agora.io.cells.Cells

class Cells(filename, path='cell_info')

Bases: object

Extracts information from an h5 file. This class accesses:

‘cell_info’, which contains ‘angles’, ‘cell_label’, ‘centres’, ‘edgemasks’, ‘ellipse_dims’, ‘mother_assign’, ‘mother_assign_dynamic’, ‘radii’, ‘timepoint’, ‘trap’. All of these except for ‘edgemasks’ are a 1D ndarray.

‘trap_info’, which contains ‘drifts’, ‘trap_locations’

Attributes

edgemasks

labels

Return all cell labels in object

max_label

max_labels

mothers

Return nested list with final prediction of mother id for each cell

mothers_daughters

Return mothers and daugters as a single array with three columns: trap, mothers and daughters

ncells_matrix

ntimepoints

ntraps

tile_size

tinterval

traps

Methods

group_by_traps(traps, cell_labels)	Returns a dict with traps as keys and list of labels as value.
labelled_in_frame(frame[, global_id])	Return labels in a ndarray with the global ids with shape (ntraps, max_nlabels, ysize, xsize) at a given frame.
matrix_trap_tp_where([min_ncells, ...])	Return a matrix of shape (ntraps x ntps - min_consecutive_tps to indicate traps and time-points where min_ncells are available for at least min_consecutive_tps
mother_assign_from_dynamic(ma, cell_label, ...)	Interpolate the list of lists containing the associated mothers from the mother_assign_dynamic feature
where(cell_id, trap_id)	Parameters

at_time
from_source
get_stacks_in_frame
labels_at_time
labels_in_trap
mask
max_labels_in_frame
mother_assign_to_mb_matrix
mothers_in_trap
nonempty_tp_in_trap
outline
random_valid_trap_tp

__init__(filename, path='cell_info')

Methods

__init__(filename[, path])
at_time(timepoint[, kind])
from_source(source)
get_stacks_in_frame(frame, tile_shape)
group_by_traps(traps, cell_labels)	Returns a dict with traps as keys and list of labels as value.
labelled_in_frame(frame[, global_id])	Return labels in a ndarray with the global ids with shape (ntraps, max_nlabels, ysize, xsize) at a given frame.
labels_at_time(timepoint)	rtype Dict[int, List[int]]
labels_in_trap(trap_id)	rtype Set[int]
mask(cell_id, trap_id)
matrix_trap_tp_where([min_ncells, ...])	Return a matrix of shape (ntraps x ntps - min_consecutive_tps to indicate traps and time-points where min_ncells are available for at least min_consecutive_tps
max_labels_in_frame(frame)	rtype List[int]
mother_assign_from_dynamic(ma, cell_label, ...)	Interpolate the list of lists containing the associated mothers from the mother_assign_dynamic feature
mother_assign_to_mb_matrix(ma)
mothers_in_trap(trap_id)
nonempty_tp_in_trap(trap_id)	rtype set
outline(cell_id, trap_id)
random_valid_trap_tp([min_ncells, ...])
where(cell_id, trap_id)	Parameters

Attributes

edgemasks	rtype List[ndarray]
labels	Return all cell labels in object We use mother_assign to list traps because it is the only property that appears even when no cells are found
max_label	rtype int
max_labels	rtype List[int]
mothers	Return nested list with final prediction of mother id for each cell
mothers_daughters	Return mothers and daugters as a single array with three columns: trap, mothers and daughters
ncells_matrix
ntimepoints	rtype int
ntraps	rtype int
tile_size	rtype Union[int, Tuple[int], None]
tinterval
traps	rtype List[int]

group_by_traps(traps, cell_labels)

Returns a dict with traps as keys and list of labels as value. Note that the total number of traps are calculated from Cells.traps.

Return type

Dict[int, List[int]]

labelled_in_frame(frame, global_id=False)

Return labels in a ndarray with the global ids with shape (ntraps, max_nlabels, ysize, xsize) at a given frame.

max_nlabels is specific for this frame, not the entire experiment.

Return type

ndarray

property labels: List[List[int]]

Return all cell labels in object We use mother_assign to list traps because it is the only property that appears even when no cells are found

Return type

List[List[int]]

matrix_trap_tp_where(min_ncells=None, min_consecutive_tps=None)

Return a matrix of shape (ntraps x ntps - min_consecutive_tps to indicate traps and time-points where min_ncells are available for at least min_consecutive_tps

Parameters

min_ncells: int Minimum number of cells min_consecutive_tps: int

Minimum number of time-points a

Returns

(ntraps x ( ntps-min_consecutive_tps )) 2D boolean numpy array where rows are trap ids and columns are timepoint windows.

If the value in a cell is true its corresponding trap and timepoint contains more than min_ncells for at least min_consecutive time-points.

static mother_assign_from_dynamic(ma, cell_label, trap, ntraps)

Interpolate the list of lists containing the associated mothers from the mother_assign_dynamic feature

property mothers

Return nested list with final prediction of mother id for each cell

property mothers_daughters: ndarray

Return mothers and daugters as a single array with three columns: trap, mothers and daughters

where(cell_id, trap_id)

Parameters

cell_id: int

Cell index

trap_id: int

Trap index

Returns

indices int array

boolean mask array

edge_ix int array