Merge pull request #54 from neuroinformatics-unit/add-snakefmt-to-precommit

Add snakefmt to precommit

Author: lauraporta

.pre-commit-config.yaml

@@ -17,6 +17,10 @@ repos:
             args: [--fix=lf]
           - id: requirements-txt-fixer
           - id: trailing-whitespace
+    - repo: https://github.com/snakemake/snakefmt
+      rev: v0.11.2
+      hooks:
+        - id: snakefmt
     - repo: https://github.com/charliermarsh/ruff-pre-commit
       rev: v0.6.3
       hooks:

photon_mosaic/workflow/Snakefile

@@ -18,14 +18,20 @@ from photon_mosaic import log_cuda_availability
 import logging
 
 # Configure logging based on config settings
-log_level = logging.DEBUG if config.get("logging", {}).get("snakemake_verbose", False) else logging.INFO
+log_level = (
+    logging.DEBUG
+    if config.get("logging", {}).get("snakemake_verbose", False)
+    else logging.INFO
+)
 logging.basicConfig(level=log_level)
 logger = logging.getLogger("snakemake.workflow")
 
+
 # CUDA availability check on workflow start
 onstart:
     log_cuda_availability()
 
+
 raw_data_base = Path(config["raw_data_base"]).resolve()
 processed_data_base = Path(config["processed_data_base"]).resolve()
 slurm_config = config.get("slurm", {})
@@ -49,7 +55,9 @@ discoverer = DatasetDiscoverer(
     exclude_datasets=config["dataset_discovery"].get("exclude_datasets"),
     exclude_sessions=config["dataset_discovery"].get("exclude_sessions"),
     tiff_patterns=config["dataset_discovery"].get("tiff_patterns"),
-    neuroblueprint_format=config["dataset_discovery"].get("neuroblueprint_format", False),
+    neuroblueprint_format=config["dataset_discovery"].get(
+        "neuroblueprint_format", False
+    ),
 )
 
 discoverer.discover()
@@ -66,7 +74,9 @@ preproc_targets = [
         / f"{output_pattern}{tiff_name}"
     )
     for i, dataset_name in enumerate(discoverer.transformed_datasets)
-    for session_idx, tiff_list in discoverer.tiff_files[discoverer.original_datasets[i]].items()
+    for session_idx, tiff_list in discoverer.tiff_files[
+        discoverer.original_datasets[i]
+    ].items()
     for tiff_name in tiff_list
 ]
 
@@ -83,17 +93,21 @@ suite2p_targets = [
         / fname
     )
     for i, dataset_name in enumerate(discoverer.transformed_datasets)
-    for session_idx, tiff_list in discoverer.tiff_files[discoverer.original_datasets[i]].items()
+    for session_idx, tiff_list in discoverer.tiff_files[
+        discoverer.original_datasets[i]
+    ].items()
     for fname in ["F.npy", "data.bin"]
     if tiff_list  # Only create targets for sessions that have files
 ]
 
 logger.info(f"Suite2p targets: {suite2p_targets}")
 
+
 include: "preprocessing.smk"
 include: "suite2p.smk"
 
+
 rule all:
     input:
         preproc_targets,
-        suite2p_targets
+        suite2p_targets,

photon_mosaic/workflow/preprocessing.smk

@@ -25,36 +25,61 @@ import os
 # Configure SLURM resources if enabled
 slurm_config = config.get("slurm", {}) if config.get("use_slurm") else {}
 
+
 # Preprocessing rule
 rule preprocessing:
     input:
-        img=lambda wildcards: cross_platform_path(raw_data_base / discoverer.original_datasets[discoverer.transformed_datasets.index(wildcards.subject_name)])
+        img=lambda wildcards: cross_platform_path(
+            raw_data_base
+            / discoverer.original_datasets[
+                discoverer.transformed_datasets.index(wildcards.subject_name)
+            ]
+        ),
     output:
         processed=cross_platform_path(
             Path(processed_data_base).resolve()
             / "{subject_name}"
             / "{session_name}"
             / "funcimg"
-            / (f"{output_pattern}"+ "{tiff}")
-        )
+            / (f"{output_pattern}" + "{tiff}")
+        ),
     params:
-        dataset_folder=lambda wildcards: cross_platform_path(raw_data_base / discoverer.original_datasets[discoverer.transformed_datasets.index(wildcards.subject_name)]),
+        dataset_folder=lambda wildcards: cross_platform_path(
+            raw_data_base
+            / discoverer.original_datasets[
+                discoverer.transformed_datasets.index(wildcards.subject_name)
+            ]
+        ),
         output_folder=lambda wildcards: cross_platform_path(
             Path(processed_data_base).resolve()
             / wildcards.subject_name
             / wildcards.session_name
             / "funcimg"
         ),
-        ses_idx=lambda wildcards: int(wildcards.session_name.split("_")[0].replace("ses-", "")),
+        ses_idx=lambda wildcards: int(
+            wildcards.session_name.split("_")[0].replace("ses-", "")
+        ),
     wildcard_constraints:
-        tiff="|".join(sorted(discoverer.tiff_files_flat)) if discoverer.tiff_files_flat else "dummy",
+        tiff=(
+            "|".join(sorted(discoverer.tiff_files_flat))
+            if discoverer.tiff_files_flat
+            else "dummy"
+        ),
         subject_name="|".join(discoverer.transformed_datasets),
-        session_name="|".join([discoverer.get_session_name(i, session_idx) for i in range(len(discoverer.transformed_datasets))
-                              for session_idx in discoverer.tiff_files[discoverer.original_datasets[i]].keys()]),
+        session_name="|".join(
+            [
+                discoverer.get_session_name(i, session_idx)
+                for i in range(len(discoverer.transformed_datasets))
+                for session_idx in discoverer.tiff_files[
+                    discoverer.original_datasets[i]
+                ].keys()
+            ]
+        ),
     resources:
         **(slurm_config if config.get("use_slurm") else {}),
     run:
         from photon_mosaic.rules.preprocessing import run_preprocessing
+
         run_preprocessing(
             Path(params.output_folder),
             config["preprocessing"],

photon_mosaic/workflow/suite2p.smk

@@ -17,6 +17,7 @@ Output: Suite2p analysis results (F.npy, data.bin) in suite2p/plane0/ directory
 import re
 from photon_mosaic.snakemake_utils import cross_platform_path
 
+
 rule suite2p:
     input:
         tiffs=lambda wildcards: [
@@ -27,9 +28,11 @@ rule suite2p:
                 / "funcimg"
                 / f"{output_pattern}{tiff_name}"
             )
-            for tiff_name in discoverer.tiff_files[discoverer.original_datasets[discoverer.transformed_datasets.index(wildcards.subject_name)]][
-                int(wildcards.session_name.split("_")[0].replace("ses-", ""))
-            ]
+            for tiff_name in discoverer.tiff_files[
+                discoverer.original_datasets[
+                    discoverer.transformed_datasets.index(wildcards.subject_name)
+                ]
+            ][int(wildcards.session_name.split("_")[0].replace("ses-", ""))]
         ],
     output:
         F=cross_platform_path(
@@ -49,7 +52,7 @@ rule suite2p:
             / "suite2p"
             / "plane0"
             / "data.bin"
-        )
+        ),
     params:
         dataset_folder=lambda wildcards: cross_platform_path(
             Path(processed_data_base).resolve()
@@ -59,8 +62,15 @@ rule suite2p:
         ),
     wildcard_constraints:
         subject_name="|".join(discoverer.transformed_datasets),
-        session_name="|".join([discoverer.get_session_name(i, session_idx) for i in range(len(discoverer.transformed_datasets))
-                              for session_idx in discoverer.tiff_files[discoverer.original_datasets[i]].keys()]),
+        session_name="|".join(
+            [
+                discoverer.get_session_name(i, session_idx)
+                for i in range(len(discoverer.transformed_datasets))
+                for session_idx in discoverer.tiff_files[
+                    discoverer.original_datasets[i]
+                ].keys()
+            ]
+        ),
     resources:
         **(slurm_config if config.get("use_slurm") else {}),
     run: