeping the desired changes, however \ +for convenience it is recommended to use a visual tool like the *Merge Editor* in VS Code. + +Resolving Conflicts in VS Code +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +When a conflict occurs, VS Code will open the *Merge Editor* to help you resolve it.\ + +1. Open the conflicting file (marked with a ``!``). +2. The *Merge Editor* will show the conflicting sections side by side. +3. Click on the ``Accept Current Change`` or ``Accept Incoming Change`` buttons to keep the desired changes, sometimes both changes will be kept or even a manual edit will be necessary. + +More details on VS Code interface and conflict solver can be found in `VS Code Docs `_. +After resolving the conflicts, save the files, make sure all conflicts are resolved, and then \ +commit the changes. + diff --git a/docs/development/docker.rst b/docs/development/docker.rst index 91aafb1d2..bc1de8745 100644 --- a/docs/development/docker.rst +++ b/docs/development/docker.rst @@ -2,7 +2,7 @@ RocketPy with docker ===================== RocketPy does not provide an official docker image, but you can build one -yourself using the provided `Dockerfile` and `docker-compose.yml` files. +yourself using the provided ``Dockerfile`` and ``docker-compose.yml`` files. Benefits -------- @@ -14,13 +14,13 @@ using docker are: worrying about dependencies. 2. **Portability**: run RocketPy on any operational system that supports docker, including the 3 main operational systems (Windows, Linux and Mac). -3. **Reproducibility**: ensure that tour code is working regardless of the +3. **Reproducibility**: ensure that our code is working regardless of the operational system. Using docker will be specially important when you are not sure if the code additions will still run on different operational systems. -Although we have a set of GitHub actions to test the code on different +Although we have a set of GitHub actions to test the code on different operational systems every time a pull request is made, it is important to submit a PR only after you are sure that the code will run flawlessly, otherwise quota limits may be reached on GitHub. @@ -28,7 +28,7 @@ otherwise quota limits may be reached on GitHub. Requirements ------------- -Before you start, you need to install on your machine: +Before you start, you need to install on your machine: 1. `Docker `__, to build and run the image. 2. `Docker Compose `__, to compose multiple images at once. @@ -40,11 +40,11 @@ Build the image To build the image, run the following command on your terminal: .. code-block:: console - + docker build -t rocketpy-image -f Dockerfile . -This will build the image and tag it as `rocketpy-image` (you can apply another +This will build the image and tag it as ``rocketpy-image`` (you can apply another name of your preference if you want). An image is a read-only template with instructions for creating a Docker @@ -82,7 +82,7 @@ Run the unit tests -------------------- You might have noticed that the container is running in an isolated environment -with no access to your machine's files, but the `Dockerfile` already copied the +with no access to your machine's files, but the ``Dockerfile`` already copied the RocketPy repository to the container. This means that you can run tests (and simulations!) as if you were running RocketPy on your machine. @@ -90,37 +90,37 @@ RocketPy on your machine. As simple as that, you can run the unit tests: .. code-block:: console - + pytest To access a list of all available execution options, see the `pytest docs `__. -Compose docker images +Compose docker images --------------------- -We also made available a `docker-compose.yml` file that allows you to compose +We also made available a ``docker-compose.yml`` file that allows you to compose multiple docker images at once. -Unfortunately, this file will not allow you to test the code on different +Unfortunately, this file will not allow you to test the code on different operational systems at once, since docker images inherits from the host operational system. However, it is still useful to run the unit tests on different python versions. -Currently, the `docker-compose.yml` file is configured to run the unit tests +Currently, the ``docker-compose.yml`` file is configured to run the unit tests on python 3.9 and 3.12. To run the unit tests on both python versions, run the following command **on your machine**: .. code-block:: console - + docker-compose up Also, you can check the logs of the containers by running: .. code-block:: console - + docker-compose logs @@ -131,7 +131,7 @@ After you're done testing, or if you wish to stop the containers and remove the services, use the command: .. code-block:: console - + docker-compose down @@ -142,31 +142,31 @@ images created by up. Changing to other operational systems ------------------------------------- -The default image in the `Dockerfile` is based on a Linux distribution. +The default image in the ``Dockerfile`` is based on a Linux distribution. However, you can alter the base image to use different operating systems, though the process may require additional steps depending on the OS's compatibility with your project setup. For instance, certain dependencies or scripts may behave differently or require different installation procedures, so use it with caution. -To change the base image, you will need to modify the `FROM` statement in the -`Dockerfile`. +To change the base image, you will need to modify the ``FROM`` statement in the +``Dockerfile``. For example, to use a Windows-based image, you might change: .. code-block:: Dockerfile - + FROM python:latest to .. code-block:: Dockerfile - + FROM mcr.microsoft.com/windows/servercore:ltsc2019 Please note, the above is just an example, and using a different OS may require -further adjustments in the `Dockerfile`. +further adjustments in the ``Dockerfile``. We recommend you to see the official Python images on the Docker Hub for different OS options: `Docker Hub Python Tags `__. diff --git a/docs/development/first_pr.rst b/docs/development/first_pr.rst new file mode 100644 index 000000000..5f3eb876c --- /dev/null +++ b/docs/development/first_pr.rst @@ -0,0 +1,139 @@ +Your first Pull Request (PR) +============================ + +This page describes a few steps to help you get started with your first PR to RocketPy. +We are excited to have you here! + +Picking an issue +---------------- + +Before you start coding, you should pick an issue to work on. You can find a +list of issues in the ``Issues`` tab on GitHub (https://github.com/RocketPy-Team/RocketPy/issues). +If you are new here, it is really recommended that you talk to maintainers +before starting to work on an issue. +That way you avoid working on something that is already being worked on or +something that is not in line with the project's goals. + +If you want to develop a new feature that is not in the issues list, please open +a new issue to discuss it with the maintainers before submitting a PR. + +Once the issue is assigned to you, you can start working on it. + +.. note:: + + In order to open an issue at our repo, you must have a GitHub account. \ + In case you do not want to open an account yet, you can contact the maintainers \ + through our Discord server. But keep in mind that you you need an account to \ + open a Pull Request (PR). + +Creating a new branch +--------------------- + +At your local machine, make sure you already have `git `_ \ +installed and set up. +In order to create a new branch, you should run the following command in your +preferred terminal: + +.. code-block:: console + + git checkout -b + +This command will create a new branch named ``branch_name`` from the current branch you are in. +Your branch name should follow the guidelines described in :doc:`/development/style_guide`. + +.. tip:: + + VS Code has a built-in integration with git, this allows you to run git commands \ + quite easily through the editor's interface. For example, you could search for \ + "git checkout" in the command palette and run the command from there. + + +Opening the PR +-------------- + +After you finish your work, you are more than welcome opening a PR to the RocketPy repository. +Here are some checks to do before opening a PR: + +* Check if the test suite is passing. +* Format your code using ``black`` and ``isort``. + +Check the :doc:`/development/pro_tips` section for more information on how to run these commands. + +When you open a PR, you should: + +* Provide a clear and concise title. +* Fill the PR description following the standard template. +* Use labels to help maintainers understand what the PR is about. +* Link any issue that may be closed when the PR is merged. + +Remember, the PR is yours, not ours! You should keep track of it and update it as needed. + +.. important:: + + See the :doc:`/development/style_guide` for more information on how to name and \ + describe your PR. + +Continuous Integration (CI) +--------------------------- + +There are several automation on our repository to help us maintain the code quality. +Currently, our CI pipeline runs the following checks: + +* **Linting**: we run ``flake8``, ``pylint``, ``black`` and ``isort`` on top of your latest commit in order to check for code style issues. To understand more about these tools, please read the :doc:`/development/pro_tips` section. +* **Testing**: we run the tests defined in the ``tests`` folder to make sure your changes do not break any existing functionality. The tests will be executed 6 times, each time with a different Python version (the oldest and newest supported versions) and with three different operating systems (Windows, Linux and MacOS). +* **Coverage**: based on the tests results, we also check the code coverage results from our test suite. There is an automation to check if the code coverage increased or decreased with your PR. It also points to potential introduced lines of code that should be tested. + +Once you open your PR or commit and push to your branch, the CI will be initialized. +Please correct any issues that may arise from the CI checks. + +.. important:: + + All the commands we run in the CI pipeline can also be run locally. It is \ + important that you run the checks locally before pushing your changes to \ + the repository. + +The CHANGELOG file +------------------ + +We keep track of the changes in the ``CHANGELOG.md`` file. +When you open a PR, you should add a new entry to the "Unreleased" section of the file. +This entry should simply be the title of your PR. + +.. note:: + + In the future we would like to automate the CHANGELOG update, but for now \ + it is a manual process, unfortunately. + + +The review process +------------------ + +After you open a PR, the maintainers will review your code. +This review process is a way to ensure that the code is in line with the +project's goals and that it is well written and documented. + +The maintainers may ask you to make changes to your code. +You should address these changes or explain why you think they are not necessary. +This is the best time to learn from the maintainers and improve your coding skills. + +In case you do not address the comments in a timely manner, the maintainers may +either close the PR or make the changes themselves. + + +Merging the PR +-------------- + +There are 3 different ways of merging a PR: + +1. **Create a merge commit**: this is the default option on GitHub. +2. **Squash and merge**: this option will squash all your commits into a single one. This is useful when you have many commits and you want to keep the history clean, therefore this is the recommended option. +3. **Rebase and merge**: this option will add your commits directly to the target branch, without creating a merge commit. This is useful to keep the history linear, however it also requires handling potential conflicts one at a time, which can be a bit more complex. + +.. note:: + + Overall, you will not have permission to merge your PR. The maintainers will \ + take care of that for you. This is here just for you to understand the process. + +All in all, there is no right or wrong way to merge a PR. +The maintainers will decide which option is the best for the project. +What you should care though is about conflicting changes, let's talk about that next in :doc:`/development/conflicts`. diff --git a/docs/development/index.rst b/docs/development/index.rst index 4837fa375..5d8cc490f 100644 --- a/docs/development/index.rst +++ b/docs/development/index.rst @@ -1,14 +1,27 @@ Contributing to RocketPy ======================== +Here you will find information on how to contribute to our code base. +RocketPy is an open-source project and we welcome contributions from everyone. +There are a few guidelines we would like to share with you to make your +contribution as smooth as possible: + .. toctree:: - :maxdepth: 2 + :maxdepth: 1 :caption: Contents: - Running RocketPy as a Developer + setting_up.rst + Your First Pull Request (PR) + Solving git conflicts Style Guide Testing Guidelines RocketPy with Docker Building the Documentation + Pro Tips + + +.. important:: -This section is still a work in progress. Here you will find information on how to contribute to our code base. \ No newline at end of file + Our Development Documentation matters as much as the Usage Documentation to us. \ + In case you find any trouble when following this guide, please let us know so \ + we can improve it. diff --git a/docs/development/pro_tips.rst b/docs/development/pro_tips.rst new file mode 100644 index 000000000..465ff7982 --- /dev/null +++ b/docs/development/pro_tips.rst @@ -0,0 +1,99 @@ +Pro Tips +======== + +Last but not least, here are some pro tips that can help you to be more +productive when developing in the project (or any other project). + + +Makefile +-------- + +We have a ``Makefile`` in the repository that contains some useful commands to +help you with the development process. + +Some examples of commands are: + +* ``make black``: runs the black formatter on the code. +* ``make format``: runs the isort and black formatters on the code. +* ``make lint``: runs the flake8 and pylint tools. +* ``make build-docs``: This will build the documentation locally, so you can check if everything is working as expected. +* ``make pytest-slow``: runs only the slow tests (marked with the ``@pytest.mark.slow`` decorator). + +These commands are meant to be system agnostic, so you can run them on any +Operational System (OS). + +Knowing your editor +------------------- + +Something we usually notice on beginners is that they don't know all the power +their editor can provide. Knowing your editor can save you a lot of time and +make you more productive. + +As stated earlier, we recommend using VS Code as your editor, as most of the +RocketPy developers use it. Therefore it is easier to help you if you are using +the same editor. + +Some of the features that can help you are: + +1. **Code navigation**: Jump to definitions, find references, etc. +2. **Integrated tests**: Run tests directly from the editor. +3. **Python Debugger**: Debug your code directly from the editor. +4. **GitHub Pull Requests**: Open, review, comment, and merge pull requests directly from the editor + +Extensions +---------- + +We have listed some recommended extensions in the ``.vscode/extensions.json`` file. +These are general recommendations based on what our developers usually install +in their VSCode. +Obviously, it is not mandatory to install them, but they can help you to be more +productive. + + +Code assistance +--------------- + +Artificial Intelligence (AI) assistance has becoming more and more common in +software development. +Some editors have AI assistance built-in. +Famous options are GitHub Copilot, JetBrains AI and TabNine. + +At this repo, the use of AI tools is welcome, we don't have any restrictions +against it. + +A few possible applications of AI tools are: + +* Writing documentation. +* Writing tests. +* Getting explanations of code sections. + +.. tip:: + + As of today, November 2024, GitHub Copilot still provides free access for \ + university email addresses. We can't guarantee this will still be the case \ + when you are reading this, so check the GitHub Copilot website for more \ + information. + + +If you are against the use of AI tools, do not worry, you can still contribute +to the project without using them. + + +Engaging with the community +--------------------------- + +The most important part of contributing to an open-source project is engaging +with the community. +Our developers are frequently available on our server, and you can +ask any questions you may have there. +Either a question about the project, or a question about how to contribute, or +even a suggestion of a new feature. +Any kind of interaction is welcome. + + +.. tip:: + + The official supported language in our server is English. \ + However, the RocketPy Team is lucky to have developers from all around the \ + world, so you may find people speaking other languages as well. \ + Don't let the language barrier keep you from engaging with the community. diff --git a/docs/development/rocketpy_as_developer.rst b/docs/development/rocketpy_as_developer.rst deleted file mode 100644 index df3608c77..000000000 --- a/docs/development/rocketpy_as_developer.rst +++ /dev/null @@ -1,277 +0,0 @@ -======================== -Introduction to RocketPy -======================== - -This tutorial part shows how to open rocketpy files and run a simple simulation. - -Opening rocketpy folder -======================= - -Go into the cloned repository folder by typing on a terminal - -.. code-block:: console - - cd - -Open your preference editor by typing on a terminal - -.. code-block:: console - - . - -For example, to open VS Code type on terminal - -.. code-block:: console - - code . - -Alternatively, you can open the folder directly through your editor's interface. - -Preparing directory for code editing -==================================== - -You may create a testing file in any directory, but you must remember that they -should not be included in the commits and pull requests unless they are part of -the proposed solution. -With that in mind, we suggest you to create a folder with all testing files, so -they can be added in the ``.gitignore`` file, which contains the name of all the -files and folders that will not be added to the commits. -To create the folder, type on the terminal: - -.. code-block:: console - - mkdir - -And, to add it on .gitignore, type: - -.. code-block:: console - - echo / >> .gitignore - -It is important to remember that all the files inside this folder will not be included in any commit so, if it is important to the solution, do not add them inside it. - -Running a simulation with RocketPy -================================== - -Importing the RocketPy files ----------------------------- - -First, create a python (or .ipynb) file to make the simulation. -To ensure you are using the local files and not the files as a python package (if you installed the library via pip for example), add - -.. code-block:: python - - pip install -e . - -Alternatively you can use the following command to pip install the local library: - -.. code-block:: console - - import sys - sys.path.append('../') # if you are using a notebook - sys.path.append('../rocketpy') # if you are using a script - -Import the classes that will be used, in case: - -.. code-block:: python - - from rocketpy import Environment, SolidMotor, Rocket, Flight, Function - -If it is the first time you are using rocketpy and you do not have all required libraries installed, you could use the command: - -.. code-block:: python - - pip install -r - -Alternatively, if you are in rocketpy folder, just type - -.. code-block:: python - - pip install -r requirements.txt - -Creating an Environment ------------------------ - -Here we create the environment object that will be used in the simulation. -It contains information about the local pressure profile, temperature, speed of sound, wind direction and intensity, etc. - -.. code-block:: python - - env = Environment(latitude=32.990254, longitude=-106.974998, elevation=1400) - -RocketPy can use local files via the Ensemble method or meteorological forecasts through OpenDAP protocol. -To work with environment files, it will be very important ensuring tha that you have the netCDF4 library installed. -Assuming we are using forecast, first we set the simulated data with: - -.. code-block:: python - - import datetime - tomorrow = datetime.date.today() + datetime.timedelta(days=1) - env.set_date((tomorrow.year, tomorrow.month, tomorrow.day, 12)) # Hour given in UTC time - -Then we set the atmospheric model, in this case, GFS forecast: - -.. code-block:: python - - env.set_atmospheric_model(type="Forecast", file="GFS") - -Weather forecast data can be visualized through two info methods. - -``env.info()`` or ``env.all_info()`` - -Creating the motor that boosts the rocket ------------------------------------------ - -Now we need to create the motor. -For example, we will use a solid motor called Pro75M1670, but other configurations are also possible. -The motor class contains information about the thrust curve and uses some geometric parameters to calculate the mass variation over time, as well as the total thrust and other important outputs. - -.. code-block:: python - - Pro75M1670 = SolidMotor( - thrust_source="../data/motors/cesaroni/Cesaroni_M1670.eng", #copy here the path to the thrust source file - burn_time=3.9, - grain_number=5, - grain_separation=5 / 1000, - grain_density=1815, - grain_outer_radius=33 / 1000, - grain_initial_inner_radius=15 / 1000, - grain_initial_height=120 / 1000, - nozzle_radius=33 / 1000, - throat_radius=11 / 1000, - interpolation_method="linear", - ) - -Motor data can be visualized through the following methods: - -``Pro75M1670.info()`` or ``Pro75M1670.all_info()`` - - -Creating the rocket -------------------- - -The Rocket class contains all information about the rocket that are necessary to the simulation, including the motor, rocket mass and inertia, aerodynamic surfaces, parachutes, etc. -The first step is to initialize the class with the vital data: - -.. code-block:: python - - calisto = Rocket( - radius=127 / 2000, - mass=19.197 - 2.956, - inertia_i=6.60, - inertia_z=0.0351, - power_off_drag="../../data/rockets/calisto/powerOffDragCurve.csv", - power_on_drag="../../data/rockets/calisto/powerOnDragCurve.csv", - center_of_dry_mass_position=0, - coordinate_system_orientation="tail_to_nose", - ) - - calisto.add_motor(Pro75M1670, position=-1.255) - -Then the rail buttons must be set: - -.. code-block:: python - - calisto.set_rail_buttons(0.2, -0.5) - -In sequence, the aerodynamic surfaces must be set. -If a lift curve for the fin set is not specified, it is assumed that they behave according to a linearized model with a coefficient calculated with Barrowman's theory. -In the example, a nosecone, one fin set and one tail were added, but each case can be designed differently. - -.. code-block:: python - - nosecone = calisto.add_nose(length=0.55829, kind="vonKarman", position=0.71971 + 0.55829) - - fin_set = calisto.add_trapezoidal_fins( - n=4, - root_chord=0.120, - tip_chord=0.040, - span=0.100, - position=-1.04956, - cant_angle=0, - radius=None, - airfoil=None, - ) - - tail = calisto.add_tail( - top_radius=0.0635, bottom_radius=0.0435, length=0.060, position=-1.194656 - ) - -If you are considering the parachutes in the simulation, they also have to be added to the rocket object. -A trigger function must be supplied to trigger the parachutes. -Currently, the pressure `(p)`, the height above ground level considering noise `(h)`, and the state-space variables `(y)` are necessary inputs for the function. -The state-space contains information about the rocket's position and velocities (translation and rotation). -For example: - -.. code-block:: python - - def drogue_trigger(p, h, y): - # p = pressure considering parachute noise signal - # h = height above ground level considering parachute noise signal - # y = [x, y, z, vx, vy, vz, e0, e1, e2, e3, w1, w2, w3] - - # activate drogue when vz < 0 m/s. - return True if y[5] < 0 else False - - - def main_trigger(p, h, y): - # p = pressure considering parachute noise signal - # h = height above ground level considering parachute noise signal - # y = [x, y, z, vx, vy, vz, e0, e1, e2, e3, w1, w2, w3] - - # activate main when vz < 0 m/s and z < 800 m - return True if y[5] < 0 and h < 800 else False - -After having the trigger functions defined, the parachute must be added to the rocket: - -.. code-block:: python - - Main = calisto.add_parachute( - "Main", - cd_s=10.0, - trigger=main_trigger, - sampling_rate=105, - lag=1.5, - noise=(0, 8.3, 0.5), - ) - - Drogue = calisto.add_parachute( - "Drogue", - cd_s=1.0, - trigger=drogue_trigger, - sampling_rate=105, - lag=1.5, - noise=(0, 8.3, 0.5), - ) - -Simulating the flight ---------------------- - -Finally, the flight can be simulated with the provided data. -The rocket and environment classes are supplied as inputs, as well as the rail length, inclination and heading angle. - -.. code-block:: python - - test_flight = Flight(rocket=calisto, environment=env, rail_length=5.2, inclination=85, heading=0) - -Flight data can be retrieved through: - -``test_flight.info()`` or ``test_flight.all_info()`` - -This function plots a comprehensive amount of flight data and graphs but, if you want to access one specific variable, for example Z position, this may be achieved by `test_flight.z`. -If you insert `test_flight.z()` the graph of the function will be plotted. -This and other features can be found in the documentation of the `Function` class, which allows data to