Merge pull request #635 from oesteban/fix/0.9.7-hotfixes

[ENH] Hotfixes to 0.9.7

.circleci/check_iqms.sh

@@ -29,4 +29,4 @@ docker run -i -v $SCRATCH:/scratch -w /scratch \
               --entrypoint="dfcheck" \
               ${DOCKER_IMAGE}:${DOCKER_TAG} \
               -i /scratch/out/${MODALITY}.csv \
-              -r /root/src/mriqc/mriqc/data/testdata/${MODALITY}.csv
+              -r /usr/local/src/mriqc/mriqc/data/testdata/${MODALITY}.csv

.circleci/participant.sh

@@ -17,7 +17,7 @@ fi
 exit_docs=0
 if [ "$CIRCLE_NODE_INDEX" == "1" ]; then
     mkdir -p ${SCRATCH}/docs
-    docker run -i --rm=false -v ${SCRATCH}:/scratch -w /root/src/mriqc/docs \
+    docker run -i --rm=false -v ${SCRATCH}:/scratch -w /usr/local/src/mriqc/docs \
                --entrypoint=sphinx-build poldracklab/mriqc:latest -T -E -W -D language=en -b html source/ /scratch/docs 2>&1 \
                | tee ${SCRATCH}/docs/builddocs.log
     exit_docs=$( grep -qi "build succeeded." ${SCRATCH}/docs/builddocs.log; echo $? )
@@ -46,7 +46,7 @@ case $CIRCLE_NODE_INDEX in
                    --entrypoint="py.test"  ${DOCKER_IMAGE}:${DOCKER_TAG} \
                    --ignore=src/ \
                    --junitxml=/scratch/tests.xml \
-                   /root/src/mriqc && \
+                   /usr/local/src/mriqc && \
         ${DOCKER_RUN} -m bold --testing --ica
         exit $(( $? + $exit_docs ))
         ;;

Dockerfile

@@ -101,9 +101,9 @@ ENV MKL_NUM_THREADS=1 \
     OMP_NUM_THREADS=1
 
 # Installing dev requirements (packages that are not in pypi)
+WORKDIR /usr/local/src/mriqc
 COPY requirements.txt requirements.txt
-RUN pip uninstall -y scikit-learn && \
-    pip install -r requirements.txt && \
+RUN pip install -r requirements.txt && \
     rm -rf ~/.cache/pip
 
 # Precaching atlases after niworkflows is available
@@ -112,10 +112,9 @@ ENV CRN_SHARED_DATA /niworkflows_data
 RUN python -c 'from niworkflows.data.getters import get_mni_icbm152_nlin_asym_09c; get_mni_icbm152_nlin_asym_09c()'
 
 # Installing MRIQC
-COPY . /root/src/mriqc
+COPY . /usr/local/src/mriqc
 ARG VERSION
-RUN cd /root/src/mriqc && \
-    echo "${VERSION}" > mriqc/VERSION && \
+RUN echo "${VERSION}" > mriqc/VERSION && \
     pip install .[all] && \
     rm -rf ~/.cache/pip
 

mriqc/__about__.py

@@ -54,6 +54,7 @@
     'numpy>=1.12.0',
     'niworkflows>=0.1.4',
     'pybids>=0.3.0',
+    'scikit-learn>=0.19.0',
     'future',
     'scipy',
     'six',
@@ -72,11 +73,8 @@
     'versioneer',
 ]
 
-LINKS_REQUIRES = [
-    'https://github.com/scikit-learn/scikit-learn/tarball/master#scikit-learn-0.19.0-dev',
-    'https://github.com/poldracklab/niworkflows.git'
-    '@0a71b0f16acec10520b6eb824649d19519e65b59#egg=niworkflows-0.1.5-dev',
-]
+LINKS_REQUIRES = []
+
 
 TESTS_REQUIRES = [
     'mock',
@@ -89,7 +87,7 @@
     'tests': TESTS_REQUIRES,
     'duecredit': ['duecredit'],
     'notebooks': ['ipython', 'jupyter'],
-    'classifier': ['scikit-learn', 'xgboost']
+    'classifier': ['xgboost']
 }
 
 # Enable a handle to install all extra dependencies at once

mriqc/qc/anatomical.py

@@ -196,10 +196,11 @@
 from builtins import zip, range, str, bytes  # pylint: disable=W0622
 from six import string_types
 
-DIETRICH_FACTOR = 1.0 / sqrt(2/(4 - pi))
+DIETRICH_FACTOR = 1.0 / sqrt(2 / (4 - pi))
 FSL_FAST_LABELS = {'csf': 1, 'gm': 2, 'wm': 3, 'bg': 0}
 PY3 = version_info[0] > 2
 
+
 def snr(mu_fg, sigma_fg, n):
     r"""
     Calculate the :abbr:`SNR (Signal-to-Noise Ratio)`.
@@ -213,16 +214,16 @@ def snr(mu_fg, sigma_fg, n):
     where :math:`\mu_F` is the mean intensity of the foreground and
     :math:`\sigma_F` is the standard deviation of the same region.
 
-    :param numpy.ndarray img: input data
-    :param numpy.ndarray fgmask: input foreground mask or segmentation
-    :param bool erode: erode masks before computations.
-    :param str fglabel: foreground label in the segmentation data.
+    :param float mu_fg: mean of foreground.
+    :param float sigma_fg: standard deviation of foreground.
+    :param int n: number of voxels in foreground mask.
 
-    :return: the computed SNR for the foreground segmentation
+    :return: the computed SNR
 
     """
     return float(mu_fg / (sigma_fg * sqrt(n / (n - 1))))
 
+
 def snr_dietrich(mu_fg, sigma_air):
     r"""
     Calculate the :abbr:`SNR (Signal-to-Noise Ratio)`.
@@ -237,11 +238,8 @@ def snr_dietrich(mu_fg, sigma_air):
         \text{SNR} = \frac{\mu_F}{\sqrt{\frac{2}{4-\pi}}\,\sigma_\text{air}}.
 
 
-    :param numpy.ndarray img: input data
-    :param numpy.ndarray smask: input foreground mask or segmentation
-    :param numpy.ndarray airmask: input background mask or segmentation
-    :param bool erode: erode masks before computations.
-    :param str fglabel: foreground label in the segmentation data.
+    :param float mu_fg: mean of foreground.
+    :param float sigma_air: standard deviation of the air surrounding the head ("hat" mask).
 
     :return: the computed SNR for the foreground segmentation
 
@@ -253,6 +251,7 @@ def snr_dietrich(mu_fg, sigma_air):
 
     return float(DIETRICH_FACTOR * mu_fg / sigma_air)
 
+
 def cnr(mu_wm, mu_gm, sigma_air):
     r"""
     Calculate the :abbr:`CNR (Contrast-to-Noise Ratio)` [Magnota2006]_.
@@ -267,8 +266,10 @@ def cnr(mu_wm, mu_gm, sigma_air):
     the air (background) mask.
 
 
-    :param numpy.ndarray img: input data
-    :param numpy.ndarray seg: input segmentation
+    :param float mu_wm: mean of signal within white-matter mask.
+    :param float mu_gm: mean of signal within gray-matter mask.
+    :param float sigma_air: standard deviation of the air surrounding the head ("hat" mask).
+
     :return: the computed CNR
 
     """
@@ -287,9 +288,11 @@ def cjv(mu_wm, mu_gm, sigma_wm, sigma_gm):
 
         \text{CJV} = \frac{\sigma_\text{WM} + \sigma_\text{GM}}{|\mu_\text{WM} - \mu_\text{GM}|}.
 
-    :param numpy.ndarray img: the input data
-    :param numpy.ndarray wmmask: the white matter mask
-    :param numpy.ndarray gmmask: the gray matter mask
+    :param float mu_wm: mean of signal within white-matter mask.
+    :param float mu_gm: mean of signal within gray-matter mask.
+    :param float sigma_wm: standard deviation of signal within white-matter mask.
+    :param float sigma_gm: standard deviation of signal within gray-matter mask.
+
     :return: the computed CJV
 
 
@@ -309,7 +312,9 @@ def fber(img, headmask, rotmask=None):
 
 
     :param numpy.ndarray img: input data
-    :param numpy.ndarray seg: input segmentation
+    :param numpy.ndarray headmask: a mask of the head (including skull, skin, etc.)
+    :param numpy.ndarray rotmask: a mask of empty voxels inserted after a rotation of
+      data
 
     """
 
@@ -325,7 +330,6 @@ def fber(img, headmask, rotmask=None):
     return float(fg_mu / bg_mu)
 
 
-
 def efc(img, framemask=None):
     r"""
     Calculate the :abbr:`EFC (Entropy Focus Criterion)` [Atkinson1997]_.
@@ -348,6 +352,8 @@ def efc(img, framemask=None):
         \text{EFC} = \left( \frac{N}{\sqrt{N}} \, \log{\sqrt{N}^{-1}} \right) \text{E}
 
