MAINT: Test on 3.8

azure-pipelines.yml

@@ -83,7 +83,6 @@ jobs:
     MNE_FORCE_SERIAL: 'true'
     OPENBLAS_NUM_THREADS: 1
     AZURE_CI_WINDOWS: 'true'
-    PYTHON_VERSION: '3.7'
     CONDA_VERSION: '>=4.3.27'
   strategy:
     maxParallel: 4
@@ -96,32 +95,45 @@ jobs:
         TEST_MODE: 'conda'
         CONDA_ENV: 'environment.yml'
         PIP_NO_UPGRADE: 'True'
+        PYTHON_VERSION: '3.7'
       Python37-64bit-full-pip:
         PYTHON_ARCH: 'x64'
         TEST_MODE: 'pip'
+        PYTHON_VERSION: '3.7'
+      Python38-64bit-pre-pip:
+        PYTHON_ARCH: 'x64'
+        TEST_MODE: 'pre-pip'
+        PYTHON_VERSION: '3.8'
   steps:
   - task: UsePythonVersion@0
     inputs:
       versionSpec: $(PYTHON_VERSION)
       architecture: $(PYTHON_ARCH)
       addToPath: true
-    condition: eq(variables['TEST_MODE'], 'pip')
-  - powershell: |
-      Set-StrictMode -Version Latest
-      $ErrorActionPreference = "Stop"
-      $PSDefaultParameterValues['*:ErrorAction']='Stop'
+    condition: in(variables['TEST_MODE'], 'pip', 'pre-pip')
+  - bash: |
+      set -e
       git clone --depth 1 git://github.com/pyvista/gl-ci-helpers.git
       powershell gl-ci-helpers/appveyor/install_opengl.ps1
     displayName: Install OpenGL
-  - powershell: |
-      Set-StrictMode -Version Latest
-      $ErrorActionPreference = "Stop"
-      $PSDefaultParameterValues['*:ErrorAction']='Stop'
+  - bash: |
+      set -e
       pip install --upgrade numpy scipy vtk
       pip install --upgrade -r requirements.txt
       pip install codecov
     condition: eq(variables['TEST_MODE'], 'pip')
     displayName: 'Install dependencies with pip'
+  # VTK does not have wheels (scheduled Q1 2020):
+  # https://github.com/KitwareMedical/VTKPythonPackage/issues/39
+  # Missing: vtk, mayavi, PySurfer, pyvista, numexpr, python-picard, numba, dipy, statsmodels (doesn't work on pre as of 2019/12/04)
+  - bash: |
+      set -e
+      python -m pip install --upgrade pip setuptools
+      python -m pip install --upgrade --pre --only-binary ":all:" -f "https://7933911d6844c6c53a7d-47bd50c35cd79bd838daf386af554a83.ssl.cf2.rackcdn.com" numpy scipy scikit-learn matplotlib h5py pandas Pillow
+      python -m pip install --upgrade nilearn pyqt5 pyqt5-sip nibabel pytest pytest-cov pytest-faulthandler pytest-mock pytest-sugar pytest-timeout joblib psutil https://api.github.com/repos/numpy/numpydoc/zipball/master neo xlrd codecov
+      python -m pip install codecov
+    condition: eq(variables['TEST_MODE'], 'pre-pip')
+    displayName: 'Install dependencies with pip --pre'
   - powershell: |
       Set-StrictMode -Version Latest
       $ErrorActionPreference = "Stop"

mne/conftest.py

@@ -75,6 +75,8 @@ def pytest_configure(config):
     ignore:.*imp.*:DeprecationWarning
     ignore:Exception creating Regex for oneOf.*:SyntaxWarning
     ignore:scipy\.gradient is deprecated.*:DeprecationWarning
+    ignore:sklearn\.externals\.joblib is deprecated.*:FutureWarning
+    ignore:The sklearn.*module.*deprecated.*:FutureWarning
     always:.*get_data.* is deprecated in favor of.*:DeprecationWarning
     """  # noqa: E501
     for warning_line in warning_lines.split('\n'):