be treated in an easier way. -The documentation of each variable used in the class can be found on `Flight.py` file. - -Further considerations -====================== - -RocketPy's classes documentation can be accessed in code via `help()` command. -For example, to access Flight class parameters, you can use: - -.. code-block:: python - - help(Flight) - -More documentation materials can be found at `read the docs `_. -It can also be found on RocketPy's GitHub page on the badge "docs". \ No newline at end of file diff --git a/docs/development/setting_up.rst b/docs/development/setting_up.rst new file mode 100644 index 000000000..9bf0981fd --- /dev/null +++ b/docs/development/setting_up.rst @@ -0,0 +1,122 @@ +Setting up RocketPy as a Developer +================================== + +Your first step to contribute to RocketPy is to set up your development environment. +If you are an experienced developer, you may skip to the final sections of this page. + +Forking the repository +---------------------- + +First you need to fork the RocketPy repository. You can do this by clicking +the **Fork** button on the top right corner of the repository page on GitHub. + +By default, the fork will only have the ``master`` branch. +You should also fork the ``develop`` branch. +You can do this by **disabling** the "Copy the master branch only" option when +forking. + +.. important:: + + Our ``master`` branch is the stable branch, and the ``develop`` branch is the \ + development branch. You should always create your PRs against the ``develop`` \ + branch, unless it is a really important hotfix. + +Alternatively, one may clone the repository directly from the RocketPy repository +and then add the fork as a remote. +However, this option is less frequently used. + +Cloning the Repository +---------------------- + +Next step is to clone the repository to your local machine. +There are different ways to do this, but most of them will involve the following command: + +.. code-block:: bash + + git clone https://github.com//RocketPy.git + + +After cloning the repository, you will have a copy of the RocketPy repository on your \ +local machine and, by default, you will be on the ``master`` branch. + +.. tip:: + + Cloning a repo means downloading the repository to your local machine and \ + creating a local copy of it. We call this local copy a "local repository", \ + and the original repository is called the "remote repository". + + When you clone a repository, you are creating a connection between your local \ + repository and the remote repository. This connection allows you to push and \ + pull changes between the two repositories. + +Navigating through the project +------------------------------ + +In order to work on your local repository, you will need to open the directory where you \ +cloned it. We recommend using VS Code as your editor, but you can use any editor you prefer. +If you are using VS Code, you can open the project by running the following command: + +.. code-block:: console + + code + +After opening the project for the first time, you will be at the ``master`` branch. +To switch to another branch, such as the ``develop`` branch, you will need to fetch \ +it from the remote repository and then checkout to it. + +.. code-block:: console + + git fetch origin develop + git checkout develop + + +Installation +------------ + +We highly recommend creating a python virtual environment first. +However, we will not describe how to do this here, since this is a common task. +Run the commands below inside Rocketpy folder to install the library and +development requirements: + +.. code-block:: console + + pip install -e . # install the rocketpy lib in editable mode + pip install -r requirements-optional.txt # install optional requirements + pip install -r requirements-tests.txt # install test/dev requirements + +.. tip:: + + When installing the ``rocketpy`` library, the requirements listed in the \ + ``requirements.txt`` file will automatically be installed. + +Running the tests +----------------- + +One simple sanity check you can do after installing rocketpy is to run the unit and integration tests. +One may achieve this by running the following commands: + +Running all tests: + +.. code-block:: console + + make pytest + +Running the slow tests only: + +.. code-block:: console + + make pytest-slow + +Creating a .html coverage report, where you could see the coverage of the tests: + +.. code-block:: console + + make coverage-report + + +.. note:: + + The slow tests are the tests marked with the ``@pytest.mark.slow`` decorator. \ + These tests are usually the ones that take longer to run, and therefore are \ + not run by default. More about the tests can be found in the \ + :ref:`testing_guidelines` section diff --git a/docs/development/style_guide.rst b/docs/development/style_guide.rst index de738d53a..510a49560 100644 --- a/docs/development/style_guide.rst +++ b/docs/development/style_guide.rst @@ -1,31 +1,41 @@ Style Guide =========== -This page describes code, documentation and git branching styles used throughout RocketPy's development. +This page describes code, documentation and git branching styles used throughout +RocketPy's development. Code Style ---------- -We mostly follow the standard Python style conventions as described here: `Style Guide for Python Code `_ +We mostly follow the standard Python style conventions as described here: +`Style Guide for Python Code `_ Naming Conventions ^^^^^^^^^^^^^^^^^^ -RocketPy was, unfortunately, initially coded using `Camel Case `_. +RocketPy was, unfortunately, initially coded using +`camelCase `_. However, PEP8 specifies the following:: - Function names should be lowercase, with words separated by underscores as necessary to improve readability. + Function names should be lowercase, with words separated by underscores as \ + necessary to improve readability. Variable names follow the same convention as function names. mixedCase is allowed only in contexts where that is already the prevailing style (e.g. threading.py), to retain backwards compatibility. -Therefore, `Snake Case `_ is preferred. -For this reason, RocketPy is being converted from Camel to Snake as of version ``1.0.0``. -New contributions should strive to follow Snake case as well. +Therefore, `snake_case `_ is preferred. +For this reason, RocketPy has being fully converted from ``CamelCase`` to ``snake_case`` as of version ``1.0.0``. -Furthermore, when it comes to naming new variables, functions, classes or anything in RocketPy, always try to use descriptive names. +.. important:: -As an example, instead of using `a` or `alpha` as a variable for a rocket's angle of attack, `angle_of_attack` is preferred. -Such descriptive names make the code significantly easier to understand, review and maintain. + New contributions should only follow ``snake_case`` naming conventions. + +Furthermore, when it comes to naming new variables, functions, classes or +anything in RocketPy, always try to use descriptive names. + +As an example, instead of using ``a`` or ``alpha`` as a variable for a rocket's +angle of attack, ``angle_of_attack`` is preferred. +Such descriptive names make the code significantly easier to understand, review +and maintain. In summary: @@ -38,18 +48,37 @@ In summary: angle_of_attack = 0.2 # in rad -Linting -^^^^^^^ -As far as line wrapping, parentheses, calling chains and other code style related matter goes, RocketPy currently employs `Black style `_. +Linting and formatting +^^^^^^^^^^^^^^^^^^^^^^ + +As far as line wrapping, parentheses, calling chains and other code style +related matter goes, RocketPy currently employs `Black style `_. Installing Black and running it before submitting a pull request is highly recommend. Currently, there are pull request tests in place to enforce that Black style is used. +Aside from Black, RocketPy also uses `Flake8 `_ +and `Pylint `_ to check for code style issues. + +Running these commands before submitting a pull request is also highly recommended: + +.. code-block:: bash + + make format + make flake8 + make pylint + +These commands will check for any code style issues in the codebase. +The ``flake8`` command will throw a report directly to the console, while the +``pylint`` command will create a ``.pylint_report.txt`` file in the root directory, +which you can open to see the report. Documentation Style ------------------- -Every class, method, attribute and function added to RocketPy should be well documented using docstrings. -RocketPy follows NumPy style docstrings, which are very well explained here: `NumPyDoc Style Guide `_. +Every class, method, attribute and function added to RocketPy should be well +documented using docstrings. +RocketPy follows NumPy style docstrings, which are very well explained here: +`NumPyDoc Style Guide `_. Git Style --------- @@ -86,7 +115,12 @@ Here are a couple of example branch names: Once you are ready to create a Pull Request for your branch, we advise you to merge with the ``develop`` branch instead of the default ``master`` branch. This way, we keep the ``master`` branch stable and use the ``develop`` branch to test out new features! -If you have any doubts, just open an issue! And don't forget that these are recommendations. Don't let them keep you from contributing. +.. important:: + + If you have any doubts, just open an issue or ask in our Discord server. \ + And don't forget that these are recommendations. \ + Don't let them keep you from contributing. + Commit Messages ^^^^^^^^^^^^^^^ @@ -103,9 +137,8 @@ Commit messages should be clear and follow a few basic rules. Example:: Describing the motivation for a change, the nature of a bug for bug fixes or some details on what an enhancement does are also good to include in a commit -message. Messages should be understandable without looking at the code -changes. A commit message like ``MNT: fixed another one`` is an example of -what not to do; the reader has to go look for context elsewhere. +message. +Messages should be understandable without looking at the code changes. Standard acronyms to start the commit message with are:: @@ -120,3 +153,27 @@ Standard acronyms to start the commit message with are:: STY: style fix (whitespace, PEP8) TST: addition or modification of tests REL: related to releasing RocketPy + +.. note:: + + A commit message like ``MNT: fixed another one`` is an example of what not to do; \ + the reader has to go look for context elsewhere. + +Pull Requests +^^^^^^^^^^^^^ + +When opening a Pull Request, the name of the PR should be clear and concise. +Similarly to the commit messages, the PR name should start with an acronym indicating the type of PR +and then a brief description of the changes. + +Here is an example of a good PR name: + +- ``BUG: fix the Frequency Response plot of the Flight class`` + +The PR description explain the changes and motivation behind them. There is a template \ +available when opening a PR that can be used to guide you through the process of both \ +describing the changes and making sure all the necessary steps were taken. Of course, \ +you can always modify the template or add more information if you think it is necessary. + + + diff --git a/docs/development/testing.rst b/docs/development/testing.rst index 032287ee3..fd521167b 100644 --- a/docs/development/testing.rst +++ b/docs/development/testing.rst @@ -1,3 +1,5 @@ +.. _testing_guidelines: + Testing Guidelines ================== @@ -68,6 +70,49 @@ Do not get caught by the size of that docstring. The only requirements it has to that the docstring contains precise information on the expected behaviour and/or behaviour to be tested. +Testing Structure +----------------- + +In order to keep the tests easily readable and maintainable, RocketPy encourages +the use of the AAA pattern (Arrange, Act, Assert) for structuring the tests. + +* **Arrange:** Set up the necessary preconditions and inputs (often done by *Fixtures* as it will be described below); +* **Act:** Execute the code under test; +* **Assert:** Verify that the code under test behaves as expected. + +The following example illustrates the AAA pattern: + +.. code-block:: python + + @pytest.mark.parametrize( + "latitude, longitude", [(-21.960641, -47.482122), (0, 0), (21.960641, 47.482122)] + ) # Arrange: Done by the fixtures and the parameters of the test + def test_location_set_location_saves_location(latitude, longitude, example_plain_env): + """Tests location is saved correctly in the environment obj. + + Parameters + ---------- + example_plain_env : rocketpy.Environment + latitude: float + The latitude in decimal degrees. + longitude: float + The longitude in decimal degrees. + """ + # Act: Set the location + example_plain_env.set_location(latitude, longitude) + # Assert: Check if the location was saved correctly + assert example_plain_env.latitude == latitude + assert example_plain_env.longitude == longitude + +This pattern is a general guideline, of course specific tests cases might +modify it to better fit the specific needs of the test. + +.. note:: + + Parameterization is a powerful feature of ``pytest.mark.parametrize`` that allows + you to run the same test with different inputs. This is highly recommended when + there multiple testing scenarios for the same method. + Directory Structure ------------------- @@ -234,7 +279,7 @@ This test contains two fundamental traits which defines it as an integration tes The motivation behind lies in the fact that it interacts and calls too many methods, being too broad to be considered an unit test. -Please be aware that Integration tests are not solely classfied when interacting with external dependencies, +Please be aware that Integration tests are not solely classified when interacting with external dependencies, but also encompass verifying the interaction between classes or too many methods at once, such as ``all_info()``. Further clarification: Even if the test contains traits of unit tests and use dependencies which are stable, such as @@ -305,5 +350,6 @@ documenting purposes, such as below: [[-10. 10.]] """ -This is not common practice, but it is optional and can be done. RocketPy however encourages -the use of other means to test its software, as described. +This is not common practice, but it is optional and can be done, specially to provide +an usage example for the function under testing. RocketPy however encourages the use +of other means to test its software, as described. diff --git a/docs/requirements.in b/docs/requirements.in index ea736a11a..b5138ee0c 100644 --- a/docs/requirements.in +++ b/docs/requirements.in @@ -6,3 +6,4 @@ ipykernel==6.29.4 pandas==2.2.2 lxml_html_clean==0.1.1 sphinx-copybutton==0.5.2 +sphinx-tabs==3.4.7 diff --git a/docs/requirements.txt b/docs/requirements.txt index a5d9c279d..c56c20cae 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -43,6 +43,7 @@ docutils==0.20.1 # nbsphinx # pydata-sphinx-theme # sphinx + # sphinx-tabs entrypoints==0.4 # via nbconvert executing==2.0.1 @@ -159,6 +160,7 @@ pygments==2.18.0 # nbconvert # pydata-sphinx-theme # sphinx + # sphinx-tabs python-dateutil==2.9.0.post0 # via # jupyter-client @@ -195,10 +197,13 @@ sphinx==7.3.7 # pydata-sphinx-theme # sphinx-copybutton # sphinx-design + # sphinx-tabs sphinx-copybutton==0.5.2 # via -r requirements.in sphinx-design==0.5.0 # via -r requirements.in +sphinx-tabs==3.4.7 + # via -r requirements.in sphinxcontrib-applehelp==1.0.8 # via sphinx sphinxcontrib-devhelp==1.0.6 diff --git a/docs/user/motors/motors.rst b/docs/user/motors/motors.rst index a6bc6ef4b..b690cf9e8 100644 --- a/docs/user/motors/motors.rst +++ b/docs/user/motors/motors.rst @@ -1,3 +1,5 @@ +.. _motors_usage: + Motors Usage ============ @@ -12,13 +14,13 @@ Motors Usage :caption: Solid Motors Solid Motor Usage - + .. toctree:: :maxdepth: 3 :caption: Hybrid Motors Hybrid Motor Usage - + .. toctree:: :maxdepth: 3 :caption: Liquid Motors @@ -30,7 +32,7 @@ Motors Usage :caption: Generic Motors Generic Motor Usage - + .. toctree:: :maxdepth: 3 :caption: Tanks and Fluid From ab6e44070f36d0950171fc158aec573d1a762ee8 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR <63590233+Gui-FernandesBR@users.noreply.github.com> Date: Sat, 30 Nov 2024 00:46:52 -0300 Subject: [PATCH 29/36] DOC: improvements to developers documentation (#732) * DOC: improvements to developers documentation * MNT: git rename a few files from the root directory * DOC: more upgrades to development docs * DEV: create script to delete local branches that no longer exist on remote * DOC: I followed the step by step, looked at other parts of the documentation, and didn't find anything to correct or improve. I think it's fine as it is. I added a model to requirements.txt, which was missing when I ran the make html command. * DOC: Update developer docs on sphinx building. * DOC: Clarify testing structure on testing docs. * DOC: Re-run docs requirements freeze. * DOC: Introduce Conflicts Guideline and update First PR. * DOC: fixing typos on development docs. * DOC: Solve review comments of dev docs. * DOC: final touches to developers documentation * DOC: update cSpell configuration to enable additional file types * DEV: move docker ignore file --------- Co-authored-by: Julio Machado Co-authored-by: Pedro Bressan Co-authored-by: Lucas de Oliveira Prates --- CITATION.cff => .github/CITATION.cff | 0 CODEOWNERS => .github/CODEOWNERS | 0 .../CODE_OF_CONDUCT.md | 0 CONTRIBUTING.md => .github/CONTRIBUTING.md | 0 .github/clear-local-branches.ps1 | 2 + .vscode/settings.json | 14 +- .dockerignore => docker/.dockerignore | 0 Dockerfile => docker/Dockerfile | 0 .../docker-compose.yml | 0 docs/conf.py | 1 + docs/development/build_docs.rst | 61 ++-- docs/development/conflicts.rst | 123 ++++++++ docs/development/docker.rst | 42 +-- docs/development/first_pr.rst | 139 +++++++++ docs/development/index.rst | 19 +- docs/development/pro_tips.rst | 99 +++++++ docs/development/rocketpy_as_developer.rst | 277 ------------------ docs/development/setting_up.rst | 122 ++++++++ docs/development/style_guide.rst | 95 ++++-- docs/development/testing.rst | 52 +++- docs/requirements.in | 1 + docs/requirements.txt | 5 + docs/user/motors/motors.rst | 8 +- 23 files changed, 699 insertions(+), 361 deletions(-) rename CITATION.cff => .github/CITATION.cff (100%) rename CODEOWNERS => .github/CODEOWNERS (100%) rename CODE_OF_CONDUCT.md => .github/CODE_OF_CONDUCT.md (100%) rename CONTRIBUTING.md => .github/CONTRIBUTING.md (100%) create mode 100644 .github/clear-local-branches.ps1 rename .dockerignore => docker/.dockerignore (100%) rename Dockerfile => docker/Dockerfile (100%) rename docker-compose.yml => docker/docker-compose.yml (100%) create mode 100644 docs/development/conflicts.rst create mode 100644 docs/development/first_pr.rst create mode 100644 docs/development/pro_tips.rst delete mode 100644 docs/development/rocketpy_as_developer.rst create mode 100644 docs/development/setting_up.rst diff --git a/CITATION.cff b/.github/CITATION.cff similarity index 100% rename from CITATION.cff rename to .github/CITATION.cff diff --git a/CODEOWNERS b/.github/CODEOWNERS similarity index 100% rename from CODEOWNERS rename to .github/CODEOWNERS diff --git a/CODE_OF_CONDUCT.md b/.github/CODE_OF_CONDUCT.md similarity index 100% rename from CODE_OF_CONDUCT.md rename to .github/CODE_OF_CONDUCT.md diff --git a/CONTRIBUTING.md b/.github/CONTRIBUTING.md similarity index 100% rename from CONTRIBUTING.md rename to .github/CONTRIBUTING.md diff --git a/.github/clear-local-branches.ps1 b/.github/clear-local-branches.ps1 new file mode 100644 index 000000000..f06055c08 --- /dev/null +++ b/.github/clear-local-branches.ps1 @@ -0,0 +1,2 @@ +# Delete local branches that no longer exists on remote (windows command line) +git branch -vv | Where-Object { $_ -match 'gone\]' } | ForEach-Object { $_.Trim().Split()[0] } | ForEach-Object { git branch -D $_ } diff --git a/.vscode/settings.json b/.vscode/settings.json index 62481957c..98010d539 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -5,12 +5,14 @@ "python.testing.unittestEnabled": false, "python.testing.pytestEnabled": true, "autoDocstring.docstringFormat": "numpy", - "cSpell.enableFiletypes": [ - "python", - "jupyter", - "markdown", - "restructuredtext" - ], + "cSpell.enabledFileTypes": { + "plaintext": true, + "python": true, + "jupyter": true, + "markdown": true, + "tex": true, + "rst": true + }, "cSpell.language": "en-US", "cSpell.words": [ "Abdulklech", diff --git a/.dockerignore b/docker/.dockerignore similarity index 100% rename from .dockerignore rename to docker/.dockerignore diff --git a/Dockerfile b/docker/Dockerfile similarity index 100% rename from Dockerfile rename to docker/Dockerfile diff --git a/docker-compose.yml b/docker/docker-compose.yml similarity index 100% rename from docker-compose.yml rename to docker/docker-compose.yml diff --git a/docs/conf.py b/docs/conf.py index e834d180e..9324ad9a6 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -40,6 +40,7 @@ "sphinx.ext.napoleon", "sphinx.ext.viewcode", "sphinx.ext.mathjax", + "sphinx_tabs.tabs", "sphinx_copybutton", "sphinx_design", "jupyter_sphinx", diff --git a/docs/development/build_docs.rst b/docs/development/build_docs.rst index f94c4f0fb..398781399 100644 --- a/docs/development/build_docs.rst +++ b/docs/development/build_docs.rst @@ -31,64 +31,67 @@ requirements that are needed to run the sphinx generator. All these requirements are listed in the ``requirements.txt`` file inside the ``docs`` folder. -To install the requirements, navigate the terminal to the ``docs`` folder and -run the following command: +The easiest way to install the requirements and build the documentation is by +RocketPy's ``Makefile`` command: .. code-block:: bash - - pip install -r requirements.txt -After installing the requirements, you can build the documentation by running -the following command in your terminal: + make build-docs -.. code-block:: bash - - make html +This command will install the requirements and generate the documentation in the +``docs/_build/html`` folder. + +.. note:: -The file named ``Makefile`` contains the commands to build the documentation. -The ``make html`` command will generate the documentation in the ``docs/_build/html`` -folder. + This requires the ``make`` command to be available in your system. Check out + GNU Make documentation on how to install it. To see the documentation, open the ``docs/_build/html/index.html`` file in your browser. -.. note:: Watch out for any warnings or errors that may appear in the terminal - when building the documentation. If you find any, fix them before - pushing the changes to the repository or at least warn the team about - them. +.. important:: + + Watch out for any introduced warnings or errors that may appear in the terminal + when building the documentation. If you find any, fix them before + pushing the changes to the repository or at least warn the team about + them. Sometimes you may face problems when building the documentation after several -times of building it. -This may happen because sphinx does not clean the ``docs/_build`` folder before -building the documentation again. -To clean the ``docs/_build`` folder, run the following command in your terminal: +times of building it. This may happen because sphinx does not clean the ``docs/_build`` +folder before building the documentation again. + +One can use the internal Makefile inside the ``docs`` folder generated by sphinx +to have specific commands to build and clean the documentation. + +To clean the ``docs/_build`` folder, run the following commands in your terminal: .. code-block:: bash + cd docs/ make clean After cleaning the ``docs/_build`` folder, you can build the documentation again -by running the ``make html`` command. +by going back to rocketpy root directory and using the ``make build-docs`` command +described above. If the error persists, it may be related to other files, such as the ``.rst`` files or the ``conf.py`` file. .. danger:: - Do not modify the Makefile or the ``make.bat`` files. These files are \ - automatically generated by sphinx and any changes will be lost. + Do not modify sphinx internal Makefile or the ``docs/make.bat`` files. + These files are automatically generated by sphinx and any changes will be lost. How to integrate the documentation with ReadTheDocs --------------------------------------------------- The documentation is automatically built and hosted by `ReadTheDocs`. -Every time a commit is pushed to the repository, `ReadTheDocs` will build the -documentation and host it automatically. -This includes other branches besides the master branch. -However, the documentation will only be available for the master branch, and you -need to configure the `ReadTheDocs` project to build the documentation for other -branches. +Every time a commit is pushed to selected branches of the repository (such as `master` and `develop`), +`ReadTheDocs` will build the documentation and host it automatically. + +However, custom branches need to be manually added. If you need to have your branch +available on `ReadTheDocs`, ask to a maintainer for help. The connection between the GitHub repository and the `ReadTheDocs` project is already configured and defined in the ``readthedocs.yml`` file, available at the diff --git a/docs/development/conflicts.rst b/docs/development/conflicts.rst new file mode 100644 index 000000000..dbdb09e61 --- /dev/null +++ b/docs/development/conflicts.rst @@ -0,0 +1,123 @@ +Solving Git Conflicts +===================== + +What Are Git Conflicts? +----------------------- + +Git conflicts occur when Git is unable to automatically merge changes made to the \ +same lines of code or when changes overlap in a file across branches. +This often occurs in RocketPy repository