MRG: Remove 2.6 (mne-tools#3530)

* MAINT: Remove 2.6

* FIX: Fix tests

* FIX: Fix more tests [circle full]

* FIX: Do not install old broken mayavi

.travis.yml

@@ -14,20 +14,18 @@ env:
     # Note that we don't run coverage on Py3k anyway because it slows our tests
     # by a factor of 2 (!), so we make this our "from install dir" run.
     #
-    # If we change the old-version run to be a different Python version
-    # from 2.6, then we need to update mne.utils.clean_warning_registry.
-    #
     # Run one test (3.5) with a non-default stim channel to make sure our
     # tests are explicit about channels.
     #
     # Must force libpng version to avoid silly libpng.so.15 error (MPL 1.1 needs it)
     #
     # Conda currently has packaging bug with mayavi/traits/numpy where 1.10 can't be used
     # but breaks sklearn on install; hopefully eventually the NUMPY=1.9 on 2.7 full can be removed
+    # Mayavi=4.3 on old 2.7 installs, but doesn't work properly due to a traits bug
     - PYTHON=2.7 DEPS=full TEST_LOCATION=src NUMPY="=1.9" SCIPY="=0.17"
     - PYTHON=2.7 DEPS=nodata TEST_LOCATION=src MNE_DONTWRITE_HOME=true MNE_FORCE_SERIAL=true MNE_SKIP_NETWORK_TEST=1  # also runs flake8
     - PYTHON=3.5 DEPS=full TEST_LOCATION=install MNE_STIM_CHANNEL=STI101
-    - PYTHON=2.6 DEPS=full TEST_LOCATION=src NUMPY="=1.7" SCIPY="=0.11" MPL="=1.1" LIBPNG="=1.5" SKLEARN="=0.11" PANDAS="=0.8"
+    - PYTHON=2.7 DEPS=full TEST_LOCATION=src NUMPY="=1.8" SCIPY="=0.12" MPL="=1.3" SKLEARN="=0.14" PANDAS="=0.12"
     - PYTHON=2.7 DEPS=minimal TEST_LOCATION=src
 
 # Setup anaconda
@@ -59,10 +57,10 @@ install:
           conda install --yes --quiet $ENSURE_PACKAGES ipython;
         else
           conda install --yes --quiet $ENSURE_PACKAGES ipython==1.1.0 statsmodels pandas$PANDAS;
-          pip install -q nitime;
-          if [ "${PYTHON}" == "2.7" ]; then
-            conda install --yes --quiet $ENSURE_PACKAGES mayavi traits;
-            pip install -q pysurfer faulthandler;
+          pip install nitime faulthandler;
+          if [ "${NUMPY}" != "=1.8" ]; then
+            conda install --yes --quiet $ENSURE_PACKAGES mayavi$MAYAVI;
+            pip install pysurfer;
           fi;
         fi;
       fi;
@@ -71,9 +69,6 @@ install:
         pip install flake8 codespell numpydoc;
       fi;
     - pip install -q codecov nose-timer
-    - if [ "${PYTHON}" == "2.6" ]; then
-          pip install -q argparse;
-      fi;
     # check our versions for the major packages
     - NP_VERSION=`python -c 'import numpy; print(numpy.__version__)'`
     - if [ -n "$NUMPY" ] && [ "${NUMPY:(-3)}" != "${NP_VERSION::3}" ]; then

README.rst

@@ -62,17 +62,16 @@ Dependencies
 
 The minimum required dependencies to run the software are:
 
-  - Python >= 2.6
-  - NumPy >= 1.6
-  - SciPy >= 0.7.2
-  - matplotlib >= 0.98.4
+  - Python >= 2.7
+  - NumPy >= 1.8
+  - SciPy >= 0.12
+  - matplotlib >= 1.3
 
 For full functionality, some functions require:
 
-  - scikit-learn >= 0.15
-  - nose >= 0.10
-  - nibabel >= 2.0.0
-  - pandas >= 0.7.3
+  - scikit-learn >= 0.18
+  - nibabel >= 2.1.0
+  - pandas >= 0.12
 
 To use NVIDIA CUDA for resampling and FFT FIR filtering, you will also need
 to install the NVIDIA CUDA SDK, pycuda, and scikits.cuda. See the

doc/Makefile

@@ -116,3 +116,5 @@ doctest:
 
 view:
 	@python -c "import webbrowser; webbrowser.open_new_tab('file://$(PWD)/_build/html/index.html')"
+
+show: view

doc/whats_new.rst

@@ -118,6 +118,8 @@ API
 
           5. Previously, if the filter was as long or longer than the signal of interest, direct FFT-based computations were used. Now a single code path (overlap-add filtering) is used for all FIR filters. This could cause minor changes in how short signals are filtered.
 
