### Install dependencies and run checks

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/contributing.md

After making changes, install all dependencies and run code checks and tests. This ensures code quality and functionality.

```bash
pixi install --all
pixi run check-code
pixi run -e dev test-api
pixi run -e dev test-full
```

--------------------------------

### Example Configuration File Structure

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/usage/data_loading.md

This YAML file defines the mapping of model marker names to internal marker names and specifies which markers and analogs to analyze. Ensure all mandatory fields are present.

```yaml
mapping:
  markers:

    # Foot
    l_heel: "ExampleHeelMarker"
    r_heel: "ExampleHeelMarker"
    l_toe: "ExampleToeMarker"
    r_toe: "ExampleToeMarker"
    l_toe_2: "ExampleSecondaryToeMarker" # optional
    r_toe_2: "ExampleSecondaryToeMarker" # optional
    l_lat_malleoli: "ExampleMalleoliMarker" # optional
    r_lat_malleoli: "ExampleMalleoliMarker" # optional


    # Hip
    l_ant_hip: "ExampleHipMarker"
    r_ant_hip: "ExampleHipMarker"
    l_post_hip: "ExampleHipMarker" # optional
    r_post_hip: "ExampleHipMarker" # optional
    sacrum: "ExampleSacrumMarker"  # optional
    xcom: "ExampleXCOMMarker" # optional

# Entities to analyse
analysis:
  markers: # Markers to analyse
    # List of modelled markers to analyse
    - "ExampleAngle1"
    - "ExampleGRF"
    - "ExampleForce2"
  analogs:
    # List of analog channels to analyse
    - "ExampleForce3"
```

--------------------------------

### Install Gaitalytics with Pip

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/README.rst

Install the Gaitalytics package using pip. This is an alternative installation method.

```shell
pip install gaitalytics

```

--------------------------------

### Gaitalytics Simple Pipeline Example

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/README.rst

A Python script demonstrating a basic Gaitalytics pipeline: loading configuration and trial data, detecting and checking events, segmenting the trial, calculating features, normalizing time, and exporting results.

```python
from gaitalytics import api
# Load configuration (yaml file from above)
config = api.load_config("./pig_config.yaml")

# Load trial from c3d file
trial = api.load_c3d_trial("./test_small.c3d", config)

# Detect events
events = api.detect_events(trial, config)
try:
    # check events
    api.check_events(events)

    # write events to c3d in the same file
    api.write_events_to_c3d("./test_small.c3d", events, './test.c3d')

    # add events to trial
    trial.events = events

    # segment trial to gait cycles. (Events are already existing in the c3d file)
    trial_segmented = api.segment_trial(trial)

    # calculate features
    features = api.calculate_features(trial_segmented, config)

    # normalise time
    trial_normalized = api.time_normalise_trial(trial_segmented)

    # save features
    features.to_netcdf("features.nc")

    # export segmented trial to netcdf
    api.export_trial(trial_segmented, "output_segments")
    api.export_trial(trial_normalized, "output_norm")

except ValueError as e:
    print(e)

```

--------------------------------

### Load c3d File with Configuration

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/usage/data_loading.md

Use this snippet to load a c3d file and its corresponding configuration file. Ensure you have the `gaitalytics` library installed and the configuration file (`config.yaml`) and c3d file (`example.c3d`) are in the specified paths.

```python
from gaitalytics import api

config = api.load_config("./config.yaml")
trial = api.load_c3d_trial("./example.c3d", config)
```

--------------------------------

### Install Gaitalytics with Conda

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/README.rst

Install the Gaitalytics package using Conda. This is a fast installation method.

```shell
conda install gaitalytics -c DartLab-LLUI

```

--------------------------------

### Load Configuration and Detect Gait Events

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/usage/event_detection.md

Illustrates the basic usage of loading configuration and C3D trial data, followed by detecting gait events. Ensure the configuration file and C3D file paths are correct.

```python
from gaitalytics import api

config = api.load_config("./config.yaml")
trial = api.load_c3d_trial("./example.c3d", config)
events = api.detect_events(trial, config)
```

--------------------------------

### Load Configuration, Trial, Segment, and Calculate Features

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/usage/features.md

This snippet demonstrates the core workflow for feature extraction. It involves loading a configuration file, a C3D trial, segmenting the trial, and then calculating the features based on the configuration.

```python
from gaitalytics import api

config = api.load_config("./config.yaml")
trial = api.load_c3d_trial("./example_with_events.c3d", config)
segmented_trial = api.segment_trial(trial)
features = api.calculate_features(segmented_trial, config)
```

--------------------------------

### Load Configuration File

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/usage/data_loading.md

Load the configuration file before loading the C3D trial. This configuration is mandatory for the library to function correctly.

```python
from gaitalytics import api
config = api.load_config("./config.yaml")
```

--------------------------------

### Clone the forked repository

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/contributing.md

Clone your forked repository locally to begin development. Replace YOURGITHUBNAME with your actual GitHub username.

```bash
git clone git@github.com:YOURGITHUBNAME/python-gaitalytics.git
```

--------------------------------

### Run Full Tests

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/CONTRIBUTING.rst

Ensure all tests pass before submitting a pull request. This includes API and full test suites.

```bash
pixi run -e dev test-full
```

```bash
pixi run -e dev test-api
```

--------------------------------

### LinearTimeNormaliser.__init__

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/reference/normalisation.md

Initializes a new instance of the LinearTimeNormaliser class, setting the target number of frames for time normalisation.

```APIDOC
## LinearTimeNormaliser.__init__

### Description
Initializes a new instance of the LinearTimeNormaliser class. This constructor sets up the normaliser with a specified number of frames to which the data will be time-normalised.

### Method
`__init__`

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
* **n_frames** (int) - Optional - The number of frames to time-normalise the data to. Defaults to 100.

### Returns
None

### Request Example
None

### Response
#### Success Response (200)
None

#### Response Example
None
```

--------------------------------

### Simple Gaitalytics Analysis Pipeline

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/index.md

This Python script demonstrates a basic workflow for gait analysis using Gaitalytics, including loading data, detecting and checking events, segmenting trials, calculating features, normalizing time, and exporting results.

```python
from gaitalytics import api
# Load configuration (yaml file from above)
config = api.load_config("./pig_config.yaml")

# Load trial from c3d file
trial = api.load_c3d_trial("./test_small.c3d", config)

# Detect events
events = api.detect_events(trial, config)
try:
    # check events
    api.check_events(events)

    # write events to c3d in the same file
    api.write_events_to_c3d("./test_small.c3d", events, './test.c3d')

    # add events to trial
    trial.events = events

    # segment trial to gait cycles. (Events are already existing in the c3d file)
    trial_segmented = api.segment_trial(trial)

    # calculate features
    features = api.calculate_features(trial_segmented, config)

    # normalise time
    trial_normalized = api.time_normalise_trial(trial_segmented)

    # save features
    features.to_netcdf("features.nc")

    # export segmented trial to netcdf
    api.export_trial(trial_segmented, "output_segments")
    api.export_trial(trial_normalized, "output_norm")

except ValueError as e:
    print(e)
```

--------------------------------

### trial_from_hdf5

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/reference/model.md

Loads trial data from an HDF5 file, capable of loading both single trials and segmented trials.

```APIDOC
## trial_from_hdf5

### Description
Loads trial data from an HDF5 file. Can be used to load a single trial or a segmented trial after saving it.

### Method
`trial_from_hdf5`

### Parameters
#### Path Parameters
- **file_path** (Path) - Required - The path to the HDF5 file or folder with the expected structure.

### Returns
- **Trial | TrialCycles**: A new instance of the Trial class if file_path is a single file, or a new instance of the TrialCycles class if file_path is a folder.
```

