bycycle.plts.plot_feature_categorical

bycycle.plts.plot_feature_categorical(df_features, param_label, group_by=None, ax=None, **kwargs)

Plot a cycle feature by one or more categories.

Parameters:

df_featurespandas.DataFrame

Dataframe output from compute_features().

param_labelstr

Column name of the parameter of interest in df_features.

group_bystr, optional

Dataframe column name of a grouping variable to split plotting by.

axmatplotlib.Axes, optional

Figure axes upon which to plot.

**kwargs

Keyword arguments to pass into matplotlib.pyplot.scatter.

Notes

Default keyword arguments include:

• xlabel: list, default: [1, 2, 3…]

• ylabel: str, default: param_label

• figsize: tuple of (float, float), default: (10, 10)

• fontsize: float, default: 20

Examples

Plot and compare the rise-decay times of two asine signals:

>>> import numpy as np
>>> import pandas as pd
>>> from neurodsp.sim import sim_oscillation
>>> from bycycle.group import compute_features_2d
>>> fs = 500
>>> sigs = np.array([sim_oscillation(5, fs, 10, cycle='asine', rdsym=0.2),
...                  sim_oscillation(5, fs, 10, cycle='asine', rdsym=0.8)])
>>> features = compute_features_2d(sigs, fs, f_range=(8, 12), return_samples=False, n_jobs=2)
>>> features[0]['group'], features[1]['group'] = 'low', 'high'
>>> df_features = pd.concat([features[0], features[1]])
>>> plot_feature_categorical(df_features, 'time_rdsym', group_by='group')

Examples using bycycle.plts.plot_feature_categorical

3. Cycle-by-cycle analysis of resting state data

3. Cycle-by-cycle analysis of resting state data

4. Running Bycycle on 2D Arrays

4. Running Bycycle on 2D Arrays

5. Running Bycycle on 3D Arrays

5. Running Bycycle on 3D Arrays