Undefined iprint variable in scipy/optimize/cobyla.py #5029
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Dup of scipy/scipy#8118 fixed in scipy/scipy#8144 will be out in SciPy 1.0.1 You can work around it at the MNE end by putting |
This was referenced Mar 28, 2018
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Can anyone reproduce this error ? I'm usin
mne-python/environment.yml(scipy 1.0.0 py36hbf646e7_0 )(mne) ❯ pytest mne/tests -k test_arithmetic Test session starts (platform: linux, Python 3.6.1, pytest 3.4.2, pytest-sugar 0.9.1) rootdir: /home/sik/code/mne-python, inifile: setup.cfg plugins: sugar-0.9.1, faulthandler-1.4.1, cov-2.5.1 mne/tests/test_cov.py ✓ 50% █████ ――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――― test_arithmetic ――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――― def test_arithmetic(): """Test evoked arithmetic.""" ev = read_evokeds(fname, condition=0) ev1 = EvokedArray(np.ones_like(ev.data), ev.info, ev.times[0], nave=20) ev2 = EvokedArray(-np.ones_like(ev.data), ev.info, ev.times[0], nave=10) # combine_evoked([ev1, ev2]) should be the same as ev1 + ev2: # data should be added according to their `nave` weights # nave = ev1.nave + ev2.nave ev = combine_evoked([ev1, ev2], weights='nave') assert_equal(ev.nave, ev1.nave + ev2.nave) assert_allclose(ev.data, 1. / 3. * np.ones_like(ev.data)) # with same trial counts, a bunch of things should be equivalent for weights in ('nave', 'equal', [0.5, 0.5]): ev = combine_evoked([ev1, ev1], weights=weights) assert_allclose(ev.data, ev1.data) assert_equal(ev.nave, 2 * ev1.nave) ev = combine_evoked([ev1, -ev1], weights=weights) assert_allclose(ev.data, 0., atol=1e-20) assert_equal(ev.nave, 2 * ev1.nave) ev = combine_evoked([ev1, -ev1], weights='equal') assert_allclose(ev.data, 0., atol=1e-20) assert_equal(ev.nave, 2 * ev1.nave) ev = combine_evoked([ev1, -ev2], weights='equal') expected = int(round(1. / (0.25 / ev1.nave + 0.25 / ev2.nave))) assert_equal(expected, 27) # this is reasonable assert_equal(ev.nave, expected) # default comment behavior if evoked.comment is None old_comment1 = ev1.comment old_comment2 = ev2.comment ev1.comment = None ev = combine_evoked([ev1, -ev2], weights=[1, -1]) assert_equal(ev.comment.count('unknown'), 2) assert_true('-unknown' in ev.comment) assert_true(' + ' in ev.comment) ev1.comment = old_comment1 ev2.comment = old_comment2 # equal weighting ev = combine_evoked([ev1, ev2], weights='equal') assert_allclose(ev.data, np.zeros_like(ev1.data)) # combine_evoked([ev1, ev2], weights=[1, 0]) should yield the same as ev1 ev = combine_evoked([ev1, ev2], weights=[1, 0]) assert_equal(ev.nave, ev1.nave) assert_allclose(ev.data, ev1.data) # simple subtraction (like in oddball) ev = combine_evoked([ev1, ev2], weights=[1, -1]) assert_allclose(ev.data, 2 * np.ones_like(ev1.data)) assert_raises(ValueError, combine_evoked, [ev1, ev2], weights='foo') assert_raises(ValueError, combine_evoked, [ev1, ev2], weights=[1]) # grand average evoked1, evoked2 = read_evokeds(fname, condition=[0, 1], proj=True) ch_names = evoked1.ch_names[2:] evoked1.info['bads'] = ['EEG 008'] # test interpolation evoked1.drop_channels(evoked1.ch_names[:1]) evoked2.drop_channels(evoked2.ch_names[1:2]) > gave = grand_average([evoked1, evoked2]) ch_names = ['MEG 0111', 'MEG 0122', 'MEG 0123', 'MEG 0121', 'MEG 0132', 'MEG 0133', ...] ev = <Evoked | comment : '1.000 * unknown + -1.000 * unknown', kind : average, time : [-0.199795, 0.499488], n_epochs : 7, n_channels x n_times : 376 x 421, ~4.8 MB> ev1 = <Evoked | comment : '', kind : average, time : [-0.199795, 0.499488], n_epochs : 20, n_channels x n_times : 376 x 421, ~4.8 MB> ev2 = <Evoked | comment : '', kind : average, time : [-0.199795, 0.499488], n_epochs : 10, n_channels x n_times : 376 x 421, ~4.8 MB> evoked1 = <Evoked | comment : 'Left Auditory', kind : average, time : [-0.199795, 0.499488], n_epochs : 3, n_channels x n_times : 375 x 421, ~4.8 MB> evoked2 = <Evoked | comment : 'Right Auditory', kind : average, time : [-0.199795, 0.499488], n_epochs : 6, n_channels x n_times : 375 x 421, ~4.8 MB> expected = 27 old_comment1 = '' old_comment2 = '' weights = [0.5, 0.5] mne/tests/test_evoked.py:493: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ mne/utils.py:2511: in grand_average else inst for inst in all_inst] mne/utils.py:2511: in <listcomp> else inst for inst in all_inst] <string>:2: in interpolate_bads ??? mne/utils.py:728: in verbose return function(*args, **kwargs) mne/channels/channels.py:920: in interpolate_bads _interpolate_bads_eeg(self) <string>:2: in _interpolate_bads_eeg ??? mne/utils.py:728: in verbose return function(*args, **kwargs) mne/channels/interpolation.py:142: in _interpolate_bads_eeg radius, center = _fit_sphere(pos_good) mne/bem.py:1001: in _fit_sphere rhoend=radius_init * 1e-6, disp=disp) ../../miniconda3/envs/mne/lib/python3.6/site-packages/scipy/optimize/cobyla.py:1 scipy/optimize/cobyla.py:167: in fmin_cobyla **opts) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ fun = <function _fit_sphere.<locals>.cost_fun at 0x7fcd69534378>, x0 = array([-0.0127069 , 0.00408325, 0.08565747, 0.06573494]), args = () constraints = ({'args': (), 'fun': <function _fit_sphere.<locals>.constraint at 0x7fcd69534730>, 'type': 'ineq'},), rhobeg = 0.065734940201687325, tol = 6.5734940201687326e-08, maxiter = 1000, disp = True catol = 0.0002, unknown_options = {}, maxfun = 1000, rhoend = 6.5734940201687326e-08, ic = 0, con = {'args': (), 'fun': <function _fit_sphere.<locals>.constraint at 0x7fcd69534730>, 'type': 'ineq'} ctype = 'ineq', c = {'args': (), 'fun': <function _fit_sphere.<locals>.constraint at 0x7fcd69534730>, 'type': 'ineq'} def _minimize_cobyla(fun, x0, args=(), constraints=(), rhobeg=1.0, tol=1e-4, maxiter=1000, disp=False, catol=2e-4, **unknown_options): """ Minimize a scalar function of one or more variables using the Constrained Optimization BY Linear Approximation (COBYLA) algorithm. Options ------- rhobeg : float Reasonable initial changes to the variables. tol : float Final accuracy in the optimization (not precisely guaranteed). This is a lower bound on the size of the trust region. disp : bool Set to True to print convergence messages. If False, `verbosity` is ignored as set to 0. maxiter : int Maximum number of function evaluations. catol : float Tolerance (absolute) for constraint violations """ _check_unknown_options(unknown_options) maxfun = maxiter rhoend = tol if not disp: iprint = 0 # check constraints if isinstance(constraints, dict): constraints = (constraints, ) for ic, con in enumerate(constraints): # check type try: ctype = con['type'].lower() except KeyError: raise KeyError('Constraint %d has no type defined.' % ic) except TypeError: raise TypeError('Constraints must be defined using a ' 'dictionary.') except AttributeError: raise TypeError("Constraint's type must be a string.") else: if ctype != 'ineq': raise ValueError("Constraints of type '%s' not handled by " "COBYLA." % con['type']) # check function if 'fun' not in con: raise KeyError('Constraint %d has no function defined.' % ic) # check extra arguments if 'args' not in con: con['args'] = () # m is the total number of constraint values # it takes into account that some constraints may be vector-valued cons_lengths = [] for c in constraints: f = c['fun'](x0, *c['args']) try: cons_length = len(f) except TypeError: cons_length = 1 cons_lengths.append(cons_length) m = sum(cons_lengths) def calcfc(x, con): f = fun(x, *args) i = 0 for size, c in izip(cons_lengths, constraints): con[i: i + size] = c['fun'](x, *c['args']) i += size return f info = np.zeros(4, np.float64) xopt, info = _cobyla.minimize(calcfc, m=m, x=np.copy(x0), rhobeg=rhobeg, > rhoend=rhoend, iprint=iprint, maxfun=maxfun, dinfo=info) E UnboundLocalError: local variable 'iprint' referenced before assignment args = () c = {'args': (), 'fun': <function _fit_sphere.<locals>.constraint at 0x7fcd69534730>, 'type': 'ineq'} calcfc = <function _minimize_cobyla.<locals>.calcfc at 0x7fcd695347b8> catol = 0.0002 con = {'args': (), 'fun': <function _fit_sphere.<locals>.constraint at 0x7fcd69534730>, 'type': 'ineq'} cons_length = 1 cons_lengths = [1] constraints = ({'args': (), 'fun': <function _fit_sphere.<locals>.constraint at 0x7fcd69534730>, 'type': 'ineq'},) ctype = 'ineq' disp = True f = 0.065734940201687325 fun = <function _fit_sphere.<locals>.cost_fun at 0x7fcd69534378> ic = 0 info = array([ 0., 0., 0., 0.]) m = 1 maxfun = 1000 maxiter = 1000 rhobeg = 0.065734940201687325 rhoend = 6.5734940201687326e-08 tol = 6.5734940201687326e-08 unknown_options = {} x0 = array([-0.0127069 , 0.00408325, 0.08565747, 0.06573494]) ../../miniconda3/envs/mne/lib/python3.6/site-packages/scipy/optimize/cobyla.py:252: UnboundLocalError ---------------------------------------------------------------------------------------------- Captured stdout call ----------------------------------------------------------------------------------------------- Reading /home/sik/code/mne-python/mne/tests/../io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Reading /home/sik/code/mne-python/mne/tests/../io/tests/data/test-ave.fif ... Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Left Auditory) 0 CTF compensation matrices available nave = 3 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied Read a total of 4 projection items: PCA-v1 (1 x 102) idle PCA-v2 (1 x 102) idle PCA-v3 (1 x 102) idle Average EEG reference (1 x 60) idle Found the data of interest: t = -199.80 ... 499.49 ms (Right Auditory) 0 CTF compensation matrices available nave = 6 - aspect type = 100 Created an SSP operator (subspace dimension = 4) 4 projection items activated SSP projectors applied... No baseline correction applied mne/tests/test_evoked.py ⨯ 100% ██████████ ============================================================================================ slowest 20 test durations ============================================================================================ 0.38s call mne/tests/test_evoked.py::test_arithmetic 0.01s call mne/tests/test_cov.py::test_arithmetic_cov 0.00s setup mne/tests/test_cov.py::test_arithmetic_cov 0.00s teardown mne/tests/test_evoked.py::test_arithmetic 0.00s teardown mne/tests/test_cov.py::test_arithmetic_cov 0.00s setup mne/tests/test_evoked.py::test_arithmetic ============================================================================================== 276 tests deselected =============================================================================================== Results (1.39s): 1 passed 1 failed - mne/tests/test_evoked.py:431 test_arithmetic 276 deselectedThe text was updated successfully, but these errors were encountered: