diff --git a/.github/workflows/pydesigner_build.yml b/.github/workflows/pydesigner_build.yml new file mode 100644 index 00000000..ef11bce5 --- /dev/null +++ b/.github/workflows/pydesigner_build.yml @@ -0,0 +1,39 @@ +# name: Docker Build and Push + +# on: +# push: +# tags: +# - '**' + +# jobs: +# docker: +# runs-on: ubuntu-latest +# steps: +# - +# name: Checkout +# uses: actions/checkout@v3 +# - +# name: Fetch version +# uses: SebRollen/toml-action@v1.0.2 +# id: version +# with: +# file: 'pyproject.toml' +# field: 'tool.poetry.version' +# - +# name: Set up QEMU +# uses: docker/setup-qemu-action@v3 +# - +# name: Set up Docker Buildx +# uses: docker/setup-buildx-action@v3 +# - +# name: Login to Docker Hub +# uses: docker/login-action@v3 +# with: +# username: ${{ secrets.DOCKERHUB_USERNAME }} +# password: ${{ secrets.DOCKERHUB_TOKEN }} +# - +# name: Build and push +# uses: docker/build-push-action@v5 +# with: +# push: true +# tags: dmri/neurodock:latest,dmri/neurodock:${{ steps.version.outputs.value }} diff --git a/.github/workflows/pydesigner_ci.yml b/.github/workflows/pydesigner_ci.yml new file mode 100644 index 00000000..5eafe0d2 --- /dev/null +++ b/.github/workflows/pydesigner_ci.yml @@ -0,0 +1,28 @@ +name: CI + +on: + pull_request: + push: + branches: [main] + + +jobs: + pre-commit: + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v3 + - name: Set up Python + uses: actions/setup-python@v3 + - name: Run Pre-Commit + uses: pre-commit/action@v3.0.0 + with: + pre-commit_version: latest + python_version: 3.8 + system: true + # - name: Auto-commit changed files + # uses: stefanzweifel/git-auto-commit-action@v5 + # with: + # commit_message: "Pre-commit fixes" + + \ No newline at end of file diff --git a/.gitignore b/.gitignore index c8958584..cbb34060 100644 --- a/.gitignore +++ b/.gitignore @@ -7,6 +7,10 @@ __pycache__/ .vscode/ /docs/build/ dist/ +.python-version +.venv/ +docs/html/ +docs/doctrees/ # Files to ignore .DS_Store diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 00000000..5d610c94 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,37 @@ +repos: +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: 'v4.5.0' + hooks: + - id: check-yaml + - id: end-of-file-fixer + - id: trailing-whitespace + +- repo: https://github.com/python-jsonschema/check-jsonschema + rev: '0.27.0' + hooks: + - id: check-readthedocs + +- repo: https://github.com/thclark/pre-commit-sphinx + rev: '0.0.3' + hooks: + - id: build-docs + args: ['--html-dir', 'docs/html', '--source-dir', 'docs/source'] + language_version: python3 + +- repo: https://github.com/psf/black + rev: '23.9.1' + hooks: + - id: black + +- repo: https://github.com/astral-sh/ruff-pre-commit + rev: 'v0.1.0' + hooks: + - id: ruff + args: [--fix, --exit-non-zero-on-fix] + +- repo: https://github.com/python-poetry/poetry + rev: '1.6.0' + hooks: + - id: poetry-check + # - id: poetry-lock + - id: poetry-export diff --git a/.readthedocs.yml b/.readthedocs.yml index cea04c20..3349b289 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -1,26 +1,25 @@ # .readthedocs.yml -# Read the Docs configuration file +# Read the Docs configuration file for Sphinx projects # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details # Required version: 2 -# Build documentation in the docs/source directory with Sphinx +# Set the OS, Python version and other tools you might need +build: + os: ubuntu-22.04 + tools: + python: "3.8" + +# Build documentation in the "docs/" directory with Sphinx sphinx: configuration: docs/source/conf.py -# Build documentation with MkDocs -#mkdocs: -# configuration: mkdocs.yml - -# Optionally build your docs in additional formats such as PDF and ePub -formats: all - -# Optionally set the version of Python and requirements required to build your docs +# Optional but recommended, declare the Python requirements required +# to build your documentation +# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html python: - version: 3.6 - install: - - requirements: docs/requirements.txt - - method: pip - path: . - system_packages: true + install: + - requirements: docs/requirements.txt + - method: pip + path: . diff --git a/CHANGELOG.rst b/CHANGELOG.rst index a9c0ddbc..197e558a 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -184,8 +184,8 @@ Dec 22, 2020 **Changed** -* Replaced ``preprocessing.util.bvec_is_fullsphere()`` and - ``preprocessing.util.vecs_are_fullsphere()`` with +* Replaced ``preprocessing.util.bvec_is_fullsphere()`` and + ``preprocessing.util.vecs_are_fullsphere()`` with ``preprocessing.mrinfoutil.is_fullsphere()``. Even though datasets may be half-shelled, it is inaccurate to label them as such because distortion relative to b-value is not linear. As such, the @@ -383,7 +383,7 @@ Apr 8, 2020 faster EPI distortion correction without degrading DTI/DKI maps. * Documentation moved to ReadTheDocs * Moved B0 production module from designer.preprocessing.brainmask to - a separate function at ``designer.preprocessing.extractmeanbzero()`` + a separate function at ``designer.preprocessing.extractmeanbzero()`` that gets called by PyDesigner main. This allows a B0.nii to be produced regardless of the ``--mask`` flag. @@ -461,4 +461,4 @@ Initial port of MATLAB code to Python. 200,000,000,000 BCE .. _v0.3: https://github.com/m-ama/PyDesigner/releases/tag/v0.3 .. _v0.2 [The Cupid Release]: https://github.com/m-ama/PyDesigner/releases/tag/v0.2 .. _dev-0.11: https://github.com/m-ama/PyDesigner/releases/tag/dev-0.11 -.. _0.1-dev: https://github.com/m-ama/PyDesigner/releases/tag/0.1-dev \ No newline at end of file +.. _0.1-dev: https://github.com/m-ama/PyDesigner/releases/tag/0.1-dev diff --git a/Dockerfile b/Dockerfile index 51eef72d..723a0cd6 100644 --- a/Dockerfile +++ b/Dockerfile @@ -14,61 +14,57 @@ # ============================================================================== # Load base Ubuntu image -FROM debian:buster-slim +FROM python:3.11-bullseye -# Add LABEL Information -# ARG BUILD_DATE -# ARG VCS_REF - -# Labels. -LABEL maintainer="Siddhartha Dhiman (dhiman@musc.edu)" +# Labels +LABEL maintainer="Siddhartha Dhiman (siddhartha.dhiman@gmail.com)" LABEL org.label-schema.schema-version="1.0.0-rc1" -LABEL org.label-schema.build-date=$BUILD_DATE LABEL org.label-schema.name="dmri/pydesigner" LABEL org.label-schema.description="A state-of-the-art difusion and kurtosis MRI processing pipeline" LABEL org.label-schema.url="https://github.com/m-ama/" LABEL org.label-schema.vcs-url="https://github.com/m-ama/NeuroDock.git" -LABEL org.label-schema.vcs-ref=$VCS_REF -LABEL org.label-schema.vendor="MAMA" +LABEL org.label-schema.vendor="MUSC BRIDGE" -ARG DEBIAN_FRONTEND=noninteractive +# ARG DEBIAN_FRONTEND=noninteractive # Initial update -RUN apt-get update && \ +RUN apt update && \ apt-get install -y \ apt-utils \ wget \ curl \ nano \ software-properties-common \ - python2.7 python-pip \ + python3 \ python3-pip \ jq \ libblas-dev \ liblapack-dev \ libatlas-base-dev \ - gfortran - -# Install MRTRIX3 dependencies -RUN apt-get install -y --no-install-recommends \ - clang \ + gfortran \ git \ + g++ \ + python \ libeigen3-dev \ zlib1g-dev \ - libqt4-opengl-dev \ + libqt5opengl5-dev \ + libqt5svg5-dev \ libgl1-mesa-dev \ libfftw3-dev \ libtiff5-dev \ - libomp-dev - -RUN rm /bin/sh && ln -s /bin/bash /bin/sh + libpng-dev # Copy and install PyDesigner -RUN mkdir -p /tmp/PyDesigner -ADD . / /tmp/PyDesigner/ -RUN pip3 install /tmp/PyDesigner -RUN echo "alias python=python3" >> ~/.bashrc && source ~/.bashrc -RUN echo "alias pip=pip3" >> ~/.bashrc && source ~/.bashrc +RUN mkdir -p /pydesigner +COPY /pydesigner /app/pydesigner +COPY pyproject.toml app/ +RUN ls +RUN ls -la /app +WORKDIR /app +ENV PYTHONPATH=${PYTHONPATH}:${PWD} +RUN pip3 install poetry +RUN poetry config virtualenvs.create false +RUN poetry install --no-dev # Install Python dependencies RUN pip3 install --upgrade setuptools && \ @@ -104,3 +100,5 @@ ENV PATH=$PATH:/usr/lib/mrtrix3/bin # Remove unwanted packages RUN apt-get autoremove && apt-get clean RUN rm /tmp/fslinstaller.py && rm -r /tmp/PyDesigner + +USER bridge diff --git a/README.rst b/README.rst index a0859dff..16fa26eb 100644 --- a/README.rst +++ b/README.rst @@ -9,11 +9,15 @@ PyDesigner .. image:: https://img.shields.io/github/downloads-pre/m-ama/pydesigner/latest/total?style=flat-square :target: https://github.com/m-ama/PyDesigner/releases/latest :alt: GitHub release (latest SemVer including pre-releases) - + .. image:: https://img.shields.io/readthedocs/pydesigner?style=flat-square :target: https://pydesigner.readthedocs.io/en/latest/?badge=latest :alt: Documentation Status +.. image:: https://results.pre-commit.ci/badge/github/muscbridge/PyDesigner/master.svg + :target: https://results.pre-commit.ci/latest/github/muscbridge/PyDesigner/master + :alt: pre-commit.ci status + Welcome to the official PyDesigner project! ******************************************* @@ -105,13 +109,13 @@ The PyDesigner software packages is based upon the the references listed below. Please be sure to cite them if PyDesigner was used in any publications. -1. Jensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K. Diffusional kurtosis imaging: the quantification of non-Gaussian water diffusion by means of MRI. Magn Reson Med 2005;53:1432-1440. doi: 10.1002/mrm.20508 -2. Jensen JH, Helpern JA. MRI Quantification of non-Gaussian water diffusion by kurtosis analysis. NMR Biomed 2010;23:698-710. doi: 10.1002/nbm.1518 -3. Fieremans E, Jensen JH, Helpern JA. White matter characterization with diffusional kurtosis imaging. Neuroimage 2011;58:177-188. doi: 10.1016/j.neuroimage.2011.06.006 -4. Tabesh A, Jensen JH, Ardekani BA, Helpern JA. Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging. Magn Reson Med 2011;65:823-836. doi: 10.1002/mrm.22655 -5. Glenn GR, Helpern JA, Tabesh A, Jensen JH. Quantitative assessment of diffusional kurtosis anisotropy. NMR Biomed 2015;28:448-459. doi: 10.1002/nbm.3271 -6. Jensen JH, Glenn GR, Helpern JA. Fiber ball imaging. Neuroimage 2016; 124:824-833. doi: 10.1016/j.neuroimage.2015.09.049 -7. McKinnon ET, Helpern JA, Jensen JH. Modeling white matter microstructure with fiber ball imaging. Neuroimage 2018;176:11-21. doi: 10.1016/j.neuroimage.2018.04.025 -8. Ades-Aron B, Veraart J, Kochunov P, McGuire S, Sherman P, Kellner E, Novikov DS, Fieremans E. Evaluation of the accuracy and precision of the diffusion parameter EStImation with Gibbs and NoisE removal pipeline. Neuroimage. 2018;183:532-543. doi: 10.1016/j.neuroimage.2018.07.066 +1. Jensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K. Diffusional kurtosis imaging: the quantification of non-Gaussian water diffusion by means of MRI. Magn Reson Med 2005;53:1432-1440. doi: 10.1002/mrm.20508 +2. Jensen JH, Helpern JA. MRI Quantification of non-Gaussian water diffusion by kurtosis analysis. NMR Biomed 2010;23:698-710. doi: 10.1002/nbm.1518 +3. Fieremans E, Jensen JH, Helpern JA. White matter characterization with diffusional kurtosis imaging. Neuroimage 2011;58:177-188. doi: 10.1016/j.neuroimage.2011.06.006 +4. Tabesh A, Jensen JH, Ardekani BA, Helpern JA. Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging. Magn Reson Med 2011;65:823-836. doi: 10.1002/mrm.22655 +5. Glenn GR, Helpern JA, Tabesh A, Jensen JH. Quantitative assessment of diffusional kurtosis anisotropy. NMR Biomed 2015;28:448-459. doi: 10.1002/nbm.3271 +6. Jensen JH, Glenn GR, Helpern JA. Fiber ball imaging. Neuroimage 2016; 124:824-833. doi: 10.1016/j.neuroimage.2015.09.049 +7. McKinnon ET, Helpern JA, Jensen JH. Modeling white matter microstructure with fiber ball imaging. Neuroimage 2018;176:11-21. doi: 10.1016/j.neuroimage.2018.04.025 +8. Ades-Aron B, Veraart J, Kochunov P, McGuire S, Sherman P, Kellner E, Novikov DS, Fieremans E. Evaluation of the accuracy and precision of the diffusion parameter EStImation with Gibbs and NoisE removal pipeline. Neuroimage. 2018;183:532-543. doi: 10.1016/j.neuroimage.2018.07.066 9. Moss H, McKinnon ET, Glenn GR, Helpern JA, Jensen JH. Optimization of data acquisition and analysis for fiber ball imaging. Neuroimage 2019;200;690-703. doi: 10.1016/j.neuroimage.2019.07.005 -10. Moss HG, Jensen JH. Optimized rectification of fiber orientation density function. Magn Reson Med. 2020 Jul 25. doi: 10.1002/mrm.28406. Online ahead of print. +10. Moss HG, Jensen JH. Optimized rectification of fiber orientation density function. Magn Reson Med. 2020 Jul 25. doi: 10.1002/mrm.28406. Online ahead of print. diff --git a/designer/._DESIGNER.py b/designer/._DESIGNER.py deleted file mode 100755 index 870966b1..00000000 Binary files a/designer/._DESIGNER.py and /dev/null differ diff --git a/designer/fitting/__init__.py b/designer/fitting/__init__.py deleted file mode 100644 index 0a8836fd..00000000 --- a/designer/fitting/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from . import dwi_fnames -from . import dwidirs -from . import dwipy -from . import thresholds \ No newline at end of file diff --git a/designer/fitting/dwi_fnames.py b/designer/fitting/dwi_fnames.py deleted file mode 100644 index f8d5fe58..00000000 --- a/designer/fitting/dwi_fnames.py +++ /dev/null @@ -1,60 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -_dti_ = { - 'md': 'dti_md', - 'rd': 'dti_rd', - 'ad': 'dti_ad', - 'fa': 'dti_fa', - 'fe': 'dti_fe', - 'trace': 'dti_trace', - 'odf': 'dti_odf' -} - -_dki_ = { - 'mk': 'dki_mk', - 'rk': 'dki_rk', - 'ak': 'dki_ak', - 'kfa': 'dki_kfa', - 'mkt': 'dki_mkt', - 'trace': 'dki_trace', - 'odf': 'dki_odf' -} - -_wmti_ = { - 'awf': 'wmti_awf', - 'eas_ad': 'wmti_eas_ad', - 'eas_rd': 'wmti_eas_rd', - 'eas_tort': 'wmti_eas_tort', - 'ias_da': 'wmti_ias_da' -} - -_fbi_ = { - 'zeta': 'fbi_zeta', - 'faa': 'fbi_faa', - 'odf': 'fbi_odf', - 'awf': 'fbwm_awf', - 'Da': 'fbwm_da', - 'De_mean': 'fbwm_de_mean', - 'De_ax': 'fbwm_de_ax', - 'De_rad': 'fbwm_de_rad', - 'fae': 'fbwm_fae', - 'min_cost': 'fbwm_minCost', - 'min_cost_fn': 'fbwm_minCost_FN' -} - -_tensor_ = { - 'DT': 'DT', - 'KT': 'KT' -} - -_outliers_ = { - 'IRLLS': 'outliers_irlls', - 'AKC': 'outliers_akc' -} - -_tractography_ = { - 'dti': 'dti_dsistudio', - 'dki': 'dki_dsistudio', - 'fbi': 'fbi_dsistudio' -} diff --git a/designer/fitting/dwidirs.py b/designer/fitting/dwidirs.py deleted file mode 100644 index a8fabd3c..00000000 --- a/designer/fitting/dwidirs.py +++ /dev/null @@ -1,15 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -import os -import numpy as np - -working_dir = os.path.abspath(os.path.dirname(__file__)) -dirs30 = np.genfromtxt(os.path.join( - working_dir,'dirs30.csv'), delimiter=",") -dirs256 = np.genfromtxt(os.path.join( - working_dir, 'dirs256.csv'), delimiter=",") -dirs10000 = np.genfromtxt(os.path.join( - working_dir, 'dirs10000.csv'), delimiter=",") -sh_grid = np.genfromtxt(os.path.join( - working_dir, 'spherical_grid.csv'), delimiter=",") diff --git a/designer/fitting/dwipy.py b/designer/fitting/dwipy.py deleted file mode 100644 index dc31dd9d..00000000 --- a/designer/fitting/dwipy.py +++ /dev/null @@ -1,2910 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -import multiprocessing -import os -import os.path as op -import random -import cvxpy as cvx -import nibabel as nib -import numpy as np -import numpy.matlib as npm -from joblib import Parallel, delayed -import scipy.linalg as sla -from scipy.special import sph_harm, gamma, hyp1f1, factorial -from tqdm import tqdm -from . import dwidirs -from . import thresholds as th -from . import dwi_fnames -from designer.plotting import outlierplot -from designer.tractography import odf, sphericalsampling, dsistudio -from designer.system.utils import vectorize, writeNii, highprecisionexp, highprecisionpower - -# Define the lowest number possible before it is considered a zero -minZero = th.__minZero__ -# Define number of directions to resample after computing all tensors -dirSample = th.__dirs__ -# Progress bar Properties -tqdmWidth = 70 # Number of columns of progress bar -# Set default numpy errorstates -np.seterr(all = 'ignore') -defaultErrorState = np.geterr() - -class DWI(object): - """ - The DWI object handles tensor estimation and parameter extraction - of dwiffusion weighted images - - Attributes - ---------- - hdr : class - Nibabel class object of input nifti file - img : ndarray - 3D or 4D input image array - grad : ndarray - [N x 4] gradient and bvalue scheme, where the first three - columns are the X, Y, and Z gradient vectors respectively, and - the fourth column is B-values - mask : ndarray(dtype=bool) - 3D array of brain mask - MaskStatus : bool - True if brain_mask.nii is present, False otherwise - workers : int - Number of CPU workers to use in processing - """ - def __init__(self, imPath, bvecPath=None, bvalPath=None, mask=None, nthreads=-1): - """ - DWI class initializer - - Parameters - ---------- - imPath : str - Path to input nifti file - - nthreads : int - Number of CPU workers to use in processing (Defaults to - all physically present workers) - """ - if not os.path.exists(imPath): - raise OSError('Input image {} not found'.format(imPath)) - self.hdr = nib.load(imPath) - self.img = np.array(self.hdr.dataobj) - truncateIdx = np.logical_or(np.isnan(self.img), - (self.img < minZero)) - self.img[truncateIdx] = minZero - # Get just NIFTI filename + extensio - (path, file) = os.path.split(imPath) - # Remove extension from NIFTI filename - fName = os.path.splitext(file)[0] - if bvecPath: - if not isinstance(bvecPath, str): - raise TypeError('Path to .bvec is not specified ' - 'as a string') - if not os.path.exists(bvecPath): - raise OSError('Path to .bvec does not exist: ' - '{}'.format(bvecPath)) - else: - bvecPath = os.path.join(path, fName + '.bvec') - if bvalPath: - if not isinstance(bvalPath, str): - raise TypeError('Path to .bval is not specified ' - 'as a string') - if not os.path.exists(bvalPath): - raise OSError('Path to .bvec does not exist: ' - '{}'.format(bvalPath)) - else: - bvalPath = os.path.join(path, fName + '.bval') - if os.path.exists(bvalPath) and os.path.exists(bvecPath): - # Load bvecs - bvecs = np.loadtxt(bvecPath) - # Load bvals - bvals = np.loadtxt(bvalPath) - # Scale bvals to ms/um2 by checking for number of digits - # in max bval - if int(np.log10(np.max(bvals)))+1 >= 3: # if no. of digits >= 3 - bvals = bvals / 1000 - # Combine bvecs and bvals into [n x 4] array where n is - # number of DWI volumes. [Gx Gy Gz Bval] - self.grad = np.c_[np.transpose(bvecs), bvals] - else: - raise OSError('Unable to locate BVAL or BVEC files') - if mask is None: - maskPath = os.path.join(path,'brain_mask.nii') - else: - maskPath = mask - if os.path.exists(maskPath): - tmp = nib.load(maskPath) - self.mask = np.array(tmp.dataobj).astype(bool) - self.maskStatus = True - else: - self.mask = np.ones((self.img.shape[0], self.img.shape[ - 1], self.img.shape[2]), order='F') - self.maskStatus = False - print('No brain mask supplied') - tqdm.write('Image ' + fName + '.nii loaded successfully') - if not nthreads is None: - if not isinstance(nthreads, int): - raise Exception('Variable nthreads need to be an integer') - if nthreads < -1 or nthreads == 0: - raise Exception('Variable nthreads is a positive integer or ' - '-1') - if nthreads is None: - self.workers = -1 - else: - self.workers = nthreads - if self.workers == -1: - tqdm.write('Processing with ' + - np.str(multiprocessing.cpu_count()) + - ' workers...') - else: - tqdm.write('Processing with ' + - np.str(self.workers) + - ' workers...') - - def getBvals(self): - """ - Returns a vector of b-values, requires no input arguments - - Returns - ------- - ndarray(dtype=float) - Vector array of b-values - - Examples - -------- - bvals = dwi.getBvals(), where dwi is the DWI class object - """ - return self.grad[:,3] - - def getBvecs(self): - """ - Returns an array of gradient vectors, requires no input - parameters - - Returns - ------- - ndarray(dtype=float) - [N x 3] array of gradient vectors - - Examples - -------- - bvecs = dwi.getBvecs(), where dwi is the DWI class object - """ - return self.grad[:,0:3] - - def maxBval(self): - """ - Returns the maximum b-value in a dataset to determine between - DTI and DKI, requires no input parameters - - Returns - ------- - float - maximum B-value in DWI - - Examples - -------- - a = dwi.maxBval(), where dwi is the DWI class object - - """ - return max(np.unique(self.grad[:,3])).astype(int) - - def maxDTIBval(self): - """ - Returns the maximum DTI b-value in a dataset - - Returns - ------- - float - maximum DTI B-value in DWI - - Examples - -------- - a = dwi.maxDKIBval(), where dwi is the DWI class object - - """ - exclude_idx = self.grad[:, 3] <= th.__maxdtibval__ - return max(np.unique(self.grad[exclude_idx,3])).astype(int) - - def maxDKIBval(self): - """ - Returns the maximum DKI b-value in a dataset - - Returns - ------- - float - maximum DKI B-value in DWI - - Examples - -------- - a = dwi.maxDKIBval(), where dwi is the DWI class object - - """ - exclude_idx = self.grad[:, 3] <= th.__maxdkibval__ - return max(np.unique(self.grad[exclude_idx,3])).astype(int) - - def maxFBIBval(self): - """ - Returns the maximum FBI b-value in a dataset - - Returns - ------- - float - maximum DKI B-value in DWI - - Examples - -------- - a = dwi.maxDKIBval(), where dwi is the DWI class object - - """ - exclude_idx = self.grad[:, 3] <= th.__maxfbibval__ - return max(np.unique(self.grad[exclude_idx,3])).astype(int) - - def idxb0(self): - """ - Returns the index of all B-zeros according to bvals - in record - - Returns - ------- - idx : bool - Index of DTI/DKI b-values - - """ - return(np.rint(self.grad[:, 3]) == 0) - - def idxdti(self): - """ - Returns the index of all DTI/DKI B-values according to bvals - in record - - Returns - ------- - idx : bool - Index of DTI/DKI b-values - - """ - idx = np.ones_like(self.grad[:, -1], dtype=bool) - if self.isdti(): - idx = self.grad[:, 3] <= self.maxDTIBval() - return idx - - def idxdki(self): - """ - Returns the index of all DTI/DKI B-values according to bvals - in record - - Returns - ------- - idx : bool - Index of DTI/DKI b-values - - """ - idx = np.ones_like(self.grad[:, -1], dtype=bool) - if self.isdki(): - idx = self.grad[:, 3] <= self.maxDKIBval() - return idx - - def idxfbi(self): - """ - Returns the index of all FBI B-values according to bvals - in record - - Returns - ------- - bool - Index of DTI/DKI b-values. - - """ - idx = np.ones_like(self.grad[:, 3], dtype=bool) - if self.isfbi(): - idx = np.rint(self.grad[:, -1]) == np.rint(self.maxFBIBval()) - else: - raise IndexError('No valid FBI sequence found.') - return idx - - def getndirs(self): - """ - Returns the number of gradient directions acquired from the - scanner - - Returns - ------- - ndarray - number of gradient directions - - Examples - -------- - n = dwi.getndirs(), where dwi is the DWI class object - """ - return np.sum(self.grad[:, 3] == self.maxDKIBval()) - - def tensorType(self): - """ - Returns whether input image is DTI or DKI compatible, requires - no input parameters - - Returns - ------- - list of str - contains list of string 'dti', 'dki', or 'fbi' based on - the protocols the input DWI represents - - Examples - -------- - a = dwi.tensorType(), where dwi is the DWI class object - """ - type = [] - if self.maxDTIBval() <= th.__maxdtibval__ and \ - self.maxDTIBval() >= th.__mindkibval__: - type.append('dti') - if self.maxDKIBval() >= th.__maxdtibval__ and \ - self.maxDKIBval() <= th.__maxdkibval__: - type.append('dki') - if self.maxBval() >= th.__minfbibval__: - type.append('fbi') - if 'fbi' in type and 'dki' in type: - type.append('fbwm') - if not type: - raise ValueError('tensortype: Error in determining maximum ' - 'BVAL') - return type - - def isdti(self): - """ - Returns logical value to answer the mystical question whether - the input image is DTI - - Returns - ------- - ans : bool - True if DTI; false otherwise - - Examples - -------- - ans = dwi.isdki(), where dwi is the DWI class object - """ - if 'dti' in self.tensorType(): - ans = True - else: - ans = False - return ans - - def isdki(self): - """ - Returns logical value to answer the mystical question whether - the input image is DKI - - Returns - ------- - ans : bool - True if DKI; false otherwise - - Examples - -------- - ans = dwi.isdki(), where dwi is the DWI class object - """ - if 'dki' in self.tensorType(): - ans = True - else: - ans = False - return ans - - def isfbi(self): - """ - Returns bool value to specify whether image input image is FBI - - Returns - ------- - and : bool - True if FBI; false otherwise - - Examples - -------- - ans = dwi.isfbi(), where dwi is the DWI class object - """ - if 'fbi' in self.tensorType(): - ans = True - else: - ans = False - return ans - - def isfbwm(self): - """ - Returns bool value to specify whether image input image is FBWM - - Returns - ------- - and : bool - True if FBWM; false otherwise - - Examples - -------- - ans = dwi.isfbi(), where dwi is the DWI class object - """ - if 'fbwm' in self.tensorType(): - ans = True - else: - ans = False - return ans - - def createTensorOrder(self, order=None): - """ - Creates tensor order array and indices - - Parameters - ---------- - order : 2 or 4 (int or None) - Tensor order number, 2 for diffusion and 4 for kurtosis. - Default: None; auto-detect - - Returns - ------- - cnt : ndarray(dtype=int) - Count of number of times a tensor appears - ind : ndarray(dtype=int) - Indices of count - - Examples - -------- - (cnt, ind) = dwi.createTensorOrder(order) - - Notes - ----- - The tensors for this pipeline are based on NYU's designer layout as - depicted in the table below. This will soon be depreciated and - updated with MRTRIX3's layout. - - .. code-block:: none - - ~~~~~~D~~~~~~ - 1 | D11 - 2 | D12 - 3 | D13 - 4 | D22 - 5 | D23 - 6 | D33 - ~~~~~~K~~~~~~ - 1 | W1111 - 2 | W1112 - 3 | W1113 - 4 | W1122 - 5 | W1123 - 6 | W1133 - 7 | W1222 - 8 | W1223 - 9 | W1233 - 10 | W1333 - 11 | W2222 - 12 | W2223 - 13 | W2233 - 14 | W2333 - 15 | W3333 - """ - if order is None: - if self.isdki(): - cnt = np.array([1, 4, 4, 6, 12, 6, 4, 12, 12, 4, 1, 4, 6, - 4, 1], - dtype=int) - ind = np.array(([1, 1, 1, 1], [1, 1, 1, 2], [1, 1, 1, 3], - [1, 1, 2, 2], [1, 1, 2, 3], [1, 1, 3, 3], - [1, 2, 2, 2], [1, 2, 2, 3], [1, 2, 3, 3], - [1, 3, 3, 3], [2, 2, 2, 2], [2, 2, 2, 3], - [2, 2, 3, 3], [2, 3, 3, 3], [3, 3, 3, 3]))\ - - 1 - else: - cnt = np.array([1, 2, 2, 1, 2, 1], dtype=int) - ind = np.array(([1, 1], [1, 2], [1, 3], [2, 2], [2, 3], - [3, 3])) - 1 - elif order == 2: - cnt = np.array([1, 2, 2, 1, 2, 1], dtype=int) - ind = np.array(([1, 1], [1, 2], [1, 3], [2, 2], [2, 3], - [3, 3])) - 1 - elif order == 4: - cnt = np.array([1, 4, 4, 6, 12, 6, 4, 12, 12, 4, 1, 4, 6, 4, - 1], - dtype=int) - ind = np.array(([1,1,1,1],[1,1,1,2],[1,1,1,3],[1,1,2,2], - [1,1,2,3],[1,1,3,3],[1,2,2,2],[1,2,2,3], - [1,2,3,3],[1,3,3,3],[2,2,2,2],[2,2,2,3], - [2,2,3,3],[2,3,3,3],[3,3,3,3])) - 1 - else: - raise ValueError('createTensorOrder: Please enter valid ' - 'order values (2 or 4).') - return cnt, ind - - def fibonacciSphere(self, samples=1, randomize=True): - """ - Returns evenly spaced points on a sphere - - Parameters - ---------- - samples : int - Number of points to compute from sphere, must be a - positive and real integer (Default: 1) - - randomize : bool - True if sampling is randomized; False otherwise - (Default: True) - - Returns - ------ - points : ndarray(dtype=float) - [3 x samples] array containing evenly spaced points - from a sphere - - Examples - -------- - dirs = dwi.fibonacciSphere(256, True) - """ - rnd = 1 - if randomize: - rnd = random.random() * samples - points = [] - offset = 2/samples - increment = np.pi * (3. - np.sqrt(5.)) - for i in range(samples): - y = ((i * offset) - 1) + (offset / 2) - r = np.sqrt(1 - pow(y,2)) - phi = ((i + rnd) % samples) * increment - x = np.cos(phi) * r - z = np.sin(phi) * r - points.append([x,y,z]) - return np.array(points) - - def radialSampling(self, dir, n): - """ - Get the radial component of a metric from a set of directions - - Parameters - ---------- - dir : ndarray(dtype=float) - [n x 3] input array of directions - n : int - number of rows, n - - Returns - ------- - dirs : Matrix containing radial components - - Examples - -------- - grad = dwi.radialSampling(dir, number_of_dirs) - - """ - dt = 2*np.pi/n - theta = np.arange(0, 2*np.pi-dt,dt) - dirs = np.vstack((np.cos(theta), np.sin(theta), 0*theta)) - v = np.hstack((-dir[1], dir[0], 0)) - s = np.sqrt(np.sum(v**2)) - c = dir[2] - V = np.array([[0, -v[2], v[1]],[v[2], 0, -v[0]],[-v[1], v[0], 0]]) - R = np.eye(3) + V + np.matmul(V,V) * (1-c)/(s**2) - dirs = np.matmul(R, dirs).T - return dirs - - def diffusionCoeff(self, dt, dir): - """ - Computes apparent diffusion coefficient (ADC) - - Parameters - ---------- - dt : ndarray(dtype=float) - [21 x nvoxel] array containing diffusion tensor - dir : ndarray(dtype=float) - [n x 3] array containing gradient directions - - Returns - ------- - adc : ndarray(dtype=float) - Array containing apparent diffusion coefficient - - Examples - -------- - adc = dwi.diffusionCoeff(dt, dir) - """ - dcnt, dind = self.createTensorOrder(2) - ndir = dir.shape[0] - bD = np.tile(dcnt,(ndir, 1)) * dir[:,dind[:, 0]] * \ - dir[:, dind[:, 1]] - adc = np.matmul(bD, dt) - return adc - - def kurtosisCoeff(self, dt, dir): - """ - Computes apparent kurtosis coefficient (AKC) - - Parameters - ---------- - dt : ndarray(dtype=float) - [21 x nvoxel] array containing diffusion tensor - dir : ndarray(dtype=float) - [n x 3] array containing gradient directions - - Returns - ------ - adc : ndarray(dtype=float) - Array containing apparent kurtosis coefficient - - Examples - -------- - adc = dwi.kurtosisCoeff(dt, dir) - """ - wcnt, wind = self.createTensorOrder(4) - ndir = dir.shape[0] - adc = self.diffusionCoeff(dt[:6], dir) - adc[adc < minZero] = minZero - md = np.sum(dt[np.array([0,3,5])], 0)/3 - bW = np.tile(wcnt,(ndir, 1)) * dir[:,wind[:, 0]] * \ - dir[:,wind[:,1]] * dir[:,wind[:, 2]] * dir[:,wind[:, 3]] - akc = np.matmul(bW, dt[6:]) - akc = (akc * np.tile(md**2, (adc.shape[0], 1)))/(adc**2) - return akc - - def dtiTensorParams(self, nn): - """ - Computes sorted DTI tensor eigenvalues and eigenvectors - - Parameters - ---------- - DT : ndarray(dtype=float) - Diffusion tensor array - - Returns - ------- - values : ndarray(dtype=float) - Array of sorted eigenvalues - vectors : ndarray(dtype=float) - Array pf sorted eigenvectors - - Examples - -------- - (values, vectors) = dwi.dtiTensorParams(DT) - """ - values, vectors = np.linalg.eig(nn) - idx = np.argsort(-values) - values = -np.sort(-values) - vectors = vectors[:, idx] - return values, vectors - - def dkiTensorParams(self, v1, dt): - """ - Uses average directional statistics to approximate axial - kurtosis(AK) and radial kurtosis (RK) - - Parameters - ---------- - v1 : ndarray(dtype=float) - Array of first eigenvectors from DWI.dtiTensorParams() - dt : ndarray(dtype=float) - Array of diffusion tensor - - Returns - ------- - rk : ndarray(dtype=float) - Radial Kurtosis - ak : ndarray(dtype=float) - Axial Kurtosis - kfa : ndarray(dtype=float) - Kurtosis Fractional Anisotropy - mkt : ndarray(dtype=float) - Mean Kurtosis Tensor - - Examples - -------- - (rk, ak, kfa, mkt) = dwi.dkiTensorParams(v1, dt) - """ - dirs = np.vstack((v1, -v1)) - akc = self.kurtosisCoeff(dt, dirs) - ak = np.mean(akc) - dirs = self.radialSampling(v1, dirSample) - akc = self.kurtosisCoeff(dt, dirs) - rk = np.mean(akc) - W_F = np.sqrt(dt[6]**2 + \ - dt[16]**2 + \ - dt[20]**2 + \ - 6 * dt[9]**2+ \ - 6 * dt[11]**2 + \ - 6 * dt[18]**2 + \ - 4 * dt[7]**2 + \ - 4 * dt[8]**2 + \ - 4 * dt[12]**2 + \ - 4 * dt[17]**2 + \ - 4 * dt[15]**2 + \ - 4 * dt[19]**2 + \ - 12 * dt[10]**2 + \ - 12 * dt[13]**2 + \ - 12 * dt[14]**2) - Wbar = 1/5 * (dt[6] + dt[16] + dt[20] + 2 * - (dt[9] + dt[11] + dt[18])) - if W_F < minZero: - kfa = 0 - else: - W_diff = np.sqrt( - (dt[6] - Wbar)**2 + \ - (dt[16] - Wbar) ** 2 + \ - (dt[20] - Wbar)**2 + \ - 6 * (dt[9] - Wbar / 3)**2 + \ - 6 * (dt[11] - Wbar / 3)**2 + \ - 6 * (dt[18] - Wbar / 3)**2 + \ - 4 * dt[7]**2 + \ - 4 * dt[8]**2 + \ - 4 * dt[12]**2 + \ - 4 * dt[17]**2 + \ - 4 * dt[15]**2 + \ - 4 * dt[19]**2 + \ - 12 * dt[10]**2 + \ - 12 * dt[13]**2 + \ - 12 * dt[14]**2) - kfa = W_diff / W_F - mkt = Wbar - return ak, rk, kfa, mkt - - def wlls(self, shat, dwi, b, cons=None, warmup=None): - """ - Estimates diffusion and kurtosis tenor at voxel with - unconstrained Moore-Penrose pseudoinverse or constrained - quadratic convex optimization. This is a helper function for - dwi.fit() so a multiprocessing parallel loop can be iterated over - voxels - - Parameters - ---------- - shat : ndarray(dtype=float) - [ndir x 1] array of S_hat, approximated signal intensity - at voxel - dwi : ndarray(dtype=float) - [ndir x 1] array of diffusion weighted intensity values at - voxel, for all b-values - b : ndarray(dtype=float) - [ndir x 1] array of b-values vector - cons : ndarray(dtype=float) - [(n * dir) x 22) array containing inequality constraints - for fitting (Default: None) - warmup : ndarray(dtype=float) - Estimated dt vector (22, 0) at each voxel for warm - starting constrianed tensor fitting (Default: None) - - Returns - ------- - dt : ndarray(dtype=float) - Diffusion tensor - - Examples - -------- - dt = dwi.wlls(shat, dwi, b, constraints) - - Notes - ----- - For Unconstrained Fitting: - In the absence of constraints, an exact formulation in the form - Cx = b is produced. This is further simplified to x_hat = C^+ * - b. One can use the Moore-Penrose method to compute the - pseudoinverse to approximate diffusion tensors. - - For Constrained Fitting: - .. code-block:: none - - The equation |Cx -b|^2 expands to 0.5*x.T(C.T*A)*x -(C.T*b).T - ~~~~~ ~~~~~ - P q - - where A is denoted by multiplier matrix (w * b) - Multiplying by a positive constant (0.5) does not change the value - of optimum x*. Similarly, the constant offset b.T*b does not - affect x*, therefore we can leave these out. - - Minimize: || C*x -b ||_2^2 - subject to A*x <= b - No lower or upper bounds - """ - w = np.diag(shat) - # Unconstrained Fitting - if cons is None: - dt = np.matmul(np.linalg.pinv(np.matmul(w, b)), - np.matmul(w, np.log(dwi))) - # Constrained fitting - else: - C = np.matmul(w, b).astype('double') - d = np.matmul(w, np.log(dwi)).astype('double').reshape(-1) - m, n = C.shape - x = cvx.Variable(n) - if warmup is not None: - x.value = warmup - objective = cvx.Minimize(0.5 * cvx.sum_squares(C @ x - d)) - constraints = [cons @ x >= np.zeros((len(cons)))] - prob = cvx.Problem(objective, constraints) - try: - prob.solve(solver=cvx.OSQP, - warm_start=True, - max_iter=20000, - polish=True, - linsys_solver='qdldl') - dt = x.value - if prob.status != 'optimal': - dt = np.full(n, minZero) - except: - dt = np.full(n, minZero) - return dt - - def fit(self, constraints=None, reject=None): - """ - Returns fitted diffusion or kurtosis tensor - - Parameters - ---------- - constraints : array_like(dtype=int) - [1 x 3] vector that specifies which constraints to use - (Default: None) - reject : ndarray(dtype=bool) - 4D array containing information on voxels to exclude - from DT estimation (Default: None) - - Examples - -------- - dwi.fit() - dwi.fit(constraints=[0,1,0], reject=irlls_output) - """ - # Handle rejected voxels from IRLLS - exclude_idx = self.idxdki() - if reject is None: - reject = np.zeros(self.img[:, :, :, exclude_idx].shape) - grad = self.grad[exclude_idx, :] - grad_orig = grad - order = np.floor(np.log(np.abs(np.max(grad[:,-1])+1))/np.log(10)) - img = self.img[:, :, :, exclude_idx] - if order >= 2: - grad[:, -1] = grad[:, -1] - img.astype(np.double) - img[img <= 0] = np.finfo(np.double).eps - grad.astype(np.double) - normgrad = np.sqrt(np.sum(grad[:,:3]**2, 1)) - normgrad[normgrad == 0] = 1 - grad[:,:3] = grad[:,:3]/np.tile(normgrad, (3,1)).T - grad[np.isnan(grad)] = 0 - dcnt, dind = self.createTensorOrder(2) - wcnt, wind = self.createTensorOrder(4) - ndwis = img.shape[-1] - bs = np.ones((ndwis, 1)) - bD = np.tile(dcnt,(ndwis, 1))*grad[:,dind[:, 0]]*grad[:,dind[:, 1]] - bW = np.tile(wcnt, (ndwis, 1)) * grad_orig[:,wind[:, 0]] * \ - grad_orig[:, wind[:, 1]] * grad_orig[:, wind[:,2]] * \ - grad_orig[:,wind[:,3]] - self.b = np.concatenate((bs, ( - np.tile(-self.grad[exclude_idx, -1], (6, 1)).T * bD), np.squeeze( - 1 / 6 * np.tile(self.grad[exclude_idx, -1], (15, 1)).T ** 2) * bW), 1) - dwi_ = vectorize(img, self.mask) - reject_ = vectorize(reject, self.mask).astype(bool) - init = np.matmul(np.linalg.pinv(self.b), np.log(dwi_)) - shat = highprecisionexp(np.matmul(self.b, init)) - if constraints is None or (constraints[0] == 0 and - constraints[1] == 0 and - constraints[2] == 0): - inputs = tqdm(range(0, dwi_.shape[1]), - desc='Unconstrained Tensor Fit', - bar_format='{desc}: [{percentage:0.0f}%]', - unit='vox', - ncols=tqdmWidth) - self.dt = Parallel(n_jobs=self.workers, prefer='processes') \ - (delayed(self.wlls)(shat[~reject_[:, i], i], \ - dwi_[~reject_[:, i], i], \ - self.b[~reject_[:, i]]) \ - for i in inputs) - else: - # C is linear inequality constraint matrix A_ub - C = self.createConstraints(constraints) - inputs = tqdm(range(0, dwi_.shape[1]), - desc='Constrained Tensor Fit', - bar_format='{desc}: [{percentage:0.0f}%]', - unit='vox', - ncols=tqdmWidth) - self.dt = Parallel(n_jobs=self.workers, - prefer='processes') \ - (delayed(self.wlls)(shat[~reject_[:, i], i], - dwi_[~reject_[:, i], i], - self.b[~reject_[:, i]], - cons=C) for i in inputs) - self.dt = np.reshape(self.dt, (dwi_.shape[1], self.b.shape[1])).T - self.s0 = highprecisionexp(self.dt[0,:]) - self.dt = self.dt[1:,:] - D_apprSq = 1/(np.sum(self.dt[(0,3,5),:], axis=0)/3)**2 - self.dt[6:,:] = self.dt[6:,:]*np.tile(D_apprSq, (15,1)) - - def createConstraints(self, constraints=[0, 1, 0]): - """ - Generates constraint array for constrained minimization quadratic - programming - - Parameters - ---------- - constraints : array_like(dtype=int) - [1 X 3] logical vector indicating which constraints - out of three to enable (Default: [0, 1, 0]) - C1 is Dapp > 0 - C1 is Kapp > 0 - C3 is Kapp < 3/(b*Dapp) - - Returns - ------- - C : ndarray(dtype=float) - Array containing constraints to consider during - minimization, C is shaped [number of constraints enforced * - number of directions, 22] - - Examples - -------- - C = dwi.createConstraints([0, 1, 0]) - """ - if sum(constraints) >= 0 and sum(constraints) <= 3: - dcnt, dind = self.createTensorOrder(2) - wcnt, wind = self.createTensorOrder(4) - cDirs = dwidirs.dirs30 - ndirs = cDirs.shape[0] - C = np.empty((0, 22)) - if constraints[0] > 0: # Dapp > 0 - C = np.append(C, np.hstack( - (np.zeros((ndirs, 1)), - np.tile(dcnt, [ndirs, 1]) * cDirs[:, dind[:, 0]] * \ - cDirs[:, dind[:, 1]],np.zeros((ndirs, 15)))), axis=0) - if constraints[1] > 0: # Kapp > 0 - C = np.append(C, np.hstack( - (np.zeros((ndirs, 7)), - np.tile(wcnt, [ndirs, 1]) * cDirs[:, wind[:, 0]] * \ - cDirs[:, wind[:, 1]] * cDirs[:,wind[:,2]] * \ - cDirs[:,wind[:,3]])),axis=0) - if constraints[2] > 0: # K < 3/(b*Dapp) - C = np.append(C, np.hstack( - (np.zeros((ndirs, 1)), - 3 / self.maxDKIBval() * \ - np.tile(dcnt, [ndirs, 1]) * cDirs[:, dind[:, 0]], - np.tile(-wcnt, [ndirs, 1]) * cDirs[:, wind[:, 1]] * \ - cDirs[:,wind[:, 2]] * cDirs[:,wind[:, 3]])),axis=0) - else: - print('Invalid constraints. Please use format "[0, 0, 0]"') - return C - - def extractDTI(self): - """ - Extract all DTI parameters from DT tensor. Warning, this can - only be run after tensor fitting dwi.fit() - - Returns - ------- - md : ndarray(dtype=float) - Mean Diffusivity - rd : ndarray(dtype=float) - Radial Diffusivity - ad : ndarray(dtype=float) - Axial Diffusivity - fa : ndarray(dtype=float) - Fractional Anisotropy - fe : ndarray(dtype=float) - First Eigenvectors - trace : ndarray(dtype=float) - Sum of first eigenvalues - - Examples - -------- - (md, rd, ad, fa) = dwi.extractDTI(), where dwi is the DWI class - object - """ - # extract all tensor parameters from dt - DT = np.reshape( - np.concatenate((self.dt[0, :], self.dt[1, :], self.dt[2, :], - self.dt[1, :], self.dt[3, :], self.dt[4, :], - self.dt[2, :], self.dt[4, :], self.dt[5, :])), - (3, 3, self.dt.shape[1])) - # get the trace - rdwi = highprecisionexp(np.matmul(self.b[:, 1:], self.dt)) - B = np.round(-(self.b[:, 0] + self.b[:, 3] + self.b[:, 5]) * 1000) - uB = np.unique(B) - trace = np.zeros((self.dt.shape[1], uB.shape[0])) - for ib in range(0, uB.shape[0]): - t = np.where(B == uB[ib]) - trace[:, ib] = np.mean(rdwi[t[0], :], axis=0) - nvox = self.dt.shape[1] - inputs = tqdm(range(0, nvox), - desc='DTI Parameters', - bar_format='{desc}: [{percentage:0.0f}%]', - unit='vox', - ncols=tqdmWidth) - values, vectors = zip( - *Parallel(n_jobs=self.workers, prefer='processes') \ - (delayed(self.dtiTensorParams)(DT[:, :, i]) for i in - inputs)) - values = np.reshape(np.abs(values), (nvox, 3)) - vectors = np.reshape(vectors, (nvox, 3, 3)) - self.DTIvectors = vectors - l1 = vectorize(values[:, 0], self.mask) - l2 = vectorize(values[:, 1], self.mask) - l3 = vectorize(values[:, 2], self.mask) - v1 = vectorize(vectors[:, :, 0].T, self.mask) - md = (l1 + l2 + l3) / 3 - rd = (l2 + l3) / 2 - ad = l1 - fa = np.sqrt(1 / 2) * \ - np.sqrt((l1 - l2) ** 2 + \ - (l2 - l3) ** 2 + \ - (l3 - l1) ** 2) / \ - np.sqrt(l1 ** 2 + l2 ** 2 + l3 ** 2) - fe = np.abs(np.stack((fa * v1[:, :, :, 0], fa * v1[:, :, :, 1], - fa * v1[:, :, :, 2]), axis=3)) - trace = vectorize(trace.T, self.mask) - return md, rd, ad, fa, fe, trace - - def extractDKI(self): - """ - Extract all DKI parameters from DT tensor. Warning, this can - only be run after tensor fitting dwi.fit() - - Returns - ------- - mk : ndarray(dtype=float) - Mean Diffusivity - rk : ndarray(dtype=float) - Radial Diffusivity - ak : ndarray(dtype=float) - Axial Diffusivity - kfa : ndarray(dtype=float) - Kurtosis Fractional Anisotropy - mkt : ndarray(dtype=float) - Mean Kurtosis Tensor - trace : ndarray(dtype=float) - Sum of first eigenvalues - - Examples - -------- - (mk, rk, ak, fe, trace) = dwi.extractDTI(), where dwi is the DWI - class object - """ - # get the trace - rdwi = highprecisionexp(np.matmul(self.b[:, 1:], self.dt)) - B = np.round(-(self.b[:, 0] + self.b[:, 3] + self.b[:, 5]) * 1000) - uB = np.unique(B) - trace = np.zeros((self.dt.shape[1], uB.shape[0])) - for ib in range(0, uB.shape[0]): - t = np.where(B == uB[ib]) - trace[:, ib] = np.mean(rdwi[t[0], :], axis=0) - dirs = dwidirs.dirs256 - akc = self.kurtosisCoeff(self.dt, dirs) - mk = np.mean(akc, 0) - nvox = self.dt.shape[1] - inputs = tqdm(range(0, nvox), - desc='DKI Parameters', - bar_format='{desc}: [{percentage:0.0f}%]', - unit='vox', - ncols=tqdmWidth) - ak, rk, kfa, mkt = zip(*Parallel(n_jobs=self.workers, - prefer='processes') \ - (delayed(self.dkiTensorParams)(self.DTIvectors[i, :, 0], - self.dt[:, i]) for i in inputs)) - ak = np.reshape(ak, (nvox)) - rk = np.reshape(rk, (nvox)) - kfa = np.reshape(kfa, (nvox)) - mkt = np.reshape(mkt, (nvox)) - trace = vectorize(trace.T, self.mask) - ak = vectorize(ak, self.mask) - rk = vectorize(rk, self.mask) - mk = vectorize(mk, self.mask) - kfa = vectorize(kfa, self.mask) - mkt = vectorize(mkt, self.mask) - return mk, rk, ak, kfa, mkt, trace - - def optimal_lmax(self): - """ - Computes the highest harmonic order (l_max) for - spherical harmonic expansion. This is adapted - from the information posted at - https://mrtrix.readthedocs.io/en/dev/concepts/sh_basis_lmax.html - - This function runs successfully only if input - DWI is an FBI or HARDI acquisition. - - Returns - ------- - int - l_max suitable for DWI - """ - if not self.isfbi(): - raise Exception('Input DWI is not an ' - 'FBI or HARDI acquisiton. Cannot compute ' - 'l_max.') - bt_unique = np.unique(self.grad[:, -1]) - fbi_vols = np.count_nonzero(self.grad[self.idxfbi(), -1]) - l_max = 0 - vols = (l_max + 1) * (l_max/2 + 1) - while vols <= fbi_vols: - l_max += 2 - vols = (l_max + 1) * (l_max/2 + 1) - return l_max - 2 - - def fbi(self, l_max=6, fbwm=True, rectify=True, res='med'): - """ - Perform fiber ball imaging (FBI) and FBI white matter model - (FBWM) analyses - - Parameters - ---------- - l_max : int - Maximum spherical harmonic degree specified as an even - integer - (Default: 6) - fbwm : bool - Perform FBWM parameterization if True - (Default: True) - rectify : bool - Perform fODF rectification if True - (Default: True) - res : str - Resolution of spherical sampling distribution (Default: 'med') - 'low' defines the spherical grid defined by 3 fold quadrisection of the - isocahedron, or 8 fold tesselation of icosahedron. - 'med' defines the spherical grid defined by 4 fold quadrisection of the - isocahedron, or 16 fold tesselation of icosahedron. - 'high' defines the spherical grid defined by 5 fold quadrisection of the - isocahedron, or 24 fold tesselation of icosahedron. - Default: "med" - - Returns - ------- - zeta : array_like(dtype=float) - Zeta parameter - faa : array_like(dtype=float) - Intra-axonal fractional anisotropy - fodf : array_like(dtype=float) - fodf from spherical harmonic expansion - min_awf : array_like(dtype=float) - Axonal water fraction - Da : array_like(dtype=float) - Intrinsic intra-axonal diffusivity - De_mean : array_like(dtype=float) - Mean extra-axonal diffusion - De_ax : array_like(dtype=float) - Axial extra-axonal diffusion - De_rad : array_like(dtype=float) - Radial extra-axonal diffusion - De_fa : array_like(dtype=float) - Extra-axonal FA - min_cost : array_like(dtype=float) - Minimum cost of the cost function (first index of - min_cost_fn) - min_cost_fn : array_like(dtype=float) - Cost function - """ - #--------------------FUNCTION SEPARATOR----------------------- - - def fbi_rectify(fodf, sh_area, iter=1000): - """ - Rectifies fODF values to eliminate all negative values while - reducing the mean square error - - Parameters - ---------- - fodf : array_like(dtype=float) - Real portion of fODF - iter : int - Number of iterations to perform - (Default: 1000) - sh_area: array_like(dtype=float) - Area of spherical sampling - - Returns - ------- - odf : float - Rectified fODF - """ - # fODF rectification - odf = fodf - fODF = fodf.real # grab real part of the fODF - fODF[np.isnan(fODF)] = 0 - Fmax = np.max(fODF) # get the max peak value of the ODF - lB = 0 # initial lower bound - uB = Fmax # initial upper bound - M = 1 # initialze iteration counter - Mmax = iter # max iterations (could probably be 100 too) - if Fmax > 0: - while M <= Mmax: - # BEGIN: bi-section algorithm - midpt = (lB + uB)/2 - fODF_lB = np.sum((np.abs(fODF - lB) - fODF - lB)*sh_area, axis=0) - fODF_midpt = np.sum((np.abs(fODF - midpt) - fODF - midpt)*sh_area, axis=0) - if fODF_midpt == 0 or (uB - lB)/2 < minZero: - EPS = midpt - break - else: - M = M + 1 - if np.sign(fODF_midpt) == np.sign(fODF_lB): - lB = midpt - else: - uB = midpt - # END: bi-section algorithm - # Subract solution from each ODF point - odf = (1/2)*(np.abs(odf - EPS) + odf - EPS) - odf = odf.real - # due to numerical error, we manually set - # very very very tiny peaks to zero after the fact... - odf[np.logical_and(odf > -minZero, odf < minZero)] = 0 - return odf - - def costCalculator(grid, BT, GT, b0, IMG, iDT, iaDT, zeta, shB, Pl0, g2l_fa_R_b, clm): - """ - Computes the cost function at voxel for FBWM calculations. - Refer to paper for additional information. - - Parameters - ---------- - grid : array_like(dtype=float) - Vector of values at which to compute cost. Usually 0 to 1 - BT : list of float - List of unique B-value shells (eg. [1, 2, 6]) - GT : list of float - List of gradient tables for each B-value shell - b0 : float - Averaged B0 signal in DWI - IMG : list of float - List of DWI signal for each B-value shell - iDT : array_like(dtype=float) - FBWM diffusion tensor - iaDT : array_like(dtype=float) - FBWM axonal diffusion tensor - zeta : float - Zeta value - shB : list of complex - SH basis sets for each B-value shell - Pl0 : array_like(dtype=float) - Legendre polynomail - g2l_fa_R_b : array_like(dtype=complex) - Information not provided - clm : array_like(dtype=complex) - fODF SH coefficients - - Returns - ------- - cost_fn : array_like(dype=float) - Cost values for input grid - """ - if grid.ndim > 1: - raise Exception('Grid needs to be a flattened 1D vector') - ndir = [len(x) for x in GT] - cost_fn = np.zeros_like(grid) - with np.errstate(all='ignore'): - for idx, awf in np.ndenumerate(grid): - for b in range(0, len(BT)): - Se = (b0 * np.exp((-BT[b] * (1-awf)**-1) * np.diag((GT[b].dot((iDT - (awf**3 * zeta**-2) * iaDT).dot(GT[b].T)))))) * (1 - awf) # Eq. 3 FBWM paper - Sa = (2*np.pi*b0*zeta*np.sqrt(np.pi/BT[b])) * (shB[b].dot((Pl0 * g2l_fa_R_b[b,idx,:][0]*clm))) # Eq. 4 FBM paper - cost_fn[idx] = cost_fn[idx] + ndir[b]**-1 * np.sum((IMG[b] - Se.real - Sa.real)**2) - cost_fn[idx] = b0**-1 * np.sqrt(len(BT)**-1 * cost_fn[idx]) # Eq. 21 FBWM paper - return cost_fn - - def fbi_helper(dwi, b0, B, H, Pl0, gl, rectify=True, - fbwm_SH1=None, fbwm_SH2=None, fbwm_B1=None, fbwm_B2=None, - fbwm_dt=None, fbwm_degs=None, sh_area=None): - """ - Computes FBI calculations for a given voxel. This function - will perform FBWM only if all optional FBWM parameters are - parsed. - - Parameters - ---------- - dwi : array_like(dtype=float) - Signal across DWI at a given voxel - b0 : float - Averaged B0 signal at a given voxel - B : array_like(dtype=complex) - FBI spherical harmonic expansion - H : array_like(dtype=complex) - ODF from spherical harmonic expansion - Pl0 : array_like(dtype=float) - Legendre polynomail - gl : array_like(dtype=float) - Correction factor - rectify : bool; optional - Specify whether to perform fODF rectification - (Default: True) - fbwm_SH1 : array_like(dtype=complex); optional - DKI spherical harmonic expansion for B1000 - fbwm_SH2 : array_like(dtype=complex); optional - DKI spherical harmonic expansion for B2000 - fbwm_degs : array_like(dtype=int); optional - Harmonics used in DKI spherical harmonic expansion - fbwm_B1 : array_like(dtype=float); optional - Signal across DWI at B1000 at a given voxel - fbwm_B2 : array_like(dtype=float); optional - Signal across DWI at B2000 at a given voxel - fbwm_dt : array_like(dtype=float); optional - Diffusion tensor at a given voxel - fbwm_degs : array_like(dtype=int) - Harmonics used in expansion of FBWM shperical - harmonics - sh_area : array_list(dtype=float) - Area of spherical sampling - - Returns - ------- - zeta : float - Zeta parameter - faa : float - Intra-axonal fractional anisotropy - clm : float - Spherical harmonic coefficients - min_awf : float - Axonal water fraction - Da : float - Intrinsic intra-axonal diffusivity - De_mean : float - Mean extra-axonal diffusion - De_ax : float - Axial extra-axonal diffusion - De_rad : float - Radial extra-axonal diffusion - De_fa : float - Extra-axonal FA - min_cost : float - Minimum cost of the cost function (first index of - min_cost_fn) - min_cost_fn : array_like(dtype=float) - Cost function vector - """ - fbwm = False - if not ((fbwm_SH1 is None) or (fbwm_SH2 is None) or - (fbwm_B1 is None) or (fbwm_B2 is None) or - (fbwm_dt is None)): - fbwm = True - if sh_area is None: - rectify = False - # For references to alm and clm see FBI papers, they (alm - # and clm) are defined in all of them - alm = np.dot(np.linalg.pinv(B),(dwi/b0)) # DWI signal SH coefficients (these are complex) - alm[np.isnan(alm)] = 0 - a00 = alm[0].real # the imaginary part is on the order of 10^-18 (this is for zeta) - clm = alm*gl[0]*highprecisionpower(np.sqrt(4*np.pi)*alm[0]*Pl0*gl,-1) # fODF SH coefficients (these are complex) - # need to figure out how to do peak detection (on this variable and then read out odf structures like in MATLAB code) - # only the real part would be read out but that would need to be done later on after the rectification process below - ODF = np.matmul(H,clm) - if rectify: - ODF = fbi_rectify(ODF.real, sh_area, iter=100) - # Re-expand the rectified fODF into SH's - clm = np.matmul(sh_area*ODF,np.conj(H)) - clm = (clm/clm[0])*(1/np.sqrt(4*np.pi)) # normalize clm - # zeta and FAA calculations - # NOTE: zeta is not affected by the rectification, only FAA - zeta = a00*np.sqrt(self.maxBval())/np.pi - faa = np.sqrt(3*np.sum(np.abs(clm[1:6]**2))/(5*np.abs(clm[0])**2 + 2 * np.sum(np.abs(clm[1:6]**2)))) - # BEGIN: construct axonal DT (aDT) - c00 = clm[0] - c2_2 = clm[1] - c2_1 = clm[2] - c20 = clm[3] - c21 = clm[4] - c22 = clm[5] - A11 = ((np.sqrt(30)/3)*c00 - (np.sqrt(6)/3)*c20 + c22 + c2_2) - A22 = ((np.sqrt(30)/3)*c00 - (np.sqrt(6)/3)*c20 - c22 - c2_2) - A33 = ((np.sqrt(30)/3)*c00 + (2*np.sqrt(6)/3)*c20) - A12 = (1j*(c22 - c2_2)) - A13 = ((-c21 + c2_1)) - A23 = (1j*(-c21 - c2_1)) - aDT = np.array([A11, A12, A13, A12, A22, A23, A13, A23, A33]).real - aDT = 1/(c00*np.sqrt(30))*aDT - iaDT = np.reshape(aDT,(3,3)).real - if fbwm: - bval = np.rint(self.grad[:, -1]) - BT = np.unique(bval) - BT = BT[BT != 0] # Exclude B0s - GT = [self.grad[bval == x, 0:3] for x in BT] - ndir = [len(x) for x in GT] - f_grid = np.linspace(0,1,100) # define AWF grid (100 pts evenly spaced between 0 (min) and 1 (max)) - int_grid = np.linspace(0,99,100, dtype=int) # define grid points to iterate over (100 of them) - awf_grid = np.linspace(0,1,100) # another AWF grid - # This holds the SH basis sets for each b-value shell - shB = [fbwm_SH1,fbwm_SH2,B] # list object: to access, shB[0] = B1 (for example) - # This hold all DWI volumes for each b-vlaue shell - IMG = [fbwm_B1, fbwm_B2, dwi] # list object: to access - # BEGIN: DT construction - iDT = np.array( - [fbwm_dt[0], - fbwm_dt[3], - fbwm_dt[4], - fbwm_dt[3], - fbwm_dt[1], - fbwm_dt[5], - fbwm_dt[4], - fbwm_dt[5], - fbwm_dt[2]] - ) - iDT = np.reshape(iDT,(3,3)) - # END: DT construction - # initialze correction factor elements that will be looped over and filled accordingly... - g2l_fa_R = np.zeros((len(Pl0),f_grid.shape[0]), order = 'F') - g2l_fa_R_b = np.zeros((len(BT),f_grid.shape[0],len(Pl0)), order = 'F') - g2l_fa_R_large = np.zeros((len(Pl0),f_grid.shape[0]), order = 'F') - # BEGIN: cost function - # Not many comments here, See McKinnon 2018 FBWm paper for details - for b in range(0,len(BT)): - idx_hyper = BT[b] * np.power(f_grid,2) * np.power(zeta,-2) < 20 # when should hypergeometric function be implemented? When b*D is small - idx_Y = 0 - for l in degs[::2]: - hypergeom_opt = np.sum((gamma((l+1)/2 + int_grid) * gamma(l+(3/2)) * ((-BT[b] * f_grid[idx_hyper]**2 * zeta**-2)*np.ones((1,len(f_grid[idx_hyper])))).T ** int_grid / (factorial(int_grid) * gamma(l+(3/2) + int_grid) * gamma((l+1)/2))),1)*np.ones((1,len(f_grid[idx_hyper]))) - g2l_fa_R[idx_Y:idx_Y+(2*l+1),np.squeeze(idx_hyper)] = npm.repmat((factorial(l/2) * (BT[b] * f_grid[idx_hyper]**2 * zeta**-2) ** ((l+1)/2) / gamma(l+(3/2)) * hypergeom_opt),(2*l+1),1) # Eq. 9 FBWM paper - idx_Y = idx_Y + (2*l+1) - g2l_fa_R_b[b,np.squeeze(idx_hyper),:] = g2l_fa_R[:,np.squeeze(idx_hyper)].T - idx_Y = 0 - for l in degs[::2]: - g2l_fa_R_large[idx_Y:idx_Y+(2*l+1), np.squeeze(~idx_hyper)] = npm.repmat((np.exp(-l/2 * (l+1) / ((2*BT[b] * (f_grid[~idx_hyper]**2 * zeta**-2))))),(2*l+1),1) # Eq. 20 FBI paper - idx_Y = idx_Y + (2*l+1) - g2l_fa_R_b[b,np.squeeze(~idx_hyper),:] = g2l_fa_R_large[:,np.squeeze(~idx_hyper)].T - cost_fn = costCalculator( - awf_grid, - BT, - GT, - b0, - IMG, - iDT, - iaDT, - zeta, - shB, - Pl0, - g2l_fa_R_b, - clm - ) - min_cost_fn_idx = np.argsort(cost_fn, axis=0) # find the indexes of the sorted cost_fn values - min_cost_fn = np.take_along_axis(cost_fn, min_cost_fn_idx, axis=0) # sort those values - min_awf = awf_grid[min_cost_fn_idx[0]] # grad the minimum AWF value based on the cost_fn sorting done immeidately prior to this... - De = (iDT - (min_awf**3 * zeta**-2) * iaDT) / (1 - min_awf) - Da = min_awf**2 / zeta**2 - iDe = De # intermeidate De - iDe[np.isnan(iDe)] = minZero - iDe[np.isinf(iDe)] = minZero - L,V = np.linalg.eig(iDe) # L : eigVals and V: eigVecs - L = np.sort(L) # sort them (this is ascending) - L = L[::-1] # reverse the order so they are descending (high -> low) - N = 1 # initialize counter - if L[0] < 0 or L[1] < 0 or L[2] < 0: - while L[0] < 0 or L[1] < 0 or L[2] < 0: # find new AWF values if L's are < 0 - N = N + 1 - if N < 100: - min_awf = awf_grid[min_cost_fn_idx[N]] - else: - min_awf = 0 - break - # update De here... - De = (iDT - (min_awf**3 * zeta**2) * iaDT) / (1 - min_awf) - Da = min_awf**2 / zeta**2 # recalculate Da too... - # Now recalculate eigVals again with correct AWF values - iDe = De - iDe[np.isnan(iDe)] = minZero - iDe[np.isinf(iDe)] = minZero - L,V = np.linalg.eig(iDe) # L : eigVals and V: eigVecs - L = np.sort(L) # again, ascending - L = L[::-1] # now, descending - with np.errstate(invalid='ignore'): - De_ax = L[0] # Eq. 24 FBWM paper, axial extra-axonal diffusivity - De_rad = (L[1] + L[2])/2 # radial De - De_fa = np.sqrt(((L[0] - L[1]) ** 2 + (L[0] - L[2]) ** 2 + (L[1] - L[2]) ** 2 ) / (2 * np.sum(L ** 2))) # extra-axonal FA - De_mean = (1/3) * (2 * De_rad + De_ax) # average De - min_cost = min_cost_fn[0] - else: - min_awf = None - Da = None - De_mean = None - De_ax = None - De_rad = None - De_fa = None - min_cost = None - min_cost_fn = None - - return zeta, faa, clm, min_awf, Da, De_mean, De_ax, De_rad, De_fa, min_cost, min_cost_fn - #--------------------FUNCTION SEPARATOR----------------------- - - if fbwm and not hasattr(self, 'dt'): - raise Exception('Cannot compute FBWM parameters ' - 'without running diffusion tensor fitting first. ' - 'Please run DWI.fit(constraints) before running DWI.fbi().') - if l_max % 2 != 0: - raise Exception('Please provide l_max as a postive ' - 'and even integer') - if l_max > self.optimal_lmax(): - print('[WARNING]: l_max value provided ({}) is ' - 'more than that supported by DWI ({}). Reverting ' - 'to l_max = {}'.format(l_max, self.optimal_lmax(), - self.optimal_lmax())) - l_max = self.optimal_lmax() - img = self.img - bt_unique = np.unique(self.grad[:, -1]) - order = self.optimal_lmax() - b0 = np.nanmean(img[:, :, :, self.idxb0()], axis=3) - b0 = np.nan_to_num(b0, nan=0, posinf=0, neginf=0) - # Vectorize images - b0 = vectorize(b0, self.mask) - img = vectorize(img, self.mask) - # Create shperical harmonic (SH) base set - degs = np.arange(l_max + 1, dtype=int) - l_num = 2 * degs + 1 # how many per degree (evens only) - harmonics = [] - sh_end = 0 # initialize the SH set for indexing - for _, phase in enumerate(l_num[::2]): - sh_start = sh_end + phase - 1 - sh_end = sh_start + phase - 1 - harmonics.extend(np.arange(sh_start, sh_end + 1)) - # Define the azimuthal (phi) and polar(theta) angles for our - # spherical expansion using the experimentally defined - # gradients from the scanner - phi = np.arccos(self.grad[self.idxfbi(), 2]) - theta = np.arctan2(self.grad[self.idxfbi() ,1], self.grad[self.idxfbi() ,0]) - # gradients for resampling from distribution - spherical_grid, idx, idx8, AREA, faces, separation_angle = sphericalsampling.odfgrid(res) - S1 = spherical_grid[:,0] # phi - S2 = spherical_grid[:,1] # theta - # B = shbasis(degs, phi, theta) - # H = shbasis(degs, S1, S2) - B = odf.shbasis(degs, phi, theta, method='tournier') - B = B[:, harmonics] - H = odf.shbasis(degs, S1, S2, method='tournier') - H = H[:, harmonics] - idx_Y = 0 - Pl0 = np.zeros((len(harmonics), 1), order ='F') # need Legendre polynomial Pl0 - gl = np.zeros((len(harmonics), 1), order ='F') # calculate correction factor (see original FBI paper, Jensen 2016) - for l in degs[::2]: - Pl0[idx_Y:idx_Y+(2*l+1), :] = (np.power(-1,l/2)* np.math.factorial(l)) / (np.power(4,l/2)*np.power(np.math.factorial(l/2),2))*np.ones((2*l+1,1)) - gl[idx_Y:idx_Y+(2*l+1), :] = (np.math.factorial(l/2)*np.power(self.maxBval()*th.__d0__,(l+1)/2))/gamma(l+3/2)*hyp1f1((l+1)/2,l+3/2,-self.maxBval()*th.__d0__)*np.ones((2*l+1,1)) - idx_Y = idx_Y + (2*l+1) - Pl0 = np.squeeze(Pl0) - gl = np.squeeze(gl) - inputs = tqdm(range(0, img.shape[1]), - desc='FBI Fit', - bar_format='{desc}: [{percentage:0.0f}%]', - unit='vox', - ncols=tqdmWidth) - if fbwm: - # Index gradients based on b1000 and b2000 shells - bval = np.rint(self.grad[:, -1]) - phi1 = np.arccos(self.grad[bval == 1, 2]) - theta1 = np.arctan2(self.grad[bval == 1, 1],self.grad[bval == 1,0]) - - phi2 = np.arccos(self.grad[bval == 2, 2]) - theta2 = np.arctan2(self.grad[bval == 2, 1], self.grad[bval == 2,0]) - # SH basis set for the two B-values in DKI - fbwm_SH1 = odf.shbasis(degs,phi1,theta1, method='tournier') - fbwm_SH2 = odf.shbasis(degs,phi2,theta2,method='tournier') - fbwm_SH1 = fbwm_SH1[:, harmonics] - fbwm_SH2 = fbwm_SH2[:, harmonics] - dt, kt = self.tensorReorder('dki') - dt = vectorize(dt, self.mask) - # for i in inputs: - # zeta, faa, fodf, min_awf, Da, De_mean, De_ax, De_rad, De_fa, min_cost, min_cost_fn = \ - # fbi_helper( - # dwi=img[self.idxfbi(), i], - # b0 = b0[i], - # B = B, - # H = H, - # Pl0=Pl0, - # gl = gl, - # rectify=rectify, - # fbwm_SH1 = fbwm_SH1, - # fbwm_SH2 = fbwm_SH2, - # fbwm_B1 = img[self.grad[:, -1] == 1, i], - # fbwm_B2 = img[self.grad[:, -1] == 2, i], - # fbwm_dt = dt[:, i], - # fbwm_degs=degs, - # sh_area=AREA - # ) - zeta, faa, fodf, min_awf, Da, De_mean, De_ax, De_rad, De_fa, min_cost, min_cost_fn = zip(*Parallel(n_jobs=self.workers, - prefer='processes') \ - (delayed(fbi_helper)( - dwi=img[self.idxfbi(), i], - b0 = b0[i], - B = B, - H = H, - Pl0=Pl0, - gl = gl, - rectify=rectify, - fbwm_SH1 = fbwm_SH1, - fbwm_SH2 = fbwm_SH2, - fbwm_B1 = img[bval == 1, i], - fbwm_B2 = img[bval == 2, i], - fbwm_dt = dt[:, i], - fbwm_degs=degs, - sh_area=AREA - ) for i in inputs)) - else: - # for i in inputs: - # zeta, faa, fodf, min_awf, Da, De_mean, De_ax, De_rad, De_fa, min_cost, min_cost_fn = \ - # fbi_helper( - # dwi=img[self.idxfbi(), i], - # b0 = b0[i], - # B = B, - # H = H, - # Pl0=Pl0, - # gl = gl, - # rectify=rectify, - # sh_area=AREA - # ) - zeta, faa, fodf, min_awf, Da, De_mean, De_ax, De_rad, De_fa, min_cost, min_cost_fn = zip(*Parallel(n_jobs=self.workers, - prefer='processes') \ - (delayed(fbi_helper)( - dwi=img[self.idxfbi(), i], - b0 = b0[i], - B = B, - H = H, - Pl0=Pl0, - gl = gl, - rectify=rectify, - sh_area=AREA - ) for i in inputs)) - - zeta = vectorize(np.array(zeta), self.mask) - faa = vectorize(np.array(faa), self.mask) - fodf = vectorize(np.array(fodf).T, self.mask) - awf = vectorize(np.array(min_awf), self.mask) - Da = vectorize(np.array(Da), self.mask) - De_mean = vectorize(np.array(De_mean), self.mask) - De_ax = vectorize(np.array(De_ax), self.mask) - De_rad = vectorize(np.array(De_rad), self.mask) - De_fa = vectorize(np.array(De_fa), self.mask) - min_cost = vectorize(np.array(min_cost), self.mask) - min_cost_fn = vectorize(np.array(min_cost_fn).T, self.mask) - return zeta, faa, fodf, awf, Da, De_mean, De_ax, De_rad, De_fa, min_cost, min_cost_fn - - def extractWMTI(self): - """ - Returns white matter tract integrity (WMTI) parameters. Warning: - this can only be run after fitting and DWI.extractDTI(). - - Returns - ------- - awf : ndarray(dtype=float) - Axonal Water Fraction - eas_ad : ndarray(dtype=float) - Extra-axonal space Axial Diffusivity - eas_rd : ndarray(dtype=float) - Extra-axonal Space Radial Diffusivity - eas_md : ndarray(dtype=float) - Extra-axonal Space Mean Diffusivity - eas_tort : ndarray(dtype=float) - Extra-axonal Space Tortuosity - ias_ad : ndarray(dtype=float) - Intra-axonal Space Axial Diffusivity - ias_rd : ndarray(dtype=float) - Intra-axonal Space Radial Diffusivity - ias_da : ndarray(dtype=float) - Intra-axonal Space Intrinsic Diffusivity - ias_tort : ndarray(dtype=float) - Intra-axonal Space Tortuosity - """ - def wmtihelper(dt, dir, adc, akc, awf, adc2dt): - # Avoid complex output. However, - # negative AKC might be taken care of by applying constraints - with np.errstate(invalid='ignore'): - akc[akc < minZero] = minZero - try: - # Eigenvalue decomposition of De(extra-axonal) - De = np.multiply( - adc, - 1 + np.sqrt( - (np.multiply(akc, awf) / (3 * (1 - awf))))) - dt_e = np.matmul(adc2dt, De) - DTe = dt_e[[0, 1, 2, 1, 3, 4, 2, 4, 5]] - DTe = np.reshape(DTe, (3, 3), order='F') - eigval = sla.eigh(DTe, eigvals_only=True) - eigval = np.sort(eigval)[::-1] - eas_ad = eigval[0] - eas_rd = 0.5 * (eigval[1] + eigval[2]) - try: - eas_tort = eas_ad / eas_rd - except: - eas_tort = minZero - except: - eas_ad = minZero - eas_rd = minZero - eas_tort = minZero - try: - # Eigenvalue decomposition of Da (intra-axonal) - Di = np.multiply( - adc, - 1 - np.sqrt( - (np.multiply(akc, (1 - awf)) / (3 * awf)))) - dt_i = np.matmul(adc2dt, Di) - DTi = dt_i[[0, 1, 2, 1, 3, 4, 2, 4, 5]] - DTi = np.reshape(DTi, (3, 3), order='F') - eigval = sla.eigh(DTi, eigvals_only=True) - eigval = np.sort(eigval)[::-1] - ias_da = np.sum(eigval) - np.seterr(invalid='raise') - except: - ias_da = minZero - return eas_ad, eas_rd, eas_tort, ias_da - dir = dwidirs.dirs10000 - nvox = self.dt.shape[1] - N = dir.shape[0] - nblocks = 10 - maxk = np.zeros((nvox, nblocks)).astype(float) - inputs = tqdm(range(nblocks), - desc='Extracting AWF', - bar_format='{desc}: [{percentage:0.0f}%]', - unit='iter', - ncols=tqdmWidth) - for i in inputs: - maxk = np.stack(self.kurtosisCoeff( - self.dt,dir[int(N/nblocks*i):int(N/nblocks*(i+1))])).astype(float) - maxk = np.nanmax(maxk, axis=0) - awf = np.divide(maxk, (maxk + 3)).astype(float) - # Changes voxels less than minZero, nans and infs to minZero - truncateIdx = np.logical_or( - np.logical_or(np.isnan(awf), np.isinf(awf)), - (awf < minZero)) - awf[truncateIdx] = minZero - dirs = dwidirs.dirs30 - adc = self.diffusionCoeff(self.dt[:6], dirs) - akc = self.kurtosisCoeff(self.dt, dirs) - (dcnt, dind) = self.createTensorOrder(2) - adc2dt = np.linalg.pinv(np.matmul( - (dirs[:, dind[:, 0]] * \ - dirs[:, dind[:, 1]]), - np.diag(dcnt))) - eas_ad = np.zeros(nvox) - eas_rd = np.zeros(nvox) - eas_md = np.zeros(nvox) - eas_tort = np.zeros(nvox) - ias_ad = np.zeros(nvox) - ias_rd = np.zeros(nvox) - ias_da = np.zeros(nvox) - ias_tort = np.zeros(nvox) - inputs = tqdm(range(nvox), - desc='Extracting EAS and IAS', - bar_format='{desc}: [{percentage:0.0f}%]', - unit='vox', - ncols=tqdmWidth) - eas_ad, eas_rd, eas_tort, ias_da = zip(*Parallel( - n_jobs=self.workers, prefer='processes')( - delayed(wmtihelper)(self.dt[:, i], - dirs, - adc[:, i], - akc[:,i], - awf[i], - adc2dt) for i in inputs)) - awf = vectorize(awf, self.mask) - eas_ad = vectorize(np.array(eas_ad), self.mask) - eas_rd = vectorize(np.array(eas_rd), self.mask) - eas_md = vectorize(np.array(eas_md), self.mask) - eas_tort = vectorize(np.array(eas_tort), self.mask) - ias_ad = vectorize(np.array(ias_ad), self.mask) - ias_rd = vectorize(np.array(ias_rd), self.mask) - ias_da = vectorize(np.array(ias_da), self.mask) - ias_tort = vectorize(np.array(ias_tort), self.mask) - return awf, eas_ad, eas_rd, eas_tort, ias_da - - def findViols(self, c=[0, 1, 0]): - """ - Returns a 3D violation map of voxels that violate constraints. - - Parameters - ---------- - img : ndarray(dtype=float) - 3D metric array such as mk or fa - c : array_like(dtype=int) - [3 x 1] vector that toggles which constraints to check - c[0]: Check D < 0 constraint - c[1]: Check K < 0 constraint (Default) - c[2]: Check K > 3/(b*D) constraint - - Returns - ------- - map : ndarray(dtype=bool) - 3D array containing locations of voxels that incur directional - violations. Voxels with values contain violaions and voxel - values represent proportion of directional violations. - - Examples - -------- - map = findViols(img, [0 1 0] - - """ - if c == None: - c = [0, 0, 0] - nvox = self.dt.shape[1] - sumViols = np.zeros(nvox) - maxB = self.maxDKIBval() - adc = self.diffusionCoeff(self.dt[:6], self.dirs) - akc = self.kurtosisCoeff(self.dt, self.dirs) - tmp = np.zeros(3) - print('...computing directional violations') - for i in range(nvox): - # C[0]: D < 0 - tmp[0] = np.size(np.nonzero(adc[:, i] < minZero)) - # C[1]: K < 0 - tmp[1] = np.size(np.nonzero(akc[:, i] < minZero)) - #c[2]: - tmp[2] = np.size(np.nonzero(akc[:, i] > \ - (3/(maxB * adc[:, i])))) - sumViols[i] = np.sum(tmp) - map = np.zeros((sumViols.shape)) - if c[0] == 0 and c[1] == 0 and c[2] == 0: - # [0 0 0] - print('0 0 0') - map = pViols - elif c[0] == 1 and c[1] == 0 and c[2] == 0: - # [1 0 0] - print('1 0 0') - map = sumViols/dirSample - elif c[0] == 0 and c[1] == 1 and c[2] == 0: - # [0 1 0] - print('0 1 0') - map = sumViols/dirSample - elif c[0] == 0 and c[1] == 0 and c[2] == 1: - # [0 0 1] - print('0 0 1') - map = sumViols/dirSample - - elif c[0] == 1 and c[1] == 1 and c[2] == 0: - # [1 1 0] - print('1 1 0') - map = sumVioms/(2 * dirSample) - elif c[0] == 1 and c[1] == 0 and c[2] == 1: - # [1 0 1] - print('1 0 1') - map = sumViols/(2 * dirSample) - elif c[0] == 0 and c[1] == 1 and c[2] == 1: - # [0 1 1] - print('0 1 1') - map = sumViols / (2 * dirSample) - elif c[0] == 1 and c[1] == 1 and c[2] == 1: - # [1 1 1] - print('1 1 1') - map = sumViols / (3 * dirSample) - map = np.reshape(map, nvox) - map = vectorize(map, self.mask) - return map - - def goodDirections(self, outliers): - """ - Creates a 3D maps of good directions from IRLLS outlier map - For any given voxel, a violation is computed using logical `or` - operant for all b-values. Whether an outlier occurs at b1000 - or b1000 and b2000, that voxel is still a violation unless - none of the b-values have outliers. - - Parameters - ---------- - outliers : ndarray(dtype=bool) - 4D maps of outliers from IRLLS - - Returns - ------- - map : ndarray(dtype=int) - 3D map of number of good directions - - Examples - -------- - map = dwi.goodDirections(outliers) - """ - # Compute number of good directions - maxB = self.maxDKIBval() - outliers_ = vectorize(outliers, self.mask) - nvox = outliers_.shape[1] - nonB0 = ~(self.grad[:, -1] < 0.01) - bvals = np.unique(self.grad[nonB0, -1]) - - # bidx is an index locator where rows are indexes of b-value and - # columns are unique b-values - bidx = np.zeros((self.getndirs(), bvals.size), dtype='int') - for i in range(bvals.size): - bidx[:, i] = np.array(np.where(self.grad[:, -1] == bvals[i])) - - tmpVals = np.zeros(bidx.shape, dtype=bool) - sumViols = np.zeros(nvox, dtype=int) - for i in range(nvox): - for j in range(bvals.size): - tmpVals[:,j] = outliers_[bidx[:,j], i] - sumViols[i] = np.sum(np.any(tmpVals, axis=1)) - - # Number of good directions - map = np.full(sumViols.shape, self.getndirs()) - sumViols - map = vectorize(map, self.mask) - return map - - def findVoxelViol(self, adcVox, akcVox, maxB, c): - """ - Returns the proportions of violations occurring at a voxel. - - Parameters - ---------- - img : ndarray(dtype=float) - 3D metric array such as mk or fa - c : array_like(dtype=int) - [3 x 1] vector that toggles which constraints to check - c[0]: Check D < 0 constraint - c[1]: Check K < 0 constraint (Default) - c[2]: Check K > 3/(b*D) constraint - - Returns - ------- - n : ndarray(dtype=float) - percentaghhe ranging from 0 to 1 that indicates proportion - of violations occuring at voxel. - - Examples - -------- - map = findViols(voxel, [0 1 0] - """ - tmp = np.zeros(3) - # C[0]: D < 0 - tmp[0] = np.size(np.nonzero(adcVox < minZero)) - # C[1]: K < 0 - tmp[1] = np.size(np.nonzero(akcVox < minZero)) - #c[2]:K > 3/(b * D) - tmp[2] = np.size(np.nonzero(akcVox > (3/(maxB * adcVox)))) - sumViols = np.sum(tmp) - - if c[0] == 0 and c[1] == 0 and c[2] == 0: - # [0 0 0] - n = 0 - elif c[0] == 1 and c[1] == 0 and c[2] == 0: - # [1 0 0] - n = sumViols/dirSample - elif c[0] == 0 and c[1] == 1 and c[2] == 0: - # [0 1 0] - n = sumViols/dirSample - elif c[0] == 0 and c[1] == 0 and c[2] == 1: - # [0 0 1] - n = sumViols/dirSample - elif c[0] == 1 and c[1] == 1 and c[2] == 0: - # [1 1 0] - n = sumViols/(2 * dirSample) - elif c[0] == 1 and c[1] == 0 and c[2] == 1: - # [1 0 1] - n = sumViols/(2 * dirSample) - elif c[0] == 0 and c[1] == 1 and c[2] == 1: - # [0 1 1] - n = sumViols/(2 * dirSample) - elif c[0] == 1 and c[1] == 1 and c[2] == 1: - # [1 1 1] - n = sumViols/(3 * dirSample) - return n - - def parfindViols(self, c=[0, 0, 0]): - if c == None: - c = [0, 0, 0] - print('...computing directional violations (parallel)') - nvox = self.dt.shape[1] - map = np.zeros(nvox) - maxB = self.maxDKIBval() - adc = self.diffusionCoeff(self.dt[:6], self.dirs) - akc = self.kurtosisCoeff(self.dt, self.dirs) - inputs = tqdm(range(0, nvox)) - map = Parallel(n_jobs=self.workers, prefer='processes') \ - (delayed(self.findVoxelViol)(adc[:,i], - akc[:,i], maxB, [0, 1, 0]) for\ - i in inputs) - map = np.reshape(pViols2, nvox) - map = self.multiplyMask(vectorize(map,self.mask)) - return map - - def multiplyMask(self, img): - """ - Multiplies a 3D image by the brain mask - - Parameters - ---------- - img : ndarray(dtype=float) - 3D image to be multiplied - - Returns - ------- - ndarray(dtype=float) - multiplied image - """ - # Returns an image multiplied by the brain mask to remove all - # values outside the brain - return np.multiply(self.mask.astype(bool), img) - - def akcoutliers(self, iter=10): - """ - Uses 100,000 direction in chunks of 10 to iteratively find - outliers. Returns a mask of locations where said violations - occur. Multiprocessing is disabled because this is a - memory-intensive task. - To be run only after tensor fitting. - - Parameters - ---------- - iter : int, optional - number of iterations to perform out of 10. Reduce this - number if your computer does not have sufficient RAM. - (Default: 10) - - Returns - ------- - akc_out : ndarray(dtype=bool) - 3D map containing outliers where AKC falls fails the - inequality test -2 < AKC < 10 - - Examples - -------- - akc_out = dwi.akoutliers(), where dwi is the DWI class object - """ - dir = dwidirs.dirs10000 - nvox = self.dt.shape[1] - akc_out = np.zeros(nvox, dtype=bool) - N = dir.shape[0] - nblocks = 10 - if iter > nblocks: - print('Entered iteration value exceeds 10...resetting to 10') - iter = 10 - inputs = tqdm(range(iter), - desc='AKC Outlier Detection', - bar_format='{desc}: [{percentage:0.0f}%]', - unit='blk', - ncols=tqdmWidth) - for i in inputs: - akc = self.kurtosisCoeff( - self.dt, dir[int(N/nblocks*i):int(N/nblocks*(i+1))]) - akc_out[np.where(np.any(np.logical_or(akc < -2, akc > 10), - axis=0))] = True - akc_out.astype('bool') - return vectorize(akc_out, self.mask) - - def akccorrect(self, akc_out, window=3, connectivity='face'): - """ - Applies AKC outlier map to DT to replace outliers with a - moving median. Run this only after tensor fitting and akc - outlier detection. - - Parameters - ---------- - akc_out : ndarray(dtype=bool) - 3D map containing outliers from DWI.akcoutliers - window : int, optional - Width of square matrix filter (Default: 5) - connectivity : str, {'face', 'all'}, optional - Specifies what kind of connected-component connectivity to - use for median determination - - Examples - -------- - dwi.akccorrect(akc_out), where dwi is the DWI class object - """ - # Get box filter properties - centralIdx = np.median(range(window)) - d2move = np.int(np.abs(window - (centralIdx + 1))) # Add 1 to - # central idx because first index starts with zero - # Vectorize and Pad - dt = np.pad(vectorize(self.dt, self.mask), - ((d2move, d2move), (d2move, d2move), - (d2move, d2move), (0, 0)), - 'constant', constant_values=np.nan) - akc_out = np.pad(akc_out, d2move, 'constant', - constant_values=False) - violIdx = np.array( - np.where(akc_out)) # Locate coordinates of violations - nvox = violIdx.shape[1] - for i in tqdm(range(dt.shape[-1]), - desc='AKC Correction', - bar_format='{desc}: [{percentage:0.0f}%]', - unit='tensor', - ncols=tqdmWidth): - for j in range(nvox): - # Index beginning and ending of patch - Ib = violIdx[0, j] - d2move - Ie = violIdx[0, j] + d2move + 1 - Jb = violIdx[1, j] - d2move - Je = violIdx[1, j] + d2move + 1 - Kb = violIdx[2, j] - d2move - Ke = violIdx[2, j] + d2move + 1 - - if connectivity == 'all': - patchViol = np.delete( - np.ravel(akc_out[Ib:Ie, Jb:Je, Kb:Ke]), - np.median(range(np.power(window,3)))) # Remove - # centroid element - patchImg = np.delete( - np.ravel(dt[Ib:Ie, Jb:Je, Kb:Ke, i]), - np.median(range(np.power(window,3)))) # Remove - # centroid element - connLimit = np.power(window,3) -1 - elif connectivity == 'face': - patchViol = np.delete(akc_out[ - Ib:Ie, violIdx[1, j], violIdx[2, j]], d2move) - patchViol = np.hstack((patchViol, np.delete(akc_out[ - violIdx[0, j], Jb:Je, violIdx[2, j]], d2move))) - patchViol = np.hstack((patchViol, np.delete(akc_out[ - violIdx[0, j], violIdx[1, j], Kb:Ke], d2move))) - patchImg = np.delete(dt[ - Ib:Ie, violIdx[1, j], violIdx[2, j], i], d2move) - patchImg = np.hstack((patchImg, np.delete(dt[ - violIdx[0, j], Jb:Je, violIdx[2, j], i], - d2move))) - patchImg = np.hstack((patchImg, np.delete(dt[ - violIdx[0, j], violIdx[1, j], Kb:Ke, i], - d2move))) - if window == 3: - connLimit = 6 - elif window == 5: - connLimit = 12 - elif window == 7: - connLimit = 18 - elif window == 9: - connLimit = 24 - else: - raise Exception( - 'Connectivity choice "{}" is invalid. Please ' - 'enter either "all" or "face".'