[SciPy-User] problem with installing on osx 10.6.6
P B
pbajk at yahoo.co.uk
Mon May 9 17:01:43 EDT 2011
Hi,this is odd. IF I put in the command >>> import numpy>>> import scipy>>> scipy.test(verbose=2)
I get the output
Running unit tests for scipy
NumPy version 1.5.1
NumPy is installed in /Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/numpy
SciPy version 0.9.0
SciPy is installed in /Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy
Python version 2.6.6 (r266:84374, Aug 31 2010, 11:00:51) [GCC 4.0.1 (Apple Inc. build 5493)]
nose version 1.0.0
******* many test results which then finish with:
OK (KNOWNFAIL=12, SKIP=42)<nose.result.TextTestResult run=4733 errors=0 failures=0>
Could it be there is a problem with the "scipy.test('1','10')" command?
The full output is:
Python 2.6.6 (r266:84374, Aug 31 2010, 11:00:51)
[GCC 4.0.1 (Apple Inc. build 5493)] on darwin
Type "copyright", "credits" or "license()" for more information.
****************************************************************
Personal firewall software may warn about the connection IDLE
makes to its subprocess using this computer's internal loopback
interface. This connection is not visible on any external
interface and no data is sent to or received from the Internet.
****************************************************************
IDLE 2.6.6
>>> import numpy
>>> import scipy
>>> scipy.test(verbose=2)
Running unit tests for scipy
NumPy version 1.5.1
NumPy is installed in /Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/numpy
SciPy version 0.9.0
SciPy is installed in /Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy
Python version 2.6.6 (r266:84374, Aug 31 2010, 11:00:51) [GCC 4.0.1 (Apple Inc. build 5493)]
nose version 1.0.0
Tests cophenet(Z) on tdist data set. ... ok
Tests cophenet(Z, Y) on tdist data set. ... ok
Tests correspond(Z, y) on linkage and CDMs over observation sets of different sizes. ... ok
Tests correspond(Z, y) on linkage and CDMs over observation sets of different sizes. Correspondance should be false. ... ok
Tests correspond(Z, y) on linkage and CDMs over observation sets of different sizes. Correspondance should be false. ... ok
Tests correspond(Z, y) with empty linkage and condensed distance matrix. ... ok
Tests num_obs_linkage with observation matrices of multiple sizes. ... ok
Tests fcluster(Z, criterion='maxclust', t=2) on a random 3-cluster data set. ... ok
Tests fcluster(Z, criterion='maxclust', t=3) on a random 3-cluster data set. ... ok
Tests fcluster(Z, criterion='maxclust', t=4) on a random 3-cluster data set. ... ok
Tests fclusterdata(X, criterion='maxclust', t=2) on a random 3-cluster data set. ... ok
Tests fclusterdata(X, criterion='maxclust', t=3) on a random 3-cluster data set. ... ok
Tests fclusterdata(X, criterion='maxclust', t=4) on a random 3-cluster data set. ... ok
Tests from_mlab_linkage on empty linkage array. ... ok
Tests from_mlab_linkage on linkage array with multiple rows. ... ok
Tests from_mlab_linkage on linkage array with single row. ... ok
Tests inconsistency matrix calculation (depth=1) on a complete linkage. ... ok
Tests inconsistency matrix calculation (depth=2) on a complete linkage. ... ok
Tests inconsistency matrix calculation (depth=3) on a complete linkage. ... ok
Tests inconsistency matrix calculation (depth=4) on a complete linkage. ... ok
Tests inconsistency matrix calculation (depth=1, dataset=Q) with single linkage. ... ok
Tests inconsistency matrix calculation (depth=2, dataset=Q) with single linkage. ... ok
Tests inconsistency matrix calculation (depth=3, dataset=Q) with single linkage. ... ok
Tests inconsistency matrix calculation (depth=4, dataset=Q) with single linkage. ... ok
Tests inconsistency matrix calculation (depth=1) on a single linkage. ... ok
Tests inconsistency matrix calculation (depth=2) on a single linkage. ... ok
Tests inconsistency matrix calculation (depth=3) on a single linkage. ... ok
Tests inconsistency matrix calculation (depth=4) on a single linkage. ... ok
Tests is_isomorphic on test case #1 (one flat cluster, different labellings) ... ok
Tests is_isomorphic on test case #2 (two flat clusters, different labelings) ... ok
Tests is_isomorphic on test case #3 (no flat clusters) ... ok
Tests is_isomorphic on test case #4A (3 flat clusters, different labelings, isomorphic) ... ok
Tests is_isomorphic on test case #4B (3 flat clusters, different labelings, nonisomorphic) ... ok
Tests is_isomorphic on test case #4C (3 flat clusters, different labelings, isomorphic) ... ok
Tests is_isomorphic on test case #5A (1000 observations, 2 random clusters, random permutation of the labeling). Run 3 times. ... ok
Tests is_isomorphic on test case #5B (1000 observations, 3 random clusters, random permutation of the labeling). Run 3 times. ... ok
Tests is_isomorphic on test case #5C (1000 observations, 5 random clusters, random permutation of the labeling). Run 3 times. ... ok
Tests is_isomorphic on test case #5A (1000 observations, 2 random clusters, random permutation of the labeling, slightly nonisomorphic.) Run 3 times. ... ok
Tests is_isomorphic on test case #5B (1000 observations, 3 random clusters, random permutation of the labeling, slightly nonisomorphic.) Run 3 times. ... ok
Tests is_isomorphic on test case #5C (1000 observations, 5 random clusters, random permutation of the labeling, slightly non-isomorphic.) Run 3 times. ... ok
Tests is_monotonic(Z) on 1x4 linkage. Expecting True. ... ok
Tests is_monotonic(Z) on 2x4 linkage. Expecting False. ... ok
Tests is_monotonic(Z) on 2x4 linkage. Expecting True. ... ok
Tests is_monotonic(Z) on 3x4 linkage (case 1). Expecting False. ... ok
Tests is_monotonic(Z) on 3x4 linkage (case 2). Expecting False. ... ok
Tests is_monotonic(Z) on 3x4 linkage (case 3). Expecting False ... ok
Tests is_monotonic(Z) on 3x4 linkage. Expecting True. ... ok
Tests is_monotonic(Z) on an empty linkage. ... ok
Tests is_monotonic(Z) on clustering generated by single linkage on Iris data set. Expecting True. ... ok
Tests is_monotonic(Z) on clustering generated by single linkage on tdist data set. Expecting True. ... ok
Tests is_monotonic(Z) on clustering generated by single linkage on tdist data set. Perturbing. Expecting False. ... ok
Tests is_valid_im(R) on im over 2 observations. ... ok
Tests is_valid_im(R) on im over 3 observations. ... ok
Tests is_valid_im(R) with 3 columns. ... ok
Tests is_valid_im(R) on im on observation sets between sizes 4 and 15 (step size 3). ... ok
Tests is_valid_im(R) on im on observation sets between sizes 4 and 15 (step size 3) with negative link counts. ... ok
Tests is_valid_im(R) on im on observation sets between sizes 4 and 15 (step size 3) with negative link height means. ... ok
Tests is_valid_im(R) on im on observation sets between sizes 4 and 15 (step size 3) with negative link height standard deviations. ... ok
Tests is_valid_im(R) with 5 columns. ... ok
Tests is_valid_im(R) with empty inconsistency matrix. ... ok
Tests is_valid_im(R) with integer type. ... ok
Tests is_valid_linkage(Z) on linkage over 2 observations. ... ok
Tests is_valid_linkage(Z) on linkage over 3 observations. ... ok
Tests is_valid_linkage(Z) with 3 columns. ... ok
Tests is_valid_linkage(Z) on linkage on observation sets between sizes 4 and 15 (step size 3). ... ok
Tests is_valid_linkage(Z) on linkage on observation sets between sizes 4 and 15 (step size 3) with negative counts. ... ok
Tests is_valid_linkage(Z) on linkage on observation sets between sizes 4 and 15 (step size 3) with negative distances. ... ok
Tests is_valid_linkage(Z) on linkage on observation sets between sizes 4 and 15 (step size 3) with negative indices (left). ... ok
Tests is_valid_linkage(Z) on linkage on observation sets between sizes 4 and 15 (step size 3) with negative indices (right). ... ok
Tests is_valid_linkage(Z) with 5 columns. ... ok
Tests is_valid_linkage(Z) with empty linkage. ... ok
Tests is_valid_linkage(Z) with integer type. ... ok
Tests leaders using a flat clustering generated by single linkage. ... ok
Tests leaves_list(Z) on a 1x4 linkage. ... ok