mne/decoding/tests/test_base.py

@@ -73,19 +73,11 @@ def test_get_coef():
 
     parameters = {'kernel': ['linear'], 'C': [1, 10]}
     lm_gs_classification = LinearModel(
-        GridSearchCV(
-            svm.SVC(), parameters,
-            cv=2, refit=True, iid=False, n_jobs=1
-        )
-    )
+        GridSearchCV(svm.SVC(), parameters, cv=2, refit=True, n_jobs=1))
     assert (is_classifier(lm_gs_classification))
 
     lm_gs_regression = LinearModel(
-        GridSearchCV(
-            svm.SVR(), parameters, cv=2,
-            refit=True, iid=False, n_jobs=1
-        )
-    )
+        GridSearchCV(svm.SVR(), parameters, cv=2, refit=True, n_jobs=1))
     assert (is_regressor(lm_gs_regression))
 
     # Define a classifier, an invertible transformer and an non-invertible one.
@@ -236,11 +228,7 @@ def test_linearmodel():
     from sklearn.model_selection import GridSearchCV
     parameters = {'kernel': ['linear'], 'C': [1, 10]}
     clf = LinearModel(
-        GridSearchCV(
-            svm.SVC(), parameters, cv=2,
-            refit=True, iid=False, n_jobs=1
-        )
-    )
+        GridSearchCV(svm.SVC(), parameters, cv=2, refit=True, n_jobs=1))
     clf.fit(X, y)
     assert_equal(clf.filters_.shape, (n_features,))
     assert_equal(clf.patterns_.shape, (n_features,))
@@ -252,11 +240,7 @@ def test_linearmodel():
     n_targets = 1
     Y = rng.rand(n, n_targets)
     clf = LinearModel(
-        GridSearchCV(
-            svm.SVR(), parameters, cv=2,
-            refit=True, iid=False, n_jobs=1
-        )
-    )
+        GridSearchCV(svm.SVR(), parameters, cv=2, refit=True, n_jobs=1))
     clf.fit(X, y)
     assert_equal(clf.filters_.shape, (n_features, ))
     assert_equal(clf.patterns_.shape, (n_features, ))

mne/epochs.py

@@ -16,6 +16,7 @@
 import json
 import operator
 import os.path as op
+import warnings
 from distutils.version import LooseVersion
 
 import numpy as np
@@ -382,7 +383,9 @@ def __init__(self, info, data, events, event_id=None, tmin=-0.2, tmax=0.5,
 
         if events is not None:  # RtEpochs can have events=None
             events_type = type(events)
-            events = np.asarray(events)
+            with warnings.catch_warnings(record=True):
+                warnings.simplefilter('ignore')  # deprecation for object array
+                events = np.asarray(events)
             if not np.issubdtype(events.dtype, np.integer):
                 raise TypeError('events should be a NumPy array of integers, '
                                 'got {}'.format(events_type))

mne/label.py

@@ -1505,7 +1505,8 @@ def grow_labels(subject, seeds, extents, hemis, subjects_dir=None, n_jobs=1,
     # make sure the inputs are arrays
     if np.isscalar(seeds):
         seeds = [seeds]
-    seeds = np.atleast_1d([np.atleast_1d(seed) for seed in seeds])
+    # these can have different sizes so need to use object array
+    seeds = np.asarray([np.atleast_1d(seed) for seed in seeds], dtype='O')
     extents = np.atleast_1d(extents)
     hemis = np.atleast_1d(hemis)
     n_seeds = len(seeds)

mne/tests/test_epochs.py

@@ -321,7 +321,7 @@ def test_reject():
             epochs.drop_bad()
             assert_equal(len(epochs), len(events))
             assert_array_equal(epochs.selection, np.arange(len(events)))
-            assert_array_equal(epochs.drop_log, [[]] * 7)
+            assert epochs.drop_log == [[]] * 7
             if proj not in data_7:
                 data_7[proj] = epochs.get_data()
             assert_array_equal(epochs.get_data(), data_7[proj])
@@ -332,7 +332,7 @@ def test_reject():
             epochs.drop_bad()
             assert_equal(len(epochs), len(events) - 4)
             assert_array_equal(epochs.selection, selection)
-            assert_array_equal(epochs.drop_log, drop_log)
+            assert epochs.drop_log == drop_log
             assert_array_equal(epochs.get_data(), data_7[proj][keep_idx])
 
