Merge branch 'develop' into feat/flight-comparison

Author: Monta120

CHANGELOG.md

@@ -44,6 +44,7 @@ Attention: The newest changes should be on top -->
 
 ### Fixed
 
+- BUG: Fix parallel Monte Carlo simulation showing incorrect iteration count [#806](https://github.com/RocketPy-Team/RocketPy/pull/806)
 - BUG: Fix CSV column header spacing in FlightDataExporter [#864](https://github.com/RocketPy-Team/RocketPy/issues/864)
 
 

docs/user/environment/1-atm-models/standard_atmosphere.rst

@@ -22,8 +22,7 @@ Other profiles can be derived from it, however, winds are automatically set to
 
     env.plots.atmospheric_model()
 
-.. skip one line with |
-| 
+|
 
 The International Standard Atmosphere can also be reset at any time by using the
 :meth:`rocketpy.Environment.set_atmospheric_model` method. For example:

docs/user/first_simulation.rst

@@ -90,6 +90,7 @@ regarding the launch site:
 
 | This roughly corresponds to the location of Spaceport America, New Mexico.
 
+
 Next, we need to specify the atmospheric model to be used. In this example,
 we will get GFS forecasts data from tomorrow.
 
@@ -110,6 +111,7 @@ of tomorrow:
 
 | Now we set the atmospheric model to be used:
 
+
 .. jupyter-execute::
 
     env.set_atmospheric_model(type="Forecast", file="GFS")
@@ -189,6 +191,7 @@ to specify several parameters:
 
 | We can see its characteristics by calling the info method:
 
+
 .. jupyter-execute::
 
     Pro75M1670.info()
@@ -637,8 +640,9 @@ and the rocket Mach number (see :meth:`rocketpy.Flight.mach_number`) to the file
     )
 
 | As you can see, the first argument is the file name to be created. The following
-arguments are the attributes to be exported. We can check the file by reading it
-with :func:`pandas.read_csv`:
+  arguments are the attributes to be exported. We can check the file by reading it
+  with :func:`pandas.read_csv`:
+
 
 .. jupyter-execute::
 
@@ -647,8 +651,9 @@ with :func:`pandas.read_csv`:
     pd.read_csv("calisto_flight_data.csv")
 
 | The file header specifies the meaning of each column. The time samples are
-obtained from the simulation solver steps. To export the data at a different
-sampling rate, use the ``time_step`` argument:
+  obtained from the simulation solver steps. To export the data at a different
+  sampling rate, use the ``time_step`` argument:
+
 
 .. jupyter-execute::
 

docs/user/three_dof_simulation.rst

@@ -67,7 +67,7 @@ The environment setup is identical to standard simulations:
         elevation=1400
     )
 
-    env.set_atmospheric_model(type="StandardAtmosphere")
+    env.set_atmospheric_model(type="standard_atmosphere")
 
 Step 2: Create a PointMassMotor
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -123,7 +123,7 @@ Or use a custom thrust function:
 .. seealso::
 
     For detailed information about :class:`rocketpy.PointMassMotor` parameters,
-    see the :ref:`API reference <api_reference>`.
+    see the :class:`rocketpy.PointMassMotor` class documentation.
 
 Step 3: Create a PointMassRocket
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -279,6 +279,14 @@ Export trajectory data to CSV:
         "vz",
     )
 
+
+.. jupyter-execute::
+    :hide-code:
+
+    import os
+    os.remove("trajectory_3dof.csv")
+
+
 Complete Example
 ----------------
 
@@ -294,7 +302,7 @@ Here's a complete 3-DOF simulation from start to finish:
         longitude=-8.2889,
         elevation=100
     )
-    env.set_atmospheric_model(type="StandardAtmosphere")
+    env.set_atmospheric_model(type="standard_atmosphere")
 
     # 2. Motor
     motor = PointMassMotor(
@@ -412,7 +420,6 @@ See Also
 - :ref:`First Simulation <firstsimulation>` - Standard 6-DOF simulation tutorial
 - :ref:`Rocket Class Usage <rocketusage>` - Full rocket modeling capabilities
 - :ref:`Flight Class Usage <flightusage>` - Complete flight simulation options
-- :doc:`../examples/3_dof_trial_sim` - Jupyter notebook example
 
 Further Reading
 ---------------

rocketpy/plots/monte_carlo_plots.py

@@ -1,8 +1,11 @@
+from pathlib import Path
+
 import matplotlib.pyplot as plt
 import numpy as np
 from matplotlib.transforms import offset_copy
 
 from ..tools import generate_monte_carlo_ellipses, import_optional_dependency
+from .plot_helpers import show_or_save_plot
 
 
 class _MonteCarloPlots:
@@ -159,7 +162,7 @@ def ellipses(
         else:
             plt.show()
 
-    def all(self, keys=None):
+    def all(self, keys=None, *, filename=None):
         """
         Plot the histograms of the Monte Carlo simulation results.
 
