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Cherry picking elliot's fork. #69

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Cherry picking elliot's fork. #69

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@jf514 jf514 commented Oct 5, 2024

Failed attempt.

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coderabbitai bot commented Oct 5, 2024

Walkthrough

The pull request introduces several changes across multiple configuration files and scripts. It updates the debugging configuration in .vscode/launch.json, adds new YAML configuration files for modeling fruit flies, and introduces new Python scripts for data processing and visualization. Additionally, it includes new MuJoCo model definitions for fruit flies and enhancements to the stac_mjx module for better data handling. The updates aim to improve the structure and functionality of the project.

Changes

File Path Change Summary
.vscode/launch.json Updated debugging configuration name from "main.py " to "Python Debugger: Current File".
configs/model/fly_tethered.yaml Introduced new configuration parameters for modeling fruit flies, including MJCF_PATH, N_FRAMES_PER_CLIP, FTOL, and others related to optimization and rendering.
configs/model/fly_treadmill.yaml Added new configuration parameters similar to those in fly_tethered.yaml, including MJCF_PATH, N_FRAMES_PER_CLIP, and optimization settings.
configs/stac/stac_fly_tethered.yaml New configuration file specifying paths and parameters for fitting and transformation processes, including fit_path, transform_path, and data_path.
configs/stac/stac_fly_treadmill.yaml New configuration file with parameters for simulation tasks, including paths for fitting and transformation, and mujoco settings.
demos/Test.py Introduced a script for biomechanical model visualization using STAC, including data loading and visualization functionalities.
demos/Test_hydra.py New script utilizing the Hydra library for configuration management, integrating data processing and visualization functionalities.
demos/stac-mjx.code-workspace Created a new Visual Studio Code workspace configuration file defining the workspace structure.
demos/wandb/settings Added a new configuration section for Weights & Biases integration, setting disabled to true and mode to offline.
models/fruitfly/fruitfly_force.xml Defined a MuJoCo model for a fruit fly, detailing physical properties, dynamics, and visual aspects.
models/fruitfly/fruitfly_force_ball.xml New MuJoCo model file for a fruit fly with defined physical and visual properties.
models/fruitfly/fruitfly_freeforce.xml New MuJoCo model file for a fruit fly, encapsulating its physical properties and dynamics.
stac_mjx/io_dict_to_hdf5.py Introduced functionality for saving and loading Python dictionaries to and from HDF5 files, preserving hierarchical structure.
stac_mjx/stac.py Enhanced the Stac class with new methods for data reshaping and error statistics, and modified existing methods for better functionality.

Possibly related PRs

  • CI workflow #22: Changes in the CI workflow may indirectly relate to modifications in .vscode/launch.json, as both involve configuration management.
  • Creating .mat and .nwb data loaders #23: Updates to the testing job in CI workflow could relate to the main PR's focus on configuration changes, particularly if testing involves debugging configurations.
  • Remove globals #43: Introduction of MJCF_PATH in configs/rodent.yaml aligns with the main PR's configuration changes for model paths.
  • Refactor configs #54: Refactor of configuration files for improved flexibility aligns with changes to .vscode/launch.json, both aiming to streamline configuration processes.
  • Merge initial mouse work.  #57: Addition of a mouse model and its configuration could relate to enhancements in debugging configurations, improving overall project structure.
  • Rename functions, classes, and modules for clarity + mjx bug fix. #64: Renaming functions and classes for clarity may relate to the focus on improving configuration clarity in .vscode/launch.json.

🐇 In a world of YAML and code,
A rabbit hops, lightening the load.
With models and scripts, we leap and bound,
Through debugging and data, new paths are found.
In the garden of code, we flourish and play,
Celebrating changes, hip-hip-hooray! 🎉


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codecov bot commented Oct 5, 2024

Codecov Report

Attention: Patch coverage is 0% with 38 lines in your changes missing coverage. Please review.

Project coverage is 40.66%. Comparing base (57174a8) to head (5c318e2).

Files with missing lines Patch % Lines
stac_mjx/io_dict_to_hdf5.py 0.00% 38 Missing ⚠️
Additional details and impacted files
@@            Coverage Diff             @@
##             main      #69      +/-   ##
==========================================
- Coverage   43.55%   40.66%   -2.89%     
==========================================
  Files           9       10       +1     
  Lines         535      573      +38     
==========================================
  Hits          233      233              
- Misses        302      340      +38     

☔ View full report in Codecov by Sentry.
📢 Have feedback on the report? Share it here.

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Actionable comments posted: 22

🧹 Outside diff range and nitpick comments (25)
configs/stac/stac_fly_treadmill.yaml (1)

1-12: Overall configuration review summary

This new configuration file for the STAC fly treadmill simulation introduces several important parameters. While it provides a good starting point, there are areas that could benefit from improvement:

  1. File paths could be made more robust and portable.
  2. Some parameter values (e.g., n_fit_frames) need justification or documentation.
  3. MuJoCo solver settings might need review to ensure optimal performance.

To enhance this configuration file:

  • Consider using environment variables or absolute paths for file locations.
  • Add comments explaining the rationale behind specific parameter values.
  • Review and potentially adjust MuJoCo solver settings based on simulation requirements.
  • Consider adding a schema or documentation for this configuration file to help users understand and correctly set these parameters.

To improve maintainability and user-friendliness, consider creating a schema for this configuration file (e.g., using JSON Schema) and adding a validation step in your application. This would help catch configuration errors early and provide better guidance to users.

.vscode/launch.json (1)

Line range hint 8-15: Consider creating separate configurations for specific use cases

The current configuration includes a specific argument "test.ik_only=True". While this might be necessary for a particular use case, it limits the configuration's flexibility for general Python debugging.

Consider creating two separate configurations:

  1. A general Python debugging configuration without specific arguments.
  2. A specific configuration for the current use case with the "test.ik_only=True" argument.

Here's a suggested implementation:

{
    "version": "0.2.0",
    "configurations": [
        {
            "name": "Python: Current File",
            "type": "debugpy",
            "request": "launch",
            "program": "${file}",
            "console": "integratedTerminal"
        },
        {
            "name": "Python: main.py with IK only",
            "type": "debugpy",
            "request": "launch",
            "program": "${workspaceFolder}/core/main.py",
            "console": "integratedTerminal",
            "args": [
                "test.ik_only=True"
            ]
        }
    ]
}

This approach provides more flexibility while maintaining the specific configuration needed for your current use case.

🧰 Tools
🪛 Biome

[error] 5-5: End of file expected

Use an array for a sequence of values: [1, 2]

(parse)


[error] 5-5: End of file expected

Use an array for a sequence of values: [1, 2]

(parse)


[error] 5-5: End of file expected

Use an array for a sequence of values: [1, 2]

(parse)


[error] 5-5: End of file expected

Use an array for a sequence of values: [1, 2]

(parse)


[error] 6-6: End of file expected

Use an array for a sequence of values: [1, 2]

(parse)


[error] 6-6: End of file expected

Use an array for a sequence of values: [1, 2]

(parse)

demos/Test.py (4)

1-9: Remove unused imports to improve code cleanliness.

The following imports are not used in the script and should be removed:

  • pandas (line 7)
  • hydra (line 8)
  • DictConfig from omegaconf (line 9)

Removing unused imports helps maintain clean code and can slightly improve script startup time.

Apply this diff to remove the unused imports:

-import pandas as pd 
-import hydra
-from omegaconf import DictConfig
🧰 Tools
🪛 Ruff

7-7: pandas imported but unused

Remove unused import: pandas

(F401)


8-8: hydra imported but unused

Remove unused import: hydra

(F401)


9-9: omegaconf.DictConfig imported but unused

Remove unused import: omegaconf.DictConfig

(F401)


21-27: Remove commented-out code to improve readability.

The commented-out code for loading data from a CSV file (lines 21-27) is not being used and may confuse readers. If this code is no longer needed, it's best to remove it entirely. If it might be needed in the future, consider moving it to a separate file or documenting it elsewhere.

Remove the following lines:

# tredmill_data = pd.read_csv(data_dir / "wt_berlin_linear_treadmill_dataset.csv")
# kp_names = ['head', 'thorax', 'abdomen', 'r1', 'r2', 'r3', 'l1', 'l2', 'l3']
# coords = ['_x', '_y', '_z']
# df_names = [kp+coord for kp in kp_names for coord in coords]
# kp_data_all = tredmill_data[df_names].values
# sorted_kp_names = kp_names
# kp_data = model_cfg['MOCAP_SCALE_FACTOR']*kp_data_all.copy()

36-37: Use underscore prefix for unused loop variable.

The loop control variable nbout is not used within the loop body. To indicate that it's intentionally unused and suppress linter warnings, prefix it with an underscore.

Apply this change:

-for nbout, key in enumerate(bout_dict.keys()):
+for _nbout, key in enumerate(bout_dict.keys()):

Alternatively, if you don't need the index at all, you can use:

for key in bout_dict.keys():
🧰 Tools
🪛 Ruff

36-36: Loop control variable nbout not used within loop body

Rename unused nbout to _nbout

(B007)


61-61: Enhance the completion message with execution details.

The current completion message is simple and doesn't provide any information about the script's execution. Consider enhancing it to include relevant details about the process, such as the number of frames processed, the output file path, or execution time.

Here's an example of how you could improve the completion message:

import time

start_time = time.time()

# Your existing code here

execution_time = time.time() - start_time
print(f'Done! Processed {n_frames} frames in {execution_time:.2f} seconds. Output saved to {save_path}')

This provides more context about what the script accomplished, which can be helpful for users and for debugging.

configs/model/fly_treadmill.yaml (6)

8-8: Address the TODO comment for optimizer loops.

There's a TODO comment indicating that the optimizer loops need to be re-implemented to use the defined tolerances (FTOL, ROOT_FTOL, LIMB_FTOL). This is an important task that should be addressed to ensure the optimization process is using the correct tolerance values.

Would you like assistance in re-implementing the optimizer loops to use these tolerances?


7-13: Remove trailing spaces.

There are trailing spaces on lines 7 and 13. While they don't affect functionality, removing them improves code cleanliness.

Apply this diff to remove the trailing spaces:

-# Tolerance for the optimizations of the full model, limb, and root. 
+# Tolerance for the optimizations of the full model, limb, and root.
 # TODO: Re-implement optimizer loops to use these tolerances
 FTOL: 5.0e-03
 ROOT_FTOL: 1.0e-05
 LIMB_FTOL: 1.0e-06

-# Number of alternating pose and offset optimization rounds. 
+# Number of alternating pose and offset optimization rounds.
🧰 Tools
🪛 yamllint

[error] 7-7: trailing spaces

(trailing-spaces)


[error] 13-13: trailing spaces

(trailing-spaces)


16-26: Remove trailing space and consider using a compact YAML list format.

There's a trailing space on line 16. Additionally, consider using a more compact YAML list format for better readability.

Apply this diff to remove the trailing space and use a compact list format:

-KP_NAMES: 
-  - 'head'
-  - 'thorax'
-  - 'abdomen'
-  - 'r1'
-  - 'r2'
-  - 'r3'
-  - 'l1'
-  - 'l2'
-  - 'l3'
+KP_NAMES: ['head', 'thorax', 'abdomen', 'r1', 'r2', 'r3', 'l1', 'l2', 'l3']
🧰 Tools
🪛 yamllint

[error] 16-16: trailing spaces

(trailing-spaces)


53-65: Remove trailing spaces and consider compact format for TRUNK_OPTIMIZATION_KEYPOINTS.

There are trailing spaces on lines 53, 57, and 58. Additionally, consider using a more compact format for TRUNK_OPTIMIZATION_KEYPOINTS.

Apply this diff to remove trailing spaces and use a compact format:

-TRUNK_OPTIMIZATION_KEYPOINTS: 
-  - 'head'
-  - 'thorax'
-  - 'abdomen'
-  
-INDIVIDUAL_PART_OPTIMIZATION: 
+TRUNK_OPTIMIZATION_KEYPOINTS: ['head', 'thorax', 'abdomen']
+
+INDIVIDUAL_PART_OPTIMIZATION:
   "T1R": ["thorax", "claw_T1_right"]
   "T2R": ["thorax", "claw_T2_right"]
   "T3R": ["thorax", "claw_T3_right"]
   "T1L": ["thorax", "claw_T1_left"]
   "T2L": ["thorax", "claw_T2_left"]
   "T3L": ["thorax", "claw_T3_left"]
🧰 Tools
🪛 yamllint

[error] 53-53: trailing spaces

(trailing-spaces)


[error] 57-57: trailing spaces

(trailing-spaces)


[error] 58-58: trailing spaces

(trailing-spaces)


66-77: Remove trailing space on line 77.

There's a trailing space on line 77. While it doesn't affect functionality, removing it improves code cleanliness.

Apply this diff to remove the trailing space:

   l2: 0 0 .3 1
   r2: 0 0 .3 1
   l3: 0 0 .3 1
   r3: 0 0 .3 1
-  
+
🧰 Tools
🪛 yamllint

[error] 77-77: trailing spaces

(trailing-spaces)


87-96: Remove trailing spaces and fix indentation.

There are trailing spaces on lines 87-89, and the list items under SITES_TO_REGULARIZE are not properly indented. Let's fix these issues for better code consistency and readability.

Apply this diff to remove trailing spaces and fix indentation:

-# If you have reason to believe that the initial offsets are correct for particular keypoints, 
-# you can regularize those sites using this with M_REG_COEF. 
-SITES_TO_REGULARIZE: 
-- l1
-- r1
-- l2
-- r2
-- l3
-- r3
+# If you have reason to believe that the initial offsets are correct for particular keypoints,
+# you can regularize those sites using this with M_REG_COEF.
+SITES_TO_REGULARIZE:
+  - l1
+  - r1
+  - l2
+  - r2
+  - l3
+  - r3
🧰 Tools
🪛 yamllint

[error] 87-87: trailing spaces

(trailing-spaces)


[error] 88-88: trailing spaces

(trailing-spaces)


[error] 89-89: trailing spaces

(trailing-spaces)


[warning] 90-90: wrong indentation: expected 2 but found 0

(indentation)

configs/model/fly_tethered.yaml (5)

83-124: Consider non-zero initial offsets and clarify TRUNK_OPTIMIZATION_KEYPOINTS.

  1. All KEYPOINT_INITIAL_OFFSETS are currently set to 0. Consider if any non-zero initial estimates could improve the optimization process, especially if you have prior knowledge about the typical posture of the fruit fly.

