tyssue.core package¶

Submodules¶

tyssue.core.history module¶

class tyssue.core.history.History(sheet, save_every=None, dt=None, extra_cols=None, save_all=True)[source]¶

Bases: object

This class handles recording and retrieving time series of sheet objects.

Creates a SheetHistory instance.

Parameters

sheet (a Sheet object which we want to record) –
save_every (float, set every time interval to save the sheet) –
dt (float, time step) –
extra_cols (dictionnary with sheet.datasets as keys and list of) – columns as values. Default None
save_all (bool) – if True, saves all the data at each time point

property cell_h¶

property edge_h¶

property face_h¶

record(to_record=None, time_stamp=None)[source]¶

Appends a copy of the sheet datasets to the history instance.

Parameters: to_report (deprecated) –

retrieve(time)[source]¶

Return datasets at time time.

If a specific dataset was not recorded at time time, the closest record before that time is used.

property time_stamps¶

to_archive(hf5file)[source]¶

Saves the history to a HDF file

This file can later be accessed again with the HistoryHdf5.from_archive class method

property vert_h¶

class tyssue.core.history.HistoryHdf5(sheet=None, save_every=None, dt=None, extra_cols=None, hf5file='', overwrite=False)[source]¶

Bases: tyssue.core.history.History

This class handles recording and retrieving time series of sheet objects.

Creates a SheetHistory instance.

Parameters

sheet (a Sheet object which we want to record) –
save_every (float, set every time interval to save the sheet) –
dt (float, time step) –
extra_cols (dictionnary with sheet.datasets as keys and list of) – columns as values. Default None
hf5file (string, define the path of the HDF5 file) –
overwrite (bool, Overwrite or not the file if it is already exist. Default False) –

classmethod from_archive(hf5file, columns=None, eptm_class=None)[source]¶

record(to_record=None, time_stamp=None, sheet=None)[source]¶

Appends a copy of the sheet datasets to the history HDF file.

Parameters

to_report (Deprecated - list of strings) – the datasets from self.sheet to be saved
sheet (a Sheet object which we want to record. This argument can) –
used if the sheet object is different at each time point. (be) –

retrieve(time)[source]¶

Return datasets at time time.

If a specific dataset was not recorded at time time, the closest record before that time is used.

property time_stamps¶

tyssue.core.monolayer module¶

Monolayer epithelium objects

class tyssue.core.monolayer.Monolayer(name, datasets, specs=None, coords=None)[source]¶

Bases: tyssue.core.objects.Epithelium

3D monolayer epithelium

Creates an epithelium

Parameters

identifier (string) –
datasets (dictionary of dataframes) –
The keys correspond to the different geometrical elements constituting the epithelium:
- vert contains a dataframe of vertices,
- edge contains a dataframe of oriented half-edges between vertices,
- face contains a dataframe of polygonal faces enclosed by half-edges,
- cell contains a dataframe of polyhedral cells delimited by faces,

specs (nested dictionnary of specifications) –

The first key designs the element name: (vert, edge, face, cell), corresponding to the respective dataframes attribute in the dataset. The second level keys design column names of the above dataframes. For exemple: .. code:

specs = {
    "face": {
        ## Face Geometry
        "perimeter": 0.,
        "area": 0.,
        ## Coordinates
        "x": 0.,
        "y": 0.,
        "z": 0.,
        ## Topology
        "num_sides": 6,
        ## Masks
        "is_alive": 1},
    "vert": {
        ## Coordinates
        "x": 0.,
        "y": 0.,
        "z": 0.,
        ## Masks
        "is_active": 1},
    "edge": {
        ## Connections
        "srce": 0,
        "trgt": 1,
        "face": 0,
        "cell": 0,
        ## Coordinates
        "dx": 0.,
        "dy": 0.,
        "dz": 0.,
        "length": 0.,
        ## Normals
        "nx": 0.,
        "ny": 0.,
        "nz": 1.}
    "settings":
        ## Custom values
        "geometry": "flat"
    }

