import warnings
import numpy as np
import logging
import pandas as pd
logger = logging.getLogger(name=__name__)
def _to_2d(df):
df_2d = to_nd(df, 2)
return df_2d
def _to_3d(df):
df_3d = to_nd(df, 3)
return df_3d
[docs]def to_nd(df, ndim):
"""
Give a new shape to an input data by duplicating its column.
Parameters
----------
df : input data that will be reshape
ndim : dimension of the new reshape data.
Returns
-------
df_nd : return array reshaped in ndim.
"""
df_nd = df[:, None]#np.asarray(df).repeat(ndim).reshape((df.size, ndim))
return df_nd
[docs]def combine_specs(*specs):
combined = {}
for spec in specs:
for key in spec:
if key in combined:
combined[key].update(spec[key])
else:
combined[key] = spec[key]
return combined
[docs]def spec_updater(specs, new):
"""
Add element to the new dictionary to the specs dictionary.
Update value if the key already exist.
Parameters
----------
specs: specification that will be modified
new: dictionary of new specification
"""
for key, spec in specs.items():
if new.get(key) is not None:
spec.update(new[key])
[docs]def set_data_columns(datasets, specs, reset=False):
"""Sets the columns of the dataframes in the datasets dictionnary to
the uniform values in the specs sub-dictionnaries.
Parameters
----------
datasets : dict of dataframes
specs : dict of dicts
reset : bool, default False
For each key in specs, the value is a dictionnary whose
keys are column names for the corresponding dataframe in
datasets. If there is no such column in the dataframe,
it is created. If the columns allready exists and reset is `True`,
the new value is used.
"""
for name, spec in specs.items():
if not len(spec):
continue
if "setting" in name:
continue
df = datasets.get(name)
if df is None:
warnings.warn(
f"There is no {name} dataset, so the {name}" " spec have no effect."
)
continue
for col, default in spec.items():
if col in df.columns and reset:
logger.warning(
"Reseting column %s of the %s" " dataset with new specs", col, name
)
if col not in df.columns or reset:
df[col] = default
[docs]def data_at_opposite(sheet, edge_data, free_value=None):
"""
Returns a pd.DataFrame with the values of the input edge_data
at the opposite edges. For free edges, optionaly replaces Nan values
with free_value
Parameters
----------
sheet: a :class:`Sheet` instance
edge_data: dataframe contain value of edge
Returns
-------
opposite: pandas series contain value of opposite edge
"""
if isinstance(edge_data, pd.Series):
opposite = pd.Series(
edge_data.reindex(sheet.edge_df["opposite"]).to_numpy(), index=edge_data.index
)
elif isinstance(edge_data, pd.DataFrame):
opposite = pd.DataFrame(
edge_data.reindex(sheet.edge_df["opposite"]).to_numpy(), index=edge_data.index,
columns=edge_data.columns
)
else:
opposite = pd.DataFrame(
np.asarray(edge_data).take(sheet.edge_df["opposite"].to_numpy(), axis=0),
index=sheet.edge_df.index
)
if free_value is not None:
opposite = opposite.replace(np.nan, free_value)
return opposite
[docs]def get_sub_eptm(eptm, edges, copy=False):
"""
Define sub-epithelium corresponding to the edges.
Parameters
----------
eptm: a :class:`Epithelium` instance
edges: list of edges includes in the sub-epithelium
Returns
-------
sub_eptm: a :class:`Epithelium` instance
"""
from ..core.objects import Epithelium
datasets = {}
edge_df = eptm.edge_df.loc[edges]
if edge_df.empty:
warnings.warn("Sub epithelium appears to be empty")
return None
datasets["edge"] = edge_df
datasets["vert"] = eptm.vert_df.loc[set(edge_df["srce"])]
datasets["face"] = eptm.face_df.loc[set(edge_df["face"])]
if "cell" in eptm.datasets:
datasets["cell"] = eptm.cell_df.loc[set(edge_df["cell"])]
if copy:
for elem, df in datasets.items():
datasets[elem] = df.copy()
sub_eptm = Epithelium("sub", datasets, eptm.specs)
sub_eptm.datasets["edge"]["edge_o"] = edges
sub_eptm.datasets["edge"]["srce_o"] = edge_df["srce"]
sub_eptm.datasets["edge"]["trgt_o"] = edge_df["trgt"]
sub_eptm.datasets["edge"]["face_o"] = edge_df["face"]
if "cell" in eptm.datasets:
sub_eptm.datasets["edge"]["cell_o"] = edge_df["cell"]
sub_eptm.datasets["vert"]["srce_o"] = set(edge_df["srce"])
sub_eptm.datasets["face"]["face_o"] = set(edge_df["face"])
if "cell" in eptm.datasets:
sub_eptm.datasets["cell"]["cell_o"] = set(edge_df["cell"])
sub_eptm.reset_index()
sub_eptm.reset_topo()
return sub_eptm
[docs]def single_cell(eptm, cell, copy=False):
"""
Define epithelium instance for all element to a define cell.