.format( - connectivity)) - nVoil = np.sum(patchViol) - - # Here a check is performed to compute the number of - # violations in a patch. If all voxels are violations, - # do nothing. Otherwise, exclude violation voxels from - # the median calculation - if nVoil == connLimit: - continue - else: - dt[violIdx[0, j], violIdx[1, j], violIdx[2, j], - i] = np.nanmedian(patchImg) - # Remove padding - dt = dt[d2move:-d2move, d2move:-d2move, - d2move:-d2move, :] - self.dt = vectorize(dt, self.mask) - - def irlls(self, excludeb0=True, maxiter=25, convcrit=1e-3, mode='DKI', - leverage=0.85, bounds=3): - """ - This functions performs outlier detection and robust parameter - estimation for diffusion MRI using the iterative reweigthed - linear least squares (IRLLS) approach. - - Parameters - ---------- - exludeb0 : bool, optional - Exlude the b0 images when removing outliers (Default: True) - maxiter : int, optional Integer; default: 25 - Maximum number of iterations in the iterative reweighting - loop (Default: 25) - convcrit : float, optional - Fraction of L2-norm of estimated diffusion parameter - vector that the L2-norm of different vector should get in - order to reach convergence in the iterative reweighted - loop (Default: 1e-3) - mode : str, {'DKI', 'DTI'}, optional - Specifies whether to use DTI or DKI model (Default: 'DKI') - leverage : float, optional - Measurement ranging from 0 to 1 where a leverage above - this threshold will not be excluded in estimation of DT - after outlier (Default: 0.85) - Bounds : int, optional - Set the threshold of the number of standard deviation - that are needed to exclude a measurement (Default: 3) - - Returns - ------- - outliers : ndarray(dtype=bool) - 4D image same size as input DWI marking voxels - that are outliers - dt : ndarray(dtype=float) - IRLLS method of DT estimation - - Examples - -------- - outliers = dwi.irlls() - """ - # if not excludeb0.dtype: - # assert('option: Excludeb0 should be set to True or False') - - if maxiter < 1 or maxiter > 200: - assert('option: Maxiter should be set to a value between 1 ' - 'and 200') - if convcrit < 0 or convcrit > 1: - assert('option: Maxiter should be set to a value between 1 ' - 'and 200') - if not (mode == 'DKI' or mode == 'DTI'): - assert('Mode should be set to DKI or DTI') - if leverage < 0 or leverage > 1: - assert('option: Leverage should be set to a value between 0 ' - 'and 1') - if bounds < 1: - assert('option: Bounds should be set to a value >= 1') - exclude_idx = np.ones_like(self.grad[:, 3], dtype=bool) - exclude_idx = self.idxdki() - # Vectorize DWI - dwi = vectorize(self.img[:, :, :, exclude_idx], self.mask) - (ndwi, nvox) = dwi.shape - b = np.array(self.grad[exclude_idx, 3]) - b = np.reshape(b, (len(b), 1)) - g = self.grad[exclude_idx, 0:3] - # Apply Scaling - scaling = False - if np.sum(dwi < 1)/np.size(dwi) < 0.001: - dwi[dwi < 1] = 1 - else: - scaling = True - if self.maxDKIBval() < 10: - tmp = dwi[dwi < 0.05] - else: - tmp = dwi[dwi < 50] - sc = np.median(tmp) - dwi[dwi < sc/1000] = sc/1000 - dwi = dwi * 1000 / sc - # Create B-matrix - (dcnt, dind) = self.createTensorOrder(2) - if mode == 'DTI': - bmat = np.hstack( - (np.ones((ndwi, 1)), - np.matmul((-np.tile(b, (1, 6)) * g[:,dind[:,0]] * \ - g[:,dind[:,1]]), np.diag(dcnt)))) - else: - (wcnt, wind) = self.createTensorOrder(4) - bmat = np.hstack( - (np.ones((ndwi,1)), - np.matmul((-np.tile(b, (1, 6)) * g[:,dind[:,0]] * \ - g[:,dind[:,1]]), np.diag(dcnt)), - (1/6)*np.matmul((np.square(np.tile(b, (1, 15))) * \ - g[:,wind[:,0]] * g[:,wind[:,1]] * \ - g[:,wind[:,2]] * g[:,wind[:,3]]), - np.diag(wcnt)))) - nparam = bmat.shape[1] - ndof = ndwi - nparam - # Initialization - b0_pos = np.zeros(b.shape,dtype=bool, order='F') - if excludeb0: - if self.maxDKIBval() < 10: - b0_pos = b < 0.01 - else: - b0_pos = b < 10 - reject = np.zeros(dwi.shape, dtype=bool, order='F') - conv = np.zeros((nvox, 1)) - dt = np.zeros((nparam, nvox)) - # Attempt basic noise estimation - try: - sigma - except NameError: - def estSigma(dwi, bmat): - dwi = np.reshape(dwi, (len(dwi), 1)) - try: - dt_ = np.linalg.lstsq(bmat, np.log(dwi), rcond=None)[0] - # dt_ = np.linalg.solve(np.dot(bmat.T, bmat), np.dot( - # bmat.T, np.log(dwi))) - except: - dt_ = np.full((bmat.shape[1], 1), minZero) - w = highprecisionexp(np.matmul(bmat, dt_)).reshape((ndwi, 1)) - try: - dt_ = np.linalg.lstsq((bmat * np.tile(w, (1, nparam))), - (np.log(dwi) * w), rcond=None)[0] - # dt_ = np.linalg.solve( - # np.dot((bmat * np.tile(w, (1, nparam))).T, - # (bmat * np.tile(w, (1, nparam)))), \ - # np.dot((bmat * np.tile(w, (1, nparam))).T, (np.log( - # dwi) * w))) - except: - dt_ = np.full((bmat.shape[1], 1), minZero) - e = np.log(dwi) - np.matmul(bmat, dt_) - m = np.median(np.abs((e * w) - np.median(e * w))) - try: - sigma_ = np.sqrt(ndwi / ndof) * 1.4826 * m - except: - sigma_ = minZero - return sigma_ - sigma_ = np.zeros((nvox,1)) - inputs = tqdm(range(nvox), - desc='IRLLS Noise Estimation', - bar_format='{desc}: [{percentage:0.0f}%]', - unit='vox', - ncols=tqdmWidth) - sigma_ = Parallel(n_jobs=self.workers, prefer='processes') \ - (delayed(estSigma)(dwi[:, i], bmat) for i in inputs) - sigma = np.median(sigma_) - sigma = np.tile(sigma,(nvox,1)) - if scaling: - sigma = sigma*1000/sc - def outlierHelper(dwi, bmat, sigma, b, b0_pos, maxiter=25, - convcrit=1e-3, leverage=3, bounds=3): - # Preliminary rough outlier check - dwi_i = dwi.reshape((len(dwi), 1)) - dwi0 = np.median(dwi_i[b.reshape(-1) < 0.01]) - out = dwi_i > (dwi0 + 3 * sigma) - if np.sum(~out[b.reshape(-1) > 0.01]) < (bmat.shape[1] - 1): - out = np.zeros((out.shape),dtype=bool) - out[b0_pos.reshape(-1)] = False - bmat_i = bmat[~out.reshape(-1)] - dwi_i = dwi_i[~out.reshape(-1)] - n_i = dwi_i.size - ndof_i = n_i - bmat_i.shape[1] - # WLLS estimation - try: - dt_i = np.linalg.lstsq(bmat_i, np.log(dwi_i), rcond=None)[0] - # dt_i = np.linalg.solve(np.dot(bmat_i.T, bmat_i), - # np.dot(bmat_i.T, np.log(dwi_i))) - except: - dt_i = np.full((bmat_i.shape[1], 1), minZero) - w = highprecisionexp(np.matmul(bmat_i, dt_i)) - try: - dt_i = np.linalg.lstsq((bmat_i * np.tile(w, (1, nparam))), - (np.log(dwi_i).reshape( - (dwi_i.shape[0], 1)) * w), - rcond=None)[0] - # dt_i = np.linalg.solve( - # np.dot((bmat_i * np.tile(w, (1, nparam))).T, - # (bmat_i * np.tile(w, (1, nparam)))), - # np.dot((bmat_i * np.tile(w, (1, nparam))).T, - # (np.log(dwi_i).reshape( - # (dwi_i.shape[0], 1)) * w))) - except: - dt_i = np.full((bmat_i.shape[1], 1), minZero) - dwi_hat = highprecisionexp(np.matmul(bmat_i, dt_i)) - # Goodness-of-fit - residu = np.log(dwi_i.reshape((dwi_i.shape[0],1))) - \ - np.log(dwi_hat) - residu_ = dwi_i.reshape((dwi_i.shape[0],1)) - dwi_hat - - try: - chi2 = np.sum((residu_ * residu_) /\ - np.square(sigma)) / (ndof_i) -1 - except: - chi2 = minZero - try: - gof = np.abs(chi2) < 3 * np.sqrt(2/ndof_i) - except: - gof = True # If ndof_i = 0, right inequality becomes inf - # and makes the logic True - gof2 = gof - # Iterative reweighning procedure - iter = 0 - np.seterr(divide='raise', invalid='raise') - while (not gof) and (iter < maxiter): - try: - C = np.sqrt(n_i/(n_i-nparam)) * \ - 1.4826 * \ - np.median(np.abs(residu_ - \ - np.median(residu_))) / dwi_hat - except: - C = np.full(dwi_hat.shape, minZero) - try: - GMM = np.square(C) / np.square(np.square(residu) + \ - np.square(C)) - except: - # The following line produces a lot of Intel MKL - # warnings that should be ignored. This is a known - # Intel and Numpy bug that has not yet been resolved. - GMM = np.full(C.shape, minZero) - w = np.sqrt(GMM) * dwi_hat - dt_imin1 = dt_i - try: - dt_i = np.linalg.lstsq( - (bmat_i * np.tile(w, (1, nparam))), - (np.log(dwi_i).reshape((dwi_i.shape[0], 1)) * w), - rcond=None)[0] - # dt_i = np.linalg.solve( - # np.dot((bmat_i * np.tile(w, (1, nparam))).T, - # (bmat_i * np.tile(w, (1, nparam)))), - # np.dot((bmat_i * np.tile(w, (1, nparam))).T, - # (np.log(dwi_i).reshape( - # (dwi_i.shape[0], 1)) * w))) - except: - dt_i = np.full((bmat_i.shape[1], 1), minZero) - dwi_hat = highprecisionexp(np.matmul(bmat_i, dt_i)) - dwi_hat[dwi_hat < 1] = 1 - residu = np.log( - dwi_i.reshape((dwi_i.shape[0],1))) - np.log(dwi_hat) - residu_ = dwi_i.reshape((dwi_i.shape[0], 1)) - dwi_hat - # Convergence check - iter = iter + 1 - gof = np.linalg.norm( - dt_i - dt_imin1) < np.linalg.norm(dt_i) * convcrit - conv = iter - np.seterr(**defaultErrorState) - # Outlier detection - if ~gof2: - try: - lev = np.diag(np.matmul(bmat_i, np.linalg.lstsq(np.matmul(np.transpose(bmat_i), - np.matmul(np.diag(np.square(w).reshape(-1)), bmat_i)), - np.matmul(np.transpose(bmat_i), np.diag(np.square(w.reshape(-1)))), rcond=None)[0])) - # lev_helper = np.linalg.solve(\ - # np.dot((np.matmul(np.transpose(bmat_i), np.matmul(np.diag(np.square(w).reshape(-1)), bmat_i))).T, \ - # (np.matmul(np.transpose(bmat_i), np.matmul(np.diag(np.square(w).reshape(-1)), bmat_i)))), \ - # np.dot((np.matmul(np.transpose(bmat_i), np.matmul(np.diag(np.square(w).reshape(-1)), bmat_i))).T, \ - # np.matmul(np.transpose(bmat_i), np.diag(np.square(w.reshape(-1)))))) - # lev = np.diag(np.matmul(bmat_i, lev_helper)) - except: - lev = np.full(residu.shape, minZero) - lev = lev.reshape((lev.shape[0], 1)) - try: - lowerbound_linear = -bounds * \ - np.lib.scimath.sqrt(1-lev) * \ - sigma / dwi_hat - except: - lowerbound_linear = np.full(lev.shape, minZero) - try: - upperbound_nonlinear = bounds * \ - np.lib.scimath.sqrt(1 -lev) * sigma - except: - upperbound_nonlinear = np.full(lev.shape, minZero) - tmp = np.zeros(residu.shape, dtype=bool, order='F') - tmp[residu < lowerbound_linear] = True - tmp[residu > upperbound_nonlinear] = True - tmp[lev > leverage] = False - tmp2 = np.ones(b.shape, dtype=bool, order='F') - tmp2[~out.reshape(-1)] = tmp - tmp2[b0_pos] = False - reject = tmp2 - else: - tmp2 = np.zeros(b.shape, dtype=bool, order='F') - tmp2[out.reshape(-1)] = True - reject = tmp2 - # Robust parameter estimation - keep = ~reject.reshape(-1) - bmat_i = bmat[keep,:] - dwi_i = dwi[keep] - try: - dt_ = np.linalg.lstsq(bmat_i, np.log(dwi_i), rcond=None)[0] - # dt_ = np.linalg.solve(np.dot(bmat_i.T, bmat_i), \ - # np.dot(bmat_i.T, np.log(dwi_i))) - except: - dt_ = np.full((bmat_i.shape[1], 1), minZero) - w = highprecisionexp(np.matmul(bmat_i, dt_)) - try: - dt = np.linalg.lstsq((bmat_i * np.tile(w.reshape((len(w),1)), (1, nparam))), (np.log(dwi_i).reshape((dwi_i.shape[0], 1)) * w.reshape((len(w),1))), - rcond=None)[0] - # dt = np.linalg.solve(\ - # np.dot((bmat_i * np.tile(w.reshape((len(w),1)), (1, nparam))).T, (bmat_i * np.tile(w.reshape((len(w),1)), (1, nparam)))), \ - # np.dot((bmat_i * np.tile(w.reshape((len(w),1)), (1, nparam))).T, \ - # (np.log(dwi_i).reshape((dwi_i.shape[0], 1)) * w.reshape((len(w),1))))) - except: - dt = np.full((bmat_i.shape[1], 1), minZero) - # dt_tmp = dt.reshape(-1) - # dt2 = np.array([[dt_tmp[1], dt_tmp[2]/2, dt_tmp[3]], - # [dt_tmp[2]/2, dt_tmp[4], dt_tmp[5]/2], - # [dt_tmp[3]/2, dt_tmp[5]/2, dt_tmp[6]]]) - # eigv, tmp = np.linalg.eig(dt2) - # fa = np.sqrt(1/2) * \ - # (np.sqrt(np.square(eigv[0] - eigv[1]) + np.square(eigv[0] - eigv[2]) + np.square(eigv[1] - eigv[2])) / \ - # np.sqrt(np.square(eigv[0]) + np.square(eigv[1]) + np.square(eigv[2]))) - # md = np.sum(eigv)/3 - return reject.reshape(-1), dt.reshape(-1)#, fa, md - inputs = tqdm(range(nvox), - desc='IRLLS Outlier Detection', - bar_format='{desc}: [{percentage:0.0f}%]', - unit='vox', - ncols=tqdmWidth) - (reject, dt) = zip(*Parallel(n_jobs=self.workers, prefer='processes') \ - (delayed(outlierHelper)(dwi[:, i], bmat, sigma[i,0], b, b0_pos) for i in inputs)) - # for i in inputs: - # reject[:,i], dt[:,i] = outlierHelper(dwi[:, i], bmat, sigma[i,0], b, b0_pos) - dt = np.array(dt) - # self.dt = dt - #Unscaling - if scaling: - dt[1, :] = dt[1, :] + np.log(sc/1000) - #Unvectorizing - reject = vectorize(np.array(reject).T, self.mask) - return reject, dt.T - - def tensorReorder(self, dwiType): - """ - Reorders tensors in DT to those of MRTRIX in accordance to - the table below - - Parameters - ---------- - dwiType : str, {'dti', 'dki'} - Indicates whether image is DTI or DKI - - Returns - ------- - DT : ndarray(dtype=float) - 4D image containing DT tensor - KT : ndarray(dtype=float) - 4D image containing KT tensor - - Examples - -------- - dt = dwi.tensorReorder() - - Notes - ----- - MRTRIX3 and Designer tensors are described below. - - .. code-block:: none - - MRTRIX3 Tensors DESIGNER Tensors - ~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~ - - 0 D0 1 1 1 1 - 1 D1 2 2 1 2 - 2 D2 3 3 1 3 - 3 D3 1 2 2 2 - 4 D4 1 3 2 3 - 5 D5 2 3 3 3 - - 6 K0 1 1 1 1 1 1 1 1 - 7 K1 2 2 2 2 1 1 1 2 - 8 K2 3 3 3 3 1 1 1 3 - 9 K3 1 1 1 2 1 1 2 2 - 10 K4 1 1 1 3 1 1 2 3 - 11 K5 1 2 2 2 1 1 3 3 - 12 K6 1 3 3 3 1 2 2 2 - 13 K7 2 2 2 3 1 2 2 3 - 14 K8 2 3 3 3 1 2 3 3 - 15 K9 1 1 2 2 1 3 3 3 - 16 K10 1 1 3 3 2 2 2 2 - 17 K11 2 2 3 3 2 2 2 3 - 18 K12 1 1 2 3 2 2 3 3 - 19 K13 1 2 2 3 2 3 3 3 - 20 K14 1 2 3 3 3 3 3 3 - - Value Assignment - ~~~~~~~~~~~~~~~~ - - MRTRIX3 DESIGNER - ~~~~~~~ ~~~~~~~~ - 0 0 - 1 3 - 2 5 - 3 1 - 4 2 - 5 4 - - 6 6 - 7 16 - 8 20 - 9 7 - 10 8 - 11 12 - 12 15 - 13 17 - 14 19 - 15 9 - 16 11 - 17 18 - 18 10 - 19 13 - 20 14 - """ - if self.dt is None: - raise Exception('Please run dwi.fit() to generate a tensor ' - 'prior to reordering tensors.') - - if dwiType == 'dti': - dt = np.zeros((6, self.dt.shape[1])) - dt[0,:] = self.dt[0, :] # D0 - dt[1, :] = self.dt[3, :] # D1 - dt[2, :] = self.dt[5, :] # D2 - dt[3, :] = self.dt[1, :] # D3 - dt[4, :] = self.dt[2, :] # D4 - dt[5, :] = self.dt[4, :] # D5 - DT = vectorize(dt[0:6, :], self.mask) - KT = None - if dwiType == 'dki': - dt = np.zeros(self.dt.shape) - dt[0, :] = self.dt[0, :] # D0 - dt[1, :] = self.dt[3, :] # D1 - dt[2, :] = self.dt[5, :] # D2 - dt[3, :] = self.dt[1, :] # D3 - dt[4, :] = self.dt[2, :] # D4 - dt[5, :] = self.dt[4, :] # D5 - dt[6, :] = self.dt[6, :] # K0 - dt[7, :] = self.dt[16, :] # K1 - dt[8, :] = self.dt[20, :] # K2 - dt[9, :] = self.dt[7, :] # K3 - dt[10, :] = self.dt[8, :] # K4 - dt[11, :] = self.dt[12, :] # K5 - dt[12, :] = self.dt[15, :] # K6 - dt[13, :] = self.dt[17, :] # K7 - dt[14, :] = self.dt[19, :] # K8 - dt[15, :] = self.dt[9, :] # K9 - dt[16, :] = self.dt[11, :] # K10 - dt[17, :] = self.dt[18, :] # K11 - dt[18, :] = self.dt[10, :] # K12 - dt[19, :] = self.dt[13, :] # K13 - dt[20, :] = self.dt[14, :] # K14 - DT = vectorize(dt[0:6, :], self.mask) - KT = vectorize(dt[6:21, :], self.mask) - return DT, KT - - def irllsviolmask(self, reject): - """ - Computes 3D violation mask of outliers detected from IRLLS - method - - Parameters - ---------- - reject : ndarray(dtype=bool) - 4D input outlier map from IRLLS - - Returns - ------- - propviol : ndarray(dtype=float) - 3D mask where voxel value is the percentage of directional - violations - - Examples - -------- - mask = dwi.irllsviolmask(outliers) - """ - img = vectorize(reject, self.mask) - (ndwi, nvox) = img.shape - b = np.array(self.grad[:, 3]) - b = np.reshape(b, (len(b), 1)) - b_pos = ~(b < 0.01).reshape(-1) - img = img[b_pos, :] - propViol = np.sum(img,axis=0).astype(int) / np.sum(b_pos) - propViol = vectorize(propViol, self.mask) - return propViol - -def fit_regime(input, output, - prefix=None, suffix=None, ext=None, - irlls=True, akc=True, qcpath=None, - fit_constraints=[0,1,0], - l_max=None, - rectify=True, - res='med', - n_fibers=5, - mask=None, - nthreads=None): - """ - Performs the entire tensor fitting regime and writes out maps. - Uses auto-detections methods to determine the types of protocols - encoded by DWI and extract their metrics. - - Parameters - ---------- - input : str - Path to DWI - output : str - Output directory to write all outputs - prefix : str - Prefix to append to output file names. - (Default: None) - suffix : str - Suffix to append to output file names. - (Default: None) - ext : str - Specify output image extension type. - (Default: None) - irlls : bool - Specify whether to perform IRLLS outlier detection. - (Default: True) - akc : bool - Specify whether to perform brute-forced AKC correction. - (Default: True) - qcpath : str - Specify output directory to write QC metrics. - (Default: None) - fit_constraints : list - List of 3 bool elements specifying which fit contraints to - use. See DWI.createConstraints() on usage. - (Default: [0, 1, 0]) - l_max : int - Maximum spherical harminic degree for FBI/FBWM fit. - (Default: None) - rectify : bool - Specify whether to rectify FBI fODF - (Default: True) - mask : str - Path to brain mask - (Default: None) - nthreads : int - Number of workers to use in processing. Default value uses all - available workers. - (Default: None) - """ - if prefix is None: - prefix = '' - if suffix is None: - suffix = '' - if ext is None: - ext = '.nii' - img = DWI(input, mask=mask, nthreads=nthreads) - protocols = img.tensorType() - print('Protocol(s) detected: {}' .format(', '.join([x.upper() for x in protocols]))) - fname_dti = {} - fname_dki = {} - fname_wmti = {} - fname_fbi = {} - fname_tensor = {} - fname_outliers = {} - fname_tractography = {} - for key, value in dwi_fnames._dti_.items(): - fname_dti[key] = prefix + value + suffix + ext - for key, value in dwi_fnames._dki_.items(): - fname_dki[key] = prefix + value + suffix + ext - for key, value in dwi_fnames._wmti_.items(): - fname_wmti[key] = prefix + value + suffix + ext - for key, value in dwi_fnames._fbi_.items(): - fname_fbi[key] = prefix + value + suffix + ext - for key, value in dwi_fnames._tensor_.items(): - fname_tensor[key] = prefix + value + suffix + ext - for key, value in dwi_fnames._outliers_.items(): - fname_outliers[key] = prefix + value + suffix + ext - for key, value in dwi_fnames._tractography_.items(): - fname_tractography[key] = prefix + value + suffix + '.fib' - if irlls: - if img.isdki(): - outliers, dt_est = img.irlls(mode='DKI', excludeb0=True) - else: - outliers, dt_est = img.irlls(mode='DTI', excludeb0=True) - if qcpath: - if op.exists(qcpath): - outlier_full = op.join(qcpath, fname_outliers['IRLLS']) - print(outlier_full) - outlier_plot_full = op.join(qcpath, - prefix + 'irlls_outliers_plot' + suffix + '.png') - bvals_outlier_full = op.join(qcpath, - prefix + 'irlls_outliers_shells' + suffix + '.bval') - if img.isdki(): - bvals_outlier = img.getBvals()[img.idxdki()].astype(int) - else: - bvals_outlier = img.getBvals()[img.idxdti()].astype(int) - bvals_outlier = bvals_outlier * 1000 - writeNii(outliers, img.hdr, outlier_full) - np.savetxt(bvals_outlier_full, bvals_outlier, newline=' ', fmt="%d") - if mask: - outlierplot.plot(input=outlier_full, - output=outlier_plot_full, - bval=bvals_outlier_full, - mask=mask) - else: - outlierplot.plot(input=outlier_full, - output=outlier_plot_full, - bval=bvals_outlier_full, - mask=None) - os.remove(bvals_outlier_full) - if irlls: - img.fit(fit_constraints, reject=outliers) - else: - img.fit(fit_constraints) - if akc and img.isdki(): - akc_out = img.akcoutliers() - img.akccorrect(akc_out) - if qcpath: - writeNii(akc_out, - img.hdr, - op.join(qcpath, fname_outliers['AKC'])) - if 'dki' in img.tensorType(): - tensorType = 'dki' - else: - tensorType = 'dti' - DT, KT = img.tensorReorder(tensorType) - if tensorType == 'dki': - writeNii(DT, img.hdr, op.join(output, fname_tensor['DT'])) - writeNii(KT, img.hdr, op.join(output, fname_tensor['KT'])) - else: - writeNii(DT, img.hdr, op.join(output, fname_tensor['DT'])) - # DTI Parameters - if img.isdti(): - md, rd, ad, fa, fe, trace = img.extractDTI() - writeNii(md, img.hdr, op.join(output, fname_dti['md'])) - writeNii(rd, img.hdr, op.join(output, fname_dti['rd'])) - writeNii(ad, img.hdr, op.join(output, fname_dti['ad'])) - writeNii(fa, img.hdr, op.join(output, fname_dti['fa'])) - writeNii(fe, img.hdr, op.join(output, fname_dti['fe'])) - writeNii(trace, img.hdr, op.join(output, fname_dti['trace'])) - dtimodel = odf.odfmodel( - dt = op.join(output, fname_tensor['DT']), - mask=mask, - l_max=2, - res=res - ) - dti_odfs = dtimodel.dtiodf() - dti_sh = dtimodel.odf2sh(dti_odfs) - dtimodel.savenii(dti_sh, op.join(output, fname_dti['odf'])) - dsistudio.makefib( - input=op.join(output, fname_dti['odf']), - output=op.join(output, fname_tractography['dti']), - map=op.join(output, fname_dti['fa']), - mask=mask, - n_fibers=n_fibers, - scale=0.5, - other_maps = [ - op.join(output, fname_dti['md']), - op.join(output, fname_dti['rd']), - op.join(output, fname_dti['ad']), - ] - ) - if img.isdki(): - # DKI Parameters - mk, rk, ak, kfa, mkt, trace = img.extractDKI() - writeNii(mk, img.hdr, op.join(output, fname_dki['mk'])) - writeNii(rk, img.hdr, op.join(output, fname_dki['rk'])) - writeNii(ak, img.hdr, op.join(output, fname_dki['ak'])) - writeNii(kfa, img.hdr, op.join(output, fname_dki['kfa'])) - writeNii(mkt, img.hdr, op.join(output, fname_dki['mkt'])) - writeNii(trace, img.hdr, op.join(output, fname_dki['trace'])) - # WMTI Parameters - awf, eas_ad, eas_rd, eas_tort, ias_da = img.extractWMTI() - writeNii(awf, img.hdr, op.join(output, fname_wmti['awf'])) - writeNii(eas_ad, img.hdr, op.join(output, fname_wmti['eas_ad'])) - writeNii(eas_rd, img.hdr, op.join(output, fname_wmti['eas_rd'])) - writeNii(eas_tort, img.hdr, op.join(output, fname_wmti['eas_tort'])) - writeNii(ias_da, img.hdr, op.join(output, fname_wmti['ias_da'])) - dkimodel = odf.odfmodel( - dt = op.join(output, fname_tensor['DT']), - kt = op.join(output, fname_tensor['KT']), - mask=mask, - l_max=6, - res=res - ) - dki_odfs = dkimodel.dkiodf(fa_t=0.90) - dki_sh = dkimodel.odf2sh(dki_odfs) - dkimodel.savenii(dki_sh, op.join(output, fname_dki['odf'])) - dsistudio.makefib( - input=op.join(output, fname_dki['odf']), - output=op.join(output, fname_tractography['dki']), - map=op.join(output, fname_dti['fa']), - mask=mask, - n_fibers=n_fibers, - scale=0.5, - other_maps = [ - op.join(output, fname_dti['md']), - op.join(output, fname_dti['rd']), - op.join(output, fname_dti['ad']), - op.join(output, fname_dki['mk']), - op.join(output, fname_dki['rk']), - op.join(output, fname_dki['ak']), - op.join(output, fname_dki['kfa']), - op.join(output, fname_wmti['awf']), - op.join(output, fname_wmti['eas_ad']), - op.join(output, fname_wmti['eas_rd']), - op.join(output, fname_wmti['eas_tort']), - op.join(output, fname_wmti['ias_da']), - ] - ) - if img.isfbi(): - if img.isfbwm(): - zeta, faa, sph, min_awf, Da, De_mean, De_ax, De_rad, \ - De_fa, min_cost, min_cost_fn = \ - img.fbi(l_max=l_max, fbwm=True, rectify=rectify) - writeNii(zeta, img.hdr, op.join(output, fname_fbi['zeta'])) - writeNii(faa, img.hdr, op.join(output, fname_fbi['faa'])) - writeNii(np.real(sph), img.hdr, op.join(output, fname_fbi['odf'])) - writeNii(min_awf, img.hdr, op.join(output, fname_fbi['awf'])) - writeNii(Da, img.hdr, op.join(output, fname_fbi['Da'])) - writeNii(De_mean, img.hdr, op.join(output, fname_fbi['De_mean'])) - writeNii(De_ax, img.hdr, op.join(output, fname_fbi['De_ax'])) - writeNii(De_rad, img.hdr, op.join(output, fname_fbi['De_rad'])) - writeNii(De_fa, img.hdr, op.join(output, fname_fbi['fae'])) - writeNii(min_cost, img.hdr, op.join(output, fname_fbi['min_cost'])) - writeNii(min_cost_fn, img.hdr, op.join(output, fname_fbi['min_cost_fn'])) - dsistudio.makefib( - input=op.join(output, fname_fbi['odf']), - output=op.join(output, fname_tractography['fbi']), - map=op.join(output, fname_fbi['faa']), - mask=mask, - n_fibers=n_fibers, - scale=1, - other_maps = [ - op.join(output, fname_dti['md']), - op.join(output, fname_dti['rd']), - op.join(output, fname_dti['ad']), - op.join(output, fname_dki['mk']), - op.join(output, fname_dki['rk']), - op.join(output, fname_dki['ak']), - op.join(output, fname_dki['kfa']), - op.join(output, fname_wmti['awf']), - op.join(output, fname_wmti['eas_ad']), - op.join(output, fname_wmti['eas_rd']), - op.join(output, fname_wmti['eas_tort']), - op.join(output, fname_wmti['ias_da']), - op.join(output, fname_fbi['zeta']), - op.join(output, fname_fbi['awf']), - op.join(output, fname_fbi['Da']), - op.join(output, fname_fbi['De_mean']), - op.join(output, fname_fbi['De_ax']), - op.join(output, fname_fbi['De_rad']), - op.join(output, fname_fbi['fae']), - ] - ) - else: - zeta, faa, sph, min_awf, Da, De_mean, De_ax, De_rad, \ - De_fa, min_cost, min_cost_fn = \ - img.fbi(l_max=l_max, fbwm=False, rectify=rectify) - writeNii(zeta, img.hdr, op.join(output, fname_fbi['zeta'])) - writeNii(faa, img.hdr, op.join(output, fname_fbi['faa'])) - writeNii(sph, img.hdr, op.join(output, fname_fbi['odf'])) - dsistudio.makefib( - input=op.join(output, fname_fbi['odf']), - output=op.join(output, fname_tractography['fbi']), - map=op.join(output, fname_fbi['faa']), - mask=mask, - n_fibers=n_fibers, - scale=1, - other_maps = [ - op.join(output, fname_fbi['zeta']) - ] - ) diff --git a/designer/fitting/thresholds.py b/designer/fitting/thresholds.py deleted file mode 100644 index 286f6def..00000000 --- a/designer/fitting/thresholds.py +++ /dev/null @@ -1,11 +0,0 @@ -__mindtibval__ = 0.5 # minimum DTI B-value -__maxdtibval__ = 1.5 # maximum DTI B-value -__mindkibval__ = 0.5 # minimum DKI B-value -__maxdkibval__ = 3.0 # maximum DKI B-value -__minfbibval__ = 4.0 # minimum FBI B-value -__maxfbibval__ = 12.0 # maximum FBI B-value -__d0__ = 3.0 # diffusivity of free water at body temp (37 deg C) for upper bound on Da -__dn__ = 1.5 # estimated intra-neurite diffusivity in mm^2/ms -__pkT__ = 0.4 # peak thresholding for white matter fiber tracking -__minZero__ = 10e-8 # threshold under which all numbers are zero -__dirs__ = 256 # Define number of directions to resample after computing all tensors diff --git a/designer/info.py b/designer/info.py deleted file mode 100644 index a3b2bfd6..00000000 --- a/designer/info.py +++ /dev/null @@ -1,70 +0,0 @@ -import inspect, os - -__execdir__ = os.path.basename( - os.path.dirname( - os.path.abspath( - inspect.getfile( - inspect.currentframe() - ) - ) - ) -) -__packagename__ = 'PyDesigner-DWI' -__version__='v1.0.0' -__author__ = 'PyDesigner developers' -__copyright__ = 'Copyright 2023, PyDesigner developers, MUSC Advanced Image Analysis (MAMA)' -__credits__ = [ - 'Siddhartha Dhiman', - 'Joshua Teves', - 'Ryn Thorn', - 'Benjamin Ades-Aron', - 'Jelle Veraart', - 'Vitria Adisetiyo', - 'Els Fieremans', - 'Jens Jensen', - 'Joseph Helpern', - 'Andreana Benitez' -] -__maintainer__ = 'Siddhartha Dhiman' -__email__ = 'bridge@musc.edu' -__url__ = 'https://github.com/m-ama/PyDesigner' -__license__='CUSTOM' -__description__ = ('Python Port of NYU\'s Designer pipeline for dMRI ' - 'processing') -# Gets folder name where this file resides -__execdir__ = os.path.basename( - os.path.dirname( - os.path.abspath( - inspect.getfile( - inspect.currentframe() - ) - ) - ) - ) - -# PyPi package requirements -REQUIRES = [ - 'numpy >= 1.19', - 'scipy >= 1.5', - 'matplotlib >= 3.3', - 'joblib >= 0.16', - 'tqdm >= 4.40', - 'multiprocess >= 0.70', - 'nibabel >= 3.2', - 'dipy >= 1.2', - 'cvxpy >= 1.1' -] - -# Python version requirements -PYTHON_REQUIRES = ">=3.6" - -# Package classifiers -CLASSIFIERS = [ - 'Programming Language :: Python :: 3.7', - 'License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)', - 'Operating System :: OS Independent', - 'Topic :: Scientific/Engineering :: Bio-Informatics', - 'Topic :: Scientific/Engineering :: Information Analysis', - 'Topic :: Scientific/Engineering :: Mathematics', - 'Topic :: Scientific/Engineering :: Physics' -] diff --git a/designer/plotting/__init__.py b/designer/plotting/__init__.py deleted file mode 100644 index a48f805c..00000000 --- a/designer/plotting/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from . import motionplot -from . import outlierplot -from . import snrplot \ No newline at end of file diff --git a/designer/plotting/motionplot.py b/designer/plotting/motionplot.py deleted file mode 100644 index 70fa7374..00000000 --- a/designer/plotting/motionplot.py +++ /dev/null @@ -1,83 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -import os.path as op -import numpy as np -import matplotlib.pyplot as plt -from matplotlib.ticker import AutoMinorLocator, FormatStrFormatter - -def plot(input, output, voxel=None): - """ - Plots subject motion from eddy_qc output file. - - Parameters - ---------- - input : str - Path to eddy_qc's - output : str - Path to brain mask - voxel : tuple of float - Size of voxel in 3D i.e. (2.7, 2.7, 2.7) for 2.7 mm isotropic - - Returns - ------- - None; writes out plot to file - - See Also - -------- - outlierplot : plots outliers from IRLLS - snrplot : plots DWI's SNR - """ - print('Plotting motion...') - if not op.exists(input): - raise OSError('Input file {} does not exist'.format(input)) - if op.isdir(output): - raise OSError('Output {} cannot be a directory. Please ' - 'define the output to be an image file.'.format(output)) - if op.splitext(output)[-1] != '.png': - raise OSError('Output path {} does not indicate a PNG file' - ' '. format(input)) - # Load file - dat = np.loadtxt(input) - if dat.shape[-1] != 2: - raise Exception('The input file containing eddy computed ' - 'movement should have only two columns. The file {} supplied ' - 'however only contains {} column(s)'.format(input, dat.size[-1])) - nvols = dat.shape[0] - if voxel is not None: - voxel = sum(voxel)/len(voxel) - dat = dat * voxel - # The datafile being read here should be - # `eddy_restricted_movement_rms`, which contians information on - # how much a subjects moved during a DWI scan, with complete - # disregard of translation in PE dir. The file has two columns - # where the first contains the RMS movement relative the first - # volume and the second column the RMS relative the previous - # volume - # Variables: - # relone: motion relative to first volume - # relbef: motion relative to previous volume - # cum: cunulative motion from relbef - relone = dat[:, 0] - relbef = dat[:, 1] - x = np.arange(start=1, stop=nvols+1, step=1) - # Plot - plt.style.use('seaborn') - fig, ax = plt.subplots() - ax.plot(x, relone, linewidth=1, label='Relative to first volume') - ax.plot(x, relbef, linewidth=1, label='Relative to previous volume') - plt.xlabel('Volume Number') - if voxel is not None: - plt.ylabel('RMS of Head Displacement [mm]') - plt.text(0, -2, '$\dag$ average voxel dimension is used in ' - 'converting voxel displacement to head displacement ' - '$V_{avg} = (V_x + V_y + V_z) / 3$', - ha='left', size=6) - else: - plt.ylabel('RMS of Voxel Displacement') - ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.10), shadow=True, ncol=2) - ax.xaxis.set_minor_locator(AutoMinorLocator()) - ax.grid(which='minor', linestyle=':', linewidth='0.5') - plt.title('Intervolume Head Motion') - plt.tight_layout() - plt.savefig(output, dpi=600) diff --git a/designer/plotting/outlierplot.py b/designer/plotting/outlierplot.py deleted file mode 100644 index 941aef42..00000000 --- a/designer/plotting/outlierplot.py +++ /dev/null @@ -1,99 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -import nibabel as nib -import os.path as op -import numpy as np -import matplotlib.pyplot as plt -from matplotlib.ticker import AutoMinorLocator - -def plot(input, output, bval=None, mask=None): - """ - Plots and saves the number of outliers in input 4D irlls output - file as a PNG file - - Parameters - ---------- - input : ndarry(dtype=float) - List of 4D DWI (nifti-format) paths to evaluate and plot - output : str - Output path of plot in .png format - bval : str - Path to relevant .bval file - mask : str - Path to brain mask in nifti format - - Returns - ------- - None; writes out plot to file - - See Also - -------- - motionplot : plots subject motion from eddy_qc output file - snrplot : plots DWI's SNR - """ - print('Plotting outliers...') - if not op.exists(input): - raise OSError('Input file {} does not exist'.format(input)) - if op.splitext(input)[-1] != '.nii': - raise OSError('Input file {} is not nifti type'. format(input)) - if op.isdir(output): - raise OSError('Output {} cannot be a directory. Please ' - 'define the output to be an image file.'.format(output)) - if op.splitext(output)[-1] != '.png': - raise OSError('Output path {} does not indicate a PNG file' - ''. format(input)) - hdr = nib.load(input) - img = np.array(hdr.dataobj) - truncateIdx = np.isnan(img) - img[truncateIdx] = 0 - dims = img.shape # size per dimension - nvox = dims[0] * dims[1] * dims[2] # no. of voxels - vols = dims[-1] # number of volumes - if np.ndim(img) != 4: - raise Exception('Only 4D nifti files can be read. ' - 'User-supplied file is not a 4D nifti.') - if mask is not None: - if op.exists(mask): - if op.splitext(mask)[-1] != '.nii': - raise OSError('Input maks {} is not nifti type ' - ''.format(mask)) - hdr_mask = nib.load(mask) - bw = np.array(hdr_mask.dataobj) - else: - raise OSError('Mask path {} does not exist'.format(mask)) - else: - bw = np.ones(dims[0:3], order='F') - if bval is None: - bvals = np.zeros(dims[-1], dtype=int) - else: - bvals = np.loadtxt(bval, dtype=int) - # multiply mask by img - for i in range(vols): - img[:,:,:,i] = np.multiply(img[:,:,:,i], bw) - # Create x-axis - x = np.arange(start=0, stop=vols, step=1) - # create y-axis - y = np.zeros_like(x) - for i in range(len(x)): - y[i] = np.count_nonzero(img[:, :, :, i]) - # Normalize to percentage of voxels - y = (y / np.count_nonzero(bw)) * 100 - # Plot - plt.style.use('seaborn') - fig, ax = plt.subplots() - plt.plot(x, y, "-", lw=1, color="black", alpha=0.40) - scat = plt.scatter(x, y, c=bvals, s=10, linewidths=0, alpha=1, cmap='Set1') - plt.xlabel('Shell Number') - plt.ylabel('Percentage of Outlier Voxels (%)') - plt.xticks(rotation=45) - ax.xaxis.set_minor_locator(AutoMinorLocator()) - ax.grid(which='minor', linestyle=':', linewidth='0.5') - if bval: - cbar = fig.colorbar(scat, ax=ax) - cbar.set_label('B-Value') - if mask is None: - plt.title('IRLLS Outlier Determination in DWI') - else: - plt.title('IRLLS Outlier Determination in DWI (Brain Masked)') - plt.savefig(output, dpi=600) diff --git a/designer/plotting/snrplot.py b/designer/plotting/snrplot.py deleted file mode 100644 index 8ea44b85..00000000 --- a/designer/plotting/snrplot.py +++ /dev/null @@ -1,360 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -import nibabel as nib -import os.path as op -import numpy as np -import matplotlib.pyplot as plt -from scipy.interpolate import make_interp_spline - -minZero = 1e-8 -np.seterr(all='ignore') - -def vectorize(img, mask): - """ - Returns vectorized image based on brain mask, requires no input - parameters - If the input is 1D or 2D, unpatch it to 3D or 4D using a mask - If the input is 3D or 4D, vectorize it using a mask - Classification: Function - - Parameters - ---------- - img : ndarray - 1D, 2D, 3D or 4D image array to vectorize - mask : ndarray - 3D image array for masking - - Returns - ------- - vec : N X number_of_voxels vector or array, where N is the number - of DWI volumes - - Usage - ----- - vec = vectorize(img) if there's no mask - vec = vectorize(img, mask) if there's a mask - """ - if mask is None: - mask = np.ones((img.shape[0], - img.shape[1], - img.shape[2]), - order='F') - mask = mask.astype(bool) - if img.ndim == 1: - n = img.shape[0] - s = np.zeros((mask.shape[0], - mask.shape[1], - mask.shape[2]), - order='F') - s[mask] = img - if img.ndim == 2: - n = img.shape[0] - s = np.zeros((mask.shape[0], - mask.shape[1], - mask.shape[2], n), - order='F') - for i in range(0, n): - s[mask, i] = img[i,:] - if img.ndim == 3: - maskind = np.ma.array(img, mask=np.logical_not(mask)) - s = np.ma.compressed(maskind) - if img.ndim == 4: - s = np.zeros((img.shape[-1], np.sum(mask).astype(int)), order='F') - for i in range(0, img.shape[-1]): - tmp = img[:,:,:,i] - # Compressed returns non-masked area, so invert the mask first - maskind = np.ma.array(tmp, mask=np.logical_not(mask)) - s[i,:] = np.ma.compressed(maskind) - return np.squeeze(s) - -class makesnr: - """ - Class object that computes and prints SNR plots for any number of - input DWIs - - Parameters - ---------- - dwilist : list of str - List of 4D DWI (nifti-format) paths to evaluate and plot - noisepath : str - Path to noise map from "dwidenoise" - maskpath : str, optional - Path to brain mask - - Methods - ---------- - __init__ : constructs makesnr class - getuniquebval : creates a list of unique B-values for the purpose of - SNR computation - computesnr : performs SNR computation - histcount : bins SNR values - makeplot : creates and saves SNR plot from bin counts - - """ - def __init__(self, dwilist, noisepath=None, maskpath=None): - """ - Constructor for makesnr class - - Parameters - ---------- - dwilist : list of str - String list of nifti paths to plot - noisepath : str - Path to noisemap (Default: None) - maskpath : str, optional - Path to nifti brain mask (Default: None) - """ - if noisepath is None: - raise Exception('Please provide the path to noise map from ' - '"dwidenoise"') - - self.nDWI = len(dwilist) # Number of input DWIs - self.DWInames = [op.split(i)[-1] for i in dwilist] - # Open the first image in list - self.hdr = nib.load(dwilist[0]) - sDWI = self.hdr.shape # Shape of input DWIs - if self.hdr.ndim != 4: - raise IOError('Input DWIs need are not 4D. Please ensure you ' - 'use 4D NifTi files only.') - # Load image into 2D array - self.img = np.array(self.hdr.dataobj) - # Load noise into a vector - self.noise = np.array(nib.load(noisepath).dataobj) - # Load BVAL - fName = op.splitext(dwilist[0])[0] - bvalPath = op.join(fName + '.bval') - if op.exists(bvalPath): - self.bval = np.rint(np.loadtxt(bvalPath) / 1000) - else: - raise IOError('BVAL file {} not found'.format(bvalPath)) - if maskpath is not None and op.exists(maskpath): - self.mask = np.array(nib.load(maskpath).dataobj).astype(bool) - self.maskStatus = True - else: - self.mask = np.ones((self.img.shape[0], self.img.shape[ - 1], self.img.shape[2]), order='F') - self.maskStatus = False - # Vectorize images - self.img = vectorize(self.img, self.mask) - self.nvox = self.img.shape[1] - self.noise = vectorize(self.noise, self.mask) - if self.nDWI > 1: - # From second image to last image - for i in range(1, self.nDWI): - try: - tmp = vectorize(np.array(nib.load(dwilist[i]).dataobj), - self.mask) - self.img = np.dstack((self.img, tmp)) - except: - raise ValueError('all input DWIs must have the same ' - 'shape.') - try: - fName = op.splitext(dwilist[i])[0] - bvalPath = op.join(fName + '.bval') - self.bval = np.stack((self.bval, - np.rint( - np.loadtxt(bvalPath) / 1000))) - except: - raise IOError('Unable to locate BVAL file for image: {' - '}'.format(dwilist[i])) - truncateIdx = np.logical_or(np.isnan(self.img), - (self.img < minZero)) - self.img[truncateIdx] = minZero - - def getuniquebval(self): - """ - Creates a list of unique B-values for the purpose of SNR - computation. In the calculation of SNR, B0 signal can be averaged - becase they are not associated to any direction. This is not true - for non-B0 values however, because every 3D volume represents a - different direction. To compute SNR appropriately, differences in - gradients have to be accounted. This function creates a list of - B-values in the order they need to appear for the calculation of - SNR. - - Returns - ------- - b_list : ndarray - Numpy vector containing list of B-values to be used in - SNR calculation - """ - - b_list = [] - for i in range(self.nDWI): - bvals = self.bval[i, :] - unibvals = np.array(np.unique(bvals),dtype=int) - bval_list = [] - for j in range(unibvals.size): - bval = unibvals[j] - # Index where entirety of bvals, given by variable - # bvals, is equal to a single unique bval - idx_bval = np.where(np.isin(bvals, bval))[-1] - if bval != 0: - bval_list.append(str(bval)) - else: - # Appends '0' to bval_list n countb0 number of times - for countb in range(len(idx_bval)): - bval_list.append(str(bval)) - b_list.append(bval_list) - return np.asarray(b_list, dtype=int) - - def computesnr(self): - """ - Computes SNR of all DWIs in class object - - Returns - ------- - snr_dwi : ndarray - Numpy array of SNR across all DWI. - """ - bval_list = self.getuniquebval() - snr_dwi = np.zeros((self.nvox, bval_list.shape[1], self.nDWI)) - for i in range(self.nDWI): - bvals = self.bval[i, :] - unibvals = np.array(np.unique(bvals),dtype=int) - print('Computing SNR: ' + self.DWInames[i]) - for j in range(unibvals.size): - bval = unibvals[j] - print(' * B' + str(bval * 1000) + '...') - # Index where entirety of bvals, given by variable - # bvals, is equal to a single unique bval - idx_bval = np.where(np.isin(bvals, bval))[-1] - idx_list = np.where(np.isin(bval_list[i, :], bval))[-1] - img = self.img[idx_bval, :, i] - if bval != 0: - snr_dwi[:, idx_list, i] = np.mean((img /self.noise), - axis=0).reshape(( - self.nvox, 1)) - else: - # Appends '0' to bval_list n countb0 number of times - for countb in range(img.shape[0]): - snr_dwi[:, idx_list, i] = \ - np.divide(img, self.noise).reshape(( - self.nvox, idx_list.size)) - truncateIdx = np.logical_or(np.isnan(snr_dwi), - (snr_dwi < minZero)) - snr_dwi[truncateIdx] = minZero - return snr_dwi - - def histcount(self, nbins=100): - """ - Bins SNR into nbins and returns various counting properties - - Parameters - ---------- - nbins : int - Number of bins to plot - - Returns - ------- - count : ndarray - Array of count of voxels in bins - binval : ndarray - Array of bin values - unibvals : ndarray - Array containing all unique B-values detected - """ - if not isinstance(nbins, int): - raise ValueError('Number of bins (nbins) entered is not an ' - 'integer. Please specify and integer.') - bval_list = self.getuniquebval() - snr = self.computesnr() - # Get min and max values of SNR - snr_ = snr.reshape(-1) - minVal = np.min(snr_[np.isfinite(snr_)]) - maxVal = np.max(snr_[np.isfinite(snr_)]) - unibvals = np.array(np.unique(bval_list), dtype=int) - count = np.zeros((nbins, unibvals.size, self.nDWI)) - edges = np.zeros((nbins+1, unibvals.size, self.nDWI)) - for j in range(self.nDWI): - for i in range(unibvals.size): - bval = unibvals[i] - idx_list = np.where(np.isin(bval_list[j, :], bval))[-1] - vals = snr[:, idx_list, j] - (count[:, i, j], edges[:, i, j]) = \ - np.histogram(vals, - bins=nbins, - range=(minVal, maxVal), - density=True) - edges = np.unique(edges) - if edges.size != nbins + 1: - raise Exception('Number of binning edges across B-values and ' - 'DWIs is not consistent. Aborting SNR ' - 'binning.') - binval = np.zeros((nbins)) - for i in range(binval.size): - binval[i] = np.median([edges[i], edges[i + 1]]) - return count, binval, unibvals - - def makeplot(self, path, smooth=True, smoothfactor=5): - """ - Creates and saves SNR plot to a path as SNR.png - - Parameters - ---------- - path : str - Directory to save the plot in - smooth : bool, optional - Specify whether to interpolate and smooth (Default: True) - smoothfactor : int, optional - Smoothing factor to apply (Default: 5) - - Returns - ------- - None: Writes out image into directory as SNR.png - """ - if not isinstance(smoothfactor, int): - raise ValueError('Please specify an integer for smooth ' - 'factor.') - if not op.isdir(path): - raise IOError('Output path defined does not exist.') - outpath = op.join(path, 'SNR.png') - (count, binval, unibvals) = self.histcount() - count = count * 100 - if smooth: - smoothFactor = smoothfactor * count.shape[0] # Number of - # points to add between histogram counts for smoothing - binval_interp = np.linspace(binval.min(), binval.max(), - smoothFactor) - (R, C, D) = count.shape - count_interp = np.zeros((smoothFactor, C, D)) - for y in range(C): - for z in range(D): - spl = make_interp_spline(binval, count[:, y, z], k=3) - count_interp[: , y, z] = spl(binval_interp) - count = count_interp - binval = binval_interp - plt.style.use('seaborn') - nplots = unibvals.size - titles = ['B' + str(i * 1000) for i in unibvals] - fig = plt.figure(figsize=(8, 10)) - fig.subplots_adjust(hspace=0.4, wspace=0.1) - fig.suptitle('SNR of Acquisitions', fontsize=20) - for i in range(nplots): - ax = fig.add_subplot(nplots, 1, i + 1) - ax.plot(binval, count[:, i , :]) - # Subplot Properties - ax.grid(True) - ax.set_title(titles[i], - loc='right') - bval = unibvals[i] - ax.set_xlabel('SNR') - ax.set_ylabel('% of voxels') - ax.set_ylim(count[np.isfinite(count)].min(), - count[np.isfinite(count)].max()) - if bval == 0: - ax.set_xlim(0, 200) - elif bval == 1: - ax.set_xlim(0, 100) - elif bval == 2: - ax.set_xlim(0, 80) - else: - ax.set_xlim(0, 60) - # Plot Properties - plt.legend(self.DWInames, - ncol=nplots, - loc='upper left', - frameon = False, - bbox_to_anchor=(0.25, -0.19)) - plt.savefig(outpath, dpi=300) diff --git a/designer/postprocessing/__init__.py b/designer/postprocessing/__init__.py deleted file mode 100644 index f9c7eae8..00000000 --- a/designer/postprocessing/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from . import filters \ No newline at end of file diff --git a/designer/postprocessing/filters.py b/designer/postprocessing/filters.py deleted file mode 100644 index e90ebd48..00000000 --- a/designer/postprocessing/filters.py +++ /dev/null @@ -1,103 +0,0 @@ -#!/usr/bin/env python -# -*- coding : utf-8 -*- - -""" -This module contains filter(s) for postprocessing DTI/DKI maps -""" - -#--------------------------------------------------------------------- -# Package Management -#--------------------------------------------------------------------- -import os.path as op -import numpy as np -from scipy.ndimage import median_filter, generate_binary_structure -import nibabel as nib - -#--------------------------------------------------------------------- -# Functions -#--------------------------------------------------------------------- -def readnii(input): - """ - Reads nifti files and returns header and numpy data array - - Parameters - ---------- - input : str - Path to nifti file - - Returns - ------- - hdr : class - Nibabel class object - img : ndarray - 3D or 4D array containing the loaded nifti image - - """ - hdr = nib.load(input) - img = np.array(hdr.dataobj) - return hdr, img - -def writenii(hdr, img, output): - """ - Write nupy array to nifti file - - Parameters - ---------- - hdr : class - Nibabel class object - img : ndarray - 3D or 4D array containing the image array - output : str - Path to save file as - - None; writes out file - """ - struct = nib.Nifti1Image(img, hdr.affine, hdr.header) - nib.save(struct, output) - -def median(input, output, mask=None): - """ - Applies median filtering to input nifti file - - Parameters - ---------- - input : str - Path to input nifti file - output : str - Path to output nifti file - mask : str, optional - Path to brainmask nifti file (Default: None) - - Returns - ------- - None; writes out file - """ - if not op.exists(input): - raise IOError('Input file {} does not exist.'.format(input)) - hdr, img = readnii(input) - if mask is not None: - if not op.exists(mask): - raise IOError('Input mask {} does not ' - 'exist.'.format(input)) - maskhdr, mask = readnii(mask) - else: - mask = np.ones((img.shape[0], img.shape[1], img.shape[2]), - order='F') - mask.astype(bool) - conn = generate_binary_structure(rank=3, connectivity=1) - if np.ndim(img) == 4: - for i in range(img.shape[-1]): - img[:, :, :, i] = median_filter(img[:, :, :, i], - footprint=conn, - mode='constant', - cval=float('nan')) \ - * mask - elif np.ndim(img) == 3: - img = median_filter(img, - footprint=conn, - mode='constant', cval=float('nan')) \ - * mask - else: - raise Exception('Input nifti image needs to be either 3D or ' - '4D. Please check the file provided.') - writenii(hdr, img, output) diff --git a/designer/preprocessing/__init__.py b/designer/preprocessing/__init__.py deleted file mode 100644 index f84f0af6..00000000 --- a/designer/preprocessing/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -from . import mrinfoutil -from . import mrpreproc -from . import preparation -from . import rician -from . import smoothing -from . import util \ No newline at end of file diff --git a/designer/preprocessing/mrinfoutil.py b/designer/preprocessing/mrinfoutil.py deleted file mode 100644 index 221c9f05..00000000 --- a/designer/preprocessing/mrinfoutil.py +++ /dev/null @@ -1,569 +0,0 @@ -#!/usr/bin/env python -# -*- coding : utf-8 -*- - -""" -Utilities for extracting information on various input files using -MRtrix3's mrinfo tool. All values are returned in basic Python data -types. -""" - -import os.path as op -import subprocess -import re - -def getconsole(path, flag): - """ - Fetches the console output of MRtrix3's mrinfo with specified - flag - - Parameters - ---------- - path : str - Path to input image or directory - flag : str - Flag to pass onto mrinfo - - Returns - ------- - str - MRtrix3's mrinfo console output - """ - if not op.exists(path): - raise OSError('Input path does not exist. Please ensure that the ' - 'folder or file specified exists.') - if not isinstance(flag, str): - raise Exception('Input flag is not a string') - arg = ['mrinfo', flag] - arg.append(path) - completion = subprocess.run(arg, stdout=subprocess.PIPE) - if completion.returncode != 0: - raise IOError('Input {} is not currently supported by ' - 'PyDesigner.'.format(path)) - console = str(completion.stdout).split('\\n')[0] - console = console.split('b')[-1] - console = console.replace("'", "") - return console - -def format(path): - """ - Returns the file format of input DWI - - Parameters - ---------- - path : str - Path to input image or directory - - Returns - ------- - str - Image file format - """ - type = getconsole(path, '-format') - return type - -def ndim(path): - """ - Returns the number of image dimensions of input DWI - - Parameters - ---------- - path : str - Path to input image or directory - - Returns - ------- - int - Number of dimensions in image - """ - num = getconsole(path, '-ndim') - return int(num) - -def size(path): - """ - Returns the size of input DWI image along each axis - - Parameters - ---------- - path : str - Path to input image or directory - - Returns - ------- - tuple of int - Number of voxels in [X, Y, Z, B-value] - """ - num = getconsole(path, '-size').split() - num = tuple(map(int, num)) - return num - -def spacing(path): - """ - Returns the voxel spacing along each of input DWI's dimensions - - Parameters - ---------- - path : str - Path to input image or directory - - Returns - ------- - tuple of int - Number of spacing between voxels [X, Y, Z, B-value] - """ - num = getconsole(path, '-spacing').split() - num = tuple(map(float, num)) - return num - -def datatype(path): - """ - Returns the data type used for storing input DWI - - Parameters - ---------- - path : str - Path to input image or directory - - Returns - ------- - str - MRtrix3 datatype - """ - return getconsole(path, '-datatype') - -def strides(path): - """ - Returns data strides of input DWI - - Parameters - ---------- - path : str - Path to input image or directory - - Returns - ------- - tuple of ints - MRtrix3's strides - """ - num = getconsole(path, '-strides').split() - num = tuple(map(int, num)) - return num - -def offset(path): - """ - Returns the input DWI's intensity offset - - Parameters - ---------- - path : str - Path to input image or directory - - Returns - ------- - float - Image intensity offset - """ - num = getconsole(path, '-offset') - num = float(num) - return num - -def multiplier(path): - """ - Returns the input DWI's intensity multiplier - - Parameters - ---------- - path : str - Path to input image or directory - - Returns - ------- - float - Image intensity multiplier - """ - num = getconsole(path, '-multiplier') - num = float(num) - return num - -def transform(path): - """ - Returns the input DWI's 4x4 voxel to image transformation matrix - - Parameters - ---------- - path : str - Path to input image or directory - - Returns - ------- - tuple of float - Image transformation matrix - """ - if not op.exists(path): - raise OSError('Input path does not exist. Please ensure that the ' - 'folder or file specified exists.') - arg = ['mrinfo', '-transform'] - arg.append(path) - completion = subprocess.run(arg, stdout=subprocess.PIPE) - if completion.returncode != 0: - raise IOError('Input {} is not currently supported by ' - 'PyDesigner.'.format(path)) - console = str(completion.stdout).split('\\n') - num = [re.findall(r"[-+]?\d*\.\d+|\d+", s) for s in console] - num = [s for s in num if s != []] - return tuple(num) - -def commandhistory(path): - """ - Returns a list of command history (manipulations or transformations) - performed on MRtrix file format .mif - - Parameters - ---------- - path : str - Path to input image or directory - - Returns - ------- - list of str - command history of input file - """ - if not op.exists(path): - raise OSError('Input path does not exist. Please ensure that the ' - 'folder or file specified exists.') - ftype = format(path) - if ftype != 'MRtrix': - raise IOError('This function only works with MRtrix (.mif) ' - 'formatted filetypes. Please ensure that the input ' - 'filetype meets this requirement') - arg = ['mrinfo', '-property', 'command_history'] - arg.append(path) - completion = subprocess.run(arg, stdout=subprocess.PIPE) - if completion.returncode != 0: - raise IOError('Input {} is not currently supported by ' - 'PyDesigner.'.format(path)) - # Remove new line delimiter - console = str(completion.stdout).split('\\n') - # Remove 'b' - console[0] = console[0][1:] - # Remove quotes - console = [s.replace("'", "") for s in console] - # Condense empty strings - console = [s.replace('"', '') for s in console] - # Remove empty strings form list - console = list(filter(None, console)) - # Remove MRtrix3 version - console = [re.sub(r'\([^)]*\)', '', s) for s in console] - # Remove whitespace to the right of string - console = [s.rstrip() for s in console] - return list(console) - -def dwscheme(path): - """ - Returns a list of input DWI's diffusion weighting scheme - - Parameters - ---------- - path : str - Path to input image or directory - - Returns - ------- - list of float - diffusion weighing scheme - """ - if not op.exists(path): - raise OSError('Input path does not exist. Please ensure that the ' - 'folder or file specified exists.') - ftype = format(path) - if ftype != 'MRtrix': - raise IOError('This function only works with MRtrix (.mif) ' - 'formatted filetypes. Please ensure that the input ' - 'filetype meets this requirement') - arg = ['mrinfo', '-dwgrad'] - arg.append(path) - completion = subprocess.run(arg, stdout=subprocess.PIPE) - if completion.returncode != 0: - raise IOError('Input {} is not currently supported by ' - 'PyDesigner.'.format(path)) - # Remove new line delimiter - console = str(completion.stdout).split('\\n') - # Remove 'b' - console[0] = console[0][1:] - # Remove quotes - console = [s.replace("'", "") for s in console] - # Condense empty strings - console = [s.replace('"', '') for s in console] - # Remove empty strings form list - console = list(filter(None, console)) - # Convert list of strings to float - dw_scheme = [] - for idx_a, line in enumerate(console): - nums = [] - for idx_b, num in enumerate(line.split()): - nums.append(float(num)) - dw_scheme.append(nums) - return dw_scheme - -def pescheme(path): - """ - Returns a list of phase encoding scheme. If len(pescheme) > 1, - the .mif DWI contains more than one directons - - Parameters - ---------- - path : str - Path to input image or directory - - Returns - ------- - nPE: int - Number of PE directions - PE: int or list of int - Phase encoding direction(s) - """ - if not op.exists(path): - raise OSError('Input path does not exist. Please ensure that the ' - 'folder or file specified exists.') - ftype = format(path) - if ftype != 'MRtrix': - raise IOError('This function only works with MRtrix (.mif) ' - 'formatted filetypes. Please ensure that the input ' - 'filetype meets this requirement') - arg = ['mrinfo', '-petable'] - arg.append(path) - completion = subprocess.run(arg, stdout=subprocess.PIPE) - if completion.returncode != 0: - raise IOError('Input {} is not currently supported by ' - 'PyDesigner.'.format(path)) - # Remove new line delimiter - console = str(completion.stdout).split('\\n') - # Remove 'b' - console[0] = console[0][1:] - # Remove quotes - console = [s.replace("'", "") for s in console] - # Condense empty strings - console = [s.replace('"', '') for s in console] - # Remove empty strings form list - console.remove('') - # Convert list of strings to float - pe_scheme = [] - for idx_a, line in enumerate(console): - nums = [] - for idx_b, num in enumerate(line.split()): - nums.append(float(num)) - pe_scheme.append(nums) - return pe_scheme - -def shells(path): - """ - Returns the number of b-value shells detected in input file - - Parameters - ---------- - path : str - Path to input image or directory - - Returns - ------- - int - Number of shells - """ - if not op.exists(path): - raise OSError('Input path does not exist. Please ensure that the ' - 'folder or file specified exists.') - ftype = format(path) - if ftype != 'MRtrix': - raise IOError('This function only works with MRtrix (.mif) ' - 'formatted filetypes. Please ensure that the input ' - 'filetype meets this requirement') - arg = ['mrinfo', '-shell_bvalues'] - arg.append(path) - completion = subprocess.run(arg, stdout=subprocess.PIPE) - if completion.returncode != 0: - raise IOError('Input {} is not currently supported by ' - 'PyDesigner.'.format(path)) - # Remove new line delimiter - console = str(completion.stdout).split('\\n') - # Remove 'b' - console[0] = console[0][1:] - # Remove quotes - console = [s.replace("'", "") for s in console] - # Condense empty strings - console = [s.replace('"', '') for s in console] - # Remove empty strings form list - console.remove('') - # Split spaces - console = [s.split(' ') for s in console] - console = [item for sublist in console for item in sublist] - console = list(filter(None, console)) - console = [int(round(float(x))) for x in console] - return console - -def num_shells(path): - """ - Returns the number of b-value shells detected in input file - - Parameters - ---------- - path : str - Path to input image or directory - - Returns - ------- - int - Number of shells - """ - if not op.exists(path): - raise OSError('Input path does not exist. Please ensure that the ' - 'folder or file specified exists.') - ftype = format(path) - if ftype != 'MRtrix': - raise IOError('This function only works with MRtrix (.mif) ' - 'formatted filetypes. Please ensure that the input ' - 'filetype meets this requirement') - console = shells(path) - return len(console) - -def max_shell(path): - """ - Returns the maximum b-value shell in DWI - - Parameters - ---------- - path : str - Path to input image or directory - - Returns - ------- - int - Max b-value - """ - if not op.exists(path): - raise OSError('Input path does not exist. Please ensure that the ' - 'folder or file specified exists.') - ftype = format(path) - if ftype != 'MRtrix': - raise IOError('This function only works with MRtrix (.mif) ' - 'formatted filetypes. Please ensure that the input ' - 'filetype meets this requirement') - arg = ['mrinfo', '-shell_bvalues'] - arg.append(path) - completion = subprocess.run(arg, stdout=subprocess.PIPE) - if completion.returncode != 0: - raise IOError('Input {} is not currently supported by ' - 'PyDesigner.'.format(path)) - # Remove new line delimiter - console = str(completion.stdout).split('\\n') - # Remove 'b' - console[0] = console[0][1:] - # Remove quotes - console = [s.replace("'", "") for s in console] - # Condense empty strings - console = [s.replace('"', '') for s in console] - # Remove empty strings form list - console.remove('') - # Split spaces - console = [s.split(' ') for s in console] - console = [item for sublist in console for item in sublist] - console = list(filter(None, console)) - console = [int(round(float(s))) for s in console] - return max(console) - -def is_fullsphere(path): - """ - Returns boolean value indicating whether input file has full - spherical sampling - - Parameters - ---------- - path : str - Path to input image or directory - - Returns - ------- - bool - True if full spherical sampling - False if half-spherical sampling - """ - if not op.exists(path): - raise OSError('Input path does not exist. Please ensure that the ' - 'folder or file specified exists.') - ftype = format(path) - if ftype != 'MRtrix': - raise IOError('This function only works with MRtrix (.mif) ' - 'formatted filetypes. Please ensure that the input ' - 'filetype meets this requirement') - arg = ['dirstat', path, '-output', 'ASYM'] - completion = subprocess.run(arg, stdout=subprocess.PIPE) - if completion.returncode != 0: - raise IOError('Input {} is not currently supported by ' - 'PyDesigner.'.format(path)) - # Remove new line delimiter - console = str(completion.stdout).split('\\n') - # Remove 'b' - console[0] = console[0][1:] - # Remove quotes - console = [s.replace("'", "") for s in console] - # Condense empty strings - console = [s.replace('"', '') for s in console] - # Remove empty strings form list - console.remove('') - # Convert strings to list - console = [float(s) for s in console] - # Find mean of all b-shells and round to nearest one decimal place - mean_dir = round(sum(console) / len(console), 1) - # Having too many b-value shells for protocols such as FBI or - # HARDI, eddy correction can create a lot of trouble. If that's - # the case, we just assume that data is fully shelled. This - # disables heuristics testing performed by eddy correction. A DWI - # will return half-shelled if and only if the norm of mean - # direction vector is greater that 0.3 AND has max b-value less - # than or equal to 3000. The linear model breaks down for high - # b-values. - if mean_dir < 0.3: - return True - else: - if max_shell(path) > 3000: - return True - else: - return False - -def echotime(path): - """ - Returns the echo time(s) of DWI in miliseconds - - Parameters - ---------- - path : str - Path to input image or directory - - Returns - ------- - int if all DWIs have the same echo time - Echo time in miliseconds - str if all DWIs have different echo time - 'variable' - """ - if not op.exists(path): - raise OSError('Input path does not exist. Please ensure that the ' - 'folder or file specified exists.') - ftype = format(path) - if ftype != 'MRtrix': - raise IOError('This function only works with MRtrix (.mif) ' - 'formatted filetypes. Please ensure that the input ' - 'filetype meets this requirement') - arg = ['mrinfo', '-property', 'EchoTime'] - arg.append(path) - completion = subprocess.run(arg, stdout=subprocess.PIPE) - if completion.returncode != 0: - raise IOError('Input {} is not currently supported by ' - 'PyDesigner.'.format(path)) - console = str(completion.stdout).split('\\n')[0] - console = console.split('b')[-1] - console = console.replace("'", "") - try: - console = float(console) - console = int(round(console * 1000, 0)) - except: - console = 'variable' - return console diff --git a/designer/preprocessing/mrpreproc.py b/designer/preprocessing/mrpreproc.py deleted file mode 100644 index 17c42ed6..00000000 --- a/designer/preprocessing/mrpreproc.py +++ /dev/null @@ -1,1454 +0,0 @@ -#!/usr/bin/env python -# -*- coding : utf-8 -*- - -""" -Utilities for running various MRtrix3's DWI preprocessing tools -""" - -import os -import os.path as op -from shutil import copyfile, which -import subprocess -import numpy as np -from designer.preprocessing import preparation, util, smoothing, rician, mrinfoutil - -def miftonii(input, output, nthreads=None, - force=True, verbose=False): - """ - Converts input `.mif` images to output `.nii` images - - Parameters - ---------- - input : str - Path to input .mif file - output : str - Path to output .nii file - nthreads : int, optional - Specify the number of threads to use in processing - (Default: all available threads) - force : bool, optional - Force overwrite of output files if pre-existing - (Default:False) - verbose : bool, optional - Specify whether to print console output (Default: False) - - Returns - ------- - None; writes out file - - See Also - -------- - niitomif - """ - if not op.exists(input): - raise OSError('Input path does not exist. Please ensure that ' - 'the folder or file specified exists.') - if not op.exists(op.dirname(output)): - raise OSError('Specifed directory for output file {} does not ' - 'exist. Please ensure that this is a valid ' - 'directory.'.format(op.dirname(output))) - if op.splitext(output)[-1] != '.nii': - raise OSError('Output specified does not possess the .nii ' - 'extension.') - if not (nthreads is None): - if not isinstance(nthreads, int): - raise Exception('Please specify the number of threads as an ' - 'integer.') - if not isinstance(force, bool): - raise Exception('Please specify whether forced overwrite is True ' - 'or False.') - if not isinstance(verbose, bool): - raise Exception('Please specify whether verbose is True or False.') - arg = ['mrconvert'] - if force: - arg.append('-force') - if not verbose: - arg.append('-quiet') - if not (nthreads is None): - arg.extend(['-nthreads', str(nthreads)]) - arg.extend(['-export_grad_fsl', - op.splitext(output)[0] + '.bvec', - op.splitext(output)[0] + '.bval']) - arg.extend(['-json_export', op.splitext(output)[0] + '.json']) - arg.extend([input, output]) - completion = subprocess.run(arg) - if completion.returncode != 0: - raise Exception('Conversion from .mif to .nii failed; check ' - 'above for errors.') - -def niitomif(input, output, nthreads=None, - force=True, verbose=False): - """ - Converts input `.nii` images to output `.nif` images provided that - all BVEC, BVAL and JSON files are provided and named same as input .nii - - Parameters - ---------- - input : str - Path to input .nii file - output : str - Path to output .mif file - nthreads : int, optional - Specify the number of threads to use in processing - (Default: all available threads) - force : bool, optional - Force overwrite of output files if pre-existing - (Default:False) - verbose : bool, optional - Specify whether to print console output (Default: False) - - Returns - ------- - None; writes out file - - See Also - -------- - miftonii - """ - if not op.exists(input): - raise OSError('Input path does not exist. Please ensure that ' - 'the folder or file specified exists.') - if not op.exists(op.dirname(output)): - raise OSError('Specifed directory for output file {} does not ' - 'exist. Please ensure that this is a valid ' - 'directory.'.format(op.dirname(output))) - if op.splitext(output)[-1] != '.mif': - raise OSError('Output specified does not possess the .mif ' - 'extension.') - if not op.exists(op.splitext(input)[0] + '.bvec'): - raise OSError('Unable to locate BVEC file" {}'.format(op.splitext( - output)[0] + '.bvec')) - if not op.exists(op.splitext(input)[0] + '.bval'): - raise OSError('Unable to locate BVAL file" {}'.format(op.splitext( - output)[0] + '.bval')) - if not op.exists(op.splitext(input)[0] + '.json'): - raise OSError('Unable to locate JSON file" {}'.format(op.splitext( - output)[0] + '.json')) - if not (nthreads is None): - if not isinstance(nthreads, int): - raise Exception('Please specify the number of threads as an ' - 'integer.') - if not isinstance(force, bool): - raise Exception('Please specify whether forced overwrite is True ' - 'or False.') - if not isinstance(verbose, bool): - raise Exception('Please specify whether verbose is True or False.') - arg = ['mrconvert'] - if force: - arg.append('-force') - if not verbose: - arg.append('-quiet') - if not (nthreads is None): - arg.extend(['-nthreads', str(nthreads)]) - arg.extend(['-fslgrad', - op.splitext(input)[0] + '.bvec', - op.splitext(input)[0] + '.bval']) - arg.extend(['-json_import', op.splitext(input)[0] + '.json']) - arg.extend([input, output]) - completion = subprocess.run(arg) - if completion.returncode != 0: - raise Exception('Conversion from .nii to .mif failed; check ' - 'above for errors.') - -def stride_match(target, moving, output, nthreads=None, force=True, verbose=False): - """ - Matches strides on inputs target and moving by converting strides - on moving image to those of target image. - - Parameters - ---------- - target : str - Path to target image .nii or .mif file - moving : str - Path to moving image .nii or .mif file - output : str - Path to output .nii or .mif file - nthreads : int, optional - Specify the number of threads to use in processing - (Default: all available threads) - force : bool, optional - Force overwrite of output files if pre-existing - (Default:False) - verbose : bool, optional - Specify whether to print console output (Default: False) - - Returns - ------- - None; writes out file - """ - if not op.exists(target): - raise OSError('Input target path does not exist. Please ensure that ' - 'the folder or file specified exists.') - if op.splitext(target)[-1] not in ['.nii', '.mif']: - raise OSError('Input target image needs to be a .nii or .mif file') - if not op.exists(moving): - raise OSError('Input moving path does not exist. Please ensure that ' - 'the folder or file specified exists.') - if op.splitext(moving)[-1] not in ['.nii', '.mif']: - raise OSError('Input moving image needs to be a .nii or .mif file') - if not op.exists(op.dirname(output)): - raise OSError('Specifed directory for output file {} does not ' - 'exist. Please ensure that this is a valid ' - 'directory.'.format(op.dirname(output))) - if op.splitext(output)[-1] not in ['.nii', '.mif']: - raise OSError('Output specified does not possess the .nii ' - 'extension.') - if not (nthreads is None): - if not isinstance(nthreads, int): - raise Exception('Please specify the number of threads as an ' - 'integer.') - if not isinstance(force, bool): - raise Exception('Please specify whether forced overwrite is True ' - 'or False.') - if not isinstance(verbose, bool): - raise Exception('Please specify whether verbose is True or False.') - arg = ['mrconvert'] - if force: - arg.append('-force') - if not verbose: - arg.append('-quiet') - if not (nthreads is None): - arg.extend(['-nthreads', str(nthreads)]) - arg.extend( - [ - '-strides', target, - moving, - output - ] - ) - completion = subprocess.run(arg) - if completion.returncode != 0: - raise Exception('Stride matching failed; check above for errors.') - -def denoise(input, output, noisemap=True, extent='5,5,5', nthreads=None, - force=True, verbose=False): - """ - Runs MRtrix3's `dwidenoise` command with optimal parameters for - PyDesigner. - - Parameters - ---------- - input : str - Path to input .mif file - output : str - Path to output .mif file - noisemap : bool, optional - Specify whether or not to save the noisemap as a - nifti file (Default: True) - extent : str, optional - Set the window size of the denoising filter. - (Default: '5,5,5') - nthreads : int, optional - Specify the number of threads to use in processing - (Default: all available threads) - force : bool, optional - Force overwrite of output files if pre-existing - (Default:False) - verbose : bool, optional - Specify whether to print console output (Default: False) - - Returns - ------- - None; writes out file - """ - if not op.exists(input): - raise OSError('Input path does not exist. Please ensure that ' - 'the folder or file specified exists.') - if not op.exists(op.dirname(output)): - raise OSError('Specifed directory for output file {} does not ' - 'exist. Please ensure that this is a valid ' - 'directory.'.format(op.dirname(output))) - if not isinstance(noisemap, bool): - raise Exception('Please specify whether noisemap generation ' - 'is True or False.') - if not isinstance(extent, str): - raise Exception('Please specify extent as a string formatted as ' - '"n,n,n".') - if not (nthreads is None): - if not isinstance(nthreads, int): - raise Exception('Please specify the number of threads as an ' - 'integer.') - if not isinstance(force, bool): - raise Exception('Please specify whether forced overwrite is True ' - 'or False.') - if not isinstance(verbose, bool): - raise Exception('Please specify whether verbose is True or False.') - noisemap_path = op.join(op.dirname(input), 'noisemap.nii') - arg = ['dwidenoise'] - if force: - arg.append('-force') - if not verbose: - arg.append('-quiet') - if not (nthreads is None): - arg.extend(['-nthreads', str(nthreads)]) - if noisemap: - arg.extend(['-noise', noisemap_path]) - if not (extent is None): - arg.extend(['-extent', extent]) - arg.extend([input, output]) - completion = subprocess.run(arg) - if completion.returncode != 0: - raise Exception('dwidenoise failed, please look above for error ' - 'sources.') - -def degibbs(input, output, nthreads=None, force=False, verbose=False): - """ - Runs MRtrix3's `mrdegibbs` command with optimal parameters for - PyDesigner. - - Parameters - ---------- - input : str - Path to input .mif file - output : str - Path to output .mif file - nthreads : int, optional - Specify the number of threads to use in processing - (Default: all available threads) - force : bool, optional - Force overwrite of output files if pre-existing - (Default:False) - verbose : bool, optional - Specify whether to print console output (Default: False) - - Returns - ------- - None; writes out file - """ - if not op.exists(input): - raise OSError('Input path does not exist. Please ensure that ' - 'the folder or file specified exists.') - if not op.exists(op.dirname(output)): - raise OSError('Specifed directory for output file {} does not ' - 'exist. Please ensure that this is a valid ' - 'directory.'.format(op.dirname(output))) - if not (nthreads is None): - if not isinstance(nthreads, int): - raise Exception('Please specify the number of threads as an ' - 'integer.') - if not isinstance(force, bool): - raise Exception('Please specify whether forced overwrite is True ' - 'or False.') - if not isinstance(verbose, bool): - raise Exception('Please specify whether verbose is True or False.') - arg = ['mrdegibbs'] - if force: - arg.append('-force') - if not verbose: - arg.append('-quiet') - if not (nthreads is None): - arg.extend(['-nthreads', str(nthreads)]) - arg.extend([input, output]) - completion = subprocess.run(arg) - if completion.returncode != 0: - raise Exception('mrdegibbs failed, please look above for error ' - 'sources.') - -def undistort(input, output, rpe='rpe_header', epib0=1, - qc=None, nthreads=None, force=False, verbose=False): - """ - Runs MRtrix3's distortion correction command with optimal - parameters for PyDesigner. - - Parameters - ---------- - input : str - Path to input .mif file - output : str - Path to output .mif file - rpe : str, {'rpe_header', 'rpe-pair', 'rpe_all, 'rpe_all'}, optional - Reverse phase encoding of the dataset. (Default: 'rpe_header') - epib0 : int - Number of reverse PE dir B0 pairs to use in TOPUP correction - (Default: 1) - qc : str - Specify path to QC directior. No QC metrics generated if None - nthreads : int, optional - Specify the number of threads to use in processing - (Default: all available threads) - force : bool, optional - Force overwrite of output files if pre-existing - (Default:False) - verbose : bool, optional - Specify whether to print console output (Default: False) - - Returns - ------- - None; writes out file - """ - if not op.exists(input): - raise OSError('Input path does not exist. Please ensure that ' - 'the folder or file specified exists.') - if not op.exists(op.dirname(output)): - raise OSError('Specifed directory for output file {} does not ' - 'exist. Please ensure that this is a valid ' - 'directory.'.format(op.dirname(output))) - if not rpe in ['rpe_none', 'rpe_pair', 'rpe_all', 'rpe_header']: - raise Exception('Entered RPE selection is not valid. Please ' - 'choose either "rpe_none", "rpe_pair", ' - '"rpe_all", or "rpe_header".') - if not isinstance(epib0, int): - raise Exception('Number of TOPUP B0s need to be specified as ' - 'as an integer.') - if not qc is None: - if not isinstance(qc, str): - raise Exception('Please specify QC directory as a string') - if not op.exists(qc): - raise OSError('Specified QC directory does not exist. ' - 'Please ensure that this is a valid ' - 'directory.') - if not (nthreads is None): - if not isinstance(nthreads, int): - raise Exception('Please specify the number of threads as an ' - 'integer.') - if not isinstance(force, bool): - raise Exception('Please specify whether forced overwrite is True ' - 'or False.') - if not isinstance(verbose, bool): - raise Exception('Please specify whether verbose is True or False.') - rpe = '-' + rpe - # Get output directory - outdir = op.dirname(output) - # Extract BVEC and BVALS for shell sampling deduction - arg_extract = ['mrinfo'] - arg_extract.extend(['-export_grad_fsl', - op.join(outdir, 'dwiec.bvec'), - op.join(outdir, 'dwiec.bval')]) - arg_extract.append(input) - completion = subprocess.run(arg_extract) - if completion.returncode != 0: - raise Exception('extracting FSL BVEC and BVEC gradients ' - 'failed during undistortion, please look ' - 'above for errors.') - # Form main undistortion argument - arg = [] - if which('dwipreproc') is None: - arg.append('dwifslpreproc') - else: - arg.append('dwipreproc') - if force: - arg.append('-force') - if not verbose: - arg.append('-quiet') - if not (nthreads is None): - arg.extend(['-nthreads', str(nthreads)]) - # Determine whether half or full sphere sampling - repol_string = '--repol ' - if mrinfoutil.is_fullsphere(input): - # is full, add appropriate dwifslpreproc option - repol_string += '--data_is_shelled' - else: - # half - repol_string += '--slm=linear' - if epib0 > 0: - try: - epi_path = op.join(outdir, 'B0_EPI.mif') - epiboost(input=input, - output=epi_path, - num=epib0, - nthreads=nthreads, - force=force, - verbose=verbose) - arg.extend(['-se_epi', epi_path]) - except: - print('[WARNING] Unable to apply TOPUPBOOST because DWI ' - 'consists of single PE direction.') - # Remove the B0_ALL.mif file that is created when epiboost - # function fails - try: - os.remove(op.join(outdir, 'B0_ALL.mif')) - except OSError: - pass - arg.extend(['-eddy_options', repol_string]) - arg.append(rpe) - if not qc is None: - arg.extend(['-eddyqc_all', qc]) - arg.extend([input, output]) - completion = subprocess.run(arg, cwd=outdir) - if completion.returncode != 0: - raise Exception('dwifslpreproc failed, please look above for ' - 'error sources.') - # Remove temporarily generated files - os.remove(op.join(outdir, 'dwiec.bvec')) - os.remove(op.join(outdir, 'dwiec.bval')) - if epib0 > 0: - try: - os.remove(epi_path) - except: - print('[Warning] unable to remove {} because it does not ' - 'exist'.format(epi_path)) - -def brainmask(input, output, thresh=0.25, nthreads=None, force=False, - verbose=False): - """ - Creates a brainmask using FSL's Brain Extraction Tool (BET) and - MRtrix3's file manipulation tools. - - Parameters - ---------- - input : str - Path to input .mif file - output : str - Path to output .nii brainmask file - thresh : float - BET threshold ranging from 0 to 1 (Default: 0.25) - nthreads : int, optional - Specify the number of threads to use in processing - (Default: all available threads) - force : bool, optional - Force overwrite of output files if pre-existing - (Default:False) - verbose : bool, optional - Specify whether to print console output (Default: False) - - Returns - ------- - None; writes out file - """ - if not op.exists(input): - raise OSError('Input path does not exist. Please ensure that ' - 'the folder or file specified exists.') - if not op.exists(op.dirname(output)): - raise OSError('Specifed directory for output file {} does not ' - 'exist. Please ensure that this is a valid ' - 'directory.'.format(op.dirname(output))) - if (thresh < 0) or (thresh > 1): - raise ValueError('BET Threshold needs to be within 0 to 1 range.') - if not (nthreads is None): - if not isinstance(nthreads, int): - raise Exception('Please specify the number of threads as an ' - 'integer.') - if not isinstance(force, bool): - raise Exception('Please specify whether forced overwrite is True ' - 'or False.') - if not isinstance(verbose, bool): - raise Exception('Please specify whether verbose is True or False.') - # Read FSL NifTi output format and change it if not '.nii' - fsl_suffix = os.getenv('FSLOUTPUTTYPE') - if fsl_suffix is None: - raise OSError('Unable to determine system environment variable ' - 'FSF_OUTPUT_FORMAT. Ensure that FSL is installed ' - 'correctly.') - if fsl_suffix == 'NIFTI_GZ': - os.environ['FSLOUTPUTTYPE'] = 'NIFTI' - outdir = op.dirname(output) - B0_nan = op.join(outdir, 'B0_nan.nii') - mask = op.join(outdir, 'brain') - tmp_brain = op.join(outdir, 'brain.nii') - # Extract averaged B0 from DWI - extractmeanbzero(input=input, - output=B0_nan, - nthreads=nthreads, - force=force, - verbose=verbose) - # Compute brain mask - arg_mask = ['bet', B0_nan, mask, '-m', '-f', str(thresh)] - completion = subprocess.run(arg_mask) - if completion.returncode != 0: - raise Exception('Unable to compute brain mask from B0. See above ' - 'for errors') - # Remove intermediary file - os.remove(B0_nan) - os.remove(tmp_brain) - os.rename(op.join(outdir, mask + '_mask.nii'), output) - -def csfmask(input, output, method='fsl', coeff=2, thresh=0.25, - nthreads=None, force=False, verbose=False): - """ - Creates a cerebral spinal fluid (CSF) mask from FSL's FAST tool. - - Parameters - ---------- - input : str - Path to input .mif file - output : str - Path to output .nii CSF mask file - method : str, optional - Define method to use for computing a CSF mask. `'fsl'` relies - on FSL FAST segmentation, and `adc` uses pseudo-diffusion - coefficient more than 2 (default) to compute a mask - coeff : float, optional - Diffusion coefficient to use in thresholding a pseudo-diffusion - map to estimate CSF (Default: 2) - thresh : float, optional - BET threshold ranging from 0 to 1 (Default: 0.25) - nthreads : int, optional - Specify the number of threads to use in processing - (Default: all available threads) - force : bool, optional - Force overwrite of output files if pre-existing - (Default:False) - verbose : bool, optional - Specify whether to print console output (Default: False) - - Returns - ------- - None; writes out file - """ - if not op.exists(input): - raise OSError('Input path does not exist. Please ensure that ' - 'the folder or file specified exists.') - if not op.exists(op.dirname(output)): - raise OSError('Specifed directory for output file {} does not ' - 'exist. Please ensure that this is a valid ' - 'directory.'.format(op.dirname(output))) - if (op.splitext(output))[-1] != '.nii': - raise IOError('Output filename {} must be specified as a ' - 'NifTi (.nii) file.') - if (thresh < 0) or (thresh > 1): - raise ValueError('BET Threshold needs to be within 0 to 1 range.') - if not (nthreads is None): - if not isinstance(nthreads, int): - raise Exception('Please specify the number of threads as an ' - 'integer.') - if not isinstance(force, bool): - raise Exception('Please specify whether forced overwrite is True ' - 'or False.') - if not isinstance(verbose, bool): - raise Exception('Please specify whether verbose is True or False.') - outdir = op.dirname(output) - if 'fsl' in method: - # Read FSL NifTi output format and change it if not '.nii' - fsl_suffix = os.getenv('FSLOUTPUTTYPE') - if fsl_suffix is None: - raise OSError('Unable to determine system environment variable ' - 'FSF_OUTPUT_FORMAT. Ensure that FSL is installed ' - 'correctly.') - if fsl_suffix == 'NIFTI_GZ': - os.environ['FSLOUTPUTTYPE'] = 'NIFTI' - f_suffix = '.nii' - B0_nan = op.join(outdir, 'B0_nan' + f_suffix) - path_brain = op.join(outdir, 'brain') - path_tissue = op.join(outdir, 'tissue') - - # Extract averaged B0 from DWI - extractmeanbzero(input=input, - output=B0_nan, - nthreads=nthreads, - force=force, - verbose=verbose) - # Compute brain mask - arg_mask = ['bet', B0_nan, path_brain, '-m', '-f', str(thresh)] - completion = subprocess.run(arg_mask) - if completion.returncode != 0: - raise Exception('Unable to compute brain mask from B0. See above ' - 'for errors') - arg = [ - 'fast' - ] - if verbose: - arg.append('-v') - arg.extend([ - '-n', '4', - '-t', '2', - '-o', path_tissue, - path_brain + f_suffix - ]) - completion = subprocess.run(arg) - if completion.returncode != 0: - raise Exception('FSL FAST segmentation of brain tissue failed. ' - 'See above for errors.') - csfclass = [] - for i in range(4): - arg = [ - 'fslmaths', - path_tissue + '_pve_' + str(i) + f_suffix, - '-thr', '0.95', - '-bin', path_tissue + '_pve_thr_' + str(i) + f_suffix - ] - completion = subprocess.run(arg) - if completion.returncode != 0: - raise Exception('FSLMATHS tissue thresholding failed. ' - 'See above for errors.') - arg = [ - 'fslstats', - path_brain + '.nii', - '-k', path_tissue + '_pve_thr_' + str(i) + f_suffix, - '-P', '95' - ] - completion = subprocess.run(arg, stdout=subprocess.PIPE) - if completion.returncode != 0: - raise Exception('FSLSTATS tissue thresholding failed. ' - 'See above for errors.') - console = str(completion.stdout).split('\\n')[0] - console = console.split('b')[-1] - console = console.replace("'", "") - csfclass.append(float(console)) - csfind = np.argmax(csfclass) - arg = [ - 'fslmaths', - path_tissue + '_pve_' + str(csfind) + f_suffix, - '-thr', '0.7', - '-bin', - # '-mul', '-1', - # '-add', '1', - '-mul', - path_brain + '_mask' + f_suffix, - output - ] - completion = subprocess.run(arg) - if completion.returncode != 0: - raise Exception('Unable to create CSF mask. ' - 'See above for errors.') - # Remove intermediate files - os.remove(B0_nan) - os.remove(op.join(outdir, path_brain + f_suffix)) - for i in range(4): - os.remove(path_tissue + '_pve_' + str(i) + f_suffix) - os.remove(path_tissue + '_pve_thr_' + str(i) + f_suffix) - os.remove(path_tissue + '_mixeltype' + f_suffix) - os.remove(path_tissue + '_pveseg' + f_suffix) - os.remove(path_tissue + '_seg' + f_suffix) - if 'adc' in method: - # Get list of b-values - bvals = mrinfoutil.shells(input) - # Find index of shell closest to b=1000 - idx = min(range(len(bvals)), key=lambda i: abs(bvals[i]-1000)) - # Specify file paths - path_b0 = op.join(outdir, 'S0.mif') - path_shell = op.join(outdir, 'S1000.mif') - # Extract mean B - # Extract averaged B0 from DWI - extractmeanbzero( - input=input, - output=path_b0, - nthreads=nthreads, - force=force, - verbose=verbose) - # Extract mean of indexed b=1000 shell - extractmeanshell( - input=input, - output=path_shell, - shell=bvals[idx], - nthreads=nthreads, - force=force, - verbose=verbose - ) - # Use the formula D_pseudo = ln(S0/S1000)/B1000 > 2 to compute - # brain mask based on pseudo-ADC - arg = ['mrcalc'] - if force: - arg.append('-force') - if not verbose: - arg.append('-quiet') - if not (nthreads is None): - arg.extend(['-nthreads', str(nthreads)]) - arg.extend( - [ - path_b0, - path_shell, - '-div', - '-log', - str(bvals[idx]/1000), - '-div', - str(coeff), - '-gt', - output - ] - ) - completion = subprocess.run(arg) - if completion.returncode != 0: - raise Exception('Unable to compute pseudo ADC. ' - 'See above for errors.') - os.remove(path_b0) - os.remove(path_shell) - -def smooth(input, output, csfname=None, fwhm=1.25, size=5): - """ - Performs Gaussian smoothing on input .mif image - - Parameters - ---------- - input : str - Path to input .mif file - output : str - Path to output .mif file - csfname : str - Path to CSF mask file in .nii format - fwhm : float - The full width half max in voxels to be smoothed - (Default: 1.25) - - Returns - ------- - None; writes out file - """ - if not op.exists(input): - raise OSError('Input path does not exist. Please ensure that ' - 'the folder or file specified exists.') - if not op.exists(op.dirname(output)): - raise OSError('Specifed directory for output file {} does not ' - 'exist. Please ensure that this is a valid ' - 'directory.'.format(op.dirname(output))) - if not (csfname is None): - if not op.exists(csfname): - raise OSError('Path to CSF mask does not exist. Please ' - 'ensure that the file specified exists.') - if fwhm < 0: - raise Exception('FWHM cannot be less than zero.') - if size < 0: - raise Exception('Size cannot be less than zero. Please ' - 'specify size as a positive integer.') - # Convert input .mif to .nii - outdir = op.dirname(output) - nii_path = op.join(outdir, 'dwism.nii') - miftonii(input=input, output=nii_path) - # Perform smoothing - smoothing.smooth_image(nii_path, - csfname=csfname, - outname=nii_path, - width=fwhm) - # Convert .nii to .mif - niitomif(input=nii_path, output=output) - # Remove converted files - os.remove(nii_path) - os.remove(op.splitext(nii_path)[0] + '.bvec') - os.remove(op.splitext(nii_path)[0] + '.bval') - os.remove(op.splitext(nii_path)[0] + '.json') - -def riciancorrect(input, output, noise=None): - """ - Performs Rician correction on input .mif - - Parameters - ---------- - input : str - Path to input .mif file - output : str - Path to output .mif file - noise : str - Path to noise map from dwidenoise in .nii format (Default: None) - - Returns - ------- - None; writes out file - """ - if not op.exists(input): - raise OSError('Input path does not exist. Please ensure that ' - 'the folder or file specified exists.') - if not op.exists(op.dirname(output)): - raise OSError('Specifed directory for output file {} does not ' - 'exist. Please ensure that this is a valid ' - 'directory.'.format(op.dirname(output))) - if noise is not None: - if not op.exists(noise): - raise OSError('Input noisemap {} does not exist.'.format( - noise)) - if op.splitext(noise)[-1] != '.nii': - raise OSError('Noisemap needs to be in NifTi format.') - else: - raise Exception('Rician correction cannot be performed without a ' - 'noisemap.') - # Convert input .mif to .nii - outdir = op.dirname(output) - nii_path = op.join(outdir, 'dwirc.nii') - miftonii(input=input, output=nii_path) - # Perform Rician correction - rician.rician_img_correct(nii_path, - noise, - outpath=nii_path) - # Convert .nii to .mif - niitomif(input=nii_path, output=output) - # Remove converted files - os.remove(nii_path) - os.remove(op.splitext(nii_path)[0] + '.bvec') - os.remove(op.splitext(nii_path)[0] + '.bval') - os.remove(op.splitext(nii_path)[0] + '.json') - -def extractbzero(input, output, nthreads=None, force=False, - verbose=False): - """ - Extracts only bzero shells from an input mif file. - - Parameters - ---------- - input : str - Path to input .mif file - output : str - Path to output .mif file - nthreads : int, optional - Specify the number of threads to use in processing - (Default: all available threads) - force : bool, optional - Force overwrite of output files if pre-existing - (Default:False) - verbose : bool, optional - Specify whether to print console output (Default: False) - - Returns - ------- - None; writes out file - """ - if not op.exists(input): - raise OSError('Input path does not exist. Please ensure that ' - 'the folder or file specified exists.') - if not op.exists(op.dirname(output)): - raise OSError('Specifed directory for output file {} does not ' - 'exist. Please ensure that this is a valid ' - 'directory.'.format(op.dirname(output))) - if not (nthreads is None): - if not isinstance(nthreads, int): - raise Exception('Please specify the number of threads as an ' - 'integer.') - if not isinstance(force, bool): - raise Exception('Please specify whether forced overwrite is True ' - 'or False.') - if not isinstance(verbose, bool): - raise Exception('Please specify whether verbose is True or False.') - arg = ['dwiextract'] - if force: - arg.append('-force') - if not verbose: - arg.append('-quiet') - if not (nthreads is None): - arg.extend(['-nthreads', str(nthreads)]) - arg.extend(['-bzero', input, output]) - completion = subprocess.run(arg) - if completion.returncode != 0: - raise Exception('Unable to extract B0s from DWI for computation ' - 'of brain mask. See above for errors.') - -def extractmeanbzero(input, output, nthreads=None, force=False, - verbose=False): - """ - Extracts average B0 from all B0 shells, with NaNs removed. - - Parameters - ---------- - input : str - Path to input .mif file - output : str - Path to output .mif or .nii file - nthreads : int, optional - Specify the number of threads to use in processing - (Default: all available threads) - force : bool, optional - Force overwrite of output files if pre-existing - (Default:False) - verbose : bool, optional - Specify whether to print console output (Default: False) - - Returns - ------- - None; writes out file - """ - if not op.exists(input): - raise OSError('Input path does not exist. Please ensure that ' - 'the folder or file specified exists.') - if not op.exists(op.dirname(output)): - raise OSError('Specifed directory for output file {} does not ' - 'exist. Please ensure that this is a valid ' - 'directory.'.format(op.dirname(output))) - if not (nthreads is None): - if not isinstance(nthreads, int): - raise Exception('Please specify the number of threads as an ' - 'integer.') - if not isinstance(force, bool): - raise Exception('Please specify whether forced overwrite is True ' - 'or False.') - if not isinstance(verbose, bool): - raise Exception('Please specify whether verbose is True or False.') - outdir = op.dirname(output) - fname_bzero = op.join(outdir, 'B0_ALL.mif') - fname_mean = op.join(outdir, 'B0_MEAN.mif') - # Extract all B0s - extractbzero(input, fname_bzero, nthreads=nthreads, force=force, - verbose=verbose) - arg_mean = ['mrmath', '-axis', '3', fname_bzero, 'mean', fname_mean] - completion = subprocess.run(arg_mean) - if completion.returncode != 0: - raise Exception('Unable to compute mean of B0s. See above for' - 'errors.') - arg_nan = ['mrcalc', fname_mean, '-finite', fname_mean, - '0', '-if', output] - completion = subprocess.run(arg_nan) - if completion.returncode != 0: - raise Exception('Unable to remove NaNs from averaged B0. See ' - 'above for errors.') - # Remove non-essential files - os.remove(fname_bzero) - os.remove(fname_mean) - -def extractnonbzero(input, output, nthreads=None, force=False, - verbose=False): - """ - Extracts only non-bzero shells from an input mif file. - - Parameters - ---------- - input : str - Path to input .mif file - output : str - Path to output .mif file - nthreads : int, optional - Specify the number of threads to use in processing - (Default: all available threads) - force : bool, optional - Force overwrite of output files if pre-existing - (Default:False) - verbose : bool, optional - Specify whether to print console output (Default: False) - - Returns - ------- - None; writes out file - """ - if not op.exists(input): - raise OSError('Input path does not exist. Please ensure that ' - 'the folder or file specified exists.') - if not op.exists(op.dirname(output)): - raise OSError('Specifed directory for output file {} does not ' - 'exist. Please ensure that this is a valid ' - 'directory.'.format(op.dirname(output))) - if not (nthreads is None): - if not isinstance(nthreads, int): - raise Exception('Please specify the number of threads as an ' - 'integer.') - if not isinstance(force, bool): - raise Exception('Please specify whether forced overwrite is True ' - 'or False.') - if not isinstance(verbose, bool): - raise Exception('Please specify whether verbose is True or False.') - arg = ['dwiextract'] - if force: - arg.append('-force') - if not verbose: - arg.append('-quiet') - if not (nthreads is None): - arg.extend(['-nthreads', str(nthreads)]) - arg.extend(['-no_bzero', input, output]) - completion = subprocess.run(arg) - if completion.returncode != 0: - raise Exception('Unable to extract B0s from DWI for computation ' - 'of brain mask. See above for errors.') - -def extractshell(input, output, shell, nthreads=None, force=False, - verbose=False): - """ - Extracts specified shell from an input mif file. - - Parameters - ---------- - input : str - Path to input .mif file - output : str - Path to output .mif file - shell : int - Approximate b-value to extract - nthreads : int, optional - Specify the number of threads to use in processing - (Default: all available threads) - force : bool, optional - Force overwrite of output files if pre-existing - (Default:False) - verbose : bool, optional - Specify whether to print console output (Default: False) - - Returns - ------- - None; writes out file - """ - if not op.exists(input): - raise OSError('Input path does not exist. Please ensure that ' - 'the folder or file specified exists.') - if not op.exists(op.dirname(output)): - raise OSError('Specifed directory for output file {} does not ' - 'exist. Please ensure that this is a valid ' - 'directory.'.format(op.dirname(output))) - if not isinstance(shell, int): - raise Exception('Please specify the shell to extract as an ' - 'integer.') - if shell < 0: - raise Exception('Please specify the shell to extract as a ' - 'positive (more than 0) integer.') - if not (nthreads is None): - if not isinstance(nthreads, int): - raise Exception('Please specify the number of threads as an ' - 'integer.') - if not isinstance(force, bool): - raise Exception('Please specify whether forced overwrite is True ' - 'or False.') - if not isinstance(verbose, bool): - raise Exception('Please specify whether verbose is True or False.') - arg = ['dwiextract'] - if force: - arg.append('-force') - if not verbose: - arg.append('-quiet') - if not (nthreads is None): - arg.extend(['-nthreads', str(nthreads)]) - arg.extend(['-no_bzero', - '-singleshell', - '-shell', str(shell), - input, output]) - completion = subprocess.run(arg) - if completion.returncode != 0: - raise Exception('Unable to extract specified shells from DWI. ' - 'See above for errors.') - -def extractmeanshell(input, output, shell, nthreads=None, force=False, - verbose=False): - """ - Extracts mean of specified from an input mif file. - - Parameters - ---------- - input : str - Path to input .mif file - output : str - Path to output .mif file - shell : int - Approximate b-value to extract - nthreads : int, optional - Specify the number of threads to use in processing - (Default: all available threads) - force : bool, optional - Force overwrite of output files if pre-existing - (Default:False) - verbose : bool, optional - Specify whether to print console output (Default: False) - - Returns - ------- - None; writes out file - """ - if not op.exists(input): - raise OSError('Input path does not exist. Please ensure that ' - 'the folder or file specified exists.') - if not op.exists(op.dirname(output)): - raise OSError('Specifed directory for output file {} does not ' - 'exist. Please ensure that this is a valid ' - 'directory.'.format(op.dirname(output))) - if not isinstance(shell, int): - raise Exception('Please specify the shell to extract as an ' - 'integer.') - if shell < 0: - raise Exception('Please specify the shell to extract as a ' - 'positive (more than 0) integer.') - if not (nthreads is None): - if not isinstance(nthreads, int): - raise Exception('Please specify the number of threads as an ' - 'integer.') - if not isinstance(force, bool): - raise Exception('Please specify whether forced overwrite is True ' - 'or False.') - if not isinstance(verbose, bool): - raise Exception('Please specify whether verbose is True or False.') - outdir = op.dirname(output) - fname_shell = op.join(outdir, 'b' + str(shell) + '_ALL.mif') - fname_mean = op.join(outdir, 'b' + str(shell) + '_MEAN.mif') - # Extract all specified shells - extractshell(input, fname_shell, shell=shell, nthreads=nthreads, - force=force, verbose=verbose) - # Compute mean - arg_mean = ['mrmath', '-axis', '3', fname_shell, 'mean', fname_mean] - completion = subprocess.run(arg_mean) - if completion.returncode != 0: - raise Exception('Unable to compute mean of B0s. See above for' - 'errors.') - arg_nan = ['mrcalc', fname_mean, '-finite', fname_mean, - '0', '-if', output] - completion = subprocess.run(arg_nan) - if completion.returncode != 0: - raise Exception('Unable to remove NaNs from averaged shell ' - 'image. See above for errors.') - # Remove non-essential files - os.remove(fname_shell) - os.remove(fname_mean) - -def epiboost(input, output, num=1, nthreads=None, force=False, - verbose=False): - """ - Analyzes an input .mif's PE direction to split into two different - phase encoding (PE) DWIs. B0s from opposing PE are then extracted - and concatenated with the DWI. This reduces the number of B0s used - in undistortion for a better and speedier estimation of the - distortion field. - - Parameters - ---------- - input : str - Path to input .mif file - output : str - Path to output .mif file - num : int - Number of B0s pairs to use in EPI correction (Default: 1) - nthreads : int, optional - Specify the number of threads to use in processing - (Default: all available threads) - force : bool, optional - Force overwrite of output files if pre-existing - (Default:False) - verbose : bool, optional - Specify whether to print console output (Default: False) - - Returns - ------- - None; writes out file - """ - print('Applying EPIBOOST') - if not op.exists(input): - raise OSError('Input path does not exist. Please ensure that ' - 'the folder or file specified exists.') - if op.splitext(output)[-1] != '.mif': - raise OSError('Output should be specified as a .mif file.') - if not op.exists(op.dirname(output)): - raise OSError('Specifed directory for output file {} does not ' - 'exist. Please ensure that this is a valid ' - 'directory.'.format(op.dirname(output))) - if not isinstance(num, int): - raise Exception('Number of B0s to use needs to be specified ' - 'as an integer.') - if not num > 0: - raise Exception('Number of B0s to use needs to be a positive ' - 'integer greater than 0.') - if not (nthreads is None): - if not isinstance(nthreads, int): - raise Exception('Please specify the number of threads as an ' - 'integer.') - if not isinstance(force, bool): - raise Exception('Please specify whether forced overwrite is True ' - 'or False.') - if not isinstance(verbose, bool): - raise Exception('Please specify whether verbose is True or False.') - outdir = op.dirname(output) - fname_bzero = op.join(outdir, 'B0_ALL.mif') - # Extract all B0s - extractbzero(input, fname_bzero, nthreads=nthreads, force=force, - verbose=verbose) - # Start by figuring out whether DWI is composed of multiple PE dirs - # or single PE dirs. If all PE dirs are the same, the dataset likely - # comes with matching phase encoding and slice timing train, - # indicating that it has a single PE direction. - dw_scheme = np.array(mrinfoutil.dwscheme(fname_bzero), dtype=int)[:, -1] - pe_scheme = np.array(mrinfoutil.pescheme(fname_bzero)) - if len(pe_scheme) != len(dw_scheme): - raise Exception('It appears that the input volume possesses a ' - 'dw_scheme of length {}, and pe_scheme of length ' - '{}. These number need to match. Please check ' - 'your dataset or contact us on GitHub'.format( - len(dw_scheme), len(pe_scheme))) - uPE, indPE, iPE = np.unique(pe_scheme, axis=0, return_index=True, - return_inverse=True) - nPE = len(uPE) - if nPE < 2: - raise Exception('DWI consists of just one PE direction. ' - 'Unable to extract B0s.') - # Index unique PE directions - bval = [] - bind = [] - iteridx = np.unique(iPE) - for i, val in enumerate(iteridx): - bind.append(np.where(iPE == val)[0].tolist()) - bval.append(dw_scheme[np.where(iPE == val)].tolist()) - # Check whether number of B0s to extract exceed those in DWI - if num > min([len(x) for x in bval]): - raise Exception('Specified number of B0s pairs to extract ' - '({}) exceed those physically present in DWI ' - '({}), please ensure that variable `num` ' - 'suitably represents the number of B0s in DWI.' - .format(num, min([len(x) for x in bval]))) - # Extract the first `num` pairs from each PE direction - num = np.arange(0, num, dtype=int).tolist() - idx_extract = [] - for idx, val in enumerate(bind): - idx_extract.extend([val[i] for i in num]) - # Extract EPI volume - str_extract = [str(x) for x in idx_extract] - arg_epi = ['mrconvert'] - if force: - arg_epi.append('-force') - if not verbose: - arg_epi.append('-quiet') - if not (nthreads is None): - arg_epi.extend(['-nthreads', str(nthreads)]) - arg_epi.extend(['-coord', '3', ','.join(str_extract)]) - arg_epi.extend([fname_bzero, output]) - completion = subprocess.run(arg_epi) - if completion.returncode != 0: - raise Exception('EPIBOOST: failed to extract specified ' - 'TOPUP B0 indices. See above for errors.') - # Remove temp files - os.remove(fname_bzero) - -def reslice(input, output, size, interp='linear', - nthreads=None, force=False, verbose=False): - """ - Reslices input image to target voxel size - - Parameters - ---------- - input : str - Path to input file; .mif or .nii - output : str - Path to output file; .mif or .nii - size : tuple of float - x, y, z voxel size in mm or output dimensions. - interp : str, {'linear', 'nearest', 'cubic' , 'sinc'}, optional - set the interpolation method to use when resizing (Default: - 'linear') - nthreads : int, optional - Specify the number of threads to use in processing - (Default: all available threads) - force : bool, optional - Force overwrite of output files if pre-existing - (Default:False) - verbose : bool, optional - Specify whether to print console output (Default: False) - - Returns - ------- - None; writes out file - - Notes - ----- - If any of the axes in ``size`` is specified to be over 9 mm, this - functions reslices to defined output dimensions, instead of voxel - size. This is done to automatically reslice with minimal user - input, and also because voxel size beyond 9 mm in unrealistic. - - Additionally, if target resolution is the same as input file's - resolution, reslicing is skipped but the output file is still - generated. - """ - dim_str = '-voxel' - if not op.exists(input): - raise OSError('Input path does not exist. Please ensure that ' - 'the folder or file specified exists.') - if not op.exists(op.dirname(output)): - raise OSError('Specifed directory for output file {} does not ' - 'exist. Please ensure that this is a valid ' - 'directory.'