diff --git a/src/agora/abc.py b/src/agora/abc.py
index 928c17098ec190b64e388208d7673a503ba50ceb..0aaefdfd8a4d315079eb1a952715e0f327667834 100644
--- a/src/agora/abc.py
+++ b/src/agora/abc.py
@@ -35,7 +35,7 @@ class ParametersABC(ABC):
"""
Return a nested dictionary of the attributes of the class instance.
- Uses recursion.
+ Use recursion.
"""
if isinstance(iterable, dict):
if any(
@@ -115,7 +115,6 @@ class ParametersABC(ABC):
If a leaf node that is to be changed is a collection, it adds the new elements.
"""
-
assert name not in (
"parameters",
"params",
diff --git a/src/aliby/pipeline.py b/src/aliby/pipeline.py
index ede77e7c9acce43356ee515fcca47a11f5178e04..c71fa237f747236fdb970bf856fc2642e514efff 100644
--- a/src/aliby/pipeline.py
+++ b/src/aliby/pipeline.py
@@ -162,20 +162,6 @@ class PipelineParameters(ParametersABC):
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.
@@ -360,13 +346,16 @@ class Pipeline(ProcessABC):
with Pool(distributed) as p:
results = p.map(
lambda x: self.run_one_position(*x),
- [(k, i) for i, k in enumerate(position_ids.items())],
+ [
+ (position_id, i)
+ for i, position_id in enumerate(position_ids.items())
+ ],
)
else:
# single core
results = [
- self.run_one_position((k, v), 1)
- for k, v in tqdm(position_ids.items())
+ self.run_one_position((position_id, position_id_path), 1)
+ for position_id, position_id_path in tqdm(position_ids.items())
]
return results
@@ -406,7 +395,7 @@ class Pipeline(ProcessABC):
session = None
run_kwargs = {"extraction": {"cell_labels": None, "masks": None}}
try:
- pipe, session = self.setup_pipeline(image_id)
+ pipe, session = self.setup_pipeline(image_id, name)
loaded_writers = {
name: writer(pipe["filename"])
for k in self.step_sequence
@@ -426,6 +415,9 @@ class Pipeline(ProcessABC):
) as image:
# initialise steps
if "tiler" not in pipe["steps"]:
+ pipe["config"]["tiler"]["position_name"] = name.split(".")[
+ 0
+ ]
pipe["steps"]["tiler"] = Tiler.from_image(
image,
TilerParameters.from_dict(pipe["config"]["tiler"]),
@@ -545,7 +537,9 @@ class Pipeline(ProcessABC):
close_session(session)
def setup_pipeline(
- self, image_id: int
+ self,
+ image_id: int,
+ name: str,
) -> t.Tuple[
Path,
MetaData,
@@ -626,8 +620,11 @@ class Pipeline(ProcessABC):
if pipe["filename"].exists():
self._log("Result file exists.", "info")
if not overwrite["tiler"]:
+ tiler_params_dict = TilerParameters.default().to_dict()
+ tiler_params_dict["position_name"] = name.split(".")[0]
+ tiler_params = TilerParameters.from_dict(tiler_params_dict)
pipe["steps"]["tiler"] = Tiler.from_h5(
- image, pipe["filename"]
+ image, pipe["filename"], tiler_params
)
try:
(
diff --git a/src/aliby/tile/tiler.py b/src/aliby/tile/tiler.py
index f03d2e25c2ff5cc87b86b85d60016b6d4bdabf11..0d77b840a4623d823b90515ffa82e1ac137d3ac0 100644
--- a/src/aliby/tile/tiler.py
+++ b/src/aliby/tile/tiler.py
@@ -37,6 +37,7 @@ import h5py
import numpy as np
from skimage.registration import phase_cross_correlation
+import aliby.global_parameters as global_parameters
from agora.abc import ParametersABC, StepABC
from agora.io.writer import BridgeH5
from aliby.tile.traps import segment_traps
@@ -214,9 +215,26 @@ class TilerParameters(ParametersABC):
"ref_channel": "Brightfield",
"ref_z": 0,
"backup_ref_channel": None,
+ "position_name": None,
}
+def find_channels_by_position(meta):
+ """Parse metadata to find the imaging channels used for each group."""
+ channels_dict = {
+ position_name: [] for position_name in meta["positions/posname"]
+ }
+ imaging_channels = meta["channels"]
+ for i, position_name in enumerate(meta["positions/posname"]):
+ for imaging_channel in imaging_channels:
+ if (
+ "positions/" + imaging_channel in meta
+ and meta["positions/" + imaging_channel][i]
+ ):
+ channels_dict[position_name].append(imaging_channel)
+ return channels_dict
+
+
class Tiler(StepABC):
"""
Divide images into smaller tiles for faster processing.
@@ -247,11 +265,14 @@ class Tiler(StepABC):
"""
super().__init__(parameters)
self.image = image
- self._metadata = metadata
- self.channels = metadata.get(
- "channels",
+ self.position_name = parameters.to_dict()["position_name"]
+ # get channels for this position
+ channel_dict = find_channels_by_position(metadata)
+ self.channels = channel_dict.get(
+ self.position_name,
list(range(metadata.get("size_c", 0))),
)
+ # get reference channel - used for segmentation
self.ref_channel = self.get_channel_index(parameters.ref_channel)
if self.ref_channel is None:
self.ref_channel = self.backup_ref_channel