* modeld: add laneline_helper for plan X indices calculation
* spacing
* keep type hints
* openpilot
* sunnypilot/models/helpers
add modeld helpers to helpers
* Send it from each fill message
---------
Co-authored-by: Jason Wen <haibin.wen3@gmail.com>
* Add model metadata lookup and update desire handling
* Bump selector version to 10
* meh
* Refactor shape mode parameters for desire handling in test buffer logic
* loop more models
* Refactor buffer handling for temporal inputs and streamline desire updates
* Refactor lateral control input handling and remove unused code
* Add modelDataV2SP and lane turn logic implementation
Note: still need to hook up to other modeld's create unit test, fix stuff, and do the UI for it
* add unit tests for lane turn logic
* Add lane turn desire controls to models panel
* use `events_sp` instead of `events`
* integrate modelDataV2SP messaging to the other modeld controllers
* move this to that
* use min for general population here, on custom branches, change this to max :)
* Update events.py
Co-authored-by: royjr <royjr96@gmail.com>
* Update events.py
Co-authored-by: royjr <royjr96@gmail.com>
* refactor lane turn value control into one method
* Update selfdrive/ui/sunnypilot/qt/offroad/settings/models_panel.cc
* add integration tests for lane turn desire
* 10 updates is possibly more representative of real life
* real objects ofc
* desc: add toggle description for clarity
---------
Co-authored-by: royjr <royjr96@gmail.com>
* modeld_v2: dynamify temporal buffer management.
* skip redundant reshaping and flattening.
* simplify MHP checks for lead and plan
* modeld_v2: add unit tests for buffer logic and refactor index mapping
* Let’s possibly fail a test :)
* Update test_buffer_logic_inspect.py
* Update test_buffer_logic_inspect.py
* modeld_v2: better temporal mapping for non-split
* Bump to 10 I guess
* Downgrade CURRENT_SELECTOR_VERSION to 9
* red diff ya know?
* add dynamic buffer update tests and compare against legacy logic.
Cover modelState.init and modelState.run
* send
* Revert "send"
This reverts commit 9e6c95fbfde134eeba952da6eef012baa0396fa0.
* format
---------
Co-authored-by: Jason Wen <haibin.wen3@gmail.com>
* wrap the params
* just 1 class and use a single param for now
* refactor
* fix
* cache itself
* no longer
* rename
* type hint
* in helpers instead
* lint
* all
* init as 0 to pass ci
* init as 0 to pass ci
* return_default
* fix init
* add LAT_SMOOTH_SECONDS directly in modeld, temp remove dynamic desc, red difffffffff
* Introduce zero inputs for Lead, and plan to conform with new SP model introduced Monday, July 7, 2025
* Clean this up
* We can revert this after dev-c3-new testing and ready to merge.
* This needs to be apart of the conditional else fail
* Add full conditional
* Update longitudinal_planner.py
* Mypy from myphone!
* red diff
* Make generation a property for clarity
* Even clearer!
* Affix to generation, while allowing older models to use this IF param is set.
* seems a bit repetitive yea?
* dynamic
* Make most outputs dynamic
* Rm toggle from refactor
* refactor(modeld): simplify MHP output parsing logic
- Introduced `_parse_mhp_output` helper to remove redundancy and streamline `parse_dynamic_outputs`.
- Ensures improved code maintainability and clarity.
* refactor(longitudinal_planner): streamline generation handling logic
- Simplified `generation` assignment with inline conditional for better readability.
- Adjusted `mlsim` logic to default to model simulation when `generation` is unset.
* for ease of syncs from now on
* fix
---------
Co-authored-by: DevTekVE <devtekve@gmail.com>
* Add live delay toggle to torqued.py and twilsonco NNLC
* Set this in init
* Clean up
* Live delay toggle refactor
* ModeldLagd -> LagdToggle
* This is for lagd_toggle.py
* Add to NNLC
* Lagd toggle:
Display current values on UI
* Add break
* LagdToggleDelay
Live edit software_delay when livedelay is toggled `off`
* Always show description
* Add description as to why values don't update offroad
---------
Co-authored-by: Kumar <36933347+rav4kumar@users.noreply.github.com>
* Model panel
* Get this outta here and use sunnylink instead
* Remove space
* 'not' because we want gas gating to use e2e/mpc blend
* Add toggles to models_panel.cc
* "keep enabled for stock behavior"
* Add this here
* Cloudlog result
* change cloudlog to debug, and add latsmooth to steer actuator delay.