             # rejection post-hoc
@@ -345,7 +345,7 @@ def test_reject():
             assert_equal(len(epochs), len(events) - 4)
             assert_equal(len(epochs), len(epochs.get_data()))
             assert_array_equal(epochs.selection, selection)
-            assert_array_equal(epochs.drop_log, drop_log)
+            assert epochs.drop_log == drop_log
             assert_array_equal(epochs.get_data(), data_7[proj][keep_idx])
 
             # rejection twice
@@ -357,7 +357,7 @@ def test_reject():
             epochs.drop_bad(reject)
             assert_equal(len(epochs), len(events) - 4)
             assert_array_equal(epochs.selection, selection)
-            assert_array_equal(epochs.drop_log, drop_log)
+            assert epochs.drop_log == drop_log
             assert_array_equal(epochs.get_data(), data_7[proj][keep_idx])
 
             # ensure that thresholds must become more stringent, not less
@@ -957,7 +957,7 @@ def test_epochs_io_preload(tmpdir, preload):
     epochs_read5 = read_epochs(temp_fname, preload=preload)
     assert_array_equal(epochs_read5.selection, epochs.selection)
     assert_equal(len(epochs_read5.selection), len(epochs_read5.events))
-    assert_array_equal(epochs_read5.drop_log, epochs.drop_log)
+    assert epochs_read5.drop_log == epochs.drop_log
 
     if preload:
         # Test that one can drop channels on read file
@@ -986,7 +986,7 @@ def test_epochs_io_preload(tmpdir, preload):
     assert_allclose(epochs.get_data(), epochs_read.get_data(), **tols)
     assert_array_equal(epochs.events, epochs_read.events)
     assert_array_equal(epochs.selection, epochs_read.selection)
-    assert_equal(epochs.drop_log, epochs_read.drop_log)
+    assert epochs.drop_log == epochs_read.drop_log
 
     # Test that having a single time point works
     epochs.load_data().crop(0, 0)

mne/viz/tests/test_3d.py

@@ -32,7 +32,7 @@
                      plot_sensors_connectivity, plot_brain_colorbar)
 from mne.viz.utils import _fake_click
 from mne.utils import (requires_mayavi, requires_pysurfer, run_tests_if_main,
-                       requires_nibabel, check_version,
+                       requires_nibabel, check_version, requires_dipy,
                        traits_test, requires_version, catch_logging)
 from mne.datasets import testing
 from mne.source_space import read_source_spaces
@@ -480,6 +480,7 @@ def test_snapshot_brain_montage(renderer):
 
 @pytest.mark.slowtest  # can be slow on OSX
 @testing.requires_testing_data
+@requires_dipy()
 @requires_nibabel()
 @requires_version('nilearn', '0.4')
 @pytest.mark.parametrize('mode, stype, init_t, want_t, init_p, want_p', [
@@ -525,6 +526,7 @@ def test_plot_volume_source_estimates(mode, stype, init_t, want_t,
 
 @pytest.mark.slowtest  # can be slow on OSX
 @testing.requires_testing_data
+@requires_dipy()
 @requires_nibabel()
 @requires_version('nilearn', '0.4')
 def test_plot_volume_source_estimates_morph():

mne/viz/topomap.py

@@ -338,7 +338,7 @@ def plot_projs_topomap(projs, layout=None, cmap=None, sensors=True,
     if vlim == 'joint':
         for _ch_type in set(types):
             idx = np.where(np.in1d(types, _ch_type))[0]
-            these_data = np.concatenate(np.array(datas)[idx])
+            these_data = np.concatenate(np.array(datas, dtype=object)[idx])
             norm = all(these_data >= 0)
             _vl = _setup_vmin_vmax(these_data, vmin=None, vmax=None, norm=norm)
             for _idx in idx: