Added central post-processing script

Author: brunosorban

rocketpy/stochastic/post_processing/scripts/stochastic_apogee.py

@@ -1,17 +1,10 @@
-from pathlib import Path
-
 import matplotlib.pyplot as plt
 import numpy as np
 
-from rocketpy.stochastic.post_processing.stochastic_cache import \
-    SimulationCache
+from rocketpy.stochastic.post_processing.stochastic_cache import SimulationCache
 
 
-def compute_apogee(file_name, batch_path):
-    cache = SimulationCache(
-        file_name,
-        batch_path,
-    )
+def compute_apogee(cache):
     apogee = cache.read_outputs('apogee')
 
     mean_apogee = float(np.nanmean(apogee, axis=0))
@@ -36,17 +29,21 @@ def plot_apogee(batch_path, apogee, mean_apogee, save=False, show=True):
     ax.legend()
 
     if save:
-        plt.savefig(batch_path / 'mean_apogee_distribution.png')
+        plt.savefig(batch_path / "Figures" / 'mean_apogee_distribution.png')
     if show:
         plt.show()
 
 
-def run(file_name, batch_path, save, show):
-    apogee, mean_apogee = compute_apogee(file_name, batch_path)
-    plot_apogee(batch_path, apogee, mean_apogee, save=save, show=show)
+def run(cache, save, show):
+    apogee, mean_apogee = compute_apogee(cache)
+    plot_apogee(cache.batch_path, apogee, mean_apogee, save=save, show=show)
 
 
 if __name__ == '__main__':
-    batch_path = Path("mc_simulations/")
+    import easygui
+
+    # configuration
     file_name = 'monte_carlo_class_example'
-    run(file_name, batch_path, save=True, show=True)
+    batch_path = easygui.diropenbox(title="Select the batch path")
+    cache = SimulationCache(file_name, batch_path)
+    run(cache, save=True, show=True)

rocketpy/stochastic/post_processing/scripts/stochastic_impact_point.py

@@ -1,11 +1,8 @@
-from pathlib import Path
-
 import matplotlib.pyplot as plt
 import numpy as np
 from matplotlib.patches import Ellipse
 
-from rocketpy.stochastic.post_processing.stochastic_cache import \
-    SimulationCache
+from rocketpy.stochastic.post_processing.stochastic_cache import SimulationCache
 
 # 1-3 sigma
 lower_percentiles = [0.16, 0.03, 0.003]
@@ -19,11 +16,9 @@ def eigsorted(cov):
     return vals[order], vecs[:, order]
 
 
-def compute_impact(file_name, batch_path, save, show):
-    cache = SimulationCache(
-        file_name,
-        batch_path,
-    )
+def compute_impact(cache, save, show):
+    batch_path = cache.batch_path
+
     x_impact = cache.read_outputs('x_impact')
     y_impact = cache.read_outputs('y_impact')
 
@@ -68,20 +63,21 @@ def compute_impact(file_name, batch_path, save, show):
     ax.grid()
 
     if save:
-        plt.savefig(batch_path / 'mean_impact_distribution.png')
+        plt.savefig(batch_path / "Figures" / 'mean_impact_distribution.png')
 
     if show:
         plt.show()
-    plt.show()
 
 
-def run(file_name, batch_path, save, show):
-    compute_impact(file_name, batch_path, save, show)
+def run(cache, save, show):
+    compute_impact(cache, save, show)
 
 
 if __name__ == '__main__':
-    # import easygui
+    import easygui
 
-    batch_path = Path("mc_simulations/")
+    # configuration
     file_name = 'monte_carlo_class_example'
-    run(file_name, batch_path, save=True, show=True)
+    batch_path = easygui.diropenbox(title="Select the batch path")
+    cache = SimulationCache(file_name, batch_path)
+    run(cache, save=True, show=True)

rocketpy/stochastic/post_processing/scripts/stochastic_mach.py

@@ -1,16 +1,11 @@
-from pathlib import Path
-
 import matplotlib.pyplot as plt
 
-from rocketpy.stochastic.post_processing.stochastic_cache import \
-    SimulationCache
+from rocketpy.stochastic.post_processing.stochastic_cache import SimulationCache
+
 