when two developers make changes to the same \ +file and try to merge them. +In this scenario, Git pauses the operation and marks the conflicting files, \ +requiring manual intervention. + +Merge vs Rebase +--------------- + +**Merge** + +The ``merge`` operation combines the changes from one branch into another. \ +It creates a new commit that marks the merging of the two branches together. + +* Retains the full history of both branches. +* Non-linear history shows how changes were combined. +* The history can become cluttered if there are many branches, due to the number of merge commits. + +**Rebase** + +The ``rebase`` operation integrates changes from one branch into another by reapplying \ +the commits on top of the target branch. It results in a more linear history. + +* Produces a cleaner, linear commit history. +* Easier to follow the sequence of changes. +* Can rewrite history, leading to potential issues when working on shared branches. + +Example Commands +---------------- + +Let's consider a common scenario from which conflicts can arise: +updating a feature branch with the latest changes from the branch it was created. +Both ``merge`` and ``rebase`` can be used to update the feature branch. +However, the ``rebase`` option is highly recommended to keep a more linear history. + +Merge +~~~~~ + +Suppose you have two branches, ``enh/feature`` that was branched off ``develop``. +It is likely that ``develop`` has been updated since you branched off ``enh/feature``, \ +therefore before merging ``enh/feature`` into ``develop``, you should update ``enh/feature`` \ +with the latest changes from ``develop``. + +One way to do this is by merging ``develop`` into ``enh/feature`` as follows: + +.. code-block:: console + + git checkout develop + git pull + git checkout enh/feature + git merge develop + +If there are conflicts, Git will pause the operation and mark the conflicting files. \ +VS Code provides a visual interface to resolve these conflicts in the **Merge Editor**. +If you want to abort the merge, you can use ``git merge --abort``. + +Rebase +~~~~~~ + +Similarly, another way to update ``enh/feature`` with the latest changes from ``develop`` \ +is by rebasing ``enh/feature`` onto ``develop`` as follows: + +.. code-block:: console + + git checkout develop + git pull + git checkout enh/feature + git rebase develop + +Differently from merge, if there are conflicts, they must be resolved commit by commit. \ +After resolving each step conflicts, you can continue the rebase with ``git rebase --continue``. \ +If you want to abort the rebase, you can use ``git rebase --abort``. + +When to Use Merge or Rebase +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Generally, the maintainers will inform you which operation to use when merging your PR. \ +Notice that there is no wrong way to merge branches, but ``rebase`` is usually preferred \ +to keep a more linear history. + +Furthermore, it is important to avoid letting conflicts accumulate, since they can become \ +harder to resolve. It is recommended to update your feature branch frequently with the latest \ +changes from the branch it was branched off. + +Solving Conflicts +----------------- + +When conflicts arise, Git marks the conflicting files. The file will contain markers like: + +.. code-block:: console + + <<<<<<< HEAD + Current branch changes + ======= + Incoming branch changes + >>>>>>> branch-name + +The ``HEAD`` section contains the changes from the current branch, while the ``branch-name`` section \ +contains the changes from the incoming branch. +The ``=======`` line separates the two sections. +One can manually edit the file to resolve the conflict, removing the markers and keeping the desired changes, however \ +for convenience it is recommended to use a visual tool like the *Merge Editor* in VS Code. + +Resolving Conflicts in VS Code +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +When a conflict occurs, VS Code will open the *Merge Editor* to help you resolve it.\ + +1. Open the conflicting file (marked with a ``!``). +2. The *Merge Editor* will show the conflicting sections side by side. +3. Click on the ``Accept Current Change`` or ``Accept Incoming Change`` buttons to keep the desired changes, sometimes both changes will be kept or even a manual edit will be necessary. + +More details on VS Code interface and conflict solver can be found in `VS Code Docs `_. +After resolving the conflicts, save the files, make sure all conflicts are resolved, and then \ +commit the changes. + diff --git a/docs/development/docker.rst b/docs/development/docker.rst index 91aafb1d2..bc1de8745 100644 --- a/docs/development/docker.rst +++ b/docs/development/docker.rst @@ -2,7 +2,7 @@ RocketPy with docker ===================== RocketPy does not provide an official docker image, but you can build one -yourself using the provided `Dockerfile` and `docker-compose.yml` files. +yourself using the provided ``Dockerfile`` and ``docker-compose.yml`` files. Benefits -------- @@ -14,13 +14,13 @@ using docker are: worrying about dependencies. 2. **Portability**: run RocketPy on any operational system that supports docker, including the 3 main operational systems (Windows, Linux and Mac). -3. **Reproducibility**: ensure that tour code is working regardless of the +3. **Reproducibility**: ensure that our code is working regardless of the operational system. Using docker will be specially important when you are not sure if the code additions will still run on different operational systems. -Although we have a set of GitHub actions to test the code on different +Although we have a set of GitHub actions to test the code on different operational systems every time a pull request is made, it is important to submit a PR only after you are sure that the code will run flawlessly, otherwise quota limits may be reached on GitHub. @@ -28,7 +28,7 @@ otherwise quota limits may be reached on GitHub. Requirements ------------- -Before you start, you need to install on your machine: +Before you start, you need to install on your machine: 1. `Docker `__, to build and run the image. 2. `Docker Compose `__, to compose multiple images at once. @@ -40,11 +40,11 @@ Build the image To build the image, run the following command on your terminal: .. code-block:: console - + docker build -t rocketpy-image -f Dockerfile . -This will build the image and tag it as `rocketpy-image` (you can apply another +This will build the image and tag it as ``rocketpy-image`` (you can apply another name of your preference if you want). An image is a read-only template with instructions for creating a Docker @@ -82,7 +82,7 @@ Run the unit tests -------------------- You might have noticed that the container is running in an isolated environment -with no access to your machine's files, but the `Dockerfile` already copied the +with no access to your machine's files, but the ``Dockerfile`` already copied the RocketPy repository to the container. This means that you can run tests (and simulations!) as if you were running RocketPy on your machine. @@ -90,37 +90,37 @@ RocketPy on your machine. As simple as that, you can run the unit tests: .. code-block:: console - + pytest To access a list of all available execution options, see the `pytest docs `__. -Compose docker images +Compose docker images --------------------- -We also made available a `docker-compose.yml` file that allows you to compose +We also made available a ``docker-compose.yml`` file that allows you to compose multiple docker images at once. -Unfortunately, this file will not allow you to test the code on different +Unfortunately, this file will not allow you to test the code on different operational systems at once, since docker images inherits from the host operational system. However, it is still useful to run the unit tests on different python versions. -Currently, the `docker-compose.yml` file is configured to run the unit tests +Currently, the ``docker-compose.yml`` file is configured to run the unit tests on python 3.9 and 3.12. To run the unit tests on both python versions, run the following command **on your machine**: .. code-block:: console - + docker-compose up Also, you can check the logs of the containers by running: .. code-block:: console - + docker-compose logs @@ -131,7 +131,7 @@ After you're done testing, or if you wish to stop the containers and remove the services, use the command: .. code-block:: console - + docker-compose down @@ -142,31 +142,31 @@ images created by up. Changing to other operational systems ------------------------------------- -The default image in the `Dockerfile` is based on a Linux distribution. +The default image in the ``Dockerfile`` is based on a Linux distribution. However, you can alter the base image to use different operating systems, though the process may require additional steps depending on the OS's compatibility with your project setup. For instance, certain dependencies or scripts may behave differently or require different installation procedures, so use it with caution. -To change the base image, you will need to modify the `FROM` statement in the -`Dockerfile`. +To change the base image, you will need to modify the ``FROM`` statement in the +``Dockerfile``. For example, to use a Windows-based image, you might change: .. code-block:: Dockerfile - + FROM python:latest to .. code-block:: Dockerfile - + FROM mcr.microsoft.com/windows/servercore:ltsc2019 Please note, the above is just an example, and using a different OS may require -further adjustments in the `Dockerfile`. +further adjustments in the ``Dockerfile``. We recommend you to see the official Python images on the Docker Hub for different OS options: `Docker Hub Python Tags `__. diff --git a/docs/development/first_pr.rst b/docs/development/first_pr.rst new file mode 100644 index 000000000..5f3eb876c --- /dev/null +++ b/docs/development/first_pr.rst @@ -0,0 +1,139 @@ +Your first Pull Request (PR) +============================ + +This page describes a few steps to help you get started with your first PR to RocketPy. +We are excited to have you here! + +Picking an issue +---------------- + +Before you start coding, you should pick an issue to work on. You can find a +list of issues in the ``Issues`` tab on GitHub (https://github.com/RocketPy-Team/RocketPy/issues). +If you are new here, it is really recommended that you talk to maintainers +before starting to work on an issue. +That way you avoid working on something that is already being worked on or +something that is not in line with the project's goals. + +If you want to develop a new feature that is not in the issues list, please open +a new issue to discuss it with the maintainers before submitting a PR. + +Once the issue is assigned to you, you can start working on it. + +.. note:: + + In order to open an issue at our repo, you must have a GitHub account. \ + In case you do not want to open an account yet, you can contact the maintainers \ + through our Discord server. But keep in mind that you you need an account to \ + open a Pull Request (PR). + +Creating a new branch +--------------------- + +At your local machine, make sure you already have `git `_ \ +installed and set up. +In order to create a new branch, you should run the following command in your +preferred terminal: + +.. code-block:: console + + git checkout -b + +This command will create a new branch named ``branch_name`` from the current branch you are in. +Your branch name should follow the guidelines described in :doc:`/development/style_guide`. + +.. tip:: + + VS Code has a built-in integration with git, this allows you to run git commands \ + quite easily through the editor's interface. For example, you could search for \ + "git checkout" in the command palette and run the command from there. + + +Opening the PR +-------------- + +After you finish your work, you are more than welcome opening a PR to the RocketPy repository. +Here are some checks to do before opening a PR: + +* Check if the test suite is passing. +* Format your code using ``black`` and ``isort``. + +Check the :doc:`/development/pro_tips` section for more information on how to run these commands. + +When you open a PR, you should: + +* Provide a clear and concise title. +* Fill the PR description following the standard template. +* Use labels to help maintainers understand what the PR is about. +* Link any issue that may be closed when the PR is merged. + +Remember, the PR is yours, not ours! You should keep track of it and update it as needed. + +.. important:: + + See the :doc:`/development/style_guide` for more information on how to name and \ + describe your PR. + +Continuous Integration (CI) +--------------------------- + +There are several automation on our repository to help us maintain the code quality. +Currently, our CI pipeline runs the following checks: + +* **Linting**: we run ``flake8``, ``pylint``, ``black`` and ``isort`` on top of your latest commit in order to check for code style issues. To understand more about these tools, please read the :doc:`/development/pro_tips` section. +* **Testing**: we run the tests defined in the ``tests`` folder to make sure your changes do not break any existing functionality. The tests will be executed 6 times, each time with a different Python version (the oldest and newest supported versions) and with three different operating systems (Windows, Linux and MacOS). +* **Coverage**: based on the tests results, we also check the code coverage results from our test suite. There is an automation to check if the code coverage increased or decreased with your PR. It also points to potential introduced lines of code that should be tested. + +Once you open your PR or commit and push to your branch, the CI will be initialized. +Please correct any issues that may arise from the CI checks. + +.. important:: + + All the commands we run in the CI pipeline can also be run locally. It is \ + important that you run the checks locally before pushing your changes to \ + the repository. + +The CHANGELOG file +------------------ + +We keep track of the changes in the ``CHANGELOG.md`` file. +When you open a PR, you should add a new entry to the "Unreleased" section of the file. +This entry should simply be the title of your PR. + +.. note:: + + In the future we would like to automate the CHANGELOG update, but for now \ + it is a manual process, unfortunately. + + +The review process +------------------ + +After you open a PR, the maintainers will review your code. +This review process is a way to ensure that the code is in line with the +project's goals and that it is well written and documented. + +The maintainers may ask you to make changes to your code. +You should address these changes or explain why you think they are not necessary. +This is the best time to learn from the maintainers and improve your coding skills. + +In case you do not address the comments in a timely manner, the maintainers may +either close the PR or make the changes themselves. + + +Merging the PR +-------------- + +There are 3 different ways of merging a PR: + +1. **Create a merge commit**: this is the default option on GitHub. +2. **Squash and merge**: this option will squash all your commits into a single one. This is useful when you have many commits and you want to keep the history clean, therefore this is the recommended option. +3. **Rebase and merge**: this option will add your commits directly to the target branch, without creating a merge commit. This is useful to keep the history linear, however it also requires handling potential conflicts one at a time, which can be a bit more complex. + +.. note:: + + Overall, you will not have permission to merge your PR. The maintainers will \ + take care of that for you. This is here just for you to understand the process. + +All in all, there is no right or wrong way to merge a PR. +The maintainers will decide which option is the best for the project. +What you should care though is about conflicting changes, let's talk about that next in :doc:`/development/conflicts`. diff --git a/docs/development/index.rst b/docs/development/index.rst index 4837fa375..5d8cc490f 100644 --- a/docs/development/index.rst +++ b/docs/development/index.rst @@ -1,14 +1,27 @@ Contributing to RocketPy ======================== +Here you will find information on how to contribute to our code base. +RocketPy is an open-source project and we welcome contributions from everyone. +There are a few guidelines we would like to share with you to make your +contribution as smooth as possible: + .. toctree:: - :maxdepth: 2 + :maxdepth: 1 :caption: Contents: - Running RocketPy as a Developer + setting_up.rst + Your First Pull Request (PR) + Solving git conflicts Style Guide Testing Guidelines RocketPy with Docker Building the Documentation + Pro Tips + + +.. important:: -This section is still a work in progress. Here you will find information on how to contribute to our code base. \ No newline at end of file + Our Development Documentation matters as much as the Usage Documentation to us. \ + In case you find any trouble when following this guide, please let us know so \ + we can improve it. diff --git a/docs/development/pro_tips.rst b/docs/development/pro_tips.rst new file mode 100644 index 000000000..465ff7982 --- /dev/null +++ b/docs/development/pro_tips.rst @@ -0,0 +1,99 @@ +Pro Tips +======== + +Last but not least, here are some pro tips that can help you to be more +productive when developing in the project (or any other project). + + +Makefile +-------- + +We have a ``Makefile`` in the repository that contains some useful commands to +help you with the development process. + +Some examples of commands are: + +* ``make black``: runs the black formatter on the code. +* ``make format``: runs the isort and black formatters on the code. +* ``make lint``: runs the flake8 and pylint tools. +* ``make build-docs``: This will build the documentation locally, so you can check if everything is working as expected. +* ``make pytest-slow``: runs only the slow tests (marked with the ``@pytest.mark.slow`` decorator). + +These commands are meant to be system agnostic, so you can run them on any +Operational System (OS). + +Knowing your editor +------------------- + +Something we usually notice on beginners is that they don't know all the power +their editor can provide. Knowing your editor can save you a lot of time and +make you more productive. + +As stated earlier, we recommend using VS Code as your editor, as most of the +RocketPy developers use it. Therefore it is easier to help you if you are using +the same editor. + +Some of the features that can help you are: + +1. **Code navigation**: Jump to definitions, find references, etc. +2. **Integrated tests**: Run tests directly from the editor. +3. **Python Debugger**: Debug your code directly from the editor. +4. **GitHub Pull Requests**: Open, review, comment, and merge pull requests directly from the editor + +Extensions +---------- + +We have listed some recommended extensions in the ``.vscode/extensions.json`` file. +These are general recommendations based on what our developers usually install +in their VSCode. +Obviously, it is not mandatory to install them, but they can help you to be more +productive. + + +Code assistance +--------------- + +Artificial Intelligence (AI) assistance has becoming more and more common in +software development. +Some editors have AI assistance built-in. +Famous options are GitHub Copilot, JetBrains AI and TabNine. + +At this repo, the use of AI tools is welcome, we don't have any restrictions +against it. + +A few possible applications of AI tools are: + +* Writing documentation. +* Writing tests. +* Getting explanations of code sections. + +.. tip:: + + As of today, November 2024, GitHub Copilot still provides free access for \ + university email addresses. We can't guarantee this will still be the case \ + when you are reading this, so check the GitHub Copilot website for more \ + information. + + +If you are against the use of AI tools, do not worry, you can still contribute +to the project without using them. + + +Engaging with the community +--------------------------- + +The most important part of contributing to an open-source project is engaging +with the community. +Our developers are frequently available on our server, and you can +ask any questions you may have there. +Either a question about the project, or a question about how to contribute, or +even a suggestion of a new feature. +Any kind of interaction is welcome. + + +.. tip:: + + The official supported language in our server is English. \ + However, the RocketPy Team is lucky to have developers from all around the \ + world, so you may find people speaking other languages as well. \ + Don't let the language barrier keep you from engaging with the community. diff --git a/docs/development/rocketpy_as_developer.rst b/docs/development/rocketpy_as_developer.rst deleted file mode 100644 index df3608c77..000000000 --- a/docs/development/rocketpy_as_developer.rst +++ /dev/null @@ -1,277 +0,0 @@ -======================== -Introduction to RocketPy -======================== - -This tutorial part shows how to open rocketpy files and run a simple simulation. - -Opening rocketpy folder -======================= - -Go into the cloned repository folder by typing on a terminal - -.. code-block:: console - - cd - -Open your preference editor by typing on a terminal - -.. code-block:: console - - . - -For example, to open VS Code type on terminal - -.. code-block:: console - - code . - -Alternatively, you can open the folder directly through your editor's interface. - -Preparing directory for code editing -==================================== - -You may create a testing file in any directory, but you must remember that they -should not be included in the commits and pull requests unless they are part of -the proposed solution. -With that in mind, we suggest you to create a folder with all testing files, so -they can be added in the ``.gitignore`` file, which contains the name of all the -files and folders that will not be added to the commits. -To create the folder, type on the terminal: - -.. code-block:: console - - mkdir - -And, to add it on .gitignore, type: - -.. code-block:: console - - echo / >> .gitignore - -It is important to remember that all the files inside this folder will not be included in any commit so, if it is important to the solution, do not add them inside it. - -Running a simulation with RocketPy -================================== - -Importing the RocketPy files ----------------------------- - -First, create a python (or .ipynb) file to make the simulation. -To ensure you are using the local files and not the files as a python package (if you installed the library via pip for example), add - -.. code-block:: python - - pip install -e . - -Alternatively you can use the following command to pip install the local library: - -.. code-block:: console - - import sys - sys.path.append('../') # if you are using a notebook - sys.path.append('../rocketpy') # if you are using a script - -Import the classes that will be used, in case: - -.. code-block:: python - - from rocketpy import Environment, SolidMotor, Rocket, Flight, Function - -If it is the first time you are using rocketpy and you do not have all required libraries installed, you could use the command: - -.. code-block:: python - - pip install -r - -Alternatively, if you are in rocketpy folder, just type - -.. code-block:: python - - pip install -r requirements.txt - -Creating an Environment ------------------------ - -Here we create the environment object that will be used in the simulation. -It contains information about the local pressure profile, temperature, speed of sound, wind direction and intensity, etc. - -.. code-block:: python - - env = Environment(latitude=32.990254, longitude=-106.974998, elevation=1400) - -RocketPy can use local files via the Ensemble method or meteorological forecasts through OpenDAP protocol. -To work with environment files, it will be very important ensuring tha that you have the netCDF4 library installed. -Assuming we are using forecast, first we set the simulated data with: - -.. code-block:: python - - import datetime - tomorrow = datetime.date.today() + datetime.timedelta(days=1) - env.set_date((tomorrow.year, tomorrow.month, tomorrow.day, 12)) # Hour given in UTC time - -Then we set the atmospheric model, in this case, GFS forecast: - -.. code-block:: python - - env.set_atmospheric_model(type="Forecast", file="GFS") - -Weather forecast data can be visualized through two info methods. - -``env.info()`` or ``env.all_info()`` - -Creating the motor that boosts the rocket ------------------------------------------ - -Now we need to create the motor. -For example, we will use a solid motor called Pro75M1670, but other configurations are also possible. -The motor class contains information about the thrust curve and uses some geometric parameters to calculate the mass variation over time, as well as the total thrust and other important outputs. - -.. code-block:: python - - Pro75M1670 = SolidMotor( - thrust_source="../data/motors/cesaroni/Cesaroni_M1670.eng", #copy here the path to the thrust source file - burn_time=3.9, - grain_number=5, - grain_separation=5 / 1000, - grain_density=1815, - grain_outer_radius=33 / 1000, - grain_initial_inner_radius=15 / 1000, - grain_initial_height=120 / 1000, - nozzle_radius=33 / 1000, - throat_radius=11 / 1000, - interpolation_method="linear", - ) - -Motor data can be visualized through the following methods: - -``Pro75M1670.info()`` or ``Pro75M1670.all_info()`` - - -Creating the rocket -------------------- - -The Rocket class contains all information about the rocket that are necessary to the