diameter_synthesis.plotting

Plotting functions.

Functions

cumulative_analysis(original_path, ...[, ...])

Make plots for cumulative distributions.

get_features_all(object1, object2, flist, ...)

Compute features from module mod.

make_cumulative_figures(original_cells, ...)

Make plots for cumulative distributions for a pair of features.

plot_cumulative_distribution(original_cells, ...)

Plot the cumulative distribution of features.

plot_diameter_diff(neuron_name, neuron_new, ...)

Plot original morphology, new one and differences.

plot_distribution_fit(data, model, neurite_types)

Plot the data distribution and its fit.

plot_violins(data[, x, y, hues, ax])

Plot the split violins of all features.

transform2DataFrame(data, pop_names, flist)

Return a DataFrame in the appropriate format from a set of features.

violin_analysis(original_path, ...[, ...])

Plot violin distributions.
diameter_synthesis.plotting.cumulative_analysis(original_path, diametrized_path, out_dir, individual=False, mtypes_file=None, neurite_types=None, ext='.png')

Make plots for cumulative distributions.

diameter_synthesis.plotting.get_features_all(object1, object2, flist, neurite_type)

Compute features from module mod.

diameter_synthesis.plotting.make_cumulative_figures(original_cells, diametrized_cells, feature1, feature2, neurite_types, out_dir, individual=False, figname_prefix='', ext='.png')

Make plots for cumulative distributions for a pair of features.

diameter_synthesis.plotting.plot_cumulative_distribution(original_cells, diametrized_cells, feature1, feature2, neurite_types, step_size=1.0, auto_limit=True)

Plot the cumulative distribution of features.

It plots feature2 with respect to the metric values determined via feature1.

Parameters:

  • original_cells – list of NeuroM objects.

  • diametrized_cells (list) – The new cells with the changed diameters.

  • feature1 – the metric feature.

  • feature2 – the cumulative distribution feature.

  • neurite_types (list[str]) – The list of neurite types to be considered.

  • step_size (float) – The step size of the cumulative histogram.

  • auto_limit (bool) – automatically compute limits.

Examples of metric features (feature1):

  • segment_radial_distances

  • segment_path_distances (not implemented yet)

Examples of cumulative distribution features (feature2):

  • segment_volumes

  • segment_surface_areas (not implemented yet)

diameter_synthesis.plotting.plot_diameter_diff(neuron_name, neuron_new, neurite_types, folder, ext='.png')

Plot original morphology, new one and differences.

diameter_synthesis.plotting.plot_distribution_fit(data, model, neurite_types, fig_name='test', ext='.png', figsize=(5, 4))

Plot the data distribution and its fit.

diameter_synthesis.plotting.plot_violins(data, x='Morphological features', y='Values', hues='Data', ax=None)

Plot the split violins of all features.

diameter_synthesis.plotting.transform2DataFrame(data, pop_names, flist)

Return a DataFrame in the appropriate format from a set of features.

diameter_synthesis.plotting.violin_analysis(original_path, diametrized_path, out_dir, mtypes_file=None, max_cells=200, with_axon=False)

Plot violin distributions.