pycellfit package

Submodules

pycellfit.utils module

Top-level utility functions for pycellfit module.

pycellfit.utils.contains_triple_junction(linestring, list_of_points)

helper function that tells you if a shapely LineString contains a triple junction

Parameters:

• linestring (shapely.geometry.LineString) – the LineString of interest
• list_of_points (list) – list containing a bunch of points of type shapely.geometry.Point

Returns:

if the linestring contains a point in the list_of_points, the first point is returned; else, None.

Return type:

shapely.geometry.Point

pycellfit.utils.extract_all_points(dataframe_of_cells, visualize=False)

generate list of all points in mesh

Parameters:

• visualize (bool) – boolean to indicate if plot of all points should be made or not
• dataframe_of_cells (geopandas dataframe) – a dataframe where each row contains a unique cell

Returns:

list_of_all_points: list of all points in the mesh

Return type:

list

pycellfit.utils.locate_triple_junctions(dataframe_of_cells, visualize=False)

generate list of triple junctions in mesh

Parameters:

• visualize (bool) – boolean to indicate if plot of all points should be made or not
• dataframe_of_cells (geopandas dataframe) – a dataframe where each row contains a unique cell

Returns:

list_of_tjs: list of all triple junctions in the mesh

Return type:

list

pycellfit.utils.make_segments(cell_boundary, list_of_triple_junctions)

recursive function that splits up a cell boundary based on triple junctions

Parameters:

• cell_boundary (shapely.geometry.LineString) – boundary of a cell that needs to be broken up into segments
• list_of_triple_junctions (list) – list of all triple junctions (of type shapely.geometry.Point) in the mesh

Return results:

list of segments (of type shapely.geometry.LineString) that make up the cell boundary

Return type:

list

pycellfit.utils.read_segmented_image(file_name, visualize=False)

Displays the segmented image using matplotlib

Parameters:

• file_name (str) – file name of a segmented image in .tif format
• visualize (bool) – if true, then image will be plotted using matplotlib.pyplot

Raises:

TypeError – only accepts tif/tiff files as input

Returns:

array of pixel values

Return type:

numpy.ndarray

pycellfit.junction module

class pycellfit.junction.Junction(coordinates)

Bases: object

add_edge(edge_label)

Adds edge label to set of edge labels

coordinates

degree

edges

set of labels of edges connected to this node

id_iter = count(0)

remove_edge(edge_label)

Remove an edge and tension vector connected to this node

Parameters:

• self –
• edge_label –

tension_vectors

returns list of Tension vectors connected to this node

x

y

pycellfit.edge module

class pycellfit.edge.Edge(start_node, end_node, radius, center)

Bases: object

center

corresponding_tension_vector

end_node

id_iter = count(0)

radius

start_node

xc

yc

pycellfit.tension_vector module

A class to define tension vectors in CellFIT

class pycellfit.tension_vector.TensionVector(edge)

Bases: object

corresponding_edge

The corresponding Edge for this tension vector

Returns:corresponding edge Return type:edge.Edge

direction

returns the direction of the tension vector in radians (or degrees) from the horizontal

Parameters:units (str) – units for the direction, either ‘rad’ or ‘deg’ Raises:ValueError – is units is not ‘rad’ or ‘deg Returns:direction Return type:float

id_iter = count(0)

label

returns the label (id number) for this tension vector

Returns:label Return type:int

magnitude

Magnitude of the tension vector

Returns:magnitude

x_component

returns the x-component of the tension vector

Returns:x-component of the vector Return type:float

y_component

returns the y-component of the tension vector

Returns:y-component of the vector Return type:float

pycellfit.cell module

class pycellfit.cell.Cell(pixel_value)

Bases: object

add_edge_point(edge_point)

approximate_cell_center()

approximates the coordinates of the center of the cell by averaging the coordinates of points on the perimeter (edge) of the cell

:return approximate center of the cell :rtype: tuple

clockwiseangle_and_distance(point)

helper function used in sorting edge points. Calculates the clockwise angle and the distance of a point from the approximate center of the cell. Source: https://stackoverflow.com/questions/41855695/sorting-list-of-two-dimensional-coordinates-by-clockwise -angle-using-python

Returns:direction (clockwise angle), length vector (distance from center) Return type:tuple

edge_points_cw

sort all edge points in clockwise order and return the sorted list

Returns:sorted list of all edge points (tuples) Return type:list

label

the label of a Cell is it’s unique pixel value. It is assigned when the Cell object is created.

Returns:

number_of_edge_points

returns the number of edge points in edge_point_list

Returns:number of edge points

pycellfit.mesh module

class pycellfit.mesh.Mesh

Bases: object

add_cell(cell_pixel_value)

number_of_cells

returns the number of cells in the mesh

Returns:number of cells in mesh Return type:int

number_of_edges

returns the number of edges in the mesh

Returns:number of edges in the mesh Return type:int

number_of_junctions

returns the number of junctions in the mesh

Returns:number of junctions in the mesh Return type:int

number_of_triple_junctions

counts and outputs the number of triple junctions in the mesh

:return number of triple junctions in mesh :rtype: int

remove_cell(cell_pixel_value)

pycellfit.constrained_circle_fit module

Constrained Circle Fit. Fit points to a circular arc when the two end points are fixed.

pycellfit.constrained_circle_fit.algebraic_circle_fit(point_1, point_2, point_3)

finds center and radius of a circle that contains three points on its edge

Parameters:

• point_1 –
• point_2 –
• point_3 –

Returns:

pycellfit.constrained_circle_fit.center_point_to_t_alpha_beta(center, point_p)

converts from standard form (center, point on circle) to parametric form (t, alpha, beta)

Parameters:

• center –
• point_p –

Returns:

pycellfit.constrained_circle_fit.constraint(ans)

first constraint for the solver: alpha^2 + beta^2 = 1

Parameters:ans – Returns:

pycellfit.constrained_circle_fit.constraint2(ans, point_a, point_p)

second constraint for the solver: distance from center to point_a = distance from center to point_p

Parameters:

• ans –
• point_a –
• point_p –

Returns:

pycellfit.constrained_circle_fit.f(ans, x, y, point_a, point_p)

cost function that needs to be minimized See https://arxiv.org/pdf/1504.06582.pdf (page 9)

Parameters:

• ans –
• x –
• y –
• point_a –
• point_p –

Returns:

pycellfit.constrained_circle_fit.fit(x, y, start_point, end_point)

performs a constrained circle fit based on points around an arc and the start and end points of the arc

Parameters:

• x –
• y –
• start_point –
• end_point –

Returns:

pycellfit.constrained_circle_fit.plot_data_and_circle_fit(x, y, xc, yc, radius, start_point, end_point)

plots the results of the circular fit

Parameters:

• x – nparray of all x coordinates of data
• y – nparray of all y coordinates of data
• xc – float of x coordinate of center of fit circle
• yc – float of y coordinate of center of fit circle
• radius – radius of fit circle
• start_point –
• end_point –

Returns:

None

pycellfit.constrained_circle_fit.r(point_a, point_p, alpha, beta, t)

calculate radius of circle

Parameters:

• point_a –
• point_p –
• alpha –
• beta –
• t –

Returns:

pycellfit.constrained_circle_fit.t_alpha_beta_to_center_radius(ans, point_a, point_p)

converts from parametric form (t, alpha, beta) to standard form (radius, center)

Parameters:

• ans –
• point_a –
• point_p –

Returns:

pycellfit.constrained_circle_fit.test1()

pycellfit.constrained_circle_fit.test2()

pycellfit.circle_fit_helpers module

pycellfit.circle_fit_helpers.M(g, h, x, y)

pycellfit.circle_fit_helpers.a0(point_a, point_p, x, y)

pycellfit.circle_fit_helpers.a1(point_a, point_p, alpha, beta, x, y)

pycellfit.circle_fit_helpers.a2(point_a, alpha, beta, x, y)

pycellfit.circle_fit_helpers.b0(point_a, point_p)

pycellfit.circle_fit_helpers.b1(point_a, point_p, alpha, beta)

pycellfit.circle_fit_helpers.b2()

pycellfit.circle_fit_helpers.distance(point_1, point_2)

calculates the euclidian distance between two points

Parameters:

• point_1 –
• point_2 –

Returns:

pycellfit.circle_fit_helpers.q(point_a, x, y)

pycellfit.circle_fit_helpers.qx(point_a, x, y)

pycellfit.circle_fit_helpers.qxx(point_a, x, y)

pycellfit.circle_fit_helpers.qxy(point_a, x, y)

pycellfit.circle_fit_helpers.qy(point_a, x, y)

pycellfit.circle_fit_helpers.qyy(point_a, x, y)

pycellfit.segmentation_transform module

functions to convert between watershed and skeleton segmented images

pycellfit.segmentation_transform.skeleton_to_watershed(skeleton_image_array, region_value=0, boundary_value=255, keep_boundaries=False)

converts a segmented skeleton image (all regions are same value with region boundaries being a second value) to a watershed segmented image (each region has a unique value and there are no boundary pixels, background region has value of zero)

Parameters:

• skeleton_image_array (np.ndarray) – 2D numpy array with pixel values of a skeleton segmented image
• region_value (float) – value of pixels in regions in skeleton_segmented images (default is 0)
• boundary_value (float) – value of boundary pixels of regions in skeleton_segmented images (default is 255)
• keep_boundaries (bool) – if True, watershed image will keep boundaries in returned result

Returns:

watershed_image_array

Rtype watershed_image_array:  

np.ndarray

pycellfit.segmentation_transform.watershed_to_skeleton(watershed_image_array, region_value=0, boundary_value=255)

converts a watershed segmented image (no boundaries between regions and each region has a different pixel value, background region has value of zero) to a skeleton segmented image (each region has the same pixel value and are separated by boundaries of a second value)

Parameters:

• watershed_image_array (numpy.ndarray) – 2D numpy array with pixel values of a watershed segmented image
• region_value (float) – desired value of all regions in the output (skeleton segmented) array
• boundary_value (float) – desired value of boundary pixels in the output (skeleton segmented) array

Returns:

skeleton_image_array

Rtype skeleton_image_array:  

np.ndarray

pycellfit.segmentation_transform_utils module

pycellfit.segmentation_transform_utils.fill_region(array_of_pixels, position, new_value)

fills a region of a 2D numpy array with the same value

Parameters:

• array_of_pixels (np.ndarray) – 2D numpy array of all pixel values
• position (tuple) – tuple with (row, col) location of pixel in the region to modify
• new_value (float) – new value for pixel at position and all pixels in same region

Returns:

None

pycellfit.segmentation_transform_utils.pad_with(vector, pad_width, iaxis, kwargs)

helper function that is called by np.pad to surround a nparray with a constant value Example: [[0,0],[0,0]] becomes [[-1,-1,-1, -1],[-1, 0, 0, -1],[-1, 0, 0, -1],[-1,-1,-1, -1]]

Module contents