reconcile bokeh html autogen with sphinx rst docs autogen

.github/pull_request_template.md

@@ -1,18 +1,104 @@
 * **Tickets addressed:** bsk-000
-* **Review:** By commit  <!-- Choose from: "by commit", "by file" -->
-* **Merge strategy:** Merge (no squash)  <!-- Choose from: "merge (no squash)", "squash and merge" -->
+* **Review:** By file
+* **Merge strategy:** Merge (no squash)
 
 ## Description
-<!-- What approach was taken to satisfy the ticket being addressed? What should reviewers be aware of? How are your
-commits organized? -->
+This branch introduces significant improvements to the Monte Carlo plotting capabilities in Basilisk, focusing on enhancing the `AnalysisBaseClass.py` file and related example scenarios. The key changes include:
+
+1. The `MonteCarloPlotter` class:
+   - Improved data loading and management
+   - Dynamic plot creation with support for multiple variables and components
+   - Interactive plot updates based on user input
+   - Efficient handling of large datasets through downsampling and streaming
+
+2. Bokeh Integration:
+   - Added support for Bokeh, enabling interactive HTML-based visualizations
+   - Implemented a Bokeh server option for real-time plot updates
+   - Created a fallback to matplotlib when Bokeh is unavailable
+
+3. User Interface Improvements:
+   - Added dropdown menus for variable and component selection
+   - Implemented a search functionality to highlight specific run numbers
+   - Created a status indicator to show plot loading/updating status
+
+4. Documentation and File Management:
+   - Improved plot saving functionality, including HTML and RST file generation
+   - Enhanced documentation generation for saved plots
+
+5. Example Scenario Updates:
+   - Modified `scenarioMonteCarloAttRW.py` to showcase the new plotting capabilities
+   - Added command-line options for data deletion and Bokeh server usage
+   - Implemented a new `scenarioRerunMonteCarlo.py` for demonstrating how to rerun specific Monte Carlo simulations
+
+6. Testing and Compatibility:
+   - Updated `test_bskMcTestScript.py` to accommodate the new features
+   - Ensured backward compatibility with existing Monte Carlo simulations
+
+These changes significantly improve the visualization and analysis capabilities for Monte Carlo simulations in Basilisk, providing users with more interactive and informative tools to explore simulation results.
 
 ## Verification
-<!-- 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. -->
+The changes were validated through the following methods:
+
+1. Manual Testing:
+   - Extensive testing of the new `MonteCarloPlotter` class functionality
+   - Verification of Bokeh integration and fallback to matplotlib
+   - Testing of user interface improvements, including dropdown menus and run number search functionality
+
+2. Automated Tests:
+   - Updated `test_bskMcTestScript.py` to cover new features and ensure backward compatibility
+   - Added new unit tests for the `MonteCarloPlotter` class to verify data loading, plot creation, and interactive updates
+
+3. Example Scenario Testing:
+   - Ran and verified the modified `scenarioMonteCarloAttRW.py` to showcase new plotting capabilities
+   - Tested the new `scenarioRerunMonteCarlo.py` to ensure proper functionality for rerunning specific Monte Carlo simulations
+
+4. Performance Testing:
+   - Conducted manual performance tests with large datasets up to 1 GB in size to verify efficient data handling and display
+
+5. Documentation Review:
+   - Verified that the updated documentation accurately reflects the new features and usage instructions in sphinx documentation
+
+No tests were removed or re-baselined. The addition and update of tests were necessary to cover the new functionality and ensure the reliability of the Monte Carlo plotting capabilities.
 
 ## Documentation
-<!-- What documentation was invalidated by these changes? Which artifacts should reviewers check for accuracy and 
-completeness? -->
+The following documentation has been affected by these changes and should be reviewed for accuracy and completeness:
+
+1. Docstrings in modified files:
+   - `AnalysisBaseClass.py`: Updated docstrings for the `MonteCarloPlotter` class and its methods, particularly those related to Bokeh integration and new plotting features
+   - `scenarioMonteCarloAttRW.py`: Revised docstrings to reflect new command-line options and usage of updated plotting capabilities
+   - `scenarioRerunMonteCarlo.py`: New file with comprehensive docstrings explaining the purpose and usage of rerunning specific Monte Carlo simulations
+
+2. `optional-requirements.txt`:
+   - Added Bokeh and Dask to the list of optional requirements
+   - Reviewers should verify these optional packages are sufficient for the new plotting capabilities introduced in this branch/pr.
+
+Reviewers should pay particular attention to:
+- Accuracy and completeness of docstrings in the modified files
+- Clarity of explanations for new features and methods
+- Consistency between code behavior and documented functionality
+- Correct specification of Bokeh and Dask in the optional requirements file
+
+Additionally, reviewers should ensure that the addition of Bokeh and Dask to the optional requirements is properly documented in any relevant README files or user guides.
 
 ## Future work
-<!-- What next steps can we anticipate from here, if any? -->
+1. Further optimization of performance for large datasets:
+   - Investigate and implement data compression techniques to improve handling of very large Monte Carlo datasets
+   - Explore more efficient data streaming methods for real-time plot updates
+
+2. Refinement of Bokeh plotting tools:
+   - Gather user feedback on the new Monte Carlo plotting capabilities
+   - Based on feedback, potentially simplify or customize Bokeh plotting tools specifically for Monte Carlo simulations
+   - Consider adding more advanced interactive features or visualization types if requested by users
+
+3. Expansion of rerun capabilities:
+   - Enhance the `scenarioRerunMonteCarlo.py` to support more complex rerun scenarios
+   - Implement a user-friendly interface for selecting specific Monte Carlo runs to rerun
+
+4. Integration with other Basilisk modules:
+   - Explore opportunities to integrate the improved Monte Carlo plotting capabilities with any other relevant Basilisk analysis and visualization tools
+
+5. Documentation and tutorials:
+   - Develop comprehensive tutorials and examples showcasing the new Monte Carlo plotting features
+   - Create video tutorials to help users quickly adopt and leverage the new capabilities
+
+Note: These future work items are based on anticipated needs and potential improvements. Priorities may be adjusted based on user feedback and project requirements.

.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()
-