+    - Support for Python 2.6 has been dropped, and the minimum supported dependencies are NumPy_ 1.8, SciPy_ 0.12, and Matplotlib_ 1.3 by `Eric Larson`_
+
     - When CTF gradient compensation is applied to raw data, it is no longer reverted on save of :meth:`mne.io.Raw.save` by `Eric Larson`_
 
     - Adds :func:`mne.time_frequency.csd_epochs` to replace :func:`mne.time_frequency.csd_compute_epochs` for naming consistency. :func:`mne.time_frequency.csd_compute_epochs` is now deprecated and will be removed in mne 0.14, by `Nick Foti`_

mne/beamformer/tests/test_dics.py

@@ -12,7 +12,7 @@
 from mne.beamformer import dics, dics_epochs, dics_source_power, tf_dics
 from mne.time_frequency import csd_epochs
 from mne.externals.six import advance_iterator
-from mne.utils import run_tests_if_main, clean_warning_registry
+from mne.utils import run_tests_if_main
 
 # Note that this is the first test file, this will apply to all subsequent
 # tests in a full nosetest:
@@ -29,9 +29,6 @@
 label = 'Aud-lh'
 fname_label = op.join(data_path, 'MEG', 'sample', 'labels', '%s.label' % label)
 
-# bit of a hack to deal with old scipy/numpy throwing warnings in tests
-clean_warning_registry()
-
 
 def read_forward_solution_meg(*args, **kwargs):
     fwd = mne.read_forward_solution(*args, **kwargs)

mne/bem.py

@@ -5,16 +5,16 @@
 #
 # License: BSD (3-clause)
 
-import sys
+from functools import partial
+import glob
 import os
 import os.path as op
 import shutil
-import glob
+import sys
 
 import numpy as np
 from scipy import linalg
 
-from .fixes import partial
 from .utils import verbose, logger, run_subprocess, get_subjects_dir, warn
 from .transforms import _ensure_trans, apply_trans
 from .io import Info

mne/channels/tests/test_channels.py

@@ -6,17 +6,18 @@
 import os.path as op
 
 from copy import deepcopy
+from functools import partial
 import warnings
 
 import numpy as np
+from scipy.io import savemat
 from numpy.testing import assert_array_equal
 from nose.tools import assert_raises, assert_true, assert_equal
 
 from mne.channels import rename_channels, read_ch_connectivity
 from mne.channels.channels import _ch_neighbor_connectivity
 from mne.io import read_info, Raw
 from mne.io.constants import FIFF
-from mne.fixes import partial, savemat
 from mne.utils import _TempDir, run_tests_if_main
 from mne import pick_types, pick_channels
 

mne/channels/tests/test_montage.py

@@ -59,9 +59,9 @@ def test_montage():
         'Site  Theta  Phi\nFp1  -92    -72\nFp2   92     72\n'
         'very_very_very_long_name   -60    -51\n',
         '346\n'
-        'EEG	      F3	 -62.027	 -50.053	      85\n'
-        'EEG	      Fz	  45.608	      90	      85\n'
-        'EEG	      F4	   62.01	  50.103	      85\n',
+        'EEG\t      F3\t -62.027\t -50.053\t      85\n'
+        'EEG\t      Fz\t  45.608\t      90\t      85\n'
+        'EEG\t      F4\t   62.01\t  50.103\t      85\n',
         'eeg Fp1 -95.0 -31.0 -3.0\neeg AF7 -81 -59 -3\neeg AF3 -87 -41 28\n'
     ]
     kinds = ['test.sfp', 'test.csd', 'test_mm.elc', 'test_m.elc', 'test.txt',

mne/chpi.py

@@ -2,6 +2,8 @@
 #
 # License: BSD (3-clause)
 
+from functools import partial
+
 import numpy as np
 from scipy import linalg, fftpack
 
@@ -14,11 +16,11 @@
                          quat_to_rot, rot_to_quat)
 from .utils import (verbose, logger, check_version, use_log_level,
                     _check_fname, warn)
-from .fixes import partial
 
 # Eventually we should add:
 #   hpicons
 #   high-passing of data during fits
+#   parsing cHPI coil information from acq pars, then to PSD if necessary
 
