deepcinac.utils.cells_map_utils

Module Contents

Classes

CellsCoord

Functions

create_cells_coord_from_suite_2p(is_cell_file_name, ...)

param is_cell_file_name

path and file_name of the file iscell.npy produce by suite2p segmentation process

get_coords_extracted_from_fiji(file_name)

Extract coords from file_name, should be z .zip or .roi file

_angle_to_point(point, centre)

calculate angle in 2-D between points and x axis

area_of_triangle(p1, p2, p3)

calculate area of any triangle given co-ordinates of the corners

convex_hull(points[, smidgen])

from: https://stackoverflow.com/questions/17553035/draw-a-smooth-polygon-around-data-points-in-a-scatter-plot-in-matplotlib
deepcinac.utils.cells_map_utils.create_cells_coord_from_suite_2p(is_cell_file_name, stat_file_name, movie_dimensions)
Parameters

  • is_cell_file_name – path and file_name of the file iscell.npy produce by suite2p segmentation process

  • None (if) –

  • None –

  • keep (means all cells in stat are cells to) –

  • stat_file_name – path and file_name of the file stat.npy produce by suite2p segmentation process

  • movie_dimensions – tuple of 2 int, dimensions of the movie n col x n line

Returns: a CellsCoord instance

deepcinac.utils.cells_map_utils.get_coords_extracted_from_fiji(file_name)

Extract coords from file_name, should be z .zip or .roi file :param file_name: .zip or .roi file

Returns: Array of len the number of contours made of array of 2 lines (x & y) and n columns, n being the number of coordinate to set the contour of each cell.

class deepcinac.utils.cells_map_utils.CellsCoord(coords=None, pixel_masks=None, nb_lines=None, nb_col=None, from_matlab=False, invert_xy_coord=False)
build_raw_traces_from_movie(movie, without_overlap=False, buffer_overlap=1)

Return a 2d array representing the fluoresence signal raw trace for each cell :param movie: 3d array n_frames x len_y x len_x :param without_overlap: (bool) if True, means the trace will be build only from the pixels from this cell :param buffer_overlap: indicate from how much pixels increasing the size of overlaping cell

Returns: A 2d array (n_cells * n_frames) of float

build_cell_polygon_from_contour(cell)

Build the (shapely) polygon representing a given cell using its contour’s coordinates. :param cell:

Returns:

get_cell_mask(cell, dimensions, without_overlap=False, buffer_overlap=1)

Return the mask of the pixels of the cell :param cell: :param dimensions: height x width :param without_overlap: if True, means with return only the pixel belonging to this cell

buffer_overlap: indicate from how much pixels increasing the size of overlaping cell

Returns

binary 2d array (movie dimension), with 1 for the pixels belonging to the cell

match_cells_indices(coord_obj, path_results, plot_title_opt='')
Parameters

coord_obj – another instanc of coord_obj

Returns

a 1d array, each index corresponds to the index of a cell of coord_obj, and map it to an index to self

or -1 if no cell match

plot_cells_map(path_results, data_id, use_pixel_masks=False, title_option='', connections_dict=None, background_color=(0, 0, 0, 1), default_cells_color=(1, 1, 1, 1.0), default_edge_color='white', dont_fill_cells_not_in_groups=False, link_connect_color='white', link_line_width=1, cell_numbers_color='dimgray', show_polygons=False, cells_to_link=None, edge_line_width=2, cells_alpha=1.0, fill_polygons=True, cells_groups=None, cells_groups_colors=None, cells_groups_alpha=None, cells_to_hide=None, img_on_background=None, real_size_image_on_bg=True, cells_groups_edge_colors=None, with_edge=False, with_cell_numbers=False, text_size=6, save_formats='png', save_plot=True, return_fig=False, ax_to_use=None, verbose=False, use_welsh_powell_coloring=False, dpi=300)
Parameters

  • path_results –

  • data_id –

  • use_pixel_masks –

  • title_option –

  • connections_dict – key is an int representing a cell number, and value is a dict representing the cells it

  • too (connects to. The key is a cell is connected) –

  • connection (and the value represent the strength of the) –

  • it) ((like how many times it connects to) –

  • background_color –

  • default_cells_color –

  • default_edge_color –

  • dont_fill_cells_not_in_groups –

  • link_connect_color –

  • link_line_width –

  • cell_numbers_color –

  • show_polygons –

  • cells_to_link –

  • edge_line_width –

  • cells_alpha –

  • fill_polygons –

  • cells_groups –

  • cells_groups_colors –

  • cells_groups_alpha –

  • cells_to_hide –

  • img_on_background –

  • real_size_image_on_bg – if True, the size of the figure will respect the original size of the background

  • image –

  • cells_groups_edge_colors –

  • with_edge –

  • with_cell_numbers –

  • text_size –

  • save_formats –

  • save_plot –

  • return_fig –

  • ax_to_use –

  • verbose – if True, some informations will be printed along the way

  • use_welsh_powell_coloring – if True, use welsh powell algorithm to color all cells that intersect with

different color. In that case, cancel cell_groups arguments.

dpi:

Returns:

add_cells_using_pixel_masks_on_ax(ax, cells_groups, cells_not_in_groups, cells_to_hide, default_cells_color, cells_groups_colors, with_cell_numbers, cell_numbers_color, text_size, background_color, cells_imshow_alpha, bg_imshow_alpha)

Using pixel mask if it exists :param ax: :param cells_groups: :param cells_not_in_groups: :param cells_to_hide: :param default_cells_color: :param cells_groups_colors: :param with_cell_numbers: :param cell_numbers_color: :param text_size: :param background_color:

Returns:

add_cells_using_polygons_on_ax(ax, cells_groups, cells_not_in_groups, cells_to_hide, with_edge, edge_line_width, default_cells_color, default_edge_color, cells_groups_edge_colors, cells_groups_colors, cells_groups_alpha, cells_alpha, with_cell_numbers, cell_numbers_color, text_size, dont_fill_cells_not_in_groups)

Add cells to a matplolib ax using the polygons representation. Arguments give parameters to apply :param ax: :param cells_groups: :param cells_not_in_groups: :param cells_to_hide: :param with_edge: :param edge_line_width: :param default_cells_color: :param default_edge_color: :param cells_groups_edge_colors: :param cells_groups_colors: :param cells_groups_alpha: :param cells_alpha: :param with_cell_numbers: :param cell_numbers_color: :param text_size: :param dont_fill_cells_not_in_groups:

Returns:

plot_text_cell(cell, ax, cell_numbers_color, text_size)

Plot the cell number on the cell :param cell: integer :param ax: matplolib axis :param cell_numbers_color: color of the text :param text_size: text size (float)

Returns:

get_cell_new_coord_in_source(cell, minx, miny)
scale_polygon_to_source(poly_gon, minx, miny)
get_source_profile(cell, tiff_movie, traces, peak_nums, spike_nums, pixels_around=0, bounds=None, buffer=None, with_full_frame=False)

Return the source profile of a cell :param cell: :param pixels_around: :param bounds: how much padding around the cell pretty much, coordinate of the frame covering the source profile 4 int list :param buffer: :param with_full_frame: Average the full frame :return:

get_transient_profile(cell, transient, tiff_movie, traces, pixels_around=0, bounds=None)
corr_between_source_and_transient(cell, transient, source_profile_dict, tiff_movie, traces, source_profile_corr_dict=None, pixels_around=1)

Measure the correlation (pearson) between a source and transient profile for a giveb cell :param cell: :param transient: :param source_profile_dict should contains cell as key, and results of get_source_profile avec values :param pixels_around: :param source_profile_corr_dict: if not None, used to save the correlation of the source profile, f for memory and computing proficiency :return:

deepcinac.utils.cells_map_utils._angle_to_point(point, centre)

calculate angle in 2-D between points and x axis

deepcinac.utils.cells_map_utils.area_of_triangle(p1, p2, p3)

calculate area of any triangle given co-ordinates of the corners

deepcinac.utils.cells_map_utils.convex_hull(points, smidgen=0.0075)

from: https://stackoverflow.com/questions/17553035/draw-a-smooth-polygon-around-data-points-in-a-scatter-plot-in-matplotlib Calculate subset of points that make a convex hull around points Recursively eliminates points that lie inside two neighbouring points until only convex hull is remaining.

Parameters

points : ndarray (2 x m) array of points for which to find hull use pylab to show progress? smidgen : float offset for graphic number labels - useful values depend on your data range

Returns

hull_points : ndarray (2 x n) convex hull surrounding points