process Namespace Reference

Functions
	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.
	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 (pd.DataFrame points, float z_upper, float z_lower)
	Given the points, and the upper and lower thresholds in the 'z'-component.
	oineus_filtration (pd.DataFrame points, oineus.ReductionParams params)
	Given a set of points, compute the oineus.filtration of the alpha complex.
	oineus_pair (pd.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 (pd.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 (pd.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

Function Documentation

calculate_APF()

process.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

compute()

process.compute

(

)

convert_simps_to_oineus()

process.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

oineus_compare()

process.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()

process.oineus_filtration	(	pd.DataFrame	points,
		oineus.ReductionParams	params )

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

Parameters

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

Returns

K oineus.filtration

oineus_kernel_image_cokernel()

process.oineus_kernel_image_cokernel	(	pd.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	pd.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()

process.oineus_pair	(	pd.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	pd.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()

process.oineus_process	(	pd.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	pd.DataFrame of the points, with colums 'x','y','z','w'
params	oineus.ReudctionParams

Returns

dgm_1 pd.DataFrame of the indexed dimension 1 diagram

dgm_2 pd.DataFrame of the indexed dimension 2 diagram

read_oineus_settings()

process.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()

process.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_at()

process.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'

sub_complex()

process.sub_complex	(	pd.DataFrame	points,
		float	z_upper,
		float	z_lower )

Given the points, and the upper and lower thresholds in the 'z'-component.

Parameters

points	pd.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()

process.test

(

)

define various functions needed for later

weighted_alpha_diode()

process.weighted_alpha_diode

(

points

)

Use diode to fill the weighted alpha shapes.

Parameters

points

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

Returns

weighted alpha shape from diode.