IMP.isd.gmm_tools Namespace Reference

Tools for handling Gaussian Mixture Models. More...

Detailed Description

Tools for handling Gaussian Mixture Models.

Functions
def	decorate_gmm_from_text
	read the output from write_gmm_to_text, decorate as Gaussian and Mass More...
def	draw_points
	given some points (and optional transform), write them to chimera 'bild' format colors flag only applies to ellipses, otherwise it'll be weird More...
def	fit_dirichlet_gmm_to_points
	fit a GMM to some points. More...
def	fit_gmm_to_points
	fit a GMM to some points. More...
def	write_gmm_to_map
	write density map from GMM. More...
def	write_gmm_to_text
	write a list of gaussians to text. More...
def	write_sklearn_gmm_to_map
	write density map directly from sklearn GMM (kinda slow) More...

Function Documentation

def IMP.isd.gmm_tools.decorate_gmm_from_text	(		in_fn,
			ps,
			mdl,
			transform = None,
			radius_scale = 1.0,
			mass_scale = 1.0
	)

read the output from write_gmm_to_text, decorate as Gaussian and Mass

Definition at line 22 of file gmm_tools.py.

def IMP.isd.gmm_tools.draw_points	(		pts,
			out_fn,
			trans = IMP.algebra.get_identity_transformation_3d(),
			use_colors = False
	)

given some points (and optional transform), write them to chimera 'bild' format colors flag only applies to ellipses, otherwise it'll be weird

Definition at line 115 of file gmm_tools.py.

def IMP.isd.gmm_tools.fit_dirichlet_gmm_to_points	(		points,
			n_components,
			mdl,
			ps = [],
			num_iter = 100,
			covariance_type = 'full',
			mass_multiplier = 1.0
	)

fit a GMM to some points.

Will return core::Gaussians. if no particles are provided, they will be created

points: list of coordinates (python) n_components: number of gaussians to create mdl: IMP Model ps: list of particles to be decorated. if empty, will add num_iter: number of EM iterations covariance_type: covar type for the gaussians. options: 'full', 'diagonal', 'sphereical' init_centers: initial coordinates of the GMM force_radii: fix the radii (spheres only) force_weight: fix the weights mass_multiplier: multiply the weights of all the gaussians by this value

Definition at line 282 of file gmm_tools.py.

def IMP.isd.gmm_tools.fit_gmm_to_points	(		points,
			n_components,
			mdl,
			ps = [],
			num_iter = 100,
			covariance_type = 'full',
			init_centers = [],
			force_radii = -1.0,
			force_weight = -1.0,
			mass_multiplier = 1.0
	)

fit a GMM to some points.

Will return core::Gaussians. if no particles are provided, they will be created

points: list of coordinates (python) n_components: number of gaussians to create mdl: IMP Model ps: list of particles to be decorated. if empty, will add num_iter: number of EM iterations covariance_type: covar type for the gaussians. options: 'full', 'diagonal', 'sphereical' init_centers: initial coordinates of the GMM force_radii: fix the radii (spheres only) force_weight: fix the weights mass_multiplier: multiply the weights of all the gaussians by this value dirichlet: use the DGMM fitting (can reduce number of components, takes longer)

Definition at line 204 of file gmm_tools.py.

def IMP.isd.gmm_tools.write_gmm_to_map	(		to_draw,
			out_fn,
			voxel_size,
			bounding_box = None
	)

write density map from GMM.

input can be either particles or gaussians

Definition at line 62 of file gmm_tools.py.

def IMP.isd.gmm_tools.write_gmm_to_text	(		ps,
			out_fn
	)

write a list of gaussians to text.

must be decorated as Gaussian and Mass

Definition at line 43 of file gmm_tools.py.

def IMP.isd.gmm_tools.write_sklearn_gmm_to_map	(		gmm,
			out_fn,
			apix = 0,
			bbox = None,
			dmap_model = None
	)

write density map directly from sklearn GMM (kinda slow)

Definition at line 93 of file gmm_tools.py.