From 1bae4b6ae612d791f8f62766e52f6d273f315c6b Mon Sep 17 00:00:00 2001 From: MateusStano Date: Thu, 8 Feb 2024 21:13:51 +0100 Subject: [PATCH 1/5] MNT: renaming to stochastic --- rocketpy/stochastic/__init__.py | 14 + .../stochastic_aero_surfaces.py} | 149 +++++----- .../stochastic_environment.py} | 32 +-- .../stochastic_flight.py} | 32 +-- .../stochastic_generic_motor.py} | 76 ++--- .../stochastic_model.py} | 7 +- .../stochastic_motor_model.py} | 8 +- .../stochastic_parachute.py} | 32 +-- .../stochastic_rocket.py} | 270 +++++++++--------- .../stochastic_solid_motor.py} | 96 +++---- 10 files changed, 358 insertions(+), 358 deletions(-) create mode 100644 rocketpy/stochastic/__init__.py rename rocketpy/{monte_carlo/mc_aero_surfaces.py => stochastic/stochastic_aero_surfaces.py} (84%) rename rocketpy/{monte_carlo/mc_environment.py => stochastic/stochastic_environment.py} (90%) rename rocketpy/{monte_carlo/mc_flight.py => stochastic/stochastic_flight.py} (87%) rename rocketpy/{monte_carlo/mc_generic_motor.py => stochastic/stochastic_generic_motor.py} (82%) rename rocketpy/{monte_carlo/dispersion_model.py => stochastic/stochastic_model.py} (99%) rename rocketpy/{monte_carlo/motor_dispersion_model.py => stochastic/stochastic_motor_model.py} (52%) rename rocketpy/{monte_carlo/mc_parachute.py => stochastic/stochastic_parachute.py} (85%) rename rocketpy/{monte_carlo/mc_rocket.py => stochastic/stochastic_rocket.py} (73%) rename rocketpy/{monte_carlo/mc_solid_motor.py => stochastic/stochastic_solid_motor.py} (80%) diff --git a/rocketpy/stochastic/__init__.py b/rocketpy/stochastic/__init__.py new file mode 100644 index 000000000..4e53b6360 --- /dev/null +++ b/rocketpy/stochastic/__init__.py @@ -0,0 +1,14 @@ +from .stochastic_model import StochasticModel +from .stochastic_aero_surfaces import ( + StochasticEllipticalFins, + StochasticNoseCone, + StochasticRailButtons, + StochasticTail, + StochasticTrapezoidalFins, +) +from .stochastic_environment import StochasticEnvironment +from .stochastic_flight import StochasticFlight +from .stochastic_parachute import StochasticParachute +from .stochastic_rocket import StochasticRocket +from .stochastic_solid_motor import StochasticSolidMotor +from .stochastic_generic_motor import StochasticGenericMotor diff --git a/rocketpy/monte_carlo/mc_aero_surfaces.py b/rocketpy/stochastic/stochastic_aero_surfaces.py similarity index 84% rename from rocketpy/monte_carlo/mc_aero_surfaces.py rename to rocketpy/stochastic/stochastic_aero_surfaces.py index c8f2aa73e..c0302203b 100644 --- a/rocketpy/monte_carlo/mc_aero_surfaces.py +++ b/rocketpy/stochastic/stochastic_aero_surfaces.py @@ -1,16 +1,11 @@ -from rocketpy.rocket.aero_surface import ( - EllipticalFins, - NoseCone, - RailButtons, - Tail, - TrapezoidalFins, -) +from rocketpy.rocket.aero_surface import (EllipticalFins, NoseCone, + RailButtons, Tail, TrapezoidalFins) -from .dispersion_model import DispersionModel +from .stochastic_model import StochasticModel -class McNoseCone(DispersionModel): - """A Monte Carlo Nose Cone class that inherits from MonteCarloModel. This +class StochasticNoseCone(StochasticModel): + """A Stochastic Nose Cone class that inherits from StochasticModel. This class is used to receive a NoseCone object and information about the dispersion of its parameters and generate a random nose cone object based on the provided information. @@ -21,19 +16,19 @@ class is used to receive a NoseCone object and information about the NoseCone object to be used for validation. length : tuple, list, int, float Length of the nose cone in meters. Follows the standard input format of - Dispersion Models. + Stochastic Models. kind : list List of strings representing the kind of nose cone. Follows the standard - input format of Dispersion Models. + input format of Stochastic Models. base_radius : tuple, list, int, float Base radius of the nose cone in meters. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. bluffness : tuple, list, int, float Bluffness of the nose cone. Follows the standard input format of - Dispersion Models. + Stochastic Models. rocket_radius : tuple, list, int, float Rocket radius of the nose cone in meters. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. name : list List of names. This attribute can not be randomized. """ @@ -47,12 +42,12 @@ def __init__( bluffness=None, rocket_radius=None, ): - """Initializes the Monte Carlo Nose Cone class. + """Initializes the Stochastic Nose Cone class. See Also -------- This should link to somewhere that explains how inputs works in - dispersion models. + stochastic models. Parameters ---------- @@ -60,19 +55,19 @@ def __init__( NoseCone object to be used for validation. length : tuple, list, int, float Length of the nose cone in meters. Follows the standard input format - of Dispersion Models. + of Stochastic Models. kind : list List of strings representing the kind of nose cone. Follows the - standard input format of Dispersion Models. + standard input format of Stochastic Models. base_radius : tuple, list, int, float Base radius of the nose cone in meters. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. bluffness : tuple, list, int, float Bluffness of the nose cone. Follows the standard input format of - Dispersion Models. + Stochastic Models. rocket_radius : tuple, list, int, float Rocket radius of the nose cone in meters. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. """ self._validate_kind(kind) super().__init__( @@ -114,8 +109,8 @@ def create_object(self): return nosecone -class McTrapezoidalFins(DispersionModel): - """A Monte Carlo Trapezoidal Fins class that inherits from MonteCarloModel. +class StochasticTrapezoidalFins(StochasticModel): + """A Stochastic Trapezoidal Fins class that inherits from StochasticModel. This class is used to receive a TrapezoidalFins object and information about the dispersion of its parameters and generate a random trapezoidal fins object based on the provided information. @@ -126,28 +121,28 @@ class McTrapezoidalFins(DispersionModel): TrapezoidalFins object to be used for validation. n : list of ints List of integers representing the number of fins. Follows the standard - input format of Dispersion Models. + input format of Stochastic Models. root_chord : tuple, list, int, float Root chord of the fins in meters. Follows the standard input format of - Dispersion Models. + Stochastic Models. tip_chord : tuple, list, int, float Tip chord of the fins in meters. Follows the standard input format of - Dispersion Models. + Stochastic Models. span : tuple, list, int, float Span of the fins in meters. Follows the standard input format of - Dispersion Models. + Stochastic Models. rocket_radius : tuple, list, int, float Rocket radius of the fins in meters. Follows the standard input format - of Dispersion Models. + of Stochastic Models. cant_angle : tuple, list, int, float Cant angle of the fins in degrees. Follows the standard input format of - Dispersion Models. + Stochastic Models. sweep_length : tuple, list, int, float Sweep length of the fins in meters. Follows the standard input format of - Dispersion Models. + Stochastic Models. sweep_angle : tuple, list, int, float Sweep angle of the fins in degrees. Follows the standard input format of - Dispersion Models. + Stochastic Models. airfoil : list List of tuples in the form of (airfoil file path, airfoil name). name : list @@ -167,12 +162,12 @@ def __init__( sweep_angle=None, airfoil=None, ): - """Initializes the Monte Carlo Trapezoidal Fins class. + """Initializes the Stochastic Trapezoidal Fins class. See Also -------- This should link to somewhere that explains how inputs works in - dispersion models. + stochastic models. Parameters ---------- @@ -180,28 +175,28 @@ def __init__( TrapezoidalFins object to be used for validation. n : list of ints List of integers representing the number of fins. Follows the - standard input format of Dispersion Models. + standard input format of Stochastic Models. root_chord : tuple, list, int, float Root chord of the fins in meters. Follows the standard input format - of Dispersion Models. + of Stochastic Models. tip_chord : tuple, list, int, float Tip chord of the fins in meters. Follows the standard input format - of Dispersion Models. + of Stochastic Models. span : tuple, list, int, float Span of the fins in meters. Follows the standard input format of - Dispersion Models. + Stochastic Models. rocket_radius : tuple, list, int, float Rocket radius of the fins in meters. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. cant_angle : tuple, list, int, float Cant angle of the fins in degrees. Follows the standard input format - of Dispersion Models. + of Stochastic Models. sweep_length : tuple, list, int, float Sweep length of the fins in meters. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. sweep_angle : tuple, list, int, float Sweep angle of the fins in degrees. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. airfoil : list List of tuples in the form of (airfoil file path, airfoil name). """ @@ -245,8 +240,8 @@ def create_object(self): return fins -class McEllipticalFins(DispersionModel): - """A Monte Carlo Elliptical Fins class that inherits from MonteCarloModel. +class StochasticEllipticalFins(StochasticModel): + """A Stochastic Elliptical Fins class that inherits from StochasticModel. This class is used to receive a EllipticalFins object and information about the dispersion of its parameters and generate a random elliptical fins object based on the provided information. @@ -257,19 +252,19 @@ class McEllipticalFins(DispersionModel): EllipticalFins object to be used for validation. n : list of ints List of integers representing the number of fins. Follows the standard - input format of Dispersion Models. + input format of Stochastic Models. root_chord : tuple, list, int, float Root chord of the fins in meters. Follows the standard input format of - Dispersion Models. + Stochastic Models. span : tuple, list, int, float Span of the fins in meters. Follows the standard input format of - Dispersion Models. + Stochastic Models. rocket_radius : tuple, list, int, float Rocket radius of the fins in meters. Follows the standard input format - of Dispersion Models. + of Stochastic Models. cant_angle : tuple, list, int, float Cant angle of the fins in degrees. Follows the standard input format of - Dispersion Models. + Stochastic Models. airfoil : list List of tuples in the form of (airfoil file path, airfoil name). name : list @@ -286,12 +281,12 @@ def __init__( cant_angle=None, airfoil=None, ): - """Initializes the Monte Carlo Elliptical Fins class. + """Initializes the Stochastic Elliptical Fins class. See Also -------- This should link to somewhere that explains how inputs works in - dispersion models. + stochastic models. Parameters ---------- @@ -299,19 +294,19 @@ def __init__( EllipticalFins object to be used for validation. n : list of ints List of integers representing the number of fins. Follows the - standard input format of Dispersion Models. + standard input format of Stochastic Models. root_chord : tuple, list, int, float Root chord of the fins in meters. Follows the standard input format - of Dispersion Models. + of Stochastic Models. span : tuple, list, int, float Span of the fins in meters. Follows the standard input format of - Dispersion Models. + Stochastic Models. rocket_radius : tuple, list, int, float Rocket radius of the fins in meters. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. cant_angle : tuple, list, int, float Cant angle of the fins in degrees. Follows the standard input format - of Dispersion Models. + of Stochastic Models. airfoil : list List of tuples in the form of (airfoil file path, airfoil name). """ @@ -350,8 +345,8 @@ def create_object(self): return fins -class McTail(DispersionModel): - """A Monte Carlo Tail class that inherits from MonteCarloModel. This class +class StochasticTail(StochasticModel): + """A Stochastic Tail class that inherits from StochasticModel. This class is used to receive a Tail object and information about the dispersion of its parameters and generate a random tail object based on the provided information. @@ -362,16 +357,16 @@ class McTail(DispersionModel): Tail object to be used for validation. top_radius : tuple, list, int, float Top radius of the tail in meters. Follows the standard input format of - Dispersion Models. + Stochastic Models. bottom_radius : tuple, list, int, float Bottom radius of the tail in meters. Follows the standard input format - of Dispersion Models. + of Stochastic Models. length : tuple, list, int, float Length of the tail in meters. Follows the standard input format of - Dispersion Models. + Stochastic Models. rocket_radius : tuple, list, int, float Rocket radius of the tail in meters. Follows the standard input format - of Dispersion Models. + of Stochastic Models. name : list List of names. This attribute can not be randomized. """ @@ -384,12 +379,12 @@ def __init__( length=None, rocket_radius=None, ): - """Initializes the Monte Carlo Tail class. + """Initializes the Stochastic Tail class. See Also -------- This should link to somewhere that explains how inputs works in - dispersion models. + stochastic models. Parameters ---------- @@ -397,16 +392,16 @@ def __init__( Tail object to be used for validation. top_radius : tuple, list, int, float Top radius of the tail in meters. Follows the standard input format - of Dispersion Models. + of Stochastic Models. bottom_radius : tuple, list, int, float Bottom radius of the tail in meters. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. length : tuple, list, int, float Length of the tail in meters. Follows the standard input format of - Dispersion Models. + Stochastic Models. rocket_radius : tuple, list, int, float Rocket radius of the tail in meters. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. """ super().__init__( tail, @@ -437,8 +432,8 @@ def create_object(self): return tail -class McRailButtons(DispersionModel): - """A Monte Carlo RailButtons class that inherits from MonteCarloModel. This +class StochasticRailButtons(StochasticModel): + """A Stochastic RailButtons class that inherits from StochasticModel. This class is used to receive a RailButtons object and information about the dispersion of its parameters and generate a random rail buttons object based on the provided information. @@ -449,13 +444,13 @@ class is used to receive a RailButtons object and information about the RailButtons object to be used for validation. rail_buttons : list List of RailButton objects. Follows the standard input format of - Dispersion Models. + Stochastic Models. buttons_distance : tuple, list, int, float Distance between the buttons in meters. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. angular_position : tuple, list, int, float Angular position of the buttons in degrees. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. name : list List of names. This attribute can not be randomized. """ @@ -466,12 +461,12 @@ def __init__( buttons_distance=None, angular_position=None, ): - """Initializes the Monte Carlo RailButtons class. + """Initializes the Stochastic RailButtons class. See Also -------- This should link to somewhere that explains how inputs works in - dispersion models. + stochastic models. Parameters ---------- @@ -479,10 +474,10 @@ def __init__( RailButtons object to be used for validation. buttons_distance : tuple, list, int, float Distance between the buttons in meters. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. angular_position : tuple, list, int, float Angular position of the buttons in degrees. Follows the standard - input format of Dispersion Models. + input format of Stochastic Models. """ super().