--------------------------------

### Minimal Gaitalytics Configuration

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/README.rst

A minimal YAML configuration file for Gaitalytics, defining markers for analysis and mapping them to specific labels.

```yaml
analysis:
  markers:
    - "LHipAngles"
    - "LKneeAngles"
    - "LAnkleAngles"
    - "LPelvisAngles"
    - "LThoraxAngles"
mapping:
  markers:
    # Foot
    l_heel: "LHEE"
    r_heel: "RHEE"
    l_toe: "LTOE"
    r_toe: "RTOE"

    # Hip
    l_ant_hip: "LASI"
    r_ant_hip: "RASI"
    l_post_hip: "LPSI"
    r_post_hip: "RPSI"
    sacrum: "SACR"

```

--------------------------------

### Commit and push changes

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/contributing.md

Commit your changes with a descriptive message and push the branch to your GitHub fork. This prepares your changes for a pull request.

```bash
git add .
git commit -m "Your detailed description of your changes."
git push origin name-of-your-bugfix-or-feature
```

--------------------------------

### TrialCycles.get_cycles_per_context

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/reference/model.md

Retrieves all cycles belonging to a specific context within the segmented trial.

```APIDOC
## TrialCycles.get_cycles_per_context

### Description
Gets all cycles from the segmented trial for a specific context.

### Method
`get_cycles_per_context`

### Parameters
#### Path Parameters
- **context** (str) - Required - The context of the cycles.

### Returns
- **dict[int, Trial]**: A dictionary containing all cycles for the specified context, keyed by cycle number.
```

--------------------------------

### Loading Trial Data from HDF5 in Gaitalytics

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/usage/export.md

Use `model.trial_from_hdf5()` to load data previously exported using `to_hdf5()`. This function reconstructs the `Trial` or `TrialCycles` objects.

```python
from gaitalytics import api, model

trial = model.trial_from_hdf5("./export_trial.h5")
segmented_trial = model.trial_from_hdf5("./export_segmented_trial.h5")
```

--------------------------------

### Store Detected Events to the Existing C3D File

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/usage/event_detection.md

Illustrates overwriting the existing C3D file with the detected gait events. Use with caution, as this modifies the original file.

```python
from gaitalytics import api

events = api.detect_events(trial)
api.write_events_to_c3d("./example.c3d", events, './example.c3d')
```

--------------------------------

### Time Normalise Trial Data

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/usage/normalisation.md

Loads configuration and C3D trial data, segments it, and then time normalises the segmented trial. This is useful for comparing trials of different durations.

```python
from gaitalytics import api

config = api.load_config("./config.yaml")
trial = api.load_c3d_trial("./example_with_events.c3d", config)
segmented_trial = api.segment_trial(trial)
normalised_trial = api.time_normalise_trial(segmented_trial)
```

--------------------------------

### Exporting Trial Data to HDF5 for Gaitalytics Usage

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/usage/export.md

Use `to_hdf5()` to export `Trial` or `TrialCycles` objects for later use within Gaitalytics. This method stores data in a format optimized for Gaitalytics.

```python
from gaitalytics import api

config = api.load_config("./config.yaml")
trial = api.load_c3d_trial("./example_with_events.c3d", config)
trial.to_hdf5("./export_trial.h5")

segmented_trial = api.segment_trial(trial)
segmented_trial.to_hdf5("./export_segmented_trial.h5")
```

--------------------------------

### Segment Gait Data (Heel Strike to Heel Strike)

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/usage/segmentation.md

Segments a loaded trial into gait cycles based on heel strike events. Ensure the trial data contains gait events.

```python
from gaitalytics import api

config = api.load_config("./config.yaml")
trial = api.load_c3d_trial("./example_with_events.c3d", config)
segmented_trial = api.segment_trial(trial)
```

--------------------------------

### Trial.get_all_data

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/reference/model.md

Retrieves all data arrays stored within the trial, organized by category.

```APIDOC
## Trial.get_all_data

### Description
Gets all data from the trial.

### Method
`get_all_data`

### Returns
- **dict[DataCategory, DataArray]**: A dictionary containing all the data arrays, keyed by their DataCategory.
```

--------------------------------

### Trial.add_data

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/reference/model.md

Adds data to the trial, categorized by DataCategory.

```APIDOC
## Trial.add_data

### Description
Adds data to the trial.

### Method
`add_data`

### Parameters
#### Path Parameters
- **category** (DataCategory) - Required - The category of the data.
- **data** (DataArray) - Required - The data array to be added.
```

