From 2747ac375fd75ac6b3917c703804800586e718f8 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Al=C3=A1n=20Mu=C3=B1oz?= <amuoz@ed.ac.uk>
Date: Sun, 10 Oct 2021 21:51:56 +0100
Subject: [PATCH] bugfixes
Former-commit-id: 6e32d26b4f402947eff408c04e2f937d8fa758d3
---
core/processes/picker.py | 97 ++++++++++++++++++++++++++++++++++------
1 file changed, 84 insertions(+), 13 deletions(-)
diff --git a/core/processes/picker.py b/core/processes/picker.py
index 375e6db1..ec0814f3 100644
--- a/core/processes/picker.py
+++ b/core/processes/picker.py
@@ -28,7 +28,8 @@ class pickerParameters(ParametersABC):
# ("lineage", "intersection", "families"),
("condition", "intersection", "any_present", 0.8),
("condition", "intersection", "growing", 50),
- ("condition", "intersection", "present", 10),
+ ("condition", "intersection", "present", 5),
+ ("condition", "intersection", "mother_buds", 5, 0.5),
# ("lineage", "full_families", "intersection"),
],
}
@@ -161,6 +162,9 @@ class picker(ProcessABC):
return idx
+ def pick_by_custom(self, signals, condition, thresh):
+ pass
+
def pick_by_condition(self, signals, condition, thresh):
idx = self.switch_case(signals, condition, thresh)
return idx
@@ -199,7 +203,7 @@ class picker(ProcessABC):
param1 = params[0]
new_indices = getattr(self, "pick_by_" + alg)(signals, param1)
else:
- param1, param2 = params
+ param1, *param2 = params
new_indices = getattr(self, "pick_by_" + alg)(signals, param1, param2)
if op is "union":
@@ -216,23 +220,20 @@ class picker(ProcessABC):
condition: str,
threshold: Union[float, int, list],
):
- threshold_asint = _as_int(threshold, signals.shape[1])
- if isinstance(threshold, list):
- thresh_presence = threshold[0]
+ if len(threshold) == 1:
+ threshold = [_as_int(*threshold, signals.shape[1])]
case_mgr = {
- "any_present": lambda s, thresh: any_present(s, threshold_asint),
- "present": lambda s, thresh: signals.notna().sum(axis=1) > threshold_asint,
+ "any_present": lambda s, thresh: any_present(s, thresh),
+ "present": lambda s, thresh: signals.notna().sum(axis=1) > thresh,
"nonstoply_present": lambda s, thresh: signals.apply(
max_nonstop_ntps, axis=1
)
- > threshold_asint,
- "growing": lambda s, thresh: signals.diff(axis=1).sum(axis=1) > threshold,
+ > thresh_asint,
+ "growing": lambda s, thresh: signals.diff(axis=1).sum(axis=1) > thresh,
+ "mother_buds": lambda s, p1, p2: mother_buds_wrap(s, p1, p2)
# "quantile": [np.quantile(signals.values[signals.notna()], threshold)],
}
- return set(signals.index[case_mgr[condition](signals, threshold)])
-
-
-from copy import copy
+ return set(signals.index[case_mgr[condition](signals, *threshold)])
def any_present(signals, threshold):
@@ -255,6 +256,76 @@ def any_present(signals, threshold):
return any_present
+from copy import copy
+
+
+def mother_buds(df, min_budgrowth_t, min_mobud_ratio):
+ """
+ Parameters
+ ----------
+ signals : pd.DataFrame
+ min_budgrowth_t: Minimal number of timepoints we lock reassignment after assigning bud
+ min_initial_size: Minimal mother-bud ratio at the assignment
+ #TODO incorporate bud-assignment data?
+
+ # If more than one bud start in the same time point pick the smallest one
+ """
+
+ ntps = df.notna().sum(axis=1)
+ mother_id = df.index[ntps.argmax()]
+ nomother = df.drop(mother_id)
+ if not len(nomother):
+ return []
+ nomother = ( # Clean short-lived cells outside our mother cell's timepoints
+ nomother.loc[
+ nomother.apply(
+ lambda x: x.first_valid_index() >= df.loc[mother_id].first_valid_index()
+ and x.last_valid_index() <= df.loc[mother_id].last_valid_index(),
+ axis=1,
+ )
+ ]
+ )
+
+ start = nomother.apply(pd.Series.first_valid_index, axis=1)
+
+ # clean duplicates
+ duplicates = start.duplicated(False)
+ if duplicates.any():
+ dup_tps = np.unique(start[duplicates])
+ idx, tps = zip(
+ *[(nomother.loc[start == tp, tp].idxmin(), tp) for tp in dup_tps]
+ )
+ start = start[~duplicates]
+ start = pd.concat(
+ (start, pd.Series(tps, index=idx, dtype="int", name="cell_label"))
+ )
+ nomother = nomother.loc[start.index]
+ nomother.index = nomother.index.astype("int")
+
+ d_to_mother = nomother[start] - df.loc[mother_id, start] * min_mobud_ratio
+ size_filter = d_to_mother[
+ d_to_mother.apply(lambda x: x.dropna().iloc[0], axis=1) < 0
+ ]
+ cols_sorted = size_filter.sort_index(axis=1)
+ if not len(cols_sorted):
+ return []
+ bud_candidates = start[[True, *(np.diff(cols_sorted.columns) > min_budgrowth_t)]]
+
+ return [mother_id] + [int(i) for i in bud_candidates.index.tolist()]
+
+
+def mother_buds_wrap(signals, *args):
+ ids = []
+ for trap in signals.index.unique(level="trap"):
+ df = signals.loc[trap]
+ selected_ids = mother_buds(df, *args)
+ ids += [(trap, i) for i in selected_ids]
+
+ idx_srs = pd.Series(False, signals.index).astype(bool)
+ idx_srs.loc[ids] = True
+ return idx_srs
+
+
def _as_int(threshold: Union[float, int], ntps: int):
if type(threshold) is float:
threshold = ntps * threshold
--
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