+def compute_mach(cache, save, show):
+    batch_path = cache.batch_path
 
-def compute_mach(file_name, batch_path, save, show):
-    cache = SimulationCache(
-        file_name,
-        batch_path,
-    )
     mach = cache.read_outputs('mach_number')
 
     fig, ax = plt.subplots()
@@ -19,24 +14,24 @@ def compute_mach(file_name, batch_path, save, show):
     ax.set_xlabel('Time [s]')
     ax.set_ylabel('Mach [-]')
     ax.set_title('Mach number Distribution')
-    ax.legend()
     ax.grid()
 
     if save:
-        plt.savefig(batch_path / 'mach_distribution.png')
+        plt.savefig(batch_path / "Figures" / 'mach_distribution.png')
 
     if show:
         plt.show()
-    plt.show()
 
 
-def run(file_name, batch_path, save, show):
-    compute_mach(file_name, batch_path, save, show)
+def run(cache, save, show):
+    compute_mach(cache, save, show)
 
 
 if __name__ == '__main__':
-    # import easygui
+    import easygui
 
-    batch_path = Path("mc_simulations/")
+    # configuration
     file_name = 'monte_carlo_class_example'
-    run(file_name, batch_path, save=True, show=True)
+    batch_path = easygui.diropenbox(title="Select the batch path")
+    cache = SimulationCache(file_name, batch_path)
+    run(cache, save=True, show=True)

rocketpy/stochastic/post_processing/scripts/stochastic_maxQ.py

@@ -1,17 +1,12 @@
-from pathlib import Path
-
 import matplotlib.pyplot as plt
 import numpy as np
 
-from rocketpy.stochastic.post_processing.stochastic_cache import \
-    SimulationCache
+from rocketpy.stochastic.post_processing.stochastic_cache import SimulationCache
+
 
+def compute_maxQ(cache, save, show):
+    batch_path = cache.batch_path
 
-def compute_maxQ(file_name, batch_path, save, show):
-    cache = SimulationCache(
-        file_name,
-        batch_path,
-    )
     dyn_press = cache.read_outputs('dynamic_pressure') / 1000
     maxQarg = np.nanargmax(dyn_press[:, :, 1], axis=1)
     maxQ = dyn_press[np.arange(len(dyn_press)), maxQarg]
@@ -27,20 +22,21 @@ def compute_maxQ(file_name, batch_path, save, show):
     ax.grid()
 
     if save:
-        plt.savefig(batch_path / 'maxQ_distribution.png')
+        plt.savefig(batch_path / "Figures" / 'maxQ_distribution.png')
 
     if show:
         plt.show()
-    plt.show()
 
 
-def run(file_name, batch_path, save, show):
-    compute_maxQ(file_name, batch_path, save, show)
+def run(cache, save, show):
+    compute_maxQ(cache, save, show)
 
 
 if __name__ == '__main__':
-    # import easygui
+    import easygui
 
-    batch_path = Path("mc_simulations/")
+    # configuration
     file_name = 'monte_carlo_class_example'
-    run(file_name, batch_path, save=True, show=True)
+    batch_path = easygui.diropenbox(title="Select the batch path")
+    cache = SimulationCache(file_name, batch_path)
+    run(cache, save=True, show=True)

rocketpy/stochastic/post_processing/stochastic_post_process.py

@@ -0,0 +1,53 @@
+# from pathlib import Path
+import importlib
+import pkgutil
+from pathlib import Path
+
+from rocketpy.stochastic.post_processing import scripts
+from rocketpy.stochastic.post_processing.stochastic_cache import SimulationCache
+
+
+def list_scripts(package):
+    scripts = []
+    for module in pkgutil.iter_modules(package.__path__):
+        scripts.append(module)
+    return scripts
+
+
+def run_all_scripts(file_name, batch_path, save, show):
+    # initialize path
+    batch_path = Path(batch_path)
+
+    figures_path = batch_path / "Figures"
+    figures_path.mkdir(parents=True, exist_ok=True)
+
+    # list all post_processing scripts
+    scripts_list = list_scripts(scripts)
+
+    # iniialize cache
+    cache = SimulationCache(file_name, batch_path)
+
+    # for each script, execute 'run' giving the cache as argument
+    for module in scripts_list:
+        print("Running script: ", module.name)
+
+        # get the module
+        # Import the module using the module name
+        module = importlib.import_module(f"{scripts.__name__}.{module.name}")
+
+        # Extract the function named 'run'
+        run_function = getattr(module, 'run')
+
+        run_function(cache, save, show)
+
+
+if __name__ == "__main__":
+    import easygui
+
+    # configuration
+    file_name = 'monte_carlo_class_example'
+    batch_path = easygui.diropenbox(title="Select the batch path")
+    save = True
+    show = False
+
+    run_all_scripts(file_name, Path(batch_path), save, show)