     :param numpy.ndarray img: input data
+    :param numpy.ndarray framemask: a mask of empty voxels inserted after a rotation of
+      data
 
     """
 

mriqc/viz/fmriplots.py

@@ -17,7 +17,11 @@
 from .utils import DINA4_LANDSCAPE
 sns.set_style("whitegrid")
 
+
 class fMRIPlot(object):
+    """
+    Generates the fMRI Summary Plot
+    """
 
     def __init__(self, func, mask, seg=None, tr=None,
                  title=None, figsize=DINA4_LANDSCAPE):
@@ -124,8 +128,8 @@ def fmricarpetplot(func_data, segmentation, outer_gs, tr=None, nskip=0):
     ax0.set_xticks([])
 
     colors1 = plt.cm.summer(np.linspace(0., 1., len(cort_gm)))
-    colors2 = plt.cm.autumn(np.linspace(0., 1., len(deep_gm) + 1))[::-1,...]
-    colors3 = plt.cm.winter(np.linspace(0., .5, len(wm_csf)))[::-1,...]
+    colors2 = plt.cm.autumn(np.linspace(0., 1., len(deep_gm) + 1))[::-1, ...]
+    colors3 = plt.cm.winter(np.linspace(0., .5, len(wm_csf)))[::-1, ...]
     cmap = LinearSegmentedColormap.from_list('my_colormap', np.vstack((colors1, colors2, colors3)))
 
     ax0.imshow(newsegm[order, np.newaxis], interpolation='nearest', aspect='auto',
@@ -151,9 +155,8 @@ def fmricarpetplot(func_data, segmentation, outer_gs, tr=None, nskip=0):
     ax1.set_yticklabels([])
 
     # Set 10 frame markers in X axis
-    interval = int(data.shape[-1] + 1) // 10
-    xticks = list(
-        range(0, data.shape[-1])[::interval]) + [data.shape[-1]-1]
+    interval = max((int(data.shape[-1] + 1) // 10, int(data.shape[-1] + 1) // 5, 1))
+    xticks = list(range(0, data.shape[-1])[::interval])
     ax1.set_xticks(xticks)
 
     if notr:
@@ -195,7 +198,7 @@ def spikesplot(ts_z, outer_gs=None, tr=None, zscored=True, spike_thresh=6., titl
     if ax is None:
         ax = plt.gca()
 
-    if not outer_gs is None:
+    if outer_gs is not None:
         gs = mgs.GridSpecFromSubplotSpec(1, 2, subplot_spec=outer_gs,
                                          width_ratios=[1, 100], wspace=0.0)
         ax = plt.subplot(gs[1])
@@ -279,7 +282,6 @@ def spikesplot(ts_z, outer_gs=None, tr=None, zscored=True, spike_thresh=6., titl
         ax.plot((0, ntsteps - 1), (yticks[0], yticks[0]), 'k:')
         ax.plot((0, ntsteps - 1), (yticks[-1], yticks[-1]), 'k:')
 
-
     for side in ["top", "right"]:
         ax.spines[side].set_color('none')
         ax.spines[side].set_visible(False)
@@ -343,8 +345,8 @@ def confoundplot(tseries, gs_ts, gs_dist=None, name=None, normalize=True,
     ax_ts.set_xlim((0, ntsteps - 1))
 
     # Set 10 frame markers in X axis
-    interval = ntsteps // 10
-    xticks = list(range(0, ntsteps)[::interval]) + [ntsteps - 1]
+    interval = max((ntsteps // 10, ntsteps // 5, 1))
+    xticks = list(range(0, ntsteps)[::interval])
     ax_ts.set_xticks(xticks)
 
     if not hide_x:
@@ -358,9 +360,9 @@ def confoundplot(tseries, gs_ts, gs_dist=None, name=None, normalize=True,
         ax_ts.set_xticklabels([])
 
     no_scale = notr or not normalize
-    if not name is None:
+    if name is not None:
         var_label = name
-        if not units is None:
+        if units is not None:
             var_label += (' [{}]' if no_scale else ' [{}/s]').format(units)
         ax_ts.set_ylabel(var_label)
 

requirements.txt

@@ -1,3 +1,3 @@
 numpy>=1.12.0
-git+https://github.com/poldracklab/niworkflows.git@0a71b0f16acec10520b6eb824649d19519e65b59#egg=niworkflows-0.1.5-dev
-git+https://github.com/scikit-learn/scikit-learn.git@master#egg=scikit-learn-0.19.0-dev
+niworkflows>=0.1.6
+scikit-learn>=0.19