  2. TRUNK_OPTIMIZATION_KEYPOINTS is empty. If this is intentional, consider adding a comment explaining why trunk optimization is not needed. If it's meant to be implemented later, consider adding a TODO comment.

The INDIVIDUAL_PART_OPTIMIZATION grouping looks comprehensive and well-organized.

🧰 Tools
🪛 yamllint

[error] 117-117: trailing spaces

(trailing-spaces)


[error] 118-118: trailing spaces

(trailing-spaces)


126-166: Consider using distinct colors for keypoint groups and clarify MOCAP_SCALE_FACTOR.

  1. All keypoints are currently set to the same color (0, 0, 0.3, 1). Consider using different colors for different keypoint groups (e.g., one color for each leg or one color for each segment type) to improve visualization and make it easier to distinguish between parts.

  2. The MOCAP_SCALE_FACTOR is set to 1, but there's a commented-out value of 0.1. Clarify when and why the 0.1 scale factor might be needed. If it's for a specific use case, consider adding a comment explaining this or using a configuration flag to switch between these values.

🧰 Tools
🪛 yamllint

[error] 158-158: trailing spaces

(trailing-spaces)


168-178: Clarify regularization usage and document rendering/sampling settings.

  1. SITES_TO_REGULARIZE is empty, but M_REG_COEF is set to 1. Clarify if regularization is intended to be used. If not, consider removing M_REG_COEF or setting it to 0. If it's for future use, add a TODO comment.

  2. Add brief comments explaining the choice of RENDER_FPS (50) and N_SAMPLE_FRAMES (100). Clarify how these values impact the overall process and what trade-offs they represent (e.g., smoothness vs. computation time).

  3. The comment for M_REG_COEF seems to be a duplicate of the comment for SITES_TO_REGULARIZE. Consider removing or updating this comment to provide unique information about M_REG_COEF.

🧰 Tools
🪛 yamllint

[error] 168-168: trailing spaces

(trailing-spaces)


[error] 169-169: trailing spaces

(trailing-spaces)


[error] 176-176: trailing spaces

(trailing-spaces)


[error] 177-177: trailing spaces

(trailing-spaces)


180-180: Add comment explaining TIME_BINS setting.

The purpose and impact of the TIME_BINS setting (0.02) are not immediately clear. Consider adding a brief comment explaining what this value represents (e.g., time step size, sampling interval) and how it affects the model or simulation.


1-180: Remove trailing spaces throughout the file.

The static analysis tool yamllint has reported trailing spaces on multiple lines throughout the file. While this doesn't affect functionality, removing trailing spaces helps maintain clean code and prevents unnecessary diff changes in version control.

Consider using an editor feature or a script to automatically remove trailing spaces from all lines in the file.

🧰 Tools
🪛 yamllint

[error] 7-7: trailing spaces

(trailing-spaces)


[error] 13-13: trailing spaces

(trailing-spaces)


[error] 16-16: trailing spaces

(trailing-spaces)


[error] 117-117: trailing spaces

(trailing-spaces)


[error] 118-118: trailing spaces

(trailing-spaces)


[error] 158-158: trailing spaces

(trailing-spaces)


[error] 168-168: trailing spaces

(trailing-spaces)


[error] 169-169: trailing spaces

(trailing-spaces)


[error] 176-176: trailing spaces

(trailing-spaces)


[error] 177-177: trailing spaces

(trailing-spaces)

stac_mjx/io_dict_to_hdf5.py (1)

39-54: Align load function capabilities with save function

The load function currently does not support mixed dictionary/list hierarchical structures when ASLIST is True, unlike the save function. This inconsistency might limit the module's usability.

Consider enhancing the load function to handle mixed hierarchical structures, providing symmetry with the save function.

🧰 Tools
🪛 Ruff

50-50: Ambiguous variable name: l

(E741)

🪛 GitHub Check: codecov/patch

[warning] 39-39: stac_mjx/io_dict_to_hdf5.py#L39
Added line #L39 was not covered by tests


[warning] 47-54: stac_mjx/io_dict_to_hdf5.py#L47-L54
Added lines #L47 - L54 were not covered by tests

demos/Test_hydra.py (2)

25-26: Update comment to reflect the code

The comment on line 25 suggests using the parent directory as base_path, but line 26 assigns base_path to an absolute path. Update the comment or modify the code to ensure consistency.


51-51: Rename unused loop variable nbout to _

The loop variable nbout is not used in the loop body. Renaming it to _ indicates that it's intentionally unused.

Apply this diff:

-for nbout, key in enumerate(bout_dict.keys()):
+for _, key in enumerate(bout_dict.keys()):
🧰 Tools
🪛 Ruff

51-51: Loop control variable nbout not used within loop body

Rename unused nbout to _nbout

(B007)

stac_mjx/stac.py (5)

Line range hint 179-186: Handle potential data loss when total frames are not a multiple of chunk size

In the _chunk_kp_data method, if total_frames is not an exact multiple of N_FRAMES_PER_CLIP, the extra frames at the end are discarded. This could lead to loss of valuable data. Consider handling the remaining frames, possibly by including them in an additional chunk or adjusting the chunking logic.

You can modify the function to handle leftover frames:

def _chunk_kp_data(self, kp_data):
    """Reshape data for parallel processing."""
    n_frames = self.cfg.model.N_FRAMES_PER_CLIP
    total_frames = kp_data.shape[0]

-   n_chunks = int(total_frames / n_frames)
+   n_chunks = int(np.ceil(total_frames / n_frames))

-   kp_data = kp_data[: int(n_chunks) * n_frames]

+   pad_size = n_chunks * n_frames - total_frames
+   if pad_size > 0:
+       padding = jp.zeros((pad_size,) + kp_data.shape[1:])
+       kp_data = jp.concatenate([kp_data, padding], axis=0)

    # Reshape the array to create chunks
    kp_data = kp_data.reshape((n_chunks, n_frames) + kp_data.shape[1:])

    return kp_data

This modification pads the kp_data with zeros if necessary, ensuring all frames are included.


Line range hint 188-194: Use flatten() for clarity when flattening arrays

In _get_error_stats, consider using the flatten() method instead of reshape(-1) for better readability when flattening the errors array.

Apply this small change:

-   flattened_errors = errors.reshape(-1)
+   flattened_errors = errors.flatten()

Line range hint 209-213: Replace print statements with logging for better practice

In both the fit_offsets and ik_only methods, using print statements for outputting mean and standard deviation is not optimal for production code. Consider using the logging module to provide better control over the logging levels and outputs.

Here's how you can modify the code:

-   print(f"Mean: {mean}")
-   print(f"Standard deviation: {std}")
+   logging.info(f"Mean error: {mean}")
+   logging.info(f"Standard deviation of error: {std}")

Remember to import the logging module and configure it appropriately at the beginning of your file.

Also applies to: 284-288


Line range hint 250-262: Avoid re-creating models inside mjx_setup to improve efficiency

In the ik_only method, the mjx_setup function re-creates mjx_model and mjx_data for each batch by calling op.mjx_load(mj_model). This can introduce unnecessary overhead. Consider initializing mjx_model once outside of vmap and updating only the necessary components within the function.

