diff --git a/core/group.py b/core/group.py
new file mode 100644
index 0000000000000000000000000000000000000000..773f151e8f92f19553a88e0a9f60734d813dbbc5
--- /dev/null
+++ b/core/group.py
@@ -0,0 +1,132 @@
+"""
+Class to group multiple positions into one using one of several criteria.
+"""
+
+from pathlib import Path
+import re
+
+import h5py
+import pandas as pd
+
+from postprocessor.core.io.base import groupsort
+from postprocessor.core.io.signal import Signal
+
+from postprocessor.core.processes.base import ParametersABC, ProcessABC
+
+
+class GroupParameters(ParametersABC):
+ def __init__(self, by="name", processes=[], signals=[]):
+ self.by = by
+ self.signals = signals
+ self.processes = processes
+
+ @classmethod
+ def default(cls):
+ return cls.from_dict({"by": "name", "signals": [], "processes": []})
+
+
+class Group(ProcessABC):
+ def __init__(self, parameters):
+ super().__init__(parameters)
+
+ def get_position_filenames(self, exp_root, poses):
+ """
+ Get filenames as a dictionary where the key is the position and value the filename.
+ """
+ central_store = Path(exp_root) / "store.h5"
+ if central_store.exists():
+ hdf = h5py.File(central_store, "r")
+ self.filenames = [pos.attrs["filename"] for pos in hdf["/positions/"]]
+ hdf.close()
+ else: # If no central store just list position files in expt root folder
+ fullfiles = [x for x in Path(exp_root).glob("*store.h5")]
+ files = [x.name for x in Path(exp_root).glob("*store.h5")]
+ filenames = [False for _ in poses]
+ for i, pos in enumerate(poses):
+ matches = [
+ True if re.match(pos + ".*.h5", fname) else False for fname in files
+ ]
+ if any(matches):
+ assert sum(matches) == 1, "More than one match"
+ filenames[i] = (pos, fullfiles[matches.index(True)])
+
+ self.filenames = {fname[0]: fname[1] for fname in filenames if fname}
+
+ self.positions = list(self.filenames.keys())
+ return self.filenames
+
+ def get_signals(self):
+ # hdf = h5py.File(central_store, "r")
+ # keys_d = groupsort(keys)
+ self.signals = {pos: {} for pos in self.filenames.keys()}
+ for pos, fname in self.filenames.items():
+ for signal in self.parameters.signals:
+ self.signals[pos][signal] = pd.read_hdf(fname, signal)
+
+ return self.signals
+
+ def gen_groups(self):
+ if self.by == "group": # Use group names in metadata
+ pass
+ elif self.by == "name": # Infer groups from signal concatenation
+ # Remove last four characters and find commonalities larger than 4
+ # characters between posnames and group them that way.
+ groupnames = list(set([x[:-3] for x in self.positions]))
+ self.group_signal_tree = {group: [] for group in groupnames}
+ self.poses_grouped = {group: [] for group in groupnames}
+ for pos in self.positions:
+ group = groupnames[groupnames.index(pos[:-3])]
+ self.group_signal_tree[group].append(self.signals[pos])
+ self.poses_grouped[group].append(pos)
+
+ elif (
+ type(self.by) == tuple
+ ): # Manually give groups as tuple or list of positions
+ pass
+
+ def concat_signals(self):
+ self.concated_signals = {group: {} for group in self.group_signal_tree}
+ for k, group in self.group_signal_tree.items():
+ for signal in self.parameters.signals:
+ self.concated_signals[k][signal] = pd.concat(
+ [g[signal] for g in group], keys=self.poses_grouped[k]
+ )
+
+ return self.concated_signals
+
+ def process_signals(self, grouped_signals):
+ pass
+
+ def run(self, central_store, poses):
+
+ self.get_position_filenames(central_store, poses)
+ self.get_signals()
+ self.gen_groups()
+ self.concat_signals()
+ # processed_signals = self.process_signals(grouped_signals)
+
+ return concated_signals
+ # return processed_signals
+
+
+# "/shared_libs/pipeline-core/scripts/data/ph_calibration_dual_phl_ura8_5_04_5_83_7_69_7_13_6_59__01"
+# simulate poses
+poses = [
+ x.name.split("store")[0]
+ for x in Path(
+ "/shared_libs/pipeline-core/scripts/data/ph_calibration_dual_phl_ura8_5_04_5_83_7_69_7_13_6_59__01"
+ ).rglob("*")
+ if x.name != "images.h5"
+]
+gr = Group(
+ GroupParameters(
+ signals=["/extraction/general/None/area", "/extraction/mCherry/np_max/median"]
+ )
+)
+gr.run(
+ central_store="/shared_libs/pipeline-core/scripts/data/ph_calibration_dual_phl_ura8_5_04_5_83_7_69_7_13_6_59__01",
+ poses=poses,
+)
+signal = Signal(
+ "/shared_libs/pipeline-core/scripts/data/ph_calibration_dual_phl_ura8_5_04_5_83_7_69_7_13_6_59__01/ph_5_04_001store.h5"
+)
diff --git a/core/io/base.py b/core/io/base.py
index 181f23b0fb08a9e3236ecda4a02cfe7fe0e73096..6812bd537afc4f442c79cb471d2343c8326bfe12 100644
--- a/core/io/base.py
+++ b/core/io/base.py
@@ -7,6 +7,11 @@ import h5py
class BridgeH5:
+ """
+ Base class to interact with h5 data stores.
+ It also contains functions useful to predict how long should segmentation take.
+ """
+
def __init__(self, file):
self._hdf = h5py.File(file, "r")
diff --git a/core/io/signal.py b/core/io/signal.py
index 05ddb6ae2fb054798277756f85bc7551870f1a29..5ae9e5058653695106074a49032b746452853b3f 100644
--- a/core/io/signal.py
+++ b/core/io/signal.py
@@ -10,21 +10,12 @@ class Signal(BridgeH5):
def __init__(self, file):
super().__init__(file)
+ self._hdf.close() # Close the file to use pandas hdf functions
+ # hdf = pd.HDFStore(file)
+ # self.file = file
def __getitem__(self, dataset):
- dset = self._hdf[dataset][()]
- attrs = self._hdf[dataset].attrs
- first_dataset = dataset.split("/")[0] + "/"
- timepoints = self._hdf[first_dataset].attrs["processed_timepoints"]
-
- if "cell_label" in self._hdf[dataset].attrs:
- ids = pd.MultiIndex.from_tuples(
- zip(attrs["trap"], attrs["cell_label"]), names=["trap", "cell_label"]
- )
- else:
- ids = pd.Index(attrs["trap"], names=["trap"])
-
- return pd.DataFrame(dset, index=ids, columns=timepoints)
+ return pd.read_hdf(self.file, dataset)
@staticmethod
def _if_ext_or_post(name):
@@ -34,4 +25,4 @@ class Signal(BridgeH5):
@property
def datasets(self):
- return signals._hdf.visit(self._if_ext_or_post)
+ return self._hdf.visit(self._if_ext_or_post)