__init__( rail_buttons, diff --git a/rocketpy/monte_carlo/mc_environment.py b/rocketpy/stochastic/stochastic_environment.py similarity index 90% rename from rocketpy/monte_carlo/mc_environment.py rename to rocketpy/stochastic/stochastic_environment.py index 51a27f865..4dcef97dc 100644 --- a/rocketpy/monte_carlo/mc_environment.py +++ b/rocketpy/stochastic/stochastic_environment.py @@ -1,10 +1,10 @@ from rocketpy.environment import Environment -from .dispersion_model import DispersionModel +from .stochastic_model import StochasticModel -class McEnvironment(DispersionModel): - """A Monte Carlo Environment class that inherits from MonteCarloModel. This +class StochasticEnvironment(StochasticModel): + """A Stochastic Environment class that inherits from StochasticModel. This class is used to receive a Environment object and information about the dispersion of its parameters and generate a random environment object based on the provided information. @@ -15,16 +15,16 @@ class is used to receive a Environment object and information about the Environment object to be used for validation. elevation : tuple, list, int, float Elevation of the launch site in meters. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. gravity : tuple, list, int, float Gravitational acceleration in meters per second squared. Follows the - standard input format of Dispersion Models. + standard input format of Stochastic Models. latitude : tuple, list, int, float Latitude of the launch site in degrees. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. longitude : tuple, list, int, float Longitude of the launch site in degrees. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. ensemble_member : list List of integers representing the ensemble member to be selected. wind_velocity_x_factor : tuple, list, int, float @@ -51,12 +51,12 @@ def __init__( wind_velocity_x_factor=(1, 0), wind_velocity_y_factor=(1, 0), ): - """Initializes the Monte Carlo Environment class. + """Initializes the Stochastic Environment class. See Also -------- This should link to somewhere that explains how inputs works in - dispersion models. + Stochastic models. Parameters ---------- @@ -67,26 +67,26 @@ def __init__( (year, month, day, hour). elevation : int, float, tuple, list, optional Elevation of the launch site in meters. Follows the standard - input format of Dispersion Models. + input format of Stochastic Models. gravity : int, float, tuple, list, optional Gravitational acceleration in meters per second squared. Follows - the standard input format of Dispersion Models. + the standard input format of Stochastic Models. latitude : int, float, tuple, list, optional Latitude of the launch site in degrees. Follows the standard - input format of Dispersion Models. + input format of Stochastic Models. longitude : int, float, tuple, list, optional Longitude of the launch site in degrees. Follows the standard - input format of Dispersion Models. + input format of Stochastic Models. ensemble_member : list, optional List of integers representing the ensemble member to be selected. wind_velocity_x_factor : int, float, tuple, list, optional Factor to be multiplied by the wind velocity in the x direction. - Follows the factor input format of Dispersion Models. + Follows the factor input format of Stochastic Models. wind_velocity_y_factor : int, float, tuple, list, optional Factor to be multiplied by the wind velocity in the y direction. - Follows the factor input format of Dispersion Models. + Follows the factor input format of Stochastic Models. """ - # Validate in DispersionModel + # Validate in StochasticModel super().__init__( environment, date=None, diff --git a/rocketpy/monte_carlo/mc_flight.py b/rocketpy/stochastic/stochastic_flight.py similarity index 87% rename from rocketpy/monte_carlo/mc_flight.py rename to rocketpy/stochastic/stochastic_flight.py index 9d00c2c09..a80fada10 100644 --- a/rocketpy/monte_carlo/mc_flight.py +++ b/rocketpy/stochastic/stochastic_flight.py @@ -1,10 +1,10 @@ from rocketpy.simulation import Flight -from .dispersion_model import DispersionModel +from .stochastic_model import StochasticModel -class McFlight(DispersionModel): - """A Monte Carlo Flight class that inherits from MonteCarloModel. This +class StochasticFlight(StochasticModel): + """A Stochastic Flight class that inherits from StochasticModel. This class is used to receive a Flight object and information about the dispersion of its parameters and generate a random flight object based on the provided information. @@ -12,16 +12,16 @@ class is used to receive a Flight object and information about the Attributes ---------- flight : Flight - The Flight object to be used as a base for the Monte Carlo flight. + The Flight object to be used as a base for the Stochastic flight. rail_length : int, float, tuple, list, optional The rail length of the flight. Follows the standard input format of - Dispersion Models. + Stochastic Models. inclination : int, float, tuple, list, optional The inclination of the flight. Follows the standard input format of - Dispersion Models. + Stochastic Models. heading : int, float, tuple, list, optional The heading of the flight. Follows the standard input format of - Dispersion Models. + Stochastic Models. initial_solution : tuple, list, optional The initial solution of the flight. This is a tuple of 14 elements that represent the initial conditions of the flight. This attribute can not @@ -40,26 +40,26 @@ def __init__( initial_solution=None, terminate_on_apogee=None, ): - """Initializes the Monte Carlo Flight class. + """Initializes the Stochastic Flight class. See Also -------- This should link to somewhere that explains how inputs works in - dispersion models. + Stochastic models. Parameters ---------- flight : Flight - The Flight object to be used as a base for the Monte Carlo flight. + The Flight object to be used as a base for the Stochastic flight. rail_length : int, float, tuple, list, optional The rail length of the flight. Follows the standard input format of - Dispersion Models. + Stochastic Models. inclination : int, float, tuple, list, optional The inclination of the flight. Follows the standard input format of - Dispersion Models. + Stochastic Models. heading : int, float, tuple, list, optional The heading of the flight. Follows the standard input format of - Dispersion Models. + Stochastic Models. initial_solution : tuple, list, optional The initial solution of the flight. This is a tuple of 14 elements that represent the initial conditions of the flight. This attribute @@ -100,21 +100,21 @@ def _validate_initial_solution(self, initial_solution): # TODO: these call dict_generator a lot of times unecessaryly def _randomize_rail_length(self): """Randomizes the rail length of the flight. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. """ generated_dict = next(self.dict_generator()) return generated_dict["rail_length"] def _randomize_inclination(self): """Randomizes the inclination of the flight. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. """ generated_dict = next(self.dict_generator()) return generated_dict["inclination"] def _randomize_heading(self): """Randomizes the heading of the flight. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. """ generated_dict = next(self.dict_generator()) return generated_dict["heading"] diff --git a/rocketpy/monte_carlo/mc_generic_motor.py b/rocketpy/stochastic/stochastic_generic_motor.py similarity index 82% rename from rocketpy/monte_carlo/mc_generic_motor.py rename to rocketpy/stochastic/stochastic_generic_motor.py index 1403d69a9..bb0415197 100644 --- a/rocketpy/monte_carlo/mc_generic_motor.py +++ b/rocketpy/stochastic/stochastic_generic_motor.py @@ -1,10 +1,10 @@ from rocketpy.motors import GenericMotor -from .motor_dispersion_model import MotorDispersionModel +from .stochastic_motor_model import StochasticMotorModel -class McGenericMotor(MotorDispersionModel): - """A Monte Carlo Generic Motor class that inherits from MonteCarloModel. +class StochasticGenericMotor(StochasticMotorModel): + """A Stochastic Generic Motor class that inherits from StochasticModel. This class is used to receive a GenericMotor object and information about the dispersion of its parameters and generate a random generic motor object based on the provided information. @@ -17,52 +17,52 @@ class McGenericMotor(MotorDispersionModel): List of strings representing the thrust source to be selected. total_impulse : int, float, tuple, list Total impulse of the motor in newton seconds. Follows the standard - input format of Dispersion Models. + input format of Stochastic Models. burn_start_time : int, float, tuple, list Burn start time of the motor in seconds. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. burn_out_time : int, float, tuple, list Burn out time of the motor in seconds. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. dry_mass : int, float, tuple, list Dry mass of the motor in kilograms. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. dry_I_11 : int, float, tuple, list Dry inertia of the motor in kilograms times meters squared. Follows the - standard input format of Dispersion Models. + standard input format of Stochastic Models. dry_I_22 : int, float, tuple, list Dry inertia of the motor in kilograms times meters squared. Follows the - standard input format of Dispersion Models. + standard input format of Stochastic Models. dry_I_33 : int, float, tuple, list Dry inertia of the motor in kilograms times meters squared. Follows the - standard input format of Dispersion Models. + standard input format of Stochastic Models. dry_I_12 : int, float, tuple, list Dry inertia of the motor in kilograms times meters squared. Follows the - standard input format of Dispersion Models. + standard input format of Stochastic Models. dry_I_13 : int, float, tuple, list Dry inertia of the motor in kilograms times meters squared. Follows the - standard input format of Dispersion Models. + standard input format of Stochastic Models. dry_I_23 : int, float, tuple, list Dry inertia of the motor in kilograms times meters squared. Follows the - standard input format of Dispersion Models. + standard input format of Stochastic Models. chamber_radius : int, float, tuple, list Chamber radius of the motor in meters. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. chamber_height : int, float, tuple, list Chamber height of the motor in meters. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. chamber_position : int, float, tuple, list Chamber position of the motor in meters. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. nozzle_radius : int, float, tuple, list Nozzle radius of the motor in meters. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. nozzle_position : int, float, tuple, list Nozzle position of the motor in meters. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. center_of_dry_mass_position : int, float, tuple, list Center of dry mass position of the motor in meters. Follows the - standard input format of Dispersion Models. + standard input format of Stochastic Models. interpolation_method : str, optional Interpolation method to be used. This attribute can not be randomized. coordinate_system_orientation : str, optional @@ -92,12 +92,12 @@ def __init__( nozzle_position=None, center_of_dry_mass_position=None, ): - """Initializes the Monte Carlo Generic Motor class. + """Initializes the Stochastic Generic Motor class. See Also -------- This should link to somewhere that explains how inputs works in - dispersion models. + Stochastic Models. Parameters ---------- @@ -105,55 +105,55 @@ def __init__( GenericMotor object to be used for validation. thrust_source : list, optional List of strings representing the thrust source to be selected. - Follows the 1d array like input format of Dispersion Models. + Follows the 1d array like input format of Stochastic Models. total_impulse : int, float, tuple, list, optional Total impulse of the motor in newton seconds. Follows the standard - input format of Dispersion Models. + input format of Stochastic Models. burn_start_time : int, float, tuple, list, optional Burn start time of the motor in seconds. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. burn_out_time : int, float, tuple, list, optional Burn out time of the motor in seconds. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. dry_mass : int, float, tuple, list, optional Dry mass of the motor in kilograms. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. dry_I_11 : int, float, tuple, list, optional Dry inertia of the motor in kilograms times meters squared. Follows - the standard input format of Dispersion Models. + the standard input format of Stochastic Models. dry_I_22 : int, float, tuple, list, optional Dry inertia of the motor in kilograms times meters squared. Follows - the standard input format of Dispersion Models. + the standard input format of Stochastic Models. dry_I_33 : int, float, tuple, list, optional Dry inertia of the motor in kilograms times meters squared. Follows - the standard input format of Dispersion Models. + the standard input format of Stochastic Models. dry_I_12 : int, float, tuple, list, optional Dry inertia of the motor in kilograms times meters squared. Follows - the standard input format of Dispersion Models. + the standard input format of Stochastic Models. dry_I_13 : int, float, tuple, list, optional Dry inertia of the motor in kilograms times meters squared. Follows - the standard input format of Dispersion Models. + the standard input format of Stochastic Models. dry_I_23 : int, float, tuple, list, optional Dry inertia of the motor in kilograms times meters squared. Follows - the standard input format of Dispersion Models. + the standard input format of Stochastic Models. chamber_radius : int, float, tuple, list, optional Chamber radius of the motor in meters. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. chamber_height : int, float, tuple, list, optional Chamber height of the motor in meters. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. chamber_position : int, float, tuple, list, optional Chamber position of the motor in meters. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. nozzle_radius : int, float, tuple, list, optional Nozzle radius of the motor in meters. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. nozzle_position : int, float, tuple, list, optional Nozzle position of the motor in meters. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. center_of_dry_mass_position : int, float, tuple, list, optional Center of dry mass position of the motor in meters. Follows the - standard input format of Dispersion Models. + standard input format of Stochastic Models. """ super().