update to latest format and document

Former-commit-id: 7704b4f09669e8610b9a7ca49e3d200c4ec9bcca

core/processes/aggregate.py

@@ -12,18 +12,22 @@ class aggregateParameters(ParametersABC):
     reduction: str to be passed to a dataframe for collapsing across columns
     """
 
-    def __init__(self, reductions):
+    def __init__(self, reductions, axis, ranges):
         super().__init__()
         self.reductions = reductions
+        self.axis = axis
+        self.ranges = ranges
 
     @classmethod
     def default(cls):
-        return cls.from_dict({"reductions": ["mean", "median", "max"]})
+        return cls.from_dict(
+            {"reductions": ["mean", "median", "max"], "axis": 1, "ranges": None}
+        )
 
 
 class aggregate(ProcessABC):
     """
-    aggregate multiple datasets
+    Aggregate multiple datasets for cell-to-cell feature comparison.
     """
 
     def __init__(self, parameters: aggregateParameters):
@@ -62,13 +66,25 @@ class aggregate(ProcessABC):
         ]
         concat = pd.concat(
             [
-                getattr(signal, red)(axis=1)
+                getattr(signal, red)(axis=self.parameters.axis)
                 for signal in signals
                 for red in self.parameters.reductions
             ],
             names=signals[0].index.names,
-            axis=1,
+            axis=self.parameters.axis,
         )
-        concat.columns = colnames
+        if self.parameters.axis:
+            concat.columns = colnames
+        else:
+            concat.columns = pd.MultiIndex.from_product(
+                (
+                    colnames,
+                    [
+                        "_".join((str(start), str(stop)))
+                        for x in self.parameters.ranges
+                        for start, stop in x
+                    ],
+                )
+            )
 
         return concat

core/processes/births.py

 #!/usr/bin/env python3
+
+import pandas as pd
+
+from agora.io.base import ParametersABC, ProcessABC
+
+
+class birthsParameters(ParametersABC):
+    """
+    :window: Number of timepoints to consider for signal.
+    """
+
+    def __init__(self):
+        pass
+
+    @classmethod
+    def default(cls):
+        return cls.from_dict({})
+
+
+class births(ProcessABC):
+    """
+    Calculate the change in a signal depending on a window
+    """
+
+    def __init__(self, parameters: birthsParameters):
+        super().__init__(parameters)
+
+    def run(self, signal: pd.DataFrame):
+        pass

core/processes/dsignal.py

 import pandas as pd
 
-from postprocessor.core.processes.base import ParametersABC, ProcessABC
+from agora.base import ParametersABC, ProcessABC
 
 
 class dsignalParameters(ParametersABC):

core/processor.py

@@ -123,9 +123,6 @@ class PostProcessorParameters(ParametersABC):
 
         return cls(targets=targets, parameters=parameters, outpaths=outpaths)
 
-    def to_dict(self):
-        return {k: _if_dict(v) for k, v in self.__dict__.items()}
-
 
 class PostProcessor:
     def __init__(self, filename, parameters):
@@ -162,8 +159,8 @@ class PostProcessor:
     @staticmethod
     def get_parameters(process):
         """
-        Dynamically import a process class from the 'processes' folder.
-        Assumes process filename and class name are the same
+        Dynamically import parameters from the 'processes' folder.
+        Assumes parameter is the same name as the file with 'Parameters' added at the end.
         """
         return locate(
             "postprocessor.core.processes." + process + "." + process + "Parameters"