def get_population(ids_el):
cell_indices = df.index.get_indexer_for(ids_el)
cell_indices = cell_indices[cell_indices > -1]
if len(cell_indices) is 0:
return None
p = np.zeros(len(df), dtype=np.float64)
p[cell_indices] = 1.0
return Population(day, p / np.sum(p))
def pull_back(self,
*populations,
to_time=None,
normalize=True,
as_list=False):
"""
Pulls the population back through the computed transport maps
Parameters
----------
*populations : wot.Population
Measure over the cells at a given timepoint to be pushed forward.
to_time : int or float, optional
Destination timepoint to pull back to.
normalize : bool, optional, default: True
Wether to normalize to a probability distribution or keep growth.
as_list : bool, optional, default: False
Wether to return a listof length 1 when a single element is passed, or a Population
Returns
-------
result : wot.Population
The pull back of the input population through the proper transport map.
Array of populations if several populations were given as input.
Raises
------
ValueError
If there is no previous timepoint to pull the population back.
ValueError
If several populations are given as input but dot live in the same timepoint.
Examples
--------
>>> self.pull_back(pop, to_time = 0) # -> wot.Population
Pushing several populations at once
>>> self.pull_back(pop1, pop2, pop3) # -> list of wot.Population
Pulling back after pushing forward
>>> self.pull_back(self.push_forward(pop))
Same, but several populations at once
>>> self.pull_back(* self.push_forward(pop1, pop2, pop3))
"""
i = self.timepoints.index(wot.tmap.unique_timepoint(*populations))
j = i - 1 if to_time is None else self.timepoints.index(to_time)
if i == -1:
raise ValueError("Timepoint not found")
if i == 0:
raise ValueError("No previous timepoints. Unable to pull back")
if j == -1:
raise ValueError("Destination timepoint not found")
if i < j:
raise ValueError(
"Destination timepoint is after source. Unable to pull back")
p = np.vstack([pop.p for pop in populations])
while i > j:
t1 = self.timepoints[i]
t0 = self.timepoints[i - 1]
tmap = self.get_coupling(t0, t1)
p = (tmap.X @ p.T).T
if normalize:
p = (p.T / np.sum(p, axis=1)).T
i -= 1
result = [
Population(self.timepoints[i], p[k, :]) for k in range(p.shape[0])
]
if len(result) == 1 and not as_list:
return result[0]
else:
return result