Tests leaves_list(Z) on a 2x4 linkage. ... ok
Tests leaves_list(Z) on the Iris data set using average linkage. ... ok
Tests leaves_list(Z) on the Iris data set using centroid linkage. ... ok
Tests leaves_list(Z) on the Iris data set using complete linkage. ... ok
Tests leaves_list(Z) on the Iris data set using median linkage. ... ok
Tests leaves_list(Z) on the Iris data set using single linkage. ... ok
Tests leaves_list(Z) on the Iris data set using ward linkage. ... ok
Tests linkage(Y, 'average') on the tdist data set. ... ok
Tests linkage(Y, 'centroid') on the Q data set. ... ok
Tests linkage(Y, 'complete') on the Q data set. ... ok
Tests linkage(Y, 'complete') on the tdist data set. ... ok
Tests linkage(Y) where Y is a 0x4 linkage matrix. Exception expected. ... ok
Tests linkage(Y, 'single') on the Q data set. ... ok
Tests linkage(Y, 'single') on the tdist data set. ... ok
Tests linkage(Y, 'weighted') on the Q data set. ... ok
Tests linkage(Y, 'weighted') on the tdist data set. ... ok
Tests maxdists(Z) on the Q data set using centroid linkage. ... ok
Tests maxdists(Z) on the Q data set using complete linkage. ... ok
Tests maxdists(Z) on the Q data set using median linkage. ... ok
Tests maxdists(Z) on the Q data set using single linkage. ... ok
Tests maxdists(Z) on the Q data set using Ward linkage. ... ok
Tests maxdists(Z) on empty linkage. Expecting exception. ... ok
Tests maxdists(Z) on linkage with one cluster. ... ok
Tests maxinconsts(Z, R) on the Q data set using centroid linkage. ... ok
Tests maxinconsts(Z, R) on the Q data set using complete linkage. ... ok
Tests maxinconsts(Z, R) on the Q data set using median linkage. ... ok
Tests maxinconsts(Z, R) on the Q data set using single linkage. ... ok
Tests maxinconsts(Z, R) on the Q data set using Ward linkage. ... ok
Tests maxinconsts(Z, R) on linkage and inconsistency matrices with different numbers of clusters. Expecting exception. ... ok
Tests maxinconsts(Z, R) on empty linkage. Expecting exception. ... ok
Tests maxinconsts(Z, R) on linkage with one cluster. ... ok
Tests maxRstat(Z, R, 0) on the Q data set using centroid linkage. ... ok
Tests maxRstat(Z, R, 0) on the Q data set using complete linkage. ... ok
Tests maxRstat(Z, R, 0) on the Q data set using median linkage. ... ok
Tests maxRstat(Z, R, 0) on the Q data set using single linkage. ... ok
Tests maxRstat(Z, R, 0) on the Q data set using Ward linkage. ... ok
Tests maxRstat(Z, R, 0) on linkage and inconsistency matrices with different numbers of clusters. Expecting exception. ... ok
Tests maxRstat(Z, R, 0) on empty linkage. Expecting exception. ... ok
Tests maxRstat(Z, R, 0) on linkage with one cluster. ... ok
Tests maxRstat(Z, R, 1) on the Q data set using centroid linkage. ... ok
Tests maxRstat(Z, R, 1) on the Q data set using complete linkage. ... ok
Tests maxRstat(Z, R, 1) on the Q data set using median linkage. ... ok
Tests maxRstat(Z, R, 1) on the Q data set using single linkage. ... ok
Tests maxRstat(Z, R, 1) on the Q data set using Ward linkage. ... ok
Tests maxRstat(Z, R, 1) on linkage and inconsistency matrices with different numbers of clusters. Expecting exception. ... ok
Tests maxRstat(Z, R, 1) on empty linkage. Expecting exception. ... ok
Tests maxRstat(Z, R, 1) on linkage with one cluster. ... ok
Tests maxRstat(Z, R, 2) on the Q data set using centroid linkage. ... ok
Tests maxRstat(Z, R, 2) on the Q data set using complete linkage. ... ok
Tests maxRstat(Z, R, 2) on the Q data set using median linkage. ... ok
Tests maxRstat(Z, R, 2) on the Q data set using single linkage. ... ok
Tests maxRstat(Z, R, 2) on the Q data set using Ward linkage. ... ok
Tests maxRstat(Z, R, 2) on linkage and inconsistency matrices with different numbers of clusters. Expecting exception. ... ok
Tests maxRstat(Z, R, 2) on empty linkage. Expecting exception. ... ok
Tests maxRstat(Z, R, 2) on linkage with one cluster. ... ok
Tests maxRstat(Z, R, 3) on the Q data set using centroid linkage. ... ok
Tests maxRstat(Z, R, 3) on the Q data set using complete linkage. ... ok
Tests maxRstat(Z, R, 3) on the Q data set using median linkage. ... ok
Tests maxRstat(Z, R, 3) on the Q data set using single linkage. ... ok
Tests maxRstat(Z, R, 3) on the Q data set using Ward linkage. ... ok
Tests maxRstat(Z, R, 3) on linkage and inconsistency matrices with different numbers of clusters. Expecting exception. ... ok
Tests maxRstat(Z, R, 3) on empty linkage. Expecting exception. ... ok
Tests maxRstat(Z, R, 3) on linkage with one cluster. ... ok
Tests maxRstat(Z, R, 3.3). Expecting exception. ... ok
Tests maxRstat(Z, R, -1). Expecting exception. ... ok
Tests maxRstat(Z, R, 4). Expecting exception. ... ok
Tests num_obs_linkage(Z) on linkage over 2 observations. ... ok
Tests num_obs_linkage(Z) on linkage over 3 observations. ... ok
Tests num_obs_linkage(Z) on linkage on observation sets between sizes 4 and 15 (step size 3). ... ok
Tests num_obs_linkage(Z) with empty linkage. ... ok
Tests to_mlab_linkage on linkage array with multiple rows. ... ok
Tests to_mlab_linkage on empty linkage array. ... ok
Tests to_mlab_linkage on linkage array with single row. ... ok
test_hierarchy.load_testing_files ... ok
Ticket #505. ... ok
Testing that kmeans2 init methods work. ... ok
Testing simple call to kmeans2 with rank 1 data. ... ok
Testing simple call to kmeans2 with rank 1 data. ... ok
Testing simple call to kmeans2 and its results. ... ok
Regression test for #546: fail when k arg is 0. ... ok
This will cause kmean to have a cluster with no points. ... ok
test_kmeans_simple (test_vq.TestKMean) ... ok
test_large_features (test_vq.TestKMean) ... ok
test_py_vq (test_vq.TestVq) ... ok
test_py_vq2 (test_vq.TestVq) ... ok
test_vq (test_vq.TestVq) ... ok
Test special rank 1 vq algo, python implementation. ... ok
test_definition (test_basic.TestDoubleFFT) ... ok
test_djbfft (test_basic.TestDoubleFFT) ... ok
test_n_argument_real (test_basic.TestDoubleFFT) ... ok
test_definition (test_basic.TestDoubleIFFT) ... ok
test_definition_real (test_basic.TestDoubleIFFT) ... ok
test_djbfft (test_basic.TestDoubleIFFT) ... ok
test_random_complex (test_basic.TestDoubleIFFT) ... ok
test_random_real (test_basic.TestDoubleIFFT) ... ok
test_size_accuracy (test_basic.TestDoubleIFFT) ... ok
test_axes_argument (test_basic.TestFftn) ... ok
test_definition (test_basic.TestFftn) ... ok
test_shape_argument (test_basic.TestFftn) ... ok
Test that fftn raises ValueError when s.shape is longer than x.shape ... ok
test_shape_axes_argument (test_basic.TestFftn) ... ok
test_shape_axes_argument2 (test_basic.TestFftn) ... ok
test_definition (test_basic.TestFftnSingle) ... ok
test_size_accuracy (test_basic.TestFftnSingle) ... ok
test_definition (test_basic.TestIRFFTDouble) ... ok
test_djbfft (test_basic.TestIRFFTDouble) ... ok
test_random_real (test_basic.TestIRFFTDouble) ... ok
test_size_accuracy (test_basic.TestIRFFTDouble) ... ok
test_definition (test_basic.TestIRFFTSingle) ... ok
test_djbfft (test_basic.TestIRFFTSingle) ... ok
test_random_real (test_basic.TestIRFFTSingle) ... ok
test_size_accuracy (test_basic.TestIRFFTSingle) ... ok
test_definition (test_basic.TestIfftnDouble) ... ok
test_random_complex (test_basic.TestIfftnDouble) ... ok
test_definition (test_basic.TestIfftnSingle) ... ok
test_random_complex (test_basic.TestIfftnSingle) ... ok
test_complex (test_basic.TestLongDoubleFailure) ... ok
test_real (test_basic.TestLongDoubleFailure) ... ok
test_basic.TestOverwrite.test_fft ... ok
test_basic.TestOverwrite.test_fftn ... ok
test_basic.TestOverwrite.test_ifft ... ok
test_basic.TestOverwrite.test_ifftn ... ok
test_basic.TestOverwrite.test_irfft ... ok
test_basic.TestOverwrite.test_rfft ... ok
test_definition (test_basic.TestRFFTDouble) ... ok
test_djbfft (test_basic.TestRFFTDouble) ... ok
test_definition (test_basic.TestRFFTSingle) ... ok
test_djbfft (test_basic.TestRFFTSingle) ... ok
test_definition (test_basic.TestSingleFFT) ... ok
test_djbfft (test_basic.TestSingleFFT) ... ok
test_n_argument_real (test_basic.TestSingleFFT) ... ok
test_notice (test_basic.TestSingleFFT) ... KNOWNFAIL: single-precision FFT implementation is partially disabled, until accuracy issues with large prime powers are resolved
test_definition (test_basic.TestSingleIFFT) ... ok
test_definition_real (test_basic.TestSingleIFFT) ... ok
test_djbfft (test_basic.TestSingleIFFT) ... ok
test_random_complex (test_basic.TestSingleIFFT) ... ok
test_random_real (test_basic.TestSingleIFFT) ... ok
test_size_accuracy (test_basic.TestSingleIFFT) ... ok
fft returns wrong result with axes parameter. ... ok
test_definition (test_helper.TestFFTFreq) ... ok
test_definition (test_helper.TestFFTShift) ... ok
test_inverse (test_helper.TestFFTShift) ... ok
test_definition (test_helper.TestRFFTFreq) ... ok
test_definition (test_pseudo_diffs.TestDiff) ... ok
test_expr (test_pseudo_diffs.TestDiff) ... ok
test_expr_large (test_pseudo_diffs.TestDiff) ... ok
test_int (test_pseudo_diffs.TestDiff) ... ok
test_period (test_pseudo_diffs.TestDiff) ... ok
test_random_even (test_pseudo_diffs.TestDiff) ... ok
test_random_odd (test_pseudo_diffs.TestDiff) ... ok
test_sin (test_pseudo_diffs.TestDiff) ... ok
test_zero_nyquist (test_pseudo_diffs.TestDiff) ... ok
test_definition (test_pseudo_diffs.TestHilbert) ... ok
test_random_even (test_pseudo_diffs.TestHilbert) ... ok
test_random_odd (test_pseudo_diffs.TestHilbert) ... ok
test_tilbert_relation (test_pseudo_diffs.TestHilbert) ... ok
test_definition (test_pseudo_diffs.TestIHilbert) ... ok
test_itilbert_relation (test_pseudo_diffs.TestIHilbert) ... ok
test_definition (test_pseudo_diffs.TestITilbert) ... ok
test_pseudo_diffs.TestOverwrite.test_cc_diff ... ok
test_pseudo_diffs.TestOverwrite.test_cs_diff ... ok
test_pseudo_diffs.TestOverwrite.test_diff ... ok
test_pseudo_diffs.TestOverwrite.test_hilbert ... ok
test_pseudo_diffs.TestOverwrite.test_itilbert ... ok
test_pseudo_diffs.TestOverwrite.test_sc_diff ... ok
test_pseudo_diffs.TestOverwrite.test_shift ... ok
test_pseudo_diffs.TestOverwrite.test_ss_diff ... ok
test_pseudo_diffs.TestOverwrite.test_tilbert ... ok
test_definition (test_pseudo_diffs.TestShift) ... ok
test_definition (test_pseudo_diffs.TestTilbert) ... ok
test_random_even (test_pseudo_diffs.TestTilbert) ... ok
test_random_odd (test_pseudo_diffs.TestTilbert) ... ok
test_axis (test_real_transforms.TestDCTIDouble) ... ok
test_definition (test_real_transforms.TestDCTIDouble) ... ok
test_axis (test_real_transforms.TestDCTIFloat) ... ok
test_definition (test_real_transforms.TestDCTIFloat) ... ok
test_axis (test_real_transforms.TestDCTIIDouble) ... ok
test_definition (test_real_transforms.TestDCTIIDouble) ... ok
Test correspondance with matlab (orthornomal mode). ... ok
test_axis (test_real_transforms.TestDCTIIFloat) ... ok
test_definition (test_real_transforms.TestDCTIIFloat) ... ok
Test correspondance with matlab (orthornomal mode). ... ok
test_axis (test_real_transforms.TestDCTIIIDouble) ... ok
test_definition (test_real_transforms.TestDCTIIIDouble) ... ok
Test orthornomal mode. ... ok
test_axis (test_real_transforms.TestDCTIIIFloat) ... ok
test_definition (test_real_transforms.TestDCTIIIFloat) ... ok
Test orthornomal mode. ... ok
test_definition (test_real_transforms.TestIDCTIDouble) ... ok
test_definition (test_real_transforms.TestIDCTIFloat) ... ok
test_definition (test_real_transforms.TestIDCTIIDouble) ... ok
test_definition (test_real_transforms.TestIDCTIIFloat) ... ok
test_definition (test_real_transforms.TestIDCTIIIDouble) ... ok
test_definition (test_real_transforms.TestIDCTIIIFloat) ... ok
test_real_transforms.TestOverwrite.test_dct ... ok
test_real_transforms.TestOverwrite.test_idct ... ok
Check the dop853 solver ... ok
Check the dopri5 solver ... ok
Check the vode solver ... ok
Check the dop853 solver ... ok
Check the dopri5 solver ... ok
Check the vode solver ... ok
Check the zvode solver ... ok
test_odeint (test_integrate.TestOdeint) ... ok
test_algebraic_log_weight (test_quadpack.TestQuad) ... ok
test_cauchypv_weight (test_quadpack.TestQuad) ... ok
test_cosine_weighted_infinite (test_quadpack.TestQuad) ... ok
test_double_integral (test_quadpack.TestQuad) ... ok
test_indefinite (test_quadpack.TestQuad) ... ok
test_sine_weighted_finite (test_quadpack.TestQuad) ... ok
test_sine_weighted_infinite (test_quadpack.TestQuad) ... ok
test_singular (test_quadpack.TestQuad) ... ok
test_triple_integral (test_quadpack.TestQuad) ... ok
test_typical (test_quadpack.TestQuad) ... ok
Test the first few degrees, for evenly spaced points. ... ok
Test newton_cotes with points that are not evenly spaced. ... ok
test_non_dtype (test_quadrature.TestQuadrature) ... ok
test_quadrature (test_quadrature.TestQuadrature) ... ok
test_quadrature_rtol (test_quadrature.TestQuadrature) ... ok
test_romb (test_quadrature.TestQuadrature) ... ok
test_romberg (test_quadrature.TestQuadrature) ... ok
test_romberg_rtol (test_quadrature.TestQuadrature) ... ok
test_bilinearity (test_fitpack.TestLSQBivariateSpline) ... ok
Test whether empty inputs returns an empty output. Ticket 1014 ... ok
test_integral (test_fitpack.TestLSQBivariateSpline) ... ok
test_linear_constant (test_fitpack.TestLSQBivariateSpline) ... ok
test_defaults (test_fitpack.TestRectBivariateSpline) ... ok
test_evaluate (test_fitpack.TestRectBivariateSpline) ... ok
test_integral (test_fitpack.TestSmoothBivariateSpline) ... ok
test_linear_1d (test_fitpack.TestSmoothBivariateSpline) ... ok
test_linear_constant (test_fitpack.TestSmoothBivariateSpline) ... ok
Test whether empty input returns an empty output. Ticket 1014 ... ok
test_linear_1d (test_fitpack.TestUnivariateSpline) ... ok
test_linear_constant (test_fitpack.TestUnivariateSpline) ... ok
test_preserve_shape (test_fitpack.TestUnivariateSpline) ... ok
test_subclassing (test_fitpack.TestUnivariateSpline) ... ok
test_interpnd.TestCloughTocher2DInterpolator.test_dense ... ok
test_interpnd.TestCloughTocher2DInterpolator.test_linear_smoketest ... ok
test_interpnd.TestCloughTocher2DInterpolator.test_quadratic_smoketest ... ok
test_interpnd.TestEstimateGradients2DGlobal.test_smoketest ... ok
test_interpnd.TestLinearNDInterpolation.test_complex_smoketest ... ok
test_interpnd.TestLinearNDInterpolation.test_smoketest ... ok
test_interpnd.TestLinearNDInterpolation.test_smoketest_alternate ... ok
test_interpnd.TestLinearNDInterpolation.test_square ... ok
test_interpolate.TestInterp1D.test_bounds ... ok
test_interpolate.TestInterp1D.test_complex ... ok
Check the actual implementation of spline interpolation. ... ok
Check that the attributes are initialized appropriately by the ... ok
Check the actual implementation of linear interpolation. ... ok
test_interpolate.TestInterp1D.test_nd ... ok
test_interpolate.TestInterp1D.test_nd_zero_spline ... KNOWNFAIL: zero-order splines fail for the last point
Check the actual implementation of nearest-neighbour interpolation. ... ok
Make sure that appropriate exceptions are raised when invalid values ... ok
Check the actual implementation of zero-order spline interpolation. ... KNOWNFAIL: zero-order splines fail for the last point
test_interp2d (test_interpolate.TestInterp2D) ... ok
test_interp2d_meshgrid_input (test_interpolate.TestInterp2D) ... ok
test_lagrange (test_interpolate.TestLagrange) ... ok
test_block_average_above (test_interpolate_wrapper.Test) ... ok
test_linear (test_interpolate_wrapper.Test) ... ok
test_linear2 (test_interpolate_wrapper.Test) ... ok
test_logarithmic (test_interpolate_wrapper.Test) ... ok
test_nearest (test_interpolate_wrapper.Test) ... ok
test_ndgriddata.TestGriddata.test_1d ... ok
test_ndgriddata.TestGriddata.test_alternative_call ... ok
test_ndgriddata.TestGriddata.test_complex_2d ... ok
test_ndgriddata.TestGriddata.test_fill_value ... ok
test_ndgriddata.TestGriddata.test_multipoint_2d ... ok
test_ndgriddata.TestGriddata.test_multivalue_2d ... ok
test_append (test_polyint.CheckBarycentric) ... ok
test_delayed (test_polyint.CheckBarycentric) ... ok
test_lagrange (test_polyint.CheckBarycentric) ... ok
test_scalar (test_polyint.CheckBarycentric) ... ok
test_shapes_1d_vectorvalue (test_polyint.CheckBarycentric) ... ok
test_shapes_scalarvalue (test_polyint.CheckBarycentric) ... ok
test_shapes_vectorvalue (test_polyint.CheckBarycentric) ... ok
test_vector (test_polyint.CheckBarycentric) ... ok
test_wrapper (test_polyint.CheckBarycentric) ... ok
test_derivative (test_polyint.CheckKrogh) ... ok
test_derivatives (test_polyint.CheckKrogh) ... ok
test_empty (test_polyint.CheckKrogh) ... ok
test_hermite (test_polyint.CheckKrogh) ... ok
test_high_derivative (test_polyint.CheckKrogh) ... ok
test_lagrange (test_polyint.CheckKrogh) ... ok
test_low_derivatives (test_polyint.CheckKrogh) ... ok
test_scalar (test_polyint.CheckKrogh) ... ok
test_shapes_1d_vectorvalue (test_polyint.CheckKrogh) ... ok
test_shapes_scalarvalue (test_polyint.CheckKrogh) ... ok
test_shapes_scalarvalue_derivative (test_polyint.CheckKrogh) ... ok
test_shapes_vectorvalue (test_polyint.CheckKrogh) ... ok
test_shapes_vectorvalue_derivative (test_polyint.CheckKrogh) ... ok
test_vector (test_polyint.CheckKrogh) ... ok
test_wrapper (test_polyint.CheckKrogh) ... ok
test_construction (test_polyint.CheckPiecewise) ... ok
test_derivative (test_polyint.CheckPiecewise) ... ok
test_derivatives (test_polyint.CheckPiecewise) ... ok
test_incremental (test_polyint.CheckPiecewise) ... ok
test_scalar (test_polyint.CheckPiecewise) ... ok
test_shapes_scalarvalue (test_polyint.CheckPiecewise) ... ok
test_shapes_scalarvalue_derivative (test_polyint.CheckPiecewise) ... ok
test_shapes_vectorvalue (test_polyint.CheckPiecewise) ... ok
test_shapes_vectorvalue_1d (test_polyint.CheckPiecewise) ... ok
test_shapes_vectorvalue_derivative (test_polyint.CheckPiecewise) ... ok
test_vector (test_polyint.CheckPiecewise) ... ok
test_wrapper (test_polyint.CheckPiecewise) ... ok
test_exponential (test_polyint.CheckTaylor) ... ok
test_rbf.test_rbf_interpolation('multiquadric',) ... ok
test_rbf.test_rbf_interpolation('multiquadric',) ... ok
test_rbf.test_rbf_interpolation('multiquadric',) ... ok
test_rbf.test_rbf_interpolation('inverse multiquadric',) ... ok
test_rbf.test_rbf_interpolation('inverse multiquadric',) ... ok
test_rbf.test_rbf_interpolation('inverse multiquadric',) ... ok
test_rbf.test_rbf_interpolation('gaussian',) ... ok
test_rbf.test_rbf_interpolation('gaussian',) ... ok
test_rbf.test_rbf_interpolation('gaussian',) ... ok
test_rbf.test_rbf_interpolation('cubic',) ... ok
test_rbf.test_rbf_interpolation('cubic',) ... ok
test_rbf.test_rbf_interpolation('cubic',) ... ok
test_rbf.test_rbf_interpolation('quintic',) ... ok
test_rbf.test_rbf_interpolation('quintic',) ... ok
test_rbf.test_rbf_interpolation('quintic',) ... ok
test_rbf.test_rbf_interpolation('thin-plate',) ... ok
test_rbf.test_rbf_interpolation('thin-plate',) ... ok
test_rbf.test_rbf_interpolation('thin-plate',) ... ok
test_rbf.test_rbf_interpolation('linear',) ... ok
test_rbf.test_rbf_interpolation('linear',) ... ok
test_rbf.test_rbf_interpolation('linear',) ... ok
test_rbf.test_rbf_regularity('multiquadric', 0.050000000000000003) ... ok
test_rbf.test_rbf_regularity('inverse multiquadric', 0.02) ... ok
test_rbf.test_rbf_regularity('gaussian', 0.01) ... ok
test_rbf.test_rbf_regularity('cubic', 0.14999999999999999) ... ok
test_rbf.test_rbf_regularity('quintic', 0.10000000000000001) ... ok
test_rbf.test_rbf_regularity('thin-plate', 0.10000000000000001) ... ok
test_rbf.test_rbf_regularity('linear', 0.20000000000000001) ... ok
Check that the Rbf class can be constructed with the default ... ok
Check that the Rbf class can be constructed with function=callable. ... ok
Ticket #629 ... ok
Parsing trivial file with nothing. ... ok
Parsing trivial file with some comments in the data section. ... ok
Test parsing wrong type of attribute from their value. ... ok
Parsing trivial header with nothing. ... ok
Test parsing type of attribute from their value. ... ok
test_missing (test_arffread.MissingDataTest) ... ok
test_byteordercodes.test_native ... ok
test_byteordercodes.test_to_numpy ... ok
test_mio.test_load('double', ['/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testdouble_4.2c_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testdouble_6.1_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testdouble_6.5.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testdouble_7.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testdouble_7.4_GLNX86.mat'], {'testdouble': array([[ 0. , 0.78539816, 1.57079633, 2.35619449, 3.14159265, ... ok
test_mio.test_load('string', ['/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/teststring_4.2c_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/teststring_6.1_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/teststring_6.5.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/teststring_7.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/teststring_7.4_GLNX86.mat'], {'teststring': array([u'"Do nine men interpret?" "Nine men," I nod.'], ... ok
test_mio.test_load('complex', ['/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testcomplex_4.2c_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testcomplex_6.1_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testcomplex_6.5.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testcomplex_7.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testcomplex_7.4_GLNX86.mat'], {'testcomplex': array([[ 1.00000000e+00 +0.00000000e+00j, ... ok
test_mio.test_load('matrix', ['/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testmatrix_4.2c_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testmatrix_6.1_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testmatrix_6.5.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testmatrix_7.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testmatrix_7.4_GLNX86.mat'], {'testmatrix': array([[ 1., 2., 3., 4., 5.], ... ok
test_mio.test_load('sparse', ['/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testsparse_4.2c_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testsparse_6.1_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testsparse_6.5.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testsparse_7.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testsparse_7.4_GLNX86.mat'], {'testsparse': <3x5 sparse matrix of type '<type 'numpy.float64'>' ... ok
test_mio.test_load('sparsecomplex', ['/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testsparsecomplex_4.2c_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testsparsecomplex_6.1_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testsparsecomplex_6.5.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testsparsecomplex_7.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testsparsecomplex_7.4_GLNX86.mat'], {'testsparsecomplex': <3x5 sparse matrix of type '<type 'numpy.complex128'>' ... ok
test_mio.test_load('multi', ['/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testmulti_4.2c_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testmulti_7.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testmulti_7.4_GLNX86.mat'], {'a': array([[ 1., 2., 3., 4., 5.], ... ok
test_mio.test_load('minus', ['/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testminus_4.2c_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testminus_6.1_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testminus_6.5.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testminus_7.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testminus_7.4_GLNX86.mat'], {'testminus': array([[-1]])}) ... ok
test_mio.test_load('onechar', ['/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testonechar_4.2c_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testonechar_6.1_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testonechar_6.5.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testonechar_7.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testonechar_7.4_GLNX86.mat'], {'testonechar': array([u'r'], ... ok
test_mio.test_load('cell', ['/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testcell_6.1_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testcell_6.5.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testcell_7.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testcell_7.4_GLNX86.mat'], {'testcell': array([[[u'This cell contains this string and 3 arrays of increasing length'], ... ok
