Functions
	check_params ()
	Check the parameters for the computation.
	read_configuration (str configuration_file, str configuration)
	import a specified structure from a configuration file
	read_computation_settings (str settings_file, settings_name)
	import a specified settings from a file
	load_configuration_settings ()
	Load the configuration settings.
	load_computation_settings ()
	Load the computation settings.
	write_dgm_csv (dgm, file_path, plot_name="")
	Save a digram as a csv file, with the option to save a plot as well.
	save_plots ()
	Save plots to a directory.
	save_dgms_as_csv ()
	Save the persistence diagrams as csv files.
	read_sample (str structure_file, str configuration)
	import a specified sample range from a configuration file
	read_oineus_settings (str structure_file, str setting_name)
	import settings for kernel/image/cokernel
	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.
	sample_all_diffusion (str file_path, str format, int sample_step=1)
	Sample all the diffusion paths from a given file.
	weighted_alpha_diode (points)
	Use diode to fill the weighted alpha shapes.
	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.
	oineus_compare (x, y)
	Comparison to compare list of simplicies to get them in the order for oineus.
	sub_complex (pandas.DataFrame points, float z_upper, float z_lower)
	Given the points, and the upper and lower thresholds in the 'z'-component.
	oineus_filtration (pandas.DataFrame points, oineus.ReductionParams params)
	Given a set of points, compute the oineus.filtration of the alpha complex.
	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.
	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.
	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.
	calculate_APF (dgm)
	Calcualte the APF from a diagram.
	compute ()
	test ()
	define various functions needed for later
	plot_APF (numpy.array APF, str name)
	Plot an accumulated persistence function.
	plot_APFs (list APFs, list APF_names, str fig_name)
	Plot a set accumulated persistence function, with automatic colour differentiation.
	plot_PD (dgm, str name)
	Plot a persistence diagram, with a specific colour.
	plot_PDs (dgms, str name)
	Plot several persistence diagrams, with automatic colour choices.
	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.
	generate_plots ()
	Generate plots for a single configuration.
	get_representative_loops (pandas.DataFrame dgm, R, filt)
	Get representative of each homology class in dimension 1.
	get_0_and_1_cycles (loop, filt)
	Get the vertices and edges of a loop.
	loop_composition (verts, filt, points, atom_types)
	Get the composition of a given representative.
	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
	get_neighbour_cells (pandas.DataFrame points, list cycle_vertices, filt)
	Get the neighbouring cells of a given cycle.
	generate_display (pandas.DataFrame points, pandas.DataFrame dgm, int id, filt, neighbours=False)
	Display a representative of a cycle.

Function Documentation

◆ calculate_APF()

toma_functions.calculate_APF ( dgm )

Calcualte the APF from a diagram.

Parameters

dgm	the diargam you want to calculate the APF for

Returns: APF the APF as a list of coordiantes

◆ check_params()

toma_functions.check_params ( )

Check the parameters for the computation.

Check the parameters for the computation

This function checks the parameters for the computation and loads them into the session state.

◆ compute()

toma_functions.compute ( )

◆ convert_simps_to_oineus()

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.

Parameters

simplices a list of simplices from diode

Returns: oin_simps a list of oineus simplices

◆ generate_display()

toma_functions.generate_display	(	pandas.DataFrame	points,
		pandas.DataFrame	dgm,
		int	id,
			filt,
			neighbours = False )

Display a representative of a cycle.

Parameters

points	pandas.DataFrame of the atoms
dgm	pandas.DataFrame of the representatives of cycles
id	int corresponding to the id of the cycle you want to visualise

Returns: fig plotly.express figure displaying the ring

◆ generate_plots()

toma_functions.generate_plots ( )

Generate plots for a single configuration.

Generate plots for a single configuration

This function generates persistence diagrams and APF plots for a single configuration. It creates empty lists to store the plots, then populates them based on enabled options.

The following plots can be generated:

Persistence diagrams in dimensions 0,1,2 if enabled via pd0, pd1, pd2 flags
Kernel/image/cokernel persistence diagrams if those computations are enabled
APF plots in dimensions 0,1,2 if enabled via apf0, apf1, apf2 flags
Kernel/image/cokernel APF plots if those computations are enabled

The plots are stored in st.session_state for later display.

Note: Requires that persistent homology has already been computed and stored in session state

◆ generate_visulisation_df()

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

Parameters

dgm	pandas.DataFrame of the diagram, with columns birth, death, birth simplex, death simplex, cycle rep

◆ get_0_and_1_cycles()

toma_functions.get_0_and_1_cycles	(		loop,
			filt )

Get the vertices and edges of a loop.

Parameters

loop	list containing the ids of the edges in the loop
filt	oineus filtration

Returns: verts, edges lists of vertices and edges in the loop

◆ get_neighbour_cells()

toma_functions.get_neighbour_cells	(	pandas.DataFrame	points,
		list	cycle_vertices,
			filt )

Get the neighbouring cells of a given cycle.