--------------------------------

### TrialCycles.get_all_cycles

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/reference/model.md

Retrieves all cycles stored within the segmented trial, organized by context and cycle ID.

```APIDOC
## TrialCycles.get_all_cycles

### Description
Gets all cycles from the segmented trial.

### Method
`get_all_cycles`

### Returns
- **dict[str, dict[int, Trial]]**: A nested dictionary containing all cycles. The outer dictionary is keyed by context, and the inner dictionary is keyed by cycle number.
```

--------------------------------

### Exporting Trial Data to NetCDF for External Use

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/usage/export.md

Use `api.export_trial()` to export `Trial` or `TrialCycles` objects to a netCDF file for use with external libraries like xarray. This is suitable for data intended for analysis outside of Gaitalytics.

```python
from gaitalytics import api

config = api.load_config("./config.yaml")
trial = api.load_c3d_trial("./example_with_events.c3d", config)
api.export_trial(trial, "./export_trial.nc")

segmented_trial = api.segment_trial(trial)
api.export_trial(segmented_trial, "./export_segmented_trial.nc")
```

--------------------------------

### Check Detected Gait Events for Errors

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/usage/event_detection.md

Demonstrates how to use the `check_events` function to validate the sequence of detected gait events. Catches and prints `ValueError` if sequence errors are found.

```python
from gaitalytics import api

events = api.detect_events(trial)
try:
    api.check_events(events)
except ValueError as e:
    print(f"Event errors {e}")
```

--------------------------------

### MappingConfigs Class

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/reference/mapping.md

A class for reading and managing marker mapping configurations from a YAML file. It allows retrieval of markers and analogs for analysis.

```APIDOC
## gaitalytics.mapping.MappingConfigs

A class for reading the mapping configuration file.

This class provides methods to read the mapping configuration file and get
the markers and analogs for analysis.

### __init__(config_path: Path)

Initializes a new instance of the MappingConfigs class.

Reads the yaml file into memory.

#### Parameters

- **config_path** (Path) - Required - The path to the configuration file.

### get_analogs_analysis() -> list[str]

Gets the analogs for analysis.

#### Returns

list[str] - A list of analog names to be used for analysis
if present in the config file, otherwise an empty list.

#### Raises

- **ValueError** - If the analysis section is missing in the config file.

### get_marker_mapping(marker: MappedMarkers) -> str

Gets the mapping of markers.

#### Parameters

- **marker** (MappedMarkers) - Required - The marker to get the mapping for.

#### Returns

str - The mapped marker name if present in the config file.

#### Raises

- **ValueError** - If sections in the mapping are missing in the config file.

### get_markers_analysis() -> list[str]

Gets the markers for analysis.

#### Returns

list[str] - A list of marker names to be used for analysis
if present in the config file, otherwise an empty list.

#### Raises

- **ValueError** - If the analysis section is missing in the config file.
```

--------------------------------

### Trial.add_channel

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/reference/model.md

Adds a channel (data array) to the trial, categorized by DataCategory.

```APIDOC
## Trial.add_channel

### Description
Adds a channel to the trial.

### Method
`add_channel`

### Parameters
#### Path Parameters
- **category** (DataCategory) - Required - The category of the data.
- **data** (DataArray) - Required - The data array to be added.
```

--------------------------------

### Loading NetCDF Data with Xarray

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/usage/export.md

Load netCDF files exported by `api.export_trial()` using the `xarray.load_dataarray` function. This allows for easy integration with xarray for further analysis.

```python
import xarray as xr

trial = xr.load_dataarray("./export_trial.nc")
segmented_trial = xr.load_dataarray("./export_segmented_trial.nc")
```

--------------------------------

### Store Detected Events to a New C3D File

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/usage/event_detection.md

Shows how to write the detected gait events into a new C3D file. The function reloads the original file and saves the events to the specified output path.

```python
from gaitalytics import api

events = api.detect_events(trial)
api.write_events_to_c3d("./example.c3d", events, './example_with_events.c3d')
```