test_mio.test_load('scalarcell', ['/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testscalarcell_7.4_GLNX86.mat'], {'testscalarcell': array([[[[1]]]], dtype=object)}) ... ok
test_mio.test_load('emptycell', ['/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testemptycell_5.3_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testemptycell_6.5.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testemptycell_7.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testemptycell_7.4_GLNX86.mat'], {'testemptycell': array([[[[1]], [[2]], [], [], [[3]]]], dtype=object)}) ... ok
test_mio.test_load('stringarray', ['/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/teststringarray_4.2c_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/teststringarray_6.1_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/teststringarray_6.5.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/teststringarray_7.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/teststringarray_7.4_GLNX86.mat'], {'teststringarray': array([u'one ', u'two ', u'three'], ... ok
test_mio.test_load('3dmatrix', ['/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/test3dmatrix_6.1_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/test3dmatrix_6.5.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/test3dmatrix_7.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/test3dmatrix_7.4_GLNX86.mat'], {'test3dmatrix': array([[[ 1, 7, 13, 19], ... ok
test_mio.test_load('struct', ['/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/teststruct_6.1_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/teststruct_6.5.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/teststruct_7.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/teststruct_7.4_GLNX86.mat'], {'teststruct': array([[ ([u'Rats live on no evil star.'], [[1.4142135623730951, 2.7182818284590451, 3.1415926535897931]], [[(1.4142135623730951+1.4142135623730951j), (2.7182818284590451+2.7182818284590451j), (3.1415926535897931+3.1415926535897931j)]])]], ... ok
test_mio.test_load('cellnest', ['/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testcellnest_6.1_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testcellnest_6.5.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testcellnest_7.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testcellnest_7.4_GLNX86.mat'], {'testcellnest': array([[[[1]], [[[[2]] [[3]] [[[[4]] [[5]]]]]]]], dtype=object)}) ... ok
test_mio.test_load('structnest', ['/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/teststructnest_6.1_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/teststructnest_6.5.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/teststructnest_7.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/teststructnest_7.4_GLNX86.mat'], {'teststructnest': array([[([[1]], [[(array([u'number 3'], ... ok
test_mio.test_load('structarr', ['/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/teststructarr_6.1_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/teststructarr_6.5.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/teststructarr_7.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/teststructarr_7.4_GLNX86.mat'], {'teststructarr': array([[([[1]], [[2]]), ([u'number 1'], [u'number 2'])]], ... ok
test_mio.test_load('object', ['/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testobject_6.1_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testobject_6.5.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testobject_7.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testobject_7.4_GLNX86.mat'], {'testobject': MatlabObject([[([u'x'], [u' x = INLINE_INPUTS_{1};'], [u'x'], [[0]], [[1]], [[1]])]], ... ok
test_mio.test_load('unicode', ['/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testunicode_7.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testunicode_7.4_GLNX86.mat'], {'testunicode': array([ u'Japanese: \n\u3059\u3079\u3066\u306e\u4eba\u9593\u306f\u3001\u751f\u307e\u308c\u306a\u304c\u3089\u306b\u3057\u3066\u81ea\u7531\u3067\u3042\u308a\u3001\n\u304b\u3064\u3001\u5c0a\u53b3\u3068\u6a29\u5229\u3068 \u306b\u3064\u3044\u3066\u5e73\u7b49\u3067\u3042\u308b\u3002\n\u4eba\u9593\u306f\u3001\u7406\u6027\u3068\u826f\u5fc3\u3068\u3092\u6388\u3051\u3089\u308c\u3066\u304a\u308a\u3001\n\u4e92\u3044\u306b\u540c\u80de\u306e\u7cbe\u795e\u3092\u3082\u3063\u3066\u884c\u52d5\u3057\u306a\u3051\u308c\u3070\u306a\u3089\u306a\u3044\u3002'], ... ok
test_mio.test_load('sparse', ['/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testsparse_4.2c_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testsparse_6.1_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testsparse_6.5.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testsparse_7.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testsparse_7.4_GLNX86.mat'], {'testsparse': <3x5 sparse matrix of type '<type 'numpy.float64'>' ... ok
test_mio.test_load('sparsecomplex', ['/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testsparsecomplex_4.2c_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testsparsecomplex_6.1_SOL2.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testsparsecomplex_6.5.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testsparsecomplex_7.1_GLNX86.mat', '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testsparsecomplex_7.4_GLNX86.mat'], {'testsparsecomplex': <3x5 sparse matrix of type '<type 'numpy.complex128'>' ... ok
test_mio.test_round_trip('double_round_trip', {'testdouble': array([[ 0. , 0.78539816, 1.57079633, 2.35619449, 3.14159265, ... ok
test_mio.test_round_trip('string_round_trip', {'teststring': array([u'"Do nine men interpret?" "Nine men," I nod.'], ... ok
test_mio.test_round_trip('complex_round_trip', {'testcomplex': array([[ 1.00000000e+00 +0.00000000e+00j, ... ok
test_mio.test_round_trip('matrix_round_trip', {'testmatrix': array([[ 1., 2., 3., 4., 5.], ... ok
test_mio.test_round_trip('sparse_round_trip', {'testsparse': <3x5 sparse matrix of type '<type 'numpy.float64'>' ... ok
test_mio.test_round_trip('sparsecomplex_round_trip', {'testsparsecomplex': <3x5 sparse matrix of type '<type 'numpy.complex128'>' ... ok
test_mio.test_round_trip('multi_round_trip', {'a': array([[ 1., 2., 3., 4., 5.], ... ok
test_mio.test_round_trip('minus_round_trip', {'testminus': array([[-1]])}, '4') ... ok
test_mio.test_round_trip('onechar_round_trip', {'testonechar': array([u'r'], ... ok
test_mio.test_round_trip('cell_round_trip', {'testcell': array([[[u'This cell contains this string and 3 arrays of increasing length'], ... ok
test_mio.test_round_trip('scalarcell_round_trip', {'testscalarcell': array([[[[1]]]], dtype=object)}, '5') ... ok
test_mio.test_round_trip('emptycell_round_trip', {'testemptycell': array([[[[1]], [[2]], [], [], [[3]]]], dtype=object)}, '5') ... ok
test_mio.test_round_trip('stringarray_round_trip', {'teststringarray': array([u'one ', u'two ', u'three'], ... ok
test_mio.test_round_trip('3dmatrix_round_trip', {'test3dmatrix': array([[[ 1, 7, 13, 19], ... ok
test_mio.test_round_trip('struct_round_trip', {'teststruct': array([[ ([u'Rats live on no evil star.'], [[1.4142135623730951, 2.7182818284590451, 3.1415926535897931]], [[(1.4142135623730951+1.4142135623730951j), (2.7182818284590451+2.7182818284590451j), (3.1415926535897931+3.1415926535897931j)]])]], ... ok
test_mio.test_round_trip('cellnest_round_trip', {'testcellnest': array([[[[1]], [[[[2]] [[3]] [[[[4]] [[5]]]]]]]], dtype=object)}, '5') ... ok
test_mio.test_round_trip('structnest_round_trip', {'teststructnest': array([[([[1]], [[(array([u'number 3'], ... ok
test_mio.test_round_trip('structarr_round_trip', {'teststructarr': array([[([[1]], [[2]]), ([u'number 1'], [u'number 2'])]], ... ok
test_mio.test_round_trip('object_round_trip', {'testobject': MatlabObject([[([u'x'], [u' x = INLINE_INPUTS_{1};'], [u'x'], [[0]], [[1]], [[1]])]], ... ok