 
 # ############################################################################

mne/connectivity/spectral.py

@@ -2,14 +2,14 @@
 #
 # License: BSD (3-clause)
 
-from ..externals.six import string_types
+from functools import partial
 from inspect import getmembers
 
 import numpy as np
 from scipy.fftpack import fftfreq
 
 from .utils import check_indices
-from ..fixes import tril_indices, partial, _get_args
+from ..fixes import _get_args
 from ..parallel import parallel_func
 from ..source_estimate import _BaseSourceEstimate
 from ..epochs import _BaseEpochs
@@ -18,6 +18,7 @@
                                          _psd_from_mt_adaptive)
 from ..time_frequency.tfr import morlet, cwt
 from ..utils import logger, verbose, _time_mask, warn
+from ..externals.six import string_types
 
 ########################################################################
 # Various connectivity estimators
@@ -789,7 +790,7 @@ def spectral_connectivity(data, method='coh', indices=None, sfreq=2 * np.pi,
 
             if indices is None:
                 # only compute r for lower-triangular region
-                indices_use = tril_indices(n_signals, -1)
+                indices_use = np.tril_indices(n_signals, -1)
             else:
                 indices_use = check_indices(indices)
 

mne/connectivity/tests/test_spectral.py

@@ -1,18 +1,20 @@
-import os
+import warnings
+
 import numpy as np
 from numpy.testing import assert_array_almost_equal
 from nose.tools import assert_true, assert_raises
-from nose.plugins.skip import SkipTest
-import warnings
 
-from mne.fixes import tril_indices
 from mne.connectivity import spectral_connectivity
 from mne.connectivity.spectral import _CohEst
 
 from mne import SourceEstimate
 from mne.utils import run_tests_if_main, slow_test
 from mne.filter import band_pass_filter
 
+trans_bandwidth = 2.5
+filt_kwargs = dict(filter_length='auto', fir_window='hamming', phase='zero',
+                   l_trans_bandwidth=trans_bandwidth,
+                   h_trans_bandwidth=trans_bandwidth)
 warnings.simplefilter('always')
 
 
@@ -35,10 +37,6 @@ def _stc_gen(data, sfreq, tmin, combo=False):
 @slow_test
 def test_spectral_connectivity():
     """Test frequency-domain connectivity methods"""
-    # XXX For some reason on 14 Oct 2015 Travis started timing out on this
-    # test, so for a quick workaround we will skip it:
-    if os.getenv('TRAVIS', 'false') == 'true':
-        raise SkipTest('Travis is broken')
     # Use a case known to have no spurious correlations (it would bad if
     # nosetests could randomly fail):
     np.random.seed(0)
@@ -55,10 +53,9 @@ def test_spectral_connectivity():
     # simulate connectivity from 5Hz..15Hz
     fstart, fend = 5.0, 15.0
     for i in range(n_epochs):
-        with warnings.catch_warnings(record=True):
-            warnings.simplefilter('always')
-            data[i, 1, :] = band_pass_filter(data[i, 0, :],
-                                             sfreq, fstart, fend)
+        data[i, 1, :] = band_pass_filter(data[i, 0, :],
+                                         sfreq, fstart, fend,
+                                         **filt_kwargs)
         # add some noise, so the spectrum is not exactly zero
         data[i, 1, :] += 1e-2 * np.random.randn(n_times)
 
@@ -117,31 +114,40 @@ def test_spectral_connectivity():
                 if mode == 'multitaper':
                     upper_t = 0.95
                     lower_t = 0.5
-                else:
+                elif mode == 'fourier':
                     # other estimates have higher variance
                     upper_t = 0.8
                     lower_t = 0.75
+                else:  # cwt_morlet
+                    upper_t = 0.64
+                    lower_t = 0.63
 
