fix(postprocessing): enforce float16

src/agora/io/writer.py

@@ -549,7 +549,7 @@ class Writer(BridgeH5):
                 compression=kwargs.get("compression", None),
             )
             dset = f[values_path]
-            dset[()] = df.values
+            dset[()] = df.values.astype("float16")
 
             # create dateset and write indices
             if not len(df):  # Only write more if not empty

src/agora/utils/indexing.py

@@ -110,22 +110,30 @@ def _assoc_indices_to_3d(ndarray: np.ndarray):
 
     This is useful when converting a signal multiindex before comparing association.
     """
-    columns = np.arange(ndarray.shape[1])
-
-    return np.stack(
-        (
-            ndarray[:, np.delete(columns, -1)],
-            ndarray[:, np.delete(columns, -2)],
-        ),
-        axis=1,
-    )
+    result = ndarray
+    if len(ndarray) and ndarray.ndim > 1:
+        columns = np.arange(ndarray.shape[1])
+
+        result = np.stack(
+            (
+                ndarray[:, np.delete(columns, -1)],
+                ndarray[:, np.delete(columns, -2)],
+            ),
+            axis=1,
+        )
+    return result
 
 
 def _3d_index_to_2d(array: np.ndarray):
     """
     Opposite to _assoc_indices_to_3d.
     """
-    return np.concatenate((array[:, 0, :], array[:, 1, 1, np.newaxis]), axis=1)
+    result = array
+    if len(array):
+        result = np.concatenate(
+            (array[:, 0, :], array[:, 1, 1, np.newaxis]), axis=1
+        )
+    return result
 
 
 def compare_indices(x: np.ndarray, y: np.ndarray) -> np.ndarray:

src/postprocessor/core/processor.py

+import logging
 import typing as t
 from itertools import takewhile
 from typing import Dict, List, Union
 
-import h5py
 import numpy as np
 import pandas as pd
 from tqdm import tqdm
@@ -12,13 +12,16 @@ from agora.io.cells import Cells
 from agora.io.signal import Signal
 from agora.io.writer import Writer
 from agora.utils.indexing import (
-    _assoc_indices_to_3d,
     _3d_index_to_2d,
+    _assoc_indices_to_3d,
     compare_indices,
 )
 from agora.utils.kymograph import get_index_as_np
 from postprocessor.core.abc import get_parameters, get_process
-from postprocessor.core.lineageprocess import LineageProcessParameters
+from postprocessor.core.lineageprocess import (
+    LineageProcess,
+    LineageProcessParameters,
+)
 from postprocessor.core.reshapers.merger import Merger, MergerParameters
 from postprocessor.core.reshapers.picker import Picker, PickerParameters
 
@@ -160,9 +163,7 @@ class PostProcessor(ProcessABC):
             "modifiers/merges", data=[np.array(x) for x in merges]
         )
 
-        lineage = self.picker.cells.mothers_daughters
-        if lineage.any():
-            lineage = _assoc_indices_to_3d(lineage)
+        lineage = _assoc_indices_to_3d(self.picker.cells.mothers_daughters)
         lineage_merged = []
 
         indices = get_index_as_np(record)
@@ -188,7 +189,7 @@ class PostProcessor(ProcessABC):
             lineage_merged = np.unique(flat_indices.reshape(-1, 2, 2), axis=0)
 
         self.lineage = _3d_index_to_2d(
-            np.array(lineage_merged if len(lineage_merged) else lineage)
+            lineage_merged if len(lineage_merged) else lineage
         )
 
         self._writer.write(
@@ -198,17 +199,14 @@ class PostProcessor(ProcessABC):
         picked_indices = self.picker.run(
             self._signal[self.targets["prepost"]["picker"][0]]
         )
-        self._writer.write(
-            "modifiers/picks",
-            data=pd.MultiIndex.from_arrays(
-                # TODO Check if multiindices are still repeated
-                np.unique(picked_indices, axis=0).T
-                if indices.any()
-                else [[], []],
-                names=["trap", "cell_label"],
-            ),
-            overwrite="overwrite",
-        )
+        if picked_indices.any():
+            self._writer.write(
+                "modifiers/picks",
+                data=pd.MultiIndex.from_arrays(
+                    picked_indices, names=["trap", "cell_label"]
+                ),
+                overwrite="overwrite",
+            )
 
     @staticmethod
     def pick_mother(a, b):
@@ -236,12 +234,13 @@ class PostProcessor(ProcessABC):
             else:
                 parameters = self.parameters_classfun[process].default()
 
+            if process == "buddings":
+                print("stop")
+
             loaded_process = self.classfun[process](parameters)
             if isinstance(parameters, LineageProcessParameters):
                 loaded_process.lineage = self.lineage
 
-            if process == "bud_metric":
-                print("stop")
             for dataset in datasets:
                 if isinstance(dataset, list):  # multisignal process
                     signal = [self._signal[d] for d in dataset]
@@ -250,7 +249,10 @@ class PostProcessor(ProcessABC):
                 else:
                     raise Exception("Unavailable record")
 
-                if len(signal):
+                if len(signal) and (
+                    not isinstance(loaded_process, LineageProcess)
+                    or len(loaded_process.lineage)
+                ):
                     result = loaded_process.run(signal)
                 else:
                     result = pd.DataFrame(
@@ -306,6 +308,8 @@ class PostProcessor(ProcessABC):
         result: Union[List, pd.DataFrame, np.ndarray],
         metadata: Dict,
     ):
+        if not result.any().any():
+            logging.getLogger("aliby").warning(f"Record {path} is empty")
         self._writer.write(path, result, meta=metadata, overwrite="overwrite")