From 2042372bdbaef2ad11fd94071595381652a020ea Mon Sep 17 00:00:00 2001 From: Tom Close Date: Tue, 28 May 2024 00:29:15 +0930 Subject: [PATCH 1/8] added file-type to in_file --- .../mriqc.workflows.anatomical.base.anat_qc_workflow.yaml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/nipype-auto-conv/specs/workflows/mriqc.workflows.anatomical.base.anat_qc_workflow.yaml b/nipype-auto-conv/specs/workflows/mriqc.workflows.anatomical.base.anat_qc_workflow.yaml index 62caab2..9350679 100644 --- a/nipype-auto-conv/specs/workflows/mriqc.workflows.anatomical.base.anat_qc_workflow.yaml +++ b/nipype-auto-conv/specs/workflows/mriqc.workflows.anatomical.base.anat_qc_workflow.yaml @@ -6,6 +6,9 @@ nipype_name: anat_qc_workflow nipype_module: mriqc.workflows.anatomical.base # Name of the node that is to be considered the input of the workflow, i.e. its outputs will be the inputs of the workflow input_node: inputnode +inputs: + in_file: + type: medimage/t1w+nifti-x-gz # name of the workflow variable that is returned workflow_variable: workflow # the names of the nested workflows that are defined in other modules and need to be imported From 3e0814269f469f8f2aa26fc5952bed8d811f7b93 Mon Sep 17 00:00:00 2001 From: Tom Close Date: Tue, 28 May 2024 17:50:14 +0930 Subject: [PATCH 2/8] fixed up type of in_file --- .../mriqc.workflows.anatomical.base.anat_qc_workflow.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nipype-auto-conv/specs/workflows/mriqc.workflows.anatomical.base.anat_qc_workflow.yaml b/nipype-auto-conv/specs/workflows/mriqc.workflows.anatomical.base.anat_qc_workflow.yaml index 9350679..c4fbd84 100644 --- a/nipype-auto-conv/specs/workflows/mriqc.workflows.anatomical.base.anat_qc_workflow.yaml +++ b/nipype-auto-conv/specs/workflows/mriqc.workflows.anatomical.base.anat_qc_workflow.yaml @@ -8,7 +8,7 @@ nipype_module: mriqc.workflows.anatomical.base input_node: inputnode inputs: in_file: - type: medimage/t1w+nifti-x-gz + type: medimage/t1w+nifti-gz-x # name of the workflow variable that is returned workflow_variable: workflow # the names of the nested workflows that are defined in other modules and need to be imported From 439eaad89c88973310ce227cf5edde7348db8f79 Mon Sep 17 00:00:00 2001 From: Tom Close Date: Sat, 1 Jun 2024 22:30:12 +0930 Subject: [PATCH 3/8] added spec find/replace --- .github/workflows/ci-cd.yaml | 6 ++++-- .../workflows/mriqc.workflows.shared.synthstrip_wf.yaml | 1 + 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/.github/workflows/ci-cd.yaml b/.github/workflows/ci-cd.yaml index 12380ac..7f860d7 100644 --- a/.github/workflows/ci-cd.yaml +++ b/.github/workflows/ci-cd.yaml @@ -9,11 +9,13 @@ name: CI/CD on: push: branches: [ main, develop ] - tags: [ '*' ] pull_request: branches: [ main, develop ] + release: + types: [published] repository_dispatch: - types: [create-release] + types: [create-post-release] + jobs: nipype-conv: diff --git a/nipype-auto-conv/specs/workflows/mriqc.workflows.shared.synthstrip_wf.yaml b/nipype-auto-conv/specs/workflows/mriqc.workflows.shared.synthstrip_wf.yaml index 942477a..d2c0fd6 100644 --- a/nipype-auto-conv/specs/workflows/mriqc.workflows.shared.synthstrip_wf.yaml +++ b/nipype-auto-conv/specs/workflows/mriqc.workflows.shared.synthstrip_wf.yaml @@ -13,6 +13,7 @@ find_replace: - ["config = NipypeConfig\\(\\)", ""] - ["iflogger = logging.getLogger\\(\"nipype.interface\"\\)", ""] - ["logging = Logging\\(config\\)", ""] + - ["save_bias=True", "bias_image=True"] # name of the workflow variable that is returned workflow_variable: workflow # the names of the nested workflows that are defined in other modules and need to be imported From 28c5b67550642a2f6841550d77fca137f46c3ba9 Mon Sep 17 00:00:00 2001 From: Tom Close Date: Sat, 1 Jun 2024 23:09:23 +0930 Subject: [PATCH 4/8] added import f/r --- nipype-auto-conv/specs/package.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/nipype-auto-conv/specs/package.yaml b/nipype-auto-conv/specs/package.yaml index ab44c0c..8385210 100644 --- a/nipype-auto-conv/specs/package.yaml +++ b/nipype-auto-conv/specs/package.yaml @@ -59,5 +59,6 @@ import_find_replace: - ["from \\.\\. import config, logging", ""] - ["_ReadDWIMetadataOutputSpec,", ""] - ["from pydra.tasks.mriqc.nipype_ports.interfaces import utility as niu", ""] + - ["\\s+config,(\\s+)fname_presuffix,", "\\1fname_presuffix,"] copy_packages: - mriqc.data From 97fe4c8a8bf35fa59700e8047293f8ce32c27b2d Mon Sep 17 00:00:00 2001 From: Tom Close Date: Sun, 2 Jun 2024 01:23:30 +0930 Subject: [PATCH 5/8] added synthstrip cli to package --- nipype-auto-conv/specs/package.yaml | 1 + pyproject.toml | 16 ++++++++++++++-- 2 files changed, 15 insertions(+), 2 deletions(-) diff --git a/nipype-auto-conv/specs/package.yaml b/nipype-auto-conv/specs/package.yaml index 8385210..11594ae 100644 --- a/nipype-auto-conv/specs/package.yaml +++ b/nipype-auto-conv/specs/package.yaml @@ -62,3 +62,4 @@ import_find_replace: - ["\\s+config,(\\s+)fname_presuffix,", "\\1fname_presuffix,"] copy_packages: - mriqc.data + - mriqc.synthstrip diff --git a/pyproject.toml b/pyproject.toml index df379ed..32e2c29 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -18,11 +18,11 @@ dependencies = [ "nilearn", "migas >= 0.4.0", "pandas ~=1.0", - "pydra >=0.22", + "pydra >=0.23", "pydra-ants", "pydra-afni", "pydra-fsl", - "pydra-mrtrix3 >=3.0.3a0", + "pydra-mrtrix3 >=3.0.4a5", "pydra-niworkflows", "pydra-nireports", "PyYAML", @@ -32,6 +32,7 @@ dependencies = [ "statsmodels", "templateflow", "nilearn", + "nitransforms", "torch", "toml", "tomli >= 1.1.0; python_version < '3.11'", @@ -103,3 +104,14 @@ per-file-ignores = ["__init__.py:F401,F403"] max-line-length = 88 select = "C,E,F,W,B,B950" extend-ignore = ['E203', 'E501', 'E129', 'W503'] + +[project.scripts] +# abide2bids = "mriqc.bin.abide2bids:main" +# dfcheck = "mriqc.bin.dfcheck:main" +# fs2gif = "mriqc.bin.fs2gif:main" +# mriqc = "mriqc.cli.run:main" +# mriqc_labeler = "mriqc.bin.labeler:main" +# mriqcwebapi_test = "mriqc.bin.mriqcwebapi_test:main" +# nib-hash = "mriqc.bin.nib_hash:main" +# participants = "mriqc.bin.subject_wrangler:main" +synthstrip = "pydra.tasks.mriqc.synthstrip.cli:main" From 652eee5b017cd6165316f87fa9f68a46033f1e83 Mon Sep 17 00:00:00 2001 From: Tom Close Date: Tue, 4 Jun 2024 20:28:38 +1000 Subject: [PATCH 6/8] debugging yaml specs --- .../interfaces/derivatives_data_sink.yaml | 94 ++++++++-------- .../specs/interfaces/iqm_file_sink.yaml | 1 + .../specs/interfaces/synth_strip.yaml | 100 +++++++++--------- nipype-auto-conv/specs/package.yaml | 11 +- ...lows.anatomical.base.anat_qc_workflow.yaml | 9 ++ 5 files changed, 114 insertions(+), 101 deletions(-) diff --git a/nipype-auto-conv/specs/interfaces/derivatives_data_sink.yaml b/nipype-auto-conv/specs/interfaces/derivatives_data_sink.yaml index 3272310..3963fb8 100644 --- a/nipype-auto-conv/specs/interfaces/derivatives_data_sink.yaml +++ b/nipype-auto-conv/specs/interfaces/derivatives_data_sink.yaml @@ -5,7 +5,7 @@ # # Docs # ---- -# +# task_name: DerivativesDataSink nipype_name: DerivativesDataSink nipype_module: mriqc.interfaces @@ -15,11 +15,11 @@ inputs: rename: # dict[str, str] - fields to rename in the Pydra interface types: - # dict[str, type] - override inferred types (use "mime-like" string for file-format types, - # e.g. 'medimage/nifti-gz'). For most fields the type will be correctly inferred - # from the nipype interface, but you may want to be more specific, particularly - # for file types, where specifying the format also specifies the file that will be - # passed to the field in the automatically generated unittests. + # dict[str, type] - override inferred types (use "mime-like" string for file-format types, + # e.g. 'medimage/nifti-gz'). For most fields the type will be correctly inferred + # from the nipype interface, but you may want to be more specific, particularly + # for file types, where specifying the format also specifies the file that will be + # passed to the field in the automatically generated unittests. base_directory: generic/directory # type=directory|default='': Path to the base directory for storing data. in_file: generic/file+list-of @@ -37,15 +37,15 @@ outputs: rename: # dict[str, str] - fields to rename in the Pydra interface types: - # dict[str, type] - override inferred types (use "mime-like" string for file-format types, - # e.g. 'medimage/nifti-gz'). For most fields the type will be correctly inferred - # from the nipype interface, but you may want to be more specific, particularly - # for file types, where specifying the format also specifies the file that will be - # passed to the field in the automatically generated unittests. + # dict[str, type] - override inferred types (use "mime-like" string for file-format types, + # e.g. 'medimage/nifti-gz'). For most fields the type will be correctly inferred + # from the nipype interface, but you may want to be more specific, particularly + # for file types, where specifying the format also specifies the file that will be + # passed to the field in the automatically generated unittests. out_file: generic/file+list-of - # type=outputmultiobject: + # type=outputmultiobject: out_meta: generic/file+list-of - # type=outputmultiobject: + # type=outputmultiobject: callables: # dict[str, str] - names of methods/callable classes defined in the adjacent `*_callables.py` # to set to the `callable` attribute of output fields @@ -54,38 +54,38 @@ outputs: requirements: # dict[str, list[str]] - input fields that are required to be provided for the output field to be present tests: -- inputs: - # dict[str, str] - values to provide to inputs fields in the task initialisation - # (if not specified, will try to choose a sensible value) - base_directory: - # type=directory|default='': Path to the base directory for storing data. - check_hdr: - # type=bool|default=True: fix headers of NIfTI outputs - compress: - # type=inputmultiobject|default=[]: whether ``in_file`` should be compressed (True), uncompressed (False) or left unmodified (None, default). - data_dtype: - # type=str|default='': NumPy datatype to coerce NIfTI data to, or `source` tomatch the input file dtype - dismiss_entities: - # type=inputmultiobject|default=[]: a list entities that will not be propagated from the source file - in_file: - # type=inputmultiobject|default=[]: the object to be saved - meta_dict: - # type=dict|default={}: an input dictionary containing metadata - source_file: - # type=inputmultiobject|default=[]: the source file(s) to extract entities from - imports: - # list[nipype2pydra.task.base.explicitimport] - list import statements required by the test, with each list item - # consisting of 'module', 'name', and optionally 'alias' keys - expected_outputs: - # dict[str, str] - expected values for selected outputs, noting that tests will typically - # be terminated before they complete for time-saving reasons, and therefore - # these values will be ignored, when running in CI - timeout: 10 - # int - the value to set for the timeout in the generated test, - # after which the test will be considered to have been initialised - # successfully. Set to 0 to disable the timeout (warning, this could - # lead to the unittests taking a very long time to complete) - xfail: true - # bool - whether the unittest is expected to fail or not. Set to false - # when you are satisfied with the edits you have made to this file + - inputs: + # dict[str, str] - values to provide to inputs fields in the task initialisation + # (if not specified, will try to choose a sensible value) + base_directory: + # type=directory|default='': Path to the base directory for storing data. + check_hdr: + # type=bool|default=True: fix headers of NIfTI outputs + compress: + # type=inputmultiobject|default=[]: whether ``in_file`` should be compressed (True), uncompressed (False) or left unmodified (None, default). + data_dtype: + # type=str|default='': NumPy datatype to coerce NIfTI data to, or `source` tomatch the input file dtype + dismiss_entities: + # type=inputmultiobject|default=[]: a list entities that will not be propagated from the source file + in_file: + # type=inputmultiobject|default=[]: the object to be saved + meta_dict: + # type=dict|default={}: an input dictionary containing metadata + source_file: + # type=inputmultiobject|default=[]: the source file(s) to extract entities from + imports: + # list[nipype2pydra.task.base.explicitimport] - list import statements required by the test, with each list item + # consisting of 'module', 'name', and optionally 'alias' keys + expected_outputs: + # dict[str, str] - expected values for selected outputs, noting that tests will typically + # be terminated before they complete for time-saving reasons, and therefore + # these values will be ignored, when running in CI + timeout: 10 + # int - the value to set for the timeout in the generated test, + # after which the test will be considered to have been initialised + # successfully. Set to 0 to disable the timeout (warning, this could + # lead to the unittests taking a very long time to complete) + xfail: true + # bool - whether the unittest is expected to fail or not. Set to false + # when you are satisfied with the edits you have made to this file doctests: [] diff --git a/nipype-auto-conv/specs/interfaces/iqm_file_sink.yaml b/nipype-auto-conv/specs/interfaces/iqm_file_sink.yaml index 6618818..2b13f7a 100644 --- a/nipype-auto-conv/specs/interfaces/iqm_file_sink.yaml +++ b/nipype-auto-conv/specs/interfaces/iqm_file_sink.yaml @@ -99,3 +99,4 @@ tests: doctests: [] find_replace: - [config\.loggers\.\w+\., logger.] + - ["value=Undefined", "value=attrs.NOTHING"] diff --git a/nipype-auto-conv/specs/interfaces/synth_strip.yaml b/nipype-auto-conv/specs/interfaces/synth_strip.yaml index bdbe066..1d017c1 100644 --- a/nipype-auto-conv/specs/interfaces/synth_strip.yaml +++ b/nipype-auto-conv/specs/interfaces/synth_strip.yaml @@ -5,7 +5,7 @@ # # Docs # ---- -# +# task_name: SynthStrip nipype_name: SynthStrip nipype_module: mriqc.interfaces.synthstrip @@ -15,11 +15,11 @@ inputs: rename: # dict[str, str] - fields to rename in the Pydra interface types: - # dict[str, type] - override inferred types (use "mime-like" string for file-format types, - # e.g. 'medimage/nifti-gz'). For most fields the type will be correctly inferred - # from the nipype interface, but you may want to be more specific, particularly - # for file types, where specifying the format also specifies the file that will be - # passed to the field in the automatically generated unittests. + # dict[str, type] - override inferred types (use "mime-like" string for file-format types, + # e.g. 'medimage/nifti-gz'). For most fields the type will be correctly inferred + # from the nipype interface, but you may want to be more specific, particularly + # for file types, where specifying the format also specifies the file that will be + # passed to the field in the automatically generated unittests. in_file: generic/file # type=file|default=: Input image to be brain extracted model: generic/file @@ -34,6 +34,8 @@ inputs: # dict[str, str] - names of methods/callable classes defined in the adjacent `*_callables.py` # to set as the `default` method of input fields metadata: + in_file: + copyfile: true # dict[str, dict[str, any]] - additional metadata to set on any of the input fields (e.g. out_file: position: 1) outputs: omit: @@ -41,11 +43,11 @@ outputs: rename: # dict[str, str] - fields to rename in the Pydra interface types: - # dict[str, type] - override inferred types (use "mime-like" string for file-format types, - # e.g. 'medimage/nifti-gz'). For most fields the type will be correctly inferred - # from the nipype interface, but you may want to be more specific, particularly - # for file types, where specifying the format also specifies the file that will be - # passed to the field in the automatically generated unittests. + # dict[str, type] - override inferred types (use "mime-like" string for file-format types, + # e.g. 'medimage/nifti-gz'). For most fields the type will be correctly inferred + # from the nipype interface, but you may want to be more specific, particularly + # for file types, where specifying the format also specifies the file that will be + # passed to the field in the automatically generated unittests. out_file: generic/file # type=file: brain-extracted image # type=file|default=: store brain-extracted input to file @@ -60,42 +62,42 @@ outputs: requirements: # dict[str, list[str]] - input fields that are required to be provided for the output field to be present tests: -- inputs: - # dict[str, str] - values to provide to inputs fields in the task initialisation - # (if not specified, will try to choose a sensible value) - in_file: - # type=file|default=: Input image to be brain extracted - use_gpu: - # type=bool|default=False: Use GPU - model: - # type=file|default=: file containing model's weights - border_mm: - # type=int|default=1: Mask border threshold in mm - out_file: - # type=file: brain-extracted image - # type=file|default=: store brain-extracted input to file - out_mask: - # type=file: brain mask - # type=file|default=: store brainmask to file - num_threads: - # type=int|default=0: Number of threads - args: - # type=str|default='': Additional parameters to the command - environ: - # type=dict|default={}: Environment variables - imports: - # list[nipype2pydra.task.base.explicitimport] - list import statements required by the test, with each list item - # consisting of 'module', 'name', and optionally 'alias' keys - expected_outputs: - # dict[str, str] - expected values for selected outputs, noting that tests will typically - # be terminated before they complete for time-saving reasons, and therefore - # these values will be ignored, when running in CI - timeout: 10 - # int - the value to set for the timeout in the generated test, - # after which the test will be considered to have been initialised - # successfully. Set to 0 to disable the timeout (warning, this could - # lead to the unittests taking a very long time to complete) - xfail: true - # bool - whether the unittest is expected to fail or not. Set to false - # when you are satisfied with the edits you have made to this file + - inputs: + # dict[str, str] - values to provide to inputs fields in the task initialisation + # (if not specified, will try to choose a sensible value) + in_file: + # type=file|default=: Input image to be brain extracted + use_gpu: + # type=bool|default=False: Use GPU + model: + # type=file|default=: file containing model's weights + border_mm: + # type=int|default=1: Mask border threshold in mm + out_file: + # type=file: brain-extracted image + # type=file|default=: store brain-extracted input to file + out_mask: + # type=file: brain mask + # type=file|default=: store brainmask to file + num_threads: + # type=int|default=0: Number of threads + args: + # type=str|default='': Additional parameters to the command + environ: + # type=dict|default={}: Environment variables + imports: + # list[nipype2pydra.task.base.explicitimport] - list import statements required by the test, with each list item + # consisting of 'module', 'name', and optionally 'alias' keys + expected_outputs: + # dict[str, str] - expected values for selected outputs, noting that tests will typically + # be terminated before they complete for time-saving reasons, and therefore + # these values will be ignored, when running in CI + timeout: 10 + # int - the value to set for the timeout in the generated test, + # after which the test will be considered to have been initialised + # successfully. Set to 0 to disable the timeout (warning, this could + # lead to the unittests taking a very long time to complete) + xfail: true + # bool - whether the unittest is expected to fail or not. Set to false + # when you are satisfied with the edits you have made to this file doctests: [] diff --git a/nipype-auto-conv/specs/package.yaml b/nipype-auto-conv/specs/package.yaml index 11594ae..455a55a 100644 --- a/nipype-auto-conv/specs/package.yaml +++ b/nipype-auto-conv/specs/package.yaml @@ -35,6 +35,12 @@ omit_constants: import_translations: - [nireports, pydra.tasks.nireports] - [niworkflows, pydra.tasks.niworkflows] +import_find_replace: + - ["from \\.\\. import config, logging", ""] + - ["_ReadDWIMetadataOutputSpec,", ""] + - ["from pydra.tasks.mriqc.nipype_ports.interfaces import utility as niu", ""] + - ["\\s+config,(\\s+)fname_presuffix,", "\\1fname_presuffix,"] + - ["from pydra.tasks.io.auto import add_traits\n", ""] find_replace: - [config\.loggers\.\w+\., logger.] - [config.to_filename\(\), ""] @@ -55,11 +61,6 @@ find_replace: # - ["\\bdict\\[", "ty.Dict["] omit_modules: - "mriqc.config" -import_find_replace: - - ["from \\.\\. import config, logging", ""] - - ["_ReadDWIMetadataOutputSpec,", ""] - - ["from pydra.tasks.mriqc.nipype_ports.interfaces import utility as niu", ""] - - ["\\s+config,(\\s+)fname_presuffix,", "\\1fname_presuffix,"] copy_packages: - mriqc.data - mriqc.synthstrip diff --git a/nipype-auto-conv/specs/workflows/mriqc.workflows.anatomical.base.anat_qc_workflow.yaml b/nipype-auto-conv/specs/workflows/mriqc.workflows.anatomical.base.anat_qc_workflow.yaml index c4fbd84..d02421e 100644 --- a/nipype-auto-conv/specs/workflows/mriqc.workflows.anatomical.base.anat_qc_workflow.yaml +++ b/nipype-auto-conv/specs/workflows/mriqc.workflows.anatomical.base.anat_qc_workflow.yaml @@ -9,6 +9,10 @@ input_node: inputnode inputs: in_file: type: medimage/t1w+nifti-gz-x + modality: + type: field/text + connections: + - [norm, modality] # name of the workflow variable that is returned workflow_variable: workflow # the names of the nested workflows that are defined in other modules and need to be imported @@ -24,3 +28,8 @@ find_replace: "# fmt: off\\n\\s*workflow.set_output\\(\\[\\('iqmswf_measures', workflow.iqmswf.lzout.measures\\)\\]\\)", "", ] + - [ + "modality=workflow.lzin.modality,(\\s+)name=\"norm\"", + "modality=workflow.lzin.modality,\\1name=\"spatial_norm\"", + ] + - ["workflow\\.norm\\b", "workflow.spatial_norm"] From 52551d9004a92aea500bf07de449a790a01e13e1 Mon Sep 17 00:00:00 2001 From: Tom Close Date: Tue, 4 Jun 2024 20:29:02 +1000 Subject: [PATCH 7/8] added scripts to help debug mriqc workflow --- test_scripts/run_anat_wf.py | 18 ++ test_scripts/run_anat_wf_orig.py | 364 +++++++++++++++++++++++++++++++ 2 files changed, 382 insertions(+) create mode 100644 test_scripts/run_anat_wf.py create mode 100644 test_scripts/run_anat_wf_orig.py diff --git a/test_scripts/run_anat_wf.py b/test_scripts/run_anat_wf.py new file mode 100644 index 0000000..e6548f2 --- /dev/null +++ b/test_scripts/run_anat_wf.py @@ -0,0 +1,18 @@ +from fileformats.medimage import NiftiGzX, T1Weighted +import logging +from pathlib import Path +from pydra.tasks.mriqc.workflows.anatomical.base import anat_qc_workflow + +log_file = Path("/Users/tclose/Data/pydra-mriqc-test.log") +log_file.unlink(missing_ok=True) + +pydra_logger = logging.getLogger("pydra") +pydra_logger.setLevel(logging.INFO) +file_handler = logging.FileHandler(str(log_file)) +pydra_logger.addHandler(file_handler) +pydra_logger.addHandler(logging.StreamHandler()) + +workflow = anat_qc_workflow(in_file=NiftiGzX[T1Weighted].sample(), modality="T1w") +workflow.cache_dir = "/Users/tclose/Data/pydra-mriqc-test-cache" +result = workflow(plugin="serial") +print(result.out) diff --git a/test_scripts/run_anat_wf_orig.py b/test_scripts/run_anat_wf_orig.py new file mode 100644 index 0000000..f6a719f --- /dev/null +++ b/test_scripts/run_anat_wf_orig.py @@ -0,0 +1,364 @@ +from fileformats.medimage import NiftiGzX, T1Weighted +import logging +from pathlib import Path +from logging import DEBUG, FileHandler + +# from niworkflows.utils.bids import DEFAULT_BIDS_QUERIES, collect_data +from mriqc._warnings import DATE_FMT, LOGGER_FMT, _LogFormatter +import atexit +import time +import tempfile +from mriqc import config +from mriqc.cli.parser import parse_args + +from mriqc.workflows.anatomical.base import anat_qc_workflow + +# from mriqc import config + + +# class Execution: +# log_dir = "/Users/tclose/Data/pydra-mriqc-test2.log" + + +class opts: + output_dir = "/Users/tclose/Data/pydra-mriqc-test2" + verbose = 0 + species = "human" + modalities = config.SUPPORTED_SUFFIXES + bids_database_wipe = False + testing = False + float32 = True + pdb = False + work_dir = Path("work").absolute() + verbose_reports = False + reports_only = False + write_graph = False + dry_run = False + profile = False + use_plugin = None + no_sub = False + email = "" + upload_strict = False + ants_float = False + # Diffusion workflow settings + min_dwi_length = config.workflow.min_len_dwi + min_bold_length = config.workflow.min_len_bold + fft_spikes_detector = False + fd_thres = 0.2 + deoblique = False + despike = False + verbose_count = 0 + + +config.execution.log_level = int(max(25 - 5 * opts.verbose_count, DEBUG)) + +config.loggers.init() + +_log_file = Path(opts.output_dir) / "logs" / f"mriqc-{config.execution.run_uuid}.log" +_log_file.parent.mkdir(exist_ok=True, parents=True) +_handler = FileHandler(_log_file) +_handler.setFormatter( + _LogFormatter( + fmt=LOGGER_FMT.format(color="", reset=""), + datefmt=DATE_FMT, + colored=False, + ) +) +config.loggers.default.addHandler(_handler) + +extra_messages = [""] + + +# config.loggers.cli.log( +# 26, +# PARTICIPANT_START.format( +# version=__version__, +# bids_dir=opts.bids_dir, +# output_dir=opts.output_dir, +# analysis_level=opts.analysis_level, +# extra_messages="\n".join(extra_messages), +# ), +# ) +# config.from_dict(vars(opts)) + +# Load base plugin_settings from file if --use-plugin +if opts.use_plugin is not None: + from yaml import safe_load as loadyml + + with open(opts.use_plugin) as f: + plugin_settings = loadyml(f) + _plugin = plugin_settings.get("plugin") + if _plugin: + config.nipype.plugin = _plugin + config.nipype.plugin_args = plugin_settings.get("plugin_args", {}) + config.nipype.nprocs = config.nipype.plugin_args.get( + "nprocs", config.nipype.nprocs + ) + +# # Load BIDS filters +# if opts.bids_filter_file: +# config.execution.bids_filters = loads(opts.bids_filter_file.read_text()) + +# bids_dir = config.execution.bids_dir +config.execution.output_dir = Path("/Users/tclose/Data/pydra-mriqc-test2-output") +output_dir = config.execution.output_dir +work_dir = config.execution.work_dir +version = config.environment.version + +# config.execution.bids_dir_datalad = ( +# config.execution.datalad_get +# and (bids_dir / ".git").exists() +# and (bids_dir / ".datalad").exists() +# ) + +# Setup directories +config.execution.log_dir = output_dir / "logs" +# Check and create output and working directories +config.execution.log_dir.mkdir(exist_ok=True, parents=True) +output_dir.mkdir(exist_ok=True, parents=True) +work_dir.mkdir(exist_ok=True, parents=True) + +# Force initialization of the BIDSLayout +# config.execution.init() + +# participant_label = [ +# d.name[4:] +# for d in config.execution.bids_dir.glob("sub-*") +# if d.is_dir() and d.exists() +# ] + + +config.execution.participant_label = "sub-01" + +# Handle analysis_level +analysis_level = set(config.workflow.analysis_level) +if not config.execution.participant_label: + analysis_level.add("group") +config.workflow.analysis_level = list(analysis_level) + +# List of files to be run +lc_modalities = "t1w" # [mod.lower() for mod in config.execution.modalities] +# bids_dataset, _ = collect_data( +# config.execution.layout, +# config.execution.participant_label, +# session_id=config.execution.session_id, +# task=config.execution.task_id, +# group_echos=True, +# bids_filters={ +# mod: config.execution.bids_filters.get(mod, {}) for mod in lc_modalities +# }, +# queries={mod: DEFAULT_BIDS_QUERIES[mod] for mod in lc_modalities}, +# ) + +# Drop empty queries +# bids_dataset = {mod: files for mod, files in bids_dataset.items() if files} +config.workflow.inputs = None # bids_dataset + + +# set specifics for alternative populations +if opts.species.lower() != "human": + config.workflow.species = opts.species + # TODO: add other species once rats are working + if opts.species.lower() == "rat": + config.workflow.template_id = "Fischer344" + # mean distance from the lateral edge to the center of the brain is + # ~ PA:10 mm, LR:7.5 mm, and IS:5 mm (see DOI: 10.1089/089771503770802853) + # roll movement is most likely to occur, so set to 7.5 mm + config.workflow.fd_radius = 7.5 + # block uploads for the moment; can be reversed before wider release + config.execution.no_sub = True + + +log_file = Path("/Users/tclose/Data/pydra-mriqc-test.log") +log_file.unlink(missing_ok=True) + +pydra_logger = logging.getLogger("pydra") +pydra_logger.setLevel(logging.INFO) +file_handler = logging.FileHandler(str(log_file)) +pydra_logger.addHandler(file_handler) +pydra_logger.addHandler(logging.StreamHandler()) + +tmp_dir = Path(tempfile.mkdtemp()) + +t1w = NiftiGzX[T1Weighted].sample() +t1w = t1w.copy(tmp_dir, new_stem="sub-01_T1w") + + +workflow = anat_qc_workflow(in_file=str(t1w), metadata=t1w.metadata) +workflow.run() +# workflow.cache_dir = "/Users/tclose/Data/pydra-mriqc-test-cache2" +result = workflow(plugin="serial") +print(result.out) + +atexit.register(config.restore_env) + +config.settings.start_time = time.time() + +# Run parser +parse_args() + +# if config.execution.pdb: +# from mriqc.utils.debug import setup_exceptionhook + +# setup_exceptionhook() +# config.nipype.plugin = "Linear" + +# # CRITICAL Save the config to a file. This is necessary because the execution graph +# # is built as a separate process to keep the memory footprint low. The most +# # straightforward way to communicate with the child process is via the filesystem. +# # The config file name needs to be unique, otherwise multiple mriqc instances +# # will create write conflicts. +# config_file = config.to_filename() +# config.loggers.cli.info(f"MRIQC config file: {config_file}.") + +# exitcode = 0 +# # Set up participant level +# _pool = None +# if config.nipype.plugin in ("MultiProc", "LegacyMultiProc"): +# import multiprocessing as mp +# import multiprocessing.forkserver +# from concurrent.futures import ProcessPoolExecutor +# from contextlib import suppress + +# os.environ["OMP_NUM_THREADS"] = "1" +# os.environ["NUMEXPR_MAX_THREADS"] = "1" + +# with suppress(RuntimeError): +# mp.set_start_method("fork") +# gc.collect() + +# _pool = ProcessPoolExecutor( +# max_workers=config.nipype.nprocs, +# initializer=config._process_initializer, +# initargs=(config_file,), +# ) + +# _resmon = None +# if config.execution.resource_monitor: +# from mriqc.instrumentation.resources import ResourceRecorder + +# _resmon = ResourceRecorder( +# pid=os.getpid(), +# log_file=mkstemp( +# dir=config.execution.work_dir, prefix=".resources.", suffix=".tsv" +# )[1], +# ) +# _resmon.start() + +# if not config.execution.notrack: +# from ..utils.telemetry import setup_migas + +# setup_migas() + +# with Manager() as mgr: +# from .workflow import build_workflow + +# retval = mgr.dict() +# p = Process(target=build_workflow, args=(str(config_file), retval)) +# p.start() +# p.join() + +# mriqc_wf = retval.get("workflow", None) +# exitcode = p.exitcode or retval.get("return_code", 0) + +# CRITICAL Load the config from the file. This is necessary because the ``build_workflow`` +# function executed constrained in a process may change the config (and thus the global +# state of MRIQC). +# config.load(config_file) + +# exitcode = exitcode or (mriqc_wf is None) * os.EX_SOFTWARE +# if exitcode != 0: +# sys.exit(exitcode) + +# # Initialize nipype config +# config.nipype.init() +# # Make sure loggers are started +# config.loggers.init() + +# if _resmon: +# config.loggers.cli.info(f"Started resource recording at {_resmon._logfile}.") + +# # Resource management options +# if config.nipype.plugin in ("MultiProc", "LegacyMultiProc") and ( +# 1 < config.nipype.nprocs < config.nipype.omp_nthreads +# ): +# config.loggers.cli.warning( +# "Per-process threads (--omp-nthreads=%d) exceed total " +# "threads (--nthreads/--n_cpus=%d)", +# config.nipype.omp_nthreads, +# config.nipype.nprocs, +# ) + +# # Check synthstrip is properly installed +# if not config.environment.synthstrip_path: +# config.loggers.cli.warning( +# ( +# "Please make sure FreeSurfer is installed and the FREESURFER_HOME " +# "environment variable is defined and pointing at the right directory." +# ) +# if config.environment.freesurfer_home is None +# else ( +# f"FreeSurfer seems to be installed at {config.environment.freesurfer_home}," +# " however SynthStrip's model is not found at the expected path." +# ) +# ) + +# if mriqc_wf is None: +# sys.exit(os.EX_SOFTWARE) + +# if mriqc_wf and config.execution.write_graph: +# mriqc_wf.write_graph(graph2use="colored", format="svg", simple_form=True) + +# if not config.execution.dry_run and not config.execution.reports_only: +# # Warn about submitting measures BEFORE +# if not config.execution.no_sub: +# config.loggers.cli.warning(config.DSA_MESSAGE) + +# # Clean up master process before running workflow, which may create forks +# gc.collect() +# # run MRIQC +# _plugin = config.nipype.get_plugin() +# if _pool: +# MultiProcPlugin + +# _plugin = { +# "plugin": MultiProcPlugin( +# pool=_pool, plugin_args=config.nipype.plugin_args +# ), +# } +# mriqc_wf.run(**_plugin) + +# # Warn about submitting measures AFTER +# if not config.execution.no_sub: +# config.loggers.cli.warning(config.DSA_MESSAGE) + +# if not config.execution.dry_run: +# from mriqc.reports.individual import generate_reports + +# generate_reports() + +# _subject_duration = time.gmtime( +# (time.time() - config.settings.start_time) +# / sum(len(files) for files in config.workflow.inputs.values()) +# ) +# config.loggers.cli.log( +# 25, +# messages.PARTICIPANT_FINISHED.format( +# duration=time.strftime("%Hh %Mmin %Ss", _subject_duration) +# ), +# ) + +# if _resmon is not None: +# from mriqc.instrumentation.viz import plot + +# _resmon.stop() +# plot( +# _resmon._logfile, +# param="mem_rss_mb", +# out_file=str(_resmon._logfile).replace(".tsv", ".rss.png"), +# ) +# plot( +# _resmon._logfile, +# param="mem_vsm_mb", +# out_file=str(_resmon._logfile).replace(".tsv", ".vsm.png"), +# ) From 8359ed14333c515c9d5f67f249ad46aa4e5b8c48 Mon Sep 17 00:00:00 2001 From: Tom Close Date: Fri, 27 Jun 2025 12:00:33 +1000 Subject: [PATCH 8/8] added auto-generated workflows --- .github/workflows/ci-cd.yaml | 2 +- .gitignore | 1 - docs/conf.py | 2 +- nipype-auto-conv/generate.py | 14 + nipype-auto-conv/specs/package.yaml | 4 + niworkflows/utils/bids.py | 65 + niworkflows/utils/images.py | 33 + niworkflows/utils/misc.py | 43 + pydra/tasks/mriqc/__init__.py | 2 +- pydra/tasks/mriqc/_post_release.py | 6 + pydra/tasks/mriqc/_version.py | 16 + pydra/tasks/mriqc/data/NOTICE | 17 + pydra/tasks/mriqc/data/__init__.py | 37 + pydra/tasks/mriqc/data/bootstrap-anat.yml | 104 + pydra/tasks/mriqc/data/bootstrap-dwi.yml | 82 + pydra/tasks/mriqc/data/bootstrap-func.yml | 97 + pydra/tasks/mriqc/data/config-example.toml | 60 + pydra/tasks/mriqc/data/config.py | 56 + pydra/tasks/mriqc/data/fsexport.tcl | 107 + pydra/tasks/mriqc/data/itk_identity.tfm | 5 + .../data/reports/embed_resources/boxplots.css | 157 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pydra/tasks/mriqc/synthstrip/model.py | 180 + pydra/tasks/mriqc/utils/__init__.py | 2 + pydra/tasks/mriqc/utils/bids.py | 95 + pydra/tasks/mriqc/utils/misc.py | 33 + pydra/tasks/mriqc/workflows/__init__.py | 31 + .../mriqc/workflows/anatomical/__init__.py | 10 + .../tasks/mriqc/workflows/anatomical/base.py | 807 + .../mriqc/workflows/anatomical/output.py | 159 + .../workflows/anatomical/tests/conftest.py | 25 + ...est_workflows_anatomical_base_airmsk_wf.py | 21 + ...kflows_anatomical_base_anat_qc_workflow.py | 22 + ..._workflows_anatomical_base_compute_iqms.py | 21 + ...st_workflows_anatomical_base_headmsk_wf.py | 21 + ...cal_base_init_brain_tissue_segmentation.py | 21 + ...s_anatomical_base_spatial_normalization.py | 21 + ...s_anatomical_output_init_anat_report_wf.py | 21 + .../mriqc/workflows/diffusion/__init__.py | 2 + pydra/tasks/mriqc/workflows/diffusion/base.py | 673 + .../tasks/mriqc/workflows/diffusion/output.py | 162 + .../workflows/diffusion/tests/conftest.py | 25 + ...t_workflows_diffusion_base_compute_iqms.py | 21 + ...rkflows_diffusion_base_dmri_qc_workflow.py | 22 + ..._workflows_diffusion_base_epi_mni_align.py | 21 + ...t_workflows_diffusion_base_hmc_workflow.py | 21 + ...ows_diffusion_output_init_dwi_report_wf.py | 21 + .../mriqc/workflows/functional/__init__.py | 2 + .../tasks/mriqc/workflows/functional/base.py | 770 + .../mriqc/workflows/functional/output.py | 276 + .../workflows/functional/tests/conftest.py | 25 + ..._workflows_functional_base_compute_iqms.py | 21 + ...workflows_functional_base_epi_mni_align.py | 21 + ...lows_functional_base_fmri_bmsk_workflow.py | 21 + ...kflows_functional_base_fmri_qc_workflow.py | 21 + .../test_workflows_functional_base_hmc.py | 21 + ...s_functional_output_init_func_report_wf.py | 21 + pydra/tasks/mriqc/workflows/shared.py | 68 + pydra/tasks/mriqc/workflows/tests/conftest.py | 25 + .../test_workflows_shared_synthstrip_wf.py | 21 + pydra/tasks/mriqc/workflows/utils.py | 176 + pyproject.toml | 2 +- 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pydra/tasks/mriqc/workflows/functional/tests/conftest.py create mode 100644 pydra/tasks/mriqc/workflows/functional/tests/test_workflows_functional_base_compute_iqms.py create mode 100644 pydra/tasks/mriqc/workflows/functional/tests/test_workflows_functional_base_epi_mni_align.py create mode 100644 pydra/tasks/mriqc/workflows/functional/tests/test_workflows_functional_base_fmri_bmsk_workflow.py create mode 100644 pydra/tasks/mriqc/workflows/functional/tests/test_workflows_functional_base_fmri_qc_workflow.py create mode 100644 pydra/tasks/mriqc/workflows/functional/tests/test_workflows_functional_base_hmc.py create mode 100644 pydra/tasks/mriqc/workflows/functional/tests/test_workflows_functional_output_init_func_report_wf.py create mode 100644 pydra/tasks/mriqc/workflows/shared.py create mode 100644 pydra/tasks/mriqc/workflows/tests/conftest.py create mode 100644 pydra/tasks/mriqc/workflows/tests/test_workflows_shared_synthstrip_wf.py create mode 100644 pydra/tasks/mriqc/workflows/utils.py create mode 100644 test_scripts/orig-affinie-init.txt create mode 100644 test_scripts/orig-mriqc-registration-cmd.txt create mode 100644 test_scripts/pydra-affine-init.txt create mode 100644 test_scripts/pydra-mriqc-registration-cmd.txt diff --git a/.github/workflows/ci-cd.yaml b/.github/workflows/ci-cd.yaml index 7f860d7..c76733b 100644 --- a/.github/workflows/ci-cd.yaml +++ b/.github/workflows/ci-cd.yaml @@ -145,7 +145,7 @@ jobs: if: ${{ always() }} with: files: coverage.xml - name: pydra-mriqc + name: pydra-tasks-mriqc deploy: needs: [test] diff --git a/.gitignore b/.gitignore index 970d75d..8483889 100644 --- a/.gitignore +++ b/.gitignore @@ -137,4 +137,3 @@ dmypy.json # Mac garbarge .DS_store -/pydra diff --git a/docs/conf.py b/docs/conf.py index 03a8f65..b2f81b1 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -17,7 +17,7 @@ # -- Project information ----------------------------------------------------- -project = "pydra-mriqc" +project = "pydra-tasks-mriqc" copyright = "2020, Xihe Xie" author = "Xihe Xie" diff --git a/nipype-auto-conv/generate.py b/nipype-auto-conv/generate.py new file mode 100644 index 0000000..343fa4b --- /dev/null +++ b/nipype-auto-conv/generate.py @@ -0,0 +1,14 @@ +from pathlib import Path +from click.testing import CliRunner +from nipype2pydra.cli import convert + + +spec_dir = Path(__file__).parent / "specs" +conv_dir = Path(__file__).parent.parent + +runner = CliRunner() +result = runner.invoke( + convert, + args=[str(spec_dir), str(conv_dir)], + catch_exceptions=False, +) diff --git a/nipype-auto-conv/specs/package.yaml b/nipype-auto-conv/specs/package.yaml index 455a55a..37adc7e 100644 --- a/nipype-auto-conv/specs/package.yaml +++ b/nipype-auto-conv/specs/package.yaml @@ -22,6 +22,10 @@ config_params: varname: config.environment type: struct module: mriqc + inputs_entities: + varname: config.workflow.inputs_entities + type: struct + module: mriqc omit_functions: - nipype.external.due.BibTeX omit_classes: diff --git a/niworkflows/utils/bids.py b/niworkflows/utils/bids.py new file mode 100644 index 0000000..4a2e6d4 --- /dev/null +++ b/niworkflows/utils/bids.py @@ -0,0 +1,65 @@ +from bids import BIDSLayout +import logging +from pathlib import Path + + +logger = logging.getLogger(__name__) + + +def relative_to_root(path): + """ + Calculate the BIDS root folder given one file path's. + + Examples + -------- + >>> str(relative_to_root( + ... "/sub-03/sourcedata/sub-01/anat/sub-01_T1.nii.gz" + ... )) + 'sub-01/anat/sub-01_T1.nii.gz' + + >>> str(relative_to_root( + ... "/sub-03/anat/sourcedata/sub-01/ses-preop/anat/sub-01_ses-preop_T1.nii.gz" + ... )) + 'sub-01/ses-preop/anat/sub-01_ses-preop_T1.nii.gz' + + >>> str(relative_to_root( + ... "sub-01/anat/sub-01_T1.nii.gz" + ... )) + 'sub-01/anat/sub-01_T1.nii.gz' + + >>> str(relative_to_root("anat/sub-01_T1.nii.gz")) + 'anat/sub-01_T1.nii.gz' + + """ + path = Path(path) + if path.name.startswith("sub-"): + parents = [path.name] + for p in path.parents: + parents.insert(0, p.name) + if p.name.startswith("sub-"): + return Path(*parents) + return path + raise ValueError( + f"Could not determine the BIDS root of <{path}>. " + "Only files under a subject directory are currently supported." + ) + + +def _init_layout(in_file=None, bids_dir=None, validate=True, database_path=None): + + if isinstance(bids_dir, BIDSLayout): + return bids_dir + if bids_dir is None: + in_file = Path(in_file) + for parent in in_file.parents: + if parent.name.startswith("sub-"): + bids_dir = parent.parent.resolve() + break + if bids_dir is None: + raise RuntimeError("Could not infer BIDS root") + layout = BIDSLayout( + str(bids_dir), + validate=validate, + database_path=database_path, + ) + return layout diff --git a/niworkflows/utils/images.py b/niworkflows/utils/images.py new file mode 100644 index 0000000..6236590 --- /dev/null +++ b/niworkflows/utils/images.py @@ -0,0 +1,33 @@ +from gzip import GzipFile +import logging +import nibabel as nb + + +logger = logging.getLogger(__name__) + + +def set_consumables(header, dataobj): + + header.set_slope_inter(dataobj.slope, dataobj.inter) + header.set_data_offset(dataobj.offset) + + +def unsafe_write_nifti_header_and_data(fname, header, data): + """Write header and data without any consistency checks or data munging + + This is almost always a bad idea, and you should not use this function + without a battery of tests for your specific use case. + + If you're not using this for NIfTI files specifically, you're playing + with Fortran-ordered fire. + """ + with open(fname, "wb") as fobj: + # Avoid setting fname or mtime, for deterministic outputs + if str(fname).endswith(".gz"): + fobj = GzipFile("", "wb", 9, fobj, 0.0) + header.write_to(fobj) + # This function serializes one block at a time to reduce memory usage a bit + # It assumes Fortran-ordered data. + nb.volumeutils.array_to_file(data, fobj, offset=header.get_data_offset()) + if str(fname).endswith(".gz"): + fobj.close() diff --git a/niworkflows/utils/misc.py b/niworkflows/utils/misc.py new file mode 100644 index 0000000..1a4f41f --- /dev/null +++ b/niworkflows/utils/misc.py @@ -0,0 +1,43 @@ +import logging +import os + + +logger = logging.getLogger(__name__) + + +def _copy_any(src, dst): + + import os + import gzip + from shutil import copyfileobj + from pydra.tasks.mriqc.nipype_ports.utils.filemanip import copyfile + + src_isgz = src.endswith(".gz") + dst_isgz = dst.endswith(".gz") + if not src_isgz and not dst_isgz: + copyfile(src, dst, copy=True, use_hardlink=True) + return False # Make sure we do not reuse the hardlink later + # Unlink target (should not exist) + if os.path.exists(dst): + os.unlink(dst) + src_open = gzip.open if src_isgz else open + with src_open(src, "rb") as f_in: + with open(dst, "wb") as f_out: + if dst_isgz: + # Remove FNAME header from gzip (nipreps/fmriprep#1480) + gz_out = gzip.GzipFile("", "wb", 9, f_out, 0.0) + copyfileobj(f_in, gz_out) + gz_out.close() + else: + copyfileobj(f_in, f_out) + return True + + +def unlink(pathlike, missing_ok=False): + """Backport of Path.unlink from Python 3.8+ with missing_ok keyword""" + # PY37 hack; drop when python_requires >= 3.8 + try: + os.unlink(pathlike) + except FileNotFoundError: + if not missing_ok: + raise diff --git a/pydra/tasks/mriqc/__init__.py b/pydra/tasks/mriqc/__init__.py index eda89fd..e1537e4 100644 --- a/pydra/tasks/mriqc/__init__.py +++ b/pydra/tasks/mriqc/__init__.py @@ -15,7 +15,7 @@ from ._version import __version__ except ImportError: raise RuntimeError( - "pydra-mriqc has not been properly installed, please run " + "pydra-tasks-mriqc has not been properly installed, please run " f"`pip install -e {str(pkg_path)}` to install a development version" ) if "post" not in __version__: diff --git a/pydra/tasks/mriqc/_post_release.py b/pydra/tasks/mriqc/_post_release.py new file mode 100644 index 0000000..506f6ee --- /dev/null +++ b/pydra/tasks/mriqc/_post_release.py @@ -0,0 +1,6 @@ +# Auto-generated by /Users/tclose/git/workflows/nipype2pydra/nipype2pydra/package.py, do not edit as it will be overwritten + +src_pkg_version = "25.1.0" +nipype2pydra_version = "0.4.5" +post_release = "2510045" + \ No newline at end of file diff --git a/pydra/tasks/mriqc/_version.py b/pydra/tasks/mriqc/_version.py new file mode 100644 index 0000000..f37f66b --- /dev/null +++ b/pydra/tasks/mriqc/_version.py @@ -0,0 +1,16 @@ +# file generated by setuptools_scm +# don't change, don't track in version control +TYPE_CHECKING = False +if TYPE_CHECKING: + from typing import Tuple, Union + VERSION_TUPLE = Tuple[Union[int, str], ...] +else: + VERSION_TUPLE = object + +version: str +__version__: str +__version_tuple__: VERSION_TUPLE +version_tuple: VERSION_TUPLE + +__version__ = version = '0.1.1.dev7+g52551d9.d20240929' +__version_tuple__ = version_tuple = (0, 1, 1, 'dev7', 'g52551d9.d20240929') diff --git a/pydra/tasks/mriqc/data/NOTICE b/pydra/tasks/mriqc/data/NOTICE new file mode 100644 index 0000000..939b4ca --- /dev/null +++ b/pydra/tasks/mriqc/data/NOTICE @@ -0,0 +1,17 @@ +MRIQC +Copyright © The NiPreps Developers. + +This product includes software developed by +the NiPreps Community (https://nipreps.org/). + +Portions of this software were developed at the Department of +Psychology at Stanford University, Stanford, CA, US. + +This software contains code ultimately derived from the +PCP Quality Assessment Protocol (QAP; +http://preprocessed-connectomes-project.org/quality-assessment-protocol) +by C. Craddock, S. Giavasis, D. Clark, Z. Shezhad, and J. Pellman. + +This software is also distributed as a Docker container image. +The bootstrapping file for the image ("Dockerfile") is licensed +under the MIT License. diff --git a/pydra/tasks/mriqc/data/__init__.py b/pydra/tasks/mriqc/data/__init__.py new file mode 100644 index 0000000..f4aed61 --- /dev/null +++ b/pydra/tasks/mriqc/data/__init__.py @@ -0,0 +1,37 @@ +# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- +# vi: set ft=python sts=4 ts=4 sw=4 et: +# +# Copyright 2024 The NiPreps Developers +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# We support and encourage derived works from this project, please read +# about our expectations at +# +# https://www.nipreps.org/community/licensing/ +# +""" +MRIQC data files + +.. autofunction:: load + +.. automethod:: load.readable + +.. automethod:: load.as_path + +.. automethod:: load.cached +""" + +from acres import Loader + +load = Loader(__package__) diff --git a/pydra/tasks/mriqc/data/bootstrap-anat.yml b/pydra/tasks/mriqc/data/bootstrap-anat.yml new file mode 100644 index 0000000..048bf23 --- /dev/null +++ b/pydra/tasks/mriqc/data/bootstrap-anat.yml @@ -0,0 +1,104 @@ +# Copyright 2023 The NiPreps Developers +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# We support and encourage derived works from this project, please read +# about our expectations at +# +# https://www.nipreps.org/community/licensing/ +# +########################################################################### +# Reports bootstrap file +# ====================== +# This is a YAML-formatted file specifying how the NiReports assembler +# will search for "reportlets" and compose them into a report file, +# typically in HTML format. +########################################################################### + +packagename: mriqc +title: '{filename} :: Anatomical MRI report' +sections: +- name: Summary + reportlets: + - bids: {datatype: figures, desc: summary, extension: [.html]} +- name: Basic visual report + reportlets: + - bids: {datatype: figures, desc: background} + caption: This panel shows a mosaic enhancing the background around the head. + Artifacts usually unveil themselves in the air surrounding the head, where no signal + sources are present. + subtitle: View of the background of the anatomical image + - bids: {datatype: figures, desc: zoomed} + caption: This panel shows a mosaic of the brain. This mosaic is the most suitable to + screen head-motion intensity inhomogeneities, global/local noise, signal leakage + (for example, from the eyeballs and across the phase-encoding axis), etc. + subtitle: Zoomed-in mosaic view of the brain +- name: Extended visual report + reportlets: + - bids: {datatype: figures, desc: airmask} + caption: The hat-mask calculated internally by MRIQC. Some metrics will use this + mask, for instance, to find out artifacts and estimate the spread of gaussian noise + added to the signal. This mask leaves out the air around the face to avoid measuring + noise sourcing from the eyeballs and their movement. + subtitle: '«Hat»-mask' + - bids: {datatype: figures, desc: noisefit} + caption: The noise fit internally estimated by MRIQC to calculate the QI1 index + proposed by Mortamet et al. (2009). + subtitle: Distribution of the noise within the hat mask + style: + max-width: 450px + - bids: {datatype: figures, desc: artifacts} + caption: Mask of artifactual intensities identified within the hat-mask. + subtitle: Artifactual intensities on the background + - bids: {datatype: figures, desc: brainmask} + caption: Brain mask as internally extracted by MRIQC. Defects on the brainmask could + indicate problematic aspects of the image quality-wise. + subtitle: Brain extraction performance + - bids: {datatype: figures, desc: head} + caption: A mask of the head calculated internally by MRIQC. + subtitle: Head mask + - bids: {datatype: figures, desc: segmentation} + caption: Brain tissue segmentation, as internally extracted by MRIQC. + Defects on this segmentation, as well as noisy tissue labels could + indicate problematic aspects of the image quality-wise. + subtitle: Brain tissue segmentation + - bids: {datatype: figures, desc: norm} + caption: This panel shows a quick-and-dirty nonlinear registration into + the MNI152NLin2009cAsym template accessed with + TemplateFlow. + subtitle: Spatial normalization of the anatomical image + static: false + +- name: About + nested: true + reportlets: + - custom: errors + path: '{reportlets_dir}/{run_uuid}' + captions: MRIQC may have recorded failure conditions. + title: Errors + - metadata: "input" + settings: + # By default, only the first dictionary will be expanded. + # If folded is true, all will be folded. If false all expanded. + folded: true + # If an ID is not provided, one should be generated automatically + id: 'about-metadata' + caption: | + Thanks for using MRIQC. The following information may assist in + reconstructing the provenance of the corresponding derivatives. + title: Reproducibility and provenance information + +# Rating widget +plugins: +- module: nireports.assembler + path: data/rating-widget/bootstrap.yml diff --git a/pydra/tasks/mriqc/data/bootstrap-dwi.yml b/pydra/tasks/mriqc/data/bootstrap-dwi.yml new file mode 100644 index 0000000..999cd11 --- /dev/null +++ b/pydra/tasks/mriqc/data/bootstrap-dwi.yml @@ -0,0 +1,82 @@ +# Copyright 2023 The NiPreps Developers +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# We support and encourage derived works from this project, please read +# about our expectations at +# +# https://www.nipreps.org/community/licensing/ +# +########################################################################### +# Reports bootstrap file +# ====================== +# This is a YAML-formatted file specifying how the NiReports assembler +# will search for "reportlets" and compose them into a report file, +# typically in HTML format. +########################################################################### + +packagename: mriqc +title: '{filename} :: Diffusion MRI MRIQC report' +sections: +- name: Summary + reportlets: + - bids: {datatype: figures, desc: summary, extension: [.html]} + - bids: {datatype: figures, desc: heatmap} + caption: This visualization divides the data by shells, and shows the joint distribution + of SNR vs. FA. At the bottom, the distributions are marginalized for SNR. + Please note that the figures of SNR provided are calculated with a coarse estimation of + the signal variability, and therefore should be interpreted with care. + subtitle: Shell-wise joint distribution of SNR vs. FA in every voxel + - bids: {datatype: figures, desc: fa} + caption: Reconstructed FA map. + subtitle: Fractional anisotropy (FA) map + - bids: {datatype: figures, desc: md} + caption: Reconstructed MD map. + subtitle: Mean diffusivity (MD) map +- name: DWI shells + ordering: bval + reportlets: + - bids: {datatype: figures, desc: avgstd} + caption: This panel shows mosaics flickering between the voxel-wise average and standard deviation + for each shell. + subtitle: Voxel-wise average and standard deviation across volumes in this DWI shell. + static: false + - bids: {datatype: figures, desc: background} + caption: This panel shows a mosaic enhancing the background around the head. + Artifacts usually unveil themselves in the air surrounding the head, where no signal + sources are present. + subtitle: View of the background of the voxel-wise average of this DWI shell + +- name: About + nested: true + reportlets: + - custom: errors + path: '{reportlets_dir}/{run_uuid}' + captions: MRIQC may have recorded failure conditions. + title: Errors + - metadata: "input" + settings: + # By default, only the first dictionary will be expanded. + # If folded is true, all will be folded. If false all expanded. + folded: true + # If an ID is not provided, one should be generated automatically + id: 'about-metadata' + caption: | + Thanks for using MRIQC. The following information may assist in + reconstructing the provenance of the corresponding derivatives. + title: Reproducibility and provenance information + +# Rating widget +plugins: +- module: nireports.assembler + path: data/rating-widget/bootstrap.yml diff --git a/pydra/tasks/mriqc/data/bootstrap-func.yml b/pydra/tasks/mriqc/data/bootstrap-func.yml new file mode 100644 index 0000000..5e34fc1 --- /dev/null +++ b/pydra/tasks/mriqc/data/bootstrap-func.yml @@ -0,0 +1,97 @@ +# Copyright 2023 The NiPreps Developers +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# We support and encourage derived works from this project, please read +# about our expectations at +# +# https://www.nipreps.org/community/licensing/ +# +########################################################################### +# Reports bootstrap file +# ====================== +# This is a YAML-formatted file specifying how the NiReports assembler +# will search for "reportlets" and compose them into a report file, +# typically in HTML format. +########################################################################### + +packagename: mriqc +title: "{filename} :: MRIQC's BOLD fMRI report" +sections: +- name: Summary + reportlets: + - bids: {datatype: figures, desc: summary, extension: [.html]} +- name: Basic echo-wise reports + ordering: echo + reportlets: + - bids: {datatype: figures, desc: stdev} + subtitle: Standard deviation of signal through time + caption: The voxel-wise standard deviation of the signal (variability along time). + - bids: {datatype: figures, desc: background} + caption: This panel shows a mosaic enhancing the background around the head. + Artifacts usually unveil themselves in the air surrounding the head, where no signal + sources are present. + subtitle: View of the background of the voxel-wise average of the BOLD timeseries + - bids: {datatype: figures, desc: zoomed} + caption: This panel shows a mosaic of the brain. This mosaic is the most suitable to + screen head-motion intensity inhomogeneities, global/local noise, signal leakage + (for example, from the eyeballs and across the phase-encoding axis), etc. + subtitle: Voxel-wise average of BOLD time-series, zoomed-in covering just the brain + - bids: {datatype: figures, desc: carpet} + subtitle: Carpetplot and nuisance signals + caption: The so-called «carpetplot» may assist in assessing head-motion + derived artifacts and respiation effects. + +- name: Extended echo-wise reports + ordering: echo + reportlets: + - bids: {datatype: figures, desc: mean} + subtitle: Voxel-wise average of BOLD time-series + caption: The average signal calculated across the last axis (time). + +- name: Extended reports shared across echos + reportlets: + - bids: {datatype: figures, desc: brainmask} + caption: Brain mask as internally extracted by MRIQC. Defects on the brainmask could + indicate problematic aspects of the image quality-wise. + subtitle: Brain extraction performance + - bids: {datatype: figures, desc: norm} + caption: This panel shows a quick-and-dirty nonlinear registration into + the MNI152NLin2009cAsym template accessed with + TemplateFlow. + subtitle: Spatial normalization of the anatomical image + static: false + +- name: About + nested: true + reportlets: + - custom: errors + path: '{reportlets_dir}/{run_uuid}' + captions: MRIQC may have recorded failure conditions. + title: Errors + - metadata: "input" + settings: + # By default, only the first dictionary will be expanded. + # If folded is true, all will be folded. If false all expanded. + folded: true + # If an ID is not provided, one should be generated automatically + id: 'about-metadata' + caption: | + Thanks for using MRIQC. The following information may assist in + reconstructing the provenance of the corresponding derivatives. + title: Reproducibility and provenance information + +# Rating widget +plugins: +- module: nireports.assembler + path: data/rating-widget/bootstrap.yml diff --git a/pydra/tasks/mriqc/data/config-example.toml b/pydra/tasks/mriqc/data/config-example.toml new file mode 100644 index 0000000..cb0519d --- /dev/null +++ b/pydra/tasks/mriqc/data/config-example.toml @@ -0,0 +1,60 @@ +[environment] +cpu_count = 8 +exec_env = "posix" +free_mem = 10.8 +overcommit_policy = "heuristic" +overcommit_limit = "50%" +nipype_version = "1.4.2" +templateflow_version = "0.5.2" +version = "0.15.2" + +[execution] +ants_float = false +bids_dir = "data/" +debug = false +dry_run = false +dsname = "ds000005" +float32 = true +layout = "BIDS Layout: data/ | Subjects: 16 | Sessions: 0 | Runs: 48" +log_dir = "derivatives/mriqc/logs" +log_level = 15 +no_sub = false +output_dir = "derivatives/" +participant_label = [ "01",] +reports_only = false +run_uuid = "20200403-185126_db5d5e64-4e98-4a75-b3d1-ab880afa0e85" +templateflow_home = "/opt/templateflow" +upload_strict = false +verbose_reports = false +webapi_url = "https://mriqc.nimh.nih.gov/api/v1" +webapi_port = 443 +work_dir = "work/" +write_graph = false + +[workflow] +analysis_level = [ "participant",] +biggest_file_gb = 0.03619009628891945 +deoblique = false +despike = false +fd_thres = 0.2 +fd_radius = 50 +fft_spikes_detector = false +ica = false +template_id = "MNI152NLin2009cAsym" + +[nipype] +crashfile_format = "txt" +get_linked_libs = false +nprocs = 8 +omp_nthreads = 8 +plugin = "MultiProc" +resource_monitor = false +stop_on_first_crash = true + +[workflow.inputs] +bold = [ "data/sub-01/func/sub-01_task-mixedgamblestask_run-01_bold.nii.gz", "data/sub-01/func/sub-01_task-mixedgamblestask_run-02_bold.nii.gz", "data/sub-01/func/sub-01_task-mixedgamblestask_run-03_bold.nii.gz",] +T1w = [ "data/sub-01/anat/sub-01_T1w.nii.gz",] + +[nipype.plugin_args] +maxtasksperchild = 1 +raise_insufficient = false diff --git a/pydra/tasks/mriqc/data/config.py b/pydra/tasks/mriqc/data/config.py new file mode 100644 index 0000000..1950d35 --- /dev/null +++ b/pydra/tasks/mriqc/data/config.py @@ -0,0 +1,56 @@ +# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- +# vi: set ft=python sts=4 ts=4 sw=4 et: +# +# Copyright 2021 The NiPreps Developers +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# We support and encourage derived works from this project, please read +# about our expectations at +# +# https://www.nipreps.org/community/licensing/ +# +"""Utilities: Jinja2 templates.""" + +from pathlib import Path + +from niworkflows.data import Loader + + +class GroupTemplate: + """Specific template for the individual report""" + + """ + Utility class for generating a config file from a jinja template. + https://github.com/oesteban/endofday/blob/f2e79c625d648ef45b08cc1f11fd0bd84342d604/endofday/core/template.py + """ + + def __init__(self): + import jinja2 + + self.template_str = Loader(__package__)('reports/group.html').absolute() + self.env = jinja2.Environment( + loader=jinja2.FileSystemLoader(searchpath='/'), + trim_blocks=True, + lstrip_blocks=True, + autoescape=False, # noqa: S701 + ) + + def compile(self, configs): + """Generates a string with the replacements""" + template = self.env.get_template(str(self.template_str)) + return template.render(configs) + + def generate_conf(self, configs, path): + """Saves the oucome after replacement on the template to file""" + Path(path).write_text(self.compile(configs)) diff --git a/pydra/tasks/mriqc/data/fsexport.tcl b/pydra/tasks/mriqc/data/fsexport.tcl new file mode 100644 index 0000000..3df1337 --- /dev/null +++ b/pydra/tasks/mriqc/data/fsexport.tcl @@ -0,0 +1,107 @@ +# Sample script for setting up and taking screen shots. Scripting +# reference is available at: + +# https://surfer.nmr.mgh.harvard.edu/fswiki/TkMeditGuide/TkMeditReference/TkMeditScripting + +# You can set the cursor or view center with the SetCursor command. + + + +# Alternatively you can set the slice number (in volume index +# coordinates). This will not change the in-plane center. + +# Use SetZoomLevel to zoom in and out. 1 is normal, >1 is zoomed in, +# and 0-1 is zoomed out. + +# SetZoomLevel level +SetZoomLevel 2 +# SetZoomCenter 0 0 0 + +# SetOrientation orientation +# orientation: +# 0 coronal +# 1 horizontal +# 2 sagittal +SetOrientation 0 + +# This command turns on and off various display flags. +# SetDisplayFlag flag value +# flag: +# 1 Aux Volume - set to 1 to show aux volume +# 2 Anatomical Volume - set to 0 to hide main and aux volume +# 3 Cursor +# 4 Main Surface +# 5 Original Surface +# 6 Pial Surface +# 7 Interpolate Surface Vertices +# 8 Surface Vertices +# 9 Control Points +# 10 Selection +# 11 Functional Overlay +# 12 Functional Color Scale Bar +# 13 Mask to Functional Overlay +# 14 Histogram Percent Change +# 15 Segmentation Volume Overlay +# 16 Aux Segmentation Volume +# 17 Segmentation Label Volume Count +# 18 DTI Overlay +# 20 Focus Frame +# 21 Undoable Voxels +# 22 Axes +# 23 Maximum Intensity Projection +# 24 Head Points +# 25 Verbose GCA DumpSetDisplayFlag + +# SetCursor coordinateSpace x y z +# coordinateSpace: +# 0 volume index +# 1 RAS +# 2 Talairach +SetCursor 0 128 128 128 + +# Turn cursor display off. +SetDisplayFlag 3 0 + +# Turn the axes on. +SetDisplayFlag 22 1 + +# Use this command to go to multiple views. This will copy the current +# view settings from the current view, so all the above commands will +# apply to all new views. +# SetDisplayConfig numberOfColumns numberOfRows linkPolicy +# linkPolicy: +# 0 none +# 1 linked cursors +# 2 linked slice changes +# SetDisplayConfig 2 2 1 + +# Use the RedrawScreen command to force a redraw after you have a view +# set up, before taking a picture. +# RedrawScreen + +# This command will save the actual screenshot. +# SaveTiff fileName +# SaveTIFF $::env(FS_OUTPUT_PATH)/screenshot.tif + + +# Use tcl loops to change orientations and take multiple +# screenshots. This will set the view to a single view, and take three +# screenshots, one of each orientation. +# SetDisplayConfig 1 1 0 +# foreach orientation {0 1 2} label {cor horiz sag} { + +# SetOrientation $orientation +# RedrawScreen +# SaveTIFF $::env(FS_OUTPUT_PATH)/screenshot-$label.tif +# } + +# Or take pictures of multiple slices for a movie. This goes through +# slices 0-255 and takes a shot at each one. +for { set slice 30 } { $slice < 226 } { incr slice } { + SetZoomCenter 160 80 $slice + SetSlice $slice + RedrawScreen + SaveTIFF $::env(FS_OUTPUT_PATH)[format "/screenshot-%03d.tif" $slice] +} + +QuitMedit diff --git a/pydra/tasks/mriqc/data/itk_identity.tfm b/pydra/tasks/mriqc/data/itk_identity.tfm new file mode 100644 index 0000000..2ba6b8a --- /dev/null +++ b/pydra/tasks/mriqc/data/itk_identity.tfm @@ -0,0 +1,5 @@ +#Insight Transform File V1.0 +#Transform 0 +Transform: IdentityTransform_double_3_3 +Parameters: +FixedParameters: diff --git a/pydra/tasks/mriqc/data/reports/embed_resources/boxplots.css b/pydra/tasks/mriqc/data/reports/embed_resources/boxplots.css new file mode 100644 index 0000000..0938a5e --- /dev/null +++ b/pydra/tasks/mriqc/data/reports/embed_resources/boxplots.css @@ -0,0 +1,157 @@ +/* Hide data table */ +.csvdata { + display: none; +} + +body { + font-family: helvetica; +} + +.text-warning { + font-weight: bold; + color: red; +} +/*Primary Chart*/ + +/*Nested divs for responsiveness*/ +.chart-wrapper { + max-width: 800px; /*Overwritten by the JS*/ + min-width: 160px; + margin-bottom: 20px; + font-family: helvetica; +} +.chart-wrapper .inner-wrapper { + position: relative; + padding-bottom: 50%; /*Overwritten by the JS*/ + width: 100%; +} +.chart-wrapper .outer-box { + position: absolute; + top: 0; + bottom: 0; + left: 0; + right: 0; +} +.chart-wrapper .inner-box { + width: 100%; + height: 100%; +} + +.chart-wrapper text { + font-family: helvetica; + font-size: 13px; +} + +.chart-wrapper .axis path, +.chart-wrapper .axis line { + fill: none; + stroke: #888; + stroke-width: 2px; + shape-rendering: crispEdges; +} + +.chart-wrapper .y.axis .tick line { + stroke: lightgrey; + opacity: 0.6; + stroke-dasharray: 2,1; + stroke-width: 1; + shape-rendering: crispEdges; + +} + +.chart-wrapper .x.axis .domain { + display: none; +} + +.chart-wrapper div.tooltip { + position: absolute; + text-align: left; + padding: 3px; + font-size: 12px; + background: #eee; + border: 0px; + border-radius: 1px; + pointer-events: none; + opacity: .7; + z-index: 10; +} + +/*Box Plot*/ +.chart-wrapper .box-plot .box { + fill-opacity: .5; + stroke-width: 2; +} +.chart-wrapper .box-plot line { + stroke-width: 2px; +} +.chart-wrapper .box-plot circle { + fill: white; + stroke: black; +} + +.chart-wrapper .box-plot .median { + stroke: black; +} + +.chart-wrapper .box-plot circle.median { + /*the script makes the circles the same color as the box, you can override this in the js*/ + fill: white !important; +} + +.chart-wrapper .box-plot .mean { + stroke: white; + stroke-dasharray: 2,1; + stroke-width: 1px; +} + +@media (max-width:500px){ + .chart-wrapper .box-plot circle {display: none;} +} + +/*Violin Plot*/ + +.chart-wrapper .violin-plot .area { + shape-rendering: geometricPrecision; + opacity: 0.4; +} + +.chart-wrapper .violin-plot .line { + fill: none; + stroke-width: 2px; + shape-rendering: geometricPrecision; +} + +/*Notch Plot*/ +.chart-wrapper .notch-plot .notch { + fill-opacity: 0.4; + stroke-width: 2; +} + +/* Point Plots*/ +.chart-wrapper .points-plot .point { + /*stroke: black; + stroke-width: 1px;*/ + fill-opacity: 0.4; +} + +.chart-wrapper .metrics-lines { + stroke-width: 4px; +} + +/* Non-Chart Styles for demo*/ +.chart-options { + min-width: 200px; + font-size: 13px; + font-family: helvetica; +} +.chart-options button { + margin: 3px; + padding: 3px; + font-size: 12px; +} +.chart-options p { + display: inline; +} +@media (max-width:500px){ + .chart-options p {display: block;} +} \ No newline at end of file diff --git a/pydra/tasks/mriqc/data/reports/embed_resources/boxplots.js b/pydra/tasks/mriqc/data/reports/embed_resources/boxplots.js new file mode 100644 index 0000000..0198b31 --- /dev/null +++ b/pydra/tasks/mriqc/data/reports/embed_resources/boxplots.js @@ -0,0 +1,1634 @@ +/** + * @fileOverview A D3 based distribution chart system. Supports: Box plots, Violin plots, Notched box plots, trend lines, beeswarm plot + * @version 3.0 + */ + + +/** + * Creates a box plot, violin plot, and or notched box plot + * @param settings Configuration options for the base plot + * @param settings.data The data for the plot + * @param settings.xName The name of the column that should be used for the x groups + * @param settings.yName The name of the column used for the y values + * @param {string} settings.selector The selector string for the main chart div + * @param [settings.axisLabels={}] Defaults to the xName and yName + * @param [settings.yTicks = 1] 1 = default ticks. 2 = double, 0.5 = half + * @param [settings.scale='linear'] 'linear' or 'log' - y scale of the chart + * @param [settings.chartSize={width:800, height:400}] The height and width of the chart itself (doesn't include the container) + * @param [settings.margin={top: 15, right: 60, bottom: 40, left: 50}] The margins around the chart (inside the main div) + * @param [settings.constrainExtremes=false] Should the y scale include outliers? + * @returns {object} chart A chart object + */ +function makeDistroChart(settings) { + + var chart = {}; + + // Defaults + chart.settings = { + data: null, + xName: null, + yName: null, + axisLabels: {xAxis: null, yAxis: null}, + labelName: "label", + unitsName: "units", + selector: null, + axisLables: null, + yTicks: 1, + scale: 'linear', + chartSize: {width: 800, height: 400}, + margin: {top: 15, right: 10, bottom: 50, left: 50}, + constrainExtremes: false, + color: d3.scale.category10(), + modality: null + }; + + for (var setting in settings) { + chart.settings[setting] = settings[setting] + } + + function formatAsFloat(d) { + if (d % 1 !== 0) { + return d3.format(".2f")(d); + } else { + return d3.format(".0f")(d); + } + } + + function logFormatNumber(d) { + var x = Math.log(d) / Math.log(10) + 1e-6; + return Math.abs(x - Math.floor(x)) < 0.6 ? formatAsFloat(d) : ""; + } + + chart.yFormatter = formatAsFloat; + + chart.data = chart.settings.data; + + iqmName = chart.data[0][chart.settings.xName] + if (iqmName.indexOf('_') > 0) { + iqmName = iqmName.substr(0, iqmName.indexOf('_')) + } + chart.settings.axisLabels.yAxis = iqmName.toUpperCase() + if (iqmName.toLowerCase().startsWith('fd') || iqmName.toLowerCase().startsWith('spikes')) { + chart.settings.constrainExtremes = true + } + + units = chart.data[0][chart.settings.unitsName] + if (units) { + chart.settings.axisLabels.yAxis += ' (' + units + ')' + } + + + chart.groupObjs = {}; //The data organized by grouping and sorted as well as any metadata for the groups + chart.objs = {mainDiv: null, chartDiv: null, g: null, xAxis: null, yAxis: null}; + chart.colorFunct = null; + + /** + * Takes an array, function, or object mapping and created a color function from it + * @param {function|[]|object} colorOptions + * @returns {function} Function to be used to determine chart colors + */ + function getColorFunct(colorOptions) { + if (typeof colorOptions == 'function') { + return colorOptions + } else if (Array.isArray(colorOptions)) { + // If an array is provided, map it to the domain + var colorMap = {}, cColor = 0; + for (var cName in chart.groupObjs) { + colorMap[cName] = colorOptions[cColor]; + cColor = (cColor + 1) % colorOptions.length; + } + return function (group) { + return colorMap[group]; + } + } else if (typeof colorOptions == 'object') { + // if an object is provided, assume it maps to the colors + return function (group) { + return colorOptions[group]; + } + } else { + return d3.scale.category10(); + } + } + + /** + * Takes a percentage as returns the values that correspond to that percentage of the group range width + * @param objWidth Percentage of range band + * @param gName The bin name to use to get the x shift + * @returns {{left: null, right: null, middle: null}} + */ + function getObjWidth(objWidth, gName) { + var objSize = {left: null, right: null, middle: null}; + var width = chart.xScale.rangeBand() * (objWidth / 100); + var padding = (chart.xScale.rangeBand() - width) / 2; + var gShift = chart.xScale(gName); + objSize.middle = chart.xScale.rangeBand() / 2 + gShift; + objSize.left = padding + gShift; + objSize.right = objSize.left + width; + return objSize; + } + + /** + * Adds jitter to the scatter point plot + * @param doJitter true or false, add jitter to the point + * @param width percent of the range band to cover with the jitter + * @returns {number} + */ + function addJitter(doJitter, width) { + if (doJitter !== true || width == 0) { + return 0 + } + return Math.floor(Math.random() * width) - width / 2; + } + + function shallowCopy(oldObj) { + var newObj = {}; + for (var i in oldObj) { + if (oldObj.hasOwnProperty(i)) { + newObj[i] = oldObj[i]; + } + } + return newObj; + } + + /** + * Closure that creates the tooltip hover function + * @param groupName Name of the x group + * @param metrics Object to use to get values for the group + * @returns {Function} A function that provides the values for the tooltip + */ + function tooltipHover(groupName, metrics) { + var tooltipString = "Group: " + groupName; + tooltipString += "Max: " + formatAsFloat(metrics.max, 0.1); + tooltipString += "Q3: " + formatAsFloat(metrics.quartile3); + tooltipString += "Median: " + formatAsFloat(metrics.median); + tooltipString += "Q1: " + formatAsFloat(metrics.quartile1); + tooltipString += "Min: " + formatAsFloat(metrics.min); + return function () { + chart.objs.tooltip.transition().duration(200).style("opacity", 0.9); + chart.objs.tooltip.html(tooltipString) + }; + } + + function axislabelHover(groupName) { + var tooltipString = "Go to definition of " + groupName; + return function () { + chart.objs.tooltip.transition().duration(200).style("opacity", 1.0); + chart.objs.tooltip.html(tooltipString) + }; + } + + /** + * Closure that creates the tooltip hover function + * @param groupName Name of the x group + * @param metrics Object to use to get values for the group + * @returns {Function} A function that provides the values for the tooltip + */ + function pointHover(label, value) { + var tooltipString = "Subject: " + label + "Measure: " + value + return function () { + chart.objs.tooltip.transition().duration(200).style("opacity", 1.0); + chart.objs.tooltip.html(tooltipString) + }; + } + + /** + * Parse the data and calculates base values for the plots + */ + !function prepareData() { + function calcMetrics(values) { + // Do not reorder in-place + values = values.slice(0).sort(d3.ascending) + + var metrics = { //These are the original non�scaled values + max: null, + upperOuterFence: null, + upperInnerFence: null, + quartile3: null, + median: null, + mean: null, + iqr: null, + quartile1: null, + lowerInnerFence: null, + lowerOuterFence: null, + min: null + }; + + metrics.min = d3.min(values); + metrics.quartile1 = d3.quantile(values, 0.25); + metrics.median = d3.median(values); + metrics.mean = d3.mean(values); + metrics.quartile3 = d3.quantile(values, 0.75); + metrics.max = d3.max(values); + metrics.iqr = metrics.quartile3 - metrics.quartile1; + + //The inner fences are the closest value to the IQR without going past it (assumes sorted lists) + var LIF = metrics.quartile1 - (1.5 * metrics.iqr); + var UIF = metrics.quartile3 + (1.5 * metrics.iqr); + for (var i = 0; i <= values.length; i++) { + if (values[i] < LIF) { + continue; + } + if (!metrics.lowerInnerFence && values[i] >= LIF) { + metrics.lowerInnerFence = values[i]; + continue; + } + if (values[i] > UIF) { + metrics.upperInnerFence = values[i - 1]; + break; + } + } + + + metrics.lowerOuterFence = metrics.quartile1 - (3 * metrics.iqr); + metrics.upperOuterFence = metrics.quartile3 + (3 * metrics.iqr); + if (!metrics.lowerInnerFence) { + metrics.lowerInnerFence = metrics.min; + } + if (!metrics.upperInnerFence) { + metrics.upperInnerFence = metrics.max; + } + return metrics + } + + var current_x = null; + var current_y = null; + var current_row; + + // Group the values + for (current_row = 0; current_row < chart.data.length; current_row++) { + current_x = chart.data[current_row][chart.settings.xName]; + current_y = chart.data[current_row][chart.settings.yName]; + current_label = chart.data[current_row][chart.settings.labelName]; + + if (chart.groupObjs.hasOwnProperty(current_x)) { + chart.groupObjs[current_x].values.push(current_y); + chart.groupObjs[current_x].labels.push(current_label); + } else { + chart.groupObjs[current_x] = {}; + chart.groupObjs[current_x].values = [current_y]; + chart.groupObjs[current_x].labels = [current_label]; + } + } + + for (var cName in chart.groupObjs) { + // chart.groupObjs[cName].values.sort(d3.ascending); + chart.groupObjs[cName].metrics = {}; + chart.groupObjs[cName].metrics = calcMetrics(chart.groupObjs[cName].values); + + } + }(); + + /** + * Prepare the chart settings and chart div and svg + */ + !function prepareSettings() { + //Set base settings + chart.margin = chart.settings.margin; + chart.divWidth = chart.settings.chartSize.width; + chart.divHeight = chart.settings.chartSize.height; + chart.width = chart.divWidth - chart.margin.left - chart.margin.right; + chart.height = chart.divHeight - chart.margin.top - chart.margin.bottom; + + if (chart.settings.axisLabels) { + chart.xAxisLable = chart.settings.axisLabels.xAxis; + chart.yAxisLable = chart.settings.axisLabels.yAxis; + } + + if (chart.settings.scale === 'log') { + chart.yScale = d3.scale.log(); + chart.yFormatter = logFormatNumber; + } else { + chart.yScale = d3.scale.linear(); + } + + if (chart.settings.constrainExtremes === true) { + var fences = []; + for (var cName in chart.groupObjs) { + fences.push(chart.groupObjs[cName].metrics.lowerInnerFence); + fences.push(chart.groupObjs[cName].metrics.upperInnerFence); + } + chart.range = d3.extent(fences); + + } else { + chart.range = d3.extent(chart.data, function (d) {return d[chart.settings.yName];}); + } + + chart.colorFunct = getColorFunct(chart.settings.colors); + + // Build Scale functions + chart.yScale.range([chart.height, 0]).domain(chart.range).nice().clamp(true); + chart.xScale = d3.scale.ordinal().domain(Object.keys(chart.groupObjs)).rangeBands([0, chart.width]); + + //Build Axes Functions + chart.objs.yAxis = d3.svg.axis() + .scale(chart.yScale) + .orient("left") + .tickFormat(chart.yFormatter) + .outerTickSize(0) + .innerTickSize(-chart.width + (chart.margin.right + chart.margin.left)); + chart.objs.yAxis.ticks(chart.objs.yAxis.ticks()*chart.settings.yTicks); + chart.objs.xAxis = d3.svg.axis().scale(chart.xScale).orient("bottom").tickSize(5); + }(); + + /** + * Updates the chart based on the current settings and window size + * @returns {*} + */ + chart.update = function () { + // Update chart size based on view port size + chart.width = parseInt(chart.objs.chartDiv.style("width"), 10) - (chart.margin.left + chart.margin.right); + chart.height = parseInt(chart.objs.chartDiv.style("height"), 10) - (chart.margin.top + chart.margin.bottom); + + // Update scale functions + chart.xScale.rangeBands([0, chart.width]); + chart.yScale.range([chart.height, 0]); + + // Update the yDomain if the Violin plot clamp is set to -1 meaning it will extend the violins to make nice points + if (chart.violinPlots && chart.violinPlots.options.show == true && chart.violinPlots.options._yDomainVP != null) { + chart.yScale.domain(chart.violinPlots.options._yDomainVP).nice().clamp(true); + } else { + chart.yScale.domain(chart.range).nice().clamp(true); + } + + //Update axes + chart.objs.g.select('.x.axis').attr("transform", "translate(0," + chart.height + ")").call(chart.objs.xAxis) + .selectAll("text") + .attr("y", 5) + .attr("x", -5) + .attr("transform", "rotate(-45)") + .style("text-anchor", "end"); + chart.objs.g.select('.x.axis .label').attr("x", chart.width / 2); + chart.objs.g.select('.y.axis').call(chart.objs.yAxis.innerTickSize(-chart.width)); + chart.objs.g.select('.y.axis .label').attr("x", -chart.height / 2); + chart.objs.chartDiv.select('svg').attr("width", chart.width + (chart.margin.left + chart.margin.right)).attr("height", chart.height + (chart.margin.top + chart.margin.bottom)); + + return chart; + }; + + /** + * Prepare the chart html elements + */ + !function prepareChart() { + // Build main div and chart div + chart.objs.mainDiv = d3.select(chart.settings.selector) + .style("width", chart.divWidth + "px") + .style("display", "inline-block"); + // Add all the divs to make it centered and responsive + chart.objs.mainDiv.append("div") + .attr("class", "inner-wrapper") + .style("padding-bottom", (chart.divHeight / chart.divWidth) * 100 + "%") + .append("div").attr("class", "outer-box") + .append("div").attr("class", "inner-box"); + // Capture the inner div for the chart (where the chart actually is) + chart.selector = chart.settings.selector + " .inner-box"; + chart.objs.chartDiv = d3.select(chart.selector); + d3.select(window).on('resize.' + chart.selector, chart.update); + + // Create the svg + chart.objs.g = chart.objs.chartDiv.append("svg") + .attr("class", "chart-area") + .attr("width", chart.width + (chart.margin.left + chart.margin.right)) + .attr("height", chart.height + (chart.margin.top + chart.margin.bottom)) + .append("g") + .attr("transform", "translate(" + chart.margin.left + "," + chart.margin.top + ")"); + + // Create axes + chart.objs.axes = chart.objs.g.append("g").attr("class", "axis"); + chart.objs.axes.append("g") + .attr("class", "x axis") + .attr("transform", "translate(0," + chart.height + ")") + .call(chart.objs.xAxis); + chart.objs.axes.append("g") + .attr("class", "y axis") + .call(chart.objs.yAxis) + .append("text") + //.attr("class", "label") + .attr("transform", "rotate(-90)") + //.attr("y", -42) + .attr("y", 6) + .attr("dy", ".71em") + //.attr("x", -chart.height / 2) + .style("text-anchor", "end") + .style("font-size", "16px") + .append("a") + .attr("xlink:href", function(d) { + label = chart.yAxisLable.toLowerCase() + charidx = label.indexOf(' ') + if (charidx > 1) { label = label.substr(0, charidx); } + charidx = label.indexOf('_') + if (charidx > 1) { label = label.substr(0, charidx); } + + return "http://mriqc.readthedocs.io/en/latest/iqms/" + chart.settings.modality.toLowerCase() + ".html#iqms-" + label + }) + .text(chart.yAxisLable) + .on("mouseover", function () { + chart.objs.tooltip + .style("display", null) + .style("left", (d3.event.pageX) + "px") + .style("top", (d3.event.pageY - 28) + "px"); + }).on("mouseout", function () { + chart.objs.tooltip.style("display", "none"); + }).on("mousemove", axislabelHover(chart.yAxisLable)); + + // Create tooltip div + chart.objs.tooltip = chart.objs.mainDiv.append('div').attr('class', 'tooltip'); + for (var cName in chart.groupObjs) { + chart.groupObjs[cName].g = chart.objs.g.append("g").attr("class", "group"); + } + chart.update(); + }(); + + /** + * Render a violin plot on the current chart + * @param options + * @param [options.showViolinPlot=true] True or False, show the violin plot + * @param [options.resolution=100 default] + * @param [options.bandwidth=10 default] May need higher bandwidth for larger data sets + * @param [options.width=50] The max percent of the group rangeBand that the violin can be + * @param [options.interpolation=''] How to render the violin + * @param [options.clamp=0 default] + * 0 = keep data within chart min and max, clamp once data = 0. May extend beyond data set min and max + * 1 = clamp at min and max of data set. Possibly no tails + * -1 = extend chart axis to make room for data to interpolate to 0. May extend axis and data set min and max + * @param [options.colors=chart default] The color mapping for the violin plot + * @returns {*} The chart object + */ + chart.renderViolinPlot = function (options) { + chart.violinPlots = {}; + + var defaultOptions = { + show: true, + showViolinPlot: true, + resolution: 100, + bandwidth: 20, + width: 50, + interpolation: 'cardinal', + clamp: 1, + colors: chart.colorFunct, + _yDomainVP: null // If the Violin plot is set to close all violin plots, it may need to extend the domain, that extended domain is stored here + }; + chart.violinPlots.options = shallowCopy(defaultOptions); + for (var option in options) { + chart.violinPlots.options[option] = options[option] + } + var vOpts = chart.violinPlots.options; + + // Create violin plot objects + for (var cName in chart.groupObjs) { + chart.groupObjs[cName].violin = {}; + chart.groupObjs[cName].violin.objs = {}; + } + + /** + * Take a new set of options and redraw the violin + * @param updateOptions + */ + chart.violinPlots.change = function (updateOptions) { + if (updateOptions) { + for (var key in updateOptions) { + vOpts[key] = updateOptions[key] + } + } + + for (var cName in chart.groupObjs) { + chart.groupObjs[cName].violin.objs.g.remove() + } + + chart.violinPlots.prepareViolin(); + chart.violinPlots.update(); + }; + + chart.violinPlots.reset = function () { + chart.violinPlots.change(defaultOptions) + }; + chart.violinPlots.show = function (opts) { + if (opts !== undefined) { + opts.show = true; + if (opts.reset) { + chart.violinPlots.reset() + } + } else { + opts = {show: true}; + } + chart.violinPlots.change(opts); + + }; + + chart.violinPlots.hide = function (opts) { + if (opts !== undefined) { + opts.show = false; + if (opts.reset) { + chart.violinPlots.reset() + } + } else { + opts = {show: false}; + } + chart.violinPlots.change(opts); + + }; + + /** + * Update the violin obj values + */ + chart.violinPlots.update = function () { + var cName, cViolinPlot; + + for (cName in chart.groupObjs) { + cViolinPlot = chart.groupObjs[cName].violin; + + // Build the violins sideways, so use the yScale for the xScale and make a new yScale + var xVScale = chart.yScale.copy(); + + + // Create the Kernel Density Estimator Function + cViolinPlot.kde = kernelDensityEstimator(eKernel(vOpts.bandwidth), xVScale.ticks(vOpts.resolution)); + cViolinPlot.kdedata = cViolinPlot.kde(chart.groupObjs[cName].values); + + var interpolateMax = chart.groupObjs[cName].metrics.max, + interpolateMin = chart.groupObjs[cName].metrics.min; + + if (vOpts.clamp == 0 || vOpts.clamp == -1) { // + // When clamp is 0, calculate the min and max that is needed to bring the violin plot to a point + // interpolateMax = the Minimum value greater than the max where y = 0 + interpolateMax = d3.min(cViolinPlot.kdedata.filter(function (d) { + return (d.x > chart.groupObjs[cName].metrics.max && d.y == 0) + }), function (d) { + return d.x; + }); + // interpolateMin = the Maximum value less than the min where y = 0 + interpolateMin = d3.max(cViolinPlot.kdedata.filter(function (d) { + return (d.x < chart.groupObjs[cName].metrics.min && d.y == 0) + }), function (d) { + return d.x; + }); + // If clamp is -1 we need to extend the axes so that the violins come to a point + if (vOpts.clamp == -1) { + kdeTester = eKernelTest(eKernel(vOpts.bandwidth), chart.groupObjs[cName].values); + if (!interpolateMax) { + var interMaxY = kdeTester(chart.groupObjs[cName].metrics.max); + var interMaxX = chart.groupObjs[cName].metrics.max; + var count = 25; // Arbitrary limit to make sure we don't get an infinite loop + while (count > 0 && interMaxY != 0) { + interMaxY = kdeTester(interMaxX); + interMaxX += 1; + count -= 1; + } + interpolateMax = interMaxX; + } + if (!interpolateMin) { + var interMinY = kdeTester(chart.groupObjs[cName].metrics.min); + var interMinX = chart.groupObjs[cName].metrics.min; + var count = 25; // Arbitrary limit to make sure we don't get an infinite loop + while (count > 0 && interMinY != 0) { + interMinY = kdeTester(interMinX); + interMinX -= 1; + count -= 1; + } + interpolateMin = interMinX; + } + + } + // Check to see if the new values are outside the existing chart range + // If they are assign them to the master _yDomainVP + if (!vOpts._yDomainVP) vOpts._yDomainVP = chart.range.slice(0); + if (interpolateMin && interpolateMin < vOpts._yDomainVP[0]) { + vOpts._yDomainVP[0] = interpolateMin; + } + if (interpolateMax && interpolateMax > vOpts._yDomainVP[1]) { + vOpts._yDomainVP[1] = interpolateMax; + } + + + } + + + if (vOpts.showViolinPlot) { + chart.update(); + xVScale = chart.yScale.copy(); + + // Need to recalculate the KDE because the xVScale changed + cViolinPlot.kde = kernelDensityEstimator(eKernel(vOpts.bandwidth), xVScale.ticks(vOpts.resolution)); + cViolinPlot.kdedata = cViolinPlot.kde(chart.groupObjs[cName].values); + } + + cViolinPlot.kdedata = cViolinPlot.kdedata + .filter(function (d) { + return (!interpolateMin || d.x >= interpolateMin) + }) + .filter(function (d) { + return (!interpolateMax || d.x <= interpolateMax) + }); + } + for (cName in chart.groupObjs) { + cViolinPlot = chart.groupObjs[cName].violin; + + // Get the violin width + var objBounds = getObjWidth(vOpts.width, cName); + var width = (objBounds.right - objBounds.left) / 2; + + var yVScale = d3.scale.linear() + .range([width, 0]) + .domain([0, d3.max(cViolinPlot.kdedata, function (d) {return d.y;})]) + .clamp(true); + + var area = d3.svg.area() + .interpolate(vOpts.interpolation) + .x(function (d) {return xVScale(d.x);}) + .y0(width) + .y1(function (d) {return yVScale(d.y);}); + + var line = d3.svg.line() + .interpolate(vOpts.interpolation) + .x(function (d) {return xVScale(d.x);}) + .y(function (d) {return yVScale(d.y)}); + + if (cViolinPlot.objs.left.area) { + cViolinPlot.objs.left.area + .datum(cViolinPlot.kdedata) + .attr("d", area); + cViolinPlot.objs.left.line + .datum(cViolinPlot.kdedata) + .attr("d", line); + + cViolinPlot.objs.right.area + .datum(cViolinPlot.kdedata) + .attr("d", area); + cViolinPlot.objs.right.line + .datum(cViolinPlot.kdedata) + .attr("d", line); + } + + // Rotate the violins + cViolinPlot.objs.left.g.attr("transform", "rotate(90,0,0) translate(0,-" + objBounds.left + ") scale(1,-1)"); + cViolinPlot.objs.right.g.attr("transform", "rotate(90,0,0) translate(0,-" + objBounds.right + ")"); + } + }; + + /** + * Create the svg elements for the violin plot + */ + chart.violinPlots.prepareViolin = function () { + var cName, cViolinPlot; + + if (vOpts.colors) { + chart.violinPlots.color = getColorFunct(vOpts.colors); + } else { + chart.violinPlots.color = chart.colorFunct + } + + if (vOpts.show == false) {return} + + for (cName in chart.groupObjs) { + cViolinPlot = chart.groupObjs[cName].violin; + + cViolinPlot.objs.g = chart.groupObjs[cName].g.append("g").attr("class", "violin-plot"); + cViolinPlot.objs.left = {area: null, line: null, g: null}; + cViolinPlot.objs.right = {area: null, line: null, g: null}; + + cViolinPlot.objs.left.g = cViolinPlot.objs.g.append("g"); + cViolinPlot.objs.right.g = cViolinPlot.objs.g.append("g"); + + if (vOpts.showViolinPlot !== false) { + //Area + cViolinPlot.objs.left.area = cViolinPlot.objs.left.g.append("path") + .attr("class", "area") + .style("fill", chart.violinPlots.color(cName)); + cViolinPlot.objs.right.area = cViolinPlot.objs.right.g.append("path") + .attr("class", "area") + .style("fill", chart.violinPlots.color(cName)); + + //Lines + cViolinPlot.objs.left.line = cViolinPlot.objs.left.g.append("path") + .attr("class", "line") + .attr("fill", 'none') + .style("stroke", chart.violinPlots.color(cName)); + cViolinPlot.objs.right.line = cViolinPlot.objs.right.g.append("path") + .attr("class", "line") + .attr("fill", 'none') + .style("stroke", chart.violinPlots.color(cName)); + } + + } + + }; + + + function kernelDensityEstimator(kernel, x) { + return function (sample) { + return x.map(function (x) { + return {x:x, y:d3.mean(sample, function (v) {return kernel(x - v);})}; + }); + }; + } + + function eKernel(scale) { + return function (u) { + return Math.abs(u /= scale) <= 1 ? .75 * (1 - u * u) / scale : 0; + }; + } + + // Used to find the roots for adjusting violin axis + // Given an array, find the value for a single point, even if it is not in the domain + function eKernelTest(kernel, array) { + return function (testX) { + return d3.mean(array, function (v) {return kernel(testX - v);}) + } + } + + chart.violinPlots.prepareViolin(); + + d3.select(window).on('resize.' + chart.selector + '.violinPlot', chart.violinPlots.update); + chart.violinPlots.update(); + return chart; + }; + + /** + * Render a box plot on the current chart + * @param options + * @param [options.show=true] Toggle the whole plot on and off + * @param [options.showBox=true] Show the box part of the box plot + * @param [options.showWhiskers=true] Show the whiskers + * @param [options.showMedian=true] Show the median line + * @param [options.showMean=false] Show the mean line + * @param [options.medianCSize=3] The size of the circle on the median + * @param [options.showOutliers=true] Plot outliers + * @param [options.boxwidth=30] The max percent of the group rangeBand that the box can be + * @param [options.lineWidth=boxWidth] The max percent of the group rangeBand that the line can be + * @param [options.outlierScatter=false] Spread out the outliers so they don't all overlap (in development) + * @param [options.outlierCSize=2] Size of the outliers + * @param [options.colors=chart default] The color mapping for the box plot + * @returns {*} The chart object + */ + chart.renderBoxPlot = function (options) { + chart.boxPlots = {}; + + // Defaults + var defaultOptions = { + show: true, + showBox: true, + showWhiskers: true, + showMedian: true, + showMean: false, + medianCSize: 3.5, + showOutliers: true, + boxWidth: 30, + lineWidth: null, + scatterOutliers: false, + outlierCSize: 2.5, + colors: chart.colorFunct, + padding: 0 + }; + chart.boxPlots.options = shallowCopy(defaultOptions); + for (var option in options) { + chart.boxPlots.options[option] = options[option] + } + var bOpts = chart.boxPlots.options; + + //Create box plot objects + for (var cName in chart.groupObjs) { + chart.groupObjs[cName].boxPlot = {}; + chart.groupObjs[cName].boxPlot.objs = {}; + } + + + /** + * Calculates all the outlier points for each group + */ + !function calcAllOutliers() { + + /** + * Create lists of the outliers for each content group + * @param cGroup The object to modify + * @return null Modifies the object in place + */ + function calcOutliers(cGroup) { + var cExtremes = []; + var cOutliers = []; + var cOut, idx; + for (idx = 0; idx <= cGroup.values.length; idx++) { + cOut = {value: cGroup.values[idx]}; + + if (cOut.value < cGroup.metrics.lowerInnerFence) { + if (cOut.value < cGroup.metrics.lowerOuterFence) { + cExtremes.push(cOut); + } else { + cOutliers.push(cOut); + } + } else if (cOut.value > cGroup.metrics.upperInnerFence) { + if (cOut.value > cGroup.metrics.upperOuterFence) { + cExtremes.push(cOut); + } else { + cOutliers.push(cOut); + } + } + } + cGroup.boxPlot.objs.outliers = cOutliers; + cGroup.boxPlot.objs.extremes = cExtremes; + } + + for (var cName in chart.groupObjs) { + calcOutliers(chart.groupObjs[cName]); + } + }(); + + /** + * Take updated options and redraw the box plot + * @param updateOptions + */ + chart.boxPlots.change = function (updateOptions) { + if (updateOptions) { + for (var key in updateOptions) { + bOpts[key] = updateOptions[key] + } + } + + for (var cName in chart.groupObjs) { + chart.groupObjs[cName].boxPlot.objs.g.remove() + } + chart.boxPlots.prepareBoxPlot(); + chart.boxPlots.update() + }; + + chart.boxPlots.reset = function () { + chart.boxPlots.change(defaultOptions) + }; + chart.boxPlots.show = function (opts) { + if (opts !== undefined) { + opts.show = true; + if (opts.reset) { + chart.boxPlots.reset() + } + } else { + opts = {show: true}; + } + chart.boxPlots.change(opts) + + }; + chart.boxPlots.hide = function (opts) { + if (opts !== undefined) { + opts.show = false; + if (opts.reset) { + chart.boxPlots.reset() + } + } else { + opts = {show: false}; + } + chart.boxPlots.change(opts) + }; + + /** + * Update the box plot obj values + */ + chart.boxPlots.update = function () { + var cName, cBoxPlot; + + for (cName in chart.groupObjs) { + cBoxPlot = chart.groupObjs[cName].boxPlot; + + // Get the box width + var objBounds = getObjWidth(bOpts.boxWidth, cName); + objBounds.middle += chart.boxPlots.options.padding + objBounds.right += chart.boxPlots.options.padding + objBounds.left += chart.boxPlots.options.padding + var width = (objBounds.right - objBounds.left); + + var sMetrics = {}; //temp var for scaled (plottable) metric values + for (var attr in chart.groupObjs[cName].metrics) { + sMetrics[attr] = null; + sMetrics[attr] = chart.yScale(chart.groupObjs[cName].metrics[attr]); + } + + // Box + if (cBoxPlot.objs.box) { + cBoxPlot.objs.box + .attr("x", objBounds.left) + .attr('width', width) + .attr("y", sMetrics.quartile3) + .attr("rx", 1) + .attr("ry", 1) + .attr("height", -sMetrics.quartile3 + sMetrics.quartile1) + } + + // Lines + var lineBounds = null; + if (bOpts.lineWidth) { + lineBounds = getObjWidth(bOpts.lineWidth, cName) + } else { + lineBounds = objBounds + } + + // Apply padding + lineBounds.middle += chart.boxPlots.options.padding + lineBounds.right += chart.boxPlots.options.padding + lineBounds.left += chart.boxPlots.options.padding + + // --Whiskers + if (cBoxPlot.objs.upperWhisker) { + cBoxPlot.objs.upperWhisker.fence + .attr("x1", lineBounds.left) + .attr("x2", lineBounds.right) + .attr('y1', sMetrics.upperInnerFence) + .attr("y2", sMetrics.upperInnerFence); + cBoxPlot.objs.upperWhisker.line + .attr("x1", lineBounds.middle) + .attr("x2", lineBounds.middle) + .attr('y1', sMetrics.quartile3) + .attr("y2", sMetrics.upperInnerFence); + + cBoxPlot.objs.lowerWhisker.fence + .attr("x1", lineBounds.left) + .attr("x2", lineBounds.right) + .attr('y1', sMetrics.lowerInnerFence) + .attr("y2", sMetrics.lowerInnerFence); + cBoxPlot.objs.lowerWhisker.line + .attr("x1", lineBounds.middle) + .attr("x2", lineBounds.middle) + .attr('y1', sMetrics.quartile1) + .attr("y2", sMetrics.lowerInnerFence); + } + + // --Median + if (cBoxPlot.objs.median) { + cBoxPlot.objs.median.line + .attr("x1", lineBounds.left) + .attr("x2", lineBounds.right) + .attr('y1', sMetrics.median) + .attr("y2", sMetrics.median); + cBoxPlot.objs.median.circle + .attr("cx", lineBounds.middle) + .attr("cy", sMetrics.median) + } + + // --Mean + if (cBoxPlot.objs.mean) { + cBoxPlot.objs.mean.line + .attr("x1", lineBounds.left) + .attr("x2", lineBounds.right) + .attr('y1', sMetrics.mean) + .attr("y2", sMetrics.mean); + cBoxPlot.objs.mean.circle + .attr("cx", lineBounds.middle) + .attr("cy", sMetrics.mean); + } + + // Outliers + + var pt; + if (cBoxPlot.objs.outliers) { + for (pt in cBoxPlot.objs.outliers) { + cBoxPlot.objs.outliers[pt].point + .attr("cx", objBounds.middle + addJitter(bOpts.scatterOutliers, width)) + .attr("cy", chart.yScale(cBoxPlot.objs.outliers[pt].value)); + } + } + if (cBoxPlot.objs.extremes) { + for (pt in cBoxPlot.objs.extremes) { + cBoxPlot.objs.extremes[pt].point + .attr("cx", objBounds.middle + addJitter(bOpts.scatterOutliers, width)) + .attr("cy", chart.yScale(cBoxPlot.objs.extremes[pt].value)); + } + } + } + }; + + /** + * Create the svg elements for the box plot + */ + chart.boxPlots.prepareBoxPlot = function () { + var cName, cBoxPlot; + + if (bOpts.colors) { + chart.boxPlots.colorFunct = getColorFunct(bOpts.colors); + } else { + chart.boxPlots.colorFunct = chart.colorFunct + } + + if (bOpts.show == false) { + return + } + + for (cName in chart.groupObjs) { + cBoxPlot = chart.groupObjs[cName].boxPlot; + + cBoxPlot.objs.g = chart.groupObjs[cName].g.append("g").attr("class", "box-plot"); + + //Plot Box (default show) + if (bOpts.showBox) { + cBoxPlot.objs.box = cBoxPlot.objs.g.append("rect") + .attr("class", "box") + .style("fill", chart.boxPlots.colorFunct(cName)) + .style("stroke", chart.boxPlots.colorFunct(cName)) + .on("mouseover", function () { + chart.objs.tooltip + .style("display", null) + .style("left", (d3.event.pageX) + "px") + .style("top", (d3.event.pageY - 28) + "px"); + }).on("mouseout", function () { + chart.objs.tooltip.style("display", "none"); + }).on("mousemove", tooltipHover(cName, chart.groupObjs[cName].metrics)); + //A stroke is added to the box with the group color, it is + // hidden by default and can be shown through css with stroke-width + } + + //Plot Median (default show) + if (bOpts.showMedian) { + cBoxPlot.objs.median = {line: null, circle: null}; + cBoxPlot.objs.median.line = cBoxPlot.objs.g.append("line") + .attr("class", "median"); + cBoxPlot.objs.median.circle = cBoxPlot.objs.g.append("circle") + .attr("class", "median") + .attr('r', bOpts.medianCSize) + .style("fill", chart.boxPlots.colorFunct(cName)); + } + + // Plot Mean (default no plot) + if (bOpts.showMean) { + cBoxPlot.objs.mean = {line: null, circle: null}; + cBoxPlot.objs.mean.line = cBoxPlot.objs.g.append("line") + .attr("class", "mean"); + cBoxPlot.objs.mean.circle = cBoxPlot.objs.g.append("circle") + .attr("class", "mean") + .attr('r', bOpts.medianCSize) + .style("fill", chart.boxPlots.colorFunct(cName)); + } + + // Plot Whiskers (default show) + if (bOpts.showWhiskers) { + cBoxPlot.objs.upperWhisker = {fence: null, line: null}; + cBoxPlot.objs.lowerWhisker = {fence: null, line: null}; + cBoxPlot.objs.upperWhisker.fence = cBoxPlot.objs.g.append("line") + .attr("class", "upper whisker") + .style("stroke", chart.boxPlots.colorFunct(cName)); + cBoxPlot.objs.upperWhisker.line = cBoxPlot.objs.g.append("line") + .attr("class", "upper whisker") + .style("stroke", chart.boxPlots.colorFunct(cName)); + + cBoxPlot.objs.lowerWhisker.fence = cBoxPlot.objs.g.append("line") + .attr("class", "lower whisker") + .style("stroke", chart.boxPlots.colorFunct(cName)); + cBoxPlot.objs.lowerWhisker.line = cBoxPlot.objs.g.append("line") + .attr("class", "lower whisker") + .style("stroke", chart.boxPlots.colorFunct(cName)); + } + + // Plot outliers (default show) + if (bOpts.showOutliers) { + if (!cBoxPlot.objs.outliers) calcAllOutliers(); + var pt; + if (cBoxPlot.objs.outliers.length) { + var outDiv = cBoxPlot.objs.g.append("g").attr("class", "boxplot outliers"); + for (pt in cBoxPlot.objs.outliers) { + cBoxPlot.objs.outliers[pt].point = outDiv.append("circle") + .attr("class", "outlier") + .attr('r', bOpts.outlierCSize) + .style("fill", chart.boxPlots.colorFunct(cName)); + } + } + + if (cBoxPlot.objs.extremes.length) { + var extDiv = cBoxPlot.objs.g.append("g").attr("class", "boxplot extremes"); + for (pt in cBoxPlot.objs.extremes) { + cBoxPlot.objs.extremes[pt].point = extDiv.append("circle") + .attr("class", "extreme") + .attr('r', bOpts.outlierCSize) + .style("stroke", chart.boxPlots.colorFunct(cName)); + } + } + } + + + } + }; + chart.boxPlots.prepareBoxPlot(); + + d3.select(window).on('resize.' + chart.selector + '.boxPlot', chart.boxPlots.update); + chart.boxPlots.update(); + return chart; + + }; + + /** + * Render a notched box on the current chart + * @param options + * @param [options.show=true] Toggle the whole plot on and off + * @param [options.showNotchBox=true] Show the notch box + * @param [options.showLines=false] Show lines at the confidence intervals + * @param [options.boxWidth=35] The width of the widest part of the box + * @param [options.medianWidth=20] The width of the narrowist part of the box + * @param [options.lineWidth=50] The width of the confidence interval lines + * @param [options.notchStyle=null] null=traditional style, 'box' cuts out the whole notch in right angles + * @param [options.colors=chart default] The color mapping for the notch boxes + * @returns {*} The chart object + */ + chart.renderNotchBoxes = function (options) { + chart.notchBoxes = {}; + + //Defaults + var defaultOptions = { + show: true, + showNotchBox: true, + showLines: false, + boxWidth: 35, + medianWidth: 20, + lineWidth: 50, + notchStyle: null, + colors: null + }; + chart.notchBoxes.options = shallowCopy(defaultOptions); + for (var option in options) { + chart.notchBoxes.options[option] = options[option] + } + var nOpts = chart.notchBoxes.options; + + //Create notch objects + for (var cName in chart.groupObjs) { + chart.groupObjs[cName].notchBox = {}; + chart.groupObjs[cName].notchBox.objs = {}; + } + + /** + * Makes the svg path string for a notched box + * @param cNotch Current notch box object + * @param notchBounds objBound object + * @returns {string} A string in the proper format for a svg polygon + */ + function makeNotchBox(cNotch, notchBounds) { + var scaledValues = []; + if (nOpts.notchStyle == 'box') { + scaledValues = [ + [notchBounds.boxLeft, chart.yScale(cNotch.metrics.quartile1)], + [notchBounds.boxLeft, chart.yScale(cNotch.metrics.lowerNotch)], + [notchBounds.medianLeft, chart.yScale(cNotch.metrics.lowerNotch)], + [notchBounds.medianLeft, chart.yScale(cNotch.metrics.median)], + [notchBounds.medianLeft, chart.yScale(cNotch.metrics.upperNotch)], + [notchBounds.boxLeft, chart.yScale(cNotch.metrics.upperNotch)], + [notchBounds.boxLeft, chart.yScale(cNotch.metrics.quartile3)], + [notchBounds.boxRight, chart.yScale(cNotch.metrics.quartile3)], + [notchBounds.boxRight, chart.yScale(cNotch.metrics.upperNotch)], + [notchBounds.medianRight, chart.yScale(cNotch.metrics.upperNotch)], + [notchBounds.medianRight, chart.yScale(cNotch.metrics.median)], + [notchBounds.medianRight, chart.yScale(cNotch.metrics.lowerNotch)], + [notchBounds.boxRight, chart.yScale(cNotch.metrics.lowerNotch)], + [notchBounds.boxRight, chart.yScale(cNotch.metrics.quartile1)] + ]; + } else { + scaledValues = [ + [notchBounds.boxLeft, chart.yScale(cNotch.metrics.quartile1)], + [notchBounds.boxLeft, chart.yScale(cNotch.metrics.lowerNotch)], + [notchBounds.medianLeft, chart.yScale(cNotch.metrics.median)], + [notchBounds.boxLeft, chart.yScale(cNotch.metrics.upperNotch)], + [notchBounds.boxLeft, chart.yScale(cNotch.metrics.quartile3)], + [notchBounds.boxRight, chart.yScale(cNotch.metrics.quartile3)], + [notchBounds.boxRight, chart.yScale(cNotch.metrics.upperNotch)], + [notchBounds.medianRight, chart.yScale(cNotch.metrics.median)], + [notchBounds.boxRight, chart.yScale(cNotch.metrics.lowerNotch)], + [notchBounds.boxRight, chart.yScale(cNotch.metrics.quartile1)] + ]; + } + return scaledValues.map(function (d) { + return [d[0], d[1]].join(","); + }).join(" "); + } + + /** + * Calculate the confidence intervals + */ + !function calcNotches() { + var cNotch, modifier; + for (var cName in chart.groupObjs) { + cNotch = chart.groupObjs[cName]; + modifier = (1.57 * (cNotch.metrics.iqr / Math.sqrt(cNotch.values.length))); + cNotch.metrics.upperNotch = cNotch.metrics.median + modifier; + cNotch.metrics.lowerNotch = cNotch.metrics.median - modifier; + } + }(); + + /** + * Take a new set of options and redraw the notch boxes + * @param updateOptions + */ + chart.notchBoxes.change = function (updateOptions) { + if (updateOptions) { + for (var key in updateOptions) { + nOpts[key] = updateOptions[key] + } + } + + for (var cName in chart.groupObjs) { + chart.groupObjs[cName].notchBox.objs.g.remove() + } + chart.notchBoxes.prepareNotchBoxes(); + chart.notchBoxes.update(); + }; + + chart.notchBoxes.reset = function () { + chart.notchBoxes.change(defaultOptions) + }; + chart.notchBoxes.show = function (opts) { + if (opts !== undefined) { + opts.show = true; + if (opts.reset) { + chart.notchBoxes.reset() + } + } else { + opts = {show: true}; + } + chart.notchBoxes.change(opts) + }; + chart.notchBoxes.hide = function (opts) { + if (opts !== undefined) { + opts.show = false; + if (opts.reset) { + chart.notchBoxes.reset() + } + } else { + opts = {show: false}; + } + chart.notchBoxes.change(opts) + }; + + /** + * Update the notch box obj values + */ + chart.notchBoxes.update = function () { + var cName, cGroup; + + for (cName in chart.groupObjs) { + cGroup = chart.groupObjs[cName]; + + // Get the box size + var boxBounds = getObjWidth(nOpts.boxWidth, cName); + var medianBounds = getObjWidth(nOpts.medianWidth, cName); + + var notchBounds = { + boxLeft: boxBounds.left, + boxRight: boxBounds.right, + middle: boxBounds.middle, + medianLeft: medianBounds.left, + medianRight: medianBounds.right + }; + + // Notch Box + if (cGroup.notchBox.objs.notch) { + cGroup.notchBox.objs.notch + .attr("points", makeNotchBox(cGroup, notchBounds)); + } + if (cGroup.notchBox.objs.upperLine) { + var lineBounds = null; + if (nOpts.lineWidth) { + lineBounds = getObjWidth(nOpts.lineWidth, cName) + } else { + lineBounds = objBounds + } + + var confidenceLines = { + upper: chart.yScale(cGroup.metrics.upperNotch), + lower: chart.yScale(cGroup.metrics.lowerNotch) + }; + cGroup.notchBox.objs.upperLine + .attr("x1", lineBounds.left) + .attr("x2", lineBounds.right) + .attr('y1', confidenceLines.upper) + .attr("y2", confidenceLines.upper); + cGroup.notchBox.objs.lowerLine + .attr("x1", lineBounds.left) + .attr("x2", lineBounds.right) + .attr('y1', confidenceLines.lower) + .attr("y2", confidenceLines.lower); + } + } + }; + + /** + * Create the svg elements for the notch boxes + */ + chart.notchBoxes.prepareNotchBoxes = function () { + var cName, cNotch; + + if (nOpts && nOpts.colors) { + chart.notchBoxes.colorFunct = getColorFunct(nOpts.colors); + } else { + chart.notchBoxes.colorFunct = chart.colorFunct + } + + if (nOpts.show == false) { + return + } + + for (cName in chart.groupObjs) { + cNotch = chart.groupObjs[cName].notchBox; + + cNotch.objs.g = chart.groupObjs[cName].g.append("g").attr("class", "notch-plot"); + + // Plot Box (default show) + if (nOpts.showNotchBox) { + cNotch.objs.notch = cNotch.objs.g.append("polygon") + .attr("class", "notch") + .style("fill", chart.notchBoxes.colorFunct(cName)) + .style("stroke", chart.notchBoxes.colorFunct(cName)); + //A stroke is added to the notch with the group color, it is + // hidden by default and can be shown through css with stroke-width + } + + //Plot Confidence Lines (default hide) + if (nOpts.showLines) { + cNotch.objs.upperLine = cNotch.objs.g.append("line") + .attr("class", "upper confidence line") + .style("stroke", chart.notchBoxes.colorFunct(cName)); + + cNotch.objs.lowerLine = cNotch.objs.g.append("line") + .attr("class", "lower confidence line") + .style("stroke", chart.notchBoxes.colorFunct(cName)); + } + } + }; + chart.notchBoxes.prepareNotchBoxes(); + + d3.select(window).on('resize.' + chart.selector + '.notchBox', chart.notchBoxes.update); + chart.notchBoxes.update(); + return chart; + }; + + /** + * Render a raw data in various forms + * @param options + * @param [options.show=true] Toggle the whole plot on and off + * @param [options.showPlot=false] True or false, show points + * @param [options.plotType='none'] Options: no scatter = (false or 'none'); scatter points= (true or [amount=% of width (default=10)]); beeswarm points = ('beeswarm') + * @param [options.pointSize=6] Diameter of the circle in pizels (not the radius) + * @param [options.showLines=['median']] Can equal any of the metrics lines + * @param [options.showbeanLines=false] Options: no lines = false + * @param [options.beanWidth=20] % width + * @param [options.colors=chart default] + * @returns {*} The chart object + * + */ + chart.renderDataPlots = function (options) { + chart.dataPlots = {}; + + + //Defaults + var defaultOptions = { + show: true, + showPlot: false, + plotType: 'none', + pointSize: 6, + showLines: false,//['median'], + showBeanLines: false, + beanWidth: 20, + colors: null, + padding: 0 + }; + chart.dataPlots.options = shallowCopy(defaultOptions); + for (var option in options) { + chart.dataPlots.options[option] = options[option] + } + var dOpts = chart.dataPlots.options; + + //Create notch objects + for (var cName in chart.groupObjs) { + chart.groupObjs[cName].dataPlots = {}; + chart.groupObjs[cName].dataPlots.objs = {}; + } + // The lines don't fit into a group bucket so they live under the dataPlot object + chart.dataPlots.objs = {}; + + /** + * Take updated options and redraw the data plots + * @param updateOptions + */ + chart.dataPlots.change = function (updateOptions) { + if (updateOptions) { + for (var key in updateOptions) { + dOpts[key] = updateOptions[key] + } + } + + chart.dataPlots.objs.g.remove(); + for (var cName in chart.groupObjs) { + chart.groupObjs[cName].dataPlots.objs.g.remove() + } + chart.dataPlots.preparePlots(); + chart.dataPlots.update() + }; + + chart.dataPlots.reset = function () { + chart.dataPlots.change(defaultOptions) + }; + chart.dataPlots.show = function (opts) { + if (opts !== undefined) { + opts.show = true; + if (opts.reset) { + chart.dataPlots.reset() + } + } else { + opts = {show: true}; + } + chart.dataPlots.change(opts) + }; + chart.dataPlots.hide = function (opts) { + if (opts !== undefined) { + opts.show = false; + if (opts.reset) { + chart.dataPlots.reset() + } + } else { + opts = {show: false}; + } + chart.dataPlots.change(opts) + }; + + /** + * Update the data plot obj values + */ + chart.dataPlots.update = function () { + var cName, cGroup, cPlot; + + // Metrics lines + if (chart.dataPlots.objs.g) { + var halfBand = chart.xScale.rangeBand() / 2; // find the middle of each band + for (var cMetric in chart.dataPlots.objs.lines) { + chart.dataPlots.objs.lines[cMetric].line + .x(function (d) { + return chart.xScale(d.x) + halfBand + }); + chart.dataPlots.objs.lines[cMetric].g + .datum(chart.dataPlots.objs.lines[cMetric].values) + .attr('d', chart.dataPlots.objs.lines[cMetric].line); + } + } + + + for (cName in chart.groupObjs) { + cGroup = chart.groupObjs[cName]; + cPlot = cGroup.dataPlots; + + if (cPlot.objs.points) { + if (dOpts.plotType == 'beeswarm') { + var swarmBounds = getObjWidth(100, cName); + var yPtScale = chart.yScale.copy() + .range([Math.floor(chart.yScale.range()[0] / dOpts.pointSize), 0]) + .interpolate(d3.interpolateRound) + .domain(chart.yScale.domain()); + var maxWidth = Math.floor(chart.xScale.rangeBand() / dOpts.pointSize); + var ptsObj = {}; + var cYBucket = null; + // Bucket points + for (var pt = 0; pt < cGroup.values.length; pt++) { + cYBucket = yPtScale(cGroup.values[pt]); + if (ptsObj.hasOwnProperty(cYBucket) !== true) { + ptsObj[cYBucket] = []; + } + ptsObj[cYBucket].push(cPlot.objs.points.pts[pt] + .attr("cx", swarmBounds.middle) + .attr("cy", yPtScale(cGroup.values[pt]) * dOpts.pointSize)); + } + // Plot buckets + var rightMax = Math.min(swarmBounds.right - dOpts.pointSize); + for (var row in ptsObj) { + var leftMin = swarmBounds.left + (Math.max((maxWidth - ptsObj[row].length) / 2, 0) * dOpts.pointSize); + var col = 0; + for (pt in ptsObj[row]) { + ptsObj[row][pt].attr("cx", Math.min(leftMin + col * dOpts.pointSize, rightMax) + dOpts.pointSize / 2); + col++ + } + } + } else { // For scatter points and points with no scatter + var plotBounds = null, + scatterWidth = 0, + width = 0; + if (dOpts.plotType == 'scatter' || typeof dOpts.plotType == 'number') { + //Default scatter percentage is 20% of box width + scatterWidth = typeof dOpts.plotType == 'number' ? dOpts.plotType : 20; + } + + plotBounds = getObjWidth(scatterWidth, cName); + plotBounds.middle += chart.dataPlots.options.padding + plotBounds.right += chart.dataPlots.options.padding + plotBounds.left += chart.dataPlots.options.padding + width = plotBounds.right - plotBounds.left; + + for (var pt = 0; pt < cGroup.values.length; pt++) { + cPlot.objs.points.pts[pt] + .attr("cx", plotBounds.middle + addJitter(true, width)) + .attr("cy", chart.yScale(cGroup.values[pt])); + } + } + } + + + if (cPlot.objs.bean) { + var beanBounds = getObjWidth(dOpts.beanWidth, cName); + for (var pt = 0; pt < cGroup.values.length; pt++) { + cPlot.objs.bean.lines[pt] + .attr("x1", beanBounds.left) + .attr("x2", beanBounds.right) + .attr('y1', chart.yScale(cGroup.values[pt])) + .attr("y2", chart.yScale(cGroup.values[pt])); + } + } + } + }; + + /** + * Create the svg elements for the data plots + */ + chart.dataPlots.preparePlots = function () { + var cName, cPlot; + + if (dOpts && dOpts.colors) { + chart.dataPlots.colorFunct = getColorFunct(dOpts.colors); + } else { + chart.dataPlots.colorFunct = chart.colorFunct + } + + if (dOpts.show == false) { + return + } + + // Metrics lines + chart.dataPlots.objs.g = chart.objs.g.append("g").attr("class", "metrics-lines"); + if (dOpts.showLines && dOpts.showLines.length > 0) { + chart.dataPlots.objs.lines = {}; + var cMetric; + for (var line in dOpts.showLines) { + cMetric = dOpts.showLines[line]; + chart.dataPlots.objs.lines[cMetric] = {}; + chart.dataPlots.objs.lines[cMetric].values = []; + for (var cGroup in chart.groupObjs) { + chart.dataPlots.objs.lines[cMetric].values.push({ + x: cGroup, + y: chart.groupObjs[cGroup].metrics[cMetric] + }) + } + chart.dataPlots.objs.lines[cMetric].line = d3.svg.line() + .interpolate("cardinal") + .y(function (d) { + return chart.yScale(d.y) + }); + chart.dataPlots.objs.lines[cMetric].g = chart.dataPlots.objs.g.append("path") + .attr("class", "line " + cMetric) + .attr("data-metric", cMetric) + .style("fill", 'none') + .style("stroke", chart.colorFunct(cMetric)); + } + + } + + for (cName in chart.groupObjs) { + + cPlot = chart.groupObjs[cName].dataPlots; + cPlot.objs.g = chart.groupObjs[cName].g.append("g").attr("class", "data-plot"); + + // Points Plot + if (dOpts.showPlot) { + cPlot.objs.points = {g: null, pts: []}; + cPlot.objs.points.g = cPlot.objs.g.append("g").attr("class", "points-plot"); + for (var pt = 0; pt < chart.groupObjs[cName].values.length; pt++) { + cPlot.objs.points.pts.push(cPlot.objs.points.g + .append("a") + .attr("xlink:href", function(d) { + return chart.groupObjs[cName].labels[pt] + ".html" + }) + .append("circle") + .attr("class", "point") + .attr('r', dOpts.pointSize / 2)// Options is diameter, r takes radius so divide by 2 + .style("fill", chart.dataPlots.colorFunct(cName)) + .on("mouseover", function() { + chart.objs.tooltip + .style("display", null) + .style("left", (d3.event.pageX) + "px") + .style("top", (d3.event.pageY - 28) + "px"); + }) + .on("mouseout", function () { + chart.objs.tooltip.style("display", "none"); + }) + .on("mousemove", pointHover(chart.groupObjs[cName].labels[pt], chart.groupObjs[cName].values[pt])) + ); + } + } + + + // Bean lines + if (dOpts.showBeanLines) { + cPlot.objs.bean = {g: null, lines: []}; + cPlot.objs.bean.g = cPlot.objs.g.append("g").attr("class", "bean-plot"); + for (var pt = 0; pt < chart.groupObjs[cName].values.length; pt++) { + cPlot.objs.bean.lines.push(cPlot.objs.bean.g.append("line") + .attr("class", "bean line") + .style("stroke-width", '1') + .style("stroke", chart.dataPlots.colorFunct(cName))); + } + } + } + + }; + chart.dataPlots.preparePlots(); + + d3.select(window).on('resize.' + chart.selector + 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0000000..85f74fc --- /dev/null +++ b/pydra/tasks/mriqc/data/reports/group.html @@ -0,0 +1,108 @@ + + + + + + + + + + MRIQC: group {{ modality }} +report + + +

MRIQC: group {{ modality }} +report

+

Summary

+
    +
  • Date and time: {{ timestamp }}.
    • +
  • MRIQC version: {{ version }}.
    • +{% if failed %} +
  • Some individual reports failed: +{% for f in failed %} +{{ f }}{% if loop.last %}.{% else %}, {% endif %} +{% endfor %} +
    • +{% endif %} +
+{% for data_csv in csv_groups %} +
+ +{% endfor %} + + + + + diff --git a/pydra/tasks/mriqc/data/reports/resources/DO_NOT_REMOVE_OR_MODIFY b/pydra/tasks/mriqc/data/reports/resources/DO_NOT_REMOVE_OR_MODIFY new file mode 100644 index 0000000..b6ae8b7 --- /dev/null +++ b/pydra/tasks/mriqc/data/reports/resources/DO_NOT_REMOVE_OR_MODIFY @@ -0,0 +1,2 @@ +Do not remove or modify the files in this folder. They are being served from +github to render group level reports in a v0.9.0. \ No newline at end of file diff --git a/pydra/tasks/mriqc/data/reports/resources/boxplots.css b/pydra/tasks/mriqc/data/reports/resources/boxplots.css new file mode 100644 index 0000000..0938a5e --- /dev/null +++ b/pydra/tasks/mriqc/data/reports/resources/boxplots.css @@ -0,0 +1,157 @@ +/* Hide data table */ +.csvdata { + display: none; +} + +body { + font-family: helvetica; +} + +.text-warning { + font-weight: bold; + color: red; +} +/*Primary Chart*/ + +/*Nested divs for responsiveness*/ +.chart-wrapper { + max-width: 800px; /*Overwritten by the JS*/ + min-width: 160px; + margin-bottom: 20px; + font-family: helvetica; +} +.chart-wrapper .inner-wrapper { + position: relative; + padding-bottom: 50%; /*Overwritten by the JS*/ + width: 100%; +} +.chart-wrapper .outer-box { + position: absolute; + top: 0; + bottom: 0; + left: 0; + right: 0; +} +.chart-wrapper .inner-box { + width: 100%; + height: 100%; +} + +.chart-wrapper text { + font-family: helvetica; + font-size: 13px; +} + +.chart-wrapper .axis path, +.chart-wrapper .axis line { + fill: none; + stroke: #888; + stroke-width: 2px; + shape-rendering: crispEdges; +} + +.chart-wrapper .y.axis .tick line { + stroke: lightgrey; + opacity: 0.6; + stroke-dasharray: 2,1; + stroke-width: 1; + shape-rendering: crispEdges; + +} + +.chart-wrapper .x.axis .domain { + display: none; +} + +.chart-wrapper div.tooltip { + position: absolute; + text-align: left; + padding: 3px; + font-size: 12px; + background: #eee; + border: 0px; + border-radius: 1px; + pointer-events: none; + opacity: .7; + z-index: 10; +} + +/*Box Plot*/ +.chart-wrapper .box-plot .box { + fill-opacity: .5; + stroke-width: 2; +} +.chart-wrapper .box-plot line { + stroke-width: 2px; +} +.chart-wrapper .box-plot circle { + fill: white; + stroke: black; +} + +.chart-wrapper .box-plot .median { + stroke: black; +} + +.chart-wrapper .box-plot circle.median { + /*the script makes the circles the same color as the box, you can override this in the js*/ + fill: white !important; +} + +.chart-wrapper .box-plot .mean { + stroke: white; + stroke-dasharray: 2,1; + stroke-width: 1px; +} + +@media (max-width:500px){ + .chart-wrapper .box-plot circle {display: none;} +} + +/*Violin Plot*/ + +.chart-wrapper .violin-plot .area { + shape-rendering: geometricPrecision; + opacity: 0.4; +} + +.chart-wrapper .violin-plot .line { + fill: none; + stroke-width: 2px; + shape-rendering: geometricPrecision; +} + +/*Notch Plot*/ +.chart-wrapper .notch-plot .notch { + fill-opacity: 0.4; + stroke-width: 2; +} + +/* Point Plots*/ +.chart-wrapper .points-plot .point { + /*stroke: black; + stroke-width: 1px;*/ + fill-opacity: 0.4; +} + +.chart-wrapper .metrics-lines { + stroke-width: 4px; +} + +/* Non-Chart Styles for demo*/ +.chart-options { + min-width: 200px; + font-size: 13px; + font-family: helvetica; +} +.chart-options button { + margin: 3px; + padding: 3px; + font-size: 12px; +} +.chart-options p { + display: inline; +} +@media (max-width:500px){ + .chart-options p {display: block;} +} \ No newline at end of file diff --git a/pydra/tasks/mriqc/data/reports/resources/boxplots.js b/pydra/tasks/mriqc/data/reports/resources/boxplots.js new file mode 100644 index 0000000..a846efa --- /dev/null +++ b/pydra/tasks/mriqc/data/reports/resources/boxplots.js @@ -0,0 +1,1630 @@ +/** + * @fileOverview A D3 based distribution chart system. Supports: Box plots, Violin plots, Notched box plots, trend lines, beeswarm plot + * @version 3.0 + */ + + +/** + * Creates a box plot, violin plot, and or notched box plot + * @param settings Configuration options for the base plot + * @param settings.data The data for the plot + * @param settings.xName The name of the column that should be used for the x groups + * @param settings.yName The name of the column used for the y values + * @param {string} settings.selector The selector string for the main chart div + * @param [settings.axisLabels={}] Defaults to the xName and yName + * @param [settings.yTicks = 1] 1 = default ticks. 2 = double, 0.5 = half + * @param [settings.scale='linear'] 'linear' or 'log' - y scale of the chart + * @param [settings.chartSize={width:800, height:400}] The height and width of the chart itself (doesn't include the container) + * @param [settings.margin={top: 15, right: 60, bottom: 40, left: 50}] The margins around the chart (inside the main div) + * @param [settings.constrainExtremes=false] Should the y scale include outliers? + * @returns {object} chart A chart object + */ +function makeDistroChart(settings) { + + var chart = {}; + + // Defaults + chart.settings = { + data: null, + xName: null, + yName: null, + axisLabels: {xAxis: null, yAxis: null}, + labelName: "label", + unitsName: "units", + selector: null, + axisLables: null, + yTicks: 1, + scale: 'linear', + chartSize: {width: 800, height: 400}, + margin: {top: 15, right: 10, bottom: 50, left: 50}, + constrainExtremes: false, + color: d3.scale.category10(), + qctype: null + }; + + for (var setting in settings) { + chart.settings[setting] = settings[setting] + } + + function formatAsFloat(d) { + if (d % 1 !== 0) { + return d3.format(".2f")(d); + } else { + return d3.format(".0f")(d); + } + } + + function logFormatNumber(d) { + var x = Math.log(d) / Math.log(10) + 1e-6; + return Math.abs(x - Math.floor(x)) < 0.6 ? formatAsFloat(d) : ""; + } + + chart.yFormatter = formatAsFloat; + + chart.data = chart.settings.data; + + iqmName = chart.data[0][chart.settings.xName] + if (iqmName.lastIndexOf('_') > 0) { + iqmName = iqmName.substr(0, iqmName.lastIndexOf('_')) + } + chart.settings.axisLabels.yAxis = iqmName.toUpperCase() + units = chart.data[0][chart.settings.unitsName] + if (units) { + chart.settings.axisLabels.yAxis += ' (' + units + ')' + } + + + chart.groupObjs = {}; //The data organized by grouping and sorted as well as any metadata for the groups + chart.objs = {mainDiv: null, chartDiv: null, g: null, xAxis: null, yAxis: null}; + chart.colorFunct = null; + + /** + * Takes an array, function, or object mapping and created a color function from it + * @param {function|[]|object} colorOptions + * @returns {function} Function to be used to determine chart colors + */ + function getColorFunct(colorOptions) { + if (typeof colorOptions == 'function') { + return colorOptions + } else if (Array.isArray(colorOptions)) { + // If an array is provided, map it to the domain + var colorMap = {}, cColor = 0; + for (var cName in chart.groupObjs) { + colorMap[cName] = colorOptions[cColor]; + cColor = (cColor + 1) % colorOptions.length; + } + return function (group) { + return colorMap[group]; + } + } else if (typeof colorOptions == 'object') { + // if an object is provided, assume it maps to the colors + return function (group) { + return colorOptions[group]; + } + } else { + return d3.scale.category10(); + } + } + + /** + * Takes a percentage as returns the values that correspond to that percentage of the group range width + * @param objWidth Percentage of range band + * @param gName The bin name to use to get the x shift + * @returns {{left: null, right: null, middle: null}} + */ + function getObjWidth(objWidth, gName) { + var objSize = {left: null, right: null, middle: null}; + var width = chart.xScale.rangeBand() * (objWidth / 100); + var padding = (chart.xScale.rangeBand() - width) / 2; + var gShift = chart.xScale(gName); + objSize.middle = chart.xScale.rangeBand() / 2 + gShift; + objSize.left = padding + gShift; + objSize.right = objSize.left + width; + return objSize; + } + + /** + * Adds jitter to the scatter point plot + * @param doJitter true or false, add jitter to the point + * @param width percent of the range band to cover with the jitter + * @returns {number} + */ + function addJitter(doJitter, width) { + if (doJitter !== true || width == 0) { + return 0 + } + return Math.floor(Math.random() * width) - width / 2; + } + + function shallowCopy(oldObj) { + var newObj = {}; + for (var i in oldObj) { + if (oldObj.hasOwnProperty(i)) { + newObj[i] = oldObj[i]; + } + } + return newObj; + } + + /** + * Closure that creates the tooltip hover function + * @param groupName Name of the x group + * @param metrics Object to use to get values for the group + * @returns {Function} A function that provides the values for the tooltip + */ + function tooltipHover(groupName, metrics) { + var tooltipString = "Group: " + groupName; + tooltipString += "Max: " + formatAsFloat(metrics.max, 0.1); + tooltipString += "Q3: " + formatAsFloat(metrics.quartile3); + tooltipString += "Median: " + formatAsFloat(metrics.median); + tooltipString += "Q1: " + formatAsFloat(metrics.quartile1); + tooltipString += "Min: " + formatAsFloat(metrics.min); + return function () { + chart.objs.tooltip.transition().duration(200).style("opacity", 0.9); + chart.objs.tooltip.html(tooltipString) + }; + } + + function axislabelHover(groupName) { + var tooltipString = "Go to definition of " + groupName; + return function () { + chart.objs.tooltip.transition().duration(200).style("opacity", 1.0); + chart.objs.tooltip.html(tooltipString) + }; + } + + /** + * Closure that creates the tooltip hover function + * @param groupName Name of the x group + * @param metrics Object to use to get values for the group + * @returns {Function} A function that provides the values for the tooltip + */ + function pointHover(label, value) { + var tooltipString = "Subject: " + label + "Measure: " + value + return function () { + chart.objs.tooltip.transition().duration(200).style("opacity", 1.0); + chart.objs.tooltip.html(tooltipString) + }; + } + + /** + * Parse the data and calculates base values for the plots + */ + !function prepareData() { + function calcMetrics(values) { + // Do not reorder in-place + values = values.slice(0).sort(d3.ascending) + + var metrics = { //These are the original non�scaled values + max: null, + upperOuterFence: null, + upperInnerFence: null, + quartile3: null, + median: null, + mean: null, + iqr: null, + quartile1: null, + lowerInnerFence: null, + lowerOuterFence: null, + min: null + }; + + metrics.min = d3.min(values); + metrics.quartile1 = d3.quantile(values, 0.25); + metrics.median = d3.median(values); + metrics.mean = d3.mean(values); + metrics.quartile3 = d3.quantile(values, 0.75); + metrics.max = d3.max(values); + metrics.iqr = metrics.quartile3 - metrics.quartile1; + + //The inner fences are the closest value to the IQR without going past it (assumes sorted lists) + var LIF = metrics.quartile1 - (1.5 * metrics.iqr); + var UIF = metrics.quartile3 + (1.5 * metrics.iqr); + for (var i = 0; i <= values.length; i++) { + if (values[i] < LIF) { + continue; + } + if (!metrics.lowerInnerFence && values[i] >= LIF) { + metrics.lowerInnerFence = values[i]; + continue; + } + if (values[i] > UIF) { + metrics.upperInnerFence = values[i - 1]; + break; + } + } + + + metrics.lowerOuterFence = metrics.quartile1 - (3 * metrics.iqr); + metrics.upperOuterFence = metrics.quartile3 + (3 * metrics.iqr); + if (!metrics.lowerInnerFence) { + metrics.lowerInnerFence = metrics.min; + } + if (!metrics.upperInnerFence) { + metrics.upperInnerFence = metrics.max; + } + return metrics + } + + var current_x = null; + var current_y = null; + var current_row; + + // Group the values + for (current_row = 0; current_row < chart.data.length; current_row++) { + current_x = chart.data[current_row][chart.settings.xName]; + current_y = chart.data[current_row][chart.settings.yName]; + current_label = chart.data[current_row][chart.settings.labelName]; + + if (chart.groupObjs.hasOwnProperty(current_x)) { + chart.groupObjs[current_x].values.push(current_y); + chart.groupObjs[current_x].labels.push(current_label); + } else { + chart.groupObjs[current_x] = {}; + chart.groupObjs[current_x].values = [current_y]; + chart.groupObjs[current_x].labels = [current_label]; + } + } + + for (var cName in chart.groupObjs) { + // chart.groupObjs[cName].values.sort(d3.ascending); + chart.groupObjs[cName].metrics = {}; + chart.groupObjs[cName].metrics = calcMetrics(chart.groupObjs[cName].values); + + } + }(); + + /** + * Prepare the chart settings and chart div and svg + */ + !function prepareSettings() { + //Set base settings + chart.margin = chart.settings.margin; + chart.divWidth = chart.settings.chartSize.width; + chart.divHeight = chart.settings.chartSize.height; + chart.width = chart.divWidth - chart.margin.left - chart.margin.right; + chart.height = chart.divHeight - chart.margin.top - chart.margin.bottom; + + if (chart.settings.axisLabels) { + chart.xAxisLable = chart.settings.axisLabels.xAxis; + chart.yAxisLable = chart.settings.axisLabels.yAxis; + } + + if (chart.settings.scale === 'log') { + chart.yScale = d3.scale.log(); + chart.yFormatter = logFormatNumber; + } else { + chart.yScale = d3.scale.linear(); + } + + if (chart.settings.constrainExtremes === true) { + var fences = []; + for (var cName in chart.groupObjs) { + fences.push(chart.groupObjs[cName].metrics.lowerInnerFence); + fences.push(chart.groupObjs[cName].metrics.upperInnerFence); + } + chart.range = d3.extent(fences); + + } else { + chart.range = d3.extent(chart.data, function (d) {return d[chart.settings.yName];}); + } + + chart.colorFunct = getColorFunct(chart.settings.colors); + + // Build Scale functions + chart.yScale.range([chart.height, 0]).domain(chart.range).nice().clamp(true); + chart.xScale = d3.scale.ordinal().domain(Object.keys(chart.groupObjs)).rangeBands([0, chart.width]); + + //Build Axes Functions + chart.objs.yAxis = d3.svg.axis() + .scale(chart.yScale) + .orient("left") + .tickFormat(chart.yFormatter) + .outerTickSize(0) + .innerTickSize(-chart.width + (chart.margin.right + chart.margin.left)); + chart.objs.yAxis.ticks(chart.objs.yAxis.ticks()*chart.settings.yTicks); + chart.objs.xAxis = d3.svg.axis().scale(chart.xScale).orient("bottom").tickSize(5); + }(); + + /** + * Updates the chart based on the current settings and window size + * @returns {*} + */ + chart.update = function () { + // Update chart size based on view port size + chart.width = parseInt(chart.objs.chartDiv.style("width"), 10) - (chart.margin.left + chart.margin.right); + chart.height = parseInt(chart.objs.chartDiv.style("height"), 10) - (chart.margin.top + chart.margin.bottom); + + // Update scale functions + chart.xScale.rangeBands([0, chart.width]); + chart.yScale.range([chart.height, 0]); + + // Update the yDomain if the Violin plot clamp is set to -1 meaning it will extend the violins to make nice points + if (chart.violinPlots && chart.violinPlots.options.show == true && chart.violinPlots.options._yDomainVP != null) { + chart.yScale.domain(chart.violinPlots.options._yDomainVP).nice().clamp(true); + } else { + chart.yScale.domain(chart.range).nice().clamp(true); + } + + //Update axes + chart.objs.g.select('.x.axis').attr("transform", "translate(0," + chart.height + ")").call(chart.objs.xAxis) + .selectAll("text") + .attr("y", 5) + .attr("x", -5) + .attr("transform", "rotate(-45)") + .style("text-anchor", "end"); + chart.objs.g.select('.x.axis .label').attr("x", chart.width / 2); + chart.objs.g.select('.y.axis').call(chart.objs.yAxis.innerTickSize(-chart.width)); + chart.objs.g.select('.y.axis .label').attr("x", -chart.height / 2); + chart.objs.chartDiv.select('svg').attr("width", chart.width + (chart.margin.left + chart.margin.right)).attr("height", chart.height + (chart.margin.top + chart.margin.bottom)); + + return chart; + }; + + /** + * Prepare the chart html elements + */ + !function prepareChart() { + // Build main div and chart div + chart.objs.mainDiv = d3.select(chart.settings.selector) + .style("width", chart.divWidth + "px") + .style("display", "inline-block"); + // Add all the divs to make it centered and responsive + chart.objs.mainDiv.append("div") + .attr("class", "inner-wrapper") + .style("padding-bottom", (chart.divHeight / chart.divWidth) * 100 + "%") + .append("div").attr("class", "outer-box") + .append("div").attr("class", "inner-box"); + // Capture the inner div for the chart (where the chart actually is) + chart.selector = chart.settings.selector + " .inner-box"; + chart.objs.chartDiv = d3.select(chart.selector); + d3.select(window).on('resize.' + chart.selector, chart.update); + + // Create the svg + chart.objs.g = chart.objs.chartDiv.append("svg") + .attr("class", "chart-area") + .attr("width", chart.width + (chart.margin.left + chart.margin.right)) + .attr("height", chart.height + (chart.margin.top + chart.margin.bottom)) + .append("g") + .attr("transform", "translate(" + chart.margin.left + "," + chart.margin.top + ")"); + + // Create axes + chart.objs.axes = chart.objs.g.append("g").attr("class", "axis"); + chart.objs.axes.append("g") + .attr("class", "x axis") + .attr("transform", "translate(0," + chart.height + ")") + .call(chart.objs.xAxis); + chart.objs.axes.append("g") + .attr("class", "y axis") + .call(chart.objs.yAxis) + .append("text") + //.attr("class", "label") + .attr("transform", "rotate(-90)") + //.attr("y", -42) + .attr("y", 6) + .attr("dy", ".71em") + //.attr("x", -chart.height / 2) + .style("text-anchor", "end") + .style("font-size", "16px") + .append("a") + .attr("xlink:href", function(d) { + return "http://mriqc.readthedocs.io/en/latest/measures.html" + }) + .text(chart.yAxisLable) + .on("mouseover", function () { + chart.objs.tooltip + .style("display", null) + .style("left", (d3.event.pageX) + "px") + .style("top", (d3.event.pageY - 28) + "px"); + }).on("mouseout", function () { + chart.objs.tooltip.style("display", "none"); + }).on("mousemove", axislabelHover(chart.yAxisLable)); + + // Create tooltip div + chart.objs.tooltip = chart.objs.mainDiv.append('div').attr('class', 'tooltip'); + for (var cName in chart.groupObjs) { + chart.groupObjs[cName].g = chart.objs.g.append("g").attr("class", "group"); + } + chart.update(); + }(); + + /** + * Render a violin plot on the current chart + * @param options + * @param [options.showViolinPlot=true] True or False, show the violin plot + * @param [options.resolution=100 default] + * @param [options.bandwidth=10 default] May need higher bandwidth for larger data sets + * @param [options.width=50] The max percent of the group rangeBand that the violin can be + * @param [options.interpolation=''] How to render the violin + * @param [options.clamp=0 default] + * 0 = keep data within chart min and max, clamp once data = 0. May extend beyond data set min and max + * 1 = clamp at min and max of data set. Possibly no tails + * -1 = extend chart axis to make room for data to interpolate to 0. May extend axis and data set min and max + * @param [options.colors=chart default] The color mapping for the violin plot + * @returns {*} The chart object + */ + chart.renderViolinPlot = function (options) { + chart.violinPlots = {}; + + var defaultOptions = { + show: true, + showViolinPlot: true, + resolution: 100, + bandwidth: 20, + width: 50, + interpolation: 'cardinal', + clamp: 1, + colors: chart.colorFunct, + _yDomainVP: null // If the Violin plot is set to close all violin plots, it may need to extend the domain, that extended domain is stored here + }; + chart.violinPlots.options = shallowCopy(defaultOptions); + for (var option in options) { + chart.violinPlots.options[option] = options[option] + } + var vOpts = chart.violinPlots.options; + + // Create violin plot objects + for (var cName in chart.groupObjs) { + chart.groupObjs[cName].violin = {}; + chart.groupObjs[cName].violin.objs = {}; + } + + /** + * Take a new set of options and redraw the violin + * @param updateOptions + */ + chart.violinPlots.change = function (updateOptions) { + if (updateOptions) { + for (var key in updateOptions) { + vOpts[key] = updateOptions[key] + } + } + + for (var cName in chart.groupObjs) { + chart.groupObjs[cName].violin.objs.g.remove() + } + + chart.violinPlots.prepareViolin(); + chart.violinPlots.update(); + }; + + chart.violinPlots.reset = function () { + chart.violinPlots.change(defaultOptions) + }; + chart.violinPlots.show = function (opts) { + if (opts !== undefined) { + opts.show = true; + if (opts.reset) { + chart.violinPlots.reset() + } + } else { + opts = {show: true}; + } + chart.violinPlots.change(opts); + + }; + + chart.violinPlots.hide = function (opts) { + if (opts !== undefined) { + opts.show = false; + if (opts.reset) { + chart.violinPlots.reset() + } + } else { + opts = {show: false}; + } + chart.violinPlots.change(opts); + + }; + + /** + * Update the violin obj values + */ + chart.violinPlots.update = function () { + var cName, cViolinPlot; + + for (cName in chart.groupObjs) { + cViolinPlot = chart.groupObjs[cName].violin; + + // Build the violins sideways, so use the yScale for the xScale and make a new yScale + var xVScale = chart.yScale.copy(); + + + // Create the Kernel Density Estimator Function + cViolinPlot.kde = kernelDensityEstimator(eKernel(vOpts.bandwidth), xVScale.ticks(vOpts.resolution)); + cViolinPlot.kdedata = cViolinPlot.kde(chart.groupObjs[cName].values); + + var interpolateMax = chart.groupObjs[cName].metrics.max, + interpolateMin = chart.groupObjs[cName].metrics.min; + + if (vOpts.clamp == 0 || vOpts.clamp == -1) { // + // When clamp is 0, calculate the min and max that is needed to bring the violin plot to a point + // interpolateMax = the Minimum value greater than the max where y = 0 + interpolateMax = d3.min(cViolinPlot.kdedata.filter(function (d) { + return (d.x > chart.groupObjs[cName].metrics.max && d.y == 0) + }), function (d) { + return d.x; + }); + // interpolateMin = the Maximum value less than the min where y = 0 + interpolateMin = d3.max(cViolinPlot.kdedata.filter(function (d) { + return (d.x < chart.groupObjs[cName].metrics.min && d.y == 0) + }), function (d) { + return d.x; + }); + // If clamp is -1 we need to extend the axes so that the violins come to a point + if (vOpts.clamp == -1) { + kdeTester = eKernelTest(eKernel(vOpts.bandwidth), chart.groupObjs[cName].values); + if (!interpolateMax) { + var interMaxY = kdeTester(chart.groupObjs[cName].metrics.max); + var interMaxX = chart.groupObjs[cName].metrics.max; + var count = 25; // Arbitrary limit to make sure we don't get an infinite loop + while (count > 0 && interMaxY != 0) { + interMaxY = kdeTester(interMaxX); + interMaxX += 1; + count -= 1; + } + interpolateMax = interMaxX; + } + if (!interpolateMin) { + var interMinY = kdeTester(chart.groupObjs[cName].metrics.min); + var interMinX = chart.groupObjs[cName].metrics.min; + var count = 25; // Arbitrary limit to make sure we don't get an infinite loop + while (count > 0 && interMinY != 0) { + interMinY = kdeTester(interMinX); + interMinX -= 1; + count -= 1; + } + interpolateMin = interMinX; + } + + } + // Check to see if the new values are outside the existing chart range + // If they are assign them to the master _yDomainVP + if (!vOpts._yDomainVP) vOpts._yDomainVP = chart.range.slice(0); + if (interpolateMin && interpolateMin < vOpts._yDomainVP[0]) { + vOpts._yDomainVP[0] = interpolateMin; + } + if (interpolateMax && interpolateMax > vOpts._yDomainVP[1]) { + vOpts._yDomainVP[1] = interpolateMax; + } + + + } + + + if (vOpts.showViolinPlot) { + chart.update(); + xVScale = chart.yScale.copy(); + + // Need to recalculate the KDE because the xVScale changed + cViolinPlot.kde = kernelDensityEstimator(eKernel(vOpts.bandwidth), xVScale.ticks(vOpts.resolution)); + cViolinPlot.kdedata = cViolinPlot.kde(chart.groupObjs[cName].values); + } + + cViolinPlot.kdedata = cViolinPlot.kdedata + .filter(function (d) { + return (!interpolateMin || d.x >= interpolateMin) + }) + .filter(function (d) { + return (!interpolateMax || d.x <= interpolateMax) + }); + } + for (cName in chart.groupObjs) { + cViolinPlot = chart.groupObjs[cName].violin; + + // Get the violin width + var objBounds = getObjWidth(vOpts.width, cName); + var width = (objBounds.right - objBounds.left) / 2; + + var yVScale = d3.scale.linear() + .range([width, 0]) + .domain([0, d3.max(cViolinPlot.kdedata, function (d) {return d.y;})]) + .clamp(true); + + var area = d3.svg.area() + .interpolate(vOpts.interpolation) + .x(function (d) {return xVScale(d.x);}) + .y0(width) + .y1(function (d) {return yVScale(d.y);}); + + var line = d3.svg.line() + .interpolate(vOpts.interpolation) + .x(function (d) {return xVScale(d.x);}) + .y(function (d) {return yVScale(d.y)}); + + if (cViolinPlot.objs.left.area) { + cViolinPlot.objs.left.area + .datum(cViolinPlot.kdedata) + .attr("d", area); + cViolinPlot.objs.left.line + .datum(cViolinPlot.kdedata) + .attr("d", line); + + cViolinPlot.objs.right.area + .datum(cViolinPlot.kdedata) + .attr("d", area); + cViolinPlot.objs.right.line + .datum(cViolinPlot.kdedata) + .attr("d", line); + } + + // Rotate the violins + cViolinPlot.objs.left.g.attr("transform", "rotate(90,0,0) translate(0,-" + objBounds.left + ") scale(1,-1)"); + cViolinPlot.objs.right.g.attr("transform", "rotate(90,0,0) translate(0,-" + objBounds.right + ")"); + } + }; + + /** + * Create the svg elements for the violin plot + */ + chart.violinPlots.prepareViolin = function () { + var cName, cViolinPlot; + + if (vOpts.colors) { + chart.violinPlots.color = getColorFunct(vOpts.colors); + } else { + chart.violinPlots.color = chart.colorFunct + } + + if (vOpts.show == false) {return} + + for (cName in chart.groupObjs) { + cViolinPlot = chart.groupObjs[cName].violin; + + cViolinPlot.objs.g = chart.groupObjs[cName].g.append("g").attr("class", "violin-plot"); + cViolinPlot.objs.left = {area: null, line: null, g: null}; + cViolinPlot.objs.right = {area: null, line: null, g: null}; + + cViolinPlot.objs.left.g = cViolinPlot.objs.g.append("g"); + cViolinPlot.objs.right.g = cViolinPlot.objs.g.append("g"); + + if (vOpts.showViolinPlot !== false) { + //Area + cViolinPlot.objs.left.area = cViolinPlot.objs.left.g.append("path") + .attr("class", "area") + .style("fill", chart.violinPlots.color(cName)); + cViolinPlot.objs.right.area = cViolinPlot.objs.right.g.append("path") + .attr("class", "area") + .style("fill", chart.violinPlots.color(cName)); + + //Lines + cViolinPlot.objs.left.line = cViolinPlot.objs.left.g.append("path") + .attr("class", "line") + .attr("fill", 'none') + .style("stroke", chart.violinPlots.color(cName)); + cViolinPlot.objs.right.line = cViolinPlot.objs.right.g.append("path") + .attr("class", "line") + .attr("fill", 'none') + .style("stroke", chart.violinPlots.color(cName)); + } + + } + + }; + + + function kernelDensityEstimator(kernel, x) { + return function (sample) { + return x.map(function (x) { + return {x:x, y:d3.mean(sample, function (v) {return kernel(x - v);})}; + }); + }; + } + + function eKernel(scale) { + return function (u) { + return Math.abs(u /= scale) <= 1 ? .75 * (1 - u * u) / scale : 0; + }; + } + + // Used to find the roots for adjusting violin axis + // Given an array, find the value for a single point, even if it is not in the domain + function eKernelTest(kernel, array) { + return function (testX) { + return d3.mean(array, function (v) {return kernel(testX - v);}) + } + } + + chart.violinPlots.prepareViolin(); + + d3.select(window).on('resize.' + chart.selector + '.violinPlot', chart.violinPlots.update); + chart.violinPlots.update(); + return chart; + }; + + /** + * Render a box plot on the current chart + * @param options + * @param [options.show=true] Toggle the whole plot on and off + * @param [options.showBox=true] Show the box part of the box plot + * @param [options.showWhiskers=true] Show the whiskers + * @param [options.showMedian=true] Show the median line + * @param [options.showMean=false] Show the mean line + * @param [options.medianCSize=3] The size of the circle on the median + * @param [options.showOutliers=true] Plot outliers + * @param [options.boxwidth=30] The max percent of the group rangeBand that the box can be + * @param [options.lineWidth=boxWidth] The max percent of the group rangeBand that the line can be + * @param [options.outlierScatter=false] Spread out the outliers so they don't all overlap (in development) + * @param [options.outlierCSize=2] Size of the outliers + * @param [options.colors=chart default] The color mapping for the box plot + * @returns {*} The chart object + */ + chart.renderBoxPlot = function (options) { + chart.boxPlots = {}; + + // Defaults + var defaultOptions = { + show: true, + showBox: true, + showWhiskers: true, + showMedian: true, + showMean: false, + medianCSize: 3.5, + showOutliers: true, + boxWidth: 30, + lineWidth: null, + scatterOutliers: false, + outlierCSize: 2.5, + colors: chart.colorFunct, + padding: 0 + }; + chart.boxPlots.options = shallowCopy(defaultOptions); + for (var option in options) { + chart.boxPlots.options[option] = options[option] + } + var bOpts = chart.boxPlots.options; + + //Create box plot objects + for (var cName in chart.groupObjs) { + chart.groupObjs[cName].boxPlot = {}; + chart.groupObjs[cName].boxPlot.objs = {}; + } + + + /** + * Calculates all the outlier points for each group + */ + !function calcAllOutliers() { + + /** + * Create lists of the outliers for each content group + * @param cGroup The object to modify + * @return null Modifies the object in place + */ + function calcOutliers(cGroup) { + var cExtremes = []; + var cOutliers = []; + var cOut, idx; + for (idx = 0; idx <= cGroup.values.length; idx++) { + cOut = {value: cGroup.values[idx]}; + + if (cOut.value < cGroup.metrics.lowerInnerFence) { + if (cOut.value < cGroup.metrics.lowerOuterFence) { + cExtremes.push(cOut); + } else { + cOutliers.push(cOut); + } + } else if (cOut.value > cGroup.metrics.upperInnerFence) { + if (cOut.value > cGroup.metrics.upperOuterFence) { + cExtremes.push(cOut); + } else { + cOutliers.push(cOut); + } + } + } + cGroup.boxPlot.objs.outliers = cOutliers; + cGroup.boxPlot.objs.extremes = cExtremes; + } + + for (var cName in chart.groupObjs) { + calcOutliers(chart.groupObjs[cName]); + } + }(); + + /** + * Take updated options and redraw the box plot + * @param updateOptions + */ + chart.boxPlots.change = function (updateOptions) { + if (updateOptions) { + for (var key in updateOptions) { + bOpts[key] = updateOptions[key] + } + } + + for (var cName in chart.groupObjs) { + chart.groupObjs[cName].boxPlot.objs.g.remove() + } + chart.boxPlots.prepareBoxPlot(); + chart.boxPlots.update() + }; + + chart.boxPlots.reset = function () { + chart.boxPlots.change(defaultOptions) + }; + chart.boxPlots.show = function (opts) { + if (opts !== undefined) { + opts.show = true; + if (opts.reset) { + chart.boxPlots.reset() + } + } else { + opts = {show: true}; + } + chart.boxPlots.change(opts) + + }; + chart.boxPlots.hide = function (opts) { + if (opts !== undefined) { + opts.show = false; + if (opts.reset) { + chart.boxPlots.reset() + } + } else { + opts = {show: false}; + } + chart.boxPlots.change(opts) + }; + + /** + * Update the box plot obj values + */ + chart.boxPlots.update = function () { + var cName, cBoxPlot; + + for (cName in chart.groupObjs) { + cBoxPlot = chart.groupObjs[cName].boxPlot; + + // Get the box width + var objBounds = getObjWidth(bOpts.boxWidth, cName); + objBounds.middle += chart.boxPlots.options.padding + objBounds.right += chart.boxPlots.options.padding + objBounds.left += chart.boxPlots.options.padding + var width = (objBounds.right - objBounds.left); + + var sMetrics = {}; //temp var for scaled (plottable) metric values + for (var attr in chart.groupObjs[cName].metrics) { + sMetrics[attr] = null; + sMetrics[attr] = chart.yScale(chart.groupObjs[cName].metrics[attr]); + } + + // Box + if (cBoxPlot.objs.box) { + cBoxPlot.objs.box + .attr("x", objBounds.left) + .attr('width', width) + .attr("y", sMetrics.quartile3) + .attr("rx", 1) + .attr("ry", 1) + .attr("height", -sMetrics.quartile3 + sMetrics.quartile1) + } + + // Lines + var lineBounds = null; + if (bOpts.lineWidth) { + lineBounds = getObjWidth(bOpts.lineWidth, cName) + } else { + lineBounds = objBounds + } + + // Apply padding + lineBounds.middle += chart.boxPlots.options.padding + lineBounds.right += chart.boxPlots.options.padding + lineBounds.left += chart.boxPlots.options.padding + + // --Whiskers + if (cBoxPlot.objs.upperWhisker) { + cBoxPlot.objs.upperWhisker.fence + .attr("x1", lineBounds.left) + .attr("x2", lineBounds.right) + .attr('y1', sMetrics.upperInnerFence) + .attr("y2", sMetrics.upperInnerFence); + cBoxPlot.objs.upperWhisker.line + .attr("x1", lineBounds.middle) + .attr("x2", lineBounds.middle) + .attr('y1', sMetrics.quartile3) + .attr("y2", sMetrics.upperInnerFence); + + cBoxPlot.objs.lowerWhisker.fence + .attr("x1", lineBounds.left) + .attr("x2", lineBounds.right) + .attr('y1', sMetrics.lowerInnerFence) + .attr("y2", sMetrics.lowerInnerFence); + cBoxPlot.objs.lowerWhisker.line + .attr("x1", lineBounds.middle) + .attr("x2", lineBounds.middle) + .attr('y1', sMetrics.quartile1) + .attr("y2", sMetrics.lowerInnerFence); + } + + // --Median + if (cBoxPlot.objs.median) { + cBoxPlot.objs.median.line + .attr("x1", lineBounds.left) + .attr("x2", lineBounds.right) + .attr('y1', sMetrics.median) + .attr("y2", sMetrics.median); + cBoxPlot.objs.median.circle + .attr("cx", lineBounds.middle) + .attr("cy", sMetrics.median) + } + + // --Mean + if (cBoxPlot.objs.mean) { + cBoxPlot.objs.mean.line + .attr("x1", lineBounds.left) + .attr("x2", lineBounds.right) + .attr('y1', sMetrics.mean) + .attr("y2", sMetrics.mean); + cBoxPlot.objs.mean.circle + .attr("cx", lineBounds.middle) + .attr("cy", sMetrics.mean); + } + + // Outliers + + var pt; + if (cBoxPlot.objs.outliers) { + for (pt in cBoxPlot.objs.outliers) { + cBoxPlot.objs.outliers[pt].point + .attr("cx", objBounds.middle + addJitter(bOpts.scatterOutliers, width)) + .attr("cy", chart.yScale(cBoxPlot.objs.outliers[pt].value)); + } + } + if (cBoxPlot.objs.extremes) { + for (pt in cBoxPlot.objs.extremes) { + cBoxPlot.objs.extremes[pt].point + .attr("cx", objBounds.middle + addJitter(bOpts.scatterOutliers, width)) + .attr("cy", chart.yScale(cBoxPlot.objs.extremes[pt].value)); + } + } + } + }; + + /** + * Create the svg elements for the box plot + */ + chart.boxPlots.prepareBoxPlot = function () { + var cName, cBoxPlot; + + if (bOpts.colors) { + chart.boxPlots.colorFunct = getColorFunct(bOpts.colors); + } else { + chart.boxPlots.colorFunct = chart.colorFunct + } + + if (bOpts.show == false) { + return + } + + for (cName in chart.groupObjs) { + cBoxPlot = chart.groupObjs[cName].boxPlot; + + cBoxPlot.objs.g = chart.groupObjs[cName].g.append("g").attr("class", "box-plot"); + + //Plot Box (default show) + if (bOpts.showBox) { + cBoxPlot.objs.box = cBoxPlot.objs.g.append("rect") + .attr("class", "box") + .style("fill", chart.boxPlots.colorFunct(cName)) + .style("stroke", chart.boxPlots.colorFunct(cName)) + .on("mouseover", function () { + chart.objs.tooltip + .style("display", null) + .style("left", (d3.event.pageX) + "px") + .style("top", (d3.event.pageY - 28) + "px"); + }).on("mouseout", function () { + chart.objs.tooltip.style("display", "none"); + }).on("mousemove", tooltipHover(cName, chart.groupObjs[cName].metrics)); + //A stroke is added to the box with the group color, it is + // hidden by default and can be shown through css with stroke-width + } + + //Plot Median (default show) + if (bOpts.showMedian) { + cBoxPlot.objs.median = {line: null, circle: null}; + cBoxPlot.objs.median.line = cBoxPlot.objs.g.append("line") + .attr("class", "median"); + cBoxPlot.objs.median.circle = cBoxPlot.objs.g.append("circle") + .attr("class", "median") + .attr('r', bOpts.medianCSize) + .style("fill", chart.boxPlots.colorFunct(cName)); + } + + // Plot Mean (default no plot) + if (bOpts.showMean) { + cBoxPlot.objs.mean = {line: null, circle: null}; + cBoxPlot.objs.mean.line = cBoxPlot.objs.g.append("line") + .attr("class", "mean"); + cBoxPlot.objs.mean.circle = cBoxPlot.objs.g.append("circle") + .attr("class", "mean") + .attr('r', bOpts.medianCSize) + .style("fill", chart.boxPlots.colorFunct(cName)); + } + + // Plot Whiskers (default show) + if (bOpts.showWhiskers) { + cBoxPlot.objs.upperWhisker = {fence: null, line: null}; + cBoxPlot.objs.lowerWhisker = {fence: null, line: null}; + cBoxPlot.objs.upperWhisker.fence = cBoxPlot.objs.g.append("line") + .attr("class", "upper whisker") + .style("stroke", chart.boxPlots.colorFunct(cName)); + cBoxPlot.objs.upperWhisker.line = cBoxPlot.objs.g.append("line") + .attr("class", "upper whisker") + .style("stroke", chart.boxPlots.colorFunct(cName)); + + cBoxPlot.objs.lowerWhisker.fence = cBoxPlot.objs.g.append("line") + .attr("class", "lower whisker") + .style("stroke", chart.boxPlots.colorFunct(cName)); + cBoxPlot.objs.lowerWhisker.line = cBoxPlot.objs.g.append("line") + .attr("class", "lower whisker") + .style("stroke", chart.boxPlots.colorFunct(cName)); + } + + // Plot outliers (default show) + if (bOpts.showOutliers) { + if (!cBoxPlot.objs.outliers) calcAllOutliers(); + var pt; + if (cBoxPlot.objs.outliers.length) { + var outDiv = cBoxPlot.objs.g.append("g").attr("class", "boxplot outliers"); + for (pt in cBoxPlot.objs.outliers) { + cBoxPlot.objs.outliers[pt].point = outDiv.append("circle") + .attr("class", "outlier") + .attr('r', bOpts.outlierCSize) + .style("fill", chart.boxPlots.colorFunct(cName)); + } + } + + if (cBoxPlot.objs.extremes.length) { + var extDiv = cBoxPlot.objs.g.append("g").attr("class", "boxplot extremes"); + for (pt in cBoxPlot.objs.extremes) { + cBoxPlot.objs.extremes[pt].point = extDiv.append("circle") + .attr("class", "extreme") + .attr('r', bOpts.outlierCSize) + .style("stroke", chart.boxPlots.colorFunct(cName)); + } + } + } + + + } + }; + chart.boxPlots.prepareBoxPlot(); + + d3.select(window).on('resize.' + chart.selector + '.boxPlot', chart.boxPlots.update); + chart.boxPlots.update(); + return chart; + + }; + + /** + * Render a notched box on the current chart + * @param options + * @param [options.show=true] Toggle the whole plot on and off + * @param [options.showNotchBox=true] Show the notch box + * @param [options.showLines=false] Show lines at the confidence intervals + * @param [options.boxWidth=35] The width of the widest part of the box + * @param [options.medianWidth=20] The width of the narrowist part of the box + * @param [options.lineWidth=50] The width of the confidence interval lines + * @param [options.notchStyle=null] null=traditional style, 'box' cuts out the whole notch in right angles + * @param [options.colors=chart default] The color mapping for the notch boxes + * @returns {*} The chart object + */ + chart.renderNotchBoxes = function (options) { + chart.notchBoxes = {}; + + //Defaults + var defaultOptions = { + show: true, + showNotchBox: true, + showLines: false, + boxWidth: 35, + medianWidth: 20, + lineWidth: 50, + notchStyle: null, + colors: null + }; + chart.notchBoxes.options = shallowCopy(defaultOptions); + for (var option in options) { + chart.notchBoxes.options[option] = options[option] + } + var nOpts = chart.notchBoxes.options; + + //Create notch objects + for (var cName in chart.groupObjs) { + chart.groupObjs[cName].notchBox = {}; + chart.groupObjs[cName].notchBox.objs = {}; + } + + /** + * Makes the svg path string for a notched box + * @param cNotch Current notch box object + * @param notchBounds objBound object + * @returns {string} A string in the proper format for a svg polygon + */ + function makeNotchBox(cNotch, notchBounds) { + var scaledValues = []; + if (nOpts.notchStyle == 'box') { + scaledValues = [ + [notchBounds.boxLeft, chart.yScale(cNotch.metrics.quartile1)], + [notchBounds.boxLeft, chart.yScale(cNotch.metrics.lowerNotch)], + [notchBounds.medianLeft, chart.yScale(cNotch.metrics.lowerNotch)], + [notchBounds.medianLeft, chart.yScale(cNotch.metrics.median)], + [notchBounds.medianLeft, chart.yScale(cNotch.metrics.upperNotch)], + [notchBounds.boxLeft, chart.yScale(cNotch.metrics.upperNotch)], + [notchBounds.boxLeft, chart.yScale(cNotch.metrics.quartile3)], + [notchBounds.boxRight, chart.yScale(cNotch.metrics.quartile3)], + [notchBounds.boxRight, chart.yScale(cNotch.metrics.upperNotch)], + [notchBounds.medianRight, chart.yScale(cNotch.metrics.upperNotch)], + [notchBounds.medianRight, chart.yScale(cNotch.metrics.median)], + [notchBounds.medianRight, chart.yScale(cNotch.metrics.lowerNotch)], + [notchBounds.boxRight, chart.yScale(cNotch.metrics.lowerNotch)], + [notchBounds.boxRight, chart.yScale(cNotch.metrics.quartile1)] + ]; + } else { + scaledValues = [ + [notchBounds.boxLeft, chart.yScale(cNotch.metrics.quartile1)], + [notchBounds.boxLeft, chart.yScale(cNotch.metrics.lowerNotch)], + [notchBounds.medianLeft, chart.yScale(cNotch.metrics.median)], + [notchBounds.boxLeft, chart.yScale(cNotch.metrics.upperNotch)], + [notchBounds.boxLeft, chart.yScale(cNotch.metrics.quartile3)], + [notchBounds.boxRight, chart.yScale(cNotch.metrics.quartile3)], + [notchBounds.boxRight, chart.yScale(cNotch.metrics.upperNotch)], + [notchBounds.medianRight, chart.yScale(cNotch.metrics.median)], + [notchBounds.boxRight, chart.yScale(cNotch.metrics.lowerNotch)], + [notchBounds.boxRight, chart.yScale(cNotch.metrics.quartile1)] + ]; + } + return scaledValues.map(function (d) { + return [d[0], d[1]].join(","); + }).join(" "); + } + + /** + * Calculate the confidence intervals + */ + !function calcNotches() { + var cNotch, modifier; + for (var cName in chart.groupObjs) { + cNotch = chart.groupObjs[cName]; + modifier = (1.57 * (cNotch.metrics.iqr / Math.sqrt(cNotch.values.length))); + cNotch.metrics.upperNotch = cNotch.metrics.median + modifier; + cNotch.metrics.lowerNotch = cNotch.metrics.median - modifier; + } + }(); + + /** + * Take a new set of options and redraw the notch boxes + * @param updateOptions + */ + chart.notchBoxes.change = function (updateOptions) { + if (updateOptions) { + for (var key in updateOptions) { + nOpts[key] = updateOptions[key] + } + } + + for (var cName in chart.groupObjs) { + chart.groupObjs[cName].notchBox.objs.g.remove() + } + chart.notchBoxes.prepareNotchBoxes(); + chart.notchBoxes.update(); + }; + + chart.notchBoxes.reset = function () { + chart.notchBoxes.change(defaultOptions) + }; + chart.notchBoxes.show = function (opts) { + if (opts !== undefined) { + opts.show = true; + if (opts.reset) { + chart.notchBoxes.reset() + } + } else { + opts = {show: true}; + } + chart.notchBoxes.change(opts) + }; + chart.notchBoxes.hide = function (opts) { + if (opts !== undefined) { + opts.show = false; + if (opts.reset) { + chart.notchBoxes.reset() + } + } else { + opts = {show: false}; + } + chart.notchBoxes.change(opts) + }; + + /** + * Update the notch box obj values + */ + chart.notchBoxes.update = function () { + var cName, cGroup; + + for (cName in chart.groupObjs) { + cGroup = chart.groupObjs[cName]; + + // Get the box size + var boxBounds = getObjWidth(nOpts.boxWidth, cName); + var medianBounds = getObjWidth(nOpts.medianWidth, cName); + + var notchBounds = { + boxLeft: boxBounds.left, + boxRight: boxBounds.right, + middle: boxBounds.middle, + medianLeft: medianBounds.left, + medianRight: medianBounds.right + }; + + // Notch Box + if (cGroup.notchBox.objs.notch) { + cGroup.notchBox.objs.notch + .attr("points", makeNotchBox(cGroup, notchBounds)); + } + if (cGroup.notchBox.objs.upperLine) { + var lineBounds = null; + if (nOpts.lineWidth) { + lineBounds = getObjWidth(nOpts.lineWidth, cName) + } else { + lineBounds = objBounds + } + + var confidenceLines = { + upper: chart.yScale(cGroup.metrics.upperNotch), + lower: chart.yScale(cGroup.metrics.lowerNotch) + }; + cGroup.notchBox.objs.upperLine + .attr("x1", lineBounds.left) + .attr("x2", lineBounds.right) + .attr('y1', confidenceLines.upper) + .attr("y2", confidenceLines.upper); + cGroup.notchBox.objs.lowerLine + .attr("x1", lineBounds.left) + .attr("x2", lineBounds.right) + .attr('y1', confidenceLines.lower) + .attr("y2", confidenceLines.lower); + } + } + }; + + /** + * Create the svg elements for the notch boxes + */ + chart.notchBoxes.prepareNotchBoxes = function () { + var cName, cNotch; + + if (nOpts && nOpts.colors) { + chart.notchBoxes.colorFunct = getColorFunct(nOpts.colors); + } else { + chart.notchBoxes.colorFunct = chart.colorFunct + } + + if (nOpts.show == false) { + return + } + + for (cName in chart.groupObjs) { + cNotch = chart.groupObjs[cName].notchBox; + + cNotch.objs.g = chart.groupObjs[cName].g.append("g").attr("class", "notch-plot"); + + // Plot Box (default show) + if (nOpts.showNotchBox) { + cNotch.objs.notch = cNotch.objs.g.append("polygon") + .attr("class", "notch") + .style("fill", chart.notchBoxes.colorFunct(cName)) + .style("stroke", chart.notchBoxes.colorFunct(cName)); + //A stroke is added to the notch with the group color, it is + // hidden by default and can be shown through css with stroke-width + } + + //Plot Confidence Lines (default hide) + if (nOpts.showLines) { + cNotch.objs.upperLine = cNotch.objs.g.append("line") + .attr("class", "upper confidence line") + .style("stroke", chart.notchBoxes.colorFunct(cName)); + + cNotch.objs.lowerLine = cNotch.objs.g.append("line") + .attr("class", "lower confidence line") + .style("stroke", chart.notchBoxes.colorFunct(cName)); + } + } + }; + chart.notchBoxes.prepareNotchBoxes(); + + d3.select(window).on('resize.' + chart.selector + '.notchBox', chart.notchBoxes.update); + chart.notchBoxes.update(); + return chart; + }; + + /** + * Render a raw data in various forms + * @param options + * @param [options.show=true] Toggle the whole plot on and off + * @param [options.showPlot=false] True or false, show points + * @param [options.plotType='none'] Options: no scatter = (false or 'none'); scatter points= (true or [amount=% of width (default=10)]); beeswarm points = ('beeswarm') + * @param [options.pointSize=6] Diameter of the circle in pizels (not the radius) + * @param [options.showLines=['median']] Can equal any of the metrics lines + * @param [options.showbeanLines=false] Options: no lines = false + * @param [options.beanWidth=20] % width + * @param [options.colors=chart default] + * @returns {*} The chart object + * + */ + chart.renderDataPlots = function (options) { + chart.dataPlots = {}; + + + //Defaults + var defaultOptions = { + show: true, + showPlot: false, + plotType: 'none', + pointSize: 6, + showLines: false,//['median'], + showBeanLines: false, + beanWidth: 20, + colors: null, + padding: 0 + }; + chart.dataPlots.options = shallowCopy(defaultOptions); + for (var option in options) { + chart.dataPlots.options[option] = options[option] + } + var dOpts = chart.dataPlots.options; + + //Create notch objects + for (var cName in chart.groupObjs) { + chart.groupObjs[cName].dataPlots = {}; + chart.groupObjs[cName].dataPlots.objs = {}; + } + // The lines don't fit into a group bucket so they live under the dataPlot object + chart.dataPlots.objs = {}; + + /** + * Take updated options and redraw the data plots + * @param updateOptions + */ + chart.dataPlots.change = function (updateOptions) { + if (updateOptions) { + for (var key in updateOptions) { + dOpts[key] = updateOptions[key] + } + } + + chart.dataPlots.objs.g.remove(); + for (var cName in chart.groupObjs) { + chart.groupObjs[cName].dataPlots.objs.g.remove() + } + chart.dataPlots.preparePlots(); + chart.dataPlots.update() + }; + + chart.dataPlots.reset = function () { + chart.dataPlots.change(defaultOptions) + }; + chart.dataPlots.show = function (opts) { + if (opts !== undefined) { + opts.show = true; + if (opts.reset) { + chart.dataPlots.reset() + } + } else { + opts = {show: true}; + } + chart.dataPlots.change(opts) + }; + chart.dataPlots.hide = function (opts) { + if (opts !== undefined) { + opts.show = false; + if (opts.reset) { + chart.dataPlots.reset() + } + } else { + opts = {show: false}; + } + chart.dataPlots.change(opts) + }; + + /** + * Update the data plot obj values + */ + chart.dataPlots.update = function () { + var cName, cGroup, cPlot; + + // Metrics lines + if (chart.dataPlots.objs.g) { + var halfBand = chart.xScale.rangeBand() / 2; // find the middle of each band + for (var cMetric in chart.dataPlots.objs.lines) { + chart.dataPlots.objs.lines[cMetric].line + .x(function (d) { + return chart.xScale(d.x) + halfBand + }); + chart.dataPlots.objs.lines[cMetric].g + .datum(chart.dataPlots.objs.lines[cMetric].values) + .attr('d', chart.dataPlots.objs.lines[cMetric].line); + } + } + + + for (cName in chart.groupObjs) { + cGroup = chart.groupObjs[cName]; + cPlot = cGroup.dataPlots; + + if (cPlot.objs.points) { + if (dOpts.plotType == 'beeswarm') { + var swarmBounds = getObjWidth(100, cName); + var yPtScale = chart.yScale.copy() + .range([Math.floor(chart.yScale.range()[0] / dOpts.pointSize), 0]) + .interpolate(d3.interpolateRound) + .domain(chart.yScale.domain()); + var maxWidth = Math.floor(chart.xScale.rangeBand() / dOpts.pointSize); + var ptsObj = {}; + var cYBucket = null; + // Bucket points + for (var pt = 0; pt < cGroup.values.length; pt++) { + cYBucket = yPtScale(cGroup.values[pt]); + if (ptsObj.hasOwnProperty(cYBucket) !== true) { + ptsObj[cYBucket] = []; + } + ptsObj[cYBucket].push(cPlot.objs.points.pts[pt] + .attr("cx", swarmBounds.middle) + .attr("cy", yPtScale(cGroup.values[pt]) * dOpts.pointSize)); + } + // Plot buckets + var rightMax = Math.min(swarmBounds.right - dOpts.pointSize); + for (var row in ptsObj) { + var leftMin = swarmBounds.left + (Math.max((maxWidth - ptsObj[row].length) / 2, 0) * dOpts.pointSize); + var col = 0; + for (pt in ptsObj[row]) { + ptsObj[row][pt].attr("cx", Math.min(leftMin + col * dOpts.pointSize, rightMax) + dOpts.pointSize / 2); + col++ + } + } + } else { // For scatter points and points with no scatter + var plotBounds = null, + scatterWidth = 0, + width = 0; + if (dOpts.plotType == 'scatter' || typeof dOpts.plotType == 'number') { + //Default scatter percentage is 20% of box width + scatterWidth = typeof dOpts.plotType == 'number' ? dOpts.plotType : 20; + } + + plotBounds = getObjWidth(scatterWidth, cName); + plotBounds.middle += chart.dataPlots.options.padding + plotBounds.right += chart.dataPlots.options.padding + plotBounds.left += chart.dataPlots.options.padding + width = plotBounds.right - plotBounds.left; + + for (var pt = 0; pt < cGroup.values.length; pt++) { + cPlot.objs.points.pts[pt] + .attr("cx", plotBounds.middle + addJitter(true, width)) + .attr("cy", chart.yScale(cGroup.values[pt])); + } + } + } + + + if (cPlot.objs.bean) { + var beanBounds = getObjWidth(dOpts.beanWidth, cName); + for (var pt = 0; pt < cGroup.values.length; pt++) { + cPlot.objs.bean.lines[pt] + .attr("x1", beanBounds.left) + .attr("x2", beanBounds.right) + .attr('y1', chart.yScale(cGroup.values[pt])) + .attr("y2", chart.yScale(cGroup.values[pt])); + } + } + } + }; + + /** + * Create the svg elements for the data plots + */ + chart.dataPlots.preparePlots = function () { + var cName, cPlot; + + if (dOpts && dOpts.colors) { + chart.dataPlots.colorFunct = getColorFunct(dOpts.colors); + } else { + chart.dataPlots.colorFunct = chart.colorFunct + } + + if (dOpts.show == false) { + return + } + + // Metrics lines + chart.dataPlots.objs.g = chart.objs.g.append("g").attr("class", "metrics-lines"); + if (dOpts.showLines && dOpts.showLines.length > 0) { + chart.dataPlots.objs.lines = {}; + var cMetric; + for (var line in dOpts.showLines) { + cMetric = dOpts.showLines[line]; + chart.dataPlots.objs.lines[cMetric] = {}; + chart.dataPlots.objs.lines[cMetric].values = []; + for (var cGroup in chart.groupObjs) { + chart.dataPlots.objs.lines[cMetric].values.push({ + x: cGroup, + y: chart.groupObjs[cGroup].metrics[cMetric] + }) + } + chart.dataPlots.objs.lines[cMetric].line = d3.svg.line() + .interpolate("cardinal") + .y(function (d) { + return chart.yScale(d.y) + }); + chart.dataPlots.objs.lines[cMetric].g = chart.dataPlots.objs.g.append("path") + .attr("class", "line " + cMetric) + .attr("data-metric", cMetric) + .style("fill", 'none') + .style("stroke", chart.colorFunct(cMetric)); + } + + } + + + for (cName in chart.groupObjs) { + + cPlot = chart.groupObjs[cName].dataPlots; + cPlot.objs.g = chart.groupObjs[cName].g.append("g").attr("class", "data-plot"); + + // Points Plot + if (dOpts.showPlot) { + cPlot.objs.points = {g: null, pts: []}; + cPlot.objs.points.g = cPlot.objs.g.append("g").attr("class", "points-plot"); + for (var pt = 0; pt < chart.groupObjs[cName].values.length; pt++) { + cPlot.objs.points.pts.push(cPlot.objs.points.g + .append("a") + .attr("xlink:href", function(d) { + if (chart.settings["qctype"].startsWith('anat')) { + return chart.groupObjs[cName].labels[pt] + "_T1w.html" + } else if (chart.settings["qctype"].startsWith('func')) { + return chart.groupObjs[cName].labels[pt] + "_bold.html" + } + + }) + .append("circle") + .attr("class", "point") + .attr('r', dOpts.pointSize / 2)// Options is diameter, r takes radius so divide by 2 + .style("fill", chart.dataPlots.colorFunct(cName)) + .on("mouseover", function() { + chart.objs.tooltip + .style("display", null) + .style("left", (d3.event.pageX) + "px") + .style("top", (d3.event.pageY - 28) + "px"); + }) + .on("mouseout", function () { + chart.objs.tooltip.style("display", "none"); + }) + .on("mousemove", pointHover(chart.groupObjs[cName].labels[pt], chart.groupObjs[cName].values[pt])) + ); + } + } + + + // Bean lines + if (dOpts.showBeanLines) { + cPlot.objs.bean = {g: null, lines: []}; + cPlot.objs.bean.g = cPlot.objs.g.append("g").attr("class", "bean-plot"); + for (var pt = 0; pt < chart.groupObjs[cName].values.length; pt++) { + cPlot.objs.bean.lines.push(cPlot.objs.bean.g.append("line") + .attr("class", "bean line") + .style("stroke-width", '1') + .style("stroke", chart.dataPlots.colorFunct(cName))); + } + } + } + + }; + chart.dataPlots.preparePlots(); + + d3.select(window).on('resize.' + chart.selector + '.dataPlot', chart.dataPlots.update); + chart.dataPlots.update(); + return chart; + }; + + return chart; +} diff --git a/pydra/tasks/mriqc/data/reports/resources/d3.min.js b/pydra/tasks/mriqc/data/reports/resources/d3.min.js new file mode 100644 index 0000000..1664873 --- /dev/null +++ b/pydra/tasks/mriqc/data/reports/resources/d3.min.js @@ -0,0 +1,5 @@ +!function(){function n(n){return n&&(n.ownerDocument||n.document||n).documentElement}function t(n){return n&&(n.ownerDocument&&n.ownerDocument.defaultView||n.document&&n||n.defaultView)}function e(n,t){return t>n?-1:n>t?1:n>=t?0:NaN}function r(n){return null===n?NaN:+n}function i(n){return!isNaN(n)}function u(n){return{left:function(t,e,r,i){for(arguments.length<3&&(r=0),arguments.length<4&&(i=t.length);i>r;){var 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714.0 19935.0 387.0 951.0 166.587 64.71407310082577 +sub-ds205s07_task-view_run-01_bold 0.00585054347826087 0.015204289673913043 0 22.382260123296703 0.9959491859340658 0.9369370696703297 0.5386 972.983 0.1520993052375479 15 16.304347826086957 2.2514825000000003 2.174985 2.38338 2.1960825 0.0282586 -0.006972935516387224 0.032188184559345245 92 53 53 27 4.313751528981998 2.200000047683716 4.0 4.0 4.0 41.5907 6.156 41.8577 23.0 56005.0 17.0 139.0 65.5813 1.8801 136.4316 706.7871 713.0 19838.0 394.0 946.0 165.2812 50.42334433761425 +sub-ds205s07_task-view_run-02_bold 0.0019870652173913047 0.014770980326086956 0 22.60577535087912 1.0123932324175822 0.9372968517582421 0.5404 965.007 0.15451170465747024 18 19.565217391304348 2.2389508333333334 2.1558375 2.3787425 2.1822725 0.0275085 -0.007005929946899414 0.03411094844341278 92 53 53 27 4.365633569269246 2.200000047683716 4.0 4.0 4.0 38.59 6.1883 42.5556 23.0 55958.0 17.0 143.0 65.669 1.7675 138.1076 711.3101 717.0 19885.0 400.0 952.0 164.2332 54.05953362467699 +sub-ds205s09_task-view_acq-LR_run-01_bold 0.005607945205479453 0.02232912082191781 0 23.14330828208334 1.043644975 0.9879293138888892 0.5131 1196.7195 0.3258302280827085 45 61.64383561643836 2.0490825 2.0376875 2.24239 1.86717 0.0367789 -0.004032289143651724 0.020073510706424713 73 53 53 27 5.010099661656575 2.200000047683716 4.0 4.0 4.0 42.9673 5.1654 39.4401 23.0 57775.0 18.0 124.0 58.7775 2.6347 119.7322 783.8782 805.0 18068.0 448.0 993.0 160.671 54.77862358093262 +sub-ds205s09_task-view_acq-LR_run-02_bold 0.006578767123287671 0.023950454794520546 0 35.81903538416666 1.5608953040277778 1.6171545502777782 0.5136 1186.884 0.3345943377548133 47 64.38356164383562 2.054238333333333 2.038455 2.2515525 1.8727075 0.0322103 -0.0038295735139399767 0.020550260320305824 73 53 53 27 5.029289310984106 2.200000047683716 4.0 4.0 4.0 44.2484 5.2449 39.9385 23.0 57802.0 18.0 126.0 59.851 2.6451 118.8398 785.5312 807.0 18041.0 451.0 994.0 160.4556 50.310420989990234 +sub-ds205s09_task-view_acq-RL_run-01_bold 0.04132 0.05151956945205479 0 34.941844608194444 1.1900752381944446 1.11412176375 0.5122 1214.6777 0.5699059645824405 53 72.6027397260274 2.103911666666667 2.0650525 2.28995 1.9567325 0.0259984 -0.003200419247150421 0.01928347535431385 73 53 53 27 5.178320854531057 2.200000047683716 4.0 4.0 4.0 42.1191 5.0721 39.7567 23.0 57912.0 18.0 126.0 60.2804 2.5073 116.7328 789.3764 810.0 17931.0 460.0 993.0 156.417 36.2927360534668 diff --git a/pydra/tasks/mriqc/data/tests/ds000005/CHANGES b/pydra/tasks/mriqc/data/tests/ds000005/CHANGES new file mode 100644 index 0000000..b5f26f4 --- /dev/null +++ b/pydra/tasks/mriqc/data/tests/ds000005/CHANGES @@ -0,0 +1,12 @@ +2.0.1 2016-10-21 + + - Added authors to dataset_discription.json + +1.0.1 2016-02-18 + + - Update orientation information in nifti header for improved left-right determination + + +1.0.0 2011-10-06 + + - initial release diff --git a/pydra/tasks/mriqc/data/tests/ds000005/README b/pydra/tasks/mriqc/data/tests/ds000005/README new file mode 100644 index 0000000..d9684e7 --- /dev/null +++ b/pydra/tasks/mriqc/data/tests/ds000005/README @@ -0,0 +1,22 @@ +This dataset was obtained from the OpenfMRI project (http://www.openfmri.org). +Accession #: ds005 +Description: Mixed-gambles task + +Please cite the following references if you use these data: + +Tom, S.M., Fox, C.R., Trepel, C., Poldrack, R.A. (2007). The neural basis of loss aversion in decision-making under risk. Science, 315(5811):515-8 + + +Release history: +10/06/2011: initial release +3/21/2013: Updated release with QA information +2/18/2016: Update orientation information in nifti header for improved left-right determination + +This dataset is made available under the Public Domain Dedication and License +v1.0, whose full text can be found at +https://opendatacommons.org/licenses/pddl/1-0/. +We hope that all users will follow the ODC Attribution/Share-Alike +Community Norms (https://opendatacommons.org/norms/odc-by-sa/); +in particular, while not legally required, we hope that all users +of the data will acknowledge the OpenfMRI project and NSF Grant +OCI-1131441 (R. Poldrack, PI) in any publications. diff --git a/pydra/tasks/mriqc/data/tests/ds000005/dataset_description.json b/pydra/tasks/mriqc/data/tests/ds000005/dataset_description.json new file mode 100644 index 0000000..1892444 --- /dev/null +++ b/pydra/tasks/mriqc/data/tests/ds000005/dataset_description.json @@ -0,0 +1,7 @@ +{ + "BIDSVersion": "1.0.0rc4", + "License": "This dataset is made available under the Public Domain Dedication and License \nv1.0, whose full text can be found at \nhttp://www.opendatacommons.org/licenses/pddl/1.0/. \n We hope that all users will follow the ODC Attribution/Share-Alike \nCommunity Norms (http://www.opendatacommons.org/norms/odc-by-sa/); \n in particular, while not legally required, we hope that all users \nof the data will acknowledge the OpenfMRI project and NSF Grant \nOCI-1131441 (R. Poldrack, PI) in any publications.", + "Name": "Mixed-gambles task", + "Authors": ["Tom, S.M.", "Fox, C.R.", "Trepel, C.", "Poldrack, R.A."], + "ReferencesAndLinks": "Tom, S.M., Fox, C.R., Trepel, C., Poldrack, R.A. (2007). The neural basis of loss aversion in decision-making under risk. 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b/pydra/tasks/mriqc/data/tests/gh921-dmd-20230319-0.oracle @@ -0,0 +1 @@ +13 \ No newline at end of file diff --git a/pydra/tasks/mriqc/interfaces/__init__.py b/pydra/tasks/mriqc/interfaces/__init__.py new file mode 100644 index 0000000..c69a565 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/__init__.py @@ -0,0 +1,55 @@ +from .anatomical import ( + ArtifactMask, + ComputeQI2, + Harmonize, + RotationMask, + StructuralQC, + artifact_mask, + fuzzy_jaccard, +) +from .bids import IQMFileSink, _process_name +from .common import ConformImage, EnsureSize, NUMPY_DTYPE, OUT_FILE +from .derivatives_data_sink import DerivativesDataSink +from .diffusion import ( + CCSegmentation, + CorrectSignalDrift, + DiffusionModel, + DiffusionQC, + ExtractOrientations, + FilterShells, + NumberOfShells, + PIESNO, + ReadDWIMetadata, + RotateVectors, + SpikingVoxelsMask, + SplitShells, + WeightedStat, + _exp_func, + _rms, + get_spike_mask, + noise_piesno, + segment_corpus_callosum, +) +from .functional import ( + FunctionalQC, + GatherTimeseries, + SelectEcho, + Spikes, + _build_timeseries_metadata, + _get_echotime, + _robust_zscore, + find_peaks, + find_spikes, + select_echo, +) +from .reports import AddProvenance +from .synthstrip import SynthStrip +from .transitional import GCOR +from .webapi import ( + HASH_BIDS, + META_WHITELIST, + PROV_WHITELIST, + UploadIQMs, + _hashfields, + upload_qc_metrics, +) diff --git a/pydra/tasks/mriqc/interfaces/anatomical/__init__.py b/pydra/tasks/mriqc/interfaces/anatomical/__init__.py new file mode 100644 index 0000000..e2f3ff4 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/anatomical/__init__.py @@ -0,0 +1,52 @@ +import attrs +from fileformats.generic import Directory, File +import json +import logging +import numpy as np +from pathlib import Path +from pydra.compose import python, shell, workflow +from .artifact_mask import ArtifactMask +from .compute_qi2 import ComputeQI2 +from .harmonize import Harmonize +from .rotation_mask import RotationMask +from .structural_qc import StructuralQC +from pydra.utils.typing import MultiInputObj +import scipy.ndimage as nd +import typing as ty +import yaml + + +logger = logging.getLogger(__name__) + + +def artifact_mask(imdata, airdata, distance, zscore=10.0): + """Compute a mask of artifacts found in the air region.""" + from statsmodels.robust.scale import mad + + qi1_msk = np.zeros(imdata.shape, dtype=bool) + bg_data = imdata[airdata] + if (bg_data > 0).sum() < 10: + return qi1_msk + # Standardize the distribution of the background + bg_spread = mad(bg_data[bg_data > 0]) + bg_data[bg_data > 0] = bg_data[bg_data > 0] / bg_spread + # Apply this threshold to the background voxels to identify voxels + # contributing artifacts. + qi1_msk[airdata] = bg_data > zscore + qi1_msk[distance < 0.10] = False + # Create a structural element to be used in an opening operation. + struct = nd.generate_binary_structure(3, 1) + qi1_msk = nd.binary_opening(qi1_msk, struct).astype(np.uint8) + return qi1_msk + + +def fuzzy_jaccard(in_tpms, in_mni_tpms): + + overlaps = [] + for tpm, mni_tpm in zip(in_tpms, in_mni_tpms): + tpm = tpm.reshape(-1) + mni_tpm = mni_tpm.reshape(-1) + num = np.min([tpm, mni_tpm], axis=0).sum() + den = np.max([tpm, mni_tpm], axis=0).sum() + overlaps.append(float(num / den)) + return overlaps diff --git a/pydra/tasks/mriqc/interfaces/anatomical/artifact_mask.py b/pydra/tasks/mriqc/interfaces/anatomical/artifact_mask.py new file mode 100644 index 0000000..789a770 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/anatomical/artifact_mask.py @@ -0,0 +1,119 @@ +import attrs +from fileformats.generic import File +import logging +import nibabel as nb +import numpy as np +import os +from pathlib import Path +from pydra.compose import python +import scipy.ndimage as nd + + +logger = logging.getLogger(__name__) + + +@python.define +class ArtifactMask(python.Task["ArtifactMask.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.interfaces.anatomical.artifact_mask import ArtifactMask + + """ + + in_file: File + head_mask: File + glabella_xyz: tuple = (0.0, 90.0, -14.0) + inion_xyz: tuple = (0.0, -120.0, -14.0) + ind2std_xfm: File + zscore: float = 10.0 + + class Outputs(python.Outputs): + out_hat_msk: File + out_art_msk: File + out_air_msk: File + + @staticmethod + def function( + in_file: File, + head_mask: File, + glabella_xyz: tuple, + inion_xyz: tuple, + ind2std_xfm: File, + zscore: float, + ) -> tuples[File, File, File]: + out_hat_msk = attrs.NOTHING + out_art_msk = attrs.NOTHING + out_air_msk = attrs.NOTHING + from nibabel.affines import apply_affine + from nitransforms.linear import Affine + + in_file = Path(in_file) + imnii = nb.as_closest_canonical(nb.load(in_file)) + imdata = np.nan_to_num(imnii.get_fdata().astype(np.float32)) + + xfm = Affine.from_filename(ind2std_xfm, fmt="itk") + + ras2ijk = np.linalg.inv(imnii.affine) + glabella_ijk, inion_ijk = apply_affine( + ras2ijk, xfm.map([glabella_xyz, inion_xyz]) + ) + + hmdata = np.bool_(nb.load(head_mask).dataobj) + + dist = nd.morphology.distance_transform_edt(~hmdata) + + hmdata[:, :, : int(inion_ijk[2])] = 1 + hmdata[:, (hmdata.shape[1] // 2) :, : int(glabella_ijk[2])] = 1 + + dist[~hmdata] = 0 + dist /= dist.max() + + qi1_img = artifact_mask(imdata, (~hmdata), dist, zscore=zscore) + + fname = in_file.relative_to(in_file.parent).stem + ext = "".join(in_file.suffixes) + + outdir = Path(os.getcwd()).absolute() + out_hat_msk = str(outdir / f"{fname}_hat{ext}") + out_art_msk = str(outdir / f"{fname}_art{ext}") + out_air_msk = str(outdir / f"{fname}_air{ext}") + + hdr = imnii.header.copy() + hdr.set_data_dtype(np.uint8) + imnii.__class__(qi1_img.astype(np.uint8), imnii.affine, hdr).to_filename( + out_art_msk + ) + + airdata = (~hmdata).astype(np.uint8) + imnii.__class__(airdata, imnii.affine, hdr).to_filename(out_hat_msk) + + airdata[qi1_img > 0] = 0 + imnii.