__init__( generic_motor, diff --git a/rocketpy/monte_carlo/dispersion_model.py b/rocketpy/stochastic/stochastic_model.py similarity index 99% rename from rocketpy/monte_carlo/dispersion_model.py rename to rocketpy/stochastic/stochastic_model.py index 8c52cf354..c24faa631 100644 --- a/rocketpy/monte_carlo/dispersion_model.py +++ b/rocketpy/stochastic/stochastic_model.py @@ -1,14 +1,13 @@ from random import choice import numpy as np - from rocketpy.mathutils.function import Function from ..tools import get_distribution -class DispersionModel: - """Base class for all Monte Carlo classes. This class is used to validate +class StochasticModel: + """Base class for all Stochastic classes. This class is used to validate the input arguments of the child classes. The input arguments are validated and saved as attributes of the class in the correct format. The attributes are then used to generate a dictionary with the randomly generated input @@ -24,7 +23,7 @@ class DispersionModel: ] def __init__(self, object, **kwargs): - """Initialize the DispersionModel class with validated input arguments. + """Initialize the StochasticModel class with validated input arguments. Parameters ---------- diff --git a/rocketpy/monte_carlo/motor_dispersion_model.py b/rocketpy/stochastic/stochastic_motor_model.py similarity index 52% rename from rocketpy/monte_carlo/motor_dispersion_model.py rename to rocketpy/stochastic/stochastic_motor_model.py index 800fdcb39..ee125068b 100644 --- a/rocketpy/monte_carlo/motor_dispersion_model.py +++ b/rocketpy/stochastic/stochastic_motor_model.py @@ -1,9 +1,9 @@ -from .dispersion_model import DispersionModel +from .stochastic_model import StochasticModel -class MotorDispersionModel(DispersionModel): - """Monte Carlo Motor Model class that inherits from MonteCarloModel. This - class is used to standardize the input of the motor dispersion model. +class StochasticMotorModel(StochasticModel): + """Stochastic Motor Model class that inherits from StochasticModel. This + class is used to standardize the input of the motor stochastic model. """ def __init__(self, object, **kwargs): diff --git a/rocketpy/monte_carlo/mc_parachute.py b/rocketpy/stochastic/stochastic_parachute.py similarity index 85% rename from rocketpy/monte_carlo/mc_parachute.py rename to rocketpy/stochastic/stochastic_parachute.py index 7c8945dcd..108c82a25 100644 --- a/rocketpy/monte_carlo/mc_parachute.py +++ b/rocketpy/stochastic/stochastic_parachute.py @@ -1,10 +1,10 @@ from rocketpy.rocket import Parachute -from .dispersion_model import DispersionModel +from .stochastic_model import StochasticModel -class McParachute(DispersionModel): - """A Monte Carlo Parachute class that inherits from MonteCarloModel. This +class StochasticParachute(StochasticModel): + """A Stochastic Parachute class that inherits from StochasticModel. This class is used to receive a Parachute object and information about the dispersion of its parameters and generate a random parachute object based on the provided information. @@ -15,19 +15,19 @@ class is used to receive a Parachute object and information about the Parachute object to be used for validation. cd_s : tuple, list, int, float Drag coefficient of the parachute. Follows the standard input format of - Dispersion Models. + Stochastic Models. trigger : list List of callables, string "apogee" or ints/floats. Follows the standard - input format of Dispersion Models. + input format of Stochastic Models. sampling_rate : tuple, list, int, float Sampling rate of the parachute in seconds. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. lag : tuple, list, int, float Lag of the parachute in seconds. Follows the standard input format of - Dispersion Models. + Stochastic Models. noise : list List of tuples in the form of (mean, standard deviation, - time-correlation). Follows the standard input format of Dispersion + time-correlation). Follows the standard input format of Stochastic Models. name : list List of names. This attribute can not be randomized. @@ -42,12 +42,12 @@ def __init__( lag=None, noise=None, ): - """Initializes the Monte Carlo Parachute class. + """Initializes the Stochastic Parachute class. See Also -------- This should link to somewhere that explains how inputs works in - dispersion models. + Stochastic Models. Parameters ---------- @@ -55,19 +55,19 @@ def __init__( Parachute object to be used for validation. cd_s : tuple, list, int, float Drag coefficient of the parachute. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. trigger : list List of callables, string "apogee" or ints/floats. Follows the - standard input format of Dispersion Models. + standard input format of Stochastic Models. sampling_rate : tuple, list, int, float Sampling rate of the parachute in seconds. Follows the standard - input format of Dispersion Models. + input format of Stochastic Models. lag : tuple, list, int, float Lag of the parachute in seconds. Follows the standard input format - of Dispersion Models. + of Stochastic Models. noise : list List of tuples in the form of (mean, standard deviation, - time-correlation). Follows the standard input format of Dispersion + time-correlation). Follows the standard input format of Stochastic Models. """ self._validate_trigger(trigger) @@ -86,7 +86,7 @@ def _validate_trigger(self, trigger): """Validates the trigger input. If the trigger input argument is not None, it must be: - a list of callables, string "apogee" or ints/floats - - a tuple that will be further validated in the DispersionModel class + - a tuple that will be further validated in the StochasticModel class """ if trigger is not None: assert isinstance(trigger, list) and all( diff --git a/rocketpy/monte_carlo/mc_rocket.py b/rocketpy/stochastic/stochastic_rocket.py similarity index 73% rename from rocketpy/monte_carlo/mc_rocket.py rename to rocketpy/stochastic/stochastic_rocket.py index 42d56e72f..10a36860c 100644 --- a/rocketpy/monte_carlo/mc_rocket.py +++ b/rocketpy/stochastic/stochastic_rocket.py @@ -1,35 +1,27 @@ import warnings from random import choice -from rocketpy.monte_carlo.mc_generic_motor import McGenericMotor -from rocketpy.monte_carlo.motor_dispersion_model import MotorDispersionModel from rocketpy.motors.motor import EmptyMotor, GenericMotor, Motor from rocketpy.motors.solid_motor import SolidMotor -from rocketpy.rocket.aero_surface import ( - EllipticalFins, - NoseCone, - RailButtons, - Tail, - TrapezoidalFins, -) +from rocketpy.rocket.aero_surface import (EllipticalFins, NoseCone, + RailButtons, Tail, TrapezoidalFins) from rocketpy.rocket.components import Components from rocketpy.rocket.parachute import Parachute from rocketpy.rocket.rocket import Rocket +from rocketpy.stochastic.stochastic_generic_motor import StochasticGenericMotor +from rocketpy.stochastic.stochastic_motor_model import StochasticMotorModel -from .dispersion_model import DispersionModel -from .mc_aero_surfaces import ( - McEllipticalFins, - McNoseCone, - McRailButtons, - McTail, - McTrapezoidalFins, -) -from .mc_parachute import McParachute -from .mc_solid_motor import McSolidMotor +from .stochastic_aero_surfaces import (StochasticEllipticalFins, + StochasticNoseCone, + StochasticRailButtons, StochasticTail, + StochasticTrapezoidalFins) +from .stochastic_model import StochasticModel +from .stochastic_parachute import StochasticParachute +from .stochastic_solid_motor import StochasticSolidMotor -class McRocket(DispersionModel): - """A Monte Carlo Rocket class that inherits from MonteCarloModel. This +class StochasticRocket(StochasticModel): + """A Stochastic Rocket class that inherits from StochasticModel. This class is used to receive a Rocket object and information about the dispersion of its parameters and generate a random rocket object based on the provided information. @@ -37,7 +29,7 @@ class is used to receive a Rocket object and information about the Attributes ---------- object : Rocket - The Rocket object to be used as a base for the Monte Carlo rocket. + The Rocket object to be used as a base for the Stochastic rocket. motors : Components A Components instance containing all the motors of the rocket. aerodynamic_surfaces : Components @@ -45,48 +37,48 @@ class is used to receive a Rocket object and information about the rocket. rail_buttons : Components A Components instance containing all the rail buttons of the rocket. - parachutes : list of McParachute - A list of McParachute instances containing all the parachutes of the + parachutes : list of StochasticParachute + A list of StochasticParachute instances containing all the parachutes of the rocket. radius : tuple, list, int, float The radius of the rocket. Follows the standard input format of - Dispersion Models. + Stochastic Models. mass : tuple, list, int, float The mass of the rocket. Follows the standard input format of - Dispersion Models. + Stochastic Models. inertia_11 : tuple, list, int, float The inertia of the rocket around the x axis. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. inertia_22 : tuple, list, int, float The inertia of the rocket around the y axis. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. inertia_33 : tuple, list, int, float The inertia of the rocket around the z axis. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. inertia_12 : tuple, list, int, float The inertia of the rocket around the xy axis. Follows the standard - input format of Dispersion Models. + input format of Stochastic Models. inertia_13 : tuple, list, int, float The inertia of the rocket around the xz axis. Follows the standard - input format of Dispersion Models. + input format of Stochastic Models. inertia_23 : tuple, list, int, float The inertia of the rocket around the yz axis. Follows the standard - input format of Dispersion Models. + input format of Stochastic Models. power_off_drag : list The power off drag of the rocket. Follows the 1d array like input format - of Dispersion Models. + of Stochastic Models. power_on_drag : list The power on drag of the rocket. Follows the 1d array like input format - of Dispersion Models. + of Stochastic Models. power_off_drag_factor : tuple, list, int, float The power off drag factor of the rocket. Follows the factor input - format of Dispersion Models. + format of Stochastic Models. power_on_drag_factor : tuple, list, int, float The power on drag factor of the rocket. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. center_of_mass_without_motor : tuple, list, int, float The center of mass of the rocket without the motor. Follows the - standard input format of Dispersion Models. + standard input format of Stochastic Models. coordinate_system_orientation : list The orientation of the coordinate system of the rocket. This attribute can not be a randomized. @@ -109,56 +101,56 @@ def __init__( power_on_drag_factor=(1, 0), center_of_mass_without_motor=None, ): - """Initializes the Monte Carlo Rocket class. + """Initializes the Stochastic Rocket class. See Also -------- This should link to somewhere that explains how inputs works in - dispersion models. + Stochastic models. Parameters ---------- rocket : Rocket - The Rocket object to be used as a base for the Monte Carlo rocket. + The Rocket object to be used as a base for the Stochastic rocket. radius : int, float, tuple, list, optional The radius of the rocket. Follows the standard input format of - Dispersion Models. + Stochastic Models. mass : int, float, tuple, list, optional The mass of the rocket. Follows the standard input format of - Dispersion Models. + Stochastic Models. inertia_11 : int, float, tuple, list, optional The inertia of the rocket around the x axis. Follows the standard - input format of Dispersion Models. + input format of Stochastic Models. inertia_22 : int, float, tuple, list, optional The inertia of the rocket around the y axis. Follows the standard - input format of Dispersion Models. + input format of Stochastic Models. inertia_33 : int, float, tuple, list, optional The inertia of the rocket around the z axis. Follows the standard - input format of Dispersion Models. + input format of Stochastic Models. inertia_12 : int, float, tuple, list, optional The inertia of the rocket around the xy axis. Follows the standard - input format of Dispersion Models. + input format of Stochastic Models. inertia_13 : int, float, tuple, list, optional The inertia of the rocket around the xz axis. Follows the standard - input format of Dispersion Models. + input format of Stochastic Models. inertia_23 : int, float, tuple, list, optional The inertia of the rocket around the yz axis. Follows the standard - input format of Dispersion Models. + input format of Stochastic Models. power_off_drag : list, optional The power off drag of the rocket. Follows the 1d array like input - format of Dispersion Models. + format of Stochastic Models. power_on_drag : list, optional The power on drag of the rocket. Follows the 1d array like input - format of Dispersion Models. + format of Stochastic Models. power_off_drag_factor : int, float, tuple, list, optional The power off drag factor of the rocket. Follows the factor input - format of Dispersion Models. + format of Stochastic Models. power_on_drag_factor : int, float, tuple, list, optional The power on drag factor of the rocket. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. center_of_mass_without_motor : int, float, tuple, list, optional The center of mass of the rocket without the motor. Follows the - standard input format of Dispersion Models. + standard input format of Stochastic Models. """ self._validate_1d_array_like("power_off_drag", power_off_drag) self._validate_1d_array_like("power_on_drag", power_on_drag) @@ -213,7 +205,7 @@ def __str__(self): # Otherwise, just use the default string representation of the value. value_str = str(value) if isinstance(value, list) and len(value) > 0: - if isinstance(value[0], (McParachute)): + if isinstance(value[0], (StochasticParachute)): value_str = "" for parachute in value: value_str += f"\n\t{parachute.name[0]} " @@ -221,157 +213,159 @@ def __str__(self): return s.strip() def add_motor(self, motor, position=None): - """Adds a monte carlo motor to the monte carlo rocket. If a motor is + """Adds a stochastic motor to the stochastic rocket. If a motor is already present, it will be replaced. Parameters ---------- - motor : McMotor or Motor - The motor to be added to the monte carlo rocket. + motor : StochasticMotor or Motor + The motor to be added to the stochastic rocket. position : tuple, list, int, float, optional The position of the motor. Follows the standard input format of - Dispersion Models. + Stochastic Models. """ # checks if there is a motor already if len(self.motors) > 0: warnings.warn( - "Only one motor can be added to the monte carlo rocket. " + "Only one motor can be added to the stochastic rocket. " "The previous motor will be replaced." ) self.motors = Components() # checks if input is a Motor - if not isinstance(motor, (Motor, MotorDispersionModel)): - raise AssertionError("`motor` must be a McMotor or Motor type") + if not isinstance(motor, (Motor, StochasticMotorModel)): + raise AssertionError("`motor` must be a StochasticMotor or Motor type") if isinstance(motor, Motor): - # create McMotor + # create StochasticMotor # TODO check motor type when hybrids and liquids are implemented if isinstance(motor, SolidMotor): - motor = McSolidMotor(solid_motor=motor) + motor = StochasticSolidMotor(solid_motor=motor) elif isinstance(motor, GenericMotor): - motor = McGenericMotor(generic_motor=motor) + motor = StochasticGenericMotor(generic_motor=motor) self.motors.add(motor, self._validate_position(motor, position)) - def _add_surfaces(self, surfaces, positions, type, monte_carlo_type, error_message): - """Adds a monte carlo aerodynamic surface to the monte carlo rocket. If + def _add_surfaces(self, surfaces, positions, type, stochastic_type, error_message): + """Adds a stochastic aerodynamic surface to the stochastic rocket. If an aerodynamic surface is already present, it will be replaced. Parameters ---------- - surfaces : McAeroSurface or AeroSurface - The aerodynamic surface to be added to the monte carlo rocket. + surfaces : StochasticAeroSurface or AeroSurface + The aerodynamic surface to be added to the stochastic rocket. positions : tuple, list, int, float, optional The position of the aerodynamic surface. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. type : type - The type of the aerodynamic surface to be added to the monte carlo + The type of the aerodynamic surface to be added to the stochastic rocket. - monte_carlo_type : type - The type of the monte carlo aerodynamic surface to be added to the - monte carlo rocket. + stochastic_type : type + The type of the stochastic aerodynamic surface to be added to the + stochastic rocket. error_message : str The error message to be raised if the input is not of the correct type. """ - if not isinstance(surfaces, (type, monte_carlo_type)): + if not isinstance(surfaces, (type, stochastic_type)): raise AssertionError(error_message) if isinstance(surfaces, type): - # create McSurfaces - surfaces = monte_carlo_type(component=surfaces) + # create StochasticSurfaces + surfaces = stochastic_type(component=surfaces) self.aerodynamic_surfaces.add( surfaces, self._validate_position(surfaces, positions) ) def add_nose(self, nose, position=None): - """Adds a monte carlo nose cone to the monte carlo rocket. + """Adds a stochastic nose cone to the stochastic rocket. Parameters ---------- - nose : McNoseCone or NoseCone - The nose cone to be added to the monte carlo rocket. + nose : StochasticNoseCone or NoseCone + The nose cone to be added to the stochastic rocket. position : tuple, list, int, float, optional The position of the nose cone. Follows the standard input format of - Dispersion Models. + Stochastic Models. """ self._add_surfaces( nose, position, NoseCone, - McNoseCone, - "`nose` must be of NoseCone or McNoseCone type", + StochasticNoseCone, + "`nose` must be of NoseCone or StochasticNoseCone type", ) def add_trapezoidal_fins(self, fins, position=None): - """Adds a monte carlo trapezoidal fins to the monte carlo rocket. + """Adds a stochastic trapezoidal fins to the stochastic rocket. Parameters ---------- - fins : McTrapezoidalFins or TrapezoidalFins - The trapezoidal fins to be added to the monte carlo rocket. + fins : StochasticTrapezoidalFins or TrapezoidalFins + The trapezoidal fins to be added to the stochastic rocket. position : tuple, list, int, float, optional The position of the trapezoidal fins. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. """ self._add_surfaces( fins, position, TrapezoidalFins, - McTrapezoidalFins, - "`fins` must be of TrapezoidalFins or McTrapezoidalFins type", + StochasticTrapezoidalFins, + "`fins` must be of TrapezoidalFins or StochasticTrapezoidalFins type", ) def add_elliptical_fins(self, fins, position=None): - """Adds a monte carlo elliptical fins to the monte carlo rocket. + """Adds a stochastic elliptical fins to the stochastic rocket. Parameters ---------- - fins : McEllipticalFins or EllipticalFins - The elliptical fins to be added to the monte carlo rocket. + fins : StochasticEllipticalFins or EllipticalFins + The elliptical fins to be added to the stochastic rocket. position : tuple, list, int, float, optional The position of the elliptical fins. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. """ self._add_surfaces( fins, position, EllipticalFins, - McEllipticalFins, - "`fins` must be of EllipticalFins or McEllipticalFins type", + StochasticEllipticalFins, + "`fins` must be of EllipticalFins or StochasticEllipticalFins type", ) def add_tail(self, tail, position=None): - """Adds a monte carlo tail to the monte carlo rocket. + """Adds a stochastic tail to the stochastic rocket. Parameters ---------- - tail : McTail or Tail - The tail to be added to the monte carlo rocket. + tail : StochasticTail or Tail + The tail to be added to the stochastic rocket. position : tuple, list, int, float, optional The position of the tail. Follows the standard input format of - Dispersion Models. + Stochastic Models. """ self._add_surfaces( tail, position, Tail, - McTail, - "`tail` must be of Tail or McTail type", + StochasticTail, + "`tail` must be of Tail or StochasticTail type", ) def add_parachute(self, parachute): - """Adds a monte carlo parachute to the monte carlo rocket. + """Adds a stochastic parachute to the stochastic rocket. Parameters ---------- - parachute : McParachute or Parachute - The parachute to be added to the monte carlo rocket. + parachute : StochasticParachute or Parachute + The parachute to be added to the stochastic rocket. """ - # checks if input is a McParachute type - if not isinstance(parachute, (Parachute, McParachute)): - raise AssertionError("`parachute` must be of Parachute or McParachute type") + # checks if input is a StochasticParachute type + if not isinstance(parachute, (Parachute, StochasticParachute)): + raise AssertionError( + "`parachute` must be of Parachute or StochasticParachute type" + ) if isinstance(parachute, Parachute): - # create McParachute - parachute = McParachute(parachute=parachute) + # create StochasticParachute + parachute = StochasticParachute(parachute=parachute) self.parachutes.append(parachute) def set_rail_buttons( @@ -379,23 +373,23 @@ def set_rail_buttons( rail_buttons, lower_button_position=None, ): - """Sets the rail buttons of the monte carlo rocket. + """Sets the rail buttons of the stochastic rocket. Parameters ---------- - rail_buttons : McRailButtons or RailButtons - The rail buttons to be added to the monte carlo rocket. + rail_buttons : StochasticRailButtons or RailButtons + The rail buttons to be added to the stochastic rocket. lower_button_position : tuple, list, int, float, optional The position of the lower button. Follows the standard input format - of Dispersion Models. + of Stochastic Models. """ - if not isinstance(rail_buttons, (McRailButtons, RailButtons)): + if not isinstance(rail_buttons, (StochasticRailButtons, RailButtons)): raise AssertionError( - "`rail_buttons` must be of RailButtons or McRailButtons type" + "`rail_buttons` must be of RailButtons or StochasticRailButtons type" ) if isinstance(rail_buttons, RailButtons): - # create McRailButtons - rail_buttons = McRailButtons(rail_buttons=rail_buttons) + # create StochasticRailButtons + rail_buttons = StochasticRailButtons(rail_buttons=rail_buttons) self.rail_buttons.add( rail_buttons, self._validate_position(rail_buttons, lower_button_position) ) @@ -470,8 +464,8 @@ def _create_get_position(self, validated_object): f"not have the same {validated_object.object.__class__.__name__} " "to get the nominal position value from." ) - # special case for motor dispersion model - if isinstance(validated_object, (MotorDispersionModel)): + # special case for motor stochastic model + if isinstance(validated_object, (StochasticMotorModel)): if isinstance(self.object.motor, EmptyMotor): raise AssertionError(error_msg) @@ -480,7 +474,7 @@ def get_motor_position(self_object, _): return get_motor_position else: - if isinstance(validated_object, McRailButtons): + if isinstance(validated_object, StochasticRailButtons): def get_surface_position(self_object, _): surfaces = self_object.rail_buttons.get_tuple_by_type( @@ -544,36 +538,34 @@ def dict_generator(self): self.last_rnd_dict = generated_dict yield generated_dict - def _create_motor(self, component_monte_carlo_motor): - monte_carlo_motor = component_monte_carlo_motor.component - motor = monte_carlo_motor.create_object() - position_rnd = self._randomize_position(component_monte_carlo_motor.position) - self.last_rnd_dict["motors"].append(monte_carlo_motor.last_rnd_dict) + def _create_motor(self, component_stochastic_motor): + stochastic_motor = component_stochastic_motor.component + motor = stochastic_motor.create_object() + position_rnd = self._randomize_position(component_stochastic_motor.position) + self.last_rnd_dict["motors"].append(stochastic_motor.last_rnd_dict) self.last_rnd_dict["motors"][-1]["position"] = position_rnd return motor, position_rnd - def _create_surface(self, component_monte_carlo_surface): - monte_carlo_surface = component_monte_carlo_surface.component - surface = monte_carlo_surface.create_object() - position_rnd = self._randomize_position(component_monte_carlo_surface.position) + def _create_surface(self, component_stochastic_surface): + stochastic_surface = component_stochastic_surface.component + surface = stochastic_surface.create_object() + position_rnd = self._randomize_position(component_stochastic_surface.position) self.last_rnd_dict["aerodynamic_surfaces"].append( - monte_carlo_surface.last_rnd_dict + stochastic_surface.last_rnd_dict ) self.last_rnd_dict["aerodynamic_surfaces"][-1]["position"] = position_rnd return surface, position_rnd - def _create_rail_buttons(self, component_monte_carlo_rail_buttons): - monte_carlo_rail_buttons = component_monte_carlo_rail_buttons.component - rail_buttons = monte_carlo_rail_buttons.create_object() + def _create_rail_buttons(self, component_stochastic_rail_buttons): + stochastic_rail_buttons = component_stochastic_rail_buttons.component + rail_buttons = stochastic_rail_buttons.create_object() lower_button_position_rnd = self._randomize_position( - component_monte_carlo_rail_buttons.position + component_stochastic_rail_buttons.position ) upper_button_position_rnd = ( rail_buttons.buttons_distance + lower_button_position_rnd ) - self.last_rnd_dict["rail_buttons"].append( - monte_carlo_rail_buttons.last_rnd_dict - ) + self.last_rnd_dict["rail_buttons"].append(stochastic_rail_buttons.last_rnd_dict) self.last_rnd_dict["rail_buttons"][-1][ "lower_button_position" ] = lower_button_position_rnd @@ -582,9 +574,9 @@ def _create_rail_buttons(self, component_monte_carlo_rail_buttons): ] = upper_button_position_rnd return rail_buttons, lower_button_position_rnd, upper_button_position_rnd - def _create_parachute(self, monte_carlo_parachute): - parachute = monte_carlo_parachute.create_object() - self.last_rnd_dict["parachutes"].append(monte_carlo_parachute.last_rnd_dict) + def _create_parachute(self, stochastic_parachute): + parachute = stochastic_parachute.create_object() + self.last_rnd_dict["parachutes"].append(stochastic_parachute.last_rnd_dict) return parachute def create_object(self): diff --git a/rocketpy/monte_carlo/mc_solid_motor.py b/rocketpy/stochastic/stochastic_solid_motor.py similarity index 80% rename from rocketpy/monte_carlo/mc_solid_motor.py rename to rocketpy/stochastic/stochastic_solid_motor.py index 1e285df1e..2b51ccc9c 100644 --- a/rocketpy/monte_carlo/mc_solid_motor.py +++ b/rocketpy/stochastic/stochastic_solid_motor.py @@ -1,10 +1,10 @@ from rocketpy.motors import SolidMotor -from .motor_dispersion_model import MotorDispersionModel +from .stochastic_motor_model import StochasticMotorModel -class McSolidMotor(MotorDispersionModel): - """A Monte Carlo Solid Motor class that inherits from MonteCarloModel. This +class StochasticSolidMotor(StochasticMotorModel): + """A Stochastic Solid Motor class that inherits from StochasticModel. This class is used to receive a SolidMotor object and information about the dispersion of its parameters and generate a random solid motor object based on the provided information. @@ -17,67 +17,67 @@ class is used to receive a SolidMotor object and information about the List of strings representing the thrust source to be selected. total_impulse : int, float, tuple, list Total impulse of the motor in newton seconds. Follows the standard - input format of Dispersion Models. + input format of Stochastic Models. burn_start_time : int, float, tuple, list Burn start time of the motor in seconds. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. burn_out_time : int, float, tuple, list Burn out time of the motor in seconds. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. dry_mass : int, float, tuple, list Dry mass of the motor in kilograms. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. dry_I_11 : int, float, tuple, list Dry inertia of the motor in kilograms times meters squared. Follows - the standard input format of Dispersion Models. + the standard input format of Stochastic Models. dry_I_22 : int, float, tuple, list Dry inertia of the motor in kilograms times meters squared. Follows - the standard input format of Dispersion Models. + the standard input format of Stochastic Models. dry_I_33 : int, float, tuple, list Dry inertia of the motor in kilograms times meters squared. Follows - the standard input format of Dispersion Models. + the standard input format of Stochastic Models. dry_I_12 : int, float, tuple, list Dry inertia of the motor in kilograms times meters squared. Follows - the standard input format of Dispersion Models. + the standard input format of Stochastic Models. dry_I_13 : int, float, tuple, list Dry inertia of the motor in kilograms times meters squared. Follows - the standard input format of Dispersion Models. + the standard input format of Stochastic Models. dry_I_23 : int, float, tuple, list Dry inertia of the motor in kilograms times meters squared. Follows - the standard input format of Dispersion Models. + the standard input format of Stochastic Models. nozzle_radius : int, float, tuple, list Nozzle radius of the motor in meters. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. grain_number : int, float, tuple, list Number of grains in the motor. Follows the standard input format of - Dispersion Models. + Stochastic Models. grain_density : int, float, tuple, list Density of the grains in the motor in kilograms per meters cubed. - Follows the standard input format of Dispersion Models. + Follows the standard input format of Stochastic Models. grain_outer_radius : int, float, tuple, list Outer radius of the grains in the motor in meters. Follows the - standard input format of Dispersion Models. + standard input format of Stochastic Models. grain_initial_inner_radius : int, float, tuple, list Initial inner radius of the grains in the motor in meters. Follows - the standard input format of Dispersion Models. + the standard input format of Stochastic Models. grain_initial_height : int, float, tuple, list Initial height of the grains in the motor in meters. Follows the - standard input format of Dispersion Models. + standard input format of Stochastic Models. grain_separation : int, float, tuple, list Separation between grains in the motor in meters. Follows the - standard input format of Dispersion Models. + standard input format of Stochastic Models. grains_center_of_mass_position : int, float, tuple, list Position of the center of mass of the grains in the motor in - meters. Follows the standard input format of Dispersion Models. + meters. Follows the standard input format of Stochastic Models. center_of_dry_mass_position : int, float, tuple, list Position of the center of mass of the dry mass in the motor in - meters. Follows the standard input format of Dispersion Models. + meters. Follows the standard input format of Stochastic Models. nozzle_position : int, float, tuple, list Position of the nozzle in the motor in meters. Follows the - standard input format of Dispersion Models. + standard input format of Stochastic Models. throat_radius : int, float, tuple, list Radius of the throat in the motor in meters. Follows the standard - input format of Dispersion Models. + input format of Stochastic Models. """ def __init__( @@ -106,12 +106,12 @@ def __init__( nozzle_position=None, throat_radius=None, ): - """Initializes the Monte Carlo Solid Motor class. + """Initializes the Stochastic Solid Motor class. See Also -------- This should link to somewhere that explains how inputs works in - dispersion models. + Stochastic Models. Parameters ---------- @@ -119,70 +119,70 @@ def __init__( SolidMotor object to be used for validation. thrust_source : list, optional List of strings representing the thrust source to be selected. - Follows the 1d array like input format of Dispersion Models. + Follows the 1d array like input format of Stochastic Models. total_impulse : int, float, tuple, list, optional Total impulse of the motor in newton seconds. Follows the standard - input format of Dispersion Models. + input format of Stochastic Models. burn_start_time : int, float, tuple, list, optional Burn start time of the motor in seconds. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. burn_out_time : int, float, tuple, list, optional Burn out time of the motor in seconds. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. dry_mass : int, float, tuple, list, optional Dry mass of the motor in kilograms. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. dry_I_11 : int, float, tuple, list, optional Dry inertia of the motor in kilograms times meters squared. Follows - the standard input format of Dispersion Models. + the standard input format of Stochastic Models. dry_I_22 : int, float, tuple, list, optional Dry inertia of the motor in kilograms times meters squared. Follows - the standard input format of Dispersion Models. + the standard input format of Stochastic Models. dry_I_33 : int, float, tuple, list, optional Dry inertia of the motor in kilograms times meters squared. Follows - the standard input format of Dispersion Models. + the standard input format of Stochastic Models. dry_I_12 : int, float, tuple, list, optional Dry inertia of the motor in kilograms times meters squared. Follows - the standard input format of Dispersion Models. + the standard input format of Stochastic Models. dry_I_13 : int, float, tuple, list, optional Dry inertia of the motor in kilograms times meters squared. Follows - the standard input format of Dispersion Models. + the standard input format of Stochastic Models. dry_I_23 : int, float, tuple, list, optional Dry inertia of the motor in kilograms times meters squared. Follows - the standard input format of Dispersion Models. + the standard input format of Stochastic Models. nozzle_radius : int, float, tuple, list, optional Nozzle radius of the motor in meters. Follows the standard input - format of Dispersion Models. + format of Stochastic Models. grain_number : int, float, tuple, list, optional Number of grains in the motor. Follows the standard input format of - Dispersion Models. + Stochastic Models. grain_density : int, float, tuple, list, optional Density of the grains in the motor in kilograms per meters cubed. - Follows the standard input format of Dispersion Models. + Follows the standard input format of Stochastic Models. grain_outer_radius : int, float, tuple, list, optional Outer radius of the grains in the motor in meters. Follows the - standard input format of Dispersion Models. + standard input format of Stochastic Models. grain_initial_inner_radius : int, float, tuple, list, optional Initial inner radius of the grains in the motor in meters. Follows - the standard input format of Dispersion Models. + the standard input format of Stochastic Models. grain_initial_height : int, float, tuple, list, optional Initial height of the grains in the motor in meters. Follows the - standard input format of Dispersion Models. + standard input format of Stochastic Models. grain_separation : int, float, tuple, list, optional Separation between grains in the motor in meters. Follows the - standard input format of Dispersion Models. + standard input format of Stochastic Models. grains_center_of_mass_position : int, float, tuple, list, optional Position of the center of mass of the grains in the motor in - meters. Follows the standard input format of Dispersion Models. + meters. Follows the standard input format of Stochastic Models. center_of_dry_mass_position : int, float, tuple, list, optional Position of the center of mass of the dry mass in the motor in - meters. Follows the standard input format of Dispersion Models. + meters. Follows the standard input format of Stochastic Models. nozzle_position : int, float, tuple, list, optional Position of the nozzle in the motor in meters. Follows the - standard input format of Dispersion Models. + standard input format of Stochastic Models. throat_radius : int, float, tuple, list, optional Radius of the throat in the motor in meters. Follows the standard - input format of Dispersion Models. + input format of Stochastic Models. """ super().__init__( solid_motor, From 3bee793a7acf9f0df2cbb75c839f275da43a0ff9 Mon Sep 17 00:00:00 2001 From: MateusStano Date: Thu, 8 Feb 2024 21:14:48 +0100 Subject: [PATCH 2/5] MNT: renaming to monte_carlo --- .../dispersion_class_usage.ipynb | 130 ++++++++---------- rocketpy/__init__.py | 22 +-- rocketpy/monte_carlo/__init__.py | 14 -- ...spersion_plots.py => monte_carlo_plots.py} | 37 ++--- ...ersion_prints.py => monte_carlo_prints.py} | 19 +-- rocketpy/simulation/__init__.py | 2 +- .../{dispersion.py => monte_carlo.py} | 72 +++++----- rocketpy/tools.py | 18 +-- tests/conftest.py | 22 +-- 9 files changed, 147 insertions(+), 189 deletions(-) delete mode 100644 rocketpy/monte_carlo/__init__.py rename rocketpy/plots/{dispersion_plots.py => monte_carlo_plots.py} (84%) rename rocketpy/prints/{dispersion_prints.py => monte_carlo_prints.py} (54%) rename rocketpy/simulation/{dispersion.py => monte_carlo.py} (92%) diff --git a/docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb b/docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb index 7c4d045fd..a9ea84e52 100644 --- a/docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb +++ b/docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb @@ -41,17 +41,17 @@ "metadata": {}, "outputs": [], "source": [ - "from rocketpy import Environment, SolidMotor, Rocket, Flight, Dispersion, Function\n", - "from rocketpy.monte_carlo import (\n", - " McEnvironment,\n", - " McSolidMotor,\n", - " McRocket,\n", - " McFlight,\n", - " McNoseCone,\n", - " McTail,\n", - " McTrapezoidalFins,\n", - " McParachute,\n", - " McRailButtons,\n", + "from rocketpy import Environment, SolidMotor, Rocket, Flight, MonteCarlo, Function\n", + "from rocketpy.stochastic import (\n", + " StochasticEnvironment,\n", + " StochasticSolidMotor,\n", + " StochasticRocket,\n", + " StochasticFlight,\n", + " StochasticNoseCone,\n", + " StochasticTail,\n", + " StochasticTrapezoidalFins,\n", + " StochasticParachute,\n", + " StochasticRailButtons,\n", ")\n", "import datetime" ] @@ -130,7 +130,7 @@ { "data": { "text/plain": [ - "object: \n", + "object: \n", "last_rnd_dict: {}\n", "elevation: [113]\n", "gravity: ['Function from R1 to R1 : (height (m)) ā†’ (gravity (m/sĀ²))']\n", @@ -149,7 +149,7 @@ } ], "source": [ - "mc_env = McEnvironment(\n", + "mc_env = StochasticEnvironment(\n", " environment=env,\n", " ensemble_member = list(range(env.num_ensemble_members)),\n", " wind_velocity_x_factor=(1.0, 0.33, \"normal\"),\n", @@ -168,7 +168,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[-2.43363182401062, -3.2080744792977374, -2.4175938202996714, -0.6800836428870567, -0.4147986561882701, -1.8612257454415737, -0.4147986561882701, -2.43363182401062, -2.77675179028226, -0.4147986561882701]\n" + "[2.8656245783020724, -1.3321216268594456, -0.5359939953111673, 2.1428841311794544, 0.40605086328475914, -0.5314461079973428, -1.9064661216740064, -3.9092318742013705, -0.6878449216372993, 1.79719671498841]\n" ] } ], @@ -244,7 +244,7 @@ { "data": { "text/plain": [ - "object: \n", + "object: \n", "last_rnd_dict: {}\n", "thrust_source: ['../../../data/motors/Cesaroni_M1670.eng', [[0, 6000], [1, 6000], [2, 6000], [3, 6000], [4, 6000]], 'Function from R1 to R1 : (Scalar) ā†’ (Scalar)']\n", "total_impulse: 6500.00000 Ā± 1000.00000 (numpy.random.normal)\n", @@ -278,7 +278,7 @@ } ], "source": [ - "mc_motor = McSolidMotor(\n", + "mc_motor = StochasticSolidMotor(\n", " solid_motor=motor,\n", " thrust_source=[\"../../../data/motors/Cesaroni_M1670.eng\",[[0,6000],[1,6000],[2,6000],[3,6000],[4,6000]], Function([[0,6000],[1,6000],[2,6000],[3,6000],[4,6000]])],\n", " burn_start_time=(0, .1),\n", @@ -305,7 +305,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[4964.74023772148, 6301.063121057903, 6874.945671519245, 7031.676385780169, 4180.972740531396, 7288.821931463088, 6355.952556067599, 7213.048653028296, 6243.881552378145, 5622.112927760851]\n" + "[4852.61864293818, 8049.503157663367, 7993.576486703936, 6209.860432796065, 6220.511807353847, 6441.7702019403805, 5486.7667676554975, 4626.165856788193, 7492.429770191062, 8801.091721035493]\n" ] } ], @@ -408,7 +408,7 @@ { "data": { "text/plain": [ - "object: \n", + "object: \n", "last_rnd_dict: {}\n", "radius: 0.06350 Ā± 0.00001 (numpy.random.normal)\n", "mass: 15.42600 Ā± 0.50000 (numpy.random.normal)\n", @@ -439,7 +439,7 @@ } ], "source": [ - "mc_rocket = McRocket(\n", + "mc_rocket = StochasticRocket(\n", " rocket=rocket,\n", " radius=0.0127 / 2000,\n", " mass=(15.426, 0.5, \"normal\"),\n", @@ -457,36 +457,36 @@ "metadata": {}, "outputs": [], "source": [ - "mc_nose_cone = McNoseCone(\n", + "mc_nose_cone = StochasticNoseCone(\n", " nosecone=nose_cone,\n", " length=0.001,\n", ")\n", "\n", - "mc_fin_set = McTrapezoidalFins(\n", + "mc_fin_set = StochasticTrapezoidalFins(\n", " trapezoidal_fins=fin_set,\n", " root_chord=0.0005,\n", " tip_chord=0.0005,\n", " span=0.0005,\n", ")\n", "\n", - "mc_tail = McTail(\n", + "mc_tail = StochasticTail(\n", " tail=tail,\n", " top_radius=0.001,\n", " bottom_radius=0.001,\n", " length=0.001,\n", ")\n", "\n", - "mc_rail_buttons = McRailButtons(\n", + "mc_rail_buttons = StochasticRailButtons(\n", " rail_buttons=rail_buttons, buttons_distance=0.001\n", ")\n", "\n", - "mc_main = McParachute(\n", + "mc_main = StochasticParachute(\n", " parachute=Main,\n", " cd_s=0.1,\n", " lag=0.1,\n", ")\n", "\n", - "mc_drogue = McParachute(\n", + "mc_drogue = StochasticParachute(\n", " parachute=Drogue,\n", " cd_s=0.07,\n", " lag=0.2,\n", @@ -516,7 +516,7 @@ { "data": { "text/plain": [ - "object: \n", + "object: \n", "last_rnd_dict: {}\n", "radius: 0.06350 Ā± 0.00001 (numpy.random.normal)\n", "mass: 15.42600 Ā± 0.50000 (numpy.random.normal)\n", @@ -533,13 +533,13 @@ "center_of_mass_without_motor: 0.00000 Ā± 0.00000 (numpy.random.normal)\n", "coordinate_system_orientation: ['tail_to_nose']\n", "motors:\n", - "\tMcSolidMotor at position: -1.25500 Ā± 0.00100 (numpy.random.normal)\n", + "\tStochasticSolidMotor at position: -1.25500 Ā± 0.00100 (numpy.random.normal)\n", "aerodynamic_surfaces:\n", - "\tMcNoseCone at position: 1.13400 Ā± 0.00100 (numpy.random.normal)\n", - "\tMcTrapezoidalFins at position: -1.04956 Ā± 0.00100 (numpy.random.normal)\n", - "\tMcTail at position: [-1.194656]\n", + "\tStochasticNoseCone at position: 1.13400 Ā± 0.00100 (numpy.random.normal)\n", + "\tStochasticTrapezoidalFins at position: -1.04956 Ā± 0.00100 (numpy.random.normal)\n", + "\tStochasticTail at position: [-1.194656]\n", "rail_buttons:\n", - "\tMcRailButtons at position: -0.61800 Ā± 0.00100 (numpy.random.normal)\n", + "\tStochasticRailButtons at position: -0.61800 Ā± 0.00100 (numpy.random.normal)\n", "parachutes: \n", "\tMain \n", "\tDrogue" @@ -580,17 +580,19 @@ "metadata": {}, "outputs": [ { - "ename": "IndexError", - "evalue": "list index out of range", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", - "Input \u001b[1;32mIn [17]\u001b[0m, in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m10\u001b[39m):\n\u001b[1;32m----> 2\u001b[0m rnd_rocket\u001b[38;5;241m=\u001b[39m\u001b[43mmc_rocket\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_object\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(rnd_rocket\u001b[38;5;241m.\u001b[39mstatic_margin(\u001b[38;5;241m0\u001b[39m))\n", - "File \u001b[1;32mC:\\Mateus\\GitHub\\RocketPy\\rocketpy\\monte_carlo\\mc_rocket.py:643\u001b[0m, in \u001b[0;36mMcRocket.create_object\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 641\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m parachute \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparachutes:\n\u001b[0;32m 642\u001b[0m parachute \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_create_parachute(parachute)\n\u001b[1;32m--> 643\u001b[0m \u001b[43mrocket\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd_parachute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 644\u001b[0m \u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparachute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 645\u001b[0m \u001b[43m \u001b[49m\u001b[43mcd_s\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparachute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcd_s\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 646\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrigger\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparachute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrigger\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 647\u001b[0m \u001b[43m \u001b[49m\u001b[43msampling_rate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparachute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msampling_rate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 648\u001b[0m \u001b[43m \u001b[49m\u001b[43mlag\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparachute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlag\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 649\u001b[0m \u001b[43m \u001b[49m\u001b[43mnoise\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparachute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnoise\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 650\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 652\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m rocket\n", - "File \u001b[1;32mC:\\Mateus\\GitHub\\RocketPy\\rocketpy\\rocket\\rocket.py:1123\u001b[0m, in \u001b[0;36mRocket.add_parachute\u001b[1;34m(self, name, cd_s, trigger, sampling_rate, lag, noise)\u001b[0m\n\u001b[0;32m 1059\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21madd_parachute\u001b[39m(\n\u001b[0;32m 1060\u001b[0m \u001b[38;5;28mself\u001b[39m, name, cd_s, trigger, sampling_rate\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m100\u001b[39m, lag\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, noise\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m 1061\u001b[0m ):\n\u001b[0;32m 1062\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Creates a new parachute, storing its parameters such as\u001b[39;00m\n\u001b[0;32m 1063\u001b[0m \u001b[38;5;124;03m opening delay, drag coefficients and trigger function.\u001b[39;00m\n\u001b[0;32m 1064\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1121\u001b[0m \u001b[38;5;124;03m Flight simulation.\u001b[39;00m\n\u001b[0;32m 1122\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1123\u001b[0m parachute \u001b[38;5;241m=\u001b[39m \u001b[43mParachute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcd_s\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrigger\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msampling_rate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlag\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnoise\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1124\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparachutes\u001b[38;5;241m.\u001b[39mappend(parachute)\n\u001b[0;32m 1125\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparachutes[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n", - "File \u001b[1;32mC:\\Mateus\\GitHub\\RocketPy\\rocketpy\\rocket\\parachute.py:163\u001b[0m, in \u001b[0;36mParachute.