Note

For efficiency reasons, we have to maintain a monotonous RangeIndex for each dataset. Thus, the index of an element can change, and should not be used as an identifier.

property apical_edges¶

property apical_faces¶

property apical_verts¶

property basal_edges¶

property basal_faces¶

property basal_verts¶

classmethod from_flat_sheet(name, apical_sheet, specs, thickness=1)[source]¶

get_sub_sheet(segment)[source]¶

Returns a Sheet object of the corresponding segment

Parameters: segment (str, the corresponding segment, wether 'apical' or 'basal') –

guess_face_segment(face)[source]¶: Infers the face segment from its surrounding edges.

guess_vert_segment(vert)[source]¶: Infers the vertex segment from its surrounding edges.

property lateral_edges¶

property lateral_faces¶

property lateral_verts¶

segment_index(segment, element)[source]¶

class tyssue.core.monolayer.MonolayerWithLamina(name, datasets, specs=None, coords=None)[source]¶

Bases: tyssue.core.monolayer.Monolayer

3D monolayer epithelium with a lamina meshing

Creates an epithelium

Parameters

identifier (string) –
datasets (dictionary of dataframes) –
The keys correspond to the different geometrical elements constituting the epithelium:
- vert contains a dataframe of vertices,
- edge contains a dataframe of oriented half-edges between vertices,
- face contains a dataframe of polygonal faces enclosed by half-edges,
- cell contains a dataframe of polyhedral cells delimited by faces,

specs (nested dictionnary of specifications) –

The first key designs the element name: (vert, edge, face, cell), corresponding to the respective dataframes attribute in the dataset. The second level keys design column names of the above dataframes. For exemple: .. code:

specs = {
    "face": {
        ## Face Geometry
        "perimeter": 0.,
        "area": 0.,
        ## Coordinates
        "x": 0.,
        "y": 0.,
        "z": 0.,
        ## Topology
        "num_sides": 6,
        ## Masks
        "is_alive": 1},
    "vert": {
        ## Coordinates
        "x": 0.,
        "y": 0.,
        "z": 0.,
        ## Masks
        "is_active": 1},
    "edge": {
        ## Connections
        "srce": 0,
        "trgt": 1,
        "face": 0,
        "cell": 0,
        ## Coordinates
        "dx": 0.,
        "dy": 0.,
        "dz": 0.,
        "length": 0.,
        ## Normals
        "nx": 0.,
        "ny": 0.,
        "nz": 1.}
    "settings":
        ## Custom values
        "geometry": "flat"
    }

Note

For efficiency reasons, we have to maintain a monotonous RangeIndex for each dataset. Thus, the index of an element can change, and should not be used as an identifier.

property lamina_edges¶

tyssue.core.multisheet module¶

class tyssue.core.multisheet.MultiSheet(name, layer_datasets, specs)[source]¶

Bases: object

property Nes¶

property Nfs¶

property Nvs¶

concat_datasets()[source]¶

property e_idxs¶

property f_idxs¶

update_interpolants()[source]¶

property v_idxs¶

tyssue.core.objects module¶

Core definitions

class tyssue.core.objects.Epithelium(identifier, datasets, specs=None, coords=None, maxbackup=5)[source]¶

Bases: object

Base class defining a connective tissue in 2D or 3D.

Creates an epithelium

Parameters

identifier (string) –
datasets (dictionary of dataframes) –
The keys correspond to the different geometrical elements constituting the epithelium:
- vert contains a dataframe of vertices,
- edge contains a dataframe of oriented half-edges between vertices,
- face contains a dataframe of polygonal faces enclosed by half-edges,
- cell contains a dataframe of polyhedral cells delimited by faces,

specs (nested dictionnary of specifications) –

The first key designs the element name: (vert, edge, face, cell), corresponding to the respective dataframes attribute in the dataset. The second level keys design column names of the above dataframes. For exemple: .. code:

specs = {
    "face": {
        ## Face Geometry
        "perimeter": 0.,
        "area": 0.,
        ## Coordinates
        "x": 0.,
        "y": 0.,
        "z": 0.,
        ## Topology
        "num_sides": 6,
        ## Masks
        "is_alive": 1},
    "vert": {
        ## Coordinates
        "x": 0.,
        "y": 0.,
        "z": 0.,
        ## Masks
        "is_active": 1},
    "edge": {
        ## Connections
        "srce": 0,
        "trgt": 1,
        "face": 0,
        "cell": 0,
        ## Coordinates
        "dx": 0.,
        "dy": 0.,
        "dz": 0.,
        "length": 0.,
        ## Normals
        "nx": 0.,
        "ny": 0.,
        "nz": 1.}
    "settings":
        ## Custom values
        "geometry": "flat"
    }

Note

For efficiency reasons, we have to maintain a monotonous RangeIndex for each dataset. Thus, the index of an element can change, and should not be used as an identifier.

property Nc¶: The number of cells in the epithelium

property Ne¶: The number of edges in the epithelium

property Nf¶: The number of faces in the epithelium

property Nv¶: The number of vertices in the epithelium

backup()[source]¶: Creates a copy of self and keeps a reference to it in the self._backups deque.

property cell_df¶: The cell pd.DataFrame containing cell associated data e.g. the position of their center or their volume

copy(deep_copy=True)[source]¶

Returns a copy of the epithelium

Parameters: deep_copy (bool, default True) – if True, use a copy of the original object’s datasets to create the new object. If False, datasets are not copied

cut_out(bbox, coords=None)[source]¶

Returns the index of edges with at least one vertex outside of the bounding box

Parameters

bbox (sequence of shape (dim, 2)) – the bounding box as (min, max) pairs for each coordinates.
coords (list of str of len dim, default None) – the coords corresponding to the bbox.

property edge_df¶: The edge pd.DataFrame containing edge associated data e.g. their length.This dataframe also contains the whole connexion of the epithelium through the “srce”, “trgt”, “face”, “cell” indices. In 2D, a half-edge is associated with a single (face, srce, trgt) positively oriented triangle. In 3D, the (cell, face, srce, trgt) positively oriented terahedron is also unique.

property face_df¶: The face pd.DataFrame containing face associated data e.g. the position of their center or their area

face_polygons(coords=None)[source]¶

Returns a pd.Series of arrays with the coordinates the face polygons

Each element of the Series is a (num_sides, num_dims) array of points ordered counterclockwise.

Note

Vertices are assumed to be ordered in a face. If you are not sure it is the case, you can run sheet.reset_index(order=True) before calling this function.

get_invalid()[source]¶: Returns a mask over self.edge_df for invalid faces

get_neighborhood(elem_id, order, elem='cell')[source]¶

Returns elem_id neighborhood up to a degree of order

For example, if order is 2, it wil return the adjacent cells (or faces) and theses cells neighbors.

Returns: neighbors – of the neighboring cell (face), and it’s neighboring order
Return type: pd.DataFrame with two colums, the index

get_neighbors(elem_id, elem='cell')[source]¶

Returns the indexes of the adjacent elements (cells or faces) of the element of index elem_id.

Parameters

elem_id (int) – the index of the central element (a face or a cell)
element ({'cell' | 'face'}, default 'cell') –

Returns

neghbors – the cells (or faces) sharing an edge with the central cell (face)

Return type

set

get_opposite_faces()[source]¶: Populates the ‘opposite’ column of self.face_df with the index of the opposite face or -1 if the face has no opposite.

get_orbits(center, periph)[source]¶

Returns a dataframe with a (center, edge) MultiIndex with periph elements.