                 # test the simulated signal
                 if method == 'coh':
-                    idx = np.searchsorted(freqs, (fstart + 1, fend - 1))
+                    idx = np.searchsorted(freqs, (fstart + trans_bandwidth,
+                                                  fend - trans_bandwidth))
                     # we see something for zero-lag
-                    assert_true(np.all(con[1, 0, idx[0]:idx[1]] > upper_t))
+                    assert_true(np.all(con[1, 0, idx[0]:idx[1]] > upper_t),
+                                con[1, 0, idx[0]:idx[1]].min())
 
                     if mode != 'cwt_morlet':
-                        idx = np.searchsorted(freqs, (fstart - 1, fend + 1))
+                        idx = np.searchsorted(freqs,
+                                              (fstart - trans_bandwidth * 2,
+                                               fend + trans_bandwidth * 2))
                         assert_true(np.all(con[1, 0, :idx[0]] < lower_t))
-                        assert_true(np.all(con[1, 0, idx[1]:] < lower_t))
+                        assert_true(np.all(con[1, 0, idx[1]:] < lower_t),
+                                    con[1, 0, idx[1:]].max())
                 elif method == 'cohy':
                     idx = np.searchsorted(freqs, (fstart + 1, fend - 1))
                     # imaginary coh will be zero
-                    assert_true(np.all(np.imag(con[1, 0, idx[0]:idx[1]]) <
-                                lower_t))
+                    check = np.imag(con[1, 0, idx[0]:idx[1]])
+                    assert_true(np.all(check < lower_t), check.max())
                     # we see something for zero-lag
                     assert_true(np.all(np.abs(con[1, 0, idx[0]:idx[1]]) >
                                 upper_t))
 
-                    idx = np.searchsorted(freqs, (fstart - 1, fend + 1))
+                    idx = np.searchsorted(freqs, (fstart - trans_bandwidth * 2,
+                                                  fend + trans_bandwidth * 2))
                     if mode != 'cwt_morlet':
                         assert_true(np.all(np.abs(con[1, 0, :idx[0]]) <
                                     lower_t))
@@ -153,10 +159,11 @@ def test_spectral_connectivity():
                     assert_true(np.all(con[1, 0, idx[0]:idx[1]] < lower_t))
                     idx = np.searchsorted(freqs, (fstart - 1, fend + 1))
                     assert_true(np.all(con[1, 0, :idx[0]] < lower_t))
-                    assert_true(np.all(con[1, 0, idx[1]:] < lower_t))
+                    assert_true(np.all(con[1, 0, idx[1]:] < lower_t),
+                                con[1, 0, idx[1]:].max())
 
                 # compute same connections using indices and 2 jobs
-                indices = tril_indices(n_signals, -1)
+                indices = np.tril_indices(n_signals, -1)
 
                 if not isinstance(method, list):
                     test_methods = (method, _CohEst)

mne/cov.py

@@ -33,7 +33,6 @@
 from .utils import (check_fname, logger, verbose, estimate_rank,
                     _compute_row_norms, check_version, _time_mask, warn,
                     copy_function_doc_to_method_doc)
-from .fixes import in1d
 from . import viz
 
 from .externals.six.moves import zip
@@ -432,7 +431,7 @@ def compute_raw_covariance(raw, tmin=0, tmax=None, tstep=0.2, reject=None,
     if picks is None:
         # Need to include all channels e.g. if eog rejection is to be used
         picks = np.arange(raw.info['nchan'])
-        pick_mask = in1d(
+        pick_mask = np.in1d(
             picks, _pick_data_channels(raw.info, with_ref_meg=False))
     else:
         pick_mask = slice(None)

mne/datasets/brainstorm/bst_auditory.py

@@ -2,9 +2,10 @@
 #
 # License: BSD (3-clause)
 
+from functools import partial
 import os.path as op
+
 from ...utils import verbose
-from ...fixes import partial
 from ..utils import (has_dataset, _data_path, _get_version, _version_doc,
                      _data_path_doc)
 

mne/datasets/brainstorm/bst_phantom_ctf.py

@@ -2,10 +2,10 @@
 #
 # License: BSD (3-clause)
 
+from functools import partial
 import os.path as op
 
 from ...utils import verbose
-from ...fixes import partial
 from ..utils import (has_dataset, _data_path, _get_version, _version_doc,
                      _data_path_doc)
 

mne/datasets/brainstorm/bst_phantom_elekta.py

@@ -2,10 +2,10 @@
 #
 # License: BSD (3-clause)
 
+from functools import partial
 import os.path as op
 
 from ...utils import verbose
-from ...fixes import partial
 from ..utils import (has_dataset, _data_path, _get_version, _version_doc,
                      _data_path_doc)
 

mne/datasets/brainstorm/bst_raw.py

@@ -2,9 +2,10 @@
 #
 # License: BSD (3-clause)
 
+from functools import partial
 import os.path as op
+
 from ...utils import verbose
-from ...fixes import partial
 from ..utils import (has_dataset, _data_path, _get_version, _version_doc,
                      _data_path_doc)