Merge branch 'master' of https://github.com/nistats/nistats into improve-reporting

* 'master' of https://github.com/nistats/nistats:
  Replace numpy's deprecated dec with pytest's skipif (nilearn#423)
  Replace Nosetest library calls with Pytest in code (nilearn#422)
  Replace Nosetests with Pytest as test runner (nilearn#419)
  Removed Appveyor config (Azure Pipelines does the windows tests now) (nilearn#420)
  All-resolution inference (nilearn#414)
  Fix: Missing variables in localizerd dataset are no longer None, but n/a (nilearn#415)

# Conflicts:
#	nistats/tests/test_thresholding.py
#	nistats/thresholding.py

Author: kchawla-pi

.travis.yml

@@ -29,7 +29,8 @@ matrix:
     - env: DISTRIB="conda" PYTHON_VERSION="2.7"
            NUMPY_VERSION="1.11.2" SCIPY_VERSION="0.19"
            SCIKIT_LEARN_VERSION="0.18" MATPLOTLIB_VERSION="1.5.1"
-           NIBABEL_VERSION="2.0.2" PANDAS_VERSION="*" COVERAGE="true"
+           NIBABEL_VERSION="2.0.2" PANDAS_VERSION="*"
+           COVERAGE="true"
     # Python 3.4 with intermediary versions
     - env: DISTRIB="conda" PYTHON_VERSION="3.4"
            NUMPY_VERSION="1.11.0" SCIPY_VERSION="0.17"
@@ -47,6 +48,7 @@ matrix:
            PANDAS_VERSION="*" BOTO3=true
 install:
     - source continuous_integration/install.sh
+    - pip install pytest>3.9 pytest-cov
 
 before_script:
     - make clean

Makefile

@@ -4,7 +4,7 @@
 
 PYTHON ?= python
 CYTHON ?= cython
-NOSETESTS ?= nosetests
+TESTRUNNER ?= nosetests
 CTAGS ?= ctags
 
 all: clean test doc-plot pdf
@@ -29,16 +29,13 @@ inplace:
 	$(PYTHON) setup.py build_ext -i
 
 test-code:
-	$(NOSETESTS) -s nistats
+	python -m pytest --pyargs nistats --cov=nistats
 test-doc:
-	$(NOSETESTS) -s --with-doctest --doctest-tests --doctest-extension=rst \
-	--doctest-extension=inc --doctest-fixtures=_fixture doc/ \
-	
+	pytest --doctest-glob='*.rst' `find doc/ -name '*.rst'`
 
 test-coverage:
 	rm -rf coverage .coverage
-	$(NOSETESTS) -s --with-coverage --cover-html --cover-html-dir=coverage \
-	--cover-package=nistats nistats
+ 	pytest --pyargs nistats --showlocals --cov=nistats --cov-report=html:coverage
 
 test: test-code test-doc
 

appveyor.yml

@@ -22,7 +22,7 @@ create_new_venv() {
     deactivate
     virtualenv --system-site-packages testvenv
     source testvenv/bin/activate
-    pip install nose
+    pip install nose pytest==3.9.1 pytest-cov
 }
 
 print_conda_requirements() {
@@ -112,7 +112,6 @@ elif [[ "$DISTRIB" == "conda" ]]; then
     fi
     pip install nilearn
 
-
 else
     echo "Unrecognized distribution ($DISTRIB); cannot setup travis environment."
     exit 1

continuous_integration/install.sh

@@ -12,7 +12,7 @@ New
 * Use :func:`nistats.reporting.make_glm_report` to easily generate HTML reports from fitted first and second level models and contrasts.
 * New dataset fetcher, :func:`nistats.datasets.fetch_language_localizer_demo_dataset` , BIDS 1.2 compatible.
 * New example showcasing the use of a GLM to get beta maps for decoding experiments (aka beta-regression).
-  See it `here :ref:sphx_glr_auto_examples_05_plot_haxby_block_classification.py`_
+* Addition of all-resolution inference, ie statistically grounded of true positive in given clusters, in :func:`nistats.thresholding.map_threshold`
 
 Changes
 -------
@@ -30,7 +30,7 @@ Fixes
 * Explicit method for fixed effects to support image-based summary
   statistics approach.
 * FIR delays are now integers.
-  
+
 Contributors
 ------------
 

doc/whats_new.rst

@@ -0,0 +1,71 @@
+"""
+Second-level fMRI model: true positive proportion in clusters
+=============================================================
+
+This script showcases the so-called "All resolution inference"
+procedure, in which the proportion of true discoveries in arbitrary
+clusters is estimated.  The clusters can be defined from the input
+image, i.e. in a circular way, as the error control accounts for
+arbitrary cluster selection.
+
+Rosenblatt JD, Finos L, Weeda WD, Solari A, Goeman JJ. All-Resolutions
+Inference for brain imaging. Neuroimage. 2018 Nov 1;181:786-796. doi:
+10.1016/j.neuroimage.2018.07.060
+
+"""
+
+#########################################################################
+# Fetch dataset
+# --------------
+# We download a list of left vs right button press contrasts from a
+# localizer dataset. Note that we fetch individual t-maps that represent the
+# BOLD activity estimate divided by the uncertainty about this estimate.
+from nilearn.datasets import fetch_localizer_contrasts
+n_subjects = 16
+data = fetch_localizer_contrasts(["left vs right button press"], n_subjects,
+                                 get_tmaps=True)
+
+from nilearn import plotting
+import matplotlib.pyplot as plt
+
+############################################################################
+# Estimate second level model
+# ---------------------------
+# We define the input maps and the design matrix for the second level model
+# and fit it.
+import pandas as pd
+second_level_input = data['cmaps']
+design_matrix = pd.DataFrame([1] * len(second_level_input),
+                             columns=['intercept'])
+
+############################################################################
+# Model specification and fit
+from nistats.second_level_model import SecondLevelModel
+second_level_model = SecondLevelModel(smoothing_fwhm=8.0)
+second_level_model = second_level_model.fit(second_level_input,
+                                            design_matrix=design_matrix)
+
+##########################################################################
+# To estimate the contrast is very simple. We can just provide the column
+# name of the design matrix.
+z_map = second_level_model.compute_contrast(output_type='z_score')
+
+###########################################################################
+# We threshold the second level contrast at uncorrected p < 0.001 and plot
+from scipy.stats import norm
+p_val = 0.001
+p001_uncorrected = norm.isf(p_val)
+
+from nistats.thresholding import cluster_level_inference
+proportion_true_discoveries_img = cluster_level_inference(z_map, threshold=[3, 4, 5], alpha=.05)
+
+plotting.plot_stat_map(
+    proportion_true_discoveries_img, threshold=0., colorbar=True, display_mode='z',
+    title='group left-right button press, proportion true positives', vmax=1)
+
+plotting.plot_stat_map(
+    z_map, threshold=p001_uncorrected, colorbar=True, display_mode='z',
+    title='group left-right button press (uncorrected p < 0.001)')
+
+
+plotting.show()

examples/03_second_level_models/plot_proportion_activated_voxels.py

@@ -34,7 +34,7 @@
 ##############################################################################
 # Quality check / Remove subjects with bad tested variate
 import numpy as np
-mask_quality_check = np.where(tested_var != b'None')[0]
+mask_quality_check = np.where(tested_var != b'n/a')[0]
 n_samples = mask_quality_check.size
 contrast_map_filenames = [localizer_dataset.cmaps[i]
                           for i in mask_quality_check]

examples/03_second_level_models/plot_second_level_association_test.py

@@ -0,0 +1,42 @@
+from distutils.version import LooseVersion
+
+import numpy as np
+import pytest
+
+from _pytest.doctest import DoctestItem
+from nilearn.datasets import func, utils
+from nilearn.datasets.tests import test_utils as tst
+
+try:
+    import matplotlib
+except ImportError:
+    collect_ignore = ['reporting',
+                      'tests/test_glm_reporter.py',
+                      'tests/test_reporting.py',
+                      'tests/test_sphinx_report.py',
+                      ]
+else:
+    matplotlib  # Prevents flake8 erring due to unused entities.
+
+
+def pytest_collection_modifyitems(items):
+    # numpy changed the str/repr formatting of numpy arrays in 1.14. We want to
+    # run doctests only for numpy >= 1.14.Adapted from scikit-learn
+    if LooseVersion(np.__version__) < LooseVersion('1.14'):
+        reason = 'doctests are only run for numpy >= 1.14'
+        skip_doctests = True
+    else:
+        skip_doctests = False
+
+    if skip_doctests:
+        skip_marker = pytest.mark.skip(reason=reason)
+        for item in items:
+            if isinstance(item, DoctestItem):
+                item.add_marker(skip_marker)
+
+
+@pytest.fixture()
+def request_mocker():
+    tst.setup_mock(utils, func)
+    yield
+    tst.teardown_mock(utils, func)

