diff --git a/core/functions/test_hdf.py b/core/functions/test_hdf.py
index bd7fd764345840e80c63bdd49f7aad4a8d5ad2ca..a9f8faca65a2c5830f8b751139079d890c3fe442 100644
--- a/core/functions/test_hdf.py
+++ b/core/functions/test_hdf.py
@@ -4,7 +4,10 @@ import h5py
from core.cells import Cells
import pandas as pd
-f = h5py.File("/home/alan/Documents/sync_docs/PhD/tmp/DO6MS2_003store.h5")
+# f = h5py.File("/home/alan/Documents/sync_docs/PhD/tmp/DO6MS2_003store.h5")
+f = h5py.File(
+ "/shared_libs/pipeline-core/scripts/data/20191026_ss_experiments_01/DO6MS2_003store.h5"
+)
tracks = f["/extraction/general/None/area"][()]
cell = Cells.from_source("/home/alan/Documents/sync_docs/PhD/tmp/DO6MS2_003store.h5")
from postprocessor.core.picker import Picker
diff --git a/core/parameters.py b/core/parameters.py
new file mode 100644
index 0000000000000000000000000000000000000000..b9e67ef3b3a61a5a70bbc1d8bbb3dd96aa4cbf78
--- /dev/null
+++ b/core/parameters.py
@@ -0,0 +1,33 @@
+from abc import ABC, abstractmethod
+
+
+class ParametersABC(ABC):
+ """
+ Base class to add yaml functionality to parameters
+
+ """
+
+ def to_dict(self):
+ return self.__dict__
+
+ @classmethod
+ def from_dict(cls, d):
+ return cls(**d)
+
+ def to_yaml(self, path=None):
+ return dump(self.__dict__, path)
+
+ @classmethod
+ def from_yaml(cls, yam):
+ with open(Path(yam)) as f:
+ params = safe_load(f)
+ return cls(**params)
+
+ @abstractmethod
+ @classmethod
+ def default(cls):
+ pass
+
+ @abstractmethod
+ def run(self):
+ pass
diff --git a/core/picker.py b/core/picker.py
index 2618ae99a99b307918eebec00a1c8fb4b10dc728..eaed8e9014ab4b4451093c58d8f848bd7e66b4bc 100644
--- a/core/picker.py
+++ b/core/picker.py
@@ -1,18 +1,36 @@
from typing import Tuple, Union, List
+from abc import ABC, abstractmethod
import numpy as np
import pandas as pd
from core.cells import CellsHDF
+
+from postprocessor.core.parameters import ParametersABC
from postprocessor.core.functions.signals import max_ntps, max_nonstop_ntps
-# def BasePicker(ABC):
-# """
-# Base class to add mother-bud filtering support
-# """
-# def __init__(self, branch=None, lineage=None):
-# self.lineage = lineage
+@ParametersABC.register
+class PickerParameters:
+ def __init__(
+ self,
+ condition: Tuple[str, Union[float, int]] = None,
+ lineage: str = None,
+ sequence: List[str] = ["lineage", "condition"],
+ ):
+ self.condition = condition
+ self.lineage = lineage
+ self.sequence = sequence
+
+ @classmethod
+ def defaults(cls):
+ return cls.from_dict(
+ {
+ "condition": ("present", 0.8),
+ "lineage": None,
+ "sequence": ["lineage", "condition"],
+ }
+ )
class Picker:
@@ -29,16 +47,15 @@ class Picker:
self,
signals: pd.DataFrame,
cells: CellsHDF,
- condition: Tuple[str, Union[float, int]] = None,
- lineage: str = None,
- sequence: List[str] = ["lineage", "condition"],
+ parameters: PickerParameters,
):
self.signals = signals
self._index = signals.index
self._cells = cells
- self.condition = condition
- self.lineage = lineage
- self.sequence = sequence
+ self.parameters = parameters
+
+ for k, v in parameters.to_dict().items(): # access parameters directly
+ setattr(self, k, v)
@staticmethod
def mother_assign_to_mb_matrix(ma: List[np.array]):
diff --git a/core/processor.py b/core/processor.py
index c4e4acee964b24d7ad332d07583dc9f8d5e6f83b..81f302de27a2f1f5cc714b6f53e12d40a65dd042 100644
--- a/core/processor.py
+++ b/core/processor.py
@@ -2,18 +2,23 @@ import pandas as pd
class Parameters:
- def __init__(self, merger=None, picker=None, processes=None, branches=None):
+ """
+ Anthology of parameters used for postprocessing
+ """
+
+ def __init__(self, merger=None, picker=None, processes=None, datasets=None):
self.merger = merger
self.picker = picker
self.processes = processes
- self.branches = branches
+
+ self.datasets = datasets
def __getitem__(self, item):
return getattr(self, item)
class PostProcessor:
- def __init__(self, parameters, signals):
+ def __init__(self, fname, parameters, signals):
self.parameters = parameters
self.merger = Merger(parameters["merger"])
@@ -21,13 +26,14 @@ class PostProcessor:
self.processes = [
self.get_process(process) for process in parameters["processes"]
]
- self.branches = parameters["branches"]
+ self.datasets = parameters["datasets"]
def run(self):
- self.merger.run(signals.get_branch(self.branches["merger"]))
- self.picker.run(signals.get_branch(self.branches["picker"]))
- for process, branch in zip(self.processes, self.branches["processes"]):
- process.run(signals.get_branch(branch))
+ self.merger.run(signals[self.datasets["merger"]])
+ self.picker.run(signals[self.datasets["picker"]])
+ for process, dataset in zip(self.processes, self.datasets["processes"]):
+ process_result = process.run(signals.get_dataset(dataset))
+ self.writer.write(process_result, dataset)
class Signals:
@@ -38,26 +44,13 @@ class Signals:
def __init__(self, file):
self._hdf = h5py.File(file, "r")
- @staticmethod
- def _if_ext_or_post(name):
- if name.startswith("extraction") or name.startswith("postprocessing"):
- if len(name.split("/")) > 3:
- return name
-
- @property
- def branches(self):
- return signals._hdf.visit(self._if_ext_or_post)
+ 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"]
- def get_branch(self, branch):
- return self._hdf[branch][()]
-
- def branch_to_df(self, branch):
- dset = self._hdf[branch][()]
- attrs = self._hdf[branch].attrs
- first_branch = "/" + branch.split("/")[0] + "/"
- timepoints = self._hdf[first_branch].attrs["processed_timepoints"]
-
- if "cell_label" in self._hdf[branch].attrs:
+ if "cell_label" in self._hdf[dataset].attrs:
ids = pd.MultiIndex.from_tuples(
zip(attrs["trap"], attrs["cell_label"]), names=["trap", "cell_label"]
)
@@ -66,5 +59,15 @@ class Signals:
return pd.DataFrame(dset, index=ids, columns=timepoints)
+ @staticmethod
+ def _if_ext_or_post(name):
+ if name.startswith("extraction") or name.startswith("postprocessing"):
+ if len(name.split("/")) > 3:
+ return name
+
+ @property
+ def datasets(self):
+ return signals._hdf.visit(self._if_ext_or_post)
+
def close(self):
self._hdf.close()