diff --git a/.github/workflows/lint-and-dryrun-testing.yml b/.github/workflows/python-testing.yml
similarity index 66%
rename from .github/workflows/lint-and-dryrun-testing.yml
rename to .github/workflows/python-testing.yml
index afb4909..674e263 100644
--- a/.github/workflows/lint-and-dryrun-testing.yml
+++ b/.github/workflows/python-testing.yml
@@ -10,58 +10,63 @@ jobs:
name: Lint code
runs-on: ubuntu-latest
steps:
- - name: Install non-python dependencies
- run: |
- sudo apt-get install -y graphviz-dev
+ - name: Clone repo
+ uses: actions/checkout@v4
+ with:
+ ref: ${{ github.event.pull_request.head.sha || github.base_ref }}
+
- name: Setup Python environment
- uses: khanlab/actions/.github/actions/action-setup_task-installPyProject@v0.3.6
+ uses: prefix-dev/setup-pixi@v0.8.14
with:
- python-version: '3.10'
- - name: yamlfix
- run: poetry run poe yamlfix-check
- - name: ruff
- run: poetry run poe ruff-lint-check
+ environments: >-
+ dev-only
+ activate-environment: dev-only
+
+ - name: isort
+ run: isort autoafids/*.py -c
+
+ - name: Black
+ run: black autoafids --check
+
- name: snakefmt
- run: poetry run poe snakefmt-check
+ run: snakefmt autoafids --check
test:
runs-on: ubuntu-latest
needs: [quality]
- defaults:
- run:
- shell: bash -l {0}
+
steps:
- name: checkout repository
uses: actions/checkout@v4
- - name: Setup Miniforge and mamba
- uses: conda-incubator/setup-miniconda@v3
+
+ - name: Setup Pixi environment
+ uses: prefix-dev/setup-pixi@v0.8.14
with:
- miniforge-variant: Miniforge3
- miniforge-version: latest
- mamba-version: '*'
- use-mamba: true
- conda-solver: libmamba
- auto-activate-base: true
- - name: Install snakebids
- run: |
- mamba install snakebids appdirs -c bioconda -c conda-forge -y
+ activate-environment: true
+
- name: Setup env for autoafids
run: |
echo "AUTOAFIDS_CACHE_DIR=`pwd`/test_data/autoafids_cache_dir" >> $GITHUB_ENV
+
- name: Test T1w modality
run: |
./autoafids/run.py test_data/bids_T1w test_out participant --participant-label 002 -np
+
- name: Test T2w modality
run: |
./autoafids/run.py test_data/bids_T2w test_out participant --modality T2w -np -c1
+
- name: Test CT modality
run: |
./autoafids/run.py test_data/bids_ct test_out participant --modality ct -np -c1
+
- name: Test stereotaxy feature with T1w modality
run: |
./autoafids/run.py test_data/bids_T1w test_out participant --participant-label 002 --stereotaxy STN -np
+
- name: Test stereotaxy feature with T2w modality
run: |
./autoafids/run.py test_data/bids_T2w test_out participant --stereotaxy STN --modality T2w -np -c1
+
- name: Test fidqc feature
run: |-
./autoafids/run.py test_data/bids_T1w test_out participant --participant-label 002 --fidqc -np
diff --git a/.github/workflows/wetrun-testing.yml b/.github/workflows/wetrun-testing.yml
index d7ac6b7..e5bc558 100644
--- a/.github/workflows/wetrun-testing.yml
+++ b/.github/workflows/wetrun-testing.yml
@@ -16,18 +16,13 @@ jobs:
steps:
- name: Checkout repository
uses: actions/checkout@v4
- - name: Setup Miniforge and mamba
- uses: conda-incubator/setup-miniconda@v3
+
+
+ - name: Setup Pixi environment
+ uses: prefix-dev/setup-pixi@v0.8.14
with:
- miniforge-variant: Miniforge3
- miniforge-version: latest
- mamba-version: '*'
- use-mamba: true
- conda-solver: libmamba
- auto-activate-base: true
- - name: Create environment with snakebids
- run: |
- mamba create -n snakebids-env snakebids scikit-learn pandas -c bioconda -c conda-forge -y
+ activate-environment: true
+
- name: Cache Snakemake Conda environments
uses: actions/cache@v4
with:
@@ -35,30 +30,32 @@ jobs:
key: conda-env-${{ hashFiles('autoafids/workflow/envs/*.yaml') }}
restore-keys: |
conda-env-
+
- name: Restore cached CNN model
uses: actions/cache@v4
with:
path: test_data/autoafids_cache_dir/models
key: static-model-cache-v1
+
- name: Set AUTOAFIDS_CACHE_DIR
run: |
echo "AUTOAFIDS_CACHE_DIR=`pwd`/test_data/autoafids_cache_dir" >> $GITHUB_ENV
+
- name: Run wet-run test for T1w modality
shell: bash -l {0}
run: |
- conda activate snakebids-env
./autoafids/run.py test_data/bids_wetrun_testing/bids_T1w test_out participant \
--cores all --force-output --stereotaxy STN --fidqc --conda-frontend mamba | tee autoafids_output.log
+
- name: Model accuracy check
shell: bash -l {0}
run: |
- conda activate snakebids-env
python ./tests/test_fcsv_output.py \
--autoafids_fcsv ./test_out/sub-001/afids-cnn/*.fcsv \
--baseline_fcsv ./test_data/bids_wetrun_testing/bids_T1w/sub-001/anat/sub-001*.fcsv | tee test_fcsv_output.log
+
- name: Run wet-run test for T2w modality
shell: bash -l {0}
run: |-
- conda activate snakebids-env
./autoafids/run.py test_data/bids_wetrun_testing/bids_T2w test_out participant \
--modality T2w --cores all --force-output --conda-frontend mamba
diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index 8fa269d..8a4fdfe 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -17,7 +17,7 @@ formats: [pdf]
build:
os: ubuntu-20.04
tools:
- python: '3.10'
+ python: '3.11'
python:
install:
- requirements: docs/requirements.txt
diff --git a/autoafids/run.py b/autoafids/run.py
index 47063de..120f5e1 100755
--- a/autoafids/run.py
+++ b/autoafids/run.py
@@ -5,9 +5,7 @@
from snakebids import bidsapp, plugins
try:
- from autoafids.workflow.lib import (
- utils as utils, # Works when run as a package
- )
+ from autoafids.workflow.lib import utils as utils # Works when run as a package
except ImportError:
from workflow.lib import utils as utils # Works when run directly
@@ -27,14 +25,13 @@
conda_prefix = Path(utils.get_download_dir()) / "conda"
# Set templateflow directory
-os.environ['TEMPLATEFLOW_HOME'] = str(
- Path(utils.get_download_dir()) / "templateflow"
- )
+os.environ["TEMPLATEFLOW_HOME"] = str(Path(utils.get_download_dir()) / "templateflow")
# Set the environment variable SNAKEMAKE_CONDA_PREFIX if not already set
if "SNAKEMAKE_CONDA_PREFIX" not in os.environ:
os.environ["SNAKEMAKE_CONDA_PREFIX"] = str(conda_prefix)
+
def get_parser():
"""Exposes parser for sphinx doc generation, cwd is the docs dir"""
return app.build_parser().parser
diff --git a/autoafids/workflow/Snakefile b/autoafids/workflow/Snakefile
index 203afe0..644c03f 100644
--- a/autoafids/workflow/Snakefile
+++ b/autoafids/workflow/Snakefile
@@ -129,7 +129,7 @@ if config["modality"] != "T1w":
bids(
root="logs",
suffix="synthsr_output.txt",
- **inputs[config["modality"]].wildcards
+ **inputs[config["modality"]].wildcards,
),
resources:
mem_mb=10000,
@@ -147,19 +147,21 @@ if "n4_bias_correction" in profile_settings and config["modality"] == "T1w":
rule n4_bias_correction:
input:
- im=bids(
- root=str(Path(config["bids_dir"])),
- datatype="anat",
- suffix="T1w.nii.gz",
- **inputs[config["modality"]].wildcards,
- )
- if config["modality"] == "T1w"
- else bids(
- root=work,
- datatype="SynthSR",
- desc=f"{config['modality']}-SynthSR",
- suffix="T1w.nii.gz",
- **inputs[config["modality"]].wildcards,
+ im=(
+ bids(
+ root=str(Path(config["bids_dir"])),
+ datatype="anat",
+ suffix="T1w.nii.gz",
+ **inputs[config["modality"]].wildcards,
+ )
+ if config["modality"] == "T1w"
+ else bids(
+ root=work,
+ datatype="SynthSR",
+ desc=f"{config['modality']}-SynthSR",
+ suffix="T1w.nii.gz",
+ **inputs[config["modality"]].wildcards,
+ )
),
output:
corrected_im=bids(
@@ -167,7 +169,7 @@ if "n4_bias_correction" in profile_settings and config["modality"] == "T1w":
datatype="n4biascorr",
desc="n4corrected",
suffix="T1w.nii.gz",
- **inputs[config["modality"]].wildcards
+ **inputs[config["modality"]].wildcards,
),
conda:
"envs/ants.yaml"
@@ -177,36 +179,42 @@ if "n4_bias_correction" in profile_settings and config["modality"] == "T1w":
rule norm_im:
input:
- im_raw=lambda wildcards: bids(
- root=work,
- datatype="synthstrip",
- desc="synthstrip",
- suffix="T1w.nii.gz",
- **inputs[config["modality"]].wildcards,
- )
- if "skull_strip" in profile_settings
- else bids(
- root=work,
- datatype="n4biascorr",
- desc="n4corrected",
- suffix="T1w.nii.gz",
- **inputs[config["modality"]].wildcards,
- )
- if "n4_bias_correction" in profile_settings
- and config["modality"] == "T1w"
- else bids(
- root=str(Path(config["bids_dir"])),
- datatype="anat",
- suffix="T1w.nii.gz",
- **inputs[config["modality"]].wildcards,
- )
- if config["modality"] == "T1w"
- else bids(
- root=work,
- datatype="SynthSR",
- desc=f"{config['modality']}-SynthSR",
- suffix="T1w.nii.gz",
- **inputs[config["modality"]].wildcards,
+ im_raw=lambda wildcards: (
+ bids(
+ root=work,
+ datatype="synthstrip",
+ desc="synthstrip",
+ suffix="T1w.nii.gz",
+ **inputs[config["modality"]].wildcards,
+ )
+ if "skull_strip" in profile_settings
+ else (
+ bids(
+ root=work,
+ datatype="n4biascorr",
+ desc="n4corrected",
+ suffix="T1w.nii.gz",
+ **inputs[config["modality"]].wildcards,
+ )
+ if "n4_bias_correction" in profile_settings
+ and config["modality"] == "T1w"
+ else (
+ bids(
+ root=str(Path(config["bids_dir"])),
+ datatype="anat",
+ suffix="T1w.nii.gz",
+ **inputs[config["modality"]].wildcards,
+ )
+ if config["modality"] == "T1w"
+ else bids(
+ root=work,
+ datatype="SynthSR",
+ desc=f"{config['modality']}-SynthSR",
+ suffix="T1w.nii.gz",
+ **inputs[config["modality"]].wildcards,
+ )
+ )
+ )
),
output:
im_norm=bids(
@@ -254,28 +262,31 @@ if "resample_im" in profile_settings and config["modality"] == "T1w":
rule regmni2sub:
input:
- im_resamp=bids(
- root=work,
- datatype="n4biascorr",
- desc="n4corrected",
- suffix="T1w.nii.gz",
- **inputs[config["modality"]].wildcards,