Parameters

center (str,) – the name of the center element for example ‘face’, ‘srce’
periph (str,) – the name of the periphery elements, for example ‘trgt’, ‘cell’

Example

>>> cell_verts = sheet.get_orbits('face', 'srce')
>>> cell_verts.loc[45]
edge
218    75
219    78
220    76
221    81
222    90
223    87
Name: srce, dtype: int64

get_valid()[source]¶: Set the ‘is_valid’ column to true if the faces are all closed polygons, and the cells closed polyhedra.

idx_lookup(elem_id, element)[source]¶

returns the current index of the element with the “id” column equal to elem_id

Parameters

elem_id (int) – id of the element to retrieve
element ({"vert"|"edge"|"face"|"cell"}) – the corresponding dataset.

remove(edge_out, trim_borders=False, order_edges=False)[source]¶

Remove the edges indexed by edge_out associated with all the cells and faces containing those edges

If trim_borders is True (defaults to False), there will be a single border edge per border face.

reset_index(order=False)[source]¶

Resets the datasets to have continuous indices

If order is True (the default), sorts the edges such that for each face, vertices are ordered clockwize

reset_topo()[source]¶: Recomputes the number of sides for the faces and the number of faces for the cells.

restore()[source]¶

Resets the eptithelium data to its last backed up state

A copy of the current state prior to restoring is kept in the _bad attribute for inspection. Calling this method multiple times (without calling backup) will go back in the epithelium backups.

sanitize(trim_borders=False, order_edges=False)[source]¶

Removes invalid faces and associated vertices

If trim_borders is True (defaults to False), there will be a single border edge per border face.

set_bbox(margin=0.0)[source]¶: Sets the attribute bbox with pairs of values bellow and above the min and max of the vert coords, with a margin.

property settings¶: Accesses the specs[‘settings’] dictionnary

sum_cell(df)[source]¶

Sums the values of the edge-indexed dataframe df grouped by the values of self.edge_df[“cell”]

Returns: summed
Return type: pd.DataFrame the summed data, indexed by the source vertices.

sum_face(df)[source]¶

Sums the values of the edge-indexed dataframe df grouped by the values of self.edge_df[“face”]

Returns: summed
Return type: pd.DataFrame the summed data, indexed by the source vertices.

sum_srce(df)[source]¶

Sums the values of the edge-indexed dataframe df grouped by the values of self.edge_df[“srce”]

Returns: summed
Return type: pd.DataFrame the summed data, indexed by the source vertices.

sum_trgt(df)[source]¶

Sums the values of the edge-indexed dataframe df grouped by the values of self.edge_df[“trgt”]

Returns: summed
Return type: pd.DataFrame the summed data, indexed by the source vertices.

triangular_mesh(coords=None, return_mask=False)[source]¶

Return a triangulation of an epithelial sheet (2D in a 3D space), with added edges between face barycenters and junction vertices.

Parameters

coords (list of str:) – pair of coordinates corresponding to column names for self.face_df and self.vert_df
return_mask (bool, optional, default True) – if True, returns face_mask

Returns

vertices ((self.Nf+self.Nv, 3) ndarray) – all the vertices’ coordinates
triangles ((self.Ne, 3) ndarray of ints) – triple of the vertices’ indexes forming the triangular elements. For each junction edge, this is simply the index (srce, trgt, face). This is correctly oriented.
face_mask ((self.Nf + self.Nv,) mask with 1 iff the vertex corresponds) – to a face center

upcast_cell(df)[source]¶

Reindexes input data to self.edge_df.index by repeating the values for each cell entry

Parameters: df (pd.DataFrame, pd.Series np.ndarray or string) – The data to be upcasted. If array like, should have self.Nc elements. If a string is passed it should be a column of self.cell_df
Returns: upcast_df – The value repeated like the values of self.edge_df[“cell”]
Return type: pd.DataFrame or pd.Series

upcast_cols(element, columns)[source]¶

Syntactic sugar to upcast from the epithelium datasets.

Parameters

element ({'srce'|'trgt'|'face'|'cell'}) – corresponding self.edge_df column over which to index if element is ‘srce’ or ‘trgt’, the upcast data will be taken form self.vert_df
columns (index) – the column(s) to be taken from the input dataset.

upcast_face(df)[source]¶

Reindexes input data to self.edge_df.index by repeating the values for each face entry

Parameters: df (pd.DataFrame, pd.Series np.ndarray or string) – The data to be upcasted. If array like, should have self.Nf elements. If a string is passed it should be a column of self.face_df
Returns: upcast_df – The value repeated like the values of self.edge_df[“face”]
Return type: pd.DataFrame or pd.Series

upcast_srce(df)[source]¶

Reindexes input data to self.edge_df.index by repeating the values for each source entry.

Parameters: df (pd.DataFrame, pd.Series np.ndarray or string) – The data to be upcasted. If array like, should have self.Nv elements. If a string is passed it should be a column of self.vert_df
Returns: upcast_df – The value repeated like the values of self.edge_df[“srce”]
Return type: pd.DataFrame or pd.Series

upcast_trgt(df)[source]¶

Reindexes input data to self.edge_df.index by repeating the values for each target entry

Parameters: df (pd.DataFrame, pd.Series np.ndarray or string) – The data to be upcasted. If array like, should have self.Nv elements. If a string is passed it should be a column of self.vert_df
Returns: upcast_df – The value repeated like the values of self.edge_df[“trgt”]
Return type: pd.DataFrame or pd.Series

update_num_faces()[source]¶: Updates the number of faces around the cells. The data is registered in the “num_faces” column of self.cell_df.

update_num_sides()[source]¶: Updates the number of half-edges around the faces. The data is registered in the “num_sides” column of self.face_df.

update_rank()[source]¶

update_specs(new, reset=False)[source]¶: Recursively updates the self.specs nested dictionnary, and set the new values to the corresponding columns in the datasets. If reset is True, existing values will be overwritten.

validate()[source]¶

returns True if the mesh is validated

e.g. has only closed polygons and polyhedra

validate_closed_cells()[source]¶

property vert_df¶: The face pd.DataFrame containing vertex associated data e.g. the position of each vertex.

vertex_mesh(coords, vertex_normals=True)[source]¶: Returns the vertex coordinates and a list of vertex indices for each face of the tissue. If vertex_normals is True, also returns the normals of each vertex (set as the average of the vertex’ edges), suitable for .OBJ export

Note

Vertices are assumed to be ordered in a face. If you are not sure it is the case, you can run sheet.reset_index() before calling this function.

tyssue.core.objects.get_next_edges(sheet)[source]¶: returns a pd.Series with the index of the next edge for each edge

tyssue.core.objects.get_opposite_faces(eptm)[source]¶

tyssue.core.objects.get_prev_edges(sheet)[source]¶: returns a pd.Series with the index of the next edge for each edge

tyssue.core.sheet module¶

An epithelial sheet, i.e a 2D mesh in a 2D or 3D space, akin to a HalfEdge data structure in CGAL.

For purely 2D the geometric properties are defined in: tyssue.geometry.planar_geometry

A dynamical model derived from Fahradifar et al. 2007 is provided in tyssue.dynamics.planar_vertex_model

For 2D in 3D, the geometric properties are defined in: tyssue.geometry.sheet_geometry

A dynamical model derived from Monier, Gettings et al. 2015 is provided in tyssue.dynamics.sheet_vertex_model

class tyssue.core.sheet.Sheet(identifier, datasets, specs=None, coords=None)[source]¶

Bases: tyssue.core.objects.Epithelium

An epithelial sheet, i.e a 2D mesh in a 2D or 3D space, akin to a HalfEdge data structure in CGAL.

The geometric properties are defined in tyssue.geometry.sheet_geometry A dynamical model derived from Fahradifar et al. 2007 is provided in tyssue.dynamics.sheet_vertex_model

Creates an epithelium sheet, such as the apical junction network.

Parameters

identifier (str, the tissue name) –
face_df (pandas.DataFrame indexed by the faces indexes) – this df holds the vertices associated with

extract(face_mask, coords=['x', 'y', 'z'])[source]¶

Extract a new sheet from the sheet that correspond to a key word that define a face.

Parameters

face_mask (column name in face composed by boolean value) –
coords –

Returns

subsheet corresponding to the fold patch area.

Return type

sheet_fold_patch_extract

extract_bounding_box(x_boundary=None, y_boundary=None, z_boundary=None, coords=['x', 'y', 'z'])[source]¶

Extracts a new sheet from the embryo sheet

that correspond to boundary coordinate define by the user.