Refactor suggestion:

# Initialize mjx_model and mjx_data once
mjx_model_base, mjx_data_base = op.mjx_load(self._mj_model)

def mjx_setup(kp_data, mjx_model_base, mjx_data_base):
    ...
    # Use copy to prevent modifying the base model and data
    mjx_model = deepcopy(mjx_model_base)
    mjx_data = deepcopy(mjx_data_base)
    ...
    return mjx_model, mjx_data

mjx_model, mjx_data = jax.vmap(
    mjx_setup, in_axes=(0, None, None)
)(batched_kp_data, mjx_model_base, mjx_data_base)

This approach avoids repeatedly loading the model and reduces overhead.


Line range hint 256-262: Complete the docstring for mjx_setup function

The mjx_setup function has incomplete docstring entries for Args and Returns. Providing detailed descriptions improves code readability and maintainability.

Update the docstring as follows:

def mjx_setup(kp_data, mj_model):
    """Create and initialize mjx_model and mjx_data for a batch.

    Args:
        kp_data (jp.ndarray): Keypoint data for the batch.
        mj_model (mujoco.MjModel): The Mujoco model to use.

    Returns:
        Tuple[mjx.MjModel, mjx.MjData]: The initialized model and data for the batch.
    """
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📥 Commits

Files that changed from the base of the PR and between 57174a8 and 5c318e2.

📒 Files selected for processing (14)
  • .vscode/launch.json (1 hunks)
  • configs/model/fly_tethered.yaml (1 hunks)
  • configs/model/fly_treadmill.yaml (1 hunks)
  • configs/stac/stac_fly_tethered.yaml (1 hunks)
  • configs/stac/stac_fly_treadmill.yaml (1 hunks)
  • demos/Test.py (1 hunks)
  • demos/Test_hydra.py (1 hunks)
  • demos/stac-mjx.code-workspace (1 hunks)
  • demos/wandb/settings (1 hunks)
  • models/fruitfly/fruitfly_force.xml (1 hunks)
  • models/fruitfly/fruitfly_force_ball.xml (1 hunks)
  • models/fruitfly/fruitfly_freeforce.xml (1 hunks)
  • stac_mjx/io_dict_to_hdf5.py (1 hunks)
  • stac_mjx/stac.py (1 hunks)
✅ Files skipped from review due to trivial changes (2)
  • demos/stac-mjx.code-workspace
  • demos/wandb/settings
🧰 Additional context used
🪛 yamllint
configs/model/fly_tethered.yaml

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configs/model/fly_treadmill.yaml

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🪛 Ruff
demos/Test.py

7-7: pandas imported but unused

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8-8: hydra imported but unused

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9-9: omegaconf.DictConfig imported but unused

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36-36: Loop control variable nbout not used within loop body

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demos/Test_hydra.py

7-7: pandas imported but unused

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18-18: Use key in dict instead of key in dict.keys()

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51-51: Loop control variable nbout not used within loop body

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stac_mjx/io_dict_to_hdf5.py

50-50: Ambiguous variable name: l

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🪛 GitHub Check: codecov/patch
stac_mjx/io_dict_to_hdf5.py

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🔇 Additional comments (34)
configs/stac/stac_fly_tethered.yaml (3)

5-7: Clarify n_fit_frames value and confirm process execution.

  1. The n_fit_frames is set to 1800. Could you provide context on why this specific number was chosen? It might be helpful to add a comment explaining the reasoning.

  2. Both skip_fit and skip_transform are set to False, meaning both processes will be executed. Please confirm if this is the intended behavior for the tethered fly experiment.

To check for other configurations that might affect these settings, run:

#!/bin/bash
# Search for other occurrences of n_fit_frames, skip_fit, and skip_transform
echo "Other occurrences of n_fit_frames:"
rg "n_fit_frames:"
echo "Other occurrences of skip_fit:"
rg "skip_fit:"
echo "Other occurrences of skip_transform:"
rg "skip_transform:"

9-12: Review MuJoCo solver settings and consider adding explanatory comments.

  1. The Newton solver is a good choice for accuracy, but it can be computationally expensive. Ensure this is the intended solver for the tethered fly experiment.

  2. Only one iteration (iterations: 1) is specified. This might not be sufficient for convergence in many cases. Consider increasing this value or explain why a single iteration is adequate for this specific scenario.

  3. Consider adding comments to explain the reasoning behind the chosen values, especially for ls_iterations: 4.

To check for other MuJoCo configurations and potential inconsistencies, run:

#!/bin/bash
# Search for other MuJoCo configurations
echo "Other MuJoCo configurations:"
rg -A 5 "mujoco:"

1-12: Overall configuration review summary

The new stac_fly_tethered.yaml configuration file provides a good starting point for the STAC tethered fly experiment. However, there are several areas that could benefit from additional clarification or adjustment:

  1. Consider using absolute paths for fit_path and transform_path.
  2. Verify the intentional use of the test dataset in data_path.
  3. Provide context for the n_fit_frames value.
  4. Confirm the intention to run both fit and transform processes.
  5. Review the MuJoCo solver settings, particularly the iteration count.

Adding comments throughout the file to explain the reasoning behind specific values would greatly improve its maintainability and ease of use for other team members.

To get an overview of similar configuration files in the project, run:

#!/bin/bash
# Find and list contents of other YAML files in the configs directory
fd -e yaml . configs | xargs -I {} sh -c 'echo "File: {}"; cat {} | sed "s/^/  /"; echo'

This will help ensure consistency across different configuration files in the project.

configs/model/fly_treadmill.yaml (5)

28-39: LGTM: KEYPOINT_MODEL_PAIRS mapping is consistent and well-defined.

The KEYPOINT_MODEL_PAIRS mapping correctly associates each keypoint with its corresponding model site. This mapping is consistent with the KP_NAMES defined earlier and provides a clear relationship between the keypoints and the model structure.


79-86: Verify scale factors for the model.

Please confirm that the following scale factors are correct for your fly treadmill model:

  1. SCALE_FACTOR: 1 (This suggests the model size matches the desired animal size)
  2. MOCAP_SCALE_FACTOR: 0.3 (This implies the mocap data is in a different scale, possibly centimeters if the model is in meters)

Ensure these values align with your expectations and the actual scales of your model and mocap data.


97-100: Verify rendering and sampling parameters.

Please confirm that the following parameters are appropriate for your use case:

  1. RENDER_FPS: 200 (This is a high frame rate, which might be necessary for capturing fast fly movements)
  2. N_SAMPLE_FRAMES: 100 (This determines the number of frames sampled for analysis or visualization)

Ensure these values provide the desired balance between detail and computational efficiency.


101-105: Verify regularization coefficient and time bins.

Please confirm that the following parameters are appropriate for your model:

  1. M_REG_COEF: 1 (This is the regularization coefficient for the sites specified in SITES_TO_REGULARIZE)
  2. TIME_BINS: 0.02 (This sets the time interval for binning, possibly in seconds)

Ensure these values align with your expectations and provide the desired behavior in your model.

Additionally, there are trailing spaces on lines 101-102. Apply this diff to remove them:

-# If you have reason to believe that the initial offsets are correct for particular keypoints, 
-# you can regularize those sites using _SITES_TO_REGULARIZE. 
+# If you have reason to believe that the initial offsets are correct for particular keypoints,
+# you can regularize those sites using _SITES_TO_REGULARIZE.
 M_REG_COEF: 1

 TIME_BINS: 0.02
🧰 Tools
🪛 yamllint

[error] 101-101: trailing spaces

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[error] 102-102: trailing spaces

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1-105: Summary: Configuration file review completed with minor improvements and verifications needed.

Overall, the fly_treadmill.yaml configuration file provides a comprehensive set of parameters for the fly treadmill model. Here's a summary of the main points to address:

  1. Implement the TODO for re-implementing optimizer loops to use the defined tolerances.
  2. Verify the zero offsets for leg keypoints in KEYPOINT_INITIAL_OFFSETS.
  3. Confirm the appropriateness of scale factors (SCALE_FACTOR and MOCAP_SCALE_FACTOR) for your model and mocap data.
  4. Verify rendering and sampling parameters (RENDER_FPS and N_SAMPLE_FRAMES) to ensure they meet your requirements.
  5. Confirm the values for M_REG_COEF and TIME_BINS are suitable for your model.
  6. Apply the suggested formatting improvements to remove trailing spaces and improve list formatting for better readability and consistency.

After addressing these points, the configuration file should be well-structured and properly tailored to your fly treadmill model.

🧰 Tools
🪛 yamllint

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configs/model/fly_tethered.yaml (1)

16-80: LGTM: Keypoint names and model pairs are well-defined.

The keypoint naming convention is consistent and intuitive. The mapping between keypoints and anatomical sites in KEYPOINT_MODEL_PAIRS is comprehensive and appears to cover all necessary parts of the fruit fly model.

🧰 Tools
🪛 yamllint

[error] 16-16: trailing spaces

(trailing-spaces)

models/fruitfly/fruitfly_force_ball.xml (8)

1-2: Root element and compiler settings look good.

The model name "fruitfly" is appropriate, and the compiler settings (autolimits=true, angle=radian) are suitable for scientific simulations of a fruit fly.


4-4: Verify gravity units and advanced settings.

  1. The gravity setting (-981 cm/s^2) appears to use centimeters. Confirm if this is intentional or if it should be -9.81 m/s^2.
  2. Please verify that the density (0.00128) and viscosity (0.000185) values are correct for the intended simulation environment.
  3. Confirm that the elliptic cone and noslip_iterations=3 settings are appropriate for this model.

6-6: Verify size limits for the simulation.

Please confirm that the following limits are sufficient for the fruit fly model:

  1. Maximum number of joints (njmax): 300
  2. Maximum number of contacts (nconmax): 100
  3. Number of keyframes (nkey): 1

Ensure these limits accommodate the model's complexity and intended simulations.


8-199: Comprehensive default settings require verification.

The default settings are extensive and detailed, covering various aspects of the fruit fly model. While they appear to be carefully crafted, it's crucial to verify:

  1. Biological accuracy of joint limits, stiffness, and damping values.
  2. Correctness of geometry specifications for each body part.
  3. Appropriateness of collision settings and groups.
  4. Accuracy of material properties and densities.

Consider having a biologist or fruit fly expert review these settings to ensure they accurately represent fruit fly physiology and biomechanics.


201-309: Visual settings look good; verify asset files.

The visual settings are well-defined for high-quality rendering. The comprehensive asset definitions cover all parts of the fruit fly's body in detail. However, please:

  1. Confirm that all referenced mesh files (e.g., "assets/head_body.obj", "assets/wing_left_brown.obj", etc.) exist in the correct location.
  2. Verify that the material properties (e.g., "body", "red", "membrane") are correctly defined for each part.
  3. Ensure the texture "groundplane" is appropriate for the intended simulations.

These checks will prevent potential runtime errors due to missing or incorrectly named assets.


311-747: Detailed worldbody structure requires anatomical verification.

The worldbody section provides an intricate definition of the fruit fly's structure, including the thorax, head, wings, abdomen, and legs. While the level of detail is impressive, it's crucial to verify:

  1. Anatomical accuracy of the body part hierarchy and connections.
  2. Correctness of joint placements and their degrees of freedom.
  3. Appropriate positioning and sizing of collision geometries.
  4. Accurate placement of sensors and tracking sites.

Consider consulting with an entomologist or fruit fly expert to ensure this model accurately represents fruit fly anatomy and biomechanics. This will be crucial for the validity of any simulations using this model.


749-912: Contact, actuator, and sensor definitions require attention.

  1. Contact Exclusions:

    • The defined exclusions seem appropriate to prevent unrealistic interactions between body parts.
    • Verify that all necessary exclusions are included to avoid any unintended collisions during simulation.
  2. Actuators:

    • The actuator definitions cover major joints of the fruit fly, which is good.
    • Confirm that the control ranges and gear ratios are biologically plausible for a fruit fly.
    • Note that some actuators are commented out (e.g., tarsus actuators). Decide if these should be included or removed.
  3. Sensors:

    • The sensor definitions are currently commented out. Determine if these should be implemented for your simulations.
    • If sensors are needed, uncomment and verify their placements and types.
  4. General:

    • Consider adding comments explaining the purpose of specific exclusions, actuators, or sensors for better maintainability.

1-912: Comprehensive fruit fly model requires expert verification and testing.

This MuJoCo model provides a detailed representation of a fruit fly, covering aspects from physical properties to visual rendering. To ensure its accuracy and usability:

  1. Consult with an entomologist or fruit fly expert to verify the anatomical accuracy and biomechanical properties.
  2. Conduct thorough testing of the model in various simulation scenarios to ensure realistic behavior.
  3. Consider implementing and testing the commented-out sensor definitions if they're needed for your simulations.
  4. Document any assumptions or simplifications made in the model for future reference.
  5. Establish a process for updating the model as new biological data becomes available.

Overall, this is an impressive and detailed model, but its complexity requires careful validation before use in scientific simulations.

models/fruitfly/fruitfly_freeforce.xml (8)

1-2: Root element and compiler settings look good.

The model name "fruitfly" is appropriate, and the compiler settings (autolimits=true, angle=radian) are suitable for a physics-based simulation of a fruit fly.


8-196: Well-structured default settings.

The default settings are organized hierarchically, providing a good balance between general consistency and part-specific customization. This structure will facilitate maintenance and future modifications of the model.


758-785: Appropriate contact exclusions defined.

The contact exclusions are well-defined, preventing unrealistic collisions between adjacent or overlapping body parts. This contributes to a more accurate and stable simulation of the fruit fly's movement.


198-305: Comprehensive visual settings and assets.

The visual settings and assets provide for a detailed and visually accurate model of the fruit fly. The high-quality settings (e.g., shadowsize, offsamples) will result in good visual output.

Note that these high-quality visual settings may impact performance. If you encounter performance issues, consider adjusting these settings. You can use the following command to check for any performance-related logs or warnings:

#!/bin/bash
# Search for performance-related logs or warnings
rg -i "performance|slow|fps"

308-756: Detailed and accurate worldbody structure.

The worldbody section provides a comprehensive and hierarchical representation of the fruit fly's anatomy. The detailed definitions for each body part, including appropriate joint types and geometries, contribute to an accurate physical model.

Given the complexity of this section, it's crucial to ensure that all body parts are correctly connected and that there are no unintended interactions. You can use the following command to check for any warnings related to body connections or constraints:

#!/bin/bash
# Search for warnings related to body connections or constraints
rg -i "warning|error" | rg -i "body|joint|connect|constraint"

1-921: Comprehensive and well-structured fruit fly model.

