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version: 0.3.0a1 - sha256: d8647cfc62b7cccf0d323cf2f9e6b1ca37a58e1708e1d130b21259fd4f7fc673 + version: 0.8.0a1 + sha256: f9b5fc77873ac5086096829d7e124cc17c3acbc14e147e082ee08e697fe023df requires_dist: - dask>=2025.5.1 + - h5py>=3.14.0 - ngff-zarr[all]>=0.13.1 - nibabel>=5.2.0 - numpy>=1.26.4 + - ome-zarr>=0.12.0 - pandas>=2.2.0 + - pyyaml>=6.0 + - scikit-image>=0.22.0 - scipy>=1.12.0 - zarr>=3.0.8 - requires_python: '>=3.11,<4.0' + - black>=24.10.0 ; extra == 'dev' + - bokeh>=3.4.1 ; extra == 'dev' + - flake8-bugbear>=24.12.12 ; extra == 'dev' + - flake8-docstrings>=1.7.0 ; extra == 'dev' + - flake8-import-order>=0.18.2 ; extra == 'dev' + - flake8>=7.3.0 ; extra == 'dev' + - isort>=5.13.2 ; extra == 'dev' + - jupyterlab>=4.2.1 ; extra == 'dev' + - matplotlib>=3.9.0 ; extra == 'dev' + - mkdocs-material>=9.5.50 ; extra == 'dev' + - mkdocs>=1.6.1 ; extra == 'dev' + - mkdocstrings-python>=1.13.0 ; extra == 'dev' + - mkdocstrings>=0.27.0 ; extra == 'dev' + - pre-commit>=4.0.0 ; extra == 'dev' + - pytest-cov>=6.0.0 ; extra == 'dev' + - pytest>=8.2.0 ; extra == 'dev' + - h5py>=3.8.0 ; extra == 'imaris' + - antspyx>=0.5.0 ; extra == 'n4' + - templateflow>=0.8.0 ; extra == 'templateflow' + requires_python: '>=3.11' - conda: https://conda.anaconda.org/conda-forge/linux-64/zfp-1.0.1-h5888daf_2.conda sha256: 0dfafc75c72f308c0200836f2b973766cdcb8741b1ab61e0b462a34dd6b6ad20 md5: e0409515c467b87176b070bff5d9442e diff --git a/pixi.toml b/pixi.toml index 4c275ae..9573fac 100644 --- a/pixi.toml +++ b/pixi.toml @@ -58,5 +58,5 @@ features = ["runtime", "dev"] features = ["dev"] [pypi-dependencies] -ngff-zarr = { version = ">=0.13.2, <0.14", extras = ["all"] } -zarrnii = "==0.3.0-a1" +ngff-zarr = { version = ">=0.18.1,<0.19", extras = ["all"] } +zarrnii = ">=0.8.0a1,<0.9.0" diff --git a/submit_jobs.py b/submit_jobs.py index ed5ffd5..e87677b 100644 --- a/submit_jobs.py +++ b/submit_jobs.py @@ -46,8 +46,6 @@ def submit_job( slurm_config["time"], "--mem", slurm_config["mem"], - "--nodelist", - "rri-cbs-h2.schulich.uwo.ca", "--cpus-per-task", slurm_config["cpus"], "--tmp", diff --git a/workflow/rules/common.smk b/workflow/rules/common.smk index a46a976..e0d2141 100644 --- a/workflow/rules/common.smk +++ b/workflow/rules/common.smk @@ -109,7 +109,7 @@ def get_all_targets(): datatype="micr", sample="{sample}", acq="{acq}", - res="{level}x", + level="{level}", stain="{stain}", suffix="SPIM.nii", expand_kwargs=dict( diff --git a/workflow/rules/ome_zarr.smk b/workflow/rules/ome_zarr.smk index 49a4c8f..2227cfb 100644 --- a/workflow/rules/ome_zarr.smk +++ b/workflow/rules/ome_zarr.smk @@ -90,12 +90,9 @@ rule tif_stacks_to_ome_zarr: rule ome_zarr_to_nii: input: - **get_storage_creds(), zarr=get_input_ome_zarr_to_nii, params: - channel_index=lambda wildcards: get_stains(wildcards).index(wildcards.stain), - uri=get_output_ome_zarr_uri(), - storage_provider_settings=workflow.storage_provider_settings, + zarrnii_kwargs={}, output: nii=bids( root=resampled, @@ -103,7 +100,7 @@ rule ome_zarr_to_nii: datatype="micr", sample="{sample}", acq="{acq}", - res="{level}x", + level="{level}", stain="{stain}", suffix="SPIM.nii", ), @@ -114,7 +111,7 @@ rule ome_zarr_to_nii: subject="{subject}", sample="{sample}", acq="{acq}", - res="{level}x", + level="{level}", stain="{stain}", suffix="benchmark.tsv", ) @@ -125,7 +122,7 @@ rule ome_zarr_to_nii: subject="{subject}", sample="{sample}", acq="{acq}", - res="{level}x", + level="{level}", stain="{stain}", suffix="log.txt", ), diff --git a/workflow/scripts/imaris_channel_to_zarr.py b/workflow/scripts/imaris_channel_to_zarr.py index 619398c..32b6718 100644 --- a/workflow/scripts/imaris_channel_to_zarr.py +++ b/workflow/scripts/imaris_channel_to_zarr.py @@ -9,14 +9,49 @@ store = zarr.ZipStore(snakemake.output.zarr, dimension_separator="/", mode="x") dest = zarr.group(store) -zarr.copy( + +def print_tree_item(name, obj): + """ + A visitor function to print the name and type of HDF5 objects. + """ + # Indentation based on group depth + shift = name.count("/") * " " + if isinstance(obj, h5py.Dataset): + print(f"{shift}Dataset: {name} (shape: {obj.shape}, dtype: {obj.dtype})") + elif isinstance(obj, h5py.Group): + print(f"{shift}Group: {name}/") + else: + print(f"{shift}Other: {name}") + + # Optionally, print attributes as well + for key, val in obj.attrs.items(): + print(f"{shift} Attribute: {key}: {val}") + + +print( + source[ + "DataSet/ResolutionLevel 0/TimePoint 0/Channel {chan}/Data".format( + chan=snakemake.params.channel + ) + ].shape +) +source.visititems(print_tree_item) +print( source[ "DataSet/ResolutionLevel 0/TimePoint 0/Channel {chan}/Data".format( chan=snakemake.params.channel ) - ], - dest, - log=stdout, - compressor=None, + ].shape ) + +# zarr.copy( +# source[ +# "DataSet/ResolutionLevel 0/TimePoint 0/Channel {chan}/Data".format( +# chan=snakemake.params.channel +# ) +# ], +# dest, +# log=stdout, +# compressor=None, +# ) source.close() diff --git a/workflow/scripts/imaris_to_metadata.py b/workflow/scripts/imaris_to_metadata.py index e0a09f1..66b854a 100644 --- a/workflow/scripts/imaris_to_metadata.py +++ b/workflow/scripts/imaris_to_metadata.py @@ -1,40 +1,189 @@ +import html import json +import re import h5py import xmltodict -with h5py.File(snakemake.input.ims, "r") as hdf5_file: - xml_data = hdf5_file["DataSetInfo/OME Image Tags/Image 0"][:] +# ----------------------------- +# Helper functions +# ----------------------------- -# Convert byte array to string and then to a dictionary -xml_str = bytes(xml_data).decode( - "utf-8", errors="ignore" -) # Decode byte array to string +def parse_xml_str(xml_str): + """Convert XML string to dict, decoding HTML entities (e.g. µ → µ).""" + + def decode_entities(obj): + """Recursively decode HTML entities in all strings.""" + if isinstance(obj, dict): + return {k: decode_entities(v) for k, v in obj.items()} + elif isinstance(obj, list): + return [decode_entities(i) for i in obj] + elif isinstance(obj, str): + return html.unescape(obj) + else: + return obj + + try: + parsed = xmltodict.parse(xml_str, namespace_separator=":") + return decode_entities(parsed) + + except Exception as e: + print(f"Error parsing XML: {e}") + return None + + +def parse_xml_bytes(xml_bytes): + """Convert raw byte array to parsed XML dict with namespace separator.""" + xml_str = bytes(xml_bytes).decode("utf-8", errors="ignore") + try: + return xmltodict.parse(f"{xml_str}", namespace_separator=":") + except Exception as e: + print(f"Error parsing XML: {e}") + return None + + +def clean_keys(obj): + """Recursively remove '@' from dict keys.""" + if isinstance(obj, dict): + return {k.replace("@", ""): clean_keys(v) for k, v in obj.items()} + elif isinstance(obj, list): + return [clean_keys(i) for i in obj] + return obj + + +def build_bids_metadata(custom_attrs): + """Convert custom attribute dict into BIDS microscopy-compliant metadata.""" + # --- PixelSize and Units --- + px_x = float(custom_attrs["DataAxis0"]["@PhysicalUnit"]) + px_y = float(custom_attrs["DataAxis1"]["@PhysicalUnit"]) + px_z = abs(float(custom_attrs["DataAxis2"]["@PhysicalUnit"])) + pixel_size = [px_x, px_y, px_z] + + unit_label = custom_attrs.get("AxesLabels", {}).get("@FirstAxis-Unit", "µm") + unit_label = unit_label.replace("µ", "u") if "µ" in unit_label else unit_label + + # --- Extract Magnification (if present) --- + obj_id = custom_attrs.get("ObjectiveID", {}).get("@ObjectiveID", "") + magnification_match = re.search(r"(\d+(?:\.\d+)?)x", obj_id) + magnification = float(magnification_match.group(1)) if magnification_match else None + + # --- Build BIDS JSON dict --- + bids_json = { + "PixelSize": pixel_size, + "PixelSizeUnits": unit_label, + "Immersion": custom_attrs.get("ObjectiveMedium", {}).get("@ObjectiveMedium"), + "NumericalAperture": float( + custom_attrs.get("ObjectiveNA", {}).get("@ObjectiveNA", 0.0) + ), + "Magnification": magnification, + "OtherAcquisitionParameters": custom_attrs.get("MeasurementMode", {}).get( + "@MeasurementMode" + ), + "InstrumentModel": custom_attrs.get("InstrumentMode", {}).get( + "@InstrumentMode" + ), + "SoftwareVersions": custom_attrs.get("ImspectorVersion", {}).get( + "@ImspectorVersion" + ), + } + + # --- Collect non-BIDS fields into ExtraMetadata --- + excluded = { + "ObjectiveMedium", + "ObjectiveNA", + "ObjectiveID", + "MeasurementMode", + "InstrumentMode", + "ImspectorVersion", + "DataAxis0", + "DataAxis1", + "DataAxis2", + "AxesLabels", + } + extra_metadata = { + k: clean_keys(v) for k, v in custom_attrs.items() if k not in excluded + } + bids_json["ExtraMetadata"] = extra_metadata + + return bids_json + + +# ----------------------------- +# Main extraction +# ----------------------------- + + +def print_h5_keys_recursive(obj, indent=""): + """ + Recursively prints the keys and their hierarchy within an h5py object. + + Args: + obj: An h5py.File or h5py.Group object. + indent: A string representing the current indentation level for printing. + """ + if isinstance(obj, h5py.File) or isinstance(obj, h5py.Group): + for key, value in obj.items(): + print(f"{indent}- {key}") + if isinstance(value, h5py.Group): + print_h5_keys_recursive(value, indent + " ") + elif isinstance(obj, h5py.Dataset): + # Datasets are typically leaf nodes, their names are already printed by the parent group + pass + else: + print(f"{indent}- (Unknown HDF5 object type: {type(obj)})") + try: - xml_dict = xmltodict.parse(f"{xml_str}", namespace_separator=":") -except Exception as e: - print(f"Error parsing XML: {e}") - - -metadata = {} -metadata["physical_size_x"] = float( - xml_dict["root"]["ca:CustomAttributes"]["DataAxis0"]["@PhysicalUnit"] -) -metadata["physical_size_y"] = float( - xml_dict["root"]["ca:CustomAttributes"]["DataAxis1"]["@PhysicalUnit"] -) -metadata["physical_size_z"] = abs( - float(xml_dict["root"]["ca:CustomAttributes"]["DataAxis3"]["@PhysicalUnit"]) -) -metadata["PixelSize"] = [ - metadata["physical_size_z"] / 1000.0, - metadata["physical_size_y"] / 1000.0, - metadata["physical_size_x"] / 1000.0, -] # zyx since OME-Zarr is ZYX -metadata["PixelSizeUnits"] = "mm" - -# write metadata to json -with open(snakemake.output.metadata_json, "w") as fp: - json.dump(metadata, fp, indent=4) + + with h5py.File(snakemake.input.ims, "r") as hdf5_file: + # print(snakemake.input.ims) + # print_h5_keys_recursive(hdf5_file) + xml_data = hdf5_file["DataSetInfo/OME Image Tags/Image 0"][:] + xml_dict = parse_xml_bytes(xml_data) + custom_attrs = xml_dict["root"]["ca:CustomAttributes"] +except: + print( + "Warning: cannot find OME metadata from imaris file, searching for tif file using standard folder structure" + ) + + from glob import glob + from pathlib import Path + + import tifffile + + p = Path(snakemake.input.ims) + + # 1) Name of the parent folder + subj = p.parent.name # -> 'B_AS161F3' + + # 2) Root path of the 'raw' folder (3 levels up from the file) + raw_root = p.parents[2] # -> '/nfs/trident3/.../raw' + + # 3) Construct the new path + new_path = str(raw_root / "tif_4x_*" / subj / "*.ome.tif") + try: + tif_file = sorted(glob(new_path))[0] + with tifffile.TiffFile(tif_file) as tif: + xml_data = tif.ome_metadata + xml_dict = parse_xml_str(xml_data) + custom_attrs = xml_dict["OME"]["Image"]["ca:CustomAttributes"] + except: + raise ( + ValueError( + "Cannot find OME metadata in imaris file or in associated tif files from standard folder structure" + ) + ) + + +if not xml_dict: + raise ValueError("Failed to parse XML from .ims file") + +print(custom_attrs) +bids_metadata = build_bids_metadata(custom_attrs) + +# ----------------------------- +# Write to JSON +# ----------------------------- +with open(snakemake.output.metadata_json, "w", encoding="utf-8") as fp: + json.dump(bids_metadata, fp, indent=4, ensure_ascii=False) diff --git a/workflow/scripts/imaris_to_ome_zarr.py b/workflow/scripts/imaris_to_ome_zarr.py index f2299ca..7440083 100644 --- a/workflow/scripts/imaris_to_ome_zarr.py +++ b/workflow/scripts/imaris_to_ome_zarr.py @@ -25,10 +25,16 @@ with open(metadata_json) as fp: metadata = json.load(fp) +units = metadata.get("PixelSizeUnits", "um") +if units == "um": + unit_convert = 1000.0 +elif units == "mm": + unit_convert = 1.0 + voxdim = [ - float(metadata["physical_size_z"]) * float(downsampling) / 1000.0, - float(metadata["physical_size_y"]) * float(downsampling) / 1000.0, - float(metadata["physical_size_x"]) * float(downsampling) / 1000.0, + float(metadata["PixelSize"][2]) * float(downsampling) / unit_convert, + float(metadata["PixelSize"][1]) * float(downsampling) / unit_convert, + float(metadata["PixelSize"][0]) * float(downsampling) / unit_convert, ] @@ -113,9 +119,15 @@ group = zarr.group(store, overwrite=True) + +# TODO: orientation was RAI for earlier scans but seems RPI for newer, need to confirm +# whether brains were placed in a different orientation, or scanned differently, or maybe +# difference the related to MACsIq vs Imaris stitching implementation.. confirm with Shaz. +# Also confirm L/R flip.. + # Add metadata for orientation -group.attrs["orientation"] = "IAR" # this is the updated orientation for imaris files -group.attrs["xyz_orientation"] = "RAI" +group.attrs["orientation"] = "IPR" # this is the updated orientation for imaris files +group.attrs["xyz_orientation"] = "RPI" scaler = Scaler(max_layer=max_layer, method=scaling_method) diff --git a/workflow/scripts/ome_zarr_to_nii.py b/workflow/scripts/ome_zarr_to_nii.py index c4eb675..c7577c1 100644 --- a/workflow/scripts/ome_zarr_to_nii.py +++ b/workflow/scripts/ome_zarr_to_nii.py @@ -1,98 +1,13 @@ -import zarr from dask.diagnostics import ProgressBar -from upath import UPath as Path from zarrnii import ZarrNii -uri = snakemake.params.uri -in_zarr = snakemake.input.zarr -channel_index = snakemake.params.channel_index - - -if Path(uri).suffix == ".zip": - store = zarr.storage.ZipStore(Path(uri).path, mode="r") -else: - store = zarr.storage.LocalStore(Path(uri).path, read_only=True) - - znimg = ZarrNii.from_ome_zarr( - store, level=int(snakemake.wildcards.level), channels=[channel_index] + snakemake.input.zarr, + level=int(snakemake.wildcards.level), + channel_labels=[snakemake.wildcards.stain], + downsample_near_isotropic=True, + **snakemake.params.zarrnii_kwargs, ) - -# before updating zarrnii ngffzarr3 branch to accommodate anisotropically downsampled data, instead -# we will calculate the z downsampling factor and downsample accordingly - TODO: move this to zarrnii - -import numpy as np - -# Get scale and axes order -scale = znimg.coordinate_transformations[0].scale - -axes = znimg.axes # list of Axis objects - -# Build a mapping from axis name to index -axis_index = {axis.name.lower(): i for i, axis in enumerate(axes)} - -# Extract x and z scales -x_scale = scale[axis_index["x"]] -z_scale = scale[axis_index["z"]] - -# Compute ratio and power -ratio = x_scale / z_scale -level = int(np.log2(round(ratio))) - - with ProgressBar(): - if level == 0: - znimg.to_nifti(snakemake.output.nii) - else: - znimg.downsample(along_z=2**level).to_nifti(snakemake.output.nii) - - -""" - -if is_remote(uri): - fs_args={'storage_provider_settings':snakemake.params.storage_provider_settings,'creds':snakemake.input.creds} -else: - fs_args={} - -fs = get_fsspec(uri,**fs_args) - -if Path(uri).suffix == '.zip': - store = zarr.storage.ZipStore(Path(uri).path,dimension_separator='/',mode='r') -else: - store = zarr.storage.FSStore(Path(uri).path,fs=fs,dimension_separator='/',mode='r') - -zi = zarr.open(store=store,mode='r') - - -attrs=zi['/'].attrs.asdict() - -level=int(snakemake.wildcards.level) - -#read coordinate transform from ome-zarr -transforms = attrs['multiscales'][0]['datasets'][level]['coordinateTransformations'] - - -#zarr uses z,y,x ordering, we reverse this for nifti -# also flip to set orientation properly -affine = np.eye(4) -affine[0,0]=-transforms[0]['scale'][3] #x -affine[1,1]=-transforms[0]['scale'][2] #y -affine[2,2]=-transforms[0]['scale'][1] #z - -if is_remote(uri): - #grab the channel index corresponding to the stain - darr = da.from_zarr(store,component=f'/{level}')[channel_index,:,:,:].squeeze() -else: - darr = da.from_zarr(in_zarr,component=f'/{level}')[channel_index,:,:,:].squeeze() - -#input array axes are ZYX -#writing to nifti we want XYZ -out_arr = np.moveaxis(darr,(0,1,2),(2,1,0)) - -nii = nib.Nifti1Image(out_arr, - affine=affine - ) - -nii.to_filename(snakemake.output.nii) -""" + znimg.to_nifti(snakemake.output.nii)