MNT: renaming to stochastic

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

rocketpy/monte_carlo/mc_environment.py → 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,

rocketpy/monte_carlo/mc_flight.py → rocketpy/stochastic/stochastic_flight.py

@@ -1,27 +1,27 @@
 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.
 
     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"]

rocketpy/monte_carlo/mc_generic_motor.py → 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
@@ -92,68 +92,68 @@ 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
         ----------
         generic_motor : GenericMotor
             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,

rocketpy/monte_carlo/dispersion_model.py → 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
         ----------