structure

class cryocat.analysis.structure.MAK

Bases: object

get_centre_from_mean_subunit_location(output_path)

class cryocat.analysis.structure.NPC

Bases: object

cluster_subunits_to_rings(mask_size, entry_mask_coord, exit_mask_coord, npc_radius, max_trace_distance, min_trace_distance=0)

static compute_diameter(input_motl, pixel_size=1.0, su_id='geom2')

Compute the average diameter of specific subunit pairs within NPCs.

Parameters:

input_motlstr

Path to the input motl file containing molecular data.

pixel_sizefloat, optional

The size of each pixel in the data, used to scale the computed distances. Default is 1.0.

su_idstr, optional

The identifier used to select subunits within the data. Default is ‘geom2’.

Returns:

numpy.ndarray

An array of average diameters for each unique (tomo_id, object_id) group in the data.

Notes

The function loads molecular data from a specified file, identifies specific subunit pairs, computes the pairwise distances between these pairs, and then calculates the average diameter for each group identified by ‘tomo_id’ and ‘object_id’. Distances are scaled by the pixel size.

static get_center_and_radius(object_motl, include_singles=False)

static get_center_with_radius(object_motl, radius)

static get_centers_as_motl(tomo_motl, tomo_id, radius)

static get_new_subunit_idx(object_motl, npc_radius, symmetry=8)

static merge_rings(input_motls, npc_radius, distance_threshold=40)

static merge_subunits(input_motl, npc_radius=55)

Merge subunits within a given radius in input_motl.

Parameters:

input_motlstr or pd.DataFrame

If a string is provided, it is assumed to be a path to the motl file. If a DataFrame is provided, it is used directly.

npc_radiusint, optional

The radius within which to search for subunits to merge. Default is 55 pixels.

Returns:

input_motlpd.DataFrame

The input motl data with merged subunits. The dataframe is modified in-place.

Notes

The function calculates the centers of each objects in the motl and merges objects with a center that’s closer than the npc_radius. All subunits were renumbered and assign back to the input_motl. The number of subunits per object was kept under geom1 in updated motl.

static unify_nn_orientations(input_motl, dist_threshold=10000)

class cryocat.analysis.structure.PleomorphicSurface

Bases: object

static assign_affiliation_distance_based(input_motl, parametric_surface, surface_type='ellipsoid', feature_id='object_id', output_file=None, unassigned_value=None)

static assign_affiliation_intersection_based(input_motl, parametric_surface, feature_id='object_id', output_file=None, keep_unassigned=True)

static assign_affiliation_mask_based(input_motl, object_motl, tomo_dim, shell_size, feature_id='object_id', output_file=None, radius_offset=0.0, motl_radius_id='geom5')

static clean_by_normals(input_motl, feature_id='object_id', compute_normals=True, surface_params=None, normals_id='geom4', threshold=None, output_file=None)

static clean_by_radius(input_motl, feature_id='object_id', threshold=None, output_file=None)

static compute_intersection(input_motl, parametric_surface, feature_id='object_id')

static compute_normals(input_motl, surface_params, feature_id='object_id', store_id='geom4', output_file=None)

static compute_point_surface_distance(input_motl, parametric_surface, surface_type='ellipsoid', feature_id='object_id', output_file=None, store_id='geom4')

static create_spherical_oversampling(input_motl, motl_radius_id, sampling_distance, sampling_angle=360, output_path=None)

static get_parametric_description(input_motl, feature_id='object_id', output_file=None)

static load_parametric_surface(parametric_surface=None, feature_id='object_id')