nistats/conftest.py

@@ -494,10 +494,8 @@ def compute_regressor(exp_condition, hrf_model, frame_times, con_id='cond',
     # fir_delays should be integers
     if fir_delays is not None:
         fir_delays = [int(x) for x in fir_delays]
-
-    # oversampling should be an integer
     oversampling = int(oversampling)
-        
+
     # this is the average tr in this session, not necessarily the true tr
     tr = float(frame_times.max()) / (np.size(frame_times) - 1)
 

nistats/hemodynamic_models.py

@@ -20,7 +20,7 @@
 from ._get_clusters_table import get_clusters_table
 from ._plot_matrices import plot_contrast_matrix, plot_design_matrix
 from .glm_reporter import make_glm_report
-from.sphinx_report import _ReportScraper
+from .sphinx_report import _ReportScraper
 
 __all__ = [compare_niimgs,
            get_clusters_table,

nistats/reporting/__init__.py

@@ -1,10 +1,7 @@
 import pandas as pd
+import pytest
 
 from nibabel.tmpdirs import InTemporaryDirectory
-from nose.tools import (assert_raises,
-                        assert_true,
-                        )
-
 from nistats.utils import _check_events_file_uses_tab_separators
 
 
@@ -34,11 +31,11 @@ def _create_test_file(temp_csv, test_data, delimiter):
 
 def _run_test_for_invalid_separator(filepath, delimiter_name):
     if delimiter_name not in ('tab', 'comma'):
-        with assert_raises(ValueError):
+        with pytest.raises(ValueError):
             _check_events_file_uses_tab_separators(events_files=filepath)
     else:
         result = _check_events_file_uses_tab_separators(events_files=filepath)
-        assert_true(result is None)
+        assert result is None
 
 
 def test_for_invalid_separator():
@@ -59,7 +56,7 @@ def test_with_2D_dataframe():
     events_pandas_dataframe = pd.DataFrame(data_for_pandas_dataframe)
     result = _check_events_file_uses_tab_separators(
             events_files=events_pandas_dataframe)
-    assert_true(result is None)
+    assert result is None
 
 
 def test_with_1D_dataframe():
@@ -68,19 +65,19 @@ def test_with_1D_dataframe():
         events_pandas_dataframe = pd.DataFrame(dataframe_)
         result = _check_events_file_uses_tab_separators(
                 events_files=events_pandas_dataframe)
-        assert_true(result is None)
+        assert result is None
 
 def test_for_invalid_filepath():
     filepath = 'junk_file_path.csv'
     result = _check_events_file_uses_tab_separators(events_files=filepath)
-    assert_true(result is None)
+    assert result is None
 
 
 def test_for_pandas_dataframe():
     events_pandas_dataframe = pd.DataFrame([['a', 'b', 'c'], [0, 1, 2]])
     result = _check_events_file_uses_tab_separators(
             events_files=events_pandas_dataframe)
-    assert_true(result is None)
+    assert result is None
 
 
 def test_binary_opening_an_image():
@@ -91,7 +88,7 @@ def test_binary_opening_an_image():
         temp_img_file = 'temp_img.gif'
         with open(temp_img_file, 'wb') as temp_img_obj:
             temp_img_obj.write(img_data)
-        with assert_raises(ValueError):
+        with pytest.raises(ValueError):
             _check_events_file_uses_tab_separators(
                     events_files=temp_img_file)
 
@@ -102,7 +99,7 @@ def test_binary_bytearray_of_ints_data():
         temp_bin_file = 'temp_bin.bin'
         with open(temp_bin_file, 'wb') as temp_bin_obj:
             temp_bin_obj.write(temp_data_bytearray_from_ints)
-        with assert_raises(ValueError):
+        with pytest.raises(ValueError):
             _check_events_file_uses_tab_separators(
                     events_files=temp_bin_file)