Parameters

x_boundary (pair of floats) –
y_boundary (pair of floats) –
z_boundary (pair of floats) –
coords (list of strings, default ['x', 'y', 'z']) – coordinates over which to crop the sheet

Returns

subsheet

Return type

a new Sheet object

get_extra_indices()[source]¶

Computes extra indices:

self.free_edges: half-edges at the epithelium boundary
self.dble_edges: half-edges inside the epithelium, with an opposite
self.east_edges: half of the dble_edges, pointing east (figuratively)
self.west_edges: half of the dble_edges, pointing west
(the order of the east and west edges is conserved, so that the ith west half-edge is the opposite of the ith east half-edge)
self.sgle_edges: joint index over free and east edges, spanning
the entire graph without double edges
self.wrpd_edges: joint index over free edges followed by the

east edges twice, such that a vector over the whole half-edge
dataframe is wrapped over the single edges
self.srtd_edges: index over the whole half-edge sorted such that
the free edges come first, then the east, then the west

Also computes: - self.Ni: the number of inside full edges

(i.e. len(self.east_edges))

self.No: the number of outside full edges (i.e. len(self.free_edges))
self.Nd: the number of double half edges (i.e. len(self.dble_edges))
self.anti_sym: pd.Series with shape (self.Ne,) with 1 at the free and east half-edges and -1 at the opposite half-edges.

Notes

East and west is resepctive to some orientation at the moment the indices are computed the partition stays valid as long as there are no changes in the topology, so due to vertex displacement, ‘east’ and ‘west’ might not stay valid. This is just a practical naming convention.
As the name suggest, this method is not working for edges in 3D pointing exactly north or south, ie iff edge[‘dx’] == edge[‘dy’] == 0. Until we need or find a better solution, we’ll just assert it worked.

get_neighborhood(face, order)[source]¶

Returns face neighborhood up to a degree of order

For example, if order is 2, it wil return the adjacent, faces and theses faces neighbors.

Returns: neighbors – of the neighboring face, and it’s neighboring order
Return type: pd.DataFrame with two colums, the index

get_neighbors(face)[source]¶: Returns the faces adjacent to face

get_opposite()[source]¶

classmethod planar_sheet_2d(identifier, nx, ny, distx, disty, noise=None)[source]¶

Creates a planar sheet from an hexagonal grid of cells.

Parameters

identifier (string) –
nx (int) – number of cells in the x and y axes
ny (int) – number of cells in the x and y axes
distx (float,) – the distances in x and y between the cells
disty (float,) – the distances in x and y between the cells
noise (float, default None) – position noise on the hexagonal grid

Returns

planar_sheet – in the (x, y) plane

Return type

a 2D Sheet instance

classmethod planar_sheet_3d(identifier, nx, ny, distx, disty, noise=None)[source]¶

Creates a planar sheet from an hexagonal grid of cells.

Parameters

identifier (string) –
nx (int) – number of cells in the x and y axes
ny (int) – number of cells in the x and y axes
distx (float,) – the distances in x and y between the cells
disty (float,) – the distances in x and y between the cells
noise (float, default None) – position noise on the hexagonal grid

Returns

flat_sheet

Return type

a 2.5D Sheet instance

reset_topo()[source]¶: Recomputes the number of sides for the faces and the number of faces for the cells.

sort_edges_eastwest()[source]¶: reorder edges such the free edges are first, then the first half of the double edges, then the other half of the double edges, this way, each subset of the edges dataframe are contiguous.

tyssue.core.sheet.get_opposite(edge_df)[source]¶: Returns the indices opposite to the edges in edge_df

tyssue.core.sheet.get_outer_sheet(eptm)[source]¶: Return a Sheet object formed by all the faces w/o an opposite face.

tyssue.core package¶

Submodules¶

tyssue.core.history module¶

tyssue.core.monolayer module¶

tyssue.core.multisheet module¶

tyssue.core.objects module¶

tyssue.core.sheet module¶

Module contents¶

Tyssue

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