__init__\u001b[1;34m(self, name, cd_s, trigger, sampling_rate, lag, noise)\u001b[0m\n\u001b[0;32m 161\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlag \u001b[38;5;241m=\u001b[39m lag\n\u001b[0;32m 162\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnoise \u001b[38;5;241m=\u001b[39m noise\n\u001b[1;32m--> 163\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnoise_signal \u001b[38;5;241m=\u001b[39m [[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1e-6\u001b[39m, np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mnormal(noise[\u001b[38;5;241m0\u001b[39m], \u001b[43mnoise\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m)]]\n\u001b[0;32m 164\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnoisy_pressure_signal \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m 165\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclean_pressure_signal \u001b[38;5;241m=\u001b[39m []\n", - "\u001b[1;31mIndexError\u001b[0m: list index out of range" + "name": "stdout", + "output_type": "stream", + "text": [ + "2.449944567018549\n", + "2.571842854527979\n", + "2.546703381357366\n", + "2.59392423412476\n", + "2.5919180256418075\n", + "2.6559017893347763\n", + "2.6812750141491803\n", + "2.381599844315245\n", + "2.556417003106341\n", + "2.721378903740952\n" ] } ], @@ -611,7 +613,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -626,13 +628,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "object: \n", + "object: \n", + "last_rnd_dict: {}\n", "rail_length: [5]\n", "inclination: 84.70000 Ā± 1.00000 (numpy.random.normal)\n", "heading: 53.00000 Ā± 2.00000 (numpy.random.normal)\n", @@ -640,13 +643,13 @@ "terminate_on_apogee: None" ] }, - "execution_count": 18, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "mc_flight = McFlight(\n", + "mc_flight = StochasticFlight(\n", " flight=test_flight,\n", " inclination=(84.7, 1),\n", " heading=(53, 2),\n", @@ -664,12 +667,12 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -702,11 +705,11 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ - "test_dispersion = Dispersion(\n", + "test_dispersion = MonteCarlo(\n", " filename=\"dispersion_analysis_outputs/disp_class_example4\",\n", " environment=mc_env,\n", " rocket=mc_rocket,\n", @@ -736,36 +739,21 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Completed 1 iterations. Total CPU time: 18.7 s. Total wall time: 79.0 sstimated time left: 0 s \r" - ] - }, - { - "ename": "SyntaxError", - "evalue": "invalid syntax (, line 1)", - "output_type": "error", - "traceback": [ - "Traceback \u001b[1;36m(most recent call last)\u001b[0m:\n", - " File \u001b[0;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\IPython\\core\\interactiveshell.py:3398\u001b[0m in \u001b[0;35mrun_code\u001b[0m\n exec(code_obj, self.user_global_ns, self.user_ns)\n", - " Input \u001b[0;32mIn [38]\u001b[0m in \u001b[0;35m\u001b[0m\n test_dispersion.run_dispersion(number_of_simulations=1,append=False)\n", - " File \u001b[0;32mC:\\Mateus\\GitHub\\RocketPy\\rocketpy\\simulation\\dispersion.py:342\u001b[0m in \u001b[0;35mrun_dispersion\u001b[0m\n self._finalize_simulation(input_file, output_file, error_file)\n", - " File \u001b[0;32mC:\\Mateus\\GitHub\\RocketPy\\rocketpy\\simulation\\dispersion.py:444\u001b[0m in \u001b[0;35m_finalize_simulation\u001b[0m\n self.input_file = f\"{self.filename}.disp_inputs.txt\"\n", - " File \u001b[0;32mC:\\Mateus\\GitHub\\RocketPy\\rocketpy\\simulation\\dispersion.py:162\u001b[0m in \u001b[0;35minput_file\u001b[0m\n self.set_inputs_log()\n", - " File \u001b[0;32mC:\\Mateus\\GitHub\\RocketPy\\rocketpy\\simulation\\dispersion.py:217\u001b[0m in \u001b[0;35mset_inputs_log\u001b[0m\n d = ast.literal_eval(line)\n", - " File \u001b[0;32mc:\\Program Files\\Python310\\lib\\ast.py:62\u001b[0m in \u001b[0;35mliteral_eval\u001b[0m\n node_or_string = parse(node_or_string.lstrip(\" \\t\"), mode='eval')\n", - "\u001b[1;36m File \u001b[1;32mc:\\Program Files\\Python310\\lib\\ast.py:50\u001b[1;36m in \u001b[1;35mparse\u001b[1;36m\u001b[0m\n\u001b[1;33m return compile(source, filename, mode, flags,\u001b[0m\n", - "\u001b[1;36m File \u001b[1;32m:1\u001b[1;36m\u001b[0m\n\u001b[1;33m {'elevation': 113, 'gravity': 'Function from R1 to R1 : (height (m)) ā†’ (gravity (m/sĀ²))', 'latitude': 39.3897, 'longitude': -8.288964, 'wind_velocity_x_factor': 0.6352139725096725, 'wind_velocity_y_factor': 1.0319467868368157, 'datum': 'SIRGAS2000', 'timezone': 'UTC', 'ensemble_member': 30, 'radius': 0.06350039157986298, 'mass': 14.98296447726694, 'I_11_without_motor': 6.321, 'I_22_without_motor': 6.3255884039545, 'I_33_without_motor': 0.0375264094622778, 'I_12_without_motor': 0, 'I_13_without_motor': 0, 'I_23_without_motor': 0, 'power_off_drag': 'Function from R1 to R1 : (Mach Number) ā†’ (Drag Coefficient with Power Off)', 'power_on_drag': 'Function from R1 to R1 : (Mach Number) ā†’ (Drag Coefficient with Power On)', 'power_off_drag_factor': 1.0, 'power_on_drag_factor': 1.0, 'center_of_mass_without_motor': 0.0, 'coordinate_system_orientation': 'nozzle_to_combustion_chamber', 'parachutes': object: Parachute Drogue with a cd_s of 1.0000 m2\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" + "Completed 1 iterations. Total CPU time: 5.0 s. Total wall time: 5.0 sEstimated time left: 0 s\n", + "Saving results. \n", + "Results saved to dispersion_analysis_outputs/disp_class_example4.disp_outputs.txt\n" ] } ], "source": [ - "test_dispersion.run_dispersion(number_of_simulations=1,append=False)" + "test_dispersion.simulate(number_of_simulations=1,append=False)" ] }, { diff --git a/rocketpy/__init__.py b/rocketpy/__init__.py index 7fc243add..e4814d135 100644 --- a/rocketpy/__init__.py +++ b/rocketpy/__init__.py @@ -5,16 +5,16 @@ funcify_method, reset_funcified_methods, ) -from .monte_carlo import ( - McEllipticalFins, - McEnvironment, - McFlight, - McNoseCone, - McParachute, - McRocket, - McSolidMotor, - McTail, - McTrapezoidalFins, +from .stochastic import ( + StochasticEllipticalFins, + StochasticEnvironment, + StochasticFlight, + StochasticNoseCone, + StochasticParachute, + StochasticRocket, + StochasticSolidMotor, + StochasticTail, + StochasticTrapezoidalFins, ) from .motors import ( CylindricalTank, @@ -45,5 +45,5 @@ Tail, TrapezoidalFins, ) -from .simulation import Flight, Dispersion +from .simulation import Flight, MonteCarlo from .plots.compare import Compare, CompareFlights diff --git a/rocketpy/monte_carlo/__init__.py b/rocketpy/monte_carlo/__init__.py deleted file mode 100644 index 2dd400d71..000000000 --- a/rocketpy/monte_carlo/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -from .dispersion_model import DispersionModel -from .mc_aero_surfaces import ( - McEllipticalFins, - McNoseCone, - McRailButtons, - McTail, - McTrapezoidalFins, -) -from .mc_environment import McEnvironment -from .mc_flight import McFlight -from .mc_parachute import McParachute -from .mc_rocket import McRocket -from .mc_solid_motor import McSolidMotor -from .mc_generic_motor import McGenericMotor diff --git a/rocketpy/plots/dispersion_plots.py b/rocketpy/plots/monte_carlo_plots.py similarity index 84% rename from rocketpy/plots/dispersion_plots.py rename to rocketpy/plots/monte_carlo_plots.py index 0291b5334..d729b5ccd 100644 --- a/rocketpy/plots/dispersion_plots.py +++ b/rocketpy/plots/monte_carlo_plots.py @@ -1,21 +1,13 @@ -__author__ = ( - "Mateus Stano Junqueira, Guilherme Fernandes Alves, Bruno Abdulklech Sorban" -) -__copyright__ = "Copyright 20XX, RocketPy Team" -__license__ = "MIT" - - import matplotlib.pyplot as plt -from ..tools import generate_dispersion_ellipses +from ..tools import generate_monte_carlo_ellipses -class _DispersionPlots: - """Class to plot the dispersion results of the dispersion analysis.""" +class _MonteCarloPlots: + """Class to plot the monte carlo analysis results.""" - def __init__(self, dispersion): - self.dispersion = dispersion - return None + def __init__(self, monte_carlo): + self.monte_carlo = monte_carlo def ellipses( self, @@ -36,7 +28,7 @@ def ellipses( The path to the image to be used as the background actual_landing_point : tuple, optional A tuple containing the actual landing point of the rocket, if known. - Useful when comparing the dispersion results with the actual landing. + Useful when comparing the monte_carlo results with the actual landing. Must be given in tuple format, such as (x, y) in meters. By default None. perimeterSize : int, optional @@ -76,7 +68,7 @@ def ellipses( apogeeY, impactX, impactY, - ) = generate_dispersion_ellipses(self.dispersion.results) + ) = generate_monte_carlo_ellipses(self.monte_carlo.results) # Create plot figure plt.figure(num=None, figsize=(8, 6), dpi=150, facecolor="w", edgecolor="k") @@ -117,7 +109,7 @@ def ellipses( # Add title and labels to plot ax.set_title( - "1$\\sigma$, 2$\\sigma$ and 3$\\sigma$ Dispersion Ellipses: Apogee and Landing Points" + "1$\\sigma$, 2$\\sigma$ and 3$\\sigma$ monte_carlo Ellipses: Apogee and Landing Points" ) ax.set_ylabel("North (m)") ax.set_xlabel("East (m)") @@ -146,16 +138,15 @@ def ellipses( # Save plot and show result if save: plt.savefig( - str(self.dispersion.filename) + ".png", + str(self.monte_carlo.filename) + ".png", bbox_inches="tight", pad_inches=0, ) else: plt.show() - return None def all_results(self, keys=None): - """Plot the results of the dispersion analysis. + """Plot the results of the monte_carlo analysis. Parameters ---------- @@ -169,11 +160,11 @@ def all_results(self, keys=None): """ if keys is None: - keys = self.dispersion.results.keys() + keys = self.monte_carlo.results.keys() elif isinstance(keys, str): keys = [keys] elif isinstance(keys, (list, tuple)): - keys = list(set(keys).intersection(self.dispersion.results.keys())) + keys = list(set(keys).intersection(self.monte_carlo.results.keys())) if len(keys) == 0: raise ValueError( "The selected 'keys' are not available in the results. " @@ -187,10 +178,8 @@ def all_results(self, keys=None): for key in keys: plt.figure() plt.hist( - self.dispersion.results[key], + self.monte_carlo.results[key], ) plt.title("Histogram of " + key) plt.ylabel("Number of Occurrences") plt.show() - - return None diff --git a/rocketpy/prints/dispersion_prints.py b/rocketpy/prints/monte_carlo_prints.py similarity index 54% rename from rocketpy/prints/dispersion_prints.py rename to rocketpy/prints/monte_carlo_prints.py index 71acf18c8..a0b30fd81 100644 --- a/rocketpy/prints/dispersion_prints.py +++ b/rocketpy/prints/monte_carlo_prints.py @@ -1,13 +1,8 @@ -__author__ = "Guilherme Fernandes Alves" -__copyright__ = "Copyright 20XX, RocketPy Team" -__license__ = "MIT" +class _MonteCarloPrints: + """Class to print the monte carlo analysis results.""" - -class _DispersionPrints: - """Class to print the dispersion results of the dispersion analysis.""" - - def __init__(self, dispersion): - self.dispersion = dispersion + def __init__(self, monte_carlo): + self.monte_carlo = monte_carlo return None def all_results(self): @@ -24,12 +19,12 @@ def all_results(self): """ print("Monte Carlo Simulation by RocketPy") - print("Data Source: ", self.dispersion.filename) - print("Number of simulations: ", self.dispersion.num_of_loaded_sims) + print("Data Source: ", self.monte_carlo.filename) + print("Number of simulations: ", self.monte_carlo.num_of_loaded_sims) print("Results: \n") print("{:>25} {:>15} {:>15}".format("Parameter", "Mean", "Std. Dev.")) print("-" * 60) - for key, value in self.dispersion.processed_results.items(): + for key, value in self.monte_carlo.processed_results.items(): print("{:>25} {:>15.3f} {:>15.3f}".format(key, value[0], value[1])) return None diff --git a/rocketpy/simulation/__init__.py b/rocketpy/simulation/__init__.py index 207fa5f33..e118c2434 100644 --- a/rocketpy/simulation/__init__.py +++ b/rocketpy/simulation/__init__.py @@ -1,3 +1,3 @@ -from .dispersion import Dispersion +from .monte_carlo import MonteCarlo from .flight import Flight from .flight_data_importer import FlightDataImporter diff --git a/rocketpy/simulation/dispersion.py b/rocketpy/simulation/monte_carlo.py similarity index 92% rename from rocketpy/simulation/dispersion.py rename to rocketpy/simulation/monte_carlo.py index 87f5175b4..10a526016 100644 --- a/rocketpy/simulation/dispersion.py +++ b/rocketpy/simulation/monte_carlo.py @@ -5,12 +5,12 @@ import simplekml from rocketpy.mathutils.function import Function -from rocketpy.plots.dispersion_plots import _DispersionPlots -from rocketpy.prints.dispersion_prints import _DispersionPrints +from rocketpy.plots.monte_carlo_plots import _MonteCarloPlots +from rocketpy.prints.monte_carlo_prints import _MonteCarloPrints from rocketpy.simulation.flight import Flight from rocketpy.tools import ( - generate_dispersion_ellipses, - generate_dispersion_ellipses_coordinates, + generate_monte_carlo_ellipses, + generate_monte_carlo_ellipses_coordinates, ) # TODO: How to save Functions? With pickle? Save just the source? @@ -21,7 +21,7 @@ # TODO: Create evolution plots to analyze convergence -class Dispersion: +class MonteCarlo: """Class to run a Monte Carlo simulation of a rocket flight. Attributes @@ -32,8 +32,7 @@ class Dispersion: is 'filename', the exported output files will be named 'filename.disp_outputs.txt'. When analyzing the results of a previous simulation, this parameter should be set to the .txt - file containing the outputs of the previous dispersion - analysis. + file containing the outputs of the previous monte carlo analysis. environment : StochasticEnvironment The stochastic environment object to be iterated over. rocket : StochasticRocket @@ -53,23 +52,23 @@ class Dispersion: num_of_loaded_sims : int Number of simulations loaded from output_file being currently used. results : dict - Dispersion results organized in a dictionary where the keys are the - names of the saved attributes, and the values are a list with all the - result number of the respective attribute + Monte carlo analysis results organized in a dictionary where the keys + are the names of the saved attributes, and the values are a list with + all the result number of the respective attribute processed_results : dict Creates a dictionary with the mean and standard deviation of each parameter available in the results - prints : _DispersionPrints - Object with methods to print information about the dispersion + prints : _MonteCarloPrints + Object with methods to print information about the monte carlo simulation. - plot : _DispersionPlots - Object with methods to plot information about the dispersion + plot : _MonteCarloPlots + Object with methods to plot information about the monte carlo simulation. """ def __init__(self, filename, environment, rocket, flight, export_list=None): """ - Initialize a Dispersion object. + Initialize a MonteCarlo object. Parameters ---------- @@ -79,7 +78,7 @@ def __init__(self, filename, environment, rocket, flight, export_list=None): is 'filename', the exported output files will be named 'filename.disp_outputs.txt'. When analyzing the results of a previous simulation, this parameter should be set to the .txt - file containing the outputs of the previous dispersion + file containing the outputs of the previous monte carlo analysis. environment : StochasticEnvironment The stochastic environment object to be iterated over. @@ -108,8 +107,8 @@ def __init__(self, filename, environment, rocket, flight, export_list=None): self.num_of_loaded_sims = 0 self.results = {} self.processed_results = {} - self.prints = _DispersionPrints(self) - self.plots = _DispersionPlots(self) + self.prints = _MonteCarloPrints(self) + self.plots = _MonteCarloPlots(self) self._inputs_dict = {} self._last_print_len = 0 # used to print on the same line @@ -134,9 +133,9 @@ def __init__(self, filename, environment, rocket, flight, export_list=None): # TODO: Initialize variables so they can be accessed by MATLAB # TODO move export_list to init - def run_dispersion(self, number_of_simulations, append=False): + def simulate(self, number_of_simulations, append=False): """ - Runs the dispersion simulation and saves all data. + Runs the monte carlo simulation and saves all data. Parameters ---------- @@ -186,7 +185,7 @@ def __run_single_simulation(self, input_file, output_file): # Update iteration count self.iteration_count += 1 # Run trajectory simulation - dispersion_flight = Flight( + monte_carlo_flight = Flight( rocket=self.rocket.create_object(), environment=self.environment.create_object(), rail_length=self.flight._randomize_rail_length(), @@ -208,7 +207,7 @@ def __run_single_simulation(self, input_file, output_file): # Export inputs and outputs to file self.__export_flight_data( - flight=dispersion_flight, + flight=monte_carlo_flight, inputs_dict=self._inputs_dict, input_file=input_file, output_file=output_file, @@ -499,7 +498,7 @@ def set_num_of_loaded_sims(self): self.num_of_loaded_sims += 1 def set_results(self): - """Dispersion results organized in a dictionary where the keys are the + """Monte carlo results organized in a dictionary where the keys are the names of the saved attributes, and the values are a list with all the result number of the respective attribute""" self.results = {} @@ -520,13 +519,14 @@ def set_processed_results(self): self.processed_results[result] = (mean, stdev) def import_outputs(self, filename=None): - """Import dispersion results from .txt file and save it into a dictionary. + """Import monte carlo results from .txt file and save it into a + dictionary. Parameters ---------- filename : str - Name or directory path to the file to be imported. If none, Dispersion - filename will be used + Name or directory path to the file to be imported. If none, + self.filename will be used. Returns ------- @@ -553,14 +553,14 @@ def import_outputs(self, filename=None): ) def import_inputs(self, filename=None): - """Import dispersion results from .txt file and save it into a + """Import monte carlo results from .txt file and save it into a dictionary. Parameters ---------- filename : str Name or directory path to the file to be imported. If none, - Dispersion filename will be used + self.filename will be used. Returns ------- @@ -583,14 +583,14 @@ def import_inputs(self, filename=None): print(f"The following input file was imported: {filepath}\n") def import_errors(self, filename=None): - """Import dispersion results from .txt file and save it into a + """Import monte carlo results from .txt file and save it into a dictionary. Parameters ---------- filename : str Name or directory path to the file to be imported. If none, - Dispersion filename will be used + self.filename will be used. Returns ------- @@ -613,14 +613,14 @@ def import_errors(self, filename=None): print(f"The following error file was imported: {filepath}\n") def import_results(self, filename=None): - """Import dispersion results from .txt file and save it into a + """Import monte carlo results from .txt file and save it into a dictionary. Parameters ---------- filename : str Name or directory path to the file to be imported. If none, - Dispersion filename will be used + self.filename will be used. Returns ------- @@ -647,7 +647,7 @@ def export_ellipses_to_kml( Parameters ---------- results : dict - Contains dispersion results from the Monte Carlo simulation. + Contains results from the Monte Carlo simulation. filename : String Name to the KML exported file. origin_lat : float @@ -676,16 +676,16 @@ def export_ellipses_to_kml( _, _, _, - ) = generate_dispersion_ellipses(self.results) + ) = generate_monte_carlo_ellipses(self.results) outputs = [] if type == "all" or type == "impact": - outputs = outputs + generate_dispersion_ellipses_coordinates( + outputs = outputs + generate_monte_carlo_ellipses_coordinates( impact_ellipses, origin_lat, origin_lon, resolution=resolution ) if type == "all" or type == "apogee": - outputs = outputs + generate_dispersion_ellipses_coordinates( + outputs = outputs + generate_monte_carlo_ellipses_coordinates( apogee_ellipses, origin_lat, origin_lon, resolution=resolution ) diff --git a/rocketpy/tools.py b/rocketpy/tools.py index 36db24ace..cc5d0105c 100644 --- a/rocketpy/tools.py +++ b/rocketpy/tools.py @@ -114,7 +114,7 @@ def bilinear_interpolation(x, y, x1, x2, y1, y2, z11, z12, z21, z22): def get_distribution(distribution_function_name): - """Sets the distribution function to be used in the dispersion analysis. + """Sets the distribution function to be used in the monte carlo analysis. Parameters ---------- @@ -281,16 +281,16 @@ def decimalDegreesToArcSeconds(angle): return deg, min, sec -# Functions for dispersion analysis -def generate_dispersion_ellipses(results): - """A function to create apogee and impact ellipses from the dispersion - results. +# Functions for monte carlo analysis +def generate_monte_carlo_ellipses(results): + """A function to create apogee and impact ellipses from the monte carlo + analysis results. Parameters ---------- results : dict - A dictionary containing the results of the dispersion analysis. It should - contain the following keys: + A dictionary containing the results of the monte carlo analysis. It + should contain the following keys: - apogeeX: an array containing the x coordinates of the apogee - apogeeY: an array containing the y coordinates of the apogee - xImpact: an array containing the x coordinates of the impact @@ -312,7 +312,7 @@ def generate_dispersion_ellipses(results): An array containing the y coordinates of the impact ellipse. """ - # Retrieve dispersion data por apogee and impact XY position + # Retrieve monte carlo data por apogee and impact XY position try: apogee_x = np.array(results["apogeeX"]) apogee_y = np.array(results["apogeeY"]) @@ -403,7 +403,7 @@ def create_ellipse_objects(x, y, n, w, h, theta, rgb): return impact_ellipses, apogee_ellipses, apogee_x, apogee_y, impact_x, impact_y -def generate_dispersion_ellipses_coordinates( +def generate_monte_carlo_ellipses_coordinates( ellipses, origin_lat, origin_lon, resolution=100 ): """Generate a list of latitude and longitude points for each ellipse in diff --git a/tests/conftest.py b/tests/conftest.py index aa9554918..5e4d3d146 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -26,17 +26,17 @@ TrapezoidalFins, UllageBasedTank, ) -from rocketpy.Dispersion import Dispersion -from rocketpy.monte_carlo import ( - McEnvironment, - McFlight, - McNoseCone, - McParachute, - McRailButtons, - McRocket, - McSolidMotor, - McTail, - McTrapezoidalFins, +from rocketpy.simulation.monte_carlo import MonteCarlo +from rocketpy.stochastic import ( + StochasticEnvironment, + StochasticFlight, + StochasticNoseCone, + StochasticParachute, + StochasticRailButtons, + StochasticRocket, + StochasticSolidMotor, + StochasticTail, + StochasticTrapezoidalFins, ) # Pytest configuration From 15c82192985a0c9c3b60c21218bac05fa2206e83 Mon Sep 17 00:00:00 2001 From: Lint Action Date: Thu, 8 Feb 2024 20:19:49 +0000 Subject: [PATCH 3/5] Fix code style issues with Black --- .../stochastic/stochastic_aero_surfaces.py | 9 +++++++-- rocketpy/stochastic/stochastic_rocket.py | 20 +++++++++++++------ 2 files changed, 21 insertions(+), 8 deletions(-) diff --git a/rocketpy/stochastic/stochastic_aero_surfaces.py b/rocketpy/stochastic/stochastic_aero_surfaces.py index c0302203b..1fc633380 100644 --- a/rocketpy/stochastic/stochastic_aero_surfaces.py +++ b/rocketpy/stochastic/stochastic_aero_surfaces.py @@ -1,5 +1,10 @@ -from rocketpy.rocket.aero_surface import (EllipticalFins, NoseCone, - RailButtons, Tail, TrapezoidalFins) +from rocketpy.rocket.aero_surface import ( + EllipticalFins, + NoseCone, + RailButtons, + Tail, + TrapezoidalFins, +) from .stochastic_model import StochasticModel diff --git a/rocketpy/stochastic/stochastic_rocket.py b/rocketpy/stochastic/stochastic_rocket.py index 10a36860c..f3c4e5fb3 100644 --- a/rocketpy/stochastic/stochastic_rocket.py +++ b/rocketpy/stochastic/stochastic_rocket.py @@ -3,18 +3,26 @@ from rocketpy.motors.motor import EmptyMotor, GenericMotor, Motor from rocketpy.motors.solid_motor import SolidMotor -from rocketpy.rocket.aero_surface import (EllipticalFins, NoseCone, - RailButtons, Tail, TrapezoidalFins) +from rocketpy.rocket.aero_surface import ( + EllipticalFins, + NoseCone, + RailButtons, + Tail, + TrapezoidalFins, +) from rocketpy.rocket.components import Components from rocketpy.rocket.parachute import Parachute from rocketpy.rocket.rocket import Rocket from rocketpy.stochastic.stochastic_generic_motor import StochasticGenericMotor from rocketpy.stochastic.stochastic_motor_model import StochasticMotorModel -from .stochastic_aero_surfaces import (StochasticEllipticalFins, - StochasticNoseCone, - StochasticRailButtons, StochasticTail, - StochasticTrapezoidalFins) +from .stochastic_aero_surfaces import ( + StochasticEllipticalFins, + StochasticNoseCone, + StochasticRailButtons, + StochasticTail, + StochasticTrapezoidalFins, +) from .stochastic_model import StochasticModel from .stochastic_parachute import StochasticParachute from .stochastic_solid_motor import StochasticSolidMotor From c60bbab418195cb70535cbbeb0cf2127b94e83e6 Mon Sep 17 00:00:00 2001 From: MateusStano Date: Thu, 8 Feb 2024 21:31:09 +0100 Subject: [PATCH 4/5] MNT: notebooks and some docs renaming --- .../monte_carlo_analysis.ipynb} | 20 +++++---- .../Cd_PowerOff.csv | 0 .../Cd_PowerOn.csv | 0 .../LASC2019_reanalysis.nc | Bin .../Valetudo_basemap_final.jpg | Bin .../thrustCurve.csv | 0 ...chute_drop_from_helicopter.disp_errors.txt | 0 ...chute_drop_from_helicopter.disp_inputs.txt | 0 ...hute_drop_from_helicopter.disp_outputs.txt | 0 .../parachute_drop_from_helicopter.svg | 0 .../valetudo_rocket_v0.disp_errors.txt | 0 .../valetudo_rocket_v0.disp_inputs.txt | 0 .../valetudo_rocket_v0.disp_outputs.txt | 0 .../valetudo_rocket_v0.svg | 0 .../monte_carlo_class_usage.ipynb} | 38 ++++++++++-------- .../parachute_drop_from_helicopter.ipynb | 16 ++++---- docs/user/first_simulation.rst | 2 +- docs/user/index.rst | 4 +- 18 files changed, 41 insertions(+), 39 deletions(-) rename docs/notebooks/{dispersion_analysis/dispersion_analysis.ipynb => monte_carlo_analysis/monte_carlo_analysis.ipynb} (99%) rename docs/notebooks/{dispersion_analysis/dispersion_analysis_inputs => monte_carlo_analysis/monte_carlo_analysis_inputs}/Cd_PowerOff.csv (100%) rename docs/notebooks/{dispersion_analysis/dispersion_analysis_inputs => monte_carlo_analysis/monte_carlo_analysis_inputs}/Cd_PowerOn.csv (100%) rename docs/notebooks/{dispersion_analysis/dispersion_analysis_inputs => monte_carlo_analysis/monte_carlo_analysis_inputs}/LASC2019_reanalysis.nc (100%) rename docs/notebooks/{dispersion_analysis/dispersion_analysis_inputs => monte_carlo_analysis/monte_carlo_analysis_inputs}/Valetudo_basemap_final.jpg (100%) rename docs/notebooks/{dispersion_analysis/dispersion_analysis_inputs => monte_carlo_analysis/monte_carlo_analysis_inputs}/thrustCurve.csv (100%) rename docs/notebooks/{dispersion_analysis/dispersion_analysis_outputs => monte_carlo_analysis/monte_carlo_analysis_outputs}/parachute_drop_from_helicopter.disp_errors.txt (100%) rename docs/notebooks/{dispersion_analysis/dispersion_analysis_outputs => monte_carlo_analysis/monte_carlo_analysis_outputs}/parachute_drop_from_helicopter.disp_inputs.txt (100%) rename docs/notebooks/{dispersion_analysis/dispersion_analysis_outputs => monte_carlo_analysis/monte_carlo_analysis_outputs}/parachute_drop_from_helicopter.disp_outputs.txt (100%) rename docs/notebooks/{dispersion_analysis/dispersion_analysis_outputs => monte_carlo_analysis/monte_carlo_analysis_outputs}/parachute_drop_from_helicopter.svg (100%) rename docs/notebooks/{dispersion_analysis/dispersion_analysis_outputs => monte_carlo_analysis/monte_carlo_analysis_outputs}/valetudo_rocket_v0.disp_errors.txt (100%) rename docs/notebooks/{dispersion_analysis/dispersion_analysis_outputs => monte_carlo_analysis/monte_carlo_analysis_outputs}/valetudo_rocket_v0.disp_inputs.txt (100%) rename docs/notebooks/{dispersion_analysis/dispersion_analysis_outputs => monte_carlo_analysis/monte_carlo_analysis_outputs}/valetudo_rocket_v0.disp_outputs.txt (100%) rename docs/notebooks/{dispersion_analysis/dispersion_analysis_outputs => monte_carlo_analysis/monte_carlo_analysis_outputs}/valetudo_rocket_v0.svg (100%) rename docs/notebooks/{dispersion_analysis/dispersion_class_usage.ipynb => monte_carlo_analysis/monte_carlo_class_usage.ipynb} (99%) rename docs/notebooks/{dispersion_analysis => monte_carlo_analysis}/parachute_drop_from_helicopter.ipynb (98%) diff --git a/docs/notebooks/dispersion_analysis/dispersion_analysis.ipynb b/docs/notebooks/monte_carlo_analysis/monte_carlo_analysis.ipynb similarity index 99% rename from docs/notebooks/dispersion_analysis/dispersion_analysis.ipynb rename to docs/notebooks/monte_carlo_analysis/monte_carlo_analysis.ipynb index a7794f7d5..71a3b6153 100644 --- a/docs/notebooks/dispersion_analysis/dispersion_analysis.ipynb +++ b/docs/notebooks/monte_carlo_analysis/monte_carlo_analysis.ipynb @@ -40,7 +40,7 @@ "!git clone https://github.com/RocketPy-Team/RocketPy.git\n", "import os\n", "\n", - "os.chdir(\"RocketPy/docs/notebooks/dispersion_analysis\")" + "os.chdir(\"RocketPy/docs/notebooks/monte_carlo_analysis\")" ] }, { @@ -382,7 +382,7 @@ ], "source": [ "# Basic analysis info\n", - "filename = \"dispersion_analysis_outputs/valetudo_rocket_v0\"\n", + "filename = \"monte_carlo_analysis_outputs/valetudo_rocket_v0\"\n", "number_of_simulations = 100\n", "\n", "# Create data files for inputs, outputs and error logging\n", @@ -402,7 +402,7 @@ "Env.max_expected_height = 1500\n", "Env.set_atmospheric_model(\n", " type=\"Ensemble\",\n", - " file=\"dispersion_analysis_inputs/LASC2019_reanalysis.nc\",\n", + " file=\"monte_carlo_analysis_inputs/LASC2019_reanalysis.nc\",\n", " dictionary=\"ECMWF\",\n", ")\n", "\n", @@ -426,7 +426,7 @@ "\n", " # Create motor\n", " Keron = SolidMotor(\n", - " thrust_source=\"dispersion_analysis_inputs/thrustCurve.csv\",\n", + " thrust_source=\"monte_carlo_analysis_inputs/thrustCurve.csv\",\n", " burn_time=5.274,\n", " reshape_thrust_curve=(setting[\"burn_time\"], setting[\"impulse\"]),\n", " nozzle_radius=setting[\"nozzle_radius\"],\n", @@ -458,8 +458,8 @@ " setting[\"rocket_inertia_11\"],\n", " setting[\"rocket_inertia_33\"],\n", " ),\n", - " power_off_drag=\"dispersion_analysis_inputs/Cd_PowerOff.csv\",\n", - " power_on_drag=\"dispersion_analysis_inputs/Cd_PowerOn.csv\",\n", + " power_off_drag=\"monte_carlo_analysis_inputs/Cd_PowerOff.csv\",\n", + " power_on_drag=\"monte_carlo_analysis_inputs/Cd_PowerOn.csv\",\n", " center_of_mass_without_motor=0,\n", " coordinate_system_orientation=\"tail_to_nose\",\n", " )\n", @@ -572,7 +572,7 @@ } ], "source": [ - "filename = \"dispersion_analysis_outputs/valetudo_rocket_v0\"\n", + "filename = \"monte_carlo_analysis_outputs/valetudo_rocket_v0\"\n", "\n", "# Initialize variable to store all results\n", "dispersion_general_results = []\n", @@ -12892,9 +12892,7 @@ ")\n", "\n", "plt.figure()\n", - "plt.hist(\n", - " dispersion_results[\"initial_static_margin\"], label=\"Initial\", bins=int(N**0.5)\n", - ")\n", + "plt.hist(dispersion_results[\"initial_static_margin\"], label=\"Initial\", bins=int(N**0.5))\n", "plt.hist(\n", " dispersion_results[\"out_of_rail_static_margin\"],\n", " label=\"Out of Rail\",\n", @@ -20354,7 +20352,7 @@ "from matplotlib.patches import Ellipse\n", "\n", "# Import background map\n", - "img = imread(\"dispersion_analysis_inputs/Valetudo_basemap_final.jpg\")\n", + "img = imread(\"monte_carlo_analysis_inputs/Valetudo_basemap_final.jpg\")\n", "\n", "# Retrieve dispersion data por apogee and impact XY position\n", "apogee_x = np.array(dispersion_results[\"apogee_x\"])\n", diff --git a/docs/notebooks/dispersion_analysis/dispersion_analysis_inputs/Cd_PowerOff.csv b/docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_inputs/Cd_PowerOff.csv similarity index 100% rename from docs/notebooks/dispersion_analysis/dispersion_analysis_inputs/Cd_PowerOff.csv rename to docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_inputs/Cd_PowerOff.csv diff --git a/docs/notebooks/dispersion_analysis/dispersion_analysis_inputs/Cd_PowerOn.csv b/docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_inputs/Cd_PowerOn.csv similarity index 100% rename from docs/notebooks/dispersion_analysis/dispersion_analysis_inputs/Cd_PowerOn.csv rename to docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_inputs/Cd_PowerOn.csv diff --git a/docs/notebooks/dispersion_analysis/dispersion_analysis_inputs/LASC2019_reanalysis.nc b/docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_inputs/LASC2019_reanalysis.nc similarity index 100% rename from docs/notebooks/dispersion_analysis/dispersion_analysis_inputs/LASC2019_reanalysis.nc rename to docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_inputs/LASC2019_reanalysis.nc