Parameters

points	pandas.DataFrame of the points (atoms) in the structure
cycle_vertices	list of the vertices in the cycle
filt	oineus filtration

Returns: neighbour_0_cells, neighbour_1_cells, neighbour_2_cells lists of neighbouring 0, 1 and 2-cells

◆ get_representative_loops()

toma_functions.get_representative_loops	(	pandas.DataFrame	dgm,
			R,
			filt )

Get representative of each homology class in dimension 1.

Parameters

dgm	pandas.DataFrame containing the diagram and the birth and death simplex id
filt	oineus filtration

Returns: dgm pandas.DataFrame with new column cycle rep

◆ load_computation_settings()

toma_functions.load_computation_settings ( )

Load the computation settings.

Load computation settings from the session state

This function reads computation settings from st.session_state and loads them into the session state. It uses read_computation_settings() to parse the settings file and extract:

n_threads: Number of threads for Oineus to use
save_plots: Whether to save plots
kernel: Whether to compute kernel persistence
image: Whether to compute image persistence
cokernel: Whether to compute cokernel persistence
thickness: Thickness settings

◆ load_configuration_settings()

toma_functions.load_configuration_settings ( )

Load the configuration settings.

Load configuration settings from the session state

This function reads configuration settings from st.session_state and loads them into the session state. It uses read_configuration() to parse the config file and extract:

atoms: ASE Atoms object containing atomic positions and types
radii: Dictionary mapping atom types to their radii
repeat_x/y/z: Number of times to repeat the cell in each direction

The settings are read from:

st.session_state["config_file"]: Path to configuration file
st.session_state["config_name"]: Name of configuration section to use

The parsed settings are stored back in st.session_state as:

st.session_state.atoms
st.session_state.radii
st.session_state.repeat_x/y/z

◆ loop_composition()

toma_functions.loop_composition	(	verts,
		filt,
		points,
		atom_types )

Get the composition of a given representative.

Parameters

loop	list containing the ids of the edges in the loop
points	pandas.DataFrame of the points (atoms) in the structure
atom_types	list of the atom types we are considering (restricting to)

Returns: comp dictionary listing the number of atoms of each type in the loop

◆ oineus_compare()

toma_functions.oineus_compare	(		x,
			y )

Comparison to compare list of simplicies to get them in the order for oineus.

Parameters

x	simplex to compare
y	simplex to compare

Returns: -1 if x<y, 1 otherwise

◆ oineus_filtration()

toma_functions.oineus_filtration	(	pandas.DataFrame	points,
		oineus.ReductionParams	params )

Given a set of points, compute the oineus.filtration of the alpha complex.

Parameters

points	pandas.DataFrame containing points and their weights
params	oineus.ReductionParams which contains the settings for oineus

Returns: K oineus.filtration

◆ oineus_kernel_image_cokernel()

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.

Parameters

points	pandas.DataFrame of the points, with columns 'x','y','z','w' corresponding to the coordinates and weights respectively
kernel	boolean parameter to set if kernel persistence is calculated
image	boolean parameter to set if image persistence is calculated
cokernel	boolean parameter to set if cokernel persistence is calculated
n_threads	number of threads to use in oineus
upper_threshold	float, z-coordinate above which points are in the subcomplex
lower_threshold	float z-coordinate below which points are in the subcomplex

Returns: kicr oineus object which contains the kernel, image, cokernel persistence diagrams as required, can also calculate ones that weren't initially specificed

◆ oineus_pair()

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.

The subcomplex L will consists of all simplices whose vertex are in the subset.

Parameters

points	pandas.DataFrame containing the points and their weights
sub	a list containing the indices of the points on which we construct the subcomplex

Returns: K list of simplices for the entire complex, as needed by oineus; L list of simplices for the subcomplex, as needed by oineus; L_to_K list which tells you how to map the simplices in L to the simplices in K

◆ oineus_process()

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.

Parameters

points	pandas.DataFrame of the points, with colums 'x','y','z','w'
params	oineus.ReudctionParams

Returns: dgm_1 pandas.DataFrame of the indexed dimension 1 diagram; dgm_2 pandas.DataFrame of the indexed dimension 2 diagram

◆ plot_APF()

toma_functions.plot_APF	(	numpy.array	APF,
		str	name )

Plot an accumulated persistence function.

Parameters

APF	- numpy.array with 2 columns of coordinates which define the APF
name	- title for the plot

Returns: a plotly.express figure

◆ plot_APFs()

toma_functions.plot_APFs	(	list	APFs,
		list	APF_names,
		str	fig_name )

Plot a set accumulated persistence function, with automatic colour differentiation.

Parameters

APFs	- accumlated persistence functions to plot

Returns: a matplotlib figure

◆ plot_kernel_image_cokernel_PD()

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.

Parameters

kicr	oineus::KerImCokReduced
d	the dimension to extract (either 1 or 2)
kernel	bool to plot kernel
image	bool to plot image
cokernel	bool to plot cokernel

Returns: figu figure with the chosen PD diagrams

◆ plot_PD()

toma_functions.plot_PD	(		dgm,
		str	name )

Plot a persistence diagram, with a specific colour.

Points at infinity are plotted at a height of 1.1 times the last finite point to die.

Parameters

dgm	- pandas.DataFrame of the diagram
name	- name to use as title of the plot

Returns: a plotly.express figure

◆ plot_PDs()

toma_functions.plot_PDs	(		dgms,
		str	name )

Plot several persistence diagrams, with automatic colour choices.

Points at infinity are plotted at a height of 1.1 times the last finite point to die.

Parameters

dgms	- list of diagrams
name	- title to use for the plot

@results a plotly.express figure

◆ read_computation_settings()

toma_functions.read_computation_settings	(	str	settings_file,
			settings_name )

import a specified settings from a file

Parameters

settings_file	path to the file to use for configurations
settings_name	name of the structure to use

Returns: n_threads, save_plots, kernel, image, cokernel, thickness the number of threads for Oineus to use, wether plots should be saved, compute kernel persistence, compute image persistence, compute cokernel persistence, thickness settings

◆ read_configuration()

toma_functions.read_configuration	(	str	configuration_file,
		str	configuration )

import a specified structure from a configuration file

Parameters

configuration_file	path to the file to use for configurations
configuration	name of the structure to use

Returns: atoms, radii, repeat_x, repeat_y, repeat_z the atom symbols, radii to use for the atoms, repetition in x-axis, repetition in y-axis, repetition in z-axis

◆ read_oineus_settings()

toma_functions.read_oineus_settings	(	str	structure_file,
		str	setting_name )

import settings for kernel/image/cokernel

Parameters

file_path	path to the ini file containing the settings
settings_name	name of the settings to use

Returns: params oineus.ReductionParams with the settings to use

◆ read_sample()

toma_functions.read_sample	(	str	structure_file,
		str	configuration )

import a specified sample range from a configuration file

Parameters

file_path	path to the file to use for configurations
sample_range	name of the structure to use

Returns: sample_start, sample_end, sample_step first sample, last sample, step between each one

◆ sample_all_diffusion()

toma_functions.sample_all_diffusion	(	str	file_path,
		str	format,
		int	sample_step = 1 )

Sample all the diffusion paths from a given file.

Parameters

file_path	: str, path to the file to use for configurations
format	: str, format of the file
backbone_list	: list, list of backbone atoms to use
flow_list	: list, list of flow atoms to use

◆ sample_at()

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.

Parameters

atoms	initial configuration
sample_index	time to sample at
repeat_x	repetition in x dir
repeat_y	repetition in y dir
repeat_z	repetition in z dir @parm atom_list list of atoms in the config
radius_list	list of radii to use for the atoms

Returns: points data frame of the points including the repetitions with columns 'Atom', 'x', 'y', 'z', 'w'

◆ save_dgms_as_csv()

toma_functions.save_dgms_as_csv ( )

Save the persistence diagrams as csv files.

Save the persistence diagrams as csv files, reading the file path from the session state.

◆ save_plots()

toma_functions.save_plots ( )

Save plots to a directory.

Save plots to a directory

This function saves plots to a specified directory. It uses the following from st.session_state:

st.session_state.file_path: Path to the file containing the data
st.session_state.sample_indices: Indices of samples to process
st.session_state.params: Computation settings
st.session_state.kicr_params: KICRcomputation settings

◆ sub_complex()

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.

Parameters

points	pandas.DataFrame containing of the points.
z_upper	float giving the upper threshold, any point above this is in the subcomplex
z_lower	float giving the lower threshold, any point below this is in the subcomplex

Returns: sub_comp list containing the indices of the points on which we build the subcomplex

◆ test()

toma_functions.test ( )

define various functions needed for later

◆ weighted_alpha_diode()

toma_functions.weighted_alpha_diode ( points )

Use diode to fill the weighted alpha shapes.

Parameters

points pandas.DataFrame with columns 'x', 'y', 'z' for coordinates, and column 'w' with the weights.

Returns: weighted alpha shape from diode.

◆ write_dgm_csv()

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.

Parameters

dgm	the diagram to be saved, and plotted
dir	directory in which to save the files
name	name of the structure
save_plot	save the plots as well

Functions

Function Documentation

◆ calculate_APF()

◆ check_params()

◆ compute()

◆ convert_simps_to_oineus()

◆ generate_display()

◆ generate_plots()

◆ generate_visulisation_df()

◆ get_0_and_1_cycles()

◆ get_neighbour_cells()

◆ get_representative_loops()

◆ load_computation_settings()

◆ load_configuration_settings()

◆ loop_composition()

◆ oineus_compare()

◆ oineus_filtration()

◆ oineus_kernel_image_cokernel()

◆ oineus_pair()

◆ oineus_process()

◆ plot_APF()

◆ plot_APFs()

◆ plot_kernel_image_cokernel_PD()

◆ plot_PD()

◆ plot_PDs()

◆ read_computation_settings()

◆ read_configuration()

◆ read_oineus_settings()

◆ read_sample()

◆ sample_all_diffusion()

◆ sample_at()

◆ save_dgms_as_csv()

◆ save_plots()

◆ sub_complex()

◆ test()

◆ weighted_alpha_diode()

◆ write_dgm_csv()