test_mio.test_round_trip('unicode_round_trip', {'testunicode': array([ u'Japanese: \n\u3059\u3079\u3066\u306e\u4eba\u9593\u306f\u3001\u751f\u307e\u308c\u306a\u304c\u3089\u306b\u3057\u3066\u81ea\u7531\u3067\u3042\u308a\u3001\n\u304b\u3064\u3001\u5c0a\u53b3\u3068\u6a29\u5229\u3068 \u306b\u3064\u3044\u3066\u5e73\u7b49\u3067\u3042\u308b\u3002\n\u4eba\u9593\u306f\u3001\u7406\u6027\u3068\u826f\u5fc3\u3068\u3092\u6388\u3051\u3089\u308c\u3066\u304a\u308a\u3001\n\u4e92\u3044\u306b\u540c\u80de\u306e\u7cbe\u795e\u3092\u3082\u3063\u3066\u884c\u52d5\u3057\u306a\u3051\u308c\u3070\u306a\u3089\u306a\u3044\u3002'], ... ok
test_mio.test_round_trip('sparse_round_trip', {'testsparse': <3x5 sparse matrix of type '<type 'numpy.float64'>' ... ok
test_mio.test_round_trip('sparsecomplex_round_trip', {'testsparsecomplex': <3x5 sparse matrix of type '<type 'numpy.complex128'>' ... ok
test_mio.test_round_trip('objectarray_round_trip', {'testobjectarray': MatlabObject([[([u'x'], [u' x = INLINE_INPUTS_{1};'], [u'x'], [[0]], [[1]], [[1]]), ... ok
test_mio.test_gzip_simple ... ok
test_mio.test_multiple_open ... ok
test_mio.test_mat73 ... ok
test_mio.test_warnings(<type 'exceptions.DeprecationWarning'>, <function find_mat_file at 0x2e9d7f0>, '/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/matlab/tests/data/testdouble_7.1_GLNX86.mat') ... ok
Regression test for #653. ... ok
test_mio.test_structname_len ... ok
test_mio.test_4_and_long_field_names_incompatible ... ok
test_mio.test_long_field_names ... ok
test_mio.test_long_field_names_in_struct ... ok
test_mio.test_cell_with_one_thing_in_it ... ok
test_mio.test_writer_properties([], []) ... ok
test_mio.test_writer_properties(['avar'], ['avar']) ... ok
test_mio.test_writer_properties(False, False) ... ok
test_mio.test_writer_properties(True, True) ... ok
test_mio.test_writer_properties(False, False) ... ok
test_mio.test_writer_properties(True, True) ... ok
test_mio.test_use_small_element(True,) ... ok
test_mio.test_use_small_element(True,) ... ok
test_mio.test_save_dict ... ok
test_mio.test_1d_shape ... ok
test_mio.test_compression(array([[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., ... ok
test_mio.test_compression(array([[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., ... ok
test_mio.test_compression(True,) ... ok
test_mio.test_compression(array([[ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., ... ok
test_mio.test_compression(array([[ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., ... ok
test_mio.test_single_object ... ok
test_mio.test_skip_variable(True,) ... ok
test_mio.test_skip_variable(True,) ... ok
test_mio.test_skip_variable(True,) ... ok
test_mio.test_empty_struct ... ok
test_mio.test_recarray(array([[ 0.5]]), 0.5) ... ok
test_mio.test_recarray(array([u'python'], ... ok
test_mio.test_recarray(array([[ 0.5]]), 0.5) ... ok
test_mio.test_recarray(array([u'python'], ... ok
test_mio.test_recarray(dtype([('f1', '|O4'), ('f2', '|O4')]), dtype([('f1', '|O4'), ('f2', '|O4')])) ... ok
test_mio.test_recarray(array([[ 99.]]), 99) ... ok
test_mio.test_recarray(array([u'not perl'], ... ok
test_mio.test_save_object ... ok
test_mio.test_read_opts ... ok
test_mio.test_empty_string ... ok
test_mio.test_mat4_3d ... ok
test_mio.test_func_read(True,) ... ok
test_mio.test_func_read(<class 'scipy.io.matlab.miobase.MatWriteError'>, <bound method MatFile5Writer.put_variables of <scipy.io.matlab.mio5.MatFile5Writer object at 0x41bd150>>, {'__version__': '1.0', '__header__': 'MATLAB 5.0 MAT-file, Platform: GLNX86, Created on: Fri Feb 20 15:26:59 2009', 'testfunc': MatlabFunction([[ ([u'/opt/matlab-2007a'], [u'/'], [u'@'], [[(array([u'afunc'], ... ok
test_mio.test_mat_dtype('u', 'u') ... ok
test_mio.test_mat_dtype('f', 'f') ... ok
test_mio.test_sparse_in_struct(matrix([[ 1., 0., 0., 0.], ... ok
test_mio.test_mat_struct_squeeze ... ok
test_mio.test_str_round ... ok
test_mio.test_fieldnames ... ok
test_mio.test_loadmat_varnames ... ok
test_mio.test_round_types ... ok
test_mio.test_varmats_from_mat ... ok
test_mio5_utils.test_byteswap(16777216L, 16777216L) ... ok
test_mio5_utils.test_byteswap(1L, 1L) ... ok
test_mio5_utils.test_byteswap(65536L, 65536L) ... ok
test_mio5_utils.test_byteswap(256L, 256L) ... ok
test_mio5_utils.test_byteswap(256L, 256L) ... ok
test_mio5_utils.test_byteswap(65536L, 65536L) ... ok
test_mio5_utils.test_read_tag(<type 'exceptions.IOError'>, <built-in method read_tag of scipy.io.matlab.mio5_utils.VarReader5 object at 0x14bdeb0>) ... ok
test_mio5_utils.test_read_tag(<type 'exceptions.ValueError'>, <built-in method read_tag of scipy.io.matlab.mio5_utils.VarReader5 object at 0x14bdeb0>) ... ok
test_mio5_utils.test_read_stream('\x05\x00\x04\x00\x01\x00\x00\x00', '\x05\x00\x04\x00\x01\x00\x00\x00') ... ok
test_mio5_utils.test_read_numeric(1, True) ... ok
test_mio5_utils.test_read_numeric(0, False) ... ok
test_mio5_utils.test_read_numeric(array([30], dtype=uint16), 30) ... ok
test_mio5_utils.test_read_numeric(array([30], dtype=uint16), 30) ... ok
test_mio5_utils.test_read_numeric(array([30], dtype=uint16), 30) ... ok
test_mio5_utils.test_read_numeric(array([30], dtype=uint16), 30) ... ok
test_mio5_utils.test_read_numeric(array([30], dtype=uint16), 30) ... ok
test_mio5_utils.test_read_numeric(array([30], dtype=uint16), 30) ... ok
test_mio5_utils.test_read_numeric(0, False) ... ok
test_mio5_utils.test_read_numeric(1, True) ... ok
test_mio5_utils.test_read_numeric(array([30], dtype=uint16), 30) ... ok
test_mio5_utils.test_read_numeric(array([30], dtype=uint16), 30) ... ok
test_mio5_utils.test_read_numeric(array([30], dtype=uint16), 30) ... ok
test_mio5_utils.test_read_numeric(array([30], dtype=uint16), 30) ... ok
test_mio5_utils.test_read_numeric(array([30], dtype=uint16), 30) ... ok
test_mio5_utils.test_read_numeric(array([30], dtype=uint16), 30) ... ok
test_mio5_utils.test_read_numeric(1, True) ... ok
test_mio5_utils.test_read_numeric(0, False) ... ok
test_mio5_utils.test_read_numeric(array([1]), 1) ... ok
test_mio5_utils.test_read_numeric(array([1]), 1) ... ok
test_mio5_utils.test_read_numeric(array([1]), 1) ... ok
test_mio5_utils.test_read_numeric(array([1]), 1) ... ok
test_mio5_utils.test_read_numeric(array([1]), 1) ... ok
test_mio5_utils.test_read_numeric(array([1]), 1) ... ok
test_mio5_utils.test_read_numeric(0, False) ... ok
test_mio5_utils.test_read_numeric(1, True) ... ok
test_mio5_utils.test_read_numeric(array([1]), 1) ... ok
test_mio5_utils.test_read_numeric(array([1]), 1) ... ok
test_mio5_utils.test_read_numeric(array([1]), 1) ... ok
test_mio5_utils.test_read_numeric(array([1]), 1) ... ok
test_mio5_utils.test_read_numeric(array([1]), 1) ... ok
test_mio5_utils.test_read_numeric(array([1]), 1) ... ok
test_mio5_utils.test_read_numeric(1, True) ... ok
test_mio5_utils.test_read_numeric(0, False) ... ok
test_mio5_utils.test_read_numeric(array([-1], dtype=int16), -1) ... ok
test_mio5_utils.test_read_numeric(array([-1], dtype=int16), -1) ... ok
test_mio5_utils.test_read_numeric(array([-1], dtype=int16), -1) ... ok
test_mio5_utils.test_read_numeric(array([-1], dtype=int16), -1) ... ok
test_mio5_utils.test_read_numeric(array([-1], dtype=int16), -1) ... ok
test_mio5_utils.test_read_numeric(array([-1], dtype=int16), -1) ... ok
test_mio5_utils.test_read_numeric(0, False) ... ok
test_mio5_utils.test_read_numeric(1, True) ... ok
test_mio5_utils.test_read_numeric(array([-1], dtype=int16), -1) ... ok
test_mio5_utils.test_read_numeric(array([-1], dtype=int16), -1) ... ok
test_mio5_utils.test_read_numeric(array([-1], dtype=int16), -1) ... ok
test_mio5_utils.test_read_numeric(array([-1], dtype=int16), -1) ... ok
test_mio5_utils.test_read_numeric(array([-1], dtype=int16), -1) ... ok
test_mio5_utils.test_read_numeric(array([-1], dtype=int16), -1) ... ok
test_mio5_utils.test_read_numeric_writeable(True,) ... ok
test_mio5_utils.test_zero_byte_string ... ok
test_mio_funcs.test_jottings ... ok
test_mio_utils.test_cproduct(1, 1) ... ok
test_mio_utils.test_cproduct(1, 1) ... ok
test_mio_utils.test_cproduct(3, 3) ... ok
test_mio_utils.test_cproduct(3, 3) ... ok
test_mio_utils.test_squeeze_element(array([ 0., 0., 0.]), array([ 0., 0., 0.])) ... ok
test_mio_utils.test_squeeze_element(True,) ... ok
test_mio_utils.test_squeeze_element(True,) ... ok
test_mio_utils.test_chars_strings(array([u'learn ', u'python', u'fast ', u'here '], ... ok
test_mio_utils.test_chars_strings(array([[u'learn ', u'python'], ... ok
test_mio_utils.test_chars_strings(array([[[u'learn ', u'python'], ... ok
test_mio_utils.test_chars_strings(array([u'learn ', u'python', u'fast ', u'here '], ... ok
test_mio_utils.test_chars_strings(array([u''], ... ok
test_pathological.test_multiple_fieldnames ... ok
test_streams.test_make_stream ... ok
test_streams.test_tell_seek(0, 0) ... ok
test_streams.test_tell_seek(0, 0) ... ok
test_streams.test_tell_seek(0, 0) ... ok
test_streams.test_tell_seek(5, 5) ... ok
test_streams.test_tell_seek(0, 0) ... ok
test_streams.test_tell_seek(7, 7) ... ok
test_streams.test_tell_seek(0, 0) ... ok
test_streams.test_tell_seek(6, 6) ... ok
test_streams.test_tell_seek(0, 0) ... ok
test_streams.test_tell_seek(0, 0) ... ok
test_streams.test_tell_seek(0, 0) ... ok
test_streams.test_tell_seek(5, 5) ... ok
test_streams.test_tell_seek(0, 0) ... ok
test_streams.test_tell_seek(7, 7) ... ok
test_streams.test_tell_seek(0, 0) ... ok
test_streams.test_tell_seek(6, 6) ... ok
test_streams.test_tell_seek(0, 0) ... ok
test_streams.test_tell_seek(0, 0) ... ok
test_streams.test_tell_seek(0, 0) ... ok
test_streams.test_tell_seek(5, 5) ... ok
test_streams.test_tell_seek(0, 0) ... ok
test_streams.test_tell_seek(7, 7) ... ok
test_streams.test_tell_seek(0, 0) ... ok
test_streams.test_tell_seek(6, 6) ... ok
test_streams.test_read('a\x00string', 'a\x00string') ... ok
test_streams.test_read('a\x00st', 'a\x00st') ... ok
test_streams.test_read('a\x00st', 'a\x00st') ... ok
test_streams.test_read('ring', 'ring') ... ok
test_streams.test_read(<type 'exceptions.IOError'>, <built-in function _read_into>, <scipy.io.matlab.streams.FileStream object at 0x4199f80>, 2) ... ok
test_streams.test_read('a\x00st', 'a\x00st') ... ok
test_streams.test_read('ring', 'ring') ... ok
test_streams.test_read(<type 'exceptions.IOError'>, <built-in function _read_string>, <scipy.io.matlab.streams.FileStream object at 0x4199f80>, 2) ... ok
test_streams.test_read('a\x00string', 'a\x00string') ... ok
test_streams.test_read('a\x00st', 'a\x00st') ... ok
test_streams.test_read('a\x00st', 'a\x00st') ... ok
test_streams.test_read('ring', 'ring') ... ok
test_streams.test_read(<type 'exceptions.IOError'>, <built-in function _read_into>, <scipy.io.matlab.streams.GenericStream object at 0x3f45670>, 2) ... ok
test_streams.test_read('a\x00st', 'a\x00st') ... ok
test_streams.test_read('ring', 'ring') ... ok
test_streams.test_read(<type 'exceptions.IOError'>, <built-in function _read_string>, <scipy.io.matlab.streams.GenericStream object at 0x3f45670>, 2) ... ok
test_streams.test_read('a\x00string', 'a\x00string') ... ok
test_streams.test_read('a\x00st', 'a\x00st') ... ok
test_streams.test_read('a\x00st', 'a\x00st') ... ok
test_streams.test_read('ring', 'ring') ... ok
test_streams.test_read(<type 'exceptions.IOError'>, <built-in function _read_into>, <scipy.io.matlab.streams.cStringStream object at 0x3f45ff0>, 2) ... ok
test_streams.test_read('a\x00st', 'a\x00st') ... ok
test_streams.test_read('ring', 'ring') ... ok
test_streams.test_read(<type 'exceptions.IOError'>, <built-in function _read_string>, <scipy.io.matlab.streams.cStringStream object at 0x3f45ff0>, 2) ... ok
test_idl.TestArrayDimensions.test_1d ... ok
test_idl.TestArrayDimensions.test_2d ... ok
test_idl.TestArrayDimensions.test_3d ... ok
test_idl.TestArrayDimensions.test_4d ... ok
test_idl.TestArrayDimensions.test_5d ... ok
test_idl.TestArrayDimensions.test_6d ... ok
test_idl.TestArrayDimensions.test_7d ... ok
test_idl.TestArrayDimensions.test_8d ... ok
test_idl.TestCompressed.test_byte ... ok
test_idl.TestCompressed.test_bytes ... ok
test_idl.TestCompressed.test_complex32 ... ok
test_idl.TestCompressed.test_complex64 ... ok
test_idl.TestCompressed.test_compressed ... ok
test_idl.TestCompressed.test_float32 ... ok
test_idl.TestCompressed.test_float64 ... ok
test_idl.TestCompressed.test_heap_pointer ... ok
test_idl.TestCompressed.test_int16 ... ok
test_idl.TestCompressed.test_int32 ... ok
test_idl.TestCompressed.test_int64 ... ok
test_idl.TestCompressed.test_object_reference ... ok
test_idl.TestCompressed.test_structure ... ok
test_idl.TestCompressed.test_uint16 ... ok
test_idl.TestCompressed.test_uint32 ... ok
test_idl.TestCompressed.test_uint64 ... ok
test_idl.TestIdict.test_idict ... ok
test_idl.TestPointers.test_pointers ... ok
test_idl.TestScalars.test_byte ... ok
test_idl.TestScalars.test_bytes ... ok
test_idl.TestScalars.test_complex32 ... ok
test_idl.TestScalars.test_complex64 ... ok
test_idl.TestScalars.test_float32 ... ok
test_idl.TestScalars.test_float64 ... ok
test_idl.TestScalars.test_heap_pointer ... ok
test_idl.TestScalars.test_int16 ... ok
test_idl.TestScalars.test_int32 ... ok
test_idl.TestScalars.test_int64 ... ok
test_idl.TestScalars.test_object_reference ... ok
test_idl.TestScalars.test_structure ... ok
test_idl.TestScalars.test_uint16 ... ok
test_idl.TestScalars.test_uint32 ... ok
test_idl.TestScalars.test_uint64 ... ok
test_idl.TestStructures.test_arrays ... ok
test_idl.TestStructures.test_arrays_replicated ... ok
test_idl.TestStructures.test_scalars ... ok
test_idl.TestStructures.test_scalars_replicated ... ok
test_random_rect_real (test_mmio.TestMMIOArray) ... ok
test_random_symmetric_real (test_mmio.TestMMIOArray) ... ok
test_simple (test_mmio.TestMMIOArray) ... ok
test_simple_complex (test_mmio.TestMMIOArray) ... ok
test_simple_hermitian (test_mmio.TestMMIOArray) ... ok
test_simple_real (test_mmio.TestMMIOArray) ... ok
test_simple_rectangular (test_mmio.TestMMIOArray) ... ok
test_simple_rectangular_real (test_mmio.TestMMIOArray) ... ok
test_simple_skew_symmetric (test_mmio.TestMMIOArray) ... ok
test_simple_skew_symmetric_float (test_mmio.TestMMIOArray) ... ok
test_simple_symmetric (test_mmio.TestMMIOArray) ... ok
test_complex_write_read (test_mmio.TestMMIOCoordinate) ... ok
test_empty_write_read (test_mmio.TestMMIOCoordinate) ... ok
read a general matrix ... ok
read a hermitian matrix ... ok
read a skew-symmetric matrix ... ok
read a symmetric matrix ... ok
read a symmetric pattern matrix ... ok
test_real_write_read (test_mmio.TestMMIOCoordinate) ... ok
test_sparse_formats (test_mmio.TestMMIOCoordinate) ... ok
test_netcdf.test_read_write_files(True,) ... ok
test_netcdf.test_read_write_files('Created for a test', 'Created for a test') ... ok
test_netcdf.test_read_write_files('days since 2008-01-01', 'days since 2008-01-01') ... ok
test_netcdf.test_read_write_files((11,), (11,)) ... ok
test_netcdf.test_read_write_files(10, 10) ... ok
test_netcdf.test_read_write_files(False,) ... ok
test_netcdf.test_read_write_files('Created for a test', 'Created for a test') ... ok
test_netcdf.test_read_write_files('days since 2008-01-01', 'days since 2008-01-01') ... ok
test_netcdf.test_read_write_files((11,), (11,)) ... ok
test_netcdf.test_read_write_files(10, 10) ... ok
test_netcdf.test_read_write_files(False,) ... ok
test_netcdf.test_read_write_files('Created for a test', 'Created for a test') ... ok
test_netcdf.test_read_write_files('days since 2008-01-01', 'days since 2008-01-01') ... ok
test_netcdf.test_read_write_files((11,), (11,)) ... ok
test_netcdf.test_read_write_files(10, 10) ... ok
test_netcdf.test_read_write_sio('Created for a test', 'Created for a test') ... ok
test_netcdf.test_read_write_sio('days since 2008-01-01', 'days since 2008-01-01') ... ok
test_netcdf.test_read_write_sio((11,), (11,)) ... ok
test_netcdf.test_read_write_sio(10, 10) ... ok
test_netcdf.test_read_write_sio(<type 'exceptions.ValueError'>, <class 'scipy.io.netcdf.netcdf_file'>, <StringIO.StringIO instance at 0x3f5f378>, 'r', True) ... ok
test_netcdf.test_read_write_sio('Created for a test', 'Created for a test') ... ok
test_netcdf.test_read_write_sio('days since 2008-01-01', 'days since 2008-01-01') ... ok
test_netcdf.test_read_write_sio((11,), (11,)) ... ok
test_netcdf.test_read_write_sio(10, 10) ... ok
test_netcdf.test_read_write_sio(2, 2) ... ok
