diff --git a/src/agora/utils/kymograph.py b/src/agora/utils/kymograph.py
index 71411e1dab85442fd986dd9c925a11f50785e3c2..f33c1c1dc1294e9cdf74aa4a2f6ad086ff2e0e7c 100644
--- a/src/agora/utils/kymograph.py
+++ b/src/agora/utils/kymograph.py
@@ -163,3 +163,15 @@ def slices_from_spans(spans: t.Tuple[int], df: pd.DataFrame) -> t.List[slice]:
for start, end in zip(cumsum[:-1], cumsum[1:])
]
return slices
+
+
+def drop_mother_label(index: pd.MultiIndex) -> np.ndarray:
+ no_mother_label = index
+ if "mother_label" in index.names:
+ no_mother_label = index.droplevel("mother_label")
+ return np.array(no_mother_label.tolist())
+
+
+def get_index_as_np(signal: pd.DataFrame):
+ # Get mother labels from multiindex dataframe
+ return np.array(signal.index.to_list())
diff --git a/src/postprocessor/core/lineageprocess.py b/src/postprocessor/core/lineageprocess.py
index f10d5b3e3fef47e23724115ab0f99669a5b6ad94..1c875020bbb9e1a92062ddc7d398b8b079b06437 100644
--- a/src/postprocessor/core/lineageprocess.py
+++ b/src/postprocessor/core/lineageprocess.py
@@ -51,9 +51,11 @@ class LineageProcess(PostProcessABC):
data, lineage=lineage, *extra_data
)
- def load_lineage(self, lineage):
- """
- Reshape the lineage information if needed
- """
- # TODO does this need to be a function?
- self.lineage = lineage
+ def get_lineage_information(self, signal):
+ if "mother_label" in signal.index.names:
+ lineage = get_index_as_np(signal)
+ elif self.cells is not None:
+ lineage = self.cells.mothers_daughters
+ else:
+ raise Exception("No linage information found")
+ return lineage
diff --git a/src/postprocessor/core/processor.py b/src/postprocessor/core/processor.py
index ec3b74abe1d8295a53d0a1f7210659852f136c50..b010efd16c8fdbddea83a8a8cf7a8a560bafbd05 100644
--- a/src/postprocessor/core/processor.py
+++ b/src/postprocessor/core/processor.py
@@ -286,7 +286,8 @@ class PostProcessor(ProcessABC):
# self.parameters.lineage_location
)
loaded_process = self.classfun[process](parameters)
- loaded_process.load_lineage(lineage)
+ loaded_process.lineage = lineage
+
else:
loaded_process = self.classfun[process](parameters)
diff --git a/src/postprocessor/core/reshapers/picker.py b/src/postprocessor/core/reshapers/picker.py
index c331c842030ae11ed5ecf0a2af639df6c700b7b5..4132cf8dbd742ebcb274efa44b97c3ab9c2476c4 100644
--- a/src/postprocessor/core/reshapers/picker.py
+++ b/src/postprocessor/core/reshapers/picker.py
@@ -6,7 +6,8 @@ import pandas as pd
from agora.abc import ParametersABC
from agora.io.cells import Cells
-from agora.utils.association import validate_association, last_col_as_rows
+from agora.utils.association import validate_association
+from agora.utils.kymograph import drop_mother_label, get_index_as_np
from postprocessor.core.lineageprocess import LineageProcess
@@ -24,14 +25,14 @@ class Picker(LineageProcess):
:cells: Cell object passed to the constructor
:condition: Tuple with condition and associated parameter(s), conditions can be
"present", "nonstoply_present" or "quantile".
- Determines the thersholds or fractions of signals/signals to use.
+ Determines the thersholds or fractions of signals to use.
:lineage: str {"mothers", "daughters", "families" (mothers AND daughters), "orphans"}. Mothers/daughters picks cells with those tags, families pick the union of both and orphans the difference between the total and families.
"""
def __init__(
self,
parameters: PickerParameters,
- cells: Cells,
+ cells: Cells or None = None,
):
super().__init__(parameters=parameters)
@@ -46,8 +47,7 @@ class Picker(LineageProcess):
cells_present = drop_mother_label(signal.index)
- if mothers_daughters is None:
- mothers_daughters = self.cells.mothers_daughters
+ mothers_daughters = self.get_lineage_information(signal)
valid_indices = slice(None)
@@ -66,15 +66,17 @@ class Picker(LineageProcess):
return signal.index[valid_indices]
- def pick_by_condition(self, signals, condition, thresh):
- idx = self.switch_case(signals, condition, thresh)
+ def pick_by_condition(self, signal, condition, thresh):
+ idx = self.switch_case(signal, condition, thresh)
return idx
- def run(self, signals):
- self.orig_signals = signals
- indices = set(signals.index)
- lineage = self.cells.mothers_daughters
- if lineage.any():
+ def run(self, signal):
+ self.orig_signal = signal
+ indices = set(signal.index)
+
+ lineage = self.get_lineage_information(signal)
+
+ if len(lineage):
self.mothers = lineage[:, :2]
self.daughters = lineage[:, [0, 2]]
@@ -84,12 +86,12 @@ class Picker(LineageProcess):
if alg == "lineage":
param1 = params[0]
new_indices = getattr(self, "pick_by_" + alg)(
- signals.loc[list(indices)], param1
+ signal.loc[list(indices)], param1
)
else:
param1, *param2 = params
new_indices = getattr(self, "pick_by_" + alg)(
- signals.loc[list(indices)], param1, param2
+ signal.loc[list(indices)], param1, param2
)
new_indices = [tuple(x) for x in new_indices]
@@ -102,12 +104,12 @@ class Picker(LineageProcess):
def switch_case(
self,
- signals: pd.DataFrame,
+ signal: pd.DataFrame,
condition: str,
threshold: t.Union[float, int, list],
):
if len(threshold) == 1:
- threshold = [_as_int(*threshold, signals.shape[1])]
+ threshold = [_as_int(*threshold, signal.shape[1])]
case_mgr = {
"any_present": lambda s, thresh: any_present(s, thresh),
"present": lambda s, thresh: s.notna().sum(axis=1) > thresh,
@@ -115,7 +117,7 @@ class Picker(LineageProcess):
> thresh,
"growing": lambda s, thresh: s.diff(axis=1).sum(axis=1) > thresh,
}
- return set(signals.index[case_mgr[condition](signals, *threshold)])
+ return set(signal.index[case_mgr[condition](signal, *threshold)])
def _as_int(threshold: t.Union[float, int], ntps: int):
@@ -124,28 +126,21 @@ def _as_int(threshold: t.Union[float, int], ntps: int):
return threshold
-def any_present(signals, threshold):
+def any_present(signal, threshold):
"""
Returns a mask for cells, True if there is a cell in that trap that was present for more than :threshold: timepoints.
"""
any_present = pd.Series(
np.sum(
[
- np.isin([x[0] for x in signals.index], i) & v
- for i, v in (signals.notna().sum(axis=1) > threshold)
+ np.isin([x[0] for x in signal.index], i) & v
+ for i, v in (signal.notna().sum(axis=1) > threshold)
.groupby("trap")
.any()
.items()
],
axis=0,
).astype(bool),
- index=signals.index,
+ index=signal.index,
)
return any_present
-
-
-def drop_mother_label(index: pd.MultiIndex) -> np.ndarray:
- no_mother_label = index
- if "mother_label" in index.names:
- no_mother_label = index.droplevel("mother_label")
- return np.array(no_mother_label.tolist())