toma_functions.py File Reference

Namespaces
namespace	toma_functions

Functions
	toma_functions.check_params ()
	Check the parameters for the computation.
	toma_functions.read_configuration (str configuration_file, str configuration)
	import a specified structure from a configuration file
	toma_functions.read_computation_settings (str settings_file, settings_name)
	import a specified settings from a file
	toma_functions.load_configuration_settings ()
	Load the configuration settings.
	toma_functions.load_computation_settings ()
	Load the computation settings.
	toma_functions.write_dgm_csv (dgm, file_path, plot_name="")
	Save a digram as a csv file, with the option to save a plot as well.
	toma_functions.save_plots ()
	Save plots to a directory.
	toma_functions.save_dgms_as_csv ()
	Save the persistence diagrams as csv files.
	toma_functions.read_sample (str structure_file, str configuration)
	import a specified sample range from a configuration file
	toma_functions.read_oineus_settings (str structure_file, str setting_name)
	import settings for kernel/image/cokernel
	toma_functions.sample_at (str file_path, str format, sample_index, int repeat_x, int repeat_y, int repeat_z, atom_list, radius_list)
	Sample a structure at a particular time, with cell repetitions.
	toma_functions.sample_all_diffusion (str file_path, str format, int sample_step=1)
	Sample all the diffusion paths from a given file.
	toma_functions.weighted_alpha_diode (points)
	Use diode to fill the weighted alpha shapes.
	toma_functions.convert_simps_to_oineus (list simplices)
	Diode is set to create simplices for dionysus, so we need to convert them to the correct type for oineus.
	toma_functions.oineus_compare (x, y)
	Comparison to compare list of simplicies to get them in the order for oineus.
	toma_functions.sub_complex (pandas.DataFrame points, float z_upper, float z_lower)
	Given the points, and the upper and lower thresholds in the 'z'-component.
	toma_functions.oineus_filtration (pandas.DataFrame points, oineus.ReductionParams params)
	Given a set of points, compute the oineus.filtration of the alpha complex.
	toma_functions.oineus_pair (pandas.DataFrame points, list sub)
	Given a set of points, and the points that are in the subset L, construct the complexes and map between them.
	toma_functions.oineus_process (pandas.DataFrame points, oineus.ReductionParams params)
	Given some points with weights, and the number of threads to use, obtain the persistent homology of the weighted alpha complex of these points, using oineus.
	toma_functions.oineus_kernel_image_cokernel (pandas.DataFrame points, oineus.ReductionParams params, float upper_threshold, float lower_threshold)
	Given points, and parameters for oineus, calculate the kernel/image/cokernel persistence as desired.
	toma_functions.calculate_APF (dgm)
	Calcualte the APF from a diagram.
	toma_functions.compute ()
	toma_functions.test ()
	define various functions needed for later
	toma_functions.plot_APF (numpy.array APF, str name)
	Plot an accumulated persistence function.
	toma_functions.plot_APFs (list APFs, list APF_names, str fig_name)
	Plot a set accumulated persistence function, with automatic colour differentiation.
	toma_functions.plot_PD (dgm, str name)
	Plot a persistence diagram, with a specific colour.
	toma_functions.plot_PDs (dgms, str name)
	Plot several persistence diagrams, with automatic colour choices.
	toma_functions.plot_kernel_image_cokernel_PD (kicr, int d, bool codomain, bool kernel, bool image, bool cokernel, str name)
	Plot kernel, image, cokernel on same figure.
	toma_functions.generate_plots ()
	Generate plots for a single configuration.
	toma_functions.get_representative_loops (pandas.DataFrame dgm, R, filt)
	Get representative of each homology class in dimension 1.
	toma_functions.get_0_and_1_cycles (loop, filt)
	Get the vertices and edges of a loop.
	toma_functions.loop_composition (verts, filt, points, atom_types)
	Get the composition of a given representative.
	toma_functions.generate_visulisation_df (pandas.DataFrame dgm, R, filt, points, atom_types)
	generate the pandas.DataFrame containing the information about the points so we can display it
	toma_functions.get_neighbour_cells (pandas.DataFrame points, list cycle_vertices, filt)
	Get the neighbouring cells of a given cycle.
	toma_functions.generate_display (pandas.DataFrame points, pandas.DataFrame dgm, int id, filt, neighbours=False)
	Display a representative of a cycle.