@@ -168,6 +171,14 @@ def all(self, keys=None):
         keys : str, list or tuple, optional
             The keys of the results to be plotted. If None, all results will be
             plotted. Default is None.
+        filename : str | None, optional
+            The path the plot should be saved to. By default None, in which case
+            the plot will be shown instead of saved. If a filename is provided,
+            each histogram will be saved with the key name appended to the
+            filename (e.g., "plots/histogram_apogee.png" for key "apogee" with
+            filename "plots/histogram.png"). Supported file endings are: eps,
+            jpg, jpeg, pdf, pgf, png, ps, raw, rgba, svg, svgz, tif, tiff and
+            webp (these are the formats supported by matplotlib).
 
         Returns
         -------
@@ -207,7 +218,16 @@ def all(self, keys=None):
             ax1.set_xticks([])
 
             plt.tight_layout()
-            plt.show()
+
+            # Generate the filename for this specific key if saving
+            if filename is not None:
+                file_path = Path(filename)
+                key_filename = str(
+                    file_path.parent / f"{file_path.stem}_{key}{file_path.suffix}"
+                )
+                show_or_save_plot(key_filename)
+            else:
+                show_or_save_plot(None)
 
     def plot_comparison(self, other_monte_carlo):
         """

rocketpy/rocket/parachute.py

@@ -25,27 +25,27 @@ class Parachute:
         system. It can be one of the following:
 
         - A callable function that takes three arguments:
-        1. Freestream pressure in pascals.
-        2. Height in meters above ground level.
-        3. The state vector of the simulation, which is defined as:
+          1. Freestream pressure in pascals.
+          2. Height in meters above ground level.
+          3. The state vector of the simulation, which is defined as:
 
-           `[x, y, z, vx, vy, vz, e0, e1, e2, e3, wx, wy, wz]`.
+             `[x, y, z, vx, vy, vz, e0, e1, e2, e3, wx, wy, wz]`.
 
-        4. A list of sensors that are attached to the rocket. The most recent
-           measurements of the sensors are provided with the
-           ``sensor.measurement`` attribute. The sensors are listed in the same
-           order as they are added to the rocket.
+          4. A list of sensors that are attached to the rocket. The most recent
+             measurements of the sensors are provided with the
+             ``sensor.measurement`` attribute. The sensors are listed in the same
+             order as they are added to the rocket.
 
-        The function should return ``True`` if the parachute ejection system
-        should be triggered and False otherwise. The function will be called
-        according to the specified sampling rate.
+          The function should return ``True`` if the parachute ejection system
+          should be triggered and False otherwise. The function will be called
+          according to the specified sampling rate.
 
         - A float value, representing an absolute height in meters. In this
-        case, the parachute will be ejected when the rocket reaches this height
-        above ground level.
+          case, the parachute will be ejected when the rocket reaches this height
+          above ground level.
 
         - The string "apogee" which triggers the parachute at apogee, i.e.,
-        when the rocket reaches its highest point and starts descending.
+          when the rocket reaches its highest point and starts descending.
 
 
     Parachute.triggerfunc : function

rocketpy/rocket/rocket.py

@@ -1667,12 +1667,11 @@ def add_air_brakes(
             6. `interactive_objects` (list): A list containing the objects that
                the controller function can interact with. The objects are
                listed in the same order as they are provided in the
-               `interactive_objects`
+               `interactive_objects` argument.
             7. `sensors` (list): A list of sensors that are attached to the
-                rocket. The most recent measurements of the sensors are provided
-                with the ``sensor.measurement`` attribute. The sensors are
-                listed in the same order as they are added to the rocket
-               ``interactive_objects``
+               rocket. The most recent measurements of the sensors are provided
+               with the ``sensor.measurement`` attribute. The sensors are
+               listed in the same order as they are added to the rocket.
 