.format(op.dirname(output))) - if not isinstance(size, str): - raise Exception('Voxel size needs to be defined as a string ' - ' of three values') - if len(size.split(',')) != 3: - raise Exception('Please specify voxel size for each axis or ' - 'single digit for all axes i.e. "3,3,3" for ' - '3 mm isotropic or "42,42,130" for output ' - 'dimensions of 42, 42, 130 voxels.') - if max([float(x) for x in size.split(',')]) > 9: - dim_str = '-size' - if not isinstance(interp, str): - raise Exception('Interpolation method needs to be specified ' - 'as a string') - if interp not in ('linear', 'nearest', 'cubic', 'sinc'): - raise Exception('User specified interpoaltion method {} is ' - 'not a valid option'.format(interp)) - if not (nthreads is None): - if not isinstance(nthreads, int): - raise Exception('Please specify the number of threads as an ' - 'integer.') - if not isinstance(force, bool): - raise Exception('Please specify whether forced overwrite is True ' - 'or False.') - if not isinstance(verbose, bool): - raise Exception('Please specify whether verbose is True or False.') - if dim_str == '-voxel': - current_size = [round(float(x), 2) for x in (mrinfoutil.spacing(input))][0:3] - elif dim_str == '-size': - current_size =[round(float(x), 2) for x in mrinfoutil.size(input)][0:3] - specified_size = [round(float(x), 2) for x in size.split(',')] - if specified_size == current_size: - print('[WARNING] target reslicing dimensions {} are the same ' - 'as input image dimensions {}, writing file without ' - 'reslicing'.format(specified_size, current_size)) - copyfile(input, output) - return - arg = [] - if which('mrresize') is None: - arg.extend( - [ - 'mrgrid', - input, - 'regrid', - dim_str, size, - '-interp', interp, - output - ] - ) - else: - arg.extend( - [ - 'mrresize', - dim_str, size, - '-interp', interp, - input, output - ] - ) - if force: - arg.append('-force') - if not verbose: - arg.append('-quiet') - if not (nthreads is None): - arg.extend(['-nthreads', str(nthreads)]) - completion = subprocess.run(arg) - if completion.returncode != 0: - raise Exception('Failed to reslice. See above for errors.') - -def dwiextract(input, output, start, end, - nthreads=None, force=False, verbose=False): - """ - Extracts a range of volumes from input .mif file in the start:end - range. Take note that the first volume starts with 0. - - Parameters - ---------- - input : str - Path to input .mif file - output : str - Path to output file, usually .mif or .nii - start : int - Starting index, inclusive - end : int - Ending index, inclusive - nthreads : int, optional - Specify the number of threads to use in processing - (Default: all available threads) - force : bool, optional - Force overwrite of output files if pre-existing - (Default:False) - verbose : bool, optional - Specify whether to print console output (Default: False) - - Returns - ------- - None; writes out file - """ - if not op.exists(input): - raise OSError('Input path does not exist. Please ensure that ' - 'the folder or file specified exists.') - if not isinstance(start, int): - raise Exception('Starting index is needs to be an integer.') - if not isinstance(end, int): - raise Exception('Ending index is needs to be an integer.') - if not (nthreads is None): - if not isinstance(nthreads, int): - raise Exception('Please specify the number of threads as an ' - 'integer.') - if not isinstance(force, bool): - raise Exception('Please specify whether forced overwrite is True ' - 'or False.') - if not isinstance(verbose, bool): - raise Exception('Please specify whether verbose is True or False.') - fname, ext = op.splitext(output) - if ext == '.gz': - fname, ext = op.splitext(fname) - ext = ext + '.gz' - arg = ['mrconvert'] - if force: - arg.append('-force') - if not verbose: - arg.append('-quiet') - if not (nthreads is None): - arg.extend(['-nthreads', str(nthreads)]) - arg.extend([input, output, - '-coord', '3', - str(start) + ':' + str(end)]) - if not '.mif' in ext: - arg.extend(['-json_export', fname + '.json', - '-export_grad_fsl', fname + '.bvec', fname + '.bval']) - completion = subprocess.run(arg) - if completion.returncode != 0: - raise Exception('Failed to extract indexed DWI volumes. See ' - 'above for errors.') diff --git a/designer/preprocessing/preparation.py b/designer/preprocessing/preparation.py deleted file mode 100644 index 8c4be13f..00000000 --- a/designer/preprocessing/preparation.py +++ /dev/null @@ -1,309 +0,0 @@ -#!/usr/bin/env python -# -*- coding : utf-8 -*- - -""" -Adds utilities for preparing the data for eddy and analysis -""" - -import os #mkdir -import os.path as op # dirname, basename, join, splitext -import shutil #rmtree -import json # decode -from enum import Enum -import nibabel as nib # various utilities for reading Nifti images -import subprocess -import re # regex substitution -from designer.preprocessing import util # preprocessing -DWIFile = util.DWIFile - -def fix_bval(bvalfile): - """ - Converts all whitespace into newlines in the file - - Parameters - ---------- - bvalfile : str - The .bval to ensure is the correct format for mrtrix - - Returns - ------- - None, overwrites bval - """ - - if not op.exists(bvalfile): - raise Exception('File '+ bvalfile + ' does not exist.') - - with open(bvalfile, 'r') as f: - data = f.read() - - # replace whitespace with lines - data = re.sub(r'\s+', '\n', data) - - # write to file - with open(bvalfile, 'w') as f: - f.write(data) - -def make_simple_mif(filetable): - """ - Makes a single .mif from the HEAD dwi file - - Parameters - ---------- - filetable : dict of str - The filetable that pydesigner.py uses to track files - - Returns - ------- - None, writes file - """ - if not op.exists(filetable['dwi'].getJSON()): - raise Exception('DWI does not have a .json file to use for eddy') - - #coerce bval into mrtrix3-friendly format - fix_bval(op.join(filetable['dwi'].getPath(), - filetable['dwi'].getName() + '.bval')) - finalpath = filetable['outpath'] - finalmif = op.join(finalpath, 'HEADmif.mif') - - dwi_convert_args = ['mrconvert', - '-json_import', - filetable['dwi'].getJSON(), - '-fslgrad', - filetable['dwi'].getBVEC(), - filetable['dwi'].getBVAL(), - filetable['dwi'].getFull(), - '-quiet', - finalmif] - - completion = subprocess.run(dwi_convert_args) - - if completion.returncode != 0: - raise Exception('topup conversion failed, please see above') - - filetable['dwimif'] = finalmif - -def make_se_epi(filetable): - """ - Makes a single spin-echo epi from the topup and the dwi - - Parameters - ---------- - filetable : dict of str - The filetable that pydesigner.py uses to track files - - Returns - ------- - None, writes file - """ - - #---------------------------------------------------------------------- - # Check inputs and coerce .bval file - #---------------------------------------------------------------------- - - # check that we actually have a .json for the original files - if not op.exists(filetable['dwi'].getJSON()): - raise Exception('DWI does not have a .json file to use for eddy') - - if not op.exists(op.join(filetable['topup'].getJSON())): - raise Exception('topup does not have a .json file to use for eddy') - - #NOTE: we assume that the DWI will have .bvec and .bval because - # pydesigner will not allow the 'dwi' entry to not have them - - # coerce .bval into having the mrtrix3-friendly format - fix_bval(op.join(filetable['dwi'].getPath(), - filetable['dwi'].getName() + '.bval')) - - finalpath = filetable['outpath'] - - # Make a temporary working directory - outpath = op.join(finalpath, 'tmp_se_epi') - if op.exists(outpath): - shutil.rmtree(outpath) - os.mkdir(outpath) - - #---------------------------------------------------------------------- - # Make dwipreproc-friendly files - #---------------------------------------------------------------------- - - # dwi - tmp_dwi = op.join(outpath, 'tmp_dwi.mif') - dwi_convert_args = ['mrconvert', - '-json_import', - filetable['dwi'].getJSON(), - '-fslgrad', - filetable['dwi'].getBVEC(), - filetable['dwi'].getBVAL(), - filetable['dwi'].getFull(), - '-quiet', - tmp_dwi] - - completion = subprocess.run(dwi_convert_args) - - # move dwi to non-temp directory as well - final_dwi = op.join(finalpath, 'tmp_dwi.mif') - shutil.copyfile(tmp_dwi, final_dwi) - - # add it to the filetable - filetable['dwimif'] = final_dwi - - if completion.returncode != 0: - raise Exception('DWI conversion failed, please see above.') - - # topup - tmp_tp = op.join(outpath, 'tmp_tp.mif') - topup_convert_args = ['mrconvert', - '-json_import', - filetable['topup'].getJSON(), - filetable['topup'].getFull(), - '-quiet', - tmp_tp] - completion = subprocess.run(topup_convert_args) - - if completion.returncode != 0: - raise Exception('topup conversion failed, please see above') - - # extract the b0 images from the dwi image - b0extracted = op.join(outpath, 'b0extracted.mif') - extract_b0_args = ['dwiextract', - '-bzero', - tmp_dwi, - '-quiet', - b0extracted] - completion = subprocess.run(extract_b0_args) - - if completion.returncode != 0: - raise Exception('b0 extraction failed, please see above') - - # separate b0 images into individual files - # start by getting number of volumes in each image - b0_info = op.join(outpath, 'b0x.txt') - get_dwi_b0_info_args = ['mrinfo', - '-size', - b0extracted] - completion = subprocess.run(get_dwi_b0_info_args, capture_output=True) - if completion.returncode != 0: - raise Exception('Extracted b0 information failed, please see above') - - # use an abomination unto good coding to extract 4th dim size - ndb0xstr = completion.stdout.decode('utf-8').rstrip().split(' ')[-1] - ndb0x = int(ndb0xstr.rstrip('"')) - - # repeat for rpe - rpe_info = op.join(outpath, 'rpex.txt') - get_rpe_info_args = ['mrinfo', - '-size', - tmp_tp] - completion = subprocess.run(get_rpe_info_args, capture_output=True) - if completion.returncode != 0: - raise Exception('Extracted topup information failed, ' - 'please see above') - - ndrpexstr = completion.stdout.decode('utf-8').rstrip().split(' ')[-1] - ndrpex = int(ndrpexstr.rstrip('"')) - - # iterate over the indices and align the data - b0_basename = op.join(outpath, 'b0x_') - regto = b0_basename+'0.mif' - to_cat = [regto] - for ii in range(ndb0x): - # iterate over b0s and register to first b0 - # extract the volume - i = str(ii) - extracted_name = b0_basename+i+'.mif' - reg_txt = b0_basename+i+'to0.txt' - reg_mif = b0_basename+i+'to0.mif' - xfm_b0 = b0_basename+i+'.mif' - extract_args = ['mrconvert', - '-force', - '-coord', - '3', - i, - b0extracted, - '-quiet', - extracted_name] - completion = subprocess.run(extract_args) - if completion.returncode != 0: - raise Exception('Failed splitting b0 volume '+i) - # if first b0, no need to register - if i == '0': - continue - # calculate the transform required - reg_args = ['mrregister', - '-type','rigid', - '-noreorientation', - '-rigid', reg_txt, - '-quiet', - extracted_name, - regto] - completion = subprocess.run(reg_args) - if completion.returncode != 0: - raise Exception('Failed registering volume '+i+' to b0') - # apply transform - transformed_basename = op.join(outpath, 'dwib0') - transform_args = ['mrtransform', - '-linear', - reg_txt, - reg_mif, - '-quiet', - xfm_b0] - to_cat.append(xfm_b0) - - # Repeat for RPE - rpe_basename = op.join(outpath, 'rpex_') - for ii in range(ndrpex): - # iterate over rpes and register to first rpe - # extract the volume - i = str(ii) - extracted_name = rpe_basename+i+'.mif' - reg_txt = rpe_basename+i+'to0.txt' - reg_mif = rpe_basename+i+'to0.mif' - xfm_rpe = rpe_basename+i+'.mif' - extract_args = ['mrconvert', - '-force', - '-coord', - '3', - i, - '-quiet', - tmp_tp, - extracted_name] - completion = subprocess.run(extract_args) - if completion.returncode != 0: - raise Exception('Failed splitting rpe volume '+i) - # calculate the transform required - reg_args = ['mrregister', - '-type','rigid', - '-noreorientation', - '-rigid', reg_txt, - '-quiet', - extracted_name, - regto] - completion = subprocess.run(reg_args) - if completion.returncode != 0: - raise Exception('Failed registering volume '+i+' to rpe') - # apply transform - transformed_basename = op.join(outpath, 'dwirpe') - transform_args = ['mrtransform', - '-linear', - reg_txt, - reg_mif, - '-quiet', - xfm_rpe] - to_cat.append(xfm_rpe) - - # Concatenate all b0 into one se-epi - # NOTE: uses finalpath instead of outpath - se_epi = op.join(finalpath, 'se-epi.mif') - mrcat_args = ['mrcat', '-force', '-quiet', '-axis', '3'] - for f in to_cat: - mrcat_args.append(f) - mrcat_args.append(se_epi) - - completion = subprocess.run(mrcat_args) - if completion.returncode != 0: - raise Exception('Concatenation of b0s into se-epi failed') - - filetable['se-epi'] = se_epi - - # Clean up temp dir - # shutil.rmtree(outpath) diff --git a/designer/pydesigner.py b/designer/pydesigner.py deleted file mode 100644 index 05b399c8..00000000 --- a/designer/pydesigner.py +++ /dev/null @@ -1,995 +0,0 @@ -""" -Runs the PyDesigner pipeline -""" - -#--------------------------------------------------------------------- -# Package Management -#--------------------------------------------------------------------- -import sys as sys -import subprocess #subprocess -import glob # recursive file search -import os # mkdir -import os.path as op # path -import shutil # which, rmtree -import gzip # handles fsl's .gz suffix -import argparse # ArgumentParser, add_argument -import textwrap # dedent -import json -import numpy as np # array, ndarray -from designer.info import __version__ -from designer.preprocessing import util, mrinfoutil, mrpreproc -from designer.plotting import snrplot, motionplot -from designer.fitting import dwipy as dp -from designer.postprocessing import filters -DWIFile = util.DWIFile -DWIParser = util.DWIParser - -# Locate mrtrix3 via which-ing dwidenoise -dwidenoise_location = shutil.which('dwidenoise') -if dwidenoise_location == None: - raise Exception('Cannot find mrtrix3, please see ' - 'https://github.com/m-ama/PyDesigner/wiki' - ' to troubleshoot.') - -# Extract mrtrix3 path from dwidenoise_location -mrtrix3path = op.dirname(dwidenoise_location) - -# Locate FSL via which-ing fsl -fsl_location = shutil.which('fsl') -if fsl_location == None: - raise Exception('Cannot find FSL, please see ' - 'https://github.com/m-ama/PyDesigner/wiki' - ' to troubleshoot.') - -# Extract FSL path from fsl_location -fslpath = op.dirname(fsl_location) - -def main(): - #----------------------------------------------------------------- - # Parse Arguments - #----------------------------------------------------------------- - # Initialize ArgumentParser - parser = argparse.ArgumentParser( - prog='pydesigner', - formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=textwrap.dedent('''\ - Appendix - -------- - Filename note: - Use the base name without the extension. This makes it easy to program - in automatic .bvec/.bval detection for Niftis and makes your shell - easier to read by others. The program will automatically search image - filenames for .nii and .nii.gz extensions. If you use the --dicom - option, then the program will assume that the entire directory - consists of dicom files, and will warn you of any files which fail to - be read in as dicoms. - - Example usage: - In order to process in the standard way: - python3 pydesigner.py \\ - --standard \\ - - - In order to process in a custom pipeline with denoising, eddy, reverse - phase encoding, and smoothing, but no diffusion metrics: - python3 pydesigner.py \\ - --denoise \\ - --undistort \\ - --smooth \\ - --nofit \\ - - - In order to just do denoising, eddy with reverse phase encode, and - diffusion metrics: - python3 pydesigner.py \\ - --denoise \\ - --undistort \\ - - - Standard pipeline steps: - 1. dwidenoise (thermal denoising) - 2. mrdegibbs (gibbs unringing) - 3. topup + eddy (undistortion) - 4. rician bias correction - 5. normalization to white matter in first b0 image - 6. IRWLLS, CWLLS DKI fit - 7. Outlier detection and removal - - See also: - GitHub https://github.com/m-ama/PyDesigner - mrtrix3 https://www.mrtrix.org/ - fsl https://fsl.fmrib.ox.ac.uk/fsl/fslwiki - - ''')) - - # Specify arguments below - - # Mandatory - parser.add_argument('dwi', - nargs='+', - help='The diffusion dataset you would like ' - 'to process. ', - type=str) - - # Optional - parser.add_argument('-o', '--output', - metavar='directory', - help='Output location. ' - 'Default: same path as dwi.', - type=str) - parser.add_argument('-s', '--standard', action='store_true', - default=False, - help='Standard preprocessing, bypasses most other ' - 'options. See Appendix:Standard pipeline steps ' - 'for more information. ') - parser.add_argument('-n', '--denoise', action='store_true', default=False, - help='Run thermal denoising with dwidenoise.') - parser.add_argument('--extent', metavar='n,n,n', default='5,5,5', - help='Denoising extent formatted n,n,n (forces ' - ' denoising. ' - 'Default: 5,5,5.') - parser.add_argument('-g', '--degibbs', action='store_true', default=False, - help='Perform gibbs unringing. Only perform if you ' - 'have full Fourier encoding. The program will check ' - 'for you if you have a .json sidecar.') - parser.add_argument('-u', '--undistort', action='store_true', default=False, - help='Run FSL eddy to perform image undistortion. ' - 'NOTE: needs a --topup to run.') - parser.add_argument('--rpe_pairs', default=0, type=int, - metavar='n', - help='Number of reverse phase encoded B0 ' - 'pairs to use in TOPUP. Using less pairs ' - 'results in faster TOPUP correction. ' - 'Specfying 0 results in using all B0 pairs.' - 'We recommend using just one pair. Default: 0') - parser.add_argument('-z', '--smooth', action='store_true', default=False, - help='Perform smoothing on the DWI data.') - parser.add_argument('--fwhm', type=float, default=1.25, - metavar='n', - help='The FWHM to use as a multiple of voxel size. ' - 'Default 1.25') - parser.add_argument('-r', '--rician', action='store_true', default=False, - help='Perform Rician noise correction on the data ' - '(requires --denoise to generate a noisemap).') - parser.add_argument('--nofit', action='store_true', default=False, - help='Do not fit DTI or DKI tensors.') - parser.add_argument('--akc', action='store_true', default=False, - help='Brute force K tensor outlier rejection. Applies ' - 'a median filter to tensor voxels that exhibit AKC ' - 'values of less than 2 and more than 10.') - parser.add_argument('--nooutliers', action='store_true', default=False, - help='Do not perform outlier correction on kurtosis ' - 'fitting metrics.') - parser.add_argument('-m', '--mask', action='store_true', default=False, - help='Compute a brain mask prior to tensor fitting ' - 'to strip skull and improve efficiency. Optionally, ' - 'use --maskthr to specify a threshold manually.') - parser.add_argument('--maskthr', metavar='n', - default=0.25, - help='FSL bet threshold used for brain masking. ' - 'Default: 0.25') - parser.add_argument('--user_mask', metavar='path', - help='Path to user-supplied brain mask.', - type=str) - parser.add_argument('-cf', '--csf_fsl', action='store_true', default=False, - help='Compute a CSF mask for CSF-excluded ' - 'smoothing to minimize partial volume ' - 'effects using FSL FAST.') - parser.add_argument('-cd', '--csf_adc', metavar='n', default=False, - help='Compute a CSF mask for CSF-excluded ' - 'smoothing to minimize partial volume ' - 'effects using thresholding a pseudo-ADC map ' - 'computed as ln(S0/S1000)/b1000. Default: 2') - parser.add_argument('--reslice', metavar='x,y,z', - help='Relices DWI to voxel resolution ' - 'specified in millimeters (mm) or output ' - 'dimensions. Performing reslicing will skip ' - 'plotting of SNR curves. Providing dimensions ' - 'greater than 9 will switch from mm voxel ' - 'reslicing to output image reslicing.') - parser.add_argument('--interp', action='store_true', default='linear', - help='Set the interpolation to use when ' - 'reslicing. Choices are linear (default), ' - 'nearest, cubic, and sinc.') - parser.add_argument('-te', '--multite', action='store_true', - default=False, - help='Specify whether input DWI consists of ' - 'multiple TEs. PyDesigner will preprocess all ' - 'TEs together, then extract metric values of ' - 'each TE separately.') - parser.add_argument('--fit_constraints', default='0,1,0', - metavar='D>0,K>0,K < 3/(b*D)', - help='Constrain the WLLS fit. ' - 'Default: 0,1,0.') - parser.add_argument('--l_max', default=6, type=int, - metavar='n', - help='Maximum spherical harmonic degree for ' - 'FBI spherical harmonic expansion') - parser.add_argument('--no_rectify', action='store_true', default=False, - help='Disable rectification of FBI fODF. Use ' - 'only when rectification of excellent ' - 'acquisitions results in degradation of FBI ' - 'or FBWM metric maps') - parser.add_argument('--t_res', type=str, default='med', - help='Resolution of directions for ODF calculation. ' - 'Higher resolution implies slower computation. Choose ' - 'between "low", "med", or "high". Default: "med"') - parser.add_argument('--t_fibers', type=int, default=5, - help='The maximum number ODF maxima to extract per ' - 'voxel for tractography. Default: 5') - parser.add_argument('--noqc', action='store_true', default=False, - help='Disable QC saving of QC metrics') - parser.add_argument('--median', action='store_true', default=False, - help='Performs postprocessing median filtering of ' - 'final maps. WARNING: Use on a case-by-case ' - 'basis for bad data only. When applied, the ' - 'filter alters the values of most voxels, so ' - 'it should be used with caution and avoided ' - 'when data quality is otherwise adequate. ' - 'While maps appear visually soother with ' - 'this flag on, they may nonetheless be less ' - 'accurate.') - parser.add_argument('--nthreads', type=int, default=None, - help='Number of threads to use for computation. ' - 'Note that using too many threads will cause a slow-' - 'down.') - parser.add_argument('--resume', action='store_true', - help='Continue from an aborted or partial previous ' - 'run of pydesigner.') - parser.add_argument('--force', action='store_true', - help='Force overwrites of existing files. Otherwise, ' - 'there will be an error at runtime.') - parser.add_argument('--verbose', action='store_true', - help='Print out all output. This is a very messy ' - 'option. We recommend piping output to a text file ' - 'if you use this option.') - parser.add_argument('--adv', action='store_true', - help='Disables safety checks for advanced users who ' - 'want to force a preprocessing step. WARNING: ' - 'THIS FLAG IS FOR ADVANCED USERS ONLY WHO FULLY ' - 'UNDERSTAND THE MRI SYSTEM AND ITS OUTPUTS. ' - 'RUNNING WITH THIS FLAG COULD POTENTIALLY ' - 'RESULT IN IMPRECISE AND INACCURATE RESULTS.') - parser.add_argument('-v', '--version', action='version', - version=__version__) - - # Use argument specification to actually get args - args = parser.parse_args() - - #----------------------------------------------------------------- - # Parse Input Image - #----------------------------------------------------------------- - image = DWIParser(args.dwi) - # Variable fType indicates the extension to raw_dwi.X, where X take the - # place of known dMRI file extensions (.mif, .nii, .nii.gz). This allows - # easy switching based on any scenario for testing. - fType = '.mif' - multi_echo = False - if not args.output: - outpath = image.getPath() - else: - outpath = args.output - image.cat(path=outpath, - ext=fType, - verbose=args.verbose, - force=args.force, - resume=args.resume) - working_path = op.join(outpath, 'working' + fType) - # Create index of DWI volumes with different TEs - if not args.resume and np.unique(image.echotime).size > 1: - multi_echo = True - multi_echo_start = [0] - multi_echo_end = [image.vols[0] - 1] - for idx, vols in enumerate(image.vols[1:]): - multi_echo_start.append(multi_echo_start[-1] + vols) - multi_echo_end.append(multi_echo_end[-1] + vols) - multi_echo_start = [int(x) for x in multi_echo_start] - multi_echo_end = [int(x) for x in multi_echo_end] - - # Make an initial conversion to nifti - init_nii = op.join(outpath, 'dwi_raw.nii') - if not (args.resume and op.exists(init_nii)): - mrpreproc.miftonii(input=working_path, - output=init_nii, - nthreads=args.nthreads, - force=args.force, - verbose=args.verbose) - - #----------------------------------------------------------------- - # Validate Arguments - #----------------------------------------------------------------- - errmsg = '' - warningmsg = '' - msgstart = 'Incompatible arguments: ' - override = '; overriding with ' - # Warn if --standard and cherry-picking - if args.standard: - stdmsg= '--standard but cherry-picking ' - override='; overriding with standard pipeline.\n' - if args.denoise: - warningmsg+=msgstart+stdmsg+'--denoise'+override - if args.undistort: - warningmsg+=msgstart+stdmsg+'--eddy'+override - if args.smooth: - warningmsg+=msgstart+stdmsg+'--smooth'+override - # Coerce all of the above to be true - args.denoise = True - args.undistort = True - args.smooth = True - args.csf_adc = 2 - args.mask = True - args.degibbs = True - args.rician = True - - # Can't do WMTI if no fit - if args.nofit: - stdmsg='--nofit given but ' - if not args.akc: - warningmsg+=msgstart+stdmsg+'--noakc'+override+'tensor fitting.\n' - args.nofit = False - if args.nooutliers: - warningmsg+=msgstart+stdmsg+'--nooutliers' - warningmsg+=override+'tensor fitting.\n' - args.nofit = False - - # (Extent or Degibbs) and no Denoise - if not args.denoise: - stdmsg='No --denoise but ' - if args.extent != '5,5,5': - warningmsg+=stdmsg+'--extent given; overriding with --denoise\n' - args.denoise = True - if args.rician: - warningmsg+=stdmsg+'--rician given; overriding with --denoise\n' - args.denoise = True - - # Cannot run --user_mask and --mask at the same time - if args.user_mask and args.mask: - errmsg+='Cannot run with both --mask and --user_mask; ' - errmsg+='--mask if you do not have a custom brain mask and ' \ - '--user_mask if you want to supply a mask.' - - # Cannot run --csf_fsl and --csf_adc at the same time - if args.csf_fsl and args.csf_adc: - errmsg+='Cannot run with both --csf_fsl and --csf_adc; ' - errmsg+='please supply only one option.' - - # Check to make sure brain mask exists if given - if args.user_mask: - if not op.exists(args.user_mask): - errmsg+='--user_mask file '+args.user_mask+' not found\n' - # Then check if it's a nifti file - if not '.nii' in op.splitext(args.user_mask)[-1]: - errmsg+='User supplied mask if not in NifTi (.nii) format.' - - # Check output directory exists if given - if args.output: - if not op.exists(args.output): - try: - os.makedirs(args.output, exist_ok=True) - except: - errmsg+='Cannot find or create output directory' - - # Check that --fit_constraints can be converted to int array - fit_constraints = np.fromstring(args.fit_constraints, - dtype=int, sep=',') - for i in fit_constraints: - if i < 0 or i > 1: - errmsg+='Invalid --fit_constraints value, should be 0 or 1\n' - break - - # Ensure l_max is an even integer - if args.l_max % 2 != 0: - errmsg+='User provided l_max = {} is not an even integer.'.format(args.l_max) - - # --force and --resume given - if args.resume and args.force: - errmsg+=msgstart+'--continue and --force\n' - - if args.output: - if not op.isdir(args.output): - try: - os.makedirs(args.output, exist_ok=True) - except: - errmsg+=('Output directory does not exist and cannot ' - 'be made.') - - # Check whether tractography variables are parsed correctly - if not args.t_res in ['low', 'med', 'high']: - warningmsg+='Specified ODF resolution not understoor. Defaulting to '\ - '"med"\n' - - # Print warnings - if warningmsg is not '': - print(warningmsg) - - # If things are unsalvageable, point out all errors and quit - if errmsg is not '': - raise Exception(errmsg) - - # Begin keeping track of nifti files - filetable = {'dwi' : DWIFile(init_nii)} - if not filetable['dwi'].isAcquisition(): - raise Exception('Input dwi does not have .bval/.bvec pair') - - # Begin composing command history - cmdtable = {'HEAD': 'none'} - cmdtable['input'] = mrinfoutil.commandhistory(working_path) - - # Check to make sure no partial fourier if --degibbs given - if args.degibbs and args.adv: - args.degibbs = True - else: - if args.degibbs and filetable['dwi'].isPartialFourier(): - print('[WARNING] Given DWI is partial fourier, overriding ' - '--degibbs; no unringing correction will be done to ' - 'avoid artifacts.Use the "--adv" flag to run forced ' - 'corrections.') - args.degibbs = False - - # Handle FBI rectification - fbi_rectify = True - if args.no_rectify: - fbi_rectify = False - - #----------------------------------------------------------------- - # Path Handling - #----------------------------------------------------------------- - qcpath = op.join(outpath, 'metrics_qc') - eddyqcpath = op.join(qcpath, 'eddy') - fitqcpath = op.join(qcpath, 'fitting') - metricpath = op.join(outpath, 'metrics') - intermediatepath = op.join(outpath, 'intermediate_nifti') - if not args.nofit: - if op.exists(metricpath): - if args.force: - shutil.rmtree(metricpath) - elif not args.resume: - raise Exception( - 'Running fitting would cause an overwrite. ' - 'In order to run this please delete the ' - 'files, use --force, use --resume, or ' - 'change output destination.') - else: - os.makedirs(metricpath, exist_ok=True) - if not args.noqc: - if op.exists(qcpath): - if args.force: - shutil.rmtree(qcpath) - elif not args.resume: - raise Exception('Running QCing would cause an overwrite. ' - 'In order to run this please delete the ' - 'files, use --force, use --resume, or ' - 'change output destination.') - else: - os.makedirs(qcpath, exist_ok=True) - if op.exists(eddyqcpath) and args.undistort: - if args.force: - shutil.rmtree(eddyqcpath) - elif not args.resume: - raise Exception('Running dwidenoise would cause an ' - 'overwrite. ' - 'In order to run this please delete the ' - 'files, use --force, or change output ' - 'destination.') - if op.exists(fitqcpath) and not args.nofit: - if args.force: - shutil.rmtree(fitqcpath) - elif not args.resume: - raise Exception('Running fitting would cause an ' - 'overwrite. ' - 'In order to run this please delete the ' - 'files, use --force, or change output ' - 'destination.') - if args.undistort: - os.makedirs(eddyqcpath, exist_ok=True) - if not args.nofit: - os.makedirs(fitqcpath, exist_ok=True) - - if not op.exists(intermediatepath): - os.makedirs(intermediatepath, exist_ok=True) - - # TODO: add non-json RPE support, additional RPE type support - - # Get naming and location information - dwiname = filetable['dwi'].getName() - if not args.output: - outpath = filetable['dwi'].getPath() - else: - outpath = args.output - filetable['outpath'] = outpath - - # Make the pipeline point to dwi as the last file since it's the only one - # so far - filetable['HEAD'] = filetable['dwi'] - - if args.nthreads and args.verbose: - print('Using ' + str(args.nthreads) + ' threads.') - - # Create processing step variable to count preprocessing stage - step_count = 0 - - #----------------------------------------------------------------- - # Run Denoising - #----------------------------------------------------------------- - if args.denoise: - step_count += 1 - denoised_name = 'dwi_denoised' - # hardcoding this to be the initial file per dwidenoise - # recommmendation - # file names - denoised_name_full = str(step_count)+ '_' + denoised_name - nii_denoised = op.join(intermediatepath, denoised_name_full + '.nii') - mif_denoised = op.join(outpath, denoised_name_full + '.mif') - # output the noise map even without user permission, space is cheap - noisemap_name = 'noisemap.nii' - nii_noisemap = op.join(outpath, noisemap_name) - # check to see if this already exists - if not (args.resume and op.exists(nii_denoised) and \ - op.exists(nii_noisemap)): - # run denoise function - mrpreproc.denoise(input=working_path, - output=mif_denoised, - noisemap=True, - extent=args.extent, - nthreads=args.nthreads, - force=args.force, - verbose=args.verbose) - mrpreproc.miftonii(input=mif_denoised, - output=nii_denoised, - nthreads=args.nthreads, - force=args.force, - verbose=False) - # remove old working.mif and replace with new corrected .mif - os.remove(working_path) - os.rename(mif_denoised, working_path) - # update command history - cmdtable['denoise'] = mrinfoutil.commandhistory(working_path)[-1] - cmdtable['HEAD'] = cmdtable['denoise'] - # update nifti file tracking - filetable['denoised'] = DWIFile(nii_denoised) - filetable['HEAD'] = filetable['denoised'] - filetable['noisemap'] = DWIFile(nii_noisemap) - - #----------------------------------------------------------------- - # Run Reslicing - #----------------------------------------------------------------- - if args.reslice: - step_count += 1 - reslice_name = 'dwi_reslice' - noise_name = 'noisemap_resliced' - # file names - reslice_name_full = str(step_count)+ '_' + reslice_name - nii_reslice = op.join(intermediatepath, reslice_name_full + '.nii') - mif_reslice = op.join(outpath, reslice_name_full + '.mif') - nii_noise = op.join(outpath, noise_name + '.nii') - # check to see if this already exists - if not (args.resume and op.exists(nii_reslice)): - # run reslice function on both DWI and noisemap - mrpreproc.reslice(input=working_path, - output=mif_reslice, - size=args.reslice, - interp=args.interp, - nthreads=args.nthreads, - force=args.force, - verbose=args.verbose) - mrpreproc.reslice(input=nii_noisemap, - output=nii_noise, - size=args.reslice, - interp=args.interp, - nthreads=args.nthreads, - force=True, - verbose=args.verbose) - mrpreproc.miftonii(input=mif_reslice, - output=nii_reslice, - nthreads=args.nthreads, - force=args.force, - verbose=False) - # remove old working.mif and replace with new corrected .mif - os.remove(working_path) - os.rename(mif_reslice, working_path) - os.rename(nii_noise, nii_noisemap) - # update command history - cmdtable['reslice'] = mrinfoutil.commandhistory(working_path)[-1] - cmdtable['HEAD'] = cmdtable['reslice'] - # update nifti file tracking - filetable['reslice'] = DWIFile(nii_reslice) - filetable['HEAD'] = filetable['reslice'] - - #----------------------------------------------------------------- - # Run Gibbs Unringing - #----------------------------------------------------------------- - if args.degibbs: - step_count += 1 - degibbs_name = 'dwi_degibbs' - # file names - degibbs_name_full = str(step_count)+ '_' + degibbs_name - nii_degibbs = op.join(intermediatepath, degibbs_name_full + '.nii') - mif_degibbs = op.join(outpath, degibbs_name_full + '.mif') - # check to see if this already exists - if not (args.resume and op.exists(nii_degibbs)): - # run degibbs function - mrpreproc.degibbs(input=working_path, - output=mif_degibbs, - nthreads=args.nthreads, - force=args.force, - verbose=args.verbose) - mrpreproc.miftonii(input=mif_degibbs, - output=nii_degibbs, - nthreads=args.nthreads, - force=args.force, - verbose=False) - # remove old working.mif and replace with new corrected .mif - os.remove(working_path) - os.rename(mif_degibbs, working_path) - # update command history - cmdtable['degibbs'] = mrinfoutil.commandhistory(working_path)[-1] - cmdtable['HEAD'] = cmdtable['degibbs'] - # update nifti file tracking - filetable['unrung'] = DWIFile(nii_degibbs) - filetable['HEAD'] = filetable['unrung'] - - #----------------------------------------------------------------- - # Undistort - #----------------------------------------------------------------- - if args.undistort: - step_count += 1 - undistorted_name = 'dwi_undistorted' - # file names - undistorted_name_full = str(step_count)+ '_' + undistorted_name - nii_undistorted = op.join(intermediatepath, undistorted_name_full + '.nii') - mif_undistorted = op.join(outpath, undistorted_name_full + '.mif') - if args.noqc: - eddyqcpath = None - # check to see if this already exists - if not (args.resume and op.exists(nii_undistorted)): - # run undistort function - mrpreproc.undistort(input=working_path, - output=mif_undistorted, - rpe='rpe_header', - qc=eddyqcpath, - epib0=args.rpe_pairs, - nthreads=args.nthreads, - force=args.force, - verbose=args.verbose) - mrpreproc.miftonii(input=mif_undistorted, - output=nii_undistorted, - nthreads=args.nthreads, - force=args.force, - verbose=False) - # remove old working.mif and replace with new corrected .mif - os.remove(working_path) - os.rename(mif_undistorted, working_path) - # update command history - cmdtable['undistort'] = mrinfoutil.commandhistory(working_path) - cmdtable['HEAD'] = cmdtable['undistort'] - # update nifti file tracking - filetable['undistorted'] = DWIFile(nii_undistorted) - filetable['HEAD'] = filetable['undistorted'] - - # Plot head motion - if not args.noqc: - plot_path_full = op.join(qcpath, 'head_motion.png') - motionplot.plot(op.join(eddyqcpath, 'eddy_restricted_movement_rms'), - plot_path_full, - voxel=mrinfoutil.spacing(working_path)) - - #----------------------------------------------------------------- - # Create CSF Mask - #----------------------------------------------------------------- - csfmask_name = 'csf_mask.nii' - csfmask_out = op.join(outpath, csfmask_name) - # FSL Method - if args.csf_fsl: - mrpreproc.csfmask(input=working_path, - output=csfmask_out, - method='fsl', - thresh=args.maskthr, - nthreads=args.nthreads, - force=args.force, - verbose=args.verbose) - filetable['csfmask'] = DWIFile(csfmask_out) - # ADC Method - if args.csf_adc: - mrpreproc.csfmask(input=working_path, - output=csfmask_out, - method='adc', - coeff=args.csf_adc, - nthreads=args.nthreads, - force=args.force, - verbose=args.verbose) - filetable['csfmask'] = DWIFile(csfmask_out) - - #----------------------------------------------------------------- - # Create Brain Mask - #----------------------------------------------------------------- - if args.mask: - brainmask_name = 'brain_mask.nii' - brainmask_out = op.join(outpath, brainmask_name) - mrpreproc.brainmask(input=working_path, - output=brainmask_out, - thresh=args.maskthr, - nthreads=args.nthreads, - force=args.force, - verbose=args.verbose) - filetable['mask'] = DWIFile(brainmask_out) - - if args.user_mask: - # Rotates user mask to same orientation as PyDesigner's working - # file to prevent incorrect masking - brainmask_name = 'brain_mask.nii' - brainmask_out = op.join(outpath, 'brain_mask.nii') - mrpreproc.stride_match( - target=working_path, - moving=args.user_mask, - output=brainmask_out, - nthreads=args.nthreads, - force=args.force, - verbose=args.verbose - ) - filetable['mask'] = DWIFile(brainmask_out) - - #----------------------------------------------------------------- - # Multiply Brain Mask with CSF Mask if both present - #----------------------------------------------------------------- - if (args.mask or args.user_mask) and (args.csf_fsl or args.csf_adc): - cmd = [ - 'mrcalc', - '-force', - brainmask_out, - csfmask_out, - '-mult', - csfmask_out - ] - completion = subprocess.run(cmd) - if completion.returncode != 0: - raise Exception('Unable to multiply CSF mask with brain ' - 'mask. See above for errors.') - #----------------------------------------------------------------- - # Smooth - #----------------------------------------------------------------- - if args.smooth: - csfname = None - if 'csfmask' in filetable: - csfname = filetable['csfmask'].getFull() - step_count += 1 - smoothing_name = 'dwi_smoothed' - # file names - smoothing_name_full = str(step_count)+ '_' + smoothing_name - nii_smoothing = op.join(intermediatepath, smoothing_name_full + '.nii') - mif_smoothing = op.join(outpath, smoothing_name_full + '.mif') - # check to see if this already exists - if not (args.resume and op.exists(nii_smoothing)): - mrpreproc.smooth(input=working_path, - csfname=csfname, - output=mif_smoothing, - fwhm=args.fwhm) - - mrpreproc.miftonii(input=mif_smoothing, - output=nii_smoothing, - nthreads=args.nthreads, - force=args.force, - verbose=False) - # remove old working.mif and replace with new corrected .mif - os.remove(working_path) - os.rename(mif_smoothing, working_path) - # update command history - cmdtable['smooth'] = ['designer.preprocessing.mrpreproc.smooth(input={}, ' - 'output={}, ' - 'fwhm={}'.format(working_path, - mif_smoothing, - args.fwhm)] - cmdtable['smooth'].append(mrinfoutil.commandhistory(working_path)[-1]) - cmdtable['HEAD'] = cmdtable['smooth'] - # update nifti file tracking - filetable['smoothed'] = DWIFile(nii_smoothing) - filetable['HEAD'] = filetable['smoothed'] - - #----------------------------------------------------------------- - # Rician Noise Correction - #----------------------------------------------------------------- - if args.rician: - step_count += 1 - rician_name = 'dwi_rician' - # file names - rician_name = str(step_count)+ '_' + rician_name - nii_rician = op.join(intermediatepath, rician_name + '.nii') - mif_rician = op.join(outpath, rician_name + '.mif') - # check to see if this already exists - if not (args.resume and op.exists(nii_rician)): - mrpreproc.riciancorrect(input=working_path, - output=mif_rician, - noise=filetable['noisemap'].getFull()) - nii_rician_name = 'r' + filetable['HEAD'].getName() + '.nii' - nii_rician_full = op.join(outpath, nii_rician_name) - mrpreproc.miftonii(input=mif_rician, - output=nii_rician, - nthreads=args.nthreads, - force=args.force, - verbose=False) - # remove old working.mif and replace with new corrected .mif - os.remove(working_path) - os.rename(mif_rician, working_path) - # update command history - cmdtable['rician'] = ['designer.preprocessing.mrpreproc.' - 'riciancorrect(input={}, ' - 'output={}, ' - 'noise={})'.format(working_path, - mif_rician, - filetable['noisemap'].getFull())] - cmdtable['rician'].append(mrinfoutil.commandhistory(working_path)[-1]) - cmdtable['HEAD'] = cmdtable['rician'] - # update nifti file tracking - filetable['rician_corrected'] = DWIFile(nii_rician) - filetable['HEAD'] = filetable['rician_corrected'] - - #----------------------------------------------------------------- - # Extract averaged B0 - #----------------------------------------------------------------- - # file names - b0_name = 'B0' - nii_b0 = op.join(outpath, b0_name + '.nii') - # check to see if this already exists - if not (args.resume and op.exists(nii_b0)): - # extract mean B0 - mrpreproc.extractmeanbzero(input=working_path, - output=nii_b0, - nthreads=args.nthreads, - force=args.force, - verbose=args.verbose) - # update command history - cmdtable['B0'] = mrinfoutil.commandhistory(working_path)[-1] - cmdtable['HEAD'] = cmdtable['B0'] - # update nifti file tracking - filetable['B0'] = DWIFile(nii_b0) - filetable['HEAD'] = filetable['B0'] - - #----------------------------------------------------------------- - # Extract averaged non-B0 shells - #----------------------------------------------------------------- - # get non B0 shells - b_shells = [x for x in mrinfoutil.shells(working_path) if x != 0] - # remove - # file names - b_names = ['B' + str(x) for x in b_shells] - b_paths = [op.join(outpath, x + '.nii') for x in b_names] - # check to see if this already exists - for b_value, b_nii in zip(b_shells, b_paths): - if not (args.resume and op.exists(b_nii)): - # extract mean shells - mrpreproc.extractmeanshell( - input=working_path, - output=b_nii, - shell=b_value, - nthreads=args.nthreads, - force=args.force, - verbose=args.verbose - ) - # update command history - cmdtable['B' + str(b_value)] = mrinfoutil.commandhistory(working_path)[-1] - cmdtable['HEAD'] = cmdtable['B' + str(b_value)] - # update nifti file tracking - filetable['B' + str(b_value)] = DWIFile(b_nii) - filetable['HEAD'] = filetable['B' + str(b_value)] - - #----------------------------------------------------------------- - # Make preprocessed file - #----------------------------------------------------------------- - preprocessed = op.join(outpath, 'dwi_preprocessed.nii') - if not (args.resume and op.exists(preprocessed)): - mrpreproc.miftonii(input=working_path, - output=preprocessed, - nthreads=args.nthreads, - force=args.force, - verbose=False) - filetable['preprocessed'] = DWIFile(preprocessed) - filetable['HEAD'] = filetable['preprocessed'] - - #----------------------------------------------------------------- - # Compute SNR - #----------------------------------------------------------------- - if (args.denoise and not args.reslice) and not args.noqc: - files = [] - files.append(init_nii) - files.append(filetable['HEAD'].getFull()) - try: - if 'mask' in filetable: - snr = snrplot.makesnr(dwilist=files, - noisepath=nii_noisemap, - maskpath=filetable['mask'].getFull()) - else: - snr = snrplot.makesnr(dwilist=files, - noisepath=filetable['noisemap'].getFull(), - maskpath=None) - snr.makeplot(path=qcpath, smooth=True, smoothfactor=3) - except: - print('[WARNING] SNR plotting failed, see above. ' - 'Proceeding with processing.') - - #----------------------------------------------------------------- - # Write logs - #----------------------------------------------------------------- - with open(op.join(outpath, 'log_command.json'), 'w') as fp: - json.dump(cmdtable, fp, indent=2) - - #----------------------------------------------------------------- - # Handle multi-echo data - #----------------------------------------------------------------- - imPath = filetable['HEAD'].getFull() - if multi_echo and args.multite: - imPath = [] - for i in range(len(image.echotime)): - echo_out = op.join(outpath, 'TE' + str(image.echotime[i]) + '_dwi_preprocessed.nii') - mrpreproc.dwiextract(working_path, - echo_out, - start=multi_echo_start[i], - end=multi_echo_end[i], - nthreads=args.nthreads, - force=args.force, - verbose=False) - imPath.append(echo_out) - - # Remove working.mif - os.remove(working_path) - - #----------------------------------------------------------------- - # Tensor Fitting - #----------------------------------------------------------------- - ext = '.nii' - fit_mask = None - if 'mask' in filetable: - fit_mask = filetable['mask'].getFull() - if not args.nofit: - # create dwi fitting object - if multi_echo and args.multite: - for path, echo in zip(imPath, image.echotime): - # qcpath = op.join(op.dirname(path), 'qc_fitting') - os.makedirs(qcpath, exist_ok=True) - dp.fit_regime( - input=path, - output=metricpath, - prefix='TE' + str(echo) + '_', - suffix=None, - ext=ext, - irlls=not args.nooutliers, - akc=args.akc, - l_max=args.l_max, - res=args.t_res, - n_fibers=args.t_fibers, - rectify = fbi_rectify, - qcpath=fitqcpath, - fit_constraints=fit_constraints, - mask=fit_mask, - nthreads=args.nthreads - ) - else: - dp.fit_regime( - input=imPath, - output=metricpath, - prefix=None, - suffix=None, - ext=ext, - irlls=not args.nooutliers, - akc=args.akc, - l_max=args.l_max, - rectify = fbi_rectify, - res=args.t_res, - n_fibers=args.t_fibers, - qcpath=fitqcpath, - fit_constraints=fit_constraints, - mask=fit_mask, - nthreads=args.nthreads - ) - - #----------------------------------------------------------------- - # Post Processing - #----------------------------------------------------------------- - if args.median: - f_metrics = glob.glob(op.join(metricpath, '*' + ext)) - f_metrics = [x for x in f_metrics if not x.endswith('DT.nii')] - f_metrics = [x for x in f_metrics if not x.endswith('KT.nii')] - f_metrics = [x for x in f_metrics if not x.endswith('fodf.nii')] - for f in f_metrics: - filters.median(f, f) - -if __name__ == '__main__': - main() diff --git a/designer/system/__init__.py b/designer/system/__init__.py deleted file mode 100644 index 9f9161bf..00000000 --- a/designer/system/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from . import utils \ No newline at end of file diff --git a/designer/tractography/__init__.py b/designer/tractography/__init__.py deleted file mode 100644 index 317b3752..00000000 --- a/designer/tractography/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from . import dsistudio -from . import odf -from . import sphericalsampling \ No newline at end of file diff --git a/designer/tractography/odf.py b/designer/tractography/odf.py deleted file mode 100644 index eea124be..00000000 --- a/designer/tractography/odf.py +++ /dev/null @@ -1,659 +0,0 @@ -#!/usr/bin/env python -# -*- coding : utf-8 -*- - -""" -Function for computing DTI and DKI spherical harmonics from diffusion and -kurtosis tensors -""" -import warnings -import multiprocessing -import os.path as op -from joblib import Parallel, delayed -import numpy as np -import nibabel as nib -from scipy.special import sph_harm -from dipy.core.geometry import sphere2cart -from designer.system.utils import vectorize, writeNii -from designer.tractography import sphericalsampling -from designer.fitting.thresholds import __minZero__ -from tqdm import tqdm - -class odfmodel(): - """ - DTI/DKI tractograpy class for computing ODFs and preparing spherical - harmonics for DTI or DKI fiber tracking. - """ - def __init__(self, dt, kt=None, mask=None, scale=None, res='med', l_max=6, - radial_weight=4, nthreads=None): - """ - Parameters - ---------- - dt : str - Path to diffusion tensor, which is a 4D NifTI volume composed of six (6) - components - kt : str; optional - Path to kurtosis tensor, which is a 4D NifTI compose of fifteen (15) - components - (Default: None) - mask : str; optional - Path to brain mask in NifTI format - scale : str; optional - Path to dMRI metric map to use for ODF scaling, where metric value - at a voxel is multiplied by the ODF. - res : str; optional, {'low', 'med', 'high'} - Resolution of directions for ODF calculation. Higher resolution - implies slower computation. - (Default: 'med') - l_max : int - Maximum spherical harmonic degree to use for spherical harmonic - expansion of ODF - (Default: 6) - radial_weight : float - Radial weighting power for detecting directional differences - (Default: 4) - """ - if not op.exists(dt): - raise OSError('Input DT path does not exist. Please ensure that ' - 'the folder or file specified exists.') - if not kt is None: - if not op.exists(kt): - raise OSError('Input KT path does not exist. Please ensure that ' - 'the folder or file specified exists.') - if not mask is None: - if not op.exists(mask): - raise OSError('Path to brain mask does not exist. Please ' - 'ensure that the file specified exists.') - if not scale is None: - if not op.exists(scale): - raise OSError('Path to scale image does not exist. Please ' - 'ensure that the file specified exists.') - if not isinstance(res, str): - raise Exception('Please specify resolution as a string. Possible ' - 'choices are "low", "med", or "high"') - # Load images - self.hdr = nib.load(dt) - self.DT = self.hdr.get_fdata() - if not kt is None: - self.KT = nib.load(kt).get_fdata() - else: - self.KT = None - if not mask is None: - self.mask_img = nib.load(mask).get_fdata() - else: - self.mask_img = None - if not scale is None: - self.scale_img = nib.load(scale).get_fdata() - else: - self.scale_img = np.ones(self.DT.shape[0:3]) - if l_max % 2 != 0: - raise Exception('Please provide l_max as a postive ' - 'and even integer') - self.l_max = l_max - if radial_weight is None: - warnings.warn('Radial weight for dODF computation not specified. ' - 'Using default value of 4.') - self.radial_weight = 4 - else: - self.radial_weight=radial_weight - self.vertices, self.idx, self.idx8, self.area, self.faces, \ - self.separation_angle = sphericalsampling.odfgrid(res) - if not nthreads is None: - if nthreads > multiprocessing.cpu_count(): - warnings.warn('Number of workers/threads specified exceed more ' - 'than available. Using the maximum workers/threads available.') - self.workers = -1 - if nthreads is None: - self.workers = -1 - else: - self.workers = nthreads - - def dkiodfhelper(self, dt, kt, radial_weight=4, fa_t=None, form='spherical'): - """ - Computes DKI fODF coefficient at a voxel. This function is intended to - parallelize computations across the brain. - - Parameters - ---------- - dt : array_like(dtype=float) - Diffusion tensor containing 6 elements - kt: array_like(dtype=float) - Kurtosis tensor containing 15 elements - radial_weighing : float; optional - Radial weighting power for detecting directional differences (Default: 4) - fa_t : float64 - In rare cases the diffusion tensor may be extremely isotropic with - very small eigenvalues, causing the kurtosis dODF to have erratic - behavior with very large values, as the kurtosis dODF evaluates the - inverse of D. Setting a threshold removes negative eigenvalues while - preserving principal orientation in voxels where FA >= threshold - form : str; optional; {'spherical', 'cartesian', 'coefficient'} - Form of ODF to return in - (Default: 'spherical') - - Returns - ------- - odf : array_like(dtype=float) - DKI ODFs in either coefficient, spherical, or cartesian form - """ - D = np.array( - [ - [dt[0], dt[3], dt[4]], - [dt[3], dt[1], dt[5]], - [dt[4], dt[5], dt[2]] - ] - ) - - W = np.zeros((3,3,3,3)) - W[0,0,0,0] = kt[0] - W[1,1,1,1] = kt[1] - W[2,2,2,2] = kt[2] - W[0,0,0,1] = kt[3]; W[0,0,1,0] = W[0,0,0,1]; W[0,1,0,0] = W[0,0,0,1]; W[1,0,0,0] = W[0,0,0,1] - W[0,0,0,2] = kt[4]; W[0,0,2,0] = W[0,0,0,2]; W[0,2,0,0] = W[0,0,0,2]; W[2,0,0,0] = W[0,0,0,2] - W[0,1,1,1] = kt[5]; W[1,0,1,1] = W[0,1,1,1]; W[1,1,0,1] = W[0,1,1,1]; W[1,1,1,0] = W[0,1,1,1] - W[0,2,2,2] = kt[6]; W[2,0,2,2] = W[0,2,2,2]; W[2,2,0,2] = W[0,2,2,2]; W[2,2,2,0] = W[0,2,2,2] - W[1,1,1,2] = kt[7]; W[1,1,2,1] = W[1,1,1,2]; W[1,2,1,1] = W[1,1,1,2]; W[2,1,1,1] = W[1,1,1,2] - W[1,2,2,2] = kt[8]; W[2,1,2,2] = W[1,2,2,2]; W[2,2,1,2] = W[1,2,2,2]; W[2,2,2,1] = W[1,2,2,2] - W[0,0,1,1] = kt[9]; W[0,1,0,1] = W[0,0,1,1]; W[0,1,1,0] = W[0,0,1,1]; W[1,0,0,1] = W[0,0,1,1]; W[1,0,1,0] = W[0,0,1,1]; W[1,1,0,0] = W[0,0,1,1] - W[0,0,2,2] = kt[10]; W[0,2,0,2] = W[0,0,2,2]; W[0,2,2,0] = W[0,0,2,2]; W[2,0,0,2] = W[0,0,2,2]; W[2,0,2,0] = W[0,0,2,2]; W[2,2,0,0] = W[0,0,2,2] - W[1,1,2,2] = kt[11]; W[1,2,1,2] = W[1,1,2,2]; W[1,2,2,1] = W[1,1,2,2]; W[2,1,1,2] = W[1,1,2,2]; W[2,1,2,1] = W[1,1,2,2]; W[2,2,1,1] = W[1,1,2,2] - W[0,0,1,2] = kt[12]; W[0,0,2,1] = W[0,0,1,2]; W[0,1,0,2] = W[0,0,1,2]; W[0,1,2,0] = W[0,0,1,2]; W[0,2,0,1] = W[0,0,1,2]; W[0,2,1,0] = W[0,0,1,2]; W[1,0,0,2] = W[0,0,1,2]; W[1,0,2,0] = W[0,0,1,2]; W[1,2,0,0] = W[0,0,1,2]; W[2,0,0,1] = W[0,0,1,2]; W[2,0,1,0] = W[0,0,1,2]; W[2,1,0,0] = W[0,0,1,2] - W[0,1,1,2] = kt[13]; W[0,1,2,1] = W[0,1,1,2]; W[0,2,1,1] = W[0,1,1,2]; W[1,0,1,2] = W[0,1,1,2]; W[1,0,2,1] = W[0,1,1,2]; W[1,1,0,2] = W[0,1,1,2]; W[1,1,2,0] = W[0,1,1,2]; W[1,2,0,1] = W[0,1,1,2]; W[1,2,1,0] = W[0,1,1,2]; W[2,0,1,1] = W[0,1,1,2]; W[2,1,0,1] = W[0,1,1,2]; W[2,1,1,0] = W[0,1,1,2] - W[0,1,2,2] = kt[14]; W[0,2,1,2] = W[0,1,2,2]; W[0,2,2,1] = W[0,1,2,2]; W[1,0,2,2] = W[0,1,2,2]; W[1,2,0,2] = W[0,1,2,2]; W[1,2,2,0] = W[0,1,2,2]; W[2,0,1,2] = W[0,1,2,2]; W[2,0,2,1] = W[0,1,2,2]; W[2,1,0,2] = W[0,1,2,2]; W[2,1,2,0] = W[0,1,2,2]; W[2,2,0,1] = W[0,1,2,2]; W[2,2,1,0] = W[0,1,2,2] - - # Reglarize tensor if fa is more than threshold specified (fa_t) - if not fa_t is None: - L, V = np.linalg.eig(D) - L[L < __minZero__] = __minZero__ - idx = np.argsort(L)[::-1] - L = L[idx] - V = V[:, idx] - fa = np.sqrt( - ( - (L[0] - L[1])**2 + \ - (L[0] - L[2])**2 + \ - (L[1] - L[2])**2 \ - ) / (2 * (np.sum(L**2))) - ) - if fa > fa_t: - x = np.roots([2*(1-2*fa_t**2)/3, -4*L[0]/3, 2*(1-fa_t**2)/3*L[0]**2]) - if x[np.logical_and(x > 0, x < L[0])].size != 0: - L[1:3] = x[np.logical_and(x > 0, x < L[0])] - else: - Davg = np.trace(D)/3 - L[L < 0.1 * Davg] = 0.1 * Davg - D = np.matmul(np.matmul(V, np.diag(L)), np.linalg.inv(V)) - W = np.zeros((3,3,3,3)) - - Davg = np.trace(D)/3 - try: - U = Davg * np.linalg.inv(D) - except: - U = Davg * np.linalg.pinv(D) - A1=0 - B11=0 - B12=0 - B13=0 - B22=0 - B23=0 - B33=0 - C1111=0 - C1112=0 - C1113=0 - C1122=0 - C1123=0 - C1133=0 - C1222=0 - C1223=0 - C1233=0 - C1333=0 - C2222=0 - C2223=0 - C2233=0 - C2333=0 - C3333=0 - - for i in [0, 1, 2]: - for j in [0, 1, 2]: - for k in [0, 1, 2]: - for l in [0, 1, 2]: - # Coefficients for: 3UijWijklUkl - A1 = A1 + 3 * U[i,j] * W[i,j,k,l] * U[k,l] - # Coefficients for: -6(a+1)UijWijklVkl - B0 = -6*(radial_weight+1) * U[i,j] * W[i,j,k,l] - B11 = B11 + B0 * (U[k,0] * U[l,0]) - B12 = B12 + B0 * (U[k,0] * U[l,1] + U[k,1] * U[l,0]) - B13 = B13 + B0 * (U[k,0] * U[l,2] + U[k,2] * U[l,0]) - B22 = B22 + B0 * (U[k,1] * U[l,1]) - B23 = B23 + B0 * (U[k,1] * U[l,2] + U[k,2] * U[l,1]) - B33 = B33 + B0 * (U[k,2] * U[l,2]) - # Coefficients for: (alpha+1)(alpha+3)W(i,j,k,l)VijVkl - C0 = (radial_weight+1) * (radial_weight+3) * W[i,j,k,l] - C1111 = C1111 + C0 * (U[i,0] * U[j,0] * U[k,0] * U[l,0]) - C1112 = C1112 + C0 * (U[i,0] * U[j,0] * U[k,0] * U[l,1] + U[i,0] * U[j,0] * U[k,1] * U[l,0] + U[i,0] * U[j,1] * U[k,0] * U[l,0] + U[i,1] * U[j,0] * U[k,0] * U[l,0]) - C1113 = C1113 + C0 * (U[i,0] * U[j,0] * U[k,0] * U[l,2] + U[i,0] * U[j,0] * U[k,2] * U[l,0] + U[i,0] * U[j,2] * U[k,0] * U[l,0] + U[i,2] * U[j,0] * U[k,0] * U[l,0]) - C1122 = C1122 + C0 * (U[i,0] * U[j,0] * U[k,1] * U[l,1] + U[i,0] * U[j,1] * U[k,0] * U[l,1] + U[i,0] * U[j,1] * U[k,1] * U[l,0] + U[i,1] * U[j,0] * U[k,0] * U[l,1] + U[i,1] * U[j,0] * U[k,1] * U[l,0] + U[i,1] * U[j,1] * U[k,0] * U[l,0]) - C1123 = C1123 + C0 * (U[i,0] * U[j,0] * U[k,1] * U[l,2] + U[i,0] * U[j,0] * U[k,2] * U[l,1] + U[i,0] * U[j,1] * U[k,0] * U[l,2] + U[i,0] * U[j,1] * U[k,2] * U[l,0] + U[i,0] * U[j,2] * U[k,0] * U[l,1] + U[i,0] * U[j,2] * U[k,1] * U[l,0] + U[i,1] * U[j,0] * U[k,0] * U[l,2] + U[i,1] * U[j,0] * U[k,2] * U[l,0] + U[i,1] * U[j,2] * U[k,0] * U[l,0] + U[i,2] * U[j,0] * U[k,0] * U[l,1] + U[i,2] * U[j,0] * U[k,1] * U[l,0] + U[i,2] * U[j,1] * U[k,0] * U[l,0]) - C1133 = C1133 + C0 * (U[i,0] * U[j,0] * U[k,2] * U[l,2] + U[i,0] * U[j,2] * U[k,0] * U[l,2] + U[i,0] * U[j,2] * U[k,2] * U[l,0] + U[i,2] * U[j,0] * U[k,0] * U[l,2] + U[i,2] * U[j,0] * U[k,2] * U[l,0] + U[i,2] * U[j,2] * U[k,0] * U[l,0]) - C1222 = C1222 + C0 * (U[i,0] * U[j,1] * U[k,1] * U[l,1] + U[i,1] * U[j,0] * U[k,1] * U[l,1] + U[i,1] * U[j,1] * U[k,0] * U[l,1] + U[i,1] * U[j,1] * U[k,1] * U[l,0]) - C1223 = C1223 + C0 * (U[i,0] * U[j,1] * U[k,1] * U[l,2] + U[i,0] * U[j,1] * U[k,2] * U[l,1] + U[i,0] * U[j,2] * U[k,1] * U[l,1] + U[i,1] * U[j,0] * U[k,1] * U[l,2] + U[i,1] * U[j,0] * U[k,2] * U[l,1] + U[i,1] * U[j,1] * U[k,0] * U[l,2] + U[i,1] * U[j,1] * U[k,2] * U[l,0] + U[i,1] * U[j,2] * U[k,0] * U[l,1] + U[i,1] * U[j,2] * U[k,1] * U[l,0] + U[i,2] * U[j,0] * U[k,1] * U[l,1] + U[i,2] * U[j,1] * U[k,0] * U[l,1] + U[i,2] * U[j,1] * U[k,1] * U[l,0]) - C1233 = C1233 + C0 * (U[i,0] * U[j,1] * U[k,2] * U[l,2] + U[i,0] * U[j,2] * U[k,1] * U[l,2] + U[i,0] * U[j,2] * U[k,2] * U[l,1] + U[i,1] * U[j,0] * U[k,2] * U[l,2] + U[i,1] * U[j,2] * U[k,0] * U[l,2] + U[i,1] * U[j,2] * U[k,2] * U[l,0] + U[i,2] * U[j,0] * U[k,1] * U[l,2] + U[i,2] * U[j,0] * U[k,2] * U[l,1] + U[i,2] * U[j,1] * U[k,0] * U[l,2] + U[i,2] * U[j,1] * U[k,2] * U[l,0] + U[i,2] * U[j,2] * U[k,0] * U[l,1] + U[i,2] * U[j,2] * U[k,1] * U[l,0]) - C1333 = C1333 + C0 * (U[i,0] * U[j,2] * U[k,2] * U[l,2] + U[i,2] * U[j,0] * U[k,2] * U[l,2] + U[i,2] * U[j,2] * U[k,0] * U[l,2] + U[i,2] * U[j,2] * U[k,2] * U[l,0]) - C2222 = C2222 + C0 * (U[i,1] * U[j,1] * U[k,1] * U[l,1]) - C2223 = C2223 + C0 * (U[i,1] * U[j,1] * U[k,1] * U[l,2] + U[i,1] * U[j,1] * U[k,2] * U[l,1] + U[i,1] * U[j,2] * U[k,1] * U[l,1] + U[i,2] * U[j,1] * U[k,1] * U[l,1]) - C2233 = C2233 + C0 * (U[i,1] * U[j,1] * U[k,2] * U[l,2] + U[i,1] * U[j,2] * U[k,1] * U[l,2] + U[i,1] * U[j,2] * U[k,2] * U[l,1] + U[i,2] * U[j,1] * U[k,1] * U[l,2] + U[i,2] * U[j,1] * U[k,2] * U[l,1] + U[i,2] * U[j,2] * U[k,1] * U[l,1]) - C2333 = C2333 + C0 * (U[i,1] * U[j,2] * U[k,2] * U[l,2] + U[i,2] * U[j,1] * U[k,2] * U[l,2] + U[i,2] * U[j,2] * U[k,1] * U[l,2] + U[i,2] * U[j,2] * U[k,2] * U[l,1]) - C3333 = C3333 + C0 * (U[i,2] * U[j,2] * U[k,2] * U[l,2]) - coeff = np.array( - [ - A1, B11, B12, B13, B22, B23, B33, C1111, C1112, C1113, C1122, C1123, C1133, C1222, C1223, C1233, C1333, C2222, C2223, C2233, C2333, C3333, U[0,0], U[1,1], U[2,2], U[0,1], U[0,2], U[1,2], radial_weight - ] - ) - if form == 'coefficient': - odf = coeff - if form == 'spherical': - odf = dkiodfspherical(coeff, self.vertices[:, 0], self.vertices[:, 1]) - if form == 'cartesian': - x, y, z = sphere2cart(1, self.vertices[:, 1], self.vertices[:, 0]) - odf = dkiodfcartesian(coeff, x, y, z) - return odf - - def dkiodf(self, form='spherical', fa_t=0.90): - """ - Computes DKI ODFs for the whole brain. - - Parameters - ---------- - form : str; optional; {'spherical', 'cartesial', 'coefficient'} - Form of ODF to return in - (Default: 'spherical') - fa_t : float64; optional - In rare cases the diffusion tensor may be extremely isotropic with - very small eigenvalues, causing the kurtosis dODF to have erratic - behavior with very large values, as the kurtosis dODF evaluates the - inverse of D. Setting a threshold removes negative eigenvalues while - preserving principal orientation in voxels where FA >= threshold - (Default: 0.95) - Returns - ------- - DKI ODF in defined form - """ - if not form in ['spherical', 'cartesian', 'coefficient']: - raise Exception('Please select a valid form of ODF to receive') - if self.KT is None: - raise AttributeError('WOAH! Cannot compute DKI ODFs without ' - 'kurtosis tensor (KT). Try using dtiodf(), Jumbo.') - # Vectorize images - DT = vectorize(self.DT, self.mask_img) - KT = vectorize(self.KT, self.mask_img) - nvox = DT.shape[-1] - inputs = tqdm(range(nvox), - desc='DKI ODF', - bar_format='{desc}: [{percentage:0.0f}%]', - unit='vox', - ncols=70) - odf = Parallel(n_jobs=self.workers, prefer='processes') (delayed(self.dkiodfhelper)\ - (DT[:, i], KT[:, i], self.radial_weight, fa_t, form) for i in inputs) - odf = np.array(odf).T - odf = vectorize(odf, self.mask_img) - return(odf) - - def dtiodfhelper(self, dt, form='spherical'): - """ - Computes DTI fODF coefficient at a voxel. This function is intended to - parallelize computations across the brain. Use only for diffusion - ellipsoids. - - Parameters - ---------- - dt : array_like(dtype=float) - Diffusion tensor containing 6 elements - radial_weighing : float; optional - Radial weighting power for detecting directional differences (Default: 4) - form : str; optional; {'spherical', 'coefficient'} - Form of ODF to return in - (Default: 'spherical') - - Returns - ------- - odf : array_like(dtype=float) - DKI ODFs in either coefficient, spherical, or cartesian form - """ - D = np.array( - [ - [dt[0], dt[3], dt[4]], - [dt[3], dt[1], dt[5]], - [dt[4], dt[5], dt[2]] - ] - ) - Davg = np.trace(D)/3 - try: - U = Davg * np.linalg.inv(D) - except: - U = Davg * np.linalg.pinv(D) - U11 = U[0,0] - U22 = U[1,1] - U33 = U[2,2] - U12 = U[0,1] - U13 = U[0,2] - U23 = U[1,2] - coeff = np.array( - [U11, U12, U13, U22, U23, U33] - ) - if form == 'coefficient': - odf = coeff - if form == 'spherical': - odf = dtiodfspherical(coeff, self.vertices[:, 0], self.vertices[:, 1], self.radial_weight) - return odf - - def dtiodf(self, form='spherical'): - """ - Computed DTI ODFs for the whole brain (ellipsoids) - - Parameters - ---------- - form : str; optional; {'spherical', 'cartesian', 'coefficient'} - Form of ODF to return in - (Default: 'spherical') - Returns - ------- - DTI ODF in defined form - """ - if self.DT is None: - raise AttributeError('WOAH! Cannot compute DTI ODFs without ' - 'diffusion tensor (DT), Jumbo.') - # Vectorize images - DT = vectorize(self.DT, self.mask_img) - nvox = DT.shape[-1] - inputs = tqdm(range(nvox), - desc='DTI ODF', - bar_format='{desc}: [{percentage:0.0f}%]', - unit='vox', - ncols=70) - odf = Parallel(n_jobs=self.workers, prefer='processes') (delayed(self.dtiodfhelper)\ - (DT[:, i], form) for i in inputs) - odf = np.array(odf).T - odf = vectorize(odf, self.mask_img) - return(odf) - - def odfmaxhelper(self, odf): - """ - Find local maxima of ODF over spherical grid at voxel - - Parameters - ---------- - odf : array_like(dtype=float64) - Spherical ODF values at a voxel - - Returns - ------- - odfmax : array_like(dtype=float64) - Local maxima of ODF over spherical grid in descending order - dirmax : array_like(dtype=float64) - Corresponding direction vector where local ODF maxima occur - """ - maxidx = self.idx[odf[self.idx[:, 1]] == np.amax(odf[self.idx], axis=1), 0] - odf_max = odf[maxidx] - dir_max = self.vertices[maxidx, :] - # Sort by magnitude in descending order - idx = np.argsort(odf_max)[::-1] - odfmax = odf_max[idx] - dirmax = dir_max[idx] - if odfmax.size == 0: - odfmax = np.array([1]) - dirmax = np.array([0, 0]) - return odfmax, dirmax - - def odf2shhelper(self, odf, B, scale): - """ - Helper function to parallelize computation spherical harmonic expansion - at a voxel. - - Parameters - ---------- - odf : array_like(dtype=float64) - Spherical ODF values at a voxel - B : array_like(dtype=complex) - Spherical harmonic basis set to compute expansion - scale : float64 - Value of dMRI metric to multiply ODF with to control stopping - criteria in tractography - - Returns - ------- - sh : Shpherical harmonic expansion of ODF at voxel - """ - odfmax, dirmax = self.odfmaxhelper(odf) - odfmax = odfmax[0] - sh = np.dot(np.linalg.pinv(B), odf / odfmax) * scale - sh[np.isnan(sh)] = __minZero__ - sh[np.isinf(sh)] = __minZero__ - return sh - - def odf2sh(self, odf): - """ - Converts whole-brain ODFs to spherical harmonics sampled at direction - set specified by resolution. Only the real portion is returned. - - Parameters - ---------- - odf : 4D ODF file containing spherical ODFs - - Returns - ------- - sh : array_like(dtype=float64) - Shperical harmonic expansion of ODF - """ - odf = vectorize(odf, self.mask_img) - scale = vectorize(self.scale_img, self.mask_img) - # Create shperical harmonic (SH) base set - degs = np.arange(self.l_max + 1, dtype=int) - l_num = 2 * degs + 1 # how many per degree (evens only) - # Variable `harmonics` holds the index of phase m where l is even i.e. - # l = 0; m = 0 --> 1 phases (even l) - # l = 1, m = -1, m = 0, m = 1 --> 3 phases (odd l) - # l = 2, m = -2, m = -1, m = 0, m = 1, m = 2 --> 5 phases (even l) - harmonics = [] - sh_end = 0 # initialize the SH set for indexing - for _, phase in enumerate(l_num[::2]): - sh_start = sh_end + phase - 1 - sh_end = sh_start + phase - 1 - harmonics.extend(np.arange(sh_start, sh_end + 1)) - B = shbasis(degs, self.vertices[:, 0], self.vertices[:, 1], 'tournier') - B = B[:, harmonics] - nvox = odf.shape[-1] - inputs = tqdm(range(nvox), - desc='ODF SH Expansion', - bar_format='{desc}: [{percentage:0.0f}%]', - unit='vox', - ncols=70) - sh = Parallel(n_jobs=self.workers, prefer='processes') (delayed(self.odf2shhelper)\ - (odf[:, i], B, scale[i]) for i in inputs) - sh = np.array(sh).T.real - sh = vectorize(sh, self.mask_img) - return sh - - def savenii(self, var, path): - """ - Write out NifTI output of associated spherical harmonic file - - Parameters - ---------- - var : array_like - variable to write out - path : str - Path to output file - - Returns - ------- - None; writes out file - """ - self.hdr.set_data_dtype(var.dtype) - writeNii(var, self.hdr, path) - -def dkiodfspherical(odf, phi, theta): - """ - Convert DKI ODFs coefficients at voxel to spherical form. - - Parameters - ---------- - odf : array_like(dtype=float64) - ODF coefficients at a voxel. There are 29 coefficients for DKI ODFs - phi : array_like(dtype=float64) - Polar phi angles - theta : array_like(dtype=float64) - Polar theta angles - - Returns - ------- - spherical : array_like(dtype=float64) - ODF in spherical form - """ - if len(theta) != len(phi): - raise Exception('Inputs theta and phi are not the same size') - try: - spherical = (1 / ((np.sin(phi) * np.cos(theta))**2 * odf[22] + (np.sin(phi) * np.sin(theta))**2 * odf[23] + np.cos(phi)**2 * odf[24] + 2 * (np.sin(phi) * np.cos(theta)) * (np.sin(phi) * np.sin(theta)) * odf[25] + \ - 2 * (np.sin(phi) * np.cos(theta)) * np.cos(phi) * odf[26] + 2 * (np.sin(phi) * np.sin(theta)) * np.cos(phi) * odf[27]))**(( odf[28] + 1) / 2) * (1 + ( odf[0] + \ - ( odf[1] * (np.sin(phi) * np.cos(theta))**2 + odf[2] * (np.sin(phi) * np.cos(theta)) * (np.sin(phi) * np.sin(theta)) + odf[3] * (np.sin(phi) * np.cos(theta)) * np.cos(phi) + odf[4] * (np.sin(phi) * np.sin(theta))**2 + \ - odf[5] * (np.sin(phi) * np.sin(theta)) * np.cos(phi) + odf[6] * np.cos(phi)**2) / ((np.sin(phi) * np.cos(theta))**2 * odf[22] + (np.sin(phi) * np.sin(theta))**2 * odf[23] + np.cos(phi)**2 * odf[24] + \ - 2 * (np.sin(phi) * np.cos(theta)) * (np.sin(phi) * np.sin(theta)) * odf[25] + 2 * (np.sin(phi) * np.cos(theta)) * np.cos(phi) * odf[26] + 2 * (np.sin(phi) * np.sin(theta)) * np.cos(phi) * odf[27]) + \ - ( odf[7] * (np.sin(phi) * np.cos(theta))**4 + odf[8] * (np.sin(phi) * np.cos(theta))**3 * (np.sin(phi) * np.sin(theta)) + odf[9] * (np.sin(phi) * np.cos(theta))**3 * np.cos(phi) + odf[10] * (np.sin(phi) * np.cos(theta))**2 * (np.sin(phi) * np.sin(theta))**2 + \ - odf[11] * (np.sin(phi) * np.cos(theta))**2 * (np.sin(phi) * np.sin(theta)) * np.cos(phi) + odf[12] * (np.sin(phi) * np.cos(theta))**2 * np.cos(phi)**2 + odf[13] * (np.sin(phi) * np.cos(theta)) * (np.sin(phi) * np.sin(theta))**3 + \ - odf[14] * (np.sin(phi) * np.cos(theta)) * (np.sin(phi) * np.sin(theta))**2 * np.cos(phi) + odf[15] * (np.sin(phi) * np.cos(theta)) * (np.sin(phi) * np.sin(theta)) * np.cos(phi)**2 + odf[16] * (np.sin(phi) * np.cos(theta)) * np.cos(phi)**3 + odf[17] * (np.sin(phi) * np.sin(theta))**4 + \ - odf[18] * (np.sin(phi) * np.sin(theta))**3 * np.cos(phi) + odf[19] * (np.sin(phi) * np.sin(theta))**2 * np.cos(phi)**2 + odf[20] * (np.sin(phi) * np.sin(theta)) * np.cos(phi)**3 + odf[21] * np.cos(phi)**4) / ((np.sin(phi) * np.cos(theta))**2 * odf[22] + (np.sin(phi) * np.sin(theta))**2 * odf[23] + \ - np.cos(phi)**2 * odf[24] + 2 * (np.sin(phi) * np.cos(theta)) * (np.sin(phi) * np.sin(theta)) * odf[25] + 2 * (np.sin(phi) * np.cos(theta)) * np.cos(phi) * odf[26] + 2 * (np.sin(phi) * np.sin(theta)) * np.cos(phi) * odf[27])**2) / 24) - except: - spherical = np.full(phi.shape, __minZero__) - return spherical - -def dkiodfcartesian(odf, x, y, z): - """ - Convert DKI ODF coefficients at voxel to Cartesian form. - - Parameters - ---------- - odf : array_like(dtype=float64) - ODF coefficients at a voxel. There are 29 coefficients for DKI ODFs - x : array_like(dtype=float64) - Cartesian x coordinates - y : array_like(dtype=float64) - Cartesian y coordinates - z : array_like(dtype=float64) - Cartesian z coordinates - - Returns - ------- - cart : array_like(dtype=float64) - ODF in cartesian form - """ - if len(x) != len(y): - raise Exception('Input x, y and z coordinates are not the same size') - if len(x) != len(z): - raise Exception('Input x, y and z coordinates are not the same size') - try: - cart = (1 / ((x)**2 * odf[22] + (y)**2 * odf[23] + z**2 * odf[24] + 2 * (x) * (y) * odf[25] + \ - 2 * (x) * z * odf[26] + 2 * (y) * z * odf[27]))**((odf[28] + 1) / 2) * (1 + (odf[0] + \ - (odf[1] * (x)**2 + odf[2] * (x) * (y) + odf[3] * (x) * z + odf[4] * (y)**2 + \ - odf[5] * (y) * z + odf[6] * z**2) / ((x)**2 * odf[22] + (y)**2 * odf[23] + z**2 * odf[24] + \ - 2 * (x) * (y) * odf[25] + 2 * (x) * z * odf[26] + 2 * (y) * z * odf[27]) + \ - (odf[7] * (x)**4 + odf[8] * (x)**3 * (y) + odf[9] * (x)**3 * z + odf[10] * (x)**2 * (y)**2 + \ - odf[11] * (x)**2 * (y) * z + odf[12] * (x)**2 * z**2 + odf[13] * (x) * (y)**3 + \ - odf[14] * (x) * (y)**2 * z + odf[15] * (x) * (y) * z**2 + odf[16] * (x) * z**3 + odf[17] * (y)**4 + \ - odf[18] * (y)**3 * z + odf[19] * (y)**2 * z**2 + odf[20] * (y) * z**3 + odf[21] * z**4) / ((x)**2 * odf[22] + (y)**2 * odf[23] + \ - z**2 * odf[24] + 2 * (x) * (y) * odf[25] + 2 * (x) * z * odf[26] + 2 * (y) * z * odf[27])**2) / 24) - except: - cart = np.full(x.shape, __minZero__) - return cart - -def dtiodfspherical(odf, phi, theta, radial_weight=4): - """ - Convert DTI ODFs coefficients at voxel to spherical form. - - Parameters - ---------- - odf : array_like(dtype=float64) - ODF coefficients at a voxel. There are 29 coefficients for DKI ODFs - phi : array_like(dtype=float64) - Polar phi angles - theta : array_like(dtype=float64) - Polar theta angles - radial_weight : float - Radial weighting power for detecting directional differences - (Default: 4) - - Returns - ------- - spherical : array_like(dtype=float64) - ODF in spherical form - """ - if len(theta) != len(phi): - raise Exception('Inputs theta and phi are not the same size') - try: - spherical = (1/((np.sin(phi) * np.cos(theta))**2 * odf[0] + (np.sin(phi) *\ - np.sin(theta))**2 * odf[3] + np.cos(phi)**2 * odf[5] + 2 * \ - (np.sin(phi) * np.cos(theta)) * (np.sin(phi) * np.sin(theta)) * \ - odf[1] + 2 * (np.sin(phi) * np.cos(theta)) * np.cos(phi) * \ - odf[2] + 2 * (np.sin(phi) * np.sin(theta)) * np.cos(phi) \ - * odf[4]))**((radial_weight + 1)/2) - except: - sherical = np.full(phi.shape, __minZero__) - return spherical - -def shbasis(deg, phi, theta, method='scipy'): - """ - Computes shperical harmonic bases for all orders (even and odd), using - functions defined by `scipy`, `Tournier`, or `Descoteaux`. - - Parameters - ---------- - deg : list of ints - Degrees of harmonic - phi : array_like - (n, ) vector denoting polar coordinates - theta : array_like - (n, ) vector denoting azimuthal coordinates - method : str; optional; {scipy, tournier, descoteaux} - Define method for SH basis set - - Returns - ------- - complex array_like - Harmonic samples at theta and phi at specified order - """ - if not any([isinstance(x, int) for x in deg]): - try: - deg = [int(x) for x in deg] - except: - raise TypeError('Please supply degree of ' - 'shperical harmonic as an integer') - if not isinstance(method, str): - raise TypeError('Please enter method as a string') - if not method in ['scipy', 'tournier', 'descoteaux']: - raise Exception('Please select a valid method for SH basis set') - SH = [] - for n in deg: - for m in range(-n, n + 1): - shb = sph_harm(m, n, theta, phi, dtype=complex) - if method == 'tournier': - # Tournier does not have Condon–Shortley phase i.e. (-1)^m in - # their formulas, so we multiply those terms by (-1)^m to reverse the - # inclusion of this phase from scipy's `sph_harm`. - - # Tournier formulas have sqrt(2) normalization term so we - # multiply Ylm. - if m < 0: - sh_ = np.sqrt(2) * shb.imag - elif m > 0: - sh_ = np.sqrt(2) * shb.real * (-1)**m - elif m == 0: - sh_ = shb - elif method == 'descoteaux': - if m < 0: - sh_ = shb.real - elif m > 0: - sh_ = shb.imag * (-1)**m - elif m == 0: - sh_ = shb - elif method == 'scipy': - sh_ = shb - SH.append(sh_) - return np.array(SH, order='F').T diff --git a/designer/tractography/sphericalsampling.py b/designer/tractography/sphericalsampling.py deleted file mode 100644 index 8bb8198e..00000000 --- a/designer/tractography/sphericalsampling.py +++ /dev/null @@ -1,35457 +0,0 @@ -#!/usr/bin/env python -# -*- coding : utf-8 -*- -""" -Various definitions of spherical sampling -""" -import os.path as op -import numpy as np - -def dsigrid(odf_key='odf8'): - """ - Reads DSIStudio's ODF geometry in odfs.mat to use in creation of DSIStudio's - .fib file. - - Parameters - ---------- - odf_keys : str; optional; {'odf4', 'odf5', 'odf6', 'odf8', 'odf12', 'odf20'} - DSIStudio's direction set to load. (Default: 'odf8') - - Returns - ------- - odf_vertices : array_like(dtype=flaot64) - ODF vertices - odf_faces : array_like(dtype=uint16) - ODF faces - """ - if not isinstance(odf_key, str): - raise Exception('Please specify ODF key as a string. Possible ' - 'choices are "odf4", "odf5", "odf6", "odf8", "odf12", or "odf20"') - if odf_key not in ['odf4', 'odf5', 'odf6', 'odf8', 'odf12', 'odf20']: - raise Exception('Invalid ODF key') - if odf_key == 'odf4': - odf_vertices = np.array( - [ - [ 0. , 0.08444399, -0.22107723, 0.27326649, -0.22107723, - 0.08444399, -0.1381966 , 0.36180341, -0.44721356, 0.36180341, - -0.1381966 , 0.16245984, -0.42532536, 0.52573109, -0.42532536, - 0.16245984, -0.05278641, -0.36180341, 0.44721359, 0.63819659, - -0.67082042, -0.67082042, 0.63819659, 0.44721359, -0.36180341, - -0.05278641, 0.2281087 , -0.59719634, 0.73817527, -0.59719634, - 0.2281087 , -0.26286554, 0.68819094, -0.85065079, 0.68819094, - -0.26286554, 0.50137526, -0.51275235, -0.51275235, 0.50137526, - 0.00703146, -0.8182736 , 0.82261926, 0.82261926, -0.8182736 , - 0.00703146, 0.2763932 , -0.72360677, 0.89442718, -0.72360677, - 0.2763932 , -0.1381966 , 0.67082042, 0.86180341, -0.94721359, - -0.94721359, 0.86180341, 0.67082042, -0.1381966 , -0.44721356, - -0.44721356, 0.44918597, 0.44918597, 0.14366472, -0.68164039, - -0.87046289, 0.9592526 , 0.9592526 , -0.87046289, -0.68164039, - 0.14366472, 0. , 0.309017 , -0.309017 , 0.58778524, - -0.58778524, 0.809017 , -0.809017 , 0.95105654, -0.95105654, - 1. , -0. , -0.08444399, 0.22107723, -0.27326649, - 0.22107723, -0.08444399, 0.1381966 , -0.36180341, 0.44721356, - -0.36180341, 0.1381966 , -0.16245984, 0.42532536, -0.52573109, - 0.42532536, -0.16245984, 0.05278641, 0.36180341, -0.44721359, - -0.63819659, 0.67082042, 0.67082042, -0.63819659, -0.44721359, - 0.36180341, 0.05278641, -0.2281087 , 0.59719634, -0.73817527, - 0.59719634, -0.2281087 , 0.26286554, -0.68819094, 0.85065079, - -0.68819094, 0.26286554, -0.50137526, 0.51275235, 0.51275235, - -0.50137526, -0.00703146, 0.8182736 , -0.82261926, -0.82261926, - 0.8182736 , -0.00703146, -0.2763932 , 0.72360677, -0.89442718, - 0.72360677, -0.2763932 , 0.1381966 , -0.67082042, -0.86180341, - 0.94721359, 0.94721359, -0.86180341, -0.67082042, 0.1381966 , - 0.44721356, 0.44721356, -0.44918597, -0.44918597, -0.14366472, - 0.68164039, 0.87046289, -0.9592526 , -0.9592526 , 0.87046289, - 0.68164039, -0.14366472, -0. , -0.309017 , 0.309017 , - -0.58778524, 0.58778524, -0.809017 , 0.809017 , -0.95105654, - 0.95105654, -1. ], - [ 0. , 0.2598919 , 0.16062202, 0. , -0.16062202, - -0.2598919 , 0.42532539, 0.26286554, 0. , -0.26286554, - -0.42532539, 0.49999997, 0.30901697, 0. , -0.30901697, - -0.49999997, 0.68819094, 0.5877853 , 0.52573115, 0.26286557, - 0.16245985, -0.16245985, -0.26286557, -0.52573115, -0.5877853 , - -0.68819094, 0.70204639, 0.43388855, 0. , -0.43388855, - -0.70204639, 0.809017 , 0.5 , 0. , -0.5 , - -0.809017 , 0.70204645, 0.69378048, -0.69378048, -0.70204645, - 0.8626684 , 0.27326649, 0.2598919 , -0.2598919 , -0.27326649, - -0.8626684 , 0.85065079, 0.52573109, 0. , -0.52573109, - -0.85065079, 0.95105654, 0.68819094, 0.42532542, 0.16245987, - -0.16245987, -0.42532542, -0.68819094, -0.95105654, 0.85065079, - -0.85065079, 0.86266845, -0.86266845, 0.96193832, 0.69378042, - 0.43388855, 0.16062203, -0.16062203, -0.43388855, -0.69378042, - -0.96193832, 1. , 0.95105654, 0.95105654, 0.809017 , - 0.809017 , 0.5877853 , 0.5877853 , 0.309017 , 0.309017 , - -0. , -0. , -0.2598919 , -0.16062202, -0. , - 0.16062202, 0.2598919 , -0.42532539, -0.26286554, -0. , - 0.26286554, 0.42532539, -0.49999997, -0.30901697, -0. , - 0.30901697, 0.49999997, -0.68819094, -0.5877853 , -0.52573115, - -0.26286557, -0.16245985, 0.16245985, 0.26286557, 0.52573115, - 0.5877853 , 0.68819094, -0.70204639, -0.43388855, -0. , - 0.43388855, 0.70204639, -0.809017 , -0.5 , -0. , - 0.5 , 0.809017 , -0.70204645, -0.69378048, 0.69378048, - 0.70204645, -0.8626684 , -0.27326649, -0.2598919 , 0.2598919 , - 0.27326649, 0.8626684 , -0.85065079, -0.52573109, -0. , - 0.52573109, 0.85065079, -0.95105654, -0.68819094, -0.42532542, - -0.16245987, 0.16245987, 0.42532542, 0.68819094, 0.95105654, - -0.85065079, 0.85065079, -0.86266845, 0.86266845, -0.96193832, - -0.69378042, -0.43388855, -0.16062203, 0.16062203, 0.43388855, - 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22, 23, 23, 24, 24, 25, 26, 26, 26, 26, 27, 27, 27, - 27, 28, 28, 28, 28, 29, 29, 29, 29, 30, 30, 30, 30, - 36, 36, 36, 37, 37, 37, 38, 39, 40, 41, 42, 43, 43, - 43, 44, 44, 44, 45, 45, 45, 46, 47, 48, 49, 50, 51, - 51, 52, 53, 53, 54, 54, 55, 56, 57, 57, 58, 59, 60, - 60, 61, 61, 62, 62, 63, 63, 64, 64, 65, 65, 66, 66, - 67, 67, 68, 68, 69, 69, 70, 70, 71, 71, 71, 72, 72, - 73, 73, 74, 75, 76, 76, 77, 77, 78, 78, 78, 79, 79, - 79, 80, 80, 81, 81, 81, 81, 81, 82, 82, 82, 83, 83, - 83, 84, 84, 84, 85, 85, 85, 86, 86, 86, 87, 87, 88, - 88, 89, 89, 90, 90, 91, 91, 92, 92, 92, 93, 93, 93, - 94, 94, 94, 95, 95, 95, 96, 96, 96, 97, 97, 98, 99, - 100, 100, 101, 101, 102, 103, 104, 104, 105, 105, 106, 107, 108, - 109, 110, 111, 112, 112, 112, 113, 113, 113, 114, 114, 114, 115, - 115, 115, 116, 116, 116, 117, 118, 119, 119, 120, 120, 121, 121, - 122, 122, 123, 123, 124, 125, 126, 127, 127, 127, 127, 128, 128, - 128, 128, 129, 129, 129, 129, 130, 130, 130, 130, 131, 131, 131, - 131, 132, 132, 133, 134, 134, 135, 135, 136, 137, 138, 138, 139, - 140, 141, 141, 142, 142, 142, 143, 143, 144, 144, 145, 145, 145, - 146, 146, 147, 147, 148, 148, 148, 149, 149, 149, 150, 150, 151, - 151, 151, 152, 153, 153, 154, 154, 155, 155, 155, 156, 156, 156, - 157, 157, 158, 158, 159, 160, 161, 161] - ] - ) - - if odf_key == 'odf5': - odf_vertices = np.array( - [ - [ 0.00000000e+00, 6.78677559e-02, -1.77680060e-01, - 2.19624668e-01, -1.77680060e-01, 6.78677559e-02, - -1.11636549e-01, 2.92268306e-01, -3.61263484e-01, - 2.92268306e-01, -1.11636549e-01, 1.32421464e-01, - -3.46683860e-01, 4.28524882e-01, -3.46683860e-01, - 1.32421464e-01, -4.95957360e-02, -2.86392897e-01, - 3.66640508e-01, 5.12989163e-01, -5.43641031e-01, - -5.43641031e-01, 5.12989223e-01, 3.66640508e-01, - -2.86392868e-01, -4.95957136e-02, 1.90508887e-01, - -4.98758733e-01, 6.16499722e-01, -4.98758733e-01, - 1.90508887e-01, -2.20943794e-01, 5.78438282e-01, - -7.14989007e-01, 5.78438282e-01, -2.20943734e-01, - -4.44179982e-01, -6.93806946e-01, -4.44179922e-01, - 1.53837129e-02, 4.19288635e-01, 7.03314543e-01, - 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] - ) - odf_faces = np.array( - [ - [ 2, 6, 4, 8, 1, 7, 5, 10, 3, 9, 12, 17, 11, - 18, 14, 21, 13, 22, 15, 25, 6, 16, 8, 20, 7, 19, - 10, 24, 9, 23, 17, 31, 27, 36, 26, 40, 18, 32, 21, - 33, 29, 43, 28, 42, 22, 34, 25, 35, 30, 45, 16, 39, - 20, 37, 19, 41, 24, 38, 23, 44, 36, 53, 40, 54, 43, - 57, 42, 60, 45, 52, 47, 33, 56, 35, 59, 31, 51, 46, - 32, 55, 48, 49, 34, 58, 50, 39, 61, 40, 64, 37, 62, - 36, 65, 41, 66, 42, 70, 38, 68, 43, 67, 44, 69, 45, - 63, 53, 54, 57, 60, 52, 51, 56, 45, 95, 87, 40, 91, - 82, 36, 92, 81, 55, 43, 94, 83, 59, 58, 42, 93, 84, - 61, 62, 66, 68, 69, 57, 83, 53, 81, 60, 84, 87, 52, - 82, 54, 65, 64, 67, 70, 76, 56, 79, 59, 63, 77, 51, - 80, 55, 78, 58, 96, 82, 97, 81, 100, 84, 98, 83, 99, - 87, 88, 61, 85, 62, 86, 68, 90, 69, 89, 66, 84, 113, - 81, 112, 83, 114, 82, 111, 103, 76, 107, 79, 87, 115, 104, - 80, 101, 78, 102, 77, 121, 122, 123, 124, 125, 110, 88, 105, - 85, 108, 89, 106, 86, 109, 90, 96, 116, 97, 117, 100, 118, - 98, 119, 99, 120, 128, 130, 127, 131, 129, 133, 137, 132, 132, - 138, 134, 135, 139, 133, 134, 140, 136, 136, 141, 135, 142, 143, - 145, 144, 146, 147, 149, 148, 150, 151, 144, 152, 142, 143, 153, - 146, 148, 154, 145, 147, 155, 150, 151, 156, 149, 165, 157, 157, - 162, 158, 166, 167, 158, 163, 159, 159, 169, 160, 168, 170, 160, - 164, 161, 161, 171, 166, 172, 165, 162, 173, 163, 168, 174, 167, - 169, 175, 164, 171, 176, 170, 177, 179, 181, 180, 182, 183, 184, - 186, 185, 178, 179, 188, 182, 194, 185, 187, 177, 180, 192, 181, - 186, 183, 195, 184, 178, 187, 188, 192, 194, 195, 179, 203, 171, - 213, 189, 162, 207, 191, 166, 208, 190, 180, 206, 183, 202, 169, - 209, 193, 186, 204, 178, 205, 168, 210, 196, 177, 214, 182, 211, - 171, 166, 162, 181, 215, 169, 185, 212, 184, 216, 168, 190, 217, - 222, 187, 191, 218, 223, 188, 196, 219, 226, 192, 193, 220, 224, - 194, 189, 221, 225, 195, 209, 229, 207, 228, 210, 227, 233, 213, - 208, 230, 238, 237, 240, 239, 202, 231, 205, 232, 241, 203, 236, - 206, 234, 204, 235, 208, 247, 222, 207, 248, 223, 210, 249, 226, - 209, 250, 224, 213, 251, 225, 214, 242, 211, 243, 212, 245, 216, - 246, 215, 244, 239, 117, 238, 120, 240, 118, 237, 119, 123, 229, - 121, 233, 241, 116, 124, 230, 122, 227, 125, 228, 247, 106, 208, - 248, 109, 207, 249, 105, 210, 250, 108, 209, 251, 110, 213, 115, - 236, 113, 231, 114, 234, 111, 232, 112, 235, 107, 242, 243, 101, - 244, 103, 245, 104, 246, 102], - [ 0, 2, 0, 4, 0, 1, 0, 5, 0, 3, 2, 12, 1, - 11, 4, 14, 3, 13, 5, 15, 1, 6, 2, 8, 3, 7, - 4, 10, 5, 9, 6, 17, 12, 27, 11, 26, 7, 18, 8, - 21, 14, 29, 13, 28, 9, 22, 10, 25, 15, 30, 11, 16, - 12, 20, 13, 19, 14, 24, 15, 23, 17, 36, 18, 40, 21, - 43, 22, 42, 25, 45, 27, 20, 33, 24, 35, 16, 31, 26, - 19, 32, 28, 29, 23, 34, 30, 26, 39, 64, 71, 27, 37, - 65, 72, 28, 41, 70, 73, 29, 38, 67, 74, 30, 44, 63, - 75, 31, 32, 33, 34, 35, 39, 37, 52, 75, 95, 54, 71, - 91, 53, 72, 92, 41, 57, 74, 94, 38, 44, 60, 73, 93, - 46, 47, 48, 49, 50, 56, 57, 51, 53, 58, 60, 52, 59, - 54, 55, 53, 54, 57, 60, 56, 62, 59, 68, 52, 51, 61, - 55, 66, 58, 69, 71, 111, 72, 112, 73, 113, 74, 114, 75, - 115, 61, 71, 62, 72, 68, 74, 69, 75, 66, 73, 78, 84, - 77, 81, 76, 83, 80, 82, 76, 85, 79, 86, 79, 87, 80, - 89, 78, 90, 77, 88, 91, 92, 93, 94, 95, 88, 96, 85, - 97, 89, 100, 86, 98, 90, 99, 91, 96, 92, 97, 93, 100, - 94, 98, 95, 99, 127, 128, 129, 130, 131, 129, 133, 137, 127, - 132, 138, 131, 135, 139, 128, 134, 140, 130, 136, 141, 137, 142, - 139, 145, 138, 146, 141, 149, 140, 150, 133, 144, 152, 132, 143, - 153, 135, 148, 154, 134, 147, 155, 136, 151, 156, 152, 165, 142, - 157, 145, 158, 154, 167, 153, 163, 146, 159, 149, 160, 156, 170, - 155, 164, 150, 161, 144, 166, 172, 143, 162, 173, 148, 168, 174, - 147, 169, 175, 151, 171, 176, 165, 177, 167, 181, 163, 182, 170, - 184, 164, 185, 157, 173, 188, 175, 194, 172, 187, 158, 174, 192, - 160, 159, 176, 195, 161, 197, 198, 199, 200, 201, 203, 214, 189, - 205, 213, 191, 203, 207, 190, 206, 208, 206, 215, 202, 211, 193, - 202, 209, 204, 216, 205, 212, 196, 204, 210, 214, 222, 211, 223, - 176, 172, 173, 215, 226, 175, 212, 224, 216, 225, 174, 172, 190, - 217, 222, 173, 191, 218, 223, 174, 196, 219, 226, 175, 193, 220, - 224, 176, 189, 221, 225, 229, 231, 228, 236, 227, 235, 232, 233, - 230, 234, 228, 230, 229, 227, 231, 243, 232, 245, 233, 236, 242, - 234, 244, 235, 246, 190, 237, 247, 191, 238, 248, 196, 239, 249, - 193, 240, 250, 189, 241, 251, 242, 247, 243, 248, 245, 250, 246, - 251, 244, 249, 117, 122, 120, 125, 118, 123, 119, 124, 113, 123, - 111, 121, 116, 121, 114, 124, 112, 122, 115, 125, 106, 119, 217, - 109, 120, 218, 105, 117, 219, 108, 118, 220, 110, 116, 221, 107, - 115, 101, 113, 103, 114, 104, 111, 102, 112, 106, 107, 101, 109, - 103, 105, 104, 108, 102, 110], - [ 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, - 7, 8, 8, 9, 9, 10, 10, 11, 11, 12, 12, 13, 13, - 14, 14, 15, 15, 16, 16, 17, 17, 18, 18, 19, 19, 20, - 20, 21, 21, 22, 22, 23, 23, 24, 24, 25, 25, 26, 26, - 27, 27, 28, 28, 29, 29, 30, 30, 31, 31, 32, 32, 33, - 33, 34, 34, 35, 35, 36, 37, 37, 38, 38, 39, 39, 40, - 41, 41, 42, 43, 44, 44, 45, 46, 46, 46, 46, 47, 47, - 47, 47, 48, 48, 48, 48, 49, 49, 49, 49, 50, 50, 50, - 50, 51, 55, 56, 58, 59, 61, 62, 63, 63, 63, 64, 64, - 64, 65, 65, 65, 66, 67, 67, 67, 68, 69, 70, 70, 70, - 71, 72, 73, 74, 75, 76, 76, 77, 77, 78, 78, 79, 79, - 80, 80, 81, 82, 83, 84, 85, 85, 86, 86, 87, 88, 88, - 89, 89, 90, 90, 91, 91, 92, 92, 93, 93, 94, 94, 95, - 95, 96, 96, 97, 97, 98, 98, 99, 99, 100, 100, 101, 101, - 102, 102, 103, 103, 104, 104, 105, 105, 106, 106, 107, 107, 108, - 108, 109, 109, 110, 110, 111, 112, 113, 114, 115, 116, 116, 117, - 117, 118, 118, 119, 119, 120, 120, 121, 121, 122, 122, 123, 123, - 124, 124, 125, 125, 126, 126, 126, 126, 126, 127, 127, 127, 128, - 128, 128, 129, 129, 129, 130, 130, 130, 131, 131, 131, 132, 132, - 133, 133, 134, 134, 135, 135, 136, 136, 137, 137, 137, 138, 138, - 138, 139, 139, 139, 140, 140, 140, 141, 141, 141, 142, 142, 143, - 143, 144, 144, 145, 145, 146, 146, 147, 147, 148, 148, 149, 149, - 150, 150, 151, 151, 152, 152, 152, 153, 153, 153, 154, 154, 154, - 155, 155, 155, 156, 156, 156, 157, 157, 158, 158, 159, 159, 160, - 160, 161, 161, 162, 163, 163, 164, 164, 165, 165, 166, 167, 167, - 168, 169, 170, 170, 171, 172, 173, 174, 175, 176, 177, 177, 178, - 178, 178, 179, 179, 179, 180, 180, 180, 181, 181, 182, 182, 183, - 183, 183, 184, 184, 185, 185, 186, 186, 186, 187, 187, 188, 188, - 189, 190, 191, 192, 192, 193, 194, 194, 195, 195, 196, 197, 197, - 197, 197, 198, 198, 198, 198, 199, 199, 199, 199, 200, 200, 200, - 200, 201, 201, 201, 201, 202, 202, 203, 203, 204, 204, 205, 205, - 206, 206, 207, 208, 209, 210, 211, 211, 212, 212, 213, 214, 214, - 215, 215, 216, 216, 217, 217, 217, 218, 218, 218, 219, 219, 219, - 220, 220, 220, 221, 221, 221, 222, 222, 223, 223, 224, 224, 225, - 225, 226, 226, 227, 227, 228, 228, 229, 229, 230, 230, 231, 231, - 232, 232, 233, 233, 234, 234, 235, 235, 236, 236, 237, 237, 237, - 238, 238, 238, 239, 239, 239, 240, 240, 240, 241, 241, 241, 242, - 242, 243, 243, 244, 244, 245, 245, 246, 246, 247, 247, 248, 248, - 249, 249, 250, 250, 251, 251] - ] - ) - - if odf_key == 'odf6': - odf_vertices = np.array( - [ - [ 0.00000000e+00, 5.66983037e-02, -1.48438066e-01, - 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3406, 3406, 3407, 3407, 3408, 3408, 3409, 3409, 3410, 3410, 3411, - 3411, 3412, 3413, 3414, 3415, 3416, 3417, 3417, 3418, 3418, 3419, - 3419, 3420, 3420, 3421, 3421, 3427, 3427, 3428, 3428, 3428, 3429, - 3429, 3430, 3430, 3431, 3431, 3431, 3432, 3432, 3432, 3433, 3433, - 3434, 3434, 3434, 3435, 3435, 3435, 3436, 3436, 3437, 3437, 3438, - 3438, 3439, 3439, 3440, 3440, 3441, 3441, 3442, 3443, 3444, 3445, - 3446, 3447, 3447, 3448, 3448, 3449, 3449, 3450, 3450, 3451, 3451, - 3454, 3454, 3454, 3457, 3457, 3457, 3458, 3458, 3458, 3460, 3460, - 3460, 3461, 3461, 3461, 3462, 3462, 3463, 3463, 3464, 3464, 3465, - 3465, 3466, 3466, 3467, 3467, 3467, 3468, 3468, 3468, 3469, 3469, - 3469, 3470, 3470, 3470, 3471, 3471, 3471, 3472, 3472, 3472, 3472, - 3473, 3473, 3473, 3473, 3474, 3474, 3474, 3474, 3475, 3475, 3475, - 3475, 3476, 3476, 3476, 3476, 3477, 3477, 3477, 3477, 3478, 3478, - 3478, 3478, 3479, 3479, 3479, 3479, 3480, 3480, 3480, 3480, 3481, - 3481, 3481, 3481, 3482, 3482, 3482, 3483, 3483, 3483, 3484, 3485, - 3486, 3487, 3487, 3487, 3488, 3489, 3490, 3490, 3490, 3491, 3491, - 3491, 3492, 3492, 3492, 3493, 3494, 3494, 3494, 3495, 3495, 3495, - 3496, 3497, 3497, 3497, 3498, 3499, 3500, 3501, 3501, 3501, 3502, - 3502, 3503, 3503, 3504, 3504, 3505, 3505, 3506, 3506, 3507, 3507, - 3508, 3508, 3509, 3509, 3510, 3510, 3511, 3511, 3512, 3512, 3513, - 3513, 3514, 3514, 3515, 3515, 3516, 3516, 3522, 3522, 3523, 3523, - 3524, 3524, 3526, 3526, 3529, 3529, 3532, 3532, 3533, 3533, 3534, - 3534, 3535, 3535, 3536, 3536, 3537, 3537, 3538, 3538, 3539, 3539, - 3540, 3540, 3541, 3541, 3542, 3542, 3542, 3543, 3543, 3543, 3544, - 3544, 3544, 3545, 3545, 3545, 3546, 3546, 3546, 3547, 3547, 3547, - 3548, 3548, 3548, 3549, 3549, 3549, 3550, 3550, 3550, 3551, 3551, - 3551, 3552, 3552, 3553, 3553, 3554, 3554, 3555, 3555, 3556, 3556, - 3557, 3558, 3559, 3559, 3560, 3561, 3561, 3562, 3563, 3564, 3564, - 3565, 3565, 3566, 3566, 3567, 3567, 3568, 3568, 3569, 3569, 3570, - 3570, 3571, 3571, 3572, 3573, 3574, 3575, 3576, 3582, 3582, 3583, - 3583, 3584, 3584, 3585, 3585, 3586, 3586, 3587, 3588, 3589, 3590, - 3591, 3592, 3592, 3593, 3593, 3594, 3594, 3595, 3595, 3596, 3596, - 3597, 3597, 3598, 3598, 3599, 3599, 3600, 3600, 3601, 3601, 3602, - 3602, 3603, 3603, 3604, 3604, 3605, 3605, 3606, 3606, 3607, 3607, - 3608, 3609, 3609, 3610, 3610, 3611, 3611, 3612, 3612, 3613, 3614, - 3615, 3616, 3617, 3617, 3618, 3618, 3619, 3619, 3620, 3620, 3621, - 3621, 3622, 3622, 3623, 3623, 3624, 3625, 3625, 3626, 3626, 3627, - 3627, 3628, 3629, 3630, 3631, 3632, 3632, 3632, 3633, 3633, 3633, - 3634, 3634, 3634, 3635, 3635, 3635, 3636, 3636, 3636, 3637, 3637, - 3637, 3638, 3638, 3638, 3639, 3639, 3639, 3640, 3640, 3640, 3641, - 3641, 3641, 3642, 3642, 3642, 3643, 3643, 3643, 3644, 3644, 3644, - 3645, 3645, 3645, 3646, 3646, 3646, 3647, 3647, 3648, 3648, 3649, - 3649, 3650, 3650, 3651, 3651, 3652, 3652, 3653, 3653, 3654, 3654, - 3655, 3655, 3656, 3656, 3657, 3657, 3658, 3658, 3659, 3659, 3660, - 3660, 3661, 3661, 3662, 3663, 3664, 3665, 3666, 3667, 3667, 3668, - 3668, 3669, 3669, 3670, 3670, 3671, 3671, 3672, 3672, 3673, 3673, - 3674, 3674, 3675, 3675, 3676, 3676, 3677, 3678, 3679, 3680, 3681, - 3682, 3682, 3682, 3682, 3683, 3683, 3683, 3683, 3684, 3684, 3685, - 3685, 3685, 3685, 3686, 3686, 3687, 3687, 3688, 3688, 3688, 3688, - 3689, 3689, 3690, 3690, 3691, 3691, 3691, 3691, 3692, 3692, 3693, - 3693, 3694, 3694, 3695, 3695, 3696, 3696, 3697, 3697, 3698, 3698, - 3699, 3699, 3700, 3700, 3701, 3701, 3702, 3702, 3703, 3703, 3704, - 3704, 3705, 3705, 3706, 3706, 3707, 3707, 3708, 3708, 3709, 3709, - 3710, 3710, 3711, 3711, 3712, 3712, 3713, 3713, 3714, 3714, 3715, - 3715, 3716, 3716, 3717, 3717, 3718, 3718, 3719, 3719, 3720, 3720, - 3721, 3721, 3722, 3723, 3724, 3724, 3725, 3726, 3726, 3727, 3728, - 3729, 3729, 3730, 3730, 3731, 3731, 3732, 3732, 3733, 3733, 3734, - 3735, 3736, 3737, 3738, 3739, 3739, 3740, 3740, 3741, 3741, 3742, - 3742, 3742, 3743, 3743, 3743, 3744, 3744, 3744, 3745, 3745, 3745, - 3746, 3746, 3746, 3747, 3747, 3748, 3748, 3749, 3749, 3750, 3750, - 3751, 3751, 3752, 3752, 3753, 3753, 3754, 3754, 3755, 3755, 3756, - 3756, 3757, 3757, 3758, 3758, 3759, 3759, 3760, 3760, 3761, 3761, - 3762, 3762, 3763, 3763, 3764, 3764, 3765, 3765, 3766, 3766, 3767, - 3767, 3768, 3768, 3769, 3769, 3770, 3770, 3771, 3771, 3772, 3772, - 3773, 3773, 3774, 3774, 3775, 3775, 3776, 3776, 3782, 3782, 3783, - 3783, 3784, 3784, 3785, 3785, 3786, 3786, 3787, 3787, 3787, 3787, - 3788, 3788, 3788, 3788, 3789, 3789, 3789, 3789, 3790, 3790, 3790, - 3790, 3791, 3791, 3791, 3791, 3792, 3792, 3793, 3793, 3794, 3794, - 3795, 3795, 3796, 3796, 3797, 3797, 3798, 3798, 3799, 3799, 3800, - 3800, 3801, 3801, 3802, 3802, 3803, 3803, 3804, 3804, 3805, 3805, - 3806, 3806, 3807, 3807, 3808, 3808, 3809, 3809, 3810, 3810, 3811, - 3811, 3812, 3812, 3813, 3813, 3814, 3814, 3815, 3815, 3816, 3816, - 3817, 3817, 3818, 3818, 3819, 3819, 3820, 3820, 3821, 3821, 3822, - 3822, 3823, 3823, 3824, 3824, 3825, 3825, 3826, 3826, 3827, 3827, - 3828, 3828, 3829, 3829, 3830, 3830, 3831, 3831, 3832, 3832, 3832, - 3833, 3833, 3833, 3834, 3834, 3834, 3835, 3835, 3835, 3836, 3836, - 3836, 3842, 3842, 3842, 3843, 3843, 3843, 3844, 3844, 3844, 3845, - 3845, 3845, 3846, 3846, 3846, 3847, 3847, 3848, 3848, 3849, 3849, - 3850, 3850, 3851, 3851, 3852, 3852, 3853, 3853, 3854, 3854, 3855, - 3855, 3856, 3856, 3857, 3857, 3858, 3858, 3859, 3859, 3860, 3860, - 3861, 3861, 3862, 3862, 3863, 3863, 3864, 3864, 3865, 3865, 3866, - 3866, 3867, 3868, 3869, 3870, 3871, 3877, 3877, 3878, 3878, 3879, - 3879, 3880, 3880, 3881, 3881, 3882, 3882, 3883, 3883, 3884, 3884, - 3885, 3885, 3886, 3886, 3887, 3887, 3888, 3888, 3889, 3889, 3890, - 3890, 3891, 3891, 3892, 3892, 3892, 3892, 3893, 3893, 3893, 3893, - 3894, 3894, 3894, 3894, 3895, 3895, 3895, 3895, 3896, 3896, 3896, - 3896, 3897, 3897, 3898, 3898, 3899, 3899, 3900, 3900, 3901, 3901, - 3902, 3902, 3903, 3903, 3904, 3904, 3905, 3905, 3906, 3906, 3907, - 3907, 3908, 3908, 3909, 3909, 3910, 3910, 3911, 3911, 3912, 3912, - 3913, 3914, 3915, 3915, 3916, 3917, 3918, 3919, 3919, 3920, 3920, - 3921, 3921, 3922, 3922, 3923, 3923, 3924, 3924, 3925, 3925, 3926, - 3926, 3927, 3927, 3928, 3928, 3929, 3929, 3930, 3930, 3931, 3931, - 3932, 3933, 3934, 3935, 3936, 3937, 3937, 3937, 3938, 3938, 3938, - 3939, 3939, 3939, 3940, 3940, 3940, 3941, 3941, 3941, 3942, 3942, - 3942, 3943, 3943, 3943, 3944, 3944, 3944, 3945, 3945, 3945, 3946, - 3946, 3946, 3947, 3947, 3948, 3948, 3949, 3949, 3950, 3950, 3951, - 3951, 3952, 3952, 3952, 3953, 3953, 3953, 3954, 3954, 3954, 3955, - 3955, 3955, 3956, 3956, 3956, 3957, 3957, 3958, 3958, 3959, 3959, - 3961, 3961, 3962, 3962, 3967, 3967, 3968, 3968, 3969, 3969, 3970, - 3970, 3971, 3971, 3982, 3982, 3983, 3983, 3984, 3984, 3985, 3985, - 3986, 3986, 3987, 3987, 3988, 3988, 3989, 3989, 3990, 3990, 3990, - 3990, 3991, 3991, 3992, 3992, 3992, 3992, 3993, 3993, 3994, 3994, - 3994, 3994, 3995, 3995, 3995, 3995, 3996, 3996, 3996, 3996, 3997, - 3997, 3997, 3998, 3998, 3998, 3999, 3999, 3999, 4000, 4000, 4000, - 4001, 4001, 4001] - ] - ) - - return odf_vertices.T, odf_faces.T - -def odfgrid(res='med'): - """ - Defines the spherical grid defined by quadrisection of the isocahedron. - There are three possible options: 'low', 'med', or 'high', where higher - resolutions cost more computational time. Use for computation of spherical - harmonics from ODFs. - - NOTE: These sampling directions were ported from DKE (original author: - Russell Glenn) - - Parameters - ---------- - res : str; optional; {'low', 'med', 'high'} - Resolution of spherical sampling distribution (Default: 'med') - 'low' defines the spherical grid defined by 3 fold quadrisection of the - isocahedron, or 8 fold tesselation of icosahedron. - 'med' defines the spherical grid defined by 4 fold quadrisection of the - isocahedron, or 16 fold tesselation of icosahedron. - 'high' defines the spherical grid defined by 5 fold quadrisection of the - isocahedron, or 24 fold tesselation of icosahedron. - - Returns - ------- - S : array_like(dtype=float64) - Coordinates for spherical grid in polar coordinates, extends slightly - over one half of the sphere to estimate local maxima on border. First - column is phi, second column is theta. - idx : aarray_like(dtype=uint16) - First column defines points in S over one half of the sphere for - candiate local maxima. Columns 2-7 define the neighbors for the - corresponding point in column 1. - idx8 : array_like(dtype=uint16) - Defines vertices to undersample for sphericalgrid3 - this is useful, for - example, when trying to save a smaler datastructure to load into DSI - studio (all coordinates are savesd in one large file) - area : array_like(dtype=float64) - Defines the area that each vertex encompasses. It is generally - impossible to spread points out isotropically over a sphere, so the area - is slightly different for each point - this can be used to update - computations that occur over the spherical grid, eg GFA. - faces : array_like(dtype=uint16) - Data structure matching S to save and load into DSI Studio for - visualization - separation_angle : float64 - Average separation angle between each peak and its nearest neighbors. - """ - if not isinstance(res, str): - raise Exception('Please specify resolution as a string. Possible ' - 'choices are "low", "med", or "high"') - if res not in ['low', 'med', 'high']: - raise Exception('. Invalid ODF resolution. Possible resolution choices ' - 'are "low", "med", or "high"') - if res == 'low': - S = np.array( - [ - [ 0.00000000e+00, 0.00000000e+00], - [ 1.38393600e-01, 1.25663705e+00], - [ 1.38393597e-01, 2.51327410e+00], - [ 1.38393616e-01, 0.00000000e+00], - [ 1.38393597e-01, -2.51327410e+00], - [ 1.38393600e-01, -1.25663705e+00], - [ 2.25901926e-01, 1.88495556e+00], - [ 2.25901932e-01, 6.28318530e-01], - [ 2.25901905e-01, 3.14159265e+00], - [ 2.25901932e-01, -6.28318530e-01], - [ 2.25901926e-01, -1.88495556e+00], - [ 2.76787143e-01, 1.25663706e+00], - [ 2.76787121e-01, 2.51327409e+00], - [ 2.76787127e-01, 0.00000000e+00], - [ 2.76787121e-01, -2.51327409e+00], - [ 2.76787143e-01, -1.25663706e+00], - [ 3.54601278e-01, 1.64908209e+00], - [ 3.54601255e-01, 2.12082903e+00], - [ 3.54601268e-01, 8.64191977e-01], - [ 3.54601264e-01, 3.92445049e-01], - [ 3.54601263e-01, 2.90571917e+00], - [ 3.54601263e-01, -2.90571917e+00], - [ 3.54601264e-01, -3.92445049e-01], - [ 3.54601268e-01, -8.64191977e-01], - [ 3.54601255e-01, -2.12082903e+00], - [ 3.54601278e-01, -1.64908209e+00], - [ 4.15180810e-01, 1.25663705e+00], - [ 4.15180788e-01, 2.51327411e+00], - [ 4.15180813e-01, 0.00000000e+00], - [ 4.15180788e-01, -2.51327411e+00], - [ 4.15180810e-01, -1.25663705e+00], - [ 4.63647656e-01, 1.88495556e+00], - [ 4.63647654e-01, 6.28318505e-01], - [ 4.63647636e-01, 3.14159265e+00], - [ 4.63647654e-01, -6.28318505e-01], - [ 4.63647656e-01, -1.88495556e+00], - [ 4.87695045e-01, 1.54458101e+00], - [ 4.87695025e-01, 9.68693018e-01], - [ 4.87695022e-01, 2.22533010e+00], - [ 4.87695052e-01, 2.80121808e+00], - [ 4.87695052e-01, -2.80121808e+00], - [ 4.87695022e-01, -2.22533010e+00], - [ 4.87695025e-01, -9.68693018e-01], - [ 4.87695045e-01, -1.54458101e+00], - [ 4.87695080e-01, 2.87943954e-01], - [ 4.87695080e-01, -2.87943954e-01], - [ 5.53574406e-01, 1.25663703e+00], - [ 5.53574398e-01, 2.51327410e+00], - [ 5.53574430e-01, 0.00000000e+00], - [ 5.53574398e-01, -2.51327410e+00], - [ 5.53574406e-01, -1.25663703e+00], - [ 6.09543626e-01, 1.74065707e+00], - [ 6.09543679e-01, 2.02925415e+00], - [ 6.09543677e-01, 7.72617086e-01], - [ 6.09543647e-01, 4.84020079e-01], - [ 6.09543632e-01, 2.99729411e+00], - [ 6.09543636e-01, -2.99729404e+00], - [ 6.09543667e-01, -4.84019938e-01], - [ 6.09543643e-01, -7.72617012e-01], - [ 6.09543712e-01, -2.02925406e+00], - [ 6.09543669e-01, -1.74065699e+00], - [ 6.48583142e-01, 1.47875427e+00], - [ 6.48583121e-01, 1.03451988e+00], - [ 6.48583133e-01, -1.03451984e+00], - [ 6.48583145e-01, -1.47875420e+00], - [ 6.48583175e-01, 2.29115694e+00], - [ 6.48583151e-01, 2.73539133e+00], - [ 6.48583179e-01, 2.22117213e-01], - [ 6.48583174e-01, -2.22117165e-01], - [ 6.48583165e-01, -2.73539126e+00], - [ 6.48583177e-01, -2.29115687e+00], - [ 6.91967994e-01, 1.25663704e+00], - [ 6.91967974e-01, 2.51327413e+00], - [ 6.91968012e-01, 0.00000000e+00], - [ 6.91967974e-01, -2.51327413e+00], - [ 6.91967994e-01, -1.25663704e+00], - [ 7.47560984e-01, 1.88495565e+00], - [ 7.47560962e-01, 6.28318585e-01], - 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[ 1.57079636e+00, 1.41371673e+00], - [ 1.57079636e+00, -2.35619441e+00], - [ 1.57079636e+00, 2.67035377e+00], - [ 1.57079636e+00, 1.57079685e-01], - [ 1.57079636e+00, -1.09955741e+00], - [ 1.57079640e+00, 2.82743343e+00], - [ 1.57079640e+00, -2.19911481e+00], - [ 1.57079640e+00, -9.42477808e-01], - [ 1.57079640e+00, 1.57079636e+00], - [ 1.57079640e+00, 3.14159294e-01], - [ 1.68841649e+00, 2.12601942e+00], - [ 1.68849751e+00, -3.05979430e+00], - [ 1.68849751e+00, -5.46520178e-01], - [ 1.68849752e+00, 7.10116891e-01], - [ 1.68849751e+00, 1.96675397e+00], - [ 1.68849751e+00, -1.80315723e+00], - [ 1.57079636e+00, 1.72787600e+00], - [ 1.57079636e+00, 4.71238939e-01], - [ 1.57079636e+00, 2.98451307e+00], - [ 1.57079636e+00, -7.85398163e-01], - [ 1.57079636e+00, -2.04203516e+00], - [ 1.57079633e+00, -2.51327405e+00], - [ 1.57079633e+00, -1.25663703e+00], - [ 1.57079633e+00, 4.70040260e-08], - [ 1.68841649e+00, 8.69382389e-01], - [ 1.68841649e+00, -2.90052883e+00], - [ 1.68841649e+00, -3.87254702e-01], - [ 1.68841649e+00, -1.64389175e+00], - [ 1.68849751e+00, 3.05979436e+00], - [ 1.68849752e+00, 1.80315727e+00], - [ 1.68849751e+00, 5.46520230e-01], - [ 1.57079633e+00, -6.28318519e-01], - [ 1.57079633e+00, -1.88495554e+00], - [ 1.71117612e+00, 1.01319142e+00], - [ 1.71131554e+00, 1.17339795e+00], - [ 1.71131555e+00, -2.59651326e+00], - [ 1.71131554e+00, 2.43003499e+00], - [ 1.71131555e+00, -1.33987618e+00], - [ 1.71131555e+00, -8.32391207e-02], - [ 1.71117614e+00, -2.75671979e+00], - [ 1.71117614e+00, 2.26982851e+00], - [ 1.71117616e+00, -2.43445647e-01], - [ 1.71117616e+00, -1.50008270e+00], - [ 1.71131558e+00, 1.33987630e+00], - [ 1.71131556e+00, 2.59651334e+00] - ] - ) - idx = np.array( - [ - [ 1, 3, 5, 2, 6, 4, 1], - [ 2, 12, 1, 7, 8, 3, 4], - [ 3, 13, 1, 9, 7, 2, 5], - [ 4, 14, 1, 8, 10, 2, 6], - [ 5, 15, 1, 9, 11, 6, 3], - [ 6, 16, 1, 11, 10, 5, 4], - [ 7, 2, 3, 18, 17, 13, 12], - [ 8, 4, 2, 19, 20, 14, 12], - [ 9, 3, 5, 21, 22, 13, 15], - [ 10, 4, 6, 24, 23, 14, 16], - [ 11, 6, 5, 25, 26, 15, 16], - [ 12, 2, 27, 17, 19, 7, 8], - [ 13, 3, 28, 18, 21, 7, 9], - [ 14, 4, 29, 20, 23, 8, 10], - [ 15, 5, 30, 25, 22, 11, 9], - [ 16, 6, 31, 26, 24, 11, 10], - [ 17, 37, 32, 12, 7, 27, 18], - [ 18, 39, 32, 13, 7, 28, 17], - [ 19, 38, 33, 12, 8, 27, 20], - [ 20, 45, 33, 14, 8, 29, 19], - [ 21, 40, 34, 13, 9, 28, 22], - [ 22, 41, 34, 15, 9, 30, 21], - [ 23, 46, 35, 14, 10, 29, 24], - [ 24, 43, 35, 16, 10, 31, 23], - [ 25, 42, 36, 15, 11, 30, 26], - [ 26, 44, 36, 16, 11, 31, 25], - [ 27, 47, 12, 37, 38, 17, 19], - [ 28, 48, 13, 39, 40, 21, 18], - [ 29, 49, 14, 45, 46, 20, 23], - [ 30, 50, 15, 42, 41, 22, 25], - [ 31, 51, 16, 44, 43, 26, 24], - [ 32, 17, 18, 39, 37, 52, 53], - [ 33, 20, 19, 38, 45, 55, 54], - [ 34, 21, 22, 40, 41, 56, 57], - [ 35, 23, 24, 43, 46, 59, 58], - [ 36, 26, 25, 42, 44, 61, 60], - [ 37, 17, 27, 32, 47, 52, 62], - [ 38, 19, 27, 33, 47, 54, 63], - [ 39, 18, 28, 32, 48, 53, 66], - [ 40, 21, 28, 48, 34, 56, 67], - [ 41, 22, 30, 50, 34, 57, 70], - [ 42, 25, 30, 36, 50, 60, 71], - [ 43, 24, 31, 35, 51, 59, 64], - [ 44, 26, 31, 36, 51, 61, 65], - [ 45, 20, 29, 49, 33, 55, 68], - [ 46, 23, 29, 49, 35, 58, 69], - [ 47, 72, 27, 63, 62, 37, 38], - [ 48, 73, 28, 66, 67, 40, 39], - [ 49, 74, 29, 69, 68, 45, 46], - [ 50, 75, 30, 70, 41, 71, 42], - [ 51, 76, 31, 65, 64, 44, 43], - [ 52, 37, 62, 32, 82, 53, 77], - [ 53, 39, 66, 32, 83, 77, 52], - [ 54, 38, 63, 84, 33, 55, 78], - [ 55, 45, 68, 33, 85, 54, 78], - [ 56, 40, 67, 34, 86, 57, 79], - [ 57, 41, 70, 34, 87, 56, 79], - [ 58, 46, 69, 35, 88, 80, 59], - [ 59, 43, 64, 35, 89, 58, 80], - [ 60, 71, 42, 91, 36, 81, 61], - [ 61, 44, 65, 36, 90, 60, 81], - [ 62, 92, 72, 82, 47, 52, 37], - [ 63, 93, 72, 47, 84, 54, 38], - [ 64, 98, 76, 51, 89, 59, 43], - [ 65, 99, 76, 51, 90, 61, 44], - [ 66, 94, 73, 48, 83, 53, 39], - [ 67, 95, 73, 86, 48, 56, 40], - [ 68, 96, 74, 85, 49, 55, 45], - [ 69, 100, 74, 49, 88, 58, 46], - [ 70, 97, 75, 50, 87, 57, 41], - [ 71, 101, 75, 91, 50, 60, 42], - [ 72, 47, 102, 63, 62, 93, 92], - [ 73, 48, 103, 66, 67, 95, 94], - [ 74, 49, 106, 69, 68, 96, 100], - [ 75, 50, 104, 70, 71, 97, 101], - [ 76, 51, 105, 65, 64, 98, 99], - [ 77, 107, 108, 83, 82, 53, 52], - [ 78, 109, 110, 85, 84, 54, 55], - [ 79, 111, 112, 87, 86, 57, 56], - [ 80, 113, 114, 88, 89, 58, 59], - [ 81, 116, 115, 90, 91, 60, 61], - [ 82, 92, 117, 62, 107, 52, 77], - [ 83, 94, 118, 108, 66, 77, 53], - [ 84, 93, 126, 63, 109, 54, 78], - [ 85, 96, 119, 68, 110, 78, 55], - [ 86, 95, 120, 67, 111, 56, 79], - [ 87, 97, 121, 70, 112, 79, 57], - [ 88, 100, 122, 69, 113, 80, 58], - [ 89, 98, 123, 64, 114, 59, 80], - [ 90, 99, 125, 65, 116, 81, 61], - [ 91, 124, 101, 115, 71, 60, 81], - [ 92, 62, 102, 82, 127, 72, 117], - [ 93, 63, 102, 84, 128, 72, 126], - [ 94, 66, 83, 103, 129, 73, 118], - [ 95, 67, 86, 103, 130, 73, 120], - [ 96, 68, 85, 106, 131, 74, 119], - [ 97, 70, 87, 104, 133, 75, 121], - [ 98, 64, 89, 105, 135, 76, 123], - [ 99, 65, 90, 105, 136, 76, 125], - [100, 69, 88, 106, 132, 74, 122], - [101, 71, 104, 91, 134, 75, 124], - [102, 72, 139, 93, 92, 128, 127], - [103, 73, 137, 94, 95, 129, 130], - [104, 75, 138, 101, 97, 133, 134], - [105, 76, 141, 99, 98, 136, 135], - [106, 74, 140, 100, 96, 131, 132], - [107, 117, 147, 142, 82, 77, 108], - [108, 118, 148, 83, 142, 77, 107], - [109, 126, 149, 143, 84, 78, 110], - [110, 119, 150, 143, 85, 78, 109], - [111, 120, 151, 144, 86, 79, 112], - [112, 121, 152, 144, 87, 79, 111], - [113, 122, 153, 146, 88, 80, 114], - [114, 123, 155, 146, 89, 80, 113], - [115, 124, 154, 91, 145, 81, 116], - [116, 125, 156, 145, 90, 81, 115], - [117, 107, 158, 82, 127, 147, 92], - [118, 108, 160, 83, 129, 148, 94], - [119, 110, 162, 85, 131, 150, 96], - [120, 111, 161, 86, 130, 151, 95], - [121, 112, 164, 87, 133, 152, 97], - [122, 113, 163, 88, 132, 153, 100], - [123, 114, 165, 89, 135, 155, 98], - [124, 115, 157, 91, 134, 154, 101], - [125, 116, 166, 90, 136, 156, 99], - [126, 109, 159, 84, 128, 149, 93], - [127, 167, 139, 117, 92, 158, 102], - [128, 171, 139, 126, 93, 159, 102], - [129, 137, 172, 118, 94, 103, 160], - [130, 168, 137, 95, 120, 103, 161], - [131, 173, 140, 119, 96, 106, 162], - [132, 140, 176, 100, 122, 106, 163], - [133, 138, 174, 97, 121, 104, 164], - [134, 169, 138, 124, 101, 157, 104], - [135, 170, 141, 123, 98, 165, 105], - [136, 175, 141, 125, 99, 166, 105], - [137, 178, 103, 129, 130, 172, 168], - [138, 180, 104, 133, 134, 174, 169], - [139, 177, 102, 128, 127, 171, 167], - [140, 179, 106, 131, 132, 176, 173], - [141, 181, 105, 136, 135, 175, 170], - [142, 148, 147, 108, 107, 183, 182], - [143, 149, 150, 110, 109, 185, 184], - [144, 152, 151, 112, 111, 187, 186], - [145, 156, 154, 116, 115, 190, 189], - [146, 153, 155, 113, 188, 114, 191], - [147, 142, 158, 182, 107, 192, 117], - [148, 142, 160, 108, 183, 118, 193], - [149, 143, 159, 109, 184, 126, 194], - [150, 143, 162, 110, 185, 119, 195], - [151, 144, 161, 111, 186, 120, 196], - [152, 144, 164, 112, 187, 121, 197], - [153, 146, 163, 113, 188, 122, 198], - [154, 145, 157, 115, 189, 124, 199], - [155, 146, 165, 114, 191, 123, 201], - [156, 145, 166, 190, 116, 200, 125], - [157, 169, 154, 124, 199, 134, 204], - [158, 167, 147, 117, 192, 127, 206], - [159, 149, 171, 126, 194, 202, 128], - [160, 172, 148, 118, 193, 129, 203], - [161, 168, 151, 120, 196, 207, 130], - [162, 173, 150, 119, 195, 131, 208], - [163, 153, 176, 198, 122, 209, 132], - [164, 152, 174, 121, 197, 210, 133], - [165, 170, 155, 123, 201, 135, 211], - [166, 156, 175, 125, 200, 136, 205], - [167, 158, 177, 127, 206, 139, 219], - [168, 161, 178, 130, 207, 137, 217], - [169, 157, 180, 204, 134, 138, 218], - [170, 165, 181, 135, 211, 141, 221], - [171, 177, 159, 128, 202, 139, 212], - [172, 178, 160, 203, 129, 213, 137], - [173, 162, 179, 131, 208, 140, 220], - [174, 180, 164, 133, 210, 215, 138], - [175, 181, 166, 136, 205, 141, 216], - [176, 179, 163, 209, 132, 214, 140], - [177, 171, 139, 212, 167, 219, 177], - [178, 172, 213, 137, 217, 168, 178], - [179, 214, 176, 140, 173, 220, 179], - [180, 215, 174, 138, 218, 169, 180], - [181, 141, 175, 216, 221, 170, 181], - [182, 192, 147, 142, 232, 222, 183], - [183, 193, 148, 228, 142, 222, 182], - [184, 194, 149, 229, 143, 225, 185], - [185, 195, 150, 143, 233, 225, 184], - [186, 196, 151, 144, 234, 223, 187], - [187, 197, 152, 227, 144, 223, 186], - [188, 198, 153, 230, 146, 224, 191], - [189, 199, 154, 236, 145, 226, 190], - [190, 200, 156, 231, 145, 226, 189], - [191, 201, 155, 235, 146, 224, 188], - [192, 182, 158, 232, 206, 147, 238], - [193, 183, 160, 228, 203, 148, 240], - [194, 184, 229, 159, 202, 149, 239], - [195, 185, 162, 233, 208, 150, 241], - [196, 186, 234, 161, 207, 151, 244], - [197, 187, 227, 164, 210, 152, 243], - [198, 188, 230, 163, 209, 153, 242], - [199, 189, 157, 236, 204, 154, 245], - [200, 190, 231, 166, 205, 156, 237], - [201, 191, 235, 165, 211, 155, 246], - [202, 212, 171, 194, 239, 159, 252], - [203, 213, 172, 240, 193, 253, 160], - [204, 169, 218, 245, 199, 259, 157], - [205, 216, 175, 237, 200, 254, 166], - [206, 219, 167, 238, 192, 255, 158], - [207, 217, 168, 196, 244, 161, 261], - [208, 220, 173, 241, 195, 256, 162], - [209, 214, 176, 242, 198, 257, 163], - [210, 215, 174, 243, 197, 258, 164], - [211, 221, 170, 246, 201, 260, 165], - [212, 177, 252, 202, 247, 171, 219], - [213, 178, 253, 203, 249, 172, 217], - [214, 179, 257, 209, 250, 176, 220], - [215, 180, 258, 251, 210, 174, 218], - [216, 181, 254, 205, 248, 175, 221], - [217, 261, 178, 249, 207, 213, 168], - [218, 259, 180, 204, 251, 215, 169], - [219, 255, 177, 247, 206, 212, 167], - [220, 256, 179, 250, 208, 214, 173], - [221, 260, 181, 211, 248, 216, 170], - [222, 182, 183, 228, 232, 262, 267], - [223, 186, 187, 227, 234, 263, 269], - [224, 191, 188, 235, 230, 270, 266], - [225, 184, 185, 229, 233, 265, 268], - [226, 189, 190, 231, 236, 264, 271], - [227, 197, 243, 187, 272, 223, 263], - [228, 193, 240, 183, 275, 222, 262], - [229, 239, 194, 276, 184, 225, 265], - [230, 198, 242, 188, 273, 266, 224], - [231, 237, 200, 190, 274, 226, 264], - [232, 192, 238, 277, 182, 267, 222], - [233, 241, 195, 185, 279, 268, 225], - [234, 196, 244, 278, 186, 223, 269], - [235, 201, 246, 280, 191, 224, 270], - [236, 245, 199, 189, 281, 271, 226], - [237, 274, 231, 254, 205, 296, 200], - [238, 277, 232, 255, 206, 300, 192], - [239, 276, 229, 252, 202, 294, 194], - [240, 275, 253, 228, 203, 292, 193], - [241, 279, 233, 256, 208, 301, 195], - [242, 273, 230, 257, 209, 295, 198], - [243, 272, 227, 258, 210, 293, 197], - [244, 278, 261, 234, 207, 297, 196], - [245, 281, 236, 259, 204, 298, 199], - [246, 280, 260, 235, 211, 299, 201], - [247, 219, 212, 284, 283, 255, 252], - [248, 221, 216, 287, 291, 260, 254], - [249, 217, 213, 288, 282, 261, 253], - [250, 220, 214, 290, 289, 256, 257], - [251, 215, 218, 285, 286, 258, 259], - [252, 294, 212, 239, 284, 202, 247], - [253, 292, 213, 240, 282, 203, 249], - [254, 296, 216, 237, 287, 205, 248], - [255, 219, 300, 283, 238, 206, 247], - [256, 220, 301, 289, 241, 250, 208], - [257, 295, 214, 242, 290, 209, 250], - [258, 293, 215, 243, 285, 251, 210], - [259, 218, 298, 286, 245, 204, 251], - [260, 221, 299, 246, 291, 211, 248], - [261, 217, 297, 288, 244, 249, 207], - [262, 314, 275, 228, 319, 267, 222], - [263, 307, 272, 317, 227, 269, 223], - [264, 316, 274, 231, 354, 226, 271], - [265, 311, 276, 229, 320, 225, 268], - [266, 273, 309, 230, 353, 270, 224], - [267, 277, 338, 319, 232, 262, 222], - [268, 279, 339, 233, 320, 225, 265], - [269, 278, 340, 317, 234, 263, 223], - [270, 280, 341, 353, 235, 224, 266], - [271, 281, 342, 236, 354, 264, 226], - [272, 243, 293, 303, 227, 263, 307], - [273, 242, 295, 230, 305, 266, 309], - [274, 237, 296, 231, 306, 264, 316], - [275, 240, 292, 304, 228, 262, 314], - [276, 239, 294, 302, 229, 265, 311], - [277, 238, 300, 232, 330, 267, 338], - [278, 244, 297, 234, 327, 269, 340], - [279, 241, 301, 331, 233, 268, 339], - [280, 246, 299, 235, 329, 270, 341], - [281, 245, 298, 328, 236, 271, 342], - [282, 312, 253, 321, 249, 292, 288], - [283, 322, 318, 255, 247, 300, 284], - [284, 308, 318, 252, 247, 294, 283], - [285, 310, 343, 258, 251, 293, 286], - [286, 323, 343, 259, 251, 298, 285], - [287, 313, 344, 254, 248, 296, 291], - [288, 324, 321, 261, 249, 297, 282], - [289, 325, 345, 256, 250, 301, 290], - [290, 315, 345, 257, 250, 295, 289], - [291, 326, 344, 260, 248, 299, 287], - [292, 253, 304, 275, 312, 282, 240], - [293, 303, 258, 272, 310, 243, 285], - [294, 252, 302, 276, 308, 284, 239], - [295, 305, 257, 273, 315, 242, 290], - [296, 254, 306, 274, 313, 287, 237], - [297, 327, 261, 324, 278, 288, 244], - [298, 328, 259, 323, 281, 286, 245], - [299, 329, 260, 326, 280, 291, 246], - [300, 330, 255, 322, 277, 283, 238], - [301, 331, 256, 325, 279, 289, 241], - [302, 294, 346, 276, 311, 355, 308], - [303, 293, 347, 307, 272, 310, 361], - [304, 332, 292, 275, 314, 312, 362], - [305, 295, 348, 273, 315, 309, 363], - [306, 296, 349, 274, 316, 313, 364], - [307, 333, 347, 303, 317, 263, 272], - [308, 284, 294, 318, 302, 356, 355], - [309, 334, 348, 353, 305, 266, 273], - [310, 285, 293, 303, 343, 357, 361], - [311, 335, 346, 302, 320, 265, 276], - [312, 282, 292, 304, 321, 358, 362], - [313, 287, 296, 344, 306, 359, 364], - [314, 336, 332, 304, 319, 262, 275], - [315, 290, 295, 345, 305, 360, 363], - [316, 337, 349, 354, 306, 264, 274], - [317, 333, 350, 340, 307, 269, 263], - [318, 283, 284, 322, 308, 356, 365], - [319, 336, 351, 314, 338, 267, 262], - [320, 335, 352, 311, 339, 268, 265], - [321, 288, 282, 324, 312, 358, 366] - ] - ) - 1 - idx8 = np.array( - [ - [ 1], - [ 2], - [ 3], - [ 4], - [ 5], - [ 6], - [ 7], - [ 8], - [ 9], - [ 10], - [ 11], - [ 12], - [ 13], - [ 14], - [ 15], - [ 16], - [ 17], - [ 18], - [ 19], - [ 20], - [ 21], - [ 22], - [ 23], - [ 24], - [ 25], - [ 26], - [ 27], - [ 28], - [ 29], - [ 30], - [ 31], - [ 32], - [ 33], - [ 34], - [ 35], - [ 36], - [ 37], - [ 38], - [ 39], - [ 40], - [ 41], - [ 42], - [ 43], - [ 44], - [ 45], - [ 46], - [ 47], - [ 48], - [ 49], - [ 50], - [ 51], - [ 52], - [ 53], - [ 54], - [ 55], - [ 56], - [ 57], - [ 58], - [ 59], - [ 60], - [ 61], - [ 62], - [ 63], - [ 64], - [ 65], - [ 66], - [ 67], - [ 68], - [ 69], - [ 70], - [ 71], - [ 72], - [ 73], - [ 74], - [ 75], - [ 76], - [ 77], - [ 78], - [ 79], - [ 80], - [ 81], - [ 82], - [ 83], - [ 84], - [ 85], - [ 86], - [ 87], - [ 88], - [ 89], - [ 90], - [ 91], - [ 92], - [ 93], - [ 94], - [ 95], - [ 96], - [ 97], - [ 98], - [ 99], - [100], - [101], - [102], - [103], - [104], - [105], - [106], - [107], - [108], - [109], - [110], - [111], - [112], - [113], - [114], - [115], - [116], - [117], - [118], - [119], - [120], - [121], - [122], - [123], - [124], - [125], - [126], - [127], - [128], - [129], - [130], - [131], - [132], - [133], - [134], - [135], - [136], - [137], - [138], - [139], - [140], - [141], - [142], - [143], - [144], - [145], - [146], - [147], - [148], - [149], - [150], - [151], - [152], - [153], - [154], - [155], - [156], - [157], - [158], - [159], - [160], - [161], - [162], - [163], - [164], - [165], - [166], - [167], - [168], - [169], - [170], - [171], - [172], - [173], - [174], - [175], - [176], - [177], - [178], - [179], - [180], - [181], - [182], - [183], - [184], - [185], - [186], - [187], - [188], - [189], - [190], - [191], - [192], - [193], - [194], - [195], - [196], - [197], - [198], - [199], - [200], - [201], - [202], - [203], - [204], - [205], - [206], - [207], - [208], - [209], - [210], - [211], - [212], - [213], - [214], - [215], - [216], - [217], - [218], - [219], - [220], - [221], - [222], - [223], - [224], - [225], - [226], - [227], - [228], - [229], - [230], - [231], - [232], - [233], - [234], - [235], - [236], - [237], - [238], - [239], - [240], - [241], - [242], - [243], - [244], - [245], - [246], - [247], - [248], - [249], - [250], - [251], - [252], - [253], - [254], - [255], - [256], - [257], - [258], - [259], - [260], - [261], - [262], - [263], - [264], - [265], - [266], - [267], - [268], - [269], - [270], - [271], - [272], - [273], - [274], - [275], - [276], - [277], - [278], - [279], - [280], - [281], - [282], - [283], - [284], - [285], - [286], - [287], - [288], - [289], - [290], - [291], - [292], - [293], - [294], - [295], - [296], - [297], - [298], - [299], - [300], - [301], - [302], - [303], - [304], - [305], - [306], - [307], - [308], - [309], - [310], - [311], - [312], - [313], - [314], - [315], - [316], - [317], - [318], - [319], - [320], - [321] - ] - ) - area = np.array( - [ - [0.00674716], - [0.01841647], - [0.01841647], - [0.01841648], - [0.01841647], - [0.01841647], - [0.01888554], - [0.01888554], - [0.01888554], - [0.01888554], - [0.01888554], - [0.01875968], - [0.01875968], - [0.01875968], - [0.01875968], - [0.01875968], - [0.01903894], - [0.01903894], - [0.01903894], - [0.01903894], - [0.01903894], - [0.01903894], - [0.01903894], - [0.01903894], - [0.01903894], - [0.01903894], - [0.01864748], - [0.01864748], - [0.01864749], - [0.01864748], - [0.01864748], - [0.02062531], - [0.02062531], - [0.02062531], - [0.02062531], - [0.02062531], - [0.02046645], - [0.02046645], - [0.02046645], - [0.02046645], - [0.02046645], - [0.02046646], - [0.02046645], - 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494, 535, 538, 461, 495, 536, 541, 542, 552, 542, 548, 545, 549, - 545, 553, 543, 554, 543, 547, 544, 550, 546, 556, 546, 551, 544, - 555, 552, 558, 548, 560, 549, 559, 553, 561, 554, 564, 547, 563, - 550, 562, 556, 565, 551, 557, 555, 566, 559, 560, 565, 579, 557, - 558, 575, 564, 581, 561, 576, 562, 563, 566, 580, 491, 567, 572, - 492, 569, 573, 496, 570, 577, 494, 571, 578, 495, 568, 574, 569, - 581, 571, 579, 567, 575, 570, 576, 568, 580, 582, 587, 583, 589, - 586, 590, 585, 588, 584, 591, 583, 592, 582, 595, 585, 596, 586, - 593, 584, 594, 587, 597, 588, 599, 589, 598, 590, 600, 591, 601, - 594, 616, 597, 620, 596, 614, 595, 612, 599, 621, 593, 615, 592, - 613, 598, 617, 601, 618, 600, 619, 603, 604, 607, 611, 602, 608, - 609, 610, 605, 606, 532, 604, 614, 533, 602, 612, 536, 607, 616, - 603, 620, 609, 621, 534, 610, 615, 535, 605, 613, 606, 618, 611, - 619, 608, 617, 634, 639, 627, 637, 317, 636, 631, 640, 320, 629, - 312, 639, 309, 640, 314, 637, 311, 320, 307, 317, 623, 627, 625, - 629, 626, 636, 624, 634, 622, 631, 305, 312, 304, 314, 302, 309, - 303, 311, 301, 307, 632, 641, 315, 638, 628, 638, 319, 630, 310, - 319, 318, 633, 308, 641, 306, 316, 316, 635, 313, 318, 624, 632, - 623, 630, 622, 628, 625, 635, 626, 633, 304, 308, 301, 310, 303, - 313, 305, 315, 302, 306, 280, 297, 278, 300, 279, 298, 277, 296, - 276, 299, 288, 300, 270, 280, 287, 296, 267, 278, 285, 297, 269, - 279, 266, 276, 290, 298, 268, 277, 282, 299, 288, 289, 263, 270, - 286, 290, 284, 285, 265, 269], - [ 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, - 7, 8, 8, 9, 9, 10, 10, 11, 11, 12, 12, 13, 13, - 14, 14, 15, 15, 16, 16, 17, 17, 18, 18, 19, 19, 20, - 20, 21, 21, 22, 22, 23, 23, 24, 24, 25, 25, 26, 26, - 27, 27, 28, 28, 29, 29, 30, 30, 31, 31, 32, 32, 33, - 33, 34, 34, 35, 35, 36, 36, 37, 37, 38, 38, 39, 39, - 40, 40, 41, 41, 42, 42, 43, 43, 44, 44, 45, 45, 46, - 46, 47, 47, 48, 48, 49, 49, 50, 50, 51, 51, 52, 52, - 53, 53, 54, 54, 55, 55, 56, 56, 57, 57, 58, 58, 59, - 59, 60, 60, 61, 61, 62, 62, 63, 63, 64, 64, 65, 65, - 66, 66, 67, 67, 68, 68, 69, 69, 70, 70, 71, 71, 72, - 72, 73, 73, 74, 74, 75, 75, 76, 76, 77, 77, 78, 78, - 79, 79, 80, 80, 81, 81, 82, 82, 83, 83, 84, 84, 85, - 85, 86, 86, 87, 87, 88, 88, 89, 89, 90, 90, 91, 91, - 92, 92, 93, 93, 94, 94, 95, 95, 96, 96, 97, 97, 98, - 98, 99, 99, 100, 100, 101, 101, 102, 102, 103, 103, 104, 104, - 105, 105, 106, 106, 107, 107, 108, 108, 109, 109, 110, 110, 111, - 111, 112, 112, 113, 113, 114, 114, 115, 115, 116, 116, 117, 117, - 118, 118, 119, 119, 120, 120, 121, 121, 122, 122, 123, 123, 124, - 124, 125, 125, 126, 126, 127, 128, 129, 129, 130, 130, 131, 132, - 133, 133, 134, 134, 135, 136, 136, 136, 136, 137, 137, 137, 137, - 138, 138, 138, 138, 139, 139, 139, 139, 140, 140, 140, 140, 141, - 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, - 155, 156, 157, 160, 161, 164, 166, 167, 168, 169, 170, 170, 170, - 171, 171, 171, 172, 173, 173, 173, 174, 174, 174, 175, 175, 175, - 176, 177, 178, 179, 180, 181, 181, 182, 182, 183, 183, 184, 184, - 185, 185, 186, 186, 187, 187, 188, 188, 189, 189, 190, 190, 191, - 191, 192, 192, 193, 193, 194, 194, 195, 195, 196, 196, 197, 197, - 198, 198, 199, 199, 200, 200, 201, 202, 203, 203, 204, 205, 205, - 206, 206, 207, 207, 208, 209, 210, 210, 211, 211, 212, 212, 213, - 213, 214, 214, 215, 215, 216, 216, 217, 217, 218, 218, 219, 219, - 220, 220, 221, 221, 222, 222, 223, 223, 224, 224, 225, 225, 226, - 226, 227, 227, 228, 228, 229, 229, 230, 230, 231, 231, 232, 232, - 233, 233, 234, 234, 235, 235, 236, 236, 237, 237, 238, 238, 239, - 239, 240, 240, 241, 241, 242, 242, 243, 243, 244, 244, 245, 245, - 246, 246, 247, 247, 248, 248, 249, 249, 250, 250, 251, 252, 253, - 254, 254, 255, 255, 256, 257, 258, 258, 259, 259, 260, 260, 261, - 261, 262, 262, 263, 263, 264, 264, 265, 265, 266, 266, 267, 267, - 268, 268, 269, 269, 270, 270, 271, 271, 272, 272, 273, 273, 274, - 274, 275, 275, 276, 276, 277, 277, 278, 278, 279, 279, 280, 280, - 281, 281, 282, 282, 283, 283, 284, 284, 285, 285, 286, 286, 287, - 287, 288, 288, 289, 289, 290, 290, 291, 291, 292, 292, 293, 293, - 294, 294, 295, 295, 296, 296, 297, 297, 298, 298, 299, 299, 300, - 300, 301, 301, 302, 302, 303, 303, 304, 304, 305, 305, 306, 306, - 307, 307, 308, 308, 309, 309, 310, 310, 311, 311, 312, 312, 313, - 313, 314, 314, 315, 315, 316, 316, 317, 317, 318, 318, 319, 319, - 320, 320, 321, 321, 321, 321, 321, 322, 322, 322, 323, 323, 323, - 324, 324, 324, 325, 325, 325, 326, 326, 326, 327, 327, 328, 328, - 329, 329, 330, 330, 331, 331, 332, 332, 332, 333, 333, 333, 334, - 334, 334, 335, 335, 335, 336, 336, 336, 337, 337, 338, 338, 339, - 339, 340, 340, 341, 341, 342, 342, 343, 343, 344, 344, 345, 345, - 346, 346, 347, 347, 347, 348, 348, 348, 349, 349, 349, 350, 350, - 350, 351, 351, 351, 352, 352, 353, 353, 354, 354, 355, 355, 356, - 356, 357, 357, 358, 358, 359, 359, 360, 360, 361, 361, 362, 362, - 363, 363, 364, 364, 365, 365, 366, 366, 367, 367, 367, 368, 368, - 368, 369, 369, 369, 370, 370, 370, 371, 371, 371, 372, 372, 373, - 373, 374, 374, 375, 375, 376, 376, 377, 377, 378, 378, 379, 379, - 380, 380, 381, 381, 382, 382, 383, 383, 384, 384, 385, 385, 386, - 386, 387, 387, 388, 388, 389, 389, 390, 390, 391, 391, 392, 392, - 392, 393, 393, 393, 394, 394, 394, 395, 395, 395, 396, 396, 396, - 397, 397, 398, 398, 399, 399, 400, 400, 401, 401, 402, 402, 403, - 403, 404, 404, 405, 405, 406, 406, 407, 407, 408, 408, 409, 409, - 410, 410, 411, 411, 412, 412, 413, 413, 414, 414, 415, 415, 416, - 416, 417, 417, 418, 418, 419, 419, 420, 420, 421, 421, 422, 422, - 422, 423, 423, 423, 424, 424, 424, 425, 425, 425, 426, 426, 426, - 427, 427, 428, 428, 429, 429, 430, 430, 431, 431, 432, 432, 433, - 433, 434, 434, 435, 435, 436, 436, 437, 437, 438, 438, 439, 439, - 440, 440, 441, 441, 442, 442, 443, 443, 444, 444, 445, 445, 446, - 446, 447, 447, 448, 449, 450, 450, 451, 451, 452, 453, 454, 454, - 455, 455, 456, 457, 458, 459, 460, 461, 462, 462, 463, 463, 464, - 464, 465, 465, 466, 466, 467, 467, 468, 468, 469, 469, 470, 470, - 471, 471, 472, 472, 473, 473, 474, 474, 475, 475, 476, 476, 477, - 477, 478, 478, 479, 479, 479, 480, 480, 480, 481, 481, 482, 482, - 483, 483, 483, 484, 484, 484, 485, 485, 486, 486, 486, 487, 487, - 488, 488, 489, 489, 490, 490, 491, 492, 493, 493, 494, 495, 496, - 497, 497, 497, 497, 498, 498, 498, 498, 499, 499, 499, 499, 500, - 500, 500, 500, 501, 501, 501, 501, 502, 502, 503, 503, 504, 504, - 505, 505, 506, 506, 507, 507, 508, 508, 509, 509, 510, 510, 511, - 511, 512, 512, 513, 513, 514, 514, 515, 515, 516, 516, 517, 517, - 518, 518, 519, 519, 520, 520, 521, 521, 522, 523, 524, 524, 525, - 526, 526, 527, 527, 528, 528, 529, 530, 531, 531, 532, 532, 532, - 533, 533, 533, 534, 534, 534, 535, 535, 535, 536, 536, 536, 537, - 537, 538, 538, 539, 539, 540, 540, 541, 541, 542, 542, 543, 543, - 544, 544, 545, 545, 546, 546, 547, 547, 548, 548, 549, 549, 550, - 550, 551, 551, 552, 552, 553, 553, 554, 554, 555, 555, 556, 556, - 557, 557, 558, 558, 559, 559, 560, 560, 561, 561, 562, 562, 563, - 563, 564, 564, 565, 565, 566, 566, 567, 567, 568, 568, 569, 569, - 570, 570, 571, 571, 572, 572, 572, 573, 573, 573, 574, 574, 574, - 575, 575, 576, 576, 577, 577, 577, 578, 578, 578, 579, 579, 580, - 580, 581, 581, 582, 582, 583, 583, 584, 584, 585, 585, 586, 586, - 587, 587, 588, 588, 589, 589, 590, 590, 591, 591, 592, 592, 593, - 593, 594, 594, 595, 595, 596, 596, 597, 597, 598, 598, 599, 599, - 600, 600, 601, 601, 602, 602, 603, 603, 604, 604, 605, 605, 606, - 606, 607, 607, 608, 608, 609, 609, 610, 610, 611, 611, 612, 612, - 613, 613, 614, 614, 615, 615, 616, 616, 617, 617, 618, 618, 619, - 619, 620, 620, 621, 621, 622, 622, 623, 623, 624, 624, 625, 625, - 626, 626, 627, 627, 628, 628, 629, 629, 630, 630, 631, 631, 632, - 632, 633, 633, 634, 634, 635, 635, 636, 636, 637, 637, 638, 638, - 639, 639, 640, 640, 641, 641] - ] - ) - separation_angle = 8.64 - - if res == 'med': - S = np.array( - [ - [ 0.00000000e+00, 0.00000000e+00], - [ 6.91966197e-02, 1.25663704e+00], - [ 6.91966162e-02, 2.51327412e+00], - [ 6.91966218e-02, 0.00000000e+00], - [ 6.91966162e-02, -2.51327412e+00], - [ 6.91966197e-02, -1.25663704e+00], - [ 1.12209757e-01, 1.88495557e+00], - [ 1.12209774e-01, 6.28318447e-01], - [ 1.12209755e-01, 3.14159265e+00], - [ 1.12209774e-01, -6.28318447e-01], - [ 1.12209757e-01, -1.88495557e+00], - [ 1.38393600e-01, 1.25663705e+00], - [ 1.38393597e-01, 2.51327410e+00], - [ 1.38393616e-01, 0.00000000e+00], - [ 1.38393597e-01, -2.51327410e+00], - [ 1.38393600e-01, -1.25663705e+00], - [ 1.73920996e-01, 1.64787724e+00], - [ 1.73920994e-01, 2.12203388e+00], - [ 1.73921005e-01, 8.65396859e-01], - [ 1.73921008e-01, 3.91240190e-01], - [ 1.73920986e-01, 2.90451430e+00], - [ 1.73920986e-01, -2.90451430e+00], - [ 1.73921008e-01, -3.91240190e-01], - [ 1.73921005e-01, -8.65396859e-01], - [ 1.73920994e-01, -2.12203388e+00], - [ 1.73920996e-01, -1.64787724e+00], - [ 2.07590303e-01, 1.25663706e+00], - [ 2.07590291e-01, 2.51327412e+00], - [ 2.07590298e-01, 0.00000000e+00], - [ 2.07590291e-01, -2.51327412e+00], - [ 2.07590303e-01, -1.25663706e+00], - [ 2.25901926e-01, 1.88495556e+00], - [ 2.25901932e-01, 6.28318530e-01], - [ 2.25901905e-01, 3.14159265e+00], - [ 2.25901932e-01, -6.28318530e-01], - [ 2.25901926e-01, -1.88495556e+00], - [ 2.39638645e-01, 1.53860570e+00], - [ 2.39638650e-01, 9.74668409e-01], - [ 2.39638634e-01, 2.23130542e+00], - [ 2.39638629e-01, 2.79524275e+00], - [ 2.39638644e-01, 2.81968672e-01], - [ 2.39638644e-01, -2.81968672e-01], - [ 2.39638629e-01, -2.79524275e+00], - [ 2.39638634e-01, -2.23130542e+00], - [ 2.39638650e-01, -9.74668409e-01], - [ 2.39638645e-01, -1.53860570e+00], - [ 2.76787143e-01, 1.25663706e+00], - [ 2.76787121e-01, 2.51327409e+00], - [ 2.76787127e-01, 0.00000000e+00], - [ 2.76787121e-01, -2.51327409e+00], - [ 2.76787143e-01, -1.25663706e+00], - [ 2.88434684e-01, 1.74146202e+00], - [ 2.88434699e-01, 2.02844889e+00], - [ 2.88434703e-01, 7.71811907e-01], - [ 2.88434700e-01, 4.84824993e-01], - [ 2.88434692e-01, 2.99809912e+00], - [ 2.88434686e-01, -2.99809927e+00], - [ 2.88434688e-01, -4.84825135e-01], - [ 2.88434703e-01, -7.71812055e-01], - [ 2.88434666e-01, -2.02844913e+00], - [ 2.88434692e-01, -1.74146221e+00], - [ 3.10101715e-01, 1.47654597e+00], - [ 3.10101691e-01, 1.03672801e+00], - [ 3.10101691e-01, 2.29336502e+00], - [ 3.10101718e-01, 2.73318307e+00], - [ 3.10101697e-01, 2.19908954e-01], - [ 3.10101675e-01, -2.19909070e-01], - [ 3.10101701e-01, -2.73318320e+00], - [ 3.10101698e-01, -2.29336519e+00], - [ 3.10101716e-01, -1.03672806e+00], - [ 3.10101711e-01, -1.47654614e+00], - [ 3.45555252e-01, 1.88495538e+00], - [ 3.45555263e-01, 6.28318426e-01], - [ 3.45555228e-01, 3.14159252e+00], - [ 3.45555250e-01, -6.28318671e-01], - [ 3.45555229e-01, -1.88495573e+00], - [ 3.45984069e-01, 1.25663705e+00], - [ 3.45984074e-01, 2.51327411e+00], - [ 3.45984092e-01, 0.00000000e+00], - [ 3.45984074e-01, -2.51327411e+00], - [ 3.45984069e-01, -1.25663705e+00], - [ 3.54601302e-01, 1.64908193e+00], - [ 3.54601257e-01, 2.12082888e+00], - [ 3.54601277e-01, 8.64191944e-01], - [ 3.54601279e-01, 3.92444936e-01], - [ 3.54601273e-01, 2.90571904e+00], - [ 3.54601254e-01, -2.90571929e+00], - [ 3.54601249e-01, -3.92445161e-01], - [ 3.54601301e-01, -8.64192121e-01], - [ 3.54601260e-01, -2.12082922e+00], - [ 3.54601254e-01, -1.64908225e+00], - [ 3.78233067e-01, 1.43799285e+00], - [ 3.78233024e-01, 1.07528116e+00], - [ 3.78233013e-01, 2.33191822e+00], - [ 3.78233060e-01, 2.69462994e+00], - [ 3.78233045e-01, 1.81355812e-01], - [ 3.78233050e-01, -1.81355891e-01], - [ 3.78233042e-01, -2.69463005e+00], - [ 3.78233032e-01, -2.33191828e+00], - [ 3.78233033e-01, -1.43799298e+00], - [ 3.78233063e-01, -1.07528123e+00], - [ 4.06626769e-01, 1.78193176e+00], - [ 4.06626739e-01, 1.98797893e+00], - [ 4.06626738e-01, 7.31341997e-01], - [ 4.06626759e-01, 5.25294781e-01], - [ 4.06626745e-01, 3.03856887e+00], - [ 4.06626732e-01, -3.03856922e+00], - [ 4.06626753e-01, -5.25295052e-01], - [ 4.06626750e-01, -1.98797937e+00], - [ 4.06626745e-01, -1.78193218e+00], - [ 4.06626815e-01, -7.31342272e-01], - 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[ 1.57079631e+00, -5.49778757e-01], - [ 1.57079631e+00, 7.06858382e-01], - [ 1.57079631e+00, -1.80641580e+00], - [ 1.57079631e+00, 1.96349534e+00], - [ 1.57079633e+00, -3.14159253e+00], - [ 1.57079633e+00, -6.28318436e-01], - [ 1.57079633e+00, -1.88495549e+00], - [ 1.57079633e+00, 6.28318651e-01], - [ 1.57079633e+00, 1.88495565e+00], - [ 1.57079634e+00, 1.33517685e+00], - [ 1.57079634e+00, 2.59181390e+00], - [ 1.57079634e+00, 7.85398095e-02], - [ 1.57079634e+00, -2.43473427e+00], - [ 1.57079634e+00, -1.17809732e+00], - [ 1.57079636e+00, 1.57079812e-01], - [ 1.57079636e+00, 1.41371685e+00], - [ 1.57079636e+00, 2.67035390e+00], - [ 1.57079638e+00, -1.02101797e+00], - [ 1.57079638e+00, 2.74889327e+00], - [ 1.57079638e+00, 2.35619174e-01], - [ 1.57079638e+00, -2.27765497e+00], - [ 1.57079638e+00, 1.49225623e+00], - [ 1.57079640e+00, 3.14159294e-01], - [ 1.57079640e+00, -2.19911481e+00], - [ 1.57079640e+00, -9.42477808e-01], - [ 1.57079640e+00, 1.57079636e+00], - [ 1.57079640e+00, 2.82743343e+00], - [ 1.62964558e+00, 9.06056882e-01], - [ 1.62964558e+00, -2.86385434e+00], - [ 1.62964558e+00, 2.16269389e+00], - [ 1.62964557e+00, -3.50580173e-01], - [ 1.62964558e+00, -1.60721727e+00], - [ 1.62965815e+00, -2.94266650e+00], - [ 1.62965816e+00, 8.27244750e-01], - [ 1.62965816e+00, -4.29392387e-01], - [ 1.62965815e+00, -1.68602943e+00], - [ 1.62965816e+00, 2.08388172e+00], - [ 1.62968852e+00, -3.02202282e+00], - [ 1.62968852e+00, 7.47888387e-01], - [ 1.62968852e+00, -5.08748689e-01], - [ 1.62968852e+00, -1.76538578e+00], - [ 1.62968852e+00, 2.00452537e+00], - [ 1.62969606e+00, -3.10083545e+00], - [ 1.62969606e+00, -5.87561360e-01], - [ 1.62969606e+00, 6.69075766e-01], - [ 1.62969606e+00, -1.84419840e+00], - [ 1.62969606e+00, 1.92571274e+00], - [ 1.62969606e+00, 5.87561543e-01], - [ 1.57079634e+00, 3.06305279e+00], - [ 1.57079634e+00, 1.80641583e+00], - [ 1.57079634e+00, 5.49778707e-01], - [ 1.57079634e+00, -7.06858428e-01], - [ 1.57079634e+00, -1.96349547e+00], - [ 1.57079636e+00, 4.71238967e-01], - [ 1.57079636e+00, -2.04203519e+00], - [ 1.57079638e+00, 1.64933616e+00], - [ 1.57079638e+00, 2.90597322e+00], - [ 1.57079638e+00, 3.92699142e-01], - [ 1.57079638e+00, -8.63937983e-01], - [ 1.57079638e+00, -2.12057500e+00], - [ 1.57079633e+00, 1.25663716e+00], - [ 1.57079633e+00, 2.51327422e+00], - [ 1.57079633e+00, 1.25344059e-07], - [ 1.57079633e+00, -2.51327400e+00], - [ 1.57079633e+00, -1.25663700e+00], - [ 1.57079636e+00, -2.35619424e+00], - [ 1.57079636e+00, -1.09955733e+00], - [ 1.62969606e+00, 3.10083564e+00], - [ 1.62969606e+00, -6.69075598e-01], - [ 1.62969606e+00, -1.92571264e+00], - [ 1.62969606e+00, 1.84419855e+00], - [ 1.57079636e+00, 2.98451306e+00], - [ 1.57079636e+00, 1.72787596e+00], - [ 1.57079636e+00, -7.85398163e-01], - [ 1.64103021e+00, 9.77659945e-01], - [ 1.64103021e+00, -2.79225128e+00], - [ 1.64103021e+00, 2.23429695e+00], - [ 1.64103022e+00, -2.78977132e-01], - [ 1.64105142e+00, -1.45668615e+00], - [ 1.64105142e+00, -2.71332320e+00], - [ 1.64105141e+00, 1.05658800e+00], - [ 1.64105141e+00, 2.31322498e+00], - [ 1.64105142e+00, -2.00049087e-01], - [ 1.64110369e+00, -1.37697935e+00], - [ 1.64110368e+00, -2.63361638e+00], - [ 1.64110368e+00, 2.39293184e+00], - [ 1.64110368e+00, 1.13629479e+00], - [ 1.64110370e+00, -1.20342268e-01], - [ 1.64111663e+00, -2.55468730e+00], - [ 1.64111663e+00, 2.47186085e+00], - [ 1.64111663e+00, 1.21522385e+00], - [ 1.64111665e+00, -4.14132234e-02], - [ 1.64111664e+00, -1.29805032e+00], - [ 1.64103022e+00, -1.53561420e+00] - ] - ) - idx = np.array( - [ - [ 1, 3, 5, 2, 6, 4, 1], - [ 2, 1, 12, 7, 8, 3, 4], - [ 3, 1, 13, 9, 7, 5, 2], - [ 4, 1, 14, 8, 10, 2, 6], - [ 5, 1, 15, 9, 11, 3, 6], - [ 6, 1, 16, 11, 10, 5, 4], - [ 7, 2, 3, 18, 17, 13, 12], - [ 8, 4, 2, 20, 19, 14, 12], - [ 9, 3, 5, 21, 22, 13, 15], - [ 10, 4, 6, 23, 24, 14, 16], - [ 11, 6, 5, 25, 26, 15, 16], - [ 12, 27, 2, 17, 19, 7, 8], - [ 13, 28, 3, 21, 18, 7, 9], - [ 14, 29, 4, 20, 23, 8, 10], - [ 15, 30, 5, 22, 25, 11, 9], - [ 16, 31, 6, 26, 24, 11, 10], - [ 17, 37, 12, 32, 7, 27, 18], - [ 18, 39, 13, 32, 7, 28, 17], - [ 19, 38, 12, 33, 8, 27, 20], - [ 20, 41, 14, 33, 8, 29, 19], - [ 21, 40, 13, 34, 9, 28, 22], - [ 22, 43, 15, 34, 9, 30, 21], - [ 23, 42, 14, 35, 10, 29, 24], - [ 24, 45, 16, 35, 10, 31, 23], - [ 25, 44, 15, 36, 11, 30, 26], - [ 26, 46, 16, 36, 11, 31, 25], - [ 27, 12, 47, 37, 38, 17, 19], - [ 28, 13, 48, 40, 39, 21, 18], - [ 29, 14, 49, 41, 42, 20, 23], - [ 30, 15, 50, 43, 44, 22, 25], - [ 31, 16, 51, 46, 45, 26, 24], - [ 32, 17, 18, 53, 52, 39, 37], - [ 33, 20, 19, 54, 55, 41, 38], - [ 34, 21, 22, 57, 56, 40, 43], - [ 35, 23, 24, 58, 59, 42, 45], - [ 36, 26, 25, 61, 60, 44, 46], - [ 37, 17, 27, 52, 62, 32, 47], - [ 38, 19, 27, 54, 63, 33, 47], - [ 39, 18, 28, 53, 64, 32, 48], - [ 40, 21, 28, 56, 65, 48, 34], - [ 41, 20, 29, 55, 66, 33, 49], - [ 42, 23, 29, 58, 67, 35, 49], - [ 43, 22, 30, 57, 68, 50, 34], - [ 44, 25, 30, 60, 69, 36, 50], - [ 45, 24, 31, 59, 70, 35, 51], - [ 46, 26, 31, 61, 71, 36, 51], - [ 47, 27, 77, 62, 63, 37, 38], - [ 48, 28, 78, 65, 64, 40, 39], - 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2220, 2222, 2222, 2223, 2223, 2227, 2227, 2228, 2228, 2229, 2229, - 2230, 2230, 2231, 2231, 2232, 2232, 2233, 2233, 2234, 2234, 2235, - 2235, 2236, 2236, 2237, 2237, 2238, 2238, 2239, 2239, 2240, 2240, - 2241, 2241, 2242, 2242, 2243, 2243, 2244, 2244, 2245, 2245, 2246, - 2246, 2247, 2247, 2248, 2248, 2249, 2249, 2250, 2250, 2251, 2251, - 2252, 2252, 2253, 2253, 2254, 2254, 2255, 2255, 2256, 2256, 2257, - 2257, 2258, 2258, 2259, 2259, 2260, 2260, 2261, 2261, 2262, 2262, - 2263, 2263, 2264, 2264, 2265, 2265, 2266, 2266, 2267, 2267, 2268, - 2268, 2269, 2269, 2270, 2270, 2271, 2271, 2272, 2272, 2273, 2273, - 2274, 2274, 2275, 2275, 2275, 2276, 2276, 2276, 2277, 2277, 2278, - 2278, 2279, 2279, 2279, 2280, 2280, 2280, 2281, 2281, 2281, 2282, - 2282, 2283, 2283, 2284, 2284, 2285, 2285, 2286, 2286, 2287, 2287, - 2288, 2288, 2289, 2289, 2290, 2290, 2291, 2291, 2292, 2292, 2293, - 2293, 2294, 2294, 2295, 2295, 2296, 2296, 2297, 2297, 2298, 2298, - 2299, 2299, 2300, 2300, 2301, 2301, 2302, 2302, 2303, 2303, 2304, - 2304, 2305, 2305, 2306, 2306, 2312, 2312, 2313, 2313, 2314, 2314, - 2315, 2315, 2316, 2316, 2317, 2317, 2318, 2318, 2319, 2319, 2320, - 2320, 2321, 2321, 2322, 2322, 2323, 2323, 2324, 2324, 2325, 2325, - 2326, 2326, 2327, 2327, 2328, 2328, 2329, 2329, 2330, 2330, 2331, - 2331, 2332, 2332, 2333, 2333, 2334, 2334, 2335, 2335, 2336, 2336, - 2337, 2337, 2338, 2338, 2339, 2339, 2340, 2340, 2341, 2341, 2342, - 2342, 2343, 2343, 2344, 2344, 2345, 2345, 2346, 2346, 2347, 2347, - 2348, 2348, 2349, 2349, 2350, 2350, 2351, 2351, 2352, 2352, 2353, - 2353, 2354, 2354, 2355, 2355, 2356, 2356, 2357, 2357, 2357, 2358, - 2358, 2358, 2359, 2359, 2359, 2360, 2360, 2360, 2361, 2361, 2361, - 2362, 2362, 2363, 2363, 2364, 2364, 2365, 2365, 2366, 2366, 2367, - 2367, 2368, 2368, 2369, 2369, 2370, 2370, 2371, 2371, 2372, 2372, - 2373, 2373, 2374, 2374, 2375, 2375, 2376, 2376, 2377, 2377, 2378, - 2378, 2379, 2379, 2380, 2380, 2381, 2381, 2382, 2382, 2383, 2383, - 2384, 2384, 2385, 2385, 2386, 2386, 2387, 2387, 2387, 2388, 2388, - 2388, 2389, 2389, 2389, 2390, 2390, 2390, 2391, 2391, 2391, 2397, - 2398, 2399, 2400, 2401, 2402, 2402, 2403, 2403, 2404, 2404, 2405, - 2405, 2406, 2406, 2407, 2407, 2408, 2408, 2409, 2409, 2410, 2410, - 2411, 2411, 2412, 2412, 2413, 2413, 2414, 2414, 2415, 2415, 2416, - 2416, 2417, 2417, 2418, 2418, 2419, 2419, 2420, 2420, 2421, 2421, - 2422, 2422, 2423, 2423, 2424, 2424, 2425, 2425, 2426, 2426, 2427, - 2427, 2428, 2428, 2429, 2429, 2430, 2430, 2431, 2431, 2432, 2432, - 2433, 2433, 2434, 2434, 2435, 2435, 2436, 2436, 2437, 2437, 2437, - 2438, 2438, 2438, 2439, 2439, 2439, 2440, 2440, 2440, 2441, 2441, - 2441, 2442, 2442, 2443, 2443, 2444, 2444, 2445, 2445, 2446, 2446, - 2447, 2447, 2448, 2448, 2449, 2449, 2450, 2450, 2451, 2451, 2452, - 2452, 2453, 2453, 2454, 2454, 2455, 2455, 2456, 2456, 2457, 2457, - 2458, 2458, 2459, 2459, 2460, 2460, 2461, 2461, 2462, 2462, 2463, - 2463, 2464, 2464, 2465, 2465, 2466, 2466, 2467, 2467, 2468, 2468, - 2469, 2469, 2470, 2470, 2471, 2471, 2477, 2477, 2477, 2478, 2478, - 2478, 2479, 2479, 2479, 2480, 2480, 2480, 2481, 2481, 2481, 2482, - 2482, 2483, 2483, 2484, 2484, 2485, 2485, 2486, 2486, 2487, 2487, - 2488, 2488, 2489, 2489, 2490, 2490, 2491, 2491, 2492, 2492, 2493, - 2493, 2494, 2494, 2495, 2495, 2496, 2496, 2497, 2497, 2498, 2498, - 2499, 2499, 2500, 2500, 2501, 2501, 2502, 2503, 2504, 2505, 2506, - 2507, 2507, 2508, 2508, 2509, 2509, 2510, 2510, 2511, 2511, 2512, - 2512, 2513, 2513, 2514, 2514, 2515, 2515, 2516, 2516, 2517, 2517, - 2517, 2518, 2518, 2518, 2519, 2519, 2519, 2520, 2520, 2520, 2521, - 2521, 2521, 2522, 2522, 2522, 2522, 2523, 2523, 2523, 2523, 2524, - 2524, 2524, 2524, 2525, 2525, 2525, 2525, 2526, 2526, 2526, 2526, - 2527, 2527, 2527, 2528, 2528, 2528, 2529, 2529, 2529, 2530, 2530, - 2530, 2531, 2531, 2531, 2532, 2533, 2534, 2535, 2536, 2537, 2538, - 2539, 2540, 2541, 2542, 2542, 2543, 2543, 2544, 2544, 2545, 2545, - 2546, 2546, 2547, 2547, 2547, 2548, 2548, 2548, 2549, 2549, 2549, - 2550, 2550, 2550, 2551, 2552, 2552, 2552, 2553, 2554, 2555, 2556, - 2557, 2558, 2559, 2560, 2561] - ] - ) - separation_angle = 4.33 - - if res == 'high': - S = np.array( - [ - [ 0.00000000e+00, 0.00000000e+00], - [ 6.91966197e-02, 1.25663704e+00], - [ 6.91966162e-02, 2.51327412e+00], - [ 6.91966218e-02, 0.00000000e+00], - [ 6.91966162e-02, -2.51327412e+00], - [ 6.91966197e-02, -1.25663704e+00], - [ 1.12209757e-01, 1.88495557e+00], - [ 1.12209774e-01, 6.28318447e-01], - [ 1.12209755e-01, 3.14159265e+00], - [ 1.12209774e-01, -6.28318447e-01], - [ 1.12209757e-01, -1.88495557e+00], - [ 1.38393600e-01, 1.25663705e+00], - [ 1.38393597e-01, 2.51327410e+00], - [ 1.38393616e-01, 0.00000000e+00], - [ 1.38393597e-01, -2.51327410e+00], - [ 1.38393600e-01, -1.25663705e+00], - [ 1.73920996e-01, 1.64787724e+00], - [ 1.73920994e-01, 2.12203388e+00], - [ 1.73921005e-01, 8.65396859e-01], - [ 1.73921008e-01, 3.91240190e-01], - [ 1.73920986e-01, 2.90451430e+00], - [ 1.73920986e-01, -2.90451430e+00], - [ 1.73921008e-01, -3.91240190e-01], - [ 1.73921005e-01, -8.65396859e-01], - [ 1.73920994e-01, -2.12203388e+00], - [ 1.73920996e-01, -1.64787724e+00], - [ 2.07590303e-01, 1.25663706e+00], - [ 2.07590291e-01, 2.51327412e+00], - [ 2.07590298e-01, 0.00000000e+00], - [ 2.07590291e-01, -2.51327412e+00], - [ 2.07590303e-01, -1.25663706e+00], - [ 2.25901926e-01, 1.88495556e+00], - [ 2.25901932e-01, 6.28318530e-01], - [ 2.25901905e-01, 3.14159265e+00], - [ 2.25901932e-01, -6.28318530e-01], - [ 2.25901926e-01, -1.88495556e+00], - [ 2.39638645e-01, 1.53860570e+00], - [ 2.39638650e-01, 9.74668409e-01], - [ 2.39638634e-01, 2.23130542e+00], - [ 2.39638629e-01, 2.79524275e+00], - [ 2.39638644e-01, 2.81968672e-01], - [ 2.39638644e-01, -2.81968672e-01], - [ 2.39638629e-01, -2.79524275e+00], - [ 2.39638634e-01, -2.23130542e+00], - [ 2.39638650e-01, -9.74668409e-01], - [ 2.39638645e-01, -1.53860570e+00], - [ 2.76787143e-01, 1.25663706e+00], - [ 2.76787121e-01, 2.51327409e+00], - [ 2.76787127e-01, 0.00000000e+00], - [ 2.76787121e-01, -2.51327409e+00], - [ 2.76787143e-01, -1.25663706e+00], - [ 2.88434684e-01, 1.74146202e+00], - [ 2.88434699e-01, 2.02844889e+00], - [ 2.88434703e-01, 7.71811907e-01], - [ 2.88434700e-01, 4.84824993e-01], - [ 2.88434692e-01, 2.99809912e+00], - [ 2.88434686e-01, -2.99809927e+00], - [ 2.88434688e-01, -4.84825135e-01], - [ 2.88434703e-01, -7.71812055e-01], - [ 2.88434666e-01, -2.02844913e+00], - [ 2.88434692e-01, -1.74146221e+00], - [ 3.10101715e-01, 1.47654597e+00], - [ 3.10101691e-01, 1.03672801e+00], - [ 3.10101691e-01, 2.29336502e+00], - [ 3.10101718e-01, 2.73318307e+00], - [ 3.10101697e-01, 2.19908954e-01], - [ 3.10101675e-01, -2.19909070e-01], - [ 3.10101701e-01, -2.73318320e+00], - [ 3.10101698e-01, -2.29336519e+00], - [ 3.10101716e-01, -1.03672806e+00], - [ 3.10101711e-01, -1.47654614e+00], - [ 3.45555252e-01, 1.88495538e+00], - [ 3.45555263e-01, 6.28318426e-01], - [ 3.45555228e-01, 3.14159252e+00], - [ 3.45555250e-01, -6.28318671e-01], - [ 3.45555229e-01, -1.88495573e+00], - [ 3.45984069e-01, 1.25663705e+00], - [ 3.45984074e-01, 2.51327411e+00], - [ 3.45984092e-01, 0.00000000e+00], - [ 3.45984074e-01, -2.51327411e+00], - [ 3.45984069e-01, -1.25663705e+00], - [ 3.54601302e-01, 1.64908193e+00], - [ 3.54601257e-01, 2.12082888e+00], - [ 3.54601277e-01, 8.64191944e-01], - [ 3.54601279e-01, 3.92444936e-01], - [ 3.54601273e-01, 2.90571904e+00], - [ 3.54601254e-01, -2.90571929e+00], - [ 3.54601249e-01, -3.92445161e-01], - [ 3.54601301e-01, -8.64192121e-01], - [ 3.54601260e-01, -2.12082922e+00], - [ 3.54601254e-01, -1.64908225e+00], - [ 3.78233067e-01, 1.43799285e+00], - [ 3.78233024e-01, 1.07528116e+00], - [ 3.78233013e-01, 2.33191822e+00], - [ 3.78233060e-01, 2.69462994e+00], - [ 3.78233045e-01, 1.81355812e-01], - [ 3.78233050e-01, -1.81355891e-01], - [ 3.78233042e-01, -2.69463005e+00], - [ 3.78233032e-01, -2.33191828e+00], - [ 3.78233033e-01, -1.43799298e+00], - [ 3.78233063e-01, -1.07528123e+00], - [ 4.06626769e-01, 1.78193176e+00], - [ 4.06626739e-01, 1.98797893e+00], - [ 4.06626738e-01, 7.31341997e-01], - [ 4.06626759e-01, 5.25294781e-01], - [ 4.06626745e-01, 3.03856887e+00], - [ 4.06626732e-01, -3.03856922e+00], - [ 4.06626753e-01, -5.25295052e-01], - [ 4.06626750e-01, -1.98797937e+00], - [ 4.06626745e-01, -1.78193218e+00], - [ 4.06626815e-01, -7.31342272e-01], - [ 4.15180810e-01, 1.25663705e+00], - [ 4.15180788e-01, 2.51327411e+00], - [ 4.15180813e-01, 0.00000000e+00], - [ 4.15180788e-01, -2.51327411e+00], - [ 4.15180810e-01, -1.25663705e+00], - [ 4.20650212e-01, 1.58905064e+00], - [ 4.20650136e-01, 2.18086011e+00], - [ 4.20650142e-01, 9.24223141e-01], - [ 4.20650215e-01, 3.32413612e-01], - [ 4.20650170e-01, 2.84568770e+00], - [ 4.20650134e-01, -2.84568802e+00], - [ 4.20650165e-01, -3.32413890e-01], - [ 4.20650213e-01, -9.24223375e-01], - [ 4.20650169e-01, -2.18086049e+00], - [ 4.20650133e-01, -1.58905099e+00], - [ 4.47391435e-01, 2.35845603e+00], - [ 4.47391443e-01, -2.66809215e+00], - [ 4.47391430e-01, -1.41145507e+00], - [ 4.47391522e-01, 1.41145489e+00], - [ 4.47391468e-01, 1.10181906e+00], - [ 4.47391476e-01, 2.66809197e+00], - [ 4.47391541e-01, 1.54817836e-01], - [ 4.47391469e-01, -1.54818004e-01], - [ 4.47391461e-01, -2.35845623e+00], - [ 4.47391504e-01, -1.10181919e+00], - [ 4.63647652e-01, 1.88495532e+00], - [ 4.63647644e-01, 6.28318358e-01], - [ 4.63647636e-01, 3.14159244e+00], - [ 4.63647664e-01, -6.28318652e-01], - [ 4.63647659e-01, -1.88495580e+00], - [ 4.69775487e-01, 1.71075693e+00], - [ 4.69775446e-01, 2.05915401e+00], - [ 4.69775490e-01, 8.02517365e-01], - [ 4.69775497e-01, 4.54119902e-01], - [ 4.69775452e-01, 2.96739403e+00], - [ 4.69775469e-01, -2.96739381e+00], - [ 4.69775504e-01, -4.54119637e-01], - [ 4.69775469e-01, -8.02517040e-01], - [ 4.69775460e-01, -2.05915421e+00], - [ 4.69775469e-01, -1.71075710e+00], - [ 4.84377630e-01, 1.25663707e+00], - [ 4.84377589e-01, 2.51327408e+00], - [ 4.84377631e-01, 0.00000000e+00], - [ 4.84377589e-01, -2.51327408e+00], - [ 4.84377630e-01, -1.25663707e+00], - [ 4.87694990e-01, 2.22533000e+00], - [ 4.87695038e-01, 9.68693070e-01], - [ 4.87695070e-01, 2.80121777e+00], - 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[ 1.57079637e+00, -2.08130509e+00], - [ 1.57079639e+00, 1.61006626e+00], - [ 1.57079639e+00, 2.86670332e+00], - [ 1.57079639e+00, 3.53429218e-01], - [ 1.57079639e+00, -9.03207895e-01], - [ 1.57079639e+00, -2.15984491e+00], - [ 1.57079634e+00, 1.33517685e+00], - [ 1.57079633e+00, 1.25663716e+00], - [ 1.57079633e+00, 2.51327422e+00], - [ 1.57079634e+00, -2.43473427e+00], - [ 1.57079638e+00, -1.02101797e+00], - [ 1.57079638e+00, 2.74889327e+00], - [ 1.57079638e+00, -2.27765497e+00], - [ 1.57079640e+00, 3.14159294e-01], - [ 1.57079640e+00, -9.42477808e-01], - [ 1.57079640e+00, 1.57079636e+00], - [ 1.57079640e+00, 2.82743343e+00], - [ 1.57079634e+00, 3.06305279e+00], - [ 1.57079634e+00, -1.96349547e+00], - [ 1.57079636e+00, 2.98451306e+00], - [ 1.57079635e+00, 3.02378293e+00], - [ 1.57079636e+00, 1.72787596e+00], - [ 1.57079635e+00, 1.76714590e+00], - [ 1.57079636e+00, 4.71238967e-01], - [ 1.57079635e+00, 5.10508837e-01], - [ 1.57079636e+00, -7.85398163e-01], - [ 1.57079635e+00, -7.46128296e-01], - [ 1.57079636e+00, -2.04203519e+00], - [ 1.57079635e+00, -2.00276533e+00], - [ 1.57079638e+00, 1.64933616e+00], - [ 1.57079638e+00, 2.90597322e+00], - [ 1.57079638e+00, 3.92699142e-01], - [ 1.57079633e+00, 1.25344059e-07], - [ 1.57079633e+00, -2.51327400e+00], - [ 1.57079633e+00, -1.25663700e+00], - [ 1.57079634e+00, 1.80641583e+00], - [ 1.57079634e+00, 5.49778707e-01], - [ 1.57079634e+00, -7.06858428e-01], - [ 1.60591866e+00, 9.60047180e-01], - [ 1.60591866e+00, -2.80986403e+00], - [ 1.60591866e+00, 2.21668418e+00], - [ 1.60591867e+00, -2.96589893e-01], - [ 1.60592940e+00, -1.47449329e+00], - [ 1.60592940e+00, -2.73113032e+00], - [ 1.60592940e+00, 1.03878086e+00], - [ 1.60592940e+00, 2.29541786e+00], - [ 1.60592940e+00, -2.17856227e-01], - [ 1.60595592e+00, -1.39537074e+00], - [ 1.60595591e+00, -2.65200775e+00], - [ 1.60595591e+00, 2.37454044e+00], - [ 1.60595591e+00, 1.11790341e+00], - [ 1.60595592e+00, -1.38733650e-01], - [ 1.60596252e+00, -2.57327360e+00], - [ 1.60596252e+00, 2.45327457e+00], - [ 1.60596252e+00, 1.19663755e+00], - [ 1.60596253e+00, -5.99995127e-02], - [ 1.60596253e+00, -1.31663654e+00], - [ 1.60592149e+00, 9.99365559e-01], - [ 1.60592149e+00, -2.77054564e+00], - [ 1.60592149e+00, 2.25600257e+00], - [ 1.60592149e+00, -2.57271523e-01], - [ 1.60592150e+00, -1.51390858e+00], - [ 1.60593196e+00, -1.43517489e+00], - [ 1.60593196e+00, -2.69181191e+00], - [ 1.60593195e+00, 1.07809927e+00], - [ 1.60593195e+00, 2.33473627e+00], - [ 1.60593196e+00, -1.78537802e-01], - [ 1.60595767e+00, -1.35605223e+00], - [ 1.60595767e+00, -2.61268931e+00], - [ 1.60595767e+00, 2.41385889e+00], - [ 1.60595767e+00, 1.15722184e+00], - [ 1.60595768e+00, -9.94152103e-02], - [ 1.60596401e+00, -2.53395503e+00], - [ 1.60596401e+00, 2.49259316e+00], - [ 1.60596401e+00, 1.23595613e+00], - [ 1.60596402e+00, -2.06809259e-02], - [ 1.60596402e+00, -1.27731804e+00], - [ 1.60591867e+00, -1.55322696e+00] - ] - ) - idx = np.array( - [ - [ 1, 1282, 1283, 1284, 1285, 1286, 1], - [ 2, 1284, 1287, 1288, 1289, 1290, 1291], - [ 3, 1282, 1292, 1293, 1294, 1295, 1290], - [ 4, 1286, 1296, 1297, 1298, 1291, 1299], - [ 5, 1283, 1300, 1301, 1302, 1295, 1303], - [ 6, 1285, 1304, 1305, 1306, 1303, 1299], - [ 7, 1288, 1294, 1307, 1308, 1309, 1310], - [ 8, 1297, 1289, 1311, 1312, 1313, 1314], - [ 9, 1293, 1301, 1315, 1316, 1317, 1318], - [ 10, 1298, 1306, 1319, 1320, 1321, 1322], - [ 11, 1305, 1302, 1323, 1324, 1325, 1326], - [ 12, 1327, 1287, 1328, 1329, 1310, 1314], - [ 13, 1330, 1292, 1331, 1332, 1309, 1317], - [ 14, 1333, 1296, 1334, 1335, 1313, 1321], - [ 15, 1336, 1300, 1337, 1338, 1325, 1318], - [ 16, 1339, 1304, 1340, 1341, 1326, 1322], - [ 17, 1342, 1328, 1343, 1308, 1344, 1345], - [ 18, 1346, 1332, 1347, 1307, 1348, 1345], - [ 19, 1349, 1329, 1350, 1312, 1351, 1352], - [ 20, 1353, 1334, 1354, 1311, 1355, 1352], - [ 21, 1356, 1331, 1357, 1315, 1358, 1359], - [ 22, 1360, 1337, 1361, 1316, 1362, 1359], - [ 23, 1363, 1335, 1364, 1319, 1365, 1366], - [ 24, 1367, 1341, 1368, 1320, 1369, 1366], - [ 25, 1370, 1338, 1371, 1323, 1372, 1373], - [ 26, 1374, 1340, 1375, 1324, 1376, 1373], - [ 27, 1327, 1377, 1378, 1379, 1344, 1351], - [ 28, 1330, 1380, 1381, 1382, 1358, 1348], - [ 29, 1333, 1383, 1384, 1385, 1355, 1365], - [ 30, 1336, 1386, 1387, 1388, 1362, 1372], - [ 31, 1339, 1389, 1390, 1391, 1376, 1369], - [ 32, 1343, 1347, 1392, 1393, 1394, 1395], - [ 33, 1354, 1350, 1396, 1397, 1398, 1399], - [ 34, 1357, 1361, 1400, 1401, 1402, 1403], - [ 35, 1364, 1368, 1404, 1405, 1406, 1407], - [ 36, 1375, 1371, 1408, 1409, 1410, 1411], - [ 37, 1342, 1378, 1412, 1413, 1395, 1414], - [ 38, 1349, 1379, 1415, 1416, 1399, 1417], - [ 39, 1346, 1382, 1418, 1419, 1394, 1420], - [ 40, 1356, 1381, 1421, 1422, 1423, 1402], - [ 41, 1353, 1384, 1424, 1425, 1398, 1426], - [ 42, 1363, 1385, 1427, 1428, 1406, 1429], - [ 43, 1360, 1387, 1430, 1431, 1432, 1403], - [ 44, 1370, 1388, 1433, 1434, 1410, 1435], - [ 45, 1367, 1391, 1436, 1437, 1407, 1438], - [ 46, 1374, 1390, 1439, 1440, 1411, 1441], - [ 47, 1377, 1442, 1443, 1444, 1414, 1417], - [ 48, 1380, 1445, 1446, 1447, 1423, 1420], - 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[ 72, 1522, 1523, 1458, 1462, 1524, 1525], - [ 73, 1526, 1527, 1469, 1465, 1528, 1529], - [ 74, 1530, 1531, 1472, 1476, 1532, 1533], - [ 75, 1534, 1535, 1483, 1479, 1536, 1537], - [ 76, 1538, 1539, 1486, 1490, 1540, 1541], - [ 77, 1542, 1442, 1543, 1544, 1494, 1497], - [ 78, 1545, 1445, 1546, 1547, 1503, 1500], - [ 79, 1548, 1448, 1549, 1550, 1506, 1509], - [ 80, 1551, 1451, 1552, 1553, 1515, 1512], - [ 81, 1554, 1454, 1555, 1556, 1518, 1521], - [ 82, 1557, 1558, 1493, 1457, 1559, 1524], - [ 83, 1560, 1561, 1499, 1461, 1562, 1525], - [ 84, 1563, 1564, 1496, 1464, 1565, 1529], - [ 85, 1566, 1567, 1505, 1468, 1568, 1528], - [ 86, 1569, 1570, 1502, 1471, 1571, 1532], - [ 87, 1572, 1573, 1511, 1475, 1574, 1533], - [ 88, 1575, 1576, 1508, 1478, 1577, 1537], - [ 89, 1578, 1517, 1579, 1482, 1580, 1536], - [ 90, 1581, 1582, 1514, 1485, 1583, 1541], - [ 91, 1584, 1585, 1520, 1489, 1586, 1540], - [ 92, 1492, 1587, 1543, 1588, 1559, 1589], - [ 93, 1495, 1590, 1544, 1591, 1565, 1592], - [ 94, 1498, 1593, 1546, 1594, 1595, 1562], - 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[2058, 2059, 252, 242, 1946, 2089, 1945], - [2059, 2058, 2197, 2089, 2057, 242, 303], - [2060, 243, 289, 2031, 2062, 2030, 2203], - [2061, 2062, 243, 253, 1949, 2092, 1948], - [2062, 2061, 2203, 2092, 2060, 304, 243], - [2063, 244, 284, 2016, 2065, 2015, 2188], - [2064, 2065, 244, 255, 1937, 2098, 1936], - [2065, 2064, 2188, 2098, 2063, 244, 300], - [2066, 245, 285, 2019, 2068, 2018, 2191], - [2067, 2068, 254, 245, 1934, 2095, 1933], - [2068, 2067, 2191, 2095, 2066, 245, 299], - [2069, 246, 290, 2022, 2071, 2021, 2206], - [2070, 2071, 257, 246, 1955, 2104, 1954], - [2071, 2070, 2206, 2104, 2069, 246, 302], - [2072, 247, 286, 2034, 2074, 2033, 2194], - [2073, 2074, 247, 256, 1940, 2101, 1939], - [2074, 2073, 2194, 2101, 2072, 247, 301], - [2075, 248, 282, 2028, 2077, 2027, 2182], - [2076, 2077, 258, 248, 1943, 2107, 1942], - [2077, 2076, 2182, 2107, 2075, 248, 305], - [2078, 249, 283, 2013, 2080, 2012, 2185], - [2079, 2080, 260, 249, 1952, 2113, 1951], - [2080, 2079, 2185, 2113, 2078, 249, 297], - [2081, 250, 288, 2037, 2083, 2036, 2200], - [2082, 2083, 250, 259, 1961, 2110, 1960], - [2083, 2082, 2200, 2110, 2081, 250, 306], - [2084, 251, 291, 2040, 2086, 2039, 2209], - [2085, 2086, 251, 261, 1958, 2116, 1957], - [2086, 2085, 2209, 2116, 2084, 298, 251], - [2087, 252, 310, 2089, 2137, 2246, 2088], - [2088, 1988, 2137, 252, 268, 1986, 2087], - [2089, 2246, 2087, 252, 303, 2059, 2058], - [2090, 311, 253, 2092, 2146, 2249, 2091], - [2091, 1991, 2146, 253, 271, 1989, 2090], - [2092, 2249, 2090, 253, 304, 2062, 2061], - [2093, 254, 313, 2095, 2119, 2234, 2094], - [2094, 1979, 2119, 254, 262, 1977, 2093], - [2095, 2234, 2093, 254, 299, 2068, 2067], - [2096, 255, 307, 2098, 2122, 2237, 2097], - [2097, 1982, 2122, 255, 263, 1980, 2096], - [2098, 2237, 2096, 255, 300, 2065, 2064], - [2099, 308, 256, 2101, 2125, 2240, 2100], - [2100, 1967, 2125, 256, 264, 1965, 2099], - [2101, 2240, 2099, 256, 301, 2074, 2073], - [2102, 257, 314, 2104, 2131, 2243, 2103], - [2103, 1970, 2131, 257, 266, 1968, 2102], - [2104, 2243, 2102, 257, 302, 2071, 2070], - [2105, 258, 309, 2107, 2134, 2252, 2106], - [2106, 1985, 2134, 258, 267, 1983, 2105], - [2107, 2252, 2105, 258, 305, 2077, 2076], - [2108, 259, 316, 2110, 2143, 2255, 2109], - [2109, 1976, 2143, 259, 270, 1974, 2108], - [2110, 2255, 2108, 259, 306, 2083, 2082], - [2111, 260, 312, 2113, 2128, 2228, 2112], - [2112, 1964, 2128, 260, 265, 1962, 2111], - [2113, 2228, 2111, 260, 297, 2080, 2079], - [2114, 315, 261, 2116, 2140, 2231, 2115], - [2115, 1973, 2140, 261, 269, 1971, 2114], - [2116, 2231, 2114, 261, 298, 2086, 2085], - [2117, 262, 317, 2213, 2119, 2118, 2276], - [2118, 2043, 2213, 262, 292, 2042, 2117], - [2119, 2094, 2276, 2093, 2117, 262, 313], - [2120, 263, 318, 2217, 2122, 2121, 2258], - [2121, 2046, 2217, 293, 263, 2045, 2120], - [2122, 2097, 2258, 2096, 2120, 263, 307], - [2123, 319, 264, 2216, 2125, 2124, 2261], - [2124, 2047, 2216, 293, 264, 2045, 2123], - [2125, 2100, 2261, 2099, 2123, 308, 264], - [2126, 322, 265, 2214, 2128, 2127, 2273], - [2127, 2044, 2214, 265, 292, 2042, 2126], - [2128, 2112, 2273, 2111, 2126, 265, 312], - [2129, 266, 323, 2220, 2131, 2130, 2279], - [2130, 2050, 2220, 266, 294, 2048, 2129], - [2131, 2103, 2279, 2102, 2129, 266, 314], - [2132, 324, 267, 2219, 2134, 2133, 2264], - [2133, 2049, 2219, 294, 267, 2048, 2132], - [2134, 2106, 2264, 2105, 2132, 267, 309], - [2135, 268, 320, 2222, 2137, 2136, 2267], - [2136, 2052, 2222, 268, 295, 2051, 2135], - [2137, 2088, 2267, 2087, 2135, 310, 268], - [2138, 269, 325, 2223, 2140, 2139, 2282], - [2139, 2053, 2223, 295, 269, 2051, 2138], - [2140, 2115, 2282, 2114, 2138, 315, 269], - [2141, 270, 326, 2226, 2143, 2142, 2285], - [2142, 2056, 2226, 296, 270, 2054, 2141], - [2143, 2109, 2285, 2108, 2141, 270, 316], - [2144, 271, 321, 2225, 2146, 2145, 2270], - [2145, 2055, 2225, 271, 296, 2054, 2144], - [2146, 2091, 2270, 2090, 2144, 271, 311], - [2147, 2017, 272, 284, 2190, 2016, 2148], - [2148, 2149, 272, 334, 2190, 2319, 2147], - [2149, 2148, 2319, 272, 327, 2150, 2178], - [2150, 2178, 2149, 280, 272, 2007, 2006], - [2151, 2020, 273, 285, 2193, 2019, 2152], - [2152, 2153, 335, 273, 2193, 2324, 2151], - [2153, 2152, 2324, 273, 328, 2154, 2181], - [2154, 2153, 2181, 273, 281, 1995, 1994], - [2155, 2157, 337, 274, 2196, 2327, 2156], - [2156, 2035, 286, 274, 2196, 2034, 2155], - [2157, 2155, 2327, 274, 329, 2158, 2161], - [2158, 2157, 2161, 275, 274, 1999, 1998], - [2159, 2161, 275, 338, 2199, 2328, 2160], - [2160, 2026, 275, 287, 2199, 2025, 2159], - [2161, 2159, 2328, 275, 329, 2158, 2157], - [2162, 2164, 339, 276, 2184, 2331, 2163], - [2163, 2029, 276, 282, 2184, 2028, 2162], - [2164, 2162, 2331, 276, 330, 2165, 2168], - [2165, 2168, 2164, 276, 277, 2003, 2002], - [2166, 2168, 277, 340, 2202, 2332, 2167], - [2167, 2038, 288, 277, 2202, 2037, 2166], - [2168, 2166, 2332, 277, 330, 2165, 2164], - [2169, 2041, 291, 278, 2211, 2040, 2170], - [2170, 2171, 341, 278, 2211, 2335, 2169], - [2171, 2170, 2335, 331, 278, 2172, 2175], - [2172, 2171, 2175, 279, 278, 2011, 2010], - [2173, 2175, 279, 332, 2205, 2336, 2174], - [2174, 2032, 279, 289, 2205, 2031, 2173], - [2175, 2173, 2336, 331, 279, 2172, 2171], - [2176, 2178, 280, 333, 2187, 2320, 2177], - [2177, 2014, 280, 283, 2187, 2013, 2176], - [2178, 2176, 2320, 280, 327, 2150, 2149], - [2179, 2181, 336, 281, 2208, 2323, 2180], - [2180, 2023, 281, 290, 2208, 2022, 2179], - [2181, 2179, 2323, 281, 328, 2154, 2153], - [2182, 2183, 2077, 305, 282, 2253, 2075], - [2183, 2182, 2253, 351, 282, 2184, 2360], - [2184, 2183, 2360, 2163, 2162, 339, 282], - [2185, 2186, 2080, 283, 297, 2229, 2078], - [2186, 2185, 2229, 283, 353, 2187, 2342], - [2187, 2186, 2342, 2177, 2176, 283, 333], - [2188, 2189, 2065, 284, 300, 2238, 2063], - [2189, 2188, 2238, 284, 347, 2190, 2345], - [2190, 2345, 2189, 2148, 2147, 284, 334], - [2191, 2192, 2068, 285, 299, 2235, 2066], - [2192, 2191, 2235, 285, 348, 2193, 2348], - [2193, 2348, 2192, 2152, 2151, 285, 335], - [2194, 2195, 2074, 286, 301, 2241, 2072], - [2195, 2194, 2241, 286, 349, 2196, 2354], - [2196, 2354, 2195, 2156, 2155, 286, 337], - [2197, 2198, 2059, 303, 287, 2247, 2057], - [2198, 2197, 2247, 287, 350, 2199, 2357], - [2199, 2198, 2357, 2160, 2159, 287, 338], - [2200, 2201, 2083, 288, 306, 2256, 2081], - [2201, 2200, 2256, 288, 355, 2202, 2363], - [2202, 2363, 2201, 2167, 2166, 288, 340], - [2203, 2204, 2062, 289, 304, 2250, 2060], - [2204, 2203, 2250, 352, 289, 2205, 2339], - [2205, 2204, 2339, 2174, 2173, 289, 332], - [2206, 2207, 2071, 290, 302, 2244, 2069], - [2207, 2206, 2244, 290, 354, 2208, 2351], - [2208, 2207, 2351, 2180, 2179, 336, 290], - [2209, 2210, 2086, 291, 298, 2232, 2084], - [2210, 2209, 2232, 291, 356, 2211, 2366], - [2211, 2366, 2210, 2170, 2169, 341, 291], - [2212, 292, 344, 2213, 2214, 2288, 2303], - [2213, 2288, 2118, 2212, 2117, 292, 317], - [2214, 2303, 2127, 2212, 2126, 292, 322], - [2215, 293, 342, 2217, 2216, 2294, 2291], - [2216, 2294, 2124, 2215, 2123, 293, 319], - [2217, 2291, 2121, 2215, 2120, 293, 318], - [2218, 294, 345, 2219, 2220, 2309, 2306], - 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[2242, 302, 366, 2244, 2280, 2404, 2243], - [2243, 2104, 2280, 302, 314, 2102, 2242], - [2244, 2404, 2242, 354, 302, 2207, 2206], - [2245, 303, 360, 2247, 2268, 2391, 2246], - [2246, 2089, 2268, 310, 303, 2087, 2245], - [2247, 2391, 2245, 303, 350, 2198, 2197], - [2248, 304, 361, 2250, 2271, 2397, 2249], - [2249, 2092, 2271, 304, 311, 2090, 2248], - [2250, 2397, 2248, 352, 304, 2204, 2203], - [2251, 305, 362, 2253, 2265, 2394, 2252], - [2252, 2107, 2265, 305, 309, 2105, 2251], - [2253, 2394, 2251, 305, 351, 2183, 2182], - [2254, 306, 364, 2256, 2286, 2407, 2255], - [2255, 2110, 2286, 316, 306, 2108, 2254], - [2256, 2407, 2254, 306, 355, 2201, 2200], - [2257, 307, 368, 2259, 2292, 2415, 2258], - [2258, 2292, 2122, 307, 318, 2257, 2120], - [2259, 2415, 2237, 2257, 2236, 307, 358], - [2260, 308, 369, 2262, 2295, 2418, 2261], - [2261, 2295, 2125, 308, 319, 2260, 2123], - [2262, 2418, 2240, 2260, 2239, 308, 359], - [2263, 374, 309, 2265, 2310, 2427, 2264], - [2264, 2310, 2134, 324, 309, 2263, 2132], - 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[5084, 5065, 5064, 1243, 1248, 5267, 5248], - [5085, 5179, 5123, 1243, 1252, 4960, 4959], - [5086, 5063, 5062, 1244, 1250, 5268, 5249], - [5087, 5180, 5124, 1256, 1244, 4954, 4953], - [5088, 5069, 5068, 1245, 1251, 5269, 5250], - [5089, 5181, 5125, 1245, 1254, 4966, 4965], - [5090, 5071, 5070, 1246, 1247, 5270, 5286], - [5091, 5182, 5126, 1246, 1255, 4963, 4962], - [5092, 5045, 5044, 1247, 1262, 5271, 5251], - [5093, 5033, 5032, 1263, 1248, 5272, 5252], - [5094, 5036, 5035, 1249, 1265, 5273, 5253], - [5095, 5039, 5038, 1250, 1264, 5274, 5254], - [5096, 5042, 5041, 1251, 1266, 5275, 5255], - [5097, 5183, 5127, 1252, 1257, 4912, 4910], - [5098, 5184, 5128, 1258, 1253, 4909, 4907], - [5099, 5185, 5129, 1259, 1254, 4921, 4919], - [5100, 5186, 5130, 1255, 1260, 4918, 4916], - [5101, 5187, 5131, 1256, 1261, 4915, 4913], - [5102, 5188, 5132, 1257, 1271, 4879, 4877], - [5103, 5189, 5133, 1258, 1274, 4882, 4880], - [5104, 5190, 5134, 1259, 1273, 4888, 4886], - [5105, 5191, 5135, 1260, 1275, 4891, 4889], - 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2398, 2399, 2400, 2401, 2402, 2402, 2403, 2403, 2404, 2404, 2405, - 2405, 2406, 2406, 2407, 2407, 2408, 2408, 2409, 2409, 2410, 2410, - 2411, 2411, 2412, 2412, 2413, 2413, 2414, 2414, 2415, 2415, 2416, - 2416, 2417, 2417, 2418, 2418, 2419, 2419, 2420, 2420, 2421, 2421, - 2422, 2422, 2423, 2423, 2424, 2424, 2425, 2425, 2426, 2426, 2427, - 2427, 2428, 2428, 2429, 2429, 2430, 2430, 2431, 2431, 2432, 2432, - 2433, 2433, 2434, 2434, 2435, 2435, 2436, 2436, 2437, 2437, 2437, - 2438, 2438, 2438, 2439, 2439, 2439, 2440, 2440, 2440, 2441, 2441, - 2441, 2442, 2442, 2443, 2443, 2444, 2444, 2445, 2445, 2446, 2446, - 2447, 2447, 2448, 2448, 2449, 2449, 2450, 2450, 2451, 2451, 2452, - 2452, 2453, 2453, 2454, 2454, 2455, 2455, 2456, 2456, 2457, 2457, - 2458, 2458, 2459, 2459, 2460, 2460, 2461, 2461, 2462, 2462, 2463, - 2463, 2464, 2464, 2465, 2465, 2466, 2466, 2467, 2467, 2468, 2468, - 2469, 2469, 2470, 2470, 2471, 2471, 2477, 2477, 2477, 2478, 2478, - 2478, 2479, 2479, 2479, 2480, 2480, 2480, 2481, 2481, 2481, 2482, - 2482, 2483, 2483, 2484, 2484, 2485, 2485, 2486, 2486, 2487, 2487, - 2488, 2488, 2489, 2489, 2490, 2490, 2491, 2491, 2492, 2492, 2493, - 2493, 2494, 2494, 2495, 2495, 2496, 2496, 2497, 2497, 2498, 2498, - 2499, 2499, 2500, 2500, 2501, 2501, 2502, 2503, 2504, 2505, 2506, - 2507, 2507, 2508, 2508, 2509, 2509, 2510, 2510, 2511, 2511, 2512, - 2512, 2513, 2513, 2514, 2514, 2515, 2515, 2516, 2516, 2517, 2517, - 2517, 2518, 2518, 2518, 2519, 2519, 2519, 2520, 2520, 2520, 2521, - 2521, 2521, 2522, 2522, 2522, 2522, 2523, 2523, 2523, 2523, 2524, - 2524, 2524, 2524, 2525, 2525, 2525, 2525, 2526, 2526, 2526, 2526, - 2527, 2527, 2527, 2528, 2528, 2528, 2529, 2529, 2529, 2530, 2530, - 2530, 2531, 2531, 2531, 2532, 2533, 2534, 2535, 2536, 2537, 2538, - 2539, 2540, 2541, 2542, 2542, 2543, 2543, 2544, 2544, 2545, 2545, - 2546, 2546, 2547, 2547, 2547, 2548, 2548, 2548, 2549, 2549, 2549, - 2550, 2550, 2550, 2551, 2552, 2552, 2552, 2553, 2554, 2555, 2556, - 2557, 2558, 2559, 2560, 2561] - ] - ) - separation_angle = 2.16 - - return S, idx, np.squeeze(idx8), np.squeeze(area), faces.T, separation_angle diff --git a/docker/install_pydesigner_release b/docker/install_pydesigner_release index 9515ea1f..f902e4f9 100644 --- a/docker/install_pydesigner_release +++ b/docker/install_pydesigner_release @@ -2,4 +2,4 @@ # Downloads and extractslatest release PyDesigner into /tmp/PyDesigner mkdir -p /tmp/PyDesigner export URL=$(curl -s https://api.github.com/repos/m-ama/PyDesigner/releases/latest | grep "tarball" | cut -d '"' -f 4) -wget -q -O - $URL | tar -xzvf - -C /tmp/PyDesigner --strip 1 \ No newline at end of file +wget -q -O - $URL | tar -xzvf - -C /tmp/PyDesigner --strip 1 diff --git a/docs/source/conf.py b/docs/source/conf.py index fe5cd0a0..21769533 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -20,12 +20,12 @@ # -- Project information ----------------------------------------------------- -project = 'PyDesigner' -copyright = '2023, MUSC Advanced MRI Analysis (MAMA)' -author = 'Siddhartha Dhiman, Joshua Teves, Ryn Thorn' +project = "PyDesigner" +copyright = "2023, MUSC Advanced MRI Analysis (MAMA)" +author = "Siddhartha Dhiman, Joshua Teves, Ryn Thorn" # The full version, including alpha/beta/rc tags -release = 'v1.0.0' +release = "v1.0.0" # -- General configuration --------------------------------------------------- @@ -33,36 +33,33 @@ # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. -extensions = [ - 'sphinx.ext.autodoc', - 'sphinx.ext.napoleon' -] +extensions = ["sphinx.ext.autodoc", "sphinx.ext.napoleon"] napoleon_google_docstring = False napoleon_use_param = False napoleon_use_ivar = True # Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] +templates_path = ["_templates"] # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. -exclude_patterns = ['build/'] +exclude_patterns = ["build/"] # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # -html_theme = 'sphinx_rtd_theme' +html_theme = "sphinx_rtd_theme" # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] +html_static_path = ["_static"] # -- User-added Options ------------------------------------------------- # This defines which which master document to use for generating docs. # We'll use `index.rst` instead of `contents.rst`. -master_doc = 'index' +master_doc = "index" diff --git a/docs/source/docker/docker_install.rst b/docs/source/docker/docker_install.rst index ff742862..576ecad0 100644 --- a/docs/source/docker/docker_install.rst +++ b/docs/source/docker/docker_install.rst @@ -25,7 +25,7 @@ Uninstall Docker installations .. code-block:: console - + $ sudo apt-get remove docker docker-engine docker.io containerd runc Don't panic if :code:`apt-get` returns an a warning about missing @@ -98,19 +98,19 @@ you may install it simply via the command: .. code-block:: console $ sudo apt-get install docker-ce docker-ce-cli containerd.io - + 6. Verify your Docker Engine installation with the command: - + .. code-block:: console - + $ sudo docker run hello-world If the following information prints in the console window, your Docker -Engine installation was sucessful. +Engine installation was sucessful. .. code-block:: console :linenos: - + Hello from Docker! This message shows that your installation appears to be working correctly. @@ -132,7 +132,7 @@ Engine installation was sucessful. For more examples and ideas, visit: https://docs.docker.com/get-started/ -You may now proceed with the fetching of NeuroDock Docker image. +You may now proceed with the fetching of NeuroDock Docker image. Mac OS @@ -155,7 +155,7 @@ by parsing the following command in Terminal: Or you may run the `hello-world` container to verify the installation: -.. code-block:: +.. code-block:: $ docker run hello-world @@ -183,7 +183,7 @@ by parsing the following command in PowerShell. Or you may run the `hello-world` container to verify the installation: -.. code-block:: +.. code-block:: $ docker run hello-world diff --git a/docs/source/docker/docker_neurodock_run.rst b/docs/source/docker/docker_neurodock_run.rst index 02aad165..d903aea0 100644 --- a/docs/source/docker/docker_neurodock_run.rst +++ b/docs/source/docker/docker_neurodock_run.rst @@ -6,7 +6,7 @@ NeuroDock, and are probably wondering how what else to do... **You're done. Not even kidding!** You can now start processing data with PyDesigner and NeuroDock. It's almost as if FSL, MRtrix3 and -PyDesigner commands are built natively into your OS - be it Linux, +PyDesigner commands are built natively into your OS - be it Linux, Mac OS, or even Windows! Intro to Docker Run @@ -54,7 +54,7 @@ The second flag. :code:`-v`, makes visible the host's local filesystem to a Docker container, which otherwise runs in a completely isolated system. By mounting a folder for NeuroDock, you are able to make it process data in said folder. The general guideline is to mount one -subject folder at a time. It is advisable that users read through +subject folder at a time. It is advisable that users read through `Docker's bind mounts`_ to understand how Docker containers handle storage. @@ -102,8 +102,8 @@ The flag to reflect this would then be: -v /Users/sid/Desktop/bond_007:/data This would make the contents of host directory :code:`bond_007` -available in the NeuroDock at :code:`\data`. Say, for example, the -nifti file :code:`bond_dwi.nii`, is located in the host system at +available in the NeuroDock at :code:`\data`. Say, for example, the +nifti file :code:`bond_dwi.nii`, is located in the host system at :code:`/Users/sid/Desktop/bond_007/nifti/bond_dwi.nii`. If the above mounting scheme is used, the NeuroDock container will see this file in :code:`/data/nifti/bond_dwi.nii` @@ -116,7 +116,7 @@ Put it all together ~~~~~~~~~~~~~~~~~~~ Considering everything on this page, it becomes incredibly easy to -process a subject using the NeuoDock container. Sticking to +process a subject using the NeuoDock container. Sticking to :code:`bond_007` example above, and combining everthing so far, one could process Mr. Bond's DWI with the command: diff --git a/docs/source/installation/fsl.rst b/docs/source/installation/fsl.rst index 758adf7e..a838e498 100644 --- a/docs/source/installation/fsl.rst +++ b/docs/source/installation/fsl.rst @@ -14,7 +14,7 @@ FSL. To check your FSL version: .. code-block:: console - + $ flirt -version A return value of at least :code:`FLIRT version 6.0` indicates diff --git a/docs/source/installation/pydesigner.rst b/docs/source/installation/pydesigner.rst index 703f0a52..3a3df593 100644 --- a/docs/source/installation/pydesigner.rst +++ b/docs/source/installation/pydesigner.rst @@ -2,7 +2,7 @@ PyDesigner ========== PyD is an installable Python package deisgned to perform pre- and -post- processing of dMRI acquisitions. +post- processing of dMRI acquisitions. Easy Install ------------ @@ -38,7 +38,7 @@ To clone the PyDesigner repository, in terminal, run: Install ------- PyDesigner can be automatically installed with all dependencies by -opening a CLI and changing directory to root PyDesigner directory, +opening a CLI and changing directory to root PyDesigner directory, followed by .. code-block:: console diff --git a/docs/source/installation/python.rst b/docs/source/installation/python.rst index 99abf1c0..3cb04c5f 100644 --- a/docs/source/installation/python.rst +++ b/docs/source/installation/python.rst @@ -26,7 +26,7 @@ Update Conda First, update conda with .. code-block:: console - + $ conda update conda Create new environment diff --git a/docs/source/installation/requirements.rst b/docs/source/installation/requirements.rst index ba484905..c114353f 100644 --- a/docs/source/installation/requirements.rst +++ b/docs/source/installation/requirements.rst @@ -20,7 +20,7 @@ Please proceed with the installation steps to configure PyD. Windows Users ------------- -FSL and MRtrix3 are currently **not** *available on the Microsoft Windows* +FSL and MRtrix3 are currently **not** *available on the Microsoft Windows* platform. Users running Windows are recommended to run the Docker image `NeuroDock`_ these interdependencies at near-native speed. diff --git a/docs/source/processing/dataset_reqs.rst b/docs/source/processing/dataset_reqs.rst index 02135a93..5c3c2eb3 100644 --- a/docs/source/processing/dataset_reqs.rst +++ b/docs/source/processing/dataset_reqs.rst @@ -28,4 +28,3 @@ their DICOM to NifTi conversion software fails to create it. PyDesigner primarily looks for partial Fourier information within a JSON information. This information if encoded in the fields :code:`PartialFourier`; or :code:`PhaseEncodingSteps` and :code:`AcquisitionMatrixPE`. Users need to have have these fields at the bare minimum to process DWIs. - diff --git a/extras/des2dke.m b/extras/des2dke.m index 5a06900a..472d4e02 100644 --- a/extras/des2dke.m +++ b/extras/des2dke.m @@ -49,7 +49,7 @@ function des2dke(inDir) else brainmask = ones(dims(1:3)); end - + if isfile(b0_Path) hdr_b0 = niftiinfo(b0_Path); b0 = niftiread(hdr_b0); @@ -132,4 +132,3 @@ function des2dke(inDir) fclose(fid); fclose(fidout); fprintf('.....Completed.....\n'); - diff --git a/extras/ft_parameters.txt b/extras/ft_parameters.txt index 830ea7f0..c28c793d 100644 --- a/extras/ft_parameters.txt +++ b/extras/ft_parameters.txt @@ -9,12 +9,12 @@ subject_list = {''}; %Specify subject numbers (i.e. {'Subject01','Subjec odf_optimization = 1; %Analyze the kurtosis dODF (1) or skip odf optimization (0). This step must be performed prior to tractography. sd = 4; %[3,4, or 5] Sampling distribution to use. See sphericalgrid3(4,5).m documentation for more info. quasiNewton = 1; %Apply non-linear optimization to increase accuracy of peak detection (1) or use orientations directly from the pre-defined sampling distribution (0). - radial_weight = 4; %Radial weighting power.Recommended radial_weight = 4. - release_memory = 0; %Do not release memory (0), release memory after each subject (1), or release memory more frequently (2). See User's Guide for additional info. + radial_weight = 4; %Radial weighting power.Recommended radial_weight = 4. + release_memory = 0; %Do not release memory (0), release memory after each subject (1), or release memory more frequently (2). See User's Guide for additional info. wrt_flg = 1; %Write outputs (1) or don't write outputs (0). pre_name = ''; %String to append to the beginning of output names. post_name = ''; %String to append to the end of output names. - + %Tractography tractography_flg = 0; %Perform tractography (1) or don't perform tractography (0). @@ -24,17 +24,17 @@ subject_list = {''}; %Specify subject numbers (i.e. {'Subject01','Subjec step_size = 1; %Step size in mm (0 defaults to half of the voxel length). trk_mask = 'dir-trk-changeme'; %Path to tracking mask to apply in addition to other tract termination criteria defined above. Should be in the same orientation as the fa.nii image. seed_mask = 'dir-seed-changeme'; %Mask within which to put seeds. Should be in the same orientation as the fa.nii image - seednum = 1E5; %Number of random seed points in the tracking mask. + seednum = 1E5; %Number of random seed points in the tracking mask. shift = 0.5; %Shift applied to voxel coordinates in .trk file. output_DTI_trks = 1; %Include tractography performed from just the diffusion tensor (1) or don't (0). - image_orientation = 'LAS'; %Orientation of input image volumes. By convention, spm writes images in 'LAS.' + image_orientation = 'LAS'; %Orientation of input image volumes. By convention, spm writes images in 'LAS.' odf_orientation = 'RAS'; %Orientation of the gradient table used to estimate the diffusion and kurtosis tensors. If SPM is used to estimate the gradient tables, this should be 'LAS.' - - + + %Make DSI Studio .fib file make_fib_file = 1; %Make .fib file to read in DSI Studio (1) or skip this step (0). - save_odfs = 1; %Save dODFS in .fib file (1) or not (0). This only affects visualization as orientations are still saved for tractography. + save_odfs = 1; %Save dODFS in .fib file (1) or not (0). This only affects visualization as orientations are still saved for tractography. scale_odf = 0.5; %Affects overall size of odfs in .fib file. This only affects visualization. odf_size = 20000; %Size of odf_n variables. Reduce this if there are issues with RAM. - odf_res = 'low'; %{'low' or 'high'}; Number of points saved in ODFs ~ only affects visualization. Use 'low' to reduce memory requirements and use 'high' for the smoothest dODF surface renderings. \ No newline at end of file + odf_res = 'low'; %{'low' or 'high'}; Number of points saved in ODFs ~ only affects visualization. Use 'low' to reduce memory requirements and use 'high' for the smoothest dODF surface renderings. diff --git a/installer b/installer index d34016b9..fd989e73 100755 --- a/installer +++ b/installer @@ -45,7 +45,7 @@ sudo apt-get install -y --no-install-recommends \ if [[ $(echo $FSLDIR) ]]; then echo -e "${GREEN}Existing FSL installation found${NC}\n" ISFSL=1 -else +else echo -e "${RED}FSL installation not found...marked for installation\n${NC}" ISFSL=0 fi @@ -53,7 +53,7 @@ fi if [[ $(which dwipreproc) ]]; then echo -e "${GREEN}Existing MRtrix3 installation found\n${NC}" ISMRTRIX=1 -else +else echo -e "${RED}MRtrix3 installation not found...marked for installation\n${NC}" ISMRTRIX=0 fi @@ -61,7 +61,7 @@ fi if [[ $(which pydesigner) ]]; then echo -e "${GREEN}Existing PyDesigner installation found\n${NC}" ISPYD=1 -else +else echo -e "${RED}PyDesigner installation not found...marked for installation\n${NC}" ISPYD=0 fi diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 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\n", + "image/png": 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rT8W1/lp3rPTV43vjQUn6YBbdnUUhDjbjLMf+bnd0eiml9DdH8UHb7okun4rP1Xs0vjerz8YR1lsH4h8NZ7bjWPUrZ8NS2XeiO/vyeLwcZbQYz397fxyPPjkc35z1b4jv6Vtuiy/g6q/e0/n6mbN7wjGiwAFuTp/8m38vrH3TX/lzYS2NQw62AE3yOZmaMro7m2Ob/FPvaKH7hFm8dXZto4W4NpvsZQfbcW08Ey/K7mp8vsmge1xvNGWEdXLdk+RvZ9OuZS+IXI+uq5SSPiOTfjKPJj7mqx1Rnz0jg61X91zwevENGwAAAIDKaNgAAAAAVEbDBgAAAKAyGjYAAAAAldGwAQAAAKiMhg0AAABAZcR6v4Gd/P44urtN4vYmc3Gm3tLhjc7Xx+O4t7fwsZWwtv9cnDH4/AfjqO39e7vnUUopg50klvzAYufr49k4YnD+SjzHXhLB3YzjWjsTr9fWvtmwNujH81w41b0mCy/E8xhszoW17Nr6w3iND30mjh6fOx9HZo8Xu3/cjOfia+5vx/mVq1+LzzWzEV/3cCVe/xOjW+JjrnbHkje9eK1O/6fvD2tH/6ePhzUA6pZFd2exzFmc86sdedxmsd5ZLZljdswocjq7rslMXJu7Gg+c2YwnOZqP92BZLPnc5WR/2b29LL0kZru/Gx9vlOx9Mtn+PptL2+s+X7YeWUx4mzzjbTKuP0zOF0SPlxI/rrPr8fF68XZ16vWH14Nv2AAAAABURsMGAAAAoDIaNgAAAACV0bABAAAAqIyGDQAAAEBlNGwAAAAAKiPW+w1s4Wwcf7dzII6r274rztRrg0P2P7kajjny6JWwduY9e8La3Hw8j4uP7w9r9z4RR36v3b3U+fp2sh4LF+N17G/Fc9w+vBDWzj0U51RO4lTpcvBzca1tuq9hMhe/jXvDOFtxd088bncp7uUunotzEvsb8XoNLm93vj5ZTNYqubZ2kMzxhe5zlVLK0c34Bmweic938cHu2sydcabk+iNbYQ2AN65p45CzWgm2I1ksdn8niY5eePWjo0fz8TEH28FcsvnvxrUslnl7bzLJTDKX3iiJEd/qrmXPwaSfZaAnpewZyWSx8MH5sjXOjtck152tSVbLMuOb6C8oiexZTdcKKuMbNgAAAACV0bABAAAAqIyGDQAAAEBlNGwAAAAAKqNhAwAAAFAZKVGVO/ND7w9ru3vjcdsH418xv7pvM6ytPdudBnXHF+L0nyzJ58oD47B2y3I8jytn4lSqJkk+Gi52/0b48Xw4JE1YCGOzSin97el+jf94Pj7m7nLcQ13Z6E4+amfiMetHF8PacDEeN3c1vrb5M/F9S+dy93Ln67sr8Zjd1fg3/I+TtK3l5+P5L56Jn+W9j8epTluHu9dy/cBcOKZ3OX5vnPqL8Xv7lp/8eFgD4AaZLmQpT6DJkneilKhJPGgSBy1Oda5S8iSf3vj643XS1KzkeElo0MukDSXnS687SSkKEqSifefLnasXb0Xy+SdrOY63I2USbEeyefTiLXyaLpXe02SNozmWUkoz6R6XrnGS+pXNfzQ37RsfXhu+YQMAAABQGQ0bAAAAgMpo2AAAAABURsMGAAAAoDIaNgAAAACV0bABAAAAqIxY7wqc+Ol3h7W5Z+NxO7fGWXz7jlwNa+868lxY+/DV+4NKPxzT241z/9rFuHbnyqWw9ql9R+Lzbe6EtSjucHdvnIO4cSR+G8xdSKKvz6yHtWMfjfMC1+9cCGv93TiCcLy3O1Z6d3+SWd7E0YSL5+I5zlyNn61mFK9l24/Xq+13z2WwFV/zwvn4+dk4Ej+Ta3dkUeFxHvjB34nv6SAq7cbnmlmLa4PN+Lpf+OE48vvWnxD5DfBqaeOPkrRWknjlXlJL5xJ8ZE9mposZziKPp46Ono3n0ht2HzSL5y5JzHN/GE9kEm8Fy86e5HN5I77wpk3i0wdT3IMpn4P0kEmMe/a8RhHdaTx3Mv80gju5p6961Hy2xskeuBnHA2eS/dlw0XcduPE8dQAAAACV0bABAAAAqIyGDQAAAEBlNGwAAAAAKqNhAwAAAFAZDRsAAACAyoj1rsDK/o2wNnc4jud+eN/5sHbn4sX4fP3tsHbk0JXO18+/PY7ZvvVju2Ftz2fjCOXP7DkW1srtW2Hp/HsPhbXFs935hBfb+FG/cl88jdWn4/zE2cvxfSu9OLp781DcJ80iFOcuds9l9mJ8Pwcb8XW3SUTlZDbOhhytxjHizSSJ6D4bPyeR/nacN7lwOl6siw+uhLWNY/F1rx9fiuey031ti8/Eazx7OSyV3b3xPDbujdfqyZ94X1i754cfjU8IwL8ljZyeMoL4VZfMMYt57g2TY04Z+T27NsWFZynPyT8d76wk+6VkGr1RXExj3JOHoRl3H7M3ymLO43NlsdhtFt2drFe2h+wHMejZvq1NYrGjmPCXO2YWj55dWxg/PuX7MIunz9ZxsB2fcDQ/RfQ7XAPfsAEAAACojIYNAAAAQGU0bAAAAAAqo2EDAAAAUBkNGwAAAIDKaNgAAAAAVEas943y4dvCUn89joh74MDpsPb2lefD2vlhHGv8Dz/xLWFt72e6swSXr8QZdzsH5sLawc/HkdNbp5fD2va+OBpv/kqcJTh3vvt8bT+Oax7eHkcorx+LY8kPPBmPm3nuQlhbvC2Oxd5KIr8v39O9zktn4rfxYDNeq909SdT5XUnuZZJauP8rcYbl4lPdkfGjvXEE+nA5zrbMYjuXT8XzWLsrvqfnH4ovbvmZ7tcXzsTzyCLEy9vXwtKde+Las2tHw9raH3tvWFv5ud+O5wJwE5vdiPcww4X4czeLXk5lUcNTJP/2d+MDzl1JIpRn4pON5qaLNU4jxoP1yo6XxXNnkevjeOuZSmPcM8G4dK2yw2XjogjreBove8zBVndxND/d85/FW2eR35NkezltRHcojZPPnv94ItnzOthJIr+T9xu8HN+wAQAAAKiMhg0AAABAZTRsAAAAACqjYQMAAABQGQ0bAAAAgMpo2AAAAABURqz3jfIdz4WlpX95d1hbHsTR0ZeGcVT1L3z1HWFt36fj2778QneW4ObBOIev3RfXskjJKGKwlFL2no8zAWcuxVHhl96+2l3oxVF78yfimO2lUzthrV1ZjGuz8Rqvfm0jrC2ciyOnN27tjri+ejx5G7dxbf5SvP7zF+P1Wk+iqq8cj2O4m/Geztd7w3geWfT47JU493L2wlZYWzgTz/HyN8Rzufje7vM16/Ecm3G8VjNJNORMP37+3/XI42HtU8vxz5KVn4vPB3Az212K/32ynfKfLrN432miu0spZXa9+zNoJni9lFKaURJZvhp/PvWTCOIs1njrQJbL3C2N0s6il5NTjZP9ZdMmNyeLJc/ix5vugdm1BUNKKaX0xvEc20k8MHtes9pwsbs4deR6cm+a4ZSR38nNCdd5ymcrixCfDLL1f7Wzx+Hl+YYNAAAAQGU0bAAAAAAqo2EDAAAAUBkNGwAAAIDKaNgAAAAAVEbDBgAAAKAyYr0r8PuOfC2s7R/EEdCfunxnWGtPxpHfK8/FmXpRJOD85Tj3L4uGzCL1miTSsL8dRzZPFpPo6CCK8sij8TyWn9uMi0k05Hh5Lqz1duL5Z7mRvd343ux5Ytj5+u7eeB7jufgGzF3sPl4ppcycuhzWlu8+ENa2DsY/UkYL3Q9XL4lPjGIoSymlNPG5ZtbjvMmDn4nv9/ylhbC2dlv3Os9ejZ+RvU/sxse7fTmsnXwgrj11RxxZfuTYpbD2+E+9J6zd9+c/EdYAbmZZrPH0B41L2Z4pii7ePJxs15MtWD+JVx5sxxcefV6XUsokmUov2vok6zFcjov93WSfmFzbeHaKeOhSSpNENkf74yymOjtXm+x9etm1Jfu6aSK6swj3tpfMMdvmZrHYSdZ59mxF15atfxrdncWSZ3HmyZpkcfLp85pE1EMpvmEDAAAAUB0NGwAAAIDKaNgAAAAAVEbDBgAAAKAyGjYAAAAAldGwAQAAAKiMWO8KXNyNI7iz2uZoNqzNrE8XEbd2e/cjsX57HEeXRfutPpnUno4jj3vDOJtw81gcvRyZvxRPMovgbgdJtOVc/PbJIgGb3fh8g7WdsDZe6r7fg63keOtJXGNybcNje8Nabze+N0svxPc0jGpPIiVnNuII92wevSQWfrh3PqwNtuJjHvpc9zH7O3Gm5Gg+zo1cfj5eq8FOfN3n2/j5nz1wJay99+ETYe3JP/m+ztcv3x8OKcf/6qNxEeANYhL/uE33N1NLtmeTKN43jRmOa7tJZPbOavz5NLMRnzDd3wQfoVm89WDr+mPOSyllkMSj9+KP1/R+Z6LzZZHMk7n4eE3ybDWTLOo8HpfFgYcR6dnzmERmZxHiw8Upo8eTWhSLPbs2XTz9eCme4ziJ/O4N41qbLWbyN+5+8Gxl7+0sOp2bj2/YAAAAAFRGwwYAAACgMho2AAAAAJXRsAEAAACojIYNAAAAQGU0bAAAAAAqI9a7Ah958r6pxi0uxhHQW7fFeYGnluO8uvGxrc7XH7zjVDjm8187FtYGX0ximZM45PFi/Gj2k+jl5Svd1z17diMcU0bxPLbu2hfWsljpmbPd61hKKc1unAk4vCWO0x4FazLYiuff34jzH5thPK5t4rjAdi5+fibz8f1ue93HTKPTZ5Lo8aXkGVmI59gbxbGXWaz3MHjfjBbjOc5ezuLkkzjw27oj3EspZXRLfE8PLqyHtafX4mf5wnd2P68LC0l+KMBNoE0ifCdTRFiXkkdfTxPHm8VbTyu97iSqOosYH813vz6ei483s5nEcycRyk0SOd2+BlHt0X3L4sWHwb6nlFLa5G9go6Q2ezVZr2R/Ez2TvSjuu5QydylerN3VZA8W//Ukj+5OHrvhQndx60D8IGeR8TNJLYtqz2Tv++x9I6Kbl+MbNgAAAACV0bABAAAAqIyGDQAAAEBlNGwAAAAAKqNhAwAAAFAZKVEVyH43+HgU99Q2ntoT1ga78VHveuTZsLZvfrPz9ScvHQzHrHx+Lqwtnol/VfzuvjgJJ5OlDQxL9Nvil+IDJr+5fXtf/NvnZ7biefS34vP1tuPfur9++0JY29nTfb7VZ+LjDS7G6VjDA/EcN2+J7+lgO16wfpJY1Rt3j4vSo0opaURHfztJx0pqk9npIkFm1ruP2SSpDJlJkra1djxek3fc9VxYOzQfp0R95eyReC6Xuu/34LcWwzEAN4M0gSnZJbfJj/40iWiaj4wpQ2SyBKZ2yn+yzfZgkWzf0JsyjDCbf3843efyaD6+tiiVKhuT/rN4lkCWJGClc0we5l6wJklQZx4llpjZjKOgsmvb2ZNMJri0LEksSzsbZ0liyRynev++zLhwf5yFtWV7Z246vmEDAAAAUBkNGwAAAIDKaNgAAAAAVEbDBgAAAKAyGjYAAAAAldGwAQAAAKiMWO8K9Ppx/F1/EGfLjUZx9HLbj/Pjnr8Sx4FvDLujtjd34vy7zfvjTL2tI3F098KZOJJu8Uy8JjNb8bUNgijBwVqcG7l293JYu3o87mnu+VpYKv3N+HzjxXhN1u6IzzcJbsHChXjMzL44lnnrSPz8XLk7jnJs4ltTFk/H45ZOda9JL4nFnswnkZJJpmqb5FRmUaDj5HzjIEqzvzNdzHl/GC/kYDMslcs7cfT7/atnwto7bnkhrJ1eXe18/bm9+8IxW4ffH9bu/JGPhzWAmqSxwPHHdWmbeA+TfS6kgkNmn1vZZ/JkEM8xiwyOYoZLKWVmPfnMDmKUR/PxudJrS5Yxi2yeXYsXZXc5OeEUty2Lqc6eg9FCEsGdHDO735noWdhNnpEm3m6ke7fBTjKR5KZmMfTRe2PST+afrVXy/E+SrWd2b7L3VPZsTYLJZO+N4XJ8suw9yhuTb9gAAAAAVEbDBgAAAKAyGjYAAAAAldGwAQAAAKiMhg0AAABAZTRsAAAAACoj1rsC+1biDN+LV+NY5nJsKyyNt+Jbe9vey/G4IENusBJn491z7Omwdsv8lbD2z0++LaxdOLE3rI0OjMJaGXfH3M2cXwmHTI7H63h0/9Wwdn7hSDyPJo5Oz6LCJ++Ozzced49bX4tjydtenKV54e3xPBYfvhDPYxKPu/rpOJdrBUwAAB13SURBVAZ6sN39TI5n42jCzaPxuRbOxbGFe55IcrGbOK+xTWIqZ891Z79O+vEctw4FWeyllLnL8XM8dzGex/Yofm/fNXcurM0n2bUrQQbn/CAe8/jkcFz7qfeEtfv+/CfCGsCN1iYRvllkcxZ9PUl219PEAqfxxEmCbxoznNTGSWT2NLHS/d3rH1NKyWO2k/nvrMafy9n8e8n2MjxfMo8sljxbk/S+ZbJU7KCWPf9pLTlXb3j90e+llDKai2vDxe5aGquerHE/iR7P3r+Z7L71pnjfZM9qFt2d/vzJnnGq5Rs2AAAAAJXRsAEAAACojIYNAAAAQGU0bAAAAAAqo2EDAAAAUBkNGwAAAIDKiPW+QeZ+/WhYu3Q6ju7es7wd1n7w7t8KaxdHcdTz//zxbw1r8y90PxLbd8bZeN/wjtNh7ZmtOOZ593NxdPe+J8NS2bh1Ni5OEYnZLMTXdmUrjsVeOJNEE8bDyuYD8T19x6E4lvnZq91R4VfeG+cu7szG+X3jYZLXmFiej7MQz+yJF3rjaPf51u+Mx9z69lNh7bkz8bO1fmwprM3Gyel5POe4+72xszeJJb8zyU9MfvouH7gS1r75yMmw9qXNW8Paxd14TbaDa9s7F0feT7biC3iL6G6gIuO5pJhF8SaxwFlk8xtCFgGdDUv+qTdak2miwF9ONo9pY7GnuafZtWXx6NWYMjp90o+LW/vj/eW075swojuLtZ/iWS2llF4SFZ4+W1M+k2Fi/CSeZBZnnr+DeSPyDRsAAACAymjYAAAAAFRGwwYAAACgMho2AAAAAJXRsAEAAACojIYNAAAAQGXEet8gT13cH9b+i3f8P2Ht4flnwtqdg2FY++/OfXNYWzkR3/Yjn9zsfP3MN8XR448dvT2sHVzcCGu9nTh2bs/JOE54z8mwVNpe9zF723H+3XMzcbz4cCWO1LvtyXj9587H8x8urYa1EyfuCWuLp7vnsrIQr+PuSlgqB0/GWZSTmYWwdvlYfL6Dz8bHXDzbncm4uxrHtD/73IGwtvC1eNz8xSQKMYng7MeJ5aU36j5mm0Rbts/F77XdB+Jn5Jtuid/3Z7fjm3pmK67N9eOI8c1h91o+dy6OTh9c8vEBvDFMBvHP6cFW/HmRxvtWEis9rTTyOPmc7GUx1vHHcnyuZI2zZczm2M4ktSnnEh7vNbjX2b2ZTBtVHe1hslWe8nmc9toy4TpncdnZPJJzTfs+TJ/l5HmN4rv78V8z0uduEqeq8wblGzYAAAAAldGwAQAAAKiMhg0AAABAZTRsAAAAACqjYQMAAABQGQ0bAAAAgMrIZX0VPfG/vTOs3bF8Max9ZeuWsHZqGEdOf/fKZ8PaYxfuCGvLz8fZcsM93VmIi2fjMZsfPhTWnjh4MKzNJBF3O3vjTMb+Tpy3N57rzrmbaeL8u5WT8fE2b4nHZTGhzW4coXzws93R6aWUMlrOrrs7L7AZxgvZjJN463E8brQcZ3POrsU/NubPd0d3l1LK4Op25+uHP53Ekj8Rn2vxTPfxSimlvxlnIfZ24nszXoqvezLbnZO48nS8jlHMfCmlPL00H9a+cuBwWDv9pbg2dynuwR/85lNh7eEDz3Wf63ISE356yixTgNfAOP6Rmu4b0lTj7MfcdIdMx80EEeOj+Sl/3r4GMeHZviK68knyt41+Fnc8Zax6L4lDziKPo+jrF0/Y/XIWAT27Fl9ctofc3htfXHbd2XMe7UeyuOn8QY6lsdLJMbP5R+vVZuea8vlP3/fTvhXTr0hE75v4AnrxVpabkG/YAAAAAFRGwwYAAACgMho2AAAAAJXRsAEAAACojIYNAAAAQGU0bAAAAAAqI9b7VXTv9/9OWBv96zhme2M8F9aW+zth7XM7x8La5c2FsLayE2f4zV7sjmUez8Z5mftOxNlyvS8mEeLLcRbfzFqc5djfis83GHT3IPtbccbj6m58rrm1OOZ5Zj2eR7Mdn2+QRlgmMdxtd20yE6/jeCFZ46vxszVzKY7Mbibx85pFhZcgWn3uQnyuXvLemMzG/ebeMFmTZL02b0nO1++e/+x6/PxcvjuOaR/fvhXWTj15KKzd9utJjPskrj134EhYm3t797PcT/JW9385fsYfjn8Uls+8M64BTKsff5S8TKTuDZbEAjeTYA+Q5AwPkijkucvxPmXrYPxXgHS9pog8Hi3Gg7JzzWxkMdXxuKmjnpNaFKOcRZZPZqa77iwqPIu+nrscf2bvrgQnTO5ntO8pJd6TllJKScZlsvWKItfbSbLGU8aLZ8/BtFHzmWjcKIl+v/xwfLz9n7/+c1E3tw0AAACgMho2AAAAAJXRsAEAAACojIYNAAAAQGU0bAAAAAAqIyXqBjm/vhTWdka3hrVvPBj/OvKV/mpYW99IUp02498if+5d3fO8cl88pp2Lf5367IU4JWf56bBUlp6PE2h2DsRJPpfu6z7f4tkkieuZOFpi5mry6+ATk6V4/bPfnj+Zi3+l/ST4bfGDzTgFYmY7rk1m43P1dpIkritxutR4KUlFWooTtyJZalb2W/z7a/E9befiOe6sxj3s7QPd67+7N4kauHc9rp1aDEvHPpakQFzoTnIrpZSrd8fpcLOX4nk+8fgtna/3luL34cbR+ONDEhRQk960yS7Thd2kesP45/twsXsySUhUGSYJTMOF+PNu2pScLHmnF3w8zV2KD7i7Gk9kZ39cm1mPj9mLP7pSbbI/2w0+srPUoJmtJPkzCdXMkqAyo+D5KaWU/m5wzCwlaiFLuZruzZE9y236t9LrP192rtSU47J0rzSdLDhf9mzt/0JyKl/HuOm4pQAAAACV0bABAAAAqIyGDQAAAEBlNGwAAAAAKqNhAwAAAFAZDRsAAACAyoj1vkGO/qEvh7Wn/6+3h7XRgTg/cZhkK/7Bt3wxrP3q7393WDv4me6cwcnJuLd39cEkOvquOIJ4eCGONe5f3Ahrg/n4sW373RGWWcRdFsPX34kz9Xq7WS2L047n34zinMde0F/dOBZHiE9m4mzCpVPxvcmubbgvjkgfz8ULHa1zGj+Y3JvZq3Fu52QxjhDfPhSv1/yleP2XTnXX1m6P7+flxfgZn7sQX/js5Tg6fbgnjmm9fF9YKuUtScT45e57uvDFOCb84tvitdqXTAPgRkujhJPaqx3TW0r+uRzF+E6ZwD21cfwxmUYNR7VBFlO9FpcmSSp5SWKxszlmx5xkfysKzpftYbb2ZZvPpJTMP4znLqW0WRx7EMOdzb83ig84GcQX0Mvmn0SdpzHcQW08O10Gd/beTn9eZLc0ue5p/h6SzaM/jC9gnPyM4Y3JN2wAAAAAKqNhAwAAAFAZDRsAAACAymjYAAAAAFRGwwYAAACgMho2AAAAAJUR612ByTjum33p0tGw9sLcnrDWa+K8w/u/6WRY++K+2zpfv+1X4/i4g5+Na898ZxwLvHF7PMe1tx0Ma/MX4jjqmbXuuYwW4oi77YNxBPRgI87oG0zi6+4l2Yq9nTiOeu3e5bC2eaQ7xr2/HZ9r4UISE55Elmfx4rMXt8LaZDaOmm+C9crO1Q7i98Z4Kc7mnMzEP9pmL8frn7Wwx3Pd13bwc0kE/fZSWNs6FJ9rZ18S/Z5EUWZxpR+4+0RYe+zs7Z2v7/uPH48PCHAzyCJ8pxs2feT3FLvy3iiZxpT/LNuLt1mp6HxZ9HI/ifwebMfnyo45jrd16edkfyeuhaaM587u9WgxPmi2n527mmSdR9HRQdx3KaXMrcUXsLsUP1yj+SnyuUspMxvx/CdBVHUaBT6lbJ+VxtpnuepNEoMexKenz7jo7jcV37ABAAAAqIyGDQAAAEBlNGwAAAAAKqNhAwAAAFAZDRsAAACAymjYAAAAAFRGrHcNkvy4p5+N461PnY6zCZeei0+3uxpHwTUPdmcoPv89cdTewY/E+Yn7vhjP4/x3xLmRz31X3Eu8/Zfi6567GkTjJfF3UZxeKaX0d+Lr7m/G82+249rurXvD2uV741jsKN5y31fic/W34/zB3jC+tt39cRz75Xvj+7198PrzUWfW4yGL5+I5ZvGPl++Of7Tt7I/Pt3A2ru19MokDD8xsxnPcKvG93jwUP//zl+LndW+c3F0+/YWHw9q+//2344EAN7EspjeLxc7ihKcN3M3ihMN5ZP/0+hok/2bnC+c/xXWVkt+bdFySbj3YygZe/7myWPLeMK5NBsnJkjXuJVuRST8+ZnTfsmduZyXep2QR1lnUfBZnvrOaPVzBua5/a1ZKKaWNLy19b/fH8XVnz+vsWrwom4f8dZycb9gAAAAAVEbDBgAAAKAyGjYAAAAAldGwAQAAAKiMhg0AAABAZTRsAAAAACojR6wC7STum73l+Omw9sTsobC2sx7HMt/6m3Gm4dYT3ZHNZx+J57hxa5x/d+RTO2Ht4tk4HnrurrWwtrVvNaxFUY6XHwiHlK2zcUz4rR+L16rZjSP6xvuX4xMmoujuUuKYwfF8EgW+Ec9xMhPf0/FcXJu7Ml2U43iu+/XRYvz8XHpLPI/tW+Pcztl9cVb4cCf+sTe8J762K2/tfl6PPLoYjlk5GT8/49n4Pbq9N77uYXy6cuhj8c+L8RNPxQMB3qyydOUpI7+zGOssRjmLE55GGsGdRF+nc0xPeP2DRvNT5qNnw5L71hslUdsz138DhskeppnEtf7udPHQaZx8ss7ZsxyeK421n/JhnfK9EY3L4rnH8V8z0jXuJ1HhWVT78j/5RFi79KH3xQeFl+EbNgAAAACV0bABAAAAqIyGDQAAAEBlNGwAAAAAKqNhAwAAAFAZDRsAAACAyoj1rsDcfJwf95eP/0pY+8zRO8La3+1/W1hbe3Y+rK2c7M6VPvKJIJO5lLK7HPf9soi+/lYcCbizE0dtLyRtxoVL3Tl9WYT19sH4eG0vy/tMjnkojmxeuz1+263fHuds9ne657J9Psk0bOL7NtiIMw0H23Ft7lIc1V4maeBnp3Ymnv/O/jiT8eJmvI7jhThWfd9TSZZpMv3z7+x+/dx3xetx/rk4g3vp+STucyeJHU1iKp/8gaNh7fhfFesN8Htl8dZpdHd60KSWfM4Mgp/9o7ksOjqZRnZtydYhjRdPatkxp5KdK7s3yXVPBq9udnoviYDOn614HoPt+CHJ7k3bT56TcfcxswjrLBY7Xccsujsu5a5/e1n6u9Odqkn2sr1RPO7iD8TR3dmzAC/HN2wAAAAAKqNhAwAAAFAZDRsAAACAymjYAAAAAFRGwwYAAACgMho2AAAAAJUR612BY9/7xbD2kc8+ENZ++ZkHw9rcF+I44cFWHNm8eUt3DPR4Ng7iWzwbZwL2dpLo6M34mKurm2Ht4oNxLPnyh7uj+JZPThc9nknjE5NIwJ39WZxzfL4Dn+8+5uoTG+GY8WIcj755JM5rzNZk4WxcG6zFFzDc233fxnPxvZlZj/MTjzwW17K4ycls8iwEsZellHLo/+3+cXn2PXF0+tL9l8Pa1vH43vQ/H8eSH/lUnFN58B88FtYA+LdlMcmjeLsxdWRwFk8cxXdnEdaTKXfyaRx4ti/K4omjcVmWc3Jt2Z6oH0Sgl1LKaH7K8OjsuoNDZms17b3JIr/TqPDsWV7qLvYvxQecuxxf3DhZ493l+KZO4q1P6U37nopMcT9LeZnI9eTvUM2q70Hw2vBkAQAAAFRGwwYAAACgMho2AAAAAJXRsAEAAACojIYNAAAAQGU0bAAAAAAqI9a7cr/9UJx/d/XHD4S19kAc03f2j26FtbsPX+h8/cRzR8Ixx34hnuP8mTieezITR4/3e/H8e8M4bi+KiB7FpyqDeIplvBRfWxbdPX86jto+/Fg8bndP/JacvdIdYz2Z7Ydj+ttx9PX8hXgdtw7H172zP64NrsQZnFHU9vb+eP6za0kE+nb8jAyX42NmBltJvOWV7ijH5aeT9+j8SlhrFuN7c/xHPx7WALgxBvF2KY3azmrZP5VGEdFZdHQTf5SU8VwyjWRcGsOdRSVPM2aamPBSSn83Lg4X4wtok+1BFtUenS2L7k4juLPnJy7l92aKcTt744nsrMaHy9Yqu+4sqn08G9ei+WfH6w3jlcyeg0k/XuSNI/7qzI3nGzYAAAAAldGwAQAAAKiMhg0AAABAZTRsAAAAACqjYQMAAABQGb/q+g2svxP/FvM/8PsfC2t3L5wLa5dGS52vP/784XBMlpaUJRht3xpHFCyO417iylNhqWzv6R63ce8wHDO4FL8N9j4Zz7/txfELbfJb/NtBfG2DzSQda9S9zqPlOKUoSmYqpZTZK/GaLD+f/Rr/aSIiSpnMdl/35pF4Pdomnv+e83FEQX8nXsfzb58Pazv7ksSqK92vj+LDlZlL8fGO/st4HAB1m8QfvVMnME3z6ZomSGUHnDZtaApZalAmS7lqkwSvmc0kHSj5p+pedxhkKSVOgxoliUKZ7BnJ5jizFV9blnS5HexvsnNlCVhZLVvHdP3jbWmcoJb8HSTaN5dSSpOca3fpBr454Br4hg0AAABAZTRsAAAAACqjYQMAAABQGQ0bAAAAgMpo2AAAAABURsMGAAAAoDJivd/A5h66FNZ++8zxsHZ6z2pYGwTZi+3FOFtxMhPH5l18cDms3X7X6bC2O07itDfCUrn0YPfrv+/Bx8MxJy4dCmsbXzkY1vY+vh1PpE2izpOYyslM3EONYrF7wzjGcfPwbFgbLsXnml3LMhnj0ng+vm+RrcPxAdfui+exeXQxrGURotuPrIe1b7jlTFh74kL3szD8avx+Wno2joZc+MVPhjUAKpck/04bY51FHk8zpkk+ytNjTplqHF13Gi8+5VpNBtNNcrAdT6a/E09md7V7f5PGWyfR3dkzktVG8/F1T/rxwzDNszVt9Hj6/EyTXZ+eLz7Z3KU4u3v9WLw/htr4hg0AAABAZTRsAAAAACqjYQMAAABQGQ0bAAAAgMpo2AAAAABURsMGAAAAoDJivd/Ajv6hL0817pEvrIW1ExtHO19vl+Jsv8v3xtF4G3fFkXp/6uhXw9ozW/vD2qPHD4e1ZtidF/jlC/GYURIhXvYk8YlBzHYppYyTWhQNWUpJY0Jnr3Tncw52sxjK+ICbR+JzrZyMfzTsOzFdnPnGrd3PyehA/Iz8u2/7Snyuh+PSuZ04Tn51Jp7/XJJh+fmNWztf3/dEPI8D//DjcRGAN6x+8lGY6Y3jz8m21/2ZnUVHZ/HKvSzWO4lXTgOzp4xlDs+VHC871e5ylqsel3pzcXFmMxkX7C+byXTx4tPEbJdSSm+UPT/J+YJpppHrmSnHZc9/ZtLvvoBxks4tupubhW/YAAAAAFRGwwYAAACgMho2AAAAAJXRsAEAAACojIYNAAAAQGU0bAAAAAAqI9b7Tehfv20lqW50vnrwl6+GIw7c3T2mlFIe2vd8WDsycyWsrSR5mY9/86Gw9uwzBztfv3hmNRzTzMSx2OWeOBNz8cxMWNv7hcthbeFU3CcdLccRhFH04vbBJFb91jg+8ei7Toe19YfiYz79pThyfbAWx1tuHe+O77799gvhmIV+HPn9tqXnwtpXtm4Ja1+8HNcWBvH5Zua6I78vvS1e4wNhBYA3oyieODVlBHcU5fxy47LzZdHR0T4lm0d6bcn2LLuA3T1xcbAZn3BmKx43CbZ8ve6twSuTrn9y4VOs5bTx4tm9yWptE8+/aeMLiJ6tuavxmOHCdJHrUBvfsAEAAACojIYNAAAAQGU0bAAAAAAqo2EDAAAAUBkNGwAAAIDKaNgAAAAAVEasN9dk0I/jrf/Z/b8Y1n5zez6sfXrreFg7vbMnrL3v8FNh7f69Zztf/42n7w7H7KzPhbV7H3ghrE3eGscFfvVLcXT00jP9sJYZB0u5cyDOT1y9+1JY+/ajJ8LaOxefDmv/5tYHwtrp7Tg+/cGVU52v95P8x2e394W1Xzz9cFg78czRsLbv0TiyfLgU39M7f/LjYQ0AXitRpHEppUyyLUUyrmSxzFk8dHbMKUwTE15KHh09dymJh07GjebjPUAUTZ5Gp2drdQPXOJOdqzeKi1mcebYmO3uzWPIkjn2r+3XR3bwZ+IYNAAAAQGU0bAAAAAAqo2EDAAAAUBkNGwAAAIDKaNgAAAAAVEbDBgAAAKAyYr25Jqt/8Mmw9n0f/Z6w9t79cQT3cn87rPWSnMGNURzDfWTuaufrB1c3wjGnduK3wcXNhbD27iPPhrXj33QxrH3tgQNhbWs4E9ZuW7nc+fq+2SDrsJRybns5rJ3eiSO4z87Gtdvn42u7f/F0WNucdMdpXxkthmM+/Phbw9o9/+HvhLX7yvNhDQDeUJLo5eFKEoW8MV0sc28cj5v0k+jr6J+Bp0xeTiOzk3jubL2yY2bzDM/3GkR3T5K/nWXXndWie9o2WZR5XEu24qnB5nTj4M3MN2wAAAAAKqNhAwAAAFAZDRsAAACAymjYAAAAAFRGwwYAAACgMho2AAAAAJUR680rNvy2U2HtN8p8WJt883vD2hMfiuOtH7g3jmy+fam7B3n3nvPhmKXv/FpYy5ycalQps2U9rD34qTgn8de+dn/n6+NnlsIx9/zwo2HtZFgp5WQ5nFRvnHtKHN0NAG92cxezXOnpTAbT5XCnUdtvAFnkd9vvfn0cb3PTCOs0snwc1zL5/KfMVgded75hAwAAAFAZDRsAAACAymjYAAAAAFRGwwYAAACgMho2AAAAAJWREsXrpvebnwlrb/nNeFz2y/NPhpWtl59QBR5/ZCesHS+fu4EzAQDelF794Kk3hGlSrrIkKIBXg2/YAAAAAFRGwwYAAACgMho2AAAAAJXRsAEAAACojIYNAAAAQGU0bAAAAAAqo2EDAAAAUBkNGwAAAIDKaNgAAAAAVEbDBgAAAKAyGjYAAAAAldGwAQA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" ] @@ -337,8 +350,8 @@ "\n", "DT, KT = myimage.tensorReorder(myimage.tensorType())\n", "\n", - "print('Shape of DT tensor is ' + np.str(DT.shape))\n", - "print('Shape of KT tensor is ' + np.str(KT.shape))\n", + "print('Shape of DT tensor is ' + str(DT.shape))\n", + "print('Shape of KT tensor is ' + str(KT.shape))\n", "\n", "fig = plt.figure(figsize=(18, 16), dpi= 80)\n", "img1 = fig.add_subplot(1,2,1)\n", @@ -352,6 +365,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -359,6 +373,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -501,6 +516,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -513,11 +529,12 @@ "metadata": {}, "outputs": [], "source": [ - "# print('min(MK): ' + np.str(np.min(mk.reshape(-1))))\n", - "# print('max(MK): ' + np.str(np.max(mk.reshape(-1))))" + "# print('min(MK): ' + str(np.min(mk.reshape(-1))))\n", + "# print('max(MK): ' + str(np.max(mk.reshape(-1))))" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -531,11 +548,12 @@ "outputs": [], "source": [ "# mk_clipped = dp.clipImage(mk, [0, 2])\n", - "# print('min(MK): ' + np.str(np.min(mk_clipped.reshape(-1))))\n", - "# print('max(MK): ' + np.str(np.max(mk_clipped.reshape(-1))))" + "# print('min(MK): ' + str(np.min(mk_clipped.reshape(-1))))\n", + "# print('max(MK): ' + str(np.max(mk_clipped.reshape(-1))))" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -564,6 +582,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ diff --git a/designer/__init__.py b/pydesigner/__init__.py similarity index 100% rename from designer/__init__.py rename to pydesigner/__init__.py diff --git a/pydesigner/fitting/__init__.py b/pydesigner/fitting/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/designer/fitting/dirs10000.csv b/pydesigner/fitting/dirs10000.csv similarity index 100% rename from designer/fitting/dirs10000.csv rename to pydesigner/fitting/dirs10000.csv diff --git a/designer/fitting/dirs256.csv b/pydesigner/fitting/dirs256.csv similarity index 100% rename from designer/fitting/dirs256.csv rename to pydesigner/fitting/dirs256.csv diff --git a/designer/fitting/dirs30.csv b/pydesigner/fitting/dirs30.csv similarity index 100% rename from designer/fitting/dirs30.csv rename to pydesigner/fitting/dirs30.csv diff --git a/pydesigner/fitting/dwi_fnames.py b/pydesigner/fitting/dwi_fnames.py new file mode 100644 index 00000000..f24d32ce --- /dev/null +++ b/pydesigner/fitting/dwi_fnames.py @@ -0,0 +1,52 @@ +_dti_ = { + "md": "dti_md", + "rd": "dti_rd", + "ad": "dti_ad", + "fa": "dti_fa", + "fe": "dti_fe", + "trace": "dti_trace", + "odf": "dti_odf", +} + +_dki_ = { + "mk": "dki_mk", + "rk": "dki_rk", + "ak": "dki_ak", + "kfa": "dki_kfa", + "mkt": "dki_mkt", + "trace": "dki_trace", + "odf": "dki_odf", +} + +_wmti_ = { + "awf": "wmti_awf", + "eas_ad": "wmti_eas_ad", + "eas_rd": "wmti_eas_rd", + "eas_tort": "wmti_eas_tort", + "ias_da": "wmti_ias_da", +} + +_fbi_ = { + "zeta": "fbi_zeta", + "faa": "fbi_faa", + "odf": "fbi_odf_raw", + "odf_mrtrix": "fbi_odf_mrtrix", + "awf": "fbwm_awf", + "Da": "fbwm_da", + "De_mean": "fbwm_de_mean", + "De_ax": "fbwm_de_ax", + "De_rad": "fbwm_de_rad", + "fae": "fbwm_fae", + "min_cost": "fbwm_minCost", + "min_cost_fn": "fbwm_minCost_FN", +} + +_tensor_ = {"DT": "DT", "KT": "KT"} + +_outliers_ = {"IRLLS": "outliers_irlls", "AKC": "outliers_akc"} + +_tractography_ = { + "dti": "dti_dsistudio", + "dki": "dki_dsistudio", + "fbi": "fbi_dsistudio", +} diff --git a/pydesigner/fitting/dwidirs.py b/pydesigner/fitting/dwidirs.py new file mode 100644 index 00000000..1292cade --- /dev/null +++ b/pydesigner/fitting/dwidirs.py @@ -0,0 +1,9 @@ +import os + +import numpy as np + +working_dir = os.path.abspath(os.path.dirname(__file__)) +dirs30 = np.genfromtxt(os.path.join(working_dir, "dirs30.csv"), delimiter=",") +dirs256 = np.genfromtxt(os.path.join(working_dir, "dirs256.csv"), delimiter=",") +dirs10000 = np.genfromtxt(os.path.join(working_dir, "dirs10000.csv"), delimiter=",") +sh_grid = np.genfromtxt(os.path.join(working_dir, "spherical_grid.csv"), delimiter=",") diff --git a/pydesigner/fitting/dwipy.py b/pydesigner/fitting/dwipy.py new file mode 100644 index 00000000..b0de0c5d --- /dev/null +++ b/pydesigner/fitting/dwipy.py @@ -0,0 +1,2991 @@ +import multiprocessing +import os +import os.path as op +import random +from typing import List, Self, Tuple, Union + +import cvxpy as cvx +import nibabel as nib +import numpy as np +import numpy.matlib as npm +import scipy.linalg as sla +from joblib import Parallel, delayed +from scipy.special import factorial, gamma, hyp1f1 +from tqdm import tqdm + +from ..plotting import outlierplot +from ..system.utils import highprecisionexp, highprecisionpower, vectorize, writeNii +from ..tractography import dsistudio, odf, sphericalsampling +from . import dwi_fnames, dwidirs +from . import thresholds as th + +# Define the lowest number possible before it is considered a zero +minZero = th.__minZero__ +# Define number of directions to resample after computing all tensors +dirSample = th.__dirs__ +# Progress bar Properties +tqdmWidth = 70 # Number of columns of progress bar +# Set default numpy errorstates +np.seterr(all="ignore") +defaultErrorState = np.geterr() + + +class DWI(object): + """The DWI object handles tensor estimation and parameter extraction + of dwiffusion weighted images. + + Attributes + ---------- + hdr: class + Nibabel class object of input nifti file. + img: ndarray + 3D or 4D input image array. + grad: ndarray + [N x 4] gradient and bvalue scheme, where the first three + columns are the X, Y, and Z gradient vectors respectively, and + the fourth column is B-values. + mask: ndarray(dtype=bool) + 3D array of brain mask. + MaskStatus: bool + True if brain_mask.nii is present, False otherwise. + workers: int + Number of CPU workers to use in processing. + """ + + def __init__( + self, + imPath: str, + bvecPath: str = None, + bvalPath: str = None, + mask: str = None, + nthreads: int = -1, + ) -> None: + """DWI class initializer + + Parameters + ---------- + imPath: str + Path to input nifti file. + bvecPath: str, optional + Path to accompanying .bvec gradient vector file. + bvalPath: str, optional + Path to accompanying .bval b-value file. + mask: str, optional + Path to brain mask in nifti format. + nthreads : int, optional + Number of CPU workers to use in processing (Defaults to + all physically present workers). + """ + if not os.path.exists(imPath): + raise OSError("Input image {} not found".format(imPath)) + self.hdr = nib.load(imPath) + self.img = np.array(self.hdr.dataobj) + truncateIdx = np.logical_or(np.isnan(self.img), (self.img < minZero)) + self.img[truncateIdx] = minZero + # Get just NIFTI filename + extensio + (path, file) = os.path.split(imPath) + # Remove extension from NIFTI filename + fName = os.path.splitext(file)[0] + if bvecPath: + if not isinstance(bvecPath, str): + raise TypeError("Path to .bvec is not specified " "as a string") + if not os.path.exists(bvecPath): + raise OSError("Path to .bvec does not exist: " "{}".format(bvecPath)) + else: + bvecPath = os.path.join(path, fName + ".bvec") + if bvalPath: + if not isinstance(bvalPath, str): + raise TypeError("Path to .bval is not specified " "as a string") + if not os.path.exists(bvalPath): + raise OSError("Path to .bvec does not exist: " "{}".format(bvalPath)) + else: + bvalPath = os.path.join(path, fName + ".bval") + if os.path.exists(bvalPath) and os.path.exists(bvecPath): + # Load bvecs + bvecs = np.loadtxt(bvecPath) + # Load bvals + bvals = np.loadtxt(bvalPath) + # Scale bvals to ms/um2 by checking for number of digits + # in max bval + if int(np.log10(np.max(bvals))) + 1 >= 3: # if no. of digits >= 3 + bvals = bvals / 1000 + # Combine bvecs and bvals into [n x 4] array where n is + # number of DWI volumes. [Gx Gy Gz Bval] + self.grad = np.c_[np.transpose(bvecs), bvals] + else: + raise OSError("Unable to locate BVAL or BVEC files") + if mask is None: + maskPath = os.path.join(path, "brain_mask.nii") + else: + maskPath = mask + if os.path.exists(maskPath): + tmp = nib.load(maskPath) + self.mask = np.array(tmp.dataobj).astype(bool) + self.maskStatus = True + else: + self.mask = np.ones((self.img.shape[0], self.img.shape[1], self.img.shape[2]), order="F") + self.maskStatus = False + print("No brain mask supplied") + tqdm.write("Image " + fName + ".nii loaded successfully") + if nthreads is not None: + if not isinstance(nthreads, int): + raise TypeError("Variable nthreads need to be an integer") + if nthreads < -1 or nthreads == 0: + raise ValueError("Variable nthreads is a positive integer or -1") + if nthreads is None: + self.workers = -1 + else: + self.workers = nthreads + if self.workers == -1: + tqdm.write("Processing with " + str(multiprocessing.cpu_count()) + " workers...") + else: + tqdm.write("Processing with " + str(self.workers) + " workers...") + + def getBvals(self) -> np.ndarray: + """Returns a vector of b-values, requires no input arguments. + + Returns + ------- + ndarray(dtype=float) + Vector array of b-values. + + Examples + -------- + bvals = dwi.getBvals(), where dwi is the DWI class object. + """ + return self.grad[:, 3] + + def getBvecs(self) -> np.ndarray: + """Returns an array of gradient vectors, requires no input + parameters. + + Returns + ------- + ndarray(dtype=float) + [N x 3] array of gradient vectors. + + Examples + -------- + bvecs = dwi.getBvecs(), where dwi is the DWI class object. + """ + return self.grad[:, 0:3] + + def maxBval(self) -> float: + """Returns the maximum b-value in a dataset to determine between + DTI and DKI, requires no input parameters. + + Returns + ------- + float + Maximum B-value in DWI. + + Examples + -------- + a = dwi.maxBval(), where dwi is the DWI class object. + + """ + return max(np.unique(self.grad[:, 3])).astype(int) + + def maxDTIBval(self) -> float: + """Returns the maximum DTI b-value in a dataset. + + Returns + ------- + float + Maximum DTI B-value in DWI. + + Examples + -------- + a = dwi.maxDKIBval(), where dwi is the DWI class object + + """ + exclude_idx = self.grad[:, 3] <= th.__maxdtibval__ + return max(np.unique(self.grad[exclude_idx, 3])).astype(int) + + def maxDKIBval(self) -> float: + """Returns the maximum DKI b-value in a dataset. + + Returns + ------- + float + Maximum DKI B-value in DWI. + + Examples + -------- + a = dwi.maxDKIBval(), where dwi is the DWI class object + + """ + exclude_idx = self.grad[:, 3] <= th.__maxdkibval__ + return max(np.unique(self.grad[exclude_idx, 3])).astype(int) + + def maxFBIBval(self) -> float: + """Returns the maximum FBI b-value in a dataset. + + Returns + ------- + float + Maximum DKI B-value in DWI. + + Examples + -------- + a = dwi.maxDKIBval(), where dwi is the DWI class object + + """ + exclude_idx = self.grad[:, 3] <= th.__maxfbibval__ + return max(np.unique(self.grad[exclude_idx, 3])).astype(int) + + def idxb0(self) -> np.ndarray[bool]: + """Returns the index of all B-zeros according to bvals + in record. + + Returns + ------- + idx: ndarray(dtype=bool) + Index of DTI/DKI b-values. + + """ + return np.rint(self.grad[:, 3]) == 0 + + def idxdti(self) -> np.ndarray[bool]: + """Returns the index of all DTI/DKI B-values according to bvals + in record. + + Returns + ------- + idx: ndarray(dtype=bool) + Index of DTI/DKI b-values. + + """ + idx = np.ones_like(self.grad[:, -1], dtype=bool) + if self.isdti(): + idx = self.grad[:, 3] <= self.maxDTIBval() + return idx + + def idxdki(self) -> np.ndarray[bool]: + """Returns the index of all DTI/DKI B-values according to bvals + in record + + Returns + ------- + idx: ndarray(dtype=bool) + Index of DTI/DKI b-values + + """ + idx = np.ones_like(self.grad[:, -1], dtype=bool) + if self.isdki(): + idx = self.grad[:, 3] <= self.maxDKIBval() + return idx + + def idxfbi(self) -> np.ndarray[int]: + """Returns the index of all FBI B-values according to bvals + in record + + Returns + ------- + idx: ndarray(dtype=bool) + Index of DTI/DKI b-values. + + """ + idx = np.ones_like(self.grad[:, 3], dtype=bool) + if self.isfbi(): + idx = np.rint(self.grad[:, -1]) == np.rint(self.maxFBIBval()) + else: + raise IndexError("No valid FBI sequence found.") + return idx + + def getndirs(self) -> int: + """Returns the number of gradient directions acquired from the + scanner + + Returns + ------- + int + Number of gradient directions + + Examples + -------- + n = dwi.getndirs(), where dwi is the DWI class object + """ + return np.sum(self.grad[:, 3] == self.maxDKIBval()) + + def tensorType(self) -> List[str]: + """Returns whether input image is DTI or DKI compatible, requires + no input parameters + + Returns + ------- + type: list[str] + Contains list of string 'dti', 'dki', or 'fbi' based on + the protocols the input DWI represents + + Examples + -------- + a = dwi.tensorType(), where dwi is the DWI class object + """ + type = [] + if self.maxDTIBval() <= th.__maxdtibval__ and self.maxDTIBval() >= th.__mindkibval__: + type.append("dti") + if self.maxDKIBval() >= th.__maxdtibval__ and self.maxDKIBval() <= th.__maxdkibval__: + type.append("dki") + if self.maxBval() >= th.__minfbibval__: + type.append("fbi") + if "fbi" in type and "dki" in type: + type.append("fbwm") + if not type: + raise ValueError("tensortype: Error in determining maximum " "BVAL") + return type + + def isdti(self) -> bool: + """Returns logical value to answer the mystical question whether + the input image is DTI. + + Returns + ------- + ans: bool + True if DTI; false otherwise. + + Examples + -------- + ans = dwi.isdki(), where dwi is the DWI class object. + """ + if "dti" in self.tensorType(): + ans = True + else: + ans = False + return ans + + def isdki(self) -> bool: + """Returns logical value to answer the mystical question whether + the input image is DKI. + + Returns + ------- + ans: bool + True if DKI; false otherwise. + + Examples + -------- + ans = dwi.isdki(), where dwi is the DWI class object. + """ + if "dki" in self.tensorType(): + ans = True + else: + ans = False + return ans + + def isfbi(self) -> bool: + """Returns bool value to specify whether image input image is + FBI. + + Returns + ------- + ans: bool + True if FBI; false otherwise. + + Examples + -------- + ans = dwi.isfbi(), where dwi is the DWI class object. + """ + if "fbi" in self.tensorType(): + ans = True + else: + ans = False + return ans + + def isfbwm(self) -> bool: + """Returns bool value to specify whether image input image is + FBWM. + + Returns + ------- + and: bool + True if FBWM; false otherwise. + + Examples + -------- + ans = dwi.isfbi(), where dwi is the DWI class object. + """ + if "fbwm" in self.tensorType(): + ans = True + else: + ans = False + return ans + + def createTensorOrder(self, order: Union[int, None] = None) -> Tuple[np.ndarray[int], np.ndarray[int]]: + """Creates tensor order array and indices. + + Parameters + ---------- + order: 2 or 4 (int or None) + Tensor order number, 2 for diffusion and 4 for kurtosis. + Default: None; auto-detect + + Returns + ------- + cnt: ndarray(dtype=int) + Count of number of times a tensor appears. + ind: ndarray(dtype=int) + Indices of count. + + Examples + -------- + (cnt, ind) = dwi.createTensorOrder(order) + + Notes + ----- + The tensors for this pipeline are based on NYU's designer layout as + depicted in the table below. This will soon be depreciated and + updated with MRTRIX3's layout. + + .. code-block:: none + + ~~~~~~D~~~~~~ + 1 | D11 + 2 | D12 + 3 | D13 + 4 | D22 + 5 | D23 + 6 | D33 + ~~~~~~K~~~~~~ + 1 | W1111 + 2 | W1112 + 3 | W1113 + 4 | W1122 + 5 | W1123 + 6 | W1133 + 7 | W1222 + 8 | W1223 + 9 | W1233 + 10 | W1333 + 11 | W2222 + 12 | W2223 + 13 | W2233 + 14 | W2333 + 15 | W3333 + """ + if order is None: + if self.isdki(): + cnt = np.array([1, 4, 4, 6, 12, 6, 4, 12, 12, 4, 1, 4, 6, 4, 1], dtype=int) + ind = ( + np.array( + ( + [1, 1, 1, 1], + [1, 1, 1, 2], + [1, 1, 1, 3], + [1, 1, 2, 2], + [1, 1, 2, 3], + [1, 1, 3, 3], + [1, 2, 2, 2], + [1, 2, 2, 3], + [1, 2, 3, 3], + [1, 3, 3, 3], + [2, 2, 2, 2], + [2, 2, 2, 3], + [2, 2, 3, 3], + [2, 3, 3, 3], + [3, 3, 3, 3], + ) + ) + - 1 + ) + else: + cnt = np.array([1, 2, 2, 1, 2, 1], dtype=int) + ind = np.array(([1, 1], [1, 2], [1, 3], [2, 2], [2, 3], [3, 3])) - 1 + elif order == 2: + cnt = np.array([1, 2, 2, 1, 2, 1], dtype=int) + ind = np.array(([1, 1], [1, 2], [1, 3], [2, 2], [2, 3], [3, 3])) - 1 + elif order == 4: + cnt = np.array([1, 4, 4, 6, 12, 6, 4, 12, 12, 4, 1, 4, 6, 4, 1], dtype=int) + ind = ( + np.array( + ( + [1, 1, 1, 1], + [1, 1, 1, 2], + [1, 1, 1, 3], + [1, 1, 2, 2], + [1, 1, 2, 3], + [1, 1, 3, 3], + [1, 2, 2, 2], + [1, 2, 2, 3], + [1, 2, 3, 3], + [1, 3, 3, 3], + [2, 2, 2, 2], + [2, 2, 2, 3], + [2, 2, 3, 3], + [2, 3, 3, 3], + [3, 3, 3, 3], + ) + ) + - 1 + ) + else: + raise ValueError("createTensorOrder: Please enter valid " "order values (2 or 4).") + return cnt, ind + + def fibonacciSphere(self, samples: int = 1, randomize: bool = True) -> np.ndarray[float]: + """Returns evenly spaced points on a sphere. + + Parameters + ---------- + samples: int + Number of points to compute from sphere, must be a + positive and real integer (Default: 1). + + randomize: bool + True if sampling is randomized; False otherwise + (Default: True). + + Returns + ------- + points: ndarray(dtype=float) + [3 x samples] array containing evenly spaced points + from a sphere. + + Examples + -------- + dirs = dwi.fibonacciSphere(256, True) + """ + rnd = 1 + if randomize: + rnd = random.random() * samples + points = [] + offset = 2 / samples + increment = np.pi * (3.0 - np.sqrt(5.0)) + for i in range(samples): + y = ((i * offset) - 1) + (offset / 2) + r = np.sqrt(1 - pow(y, 2)) + phi = ((i + rnd) % samples) * increment + x = np.cos(phi) * r + z = np.sin(phi) * r + points.append([x, y, z]) + return np.array(points) + + def radialSampling(self, dir: np.ndarray[float], n: int) -> np.ndarray[float]: + """Get the radial component of a metric from a set of directions. + + Parameters + ---------- + dir: ndarray(dtype=float) + [n x 3] input array of directions. + n: int + Number of rows, n. + + Returns + ------- + dirs: ndarray(dtype=float) + Matrix containing radial components. + + Examples + -------- + grad = dwi.radialSampling(dir, number_of_dirs). + + """ + dt = 2 * np.pi / n + theta = np.arange(0, 2 * np.pi - dt, dt) + dirs = np.vstack((np.cos(theta), np.sin(theta), 0 * theta)) + v = np.hstack((-dir[1], dir[0], 0)) + s = np.sqrt(np.sum(v**2)) + c = dir[2] + V = np.array([[0, -v[2], v[1]], [v[2], 0, -v[0]], [-v[1], v[0], 0]]) + R = np.eye(3) + V + np.matmul(V, V) * (1 - c) / (s**2) + dirs = np.matmul(R, dirs).T + return dirs + + def diffusionCoeff(self, dt: np.ndarray[float], dir: np.ndarray[float]) -> np.ndarray[float]: + """Computes apparent diffusion coefficient (ADC). + + Parameters + ---------- + ----------S + dt: ndarray(dtype=float) + [21 x nvoxel] array containing diffusion tensor. + dir: ndarray(dtype=float) + [n x 3] array containing gradient directions. + + Returns + ------- + adc : ndarray(dtype=float) + Array containing apparent diffusion coefficient. + + Examples + -------- + adc = dwi.diffusionCoeff(dt, dir) + """ + dcnt, dind = self.createTensorOrder(2) + ndir = dir.shape[0] + bD = np.tile(dcnt, (ndir, 1)) * dir[:, dind[:, 0]] * dir[:, dind[:, 1]] + adc = np.matmul(bD, dt) + return adc + + def kurtosisCoeff(self, dt: np.ndarray[float], dir: np.ndarray[float]) -> np.ndarray[float]: + """Computes apparent kurtosis coefficient (AKC) + + Parameters + ---------- + dt : ndarray(dtype=float) + [21 x nvoxel] array containing diffusion tensor + dir : ndarray(dtype=float) + [n x 3] array containing gradient directions + + Returns + ------- + adc : ndarray(dtype=float) + Array containing apparent kurtosis coefficient + + Examples + -------- + adc = dwi.kurtosisCoeff(dt, dir) + """ + wcnt, wind = self.createTensorOrder(4) + ndir = dir.shape[0] + adc = self.diffusionCoeff(dt[:6], dir) + adc[adc < minZero] = minZero + md = np.sum(dt[np.array([0, 3, 5])], 0) / 3 + bW = ( + np.tile(wcnt, (ndir, 1)) * dir[:, wind[:, 0]] * dir[:, wind[:, 1]] * dir[:, wind[:, 2]] * dir[:, wind[:, 3]] + ) + akc = np.matmul(bW, dt[6:]) + akc = (akc * np.tile(md**2, (adc.shape[0], 1))) / (adc**2) + return akc + + def dtiTensorParams(self, dt: np.ndarray[float]) -> Tuple[np.ndarray[float], np.ndarray[float]]: + """Computes sorted DTI tensor eigenvalues and eigenvectors. + + Parameters + ---------- + DT: ndarray(dtype=float) + Diffusion tensor array. + + Returns + ------- + values: ndarray(dtype=float) + Array of sorted eigenvalues. + vectors: ndarray(dtype=float) + Array lf sorted eigenvectors. + + Examples + -------- + (values, vectors) = dwi.dtiTensorParams(dt) + """ + values, vectors = np.linalg.eig(dt) + idx = np.argsort(-values) + values = -np.sort(-values) + vectors = vectors[:, idx] + return values, vectors + + def dkiTensorParams( + self, v1: np.ndarray[float], dt: np.ndarray[float] + ) -> Tuple[np.ndarray[float], np.ndarray[float], np.ndarray[float], np.ndarray[float]]: + """Uses average directional statistics to approximate axial + kurtosis(AK) and radial kurtosis (RK). + + Parameters + ---------- + v1 : ndarray(dtype=float) + Array of first eigenvectors from DWI.dtiTensorParams(). + dt : ndarray(dtype=float) + Array of diffusion tensor. + + Returns + ------- + rk : ndarray(dtype=float) + Radial Kurtosis. + ak : ndarray(dtype=float) + Axial Kurtosis. + kfa : ndarray(dtype=float) + Kurtosis Fractional Anisotropy. + mkt : ndarray(dtype=float) + Mean Kurtosis Tensor. + + Examples + -------- + (rk, ak, kfa, mkt) = dwi.dkiTensorParams(v1, dt) + """ + dirs = np.vstack((v1, -v1)) + akc = self.kurtosisCoeff(dt, dirs) + ak = np.mean(akc) + dirs = self.radialSampling(v1, dirSample) + akc = self.kurtosisCoeff(dt, dirs) + rk = np.mean(akc) + W_F = np.sqrt( + dt[6] ** 2 + + dt[16] ** 2 + + dt[20] ** 2 + + 6 * dt[9] ** 2 + + 6 * dt[11] ** 2 + + 6 * dt[18] ** 2 + + 4 * dt[7] ** 2 + + 4 * dt[8] ** 2 + + 4 * dt[12] ** 2 + + 4 * dt[17] ** 2 + + 4 * dt[15] ** 2 + + 4 * dt[19] ** 2 + + 12 * dt[10] ** 2 + + 12 * dt[13] ** 2 + + 12 * dt[14] ** 2 + ) + Wbar = 1 / 5 * (dt[6] + dt[16] + dt[20] + 2 * (dt[9] + dt[11] + dt[18])) + if W_F < minZero: + kfa = 0 + else: + W_diff = np.sqrt( + (dt[6] - Wbar) ** 2 + + (dt[16] - Wbar) ** 2 + + (dt[20] - Wbar) ** 2 + + 6 * (dt[9] - Wbar / 3) ** 2 + + 6 * (dt[11] - Wbar / 3) ** 2 + + 6 * (dt[18] - Wbar / 3) ** 2 + + 4 * dt[7] ** 2 + + 4 * dt[8] ** 2 + + 4 * dt[12] ** 2 + + 4 * dt[17] ** 2 + + 4 * dt[15] ** 2 + + 4 * dt[19] ** 2 + + 12 * dt[10] ** 2 + + 12 * dt[13] ** 2 + + 12 * dt[14] ** 2 + ) + kfa = W_diff / W_F + mkt = Wbar + return ak, rk, kfa, mkt + + def wlls( + self, + shat: np.ndarray[float], + dwi: np.ndarray[float], + b: np.ndarray[float], + cons: Union[np.ndarray[float], None] = None, + warmup: bool = None, + ) -> np.ndarray[float]: + """Estimates diffusion and kurtosis tenor at voxel with + unconstrained Moore-Penrose pseudoinverse or constrained + quadratic convex optimization. This is a helper function for + dwi.fit() so a multiprocessing parallel loop can be iterated over + voxels. + + Parameters + ---------- + shat: ndarray(dtype=float) + [ndir x 1] array of S_hat, approximated signal intensity + at voxel. + dwiL: ndarray(dtype=float) + [ndir x 1] array of diffusion weighted intensity values at + voxel, for all b-values. + b: ndarray(dtype=float) + [ndir x 1] array of b-values vector. + cons: ndarray(dtype=float) + [(n * dir) x 22) array containing inequality constraints + for fitting (Default: None). + warmup: ndarray(dtype=float) + Estimated dt vector (22, 0) at each voxel for warm + starting constrianed tensor fitting (Default: None). + + Returns + ------- + dt: ndarray(dtype=float) + Diffusion tensor. + + Examples + -------- + dt = dwi.wlls(shat, dwi, b, constraints) + + Notes + ----- + For Unconstrained Fitting: + In the absence of constraints, an exact formulation in the form + Cx = b is produced. This is further simplified to x_hat = C^+ * + b. One can use the Moore-Penrose method to compute the + pseudoinverse to approximate diffusion tensors. + + For Constrained Fitting: + .. code-block:: none + + The equation |Cx -b|^2 expands to 0.5*x.T(C.T*A)*x -(C.T*b).T + ~~~~~ ~~~~~ + P q + + where A is denoted by multiplier matrix (w * b) + Multiplying by a positive constant (0.5) does not change the value + of optimum x*. Similarly, the constant offset b.T*b does not + affect x*, therefore we can leave these out. + + Minimize: || C*x -b ||_2^2 + subject to A*x <= b + No lower or upper bounds + """ + w = np.diag(shat) + # Unconstrained Fitting + if cons is None: + dt = np.matmul(np.linalg.pinv(np.matmul(w, b)), np.matmul(w, np.log(dwi))) + # Constrained fitting + else: + C = np.matmul(w, b).astype("double") + d = np.matmul(w, np.log(dwi)).astype("double").reshape(-1) + m, n = C.shape + x = cvx.Variable(n) + if warmup is not None: + x.value = warmup + objective = cvx.Minimize(0.5 * cvx.sum_squares(C @ x - d)) + constraints = [cons @ x >= np.zeros((len(cons)))] + prob = cvx.Problem(objective, constraints) + try: + prob.solve( + solver=cvx.OSQP, + warm_start=True, + max_iter=20000, + polish=True, + linsys_solver="qdldl", + ) + dt = x.value + if prob.status != "optimal": + dt = np.full(n, minZero) + except: # noqa: E722 + dt = np.full(n, minZero) + return dt + + def fit(self, constraints: Union[np.ndarray[float], None] = None, reject: bool = None) -> Self: + """Returns fitted diffusion or kurtosis tensor + + Parameters + ---------- + constraints: array_like(dtype=int) + [1 x 3] vector that specifies which constraints to use + (Default: None) + reject: ndarray(dtype=bool) + 4D array containing information on voxels to exclude + from DT estimation (Default: None) + + Examples + -------- + dwi.fit() + dwi.fit(constraints=[0,1,0], reject=irlls_output) + """ + # Handle rejected voxels from IRLLS + exclude_idx = self.idxdki() + if reject is None: + reject = np.zeros(self.img[:, :, :, exclude_idx].shape) + grad = self.grad[exclude_idx, :] + grad_orig = grad + order = np.floor(np.log(np.abs(np.max(grad[:, -1]) + 1)) / np.log(10)) + img = self.img[:, :, :, exclude_idx] + if order >= 2: + grad[:, -1] = grad[:, -1] + img.astype(np.double) + img[img <= 0] = np.finfo(np.double).eps + grad.astype(np.double) + normgrad = np.sqrt(np.sum(grad[:, :3] ** 2, 1)) + normgrad[normgrad == 0] = 1 + grad[:, :3] = grad[:, :3] / np.tile(normgrad, (3, 1)).T + grad[np.isnan(grad)] = 0 + dcnt, dind = self.createTensorOrder(2) + wcnt, wind = self.createTensorOrder(4) + ndwis = img.shape[-1] + bs = np.ones((ndwis, 1)) + bD = np.tile(dcnt, (ndwis, 1)) * grad[:, dind[:, 0]] * grad[:, dind[:, 1]] + bW = ( + np.tile(wcnt, (ndwis, 1)) + * grad_orig[:, wind[:, 0]] + * grad_orig[:, wind[:, 1]] + * grad_orig[:, wind[:, 2]] + * grad_orig[:, wind[:, 3]] + ) + self.b = np.concatenate( + ( + bs, + (np.tile(-self.grad[exclude_idx, -1], (6, 1)).T * bD), + np.squeeze(1 / 6 * np.tile(self.grad[exclude_idx, -1], (15, 1)).T ** 2) * bW, + ), + 1, + ) + dwi_ = vectorize(img, self.mask) + reject_ = vectorize(reject, self.mask).astype(bool) + init = np.matmul(np.linalg.pinv(self.b), np.log(dwi_)) + shat = highprecisionexp(np.matmul(self.b, init)) + if constraints is None or (constraints[0] == 0 and constraints[1] == 0 and constraints[2] == 0): + inputs = tqdm( + range(0, dwi_.shape[1]), + desc="Unconstrained Tensor Fit", + bar_format="{desc}: [{percentage:0.0f}%]", + unit="vox", + ncols=tqdmWidth, + ) + self.dt = Parallel(n_jobs=self.workers, prefer="processes")( + delayed(self.wlls)( + shat[~reject_[:, i], i], + dwi_[~reject_[:, i], i], + self.b[~reject_[:, i]], + ) + for i in inputs + ) + else: + # C is linear inequality constraint matrix A_ub + C = self.createConstraints(constraints) + inputs = tqdm( + range(0, dwi_.shape[1]), + desc="Constrained Tensor Fit", + bar_format="{desc}: [{percentage:0.0f}%]", + unit="vox", + ncols=tqdmWidth, + ) + self.dt = Parallel(n_jobs=self.workers, prefer="processes")( + delayed(self.wlls)( + shat[~reject_[:, i], i], + dwi_[~reject_[:, i], i], + self.b[~reject_[:, i]], + cons=C, + ) + for i in inputs + ) + self.dt = np.reshape(self.dt, (dwi_.shape[1], self.b.shape[1])).T + self.s0 = highprecisionexp(self.dt[0, :]) + self.dt = self.dt[1:, :] + D_apprSq = 1 / (np.sum(self.dt[(0, 3, 5), :], axis=0) / 3) ** 2 + self.dt[6:, :] = self.dt[6:, :] * np.tile(D_apprSq, (15, 1)) + + def createConstraints(self, constraints: List[int] = [0, 1, 0]) -> np.ndarray[float]: + """Generates constraint array for constrained minimization quadratic + programming. + + Parameters + ---------- + constraints: array_like(dtype=int) + [1 X 3] logical vector indicating which constraints + out of three to enable (Default: [0, 1, 0]) + C1 is Dapp > 0 + C1 is Kapp > 0 + C3 is Kapp < 3/(b*Dapp) + + Returns + ------- + C: ndarray(dtype=float) + Array containing constraints to consider during + minimization, C is shaped [number of constraints enforced * + number of directions, 22]. + + Examples + -------- + C = dwi.createConstraints([0, 1, 0]) + """ + if sum(constraints) >= 0 and sum(constraints) <= 3: + dcnt, dind = self.createTensorOrder(2) + wcnt, wind = self.createTensorOrder(4) + cDirs = dwidirs.dirs30 + ndirs = cDirs.shape[0] + C = np.empty((0, 22)) + if constraints[0] > 0: # Dapp > 0 + C = np.append( + C, + np.hstack( + ( + np.zeros((ndirs, 1)), + np.tile(dcnt, [ndirs, 1]) * cDirs[:, dind[:, 0]] * cDirs[:, dind[:, 1]], + np.zeros((ndirs, 15)), + ) + ), + axis=0, + ) + if constraints[1] > 0: # Kapp > 0 + C = np.append( + C, + np.hstack( + ( + np.zeros((ndirs, 7)), + np.tile(wcnt, [ndirs, 1]) + * cDirs[:, wind[:, 0]] + * cDirs[:, wind[:, 1]] + * cDirs[:, wind[:, 2]] + * cDirs[:, wind[:, 3]], + ) + ), + axis=0, + ) + if constraints[2] > 0: # K < 3/(b*Dapp) + C = np.append( + C, + np.hstack( + ( + np.zeros((ndirs, 1)), + 3 / self.maxDKIBval() * np.tile(dcnt, [ndirs, 1]) * cDirs[:, dind[:, 0]], + np.tile(-wcnt, [ndirs, 1]) + * cDirs[:, wind[:, 1]] + * cDirs[:, wind[:, 2]] + * cDirs[:, wind[:, 3]], + ) + ), + axis=0, + ) + else: + print('Invalid constraints. Please use format "[0, 0, 0]"') + return C + + def extractDTI( + self, + ) -> Tuple[ + np.ndarray[float], + np.ndarray[float], + np.ndarray[float], + np.ndarray[float], + np.ndarray[float], + np.ndarray[float], + ]: + """Extract all DTI parameters from DT tensor. Warning, this can + only be run after tensor fitting dwi.fit(). + + Returns + ------- + md: ndarray(dtype=float) + Mean Diffusivity. + rd: ndarray(dtype=float) + Radial Diffusivity. + ad: ndarray(dtype=float) + Axial Diffusivity. + fa: ndarray(dtype=float) + Fractional Anisotropy. + fe: ndarray(dtype=float) + First Eigenvectors. + trace: ndarray(dtype=float) + Sum of first eigenvalues. + + Examples + -------- + (md, rd, ad, fa) = dwi.extractDTI(), where dwi is the DWI class + object + """ + # extract all tensor parameters from dt + DT = np.reshape( + np.concatenate( + ( + self.dt[0, :], + self.dt[1, :], + self.dt[2, :], + self.dt[1, :], + self.dt[3, :], + self.dt[4, :], + self.dt[2, :], + self.dt[4, :], + self.dt[5, :], + ) + ), + (3, 3, self.dt.shape[1]), + ) + # get the trace + rdwi = highprecisionexp(np.matmul(self.b[:, 1:], self.dt)) + B = np.round(-(self.b[:, 0] + self.b[:, 3] + self.b[:, 5]) * 1000) + uB = np.unique(B) + trace = np.zeros((self.dt.shape[1], uB.shape[0])) + for ib in range(0, uB.shape[0]): + t = np.where(B == uB[ib]) + trace[:, ib] = np.mean(rdwi[t[0], :], axis=0) + nvox = self.dt.shape[1] + inputs = tqdm( + range(0, nvox), + desc="DTI Parameters", + bar_format="{desc}: [{percentage:0.0f}%]", + unit="vox", + ncols=tqdmWidth, + ) + values, vectors = zip( + *Parallel(n_jobs=self.workers, prefer="processes")( + delayed(self.dtiTensorParams)(DT[:, :, i]) for i in inputs + ) + ) + values = np.reshape(np.abs(values), (nvox, 3)) + vectors = np.reshape(vectors, (nvox, 3, 3)) + self.DTIvectors = vectors + l1 = vectorize(values[:, 0], self.mask) + l2 = vectorize(values[:, 1], self.mask) + l3 = vectorize(values[:, 2], self.mask) + v1 = vectorize(vectors[:, :, 0].T, self.mask) + md = (l1 + l2 + l3) / 3 + rd = (l2 + l3) / 2 + ad = l1 + fa = ( + np.sqrt(1 / 2) + * np.sqrt((l1 - l2) ** 2 + (l2 - l3) ** 2 + (l3 - l1) ** 2) + / np.sqrt(l1**2 + l2**2 + l3**2) + ) + fe = np.abs(np.stack((fa * v1[:, :, :, 0], fa * v1[:, :, :, 1], fa * v1[:, :, :, 2]), axis=3)) + trace = vectorize(trace.T, self.mask) + return md, rd, ad, fa, fe, trace + + def extractDKI( + self, + ) -> Tuple[ + np.ndarray[float], + np.ndarray[float], + np.ndarray[float], + np.ndarray[float], + np.ndarray[float], + np.ndarray[float], + ]: + """Extract all DKI parameters from DT tensor. Warning, this can + only be run after tensor fitting dwi.fit(). + + Returns + ------- + mk: ndarray(dtype=float) + Mean Diffusivity. + rk: ndarray(dtype=float) + Radial Diffusivity. + ak: ndarray(dtype=float) + Axial Diffusivity. + kfa: ndarray(dtype=float) + Kurtosis Fractional Anisotropy. + mkt: ndarray(dtype=float) + Mean Kurtosis Tensor. + trace: ndarray(dtype=float) + Sum of first eigenvalues. + + Examples + -------- + (mk, rk, ak, fe, trace) = dwi.extractDTI(), where dwi is the DWI + class object + """ + # get the trace + rdwi = highprecisionexp(np.matmul(self.b[:, 1:], self.dt)) + B = np.round(-(self.b[:, 0] + self.b[:, 3] + self.b[:, 5]) * 1000) + uB = np.unique(B) + trace = np.zeros((self.dt.shape[1], uB.shape[0])) + for ib in range(0, uB.shape[0]): + t = np.where(B == uB[ib]) + trace[:, ib] = np.mean(rdwi[t[0], :], axis=0) + dirs = dwidirs.dirs256 + akc = self.kurtosisCoeff(self.dt, dirs) + mk = np.mean(akc, 0) + nvox = self.dt.shape[1] + inputs = tqdm( + range(0, nvox), + desc="DKI Parameters", + bar_format="{desc}: [{percentage:0.0f}%]", + unit="vox", + ncols=tqdmWidth, + ) + ak, rk, kfa, mkt = zip( + *Parallel(n_jobs=self.workers, prefer="processes")( + delayed(self.dkiTensorParams)(self.DTIvectors[i, :, 0], self.dt[:, i]) for i in inputs + ) + ) + ak = np.reshape(ak, (nvox)) + rk = np.reshape(rk, (nvox)) + kfa = np.reshape(kfa, (nvox)) + mkt = np.reshape(mkt, (nvox)) + trace = vectorize(trace.T, self.mask) + ak = vectorize(ak, self.mask) + rk = vectorize(rk, self.mask) + mk = vectorize(mk, self.mask) + kfa = vectorize(kfa, self.mask) + mkt = vectorize(mkt, self.mask) + return mk, rk, ak, kfa, mkt, trace + + def optimal_lmax(self) -> int: + """Computes the highest harmonic order (l_max) for + spherical harmonic expansion. This is adapted + from the information posted at + https://mrtrix.readthedocs.io/en/dev/concepts/sh_basis_lmax.html + + This function runs successfully only if input + DWI is an FBI or HARDI acquisition. + + Returns + ------- + int + l_max suitable for DWI. + """ + if not self.isfbi(): + raise Exception("Input DWI is not an " "FBI or HARDI acquisiton. Cannot compute " "l_max.") + fbi_vols = np.count_nonzero(self.grad[self.idxfbi(), -1]) + l_max = 0 + vols = (l_max + 1) * (l_max / 2 + 1) + while vols <= fbi_vols: + l_max += 2 + vols = (l_max + 1) * (l_max / 2 + 1) + return l_max - 2 + + def fbi( + self, l_max: int = 6, fbwm: bool = True, rectify: bool = True, res: str = "med" + ) -> Tuple[ + np.ndarray[float], + np.ndarray[float], + np.ndarray[float], + np.ndarray[float], + np.ndarray[float], + np.ndarray[float], + np.ndarray[float], + np.ndarray[float], + np.ndarray[float], + np.ndarray[float], + np.ndarray[float], + ]: + """Perform fiber ball imaging (FBI) and FBI white matter model + (FBWM) analyses + + Parameters + ---------- + l_max: int + Maximum spherical harmonic degree specified as an even + integer + (Default: 6) + fbwm: bool + Perform FBWM parameterization if True + (Default: True) + rectify: bool + Perform fODF rectification if True + (Default: True) + res: str + Resolution of spherical sampling distribution (Default: 'med') + 'low' defines the spherical grid defined by 3 fold quadrisection of the + isocahedron, or 8 fold tesselation of icosahedron. + 'med' defines the spherical grid defined by 4 fold quadrisection of the + isocahedron, or 16 fold tesselation of icosahedron. + 'high' defines the spherical grid defined by 5 fold quadrisection of the + isocahedron, or 24 fold tesselation of icosahedron. + Default: "med" + + Returns + ------- + zeta: array_like(dtype=float) + Zeta parameter + faa: array_like(dtype=float) + Intra-axonal fractional anisotropy + fodf: array_like(dtype=float) + fodf from spherical harmonic expansion + min_awf: array_like(dtype=float) + Axonal water fraction + Da: array_like(dtype=float) + Intrinsic intra-axonal diffusivity + De_mean: array_like(dtype=float) + Mean extra-axonal diffusion + De_ax: array_like(dtype=float) + Axial extra-axonal diffusion + De_rad: array_like(dtype=float) + Radial extra-axonal diffusion + De_fa: array_like(dtype=float) + Extra-axonal FA + min_cost: array_like(dtype=float) + Minimum cost of the cost function (first index of + min_cost_fn) + min_cost_fn: array_like(dtype=float) + Cost function + """ + # --------------------FUNCTION SEPARATOR----------------------- + + def __fbi_rectify(fodf, sh_area, iter=1000): + """Rectifies fODF values to eliminate all negative values while + reducing the mean square error + + Parameters + ---------- + fodf : array_like(dtype=float) + Real portion of fODF + iter : int + Number of iterations to perform + (Default: 1000) + sh_area: array_like(dtype=float) + Area of spherical sampling + + Returns + ------- + odf : float + Rectified fODF + """ + # fODF rectification + odf = fodf + fODF = fodf.real # grab real part of the fODF + fODF[np.isnan(fODF)] = 0 + Fmax = np.max(fODF) # get the max peak value of the ODF + lB = 0 # initial lower bound + uB = Fmax # initial upper bound + M = 1 # initialze iteration counter + Mmax = iter # max iterations (could probably be 100 too) + if Fmax > 0: + while M <= Mmax: + # BEGIN: bi-section algorithm + midpt = (lB + uB) / 2 + fODF_lB = np.sum((np.abs(fODF - lB) - fODF - lB) * sh_area, axis=0) + fODF_midpt = np.sum((np.abs(fODF - midpt) - fODF - midpt) * sh_area, axis=0) + if fODF_midpt == 0 or (uB - lB) / 2 < minZero: + EPS = midpt + break + else: + M = M + 1 + if np.sign(fODF_midpt) == np.sign(fODF_lB): + lB = midpt + else: + uB = midpt + # END: bi-section algorithm + # Subract solution from each ODF point + odf = (1 / 2) * (np.abs(odf - EPS) + odf - EPS) + odf = odf.real + # due to numerical error, we manually set + # very very very tiny peaks to zero after the fact... + odf[np.logical_and(odf > -minZero, odf < minZero)] = 0 + return odf + + def __costCalculator(grid, BT, GT, b0, IMG, iDT, iaDT, zeta, shB, Pl0, g2l_fa_R_b, clm): + """Computes the cost function at voxel for FBWM calculations. + Refer to paper for additional information. + + Parameters + ---------- + grid : array_like(dtype=float) + Vector of values at which to compute cost. Usually 0 to 1 + BT : list of float + List of unique B-value shells (eg. [1, 2, 6]) + GT : list of float + List of gradient tables for each B-value shell + b0 : float + Averaged B0 signal in DWI + IMG : list of float + List of DWI signal for each B-value shell + iDT : array_like(dtype=float) + FBWM diffusion tensor + iaDT : array_like(dtype=float) + FBWM axonal diffusion tensor + zeta : float + Zeta value + shB : list of complex + SH basis sets for each B-value shell + Pl0 : array_like(dtype=float) + Legendre polynomail + g2l_fa_R_b : array_like(dtype=complex) + Information not provided + clm : array_like(dtype=complex) + fODF SH coefficients + + Returns + ------- + cost_fn : array_like(dype=float) + Cost values for input grid + """ + if grid.ndim > 1: + raise Exception("Grid needs to be a flattened 1D vector") + ndir = [len(x) for x in GT] + cost_fn = np.zeros_like(grid) + with np.errstate(all="ignore"): + for idx, awf in np.ndenumerate(grid): + for b in range(0, len(BT)): + Se = ( + b0 + * np.exp( + (-BT[b] * (1 - awf) ** -1) + * np.diag((GT[b].dot((iDT - (awf**3 * zeta**-2) * iaDT).dot(GT[b].T)))) + ) + ) * ( + 1 - awf + ) # Eq. 3 FBWM paper + Sa = (2 * np.pi * b0 * zeta * np.sqrt(np.pi / BT[b])) * ( + shB[b].dot((Pl0 * g2l_fa_R_b[b, idx, :][0] * clm)) + ) # Eq. 4 FBM paper + cost_fn[idx] = cost_fn[idx] + ndir[b] ** -1 * np.sum((IMG[b] - Se.real - Sa.real) ** 2) + cost_fn[idx] = b0**-1 * np.sqrt(len(BT) ** -1 * cost_fn[idx]) # Eq. 21 FBWM paper + return cost_fn + + def fbi_helper( + dwi, + b0, + B, + H, + Pl0, + gl, + rectify=True, + fbwm_SH1=None, + fbwm_SH2=None, + fbwm_B1=None, + fbwm_B2=None, + fbwm_dt=None, + fbwm_degs=None, + sh_area=None, + ): + """Computes FBI calculations for a given voxel. This function + will perform FBWM only if all optional FBWM parameters are + parsed. + + Parameters + ---------- + dwi: array_like(dtype=float) + Signal across DWI at a given voxel. + b0: float + Averaged B0 signal at a given voxel. + B: array_like(dtype=complex) + FBI spherical harmonic expansion. + H: array_like(dtype=complex) + ODF from spherical harmonic expansion. + Pl0: array_like(dtype=float) + Legendre polynomail. + gl: array_like(dtype=float) + Correction factor. + rectify: bool; optional + Specify whether to perform fODF rectification + (Default: True). + fbwm_SH1: array_like(dtype=complex); optional + DKI spherical harmonic expansion for B1000. + fbwm_SH2: array_like(dtype=complex); optional + DKI spherical harmonic expansion for B2000. + fbwm_degs: array_like(dtype=int); optional + Harmonics used in DKI spherical harmonic expansion. + fbwm_B1: array_like(dtype=float); optional + Signal across DWI at B1000 at a given voxel. + fbwm_B2: array_like(dtype=float); optional + Signal across DWI at B2000 at a given voxel. + fbwm_dt: array_like(dtype=float); optional + Diffusion tensor at a given voxel. + fbwm_degs: array_like(dtype=int) + Harmonics used in expansion of FBWM shperical + harmonics. + sh_area: array_list(dtype=float) + Area of spherical sampling. + + Returns + ------- + zeta: float + Zeta parameter + faa: float + Intra-axonal fractional anisotropy. + clm: float + Spherical harmonic coefficients. + min_awf : float + Axonal water fraction. + Da: float + Intrinsic intra-axonal diffusivity. + De_mean: float + Mean extra-axonal diffusion. + De_ax: float + Axial extra-axonal diffusion. + De_rad: float + Radial extra-axonal diffusion. + De_fa: float + Extra-axonal FA. + min_cost: float + Minimum cost of the cost function (first index of + min_cost_fn). + min_cost_fn: array_like(dtype=float) + Cost function vector. + """ + fbwm = False + if not ( + (fbwm_SH1 is None) or (fbwm_SH2 is None) or (fbwm_B1 is None) or (fbwm_B2 is None) or (fbwm_dt is None) + ): + fbwm = True + if sh_area is None: + rectify = False + # For references to alm and clm see FBI papers, they (alm + # and clm) are defined in all of them + alm = np.dot(np.linalg.pinv(B), (dwi / b0)) # DWI signal SH coefficients (these are complex) + alm[np.isnan(alm)] = 0 + a00 = alm[0].real # the imaginary part is on the order of 10^-18 (this is for zeta) + clm = ( + alm * gl[0] * highprecisionpower(np.sqrt(4 * np.pi) * alm[0] * Pl0 * gl, -1) + ) # fODF SH coefficients (these are complex) + # need to figure out how to do peak detection (on this variable and + # then read out odf structures like in MATLAB code) only the real + # part would be read out but that would need to be done later on + # after the rectification process below + ODF = np.matmul(H, clm) + if rectify: + ODF = __fbi_rectify(ODF.real, sh_area, iter=100) + # Re-expand the rectified fODF into SH's + clm = np.matmul(sh_area * ODF, np.conj(H)) + clm = (clm / clm[0]) * (1 / np.sqrt(4 * np.pi)) # normalize clm + # zeta and FAA calculations + # NOTE: zeta is not affected by the rectification, only FAA + zeta = a00 * np.sqrt(self.maxBval()) / np.pi + faa = np.sqrt( + 3 * np.sum(np.abs(clm[1:6] ** 2)) / (5 * np.abs(clm[0]) ** 2 + 2 * np.sum(np.abs(clm[1:6] ** 2))) + ) + # BEGIN: construct axonal DT (aDT) + c00 = clm[0] + c2_2 = clm[1] + c2_1 = clm[2] + c20 = clm[3] + c21 = clm[4] + c22 = clm[5] + A11 = (np.sqrt(30) / 3) * c00 - (np.sqrt(6) / 3) * c20 + c22 + c2_2 + A22 = (np.sqrt(30) / 3) * c00 - (np.sqrt(6) / 3) * c20 - c22 - c2_2 + A33 = (np.sqrt(30) / 3) * c00 + (2 * np.sqrt(6) / 3) * c20 + A12 = 1j * (c22 - c2_2) + A13 = -c21 + c2_1 + A23 = 1j * (-c21 - c2_1) + aDT = np.array([A11, A12, A13, A12, A22, A23, A13, A23, A33]).real + aDT = 1 / (c00 * np.sqrt(30)) * aDT + iaDT = np.reshape(aDT, (3, 3)).real + if fbwm: + bval = np.rint(self.grad[:, -1]) + BT = np.unique(bval) + BT = BT[BT != 0] # Exclude B0s + GT = [self.grad[bval == x, 0:3] for x in BT] + f_grid = np.linspace(0, 1, 100) # define AWF grid (100 pts evenly spaced between 0 (min) and 1 (max)) + int_grid = np.linspace(0, 99, 100, dtype=int) # define grid points to iterate over (100 of them) + awf_grid = np.linspace(0, 1, 100) # another AWF grid + # This holds the SH basis sets for each b-value shell + shB = [ + fbwm_SH1, + fbwm_SH2, + B, + ] # list object: to access, shB[0] = B1 (for example) + # This hold all DWI volumes for each b-vlaue shell + IMG = [fbwm_B1, fbwm_B2, dwi] # list object: to access + # BEGIN: DT construction + iDT = np.array( + [ + fbwm_dt[0], + fbwm_dt[3], + fbwm_dt[4], + fbwm_dt[3], + fbwm_dt[1], + fbwm_dt[5], + fbwm_dt[4], + fbwm_dt[5], + fbwm_dt[2], + ] + ) + iDT = np.reshape(iDT, (3, 3)) + # END: DT construction + # initialze correction factor elements that will be looped over and filled accordingly... + g2l_fa_R = np.zeros((len(Pl0), f_grid.shape[0]), order="F") + g2l_fa_R_b = np.zeros((len(BT), f_grid.shape[0], len(Pl0)), order="F") + g2l_fa_R_large = np.zeros((len(Pl0), f_grid.shape[0]), order="F") + # BEGIN: cost function + # Not many comments here, See McKinnon 2018 FBWm paper for details + for b in range(0, len(BT)): + idx_hyper = ( + BT[b] * np.power(f_grid, 2) * np.power(zeta, -2) < 20 + ) # when should hypergeometric function be implemented? When b*D is small + idx_Y = 0 + for l_num in degs[::2]: + hypergeom_opt = np.sum( + ( + gamma((l_num + 1) / 2 + int_grid) + * gamma(l_num + (3 / 2)) + * ( + (-BT[b] * f_grid[idx_hyper] ** 2 * zeta**-2) + * np.ones((1, len(f_grid[idx_hyper]))) + ).T + ** int_grid + / (factorial(int_grid) * gamma(l_num + (3 / 2) + int_grid) * gamma((l_num + 1) / 2)) + ), + 1, + ) * np.ones((1, len(f_grid[idx_hyper]))) + g2l_fa_R[idx_Y : idx_Y + (2 * l_num + 1), np.squeeze(idx_hyper)] = npm.repmat( + ( + factorial(l_num // 2) + * (BT[b] * f_grid[idx_hyper] ** 2 * zeta**-2) ** ((l_num + 1) / 2) + / gamma(l_num + (3 / 2)) + * hypergeom_opt + ), + (2 * l_num + 1), + 1, + ) # Eq. 9 FBWM paper + idx_Y = idx_Y + (2 * l_num + 1) + g2l_fa_R_b[b, np.squeeze(idx_hyper), :] = g2l_fa_R[:, np.squeeze(idx_hyper)].T + idx_Y = 0 + for l_num in degs[::2]: + g2l_fa_R_large[idx_Y : idx_Y + (2 * l_num + 1), np.squeeze(~idx_hyper)] = npm.repmat( + ( + np.exp( + -l_num // 2 * (l_num + 1) / ((2 * BT[b] * (f_grid[~idx_hyper] ** 2 * zeta**-2))) + ) + ), + (2 * l_num + 1), + 1, + ) # Eq. 20 FBI paper + idx_Y = idx_Y + (2 * l_num + 1) + g2l_fa_R_b[b, np.squeeze(~idx_hyper), :] = g2l_fa_R_large[:, np.squeeze(~idx_hyper)].T + cost_fn = __costCalculator( + awf_grid, + BT, + GT, + b0, + IMG, + iDT, + iaDT, + zeta, + shB, + Pl0, + g2l_fa_R_b, + clm, + ) + min_cost_fn_idx = np.argsort(cost_fn, axis=0) # find the indexes of the sorted cost_fn values + min_cost_fn = np.take_along_axis(cost_fn, min_cost_fn_idx, axis=0) # sort those values + min_awf = awf_grid[ + min_cost_fn_idx[0] + ] # grad the minimum AWF value based on the cost_fn sorting done immeidately prior to this... + De = (iDT - (min_awf**3 * zeta**-2) * iaDT) / (1 - min_awf) + Da = min_awf**2 / zeta**2 + iDe = De # intermeidate De + iDe[np.isnan(iDe)] = minZero + iDe[np.isinf(iDe)] = minZero + L, V = np.linalg.eig(iDe) # L : eigVals and V: eigVecs + L = np.sort(L) # sort them (this is ascending) + L = L[::-1] # reverse the order so they are descending (high -> low) + N = 1 # initialize counter + if L[0] < 0 or L[1] < 0 or L[2] < 0: + while L[0] < 0 or L[1] < 0 or L[2] < 0: # find new AWF values if L's are < 0 + N = N + 1 + if N < 100: + min_awf = awf_grid[min_cost_fn_idx[N]] + else: + min_awf = 0 + break + # update De here... + De = (iDT - (min_awf**3 * zeta**2) * iaDT) / (1 - min_awf) + Da = min_awf**2 / zeta**2 # recalculate Da too... + # Now recalculate eigVals again with correct AWF values + iDe = De + iDe[np.isnan(iDe)] = minZero + iDe[np.isinf(iDe)] = minZero + L, V = np.linalg.eig(iDe) # L : eigVals and V: eigVecs + L = np.sort(L) # again, ascending + L = L[::-1] # now, descending + with np.errstate(invalid="ignore"): + De_ax = L[0] # Eq. 24 FBWM paper, axial extra-axonal diffusivity + De_rad = (L[1] + L[2]) / 2 # radial De + De_fa = np.sqrt( + ((L[0] - L[1]) ** 2 + (L[0] - L[2]) ** 2 + (L[1] - L[2]) ** 2) / (2 * np.sum(L**2)) + ) # extra-axonal FA + De_mean = (1 / 3) * (2 * De_rad + De_ax) # average De + min_cost = min_cost_fn[0] + else: + min_awf = None + Da = None + De_mean = None + De_ax = None + De_rad = None + De_fa = None + min_cost = None + min_cost_fn = None + + return ( + zeta, + faa, + clm, + min_awf, + Da, + De_mean, + De_ax, + De_rad, + De_fa, + min_cost, + min_cost_fn, + ) + + # --------------------FUNCTION SEPARATOR----------------------- + + if fbwm and not hasattr(self, "dt"): + raise Exception( + "Cannot compute FBWM parameters " + "without running diffusion tensor fitting first. " + "Please run DWI.fit(constraints) before running DWI.fbi()." + ) + if l_max % 2 != 0: + raise Exception("Please provide l_max as a postive " "and even integer") + if l_max > self.optimal_lmax(): + print( + "[WARNING]: l_max value provided ({}) is " + "more than that supported by DWI ({}). Reverting " + "to l_max = {}".format(l_max, self.optimal_lmax(), self.optimal_lmax()) + ) + l_max = self.optimal_lmax() + img = self.img + np.unique(self.grad[:, -1]) + self.optimal_lmax() + b0 = np.nanmean(img[:, :, :, self.idxb0()], axis=3) + b0 = np.nan_to_num(b0, nan=0, posinf=0, neginf=0) + # Vectorize images + b0 = vectorize(b0, self.mask) + img = vectorize(img, self.mask) + # Create shperical harmonic (SH) base set + degs = np.arange(l_max + 1, dtype=int) + l_num = 2 * degs + 1 # how many per degree (evens only) + harmonics = [] + sh_end = 0 # initialize the SH set for indexing + for _, phase in enumerate(l_num[::2]): + sh_start = sh_end + phase - 1 + sh_end = sh_start + phase - 1 + harmonics.extend(np.arange(sh_start, sh_end + 1)) + # Define the azimuthal (phi) and polar(theta) angles for our + # spherical expansion using the experimentally defined + # gradients from the scanner + phi = np.arccos(self.grad[self.idxfbi(), 2]) + theta = np.arctan2(self.grad[self.idxfbi(), 1], self.grad[self.idxfbi(), 0]) + # gradients for resampling from distribution + ( + spherical_grid, + idx, + idx8, + AREA, + faces, + separation_angle, + ) = sphericalsampling.odfgrid(res) + S1 = spherical_grid[:, 0] # phi + S2 = spherical_grid[:, 1] # theta + # B = shbasis(degs, phi, theta) + # H = shbasis(degs, S1, S2) + B = odf.shbasis(degs, phi, theta, method="scipy") + B = B[:, harmonics] + H = odf.shbasis(degs, S1, S2, method="scipy") + H = H[:, harmonics] + idx_Y = 0 + Pl0 = np.zeros((len(harmonics), 1), order="F") # need Legendre polynomial Pl0 + gl = np.zeros( + (len(harmonics), 1), order="F" + ) # calculate correction factor (see original FBI paper, Jensen 2016) + for l_num in degs[::2]: + Pl0[idx_Y : idx_Y + (2 * l_num + 1), :] = ( + (np.power(-1, l_num // 2) * np.math.factorial(l_num)) + / (np.power(4, l_num // 2) * np.power(np.math.factorial(l_num // 2), 2)) + * np.ones((2 * l_num + 1, 1)) + ) + gl[idx_Y : idx_Y + (2 * l_num + 1), :] = ( + (np.math.factorial(l_num // 2) * np.power(self.maxBval() * th.__d0__, (l_num + 1) / 2)) + / gamma(l_num + 3 / 2) + * hyp1f1((l_num + 1) / 2, l_num + 3 / 2, -self.maxBval() * th.__d0__) + * np.ones((2 * l_num + 1, 1)) + ) + idx_Y = idx_Y + (2 * l_num + 1) + Pl0 = np.squeeze(Pl0) + gl = np.squeeze(gl) + inputs = tqdm( + range(0, img.shape[1]), + desc="FBI Fit", + bar_format="{desc}: [{percentage:0.0f}%]", + unit="vox", + ncols=tqdmWidth, + ) + if fbwm: + # Index gradients based on b1000 and b2000 shells + bval = np.rint(self.grad[:, -1]) + phi1 = np.arccos(self.grad[bval == 1, 2]) + theta1 = np.arctan2(self.grad[bval == 1, 1], self.grad[bval == 1, 0]) + + phi2 = np.arccos(self.grad[bval == 2, 2]) + theta2 = np.arctan2(self.grad[bval == 2, 1], self.grad[bval == 2, 0]) + # SH basis set for the two B-values in DKI + fbwm_SH1 = odf.shbasis(degs, phi1, theta1, method="scipy") + fbwm_SH2 = odf.shbasis(degs, phi2, theta2, method="scipy") + fbwm_SH1 = fbwm_SH1[:, harmonics] + fbwm_SH2 = fbwm_SH2[:, harmonics] + dt, kt = self.tensorReorder("dki") + dt = vectorize(dt, self.mask) + # for i in inputs: + # zeta, faa, fodf, min_awf, Da, De_mean, De_ax, De_rad, De_fa, min_cost, min_cost_fn = \ + # fbi_helper( + # dwi=img[self.idxfbi(), i], + # b0 = b0[i], + # B = B, + # H = H, + # Pl0=Pl0, + # gl = gl, + # rectify=rectify, + # fbwm_SH1 = fbwm_SH1, + # fbwm_SH2 = fbwm_SH2, + # fbwm_B1 = img[self.grad[:, -1] == 1, i], + # fbwm_B2 = img[self.grad[:, -1] == 2, i], + # fbwm_dt = dt[:, i], + # fbwm_degs=degs, + # sh_area=AREA + # ) + ( + zeta, + faa, + fodf, + min_awf, + Da, + De_mean, + De_ax, + De_rad, + De_fa, + min_cost, + min_cost_fn, + ) = zip( + *Parallel(n_jobs=self.workers, prefer="processes")( + delayed(fbi_helper)( + dwi=img[self.idxfbi(), i], + b0=b0[i], + B=B, + H=H, + Pl0=Pl0, + gl=gl, + rectify=rectify, + fbwm_SH1=fbwm_SH1, + fbwm_SH2=fbwm_SH2, + fbwm_B1=img[bval == 1, i], + fbwm_B2=img[bval == 2, i], + fbwm_dt=dt[:, i], + fbwm_degs=degs, + sh_area=AREA, + ) + for i in inputs + ) + ) + else: + # for i in inputs: + # zeta, faa, fodf, min_awf, Da, De_mean, De_ax, De_rad, De_fa, min_cost, min_cost_fn = \ + # fbi_helper( + # dwi=img[self.idxfbi(), i], + # b0 = b0[i], + # B = B, + # H = H, + # Pl0=Pl0, + # gl = gl, + # rectify=rectify, + # sh_area=AREA + # ) + ( + zeta, + faa, + fodf, + min_awf, + Da, + De_mean, + De_ax, + De_rad, + De_fa, + min_cost, + min_cost_fn, + ) = zip( + *Parallel(n_jobs=self.workers, prefer="processes")( + delayed(fbi_helper)( + dwi=img[self.idxfbi(), i], + b0=b0[i], + B=B, + H=H, + Pl0=Pl0, + gl=gl, + rectify=rectify, + sh_area=AREA, + ) + for i in inputs + ) + ) + fodf_mrtrix = odf.odf_conversion(np.array(fodf).T, target="tournier") + zeta = vectorize(np.array(zeta), self.mask) + faa = vectorize(np.array(faa), self.mask) + fodf = vectorize(np.array(fodf).T, self.mask) + fodf_mrtrix = vectorize(fodf_mrtrix, self.mask) + awf = vectorize(np.array(min_awf), self.mask) + Da = vectorize(np.array(Da), self.mask) + De_mean = vectorize(np.array(De_mean), self.mask) + De_ax = vectorize(np.array(De_ax), self.mask) + De_rad = vectorize(np.array(De_rad), self.mask) + De_fa = vectorize(np.array(De_fa), self.mask) + min_cost = vectorize(np.array(min_cost), self.mask) + min_cost_fn = vectorize(np.array(min_cost_fn).T, self.mask) + return ( + zeta, + faa, + fodf, + fodf_mrtrix, + awf, + Da, + De_mean, + De_ax, + De_rad, + De_fa, + min_cost, + min_cost_fn, + ) + + def extractWMTI( + self, + ) -> Tuple[np.ndarray[float], np.ndarray[float], np.ndarray[float], np.ndarray[float], np.ndarray[float],]: + """Returns white matter tract integrity (WMTI) parameters. Warning: + this can only be run after fitting and DWI.extractDTI(). + + Returns + ------- + awf: ndarray(dtype=float) + Axonal Water Fraction + eas_ad: ndarray(dtype=float) + Extra-axonal space Axial Diffusivity + eas_rd: ndarray(dtype=float) + Extra-axonal Space Radial Diffusivity + eas_md: ndarray(dtype=float) + Extra-axonal Space Mean Diffusivity + eas_tort: ndarray(dtype=float) + Extra-axonal Space Tortuosity + ias_ad: ndarray(dtype=float) + Intra-axonal Space Axial Diffusivity + ias_rd: ndarray(dtype=float) + Intra-axonal Space Radial Diffusivity + ias_da: ndarray(dtype=float) + Intra-axonal Space Intrinsic Diffusivity + ias_tort: ndarray(dtype=float) + Intra-axonal Space Tortuosity + """ + + def wmtihelper(dt, dir, adc, akc, awf, adc2dt): + # Avoid complex output. However, + # negative AKC might be taken care of by applying constraints + with np.errstate(invalid="ignore"): + akc[akc < minZero] = minZero + try: + # Eigenvalue decomposition of De(extra-axonal) + De = np.multiply(adc, 1 + np.sqrt((np.multiply(akc, awf) / (3 * (1 - awf))))) + dt_e = np.matmul(adc2dt, De) + DTe = dt_e[[0, 1, 2, 1, 3, 4, 2, 4, 5]] + DTe = np.reshape(DTe, (3, 3), order="F") + eigval = sla.eigh(DTe, eigvals_only=True) + eigval = np.sort(eigval)[::-1] + eas_ad = eigval[0] + eas_rd = 0.5 * (eigval[1] + eigval[2]) + try: + eas_tort = eas_ad / eas_rd + except: # noqa: E722 + eas_tort = minZero + except: # noqa: E722 + eas_ad = minZero + eas_rd = minZero + eas_tort = minZero + try: + # Eigenvalue decomposition of Da (intra-axonal) + Di = np.multiply(adc, 1 - np.sqrt((np.multiply(akc, (1 - awf)) / (3 * awf)))) + dt_i = np.matmul(adc2dt, Di) + DTi = dt_i[[0, 1, 2, 1, 3, 4, 2, 4, 5]] + DTi = np.reshape(DTi, (3, 3), order="F") + eigval = sla.eigh(DTi, eigvals_only=True) + eigval = np.sort(eigval)[::-1] + ias_da = np.sum(eigval) + np.seterr(invalid="raise") + except: # noqa: E722 + ias_da = minZero + return eas_ad, eas_rd, eas_tort, ias_da + + dir = dwidirs.dirs10000 + nvox = self.dt.shape[1] + N = dir.shape[0] + nblocks = 10 + maxk = np.zeros((nvox, nblocks)).astype(float) + inputs = tqdm( + range(nblocks), + desc="Extracting AWF", + bar_format="{desc}: [{percentage:0.0f}%]", + unit="iter", + ncols=tqdmWidth, + ) + for i in inputs: + maxk = np.stack(self.kurtosisCoeff(self.dt, dir[int(N / nblocks * i) : int(N / nblocks * (i + 1))])).astype( + float + ) + maxk = np.nanmax(maxk, axis=0) + awf = np.divide(maxk, (maxk + 3)).astype(float) + # Changes voxels less than minZero, nans and infs to minZero + truncateIdx = np.logical_or(np.logical_or(np.isnan(awf), np.isinf(awf)), (awf < minZero)) + awf[truncateIdx] = minZero + dirs = dwidirs.dirs30 + adc = self.diffusionCoeff(self.dt[:6], dirs) + akc = self.kurtosisCoeff(self.dt, dirs) + (dcnt, dind) = self.createTensorOrder(2) + adc2dt = np.linalg.pinv(np.matmul((dirs[:, dind[:, 0]] * dirs[:, dind[:, 1]]), np.diag(dcnt))) + eas_ad = np.zeros(nvox) + eas_rd = np.zeros(nvox) + eas_md = np.zeros(nvox) + eas_tort = np.zeros(nvox) + ias_ad = np.zeros(nvox) + ias_rd = np.zeros(nvox) + ias_da = np.zeros(nvox) + ias_tort = np.zeros(nvox) + inputs = tqdm( + range(nvox), + desc="Extracting EAS and IAS", + bar_format="{desc}: [{percentage:0.0f}%]", + unit="vox", + ncols=tqdmWidth, + ) + eas_ad, eas_rd, eas_tort, ias_da = zip( + *Parallel(n_jobs=self.workers, prefer="processes")( + delayed(wmtihelper)(self.dt[:, i], dirs, adc[:, i], akc[:, i], awf[i], adc2dt) for i in inputs + ) + ) + awf = vectorize(awf, self.mask) + eas_ad = vectorize(np.array(eas_ad), self.mask) + eas_rd = vectorize(np.array(eas_rd), self.mask) + eas_md = vectorize(np.array(eas_md), self.mask) + eas_tort = vectorize(np.array(eas_tort), self.mask) + ias_ad = vectorize(np.array(ias_ad), self.mask) + ias_rd = vectorize(np.array(ias_rd), self.mask) + ias_da = vectorize(np.array(ias_da), self.mask) + ias_tort = vectorize(np.array(ias_tort), self.mask) + return awf, eas_ad, eas_rd, eas_tort, ias_da + + def multiplyMask(self, img: np.ndarray[float]) -> np.ndarray[float]: + """Multiplies a 3D image by the brain mask. + + Parameters + ---------- + img: ndarray(dtype=float) + 3D image to be multiplied. + + Returns + ------- + ndarray(dtype=float) + Multiplied image. + """ + # Returns an image multiplied by the brain mask to remove all + # values outside the brain + return np.multiply(self.mask.astype(bool), img) + + def akcoutliers(self, iter: int = 10) -> np.ndarray[bool]: + """Uses 100,000 direction in chunks of 10 to iteratively find + outliers. Returns a mask of locations where said violations + occur. Multiprocessing is disabled because this is a + memory-intensive task. + To be run only after tensor fitting. + + Parameters + ---------- + iter: int, optional + Number of iterations to perform out of 10. Reduce this + number if your computer does not have sufficient RAM. + (Default: 10). + + Returns + ------- + akc_out: ndarray(dtype=bool) + 3D map containing outliers where AKC falls fails the + inequality test -2 < AKC < 10. + + Examples + -------- + akc_out = dwi.akoutliers(), where dwi is the DWI class object. + """ + dir = dwidirs.dirs10000 + nvox = self.dt.shape[1] + akc_out = np.zeros(nvox, dtype=bool) + N = dir.shape[0] + nblocks = 10 + if iter > nblocks: + print("Entered iteration value exceeds 10...resetting to 10") + iter = 10 + inputs = tqdm( + range(iter), + desc="AKC Outlier Detection", + bar_format="{desc}: [{percentage:0.0f}%]", + unit="blk", + ncols=tqdmWidth, + ) + for i in inputs: + akc = self.kurtosisCoeff(self.dt, dir[int(N / nblocks * i) : int(N / nblocks * (i + 1))]) + akc_out[np.where(np.any(np.logical_or(akc < -2, akc > 10), axis=0))] = True + akc_out.astype("bool") + return vectorize(akc_out, self.mask) + + def akccorrect(self, akc_out: np.ndarray[bool], window: int = 3, connectivity: str = "face") -> Self: + """Applies AKC outlier map to DT to replace outliers with a + moving median. Run this only after tensor fitting and akc + outlier detection. + + Parameters + ---------- + akc_out : ndarray(dtype=bool) + 3D map containing outliers from DWI.akcoutliers + window : int, optional + Width of square matrix filter (Default: 5) + connectivity : str, {'face', 'all'}, optional + Specifies what kind of connected-component connectivity to + use for median determination + + Examples + -------- + dwi.akccorrect(akc_out), where dwi is the DWI class object + """ + # Get box filter properties + centralIdx = np.median(range(window)) + d2move = np.int(np.abs(window - (centralIdx + 1))) # Add 1 to + # central idx because first index starts with zero + # Vectorize and Pad + dt = np.pad( + vectorize(self.dt, self.mask), + ((d2move, d2move), (d2move, d2move), (d2move, d2move), (0, 0)), + "constant", + constant_values=np.nan, + ) + akc_out = np.pad(akc_out, d2move, "constant", constant_values=False) + violIdx = np.array(np.where(akc_out)) # Locate coordinates of violations + nvox = violIdx.shape[1] + for i in tqdm( + range(dt.shape[-1]), + desc="AKC Correction", + bar_format="{desc}: [{percentage:0.0f}%]", + unit="tensor", + ncols=tqdmWidth, + ): + for j in range(nvox): + # Index beginning and ending of patch + Ib = violIdx[0, j] - d2move + Ie = violIdx[0, j] + d2move + 1 + Jb = violIdx[1, j] - d2move + Je = violIdx[1, j] + d2move + 1 + Kb = violIdx[2, j] - d2move + Ke = violIdx[2, j] + d2move + 1 + + if connectivity == "all": + patchViol = np.delete( + np.ravel(akc_out[Ib:Ie, Jb:Je, Kb:Ke]), + np.median(range(np.power(window, 3))), + ) # Remove + # centroid element + patchImg = np.delete( + np.ravel(dt[Ib:Ie, Jb:Je, Kb:Ke, i]), + np.median(range(np.power(window, 3))), + ) # Remove + # centroid element + connLimit = np.power(window, 3) - 1 + elif connectivity == "face": + patchViol = np.delete(akc_out[Ib:Ie, violIdx[1, j], violIdx[2, j]], d2move) + patchViol = np.hstack( + ( + patchViol, + np.delete(akc_out[violIdx[0, j], Jb:Je, violIdx[2, j]], d2move), + ) + ) + patchViol = np.hstack( + ( + patchViol, + np.delete(akc_out[violIdx[0, j], violIdx[1, j], Kb:Ke], d2move), + ) + ) + patchImg = np.delete(dt[Ib:Ie, violIdx[1, j], violIdx[2, j], i], d2move) + patchImg = np.hstack( + ( + patchImg, + np.delete(dt[violIdx[0, j], Jb:Je, violIdx[2, j], i], d2move), + ) + ) + patchImg = np.hstack( + ( + patchImg, + np.delete(dt[violIdx[0, j], violIdx[1, j], Kb:Ke, i], d2move), + ) + ) + if window == 3: + connLimit = 6 + elif window == 5: + connLimit = 12 + elif window == 7: + connLimit = 18 + elif window == 9: + connLimit = 24 + else: + raise Exception( + 'Connectivity choice "{}" is invalid. Please ' + 'enter either "all" or "face".'.format(connectivity) + ) + nVoil = np.sum(patchViol) + + # Here a check is performed to compute the number of + # violations in a patch. If all voxels are violations, + # do nothing. Otherwise, exclude violation voxels from + # the median calculation + if nVoil == connLimit: + continue + else: + dt[violIdx[0, j], violIdx[1, j], violIdx[2, j], i] = np.nanmedian(patchImg) + # Remove padding + dt = dt[d2move:-d2move, d2move:-d2move, d2move:-d2move, :] + self.dt = vectorize(dt, self.mask) + + def irlls( + self, + excludeb0: bool = True, + maxiter: int = 25, + convcrit: float = 1e-3, + mode: str = "DKI", + leverage: float = 0.85, + bounds: int = 3, + ) -> Tuple[np.ndarray[bool], np.ndarray[float]]: + """This functions performs outlier detection and robust parameter + estimation for diffusion MRI using the iterative reweigthed + linear least squares (IRLLS) approach. + + Parameters + ---------- + exludeb0: bool, optional + Exlude the b0 images when removing outliers (Default: True) + maxiter: int, optional + Maximum number of iterations in the iterative reweighting + loop (Default: 25). + convcrit: float, optional + Fraction of L2-norm of estimated diffusion parameter + vector that the L2-norm of different vector should get in + order to reach convergence in the iterative reweighted + loop (Default: 1e-3). + mode: str, {'DKI', 'DTI'}, optional + Specifies whether to use DTI or DKI model (Default: 'DKI') + leverage: float, optional. + Measurement ranging from 0 to 1 where a leverage above + this threshold will not be excluded in estimation of DT + after outlier (Default: 0.85). + Bounds: int, optional + Set the threshold of the number of standard deviation + that are needed to exclude a measurement (Default: 3). + + Returns + ------- + outliers: ndarray(dtype=bool) + 4D image same size as input DWI marking voxels + that are outliers. + dt: ndarray(dtype=float) + IRLLS method of DT estimation. + + Examples + -------- + outliers = dwi.irlls() + """ + # if not excludeb0.dtype: + # assert('option: Excludeb0 should be set to True or False') + + if maxiter < 1 or maxiter > 200: + assert "option: Maxiter should be set to a value between 1 " "and 200" + if convcrit < 0 or convcrit > 1: + assert "option: Maxiter should be set to a value between 1 " "and 200" + if not (mode == "DKI" or mode == "DTI"): + assert "Mode should be set to DKI or DTI" + if leverage < 0 or leverage > 1: + assert "option: Leverage should be set to a value between 0 " "and 1" + if bounds < 1: + assert "option: Bounds should be set to a value >= 1" + exclude_idx = np.ones_like(self.grad[:, 3], dtype=bool) + exclude_idx = self.idxdki() + # Vectorize DWI + dwi = vectorize(self.img[:, :, :, exclude_idx], self.mask) + (ndwi, nvox) = dwi.shape + b = np.array(self.grad[exclude_idx, 3]) + b = np.reshape(b, (len(b), 1)) + g = self.grad[exclude_idx, 0:3] + # Apply Scaling + scaling = False + if np.sum(dwi < 1) / np.size(dwi) < 0.001: + dwi[dwi < 1] = 1 + else: + scaling = True + if self.maxDKIBval() < 10: + tmp = dwi[dwi < 0.05] + else: + tmp = dwi[dwi < 50] + sc = np.median(tmp) + dwi[dwi < sc / 1000] = sc / 1000 + dwi = dwi * 1000 / sc + # Create B-matrix + (dcnt, dind) = self.createTensorOrder(2) + if mode == "DTI": + bmat = np.hstack( + ( + np.ones((ndwi, 1)), + np.matmul( + (-np.tile(b, (1, 6)) * g[:, dind[:, 0]] * g[:, dind[:, 1]]), + np.diag(dcnt), + ), + ) + ) + else: + (wcnt, wind) = self.createTensorOrder(4) + bmat = np.hstack( + ( + np.ones((ndwi, 1)), + np.matmul( + (-np.tile(b, (1, 6)) * g[:, dind[:, 0]] * g[:, dind[:, 1]]), + np.diag(dcnt), + ), + (1 / 6) + * np.matmul( + ( + np.square(np.tile(b, (1, 15))) + * g[:, wind[:, 0]] + * g[:, wind[:, 1]] + * g[:, wind[:, 2]] + * g[:, wind[:, 3]] + ), + np.diag(wcnt), + ), + ) + ) + nparam = bmat.shape[1] + ndof = ndwi - nparam + # Initialization + b0_pos = np.zeros(b.shape, dtype=bool, order="F") + if excludeb0: + if self.maxDKIBval() < 10: + b0_pos = b < 0.01 + else: + b0_pos = b < 10 + reject = np.zeros(dwi.shape, dtype=bool, order="F") + np.zeros((nvox, 1)) + dt = np.zeros((nparam, nvox)) + # Attempt basic noise estimation + try: + sigma + except NameError: + + def estSigma(dwi, bmat): + dwi = np.reshape(dwi, (len(dwi), 1)) + try: + dt_ = np.linalg.lstsq(bmat, np.log(dwi), rcond=None)[0] + # dt_ = np.linalg.solve(np.dot(bmat.T, bmat), np.dot( + # bmat.T, np.log(dwi))) + except: # noqa: E722 + dt_ = np.full((bmat.shape[1], 1), minZero) + w = highprecisionexp(np.matmul(bmat, dt_)).reshape((ndwi, 1)) + try: + dt_ = np.linalg.lstsq((bmat * np.tile(w, (1, nparam))), (np.log(dwi) * w), rcond=None)[0] + # dt_ = np.linalg.solve( + # np.dot((bmat * np.tile(w, (1, nparam))).T, + # (bmat * np.tile(w, (1, nparam)))), \ + # np.dot((bmat * np.tile(w, (1, nparam))).T, (np.log( + # dwi) * w))) + except: # noqa: E722 + dt_ = np.full((bmat.shape[1], 1), minZero) + e = np.log(dwi) - np.matmul(bmat, dt_) + m = np.median(np.abs((e * w) - np.median(e * w))) + try: + sigma_ = np.sqrt(ndwi / ndof) * 1.4826 * m + except: # noqa: E722 + sigma_ = minZero + return sigma_ + + sigma_ = np.zeros((nvox, 1)) + inputs = tqdm( + range(nvox), + desc="IRLLS Noise Estimation", + bar_format="{desc}: [{percentage:0.0f}%]", + unit="vox", + ncols=tqdmWidth, + ) + sigma_ = Parallel(n_jobs=self.workers, prefer="processes")( + delayed(estSigma)(dwi[:, i], bmat) for i in inputs + ) + sigma = np.median(sigma_) + sigma = np.tile(sigma, (nvox, 1)) + if scaling: + sigma = sigma * 1000 / sc + + def outlierHelper(dwi, bmat, sigma, b, b0_pos, maxiter=25, convcrit=1e-3, leverage=3, bounds=3): + # Preliminary rough outlier check + dwi_i = dwi.reshape((len(dwi), 1)) + dwi0 = np.median(dwi_i[b.reshape(-1) < 0.01]) + out = dwi_i > (dwi0 + 3 * sigma) + if np.sum(~out[b.reshape(-1) > 0.01]) < (bmat.shape[1] - 1): + out = np.zeros((out.shape), dtype=bool) + out[b0_pos.reshape(-1)] = False + bmat_i = bmat[~out.reshape(-1)] + dwi_i = dwi_i[~out.reshape(-1)] + n_i = dwi_i.size + ndof_i = n_i - bmat_i.shape[1] + # WLLS estimation + try: + dt_i = np.linalg.lstsq(bmat_i, np.log(dwi_i), rcond=None)[0] + # dt_i = np.linalg.solve(np.dot(bmat_i.T, bmat_i), + # np.dot(bmat_i.T, np.log(dwi_i))) + except: # noqa: E722 + dt_i = np.full((bmat_i.shape[1], 1), minZero) + w = highprecisionexp(np.matmul(bmat_i, dt_i)) + try: + dt_i = np.linalg.lstsq( + (bmat_i * np.tile(w, (1, nparam))), + (np.log(dwi_i).reshape((dwi_i.shape[0], 1)) * w), + rcond=None, + )[0] + # dt_i = np.linalg.solve( + # np.dot((bmat_i * np.tile(w, (1, nparam))).T, + # (bmat_i * np.tile(w, (1, nparam)))), + # np.dot((bmat_i * np.tile(w, (1, nparam))).T, + # (np.log(dwi_i).reshape( + # (dwi_i.shape[0], 1)) * w))) + except: # noqa: E722 + dt_i = np.full((bmat_i.shape[1], 1), minZero) + dwi_hat = highprecisionexp(np.matmul(bmat_i, dt_i)) + # Goodness-of-fit + residu = np.log(dwi_i.reshape((dwi_i.shape[0], 1))) - np.log(dwi_hat) + residu_ = dwi_i.reshape((dwi_i.shape[0], 1)) - dwi_hat + + try: + chi2 = np.sum((residu_ * residu_) / np.square(sigma)) / (ndof_i) - 1 + except: # noqa: E722 + chi2 = minZero + try: + gof = np.abs(chi2) < 3 * np.sqrt(2 / ndof_i) + except: # noqa: E722 + gof = True # If ndof_i = 0, right inequality becomes inf + # and makes the logic True + gof2 = gof + # Iterative reweighning procedure + iter = 0 + np.seterr(divide="raise", invalid="raise") + while (not gof) and (iter < maxiter): + try: + C = ( + np.sqrt(n_i / (n_i - nparam)) + * 1.4826 + * np.median(np.abs(residu_ - np.median(residu_))) + / dwi_hat + ) + except: # noqa: E722 + C = np.full(dwi_hat.shape, minZero) + try: + GMM = np.square(C) / np.square(np.square(residu) + np.square(C)) + except: # noqa: E722 + # The following line produces a lot of Intel MKL + # warnings that should be ignored. This is a known + # Intel and Numpy bug that has not yet been resolved. + GMM = np.full(C.shape, minZero) + w = np.sqrt(GMM) * dwi_hat + dt_imin1 = dt_i + try: + dt_i = np.linalg.lstsq( + (bmat_i * np.tile(w, (1, nparam))), + (np.log(dwi_i).reshape((dwi_i.shape[0], 1)) * w), + rcond=None, + )[0] + # dt_i = np.linalg.solve( + # np.dot((bmat_i * np.tile(w, (1, nparam))).T, + # (bmat_i * np.tile(w, (1, nparam)))), + # np.dot((bmat_i * np.tile(w, (1, nparam))).T, + # (np.log(dwi_i).reshape( + # (dwi_i.shape[0], 1)) * w))) + except: # noqa: E722 + dt_i = np.full((bmat_i.shape[1], 1), minZero) + dwi_hat = highprecisionexp(np.matmul(bmat_i, dt_i)) + dwi_hat[dwi_hat < 1] = 1 + residu = np.log(dwi_i.reshape((dwi_i.shape[0], 1))) - np.log(dwi_hat) + residu_ = dwi_i.reshape((dwi_i.shape[0], 1)) - dwi_hat + # Convergence check + iter = iter + 1 + gof = np.linalg.norm(dt_i - dt_imin1) < np.linalg.norm(dt_i) * convcrit + np.seterr(**defaultErrorState) + # Outlier detection + if ~gof2: + try: + lev = np.diag( + np.matmul( + bmat_i, + np.linalg.lstsq( + np.matmul( + np.transpose(bmat_i), + np.matmul(np.diag(np.square(w).reshape(-1)), bmat_i), + ), + np.matmul( + np.transpose(bmat_i), + np.diag(np.square(w.reshape(-1))), + ), + rcond=None, + )[0], + ) + ) + except: # noqa: E722 + lev = np.full(residu.shape, minZero) + lev = lev.reshape((lev.shape[0], 1)) + try: + lowerbound_linear = -bounds * np.lib.scimath.sqrt(1 - lev) * sigma / dwi_hat + except: # noqa: E722 + lowerbound_linear = np.full(lev.shape, minZero) + try: + upperbound_nonlinear = bounds * np.lib.scimath.sqrt(1 - lev) * sigma + except: # noqa: E722 + upperbound_nonlinear = np.full(lev.shape, minZero) + tmp = np.zeros(residu.shape, dtype=bool, order="F") + tmp[residu < lowerbound_linear] = True + tmp[residu > upperbound_nonlinear] = True + tmp[lev > leverage] = False + tmp2 = np.ones(b.shape, dtype=bool, order="F") + tmp2[~out.reshape(-1)] = tmp + tmp2[b0_pos] = False + reject = tmp2 + else: + tmp2 = np.zeros(b.shape, dtype=bool, order="F") + tmp2[out.reshape(-1)] = True + reject = tmp2 + # Robust parameter estimation + keep = ~reject.reshape(-1) + bmat_i = bmat[keep, :] + dwi_i = dwi[keep] + try: + dt_ = np.linalg.lstsq(bmat_i, np.log(dwi_i), rcond=None)[0] + except: # noqa: E722 + dt_ = np.full((bmat_i.shape[1], 1), minZero) + w = highprecisionexp(np.matmul(bmat_i, dt_)) + try: + dt = np.linalg.lstsq( + (bmat_i * np.tile(w.reshape((len(w), 1)), (1, nparam))), + (np.log(dwi_i).reshape((dwi_i.shape[0], 1)) * w.reshape((len(w), 1))), + rcond=None, + )[0] + except: # noqa: E722 + dt = np.full((bmat_i.shape[1], 1), minZero) + return reject.reshape(-1), dt.reshape(-1) # , fa, md + + inputs = tqdm( + range(nvox), + desc="IRLLS Outlier Detection", + bar_format="{desc}: [{percentage:0.0f}%]", + unit="vox", + ncols=tqdmWidth, + ) + (reject, dt) = zip( + *Parallel(n_jobs=self.workers, prefer="processes")( + delayed(outlierHelper)(dwi[:, i], bmat, sigma[i, 0], b, b0_pos) for i in inputs + ) + ) + # for i in inputs: + # reject[:,i], dt[:,i] = outlierHelper(dwi[:, i], bmat, sigma[i,0], b, b0_pos) + dt = np.array(dt) + # self.dt = dt + # Unscaling + if scaling: + dt[1, :] = dt[1, :] + np.log(sc / 1000) + # Unvectorizing + reject = vectorize(np.array(reject).T, self.mask) + return reject, dt.T + + def tensorReorder(self, dwiType: str) -> Tuple[np.ndarray[float], np.ndarray[float]]: + """Reorders tensors in DT to those of MRTRIX in accordance to + the table below. + + Parameters + ---------- + dwiType: str, {'dti', 'dki'} + Indicates whether image is DTI or DKI. + + Returns + ------- + DT: ndarray(dtype=float) + 4D image containing DT tensor. + KT: ndarray(dtype=float) + 4D image containing KT tensor. + + Examples + -------- + dt = dwi.tensorReorder() + + Notes + ----- + MRTRIX3 and Designer tensors are described below. + + .. code-block:: none + + MRTRIX3 Tensors DESIGNER Tensors + ~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~ + + 0 D0 1 1 1 1 + 1 D1 2 2 1 2 + 2 D2 3 3 1 3 + 3 D3 1 2 2 2 + 4 D4 1 3 2 3 + 5 D5 2 3 3 3 + + 6 K0 1 1 1 1 1 1 1 1 + 7 K1 2 2 2 2 1 1 1 2 + 8 K2 3 3 3 3 1 1 1 3 + 9 K3 1 1 1 2 1 1 2 2 + 10 K4 1 1 1 3 1 1 2 3 + 11 K5 1 2 2 2 1 1 3 3 + 12 K6 1 3 3 3 1 2 2 2 + 13 K7 2 2 2 3 1 2 2 3 + 14 K8 2 3 3 3 1 2 3 3 + 15 K9 1 1 2 2 1 3 3 3 + 16 K10 1 1 3 3 2 2 2 2 + 17 K11 2 2 3 3 2 2 2 3 + 18 K12 1 1 2 3 2 2 3 3 + 19 K13 1 2 2 3 2 3 3 3 + 20 K14 1 2 3 3 3 3 3 3 + + Value Assignment + ~~~~~~~~~~~~~~~~ + + MRTRIX3 DESIGNER + ~~~~~~~ ~~~~~~~~ + 0 0 + 1 3 + 2 5 + 3 1 + 4 2 + 5 4 + + 6 6 + 7 16 + 8 20 + 9 7 + 10 8 + 11 12 + 12 15 + 13 17 + 14 19 + 15 9 + 16 11 + 17 18 + 18 10 + 19 13 + 20 14 + """ + if self.dt is None: + raise Exception("Please run dwi.fit() to generate a tensor " "prior to reordering tensors.") + + if dwiType == "dti": + dt = np.zeros((6, self.dt.shape[1])) + dt[0, :] = self.dt[0, :] # D0 + dt[1, :] = self.dt[3, :] # D1 + dt[2, :] = self.dt[5, :] # D2 + dt[3, :] = self.dt[1, :] # D3 + dt[4, :] = self.dt[2, :] # D4 + dt[5, :] = self.dt[4, :] # D5 + DT = vectorize(dt[0:6, :], self.mask) + KT = None + if dwiType == "dki": + dt = np.zeros(self.dt.shape) + dt[0, :] = self.dt[0, :] # D0 + dt[1, :] = self.dt[3, :] # D1 + dt[2, :] = self.dt[5, :] # D2 + dt[3, :] = self.dt[1, :] # D3 + dt[4, :] = self.dt[2, :] # D4 + dt[5, :] = self.dt[4, :] # D5 + dt[6, :] = self.dt[6, :] # K0 + dt[7, :] = self.dt[16, :] # K1 + dt[8, :] = self.dt[20, :] # K2 + dt[9, :] = self.dt[7, :] # K3 + dt[10, :] = self.dt[8, :] # K4 + dt[11, :] = self.dt[12, :] # K5 + dt[12, :] = self.dt[15, :] # K6 + dt[13, :] = self.dt[17, :] # K7 + dt[14, :] = self.dt[19, :] # K8 + dt[15, :] = self.dt[9, :] # K9 + dt[16, :] = self.dt[11, :] # K10 + dt[17, :] = self.dt[18, :] # K11 + dt[18, :] = self.dt[10, :] # K12 + dt[19, :] = self.dt[13, :] # K13 + dt[20, :] = self.dt[14, :] # K14 + DT = vectorize(dt[0:6, :], self.mask) + KT = vectorize(dt[6:21, :], self.mask) + return DT, KT + + def irllsviolmask(self, reject: np.ndarray[bool]) -> np.ndarray[float]: + """Computes 3D violation mask of outliers detected from IRLLS + method. + + Parameters + ---------- + reject: ndarray(dtype=bool) + 4D input outlier map from IRLLS. + + Returns + ------- + propviol: ndarray(dtype=float) + 3D mask where voxel value is the percentage of directional + violations. + + Examples + -------- + mask = dwi.irllsviolmask(outliers) + """ + img = vectorize(reject, self.mask) + (ndwi, nvox) = img.shape + b = np.array(self.grad[:, 3]) + b = np.reshape(b, (len(b), 1)) + b_pos = ~(b < 0.01).reshape(-1) + img = img[b_pos, :] + propViol = np.sum(img, axis=0).astype(int) / np.sum(b_pos) + propViol = vectorize(propViol, self.mask) + return propViol + + +def fit_regime( + input: str, + output: str, + prefix: str = None, + suffix: str = None, + ext: str = None, + irlls: bool = True, + akc: bool = True, + qcpath: Union[str, None] = None, + fit_constraints: List[int] = [0, 1, 0], + l_max: Union[int, None] = None, + rectify: bool = True, + res: str = "med", + n_fibers: int = 5, + mask: Union[str, None] = None, + nthreads: Union[int, None] = None, +) -> None: + """Performs the entire tensor fitting regime and writes out maps. + Uses auto-detections methods to determine the types of protocols + encoded by DWI and extract their metrics. + + Parameters + ---------- + input : str + Path to DWI + output : str + Output directory to write all outputs + prefix : str + Prefix to append to output file names. + (Default: None) + suffix : str + Suffix to append to output file names. + (Default: None) + ext : str + Specify output image extension type. + (Default: None) + irlls : bool + Specify whether to perform IRLLS outlier detection. + (Default: True) + akc : bool + Specify whether to perform brute-forced AKC correction. + (Default: True) + qcpath : str + Specify output directory to write QC metrics. + (Default: None) + fit_constraints : list + List of 3 bool elements specifying which fit contraints to + use. See DWI.createConstraints() on usage. + (Default: [0, 1, 0]) + l_max : int + Maximum spherical harminic degree for FBI/FBWM fit. + (Default: None) + rectify : bool + Specify whether to rectify FBI fODF + (Default: True) + mask : str + Path to brain mask + (Default: None) + nthreads : int + Number of workers to use in processing. Default value uses all + available workers. + (Default: None) + """ + if prefix is None: + prefix = "" + if suffix is None: + suffix = "" + if ext is None: + ext = ".nii" + img = DWI(input, mask=mask, nthreads=nthreads) + protocols = img.tensorType() + print("Protocol(s) detected: {}".format(", ".join([x.upper() for x in protocols]))) + fname_dti = {} + fname_dki = {} + fname_wmti = {} + fname_fbi = {} + fname_tensor = {} + fname_outliers = {} + fname_tractography = {} + for key, value in dwi_fnames._dti_.items(): + fname_dti[key] = prefix + value + suffix + ext + for key, value in dwi_fnames._dki_.items(): + fname_dki[key] = prefix + value + suffix + ext + for key, value in dwi_fnames._wmti_.items(): + fname_wmti[key] = prefix + value + suffix + ext + for key, value in dwi_fnames._fbi_.items(): + fname_fbi[key] = prefix + value + suffix + ext + for key, value in dwi_fnames._tensor_.items(): + fname_tensor[key] = prefix + value + suffix + ext + for key, value in dwi_fnames._outliers_.items(): + fname_outliers[key] = prefix + value + suffix + ext + for key, value in dwi_fnames._tractography_.items(): + fname_tractography[key] = prefix + value + suffix + ".fib" + if irlls: + if img.isdki(): + outliers, dt_est = img.irlls(mode="DKI", excludeb0=True) + else: + outliers, dt_est = img.irlls(mode="DTI", excludeb0=True) + if qcpath: + if op.exists(qcpath): + outlier_full = op.join(qcpath, fname_outliers["IRLLS"]) + print(outlier_full) + outlier_plot_full = op.join(qcpath, prefix + "irlls_outliers_plot" + suffix + ".png") + bvals_outlier_full = op.join(qcpath, prefix + "irlls_outliers_shells" + suffix + ".bval") + if img.isdki(): + bvals_outlier = img.getBvals()[img.idxdki()].astype(int) + else: + bvals_outlier = img.getBvals()[img.idxdti()].astype(int) + bvals_outlier = bvals_outlier * 1000 + writeNii(outliers, img.hdr, outlier_full) + np.savetxt(bvals_outlier_full, bvals_outlier, newline=" ", fmt="%d") + if mask: + outlierplot.plot( + input=outlier_full, + output=outlier_plot_full, + bval=bvals_outlier_full, + mask=mask, + ) + else: + outlierplot.plot( + input=outlier_full, + output=outlier_plot_full, + bval=bvals_outlier_full, + mask=None, + ) + os.remove(bvals_outlier_full) + if irlls: + img.fit(fit_constraints, reject=outliers) + else: + img.fit(fit_constraints) + if akc and img.isdki(): + akc_out = img.akcoutliers() + img.akccorrect(akc_out) + if qcpath: + writeNii(akc_out, img.hdr, op.join(qcpath, fname_outliers["AKC"])) + if "dki" in img.tensorType(): + tensorType = "dki" + else: + tensorType = "dti" + DT, KT = img.tensorReorder(tensorType) + if tensorType == "dki": + writeNii(DT, img.hdr, op.join(output, fname_tensor["DT"])) + writeNii(KT, img.hdr, op.join(output, fname_tensor["KT"])) + else: + writeNii(DT, img.hdr, op.join(output, fname_tensor["DT"])) + # DTI Parameters + if img.isdti(): + md, rd, ad, fa, fe, trace = img.extractDTI() + writeNii(md, img.hdr, op.join(output, fname_dti["md"])) + writeNii(rd, img.hdr, op.join(output, fname_dti["rd"])) + writeNii(ad, img.hdr, op.join(output, fname_dti["ad"])) + writeNii(fa, img.hdr, op.join(output, fname_dti["fa"])) + writeNii(fe, img.hdr, op.join(output, fname_dti["fe"])) + writeNii(trace, img.hdr, op.join(output, fname_dti["trace"])) + dtimodel = odf.odfmodel(dt=op.join(output, fname_tensor["DT"]), mask=mask, l_max=2, res=res) + dti_odfs = dtimodel.dtiodf() + dti_sh = dtimodel.odf2sh(dti_odfs) + dtimodel.savenii(dti_sh, op.join(output, fname_dti["odf"])) + dsistudio.makefib( + input=op.join(output, fname_dti["odf"]), + output=op.join(output, fname_tractography["dti"]), + map=op.join(output, fname_dti["fa"]), + mask=mask, + n_fibers=n_fibers, + scale=0.5, + other_maps=[ + op.join(output, fname_dti["md"]), + op.join(output, fname_dti["rd"]), + op.join(output, fname_dti["ad"]), + ], + ) + if img.isdki(): + # DKI Parameters + mk, rk, ak, kfa, mkt, trace = img.extractDKI() + writeNii(mk, img.hdr, op.join(output, fname_dki["mk"])) + writeNii(rk, img.hdr, op.join(output, fname_dki["rk"])) + writeNii(ak, img.hdr, op.join(output, fname_dki["ak"])) + writeNii(kfa, img.hdr, op.join(output, fname_dki["kfa"])) + writeNii(mkt, img.hdr, op.join(output, fname_dki["mkt"])) + writeNii(trace, img.hdr, op.join(output, fname_dki["trace"])) + # WMTI Parameters + awf, eas_ad, eas_rd, eas_tort, ias_da = img.extractWMTI() + writeNii(awf, img.hdr, op.join(output, fname_wmti["awf"])) + writeNii(eas_ad, img.hdr, op.join(output, fname_wmti["eas_ad"])) + writeNii(eas_rd, img.hdr, op.join(output, fname_wmti["eas_rd"])) + writeNii(eas_tort, img.hdr, op.join(output, fname_wmti["eas_tort"])) + writeNii(ias_da, img.hdr, op.join(output, fname_wmti["ias_da"])) + dkimodel = odf.odfmodel( + dt=op.join(output, fname_tensor["DT"]), + kt=op.join(output, fname_tensor["KT"]), + mask=mask, + l_max=6, + res=res, + ) + dki_odfs = dkimodel.dkiodf(fa_t=0.90) + dki_sh = dkimodel.odf2sh(dki_odfs) + dkimodel.savenii(dki_sh, op.join(output, fname_dki["odf"])) + dsistudio.makefib( + input=op.join(output, fname_dki["odf"]), + output=op.join(output, fname_tractography["dki"]), + map=op.join(output, fname_dti["fa"]), + mask=mask, + n_fibers=n_fibers, + scale=0.5, + other_maps=[ + op.join(output, fname_dti["md"]), + op.join(output, fname_dti["rd"]), + op.join(output, fname_dti["ad"]), + op.join(output, fname_dki["mk"]), + op.join(output, fname_dki["rk"]), + op.join(output, fname_dki["ak"]), + op.join(output, fname_dki["kfa"]), + op.join(output, fname_wmti["awf"]), + op.join(output, fname_wmti["eas_ad"]), + op.join(output, fname_wmti["eas_rd"]), + op.join(output, fname_wmti["eas_tort"]), + op.join(output, fname_wmti["ias_da"]), + ], + ) + if img.isfbi(): + if img.isfbwm(): + ( + zeta, + faa, + sph, + sph_mrtrix, + min_awf, + Da, + De_mean, + De_ax, + De_rad, + De_fa, + min_cost, + min_cost_fn, + ) = img.fbi(l_max=l_max, fbwm=True, rectify=rectify) + writeNii(zeta, img.hdr, op.join(output, fname_fbi["zeta"])) + writeNii(faa, img.hdr, op.join(output, fname_fbi["faa"])) + writeNii(np.real(sph), img.hdr, op.join(output, fname_fbi["odf"])) + writeNii(np.real(sph_mrtrix), img.hdr, op.join(output, fname_fbi["odf_mrtrix"])) + writeNii(min_awf, img.hdr, op.join(output, fname_fbi["awf"])) + writeNii(Da, img.hdr, op.join(output, fname_fbi["Da"])) + writeNii(De_mean, img.hdr, op.join(output, fname_fbi["De_mean"])) + writeNii(De_ax, img.hdr, op.join(output, fname_fbi["De_ax"])) + writeNii(De_rad, img.hdr, op.join(output, fname_fbi["De_rad"])) + writeNii(De_fa, img.hdr, op.join(output, fname_fbi["fae"])) + writeNii(min_cost, img.hdr, op.join(output, fname_fbi["min_cost"])) + writeNii(min_cost_fn, img.hdr, op.join(output, fname_fbi["min_cost_fn"])) + dsistudio.makefib( + input=op.join(output, fname_fbi["odf_mrtrix"]), + output=op.join(output, fname_tractography["fbi"]), + map=op.join(output, fname_fbi["faa"]), + mask=mask, + n_fibers=n_fibers, + scale=1, + other_maps=[ + op.join(output, fname_dti["md"]), + op.join(output, fname_dti["rd"]), + op.join(output, fname_dti["ad"]), + op.join(output, fname_dki["mk"]), + op.join(output, fname_dki["rk"]), + op.join(output, fname_dki["ak"]), + op.join(output, fname_dki["kfa"]), + op.join(output, fname_wmti["awf"]), + op.join(output, fname_wmti["eas_ad"]), + op.join(output, fname_wmti["eas_rd"]), + op.join(output, fname_wmti["eas_tort"]), + op.join(output, fname_wmti["ias_da"]), + op.join(output, fname_fbi["zeta"]), + op.join(output, fname_fbi["awf"]), + op.join(output, fname_fbi["Da"]), + op.join(output, fname_fbi["De_mean"]), + op.join(output, fname_fbi["De_ax"]), + op.join(output, fname_fbi["De_rad"]), + op.join(output, fname_fbi["fae"]), + ], + ) + else: + ( + zeta, + faa, + sph, + sph_mrtrix, + min_awf, + Da, + De_mean, + De_ax, + De_rad, + De_fa, + min_cost, + min_cost_fn, + ) = img.fbi(l_max=l_max, fbwm=False, rectify=rectify) + writeNii(zeta, img.hdr, op.join(output, fname_fbi["zeta"])) + writeNii(faa, img.hdr, op.join(output, fname_fbi["faa"])) + writeNii(np.real(sph), img.hdr, op.join(output, fname_fbi["odf"])) + writeNii(np.real(sph_mrtrix), img.hdr, op.join(output, fname_fbi["odf_mrtrix"])) + dsistudio.makefib( + input=op.join(output, fname_fbi["odf"]), + output=op.join(output, fname_tractography["fbi"]), + map=op.join(output, fname_fbi["faa"]), + mask=mask, + n_fibers=n_fibers, + scale=1, + other_maps=[op.join(output, fname_fbi["zeta"])], + ) diff --git a/designer/fitting/spherical_grid.csv b/pydesigner/fitting/spherical_grid.csv similarity index 100% rename from designer/fitting/spherical_grid.csv rename to pydesigner/fitting/spherical_grid.csv diff --git a/pydesigner/fitting/thresholds.py b/pydesigner/fitting/thresholds.py new file mode 100644 index 00000000..e6c2fb11 --- /dev/null +++ b/pydesigner/fitting/thresholds.py @@ -0,0 +1,11 @@ +__mindtibval__ = 0.5 # minimum DTI B-value +__maxdtibval__ = 1.5 # maximum DTI B-value +__mindkibval__ = 0.5 # minimum DKI B-value +__maxdkibval__ = 3.0 # maximum DKI B-value +__minfbibval__ = 4.0 # minimum FBI B-value +__maxfbibval__ = 12.0 # maximum FBI B-value +__d0__ = 3.0 # diffusivity of free water at body temp (37 deg C) for upper bound on Da +__dn__ = 1.5 # estimated intra-neurite diffusivity in mm^2/ms +__pkT__ = 0.4 # peak thresholding for white matter fiber tracking +__minZero__ = 10e-8 # threshold under which all numbers are zero +__dirs__ = 256 # Define number of directions to resample after computing all tensors diff --git a/pydesigner/main.py b/pydesigner/main.py new file mode 100644 index 00000000..6b7070b2 --- /dev/null +++ b/pydesigner/main.py @@ -0,0 +1,1151 @@ +"""Runs the PyDesigner pipeline +""" + +import argparse # ArgumentParser, add_argument +import glob # recursive file search +import json +import os # mkdir +import os.path as op # path +import shutil # which, rmtree +import subprocess # subprocess +import sys as sys +import textwrap # dedent + +import numpy as np # array, ndarray + +from .fitting import dwipy as dp +from .info import __version__ +from .plotting import motionplot, snrplot +from .postprocessing import filters +from .preprocessing import mrinfoutil, mrpreproc, util + +DWIFile = util.DWIFile +DWIParser = util.DWIParser + +# Locate mrtrix3 via which-ing dwidenoise +dwidenoise_location = shutil.which("dwidenoise") +if dwidenoise_location is None: + raise Exception("Cannot find mrtrix3, please see " "https://github.com/m-ama/PyDesigner/wiki" " to troubleshoot.") + +# Extract mrtrix3 path from dwidenoise_location +mrtrix3path = op.dirname(dwidenoise_location) + +# Locate FSL via which-ing fsl +fsl_location = shutil.which("fsl") +if fsl_location is None: + raise Exception("Cannot find FSL, please see " "https://github.com/m-ama/PyDesigner/wiki" " to troubleshoot.") + +# Extract FSL path from fsl_location +fslpath = op.dirname(fsl_location) + + +def main(): + # ----------------------------------------------------------------- + # Parse Arguments + # ----------------------------------------------------------------- + # Initialize ArgumentParser + parser = argparse.ArgumentParser( + prog="pydesigner", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=textwrap.dedent( + """\ + Appendix + -------- + Filename note: + Use the base name without the extension. This makes it easy to program + in automatic .bvec/.bval detection for Niftis and makes your shell + easier to read by others. The program will automatically search image + filenames for .nii and .nii.gz extensions. If you use the --dicom + option, then the program will assume that the entire directory + consists of dicom files, and will warn you of any files which fail to + be read in as dicoms. + + Example usage: + In order to process in the standard way: + python3 pydesigner.py \\ + --standard \\ + + + In order to process in a custom pipeline with denoising, eddy, reverse + phase encoding, and smoothing, but no diffusion metrics: + python3 pydesigner.py \\ + --denoise \\ + --undistort \\ + --smooth \\ + --nofit \\ + + + In order to just do denoising, eddy with reverse phase encode, and + diffusion metrics: + python3 pydesigner.py \\ + --denoise \\ + --undistort \\ + + + Standard pipeline steps: + 1. dwidenoise (thermal denoising) + 2. mrdegibbs (gibbs unringing) + 3. topup + eddy (undistortion) + 4. rician bias correction + 5. normalization to white matter in first b0 image + 6. IRWLLS, CWLLS DKI fit + 7. Outlier detection and removal + + See also: + GitHub https://github.com/m-ama/PyDesigner + mrtrix3 https://www.mrtrix.org/ + fsl https://fsl.fmrib.ox.ac.uk/fsl/fslwiki + + """ + ), + ) + + # Specify arguments below + + # Mandatory + parser.add_argument( + "dwi", + nargs="+", + help="The diffusion dataset you would like " "to process. ", + type=str, + ) + + # Optional + parser.add_argument( + "-o", + "--output", + metavar="directory", + help="Output location. " "Default: same path as dwi.", + type=str, + ) + parser.add_argument( + "-s", + "--standard", + action="store_true", + default=False, + help="Standard preprocessing, bypasses most other " + "options. See Appendix:Standard pipeline steps " + "for more information. ", + ) + parser.add_argument( + "-n", + "--denoise", + action="store_true", + default=False, + help="Run thermal denoising with dwidenoise.", + ) + parser.add_argument( + "--extent", + metavar="n,n,n", + default="5,5,5", + help="Denoising extent formatted n,n,n (forces " " denoising. " "Default: 5,5,5.", + ) + parser.add_argument( + "-g", + "--degibbs", + action="store_true", + default=False, + help="Perform gibbs unringing. Only perform if you " + "have full Fourier encoding. The program will check " + "for you if you have a .json sidecar.", + ) + parser.add_argument( + "-u", + "--undistort", + action="store_true", + default=False, + help="Run FSL eddy to perform image undistortion. " "NOTE: needs a --topup to run.", + ) + parser.add_argument( + "--rpe_pairs", + default=0, + type=int, + metavar="n", + help="Number of reverse phase encoded B0 " + "pairs to use in TOPUP. Using less pairs " + "results in faster TOPUP correction. " + "Specfying 0 results in using all B0 pairs." + "We recommend using just one pair. Default: 0", + ) + parser.add_argument( + "-z", + "--smooth", + action="store_true", + default=False, + help="Perform smoothing on the DWI data.", + ) + parser.add_argument( + "--fwhm", + type=float, + default=1.25, + metavar="n", + help="The FWHM to use as a multiple of voxel size. " "Default 1.25", + ) + parser.add_argument( + "-r", + "--rician", + action="store_true", + default=False, + help="Perform Rician noise correction on the data " "(requires --denoise to generate a noisemap).", + ) + parser.add_argument( + "--nofit", + action="store_true", + default=False, + help="Do not fit DTI or DKI tensors.", + ) + parser.add_argument( + "--akc", + action="store_true", + default=False, + help="Brute force K tensor outlier rejection. Applies " + "a median filter to tensor voxels that exhibit AKC " + "values of less than 2 and more than 10.", + ) + parser.add_argument( + "--nooutliers", + action="store_true", + default=False, + help="Do not perform outlier correction on kurtosis " "fitting metrics.", + ) + parser.add_argument( + "-m", + "--mask", + action="store_true", + default=False, + help="Compute a brain mask prior to tensor fitting " + "to strip skull and improve efficiency. Optionally, " + "use --maskthr to specify a threshold manually.", + ) + parser.add_argument( + "--maskthr", + metavar="n", + default=0.25, + help="FSL bet threshold used for brain masking. " "Default: 0.25", + ) + parser.add_argument( + "--user_mask", + metavar="path", + help="Path to user-supplied brain mask.", + type=str, + ) + parser.add_argument( + "-cf", + "--csf_fsl", + action="store_true", + default=False, + help="Compute a CSF mask for CSF-excluded " "smoothing to minimize partial volume " "effects using FSL FAST.", + ) + parser.add_argument( + "-cd", + "--csf_adc", + metavar="n", + default=False, + help="Compute a CSF mask for CSF-excluded " + "smoothing to minimize partial volume " + "effects using thresholding a pseudo-ADC map " + "computed as ln(S0/S1000)/b1000. Default: 2", + ) + parser.add_argument( + "--reslice", + metavar="x,y,z", + help="Relices DWI to voxel resolution " + "specified in millimeters (mm) or output " + "dimensions. Performing reslicing will skip " + "plotting of SNR curves. Providing dimensions " + "greater than 9 will switch from mm voxel " + "reslicing to output image reslicing.", + ) + parser.add_argument( + "--interp", + action="store_true", + default="linear", + help="Set the interpolation to use when " + "reslicing. Choices are linear (default), " + "nearest, cubic, and sinc.", + ) + parser.add_argument( + "-te", + "--multite", + action="store_true", + default=False, + help="Specify whether input DWI consists of " + "multiple TEs. PyDesigner will preprocess all " + "TEs together, then extract metric values of " + "each TE separately.", + ) + parser.add_argument( + "--fit_constraints", + default="0,1,0", + metavar="D>0,K>0,K < 3/(b*D)", + help="Constrain the WLLS fit. " "Default: 0,1,0.", + ) + parser.add_argument( + "--l_max", + default=6, + type=int, + metavar="n", + help="Maximum spherical harmonic degree for " "FBI spherical harmonic expansion", + ) + parser.add_argument( + "--no_rectify", + action="store_true", + default=False, + help="Disable rectification of FBI fODF. Use " + "only when rectification of excellent " + "acquisitions results in degradation of FBI " + "or FBWM metric maps", + ) + parser.add_argument( + "--t_res", + type=str, + default="med", + help="Resolution of directions for ODF calculation. " + "Higher resolution implies slower computation. Choose " + 'between "low", "med", or "high". Default: "med"', + ) + parser.add_argument( + "--t_fibers", + type=int, + default=5, + help="The maximum number ODF maxima to extract per " "voxel for tractography. Default: 5", + ) + parser.add_argument( + "--noqc", + action="store_true", + default=False, + help="Disable QC saving of QC metrics", + ) + parser.add_argument( + "--median", + action="store_true", + default=False, + help="Performs postprocessing median filtering of " + "final maps. WARNING: Use on a case-by-case " + "basis for bad data only. When applied, the " + "filter alters the values of most voxels, so " + "it should be used with caution and avoided " + "when data quality is otherwise adequate. " + "While maps appear visually soother with " + "this flag on, they may nonetheless be less " + "accurate.", + ) + parser.add_argument( + "--nthreads", + type=int, + default=None, + help="Number of threads to use for computation. " "Note that using too many threads will cause a slow-" "down.", + ) + parser.add_argument( + "--resume", + action="store_true", + help="Continue from an aborted or partial previous " "run of pydesigner.", + ) + parser.add_argument( + "--force", + action="store_true", + help="Force overwrites of existing files. Otherwise, " "there will be an error at runtime.", + ) + parser.add_argument( + "--verbose", + action="store_true", + help="Print out all output. This is a very messy " + "option. We recommend piping output to a text file " + "if you use this option.", + ) + parser.add_argument( + "--adv", + action="store_true", + help="Disables safety checks for advanced users who " + "want to force a preprocessing step. WARNING: " + "THIS FLAG IS FOR ADVANCED USERS ONLY WHO FULLY " + "UNDERSTAND THE MRI SYSTEM AND ITS OUTPUTS. " + "RUNNING WITH THIS FLAG COULD POTENTIALLY " + "RESULT IN IMPRECISE AND INACCURATE RESULTS.", + ) + parser.add_argument("-v", "--version", action="version", version=__version__) + + # Use argument specification to actually get args + args = parser.parse_args() + + # ----------------------------------------------------------------- + # Parse Input Image + # ----------------------------------------------------------------- + image = DWIParser(args.dwi) + # Variable fType indicates the extension to raw_dwi.X, where X take the + # place of known dMRI file extensions (.mif, .nii, .nii.gz). This allows + # easy switching based on any scenario for testing. + fType = ".mif" + multi_echo = False + if not args.output: + outpath = image.getPath() + else: + outpath = args.output + image.cat( + path=outpath, + ext=fType, + verbose=args.verbose, + force=args.force, + resume=args.resume, + ) + working_path = op.join(outpath, "working" + fType) + # Create index of DWI volumes with different TEs + if not args.resume and np.unique(image.echotime).size > 1: + multi_echo = True + multi_echo_start = [0] + multi_echo_end = [image.vols[0] - 1] + for idx, vols in enumerate(image.vols[1:]): + multi_echo_start.append(multi_echo_start[-1] + vols) + multi_echo_end.append(multi_echo_end[-1] + vols) + multi_echo_start = [int(x) for x in multi_echo_start] + multi_echo_end = [int(x) for x in multi_echo_end] + + # Make an initial conversion to nifti + init_nii = op.join(outpath, "dwi_raw.nii") + if not (args.resume and op.exists(init_nii)): + mrpreproc.miftonii( + input=working_path, + output=init_nii, + nthreads=args.nthreads, + force=args.force, + verbose=args.verbose, + ) + + # ----------------------------------------------------------------- + # Validate Arguments + # ----------------------------------------------------------------- + errmsg = "" + warningmsg = "" + msgstart = "Incompatible arguments: " + override = "; overriding with " + # Warn if --standard and cherry-picking + if args.standard: + stdmsg = "--standard but cherry-picking " + override = "; overriding with standard pipeline.\n" + if args.denoise: + warningmsg += msgstart + stdmsg + "--denoise" + override + if args.undistort: + warningmsg += msgstart + stdmsg + "--eddy" + override + if args.smooth: + warningmsg += msgstart + stdmsg + "--smooth" + override + # Coerce all of the above to be true + args.denoise = True + args.undistort = True + args.smooth = True + args.csf_adc = 2 + args.mask = True + args.degibbs = True + args.rician = True + + # Can't do WMTI if no fit + if args.nofit: + stdmsg = "--nofit given but " + if not args.akc: + warningmsg += msgstart + stdmsg + "--noakc" + override + "tensor fitting.\n" + args.nofit = False + if args.nooutliers: + warningmsg += msgstart + stdmsg + "--nooutliers" + warningmsg += override + "tensor fitting.\n" + args.nofit = False + + # (Extent or Degibbs) and no Denoise + if not args.denoise: + stdmsg = "No --denoise but " + if args.extent != "5,5,5": + warningmsg += stdmsg + "--extent given; overriding with --denoise\n" + args.denoise = True + if args.rician: + warningmsg += stdmsg + "--rician given; overriding with --denoise\n" + args.denoise = True + + # Cannot run --user_mask and --mask at the same time + if args.user_mask and args.mask: + errmsg += "Cannot run with both --mask and --user_mask; " + errmsg += "--mask if you do not have a custom brain mask and " "--user_mask if you want to supply a mask." + + # Cannot run --csf_fsl and --csf_adc at the same time + if args.csf_fsl and args.csf_adc: + errmsg += "Cannot run with both --csf_fsl and --csf_adc; " + errmsg += "please supply only one option." + + # Check to make sure brain mask exists if given + if args.user_mask: + if not op.exists(args.user_mask): + errmsg += "--user_mask file " + args.user_mask + " not found\n" + # Then check if it's a nifti file + if ".nii" not in op.splitext(args.user_mask)[-1]: + errmsg += "User supplied mask if not in NifTi (.nii) format." + + # Check output directory exists if given + if args.output: + if not op.exists(args.output): + try: + os.makedirs(args.output, exist_ok=True) + except: # noqa: E722 + errmsg += "Cannot find or create output directory" + + # Check that --fit_constraints can be converted to int array + fit_constraints = np.fromstring(args.fit_constraints, dtype=int, sep=",") + for i in fit_constraints: + if i < 0 or i > 1: + errmsg += "Invalid --fit_constraints value, should be 0 or 1\n" + break + + # Ensure l_max is an even integer + if args.l_max % 2 != 0: + errmsg += "User provided l_max = {} is not an even integer.".format(args.l_max) + + # --force and --resume given + if args.resume and args.force: + errmsg += msgstart + "--continue and --force\n" + + if args.output: + if not op.isdir(args.output): + try: + os.makedirs(args.output, exist_ok=True) + except: # noqa: E722 + errmsg += "Output directory does not exist and cannot be made." + + # Check whether tractography variables are parsed correctly + if args.t_res not in ["low", "med", "high"]: + warningmsg += "Specified ODF resolution not understoord. Defaulting to med\n" + + # Print warnings + if warningmsg != "": + print(warningmsg) + + # If things are unsalvageable, point out all errors and quit + if errmsg != "": + print(errmsg) + + # Begin keeping track of nifti files + filetable = {"dwi": DWIFile(init_nii)} + if not filetable["dwi"].isAcquisition(): + raise FileNotFoundError("Input dwi does not have .bval/.bvec pair") + + # Begin composing command history + cmdtable = {"HEAD": "none"} + cmdtable["input"] = mrinfoutil.commandhistory(working_path) + + # Check to make sure no partial fourier if --degibbs given + if args.degibbs and args.adv: + args.degibbs = True + else: + if args.degibbs and filetable["dwi"].isPartialFourier(): + print( + "[WARNING] Given DWI is partial fourier, overriding " + "--degibbs; no unringing correction will be done to " + 'avoid artifacts.Use the "--adv" flag to run forced ' + "corrections." + ) + args.degibbs = False + + # Handle FBI rectification + fbi_rectify = True + if args.no_rectify: + fbi_rectify = False + + # ----------------------------------------------------------------- + # Path Handling + # ----------------------------------------------------------------- + qcpath = op.join(outpath, "metrics_qc") + eddyqcpath = op.join(qcpath, "eddy") + fitqcpath = op.join(qcpath, "fitting") + metricpath = op.join(outpath, "metrics") + intermediatepath = op.join(outpath, "intermediate_nifti") + if not args.nofit: + if op.exists(metricpath): + if args.force: + shutil.rmtree(metricpath) + elif not args.resume: + raise Exception( + "Running fitting would cause an overwrite. " + "In order to run this please delete the " + "files, use --force, use --resume, or " + "change output destination." + ) + else: + os.makedirs(metricpath, exist_ok=True) + if not args.noqc: + if op.exists(qcpath): + if args.force: + shutil.rmtree(qcpath) + elif not args.resume: + raise Exception( + "Running QCing would cause an overwrite. " + "In order to run this please delete the " + "files, use --force, use --resume, or " + "change output destination." + ) + else: + os.makedirs(qcpath, exist_ok=True) + if op.exists(eddyqcpath) and args.undistort: + if args.force: + shutil.rmtree(eddyqcpath) + elif not args.resume: + raise Exception( + "Running dwidenoise would cause an " + "overwrite. " + "In order to run this please delete the " + "files, use --force, or change output " + "destination." + ) + if op.exists(fitqcpath) and not args.nofit: + if args.force: + shutil.rmtree(fitqcpath) + elif not args.resume: + raise Exception( + "Running fitting would cause an " + "overwrite. " + "In order to run this please delete the " + "files, use --force, or change output " + "destination." + ) + if args.undistort: + os.makedirs(eddyqcpath, exist_ok=True) + if not args.nofit: + os.makedirs(fitqcpath, exist_ok=True) + + if not op.exists(intermediatepath): + os.makedirs(intermediatepath, exist_ok=True) + + # TODO: add non-json RPE support, additional RPE type support + + # Get naming and location information + filetable["dwi"].getName() + if not args.output: + outpath = filetable["dwi"].getPath() + else: + outpath = args.output + filetable["outpath"] = outpath + + # Make the pipeline point to dwi as the last file since it's the only one + # so far + filetable["HEAD"] = filetable["dwi"] + + if args.nthreads and args.verbose: + print("Using " + str(args.nthreads) + " threads.") + + # Create processing step variable to count preprocessing stage + step_count = 0 + + # ----------------------------------------------------------------- + # Run Denoising + # ----------------------------------------------------------------- + if args.denoise: + step_count += 1 + denoised_name = "dwi_denoised" + # hardcoding this to be the initial file per dwidenoise + # recommmendation + # file names + denoised_name_full = str(step_count) + "_" + denoised_name + nii_denoised = op.join(intermediatepath, denoised_name_full + ".nii") + mif_denoised = op.join(outpath, denoised_name_full + ".mif") + # output the noise map even without user permission, space is cheap + noisemap_name = "noisemap.nii" + nii_noisemap = op.join(outpath, noisemap_name) + # check to see if this already exists + if not (args.resume and op.exists(nii_denoised) and op.exists(nii_noisemap)): + # run denoise function + mrpreproc.denoise( + input=working_path, + output=mif_denoised, + noisemap=True, + extent=args.extent, + nthreads=args.nthreads, + force=args.force, + verbose=args.verbose, + ) + mrpreproc.miftonii( + input=mif_denoised, + output=nii_denoised, + nthreads=args.nthreads, + force=args.force, + verbose=False, + ) + # remove old working.mif and replace with new corrected .mif + os.remove(working_path) + os.rename(mif_denoised, working_path) + # update command history + cmdtable["denoise"] = mrinfoutil.commandhistory(working_path)[-1] + cmdtable["HEAD"] = cmdtable["denoise"] + # update nifti file tracking + filetable["denoised"] = DWIFile(nii_denoised) + filetable["HEAD"] = filetable["denoised"] + filetable["noisemap"] = DWIFile(nii_noisemap) + + # ----------------------------------------------------------------- + # Run Reslicing + # ----------------------------------------------------------------- + if args.reslice: + step_count += 1 + reslice_name = "dwi_reslice" + noise_name = "noisemap_resliced" + # file names + reslice_name_full = str(step_count) + "_" + reslice_name + nii_reslice = op.join(intermediatepath, reslice_name_full + ".nii") + mif_reslice = op.join(outpath, reslice_name_full + ".mif") + nii_noise = op.join(outpath, noise_name + ".nii") + # check to see if this already exists + if not (args.resume and op.exists(nii_reslice)): + # run reslice function on both DWI and noisemap + mrpreproc.reslice( + input=working_path, + output=mif_reslice, + size=args.reslice, + interp=args.interp, + nthreads=args.nthreads, + force=args.force, + verbose=args.verbose, + ) + mrpreproc.reslice( + input=nii_noisemap, + output=nii_noise, + size=args.reslice, + interp=args.interp, + nthreads=args.nthreads, + force=True, + verbose=args.verbose, + ) + mrpreproc.miftonii( + input=mif_reslice, + output=nii_reslice, + nthreads=args.nthreads, + force=args.force, + verbose=False, + ) + # remove old working.mif and replace with new corrected .mif + os.remove(working_path) + os.rename(mif_reslice, working_path) + os.rename(nii_noise, nii_noisemap) + # update command history + cmdtable["reslice"] = mrinfoutil.commandhistory(working_path)[-1] + cmdtable["HEAD"] = cmdtable["reslice"] + # update nifti file tracking + filetable["reslice"] = DWIFile(nii_reslice) + filetable["HEAD"] = filetable["reslice"] + + # ----------------------------------------------------------------- + # Run Gibbs Unringing + # ----------------------------------------------------------------- + if args.degibbs: + step_count += 1 + degibbs_name = "dwi_degibbs" + # file names + degibbs_name_full = str(step_count) + "_" + degibbs_name + nii_degibbs = op.join(intermediatepath, degibbs_name_full + ".nii") + mif_degibbs = op.join(outpath, degibbs_name_full + ".mif") + # check to see if this already exists + if not (args.resume and op.exists(nii_degibbs)): + # run degibbs function + mrpreproc.degibbs( + input=working_path, + output=mif_degibbs, + nthreads=args.nthreads, + force=args.force, + verbose=args.verbose, + ) + mrpreproc.miftonii( + input=mif_degibbs, + output=nii_degibbs, + nthreads=args.nthreads, + force=args.force, + verbose=False, + ) + # remove old working.mif and replace with new corrected .mif + os.remove(working_path) + os.rename(mif_degibbs, working_path) + # update command history + cmdtable["degibbs"] = mrinfoutil.commandhistory(working_path)[-1] + cmdtable["HEAD"] = cmdtable["degibbs"] + # update nifti file tracking + filetable["unrung"] = DWIFile(nii_degibbs) + filetable["HEAD"] = filetable["unrung"] + + # ----------------------------------------------------------------- + # Undistort + # ----------------------------------------------------------------- + if args.undistort: + step_count += 1 + undistorted_name = "dwi_undistorted" + # file names + undistorted_name_full = str(step_count) + "_" + undistorted_name + nii_undistorted = op.join(intermediatepath, undistorted_name_full + ".nii") + mif_undistorted = op.join(outpath, undistorted_name_full + ".mif") + if args.noqc: + eddyqcpath = None + # check to see if this already exists + if not (args.resume and op.exists(nii_undistorted)): + # run undistort function + mrpreproc.undistort( + input=working_path, + output=mif_undistorted, + rpe="rpe_header", + qc=eddyqcpath, + epib0=args.rpe_pairs, + nthreads=args.nthreads, + force=args.force, + verbose=args.verbose, + ) + mrpreproc.miftonii( + input=mif_undistorted, + output=nii_undistorted, + nthreads=args.nthreads, + force=args.force, + verbose=False, + ) + # remove old working.mif and replace with new corrected .mif + os.remove(working_path) + os.rename(mif_undistorted, working_path) + # update command history + cmdtable["undistort"] = mrinfoutil.commandhistory(working_path) + cmdtable["HEAD"] = cmdtable["undistort"] + # update nifti file tracking + filetable["undistorted"] = DWIFile(nii_undistorted) + filetable["HEAD"] = filetable["undistorted"] + + # Plot head motion + if not args.noqc: + plot_path_full = op.join(qcpath, "head_motion.png") + motionplot.plot( + op.join(eddyqcpath, "eddy_restricted_movement_rms"), + plot_path_full, + voxel=mrinfoutil.spacing(working_path), + ) + + # ----------------------------------------------------------------- + # Create CSF Mask + # ----------------------------------------------------------------- + csfmask_name = "csf_mask.nii" + csfmask_out = op.join(outpath, csfmask_name) + # FSL Method + if args.csf_fsl: + mrpreproc.csfmask( + input=working_path, + output=csfmask_out, + method="fsl", + thresh=args.maskthr, + nthreads=args.nthreads, + force=args.force, + verbose=args.verbose, + ) + filetable["csfmask"] = DWIFile(csfmask_out) + # ADC Method + if args.csf_adc: + mrpreproc.csfmask( + input=working_path, + output=csfmask_out, + method="adc", + coeff=args.csf_adc, + nthreads=args.nthreads, + force=args.force, + verbose=args.verbose, + ) + filetable["csfmask"] = DWIFile(csfmask_out) + + # ----------------------------------------------------------------- + # Create Brain Mask + # ----------------------------------------------------------------- + if args.mask: + brainmask_name = "brain_mask.nii" + brainmask_out = op.join(outpath, brainmask_name) + mrpreproc.brainmask( + input=working_path, + output=brainmask_out, + thresh=args.maskthr, + nthreads=args.nthreads, + force=args.force, + verbose=args.verbose, + ) + filetable["mask"] = DWIFile(brainmask_out) + + if args.user_mask: + # Rotates user mask to same orientation as PyDesigner's working + # file to prevent incorrect masking + brainmask_name = "brain_mask.nii" + brainmask_out = op.join(outpath, "brain_mask.nii") + mrpreproc.stride_match( + target=working_path, + moving=args.user_mask, + output=brainmask_out, + nthreads=args.nthreads, + force=args.force, + verbose=args.verbose, + ) + filetable["mask"] = DWIFile(brainmask_out) + + # ----------------------------------------------------------------- + # Multiply Brain Mask with CSF Mask if both present + # ----------------------------------------------------------------- + if (args.mask or args.user_mask) and (args.csf_fsl or args.csf_adc): + cmd = ["mrcalc", "-force", brainmask_out, csfmask_out, "-mult", csfmask_out] + completion = subprocess.run(cmd) + if completion.returncode != 0: + raise Exception("Unable to multiply CSF mask with brain " "mask. See above for errors.") + # ----------------------------------------------------------------- + # Smooth + # ----------------------------------------------------------------- + if args.smooth: + csfname = None + if "csfmask" in filetable: + csfname = filetable["csfmask"].getFull() + step_count += 1 + smoothing_name = "dwi_smoothed" + # file names + smoothing_name_full = str(step_count) + "_" + smoothing_name + nii_smoothing = op.join(intermediatepath, smoothing_name_full + ".nii") + mif_smoothing = op.join(outpath, smoothing_name_full + ".mif") + # check to see if this already exists + if not (args.resume and op.exists(nii_smoothing)): + mrpreproc.smooth( + input=working_path, + csfname=csfname, + output=mif_smoothing, + fwhm=args.fwhm, + ) + + mrpreproc.miftonii( + input=mif_smoothing, + output=nii_smoothing, + nthreads=args.nthreads, + force=args.force, + verbose=False, + ) + # remove old working.mif and replace with new corrected .mif + os.remove(working_path) + os.rename(mif_smoothing, working_path) + # update command history + cmdtable["smooth"] = [ + "designer.preprocessing.mrpreproc.smooth(input={}, " + "output={}, " + "fwhm={}".format(working_path, mif_smoothing, args.fwhm) + ] + cmdtable["smooth"].append(mrinfoutil.commandhistory(working_path)[-1]) + cmdtable["HEAD"] = cmdtable["smooth"] + # update nifti file tracking + filetable["smoothed"] = DWIFile(nii_smoothing) + filetable["HEAD"] = filetable["smoothed"] + + # ----------------------------------------------------------------- + # Rician Noise Correction + # ----------------------------------------------------------------- + if args.rician: + step_count += 1 + rician_name = "dwi_rician" + # file names + rician_name = str(step_count) + "_" + rician_name + nii_rician = op.join(intermediatepath, rician_name + ".nii") + mif_rician = op.join(outpath, rician_name + ".mif") + # check to see if this already exists + if not (args.resume and op.exists(nii_rician)): + mrpreproc.riciancorrect( + input=working_path, + output=mif_rician, + noise=filetable["noisemap"].getFull(), + ) + nii_rician_name = "r" + filetable["HEAD"].getName() + ".nii" + op.join(outpath, nii_rician_name) + mrpreproc.miftonii( + input=mif_rician, + output=nii_rician, + nthreads=args.nthreads, + force=args.force, + verbose=False, + ) + # remove old working.mif and replace with new corrected .mif + os.remove(working_path) + os.rename(mif_rician, working_path) + # update command history + cmdtable["rician"] = [ + "designer.preprocessing.mrpreproc." + "riciancorrect(input={}, " + "output={}, " + "noise={})".format(working_path, mif_rician, filetable["noisemap"].getFull()) + ] + cmdtable["rician"].append(mrinfoutil.commandhistory(working_path)[-1]) + cmdtable["HEAD"] = cmdtable["rician"] + # update nifti file tracking + filetable["rician_corrected"] = DWIFile(nii_rician) + filetable["HEAD"] = filetable["rician_corrected"] + + # ----------------------------------------------------------------- + # Extract averaged B0 + # ----------------------------------------------------------------- + # file names + b0_name = "B0" + nii_b0 = op.join(outpath, b0_name + ".nii") + # check to see if this already exists + if not (args.resume and op.exists(nii_b0)): + # extract mean B0 + mrpreproc.extractmeanbzero( + input=working_path, + output=nii_b0, + nthreads=args.nthreads, + force=args.force, + verbose=args.verbose, + ) + # update command history + cmdtable["B0"] = mrinfoutil.commandhistory(working_path)[-1] + cmdtable["HEAD"] = cmdtable["B0"] + # update nifti file tracking + filetable["B0"] = DWIFile(nii_b0) + filetable["HEAD"] = filetable["B0"] + + # ----------------------------------------------------------------- + # Extract averaged non-B0 shells + # ----------------------------------------------------------------- + # get non B0 shells + b_shells = [x for x in mrinfoutil.shells(working_path) if x != 0] + # remove + # file names + b_names = ["B" + str(x) for x in b_shells] + b_paths = [op.join(outpath, x + ".nii") for x in b_names] + # check to see if this already exists + for b_value, b_nii in zip(b_shells, b_paths): + if not (args.resume and op.exists(b_nii)): + # extract mean shells + mrpreproc.extractmeanshell( + input=working_path, + output=b_nii, + shell=b_value, + nthreads=args.nthreads, + force=args.force, + verbose=args.verbose, + ) + # update command history + cmdtable["B" + str(b_value)] = mrinfoutil.commandhistory(working_path)[-1] + cmdtable["HEAD"] = cmdtable["B" + str(b_value)] + # update nifti file tracking + filetable["B" + str(b_value)] = DWIFile(b_nii) + filetable["HEAD"] = filetable["B" + str(b_value)] + + # ----------------------------------------------------------------- + # Make preprocessed file + # ----------------------------------------------------------------- + preprocessed = op.join(outpath, "dwi_preprocessed.nii") + if not (args.resume and op.exists(preprocessed)): + mrpreproc.miftonii( + input=working_path, + output=preprocessed, + nthreads=args.nthreads, + force=args.force, + verbose=False, + ) + filetable["preprocessed"] = DWIFile(preprocessed) + filetable["HEAD"] = filetable["preprocessed"] + + # ----------------------------------------------------------------- + # Compute SNR + # ----------------------------------------------------------------- + if (args.denoise and not args.reslice) and not args.noqc: + files = [] + files.append(init_nii) + files.append(filetable["HEAD"].getFull()) + try: + if "mask" in filetable: + snr = snrplot.makesnr( + dwilist=files, + noisepath=nii_noisemap, + maskpath=filetable["mask"].getFull(), + ) + else: + snr = snrplot.makesnr( + dwilist=files, + noisepath=filetable["noisemap"].getFull(), + maskpath=None, + ) + snr.makeplot(path=qcpath, smooth=True, smoothfactor=3) + except: # noqa: E722 + print("[WARNING] SNR plotting failed, see above. " "Proceeding with processing.") + + # ----------------------------------------------------------------- + # Write logs + # ----------------------------------------------------------------- + with open(op.join(outpath, "log_command.json"), "w") as fp: + json.dump(cmdtable, fp, indent=2) + + # ----------------------------------------------------------------- + # Handle multi-echo data + # ----------------------------------------------------------------- + imPath = filetable["HEAD"].getFull() + if multi_echo and args.multite: + imPath = [] + for i in range(len(image.echotime)): + echo_out = op.join(outpath, "TE" + str(image.echotime[i]) + "_dwi_preprocessed.nii") + mrpreproc.dwiextract( + working_path, + echo_out, + start=multi_echo_start[i], + end=multi_echo_end[i], + nthreads=args.nthreads, + force=args.force, + verbose=False, + ) + imPath.append(echo_out) + + # Remove working.mif + os.remove(working_path) + + # ----------------------------------------------------------------- + # Tensor Fitting + # ----------------------------------------------------------------- + ext = ".nii" + fit_mask = None + if "mask" in filetable: + fit_mask = filetable["mask"].getFull() + if not args.nofit: + # create dwi fitting object + if multi_echo and args.multite: + for path, echo in zip(imPath, image.echotime): + # qcpath = op.join(op.dirname(path), 'qc_fitting') + os.makedirs(qcpath, exist_ok=True) + dp.fit_regime( + input=path, + output=metricpath, + prefix="TE" + str(echo) + "_", + suffix=None, + ext=ext, + irlls=not args.nooutliers, + akc=args.akc, + l_max=args.l_max, + res=args.t_res, + n_fibers=args.t_fibers, + rectify=fbi_rectify, + qcpath=fitqcpath, + fit_constraints=fit_constraints, + mask=fit_mask, + nthreads=args.nthreads, + ) + else: + dp.fit_regime( + input=imPath, + output=metricpath, + prefix=None, + suffix=None, + ext=ext, + irlls=not args.nooutliers, + akc=args.akc, + l_max=args.l_max, + rectify=fbi_rectify, + res=args.t_res, + n_fibers=args.t_fibers, + qcpath=fitqcpath, + fit_constraints=fit_constraints, + mask=fit_mask, + nthreads=args.nthreads, + ) + + # ----------------------------------------------------------------- + # Post Processing + # ----------------------------------------------------------------- + if args.median: + f_metrics = glob.glob(op.join(metricpath, "*" + ext)) + f_metrics = [x for x in f_metrics if not x.endswith("DT.nii")] + f_metrics = [x for x in f_metrics if not x.endswith("KT.nii")] + f_metrics = [x for x in f_metrics if not x.endswith("fodf.nii")] + for f in f_metrics: + filters.median(f, f) + + +if __name__ == "__main__": + main() diff --git a/pydesigner/plotting/__init__.py b/pydesigner/plotting/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/pydesigner/plotting/motionplot.py b/pydesigner/plotting/motionplot.py new file mode 100644 index 00000000..49de075a --- /dev/null +++ b/pydesigner/plotting/motionplot.py @@ -0,0 +1,89 @@ +import os.path as op +from typing import Tuple, Union + +import matplotlib.pyplot as plt +import numpy as np +from matplotlib.ticker import AutoMinorLocator + + +def plot(input: str, output: str, voxel: Union[Tuple[float], None] = None) -> None: + """Plots subject motion from eddy_qc output file. + + Parameters + ---------- + input : str + Path to eddy_qc's + output : str + Path to brain mask + voxel : tuple of float + Size of voxel in 3D i.e. (2.7, 2.7, 2.7) for 2.7 mm isotropic + + Returns + ------- + None; writes out plot to file + + See Also + -------- + outlierplot : plots outliers from IRLLS + snrplot : plots DWI's SNR + """ + print("Plotting motion...") + if not op.exists(input): + raise OSError("Input file {} does not exist".format(input)) + if op.isdir(output): + raise OSError( + "Output {} cannot be a directory. Please " "define the output to be an image file.".format(output) + ) + if op.splitext(output)[-1] != ".png": + raise OSError("Output path {} does not indicate a PNG file" " ".format(input)) + # Load file + dat = np.loadtxt(input) + if dat.shape[-1] != 2: + raise Exception( + "The input file containing eddy computed " + "movement should have only two columns. The file {} supplied " + "however only contains {} column(s)".format(input, dat.size[-1]) + ) + nvols = dat.shape[0] + if voxel is not None: + voxel = sum(voxel) / len(voxel) + dat = dat * voxel + # The datafile being read here should be + # `eddy_restricted_movement_rms`, which contians information on + # how much a subjects moved during a DWI scan, with complete + # disregard of translation in PE dir. The file has two columns + # where the first contains the RMS movement relative the first + # volume and the second column the RMS relative the previous + # volume + # Variables: + # relone: motion relative to first volume + # relbef: motion relative to previous volume + # cum: cunulative motion from relbef + relone = dat[:, 0] + relbef = dat[:, 1] + x = np.arange(start=1, stop=nvols + 1, step=1) + # Plot + plt.style.use("seaborn") + fig, ax = plt.subplots() + ax.plot(x, relone, linewidth=1, label="Relative to first volume") + ax.plot(x, relbef, linewidth=1, label="Relative to previous volume") + plt.xlabel("Volume Number") + if voxel is not None: + plt.ylabel("RMS of Head Displacement [mm]") + plt.text( + 0, + -2, + "$\dag$ average voxel dimension is used in " + "converting voxel displacement to head displacement " + "$V_{avg} = (V_x + V_y + V_z) / 3$", + ha="left", + size=6, + ) + else: + plt.ylabel("RMS of Voxel Displacement") + ax.legend(loc="upper center", bbox_to_anchor=(0.5, -0.10), shadow=True, ncol=2) + ax.xaxis.set_minor_locator(AutoMinorLocator()) + ax.grid(which="minor", linestyle=":", linewidth="0.5") + plt.title("Intervolume Head Motion") + plt.tight_layout() + plt.savefig(output, dpi=600) diff --git a/pydesigner/plotting/outlierplot.py b/pydesigner/plotting/outlierplot.py new file mode 100644 index 00000000..94289645 --- /dev/null +++ b/pydesigner/plotting/outlierplot.py @@ -0,0 +1,101 @@ +import os.path as op +from typing import Union + +import matplotlib.pyplot as plt +import nibabel as nib +import numpy as np +from matplotlib.ticker import AutoMinorLocator + + +def plot( + input: str, + output: str, + bval: Union[str, None] = None, + mask: Union[str, None] = None, +) -> None: + """Plots and saves the number of outliers in input 4D irlls output + file as a PNG file. + + Parameters + ---------- + input: ndarry(dtype=float) + List of 4D DWI (nifti-format) paths to evaluate and plot. + output: str + Output path of plot in .png format. + bval: str + Path to relevant .bval file. + mask: str + Path to brain mask in nifti format. + + Returns + ------- + None; writes out plot to file. + + See Also + -------- + motionplot: plots subject motion from eddy_qc output file. + snrplot : plots DWI's SNR. + """ + print("Plotting outliers...") + if not op.exists(input): + raise OSError("Input file {} does not exist".format(input)) + if op.splitext(input)[-1] != ".nii": + raise OSError("Input file {} is not nifti type".format(input)) + if op.isdir(output): + raise OSError( + "Output {} cannot be a directory. Please " "define the output to be an image file.".format(output) + ) + if op.splitext(output)[-1] != ".png": + raise OSError("Output path {} does not indicate a PNG file" "".format(input)) + hdr = nib.load(input) + img = np.array(hdr.dataobj) + truncateIdx = np.isnan(img) + img[truncateIdx] = 0 + dims = img.shape # size per dimension + dims[0] * dims[1] * dims[2] # no. of voxels + vols = dims[-1] # number of volumes + if np.ndim(img) != 4: + raise Exception("Only 4D nifti files can be read. " "User-supplied file is not a 4D nifti.") + if mask is not None: + if op.exists(mask): + if op.splitext(mask)[-1] != ".nii": + raise OSError("Input maks {} is not nifti type " "".format(mask)) + hdr_mask = nib.load(mask) + bw = np.array(hdr_mask.dataobj) + else: + raise OSError("Mask path {} does not exist".format(mask)) + else: + bw = np.ones(dims[0:3], order="F") + if bval is None: + bvals = np.zeros(dims[-1], dtype=int) + else: + bvals = np.loadtxt(bval, dtype=int) + # multiply mask by img + for i in range(vols): + img[:, :, :, i] = np.multiply(img[:, :, :, i], bw) + # Create x-axis + x = np.arange(start=0, stop=vols, step=1) + # create y-axis + y = np.zeros_like(x) + for i in range(len(x)): + y[i] = np.count_nonzero(img[:, :, :, i]) + # Normalize to percentage of voxels + y = (y / np.count_nonzero(bw)) * 100 + # Plot + plt.style.use("seaborn") + fig, ax = plt.subplots() + plt.plot(x, y, "-", lw=1, color="black", alpha=0.40) + scat = plt.scatter(x, y, c=bvals, s=10, linewidths=0, alpha=1, cmap="Set1") + plt.xlabel("Shell Number") + plt.ylabel("Percentage of Outlier Voxels (%)") + plt.xticks(rotation=45) + ax.xaxis.set_minor_locator(AutoMinorLocator()) + ax.grid(which="minor", linestyle=":", linewidth="0.5") + if bval: + cbar = fig.colorbar(scat, ax=ax) + cbar.set_label("B-Value") + if mask is None: + plt.title("IRLLS Outlier Determination in DWI") + else: + plt.title("IRLLS Outlier Determination in DWI (Brain Masked)") + plt.savefig(output, dpi=600) diff --git a/pydesigner/plotting/snrplot.py b/pydesigner/plotting/snrplot.py new file mode 100644 index 00000000..4b0d6979 --- /dev/null +++ b/pydesigner/plotting/snrplot.py @@ -0,0 +1,281 @@ +import os.path as op +from typing import List, Self, Tuple, Union + +import matplotlib.pyplot as plt +import nibabel as nib +import numpy as np +from scipy.interpolate import make_interp_spline + +from ..system.utils import vectorize + +minZero = 1e-8 +np.seterr(all="ignore") + + +class makesnr: + """Class object that computes and prints SNR plots for any number of + input DWIs. + + Parameters + ---------- + dwilist: list of str + List of 4D DWI (nifti-format) paths to evaluate and plot. + noisepath: str + Path to noise map from "dwidenoise". + maskpath: str, optional + Path to brain mask. + + Methods + ------- + __init__: constructs makesnr class + getuniquebval : creates a list of unique B-values for the purpose of + SNR computation. + computesnr: performs SNR computation. + histcount: bins SNR values. + makeplot: creates and saves SNR plot from bin counts. + + """ + + def __init__( + self, + dwilist: List[str], + noisepath: Union[str, None] = None, + maskpath: Union[str, None] = None, + ) -> Self: + """Constructor for makesnr class + + Parameters + ---------- + dwilist: list of str + String list of nifti paths to plot. + noisepath: str + Path to noisemap (Default: None). + maskpath: str, optional + Path to nifti brain mask (Default: None). + """ + if noisepath is None: + raise Exception("Please provide the path to noise map from " '"dwidenoise"') + + self.nDWI = len(dwilist) # Number of input DWIs + self.DWInames = [op.split(i)[-1] for i in dwilist] + # Open the first image in list + self.hdr = nib.load(dwilist[0]) + if self.hdr.ndim != 4: + raise IOError("Input DWIs need are not 4D. Please ensure you " "use 4D NifTi files only.") + # Load image into 2D array + self.img = np.array(self.hdr.dataobj) + # Load noise into a vector + self.noise = np.array(nib.load(noisepath).dataobj) + # Load BVAL + fName = op.splitext(dwilist[0])[0] + bvalPath = op.join(fName + ".bval") + if op.exists(bvalPath): + self.bval = np.rint(np.loadtxt(bvalPath) / 1000) + else: + raise IOError("BVAL file {} not found".format(bvalPath)) + if maskpath is not None and op.exists(maskpath): + self.mask = np.array(nib.load(maskpath).dataobj).astype(bool) + self.maskStatus = True + else: + self.mask = np.ones((self.img.shape[0], self.img.shape[1], self.img.shape[2]), order="F") + self.maskStatus = False + # Vectorize images + self.img = vectorize(self.img, self.mask) + self.nvox = self.img.shape[1] + self.noise = vectorize(self.noise, self.mask) + if self.nDWI > 1: + # From second image to last image + for i in range(1, self.nDWI): + try: + tmp = vectorize(np.array(nib.load(dwilist[i]).dataobj), self.mask) + self.img = np.dstack((self.img, tmp)) + except: # noqa: E722 + raise ValueError("all input DWIs must have the same " "shape.") + try: + fName = op.splitext(dwilist[i])[0] + bvalPath = op.join(fName + ".bval") + self.bval = np.stack((self.bval, np.rint(np.loadtxt(bvalPath) / 1000))) + except: # noqa: E722 + raise IOError("Unable to locate BVAL file for image: {" "}".format(dwilist[i])) + truncateIdx = np.logical_or(np.isnan(self.img), (self.img < minZero)) + self.img[truncateIdx] = minZero + + def getuniquebval(self) -> np.ndarray[float]: + """Creates a list of unique B-values for the purpose of SNR + computation. In the calculation of SNR, B0 signal can be averaged + becase they are not associated to any direction. This is not true + for non-B0 values however, because every 3D volume represents a + different direction. To compute SNR appropriately, differences in + gradients have to be accounted. This function creates a list of + B-values in the order they need to appear for the calculation of + SNR. + + Returns + ------- + b_list : ndarray(dtype=float) + Numpy vector containing list of B-values to be used in + SNR calculation. + """ + b_list = [] + for i in range(self.nDWI): + bvals = self.bval[i, :] + unibvals = np.array(np.unique(bvals), dtype=int) + bval_list = [] + for j in range(unibvals.size): + bval = unibvals[j] + # Index where entirety of bvals, given by variable + # bvals, is equal to a single unique bval + idx_bval = np.where(np.isin(bvals, bval))[-1] + if bval != 0: + bval_list.append(str(bval)) + else: + # Appends '0' to bval_list n countb0 number of times + for countb in range(len(idx_bval)): + bval_list.append(str(bval)) + b_list.append(bval_list) + return np.asarray(b_list, dtype=int) + + def computesnr(self) -> np.ndarray[float]: + """Computes SNR of all DWIs in class object. + + Returns + ------- + snr_dwi: ndarray(dtype=float) + Numpy array of SNR across all DWI. + """ + bval_list = self.getuniquebval() + snr_dwi = np.zeros((self.nvox, bval_list.shape[1], self.nDWI)) + for i in range(self.nDWI): + bvals = self.bval[i, :] + unibvals = np.array(np.unique(bvals), dtype=int) + print("Computing SNR: " + self.DWInames[i]) + for j in range(unibvals.size): + bval = unibvals[j] + print(" * B" + str(bval * 1000) + "...") + # Index where entirety of bvals, given by variable + # bvals, is equal to a single unique bval + idx_bval = np.where(np.isin(bvals, bval))[-1] + idx_list = np.where(np.isin(bval_list[i, :], bval))[-1] + img = self.img[idx_bval, :, i] + if bval != 0: + snr_dwi[:, idx_list, i] = np.mean((img / self.noise), axis=0).reshape((self.nvox, 1)) + else: + # Appends '0' to bval_list n countb0 number of times + for countb in range(img.shape[0]): + snr_dwi[:, idx_list, i] = np.divide(img, self.noise).reshape((self.nvox, idx_list.size)) + truncateIdx = np.logical_or(np.isnan(snr_dwi), (snr_dwi < minZero)) + snr_dwi[truncateIdx] = minZero + return snr_dwi + + def histcount(self, nbins: int = 100) -> Tuple[np.ndarray[int], np.ndarray[float], np.ndarray[float]]: + """Bins SNR into nbins and returns various counting properties. + + Parameters + ---------- + nbins: int + Number of bins to plot. + + Returns + ------- + count: ndarray(dtype=int) + Array of count of voxels in bins. + binval: ndarray(dtype=float) + Array of bin values. + unibvals: ndarray(dtype=float) + Array containing all unique B-values detected. + """ + if not isinstance(nbins, int): + raise ValueError("Number of bins (nbins) entered is not an " "integer. Please specify and integer.") + bval_list = self.getuniquebval() + snr = self.computesnr() + # Get min and max values of SNR + snr_ = snr.reshape(-1) + minVal = np.min(snr_[np.isfinite(snr_)]) + maxVal = np.max(snr_[np.isfinite(snr_)]) + unibvals = np.array(np.unique(bval_list), dtype=int) + count = np.zeros((nbins, unibvals.size, self.nDWI)) + edges = np.zeros((nbins + 1, unibvals.size, self.nDWI)) + for j in range(self.nDWI): + for i in range(unibvals.size): + bval = unibvals[i] + idx_list = np.where(np.isin(bval_list[j, :], bval))[-1] + vals = snr[:, idx_list, j] + (count[:, i, j], edges[:, i, j]) = np.histogram(vals, bins=nbins, range=(minVal, maxVal), density=True) + edges = np.unique(edges) + if edges.size != nbins + 1: + raise Exception( + "Number of binning edges across B-values and " "DWIs is not consistent. Aborting SNR " "binning." + ) + binval = np.zeros((nbins)) + for i in range(binval.size): + binval[i] = np.median([edges[i], edges[i + 1]]) + return count, binval, unibvals + + def makeplot(self, path: str, smooth: bool = True, smoothfactor: int = 5) -> None: + """Creates and saves SNR plot to a path as SNR.png + + Parameters + ---------- + path : str + Directory to save the plot in + smooth : bool, optional + Specify whether to interpolate and smooth (Default: True) + smoothfactor : int, optional + Smoothing factor to apply (Default: 5) + + Returns + ------- + None: Writes out image into directory as SNR.png + """ + if not isinstance(smoothfactor, int): + raise ValueError("Please specify an integer for smooth " "factor.") + if not op.isdir(path): + raise IOError("Output path defined does not exist.") + outpath = op.join(path, "SNR.png") + (count, binval, unibvals) = self.histcount() + count = count * 100 + if smooth: + smoothFactor = smoothfactor * count.shape[0] # Number of + # points to add between histogram counts for smoothing + binval_interp = np.linspace(binval.min(), binval.max(), smoothFactor) + (R, C, D) = count.shape + count_interp = np.zeros((smoothFactor, C, D)) + for y in range(C): + for z in range(D): + spl = make_interp_spline(binval, count[:, y, z], k=3) + count_interp[:, y, z] = spl(binval_interp) + count = count_interp + binval = binval_interp + plt.style.use("seaborn") + nplots = unibvals.size + titles = ["B" + str(i * 1000) for i in unibvals] + fig = plt.figure(figsize=(8, 10)) + fig.subplots_adjust(hspace=0.4, wspace=0.1) + fig.suptitle("SNR of Acquisitions", fontsize=20) + for i in range(nplots): + ax = fig.add_subplot(nplots, 1, i + 1) + ax.plot(binval, count[:, i, :]) + # Subplot Properties + ax.grid(True) + ax.set_title(titles[i], loc="right") + bval = unibvals[i] + ax.set_xlabel("SNR") + ax.set_ylabel("% of voxels") + ax.set_ylim(count[np.isfinite(count)].min(), count[np.isfinite(count)].max()) + if bval == 0: + ax.set_xlim(0, 200) + elif bval == 1: + ax.set_xlim(0, 100) + elif bval == 2: + ax.set_xlim(0, 80) + else: + ax.set_xlim(0, 60) + # Plot Properties + plt.legend( + self.DWInames, + ncol=nplots, + loc="upper left", + frameon=False, + bbox_to_anchor=(0.25, -0.19), + ) + plt.savefig(outpath, dpi=300) diff --git a/pydesigner/postprocessing/__init__.py b/pydesigner/postprocessing/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/pydesigner/postprocessing/filters.py b/pydesigner/postprocessing/filters.py new file mode 100644 index 00000000..f7d2fa3d --- /dev/null +++ b/pydesigner/postprocessing/filters.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python +# -*- coding : utf-8 -*- + +"""This module contains filter(s) for postprocessing DTI/DKI maps +""" + +# --------------------------------------------------------------------- +# Package Management +# --------------------------------------------------------------------- +import os.path as op +from typing import Union + +import nibabel as nib +import numpy as np +from scipy.ndimage import generate_binary_structure, median_filter + + +# --------------------------------------------------------------------- +# Functions +# --------------------------------------------------------------------- +def readnii(input: str): + """Reads nifti files and returns header and numpy data array + + Parameters + ---------- + input : str + Path to nifti file + + Returns + ------- + hdr : class + Nibabel class object + img : ndarray + 3D or 4D array containing the loaded nifti image + + """ + hdr = nib.load(input) + img = np.array(hdr.dataobj) + return hdr, img + + +def writenii(hdr, img: np.ndarray[float], output: str) -> None: + """Write nupy array to nifti file + + Parameters + ---------- + hdr : class + Nibabel class object + img : ndarray + 3D or 4D array containing the image array + output : str + Path to save file as + + None; writes out file + """ + struct = nib.Nifti1Image(img, hdr.affine, hdr.header) + nib.save(struct, output) + + +def median(input: str, output: str, mask: Union[str, None] = None) -> None: + """Applies median filtering to input nifti file + + Parameters + ---------- + input : str + Path to input nifti file + output : str + Path to output nifti file + mask : str, optional + Path to brainmask nifti file (Default: None) + + Returns + ------- + None; writes out file + """ + if not op.exists(input): + raise IOError("Input file {} does not exist.".format(input)) + hdr, img = readnii(input) + if mask is not None: + if not op.exists(mask): + raise IOError("Input mask {} does not " "exist.".format(input)) + maskhdr, mask = readnii(mask) + else: + mask = np.ones((img.shape[0], img.shape[1], img.shape[2]), order="F") + mask.astype(bool) + conn = generate_binary_structure(rank=3, connectivity=1) + if np.ndim(img) == 4: + for i in range(img.shape[-1]): + img[:, :, :, i] = median_filter(img[:, :, :, i], footprint=conn, mode="constant", cval=float("nan")) * mask + elif np.ndim(img) == 3: + img = median_filter(img, footprint=conn, mode="constant", cval=float("nan")) * mask + else: + raise Exception("Input nifti image needs to be either 3D or " "4D. Please check the file provided.") + writenii(hdr, img, output) diff --git a/pydesigner/preprocessing/__init__.py b/pydesigner/preprocessing/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/pydesigner/preprocessing/mrinfoutil.py b/pydesigner/preprocessing/mrinfoutil.py new file mode 100644 index 00000000..20fe04a4 --- /dev/null +++ b/pydesigner/preprocessing/mrinfoutil.py @@ -0,0 +1,564 @@ +#!/usr/bin/env python +# -*- coding : utf-8 -*- + +"""Utilities for extracting information on various input files using +MRtrix3's mrinfo tool. All values are returned in basic Python data +types. +""" + +import os.path as op +import re +import subprocess +from typing import List, Tuple, Union + + +def getconsole(path: int, flag: str) -> str: + """Fetches the console output of MRtrix3's mrinfo with specified + flag. + + Parameters + ---------- + path: str + Path to input image or directory. + flag: str + Flag to pass onto mrinfo. + + Returns + ------- + str + MRtrix3's mrinfo console output. + """ + if not op.exists(path): + raise OSError("Input path does not exist. Please ensure that the " "folder or file specified exists.") + if not isinstance(flag, str): + raise TypeError("Input flag is not a string") + arg = ["mrinfo", flag] + arg.append(path) + completion = subprocess.run(arg, stdout=subprocess.PIPE) + if completion.returncode != 0: + raise IOError("Input {} is not currently supported by " "PyDesigner.".format(path)) + console = str(completion.stdout).split("\\n")[0] + console = console.split("b")[-1] + console = console.replace("'", "") + return console + + +def format(path: str) -> str: + """Returns the file format of input DWI. + + Parameters + ---------- + path: str + Path to input image or directory. + + Returns + ------- + str + Image file format. + """ + type = getconsole(path, "-format") + return type + + +def ndim(path: str) -> int: + """Returns the number of image dimensions of input DWI. + + Parameters + ---------- + path: str + Path to input image or directory. + + Returns + ------- + int + Number of dimensions in image. + """ + num = getconsole(path, "-ndim") + return int(num) + + +def size(path: str) -> Tuple[int]: + """Returns the size of input DWI image along each axis. + + Parameters + ---------- + path: str + Path to input image or directory. + + Returns + ------- + Tuple[int] + Number of voxels in [X, Y, Z, B-value]. + """ + num = getconsole(path, "-size").split() + num = tuple(map(int, num)) + return num + + +def spacing(path: str) -> Tuple[int]: + """Returns the voxel spacing along each of input DWI's dimensions. + + Parameters + ---------- + path : str + Path to input image or directory. + + Returns + ------- + Tuple[int] + Number of spacing between voxels [X, Y, Z, B-value]. + """ + num = getconsole(path, "-spacing").split() + num = tuple(map(float, num)) + return num + + +def datatype(path: str) -> str: + """Returns the data type used for storing input DWI. + + Parameters + ---------- + path : str + Path to input image or directory. + + Returns + ------- + str + MRtrix3 datatype. + """ + return getconsole(path, "-datatype") + + +def strides(path: str) -> Tuple[int]: + """Returns data strides of input DWI. + + Parameters + ---------- + path : str + Path to input image or directory. + + Returns + ------- + num: Tuple[int] + MRtrix3's strides. + """ + num = getconsole(path, "-strides").split() + num = tuple(map(int, num)) + return num + + +def offset(path: str) -> float: + """Returns the input DWI's intensity offset. + + Parameters + ---------- + path: str + Path to input image or directory. + + Returns + ------- + num: float + Image intensity offset. + """ + num = getconsole(path, "-offset") + num = float(num) + return num + + +def multiplier(path: str) -> float: + """Returns the input DWI's intensity multiplier. + + Parameters + ---------- + path: str + Path to input image or directory. + + Returns + ------- + num: float + Image intensity multiplier. + """ + num = getconsole(path, "-multiplier") + num = float(num) + return num + + +def transform(path: str) -> Tuple[float]: + """Returns the input DWI's 4x4 voxel to image transformation matrix. + + Parameters + ---------- + path: str + Path to input image or directory. + + Returns + ------- + num: Tuple(float) + Image transformation matrix. + """ + if not op.exists(path): + raise OSError("Input path does not exist. Please ensure that the " "folder or file specified exists.") + arg = ["mrinfo", "-transform"] + arg.append(path) + completion = subprocess.run(arg, stdout=subprocess.PIPE) + if completion.returncode != 0: + raise IOError("Input {} is not currently supported by " "PyDesigner.".format(path)) + console = str(completion.stdout).split("\\n") + num = [re.findall(r"[-+]?\d*\.\d+|\d+", s) for s in console] + num = [s for s in num if s != []] + return tuple(num) + + +def commandhistory(path: str) -> List[str]: + """Returns a list of command history (manipulations or transformations) + performed on MRtrix file format .mif + + Parameters + ---------- + path: str + Path to input image or directory + + Returns + ------- + list(str) + command history of input file + """ + if not op.exists(path): + raise OSError("Input path does not exist. Please ensure that the " "folder or file specified exists.") + ftype = format(path) + if ftype != "MRtrix": + raise IOError( + "This function only works with MRtrix (.mif) " + "formatted filetypes. Please ensure that the input " + "filetype meets this requirement" + ) + arg = ["mrinfo", "-property", "command_history"] + arg.append(path) + completion = subprocess.run(arg, stdout=subprocess.PIPE) + if completion.returncode != 0: + raise IOError("Input {} is not currently supported by " "PyDesigner.".format(path)) + # Remove new line delimiter + console = str(completion.stdout).split("\\n") + # Remove 'b' + console[0] = console[0][1:] + # Remove quotes + console = [s.replace("'", "") for s in console] + # Condense empty strings + console = [s.replace('"', "") for s in console] + # Remove empty strings form list + console = list(filter(None, console)) + # Remove MRtrix3 version + console = [re.sub(r"\([^)]*\)", "", s) for s in console] + # Remove whitespace to the right of string + console = [s.rstrip() for s in console] + return list(console) + + +def dwscheme(path: str) -> List[float]: + """Returns a list of input DWI's diffusion weighting scheme. + + Parameters + ---------- + path: str + Path to input image or directory. + + Returns + ------- + dw_scheme: list(float) + diffusion weighing scheme. + """ + if not op.exists(path): + raise OSError("Input path does not exist. Please ensure that the " "folder or file specified exists.") + ftype = format(path) + if ftype != "MRtrix": + raise IOError( + "This function only works with MRtrix (.mif) " + "formatted filetypes. Please ensure that the input " + "filetype meets this requirement" + ) + arg = ["mrinfo", "-dwgrad"] + arg.append(path) + completion = subprocess.run(arg, stdout=subprocess.PIPE) + if completion.returncode != 0: + raise IOError("Input {} is not currently supported by " "PyDesigner.".format(path)) + # Remove new line delimiter + console = str(completion.stdout).split("\\n") + # Remove 'b' + console[0] = console[0][1:] + # Remove quotes + console = [s.replace("'", "") for s in console] + # Condense empty strings + console = [s.replace('"', "") for s in console] + # Remove empty strings form list + console = list(filter(None, console)) + # Convert list of strings to float + dw_scheme = [] + for idx_a, line in enumerate(console): + nums = [] + for idx_b, num in enumerate(line.split()): + nums.append(float(num)) + dw_scheme.append(nums) + return dw_scheme + + +def pescheme(path: str) -> List[float]: + """Returns a list of phase encoding scheme. If len(pescheme) > 1, + the .mif DWI contains more than one directons. + + Parameters + ---------- + path: str + Path to input image or directory. + + Returns + ------- + pe_scheme: list(float) + Phase encoding scheme. + """ + if not op.exists(path): + raise OSError("Input path does not exist. Please ensure that the " "folder or file specified exists.") + ftype = format(path) + if ftype != "MRtrix": + raise IOError( + "This function only works with MRtrix (.mif) " + "formatted filetypes. Please ensure that the input " + "filetype meets this requirement" + ) + arg = ["mrinfo", "-petable"] + arg.append(path) + completion = subprocess.run(arg, stdout=subprocess.PIPE) + if completion.returncode != 0: + raise IOError("Input {} is not currently supported by " "PyDesigner.".format(path)) + # Remove new line delimiter + console = str(completion.stdout).split("\\n") + # Remove 'b' + console[0] = console[0][1:] + # Remove quotes + console = [s.replace("'", "") for s in console] + # Condense empty strings + console = [s.replace('"', "") for s in console] + # Remove empty strings form list + console.remove("") + # Convert list of strings to float + pe_scheme = [] + for idx_a, line in enumerate(console): + nums = [] + for idx_b, num in enumerate(line.split()): + nums.append(float(num)) + pe_scheme.append(nums) + return pe_scheme + + +def shells(path: str) -> int: + """Returns the number of b-value shells detected in input file. + + Parameters + ---------- + path: str + Path to input image or directory. + + Returns + ------- + console: int + Number of shells. + """ + if not op.exists(path): + raise OSError("Input path does not exist. Please ensure that the " "folder or file specified exists.") + ftype = format(path) + if ftype != "MRtrix": + raise IOError( + "This function only works with MRtrix (.mif) " + "formatted filetypes. Please ensure that the input " + "filetype meets this requirement" + ) + arg = ["mrinfo", "-shell_bvalues"] + arg.append(path) + completion = subprocess.run(arg, stdout=subprocess.PIPE) + if completion.returncode != 0: + raise IOError("Input {} is not currently supported by " "PyDesigner.".format(path)) + # Remove new line delimiter + console = str(completion.stdout).split("\\n") + # Remove 'b' + console[0] = console[0][1:] + # Remove quotes + console = [s.replace("'", "") for s in console] + # Condense empty strings + console = [s.replace('"', "") for s in console] + # Remove empty strings form list + console.remove("") + # Split spaces + console = [s.split(" ") for s in console] + console = [item for sublist in console for item in sublist] + console = list(filter(None, console)) + console = [int(round(float(x))) for x in console] + return console + + +def num_shells(path: str) -> int: + """Returns the number of b-value shells detected in input file. + + Parameters + ---------- + path: str + Path to input image or directory. + + Returns + ------- + int + Number of shells. + """ + if not op.exists(path): + raise OSError("Input path does not exist. Please ensure that the " "folder or file specified exists.") + ftype = format(path) + if ftype != "MRtrix": + raise IOError( + "This function only works with MRtrix (.mif) " + "formatted filetypes. Please ensure that the input " + "filetype meets this requirement" + ) + console = shells(path) + return len(console) + + +def max_shell(path: str) -> int: + """Returns the maximum b-value shell in DWI + + Parameters + ---------- + path: str + Path to input image or directory + + Returns + ------- + int + Max b-value + """ + if not op.exists(path): + raise OSError("Input path does not exist. Please ensure that the " "folder or file specified exists.") + ftype = format(path) + if ftype != "MRtrix": + raise IOError( + "This function only works with MRtrix (.mif) " + "formatted filetypes. Please ensure that the input " + "filetype meets this requirement" + ) + arg = ["mrinfo", "-shell_bvalues"] + arg.append(path) + completion = subprocess.run(arg, stdout=subprocess.PIPE) + if completion.returncode != 0: + raise IOError("Input {} is not currently supported by " "PyDesigner.".format(path)) + # Remove new line delimiter + console = str(completion.stdout).split("\\n") + # Remove 'b' + console[0] = console[0][1:] + # Remove quotes + console = [s.replace("'", "") for s in console] + # Condense empty strings + console = [s.replace('"', "") for s in console] + # Remove empty strings form list + console.remove("") + # Split spaces + console = [s.split(" ") for s in console] + console = [item for sublist in console for item in sublist] + console = list(filter(None, console)) + console = [int(round(float(s))) for s in console] + return max(console) + + +def is_fullsphere(path: str) -> bool: + """Returns boolean value indicating whether input file has full + spherical sampling. + + Parameters + ---------- + path: str + Path to input image or directory. + + Returns + ------- + bool + True if full spherical sampling. + False if half-spherical sampling. + """ + if not op.exists(path): + raise OSError("Input path does not exist. Please ensure that the " "folder or file specified exists.") + ftype = format(path) + if ftype != "MRtrix": + raise IOError( + "This function only works with MRtrix (.mif) " + "formatted filetypes. Please ensure that the input " + "filetype meets this requirement" + ) + arg = ["dirstat", path, "-output", "ASYM"] + completion = subprocess.run(arg, stdout=subprocess.PIPE) + if completion.returncode != 0: + raise IOError("Input {} is not currently supported by " "PyDesigner.".format(path)) + # Remove new line delimiter + console = str(completion.stdout).split("\\n") + # Remove 'b' + console[0] = console[0][1:] + # Remove quotes + console = [s.replace("'", "") for s in console] + # Condense empty strings + console = [s.replace('"', "") for s in console] + # Remove empty strings form list + console.remove("") + # Convert strings to list + console = [float(s) for s in console] + # Find mean of all b-shells and round to nearest one decimal place + mean_dir = round(sum(console) / len(console), 1) + # Having too many b-value shells for protocols such as FBI or + # HARDI, eddy correction can create a lot of trouble. If that's + # the case, we just assume that data is fully shelled. This + # disables heuristics testing performed by eddy correction. A DWI + # will return half-shelled if and only if the norm of mean + # direction vector is greater that 0.3 AND has max b-value less + # than or equal to 3000. The linear model breaks down for high + # b-values. + if mean_dir < 0.3: + return True + else: + if max_shell(path) > 3000: + return True + else: + return False + + +def echotime(path: str) -> Union[int, str]: + """Returns the echo time(s) of DWI in miliseconds + + Parameters + ---------- + path: str + Path to input image or directory + + Returns + ------- + int if all DWIs have the same echo time + Echo time in miliseconds + str if all DWIs have different echo time + 'variable' + """ + if not op.exists(path): + raise OSError("Input path does not exist. Please ensure that the " "folder or file specified exists.") + ftype = format(path) + if ftype != "MRtrix": + raise IOError( + "This function only works with MRtrix (.mif) " + "formatted filetypes. Please ensure that the input " + "filetype meets this requirement" + ) + arg = ["mrinfo", "-property", "EchoTime"] + arg.append(path) + completion = subprocess.run(arg, stdout=subprocess.PIPE) + if completion.returncode != 0: + raise IOError("Input {} is not currently supported by " "PyDesigner.".format(path)) + console = str(completion.stdout).split("\\n")[0] + console = console.split("b")[-1] + console = console.replace("'", "") + try: + console = float(console) + console = int(round(console * 1000, 0)) + except: # noqa: E722 + console = "variable" + return console diff --git a/pydesigner/preprocessing/mrpreproc.py b/pydesigner/preprocessing/mrpreproc.py new file mode 100644 index 00000000..c40f1dbb --- /dev/null +++ b/pydesigner/preprocessing/mrpreproc.py @@ -0,0 +1,1375 @@ +#!