This MuJoCo model provides a detailed and accurate representation of a fruit fly, including appropriate physics settings, visual assets, body structure, contact dynamics, and actuation. The hierarchical organization and specific customizations for different body parts contribute to a robust and flexible simulation framework.

While the model is complex, this level of detail is necessary for accurately simulating the intricate movements and physics of a fruit fly. The commented-out sections provide alternative configurations that may be useful for different simulation scenarios.

To ensure the model's completeness and consistency, run the following checks:

#!/bin/bash
# Check for any TODO comments or unfinished sections
echo "Checking for TODO comments:"
rg -i "todo|fixme|xxx" models/fruitfly/fruitfly_freeforce.xml

# Verify that all referenced mesh files exist
echo "Verifying mesh file references:"
meshes=$(rg 'file="assets/' models/fruitfly/fruitfly_freeforce.xml | awk -F'"' '{print $2}')
for mesh in $meshes; do
    if [ ! -f "$mesh" ]; then
        echo "Missing mesh file: $mesh"
    fi
done

# Check for any unused assets
echo "Checking for unused assets:"
assets=$(rg '<mesh name="([^"]+)"' models/fruitfly/fruitfly_freeforce.xml -or '$1')
for asset in $assets; do
    if ! rg "mesh=\"$asset\"" models/fruitfly/fruitfly_freeforce.xml > /dev/null; then
        echo "Unused asset: $asset"
    fi
done

These checks will help identify any remaining tasks, verify the existence of required mesh files, and detect any unused assets in the model.


844-921: Well-defined actuators with some alternatives.

The actuator section appropriately defines motors for controlling the fruit fly's movement. The definitions cover major joint movements necessary for simulating the insect's locomotion.

There are several commented-out motor and adhesion definitions. Consider reviewing these to determine if they should be removed or uncommented based on the current simulation requirements.

To ensure that all necessary actuators are properly defined and there are no unused definitions, you can use the following command:

#!/bin/bash
# List all defined actuators and check for their usage in the model
actuators=$(rg '<motor' models/fruitfly/fruitfly_freeforce.xml | awk -F'"' '{print $2}')
for actuator in $actuators; do
    echo "Checking usage of actuator: $actuator"
    rg "$actuator" --type xml
done

4-6: Physics and size settings are appropriate.

The gravity, density, and viscosity settings are suitable for simulating a fruit fly in air. The size parameters (njmax, nconmax, nkey) seem reasonable, but their adequacy should be monitored during simulation.

If performance issues arise, consider reviewing these size parameters. You can use the following command to check for any warnings or errors related to these parameters during simulation:

✅ Verification successful

[/run_scripts]


[/run_scripts]


Size parameters are appropriate and no related warnings or errors were found.

🏁 Scripts executed

The following scripts were executed for the analysis:

Script:

#!/bin/bash
# Search for warnings or errors related to joint, contact, or key limits
rg -i "warning|error|exceed" | rg -i "joint|contact|key"

Length of output: 511


Script:

#!/bin/bash
# Search for warnings or errors related to joint, contact, or key limits
rg -i -E "(warning|error|exceed).*(joint|contact|key)" .

Length of output: 139


Script:

#!/bin/bash
# Search for warnings, errors, or exceed related to joint, contact, or key limits
rg -i 'warning|error|exceed' . | rg -i 'joint|contact|key'

Length of output: 3439

models/fruitfly/fruitfly_force.xml (7)

1-2: Root element and compiler settings look good.

The use of radians for angles and enabling autolimits are appropriate choices for a scientific simulation of a fruit fly. These settings will help ensure accurate and realistic movements in the model.


1-932: Impressive and detailed fruit fly model; consider performance and expert validation.

This MuJoCo model of a fruit fly is remarkably comprehensive, covering all aspects of the insect's anatomy and biomechanics in great detail. The model includes well-thought-out definitions for body structure, materials, joints, actuators, and visual properties.

Key points to consider:

  1. The level of detail, while impressive, may impact simulation performance. Consider profiling and optimizing if real-time performance is required.
  2. Given the model's complexity and specialized nature, validation by an expert in fruit fly anatomy and biomechanics is highly recommended.
  3. Sensor definitions are currently commented out. Evaluate if these should be enabled based on your simulation requirements.
  4. The visual settings are configured for high quality. Adjust these if faster rendering is needed for real-time applications.

Overall, this model provides an excellent foundation for detailed fruit fly simulations, but may require some tuning based on specific performance requirements and research goals.


763-932: Contact and actuator definitions look good; consider sensor usage.

The extensive list of contact exclusions is necessary and well-thought-out, preventing unrealistic collisions between adjacent body parts. The actuator definitions align properly with the joint definitions, providing appropriate control over the model's movements.

Note that the sensor definitions are currently commented out. If these sensors (accelerometer, gyro, velocimeter, force sensors, touch sensors) are relevant to your current simulation goals, consider uncommenting and utilizing them.

To help decide on sensor usage, you can run this script to summarize the available sensors:

#!/bin/bash
# Summarize sensor definitions

echo "Sensor Summary:"
sed -n '/<sensor>/,/<\/sensor>/p' models/fruitfly/fruitfly_force.xml | \
grep '<[a-z]' | sed -E 's/.*name="([^"]+)".*/\1/' | sort | uniq -c

echo "\nActuator Summary:"
grep '<motor' models/fruitfly/fruitfly_force.xml | \
sed -E 's/.*name="([^"]+)".*/\1/' | sort | uniq -c

This will give you an overview of the sensors and actuators available in the model, helping you decide which ones to enable for your specific simulation needs.


208-307: Comprehensive asset definitions; monitor performance impact.

The asset definitions are impressively detailed, with specific textures, materials, and meshes for each part of the fruit fly. This level of detail will contribute to a highly realistic model. The separation of materials and meshes allows for flexible texturing and coloring.

However, with over 80 mesh files referenced, there could be an impact on load times and memory usage, especially on less powerful systems.

To assess the potential performance impact, you can run this script to count and summarize the asset definitions:

#!/bin/bash
# Count and summarize asset definitions

echo "Asset Summary:"
echo "Textures: $(grep -c '<texture' models/fruitfly/fruitfly_force.xml)"
echo "Materials: $(grep -c '<material' models/fruitfly/fruitfly_force.xml)"
echo "Meshes: $(grep -c '<mesh' models/fruitfly/fruitfly_force.xml)"

echo "\nMesh file sizes:"
grep '<mesh' models/fruitfly/fruitfly_force.xml | \
sed -E 's/.*file="([^"]+)".*/\1/' | \
xargs -I {} sh -c 'echo "{}: $(du -h {} 2>/dev/null | cut -f1)"'

This will help you identify if any mesh files are particularly large and might need optimization.


310-761: Highly detailed worldbody; consider expert review and performance testing.

The worldbody definition provides an exceptionally detailed representation of the fruit fly's anatomy. The hierarchy and relationships between body parts appear to be anatomically accurate. The inclusion of multiple camera views and tracking sites is well-considered for comprehensive simulation analysis.

However, given the complexity of this structure:

  1. An expert in fruit fly anatomy should verify the accuracy of the model.
  2. Performance testing is recommended to ensure the simulation runs efficiently with this level of detail.

