diff --git a/src/extraction/core/extractor.py b/src/extraction/core/extractor.py
index 7cb11443b2e9b6197382911230ce82ee90dc9cec..95ba13c6191b8951c0c60c5a06f674702757fe9c 100644
--- a/src/extraction/core/extractor.py
+++ b/src/extraction/core/extractor.py
@@ -1,22 +1,22 @@
import typing as t
+from pathlib import Path
import bottleneck as bn
import h5py
import numpy as np
import pandas as pd
+import aliby.global_parameters as global_parameters
from agora.abc import ParametersABC, StepABC
from agora.io.cells import Cells
from agora.io.writer import Writer, load_meta
-from aliby.tile.tiler import Tiler
-from extraction.core.functions.defaults import exparams_from_meta
+from aliby.tile.tiler import Tiler, find_channel_name
from extraction.core.functions.distributors import reduce_z, trap_apply
from extraction.core.functions.loaders import (
load_custom_args,
load_funs,
load_redfuns,
)
-import aliby.global_parameters as global_parameters
# define types
reduction_method = t.Union[t.Callable, str, None]
@@ -68,6 +68,7 @@ class ExtractorParameters(ParametersABC):
@classmethod
def from_meta(cls, meta):
+ """Instantiate using meta data."""
return cls(**exparams_from_meta(meta))
@@ -182,8 +183,7 @@ class Extractor(StepABC):
def load_custom_funs(self):
"""
- Incorporate the extra arguments of custom functions into their
- definitions.
+ Incorporate the extra arguments of custom functions into their definitions.
Normal functions only have cell_masks and trap_image as their
arguments, and here custom functions are made the same by
@@ -354,7 +354,7 @@ class Extractor(StepABC):
**kwargs,
) -> t.Dict[str, t.Dict[reduction_method, t.Dict[str, pd.Series]]]:
"""
- Wrapper to reduce to a 2D image and then extract.
+ Reduce to a 2D image and then extract.
Parameters
----------
@@ -374,9 +374,6 @@ class Extractor(StepABC):
------
Dict of dataframes with the corresponding reductions and metrics nested.
"""
- # FIXME hack to pass tests
- if "labels" in kwargs:
- kwargs["cell_labels"] = kwargs.pop("labels")
# create dict with keys naming the reduction in the z-direction
# and the reduced data as values
reduced_tiles = {}
@@ -484,7 +481,7 @@ class Extractor(StepABC):
self, tree_bits, cell_labels, tiles, masks, bgs, **kwargs
):
"""
- Extract using all metrics requiring a single channel.
+ Extract all metrics requiring only a single channel.
Apply first without and then with background subtraction.
@@ -496,8 +493,8 @@ class Extractor(StepABC):
for ch, tree_branch in tree_bits["tree"].items():
# NB ch != is necessary for threading
if ch != "general" and tiles is not None and len(tiles):
- # image data for all traps for a particular channel and time point
- # arranged as (traps, Z, X, Y)
+ # image data for all traps for a particular channel and
+ # time point arranged as (traps, Z, X, Y)
# we use 0 here to access the single time point available
img = tiles[:, tree_bits["tree_channels"].index(ch), 0]
else:
@@ -530,9 +527,9 @@ class Extractor(StepABC):
img_bgsub[ch_bs] = np.moveaxis(mapping_result, -1, 1)
# apply metrics to background-corrected data
d[ch_bs] = self.reduce_extract(
- tree_branch=tree_bits["channel_tree"][ch],
traps=img_bgsub[ch_bs],
masks=masks,
+ tree_branch=tree_bits["channel_tree"][ch],
cell_labels=cell_labels,
**kwargs,
)
@@ -542,8 +539,9 @@ class Extractor(StepABC):
self, tree_bits, cell_labels, tiles, masks, **kwargs
):
"""
- Extract using all metrics requiring multiple channels.
+ Extract all metrics requiring multiple channels.
"""
+ # channels and background corrected channels
available_chs = set(self.img_bgsub.keys()).union(
tree_bits["tree_channels"]
)
@@ -767,7 +765,6 @@ class Extractor(StepABC):
}
-### Helpers
def flatten_nesteddict(
nest: dict, to="series", tp: int = None
) -> t.Dict[str, pd.Series]:
@@ -799,3 +796,43 @@ def flatten_nesteddict(
pd.Series(*v2, name=tp) if to == "series" else v2
)
return d
+
+
+def extraction_params_from_meta(
+ meta: t.Union[dict, Path, str], extras: t.Collection[str] = ["ph"]
+):
+ """
+ Obtain parameters from metadata of the h5 file.
+
+ Compares a list of candidate channels using case-insensitive
+ regex to identify valid channels.