/usr/bin/env python +# -*- coding : utf-8 -*- + +"""Utilities for running various MRtrix3's DWI preprocessing tools +""" + +import os +import os.path as op +import subprocess +from shutil import copyfile, which + +import numpy as np + +from ..preprocessing import mrinfoutil, rician, smoothing + + +def miftonii(input, output, nthreads=None, force=True, verbose=False): + """Converts input `.mif` images to output `.nii` images + + Parameters + ---------- + input : str + Path to input .mif file + output : str + Path to output .nii file + nthreads : int, optional + Specify the number of threads to use in processing + (Default: all available threads) + force : bool, optional + Force overwrite of output files if pre-existing + (Default:False) + verbose : bool, optional + Specify whether to print console output (Default: False) + + Returns + ------- + None; writes out file + + See Also + -------- + niitomif + """ + if not op.exists(input): + raise OSError("Input path does not exist. Please ensure that " "the folder or file specified exists.") + if not op.exists(op.dirname(output)): + raise OSError( + "Specifed directory for output file {} does not " + "exist. Please ensure that this is a valid " + "directory.".format(op.dirname(output)) + ) + if op.splitext(output)[-1] != ".nii": + raise OSError("Output specified does not possess the .nii " "extension.") + if nthreads is not None: + if not isinstance(nthreads, int): + raise Exception("Please specify the number of threads as an " "integer.") + if not isinstance(force, bool): + raise Exception("Please specify whether forced overwrite is True " "or False.") + if not isinstance(verbose, bool): + raise Exception("Please specify whether verbose is True or False.") + arg = ["mrconvert"] + if force: + arg.append("-force") + if not verbose: + arg.append("-quiet") + if nthreads is not None: + arg.extend(["-nthreads", str(nthreads)]) + arg.extend( + [ + "-export_grad_fsl", + op.splitext(output)[0] + ".bvec", + op.splitext(output)[0] + ".bval", + ] + ) + arg.extend(["-json_export", op.splitext(output)[0] + ".json"]) + arg.extend([input, output]) + completion = subprocess.run(arg) + if completion.returncode != 0: + raise Exception("Conversion from .mif to .nii failed; check " "above for errors.") + + +def niitomif(input, output, nthreads=None, force=True, verbose=False): + """Converts input `.nii` images to output `.nif` images provided that + all BVEC, BVAL and JSON files are provided and named same as input .nii + + Parameters + ---------- + input : str + Path to input .nii file + output : str + Path to output .mif file + nthreads : int, optional + Specify the number of threads to use in processing + (Default: all available threads) + force : bool, optional + Force overwrite of output files if pre-existing + (Default:False) + verbose : bool, optional + Specify whether to print console output (Default: False) + + Returns + ------- + None; writes out file + + See Also + -------- + miftonii + """ + if not op.exists(input): + raise OSError("Input path does not exist. Please ensure that " "the folder or file specified exists.") + if not op.exists(op.dirname(output)): + raise OSError( + "Specifed directory for output file {} does not " + "exist. Please ensure that this is a valid " + "directory.".format(op.dirname(output)) + ) + if op.splitext(output)[-1] != ".mif": + raise OSError("Output specified does not possess the .mif " "extension.") + if not op.exists(op.splitext(input)[0] + ".bvec"): + raise OSError('Unable to locate BVEC file" {}'.format(op.splitext(output)[0] + ".bvec")) + if not op.exists(op.splitext(input)[0] + ".bval"): + raise OSError('Unable to locate BVAL file" {}'.format(op.splitext(output)[0] + ".bval")) + if not op.exists(op.splitext(input)[0] + ".json"): + raise OSError('Unable to locate JSON file" {}'.format(op.splitext(output)[0] + ".json")) + if nthreads is not None: + if not isinstance(nthreads, int): + raise Exception("Please specify the number of threads as an " "integer.") + if not isinstance(force, bool): + raise Exception("Please specify whether forced overwrite is True " "or False.") + if not isinstance(verbose, bool): + raise Exception("Please specify whether verbose is True or False.") + arg = ["mrconvert"] + if force: + arg.append("-force") + if not verbose: + arg.append("-quiet") + if nthreads is not None: + arg.extend(["-nthreads", str(nthreads)]) + arg.extend(["-fslgrad", op.splitext(input)[0] + ".bvec", op.splitext(input)[0] + ".bval"]) + arg.extend(["-json_import", op.splitext(input)[0] + ".json"]) + arg.extend([input, output]) + completion = subprocess.run(arg) + if completion.returncode != 0: + raise Exception("Conversion from .nii to .mif failed; check " "above for errors.") + + +def stride_match(target, moving, output, nthreads=None, force=True, verbose=False): + """Matches strides on inputs target and moving by converting strides + on moving image to those of target image. + + Parameters + ---------- + target : str + Path to target image .nii or .mif file + moving : str + Path to moving image .nii or .mif file + output : str + Path to output .nii or .mif file + nthreads : int, optional + Specify the number of threads to use in processing + (Default: all available threads) + force : bool, optional + Force overwrite of output files if pre-existing + (Default:False) + verbose : bool, optional + Specify whether to print console output (Default: False) + + Returns + ------- + None; writes out file + """ + if not op.exists(target): + raise OSError("Input target path does not exist. Please ensure that " "the folder or file specified exists.") + if op.splitext(target)[-1] not in [".nii", ".mif"]: + raise OSError("Input target image needs to be a .nii or .mif file") + if not op.exists(moving): + raise OSError("Input moving path does not exist. Please ensure that " "the folder or file specified exists.") + if op.splitext(moving)[-1] not in [".nii", ".mif"]: + raise OSError("Input moving image needs to be a .nii or .mif file") + if not op.exists(op.dirname(output)): + raise OSError( + "Specifed directory for output file {} does not " + "exist. Please ensure that this is a valid " + "directory.".format(op.dirname(output)) + ) + if op.splitext(output)[-1] not in [".nii", ".mif"]: + raise OSError("Output specified does not possess the .nii " "extension.") + if nthreads is not None: + if not isinstance(nthreads, int): + raise Exception("Please specify the number of threads as an " "integer.") + if not isinstance(force, bool): + raise Exception("Please specify whether forced overwrite is True " "or False.") + if not isinstance(verbose, bool): + raise Exception("Please specify whether verbose is True or False.") + arg = ["mrconvert"] + if force: + arg.append("-force") + if not verbose: + arg.append("-quiet") + if nthreads is not None: + arg.extend(["-nthreads", str(nthreads)]) + arg.extend(["-strides", target, moving, output]) + completion = subprocess.run(arg) + if completion.returncode != 0: + raise Exception("Stride matching failed; check above for errors.") + + +def denoise( + input, + output, + noisemap=True, + extent="5,5,5", + nthreads=None, + force=True, + verbose=False, +): + """Runs MRtrix3's `dwidenoise` command with optimal parameters for + PyDesigner. + + Parameters + ---------- + input : str + Path to input .mif file + output : str + Path to output .mif file + noisemap : bool, optional + Specify whether or not to save the noisemap as a + nifti file (Default: True) + extent : str, optional + Set the window size of the denoising filter. + (Default: '5,5,5') + nthreads : int, optional + Specify the number of threads to use in processing + (Default: all available threads) + force : bool, optional + Force overwrite of output files if pre-existing + (Default:False) + verbose : bool, optional + Specify whether to print console output (Default: False) + + Returns + ------- + None; writes out file + """ + if not op.exists(input): + raise OSError("Input path does not exist. Please ensure that " "the folder or file specified exists.") + if not op.exists(op.dirname(output)): + raise OSError( + "Specifed directory for output file {} does not " + "exist. Please ensure that this is a valid " + "directory.".format(op.dirname(output)) + ) + if not isinstance(noisemap, bool): + raise Exception("Please specify whether noisemap generation " "is True or False.") + if not isinstance(extent, str): + raise Exception("Please specify extent as a string formatted as " '"n,n,n".') + if nthreads is not None: + if not isinstance(nthreads, int): + raise Exception("Please specify the number of threads as an " "integer.") + if not isinstance(force, bool): + raise Exception("Please specify whether forced overwrite is True " "or False.") + if not isinstance(verbose, bool): + raise Exception("Please specify whether verbose is True or False.") + noisemap_path = op.join(op.dirname(input), "noisemap.nii") + arg = ["dwidenoise"] + if force: + arg.append("-force") + if not verbose: + arg.append("-quiet") + if nthreads is not None: + arg.extend(["-nthreads", str(nthreads)]) + if noisemap: + arg.extend(["-noise", noisemap_path]) + if extent is not None: + arg.extend(["-extent", extent]) + arg.extend([input, output]) + completion = subprocess.run(arg) + if completion.returncode != 0: + raise Exception("dwidenoise failed, please look above for error " "sources.") + + +def degibbs(input, output, nthreads=None, force=False, verbose=False): + """Runs MRtrix3's `mrdegibbs` command with optimal parameters for + PyDesigner. + + Parameters + ---------- + input : str + Path to input .mif file + output : str + Path to output .mif file + nthreads : int, optional + Specify the number of threads to use in processing + (Default: all available threads) + force : bool, optional + Force overwrite of output files if pre-existing + (Default:False) + verbose : bool, optional + Specify whether to print console output (Default: False) + + Returns + ------- + None; writes out file + """ + if not op.exists(input): + raise OSError("Input path does not exist. Please ensure that " "the folder or file specified exists.") + if not op.exists(op.dirname(output)): + raise OSError( + "Specifed directory for output file {} does not " + "exist. Please ensure that this is a valid " + "directory.".format(op.dirname(output)) + ) + if nthreads is not None: + if not isinstance(nthreads, int): + raise Exception("Please specify the number of threads as an " "integer.") + if not isinstance(force, bool): + raise Exception("Please specify whether forced overwrite is True " "or False.") + if not isinstance(verbose, bool): + raise Exception("Please specify whether verbose is True or False.") + arg = ["mrdegibbs"] + if force: + arg.append("-force") + if not verbose: + arg.append("-quiet") + if nthreads is not None: + arg.extend(["-nthreads", str(nthreads)]) + arg.extend([input, output]) + completion = subprocess.run(arg) + if completion.returncode != 0: + raise Exception("mrdegibbs failed, please look above for error " "sources.") + + +def undistort( + input, + output, + rpe="rpe_header", + epib0=1, + qc=None, + nthreads=None, + force=False, + verbose=False, +): + """Runs MRtrix3's distortion correction command with optimal + parameters for PyDesigner. + + Parameters + ---------- + input : str + Path to input .mif file + output : str + Path to output .mif file + rpe : str, {'rpe_header', 'rpe-pair', 'rpe_all, 'rpe_all'}, optional + Reverse phase encoding of the dataset. (Default: 'rpe_header') + epib0 : int + Number of reverse PE dir B0 pairs to use in TOPUP correction + (Default: 1) + qc : str + Specify path to QC directior. No QC metrics generated if None + nthreads : int, optional + Specify the number of threads to use in processing + (Default: all available threads) + force : bool, optional + Force overwrite of output files if pre-existing + (Default:False) + verbose : bool, optional + Specify whether to print console output (Default: False) + + Returns + ------- + None; writes out file + """ + if not op.exists(input): + raise OSError("Input path does not exist. Please ensure that " "the folder or file specified exists.") + if not op.exists(op.dirname(output)): + raise OSError( + "Specifed directory for output file {} does not " + "exist. Please ensure that this is a valid " + "directory.".format(op.dirname(output)) + ) + if rpe not in ["rpe_none", "rpe_pair", "rpe_all", "rpe_header"]: + raise Exception( + "Entered RPE selection is not valid. Please " + 'choose either "rpe_none", "rpe_pair", ' + '"rpe_all", or "rpe_header".' + ) + if not isinstance(epib0, int): + raise Exception("Number of TOPUP B0s need to be specified as " "as an integer.") + if qc is not None: + if not isinstance(qc, str): + raise Exception("Please specify QC directory as a string") + if not op.exists(qc): + raise OSError("Specified QC directory does not exist. " "Please ensure that this is a valid " "directory.") + if nthreads is not None: + if not isinstance(nthreads, int): + raise Exception("Please specify the number of threads as an " "integer.") + if not isinstance(force, bool): + raise Exception("Please specify whether forced overwrite is True " "or False.") + if not isinstance(verbose, bool): + raise Exception("Please specify whether verbose is True or False.") + rpe = "-" + rpe + # Get output directory + outdir = op.dirname(output) + # Extract BVEC and BVALS for shell sampling deduction + arg_extract = ["mrinfo"] + arg_extract.extend( + [ + "-export_grad_fsl", + op.join(outdir, "dwiec.bvec"), + op.join(outdir, "dwiec.bval"), + ] + ) + arg_extract.append(input) + completion = subprocess.run(arg_extract) + if completion.returncode != 0: + raise Exception( + "extracting FSL BVEC and BVEC gradients " "failed during undistortion, please look " "above for errors." + ) + # Form main undistortion argument + arg = [] + if which("dwipreproc") is None: + arg.append("dwifslpreproc") + else: + arg.append("dwipreproc") + if force: + arg.append("-force") + if not verbose: + arg.append("-quiet") + if nthreads is not None: + arg.extend(["-nthreads", str(nthreads)]) + # Determine whether half or full sphere sampling + repol_string = "--repol " + if mrinfoutil.is_fullsphere(input): + # is full, add appropriate dwifslpreproc option + repol_string += "--data_is_shelled" + else: + # half + repol_string += "--slm=linear" + if epib0 > 0: + try: + epi_path = op.join(outdir, "B0_EPI.mif") + epiboost( + input=input, + output=epi_path, + num=epib0, + nthreads=nthreads, + force=force, + verbose=verbose, + ) + arg.extend(["-se_epi", epi_path]) + except: # noqa: E722 + print("[WARNING] Unable to apply TOPUPBOOST because DWI " "consists of single PE direction.") + # Remove the B0_ALL.mif file that is created when epiboost + # function fails + try: + os.remove(op.join(outdir, "B0_ALL.mif")) + except OSError: + pass + arg.extend(["-eddy_options", repol_string]) + arg.append(rpe) + if qc is not None: + arg.extend(["-eddyqc_all", qc]) + arg.extend([input, output]) + completion = subprocess.run(arg, cwd=outdir) + if completion.returncode != 0: + raise Exception("dwifslpreproc failed, please look above for " "error sources.") + # Remove temporarily generated files + os.remove(op.join(outdir, "dwiec.bvec")) + os.remove(op.join(outdir, "dwiec.bval")) + if epib0 > 0: + try: + os.remove(epi_path) + except: # noqa: E722 + print("[Warning] unable to remove {} because it does not " "exist".format(epi_path)) + + +def brainmask(input, output, thresh=0.25, nthreads=None, force=False, verbose=False): + """Creates a brainmask using FSL's Brain Extraction Tool (BET) and + MRtrix3's file manipulation tools. + + Parameters + ---------- + input : str + Path to input .mif file + output : str + Path to output .nii brainmask file + thresh : float + BET threshold ranging from 0 to 1 (Default: 0.25) + nthreads : int, optional + Specify the number of threads to use in processing + (Default: all available threads) + force : bool, optional + Force overwrite of output files if pre-existing + (Default:False) + verbose : bool, optional + Specify whether to print console output (Default: False) + + Returns + ------- + None; writes out file + """ + if not op.exists(input): + raise OSError("Input path does not exist. Please ensure that " "the folder or file specified exists.") + if not op.exists(op.dirname(output)): + raise OSError( + "Specifed directory for output file {} does not " + "exist. Please ensure that this is a valid " + "directory.".format(op.dirname(output)) + ) + if (thresh < 0) or (thresh > 1): + raise ValueError("BET Threshold needs to be within 0 to 1 range.") + if nthreads is not None: + if not isinstance(nthreads, int): + raise Exception("Please specify the number of threads as an " "integer.") + if not isinstance(force, bool): + raise Exception("Please specify whether forced overwrite is True " "or False.") + if not isinstance(verbose, bool): + raise Exception("Please specify whether verbose is True or False.") + # Read FSL NifTi output format and change it if not '.nii' + fsl_suffix = os.getenv("FSLOUTPUTTYPE") + if fsl_suffix is None: + raise OSError( + "Unable to determine system environment variable " + "FSF_OUTPUT_FORMAT. Ensure that FSL is installed " + "correctly." + ) + if fsl_suffix == "NIFTI_GZ": + os.environ["FSLOUTPUTTYPE"] = "NIFTI" + outdir = op.dirname(output) + B0_nan = op.join(outdir, "B0_nan.nii") + mask = op.join(outdir, "brain") + tmp_brain = op.join(outdir, "brain.nii") + # Extract averaged B0 from DWI + extractmeanbzero(input=input, output=B0_nan, nthreads=nthreads, force=force, verbose=verbose) + # Compute brain mask + arg_mask = ["bet", B0_nan, mask, "-m", "-f", str(thresh)] + completion = subprocess.run(arg_mask) + if completion.returncode != 0: + raise Exception("Unable to compute brain mask from B0. See above " "for errors") + # Remove intermediary file + os.remove(B0_nan) + os.remove(tmp_brain) + os.rename(op.join(outdir, mask + "_mask.nii"), output) + + +def csfmask( + input, + output, + method="fsl", + coeff=2, + thresh=0.25, + nthreads=None, + force=False, + verbose=False, +): + """Creates a cerebral spinal fluid (CSF) mask from FSL's FAST tool. + + Parameters + ---------- + input : str + Path to input .mif file + output : str + Path to output .nii CSF mask file + method : str, optional + Define method to use for computing a CSF mask. `'fsl'` relies + on FSL FAST segmentation, and `adc` uses pseudo-diffusion + coefficient more than 2 (default) to compute a mask + coeff : float, optional + Diffusion coefficient to use in thresholding a pseudo-diffusion + map to estimate CSF (Default: 2) + thresh : float, optional + BET threshold ranging from 0 to 1 (Default: 0.25) + nthreads : int, optional + Specify the number of threads to use in processing + (Default: all available threads) + force : bool, optional + Force overwrite of output files if pre-existing + (Default:False) + verbose : bool, optional + Specify whether to print console output (Default: False) + + Returns + ------- + None; writes out file + """ + if not op.exists(input): + raise OSError("Input path does not exist. Please ensure that " "the folder or file specified exists.") + if not op.exists(op.dirname(output)): + raise OSError( + "Specifed directory for output file {} does not " + "exist. Please ensure that this is a valid " + "directory.".format(op.dirname(output)) + ) + if (op.splitext(output))[-1] != ".nii": + raise IOError("Output filename {} must be specified as a " "NifTi (.nii) file.") + if (thresh < 0) or (thresh > 1): + raise ValueError("BET Threshold needs to be within 0 to 1 range.") + if nthreads is not None: + if not isinstance(nthreads, int): + raise Exception("Please specify the number of threads as an " "integer.") + if not isinstance(force, bool): + raise Exception("Please specify whether forced overwrite is True " "or False.") + if not isinstance(verbose, bool): + raise Exception("Please specify whether verbose is True or False.") + outdir = op.dirname(output) + if "fsl" in method: + # Read FSL NifTi output format and change it if not '.nii' + fsl_suffix = os.getenv("FSLOUTPUTTYPE") + if fsl_suffix is None: + raise OSError( + "Unable to determine system environment variable " + "FSF_OUTPUT_FORMAT. Ensure that FSL is installed " + "correctly." + ) + if fsl_suffix == "NIFTI_GZ": + os.environ["FSLOUTPUTTYPE"] = "NIFTI" + f_suffix = ".nii" + B0_nan = op.join(outdir, "B0_nan" + f_suffix) + path_brain = op.join(outdir, "brain") + path_tissue = op.join(outdir, "tissue") + + # Extract averaged B0 from DWI + extractmeanbzero(input=input, output=B0_nan, nthreads=nthreads, force=force, verbose=verbose) + # Compute brain mask + arg_mask = ["bet", B0_nan, path_brain, "-m", "-f", str(thresh)] + completion = subprocess.run(arg_mask) + if completion.returncode != 0: + raise Exception("Unable to compute brain mask from B0. See above " "for errors") + arg = ["fast"] + if verbose: + arg.append("-v") + arg.extend(["-n", "4", "-t", "2", "-o", path_tissue, path_brain + f_suffix]) + completion = subprocess.run(arg) + if completion.returncode != 0: + raise Exception("FSL FAST segmentation of brain tissue failed. " "See above for errors.") + csfclass = [] + for i in range(4): + arg = [ + "fslmaths", + path_tissue + "_pve_" + str(i) + f_suffix, + "-thr", + "0.95", + "-bin", + path_tissue + "_pve_thr_" + str(i) + f_suffix, + ] + completion = subprocess.run(arg) + if completion.returncode != 0: + raise Exception("FSLMATHS tissue thresholding failed. " "See above for errors.") + arg = [ + "fslstats", + path_brain + ".nii", + "-k", + path_tissue + "_pve_thr_" + str(i) + f_suffix, + "-P", + "95", + ] + completion = subprocess.run(arg, stdout=subprocess.PIPE) + if completion.returncode != 0: + raise Exception("FSLSTATS tissue thresholding failed. " "See above for errors.") + console = str(completion.stdout).split("\\n")[0] + console = console.split("b")[-1] + console = console.replace("'", "") + csfclass.append(float(console)) + csfind = np.argmax(csfclass) + arg = [ + "fslmaths", + path_tissue + "_pve_" + str(csfind) + f_suffix, + "-thr", + "0.7", + "-bin", + # '-mul', '-1', + # '-add', '1', + "-mul", + path_brain + "_mask" + f_suffix, + output, + ] + completion = subprocess.run(arg) + if completion.returncode != 0: + raise Exception("Unable to create CSF mask. " "See above for errors.") + # Remove intermediate files + os.remove(B0_nan) + os.remove(op.join(outdir, path_brain + f_suffix)) + for i in range(4): + os.remove(path_tissue + "_pve_" + str(i) + f_suffix) + os.remove(path_tissue + "_pve_thr_" + str(i) + f_suffix) + os.remove(path_tissue + "_mixeltype" + f_suffix) + os.remove(path_tissue + "_pveseg" + f_suffix) + os.remove(path_tissue + "_seg" + f_suffix) + if "adc" in method: + # Get list of b-values + bvals = mrinfoutil.shells(input) + # Find index of shell closest to b=1000 + idx = min(range(len(bvals)), key=lambda i: abs(bvals[i] - 1000)) + # Specify file paths + path_b0 = op.join(outdir, "S0.mif") + path_shell = op.join(outdir, "S1000.mif") + # Extract mean B + # Extract averaged B0 from DWI + extractmeanbzero(input=input, output=path_b0, nthreads=nthreads, force=force, verbose=verbose) + # Extract mean of indexed b=1000 shell + extractmeanshell( + input=input, + output=path_shell, + shell=bvals[idx], + nthreads=nthreads, + force=force, + verbose=verbose, + ) + # Use the formula D_pseudo = ln(S0/S1000)/B1000 > 2 to compute + # brain mask based on pseudo-ADC + arg = ["mrcalc"] + if force: + arg.append("-force") + if not verbose: + arg.append("-quiet") + if nthreads is not None: + arg.extend(["-nthreads", str(nthreads)]) + arg.extend( + [ + path_b0, + path_shell, + "-div", + "-log", + str(bvals[idx] / 1000), + "-div", + str(coeff), + "-gt", + output, + ] + ) + completion = subprocess.run(arg) + if completion.returncode != 0: + raise Exception("Unable to compute pseudo ADC. " "See above for errors.") + os.remove(path_b0) + os.remove(path_shell) + + +def smooth(input, output, csfname=None, fwhm=1.25, size=5): + """Performs Gaussian smoothing on input .mif image + + Parameters + ---------- + input : str + Path to input .mif file + output : str + Path to output .mif file + csfname : str + Path to CSF mask file in .nii format + fwhm : float + The full width half max in voxels to be smoothed + (Default: 1.25) + + Returns + ------- + None; writes out file + """ + if not op.exists(input): + raise OSError("Input path does not exist. Please ensure that " "the folder or file specified exists.") + if not op.exists(op.dirname(output)): + raise OSError( + "Specifed directory for output file {} does not " + "exist. Please ensure that this is a valid " + "directory.".format(op.dirname(output)) + ) + if csfname is not None: + if not op.exists(csfname): + raise OSError("Path to CSF mask does not exist. Please " "ensure that the file specified exists.") + if fwhm < 0: + raise Exception("FWHM cannot be less than zero.") + if size < 0: + raise Exception("Size cannot be less than zero. Please " "specify size as a positive integer.") + # Convert input .mif to .nii + outdir = op.dirname(output) + nii_path = op.join(outdir, "dwism.nii") + miftonii(input=input, output=nii_path) + # Perform smoothing + smoothing.smooth_image(nii_path, csfname=csfname, outname=nii_path, width=fwhm) + # Convert .nii to .mif + niitomif(input=nii_path, output=output) + # Remove converted files + os.remove(nii_path) + os.remove(op.splitext(nii_path)[0] + ".bvec") + os.remove(op.splitext(nii_path)[0] + ".bval") + os.remove(op.splitext(nii_path)[0] + ".json") + + +def riciancorrect(input, output, noise=None): + """Performs Rician correction on input .mif + + Parameters + ---------- + input : str + Path to input .mif file + output : str + Path to output .mif file + noise : str + Path to noise map from dwidenoise in .nii format (Default: None) + + Returns + ------- + None; writes out file + """ + if not op.exists(input): + raise OSError("Input path does not exist. Please ensure that " "the folder or file specified exists.") + if not op.exists(op.dirname(output)): + raise OSError( + "Specifed directory for output file {} does not " + "exist. Please ensure that this is a valid " + "directory.".format(op.dirname(output)) + ) + if noise is not None: + if not op.exists(noise): + raise OSError("Input noisemap {} does not exist.".format(noise)) + if op.splitext(noise)[-1] != ".nii": + raise OSError("Noisemap needs to be in NifTi format.") + else: + raise Exception("Rician correction cannot be performed without a " "noisemap.") + # Convert input .mif to .nii + outdir = op.dirname(output) + nii_path = op.join(outdir, "dwirc.nii") + miftonii(input=input, output=nii_path) + # Perform Rician correction + rician.rician_img_correct(nii_path, noise, outpath=nii_path) + # Convert .nii to .mif + niitomif(input=nii_path, output=output) + # Remove converted files + os.remove(nii_path) + os.remove(op.splitext(nii_path)[0] + ".bvec") + os.remove(op.splitext(nii_path)[0] + ".bval") + os.remove(op.splitext(nii_path)[0] + ".json") + + +def extractbzero(input, output, nthreads=None, force=False, verbose=False): + """Extracts only bzero shells from an input mif file. + + Parameters + ---------- + input : str + Path to input .mif file + output : str + Path to output .mif file + nthreads : int, optional + Specify the number of threads to use in processing + (Default: all available threads) + force : bool, optional + Force overwrite of output files if pre-existing + (Default:False) + verbose : bool, optional + Specify whether to print console output (Default: False) + + Returns + ------- + None; writes out file + """ + if not op.exists(input): + raise OSError("Input path does not exist. Please ensure that " "the folder or file specified exists.") + if not op.exists(op.dirname(output)): + raise OSError( + "Specifed directory for output file {} does not " + "exist. Please ensure that this is a valid " + "directory.".format(op.dirname(output)) + ) + if nthreads is not None: + if not isinstance(nthreads, int): + raise Exception("Please specify the number of threads as an " "integer.") + if not isinstance(force, bool): + raise Exception("Please specify whether forced overwrite is True " "or False.") + if not isinstance(verbose, bool): + raise Exception("Please specify whether verbose is True or False.") + arg = ["dwiextract"] + if force: + arg.append("-force") + if not verbose: + arg.append("-quiet") + if nthreads is not None: + arg.extend(["-nthreads", str(nthreads)]) + arg.extend(["-bzero", input, output]) + completion = subprocess.run(arg) + if completion.returncode != 0: + raise Exception("Unable to extract B0s from DWI for computation " "of brain mask. See above for errors.") + + +def extractmeanbzero(input, output, nthreads=None, force=False, verbose=False): + """Extracts average B0 from all B0 shells, with NaNs removed. + + Parameters + ---------- + input : str + Path to input .mif file + output : str + Path to output .mif or .nii file + nthreads : int, optional + Specify the number of threads to use in processing + (Default: all available threads) + force : bool, optional + Force overwrite of output files if pre-existing + (Default:False) + verbose : bool, optional + Specify whether to print console output (Default: False) + + Returns + ------- + None; writes out file + """ + if not op.exists(input): + raise OSError("Input path does not exist. Please ensure that " "the folder or file specified exists.") + if not op.exists(op.dirname(output)): + raise OSError( + "Specifed directory for output file {} does not " + "exist. Please ensure that this is a valid " + "directory.".format(op.dirname(output)) + ) + if nthreads is not None: + if not isinstance(nthreads, int): + raise Exception("Please specify the number of threads as an " "integer.") + if not isinstance(force, bool): + raise Exception("Please specify whether forced overwrite is True " "or False.") + if not isinstance(verbose, bool): + raise Exception("Please specify whether verbose is True or False.") + outdir = op.dirname(output) + fname_bzero = op.join(outdir, "B0_ALL.mif") + fname_mean = op.join(outdir, "B0_MEAN.mif") + # Extract all B0s + extractbzero(input, fname_bzero, nthreads=nthreads, force=force, verbose=verbose) + arg_mean = ["mrmath", "-axis", "3", fname_bzero, "mean", fname_mean] + completion = subprocess.run(arg_mean) + if completion.returncode != 0: + raise Exception("Unable to compute mean of B0s. See above for" "errors.") + arg_nan = ["mrcalc", fname_mean, "-finite", fname_mean, "0", "-if", output] + completion = subprocess.run(arg_nan) + if completion.returncode != 0: + raise Exception("Unable to remove NaNs from averaged B0. See " "above for errors.") + # Remove non-essential files + os.remove(fname_bzero) + os.remove(fname_mean) + + +def extractnonbzero(input, output, nthreads=None, force=False, verbose=False): + """Extracts only non-bzero shells from an input mif file. + + Parameters + ---------- + input : str + Path to input .mif file + output : str + Path to output .mif file + nthreads : int, optional + Specify the number of threads to use in processing + (Default: all available threads) + force : bool, optional + Force overwrite of output files if pre-existing + (Default:False) + verbose : bool, optional + Specify whether to print console output (Default: False) + + Returns + ------- + None; writes out file + """ + if not op.exists(input): + raise OSError("Input path does not exist. Please ensure that " "the folder or file specified exists.") + if not op.exists(op.dirname(output)): + raise OSError( + "Specifed directory for output file {} does not " + "exist. Please ensure that this is a valid " + "directory.".format(op.dirname(output)) + ) + if nthreads is not None: + if not isinstance(nthreads, int): + raise Exception("Please specify the number of threads as an " "integer.") + if not isinstance(force, bool): + raise Exception("Please specify whether forced overwrite is True " "or False.") + if not isinstance(verbose, bool): + raise Exception("Please specify whether verbose is True or False.") + arg = ["dwiextract"] + if force: + arg.append("-force") + if not verbose: + arg.append("-quiet") + if nthreads is not None: + arg.extend(["-nthreads", str(nthreads)]) + arg.extend(["-no_bzero", input, output]) + completion = subprocess.run(arg) + if completion.returncode != 0: + raise Exception("Unable to extract B0s from DWI for computation " "of brain mask. See above for errors.") + + +def extractshell(input, output, shell, nthreads=None, force=False, verbose=False): + """Extracts specified shell from an input mif file. + + Parameters + ---------- + input : str + Path to input .mif file + output : str + Path to output .mif file + shell : int + Approximate b-value to extract + nthreads : int, optional + Specify the number of threads to use in processing + (Default: all available threads) + force : bool, optional + Force overwrite of output files if pre-existing + (Default:False) + verbose : bool, optional + Specify whether to print console output (Default: False) + + Returns + ------- + None; writes out file + """ + if not op.exists(input): + raise OSError("Input path does not exist. Please ensure that " "the folder or file specified exists.") + if not op.exists(op.dirname(output)): + raise OSError( + "Specifed directory for output file {} does not " + "exist. Please ensure that this is a valid " + "directory.".format(op.dirname(output)) + ) + if not isinstance(shell, int): + raise Exception("Please specify the shell to extract as an " "integer.") + if shell < 0: + raise Exception("Please specify the shell to extract as a " "positive (more than 0) integer.") + if nthreads is not None: + if not isinstance(nthreads, int): + raise Exception("Please specify the number of threads as an " "integer.") + if not isinstance(force, bool): + raise Exception("Please specify whether forced overwrite is True " "or False.") + if not isinstance(verbose, bool): + raise Exception("Please specify whether verbose is True or False.") + arg = ["dwiextract"] + if force: + arg.append("-force") + if not verbose: + arg.append("-quiet") + if nthreads is not None: + arg.extend(["-nthreads", str(nthreads)]) + arg.extend(["-no_bzero", "-singleshell", "-shell", str(shell), input, output]) + completion = subprocess.run(arg) + if completion.returncode != 0: + raise Exception("Unable to extract specified shells from DWI. " "See above for errors.") + + +def extractmeanshell(input, output, shell, nthreads=None, force=False, verbose=False): + """Extracts mean of specified from an input mif file. + + Parameters + ---------- + input : str + Path to input .mif file + output : str + Path to output .mif file + shell : int + Approximate b-value to extract + nthreads : int, optional + Specify the number of threads to use in processing + (Default: all available threads) + force : bool, optional + Force overwrite of output files if pre-existing + (Default:False) + verbose : bool, optional + Specify whether to print console output (Default: False) + + Returns + ------- + None; writes out file + """ + if not op.exists(input): + raise OSError("Input path does not exist. Please ensure that " "the folder or file specified exists.") + if not op.exists(op.dirname(output)): + raise OSError( + "Specifed directory for output file {} does not " + "exist. Please ensure that this is a valid " + "directory.".format(op.dirname(output)) + ) + if not isinstance(shell, int): + raise Exception("Please specify the shell to extract as an " "integer.") + if shell < 0: + raise Exception("Please specify the shell to extract as a " "positive (more than 0) integer.") + if nthreads is not None: + if not isinstance(nthreads, int): + raise Exception("Please specify the number of threads as an " "integer.") + if not isinstance(force, bool): + raise Exception("Please specify whether forced overwrite is True " "or False.") + if not isinstance(verbose, bool): + raise Exception("Please specify whether verbose is True or False.") + outdir = op.dirname(output) + fname_shell = op.join(outdir, "b" + str(shell) + "_ALL.mif") + fname_mean = op.join(outdir, "b" + str(shell) + "_MEAN.mif") + # Extract all specified shells + extractshell(input, fname_shell, shell=shell, nthreads=nthreads, force=force, verbose=verbose) + # Compute mean + arg_mean = ["mrmath", "-axis", "3", fname_shell, "mean", fname_mean] + completion = subprocess.run(arg_mean) + if completion.returncode != 0: + raise Exception("Unable to compute mean of B0s. See above for" "errors.") + arg_nan = ["mrcalc", fname_mean, "-finite", fname_mean, "0", "-if", output] + completion = subprocess.run(arg_nan) + if completion.returncode != 0: + raise Exception("Unable to remove NaNs from averaged shell " "image. See above for errors.") + # Remove non-essential files + os.remove(fname_shell) + os.remove(fname_mean) + + +def epiboost(input, output, num=1, nthreads=None, force=False, verbose=False): + """Analyzes an input .mif's PE direction to split into two different + phase encoding (PE) DWIs. B0s from opposing PE are then extracted + and concatenated with the DWI. This reduces the number of B0s used + in undistortion for a better and speedier estimation of the + distortion field. + + Parameters + ---------- + input : str + Path to input .mif file + output : str + Path to output .mif file + num : int + Number of B0s pairs to use in EPI correction (Default: 1) + nthreads : int, optional + Specify the number of threads to use in processing + (Default: all available threads) + force : bool, optional + Force overwrite of output files if pre-existing + (Default:False) + verbose : bool, optional + Specify whether to print console output (Default: False) + + Returns + ------- + None; writes out file + """ + print("Applying EPIBOOST") + if not op.exists(input): + raise OSError("Input path does not exist. Please ensure that " "the folder or file specified exists.") + if op.splitext(output)[-1] != ".mif": + raise OSError("Output should be specified as a .mif file.") + if not op.exists(op.dirname(output)): + raise OSError( + "Specifed directory for output file {} does not " + "exist. Please ensure that this is a valid " + "directory.".format(op.dirname(output)) + ) + if not isinstance(num, int): + raise Exception("Number of B0s to use needs to be specified " "as an integer.") + if not num > 0: + raise Exception("Number of B0s to use needs to be a positive " "integer greater than 0.") + if nthreads is not None: + if not isinstance(nthreads, int): + raise Exception("Please specify the number of threads as an " "integer.") + if not isinstance(force, bool): + raise Exception("Please specify whether forced overwrite is True " "or False.") + if not isinstance(verbose, bool): + raise Exception("Please specify whether verbose is True or False.") + outdir = op.dirname(output) + fname_bzero = op.join(outdir, "B0_ALL.mif") + # Extract all B0s + extractbzero(input, fname_bzero, nthreads=nthreads, force=force, verbose=verbose) + # Start by figuring out whether DWI is composed of multiple PE dirs + # or single PE dirs. If all PE dirs are the same, the dataset likely + # comes with matching phase encoding and slice timing train, + # indicating that it has a single PE direction. + dw_scheme = np.array(mrinfoutil.dwscheme(fname_bzero), dtype=int)[:, -1] + pe_scheme = np.array(mrinfoutil.pescheme(fname_bzero)) + if len(pe_scheme) != len(dw_scheme): + raise Exception( + "It appears that the input volume possesses a " + "dw_scheme of length {}, and pe_scheme of length " + "{}. These number need to match. Please check " + "your dataset or contact us on GitHub".format(len(dw_scheme), len(pe_scheme)) + ) + uPE, indPE, iPE = np.unique(pe_scheme, axis=0, return_index=True, return_inverse=True) + nPE = len(uPE) + if nPE < 2: + raise Exception("DWI consists of just one PE direction. " "Unable to extract B0s.") + # Index unique PE directions + bval = [] + bind = [] + iteridx = np.unique(iPE) + for i, val in enumerate(iteridx): + bind.append(np.where(iPE == val)[0].tolist()) + bval.append(dw_scheme[np.where(iPE == val)].tolist()) + # Check whether number of B0s to extract exceed those in DWI + if num > min([len(x) for x in bval]): + raise Exception( + "Specified number of B0s pairs to extract " + "({}) exceed those physically present in DWI " + "({}), please ensure that variable `num` " + "suitably represents the number of B0s in DWI.".format(num, min([len(x) for x in bval])) + ) + # Extract the first `num` pairs from each PE direction + num = np.arange(0, num, dtype=int).tolist() + idx_extract = [] + for idx, val in enumerate(bind): + idx_extract.extend([val[i] for i in num]) + # Extract EPI volume + str_extract = [str(x) for x in idx_extract] + arg_epi = ["mrconvert"] + if force: + arg_epi.append("-force") + if not verbose: + arg_epi.append("-quiet") + if nthreads is not None: + arg_epi.extend(["-nthreads", str(nthreads)]) + arg_epi.extend(["-coord", "3", ",".join(str_extract)]) + arg_epi.extend([fname_bzero, output]) + completion = subprocess.run(arg_epi) + if completion.returncode != 0: + raise Exception("EPIBOOST: failed to extract specified " "TOPUP B0 indices. See above for errors.") + # Remove temp files + os.remove(fname_bzero) + + +def reslice(input, output, size, interp="linear", nthreads=None, force=False, verbose=False): + """Reslices input image to target voxel size + + Parameters + ---------- + input : str + Path to input file; .mif or .nii + output : str + Path to output file; .mif or .nii + size : tuple of float + x, y, z voxel size in mm or output dimensions. + interp : str, {'linear', 'nearest', 'cubic' , 'sinc'}, optional + set the interpolation method to use when resizing (Default: + 'linear') + nthreads : int, optional + Specify the number of threads to use in processing + (Default: all available threads) + force : bool, optional + Force overwrite of output files if pre-existing + (Default:False) + verbose : bool, optional + Specify whether to print console output (Default: False) + + Returns + ------- + None; writes out file + + Notes + ----- + If any of the axes in ``size`` is specified to be over 9 mm, this + functions reslices to defined output dimensions, instead of voxel + size. This is done to automatically reslice with minimal user + input, and also because voxel size beyond 9 mm in unrealistic. + + Additionally, if target resolution is the same as input file's + resolution, reslicing is skipped but the output file is still + generated. + """ + dim_str = "-voxel" + if not op.exists(input): + raise OSError("Input path does not exist. Please ensure that " "the folder or file specified exists.") + if not op.exists(op.dirname(output)): + raise OSError( + "Specifed directory for output file {} does not " + "exist. Please ensure that this is a valid " + "directory.".format(op.dirname(output)) + ) + if not isinstance(size, str): + raise Exception("Voxel size needs to be defined as a string " " of three values") + if len(size.split(",")) != 3: + raise Exception( + "Please specify voxel size for each axis or " + 'single digit for all axes i.e. "3,3,3" for ' + '3 mm isotropic or "42,42,130" for output ' + "dimensions of 42, 42, 130 voxels." + ) + if max([float(x) for x in size.split(",")]) > 9: + dim_str = "-size" + if not isinstance(interp, str): + raise Exception("Interpolation method needs to be specified " "as a string") + if interp not in ("linear", "nearest", "cubic", "sinc"): + raise Exception("User specified interpoaltion method {} is " "not a valid option".format(interp)) + if nthreads is not None: + if not isinstance(nthreads, int): + raise Exception("Please specify the number of threads as an " "integer.") + if not isinstance(force, bool): + raise Exception("Please specify whether forced overwrite is True " "or False.") + if not isinstance(verbose, bool): + raise Exception("Please specify whether verbose is True or False.") + if dim_str == "-voxel": + current_size = [round(float(x), 2) for x in (mrinfoutil.spacing(input))][0:3] + elif dim_str == "-size": + current_size = [round(float(x), 2) for x in mrinfoutil.size(input)][0:3] + specified_size = [round(float(x), 2) for x in size.split(",")] + if specified_size == current_size: + print( + "[WARNING] target reslicing dimensions {} are the same " + "as input image dimensions {}, writing file without " + "reslicing".format(specified_size, current_size) + ) + copyfile(input, output) + return + arg = [] + if which("mrresize") is None: + arg.extend(["mrgrid", input, "regrid", dim_str, size, "-interp", interp, output]) + else: + arg.extend(["mrresize", dim_str, size, "-interp", interp, input, output]) + if force: + arg.append("-force") + if not verbose: + arg.append("-quiet") + if nthreads is not None: + arg.extend(["-nthreads", str(nthreads)]) + completion = subprocess.run(arg) + if completion.returncode != 0: + raise Exception("Failed to reslice. See above for errors.") + + +def dwiextract(input, output, start, end, nthreads=None, force=False, verbose=False): + """Extracts a range of volumes from input .mif file in the start:end + range. Take note that the first volume starts with 0. + + Parameters + ---------- + input : str + Path to input .mif file + output : str + Path to output file, usually .mif or .nii + start : int + Starting index, inclusive + end : int + Ending index, inclusive + nthreads : int, optional + Specify the number of threads to use in processing + (Default: all available threads) + force : bool, optional + Force overwrite of output files if pre-existing + (Default:False) + verbose : bool, optional + Specify whether to print console output (Default: False) + + Returns + ------- + None; writes out file + """ + if not op.exists(input): + raise OSError("Input path does not exist. Please ensure that " "the folder or file specified exists.") + if not isinstance(start, int): + raise Exception("Starting index is needs to be an integer.") + if not isinstance(end, int): + raise Exception("Ending index is needs to be an integer.") + if nthreads is not None: + if not isinstance(nthreads, int): + raise Exception("Please specify the number of threads as an " "integer.") + if not isinstance(force, bool): + raise Exception("Please specify whether forced overwrite is True " "or False.") + if not isinstance(verbose, bool): + raise Exception("Please specify whether verbose is True or False.") + fname, ext = op.splitext(output) + if ext == ".gz": + fname, ext = op.splitext(fname) + ext = ext + ".gz" + arg = ["mrconvert"] + if force: + arg.append("-force") + if not verbose: + arg.append("-quiet") + if nthreads is not None: + arg.extend(["-nthreads", str(nthreads)]) + arg.extend([input, output, "-coord", "3", str(start) + ":" + str(end)]) + if ".mif" not in ext: + arg.extend( + [ + "-json_export", + fname + ".json", + "-export_grad_fsl", + fname + ".bvec", + fname + ".bval", + ] + ) + completion = subprocess.run(arg) + if completion.returncode != 0: + raise Exception("Failed to extract indexed DWI volumes. See " "above for errors.") diff --git a/pydesigner/preprocessing/preparation.py b/pydesigner/preprocessing/preparation.py new file mode 100644 index 00000000..6026ec00 --- /dev/null +++ b/pydesigner/preprocessing/preparation.py @@ -0,0 +1,300 @@ +#!/usr/bin/env python +# -*- coding : utf-8 -*- + +"""Adds utilities for preparing the data for eddy and analysis +""" + +import os # mkdir +import os.path as op # dirname, basename, join, splitext +import re # regex substitution +import shutil # rmtree +import subprocess + +from ..preprocessing import util # preprocessing + +DWIFile = util.DWIFile + + +def fix_bval(bvalfile): + """Converts all whitespace into newlines in the file + + Parameters + ---------- + bvalfile : str + The .bval to ensure is the correct format for mrtrix + + Returns + ------- + None, overwrites bval + """ + if not op.exists(bvalfile): + raise Exception("File " + bvalfile + " does not exist.") + + with open(bvalfile, "r") as f: + data = f.read() + + # replace whitespace with lines + data = re.sub(r"\s+", "\n", data) + + # write to file + with open(bvalfile, "w") as f: + f.write(data) + + +def make_simple_mif(filetable): + """Makes a single .mif from the HEAD dwi file + + Parameters + ---------- + filetable : dict of str + The filetable that pydesigner.py uses to track files + + Returns + ------- + None, writes file + """ + if not op.exists(filetable["dwi"].getJSON()): + raise Exception("DWI does not have a .json file to use for eddy") + + # coerce bval into mrtrix3-friendly format + fix_bval(op.join(filetable["dwi"].getPath(), filetable["dwi"].getName() + ".bval")) + finalpath = filetable["outpath"] + finalmif = op.join(finalpath, "HEADmif.mif") + + dwi_convert_args = [ + "mrconvert", + "-json_import", + filetable["dwi"].getJSON(), + "-fslgrad", + filetable["dwi"].getBVEC(), + filetable["dwi"].getBVAL(), + filetable["dwi"].getFull(), + "-quiet", + finalmif, + ] + + completion = subprocess.run(dwi_convert_args) + + if completion.returncode != 0: + raise Exception("topup conversion failed, please see above") + + filetable["dwimif"] = finalmif + + +def make_se_epi(filetable): + """Makes a single spin-echo epi from the topup and the dwi + + Parameters + ---------- + filetable : dict of str + The filetable that pydesigner.py uses to track files + + Returns + ------- + None, writes file + """ + # ---------------------------------------------------------------------- + # Check inputs and coerce .bval file + # ---------------------------------------------------------------------- + + # check that we actually have a .json for the original files + if not op.exists(filetable["dwi"].getJSON()): + raise Exception("DWI does not have a .json file to use for eddy") + + if not op.exists(op.join(filetable["topup"].getJSON())): + raise Exception("topup does not have a .json file to use for eddy") + + # NOTE: we assume that the DWI will have .bvec and .bval because + # pydesigner will not allow the 'dwi' entry to not have them + + # coerce .bval into having the mrtrix3-friendly format + fix_bval(op.join(filetable["dwi"].getPath(), filetable["dwi"].getName() + ".bval")) + + finalpath = filetable["outpath"] + + # Make a temporary working directory + outpath = op.join(finalpath, "tmp_se_epi") + if op.exists(outpath): + shutil.rmtree(outpath) + os.mkdir(outpath) + + # ---------------------------------------------------------------------- + # Make dwipreproc-friendly files + # ---------------------------------------------------------------------- + + # dwi + tmp_dwi = op.join(outpath, "tmp_dwi.mif") + dwi_convert_args = [ + "mrconvert", + "-json_import", + filetable["dwi"].getJSON(), + "-fslgrad", + filetable["dwi"].getBVEC(), + filetable["dwi"].getBVAL(), + filetable["dwi"].getFull(), + "-quiet", + tmp_dwi, + ] + + completion = subprocess.run(dwi_convert_args) + + # move dwi to non-temp directory as well + final_dwi = op.join(finalpath, "tmp_dwi.mif") + shutil.copyfile(tmp_dwi, final_dwi) + + # add it to the filetable + filetable["dwimif"] = final_dwi + + if completion.returncode != 0: + raise Exception("DWI conversion failed, please see above.") + + # topup + tmp_tp = op.join(outpath, "tmp_tp.mif") + topup_convert_args = [ + "mrconvert", + "-json_import", + filetable["topup"].getJSON(), + filetable["topup"].getFull(), + "-quiet", + tmp_tp, + ] + completion = subprocess.run(topup_convert_args) + + if completion.returncode != 0: + raise Exception("topup conversion failed, please see above") + + # extract the b0 images from the dwi image + b0extracted = op.join(outpath, "b0extracted.mif") + extract_b0_args = ["dwiextract", "-bzero", tmp_dwi, "-quiet", b0extracted] + completion = subprocess.run(extract_b0_args) + + if completion.returncode != 0: + raise Exception("b0 extraction failed, please see above") + + # separate b0 images into individual files + # start by getting number of volumes in each image + op.join(outpath, "b0x.txt") + get_dwi_b0_info_args = ["mrinfo", "-size", b0extracted] + completion = subprocess.run(get_dwi_b0_info_args, capture_output=True) + if completion.returncode != 0: + raise Exception("Extracted b0 information failed, please see above") + + # use an abomination unto good coding to extract 4th dim size + ndb0xstr = completion.stdout.decode("utf-8").rstrip().split(" ")[-1] + ndb0x = int(ndb0xstr.rstrip('"')) + + # repeat for rpe + op.join(outpath, "rpex.txt") + get_rpe_info_args = ["mrinfo", "-size", tmp_tp] + completion = subprocess.run(get_rpe_info_args, capture_output=True) + if completion.returncode != 0: + raise Exception("Extracted topup information failed, " "please see above") + + ndrpexstr = completion.stdout.decode("utf-8").rstrip().split(" ")[-1] + ndrpex = int(ndrpexstr.rstrip('"')) + + # iterate over the indices and align the data + b0_basename = op.join(outpath, "b0x_") + regto = b0_basename + "0.mif" + to_cat = [regto] + for ii in range(ndb0x): + # iterate over b0s and register to first b0 + # extract the volume + i = str(ii) + extracted_name = b0_basename + i + ".mif" + reg_txt = b0_basename + i + "to0.txt" + b0_basename + i + "to0.mif" + xfm_b0 = b0_basename + i + ".mif" + extract_args = [ + "mrconvert", + "-force", + "-coord", + "3", + i, + b0extracted, + "-quiet", + extracted_name, + ] + completion = subprocess.run(extract_args) + if completion.returncode != 0: + raise Exception("Failed splitting b0 volume " + i) + # if first b0, no need to register + if i == "0": + continue + # calculate the transform required + reg_args = [ + "mrregister", + "-type", + "rigid", + "-noreorientation", + "-rigid", + reg_txt, + "-quiet", + extracted_name, + regto, + ] + completion = subprocess.run(reg_args) + if completion.returncode != 0: + raise Exception("Failed registering volume " + i + " to b0") + # apply transform + op.join(outpath, "dwib0") + to_cat.append(xfm_b0) + + # Repeat for RPE + rpe_basename = op.join(outpath, "rpex_") + for ii in range(ndrpex): + # iterate over rpes and register to first rpe + # extract the volume + i = str(ii) + extracted_name = rpe_basename + i + ".mif" + reg_txt = rpe_basename + i + "to0.txt" + rpe_basename + i + "to0.mif" + xfm_rpe = rpe_basename + i + ".mif" + extract_args = [ + "mrconvert", + "-force", + "-coord", + "3", + i, + "-quiet", + tmp_tp, + extracted_name, + ] + completion = subprocess.run(extract_args) + if completion.returncode != 0: + raise Exception("Failed splitting rpe volume " + i) + # calculate the transform required + reg_args = [ + "mrregister", + "-type", + "rigid", + "-noreorientation", + "-rigid", + reg_txt, + "-quiet", + extracted_name, + regto, + ] + completion = subprocess.run(reg_args) + if completion.returncode != 0: + raise Exception("Failed registering volume " + i + " to rpe") + # apply transform + op.join(outpath, "dwirpe") + to_cat.append(xfm_rpe) + + # Concatenate all b0 into one se-epi + # NOTE: uses finalpath instead of outpath + se_epi = op.join(finalpath, "se-epi.mif") + mrcat_args = ["mrcat", "-force", "-quiet", "-axis", "3"] + for f in to_cat: + mrcat_args.append(f) + mrcat_args.append(se_epi) + + completion = subprocess.run(mrcat_args) + if completion.returncode != 0: + raise Exception("Concatenation of b0s into se-epi failed") + + filetable["se-epi"] = se_epi + + # Clean up temp dir + # shutil.rmtree(outpath) diff --git a/designer/preprocessing/rician.py b/pydesigner/preprocessing/rician.py similarity index 79% rename from designer/preprocessing/rician.py rename to pydesigner/preprocessing/rician.py index 09ee7634..0689233e 100644 --- a/designer/preprocessing/rician.py +++ b/pydesigner/preprocessing/rician.py @@ -1,15 +1,11 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- +import os.path as op -import numpy as np import nibabel as nib -import scipy as sc -from scipy import ndimage -import os.path as op +import numpy as np + def rician_img_correct(dwiname, noisemapname, outpath=None): - """ - Performs Rician correction on a dataset with a noisemap + """Performs Rician correction on a dataset with a noisemap Parameters ---------- @@ -28,8 +24,7 @@ def rician_img_correct(dwiname, noisemapname, outpath=None): -------- rician_correct(dwi, noise) is wrapped by this function """ - - print('Running Rician correction...') + print("Running Rician correction...") # load files dwiimg = nib.load(dwiname) @@ -43,24 +38,24 @@ def rician_img_correct(dwiname, noisemapname, outpath=None): # determine the output name path = op.dirname(outpath) - print('Path: ' + path) + print("Path: " + path) [name, ext] = op.splitext(op.basename(outpath)) - print('Name: ' + name) + print("Name: " + name) if not name: - name = 'rdwi.nii' + name = "rdwi.nii" else: - name += '.nii' + name += ".nii" out = op.join(path, name) - print('Full: ' + out) + print("Full: " + out) nib.save(newimg, out) return + def rician_correct(dwi, noisemap): - """ - Smooths a DWI dataset + """Smooths a DWI dataset Parameters ---------- @@ -71,13 +66,12 @@ def rician_correct(dwi, noisemap): The noise map from dwidenoise width : float, optional The full width half max in voxels to be smoothed. Default: 1.25 - + Returns ------- corrected : (X x Y x Z x N) array_like or img_like object The rician-corrected version of dwi """ - # Replace NaN with 0 minZero = 1e-8 # dwi @@ -90,8 +84,8 @@ def rician_correct(dwi, noisemap): sqr_noise = np.square(noisemap) sqr_data = np.square(dwi) difference = np.zeros(sqr_data.shape) - for i in range(len(sqr_data[1,1,1,:])): - difference[:,:,:,i] = sqr_noise - sqr_data[:,:,:,i] + for i in range(len(sqr_data[1, 1, 1, :])): + difference[:, :, :, i] = sqr_noise - sqr_data[:, :, :, i] result = np.sqrt(np.absolute(difference)) return result diff --git a/designer/preprocessing/smoothing.py b/pydesigner/preprocessing/smoothing.py similarity index 82% rename from designer/preprocessing/smoothing.py rename to pydesigner/preprocessing/smoothing.py index 63e986ce..09f44352 100644 --- a/designer/preprocessing/smoothing.py +++ b/pydesigner/preprocessing/smoothing.py @@ -1,14 +1,12 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -import numpy as np import nibabel as nib +import numpy as np + # import scipy as sc from scipy.ndimage import gaussian_filter -def smooth_image(dwiname, csfname=None, outname='dwism.nii', width=1.25, size=5): - """ - Smooths a DWI dataset + +def smooth_image(dwiname, csfname=None, outname="dwism.nii", width=1.25, size=5): + """Smooths a DWI dataset Parameters ---------- @@ -32,9 +30,9 @@ def smooth_image(dwiname, csfname=None, outname='dwism.nii', width=1.25, size=5) smooth(dwi, csfmask=None, width=1.25) is wrapped by this function """ if csfname is None: - print('Running smoothing at FWHM = {}...'.format(width)) + print("Running smoothing at FWHM = {}...".format(width)) else: - print('Running CSF-excluded smoothing at FWHM = {}...'.format(width)) + print("Running CSF-excluded smoothing at FWHM = {}...".format(width)) dwiimg = nib.load(dwiname) @@ -49,9 +47,9 @@ def smooth_image(dwiname, csfname=None, outname='dwism.nii', width=1.25, size=5) return + def smooth(dwi, csfmask=None, width=1.25, size=5): - """ - Smooths a DWI dataset + """Smooths a DWI dataset Parameters ---------- @@ -64,7 +62,7 @@ def smooth(dwi, csfmask=None, width=1.25, size=5): The full width half max in voxels to be smoothed. Default: 1.25 size : int, optional The size of 2D Gaussian kernel [size, size]. Default: 5 - + Returns ------- smoothed : (X x Y x Z x N) array_like or img_like object @@ -75,31 +73,30 @@ def smooth(dwi, csfmask=None, width=1.25, size=5): This is done mainly to reduce the Gibbs ringing. It might be recommended to only smooth the high SNR (or b-valued) data in order not to alter the Rice distribution of the low SNR data. This is - important to maintain the high accuracy of WLLS. If a CSF mask is + important to maintain the high accuracy of WLLS. If a CSF mask is given as an additional argument, CSF infiltration in microstructural signal is avoided during smoothing. """ - if dwi.ndim != 4: - raise ValueError('Input dwi dataset is not 4-D') + raise ValueError("Input dwi dataset is not 4-D") dwidata = dwi.get_fdata() if csfmask: - csfdata = csfmask.get_fdata().astype('bool') + csfdata = csfmask.get_fdata().astype("bool") if csfmask is None: smoothed = dwidata.copy() for i in range(dwi.shape[-1]): for z in range(dwi.shape[-2]): - currslice = dwidata[:,:,z,i] - smoothed[:,:,z,i] = nansmooth(currslice, width, size=size) + currslice = dwidata[:, :, z, i] + smoothed[:, :, z, i] = nansmooth(currslice, width, size=size) else: bgmask = np.isnan(dwidata) smoothed = dwidata.copy() for i in range(dwi.shape[-1]): for z in range(dwi.shape[-2]): - currslice = dwidata[:,:,z,i] - currcsf = csfdata[:,:,z] + currslice = dwidata[:, :, z, i] + currcsf = csfdata[:, :, z] wmgm = currslice.copy() wmgm[currcsf] = np.nan wmgm_ = nansmooth(wmgm, width, size=size) @@ -107,10 +104,11 @@ def smooth(dwi, csfmask=None, width=1.25, size=5): csf[np.logical_not(currcsf)] = np.nan csf_ = nansmooth(csf, width, size=size) total = np.nansum(np.dstack((wmgm_, csf_)), 2) - smoothed[:,:,z,i] = total + smoothed[:, :, z, i] = total smoothed[bgmask] = np.nan return smoothed + def nansmooth(imgslice, fwhm, size=5): """Smooths an image slice while ignoring NaNs @@ -146,22 +144,19 @@ def nansmooth(imgslice, fwhm, size=5): stddev = fwhm / np.sqrt(8 * np.log(2)) # Scipy required truncate instead of size - truncate = (((size - 1)/2)-0.5)/stddev + truncate = (((size - 1) / 2) - 0.5) / stddev nan_msk = np.isnan(imgslice) loss = np.zeros(imgslice.shape) loss[nan_msk] = 1 - loss = gaussian_filter( - loss, sigma=stddev, mode='constant', truncate=truncate) + loss = gaussian_filter(loss, sigma=stddev, mode="constant", truncate=truncate) gauss = imgslice.copy() gauss[nan_msk] = 0 - gauss = gaussian_filter( - gauss, sigma=stddev, mode='constant', truncate=truncate) + gauss = gaussian_filter(gauss, sigma=stddev, mode="constant", truncate=truncate) gauss[nan_msk] = np.nan gauss += loss * imgslice return gauss - \ No newline at end of file diff --git a/designer/preprocessing/util.py b/pydesigner/preprocessing/util.py similarity index 52% rename from designer/preprocessing/util.py rename to pydesigner/preprocessing/util.py index b1117cb5..9954a23a 100644 --- a/designer/preprocessing/util.py +++ b/pydesigner/preprocessing/util.py @@ -1,21 +1,20 @@ -""" -Adds utilities for the command-line interface +"""Adds utilities for the command-line interface """ -import os.path as op # dirname, basename, join, splitext +import json # decode import os -import sys # exit +import os.path as op # dirname, basename, join, splitext +import pprint # pprint import subprocess -import json # decode -import pprint #pprint -import numpy as np -import math import warnings -from designer.preprocessing import mrinfoutil + +import numpy as np + +from pydesigner.preprocessing import mrinfoutil + def find_valid_ext(pathname): - """ - Finds valid extensions for dwifile, helper function + """Finds valid extensions for dwifile, helper function Parameters ---------- @@ -28,7 +27,7 @@ def find_valid_ext(pathname): Array of valid file extensions for the basename """ # Go ahead and return blank if the pathname is blank - if pathname is '': + if pathname == "": return [] # Coerce into a basename only in case somebody is lazy before calling @@ -37,17 +36,16 @@ def find_valid_ext(pathname): exts = [] # Figure out which extensions are valid - valid_extensions = ('.nii.gz', '.nii', '.json', '.bval', - '.bvec') + valid_extensions = (".nii.gz", ".nii", ".json", ".bval", ".bvec") for ext in valid_extensions: if op.exists(pathname + ext): exts.append(ext) - + return exts + def json2fslgrad(path): - """ - Creates FSL .bvec and .bval for series missing that information. + """Creates FSL .bvec and .bval for series missing that information. Some datasets have their B0s separately that do not produce fsl gradients upon conversion to NifTi. This function creates those missing features for complete concatenation from .json file. Use @@ -65,10 +63,12 @@ def json2fslgrad(path): image = DWIFile(path) if (image.getBVAL() is None) or (image.getBVEC() is None): if not image.hasJSON(): - raise Exception('It is not advisable to run multi-series ' - 'processing without `.json` files. Please ' - 'ensure your NifTi files come with .json ' - 'files.') + raise Exception( + "It is not advisable to run multi-series " + "processing without `.json` files. Please " + "ensure your NifTi files come with .json " + "files." + ) # Get number of 3D volumes in image ndim = mrinfoutil.ndim(image.getFull()) if ndim == 4: @@ -78,22 +78,32 @@ def json2fslgrad(path): if nDWI <= 15: bval = np.zeros((1, nDWI), dtype=int) bvec = np.zeros((3, nDWI), dtype=int) - fPath = op.splitext(path)[0] - np.savetxt(op.join(image.getPath(), image.getName() + '.bvec'), - bvec, delimiter=' ', fmt='%d') - np.savetxt(op.join(image.getPath(), image.getName() + '.bval'), - np.c_[bval], delimiter=' ', fmt='%d') + op.splitext(path)[0] + np.savetxt( + op.join(image.getPath(), image.getName() + ".bvec"), + bvec, + delimiter=" ", + fmt="%d", + ) + np.savetxt( + op.join(image.getPath(), image.getName() + ".bval"), + np.c_[bval], + delimiter=" ", + fmt="%d", + ) else: - raise Exception('PyDesigner currently only supports ' - 'B0s without BVAL or BVEC pairs if ' - 'the number of volumes is less than ' - '15. Please ensure all your input ' - 'volumes come with valid BVEC/BVAL ' - 'pairs and JSON.') + raise Exception( + "PyDesigner currently only supports " + "B0s without BVAL or BVEC pairs if " + "the number of volumes is less than " + "15. Please ensure all your input " + "volumes come with valid BVEC/BVAL " + "pairs and JSON." + ) + class DWIFile: - """ - Diffusion data file object, used for handling paths and extensions. + """Diffusion data file object, used for handling paths and extensions. Helps interface different extensions, group .bval/.bvec seamlessly to the programmer. Offers interactive tools to try and locate a file if @@ -115,8 +125,7 @@ class DWIFile: """ def __init__(self, name): - """ - Constructor for dwifile + """Constructor for dwifile Attempts to find the file and launches interactive file-finder if it doesn't exist or can't be found. @@ -129,36 +138,33 @@ def __init__(self, name): """ full = name [pathname, ext] = op.splitext(full) - if ext == '.gz': + if ext == ".gz": # split again pathname = op.splitext(pathname)[0] - ext = '.nii.gz' + ext = ".nii.gz" self.path = op.dirname(pathname) self.name = op.basename(pathname) # Check for existence of the name self.ext = find_valid_ext(pathname) if not self.ext: - raise Exception('File '+name+' is not a valid file.') + raise Exception("File " + name + " is not a valid file.") # Figure out if dwi acquisition - if (('.bval' in self.ext) and - ('.bvec' in self.ext) and - (('.nii.gz' in self.ext) or ('.nii' in self.ext))): + if (".bval" in self.ext) and (".bvec" in self.ext) and ((".nii.gz" in self.ext) or (".nii" in self.ext)): self.acquisition = True else: self.acquisition = False # If JSON available, load it - if ('.json' in self.ext): - with open(op.join(self.path, self.name + '.json')) as f: + if ".json" in self.ext: + with open(op.join(self.path, self.name + ".json")) as f: self.json = json.load(f) else: self.json = None def getName(self): - """ - Get the name without the path for this dwifile + """Get the name without the path for this dwifile Returns ------- @@ -168,8 +174,7 @@ def getName(self): return self.name def getPath(self): - """ - Get the path without the name for this dwifile + """Get the path without the name for this dwifile Returns ------- @@ -179,35 +184,30 @@ def getPath(self): return self.path def getFull(self): - """ - Get the path and name combined for this dwifile + """Get the path and name combined for this dwifile Returns ------- str The full path and filename with extension """ - - if '.nii' in self.ext: - return op.join(self.path, self.name + '.nii') + if ".nii" in self.ext: + return op.join(self.path, self.name + ".nii") else: - return op.join(self.path, self.name + '.nii.gz') + return op.join(self.path, self.name + ".nii.gz") def isAcquisition(self): - """ - Check if this object is an acquisition + """Check if this object is an acquisition Returns ------- bool True if acquisition, False if not """ - return self.acquisition def hasJSON(self): - """ - Checks if this object has a .json file + """Checks if this object has a .json file Returns ------- @@ -220,8 +220,7 @@ def hasJSON(self): return False def getJSON(self): - """ - Returns the .json filename for this DWIFile + """Returns the .json filename for this DWIFile Returns ------- @@ -230,15 +229,14 @@ def getJSON(self): """ if self.hasJSON(): if self.path: - return op.join(self.path, self.name + '.json') + return op.join(self.path, self.name + ".json") else: - return self.name + '.json' + return self.name + ".json" else: return None def getBVAL(self): - """ - Returns the .bval filename for this DWIFile + """Returns the .bval filename for this DWIFile Returns ------- @@ -247,77 +245,75 @@ def getBVAL(self): """ if self.isAcquisition(): if self.path: - return op.join(self.path, self.name + '.bval') + return op.join(self.path, self.name + ".bval") else: - return self.name + '.bval' + return self.name + ".bval" else: return None def getBVEC(self): - """ - Returns the .bvec filename for this DWIFile + """Returns the .bvec filename for this DWIFile Returns ------- str The full path to the .bvec """ - if self.isAcquisition(): if self.path: - return op.join(self.path, self.name + '.bvec') + return op.join(self.path, self.name + ".bvec") else: - return self.name + '.bvec' + return self.name + ".bvec" else: return None + def isPartialFourier(self): - """ - Returns whether the volume is partial fourier encoded + """Returns whether the volume is partial fourier encoded Returns ------- bool True if encoding is partial fourier; False otherwise """ - if not self.isAcquisition(): - raise Exception('Volume is not an acquisition volume.') + raise Exception("Volume is not an acquisition volume.") else: if not self.getJSON(): - raise Exception('No access to Partial Fourier information.') + raise Exception("No access to Partial Fourier information.") else: - if 'PartialFourier' in self.json: - encoding = self.json['PartialFourier'] + if "PartialFourier" in self.json: + encoding = self.json["PartialFourier"] encodingnumber = float(encoding) if encodingnumber != 1: return True else: return False - elif ('PhaseEncodingSteps' in self.json) and \ - ('AcquisitionMatrixPE' in self.json): - steps = int(self.json['PhaseEncodingSteps']) - acqmat = int(self.json['AcquisitionMatrixPE']) + elif ("PhaseEncodingSteps" in self.json) and ("AcquisitionMatrixPE" in self.json): + steps = int(self.json["PhaseEncodingSteps"]) + acqmat = int(self.json["AcquisitionMatrixPE"]) if steps != acqmat: return False else: return True else: - print('Insufficient information in .json file to ' - 'determine Fourier status. Assuming DWI is ' - 'partial Fourier') + print( + "Insufficient information in .json file to " + "determine Fourier status. Assuming DWI is " + "partial Fourier" + ) return True def print(self, json=False): - print('Path: ' + self.path) - print('Name: ' + self.name) - print('Extensions: ' + str(self.ext)) - print('Acquisition: ' + str(self.acquisition)) + print("Path: " + self.path) + print("Name: " + self.name) + print("Extensions: " + str(self.ext)) + print("Acquisition: " + str(self.acquisition)) if json: pprint.pprint(self.json) + class DWIParser: - """ - Parses a list of DWIs and concatenates them into a single 4D NifTi + """Parses a list of DWIs and concatenates them into a single 4D NifTi with appropriate BVEC, BVALS. Attributes @@ -341,9 +337,9 @@ class DWIParser: vols : list of ints Number of volumes in each input DWI """ + def __init__(self, path): - """ - DWIParser class initiator + """DWIParser class initiator Parameters ---------- @@ -365,32 +361,31 @@ def __init__(self, path): i += 1 self.InputType = np.unique(ftype) if self.InputType.size > 1: - raise IOError('It appears that multiple types of files ' - 'have been parsed as input. Please input ' - 'a specific type of input exclusively. ' - 'Detected inputs were: {}'.format( - self.InputType)) + raise IOError( + "It appears that multiple types of files " + "have been parsed as input. Please input " + "a specific type of input exclusively. " + "Detected inputs were: {}".format(self.InputType) + ) self.InputType = self.InputType[0] DWIflist = [op.splitext(i) for i in self.DWIlist] # Compressed nifti (.nii.gz) have double extensions, and so # require double ext-splitting. The following loop takes care of # that. - if '.gz' in np.unique(np.array(DWIflist)[:,-1])[0]: + if ".gz" in np.unique(np.array(DWIflist)[:, -1])[0]: for idx, i in enumerate(DWIflist): - DWIflist[idx] = (op.splitext(i[0])[0], '.nii.gz') + DWIflist[idx] = (op.splitext(i[0])[0], ".nii.gz") self.DWInlist = [i[0] for i in DWIflist] - if 'nifti' in self.InputType: - self.BVALlist = [i + '.bval' for i in self.DWInlist] - self.BVEClist = [i + '.bvec' for i in self.DWInlist] - self.JSONlist = [i + '.json' for i in self.DWInlist] + if "nifti" in self.InputType: + self.BVALlist = [i + ".bval" for i in self.DWInlist] + self.BVEClist = [i + ".bvec" for i in self.DWInlist] + self.JSONlist = [i + ".json" for i in self.DWInlist] self.DWIext = [i[1] for i in DWIflist] self.nDWI = len(self.DWIlist) - def cat(self, path, ext='.nii', verbose=False, force=False, - resume=False): - """ - Concatenates all input series when nDWI > 1 into a 4D NifTi + def cat(self, path, ext=".nii", verbose=False, force=False, resume=False): + """Concatenates all input series when nDWI > 1 into a 4D NifTi along with a appropriate BVAL, BVEC and JSON files. Concatenation of series via MRTRIX3 requires every NifTi file to come with BVAL/BVEC to produce a .json with `dw_scheme`. @@ -410,134 +405,138 @@ def cat(self, path, ext='.nii', verbose=False, force=False, resume : bool, optional Continue from an aborted or partial previous run of pydesigner (Default: False) - + Returns ------- None; writes out file """ # Check whether working.(ext) exists - if op.exists(op.join(path, 'working' + ext)): + if op.exists(op.join(path, "working" + ext)): if force: - os.remove(op.join(path, 'working' + ext)) + os.remove(op.join(path, "working" + ext)) for i in range(self.nDWI): - if op.exists(op.join(path, ('dwi' + str(i) + '.mif'))): - os.remove(op.join(path, ('dwi' + str(i) + '.mif'))) + if op.exists(op.join(path, ("dwi" + str(i) + ".mif"))): + os.remove(op.join(path, ("dwi" + str(i) + ".mif"))) elif not resume: raise IOError( - 'Concatenated series already exists. ' - 'In order to run this please delete the ' - 'file working, use --force, use --resume, or ' - 'change output destination.') + "Concatenated series already exists. " + "In order to run this please delete the " + "file working, use --force, use --resume, or " + "change output destination." + ) - if not (resume and op.exists(op.join(path, 'working' + ext))): + if not (resume and op.exists(op.join(path, "working" + ext))): miflist = [] # The following loop converts input file into .mif echotime = [] - for (idx, i) in enumerate(self.DWIlist): - if 'nifti' in self.InputType and \ - (not (op.exists(self.BVEClist[idx])) or \ - (op.exists(self.BVALlist[idx]))): + for idx, i in enumerate(self.DWIlist): + if "nifti" in self.InputType and ( + not (op.exists(self.BVEClist[idx])) or (op.exists(self.BVALlist[idx])) + ): try: json2fslgrad(i) - except: - raise IOError('Please supply a valid JSON file ' - 'accompanying {}'.format(i)) - convert_args = ['mrconvert'] + except: # noqa: E722 + raise IOError("Please supply a valid JSON file " "accompanying {}".format(i)) + convert_args = ["mrconvert"] if verbose is False: - convert_args.append('-quiet') + convert_args.append("-quiet") if force is True: - convert_args.append('-force') - if hasattr(self, 'BVEClist') or hasattr(self, 'BVALlist'): - if op.exists(self.BVEClist[idx]) or \ - op.exists(self.BVALlist[idx]): - convert_args.append('-fslgrad') + convert_args.append("-force") + if hasattr(self, "BVEClist") or hasattr(self, "BVALlist"): + if op.exists(self.BVEClist[idx]) or op.exists(self.BVALlist[idx]): + convert_args.append("-fslgrad") convert_args.append(self.BVEClist[idx]) convert_args.append(self.BVALlist[idx]) else: - raise FileNotFoundError('BVEC and BVAL pairs for the ' - 'input paths do not exist. ' - 'Ensure that they exist or ' - 'have the same name as DWI.') - if hasattr(self, 'JSONlist'): + raise FileNotFoundError( + "BVEC and BVAL pairs for the " + "input paths do not exist. " + "Ensure that they exist or " + "have the same name as DWI." + ) + if hasattr(self, "JSONlist"): if op.exists(self.JSONlist[idx]): - convert_args.append('-json_import') + convert_args.append("-json_import") convert_args.append(self.JSONlist[idx]) else: - warnings.warn('JSON file(s) {} not found. ' - 'Attempting to process without. ' - 'If processing fails, please use the ' - '"--adv" flag'.format(JSONlist)) + warnings.warn( + "JSON file(s) {} not found. " + "Attempting to process without. " + "If processing fails, please use the " + '"--adv" flag'.format(self.JSONlist) + ) convert_args.append(i) - convert_args.append( - op.join(path, ('dwi' + str(idx) + '.mif'))) - miflist.append(op.join(path, ('dwi' + str(idx) + '.mif'))) - cmd = ' '.join(str(e) for e in convert_args) + convert_args.append(op.join(path, ("dwi" + str(idx) + ".mif"))) + miflist.append(op.join(path, ("dwi" + str(idx) + ".mif"))) + cmd = " ".join(str(e) for e in convert_args) completion = subprocess.run(cmd, shell=True) if completion.returncode != 0: - raise Exception('Please use the "--force" flag to ' - 'overwrite existing outputs, or clear ' - 'the output directory') + raise Exception( + 'Please use the "--force" flag to ' + "overwrite existing outputs, or clear " + "the output directory" + ) # The following command concatenates all DWI(i) into a single # .mif file if nDWI > 1 if self.nDWI > 1: - cat_arg = ['mrcat -axis 3'] + cat_arg = ["mrcat -axis 3"] if verbose is False: - cat_arg.append('-quiet') + cat_arg.append("-quiet") if force is True: - cat_arg.append('-force') - for i,fname in enumerate(miflist): + cat_arg.append("-force") + for i, fname in enumerate(miflist): cat_arg.append(fname) - cat_arg.append( - op.join(path, ('working' + '.mif'))) - cmd = ' '.join(str(e) for e in cat_arg) + cat_arg.append(op.join(path, ("working" + ".mif"))) + cmd = " ".join(str(e) for e in cat_arg) completion = subprocess.run(cmd, shell=True) if completion.returncode != 0: - raise Exception('Failed to concatenate multiple ' - 'series.') + raise Exception("Failed to concatenate multiple " "series.") else: - cat_arg = ['mrconvert'] + cat_arg = ["mrconvert"] if verbose is False: - cat_arg.append('-quiet') + cat_arg.append("-quiet") if force is True: - cat_arg.append('-force') - cat_arg.append(op.join(path, 'dwi0.mif')) - cat_arg.append(op.join(path, 'working.mif')) - cmd = ' '.join(str(e) for e in cat_arg) + cat_arg.append("-force") + cat_arg.append(op.join(path, "dwi0.mif")) + cat_arg.append(op.join(path, "working.mif")) + cmd = " ".join(str(e) for e in cat_arg) print(cmd) completion = subprocess.run(cmd, shell=True) if completion.returncode != 0: - raise Exception('Failed to convert single series') - if '.mif' not in ext: - miflist.append(op.join(path, 'working' + '.mif')) + raise Exception("Failed to convert single series") + if ".mif" not in ext: + miflist.append(op.join(path, "working" + ".mif")) # Output concatenated .mif into other formats - if '.mif' not in ext: - convert_args = ['mrconvert'] + if ".mif" not in ext: + convert_args = ["mrconvert"] if verbose is False: - convert_args.append('-quiet') + convert_args.append("-quiet") if force is True: - convert_args.append('-force') - convert_args.append('-export_grad_fsl') - convert_args.append(op.join(path, 'working.bvec')) - convert_args.append(op.join(path, 'working.bval')) - convert_args.append('-json_export') - convert_args.append(op.join(path, 'working.json')) - convert_args.append(op.join(path, 'working.mif')) - convert_args.append(op.join(path, 'working' + ext)) - cmd = ' '.join(str(e) for e in convert_args) + convert_args.append("-force") + convert_args.append("-export_grad_fsl") + convert_args.append(op.join(path, "working.bvec")) + convert_args.append(op.join(path, "working.bval")) + convert_args.append("-json_export") + convert_args.append(op.join(path, "working.json")) + convert_args.append(op.join(path, "working.mif")) + convert_args.append(op.join(path, "working" + ext)) + cmd = " ".join(str(e) for e in convert_args) completion = subprocess.run(cmd, shell=True) if completion.returncode != 0: for i, fname in enumerate(miflist): os.remove(fname) - os.remove(op.join(path, 'working' + ext)) - raise Exception('Concatenation to ' + str(ext) + ' ' - 'failed. Please ensure that your input ' - 'NifTi files have the same phase ' - 'encoding directions, and are ' - 'accompanied by valid .bval, .bvec, ' - 'and .json. If this is not possible, ' - 'please provide manually concatenated ' - 'DWIs or run with single series input.') + os.remove(op.join(path, "working" + ext)) + raise Exception( + "Concatenation to " + str(ext) + " " + "failed. Please ensure that your input " + "NifTi files have the same phase " + "encoding directions, and are " + "accompanied by valid .bval, .bvec, " + "and .json. If this is not possible, " + "please provide manually concatenated " + "DWIs or run with single series input." + ) echotime = [] vols = [] for i, fname in enumerate(miflist): @@ -548,8 +547,7 @@ def cat(self, path, ext='.nii', verbose=False, force=False, self.vols = vols def getPath(self): - """ - Returns directory where first file in DWI list is stored + """Returns directory where first file in DWI list is stored Returns ------- diff --git a/pydesigner/system/__init__.py b/pydesigner/system/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/designer/system/utils.py b/pydesigner/system/utils.py similarity index 67% rename from designer/system/utils.py rename to pydesigner/system/utils.py index ee1e39f1..108020c7 100644 --- a/designer/system/utils.py +++ b/pydesigner/system/utils.py @@ -1,12 +1,9 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -import numpy as np import nibabel as nib +import numpy as np -def vectorize(img, mask): - """ - Returns vectorized image based on brain mask, requires no input + +def vectorize(img, mask) -> np.ndarray[float]: + """Returns vectorized image based on brain mask, requires no input parameters If the input is 1D or 2D, unpatch it to 3D or 4D using a mask If the input is 3D or 4D, vectorize it using a mask @@ -30,46 +27,32 @@ def vectorize(img, mask): vec = vectorize(img, mask) if there's a mask """ if mask is None: - mask = np.ones((img.shape[0], - img.shape[1], - img.shape[2]), - order='F', - dtype=img.dtype) + mask = np.ones((img.shape[0], img.shape[1], img.shape[2]), order="F", dtype=img.dtype) mask = mask.astype(bool) if img.ndim == 1: n = img.shape[0] - s = np.zeros((mask.shape[0], - mask.shape[1], - mask.shape[2]), - order='F', - dtype=img.dtype) + s = np.zeros((mask.shape[0], mask.shape[1], mask.shape[2]), order="F", dtype=img.dtype) s[mask] = img if img.ndim == 2: n = img.shape[0] - s = np.zeros((mask.shape[0], - mask.shape[1], - mask.shape[2], n), - order='F', - dtype=img.dtype) + s = np.zeros((mask.shape[0], mask.shape[1], mask.shape[2], n), order="F", dtype=img.dtype) for i in range(0, n): - s[mask, i] = img[i,:] + s[mask, i] = img[i, :] if img.ndim == 3: - maskind = np.ma.array(img, mask=np.logical_not(mask), - dtype=img.dtype, order='F') + maskind = np.ma.array(img, mask=np.logical_not(mask), dtype=img.dtype, order="F") s = np.ma.compressed(maskind) if img.ndim == 4: - s = np.zeros((img.shape[-1], np.sum(mask).astype(int)), - order='F', dtype=img.dtype) + s = np.zeros((img.shape[-1], np.sum(mask).astype(int)), order="F", dtype=img.dtype) for i in range(0, img.shape[-1]): - tmp = img[:,:,:,i] + tmp = img[:, :, :, i] # Compressed returns non-masked area, so invert the mask first maskind = np.ma.array(tmp, mask=np.logical_not(mask)) - s[i,:] = np.ma.compressed(maskind) + s[i, :] = np.ma.compressed(maskind) return np.squeeze(s) -def writeNii(map, hdr, outDir, range=None): - """ - Write clipped NifTi images + +def writeNii(map, hdr, outDir, range=None) -> None: + """Write clipped NifTi images Parameters ---------- @@ -92,18 +75,19 @@ def writeNii(map, hdr, outDir, range=None): writeNii(matrix, header, output_directory, [0, 2]) See Also + -------- clipImage(img, range) : this function is wrapped around """ - if range == None: + if range is None: clipped_img = nib.Nifti1Image(map, hdr.affine, hdr.header) else: clipped_img = clipImage(map, range) clipped_img = nib.Nifti1Image(clipped_img, hdr.affine, hdr.header) nib.save(clipped_img, outDir) -def clipImage(img, range): - """ - Clips input matrix within desired range. Min and max values are + +def clipImage(img, range) -> np.ndarray[float]: + """Clips input matrix within desired range. Min and max values are inclusive of range Classification: Function @@ -127,9 +111,9 @@ def clipImage(img, range): img[img < range[0]] = range[0] return img -def highprecisionexp(array, maxp=1e32): - """ - Prevents overflow warning with numpy.exp by assigning overflows + +def highprecisionexp(array, maxp=1e32) -> np.ndarray[float]: + """Prevents overflow warning with numpy.exp by assigning overflows to a maxumum precision value Classification: Function @@ -148,19 +132,19 @@ def highprecisionexp(array, maxp=1e32): -------- a = highprecisionexp(array) """ - np.seterr(all='ignore') + np.seterr(all="ignore") defaultErrorState = np.geterr() - np.seterr(over='raise', invalid='raise') + np.seterr(over="raise", invalid="raise") try: ans = np.exp(array) - except: + except: # noqa: E722 ans = np.full(array.shape, maxp) np.seterr(**defaultErrorState) return ans -def highprecisionpower(x1, x2, maxp=1e32): - """ - Prevents overflow warning with numpy.powerr by assigning overflows + +def highprecisionpower(x1, x2, maxp=1e32) -> np.ndarray[float]: + """Prevents overflow warning with numpy.powerr by assigning overflows to a maxumum precision value Classification: Function @@ -181,12 +165,12 @@ def highprecisionpower(x1, x2, maxp=1e32): -------- a = highprecisionexp(array) """ - np.seterr(all='ignore') + np.seterr(all="ignore") defaultErrorState = np.geterr() - np.seterr(over='raise', invalid='raise') + np.seterr(over="raise", invalid="raise") try: ans = np.power(x1, x2) - except: + except: # noqa: E722 ans = np.full(x1.shape, maxp) np.seterr(**defaultErrorState) return ans diff --git a/pydesigner/tractography/__init__.py b/pydesigner/tractography/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/designer/tractography/dsistudio.py b/pydesigner/tractography/dsistudio.py similarity index 57% rename from designer/tractography/dsistudio.py rename to pydesigner/tractography/dsistudio.py index 7274c9fc..09940594 100644 --- a/designer/tractography/dsistudio.py +++ b/pydesigner/tractography/dsistudio.py @@ -1,33 +1,38 @@ #!/usr/bin/env python # -*- coding : utf-8 -*- -""" -Tools for exporting DSIStudio-compatible outputs. Adapted from +"""Tools for exporting DSIStudio-compatible outputs. Adapted from mattcieslak/dmri_convert/mrtrix_to_dsistudio.py to suit PyDesigner's needs. -References: +References +---------- 1. https://github.com/mattcieslak/dmri_convert/blob/master/mrtrix_to_dsistudio.py + """ import os import os.path as op import subprocess -import numpy as np +from typing import Tuple + import nibabel as nib +import numpy as np from dipy.core.geometry import cart2sphere from dipy.core.sphere import HemiSphere -from dipy.direction import peak_directions, gfa +from dipy.direction import gfa, peak_directions from scipy.io.matlab import loadmat, savemat -from designer.tractography import sphericalsampling from tqdm import tqdm + +from . import sphericalsampling + ODF_COLS = 20000 # Number of columns in DSI Studio odf split tqdmWidth = 70 -def get_dsi_studio_ODF_geometry(odf_key): - """ - Reads DSIStudio's ODF geometry in odfs.mat + +def get_dsi_studio_ODF_geometry(odf_key) -> Tuple[np.ndarray[int], np.ndarray[float]]: + """Reads DSIStudio's ODF geometry in odfs.mat Parameters ---------- @@ -42,14 +47,14 @@ def get_dsi_studio_ODF_geometry(odf_key): ODF faces """ working_dir = os.path.abspath(os.path.dirname(__file__)) - m = loadmat(op.join(working_dir, 'odfs.mat')) - odf_vertices = m[odf_key + '_vertices'].T - odf_faces = m[odf_key + '_faces'].T + m = loadmat(op.join(working_dir, "odfs.mat")) + odf_vertices = m[odf_key + "_vertices"].T + odf_faces = m[odf_key + "_faces"].T return odf_vertices, odf_faces -def convertLPS(input, output): - """ - Converts a nifti file to LPS for compatibility with DSIStudio + +def convertLPS(input, output) -> None: + """Converts a nifti file to LPS for compatibility with DSIStudio Parameters ---------- @@ -63,35 +68,25 @@ def convertLPS(input, output): None, writes out file """ if not op.exists(input): - raise OSError('Input path does not exist. Please ensure that ' - 'the folder or file specified exists.') + raise OSError("Input path does not exist. Please ensure that " "the folder or file specified exists.") if not op.exists(op.dirname(output)): - raise OSError('Specifed directory for output file {} does not ' - 'exist. Please ensure that this is a valid ' - 'directory.'.format(op.dirname(output))) - if op.splitext(input)[-1] != '.nii': - raise IOError('Input file needs to specified as a NifTI ' - '(.nii)') - if op.splitext(output)[-1] != '.nii': - raise IOError('Output file needs to specified as a NifTI ' - '(.nii)') - arg = [ - 'mrconvert', - '-quiet', - '-force', - input, - '-strides', '-1,-2,3,4', - output - ] + raise OSError( + "Specifed directory for output file {} does not " + "exist. Please ensure that this is a valid " + "directory.".format(op.dirname(output)) + ) + if op.splitext(input)[-1] != ".nii": + raise IOError("Input file needs to specified as a NifTI " "(.nii)") + if op.splitext(output)[-1] != ".nii": + raise IOError("Output file needs to specified as a NifTI " "(.nii)") + arg = ["mrconvert", "-quiet", "-force", input, "-strides", "-1,-2,3,4", output] completion = subprocess.run(arg) if completion.returncode != 0: - raise Exception('Conversion of NifTI file to LPS failed. ' - 'Check above for errors.') + raise Exception("Conversion of NifTI file to LPS failed. " "Check above for errors.") -def makefib(input, output, map=None, mask=None, n_fibers=5, scale=1, - other_maps=None): - """ - Converts a NifTi ``.nii`` file containing sh coefficients to a DSI + +def makefib(input, output, map=None, mask=None, n_fibers=5, scale=1, other_maps=None) -> None: + """Converts a NifTi ``.nii`` file containing sh coefficients to a DSI Studio fib file This function uses ``sh2amp`` to get amplitude values for each @@ -114,7 +109,7 @@ def makefib(input, output, map=None, mask=None, n_fibers=5, scale=1, n_fibers : int, optional The maximum number ODF maxima to extract per voxel (Default: 3) - scale: float; optional + scale: float; optional Affects overall size of odfs in .fib file. This only affects visualization. (Default: 1) @@ -129,51 +124,49 @@ def makefib(input, output, map=None, mask=None, n_fibers=5, scale=1, None, writes out file """ if not op.exists(input): - raise OSError('Input path does not exist. Please ensure that ' - 'the folder or file specified exists.') + raise OSError("Input path does not exist. Please ensure that " "the folder or file specified exists.") if not op.exists(op.dirname(output)): - raise OSError('Specifed directory for output file {} does not ' - 'exist. Please ensure that this is a valid ' - 'directory.'.format(op.dirname(output))) - if op.splitext(input)[-1] != '.nii': - raise IOError('Input file needs to specified as a NifTI ' - '(.nii)') - if op.splitext(output)[-1] != '.fib': - raise IOError('Output file needs to specified as a .fib file') - if not map is None: + raise OSError( + "Specifed directory for output file {} does not " + "exist. Please ensure that this is a valid " + "directory.".format(op.dirname(output)) + ) + if op.splitext(input)[-1] != ".nii": + raise IOError("Input file needs to specified as a NifTI " "(.nii)") + if op.splitext(output)[-1] != ".fib": + raise IOError("Output file needs to specified as a .fib file") + if map is not None: if not op.exists(map): - raise OSError('Path to map image does not exist. Please ' - 'ensure that the folder specified exists.') - if not mask is None: + raise OSError("Path to map image does not exist. Please " "ensure that the folder specified exists.") + if mask is not None: if not op.exists(mask): - raise OSError('Path to brain mask does not exist. Please ' - 'ensure that the folder specified exists.') - if type(other_maps) == list or other_maps == None: - if type(other_maps) == list: + raise OSError("Path to brain mask does not exist. Please " "ensure that the folder specified exists.") + if isinstance(other_maps, list) or other_maps is None: + if isinstance(other_maps, list): if any([not op.exists(x) for x in other_maps]): - raise OSError('One of the paths defined in other maps does not ' - 'exist Please ensure all files exist.') + raise OSError("One of the paths defined in other maps does not " "exist Please ensure all files exist.") else: - raise TypeError('Path to other maps needs to be entered as a list of ' - 'strings defining paths to other metric files.') + raise TypeError( + "Path to other maps needs to be entered as a list of " "strings defining paths to other metric files." + ) outdir = op.dirname(output) # Convert to LPS fname, ext = op.splitext(op.basename(input)) - fname = fname +'_lps' + fname = fname + "_lps" input_ = op.join(outdir, fname + ext) convertLPS(input, input_) - if not map is None: + if map is not None: fname, ext = op.splitext(op.basename(map)) - fname = fname +'_lps' + fname = fname + "_lps" map_ = op.join(outdir, fname + ext) convertLPS(map, map_) - if not mask is None: + if mask is not None: fname, ext = op.splitext(op.basename(mask)) - fname = fname +'_lps' + fname = fname + "_lps" mask_ = op.join(outdir, fname + ext) convertLPS(mask, mask_) # Get ODF geometry - verts, faces = sphericalsampling.dsigrid('odf8') + verts, faces = sphericalsampling.dsigrid("odf8") num_dirs, _ = verts.shape hemisphere = num_dirs // 2 x, y, z = verts[:hemisphere].T @@ -183,47 +176,43 @@ def makefib(input, output, map=None, mask=None, n_fibers=5, scale=1, dirs_txt = op.join(outdir, "directions.txt") np.savetxt(dirs_txt, np.column_stack([phi, theta])) # Get SH amplitude - odf_amplitudes_nii = op.join(outdir, 'amplitudes.nii') + odf_amplitudes_nii = op.join(outdir, "amplitudes.nii") arg = [ - 'sh2amp', - '-quiet', - '-force', - '-nonnegative', - input_, dirs_txt, odf_amplitudes_nii + "sh2amp", + "-quiet", + "-force", + "-nonnegative", + input_, + dirs_txt, + odf_amplitudes_nii, ] completion = subprocess.run(arg) if completion.returncode != 0: - raise Exception('Failed to determine amplitude of SH ' - 'coefficients. Check above for errors.') + raise Exception("Failed to determine amplitude of SH " "coefficients. Check above for errors.") # Load images amplitudes_img = nib.load(odf_amplitudes_nii) ampl_data = amplitudes_img.get_fdata() - if not mask is None: + if mask is not None: mask_img = nib.load(mask_) if not np.allclose(mask_img.affine, amplitudes_img.affine): - raise ValueError('Differing orientation between mask and ' - 'amplitudes.') + raise ValueError("Differing orientation between mask and " "amplitudes.") if not mask_img.shape == amplitudes_img.shape[:3]: - raise ValueError('Differing grid between mask and ' - 'amplitudes') + raise ValueError("Differing grid between mask and " "amplitudes") mask_img = mask_img.get_fdata() else: - mask_img = np.ones((ampl_data.shape[0], ampl_data.shape[1], - ampl_data.shape[2]), order='F') + mask_img = np.ones((ampl_data.shape[0], ampl_data.shape[1], ampl_data.shape[2]), order="F") # Make flat mask - flat_mask = mask_img.flatten(order='F') > 0 - odf_array = ampl_data.reshape(-1, ampl_data.shape[3], order='F') + flat_mask = mask_img.flatten(order="F") > 0 + odf_array = ampl_data.reshape(-1, ampl_data.shape[3], order="F") masked_odfs = odf_array[flat_mask, :] masked_odfs = np.nan_to_num(masked_odfs) - if not map is None: + if map is not None: map_img = nib.load(map_) if not np.allclose(map_img.affine, amplitudes_img.affine): - raise ValueError('Differing orientation between map image and ' - 'amplitudes.') + raise ValueError("Differing orientation between map image and " "amplitudes.") if not map_img.shape == amplitudes_img.shape[:3]: - raise ValueError('Differing grid between map image and ' - 'amplitudes') - map_img = map_img.get_fdata().flatten(order='F') + raise ValueError("Differing grid between map image and " "amplitudes") + map_img = map_img.get_fdata().flatten(order="F") map_img[map_img < 0] = 0 masked_map = map_img[flat_mask] @@ -232,39 +221,43 @@ def makefib(input, output, map=None, mask=None, n_fibers=5, scale=1, # Compute GFA masked_gfa = np.zeros(masked_odfs.shape[0]) - for vox in tqdm(range(masked_odfs.shape[0]), - desc='Computing GFA', - bar_format='{desc}: [{percentage:0.0f}%]', - unit='vox', - ncols=tqdmWidth): + for vox in tqdm( + range(masked_odfs.shape[0]), + desc="Computing GFA", + bar_format="{desc}: [{percentage:0.0f}%]", + unit="vox", + ncols=tqdmWidth, + ): masked_gfa[vox] = gfa(masked_odfs[vox, :]) - + # Remove voxels possibly containing errors in ODF calculations. This is # particularly important for volumes that were co-registered as it fixes # large amplitudes ODF amplitude at border voxels - if not map is None: + if map is not None: idx = np.logical_or(masked_map == 0, np.sum(masked_odfs, axis=1) == 0) masked_map[idx] = 0 else: idx = np.sum(masked_odfs, axis=1) == 0 masked_odfs[idx, :] = 0 masked_gfa[idx] = 0 - if not map is None: - masked_map[idx] = 0 + if map is not None: + masked_map[idx] = 0 n_odfs = masked_odfs.shape[0] peak_indices = np.zeros((n_odfs, n_fibers), dtype=int) peak_vals = np.zeros((n_odfs, n_fibers)) dsi_mat = {} # Create matfile that can be read by dsi Studio - dsi_mat['dimension'] = np.array(amplitudes_img.shape[:3]) - dsi_mat['voxel_size'] = np.array(amplitudes_img.header.get_zooms()[:3]) - n_voxels = int(np.prod(dsi_mat['dimension'])) - for odfnum in tqdm(range(masked_odfs.shape[0]), - desc='ODF Peak Detection', - bar_format='{desc}: [{percentage:0.0f}%]', - unit='vox', - ncols=tqdmWidth): + dsi_mat["dimension"] = np.array(amplitudes_img.shape[:3]) + dsi_mat["voxel_size"] = np.array(amplitudes_img.header.get_zooms()[:3]) + n_voxels = int(np.prod(dsi_mat["dimension"])) + for odfnum in tqdm( + range(masked_odfs.shape[0]), + desc="ODF Peak Detection", + bar_format="{desc}: [{percentage:0.0f}%]", + unit="vox", + ncols=tqdmWidth, + ): dirs, vals, indices = peak_directions(masked_odfs[odfnum], hs) for dirnum, (val, idx) in enumerate(zip(vals, indices)): if dirnum == n_fibers: @@ -274,50 +267,49 @@ def makefib(input, output, map=None, mask=None, n_fibers=5, scale=1, for nfib in range(n_fibers): # fill in the "fa" values fa_n = np.zeros(n_voxels) - if not map is None: + if map is not None: fa_fromidx = np.zeros(masked_map.shape) np.putmask(fa_fromidx, peak_vals[:, nfib] != 0, masked_map) fa_n[flat_mask] = fa_fromidx else: fa_n[flat_mask] = peak_vals[:, nfib] - dsi_mat['fa%d' % nfib] = fa_n.astype(np.float32) + dsi_mat["fa%d" % nfib] = fa_n.astype(np.float32) # Fill in the index values index_n = np.zeros(n_voxels) index_n[flat_mask] = peak_indices[:, nfib] - dsi_mat['index%d' % nfib] = index_n.astype(np.int16) + dsi_mat["index%d" % nfib] = index_n.astype(np.int16) # Add in the ODFs num_odf_matrices = n_odfs // ODF_COLS split_indices = (np.arange(num_odf_matrices) + 1) * ODF_COLS odf_splits = np.array_split(masked_odfs, split_indices, axis=0) for splitnum, odfs in enumerate(odf_splits): - dsi_mat['odf%d' % splitnum] = odfs.T.astype(np.float32) - dsi_mat['odf_vertices'] = verts.T - dsi_mat['odf_faces'] = faces.T - dsi_mat['z0'] = np.array([1.]) + dsi_mat["odf%d" % splitnum] = odfs.T.astype(np.float32) + dsi_mat["odf_vertices"] = verts.T + dsi_mat["odf_faces"] = faces.T + dsi_mat["z0"] = np.array([1.0]) # Fill in GFA g_fa = np.zeros(n_voxels) g_fa[flat_mask] = masked_gfa - dsi_mat['gfa'] = g_fa.astype(np.float32) + dsi_mat["gfa"] = g_fa.astype(np.float32) # Fill in other metrics maps - if not other_maps is None: + if other_maps is not None: for path_map in other_maps: map_name, ext = op.splitext(op.basename(path_map)) map_dir = op.dirname(path_map) - map_lps = op.join(map_dir, map_name + '_lps' + ext) + map_lps = op.join(map_dir, map_name + "_lps" + ext) convertLPS(path_map, map_lps) other_img = nib.load(map_lps) if not other_img.shape == amplitudes_img.shape[:3]: - raise ValueError('Differing grid between other map image: {} ' - 'and amplitudes'.format(path_map)) - gmap = other_img.get_fdata().flatten(order='F') + raise ValueError("Differing grid between other map image: {} " "and amplitudes".format(path_map)) + gmap = other_img.get_fdata().flatten(order="F") dsi_mat[map_name] = gmap.astype(np.float32) os.remove(map_lps) - savemat(output, dsi_mat, format='4', appendmat=False) + savemat(output, dsi_mat, format="4", appendmat=False) # Remove unwanted files os.remove(input_) - if not mask is None: + if mask is not None: os.remove(mask_) - if not map is None: + if map is not None: os.remove(map_) os.remove(dirs_txt) os.remove(odf_amplitudes_nii) diff --git a/pydesigner/tractography/odf.py b/pydesigner/tractography/odf.py new file mode 100644 index 00000000..fd432891 --- /dev/null +++ b/pydesigner/tractography/odf.py @@ -0,0 +1,993 @@ +#!/usr/bin/env python +# -*- coding : utf-8 -*- + +"""Function for computing DTI and DKI spherical harmonics from diffusion and +kurtosis tensors +""" +import multiprocessing +import os.path as op +import warnings +from typing import Self, Tuple + +import nibabel as nib +import numpy as np +from dipy.core.geometry import sphere2cart +from joblib import Parallel, delayed +from scipy.special import sph_harm +from tqdm import tqdm + +from ..fitting.thresholds import __minZero__ +from ..system.utils import vectorize, writeNii +from . import sphericalsampling + + +class odfmodel: + """DTI/DKI tractograpy class for computing ODFs and preparing spherical + harmonics for DTI or DKI fiber tracking. + """ + + def __init__( + self, + dt, + kt=None, + mask=None, + scale=None, + res="med", + l_max=6, + radial_weight=4, + nthreads=None, + ) -> Self: + """Parameters + ---------- + dt : str + Path to diffusion tensor, which is a 4D NifTI volume composed of six (6) + components + kt : str; optional + Path to kurtosis tensor, which is a 4D NifTI compose of fifteen (15) + components + (Default: None) + mask : str; optional + Path to brain mask in NifTI format + scale : str; optional + Path to dMRI metric map to use for ODF scaling, where metric value + at a voxel is multiplied by the ODF. + res : str; optional, {'low', 'med', 'high'} + Resolution of directions for ODF calculation. Higher resolution + implies slower computation. + (Default: 'med') + l_max : int + Maximum spherical harmonic degree to use for spherical harmonic + expansion of ODF + (Default: 6) + radial_weight : float + Radial weighting power for detecting directional differences + (Default: 4) + """ + if not op.exists(dt): + raise OSError("Input DT path does not exist. Please ensure that " "the folder or file specified exists.") + if kt is not None: + if not op.exists(kt): + raise OSError( + "Input KT path does not exist. Please ensure that " "the folder or file specified exists." + ) + if mask is not None: + if not op.exists(mask): + raise OSError("Path to brain mask does not exist. Please " "ensure that the file specified exists.") + if scale is not None: + if not op.exists(scale): + raise OSError("Path to scale image does not exist. Please " "ensure that the file specified exists.") + if not isinstance(res, str): + raise Exception("Please specify resolution as a string. Possible " 'choices are "low", "med", or "high"') + # Load images + self.hdr = nib.load(dt) + self.DT = self.hdr.get_fdata() + if kt is not None: + self.KT = nib.load(kt).get_fdata() + else: + self.KT = None + if mask is not None: + self.mask_img = nib.load(mask).get_fdata() + else: + self.mask_img = None + if scale is not None: + self.scale_img = nib.load(scale).get_fdata() + else: + self.scale_img = np.ones(self.DT.shape[0:3]) + if l_max % 2 != 0: + raise Exception("Please provide l_max as a postive " "and even integer") + self.l_max = l_max + if radial_weight is None: + warnings.warn("Radial weight for dODF computation not specified. " "Using default value of 4.") + self.radial_weight = 4 + else: + self.radial_weight = radial_weight + ( + self.vertices, + self.idx, + self.idx8, + self.area, + self.faces, + self.separation_angle, + ) = sphericalsampling.odfgrid(res) + if nthreads is not None: + if nthreads > multiprocessing.cpu_count(): + warnings.warn( + "Number of workers/threads specified exceed more " + "than available. Using the maximum workers/threads available." + ) + self.workers = -1 + if nthreads is None: + self.workers = -1 + else: + self.workers = nthreads + + def dkiodfhelper(self, dt, kt, radial_weight=4, fa_t=None, form="spherical") -> np.ndarray[float]: + """Computes DKI fODF coefficient at a voxel. This function is intended to + parallelize computations across the brain. + + Parameters + ---------- + dt : array_like(dtype=float) + Diffusion tensor containing 6 elements + kt: array_like(dtype=float) + Kurtosis tensor containing 15 elements + radial_weighing : float; optional + Radial weighting power for detecting directional differences (Default: 4) + fa_t : float64 + In rare cases the diffusion tensor may be extremely isotropic with + very small eigenvalues, causing the kurtosis dODF to have erratic + behavior with very large values, as the kurtosis dODF evaluates the + inverse of D. Setting a threshold removes negative eigenvalues while + preserving principal orientation in voxels where FA >= threshold + form : str; optional; {'spherical', 'cartesian', 'coefficient'} + Form of ODF to return in + (Default: 'spherical') + + Returns + ------- + odf : array_like(dtype=float) + DKI ODFs in either coefficient, spherical, or cartesian form + """ + D = np.array([[dt[0], dt[3], dt[4]], [dt[3], dt[1], dt[5]], [dt[4], dt[5], dt[2]]]) + + W = np.zeros((3, 3, 3, 3)) + W[0, 0, 0, 0] = kt[0] + W[1, 1, 1, 1] = kt[1] + W[2, 2, 2, 2] = kt[2] + W[0, 0, 0, 1] = kt[3] + W[0, 0, 1, 0] = W[0, 0, 0, 1] + W[0, 1, 0, 0] = W[0, 0, 0, 1] + W[1, 0, 0, 0] = W[0, 0, 0, 1] + W[0, 0, 0, 2] = kt[4] + W[0, 0, 2, 0] = W[0, 0, 0, 2] + W[0, 2, 0, 0] = W[0, 0, 0, 2] + W[2, 0, 0, 0] = W[0, 0, 0, 2] + W[0, 1, 1, 1] = kt[5] + W[1, 0, 1, 1] = W[0, 1, 1, 1] + W[1, 1, 0, 1] = W[0, 1, 1, 1] + W[1, 1, 1, 0] = W[0, 1, 1, 1] + W[0, 2, 2, 2] = kt[6] + W[2, 0, 2, 2] = W[0, 2, 2, 2] + W[2, 2, 0, 2] = W[0, 2, 2, 2] + W[2, 2, 2, 0] = W[0, 2, 2, 2] + W[1, 1, 1, 2] = kt[7] + W[1, 1, 2, 1] = W[1, 1, 1, 2] + W[1, 2, 1, 1] = W[1, 1, 1, 2] + W[2, 1, 1, 1] = W[1, 1, 1, 2] + W[1, 2, 2, 2] = kt[8] + W[2, 1, 2, 2] = W[1, 2, 2, 2] + W[2, 2, 1, 2] = W[1, 2, 2, 2] + W[2, 2, 2, 1] = W[1, 2, 2, 2] + W[0, 0, 1, 1] = kt[9] + W[0, 1, 0, 1] = W[0, 0, 1, 1] + W[0, 1, 1, 0] = W[0, 0, 1, 1] + W[1, 0, 0, 1] = W[0, 0, 1, 1] + W[1, 0, 1, 0] = W[0, 0, 1, 1] + W[1, 1, 0, 0] = W[0, 0, 1, 1] + W[0, 0, 2, 2] = kt[10] + W[0, 2, 0, 2] = W[0, 0, 2, 2] + W[0, 2, 2, 0] = W[0, 0, 2, 2] + W[2, 0, 0, 2] = W[0, 0, 2, 2] + W[2, 0, 2, 0] = W[0, 0, 2, 2] + W[2, 2, 0, 0] = W[0, 0, 2, 2] + W[1, 1, 2, 2] = kt[11] + W[1, 2, 1, 2] = W[1, 1, 2, 2] + W[1, 2, 2, 1] = W[1, 1, 2, 2] + W[2, 1, 1, 2] = W[1, 1, 2, 2] + W[2, 1, 2, 1] = W[1, 1, 2, 2] + W[2, 2, 1, 1] = W[1, 1, 2, 2] + W[0, 0, 1, 2] = kt[12] + W[0, 0, 2, 1] = W[0, 0, 1, 2] + W[0, 1, 0, 2] = W[0, 0, 1, 2] + W[0, 1, 2, 0] = W[0, 0, 1, 2] + W[0, 2, 0, 1] = W[0, 0, 1, 2] + W[0, 2, 1, 0] = W[0, 0, 1, 2] + W[1, 0, 0, 2] = W[0, 0, 1, 2] + W[1, 0, 2, 0] = W[0, 0, 1, 2] + W[1, 2, 0, 0] = W[0, 0, 1, 2] + W[2, 0, 0, 1] = W[0, 0, 1, 2] + W[2, 0, 1, 0] = W[0, 0, 1, 2] + W[2, 1, 0, 0] = W[0, 0, 1, 2] + W[0, 1, 1, 2] = kt[13] + W[0, 1, 2, 1] = W[0, 1, 1, 2] + W[0, 2, 1, 1] = W[0, 1, 1, 2] + W[1, 0, 1, 2] = W[0, 1, 1, 2] + W[1, 0, 2, 1] = W[0, 1, 1, 2] + W[1, 1, 0, 2] = W[0, 1, 1, 2] + W[1, 1, 2, 0] = W[0, 1, 1, 2] + W[1, 2, 0, 1] = W[0, 1, 1, 2] + W[1, 2, 1, 0] = W[0, 1, 1, 2] + W[2, 0, 1, 1] = W[0, 1, 1, 2] + W[2, 1, 0, 1] = W[0, 1, 1, 2] + W[2, 1, 1, 0] = W[0, 1, 1, 2] + W[0, 1, 2, 2] = kt[14] + W[0, 2, 1, 2] = W[0, 1, 2, 2] + W[0, 2, 2, 1] = W[0, 1, 2, 2] + W[1, 0, 2, 2] = W[0, 1, 2, 2] + W[1, 2, 0, 2] = W[0, 1, 2, 2] + W[1, 2, 2, 0] = W[0, 1, 2, 2] + W[2, 0, 1, 2] = W[0, 1, 2, 2] + W[2, 0, 2, 1] = W[0, 1, 2, 2] + W[2, 1, 0, 2] = W[0, 1, 2, 2] + W[2, 1, 2, 0] = W[0, 1, 2, 2] + W[2, 2, 0, 1] = W[0, 1, 2, 2] + W[2, 2, 1, 0] = W[0, 1, 2, 2] + + # Reglarize tensor if fa is more than threshold specified (fa_t) + if fa_t is not None: + L, V = np.linalg.eig(D) + L[L < __minZero__] = __minZero__ + idx = np.argsort(L)[::-1] + L = L[idx] + V = V[:, idx] + fa = np.sqrt(((L[0] - L[1]) ** 2 + (L[0] - L[2]) ** 2 + (L[1] - L[2]) ** 2) / (2 * (np.sum(L**2)))) + if fa > fa_t: + x = np.roots( + [ + 2 * (1 - 2 * fa_t**2) / 3, + -4 * L[0] / 3, + 2 * (1 - fa_t**2) / 3 * L[0] ** 2, + ] + ) + if x[np.logical_and(x > 0, x < L[0])].size != 0: + L[1:3] = x[np.logical_and(x > 0, x < L[0])] + else: + Davg = np.trace(D) / 3 + L[L < 0.1 * Davg] = 0.1 * Davg + D = np.matmul(np.matmul(V, np.diag(L)), np.linalg.inv(V)) + W = np.zeros((3, 3, 3, 3)) + + Davg = np.trace(D) / 3 + try: + U = Davg * np.linalg.inv(D) + except: # noqa: E722 + U = Davg * np.linalg.pinv(D) + A1 = 0 + B11 = 0 + B12 = 0 + B13 = 0 + B22 = 0 + B23 = 0 + B33 = 0 + C1111 = 0 + C1112 = 0 + C1113 = 0 + C1122 = 0 + C1123 = 0 + C1133 = 0 + C1222 = 0 + C1223 = 0 + C1233 = 0 + C1333 = 0 + C2222 = 0 + C2223 = 0 + C2233 = 0 + C2333 = 0 + C3333 = 0 + + for i in [0, 1, 2]: + for j in [0, 1, 2]: + for k in [0, 1, 2]: + for l in [0, 1, 2]: # noqa: E741 + # Coefficients for: 3UijWijklUkl + A1 = A1 + 3 * U[i, j] * W[i, j, k, l] * U[k, l] + # Coefficients for: -6(a+1)UijWijklVkl + B0 = -6 * (radial_weight + 1) * U[i, j] * W[i, j, k, l] + B11 = B11 + B0 * (U[k, 0] * U[l, 0]) + B12 = B12 + B0 * (U[k, 0] * U[l, 1] + U[k, 1] * U[l, 0]) + B13 = B13 + B0 * (U[k, 0] * U[l, 2] + U[k, 2] * U[l, 0]) + B22 = B22 + B0 * (U[k, 1] * U[l, 1]) + B23 = B23 + B0 * (U[k, 1] * U[l, 2] + U[k, 2] * U[l, 1]) + B33 = B33 + B0 * (U[k, 2] * U[l, 2]) + # Coefficients for: (alpha+1)(alpha+3)W(i,j,k,l)VijVkl + C0 = (radial_weight + 1) * (radial_weight + 3) * W[i, j, k, l] + C1111 = C1111 + C0 * (U[i, 0] * U[j, 0] * U[k, 0] * U[l, 0]) + C1112 = C1112 + C0 * ( + U[i, 0] * U[j, 0] * U[k, 0] * U[l, 1] + + U[i, 0] * U[j, 0] * U[k, 1] * U[l, 0] + + U[i, 0] * U[j, 1] * U[k, 0] * U[l, 0] + + U[i, 1] * U[j, 0] * U[k, 0] * U[l, 0] + ) + C1113 = C1113 + C0 * ( + U[i, 0] * U[j, 0] * U[k, 0] * U[l, 2] + + U[i, 0] * U[j, 0] * U[k, 2] * U[l, 0] + + U[i, 0] * U[j, 2] * U[k, 0] * U[l, 0] + + U[i, 2] * U[j, 0] * U[k, 0] * U[l, 0] + ) + C1122 = C1122 + C0 * ( + U[i, 0] * U[j, 0] * U[k, 1] * U[l, 1] + + U[i, 0] * U[j, 1] * U[k, 0] * U[l, 1] + + U[i, 0] * U[j, 1] * U[k, 1] * U[l, 0] + + U[i, 1] * U[j, 0] * U[k, 0] * U[l, 1] + + U[i, 1] * U[j, 0] * U[k, 1] * U[l, 0] + + U[i, 1] * U[j, 1] * U[k, 0] * U[l, 0] + ) + C1123 = C1123 + C0 * ( + U[i, 0] * U[j, 0] * U[k, 1] * U[l, 2] + + U[i, 0] * U[j, 0] * U[k, 2] * U[l, 1] + + U[i, 0] * U[j, 1] * U[k, 0] * U[l, 2] + + U[i, 0] * U[j, 1] * U[k, 2] * U[l, 0] + + U[i, 0] * U[j, 2] * U[k, 0] * U[l, 1] + + U[i, 0] * U[j, 2] * U[k, 1] * U[l, 0] + + U[i, 1] * U[j, 0] * U[k, 0] * U[l, 2] + + U[i, 1] * U[j, 0] * U[k, 2] * U[l, 0] + + U[i, 1] * U[j, 2] * U[k, 0] * U[l, 0] + + U[i, 2] * U[j, 0] * U[k, 0] * U[l, 1] + + U[i, 2] * U[j, 0] * U[k, 1] * U[l, 0] + + U[i, 2] * U[j, 1] * U[k, 0] * U[l, 0] + ) + C1133 = C1133 + C0 * ( + U[i, 0] * U[j, 0] * U[k, 2] * U[l, 2] + + U[i, 0] * U[j, 2] * U[k, 0] * U[l, 2] + + U[i, 0] * U[j, 2] * U[k, 2] * U[l, 0] + + U[i, 2] * U[j, 0] * U[k, 0] * U[l, 2] + + U[i, 2] * U[j, 0] * U[k, 2] * U[l, 0] + + U[i, 2] * U[j, 2] * U[k, 0] * U[l, 0] + ) + C1222 = C1222 + C0 * ( + U[i, 0] * U[j, 1] * U[k, 1] * U[l, 1] + + U[i, 1] * U[j, 0] * U[k, 1] * U[l, 1] + + U[i, 1] * U[j, 1] * U[k, 0] * U[l, 1] + + U[i, 1] * U[j, 1] * U[k, 1] * U[l, 0] + ) + C1223 = C1223 + C0 * ( + U[i, 0] * U[j, 1] * U[k, 1] * U[l, 2] + + U[i, 0] * U[j, 1] * U[k, 2] * U[l, 1] + + U[i, 0] * U[j, 2] * U[k, 1] * U[l, 1] + + U[i, 1] * U[j, 0] * U[k, 1] * U[l, 2] + + U[i, 1] * U[j, 0] * U[k, 2] * U[l, 1] + + U[i, 1] * U[j, 1] * U[k, 0] * U[l, 2] + + U[i, 1] * U[j, 1] * U[k, 2] * U[l, 0] + + U[i, 1] * U[j, 2] * U[k, 0] * U[l, 1] + + U[i, 1] * U[j, 2] * U[k, 1] * U[l, 0] + + U[i, 2] * U[j, 0] * U[k, 1] * U[l, 1] + + U[i, 2] * U[j, 1] * U[k, 0] * U[l, 1] + + U[i, 2] * U[j, 1] * U[k, 1] * U[l, 0] + ) + C1233 = C1233 + C0 * ( + U[i, 0] * U[j, 1] * U[k, 2] * U[l, 2] + + U[i, 0] * U[j, 2] * U[k, 1] * U[l, 2] + + U[i, 0] * U[j, 2] * U[k, 2] * U[l, 1] + + U[i, 1] * U[j, 0] * U[k, 2] * U[l, 2] + + U[i, 1] * U[j, 2] * U[k, 0] * U[l, 2] + + U[i, 1] * U[j, 2] * U[k, 2] * U[l, 0] + + U[i, 2] * U[j, 0] * U[k, 1] * U[l, 2] + + U[i, 2] * U[j, 0] * U[k, 2] * U[l, 1] + + U[i, 2] * U[j, 1] * U[k, 0] * U[l, 2] + + U[i, 2] * U[j, 1] * U[k, 2] * U[l, 0] + + U[i, 2] * U[j, 2] * U[k, 0] * U[l, 1] + + U[i, 2] * U[j, 2] * U[k, 1] * U[l, 0] + ) + C1333 = C1333 + C0 * ( + U[i, 0] * U[j, 2] * U[k, 2] * U[l, 2] + + U[i, 2] * U[j, 0] * U[k, 2] * U[l, 2] + + U[i, 2] * U[j, 2] * U[k, 0] * U[l, 2] + + U[i, 2] * U[j, 2] * U[k, 2] * U[l, 0] + ) + C2222 = C2222 + C0 * (U[i, 1] * U[j, 1] * U[k, 1] * U[l, 1]) + C2223 = C2223 + C0 * ( + U[i, 1] * U[j, 1] * U[k, 1] * U[l, 2] + + U[i, 1] * U[j, 1] * U[k, 2] * U[l, 1] + + U[i, 1] * U[j, 2] * U[k, 1] * U[l, 1] + + U[i, 2] * U[j, 1] * U[k, 1] * U[l, 1] + ) + C2233 = C2233 + C0 * ( + U[i, 1] * U[j, 1] * U[k, 2] * U[l, 2] + + U[i, 1] * U[j, 2] * U[k, 1] * U[l, 2] + + U[i, 1] * U[j, 2] * U[k, 2] * U[l, 1] + + U[i, 2] * U[j, 1] * U[k, 1] * U[l, 2] + + U[i, 2] * U[j, 1] * U[k, 2] * U[l, 1] + + U[i, 2] * U[j, 2] * U[k, 1] * U[l, 1] + ) + C2333 = C2333 + C0 * ( + U[i, 1] * U[j, 2] * U[k, 2] * U[l, 2] + + U[i, 2] * U[j, 1] * U[k, 2] * U[l, 2] + + U[i, 2] * U[j, 2] * U[k, 1] * U[l, 2] + + U[i, 2] * U[j, 2] * U[k, 2] * U[l, 1] + ) + C3333 = C3333 + C0 * (U[i, 2] * U[j, 2] * U[k, 2] * U[l, 2]) + coeff = np.array( + [ + A1, + B11, + B12, + B13, + B22, + B23, + B33, + C1111, + C1112, + C1113, + C1122, + C1123, + C1133, + C1222, + C1223, + C1233, + C1333, + C2222, + C2223, + C2233, + C2333, + C3333, + U[0, 0], + U[1, 1], + U[2, 2], + U[0, 1], + U[0, 2], + U[1, 2], + radial_weight, + ] + ) + if form == "coefficient": + odf = coeff + if form == "spherical": + odf = dkiodfspherical(coeff, self.vertices[:, 0], self.vertices[:, 1]) + if form == "cartesian": + x, y, z = sphere2cart(1, self.vertices[:, 1], self.vertices[:, 0]) + odf = dkiodfcartesian(coeff, x, y, z) + return odf + + def dkiodf(self, form="spherical", fa_t=0.90) -> np.ndarray[float]: + """Computes DKI ODFs for the whole brain. + + Parameters + ---------- + form : str; optional; {'spherical', 'cartesial', 'coefficient'} + Form of ODF to return in + (Default: 'spherical') + fa_t : float64; optional + In rare cases the diffusion tensor may be extremely isotropic with + very small eigenvalues, causing the kurtosis dODF to have erratic + behavior with very large values, as the kurtosis dODF evaluates the + inverse of D. Setting a threshold removes negative eigenvalues while + preserving principal orientation in voxels where FA >= threshold + (Default: 0.95) + + Returns + ------- + DKI ODF in defined form + """ + if form not in ["spherical", "cartesian", "coefficient"]: + raise Exception("Please select a valid form of ODF to receive") + if self.KT is None: + raise AttributeError( + "WOAH! Cannot compute DKI ODFs without " "kurtosis tensor (KT). Try using dtiodf(), Jumbo." + ) + # Vectorize images + DT = vectorize(self.DT, self.mask_img) + KT = vectorize(self.KT, self.mask_img) + nvox = DT.shape[-1] + inputs = tqdm( + range(nvox), + desc="DKI ODF", + bar_format="{desc}: [{percentage:0.0f}%]", + unit="vox", + ncols=70, + ) + odf = Parallel(n_jobs=self.workers, prefer="processes")( + delayed(self.dkiodfhelper)(DT[:, i], KT[:, i], self.radial_weight, fa_t, form) for i in inputs + ) + odf = np.array(odf).T + odf = vectorize(odf, self.mask_img) + return odf + + def dtiodfhelper(self, dt, form="spherical") -> np.ndarray[float]: + """Computes DTI fODF coefficient at a voxel. This function is intended to + parallelize computations across the brain. Use only for diffusion + ellipsoids. + + Parameters + ---------- + dt : array_like(dtype=float) + Diffusion tensor containing 6 elements + radial_weighing : float; optional + Radial weighting power for detecting directional differences (Default: 4) + form : str; optional; {'spherical', 'coefficient'} + Form of ODF to return in + (Default: 'spherical') + + Returns + ------- + odf : array_like(dtype=float) + DKI ODFs in either coefficient, spherical, or cartesian form + """ + D = np.array([[dt[0], dt[3], dt[4]], [dt[3], dt[1], dt[5]], [dt[4], dt[5], dt[2]]]) + Davg = np.trace(D) / 3 + try: + U = Davg * np.linalg.inv(D) + except: # noqa: E722 + U = Davg * np.linalg.pinv(D) + U11 = U[0, 0] + U22 = U[1, 1] + U33 = U[2, 2] + U12 = U[0, 1] + U13 = U[0, 2] + U23 = U[1, 2] + coeff = np.array([U11, U12, U13, U22, U23, U33]) + if form == "coefficient": + odf = coeff + if form == "spherical": + odf = dtiodfspherical(coeff, self.vertices[:, 0], self.vertices[:, 1], self.radial_weight) + return odf + + def dtiodf(self, form="spherical") -> np.ndarray[float]: + """Computed DTI ODFs for the whole brain (ellipsoids) + + Parameters + ---------- + form : str; optional; {'spherical', 'cartesian', 'coefficient'} + Form of ODF to return in + (Default: 'spherical') + + Returns + ------- + DTI ODF in defined form + """ + if self.DT is None: + raise AttributeError("WOAH! Cannot compute DTI ODFs without " "diffusion tensor (DT), Jumbo.") + # Vectorize images + DT = vectorize(self.DT, self.mask_img) + nvox = DT.shape[-1] + inputs = tqdm( + range(nvox), + desc="DTI ODF", + bar_format="{desc}: [{percentage:0.0f}%]", + unit="vox", + ncols=70, + ) + odf = Parallel(n_jobs=self.workers, prefer="processes")( + delayed(self.dtiodfhelper)(DT[:, i], form) for i in inputs + ) + odf = np.array(odf).T + odf = vectorize(odf, self.mask_img) + return odf + + def odfmaxhelper(self, odf) -> Tuple[np.ndarray[float], np.ndarray[float]]: + """Find local maxima of ODF over spherical grid at voxel + + Parameters + ---------- + odf : array_like(dtype=float64) + Spherical ODF values at a voxel + + Returns + ------- + odfmax : array_like(dtype=float64) + Local maxima of ODF over spherical grid in descending order + dirmax : array_like(dtype=float64) + Corresponding direction vector where local ODF maxima occur + """ + maxidx = self.idx[odf[self.idx[:, 1]] == np.amax(odf[self.idx], axis=1), 0] + odf_max = odf[maxidx] + dir_max = self.vertices[maxidx, :] + # Sort by magnitude in descending order + idx = np.argsort(odf_max)[::-1] + odfmax = odf_max[idx] + dirmax = dir_max[idx] + if odfmax.size == 0: + odfmax = np.array([1]) + dirmax = np.array([0, 0]) + return odfmax, dirmax + + def odf2shhelper(self, odf, B, scale) -> np.ndarray[complex]: + """Helper function to parallelize computation spherical harmonic expansion + at a voxel. + + Parameters + ---------- + odf : array_like(dtype=float64) + Spherical ODF values at a voxel + B : array_like(dtype=complex) + Spherical harmonic basis set to compute expansion + scale : float64 + Value of dMRI metric to multiply ODF with to control stopping + criteria in tractography + + Returns + ------- + sh : Shpherical harmonic expansion of ODF at voxel + """ + odfmax, dirmax = self.odfmaxhelper(odf) + odfmax = odfmax[0] + sh = np.dot(np.linalg.pinv(B), odf / odfmax) * scale + sh[np.isnan(sh)] = __minZero__ + sh[np.isinf(sh)] = __minZero__ + return sh + + def odf2sh(self, odf) -> np.ndarray[complex]: + """Converts whole-brain ODFs to spherical harmonics sampled at direction + set specified by resolution. Only the real portion is returned. + + Parameters + ---------- + odf : 4D ODF file containing spherical ODFs + + Returns + ------- + sh : array_like(dtype=float64) + Shperical harmonic expansion of ODF + """ + odf = vectorize(odf, self.mask_img) + scale = vectorize(self.scale_img, self.mask_img) + # Create shperical harmonic (SH) base set + degs = np.arange(self.l_max + 1, dtype=int) + l_num = 2 * degs + 1 # how many per degree (evens only) + # Variable `harmonics` holds the index of phase m where l is even i.e. + # l = 0; m = 0 --> 1 phases (even l) + # l = 1, m = -1, m = 0, m = 1 --> 3 phases (odd l) + # l = 2, m = -2, m = -1, m = 0, m = 1, m = 2 --> 5 phases (even l) + harmonics = [] + sh_end = 0 # initialize the SH set for indexing + for _, phase in enumerate(l_num[::2]): + sh_start = sh_end + phase - 1 + sh_end = sh_start + phase - 1 + harmonics.extend(np.arange(sh_start, sh_end + 1)) + B = shbasis(degs, self.vertices[:, 0], self.vertices[:, 1], "tournier") + B = B[:, harmonics] + nvox = odf.shape[-1] + inputs = tqdm( + range(nvox), + desc="ODF SH Expansion", + bar_format="{desc}: [{percentage:0.0f}%]", + unit="vox", + ncols=70, + ) + sh = Parallel(n_jobs=self.workers, prefer="processes")( + delayed(self.odf2shhelper)(odf[:, i], B, scale[i]) for i in inputs + ) + sh = np.array(sh).T.real + sh = vectorize(sh, self.mask_img) + return sh + + def savenii(self, var, path) -> None: + """Write out NifTI output of associated spherical harmonic file + + Parameters + ---------- + var : array_like + variable to write out + path : str + Path to output file + + Returns + ------- + None; writes out file + """ + self.hdr.set_data_dtype(var.dtype) + writeNii(var, self.hdr, path) + + +def dkiodfspherical(odf, phi, theta) -> np.ndarray[float]: + """Convert DKI ODFs coefficients at voxel to spherical form. + + Parameters + ---------- + odf : array_like(dtype=float64) + ODF coefficients at a voxel. There are 29 coefficients for DKI ODFs + phi : array_like(dtype=float64) + Polar phi angles + theta : array_like(dtype=float64) + Polar theta angles + + Returns + ------- + spherical : array_like(dtype=float64) + ODF in spherical form + """ + if len(theta) != len(phi): + raise Exception("Inputs theta and phi are not the same size") + try: + spherical = ( + 1 + / ( + (np.sin(phi) * np.cos(theta)) ** 2 * odf[22] + + (np.sin(phi) * np.sin(theta)) ** 2 * odf[23] + + np.cos(phi) ** 2 * odf[24] + + 2 * (np.sin(phi) * np.cos(theta)) * (np.sin(phi) * np.sin(theta)) * odf[25] + + 2 * (np.sin(phi) * np.cos(theta)) * np.cos(phi) * odf[26] + + 2 * (np.sin(phi) * np.sin(theta)) * np.cos(phi) * odf[27] + ) + ) ** ((odf[28] + 1) / 2) * ( + 1 + + ( + odf[0] + + ( + odf[1] * (np.sin(phi) * np.cos(theta)) ** 2 + + odf[2] * (np.sin(phi) * np.cos(theta)) * (np.sin(phi) * np.sin(theta)) + + odf[3] * (np.sin(phi) * np.cos(theta)) * np.cos(phi) + + odf[4] * (np.sin(phi) * np.sin(theta)) ** 2 + + odf[5] * (np.sin(phi) * np.sin(theta)) * np.cos(phi) + + odf[6] * np.cos(phi) ** 2 + ) + / ( + (np.sin(phi) * np.cos(theta)) ** 2 * odf[22] + + (np.sin(phi) * np.sin(theta)) ** 2 * odf[23] + + np.cos(phi) ** 2 * odf[24] + + 2 * (np.sin(phi) * np.cos(theta)) * (np.sin(phi) * np.sin(theta)) * odf[25] + + 2 * (np.sin(phi) * np.cos(theta)) * np.cos(phi) * odf[26] + + 2 * (np.sin(phi) * np.sin(theta)) * np.cos(phi) * odf[27] + ) + + ( + odf[7] * (np.sin(phi) * np.cos(theta)) ** 4 + + odf[8] * (np.sin(phi) * np.cos(theta)) ** 3 * (np.sin(phi) * np.sin(theta)) + + odf[9] * (np.sin(phi) * np.cos(theta)) ** 3 * np.cos(phi) + + odf[10] * (np.sin(phi) * np.cos(theta)) ** 2 * (np.sin(phi) * np.sin(theta)) ** 2 + + odf[11] * (np.sin(phi) * np.cos(theta)) ** 2 * (np.sin(phi) * np.sin(theta)) * np.cos(phi) + + odf[12] * (np.sin(phi) * np.cos(theta)) ** 2 * np.cos(phi) ** 2 + + odf[13] * (np.sin(phi) * np.cos(theta)) * (np.sin(phi) * np.sin(theta)) ** 3 + + odf[14] * (np.sin(phi) * np.cos(theta)) * (np.sin(phi) * np.sin(theta)) ** 2 * np.cos(phi) + + odf[15] * (np.sin(phi) * np.cos(theta)) * (np.sin(phi) * np.sin(theta)) * np.cos(phi) ** 2 + + odf[16] * (np.sin(phi) * np.cos(theta)) * np.cos(phi) ** 3 + + odf[17] * (np.sin(phi) * np.sin(theta)) ** 4 + + odf[18] * (np.sin(phi) * np.sin(theta)) ** 3 * np.cos(phi) + + odf[19] * (np.sin(phi) * np.sin(theta)) ** 2 * np.cos(phi) ** 2 + + odf[20] * (np.sin(phi) * np.sin(theta)) * np.cos(phi) ** 3 + + odf[21] * np.cos(phi) ** 4 + ) + / ( + (np.sin(phi) * np.cos(theta)) ** 2 * odf[22] + + (np.sin(phi) * np.sin(theta)) ** 2 * odf[23] + + np.cos(phi) ** 2 * odf[24] + + 2 * (np.sin(phi) * np.cos(theta)) * (np.sin(phi) * np.sin(theta)) * odf[25] + + 2 * (np.sin(phi) * np.cos(theta)) * np.cos(phi) * odf[26] + + 2 * (np.sin(phi) * np.sin(theta)) * np.cos(phi) * odf[27] + ) + ** 2 + ) + / 24 + ) + except: # noqa: E722 + spherical = np.full(phi.shape, __minZero__) + return spherical + + +def dkiodfcartesian(odf, x, y, z) -> np.ndarray[float]: + """Convert DKI ODF coefficients at voxel to Cartesian form. + + Parameters + ---------- + odf : array_like(dtype=float64) + ODF coefficients at a voxel. There are 29 coefficients for DKI ODFs + x : array_like(dtype=float64) + Cartesian x coordinates + y : array_like(dtype=float64) + Cartesian y coordinates + z : array_like(dtype=float64) + Cartesian z coordinates + + Returns + ------- + cart : array_like(dtype=float64) + ODF in cartesian form + """ + if len(x) != len(y): + raise Exception("Input x, y and z coordinates are not the same size") + if len(x) != len(z): + raise Exception("Input x, y and z coordinates are not the same size") + try: + cart = ( + 1 + / ( + (x) ** 2 * odf[22] + + (y) ** 2 * odf[23] + + z**2 * odf[24] + + 2 * (x) * (y) * odf[25] + + 2 * (x) * z * odf[26] + + 2 * (y) * z * odf[27] + ) + ) ** ((odf[28] + 1) / 2) * ( + 1 + + ( + odf[0] + + ( + odf[1] * (x) ** 2 + + odf[2] * (x) * (y) + + odf[3] * (x) * z + + odf[4] * (y) ** 2 + + odf[5] * (y) * z + + odf[6] * z**2 + ) + / ( + (x) ** 2 * odf[22] + + (y) ** 2 * odf[23] + + z**2 * odf[24] + + 2 * (x) * (y) * odf[25] + + 2 * (x) * z * odf[26] + + 2 * (y) * z * odf[27] + ) + + ( + odf[7] * (x) ** 4 + + odf[8] * (x) ** 3 * (y) + + odf[9] * (x) ** 3 * z + + odf[10] * (x) ** 2 * (y) ** 2 + + odf[11] * (x) ** 2 * (y) * z + + odf[12] * (x) ** 2 * z**2 + + odf[13] * (x) * (y) ** 3 + + odf[14] * (x) * (y) ** 2 * z + + odf[15] * (x) * (y) * z**2 + + odf[16] * (x) * z**3 + + odf[17] * (y) ** 4 + + odf[18] * (y) ** 3 * z + + odf[19] * (y) ** 2 * z**2 + + odf[20] * (y) * z**3 + + odf[21] * z**4 + ) + / ( + (x) ** 2 * odf[22] + + (y) ** 2 * odf[23] + + z**2 * odf[24] + + 2 * (x) * (y) * odf[25] + + 2 * (x) * z * odf[26] + + 2 * (y) * z * odf[27] + ) + ** 2 + ) + / 24 + ) + except: # noqa: E722 + cart = np.full(x.shape, __minZero__) + return cart + + +def dtiodfspherical(odf, phi, theta, radial_weight=4) -> np.ndarray[float]: + """Convert DTI ODFs coefficients at voxel to spherical form. + + Parameters + ---------- + odf : array_like(dtype=float64) + ODF coefficients at a voxel. There are 29 coefficients for DKI ODFs + phi : array_like(dtype=float64) + Polar phi angles + theta : array_like(dtype=float64) + Polar theta angles + radial_weight : float + Radial weighting power for detecting directional differences + (Default: 4) + + Returns + ------- + spherical : array_like(dtype=float64) + ODF in spherical form + """ + if len(theta) != len(phi): + raise Exception("Inputs theta and phi are not the same size") + try: + spherical = ( + 1 + / ( + (np.sin(phi) * np.cos(theta)) ** 2 * odf[0] + + (np.sin(phi) * np.sin(theta)) ** 2 * odf[3] + + np.cos(phi) ** 2 * odf[5] + + 2 * (np.sin(phi) * np.cos(theta)) * (np.sin(phi) * np.sin(theta)) * odf[1] + + 2 * (np.sin(phi) * np.cos(theta)) * np.cos(phi) * odf[2] + + 2 * (np.sin(phi) * np.sin(theta)) * np.cos(phi) * odf[4] + ) + ) ** ((radial_weight + 1) / 2) + except: # noqa: E722 + np.full(phi.shape, __minZero__) + return spherical + + +def shbasis(deg, phi, theta, method="scipy") -> np.ndarray[complex]: + """Computes shperical harmonic bases for all orders (even and odd), using + functions defined by `scipy`, `tournier`, or `descoteaux`. + + Parameters + ---------- + deg : list of ints + Degrees of harmonic + phi : array_like + (n, ) vector denoting polar coordinates + theta : array_like + (n, ) vector denoting azimuthal coordinates + method : str; optional; {scipy, tournier, descoteaux} + Define method for SH basis set + + Returns + ------- + complex array_like + Harmonic samples at theta and phi at specified order + """ + if not any([isinstance(x, int) for x in deg]): + try: + deg = [int(x) for x in deg] + except: # noqa: E722 + raise TypeError("Please supply degree of " "shperical harmonic as an integer") + if not isinstance(method, str): + raise TypeError("Please enter method as a string") + if method not in ["scipy", "tournier", "descoteaux"]: + raise Exception("Please select a valid method for SH basis set") + SH = [] + for n in deg: + for m in range(-n, n + 1): + shb = sph_harm(m, n, theta, phi, dtype=complex) + if method == "tournier": + # Tournier does not have Condon–Shortley phase i.e. (-1)^m in + # their formulas, so we multiply those terms by (-1)^m to reverse the + # inclusion of this phase from scipy's `sph_harm`. + + # Tournier formulas have sqrt(2) normalization term so we + # multiply Ylm. + if m < 0: + sh_ = np.sqrt(2) * shb.imag + elif m > 0: + sh_ = np.sqrt(2) * shb.real * (-1) ** m + elif m == 0: + sh_ = shb + elif method == "descoteaux": + if m < 0: + sh_ = shb.real + elif m > 0: + sh_ = shb.imag * (-1) ** m + elif m == 0: + sh_ = shb + elif method == "scipy": + sh_ = shb + SH.append(sh_) + return np.array(SH, order="F").T + + +def odf_conversion(odf, target="tournier"): + """Converts ODFs from scipy to tournier or descoteaux. + + Parameters + ---------- + odf: array_like + ODF from even number spherical harmonics + + target: str; optional; {tournier, descoteaux} + Define method to convert ODFs + + """ + if not isinstance(target, str): + raise TypeError("Please enter method as a string") + if target not in ["tournier", "descoteaux"]: + raise Exception("Please select a valid method for SH basis set") + l_max_table = np.arange(0, 42, 2, dtype=int) # create array of even l_max allowed + vol_table = np.array([0.5 * (x + 1) * (x + 2) for x in l_max_table], dtype=int) + vols = odf.shape[0] + l_max = l_max_table[np.where(vol_table == vols)[0]] + degs = np.arange(0, l_max + 1, 2, dtype=int) + curr_vol = 0 + odf_ = np.zeros_like(odf) + for n in degs: + for m in range(-n, n + 1): + shb = odf[curr_vol, :] + if target == "tournier": + if m < 0: + sh_ = np.sqrt(2) * shb.imag + elif m > 0: + sh_ = np.sqrt(2) * shb.real * (-1) ** m + elif m == 0: + sh_ = shb + elif target == "descoteaux": + if m < 0: + sh_ = shb.real + elif m > 0: + sh_ = shb.imag * (-1) ** m + elif m == 0: + sh_ = shb + odf_[curr_vol, :] = sh_ + curr_vol = curr_vol + 1 + return odf_ diff --git a/pydesigner/tractography/sphericalsampling.py b/pydesigner/tractography/sphericalsampling.py new file mode 100644 index 00000000..d8864f8c --- /dev/null +++ b/pydesigner/tractography/sphericalsampling.py @@ -0,0 +1,118280 @@ +#!/usr/bin/env python +# -*- coding : utf-8 -*- +"""Various definitions of spherical sampling +""" + +import numpy as np + + +def dsigrid(odf_key="odf8") -> np.ndarray[float]: + """Reads DSIStudio's ODF geometry in odfs.mat to use in creation of DSIStudio's + .fib file. + + Parameters + ---------- + odf_keys : str; optional; {'odf4', 'odf5', 'odf6', 'odf8', 'odf12', 'odf20'} + DSIStudio's direction set to load. (Default: 'odf8') + + Returns + ------- + odf_vertices : array_like(dtype=flaot64) + ODF vertices + odf_faces : array_like(dtype=uint16) + ODF faces + """ + if not isinstance(odf_key, str): + raise Exception( + "Please specify ODF key as a string. Possible " + 'choices are "odf4", "odf5", "odf6", "odf8", "odf12", or "odf20"' + ) + if odf_key not in ["odf4", "odf5", "odf6", "odf8", "odf12", "odf20"]: + raise Exception("Invalid ODF key") + if odf_key == "odf4": + odf_vertices = np.array( + [ + [ + 0.0, + 0.08444399, + -0.22107723, + 0.27326649, + -0.22107723, + 0.08444399, + -0.1381966, + 0.36180341, + -0.44721356, + 0.36180341, + -0.1381966, + 0.16245984, + -0.42532536, + 0.52573109, + -0.42532536, + 0.16245984, + -0.05278641, + -0.36180341, + 0.44721359, + 0.63819659, + -0.67082042, + -0.67082042, + 0.63819659, + 0.44721359, + -0.36180341, + -0.05278641, + 0.2281087, + -0.59719634, + 0.73817527, + -0.59719634, + 0.2281087, + -0.26286554, + 0.68819094, + -0.85065079, + 0.68819094, + -0.26286554, + 0.50137526, + -0.51275235, + -0.51275235, + 0.50137526, + 0.00703146, + -0.8182736, + 0.82261926, + 0.82261926, + -0.8182736, + 0.00703146, + 0.2763932, + -0.72360677, + 0.89442718, + -0.72360677, + 0.2763932, + -0.1381966, + 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158, + 158, + 159, + 160, + 161, + 161, + ], + ] + ) + + if odf_key == "odf5": + odf_vertices = np.array( + [ + [ + 0.00000000e00, + 6.78677559e-02, + -1.77680060e-01, + 2.19624668e-01, + -1.77680060e-01, + 6.78677559e-02, + -1.11636549e-01, + 2.92268306e-01, + -3.61263484e-01, + 2.92268306e-01, + -1.11636549e-01, + 1.32421464e-01, + -3.46683860e-01, + 4.28524882e-01, + -3.46683860e-01, + 1.32421464e-01, + -4.95957360e-02, + -2.86392897e-01, + 3.66640508e-01, + 5.12989163e-01, + -5.43641031e-01, + -5.43641031e-01, + 5.12989223e-01, + 3.66640508e-01, + -2.86392868e-01, + -4.95957136e-02, + 1.90508887e-01, + -4.98758733e-01, + 6.16499722e-01, + -4.98758733e-01, + 1.90508887e-01, + -2.20943794e-01, + 5.78438282e-01, + -7.14989007e-01, + 5.78438282e-01, + -2.20943734e-01, + -4.44179982e-01, + -6.93806946e-01, + -4.44179922e-01, + 1.53837129e-02, + 4.19288635e-01, + 7.03314543e-01, + 7.03314602e-01, + -6.93806887e-01, + 4.19288635e-01, + 1.53837474e-02, + 2.39293560e-01, + -6.26478612e-01, 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3967, + 3968, + 3968, + 3969, + 3969, + 3970, + 3970, + 3971, + 3971, + 3982, + 3982, + 3983, + 3983, + 3984, + 3984, + 3985, + 3985, + 3986, + 3986, + 3987, + 3987, + 3988, + 3988, + 3989, + 3989, + 3990, + 3990, + 3990, + 3990, + 3991, + 3991, + 3992, + 3992, + 3992, + 3992, + 3993, + 3993, + 3994, + 3994, + 3994, + 3994, + 3995, + 3995, + 3995, + 3995, + 3996, + 3996, + 3996, + 3996, + 3997, + 3997, + 3997, + 3998, + 3998, + 3998, + 3999, + 3999, + 3999, + 4000, + 4000, + 4000, + 4001, + 4001, + 4001, + ], + ] + ) + + return odf_vertices.T, odf_faces.T + + +def odfgrid(res="med") -> np.ndarray[float]: + """Defines the spherical grid defined by quadrisection of the isocahedron. + There are three possible options: 'low', 'med', or 'high', where higher + resolutions cost more computational time. Use for computation of spherical + harmonics from ODFs. + + NOTE: These sampling directions were ported from DKE (original author: + Russell Glenn) + + Parameters + ---------- + res : str; optional; {'low', 'med', 'high'} + Resolution of spherical sampling distribution (Default: 'med') + 'low' defines the spherical grid defined by 3 fold quadrisection of the + isocahedron, or 8 fold tesselation of icosahedron. + 'med' defines the spherical grid defined by 4 fold quadrisection of the + isocahedron, or 16 fold tesselation of icosahedron. + 'high' defines the spherical grid defined by 5 fold quadrisection of the + isocahedron, or 24 fold tesselation of icosahedron. + + Returns + ------- + S : array_like(dtype=float64) + Coordinates for spherical grid in polar coordinates, extends slightly + over one half of the sphere to estimate local maxima on border. First + column is phi, second column is theta. + idx : aarray_like(dtype=uint16) + First column defines points in S over one half of the sphere for + candiate local maxima. Columns 2-7 define the neighbors for the + corresponding point in column 1. + idx8 : array_like(dtype=uint16) + Defines vertices to undersample for sphericalgrid3 - this is useful, for + example, when trying to save a smaler datastructure to load into DSI + studio (all coordinates are savesd in one large file) + area : array_like(dtype=float64) + Defines the area that each vertex encompasses. It is generally + impossible to spread points out isotropically over a sphere, so the area + is slightly different for each point - this can be used to update + computations that occur over the spherical grid, eg GFA. + faces : array_like(dtype=uint16) + Data structure matching S to save and load into DSI Studio for + visualization + separation_angle : float64 + Average separation angle between each peak and its nearest neighbors. + """ + if not isinstance(res, str): + raise Exception("Please specify resolution as a string. Possible " 'choices are "low", "med", or "high"') + if res not in ["low", "med", "high"]: + raise Exception(". Invalid ODF resolution. Possible resolution choices " 'are "low", "med", or "high"') + if res == "low": + S = np.array( + [ + [0.00000000e00, 0.00000000e00], + [1.38393600e-01, 1.25663705e00], + [1.38393597e-01, 2.51327410e00], + [1.38393616e-01, 0.00000000e00], + [1.38393597e-01, -2.51327410e00], + [1.38393600e-01, -1.25663705e00], + [2.25901926e-01, 1.88495556e00], + [2.25901932e-01, 6.28318530e-01], + [2.25901905e-01, 3.14159265e00], + [2.25901932e-01, -6.28318530e-01], + [2.25901926e-01, -1.88495556e00], + [2.76787143e-01, 1.25663706e00], + [2.76787121e-01, 2.51327409e00], + [2.76787127e-01, 0.00000000e00], + [2.76787121e-01, -2.51327409e00], + [2.76787143e-01, -1.25663706e00], + [3.54601278e-01, 1.64908209e00], + [3.54601255e-01, 2.12082903e00], + [3.54601268e-01, 8.64191977e-01], + [3.54601264e-01, 3.92445049e-01], + [3.54601263e-01, 2.90571917e00], + [3.54601263e-01, -2.90571917e00], + [3.54601264e-01, -3.92445049e-01], + [3.54601268e-01, -8.64191977e-01], + [3.54601255e-01, -2.12082903e00], + [3.54601278e-01, -1.64908209e00], + [4.15180810e-01, 1.25663705e00], + [4.15180788e-01, 2.51327411e00], + [4.15180813e-01, 0.00000000e00], + [4.15180788e-01, -2.51327411e00], + [4.15180810e-01, -1.25663705e00], + [4.63647656e-01, 1.88495556e00], + [4.63647654e-01, 6.28318505e-01], + [4.63647636e-01, 3.14159265e00], + [4.63647654e-01, -6.28318505e-01], + [4.63647656e-01, -1.88495556e00], + [4.87695045e-01, 1.54458101e00], + [4.87695025e-01, 9.68693018e-01], + [4.87695022e-01, 2.22533010e00], + [4.87695052e-01, 2.80121808e00], + [4.87695052e-01, -2.80121808e00], + [4.87695022e-01, -2.22533010e00], + [4.87695025e-01, -9.68693018e-01], + [4.87695045e-01, -1.54458101e00], + [4.87695080e-01, 2.87943954e-01], + [4.87695080e-01, -2.87943954e-01], + [5.53574406e-01, 1.25663703e00], + [5.53574398e-01, 2.51327410e00], + [5.53574430e-01, 0.00000000e00], + [5.53574398e-01, -2.51327410e00], + [5.53574406e-01, -1.25663703e00], + [6.09543626e-01, 1.74065707e00], + [6.09543679e-01, 2.02925415e00], + 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111, 161, 86, 130, 151, 95], + [121, 112, 164, 87, 133, 152, 97], + [122, 113, 163, 88, 132, 153, 100], + [123, 114, 165, 89, 135, 155, 98], + [124, 115, 157, 91, 134, 154, 101], + [125, 116, 166, 90, 136, 156, 99], + [126, 109, 159, 84, 128, 149, 93], + [127, 167, 139, 117, 92, 158, 102], + [128, 171, 139, 126, 93, 159, 102], + [129, 137, 172, 118, 94, 103, 160], + [130, 168, 137, 95, 120, 103, 161], + [131, 173, 140, 119, 96, 106, 162], + [132, 140, 176, 100, 122, 106, 163], + [133, 138, 174, 97, 121, 104, 164], + [134, 169, 138, 124, 101, 157, 104], + [135, 170, 141, 123, 98, 165, 105], + [136, 175, 141, 125, 99, 166, 105], + [137, 178, 103, 129, 130, 172, 168], + [138, 180, 104, 133, 134, 174, 169], + [139, 177, 102, 128, 127, 171, 167], + [140, 179, 106, 131, 132, 176, 173], + [141, 181, 105, 136, 135, 175, 170], + [142, 148, 147, 108, 107, 183, 182], + [143, 149, 150, 110, 109, 185, 184], + [144, 152, 151, 112, 111, 187, 186], + [145, 156, 154, 116, 115, 190, 189], + [146, 153, 155, 113, 188, 114, 191], + [147, 142, 158, 182, 107, 192, 117], + [148, 142, 160, 108, 183, 118, 193], + [149, 143, 159, 109, 184, 126, 194], + [150, 143, 162, 110, 185, 119, 195], + [151, 144, 161, 111, 186, 120, 196], + [152, 144, 164, 112, 187, 121, 197], + [153, 146, 163, 113, 188, 122, 198], + [154, 145, 157, 115, 189, 124, 199], + [155, 146, 165, 114, 191, 123, 201], + [156, 145, 166, 190, 116, 200, 125], + [157, 169, 154, 124, 199, 134, 204], + [158, 167, 147, 117, 192, 127, 206], + [159, 149, 171, 126, 194, 202, 128], + [160, 172, 148, 118, 193, 129, 203], + [161, 168, 151, 120, 196, 207, 130], + [162, 173, 150, 119, 195, 131, 208], + [163, 153, 176, 198, 122, 209, 132], + [164, 152, 174, 121, 197, 210, 133], + [165, 170, 155, 123, 201, 135, 211], + [166, 156, 175, 125, 200, 136, 205], + [167, 158, 177, 127, 206, 139, 219], + [168, 161, 178, 130, 207, 137, 217], + [169, 157, 180, 204, 134, 138, 218], + [170, 165, 181, 135, 211, 141, 221], + [171, 177, 159, 128, 202, 139, 212], + [172, 178, 160, 203, 129, 213, 137], + [173, 162, 179, 131, 208, 140, 220], + [174, 180, 164, 133, 210, 215, 138], + [175, 181, 166, 136, 205, 141, 216], + [176, 179, 163, 209, 132, 214, 140], + [177, 171, 139, 212, 167, 219, 177], + [178, 172, 213, 137, 217, 168, 178], + [179, 214, 176, 140, 173, 220, 179], + [180, 215, 174, 138, 218, 169, 180], + [181, 141, 175, 216, 221, 170, 181], + [182, 192, 147, 142, 232, 222, 183], + [183, 193, 148, 228, 142, 222, 182], + [184, 194, 149, 229, 143, 225, 185], + [185, 195, 150, 143, 233, 225, 184], + [186, 196, 151, 144, 234, 223, 187], + [187, 197, 152, 227, 144, 223, 186], + [188, 198, 153, 230, 146, 224, 191], + [189, 199, 154, 236, 145, 226, 190], + [190, 200, 156, 231, 145, 226, 189], + [191, 201, 155, 235, 146, 224, 188], + [192, 182, 158, 232, 206, 147, 238], + [193, 183, 160, 228, 203, 148, 240], + [194, 184, 229, 159, 202, 149, 239], + [195, 185, 162, 233, 208, 150, 241], + [196, 186, 234, 161, 207, 151, 244], + [197, 187, 227, 164, 210, 152, 243], + [198, 188, 230, 163, 209, 153, 242], + [199, 189, 157, 236, 204, 154, 245], + [200, 190, 231, 166, 205, 156, 237], + [201, 191, 235, 165, 211, 155, 246], + [202, 212, 171, 194, 239, 159, 252], + [203, 213, 172, 240, 193, 253, 160], + [204, 169, 218, 245, 199, 259, 157], + [205, 216, 175, 237, 200, 254, 166], + [206, 219, 167, 238, 192, 255, 158], + [207, 217, 168, 196, 244, 161, 261], + [208, 220, 173, 241, 195, 256, 162], + [209, 214, 176, 242, 198, 257, 163], + [210, 215, 174, 243, 197, 258, 164], + [211, 221, 170, 246, 201, 260, 165], + [212, 177, 252, 202, 247, 171, 219], + [213, 178, 253, 203, 249, 172, 217], + [214, 179, 257, 209, 250, 176, 220], + [215, 180, 258, 251, 210, 174, 218], + [216, 181, 254, 205, 248, 175, 221], + [217, 261, 178, 249, 207, 213, 168], + [218, 259, 180, 204, 251, 215, 169], + [219, 255, 177, 247, 206, 212, 167], + [220, 256, 179, 250, 208, 214, 173], + [221, 260, 181, 211, 248, 216, 170], + [222, 182, 183, 228, 232, 262, 267], + [223, 186, 187, 227, 234, 263, 269], + [224, 191, 188, 235, 230, 270, 266], + [225, 184, 185, 229, 233, 265, 268], + [226, 189, 190, 231, 236, 264, 271], + [227, 197, 243, 187, 272, 223, 263], + [228, 193, 240, 183, 275, 222, 262], + [229, 239, 194, 276, 184, 225, 265], + [230, 198, 242, 188, 273, 266, 224], + [231, 237, 200, 190, 274, 226, 264], + [232, 192, 238, 277, 182, 267, 222], + [233, 241, 195, 185, 279, 268, 225], + [234, 196, 244, 278, 186, 223, 269], + [235, 201, 246, 280, 191, 224, 270], + [236, 245, 199, 189, 281, 271, 226], + [237, 274, 231, 254, 205, 296, 200], + [238, 277, 232, 255, 206, 300, 192], + [239, 276, 229, 252, 202, 294, 194], + [240, 275, 253, 228, 203, 292, 193], + [241, 279, 233, 256, 208, 301, 195], + [242, 273, 230, 257, 209, 295, 198], + [243, 272, 227, 258, 210, 293, 197], + [244, 278, 261, 234, 207, 297, 196], + [245, 281, 236, 259, 204, 298, 199], + [246, 280, 260, 235, 211, 299, 201], + [247, 219, 212, 284, 283, 255, 252], + [248, 221, 216, 287, 291, 260, 254], + [249, 217, 213, 288, 282, 261, 253], + [250, 220, 214, 290, 289, 256, 257], + [251, 215, 218, 285, 286, 258, 259], + [252, 294, 212, 239, 284, 202, 247], + [253, 292, 213, 240, 282, 203, 249], + [254, 296, 216, 237, 287, 205, 248], + [255, 219, 300, 283, 238, 206, 247], + [256, 220, 301, 289, 241, 250, 208], + [257, 295, 214, 242, 290, 209, 250], + [258, 293, 215, 243, 285, 251, 210], + [259, 218, 298, 286, 245, 204, 251], + [260, 221, 299, 246, 291, 211, 248], + [261, 217, 297, 288, 244, 249, 207], + [262, 314, 275, 228, 319, 267, 222], + [263, 307, 272, 317, 227, 269, 223], + [264, 316, 274, 231, 354, 226, 271], + [265, 311, 276, 229, 320, 225, 268], + [266, 273, 309, 230, 353, 270, 224], + [267, 277, 338, 319, 232, 262, 222], + [268, 279, 339, 233, 320, 225, 265], + [269, 278, 340, 317, 234, 263, 223], + [270, 280, 341, 353, 235, 224, 266], + [271, 281, 342, 236, 354, 264, 226], + [272, 243, 293, 303, 227, 263, 307], + [273, 242, 295, 230, 305, 266, 309], + [274, 237, 296, 231, 306, 264, 316], + [275, 240, 292, 304, 228, 262, 314], + [276, 239, 294, 302, 229, 265, 311], + [277, 238, 300, 232, 330, 267, 338], + [278, 244, 297, 234, 327, 269, 340], + [279, 241, 301, 331, 233, 268, 339], + [280, 246, 299, 235, 329, 270, 341], + [281, 245, 298, 328, 236, 271, 342], + [282, 312, 253, 321, 249, 292, 288], + [283, 322, 318, 255, 247, 300, 284], + [284, 308, 318, 252, 247, 294, 283], + [285, 310, 343, 258, 251, 293, 286], + [286, 323, 343, 259, 251, 298, 285], + [287, 313, 344, 254, 248, 296, 291], + [288, 324, 321, 261, 249, 297, 282], + [289, 325, 345, 256, 250, 301, 290], + [290, 315, 345, 257, 250, 295, 289], + [291, 326, 344, 260, 248, 299, 287], + [292, 253, 304, 275, 312, 282, 240], + [293, 303, 258, 272, 310, 243, 285], + [294, 252, 302, 276, 308, 284, 239], + [295, 305, 257, 273, 315, 242, 290], + [296, 254, 306, 274, 313, 287, 237], + [297, 327, 261, 324, 278, 288, 244], + [298, 328, 259, 323, 281, 286, 245], + [299, 329, 260, 326, 280, 291, 246], + [300, 330, 255, 322, 277, 283, 238], + [301, 331, 256, 325, 279, 289, 241], + [302, 294, 346, 276, 311, 355, 308], + [303, 293, 347, 307, 272, 310, 361], + [304, 332, 292, 275, 314, 312, 362], + [305, 295, 348, 273, 315, 309, 363], + [306, 296, 349, 274, 316, 313, 364], + [307, 333, 347, 303, 317, 263, 272], + [308, 284, 294, 318, 302, 356, 355], + [309, 334, 348, 353, 305, 266, 273], + [310, 285, 293, 303, 343, 357, 361], + [311, 335, 346, 302, 320, 265, 276], + [312, 282, 292, 304, 321, 358, 362], + [313, 287, 296, 344, 306, 359, 364], + [314, 336, 332, 304, 319, 262, 275], + [315, 290, 295, 345, 305, 360, 363], + [316, 337, 349, 354, 306, 264, 274], + [317, 333, 350, 340, 307, 269, 263], + [318, 283, 284, 322, 308, 356, 365], + [319, 336, 351, 314, 338, 267, 262], + [320, 335, 352, 311, 339, 268, 265], + [321, 288, 282, 324, 312, 358, 366], + ] + ) + - 1 + ) + idx8 = np.array( + [ + [1], + [2], + [3], + [4], + [5], + [6], + [7], + [8], + [9], + [10], + [11], + [12], + [13], + [14], + [15], + [16], + [17], + [18], + [19], + [20], + [21], + [22], + [23], + [24], + [25], + [26], + [27], + [28], + [29], + [30], + [31], + [32], + [33], + [34], + [35], + [36], + [37], + [38], + [39], + [40], + [41], + [42], + [43], + [44], + [45], + [46], + [47], + [48], + [49], + [50], + [51], + [52], + [53], + [54], + [55], + [56], + [57], + [58], + [59], + [60], + [61], + [62], + [63], + [64], + [65], + [66], + [67], + [68], + [69], + [70], + [71], + [72], + [73], + [74], + [75], + [76], + [77], + [78], + [79], + [80], + [81], + [82], + [83], + [84], + [85], + [86], + [87], + [88], + [89], + [90], + [91], + [92], + [93], + [94], + [95], + [96], + [97], + [98], + [99], + [100], + [101], + [102], + [103], + [104], + [105], + [106], + [107], + [108], + [109], + [110], + [111], + [112], + [113], + [114], + [115], + [116], + [117], + [118], + [119], + [120], + [121], + [122], + [123], + [124], + [125], + [126], + [127], + [128], + [129], + [130], + [131], + [132], + [133], + [134], + [135], + [136], + [137], + [138], + [139], + [140], + [141], + [142], + [143], + [144], + [145], + [146], + [147], + [148], + [149], + [150], + [151], + [152], + [153], + [154], + [155], + [156], + [157], + [158], + [159], + [160], + [161], + [162], + [163], + [164], + [165], + [166], + [167], + [168], + [169], + [170], + [171], + [172], + [173], + [174], + [175], + [176], + [177], + [178], + [179], + [180], + [181], + [182], + [183], + [184], + [185], + [186], + [187], + [188], + [189], + [190], + [191], + [192], + [193], + [194], + [195], + [196], + [197], + [198], + [199], + [200], + [201], + [202], + [203], + [204], + [205], + [206], + [207], + [208], + [209], + [210], + [211], + [212], + [213], + [214], + [215], + [216], + [217], + [218], + [219], + [220], + [221], + [222], + [223], + [224], + [225], + [226], + [227], + [228], + [229], + [230], + [231], + [232], + [233], + [234], + [235], + [236], + [237], + [238], + [239], + [240], + [241], + [242], + [243], + [244], + [245], + [246], + [247], + [248], + [249], + [250], + [251], + [252], + [253], + [254], + [255], + [256], + [257], + [258], + [259], + [260], + [261], + [262], + [263], + [264], + [265], + [266], + [267], + [268], + [269], + [270], + [271], + [272], + [273], + [274], + [275], + [276], + [277], + [278], + [279], + [280], + [281], + [282], + [283], + [284], + [285], + [286], + [287], + [288], + [289], + [290], + [291], + [292], + [293], + [294], + [295], + [296], + [297], + [298], + [299], + [300], + [301], + [302], + [303], + [304], + [305], + [306], + [307], + [308], + [309], + [310], + [311], + [312], + [313], + [314], + [315], + [316], + [317], + [318], + [319], + [320], + [321], + ] + ) + area = np.array( + [ + [0.00674716], + [0.01841647], + [0.01841647], + [0.01841648], + [0.01841647], + [0.01841647], + [0.01888554], + [0.01888554], + [0.01888554], + [0.01888554], + [0.01888554], + [0.01875968], + [0.01875968], + [0.01875968], + [0.01875968], + [0.01875968], + [0.01903894], + [0.01903894], + [0.01903894], + [0.01903894], + [0.01903894], + [0.01903894], + [0.01903894], + [0.01903894], + [0.01903894], + [0.01903894], + [0.01864748], + [0.01864748], + [0.01864749], + [0.01864748], + [0.01864748], + [0.02062531], + [0.02062531], + [0.02062531], + [0.02062531], + [0.02062531], + [0.02046645], + [0.02046645], + [0.02046645], + [0.02046645], + [0.02046645], + [0.02046646], + [0.02046645], + [0.02046645], + [0.02046645], + [0.02046645], + [0.01943838], + [0.01943838], + [0.01943838], + [0.01943838], + [0.01943838], + [0.02290303], + [0.02290303], + [0.02290302], + [0.02290303], + [0.02290303], + [0.02290303], + [0.02290303], + [0.02290303], + [0.02290303], + [0.02290303], + [0.02046645], + [0.02046645], + [0.02046645], + [0.02046645], + [0.02046645], + [0.02046645], + [0.02046645], + [0.02046645], + [0.02046645], + [0.02046645], + [0.01864748], + [0.01864747], + [0.01864748], + [0.01864747], + [0.01864747], + [0.02290303], + [0.02290303], + [0.02290303], + [0.02290303], + [0.02290303], + [0.02062532], + [0.02062531], + [0.02062532], + [0.02062531], + [0.02062531], + [0.02062531], + [0.02062531], + [0.02062531], + [0.02062531], + [0.02062531], + [0.01903894], + [0.01903894], + [0.01903893], + [0.01903894], + [0.01903894], + [0.01903893], + [0.01903894], + [0.01903893], + [0.01903893], + [0.01903894], + [0.01875968], + [0.01875968], + [0.01875968], + [0.01875968], + [0.01875968], + [0.02046645], + [0.02046645], + [0.02046645], + [0.02046645], + [0.02046645], + [0.02046645], + [0.02046645], + [0.02046645], + [0.02046645], + [0.02046645], + [0.01903894], + [0.01903893], + [0.01903894], + [0.01903894], + [0.01903893], + [0.01903893], + [0.01903894], + [0.01903893], + [0.01903893], + [0.01903894], + [0.01888555], + [0.01888554], + [0.01888554], + [0.01888555], + [0.01888555], + [0.01888555], + [0.01888555], + [0.01888554], + [0.01888554], + [0.01888555], + [0.01841646], + [0.01841646], + [0.01841646], + [0.01841646], + [0.01841646], + [0.01943838], + 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1242, 1182, 1198], + [1254, 1307, 1303, 1259, 1245, 1186, 1201], + [1255, 1308, 1304, 1260, 1246, 1185, 1200], + [1256, 1309, 1302, 1261, 1244, 1184, 1197], + [1257, 1310, 1305, 1252, 1271, 1172, 1183], + [1258, 1311, 1306, 1253, 1274, 1173, 1182], + [1259, 1312, 1307, 1254, 1273, 1175, 1186], + [1260, 1313, 1308, 1255, 1275, 1176, 1185], + [1261, 1314, 1309, 1256, 1276, 1174, 1184], + [1262, 1212, 1226, 1247, 1272, 1356, 1351], + [1263, 1216, 1222, 1267, 1248, 1357, 1352], + [1264, 1214, 1224, 1268, 1250, 1358, 1354], + [1265, 1213, 1223, 1269, 1249, 1359, 1353], + [1266, 1215, 1225, 1270, 1251, 1360, 1355], + [1267, 1208, 1216, 1263, 1336, 1361, 1357], + [1268, 1204, 1214, 1264, 1334, 1362, 1358], + [1269, 1203, 1213, 1265, 1333, 1363, 1359], + [1270, 1207, 1215, 1266, 1335, 1364, 1360], + [1271, 1315, 1310, 1277, 1257, 1162, 1172], + [1272, 1210, 1212, 1337, 1262, 1365, 1356], + [1273, 1316, 1312, 1278, 1259, 1166, 1175], + [1274, 1317, 1311, 1280, 1258, 1164, 1173], + [1275, 1318, 1313, 1279, 1260, 1168, 1176], + [1276, 1319, 1314, 1281, 1261, 1163, 1174], + [1277, 1315, 1340, 1271, 1321, 1165, 1162], + [1278, 1316, 1341, 1273, 1324, 1170, 1166], + [1279, 1318, 1342, 1275, 1325, 1171, 1168], + [1280, 1320, 1317, 1274, 1323, 1169, 1164], + [1281, 1319, 1343, 1276, 1322, 1167, 1163], + ] + ) + - 1 + ) + idx8 = np.array( + [ + [1], + [12], + [13], + [14], + [15], + [16], + [32], + [33], + [34], + [35], + [36], + [47], + [48], + [49], + [50], + [51], + [82], + [83], + [84], + [85], + [86], + [87], + [88], + [89], + [90], + [91], + [112], + [113], + [114], + [115], + [116], + [137], + [138], + [139], + [140], + [141], + [165], + [158], + [157], + [159], + [161], + [162], + [163], + [164], + [166], + [160], + [197], + [198], + [199], + [200], + [201], + [227], + [228], + [229], + [230], + [234], + [231], + [232], + [235], + [236], + [233], + [260], + [254], + [259], + [253], + [255], + [256], + [257], + [258], + [252], + [261], + [292], + [293], + [294], + [295], + [296], + [327], + [328], + [329], + [330], + [331], + [353], + [347], + [348], + [354], + [349], + [350], + [351], + [355], + [352], + [356], + [372], + [367], + [368], + [369], + [373], + [370], + [376], + [371], + [374], + [375], + [407], + [408], + [409], + [410], + [411], + [458], + [459], + [460], + [464], + [461], + [462], + [463], + [465], + [466], + [457], + [482], + [483], + [484], + [485], + [486], + [487], + [488], + [489], + [490], + [491], + [502], + [503], + [504], + [505], + [506], + [507], + [508], + [509], + [510], + [511], + [552], + [553], + [554], + [555], + [556], + [587], + [588], + [589], + [590], + [591], + [612], + [613], + [614], + [615], + [616], + [617], + [618], + [619], + [620], + [621], + [637], + [638], + [639], + [640], + [641], + [642], + [643], + [644], + [645], + [646], + [657], + [658], + [659], + [660], + [661], + [662], + [663], + [664], + [665], + [666], + [717], + [718], + [719], + [720], + [721], + [763], + [760], + [757], + [764], + [762], + [758], + [761], + [765], + [759], + [766], + [789], + [790], + [787], + [791], + [792], + [788], + [793], + [795], + [796], + [794], + [807], + [808], + [809], + [810], + [811], + [812], + [813], + [814], + [815], + [816], + [842], + [843], + [844], + [845], + [846], + [847], + [848], + [849], + [850], + [851], + [923], + [922], + [925], + [924], + [926], + [937], + [940], + [938], + [942], + [943], + [939], + [944], + [941], + [945], + [946], + [963], + [957], + [958], + [959], + [964], + [961], + [962], + [960], + [965], + [966], + [977], + [978], + [979], + [980], + [981], + [992], + [993], + [994], + [995], + [996], + [997], + [998], + [999], + [1000], + [1001], + [1099], + [1097], + [1103], + [1098], + [1100], + [1101], + [1104], + [1102], + [1105], + [1106], + [1108], + [1111], + [1109], + [1110], + [1107], + [1112], + [1114], + [1113], + [1115], + [1116], + [1144], + [1142], + [1143], + [1147], + [1148], + [1149], + [1145], + [1150], + [1146], + [1151], + [1152], + [1153], + [1154], + [1155], + [1156], + [1157], + [1158], + [1159], + [1160], + [1161], + [1242], + [1243], + [1244], + [1245], + [1246], + [1257], + [1265], + [1259], + [1263], + [1258], + [1264], + [1262], + [1261], + [1266], + [1260], + [1277], + [1333], + [1281], + [1280], + [1334], + ] + ) + area = np.array( + [ + [0.00166744], + [0.00456639], + [0.00456639], + [0.0045664], + [0.00456639], + [0.00456639], + [0.00459535], + [0.00459535], + [0.00459535], + [0.00459535], + [0.00459535], + [0.00458837], + [0.00458837], + [0.00458837], + [0.00458837], + [0.00458837], + [0.00460713], + [0.00460713], + [0.00460713], + [0.00460713], + [0.00460713], + [0.00460713], + [0.00460713], + [0.00460713], + [0.00460713], + [0.00460713], + [0.00458262], + [0.00458262], + [0.00458262], + [0.00458262], + [0.00458262], + [0.0047025], + [0.0047025], + [0.0047025], + [0.0047025], + [0.0047025], + [0.00470162], + [0.00470162], + [0.00470162], + [0.00470162], + [0.00470162], + [0.00470162], + [0.00470162], + 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[5104, 5190, 5134, 1259, 1273, 4888, 4886], + [5105, 5191, 5135, 1260, 1275, 4891, 4889], + [5106, 5192, 5136, 1261, 1276, 4885, 4883], + [5107, 5003, 5002, 1262, 1272, 5276, 5256], + [5108, 5015, 5014, 1263, 1267, 5277, 5257], + [5109, 5009, 5008, 1264, 1268, 5278, 5258], + [5110, 5006, 5005, 1269, 1265, 5279, 5259], + [5111, 5012, 5011, 1270, 1266, 5280, 5260], + [5112, 4989, 4988, 1267, 5242, 5281, 5261], + [5113, 4975, 4974, 5217, 1268, 5282, 5262], + [5114, 4972, 4971, 1269, 5216, 5283, 5263], + [5115, 4986, 4985, 1270, 5241, 5284, 5264], + [5116, 5142, 5137, 1271, 1277, 4844, 4843], + [5117, 4996, 4995, 1272, 5243, 5285, 5265], + [5118, 5143, 5138, 1278, 1273, 4859, 4858], + [5119, 5193, 5139, 1274, 1280, 4852, 4851], + [5120, 5144, 5140, 1279, 1275, 4866, 4865], + [5121, 5146, 5141, 1276, 1281, 4848, 4847], + ] + ) + - 1 + ) + idx8 = np.array( + [ + [1], + [12], + [13], + [14], + [15], + [16], + [32], + [33], + [34], + [35], + [36], + [47], + [48], + [49], + [50], + [51], + [82], + [83], + [84], + [85], + [86], + [87], + [88], + [89], + [90], + [91], + [112], + [113], + [114], + [115], + [116], + [137], + [138], + [139], + [140], + [141], + [165], + [158], + [157], + [159], + [161], + [162], + [163], + [164], + [166], + [160], + [197], + [198], + [199], + [200], + [201], + [227], + [228], + [229], + [230], + [234], + [231], + [232], + [235], + [236], + [233], + [260], + [254], + [259], + [253], + [255], + [256], + [257], + [258], + [252], + [261], + [292], + [293], + [294], + [295], + [296], + [327], + [328], + [329], + [330], + [331], + [353], + [347], + [348], + [354], + [349], + [350], + [351], + [355], + [352], + [356], + [372], + [367], + [368], + [369], + [373], + [370], + [376], + [371], + [374], + [375], + [407], + [408], + [409], + [410], + [411], + [458], + [459], + [460], + [464], + [461], + [462], + [463], + [465], + [466], + [457], + [482], + [483], + [484], + [485], + [486], + [487], + [488], + [489], + [490], + [491], + [502], + [503], + [504], + [505], + [506], + [507], + [508], + [509], + [510], + [511], + [552], + [553], + [554], + [555], + [556], + [587], + [588], + [589], + [590], + [591], + [612], + [613], + [614], + [615], + [616], + [617], + [618], + [619], + [620], + [621], + [637], + [638], + [639], + [640], + [641], + [642], + [643], + [644], + [645], + [646], + [657], + [658], + [659], + [660], + [661], + [662], + [663], + [664], + [665], + [666], + [717], + [718], + [719], + [720], + [721], + [763], + [760], + [757], + [764], + [762], + [758], + [761], + [765], + [759], + [766], + [789], + [790], + [787], + [791], + [792], + [788], + [793], + [795], + [796], + [794], + [807], + [808], + [809], + [810], + [811], + [812], + [813], + [814], + [815], + [816], + [842], + [843], + [844], + [845], + [846], + [847], + [848], + [849], + [850], + [851], + [923], + [922], + [925], + [924], + [926], + [937], + [940], + [938], + [942], + [943], + [939], + [944], + [941], + [945], + [946], + [963], + [957], + [958], + [959], + [964], + [961], + [962], + [960], + [965], + [966], + [977], + [978], + [979], + [980], + [981], + [992], + [993], + [994], + [995], + [996], + [997], + [998], + [999], + [1000], + [1001], + [1099], + [1097], + [1103], + [1098], + [1100], + [1101], + [1104], + [1102], + [1105], + [1106], + [1108], + [1111], + [1109], + [1110], + [1107], + [1112], + [1114], + [1113], + [1115], + [1116], + [1144], + [1142], + [1143], + [1147], + [1148], + [1149], + [1145], + [1150], + [1146], + [1151], + [1152], + [1153], + [1154], + [1155], + [1156], + [1157], + [1158], + [1159], + [1160], + [1161], + [1242], + [1243], + [1244], + [1245], + [1246], + [1257], + [1265], + [1259], + [1263], + [1258], + [1264], + [1262], + [1261], + [1266], + [1260], + [1277], + [5216], + [1281], + [1280], + [5217], + ] + ) + area = np.array( + [ + [0.00041565], + [0.00114062], + [0.00114062], + [0.00114062], + [0.00114062], + [0.00114062], + [0.00114767], + [0.00114767], + [0.00114767], + [0.00114767], + 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+ 2544, + 2545, + 2545, + 2546, + 2546, + 2547, + 2547, + 2547, + 2548, + 2548, + 2548, + 2549, + 2549, + 2549, + 2550, + 2550, + 2550, + 2551, + 2552, + 2552, + 2552, + 2553, + 2554, + 2555, + 2556, + 2557, + 2558, + 2559, + 2560, + 2561, + ], + ] + ) + separation_angle = 2.16 + + return S, idx, np.squeeze(idx8), np.squeeze(area), faces.T, separation_angle diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..fb89f3c9 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,110 @@ +[tool.poetry] +name = "pydesigner" +version = "2.0.0" +description = "A hands-free DTI, DKI, FBI and FBWM preprocessing pipeline" +license = "Proprietary" +authors = ["MUSC Brain Imaging Diffusion Group (BRIDGE) Lab "] +maintainers = [ + "Siddhartha Dhiman ", + "Ryn Thorn ", + "Hunter Moss " +] +readme = "README.rst" +homepage = "https://pypi.org/project/PyDesigner-DWI/" +repository = "https://github.com/muscbridge/PyDesigner" +documentation = "https://pydesigner.readthedocs.io/en/latest/" +keywords = ["python", "mrtrix", "dipy", "diffusion", "imaging", "dti", "dki", "fbi", "fbwm"] +classifiers = [ + "Programming Language :: Python", + "License :: Free To Use But Restricted", + "Operating System :: OS Independent", + "Topic :: Scientific/Engineering :: Bio-Informatics", + "Topic :: Scientific/Engineering :: Information Analysis", + "Topic :: Scientific/Engineering :: Mathematics", + "Topic :: Scientific/Engineering :: Physics", + "Topic :: Scientific/Engineering :: Medical Science Apps.", + "Topic :: Software Development :: Libraries :: Python Modules" +] + +[tool.poetry.dependencies] +python = ">=3.11, <4.0" +numpy = ">=1.20.0, <1.26.0" +scipy = ">=1.5.0, <2.0.0" +joblib = ">=1.2.0, <2.0.0" +tqdm = ">=4.65.0, <5.0.0" +multiprocess = ">=0.70.00, <0.80.00" +nibabel = ">=5.0.0, <6.0.0" +dipy = ">=1.7.0, <2.0.0" +cvxpy = ">1.3.0, <2.0.0" +matplotlib = ">=3.7.0, <4.0.0" + +[tool.poetry.scripts] +pydesigner = "pydesigner.main:main" + +[tool.poetry.group.dev.dependencies] +pytest = ">=7.4.0, <8.0.0" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.black] +line-length = 120 +target-version = ["py311", "py312"] +exclude = "docs/" + +[tool.ruff] +# Enable the pycodestyle (`E`) and Pyflakes (`F`) rules by default. +# Unlike Flake8, Ruff doesn't enable pycodestyle warnings (`W`) or +# McCabe complexity (`C901`) by default. +select = ["E", "F", "I"] +ignore = [] + +# Allow fix for all enabled rules (when `--fix`) is provided. +fixable = ["ALL"] +unfixable = [] + +# Exclude a variety of commonly ignored directories. +exclude = [ + ".bzr", + ".direnv", + ".eggs", + ".git", + ".git-rewrite", + ".hg", + ".mypy_cache", + ".nox", + ".pants.d", + ".pytype", + ".ruff_cache", + ".svn", + ".tox", + ".venv", + "__pypackages__", + "_build", + "buck-out", + "build", + "dist", + "node_modules", + "venv", + "docs", + "setup.py", +] + +# Same as Black +line-length = 120 + +# Allow unused variables when underscore-prefixed. +dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$" + +# Assume Python 3.11 +target-version = "py311" + +[tool.ruff.per-file-ignores] +"pydesigner/fitting/__init__.py" = ["F401"] +"pydesigner/plotting/__init__.py" = ["F401"] +"pydesigner/postprocessing/__init__.py" = ["F401"] +"pydesigner/preprocessing/__init__.py" = ["F401"] +"pydesigner/system/__init__.py" = ["F401"] +"pydesigner/tractography/__init__.py" = ["F401"] +"tests/test_preprocessing_mrinfoutil.py" = ["E501"] diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 00000000..23f17380 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,548 @@ +clarabel==0.6.0 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:2e0b1891d8e507eb0bfc7e0b981584c388b2ab28658056e600997dbbc23f1ab4 \ + --hash=sha256:4f366de79b8bc66bef8dc170987840b672ccab9222e710c09536d78ef47f606d \ + --hash=sha256:5a6be4df9fed98b6f73f034836def913a1ecd52e8b79ca230ddf7cd66ebcdee7 \ + --hash=sha256:73ed408c975a8ea021c3d8262d5d023a18e1ac3f6bb59a37cd69a11dba8f86ed \ + --hash=sha256:9946d3b5db346421b6d839d868e7b1151b590f871344fe95113bfd55b5be2433 \ + --hash=sha256:e737d2818b9ca10e92ccd3fa9ad1a805b039976016415a0c45adef3427d70792 \ + --hash=sha256:edcebbfc14073cd32bfb664317fd2555716c96be8b2a54efdb2b728453582bea \ + --hash=sha256:ef909a393e72981ca10b1d866d9cc7fb6295ece20ae035def764338894961184 +colorama==0.4.6 ; python_version >= "3.11" and python_version < "4.0" and platform_system == "Windows" \ + --hash=sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44 \ + --hash=sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6 +contourpy==1.1.1 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:059c3d2a94b930f4dafe8105bcdc1b21de99b30b51b5bce74c753686de858cb6 \ + --hash=sha256:0683e1ae20dc038075d92e0e0148f09ffcefab120e57f6b4c9c0f477ec171f33 \ + 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--hash=sha256:46e24f5412c948d81736509377e255f6040e94216bf1a9b5ea1eaa9d29f6ec1b \ + --hash=sha256:498e53573e8b94b1caeb9e62d7c2d053c263ebb6aa259c81050766beb50ff8d9 \ + --hash=sha256:4ebf42695f75ee1a952f98ce9775c873e4971732a87334b099dde90b6af6a916 \ + --hash=sha256:4f9147051cb8fdb29a51dc2482d792b3b23e50f8f57e3720ca2e3d438b7adf23 \ + --hash=sha256:549174b0713d49871c6dee90a4b499d3f12f5e5f69641cd23c50a4542e2ca1eb \ + --hash=sha256:560f1d68a33e89c62da5da4077ba98137a5e4d3a271b29f2f195d0fba2adcb6a \ + --hash=sha256:566f0e41df06dfef2431defcfaa155f0acfa1ca4acbf8fd80895b1e7e2ada40e \ + --hash=sha256:56de98a2fb23025882a18b60c7f0ea2d2d70bbbcfcf878f9067234b1c4818442 \ + --hash=sha256:66544f853bfa85c0d07a68f6c648b2ec81dafd30f272565c37ab47a33b220684 \ + --hash=sha256:6c06e4c6e234fcc65435223c7b2a90f286b7f1b2733058bdf1345d218cc59e34 \ + --hash=sha256:6d0a8efc258659edc5299f9ef32d8d81de8b53b45d67bf4bfa3067f31366764d \ + --hash=sha256:70e5a10f8093d228bb2b552beeb318b8928b8a94763ef03b858ef3612b29395d \ + 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--hash=sha256:ca6fab080484e419528e98624fb5c4282148b847e3602dc8dbe0cb0669469887 \ + --hash=sha256:d0c188ae66b772d9d61d43c6030500344c13e3f73a00d1dc241da896f379bb62 \ + --hash=sha256:d6ab42f223e58b7dac1bb0af32194a7b9311065583cc75ff59dcf301afd8a431 \ + --hash=sha256:dfe80c017973e6a4c367e037cb31601044dd55e6bfacd57370674867d15a899b \ + --hash=sha256:e0c02b75acfea5cab07585d25069207e478d12309557f90a61b5a3b4f77f46ce \ + --hash=sha256:e30aaf2b8a2bac57eb7e1650df1b3a4130e8d0c66fc2f861039d507a11760e1b \ + --hash=sha256:eafbef886566dc1047d7b3d4b14db0d5b7deb99638d8e1be4e23a7c7ac59ff0f \ + --hash=sha256:efe0fab26d598e1ec07d72cf03eaeeba8e42b4ecf6b9ccb5a356fde60ff08b85 \ + --hash=sha256:f08e469821a5e4751c97fcd34bcb586bc243c39c2e39321822060ba902eac49e \ + --hash=sha256:f1eaac5257a8f8a047248d60e8f9315c6cff58f7803971170d952555ef6344a7 \ + --hash=sha256:f29fb0b3f1217dfe9362ec55440d0743fe868497359f2cf93293f4b2701b8251 \ + --hash=sha256:f44d78b61740e4e8c71db1cf1fd56d9050a4747681c59ec1094750a658ceb970 \ + --hash=sha256:f6aec19457617ef468ff091669cca01fa7ea557b12b59a7908b9474bb9674cf0 \ + --hash=sha256:f9dc7f933975367251c1b34da882c4f0e0b2e24bb35dc906d2f598a40b72bfc7 +cvxpy==1.4.1 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:03588055b660c043848f5281fe24dbd21f005b34bd8bd3b56906d8ad457c14ae \ + --hash=sha256:0f1482558b785f2db51c76b9c6e91cc85dbd146675b126a799e7d7aab5b15354 \ + --hash=sha256:163caffd7f7f27b6cb151f4ccff283068e063c3673158793048761690cbe4bbe \ + --hash=sha256:2f84687d15d11f9b49ca902f20103a2076efd47773c399cace71237ef53cdadc \ + --hash=sha256:315609ff96adeda4970471b349bc19d44ff4043e15630cf5ac70c029658fe8fc \ + --hash=sha256:372c0825cc6e6bb03ecc550d83718761a1bbdbbb48010fec6f9718581ebd45b5 \ + --hash=sha256:41cfaecf86f85162ca53c7be7377b4143e316204fb9b6a7df8b7a08c826e3806 \ + --hash=sha256:55e08ffb973d62b3fabc675ad464cb6013ea5ce69799f330b33a084a2e580d8d \ + --hash=sha256:57593a852c563ce77bdb075a3e75f23d36d4b3162ebf3199b54cc7fe75088ef2 \ + --hash=sha256:5a3ec054279880a9ebf5fd9d2ac4109acf944b8c45ea8b24e461680e34f3d7b5 \ + --hash=sha256:6b0f17dca85b2a410e73f5d84b28f35f57a20cfec1b0adc9b16f0f8aabff9961 \ + --hash=sha256:71a95aaccf22431fd25a63bcb12d583e1b0baeaeb4fafa3e25857cec03b9e2f3 \ + --hash=sha256:7a46ef722c8d1590875e86360d5781703dfcbd08be73eb98a2fc91a280870064 \ + --hash=sha256:7a9ef34e3c57ff8c844d86f0a3834fb5575af19233947639de0ba577c6122e3e \ + --hash=sha256:9318c4e679b3db470e76e7f23cce362b038bd2d68c4a7326a7c21577ddbdc542 \ + --hash=sha256:9d3bae3bf31e4eb6ed6407f78c6bc3c7bc4b4145cdbbb9ba8c61c3fc541d7067 \ + --hash=sha256:d220a7ee55907da9b55b98e5238d03735118d03b82855ba87b872cb2e6977367 \ + --hash=sha256:d6bfbd535fdaabc5fa55f28de7a1d40f3a803a27fe3fec86e90700fa159a3afc \ + --hash=sha256:db89b55025514bad821b1f1781bed373cbb6aa22fe84420431efd510dbe7f858 \ + --hash=sha256:edf66010e49b64d3f2dd1a7abde8fa3e615ce7a2b3eb185ab744b0beb3a6adb9 \ + --hash=sha256:f24067c54979b09910aea0a03256247121d8a8169538facf087c1923e9e2701a 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--hash=sha256:f1b24176de97ecedf698596086f85da6dad472fe38a4b21cf4b460f87cae2c37 \ + --hash=sha256:fcf4b985a787135b3e83682a4c5b9bce9c6290cfead1d7225c38f34f5ead7187 +setuptools-scm==8.0.4 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:b47844cd2a84b83b3187a5782c71128c28b4c94cad8bfb871da2784a5cb54c4f \ + --hash=sha256:b5f43ff6800669595193fd09891564ee9d1d7dcb196cab4b2506d53a2e1c95c7 +setuptools==68.2.2 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:4ac1475276d2f1c48684874089fefcd83bd7162ddaafb81fac866ba0db282a87 \ + --hash=sha256:b454a35605876da60632df1a60f736524eb73cc47bbc9f3f1ef1b644de74fd2a +six==1.16.0 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926 \ + --hash=sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254 +tqdm==4.66.1 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:d302b3c5b53d47bce91fea46679d9c3c6508cf6332229aa1e7d8653723793386 \ + --hash=sha256:d88e651f9db8d8551a62556d3cff9e3034274ca5d66e93197cf2490e2dcb69c7 +typing-extensions==4.8.0 ; python_version >= "3.11" and python_version < "4.0" \ + --hash=sha256:8f92fc8806f9a6b641eaa5318da32b44d401efaac0f6678c9bc448ba3605faa0 \ + --hash=sha256:df8e4339e9cb77357558cbdbceca33c303714cf861d1eef15e1070055ae8b7ef diff --git a/setup.py b/setup.py deleted file mode 100644 index af8966ec..00000000 --- a/setup.py +++ /dev/null @@ -1,43 +0,0 @@ -from setuptools import setup, find_packages -from designer.info import ( - __packagename__, - __version__, - __author__, - __copyright__, - __credits__, - __execdir__, - __maintainer__, - __email__, - __url__, - __license__, - __description__, - REQUIRES, - PYTHON_REQUIRES, - CLASSIFIERS -) - -with open("README.rst", "r") as fh: - long_description = fh.read() - -setup( - name=__packagename__, - packages=find_packages(), - version=__version__, - author=__author__, - author_email=__email__, - maintainer=__maintainer__, - description=__description__, - long_description=long_description, - long_description_content_type='text/x-rst', - url=__url__, - license=__license__, - include_package_data=True, - classifiers=CLASSIFIERS, - python_requires=PYTHON_REQUIRES, - install_requires=REQUIRES, - entry_points={ - 'console_scripts': [ - 'pydesigner = designer.pydesigner:main', - ] - } -) diff --git a/tests/data/brain_mask.nii b/tests/data/brain_mask.nii new file mode 100644 index 00000000..fbfee071 Binary files /dev/null and b/tests/data/brain_mask.nii differ diff --git a/tests/data/hifi_splenium_4vox no_sidecar.nii b/tests/data/hifi_splenium_4vox no_sidecar.nii new file mode 100644 index 00000000..047688bb Binary files /dev/null and b/tests/data/hifi_splenium_4vox no_sidecar.nii differ diff --git a/tests/data/hifi_splenium_4vox.bval b/tests/data/hifi_splenium_4vox.bval new file mode 100755 index 00000000..8f84e817 --- /dev/null +++ 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+prior_pe_scheme: 0.0,-1.0,0.0,0.027 +prior_pe_scheme: 0.0,-1.0,0.0,0.027 +prior_pe_scheme: 0.0,-1.0,0.0,0.027 +file: . 17608 +END +DrBIBB=BHBBB/B diff --git a/tests/test_fitting_dwipy.py b/tests/test_fitting_dwipy.py new file mode 100644 index 00000000..95430ac8 --- /dev/null +++ b/tests/test_fitting_dwipy.py @@ -0,0 +1,245 @@ +import os +from pathlib import Path + +import numpy as np +import pytest + +from pydesigner.fitting.dwipy import DWI + +TEST_DIR = Path(__file__).parent +PATH_DWI = os.path.join(TEST_DIR, "data", "hifi_splenium_4vox.nii") +PATH_DWI_NOSIDECAR = os.path.join(TEST_DIR, "data", "hifi_splenium_4vox_nosidecar.nii") +PATH_BVEC = os.path.join(TEST_DIR, "data", "hifi_splenium_4vox.bvec") +PATH_BVAL = os.path.join(TEST_DIR, "data", "hifi_splenium_4vox.bval") +PATH_JSON = os.path.join(TEST_DIR, "data", "hifi_splenium_4vox.json") +PATH_MIF = os.path.join(TEST_DIR, "data", "hifi_splenium_mrgrid.mif") +PATH_MASK = os.path.join(TEST_DIR, "data", "brain_mask.nii") + + +def test_dwi_image_path_nonexistent(): + """Tests whether function raises OSError when input is not found""" + with pytest.raises(OSError): + DWI("foo") + + +def test_dwi_bvec_path_invalid(): + """Tests whether function raises TypeError when bvec file input is invalid""" + with pytest.raises(TypeError): + DWI(PATH_DWI, bvecPath=10) + + +def test_dwi_bvec_path_nonexistent(): + """Tests whether function raises OSError when bvec file is not found""" + with pytest.raises(OSError): + DWI(PATH_DWI, bvecPath="foo") + + +def test_dwi_bval_path_invalid(): + """Tests whether function raises TypeError when bval file input is invalid""" + with pytest.raises(TypeError): + DWI(PATH_DWI, bvalPath=10) + + +def test_dwi_bval_path_nonexistent(): + """Tests whether function raises OSError when bval file is not found""" + with pytest.raises(OSError): + DWI(PATH_DWI, bvalPath="foo") + + +def test_dwi_mask_path_nonexistent(capsys): + """Tests whether function raises OSError when mask file is not found""" + DWI(PATH_DWI, mask="foo") + captured = capsys.readouterr() + assert "No brain mask supplied" in captured.out + + +def test_dwi_path_nosidecar(): + """Tests whether function raises OSError when sidecar files are not found""" + with pytest.raises(OSError): + DWI(PATH_DWI_NOSIDECAR) + + +def test_dwi_nthreads_nonint(): + """Tests whether function raises TypeError when nthreads is not an int""" + with pytest.raises(TypeError): + DWI(PATH_DWI, nthreads="foo") + + +def test_dwi_nthreads_negative_int(): + """Tests whether function raises ValueError when nthreads is negative""" + with pytest.raises(ValueError): + DWI(PATH_DWI, nthreads=-5) + + +def test_dwi_paths_valid(capsys): + """Tests whether function responds normally when all paths are valid""" + dwi = DWI(PATH_DWI, bvecPath=PATH_BVEC, bvalPath=PATH_BVAL, mask=PATH_MASK) + captured = capsys.readouterr() + print(captured.out) + assert dwi is not None + assert "Image hifi_splenium_4vox.nii loaded successfully" in captured.out + assert "Processing with" in captured.out + assert "workers..." in captured.out + + +def test_dwi_get_bvals(): + """Tests whether function returns correct bvals""" + dwi = DWI(PATH_DWI, bvecPath=PATH_BVEC, bvalPath=PATH_BVAL, mask=PATH_MASK) + bvals = dwi.getBvals() + assert bvals.dtype == np.float64 + assert len(bvals) == 337 + assert 0 in bvals + assert 1 in bvals + assert 2 in bvals + assert 8 in bvals + + +def test_dwi_get_bvecs(): + """Tests whether function returns correct bvecs""" + dwi = DWI(PATH_DWI, bvecPath=PATH_BVEC, bvalPath=PATH_BVAL, mask=PATH_MASK) + bvecs = dwi.getBvecs() + assert bvecs.dtype == np.float64 + assert bvecs.shape == (337, 3) + + +def test_dwi_max_bval(): + """Tests whether function returns correct max bval""" + dwi = DWI(PATH_DWI, bvecPath=PATH_BVEC, bvalPath=PATH_BVAL, mask=PATH_MASK) + dwi.maxBval() == float + assert dwi.maxBval() == 8 + + +def test_dwi_max_dti_bval(): + """Tests whether function returns correct max DTI bval""" + dwi = DWI(PATH_DWI, bvecPath=PATH_BVEC, bvalPath=PATH_BVAL, mask=PATH_MASK) + dwi.maxDTIBval() == float + assert dwi.maxDTIBval() == 1 + + +def test_dwi_max_dki_bval(): + """Tests whether function returns correct max DKI bval""" + dwi = DWI(PATH_DWI, bvecPath=PATH_BVEC, bvalPath=PATH_BVAL, mask=PATH_MASK) + dwi.maxDKIBval() == float + assert dwi.maxDKIBval() == 2 + + +def test_max_fbi_bval(): + """Tests whether function returns correct max FBI bval""" + dwi = DWI(PATH_DWI, bvecPath=PATH_BVEC, bvalPath=PATH_BVAL, mask=PATH_MASK) + dwi.maxFBIBval() == float + assert dwi.maxFBIBval() == 8 + + +def test_dwi_idx_b0(): + """Tests whether function returns correct index of b0""" + dwi = DWI(PATH_DWI, bvecPath=PATH_BVEC, bvalPath=PATH_BVAL, mask=PATH_MASK) + idx = dwi.idxb0() + assert idx.dtype == bool + assert len(idx) == 337 + assert sum(idx) == 21 + + +def test_dwi_idx_dti(): + """Tests whether function returns correct index of DTI b-values""" + dwi = DWI(PATH_DWI, bvecPath=PATH_BVEC, bvalPath=PATH_BVAL, mask=PATH_MASK) + idx = dwi.idxdti() + assert idx.dtype == bool + assert len(idx) == 337 + assert sum(idx) == 51 + + +def test_dwi_idx_dki(): + """Tests whether function returns correct index of DKI b-values""" + dwi = DWI(PATH_DWI, bvecPath=PATH_BVEC, bvalPath=PATH_BVAL, mask=PATH_MASK) + idx = dwi.idxdki() + assert idx.dtype == bool + assert len(idx) == 337 + assert sum(idx) == 81 + + +def test_idx_fbi(): + """Tests whether function returns correct index of FBI b-values""" + dwi = DWI(PATH_DWI, bvecPath=PATH_BVEC, bvalPath=PATH_BVAL, mask=PATH_MASK) + idx = dwi.idxfbi() + assert idx.dtype == bool + assert len(idx) == 337 + assert sum(idx) == 256 + + +def test_dwi_n_dirs(): + """Tests whether function returns correct number of directions""" + dwi = DWI(PATH_DWI, bvecPath=PATH_BVEC, bvalPath=PATH_BVAL, mask=PATH_MASK) + assert dwi.getndirs() == 30 + + +def test_dwi_tensor_type(): + """Tests whether function returns correct tensor type""" + dwi = DWI(PATH_DWI, bvecPath=PATH_BVEC, bvalPath=PATH_BVAL, mask=PATH_MASK) + tensor = dwi.tensorType() + assert isinstance(tensor, list) + assert "dti" in tensor + assert "dki" in tensor + assert "fbi" in tensor + assert "fbwm" in tensor + + +def test_dwi_is_dti(): + """Tests whether function returns correct boolean for DTI dataset""" + dwi = DWI(PATH_DWI, bvecPath=PATH_BVEC, bvalPath=PATH_BVAL, mask=PATH_MASK) + assert dwi.isdti() is True + + +def test_dwi_is_dki(): + """Tests whether function returns correct boolean for DKI dataset""" + dwi = DWI(PATH_DWI, bvecPath=PATH_BVEC, bvalPath=PATH_BVAL, mask=PATH_MASK) + assert dwi.isdki() is True + + +def test_dwi_is_fbi(): + """Tests whether function returns correct boolean for FBI dataset""" + dwi = DWI(PATH_DWI, bvecPath=PATH_BVEC, bvalPath=PATH_BVAL, mask=PATH_MASK) + assert dwi.isfbi() is True + + +def test_dwi_is_fbwm(): + """Tests whether function returns correct boolean for FBWM dataset""" + dwi = DWI(PATH_DWI, bvecPath=PATH_BVEC, bvalPath=PATH_BVAL, mask=PATH_MASK) + assert dwi.isfbwm() is True + + +def test_dwi_tensor_order_invalid_order(): + """Tests whether function returns correct tensor order""" + dwi = DWI(PATH_DWI, bvecPath=PATH_BVEC, bvalPath=PATH_BVAL, mask=PATH_MASK) + with pytest.raises(ValueError): + cnt, ind = dwi.createTensorOrder(5) + + +def test_dwi_tensor_order_valid_order(): + """Tests whether function returns correct tensor order""" + dwi = DWI(PATH_DWI, bvecPath=PATH_BVEC, bvalPath=PATH_BVAL, mask=PATH_MASK) + cnt, ind = dwi.createTensorOrder(2) + assert len(cnt) == 6 + assert np.shape(ind) == (6, 2) + + +def test_dwi_tensor_order_auto_detect(): + """Tests whether function returns correct tensor order""" + dwi = DWI(PATH_DWI, bvecPath=PATH_BVEC, bvalPath=PATH_BVAL, mask=PATH_MASK) + cnt, ind = dwi.createTensorOrder() + assert len(cnt) == 15 + assert np.shape(ind) == (15, 4) + + +def test_fibonacci_sphere_invalid_samnples(): + """Tests whether function returns correct response from invalid samples type""" + dwi = DWI(PATH_DWI, bvecPath=PATH_BVEC, bvalPath=PATH_BVAL, mask=PATH_MASK) + with pytest.raises(TypeError): + dwi.fibonacciSphere(samples=5.2) + + +def test_fibonacci_sphere(): + """Tests whether function returns correct response""" + dwi = DWI(PATH_DWI, bvecPath=PATH_BVEC, bvalPath=PATH_BVAL, mask=PATH_MASK) + sphere = dwi.fibonacciSphere(samples=5) + assert sphere.dtype == np.float64 + assert np.shape(sphere) == (5, 3) diff --git a/tests/test_preprocessing_mrinfoutil.py b/tests/test_preprocessing_mrinfoutil.py new file mode 100644 index 00000000..b0739bbc --- /dev/null +++ b/tests/test_preprocessing_mrinfoutil.py @@ -0,0 +1,164 @@ +import os +from pathlib import Path + +import pytest + +from pydesigner.preprocessing import mrinfoutil + +TEST_DIR = Path(__file__).parent +PATH_DWI = os.path.join(TEST_DIR, "data", "hifi_splenium_4vox.nii") +PATH_BVEC = os.path.join(TEST_DIR, "data", "hifi_splenium_4vox.bvec") +PATH_BVAL = os.path.join(TEST_DIR, "data", "hifi_splenium_4vox.bval") +PATH_JSON = os.path.join(TEST_DIR, "data", "hifi_splenium_4vox.json") +PATH_MIF = os.path.join(TEST_DIR, "data", "hifi_splenium_mrgrid.mif") + + +def test_getconsole_error_exists(): + """Tests whether function raises OSError when input is not found""" + with pytest.raises(OSError): + mrinfoutil.getconsole("nonexistentfile", "--size") + + +def test_getconsole_error_flag_non_string(): + """Tests whether function raises TypeError when flag is not a string""" + with pytest.raises(TypeError): + mrinfoutil.getconsole(PATH_DWI, 420) + + +def test_getconsole_invalid_flag(): + """Tests whether function raises ValueError when flag is not valid""" + with pytest.raises(OSError): + mrinfoutil.getconsole(PATH_DWI, "--foo") + + +def test_getconsole_valid_flag(): + """Test normal function of getconsole""" + assert mrinfoutil.getconsole(PATH_DWI, "--format") == "NIfTI-1.1" + + +def test_console_dtype(): + """Test whether function returns string type""" + assert isinstance(mrinfoutil.getconsole(PATH_DWI, "--format"), str) + + +def test_format(): + """Test whether function returns correct format""" + assert mrinfoutil.format(PATH_DWI) == "NIfTI-1.1" + + +def test_format_dtype(): + """Test whether function returns string type""" + assert isinstance(mrinfoutil.format(PATH_DWI), str) + + +def test_ndim(): + """Test whether function returns correct number of dimensions""" + assert mrinfoutil.ndim(PATH_DWI) == 4 + + +def test_ndim_dtype(): + """Test whether function returns int type""" + assert isinstance(mrinfoutil.ndim(PATH_DWI), int) + + +def test_size(): + """Test whether function returns correct size""" + assert mrinfoutil.size(PATH_DWI) == (2, 2, 2, 337) + + +def test_size_dtype(): + """Test whether function returns tuple type""" + assert isinstance(mrinfoutil.size(PATH_DWI), tuple) + + +def test_spacing(): + """Test whether function returns correct spacing""" + assert any(t in (3.0, 3.0, 3.0) for t in mrinfoutil.spacing(PATH_DWI)) is True + + +def test_spacing_dtype(): + """Test whether function returns tuple type""" + assert isinstance(mrinfoutil.spacing(PATH_DWI), tuple) + + +def test_datatype(): + """Test whether function returns correct datatype""" + assert mrinfoutil.datatype(PATH_DWI) == "Float32LE" + + +def test_datatype_dtype(): + """Test whether function returns string type""" + assert isinstance(mrinfoutil.datatype(PATH_DWI), str) + + +def test_strides(): + """Test whether function returns correct strides""" + assert mrinfoutil.strides(PATH_DWI) == (1, 2, 3, 4) + + +def test_strides_dtype(): + """Test whether function returns tuple type""" + assert isinstance(mrinfoutil.strides(PATH_DWI), tuple) + + +def test_offset(): + """Test whether function returns correct offset""" + assert mrinfoutil.offset(PATH_DWI) == 0 + + +def test_offset_dtype(): + """Test whether function returns int type""" + assert isinstance(mrinfoutil.offset(PATH_DWI), float) + + +def test_multiplier(): + """Test whether function returns correct multiplier""" + assert mrinfoutil.multiplier(PATH_DWI) == 1.0 + + +def test_multiplier_dtype(): + """Test whether function returns float type""" + assert isinstance(mrinfoutil.multiplier(PATH_DWI), float) + + +def test_transform(): + """Test whether function returns correct transform""" + result = ( + ["0.994345310289832", "2.73896740248844", "08", "-0.106195121171422", "-7.16012287139893"], + ["-0.0163522207646128", "0.988073569252652", "-0.153111814252808", "-15.8305568695068"], + ["0.104928588957842", "0.153982537140822", "0.982486319790561", "-10.7536220550537"], + ["0", "0", "0", "1"], + ) + assert mrinfoutil.transform(PATH_DWI) == result + + +def test_transform_dtype(): + """Test whether function returns tuple type""" + assert isinstance(mrinfoutil.transform(PATH_DWI), tuple) + + +def test_commandhistory_invalid(): + """Test whether function raises OSError when input is invalid""" + with pytest.raises(OSError): + mrinfoutil.commandhistory(PATH_DWI) + + +def test_commandhistory_valid(): + """Test normal function of commandhistory""" + result = [ + "variable", + "mrcat -axis 3 /media/sid/Secondary/Datasets/IAM_HiFI/out/pydesigner/dwi0.mif /media/sid/Secondary/Datasets/IAM_HiFI/out/pydesigner/dwi1.mif /media/sid/Secondary/Datasets/IAM_HiFI/out/pydesigner/dwi2.mif /media/sid/Secondary/Datasets/IAM_HiFI/out/pydesigner/working.mif", + "dwidenoise -noise /media/sid/Secondary/Datasets/IAM_HiFI/out/pydesigner/noisemap.nii -extent 5,5,5 /media/sid/Secondary/Datasets/IAM_HiFI/out/pydesigner/working.mif /media/sid/Secondary/Datasets/IAM_HiFI/out/pydesigner/1_dwi_denoised.mif", + "mrdegibbs /media/sid/Secondary/Datasets/IAM_HiFI/out/pydesigner/working.mif /media/sid/Secondary/Datasets/IAM_HiFI/out/pydesigner/2_dwi_degibbs.mif", + "/usr/local/mrtrix3/bin/dwifslpreproc -se_epi /media/sid/Secondary/Datasets/IAM_HiFI/out/pydesigner/B0_EPI.mif -eddy_options --repol --data_is_shelled -rpe_header -eddyqc_all /media/sid/Secondary/Datasets/IAM_HiFI/out/pydesigner/metrics_qc/eddy /media/sid/Secondary/Datasets/IAM_HiFI/out/pydesigner/working.mif /media/sid/Secondary/Datasets/IAM_HiFI/out/pydesigner/3_dwi_undistorted.mif", + "mrconvert -force -quiet -fslgrad /media/sid/Secondary/Datasets/IAM_HiFI/out/pydesigner/dwism.bvec /media/sid/Secondary/Datasets/IAM_HiFI/out/pydesigner/dwism.bval -json_import /media/sid/Secondary/Datasets/IAM_HiFI/out/pydesigner/dwism.json -strides 1,2,3,4 /media/sid/Secondary/Datasets/IAM_HiFI/out/pydesigner/dwism.nii /media/sid/Secondary/Datasets/IAM_HiFI/out/pydesigner/4_dwi_smoothed.mif", + "mrconvert -force -quiet -fslgrad /media/sid/Secondary/Datasets/IAM_HiFI/out/pydesigner/dwirc.bvec /media/sid/Secondary/Datasets/IAM_HiFI/out/pydesigner/dwirc.bval -json_import /media/sid/Secondary/Datasets/IAM_HiFI/out/pydesigner/dwirc.json -strides 1,2,3,4 /media/sid/Secondary/Datasets/IAM_HiFI/out/pydesigner/dwirc.nii /media/sid/Secondary/Datasets/IAM_HiFI/out/pydesigner/5_dwi_rician.mif", + "mrconvert -fslgrad /media/sid/Secondary/Datasets/IAM_HiFI/out/pydesigner/dwi_preprocessed.bvec /media/sid/Secondary/Datasets/IAM_HiFI/out/pydesigner/dwi_preprocessed.bval -json_import /media/sid/Secondary/Datasets/IAM_HiFI/out/pydesigner/dwi_preprocessed.json /media/sid/Secondary/Datasets/IAM_HiFI/out/pydesigner/dwi_preprocessed.nii /media/sid/Secondary/Datasets/IAM_HiFI/out/pydesigner/working.mif", + "mrgrid /Users/siddhiman/Datasets/IAM_HiFI/out/pydesigner/working.mif regrid -size 1,1,1 /Users/siddhiman/Repos/PyDesigner/tests/data/hifi_splenium_mrgrid.mif", + ] + assert mrinfoutil.commandhistory(PATH_MIF) == result + + +def test_commandhistory_dtype(): + """Test whether function returns list type""" + assert isinstance(mrinfoutil.commandhistory(PATH_MIF), list) diff --git a/tests/test_preprocessing_mrpreproc.py b/tests/test_preprocessing_mrpreproc.py new file mode 100644 index 00000000..2a1d90ca --- /dev/null +++ b/tests/test_preprocessing_mrpreproc.py @@ -0,0 +1,9 @@ +import os +from pathlib import Path + +TEST_DIR = Path(__file__).parent +PATH_DWI = os.path.join(TEST_DIR, "data", "hifi_splenium_4vox.nii") +PATH_BVEC = os.path.join(TEST_DIR, "data", "hifi_splenium_4vox.bvec") +PATH_BVAL = os.path.join(TEST_DIR, "data", "hifi_splenium_4vox.bval") +PATH_JSON = os.path.join(TEST_DIR, "data", "hifi_splenium_4vox.json") +PATH_MIF = os.path.join(TEST_DIR, "data", "hifi_splenium_mrgrid.mif")