diff --git a/core/functions/tracks.py b/core/functions/tracks.py
index 928fc2b177c63857d0ff4149771b5f04b581478c..e47e60231da31e2c23acf5e97f5b721edbffdfee 100644
--- a/core/functions/tracks.py
+++ b/core/functions/tracks.py
@@ -388,6 +388,7 @@ def get_contiguous_pairs(tracks: pd.DataFrame) -> list:
mins_d = mins.groupby(mins).apply(lambda x: x.index.tolist())
mins_d.index = mins_d.index - 1 # make indices equal
+ # TODO add support for skipping time points
maxes_d = maxes.groupby(maxes).apply(lambda x: x.index.tolist())
common = sorted(set(mins_d.index).intersection(maxes_d.index), reverse=True)
diff --git a/core/processes/picker.py b/core/processes/picker.py
index 7649ab5995683a5e1ad14b3980e78504403704e0..2a26f2ebe2e5fa540d4c9a797dfa7a87dd7f0820 100644
--- a/core/processes/picker.py
+++ b/core/processes/picker.py
@@ -87,42 +87,64 @@ class picker(ProcessABC):
return nested_massign
def pick_by_lineage(self, signals, how):
+
idx = signals.index
if how:
- ma = self._cells["mother_assign_dynamic"]
- trap = self._cells["trap"]
- label = self._cells["cell_label"]
- nested_massign = self.mother_assign_from_dynamic(
- ma, label, trap, self._cells.ntraps
- )
- # mother_bud_mat = self.mother_assign_to_mb_matrix(nested_massign)
-
- idx = set(
- [
- (tid, i + 1)
- for tid, x in enumerate(nested_massign)
- for i in range(len(x))
- ]
- )
- mothers, daughters = zip(
- *[
- ((tid, m), (tid, d))
- for tid, trapcells in enumerate(nested_massign)
- for d, m in enumerate(trapcells, 1)
- if m
- ]
- )
- self.mothers = mothers
- self.daughters = daughters
-
- mothers = set(mothers)
- daughters = set(daughters)
+ mothers = set(self.mothers)
+ daughters = set(self.daughters)
# daughters, mothers = np.where(mother_bud_mat)
+
+ search = lambda a, b: np.where(
+ np.in1d(
+ np.ravel_multi_index(np.array(a).T, np.array(a).max(0) + 1),
+ np.ravel_multi_index(np.array(b).T, np.array(a).max(0) + 1),
+ )
+ )
if how == "mothers":
idx = mothers
elif how == "daughters":
idx = daughters
+ elif how == "daughters_w_mothers":
+ present_mothers = idx.intersection(mothers)
+ idx = set(
+ [
+ tuple(x)
+ for m in present_mothers
+ for x in np.array(self.daughters)[search(self.mothers, m)]
+ ]
+ )
+ elif how == "mothers_w_daughters":
+ present_daughters = idx.intersection(daughters)
+ idx = set(
+ [
+ tuple(x)
+ for d in present_daughters
+ for x in np.array(self.mothers)[search(self.daughters, d)]
+ ]
+ )
+ elif how == "full_families":
+ present_mothers = idx.intersection(mothers)
+ dwm_idx = set(
+ [
+ tuple(x)
+ for m in present_mothers
+ for x in np.array(self.daughters)[
+ search(np.array(self.mothers), m)
+ ]
+ ]
+ )
+ present_daughters = idx.intersection(daughters)
+ mwd_idx = set(
+ [
+ tuple(x)
+ for d in present_daughters
+ for x in np.array(self.mothers)[
+ search(np.array(self.daughters), d)
+ ]
+ ]
+ )
+ idx = mwd_idx.union(dwm_idx)
elif how == "families" or how == "orphans":
families = mothers.union(daughters)
if how == "families":
@@ -138,9 +160,35 @@ class picker(ProcessABC):
idx = self.switch_case(signals, condition, thresh)
return idx
+ def get_mothers_daughters(self):
+ ma = self._cells["mother_assign_dynamic"]
+ trap = self._cells["trap"]
+ label = self._cells["cell_label"]
+ nested_massign = self.mother_assign_from_dynamic(
+ ma, label, trap, self._cells.ntraps
+ )
+ # mother_bud_mat = self.mother_assign_to_mb_matrix(nested_massign)
+
+ idx = set(
+ [
+ (tid, i + 1)
+ for tid, x in enumerate(nested_massign)
+ for i in range(len(x))
+ ]
+ )
+ mothers, daughters = zip(
+ *[
+ ((tid, m), (tid, d))
+ for tid, trapcells in enumerate(nested_massign)
+ for d, m in enumerate(trapcells, 1)
+ if m
+ ]
+ )
+ return mothers, daughters
+
def run(self, signals):
indices = set(signals.index)
- daughters, mothers = (None, None)
+ self.mothers, self.daughters = self.get_mothers_daughters()
for alg, param1, param2 in self.sequence:
if alg is "lineage":
new_indices = getattr(self, "pick_by_" + alg)(signals, param1)
@@ -149,22 +197,25 @@ class picker(ProcessABC):
indices = indices.union(set(new_indices))
else:
new_indices = getattr(self, "pick_by_" + alg)(signals, param1, param2)
- indices = indices.intersection(new_indices)
+ indices = indices.intersection(new_indices)
- daughters, mothers = self.daughters, self.mothers
- return np.array(daughters), np.array(mothers), np.array(list(indices))
+ mothers, daughters = self.mothers, self.daughters
+ return np.array(mothers), np.array(daughters), np.array(list(indices))
@staticmethod
def switch_case(
signals: pd.DataFrame,
condition: str,
- threshold: Union[float, int],
+ threshold: Union[float, int, list],
):
threshold_asint = _as_int(threshold, signals.shape[1])
+ if isinstance(threshold, list):
+ thresh_presence = threshold[0]
case_mgr = {
"present": signals.notna().sum(axis=1) > threshold_asint,
"nonstoply_present": signals.apply(max_nonstop_ntps, axis=1)
> threshold_asint,
+ "growing": signals.diff(axis=1).sum(axis=1) > threshold,
# "quantile": [np.quantile(signals.values[signals.notna()], threshold)],
}
return set(signals.index[case_mgr[condition]])