             This function will be called during the simulation at the specified
             sampling rate. The function should evaluate and change the observed

rocketpy/simulation/monte_carlo.py

@@ -292,7 +292,7 @@ def __run_in_serial(self):
                 with open(self.output_file, "a", encoding="utf-8") as f:
                     f.write(outputs_json)
 
-                sim_monitor.print_update_status(sim_monitor.count)
+                sim_monitor.print_update_status()
 
             sim_monitor.print_final_status()
 
@@ -430,7 +430,7 @@ def __sim_producer(self, seed, sim_monitor, mutex, error_event):  # pylint: disa
                     with open(self.output_file, "a", encoding="utf-8") as f:
                         f.write(outputs_json)
 
-                    sim_monitor.print_update_status(sim_idx)
+                    sim_monitor.print_update_status()
                 finally:
                     mutex.release()
 
@@ -1208,6 +1208,7 @@ def __init__(self, initial_count, n_simulations, start_time):
         self.count = initial_count
         self.n_simulations = n_simulations
         self.start_time = start_time
+        self.completed_count = 0
 
     def keep_simulating(self):
         return self.count < self.n_simulations
@@ -1216,25 +1217,24 @@ def increment(self):
         self.count += 1
         return self.count
 
-    def print_update_status(self, sim_idx):
+    def print_update_status(self):
         """Prints a message on the same line as the previous one and replaces
         the previous message with the new one, deleting the extra characters
-        from the previous message.
-
-        Parameters
-        ----------
-        sim_idx : int
-            Index of the current simulation.
+        from the previous message. This method increments the completed_count
+        to track how many simulations have finished (thread-safe when called
+        within a mutex-protected section).
 
         Returns
         -------
         None
         """
+        self.completed_count += 1
 
-        average_time = (time() - self.start_time) / (self.count - self.initial_count)
-        estimated_time = int((self.n_simulations - self.count) * average_time)
+        average_time = (time() - self.start_time) / self.completed_count
+        remaining = self.n_simulations - self.initial_count - self.completed_count
+        estimated_time = int(remaining * average_time)
 
-        msg = f"Current iteration: {sim_idx:06d}"
+        msg = f"Iterations completed: {self.completed_count:06d}"
         msg += f" | Average Time per Iteration: {average_time:.3f} s"
         msg += f" | Estimated time left: {estimated_time} s"
 

rocketpy/tools.py

@@ -1312,7 +1312,7 @@ def find_obj_from_hash(obj, hash_, depth_limit=None):
     Returns
     -------
     object
-        The object whose '__rpy_hash' matches hash_, or None if not found.
+        The object whose '__rpy_hash' matches ``hash_``, or None if not found.
     """
 
     stack = [(obj, 0)]

tests/integration/simulation/test_monte_carlo.py

@@ -11,14 +11,18 @@
 
 def _post_test_file_cleanup():
     """Clean monte carlo files after test session if they exist."""
-    if os.path.exists("monte_carlo_class_example.kml"):
-        os.remove("monte_carlo_class_example.kml")
-    if os.path.exists("monte_carlo_test.errors.txt"):
-        os.remove("monte_carlo_test.errors.txt")
-    if os.path.exists("monte_carlo_test.inputs.txt"):
-        os.remove("monte_carlo_test.inputs.txt")
-    if os.path.exists("monte_carlo_test.outputs.txt"):
-        os.remove("monte_carlo_test.outputs.txt")
+    files_to_cleanup = [
+        "monte_carlo_class_example.kml",
+        "monte_carlo_test.errors.txt",
+        "monte_carlo_test.inputs.txt",
+        "monte_carlo_test.outputs.txt",
+        "test_histogram_apogee.png",
+        "test_multi_apogee.png",
+        "test_multi_x_impact.png",
+    ]
+    for filepath in files_to_cleanup:
+        if os.path.exists(filepath):
+            os.remove(filepath)
 
 
 @pytest.mark.slow
@@ -134,6 +138,31 @@ def test_monte_carlo_plots(mock_show, monte_carlo_calisto_pre_loaded):
         _post_test_file_cleanup()
 
 
+def test_monte_carlo_plots_all_save(monte_carlo_calisto_pre_loaded):
+    """Tests the plots.all method with save functionality.
+
+    Parameters
+    ----------
+    monte_carlo_calisto_pre_loaded : MonteCarlo
+        The MonteCarlo object, this is a pytest fixture.
+    """
+    try:
+        # Test saving with a single key
+        monte_carlo_calisto_pre_loaded.plots.all(
+            keys="apogee", filename="test_histogram.png"
+        )
+        assert os.path.exists("test_histogram_apogee.png")
+
+        # Test saving with multiple keys
+        monte_carlo_calisto_pre_loaded.plots.all(
+            keys=["apogee", "x_impact"], filename="test_multi.png"
+        )
+        assert os.path.exists("test_multi_apogee.png")
+        assert os.path.exists("test_multi_x_impact.png")
+    finally:
+        _post_test_file_cleanup()
+
+
 def test_monte_carlo_export_ellipses_to_kml(monte_carlo_calisto_pre_loaded):
     """Tests the export_ellipses_to_kml method of the MonteCarlo class.