- )
- if "n4_bias_correction" in profile_settings
- and config["modality"] == "T1w"
- else bids(
- root=str(Path(config["bids_dir"])),
- datatype="anat",
- suffix="T1w.nii.gz",
- **inputs[config["modality"]].wildcards,
- )
- if config["modality"] == "T1w"
- else bids(
- root=work,
- datatype="SynthSR",
- desc=f"{config['modality']}-SynthSR",
- suffix="T1w.nii.gz",
- **inputs[config["modality"]].wildcards,
+ im_resamp=(
+ bids(
+ root=work,
+ datatype="n4biascorr",
+ desc="n4corrected",
+ suffix="T1w.nii.gz",
+ **inputs[config["modality"]].wildcards,
+ )
+ if "n4_bias_correction" in profile_settings and config["modality"] == "T1w"
+ else (
+ bids(
+ root=str(Path(config["bids_dir"])),
+ datatype="anat",
+ suffix="T1w.nii.gz",
+ **inputs[config["modality"]].wildcards,
+ )
+ if config["modality"] == "T1w"
+ else bids(
+ root=work,
+ datatype="SynthSR",
+ desc=f"{config['modality']}-SynthSR",
+ suffix="T1w.nii.gz",
+ **inputs[config["modality"]].wildcards,
+ )
+ )
),
output:
xfm_slicer=bids(
@@ -357,43 +368,51 @@ rule all:
datatype="afids-cnn",
desc="afidscnn",
suffix="afids.fcsv",
- **inputs[config["modality"]].wildcards
+ **inputs[config["modality"]].wildcards,
),
),
- fidqc=inputs[config["modality"]].expand(
- bids(
- root=root,
- datatype="fidqc",
- desc="fidqc",
- suffix="afids.html",
- **inputs[config["modality"]].wildcards
+ fidqc=(
+ inputs[config["modality"]].expand(
+ bids(
+ root=root,
+ datatype="fidqc",
+ desc="fidqc",
+ suffix="afids.html",
+ **inputs[config["modality"]].wildcards,
+ )
)
- )
- if config["fidqc"]
- else [],
- regqc=inputs[config["modality"]].expand(
- bids(
- root=root,
- datatype="regqc",
- desc="reg",
- suffix="qc.html",
- **inputs[config["modality"]].wildcards
+ if config["fidqc"]
+ else []
+ ),
+ regqc=(
+ inputs[config["modality"]].expand(
+ bids(
+ root=root,
+ datatype="regqc",
+ desc="reg",
+ suffix="qc.html",
+ **inputs[config["modality"]].wildcards,
+ )
)
- )
- if config["LEAD_DBS_DIR"] or config["FMRIPREP_DIR"]
- else [],
- regqc_summary=[os.path.join(root, "dataset_desc-reg_qc_summary.html")]
- if config["LEAD_DBS_DIR"] or config["FMRIPREP_DIR"]
- else [],
- afidspred=inputs[config["modality"]].expand(
- bids(
- root=root,
- datatype="stereotaxy",
- desc=stereotaxy_target,
- suffix="native.fcsv",
- **inputs[config["modality"]].wildcards
+ if config["LEAD_DBS_DIR"] or config["FMRIPREP_DIR"]
+ else []
+ ),
+ regqc_summary=(
+ [os.path.join(root, "dataset_desc-reg_qc_summary.html")]
+ if config["LEAD_DBS_DIR"] or config["FMRIPREP_DIR"]
+ else []
+ ),
+ afidspred=(
+ inputs[config["modality"]].expand(
+ bids(
+ root=root,
+ datatype="stereotaxy",
+ desc=stereotaxy_target,
+ suffix="native.fcsv",
+ **inputs[config["modality"]].wildcards,
+ )
)
- )
- if config["stereotaxy"]
- else [],
+ if config["stereotaxy"]
+ else []
+ ),
default_target: True
diff --git a/autoafids/workflow/rules/cnn.smk b/autoafids/workflow/rules/cnn.smk
index 1abb9e0..884f011 100644
--- a/autoafids/workflow/rules/cnn.smk
+++ b/autoafids/workflow/rules/cnn.smk
@@ -25,7 +25,7 @@ rule applyfidmodel:
datatype="normalize",
desc=chosen_norm_method,
suffix="T1w.nii.gz",
- **inputs[config["modality"]].wildcards
+ **inputs[config["modality"]].wildcards,
)
if config["modality"] != "T1w"
else bids(
@@ -34,7 +34,7 @@ rule applyfidmodel:
desc=chosen_norm_method,
res=config["res"],
suffix="T1w.nii.gz",
- **inputs[config["modality"]].wildcards
+ **inputs[config["modality"]].wildcards,
)
),
prior=bids(
@@ -52,14 +52,10 @@ rule applyfidmodel:
datatype="afids-cnn",
desc="afidscnn",
suffix="afids.fcsv",
- **inputs[config["modality"]].wildcards
+ **inputs[config["modality"]].wildcards,
),
log:
- bids(
- root="logs",
- suffix="landmark.log",
- **inputs[config["modality"]].wildcards
- ),
+ bids(root="logs", suffix="landmark.log", **inputs[config["modality"]].wildcards),
conda:
"../envs/tensorflow.yaml"
script:
diff --git a/autoafids/workflow/rules/fidqc.smk b/autoafids/workflow/rules/fidqc.smk
index e0f5190..820a98e 100644
--- a/autoafids/workflow/rules/fidqc.smk
+++ b/autoafids/workflow/rules/fidqc.smk
@@ -5,7 +5,7 @@ rule fidqc:
datatype="afids-cnn",
desc="afidscnn",
suffix="afids.fcsv",
- **inputs[config["modality"]].wildcards
+ **inputs[config["modality"]].wildcards,
),
im=bids(
root=str(Path(config["bids_dir"])),
@@ -19,7 +19,7 @@ rule fidqc:
datatype="fidqc",
desc="fidqc",
suffix="afids.html",
- **inputs[config["modality"]].wildcards
+ **inputs[config["modality"]].wildcards,
),
params:
refim=str(Path(workflow.basedir).parent / config["templatet1w"]),
diff --git a/autoafids/workflow/rules/regqc.smk b/autoafids/workflow/rules/regqc.smk
index 9ffa2d1..d347bd4 100644
--- a/autoafids/workflow/rules/regqc.smk
+++ b/autoafids/workflow/rules/regqc.smk
@@ -23,10 +23,7 @@ template_dict = {
def get_warp_path(subject):
if lead_dbs_dir:
trans_dir = (
- Path(lead_dbs_dir)
- / f"sub-{subject}"
- / "normalization"
- / "transformations"
+ Path(lead_dbs_dir) / f"sub-{subject}" / "normalization" / "transformations"
)
pattern = f"sub-{subject}*_from-MNI*_to-anchorNative_desc-ants.nii.gz"
matches = list(trans_dir.glob(pattern))
@@ -43,18 +40,13 @@ def get_warp_path(subject):
return None
else:
- raise ValueError(
- "No LEAD-DBS or fMRIPrep directory provided for warp."
- )
+ raise ValueError("No LEAD-DBS or fMRIPrep directory provided for warp.")
def get_optional_matrix_path(subject):
if lead_dbs_dir:
trans_dir = (
- Path(lead_dbs_dir)
- / f"sub-{subject}"
- / "coregistration"
- / "transformations"
+ Path(lead_dbs_dir) / f"sub-{subject}" / "coregistration" / "transformations"
)
pattern = f"sub-{subject}_desc-precoreg_*T1w.mat"
matches = list(trans_dir.glob(pattern))
@@ -112,19 +104,11 @@ def get_ref_paths():
)
else:
if lead_dbs_dir:
- refimage = str(
- Path(workflow.basedir).parent / config["templatet1w_lead"]
- )
- refcoordinate = str(
- Path(workflow.basedir).parent / config["fcsv_mni_lead"]
- )
+ refimage = str(Path(workflow.basedir).parent / config["templatet1w_lead"])
+ refcoordinate = str(Path(workflow.basedir).parent / config["fcsv_mni_lead"])
else:
- refimage = str(
- Path(workflow.basedir).parent / config["templatet1w"]
- )
- refcoordinate = str(
- Path(workflow.basedir).parent / config["fcsv_mni"]
- )
+ refimage = str(Path(workflow.basedir).parent / config["templatet1w"])
+ refcoordinate = str(Path(workflow.basedir).parent / config["fcsv_mni"])
return refimage, refcoordinate
@@ -146,13 +130,11 @@ rule regqc:
datatype="afids-cnn",
desc="afidscnn",
suffix="afids.fcsv",
- **inputs[config["modality"]].wildcards
+ **inputs[config["modality"]].wildcards,
),
im=lambda wildcards: get_resampled_im(wildcards.subject),
warp=lambda wildcards: get_warp_path(wildcards.subject),
- optional_matrix=lambda wildcards: get_optional_matrix_path(
- wildcards.subject
- ),
+ optional_matrix=lambda wildcards: get_optional_matrix_path(wildcards.subject),
refim=lambda wildcards: get_ref_paths()[0],
refcoord=lambda wildcards: get_ref_paths()[1],
output:
@@ -161,21 +143,21 @@ rule regqc:
datatype="regqc",
desc="reg",
suffix="qc.html",
- **inputs[config["modality"]].wildcards
+ **inputs[config["modality"]].wildcards,
),
csv=bids(
root=root,
datatype="regqc",
desc="reg",
suffix="qc.csv",
- **inputs[config["modality"]].wildcards
+ **inputs[config["modality"]].wildcards,
),
fcsv=bids(
root=root,
datatype="regqc",
desc="reg",
suffix="afids.fcsv",
- **inputs[config["modality"]].wildcards
+ **inputs[config["modality"]].wildcards,
),
params:
template=template_name,
diff --git a/autoafids/workflow/rules/stereotaxy.smk b/autoafids/workflow/rules/stereotaxy.smk
index f439c97..cc9515d 100644
--- a/autoafids/workflow/rules/stereotaxy.smk
+++ b/autoafids/workflow/rules/stereotaxy.smk
@@ -8,7 +8,7 @@ rule stereotaxy:
datatype="afids-cnn",
desc="afidscnn",
suffix="afids.fcsv",
- **inputs[config["modality"]].wildcards
+ **inputs[config["modality"]].wildcards,
),
output:
fcsv_native=bids(
@@ -16,28 +16,26 @@ rule stereotaxy:
datatype="stereotaxy",
desc=stereotaxy_target,
suffix="native.fcsv",
- **inputs[config["modality"]].wildcards
+ **inputs[config["modality"]].wildcards,
),
fcsv_mcp=bids(
root=root,
datatype="stereotaxy",
desc=stereotaxy_target,
suffix="mcp.fcsv",
- **inputs[config["modality"]].wildcards
+ **inputs[config["modality"]].wildcards,
),
ACPC_txt=bids(
root=work,
datatype="ACPCtransforms",
desc="transform",
suffix="ACPC.txt",
- **inputs[config["modality"]].wildcards
+ **inputs[config["modality"]].wildcards,
),
params:
model=str(Path(workflow.basedir).parent / config[stereotaxy_target]),
midpoint="PMJ",
- target_fcsv=str(
- Path(workflow.basedir).parent / config["template_fcsv"]
- ),
+ target_fcsv=str(Path(workflow.basedir).parent / config["template_fcsv"]),
conda:
"../envs/skimage.yaml"
script:
diff --git a/autoafids/workflow/scripts/apply.py b/autoafids/workflow/scripts/apply.py
index 8f9a1eb..40ea15e 100644
--- a/autoafids/workflow/scripts/apply.py
+++ b/autoafids/workflow/scripts/apply.py
@@ -85,9 +85,7 @@ def afids_to_fcsv(
row["z"] = afid_coords[label][2]
# Write output fcsv
- with Path(fcsv_output).open(
- "w", encoding="utf-8", newline=""
- ) as out_fcsv_file:
+ with Path(fcsv_output).open("w", encoding="utf-8", newline="") as out_fcsv_file:
for line in header:
out_fcsv_file.write(line)
writer = csv.DictWriter(out_fcsv_file, fieldnames=AFIDS_FIELDNAMES)
@@ -95,7 +93,6 @@ def afids_to_fcsv(
writer.writerow(row)
-
def load_fcsv(fcsv_path: Union[PathLike[str], str]) -> pd.DataFrame:
"""
Loads fcsv.
@@ -187,10 +184,7 @@ def fid_world2voxel(
return fid_voxel.astype(int)
-def gen_patch_slices(
- centre: NDArray,
- radius: int
- ) -> tuple[slice, slice, slice]:
+def gen_patch_slices(centre: NDArray, radius: int) -> tuple[slice, slice, slice]:
"""
Generate patch slices
@@ -206,10 +200,7 @@ def gen_patch_slices(
-------
Image patches around center coordinate
"""
- return tuple(
- slice(coord - radius, coord + radius + 1
- ) for coord in centre[:3]
- )
+ return tuple(slice(coord - radius, coord + radius + 1) for coord in centre[:3])
def slice_img(img: NDArray, centre: NDArray, radius: int) -> NDArray:
@@ -311,7 +302,7 @@ def process_distances(
try:
new = skimage.measure.regionprops(thresholded)
except MemoryError:
- new=None
+ new = None
if not new:
print("No centroid found for this afid. Results may be suspect.")
return np.array(
diff --git a/autoafids/workflow/scripts/norm_schemes.py b/autoafids/workflow/scripts/norm_schemes.py
index ef40ed8..b5a9cba 100644
--- a/autoafids/workflow/scripts/norm_schemes.py
+++ b/autoafids/workflow/scripts/norm_schemes.py
@@ -35,12 +35,7 @@ def normalize(method, input_im, output_im):
elif method == "zscore":
nii_data_normalized = (nii_data - nii_data.mean()) / (nii_data.std())
- nib.save(
- nib.Nifti1Image(
- nii_data_normalized,
- affine=nii_affine), output_im
- )
-
+ nib.save(nib.Nifti1Image(nii_data_normalized, affine=nii_affine), output_im)
normalize(
diff --git a/autoafids/workflow/scripts/qc.py b/autoafids/workflow/scripts/qc.py
index 7a4e5c2..bc73be5 100644
--- a/autoafids/workflow/scripts/qc.py
+++ b/autoafids/workflow/scripts/qc.py
@@ -97,8 +97,7 @@ def generate_html_with_keypress(
for label, images in subject_images.items():
num_slices = len(images)
- has_reference = ( reference_images is not None
- and label in reference_images )
+ has_reference = reference_images is not None and label in reference_images
html_content += f"""
@@ -119,10 +118,7 @@ def generate_html_with_keypress(
"""
for label, images in subject_images.items():
- has_reference = (
- reference_images is not None
- and label in reference_images
- )
+ has_reference = reference_images is not None and label in reference_images
html_content += f"""
subjects["{label}"] = {{
targetImages: {images},
@@ -169,19 +165,12 @@ def generate_html_with_keypress(
f.write(html_content)
-def save_single_slice_in_memory(
- data, x, y, z,
- offset,
- zoom_radius,
- show_crosshairs
- ):
+def save_single_slice_in_memory(data, x, y, z, offset, zoom_radius, show_crosshairs):
fig, axes = plt.subplots(1, 3, figsize=(15, 5))
for i, (slice_data, coord, title) in enumerate(
zip(
- [data[x + offset, :, :],
- data[:, y + offset, :],
- data[:, :, z + offset]],
+ [data[x + offset, :, :], data[:, y + offset, :], data[:, :, z + offset]],
[(y, z), (x, z), (x, y)],
["Sagittal", "Coronal", "Axial"],
)
@@ -203,11 +192,7 @@ def save_single_slice_in_memory(
return base64.b64encode(buffer.read()).decode()
-def save_mri_slices_as_images(
- data, x, y, z,
- jitter,
- zoom_radius,
- show_crosshairs=True):
+def save_mri_slices_as_images(data, x, y, z, jitter, zoom_radius, show_crosshairs=True):
return Parallel(n_jobs=-1)(
delayed(save_single_slice_in_memory)(
data, x, y, z, offset, zoom_radius, show_crosshairs
@@ -216,7 +201,6 @@ def save_mri_slices_as_images(
)
-
def extract_coordinates_from_fcsv(file_path, label_description):
df = pd.read_csv(file_path, comment="#", header=None)
row = df[df[11] == label_description]
diff --git a/autoafids/workflow/scripts/regqc.py b/autoafids/workflow/scripts/regqc.py
index 3b7835b..4045841 100644
--- a/autoafids/workflow/scripts/regqc.py
+++ b/autoafids/workflow/scripts/regqc.py
@@ -24,9 +24,7 @@
)
-def afids_to_fcsv(
- transformed_coords, output_path, template_path=FCSV_TEMPLATE
- ):
+def afids_to_fcsv(transformed_coords, output_path, template_path=FCSV_TEMPLATE):
"""
Writes transformed AFID coordinates
to a new FCSV file using a template FCSV.
@@ -59,14 +57,14 @@ def afids_to_fcsv(
"lock",
"label",
"desc",
- "associatedNodeID"
- ]
- )
+ "associatedNodeID",
+ ],
+ )
rows = list(reader)
# Update coordinates
for i, row in enumerate(rows):
- row['x'], row['y'], row['z'] = map(str, transformed_coords[i])
+ row["x"], row["y"], row["z"] = map(str, transformed_coords[i])
# Write updated FCSV
with open(output_path, "w", newline="") as out_file:
@@ -74,6 +72,7 @@ def afids_to_fcsv(
writer = csv.DictWriter(out_file, fieldnames=reader.fieldnames)
writer.writerows(rows)
+
# --- LOAD FCSV ---
def load_fcsv(path):
"""
@@ -84,10 +83,11 @@ def load_fcsv(path):
- labels: list of coordinate labels
"""
df = pd.read_csv(path, skiprows=2)
- coords = df[['x', 'y', 'z']].to_numpy()
- labels = df['label'].tolist()
+ coords = df[["x", "y", "z"]].to_numpy()
+ labels = df["label"].tolist()
return coords, labels
+
# --- APPLY 4X4 ---
def apply_affine_transform(mat_path, coords):
"""
@@ -101,19 +101,16 @@ def apply_affine_transform(mat_path, coords):
- transformed_coords: np.ndarray of shape (N, 3)
"""
mat_contents = scipy.io.loadmat(mat_path)
- transformation_matrix = mat_contents['tmat']
+ transformation_matrix = mat_contents["tmat"]
homogeneous_coords = np.hstack((coords, np.ones((coords.shape[0], 1))))
transformed_homogeneous = (
np.linalg.inv(transformation_matrix) @ homogeneous_coords.T
).T
return transformed_homogeneous[:, :3]
+
# --- APPLY WARP (HARMONIZED) ---
-def apply_warp_deformation(
- transform_path,
- coords,
- flip_ras_lps=True,
- leaddbs=True):
+def apply_warp_deformation(transform_path, coords, flip_ras_lps=True, leaddbs=True):
"""
Applies a non-linear warp deformation to coordinates
using a SimpleITK displacement field.
@@ -132,20 +129,18 @@ def apply_warp_deformation(
if leaddbs:
transform_image = SimpleITK.ReadImage(transform_path)
- transform_image = SimpleITK.Cast(
- transform_image, SimpleITK.sitkVectorFloat64
- )
+ transform_image = SimpleITK.Cast(transform_image, SimpleITK.sitkVectorFloat64)
transform = SimpleITK.Transform(transform_image)
- transformed_coords = np.array([
- transform.TransformPoint(point.tolist()) for point in coords
- ])
+ transformed_coords = np.array(
+ [transform.TransformPoint(point.tolist()) for point in coords]
+ )
else:
- d = pd.DataFrame(data=coords, columns=['x','y','z'])
- transformed_coords_dataframe = ants.apply_transforms_to_points( 3,
- d,
- transform_path)
+ d = pd.DataFrame(data=coords, columns=["x", "y", "z"])
+ transformed_coords_dataframe = ants.apply_transforms_to_points(
+ 3, d, transform_path
+ )
transformed_coords = transformed_coords_dataframe.to_numpy()
if flip_ras_lps:
@@ -153,6 +148,7 @@ def apply_warp_deformation(
return transformed_coords
+
# --- COMPUTE ERROR ---
def compute_error_components(gt, pred):
dx = np.abs(pred[:, 0] - gt[:, 0])
@@ -167,19 +163,20 @@ def make_toggleable_heatmap(error_components, afid_ids):
components = ["x", "y", "z", "ED"]
data_matrix = np.stack(error_components, axis=1)
y_labels = [f"AFID {i}" for i in afid_ids]
- fig = go.Figure(data=go.Heatmap(
- z=data_matrix, x=components, y=y_labels,
- colorscale=[[0.0, 'rgb(255,255,255)'], [1.0, 'rgb(220,0,0)']],
- colorbar=dict(title="Error (mm)"),
- hovertemplate="AFID %{y}
%{x} Error = %{z:.2f} mm"
- ))
+ fig = go.Figure(
+ data=go.Heatmap(
+ z=data_matrix,
+ x=components,
+ y=y_labels,
+ colorscale=[[0.0, "rgb(255,255,255)"], [1.0, "rgb(220,0,0)"]],
+ colorbar=dict(title="Error (mm)"),
+ hovertemplate="AFID %{y}
%{x} Error = %{z:.2f} mm",
+ )
+ )
fig.update_layout(
- height=600,
- width=500,
- margin=dict(t=0, b=0, l=0, r=0),
- template='plotly_white'
+ height=600, width=500, margin=dict(t=0, b=0, l=0, r=0), template="plotly_white"
)
- return pio.to_html(fig, full_html=False, include_plotlyjs='cdn')
+ return pio.to_html(fig, full_html=False, include_plotlyjs="cdn")
# --- 3D SCATTER PLOT ---
@@ -190,12 +187,10 @@ def make_3d_plot(gt, pred, afid_ids):
x=gt[:, 0],
y=gt[:, 1],
z=gt[:, 2],
- mode='markers',
- name='Stereotactic Space',
- marker=dict(
- size=6, color='green', opacity=0.7, symbol='diamond'
- ),
- text=afid_ids
+ mode="markers",
+ name="Stereotactic Space",
+ marker=dict(size=6, color="green", opacity=0.7, symbol="diamond"),
+ text=afid_ids,
)
)
fig.add_trace(
@@ -203,12 +198,10 @@ def make_3d_plot(gt, pred, afid_ids):
x=pred[:, 0],
y=pred[:, 1],
z=pred[:, 2],
- mode='markers',
- name='Registered Subject',
- marker=dict(
- size=6, color='red', opacity=0.7, symbol='cross'
- ),
- text=afid_ids
+ mode="markers",
+ name="Registered Subject",
+ marker=dict(size=6, color="red", opacity=0.7, symbol="cross"),
+ text=afid_ids,
)
)
for i in range(gt.shape[0]):
@@ -217,25 +210,26 @@ def make_3d_plot(gt, pred, afid_ids):
x=[gt[i, 0], pred[i, 0]],
y=[gt[i, 1], pred[i, 1]],
z=[gt[i, 2], pred[i, 2]],
- mode='lines',
- line=dict(color='orange', width=7),
- showlegend=False
+ mode="lines",
+ line=dict(color="orange", width=7),
+ showlegend=False,
)
)
fig.update_layout(
scene=dict(
- xaxis_title='X',
- yaxis_title='Y',
- zaxis_title='Z',
- bgcolor='rgb(250,250,250)'
+ xaxis_title="X",
+ yaxis_title="Y",
+ zaxis_title="Z",
+ bgcolor="rgb(250,250,250)",
),
height=600,
width=800,
margin=dict(t=0, b=0, l=0, r=0),
- template='plotly_white',
- legend=dict(x=0.7, y=0.9)
+ template="plotly_white",
+ legend=dict(x=0.7, y=0.9),
)
- return pio.to_html(fig, full_html=False, include_plotlyjs='cdn')
+ return pio.to_html(fig, full_html=False, include_plotlyjs="cdn")
+
# --- Coordinate Utilities ---
def world_to_voxel_coords(img, world_coords):
@@ -244,16 +238,18 @@ def world_to_voxel_coords(img, world_coords):
affine_inv = np.linalg.inv(img.affine)
return np.round(affine_inv.dot(np.append(world_coords, 1))).astype(int)
+
def get_afid_world_and_voxel_coords(img, fcsv_path, label_num):
"""Get both world and voxel coordinates for a given AFID label."""
df = pd.read_csv(fcsv_path, skiprows=2)
- row = df[df['label'] == label_num]
+ row = df[df["label"] == label_num]
if row.empty:
return None, None
- world_coords = row[['x', 'y', 'z']].to_numpy()[0]
+ world_coords = row[["x", "y", "z"]].to_numpy()[0]
voxel_coords = world_to_voxel_coords(img, world_coords)
return world_coords, voxel_coords
+
# --- Visualization Utilities ---
def render_mri_planes(data, coords, zoom=50, show_crosshairs=True):
"""
@@ -296,6 +292,7 @@ def render_mri_planes(data, coords, zoom=50, show_crosshairs=True):
plt.close(fig)
return base64.b64encode(buf.getvalue()).decode()
+
# --- HTML Utilities ---
def build_afid_viewer_block(label, error_val, subject_image, ref_image):
return f"""
@@ -383,15 +380,11 @@ def build_afid_viewer_block(label, error_val, subject_image, ref_image):
"""
+
# --- Main Viewer Builder ---
def generate_qualitative_mri_html(
- subject_nii,
- ref_nii,
- ref_fcsv,
- ed_errors,
- labels=list(range(1, 33)),
- zoom=40
- ):
+ subject_nii, ref_nii, ref_fcsv, ed_errors, labels=list(range(1, 33)), zoom=40
+):
"""Generates HTML for visual comparison of AFID localization
on subject vs template MRI."""
subject_img = nib.as_closest_canonical(nib.load(subject_nii))
@@ -407,14 +400,10 @@ def generate_qualitative_mri_html(
if world_coords is None:
continue
subject_voxel = world_to_voxel_coords(subject_img, world_coords)
- subject_image = render_mri_planes(
- subject_data, subject_voxel[:3], zoom=zoom
- )
+ subject_image = render_mri_planes(subject_data, subject_voxel[:3], zoom=zoom)
ref_image = render_mri_planes(ref_data, template_voxel[:3], zoom=zoom)
viewer_blocks.append(
- build_afid_viewer_block(
- label, ed_errors[i], subject_image, ref_image
- )
+ build_afid_viewer_block(label, ed_errors[i], subject_image, ref_image)
)
return f"""
@@ -450,13 +439,15 @@ def generate_qualitative_mri_html(
"""
+
# --- RENDER HTML DASHBOARD ---
def render_dashboard_html(
- output_path, heatmap_html, scatter_html, ed_errors, qualitative_html
- ):
- stats = pd.Series(ed_errors).describe(percentiles=[.25, .5, .75]).to_dict()
+ output_path, heatmap_html, scatter_html, ed_errors, qualitative_html
+):
+ stats = pd.Series(ed_errors).describe(percentiles=[0.25, 0.5, 0.75]).to_dict()
std = np.std(ed_errors)
- template = Template("""
+ template = Template(
+ """
AFID Inspector
@@ -669,7 +660,8 @@ def render_dashboard_html(
- """)
+ """
+ )
html = template.render(
stats={
"Min": stats["min"],
@@ -677,12 +669,12 @@ def render_dashboard_html(
"Median": stats["50%"],
"75%": stats["75%"],
"Max": stats["max"],
- "Mean": stats["mean"]
+ "Mean": stats["mean"],
},
std=std,
heatmap=heatmap_html,
scatter=scatter_html,
- qualitative=qualitative_html
+ qualitative=qualitative_html,
)
Path(output_path).write_text(html)
return output_path
@@ -690,66 +682,68 @@ def render_dashboard_html(
# --- MAIN WRAPPER ---
def generate_afid_qc_dashboard(
- template_name,
- gt_fcsv_path,
- pred_fcsv_path,
- output_html_path,
- output_fcsv_path,
- output_csv_path,subject_nii,
- template_path_or_dir,
- matfile_path,
- warpfile_path
- ):
+ template_name,
+ gt_fcsv_path,
+ pred_fcsv_path,
+ output_html_path,
+ output_fcsv_path,
+ output_csv_path,
+ subject_nii,
+ template_path_or_dir,
+ matfile_path,
+ warpfile_path,
+):
native_coords, afid_ids = load_fcsv(gt_fcsv_path)
if matfile_path:
- gt_coords_a= apply_affine_transform(matfile_path,native_coords)
- gt_coords= apply_warp_deformation(warpfile_path,gt_coords_a)
+ gt_coords_a = apply_affine_transform(matfile_path, native_coords)
+ gt_coords = apply_warp_deformation(warpfile_path, gt_coords_a)
else:
- gt_coords= apply_warp_deformation(warpfile_path,
- native_coords,
- leaddbs=False)
+ gt_coords = apply_warp_deformation(warpfile_path, native_coords, leaddbs=False)
pred_coords, _ = load_fcsv(pred_fcsv_path)
afids_to_fcsv(gt_coords, output_fcsv_path)
dx, dy, dz, ed = compute_error_components(gt_coords, pred_coords)
# --- EXPORT TO CSV ---
- error_df = pd.DataFrame({
- "AFID": afid_ids,
- "dx (mm)": dx,
- "dy (mm)": dy,
- "dz (mm)": dz,
- "ED (mm)": ed
- })
+ error_df = pd.DataFrame(
+ {"AFID": afid_ids, "dx (mm)": dx, "dy (mm)": dy, "dz (mm)": dz, "ED (mm)": ed}
+ )
if template_name:
- if template_name in ['MNI152NLin2009bAsym','MNI152NLin2009bSym']:
- template_nii = str(Path(template_path_or_dir) /
- f'tpl-{template_name}' /
- f'tpl-{template_name}_res-1_T1w.nii.gz')
- elif template_name in ['MNI305', 'MNIColin27']:
- template_nii = str(Path(template_path_or_dir) /
- f'tpl-{template_name}' /
- f'tpl-{template_name}_T1w.nii.gz')
+ if template_name in ["MNI152NLin2009bAsym", "MNI152NLin2009bSym"]:
+ template_nii = str(
+ Path(template_path_or_dir)
+ / f"tpl-{template_name}"
+ / f"tpl-{template_name}_res-1_T1w.nii.gz"
+ )
+ elif template_name in ["MNI305", "MNIColin27"]:
+ template_nii = str(
+ Path(template_path_or_dir)
+ / f"tpl-{template_name}"
+ / f"tpl-{template_name}_T1w.nii.gz"
+ )
else:
- template_nii = str(Path(template_path_or_dir) /
- f'tpl-{template_name}' /
- f'tpl-{template_name}_res-01_T1w.nii.gz')
+ template_nii = str(
+ Path(template_path_or_dir)
+ / f"tpl-{template_name}"
+ / f"tpl-{template_name}_res-01_T1w.nii.gz"
+ )
else:
template_nii = template_path_or_dir
-
error_df.to_csv(output_csv_path, index=False)
heatmap_html = make_toggleable_heatmap([dx, dy, dz, ed], afid_ids)
scatter_html = make_3d_plot(gt_coords, pred_coords, afid_ids)
qualitative_html = generate_qualitative_mri_html(
- subject_nii, template_nii, pred_fcsv_path, ed,zoom=50)
+ subject_nii, template_nii, pred_fcsv_path, ed, zoom=50
+ )
return render_dashboard_html(
- output_html_path, heatmap_html, scatter_html, ed, qualitative_html)
+ output_html_path, heatmap_html, scatter_html, ed, qualitative_html
+ )
if __name__ == "__main__":
generate_afid_qc_dashboard(
- template_name=snakemake.params['template'],
+ template_name=snakemake.params["template"],
gt_fcsv_path=snakemake.input["afidfcsv"],
pred_fcsv_path=snakemake.input["refcoord"],
output_html_path=snakemake.output["html"],
@@ -758,5 +752,5 @@ def generate_afid_qc_dashboard(
subject_nii=snakemake.input["im"],
template_path_or_dir=snakemake.input["refim"],
matfile_path=snakemake.input["optional_matrix"],
- warpfile_path=snakemake.input["warp"]
+ warpfile_path=snakemake.input["warp"],
)
diff --git a/autoafids/workflow/scripts/regqc_summary.py b/autoafids/workflow/scripts/regqc_summary.py
index b842b99..12d69fa 100644
--- a/autoafids/workflow/scripts/regqc_summary.py
+++ b/autoafids/workflow/scripts/regqc_summary.py
@@ -28,6 +28,7 @@
# Helpers
# ---------------------------------------------------------------------------
+
def _parse_bids_entities(filename: str) -> dict:
"""Extract BIDS key-value entities from a filename."""
entities = {}
@@ -92,7 +93,7 @@ def load_fcsv(path: str) -> tuple:
def load_all_fcsvs(
- fcsv_paths: list,
+ fcsv_paths: list,
) -> list:
"""Load all per-subject FCSVs.
@@ -106,18 +107,20 @@ def load_all_fcsvs(
coords, afid_labels = load_fcsv(fp)
except Exception:
continue
- result.append({
- "subject_label": label,
- "coords": coords,
- "labels": afid_labels,
- })
+ result.append(
+ {
+ "subject_label": label,
+ "coords": coords,
+ "labels": afid_labels,
+ }
+ )
return result
def compute_error_decomposition(
- subject_fcsvs: list,
- gt_coords: np.ndarray,
- gt_labels: list,
+ subject_fcsvs: list,
+ gt_coords: np.ndarray,
+ gt_labels: list,
) -> pd.DataFrame:
"""Decompose per-AFID error into systematic bias and random scatter.
@@ -157,59 +160,61 @@ def compute_error_decomposition(
errors = np.stack(all_errors, axis=0) # (S, N, 3)
# Per-AFID statistics
- bias = errors.mean(axis=0) # (N, 3) — systematic
- scatter = errors.std(axis=0, ddof=1) if errors.shape[0] > 1 \
- else np.zeros_like(bias) # (N, 3) — random
+ bias = errors.mean(axis=0) # (N, 3) — systematic
+ scatter = (
+ errors.std(axis=0, ddof=1) if errors.shape[0] > 1 else np.zeros_like(bias)
+ ) # (N, 3) — random
- bias_mag = np.linalg.norm(bias, axis=1) # (N,)
- scatter_rms = np.sqrt((scatter ** 2).mean(axis=1)) # (N,)
+ bias_mag = np.linalg.norm(bias, axis=1) # (N,)
+ scatter_rms = np.sqrt((scatter**2).mean(axis=1)) # (N,)
total_error = np.linalg.norm(errors, axis=2).mean(axis=0) # (N,)
bias_ratio = bias_mag / (bias_mag + scatter_rms + 1e-9)
- return pd.DataFrame({
- "label": gt_labels,
- "bias_x": bias[:, 0],
- "bias_y": bias[:, 1],
- "bias_z": bias[:, 2],
- "bias_mag": bias_mag,
- "scatter_x": scatter[:, 0],
- "scatter_y": scatter[:, 1],
- "scatter_z": scatter[:, 2],
- "scatter_rms": scatter_rms,
- "total_error": total_error,
- "bias_ratio": bias_ratio,
- "n_subjects": errors.shape[0],
- "gt_x": gt_coords[:, 0],
- "gt_y": gt_coords[:, 1],
- "gt_z": gt_coords[:, 2],
- })
+ return pd.DataFrame(
+ {
+ "label": gt_labels,
+ "bias_x": bias[:, 0],
+ "bias_y": bias[:, 1],
+ "bias_z": bias[:, 2],
+ "bias_mag": bias_mag,
+ "scatter_x": scatter[:, 0],
+ "scatter_y": scatter[:, 1],
+ "scatter_z": scatter[:, 2],
+ "scatter_rms": scatter_rms,
+ "total_error": total_error,
+ "bias_ratio": bias_ratio,
+ "n_subjects": errors.shape[0],
+ "gt_x": gt_coords[:, 0],
+ "gt_y": gt_coords[:, 1],
+ "gt_z": gt_coords[:, 2],
+ }
+ )
# ---------------------------------------------------------------------------
# Plotly chart builders
# ---------------------------------------------------------------------------
+
def make_heatmap(long_df: pd.DataFrame) -> str:
"""
Subjects (rows) x AFIDs (cols), colour = ED (mm).
Rows sorted by mean ED descending (worst on top).
"""
- pivot = long_df.pivot_table(
- index="subject_label", columns="AFID", values="ED (mm)"
- )
+ pivot = long_df.pivot_table(index="subject_label", columns="AFID", values="ED (mm)")
row_order = pivot.mean(axis=1).sort_values(ascending=False).index
pivot = pivot.loc[row_order]
- fig = go.Figure(data=go.Heatmap(
- z=pivot.values,
- x=[f"AFID {c}" for c in pivot.columns],
- y=list(pivot.index),
- colorscale=[[0.0, "rgb(255,255,255)"], [1.0, "rgb(220,0,0)"]],
- colorbar=dict(title="ED (mm)"),
- hovertemplate=(
- "%{y}
AFID %{x}
ED = %{z:.2f} mm"
- ),
- ))
+ fig = go.Figure(
+ data=go.Heatmap(
+ z=pivot.values,
+ x=[f"AFID {c}" for c in pivot.columns],
+ y=list(pivot.index),
+ colorscale=[[0.0, "rgb(255,255,255)"], [1.0, "rgb(220,0,0)"]],
+ colorbar=dict(title="ED (mm)"),
+ hovertemplate=("%{y}
AFID %{x}
ED = %{z:.2f} mm"),
+ )
+ )
fig.update_layout(
title="Registration Error (ED mm) — Subjects x AFIDs",
height=max(300, 30 * len(pivot)),
@@ -229,15 +234,17 @@ def make_afid_boxplot(long_df: pd.DataFrame) -> str:
fig = go.Figure()
for afid in afid_ids:
vals = long_df.loc[long_df["AFID"] == afid, "ED (mm)"]
- fig.add_trace(go.Box(
- y=vals,
- name=f"AFID {afid}",
- boxpoints=show_points,
- jitter=0.4,
- pointpos=0,
- marker=dict(size=4, opacity=0.5),
- line=dict(width=1.5),
- ))
+ fig.add_trace(
+ go.Box(
+ y=vals,
+ name=f"AFID {afid}",
+ boxpoints=show_points,
+ jitter=0.4,
+ pointpos=0,
+ marker=dict(size=4, opacity=0.5),
+ line=dict(width=1.5),
+ )
+ )
fig.update_layout(
title="ED Distribution per AFID (across subjects)",
yaxis_title="ED (mm)",
@@ -256,19 +263,17 @@ def make_subject_bar(subject_stats: pd.DataFrame) -> str:
"""
df = subject_stats.sort_values("mean_ED", ascending=True)
colorscale_vals = df["mean_ED"].values
- norm = (colorscale_vals - colorscale_vals.min()) / (
- np.ptp(colorscale_vals) + 1e-9
+ norm = (colorscale_vals - colorscale_vals.min()) / (np.ptp(colorscale_vals) + 1e-9)
+ colours = [f"rgb({int(255*v)},{int(255*(1-v))},80)" for v in norm]
+ fig = go.Figure(
+ go.Bar(
+ x=df["mean_ED"],
+ y=df["subject_label"],
+ orientation="h",
+ marker_color=colours,
+ hovertemplate="%{y}
Mean ED = %{x:.2f} mm",
+ )
)
- colours = [
- f"rgb({int(255*v)},{int(255*(1-v))},80)" for v in norm
- ]
- fig = go.Figure(go.Bar(
- x=df["mean_ED"],
- y=df["subject_label"],
- orientation="h",
- marker_color=colours,
- hovertemplate="%{y}
Mean ED = %{x:.2f} mm",
- ))
fig.update_layout(
title="Mean ED per Subject (sorted best → worst)",
xaxis_title="Mean ED (mm)",
@@ -280,9 +285,9 @@ def make_subject_bar(subject_stats: pd.DataFrame) -> str:
def make_3d_landmark_scatter(
- subject_fcsvs: list,
- gt_coords: np.ndarray = None,
- gt_labels: list | None = None,
+ subject_fcsvs: list,
+ gt_coords: np.ndarray = None,
+ gt_labels: list | None = None,
) -> str:
"""Interactive 3D scatter of all subjects' AFIDs in MNI space.
@@ -296,31 +301,50 @@ def make_3d_landmark_scatter(
# Ground truth (template) as large green diamonds
if gt_coords is not None:
- hover = [
- f"AFID {i}" for i in (gt_labels or range(1, len(gt_coords) + 1))
- ]
- fig.add_trace(go.Scatter3d(
- x=gt_coords[:, 0],
- y=gt_coords[:, 1],
- z=gt_coords[:, 2],
- mode="markers",
- name="MNI Ground Truth",
- marker=dict(
- size=6, color="green", opacity=0.9, symbol="diamond",
- ),
- text=hover,
- hovertemplate="%{text}
x=%{x:.1f} y=%{y:.1f} z=%{z:.1f}"
- "MNI Ground Truth",
- ))
+ hover = [f"AFID {i}" for i in (gt_labels or range(1, len(gt_coords) + 1))]
+ fig.add_trace(
+ go.Scatter3d(
+ x=gt_coords[:, 0],
+ y=gt_coords[:, 1],
+ z=gt_coords[:, 2],
+ mode="markers",
+ name="MNI Ground Truth",
+ marker=dict(
+ size=6,
+ color="green",
+ opacity=0.9,
+ symbol="diamond",
+ ),
+ text=hover,
+ hovertemplate="%{text}
x=%{x:.1f} y=%{y:.1f} z=%{z:.1f}"
+ "MNI Ground Truth",
+ )
+ )
# Per-subject data — merge into a single trace for scalability
# (avoids creating N traces which kills performance at N>30)
all_x, all_y, all_z, all_hover, all_colour = [], [], [], [], []
colours = [
- "#e6194b", "#3cb44b", "#4363d8", "#f58231", "#911eb4",
- "#42d4f4", "#f032e6", "#bfef45", "#fabed4", "#469990",
- "#dcbeff", "#9A6324", "#fffac8", "#800000", "#aaffc3",
- "#808000", "#ffd8b1", "#000075", "#a9a9a9", "#000000",
+ "#e6194b",
+ "#3cb44b",
+ "#4363d8",
+ "#f58231",
+ "#911eb4",
+ "#42d4f4",
+ "#f032e6",
+ "#bfef45",
+ "#fabed4",
+ "#469990",
+ "#dcbeff",
+ "#9A6324",
+ "#fffac8",
+ "#800000",
+ "#aaffc3",
+ "#808000",
+ "#ffd8b1",
+ "#000075",
+ "#a9a9a9",
+ "#000000",
]
for i, sub in enumerate(subject_fcsvs):
colour = colours[i % len(colours)]
@@ -328,22 +352,26 @@ def make_3d_landmark_scatter(
all_x.extend(sub["coords"][:, 0].tolist())
all_y.extend(sub["coords"][:, 1].tolist())
all_z.extend(sub["coords"][:, 2].tolist())
- all_hover.extend(
- f"{sub['subject_label']} — AFID {i}" for i in sub["labels"]
- )
+ all_hover.extend(f"{sub['subject_label']} — AFID {i}" for i in sub["labels"])
all_colour.extend([colour] * n)
- fig.add_trace(go.Scatter3d(
- x=all_x, y=all_y, z=all_z,
- mode="markers",
- name=f"Subjects (n={len(subject_fcsvs)})",
- marker=dict(
- size=3, color=all_colour, opacity=0.5,
- ),
- text=all_hover,
- hovertemplate="%{text}
x=%{x:.1f} y=%{y:.1f} z=%{z:.1f}"
- "",
- ))
+ fig.add_trace(
+ go.Scatter3d(
+ x=all_x,
+ y=all_y,
+ z=all_z,
+ mode="markers",
+ name=f"Subjects (n={len(subject_fcsvs)})",
+ marker=dict(
+ size=3,
+ color=all_colour,
+ opacity=0.5,
+ ),
+ text=all_hover,
+ hovertemplate="%{text}
x=%{x:.1f} y=%{y:.1f} z=%{z:.1f}"
+ "",
+ )
+ )
fig.update_layout(
scene=dict(
@@ -358,16 +386,18 @@ def make_3d_landmark_scatter(
template="plotly_white",
title="AFID Landmarks in MNI Space (all subjects)",
legend=dict(
- x=0.82, y=0.95,
+ x=0.82,
+ y=0.95,
bgcolor="rgba(255,255,255,0.8)",
- bordercolor="#ccc", borderwidth=1,
+ bordercolor="#ccc",
+ borderwidth=1,
),
)
return pio.to_html(fig, full_html=False, include_plotlyjs=False)
def make_bias_vector_3d(
- decomp_df: pd.DataFrame,
+ decomp_df: pd.DataFrame,
) -> str:
"""3D cone plot showing bias direction and magnitude at each AFID.
@@ -382,64 +412,83 @@ def make_bias_vector_3d(
fig = go.Figure()
# Ground-truth markers
- fig.add_trace(go.Scatter3d(
- x=df["gt_x"], y=df["gt_y"], z=df["gt_z"],
- mode="markers",
- name="AFID Location (MNI)",
- marker=dict(size=4, color="#888", opacity=0.6, symbol="diamond"),
- text=[f"AFID {i}" for i in df["label"]],
- hovertemplate="%{text}Ground Truth",
- ))
+ fig.add_trace(
+ go.Scatter3d(
+ x=df["gt_x"],
+ y=df["gt_y"],
+ z=df["gt_z"],
+ mode="markers",
+ name="AFID Location (MNI)",
+ marker=dict(size=4, color="#888", opacity=0.6, symbol="diamond"),
+ text=[f"AFID {i}" for i in df["label"]],
+ hovertemplate="%{text}Ground Truth",
+ )
+ )
# Bias cones — Plotly Cone trace
- fig.add_trace(go.Cone(
- x=df["gt_x"], y=df["gt_y"], z=df["gt_z"],
- u=df["bias_x"], v=df["bias_y"], w=df["bias_z"],
- sizemode="absolute",
- sizeref=max_bias * 0.8,
- colorscale=[
- [0.0, "rgb(49,130,189)"], # blue = mostly random
- [0.5, "rgb(255,255,100)"], # yellow
- [1.0, "rgb(215,48,39)"], # red = mostly systematic
- ],
- cmin=0, cmax=1,
- colorbar=dict(
- title="Bias Ratio",
- tickvals=[0, 0.5, 1],
- ticktext=["Random", "Mixed", "Systematic"],
- len=0.6, y=0.5,
- ),
- name="Bias Vector",
- text=[
- f"AFID {row.label}
"
- f"Bias: {row.bias_mag:.2f} mm
"
- f"Scatter: {row.scatter_rms:.2f} mm
"
- f"Ratio: {row.bias_ratio:.0%} systematic"
- for _, row in df.iterrows()
- ],
- hoverinfo="text",
- ))
+ fig.add_trace(
+ go.Cone(
+ x=df["gt_x"],
+ y=df["gt_y"],
+ z=df["gt_z"],
+ u=df["bias_x"],
+ v=df["bias_y"],
+ w=df["bias_z"],
+ sizemode="absolute",
+ sizeref=max_bias * 0.8,
+ colorscale=[
+ [0.0, "rgb(49,130,189)"], # blue = mostly random
+ [0.5, "rgb(255,255,100)"], # yellow
+ [1.0, "rgb(215,48,39)"], # red = mostly systematic
+ ],
+ cmin=0,
+ cmax=1,
+ colorbar=dict(
+ title="Bias Ratio",
+ tickvals=[0, 0.5, 1],
+ ticktext=["Random", "Mixed", "Systematic"],
+ len=0.6,
+ y=0.5,
+ ),
+ name="Bias Vector",
+ text=[
+ f"AFID {row.label}
"
+ f"Bias: {row.bias_mag:.2f} mm
"
+ f"Scatter: {row.scatter_rms:.2f} mm
"
+ f"Ratio: {row.bias_ratio:.0%} systematic"
+ for _, row in df.iterrows()
+ ],
+ hoverinfo="text",
+ )
+ )
# Endpoint markers (bias tip) for error lines
tip_x = df["gt_x"] + df["bias_x"]
tip_y = df["gt_y"] + df["bias_y"]
tip_z = df["gt_z"] + df["bias_z"]
- fig.add_trace(go.Scatter3d(
- x=tip_x, y=tip_y, z=tip_z,
- mode="markers",
- name="Bias Endpoint",
- marker=dict(size=2, color="red", opacity=0.5),
- hoverinfo="skip",
- showlegend=False,
- ))
+ fig.add_trace(
+ go.Scatter3d(
+ x=tip_x,
+ y=tip_y,
+ z=tip_z,
+ mode="markers",
+ name="Bias Endpoint",
+ marker=dict(size=2, color="red", opacity=0.5),
+ hoverinfo="skip",
+ showlegend=False,
+ )
+ )
fig.update_layout(
scene=dict(
- xaxis_title="X (mm)", yaxis_title="Y (mm)", zaxis_title="Z (mm)",
+ xaxis_title="X (mm)",
+ yaxis_title="Y (mm)",
+ zaxis_title="Z (mm)",
bgcolor="rgb(250,250,250)",
aspectmode="data",
),
- height=700, width=950,
+ height=700,
+ width=950,
margin=dict(t=50, b=30, l=20, r=20),
template="plotly_white",
title="Systematic Bias Vectors at Each AFID",
@@ -454,18 +503,24 @@ def make_decomposition_bar(decomp_df: pd.DataFrame) -> str:
labels = [f"AFID {i}" for i in df["label"]]
fig = go.Figure()
- fig.add_trace(go.Bar(
- x=labels, y=df["bias_mag"],
- name="Systematic Bias |μ|",
- marker_color="rgb(215,48,39)",
- hovertemplate="%{x}
Bias = %{y:.2f} mm",
- ))
- fig.add_trace(go.Bar(
- x=labels, y=df["scatter_rms"],
- name="Random Scatter o",
- marker_color="rgb(49,130,189)",
- hovertemplate="%{x}
Scatter = %{y:.2f} mm",
- ))
+ fig.add_trace(
+ go.Bar(
+ x=labels,
+ y=df["bias_mag"],
+ name="Systematic Bias |μ|",
+ marker_color="rgb(215,48,39)",
+ hovertemplate="%{x}
Bias = %{y:.2f} mm",
+ )
+ )
+ fig.add_trace(
+ go.Bar(
+ x=labels,
+ y=df["scatter_rms"],
+ name="Random Scatter o",
+ marker_color="rgb(49,130,189)",
+ hovertemplate="%{x}
Scatter = %{y:.2f} mm",
+ )
+ )
fig.update_layout(
barmode="group",
title="Error Decomposition per AFID: Bias (red) vs Scatter (blue)",
@@ -483,7 +538,8 @@ def make_decomposition_bar(decomp_df: pd.DataFrame) -> str:
# HTML rendering
# ---------------------------------------------------------------------------
-SUMMARY_TEMPLATE = Template("""\
+SUMMARY_TEMPLATE = Template(
+ """\
@@ -786,7 +842,8 @@ def make_decomposition_bar(decomp_df: pd.DataFrame) -> str:
-""")
+"""
+)
def _ed_badge(mean_ed: float) -> str:
@@ -798,36 +855,35 @@ def _ed_badge(mean_ed: float) -> str:
def render_summary_html(
- output_path: str,
- long_df: pd.DataFrame,
- output_dir: str,
- subject_fcsvs: list | None = None,
- gt_coords: np.ndarray = None,
- gt_labels: list | None = None,
+ output_path: str,
+ long_df: pd.DataFrame,
+ output_dir: str,
+ subject_fcsvs: list | None = None,
+ gt_coords: np.ndarray = None,
+ gt_labels: list | None = None,
) -> str:
"""Build and write the dataset summary HTML file."""
# Per-subject stats
grp = long_df.groupby("subject_label")
- stats = grp["ED (mm)"].agg(
- mean_ED="mean", median_ED="median", max_ED="max"
- ).reset_index()
- worst_afid = (
- long_df.loc[long_df.groupby("subject_label")["ED (mm)"].idxmax()]
- [["subject_label", "AFID"]].rename(columns={"AFID": "worst_afid"})
- )
- html_paths_ser = (
- long_df.groupby("subject_label")["html_path"].first().reset_index()
+ stats = (
+ grp["ED (mm)"]
+ .agg(mean_ED="mean", median_ED="median", max_ED="max")
+ .reset_index()
)
+ worst_afid = long_df.loc[long_df.groupby("subject_label")["ED (mm)"].idxmax()][
+ ["subject_label", "AFID"]
+ ].rename(columns={"AFID": "worst_afid"})
+ html_paths_ser = long_df.groupby("subject_label")["html_path"].first().reset_index()
subject_stats = (
- stats
- .merge(worst_afid, on="subject_label")
+ stats.merge(worst_afid, on="subject_label")
.merge(html_paths_ser, on="subject_label")
.sort_values("mean_ED", ascending=False)
)
# Make relative paths from the output HTML's directory
out_dir = Path(output_path).parent
+
def _relpath(p):
if not p:
return ""
@@ -841,25 +897,23 @@ def _relpath(p):
# Dataset-level stats (all subjects x all AFIDs)
all_ed = long_df["ED (mm)"]
- dataset_mean = float(all_ed.mean())
+ dataset_mean = float(all_ed.mean())
dataset_median = float(all_ed.median())
- dataset_std = float(all_ed.std())
- best_ed = float(subject_stats["mean_ED"].min())
+ dataset_std = float(all_ed.std())
+ best_ed = float(subject_stats["mean_ED"].min())
worst_ed = float(subject_stats["mean_ED"].max())
n_subjects = int(subject_stats["subject_label"].nunique())
- n_afids = int(long_df["AFID"].nunique())
+ n_afids = int(long_df["AFID"].nunique())
# Build charts
heatmap_html = make_heatmap(long_df)
boxplot_html = make_afid_boxplot(long_df)
- bar_html = make_subject_bar(subject_stats)
+ bar_html = make_subject_bar(subject_stats)
# 3D landmark scatter (optional — only if fcsv data provided)
scatter3d_html = ""
if subject_fcsvs:
- scatter3d_html = make_3d_landmark_scatter(
- subject_fcsvs, gt_coords, gt_labels
- )
+ scatter3d_html = make_3d_landmark_scatter(subject_fcsvs, gt_coords, gt_labels)
# Error decomposition (requires ≥1 subject fcsv + gt)
decomp_html = False
@@ -873,9 +927,7 @@ def _relpath(p):
decomp_worst_scatter_afid = "-"
decomp_worst_scatter_val = 0.0
if subject_fcsvs and gt_coords is not None:
- decomp_df = compute_error_decomposition(
- subject_fcsvs, gt_coords, gt_labels
- )
+ decomp_df = compute_error_decomposition(subject_fcsvs, gt_coords, gt_labels)
if not decomp_df.empty:
decomp_html = True
decomp_bar_html = make_decomposition_bar(decomp_df)
@@ -883,24 +935,14 @@ def _relpath(p):
decomp_bias_mean = float(decomp_df["bias_mag"].mean())
decomp_scatter_mean = float(decomp_df["scatter_rms"].mean())
decomp_bias_pct = (
- decomp_bias_mean
- / (decomp_bias_mean + decomp_scatter_mean + 1e-9)
- * 100
+ decomp_bias_mean / (decomp_bias_mean + decomp_scatter_mean + 1e-9) * 100
)
worst_bias_idx = decomp_df["bias_mag"].idxmax()
- decomp_worst_bias_afid = decomp_df.loc[
- worst_bias_idx, "label"
- ]
- decomp_worst_bias_val = decomp_df.loc[
- worst_bias_idx, "bias_mag"
- ]
+ decomp_worst_bias_afid = decomp_df.loc[worst_bias_idx, "label"]
+ decomp_worst_bias_val = decomp_df.loc[worst_bias_idx, "bias_mag"]
worst_scatter_idx = decomp_df["scatter_rms"].idxmax()
- decomp_worst_scatter_afid = decomp_df.loc[
- worst_scatter_idx, "label"
- ]
- decomp_worst_scatter_val = decomp_df.loc[
- worst_scatter_idx, "scatter_rms"
- ]
+ decomp_worst_scatter_afid = decomp_df.loc[worst_scatter_idx, "label"]
+ decomp_worst_scatter_val = decomp_df.loc[worst_scatter_idx, "scatter_rms"]
# Render template
html = SUMMARY_TEMPLATE.render(
@@ -937,8 +979,8 @@ def _relpath(p):
# Snakemake entry point
# ---------------------------------------------------------------------------
-def main(csv_paths, html_paths, output_html,
- fcsv_paths=None, gt_fcsv_path=None):
+
+def main(csv_paths, html_paths, output_html, fcsv_paths=None, gt_fcsv_path=None):
long_df = load_all_csvs(csv_paths, html_paths)
out_dir = str(Path(output_html).parent)
@@ -954,13 +996,16 @@ def main(csv_paths, html_paths, output_html,
pass
render_summary_html(
- output_html, long_df, out_dir,
+ output_html,
+ long_df,
+ out_dir,
subject_fcsvs=subject_fcsvs,
gt_coords=gt_coords,
gt_labels=gt_labels,
)
print(f"[regqc_summary] Written → {output_html}")
+
# Snakemake script entry point
fcsv_list = list(snakemake.input.get("fcsvs", []))
gt_path = snakemake.params.get("gt_fcsv", None)
@@ -971,4 +1016,3 @@ def main(csv_paths, html_paths, output_html,
fcsv_paths=fcsv_list,
gt_fcsv_path=gt_path,
)
-
diff --git a/autoafids/workflow/scripts/run_synthsr.py b/autoafids/workflow/scripts/run_synthsr.py
index c202e7d..845e834 100644
--- a/autoafids/workflow/scripts/run_synthsr.py
+++ b/autoafids/workflow/scripts/run_synthsr.py
@@ -4,12 +4,7 @@
# run SynthSR
-def run_program(
- input_img,
- output_img,
- modality,
- log_file
- ):
+def run_program(input_img, output_img, modality, log_file):
cmd = [
"python",
@@ -17,7 +12,7 @@ def run_program(
"SynthSR.scripts.predict_command_line",
input_img,
output_img,
- "--cpu"
+ "--cpu",
]
if modality == "ct":
@@ -25,12 +20,7 @@ def run_program(
with open(log_file, "a") as log:
try:
- subprocess.run(
- cmd,
- check=True,
- stdout=log,
- stderr=log
- )
+ subprocess.run(cmd, check=True, stdout=log, stderr=log)
logging.info(
f"SynthSR processing completed for {input_img}."
f"Output saved to {output_img}"
@@ -40,15 +30,12 @@ def run_program(
raise
-log_file=snakemake.log[0]
-download_dir=snakemake.params["download_dir"]
+log_file = snakemake.log[0]
+download_dir = snakemake.params["download_dir"]
run_program(
input_img=snakemake.input["im"],
output_img=snakemake.output["SynthSR"],
modality=snakemake.params["modality"],
- log_file=log_file
+ log_file=log_file,
)
-
-
-
diff --git a/autoafids/workflow/scripts/stereotaxy.py b/autoafids/workflow/scripts/stereotaxy.py
index 315e433..24d5c64 100644
--- a/autoafids/workflow/scripts/stereotaxy.py
+++ b/autoafids/workflow/scripts/stereotaxy.py
@@ -204,12 +204,14 @@ def fids_to_fcsv(fids, fcsv_template, fcsv_output):
with open(fcsv_output, "w") as f:
f.write("\n".join(line for line in fcsv))
+
def make_zero(num, threshold=0.0001):
if abs(num) < threshold:
return 0
else:
return num
+
def mcp_origin(df_afids):
"""
sets MCP as the origin point for each of the subjects
@@ -241,6 +243,7 @@ def mcp_origin(df_afids):
np.array([mcp_x.to_numpy(), mcp_y.to_numpy(), mcp_z.to_numpy()])
)
+
def get_fiducial_index(fid):
"""
Retrieve the index corresponding to the fiducial name or integer.
@@ -278,7 +281,7 @@ def fcsvtodf(fcsv_path):
try:
subject_id = re.search(r"(sub-\w+)", fcsv_path).group(1)
except Exception as e:
- print("no subject id found")
+ print("no subject id found")
# Read in .fcsv file, skip header
df_raw = pd.read_table(fcsv_path, sep=",", header=2)
@@ -304,6 +307,7 @@ def fcsvtodf(fcsv_path):
return df_xyz_clean, df_raw.shape[0]
+
def compute_distance(fcsv_path, fid1, fid2):
"""
Compute the Euclidean distance between two fiducials.
@@ -335,6 +339,7 @@ def compute_distance(fcsv_path, fid1, fid2):
return xyz_diff.flatten(), distance
+
def compute_average(fcsv_path, fid1, fid2):
"""
Compute the average position between two fiducials.
@@ -400,11 +405,7 @@ def generate_slicer_file(matrix, filename):
def acpcmatrix(
- fcsv_path,
- midline,
- center_on_mcp=False,
- write_matrix=True,
- transform_file_name=None
+ fcsv_path, midline, center_on_mcp=False, write_matrix=True, transform_file_name=None
):
"""
Computes a 4x4 transformation matrix aligning with the AC-PC axis.
@@ -509,12 +510,12 @@ def transform_afids(fcsv_path, slicer_tfm, midpoint):
fcsv_df.loc[:, "z"] = tcoords[:, 2]
else:
raise ValueError(
- "New coordinates do not match " +
- "the number of rows in the original fcsv."
+ "New coordinates do not match " + "the number of rows in the original fcsv."
)
return fcsv_df, xfm_txt
+
def model_pred(
in_fcsv: str,
model: str,
@@ -584,12 +585,10 @@ def model_pred(
num_cols = df_sub_mcp.select_dtypes(include="number")
cols_to_modify = (num_cols > -0.0001).all() & (num_cols < 0.0001).all()
- df_sub_mcp.loc[:, cols_to_modify] = (
- df_sub_mcp.loc[:, cols_to_modify]
- .applymap(make_zero)
+ df_sub_mcp.loc[:, cols_to_modify] = df_sub_mcp.loc[:, cols_to_modify].applymap(
+ make_zero
)
-
# Load the trained model components from the pickle file
with open(model, "rb") as file:
objects_dict = pickle.load(file)
@@ -604,11 +603,7 @@ def model_pred(
df_sub_mcp = pca.transform(df_sub_mcp)
# Make predictions using Ridge regression models for x, y, z coordinates
- y_sub = np.column_stack(
- [
- ridge.predict(df_sub_mcp) for ridge in ridge_inference
- ]
- )
+ y_sub = np.column_stack([ridge.predict(df_sub_mcp) for ridge in ridge_inference])
# Adjust the second predicted x-coordinate to reflect the left hemisphere
y_sub[1, 0] *= -1
diff --git a/autoafids/workflow/scripts/template_flow.py b/autoafids/workflow/scripts/template_flow.py
index c1dd350..1baf716 100644
--- a/autoafids/workflow/scripts/template_flow.py
+++ b/autoafids/workflow/scripts/template_flow.py
@@ -2,18 +2,11 @@
template_name = snakemake.params["template"]
-if template_name in ['MNI305', 'MNIColin27']:
+if template_name in ["MNI305", "MNIColin27"]:
template_path = tflow.get(
- str(template_name),
- desc=None,
- suffix='T1w',
- extension='nii.gz'
- )
+ str(template_name), desc=None, suffix="T1w", extension="nii.gz"
+ )
else:
template_path = tflow.get(
- str(template_name),
- desc=None,
- resolution=1,
- suffix='T1w',
- extension='nii.gz'
- )
+ str(template_name), desc=None, resolution=1, suffix="T1w", extension="nii.gz"
+ )
diff --git a/docs/contributing/contributing.md b/docs/contributing/contributing.md
index e2f3580..ed0e998 100644
--- a/docs/contributing/contributing.md
+++ b/docs/contributing/contributing.md
@@ -1,6 +1,6 @@
# Contributing to AutoAFIDs
-AutoAFIDs is a Python-based BIDS App for automatic anatomical fiducial detection. Development is managed using **Conda** for dependency and environment management.
+AutoAFIDs is a Python-based BIDS App for automatic anatomical fiducial detection. Development is managed using **pixi** for dependency and environment management.
> 🛠️ These instructions are intended for contributors making code changes to the AutoAFIDs codebase or using advanced features such as Snakemake cluster execution profiles.
@@ -8,8 +8,8 @@ AutoAFIDs is a Python-based BIDS App for automatic anatomical fiducial detection
## 📦 Prerequisites
-Ensure **Conda** is installed on your system. You can install Miniconda or Anaconda by following the official guide:
-👉 [https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html)
+Ensure **pixi** is installed on your system. You can install pixi by following the official guide:
+👉 [https://pixi.prefix.dev/latest/installation/](https://pixi.prefix.dev/latest/installation/)
Note: AutoAFIDs primarily supports T1-weighted images. For additional modalities (e.g., T2w), SynthSR will be triggered, though compatibility varies by operating system.
@@ -25,9 +25,7 @@ cd autoafids
2. Create and activate the development environment:
```bash
-mamba env create -f autoafids-dev.yml
-eval "$(mamba shell hook --shell zsh)"
-mamba activate autoafids-dev
+pixi shell
```
3. Run AutoAFIDs with development dependencies:
@@ -43,24 +41,32 @@ mamba activate autoafids-dev
AutoAFIDs uses several tools to ensure clean and consistent code:
-- [`ruff`](https://github.com/charliermarsh/ruff) – for linting and formatting
+- [`isort`](https://github.com/pycqa/isort) – for sorting imports.
- [`snakefmt`](https://github.com/snakemake/snakefmt) – for formatting Snakemake files
-- [`yamlfix`](https://github.com/lyz-code/yamlfix) – for YAML cleanup
+- [`black`](https://github.com/psf/black) – for formatting Python code
### Check formatting:
+1. Activate the development environment:
+
+```bash
+pixi shell --environment dev
+```
+
+2. Run tools
+
```bash
-ruff check .
-snakefmt --check Snakefile
-yamlfix --check config/
+isort autoafids/*.py -c
+snakefmt autoafids --check
+black autoafids --check
```
### Auto-fix formatting:
```bash
-ruff check . --fix
-snakefmt Snakefile
-yamlfix config/
+isort autoafids/*.py
+snakefmt autoafids
+black autoafids
```
---
diff --git a/docs/images/dag.dot b/docs/images/dag.dot
deleted file mode 100644
index 19afbf8..0000000
--- a/docs/images/dag.dot
+++ /dev/null
@@ -1,40 +0,0 @@
-digraph snakemake_dag {
- graph[bgcolor=white, margin=0.3];
- node[shape=box, style=rounded, fontname=sans, fontsize=10, penwidth=2];
- edge[penwidth=2, color=grey];
-
- synthsr[label = "synthsr", color = "0.75 0.6 0.85", style="rounded"];
-
- 1[label = "applyfidmodel", color = "0.42 0.6 0.85", style="rounded"];
- 2[label = "resample_im", color = "0.00 0.6 0.85", style="rounded"];
- 3[label = "norm_im", color = "0.58 0.6 0.85", style="rounded"];
- 5[label = "mni2subfids", color = "0.08 0.6 0.85", style="rounded"];
- 6[label = "regmni2sub", color = "0.25 0.6 0.85", style="rounded"];
- 7[label = "download_cnn_model", color = "0.33 0.6 0.85", style="rounded"];
- 0[label = "all", color = "0.50 0.6 0.85", style="rounded"];
- 4[label = "n4_bias_correction", color = "0.17 0.6 0.85", style="rounded"];
- 8[label = "stereotaxy", color = "0.91 0.6 0.85", style="rounded"];
- 9[label = "fidqc", color = "0.21 0.6 0.85", style="rounded"];
-
- 7 -> 1
- 2 -> 1
- 5 -> 1
- 3 -> 2
- 6 -> 5
- 4 -> 6
- 4 -> 3
- 1 -> 0
- 1 -> 8
- 8 -> 0
- 1 -> 9
- 9 -> 0
-
- 3 -> 1 [label = "if SynthSR", style = "dashed", color = "grey"];
-
- T1w -> 4[style=dashed];
- T2w -> synthsr[style=dashed];
- FLAIR -> synthsr[style=dashed];
- ct -> synthsr[style=dashed];
- synthsr -> 3;
- synthsr -> 6;
- }
\ No newline at end of file
diff --git a/docs/requirements.txt b/docs/requirements.txt
index 4d745bf..8adf8a5 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -5,3 +5,4 @@ snakebids>=0.4.0
myst-parser==1.0.0
sphinxcontrib.asciinema==0.3.7
pulp<2.8.0
+appdirs>=1.4.4
diff --git a/pixi.lock b/pixi.lock
new file mode 100644
index 0000000..d5d7131
--- /dev/null
+++ b/pixi.lock
@@ -0,0 +1,16745 @@
+version: 6
+environments:
+ default:
+ channels:
+ - url: https://conda.anaconda.org/conda-forge/
+ - url: https://conda.anaconda.org/bioconda/
+ indexes:
+ - https://pypi.org/simple
+ options:
+ pypi-prerelease-mode: if-necessary-or-explicit
+ packages:
+ linux-64:
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/acres-0.5.0-pyhcf101f3_1.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/aiohappyeyeballs-2.6.1-pyhd8ed1ab_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/aiohttp-3.13.5-py312h5d8c7f2_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/aiosignal-1.4.0-pyhd8ed1ab_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.15.3-hb03c661_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/amply-0.1.6-pyhd8ed1ab_1.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/annotated-doc-0.0.4-pyhcf101f3_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/annotated-types-0.7.0-pyhd8ed1ab_1.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/aom-3.9.1-hac33072_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/appdirs-1.4.4-pyhd8ed1ab_1.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/archspec-0.2.5-pyhd8ed1ab_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/argparse-dataclass-2.0.0-pyhd8ed1ab_1.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/arrow-1.4.0-pyhcf101f3_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/astor-0.8.1-pyhd8ed1ab_1.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-26.1.0-pyhcf101f3_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/backports.zstd-1.4.0-py312h90b7ffd_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/bcrypt-5.0.0-py312h868fb18_1.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/bids-validator-1.14.7.post0-pyhd8ed1ab_1.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/bidsschematools-1.2.1-pyhcf101f3_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/blinker-1.9.0-pyhff2d567_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/blosc-1.21.6-he440d0b_1.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/boltons-25.0.0-pyhd8ed1ab_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/boutiques-0.5.31-pyhd8ed1ab_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-1.2.0-hed03a55_1.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.2.0-hb03c661_1.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.2.0-py312hdb49522_1.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_9.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.6-hb03c661_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.4.22-hbd8a1cb_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/cached_property-1.5.2-pyha770c72_1.tar.bz2
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.4-h3394656_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2026.4.22-pyhd8ed1ab_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-2.0.0-py312h460c074_1.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.7-pyhd8ed1ab_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.3.3-pyhc90fa1f_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/coin-or-cbc-2.10.13-h4d16d09_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/coin-or-cgl-0.60.10-hc46dffc_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/coin-or-clp-1.17.11-hc03379b_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/coin-or-osi-0.108.12-hf4fecb4_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/coin-or-utils-2.11.13-hc93afbd_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/coloredlogs-15.0.1-pyhd8ed1ab_4.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/conda-25.11.1-py312h7900ff3_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/conda-inject-1.3.2-pyhd8ed1ab_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/conda-libmamba-solver-26.4.1-pyhd8ed1ab_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/conda-package-handling-2.4.0-pyh7900ff3_2.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/conda-package-streaming-0.12.0-pyhd8ed1ab_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/configargparse-1.7.5-pyhcf101f3_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/connection_pool-0.0.3-pyhd3deb0d_0.tar.bz2
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.3-py312h0a2e395_4.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/copier-9.15.0-pyhcf101f3_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/cpp-expected-1.3.1-h171cf75_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/cryptography-48.0.0-py312ha4b625e_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhcf101f3_2.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/cyrus-sasl-2.1.28-hd9c7081_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/dav1d-1.2.1-hd590300_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/dbus-1.13.6-h5008d03_3.tar.bz2
+ - conda: https://conda.anaconda.org/conda-forge/noarch/distro-1.9.0-pyhd8ed1ab_1.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/docopt-0.6.2-pyhd8ed1ab_2.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/docutils-0.22.4-pyhd8ed1ab_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/double-conversion-3.3.1-h5888daf_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/dpath-2.2.0-pyha770c72_1.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/dunamai-1.26.1-pyhd8ed1ab_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/eido-0.2.5-pyhd8ed1ab_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/eval-type-backport-0.2.2-pyhd8ed1ab_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/eval_type_backport-0.2.2-pyha770c72_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/expat-2.8.0-hecca717_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/ffmpeg-7.1.1-gpl_h0b79d52_704.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/fmt-11.2.0-h07f6e7f_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2
+ - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2
+ - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2
+ - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.17.1-h27c8c51_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2
+ - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-hc364b38_1.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.62.1-py312h8a5da7c_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/formulaic-0.5.2-pyhd8ed1ab_0.tar.bz2
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.14.3-ha770c72_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/fribidi-1.0.16-hb03c661_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/frozendict-2.4.7-py312h4c3975b_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/frozenlist-1.7.0-py312h447239a_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/funcy-2.0-pyhd8ed1ab_1.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/gdk-pixbuf-2.42.12-hb9ae30d_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/gitdb-4.0.12-pyhd8ed1ab_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/gitpython-3.1.49-pyhd8ed1ab_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/gl2ps-1.4.2-h36e74d4_2.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/glew-2.1.0-h9c3ff4c_2.tar.bz2
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/gmp-6.3.0-hac33072_2.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.14-hecca717_2.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/graphlib-backport-1.0.3-pyhd8ed1ab_0.tar.bz2
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/greenlet-3.5.0-py312h8285ef7_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/h2-4.3.0-pyhcf101f3_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-11.0.0-h76408a6_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/hdf4-4.2.15-h2a13503_7.conda
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+ - conda: https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.1.0-pyhd8ed1ab_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-75.1-he02047a_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.13-pyhcf101f3_0.conda
+ - conda: https://conda.anaconda.org/conda-forge/linux-64/immutables-0.21-py312h4c3975b_2.conda
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- {file = "wrapt-1.17.2.tar.gz", hash = "sha256:41388e9d4d1522446fe79d3213196bd9e3b301a336965b9e27ca2788ebd122f3"},
-]
-
-[[package]]
-name = "yamlfix"
-version = "1.16.0"
-description = "A simple opionated yaml formatter that keeps your comments!"
-optional = false
-python-versions = ">=3.8"
-files = [
- {file = "yamlfix-1.16.0-py3-none-any.whl", hash = "sha256:d92bf8a6d5b6f186bd9d643d633549a1c2424555cb8d176a5d38bce3e678b2b0"},
- {file = "yamlfix-1.16.0.tar.gz", hash = "sha256:72f7990e5b2b4459ef3249df4724dacbd85ce7b87f4ea3503d8a72c48574cc32"},
-]
-
-[package.dependencies]
-click = ">=8.1.3"
-maison = ">=1.4.0"
-ruyaml = ">=0.91.0"
-
-[[package]]
-name = "yte"
-version = "1.8.1"
-description = "A YAML template engine with Python expressions"
-optional = false
-python-versions = "<4.0,>=3.7"
-files = [
- {file = "yte-1.8.1-py3-none-any.whl", hash = "sha256:4fa0ddfff047984bda0bc521d85cba32a8a7ca4c4cdb16938b6911c049485733"},
- {file = "yte-1.8.1.tar.gz", hash = "sha256:6eefbdceae56e156ba9881ecb63f3c9217cfe5d5cc6f85fdb061c266a8eff112"},
-]
-
-[package.dependencies]
-dpath = ">=2.1,<3.0"
-plac = ">=1.3.4,<2.0"
-pyyaml = ">=6.0,<7.0"
-
-[[package]]
-name = "zipp"
-version = "3.23.0"
-description = "Backport of pathlib-compatible object wrapper for zip files"
-optional = false
-python-versions = ">=3.9"
-files = [
- {file = "zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e"},
- {file = "zipp-3.23.0.tar.gz", hash = "sha256:a07157588a12518c9d4034df3fbbee09c814741a33ff63c05fa29d26a2404166"},
-]
-
-[package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
-cover = ["pytest-cov"]
-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-enabler = ["pytest-enabler (>=2.2)"]
-test = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more_itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
-type = ["pytest-mypy"]
-
-[metadata]
-lock-version = "2.0"
-python-versions = ">=3.9,<4.0"
-content-hash = "0e260bc56576fa1834a3da9ce8ac0d28d87238aa459802b1567f5bcc5e6e0bef"
diff --git a/pyproject.toml b/pyproject.toml
index 2be7d05..545175d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,72 +1,61 @@
-[tool.poetry]
+[project]
name = "autoafids"
-version = "1.1.0"
+version = "0.0.1"
description = "End-to-end SnakeBIDS workflow for landmark regression and derivative apps"
readme = "README.md"
license = "MIT"
-authors = [
- "Alaa Taha "
-]
-classifiers = [
- "Programming Language :: Python :: 3",
- "Operating System :: OS Independent",
-]
-
-[tool.poetry.dependencies]
-python = ">=3.9,<4.0"
-snakebids = "0.14.0"
-appdirs = "^1.4.4"
-pulp = { version="<2.8.0", python = "<3.11" }
-pyvista = "^0.44.2"
-
-[tool.poetry.group.dev.dependencies]
-black = "^23.7.0"
-isort = "^5.12.0"
-poethepoet = "^0.21.1"
-ruff = "^0.0.282"
-snakefmt = "^0.8.4"
-yamlfix = "^1.11.0"
-pygraphviz = "1.7"
-Jinja2 = "^3.0.3"
-
-[tool.poetry.scripts]
-autoafids = "autoafids.run:app.run"
-
+authors = [{name = "Alaa Taha", email = "ataha24@uwo.ca"}]
+requires-python = ">= 3.11, <3.13"
+dependencies = []
+scripts = { autoafids = "autoafids.run:app.run" }
[build-system]
-requires = ["poetry-core>=1.0.0"]
-build-backend = "poetry.core.masonry.api"
-
-[tool.poe.tasks]
-setup = "pre-commit install"
-ruff-lint = "ruff check --fix autoafids"
-ruff-lint-check = "ruff check autoafids"
-snakefmt = "snakefmt autoafids"
-snakefmt-check = "snakefmt --check autoafids"
-quality = ["yamlfix", "ruff-lint", "snakefmt"]
-quality-check = ["yamlfix-check", "ruff-lint-check", "snakefmt-check"]
-
-[tool.poe.tasks.yamlfix]
-shell = """
-find . -type f \\( \
- -iname \\*.yaml -o -iname \\*.yml ! -iname snakemake* \
- ! -iname snakebids* \
-\\) -exec yamlfix {} \\;
-"""
-
-[tool.poe.tasks.yamlfix-check]
-shell = """
-find . -type f \\( \
- -iname \\*.yaml -o -iname \\*.yml ! -iname snakemake* \
- ! -iname snakebids* \
-\\) -exec yamlfix --check {} \\;
-"""
-
-[tool.ruff]
-select = ["E", "W", "N", "RUF", "I", "UP"]
-line-length = 79
-builtins = ["snakemake"]
-
-[tool.snakefmt]
-line_length = 79
-include = '\.smk$|^Snakefile'
\ No newline at end of file
+build-backend = "hatchling.build"
+requires = ["hatchling"]
+
+[tool.pixi.workspace]
+channels = ["conda-forge", "bioconda"]
+platforms = ["linux-64", "osx-arm64", "osx-64"]
+
+[tool.pixi.feature.runtime.dependencies]
+snakebids = ">=0.14.0,<0.15"
+appdirs = ">=1.4.4,<2.0.0"
+pyvista = ">=0.44.2,<0.45.0"
+conda = ">=25.5.0,<26"
+
+[tool.pixi.feature.dev.dependencies]
+flake8 = ">=4.0.1,<5.0.0"
+pytest-console-scripts = ">=1.2.1,<2.0.0"
+pytest = "*"
+black = ">=24.0.0,<25.0.0"
+pylint = ">=2.11.1,<3.0.0"
+isort = ">=5.10.1,<6.0.0"
+poethepoet = ">=0.10.0,<0.11.0"
+snakefmt = ">=0.10.0,<0.11.0"
+pygraphviz = "*"
+Jinja2 = ">=3.0.3,<4.0.0"
+
+[tool.pixi.environments.default]
+features = ["runtime"]
+
+[tool.pixi.environments.dev]
+features = ["runtime", "dev"]
+
+[tool.pixi.environments.dev-only]
+features = ["dev"]
+
+[tool.isort]
+profile = "black"
+multi_line_output = 3
+
+[tool.pixi.pypi-dependencies]
+autoafids = { path = ".", editable = true }
+
+[tool.pixi.dependencies]
+snakemake-storage-plugin-http = ">=0.3.0,<0.4"
+unzip = ">=6.0,<7"
+
+
+[tool.pixi.feature.dev.tasks]
+quality_check = "isort autoafids/*.py && black autoafids --check && snakefmt autoafids --check"
+quality_fix = "isort autoafids/*.py && black autoafids && snakefmt autoafids"
\ No newline at end of file