--------------------------------

### BaseTrial.to_hdf5

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/reference/model.md

Saves the trial data to an HDF5 file. This method is part of the abstract base class for trials, providing a common interface for saving data.

```APIDOC
## BaseTrial.to_hdf5

### Description
Saves the trial data to an HDF5 file.

### Method
`to_hdf5`

### Parameters
#### Path Parameters
- **file_path** (Path) - Required - The path to the HDF5 file.
- **base_group** (str | None) - Optional - The base group to save the data. Defaults to None, saving data to the root of the file.

### Raises
- **FileExistsError**: If the file already exists.
- **ValueError**: If the trial is a segmented trial and the file path is a single file.
- **ValueError**: If the trial is a trial and the file path is a folder.
```

--------------------------------

### Create a new branch for development

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/contributing.md

Create a new branch for your bug fix or feature development. This isolates your changes from the main codebase.

```bash
git checkout -b name-of-your-bugfix-or-feature
```

--------------------------------

### Trial.get_data

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/reference/model.md

Retrieves a specific data array from the trial based on its category.

```APIDOC
## Trial.get_data

### Description
Gets the data from the trial for a specific category.

### Method
`get_data`

### Parameters
#### Path Parameters
- **category** (DataCategory) - Required - The category of the data to retrieve.
```

--------------------------------

### Time Normalisation Function

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/usage/normalisation.md

Demonstrates how to use the `time_normalise_trial` function to normalise segmented trial data. The function returns a `TrialCycles` object containing normalised trials.

```APIDOC
## `gaitalytics.api.time_normalise_trial()`

### Description
Normalises segmented trial data to a specified number of frames. This function is useful for comparing trials of different durations.

### Method
`api.time_normalise_trial(segmented_trial, n_frames=100)`

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
- **segmented_trial** (`gaitalytics.model.TrialCycles`) - Required - The segmented trial data to be normalised.
- **n_frames** (int) - Optional - The target number of frames for the normalised time series. Defaults to 100.

### Request Example
```python
from gaitalytics import api

config = api.load_config("./config.yaml")
trial = api.load_c3d_trial("./example_with_events.c3d", config)
segmented_trial = api.segment_trial(trial)
normalised_trial = api.time_normalise_trial(segmented_trial, n_frames=150)
```

### Response
#### Success Response (200)
- **normalised_trial** (`gaitalytics.model.TrialCycles`) - A dictionary where keys are cycle numbers and values are `Trial` objects, each normalised to `n_frames`.

#### Response Example
```json
{
  "1": {
    "frames": [
      { "marker_data": { "L.Shoulder": [1.0, 2.0, 3.0] } },
      { "marker_data": { "L.Shoulder": [1.1, 2.1, 3.1] } },
      // ... more frames up to n_frames
    ]
  },
  "2": {
    "frames": [
      { "marker_data": { "L.Shoulder": [1.5, 2.5, 3.5] } },
      // ... more frames up to n_frames
    ]
  }
}
```
```

--------------------------------

### TrialCycles.add_cycle

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/reference/model.md

Adds a cycle segment to the segmented trial, identified by context and cycle ID.

```APIDOC
## TrialCycles.add_cycle

### Description
Adds a Cycle to the segmented trial.

### Method
`add_cycle`

### Parameters
#### Path Parameters
- **context** (str) - Required - The context of the cycle.
- **cycle_id** (int) - Required - The id of the cycle.
- **segment** (Trial) - Required - The segment (Trial object) to be added.
```

--------------------------------

### Trial.events

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/reference/model.md

Retrieves the events associated with the trial as a pandas DataFrame.

```APIDOC
## Trial.events

### Description
Gets the events in the trial.

### Method
`events` (property)

### Returns
- **DataFrame | None**: A pandas DataFrame containing the events if present, otherwise None.
```

--------------------------------

### TrialCycles.get_cycle

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/reference/model.md

Retrieves a specific cycle from the segmented trial using its context and cycle ID.

```APIDOC
## TrialCycles.get_cycle

### Description
Gets a specific cycle from the segmented trial.

### Method
`get_cycle`

### Parameters
#### Path Parameters
- **context** (str) - Required - The context of the cycle.
- **cycle_id** (int) - Required - The id of the cycle.

### Raises
- **KeyError**: If the cycle does not exist.
```

--------------------------------

### BaseNormaliser.normalise

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/reference/normalisation.md

Abstract method to normalise input data. This serves as a common interface for all normalisers.

```APIDOC
## BaseNormaliser.normalise

### Description
Normalises the input data. This method is intended to be implemented by subclasses to provide specific normalisation strategies.

### Method
`normalise`

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
* **trial** ([Trial](model.md#gaitalytics.model.Trial) | [TrialCycles](model.md#gaitalytics.model.TrialCycles)) - The trial to be normalised.

### Returns
* **Trial** | **TrialCycles** - A new trial containing the normalised data.

### Request Example
None

### Response
#### Success Response (200)
None

#### Response Example
None
```

--------------------------------

### MappedMarkers Enum

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/reference/mapping.md

Defines the standard markers that can be mapped in the Gaitalytics configuration file. Each marker is represented as a string.

```APIDOC
## gaitalytics.mapping.MappedMarkers

This defines the markers that are mapped in the configuration file.

### Members

- **L_HEEL** (str) - Left heel marker
- **R_HEEL** (str) - Right heel marker
- **L_TOE** (str) - Left toe marker
- **R_TOE** (str) - Right toe marker
- **L_ANKLE** (str) - Left ankle marker
- **R_ANKLE** (str) - Right ankle marker
- **L_TOE_2** (str) - Left toe 2 marker
- **R_TOE_2** (str) - Right toe 2 marker
- **L_ANT_HIP** (str) - Left anterior hip marker
- **R_ANT_HIP** (str) - Right anterior hip marker
- **L_POST_HIP** (str) - Left posterior hip marker
- **R_POST_HIP** (str) - Right posterior hip marker
- **SACRUM** (str) - Sacrum marker
- **XCOM** (str) - Extrapolated center of mass marker
```

--------------------------------

### Define a New Feature Class

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/extend/features.md

Create a new class that inherits from PointDependentFeature to implement custom feature calculations. Override the _calculate method to define your feature's logic.

```python
from gaitalytics.features import PointDependentFeature

class NewFeature(PointDependentFeature):

    def _calculate(self, cycle):
        # Your feature calculation here
        return feature_value
```

--------------------------------

### LinearTimeNormaliser.normalise

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/reference/normalisation.md

Normalises the gait data based on time to a fixed number of frames.

```APIDOC
## LinearTimeNormaliser.normalise

### Description
Normalises the data based on time, resampling it to a consistent number of frames. This is useful for comparing trials of different durations.

### Method
`normalise`

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
* **trial** ([Trial](model.md#gaitalytics.model.Trial) | [TrialCycles](model.md#gaitalytics.model.TrialCycles)) - The trial to be normalised.

### Returns
* **Trial** | **TrialCycles** - A new trial containing the time-normalised data.

### Request Example
None

### Response
#### Success Response (200)
None

#### Response Example
None
```

--------------------------------

### Calculate Custom Features

Source: https://github.com/dart-lab-llui/python-gaitalytics/blob/main/docs/extend/features.md

Include your custom feature class in the list of features to be calculated by gaitalytics.api.calculate_features. This allows Gaitalytics to compute your new feature alongside existing ones.

```python
from gaitalytics import api
from gaitalytics.features import NewFeature

config = api.load_config("./config.yaml")
trial = api.load_c3d_trial("./example.c3d", config)

# Calculate the only the new feature
features = api.calculate_features(trial, config, [NewFeature])

# Calculate all features including the new feature
features = api.calculate_features(trial, config, [gaitalytics.features.TimeSeriesFeatures,
                                                  gaitalytics.features.PhaseTimeSeriesFeatures,
                                                  gaitalytics.features.TemporalFeatures,
                                                  gaitalytics.features.SpatialFeatures,
                                                  NewFeature])
```

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