Need to edit json. Will do locally so when this is merged its a simple ready to go push.
* Cleanup
* Update longitudinal_planner.py
* Remove gasgating for now.. may need to be placed in model
---------
Co-authored-by: DevTekVE <devtekve@gmail.com>
* Refactor model runner methods for improved abstraction.
Moved slicing logic to a private `_slice_outputs` method and decoupled `_run_model` for clearer subclass implementation. Removed redundant `output` attribute in `ModelState` to streamline data handling.
* Add output parsing to model_runner and remove duplicate logic
Integrates an output parser directly into `model_runner` for streamlined inference and parsing. Removes redundant parser initialization from `modeld` to avoid duplication and enhance maintainability.
* Reordering
* linter
* linter
* Refactor model handling with `ModelData` abstraction
Introduce a `ModelData` class to encapsulate model and metadata logic, improving code clarity and modularity. Refactor `ModelRunner` to manage multiple models and add conditional handling for fallback scenarios. Adjust `TinygradRunner` to validate and use the new `ModelData` structure.
* Refactor model handling to use dictionary-based structure
Replaces the model list with a dictionary keyed by model type to improve clarity and maintainability. Updates related logic and ensures consistent handling of model metadata and inputs. Adds `slice_outputs` implementation to the `TinygradRunner` for proper output parsing.
* Refactor model runners to support policy and vision separation
Introduced `TinygradPolicyRunner`, `TinygradVisionRunner`, and `TinygradSplitRunner` to enable separate handling of policy and vision models. Updated `TinygradRunner` initialization and input preparation to accommodate modular processing. Adjusted `modeld` to utilize the new runners, ensuring compatibility with separated model workflows.
* Refactor model runner initialization and simplify logic
Introduce `get_model_runner` to centralize model runner selection logic, replacing multiple conditional instantiations. Simplify the handling of model metadata by removing fallback logic and restructuring output slicing to enforce proper loading of model data. These changes improve code maintainability and clarity.
* Refactor model data access for TinygradRunner initialization
Update references to access nested artifact properties and align with structural changes to the model data schema. This simplifies input shapes handling and ensures compatibility with updated model attributes.
* Refactor imports and clean up redundant code.
Removed unused imports and improved formatting for clarity and maintainability. These changes simplify the codebase by eliminating unnecessary dependencies and ensuring consistency.
* Refactor model output parsing with specialized parsers.
Introduce abstract `_parse_outputs` method to standardize parsing logic. Add `SplitParser` for specialized parsing in `TinygradVisionRunner` and `TinygradPolicyRunner`. This improves modularity and paves the way for easier parser customization.
* Add parser for model output processing in modeld_v2
Introduce a new `Parser` class to handle parsing and processing of model outputs, including MDN, binary cross-entropy, and categorical cross-entropy outputs. This modularizes the logic, improves clarity, and prepares for handling various types of model data.
* Add `input_shapes` property to model runners
Introduce a new `input_shapes` property in the abstract base class and its implementation in derived classes. This provides a standardized way to access the input shapes of models, improving clarity and consistency in the model runners.
* Refactor model runner to use private `_model_data` attribute
Replaced public `model_data` with private `_model_data` for improved encapsulation. Updated all references and property accessors accordingly. Simplified model type handling by using raw types where applicable.
* Remove debug print statement from model_runner.py
The unnecessary `print(model_type)` statement was removed as it served no functional purpose in the code. This improves code cleanliness and avoids unintended console output during execution.
* Refactor `_parse_outputs` call in `run_model`.
Replaced the use of `self.parser.parse_outputs` with `self._parse_outputs` for clarity and consistency. Updated method signature to align with the revised usage.
* Refactor model output parsing for clarity and scope separation
Moved specific parsing logic (e.g., lane_lines, lead) from `parse_model_outputs` to `parse_policy_outputs` to better align with functional responsibilities. This improves modularity and readability while maintaining existing functionality.
* Refactor model_runner to simplify result handling
Renamed variable `result` to `parsed_result` for clarity and removed unnecessary slicing during model output parsing. These changes improve code readability and maintain consistency within the `run_model` method.
* Adjust _parse_outputs method signature in model_runner
Updated the method signature of _parse_outputs to accept a single np.ndarray instead of a dictionary. This aligns with the intended data structure and ensures consistency across subclasses implementing this abstract method.
* Refactor ModelRunner to enforce abstract base class compliance
Updated `ModelRunner` and its subclasses to properly inherit from `ABC` while refactoring methods to ensure compliance with Python's abstract base class standards. Streamlined the handling of `_parse_outputs` and added a new `input_shapes` property for improved functionality.
