IMP  2.4.0
The Integrative Modeling Platform
Functions
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.