File visuals.py has 3428 lines of code (exceeds 250 allowed). Consider refactoring.

Open

"""General plotting functions for the deepof package."""
# @author lucasmiranda42
# encoding: utf-8
# module deepof

File data.py has 2178 lines of code (exceeds 250 allowed). Consider refactoring.

Open

"""Data structures for preprocessing and wrangling of motion tracking output data. This is the main module handled by the user.

There are three main data structures to pay attention to:
- :class:`~deepof.data.Project`, which serves as a configuration hub for the whole pipeline
- :class:`~deepof.data.Coordinates`, which acts as an intermediary between project configuration and data, and contains

File utils.py has 2050 lines of code (exceeds 250 allowed). Consider refactoring.

Open

# @author lucasmiranda42
# encoding: utf-8
# module deepof

"""Functions and general utilities for the deepof package."""

File models.py has 1650 lines of code (exceeds 250 allowed). Consider refactoring.

Open

"""deep autoencoder models for unsupervised pose detection.

- VQ-VAE: a variational autoencoder with a vector quantization latent-space (https://arxiv.org/abs/1711.00937).
- VaDE: a variational autoencoder with a Gaussian mixture latent-space.
- Contrastive: an embedding model consisting of a single encoder, trained using a contrastive loss.

File model_utils.py has 1355 lines of code (exceeds 250 allowed). Consider refactoring.

Open

# @author lucasmiranda42
# encoding: utf-8
# module deepof

"""Utility functions for both training autoencoder models in deepof.models and tuning hyperparameters with deepof.hypermodels."""

File post_hoc.py has 973 lines of code (exceeds 250 allowed). Consider refactoring.

Open

# @author lucasmiranda42
# encoding: utf-8
# module deepof

"""Data structures and functions for analyzing supervised and unsupervised model results."""

File annotation_utils.py has 763 lines of code (exceeds 250 allowed). Consider refactoring.

Open

# @author lucasmiranda42
# encoding: utf-8
# module deepof

"""Functions and general utilities for supervised pose estimation. See documentation for details."""

Function plot_embeddings has a Cognitive Complexity of 72 (exceeds 5 allowed). Consider refactoring.

Open

def plot_embeddings(
    coordinates: coordinates,
    embeddings: table_dict = None,
    soft_counts: table_dict = None,
    breaks: table_dict = None,

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Function get_coords has a Cognitive Complexity of 68 (exceeds 5 allowed). Consider refactoring.

Open

    def get_coords(
        self,
        center: str = False,
        polar: bool = False,
        speed: int = 0,

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Function preprocess has a Cognitive Complexity of 64 (exceeds 5 allowed). Consider refactoring.

Open

    def preprocess(
        self,
        automatic_changepoints=False,
        handle_ids: str = "concat",
        window_size: int = 25,

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Function tag_annotated_frames has a Cognitive Complexity of 62 (exceeds 5 allowed). Consider refactoring.

Open

def tag_annotated_frames(
    frame,
    font,
    frame_speeds,
    animal_ids,

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Function plot_enrichment has a Cognitive Complexity of 59 (exceeds 5 allowed). Consider refactoring.

Open

def plot_enrichment(
    coordinates: coordinates,
    embeddings: table_dict = None,
    soft_counts: table_dict = None,
    breaks: table_dict = None,

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Function align_deepof_kinematics_with_unsupervised_labels has a Cognitive Complexity of 44 (exceeds 5 allowed). Consider refactoring.

Open

def align_deepof_kinematics_with_unsupervised_labels(
    deepof_project: coordinates,
    kin_derivative: int = 1,
    center: str = "Center",
    align: str = "Spine_1",

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Function retrieve_corners_from_image has a Cognitive Complexity of 43 (exceeds 5 allowed). Consider refactoring.

Open

def retrieve_corners_from_image(
    frame: np.ndarray, arena_type: str, cur_vid: int, videos: list
):  # pragma: no cover
    """Open a window and waits for the user to click on all corners of the polygonal arena.

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Function plot_gantt has a Cognitive Complexity of 39 (exceeds 5 allowed). Consider refactoring.

Open

def plot_gantt(
    coordinates: project,
    experiment_id: str,
    # Time selection parameters
    bin_index: Union[int, str] = None,

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Function _preprocess_time_bins has a Cognitive Complexity of 39 (exceeds 5 allowed). Consider refactoring.

Open

def _preprocess_time_bins(
    coordinates: coordinates,
    bin_size: Union[int, str],
    bin_index: Union[int, str],
    precomputed_bins: np.ndarray = None,

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File deepof_train_embeddings.py has 412 lines of code (exceeds 250 allowed). Consider refactoring.

Open

# @author lucasmiranda42
# encoding: utf-8
# module deepof

"""

Function get_arenas has a Cognitive Complexity of 38 (exceeds 5 allowed). Consider refactoring.

Open

def get_arenas(
    coordinates: coordinates,
    tables: table_dict,
    arena: str,
    arena_dims: int,

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Function animate_skeleton has a Cognitive Complexity of 36 (exceeds 5 allowed). Consider refactoring.

Open

def animate_skeleton(
    coordinates: coordinates,
    experiment_id: str,
    animal_id: list = None,
    center: str = "arena",

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Function load_tables has a Cognitive Complexity of 35 (exceeds 5 allowed). Consider refactoring.

Open

    def load_tables(self, verbose: bool = True) -> Tuple:
        """Load videos and tables into dictionaries.

        Args:
            verbose (bool): If True, prints the progress of data loading.

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