* Fix buffer length issue in 20Hz model initialization
Adjusted `FULL_HISTORY_BUFFER_LEN` by adding +1 for `full_features_20Hz` to address compatibility issues with the current FoF model. Added a comment noting potential failure for other models with this adjustment.
* Refactor TinygradRunner to remove abstract methods.
Simplified the TinygradRunner class by removing unnecessary @abstractmethod decorators and redundant method definitions. This streamlines the code and aligns it more effectively with its current usage and implementation.
* Refactor model runner classes and enhance type annotations
Simplified model runner implementations, added type annotations, and improved code readability and maintainability. Introduced new type definitions, updated metadata handling, and standardized input/output parsing across all runner classes. Minor comment update in `modeld.py` for clarity.
* Refactor model runner classes with detailed docstrings.
Enhanced class and function docstrings across model_runner.py for better clarity and maintainability. Descriptions now include detailed explanations of attributes, purposes, and workflows to aid understanding and future development.
* Refactor model runner classes and add TICI hardware optimization
Simplified and clarified class definitions, comments, and functionality for ModelRunner subclasses. Introduced the use of QCOM environment variable on TICI for potential hardware acceleration. Enhanced input/output handling and error reporting across Tinygrad and ONNX implementations.
* Update parser import and usage to use CombinedParser
Replaced the Parser class with CombinedParser in model_runner.py. This change ensures consistency with the updated parsing logic, aligning with the latest requirements for combined model output handling.
* Refactor TinygradRunner hierarchy for modular parsers
Reorganized the TinygradRunner and its specialized runners (Vision, Policy, and Supercombo) into a cleaner, modular structure using composable classes. This consolidates parser logic, removes redundancy, and simplifies initialization by leveraging a shared base class with a dictionary-based parser method.
* Refactor model runners to use ModularRunner as abstract base.
Introduce a new `ModularRunner` class to enforce a consistent interface across model runners. Updated existing runners, including `ModelRunner`, `SupercomboTinygrad`, `PolicyTinygrad`, and `VisionTinygrad`, to extend `ModularRunner`. Added abstract methods and properties to enhance modularity and code maintainability.
* Refactor model runners into modular components.
This commit separates the logic for Tinygrad, ONNX, and split runners into clearly defined modules and components. It introduces `PolicyTinygrad`, `VisionTinygrad`, `SupercomboTinygrad`, and centralized helpers for cleaner architecture. The changes improve modularity and maintainability of the model running and parsing workflows.
* Simplify imports and clean up unused code in ONNXRunner.
Removed unused imports and redundant environmental variables to streamline the codebase. Consolidated necessary imports and organized type definitions for improved readability and maintenance.
* Standardize imports and add model data validation.
Updated import paths to ensure consistency across modules by using `openpilot` as the base. Introduced validation in `_parse_outputs` methods to handle cases where `_model_data` is not initialized, preventing potential runtime errors.
* Remove unused import and fix whitespace in runners
The unused import `ModelData` was removed from `tinygrad_runner.py` to clean up the code. Additionally, extraneous whitespace was corrected in `onnx_runner.py` for improved readability and consistency.
* Remove unnecessary blank line in import statements
Cleaned up import section by removing an extra blank line. This helps maintain consistency and adheres to code style conventions.
* BROKEN!! Staging code but its not gonna work. Also I realized we need to run the split models in 2 stages because the output of one is immediately needed for the input of the other. We might handle it inside of the model_runner instead
* update smooth
* Revert "update smooth"
This reverts commit c335712e6e1ee189459ce34dfc9d4028feb9470f.
* match case made this very hard to read.
* shouldnt be there
* Refactor to allow TR (soon TM)
* TR 7 is .1
* metadata
* .2=3
* Remove redundant comments and clean up conditional blocks in modeld.py.
* Undoing wrong buffer
* Refactor model initialization and adjust ONNX runner import
Reorganized numpy input buffer initialization and updated `temporal_idxs` logic for better clarity and efficiency. Conditional import for ONNXRunner added for non-TICI platforms to optimize imports. These changes improve maintainability and compatibility across platforms.
* Update CURRENT_SELECTOR_VERSION to 4
Bump the CURRENT_SELECTOR_VERSION constant from 3 to 4 to reflect changes in the selector logic or requirements. This ensures compatibility with the updated selector version while maintaining the minimum required version as 2.
