reconcile bokeh html autogen with sphinx rst docs autogen

.github/pull_request_template.md

@@ -7,11 +7,11 @@
 commits organized? -->
 
 ## Verification
-<!-- How were the changes validated? Were any automated tests added, updated, removed, or re-baselined? If you didn't 
+<!-- How were the changes validated? Were any automated tests added, updated, removed, or re-baselined? If you didn't
 add or update any tests justify this choice. -->
 
 ## Documentation
-<!-- What documentation was invalidated by these changes? Which artifacts should reviewers check for accuracy and 
+<!-- What documentation was invalidated by these changes? Which artifacts should reviewers check for accuracy and
 completeness? -->
 
 ## Future work

.gitignore

@@ -60,6 +60,6 @@ venv/
 build/
 
 # Monte Carlo ignore generated data files and test init file.
-examples/MonteCarloExamples/scenario_AttFeedbackMC/
+examples/MonteCarloExamples/scenarioBskSimAttFeedbackMC/
 src/utilities/tests/__init__.py
 examples/montecarlo_test*/

docs/source/Support/bskReleaseNotes.rst

@@ -728,7 +728,7 @@ Version 2.1.0 (Nov. 13, 2021)
 - Fixed a rare issue where RW data didn't stick
 - Fixed an issue subscribing to a C++ wrapped message object from python
 - Cleaned up documentation on using datashaders and bokeh to interactively plot large simulation data sets.
-  The script :ref:`scenarioAnalyzeMonteCarlo` is updated to discuss the particular challenges in running this
+  The script :ref:`scenarioVisualizeMonteCarlo` is updated to discuss the particular challenges in running this
   datashader example of plotting data.
 - enable Monte Carlo ``pytest`` test scripts to run on macOS if Python 3.9 or higher is used
 - enable opNav scenario ``pytest`` test scripts to be tested by ``pytest`` if the build flag ``--opNav``

examples/BskSim/scenarios/scenario_AttFeedback.py

@@ -47,7 +47,7 @@
 class scenario_AttFeedback(BSKSim, BSKScenario):
     def __init__(self):
         super(scenario_AttFeedback, self).__init__()
-        self.name = 'scenario_AttFeedbackMC'
+        self.name = 'scenarioBskSimAttFeedbackMC'
 
         # declare additional class variables
         self.msgRecList = {}

examples/MonteCarloExamples/scenario_AttFeedbackMC.py → examples/MonteCarloExamples/scenarioBskSimAttFeedbackMC.py

@@ -35,18 +35,18 @@
 -----------
 1. Ensure you have Basilisk installed with all required dependencies.
 2. Run this script directly to execute the Monte Carlo simulations:
-   
-   python scenario_AttFeedbackMC.py
+
+   python scenarioBskSimAttFeedbackMC.py
 
 3. The script will run 20 Monte Carlo simulations by default.
-4. Results will be saved in the 'scenario_AttFeedbackMC' directory within the script's location.
+4. Results will be saved in the 'scenarioBskSimAttFeedbackMC' directory within the script's location.
 
 Monte Carlo Configuration:
 --------------------------
 - Simulation Function: Uses `scenario_AttFeedback.scenario_AttFeedback` to set up the base scenario.
 - Execution Function: Uses `scenario_AttFeedback.runScenario` to run each simulation.
 - Execution Count: Set to 20 simulations.
-- Archive Directory: Results are saved in 'scenario_AttFeedbackMC'.
+- Archive Directory: Results are saved in 'scenarioBskSimAttFeedbackMC'.
 - Seed Dispersion: Enabled to randomize the seed for each module.
 - Variable Casting: Downcasts retained numbers to float32 to save storage space.
 - Dispersion Magnitude File: Produces a .txt file showing dispersion in standard deviation units.
@@ -110,9 +110,9 @@ def run(show_plots):
     monteCarlo = Controller()
     monteCarlo.setSimulationFunction(scenario_AttFeedback.scenario_AttFeedback)  # Required: function that configures the base scenario
     monteCarlo.setExecutionFunction(scenario_AttFeedback.runScenario)  # Required: function that runs the scenario
-    monteCarlo.setExecutionCount(20)  # Required: Number of MCs to run
+    monteCarlo.setExecutionCount(4)  # Required: Number of MCs to run
 
-    monteCarlo.setArchiveDir(path + "/scenario_AttFeedbackMC")  # Optional: If/where to save retained data.
+    monteCarlo.setArchiveDir(path + "/scenarioBskSimAttFeedbackMC")  # Optional: If/where to save retained data.
     monteCarlo.setShouldDisperseSeeds(True)  # Optional: Randomize the seed for each module
     # monteCarlo.setThreadCount(2)  # Optional: Number of processes to spawn MCs on, automatically sizes for personal computer.
     # monteCarlo.setVerbose(True)  # Optional: Produce supplemental text output in console describing status

examples/MonteCarloExamples/scenarioRerunMonteCarlo.py

@@ -21,7 +21,7 @@
 This script is a basic demonstration of a script that can be used to rerun a set or subset of Monte Carlo simulations.
 
 .. important::
-   This script can only be run once there exists data produced by the ``scenario_AttFeedbackMC.py`` script.
+   This script can only be run once there exists data produced by the ``scenarioBskSimAttFeedbackMC.py`` script.
 
 
 """
@@ -88,7 +88,7 @@ def run(time=None):
     # Step 4: Add any additional retention policies desired
     retentionPolicy = RetentionPolicy()
     retentionPolicy.logRate = int(2E9)
-    retentionPolicy.addMessageLog("attGuidMsg", ["sigma_BR"]) 
+    retentionPolicy.addMessageLog("attGuidMsg", ["sigma_BR"])
     monteCarlo.addRetentionPolicy(retentionPolicy)
 
 
@@ -99,4 +99,3 @@ def run(time=None):
 
 if __name__ == "__main__":
     run()
-