To help with the performance assessment, you can run this script to count the number of bodies and geoms:

#!/bin/bash
# Count bodies and geoms in the worldbody

echo "Worldbody Structure Summary:"
echo "Number of bodies: $(grep -c '<body' models/fruitfly/fruitfly_force.xml)"
echo "Number of geoms: $(grep -c '<geom' models/fruitfly/fruitfly_force.xml)"
echo "Number of joints: $(grep -c '<joint' models/fruitfly/fruitfly_force.xml)"
echo "Number of cameras: $(grep -c '<camera' models/fruitfly/fruitfly_force.xml)"

echo "\nBody Hierarchy:"
grep '<body' models/fruitfly/fruitfly_force.xml | sed -E 's/.*name="([^"]+)".*/\1/' | \
awk '{print sprintf("%*s%s", 2*length($0), "", $0)}'

This will give you an overview of the model's complexity and help identify areas that might need optimization if performance issues arise.


198-206: High-quality visual settings; consider performance for real-time use.

The visual settings are configured for high-quality rendering, with a 4K offscreen buffer and 2048px shadow resolution. The lighting and force visualization settings are well-tuned for clear model visualization. These are excellent for detailed analysis or high-quality video output.

However, if real-time performance is a requirement, these high-quality settings might be computationally expensive.

To assess the impact of these visual settings, you could run performance tests with different quality levels. Here's a script to extract the current visual settings for reference:

✅ Verification successful

Visual settings verified; no changes detected.

The visual settings in models/fruitfly/fruitfly_force.xml (lines 198-206) match the expected high-quality configuration:

  • 4K offscreen buffer (offwidth="3840", offheight="2160")
  • Shadow size of 2048 and offsamples of 24
  • Well-configured headlight and force visualization settings
  • Appropriate scaling for joints and frames

These settings are ideal for detailed analysis and high-quality rendering. If real-time performance becomes a requirement, consider optimizing these parameters to balance quality and computational efficiency.

🏁 Scripts executed

The following scripts were executed for the analysis:

Script:

#!/bin/bash
# Extract visual settings

echo "Visual Settings Summary:"
sed -n '/<visual>/,/<\/visual>/p' models/fruitfly/fruitfly_force.xml | \
grep -v '<visual>\|</visual>' | sed 's/^[ \t]*//'

Length of output: 3318


Script:

#!/bin/bash
# Extract visual settings

echo "Visual Settings Summary:"
sed -n '/<visual>/,/<\/visual>/p' models/fruitfly/fruitfly_force.xml | \
grep -v '<visual>\|</visual>' | sed 's/^[ \t]*//'

Length of output: 454


8-196: Detailed default settings require expert verification.

The default settings are impressively detailed, covering various aspects of the fruit fly's anatomy including the body, head, wings, legs, and joints. The use of classes for different body parts promotes good organization and reusability. The joint settings (stiffness, damping, range limits) appear to be carefully chosen.

However, given the complexity and specificity of these settings, it would be beneficial to have them reviewed by an expert in fruit fly anatomy and biomechanics to ensure their accuracy and appropriateness for the simulation goals.

Consider having a domain expert review these settings. In the meantime, you can run this script to extract and summarize the joint ranges for a quick overview:

demos/Test_hydra.py (1)

70-70: Inconsistent base_path usage in function call

In the call to stac_mjx.viz_stac, the base_path is set to Path.cwd().parent, which differs from the base_path defined earlier. Ensure this is intentional or update it for consistency.

stac_mjx/stac.py (1)

Line range hint 264-266: Ensure correct batching behavior in vmap for mjx_setup function

In the vmap call for mjx_setup, using in_axes=(0, None) implies that kp_data is batched and mj_model is shared across batches. Since mjx_model and mjx_data are being returned for each batch, verify that this is necessary and that it does not introduce significant memory overhead. If mjx_model remains the same across batches, consider returning only mjx_data.

Comment on lines 1 to 3
fit_path: "fit_free.p"
transform_path: "transform_free.p"
data_path: "tests/data/test_rodent_mocap_1000_frames.nwb"
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💡 Codebase verification

Issues found with path configurations and data paths.

  1. Relative Paths Usage:

    • Both fit_path and transform_path are defined using relative paths in:
      • configs/stac/stac_fly_treadmill.yaml
      • configs/stac/stac_fly_tethered.yaml

    Recommendation: Use absolute paths to ensure consistency regardless of the working directory.

  2. Data Path Pointing to Test Dataset:

    • The data_path is set to tests/data/test_rodent_mocap_1000_frames.nwb in:
      • tests/fixtures/datasets.py
      • tests/configs/stac/test_stac.yaml
      • demos/view_nwb.ipynb
      • configs/stac/stac_fly_treadmill.yaml
      • configs/stac/stac.yaml
      • configs/stac/stac_fly_tethered.yaml

    Recommendation: Verify if using a test dataset in these configurations is intentional. If these files are meant for production, update the data_path to reference the appropriate production dataset.

🔗 Analysis chain

Consider using absolute paths and verify the use of test data.

  1. The fit_path and transform_path use relative paths. Consider using absolute paths to ensure consistency regardless of the working directory.

  2. The data_path points to a test dataset (test_rodent_mocap_1000_frames.nwb). Verify if this is intentional or if it should be replaced with a production dataset for the tethered fly experiment.

To check for other occurrences of these paths and potential inconsistencies, run:

🏁 Scripts executed

The following scripts were executed for the analysis:

Script:

#!/bin/bash
# Search for occurrences of the defined paths
echo "Occurrences of fit_path:"
rg "fit_free\.p"
echo "Occurrences of transform_path:"
rg "transform_free\.p"
echo "Occurrences of data_path:"
rg "test_rodent_mocap_1000_frames\.nwb"

Length of output: 19585


Script:

#!/bin/bash
# Corrected script to search for occurrences of the defined paths
echo "Occurrences of fit_path:"
rg "fit_free\.p"
echo "Occurrences of transform_path:"
rg "transform_free\.p"
echo "Occurrences of data_path:"
rg "test_rodent_mocap_1000_frames\.nwb"

Length of output: 1158

Comment on lines 1 to 3
fit_path: "fit_free.p"
transform_path: "transform_free.p"
data_path: "tests/data/test_rodent_mocap_1000_frames.nwb"
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🛠️ Refactor suggestion

Consider using absolute paths and more portable file formats.

The current configuration uses relative paths, which might cause issues if the working directory changes. Additionally, the use of pickle files (.p) for fit and transform paths may not be the most portable option.

Suggestions:

  1. Use absolute paths or environment variables for better reliability.
  2. Consider using a more portable format like JSON or YAML for fit and transform files.
  3. Clarify why a test data file is being used in what appears to be a production configuration.

Example of using environment variables:

fit_path: ${STAC_FIT_PATH:-"fit_free.json"}
transform_path: ${STAC_TRANSFORM_PATH:-"transform_free.json"}
data_path: ${STAC_DATA_PATH:-"/path/to/production/data.nwb"}

This allows for easy overrides while providing default values.

Comment on lines 9 to 12
mujoco:
solver: "newton"
iterations: 1
ls_iterations: 4
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💡 Codebase verification

🛠️ Refactor suggestion

Add documentation for MuJoCo solver settings.

