feat(grouper): add mode "families" and "daughter"

src/agora/io/signal.py

@@ -41,11 +41,13 @@ class Signal(BridgeH5):
 
     def __getitem__(self, dsets: t.Union[str, t.Collection]):
 
-        if isinstance(dsets, str) and dsets.endswith("imBackground"):
+        if isinstance(
+            dsets, str
+        ):  # or  isinstance(Dsets,dsets.endswith("imBackground"):
             df = self.get_raw(dsets)
 
-        elif isinstance(dsets, str):
-            df = self.apply_prepost(dsets)
+        # elif isinstance(dsets, str):
+        #     df = self.apply_prepost(dsets)
 
         elif isinstance(dsets, list):
             is_bgd = [dset.endswith("imBackground") for dset in dsets]

src/agora/utils/kymograph.py

@@ -2,8 +2,11 @@
 import typing as t
 from copy import copy
 
+import numpy as np
 import pandas as pd
 
+index_row = t.Tuple[str, str, int, int]
+
 
 def add_index_levels(
     df: pd.DataFrame, additional_ids: t.Dict[str, pd.Series] = {}
@@ -16,3 +19,42 @@ def add_index_levels(
         new_df[k] = srs
         new_df.set_index(k, inplace=True, append=True)
     return new_df
+
+
+def drop_level(
+    df: pd.DataFrame, name: str = "mother_label", as_list: bool = True
+) -> t.Union[t.List[index_row], pd.Index]:
+    """Drop index level
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        dataframe whose multiindex we will drop
+    name : str
+        name of index level to drop
+    as_list : bool
+        Whether to return as a list instead of an index
+
+    Examples
+    --------
+    FIXME: Add docs.
+
+    """
+    short_index = df.index.droplevel(name)
+    if as_list:
+        short_index = short_index.to_list()
+    return short_index
+
+
+def intersection_matrix(
+    index1: pd.MultiIndex, index2: pd.MultiIndex
+) -> np.ndarray:
+    """
+    Use casting to obtain the boolean mask of the intersection of two multiindices
+    """
+    if not isinstance(index1, np.ndarray):
+        index1 = np.array(index1.to_list())
+    if not isinstance(index2, np.ndarray):
+        index2 = np.array(index2.to_list())
+
+    return (index1[..., None] == index2.T).all(axis=1)

src/postprocessor/grouper.py

@@ -13,9 +13,11 @@ import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import seaborn as sns
-from postprocessor.chainer import Chainer
 from pathos.multiprocessing import Pool
 
+from agora.utils.kymograph import drop_level, intersection_matrix
+from postprocessor.chainer import Chainer
+
 
 class Grouper(ABC):
     """Base grouper class."""
@@ -83,7 +85,7 @@ class Grouper(ABC):
         standard: t.Optional[bool] = False,
         **kwargs,
     ):
-        """Concate
+        """Concatenate multiple signals
 
                Parameters
                ----------
@@ -390,12 +392,31 @@ def concat_signal_ind(
         raise (NotImplementedError)
     elif mode == "raw":
         combined = chainer.get_raw(path, **kwargs)
+    elif mode == "daughters":
+        combined = chainer.get_raw(path, **kwargs)
+        combined = combined.loc[
+            combined.index.get_level_values("mother_label") > 0
+        ]
     elif mode == "families":
-        combined = chainer[path]
+        combined = chainer.get_raw(path, **kwargs)
+        daughter_ids = combined.index[
+            combined.index.get_level_values("mother_label") > 0
+        ]
+        mother_id_mask = intersection_matrix(
+            daughter_ids.droplevel("cell_label"),
+            drop_level(combined, "mother_label", as_list=False),
+        ).any(axis=0)
+        combined = combined.loc[
+            combined.index[mother_id_mask].union(daughter_ids)
+        ]
+
     combined["position"] = position
     combined["group"] = group
     combined.set_index(["group", "position"], inplace=True, append=True)
-    combined.index = combined.index.swaplevel(-2, 0).swaplevel(-1, 1)
+    # combined.index = combined.index.swaplevel(-2, 0).swaplevel(-1, 1)
+    combined.index = combined.index.reorder_levels(
+        ("group", "position", "trap", "cell_label", "mother_label")
+    )
 
     return combined