+ """
+ meta = meta if isinstance(meta, dict) else load_metadata(meta)
+ base = {
+ "tree": {"general": {"None": ["area", "volume", "eccentricity"]}},
+ "multichannel_ops": {},
+ }
+ candidate_channels = set(global_parameters.possible_imaging_channels)
+ default_reductions = {"max"}
+ default_metrics = set(global_parameters.fluorescence_functions)
+ default_reduction_metrics = {
+ r: default_metrics for r in default_reductions
+ }
+ # default_rm["None"] = ["nuc_conv_3d"] # Uncomment this to add nuc_conv_3d (slow)
+ extant_fluorescence_ch = []
+ for av_channel in candidate_channels:
+ # find matching channels in metadata
+ found_channel = find_channel_name(meta.get("channels", []), av_channel)
+ if found_channel is not None:
+ extant_fluorescence_ch.append(found_channel)
+ for ch in extant_fluorescence_ch:
+ base["tree"][ch] = default_reduction_metrics
+ base["sub_bg"] = extant_fluorescence_ch
+ return base
+
+
+def load_metadata(file: t.Union[str, Path], group="/"):
+ """Get meta data from an h5 file."""
+ with h5py.File(file, "r") as f:
+ meta = dict(f[group].attrs.items())
+ return meta
diff --git a/src/extraction/core/functions/defaults.py b/src/extraction/core/functions/defaults.py
deleted file mode 100644
index bc9107b7f86fd00a510d443596171d5607525191..0000000000000000000000000000000000000000
--- a/src/extraction/core/functions/defaults.py
+++ /dev/null
@@ -1,91 +0,0 @@
-# File with defaults for ease of use
-import typing as t
-from pathlib import Path
-
-import h5py
-
-import aliby.global_parameters as global_parameters
-from aliby.tile.tiler import find_channel_name
-
-
-def exparams_from_meta(
- meta: t.Union[dict, Path, str], extras: t.Collection[str] = ["ph"]
-):
- """
- Obtain parameters from metadata of the h5 file.
-
- Compares a list of candidate channels using case-insensitive
- REGEX to identify valid channels.
- """
- meta = meta if isinstance(meta, dict) else load_metadata(meta)
- base = {
- "tree": {"general": {"None": ["area", "volume", "eccentricity"]}},
- "multichannel_ops": {},
- }
- candidate_channels = set(global_parameters.possible_imaging_channels)
- default_reductions = {"max"}
- default_metrics = set(global_parameters.fluorescence_functions)
-
- # define ratiometric combinations
- # key is numerator and value is denominator
- # add more to support additional channel names
- ratiometric_combinations = {"phluorin405": ("phluorin488", "gfpfast")}
- default_reduction_metrics = {
- r: default_metrics for r in default_reductions
- }
- # default_rm["None"] = ["nuc_conv_3d"] # Uncomment this to add nuc_conv_3d (slow)
- extant_fluorescence_ch = []
- for av_channel in candidate_channels:
- # find matching channels in metadata
- found_channel = find_channel_name(meta.get("channels", []), av_channel)
- if found_channel is not None:
- extant_fluorescence_ch.append(found_channel)
- for ch in extant_fluorescence_ch:
- base["tree"][ch] = default_reduction_metrics
- base["sub_bg"] = extant_fluorescence_ch
-
- # additional extraction defaults if the channels are available
- if "ph" in extras:
- # SWAINLAB specific names
- # find first valid combination of ratiometric fluorescence channels
- numerator_channel, denominator_channel = (None, None)
- for ch1, chs2 in ratiometric_combinations.items():
- found_channel1 = find_channel_name(extant_fluorescence_ch, ch1)
- if found_channel1 is not None:
- numerator_channel = found_channel1
- for ch2 in chs2:
- found_channel2 = find_channel_name(
- extant_fluorescence_ch, ch2
- )
- if found_channel2:
- denominator_channel = found_channel2
- break
- # if two compatible ratiometric channels are available
- if numerator_channel is not None and denominator_channel is not None:
- sets = {
- b + a: (x, y)
- for a, x in zip(
- ["", "_bgsub"],
- (
- [numerator_channel, denominator_channel],
- [
- f"{numerator_channel}_bgsub",
- f"{denominator_channel}_bgsub",
- ],
- ),
- )
- for b, y in zip(["em_ratio", "gsum"], ["div0", "add"])
- }
- for i, v in sets.items():
- base["multichannel_ops"][i] = [
- *v,
- default_reduction_metrics,
- ]
- return base
-
-
-def load_metadata(file: t.Union[str, Path], group="/"):
- """Get meta data from an h5 file."""
- with h5py.File(file, "r") as f:
- meta = dict(f[group].attrs.items())
- return meta
diff --git a/src/postprocessor/core/postprocessing.py b/src/postprocessor/core/postprocessing.py
index 2b13b68cdbbfd4091287b0c4223a45be6421344b..8f77568dc02550ff717832c3d806d15e29301c78 100644
--- a/src/postprocessor/core/postprocessing.py
+++ b/src/postprocessor/core/postprocessing.py
@@ -45,15 +45,8 @@ class PostProcessorParameters(ParametersABC):
return getattr(self, item)
@classmethod
- def default(cls, kind=[]):
- """
- Include buddings and bud_metric and estimates of their time derivatives.
-
- Parameters
- ----------
- kind: list of str
- If "ph_batman" included, add targets for experiments using pHlourin.
- """
+ def default(cls):
+ """Include buddings and bud volumes."""
# each subitem specifies the function to be called
# and the h5-file location for the results
targets = {