Add dvars

.github/workflows/testing.yml

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+name: Build and run tests
+
+on:
+  push:
+  pull_request:
+
+jobs:
+  tests:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@master
+        with:
+          submodules: true
+      - uses: actions/setup-python@v2
+        with:
+          python-version: "3.x"
+      - name: Install test dependencies
+        run: |
+          pip install -r test_requirements.txt
+          pip install pytest-cov
+      - name: Install package
+        run: |
+          pip install flit
+          flit install
+      - name: Run tests
+        run: |
+          pytest . --log-level DEBUG --cov-config=.coveragerc --cov=findoutlie --doctest-modules
+      - name: Collect code coverage data
+        run: |
+          coverage xml
+      - name: Upload coverage to Codecov
+        uses: codecov/codecov-action@v2
+        with:
+          fail_ci_if_error: true

findoutlie/__init__.py

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+""" Init for findoutlie module
+"""
+
+__version__ = '0.1a0'

findoutlie/metrics.py

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+""" Scan outlier metrics
+"""
+
+import numpy as np
+
+
+def dvars(img):
+    """ Calculate dvars metric on Nibabel image `img`
+
+    The dvars calculation between two volumes is defined as the square root of
+    (the sum of the (voxel differences squared) divided by the number of
+    voxels).
+
+    Parameters
+    ----------
+    img : nibabel image
+
+    Returns
+    -------
+    dvals : 1D array
+        One-dimensional array with n-1 elements, where n is the number of
+        volumnes in `img`.
+    """
+    # Hint: remember 'axis='.  For example:
+    # In [2]: arr = np.array([[2, 3, 4], [5, 6, 7]])
+    # In [3]: np.mean(arr, axis=1)
+    # Out[2]: array([3., 6.])
+    #
+    # You may be be able to solve this in four lines, without a loop.
+    # But solve it any way you can.
+
+    data = img.get_fdata()
+    first = data[:,:,:,1:]
+    second = data[:,:,:,:-1]
+
+    diff = first - second
+    dvar_val = np.sqrt(np.mean(diff ** 2, axis = (0,1,2)))
+    #import pdb;
+    #pdb.set_trace()
+    return(dvar_val)

findoutlie/tests/test_dvars.py

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+""" Test dvars implementation
+
+You can run the tests from the root directory (containing ``README.md``) with::
+
+    python3 -m pytest .
+"""
+
+import numpy as np
+
+import nibabel as nib
+
+import nipraxis as npx
+
+from findoutlie.metrics import dvars
+
+
+TEST_FNAME = npx.fetch_file('ds114_sub009_t2r1.nii')
+
+
+def test_dvars():
+    img = nib.load(TEST_FNAME)
+    n_trs = img.shape[-1]
+    n_voxels = np.prod(img.shape[:-1])
+    dvals = dvars(img)
+    assert len(dvals) == n_trs - 1
+    # Calculate the values the long way round
+    data = img.get_fdata()
+    prev_vol = data[..., 0]
+    long_dvals = []
+    for i in range(1, n_trs):
+        this_vol = data[..., i]
+        d = this_vol - prev_vol
+        long_dvals.append(np.sqrt(np.sum(d ** 2) / n_voxels))
+        prev_vol = this_vol
+    assert np.allclose(dvals, long_dvals)

pyproject.toml

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+[build-system]
+requires = ["flit_core >=2,<4"]
+build-backend = "flit_core.buildapi"
+
+[tool.flit.metadata]
+module = "findoutlie"
+author = "The team"
+author-email = "team@email"
+home-page = "https://github.com/nipraxis-spring-2022/diagnostics-example"
+classifiers = ["License :: OSI Approved :: BSD License",
+               "Programming Language :: Python :: 3"]
+description-file="README.md"
+# Check against requirements.txt
+requires = [
+    'nibabel',
+    'numpy',
+    'scipy',
+    'matplotlib',
+]
+requires-python=">=3.6"

pytest.ini

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+[pytest]
+addopts = --ignore-glob=solutions/*

requirements.txt

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+nibabel
+numpy
+scipy
+matplotlib
+nipraxis

test_requirements.txt

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+# Test requirements
+-r requirements.txt
+pytest