[FIX] Ensure build fails in Circle when tests fail

.circle/tests.sh

@@ -19,25 +19,33 @@ case ${CIRCLE_NODE_INDEX} in
   0)
     docker run --rm=false -it -e FSL_COURSE_DATA="/data/examples/nipype-fsl_course_data" -v $HOME/examples:/data/examples:ro -v $WORKDIR:/work -w /work nipype/nipype:py36 /usr/bin/run_pytests.sh && \
     docker run --rm=false -it -e FSL_COURSE_DATA="/data/examples/nipype-fsl_course_data" -v $HOME/examples:/data/examples:ro -v $WORKDIR:/work -w /work nipype/nipype:py27 /usr/bin/run_pytests.sh && \
-    docker run --rm=false -it -v $WORKDIR:/work -w /src/nipype/doc nipype/nipype:py36 /usr/bin/run_builddocs.sh && \
+    docker run --rm=false -it -v $WORKDIR:/work -w /src/nipype/doc --entrypoint=/usr/bin/run_builddocs.sh nipype/nipype:py36 /usr/bin/run_builddocs.sh && \
     docker run --rm=false -it -v $HOME/examples:/data/examples:ro -v $WORKDIR:/work -w /work nipype/nipype:py36 /usr/bin/run_examples.sh test_spm Linear /data/examples/ workflow3d && \
     docker run --rm=false -it -v $HOME/examples:/data/examples:ro -v $WORKDIR:/work -w /work nipype/nipype:py36 /usr/bin/run_examples.sh test_spm Linear /data/examples/ workflow4d
+    exitcode=$?
     ;;
   1)
     docker run --rm=false -it -v $HOME/examples:/data/examples:ro -v $WORKDIR:/work -w /work nipype/nipype:py36 /usr/bin/run_examples.sh fmri_spm_dartel Linear /data/examples/ level1 && \
     docker run --rm=false -it -v $HOME/examples:/data/examples:ro -v $WORKDIR:/work -w /work nipype/nipype:py36 /usr/bin/run_examples.sh fmri_spm_dartel Linear /data/examples/ l2pipeline
+    exitcode=$?
     ;;
   2)
     docker run --rm=false -it -e NIPYPE_NUMBER_OF_CPUS=4 -v $HOME/examples:/data/examples:ro -v $WORKDIR:/work -w /work nipype/nipype:py27 /usr/bin/run_examples.sh fmri_spm_nested MultiProc /data/examples/ level1 && \
     docker run --rm=false -it -e NIPYPE_NUMBER_OF_CPUS=4 -v $HOME/examples:/data/examples:ro -v $WORKDIR:/work -w /work nipype/nipype:py36 /usr/bin/run_examples.sh fmri_spm_nested MultiProc /data/examples/ l2pipeline
+    exitcode=$?
     ;;
   3)
     docker run --rm=false -it -e NIPYPE_NUMBER_OF_CPUS=4 -v $HOME/examples:/data/examples:ro -v $WORKDIR:/work -w /work nipype/nipype:py36 /usr/bin/run_examples.sh fmri_spm_nested MultiProc /data/examples/ level1 && \
     docker run --rm=false -it -v $HOME/examples:/data/examples:ro -v $WORKDIR:/work -w /work nipype/nipype:py36 /usr/bin/run_examples.sh fmri_fsl_feeds Linear /data/examples/ l1pipeline && \
     docker run --rm=false -it -v $HOME/examples:/data/examples:ro -v $WORKDIR:/work -w /work nipype/nipype:py36 /usr/bin/run_examples.sh fmri_fsl_reuse Linear /data/examples/ level1_workflow
+    exitcode=$?
     ;;
 esac
 
 cp ${WORKDIR}/tests/*.xml ${CIRCLE_TEST_REPORTS}/tests/
+
+# Exit with error if any of the tests failed
+if [ "$exitcode" != "0" ]; then exit 1; fi
 codecov -f "coverage*.xml" -s "${WORKDIR}/tests/" -R "${HOME}/nipype/" -F unittests -e CIRCLE_NODE_INDEX
 codecov -f "smoketest*.xml" -s "${WORKDIR}/tests/" -R "${HOME}/nipype/" -F smoketests -e CIRCLE_NODE_INDEX
+

CHANGES

@@ -1,6 +1,7 @@
 Upcoming Release
 =====================
 
+* FIX: Ensure build fails in Circle when tests fail (https://github.com/nipy/nipype/pull/1981)
 * ENH: Add interface to antsAffineInitializer (https://github.com/nipy/nipype/pull/1980)
 * ENH: AFNI motion parameter support for FrameWiseDisplacement (https://github.com/nipy/nipype/pull/1840)
 * ENH: Add ANTs KellyKapowski interface (https://github.com/nipy/nipype/pull/1845)

docker/files/run_pytests.sh

@@ -40,4 +40,6 @@ fi
 # Collect crashfiles
 find ${WORKDIR} -name "crash-*" -maxdepth 1 -exec mv {} ${WORKDIR}/crashfiles/ \;
 
+echo "Unit tests finished with exit code ${exit_code}"
 exit ${exit_code}
+

nipype/algorithms/confounds.py

@@ -330,7 +330,7 @@ class CompCorOutputSpec(TraitedSpec):
         desc='text file containing the noise components')
 
 class CompCor(BaseInterface):
-    '''
+    """
     Interface with core CompCor computation, used in aCompCor and tCompCor
 
     Example
@@ -342,7 +342,8 @@ class CompCor(BaseInterface):
     >>> ccinterface.inputs.num_components = 1
     >>> ccinterface.inputs.use_regress_poly = True
     >>> ccinterface.inputs.regress_poly_degree = 2
-    '''
+
+    """
     input_spec = CompCorInputSpec
     output_spec = CompCorOutputSpec
     references_ = [{'entry': BibTeX("@article{compcor_2007,"
@@ -465,8 +466,11 @@ def _make_headers(self, num_col):
 
 
 class ACompCor(CompCor):
-    ''' Anatomical compcor; for input/output, see CompCor.
-    If the mask provided is an anatomical mask, CompCor == ACompCor '''
+    """
+    Anatomical compcor: for inputs and outputs, see CompCor.
+    When the mask provided is an anatomical mask, then CompCor
+    is equivalent to ACompCor.
+    """
 
     def __init__(self, *args, **kwargs):
         ''' exactly the same as compcor except the header '''
@@ -492,7 +496,7 @@ class TCompCorOutputSpec(CompCorInputSpec):
                          desc="voxels excedding the variance threshold"))
 
 class TCompCor(CompCor):
-    '''
+    """
     Interface for tCompCor. Computes a ROI mask based on variance of voxels.
 
     Example
@@ -505,7 +509,8 @@ class TCompCor(CompCor):
     >>> ccinterface.inputs.use_regress_poly = True
     >>> ccinterface.inputs.regress_poly_degree = 2
     >>> ccinterface.inputs.percentile_threshold = .03
-    '''
+
+    """
 
     input_spec = TCompCorInputSpec
     output_spec = TCompCorOutputSpec
@@ -634,7 +639,8 @@ class TSNROutputSpec(TraitedSpec):
 
 
 class TSNR(BaseInterface):
-    """Computes the time-course SNR for a time series
+    """
+    Computes the time-course SNR for a time series
 
     Typically you want to run this on a realigned time-series.
 
@@ -719,80 +725,6 @@ def _run_interface(self, runtime):
     def _list_outputs(self):
         return self._results
 
-def is_outlier(points, thresh=3.5):
-    """
-    Returns a boolean array with True if points are outliers and False
-    otherwise.
-
-    Parameters:
-    -----------
-        points : An numobservations by numdimensions array of observations
-        thresh : The modified z-score to use as a threshold. Observations with
-            a modified z-score (based on the median absolute deviation) greater
-            than this value will be classified as outliers.
-
-    Returns:
-    --------
-        mask : A numobservations-length boolean array.
-
-    References:
-    ----------
-        Boris Iglewicz and David Hoaglin (1993), "Volume 16: How to Detect and
-        Handle Outliers", The ASQC Basic References in Quality Control:
-        Statistical Techniques, Edward F. Mykytka, Ph.D., Editor.
-    """
-    if len(points.shape) == 1:
-        points = points[:, None]
-    median = np.median(points, axis=0)
-    diff = np.sum((points - median) ** 2, axis=-1)
-    diff = np.sqrt(diff)
-    med_abs_deviation = np.median(diff)
-
-    modified_z_score = 0.6745 * diff / med_abs_deviation
-
-    timepoints_to_discard = 0
-    for i in range(len(modified_z_score)):
-        if modified_z_score[i] <= thresh:
-            break
-        else:
-            timepoints_to_discard += 1
-
-    return timepoints_to_discard
-
-
-def regress_poly(degree, data, remove_mean=True, axis=-1):
-    ''' returns data with degree polynomial regressed out.
-    Be default it is calculated along the last axis (usu. time).
-    If remove_mean is True (default), the data is demeaned (i.e. degree 0).
-    If remove_mean is false, the data is not.
-    '''
-    IFLOG.debug('Performing polynomial regression on data of shape ' + str(data.shape))
-
-    datashape = data.shape
-    timepoints = datashape[axis]
-
-    # Rearrange all voxel-wise time-series in rows
-    data = data.reshape((-1, timepoints))
-
-    # Generate design matrix
-    X = np.ones((timepoints, 1)) # quick way to calc degree 0
-    for i in range(degree):
-        polynomial_func = Legendre.basis(i + 1)
-        value_array = np.linspace(-1, 1, timepoints)
-        X = np.hstack((X, polynomial_func(value_array)[:, np.newaxis]))
-
-    # Calculate coefficients
-    betas = np.linalg.pinv(X).dot(data.T)
-
-    # Estimation
-    if remove_mean:
-        datahat = X.dot(betas).T
-    else: # disregard the first layer of X, which is degree 0
-        datahat = X[:, 1:].dot(betas[1:, ...]).T
-    regressed_data = data - datahat
-
-    # Back to original shape
-    return regressed_data.reshape(datashape)
 
 def compute_dvars(in_file, in_mask, remove_zerovariance=False,
                   intensity_normalization=1000):
@@ -921,3 +853,78 @@ def plot_confound(tseries, figsize, name, units=None,
     ax.set_ylim(ylim)
     ax.set_yticklabels([])
     return fig
+
+def is_outlier(points, thresh=3.5):
+    """
+    Returns a boolean array with True if points are outliers and False
+    otherwise.
+
+    :param nparray points: an numobservations by numdimensions numpy array of observations
+    :param float thresh: the modified z-score to use as a threshold. Observations with
+        a modified z-score (based on the median absolute deviation) greater
+        than this value will be classified as outliers.
+
+    :return: A bolean mask, of size numobservations-length array.
+
+    .. note:: References
+
+        Boris Iglewicz and David Hoaglin (1993), "Volume 16: How to Detect and
+        Handle Outliers", The ASQC Basic References in Quality Control:
+        Statistical Techniques, Edward F. Mykytka, Ph.D., Editor.
+
+    """
+    if len(points.shape) == 1:
+        points = points[:, None]
+    median = np.median(points, axis=0)
+    diff = np.sum((points - median) ** 2, axis=-1)
+    diff = np.sqrt(diff)
+    med_abs_deviation = np.median(diff)
+
+    modified_z_score = 0.6745 * diff / med_abs_deviation
+
+    timepoints_to_discard = 0
+    for i in range(len(modified_z_score)):
+        if modified_z_score[i] <= thresh:
+            break
+        else:
+            timepoints_to_discard += 1
+
+    return timepoints_to_discard
+
+
+def regress_poly(degree, data, remove_mean=True, axis=-1):
+    """
+    Returns data with degree polynomial regressed out.
+
+    :param bool remove_mean: whether or not demean data (i.e. degree 0),
+    :param int axis: numpy array axes along which regression is performed
+
+    """
+    IFLOG.debug('Performing polynomial regression on data of shape ' + str(data.shape))
+
+    datashape = data.shape
+    timepoints = datashape[axis]
+
+    # Rearrange all voxel-wise time-series in rows
+    data = data.reshape((-1, timepoints))
+
+    # Generate design matrix
+    X = np.ones((timepoints, 1)) # quick way to calc degree 0
+    for i in range(degree):
+        polynomial_func = Legendre.basis(i + 1)
+        value_array = np.linspace(-1, 1, timepoints)
+        X = np.hstack((X, polynomial_func(value_array)[:, np.newaxis]))
+
+    # Calculate coefficients
+    betas = np.linalg.pinv(X).dot(data.T)
+
+    # Estimation
+    if remove_mean:
+        datahat = X.dot(betas).T
+    else: # disregard the first layer of X, which is degree 0
+        datahat = X[:, 1:].dot(betas[1:, ...]).T
+    regressed_data = data - datahat
+
+    # Back to original shape
+    return regressed_data.reshape(datashape)
+

nipype/tests/test_nipype.py

@@ -6,4 +6,8 @@ def test_nipype_info():
         get_info()
     except Exception as e:
         exception_not_raised = False
-    assert exception_not_raised
+    assert exception_not_raised
+
+# def test_fail_always():
+#     assert False
+

nipype/workflows/smri/niftyreg/groupwise.py

@@ -1,12 +1,10 @@
 # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
 # vi: set ft=python sts=4 ts=4 sw=4 et:
 
-'''
-This file provides some common registration routines useful for a variety of
-pipelines.
-
-Including linear and non-linear image co-registration
-'''
+"""
+Example of registration workflows using niftyreg, useful for a variety of
+pipelines. Including linear and non-linear image co-registration
+"""
 
 from builtins import str, range
 import nipype.interfaces.utility as niu
@@ -20,7 +18,7 @@ def create_linear_gw_step(name="linear_gw_niftyreg",
                           use_mask=False,
                           verbose=False):
     """
-    Creates a workflow that perform linear co-registration of a set of images
+    Creates a workflow that performs linear co-registration of a set of images
     using RegAladin, producing an average image and a set of affine
     transformation matrices linking each of the floating images to the average.
 
@@ -38,6 +36,7 @@ def create_linear_gw_step(name="linear_gw_niftyreg",
         outputspec.aff_files - The affine transformation files
 
     Optional arguments::
+
         linear_options_hash - An options dictionary containing a list of
                               parameters for RegAladin that take
         the same form as given in the interface (default None)
@@ -51,8 +50,8 @@ def create_linear_gw_step(name="linear_gw_niftyreg",
 
     >>> from nipype.workflows.smri.niftyreg import create_linear_gw_step
     >>> lgw = create_linear_gw_step('my_linear_coreg')  # doctest: +SKIP
-    >>> lgw.inputs.inputspec.in_files = ['file1.nii.gz', 'file2.nii.gz']  \
-# doctest: +SKIP
+    >>> lgw.inputs.inputspec.in_files = [
+    ...     'file1.nii.gz', 'file2.nii.gz']  # doctest: +SKIP
     >>> lgw.inputs.inputspec.ref_file = ['ref.nii.gz']  # doctest: +SKIP
     >>> lgw.run()  # doctest: +SKIP
 
@@ -121,6 +120,7 @@ def create_nonlinear_gw_step(name="nonlinear_gw_niftyreg",
     cpp transformation linking each of the floating images to the average.
 
     Inputs::
+
         inputspec.in_files - The input files to be registered
         inputspec.ref_file - The initial reference image that the input files
                               are registered to
@@ -134,6 +134,7 @@ def create_nonlinear_gw_step(name="nonlinear_gw_niftyreg",
         outputspec.cpp_files - The bspline transformation files
 
     Optional arguments::
+
         nonlinear_options_hash - An options dictionary containing a list of
                                  parameters for RegAladin that take the
         same form as given in the interface (default None)
@@ -144,8 +145,8 @@ def create_nonlinear_gw_step(name="nonlinear_gw_niftyreg",
     -------
     >>> from nipype.workflows.smri.niftyreg import create_nonlinear_gw_step
     >>> nlc = create_nonlinear_gw_step('nonlinear_coreg')  # doctest: +SKIP
-    >>> nlc.inputs.inputspec.in_files = ['file1.nii.gz', 'file2.nii.gz']  \
-# doctest: +SKIP
+    >>> nlc.inputs.inputspec.in_files = [
+    ...     'file1.nii.gz', 'file2.nii.gz']  # doctest: +SKIP
     >>> nlc.inputs.inputspec.ref_file = ['ref.nii.gz']  # doctest: +SKIP
     >>> nlc.run()  # doctest: +SKIP
 
@@ -246,6 +247,7 @@ def create_groupwise_average(name="atlas_creation",
     non-linear components.
 
     Inputs::
+
         inputspec.in_files - The input files to be registered
         inputspec.ref_file - The initial reference image that the input files
                               are registered to
@@ -258,12 +260,14 @@ def create_groupwise_average(name="atlas_creation",
         outputspec.average_image - The average image
         outputspec.cpp_files - The bspline transformation files
 
+
     Example
     -------
+
     >>> from nipype.workflows.smri.niftyreg import create_groupwise_average
     >>> node = create_groupwise_average('groupwise_av')  # doctest: +SKIP
-    >>> node.inputs.inputspec.in_files = ['file1.nii.gz', 'file2.nii.gz']  \
-# doctest: +SKIP
+    >>> node.inputs.inputspec.in_files = [
+    ...     'file1.nii.gz', 'file2.nii.gz']  # doctest: +SKIP
     >>> node.inputs.inputspec.ref_file = ['ref.nii.gz']  # doctest: +SKIP
     >>> node.inputs.inputspec.rmask_file = ['mask.nii.gz']  # doctest: +SKIP
     >>> node.run()  # doctest: +SKIP
@@ -384,3 +388,4 @@ def create_groupwise_average(name="atlas_creation",
                      ])
 
     return workflow
+