__class__(airdata.astype(np.uint8), imnii.affine, hdr).to_filename( + out_air_msk + ) + + return out_hat_msk, out_art_msk, out_air_msk + + +def artifact_mask(imdata, airdata, distance, zscore=10.0): + """Compute a mask of artifacts found in the air region.""" + from statsmodels.robust.scale import mad + + qi1_msk = np.zeros(imdata.shape, dtype=bool) + bg_data = imdata[airdata] + if (bg_data > 0).sum() < 10: + return qi1_msk + # Standardize the distribution of the background + bg_spread = mad(bg_data[bg_data > 0]) + bg_data[bg_data > 0] = bg_data[bg_data > 0] / bg_spread + # Apply this threshold to the background voxels to identify voxels + # contributing artifacts. + qi1_msk[airdata] = bg_data > zscore + qi1_msk[distance < 0.10] = False + # Create a structural element to be used in an opening operation. + struct = nd.generate_binary_structure(3, 1) + qi1_msk = nd.binary_opening(qi1_msk, struct).astype(np.uint8) + return qi1_msk diff --git a/pydra/tasks/mriqc/interfaces/anatomical/compute_qi2.py b/pydra/tasks/mriqc/interfaces/anatomical/compute_qi2.py new file mode 100644 index 0000000..a0a2cb6 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/anatomical/compute_qi2.py @@ -0,0 +1,40 @@ +import attrs +from fileformats.generic import File +import logging +from pydra.tasks.mriqc.qc.anatomical import art_qi2 +import nibabel as nb +from pydra.compose import python + + +logger = logging.getLogger(__name__) + + +@python.define +class ComputeQI2(python.Task["ComputeQI2.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.interfaces.anatomical.compute_qi2 import ComputeQI2 + + """ + + in_file: File + air_msk: File + + class Outputs(python.Outputs): + qi2: float + out_file: File + + @staticmethod + def function(in_file: File, air_msk: File) -> tuples[float, File]: + qi2 = attrs.NOTHING + out_file = attrs.NOTHING + imdata = nb.load(in_file).get_fdata() + airdata = nb.load(air_msk).get_fdata() + qi2, out_file = art_qi2(imdata, airdata) + qi2 = qi2 + out_file = out_file + + return qi2, out_file diff --git a/pydra/tasks/mriqc/interfaces/anatomical/harmonize.py b/pydra/tasks/mriqc/interfaces/anatomical/harmonize.py new file mode 100644 index 0000000..dd25f49 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/anatomical/harmonize.py @@ -0,0 +1,74 @@ +import attrs +from fileformats.generic import File +import logging +import nibabel as nb +from pydra.tasks.mriqc.nipype_ports.utils.filemanip import fname_presuffix +import numpy as np +from pydra.compose import python +import scipy.ndimage as nd + + +logger = logging.getLogger(__name__) + + +@python.define +class Harmonize(python.Task["Harmonize.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.interfaces.anatomical.harmonize import Harmonize + + """ + + in_file: File + wm_mask: File + brain_mask: File + erodemsk: bool = True + thresh: float = 0.9 + min_size: int = 30 + + class Outputs(python.Outputs): + out_file: File + + @staticmethod + def function( + in_file: File, + wm_mask: File, + brain_mask: File, + erodemsk: bool, + thresh: float, + min_size: int, + ) -> File: + out_file = attrs.NOTHING + in_file = nb.load(in_file) + data = in_file.get_fdata() + + wm_mask = nb.load(wm_mask).get_fdata() + wm_mask[wm_mask < thresh] = 0 + wm_mask[wm_mask > 0] = 1 + wm_mask = wm_mask.astype(bool) + wm_mask_size = wm_mask.sum() + + if wm_mask_size < min_size: + brain_mask = nb.load(brain_mask).get_fdata() > 0.5 + wm_mask = brain_mask.copy() + wm_mask[data < np.percentile(data[brain_mask], 75)] = False + wm_mask[data > np.percentile(data[brain_mask], 95)] = False + elif erodemsk: + + struct = nd.generate_binary_structure(3, 2) + + wm_mask = nd.binary_erosion( + wm_mask.astype(np.uint8), structure=struct + ).astype(bool) + + data *= 1000.0 / np.median(data[wm_mask]) + + out_file = fname_presuffix(in_file, suffix="_harmonized", newpath=".") + in_file.__class__(data, in_file.affine, in_file.header).to_filename(out_file) + + out_file = out_file + + return out_file diff --git a/pydra/tasks/mriqc/interfaces/anatomical/rotation_mask.py b/pydra/tasks/mriqc/interfaces/anatomical/rotation_mask.py new file mode 100644 index 0000000..9cd148c --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/anatomical/rotation_mask.py @@ -0,0 +1,61 @@ +import attrs +from fileformats.generic import File +import logging +import nibabel as nb +from pydra.tasks.mriqc.nipype_ports.utils.filemanip import fname_presuffix +import numpy as np +from pydra.compose import python +import scipy.ndimage as nd + + +logger = logging.getLogger(__name__) + + +@python.define +class RotationMask(python.Task["RotationMask.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.interfaces.anatomical.rotation_mask import RotationMask + + """ + + in_file: File + + class Outputs(python.Outputs): + out_file: File + + @staticmethod + def function(in_file: File) -> File: + out_file = attrs.NOTHING + in_file = nb.load(in_file) + data = in_file.get_fdata() + mask = data <= 0 + + mask = np.pad(mask, pad_width=(1,), mode="constant", constant_values=1) + + struct = nd.generate_binary_structure(3, 2) + mask = nd.binary_opening(mask, structure=struct).astype(np.uint8) + + label_im, nb_labels = nd.label(mask) + if nb_labels > 2: + sizes = nd.sum(mask, label_im, list(range(nb_labels + 1))) + ordered = sorted(zip(sizes, list(range(nb_labels + 1))), reverse=True) + for _, label in ordered[2:]: + mask[label_im == label] = 0 + + mask = mask[1:-1, 1:-1, 1:-1] + + if mask.sum() < 500: + mask = np.zeros_like(mask, dtype=np.uint8) + + out_img = in_file.__class__(mask, in_file.affine, in_file.header) + out_img.header.set_data_dtype(np.uint8) + + out_file = fname_presuffix(in_file, suffix="_rotmask", newpath=".") + out_img.to_filename(out_file) + out_file = out_file + + return out_file diff --git a/pydra/tasks/mriqc/interfaces/anatomical/structural_qc.py b/pydra/tasks/mriqc/interfaces/anatomical/structural_qc.py new file mode 100644 index 0000000..b7e9268 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/anatomical/structural_qc.py @@ -0,0 +1,295 @@ +import attrs +from fileformats.generic import File +import logging +from pydra.tasks.mriqc.qc.anatomical import ( + art_qi1, + cjv, + cnr, + efc, + fber, + rpve, + snr, + snr_dietrich, + summary_stats, + volume_fraction, + wm2max, +) +from pydra.tasks.mriqc.utils.misc import _flatten_dict +import nibabel as nb +import numpy as np +from pydra.compose import python + + +logger = logging.getLogger(__name__) + + +@python.define +class StructuralQC(python.Task["StructuralQC.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.interfaces.anatomical.structural_qc import StructuralQC + + """ + + in_file: File + in_noinu: File + in_segm: File + in_bias: File + head_msk: File + air_msk: File + rot_msk: File + artifact_msk: File + in_pvms: List + in_tpms: List + mni_tpms: List + in_fwhm: list + human: bool = True + + class Outputs(python.Outputs): + summary: dict + icvs: dict + rpve: dict + size: dict + spacing: dict + fwhm: dict + inu: dict + snr: dict + snrd: dict + cnr: float + fber: float + efc: float + qi_1: float + wm2max: float + cjv: float + out_qc: dict + out_noisefit: File + tpm_overlap: dict + + @staticmethod + def function( + in_file: File, + in_noinu: File, + in_segm: File, + in_bias: File, + head_msk: File, + air_msk: File, + rot_msk: File, + artifact_msk: File, + in_pvms: List, + in_tpms: List, + mni_tpms: List, + in_fwhm: list, + human: bool, + ) -> tuples[ + dict, + dict, + dict, + dict, + dict, + dict, + dict, + dict, + dict, + float, + float, + float, + float, + float, + float, + dict, + File, + dict, + ]: + summary = attrs.NOTHING + icvs = attrs.NOTHING + rpve = attrs.NOTHING + size = attrs.NOTHING + spacing = attrs.NOTHING + fwhm = attrs.NOTHING + inu = attrs.NOTHING + snr = attrs.NOTHING + snrd = attrs.NOTHING + cnr = attrs.NOTHING + fber = attrs.NOTHING + efc = attrs.NOTHING + qi_1 = attrs.NOTHING + wm2max = attrs.NOTHING + cjv = attrs.NOTHING + out_qc = attrs.NOTHING + out_noisefit = attrs.NOTHING + tpm_overlap = attrs.NOTHING + self_dict = {} + imnii = nb.load(in_noinu) + + inudata = np.nan_to_num(imnii.get_fdata()) + inudata[inudata < 0] = 0 + + if np.all(inudata < 1e-5): + raise RuntimeError( + "Input inhomogeneity-corrected data seem empty. " + "MRIQC failed to process this dataset." + ) + + segnii = nb.load(in_segm) + segdata = np.asanyarray(segnii.dataobj).astype(np.uint8) + + if np.sum(segdata > 0) < 1e3: + raise RuntimeError( + "Input segmentation data is likely corrupt. MRIQC failed to process this dataset." + ) + + airdata = np.asanyarray(nb.load(air_msk).dataobj).astype(np.uint8) + artdata = np.asanyarray(nb.load(artifact_msk).dataobj).astype(np.uint8) + + headdata = np.asanyarray(nb.load(head_msk).dataobj).astype(np.uint8) + if np.sum(headdata > 0) < 100: + raise RuntimeError( + "Detected less than 100 voxels belonging to the head mask. " + "MRIQC failed to process this dataset." + ) + + rotdata = np.asanyarray(nb.load(rot_msk).dataobj).astype(np.uint8) + + pvms = { + label: nb.load(fname).get_fdata() + for label, fname in zip(("csf", "gm", "wm"), in_pvms) + } + pvmdata = list(pvms.values()) + + pvms["bg"] = airdata + + stats = summary_stats(inudata, pvms) + summary = stats + + snrvals = [] + snr = {} + for tlabel in ("csf", "wm", "gm"): + snrvals.append( + snr( + stats[tlabel]["median"], + stats[tlabel]["stdv"], + stats[tlabel]["n"], + ) + ) + snr[tlabel] = snrvals[-1] + snr["total"] = float(np.mean(snrvals)) + + snrvals = [] + snrd = { + tlabel: snr_dietrich( + stats[tlabel]["median"], + mad_air=stats["bg"]["mad"], + sigma_air=stats["bg"]["stdv"], + ) + for tlabel in ["csf", "wm", "gm"] + } + snrd["total"] = float(np.mean([val for _, val in list(snrd.items())])) + + cnr = cnr( + stats["wm"]["median"], + stats["gm"]["median"], + stats["bg"]["stdv"], + stats["wm"]["stdv"], + stats["gm"]["stdv"], + ) + + fber = fber(inudata, headdata, rotdata) + + efc = efc(inudata, rotdata) + + wm2max = wm2max(inudata, stats["wm"]["median"]) + + qi_1 = art_qi1(airdata, artdata) + + cjv = cjv( + stats["wm"]["median"], + stats["gm"]["median"], + stats["wm"]["mad"], + stats["gm"]["mad"], + ) + + fwhm = np.array(in_fwhm[:3]) / np.array(imnii.header.get_zooms()[:3]) + fwhm = { + "x": float(fwhm[0]), + "y": float(fwhm[1]), + "z": float(fwhm[2]), + "avg": float(np.average(fwhm)), + } + + icvs = volume_fraction(pvmdata) + + rpve = rpve(pvmdata, segdata) + + size = { + "x": int(inudata.shape[0]), + "y": int(inudata.shape[1]), + "z": int(inudata.shape[2]), + } + spacing = { + i: float(v) for i, v in zip(["x", "y", "z"], imnii.header.get_zooms()[:3]) + } + + try: + size["t"] = int(inudata.shape[3]) + except IndexError: + pass + + try: + spacing["tr"] = float(imnii.header.get_zooms()[3]) + except IndexError: + pass + + bias = nb.load(in_bias).get_fdata()[segdata > 0] + inu = { + "range": float( + np.abs(np.percentile(bias, 95.0) - np.percentile(bias, 5.0)) + ), + "med": float(np.median(bias)), + } # pylint: disable=E1101 + + mni_tpms = [nb.load(tpm).get_fdata() for tpm in mni_tpms] + in_tpms = [nb.load(tpm).get_fdata() for tpm in in_pvms] + overlap = fuzzy_jaccard(in_tpms, mni_tpms) + tpm_overlap = { + "csf": overlap[0], + "gm": overlap[1], + "wm": overlap[2], + } + + out_qc = _flatten_dict(self_dict["_results"]) + + return ( + summary, + icvs, + rpve, + size, + spacing, + fwhm, + inu, + snr, + snrd, + cnr, + fber, + efc, + qi_1, + wm2max, + cjv, + out_qc, + # out_noisefit, + tpm_overlap, + ) + + +def fuzzy_jaccard(in_tpms, in_mni_tpms): + + overlaps = [] + for tpm, mni_tpm in zip(in_tpms, in_mni_tpms): + tpm = tpm.reshape(-1) + mni_tpm = mni_tpm.reshape(-1) + num = np.min([tpm, mni_tpm], axis=0).sum() + den = np.max([tpm, mni_tpm], axis=0).sum() + overlaps.append(float(num / den)) + return overlaps diff --git a/pydra/tasks/mriqc/interfaces/anatomical/tests/conftest.py b/pydra/tasks/mriqc/interfaces/anatomical/tests/conftest.py new file mode 100644 index 0000000..751042d --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/anatomical/tests/conftest.py @@ -0,0 +1,25 @@ + +# For debugging in IDE's don't catch raised exceptions and let the IDE +# break at it +import os +import pytest + + +if os.getenv("_PYTEST_RAISE", "0") != "0": + + @pytest.hookimpl(tryfirst=True) + def pytest_exception_interact(call): + raise call.excinfo.value # raise internal errors instead of capturing them + + @pytest.hookimpl(tryfirst=True) + def pytest_internalerror(excinfo): + raise excinfo.value # raise internal errors instead of capturing them + + def pytest_configure(config): + config.option.capture = 'no' # allow print statements to show up in the console + config.option.log_cli = True # show log messages in the console + config.option.log_level = "INFO" # set the log level to INFO + + CATCH_CLI_EXCEPTIONS = False +else: + CATCH_CLI_EXCEPTIONS = True diff --git a/pydra/tasks/mriqc/interfaces/anatomical/tests/test_artifactmask.py b/pydra/tasks/mriqc/interfaces/anatomical/tests/test_artifactmask.py new file mode 100644 index 0000000..4b624a4 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/anatomical/tests/test_artifactmask.py @@ -0,0 +1,21 @@ +from fileformats.generic import File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.interfaces.anatomical.artifact_mask import ArtifactMask +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_artifactmask_1(): + task = ArtifactMask() + task.inputs.in_file = File.sample(seed=0) + task.inputs.head_mask = File.sample(seed=1) + task.inputs.glabella_xyz = [0.0, 90.0, -14.0] + task.inputs.inion_xyz = [0.0, -120.0, -14.0] + task.inputs.ind2std_xfm = File.sample(seed=4) + task.inputs.zscore = 10.0 + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/interfaces/anatomical/tests/test_computeqi2.py b/pydra/tasks/mriqc/interfaces/anatomical/tests/test_computeqi2.py new file mode 100644 index 0000000..c8bb604 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/anatomical/tests/test_computeqi2.py @@ -0,0 +1,17 @@ +from fileformats.generic import File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.interfaces.anatomical.compute_qi2 import ComputeQI2 +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_computeqi2_1(): + task = ComputeQI2() + task.inputs.in_file = File.sample(seed=0) + task.inputs.air_msk = File.sample(seed=1) + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/interfaces/anatomical/tests/test_harmonize.py b/pydra/tasks/mriqc/interfaces/anatomical/tests/test_harmonize.py new file mode 100644 index 0000000..da36f9c --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/anatomical/tests/test_harmonize.py @@ -0,0 +1,19 @@ +from fileformats.generic import File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.interfaces.anatomical.harmonize import Harmonize +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_harmonize_1(): + task = Harmonize() + task.inputs.in_file = File.sample(seed=0) + task.inputs.wm_mask = File.sample(seed=1) + task.inputs.erodemsk = True + task.inputs.thresh = 0.9 + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/interfaces/anatomical/tests/test_rotationmask.py b/pydra/tasks/mriqc/interfaces/anatomical/tests/test_rotationmask.py new file mode 100644 index 0000000..e3a88db --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/anatomical/tests/test_rotationmask.py @@ -0,0 +1,16 @@ +from fileformats.generic import File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.interfaces.anatomical.rotation_mask import RotationMask +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_rotationmask_1(): + task = RotationMask() + task.inputs.in_file = File.sample(seed=0) + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/interfaces/anatomical/tests/test_structuralqc.py b/pydra/tasks/mriqc/interfaces/anatomical/tests/test_structuralqc.py new file mode 100644 index 0000000..37b3207 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/anatomical/tests/test_structuralqc.py @@ -0,0 +1,27 @@ +from fileformats.generic import File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.interfaces.anatomical.structural_qc import StructuralQC +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_structuralqc_1(): + task = StructuralQC() + task.inputs.in_file = File.sample(seed=0) + task.inputs.in_noinu = File.sample(seed=1) + task.inputs.in_segm = File.sample(seed=2) + task.inputs.in_bias = File.sample(seed=3) + task.inputs.head_msk = File.sample(seed=4) + task.inputs.air_msk = File.sample(seed=5) + task.inputs.rot_msk = File.sample(seed=6) + task.inputs.artifact_msk = File.sample(seed=7) + task.inputs.in_pvms = [File.sample(seed=8)] + task.inputs.in_tpms = [File.sample(seed=9)] + task.inputs.mni_tpms = [File.sample(seed=10)] + task.inputs.human = True + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/interfaces/bids/__init__.py b/pydra/tasks/mriqc/interfaces/bids/__init__.py new file mode 100644 index 0000000..e6d1c44 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/bids/__init__.py @@ -0,0 +1,25 @@ +import attrs +from fileformats.generic import Directory, File +import json +import logging +from pathlib import Path +from pydra.compose import python, shell, workflow +from .iqm_file_sink import IQMFileSink +from pydra.utils.typing import MultiInputObj +import typing as ty +import yaml + + +logger = logging.getLogger(__name__) + + +def _process_name(name, val): + + if "." in name: + newkeys = name.split(".") + name = newkeys.pop(0) + nested_dict = {newkeys.pop(): val} + for nk in reversed(newkeys): + nested_dict = {nk: nested_dict} + val = nested_dict + return name, val diff --git a/pydra/tasks/mriqc/interfaces/bids/iqm_file_sink.py b/pydra/tasks/mriqc/interfaces/bids/iqm_file_sink.py new file mode 100644 index 0000000..10dce50 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/bids/iqm_file_sink.py @@ -0,0 +1,197 @@ +import attrs +from fileformats.generic import File +import json +import logging +from pydra.tasks.mriqc.utils.misc import BIDS_COMP +import orjson as json +from pathlib import Path +from pydra.compose import python +import typing as ty + + +logger = logging.getLogger(__name__) + + +@python.define +class IQMFileSink(python.Task["IQMFileSink.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.interfaces.bids.iqm_file_sink import IQMFileSink + + """ + + in_file: str + modality: str + entities: dict + subject_id: str + session_id: ty.Any + task_id: ty.Any + acq_id: ty.Any + rec_id: ty.Any + run_id: ty.Any + dataset: str + dismiss_entities: list = ["datatype", "part", "echo", "extension", "suffix"] + metadata: dict + provenance: dict + root: dict + out_dir: Path + _outputs: dict = {} + + class Outputs(python.Outputs): + out_file: File + + @staticmethod + def function( + in_file: str, + modality: str, + entities: dict, + subject_id: str, + session_id: ty.Any, + task_id: ty.Any, + acq_id: ty.Any, + rec_id: ty.Any, + run_id: ty.Any, + dataset: str, + dismiss_entities: list, + metadata: dict, + provenance: dict, + root: dict, + out_dir: Path, + _outputs: dict, + ) -> File: + out_file = attrs.NOTHING + self_dict = {} + + if fields is None: + fields = [] + + self_dict["_out_dict"] = {} + + fields = list(set(fields) - set(self_dict["inputs"].copyable_trait_names())) + self_dict["_input_names"] = fields + undefined_traits = { + key: _add_field(key, add_trait=add_trait, _outputs=_outputs) + for key in fields + } + self_dict["inputs"].trait_set(trait_change_notify=False, **undefined_traits) + + if force_run: + self_dict["_always_run"] = True + self_dict = {} + out_file = _gen_outfile( + in_file=in_file, out_dir=out_dir, dismiss_entities=dismiss_entities + ) + + if root is not attrs.NOTHING: + self_dict["_out_dict"] = root + + root_adds = [] + for key, val in list(_outputs.items()): + if (val is attrs.NOTHING) or key == "trait_added": + continue + + if self_dict["expr"].match(key) is not None: + root_adds.append(key) + continue + + key, val = _process_name(key, val) + self_dict["_out_dict"][key] = val + + for root_key in root_adds: + val = _outputs.get(root_key, None) + if isinstance(val, dict): + self_dict["_out_dict"].update(val) + else: + logger.warning( + 'Output "%s" is not a dictionary (value="%s"), discarding output.', + root_key, + str(val), + ) + + id_dict = entities if (entities is not attrs.NOTHING) else {} + for comp in BIDS_COMP: + comp_val = getattr(self_dict["inputs"], comp, None) + if (comp_val is not attrs.NOTHING) and comp_val is not None: + id_dict[comp] = comp_val + id_dict["modality"] = modality + + if (metadata is not attrs.NOTHING) and metadata: + id_dict.update(metadata) + + if self_dict["_out_dict"].get("bids_meta") is None: + self_dict["_out_dict"]["bids_meta"] = {} + self_dict["_out_dict"]["bids_meta"].update(id_dict) + + if dataset is not attrs.NOTHING: + self_dict["_out_dict"]["bids_meta"]["dataset"] = dataset + + prov_dict = {} + if (provenance is not attrs.NOTHING) and provenance: + prov_dict.update(provenance) + + if self_dict["_out_dict"].get("provenance") is None: + self_dict["_out_dict"]["provenance"] = {} + self_dict["_out_dict"]["provenance"].update(prov_dict) + + Path(out_file).write_bytes( + json.dumps( + self_dict["_out_dict"], + option=( + json.OPT_SORT_KEYS + | json.OPT_INDENT_2 + | json.OPT_APPEND_NEWLINE + | json.OPT_SERIALIZE_NUMPY + ), + ) + ) + + return out_file + + +def _add_field(name, value=attrs.NOTHING, add_trait=None, _outputs=None): + self_dict = {} + self_dict["inputs"].add_trait(name, traits.Any) + _outputs[name] = value + return value + + +def _gen_outfile(in_file=None, out_dir=None, dismiss_entities=None): + out_dir = Path() + if out_dir is not attrs.NOTHING: + out_dir = Path(out_dir) + + path = Path(in_file) + for i in range(1, 4): + if str(path.parents[i].name).startswith("sub-"): + bids_root = path.parents[i + 1] + break + in_file = str(path.relative_to(bids_root)) + + if (dismiss_entities is not attrs.NOTHING) and (dismiss := dismiss_entities): + for entity in dismiss: + bids_chunks = [ + chunk + for chunk in path.name.split("_") + if not chunk.startswith(f"{entity}-") + ] + path = path.parent / "_".join(bids_chunks) + + bids_path = out_dir / in_file.replace("".join(Path(in_file).suffixes), ".json") + bids_path.parent.mkdir(parents=True, exist_ok=True) + out_file = str(bids_path) + return out_file + + +def _process_name(name, val): + + if "." in name: + newkeys = name.split(".") + name = newkeys.pop(0) + nested_dict = {newkeys.pop(): val} + for nk in reversed(newkeys): + nested_dict = {nk: nested_dict} + val = nested_dict + return name, val diff --git a/pydra/tasks/mriqc/interfaces/bids/tests/conftest.py b/pydra/tasks/mriqc/interfaces/bids/tests/conftest.py new file mode 100644 index 0000000..751042d --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/bids/tests/conftest.py @@ -0,0 +1,25 @@ + +# For debugging in IDE's don't catch raised exceptions and let the IDE +# break at it +import os +import pytest + + +if os.getenv("_PYTEST_RAISE", "0") != "0": + + @pytest.hookimpl(tryfirst=True) + def pytest_exception_interact(call): + raise call.excinfo.value # raise internal errors instead of capturing them + + @pytest.hookimpl(tryfirst=True) + def pytest_internalerror(excinfo): + raise excinfo.value # raise internal errors instead of capturing them + + def pytest_configure(config): + config.option.capture = 'no' # allow print statements to show up in the console + config.option.log_cli = True # show log messages in the console + config.option.log_level = "INFO" # set the log level to INFO + + CATCH_CLI_EXCEPTIONS = False +else: + CATCH_CLI_EXCEPTIONS = True diff --git a/pydra/tasks/mriqc/interfaces/bids/tests/test_iqmfilesink.py b/pydra/tasks/mriqc/interfaces/bids/tests/test_iqmfilesink.py new file mode 100644 index 0000000..98d9473 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/bids/tests/test_iqmfilesink.py @@ -0,0 +1,16 @@ +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.interfaces.bids.iqm_file_sink import IQMFileSink +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_iqmfilesink_1(): + task = IQMFileSink() + task.inputs.dismiss_entities = ["datatype", "part", "echo", "extension", "suffix"] + task.inputs._outputs = {} + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/interfaces/common/__init__.py b/pydra/tasks/mriqc/interfaces/common/__init__.py new file mode 100644 index 0000000..c76fa8e --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/common/__init__.py @@ -0,0 +1,2 @@ +from .conform_image import ConformImage, NUMPY_DTYPE, OUT_FILE +from .ensure_size import EnsureSize diff --git a/pydra/tasks/mriqc/interfaces/common/conform_image/__init__.py b/pydra/tasks/mriqc/interfaces/common/conform_image/__init__.py new file mode 100644 index 0000000..a8c2e47 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/common/conform_image/__init__.py @@ -0,0 +1,29 @@ +import attrs +from fileformats.generic import Directory, File +import json +import logging +import numpy as np +from pathlib import Path +from pydra.compose import python, shell, workflow +from .conform_image import ConformImage +from pydra.utils.typing import MultiInputObj +import typing as ty +import yaml + + +logger = logging.getLogger(__name__) + + +NUMPY_DTYPE = { + 1: np.uint8, + 2: np.uint8, + 4: np.uint16, + 8: np.uint32, + 64: np.float32, + 256: np.uint8, + 1024: np.uint32, + 1280: np.uint32, + 1536: np.float32, +} + +OUT_FILE = "{prefix}_conformed{ext}" diff --git a/pydra/tasks/mriqc/interfaces/common/conform_image/conform_image.py b/pydra/tasks/mriqc/interfaces/common/conform_image/conform_image.py new file mode 100644 index 0000000..ff75f7a --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/common/conform_image/conform_image.py @@ -0,0 +1,126 @@ +import attrs +from fileformats.generic import File +import logging +import nibabel as nib +import numpy as np +from os import path as op +from pydra.compose import python + + +logger = logging.getLogger(__name__) + + +@python.define +class ConformImage(python.Task["ConformImage.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.interfaces.common.conform_image.conform_image import ConformImage + + """ + + in_file: File + check_ras: bool = True + check_dtype: bool = True + + class Outputs(python.Outputs): + out_file: File + + @staticmethod + def function(in_file: File, check_ras: bool, check_dtype: bool) -> File: + out_file = attrs.NOTHING + """ + Execute this interface with the provided runtime. + + TODO: Is the *runtime* argument required? It doesn't seem to be used + anywhere. + + Parameters + ---------- + runtime : Any + Execution runtime ? + + Returns + ------- + Any + Execution runtime ? + """ + + nii = nib.squeeze_image(nib.load(in_file)) + + if check_ras: + nii = nib.as_closest_canonical(nii) + + if check_dtype: + nii = _check_dtype(nii, in_file=in_file) + + out_file, ext = op.splitext(op.basename(in_file)) + if ext == ".gz": + out_file, ext2 = op.splitext(out_file) + ext = ext2 + ext + out_file_name = OUT_FILE.format(prefix=out_file, ext=ext) + out_file = op.abspath(out_file_name) + nii.to_filename(out_file) + + return out_file + + +def _check_dtype(nii: nib.Nifti1Image, in_file=None) -> nib.Nifti1Image: + """ + Checks the NIfTI header datatype and converts the data to the matching + numpy dtype. + + Parameters + ---------- + nii : nib.Nifti1Image + Input image + + Returns + ------- + nib.Nifti1Image + Converted input image + """ + header = nii.header.copy() + datatype = int(header["datatype"]) + _warn_suspicious_dtype(datatype, in_file=in_file) + try: + dtype = NUMPY_DTYPE[datatype] + except KeyError: + return nii + else: + header.set_data_dtype(dtype) + converted = np.asanyarray(nii.dataobj, dtype=dtype) + return nib.Nifti1Image(converted, nii.affine, header) + + +def _warn_suspicious_dtype(dtype: int, in_file=None) -> None: + """ + Warns about binary type *nii* images. + + Parameters + ---------- + dtype : int + NIfTI header datatype + """ + if dtype == 1: + dtype_message = "Input image {in_file} has a suspicious data type: '{dtype}'".format( + in_file=in_file, dtype=dtype + ) + logger.warning(dtype_message) + + +NUMPY_DTYPE = { + 1: np.uint8, + 2: np.uint8, + 4: np.uint16, + 8: np.uint32, + 64: np.float32, + 256: np.uint8, + 1024: np.uint32, + 1280: np.uint32, + 1536: np.float32, +} + +OUT_FILE = "{prefix}_conformed{ext}" diff --git a/pydra/tasks/mriqc/interfaces/common/conform_image/tests/conftest.py b/pydra/tasks/mriqc/interfaces/common/conform_image/tests/conftest.py new file mode 100644 index 0000000..751042d --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/common/conform_image/tests/conftest.py @@ -0,0 +1,25 @@ + +# For debugging in IDE's don't catch raised exceptions and let the IDE +# break at it +import os +import pytest + + +if os.getenv("_PYTEST_RAISE", "0") != "0": + + @pytest.hookimpl(tryfirst=True) + def pytest_exception_interact(call): + raise call.excinfo.value # raise internal errors instead of capturing them + + @pytest.hookimpl(tryfirst=True) + def pytest_internalerror(excinfo): + raise excinfo.value # raise internal errors instead of capturing them + + def pytest_configure(config): + config.option.capture = 'no' # allow print statements to show up in the console + config.option.log_cli = True # show log messages in the console + config.option.log_level = "INFO" # set the log level to INFO + + CATCH_CLI_EXCEPTIONS = False +else: + CATCH_CLI_EXCEPTIONS = True diff --git a/pydra/tasks/mriqc/interfaces/common/conform_image/tests/test_conformimage.py b/pydra/tasks/mriqc/interfaces/common/conform_image/tests/test_conformimage.py new file mode 100644 index 0000000..2cf3a42 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/common/conform_image/tests/test_conformimage.py @@ -0,0 +1,18 @@ +from fileformats.generic import File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.interfaces.common.conform_image.conform_image import ConformImage +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_conformimage_1(): + task = ConformImage() + task.inputs.in_file = File.sample(seed=0) + task.inputs.check_ras = True + task.inputs.check_dtype = True + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/interfaces/common/ensure_size/__init__.py b/pydra/tasks/mriqc/interfaces/common/ensure_size/__init__.py new file mode 100644 index 0000000..c423b12 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/common/ensure_size/__init__.py @@ -0,0 +1 @@ +from .ensure_size import EnsureSize diff --git a/pydra/tasks/mriqc/interfaces/common/ensure_size/ensure_size.py b/pydra/tasks/mriqc/interfaces/common/ensure_size/ensure_size.py new file mode 100644 index 0000000..1987546 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/common/ensure_size/ensure_size.py @@ -0,0 +1,139 @@ +import attrs +from fileformats.generic import File +import logging +import nibabel as nib +from pydra.tasks.ants.auto import ApplyTransforms +from pydra.tasks.niworkflows.data import Loader +import numpy as np +from os import path as op +from pydra.compose import python + + +logger = logging.getLogger(__name__) + + +@python.define +class EnsureSize(python.Task["EnsureSize.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.interfaces.common.ensure_size.ensure_size import EnsureSize + + """ + + in_file: File + in_mask: File + pixel_size: float = 2.0 + + class Outputs(python.Outputs): + out_file: File + out_mask: File + + @staticmethod + def function(in_file: File, in_mask: File, pixel_size: float) -> tuples[File, File]: + out_file = attrs.NOTHING + out_mask = attrs.NOTHING + nii = nib.load(in_file) + size_ok = _check_size(nii, pixel_size=pixel_size) + if size_ok: + out_file = in_file + if in_mask is not attrs.NOTHING: + out_mask = in_mask + else: + + aff_base = nii.header.get_base_affine() + aff_base_inv = np.linalg.inv(aff_base) + + center_idx = (np.array(nii.shape[:3]) - 1) * 0.5 + center_mm = aff_base.dot(center_idx.tolist() + [1]) + + min_mm = aff_base.dot([-0.5, -0.5, -0.5, 1]) + max_mm = aff_base.dot((np.array(nii.shape[:3]) - 0.5).tolist() + [1]) + extent_mm = np.abs(max_mm - min_mm)[:3] + + new_size = np.array(extent_mm / pixel_size, dtype=int) + + new_base = aff_base[:3, :3] * np.abs(aff_base_inv[:3, :3]) * pixel_size + + new_center_idx = (new_size - 1) * 0.5 + new_affine_base = np.eye(4) + new_affine_base[:3, :3] = new_base + new_affine_base[:3, 3] = center_mm[:3] - new_base.dot(new_center_idx) + + rotation = nii.affine.dot(aff_base_inv) + new_affine = rotation.dot(new_affine_base) + + hdr = nii.header.copy() + hdr.set_data_shape(new_size) + nib.Nifti1Image( + np.zeros(new_size, dtype=nii.get_data_dtype()), new_affine, hdr + ).to_filename(REF_FILE_NAME) + + out_prefix, ext = op.splitext(op.basename(in_file)) + if ext == ".gz": + out_prefix, ext2 = op.splitext(out_prefix) + ext = ext2 + ext + + out_file_name = OUT_FILE_NAME.format(prefix=out_prefix, ext=ext) + out_file = op.abspath(out_file_name) + + ApplyTransforms( + dimension=3, + input_image=in_file, + reference_image=REF_FILE_NAME, + interpolation="LanczosWindowedSinc", + transforms=[str(load_data("data/itk_identity.tfm").absolute())], + output_image=out_file, + ).run() + + out_file = out_file + + if in_mask is not attrs.NOTHING: + hdr = nii.header.copy() + hdr.set_data_shape(new_size) + hdr.set_data_dtype(np.uint8) + nib.Nifti1Image( + np.zeros(new_size, dtype=np.uint8), new_affine, hdr + ).to_filename(REF_MASK_NAME) + + out_mask_name = OUT_MASK_NAME.format(prefix=out_prefix, ext=ext) + out_mask = op.abspath(out_mask_name) + ApplyTransforms( + dimension=3, + input_image=in_mask, + reference_image=REF_MASK_NAME, + interpolation="NearestNeighbor", + transforms=[str(load_data("data/itk_identity.tfm").absolute())], + output_image=out_mask, + ).run() + + out_mask = out_mask + + return out_file, out_mask + + +def _check_size(nii: nib.Nifti1Image, pixel_size=None) -> bool: + zooms = nii.header.get_zooms() + size_diff = np.array(zooms[:3]) - (pixel_size - 0.1) + if np.all(size_diff >= -1e-3): + logger.info('Voxel size is large enough.') + return True + else: + small_voxel_message = 'One or more voxel dimensions (%f, %f, %f) are smaller than the requested voxel size (%f) - diff=(%f, %f, %f)'.format( + *zooms[:3], pixel_size, *size_diff + ) + logger.info(small_voxel_message) + return False + + +OUT_FILE_NAME = "{prefix}_resampled{ext}" + +OUT_MASK_NAME = "{prefix}_resmask{ext}" + +REF_FILE_NAME = "resample_ref.nii.gz" + +REF_MASK_NAME = "mask_ref.nii.gz" + +load_data = Loader("pydra.tasks.mriqc") diff --git a/pydra/tasks/mriqc/interfaces/common/ensure_size/tests/conftest.py b/pydra/tasks/mriqc/interfaces/common/ensure_size/tests/conftest.py new file mode 100644 index 0000000..751042d --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/common/ensure_size/tests/conftest.py @@ -0,0 +1,25 @@ + +# For debugging in IDE's don't catch raised exceptions and let the IDE +# break at it +import os +import pytest + + +if os.getenv("_PYTEST_RAISE", "0") != "0": + + @pytest.hookimpl(tryfirst=True) + def pytest_exception_interact(call): + raise call.excinfo.value # raise internal errors instead of capturing them + + @pytest.hookimpl(tryfirst=True) + def pytest_internalerror(excinfo): + raise excinfo.value # raise internal errors instead of capturing them + + def pytest_configure(config): + config.option.capture = 'no' # allow print statements to show up in the console + config.option.log_cli = True # show log messages in the console + config.option.log_level = "INFO" # set the log level to INFO + + CATCH_CLI_EXCEPTIONS = False +else: + CATCH_CLI_EXCEPTIONS = True diff --git a/pydra/tasks/mriqc/interfaces/common/ensure_size/tests/test_ensuresize.py b/pydra/tasks/mriqc/interfaces/common/ensure_size/tests/test_ensuresize.py new file mode 100644 index 0000000..2502465 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/common/ensure_size/tests/test_ensuresize.py @@ -0,0 +1,18 @@ +from fileformats.generic import File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.interfaces.common.ensure_size.ensure_size import EnsureSize +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_ensuresize_1(): + task = EnsureSize() + task.inputs.in_file = File.sample(seed=0) + task.inputs.in_mask = File.sample(seed=1) + task.inputs.pixel_size = 2.0 + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/interfaces/derivatives_data_sink.py b/pydra/tasks/mriqc/interfaces/derivatives_data_sink.py new file mode 100644 index 0000000..77cd450 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/derivatives_data_sink.py @@ -0,0 +1,300 @@ +import attrs +from contextlib import suppress +from fileformats.generic import Directory +from json import dumps +import logging +import nibabel as nb +from pydra.tasks.niworkflows import data +from pydra.tasks.niworkflows.utils.bids import relative_to_root +from pydra.tasks.niworkflows.utils.images import ( + set_consumables, + unsafe_write_nifti_header_and_data, +) +from pydra.tasks.niworkflows.utils.misc import _copy_any, unlink +import numpy as np +import os +from pathlib import Path +from pydra.compose import python +from pydra.utils.typing import MultiInputObj +import re + + +logger = logging.getLogger(__name__) + + +@python.define +class DerivativesDataSink(python.Task["DerivativesDataSink.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import Directory, File + >>> from pydra.tasks.mriqc.interfaces.derivatives_data_sink import DerivativesDataSink + >>> from pydra.utils.typing import MultiInputObj, MultiOutputType + + """ + + base_directory: Directory + check_hdr: bool = True + compress: MultiInputObj = [] + data_dtype: str + dismiss_entities: MultiInputObj = [] + in_file: List + meta_dict: dict + source_file: List + + class Outputs(python.Outputs): + out_file: List + out_meta: List + compression: Union + fixed_hdr: list + + @staticmethod + def function( + base_directory: Directory, + check_hdr: bool, + compress: MultiInputObj, + data_dtype: str, + dismiss_entities: MultiInputObj, + in_file: List, + meta_dict: dict, + source_file: List, + ) -> tuples[List, List, Union, list]: + out_file = attrs.NOTHING + out_meta = attrs.NOTHING + compression = attrs.NOTHING + fixed_hdr = attrs.NOTHING + self_dict = {} + """Initialize the SimpleInterface and extend inputs with custom entities.""" + self_dict["_allowed_entities"] = set(allowed_entities or []).union( + set(self_dict["_config_entities"]) + ) + if out_path_base: + self_dict["out_path_base"] = out_path_base + + self_dict["_metadata"] = {} + self_dict["_static_traits"] = self_dict[ + "input_spec" + ].class_editable_traits() + sorted(self_dict["_allowed_entities"]) + for dynamic_input in set(inputs) - set(self_dict["_static_traits"]): + self_dict["_metadata"][dynamic_input] = inputs.pop(dynamic_input) + + add_traits(self_dict["inputs"], self_dict["_allowed_entities"]) + for k in self_dict["_allowed_entities"].intersection(list(inputs.keys())): + + setattr(self_dict["inputs"], k, inputs[k]) + self_dict = {} + from bids.layout import parse_file_entities, Config + from bids.layout.writing import build_path + from bids.utils import listify + + base_directory = os.getcwd() + if base_directory is not attrs.NOTHING: + base_directory = base_directory + base_directory = Path(base_directory).absolute() + out_path = base_directory / self_dict["out_path_base"] + out_path.mkdir(exist_ok=True, parents=True) + + in_file = listify(in_file) + + if meta_dict is not attrs.NOTHING: + meta = meta_dict + + meta.update(self_dict["_metadata"]) + self_dict["_metadata"] = meta + + custom_config = Config( + name="custom", + entities=self_dict["_config_entities_dict"], + default_path_patterns=self_dict["_file_patterns"], + ) + in_entities = [ + parse_file_entities( + str(relative_to_root(source_file)), + config=["bids", "derivatives", custom_config], + ) + for source_file in source_file + ] + out_entities = { + k: v + for k, v in in_entities[0].items() + if all(ent.get(k) == v for ent in in_entities[1:]) + } + for drop_entity in listify(dismiss_entities or []): + out_entities.pop(drop_entity, None) + + out_entities["extension"] = [ + "".join(Path(orig_file).suffixes).lstrip(".") for orig_file in in_file + ] + + compress = listify(compress) or [None] + if len(compress) == 1: + compress = compress * len(in_file) + for i, ext in enumerate(out_entities["extension"]): + if compress[i] is not None: + ext = regz.sub("", ext) + out_entities["extension"][i] = f"{ext}.gz" if compress[i] else ext + + for key in self_dict["_allowed_entities"]: + value = getattr(self_dict["inputs"], key) + if value is not None and (value is not attrs.NOTHING): + out_entities[key] = value + + if out_entities.get("resolution") == "native" and out_entities.get("space"): + out_entities.pop("resolution", None) + + resolution = out_entities.get("resolution") + space = out_entities.get("space") + if resolution: + + if space in self_dict["_standard_spaces"]: + res = _get_tf_resolution(space, resolution) + else: # TODO: Nonstandard? + res = "Unknown" + self_dict["_metadata"]["Resolution"] = res + + if len(set(out_entities["extension"])) == 1: + out_entities["extension"] = out_entities["extension"][0] + + custom_entities = set(out_entities) - set(self_dict["_config_entities"]) + patterns = self_dict["_file_patterns"] + if custom_entities: + + custom_pat = "_".join(f"{key}-{{{key}}}" for key in sorted(custom_entities)) + patterns = [ + pat.replace("_{suffix", "_".join(("", custom_pat, "{suffix"))) + for pat in patterns + ] + + out_file = [] + compression = [] + fixed_hdr = [False] * len(in_file) + + dest_files = build_path(out_entities, path_patterns=patterns) + if not dest_files: + raise ValueError(f"Could not build path with entities {out_entities}.") + + dest_files = listify(dest_files) + if len(in_file) != len(dest_files): + raise ValueError( + f"Input files ({len(in_file)}) not matched " + f"by interpolated patterns ({len(dest_files)})." + ) + + for i, (orig_file, dest_file) in enumerate(zip(in_file, dest_files)): + out_file = out_path / dest_file + out_file.parent.mkdir(exist_ok=True, parents=True) + out_file.append(str(out_file)) + compression.append(str(dest_file).endswith(".gz")) + + try: + if os.path.samefile(orig_file, out_file): + continue + except FileNotFoundError: + pass + + new_data, new_header = None, None + + is_nifti = False + with suppress(nb.filebasedimages.ImageFileError): + is_nifti = isinstance(nb.load(orig_file), nb.Nifti1Image) + + data_dtype = data_dtype or self_dict["_default_dtypes"][suffix] + if is_nifti and any((check_hdr, data_dtype)): + nii = nb.load(orig_file) + + if check_hdr: + hdr = nii.header + curr_units = tuple( + [None if u == "unknown" else u for u in hdr.get_xyzt_units()] + ) + curr_codes = (int(hdr["qform_code"]), int(hdr["sform_code"])) + + units = ( + curr_units[0] or "mm", + "sec" if out_entities["suffix"] == "bold" else None, + ) + xcodes = (1, 1) # Derivative in its original scanner space + if space: + xcodes = ( + (4, 4) if space in self_dict["_standard_spaces"] else (2, 2) + ) + + curr_zooms = zooms = hdr.get_zooms() + if "RepetitionTime" in self_dict["inputs"].get(): + zooms = curr_zooms[:3] + (RepetitionTime,) + + if (curr_codes, curr_units, curr_zooms) != (xcodes, units, zooms): + fixed_hdr[i] = True + new_header = hdr.copy() + new_header.set_qform(nii.affine, xcodes[0]) + new_header.set_sform(nii.affine, xcodes[1]) + new_header.set_xyzt_units(*units) + new_header.set_zooms(zooms) + + if data_dtype == "source": # match source dtype + try: + data_dtype = nb.load(source_file[0]).get_data_dtype() + except Exception: + LOGGER.warning( + f"Could not get data type of file {source_file[0]}" + ) + data_dtype = None + + if data_dtype: + data_dtype = np.dtype(data_dtype) + orig_dtype = nii.get_data_dtype() + if orig_dtype != data_dtype: + LOGGER.warning( + f"Changing {out_file} dtype from {orig_dtype} to {data_dtype}" + ) + + if np.issubdtype(data_dtype, np.integer): + new_data = np.rint(nii.dataobj).astype(data_dtype) + else: + new_data = np.asanyarray(nii.dataobj, dtype=data_dtype) + + if new_header is None: + new_header = nii.header.copy() + new_header.set_data_dtype(data_dtype) + del nii + + unlink(out_file, missing_ok=True) + if new_data is new_header is None: + _copy_any(orig_file, str(out_file)) + else: + orig_img = nb.load(orig_file) + if new_data is None: + set_consumables(new_header, orig_img.dataobj) + new_data = orig_img.dataobj.get_unscaled() + else: + + new_header.set_slope_inter(slope=1.0, inter=0.0) + unsafe_write_nifti_header_and_data( + fname=out_file, header=new_header, data=new_data + ) + del orig_img + + if len(out_file) == 1: + meta_fields = self_dict["inputs"].copyable_trait_names() + self_dict["_metadata"].update( + { + k: getattr(self_dict["inputs"], k) + for k in meta_fields + if k not in self_dict["_static_traits"] + } + ) + if self_dict["_metadata"]: + sidecar = out_file.parent / f"{out_file.name.split('.', 1)[0]}.json" + unlink(sidecar, missing_ok=True) + sidecar.write_text( + dumps(self_dict["_metadata"], sort_keys=True, indent=2) + ) + out_meta = str(sidecar) + + return out_file, out_meta, compression, fixed_hdr + + +LOGGER = logging.getLogger("nipype.interface") + +regz = re.compile(r"\.gz$") diff --git a/pydra/tasks/mriqc/interfaces/diffusion/__init__.py b/pydra/tasks/mriqc/interfaces/diffusion/__init__.py new file mode 100644 index 0000000..3e80db9 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/diffusion/__init__.py @@ -0,0 +1,197 @@ +import attrs +from fileformats.generic import Directory, File +import json +import logging +import numpy as np +from pathlib import Path +from pydra.compose import python, shell, workflow +from .cc_segmentation import CCSegmentation +from .correct_signal_drift import CorrectSignalDrift +from .diffusion_model import DiffusionModel +from .diffusion_qc import DiffusionQC +from .extract_orientations import ExtractOrientations +from .filter_shells import FilterShells +from .number_of_shells import NumberOfShells +from .piesno import PIESNO +from .read_dwi_metadata import ReadDWIMetadata +from .rotate_vectors import RotateVectors +from .spiking_voxels_mask import SpikingVoxelsMask +from .split_shells import SplitShells +from .weighted_stat import WeightedStat +from pydra.utils.typing import MultiInputObj +import scipy.ndimage as nd +import typing as ty +import yaml + + +logger = logging.getLogger(__name__) + + +def _exp_func(t, A, K, C): + + return A * np.exp(K * t) + C + + +def _rms(estimator, X): + """ + Callable to pass to GridSearchCV that will calculate a distance score. + + To consider: using `MDL + `__ + + """ + if len(np.unique(estimator.cluster_centers_)) < estimator.n_clusters: + return -np.inf + # Calculate distance from assigned shell centroid + distance = X - estimator.cluster_centers_[estimator.predict(X)] + # Make negative so CV optimizes minimizes the error + return -np.sqrt(distance**2).sum() + + +def get_spike_mask( + data: np.ndarray, shell_masks: list, brainmask: np.ndarray, z_threshold: float = 3.0 +) -> np.ndarray: + """ + Creates a binary mask classifying voxels in the data array as spike or non-spike. + + This function identifies voxels with signal intensities exceeding a threshold based + on standard deviations above the mean. The threshold can be applied globally to + the entire data array, or it can be calculated for groups of voxels defined by + the ``grouping_vals`` parameter. + + Parameters + ---------- + data : :obj:`~numpy.ndarray` + The data array to be thresholded. + z_threshold : :obj:`float`, optional (default=3.0) + The number of standard deviations to use above the mean as the threshold + multiplier. + brainmask : :obj:`~numpy.ndarray` + The brain mask. + shell_masks : :obj:`list` + A list of :obj:`~numpy.ndarray` objects + + Returns: + ------- + spike_mask : :obj:`~numpy.ndarray` + A binary mask where ``True`` values indicate voxels classified as spikes and + ``False`` values indicate non-spikes. The mask has the same shape as the input + data array. + + """ + spike_mask = np.zeros_like(data, dtype=bool) + brainmask = brainmask >= 0.5 + for b_mask in shell_masks: + shelldata = data[..., b_mask] + a_thres = z_threshold * shelldata[brainmask].std() + shelldata[brainmask].mean() + spike_mask[..., b_mask] = shelldata > a_thres + return spike_mask + + +def noise_piesno(data: np.ndarray, n_channels: int = 4) -> (np.ndarray, np.ndarray): + """ + Estimates noise in raw diffusion MRI (dMRI) data using the PIESNO algorithm. + + This function implements the PIESNO (Probabilistic Identification and Estimation + of Noise) algorithm [Koay2009]_ to estimate the standard deviation (sigma) of the + noise in each voxel of a 4D dMRI data array. The PIESNO algorithm assumes Rician + distributed signal and exploits the statistical properties of the noise to + separate it from the underlying signal. + + Parameters + ---------- + data : :obj:`~numpy.ndarray` + The 4D raw dMRI data array. + n_channels : :obj:`int`, optional (default=4) + The number of diffusion-encoding channels in the data. This value is used + internally by the PIESNO algorithm. + + Returns + ------- + sigma : :obj:`~numpy.ndarray` + The estimated noise standard deviation for each voxel in the data array. + mask : :obj:`~numpy.ndarray` + A brain mask estimated by PIESNO. This mask identifies voxels containing + mostly noise and can be used for further processing. + + """ + from dipy.denoise.noise_estimate import piesno + + sigma, mask = piesno(data, N=n_channels, return_mask=True) + return sigma, mask + + +def segment_corpus_callosum( + in_cfa: np.ndarray, + mask: np.ndarray, + min_rgb: tuple[float, float, float] = (0.6, 0.0, 0.0), + max_rgb: tuple[float, float, float] = (1.0, 0.1, 0.1), + clean_mask: bool = False, +) -> tuple[np.ndarray, np.ndarray]: + """ + Segments the corpus callosum (CC) from a color FA map. + + Parameters + ---------- + in_cfa : :obj:`~numpy.ndarray` + The color FA (cFA) map. + mask : :obj:`~numpy.ndarray` (bool, 3D) + A white matter mask used to define the initial bounding box. + min_rgb : :obj:`tuple`, optional + Minimum RGB values. + max_rgb : :obj:`tuple`, optional + Maximum RGB values. + clean_mask : :obj:`bool`, optional + Whether the CC mask is finally cleaned-up for spurious off voxels with + :obj:`dipy.segment.mask.clean_cc_mask` + + Returns + ------- + cc_mask: :obj:`~numpy.ndarray` + The final binary mask of the segmented CC. + + Notes + ----- + This implementation was derived from + :obj:`dipy.segment.mask.segment_from_cfa`. + + """ + from dipy.segment.mask import bounding_box + + # Prepare a bounding box of the CC + cc_box = np.zeros_like(mask, dtype=bool) + mins, maxs = bounding_box(mask) # mask needs to be volume + mins = np.array(mins) + maxs = np.array(maxs) + diff = (maxs - mins) // 5 + bounds_min = mins + diff + bounds_max = maxs - diff + cc_box[ + bounds_min[0] : bounds_max[0], + bounds_min[1] : bounds_max[1], + bounds_min[2] : bounds_max[2], + ] = True + min_rgb = np.array(min_rgb) + max_rgb = np.array(max_rgb) + # Threshold color FA + cc_mask = np.all( + (in_cfa >= min_rgb[None, :]) & (in_cfa <= max_rgb[None, :]), + axis=-1, + ) + # Apply bounding box and WM mask + cc_mask *= cc_box & mask + struct = nd.generate_binary_structure(cc_mask.ndim, cc_mask.ndim - 1) + # Perform a closing followed by opening operations on the FA. + cc_mask = nd.binary_closing( + cc_mask, + structure=struct, + ) + cc_mask = nd.binary_opening( + cc_mask, + structure=struct, + ) + if clean_mask: + from dipy.segment.mask import clean_cc_mask + + cc_mask = clean_cc_mask(cc_mask) + return cc_mask diff --git a/pydra/tasks/mriqc/interfaces/diffusion/cc_segmentation.py b/pydra/tasks/mriqc/interfaces/diffusion/cc_segmentation.py new file mode 100644 index 0000000..22818e8 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/diffusion/cc_segmentation.py @@ -0,0 +1,215 @@ +import attrs +from fileformats.generic import File +import logging +import nibabel as nb +from pydra.tasks.mriqc.nipype_ports.utils.filemanip import fname_presuffix +import numpy as np +import os +from pydra.compose import python +import scipy.ndimage as nd + + +logger = logging.getLogger(__name__) + + +@python.define +class CCSegmentation(python.Task["CCSegmentation.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.interfaces.diffusion.cc_segmentation import CCSegmentation + + """ + + in_fa: File + in_cfa: File + min_rgb: tuple = (0.4, 0.008, 0.008) + max_rgb: tuple = (1.1, 0.25, 0.25) + wm_threshold: float = 0.35 + clean_mask: bool = False + + class Outputs(python.Outputs): + out_mask: File + wm_mask: File + wm_finalmask: File + + @staticmethod + def function( + in_fa: File, + in_cfa: File, + min_rgb: tuple, + max_rgb: tuple, + wm_threshold: float, + clean_mask: bool, + ) -> tuples[File, File, File]: + out_mask = attrs.NOTHING + wm_mask = attrs.NOTHING + wm_finalmask = attrs.NOTHING + from skimage.measure import label + + out_mask = fname_presuffix( + in_cfa, + suffix="ccmask", + newpath=os.getcwd(), + ) + wm_mask = fname_presuffix( + in_cfa, + suffix="wmmask", + newpath=os.getcwd(), + ) + wm_finalmask = fname_presuffix( + in_cfa, + suffix="wmfinalmask", + newpath=os.getcwd(), + ) + + fa_nii = nb.load(in_fa) + fa_data = np.round(fa_nii.get_fdata(dtype="float32"), 4) + fa_labels = label((fa_data > wm_threshold).astype(np.uint8)) + wm_mask = fa_labels == np.argmax(np.bincount(fa_labels.flat)[1:]) + 1 + + wm_mask_nii = nb.Nifti1Image( + wm_mask.astype(np.uint8), + fa_nii.affine, + None, + ) + wm_mask_nii.header.set_xyzt_units("mm") + wm_mask_nii.header.set_intent( + "estimate", name="white-matter mask (FA thresholded)" + ) + wm_mask_nii.header["cal_max"] = 1 + wm_mask_nii.header["cal_min"] = 0 + wm_mask_nii.to_filename(wm_mask) + + struct = nd.generate_binary_structure(wm_mask.ndim, wm_mask.ndim - 1) + + wm_mask = nd.grey_closing( + fa_data, + structure=struct, + ) + wm_mask = nd.grey_opening( + wm_mask, + structure=struct, + ) + + fa_labels = label((np.round(wm_mask, 4) > wm_threshold).astype(np.uint8)) + wm_mask = fa_labels == np.argmax(np.bincount(fa_labels.flat)[1:]) + 1 + + wm_mask_nii = nb.Nifti1Image( + wm_mask.astype(np.uint8), + fa_nii.affine, + wm_mask_nii.header, + ) + wm_mask_nii.header.set_intent( + "estimate", name="white-matter mask after binary opening" + ) + wm_mask_nii.to_filename(wm_finalmask) + + cfa_data = np.round(nb.load(in_cfa).get_fdata(dtype="float32"), 4) + for i in range(cfa_data.shape[-1]): + cfa_data[..., i] = nd.grey_closing( + cfa_data[..., i], + structure=struct, + ) + cfa_data[..., i] = nd.grey_opening( + cfa_data[..., i], + structure=struct, + ) + + cc_mask = segment_corpus_callosum( + in_cfa=cfa_data, + mask=wm_mask, + min_rgb=min_rgb, + max_rgb=max_rgb, + clean_mask=clean_mask, + ) + cc_mask_nii = nb.Nifti1Image( + cc_mask.astype(np.uint8), + fa_nii.affine, + None, + ) + cc_mask_nii.header.set_xyzt_units("mm") + cc_mask_nii.header.set_intent("estimate", name="corpus callosum mask") + cc_mask_nii.header["cal_max"] = 1 + cc_mask_nii.header["cal_min"] = 0 + cc_mask_nii.to_filename(out_mask) + + return out_mask, wm_mask, wm_finalmask + + +def segment_corpus_callosum( + in_cfa: np.ndarray, + mask: np.ndarray, + min_rgb: tuple[float, float, float] = (0.6, 0.0, 0.0), + max_rgb: tuple[float, float, float] = (1.0, 0.1, 0.1), + clean_mask: bool = False, +) -> tuple[np.ndarray, np.ndarray]: + """ + Segments the corpus callosum (CC) from a color FA map. + + Parameters + ---------- + in_cfa : :obj:`~numpy.ndarray` + The color FA (cFA) map. + mask : :obj:`~numpy.ndarray` (bool, 3D) + A white matter mask used to define the initial bounding box. + min_rgb : :obj:`tuple`, optional + Minimum RGB values. + max_rgb : :obj:`tuple`, optional + Maximum RGB values. + clean_mask : :obj:`bool`, optional + Whether the CC mask is finally cleaned-up for spurious off voxels with + :obj:`dipy.segment.mask.clean_cc_mask` + + Returns + ------- + cc_mask: :obj:`~numpy.ndarray` + The final binary mask of the segmented CC. + + Notes + ----- + This implementation was derived from + :obj:`dipy.segment.mask.segment_from_cfa`. + + """ + from dipy.segment.mask import bounding_box + + # Prepare a bounding box of the CC + cc_box = np.zeros_like(mask, dtype=bool) + mins, maxs = bounding_box(mask) # mask needs to be volume + mins = np.array(mins) + maxs = np.array(maxs) + diff = (maxs - mins) // 5 + bounds_min = mins + diff + bounds_max = maxs - diff + cc_box[ + bounds_min[0] : bounds_max[0], + bounds_min[1] : bounds_max[1], + bounds_min[2] : bounds_max[2], + ] = True + min_rgb = np.array(min_rgb) + max_rgb = np.array(max_rgb) + # Threshold color FA + cc_mask = np.all( + (in_cfa >= min_rgb[None, :]) & (in_cfa <= max_rgb[None, :]), + axis=-1, + ) + # Apply bounding box and WM mask + cc_mask *= cc_box & mask + struct = nd.generate_binary_structure(cc_mask.ndim, cc_mask.ndim - 1) + # Perform a closing followed by opening operations on the FA. + cc_mask = nd.binary_closing( + cc_mask, + structure=struct, + ) + cc_mask = nd.binary_opening( + cc_mask, + structure=struct, + ) + if clean_mask: + from dipy.segment.mask import clean_cc_mask + + cc_mask = clean_cc_mask(cc_mask) + return cc_mask diff --git a/pydra/tasks/mriqc/interfaces/diffusion/correct_signal_drift.py b/pydra/tasks/mriqc/interfaces/diffusion/correct_signal_drift.py new file mode 100644 index 0000000..f3a45e8 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/diffusion/correct_signal_drift.py @@ -0,0 +1,129 @@ +import attrs +from fileformats.generic import File +import logging +import nibabel as nb +from pydra.tasks.mriqc.nipype_ports.utils.filemanip import fname_presuffix +import numpy as np +import os +from pydra.compose import python + + +logger = logging.getLogger(__name__) + + +@python.define +class CorrectSignalDrift(python.Task["CorrectSignalDrift.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.interfaces.diffusion.correct_signal_drift import CorrectSignalDrift + + """ + + in_file: File + bias_file: File + brainmask_file: File + b0_ixs: list + bval_file: File + full_epi: File + + class Outputs(python.Outputs): + out_file: File + out_full_file: File + b0_drift: list + signal_drift: list + + @staticmethod + def function( + in_file: File, + bias_file: File, + brainmask_file: File, + b0_ixs: list, + bval_file: File, + full_epi: File, + ) -> tuples[File, File, list, list]: + out_file = attrs.NOTHING + out_full_file = attrs.NOTHING + b0_drift = attrs.NOTHING + signal_drift = attrs.NOTHING + from mriqc import config + + bvals = np.loadtxt(bval_file) + len_dmri = bvals.size + + img = nb.load(in_file) + data = img.get_fdata() + bmask = np.ones_like(data[..., 0], dtype=bool) + + if bias_file is not attrs.NOTHING: + data *= nb.load(bias_file).get_fdata()[..., np.newaxis] + + if brainmask_file is not attrs.NOTHING: + bmask = np.round(nb.load(brainmask_file).get_fdata(), 2) > 0.5 + + out_file = fname_presuffix(in_file, suffix="_nodrift", newpath=os.getcwd()) + + if (b0len := int(data.ndim < 4)) or (b0len := data.shape[3]) < 3: + logger.warn( + f"Insufficient number of low-b orientations ({b0len}) " + "to safely calculate signal drift." + ) + + img.__class__( + np.round(data.astype("float32"), 4), + img.affine, + img.header, + ).to_filename(out_file) + + if full_epi is not attrs.NOTHING: + out_full_file = full_epi + + b0_drift = [1.0] * b0len + signal_drift = [1.0] * len_dmri + + global_signal = np.array( + [np.median(data[..., n_b0][bmask]) for n_b0 in range(img.shape[-1])] + ).astype("float32") + + global_signal /= global_signal[0] + b0_drift = [round(float(gs), 4) for gs in global_signal] + + logger.info( + f"Correcting drift with {len(global_signal)} b=0 volumes, with " + "global signal estimated at " + f'{", ".join([str(v) for v in b0_drift])}.' + ) + + data *= 1.0 / global_signal[np.newaxis, np.newaxis, np.newaxis, :] + + img.__class__( + data.astype(img.header.get_data_dtype()), + img.affine, + img.header, + ).to_filename(out_file) + + K, A_log = np.polyfit(b0_ixs, np.log(global_signal), 1) + + t_points = np.arange(len_dmri, dtype=int) + fitted = np.squeeze(_exp_func(t_points, np.exp(A_log), K, 0)) + signal_drift = fitted.astype(float).tolist() + + if full_epi is not attrs.NOTHING: + out_full_file = fname_presuffix( + full_epi, suffix="_nodriftfull", newpath=os.getcwd() + ) + full_img = nb.load(full_epi) + full_img.__class__( + full_img.get_fdata() * fitted[np.newaxis, np.newaxis, np.newaxis, :], + full_img.affine, + full_img.header, + ).to_filename(out_full_file) + + return out_file, out_full_file, b0_drift, signal_drift + + +def _exp_func(t, A, K, C): + + return A * np.exp(K * t) + C diff --git a/pydra/tasks/mriqc/interfaces/diffusion/diffusion_model.py b/pydra/tasks/mriqc/interfaces/diffusion/diffusion_model.py new file mode 100644 index 0000000..3d04e96 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/diffusion/diffusion_model.py @@ -0,0 +1,182 @@ +import attrs +from fileformats.generic import File +import logging +import nibabel as nb +from pydra.tasks.mriqc.nipype_ports.utils.filemanip import fname_presuffix +import numpy as np +import os +from pydra.compose import python + + +logger = logging.getLogger(__name__) + + +@python.define +class DiffusionModel(python.Task["DiffusionModel.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.interfaces.diffusion.diffusion_model import DiffusionModel + + """ + + in_file: File + bvals: list + bvec_file: File + brain_mask: File + decimals: int = 3 + n_shells: int + + class Outputs(python.Outputs): + out_fa: File + out_fa_nans: File + out_fa_degenerate: File + out_cfa: File + out_md: File + + @staticmethod + def function( + in_file: File, + bvals: list, + bvec_file: File, + brain_mask: File, + decimals: int, + n_shells: int, + ) -> tuples[File, File, File, File, File]: + out_fa = attrs.NOTHING + out_fa_nans = attrs.NOTHING + out_fa_degenerate = attrs.NOTHING + out_cfa = attrs.NOTHING + out_md = attrs.NOTHING + from dipy.core.gradients import gradient_table_from_bvals_bvecs + from nipype.utils.filemanip import fname_presuffix + + bvals = np.array(bvals) + + gtab = gradient_table_from_bvals_bvecs( + bvals=bvals, + bvecs=np.loadtxt(bvec_file).T, + ) + + img = nb.load(in_file) + data = img.get_fdata(dtype="float32") + + brainmask = np.ones_like(data[..., 0], dtype=bool) + + if brain_mask is not attrs.NOTHING: + brainmask = ( + np.round( + nb.load(brain_mask).get_fdata(), + 3, + ) + > 0.5 + ) + + if n_shells == 1: + from dipy.reconst.dti import TensorModel as Model + else: + from dipy.reconst.dki import DiffusionKurtosisModel as Model + + fwdtifit = Model(gtab).fit( + data, + mask=brainmask, + ) + + fa_data = fwdtifit.fa + fa_nan_msk = np.isnan(fa_data) + fa_data[fa_nan_msk] = 0 + + fa_data = np.round(fa_data, decimals) + degenerate_msk = (fa_data < 0) | (fa_data > 1.0) + + fa_data = np.clip(fa_data, 0, 1) + + fa_nii = nb.Nifti1Image( + fa_data, + img.affine, + None, + ) + + fa_nii.header.set_xyzt_units("mm") + fa_nii.header.set_intent("estimate", name="Fractional Anisotropy (FA)") + + out_fa = fname_presuffix( + in_file, + suffix="fa", + newpath=os.getcwd(), + ) + + fa_nii.to_filename(out_fa) + + fa_nan_nii = nb.Nifti1Image( + fa_nan_msk.astype(np.uint8), + img.affine, + None, + ) + + fa_nan_nii.header.set_xyzt_units("mm") + fa_nan_nii.header.set_intent("estimate", name="NaNs in the FA map mask") + fa_nan_nii.header["cal_max"] = 1 + fa_nan_nii.header["cal_min"] = 0 + + out_fa_nans = fname_presuffix( + in_file, + suffix="desc-fanans_mask", + newpath=os.getcwd(), + ) + fa_nan_nii.to_filename(out_fa_nans) + + fa_degenerate_nii = nb.Nifti1Image( + degenerate_msk.astype(np.uint8), + img.affine, + None, + ) + + fa_degenerate_nii.header.set_xyzt_units("mm") + fa_degenerate_nii.header.set_intent( + "estimate", name="degenerate vectors in the FA map mask" + ) + fa_degenerate_nii.header["cal_max"] = 1 + fa_degenerate_nii.header["cal_min"] = 0 + + out_fa_degenerate = fname_presuffix( + in_file, + suffix="desc-fadegenerate_mask", + newpath=os.getcwd(), + ) + fa_degenerate_nii.to_filename(out_fa_degenerate) + + cfa_data = fwdtifit.color_fa + cfa_nii = nb.Nifti1Image( + np.clip(cfa_data, a_min=0.0, a_max=1.0), + img.affine, + None, + ) + + cfa_nii.header.set_xyzt_units("mm") + cfa_nii.header.set_intent("estimate", name="Fractional Anisotropy (FA)") + cfa_nii.header["cal_max"] = 1.0 + cfa_nii.header["cal_min"] = 0.0 + + out_cfa = fname_presuffix( + in_file, + suffix="cfa", + newpath=os.getcwd(), + ) + cfa_nii.to_filename(out_cfa) + + out_md = fname_presuffix( + in_file, + suffix="md", + newpath=os.getcwd(), + ) + md_data = np.array(fwdtifit.md, dtype="float32") + md_data[np.isnan(md_data)] = 0 + md_data = np.clip(md_data, 0, 1) + md_hdr = fa_nii.header.copy() + md_hdr.set_intent("estimate", name="Mean diffusivity (MD)") + nb.Nifti1Image(md_data, img.affine, md_hdr).to_filename(out_md) + + return out_fa, out_fa_nans, out_fa_degenerate, out_cfa, out_md diff --git a/pydra/tasks/mriqc/interfaces/diffusion/diffusion_qc.py b/pydra/tasks/mriqc/interfaces/diffusion/diffusion_qc.py new file mode 100644 index 0000000..09cfe87 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/diffusion/diffusion_qc.py @@ -0,0 +1,229 @@ +import attrs +from fileformats.generic import File +import logging +from pydra.tasks.mriqc.utils.misc import _flatten_dict +import nibabel as nb +import numpy as np +from pydra.compose import python +import typing as ty + + +logger = logging.getLogger(__name__) + + +@python.define +class DiffusionQC(python.Task["DiffusionQC.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.interfaces.diffusion.diffusion_qc import DiffusionQC + + """ + + in_file: File + in_b0: File + in_shells: List + in_shells_bval: list + in_bval_file: File + in_bvec: list + in_bvec_rotated: list + in_bvec_diff: list + in_fa: File + in_fa_nans: File + in_fa_degenerate: File + in_cfa: File + in_md: File + brain_mask: File + wm_mask: File + cc_mask: File + spikes_mask: File + noise_floor: float + direction: ty.Any = all + in_fd: File + fd_thres: float = 0.2 + in_fwhm: list + qspace_neighbors: list + piesno_sigma: float = -1.0 + + class Outputs(python.Outputs): + bdiffs: dict + efc: dict + fa_degenerate: float + fa_nans: float + fber: dict + fd: dict + ndc: float + sigma: dict + spikes: dict + snr_cc: dict + summary: dict + out_qc: dict + + @staticmethod + def function( + in_file: File, + in_b0: File, + in_shells: List, + in_shells_bval: list, + in_bval_file: File, + in_bvec: list, + in_bvec_rotated: list, + in_bvec_diff: list, + in_fa: File, + in_fa_nans: File, + in_fa_degenerate: File, + in_cfa: File, + in_md: File, + brain_mask: File, + wm_mask: File, + cc_mask: File, + spikes_mask: File, + noise_floor: float, + direction: ty.Any, + in_fd: File, + fd_thres: float, + in_fwhm: list, + qspace_neighbors: list, + piesno_sigma: float, + ) -> tuples[ + dict, dict, float, float, dict, dict, float, dict, dict, dict, dict, dict + ]: + bdiffs = attrs.NOTHING + efc = attrs.NOTHING + fa_degenerate = attrs.NOTHING + fa_nans = attrs.NOTHING + fber = attrs.NOTHING + fd = attrs.NOTHING + ndc = attrs.NOTHING + sigma = attrs.NOTHING + spikes = attrs.NOTHING + snr_cc = attrs.NOTHING + summary = attrs.NOTHING + out_qc = attrs.NOTHING + self_dict = {} + from mriqc.qc import anatomical as aqc + from mriqc.qc import diffusion as dqc + + b0nii = nb.load(in_b0) + b0data = np.round( + np.nan_to_num(np.asanyarray(b0nii.dataobj)), + 3, + ) + b0data[b0data < 0] = 0 + + msknii = nb.load(brain_mask) + mskdata = np.round( # Protect the thresholding with a rounding for stability + msknii.get_fdata(), + 3, + ) + if np.sum(mskdata) < 100: + raise RuntimeError( + "Detected less than 100 voxels belonging to the brain mask. " + "MRIQC failed to process this dataset." + ) + + wmnii = nb.load(wm_mask) + wmdata = np.round( # Protect the thresholding with a rounding for stability + np.asanyarray(wmnii.dataobj), + 3, + ) + + ccnii = nb.load(cc_mask) + ccdata = np.round( # Protect the thresholding with a rounding for stability + np.asanyarray(ccnii.dataobj), + 3, + ) + + shelldata = [ + np.round( + np.asanyarray(nb.load(s).dataobj), + 4, + ) + for s in in_shells + ] + + rois = { + "fg": mskdata, + "bg": 1.0 - mskdata, + "wm": wmdata, + } + stats = aqc.summary_stats(b0data, rois) + summary = stats + + snr_cc, cc_sigma = dqc.cc_snr( + in_b0=b0data, + dwi_shells=shelldata, + cc_mask=ccdata, + b_values=in_shells_bval, + b_vectors=in_bvec, + ) + + fa_nans_mask = np.asanyarray(nb.load(in_fa_nans).dataobj) > 0.0 + fa_nans = round(float(1e6 * fa_nans_mask[mskdata > 0.5].mean()), 2) + + fa_degenerate_mask = np.asanyarray(nb.load(in_fa_degenerate).dataobj) > 0.0 + fa_degenerate = round( + float(1e6 * fa_degenerate_mask[mskdata > 0.5].mean()), + 2, + ) + + spmask = np.asanyarray(nb.load(spikes_mask).dataobj) > 0.0 + spikes = dqc.spike_ppm(spmask) + + fber = { + f"shell{i + 1:02d}": aqc.fber(bdata, mskdata.astype(np.uint8)) + for i, bdata in enumerate(shelldata) + } + + efc = {f"shell{i + 1:02d}": aqc.efc(bdata) for i, bdata in enumerate(shelldata)} + + fd_data = np.loadtxt(in_fd, skiprows=1) + num_fd = (fd_data > fd_thres).sum() + fd = { + "mean": round(float(fd_data.mean()), 4), + "num": int(num_fd), + "perc": float(num_fd * 100 / (len(fd_data) + 1)), + } + + dwidata = np.round( + np.nan_to_num(nb.load(in_file).get_fdata()), + 3, + ) + ndc = dqc.neighboring_dwi_correlation( + dwidata, + neighbor_indices=qspace_neighbors, + mask=mskdata > 0.5, + ) + + sigma = { + "cc": round(float(cc_sigma), 4), + "piesno": round(piesno_sigma, 4), + "pca": round(noise_floor, 4), + } + + diffs = np.array(in_bvec_diff) + bdiffs = { + "mean": round(float(diffs[diffs > 1e-4].mean()), 4), + "median": round(float(np.median(diffs[diffs > 1e-4])), 4), + "max": round(float(diffs[diffs > 1e-4].max()), 4), + "min": round(float(diffs[diffs > 1e-4].min()), 4), + } + + out_qc = _flatten_dict(self_dict["_results"]) + + return ( + bdiffs, + efc, + fa_degenerate, + fa_nans, + fber, + fd, + ndc, + sigma, + spikes, + snr_cc, + summary, + out_qc, + ) diff --git a/pydra/tasks/mriqc/interfaces/diffusion/extract_orientations.py b/pydra/tasks/mriqc/interfaces/diffusion/extract_orientations.py new file mode 100644 index 0000000..4152ed7 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/diffusion/extract_orientations.py @@ -0,0 +1,61 @@ +import attrs +from fileformats.generic import File +import logging +import nibabel as nb +from pydra.tasks.mriqc.nipype_ports.utils.filemanip import fname_presuffix +import numpy as np +import os +from pydra.compose import python + + +logger = logging.getLogger(__name__) + + +@python.define +class ExtractOrientations(python.Task["ExtractOrientations.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.interfaces.diffusion.extract_orientations import ExtractOrientations + + """ + + in_file: File + indices: list + in_bvec_file: File + + class Outputs(python.Outputs): + out_file: File + out_bvec: list + + @staticmethod + def function( + in_file: File, indices: list, in_bvec_file: File + ) -> tuples[File, list]: + out_file = attrs.NOTHING + out_bvec = attrs.NOTHING + from nipype.utils.filemanip import fname_presuffix + + out_file = fname_presuffix( + in_file, + suffix="_subset", + newpath=os.getcwd(), + ) + + out_file = out_file + + img = nb.load(in_file) + bzeros = np.squeeze(np.asanyarray(img.dataobj)[..., indices]) + + hdr = img.header.copy() + hdr.set_data_shape(bzeros.shape) + hdr.set_xyzt_units("mm") + nb.Nifti1Image(bzeros, img.affine, hdr).to_filename(out_file) + + if in_bvec_file is not attrs.NOTHING: + bvecs = np.loadtxt(in_bvec_file)[:, indices].T + out_bvec = [tuple(row) for row in bvecs] + + return out_file, out_bvec diff --git a/pydra/tasks/mriqc/interfaces/diffusion/filter_shells.py b/pydra/tasks/mriqc/interfaces/diffusion/filter_shells.py new file mode 100644 index 0000000..cb308aa --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/diffusion/filter_shells.py @@ -0,0 +1,83 @@ +import attrs +from fileformats.generic import File +import logging +import nibabel as nb +from pydra.tasks.mriqc.nipype_ports.utils.filemanip import fname_presuffix +import numpy as np +import os +from pydra.compose import python + + +logger = logging.getLogger(__name__) + + +@python.define +class FilterShells(python.Task["FilterShells.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.interfaces.diffusion.filter_shells import FilterShells + + """ + + in_file: File + bvals: list + bvec_file: File + b_threshold: float = 1100 + + class Outputs(python.Outputs): + out_file: File + out_bvals: list + out_bvec_file: File + out_bval_file: File + + @staticmethod + def function( + in_file: File, bvals: list, bvec_file: File, b_threshold: float + ) -> tuples[File, list, File, File]: + out_file = attrs.NOTHING + out_bvals = attrs.NOTHING + out_bvec_file = attrs.NOTHING + out_bval_file = attrs.NOTHING + from nipype.utils.filemanip import fname_presuffix + + bvals = np.array(bvals) + bval_mask = bvals < b_threshold + bvecs = np.loadtxt(bvec_file)[:, bval_mask] + + out_bvals = bvals[bval_mask].astype(float).tolist() + out_bvec_file = fname_presuffix( + in_file, + suffix="_dti.bvec", + newpath=os.getcwd(), + use_ext=False, + ) + np.savetxt(out_bvec_file, bvecs) + + out_bval_file = fname_presuffix( + in_file, + suffix="_dti.bval", + newpath=os.getcwd(), + use_ext=False, + ) + np.savetxt(out_bval_file, bvals) + + out_file = fname_presuffix( + in_file, + suffix="_dti", + newpath=os.getcwd(), + ) + + dwi_img = nb.load(in_file) + data = np.array(dwi_img.dataobj, dtype=dwi_img.header.get_data_dtype())[ + ..., bval_mask + ] + dwi_img.__class__( + data, + dwi_img.affine, + dwi_img.header, + ).to_filename(out_file) + + return out_file, out_bvals, out_bvec_file, out_bval_file diff --git a/pydra/tasks/mriqc/interfaces/diffusion/number_of_shells.py b/pydra/tasks/mriqc/interfaces/diffusion/number_of_shells.py new file mode 100644 index 0000000..fae13e1 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/diffusion/number_of_shells.py @@ -0,0 +1,130 @@ +import attrs +from fileformats.generic import File +import logging +import numpy as np +from pydra.compose import python +from sklearn.cluster import KMeans +from sklearn.model_selection import GridSearchCV + + +logger = logging.getLogger(__name__) + + +@python.define +class NumberOfShells(python.Task["NumberOfShells.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.interfaces.diffusion.number_of_shells import NumberOfShells + + """ + + in_bvals: File + b0_threshold: float = 50 + dsi_threshold: int = 11 + + class Outputs(python.Outputs): + models: list + n_shells: int + out_data: list + b_values: list + b_masks: list + b_indices: list + b_dict: dict + + @staticmethod + def function( + in_bvals: File, b0_threshold: float, dsi_threshold: int + ) -> tuples[list, int, list, list, list, list, dict]: + models = attrs.NOTHING + n_shells = attrs.NOTHING + out_data = attrs.NOTHING + b_values = attrs.NOTHING + b_masks = attrs.NOTHING + b_indices = attrs.NOTHING + b_dict = attrs.NOTHING + in_data = np.squeeze(np.loadtxt(in_bvals)) + highb_mask = in_data > b0_threshold + + original_bvals = sorted(set(np.rint(in_data[highb_mask]).astype(int))) + round_bvals = np.round(in_data, -2).astype(int) + shell_bvals = sorted(set(round_bvals[highb_mask])) + + if len(shell_bvals) <= dsi_threshold: + n_shells = len(shell_bvals) + models = [n_shells] + out_data = round_bvals.tolist() + b_values = shell_bvals + else: + + grid_search = GridSearchCV( + KMeans(), param_grid={"n_clusters": range(1, 10)}, scoring=_rms + ).fit(in_data[highb_mask].reshape(-1, 1)) + + results = np.array( + sorted( + zip( + grid_search.cv_results_["mean_test_score"] * -1.0, + grid_search.cv_results_["param_n_clusters"], + ) + ) + ) + + models = results[:, 1].astype(int).tolist() + n_shells = int(grid_search.best_params_["n_clusters"]) + + out_data = np.zeros_like(in_data) + predicted_shell = np.rint( + np.squeeze( + grid_search.best_estimator_.cluster_centers_[ + grid_search.best_estimator_.predict( + in_data[highb_mask].reshape(-1, 1) + ) + ], + ) + ).astype(int) + + if len(original_bvals) == n_shells: + + indices = np.abs( + predicted_shell[:, np.newaxis] - original_bvals + ).argmin(axis=1) + predicted_shell = original_bvals[indices] + + out_data[highb_mask] = predicted_shell + out_data = np.round(out_data.astype(float), 2).tolist() + b_values = sorted( + np.unique(np.round(predicted_shell.astype(float), 2)).tolist() + ) + + b_masks = [(~highb_mask).tolist()] + [ + np.isclose(out_data, bvalue).tolist() for bvalue in b_values + ] + b_indices = [ + np.atleast_1d(np.squeeze(np.argwhere(b_mask)).astype(int)).tolist() + for b_mask in b_masks + ] + + b_dict = { + int(round(k, 0)): value for k, value in zip([0] + b_values, b_indices) + } + + return models, n_shells, out_data, b_values, b_masks, b_indices, b_dict + + +def _rms(estimator, X): + """ + Callable to pass to GridSearchCV that will calculate a distance score. + + To consider: using `MDL + `__ + + """ + if len(np.unique(estimator.cluster_centers_)) < estimator.n_clusters: + return -np.inf + # Calculate distance from assigned shell centroid + distance = X - estimator.cluster_centers_[estimator.predict(X)] + # Make negative so CV optimizes minimizes the error + return -np.sqrt(distance**2).sum() diff --git a/pydra/tasks/mriqc/interfaces/diffusion/piesno.py b/pydra/tasks/mriqc/interfaces/diffusion/piesno.py new file mode 100644 index 0000000..cdfb238 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/diffusion/piesno.py @@ -0,0 +1,95 @@ +import attrs +from fileformats.generic import File +import logging +import nibabel as nb +from pydra.tasks.mriqc.nipype_ports.utils.filemanip import fname_presuffix +import numpy as np +import os +from pydra.compose import python + + +logger = logging.getLogger(__name__) + + +@python.define +class PIESNO(python.Task["PIESNO.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.interfaces.diffusion.piesno import PIESNO + + """ + + in_file: File + n_channels: int = 4 + + class Outputs(python.Outputs): + sigma: float + out_mask: File + + @staticmethod + def function(in_file: File, n_channels: int) -> tuples[float, File]: + sigma = attrs.NOTHING + out_mask = attrs.NOTHING + out_mask = fname_presuffix( + in_file, + suffix="piesno", + newpath=os.getcwd(), + ) + + in_nii = nb.load(in_file) + data = np.round(in_nii.get_fdata(), 4).astype("float32") + + sigma, maskdata = noise_piesno(data) + + header = in_nii.header.copy() + header.set_data_dtype(np.uint8) + header.set_xyzt_units("mm") + header.set_intent("estimate", name="PIESNO noise voxels mask") + header["cal_max"] = 1 + header["cal_min"] = 0 + + nb.Nifti1Image( + maskdata.astype(np.uint8), + in_nii.affine, + header, + ).to_filename(out_mask) + + sigma = round(float(np.median(sigma)), 5) + + return sigma, out_mask + + +def noise_piesno(data: np.ndarray, n_channels: int = 4) -> (np.ndarray, np.ndarray): + """ + Estimates noise in raw diffusion MRI (dMRI) data using the PIESNO algorithm. + + This function implements the PIESNO (Probabilistic Identification and Estimation + of Noise) algorithm [Koay2009]_ to estimate the standard deviation (sigma) of the + noise in each voxel of a 4D dMRI data array. The PIESNO algorithm assumes Rician + distributed signal and exploits the statistical properties of the noise to + separate it from the underlying signal. + + Parameters + ---------- + data : :obj:`~numpy.ndarray` + The 4D raw dMRI data array. + n_channels : :obj:`int`, optional (default=4) + The number of diffusion-encoding channels in the data. This value is used + internally by the PIESNO algorithm. + + Returns + ------- + sigma : :obj:`~numpy.ndarray` + The estimated noise standard deviation for each voxel in the data array. + mask : :obj:`~numpy.ndarray` + A brain mask estimated by PIESNO. This mask identifies voxels containing + mostly noise and can be used for further processing. + + """ + from dipy.denoise.noise_estimate import piesno + + sigma, mask = piesno(data, N=n_channels, return_mask=True) + return sigma, mask diff --git a/pydra/tasks/mriqc/interfaces/diffusion/read_dwi_metadata.py b/pydra/tasks/mriqc/interfaces/diffusion/read_dwi_metadata.py new file mode 100644 index 0000000..844a54d --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/diffusion/read_dwi_metadata.py @@ -0,0 +1,120 @@ +import attrs +from dipy.core.gradients import gradient_table +from dipy.stats.qc import find_qspace_neighbors +from fileformats.generic import Directory, File +import logging +from pydra.tasks.niworkflows.utils.bids import _init_layout +import numpy as np +from pydra.compose import python +import typing as ty + + +logger = logging.getLogger(__name__) + + +@python.define +class ReadDWIMetadata(python.Task["ReadDWIMetadata.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import Directory, File + >>> from pydra.tasks.mriqc.interfaces.diffusion.read_dwi_metadata import ReadDWIMetadata + + """ + + in_file: File + bids_dir: ty.Any + bids_validate: bool = True + index_db: Directory + + class Outputs(python.Outputs): + out_bvec_file: File + out_bval_file: File + out_bmatrix: list + qspace_neighbors: list + out_dict: dict + subject: str + session: str + task: str + acquisition: str + reconstruction: str + run: int + suffix: str + + @staticmethod + def function( + in_file: File, bids_dir: ty.Any, bids_validate: bool, index_db: Directory + ) -> tuples[File, File, list, list, dict, str, str, str, str, str, int, str]: + out_bvec_file = attrs.NOTHING + out_bval_file = attrs.NOTHING + out_bmatrix = attrs.NOTHING + qspace_neighbors = attrs.NOTHING + out_dict = attrs.NOTHING + subject = attrs.NOTHING + session = attrs.NOTHING + task = attrs.NOTHING + acquisition = attrs.NOTHING + reconstruction = attrs.NOTHING + run = attrs.NOTHING + suffix = attrs.NOTHING + self_dict = {} + from bids.utils import listify + + self_dict["_fields"] = listify(fields or []) + self_dict["_undef_fields"] = undef_fields + self_dict = {} + runtime = niworkflows_interfaces_bids__ReadSidecarJSON___run_interface(runtime) + + out_bvec_file = str(self_dict["layout"].get_bvec(in_file)) + out_bval_file = str(self_dict["layout"].get_bval(in_file)) + + bvecs = np.loadtxt(out_bvec_file).T + bvals = np.loadtxt(out_bval_file) + + gtab = gradient_table(bvals, bvecs=bvecs) + + qspace_neighbors = find_qspace_neighbors(gtab) + out_bmatrix = np.hstack((bvecs, bvals[:, np.newaxis])).tolist() + + return ( + out_bvec_file, + out_bval_file, + out_bmatrix, + qspace_neighbors, + out_dict, + subject, + session, + task, + acquisition, + reconstruction, + run, + suffix, + ) + + +def niworkflows_interfaces_bids__ReadSidecarJSON___run_interface(): + self_dict = {} + self_dict["layout"] = bids_dir or self_dict["layout"] + self_dict["layout"] = _init_layout( + in_file, + self_dict["layout"], + bids_validate, + database_path=(index_db if (index_db is not attrs.NOTHING) else None), + ) + + output_keys = list(_BIDSInfoOutputSpec().get().keys()) + params = self_dict["layout"].parse_file_entities(in_file) + self_dict["_results"] = { + key: params.get(key.split("_")[0], type(attrs.NOTHING)) for key in output_keys + } + + metadata = self_dict["layout"].get_metadata(in_file) + out_dict = metadata + + for fname in self_dict["_fields"]: + if not self_dict["_undef_fields"] and fname not in metadata: + raise KeyError( + 'Metadata field "%s" not found for file %s' % (fname, in_file) + ) + self_dict["_results"][fname] = metadata.get(fname, type(attrs.NOTHING)) diff --git a/pydra/tasks/mriqc/interfaces/diffusion/rotate_vectors.py b/pydra/tasks/mriqc/interfaces/diffusion/rotate_vectors.py new file mode 100644 index 0000000..743d65e --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/diffusion/rotate_vectors.py @@ -0,0 +1,70 @@ +import attrs +from fileformats.generic import File +import logging +import nibabel as nb +import numpy as np +from pydra.compose import python + + +logger = logging.getLogger(__name__) + + +@python.define +class RotateVectors(python.Task["RotateVectors.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.interfaces.diffusion.rotate_vectors import RotateVectors + + """ + + in_file: File + reference: File + transforms: File + + class Outputs(python.Outputs): + out_bvec: list + out_diff: list + + @staticmethod + def function( + in_file: File, reference: File, transforms: File + ) -> tuples[list, list]: + out_bvec = attrs.NOTHING + out_diff = attrs.NOTHING + from nitransforms.linear import load + + vox2ras = nb.load(reference).affine + ras2vox = np.linalg.inv(vox2ras) + + ijk = np.loadtxt(in_file).T + nonzero = np.linalg.norm(ijk, axis=1) > 1e-3 + + xyz = (vox2ras[:3, :3] @ ijk.T).T + + xyz_norms = np.linalg.norm(xyz, axis=1) + xyz[nonzero] = xyz[nonzero] / xyz_norms[nonzero, np.newaxis] + + hmc_rot = load(transforms).matrix[:, :3, :3] + ijk_rotated = ( + ras2vox[:3, :3] @ np.einsum("ijk,ik->ij", hmc_rot, xyz).T + ).T.astype("float32") + ijk_rotated_norm = np.linalg.norm(ijk_rotated, axis=1) + ijk_rotated[nonzero] = ( + ijk_rotated[nonzero] / ijk_rotated_norm[nonzero, np.newaxis] + ) + ijk_rotated[~nonzero] = ijk[~nonzero] + + out_bvec = list(zip(ijk_rotated[:, 0], ijk_rotated[:, 1], ijk_rotated[:, 2])) + + diffs = np.zeros_like(ijk[:, 0]) + diffs[nonzero] = np.arccos( + np.clip( + np.einsum("ij, ij->i", ijk[nonzero], ijk_rotated[nonzero]), -1.0, 1.0 + ) + ) + out_diff = [round(float(v), 6) for v in diffs] + + return out_bvec, out_diff diff --git a/pydra/tasks/mriqc/interfaces/diffusion/spiking_voxels_mask.py b/pydra/tasks/mriqc/interfaces/diffusion/spiking_voxels_mask.py new file mode 100644 index 0000000..58664a3 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/diffusion/spiking_voxels_mask.py @@ -0,0 +1,111 @@ +import attrs +from fileformats.generic import File +import logging +import nibabel as nb +from pydra.tasks.mriqc.nipype_ports.utils.filemanip import fname_presuffix +import numpy as np +import os +from pydra.compose import python + + +logger = logging.getLogger(__name__) + + +@python.define +class SpikingVoxelsMask(python.Task["SpikingVoxelsMask.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.interfaces.diffusion.spiking_voxels_mask import SpikingVoxelsMask + + """ + + in_file: File + brain_mask: File + z_threshold: float = 3.0 + b_masks: list + + class Outputs(python.Outputs): + out_mask: File + + @staticmethod + def function( + in_file: File, brain_mask: File, z_threshold: float, b_masks: list + ) -> File: + out_mask = attrs.NOTHING + out_mask = fname_presuffix( + in_file, + suffix="spikesmask", + newpath=os.getcwd(), + ) + + in_nii = nb.load(in_file) + data = np.round(in_nii.get_fdata(), 4).astype("float32") + + bmask_nii = nb.load(brain_mask) + brainmask = np.round(bmask_nii.get_fdata(), 2).astype("float32") + + spikes_mask = get_spike_mask( + data, + shell_masks=b_masks, + brainmask=brainmask, + z_threshold=z_threshold, + ) + + header = bmask_nii.header.copy() + header.set_data_dtype(np.uint8) + header.set_xyzt_units("mm") + header.set_intent("estimate", name="spiking voxels mask") + header["cal_max"] = 1 + header["cal_min"] = 0 + + spikes_mask_nii = nb.Nifti1Image( + spikes_mask.astype(np.uint8), + bmask_nii.affine, + header, + ) + spikes_mask_nii.to_filename(out_mask) + + return out_mask + + +def get_spike_mask( + data: np.ndarray, shell_masks: list, brainmask: np.ndarray, z_threshold: float = 3.0 +) -> np.ndarray: + """ + Creates a binary mask classifying voxels in the data array as spike or non-spike. + + This function identifies voxels with signal intensities exceeding a threshold based + on standard deviations above the mean. The threshold can be applied globally to + the entire data array, or it can be calculated for groups of voxels defined by + the ``grouping_vals`` parameter. + + Parameters + ---------- + data : :obj:`~numpy.ndarray` + The data array to be thresholded. + z_threshold : :obj:`float`, optional (default=3.0) + The number of standard deviations to use above the mean as the threshold + multiplier. + brainmask : :obj:`~numpy.ndarray` + The brain mask. + shell_masks : :obj:`list` + A list of :obj:`~numpy.ndarray` objects + + Returns: + ------- + spike_mask : :obj:`~numpy.ndarray` + A binary mask where ``True`` values indicate voxels classified as spikes and + ``False`` values indicate non-spikes. The mask has the same shape as the input + data array. + + """ + spike_mask = np.zeros_like(data, dtype=bool) + brainmask = brainmask >= 0.5 + for b_mask in shell_masks: + shelldata = data[..., b_mask] + a_thres = z_threshold * shelldata[brainmask].std() + shelldata[brainmask].mean() + spike_mask[..., b_mask] = shelldata > a_thres + return spike_mask diff --git a/pydra/tasks/mriqc/interfaces/diffusion/split_shells.py b/pydra/tasks/mriqc/interfaces/diffusion/split_shells.py new file mode 100644 index 0000000..d38e2b5 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/diffusion/split_shells.py @@ -0,0 +1,55 @@ +import attrs +from fileformats.generic import File +import logging +import nibabel as nb +from pydra.tasks.mriqc.nipype_ports.utils.filemanip import fname_presuffix +import numpy as np +import os +from pydra.compose import python + + +logger = logging.getLogger(__name__) + + +@python.define +class SplitShells(python.Task["SplitShells.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.interfaces.diffusion.split_shells import SplitShells + + """ + + in_file: File + bvals: list + + class Outputs(python.Outputs): + out_file: List + + @staticmethod + def function(in_file: File, bvals: list) -> List: + out_file = attrs.NOTHING + from nipype.utils.filemanip import fname_presuffix + + bval_list = np.rint(bvals).astype(int) + bvals = np.unique(bval_list) + img = nb.load(in_file) + data = np.asanyarray(img.dataobj) + + out_file = [] + + for bval in bvals: + fname = fname_presuffix( + in_file, suffix=f"_b{bval:05d}", newpath=os.getcwd() + ) + out_file.append(fname) + + img.__class__( + data[..., np.argwhere(bval_list == bval)], + img.affine, + img.header, + ).to_filename(fname) + + return out_file diff --git a/pydra/tasks/mriqc/interfaces/diffusion/tests/conftest.py b/pydra/tasks/mriqc/interfaces/diffusion/tests/conftest.py new file mode 100644 index 0000000..751042d --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/diffusion/tests/conftest.py @@ -0,0 +1,25 @@ + +# For debugging in IDE's don't catch raised exceptions and let the IDE +# break at it +import os +import pytest + + +if os.getenv("_PYTEST_RAISE", "0") != "0": + + @pytest.hookimpl(tryfirst=True) + def pytest_exception_interact(call): + raise call.excinfo.value # raise internal errors instead of capturing them + + @pytest.hookimpl(tryfirst=True) + def pytest_internalerror(excinfo): + raise excinfo.value # raise internal errors instead of capturing them + + def pytest_configure(config): + config.option.capture = 'no' # allow print statements to show up in the console + config.option.log_cli = True # show log messages in the console + config.option.log_level = "INFO" # set the log level to INFO + + CATCH_CLI_EXCEPTIONS = False +else: + CATCH_CLI_EXCEPTIONS = True diff --git a/pydra/tasks/mriqc/interfaces/diffusion/tests/test_ccsegmentation.py b/pydra/tasks/mriqc/interfaces/diffusion/tests/test_ccsegmentation.py new file mode 100644 index 0000000..4cd0c0d --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/diffusion/tests/test_ccsegmentation.py @@ -0,0 +1,21 @@ +from fileformats.generic import File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.interfaces.diffusion.cc_segmentation import CCSegmentation +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_ccsegmentation_1(): + task = CCSegmentation() + task.inputs.in_fa = File.sample(seed=0) + task.inputs.in_cfa = File.sample(seed=1) + task.inputs.min_rgb = [0.4, 0.008, 0.008] + task.inputs.max_rgb = [1.1, 0.25, 0.25] + task.inputs.wm_threshold = 0.35 + task.inputs.clean_mask = False + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/interfaces/diffusion/tests/test_correctsignaldrift.py b/pydra/tasks/mriqc/interfaces/diffusion/tests/test_correctsignaldrift.py new file mode 100644 index 0000000..9500900 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/diffusion/tests/test_correctsignaldrift.py @@ -0,0 +1,22 @@ +from fileformats.generic import File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.interfaces.diffusion.correct_signal_drift import ( + CorrectSignalDrift, +) +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_correctsignaldrift_1(): + task = CorrectSignalDrift() + task.inputs.in_file = File.sample(seed=0) + task.inputs.bias_file = File.sample(seed=1) + task.inputs.brainmask_file = File.sample(seed=2) + task.inputs.bval_file = File.sample(seed=4) + task.inputs.full_epi = File.sample(seed=5) + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/interfaces/diffusion/tests/test_diffusionmodel.py b/pydra/tasks/mriqc/interfaces/diffusion/tests/test_diffusionmodel.py new file mode 100644 index 0000000..ffa0b8c --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/diffusion/tests/test_diffusionmodel.py @@ -0,0 +1,19 @@ +from fileformats.generic import File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.interfaces.diffusion.diffusion_model import DiffusionModel +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_diffusionmodel_1(): + task = DiffusionModel() + task.inputs.in_file = File.sample(seed=0) + task.inputs.bvec_file = File.sample(seed=2) + task.inputs.brain_mask = File.sample(seed=3) + task.inputs.decimals = 3 + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/interfaces/diffusion/tests/test_diffusionqc.py b/pydra/tasks/mriqc/interfaces/diffusion/tests/test_diffusionqc.py new file mode 100644 index 0000000..3a57024 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/diffusion/tests/test_diffusionqc.py @@ -0,0 +1,32 @@ +from fileformats.generic import File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.interfaces.diffusion.diffusion_qc import DiffusionQC +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_diffusionqc_1(): + task = DiffusionQC() + task.inputs.in_file = File.sample(seed=0) + task.inputs.in_b0 = File.sample(seed=1) + task.inputs.in_shells = [File.sample(seed=2)] + task.inputs.in_bval_file = File.sample(seed=4) + task.inputs.in_fa = File.sample(seed=8) + task.inputs.in_fa_nans = File.sample(seed=9) + task.inputs.in_fa_degenerate = File.sample(seed=10) + task.inputs.in_cfa = File.sample(seed=11) + task.inputs.in_md = File.sample(seed=12) + task.inputs.brain_mask = File.sample(seed=13) + task.inputs.wm_mask = File.sample(seed=14) + task.inputs.cc_mask = File.sample(seed=15) + task.inputs.spikes_mask = File.sample(seed=16) + task.inputs.direction = "all" + task.inputs.in_fd = File.sample(seed=19) + task.inputs.fd_thres = 0.2 + task.inputs.piesno_sigma = -1.0 + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/interfaces/diffusion/tests/test_extractorientations.py b/pydra/tasks/mriqc/interfaces/diffusion/tests/test_extractorientations.py new file mode 100644 index 0000000..c720475 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/diffusion/tests/test_extractorientations.py @@ -0,0 +1,19 @@ +from fileformats.generic import File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.interfaces.diffusion.extract_orientations import ( + ExtractOrientations, +) +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_extractorientations_1(): + task = ExtractOrientations() + task.inputs.in_file = File.sample(seed=0) + task.inputs.in_bvec_file = File.sample(seed=2) + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/interfaces/diffusion/tests/test_filtershells.py b/pydra/tasks/mriqc/interfaces/diffusion/tests/test_filtershells.py new file mode 100644 index 0000000..2c63852 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/diffusion/tests/test_filtershells.py @@ -0,0 +1,18 @@ +from fileformats.generic import File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.interfaces.diffusion.filter_shells import FilterShells +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_filtershells_1(): + task = FilterShells() + task.inputs.in_file = File.sample(seed=0) + task.inputs.bvec_file = File.sample(seed=2) + task.inputs.b_threshold = 1100 + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/interfaces/diffusion/tests/test_numberofshells.py b/pydra/tasks/mriqc/interfaces/diffusion/tests/test_numberofshells.py new file mode 100644 index 0000000..b5b8fff --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/diffusion/tests/test_numberofshells.py @@ -0,0 +1,18 @@ +from fileformats.generic import File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.interfaces.diffusion.number_of_shells import NumberOfShells +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_numberofshells_1(): + task = NumberOfShells() + task.inputs.in_bvals = File.sample(seed=0) + task.inputs.b0_threshold = 50 + task.inputs.dsi_threshold = 11 + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/interfaces/diffusion/tests/test_piesno.py b/pydra/tasks/mriqc/interfaces/diffusion/tests/test_piesno.py new file mode 100644 index 0000000..eff5f4b --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/diffusion/tests/test_piesno.py @@ -0,0 +1,17 @@ +from fileformats.generic import File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.interfaces.diffusion.piesno import PIESNO +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_piesno_1(): + task = PIESNO() + task.inputs.in_file = File.sample(seed=0) + task.inputs.n_channels = 4 + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/interfaces/diffusion/tests/test_readdwimetadata.py b/pydra/tasks/mriqc/interfaces/diffusion/tests/test_readdwimetadata.py new file mode 100644 index 0000000..68cb47c --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/diffusion/tests/test_readdwimetadata.py @@ -0,0 +1,18 @@ +from fileformats.generic import Directory, File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.interfaces.diffusion.read_dwi_metadata import ReadDWIMetadata +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_readdwimetadata_1(): + task = ReadDWIMetadata() + task.inputs.in_file = File.sample(seed=0) + task.inputs.bids_validate = True + task.inputs.index_db = Directory.sample(seed=3) + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/interfaces/diffusion/tests/test_rotatevectors.py b/pydra/tasks/mriqc/interfaces/diffusion/tests/test_rotatevectors.py new file mode 100644 index 0000000..d951a5e --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/diffusion/tests/test_rotatevectors.py @@ -0,0 +1,18 @@ +from fileformats.generic import File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.interfaces.diffusion.rotate_vectors import RotateVectors +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_rotatevectors_1(): + task = RotateVectors() + task.inputs.in_file = File.sample(seed=0) + task.inputs.reference = File.sample(seed=1) + task.inputs.transforms = File.sample(seed=2) + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/interfaces/diffusion/tests/test_spikingvoxelsmask.py b/pydra/tasks/mriqc/interfaces/diffusion/tests/test_spikingvoxelsmask.py new file mode 100644 index 0000000..cfb1fff --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/diffusion/tests/test_spikingvoxelsmask.py @@ -0,0 +1,18 @@ +from fileformats.generic import File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.interfaces.diffusion.spiking_voxels_mask import SpikingVoxelsMask +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_spikingvoxelsmask_1(): + task = SpikingVoxelsMask() + task.inputs.in_file = File.sample(seed=0) + task.inputs.brain_mask = File.sample(seed=1) + task.inputs.z_threshold = 3.0 + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/interfaces/diffusion/tests/test_splitshells.py b/pydra/tasks/mriqc/interfaces/diffusion/tests/test_splitshells.py new file mode 100644 index 0000000..a9cbc5c --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/diffusion/tests/test_splitshells.py @@ -0,0 +1,16 @@ +from fileformats.generic import File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.interfaces.diffusion.split_shells import SplitShells +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_splitshells_1(): + task = SplitShells() + task.inputs.in_file = File.sample(seed=0) + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/interfaces/diffusion/tests/test_weightedstat.py b/pydra/tasks/mriqc/interfaces/diffusion/tests/test_weightedstat.py new file mode 100644 index 0000000..c0b9266 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/diffusion/tests/test_weightedstat.py @@ -0,0 +1,17 @@ +from fileformats.generic import File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.interfaces.diffusion.weighted_stat import WeightedStat +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_weightedstat_1(): + task = WeightedStat() + task.inputs.in_file = File.sample(seed=0) + task.inputs.stat = "mean" + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/interfaces/diffusion/weighted_stat.py b/pydra/tasks/mriqc/interfaces/diffusion/weighted_stat.py new file mode 100644 index 0000000..5d2dc07 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/diffusion/weighted_stat.py @@ -0,0 +1,57 @@ +import attrs +from fileformats.generic import File +import logging +import nibabel as nb +from pydra.tasks.mriqc.nipype_ports.utils.filemanip import fname_presuffix +import numpy as np +import os +from pydra.compose import python +import typing as ty + + +logger = logging.getLogger(__name__) + + +@python.define +class WeightedStat(python.Task["WeightedStat.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.interfaces.diffusion.weighted_stat import WeightedStat + + """ + + in_file: File + in_weights: list + stat: ty.Any = mean + + class Outputs(python.Outputs): + out_file: File + + @staticmethod + def function(in_file: File, in_weights: list, stat: ty.Any) -> File: + out_file = attrs.NOTHING + img = nb.load(in_file) + weights = [float(w) for w in in_weights] + data = np.asanyarray(img.dataobj) + statmap = np.average(data, weights=weights, axis=-1) + + out_file = fname_presuffix(in_file, suffix=f"_{stat}", newpath=os.getcwd()) + + if stat == "std": + statmap = np.sqrt( + np.average( + (data - statmap[..., np.newaxis]) ** 2, weights=weights, axis=-1 + ) + ) + + hdr = img.header.copy() + img.__class__( + statmap.astype(hdr.get_data_dtype()), + img.affine, + hdr, + ).to_filename(out_file) + + return out_file diff --git a/pydra/tasks/mriqc/interfaces/functional/__init__.py b/pydra/tasks/mriqc/interfaces/functional/__init__.py new file mode 100644 index 0000000..293bd8b --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/functional/__init__.py @@ -0,0 +1,214 @@ +import attrs +from fileformats.generic import Directory, File +import json +import logging +import numpy as np +from pathlib import Path +from pydra.compose import python, shell, workflow +from .functional_qc import FunctionalQC +from .gather_timeseries import GatherTimeseries +from .select_echo import SelectEcho +from .spikes import Spikes +from pydra.utils.typing import MultiInputObj +import typing as ty +import yaml + + +logger = logging.getLogger(__name__) + + +def _build_timeseries_metadata(): + + return { + "trans_x": { + "LongName": "Translation Along X Axis", + "Description": "Estimated Motion Parameter", + "Units": "mm", + }, + "trans_y": { + "LongName": "Translation Along Y Axis", + "Description": "Estimated Motion Parameter", + "Units": "mm", + }, + "trans_z": { + "LongName": "Translation Along Z Axis", + "Description": "Estimated Motion Parameter", + "Units": "mm", + }, + "rot_x": { + "LongName": "Rotation Around X Axis", + "Description": "Estimated Motion Parameter", + "Units": "rad", + }, + "rot_y": { + "LongName": "Rotation Around X Axis", + "Description": "Estimated Motion Parameter", + "Units": "rad", + }, + "rot_z": { + "LongName": "Rotation Around X Axis", + "Description": "Estimated Motion Parameter", + "Units": "rad", + }, + "dvars_std": { + "LongName": "Derivative of RMS Variance over Voxels, Standardized", + "Description": ( + "Indexes the rate of change of BOLD signal across" + "the entire brain at each frame of data, normalized with the" + "standard deviation of the temporal difference time series" + ), + }, + "dvars_nstd": { + "LongName": ("Derivative of RMS Variance over Voxels, Non-Standardized"), + "Description": ( + "Indexes the rate of change of BOLD signal across" + "the entire brain at each frame of data, not normalized." + ), + }, + "dvars_vstd": { + "LongName": "Derivative of RMS Variance over Voxels, Standardized", + "Description": ( + "Indexes the rate of change of BOLD signal across" + "the entire brain at each frame of data, normalized across" + "time by that voxel standard deviation across time," + "before computing the RMS of the temporal difference" + ), + }, + "framewise_displacement": { + "LongName": "Framewise Displacement", + "Description": ( + "A quantification of the estimated bulk-head" + "motion calculated using formula proposed by Power (2012)" + ), + "Units": "mm", + }, + "aqi": { + "LongName": "AFNI's Quality Index", + "Description": "Mean quality index as computed by AFNI's 3dTqual", + }, + "aor": { + "LongName": "AFNI's Fraction of Outliers per Volume", + "Description": ( + "Mean fraction of outliers per fMRI volume as given by AFNI's 3dToutcount" + ), + }, + } + + +def _get_echotime(inlist): + + if isinstance(inlist, list): + retval = [_get_echotime(el) for el in inlist] + return retval[0] if len(retval) == 1 else retval + echo_time = inlist.get("EchoTime", None) if inlist else None + if echo_time: + return float(echo_time) + + +def _robust_zscore(data): + + return (data - np.atleast_2d(np.median(data, axis=1)).T) / np.atleast_2d( + data.std(axis=1) + ).T + + +def find_peaks(data): + + t_z = [data[:, :, i, :].mean(axis=0).mean(axis=0) for i in range(data.shape[2])] + return t_z + + +def find_spikes(data, spike_thresh): + + data -= np.median(np.median(np.median(data, axis=0), axis=0), axis=0) + slice_mean = np.median(np.median(data, axis=0), axis=0) + t_z = _robust_zscore(slice_mean) + spikes = np.abs(t_z) > spike_thresh + spike_inds = np.transpose(spikes.nonzero()) + # mask out the spikes and recompute z-scores using variance uncontaminated with spikes. + # This will catch smaller spikes that may have been swamped by big + # ones. + data.mask[:, :, spike_inds[:, 0], spike_inds[:, 1]] = True + slice_mean2 = np.median(np.median(data, axis=0), axis=0) + t_z = _robust_zscore(slice_mean2) + spikes = np.logical_or(spikes, np.abs(t_z) > spike_thresh) + spike_inds = [tuple(i) for i in np.transpose(spikes.nonzero())] + return spike_inds, t_z + + +def select_echo( + in_files: str | list[str], + te_echos: list[float | type(attrs.NOTHING) | None] | None = None, + te_reference: float = 0.030, +) -> str: + """ + Select the echo file with the closest echo time to the reference echo time. + + Used to grab the echo file when processing multi-echo data through workflows + that only accept a single file. + + Parameters + ---------- + in_files : :obj:`str` or :obj:`list` + A single filename or a list of filenames. + te_echos : :obj:`list` of :obj:`float` + List of echo times corresponding to each file. + If not a number (typically, a :obj:`~nipype.interfaces.base.type(attrs.NOTHING)`), + the function selects the second echo. + te_reference : float, optional + Reference echo time used to find the closest echo time. + + Returns + ------- + str + The selected echo file. + + Examples + -------- + >>> select_echo("single-echo.nii.gz") + ('single-echo.nii.gz', -1) + + >>> select_echo(["single-echo.nii.gz"]) + ('single-echo.nii.gz', -1) + + >>> select_echo( + ... [f"echo{n}.nii.gz" for n in range(1,7)], + ... ) + ('echo2.nii.gz', 1) + + >>> select_echo( + ... [f"echo{n}.nii.gz" for n in range(1,7)], + ... te_echos=[12.5, 28.5, 34.2, 45.0, 56.1, 68.4], + ... te_reference=33.1, + ... ) + ('echo3.nii.gz', 2) + + >>> select_echo( + ... [f"echo{n}.nii.gz" for n in range(1,7)], + ... te_echos=[12.5, 28.5, 34.2, 45.0, 56.1], + ... te_reference=33.1, + ... ) + ('echo2.nii.gz', 1) + + >>> select_echo( + ... [f"echo{n}.nii.gz" for n in range(1,7)], + ... te_echos=[12.5, 28.5, 34.2, 45.0, 56.1, None], + ... te_reference=33.1, + ... ) + ('echo2.nii.gz', 1) + + """ + if not isinstance(in_files, (list, tuple)): + return in_files, -1 + if len(in_files) == 1: + return in_files[0], -1 + import numpy as np + + n_echos = len(in_files) + if te_echos is not None and len(te_echos) == n_echos: + try: + index = np.argmin(np.abs(np.array(te_echos) - te_reference)) + return in_files[index], index + except TypeError: + pass + return in_files[1], 1 diff --git a/pydra/tasks/mriqc/interfaces/functional/functional_qc.py b/pydra/tasks/mriqc/interfaces/functional/functional_qc.py new file mode 100644 index 0000000..ec079d0 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/functional/functional_qc.py @@ -0,0 +1,173 @@ +import attrs +from fileformats.generic import File +import logging +from pydra.tasks.mriqc.qc.anatomical import efc, fber, snr, summary_stats +from pydra.tasks.mriqc.qc.functional import gsr +from pydra.tasks.mriqc.utils.misc import _flatten_dict +import nibabel as nb +import numpy as np +from pydra.compose import python +import typing as ty + + +logger = logging.getLogger(__name__) + + +@python.define +class FunctionalQC(python.Task["FunctionalQC.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.interfaces.functional.functional_qc import FunctionalQC + + """ + + in_epi: File + in_hmc: File + in_tsnr: File + in_mask: File + direction: ty.Any = all + in_fd: File + fd_thres: float = 0.2 + in_dvars: File + in_fwhm: list + + class Outputs(python.Outputs): + fber: float + efc: float + snr: float + gsr: dict + tsnr: float + dvars: dict + fd: dict + fwhm: dict + size: dict + spacing: dict + summary: dict + out_qc: dict + + @staticmethod + def function( + in_epi: File, + in_hmc: File, + in_tsnr: File, + in_mask: File, + direction: ty.Any, + in_fd: File, + fd_thres: float, + in_dvars: File, + in_fwhm: list, + ) -> tuples[ + float, float, float, dict, float, dict, dict, dict, dict, dict, dict, dict + ]: + fber = attrs.NOTHING + efc = attrs.NOTHING + snr = attrs.NOTHING + gsr = attrs.NOTHING + tsnr = attrs.NOTHING + dvars = attrs.NOTHING + fd = attrs.NOTHING + fwhm = attrs.NOTHING + size = attrs.NOTHING + spacing = attrs.NOTHING + summary = attrs.NOTHING + out_qc = attrs.NOTHING + self_dict = {} + + epinii = nb.load(in_epi) + epidata = np.nan_to_num(np.float32(epinii.dataobj)) + epidata[epidata < 0] = 0 + + hmcnii = nb.load(in_hmc) + hmcdata = np.nan_to_num(np.float32(hmcnii.dataobj)) + hmcdata[hmcdata < 0] = 0 + + msknii = nb.load(in_mask) + mskdata = np.asanyarray(msknii.dataobj) > 0 + if np.sum(mskdata) < 100: + raise RuntimeError( + "Detected less than 100 voxels belonging to the brain mask. " + "MRIQC failed to process this dataset." + ) + + rois = {"fg": mskdata.astype(np.uint8), "bg": (~mskdata).astype(np.uint8)} + stats = summary_stats(epidata, rois) + summary = stats + + snr = snr(stats["fg"]["median"], stats["fg"]["stdv"], stats["fg"]["n"]) + + fber = fber(epidata, mskdata.astype(np.uint8)) + + efc = efc(epidata) + + gsr = {} + if direction == "all": + epidir = ["x", "y"] + else: + epidir = [direction] + + for axis in epidir: + gsr[axis] = gsr(epidata, mskdata.astype(np.uint8), direction=axis) + + dvars_avg = np.loadtxt(in_dvars, skiprows=1, usecols=list(range(3))).mean( + axis=0 + ) + dvars_col = ["std", "nstd", "vstd"] + dvars = {key: float(val) for key, val in zip(dvars_col, dvars_avg)} + + tsnr_data = nb.load(in_tsnr).get_fdata() + tsnr = float(np.median(tsnr_data[mskdata])) + + fd_data = np.loadtxt(in_fd, skiprows=1) + num_fd = (fd_data > fd_thres).sum() + fd = { + "mean": float(fd_data.mean()), + "num": int(num_fd), + "perc": float(num_fd * 100 / (len(fd_data) + 1)), + } + + fwhm = np.array(in_fwhm[:3]) / np.array(hmcnii.header.get_zooms()[:3]) + fwhm = { + "x": float(fwhm[0]), + "y": float(fwhm[1]), + "z": float(fwhm[2]), + "avg": float(np.average(fwhm)), + } + + size = { + "x": int(hmcdata.shape[0]), + "y": int(hmcdata.shape[1]), + "z": int(hmcdata.shape[2]), + } + spacing = { + i: float(v) for i, v in zip(["x", "y", "z"], hmcnii.header.get_zooms()[:3]) + } + + try: + size["t"] = int(hmcdata.shape[3]) + except IndexError: + pass + + try: + spacing["tr"] = float(hmcnii.header.get_zooms()[3]) + except IndexError: + pass + + out_qc = _flatten_dict(self_dict["_results"]) + + return ( + fber, + efc, + snr, + gsr, + tsnr, + dvars, + fd, + fwhm, + size, + spacing, + summary, + out_qc, + ) diff --git a/pydra/tasks/mriqc/interfaces/functional/gather_timeseries.py b/pydra/tasks/mriqc/interfaces/functional/gather_timeseries.py new file mode 100644 index 0000000..d1d2aed --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/functional/gather_timeseries.py @@ -0,0 +1,163 @@ +import attrs +from fileformats.generic import File +import logging +from pydra.tasks.mriqc.nipype_ports.utils.misc import normalize_mc_params +import numpy as np +from os import path as op +import os +import pandas as pd +from pydra.compose import python +import typing as ty + + +logger = logging.getLogger(__name__) + + +@python.define +class GatherTimeseries(python.Task["GatherTimeseries.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.interfaces.functional.gather_timeseries import GatherTimeseries + + """ + + dvars: File + fd: File + mpars: File + mpars_source: ty.Any + outliers: File + quality: File + + class Outputs(python.Outputs): + timeseries_file: File + timeseries_metadata: dict + + @staticmethod + def function( + dvars: File, + fd: File, + mpars: File, + mpars_source: ty.Any, + outliers: File, + quality: File, + ) -> tuples[File, dict]: + timeseries_file = attrs.NOTHING + timeseries_metadata = attrs.NOTHING + + mpars = np.apply_along_axis( + func1d=normalize_mc_params, + axis=1, + arr=np.loadtxt(mpars), # mpars is N_t x 6 + source=mpars_source, + ) + timeseries = pd.DataFrame( + mpars, columns=["trans_x", "trans_y", "trans_z", "rot_x", "rot_y", "rot_z"] + ) + + dvars = pd.read_csv( + dvars, + sep=r"\s+", + skiprows=1, # column names have spaces + header=None, + names=["dvars_std", "dvars_nstd", "dvars_vstd"], + ) + dvars.index = pd.RangeIndex(1, timeseries.index.max() + 1) + + fd = pd.read_csv(fd, sep=r"\s+", header=0, names=["framewise_displacement"]) + fd.index = pd.RangeIndex(1, timeseries.index.max() + 1) + + aqi = pd.read_csv(quality, sep=r"\s+", header=None, names=["aqi"]) + + aor = pd.read_csv(outliers, sep=r"\s+", header=None, names=["aor"]) + + timeseries = pd.concat((timeseries, dvars, fd, aqi, aor), axis=1) + + timeseries_file = op.join(os.getcwd(), "timeseries.tsv") + + timeseries.to_csv(timeseries_file, sep="\t", index=False, na_rep="n/a") + + timeseries_file = timeseries_file + timeseries_metadata = _build_timeseries_metadata() + + return timeseries_file, timeseries_metadata + + +def _build_timeseries_metadata(): + + return { + "trans_x": { + "LongName": "Translation Along X Axis", + "Description": "Estimated Motion Parameter", + "Units": "mm", + }, + "trans_y": { + "LongName": "Translation Along Y Axis", + "Description": "Estimated Motion Parameter", + "Units": "mm", + }, + "trans_z": { + "LongName": "Translation Along Z Axis", + "Description": "Estimated Motion Parameter", + "Units": "mm", + }, + "rot_x": { + "LongName": "Rotation Around X Axis", + "Description": "Estimated Motion Parameter", + "Units": "rad", + }, + "rot_y": { + "LongName": "Rotation Around X Axis", + "Description": "Estimated Motion Parameter", + "Units": "rad", + }, + "rot_z": { + "LongName": "Rotation Around X Axis", + "Description": "Estimated Motion Parameter", + "Units": "rad", + }, + "dvars_std": { + "LongName": "Derivative of RMS Variance over Voxels, Standardized", + "Description": ( + "Indexes the rate of change of BOLD signal across" + "the entire brain at each frame of data, normalized with the" + "standard deviation of the temporal difference time series" + ), + }, + "dvars_nstd": { + "LongName": ("Derivative of RMS Variance over Voxels, Non-Standardized"), + "Description": ( + "Indexes the rate of change of BOLD signal across" + "the entire brain at each frame of data, not normalized." + ), + }, + "dvars_vstd": { + "LongName": "Derivative of RMS Variance over Voxels, Standardized", + "Description": ( + "Indexes the rate of change of BOLD signal across" + "the entire brain at each frame of data, normalized across" + "time by that voxel standard deviation across time," + "before computing the RMS of the temporal difference" + ), + }, + "framewise_displacement": { + "LongName": "Framewise Displacement", + "Description": ( + "A quantification of the estimated bulk-head" + "motion calculated using formula proposed by Power (2012)" + ), + "Units": "mm", + }, + "aqi": { + "LongName": "AFNI's Quality Index", + "Description": "Mean quality index as computed by AFNI's 3dTqual", + }, + "aor": { + "LongName": "AFNI's Fraction of Outliers per Volume", + "Description": ( + "Mean fraction of outliers per fMRI volume as given by AFNI's 3dToutcount" + ), + }, + } diff --git a/pydra/tasks/mriqc/interfaces/functional/select_echo.py b/pydra/tasks/mriqc/interfaces/functional/select_echo.py new file mode 100644 index 0000000..91b4a2c --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/functional/select_echo.py @@ -0,0 +1,140 @@ +import attrs +from fileformats.generic import File +import logging +import numpy as np +from pydra.compose import python +from pydra.utils.typing import MultiInputObj + + +logger = logging.getLogger(__name__) + + +@python.define +class SelectEcho(python.Task["SelectEcho.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.interfaces.functional.select_echo import SelectEcho + >>> from pydra.utils.typing import MultiInputObj + + """ + + in_files: List + metadata: MultiInputObj + te_reference: float = 0.03 + + class Outputs(python.Outputs): + out_file: File + echo_index: int + is_multiecho: bool + + @staticmethod + def function( + in_files: List, metadata: MultiInputObj, te_reference: float + ) -> tuples[File, int, bool]: + out_file = attrs.NOTHING + echo_index = attrs.NOTHING + is_multiecho = attrs.NOTHING + ( + out_file, + echo_index, + ) = select_echo( + in_files, + te_echos=( + _get_echotime(metadata) if (metadata is not attrs.NOTHING) else None + ), + te_reference=te_reference, + ) + is_multiecho = echo_index != -1 + + return out_file, echo_index, is_multiecho + + +def _get_echotime(inlist): + + if isinstance(inlist, list): + retval = [_get_echotime(el) for el in inlist] + return retval[0] if len(retval) == 1 else retval + echo_time = inlist.get("EchoTime", None) if inlist else None + if echo_time: + return float(echo_time) + + +def select_echo( + in_files: str | list[str], + te_echos: list[float | type(attrs.NOTHING) | None] | None = None, + te_reference: float = 0.030, +) -> str: + """ + Select the echo file with the closest echo time to the reference echo time. + + Used to grab the echo file when processing multi-echo data through workflows + that only accept a single file. + + Parameters + ---------- + in_files : :obj:`str` or :obj:`list` + A single filename or a list of filenames. + te_echos : :obj:`list` of :obj:`float` + List of echo times corresponding to each file. + If not a number (typically, a :obj:`~nipype.interfaces.base.type(attrs.NOTHING)`), + the function selects the second echo. + te_reference : float, optional + Reference echo time used to find the closest echo time. + + Returns + ------- + str + The selected echo file. + + Examples + -------- + >>> select_echo("single-echo.nii.gz") + ('single-echo.nii.gz', -1) + + >>> select_echo(["single-echo.nii.gz"]) + ('single-echo.nii.gz', -1) + + >>> select_echo( + ... [f"echo{n}.nii.gz" for n in range(1,7)], + ... ) + ('echo2.nii.gz', 1) + + >>> select_echo( + ... [f"echo{n}.nii.gz" for n in range(1,7)], + ... te_echos=[12.5, 28.5, 34.2, 45.0, 56.1, 68.4], + ... te_reference=33.1, + ... ) + ('echo3.nii.gz', 2) + + >>> select_echo( + ... [f"echo{n}.nii.gz" for n in range(1,7)], + ... te_echos=[12.5, 28.5, 34.2, 45.0, 56.1], + ... te_reference=33.1, + ... ) + ('echo2.nii.gz', 1) + + >>> select_echo( + ... [f"echo{n}.nii.gz" for n in range(1,7)], + ... te_echos=[12.5, 28.5, 34.2, 45.0, 56.1, None], + ... te_reference=33.1, + ... ) + ('echo2.nii.gz', 1) + + """ + if not isinstance(in_files, (list, tuple)): + return in_files, -1 + if len(in_files) == 1: + return in_files[0], -1 + import numpy as np + + n_echos = len(in_files) + if te_echos is not None and len(te_echos) == n_echos: + try: + index = np.argmin(np.abs(np.array(te_echos) - te_reference)) + return in_files[index], index + except TypeError: + pass + return in_files[1], 1 diff --git a/pydra/tasks/mriqc/interfaces/functional/spikes.py b/pydra/tasks/mriqc/interfaces/functional/spikes.py new file mode 100644 index 0000000..2a09ae7 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/functional/spikes.py @@ -0,0 +1,124 @@ +import attrs +from fileformats.generic import File +import logging +import nibabel as nb +import numpy as np +from os import path as op +from pathlib import Path +from pydra.compose import python + + +logger = logging.getLogger(__name__) + + +@python.define +class Spikes(python.Task["Spikes.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.interfaces.functional.spikes import Spikes + + """ + + in_file: File + in_mask: File + invert_mask: bool = False + no_zscore: bool = False + detrend: bool = True + spike_thresh: float = 6.0 + skip_frames: int = 0 + out_tsz: Path = spikes_tsz.txt + out_spikes: Path = spikes_idx.txt + + class Outputs(python.Outputs): + out_tsz: File + out_spikes: File + num_spikes: int + + @staticmethod + def function( + in_file: File, + in_mask: File, + invert_mask: bool, + no_zscore: bool, + detrend: bool, + spike_thresh: float, + skip_frames: int, + out_tsz: Path, + out_spikes: Path, + ) -> tuples[File, File, int]: + out_tsz = attrs.NOTHING + out_spikes = attrs.NOTHING + num_spikes = attrs.NOTHING + func_nii = nb.load(in_file) + func_data = func_nii.get_fdata(dtype="float32") + func_shape = func_data.shape + ntsteps = func_shape[-1] + tr = func_nii.header.get_zooms()[-1] + nskip = skip_frames + + mask_data = np.bool_(nb.load(in_mask).dataobj) + mask_data[..., :nskip] = 0 + mask_data = np.stack([mask_data] * ntsteps, axis=-1) + + if not invert_mask: + brain = np.ma.array(func_data, mask=(mask_data != 1)) + else: + mask_data[..., :skip_frames] = 1 + brain = np.ma.array(func_data, mask=(mask_data == 1)) + + if detrend: + from nilearn.signal import clean + + brain = clean(brain[:, nskip:].T, t_r=tr, standardize=False).T + + if no_zscore: + ts_z = find_peaks(brain) + total_spikes = [] + else: + total_spikes, ts_z = find_spikes(brain, spike_thresh) + total_spikes = list(set(total_spikes)) + + out_tsz = op.abspath(out_tsz) + out_tsz = out_tsz + np.savetxt(out_tsz, ts_z) + + out_spikes = op.abspath(out_spikes) + out_spikes = out_spikes + np.savetxt(out_spikes, total_spikes) + num_spikes = len(total_spikes) + + return out_tsz, out_spikes, num_spikes + + +def _robust_zscore(data): + + return (data - np.atleast_2d(np.median(data, axis=1)).T) / np.atleast_2d( + data.std(axis=1) + ).T + + +def find_peaks(data): + + t_z = [data[:, :, i, :].mean(axis=0).mean(axis=0) for i in range(data.shape[2])] + return t_z + + +def find_spikes(data, spike_thresh): + + data -= np.median(np.median(np.median(data, axis=0), axis=0), axis=0) + slice_mean = np.median(np.median(data, axis=0), axis=0) + t_z = _robust_zscore(slice_mean) + spikes = np.abs(t_z) > spike_thresh + spike_inds = np.transpose(spikes.nonzero()) + # mask out the spikes and recompute z-scores using variance uncontaminated with spikes. + # This will catch smaller spikes that may have been swamped by big + # ones. + data.mask[:, :, spike_inds[:, 0], spike_inds[:, 1]] = True + slice_mean2 = np.median(np.median(data, axis=0), axis=0) + t_z = _robust_zscore(slice_mean2) + spikes = np.logical_or(spikes, np.abs(t_z) > spike_thresh) + spike_inds = [tuple(i) for i in np.transpose(spikes.nonzero())] + return spike_inds, t_z diff --git a/pydra/tasks/mriqc/interfaces/functional/tests/conftest.py b/pydra/tasks/mriqc/interfaces/functional/tests/conftest.py new file mode 100644 index 0000000..751042d --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/functional/tests/conftest.py @@ -0,0 +1,25 @@ + +# For debugging in IDE's don't catch raised exceptions and let the IDE +# break at it +import os +import pytest + + +if os.getenv("_PYTEST_RAISE", "0") != "0": + + @pytest.hookimpl(tryfirst=True) + def pytest_exception_interact(call): + raise call.excinfo.value # raise internal errors instead of capturing them + + @pytest.hookimpl(tryfirst=True) + def pytest_internalerror(excinfo): + raise excinfo.value # raise internal errors instead of capturing them + + def pytest_configure(config): + config.option.capture = 'no' # allow print statements to show up in the console + config.option.log_cli = True # show log messages in the console + config.option.log_level = "INFO" # set the log level to INFO + + CATCH_CLI_EXCEPTIONS = False +else: + CATCH_CLI_EXCEPTIONS = True diff --git a/pydra/tasks/mriqc/interfaces/functional/tests/test_functionalqc.py b/pydra/tasks/mriqc/interfaces/functional/tests/test_functionalqc.py new file mode 100644 index 0000000..85839bf --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/functional/tests/test_functionalqc.py @@ -0,0 +1,23 @@ +from fileformats.generic import File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.interfaces.functional.functional_qc import FunctionalQC +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_functionalqc_1(): + task = FunctionalQC() + task.inputs.in_epi = File.sample(seed=0) + task.inputs.in_hmc = File.sample(seed=1) + task.inputs.in_tsnr = File.sample(seed=2) + task.inputs.in_mask = File.sample(seed=3) + task.inputs.direction = "all" + task.inputs.in_fd = File.sample(seed=5) + task.inputs.fd_thres = 0.2 + task.inputs.in_dvars = File.sample(seed=7) + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/interfaces/functional/tests/test_gathertimeseries.py b/pydra/tasks/mriqc/interfaces/functional/tests/test_gathertimeseries.py new file mode 100644 index 0000000..677c5a7 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/functional/tests/test_gathertimeseries.py @@ -0,0 +1,20 @@ +from fileformats.generic import File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.interfaces.functional.gather_timeseries import GatherTimeseries +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_gathertimeseries_1(): + task = GatherTimeseries() + task.inputs.dvars = File.sample(seed=0) + task.inputs.fd = File.sample(seed=1) + task.inputs.mpars = File.sample(seed=2) + task.inputs.outliers = File.sample(seed=4) + task.inputs.quality = File.sample(seed=5) + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/interfaces/functional/tests/test_selectecho.py b/pydra/tasks/mriqc/interfaces/functional/tests/test_selectecho.py new file mode 100644 index 0000000..08d3602 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/functional/tests/test_selectecho.py @@ -0,0 +1,17 @@ +from fileformats.generic import File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.interfaces.functional.select_echo import SelectEcho +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_selectecho_1(): + task = SelectEcho() + task.inputs.in_files = [File.sample(seed=0)] + task.inputs.te_reference = 0.03 + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/interfaces/functional/tests/test_spikes.py b/pydra/tasks/mriqc/interfaces/functional/tests/test_spikes.py new file mode 100644 index 0000000..6394182 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/functional/tests/test_spikes.py @@ -0,0 +1,24 @@ +from fileformats.generic import File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.interfaces.functional.spikes import Spikes +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_spikes_1(): + task = Spikes() + task.inputs.in_file = File.sample(seed=0) + task.inputs.in_mask = File.sample(seed=1) + task.inputs.invert_mask = False + task.inputs.no_zscore = False + task.inputs.detrend = True + task.inputs.spike_thresh = 6.0 + task.inputs.skip_frames = 0 + task.inputs.out_tsz = "spikes_tsz.txt" + task.inputs.out_spikes = "spikes_idx.txt" + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/interfaces/reports/__init__.py b/pydra/tasks/mriqc/interfaces/reports/__init__.py new file mode 100644 index 0000000..8d9323e --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/reports/__init__.py @@ -0,0 +1 @@ +from .add_provenance import AddProvenance diff --git a/pydra/tasks/mriqc/interfaces/reports/add_provenance.py b/pydra/tasks/mriqc/interfaces/reports/add_provenance.py new file mode 100644 index 0000000..65e9143 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/reports/add_provenance.py @@ -0,0 +1,60 @@ +import attrs +from fileformats.generic import File +import logging +import nibabel as nb +import numpy as np +from pydra.compose import python + + +logger = logging.getLogger(__name__) + + +@python.define +class AddProvenance(python.Task["AddProvenance.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.interfaces.reports.add_provenance import AddProvenance + + """ + + in_file: File + air_msk: File + rot_msk: File + modality: str + + class Outputs(python.Outputs): + out_prov: dict + + @staticmethod + def function(in_file: File, air_msk: File, rot_msk: File, modality: str) -> dict: + out_prov = attrs.NOTHING + from nipype.utils.filemanip import hash_infile + + out_prov = { + "md5sum": hash_infile(in_file), + "version": '', + "software": "mriqc", + "settings": { + "testing": False, + }, + } + + if modality in ("T1w", "T2w"): + air_msk_size = np.asanyarray(nb.load(air_msk).dataobj).astype(bool).sum() + rot_msk_size = np.asanyarray(nb.load(rot_msk).dataobj).astype(bool).sum() + out_prov["warnings"] = { + "small_air_mask": bool(air_msk_size < 5e5), + "large_rot_frame": bool(rot_msk_size > 500), + } + + if modality == "bold": + out_prov["settings"].update( + { + "fd_thres": 0.2, # .fd_thres + } + ) + + return out_prov diff --git a/pydra/tasks/mriqc/interfaces/reports/tests/conftest.py b/pydra/tasks/mriqc/interfaces/reports/tests/conftest.py new file mode 100644 index 0000000..751042d --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/reports/tests/conftest.py @@ -0,0 +1,25 @@ + +# For debugging in IDE's don't catch raised exceptions and let the IDE +# break at it +import os +import pytest + + +if os.getenv("_PYTEST_RAISE", "0") != "0": + + @pytest.hookimpl(tryfirst=True) + def pytest_exception_interact(call): + raise call.excinfo.value # raise internal errors instead of capturing them + + @pytest.hookimpl(tryfirst=True) + def pytest_internalerror(excinfo): + raise excinfo.value # raise internal errors instead of capturing them + + def pytest_configure(config): + config.option.capture = 'no' # allow print statements to show up in the console + config.option.log_cli = True # show log messages in the console + config.option.log_level = "INFO" # set the log level to INFO + + CATCH_CLI_EXCEPTIONS = False +else: + CATCH_CLI_EXCEPTIONS = True diff --git a/pydra/tasks/mriqc/interfaces/reports/tests/test_addprovenance.py b/pydra/tasks/mriqc/interfaces/reports/tests/test_addprovenance.py new file mode 100644 index 0000000..61e3ea5 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/reports/tests/test_addprovenance.py @@ -0,0 +1,18 @@ +from fileformats.generic import File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.interfaces.reports.add_provenance import AddProvenance +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_addprovenance_1(): + task = AddProvenance() + task.inputs.in_file = File.sample(seed=0) + task.inputs.air_msk = File.sample(seed=1) + task.inputs.rot_msk = File.sample(seed=2) + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/interfaces/synthstrip/__init__.py b/pydra/tasks/mriqc/interfaces/synthstrip/__init__.py new file mode 100644 index 0000000..63e821d --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/synthstrip/__init__.py @@ -0,0 +1 @@ +from .synth_strip import SynthStrip diff --git a/pydra/tasks/mriqc/interfaces/synthstrip/synth_strip.py b/pydra/tasks/mriqc/interfaces/synthstrip/synth_strip.py new file mode 100644 index 0000000..cbeab14 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/synthstrip/synth_strip.py @@ -0,0 +1,43 @@ +from fileformats.generic import File +import logging +from pathlib import Path +from pydra.compose import shell + + +logger = logging.getLogger(__name__) + + +@shell.define +class SynthStrip(shell.Task["SynthStrip.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.interfaces.synthstrip.synth_strip import SynthStrip + + """ + + executable = "synthstrip" + in_file: File = shell.arg( + help="Input image to be brain extracted", + argstr="-i {in_file}", + copy_mode="File.CopyMode.copy", + ) + use_gpu: bool = shell.arg(help="Use GPU", argstr="-g", default=False) + model: File = shell.arg( + help="file containing model's weights", + argstr="--model {model}", + default="/Applications/freesurfer/7.4.1/models/synthstrip.1.pt", + ) + border_mm: int = shell.arg( + help="Mask border threshold in mm", argstr="-b {border_mm}", default=1 + ) + num_threads: int = shell.arg(help="Number of threads", argstr="-n {num_threads}") + + class Outputs(shell.Outputs): + out_mask: Path = shell.outarg( + help="store brainmask to file", + argstr="-m {out_mask}", + path_template="{in_file}_desc-brain_mask.nii.gz", + ) diff --git a/pydra/tasks/mriqc/interfaces/synthstrip/tests/conftest.py b/pydra/tasks/mriqc/interfaces/synthstrip/tests/conftest.py new file mode 100644 index 0000000..751042d --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/synthstrip/tests/conftest.py @@ -0,0 +1,25 @@ + +# For debugging in IDE's don't catch raised exceptions and let the IDE +# break at it +import os +import pytest + + +if os.getenv("_PYTEST_RAISE", "0") != "0": + + @pytest.hookimpl(tryfirst=True) + def pytest_exception_interact(call): + raise call.excinfo.value # raise internal errors instead of capturing them + + @pytest.hookimpl(tryfirst=True) + def pytest_internalerror(excinfo): + raise excinfo.value # raise internal errors instead of capturing them + + def pytest_configure(config): + config.option.capture = 'no' # allow print statements to show up in the console + config.option.log_cli = True # show log messages in the console + config.option.log_level = "INFO" # set the log level to INFO + + CATCH_CLI_EXCEPTIONS = False +else: + CATCH_CLI_EXCEPTIONS = True diff --git a/pydra/tasks/mriqc/interfaces/synthstrip/tests/test_synthstrip.py b/pydra/tasks/mriqc/interfaces/synthstrip/tests/test_synthstrip.py new file mode 100644 index 0000000..ab311e4 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/synthstrip/tests/test_synthstrip.py @@ -0,0 +1,20 @@ +from fileformats.generic import File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.interfaces.synthstrip.synth_strip import SynthStrip +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_synthstrip_1(): + task = SynthStrip() + task.inputs.in_file = File.sample(seed=0) + task.inputs.use_gpu = False + task.inputs.model = File.sample(seed=2) + task.inputs.border_mm = 1 + print(f"CMDLINE: {task.cmdline}\n\n") + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/interfaces/tests/conftest.py b/pydra/tasks/mriqc/interfaces/tests/conftest.py new file mode 100644 index 0000000..751042d --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/tests/conftest.py @@ -0,0 +1,25 @@ + +# For debugging in IDE's don't catch raised exceptions and let the IDE +# break at it +import os +import pytest + + +if os.getenv("_PYTEST_RAISE", "0") != "0": + + @pytest.hookimpl(tryfirst=True) + def pytest_exception_interact(call): + raise call.excinfo.value # raise internal errors instead of capturing them + + @pytest.hookimpl(tryfirst=True) + def pytest_internalerror(excinfo): + raise excinfo.value # raise internal errors instead of capturing them + + def pytest_configure(config): + config.option.capture = 'no' # allow print statements to show up in the console + config.option.log_cli = True # show log messages in the console + config.option.log_level = "INFO" # set the log level to INFO + + CATCH_CLI_EXCEPTIONS = False +else: + CATCH_CLI_EXCEPTIONS = True diff --git a/pydra/tasks/mriqc/interfaces/tests/test_derivativesdatasink.py b/pydra/tasks/mriqc/interfaces/tests/test_derivativesdatasink.py new file mode 100644 index 0000000..1e144d1 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/tests/test_derivativesdatasink.py @@ -0,0 +1,21 @@ +from fileformats.generic import Directory, File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.interfaces.derivatives_data_sink import DerivativesDataSink +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_derivativesdatasink_1(): + task = DerivativesDataSink() + task.inputs.base_directory = Directory.sample(seed=0) + task.inputs.check_hdr = True + task.inputs.compress = [] + task.inputs.dismiss_entities = [] + task.inputs.in_file = [File.sample(seed=5)] + task.inputs.source_file = [File.sample(seed=7)] + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/interfaces/transitional/__init__.py b/pydra/tasks/mriqc/interfaces/transitional/__init__.py new file mode 100644 index 0000000..0ef3ac2 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/transitional/__init__.py @@ -0,0 +1 @@ +from .gcor import GCOR diff --git a/pydra/tasks/mriqc/interfaces/transitional/gcor.py b/pydra/tasks/mriqc/interfaces/transitional/gcor.py new file mode 100644 index 0000000..53f6aa2 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/transitional/gcor.py @@ -0,0 +1,63 @@ +import attrs +from fileformats.generic import File +from fileformats.medimage import Nifti1 +import logging +from pydra.compose import shell + + +logger = logging.getLogger(__name__) + + +def _list_outputs(inputs=None, stdout=None, stderr=None, output_dir=None): + inputs = attrs.asdict(inputs) + + return {"out": parsed_inputs["_gcor"]} + + +def out_callable(output_dir, inputs, stdout, stderr): + outputs = _list_outputs( + output_dir=output_dir, inputs=inputs, stdout=stdout, stderr=stderr + ) + return outputs.get("out") + + +@shell.define +class GCOR(shell.Task["GCOR.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from fileformats.medimage import Nifti1 + >>> from pydra.tasks.mriqc.interfaces.transitional.gcor import GCOR + + >>> task = GCOR() + >>> task.inputs.in_file = Nifti1.mock("func.nii") + >>> task.inputs.mask = File.mock() + >>> task.inputs.nfirst = 4 + >>> task.cmdline + '@compute_gcor -nfirst 4 -input func.nii' + + + """ + + executable = "@compute_gcor" + in_file: Nifti1 = shell.arg( + help="input dataset to compute the GCOR over", + argstr="-input {in_file}", + position=-1, + ) + mask: File = shell.arg( + help="mask dataset, for restricting the computation", argstr="-mask {mask}" + ) + nfirst: int = shell.arg( + help="specify number of initial TRs to ignore", argstr="-nfirst {nfirst}" + ) + no_demean: bool = shell.arg( + help="do not (need to) demean as first step", argstr="-no_demean" + ) + + class Outputs(shell.Outputs): + out: float | None = shell.out( + help="global correlation value", callable=out_callable + ) diff --git a/pydra/tasks/mriqc/interfaces/transitional/tests/conftest.py b/pydra/tasks/mriqc/interfaces/transitional/tests/conftest.py new file mode 100644 index 0000000..751042d --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/transitional/tests/conftest.py @@ -0,0 +1,25 @@ + +# For debugging in IDE's don't catch raised exceptions and let the IDE +# break at it +import os +import pytest + + +if os.getenv("_PYTEST_RAISE", "0") != "0": + + @pytest.hookimpl(tryfirst=True) + def pytest_exception_interact(call): + raise call.excinfo.value # raise internal errors instead of capturing them + + @pytest.hookimpl(tryfirst=True) + def pytest_internalerror(excinfo): + raise excinfo.value # raise internal errors instead of capturing them + + def pytest_configure(config): + config.option.capture = 'no' # allow print statements to show up in the console + config.option.log_cli = True # show log messages in the console + config.option.log_level = "INFO" # set the log level to INFO + + CATCH_CLI_EXCEPTIONS = False +else: + CATCH_CLI_EXCEPTIONS = True diff --git a/pydra/tasks/mriqc/interfaces/transitional/tests/test_gcor.py b/pydra/tasks/mriqc/interfaces/transitional/tests/test_gcor.py new file mode 100644 index 0000000..5d72b01 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/transitional/tests/test_gcor.py @@ -0,0 +1,29 @@ +from fileformats.generic import File +from fileformats.medimage import Nifti1 +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.interfaces.transitional.gcor import GCOR +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_gcor_1(): + task = GCOR() + task.inputs.in_file = Nifti1.sample(seed=0) + task.inputs.mask = File.sample(seed=1) + print(f"CMDLINE: {task.cmdline}\n\n") + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) + + +@pytest.mark.xfail +def test_gcor_2(): + task = GCOR() + task.inputs.in_file = Nifti1.sample(seed=0) + task.inputs.nfirst = 4 + print(f"CMDLINE: {task.cmdline}\n\n") + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/interfaces/webapi/__init__.py b/pydra/tasks/mriqc/interfaces/webapi/__init__.py new file mode 100644 index 0000000..c813019 --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/webapi/__init__.py @@ -0,0 +1,182 @@ +import attrs +from fileformats.generic import Directory, File +import json +import logging +from pydra.tasks.mriqc import messages +import orjson +from pathlib import Path +from pydra.compose import python, shell, workflow +from .upload_iq_ms import UploadIQMs +from pydra.utils.typing import MultiInputObj +import typing as ty +import yaml + + +logger = logging.getLogger(__name__) + + +def _hashfields(data): + + from hashlib import sha256 + + for name in HASH_BIDS: + if name in data: + data[name] = sha256(data[name].encode()).hexdigest() + return data + + +def upload_qc_metrics( + in_iqms, endpoint=None, email=None, auth_token=None, modality=None +): + """ + Upload qc metrics to remote repository. + + :param str in_iqms: Path to the qc metric json file as a string + :param str webapi_url: the protocol (either http or https) + :param str email: email address to be included with the metric submission + :param str auth_token: authentication token + + :return: either the response object if a response was successfully sent + or it returns the string "No Response" + :rtype: object + + + """ + from copy import deepcopy + import requests + + if not endpoint or not auth_token: + # If endpoint unknown, do not even report what happens to the token. + errmsg = "Unknown API endpoint" if not endpoint else "Authentication failed." + return Bunch(status_code=1, text=errmsg) + in_data = orjson.loads(Path(in_iqms).read_bytes()) + # Extract metadata and provenance + meta = in_data.pop("bids_meta") + prov = in_data.pop("provenance") + # At this point, data should contain only IQMs + data = deepcopy(in_data) + # Check modality + modality = meta.get("modality", None) or meta.get("suffix", None) or modality + if modality not in ("T1w", "bold", "T2w"): + errmsg = ( + 'Submitting to MRIQCWebAPI: image modality should be "bold", "T1w", or "T2w", ' + f'(found "{modality}")' + ) + return Bunch(status_code=1, text=errmsg) + # Filter metadata values that aren't in whitelist + data["bids_meta"] = {k: meta[k] for k in META_WHITELIST if k in meta} + # Check for fields with appended _id + bids_meta_names = { + k: k.replace("_id", "") for k in META_WHITELIST if k.endswith("_id") + } + data["bids_meta"].update( + {k: meta[v] for k, v in bids_meta_names.items() if v in meta} + ) + # For compatibility with WebAPI. Should be rolled back to int + if (run_id := data["bids_meta"].get("run_id", None)) is not None: + data["bids_meta"]["run_id"] = f"{run_id}" + # One more chance for spelled-out BIDS entity acquisition + if (acq_id := meta.get("acquisition", None)) is not None: + data["bids_meta"]["acq_id"] = acq_id + # Filter provenance values that aren't in whitelist + data["provenance"] = {k: prov[k] for k in PROV_WHITELIST if k in prov} + # Hash fields that may contain personal information + data["bids_meta"] = _hashfields(data["bids_meta"]) + data["bids_meta"]["modality"] = modality + if email: + data["provenance"]["email"] = email + headers = {"Authorization": auth_token, "Content-Type": "application/json"} + start_message = messages.QC_UPLOAD_START.format(url=endpoint) + logger.info(start_message) + errmsg = None + try: + # if the modality is bold, call "bold" endpoint + response = requests.post( + f"{endpoint}/{modality}", + headers=headers, + data=orjson.dumps(data, option=orjson.OPT_SERIALIZE_NUMPY), + timeout=15, + ) + except requests.ConnectionError as err: + errmsg = ("Error uploading IQMs: Connection error:", f"{err}") + except requests.exceptions.ReadTimeout as err: + errmsg = (f"Error uploading IQMs: Server {endpoint} is down.", f"{err}") + if errmsg is not None: + response = Bunch(status_code=1, text="\n".join(errmsg)) + return response, data + + +HASH_BIDS = ["subject_id", "session_id"] + +META_WHITELIST = [ + "AccelNumReferenceLines", + "AccelerationFactorPE", + "AcquisitionMatrix", + "CogAtlasID", + "CogPOID", + "CoilCombinationMethod", + "ContrastBolusIngredient", + "ConversionSoftware", + "ConversionSoftwareVersion", + "DelayTime", + "DeviceSerialNumber", + "EchoTime", + "EchoTrainLength", + "EffectiveEchoSpacing", + "FlipAngle", + "GradientSetType", + "HardcopyDeviceSoftwareVersion", + "ImageType", + "ImagingFrequency", + "InPlanePhaseEncodingDirection", + "InstitutionAddress", + "InstitutionName", + "Instructions", + "InversionTime", + "MRAcquisitionType", + "MRTransmitCoilSequence", + "MagneticFieldStrength", + "Manufacturer", + "ManufacturersModelName", + "MatrixCoilMode", + "MultibandAccelerationFactor", + "NumberOfAverages", + "NumberOfPhaseEncodingSteps", + "NumberOfVolumesDiscardedByScanner", + "NumberOfVolumesDiscardedByUser", + "NumberShots", + "ParallelAcquisitionTechnique", + "ParallelReductionFactorInPlane", + "PartialFourier", + "PartialFourierDirection", + "PatientPosition", + "PercentPhaseFieldOfView", + "PercentSampling", + "PhaseEncodingDirection", + "PixelBandwidth", + "ProtocolName", + "PulseSequenceDetails", + "PulseSequenceType", + "ReceiveCoilName", + "RepetitionTime", + "ScanOptions", + "ScanningSequence", + "SequenceName", + "SequenceVariant", + "SliceEncodingDirection", + "SoftwareVersions", + "TaskDescription", + "TaskName", + "TotalReadoutTime", + "TotalScanTimeSec", + "TransmitCoilName", + "VariableFlipAngleFlag", + "acq_id", + "modality", + "run_id", + "subject_id", + "task_id", + "session_id", +] + +PROV_WHITELIST = ["version", "md5sum", "software", "settings"] diff --git a/pydra/tasks/mriqc/interfaces/webapi/tests/conftest.py b/pydra/tasks/mriqc/interfaces/webapi/tests/conftest.py new file mode 100644 index 0000000..751042d --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/webapi/tests/conftest.py @@ -0,0 +1,25 @@ + +# For debugging in IDE's don't catch raised exceptions and let the IDE +# break at it +import os +import pytest + + +if os.getenv("_PYTEST_RAISE", "0") != "0": + + @pytest.hookimpl(tryfirst=True) + def pytest_exception_interact(call): + raise call.excinfo.value # raise internal errors instead of capturing them + + @pytest.hookimpl(tryfirst=True) + def pytest_internalerror(excinfo): + raise excinfo.value # raise internal errors instead of capturing them + + def pytest_configure(config): + config.option.capture = 'no' # allow print statements to show up in the console + config.option.log_cli = True # show log messages in the console + config.option.log_level = "INFO" # set the log level to INFO + + CATCH_CLI_EXCEPTIONS = False +else: + CATCH_CLI_EXCEPTIONS = True diff --git a/pydra/tasks/mriqc/interfaces/webapi/tests/test_uploadiqms.py b/pydra/tasks/mriqc/interfaces/webapi/tests/test_uploadiqms.py new file mode 100644 index 0000000..828e34c --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/webapi/tests/test_uploadiqms.py @@ -0,0 +1,15 @@ +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.interfaces.webapi.upload_iq_ms import UploadIQMs +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_uploadiqms_1(): + task = UploadIQMs() + task.inputs.strict = False + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/interfaces/webapi/upload_iq_ms.py b/pydra/tasks/mriqc/interfaces/webapi/upload_iq_ms.py new file mode 100644 index 0000000..2a8e78f --- /dev/null +++ b/pydra/tasks/mriqc/interfaces/webapi/upload_iq_ms.py @@ -0,0 +1,269 @@ +import attrs +from fileformats.generic import File +import logging +import orjson +from pathlib import Path +from pydra.compose import python +import typing as ty + + +logger = logging.getLogger(__name__) + + +@python.define +class UploadIQMs(python.Task["UploadIQMs.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.interfaces.webapi.upload_iq_ms import UploadIQMs + + """ + + in_iqms: dict + endpoint: str + auth_token: str + email: str + strict: bool = False + modality: str = undefined + + class Outputs(python.Outputs): + api_id: ty.Any + payload_file: File + + @staticmethod + def function( + in_iqms: dict, + endpoint: str, + auth_token: str, + email: str, + strict: bool, + modality: str, + ) -> tuples[ty.Any, File]: + api_id = attrs.NOTHING + payload_file = attrs.NOTHING + email = None + if email is not attrs.NOTHING: + email = email + + api_id = None + + response, payload = upload_qc_metrics( + in_iqms, + endpoint=endpoint, + auth_token=auth_token, + email=email, + modality=modality, + ) + + payload_str = orjson.dumps( + payload, + option=( + orjson.OPT_SORT_KEYS + | orjson.OPT_INDENT_2 + | orjson.OPT_APPEND_NEWLINE + | orjson.OPT_SERIALIZE_NUMPY + ), + ).decode("utf-8") + Path("payload.json").write_text(payload_str) + payload_file = str(Path("payload.json").absolute()) + + try: + api_id = response.json()["_id"] + except (AttributeError, KeyError, ValueError): + + errmsg = ( + "QC metrics upload failed to create an ID for the record " + f"uploaded. Response from server follows: {response.text}" + "\n\nPayload:\n" + f"{payload_str}" + ) + logger.warning(errmsg) + + if response.status_code == 201: + logger.info('QC metrics successfully uploaded.') + + errmsg = "\n".join( + [ + "Unsuccessful upload.", + f"Server response status {response.status_code}:", + response.text, + "", + "", + "Payload:", + f"{payload_str}", + ] + ) + logger.warning(errmsg) + if strict: + raise RuntimeError(errmsg) + + return api_id, payload_file + + +def _hashfields(data): + + from hashlib import sha256 + + for name in HASH_BIDS: + if name in data: + data[name] = sha256(data[name].encode()).hexdigest() + return data + + +def upload_qc_metrics( + in_iqms, endpoint=None, email=None, auth_token=None, modality=None +): + """ + Upload qc metrics to remote repository. + + :param str in_iqms: Path to the qc metric json file as a string + :param str webapi_url: the protocol (either http or https) + :param str email: email address to be included with the metric submission + :param str auth_token: authentication token + + :return: either the response object if a response was successfully sent + or it returns the string "No Response" + :rtype: object + + + """ + from copy import deepcopy + import requests + + if not endpoint or not auth_token: + # If endpoint unknown, do not even report what happens to the token. + errmsg = "Unknown API endpoint" if not endpoint else "Authentication failed." + return Bunch(status_code=1, text=errmsg) + in_data = orjson.loads(Path(in_iqms).read_bytes()) + # Extract metadata and provenance + meta = in_data.pop("bids_meta") + prov = in_data.pop("provenance") + # At this point, data should contain only IQMs + data = deepcopy(in_data) + # Check modality + modality = meta.get("modality", None) or meta.get("suffix", None) or modality + if modality not in ("T1w", "bold", "T2w"): + errmsg = ( + 'Submitting to MRIQCWebAPI: image modality should be "bold", "T1w", or "T2w", ' + f'(found "{modality}")' + ) + return Bunch(status_code=1, text=errmsg) + # Filter metadata values that aren't in whitelist + data["bids_meta"] = {k: meta[k] for k in META_WHITELIST if k in meta} + # Check for fields with appended _id + bids_meta_names = { + k: k.replace("_id", "") for k in META_WHITELIST if k.endswith("_id") + } + data["bids_meta"].update( + {k: meta[v] for k, v in bids_meta_names.items() if v in meta} + ) + # For compatibility with WebAPI. Should be rolled back to int + if (run_id := data["bids_meta"].get("run_id", None)) is not None: + data["bids_meta"]["run_id"] = f"{run_id}" + # One more chance for spelled-out BIDS entity acquisition + if (acq_id := meta.get("acquisition", None)) is not None: + data["bids_meta"]["acq_id"] = acq_id + # Filter provenance values that aren't in whitelist + data["provenance"] = {k: prov[k] for k in PROV_WHITELIST if k in prov} + # Hash fields that may contain personal information + data["bids_meta"] = _hashfields(data["bids_meta"]) + data["bids_meta"]["modality"] = modality + if email: + data["provenance"]["email"] = email + headers = {"Authorization": auth_token, "Content-Type": "application/json"} + start_message = 'MRIQC Web API: submitting to <{url}>'.format(url=endpoint) + logger.info(start_message) + errmsg = None + try: + # if the modality is bold, call "bold" endpoint + response = requests.post( + f"{endpoint}/{modality}", + headers=headers, + data=orjson.dumps(data, option=orjson.OPT_SERIALIZE_NUMPY), + timeout=15, + ) + except requests.ConnectionError as err: + errmsg = ("Error uploading IQMs: Connection error:", f"{err}") + except requests.exceptions.ReadTimeout as err: + errmsg = (f"Error uploading IQMs: Server {endpoint} is down.", f"{err}") + if errmsg is not None: + response = Bunch(status_code=1, text="\n".join(errmsg)) + return response, data + + +HASH_BIDS = ["subject_id", "session_id"] + +META_WHITELIST = [ + "AccelNumReferenceLines", + "AccelerationFactorPE", + "AcquisitionMatrix", + "CogAtlasID", + "CogPOID", + "CoilCombinationMethod", + "ContrastBolusIngredient", + "ConversionSoftware", + "ConversionSoftwareVersion", + "DelayTime", + "DeviceSerialNumber", + "EchoTime", + "EchoTrainLength", + "EffectiveEchoSpacing", + "FlipAngle", + "GradientSetType", + "HardcopyDeviceSoftwareVersion", + "ImageType", + "ImagingFrequency", + "InPlanePhaseEncodingDirection", + "InstitutionAddress", + "InstitutionName", + "Instructions", + "InversionTime", + "MRAcquisitionType", + "MRTransmitCoilSequence", + "MagneticFieldStrength", + "Manufacturer", + "ManufacturersModelName", + "MatrixCoilMode", + "MultibandAccelerationFactor", + "NumberOfAverages", + "NumberOfPhaseEncodingSteps", + "NumberOfVolumesDiscardedByScanner", + "NumberOfVolumesDiscardedByUser", + "NumberShots", + "ParallelAcquisitionTechnique", + "ParallelReductionFactorInPlane", + "PartialFourier", + "PartialFourierDirection", + "PatientPosition", + "PercentPhaseFieldOfView", + "PercentSampling", + "PhaseEncodingDirection", + "PixelBandwidth", + "ProtocolName", + "PulseSequenceDetails", + "PulseSequenceType", + "ReceiveCoilName", + "RepetitionTime", + "ScanOptions", + "ScanningSequence", + "SequenceName", + "SequenceVariant", + "SliceEncodingDirection", + "SoftwareVersions", + "TaskDescription", + "TaskName", + "TotalReadoutTime", + "TotalScanTimeSec", + "TransmitCoilName", + "VariableFlipAngleFlag", + "acq_id", + "modality", + "run_id", + "subject_id", + "task_id", + "session_id", +] + +PROV_WHITELIST = ["version", "md5sum", "software", "settings"] diff --git a/pydra/tasks/mriqc/messages.py b/pydra/tasks/mriqc/messages.py new file mode 100644 index 0000000..0574250 --- /dev/null +++ b/pydra/tasks/mriqc/messages.py @@ -0,0 +1,21 @@ +import logging +from pydra.compose import workflow + + +logger = logging.getLogger(__name__) + + +BUILDING_WORKFLOW = "Building {modality} MRIQC workflow {detail}." + +QC_UPLOAD_COMPLETE = "QC metrics successfully uploaded." + +QC_UPLOAD_START = "MRIQC Web API: submitting to <{url}>" + +SUSPICIOUS_DATA_TYPE = "Input image {in_file} has a suspicious data type: '{dtype}'" + +VOXEL_SIZE_OK = "Voxel size is large enough." + +VOXEL_SIZE_SMALL = ( + "One or more voxel dimensions (%f, %f, %f) are smaller than the " + "requested voxel size (%f) - diff=(%f, %f, %f)" +) diff --git a/pydra/tasks/mriqc/nipype_ports/__init__.py b/pydra/tasks/mriqc/nipype_ports/__init__.py new file mode 100644 index 0000000..3b6ef55 --- /dev/null +++ b/pydra/tasks/mriqc/nipype_ports/__init__.py @@ -0,0 +1,27 @@ +from .algorithms import ( + ComputeDVARS, + FramewiseDisplacement, + IFLOGGER, + NonSteadyStateDetector, + TSNR, + _AR_est_YW, + compute_dvars, + is_outlier, + plot_confound, + regress_poly, +) +from .utils import ( + _cifs_table, + _generate_cifs_table, + _parse_mount_table, + copyfile, + fmlogger, + fname_presuffix, + get_related_files, + hash_infile, + hash_timestamp, + normalize_mc_params, + on_cifs, + related_filetype_sets, + split_filename, +) diff --git a/pydra/tasks/mriqc/nipype_ports/algorithms/__init__.py b/pydra/tasks/mriqc/nipype_ports/algorithms/__init__.py new file mode 100644 index 0000000..fb9df24 --- /dev/null +++ b/pydra/tasks/mriqc/nipype_ports/algorithms/__init__.py @@ -0,0 +1,12 @@ +from .confounds import ( + ComputeDVARS, + FramewiseDisplacement, + IFLOGGER, + NonSteadyStateDetector, + TSNR, + _AR_est_YW, + compute_dvars, + is_outlier, + plot_confound, + regress_poly, +) diff --git a/pydra/tasks/mriqc/nipype_ports/algorithms/confounds/__init__.py b/pydra/tasks/mriqc/nipype_ports/algorithms/confounds/__init__.py new file mode 100644 index 0000000..49214ce --- /dev/null +++ b/pydra/tasks/mriqc/nipype_ports/algorithms/confounds/__init__.py @@ -0,0 +1,22 @@ +import attrs +from fileformats.generic import Directory, File +import json +import logging +import nibabel as nb +import numpy as np +from numpy.polynomial import Legendre +from pathlib import Path +from pydra.compose import python, shell, workflow +from .compute_dvars import ComputeDVARS +from .framewise_displacement import FramewiseDisplacement +from .non_steady_state_detector import NonSteadyStateDetector +from .tsnr import TSNR +from pydra.utils.typing import MultiInputObj +import typing as ty +import yaml + + +logger = logging.getLogger(__name__) + + +IFLOGGER = logging.getLogger("nipype.interface") diff --git a/pydra/tasks/mriqc/nipype_ports/algorithms/confounds/compute_dvars.py b/pydra/tasks/mriqc/nipype_ports/algorithms/confounds/compute_dvars.py new file mode 100644 index 0000000..804deb1 --- /dev/null +++ b/pydra/tasks/mriqc/nipype_ports/algorithms/confounds/compute_dvars.py @@ -0,0 +1,195 @@ +import attrs +from fileformats.generic import File +import logging +import numpy as np +import os.path as op +from pydra.compose import python +import typing as ty + + +logger = logging.getLogger(__name__) + + +@python.define +class ComputeDVARS(python.Task["ComputeDVARS.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.nipype_ports.algorithms.confounds.compute_dvars import ComputeDVARS + + """ + + in_file: File + in_mask: File + remove_zerovariance: bool = True + variance_tol: float = 1e-07 + save_std: bool = True + save_nstd: bool = False + save_vxstd: bool = False + save_all: bool = False + series_tr: float + save_plot: bool = False + figdpi: int = 100 + figsize: ty.Any = (11.7, 2.3) + figformat: ty.Any = png + intensity_normalization: float = 1000.0 + + class Outputs(python.Outputs): + out_std: File + out_nstd: File + out_vxstd: File + out_all: File + avg_std: float + avg_nstd: float + avg_vxstd: float + fig_std: File + fig_nstd: File + fig_vxstd: File + + @staticmethod + def function( + in_file: File, + in_mask: File, + remove_zerovariance: bool, + variance_tol: float, + save_std: bool, + save_nstd: bool, + save_vxstd: bool, + save_all: bool, + series_tr: float, + save_plot: bool, + figdpi: int, + figsize: ty.Any, + figformat: ty.Any, + intensity_normalization: float, + ) -> tuples[File, File, File, File, float, float, float, File, File, File]: + out_std = attrs.NOTHING + out_nstd = attrs.NOTHING + out_vxstd = attrs.NOTHING + out_all = attrs.NOTHING + avg_std = attrs.NOTHING + avg_nstd = attrs.NOTHING + avg_vxstd = attrs.NOTHING + fig_std = attrs.NOTHING + fig_nstd = attrs.NOTHING + fig_vxstd = attrs.NOTHING + self_dict = {} + self_dict["_results"] = {} + + dvars = compute_dvars( + in_file, + in_mask, + remove_zerovariance=remove_zerovariance, + variance_tol=variance_tol, + intensity_normalization=intensity_normalization, + ) + + ( + avg_std, + avg_nstd, + avg_vxstd, + ) = np.mean( + dvars, axis=1 + ).astype(float) + + tr = None + if series_tr is not attrs.NOTHING: + tr = series_tr + + if save_std: + out_file = _gen_fname("dvars_std", ext="tsv", in_file=in_file) + np.savetxt(out_file, dvars[0], fmt="%0.6f") + out_std = out_file + + if save_plot: + fig_std = _gen_fname("dvars_std", ext=figformat, in_file=in_file) + fig = plot_confound( + dvars[0], figsize, "Standardized DVARS", series_tr=tr + ) + fig.savefig( + fig_std, + dpi=float(figdpi), + format=figformat, + bbox_inches="tight", + ) + fig.clf() + + if save_nstd: + out_file = _gen_fname("dvars_nstd", ext="tsv", in_file=in_file) + np.savetxt(out_file, dvars[1], fmt="%0.6f") + out_nstd = out_file + + if save_plot: + fig_nstd = _gen_fname("dvars_nstd", ext=figformat, in_file=in_file) + fig = plot_confound(dvars[1], figsize, "DVARS", series_tr=tr) + fig.savefig( + fig_nstd, + dpi=float(figdpi), + format=figformat, + bbox_inches="tight", + ) + fig.clf() + + if save_vxstd: + out_file = _gen_fname("dvars_vxstd", ext="tsv", in_file=in_file) + np.savetxt(out_file, dvars[2], fmt="%0.6f") + out_vxstd = out_file + + if save_plot: + fig_vxstd = _gen_fname("dvars_vxstd", ext=figformat, in_file=in_file) + fig = plot_confound( + dvars[2], figsize, "Voxelwise std DVARS", series_tr=tr + ) + fig.savefig( + fig_vxstd, + dpi=float(figdpi), + format=figformat, + bbox_inches="tight", + ) + fig.clf() + + if save_all: + out_file = _gen_fname("dvars", ext="tsv", in_file=in_file) + np.savetxt( + out_file, + np.vstack(dvars).T, + fmt="%0.8f", + delimiter="\t", + header="std DVARS\tnon-std DVARS\tvx-wise std DVARS", + comments="", + ) + out_all = out_file + + return ( + out_std, + out_nstd, + out_vxstd, + out_all, + avg_std, + avg_nstd, + avg_vxstd, + fig_std, + fig_nstd, + fig_vxstd, + ) + + +def _gen_fname(suffix, ext=None, in_file=None): + fname, in_ext = op.splitext(op.basename(in_file)) + + if in_ext == ".gz": + fname, in_ext2 = op.splitext(fname) + in_ext = in_ext2 + in_ext + + if ext is None: + ext = in_ext + + if ext.startswith("."): + ext = ext[1:] + + return op.abspath(f"{fname}_{suffix}.{ext}") + + +IFLOGGER = logging.getLogger("nipype.interface") diff --git a/pydra/tasks/mriqc/nipype_ports/algorithms/confounds/framewise_displacement.py b/pydra/tasks/mriqc/nipype_ports/algorithms/confounds/framewise_displacement.py new file mode 100644 index 0000000..779dbab --- /dev/null +++ b/pydra/tasks/mriqc/nipype_ports/algorithms/confounds/framewise_displacement.py @@ -0,0 +1,105 @@ +import attrs +from fileformats.generic import File +import logging +from pydra.tasks.mriqc.nipype_ports.utils.misc import normalize_mc_params +import numpy as np +import os.path as op +from pathlib import Path +from pydra.compose import python +from pydra.tasks.mriqc.nipype_ports.utils.misc import normalize_mc_params +import typing as ty + + +logger = logging.getLogger(__name__) + + +@python.define +class FramewiseDisplacement(python.Task["FramewiseDisplacement.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.nipype_ports.algorithms.confounds.framewise_displacement import FramewiseDisplacement + + """ + + in_file: File + parameter_source: ty.Any + radius: float = 50 + out_file: Path = fd_power_2012.txt + out_figure: Path = fd_power_2012.pdf + series_tr: float + save_plot: bool = False + normalize: bool = False + figdpi: int = 100 + figsize: ty.Any = (11.7, 2.3) + + class Outputs(python.Outputs): + out_file: File + out_figure: File + fd_average: float + + @staticmethod + def function( + in_file: File, + parameter_source: ty.Any, + radius: float, + out_file: Path, + out_figure: Path, + series_tr: float, + save_plot: bool, + normalize: bool, + figdpi: int, + figsize: ty.Any, + ) -> tuples[File, File, float]: + out_file = attrs.NOTHING + out_figure = attrs.NOTHING + fd_average = attrs.NOTHING + self_dict = {} + mpars = np.loadtxt(in_file) # mpars is N_t x 6 + mpars = np.apply_along_axis( + func1d=normalize_mc_params, + axis=1, + arr=mpars, + source=parameter_source, + ) + diff = mpars[:-1, :6] - mpars[1:, :6] + diff[:, 3:6] *= radius + fd_res = np.abs(diff).sum(axis=1) + + self_dict["_results"] = { + "out_file": op.abspath(out_file), + "fd_average": float(fd_res.mean()), + } + np.savetxt(out_file, fd_res, header="FramewiseDisplacement", comments="") + + if save_plot: + tr = None + if series_tr is not attrs.NOTHING: + tr = series_tr + + if normalize and tr is None: + IFLOGGER.warning("FD plot cannot be normalized if TR is not set") + + out_figure = op.abspath(out_figure) + fig = plot_confound( + fd_res, + figsize, + "FD", + units="mm", + series_tr=tr, + normalize=normalize, + ) + fig.savefig( + out_figure, + dpi=float(figdpi), + format=out_figure[-3:], + bbox_inches="tight", + ) + fig.clf() + + return out_file, out_figure, fd_average + + +IFLOGGER = logging.getLogger("nipype.interface") diff --git a/pydra/tasks/mriqc/nipype_ports/algorithms/confounds/non_steady_state_detector.py b/pydra/tasks/mriqc/nipype_ports/algorithms/confounds/non_steady_state_detector.py new file mode 100644 index 0000000..99b1b43 --- /dev/null +++ b/pydra/tasks/mriqc/nipype_ports/algorithms/confounds/non_steady_state_detector.py @@ -0,0 +1,38 @@ +import attrs +from fileformats.generic import File +import logging +import nibabel as nb +from pydra.compose import python + + +logger = logging.getLogger(__name__) + + +@python.define +class NonSteadyStateDetector(python.Task["NonSteadyStateDetector.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.nipype_ports.algorithms.confounds.non_steady_state_detector import NonSteadyStateDetector + + """ + + in_file: File + + class Outputs(python.Outputs): + n_volumes_to_discard: int + + @staticmethod + def function(in_file: File) -> int: + n_volumes_to_discard = attrs.NOTHING + self_dict = {} + in_nii = nb.load(in_file) + global_signal = ( + in_nii.dataobj[:, :, :, :50].mean(axis=0).mean(axis=0).mean(axis=0) + ) + + self_dict["_results"] = {"n_volumes_to_discard": is_outlier(global_signal)} + + return n_volumes_to_discard diff --git a/pydra/tasks/mriqc/nipype_ports/algorithms/confounds/tests/conftest.py b/pydra/tasks/mriqc/nipype_ports/algorithms/confounds/tests/conftest.py new file mode 100644 index 0000000..751042d --- /dev/null +++ b/pydra/tasks/mriqc/nipype_ports/algorithms/confounds/tests/conftest.py @@ -0,0 +1,25 @@ + +# For debugging in IDE's don't catch raised exceptions and let the IDE +# break at it +import os +import pytest + + +if os.getenv("_PYTEST_RAISE", "0") != "0": + + @pytest.hookimpl(tryfirst=True) + def pytest_exception_interact(call): + raise call.excinfo.value # raise internal errors instead of capturing them + + @pytest.hookimpl(tryfirst=True) + def pytest_internalerror(excinfo): + raise excinfo.value # raise internal errors instead of capturing them + + def pytest_configure(config): + config.option.capture = 'no' # allow print statements to show up in the console + config.option.log_cli = True # show log messages in the console + config.option.log_level = "INFO" # set the log level to INFO + + CATCH_CLI_EXCEPTIONS = False +else: + CATCH_CLI_EXCEPTIONS = True diff --git a/pydra/tasks/mriqc/nipype_ports/algorithms/confounds/tests/test_computedvars.py b/pydra/tasks/mriqc/nipype_ports/algorithms/confounds/tests/test_computedvars.py new file mode 100644 index 0000000..7f92a0e --- /dev/null +++ b/pydra/tasks/mriqc/nipype_ports/algorithms/confounds/tests/test_computedvars.py @@ -0,0 +1,30 @@ +from fileformats.generic import File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.nipype_ports.algorithms.confounds.compute_dvars import ( + ComputeDVARS, +) +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_computedvars_1(): + task = ComputeDVARS() + task.inputs.in_file = File.sample(seed=0) + task.inputs.in_mask = File.sample(seed=1) + task.inputs.remove_zerovariance = True + task.inputs.variance_tol = 1e-07 + task.inputs.save_std = True + task.inputs.save_nstd = False + task.inputs.save_vxstd = False + task.inputs.save_all = False + task.inputs.save_plot = False + task.inputs.figdpi = 100 + task.inputs.figsize = [11.7, 2.3] + task.inputs.figformat = "png" + task.inputs.intensity_normalization = 1000.0 + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/nipype_ports/algorithms/confounds/tests/test_framewisedisplacement.py b/pydra/tasks/mriqc/nipype_ports/algorithms/confounds/tests/test_framewisedisplacement.py new file mode 100644 index 0000000..0ca9a28 --- /dev/null +++ b/pydra/tasks/mriqc/nipype_ports/algorithms/confounds/tests/test_framewisedisplacement.py @@ -0,0 +1,25 @@ +from fileformats.generic import File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.nipype_ports.algorithms.confounds.framewise_displacement import ( + FramewiseDisplacement, +) +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_framewisedisplacement_1(): + task = FramewiseDisplacement() + task.inputs.in_file = File.sample(seed=0) + task.inputs.radius = 50 + task.inputs.out_file = "fd_power_2012.txt" + task.inputs.out_figure = "fd_power_2012.pdf" + task.inputs.save_plot = False + task.inputs.normalize = False + task.inputs.figdpi = 100 + task.inputs.figsize = [11.7, 2.3] + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/nipype_ports/algorithms/confounds/tests/test_nonsteadystatedetector.py b/pydra/tasks/mriqc/nipype_ports/algorithms/confounds/tests/test_nonsteadystatedetector.py new file mode 100644 index 0000000..74ffbc5 --- /dev/null +++ b/pydra/tasks/mriqc/nipype_ports/algorithms/confounds/tests/test_nonsteadystatedetector.py @@ -0,0 +1,18 @@ +from fileformats.generic import File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.nipype_ports.algorithms.confounds.non_steady_state_detector import ( + NonSteadyStateDetector, +) +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_nonsteadystatedetector_1(): + task = NonSteadyStateDetector() + task.inputs.in_file = File.sample(seed=0) + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/nipype_ports/algorithms/confounds/tests/test_tsnr.py b/pydra/tasks/mriqc/nipype_ports/algorithms/confounds/tests/test_tsnr.py new file mode 100644 index 0000000..49f6e47 --- /dev/null +++ b/pydra/tasks/mriqc/nipype_ports/algorithms/confounds/tests/test_tsnr.py @@ -0,0 +1,20 @@ +from fileformats.generic import File +import logging +from nipype2pydra.testing import PassAfterTimeoutWorker +from pydra.tasks.mriqc.nipype_ports.algorithms.confounds.tsnr import TSNR +import pytest + + +logger = logging.getLogger(__name__) + + +@pytest.mark.xfail +def test_tsnr_1(): + task = TSNR() + task.inputs.in_file = [File.sample(seed=0)] + task.inputs.tsnr_file = "tsnr.nii.gz" + task.inputs.mean_file = "mean.nii.gz" + task.inputs.stddev_file = "stdev.nii.gz" + task.inputs.detrended_file = "detrend.nii.gz" + res = task(worker=PassAfterTimeoutWorker) + print("RESULT: ", res) diff --git a/pydra/tasks/mriqc/nipype_ports/algorithms/confounds/tsnr.py b/pydra/tasks/mriqc/nipype_ports/algorithms/confounds/tsnr.py new file mode 100644 index 0000000..a9eff44 --- /dev/null +++ b/pydra/tasks/mriqc/nipype_ports/algorithms/confounds/tsnr.py @@ -0,0 +1,97 @@ +import attrs +from fileformats.generic import File +import logging +import nibabel as nb +import numpy as np +import os.path as op +from pathlib import Path +from pydra.compose import python +import typing as ty + + +logger = logging.getLogger(__name__) + + +@python.define +class TSNR(python.Task["TSNR.Outputs"]): + """ + Examples + ------- + + >>> from fileformats.generic import File + >>> from pydra.tasks.mriqc.nipype_ports.algorithms.confounds.tsnr import TSNR + + """ + + in_file: List + regress_poly: ty.Any + tsnr_file: Path = tsnr.nii.gz + mean_file: Path = mean.nii.gz + stddev_file: Path = stdev.nii.gz + detrended_file: Path = detrend.nii.gz + + class Outputs(python.Outputs): + tsnr_file: File + mean_file: File + stddev_file: File + detrended_file: File + + @staticmethod + def function( + in_file: List, + regress_poly: ty.Any, + tsnr_file: Path, + mean_file: Path, + stddev_file: Path, + detrended_file: Path, + ) -> tuples[File, File, File, File]: + tsnr_file = attrs.NOTHING + mean_file = attrs.NOTHING + stddev_file = attrs.NOTHING + detrended_file = attrs.NOTHING + img = nb.load(in_file[0]) + header = img.header.copy() + vollist = [nb.load(filename) for filename in in_file] + data = np.concatenate( + [ + vol.get_fdata(dtype=np.float32).reshape(vol.shape[:3] + (-1,)) + for vol in vollist + ], + axis=3, + ) + data = np.nan_to_num(data) + + if data.dtype.kind == "i": + header.set_data_dtype(np.float32) + data = data.astype(np.float32) + + if regress_poly is not attrs.NOTHING: + data = regress_poly(regress_poly, data, remove_mean=False)[0] + img = nb.Nifti1Image(data, img.affine, header) + nb.save(img, op.abspath(detrended_file)) + + meanimg = np.mean(data, axis=3) + stddevimg = np.std(data, axis=3) + tsnr = np.zeros_like(meanimg) + stddevimg_nonzero = stddevimg > 1.0e-3 + tsnr[stddevimg_nonzero] = ( + meanimg[stddevimg_nonzero] / stddevimg[stddevimg_nonzero] + ) + img = nb.Nifti1Image(tsnr, img.affine, header) + nb.save(img, op.abspath(tsnr_file)) + img = nb.Nifti1Image(meanimg, img.affine, header) + nb.save(img, op.abspath(mean_file)) + img = nb.Nifti1Image(stddevimg, img.affine, header) + nb.save(img, op.abspath(stddev_file)) + self_dict = {} + outputs = {} + for k in ["tsnr_file", "mean_file", "stddev_file"]: + outputs[k] = op.abspath(getattr(self_dict["inputs"], k)) + + if regress_poly is not attrs.NOTHING: + detrended_file = op.abspath(detrended_file) + + return tsnr_file, mean_file, stddev_file, detrended_file + + +IFLOGGER = logging.getLogger("nipype.interface") diff --git a/pydra/tasks/mriqc/nipype_ports/utils/__init__.py b/pydra/tasks/mriqc/nipype_ports/utils/__init__.py new file mode 100644 index 0000000..ea49722 --- /dev/null +++ b/pydra/tasks/mriqc/nipype_ports/utils/__init__.py @@ -0,0 +1,15 @@ +from .filemanip import ( + _cifs_table, + _generate_cifs_table, + _parse_mount_table, + copyfile, + fmlogger, + fname_presuffix, + get_related_files, + hash_infile, + hash_timestamp, + on_cifs, + related_filetype_sets, + split_filename, +) +from .misc import normalize_mc_params diff --git a/pydra/tasks/mriqc/nipype_ports/utils/filemanip.py b/pydra/tasks/mriqc/nipype_ports/utils/filemanip.py new file mode 100644 index 0000000..5c2271e --- /dev/null +++ b/pydra/tasks/mriqc/nipype_ports/utils/filemanip.py @@ -0,0 +1,11 @@ +import logging + + +logger = logging.getLogger(__name__) + + +_cifs_table = _generate_cifs_table() + +fmlogger = logging.getLogger("nipype.utils") + +related_filetype_sets = [(".hdr", ".img", ".mat"), (".nii", ".mat"), (".BRIK", ".HEAD")] diff --git a/pydra/tasks/mriqc/nipype_ports/utils/misc.py b/pydra/tasks/mriqc/nipype_ports/utils/misc.py new file mode 100644 index 0000000..f29d7d9 --- /dev/null +++ b/pydra/tasks/mriqc/nipype_ports/utils/misc.py @@ -0,0 +1,4 @@ +import logging + + +logger = logging.getLogger(__name__) diff --git a/pydra/tasks/mriqc/qc/__init__.py b/pydra/tasks/mriqc/qc/__init__.py new file mode 100644 index 0000000..bcbb7a6 --- /dev/null +++ b/pydra/tasks/mriqc/qc/__init__.py @@ -0,0 +1,24 @@ +from .anatomical import ( + DIETRICH_FACTOR, + FSL_FAST_LABELS, + art_qi1, + art_qi2, + cjv, + cnr, + efc, + fber, + rpve, + snr, + snr_dietrich, + summary_stats, + volume_fraction, + wm2max, +) +from .diffusion import ( + ExtremeValueWarning, + MIN_NUM_CC_MASK, + cc_snr, + neighboring_dwi_correlation, + spike_ppm, +) +from .functional import RAS_AXIS_ORDER, gsr diff --git a/pydra/tasks/mriqc/qc/anatomical.py b/pydra/tasks/mriqc/qc/anatomical.py new file mode 100644 index 0000000..71b2a9d --- /dev/null +++ b/pydra/tasks/mriqc/qc/anatomical.py @@ -0,0 +1,417 @@ +import logging +from math import sqrt +import numpy as np +import os.path as op +from scipy.stats import kurtosis + + +logger = logging.getLogger(__name__) + + +def art_qi1(airmask, artmask): + r""" + Detect artifacts in the image using the method described in [Mortamet2009]_. + Calculates :math:`\text{QI}_1`, as the proportion of voxels with intensity + corrupted by artifacts normalized by the number of voxels in the "*hat*" + mask (i.e., the background region above the nasio-occipital plane): + + .. math :: + + \text{QI}_1 = \frac{1}{N} \sum\limits_{x\in X_\text{art}} 1 + + Near-zero values are better. + If :math:`\text{QI}_1 = -1`, then the "*hat*" mask (background) was empty + and the dataset is likely a skull-stripped image or has been heavily + post-processed. + + :param numpy.ndarray airmask: input air mask, without artifacts + :param numpy.ndarray artmask: input artifacts mask + + """ + if airmask.sum() < 1: + return -1.0 + # Count the ratio between artifacts and the total voxels in "hat" mask + return float(artmask.sum() / (airmask.sum() + artmask.sum())) + + +def art_qi2( + img, + airmask, + min_voxels=int(1e3), + max_voxels=int(3e5), + save_plot=True, + coil_elements=32, +): + r""" + Calculates :math:`\text{QI}_2`, based on the goodness-of-fit of a centered + :math:`\chi^2` distribution onto the intensity distribution of + non-artifactual background (within the "hat" mask): + + + .. math :: + + \chi^2_n = \frac{2}{(\sigma \sqrt{2})^{2n} \, (n - 1)!}x^{2n - 1}\, e^{-\frac{x}{2}} + + where :math:`n` is the number of coil elements. + + :param numpy.ndarray img: input data + :param numpy.ndarray airmask: input air mask without artifacts + + """ + from pydra.tasks.nireports.reportlets.nuisance import plot_qi2 + from scipy.stats import chi2 + from sklearn.neighbors import KernelDensity + + # S. Ogawa was born + np.random.seed(1191935) + data = np.nan_to_num(img[airmask > 0], posinf=0.0) + data[data < 0] = 0 + # Write out figure of the fitting + out_file = op.abspath("error.svg") + with open(out_file, "w") as ofh: + ofh.write("

Background noise fitting could not be plotted.

") + if (data > 0).sum() < min_voxels: + return 0.0, out_file + data *= 100 / np.percentile(data, 99) + modelx = data if len(data) < max_voxels else np.random.choice(data, size=max_voxels) + x_grid = np.linspace(0.0, 110, 1000) + # Estimate data pdf with KDE on a random subsample + kde_skl = KernelDensity(kernel="gaussian", bandwidth=4.0).fit(modelx[:, np.newaxis]) + kde = np.exp(kde_skl.score_samples(x_grid[:, np.newaxis])) + # Find cutoff + kdethi = np.argmax(kde[::-1] > kde.max() * 0.5) + # Fit X^2 + param = chi2.fit(modelx, coil_elements) + chi_pdf = chi2.pdf(x_grid, *param[:-2], loc=param[-2], scale=param[-1]) + # Compute goodness-of-fit (gof) + gof = float(np.abs(kde[-kdethi:] - chi_pdf[-kdethi:]).mean()) + if save_plot: + out_file = plot_qi2(x_grid, kde, chi_pdf, modelx, kdethi) + return gof, out_file + + +def cjv(mu_wm, mu_gm, sigma_wm, sigma_gm): + r""" + Calculate the :abbr:`CJV (coefficient of joint variation)`, a measure + related to :abbr:`SNR (Signal-to-Noise Ratio)` and + :abbr:`CNR (Contrast-to-Noise Ratio)` that is presented as a proxy for + the :abbr:`INU (intensity non-uniformity)` artifact [Ganzetti2016]_. + Lower is better. + + .. math:: + + \text{CJV} = \frac{\sigma_\text{WM} + \sigma_\text{GM}}{|\mu_\text{WM} - \mu_\text{GM}|}. + + :param float mu_wm: mean of signal within white-matter mask. + :param float mu_gm: mean of signal within gray-matter mask. + :param float sigma_wm: standard deviation of signal within white-matter mask. + :param float sigma_gm: standard deviation of signal within gray-matter mask. + + :return: the computed CJV + + + """ + return float((sigma_wm + sigma_gm) / abs(mu_wm - mu_gm)) + + +def cnr(mu_wm, mu_gm, sigma_air, sigma_wm, sigma_gm): + r""" + Calculate the :abbr:`CNR (Contrast-to-Noise Ratio)` [Magnota2006]_. + Higher values are better. + + .. math:: + + \text{CNR} = \frac{|\mu_\text{GM} - \mu_\text{WM} |}{\sqrt{\sigma_B^2 + + \sigma_\text{WM}^2 + \sigma_\text{GM}^2}}, + + where :math:`\sigma_B` is the standard deviation of the noise distribution within + the air (background) mask. + + + :param float mu_wm: mean of signal within white-matter mask. + :param float mu_gm: mean of signal within gray-matter mask. + :param float sigma_air: standard deviation of the air surrounding the head ("hat" mask). + :param float sigma_wm: standard deviation within white-matter mask. + :param float sigma_gm: standard within gray-matter mask. + + :return: the computed CNR + + """ + return float(abs(mu_wm - mu_gm) / sqrt(sigma_air**2 + sigma_gm**2 + sigma_wm**2)) + + +def efc(img, framemask=None, decimals=4): + r""" + Calculate the :abbr:`EFC (Entropy Focus Criterion)` [Atkinson1997]_. + Uses the Shannon entropy of voxel intensities as an indication of ghosting + and blurring induced by head motion. A range of low values is better, + with EFC = 0 for all the energy concentrated in one pixel. + + .. math:: + + \text{E} = - \sum_{j=1}^N \frac{x_j}{x_\text{max}} + \ln \left[\frac{x_j}{x_\text{max}}\right] + + with :math:`x_\text{max} = \sqrt{\sum_{j=1}^N x^2_j}`. + + The original equation is normalized by the maximum entropy, so that the + :abbr:`EFC (Entropy Focus Criterion)` can be compared across images with + different dimensions: + + .. math:: + + \text{EFC} = \left( \frac{N}{\sqrt{N}} \, \log{\sqrt{N}^{-1}} \right) \text{E} + + :param numpy.ndarray img: input data + :param numpy.ndarray framemask: a mask of empty voxels inserted after a rotation of + data + + """ + if framemask is None: + framemask = np.zeros_like(img, dtype=np.uint8) + n_vox = np.sum(1 - framemask) + # Calculate the maximum value of the EFC (which occurs any time all + # voxels have the same value) + efc_max = 1.0 * n_vox * (1.0 / np.sqrt(n_vox)) * np.log(1.0 / np.sqrt(n_vox)) + # Calculate the total image energy + b_max = np.sqrt((img[framemask == 0] ** 2).sum()) + # Calculate EFC (add 1e-16 to the image data to keep log happy) + return round( + float( + (1.0 / efc_max) + * np.sum( + (img[framemask == 0] / b_max) + * np.log((img[framemask == 0] + 1e-16) / b_max) + ), + ), + decimals, + ) + + +def fber(img, headmask, rotmask=None, decimals=4): + r""" + Calculate the :abbr:`FBER (Foreground-Background Energy Ratio)` [Shehzad2015]_, + defined as the mean energy of image values within the head relative + to outside the head. + Higher values are better, and an FBER=-1.0 indicates that there is no signal + outside the head mask (e.g., a skull-stripped dataset). + + .. math:: + + \text{FBER} = \frac{E[|F|^2]}{E[|B|^2]} + + + :param numpy.ndarray img: input data + :param numpy.ndarray headmask: a mask of the head (including skull, skin, etc.) + :param numpy.ndarray rotmask: a mask of empty voxels inserted after a rotation of + data + + """ + fg_mu = np.median(np.abs(img[headmask > 0]) ** 2) + airmask = np.ones_like(headmask, dtype=np.uint8) + airmask[headmask > 0] = 0 + if rotmask is not None: + airmask[rotmask > 0] = 0 + bg_mu = np.median(np.abs(img[airmask == 1]) ** 2) + if bg_mu < 1.0e-3: + return -1.0 + return round(float(fg_mu / bg_mu), decimals) + + +def rpve(pvms, seg): + """ + Computes the :abbr:`rPVe (residual partial voluming error)` + of each tissue class. + + .. math :: + + \\text{rPVE}^k = \\frac{1}{N} \\left[ \\sum\\limits_{p^k_i \\in [0.5, P_{98}]} p^k_i + \\sum\\limits_{p^k_i \\in [P_{2}, 0.5)} 1 - p^k_i \\right] + + """ + pvfs = {} + for k, lid in list(FSL_FAST_LABELS.items()): + if lid == 0: + continue + pvmap = pvms[lid - 1] + pvmap[pvmap < 0.0] = 0.0 + pvmap[pvmap >= 1.0] = 1.0 + totalvol = np.sum(pvmap > 0.0) + upth = np.percentile(pvmap[pvmap > 0], 98) + loth = np.percentile(pvmap[pvmap > 0], 2) + pvmap[pvmap < loth] = 0 + pvmap[pvmap > upth] = 0 + pvfs[k] = ( + pvmap[pvmap > 0.5].sum() + (1.0 - pvmap[pvmap <= 0.5]).sum() + ) / totalvol + return {k: float(v) for k, v in list(pvfs.items())} + + +def snr(mu_fg, sigma_fg, n): + r""" + Calculate the :abbr:`SNR (Signal-to-Noise Ratio)`. + The estimation may be provided with only one foreground region in + which the noise is computed as follows: + + .. math:: + + \text{SNR} = \frac{\mu_F}{\sigma_F\sqrt{n/(n-1)}}, + + where :math:`\mu_F` is the mean intensity of the foreground and + :math:`\sigma_F` is the standard deviation of the same region. + + :param float mu_fg: mean of foreground. + :param float sigma_fg: standard deviation of foreground. + :param int n: number of voxels in foreground mask. + + :return: the computed SNR + + """ + return float(mu_fg / (sigma_fg * sqrt(n / (n - 1)))) + + +def snr_dietrich(mu_fg, mad_air=0.0, sigma_air=1.0): + r""" + Calculate the :abbr:`SNR (Signal-to-Noise Ratio)`. + + This must be an air mask around the head, and it should not contain artifacts. + The computation is done following the eq. A.12 of [Dietrich2007]_, which + includes a correction factor in the estimation of the standard deviation of + air and its Rayleigh distribution: + + .. math:: + + \text{SNR} = \frac{\mu_F}{\sqrt{\frac{2}{4-\pi}}\,\sigma_\text{air}}. + + + :param float mu_fg: mean of foreground. + :param float sigma_air: standard deviation of the air surrounding the head ("hat" mask). + + :return: the computed SNR for the foreground segmentation + + """ + if mad_air > 1.0: + return float(DIETRICH_FACTOR * mu_fg / mad_air) + logger.warning( + "Estimated signal variation in the background was too small " + f"(MAD={mad_air}, sigma={sigma_air})", + ) + return float(DIETRICH_FACTOR * mu_fg / sigma_air) if sigma_air > 1e-3 else -1.0 + + +def summary_stats( + data: np.ndarray, + pvms: dict[str, np.ndarray], + rprec_data: int = 0, + rprec_prob: int = 3, + decimals: int = 4, +) -> dict[str, dict[str, float]]: + """ + Estimates weighted summary statistics for each tissue distribution in the data. + + This function calculates the mean, median, standard deviation, kurtosis, median + absolute deviation (MAD), the 95th and 5th percentiles, and the number of voxels for + each tissue distribution defined by a label in the provided partial volume maps (pvms). + + Parameters + ---------- + data : :obj:`~numpy.ndarray` (float, 3D) + A three-dimensional array of data from which summary statistics will be extracted. + pvms : :obj:`dict` of :obj:`str` keys and :obj:`~numpy.ndarray` (float, 3D) values + A dictionary of partial volume maps where the key indicates the label of a + region-of-interest (ROI) and the values are three-dimensional arrays matched in size + with `data` and containing the probability/fraction of the voxel containing the given + label. + rprec_data : :obj:`int`, optional (default=0) + Number of decimal places to round the data array before calculation. Rounding + alleviates floating-point error variability by explicitly rounding before + quantification operations. + rprec_prob : :obj:`int`, optional (default=3) + Number of decimal places to round the probability maps before calculation. Rounding + alleviates floating-point error variability by explicitly rounding before + quantification operations. + + Returns + ------- + :obj:`dict` + A dictionary where the keys are labels from the ``pvms`` dictionary and the values + are dictionaries containing the following keys for each tissue distribution: + + * ``'mean'``: :obj:`float` - Mean value + * ``'median'``: :obj:`float` - Median value + * ``'p95'``: :obj:`float` - 95th percentile + * ``'p05'``: :obj:`float` - 5th percentile + * ``'k'``: :obj:`float` - Kurtosis + * ``'stdv'``: :obj:`float` - Standard deviation + * ``'mad'``: :obj:`float` - Median absolute deviation + * ``'n'``: :obj:`int` - Number of voxels in the tissue distribution + + """ + from statsmodels.robust.scale import mad + from statsmodels.stats.weightstats import DescrStatsW + + output = {} + for label, probmap in pvms.items(): + wstats = DescrStatsW( + data=np.round(data.reshape(-1), rprec_data), + weights=np.round(probmap.astype(np.float32).reshape(-1), rprec_prob), + ) + nvox = probmap.sum() + p05, median, p95 = wstats.quantile( + np.array([0.05, 0.50, 0.95]), return_pandas=False + ) + thresholded = data[probmap > (0.5 * probmap.max())] + output[label] = { + "mean": round(float(wstats.mean), decimals), + "median": round(float(median), decimals), + "p95": round(float(p95), decimals), + "p05": round(float(p05), decimals), + "k": round(float(kurtosis(thresholded)), decimals), + "stdv": round(float(wstats.std), decimals), + "mad": round(float(mad(thresholded, center=median)), decimals), + "n": float(nvox), + } + return output + + +def volume_fraction(pvms): + r""" + Computes the :abbr:`ICV (intracranial volume)` fractions + corresponding to the (partial volume maps). + + .. math :: + + \text{ICV}^k = \frac{\sum_i p^k_i}{\sum\limits_{x \in X_\text{brain}} 1} + + :param list pvms: list of :code:`numpy.ndarray` of partial volume maps. + + """ + tissue_vfs = {} + total = 0 + for k, lid in list(FSL_FAST_LABELS.items()): + if lid == 0: + continue + tissue_vfs[k] = pvms[lid - 1].sum() + total += tissue_vfs[k] + for k in list(tissue_vfs.keys()): + tissue_vfs[k] /= total + return {k: float(v) for k, v in list(tissue_vfs.items())} + + +def wm2max(img, mu_wm): + r""" + Calculate the :abbr:`WM2MAX (white-matter-to-max ratio)`, + defined as the maximum intensity found in the volume w.r.t. the + mean value of the white matter tissue. Values close to 1.0 are + better: + + .. math :: + + \text{WM2MAX} = \frac{\mu_\text{WM}}{P_{99.95}(X)} + + """ + return float(mu_wm / np.percentile(img.reshape(-1), 99.95)) + + +DIETRICH_FACTOR = 0.6551364 # 1.0 / sqrt(2 / (4 - pi)) + +FSL_FAST_LABELS = {"csf": 1, "gm": 2, "wm": 3, "bg": 0} diff --git a/pydra/tasks/mriqc/qc/diffusion.py b/pydra/tasks/mriqc/qc/diffusion.py new file mode 100644 index 0000000..ec81a69 --- /dev/null +++ b/pydra/tasks/mriqc/qc/diffusion.py @@ -0,0 +1,210 @@ +from contextlib import suppress +import logging +import numpy as np +from statsmodels.robust.scale import mad +from warnings import warn + + +logger = logging.getLogger(__name__) + + +class ExtremeValueWarning(UserWarning): + """A warning type for dubious metric values.""" + + +def cc_snr( + in_b0: np.ndarray, + dwi_shells: list[np.ndarray], + cc_mask: np.ndarray, + b_values: np.ndarray, + b_vectors: np.ndarray, + bval_thres: int = 50, + decimals: int = 2, +) -> dict[int, (float, float)]: + """ + Calculates the worst-case and best-case signal-to-noise ratio (SNR) within the corpus callosum. + + This function estimates the SNR in the corpus callosum (CC) by comparing the + mean signal intensity within the CC mask to the standard deviation of the background + signal (extracted from the b0 image). It performs separate calculations for + each diffusion-weighted imaging (DWI) shell. + + **Worst-case SNR:** The mean signal intensity along the diffusion direction with the + lowest signal is considered the worst-case scenario. + + **Best-case SNR:** The mean signal intensity averaged across the two diffusion + directions with the highest signal is considered the best-case scenario. + + Parameters + ---------- + in_b0 : :obj:`~numpy.ndarray` (float, 3D) + T1-weighted or b0 image used for background signal estimation. + dwi_shells : list[:obj:`~numpy.ndarray` (float, 4D)] + List of DWI data for each diffusion shell. + cc_mask : :obj:`~numpy.ndarray` (bool, 3D) + Boolean mask of the corpus callosum. + b_values : :obj:`~numpy.ndarray` (int) + Array of b-values for each DWI volume in ``dwi_shells``. + b_vectors : :obj:`~numpy.ndarray` (float) + Array of diffusion-encoding vectors for each DWI volume in ``dwi_shells``. + + Returns + ------- + cc_snr_estimates : :obj:`dict` + Dictionary containing SNR estimates for each b-value. Keys are the b-values + (integers), and values are tuples containing two elements: + + * The first element is the worst-case SNR (float). + * The second element is the best-case SNR (float). + + The SNR estimates are zero if there are no sufficient voxels to calculate them + (may occur if the number of orientations in the file is very low). + + """ + cc_mask = cc_mask > 0 # Ensure it's a boolean mask + b_values = np.rint(b_values).astype(np.uint16) + n_shells = len(b_values) + if (nvox_cc := cc_mask.sum()) < MIN_NUM_CC_MASK: + warn( + f"CC mask is too small ({nvox_cc} voxels)", + ExtremeValueWarning, + stacklevel=1, + ) + cc_snr_estimates = {"shell0": 0} + cc_snr_estimates = cc_snr_estimates | { + f"shell{shell_index:d}_worst": 0 for shell_index in range(1, n_shells + 1) + } + cc_snr_estimates = cc_snr_estimates | { + f"shell{shell_index:d}_best": 0 for shell_index in range(1, n_shells + 1) + } + return cc_snr_estimates, 0 + std_signal = mad(in_b0[cc_mask]) + xyz = np.eye(3) + cc_snr_estimates = {} + cc_snr_estimates["shell0"] = round( + float(in_b0[cc_mask].mean() / std_signal), decimals + ) + # Shell-wise calculation + for shell_index, bvecs, shell_data in zip( + range(1, n_shells + 1), b_vectors, dwi_shells + ): + shell_data = shell_data[cc_mask] + # Find main directions of diffusion + axis_X = np.argmin(np.sum((bvecs - xyz[0, :]) ** 2, axis=-1)) + axis_Y = np.argmin(np.sum((bvecs - xyz[1, :]) ** 2, axis=-1)) + axis_Z = np.argmin(np.sum((bvecs - xyz[2, :]) ** 2, axis=-1)) + mean_signal_worst = 0 + with suppress(IndexError): + data_X = shell_data[..., axis_X] + mean_signal_worst = np.mean(data_X) + mean_signal_best = 0 + with suppress(IndexError): + data_Y = shell_data[..., axis_Y] + data_Z = shell_data[..., axis_Z] + mean_signal_best = 0.5 * (np.mean(data_Y) + np.mean(data_Z)) + cc_snr_estimates[f"shell{shell_index:d}_worst"] = round( + float(np.mean(mean_signal_worst / std_signal)), decimals + ) + cc_snr_estimates[f"shell{shell_index:d}_best"] = round( + float(np.mean(mean_signal_best / std_signal)), decimals + ) + return cc_snr_estimates, std_signal + + +def neighboring_dwi_correlation( + dwi_data: np.ndarray, + neighbor_indices: list[tuple[int, int]], + mask: np.ndarray | None = None, + decimals: int = 4, +) -> float: + """ + Calculates the Neighboring DWI Correlation (NDC) from diffusion MRI (dMRI) data. + + The NDC is a measure of the correlation between signal intensities in neighboring + diffusion-weighted images (DWIs) within a mask. A low NDC (typically below 0.4) + can indicate poor image quality, according to Yeh et al. [Yeh2019]_. + + Parameters + ---------- + dwi_data : 4D :obj:`~numpy.ndarray` + DWI data on which to calculate NDC + neighbor_indices : :obj:`list` of :obj:`tuple` + List of (from, to) index neighbors. + mask : 3D :obj:`~numpy.ndarray`, optional + optional mask of voxels to include in the NDC calculation + + Returns + ------- + :obj:`float` + The NDC value. + + References + ---------- + .. [Yeh2019] Yeh, Fang-Cheng, et al. "Differential tractography as a + track-based biomarker for neuronal injury." + NeuroImage 202 (2019): 116131. + + Notes + ----- + This is a copy of DIPY's code to be removed (and just imported) as soon as + a new release of DIPY is cut including + `dipy/dipy#3156 `__. + + """ + neighbor_correlations = [] + mask = np.ones_like(dwi_data[..., 0], dtype=bool) if mask is None else mask + dwi_data = dwi_data[mask] + for from_index, to_index in neighbor_indices: + flat_from_image = dwi_data[from_index] + flat_to_image = dwi_data[to_index] + neighbor_correlations.append(np.corrcoef(flat_from_image, flat_to_image)[0, 1]) + return round(float(np.mean(neighbor_correlations)), decimals) + + +def spike_ppm( + spike_mask: np.ndarray, slice_threshold: float = 0.05, decimals: int = 2 +) -> dict[str, float | np.ndarray]: + """ + Calculates fractions (global and slice-wise) of voxels classified as spikes in ppm. + + This function computes two metrics: + + * Global spike parts-per-million [ppm]: Fraction of voxels exceeding the spike + threshold across the entire data array. + * Slice-wise spiking [ppm]: The fraction of slices along each dimension of + the data array where the average fraction of spiking voxels within the slice + exceeds a user-defined threshold (``slice_threshold``). + + Parameters + ---------- + spike_mask : :obj:`~numpy.ndarray` (bool, same shape as data) + The binary mask indicating spike voxels (True) and non-spike voxels (False). + slice_threshold : :obj:`float`, optional (default=0.05) + The minimum fraction of voxels in a slice that must be classified as spikes + for the slice to be considered spiking. + decimals : :obj:`int` + The number of decimals to round the fractions. + + Returns + ------- + :obj:`dict` + A dictionary containing the calculated spike percentages: + + * 'global': :obj:`float` - global spiking voxels ppm. + * 'slice_{i,j,k,t}': :obj:`float` - Slice-wise spiking voxel + fractions in ppm for each dimension of the data array. + + """ + axisnames = "ijkt" + spike_global = round(float(1e6 * np.mean(np.ravel(spike_mask))), decimals) + spike_slice = { + f"slice_{axisnames[axis]}": round( + float(1e6 * np.mean(np.mean(spike_mask, axis=axis) > slice_threshold)), + decimals, + ) + for axis in range(min(spike_mask.ndim, 3)) + } + return {"global": spike_global} | spike_slice + + +MIN_NUM_CC_MASK = 5 diff --git a/pydra/tasks/mriqc/qc/functional.py b/pydra/tasks/mriqc/qc/functional.py new file mode 100644 index 0000000..4100a00 --- /dev/null +++ b/pydra/tasks/mriqc/qc/functional.py @@ -0,0 +1,62 @@ +import logging +import numpy as np +import os.path as op + + +logger = logging.getLogger(__name__) + + +def gsr(epi_data, mask, direction="y", ref_file=None, out_file=None): + """ + Compute the :abbr:`GSR (ghost to signal ratio)` [Giannelli2010]_. + + The procedure is as follows: + + + + + + + + .. warning :: + + This should be used with EPI images for which the phase + encoding direction is known. + + :param str epi_file: path to epi file + :param str mask_file: path to brain mask + :param str direction: the direction of phase encoding (x, y, all) + :return: the computed gsr + + """ + direction = direction.lower() + if direction[-1] not in ("x", "y", "all"): + raise Exception( + f"Unknown direction {direction}, should be one of x, -x, y, -y, all" + ) + if direction == "all": + result = [] + for newdir in ("x", "y"): + ofile = None + if out_file is not None: + fname, ext = op.splitext(ofile) + if ext == ".gz": + fname, ext2 = op.splitext(fname) + ext = ext2 + ext + ofile = f"{fname}_{newdir}{ext}" + result += [gsr(epi_data, mask, newdir, ref_file=ref_file, out_file=ofile)] + return result + # Roll data of mask through the appropriate axis + axis = RAS_AXIS_ORDER[direction] + n2_mask = np.roll(mask, mask.shape[axis] // 2, axis=axis) + # Step 3: remove from n2_mask pixels inside the brain + n2_mask = n2_mask * (1 - mask) + # Step 4: non-ghost background region is labeled as 2 + n2_mask = n2_mask + 2 * (1 - n2_mask - mask) + # Step 5: signal is the entire foreground image + ghost = np.mean(epi_data[n2_mask == 1]) - np.mean(epi_data[n2_mask == 2]) + signal = np.median(epi_data[n2_mask == 0]) + return float(ghost / signal) + + +RAS_AXIS_ORDER = {"x": 0, "y": 1, "z": 2} diff --git a/pydra/tasks/mriqc/synthstrip/ORIGINAL_LICENSE b/pydra/tasks/mriqc/synthstrip/ORIGINAL_LICENSE new file mode 100644 index 0000000..2a8c7f8 --- /dev/null +++ b/pydra/tasks/mriqc/synthstrip/ORIGINAL_LICENSE @@ -0,0 +1,191 @@ + FreeSurfer Software License Agreement ("Agreement") + Version 1.0 (February 2011) + +This Agreement covers contributions to and downloads from the +FreeSurfer project ("FreeSurfer") maintained by The General Hospital +Corporation, Boston MA, USA ("MGH"). 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This Agreement shall supercede and +replace any license terms that you may have agreed to previously with +respect to FreeSurfer. + diff --git a/pydra/tasks/mriqc/synthstrip/__init__.py b/pydra/tasks/mriqc/synthstrip/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/pydra/tasks/mriqc/synthstrip/__main__.py b/pydra/tasks/mriqc/synthstrip/__main__.py new file mode 100644 index 0000000..8c08b16 --- /dev/null +++ b/pydra/tasks/mriqc/synthstrip/__main__.py @@ -0,0 +1,34 @@ +# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- +# vi: set ft=python sts=4 ts=4 sw=4 et: +# +# Copyright 2022 The NiPreps Developers +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# We support and encourage derived works from this project, please read +# about our expectations at +# +# https://www.nipreps.org/community/licensing/ +# + +if __name__ == '__main__': + import sys + + from mriqc.synthstrip.cli import main + + from . import __name__ as module + + # `python -m ` typically displays the command as __main__.py + if '__main__.py' in sys.argv[0]: + sys.argv[0] = f'{sys.executable} -m {module}' + main() diff --git a/pydra/tasks/mriqc/synthstrip/cli.py b/pydra/tasks/mriqc/synthstrip/cli.py new file mode 100644 index 0000000..ee42b7d --- /dev/null +++ b/pydra/tasks/mriqc/synthstrip/cli.py @@ -0,0 +1,235 @@ +# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- +# vi: set ft=python sts=4 ts=4 sw=4 et: +# +# Copyright 2022 The NiPreps Developers +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# We support and encourage derived works from this project, please read +# about our expectations at +# +# https://www.nipreps.org/community/licensing/ +# +# STATEMENT OF CHANGES: This file is derived from sources licensed under the FreeSurfer 1.0 license +# terms, and this file has been changed. +# The full licensing terms of the original work are found at: +# https://github.com/freesurfer/freesurfer/blob/2995ded957961a7f3704de57eee88eb6cc30d52d/LICENSE.txt +# A copy of the license has been archived in the ORIGINAL_LICENSE file +# found within this redistribution. +# +# The original file this work derives from is found at: +# https://github.com/freesurfer/freesurfer/blob/2995ded957961a7f3704de57eee88eb6cc30d52d/mri_synthstrip/mri_synthstrip +# +# [April 2022] CHANGES: +# * MAINT: Split the monolithic file into model and CLI submodules +# * ENH: Replace freesurfer Python bundle with in-house code. +# +""" +Robust, universal skull-stripping for brain images of any type. +If you use SynthStrip in your analysis, please cite: + + A Hoopes, JS Mora, AV Dalca, B Fischl, M Hoffmann. + SynthStrip: Skull-Stripping for Any Brain Image. + https://arxiv.org/abs/2203.09974 + +""" + + +def main(): + """Entry point to SynthStrip.""" + import os + from argparse import ArgumentParser + + import nibabel as nb + import numpy as np + import scipy + import torch + + from .model import StripModel + + # parse command line + parser = ArgumentParser(description=__doc__) + parser.add_argument( + '-i', + '--image', + metavar='file', + required=True, + help='Input image to skullstrip.', + ) + parser.add_argument('-o', '--out', metavar='file', help='Save stripped image to path.') + parser.add_argument('-m', '--mask', metavar='file', help='Save binary brain mask to path.') + parser.add_argument('-g', '--gpu', action='store_true', help='Use the GPU.') + parser.add_argument('-n', '--num-threads', action='store', type=int, help='number of threads') + parser.add_argument( + '-b', + '--border', + default=1, + type=int, + help='Mask border threshold in mm. Default is 1.', + ) + parser.add_argument('--model', metavar='file', help='Alternative model weights.') + args = parser.parse_args() + + # sanity check on the inputs + if not args.out and not args.mask: + parser.fatal('Must provide at least --out or --mask output flags.') + + # necessary for speed gains (I think) + torch.backends.cudnn.benchmark = True + torch.backends.cudnn.deterministic = True + + # configure GPU device + if args.gpu: + os.environ['CUDA_VISIBLE_DEVICES'] = '0' + device = torch.device('cuda') + device_name = 'GPU' + else: + os.environ['CUDA_VISIBLE_DEVICES'] = '-1' + device = torch.device('cpu') + device_name = 'CPU' + + if args.num_threads and args.num_threads > 0: + torch.set_num_threads(args.num_threads) + + # configure model + print(f'Configuring model on the {device_name}') + + with torch.no_grad(): + model = StripModel() + model.to(device) + model.eval() + + # load model weights + if args.model is not None: + modelfile = args.model + print('Using custom model weights') + else: + raise RuntimeError('A model must be provided.') + + checkpoint = torch.load(modelfile, map_location=device) + model.load_state_dict(checkpoint['model_state_dict']) + + # load input volume + print(f'Input image read from: {args.image}') + + # normalize intensities + image = nb.load(args.image) + conformed = conform(image) + in_data = conformed.get_fdata(dtype='float32') + in_data -= in_data.min() + in_data = np.clip(in_data / np.percentile(in_data, 99), 0, 1) + in_data = in_data[np.newaxis, np.newaxis] + + # predict the surface distance transform + input_tensor = torch.from_numpy(in_data).to(device) + with torch.no_grad(): + sdt = model(input_tensor).cpu().numpy().squeeze() + + # unconform the sdt and extract mask + sdt_target = resample_like( + nb.Nifti1Image(sdt, conformed.affine, None), + image, + output_dtype='int16', + cval=100, + ) + sdt_data = np.asanyarray(sdt_target.dataobj).astype('int16') + + # find largest CC (just do this to be safe for now) + components = scipy.ndimage.label(sdt_data.squeeze() < args.border)[0] + bincount = np.bincount(components.flatten())[1:] + mask = components == (np.argmax(bincount) + 1) + mask = scipy.ndimage.morphology.binary_fill_holes(mask) + + # write the masked output + if args.out: + img_data = image.get_fdata() + bg = np.min([0, img_data.min()]) + img_data[mask == 0] = bg + nb.Nifti1Image(img_data, image.affine, image.header).to_filename( + args.out, + ) + print(f'Masked image saved to: {args.out}') + + # write the brain mask + if args.mask: + hdr = image.header.copy() + hdr.set_data_dtype('uint8') + nb.Nifti1Image(mask, image.affine, hdr).to_filename(args.mask) + print(f'Binary brain mask saved to: {args.mask}') + + print('If you use SynthStrip in your analysis, please cite:') + print('----------------------------------------------------') + print('SynthStrip: Skull-Stripping for Any Brain Image.') + print('A Hoopes, JS Mora, AV Dalca, B Fischl, M Hoffmann.') + + +def conform(input_nii): + """Resample image as SynthStrip likes it.""" + import nibabel as nb + import numpy as np + from nitransforms.linear import Affine + + shape = np.array(input_nii.shape[:3]) + affine = input_nii.affine + + # Get corner voxel centers in index coords + corner_centers_ijk = ( + np.array( + [ + (i, j, k) + for k in (0, shape[2] - 1) + for j in (0, shape[1] - 1) + for i in (0, shape[0] - 1) + ] + ) + + 0.5 + ) + + # Get corner voxel centers in mm + corners_xyz = affine @ np.hstack((corner_centers_ijk, np.ones((len(corner_centers_ijk), 1)))).T + + # Target affine is 1mm voxels in LIA orientation + target_affine = np.diag([-1.0, 1.0, -1.0, 1.0])[:, (0, 2, 1, 3)] + + # Target shape + extent = corners_xyz.min(1)[:3], corners_xyz.max(1)[:3] + target_shape = ((extent[1] - extent[0]) / 1.0 + 0.999).astype(int) + + # SynthStrip likes dimensions be multiple of 64 (192, 256, or 320) + target_shape = np.clip(np.ceil(np.array(target_shape) / 64).astype(int) * 64, 192, 320) + + # Ensure shape ordering is LIA too + target_shape[2], target_shape[1] = target_shape[1:3] + + # Coordinates of center voxel do not change + input_c = affine @ np.hstack((0.5 * (shape - 1), 1.0)) + target_c = target_affine @ np.hstack((0.5 * (target_shape - 1), 1.0)) + + # Rebase the origin of the new, plumb affine + target_affine[:3, 3] -= target_c[:3] - input_c[:3] + + nii = Affine( + reference=nb.Nifti1Image(np.zeros(target_shape), target_affine, None), + ).apply(input_nii) + return nii + + +def resample_like(image, target, output_dtype=None, cval=0): + """Resample the input image to be in the target's grid via identity transform.""" + from nitransforms.linear import Affine + + return Affine(reference=target).apply(image, output_dtype=output_dtype, cval=cval) + + +if __name__ == '__main__': + main() diff --git a/pydra/tasks/mriqc/synthstrip/model.py b/pydra/tasks/mriqc/synthstrip/model.py new file mode 100644 index 0000000..3081d2d --- /dev/null +++ b/pydra/tasks/mriqc/synthstrip/model.py @@ -0,0 +1,180 @@ +# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- +# vi: set ft=python sts=4 ts=4 sw=4 et: +# +# Copyright 2022 The NiPreps Developers +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# We support and encourage derived works from this project, please read +# about our expectations at +# +# https://www.nipreps.org/community/licensing/ +# +# STATEMENT OF CHANGES: This file is derived from sources licensed under the FreeSurfer 1.0 license +# terms, and this file has been changed. +# The full licensing terms of the original work are found at: +# https://github.com/freesurfer/freesurfer/blob/2995ded957961a7f3704de57eee88eb6cc30d52d/LICENSE.txt +# A copy of the license has been archived in the ORIGINAL_LICENSE file +# found within this redistribution. +# +# The original file this work derives from is found at: +# https://github.com/freesurfer/freesurfer/blob/2995ded957961a7f3704de57eee88eb6cc30d52d/mri_synthstrip/mri_synthstrip +# +# [April 2022] CHANGES: +# * MAINT: Split the monolithic file into model and CLI submodules +# * ENH: Replace freesurfer Python bundle with in-house code. +# +""" +Robust, universal skull-stripping for brain images of any type. +If you use SynthStrip in your analysis, please cite: + + A Hoopes, JS Mora, AV Dalca, B Fischl, M Hoffmann. + SynthStrip: Skull-Stripping for Any Brain Image. + https://arxiv.org/abs/2203.09974 + +""" + +import numpy as np +import torch +import torch.nn as nn + + +class StripModel(nn.Module): + def __init__( + self, + nb_features=16, + nb_levels=7, + feat_mult=2, + max_features=64, + nb_conv_per_level=2, + max_pool=2, + return_mask=False, + ): + super().__init__() + + # dimensionality + ndims = 3 + + # build feature list automatically + if isinstance(nb_features, int): + if nb_levels is None: + raise ValueError('must provide unet nb_levels if nb_features is an integer') + feats = np.round(nb_features * feat_mult ** np.arange(nb_levels)).astype(int) + feats = np.clip(feats, 1, max_features) + nb_features = [ + np.repeat(feats[:-1], nb_conv_per_level), + np.repeat(np.flip(feats), nb_conv_per_level), + ] + elif nb_levels is not None: + raise ValueError('cannot use nb_levels if nb_features is not an integer') + + # extract any surplus (full resolution) decoder convolutions + enc_nf, dec_nf = nb_features + nb_dec_convs = len(enc_nf) + final_convs = dec_nf[nb_dec_convs:] + dec_nf = dec_nf[:nb_dec_convs] + self.nb_levels = int(nb_dec_convs / nb_conv_per_level) + 1 + + if isinstance(max_pool, int): + max_pool = [max_pool] * self.nb_levels + + # cache downsampling / upsampling operations + MaxPooling = getattr(nn, 'MaxPool%dd' % ndims) + self.pooling = [MaxPooling(s) for s in max_pool] + self.upsampling = [nn.Upsample(scale_factor=s, mode='nearest') for s in max_pool] + + # configure encoder (down-sampling path) + prev_nf = 1 + encoder_nfs = [prev_nf] + self.encoder = nn.ModuleList() + for level in range(self.nb_levels - 1): + convs = nn.ModuleList() + for conv in range(nb_conv_per_level): + nf = enc_nf[level * nb_conv_per_level + conv] + convs.append(ConvBlock(ndims, prev_nf, nf)) + prev_nf = nf + self.encoder.append(convs) + encoder_nfs.append(prev_nf) + + # configure decoder (up-sampling path) + encoder_nfs = np.flip(encoder_nfs) + self.decoder = nn.ModuleList() + for level in range(self.nb_levels - 1): + convs = nn.ModuleList() + for conv in range(nb_conv_per_level): + nf = dec_nf[level * nb_conv_per_level + conv] + convs.append(ConvBlock(ndims, prev_nf, nf)) + prev_nf = nf + self.decoder.append(convs) + if level < (self.nb_levels - 1): + prev_nf += encoder_nfs[level] + + # now we take care of any remaining convolutions + self.remaining = nn.ModuleList() + for nf in final_convs: + self.remaining.append(ConvBlock(ndims, prev_nf, nf)) + prev_nf = nf + + # final convolutions + if return_mask: + self.remaining.append(ConvBlock(ndims, prev_nf, 2, activation=None)) + self.remaining.append(nn.Softmax(dim=1)) + else: + self.remaining.append(ConvBlock(ndims, prev_nf, 1, activation=None)) + + def forward(self, x): + # encoder forward pass + x_history = [x] + for level, convs in enumerate(self.encoder): + for conv in convs: + x = conv(x) + x_history.append(x) + x = self.pooling[level](x) + + # decoder forward pass with upsampling and concatenation + for level, convs in enumerate(self.decoder): + for conv in convs: + x = conv(x) + if level < (self.nb_levels - 1): + x = self.upsampling[level](x) + x = torch.cat([x, x_history.pop()], dim=1) + + # remaining convs at full resolution + for conv in self.remaining: + x = conv(x) + + return x + + +class ConvBlock(nn.Module): + """ + Specific convolutional block followed by leakyrelu for unet. + """ + + def __init__(self, ndims, in_channels, out_channels, stride=1, activation='leaky'): + super().__init__() + + Conv = getattr(nn, 'Conv%dd' % ndims) + self.conv = Conv(in_channels, out_channels, 3, stride, 1) + if activation == 'leaky': + self.activation = nn.LeakyReLU(0.2) + elif activation is None: + self.activation = None + else: + raise ValueError(f'Unknown activation: {activation}') + + def forward(self, x): + out = self.conv(x) + if self.activation is not None: + out = self.activation(out) + return out diff --git a/pydra/tasks/mriqc/utils/__init__.py b/pydra/tasks/mriqc/utils/__init__.py new file mode 100644 index 0000000..f0c40d8 --- /dev/null +++ b/pydra/tasks/mriqc/utils/__init__.py @@ -0,0 +1,2 @@ +from .bids import derive_bids_fname +from .misc import BIDS_COMP, _flatten_dict diff --git a/pydra/tasks/mriqc/utils/bids.py b/pydra/tasks/mriqc/utils/bids.py new file mode 100644 index 0000000..2bba31c --- /dev/null +++ b/pydra/tasks/mriqc/utils/bids.py @@ -0,0 +1,95 @@ +import logging +from pathlib import Path + + +logger = logging.getLogger(__name__) + + +def derive_bids_fname( + orig_path: str | Path, + entity: str | None = None, + newsuffix: str | None = None, + newpath: str | Path | None = None, + newext: str | None = None, + position: int = -1, + absolute: bool = True, +) -> Path | str: + """ + Derive a new file name from a BIDS-formatted path. + + Parameters + ---------- + orig_path : :obj:`str` or :obj:`os.pathlike` + A filename (may or may not include path). + entity : :obj:`str`, optional + A new BIDS-like key-value pair. + newsuffix : :obj:`str`, optional + Replace the BIDS suffix. + newpath : :obj:`str` or :obj:`os.pathlike`, optional + Path to replace the path of the input orig_path. + newext : :obj:`str`, optional + Replace the extension of the file. + position : :obj:`int`, optional + Position to insert the entity in the filename. + absolute : :obj:`bool`, optional + If True (default), returns the absolute path of the modified filename. + + Returns + ------- + Absolute path of the modified filename + + Examples + -------- + >>> derive_bids_fname( + ... 'sub-001/ses-01/anat/sub-001_ses-01_T1w.nii.gz', + ... entity='desc-preproc', + ... absolute=False, + ... ) + PosixPath('sub-001/ses-01/anat/sub-001_ses-01_desc-preproc_T1w.nii.gz') + + >>> derive_bids_fname( + ... 'sub-001/ses-01/anat/sub-001_ses-01_T1w.nii.gz', + ... entity='desc-brain', + ... newsuffix='mask', + ... newext=".nii", + ... absolute=False, + ... ) # doctest: +ELLIPSIS + PosixPath('sub-001/ses-01/anat/sub-001_ses-01_desc-brain_mask.nii') + + >>> derive_bids_fname( + ... 'sub-001/ses-01/anat/sub-001_ses-01_T1w.nii.gz', + ... entity='desc-brain', + ... newsuffix='mask', + ... newext=".nii", + ... newpath="/output/node", + ... absolute=True, + ... ) # doctest: +ELLIPSIS + PosixPath('/output/node/sub-001_ses-01_desc-brain_mask.nii') + + >>> derive_bids_fname( + ... 'sub-001/ses-01/anat/sub-001_ses-01_T1w.nii.gz', + ... entity='desc-brain', + ... newsuffix='mask', + ... newext=".nii", + ... newpath=".", + ... absolute=False, + ... ) # doctest: +ELLIPSIS + PosixPath('sub-001_ses-01_desc-brain_mask.nii') + + """ + orig_path = Path(orig_path) + newpath = orig_path.parent if newpath is None else Path(newpath) + ext = "".join(orig_path.suffixes) + newext = newext if newext is not None else ext + orig_stem = orig_path.name.replace(ext, "") + suffix = orig_stem.rsplit("_", maxsplit=1)[-1].strip("_") + newsuffix = newsuffix.strip("_") if newsuffix is not None else suffix + orig_stem = orig_stem.replace(suffix, "").strip("_") + bidts = [bit for bit in orig_stem.split("_") if bit] + if entity: + if position == -1: + bidts.append(entity) + else: + bidts.insert(position, entity.strip("_")) + retval = newpath / f'{"_".join(bidts)}_{newsuffix}.{newext.strip(".")}' + return retval.absolute() if absolute else retval diff --git a/pydra/tasks/mriqc/utils/misc.py b/pydra/tasks/mriqc/utils/misc.py new file mode 100644 index 0000000..4b4f475 --- /dev/null +++ b/pydra/tasks/mriqc/utils/misc.py @@ -0,0 +1,33 @@ +from collections import OrderedDict +import logging + + +logger = logging.getLogger(__name__) + + +def _flatten_dict(indict): + + out_qc = {} + for k, value in list(indict.items()): + if not isinstance(value, dict): + out_qc[k] = value + else: + for subk, subval in list(value.items()): + if not isinstance(subval, dict): + out_qc["_".join([k, subk])] = subval + else: + for ssubk, ssubval in list(subval.items()): + out_qc["_".join([k, subk, ssubk])] = ssubval + return out_qc + + +BIDS_COMP = OrderedDict( + [ + ("subject_id", "sub"), + ("session_id", "ses"), + ("task_id", "task"), + ("acq_id", "acq"), + ("rec_id", "rec"), + ("run_id", "run"), + ] +) diff --git a/pydra/tasks/mriqc/workflows/__init__.py b/pydra/tasks/mriqc/workflows/__init__.py new file mode 100644 index 0000000..e5b76cf --- /dev/null +++ b/pydra/tasks/mriqc/workflows/__init__.py @@ -0,0 +1,31 @@ +from .anatomical import ( + _binarize, + airmsk_wf, + anat_qc_workflow, + compute_iqms, + headmsk_wf, + init_anat_report_wf, + init_brain_tissue_segmentation, + spatial_normalization, +) +from .diffusion import ( + _get_wm, + compute_iqms, + dmri_qc_workflow, + epi_mni_align, + hmc_workflow, + init_dwi_report_wf, +) +from .functional import ( + _carpet_parcellation, + _get_tr, + compute_iqms, + epi_mni_align, + fmri_bmsk_workflow, + fmri_qc_workflow, + hmc, + init_func_report_wf, + spikes_mask, +) +from .shared import synthstrip_wf +from .utils import _tofloat, generate_filename, get_fwhmx, slice_wise_fft, spectrum_mask diff --git a/pydra/tasks/mriqc/workflows/anatomical/__init__.py b/pydra/tasks/mriqc/workflows/anatomical/__init__.py new file mode 100644 index 0000000..c82ce0a --- /dev/null +++ b/pydra/tasks/mriqc/workflows/anatomical/__init__.py @@ -0,0 +1,10 @@ +from .base import ( + _binarize, + airmsk_wf, + anat_qc_workflow, + compute_iqms, + headmsk_wf, + init_brain_tissue_segmentation, + spatial_normalization, +) +from .output import init_anat_report_wf diff --git a/pydra/tasks/mriqc/workflows/anatomical/base.py b/pydra/tasks/mriqc/workflows/anatomical/base.py new file mode 100644 index 0000000..1795675 --- /dev/null +++ b/pydra/tasks/mriqc/workflows/anatomical/base.py @@ -0,0 +1,807 @@ +import attrs +from itertools import chain +import logging +from pathlib import Path +from pydra.compose import python, workflow +from pydra.tasks.mriqc.interfaces import ( + ArtifactMask, + ComputeQI2, + ConformImage, + RotationMask, + StructuralQC, +) +from pydra.tasks.mriqc.workflows.anatomical.output import init_anat_report_wf +from pydra.tasks.mriqc.workflows.utils import get_fwhmx +from pydra.tasks.niworkflows.interfaces.fixes import ( + FixHeaderApplyTransforms as ApplyTransforms, +) +from templateflow.api import get as get_template +import typing as ty + + +logger = logging.getLogger(__name__) + + +@workflow.define( + outputs=[ + "norm_report", + "iqmswf_noise_report", + "anat_report_wf_zoom_report", + "anat_report_wf_bg_report", + "anat_report_wf_segm_report", + "anat_report_wf_bmask_report", + "anat_report_wf_artmask_report", + "anat_report_wf_airmask_report", + "anat_report_wf_headmask_report", + ] +) +def anat_qc_workflow( + exec_ants_float=False, + exec_debug=False, + exec_no_sub=False, + exec_verbose_reports=False, + exec_work_dir=None, + in_file: ty.Any = attrs.NOTHING, + modality: ty.Any = attrs.NOTHING, + name="anatMRIQC", + nipype_omp_nthreads=12, + wf_biggest_file_gb=1, + wf_inputs=None, + wf_inputs_entities={}, + wf_inputs_metadata=None, + wf_species="human", + wf_template_id="MNI152NLin2009cAsym", +) -> [ + "ty.Any", + "ty.Any", + "ty.Any", + "ty.Any", + "ty.Any", + "ty.Any", + "ty.Any", + "ty.Any", + "ty.Any", +]: + """ + One-subject-one-session-one-run pipeline to extract the NR-IQMs from + anatomical images + + .. workflow:: + + import os.path as op + from mriqc.workflows.anatomical.base import anat_qc_workflow + from mriqc.testing import mock_config + with mock_config(): + wf = anat_qc_workflow() + + """ + from pydra.tasks.mriqc.workflows.shared import synthstrip_wf + + if exec_work_dir is None: + exec_work_dir = Path.cwd() + + outputs_ = { + "norm_report": attrs.NOTHING, + "iqmswf_noise_report": attrs.NOTHING, + "anat_report_wf_zoom_report": attrs.NOTHING, + "anat_report_wf_bg_report": attrs.NOTHING, + "anat_report_wf_segm_report": attrs.NOTHING, + "anat_report_wf_bmask_report": attrs.NOTHING, + "anat_report_wf_artmask_report": attrs.NOTHING, + "anat_report_wf_airmask_report": attrs.NOTHING, + "anat_report_wf_headmask_report": attrs.NOTHING, + } + + # Enable if necessary + # mem_gb = max( + # wf_biggest_file_gb['t1w'], + # wf_biggest_file_gb['t2w'], + # ) + dataset = list( + chain( + wf_inputs.get("t1w", []), + wf_inputs.get("t2w", []), + ) + ) + metadata = list( + chain( + wf_inputs_metadata.get("t1w", []), + wf_inputs_metadata.get("t2w", []), + ) + ) + entities = list( + chain( + wf_inputs_entities.get("t1w", []), + wf_inputs_entities.get("t2w", []), + ) + ) + message = "Building {modality} MRIQC workflow {detail}.".format( + modality="anatomical", + detail=f"for {len(dataset)} NIfTI files.", + ) + logger.info(message) + # Initialize workflow + # Define workflow, inputs and outputs + # 0. Get data + + # 1. Reorient anatomical image + to_ras = workflow.add( + ConformImage(check_dtype=False, in_file=in_file), name="to_ras" + ) + # 2. species specific skull-stripping + if wf_species.lower() == "human": + skull_stripping = workflow.add( + synthstrip_wf( + omp_nthreads=nipype_omp_nthreads, + in_files=to_ras.out_file, + name="skull_stripping", + ) + ) + ss_bias_field = "outputnode.bias_image" + else: + from nirodents.workflows.brainextraction import init_rodent_brain_extraction_wf + + skull_stripping = init_rodent_brain_extraction_wf(template_id=wf_template_id) + ss_bias_field = "final_n4.bias_image" + # 3. Head mask + hmsk = workflow.add( + headmsk_wf(omp_nthreads=nipype_omp_nthreads, wf_species=wf_species, name="hmsk") + ) + # 4. Spatial Normalization, using ANTs + norm = workflow.add( + spatial_normalization( + wf_species=wf_species, + wf_template_id=wf_template_id, + exec_ants_float=exec_ants_float, + exec_debug=exec_debug, + nipype_omp_nthreads=nipype_omp_nthreads, + modality=modality, + name="norm", + ) + ) + # 5. Air mask (with and without artifacts) + amw = workflow.add( + airmsk_wf( + ind2std_xfm=norm.ind2std_xfm, + in_file=to_ras.out_file, + head_mask=hmsk.out_file, + name="amw", + ) + ) + # 6. Brain tissue segmentation + bts = workflow.add( + init_brain_tissue_segmentation( + nipype_omp_nthreads=nipype_omp_nthreads, + std_tpms=norm.out_tpms, + in_file=hmsk.out_denoised, + name="bts", + ) + ) + # 7. Compute IQMs + iqmswf = workflow.add( + compute_iqms( + wf_species=wf_species, + std_tpms=norm.out_tpms, + in_ras=to_ras.out_file, + airmask=amw.air_mask, + hatmask=amw.hat_mask, + artmask=amw.art_mask, + rotmask=amw.rot_mask, + segmentation=bts.out_segm, + pvms=bts.out_pvms, + headmask=hmsk.out_file, + name="iqmswf", + ) + ) + # Reports + anat_report_wf = workflow.add( + init_anat_report_wf( + wf_species=wf_species, + exec_verbose_reports=exec_verbose_reports, + exec_work_dir=exec_work_dir, + in_ras=to_ras.out_file, + headmask=hmsk.out_file, + airmask=amw.air_mask, + artmask=amw.art_mask, + segmentation=bts.out_segm, + name="anat_report_wf", + ) + ) + # Connect all nodes + # fmt: off + + hmsk.inputs.in_file = skull_stripping.out_corrected + hmsk.inputs.brainmask = skull_stripping.out_mask + bts.inputs.brainmask = skull_stripping.out_mask + norm.inputs.moving_image = skull_stripping.out_corrected + norm.inputs.moving_mask = skull_stripping.out_mask + hmsk.inputs.in_tpms = norm.out_tpms + + iqmswf.inputs.inu_corrected = skull_stripping.out_corrected + iqmswf.inputs.in_inu = skull_stripping.bias_image + iqmswf.inputs.brainmask = skull_stripping.out_mask + + anat_report_wf.inputs.brainmask = skull_stripping.out_mask + + # fmt: on + # Upload metrics + if not exec_no_sub: + from pydra.tasks.mriqc.interfaces.webapi import UploadIQMs + + pass + # fmt: off + pass + pass + # fmt: on + outputs_["norm_report"] = norm.report + outputs_["iqmswf_noise_report"] = iqmswf.noise_report + outputs_["anat_report_wf_bmask_report"] = anat_report_wf.bmask_report + outputs_["anat_report_wf_artmask_report"] = anat_report_wf.artmask_report + outputs_["anat_report_wf_headmask_report"] = anat_report_wf.headmask_report + outputs_["anat_report_wf_bg_report"] = anat_report_wf.bg_report + outputs_["anat_report_wf_airmask_report"] = anat_report_wf.airmask_report + outputs_["anat_report_wf_zoom_report"] = anat_report_wf.zoom_report + outputs_["anat_report_wf_segm_report"] = anat_report_wf.segm_report + + return tuple(outputs_) + + +@workflow.define(outputs=["hat_mask", "air_mask", "art_mask", "rot_mask"]) +def airmsk_wf( + head_mask: ty.Any = attrs.NOTHING, + in_file: ty.Any = attrs.NOTHING, + ind2std_xfm: ty.Any = attrs.NOTHING, + name="AirMaskWorkflow", +) -> ["ty.Any", "ty.Any", "ty.Any", "ty.Any"]: + """ + Calculate air, artifacts and "hat" masks to evaluate noise in the background. + + This workflow mostly addresses the implementation of Step 1 in [Mortamet2009]_. + This work proposes to look at the signal distribution in the background, where + no signals are expected, to evaluate the spread of the noise. + It is in the background where [Mortamet2009]_ proposed to also look at the presence + of ghosts and artifacts, where they are very easy to isolate. + + However, [Mortamet2009]_ proposes not to look at the background around the face + because of the likely signal leakage through the phase-encoding axis sourcing from + eyeballs (and their motion). + To avoid that, [Mortamet2009]_ proposed atlas-based identification of two landmarks + (nasion and cerebellar projection on to the occipital bone). + MRIQC, for simplicity, used a such a mask created in MNI152NLin2009cAsym space and + projected it on to the individual. + Such a solution is inadequate because it doesn't drop full in-plane slices as there + will be a large rotation of the individual's tilt of the head with respect to the + template. + The new implementation (23.1.x series) follows [Mortamet2009]_ more closely, + projecting the two landmarks from the template space and leveraging + *NiTransforms* to do that. + + .. workflow:: + + from mriqc.testing import mock_config + from mriqc.workflows.anatomical.base import airmsk_wf + with mock_config(): + wf = airmsk_wf() + + """ + outputs_ = { + "hat_mask": attrs.NOTHING, + "air_mask": attrs.NOTHING, + "art_mask": attrs.NOTHING, + "rot_mask": attrs.NOTHING, + } + + rotmsk = workflow.add(RotationMask(in_file=in_file), name="rotmsk") + qi1 = workflow.add( + ArtifactMask(head_mask=head_mask, in_file=in_file, ind2std_xfm=ind2std_xfm), + name="qi1", + ) + # fmt: off + outputs_['hat_mask'] = qi1.out_hat_msk + outputs_['air_mask'] = qi1.out_air_msk + outputs_['art_mask'] = qi1.out_art_msk + outputs_['rot_mask'] = rotmsk.out_file + # fmt: on + + return tuple(outputs_) + + +@workflow.define(outputs=["out_file", "out_denoised"]) +def headmsk_wf( + brainmask: ty.Any = attrs.NOTHING, + in_file: ty.Any = attrs.NOTHING, + in_tpms: ty.Any = attrs.NOTHING, + name="HeadMaskWorkflow", + omp_nthreads=1, + wf_species="human", +) -> ["ty.Any", "ty.Any"]: + """ + Computes a head mask as in [Mortamet2009]_. + + .. workflow:: + + from mriqc.testing import mock_config + from mriqc.workflows.anatomical.base import headmsk_wf + with mock_config(): + wf = headmsk_wf() + + """ + from pydra.tasks.niworkflows.interfaces.nibabel import ApplyMask + + outputs_ = { + "out_file": attrs.NOTHING, + "out_denoised": attrs.NOTHING, + } + + def _select_wm(inlist): + return [f for f in inlist if "WM" in f][0] + + enhance = workflow.add( + FunctionTask( + func=_enhance, + input_spec=SpecInfo( + name="FunctionIn", + bases=(BaseSpec,), + fields=[("in_file", ty.Any), ("wm_tpm", ty.Any)], + ), + output_spec=SpecInfo( + name="FunctionOut", bases=(BaseSpec,), fields=[("out_file", ty.Any)] + ), + in_file=in_file, + wm_tpm=in_tpms, + ), + name="enhance", + ) + gradient = workflow.add( + FunctionTask( + func=image_gradient, + input_spec=SpecInfo( + name="FunctionIn", + bases=(BaseSpec,), + fields=[("in_file", ty.Any), ("brainmask", ty.Any), ("sigma", ty.Any)], + ), + output_spec=SpecInfo( + name="FunctionOut", bases=(BaseSpec,), fields=[("out_file", ty.Any)] + ), + brainmask=brainmask, + in_file=enhance.out_file, + ), + name="gradient", + ) + thresh = workflow.add( + FunctionTask( + func=gradient_threshold, + input_spec=SpecInfo( + name="FunctionIn", + bases=(BaseSpec,), + fields=[ + ("in_file", ty.Any), + ("brainmask", ty.Any), + ("aniso", ty.Any), + ("thresh", ty.Any), + ], + ), + output_spec=SpecInfo( + name="FunctionOut", bases=(BaseSpec,), fields=[("out_file", ty.Any)] + ), + brainmask=brainmask, + in_file=gradient.out_file, + ), + name="thresh", + ) + if wf_species != "human": + gradient.inputs.inputs.sigma = 3.0 + thresh.inputs.inputs.aniso = True + thresh.inputs.inputs.thresh = 4.0 + apply_mask = workflow.add( + ApplyMask(in_file=enhance.out_file, in_mask=brainmask), name="apply_mask" + ) + # fmt: off + enhance.inputs.wm_tpm = in_tpms + outputs_['out_file'] = thresh.out_file + outputs_['out_denoised'] = apply_mask.out_file + # fmt: on + + return tuple(outputs_) + + +@workflow.define(outputs=["out_segm", "out_pvms"]) +def init_brain_tissue_segmentation( + brainmask: ty.Any = attrs.NOTHING, + in_file: ty.Any = attrs.NOTHING, + name="brain_tissue_segmentation", + nipype_omp_nthreads=12, + std_tpms: ty.Any = attrs.NOTHING, +) -> ["ty.Any", "ty.Any"]: + """ + Setup a workflow for brain tissue segmentation. + + .. workflow:: + + from mriqc.workflows.anatomical.base import init_brain_tissue_segmentation + from mriqc.testing import mock_config + with mock_config(): + wf = init_brain_tissue_segmentation() + + """ + from pydra.tasks.ants.auto import Atropos + + outputs_ = { + "out_segm": attrs.NOTHING, + "out_pvms": attrs.NOTHING, + } + + def _format_tpm_names(in_files, fname_string=None): + import glob + from pathlib import Path + import nibabel as nb + + out_path = Path.cwd().absolute() + # copy files to cwd and rename iteratively + for count, fname in enumerate(in_files): + img = nb.load(fname) + extension = "".join(Path(fname).suffixes) + out_fname = f"priors_{1 + count:02}{extension}" + nb.save(img, Path(out_path, out_fname)) + if fname_string is None: + fname_string = f"priors_%02d{extension}" + out_files = [ + str(prior) + for prior in glob.glob(str(Path(out_path, f"priors*{extension}"))) + ] + # return path with c-style format string for Atropos + file_format = str(Path(out_path, fname_string)) + return file_format, out_files + + format_tpm_names = workflow.add( + FunctionTask( + execution={"keep_inputs": True, "remove_unnecessary_outputs": False}, + func=_format_tpm_names, + input_spec=SpecInfo( + name="FunctionIn", bases=(BaseSpec,), fields=[("in_files", ty.Any)] + ), + output_spec=SpecInfo( + name="FunctionOut", bases=(BaseSpec,), fields=[("file_format", ty.Any)] + ), + in_files=std_tpms, + ), + name="format_tpm_names", + ) + segment = workflow.add( + Atropos( + initialization="PriorProbabilityImages", + mrf_radius=[1, 1, 1], + mrf_smoothing_factor=0.01, + num_threads=nipype_omp_nthreads, + number_of_tissue_classes=3, + out_classified_image_name="segment.nii.gz", + output_posteriors_name_template="segment_%02d.nii.gz", + prior_weighting=0.1, + save_posteriors=True, + intensity_images=in_file, + mask_image=brainmask, + ), + name="segment", + ) + # fmt: off + + @python.define + def format_tpm_names_file_format_to_segment_prior_image_callable(in_: ty.Any) -> ty.Any: + return _pop(in_) + + format_tpm_names_file_format_to_segment_prior_image_callable = workflow.add(format_tpm_names_file_format_to_segment_prior_image_callable(in_=format_tpm_names.file_format), name="format_tpm_names_file_format_to_segment_prior_image_callable") + + segment.inputs.prior_image = format_tpm_names_file_format_to_segment_prior_image_callable.out + outputs_['out_segm'] = segment.classified_image + outputs_['out_pvms'] = segment.posteriors + # fmt: on + + return tuple(outputs_) + + +@workflow.define(outputs=["report", "ind2std_xfm", "out_tpms"]) +def spatial_normalization( + exec_ants_float=False, + exec_debug=False, + modality: ty.Any = attrs.NOTHING, + moving_image: ty.Any = attrs.NOTHING, + moving_mask: ty.Any = attrs.NOTHING, + name="SpatialNormalization", + nipype_omp_nthreads=12, + wf_species="human", + wf_template_id="MNI152NLin2009cAsym", +) -> ["ty.Any", "ty.Any", "ty.Any"]: + """Create a simplified workflow to perform fast spatial normalization.""" + from pydra.tasks.niworkflows.interfaces.reportlets.registration import ( + SpatialNormalizationRPT as RobustMNINormalization, + ) + + outputs_ = { + "report": attrs.NOTHING, + "ind2std_xfm": attrs.NOTHING, + "out_tpms": attrs.NOTHING, + } + + # Have the template id handy + tpl_id = wf_template_id + # Define workflow interface + + # Spatial normalization + norm = workflow.add( + RobustMNINormalization( + flavor=["testing", "fast"][exec_debug], + float=exec_ants_float, + generate_report=True, + num_threads=nipype_omp_nthreads, + template=tpl_id, + moving_image=moving_image, + moving_mask=moving_mask, + reference=modality, + ), + name="norm", + ) + if wf_species.lower() == "human": + norm.inputs.inputs.reference_mask = str( + get_template(tpl_id, resolution=2, desc="brain", suffix="mask") + ) + else: + norm.inputs.inputs.reference_image = str(get_template(tpl_id, suffix="T2w")) + norm.inputs.inputs.reference_mask = str( + get_template(tpl_id, desc="brain", suffix="mask")[0] + ) + # Project standard TPMs into T1w space + tpms_std2t1w = workflow.add( + ApplyTransforms( + default_value=0, + dimension=3, + float=exec_ants_float, + interpolation="Gaussian", + reference_image=moving_image, + transforms=norm.inverse_composite_transform, + ), + name="tpms_std2t1w", + ) + tpms_std2t1w.inputs.inputs.input_image = [ + str(p) + for p in get_template( + wf_template_id, + suffix="probseg", + resolution=(1 if wf_species.lower() == "human" else None), + label=["CSF", "GM", "WM"], + ) + ] + # fmt: off + outputs_['ind2std_xfm'] = norm.composite_transform + outputs_['report'] = norm.out_report + outputs_['out_tpms'] = tpms_std2t1w.output_image + # fmt: on + + return tuple(outputs_) + + +@workflow.define(outputs=["measures", "noise_report"]) +def compute_iqms( + airmask: ty.Any = attrs.NOTHING, + artmask: ty.Any = attrs.NOTHING, + brainmask: ty.Any = attrs.NOTHING, + hatmask: ty.Any = attrs.NOTHING, + headmask: ty.Any = attrs.NOTHING, + in_inu: ty.Any = attrs.NOTHING, + in_ras: ty.Any = attrs.NOTHING, + inu_corrected: ty.Any = attrs.NOTHING, + name="ComputeIQMs", + pvms: ty.Any = attrs.NOTHING, + rotmask: ty.Any = attrs.NOTHING, + segmentation: ty.Any = attrs.NOTHING, + std_tpms: ty.Any = attrs.NOTHING, + wf_species="human", +) -> ["ty.Any", "ty.Any"]: + """ + Setup the workflow that actually computes the IQMs. + + .. workflow:: + + from mriqc.workflows.anatomical.base import compute_iqms + from mriqc.testing import mock_config + with mock_config(): + wf = compute_iqms() + + """ + from pydra.tasks.mriqc.interfaces.anatomical import Harmonize + + outputs_ = { + "measures": attrs.NOTHING, + "noise_report": attrs.NOTHING, + } + + from pydra.tasks.mriqc.workflows.utils import _tofloat + + # Add provenance + + # AFNI check smoothing + fwhm_interface = get_fwhmx() + fwhm = workflow.add(fwhm_task, name="fwhm") + # Harmonize + homog = workflow.add( + Harmonize(brain_mask=brainmask, in_file=inu_corrected, wm_mask=pvms), + name="homog", + ) + if wf_species.lower() != "human": + homog.inputs.inputs.erodemsk = False + homog.inputs.inputs.thresh = 0.8 + # Mortamet's QI2 + getqi2 = workflow.add(ComputeQI2(air_msk=hatmask, in_file=in_ras), name="getqi2") + # Compute python-coded measures + measures = workflow.add( + StructuralQC( + human=wf_species.lower() == "human", + air_msk=airmask, + artifact_msk=artmask, + head_msk=headmask, + in_bias=in_inu, + in_file=in_ras, + in_noinu=homog.out_file, + in_pvms=pvms, + in_segm=segmentation, + mni_tpms=std_tpms, + rot_msk=rotmask, + ), + name="measures", + ) + + def _getwm(inlist): + return inlist[-1] + + # fmt: off + + + homog.inputs.wm_mask = pvms + + @python.define + def fwhm_fwhm_to_measures_in_fwhm_callable(in_: ty.Any) -> ty.Any: + return _tofloat(in_) + + fwhm_fwhm_to_measures_in_fwhm_callable = workflow.add(fwhm_fwhm_to_measures_in_fwhm_callable(in_=fwhm.fwhm), name="fwhm_fwhm_to_measures_in_fwhm_callable") + + measures.inputs.in_fwhm = fwhm_fwhm_to_measures_in_fwhm_callable.out + outputs_['measures'] = measures.out_qc + outputs_['noise_report'] = getqi2.out_file + + # fmt: on + + return tuple(outputs_) + + +def _enhance(in_file, wm_tpm, out_file=None): + + import nibabel as nb + import numpy as np + from pydra.tasks.mriqc.workflows.utils import generate_filename + + imnii = nb.load(in_file) + data = imnii.get_fdata(dtype=np.float32) + range_max = np.percentile(data[data > 0], 99.98) + excess = data > range_max + wm_prob = nb.load(wm_tpm).get_fdata() + wm_prob[wm_prob < 0] = 0 # Ensure no negative values + wm_prob[excess] = 0 # Ensure no outliers are considered + # Calculate weighted mean and standard deviation + wm_mu = np.average(data, weights=wm_prob) + wm_sigma = np.sqrt(np.average((data - wm_mu) ** 2, weights=wm_prob)) + # Resample signal excess pixels + data[excess] = np.random.normal(loc=wm_mu, scale=wm_sigma, size=excess.sum()) + out_file = out_file or str(generate_filename(in_file, suffix="enhanced").absolute()) + nb.Nifti1Image(data, imnii.affine, imnii.header).to_filename(out_file) + return out_file + + +def _get_mod(in_file): + + from pathlib import Path + + in_file = Path(in_file) + extension = "".join(in_file.suffixes) + return in_file.name.replace(extension, "").split("_")[-1] + + +def _pop(inlist): + + if isinstance(inlist, (list, tuple)): + return inlist[0] + return inlist + + +def gradient_threshold(in_file, brainmask, thresh=15.0, out_file=None, aniso=False): + """Compute a threshold from the histogram of the magnitude gradient image""" + import nibabel as nb + import numpy as np + from scipy import ndimage as sim + from pydra.tasks.mriqc.workflows.utils import generate_filename + + if not aniso: + struct = sim.iterate_structure(sim.generate_binary_structure(3, 2), 2) + else: + # Generate an anisotropic binary structure, taking into account slice thickness + img = nb.load(in_file) + zooms = img.header.get_zooms() + dist = max(zooms) + dim = img.header["dim"][0] + x = np.ones((5) * np.ones(dim, dtype=np.int8)) + np.put(x, x.size // 2, 0) + dist_matrix = np.round(sim.distance_transform_edt(x, sampling=zooms), 5) + struct = dist_matrix <= dist + imnii = nb.load(in_file) + hdr = imnii.header.copy() + hdr.set_data_dtype(np.uint8) + data = imnii.get_fdata(dtype=np.float32) + mask = np.zeros_like(data, dtype=np.uint8) + mask[data > thresh] = 1 + mask = sim.binary_closing(mask, struct, iterations=2).astype(np.uint8) + mask = sim.binary_erosion(mask, sim.generate_binary_structure(3, 2)).astype( + np.uint8 + ) + segdata = np.asanyarray(nb.load(brainmask).dataobj) > 0 + segdata = sim.binary_dilation(segdata, struct, iterations=2, border_value=1).astype( + np.uint8 + ) + mask[segdata] = 1 + # Remove small objects + label_im, nb_labels = sim.label(mask) + artmsk = np.zeros_like(mask) + if nb_labels > 2: + sizes = sim.sum(mask, label_im, list(range(nb_labels + 1))) + ordered = sorted(zip(sizes, list(range(nb_labels + 1))), reverse=True) + for _, label in ordered[2:]: + mask[label_im == label] = 0 + artmsk[label_im == label] = 1 + mask = sim.binary_fill_holes(mask, struct).astype( + np.uint8 + ) # pylint: disable=no-member + out_file = out_file or str(generate_filename(in_file, suffix="gradmask").absolute()) + nb.Nifti1Image(mask, imnii.affine, hdr).to_filename(out_file) + return out_file + + +def image_gradient(in_file, brainmask, sigma=4.0, out_file=None): + """Computes the magnitude gradient of an image using numpy""" + import nibabel as nb + import numpy as np + from scipy.ndimage import gaussian_gradient_magnitude as gradient + from pydra.tasks.mriqc.workflows.utils import generate_filename + + imnii = nb.load(in_file) + mask = np.bool_(nb.load(brainmask).dataobj) + data = imnii.get_fdata(dtype=np.float32) + datamax = np.percentile(data.reshape(-1), 99.5) + data *= 100 / datamax + data[mask] = 100 + zooms = np.array(imnii.header.get_zooms()[:3]) + sigma_xyz = 2 - zooms / min(zooms) + grad = gradient(data, sigma * sigma_xyz) + gradmax = np.percentile(grad.reshape(-1), 99.5) + grad *= 100.0 + grad /= gradmax + grad[mask] = 100 + out_file = out_file or str(generate_filename(in_file, suffix="grad").absolute()) + nb.Nifti1Image(grad, imnii.affine, imnii.header).to_filename(out_file) + return out_file + + +def _binarize(in_file, threshold=0.5, out_file=None): + + import os.path as op + import nibabel as nb + import numpy as np + + if out_file is None: + fname, ext = op.splitext(op.basename(in_file)) + if ext == ".gz": + fname, ext2 = op.splitext(fname) + ext = ext2 + ext + out_file = op.abspath(f"{fname}_bin{ext}") + nii = nb.load(in_file) + data = nii.get_fdata() > threshold + hdr = nii.header.copy() + hdr.set_data_dtype(np.uint8) + nb.Nifti1Image(data.astype(np.uint8), nii.affine, hdr).to_filename(out_file) + return out_file diff --git a/pydra/tasks/mriqc/workflows/anatomical/output.py b/pydra/tasks/mriqc/workflows/anatomical/output.py new file mode 100644 index 0000000..e2ba900 --- /dev/null +++ b/pydra/tasks/mriqc/workflows/anatomical/output.py @@ -0,0 +1,159 @@ +import attrs +import logging +from pathlib import Path +from pydra.compose import workflow +import typing as ty + + +logger = logging.getLogger(__name__) + + +@workflow.define( + outputs=[ + "zoom_report", + "bg_report", + "segm_report", + "bmask_report", + "artmask_report", + "airmask_report", + "headmask_report", + ] +) +def init_anat_report_wf( + airmask: ty.Any = attrs.NOTHING, + artmask: ty.Any = attrs.NOTHING, + brainmask: ty.Any = attrs.NOTHING, + exec_verbose_reports=False, + exec_work_dir=None, + headmask: ty.Any = attrs.NOTHING, + in_ras: ty.Any = attrs.NOTHING, + name: str = "anat_report_wf", + segmentation: ty.Any = attrs.NOTHING, + wf_species="human", +) -> ["ty.Any", "ty.Any", "ty.Any", "ty.Any", "ty.Any", "ty.Any", "ty.Any"]: + """ + Generate the components of the individual report. + + .. workflow:: + + from mriqc.workflows.anatomical.output import init_anat_report_wf + from mriqc.testing import mock_config + with mock_config(): + wf = init_anat_report_wf() + + """ + from pydra.tasks.nireports.interfaces import PlotMosaic + + if exec_work_dir is None: + exec_work_dir = Path.cwd() + + outputs_ = { + "zoom_report": attrs.NOTHING, + "bg_report": attrs.NOTHING, + "segm_report": attrs.NOTHING, + "bmask_report": attrs.NOTHING, + "artmask_report": attrs.NOTHING, + "airmask_report": attrs.NOTHING, + "headmask_report": attrs.NOTHING, + } + + # from mriqc.interfaces.reports import IndividualReport + verbose = exec_verbose_reports + reportlets_dir = exec_work_dir / "reportlets" + + mosaic_zoom = workflow.add( + PlotMosaic(cmap="Greys_r", bbox_mask_file=brainmask, in_file=in_ras), + name="mosaic_zoom", + ) + mosaic_noise = workflow.add( + PlotMosaic(cmap="viridis_r", only_noise=True, in_file=in_ras), + name="mosaic_noise", + ) + if wf_species.lower() in ("rat", "mouse"): + mosaic_zoom.inputs.inputs.view = ["coronal", "axial"] + mosaic_noise.inputs.inputs.view = ["coronal", "axial"] + + # fmt: off + outputs_['zoom_report'] = mosaic_zoom.out_file + outputs_['bg_report'] = mosaic_noise.out_file + # fmt: on + + from pydra.tasks.nireports.interfaces import PlotContours + + display_mode = "y" if wf_species.lower() in ("rat", "mouse") else "z" + plot_segm = workflow.add( + PlotContours( + colors=["r", "g", "b"], + cut_coords=10, + display_mode=display_mode, + levels=[0.5, 1.5, 2.5], + in_contours=segmentation, + in_file=in_ras, + ), + name="plot_segm", + ) + + plot_bmask = workflow.add( + PlotContours( + colors=["r"], + cut_coords=10, + display_mode=display_mode, + levels=[0.5], + out_file="bmask", + in_contours=brainmask, + in_file=in_ras, + ), + name="plot_bmask", + ) + + plot_artmask = workflow.add( + PlotContours( + colors=["r"], + cut_coords=10, + display_mode=display_mode, + levels=[0.5], + out_file="artmask", + saturate=True, + in_contours=artmask, + in_file=in_ras, + ), + name="plot_artmask", + ) + + # NOTE: humans switch on these two to coronal view. + display_mode = "y" if wf_species.lower() in ("rat", "mouse") else "x" + plot_airmask = workflow.add( + PlotContours( + colors=["r"], + cut_coords=6, + display_mode=display_mode, + levels=[0.5], + out_file="airmask", + in_contours=airmask, + in_file=in_ras, + ), + name="plot_airmask", + ) + + plot_headmask = workflow.add( + PlotContours( + colors=["r"], + cut_coords=6, + display_mode=display_mode, + levels=[0.5], + out_file="headmask", + in_contours=headmask, + in_file=in_ras, + ), + name="plot_headmask", + ) + + # fmt: off + outputs_['bmask_report'] = plot_bmask.out_file + outputs_['segm_report'] = plot_segm.out_file + outputs_['artmask_report'] = plot_artmask.out_file + outputs_['headmask_report'] = plot_headmask.out_file + outputs_['airmask_report'] = plot_airmask.out_file + # fmt: on + + return tuple(outputs_) diff --git a/pydra/tasks/mriqc/workflows/anatomical/tests/conftest.py b/pydra/tasks/mriqc/workflows/anatomical/tests/conftest.py new file mode 100644 index 0000000..751042d --- /dev/null +++ b/pydra/tasks/mriqc/workflows/anatomical/tests/conftest.py @@ -0,0 +1,25 @@ + +# For debugging in IDE's don't catch raised exceptions and let the IDE +# break at it +import os +import pytest + + +if os.getenv("_PYTEST_RAISE", "0") != "0": + + @pytest.hookimpl(tryfirst=True) + def pytest_exception_interact(call): + raise call.excinfo.value # raise internal errors instead of capturing them + + @pytest.hookimpl(tryfirst=True) + def pytest_internalerror(excinfo): + raise excinfo.value # raise internal errors instead of capturing them + + def pytest_configure(config): + config.option.capture = 'no' # allow print statements to show up in the console + config.option.log_cli = True # show log messages in the console + config.option.log_level = "INFO" # set the log level to INFO + + CATCH_CLI_EXCEPTIONS = False +else: + CATCH_CLI_EXCEPTIONS = True diff --git a/pydra/tasks/mriqc/workflows/anatomical/tests/test_workflows_anatomical_base_airmsk_wf.py b/pydra/tasks/mriqc/workflows/anatomical/tests/test_workflows_anatomical_base_airmsk_wf.py new file mode 100644 index 0000000..2bba00d --- /dev/null +++ b/pydra/tasks/mriqc/workflows/anatomical/tests/test_workflows_anatomical_base_airmsk_wf.py @@ -0,0 +1,21 @@ +import logging +from pydra.compose import workflow +from pydra.tasks.mriqc.workflows.anatomical.base import airmsk_wf +import pytest + + +logger = logging.getLogger(__name__) + + +def test_airmsk_wf_build(): + workflow = airmsk_wf() + assert isinstance(workflow, Workflow) + + +@pytest.mark.skip( + reason="Appropriate inputs for this workflow haven't been specified yet" +) +def test_airmsk_wf_run(): + workflow = airmsk_wf() + result = workflow(worker="debug") + print(result.out) diff --git a/pydra/tasks/mriqc/workflows/anatomical/tests/test_workflows_anatomical_base_anat_qc_workflow.py b/pydra/tasks/mriqc/workflows/anatomical/tests/test_workflows_anatomical_base_anat_qc_workflow.py new file mode 100644 index 0000000..4a722fe --- /dev/null +++ b/pydra/tasks/mriqc/workflows/anatomical/tests/test_workflows_anatomical_base_anat_qc_workflow.py @@ -0,0 +1,22 @@ +from fileformats.medimage import NiftiGzX, T1Weighted +import logging +from pydra.compose import workflow +from pydra.tasks.mriqc.workflows.anatomical.base import anat_qc_workflow +import pytest + + +logger = logging.getLogger(__name__) + + +def test_anat_qc_workflow_build(): + workflow = anat_qc_workflow() + assert isinstance(workflow, Workflow) + + +@pytest.mark.skip( + reason="Appropriate inputs for this workflow haven't been specified yet" +) +def test_anat_qc_workflow_run(): + workflow = anat_qc_workflow(in_file=NiftiGzX[T1Weighted].sample()) + result = workflow(worker="debug") + print(result.out) diff --git a/pydra/tasks/mriqc/workflows/anatomical/tests/test_workflows_anatomical_base_compute_iqms.py b/pydra/tasks/mriqc/workflows/anatomical/tests/test_workflows_anatomical_base_compute_iqms.py new file mode 100644 index 0000000..507dc77 --- /dev/null +++ b/pydra/tasks/mriqc/workflows/anatomical/tests/test_workflows_anatomical_base_compute_iqms.py @@ -0,0 +1,21 @@ +import logging +from pydra.compose import workflow +from pydra.tasks.mriqc.workflows.anatomical.base import compute_iqms +import pytest + + +logger = logging.getLogger(__name__) + + +def test_compute_iqms_build(): + workflow = compute_iqms() + assert isinstance(workflow, Workflow) + + +@pytest.mark.skip( + reason="Appropriate inputs for this workflow haven't been specified yet" +) +def test_compute_iqms_run(): + workflow = compute_iqms() + result = workflow(worker="debug") + print(result.out) diff --git a/pydra/tasks/mriqc/workflows/anatomical/tests/test_workflows_anatomical_base_headmsk_wf.py b/pydra/tasks/mriqc/workflows/anatomical/tests/test_workflows_anatomical_base_headmsk_wf.py new file mode 100644 index 0000000..26b8172 --- /dev/null +++ b/pydra/tasks/mriqc/workflows/anatomical/tests/test_workflows_anatomical_base_headmsk_wf.py @@ -0,0 +1,21 @@ +import logging +from pydra.compose import workflow +from pydra.tasks.mriqc.workflows.anatomical.base import headmsk_wf +import pytest + + +logger = logging.getLogger(__name__) + + +def test_headmsk_wf_build(): + workflow = headmsk_wf() + assert isinstance(workflow, Workflow) + + +@pytest.mark.skip( + reason="Appropriate inputs for this workflow haven't been specified yet" +) +def test_headmsk_wf_run(): + workflow = headmsk_wf() + result = workflow(worker="debug") + print(result.out) diff --git a/pydra/tasks/mriqc/workflows/anatomical/tests/test_workflows_anatomical_base_init_brain_tissue_segmentation.py b/pydra/tasks/mriqc/workflows/anatomical/tests/test_workflows_anatomical_base_init_brain_tissue_segmentation.py new file mode 100644 index 0000000..94bbea9 --- /dev/null +++ b/pydra/tasks/mriqc/workflows/anatomical/tests/test_workflows_anatomical_base_init_brain_tissue_segmentation.py @@ -0,0 +1,21 @@ +import logging +from pydra.compose import workflow +from pydra.tasks.mriqc.workflows.anatomical.base import init_brain_tissue_segmentation +import pytest + + +logger = logging.getLogger(__name__) + + +def test_init_brain_tissue_segmentation_build(): + workflow = init_brain_tissue_segmentation() + assert isinstance(workflow, Workflow) + + +@pytest.mark.skip( + reason="Appropriate inputs for this workflow haven't been specified yet" +) +def test_init_brain_tissue_segmentation_run(): + workflow = init_brain_tissue_segmentation() + result = workflow(worker="debug") + print(result.out) diff --git a/pydra/tasks/mriqc/workflows/anatomical/tests/test_workflows_anatomical_base_spatial_normalization.py b/pydra/tasks/mriqc/workflows/anatomical/tests/test_workflows_anatomical_base_spatial_normalization.py new file mode 100644 index 0000000..6b6c3ed --- /dev/null +++ b/pydra/tasks/mriqc/workflows/anatomical/tests/test_workflows_anatomical_base_spatial_normalization.py @@ -0,0 +1,21 @@ +import logging +from pydra.compose import workflow +from pydra.tasks.mriqc.workflows.anatomical.base import spatial_normalization +import pytest + + +logger = logging.getLogger(__name__) + + +def test_spatial_normalization_build(): + workflow = spatial_normalization() + assert isinstance(workflow, Workflow) + + +@pytest.mark.skip( + reason="Appropriate inputs for this workflow haven't been specified yet" +) +def test_spatial_normalization_run(): + workflow = spatial_normalization() + result = workflow(worker="debug") + print(result.out) diff --git a/pydra/tasks/mriqc/workflows/anatomical/tests/test_workflows_anatomical_output_init_anat_report_wf.py b/pydra/tasks/mriqc/workflows/anatomical/tests/test_workflows_anatomical_output_init_anat_report_wf.py new file mode 100644 index 0000000..c4ebae4 --- /dev/null +++ b/pydra/tasks/mriqc/workflows/anatomical/tests/test_workflows_anatomical_output_init_anat_report_wf.py @@ -0,0 +1,21 @@ +import logging +from pydra.compose import workflow +from pydra.tasks.mriqc.workflows.anatomical.output import init_anat_report_wf +import pytest + + +logger = logging.getLogger(__name__) + + +def test_init_anat_report_wf_build(): + workflow = init_anat_report_wf() + assert isinstance(workflow, Workflow) + + +@pytest.mark.skip( + reason="Appropriate inputs for this workflow haven't been specified yet" +) +def test_init_anat_report_wf_run(): + workflow = init_anat_report_wf() + result = workflow(worker="debug") + print(result.out) diff --git a/pydra/tasks/mriqc/workflows/diffusion/__init__.py b/pydra/tasks/mriqc/workflows/diffusion/__init__.py new file mode 100644 index 0000000..e23fd8a --- /dev/null +++ b/pydra/tasks/mriqc/workflows/diffusion/__init__.py @@ -0,0 +1,2 @@ +from .base import compute_iqms, dmri_qc_workflow, epi_mni_align, hmc_workflow +from .output import _get_wm, init_dwi_report_wf diff --git a/pydra/tasks/mriqc/workflows/diffusion/base.py b/pydra/tasks/mriqc/workflows/diffusion/base.py new file mode 100644 index 0000000..4a67a5e --- /dev/null +++ b/pydra/tasks/mriqc/workflows/diffusion/base.py @@ -0,0 +1,673 @@ +import attrs +import logging +from pathlib import Path +from pydra.compose import python, workflow +from pydra.tasks.mriqc.workflows.diffusion.output import init_dwi_report_wf +import typing as ty + + +logger = logging.getLogger(__name__) + + +@workflow.define( + outputs=[ + "iqms_wf_out_file", + "iqms_wf_noise_floor", + "dwi_report_wf_snr_report", + "dwi_report_wf_noise_report", + "dwi_report_wf_fa_report", + "dwi_report_wf_md_report", + "dwi_report_wf_heatmap_report", + "dwi_report_wf_spikes_report", + "dwi_report_wf_carpet_report", + "dwi_report_wf_bmask_report", + ] +) +def dmri_qc_workflow( + bvals: ty.Any = attrs.NOTHING, + bvecs: ty.Any = attrs.NOTHING, + exec_ants_float=False, + exec_debug=False, + exec_float32=True, + exec_verbose_reports=False, + exec_work_dir=None, + in_file: ty.Any = attrs.NOTHING, + name="dwiMRIQC", + nipype_nprocs=12, + nipype_omp_nthreads=12, + qspace_neighbors: ty.Any = attrs.NOTHING, + wf_biggest_file_gb=1, + wf_fd_radius=50, + wf_fd_thres=0.2, + wf_inputs=None, + wf_inputs_entities={}, + wf_inputs_metadata=None, + wf_species="human", + wf_template_id="MNI152NLin2009cAsym", +) -> [ + "ty.Any", + "ty.Any", + "ty.Any", + "ty.Any", + "ty.Any", + "ty.Any", + "ty.Any", + "ty.Any", + "ty.Any", + "ty.Any", +]: + """ + Initialize the dMRI-QC workflow. + + .. workflow:: + + import os.path as op + from mriqc.workflows.diffusion.base import dmri_qc_workflow + from mriqc.testing import mock_config + with mock_config(): + wf = dmri_qc_workflow() + + """ + from pydra.tasks.afni.auto import Volreg + + if exec_work_dir is None: + exec_work_dir = Path.cwd() + + outputs_ = { + "iqms_wf_out_file": attrs.NOTHING, + "iqms_wf_noise_floor": attrs.NOTHING, + "dwi_report_wf_snr_report": attrs.NOTHING, + "dwi_report_wf_noise_report": attrs.NOTHING, + "dwi_report_wf_fa_report": attrs.NOTHING, + "dwi_report_wf_md_report": attrs.NOTHING, + "dwi_report_wf_heatmap_report": attrs.NOTHING, + "dwi_report_wf_spikes_report": attrs.NOTHING, + "dwi_report_wf_carpet_report": attrs.NOTHING, + "dwi_report_wf_bmask_report": attrs.NOTHING, + } + + from pydra.tasks.mrtrix3.v3_0 import DwiDenoise + from pydra.tasks.niworkflows.interfaces.header import SanitizeImage + from pydra.tasks.niworkflows.interfaces.images import RobustAverage + from pydra.tasks.mriqc.interfaces.diffusion import ( + CCSegmentation, + CorrectSignalDrift, + DiffusionModel, + ExtractOrientations, + NumberOfShells, + PIESNO, + ReadDWIMetadata, + SpikingVoxelsMask, + WeightedStat, + ) + + from pydra.tasks.mriqc.workflows.shared import synthstrip_wf as dmri_bmsk_workflow + + # Enable if necessary + # mem_gb = wf_biggest_file_gb['dwi'] + dataset = wf_inputs["dwi"] + metadata = wf_inputs_metadata["dwi"] + entities = wf_inputs_entities["dwi"] + message = "Building {modality} MRIQC workflow {detail}.".format( + modality="diffusion", + detail=f"for {len(dataset)} NIfTI files.", + ) + logger.info(message) + # Define workflow, inputs and outputs + # 0. Get data, put it in RAS orientation + + sanitize = workflow.add( + SanitizeImage(max_32bit=exec_float32, n_volumes_to_discard=0, in_file=in_file), + name="sanitize", + ) + # Workflow -------------------------------------------------------- + # Read metadata & bvec/bval, estimate number of shells, extract and split B0s + + shells = workflow.add(NumberOfShells(in_bvals=bvals), name="shells") + get_lowb = workflow.add( + ExtractOrientations(in_file=sanitize.out_file), name="get_lowb" + ) + # Generate B0 reference + dwi_ref = workflow.add( + RobustAverage(mc_method=None, in_file=sanitize.out_file), name="dwi_ref" + ) + hmc_b0 = workflow.add( + Volreg( + args="-Fourier -twopass", + outputtype="NIFTI_GZ", + zpad=4, + basefile=dwi_ref.out_file, + in_file=get_lowb.out_file, + ), + name="hmc_b0", + ) + # Calculate brainmask + dmri_bmsk = workflow.add( + dmri_bmsk_workflow( + omp_nthreads=nipype_omp_nthreads, + in_files=dwi_ref.out_file, + name="dmri_bmsk", + ) + ) + # HMC: head motion correct + hmcwf = workflow.add( + hmc_workflow(wf_fd_radius=wf_fd_radius, in_bvec=bvecs, name="hmcwf") + ) + get_hmc_shells = workflow.add( + ExtractOrientations( + in_bvec_file=bvecs, in_file=hmcwf.out_file, indices=shells.b_indices + ), + name="get_hmc_shells", + ) + # Split shells and compute some stats + averages = workflow.add(WeightedStat(in_weights=shells.b_masks), name="averages") + stddev = workflow.add( + WeightedStat(stat="std", in_weights=shells.b_masks), name="stddev" + ) + dwidenoise = workflow.add( + DwiDenoise( + noise="noisemap.nii.gz", + nthreads=nipype_omp_nthreads, + mask=dmri_bmsk.out_mask, + ), + name="dwidenoise", + ) + drift = workflow.add( + CorrectSignalDrift( + brainmask_file=dmri_bmsk.out_mask, + bval_file=bvals, + full_epi=sanitize.out_file, + in_file=hmc_b0.out_file, + ), + name="drift", + ) + sp_mask = workflow.add( + SpikingVoxelsMask( + b_masks=shells.b_masks, + brain_mask=dmri_bmsk.out_mask, + in_file=sanitize.out_file, + ), + name="sp_mask", + ) + # Fit DTI/DKI model + dwimodel = workflow.add( + DiffusionModel( + brain_mask=dmri_bmsk.out_mask, + bvals=shells.out_data, + bvec_file=bvecs, + in_file=dwidenoise.out_file, + n_shells=shells.n_shells, + ), + name="dwimodel", + ) + # Calculate CC mask + cc_mask = workflow.add( + CCSegmentation(in_cfa=dwimodel.out_cfa, in_fa=dwimodel.out_fa), name="cc_mask" + ) + # Run PIESNO noise estimation + piesno = workflow.add(PIESNO(in_file=sanitize.out_file), name="piesno") + # EPI to MNI registration + spatial_norm = workflow.add( + epi_mni_align( + nipype_omp_nthreads=nipype_omp_nthreads, + wf_species=wf_species, + wf_template_id=wf_template_id, + nipype_nprocs=nipype_nprocs, + exec_debug=exec_debug, + exec_ants_float=exec_ants_float, + epi_mean=dwi_ref.out_file, + epi_mask=dmri_bmsk.out_mask, + name="spatial_norm", + ) + ) + # Compute IQMs + iqms_wf = workflow.add( + compute_iqms( + in_file=in_file, + b_values_file=bvals, + qspace_neighbors=qspace_neighbors, + spikes_mask=sp_mask.out_mask, + piesno_sigma=piesno.sigma, + framewise_displacement=hmcwf.out_fd, + in_bvec_rotated=hmcwf.out_bvec, + in_bvec_diff=hmcwf.out_bvec_diff, + in_fa=dwimodel.out_fa, + in_cfa=dwimodel.out_cfa, + in_fa_nans=dwimodel.out_fa_nans, + in_fa_degenerate=dwimodel.out_fa_degenerate, + in_md=dwimodel.out_md, + brain_mask=dmri_bmsk.out_mask, + cc_mask=cc_mask.out_mask, + wm_mask=cc_mask.wm_finalmask, + b_values_shells=shells.b_values, + in_shells=get_hmc_shells.out_file, + in_bvec=get_hmc_shells.out_bvec, + in_noise=dwidenoise.noise, + name="iqms_wf", + ) + ) + # Generate outputs + dwi_report_wf = workflow.add( + init_dwi_report_wf( + wf_species=wf_species, + wf_biggest_file_gb=wf_biggest_file_gb, + exec_verbose_reports=exec_verbose_reports, + wf_fd_thres=wf_fd_thres, + exec_work_dir=exec_work_dir, + in_bdict=shells.b_dict, + brain_mask=dmri_bmsk.out_mask, + in_avgmap=averages.out_file, + in_stdmap=stddev.out_file, + in_epi=drift.out_full_file, + in_fa=dwimodel.out_fa, + in_md=dwimodel.out_md, + in_parcellation=spatial_norm.epi_parc, + name="dwi_report_wf", + ) + ) + # fmt: off + + @python.define + def shells_b_masks_to_dwi_ref_t_mask_callable(in_: ty.Any) -> ty.Any: + return _first(in_) + + shells_b_masks_to_dwi_ref_t_mask_callable = workflow.add(shells_b_masks_to_dwi_ref_t_mask_callable(in_=shells.b_masks), name="shells_b_masks_to_dwi_ref_t_mask_callable") + + dwi_ref.inputs.t_mask = shells_b_masks_to_dwi_ref_t_mask_callable.out + + @python.define + def shells_b_indices_to_get_lowb_indices_callable(in_: ty.Any) -> ty.Any: + return _first(in_) + + shells_b_indices_to_get_lowb_indices_callable = workflow.add(shells_b_indices_to_get_lowb_indices_callable(in_=shells.b_indices), name="shells_b_indices_to_get_lowb_indices_callable") + + get_lowb.inputs.indices = shells_b_indices_to_get_lowb_indices_callable.out + + @python.define + def shells_b_indices_to_drift_b0_ixs_callable(in_: ty.Any) -> ty.Any: + return _first(in_) + + shells_b_indices_to_drift_b0_ixs_callable = workflow.add(shells_b_indices_to_drift_b0_ixs_callable(in_=shells.b_indices), name="shells_b_indices_to_drift_b0_ixs_callable") + + drift.inputs.b0_ixs = shells_b_indices_to_drift_b0_ixs_callable.out + hmcwf.inputs.in_file = drift.out_full_file + averages.inputs.in_file = drift.out_full_file + stddev.inputs.in_file = drift.out_full_file + + @python.define + def averages_out_file_to_hmcwf_reference_callable(in_: ty.Any) -> ty.Any: + return _first(in_) + + averages_out_file_to_hmcwf_reference_callable = workflow.add(averages_out_file_to_hmcwf_reference_callable(in_=averages.out_file), name="averages_out_file_to_hmcwf_reference_callable") + + hmcwf.inputs.reference = averages_out_file_to_hmcwf_reference_callable.out + dwidenoise.inputs.in_file = drift.out_full_file + + @python.define + def averages_out_file_to_iqms_wf_in_b0_callable(in_: ty.Any) -> ty.Any: + return _first(in_) + + averages_out_file_to_iqms_wf_in_b0_callable = workflow.add(averages_out_file_to_iqms_wf_in_b0_callable(in_=averages.out_file), name="averages_out_file_to_iqms_wf_in_b0_callable") + + iqms_wf.inputs.in_b0 = averages_out_file_to_iqms_wf_in_b0_callable.out + # fmt: on + outputs_["iqms_wf_out_file"] = iqms_wf.out_file + outputs_["iqms_wf_noise_floor"] = iqms_wf.noise_floor + outputs_["dwi_report_wf_noise_report"] = dwi_report_wf.noise_report + outputs_["dwi_report_wf_md_report"] = dwi_report_wf.md_report + outputs_["dwi_report_wf_bmask_report"] = dwi_report_wf.bmask_report + outputs_["dwi_report_wf_snr_report"] = dwi_report_wf.snr_report + outputs_["dwi_report_wf_carpet_report"] = dwi_report_wf.carpet_report + outputs_["dwi_report_wf_fa_report"] = dwi_report_wf.fa_report + outputs_["dwi_report_wf_spikes_report"] = dwi_report_wf.spikes_report + outputs_["dwi_report_wf_heatmap_report"] = dwi_report_wf.heatmap_report + + return tuple(outputs_) + + +@workflow.define(outputs=["out_file", "out_fd", "out_bvec", "out_bvec_diff"]) +def hmc_workflow( + in_bvec: ty.Any = attrs.NOTHING, + in_file: ty.Any = attrs.NOTHING, + name="dMRI_HMC", + reference: ty.Any = attrs.NOTHING, + wf_fd_radius=50, +) -> ["ty.Any", "ty.Any", "ty.Any", "ty.Any"]: + """ + Create a :abbr:`HMC (head motion correction)` workflow for dMRI. + + .. workflow:: + + from mriqc.workflows.diffusion.base import hmc + from mriqc.testing import mock_config + with mock_config(): + wf = hmc() + + """ + from pydra.tasks.mriqc.nipype_ports.algorithms.confounds import ( + FramewiseDisplacement, + ) + + outputs_ = { + "out_file": attrs.NOTHING, + "out_fd": attrs.NOTHING, + "out_bvec": attrs.NOTHING, + "out_bvec_diff": attrs.NOTHING, + } + + from pydra.tasks.afni.auto import Volreg + from pydra.tasks.mriqc.interfaces.diffusion import RotateVectors + + # calculate hmc parameters + hmc = workflow.add( + Volreg( + args="-Fourier -twopass", + outputtype="NIFTI_GZ", + zpad=4, + basefile=reference, + in_file=in_file, + ), + name="hmc", + ) + bvec_rot = workflow.add( + RotateVectors( + in_file=in_bvec, reference=reference, transforms=hmc.oned_matrix_save + ), + name="bvec_rot", + ) + # Compute the frame-wise displacement + fdnode = workflow.add( + FramewiseDisplacement( + normalize=False, + parameter_source="AFNI", + radius=wf_fd_radius, + in_file=hmc.oned_file, + ), + name="fdnode", + ) + # fmt: off + outputs_['out_file'] = hmc.out_file + outputs_['out_fd'] = fdnode.out_file + outputs_['out_bvec'] = bvec_rot.out_bvec + outputs_['out_bvec_diff'] = bvec_rot.out_diff + # fmt: on + + return tuple(outputs_) + + +@workflow.define(outputs=["epi_parc", "epi_mni", "report"]) +def epi_mni_align( + epi_mask: ty.Any = attrs.NOTHING, + epi_mean: ty.Any = attrs.NOTHING, + exec_ants_float=False, + exec_debug=False, + name="SpatialNormalization", + nipype_nprocs=12, + nipype_omp_nthreads=12, + wf_species="human", + wf_template_id="MNI152NLin2009cAsym", +) -> ["ty.Any", "ty.Any", "ty.Any"]: + """ + Estimate the transform that maps the EPI space into MNI152NLin2009cAsym. + + The input epi_mean is the averaged and brain-masked EPI timeseries + + Returns the EPI mean resampled in MNI space (for checking out registration) and + the associated "lobe" parcellation in EPI space. + + .. workflow:: + + from mriqc.workflows.diffusion.base import epi_mni_align + from mriqc.testing import mock_config + with mock_config(): + wf = epi_mni_align() + + """ + from pydra.tasks.ants.auto import ApplyTransforms, N4BiasFieldCorrection + + outputs_ = { + "epi_parc": attrs.NOTHING, + "epi_mni": attrs.NOTHING, + "report": attrs.NOTHING, + } + + from pydra.tasks.niworkflows.interfaces.reportlets.registration import ( + SpatialNormalizationRPT as RobustMNINormalization, + ) + from templateflow.api import get as get_template + + # Get settings + testing = exec_debug + n_procs = nipype_nprocs + ants_nthreads = nipype_omp_nthreads + + n4itk = workflow.add( + N4BiasFieldCorrection(copy_header=True, dimension=3, input_image=epi_mean), + name="n4itk", + ) + norm = workflow.add( + RobustMNINormalization( + explicit_masking=False, + flavor="testing" if testing else "precise", + float=exec_ants_float, + generate_report=True, + moving="boldref", + num_threads=ants_nthreads, + reference="boldref", + template=wf_template_id, + moving_image=n4itk.output_image, + ), + name="norm", + ) + if wf_species.lower() == "human": + norm.inputs.inputs.reference_image = str( + get_template(wf_template_id, resolution=2, suffix="boldref") + ) + norm.inputs.inputs.reference_mask = str( + get_template( + wf_template_id, + resolution=2, + desc="brain", + suffix="mask", + ) + ) + # adapt some population-specific settings + else: + from nirodents.workflows.brainextraction import _bspline_grid + + n4itk.inputs.inputs.shrink_factor = 1 + n4itk.inputs.inputs.n_iterations = [50] * 4 + norm.inputs.inputs.reference_image = str( + get_template(wf_template_id, suffix="T2w") + ) + norm.inputs.inputs.reference_mask = str( + get_template( + wf_template_id, + desc="brain", + suffix="mask", + )[0] + ) + bspline_grid = workflow.add( + FunctionTask(func=_bspline_grid), name="bspline_grid" + ) + # fmt: off + bspline_grid.inputs.in_file = epi_mean + n4itk.inputs.args = bspline_grid.out + # fmt: on + # Warp segmentation into EPI space + invt = workflow.add( + ApplyTransforms( + default_value=0, + dimension=3, + float=True, + interpolation="MultiLabel", + reference_image=epi_mean, + transforms=norm.inverse_composite_transform, + ), + name="invt", + ) + if wf_species.lower() == "human": + invt.inputs.inputs.input_image = str( + get_template( + wf_template_id, + resolution=1, + desc="carpet", + suffix="dseg", + ) + ) + else: + invt.inputs.inputs.input_image = str( + get_template( + wf_template_id, + suffix="dseg", + )[-1] + ) + # fmt: off + outputs_['epi_parc'] = invt.output_image + outputs_['epi_mni'] = norm.warped_image + outputs_['report'] = norm.out_report + # fmt: on + if wf_species.lower() == "human": + norm.inputs.moving_mask = epi_mask + + return tuple(outputs_) + + +@workflow.define(outputs=["out_file", "noise_floor"]) +def compute_iqms( + b_values_file: ty.Any = attrs.NOTHING, + b_values_shells: ty.Any = attrs.NOTHING, + brain_mask: ty.Any = attrs.NOTHING, + cc_mask: ty.Any = attrs.NOTHING, + framewise_displacement: ty.Any = attrs.NOTHING, + in_b0: ty.Any = attrs.NOTHING, + in_bvec: ty.Any = attrs.NOTHING, + in_bvec_diff: ty.Any = attrs.NOTHING, + in_bvec_rotated: ty.Any = attrs.NOTHING, + in_cfa: ty.Any = attrs.NOTHING, + in_fa: ty.Any = attrs.NOTHING, + in_fa_degenerate: ty.Any = attrs.NOTHING, + in_fa_nans: ty.Any = attrs.NOTHING, + in_file: ty.Any = attrs.NOTHING, + in_md: ty.Any = attrs.NOTHING, + in_noise: ty.Any = attrs.NOTHING, + in_shells: ty.Any = attrs.NOTHING, + name="ComputeIQMs", + piesno_sigma: ty.Any = attrs.NOTHING, + qspace_neighbors: ty.Any = attrs.NOTHING, + spikes_mask: ty.Any = attrs.NOTHING, + wm_mask: ty.Any = attrs.NOTHING, +) -> ["ty.Any", "ty.Any"]: + """ + Initialize the workflow that actually computes the IQMs. + + .. workflow:: + + from mriqc.workflows.diffusion.base import compute_iqms + from mriqc.testing import mock_config + with mock_config(): + wf = compute_iqms() + + """ + from pydra.tasks.mriqc.interfaces import IQMFileSink + + outputs_ = { + "out_file": attrs.NOTHING, + "noise_floor": attrs.NOTHING, + } + + from pydra.tasks.mriqc.interfaces.diffusion import DiffusionQC + from pydra.tasks.mriqc.interfaces.reports import AddProvenance + + # from mriqc.workflows.utils import _tofloat, get_fwhmx + + estimate_sigma = workflow.add( + FunctionTask(func=_estimate_sigma, in_file=in_noise, mask=brain_mask), + name="estimate_sigma", + ) + measures = workflow.add( + DiffusionQC( + brain_mask=brain_mask, + cc_mask=cc_mask, + in_b0=in_b0, + in_bval_file=b_values_file, + in_bvec=in_bvec, + in_bvec_diff=in_bvec_diff, + in_bvec_rotated=in_bvec_rotated, + in_cfa=in_cfa, + in_fa=in_fa, + in_fa_degenerate=in_fa_degenerate, + in_fa_nans=in_fa_nans, + in_fd=framewise_displacement, + in_file=in_file, + in_md=in_md, + in_shells=in_shells, + in_shells_bval=b_values_shells, + piesno_sigma=piesno_sigma, + qspace_neighbors=qspace_neighbors, + spikes_mask=spikes_mask, + wm_mask=wm_mask, + ), + name="measures", + ) + + # Save to JSON file + + # fmt: off + + + + outputs_['out_file'] = measures.out_qc + outputs_['noise_floor'] = estimate_sigma.out + # fmt: on + + return tuple(outputs_) + + +def _bvals_report(in_file): + + import numpy as np + + bvals = [ + round(float(val), 2) for val in np.unique(np.round(np.loadtxt(in_file), 2)) + ] + if len(bvals) > 10: + return "Likely DSI" + return bvals + + +def _estimate_sigma(in_file, mask): + + import nibabel as nb + from numpy import median + + msk = nb.load(mask).get_fdata() > 0.5 + return round( + float(median(nb.load(in_file).get_fdata()[msk])), + 6, + ) + + +def _filter_metadata( + in_dict, + keys=( + "global", + "dcmmeta_affine", + "dcmmeta_reorient_transform", + "dcmmeta_shape", + "dcmmeta_slice_dim", + "dcmmeta_version", + "time", + ), +): + """Drop large and partially redundant objects generated by dcm2niix.""" + for key in keys: + in_dict.pop(key, None) + return in_dict + + +def _first(inlist): + + if isinstance(inlist, (list, tuple)): + return inlist[0] + return inlist diff --git a/pydra/tasks/mriqc/workflows/diffusion/output.py b/pydra/tasks/mriqc/workflows/diffusion/output.py new file mode 100644 index 0000000..59d5766 --- /dev/null +++ b/pydra/tasks/mriqc/workflows/diffusion/output.py @@ -0,0 +1,162 @@ +import attrs +import logging +from pathlib import Path +from pydra.compose import workflow +from pydra.tasks.mriqc.workflows.diffusion.output import init_dwi_report_wf +from pydra.tasks.nireports.interfaces.dmri import DWIHeatmap +from pydra.tasks.nireports.interfaces.reporting.base import ( + SimpleBeforeAfterRPT as SimpleBeforeAfter, +) +import typing as ty + + +logger = logging.getLogger(__name__) + + +@workflow.define( + outputs=[ + "snr_report", + "noise_report", + "fa_report", + "md_report", + "heatmap_report", + "spikes_report", + "carpet_report", + "bmask_report", + ] +) +def init_dwi_report_wf( + brain_mask: ty.Any = attrs.NOTHING, + exec_verbose_reports=False, + exec_work_dir=None, + in_avgmap: ty.Any = attrs.NOTHING, + in_bdict: ty.Any = attrs.NOTHING, + in_epi: ty.Any = attrs.NOTHING, + in_fa: ty.Any = attrs.NOTHING, + in_md: ty.Any = attrs.NOTHING, + in_parcellation: ty.Any = attrs.NOTHING, + in_stdmap: ty.Any = attrs.NOTHING, + name="dwi_report_wf", + noise_floor: ty.Any = attrs.NOTHING, + wf_biggest_file_gb=1, + wf_fd_thres=0.2, + wf_species="human", +) -> ["ty.Any", "ty.Any", "ty.Any", "ty.Any", "ty.Any", "ty.Any", "ty.Any", "ty.Any"]: + """ + Write out individual reportlets. + + .. workflow:: + + from mriqc.workflows.diffusion.output import init_dwi_report_wf + from mriqc.testing import mock_config + with mock_config(): + wf = init_dwi_report_wf() + + """ + from pydra.tasks.nireports.interfaces import PlotMosaic + + if exec_work_dir is None: + exec_work_dir = Path.cwd() + + outputs_ = { + "snr_report": attrs.NOTHING, + "noise_report": attrs.NOTHING, + "fa_report": attrs.NOTHING, + "md_report": attrs.NOTHING, + "heatmap_report": attrs.NOTHING, + "spikes_report": attrs.NOTHING, + "carpet_report": attrs.NOTHING, + "bmask_report": attrs.NOTHING, + } + + # from mriqc.interfaces.reports import IndividualReport + verbose = exec_verbose_reports + mem_gb = wf_biggest_file_gb + reportlets_dir = exec_work_dir / "reportlets" + + # Set FD threshold + # inputnode.inputs.fd_thres = wf_fd_thres + mosaic_fa = workflow.add( + PlotMosaic(cmap="Greys_r", bbox_mask_file=brain_mask, in_file=in_fa), + name="mosaic_fa", + ) + mosaic_md = workflow.add( + PlotMosaic(cmap="Greys_r", bbox_mask_file=brain_mask, in_file=in_md), + name="mosaic_md", + ) + mosaic_snr = workflow.add( + SimpleBeforeAfter( + after_label="Standard Deviation", + before_label="Average", + dismiss_affine=True, + fixed_params={"cmap": "viridis"}, + moving_params={"cmap": "Greys_r"}, + after=in_stdmap, + before=in_avgmap, + wm_seg=brain_mask, + ), + name="mosaic_snr", + ) + mosaic_noise = workflow.add( + PlotMosaic(cmap="viridis_r", only_noise=True, in_file=in_avgmap), + name="mosaic_noise", + ) + if wf_species.lower() in ("rat", "mouse"): + mosaic_noise.inputs.inputs.view = ["coronal", "axial"] + mosaic_fa.inputs.inputs.view = ["coronal", "axial"] + mosaic_md.inputs.inputs.view = ["coronal", "axial"] + + def _gen_entity(inlist): + return ["00000"] + [f"{int(round(bval, 0)):05d}" for bval in inlist] + + # fmt: off + + + outputs_['snr_report'] = mosaic_snr.out_report + outputs_['noise_report'] = mosaic_noise.out_file + outputs_['fa_report'] = mosaic_fa.out_file + outputs_['md_report'] = mosaic_md.out_file + # fmt: on + get_wm = workflow.add( + FunctionTask(func=_get_wm, in_file=in_parcellation), name="get_wm" + ) + plot_heatmap = workflow.add( + DWIHeatmap( + scalarmap_label="Shell-wise Fractional Anisotropy (FA)", + b_indices=in_bdict, + in_file=in_epi, + mask_file=get_wm.out, + scalarmap=in_fa, + sigma=noise_floor, + ), + name="plot_heatmap", + ) + + # fmt: off + outputs_['heatmap_report'] = plot_heatmap.out_file + # fmt: on + + return tuple(outputs_) + + +def _get_wm(in_file, radius=2): + + from pathlib import Path + import nibabel as nb + import numpy as np + from pydra.tasks.mriqc.nipype_ports.utils.filemanip import fname_presuffix + from scipy import ndimage as ndi + from skimage.morphology import ball + + parc = nb.load(in_file) + hdr = parc.header.copy() + data = np.array(parc.dataobj, dtype=hdr.get_data_dtype()) + wm_mask = ndi.binary_erosion((data == 1) | (data == 2), ball(radius)) + hdr.set_data_dtype(np.uint8) + out_wm = fname_presuffix(in_file, suffix="wm", newpath=str(Path.cwd())) + parc.__class__( + wm_mask.astype(np.uint8), + parc.affine, + hdr, + ).to_filename(out_wm) + return out_wm diff --git a/pydra/tasks/mriqc/workflows/diffusion/tests/conftest.py b/pydra/tasks/mriqc/workflows/diffusion/tests/conftest.py new file mode 100644 index 0000000..751042d --- /dev/null +++ b/pydra/tasks/mriqc/workflows/diffusion/tests/conftest.py @@ -0,0 +1,25 @@ + +# For debugging in IDE's don't catch raised exceptions and let the IDE +# break at it +import os +import pytest + + +if os.getenv("_PYTEST_RAISE", "0") != "0": + + @pytest.hookimpl(tryfirst=True) + def pytest_exception_interact(call): + raise call.excinfo.value # raise internal errors instead of capturing them + + @pytest.hookimpl(tryfirst=True) + def pytest_internalerror(excinfo): + raise excinfo.value # raise internal errors instead of capturing them + + def pytest_configure(config): + config.option.capture = 'no' # allow print statements to show up in the console + config.option.log_cli = True # show log messages in the console + config.option.log_level = "INFO" # set the log level to INFO + + CATCH_CLI_EXCEPTIONS = False +else: + CATCH_CLI_EXCEPTIONS = True diff --git a/pydra/tasks/mriqc/workflows/diffusion/tests/test_workflows_diffusion_base_compute_iqms.py b/pydra/tasks/mriqc/workflows/diffusion/tests/test_workflows_diffusion_base_compute_iqms.py new file mode 100644 index 0000000..38501b2 --- /dev/null +++ b/pydra/tasks/mriqc/workflows/diffusion/tests/test_workflows_diffusion_base_compute_iqms.py @@ -0,0 +1,21 @@ +import logging +from pydra.compose import workflow +from pydra.tasks.mriqc.workflows.diffusion.base import compute_iqms +import pytest + + +logger = logging.getLogger(__name__) + + +def test_compute_iqms_build(): + workflow = compute_iqms() + assert isinstance(workflow, Workflow) + + +@pytest.mark.skip( + reason="Appropriate inputs for this workflow haven't been specified yet" +) +def test_compute_iqms_run(): + workflow = compute_iqms() + result = workflow(worker="debug") + print(result.out) diff --git a/pydra/tasks/mriqc/workflows/diffusion/tests/test_workflows_diffusion_base_dmri_qc_workflow.py b/pydra/tasks/mriqc/workflows/diffusion/tests/test_workflows_diffusion_base_dmri_qc_workflow.py new file mode 100644 index 0000000..9c1be77 --- /dev/null +++ b/pydra/tasks/mriqc/workflows/diffusion/tests/test_workflows_diffusion_base_dmri_qc_workflow.py @@ -0,0 +1,22 @@ +from fileformats.medimage import Bval, Bvec +import logging +from pydra.compose import workflow +from pydra.tasks.mriqc.workflows.diffusion.base import dmri_qc_workflow +import pytest + + +logger = logging.getLogger(__name__) + + +def test_dmri_qc_workflow_build(): + workflow = dmri_qc_workflow() + assert isinstance(workflow, Workflow) + + +@pytest.mark.skip( + reason="Appropriate inputs for this workflow haven't been specified yet" +) +def test_dmri_qc_workflow_run(): + workflow = dmri_qc_workflow(bvals=Bval.sample(), bvecs=Bvec.sample()) + result = workflow(worker="debug") + print(result.out) diff --git a/pydra/tasks/mriqc/workflows/diffusion/tests/test_workflows_diffusion_base_epi_mni_align.py b/pydra/tasks/mriqc/workflows/diffusion/tests/test_workflows_diffusion_base_epi_mni_align.py new file mode 100644 index 0000000..110d050 --- /dev/null +++ b/pydra/tasks/mriqc/workflows/diffusion/tests/test_workflows_diffusion_base_epi_mni_align.py @@ -0,0 +1,21 @@ +import logging +from pydra.compose import workflow +from pydra.tasks.mriqc.workflows.diffusion.base import epi_mni_align +import pytest + + +logger = logging.getLogger(__name__) + + +def test_epi_mni_align_build(): + workflow = epi_mni_align() + assert isinstance(workflow, Workflow) + + +@pytest.mark.skip( + reason="Appropriate inputs for this workflow haven't been specified yet" +) +def test_epi_mni_align_run(): + workflow = epi_mni_align() + result = workflow(worker="debug") + print(result.out) diff --git a/pydra/tasks/mriqc/workflows/diffusion/tests/test_workflows_diffusion_base_hmc_workflow.py b/pydra/tasks/mriqc/workflows/diffusion/tests/test_workflows_diffusion_base_hmc_workflow.py new file mode 100644 index 0000000..fca72de --- /dev/null +++ b/pydra/tasks/mriqc/workflows/diffusion/tests/test_workflows_diffusion_base_hmc_workflow.py @@ -0,0 +1,21 @@ +import logging +from pydra.compose import workflow +from pydra.tasks.mriqc.workflows.diffusion.base import hmc_workflow +import pytest + + +logger = logging.getLogger(__name__) + + +def test_hmc_workflow_build(): + workflow = hmc_workflow() + assert isinstance(workflow, Workflow) + + +@pytest.mark.skip( + reason="Appropriate inputs for this workflow haven't been specified yet" +) +def test_hmc_workflow_run(): + workflow = hmc_workflow() + result = workflow(worker="debug") + print(result.out) diff --git a/pydra/tasks/mriqc/workflows/diffusion/tests/test_workflows_diffusion_output_init_dwi_report_wf.py b/pydra/tasks/mriqc/workflows/diffusion/tests/test_workflows_diffusion_output_init_dwi_report_wf.py new file mode 100644 index 0000000..83711c8 --- /dev/null +++ b/pydra/tasks/mriqc/workflows/diffusion/tests/test_workflows_diffusion_output_init_dwi_report_wf.py @@ -0,0 +1,21 @@ +import logging +from pydra.compose import workflow +from pydra.tasks.mriqc.workflows.diffusion.output import init_dwi_report_wf +import pytest + + +logger = logging.getLogger(__name__) + + +def test_init_dwi_report_wf_build(): + workflow = init_dwi_report_wf() + assert isinstance(workflow, Workflow) + + +@pytest.mark.skip( + reason="Appropriate inputs for this workflow haven't been specified yet" +) +def test_init_dwi_report_wf_run(): + workflow = init_dwi_report_wf() + result = workflow(worker="debug") + print(result.out) diff --git a/pydra/tasks/mriqc/workflows/functional/__init__.py b/pydra/tasks/mriqc/workflows/functional/__init__.py new file mode 100644 index 0000000..81e4f91 --- /dev/null +++ b/pydra/tasks/mriqc/workflows/functional/__init__.py @@ -0,0 +1,2 @@ +from .base import compute_iqms, epi_mni_align, fmri_bmsk_workflow, fmri_qc_workflow, hmc +from .output import _carpet_parcellation, _get_tr, init_func_report_wf, spikes_mask diff --git a/pydra/tasks/mriqc/workflows/functional/base.py b/pydra/tasks/mriqc/workflows/functional/base.py new file mode 100644 index 0000000..89290b5 --- /dev/null +++ b/pydra/tasks/mriqc/workflows/functional/base.py @@ -0,0 +1,770 @@ +import attrs +import logging +from pydra.tasks.mriqc.workflows.functional.output import init_func_report_wf +from pydra.tasks.niworkflows.utils.connections import pop_file as _pop +from pathlib import Path +from pydra.compose import python, workflow +from pydra.tasks.niworkflows.utils.connections import pop_file as _pop +import typing as ty + + +logger = logging.getLogger(__name__) + + +@workflow.define(outputs=["out_file"]) +def fmri_bmsk_workflow( + in_file: ty.Any = attrs.NOTHING, name="fMRIBrainMask" +) -> ["ty.Any"]: + """ + Compute a brain mask for the input :abbr:`fMRI (functional MRI)` dataset. + + .. workflow:: + + from mriqc.workflows.functional.base import fmri_bmsk_workflow + from mriqc.testing import mock_config + with mock_config(): + wf = fmri_bmsk_workflow() + + + """ + from pydra.tasks.afni.auto import Automask + + outputs_ = { + "out_file": attrs.NOTHING, + } + + afni_msk = workflow.add( + Automask(outputtype="NIFTI_GZ", in_file=in_file), name="afni_msk" + ) + # Connect brain mask extraction + # fmt: off + outputs_['out_file'] = afni_msk.out_file + # fmt: on + + return tuple(outputs_) + + +@workflow.define(outputs=["epi_parc", "epi_mni", "report"]) +def epi_mni_align( + epi_mask: ty.Any = attrs.NOTHING, + epi_mean: ty.Any = attrs.NOTHING, + exec_ants_float=False, + exec_debug=False, + name="SpatialNormalization", + nipype_nprocs=12, + nipype_omp_nthreads=12, + wf_species="human", + wf_template_id="MNI152NLin2009cAsym", +) -> ["ty.Any", "ty.Any", "ty.Any"]: + """ + Estimate the transform that maps the EPI space into MNI152NLin2009cAsym. + + The input epi_mean is the averaged and brain-masked EPI timeseries + + Returns the EPI mean resampled in MNI space (for checking out registration) and + the associated "lobe" parcellation in EPI space. + + .. workflow:: + + from mriqc.workflows.functional.base import epi_mni_align + from mriqc.testing import mock_config + with mock_config(): + wf = epi_mni_align() + + """ + from pydra.tasks.ants.auto import ApplyTransforms, N4BiasFieldCorrection + + outputs_ = { + "epi_parc": attrs.NOTHING, + "epi_mni": attrs.NOTHING, + "report": attrs.NOTHING, + } + + from pydra.tasks.niworkflows.interfaces.reportlets.registration import ( + SpatialNormalizationRPT as RobustMNINormalization, + ) + from templateflow.api import get as get_template + + # Get settings + testing = exec_debug + n_procs = nipype_nprocs + ants_nthreads = nipype_omp_nthreads + + n4itk = workflow.add( + N4BiasFieldCorrection(copy_header=True, dimension=3, input_image=epi_mean), + name="n4itk", + ) + norm = workflow.add( + RobustMNINormalization( + explicit_masking=False, + flavor="testing" if testing else "precise", + float=exec_ants_float, + generate_report=True, + moving="boldref", + num_threads=ants_nthreads, + reference="boldref", + template=wf_template_id, + moving_image=n4itk.output_image, + ), + name="norm", + ) + if wf_species.lower() == "human": + norm.inputs.inputs.reference_image = str( + get_template(wf_template_id, resolution=2, suffix="boldref") + ) + norm.inputs.inputs.reference_mask = str( + get_template( + wf_template_id, + resolution=2, + desc="brain", + suffix="mask", + ) + ) + # adapt some population-specific settings + else: + from nirodents.workflows.brainextraction import _bspline_grid + + n4itk.inputs.inputs.shrink_factor = 1 + n4itk.inputs.inputs.n_iterations = [50] * 4 + norm.inputs.inputs.reference_image = str( + get_template(wf_template_id, suffix="T2w") + ) + norm.inputs.inputs.reference_mask = str( + get_template( + wf_template_id, + desc="brain", + suffix="mask", + )[0] + ) + bspline_grid = workflow.add( + FunctionTask(func=_bspline_grid), name="bspline_grid" + ) + # fmt: off + bspline_grid.inputs.in_file = epi_mean + n4itk.inputs.args = bspline_grid.out + # fmt: on + # Warp segmentation into EPI space + invt = workflow.add( + ApplyTransforms( + default_value=0, + dimension=3, + float=True, + interpolation="MultiLabel", + reference_image=epi_mean, + transforms=norm.inverse_composite_transform, + ), + name="invt", + ) + if wf_species.lower() == "human": + invt.inputs.inputs.input_image = str( + get_template( + wf_template_id, + resolution=1, + desc="carpet", + suffix="dseg", + ) + ) + else: + invt.inputs.inputs.input_image = str( + get_template( + wf_template_id, + suffix="dseg", + )[-1] + ) + # fmt: off + outputs_['epi_parc'] = invt.output_image + outputs_['epi_mni'] = norm.warped_image + outputs_['report'] = norm.out_report + # fmt: on + if wf_species.lower() == "human": + norm.inputs.moving_mask = epi_mask + + return tuple(outputs_) + + +@workflow.define(outputs=["out_file", "mpars", "out_fd"]) +def hmc( + fd_radius: ty.Any = attrs.NOTHING, + in_file: ty.Any = attrs.NOTHING, + name="fMRI_HMC", + omp_nthreads=None, + wf_biggest_file_gb=1, + wf_deoblique=False, + wf_despike=False, +) -> ["ty.Any", "ty.Any", "ty.Any"]: + """ + Create a :abbr:`HMC (head motion correction)` workflow for fMRI. + + .. workflow:: + + from mriqc.workflows.functional.base import hmc + from mriqc.testing import mock_config + with mock_config(): + wf = hmc() + + """ + from pydra.tasks.mriqc.nipype_ports.algorithms.confounds import ( + FramewiseDisplacement, + ) + + outputs_ = { + "out_file": attrs.NOTHING, + "mpars": attrs.NOTHING, + "out_fd": attrs.NOTHING, + } + + from pydra.tasks.afni.auto import Despike, Refit, Volreg + + mem_gb = wf_biggest_file_gb["bold"] + + # calculate hmc parameters + estimate_hm = workflow.add( + Volreg(args="-Fourier -twopass", outputtype="NIFTI_GZ", zpad=4), + name="estimate_hm", + ) + # Compute the frame-wise displacement + fdnode = workflow.add( + FramewiseDisplacement( + normalize=False, + parameter_source="AFNI", + in_file=estimate_hm.oned_file, + radius=fd_radius, + ), + name="fdnode", + ) + # Apply transforms to other echos + apply_hmc = workflow.add( + FunctionTask( + func=_apply_transforms, + input_spec=SpecInfo( + name="FunctionIn", + bases=(BaseSpec,), + fields=[ + ("in_file", ty.Any), + ("in_xfm", ty.Any), + ("max_concurrent", ty.Any), + ], + ), + in_xfm=estimate_hm.oned_matrix_save, + ), + name="apply_hmc", + ) + apply_hmc.inputs.inputs.max_concurrent = 4 + # fmt: off + outputs_['out_file'] = apply_hmc.out + outputs_['mpars'] = estimate_hm.oned_file + outputs_['out_fd'] = fdnode.out_file + # fmt: on + if not (wf_despike or wf_deoblique): + # fmt: off + estimate_hm.inputs.in_file = in_file + apply_hmc.inputs.in_file = in_file + # fmt: on + return workflow + # despiking, and deoblique + deoblique_node = workflow.add(Refit(deoblique=True), name="deoblique_node") + despike_node = workflow.add(Despike(outputtype="NIFTI_GZ"), name="despike_node") + if wf_despike and wf_deoblique: + # fmt: off + despike_node.inputs.in_file = in_file + deoblique_node.inputs.in_file = despike_node.out_file + + @python.define + def deoblique_node_out_file_to_estimate_hm_in_file_callable(in_: ty.Any) -> ty.Any: + return _pop(in_) + + deoblique_node_out_file_to_estimate_hm_in_file_callable = workflow.add(deoblique_node_out_file_to_estimate_hm_in_file_callable(in_=deoblique_node.out_file), name="deoblique_node_out_file_to_estimate_hm_in_file_callable") + + estimate_hm.inputs.in_file = deoblique_node_out_file_to_estimate_hm_in_file_callable.out + apply_hmc.inputs.in_file = deoblique_node.out_file + # fmt: on + elif wf_despike: + # fmt: off + despike_node.inputs.in_file = in_file + + @python.define + def despike_node_out_file_to_estimate_hm_in_file_callable(in_: ty.Any) -> ty.Any: + return _pop(in_) + + despike_node_out_file_to_estimate_hm_in_file_callable = workflow.add(despike_node_out_file_to_estimate_hm_in_file_callable(in_=despike_node.out_file), name="despike_node_out_file_to_estimate_hm_in_file_callable") + + estimate_hm.inputs.in_file = despike_node_out_file_to_estimate_hm_in_file_callable.out + apply_hmc.inputs.in_file = despike_node.out_file + # fmt: on + elif wf_deoblique: + # fmt: off + deoblique_node.inputs.in_file = in_file + + @python.define + def deoblique_node_out_file_to_estimate_hm_in_file_callable(in_: ty.Any) -> ty.Any: + return _pop(in_) + + deoblique_node_out_file_to_estimate_hm_in_file_callable = workflow.add(deoblique_node_out_file_to_estimate_hm_in_file_callable(in_=deoblique_node.out_file), name="deoblique_node_out_file_to_estimate_hm_in_file_callable") + + estimate_hm.inputs.in_file = deoblique_node_out_file_to_estimate_hm_in_file_callable.out + apply_hmc.inputs.in_file = deoblique_node.out_file + # fmt: on + else: + raise NotImplementedError + + return tuple(outputs_) + + +def _apply_transforms(in_file, in_xfm, max_concurrent): + + from pathlib import Path + from nitransforms.linear import load + from nitransforms.resampling import apply + from pydra.tasks.mriqc.utils.bids import derive_bids_fname + + realigned = apply( + load(in_xfm, fmt="afni", reference=in_file, moving=in_file), + in_file, + dtype_width=4, + serialize_nvols=2, + max_concurrent=max_concurrent, + mode="reflect", + ) + out_file = derive_bids_fname( + in_file, + entity="desc-realigned", + newpath=Path.cwd(), + absolute=True, + ) + realigned.to_filename(out_file) + return str(out_file) + + +@workflow.define( + outputs=["out_file", "spikes", "fft", "spikes_num", "outliers", "dvars"] +) +def compute_iqms( + brainmask: ty.Any = attrs.NOTHING, + epi_mean: ty.Any = attrs.NOTHING, + fd_thres: ty.Any = attrs.NOTHING, + hmc_epi: ty.Any = attrs.NOTHING, + hmc_fd: ty.Any = attrs.NOTHING, + in_ras: ty.Any = attrs.NOTHING, + in_tsnr: ty.Any = attrs.NOTHING, + name="ComputeIQMs", + wf_biggest_file_gb=1, + wf_fft_spikes_detector=False, +) -> ["ty.Any", "ty.Any", "ty.Any", "Integer", "ty.Any", "ty.Any"]: + """ + Initialize the workflow that actually computes the IQMs. + + .. workflow:: + + from mriqc.workflows.functional.base import compute_iqms + from mriqc.testing import mock_config + with mock_config(): + wf = compute_iqms() + + """ + from pydra.tasks.mriqc.nipype_ports.algorithms.confounds import ComputeDVARS + + outputs_ = { + "out_file": attrs.NOTHING, + "spikes": attrs.NOTHING, + "fft": attrs.NOTHING, + "spikes_num": attrs.NOTHING, + "outliers": attrs.NOTHING, + "dvars": attrs.NOTHING, + } + + from pydra.tasks.afni.auto import OutlierCount, QualityIndex + from pydra.tasks.mriqc.interfaces import ( + DerivativesDataSink, + FunctionalQC, + GatherTimeseries, + IQMFileSink, + ) + from pydra.tasks.mriqc.interfaces.reports import AddProvenance + from pydra.tasks.mriqc.interfaces.transitional import GCOR + from pydra.tasks.mriqc.workflows.utils import _tofloat, get_fwhmx + + mem_gb = wf_biggest_file_gb["bold"] + + # Set FD threshold + + # Compute DVARS + dvnode = workflow.add( + ComputeDVARS( + save_all=True, save_plot=False, in_file=hmc_epi, in_mask=brainmask + ), + name="dvnode", + ) + # AFNI quality measures + fwhm = workflow.add(fwhm_task, name="fwhm") + fwhm.inputs.inputs.acf = True # Only AFNI >= 16 + outliers = workflow.add( + OutlierCount( + fraction=True, out_file="outliers.out", in_file=hmc_epi, mask=brainmask + ), + name="outliers", + ) + + measures = workflow.add( + FunctionalQC( + fd_thres=fd_thres, + in_epi=epi_mean, + in_fd=hmc_fd, + in_hmc=hmc_epi, + in_mask=brainmask, + in_tsnr=in_tsnr, + ), + name="measures", + ) + + # fmt: off + outputs_['dvars'] = dvnode.out_all + + @python.define + def fwhm_fwhm_to_measures_in_fwhm_callable(in_: ty.Any) -> ty.Any: + return _tofloat(in_) + + fwhm_fwhm_to_measures_in_fwhm_callable = workflow.add(fwhm_fwhm_to_measures_in_fwhm_callable(in_=fwhm.fwhm), name="fwhm_fwhm_to_measures_in_fwhm_callable") + + measures.inputs.in_fwhm = fwhm_fwhm_to_measures_in_fwhm_callable.out + outputs_['outliers'] = outliers.out_file + # fmt: on + + # Save to JSON file + + # Save timeseries TSV file + + # fmt: off + + + outputs_['out_file'] = measures.out_qc + + # fmt: on + # FFT spikes finder + if True: # wf_fft_spikes_detector: - disabled to ensure all outputs are generated + from pydra.tasks.mriqc.workflows.utils import slice_wise_fft + + spikes_fft = workflow.add( + FunctionTask( + func=slice_wise_fft, + input_spec=SpecInfo( + name="FunctionIn", bases=(BaseSpec,), fields=[("in_file", ty.Any)] + ), + output_spec=SpecInfo( + name="FunctionOut", + bases=(BaseSpec,), + fields=[ + ("n_spikes", ty.Any), + ("out_spikes", ty.Any), + ("out_fft", ty.Any), + ], + ), + ), + name="spikes_fft", + ) + # fmt: off + spikes_fft.inputs.in_file = in_ras + outputs_['spikes'] = spikes_fft.out_spikes + outputs_['fft'] = spikes_fft.out_fft + outputs_['spikes_num'] = spikes_fft.n_spikes + # fmt: on + + return tuple(outputs_) + + +def _parse_tout(in_file): + + if isinstance(in_file, (list, tuple)): + return ( + [_parse_tout(f) for f in in_file] + if len(in_file) > 1 + else _parse_tout(in_file[0]) + ) + import numpy as np + + data = np.loadtxt(in_file) # pylint: disable=no-member + return data.mean() + + +def _parse_tqual(in_file): + + if isinstance(in_file, (list, tuple)): + return ( + [_parse_tqual(f) for f in in_file] + if len(in_file) > 1 + else _parse_tqual(in_file[0]) + ) + import numpy as np + + with open(in_file) as fin: + lines = fin.readlines() + return np.mean([float(line.strip()) for line in lines if not line.startswith("++")]) + + +@workflow.define( + outputs=[ + "ema_report", + "iqmswf_out_file", + "iqmswf_spikes", + "iqmswf_fft", + "iqmswf_spikes_num", + "iqmswf_outliers", + "iqmswf_dvars", + "func_report_wf_mean_report", + "func_report_wf_stdev_report", + "func_report_wf_background_report", + "func_report_wf_zoomed_report", + "func_report_wf_carpet_report", + "func_report_wf_spikes_report", + ] +) +def fmri_qc_workflow( + exec_ants_float=False, + exec_debug=False, + exec_float32=True, + exec_no_sub=False, + exec_verbose_reports=False, + exec_work_dir=None, + in_file: ty.Any = attrs.NOTHING, + metadata: ty.Any = attrs.NOTHING, + name="funcMRIQC", + nipype_nprocs=12, + nipype_omp_nthreads=12, + wf_biggest_file_gb=1, + wf_deoblique=False, + wf_despike=False, + wf_fd_radius=50, + wf_fft_spikes_detector=False, + wf_inputs=None, + wf_inputs_entities={}, + wf_inputs_metadata=None, + wf_species="human", + wf_template_id="MNI152NLin2009cAsym", +) -> [ + "ty.Any", + "ty.Any", + "ty.Any", + "ty.Any", + "ty.Any", + "ty.Any", + "ty.Any", + "ty.Any", + "ty.Any", + "ty.Any", + "ty.Any", + "ty.Any", + "ty.Any", +]: + """ + Initialize the (f)MRIQC workflow. + + .. workflow:: + + import os.path as op + from mriqc.workflows.functional.base import fmri_qc_workflow + from mriqc.testing import mock_config + with mock_config(): + wf = fmri_qc_workflow() + + """ + from pydra.tasks.mriqc.nipype_ports.algorithms.confounds import ( + NonSteadyStateDetector, + TSNR, + ) + + if exec_work_dir is None: + exec_work_dir = Path.cwd() + + outputs_ = { + "ema_report": attrs.NOTHING, + "iqmswf_out_file": attrs.NOTHING, + "iqmswf_spikes": attrs.NOTHING, + "iqmswf_fft": attrs.NOTHING, + "iqmswf_spikes_num": attrs.NOTHING, + "iqmswf_outliers": attrs.NOTHING, + "iqmswf_dvars": attrs.NOTHING, + "func_report_wf_mean_report": attrs.NOTHING, + "func_report_wf_stdev_report": attrs.NOTHING, + "func_report_wf_background_report": attrs.NOTHING, + "func_report_wf_zoomed_report": attrs.NOTHING, + "func_report_wf_carpet_report": attrs.NOTHING, + "func_report_wf_spikes_report": attrs.NOTHING, + } + + from pydra.tasks.afni.auto import TStat + from pydra.tasks.niworkflows.interfaces.header import SanitizeImage + from pydra.tasks.mriqc.interfaces.functional import SelectEcho + + mem_gb = wf_biggest_file_gb["bold"] + dataset = wf_inputs["bold"] + metadata = wf_inputs_metadata["bold"] + entities = wf_inputs_entities["bold"] + message = "Building {modality} MRIQC workflow {detail}.".format( + modality="functional", + detail=f"for {len(dataset)} BOLD runs.", + ) + logger.info(message) + # Define workflow, inputs and outputs + # 0. Get data, put it in RAS orientation + + pick_echo = workflow.add( + SelectEcho(in_files=in_file, metadata=metadata), name="pick_echo" + ) + non_steady_state_detector = workflow.add( + NonSteadyStateDetector(in_file=pick_echo.out_file), + name="non_steady_state_detector", + ) + sanitize = workflow.add( + SanitizeImage( + max_32bit=exec_float32, + in_file=in_file, + n_volumes_to_discard=non_steady_state_detector.n_volumes_to_discard, + ), + name="sanitize", + ) + # Workflow -------------------------------------------------------- + # 1. HMC: head motion correct + hmcwf = workflow.add( + hmc( + omp_nthreads=nipype_omp_nthreads, + wf_biggest_file_gb=wf_biggest_file_gb, + wf_deoblique=wf_deoblique, + wf_despike=wf_despike, + in_file=sanitize.out_file, + name="hmcwf", + ) + ) + # Set HMC settings + hmcwf.inputs.inputs.inputnode.fd_radius = wf_fd_radius + # 2. Compute mean fmri + mean = workflow.add( + TStat(options="-mean", outputtype="NIFTI_GZ", in_file=hmcwf.out_file), + name="mean", + ) + # Compute TSNR using nipype implementation + tsnr = workflow.add(TSNR(in_file=hmcwf.out_file), name="tsnr") + # EPI to MNI registration + ema = workflow.add( + epi_mni_align( + nipype_omp_nthreads=nipype_omp_nthreads, + wf_species=wf_species, + wf_template_id=wf_template_id, + nipype_nprocs=nipype_nprocs, + exec_debug=exec_debug, + exec_ants_float=exec_ants_float, + name="ema", + ) + ) + # 7. Compute IQMs + iqmswf = workflow.add( + compute_iqms( + wf_fft_spikes_detector=wf_fft_spikes_detector, + wf_biggest_file_gb=wf_biggest_file_gb, + in_ras=sanitize.out_file, + epi_mean=mean.out_file, + hmc_epi=hmcwf.out_file, + hmc_fd=hmcwf.out_fd, + in_tsnr=tsnr.tsnr_file, + name="iqmswf", + ) + ) + # Reports + func_report_wf = workflow.add( + init_func_report_wf( + wf_fft_spikes_detector=wf_fft_spikes_detector, + wf_species=wf_species, + wf_biggest_file_gb=wf_biggest_file_gb, + exec_verbose_reports=exec_verbose_reports, + exec_work_dir=exec_work_dir, + meta_sidecar=metadata, + in_ras=sanitize.out_file, + epi_mean=mean.out_file, + in_stddev=tsnr.stddev_file, + hmc_fd=hmcwf.out_fd, + hmc_epi=hmcwf.out_file, + epi_parc=ema.epi_parc, + name="func_report_wf", + ) + ) + # fmt: off + + @python.define + def mean_out_file_to_ema_epi_mean_callable(in_: ty.Any) -> ty.Any: + return _pop(in_) + + mean_out_file_to_ema_epi_mean_callable = workflow.add(mean_out_file_to_ema_epi_mean_callable(in_=mean.out_file), name="mean_out_file_to_ema_epi_mean_callable") + + ema.inputs.epi_mean = mean_out_file_to_ema_epi_mean_callable.out + + # fmt: on + if wf_fft_spikes_detector: + # fmt: off + outputs_['iqmswf_spikes'] = iqmswf.spikes + outputs_['iqmswf_fft'] = iqmswf.fft + # fmt: on + # population specific changes to brain masking + if wf_species == "human": + from pydra.tasks.mriqc.workflows.shared import ( + synthstrip_wf as fmri_bmsk_workflow, + ) + + skullstrip_epi = workflow.add( + fmri_bmsk_workflow(omp_nthreads=nipype_omp_nthreads, name="skullstrip_epi") + ) + # fmt: off + + @python.define + def mean_out_file_to_skullstrip_epi_in_files_callable(in_: ty.Any) -> ty.Any: + return _pop(in_) + + mean_out_file_to_skullstrip_epi_in_files_callable = workflow.add(mean_out_file_to_skullstrip_epi_in_files_callable(in_=mean.out_file), name="mean_out_file_to_skullstrip_epi_in_files_callable") + + skullstrip_epi.inputs.in_files = mean_out_file_to_skullstrip_epi_in_files_callable.out + ema.inputs.epi_mask = skullstrip_epi.out_mask + iqmswf.inputs.brainmask = skullstrip_epi.out_mask + func_report_wf.inputs.brainmask = skullstrip_epi.out_mask + # fmt: on + else: + from pydra.tasks.mriqc.workflows.anatomical.base import _binarize + + binarise_labels = workflow.add( + FunctionTask( + func=_binarize, + input_spec=SpecInfo( + name="FunctionIn", + bases=(BaseSpec,), + fields=[("in_file", ty.Any), ("threshold", ty.Any)], + ), + output_spec=SpecInfo( + name="FunctionOut", bases=(BaseSpec,), fields=[("out_file", ty.Any)] + ), + ), + name="binarise_labels", + ) + # fmt: off + binarise_labels.inputs.in_file = ema.epi_parc + iqmswf.inputs.brainmask = binarise_labels.out_file + func_report_wf.inputs.brainmask = binarise_labels.out_file + # fmt: on + # Upload metrics + if not exec_no_sub: + from pydra.tasks.mriqc.interfaces.webapi import UploadIQMs + + pass + # fmt: off + outputs_['iqmswf_out_file'] = iqmswf.out_file + # fmt: on + outputs_["ema_report"] = ema.report + outputs_["iqmswf_spikes_num"] = iqmswf.spikes_num + outputs_["iqmswf_fft"] = iqmswf.fft + outputs_["iqmswf_dvars"] = iqmswf.dvars + outputs_["iqmswf_spikes"] = iqmswf.spikes + outputs_["iqmswf_out_file"] = iqmswf.out_file + outputs_["iqmswf_outliers"] = iqmswf.outliers + outputs_["func_report_wf_carpet_report"] = func_report_wf.carpet_report + outputs_["func_report_wf_zoomed_report"] = func_report_wf.zoomed_report + outputs_["func_report_wf_mean_report"] = func_report_wf.mean_report + outputs_["func_report_wf_spikes_report"] = func_report_wf.spikes_report + outputs_["func_report_wf_background_report"] = func_report_wf.background_report + outputs_["func_report_wf_stdev_report"] = func_report_wf.stdev_report + + return tuple(outputs_) diff --git a/pydra/tasks/mriqc/workflows/functional/output.py b/pydra/tasks/mriqc/workflows/functional/output.py new file mode 100644 index 0000000..dfc51d0 --- /dev/null +++ b/pydra/tasks/mriqc/workflows/functional/output.py @@ -0,0 +1,276 @@ +import attrs +import logging +from pathlib import Path +from pydra.compose import workflow +import typing as ty + + +logger = logging.getLogger(__name__) + + +@workflow.define( + outputs=[ + "mean_report", + "stdev_report", + "background_report", + "zoomed_report", + "carpet_report", + "spikes_report", + ] +) +def init_func_report_wf( + brainmask: ty.Any = attrs.NOTHING, + epi_mean: ty.Any = attrs.NOTHING, + epi_parc: ty.Any = attrs.NOTHING, + exec_verbose_reports=False, + exec_work_dir=None, + fd_thres: ty.Any = attrs.NOTHING, + hmc_epi: ty.Any = attrs.NOTHING, + hmc_fd: ty.Any = attrs.NOTHING, + in_dvars: ty.Any = attrs.NOTHING, + in_fft: ty.Any = attrs.NOTHING, + in_ras: ty.Any = attrs.NOTHING, + in_spikes: ty.Any = attrs.NOTHING, + in_stddev: ty.Any = attrs.NOTHING, + meta_sidecar: ty.Any = attrs.NOTHING, + name="func_report_wf", + outliers: ty.Any = attrs.NOTHING, + wf_biggest_file_gb=1, + wf_fft_spikes_detector=False, + wf_species="human", +) -> ["ty.Any", "ty.Any", "ty.Any", "ty.Any", "ty.Any", "ty.Any"]: + """ + Write out individual reportlets. + + .. workflow:: + + from mriqc.workflows.functional.output import init_func_report_wf + from mriqc.testing import mock_config + with mock_config(): + wf = init_func_report_wf() + + """ + from pydra.tasks.nireports.interfaces import FMRISummary, PlotMosaic, PlotSpikes + + if exec_work_dir is None: + exec_work_dir = Path.cwd() + + outputs_ = { + "mean_report": attrs.NOTHING, + "stdev_report": attrs.NOTHING, + "background_report": attrs.NOTHING, + "zoomed_report": attrs.NOTHING, + "carpet_report": attrs.NOTHING, + "spikes_report": attrs.NOTHING, + } + + from pydra.tasks.niworkflows.interfaces.morphology import ( + BinaryDilation, + BinarySubtraction, + ) + from pydra.tasks.mriqc.interfaces.functional import Spikes + + # from mriqc.interfaces.reports import IndividualReport + verbose = exec_verbose_reports + mem_gb = wf_biggest_file_gb["bold"] + reportlets_dir = exec_work_dir / "reportlets" + + # Set FD threshold + + spmask = workflow.add( + FunctionTask( + func=spikes_mask, + input_spec=SpecInfo( + name="FunctionIn", + bases=(BaseSpec,), + fields=[("in_file", ty.Any), ("in_mask", ty.Any)], + ), + output_spec=SpecInfo( + name="FunctionOut", + bases=(BaseSpec,), + fields=[("out_file", ty.Any), ("out_plot", ty.Any)], + ), + in_file=in_ras, + ), + name="spmask", + ) + spikes_bg = workflow.add( + Spikes(detrend=False, no_zscore=True, in_file=in_ras, in_mask=spmask.out_file), + name="spikes_bg", + ) + # Generate crown mask + # Create the crown mask + dilated_mask = workflow.add(BinaryDilation(in_mask=brainmask), name="dilated_mask") + subtract_mask = workflow.add( + BinarySubtraction(in_base=dilated_mask.out_mask, in_subtract=brainmask), + name="subtract_mask", + ) + parcels = workflow.add( + FunctionTask( + func=_carpet_parcellation, + crown_mask=subtract_mask.out_mask, + segmentation=epi_parc, + ), + name="parcels", + ) + bigplot = workflow.add( + FMRISummary( + dvars=in_dvars, + fd=hmc_fd, + fd_thres=fd_thres, + in_func=hmc_epi, + in_segm=parcels.out, + in_spikes_bg=spikes_bg.out_tsz, + outliers=outliers, + tr=meta_sidecar, + ), + name="bigplot", + ) + # fmt: off + bigplot.inputs.tr = meta_sidecar + # fmt: on + mosaic_mean = workflow.add( + PlotMosaic( + cmap="Greys_r", out_file="plot_func_mean_mosaic1.svg", in_file=epi_mean + ), + name="mosaic_mean", + ) + mosaic_stddev = workflow.add( + PlotMosaic( + cmap="viridis", + out_file="plot_func_stddev_mosaic2_stddev.svg", + in_file=in_stddev, + ), + name="mosaic_stddev", + ) + mosaic_zoom = workflow.add( + PlotMosaic(cmap="Greys_r", bbox_mask_file=brainmask, in_file=epi_mean), + name="mosaic_zoom", + ) + mosaic_noise = workflow.add( + PlotMosaic(cmap="viridis_r", only_noise=True, in_file=epi_mean), + name="mosaic_noise", + ) + if wf_species.lower() in ("rat", "mouse"): + mosaic_mean.inputs.inputs.view = ["coronal", "axial"] + mosaic_stddev.inputs.inputs.view = ["coronal", "axial"] + mosaic_zoom.inputs.inputs.view = ["coronal", "axial"] + mosaic_noise.inputs.inputs.view = ["coronal", "axial"] + + # fmt: off + outputs_['mean_report'] = mosaic_mean.out_file + outputs_['stdev_report'] = mosaic_stddev.out_file + outputs_['background_report'] = mosaic_noise.out_file + outputs_['zoomed_report'] = mosaic_zoom.out_file + outputs_['carpet_report'] = bigplot.out_file + # fmt: on + if True: # wf_fft_spikes_detector: - disabled so output is always created + mosaic_spikes = workflow.add( + PlotSpikes( + cmap="viridis", + out_file="plot_spikes.svg", + title="High-Frequency spikes", + ), + name="mosaic_spikes", + ) + pass + # fmt: off + pass + mosaic_spikes.inputs.in_file = in_ras + mosaic_spikes.inputs.in_spikes = in_spikes + mosaic_spikes.inputs.in_fft = in_fft + outputs_['spikes_report'] = mosaic_spikes.out_file + # fmt: on + if not verbose: + return workflow + # Verbose-reporting goes here + from pydra.tasks.nireports.interfaces import PlotContours + from pydra.tasks.niworkflows.utils.connections import pop_file as _pop + + # fmt: off + + # fmt: on + + return tuple(outputs_) + + +def _carpet_parcellation(segmentation, crown_mask): + """Generate the union of two masks.""" + from pathlib import Path + import nibabel as nb + import numpy as np + + img = nb.load(segmentation) + lut = np.zeros((256,), dtype="uint8") + lut[100:201] = 1 # Ctx GM + lut[30:99] = 2 # dGM + lut[1:11] = 3 # WM+CSF + lut[255] = 4 # Cerebellum + # Apply lookup table + seg = lut[np.asanyarray(img.dataobj, dtype="uint16")] + seg[np.asanyarray(nb.load(crown_mask).dataobj, dtype=int) > 0] = 5 + outimg = img.__class__(seg.astype("uint8"), img.affine, img.header) + outimg.set_data_dtype("uint8") + out_file = Path("segments.nii.gz").absolute() + outimg.to_filename(out_file) + return str(out_file) + + +def _get_tr(meta_dict): + + if isinstance(meta_dict, (list, tuple)): + meta_dict = meta_dict[0] + return meta_dict.get("RepetitionTime", None) + + +def spikes_mask(in_file, in_mask=None, out_file=None): + """Calculate a mask in which check for :abbr:`EM (electromagnetic)` spikes.""" + import os.path as op + import nibabel as nb + import numpy as np + from nilearn.image import mean_img + from nilearn.plotting import plot_roi + from scipy import ndimage as nd + + if out_file is None: + fname, ext = op.splitext(op.basename(in_file)) + if ext == ".gz": + fname, ext2 = op.splitext(fname) + ext = ext2 + ext + out_file = op.abspath(f"{fname}_spmask{ext}") + out_plot = op.abspath(f"{fname}_spmask.pdf") + in_4d_nii = nb.load(in_file) + orientation = nb.aff2axcodes(in_4d_nii.affine) + if in_mask: + mask_data = np.asanyarray(nb.load(in_mask).dataobj) + a = np.where(mask_data != 0) + bbox = ( + np.max(a[0]) - np.min(a[0]), + np.max(a[1]) - np.min(a[1]), + np.max(a[2]) - np.min(a[2]), + ) + longest_axis = np.argmax(bbox) + # Input here is a binarized and intersected mask data from previous section + dil_mask = nd.binary_dilation( + mask_data, iterations=int(mask_data.shape[longest_axis] / 9) + ) + rep = list(mask_data.shape) + rep[longest_axis] = -1 + new_mask_2d = dil_mask.max(axis=longest_axis).reshape(rep) + rep = [1, 1, 1] + rep[longest_axis] = mask_data.shape[longest_axis] + new_mask_3d = np.logical_not(np.tile(new_mask_2d, rep)) + else: + new_mask_3d = np.zeros(in_4d_nii.shape[:3]) == 1 + if orientation[0] in ("L", "R"): + new_mask_3d[0:2, :, :] = True + new_mask_3d[-3:-1, :, :] = True + else: + new_mask_3d[:, 0:2, :] = True + new_mask_3d[:, -3:-1, :] = True + mask_nii = nb.Nifti1Image( + new_mask_3d.astype(np.uint8), in_4d_nii.affine, in_4d_nii.header + ) + mask_nii.to_filename(out_file) + plot_roi(mask_nii, mean_img(in_4d_nii), output_file=out_plot) + return out_file, out_plot diff --git a/pydra/tasks/mriqc/workflows/functional/tests/conftest.py b/pydra/tasks/mriqc/workflows/functional/tests/conftest.py new file mode 100644 index 0000000..751042d --- /dev/null +++ b/pydra/tasks/mriqc/workflows/functional/tests/conftest.py @@ -0,0 +1,25 @@ + +# For debugging in IDE's don't catch raised exceptions and let the IDE +# break at it +import os +import pytest + + +if os.getenv("_PYTEST_RAISE", "0") != "0": + + @pytest.hookimpl(tryfirst=True) + def pytest_exception_interact(call): + raise call.excinfo.value # raise internal errors instead of capturing them + + @pytest.hookimpl(tryfirst=True) + def pytest_internalerror(excinfo): + raise excinfo.value # raise internal errors instead of capturing them + + def pytest_configure(config): + config.option.capture = 'no' # allow print statements to show up in the console + config.option.log_cli = True # show log messages in the console + config.option.log_level = "INFO" # set the log level to INFO + + CATCH_CLI_EXCEPTIONS = False +else: + CATCH_CLI_EXCEPTIONS = True diff --git a/pydra/tasks/mriqc/workflows/functional/tests/test_workflows_functional_base_compute_iqms.py b/pydra/tasks/mriqc/workflows/functional/tests/test_workflows_functional_base_compute_iqms.py new file mode 100644 index 0000000..10c63c3 --- /dev/null +++ b/pydra/tasks/mriqc/workflows/functional/tests/test_workflows_functional_base_compute_iqms.py @@ -0,0 +1,21 @@ +import logging +from pydra.compose import workflow +from pydra.tasks.mriqc.workflows.functional.base import compute_iqms +import pytest + + +logger = logging.getLogger(__name__) + + +def test_compute_iqms_build(): + workflow = compute_iqms() + assert isinstance(workflow, Workflow) + + +@pytest.mark.skip( + reason="Appropriate inputs for this workflow haven't been specified yet" +) +def test_compute_iqms_run(): + workflow = compute_iqms() + result = workflow(worker="debug") + print(result.out) diff --git a/pydra/tasks/mriqc/workflows/functional/tests/test_workflows_functional_base_epi_mni_align.py b/pydra/tasks/mriqc/workflows/functional/tests/test_workflows_functional_base_epi_mni_align.py new file mode 100644 index 0000000..08e538a --- /dev/null +++ b/pydra/tasks/mriqc/workflows/functional/tests/test_workflows_functional_base_epi_mni_align.py @@ -0,0 +1,21 @@ +import logging +from pydra.compose import workflow +from pydra.tasks.mriqc.workflows.functional.base import epi_mni_align +import pytest + + +logger = logging.getLogger(__name__) + + +def test_epi_mni_align_build(): + workflow = epi_mni_align() + assert isinstance(workflow, Workflow) + + +@pytest.mark.skip( + reason="Appropriate inputs for this workflow haven't been specified yet" +) +def test_epi_mni_align_run(): + workflow = epi_mni_align() + result = workflow(worker="debug") + print(result.out) diff --git a/pydra/tasks/mriqc/workflows/functional/tests/test_workflows_functional_base_fmri_bmsk_workflow.py b/pydra/tasks/mriqc/workflows/functional/tests/test_workflows_functional_base_fmri_bmsk_workflow.py new file mode 100644 index 0000000..e77c84b --- /dev/null +++ b/pydra/tasks/mriqc/workflows/functional/tests/test_workflows_functional_base_fmri_bmsk_workflow.py @@ -0,0 +1,21 @@ +import logging +from pydra.compose import workflow +from pydra.tasks.mriqc.workflows.functional.base import fmri_bmsk_workflow +import pytest + + +logger = logging.getLogger(__name__) + + +def test_fmri_bmsk_workflow_build(): + workflow = fmri_bmsk_workflow() + assert isinstance(workflow, Workflow) + + +@pytest.mark.skip( + reason="Appropriate inputs for this workflow haven't been specified yet" +) +def test_fmri_bmsk_workflow_run(): + workflow = fmri_bmsk_workflow() + result = workflow(worker="debug") + print(result.out) diff --git a/pydra/tasks/mriqc/workflows/functional/tests/test_workflows_functional_base_fmri_qc_workflow.py b/pydra/tasks/mriqc/workflows/functional/tests/test_workflows_functional_base_fmri_qc_workflow.py new file mode 100644 index 0000000..13211d7 --- /dev/null +++ b/pydra/tasks/mriqc/workflows/functional/tests/test_workflows_functional_base_fmri_qc_workflow.py @@ -0,0 +1,21 @@ +import logging +from pydra.compose import workflow +from pydra.tasks.mriqc.workflows.functional.base import fmri_qc_workflow +import pytest + + +logger = logging.getLogger(__name__) + + +def test_fmri_qc_workflow_build(): + workflow = fmri_qc_workflow() + assert isinstance(workflow, Workflow) + + +@pytest.mark.skip( + reason="Appropriate inputs for this workflow haven't been specified yet" +) +def test_fmri_qc_workflow_run(): + workflow = fmri_qc_workflow() + result = workflow(worker="debug") + print(result.out) diff --git a/pydra/tasks/mriqc/workflows/functional/tests/test_workflows_functional_base_hmc.py b/pydra/tasks/mriqc/workflows/functional/tests/test_workflows_functional_base_hmc.py new file mode 100644 index 0000000..bff3d78 --- /dev/null +++ b/pydra/tasks/mriqc/workflows/functional/tests/test_workflows_functional_base_hmc.py @@ -0,0 +1,21 @@ +import logging +from pydra.compose import workflow +from pydra.tasks.mriqc.workflows.functional.base import hmc +import pytest + + +logger = logging.getLogger(__name__) + + +def test_hmc_build(): + workflow = hmc() + assert isinstance(workflow, Workflow) + + +@pytest.mark.skip( + reason="Appropriate inputs for this workflow haven't been specified yet" +) +def test_hmc_run(): + workflow = hmc() + result = workflow(worker="debug") + print(result.out) diff --git a/pydra/tasks/mriqc/workflows/functional/tests/test_workflows_functional_output_init_func_report_wf.py b/pydra/tasks/mriqc/workflows/functional/tests/test_workflows_functional_output_init_func_report_wf.py new file mode 100644 index 0000000..9b0b756 --- /dev/null +++ b/pydra/tasks/mriqc/workflows/functional/tests/test_workflows_functional_output_init_func_report_wf.py @@ -0,0 +1,21 @@ +import logging +from pydra.compose import workflow +from pydra.tasks.mriqc.workflows.functional.output import init_func_report_wf +import pytest + + +logger = logging.getLogger(__name__) + + +def test_init_func_report_wf_build(): + workflow = init_func_report_wf() + assert isinstance(workflow, Workflow) + + +@pytest.mark.skip( + reason="Appropriate inputs for this workflow haven't been specified yet" +) +def test_init_func_report_wf_run(): + workflow = init_func_report_wf() + result = workflow(worker="debug") + print(result.out) diff --git a/pydra/tasks/mriqc/workflows/shared.py b/pydra/tasks/mriqc/workflows/shared.py new file mode 100644 index 0000000..c8e199c --- /dev/null +++ b/pydra/tasks/mriqc/workflows/shared.py @@ -0,0 +1,68 @@ +import attrs +import logging +from pydra.compose import workflow +import typing as ty + + +logger = logging.getLogger(__name__) + + +@workflow.define(outputs=["out_brain", "bias_image", "out_mask", "out_corrected"]) +def synthstrip_wf( + in_files: ty.Any = attrs.NOTHING, name="synthstrip_wf", omp_nthreads=None +) -> ["ty.Any", "ty.Any", "ty.Any", "ty.Any"]: + """Create a brain-extraction workflow using SynthStrip.""" + from pydra.tasks.ants.auto import N4BiasFieldCorrection + + outputs_ = { + "out_brain": attrs.NOTHING, + "bias_image": attrs.NOTHING, + "out_mask": attrs.NOTHING, + "out_corrected": attrs.NOTHING, + } + + from pydra.tasks.niworkflows.interfaces.nibabel import ApplyMask, IntensityClip + from pydra.tasks.mriqc.interfaces.synthstrip import SynthStrip + + # truncate target intensity for N4 correction + pre_clip = workflow.add( + IntensityClip(p_max=99.9, p_min=10, in_file=in_files), name="pre_clip" + ) + pre_n4 = workflow.add( + N4BiasFieldCorrection( + copy_header=True, + dimension=3, + num_threads=omp_nthreads, + rescale_intensities=True, + input_image=pre_clip.out_file, + ), + name="pre_n4", + ) + post_n4 = workflow.add( + N4BiasFieldCorrection( + copy_header=True, + dimension=3, + n_iterations=[50] * 4, + num_threads=omp_nthreads, + bias_image=True, + input_image=pre_clip.out_file, + ), + name="post_n4", + ) + synthstrip = workflow.add( + SynthStrip(num_threads=omp_nthreads, in_file=pre_n4.output_image), + name="synthstrip", + ) + final_masked = workflow.add( + ApplyMask(in_file=post_n4.output_image, in_mask=synthstrip.out_mask), + name="final_masked", + ) + # fmt: off + post_n4.inputs.weight_image = synthstrip.out_mask + outputs_['out_brain'] = final_masked.out_file + outputs_['bias_image'] = post_n4.bias_image + outputs_['out_mask'] = synthstrip.out_mask + outputs_['out_corrected'] = post_n4.output_image + # fmt: on + + return tuple(outputs_) diff --git a/pydra/tasks/mriqc/workflows/tests/conftest.py b/pydra/tasks/mriqc/workflows/tests/conftest.py new file mode 100644 index 0000000..751042d --- /dev/null +++ b/pydra/tasks/mriqc/workflows/tests/conftest.py @@ -0,0 +1,25 @@ + +# For debugging in IDE's don't catch raised exceptions and let the IDE +# break at it +import os +import pytest + + +if os.getenv("_PYTEST_RAISE", "0") != "0": + + @pytest.hookimpl(tryfirst=True) + def pytest_exception_interact(call): + raise call.excinfo.value # raise internal errors instead of capturing them + + @pytest.hookimpl(tryfirst=True) + def pytest_internalerror(excinfo): + raise excinfo.value # raise internal errors instead of capturing them + + def pytest_configure(config): + config.option.capture = 'no' # allow print statements to show up in the console + config.option.log_cli = True # show log messages in the console + config.option.log_level = "INFO" # set the log level to INFO + + CATCH_CLI_EXCEPTIONS = False +else: + CATCH_CLI_EXCEPTIONS = True diff --git a/pydra/tasks/mriqc/workflows/tests/test_workflows_shared_synthstrip_wf.py b/pydra/tasks/mriqc/workflows/tests/test_workflows_shared_synthstrip_wf.py new file mode 100644 index 0000000..6e5b5c0 --- /dev/null +++ b/pydra/tasks/mriqc/workflows/tests/test_workflows_shared_synthstrip_wf.py @@ -0,0 +1,21 @@ +import logging +from pydra.compose import workflow +from pydra.tasks.mriqc.workflows.shared import synthstrip_wf +import pytest + + +logger = logging.getLogger(__name__) + + +def test_synthstrip_wf_build(): + workflow = synthstrip_wf(omp_nthreads=1) + assert isinstance(workflow, Workflow) + + +@pytest.mark.skip( + reason="Appropriate inputs for this workflow haven't been specified yet" +) +def test_synthstrip_wf_run(): + workflow = synthstrip_wf() + result = workflow(worker="debug") + print(result.out) diff --git a/pydra/tasks/mriqc/workflows/utils.py b/pydra/tasks/mriqc/workflows/utils.py new file mode 100644 index 0000000..193cb2f --- /dev/null +++ b/pydra/tasks/mriqc/workflows/utils.py @@ -0,0 +1,176 @@ +import logging +from pathlib import Path + + +logger = logging.getLogger(__name__) + + +def _tofloat(inlist): + + if isinstance(inlist, (list, tuple)): + return ( + [_tofloat(el) for el in inlist] if len(inlist) > 1 else _tofloat(inlist[0]) + ) + return float(inlist) + + +def generate_filename(in_file, dirname=None, suffix="", extension=None): + """ + Generate a nipype-like filename. + + >>> str(generate_filename("/path/to/input.nii.gz").relative_to(Path.cwd())) + 'input.nii.gz' + + >>> str(generate_filename( + ... "/path/to/input.nii.gz", dirname="/other/path", + ... )) + '/other/path/input.nii.gz' + + >>> str(generate_filename( + ... "/path/to/input.nii.gz", dirname="/other/path", extension="tsv", + ... )) + '/other/path/input.tsv' + + >>> str(generate_filename( + ... "/path/to/input.nii.gz", dirname="/other/path", extension=".tsv", + ... )) + '/other/path/input.tsv' + + >>> str(generate_filename( + ... "/path/to/input.nii.gz", dirname="/other/path", extension="", + ... )) + '/other/path/input' + + >>> str(generate_filename( + ... "/path/to/input.nii.gz", dirname="/other/path", extension="", suffix="_mod", + ... )) + '/other/path/input_mod' + + >>> str(generate_filename( + ... "/path/to/input.nii.gz", dirname="/other/path", extension="", suffix="mod", + ... )) + '/other/path/input_mod' + + >>> str(generate_filename( + ... "/path/to/input", dirname="/other/path", extension="tsv", suffix="mod", + ... )) + '/other/path/input_mod.tsv' + + """ + from pathlib import Path + + in_file = Path(in_file) + in_ext = "".join(in_file.suffixes) + dirname = Path.cwd() if dirname is None else Path(dirname) + if extension is not None: + extension = ( + extension if not extension or extension.startswith(".") else f".{extension}" + ) + else: + extension = in_ext + stem = in_file.name[: -len(in_ext)] if in_ext else in_file.name + if suffix and not suffix.startswith("_"): + suffix = f"_{suffix}" + return dirname / f"{stem}{suffix}{extension}" + + +def get_fwhmx(): + + from pydra.tasks.afni.auto import FWHMx, Info + + fwhm_args = {"combine": True, "detrend": True} + afni_version = Info.version() + if afni_version and afni_version >= (2017, 2, 3): + fwhm_args["args"] = "-ShowMeClassicFWHM" + fwhm_interface = FWHMx(**fwhm_args) + return fwhm_interface + + +def slice_wise_fft(in_file, ftmask=None, spike_thres=3.0, out_prefix=None): + """Search for spikes in slices using the 2D FFT""" + import os.path as op + import nibabel as nb + import numpy as np + from scipy.ndimage import binary_erosion, generate_binary_structure + from scipy.ndimage.filters import median_filter + from statsmodels.robust.scale import mad + from pydra.tasks.mriqc.workflows.utils import spectrum_mask + + if out_prefix is None: + fname, ext = op.splitext(op.basename(in_file)) + if ext == ".gz": + fname, _ = op.splitext(fname) + out_prefix = op.abspath(fname) + func_data = nb.load(in_file).get_fdata() + if ftmask is None: + ftmask = spectrum_mask(tuple(func_data.shape[:2])) + fft_data = [] + for t in range(func_data.shape[-1]): + func_frame = func_data[..., t] + fft_slices = [] + for z in range(func_frame.shape[2]): + sl = func_frame[..., z] + fftsl = ( + median_filter( + np.real(np.fft.fft2(sl)).astype(np.float32), + size=(5, 5), + mode="constant", + ) + * ftmask + ) + fft_slices.append(fftsl) + fft_data.append(np.stack(fft_slices, axis=-1)) + # Recompose the 4D FFT timeseries + fft_data = np.stack(fft_data, -1) + # Z-score across t, using robust statistics + mu = np.median(fft_data, axis=3) + sigma = np.stack([mad(fft_data, axis=3)] * fft_data.shape[-1], -1) + idxs = np.where(np.abs(sigma) > 1e-4) + fft_zscored = fft_data - mu[..., np.newaxis] + fft_zscored[idxs] /= sigma[idxs] + # save fft z-scored + out_fft = op.abspath(out_prefix + "_zsfft.nii.gz") + nii = nb.Nifti1Image(fft_zscored.astype(np.float32), np.eye(4), None) + nii.to_filename(out_fft) + # Find peaks + spikes_list = [] + for t in range(fft_zscored.shape[-1]): + fft_frame = fft_zscored[..., t] + for z in range(fft_frame.shape[-1]): + sl = fft_frame[..., z] + if np.all(sl < spike_thres): + continue + # Any zscore over spike_thres will be called a spike + sl[sl <= spike_thres] = 0 + sl[sl > 0] = 1 + # Erode peaks and see how many survive + struct = generate_binary_structure(2, 2) + sl = binary_erosion(sl.astype(np.uint8), structure=struct).astype(np.uint8) + if sl.sum() > 10: + spikes_list.append((t, z)) + out_spikes = op.abspath(out_prefix + "_spikes.tsv") + np.savetxt(out_spikes, spikes_list, fmt=b"%d", delimiter=b"\t", header="TR\tZ") + return len(spikes_list), out_spikes, out_fft + + +def spectrum_mask(size): + """Creates a mask to filter the image of size size""" + import numpy as np + from scipy.ndimage.morphology import distance_transform_edt as distance + + ftmask = np.ones(size) + # Set zeros on corners + # ftmask[0, 0] = 0 + # ftmask[size[0] - 1, size[1] - 1] = 0 + # ftmask[0, size[1] - 1] = 0 + # ftmask[size[0] - 1, 0] = 0 + ftmask[size[0] // 2, size[1] // 2] = 0 + # Distance transform + ftmask = distance(ftmask) + ftmask /= ftmask.max() + # Keep this just in case we want to switch to the opposite filter + ftmask *= -1.0 + ftmask += 1.0 + ftmask[ftmask >= 0.4] = 1 + ftmask[ftmask < 1] = 0 + return ftmask diff --git a/pyproject.toml b/pyproject.toml index 32e2c29..31fd154 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,7 @@ requires = ["hatchling", "hatch-vcs"] build-backend = "hatchling.build" [project] -name = "pydra-mriqc" +name = "pydra-tasks-mriqc" description = "Pydra tasks package for mriqc" readme = "README.rst" requires-python = ">=3.10" diff --git a/test_scripts/orig-affinie-init.txt b/test_scripts/orig-affinie-init.txt new file mode 100644 index 0000000..43dfcea --- /dev/null +++ b/test_scripts/orig-affinie-init.txt @@ -0,0 +1,9 @@ +antsAffineInitializer +3 +/Users/tclose/git/workflows/mriqc/work/mriqc_wf/anatMRIQC/SpatialNormalization/_in_file_..Users..tclose..Data..openneuro..ds000114..sub-03..ses-retest..anat..sub-03_ses-retest_T1w.nii.gz/SpatialNormalization/fixed_masked.nii.gz +/Users/tclose/git/workflows/mriqc/work/mriqc_wf/anatMRIQC/SpatialNormalization/_in_file_..Users..tclose..Data..openneuro..ds000114..sub-03..ses-retest..anat..sub-03_ses-retest_T1w.nii.gz/SpatialNormalization/moving_masked.nii.gz +transform.mat +15.000000 +0.100000 +0 +10 \ No newline at end of file diff --git a/test_scripts/orig-mriqc-registration-cmd.txt b/test_scripts/orig-mriqc-registration-cmd.txt new file mode 100644 index 0000000..bee114a --- /dev/null +++ b/test_scripts/orig-mriqc-registration-cmd.txt @@ -0,0 +1,79 @@ +antsRegistration +--dimensionality +3 +--float +0 +--initial-moving-transform +[ +/Users/tclose/git/workflows/mriqc/work/mriqc_wf/anatMRIQC/SpatialNormalization/_in_file_..Users..tclose..Data..openneuro..ds000114..sub-02..ses-test..anat..sub-02_ses-test_T1w.nii.gz/SpatialNormalization/transform.mat, +0 +] +--initialize-transforms-per-stage +0 +--interpolation +LanczosWindowedSinc +--output +[ +ants_t1_to_mni, +ants_t1_to_mni_Warped.nii.gz +] +--transform +Rigid[ +1.0 +] +--metric +Mattes[ +/Users/tclose/git/workflows/mriqc/work/mriqc_wf/anatMRIQC/SpatialNormalization/_in_file_..Users..tclose..Data..openneuro..ds000114..sub-02..ses-test..anat..sub-02_ses-test_T1w.nii.gz/SpatialNormalization/fixed_masked.nii.gz, +/Users/tclose/git/workflows/mriqc/work/mriqc_wf/anatMRIQC/SpatialNormalization/_in_file_..Users..tclose..Data..openneuro..ds000114..sub-02..ses-test..anat..sub-02_ses-test_T1w.nii.gz/SpatialNormalization/moving_masked.nii.gz, +1, +56, +Random, +0.2 +] +--convergence +[ +20, +1e-07, +15 +] +--smoothing-sigmas +4.0vox +--shrink-factors +2 +--use-histogram-matching +0 +--transform +Affine[ +1.0 +] +--metric +Mattes[ +/Users/tclose/git/workflows/mriqc/work/mriqc_wf/anatMRIQC/SpatialNormalization/_in_file_..Users..tclose..Data..openneuro..ds000114..sub-02..ses-test..anat..sub-02_ses-test_T1w.nii.gz/SpatialNormalization/fixed_masked.nii.gz, +/Users/tclose/git/workflows/mriqc/work/mriqc_wf/anatMRIQC/SpatialNormalization/_in_file_..Users..tclose..Data..openneuro..ds000114..sub-02..ses-test..anat..sub-02_ses-test_T1w.nii.gz/SpatialNormalization/moving_masked.nii.gz, +1, +56, +Random, +0.1 +] +--convergence +[ +15, +1e-08, +5 +] +--smoothing-sigmas +2.0vox +--shrink-factors +1 +--use-histogram-matching +1 +--winsorize-image-intensities +[ +0.005, +0.995 +] + +--write-composite-transform +1 +--collapse-output-transforms +1 diff --git a/test_scripts/pydra-affine-init.txt b/test_scripts/pydra-affine-init.txt new file mode 100644 index 0000000..b967dd5 --- /dev/null +++ b/test_scripts/pydra-affine-init.txt @@ -0,0 +1,9 @@ +antsAffineInitializer +3 +/Users/tclose/Data/pydra-mriqc-test-cache/FunctionTask_9e6afe7c28f8064822f15f48c4fc3aa8/fixed_masked.nii.gz +/Users/tclose/Data/pydra-mriqc-test-cache/FunctionTask_9e6afe7c28f8064822f15f48c4fc3aa8/moving_masked.nii.gz +transform.mat +15.0 +0.1 +False +10 \ No newline at end of file diff --git a/test_scripts/pydra-mriqc-registration-cmd.txt b/test_scripts/pydra-mriqc-registration-cmd.txt new file mode 100644 index 0000000..c4f3488 --- /dev/null +++ b/test_scripts/pydra-mriqc-registration-cmd.txt @@ -0,0 +1,77 @@ +antsRegistration +--dimensionality +3 +--initial-moving-transform +[ +/private/var/folders/mz/yn83q2fd3s758w1j75d2nnw80000gn/T/tmpl8nw1lip/AffineInitializer_2208bf8e3d355eaa5ae846eb0cfb6c1b/transform.mat, +0 +] +--interpolation +LanczosWindowedSinc +--output +[ +ants_t1_to_mni, +ants_t1_to_mni_Warped.nii.gz +] +--transform +Rigid[ +1.0 +] +--metric +Mattes[ +/Users/tclose/Data/pydra-mriqc-test-cache/FunctionTask_c9201346e6b94a8d35e4d8c1b43adb53/fixed_masked.nii.gz, +/Users/tclose/Data/pydra-mriqc-test-cache/FunctionTask_c9201346e6b94a8d35e4d8c1b43adb53/moving_masked.nii.gz, +1, +56, +Random, +0.2 +] +--convergence +[ +20, +1e-07, +15 +] +--smoothing-sigmas +4vox +--shrink-factors +2 +--use-histogram-matching +0 +--transform +Affine[ +1.0 +] +--metric +a[ +/Users/tclose/Data/pydra-mriqc-test-cache/FunctionTask_c9201346e6b94a8d35e4d8c1b43adb53/fixed_masked.nii.gz, +/Users/tclose/Data/pydra-mriqc-test-cache/FunctionTask_c9201346e6b94a8d35e4d8c1b43adb53/moving_masked.nii.gz, +1, +56, +Random, +0.1 +] +--convergence +[ +15, +1e-08, +5 +] +--smoothing-sigmas +2vox +--shrink-factors +1 +--use-histogram-matching +1 +--winsorize-image-intensities +[ +0.005, +0.995 +] +--winsorize-image-intensities +[ +0.005, +0.995 +] +--write-composite-transform +--collapse-output-transforms diff --git a/test_scripts/run_anat_wf.py b/test_scripts/run_anat_wf.py index e6548f2..bde3b2b 100644 --- a/test_scripts/run_anat_wf.py +++ b/test_scripts/run_anat_wf.py @@ -1,5 +1,6 @@ from fileformats.medimage import NiftiGzX, T1Weighted import logging +import tempfile from pathlib import Path from pydra.tasks.mriqc.workflows.anatomical.base import anat_qc_workflow @@ -12,7 +13,13 @@ pydra_logger.addHandler(file_handler) pydra_logger.addHandler(logging.StreamHandler()) -workflow = anat_qc_workflow(in_file=NiftiGzX[T1Weighted].sample(), modality="T1w") +in_file = NiftiGzX[T1Weighted].sample() + +tmp_dir = Path(tempfile.mkdtemp()) + +in_file = in_file.copy(tmp_dir, new_stem="sub-01_T1w") + +workflow = anat_qc_workflow(in_file=in_file, modality="T1w") workflow.cache_dir = "/Users/tclose/Data/pydra-mriqc-test-cache" result = workflow(plugin="serial") print(result.out)