simulation, including the motor, rocket mass and inertia, aerodynamic surfaces, parachutes, etc. -The first step is to initialize the class with the vital data: - -.. code-block:: python - - calisto = Rocket( - radius=127 / 2000, - mass=19.197 - 2.956, - inertia_i=6.60, - inertia_z=0.0351, - power_off_drag="../../data/rockets/calisto/powerOffDragCurve.csv", - power_on_drag="../../data/rockets/calisto/powerOnDragCurve.csv", - center_of_dry_mass_position=0, - coordinate_system_orientation="tail_to_nose", - ) - - calisto.add_motor(Pro75M1670, position=-1.255) - -Then the rail buttons must be set: - -.. code-block:: python - - calisto.set_rail_buttons(0.2, -0.5) - -In sequence, the aerodynamic surfaces must be set. -If a lift curve for the fin set is not specified, it is assumed that they behave according to a linearized model with a coefficient calculated with Barrowman's theory. -In the example, a nosecone, one fin set and one tail were added, but each case can be designed differently. - -.. code-block:: python - - nosecone = calisto.add_nose(length=0.55829, kind="vonKarman", position=0.71971 + 0.55829) - - fin_set = calisto.add_trapezoidal_fins( - n=4, - root_chord=0.120, - tip_chord=0.040, - span=0.100, - position=-1.04956, - cant_angle=0, - radius=None, - airfoil=None, - ) - - tail = calisto.add_tail( - top_radius=0.0635, bottom_radius=0.0435, length=0.060, position=-1.194656 - ) - -If you are considering the parachutes in the simulation, they also have to be added to the rocket object. -A trigger function must be supplied to trigger the parachutes. -Currently, the pressure `(p)`, the height above ground level considering noise `(h)`, and the state-space variables `(y)` are necessary inputs for the function. -The state-space contains information about the rocket's position and velocities (translation and rotation). -For example: - -.. code-block:: python - - def drogue_trigger(p, h, y): - # p = pressure considering parachute noise signal - # h = height above ground level considering parachute noise signal - # y = [x, y, z, vx, vy, vz, e0, e1, e2, e3, w1, w2, w3] - - # activate drogue when vz < 0 m/s. - return True if y[5] < 0 else False - - - def main_trigger(p, h, y): - # p = pressure considering parachute noise signal - # h = height above ground level considering parachute noise signal - # y = [x, y, z, vx, vy, vz, e0, e1, e2, e3, w1, w2, w3] - - # activate main when vz < 0 m/s and z < 800 m - return True if y[5] < 0 and h < 800 else False - -After having the trigger functions defined, the parachute must be added to the rocket: - -.. code-block:: python - - Main = calisto.add_parachute( - "Main", - cd_s=10.0, - trigger=main_trigger, - sampling_rate=105, - lag=1.5, - noise=(0, 8.3, 0.5), - ) - - Drogue = calisto.add_parachute( - "Drogue", - cd_s=1.0, - trigger=drogue_trigger, - sampling_rate=105, - lag=1.5, - noise=(0, 8.3, 0.5), - ) - -Simulating the flight ---------------------- - -Finally, the flight can be simulated with the provided data. -The rocket and environment classes are supplied as inputs, as well as the rail length, inclination and heading angle. - -.. code-block:: python - - test_flight = Flight(rocket=calisto, environment=env, rail_length=5.2, inclination=85, heading=0) - -Flight data can be retrieved through: - -``test_flight.info()`` or ``test_flight.all_info()`` - -This function plots a comprehensive amount of flight data and graphs but, if you want to access one specific variable, for example Z position, this may be achieved by `test_flight.z`. -If you insert `test_flight.z()` the graph of the function will be plotted. -This and other features can be found in the documentation of the `Function` class, which allows data to be treated in an easier way. -The documentation of each variable used in the class can be found on `Flight.py` file. - -Further considerations -====================== - -RocketPy's classes documentation can be accessed in code via `help()` command. -For example, to access Flight class parameters, you can use: - -.. code-block:: python - - help(Flight) - -More documentation materials can be found at `read the docs `_. -It can also be found on RocketPy's GitHub page on the badge "docs". \ No newline at end of file diff --git a/docs/development/setting_up.rst b/docs/development/setting_up.rst new file mode 100644 index 000000000..9bf0981fd --- /dev/null +++ b/docs/development/setting_up.rst @@ -0,0 +1,122 @@ +Setting up RocketPy as a Developer +================================== + +Your first step to contribute to RocketPy is to set up your development environment. +If you are an experienced developer, you may skip to the final sections of this page. + +Forking the repository +---------------------- + +First you need to fork the RocketPy repository. You can do this by clicking +the **Fork** button on the top right corner of the repository page on GitHub. + +By default, the fork will only have the ``master`` branch. +You should also fork the ``develop`` branch. +You can do this by **disabling** the "Copy the master branch only" option when +forking. + +.. important:: + + Our ``master`` branch is the stable branch, and the ``develop`` branch is the \ + development branch. You should always create your PRs against the ``develop`` \ + branch, unless it is a really important hotfix. + +Alternatively, one may clone the repository directly from the RocketPy repository +and then add the fork as a remote. +However, this option is less frequently used. + +Cloning the Repository +---------------------- + +Next step is to clone the repository to your local machine. +There are different ways to do this, but most of them will involve the following command: + +.. code-block:: bash + + git clone https://github.com//RocketPy.git + + +After cloning the repository, you will have a copy of the RocketPy repository on your \ +local machine and, by default, you will be on the ``master`` branch. + +.. tip:: + + Cloning a repo means downloading the repository to your local machine and \ + creating a local copy of it. We call this local copy a "local repository", \ + and the original repository is called the "remote repository". + + When you clone a repository, you are creating a connection between your local \ + repository and the remote repository. This connection allows you to push and \ + pull changes between the two repositories. + +Navigating through the project +------------------------------ + +In order to work on your local repository, you will need to open the directory where you \ +cloned it. We recommend using VS Code as your editor, but you can use any editor you prefer. +If you are using VS Code, you can open the project by running the following command: + +.. code-block:: console + + code + +After opening the project for the first time, you will be at the ``master`` branch. +To switch to another branch, such as the ``develop`` branch, you will need to fetch \ +it from the remote repository and then checkout to it. + +.. code-block:: console + + git fetch origin develop + git checkout develop + + +Installation +------------ + +We highly recommend creating a python virtual environment first. +However, we will not describe how to do this here, since this is a common task. +Run the commands below inside Rocketpy folder to install the library and +development requirements: + +.. code-block:: console + + pip install -e . # install the rocketpy lib in editable mode + pip install -r requirements-optional.txt # install optional requirements + pip install -r requirements-tests.txt # install test/dev requirements + +.. tip:: + + When installing the ``rocketpy`` library, the requirements listed in the \ + ``requirements.txt`` file will automatically be installed. + +Running the tests +----------------- + +One simple sanity check you can do after installing rocketpy is to run the unit and integration tests. +One may achieve this by running the following commands: + +Running all tests: + +.. code-block:: console + + make pytest + +Running the slow tests only: + +.. code-block:: console + + make pytest-slow + +Creating a .html coverage report, where you could see the coverage of the tests: + +.. code-block:: console + + make coverage-report + + +.. note:: + + The slow tests are the tests marked with the ``@pytest.mark.slow`` decorator. \ + These tests are usually the ones that take longer to run, and therefore are \ + not run by default. More about the tests can be found in the \ + :ref:`testing_guidelines` section diff --git a/docs/development/style_guide.rst b/docs/development/style_guide.rst index de738d53a..510a49560 100644 --- a/docs/development/style_guide.rst +++ b/docs/development/style_guide.rst @@ -1,31 +1,41 @@ Style Guide =========== -This page describes code, documentation and git branching styles used throughout RocketPy's development. +This page describes code, documentation and git branching styles used throughout +RocketPy's development. Code Style ---------- -We mostly follow the standard Python style conventions as described here: `Style Guide for Python Code `_ +We mostly follow the standard Python style conventions as described here: +`Style Guide for Python Code `_ Naming Conventions ^^^^^^^^^^^^^^^^^^ -RocketPy was, unfortunately, initially coded using `Camel Case `_. +RocketPy was, unfortunately, initially coded using +`camelCase `_. However, PEP8 specifies the following:: - Function names should be lowercase, with words separated by underscores as necessary to improve readability. + Function names should be lowercase, with words separated by underscores as \ + necessary to improve readability. Variable names follow the same convention as function names. mixedCase is allowed only in contexts where that is already the prevailing style (e.g. threading.py), to retain backwards compatibility. -Therefore, `Snake Case `_ is preferred. -For this reason, RocketPy is being converted from Camel to Snake as of version ``1.0.0``. -New contributions should strive to follow Snake case as well. +Therefore, `snake_case `_ is preferred. +For this reason, RocketPy has being fully converted from ``CamelCase`` to ``snake_case`` as of version ``1.0.0``. -Furthermore, when it comes to naming new variables, functions, classes or anything in RocketPy, always try to use descriptive names. +.. important:: -As an example, instead of using `a` or `alpha` as a variable for a rocket's angle of attack, `angle_of_attack` is preferred. -Such descriptive names make the code significantly easier to understand, review and maintain. + New contributions should only follow ``snake_case`` naming conventions. + +Furthermore, when it comes to naming new variables, functions, classes or +anything in RocketPy, always try to use descriptive names. + +As an example, instead of using ``a`` or ``alpha`` as a variable for a rocket's +angle of attack, ``angle_of_attack`` is preferred. +Such descriptive names make the code significantly easier to understand, review +and maintain. In summary: @@ -38,18 +48,37 @@ In summary: angle_of_attack = 0.2 # in rad -Linting -^^^^^^^ -As far as line wrapping, parentheses, calling chains and other code style related matter goes, RocketPy currently employs `Black style `_. +Linting and formatting +^^^^^^^^^^^^^^^^^^^^^^ + +As far as line wrapping, parentheses, calling chains and other code style +related matter goes, RocketPy currently employs `Black style `_. Installing Black and running it before submitting a pull request is highly recommend. Currently, there are pull request tests in place to enforce that Black style is used. +Aside from Black, RocketPy also uses `Flake8 `_ +and `Pylint `_ to check for code style issues. + +Running these commands before submitting a pull request is also highly recommended: + +.. code-block:: bash + + make format + make flake8 + make pylint + +These commands will check for any code style issues in the codebase. +The ``flake8`` command will throw a report directly to the console, while the +``pylint`` command will create a ``.pylint_report.txt`` file in the root directory, +which you can open to see the report. Documentation Style ------------------- -Every class, method, attribute and function added to RocketPy should be well documented using docstrings. -RocketPy follows NumPy style docstrings, which are very well explained here: `NumPyDoc Style Guide `_. +Every class, method, attribute and function added to RocketPy should be well +documented using docstrings. +RocketPy follows NumPy style docstrings, which are very well explained here: +`NumPyDoc Style Guide `_. Git Style --------- @@ -86,7 +115,12 @@ Here are a couple of example branch names: Once you are ready to create a Pull Request for your branch, we advise you to merge with the ``develop`` branch instead of the default ``master`` branch. This way, we keep the ``master`` branch stable and use the ``develop`` branch to test out new features! -If you have any doubts, just open an issue! And don't forget that these are recommendations. Don't let them keep you from contributing. +.. important:: + + If you have any doubts, just open an issue or ask in our Discord server. \ + And don't forget that these are recommendations. \ + Don't let them keep you from contributing. + Commit Messages ^^^^^^^^^^^^^^^ @@ -103,9 +137,8 @@ Commit messages should be clear and follow a few basic rules. Example:: Describing the motivation for a change, the nature of a bug for bug fixes or some details on what an enhancement does are also good to include in a commit -message. Messages should be understandable without looking at the code -changes. A commit message like ``MNT: fixed another one`` is an example of -what not to do; the reader has to go look for context elsewhere. +message. +Messages should be understandable without looking at the code changes. Standard acronyms to start the commit message with are:: @@ -120,3 +153,27 @@ Standard acronyms to start the commit message with are:: STY: style fix (whitespace, PEP8) TST: addition or modification of tests REL: related to releasing RocketPy + +.. note:: + + A commit message like ``MNT: fixed another one`` is an example of what not to do; \ + the reader has to go look for context elsewhere. + +Pull Requests +^^^^^^^^^^^^^ + +When opening a Pull Request, the name of the PR should be clear and concise. +Similarly to the commit messages, the PR name should start with an acronym indicating the type of PR +and then a brief description of the changes. + +Here is an example of a good PR name: + +- ``BUG: fix the Frequency Response plot of the Flight class`` + +The PR description explain the changes and motivation behind them. There is a template \ +available when opening a PR that can be used to guide you through the process of both \ +describing the changes and making sure all the necessary steps were taken. Of course, \ +you can always modify the template or add more information if you think it is necessary. + + + diff --git a/docs/development/testing.rst b/docs/development/testing.rst index 032287ee3..fd521167b 100644 --- a/docs/development/testing.rst +++ b/docs/development/testing.rst @@ -1,3 +1,5 @@ +.. _testing_guidelines: + Testing Guidelines ================== @@ -68,6 +70,49 @@ Do not get caught by the size of that docstring. The only requirements it has to that the docstring contains precise information on the expected behaviour and/or behaviour to be tested. +Testing Structure +----------------- + +In order to keep the tests easily readable and maintainable, RocketPy encourages +the use of the AAA pattern (Arrange, Act, Assert) for structuring the tests. + +* **Arrange:** Set up the necessary preconditions and inputs (often done by *Fixtures* as it will be described below); +* **Act:** Execute the code under test; +* **Assert:** Verify that the code under test behaves as expected. + +The following example illustrates the AAA pattern: + +.. code-block:: python + + @pytest.mark.parametrize( + "latitude, longitude", [(-21.960641, -47.482122), (0, 0), (21.960641, 47.482122)] + ) # Arrange: Done by the fixtures and the parameters of the test + def test_location_set_location_saves_location(latitude, longitude, example_plain_env): + """Tests location is saved correctly in the environment obj. + + Parameters + ---------- + example_plain_env : rocketpy.Environment + latitude: float + The latitude in decimal degrees. + longitude: float + The longitude in decimal degrees. + """ + # Act: Set the location + example_plain_env.set_location(latitude, longitude) + # Assert: Check if the location was saved correctly + assert example_plain_env.latitude == latitude + assert example_plain_env.longitude == longitude + +This pattern is a general guideline, of course specific tests cases might +modify it to better fit the specific needs of the test. + +.. note:: + + Parameterization is a powerful feature of ``pytest.mark.parametrize`` that allows + you to run the same test with different inputs. This is highly recommended when + there multiple testing scenarios for the same method. + Directory Structure ------------------- @@ -234,7 +279,7 @@ This test contains two fundamental traits which defines it as an integration tes The motivation behind lies in the fact that it interacts and calls too many methods, being too broad to be considered an unit test. -Please be aware that Integration tests are not solely classfied when interacting with external dependencies, +Please be aware that Integration tests are not solely classified when interacting with external dependencies, but also encompass verifying the interaction between classes or too many methods at once, such as ``all_info()``. Further clarification: Even if the test contains traits of unit tests and use dependencies which are stable, such as @@ -305,5 +350,6 @@ documenting purposes, such as below: [[-10. 10.]] """ -This is not common practice, but it is optional and can be done. RocketPy however encourages -the use of other means to test its software, as described. +This is not common practice, but it is optional and can be done, specially to provide +an usage example for the function under testing. RocketPy however encourages the use +of other means to test its software, as described. diff --git a/docs/requirements.in b/docs/requirements.in index ea736a11a..b5138ee0c 100644 --- a/docs/requirements.in +++ b/docs/requirements.in @@ -6,3 +6,4 @@ ipykernel==6.29.4 pandas==2.2.2 lxml_html_clean==0.1.1 sphinx-copybutton==0.5.2 +sphinx-tabs==3.4.7 diff --git a/docs/requirements.txt b/docs/requirements.txt index a5d9c279d..c56c20cae 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -43,6 +43,7 @@ docutils==0.20.1 # nbsphinx # pydata-sphinx-theme # sphinx + # sphinx-tabs entrypoints==0.4 # via nbconvert executing==2.0.1 @@ -159,6 +160,7 @@ pygments==2.18.0 # nbconvert # pydata-sphinx-theme # sphinx + # sphinx-tabs python-dateutil==2.9.0.post0 # via # jupyter-client @@ -195,10 +197,13 @@ sphinx==7.3.7 # pydata-sphinx-theme # sphinx-copybutton # sphinx-design + # sphinx-tabs sphinx-copybutton==0.5.2 # via -r requirements.in sphinx-design==0.5.0 # via -r requirements.in +sphinx-tabs==3.4.7 + # via -r requirements.in sphinxcontrib-applehelp==1.0.8 # via sphinx sphinxcontrib-devhelp==1.0.6 diff --git a/docs/user/motors/motors.rst b/docs/user/motors/motors.rst index a6bc6ef4b..b690cf9e8 100644 --- a/docs/user/motors/motors.rst +++ b/docs/user/motors/motors.rst @@ -1,3 +1,5 @@ +.. _motors_usage: + Motors Usage ============ @@ -12,13 +14,13 @@ Motors Usage :caption: Solid Motors Solid Motor Usage - + .. toctree:: :maxdepth: 3 :caption: Hybrid Motors Hybrid Motor Usage - + .. toctree:: :maxdepth: 3 :caption: Liquid Motors @@ -30,7 +32,7 @@ Motors Usage :caption: Generic Motors Generic Motor Usage - + .. toctree:: :maxdepth: 3 :caption: Tanks and Fluid From e66f79f05753869d44824829005c6fc58b1b0cdb Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Sat, 30 Nov 2024 03:59:05 -0300 Subject: [PATCH 30/36] DEV: move the citation.cff file back to root --- .github/CITATION.cff => CITATION.cff | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename .github/CITATION.cff => CITATION.cff (100%) diff --git a/.github/CITATION.cff b/CITATION.cff similarity index 100% rename from .github/CITATION.cff rename to CITATION.cff From 42ef34fcf432440786ffadc1de36c2ce6085d3c2 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Sat, 30 Nov 2024 10:39:34 -0300 Subject: [PATCH 31/36] BUG: update draw method calls to use keyword argument for filename --- rocketpy/motors/hybrid_motor.py | 2 +- rocketpy/motors/liquid_motor.py | 2 +- rocketpy/motors/solid_motor.py | 2 +- rocketpy/motors/tank.py | 2 +- rocketpy/rocket/aero_surface/fins/fins.py | 2 +- rocketpy/rocket/aero_surface/nose_cone.py | 2 +- 6 files changed, 6 insertions(+), 6 deletions(-) diff --git a/rocketpy/motors/hybrid_motor.py b/rocketpy/motors/hybrid_motor.py index 58c2fab8e..163cdd811 100644 --- a/rocketpy/motors/hybrid_motor.py +++ b/rocketpy/motors/hybrid_motor.py @@ -616,4 +616,4 @@ def draw(self, *, filename=None): ------- None """ - self.plots.draw(filename) + self.plots.draw(filename=filename) diff --git a/rocketpy/motors/liquid_motor.py b/rocketpy/motors/liquid_motor.py index d99d0165b..d9a3220ae 100644 --- a/rocketpy/motors/liquid_motor.py +++ b/rocketpy/motors/liquid_motor.py @@ -478,4 +478,4 @@ def draw(self, *, filename=None): ------- None """ - self.plots.draw(filename) + self.plots.draw(filename=filename) diff --git a/rocketpy/motors/solid_motor.py b/rocketpy/motors/solid_motor.py index 9f8622cea..f0862e26a 100644 --- a/rocketpy/motors/solid_motor.py +++ b/rocketpy/motors/solid_motor.py @@ -742,4 +742,4 @@ def draw(self, *, filename=None): ------- None """ - self.plots.draw(filename) + self.plots.draw(filename=filename) diff --git a/rocketpy/motors/tank.py b/rocketpy/motors/tank.py index 14511eab0..29aebfddc 100644 --- a/rocketpy/motors/tank.py +++ b/rocketpy/motors/tank.py @@ -491,7 +491,7 @@ def draw(self, *, filename=None): ------- None """ - self.plots.draw(filename) + self.plots.draw(filename=filename) class MassFlowRateBasedTank(Tank): diff --git a/rocketpy/rocket/aero_surface/fins/fins.py b/rocketpy/rocket/aero_surface/fins/fins.py index 3c98855d0..b2af4f55f 100644 --- a/rocketpy/rocket/aero_surface/fins/fins.py +++ b/rocketpy/rocket/aero_surface/fins/fins.py @@ -442,4 +442,4 @@ def draw(self, *, filename=None): ------- None """ - self.plots.draw(filename) + self.plots.draw(filename=filename) diff --git a/rocketpy/rocket/aero_surface/nose_cone.py b/rocketpy/rocket/aero_surface/nose_cone.py index 6c295d1c5..c8f3a2062 100644 --- a/rocketpy/rocket/aero_surface/nose_cone.py +++ b/rocketpy/rocket/aero_surface/nose_cone.py @@ -502,7 +502,7 @@ def draw(self, *, filename=None): ------- None """ - self.plots.draw(filename) + self.plots.draw(filename=filename) def info(self): """Prints and plots summarized information of the nose cone. From 14712fcadd059e7c2ec382d57d8f11feec47eeff Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Sat, 30 Nov 2024 10:39:55 -0300 Subject: [PATCH 32/36] DOC: fix wrong directive --- rocketpy/simulation/monte_carlo.py | 1 + 1 file changed, 1 insertion(+) diff --git a/rocketpy/simulation/monte_carlo.py b/rocketpy/simulation/monte_carlo.py index 70584838a..926576831 100644 --- a/rocketpy/simulation/monte_carlo.py +++ b/rocketpy/simulation/monte_carlo.py @@ -120,6 +120,7 @@ def __init__( a Flight object and returns a value of that variable. For instance .. code-block:: python + custom_data_collector = { "max_acceleration": lambda flight: max(flight.acceleration(flight.time)), "date": lambda flight: flight.env.date, From 36ff7071c384d8ff18f610a8ea6b45151bafac23 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Sat, 30 Nov 2024 12:06:01 -0300 Subject: [PATCH 33/36] DOC: comment slow code execution --- docs/user/environment/1-atm-models/ensemble.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/user/environment/1-atm-models/ensemble.rst b/docs/user/environment/1-atm-models/ensemble.rst index c1d19722c..97c247f68 100644 --- a/docs/user/environment/1-atm-models/ensemble.rst +++ b/docs/user/environment/1-atm-models/ensemble.rst @@ -24,7 +24,7 @@ Global Ensemble Forecast System (GEFS) The ``GEFS`` model is a global ensemble forecast model ... -.. jupyter-execute:: +.. code-block:: python from datetime import date, timedelta from rocketpy import Environment @@ -45,7 +45,7 @@ The ``GEFS`` model is a global ensemble forecast model ... .. forecasts are kept away stored. .. Changing this is as easy as the following line of code. -.. jupyter-execute:: +.. code-block:: python env_gefs.select_ensemble_member(3) # selects the 3rd forecast member @@ -56,7 +56,7 @@ A very useful thing is to compare the forecast of all ensemble members, as well as confirming how many are there. All of this can be done by calling the :meth:`rocketpy.Environment.all_info` method. -.. jupyter-execute:: +.. code-block:: python env_gefs.all_info() From 10fc1a3c285b40ccc0e4ff7eda1baa3ae14e89d4 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Sat, 30 Nov 2024 12:39:12 -0300 Subject: [PATCH 34/36] REL: update version to 1.7.0 in configuration files --- docs/conf.py | 2 +- docs/user/installation.rst | 2 +- pyproject.toml | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 9324ad9a6..62334799d 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -27,7 +27,7 @@ author = "RocketPy Team" # The full version, including alpha/beta/rc tags -release = "1.6.2" +release = "1.7.0" # -- General configuration --------------------------------------------------- diff --git a/docs/user/installation.rst b/docs/user/installation.rst index b8f0d73b7..ea36f1730 100644 --- a/docs/user/installation.rst +++ b/docs/user/installation.rst @@ -19,7 +19,7 @@ If you want to choose a specific version to guarantee compatibility, you may ins .. code-block:: shell - pip install rocketpy==1.6.2 + pip install rocketpy==1.7.0 Optional Installation Method: ``conda`` diff --git a/pyproject.toml b/pyproject.toml index 244de58d5..024242696 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "rocketpy" -version = "1.6.2" +version = "1.7.0" description="Advanced 6-DOF trajectory simulation for High-Power Rocketry." dynamic = ["dependencies"] readme = "README.md" From 5cb88c359de6369115bdaa89c91ab83b579d0528 Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Sat, 30 Nov 2024 12:40:36 -0300 Subject: [PATCH 35/36] DEV: update CHANGELOG --- CHANGELOG.md | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index b1197708a..d89921da5 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -32,6 +32,17 @@ Attention: The newest changes should be on top --> ### Added + +### Changed + + +### Fixed + + +## [v1.7.0] - 2024-11-30 + +### Added + - DOC: GENESIS Flight Example [#734](https://github.com/RocketPy-Team/RocketPy/pull/734) - DOC: Camoes Flight Example [#733](https://github.com/RocketPy-Team/RocketPy/pull/733) - ENH: Callback function for collecting additional data from Monte Carlo sims [#702](https://github.com/RocketPy-Team/RocketPy/pull/702) @@ -39,7 +50,9 @@ Attention: The newest changes should be on top --> ### Changed +- REL: update version to 1.7.0 in configuration files [#741](https://github.com/RocketPy-Team/RocketPy/pull/741) - MNT: Place filename save parameter to the end. [#739](https://github.com/RocketPy-Team/RocketPy/pull/739) +- DOC: improvements to developers documentation [#732](https://github.com/RocketPy-Team/RocketPy/pull/732) ### Fixed @@ -47,6 +60,7 @@ Attention: The newest changes should be on top --> - BUG: forecast and reanalysis models - Update ECMWF dictionary values [#736](https://github.com/RocketPy-Team/RocketPy/pull/736) - BUG: forecast and reanalysis models - move wind_speed to correct position [#735](https://github.com/RocketPy-Team/RocketPy/pull/735) - BUG: Sideslip Angle and Damping Coefficient Calculation [#729](https://github.com/RocketPy-Team/RocketPy/pull/729) +- DOC: fixed documentation about spherical caps [#728](https://github.com/RocketPy-Team/RocketPy/pull/728) ## [v1.6.2] - 2024-11-08 From d93666af447114103996a2688e4fa4edf59b9f76 Mon Sep 17 00:00:00 2001 From: Pedro Henrique Marinho Bressan <87212571+phmbressan@users.noreply.github.com> Date: Sat, 30 Nov 2024 18:32:52 +0100 Subject: [PATCH 36/36] MNT: Refactor Tank's testing Assertion with CAD data. (#678) * MNT: Add CAD data for tank geometry validation. * MNT: Refactor tank testing and improve file structure. * STY: fix pylint suggestions. * ENH: implement Tanks prints and plots. * MNT: add recent changes to CHANGELOG. * TST: tighten tolerances for tank geometry tests. * DOC: improve description of cad notebook validator. * MNT: remove duplicated fixtures. * DOC: improve flux_time docstring description. * MNT: add info and all_info methods to tanks. --------- Co-authored-by: Gui-FernandesBR <63590233+Gui-FernandesBR@users.noreply.github.com> --- .vscode/settings.json | 2 + CHANGELOG.md | 1 + rocketpy/motors/tank.py | 185 +-- rocketpy/plots/tank_geometry_plots.py | 6 +- rocketpy/plots/tank_plots.py | 86 ++ rocketpy/prints/fluid_prints.py | 2 + rocketpy/prints/tank_prints.py | 35 + tests/conftest.py | 1 + tests/fixtures/hybrid/__init__.py | 0 tests/fixtures/hybrid/hybrid_fixtures.py | 276 ---- .../cylindrical_oxidizer_tank_expected.csv | 26 + .../cylindrical_pressurant_tank_expected.csv | 26 + .../data/spherical_oxidizer_tank_expected.csv | 26 + .../tank_cad_validation_data_generator.ipynb | 265 ++++ tests/fixtures/motor/liquid_fixtures.py | 26 + .../fixtures/motor/tank_geometry_fixtures.py | 41 + tests/fixtures/motor/tanks_fixtures.py | 354 +++++- tests/integration/test_tank.py | 35 + tests/unit/test_tank.py | 1132 ++++------------- tests/unit/test_tank_geometry.py | 126 ++ 20 files changed, 1405 insertions(+), 1246 deletions(-) delete mode 100644 tests/fixtures/hybrid/__init__.py delete mode 100644 tests/fixtures/hybrid/hybrid_fixtures.py create mode 100644 tests/fixtures/motor/data/cylindrical_oxidizer_tank_expected.csv create mode 100644 tests/fixtures/motor/data/cylindrical_pressurant_tank_expected.csv create mode 100644 tests/fixtures/motor/data/spherical_oxidizer_tank_expected.csv create mode 100644 tests/fixtures/motor/data/tank_cad_validation_data_generator.ipynb create mode 100644 tests/fixtures/motor/tank_geometry_fixtures.py create mode 100644 tests/integration/test_tank.py create mode 100644 tests/unit/test_tank_geometry.py diff --git a/.vscode/settings.json b/.vscode/settings.json index 98010d539..e36a259e0 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -233,6 +233,7 @@ "polystyle", "powerseries", "Prandtl", + "pressurant", "prettytable", "Projeto", "prometheus", @@ -265,6 +266,7 @@ "SBMT", "scilimits", "searchsorted", + "seblm", "seealso", "setrail", "simplekml", diff --git a/CHANGELOG.md b/CHANGELOG.md index d89921da5..bfd5501ae 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -35,6 +35,7 @@ Attention: The newest changes should be on top --> ### Changed +- MNT: Refactor Tank's testing Assertion with CAD data. [#678](https://github.com/RocketPy-Team/RocketPy/pull/678) ### Fixed diff --git a/rocketpy/motors/tank.py b/rocketpy/motors/tank.py index 29aebfddc..982380891 100644 --- a/rocketpy/motors/tank.py +++ b/rocketpy/motors/tank.py @@ -23,7 +23,7 @@ class Tank(ABC): Name of the tank. Tank.geometry : TankGeometry Geometry of the tank. - Tank.flux_time : float, tuple of float, optional + Tank.flux_time : float, tuple of float Tank flux time in seconds. Tank.liquid : Fluid Liquid inside the tank as a Fluid object. @@ -84,13 +84,14 @@ def __init__(self, name, geometry, flux_time, liquid, gas, discretize=100): Name of the tank. geometry : TankGeometry Geometry of the tank. - flux_time : float, tuple of float, optional - Tank flux time in seconds. It is the time range in which the tank - flux is being analyzed. In general, during this time, the tank is - being filled or emptied.If a float is given, the flux time is - assumed to be between 0 and the given float, in seconds. If a tuple - of float is given, the flux time is assumed to be between the first - and second elements of the tuple. + flux_time : float, tuple of float + Tank flux time in seconds. Time interval that the fluid flux is + being analyzed. If a float is given, the flux time is assumed to + be between 0 and the given float, in seconds. If a tuple of float + is given, the flux time is assumed to be between the first and + second elements of the tuple. + Before the start time, the tank properties are kept at their + initial state. After the final time, their final state is kept. gas : Fluid Gas inside the tank as a Fluid object. liquid : Fluid @@ -335,7 +336,7 @@ def center_of_mass(self): return center_of_mass - @funcify_method("Time (s)", "Inertia tensor of liquid (kg*m²)") + @funcify_method("Time (s)", "Liquid Inertia (kg*m²)") def liquid_inertia(self): """ Returns the inertia tensor of the liquid portion of the tank @@ -361,7 +362,7 @@ def liquid_inertia(self): return self.liquid.density * Ix_volume - @funcify_method("Time (s)", "inertia tensor of gas (kg*m^2)") + @funcify_method("Time (s)", "Gas Inertia (kg*m^2)") def gas_inertia(self): """ Returns the inertia tensor of the gas portion of the tank @@ -387,7 +388,7 @@ def gas_inertia(self): return self.gas.density * inertia_volume - @funcify_method("Time (s)", "inertia tensor (kg*m^2)") + @funcify_method("Time (s)", "Fluid Inertia (kg*m^2)") def inertia(self): """ Returns the inertia tensor of the tank's fluids as a function of @@ -493,6 +494,17 @@ def draw(self, *, filename=None): """ self.plots.draw(filename=filename) + def info(self): + """Prints out a summary of the tank properties.""" + self.prints.all() + + def all_info(self): + """Prints out detailed information and plots of the tank + properties. + """ + self.prints.all() + self.plots.all() + class MassFlowRateBasedTank(Tank): """Class to define a tank based on mass flow rates inputs. This class @@ -527,14 +539,14 @@ def __init__( Name of the tank. geometry : TankGeometry Geometry of the tank. - flux_time : float, tuple of float, optional - Tank flux time in seconds. It is the time range in which the tank - flux is being analyzed. In general, during this time, the tank is - being filled or emptied. - If a float is given, the flux time is assumed to be between 0 and - the given float, in seconds. If a tuple of float is given, the flux - time is assumed to be between the first and second elements of the - tuple. + flux_time : float, tuple of float + Tank flux time in seconds. Time interval that the fluid flux is + being analyzed. If a float is given, the flux time is assumed to + be between 0 and the given float, in seconds. If a tuple of float + is given, the flux time is assumed to be between the first and + second elements of the tuple. + Before the start time, the tank properties are kept at their + initial state. After the final time, their final state is kept. liquid : Fluid Liquid inside the tank as a Fluid object. gas : Fluid @@ -586,28 +598,28 @@ def __init__( self.liquid_mass_flow_rate_in = Function( liquid_mass_flow_rate_in, inputs="Time (s)", - outputs="Mass Flow Rate (kg/s)", + outputs="Liquid Mass Flow Rate In (kg/s)", interpolation="linear", extrapolation="zero", ) self.gas_mass_flow_rate_in = Function( gas_mass_flow_rate_in, inputs="Time (s)", - outputs="Mass Flow Rate (kg/s)", + outputs="Gas Mass Flow Rate In (kg/s)", interpolation="linear", extrapolation="zero", ) self.liquid_mass_flow_rate_out = Function( liquid_mass_flow_rate_out, inputs="Time (s)", - outputs="Mass Flow Rate (kg/s)", + outputs="Liquid Mass Flow Rate Out (kg/s)", interpolation="linear", extrapolation="zero", ) self.gas_mass_flow_rate_out = Function( gas_mass_flow_rate_out, inputs="Time (s)", - outputs="Mass Flow Rate (kg/s)", + outputs="Gas Mass Flow Rate Out (kg/s)", interpolation="linear", extrapolation="zero", ) @@ -620,7 +632,7 @@ def __init__( self._check_volume_bounds() self._check_height_bounds() - @funcify_method("Time (s)", "Mass (kg)") + @funcify_method("Time (s)", "Fluid Mass (kg)") def fluid_mass(self): """ Returns the total mass of liquid and gases inside the tank as a @@ -633,7 +645,7 @@ def fluid_mass(self): """ return self.liquid_mass + self.gas_mass - @funcify_method("Time (s)", "Mass (kg)") + @funcify_method("Time (s)", "Liquid Mass (kg)") def liquid_mass(self): """ Returns the mass of the liquid as a function of time by integrating @@ -644,7 +656,9 @@ def liquid_mass(self): Function Mass of the liquid as a function of time. """ - liquid_flow = self.net_liquid_flow_rate.integral_function() + liquid_flow = self.net_liquid_flow_rate.integral_function( + datapoints=self.discretize + ) liquid_mass = self.initial_liquid_mass + liquid_flow if (liquid_mass < 0).any(): raise ValueError( @@ -657,7 +671,7 @@ def liquid_mass(self): ) return liquid_mass - @funcify_method("Time (s)", "Mass (kg)") + @funcify_method("Time (s)", "Gas Mass (kg)") def gas_mass(self): """ Returns the mass of the gas as a function of time by integrating @@ -668,7 +682,7 @@ def gas_mass(self): Function Mass of the gas as a function of time. """ - gas_flow = self.net_gas_flow_rate.integral_function() + gas_flow = self.net_gas_flow_rate.integral_function(datapoints=self.discretize) gas_mass = self.initial_gas_mass + gas_flow if (gas_mass < -1e-6).any(): # -1e-6 is to avoid numerical errors raise ValueError( @@ -682,7 +696,7 @@ def gas_mass(self): return gas_mass - @funcify_method("Time (s)", "liquid mass flow rate (kg/s)", extrapolation="zero") + @funcify_method("Time (s)", "Liquid Mass Flow Rate (kg/s)", extrapolation="zero") def net_liquid_flow_rate(self): """ Returns the net mass flow rate of liquid as a function of time. @@ -696,7 +710,7 @@ def net_liquid_flow_rate(self): """ return self.liquid_mass_flow_rate_in - self.liquid_mass_flow_rate_out - @funcify_method("Time (s)", "gas mass flow rate (kg/s)", extrapolation="zero") + @funcify_method("Time (s)", "Gas Mass Flow Rate (kg/s)", extrapolation="zero") def net_gas_flow_rate(self): """ Returns the net mass flow rate of gas as a function of time. @@ -710,7 +724,7 @@ def net_gas_flow_rate(self): """ return self.gas_mass_flow_rate_in - self.gas_mass_flow_rate_out - @funcify_method("Time (s)", "mass flow rate (kg/s)", extrapolation="zero") + @funcify_method("Time (s)", "Net Mass Flow Rate (kg/s)", extrapolation="zero") def net_mass_flow_rate(self): """ Returns the net mass flow rate of the tank as a function of time. @@ -724,7 +738,7 @@ def net_mass_flow_rate(self): """ return self.net_liquid_flow_rate + self.net_gas_flow_rate - @funcify_method("Time (s)", "Volume (m³)") + @funcify_method("Time (s)", "Fluid Volume (m³)") def fluid_volume(self): """ Returns the volume total fluid volume inside the tank as a @@ -738,7 +752,7 @@ def fluid_volume(self): """ return self.liquid_volume + self.gas_volume - @funcify_method("Time (s)", "Volume (m³)") + @funcify_method("Time (s)", "Liquid Volume (m³)") def liquid_volume(self): """ Returns the volume of the liquid as a function of time. @@ -750,7 +764,7 @@ def liquid_volume(self): """ return self.liquid_mass / self.liquid.density - @funcify_method("Time (s)", "Volume (m³)") + @funcify_method("Time (s)", "Gas Volume (m³)") def gas_volume(self): """ Returns the volume of the gas as a function of time. @@ -762,7 +776,7 @@ def gas_volume(self): """ return self.gas_mass / self.gas.density - @funcify_method("Time (s)", "Height (m)") + @funcify_method("Time (s)", "Liquid Height (m)") def liquid_height(self): """ Returns the liquid level as a function of time. This @@ -799,7 +813,7 @@ def liquid_height(self): return liquid_height - @funcify_method("Time (s)", "Height (m)") + @funcify_method("Time (s)", "Gas Height (m)") def gas_height(self): """ Returns the gas level as a function of time. This @@ -864,13 +878,14 @@ def __init__( Name of the tank. geometry : TankGeometry Geometry of the tank. - flux_time : float, tuple of float, optional - Tank flux time in seconds. It is the time range in which the tank - flux is being analyzed. In general, during this time, the tank is - being filled or emptied. If a float is given, the flux time is - assumed to be between 0 and the given float, in seconds. If a tuple - of float is given, the flux time is assumed to be between the first - and second elements of the tuple. + flux_time : float, tuple of float + Tank flux time in seconds. Time interval that the fluid flux is + being analyzed. If a float is given, the flux time is assumed to + be between 0 and the given float, in seconds. If a tuple of float + is given, the flux time is assumed to be between the first and + second elements of the tuple. + Before the start time, the tank properties are kept at their + initial state. After the final time, their final state is kept. liquid : Fluid Liquid inside the tank as a Fluid object. gas : Fluid @@ -902,7 +917,7 @@ def __init__( self._check_volume_bounds() self._check_height_bounds() - @funcify_method("Time (s)", "Mass (kg)") + @funcify_method("Time (s)", "Fluid Mass (kg)") def fluid_mass(self): """ Returns the total mass of liquid and gases inside the tank as a @@ -915,7 +930,7 @@ def fluid_mass(self): """ return self.liquid_mass + self.gas_mass - @funcify_method("Time (s)", "Mass flow rate (kg/s)") + @funcify_method("Time (s)", "Net Mass Flow Rate (kg/s)") def net_mass_flow_rate(self): """ Returns the net mass flow rate of the tank as a function of time by @@ -928,7 +943,7 @@ def net_mass_flow_rate(self): """ return self.fluid_mass.derivative_function() - @funcify_method("Time (s)", "Volume (m³)") + @funcify_method("Time (s)", "Fluid Volume (m³)") def fluid_volume(self): """ Returns the volume total fluid volume inside the tank as a @@ -944,7 +959,7 @@ def fluid_volume(self): self.gas_volume ) - @funcify_method("Time (s)", "Volume (m³)") + @funcify_method("Time (s)", "Liquid Volume (m³)") def liquid_volume(self): """ Returns the volume of the liquid as a function of time. The @@ -958,7 +973,7 @@ def liquid_volume(self): """ return -(self.ullage - self.geometry.total_volume) - @funcify_method("Time (s)", "Volume (m³)") + @funcify_method("Time (s)", "Gas Volume (m³)") def gas_volume(self): """ Returns the volume of the gas as a function of time. From the @@ -971,7 +986,7 @@ def gas_volume(self): """ return self.ullage - @funcify_method("Time (s)", "Mass (kg)") + @funcify_method("Time (s)", "Gas Mass (kg)") def gas_mass(self): """ Returns the mass of the gas as a function of time. @@ -983,7 +998,7 @@ def gas_mass(self): """ return self.gas_volume * self.gas.density - @funcify_method("Time (s)", "Mass (kg)") + @funcify_method("Time (s)", "Liquid Mass (kg)") def liquid_mass(self): """ Returns the mass of the liquid as a function of time. @@ -995,7 +1010,7 @@ def liquid_mass(self): """ return self.liquid_volume * self.liquid.density - @funcify_method("Time (s)", "Height (m)") + @funcify_method("Time (s)", "Liquid Height (m)") def liquid_height(self): """ Returns the liquid level as a function of time. This @@ -1009,7 +1024,7 @@ def liquid_height(self): """ return self.geometry.inverse_volume.compose(self.liquid_volume) - @funcify_method("Time (s)", "Height (m)", "linear") + @funcify_method("Time (s)", "Gas Height (m)", "linear") def gas_height(self): """ Returns the gas level as a function of time. This height is measured @@ -1059,13 +1074,14 @@ def __init__( Name of the tank. geometry : TankGeometry Geometry of the tank. - flux_time : float, tuple of float, optional - Tank flux time in seconds. It is the time range in which the tank - flux is being analyzed. In general, during this time, the tank is - being filled or emptied. If a float is given, the flux time is - assumed to be between 0 and the given float, in seconds. If a tuple - of float is given, the flux time is assumed to be between the first - and second elements of the tuple. + flux_time : float, tuple of float + Tank flux time in seconds. Time interval that the fluid flux is + being analyzed. If a float is given, the flux time is assumed to + be between 0 and the given float, in seconds. If a tuple of float + is given, the flux time is assumed to be between the first and + second elements of the tuple. + Before the start time, the tank properties are kept at their + initial state. After the final time, their final state is kept. liquid : Fluid Liquid inside the tank as a Fluid object. gas : Fluid @@ -1096,7 +1112,7 @@ def __init__( self._check_height_bounds() self._check_volume_bounds() - @funcify_method("Time (s)", "Mass (kg)") + @funcify_method("Time (s)", "Fluid Mass (kg)") def fluid_mass(self): """ Returns the total mass of liquid and gases inside the tank as a @@ -1112,7 +1128,7 @@ def fluid_mass(self): sum_mass.set_discrete_based_on_model(self.liquid_level) return sum_mass - @funcify_method("Time (s)", "Mass flow rate (kg/s)") + @funcify_method("Time (s)", "Net Mass Flow Rate (kg/s)") def net_mass_flow_rate(self): """ Returns the net mass flow rate of the tank as a function of time by @@ -1125,7 +1141,7 @@ def net_mass_flow_rate(self): """ return self.fluid_mass.derivative_function() - @funcify_method("Time (s)", "Volume (m³)") + @funcify_method("Time (s)", "Fluid Volume (m³)") def fluid_volume(self): """ Returns the volume total fluid volume inside the tank as a @@ -1148,7 +1164,7 @@ def fluid_volume(self): ) return volume - @funcify_method("Time (s)", "Volume (m³)") + @funcify_method("Time (s)", "Liquid Volume (m³)") def liquid_volume(self): """ Returns the volume of the liquid as a function of time. @@ -1160,7 +1176,7 @@ def liquid_volume(self): """ return self.geometry.volume.compose(self.liquid_height) - @funcify_method("Time (s)", "Volume (m³)") + @funcify_method("Time (s)", "Gas Volume (m³)") def gas_volume(self): """ Returns the volume of the gas as a function of time. The gas volume @@ -1178,7 +1194,7 @@ def gas_volume(self): func -= self.liquid_volume return func - @funcify_method("Time (s)", "Height (m)") + @funcify_method("Time (s)", "Liquid Height (m)") def liquid_height(self): """ Returns the liquid level as a function of time. This height is @@ -1191,7 +1207,7 @@ def liquid_height(self): """ return self.liquid_level - @funcify_method("Time (s)", "Mass (kg)") + @funcify_method("Time (s)", "Gas Mass (kg)") def gas_mass(self): """ Returns the mass of the gas as a function of time. @@ -1203,7 +1219,7 @@ def gas_mass(self): """ return self.gas_volume * self.gas.density - @funcify_method("Time (s)", "Mass (kg)") + @funcify_method("Time (s)", "Liquid Mass (kg)") def liquid_mass(self): """ Returns the mass of the liquid as a function of time. @@ -1215,7 +1231,7 @@ def liquid_mass(self): """ return self.liquid_volume * self.liquid.density - @funcify_method("Time (s)", "Height (m)", "linear") + @funcify_method("Time (s)", "Gas Height (m)", "linear") def gas_height(self): """ Returns the gas level as a function of time. This @@ -1268,13 +1284,14 @@ def __init__( Name of the tank. geometry : TankGeometry Geometry of the tank. - flux_time : float, tuple of float, optional - Tank flux time in seconds. It is the time range in which the tank - flux is being analyzed. In general, during this time, the tank is - being filled or emptied. If a float is given, the flux time is - assumed to be between 0 and the given float, in seconds. If a tuple - of float is given, the flux time is assumed to be between the first - and second elements of the tuple. + flux_time : float, tuple of float + Tank flux time in seconds. Time interval that the fluid flux is + being analyzed. If a float is given, the flux time is assumed to + be between 0 and the given float, in seconds. If a tuple of float + is given, the flux time is assumed to be between the first and + second elements of the tuple. + Before the start time, the tank properties are kept at their + initial state. After the final time, their final state is kept. liquid : Fluid Liquid inside the tank as a Fluid object. gas : Fluid @@ -1311,7 +1328,7 @@ def __init__( self._check_volume_bounds() self._check_height_bounds() - @funcify_method("Time (s)", "Mass (kg)") + @funcify_method("Time (s)", "Fluid Mass (kg)") def fluid_mass(self): """ Returns the total mass of liquid and gases inside the tank as @@ -1324,7 +1341,7 @@ def fluid_mass(self): """ return self.liquid_mass + self.gas_mass - @funcify_method("Time (s)", "Mass flow rate (kg/s)") + @funcify_method("Time (s)", "Net Mass Flow Rate (kg/s)") def net_mass_flow_rate(self): """ Returns the net mass flow rate of the tank as a function of time @@ -1337,7 +1354,7 @@ def net_mass_flow_rate(self): """ return self.fluid_mass.derivative_function() - @funcify_method("Time (s)", "Mass (kg)") + @funcify_method("Time (s)", "Liquid Mass (kg)") def liquid_mass(self): """ Returns the mass of the liquid as a function of time. @@ -1349,7 +1366,7 @@ def liquid_mass(self): """ return self.liquid_mass - @funcify_method("Time (s)", "Mass (kg)") + @funcify_method("Time (s)", "Gas Mass (kg)") def gas_mass(self): """ Returns the mass of the gas as a function of time. @@ -1361,7 +1378,7 @@ def gas_mass(self): """ return self.gas_mass - @funcify_method("Time (s)", "Volume (m³)") + @funcify_method("Time (s)", "Fluid Volume (m³)") def fluid_volume(self): """ Returns the volume total fluid volume inside the tank as a @@ -1389,7 +1406,7 @@ def fluid_volume(self): return fluid_volume - @funcify_method("Time (s)", "Volume (m³)") + @funcify_method("Time (s)", "Gas Volume (m³)") def gas_volume(self): """ Returns the volume of the gas as a function of time. @@ -1401,7 +1418,7 @@ def gas_volume(self): """ return self.gas_mass / self.gas.density - @funcify_method("Time (s)", "Volume (m³)") + @funcify_method("Time (s)", "Liquid Volume (m³)") def liquid_volume(self): """ Returns the volume of the liquid as a function of time. @@ -1413,7 +1430,7 @@ def liquid_volume(self): """ return self.liquid_mass / self.liquid.density - @funcify_method("Time (s)", "Height (m)") + @funcify_method("Time (s)", "Liquid Height (m)") def liquid_height(self): """ Returns the liquid level as a function of time. This @@ -1448,7 +1465,7 @@ def liquid_height(self): return liquid_height - @funcify_method("Time (s)", "Height (m)") + @funcify_method("Time (s)", "Gas Height (m)") def gas_height(self): """ Returns the gas level as a function of time. This diff --git a/rocketpy/plots/tank_geometry_plots.py b/rocketpy/plots/tank_geometry_plots.py index d9bf141bf..8c6ca9afa 100644 --- a/rocketpy/plots/tank_geometry_plots.py +++ b/rocketpy/plots/tank_geometry_plots.py @@ -40,6 +40,6 @@ def all(self): ------- None """ - self.radius() - self.area() - self.volume() + self.tank_geometry.radius.plot(equal_axis=True) + self.tank_geometry.area() + self.tank_geometry.volume() diff --git a/rocketpy/plots/tank_plots.py b/rocketpy/plots/tank_plots.py index 6f1c0e5f2..9c6ebb78a 100644 --- a/rocketpy/plots/tank_plots.py +++ b/rocketpy/plots/tank_plots.py @@ -2,6 +2,8 @@ import numpy as np from matplotlib.patches import Polygon +from rocketpy.mathutils.function import Function + from .plot_helpers import show_or_save_plot @@ -30,6 +32,7 @@ def __init__(self, tank): self.tank = tank self.name = tank.name + self.flux_time = tank.flux_time self.geometry = tank.geometry def _generate_tank(self, translate=(0, 0), csys=1): @@ -101,6 +104,82 @@ def draw(self, *, filename=None): ax.set_ylim(-1.5 * y_max, 1.5 * y_max) show_or_save_plot(filename) + def fluid_volume(self, filename=None): + """Plots both the liquid and gas fluid volumes. + + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + """ + _, ax = Function.compare_plots( + [self.tank.liquid_volume, self.tank.gas_volume], + *self.flux_time, + title="Fluid Volume (m^3) x Time (s)", + xlabel="Time (s)", + ylabel="Volume (m^3)", + show=False, + return_object=True, + ) + ax.legend(["Liquid", "Gas"]) + show_or_save_plot(filename) + + def fluid_height(self, filename=None): + """Plots both the liquid and gas fluid height. + + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + """ + _, ax = Function.compare_plots( + [self.tank.liquid_height, self.tank.gas_height], + *self.flux_time, + title="Fluid Height (m) x Time (s)", + xlabel="Time (s)", + ylabel="Height (m)", + show=False, + return_object=True, + ) + ax.legend(["Liquid", "Gas"]) + show_or_save_plot(filename) + + def fluid_center_of_mass(self, filename=None): + """Plots the gas, liquid and combined center of mass. + + Parameters + ---------- + filename : str | None, optional + The path the plot should be saved to. By default None, in which case + the plot will be shown instead of saved. Supported file endings are: + eps, jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff + and webp (these are the formats supported by matplotlib). + """ + _, ax = Function.compare_plots( + [ + self.tank.liquid_center_of_mass, + self.tank.gas_center_of_mass, + self.tank.center_of_mass, + ], + *self.flux_time, + title="Fluid Center of Mass (m) x Time (s)", + xlabel="Time (s)", + ylabel="Center of Mass (m)", + show=False, + return_object=True, + ) + # Change style of lines + ax.lines[0].set_linestyle("--") + ax.lines[1].set_linestyle("-.") + ax.legend(["Liquid", "Gas", "Total"]) + show_or_save_plot(filename) + def all(self): """Prints out all graphs available about the Tank. It simply calls all the other plotter methods in this class. @@ -109,3 +188,10 @@ def all(self): ------- None """ + self.draw() + self.tank.fluid_mass.plot(*self.flux_time) + self.tank.net_mass_flow_rate.plot(*self.flux_time) + self.fluid_height() + self.fluid_volume() + self.fluid_center_of_mass() + self.tank.inertia.plot(*self.flux_time) diff --git a/rocketpy/prints/fluid_prints.py b/rocketpy/prints/fluid_prints.py index a90aac229..7b2d7cac8 100644 --- a/rocketpy/prints/fluid_prints.py +++ b/rocketpy/prints/fluid_prints.py @@ -32,3 +32,5 @@ def all(self): ------- None """ + print(f"Name: {self.fluid.name}") + print(f"Density: {self.fluid.density:.4f} kg/m^3") diff --git a/rocketpy/prints/tank_prints.py b/rocketpy/prints/tank_prints.py index 41732c6cf..7c61aa67f 100644 --- a/rocketpy/prints/tank_prints.py +++ b/rocketpy/prints/tank_prints.py @@ -25,6 +25,37 @@ def __init__( """ self.tank = tank + def fluid_parameters(self): + """Prints out the fluid parameters of the Tank. + + Returns + ------- + None + """ + print(f"Tank '{self.tank.name}' Fluid Parameters:") + print("\nLiquid Fluid") + self.tank.liquid.prints.all() + print("\nGas Fluid") + self.tank.gas.prints.all() + + def mass_flux(self): + """Prints out the mass flux of the Tank. + + Returns + ------- + None + """ + initial_time, final_time = self.tank.flux_time + print(f"\nTank '{self.tank.name}' Mass Flux Data:") + print(f"\nInitial Quantities at t = {initial_time:.2f} s:") + print(f"Initial Fluid Mass: {self.tank.fluid_mass(initial_time):.3e} kg") + print(f"Initial Liquid Volume: {self.tank.liquid_volume(initial_time):.3e} m^3") + print(f"Initial Liquid Level: {self.tank.liquid_height(initial_time):.3e} m") + print(f"\nFinal Quantities at t = {final_time:.2f} s:") + print(f"Final Fluid Mass: {self.tank.fluid_mass(final_time):.3e} kg") + print(f"Final Liquid Volume: {self.tank.liquid_volume(final_time):.3e} m^3") + print(f"Final Liquid Level: {self.tank.liquid_height(final_time):.3e} m") + def all(self): """Prints out all data available about the Tank. @@ -32,3 +63,7 @@ def all(self): ------- None """ + print(f"\nTank '{self.tank.name}' Data:\n") + self.tank.geometry.prints.all() + self.fluid_parameters() + self.mass_flux() diff --git a/tests/conftest.py b/tests/conftest.py index a23f5fd89..31f53484a 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -9,6 +9,7 @@ "tests.fixtures.motor.hybrid_fixtures", "tests.fixtures.motor.solid_motor_fixtures", "tests.fixtures.motor.tanks_fixtures", + "tests.fixtures.motor.tank_geometry_fixtures", "tests.fixtures.motor.generic_motor_fixtures", "tests.fixtures.parachutes.parachute_fixtures", "tests.fixtures.rockets.rocket_fixtures", diff --git a/tests/fixtures/hybrid/__init__.py b/tests/fixtures/hybrid/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/tests/fixtures/hybrid/hybrid_fixtures.py b/tests/fixtures/hybrid/hybrid_fixtures.py deleted file mode 100644 index 0f5a872fb..000000000 --- a/tests/fixtures/hybrid/hybrid_fixtures.py +++ /dev/null @@ -1,276 +0,0 @@ -import numpy as np -import pytest - -from rocketpy import ( - CylindricalTank, - Fluid, - Function, - HybridMotor, - LevelBasedTank, - LiquidMotor, - MassBasedTank, - SphericalTank, - UllageBasedTank, -) - - -@pytest.fixture -def pressurant_fluid(): - """An example of a pressurant fluid as N2 gas at - 273.15K and 30MPa. - - Returns - ------- - rocketpy.Fluid - An object of the Fluid class. - """ - return Fluid(name="N2", density=300) - - -@pytest.fixture -def fuel_pressurant(): - """An example of a pressurant fluid as N2 gas at - 273.15K and 2MPa. - - Returns - ------- - rocketpy.Fluid - An object of the Fluid class. - """ - return Fluid(name="N2", density=25) - - -@pytest.fixture -def oxidizer_pressurant(): - """An example of a pressurant fluid as N2 gas at - 273.15K and 3MPa. - - Returns - ------- - rocketpy.Fluid - An object of the Fluid class. - """ - return Fluid(name="N2", density=35) - - -@pytest.fixture -def fuel_fluid(): - """An example of propane as fuel fluid at - 273.15K and 2MPa. - - Returns - ------- - rocketpy.Fluid - An object of the Fluid class. - """ - return Fluid(name="Propane", density=500) - - -@pytest.fixture -def oxidizer_fluid(): - """An example of liquid oxygen as oxidizer fluid at - 100K and 3MPa. - - Returns - ------- - rocketpy.Fluid - An object of the Fluid class. - """ - return Fluid(name="O2", density=1000) - - -@pytest.fixture -def pressurant_tank(pressurant_fluid): - """An example of a pressurant cylindrical tank with spherical - caps. - - Parameters - ---------- - pressurant_fluid : rocketpy.Fluid - Pressurizing fluid. This is a pytest fixture. - - Returns - ------- - rocketpy.MassBasedTank - An object of the CylindricalTank class. - """ - geometry = CylindricalTank(0.135 / 2, 0.981, spherical_caps=True) - pressurant_tank = MassBasedTank( - name="Pressure Tank", - geometry=geometry, - liquid_mass=0, - flux_time=(8, 20), - gas_mass="data/rockets/berkeley/pressurantMassFiltered.csv", - gas=pressurant_fluid, - liquid=pressurant_fluid, - ) - - return pressurant_tank - - -@pytest.fixture -def fuel_tank(fuel_fluid, fuel_pressurant): - """An example of a fuel cylindrical tank with spherical - caps. - - Parameters - ---------- - fuel_fluid : rocketpy.Fluid - Fuel fluid of the tank. This is a pytest fixture. - fuel_pressurant : rocketpy.Fluid - Pressurizing fluid of the fuel tank. This is a pytest - fixture. - - Returns - ------- - rocketpy.UllageBasedTank - """ - geometry = CylindricalTank(0.0744, 0.8068, spherical_caps=True) - ullage = ( - -Function("data/rockets/berkeley/test124_Propane_Volume.csv") * 1e-3 - + geometry.total_volume - ) - fuel_tank = UllageBasedTank( - name="Propane Tank", - flux_time=(8, 20), - geometry=geometry, - liquid=fuel_fluid, - gas=fuel_pressurant, - ullage=ullage, - ) - - return fuel_tank - - -@pytest.fixture -def oxidizer_tank(oxidizer_fluid, oxidizer_pressurant): - """An example of a oxidizer cylindrical tank with spherical - caps. - - Parameters - ---------- - oxidizer_fluid : rocketpy.Fluid - Oxidizer fluid of the tank. This is a pytest fixture. - oxidizer_pressurant : rocketpy.Fluid - Pressurizing fluid of the oxidizer tank. This is a pytest - fixture. - - Returns - ------- - rocketpy.UllageBasedTank - """ - geometry = CylindricalTank(0.0744, 0.8068, spherical_caps=True) - ullage = ( - -Function("data/rockets/berkeley/test124_Lox_Volume.csv") * 1e-3 - + geometry.total_volume - ) - oxidizer_tank = UllageBasedTank( - name="Lox Tank", - flux_time=(8, 20), - geometry=geometry, - liquid=oxidizer_fluid, - gas=oxidizer_pressurant, - ullage=ullage, - ) - - return oxidizer_tank - - -@pytest.fixture -def liquid_motor(pressurant_tank, fuel_tank, oxidizer_tank): - """An example of a liquid motor with pressurant, fuel and oxidizer tanks. - - Parameters - ---------- - pressurant_tank : rocketpy.MassBasedTank - Tank that contains pressurizing fluid. This is a pytest fixture. - fuel_tank : rocketpy.UllageBasedTank - Tank that contains the motor fuel. This is a pytest fixture. - oxidizer_tank : rocketpy.UllageBasedTank - Tank that contains the motor oxidizer. This is a pytest fixture. - - Returns - ------- - rocketpy.LiquidMotor - """ - liquid_motor = LiquidMotor( - thrust_source="data/rockets/berkeley/test124_Thrust_Curve.csv", - burn_time=(8, 20), - dry_mass=10, - dry_inertia=(5, 5, 0.2), - center_of_dry_mass_position=0, - nozzle_position=-1.364, - nozzle_radius=0.069 / 2, - ) - liquid_motor.add_tank(pressurant_tank, position=2.007) - liquid_motor.add_tank(fuel_tank, position=-1.048) - liquid_motor.add_tank(oxidizer_tank, position=0.711) - - return liquid_motor - - -@pytest.fixture -def spherical_oxidizer_tank(oxidizer_fluid, oxidizer_pressurant): - """An example of a oxidizer spherical tank. - - Parameters - ---------- - oxidizer_fluid : rocketpy.Fluid - Oxidizer fluid of the tank. This is a pytest fixture. - oxidizer_pressurant : rocketpy.Fluid - Pressurizing fluid of the oxidizer tank. This is a pytest - fixture. - - Returns - ------- - rocketpy.UllageBasedTank - """ - geometry = SphericalTank(0.05) - liquid_level = Function(lambda t: 0.1 * np.exp(-t / 2) - 0.05) - oxidizer_tank = LevelBasedTank( - name="Lox Tank", - flux_time=10, - geometry=geometry, - liquid=oxidizer_fluid, - gas=oxidizer_pressurant, - liquid_height=liquid_level, - ) - - return oxidizer_tank - - -@pytest.fixture -def hybrid_motor(spherical_oxidizer_tank): - """An example of a hybrid motor with spherical oxidizer - tank and fuel grains. - - Parameters - ---------- - spherical_oxidizer_tank : rocketpy.LevelBasedTank - Example Tank that contains the motor oxidizer. This is a - pytest fixture. - - Returns - ------- - rocketpy.HybridMotor - """ - motor = HybridMotor( - thrust_source=lambda t: 2000 - 100 * t, - burn_time=10, - center_of_dry_mass_position=0, - dry_inertia=(4, 4, 0.1), - dry_mass=8, - grain_density=1700, - grain_number=4, - grain_initial_height=0.1, - grain_separation=0, - grain_initial_inner_radius=0.04, - grain_outer_radius=0.1, - nozzle_position=-0.4, - nozzle_radius=0.07, - grains_center_of_mass_position=-0.1, - ) - - motor.add_tank(spherical_oxidizer_tank, position=0.3) - - return motor diff --git a/tests/fixtures/motor/data/cylindrical_oxidizer_tank_expected.csv b/tests/fixtures/motor/data/cylindrical_oxidizer_tank_expected.csv new file mode 100644 index 000000000..22652a6ce --- /dev/null +++ b/tests/fixtures/motor/data/cylindrical_oxidizer_tank_expected.csv @@ -0,0 +1,26 @@ +level_height,volume,center_of_mass,inertia +-0.4034,0.0,0.0,0.0 +-0.3697833333333333,0.00022435619637196954,-0.38148562747252746,3.2817704908519726e-05 +-0.33616666666666667,0.0007382946059424683,-0.36099299529814066,9.719213246759829e-05 +-0.30255,0.0013224978119496303,-0.3425968277449047,0.00015763404974113978 +-0.26893333333333336,0.0019070864787702323,-0.3251687335359676,0.00020622875048451338 +-0.23531666666666667,0.002491675145590835,-0.30803145186305303,0.0002442533372601051 +-0.2017,0.0030762638124114438,-0.29101919163369117,0.00027302907431388793 +-0.16808333333333333,0.003660852479232046,-0.27407206014303415,0.00029387722589183405 +-0.13446666666666668,0.004245441146052648,-0.2571631531039504,0.0003081190562399166 +-0.10085,0.004830029812873251,-0.24027859135913576,0.0003170758296041081 +-0.06723333333333337,0.005414618479693852,-0.22341048959783028,0.0003220688102303815 +-0.033616666666666684,0.005999207146514448,-0.20655403602428474,0.0003244192623647094 +0.0,0.006583795813335056,-0.1897061278394083,0.00032544845025306453 +0.03361666666666663,0.007168384480155651,-0.1728646743891429,0.0003264776381414197 +0.06723333333333331,0.00775297314697626,-0.156028215576324,0.0003288280902757475 +0.10085,0.008337561813796869,-0.13919570080300325,0.0003338210709020209 +0.13446666666666662,0.008922150480617464,-0.1223663548163549,0.00034277784426621244 +0.16808333333333325,0.009506739147438074,-0.10553959305102469,0.00035701967461429507 +0.2017,0.010091327814258668,-0.08871496639669146,0.00037786782619224097 +0.23531666666666662,0.010675916481079278,-0.07189212411203103,0.00040664356324602403 +0.26893333333333325,0.011260505147899872,-0.05507078829060723,0.000444668150021615 +0.30255,0.011845093814720481,-0.038250735887836625,0.0004932628507649892 +0.3361666666666666,0.012429297020727635,-0.021442820198694704,0.0005537047680385298 +0.36978333333333324,0.012943235430298142,-0.006612617441073541,0.0006180791955976091 +0.4034,0.013167591626670111,-8.233868449333761e-18,0.0006508969005061287 diff --git a/tests/fixtures/motor/data/cylindrical_pressurant_tank_expected.csv b/tests/fixtures/motor/data/cylindrical_pressurant_tank_expected.csv new file mode 100644 index 000000000..caf9862e7 --- /dev/null +++ b/tests/fixtures/motor/data/cylindrical_pressurant_tank_expected.csv @@ -0,0 +1,26 @@ +level_height,volume,center_of_mass,inertia +-0.4905,0.0,0.0,0.0 +-0.449625,0.0002827826025953255,-0.46411144141531335,6.113435649768482e-05 +-0.40875,0.0008480974805291653,-0.4405110759493672,0.0001657333255803535 +-0.36787499999999995,0.0014331773879828432,-0.41920154494382006,0.00025470343771418223 +-0.32699999999999996,0.0020182572954365085,-0.3983976063829787,0.00032607798469747707 +-0.28612499999999996,0.0026033372028901733,-0.3777583762886599,0.00038181202932474384 +-0.24524999999999997,0.003188417110343852,-0.3571931818181816,0.00042386063439048673 +-0.20437499999999997,0.003773497017797517,-0.33666758534850627,0.00045417886268920747 +-0.16349999999999998,0.004358576925251182,-0.3161656403940886,0.00047472177701541056 +-0.12262499999999998,0.004943656832704848,-0.2956789495114007,0.00048744444016359965 +-0.08174999999999999,0.005528736740158513,-0.27520266990291287,0.0004943019149282782 +-0.040874999999999995,0.006113816647612191,-0.2547338125548726,0.0004972492641039496 +5.551115123125783e-17,0.006698896555065856,-0.23427043269230766,0.0004982415504851175 +0.04087500000000005,0.007283976462519522,-0.21381121039056752,0.0004992338368662853 +0.08175000000000004,0.0078690563699732,-0.19335521828103658,0.0005021811860419568 +0.12262500000000004,0.008454136277426865,-0.17290178571428558,0.0005090386608066354 +0.16350000000000003,0.009039216184880532,-0.15245041567695955,0.0005217613239548245 +0.20437500000000003,0.009624296092334197,-0.13200073201338547,0.0005423042382810275 +0.24525000000000002,0.010209375999787874,-0.11155244479495245,0.000572622466579749 +0.286125,0.010794455907241539,-0.09110532695176517,0.000614671071645491 +0.327,0.011379535814695205,-0.07065919811320753,0.0006704051162727579 +0.367875,0.01196461572214887,-0.050213913189771295,0.0007417796632560529 +0.40875,0.01254969562960255,-0.029769354148844927,0.0008307497753898814 +0.449625,0.013115010507536386,-0.010007055749004045,0.0009353487444725499 +0.4905,0.013397793110131711,0.0,0.0009964831009702348 diff --git a/tests/fixtures/motor/data/spherical_oxidizer_tank_expected.csv b/tests/fixtures/motor/data/spherical_oxidizer_tank_expected.csv new file mode 100644 index 000000000..9d1cd0c62 --- /dev/null +++ b/tests/fixtures/motor/data/spherical_oxidizer_tank_expected.csv @@ -0,0 +1,26 @@ +level_height,volume,center_of_mass,inertia +-0.05,0.0,0.0,0.0 +-0.04583333333333334,2.6513248185677844e-06,-0.047232142857142854,6.095088014828042e-09 +-0.04166666666666667,1.0302290723577582e-05,-0.044485294117647074,2.175980154667401e-08 +-0.037500000000000006,2.2498384888989377e-05,-0.04176136363636364,4.363855762203842e-08 +-0.03333333333333334,3.87850944887629e-05,-0.0390625,6.908594955810907e-08 +-0.02916666666666667,5.8707906696857845e-05,-0.036391129032258066,9.609572933369276e-08 +-0.025,8.181230868723432e-05,-0.03374999999999999,1.2322978996014713e-07 +-0.020833333333333336,0.00010764378763385171,-0.031142241379310345,1.4954714785231024e-07 +-0.01666666666666667,0.00013574783071067004,-0.028571428571428584,1.7453292519943379e-07 +-0.012500000000000004,0.0001656699250916491,-0.026041666666666678,1.9802733233611288e-07 +-0.008333333333333338,0.00019695555795074912,-0.0235576923076923,2.201546501132184e-07 +-0.004166666666666673,0.00022915021646192904,-0.02112500000000001,2.412522122688262e-07 +0.0,0.00026179938779914946,-0.018749999999999996,2.61799387799151e-07 +0.004166666666666666,0.0002944485591363695,-0.01644021739130436,2.8234656332947566e-07 +0.008333333333333331,0.00032664321764755015,-0.014204545454545458,3.034441254850839e-07 +0.012499999999999997,0.0003579288505066499,-0.012053571428571419,3.2557144326218885e-07 +0.016666666666666663,0.0003878509448876289,-0.009999999999999992,3.490658503988677e-07 +0.02083333333333333,0.0004159549879644476,-0.008059210526315778,3.740516277459912e-07 +0.024999999999999994,0.0004417864669110648,-0.00624999999999999,4.0036898563815394e-07 +0.02916666666666666,0.0004648908689014413,-0.004595588235294098,4.2750304626460787e-07 +0.033333333333333326,0.0004848136811095362,-0.0031249999999999993,4.54512826040191e-07 +0.03749999999999999,0.0005011003907093094,-0.0018750000000000101,4.799602179762609e-07 +0.04166666666666666,0.000513296484874721,-0.0008928571428571531,5.018389740516248e-07 +0.04583333333333332,0.000520947450779731,-0.0002403846153846192,5.17503687583471e-07 +0.05,0.0005235987755982989,6.229927327055379e-19,5.235987755982989e-07 diff --git a/tests/fixtures/motor/data/tank_cad_validation_data_generator.ipynb b/tests/fixtures/motor/data/tank_cad_validation_data_generator.ipynb new file mode 100644 index 000000000..63131969d --- /dev/null +++ b/tests/fixtures/motor/data/tank_cad_validation_data_generator.ipynb @@ -0,0 +1,265 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# CadQuery Validation of Tank Geometry Parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook is an example of using CadQuery to compute properties, such as volume, center of mass and inertia of geometric shapes. Therefore, its main purpose is to validate the `rocketpy.TankGeometry` calculations for different fluid heights." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import cadquery as cq\n", + "import numpy as np\n", + "import csv\n", + "from rocketpy import CylindricalTank, SphericalTank" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Adding spherical caps to the tank will not modify the total height of the tank 0.8068 m. Its cylindrical portion height will be reduced to 0.6579999999999999 m.\n", + "Warning: Adding spherical caps to the tank will not modify the total height of the tank 0.981 m. Its cylindrical portion height will be reduced to 0.846 m.\n" + ] + } + ], + "source": [ + "# Create fixtures geometries\n", + "geometry_map = {}\n", + "geometry_map[\"sphere\"] = {\"spherical_oxidizer_tank\": SphericalTank(0.05)}\n", + "geometry_map[\"cylinder\"] = {\n", + " \"cylindrical_oxidizer_tank\": CylindricalTank(0.0744, 0.8068, True),\n", + " \"cylindrical_pressurant_tank\": CylindricalTank(0.135 / 2, 0.981, True),\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def export_expected_parameters(name, datapoints):\n", + " with open(f'{name}_expected.csv', mode='w') as output_file:\n", + " writer = csv.writer(output_file)\n", + " writer.writerow(['level_height', 'volume', 'center_of_mass', 'inertia'])\n", + " for data_row in datapoints:\n", + " writer.writerow([*data_row])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## CadQuery of a Spherical Tank" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A spherical shape is easily defined in CadQuery by using the `sphere` function. Furthermore, different levels of fluid height can be defined by using the `cut` function, i.e., subtracting a cylinder from the desired height and above." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def evaluate_sphere_parameters(radius, level_height):\n", + " \"\"\"Computes the volume, center of mass and inertia\n", + " (with respect to the origin) of a sphere 'filled' up\n", + " to a certain level height.\n", + "\n", + " Parameters\n", + " ----------\n", + " radius : float\n", + " The radius of the sphere.\n", + " level_height : float\n", + " The height of the liquid inside the sphere.\n", + "\n", + " Returns\n", + " -------\n", + " volume : float\n", + " The volume of the sphere.\n", + " center_of_mass : float\n", + " The center of mass of the sphere.\n", + " inertia : float\n", + " The inertia of the sphere with respect to the origin.\n", + " \"\"\"\n", + " sphere = cq.Workplane(\"XY\").sphere(radius)\n", + "\n", + " # Cut the sphere to the level height\n", + " drill_height = 10 * radius\n", + " sphere = sphere.cut(\n", + " cq.Workplane(\"XY\")\n", + " .cylinder(drill_height, radius)\n", + " .translate((0, 0, drill_height / 2 + level_height))\n", + " )\n", + "\n", + " # Uncomment to display the CAD\n", + " # display(sphere)\n", + "\n", + " primitive = sphere.val()\n", + "\n", + " volume = primitive.Volume()\n", + " center_of_mass = primitive.centerOfMass(primitive)\n", + " inertia_tensor = primitive.matrixOfInertia(primitive)\n", + "\n", + " return volume, center_of_mass.z, inertia_tensor[0][0] + volume * center_of_mass.z**2" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "for name, sphere_geometry in geometry_map[\"sphere\"].items():\n", + " radius = sphere_geometry.total_height / 2\n", + " datapoints = []\n", + " for h in np.linspace(-radius, radius, 25):\n", + " datapoints.append([h, *evaluate_sphere_parameters(radius, h)])\n", + "\n", + " export_expected_parameters(name, datapoints)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## CadQuery of a Cylindrical Tank with Caps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A cylindrical shape with caps is defined by using the `cylinder` function for the main body, the `sphere` function for the caps and uniting the three shapes into one. Similarly to the sphere case, the fluid height is defined by using the `cut` function, i.e., subtracting a cylinder from the desired height and above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def evaluate_cylinder_with_caps(total_length, radius, level_height):\n", + " \"\"\"Computes the volume, center of mass and inertia (with respect\n", + " to the origin) of a cylinder with spherical caps 'filled' up to a\n", + " certain level height.\n", + "\n", + " Parameters\n", + " ----------\n", + " total_length : float\n", + " The total length of the cylinder (with caps).\n", + " radius : float\n", + " The radius of the cylinder.\n", + "\n", + " Returns\n", + " -------\n", + " volume : float\n", + " The volume of the cylinder with caps.\n", + " center_of_mass : float\n", + " The z-coordinate of the center of mass.\n", + " inertia : float\n", + " The inertia of the cylinder with caps with respect to the origin.\n", + " \"\"\"\n", + " cylinder_height = total_length - 2 * radius\n", + "\n", + " cylinder = cq.Workplane(\"XY\").cylinder(cylinder_height, radius)\n", + " top_cap = (\n", + " cq.Workplane(\"XY\").sphere(radius).translate((0, 0, total_length / 2 - radius))\n", + " )\n", + " bottom_cap = (\n", + " cq.Workplane(\"XY\").sphere(radius).translate((0, 0, -total_length / 2 + radius))\n", + " )\n", + "\n", + " solid = cylinder.union(top_cap).union(bottom_cap)\n", + "\n", + " # Remove solid above the level_height\n", + " drill_height = 10 * total_length\n", + " solid = solid.cut(\n", + " cq.Workplane(\"XY\")\n", + " .cylinder(drill_height, radius)\n", + " .translate((0, 0, drill_height / 2 + level_height))\n", + " )\n", + " # Uncomment to display the CAD\n", + " # display(solid)\n", + "\n", + " primitive = solid.val()\n", + "\n", + " volume = primitive.Volume()\n", + " center_of_mass = primitive.centerOfMass(primitive)\n", + " inertia_tensor = primitive.matrixOfInertia(primitive)\n", + "\n", + " return volume, center_of_mass.z, inertia_tensor[0][0] + volume * center_of_mass.z**2" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "for name, cylinder_geometry in geometry_map[\"cylinder\"].items():\n", + " radius = cylinder_geometry.radius(0)\n", + " length = cylinder_geometry.total_height\n", + " datapoints = []\n", + " for h in np.linspace(-length / 2, length / 2, 25):\n", + " datapoints.append([h, *evaluate_cylinder_with_caps(length, radius, h)])\n", + "\n", + " export_expected_parameters(name, datapoints)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv312", + "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": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tests/fixtures/motor/liquid_fixtures.py b/tests/fixtures/motor/liquid_fixtures.py index 7cd708a48..0711f56d6 100644 --- a/tests/fixtures/motor/liquid_fixtures.py +++ b/tests/fixtures/motor/liquid_fixtures.py @@ -68,6 +68,32 @@ def oxidizer_fluid(): return Fluid(name="O2", density=1000) +@pytest.fixture +def lox_fluid_seblm(): + """A liquid oxygen fixture whose density comes + from testing data. + + Returns + ------- + rocketpy.Fluid + An object of the Fluid class. + """ + return Fluid(name="O2", density=1141.7) + + +@pytest.fixture +def nitrogen_fluid_seblm(): + """A nitrogen gas fixture whose density comes + from testing data. + + Returns + ------- + rocketpy.Fluid + An object of the Fluid class. + """ + return Fluid(name="N2", density=51.75) + + @pytest.fixture def liquid_motor(pressurant_tank, fuel_tank, oxidizer_tank): """An example of a liquid motor with pressurant, fuel and oxidizer tanks. diff --git a/tests/fixtures/motor/tank_geometry_fixtures.py b/tests/fixtures/motor/tank_geometry_fixtures.py new file mode 100644 index 000000000..c664fec21 --- /dev/null +++ b/tests/fixtures/motor/tank_geometry_fixtures.py @@ -0,0 +1,41 @@ +import pytest + +from rocketpy import CylindricalTank, SphericalTank + + +@pytest.fixture +def pressurant_tank_geometry(): + """An example of a pressurant cylindrical tank with spherical + caps. + + Returns + ------- + rocketpy.CylindricalTank + An object of the CylindricalTank class. + """ + return CylindricalTank(0.135 / 2, 0.981, spherical_caps=True) + + +@pytest.fixture +def propellant_tank_geometry(): + """An example of a cylindrical tank with spherical + caps. + + Returns + ------- + rocketpy.CylindricalTank + An object of the CylindricalTank class. + """ + return CylindricalTank(0.0744, 0.8068, spherical_caps=True) + + +@pytest.fixture +def spherical_oxidizer_geometry(): + """An example of a spherical tank. + + Returns + ------- + rocketpy.SphericalTank + An object of the SphericalTank class. + """ + return SphericalTank(0.05) diff --git a/tests/fixtures/motor/tanks_fixtures.py b/tests/fixtures/motor/tanks_fixtures.py index 7dc1904b5..40417c8a4 100644 --- a/tests/fixtures/motor/tanks_fixtures.py +++ b/tests/fixtures/motor/tanks_fixtures.py @@ -3,16 +3,334 @@ from rocketpy import ( CylindricalTank, + Fluid, Function, LevelBasedTank, MassBasedTank, - SphericalTank, + MassFlowRateBasedTank, + TankGeometry, UllageBasedTank, ) @pytest.fixture -def pressurant_tank(pressurant_fluid): +def sample_full_mass_flow_rate_tank(): + """An example of a full MassFlowRateBasedTank. + + Returns + ------- + rocketpy.MassFlowRateBasedTank + An object of the MassFlowRateBasedTank class. + """ + full_tank = MassFlowRateBasedTank( + name="Full Tank", + geometry=CylindricalTank(0.1, 1 / np.pi), + initial_liquid_mass=9, + initial_gas_mass=0.001, + liquid=Fluid("Water", 1000), + gas=Fluid("Air", 1), + liquid_mass_flow_rate_in=0, + gas_mass_flow_rate_in=0, + gas_mass_flow_rate_out=0, + liquid_mass_flow_rate_out=0, + flux_time=(0, 10), + ) + + return full_tank + + +@pytest.fixture +def sample_empty_mass_flow_rate_tank(): + """An example of an empty MassFlowRateBasedTank. + + Returns + ------- + rocketpy.MassFlowRateBasedTank + An object of the MassFlowRateBasedTank class. + """ + empty_tank = MassFlowRateBasedTank( + name="Empty Tank", + geometry=CylindricalTank(0.1, 1 / np.pi), + initial_liquid_mass=0, + initial_gas_mass=0, + liquid=Fluid("Water", 1000), + gas=Fluid("Air", 1), + liquid_mass_flow_rate_in=0, + gas_mass_flow_rate_in=0, + gas_mass_flow_rate_out=0, + liquid_mass_flow_rate_out=0, + flux_time=(0, 10), + ) + + return empty_tank + + +@pytest.fixture +def sample_full_ullage_tank(): + """An example of a UllageBasedTank full of liquid. + + Returns + ------- + rocketpy.UllageBasedTank + An object of the UllageBasedTank class. + """ + full_tank = UllageBasedTank( + name="Full Tank", + geometry=CylindricalTank(0.1, 1 / np.pi), + liquid=Fluid("Water", 1000), + gas=Fluid("Air", 1), + ullage=0, + flux_time=(0, 10), + ) + + return full_tank + + +@pytest.fixture +def sample_empty_ullage_tank(): + """An example of a UllageBasedTank with no liquid. + + Returns + ------- + rocketpy.UllageBasedTank + An object of the UllageBasedTank class. + """ + empty_tank = UllageBasedTank( + name="Empty Tank", + geometry=CylindricalTank(0.1, 1 / np.pi), + liquid=Fluid("Water", 1000), + gas=Fluid("Air", 1), + ullage=0.01, + flux_time=(0, 10), + ) + + return empty_tank + + +@pytest.fixture +def sample_full_level_tank(): + """An example of a LevelBasedTank full of liquid. + + Returns + ------- + rocketpy.LevelBasedTank + An object of the LevelBasedTank class. + """ + full_tank = LevelBasedTank( + name="Full Tank", + geometry=CylindricalTank(0.1, 1 / np.pi), + liquid=Fluid("Water", 1000), + gas=Fluid("Air", 1), + liquid_height=1 / (2 * np.pi), + flux_time=(0, 10), + ) + + return full_tank + + +@pytest.fixture +def sample_empty_level_tank(): + """An example of a LevelBasedTank with no liquid. + + Returns + ------- + rocketpy.LevelBasedTank + An object of the LevelBasedTank class. + """ + empty_tank = LevelBasedTank( + name="Empty Tank", + geometry=CylindricalTank(0.1, 1 / np.pi), + liquid=Fluid("Water", 1000), + gas=Fluid("Air", 1), + liquid_height=0, + flux_time=(0, 10), + ) + + return empty_tank + + +@pytest.fixture +def sample_full_mass_tank(): + """An example of a full MassBasedTank. + + Returns + ------- + rocketpy.MassBasedTank + An object of the MassBasedTank class. + """ + full_tank = MassBasedTank( + name="Full Tank", + geometry=CylindricalTank(0.1, 1 / np.pi), + liquid=Fluid("Water", 1000), + gas=Fluid("Air", 1), + liquid_mass=9, + gas_mass=0.001, + flux_time=(0, 10), + ) + + return full_tank + + +@pytest.fixture +def sample_empty_mass_tank(): + """An example of an empty MassBasedTank. + + Returns + ------- + rocketpy.MassBasedTank + An object of the MassBasedTank class. + """ + empty_tank = MassBasedTank( + name="Empty Tank", + geometry=CylindricalTank(0.1, 1 / np.pi), + liquid=Fluid("Water", 1000), + gas=Fluid("Air", 1), + liquid_mass=0, + gas_mass=0, + flux_time=(0, 10), + ) + + return empty_tank + + +@pytest.fixture +def real_mass_based_tank_seblm(lox_fluid_seblm, nitrogen_fluid_seblm): + """An instance of a real cylindrical tank with spherical caps. + + Parameters + ---------- + lox_fluid_seblm : rocketpy.Fluid + Liquid oxygen fluid. This is a pytest fixture. + nitrogen_fluid_seblm : rocketpy.Fluid + Nitrogen gas fluid. This is a pytest fixture. + + Returns + ------- + rocketpy.MassBasedTank + An object of the MassBasedTank class. + """ + geometry = CylindricalTank(0.0744, 0.8698, True) + + lox_tank = MassBasedTank( + name="Real Tank", + geometry=geometry, + flux_time=(0, 15.583), + liquid_mass="./data/rockets/berkeley/Test135LoxMass.csv", + gas_mass="./data/rockets/berkeley/Test135GasMass.csv", + liquid=lox_fluid_seblm, + gas=nitrogen_fluid_seblm, + discretize=200, + ) + + return lox_tank + + +@pytest.fixture +def example_mass_based_tank_seblm(lox_fluid_seblm, nitrogen_fluid_seblm): + """Example data of a cylindrical tank with spherical caps. + + Parameters + ---------- + lox_fluid_seblm : rocketpy.Fluid + Liquid oxygen fluid. This is a pytest fixture. + nitrogen_fluid_seblm : rocketpy.Fluid + Nitrogen gas fluid. This is a pytest fixture. + + Returns + ------- + rocketpy.MassBasedTank + An object of the MassBasedTank class. + """ + geometry = TankGeometry({(0, 5): 1}) + + example_tank = MassBasedTank( + name="Example Tank", + geometry=geometry, + flux_time=(0, 10), + liquid_mass="./data/rockets/berkeley/ExampleTankLiquidMassData.csv", + gas_mass="./data/rockets/berkeley/ExampleTankGasMassData.csv", + liquid=lox_fluid_seblm, + gas=nitrogen_fluid_seblm, + discretize=None, + ) + + return example_tank + + +@pytest.fixture +def real_level_based_tank_seblm(lox_fluid_seblm, nitrogen_fluid_seblm): + """An instance of a real cylindrical tank with spherical caps. + + Parameters + ---------- + lox_fluid_seblm : rocketpy.Fluid + Liquid oxygen fluid. This is a pytest fixture. + nitrogen_fluid_seblm : rocketpy.Fluid + Nitrogen gas fluid. This is a pytest fixture. + + Returns + ------- + rocketpy.LevelBasedTank + An object of the LevelBasedTank class. + """ + geometry = TankGeometry( + { + (0, 0.0559): lambda h: np.sqrt(0.0775**2 - (0.0775 - h) ** 2), + (0.0559, 0.7139): 0.0744, + (0.7139, 0.7698): lambda h: np.sqrt(0.0775**2 - (h - 0.6924) ** 2), + } + ) + + level_tank = LevelBasedTank( + name="Level Tank", + geometry=geometry, + flux_time=(0, 15.583), + gas=nitrogen_fluid_seblm, + liquid=lox_fluid_seblm, + liquid_height="./data/rockets/berkeley/loxUllage.csv", + discretize=None, + ) + + return level_tank + + +@pytest.fixture +def example_mass_flow_rate_based_tank_seblm(lox_fluid_seblm, nitrogen_fluid_seblm): + """An instance of a example cylindrical tank whose flux + is given by mass flow rates. + + Parameters + ---------- + lox_fluid_seblm : rocketpy.Fluid + Liquid oxygen fluid. This is a pytest fixture. + nitrogen_fluid_seblm : rocketpy.Fluid + Nitrogen gas fluid. This is a pytest fixture. + + Returns + ------- + rocketpy.MassFlowRateBasedTank + An object of the MassFlowRateBasedTank class. + """ + mass_flow_rate_tank = MassFlowRateBasedTank( + name="Test Tank", + geometry=TankGeometry({(0, 1): 1}), + flux_time=(0, 10), + initial_liquid_mass=5, + initial_gas_mass=0.1, + liquid_mass_flow_rate_in=0.1, + gas_mass_flow_rate_in=0.01, + liquid_mass_flow_rate_out=0.2, + gas_mass_flow_rate_out=0.02, + liquid=lox_fluid_seblm, + gas=nitrogen_fluid_seblm, + discretize=11, + ) + + return mass_flow_rate_tank + + +@pytest.fixture +def pressurant_tank(pressurant_fluid, pressurant_tank_geometry): """An example of a pressurant cylindrical tank with spherical caps. @@ -20,16 +338,17 @@ def pressurant_tank(pressurant_fluid): ---------- pressurant_fluid : rocketpy.Fluid Pressurizing fluid. This is a pytest fixture. + pressurant_tank_geometry : rocketpy.CylindricalTank + Geometry of the pressurant tank. This is a pytest fixture. Returns ------- rocketpy.MassBasedTank An object of the CylindricalTank class. """ - geometry = CylindricalTank(0.135 / 2, 0.981, spherical_caps=True) pressurant_tank = MassBasedTank( name="Pressure Tank", - geometry=geometry, + geometry=pressurant_tank_geometry, liquid_mass=0, flux_time=(8, 20), gas_mass="data/rockets/berkeley/pressurantMassFiltered.csv", @@ -41,7 +360,7 @@ def pressurant_tank(pressurant_fluid): @pytest.fixture -def fuel_tank(fuel_fluid, fuel_pressurant): +def fuel_tank(fuel_fluid, fuel_pressurant, propellant_tank_geometry): """An example of a fuel cylindrical tank with spherical caps. @@ -52,20 +371,21 @@ def fuel_tank(fuel_fluid, fuel_pressurant): fuel_pressurant : rocketpy.Fluid Pressurizing fluid of the fuel tank. This is a pytest fixture. + propellant_tank_geometry : rocketpy.CylindricalTank + Geometry of the fuel tank. This is a pytest fixture. Returns ------- rocketpy.UllageBasedTank """ - geometry = CylindricalTank(0.0744, 0.8068, spherical_caps=True) ullage = ( -Function("data/rockets/berkeley/test124_Propane_Volume.csv") * 1e-3 - + geometry.total_volume + + propellant_tank_geometry.total_volume ) fuel_tank = UllageBasedTank( name="Propane Tank", flux_time=(8, 20), - geometry=geometry, + geometry=propellant_tank_geometry, liquid=fuel_fluid, gas=fuel_pressurant, ullage=ullage, @@ -75,7 +395,7 @@ def fuel_tank(fuel_fluid, fuel_pressurant): @pytest.fixture -def oxidizer_tank(oxidizer_fluid, oxidizer_pressurant): +def oxidizer_tank(oxidizer_fluid, oxidizer_pressurant, propellant_tank_geometry): """An example of a oxidizer cylindrical tank with spherical caps. @@ -86,20 +406,21 @@ def oxidizer_tank(oxidizer_fluid, oxidizer_pressurant): oxidizer_pressurant : rocketpy.Fluid Pressurizing fluid of the oxidizer tank. This is a pytest fixture. + propellant_tank_geometry : rocketpy.CylindricalTank + Geometry of the oxidizer tank. This is a pytest fixture. Returns ------- rocketpy.UllageBasedTank """ - geometry = CylindricalTank(0.0744, 0.8068, spherical_caps=True) ullage = ( -Function("data/rockets/berkeley/test124_Lox_Volume.csv") * 1e-3 - + geometry.total_volume + + propellant_tank_geometry.total_volume ) oxidizer_tank = UllageBasedTank( name="Lox Tank", flux_time=(8, 20), - geometry=geometry, + geometry=propellant_tank_geometry, liquid=oxidizer_fluid, gas=oxidizer_pressurant, ullage=ullage, @@ -109,7 +430,9 @@ def oxidizer_tank(oxidizer_fluid, oxidizer_pressurant): @pytest.fixture -def spherical_oxidizer_tank(oxidizer_fluid, oxidizer_pressurant): +def spherical_oxidizer_tank( + oxidizer_fluid, oxidizer_pressurant, spherical_oxidizer_geometry +): """An example of a oxidizer spherical tank. Parameters @@ -122,14 +445,13 @@ def spherical_oxidizer_tank(oxidizer_fluid, oxidizer_pressurant): Returns ------- - rocketpy.UllageBasedTank + rocketpy.LevelBasedTank """ - geometry = SphericalTank(0.05) liquid_level = Function(lambda t: 0.1 * np.exp(-t / 2) - 0.05) oxidizer_tank = LevelBasedTank( name="Lox Tank", flux_time=10, - geometry=geometry, + geometry=spherical_oxidizer_geometry, liquid=oxidizer_fluid, gas=oxidizer_pressurant, liquid_height=liquid_level, diff --git a/tests/integration/test_tank.py b/tests/integration/test_tank.py new file mode 100644 index 000000000..f79d670ab --- /dev/null +++ b/tests/integration/test_tank.py @@ -0,0 +1,35 @@ +# pylint: disable=unused-argument + +from unittest.mock import patch + +import pytest + + +@patch("matplotlib.pyplot.show") +@pytest.mark.parametrize( + "fixture_name", + [ + "sample_full_mass_flow_rate_tank", + "sample_empty_mass_flow_rate_tank", + "sample_full_ullage_tank", + "sample_empty_ullage_tank", + "sample_full_level_tank", + "sample_empty_level_tank", + "sample_full_mass_tank", + "sample_empty_mass_tank", + "real_mass_based_tank_seblm", + "pressurant_tank", + "fuel_tank", + "oxidizer_tank", + "spherical_oxidizer_tank", + ], +) +def test_tank_all_info(mock_show, fixture_name, request): + tank = request.getfixturevalue(fixture_name) + + assert tank.info() is None + assert tank.all_info() is None + + assert (tank.gas_height <= tank.geometry.top).all + assert (tank.liquid_height <= tank.geometry.top).all + assert (tank.fluid_volume <= tank.geometry.total_volume).all diff --git a/tests/unit/test_tank.py b/tests/unit/test_tank.py index 3002bee43..a313caf20 100644 --- a/tests/unit/test_tank.py +++ b/tests/unit/test_tank.py @@ -1,959 +1,357 @@ -# TODO: This file must be refactored to improve readability and maintainability. -# pylint: disable=too-many-statements -import os from math import isclose +from pathlib import Path import numpy as np import pytest +import scipy.integrate as spi -from rocketpy import ( - Fluid, - Function, - LevelBasedTank, - MassBasedTank, - MassFlowRateBasedTank, - TankGeometry, -) - -pressurant_params = (0.135 / 2, 0.981) -fuel_params = (0.0744, 0.8068) -oxidizer_params = (0.0744, 0.8068) +BASE_PATH = Path("./data/rockets/berkeley/") -parametrize_fixtures = pytest.mark.parametrize( +@pytest.mark.parametrize( "params", [ - ("pressurant_tank", pressurant_params), - ("fuel_tank", fuel_params), - ("oxidizer_tank", oxidizer_params), + ( + "real_mass_based_tank_seblm", + BASE_PATH / "Test135LoxMass.csv", + BASE_PATH / "Test135GasMass.csv", + ), + ( + "example_mass_based_tank_seblm", + BASE_PATH / "ExampleTankLiquidMassData.csv", + BASE_PATH / "ExampleTankGasMassData.csv", + ), ], ) +def test_mass_based_tank_fluid_mass(params, request): + """Test the fluid_mass property of the MassBasedTank subclass of Tank + class. - -@parametrize_fixtures -def test_tank_bounds(params, request): - """Test basic geometric properties of the tanks.""" - tank, (expected_radius, expected_height) = params - tank = request.getfixturevalue(tank) - - expected_total_height = expected_height - - assert tank.geometry.radius(0) == pytest.approx(expected_radius, abs=1e-6) - assert tank.geometry.total_height == pytest.approx(expected_total_height, abs=1e-6) - - -@parametrize_fixtures -def test_tank_coordinates(params, request): - """Test basic coordinate values of the tanks.""" - tank, (_, height) = params - tank = request.getfixturevalue(tank) - - expected_bottom = -height / 2 - expected_top = height / 2 - - assert tank.geometry.bottom == pytest.approx(expected_bottom, abs=1e-6) - assert tank.geometry.top == pytest.approx(expected_top, abs=1e-6) - - -@parametrize_fixtures -def test_tank_total_volume(params, request): - """Test the total volume of the tanks comparing to the analytically - calculated values. + Parameters + ---------- + params : tuple + A tuple containing test parameters. + request : _pytest.fixtures.FixtureRequest + A pytest fixture request object. """ - tank, (radius, height) = params + tank, liq_path, gas_path = params tank = request.getfixturevalue(tank) - - expected_total_volume = ( - np.pi * radius**2 * (height - 2 * radius) + 4 / 3 * np.pi * radius**3 + expected_liquid_mass = np.loadtxt(liq_path, skiprows=1, delimiter=",") + expected_gas_mass = np.loadtxt(gas_path, skiprows=1, delimiter=",") + + assert np.allclose( + expected_liquid_mass[:, 1], + tank.liquid_mass(expected_liquid_mass[:, 0]), + rtol=1e-2, + atol=1e-4, + ) + assert np.allclose( + expected_gas_mass[:, 1], + tank.gas_mass(expected_gas_mass[:, 0]), + rtol=1e-1, + atol=1e-4, ) - assert tank.geometry.total_volume == pytest.approx(expected_total_volume, abs=1e-6) - - -@parametrize_fixtures -def test_tank_volume(params, request): - """Test the volume of the tanks at different heights comparing to the - analytically calculated values. - """ - tank, (radius, height) = params - tank = request.getfixturevalue(tank) - - total_height = height - bottom = -height / 2 - top = height / 2 - - expected_volume = tank_volume_function(radius, total_height, bottom) - - for h in np.linspace(bottom, top, 101): - assert tank.geometry.volume(h) == pytest.approx(expected_volume(h), abs=1e-6) - - -@parametrize_fixtures -def test_tank_centroid(params, request): - """Test the centroid of the tanks at different heights comparing to the - analytically calculated values. - """ - tank, (radius, height) = params - tank = request.getfixturevalue(tank) - - total_height = height - bottom = -height / 2 - - expected_centroid = tank_centroid_function(radius, total_height, bottom) - - for h, liquid_com in zip( - tank.liquid_height.y_array, tank.liquid_center_of_mass.y_array - ): - # Loss of accuracy to 1e-3 when liquid height is close to zero - assert liquid_com == pytest.approx(expected_centroid(h), abs=1e-3) +@pytest.mark.parametrize( + "params", + [ + ( + "real_mass_based_tank_seblm", + BASE_PATH / "Test135LoxMass.csv", + BASE_PATH / "Test135GasMass.csv", + ), + ( + "example_mass_based_tank_seblm", + BASE_PATH / "ExampleTankLiquidMassData.csv", + BASE_PATH / "ExampleTankGasMassData.csv", + ), + ], +) +def test_mass_based_tank_net_mass_flow_rate(params, request): + """Test the net_mass_flow_rate property of the MassBasedTank + subclass of Tank. -@parametrize_fixtures -def test_tank_inertia(params, request): - """Test the inertia of the tanks at different heights comparing to the - analytically calculated values. + Parameters + ---------- + params : tuple + A tuple containing test parameters. + request : _pytest.fixtures.FixtureRequest + A pytest fixture request object. """ - tank, (radius, height) = params + tank, liq_path, gas_path = params tank = request.getfixturevalue(tank) + expected_liquid_mass = np.loadtxt(liq_path, skiprows=1, delimiter=",") + expected_gas_mass = np.loadtxt(gas_path, skiprows=1, delimiter=",") - total_height = height - bottom = -height / 2 - - expected_inertia = tank_inertia_function(radius, total_height, bottom) - - for h in tank.liquid_height.y_array: - assert tank.geometry.Ix_volume(tank.geometry.bottom, h)(h) == pytest.approx( - expected_inertia(h)[0], abs=1e-5 - ) - - -def test_mass_based_tank(): - """Tests the MassBasedTank subclass of Tank regarding its mass and - net mass flow rate properties. The test is performed on both a real - tank and a simplified tank. - """ - lox = Fluid(name="LOx", density=1141.7) - n2 = Fluid( - name="Nitrogen Gas", - density=51.75, - ) # density value may be estimate - - def top_endcap(y): - """Calculate the top endcap based on hemisphere equation. - - Parameters: - y (float): The y-coordinate. - - Returns: - float: The result of the hemisphere equation for the top endcap. - """ - return np.sqrt(0.0775**2 - (y - 0.7924) ** 2) - - def bottom_endcap(y): - """Calculate the bottom endcap based on hemisphere equation. - - Parameters: - y (float): The y-coordinate. - - Returns: - float: The result of the hemisphere equation for the bottom endcap. - """ - return np.sqrt(0.0775**2 - (0.0775 - y) ** 2) - - # Generate tank geometry {radius: height, ...} - real_geometry = TankGeometry( - { - (0, 0.0559): bottom_endcap, - (0.0559, 0.8039): lambda y: 0.0744, - (0.8039, 0.8698): top_endcap, - } + # Noisy derivatives, assert integrals + initial_mass = expected_liquid_mass[0, 1] + expected_gas_mass[0, 1] + expected_mass_variation = ( + expected_liquid_mass[-1, 1] + expected_gas_mass[-1, 1] - initial_mass ) - - # Import liquid mass data - lox_masses = "./data/rockets/berkeley/Test135LoxMass.csv" - example_liquid_masses = "./data/rockets/berkeley/ExampleTankLiquidMassData.csv" - - # Import gas mass data - gas_masses = "./data/rockets/berkeley/Test135GasMass.csv" - example_gas_masses = "./data/rockets/berkeley/ExampleTankGasMassData.csv" - - # Generate tanks based on Berkeley SEB team's real tank geometries - real_tank_lox = MassBasedTank( - name="Real Tank", - geometry=real_geometry, - flux_time=(0, 10), - liquid_mass=lox_masses, - gas_mass=gas_masses, - liquid=lox, - gas=n2, + computed_final_mass = spi.simpson( + tank.net_mass_flow_rate.y_array, + x=tank.net_mass_flow_rate.x_array, ) - # Generate tank geometry {radius: height, ...} - example_geometry = TankGeometry({(0, 5): 1}) - - # Generate tanks based on simplified tank geometry - example_tank_lox = MassBasedTank( - name="Example Tank", - geometry=example_geometry, - flux_time=(0, 10), - liquid_mass=example_liquid_masses, - gas_mass=example_gas_masses, - liquid=lox, - gas=n2, - discretize=None, - ) - - # Assert volume bounds - # pylint: disable=comparison-with-callable - assert (real_tank_lox.gas_height <= real_tank_lox.geometry.top).all - assert (real_tank_lox.fluid_volume <= real_tank_lox.geometry.total_volume).all - assert (example_tank_lox.gas_height <= example_tank_lox.geometry.top).all - assert (example_tank_lox.fluid_volume <= example_tank_lox.geometry.total_volume).all - - initial_liquid_mass = 5 - initial_gas_mass = 0 - liquid_mass_flow_rate_in = 0.1 - gas_mass_flow_rate_in = 0.1 - liquid_mass_flow_rate_out = 0.2 - gas_mass_flow_rate_out = 0.05 - - def test(calculated, expected, t, real=False): - """Iterate over time range and test that calculated value is close to actual value""" - j = 0 - for i in np.arange(0, t, 0.1): - try: - print(calculated.get_value(i), expected(i)) - assert isclose(calculated.get_value(i), expected(i), rel_tol=5e-2) - except IndexError: - break - - if real: - j += 4 - else: - j += 1 - - def test_mass(): - """Test mass function of MassBasedTank subclass of Tank""" - - def example_expected(t): - return ( - initial_liquid_mass - + t * (liquid_mass_flow_rate_in - liquid_mass_flow_rate_out) - + initial_gas_mass - + t * (gas_mass_flow_rate_in - gas_mass_flow_rate_out) - ) - - example_calculated = example_tank_lox.fluid_mass - - lox_vals = Function(lox_masses).y_array - - def real_expected(t): - return lox_vals[t] - - real_calculated = real_tank_lox.fluid_mass - - test(example_calculated, example_expected, 5) - test(real_calculated, real_expected, 15.5, real=True) - - def test_net_mfr(): - """Test net_mass_flow_rate function of MassBasedTank subclass of Tank""" - - def example_expected(_): - return ( - liquid_mass_flow_rate_in - - liquid_mass_flow_rate_out - + gas_mass_flow_rate_in - - gas_mass_flow_rate_out - ) - - example_calculated = example_tank_lox.net_mass_flow_rate + assert isclose(expected_mass_variation, computed_final_mass, rel_tol=1e-2) - liquid_mfrs = Function(example_liquid_masses).y_array - gas_mfrs = Function(example_gas_masses).y_array - - def real_expected(t): - return (liquid_mfrs[t] + gas_mfrs[t]) / t - - real_calculated = real_tank_lox.net_mass_flow_rate - - test(example_calculated, example_expected, 10) - test(real_calculated, real_expected, 15.5, real=True) - - test_mass() - test_net_mfr() - - -def test_level_based_tank(): - """Test LevelBasedTank subclass of Tank class using Berkeley SEB team's - tank data of fluid level. - """ - lox = Fluid(name="LOx", density=1141.7) - n2 = Fluid(name="Nitrogen Gas", density=51.75) - - test_dir = "./data/rockets/berkeley/" - - def top_endcap(y): - return np.sqrt(0.0775**2 - (y - 0.692300000000001) ** 2) - - def bottom_endcap(y): - return np.sqrt(0.0775**2 - (0.0775 - y) ** 2) - - tank_geometry = TankGeometry( - { - (0, 0.0559): bottom_endcap, - (0.0559, 0.7139): lambda y: 0.0744, - (0.7139, 0.7698): top_endcap, - } - ) - - ullage_data = Function(os.path.abspath(test_dir + "loxUllage.csv")).get_source() - level_tank = LevelBasedTank( - name="LevelTank", - geometry=tank_geometry, - flux_time=(0, 10), - gas=n2, - liquid=lox, - liquid_height=ullage_data, - discretize=None, - ) - - mass_data = Function(test_dir + "loxMass.csv").get_source() - mass_flow_rate_data = Function(test_dir + "loxMFR.csv").get_source() - - def align_time_series(small_source, large_source): - assert isinstance(small_source, np.ndarray) and isinstance( - large_source, np.ndarray - ), "Must be np.ndarrays" - if small_source.shape[0] > large_source.shape[0]: - small_source, large_source = large_source, small_source - - result_larger_source = np.ndarray(small_source.shape) - result_smaller_source = np.ndarray(small_source.shape) - tolerance = 0.1 - curr_ind = 0 - for val in small_source: - time = val[0] - delta_time_vector = abs(time - large_source[:, 0]) - large_index = np.argmin(delta_time_vector) - delta_time = abs(time - large_source[large_index][0]) - - if delta_time < tolerance: - result_larger_source[curr_ind] = large_source[large_index] - result_smaller_source[curr_ind] = val - curr_ind += 1 - return result_larger_source, result_smaller_source - - assert np.allclose(level_tank.liquid_height, ullage_data) - - calculated_mass = level_tank.liquid_mass.set_discrete( - mass_data[0][0], mass_data[0][-1], len(mass_data[0]) - ) - calculated_mass, mass_data = align_time_series( - calculated_mass.get_source(), mass_data - ) - assert np.allclose(calculated_mass, mass_data, rtol=1, atol=2) - - calculated_mfr = level_tank.net_mass_flow_rate.set_discrete( - mass_flow_rate_data[0][0], - mass_flow_rate_data[0][-1], - len(mass_flow_rate_data[0]), - ) - calculated_mfr, _ = align_time_series( - calculated_mfr.get_source(), mass_flow_rate_data - ) +def test_level_based_tank_liquid_level(real_level_based_tank_seblm): + """Test the liquid_level property of LevelBasedTank + subclass of Tank. - -def test_mfr_tank_basic(): - """Test MassFlowRateTank subclass of Tank class regarding its properties, - such as net_mass_flow_rate, fluid_mass, center_of_mass and inertia. + Parameters + ---------- + real_level_based_tank_seblm : LevelBasedTank + The LevelBasedTank to be tested. """ + tank = real_level_based_tank_seblm + level_data = np.loadtxt(BASE_PATH / "loxUllage.csv", delimiter=",") - def test(t, a, tol=1e-4): - for i in np.arange(0, 10, 1): - print(t.get_value(i), a(i)) - assert isclose(t.get_value(i), a(i), abs_tol=tol) - - def test_nmfr(): - def nmfr(_): - return ( - liquid_mass_flow_rate_in - + gas_mass_flow_rate_in - - liquid_mass_flow_rate_out - - gas_mass_flow_rate_out - ) - - test(t.net_mass_flow_rate, nmfr) - - def test_mass(): - def m(x): - return ( - initial_liquid_mass - + (liquid_mass_flow_rate_in - liquid_mass_flow_rate_out) * x - ) + ( - initial_gas_mass + (gas_mass_flow_rate_in - gas_mass_flow_rate_out) * x - ) - - lm = t.fluid_mass - test(lm, m) - - def test_liquid_height(): - def alv(x): - return ( - initial_liquid_mass - + (liquid_mass_flow_rate_in - liquid_mass_flow_rate_out) * x - ) / lox.density - - def alh(x): - return alv(x) / (np.pi) - - tlh = t.liquid_height - test(tlh, alh) - - def test_com(): - def liquid_mass(x): - return ( - initial_liquid_mass - + (liquid_mass_flow_rate_in - liquid_mass_flow_rate_out) * x - ) - - def liquid_volume(x): - return liquid_mass(x) / lox.density - - def liquid_height(x): - return liquid_volume(x) / (np.pi) - - def gas_mass(x): - return ( - initial_gas_mass + (gas_mass_flow_rate_in - gas_mass_flow_rate_out) * x - ) - - def gas_volume(x): - return gas_mass(x) / n2.density - - def gas_height(x): - return gas_volume(x) / np.pi + liquid_height(x) - - def liquid_com(x): - return liquid_height(x) / 2 - - def gas_com(x): - return (gas_height(x) - liquid_height(x)) / 2 + liquid_height(x) - - def acom(x): - return (liquid_mass(x) * liquid_com(x) + gas_mass(x) * gas_com(x)) / ( - liquid_mass(x) + gas_mass(x) - ) - - tcom = t.center_of_mass - test(tcom, acom) - - def test_inertia(): - def liquid_mass(x): - return ( - initial_liquid_mass - + (liquid_mass_flow_rate_in - liquid_mass_flow_rate_out) * x - ) - - def liquid_volume(x): - return liquid_mass(x) / lox.density + assert np.allclose(level_data, tank.liquid_height.get_source()) - def liquid_height(x): - return liquid_volume(x) / (np.pi) - def gas_mass(x): - return ( - initial_gas_mass + (gas_mass_flow_rate_in - gas_mass_flow_rate_out) * x - ) - - def gas_volume(x): - return gas_mass(x) / n2.density - - def gas_height(x): - return gas_volume(x) / np.pi + liquid_height(x) - - def liquid_com(x): - return liquid_height(x) / 2 - - def gas_com(x): - return (gas_height(x) - liquid_height(x)) / 2 + liquid_height(x) - - def acom(x): - return (liquid_mass(x) * liquid_com(x) + gas_mass(x) * gas_com(x)) / ( - liquid_mass(x) + gas_mass(x) - ) - - r = 1 - - def ixy_gas(x): - return ( - 1 / 4 * gas_mass(x) * r**2 - + 1 / 12 * gas_mass(x) * (gas_height(x) - liquid_height(x)) ** 2 - + gas_mass(x) * (gas_com(x) - acom(x)) ** 2 - ) - - def ixy_liq(x): - return ( - 1 / 4 * liquid_mass(x) * r**2 - + 1 / 12 * liquid_mass(x) * (liquid_height(x) - t.geometry.bottom) ** 2 - + liquid_mass(x) * (liquid_com(x) - acom(x)) ** 2 - ) - - def ixy(x): - return ixy_gas(x) + ixy_liq(x) - - test(t.gas_inertia, ixy_gas, tol=1e-3) - test(t.liquid_inertia, ixy_liq, tol=1e-3) - test(t.inertia, ixy, tol=1e-3) - - tank_radius_function = TankGeometry({(0, 5): 1}) - lox = Fluid( - name="LOx", - density=1141, - ) - n2 = Fluid( - name="Nitrogen Gas", - density=51.75, - ) # density value may be estimate - initial_liquid_mass = 5 - initial_gas_mass = 0.1 - liquid_mass_flow_rate_in = 0.1 - gas_mass_flow_rate_in = 0.01 - liquid_mass_flow_rate_out = 0.2 - gas_mass_flow_rate_out = 0.02 - - t = MassFlowRateBasedTank( - name="Test Tank", - geometry=tank_radius_function, - flux_time=(0, 10), - initial_liquid_mass=initial_liquid_mass, - initial_gas_mass=initial_gas_mass, - liquid_mass_flow_rate_in=Function(0.1).set_discrete(0, 10, 1000), - gas_mass_flow_rate_in=Function(0.01).set_discrete(0, 10, 1000), - liquid_mass_flow_rate_out=Function(0.2).set_discrete(0, 10, 1000), - gas_mass_flow_rate_out=Function(0.02).set_discrete(0, 10, 1000), - liquid=lox, - gas=n2, - discretize=None, - ) - - test_nmfr() - test_mass() - test_liquid_height() - test_com() - test_inertia() - - -# Auxiliary testing functions - - -def cylinder_volume(radius, height): - """Returns the volume of a cylinder with the given radius and height. +def test_level_based_tank_mass(real_level_based_tank_seblm): + """Test the mass property of LevelBasedTank subclass of Tank. Parameters ---------- - radius : float - The radius of the cylinder. - height : float - The height of the cylinder. - - Returns - ------- - float - The volume of the cylinder. + real_level_based_tank_seblm : LevelBasedTank + The LevelBasedTank to be tested. """ - return np.pi * radius**2 * height + tank = real_level_based_tank_seblm + mass_data = np.loadtxt(BASE_PATH / "loxMass.csv", delimiter=",") + # Soft tolerances for the whole curve + assert np.allclose(mass_data, tank.fluid_mass.get_source(), rtol=1e-1, atol=6e-1) -def lower_spherical_cap_volume(radius, height=None): - """Returns the volume of a spherical cap with the given radius and filled - height that is filled from its convex side. - - Parameters - ---------- - radius : float - The radius of the spherical cap. - height : float, optional - The height of the spherical cap. If not given, the radius is used. - - Returns - ------- - float - The volume of the spherical cap. - """ - if height is None: - height = radius - return np.pi / 3 * height**2 * (3 * radius - height) + # Tighter tolerances for middle of the curve + assert np.allclose( + mass_data[100:401], tank.fluid_mass.get_source()[100:401], rtol=5e-2, atol=1e-1 + ) -def upper_spherical_cap_volume(radius, height=None): - """Returns the volume of a spherical cap with the given radius and filled - height that is filled from its concave side. +def test_mass_flow_rate_tank_mass_flow_rate(example_mass_flow_rate_based_tank_seblm): + """Test the mass_flow_rate property of the MassFlowRateBasedTank + subclass of Tank. Parameters ---------- - radius : float - The radius of the spherical cap. - height : float, optional - The height of the spherical cap. If not given, the radius is used. - - Returns - ------- - float - The volume of the spherical cap. + example_mass_flow_rate_based_tank_seblm : MassFlowRateBasedTank + The MassFlowRateBasedTank to be tested. """ - if height is None: - height = radius - return np.pi / 3 * height * (3 * radius**2 - height**2) + tank = example_mass_flow_rate_based_tank_seblm + expected_mass_flow_rate = 0.1 - 0.2 + 0.01 - 0.02 -def tank_volume_function(tank_radius, tank_height, zero_height=0): - """Returns a function that calculates the volume of a cylindrical tank - with spherical caps. + assert np.allclose(expected_mass_flow_rate, tank.net_mass_flow_rate.y_array) - Parameters - ---------- - tank_radius : float - The radius of the cylindrical part of the tank. - tank_height : float - The height of the tank including caps. - zero_height : float, optional - The coordinate of the bottom of the tank. Defaults to 0. - - Returns - ------- - function - A function that calculates the volume of the tank for a given height. - """ - def tank_volume(h): - # Coordinate shift to the bottom of the tank - h = h - zero_height - if h < tank_radius: - return lower_spherical_cap_volume(tank_radius, h) - elif tank_radius <= h < tank_height - tank_radius: - return lower_spherical_cap_volume(tank_radius) + cylinder_volume( - tank_radius, h - tank_radius - ) - else: - return ( - lower_spherical_cap_volume(tank_radius) - + cylinder_volume(tank_radius, tank_height - 2 * tank_radius) - + upper_spherical_cap_volume( - tank_radius, h - (tank_height - tank_radius) - ) - ) - - return tank_volume - - -def cylinder_centroid(height): - """Returns the centroid of a cylinder with the given height. +def test_mass_flow_rate_tank_fluid_mass(example_mass_flow_rate_based_tank_seblm): + """Test the fluid_mass property of the MassFlowRateBasedTank + subclass of Tank. Parameters ---------- - height : float - The height of the cylinder. - - Returns - ------- - float - The centroid of the cylinder. + example_mass_flow_rate_based_tank_seblm : MassFlowRateBasedTank + The MassFlowRateBasedTank to be tested. """ - return height / 2 + tank = example_mass_flow_rate_based_tank_seblm + expected_initial_liquid_mass = 5 + expected_initial_gas_mass = 0.1 + expected_initial_mass = expected_initial_liquid_mass + expected_initial_gas_mass + expected_liquid_mass_flow = 0.1 - 0.2 + expected_gas_mass_flow = 0.01 - 0.02 + expected_total_mass_flow = expected_liquid_mass_flow + expected_gas_mass_flow -def lower_spherical_cap_centroid(radius, height=None): - """Returns the centroid of a spherical cap with the given radius and filled - height that is filled from its convex side. + times = np.linspace(0, 10, 11) - Parameters - ---------- - radius : float - The radius of the spherical cap. - height : float, optional - The height of the spherical cap. If not given, the radius is used. - - Returns - ------- - float - The centroid of the spherical cap. - """ - if height is None: - height = radius - return radius - (0.75 * (2 * radius - height) ** 2 / (3 * radius - height)) + assert np.allclose( + expected_initial_liquid_mass + expected_liquid_mass_flow * times, + tank.liquid_mass.y_array, + ) + assert np.allclose( + expected_initial_gas_mass + expected_gas_mass_flow * times, + tank.gas_mass.y_array, + ) + assert np.allclose( + expected_initial_mass + expected_total_mass_flow * times, + tank.fluid_mass.y_array, + ) -def upper_spherical_cap_centroid(radius, height=None): - """Returns the centroid of a spherical cap with the given radius and filled - height that is filled from its concave side. +def test_mass_flow_rate_tank_liquid_height( + example_mass_flow_rate_based_tank_seblm, lox_fluid_seblm, nitrogen_fluid_seblm +): + """Test the liquid height properties of the MassFlowRateBasedTank + subclass of Tank. Parameters ---------- - radius : float - The radius of the spherical cap. - height : float, optional - The height of the spherical cap. If not given, the radius is used. - - Returns - ------- - float - The centroid of the spherical cap. + example_mass_flow_rate_based_tank_seblm : MassFlowRateBasedTank + The MassFlowRateBasedTank to be tested. + lox_fluid_seblm : Fluid + The Fluid object representing liquid oxygen. + nitrogen_fluid_seblm : Fluid + The Fluid object representing nitrogen gas. """ - if height is None: - height = radius - return 0.75 * (height**3 - 2 * height * radius**2) / (height**2 - 3 * radius**2) + tank = example_mass_flow_rate_based_tank_seblm + def expected_liquid_volume(t): + return (5 + (0.1 - 0.2) * t) / lox_fluid_seblm.density -def tank_centroid_function(tank_radius, tank_height, zero_height=0): - """Returns a function that calculates the centroid of a cylindrical tank - with spherical caps. + def expected_gas_volume(t): + return (0.1 + (0.01 - 0.02) * t) / nitrogen_fluid_seblm.density - Parameters - ---------- - tank_radius : float - The radius of the cylindrical part of the tank. - tank_height : float - The height of the tank including caps. - zero_height : float, optional - The coordinate of the bottom of the tank. Defaults to 0. - - Returns - ------- - function - A function that calculates the centroid of the tank for a given height. - """ + times = np.linspace(0, 10, 11) - def tank_centroid(h): - # Coordinate shift to the bottom of the tank - h = h - zero_height - cylinder_height = tank_height - 2 * tank_radius - - if h < tank_radius: - centroid = lower_spherical_cap_centroid(tank_radius, h) - - elif tank_radius <= h < tank_height - tank_radius: - # Fluid height from cylinder base - base = tank_radius - height = h - base - - balance = lower_spherical_cap_volume( - tank_radius - ) * lower_spherical_cap_centroid(tank_radius) + cylinder_volume( - tank_radius, height - ) * ( - cylinder_centroid(height) + tank_radius - ) - volume = lower_spherical_cap_volume(tank_radius) + cylinder_volume( - tank_radius, height - ) - centroid = balance / volume - - else: - # Fluid height from upper cap base - base = tank_height - tank_radius - height = h - base - - balance = ( - lower_spherical_cap_volume(tank_radius) - * lower_spherical_cap_centroid(tank_radius) - + cylinder_volume(tank_radius, cylinder_height) - * (cylinder_centroid(cylinder_height) + tank_radius) - + upper_spherical_cap_volume(tank_radius, height) - * (upper_spherical_cap_centroid(tank_radius, height) + base) - ) - volume = ( - lower_spherical_cap_volume(tank_radius) - + cylinder_volume(tank_radius, cylinder_height) - + upper_spherical_cap_volume(tank_radius, height) - ) - centroid = balance / volume - - return centroid + zero_height - - return tank_centroid - - -def cylinder_inertia(radius, height, reference=0): - """Returns the inertia of a cylinder with the given radius and height. - - Parameters - ---------- - radius : float - The radius of the cylinder. - height : float - The height of the cylinder. - reference : float, optional - The coordinate of the axis of rotation. - - Returns - ------- - numpy.ndarray - The inertia of the cylinder in the x, y, and z directions. - """ - # Evaluate inertia and perform coordinate shift to the reference point - inertia_x = cylinder_volume(radius, height) * ( - radius**2 / 4 + height**2 / 12 + (height / 2 - reference) ** 2 + assert np.allclose(expected_liquid_volume(times), tank.liquid_volume.y_array) + assert np.allclose( + expected_liquid_volume(times) / tank.geometry.area(0), + tank.liquid_height.y_array, + ) + assert np.allclose( + expected_gas_volume(times), + tank.gas_volume.y_array, + ) + assert np.allclose( + (expected_gas_volume(times) + expected_liquid_volume(times)) + / tank.geometry.area(0), + tank.gas_height.y_array, ) - inertia_y = inertia_x - inertia_z = cylinder_volume(radius, height) * radius**2 / 2 - - return np.array([inertia_x, inertia_y, inertia_z]) -def lower_spherical_cap_inertia(radius, height=None, reference=0): - """Returns the inertia of a spherical cap with the given radius and filled - height that is filled from its convex side. +def test_mass_flow_rate_tank_center_of_mass( + example_mass_flow_rate_based_tank_seblm, lox_fluid_seblm, nitrogen_fluid_seblm +): + """Test the center of mass properties of the MassFlowRateBasedTank + subclass of Tank. Parameters ---------- - radius : float - The radius of the spherical cap. - height : float - The height of the spherical cap. If not given, the radius is used. - reference : float, optional - The coordinate of the axis of rotation. - - Returns - ------- - numpy.ndarray - The inertia of the spherical cap in the x, y, and z directions. + example_mass_flow_rate_based_tank_seblm : MassFlowRateBasedTank + The MassFlowRateBasedTank to be tested. + lox_fluid_seblm : Fluid + The Fluid object representing liquid oxygen. + nitrogen_fluid_seblm : Fluid + The Fluid object representing nitrogen gas. """ - if height is None: - height = radius + # TODO: improve code context and repetition + tank = example_mass_flow_rate_based_tank_seblm - centroid = lower_spherical_cap_centroid(radius, height) + def expected_liquid_center_of_mass(t): + liquid_height = (5 + (0.1 - 0.2) * t) / lox_fluid_seblm.density / np.pi - # Evaluate inertia and perform coordinate shift to the reference point - inertia_x = lower_spherical_cap_volume(radius, height) * ( - ( - np.pi - * height**2 - * ( - -9 * height**3 - + 45 * height**2 * radius - - 80 * height * radius**2 - + 60 * radius**3 - ) - / 60 - ) - - (radius - centroid) ** 2 - + (centroid - reference) ** 2 - ) - inertia_y = inertia_x - inertia_z = lower_spherical_cap_volume(radius, height) * ( - np.pi * height**3 * (3 * height**2 - 15 * height * radius + 20 * radius**2) / 30 - ) - return np.array([inertia_x, inertia_y, inertia_z]) + return liquid_height / 2 + def expected_gas_center_of_mass(t): + liquid_height = (5 + (0.1 - 0.2) * t) / lox_fluid_seblm.density / np.pi + gas_height = (0.1 + (0.01 - 0.02) * t) / nitrogen_fluid_seblm.density / np.pi -def upper_spherical_cap_inertia(radius, height=None, reference=0): - """Returns the inertia of a spherical cap with the given radius and filled - height that is filled from its concave side. + return gas_height / 2 + liquid_height - Parameters - ---------- - radius : float - The radius of the spherical cap. - height : float - The height of the spherical cap. If not given, the radius is used. - reference : float, optional - The coordinate of the axis of rotation. - - Returns - ------- - numpy.ndarray - The inertia of the spherical cap in the x, y, and z directions. - """ - if height is None: - height = radius + def expected_center_of_mass(t): + liquid_mass = 5 + (0.1 - 0.2) * t + gas_mass = 0.1 + (0.01 - 0.02) * t - centroid = upper_spherical_cap_centroid(radius, height) + return ( + liquid_mass * expected_liquid_center_of_mass(t) + + gas_mass * expected_gas_center_of_mass(t) + ) / (liquid_mass + gas_mass) - # Evaluate inertia and perform coordinate shift to the reference point - inertia_x = upper_spherical_cap_volume(radius, height) * ( - ( - ( - np.pi - * height - * (-9 * height**4 + 10 * height**2 * radius**2 + 15 * radius**4) - ) - / 60 - ) - - centroid**2 - + (centroid - reference) ** 2 + times = np.linspace(0, 10, 11) + + assert np.allclose( + expected_liquid_center_of_mass(times), + tank.liquid_center_of_mass.y_array, + atol=1e-4, + rtol=1e-3, + ) + assert np.allclose( + expected_gas_center_of_mass(times), + tank.gas_center_of_mass.y_array, + atol=1e-4, + rtol=1e-3, ) - inertia_y = inertia_x - inertia_z = upper_spherical_cap_volume(radius, height) * ( - np.pi - * height - * (3 * height**4 - 10 * height**2 * radius**2 + 15 * radius**4) - / 30 + assert np.allclose( + expected_center_of_mass(times), + tank.center_of_mass.y_array, + atol=1e-4, + rtol=1e-3, ) - return np.array([inertia_x, inertia_y, inertia_z]) -def tank_inertia_function(tank_radius, tank_height, zero_height=0): - """Returns a function that calculates the inertia of a cylindrical tank - with spherical caps. The reference point is the tank centroid. +def test_mass_flow_rate_tank_inertia( + example_mass_flow_rate_based_tank_seblm, lox_fluid_seblm, nitrogen_fluid_seblm +): + """Test the inertia properties of the MassFlowRateBasedTank + subclass of Tank. Parameters ---------- - tank_radius : float - The radius of the cylindrical part of the tank. - tank_height : float - The height of the tank including caps. - zero_height : float, optional - The coordinate of the bottom of the tank. Defaults to 0. - - Returns - ------- - function - A function that calculates the inertia of the tank for a given height. + example_mass_flow_rate_based_tank_seblm : MassFlowRateBasedTank + The MassFlowRateBasedTank to be tested. + lox_fluid_seblm : Fluid + The Fluid object representing liquid oxygen. + nitrogen_fluid_seblm : Fluid + The Fluid object representing nitrogen gas. """ + # TODO: improve code context and repetition + tank = example_mass_flow_rate_based_tank_seblm - def tank_inertia(h): - # Coordinate shift to the bottom of the tank - h = h - zero_height - center = tank_height / 2 - cylinder_height = tank_height - 2 * tank_radius - - if h < tank_radius: - inertia = lower_spherical_cap_inertia(tank_radius, h, center) - - elif tank_radius <= h < tank_height - tank_radius: - # Fluid height from cylinder base - base = tank_radius - height = h - base + def expected_center_of_mass(t): + liquid_mass = 5 + (0.1 - 0.2) * t + gas_mass = 0.1 + (0.01 - 0.02) * t + liquid_height = liquid_mass / lox_fluid_seblm.density / np.pi + gas_height = gas_mass / nitrogen_fluid_seblm.density / np.pi - lower_centroid = lower_spherical_cap_centroid(tank_radius) - cyl_centroid = cylinder_centroid(height) + base - lower_inertia = lower_spherical_cap_inertia(tank_radius, reference=center) - cyl_inertia = cylinder_inertia(tank_radius, height, reference=center - base) + return ( + liquid_mass * liquid_height / 2 + + gas_mass * (gas_height / 2 + liquid_height) + ) / (liquid_mass + gas_mass) - inertia = lower_inertia + cyl_inertia - - else: - # Fluid height from upper cap base - base = tank_height - tank_radius - height = h - base - - lower_centroid = lower_spherical_cap_centroid(tank_radius) - cyl_centroid = cylinder_centroid(cylinder_height) + tank_radius - upper_centroid = upper_spherical_cap_centroid(tank_radius, height) + base - - lower_inertia = lower_spherical_cap_inertia( - tank_radius, reference=lower_centroid - center - ) - cyl_inertia = cylinder_inertia( - tank_radius, cylinder_height, cyl_centroid - tank_radius - ) - upper_inertia = upper_spherical_cap_inertia( - tank_radius, height, upper_centroid - base - ) + def expected_liquid_inertia(t): + r = 1 + liquid_mass = 5 + (0.1 - 0.2) * t + liquid_height = liquid_mass / lox_fluid_seblm.density / np.pi + liquid_com = liquid_height / 2 + + return ( + 1 / 4 * liquid_mass * r**2 + + 1 / 12 * liquid_mass * liquid_height**2 + + liquid_mass * (liquid_com - expected_center_of_mass(t)) ** 2 + ) - inertia = lower_inertia + cyl_inertia + upper_inertia + def expected_gas_inertia(t): + r = 1 + liquid_mass = 5 + (0.1 - 0.2) * t + gas_mass = 0.1 + (0.01 - 0.02) * t + liquid_height = liquid_mass / lox_fluid_seblm.density / np.pi + gas_height = gas_mass / nitrogen_fluid_seblm.density / np.pi + gas_com = gas_height / 2 + liquid_height + + return ( + 1 / 4 * gas_mass * r**2 + + 1 / 12 * gas_mass * (gas_height - liquid_height) ** 2 + + gas_mass * (gas_com - expected_center_of_mass(t)) ** 2 + ) - return inertia + times = np.linspace(0, 10, 11) - return tank_inertia + assert np.allclose( + expected_liquid_inertia(times), + tank.liquid_inertia.y_array, + atol=1e-3, + rtol=1e-2, + ) + assert np.allclose( + expected_gas_inertia(times), tank.gas_inertia.y_array, atol=1e-3, rtol=1e-2 + ) + assert np.allclose( + expected_liquid_inertia(times) + expected_gas_inertia(times), + tank.inertia.y_array, + atol=1e-3, + rtol=1e-2, + ) diff --git a/tests/unit/test_tank_geometry.py b/tests/unit/test_tank_geometry.py new file mode 100644 index 000000000..54e3671c2 --- /dev/null +++ b/tests/unit/test_tank_geometry.py @@ -0,0 +1,126 @@ +from pathlib import Path + +import numpy as np +import pytest + +PRESSURANT_PARAMS = (0.135 / 2, 0.981) +PROPELLANT_PARAMS = (0.0744, 0.8068) +SPHERICAL_PARAMS = (0.05, 0.1) + +BASE_PATH = Path("tests/fixtures/motor/data/") + +parametrize_fixtures = pytest.mark.parametrize( + "params", + [ + ( + "pressurant_tank_geometry", + PRESSURANT_PARAMS, + BASE_PATH / "cylindrical_pressurant_tank_expected.csv", + ), + ( + "propellant_tank_geometry", + PROPELLANT_PARAMS, + BASE_PATH / "cylindrical_oxidizer_tank_expected.csv", + ), + ( + "spherical_oxidizer_geometry", + SPHERICAL_PARAMS, + BASE_PATH / "spherical_oxidizer_tank_expected.csv", + ), + ], +) + + +@parametrize_fixtures +def test_tank_bounds(params, request): + """Test basic geometric properties of the tanks.""" + geometry, (expected_radius, expected_height), _ = params + geometry = request.getfixturevalue(geometry) + + expected_total_height = expected_height + + assert np.isclose(geometry.radius(0), expected_radius) + assert np.isclose(geometry.total_height, expected_total_height) + + +@parametrize_fixtures +def test_tank_coordinates(params, request): + """Test basic coordinate values of the tanks.""" + geometry, (_, height), _ = params + geometry = request.getfixturevalue(geometry) + + expected_bottom = -height / 2 + expected_top = height / 2 + + assert np.isclose(geometry.bottom, expected_bottom) + assert np.isclose(geometry.top, expected_top) + + +@parametrize_fixtures +def test_tank_total_volume(params, request): + """Test the total volume of the tanks comparing to the analytically + calculated values. + """ + geometry, (radius, height), _ = params + geometry = request.getfixturevalue(geometry) + + expected_total_volume = ( + np.pi * radius**2 * (height - 2 * radius) + 4 / 3 * np.pi * radius**3 + ) + + assert np.isclose(geometry.total_volume, expected_total_volume) + + +@parametrize_fixtures +def test_tank_volume(params, request): + """Test the volume of the tanks at different heights comparing to the + CAD generated values. + """ + geometry, *_, file_path = params + geometry = request.getfixturevalue(geometry) + + heights, expected_volumes = np.loadtxt( + file_path, delimiter=",", skiprows=1, usecols=(0, 1), unpack=True + ) + + assert np.allclose(expected_volumes, geometry.volume(heights)) + + +@parametrize_fixtures +def test_tank_centroid(params, request): + """Test the centroid of the tanks at different heights comparing to the + analytically calculated values. + """ + geometry, *_, file_path = params + geometry = request.getfixturevalue(geometry) + + heights, expected_volumes, expected_centroids = np.loadtxt( + file_path, delimiter=",", skiprows=1, usecols=(0, 1, 2), unpack=True + ) + + # For higher accuracy: geometry.volume_moment(geometry.bottom, h)(h) + assert np.allclose( + expected_centroids * expected_volumes, + geometry.volume_moment(geometry.bottom, geometry.top)(heights), + ) + + +@parametrize_fixtures +def test_tank_inertia(params, request): + """Test the inertia of the tanks at different heights comparing to the + analytically calculated values. + """ + geometry, *_, file_path = params + geometry = request.getfixturevalue(geometry) + + heights, expected_inertia = np.loadtxt( + file_path, delimiter=",", skiprows=1, usecols=(0, 3), unpack=True + ) + + # For higher accuracy: geometry.Ix_volume(geometry.bottom, h)(h) + assert np.allclose( + expected_inertia[1:], + geometry.Ix_volume(geometry.bottom, geometry.top)(heights[1:]), + rtol=1e-5, + atol=1e-9, + )

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