API Reference#

This is CRISPy’s class and function reference. In addition to the table below, you can also search with the search bar located on the top right corner of the webpage.

Object	Description
`clean_grid`	Process and grid CRISPy coordinates onto a reference image, labeling and cleaning skeleton structures. `crispy.grid_ridge`
`clean_grid_ppv`	Process and grid CRISPy coordinates in PPV space onto a reference image, labeling and cleaning skeleton structures. `crispy.grid_ridge`
`get_2d_length`	Calculate the sky-projected length of a 3D skeleton. `crispy.grid_ridge`
`grid_skel`	Map raw CRISPy results onto a reference image grid and save the gridded results. `crispy.grid_ridge`
`grid_skeleton`	Map CRISPy skeleton coordinates onto a reference image grid. `crispy.grid_ridge`
`label_ridge`	Label unconnected ridges using DBSCAN clustering. `crispy.grid_ridge`
`make_skeleton`	Map CRISPy skeleton coordinates onto a reference grid and clean the skeleton. `crispy.grid_ridge`
`read_table`	Read filament skeleton data from a file. `crispy.grid_ridge`
`uniq_per_pix`	Reduce a list of ridge coordinates to one unique point per pixel. `crispy.grid_ridge`
`write_skel`	Write a gridded image to a FITS file. `crispy.grid_ridge`
`image2data`	Convert an image into a format compatible with the SCMS algorithm. `crispy.image_ridge_find`
`read_output`	Read SCMS output files and retrieve walker coordinates. `crispy.image_ridge_find`
`run`	Identify density ridges in a gridded image using the SCMS algorithm. `crispy.image_ridge_find`
`threshold_local`	Apply a local thresholding method to an image for binarization. `crispy.image_ridge_find`
`write_output`	Write SCMS output coordinates to a file. `crispy.image_ridge_find`
`Skeleton`	Represents a skeletonized structure with tools for pruning, analyzing, and processing filament data. `crispy.pruning.Skeleton`
`bodyPoints`	Identify body points in a skeletonized structure. `crispy.pruning.pruning`
`branchedPoints`	Identify branch points in a skeletonized structure. `crispy.pruning.pruning`
`classify_structure`	Classify the components of a skeleton into labeled branches, intersections, and endpoints. `crispy.pruning.pruning`
`endPoints`	Identify endpoints in a skeletonized structure. `crispy.pruning.pruning`
`init_branch_properties`	Initialize branch properties for 2D or 3D skeletons. `crispy.pruning.pruning`
`init_lengths_3D`	Compute lengths and intensities for branches in 3D skeletons. `crispy.pruning.pruning`
`main_length_3D`	Compute the main lengths of 3D skeletons and generate longest path arrays. `crispy.pruning.pruning`
`pre_graph_3D`	Convert 3D skeletons into graph representations with weighted edges. `crispy.pruning.pruning`
`remove_bad_ppv_branches`	Remove unphysical branches from a labeled 3D skeleton in PPV space. `crispy.pruning.pruning`
`segment_len`	Calculate the length of a skeleton segment. `crispy.pruning.pruning`
`walk_through_segment_3D`	Traverse a 3D skeleton segment to obtain an ordered list of pixel coordinates. `crispy.pruning.pruning`
`get_base_block`	Generate a base block array for 2D or 3D skeleton structures. `crispy.pruning.structures`
`get_footprints`	Generate a footprint array representing connectivity in 2D or 3D space. `crispy.pruning.structures`
`chunk_data`	Divide data into chunks for multiprocessing. `crispy.scms`
`euclidean_dist`	Compute the Euclidean distances and differences between data points and walkers. `crispy.scms`
`find_ridge`	Identify density ridges in data using the Subspace Constrained Mean Shift (SCMS) algorithm. `crispy.scms`
`shift_particles`	Shift walkers toward density ridges using the Subspace Constrained Mean Shift (SCMS) algorithm. `crispy.scms`
`shift_wakers_multiproc`	Shift walkers towards density ridges using the SCMS algorithm with multiprocessing. `crispy.scms`
`shift_walkers`	Shift walkers towards density ridges using the Subspace Constrained Mean Shift (SCMS) algorithm. `crispy.scms`
`vectorized_gaussian`	Compute Gaussian kernel values for data points relative to walker positions. `crispy.scms`
`wgauss_n_filtered_points`	Compute weighted Gaussian values for data points relative to walker positions, `crispy.scms`
`wgauss_n_filtered_points_multiproc`	Compute weighted Gaussian values for data points relative to walker positions `crispy.scms`
`render_point_cloud`	Render a 3D scatter plot from a 3D data cube with efficient visualization. `crispy.visualize`
`render_volume`	Render a 3D volume using layers of isosurfaces. `crispy.visualize`
`ridge_trace_3D`	Create a 3D scatter trace for visualizing ridge points. `crispy.visualize`
`skel_volume`	Render a 3D skeleton volume using isosurface visualization. `crispy.visualize`