Parameters
----------
eptm : a :class:`Epithelium` instance
cell : identifier of a cell
copy : bool, default `False`
Returns
-------
sub_etpm: class:'Epithelium' instance corresponding to the cell
"""
edges = eptm.edge_df[eptm.edge_df["cell"] == cell].index
return get_sub_eptm(eptm, edges, copy)
[docs]def scaled_unscaled(func, scale, eptm, geom, args=(), kwargs={}, coords=None):
"""Scales the epithelium by an homotetic factor `scale`, applies
the function `func`, and scales back to original size.
Parameters
----------
func: the function to apply to the scaled epithelium
scale: float, the scale to apply
eptm: a :class:`Epithelium` instance
geom: a :class:`Geometry` class
args: sequence, the arguments to pass to func
kwargs: dictionary, the keywords arguments
to pass to func
coords: the coordinates on which the scaling applies
If the execution of function fails, the scaling is still reverted
Returns
-------
res: the result of the function func
"""
if coords is None:
coords = eptm.coords
geom.scale(eptm, scale, coords)
geom.update_all(eptm)
try:
res = func(*args, **kwargs)
except:
raise
finally:
geom.scale(eptm, 1 / scale, coords)
geom.update_all(eptm)
return res
[docs]def modify_segments(eptm, modifiers):
"""Modifies the datasets of a segmented epithelium
according to the passed modifiers.
Parameters
----------
eptm : :class:`tyssue.Epithelium`
modifiers : nested dictionnary
Note
----
This functions assumes that the epithelium has a `segment_index`
method as implemented in the :class:`tyssue.Monolayer`.
Example
-------
>>> modifiers = {
>>> 'apical' : {
>>> 'edge': {'line_tension': 1.},
>>> 'face': {'prefered_area': 0.2},
>>> },
>>> 'basal' : {
>>> 'edge': {'line_tension': 3.},
>>> 'face': {'prefered_area': 0.1},
>>> }
>>> modify_segments(monolayer, modifiers)
>>> monolayer.ver_df.loc[monolayer.apical_edges,
>>> 'line_tension'].unique()[0] == 1.
True
"""
for segment, spec in modifiers.items():
for element, parameters in spec.items():
idx = eptm.segment_index(segment, element)
for param_name, param_value in parameters.items():
eptm.datasets[element].loc[idx, param_name] = param_value
def _compute_ar(df, coords):
u, v = coords
major = np.ptp(df[u].values)
minor = np.ptp(df[v].values)
if major < minor:
minor, major = major, minor
return 0 if minor == 0 else major / minor
[docs]def ar_calculation(sheet, coords=["x", "y"]):
""" Calculates the aspect ratio of each face of the sheet
Parameters
----------
eptm : a :class:`Sheet` object
coords : list of str, optional, default ['x', 'y']
the coordinates on which to compute the aspect ratio
Returns
-------
AR: pandas series of aspect ratio for all faces.
Note
----
As is the case in ImageJ, the returned aspect ratio is always higher than 1
"""
srce_pos = sheet.upcast_srce(sheet.vert_df[sheet.coords])
srce_pos["face"] = sheet.edge_df["face"]
return srce_pos.groupby("face").apply(_compute_ar, coords)
[docs]def get_next(eptm):
"""
Returns the indices of the next edge for each edge
"""
fs_indexed = (
eptm.edge_df[["face", "srce"]]
.reset_index()
.set_index(["face", "srce"], drop=False)
)
ft_index = pd.MultiIndex.from_frame(
eptm.edge_df[["face", "trgt"]], names=["face", "srce"]
)
next_ = fs_indexed.loc[ft_index, "edge"].values
return next_
## small utlity to swap apical and basal segments
[docs]def swap_apico_basal(organo):
"""Swap apical and basal segments of an organoid
"""
for elem in ["vert", "face", "edge"]:
swaped = organo.datasets[elem]["segment"].copy()
swaped.loc[organo.segment_index("apical", elem)] = "basal"
swaped.loc[organo.segment_index("basal", elem)] = "apical"
organo.datasets[elem]["segment"] = swaped