diff --git a/src/agora/io/signal.py b/src/agora/io/signal.py
index 20c7b950d3a3e4dbf1cb21dec29947467b938bd7..6b9457bd77b91d9303758168188603ce7d5b728f 100644
--- a/src/agora/io/signal.py
+++ b/src/agora/io/signal.py
@@ -9,6 +9,7 @@ import h5py
import numpy as np
import pandas as pd
+import aliby.global_parameters as global_parameters
from agora.io.bridge import BridgeH5
from agora.io.decorators import _first_arg_str_to_raw_df
from agora.utils.indexing import validate_lineage
@@ -36,16 +37,7 @@ class Signal(BridgeH5):
"cell_label",
"mother_label",
)
- self.candidate_channels = (
- "GFP",
- "GFPFast",
- "mCherry",
- "Flavin",
- "Citrine",
- "mKO2",
- "Cy5",
- "pHluorin405",
- )
+ self.candidate_channels = global_parameters.possible_imaging_channels
def __getitem__(self, dsets: t.Union[str, t.Collection]):
"""Get and potentially pre-process data from h5 file and return as a dataframe."""
diff --git a/src/aliby/global_parameters.py b/src/aliby/global_parameters.py
new file mode 100644
index 0000000000000000000000000000000000000000..92b58f8969de09ee31c558019ccabe52a01338c5
--- /dev/null
+++ b/src/aliby/global_parameters.py
@@ -0,0 +1,38 @@
+# parameters to stop the pipeline when exceeded
+earlystop = dict(
+ min_tp=100,
+ thresh_pos_clogged=0.4,
+ thresh_trap_ncells=8,
+ thresh_trap_area=0.9,
+ ntps_to_eval=5,
+)
+
+# imaging properties of the microscope
+imaging_specifications = {
+ "pixel_size": 0.236,
+ "z_size": 0.6,
+ "spacing": 0.6,
+}
+
+# possible imaging channels
+possible_imaging_channels = [
+ "Citrine",
+ "GFP",
+ "GFPFast",
+ "mCherry",
+ "Flavin",
+ "Citrine",
+ "mKO2",
+ "Cy5",
+ "pHluorin405",
+ "pHluorin488",
+]
+
+# functions to apply to the fluorescence of each cell
+fluorescence_functions = [
+ "mean",
+ "median",
+ "std",
+ "imBackground",
+ "max5px_median",
+]
diff --git a/src/aliby/pipeline.py b/src/aliby/pipeline.py
index 0d73c3356455040e0712a62a669b9de9e186c96f..ede77e7c9acce43356ee515fcca47a11f5178e04 100644
--- a/src/aliby/pipeline.py
+++ b/src/aliby/pipeline.py
@@ -14,6 +14,7 @@ import numpy as np
from pathos.multiprocessing import Pool
from tqdm import tqdm
+import aliby.global_parameters as global_parameters
from agora.abc import ParametersABC, ProcessABC
from agora.io.metadata import MetaData, parse_logfiles
from agora.io.reader import StateReader
@@ -119,13 +120,7 @@ class PipelineParameters(ParametersABC):
tps=tps,
directory=str(directory.parent),
filter="",
- earlystop=dict(
- min_tp=100,
- thresh_pos_clogged=0.4,
- thresh_trap_ncells=8,
- thresh_trap_area=0.9,
- ntps_to_eval=5,
- ),
+ earlystop=global_parameters.earlystop,
logfile_level="INFO",
use_explog=True,
)
@@ -162,10 +157,25 @@ class PipelineParameters(ParametersABC):
return cls(**{k: v for k, v in defaults.items()})
def load_logs(self):
+ """Load and parse log files."""
parsed_flattened = parse_logfiles(self.log_dir)
return parsed_flattened
+def find_channels_by_group(meta):
+ """Parse meta data to find which imaging channels are used for each group."""
+ channels_dict = {group_no: [] for group_no in meta["positions/group"]}
+ imaging_channels = global_parameters.possible_imaging_channels
+ for i, group_no in enumerate(meta["positions/group"]):
+ for imaging_channel in imaging_channels:
+ if (
+ "positions/" + imaging_channel in meta
+ and meta["positions/" + imaging_channel][i]
+ ):
+ channels_dict[group_no].append(imaging_channel)
+ return channels_dict
+
+
class Pipeline(ProcessABC):
"""
Initialise and run tiling, segmentation, extraction and post-processing.
@@ -224,11 +234,6 @@ class Pipeline(ProcessABC):
fh.setFormatter(formatter)
logger.addHandler(fh)
- @classmethod
- def from_yaml(cls, fpath):
- # an unfinished convenience function
- return cls(parameters=PipelineParameters.from_yaml(fpath))
-
@classmethod
def from_folder(cls, dir_path):
"""
@@ -324,7 +329,7 @@ class Pipeline(ProcessABC):
directory = self.store or root_dir / conn.unique_name
if not directory.exists():
directory.mkdir(parents=True)
- # download logs to use for metadata
+ # get logs to use for metadata
conn.cache_logs(directory)
print("Positions available:")
for i, pos in enumerate(position_ids.keys()):
diff --git a/src/extraction/core/extractor.py b/src/extraction/core/extractor.py
index 0192434d554218d7286be1dbbb1680bb5b82fdbe..7cb11443b2e9b6197382911230ce82ee90dc9cec 100644
--- a/src/extraction/core/extractor.py
+++ b/src/extraction/core/extractor.py
@@ -16,6 +16,7 @@ from extraction.core.functions.loaders import (
load_funs,
load_redfuns,
)
+import aliby.global_parameters as global_parameters
# define types
reduction_method = t.Union[t.Callable, str, None]
@@ -89,12 +90,7 @@ class Extractor(StepABC):
or leaf level.
"""
- # TODO Move this to a location with the SwainLab defaults
- default_meta = {
- "pixel_size": 0.236,
- "z_size": 0.6,
- "spacing": 0.6,
- }
+ default_meta = global_parameters.imaging_specifications
def __init__(
self,
diff --git a/src/extraction/core/functions/defaults.py b/src/extraction/core/functions/defaults.py
index 1c32d0e2c7c2ba652a35419e78d8bcfa94eab00b..bc9107b7f86fd00a510d443596171d5607525191 100644
--- a/src/extraction/core/functions/defaults.py
+++ b/src/extraction/core/functions/defaults.py
@@ -4,6 +4,7 @@ from pathlib import Path
import h5py
+import aliby.global_parameters as global_parameters
from aliby.tile.tiler import find_channel_name
@@ -21,26 +22,10 @@ def exparams_from_meta(
"tree": {"general": {"None": ["area", "volume", "eccentricity"]}},
"multichannel_ops": {},
}
- candidate_channels = {
- "Citrine",
- "GFP",
- "GFPFast",
- "mCherry",
- "pHluorin405",
- "pHluorin488",
- "Flavin",
- "Cy5",
- "mKO2",
- }
+ candidate_channels = set(global_parameters.possible_imaging_channels)
default_reductions = {"max"}
- default_metrics = {
- "mean",
- "median",
- "std",
- "imBackground",
- "max5px_median",
- # "nuc_est_conv",
- }
+ default_metrics = set(global_parameters.fluorescence_functions)
+
# define ratiometric combinations
# key is numerator and value is denominator
# add more to support additional channel names