The MuJoCo configuration parameters in stac_mjx/stac.py are currently set as follows:

mujoco:
  solver: "newton"
  iterations: 1
  ls_iterations: 4

To enhance clarity and maintainability:

  • Solver Choice: Add a comment explaining why the "newton" solver was selected, considering its accuracy and computational implications.
  • Iterations: Clarify why iterations is set to 1 and how it affects convergence.
  • LS Iterations: Provide context for the purpose of ls_iterations and its impact on the simulation.

This documentation will aid in understanding the configuration choices and facilitate future reviews and potential adjustments.

🔗 Analysis chain

Review and document MuJoCo solver settings.

The current MuJoCo configuration uses the Newton solver with only 1 iteration and 4 ls_iterations (presumably line search iterations). This configuration might need review:

  1. The Newton solver is accurate but can be computationally expensive. Is this the best choice for your simulation?
  2. Setting iterations to 1 is unusual and might not provide sufficient convergence. Was this intentional?
  3. The purpose of ls_iterations is not clear without context.

Let's search for documentation or usage of these MuJoCo parameters:

Consider adding comments to explain the rationale behind these settings:

mujoco:
  # Newton solver chosen for accuracy at the cost of computation time
  solver: "newton"
  # Single iteration used to [explain reason]
  iterations: 1
  # 4 line search iterations to [explain reason]
  ls_iterations: 4
🏁 Scripts executed

The following scripts were executed for the analysis:

Script:

#!/bin/bash
# Search for MuJoCo solver documentation or usage
rg --type python -A 10 "mujoco.*solver|mujoco.*iterations|mujoco.*ls_iterations"

Length of output: 187


Script:

#!/bin/bash
# Search for MuJoCo solver documentation or usage across all file types
rg "mujoco.*solver|mujoco.*iterations|mujoco.*ls_iterations" -A 10

Length of output: 819

Comment on lines 5 to 7
n_fit_frames: 1800
skip_fit: False
skip_transform: False
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💡 Codebase verification

Inconsistent Parameter Naming for skip_fit and skip_transform

The configuration configs/stac/stac_fly_treadmill.yaml uses skip_fit and skip_transform, which are inconsistent with other configuration files that utilize skip_fit_offsets and skip_ik_only.

Suggested Actions:

  • Rename skip_fit to skip_fit_offsets and skip_transform to skip_ik_only in stac_fly_treadmill.yaml and stac_fly_tethered.yaml to align with the rest of the codebase.
  • Verify that these changes are reflected correctly in the implementation to prevent potential bugs.
🔗 Analysis chain

Clarify the n_fit_frames value and the purpose of skip flags.

The current configuration sets n_fit_frames to 1800 and both skip_fit and skip_transform to False.

Questions for clarification:

  1. What is the significance of 1800 frames for fitting? Is this value based on specific requirements or constraints?
  2. Under what circumstances would skip_fit or skip_transform be set to True?
  3. Are there any performance implications of always performing both fitting and transformation?

To better understand the usage of these parameters, let's search for their occurrences in the codebase:

This will help us understand how these parameters are used and potentially provide context for their values.

🏁 Scripts executed

The following scripts were executed for the analysis:

Script:

#!/bin/bash
# Search for usage of n_fit_frames, skip_fit, and skip_transform
rg --type python -A 5 "n_fit_frames|skip_fit|skip_transform"

Length of output: 187


Script:

#!/bin/bash
# Search for usage of n_fit_frames, skip_fit, and skip_transform across all files
rg -A 5 "n_fit_frames|skip_fit|skip_transform"

Length of output: 2484


Script:

#!/bin/bash
# Search for usage of skip_fit_offsets and skip_ik_only across all files
rg -A 5 "skip_fit_offsets|skip_ik_only"

Length of output: 2001

@@ -5,7 +5,7 @@
"version": "0.2.0",
"configurations": [
{
"name": "main.py ",
"name": "Python Debugger: Current File",
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⚠️ Potential issue

Consider aligning the configuration name with its actual behavior

The configuration name has been changed to "Python Debugger: Current File", which suggests that it can debug any current Python file. However, the "program" field still specifically points to "${workspaceFolder}/core/main.py".

This inconsistency might lead to confusion for developers using this configuration. Consider either:

  1. Keeping the new name and updating the "program" field to use the current file variable, like so: "program": "${file}", or
  2. Reverting the name to something more specific like "Debug main.py" to accurately reflect the current configuration.

Here's a suggested change for option 1:

 {
     "configurations": [
         {
-            "name": "Python Debugger: Current File",
+            "name": "Python: Current File",
             "type": "debugpy",
             "request": "launch",
-            "program": "${workspaceFolder}/core/main.py",
+            "program": "${file}",
             "console": "integratedTerminal",
             "args": [
                 "test.ik_only=True"
             ]
         }
     ]
 }

This change would make the configuration truly work for the current file, aligning with the new name.

📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
"name": "Python Debugger: Current File",
"name": "Python: Current File",
"type": "debugpy",
"request": "launch",
"program": "${file}",
"console": "integratedTerminal",
"args": [
"test.ik_only=True"
]


os.environ['CUDA_VISIBLE_DEVICES'] = cfg.gpu # Use GPU 1
# Choose parent directory as base path to make relative pathing easier
base_path = Path('/home/eabe/Research/MyRepos/stac-mjx') #Path.cwd().parent
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🛠️ Refactor suggestion

Avoid hardcoded file paths; make base_path configurable

Using a hardcoded absolute path for base_path may cause portability issues. Consider retrieving base_path from the configuration or computing it relative to the current working directory.

Optionally, you can use:

base_path = Path.cwd().parent

Or retrieve from the configuration:

base_path = Path(cfg.base_path)

# sorted_kp_names = kp_names
# kp_data = model_cfg['MOCAP_SCALE_FACTOR']*kp_data_all.copy()

import stac_mjx.io_dict_to_hdf5 as ioh5
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🛠️ Refactor suggestion

Move import statement to the top of the file

It's best practice to place all import statements at the top of the file. This improves readability and complies with PEP8 guidelines.

Move the import to the top of the file:

import stac_mjx.io_dict_to_hdf5 as ioh5

# kp_data = model_cfg['MOCAP_SCALE_FACTOR']*kp_data_all.copy()

import stac_mjx.io_dict_to_hdf5 as ioh5
data_dir = Path('/data/users/eabe/biomech_model/Flybody/datasets/Tuthill_data/')
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🛠️ Refactor suggestion

Avoid hardcoded file paths; make data_dir configurable

The hardcoded path for data_dir may not exist on other systems. Consider adding data_dir to your configuration and accessing it via cfg.

Example:

data_dir = Path(cfg.dataset.data_dir)

)

# set args
data_path = base_path / "transform.p"
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🛠️ Refactor suggestion

Consider using transform_path instead of redefining data_path

Since transform_path is already returned from stac_mjx.run_stac, consider using it directly instead of reconstructing data_path.

Update the code:

data_path = transform_path

from pathlib import Path
from jax import numpy as jp
import mediapy as media
import pandas as pd
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⚠️ Potential issue

Remove unused import of pandas

The import pandas as pd at line 7 is unused in this script. Removing it will improve code cleanliness.

Apply this diff to remove the unused import:

-import pandas as pd 
📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
import pandas as pd
🧰 Tools
🪛 Ruff

7-7: pandas imported but unused

Remove unused import: pandas

(F401)

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