emodel_generalisation.exemplars

“Module to create exemplar morphologies from a morphological population.

Functions

bin_data(distances, data, path_bins[, tpe])	Bin data using distances.
build_ais_diameter_model(morphology_paths[, ...])	Build the AIS model by fitting first sections of axons.
build_soma_model(morphology_paths)	Build soma model.
extract_ais_diameters(morphologies)	Produce an iterator on ais diameters.
extract_ais_path_distances(morphologies)	Produce an iterator on ais diameters.
generate_exemplars(df[, figure_folder, ...])	Create a yaml file with data to produce exemplar morphologies to optimise.
get_ais(neuron)	Get the axon initial section of a neuron.
get_best_exemplar(df[, bin_params, ...])	Find the best exemplar morphology to be most average in surface area profile.
get_bins(bin_params)	Compute path lengths bins from parameters.
get_surface_density(neuron_path, path_bins)	Compute the binned surface densities of a neuron.
get_surface_profile(df, path_bins[, ...])	Get surface profile.
plot_ais_taper(data, model, ax)	Plot AIS taper.
plot_ais_taper_models(models[, pdf_filename])	Create a pdf with all models of AIS and datapoints.
plot_soma_shape_models(models[, pdf_filename])	Plot soma shape models (surface area and radii).
plot_surface_comparison(surf_df, df[, ...])	Plot comparison of surface areas and median scores.
taper_function(length, strength, ...)	Function to model tappers AIS.

emodel_generalisation.exemplars.bin_data(distances, data, path_bins, tpe='area')

Bin data using distances.

emodel_generalisation.exemplars.build_ais_diameter_model(morphology_paths, bin_size=2, total_length=60, with_taper=False)

Build the AIS model by fitting first sections of axons.

Parameters:

• morphology_paths (list) – list of paths to morphologies

• bin_size (float) – size of bins (in unit length) for smoothing of diameters

• total_length (flow) – length of AIS

emodel_generalisation.exemplars.build_soma_model(morphology_paths)

Build soma model.

Using only surface area for now.

emodel_generalisation.exemplars.extract_ais_diameters(morphologies)

Produce an iterator on ais diameters.

emodel_generalisation.exemplars.extract_ais_path_distances(morphologies)

Produce an iterator on ais diameters.

emodel_generalisation.exemplars.generate_exemplars(df, figure_folder='exemplar_figures', with_plots=True, surface_percentile=50, bin_params=None)

Create a yaml file with data to produce exemplar morphologies to optimise.

emodel_generalisation.exemplars.get_ais(neuron)

Get the axon initial section of a neuron.

emodel_generalisation.exemplars.get_best_exemplar(df, bin_params=None, surface_percentile=50)

Find the best exemplar morphology to be most average in surface area profile.

emodel_generalisation.exemplars.get_bins(bin_params)

Compute path lengths bins from parameters.

emodel_generalisation.exemplars.get_surface_density(neuron_path, path_bins, neurite_type='basal', tpe='area')

Compute the binned surface densities of a neuron.

emodel_generalisation.exemplars.get_surface_profile(df, path_bins, neurite_type='basal', morphology_path='path', tpe='area')

Get surface profile.

emodel_generalisation.exemplars.plot_ais_taper(data, model, ax)

Plot AIS taper.

emodel_generalisation.exemplars.plot_ais_taper_models(models, pdf_filename='AIS_models.pdf')

Create a pdf with all models of AIS and datapoints.

emodel_generalisation.exemplars.plot_soma_shape_models(models, pdf_filename='soma_shape_models.pdf')

Plot soma shape models (surface area and radii).

emodel_generalisation.exemplars.plot_surface_comparison(surf_df, df, pdf_filename='surface_profile.pdf', surface_percentile=50)

Plot comparison of surface areas and median scores.

emodel_generalisation.exemplars.taper_function(length, strength, taper_scale, terminal_diameter)

Function to model tappers AIS.