diff --git a/docs/notebooks/dispersion_analysis/dispersion_analysis_inputs/Valetudo_basemap_final.jpg b/docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_inputs/Valetudo_basemap_final.jpg similarity index 100% rename from docs/notebooks/dispersion_analysis/dispersion_analysis_inputs/Valetudo_basemap_final.jpg rename to docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_inputs/Valetudo_basemap_final.jpg diff --git a/docs/notebooks/dispersion_analysis/dispersion_analysis_inputs/thrustCurve.csv b/docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_inputs/thrustCurve.csv similarity index 100% rename from docs/notebooks/dispersion_analysis/dispersion_analysis_inputs/thrustCurve.csv rename to docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_inputs/thrustCurve.csv diff --git a/docs/notebooks/dispersion_analysis/dispersion_analysis_outputs/parachute_drop_from_helicopter.disp_errors.txt b/docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_outputs/parachute_drop_from_helicopter.disp_errors.txt similarity index 100% rename from docs/notebooks/dispersion_analysis/dispersion_analysis_outputs/parachute_drop_from_helicopter.disp_errors.txt rename to docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_outputs/parachute_drop_from_helicopter.disp_errors.txt diff --git a/docs/notebooks/dispersion_analysis/dispersion_analysis_outputs/parachute_drop_from_helicopter.disp_inputs.txt b/docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_outputs/parachute_drop_from_helicopter.disp_inputs.txt similarity index 100% rename from docs/notebooks/dispersion_analysis/dispersion_analysis_outputs/parachute_drop_from_helicopter.disp_inputs.txt rename to docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_outputs/parachute_drop_from_helicopter.disp_inputs.txt diff --git a/docs/notebooks/dispersion_analysis/dispersion_analysis_outputs/parachute_drop_from_helicopter.disp_outputs.txt b/docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_outputs/parachute_drop_from_helicopter.disp_outputs.txt similarity index 100% rename from docs/notebooks/dispersion_analysis/dispersion_analysis_outputs/parachute_drop_from_helicopter.disp_outputs.txt rename to docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_outputs/parachute_drop_from_helicopter.disp_outputs.txt diff --git a/docs/notebooks/dispersion_analysis/dispersion_analysis_outputs/parachute_drop_from_helicopter.svg b/docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_outputs/parachute_drop_from_helicopter.svg similarity index 100% rename from docs/notebooks/dispersion_analysis/dispersion_analysis_outputs/parachute_drop_from_helicopter.svg rename to docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_outputs/parachute_drop_from_helicopter.svg diff --git a/docs/notebooks/dispersion_analysis/dispersion_analysis_outputs/valetudo_rocket_v0.disp_errors.txt b/docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_outputs/valetudo_rocket_v0.disp_errors.txt similarity index 100% rename from docs/notebooks/dispersion_analysis/dispersion_analysis_outputs/valetudo_rocket_v0.disp_errors.txt rename to docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_outputs/valetudo_rocket_v0.disp_errors.txt diff --git a/docs/notebooks/dispersion_analysis/dispersion_analysis_outputs/valetudo_rocket_v0.disp_inputs.txt b/docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_outputs/valetudo_rocket_v0.disp_inputs.txt similarity index 100% rename from docs/notebooks/dispersion_analysis/dispersion_analysis_outputs/valetudo_rocket_v0.disp_inputs.txt rename to docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_outputs/valetudo_rocket_v0.disp_inputs.txt diff --git a/docs/notebooks/dispersion_analysis/dispersion_analysis_outputs/valetudo_rocket_v0.disp_outputs.txt b/docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_outputs/valetudo_rocket_v0.disp_outputs.txt similarity index 100% rename from docs/notebooks/dispersion_analysis/dispersion_analysis_outputs/valetudo_rocket_v0.disp_outputs.txt rename to docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_outputs/valetudo_rocket_v0.disp_outputs.txt diff --git a/docs/notebooks/dispersion_analysis/dispersion_analysis_outputs/valetudo_rocket_v0.svg b/docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_outputs/valetudo_rocket_v0.svg similarity index 100% rename from docs/notebooks/dispersion_analysis/dispersion_analysis_outputs/valetudo_rocket_v0.svg rename to docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_outputs/valetudo_rocket_v0.svg diff --git a/docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb b/docs/notebooks/monte_carlo_analysis/monte_carlo_class_usage.ipynb similarity index 99% rename from docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb rename to docs/notebooks/monte_carlo_analysis/monte_carlo_class_usage.ipynb index a9ea84e52..adf040b0c 100644 --- a/docs/notebooks/dispersion_analysis/dispersion_class_usage.ipynb +++ b/docs/notebooks/monte_carlo_analysis/monte_carlo_class_usage.ipynb @@ -111,15 +111,15 @@ "metadata": {}, "outputs": [], "source": [ - "env = Environment(\n", - " latitude=39.389700, longitude=-8.288964, elevation=113\n", - ")\n", + "env = Environment(latitude=39.389700, longitude=-8.288964, elevation=113)\n", "\n", "tomorrow = datetime.date.today() + datetime.timedelta(days=1)\n", "\n", - "env.set_date((tomorrow.year, tomorrow.month, tomorrow.day, 12)) # Hour given in UTC time\n", + "env.set_date(\n", + " (tomorrow.year, tomorrow.month, tomorrow.day, 12)\n", + ") # Hour given in UTC time\n", "\n", - "env.set_atmospheric_model(type=\"Ensemble\", file=\"GEFS\")\n" + "env.set_atmospheric_model(type=\"Ensemble\", file=\"GEFS\")" ] }, { @@ -151,7 +151,7 @@ "source": [ "mc_env = StochasticEnvironment(\n", " environment=env,\n", - " ensemble_member = list(range(env.num_ensemble_members)),\n", + " ensemble_member=list(range(env.num_ensemble_members)),\n", " wind_velocity_x_factor=(1.0, 0.33, \"normal\"),\n", " wind_velocity_y_factor=(1.0, 0.33, \"normal\"),\n", ")\n", @@ -175,9 +175,9 @@ "source": [ "wind_x_at_1000m = []\n", "for i in range(10):\n", - " rnd_env=mc_env.create_object()\n", + " rnd_env = mc_env.create_object()\n", " wind_x_at_1000m.append(rnd_env.wind_velocity_x(1000))\n", - " \n", + "\n", "print(wind_x_at_1000m)" ] }, @@ -280,8 +280,12 @@ "source": [ "mc_motor = StochasticSolidMotor(\n", " solid_motor=motor,\n", - " thrust_source=[\"../../../data/motors/Cesaroni_M1670.eng\",[[0,6000],[1,6000],[2,6000],[3,6000],[4,6000]], Function([[0,6000],[1,6000],[2,6000],[3,6000],[4,6000]])],\n", - " burn_start_time=(0, .1),\n", + " thrust_source=[\n", + " \"../../../data/motors/Cesaroni_M1670.eng\",\n", + " [[0, 6000], [1, 6000], [2, 6000], [3, 6000], [4, 6000]],\n", + " Function([[0, 6000], [1, 6000], [2, 6000], [3, 6000], [4, 6000]]),\n", + " ],\n", + " burn_start_time=(0, 0.1),\n", " grains_center_of_mass_position=0.001,\n", " grain_density=50,\n", " grain_separation=1 / 1000,\n", @@ -312,9 +316,9 @@ "source": [ "total_impulse = []\n", "for i in range(10):\n", - " rnd_motor=mc_motor.create_object()\n", + " rnd_motor = mc_motor.create_object()\n", " total_impulse.append(rnd_motor.total_impulse)\n", - " \n", + "\n", "print(total_impulse)" ] }, @@ -503,7 +507,7 @@ "mc_rocket.add_nose(mc_nose_cone, position=(1.134, 0.001))\n", "mc_rocket.add_trapezoidal_fins(mc_fin_set, position=(0.001, \"normal\"))\n", "mc_rocket.add_tail(mc_tail)\n", - "mc_rocket.set_rail_buttons(mc_rail_buttons,lower_button_position=(0.001, \"normal\"))\n", + "mc_rocket.set_rail_buttons(mc_rail_buttons, lower_button_position=(0.001, \"normal\"))\n", "mc_rocket.add_parachute(mc_main)\n", "mc_rocket.add_parachute(mc_drogue)" ] @@ -598,7 +602,7 @@ ], "source": [ "for i in range(10):\n", - " rnd_rocket=mc_rocket.create_object()\n", + " rnd_rocket = mc_rocket.create_object()\n", " print(rnd_rocket.static_margin(0))" ] }, @@ -710,7 +714,7 @@ "outputs": [], "source": [ "test_dispersion = MonteCarlo(\n", - " filename=\"dispersion_analysis_outputs/disp_class_example4\",\n", + " filename=\"monte_carlo_analysis_outputs/disp_class_example4\",\n", " environment=mc_env,\n", " rocket=mc_rocket,\n", " flight=mc_flight,\n", @@ -753,7 +757,7 @@ } ], "source": [ - "test_dispersion.simulate(number_of_simulations=1,append=False)" + "test_dispersion.simulate(number_of_simulations=1, append=False)" ] }, { @@ -1091,7 +1095,7 @@ "outputs": [], "source": [ "test_dispersion.exportEllipsesToKML(\n", - " filename=\"dispersion_analysis_outputs/disp_class_example.kml\",\n", + " filename=\"monte_carlo_analysis_outputs/disp_class_example.kml\",\n", " origin_lat=env.latitude,\n", " origin_lon=env.longitude,\n", " type=\"impact\",\n", diff --git a/docs/notebooks/dispersion_analysis/parachute_drop_from_helicopter.ipynb b/docs/notebooks/monte_carlo_analysis/parachute_drop_from_helicopter.ipynb similarity index 98% rename from docs/notebooks/dispersion_analysis/parachute_drop_from_helicopter.ipynb rename to docs/notebooks/monte_carlo_analysis/parachute_drop_from_helicopter.ipynb index e9709ff14..9cf7bdbc4 100644 --- a/docs/notebooks/dispersion_analysis/parachute_drop_from_helicopter.ipynb +++ b/docs/notebooks/monte_carlo_analysis/parachute_drop_from_helicopter.ipynb @@ -34,7 +34,7 @@ "!git clone https://github.com/giovaniceotto/RocketPy.git\n", "import os\n", "\n", - "os.chdir(\"RocketPy/docs/notebooks/dispersion_analysis\")" + "os.chdir(\"RocketPy/docs/notebooks/monte_carlo_analysis\")" ] }, { @@ -391,7 +391,7 @@ "outputs": [], "source": [ "# Basic analysis info\n", - "filename = \"dispersion_analysis_outputs/parachute_drop_from_helicopter\"\n", + "filename = \"monte_carlo_analysis_outputs/parachute_drop_from_helicopter\"\n", "number_of_simulations = 4000\n", "\n", "# Create data files for inputs, outputs and error logging\n", @@ -413,7 +413,7 @@ "Env.maxExpectedHeight = 1500\n", "Env.setAtmosphericModel(\n", " type=\"Ensemble\",\n", - " file=\"dispersion_analysis_inputs/LASC2019_reanalysis.nc\",\n", + " file=\"monte_carlo_analysis_inputs/LASC2019_reanalysis.nc\",\n", " dictionary=\"ECMWF\",\n", ")\n", "\n", @@ -436,7 +436,7 @@ "\n", " # Create motor\n", " Keron = SolidMotor(\n", - " thrustSource=\"dispersion_analysis_inputs/thrustCurve.csv\",\n", + " thrustSource=\"monte_carlo_analysis_inputs/thrustCurve.csv\",\n", " burn_time=5.274,\n", " reshapeThrustCurve=(setting[\"burn_time\"], setting[\"impulse\"]),\n", " nozzle_radius=setting[\"nozzle_radius\"],\n", @@ -459,8 +459,8 @@ " mass=setting[\"rocketMass\"],\n", " inertiaI=setting[\"inertiaI\"],\n", " inertiaZ=setting[\"inertiaZ\"],\n", - " powerOffDrag=\"dispersion_analysis_inputs/Cd_PowerOff.csv\",\n", - " powerOnDrag=\"dispersion_analysis_inputs/Cd_PowerOn.csv\",\n", + " powerOffDrag=\"monte_carlo_analysis_inputs/Cd_PowerOff.csv\",\n", + " powerOnDrag=\"monte_carlo_analysis_inputs/Cd_PowerOn.csv\",\n", " centerOfDryMassPosition=0,\n", " coordinateSystemOrientation=\"tailToNose\",\n", " )\n", @@ -581,7 +581,7 @@ }, "outputs": [], "source": [ - "filename = \"dispersion_analysis_outputs/parachute_drop_from_helicopter\"\n", + "filename = \"monte_carlo_analysis_outputs/parachute_drop_from_helicopter\"\n", "\n", "# Initialize variable to store all results\n", "dispersion_general_results = []\n", @@ -1165,7 +1165,7 @@ "from matplotlib.patches import Ellipse\n", "\n", "# Import background map\n", - "img = imread(\"dispersion_analysis_inputs/Valetudo_basemap_final.jpg\")\n", + "img = imread(\"monte_carlo_analysis_inputs/Valetudo_basemap_final.jpg\")\n", "\n", "# Retrieve dispersion data por apogee and impact XY position\n", "apogeeX = np.array(dispersion_results[\"apogeeX\"])\n", diff --git a/docs/user/first_simulation.rst b/docs/user/first_simulation.rst index 9cfa5ae43..1e73ca347 100644 --- a/docs/user/first_simulation.rst +++ b/docs/user/first_simulation.rst @@ -661,7 +661,7 @@ analysis. Here we will show some examples, but much more can be done! .. seealso:: *RocketPy* can be used to perform a Monte Carlo Dispersion Analysis. See - `Monte Carlo Simulations `_ + `Monte Carlo Simulations `_ for more information. Apogee as a Function of Mass diff --git a/docs/user/index.rst b/docs/user/index.rst index cc7591631..99f5a8de8 100644 --- a/docs/user/index.rst +++ b/docs/user/index.rst @@ -29,8 +29,8 @@ RocketPy's User Guide :maxdepth: 2 :caption: Dispersion Analysis - ../notebooks/dispersion_analysis/dispersion_analysis.ipynb - ../notebooks/dispersion_analysis/parachute_drop_from_helicopter.ipynb + ../notebooks/monte_carlo_analysis/monte_carlo_analysis.ipynb + ../notebooks/monte_carlo_analysis/parachute_drop_from_helicopter.ipynb .. toctree:: :maxdepth: 2 From f234624050f4b86aad2b7ab64af2b9b5765a5f1a Mon Sep 17 00:00:00 2001 From: Gui-FernandesBR Date: Sat, 10 Feb 2024 12:05:32 -0500 Subject: [PATCH 5/5] MNT: Fix typos --- rocketpy/plots/monte_carlo_plots.py | 7 ++++--- rocketpy/simulation/monte_carlo.py | 2 +- rocketpy/stochastic/stochastic_aero_surfaces.py | 8 ++++---- 3 files changed, 9 insertions(+), 8 deletions(-) diff --git a/rocketpy/plots/monte_carlo_plots.py b/rocketpy/plots/monte_carlo_plots.py index d729b5ccd..5ff028387 100644 --- a/rocketpy/plots/monte_carlo_plots.py +++ b/rocketpy/plots/monte_carlo_plots.py @@ -28,7 +28,7 @@ def ellipses( The path to the image to be used as the background actual_landing_point : tuple, optional A tuple containing the actual landing point of the rocket, if known. - Useful when comparing the monte_carlo results with the actual landing. + Useful when comparing the Monte Carlo results with the actual landing. Must be given in tuple format, such as (x, y) in meters. By default None. perimeterSize : int, optional @@ -109,7 +109,8 @@ def ellipses( # Add title and labels to plot ax.set_title( - "1$\\sigma$, 2$\\sigma$ and 3$\\sigma$ monte_carlo Ellipses: Apogee and Landing Points" + "1$\\sigma$, 2$\\sigma$ and 3$\\sigma$ " + + "Monte Carlo Ellipses: Apogee and Landing Points" ) ax.set_ylabel("North (m)") ax.set_xlabel("East (m)") @@ -146,7 +147,7 @@ def ellipses( plt.show() def all_results(self, keys=None): - """Plot the results of the monte_carlo analysis. + """Plot the results of the Monte Carlo analysis. Parameters ---------- diff --git a/rocketpy/simulation/monte_carlo.py b/rocketpy/simulation/monte_carlo.py index 10a526016..b21bbfa8a 100644 --- a/rocketpy/simulation/monte_carlo.py +++ b/rocketpy/simulation/monte_carlo.py @@ -61,7 +61,7 @@ class MonteCarlo: prints : _MonteCarloPrints Object with methods to print information about the monte carlo simulation. - plot : _MonteCarloPlots + plots : _MonteCarloPlots Object with methods to plot information about the monte carlo simulation. """ diff --git a/rocketpy/stochastic/stochastic_aero_surfaces.py b/rocketpy/stochastic/stochastic_aero_surfaces.py index 1fc633380..8b406fe6a 100644 --- a/rocketpy/stochastic/stochastic_aero_surfaces.py +++ b/rocketpy/stochastic/stochastic_aero_surfaces.py @@ -227,7 +227,7 @@ def create_object(self): Returns ------- - trapezoidalfins : TrapezoidalFins + fins : TrapezoidalFins TrapezoidalFins object with the randomly generated input arguments. """ generated_dict = next(self.dict_generator()) @@ -334,7 +334,7 @@ def create_object(self): Returns ------- - ellipticalfins : EllipticalFins + fins : EllipticalFins EllipticalFins object with the randomly generated input arguments. """ generated_dict = next(self.dict_generator()) @@ -388,7 +388,7 @@ def __init__( See Also -------- - This should link to somewhere that explains how inputs works in + TODO: This should link to somewhere that explains how inputs works in stochastic models. Parameters @@ -497,7 +497,7 @@ def create_object(self): Returns ------- - railbuttons : RailButtons + rail_buttons : RailButtons RailButtons object with the randomly generated input arguments. """ generated_dict = next(self.dict_generator())