* Add output_slices property to model runners
Introduce output_slices property to provide access to the output slices for individual and combined models. This ensures consistent handling of output slices across vision and policy models, improving modularity and usability.
* Refactor imports to use SplitModelConstants consistently
Updated import references to use the renamed `SplitModelConstants` class for consistency across files. This change ensures clarity and better alignment with the updated class naming convention.
* Refactor buffer initialization and desired curvature handling
Refactored model input buffer initialization for improved clarity and consistency, leveraging dynamic shape calculations. Extracted `process_desired_curvature` method to encapsulate logic for handling 3D and non-3D cases. Simplified temporal index generation and related calculations for better maintainability.
* Refactor desire reshape dims logic in modeld.py
Adjust logic for setting desire reshape dimensions to handle `is_20hz_3d` separately. This improves clarity and ensures proper handling of different model runner configurations.
* Fix off-by-one error in full_desire buffer initialization
The full_desire buffer length was mistakenly set to full_history_buffer_len + 1. This change corrects it to match the intended full_history_buffer_len, ensuring proper alignment with other buffers.
* Simplify desire reshape logic by removing unused condition.
Removed the `is_20hz_3d` condition and associated reshape logic since it is no longer needed. This streamlines the code and avoids unnecessary checks for unused configurations.
* Refactor buffer initialization for 20Hz model variants
Reorganized buffer initialization logic to prioritize the 20Hz_3D condition. This improves clarity and ensures specific handling of different 20Hz configurations. Adjusted the order of conditions to streamline execution flow.
* Refactor: Move `get_action_from_model` and constants to `Model` class to improve encapsulation and readability.
* 12 line reshaping red diff
* .2 to match old models delay
* mypy fixes
* Revert "12 line reshaping red diff"
This reverts commit 8c7280f629043f0485749e2536a20af74c9209b2.
* mypy
* remove this
* Fix desired curvature for models which do not output desired_curvature
* fix FoF
* flip policy and vision outs to allow FoF and tomb raider to live in harmony using conditional `if 'this' in outs:'`
* noqa
* single
* sunnypilot modeld.py
* action
* overrides methodology
* combine split outputs to its own method
* comments
* Fix static checker line length
* static will fail on line length
lines:
286,
206,
70 - 77,
159,
168
* Address E501 line length violations
* This will make TR better while not effecting FoF/VFF at all
* Reduce this to one conditional and just call normally in vision/policy
* Align with upstream in our own way.
* check for desired curvature in outputs first
* outputs
* Use a cleaner import method
* Fix output
* Clean up some values
* Only call on init
* slight cleanup
* names!!!!!!!!!
* Refactor overrides structure to support key-value pairs.
The overrides structure now uses a list of key-value pairs instead of fixed lat/long fields. This change improves flexibility, allowing dynamic addition of override parameters. Code adjustments ensure backward compatibility and consistent behavior throughout the application.
* Refactor: Use local variable for SplitModelConstants
Introduce a local `constants` variable to replace repeated access to `SplitModelConstants`. This simplifies code readability and adheres to linter recommendations for line length.
* Refactor model constant handling to improve modularity
Replaced direct usage of model constants with dynamic access through model runners for better scalability and maintainability. This change centralizes constant definitions, reduces redundancy, and ensures clearer integration with different model types.
---------
Co-authored-by: discountchubbs <alexgrant990@gmail.com>
Co-authored-by: Discountchubbs <159560811+Discountchubbs@users.noreply.github.com>
Co-authored-by: Jason Wen <haibin.wen3@gmail.com>
Refactor variable names for clarity in 20Hz model logic.
Renamed `full_features_20Hz` to `full_features_buffer` and `desire_20Hz` to `full_desire` for better readability and consistency. This improves code maintainability and aligns variable names with their intended purpose.
* Refactor model and artifact structures with version compatibility filtering
- Introduced `Artifact` struct and nested it within the `Model` struct for improved clarity and organization.
- Updated enums, logic, and parsing to align with the new struct definitions.
- Implemented version compatibility filtering for model bundles using the `is_bundle_version_compatible` helper.
- Enhanced artifact download handling by adding checks for missing URIs, better error management, and improved logging.
- Adjusted model fetching to point to the latest endpoint (`v3`).
* Make linter happy
* Make linter happy
* Refactor model data parsing to improve readability.
Replaced kwargs-based data extraction with explicit parameter passing for clarity. This enhances code readability and reduces ambiguities in method calls, making the parsing logic more maintainable and straightforward.
* Refactor error handling in active model bundle retrieval.
Wrapped the logic to fetch the active model bundle in a try-except block to prevent unhandled exceptions. This ensures more robust error handling and avoids potential crashes when retrieving or processing model data.
* Refactor exception handling in get_active_model_bundle
Replace bare except with Exception to improve specificity and clarity. This ensures better debugging practices and aligns with recommended coding standards. Other minor whitespace adjustments were made for improved readability.
* Update model path to use artifact fileName property
Replaced `fileName` with `artifact.fileName` in the custom model path construction. This ensures compatibility with updated drive model structures and avoids potential file resolution issues.
* Refactor model runner methods for improved abstraction.
Moved slicing logic to a private `_slice_outputs` method and decoupled `_run_model` for clearer subclass implementation. Removed redundant `output` attribute in `ModelState` to streamline data handling.
* Add output parsing to model_runner and remove duplicate logic
Integrates an output parser directly into `model_runner` for streamlined inference and parsing. Removes redundant parser initialization from `modeld` to avoid duplication and enhance maintainability.
* Reordering
* linter
* linter
* Remove supercombo model ONNX file.
Deleted the large `supercombo.onnx` model file from the repository. This cleanup reduces repository size and dependency on unused or outdated files for this version.
* Disable tinygrad model compilation on macos temporarily
* Remove unused dmonitoring model file.
Deleted the ONNX model for dmonitoring as it is no longer required. This eliminates unnecessary assets and reduces repository size.
* Removing the model also from the snpe build, we have them, prebuilt
* Add support for TinyGrad model runner processing
Introduced a new function `is_tinygrad_model` to detect TinyGrad as an active model runner. Updated the `is_stock_model` logic to account for TinyGrad models and added a new process entry for TinyGrad in the model manager. This enables handling TinyGrad models alongside existing configurations.
adding modeld back
Add support for `modeld_v2` and update paths for consistency
Updated `SConscript` files to integrate `modeld_v2` alongside `modeld` and adjusted script paths for correct metadata handling. Adjusted various configurations and scripts, such as `labeler.yaml` and `build_release.sh`, to include `modeld_v2` and ensure cohesive project structure.
Refactor imports to use updated `modeld_v2` paths.
Replaced outdated `modeld` references with their `modeld_v2` counterparts for consistency and clarity across the codebase. Also updated `.gitignore` to accommodate new directory structure. This change ensures better maintainability and alignment with the new directory schema.
Refactor and reorganize modeld to sunnypilot/modeld_v2 structure.
Moved and renamed `modeld` components to the new `sunnypilot/modeld_v2` directory for better organization and modularity. Updated imports and file references to align with the new structure, ensuring compatibility and functionality. Streamlined project structure to improve maintainability and future development.
* typo
* Use `stock` model runner and refactor model checks.
Replaces outdated model detection logic with unified `stock` runner integration, simplifying the decision flow for model selection. Includes `stock` as a new enum in the `Runner` type and updates affected references accordingly.
* Handle missing 'sim_pose' in model outputs gracefully.
Added conditional checks to ensure the code handles cases where 'sim_pose' is absent in the model outputs. Fallback behaviors use 'plan' data when 'sim_pose' is unavailable, preventing potential errors and enhancing robustness.
* Add buffer length parameter for enhanced frame handling
Introduce a configurable `buffer_length` parameter to `DrivingModelFrame` to support dynamic buffer sizes, enabling better handling of different frame rates like 20Hz. Updates include necessary adjustments in buffer initialization, copying logic, and related model inputs for improved compatibility and flexibility.
* Rename variable `len` to `length` to avoid shadowing built-in.
Replaced the usage of `len` with `length` across the code to prevent conflicts with Python's built-in `len` function. This improves code clarity and reduces potential errors or misunderstandings in variable usage.
* Fix spacing inconsistency in modeld.py
Added a missing newline for better code readability and consistency. This change has no impact on functionality but improves code formatting.
* Move numpy_inputs initialization to correct position
Repositioned the `numpy_inputs` initialization to align with the input shape processing logic. This ensures consistency in buffer management and clarifies the flow of code execution related to input handling.
* Add 20Hz model state, smart input, and model switcher classes
Introduce `ModelState20Hz`, `ModelSmartInput`, and `ModelSwitcher` for enhanced modularity and flexibility in modeld. Refactor `ModelState` to inherit from these new classes, enabling support for 20Hz processing and smart input initialization. Update associated files to handle the new buffer length parameter and metadata management.
* Refactor `modeld` to streamline feature handling logic
Simplified feature processing for both standard and "smart input" modes by consolidating logic into reusable methods. Updated variable naming, formatting, and spacing for consistency and readability. This refactor enhances maintainability and reduces redundancy in feature update operations.
* Silence debug print statements and use cloudlog for warnings.
Commented out a debug print statement in `commonmodel.cc` to reduce noise. Replaced `print` statements with `cloudlog.warning` in `model_smart_input.py` for improved logging consistency and better integration with the logging system.
* Clean up formatting and fix minor style inconsistencies
Removed unnecessary blank lines and adjusted spacing to standardize code style across the file. These changes improve readability without altering functionality or logic.
* Refactor modeld logic and remove unused 20Hz and smart inputs
Eliminated `ModelSmartInput`, `ModelSwitcher`, and `ModelState20Hz` classes, simplifying model state handling. Centralized model processing within a unified `ModelState` class and moved related code into `sunnypilot/modeld_20hz`. This improves maintainability by removing unused features and consolidating model execution logic, aligning with current system requirements.
* clean
* Remove debug print statement in commonmodel.cc
The `printf` statement logging buffer movement details was removed as it is unnecessary for release builds. This helps streamline the code and avoid excessive console output during execution.
* Refactor model handling for 20Hz and introduce model runners
Introduce ModelRunner abstraction with TinygradRunner and ONNXRunner to streamline model handling for TICI and non-TICI hardware. Added support for dynamic input preparation and 20Hz models while simplifying the model parsing logic. This improves modularity, readability, and extensibility for future updates.
* Remove unused import and fix import order in model_runner.py
This commit removes the unused 'dtypes' import from tinygrad.tensor and adjusts the import order for cleaner code. These changes enhance readability and maintain coding standards.
* Add is20hz field to custom.capnp schema
Introduce a new boolean field `is20hz` to the `custom.capnp` schema. This allows the system to identify 20Hz-specific configurations or data processing. No changes to existing behavior are introduced for non-20Hz cases.
* Add Meta20hz class for 20Hz model message handling.
Introduces a new Meta20hz class for filling 20Hz model messages, encapsulating functionality for curvature, lane lines, road edges, and more. Refactored `modeld.py` to utilize the new class, improving modularity and maintainability. Minor adjustments were made to initialize and handle model metadata.
* Refactor import paths to align with `openpilot` structure.
Updated several import statements to use the `openpilot` namespace for better consistency and organization. This aligns the sunnypilot components more closely with the overall project structure.
* Refactor modeld to support 20Hz models and modularize runners
Replaced legacy runner logic with a unified ONNX and Tinygrad runner to support 20Hz models. Centralized model metadata management and optimized input preparation for adaptability. Updated curvature handling and output parsing for improved modularity and maintainability.
* Add 20Hz metadata handling for model predictions
Introduce `Meta20hz` class for 20Hz-specific metadata and implement dynamic loading of meta model classes in `meta_helper.py`. Update `fill_model_msg.py` to use the new metadata structure, ensuring seamless integration with 20Hz models. Adjust imports in `model_runner.py` to align with project structure.
* "Refactor modeld_20hz to modeld_v2 with cleanup"
Refactored `modeld_20hz` module to `modeld_v2` for improved clarity and consistency. Removed unused code and aligned imports across modules to reflect the new structure. Enhanced maintainability by restructuring model-related files and updating references accordingly.
* Refactor variable names and adjust imports for clarity.
Renamed `len` to `length` to avoid conflict with the built-in function and improve readability. Reorganized imports in `fill_model_msg.py` for better structure and consistency.
* "Add missing newline at end of file in __init__.py
Ensure proper formatting by adding a newline at the end of the file. This adheres to POSIX standards and improves compatibility with some tools and version control systems."
* Handle model runner initialization errors gracefully
Wrap the model runner initialization in a try-except block to catch and log exceptions. This ensures that failures during initialization are logged with detailed information, improving debugging and error tracing.
* Refactor curvature calculation for clarity and reuse.
Introduce a dedicated `get_curvature_from_output` function to handle desired curvature retrieval, improving code readability and reusability. Replace redundant logic in curvature calculation with the new function to streamline the flow.
* Make 20Hz-specific variables conditional in modeld.py
Moved the initialization of 20Hz-specific variables to be conditional based on the `is_20hz` flag. This ensures that unnecessary memory allocations are avoided when the model is not running at 20Hz, improving efficiency and clarity.
* cleanup
---------
Co-authored-by: Jason Wen <haibin.wen3@gmail.com>
James Vecellio-Grant