test_netcdf.test_read_write_sio('Created for a test', 'Created for a test') ... ok
test_netcdf.test_read_write_sio('days since 2008-01-01', 'days since 2008-01-01') ... ok
test_netcdf.test_read_write_sio((11,), (11,)) ... ok
test_netcdf.test_read_write_sio(10, 10) ... ok
test_netcdf.test_read_write_sio(2, 2) ... ok
test_netcdf.test_read_example_data ... ok
test_cast_to_fp (test_recaster.TestRecaster) ...
Warning (from warnings module):
File "/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/recaster.py", line 328
test_arr = arr.astype(T)
ComplexWarning: Casting complex values to real discards the imaginary part
ok
test_init (test_recaster.TestRecaster) ... ok
test_recasts (test_recaster.TestRecaster) ...
Warning (from warnings module):
File "/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/io/recaster.py", line 375
return arr.astype(idt)
ComplexWarning: Casting complex values to real discards the imaginary part
ok
test_smallest_int_sctype (test_recaster.TestRecaster) ... ok
test_wavfile.test_read_1 ... ok
test_wavfile.test_read_2 ... ok
test_wavfile.test_read_fail ... ok
test_wavfile.test_write_roundtrip(8000, dtype('>i2'), 1) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('>i2'), 2) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('>i2'), 5) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('>i2'), 1) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('>i2'), 2) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('>i2'), 5) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('int16'), 1) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('int16'), 2) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('int16'), 5) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('int16'), 1) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('int16'), 2) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('int16'), 5) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('>i4'), 1) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('>i4'), 2) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('>i4'), 5) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('>i4'), 1) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('>i4'), 2) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('>i4'), 5) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('int32'), 1) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('int32'), 2) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('int32'), 5) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('int32'), 1) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('int32'), 2) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('int32'), 5) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('>i8'), 1) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('>i8'), 2) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('>i8'), 5) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('>i8'), 1) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('>i8'), 2) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('>i8'), 5) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('int64'), 1) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('int64'), 2) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('int64'), 5) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('int64'), 1) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('int64'), 2) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('int64'), 5) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('uint8'), 1) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('uint8'), 2) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('uint8'), 5) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('uint8'), 1) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('uint8'), 2) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('uint8'), 5) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('>u2'), 1) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('>u2'), 2) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('>u2'), 5) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('>u2'), 1) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('>u2'), 2) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('>u2'), 5) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('uint16'), 1) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('uint16'), 2) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('uint16'), 5) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('uint16'), 1) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('uint16'), 2) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('uint16'), 5) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('>u4'), 1) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('>u4'), 2) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('>u4'), 5) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('>u4'), 1) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('>u4'), 2) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('>u4'), 5) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('uint32'), 1) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('uint32'), 2) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('uint32'), 5) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('uint32'), 1) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('uint32'), 2) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('uint32'), 5) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('>u8'), 1) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('>u8'), 2) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('>u8'), 5) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('>u8'), 1) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('>u8'), 2) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('>u8'), 5) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('uint64'), 1) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('uint64'), 2) ... ok
test_wavfile.test_write_roundtrip(8000, dtype('uint64'), 5) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('uint64'), 1) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('uint64'), 2) ... ok
test_wavfile.test_write_roundtrip(32000, dtype('uint64'), 5) ... ok
test_blas (test_blas.TestBLAS) ... ok
test_axpy (test_blas.TestCBLAS1Simple) ... ok
test_amax (test_blas.TestFBLAS1Simple) ... ok
test_asum (test_blas.TestFBLAS1Simple) ... ok
test_axpy (test_blas.TestFBLAS1Simple) ... ok
test_copy (test_blas.TestFBLAS1Simple) ... ok
test_dot (test_blas.TestFBLAS1Simple) ... ok
test_nrm2 (test_blas.TestFBLAS1Simple) ... ok
test_scal (test_blas.TestFBLAS1Simple) ... ok
test_swap (test_blas.TestFBLAS1Simple) ... ok
test_gemv (test_blas.TestFBLAS2Simple) ... ok
test_ger (test_blas.TestFBLAS2Simple) ... ok
test_gemm (test_blas.TestFBLAS3Simple) ... ok
test_gemm2 (test_blas.TestFBLAS3Simple) ... ok
test_default_a (test_fblas.TestCaxpy) ... ok
test_simple (test_fblas.TestCaxpy) ... ok
test_x_and_y_stride (test_fblas.TestCaxpy) ... ok
test_x_bad_size (test_fblas.TestCaxpy) ... ok
test_x_stride (test_fblas.TestCaxpy) ... ok
test_y_bad_size (test_fblas.TestCaxpy) ... ok
test_y_stride (test_fblas.TestCaxpy) ... ok
test_simple (test_fblas.TestCcopy) ... ok
test_x_and_y_stride (test_fblas.TestCcopy) ... ok
test_x_bad_size (test_fblas.TestCcopy) ... ok
test_x_stride (test_fblas.TestCcopy) ... ok
test_y_bad_size (test_fblas.TestCcopy) ... ok
test_y_stride (test_fblas.TestCcopy) ... ok
test_default_beta_y (test_fblas.TestCgemv) ... ok
test_simple (test_fblas.TestCgemv) ... ok
test_simple_transpose (test_fblas.TestCgemv) ... ok
test_simple_transpose_conj (test_fblas.TestCgemv) ... ok
test_x_stride (test_fblas.TestCgemv) ... ok
test_x_stride_assert (test_fblas.TestCgemv) ... ok
test_x_stride_transpose (test_fblas.TestCgemv) ... ok
test_y_stride (test_fblas.TestCgemv) ... ok
test_y_stride_assert (test_fblas.TestCgemv) ... ok
test_y_stride_transpose (test_fblas.TestCgemv) ... ok
test_simple (test_fblas.TestCscal) ... ok
test_x_bad_size (test_fblas.TestCscal) ... ok
test_x_stride (test_fblas.TestCscal) ... ok
test_simple (test_fblas.TestCswap) ... ok
test_x_and_y_stride (test_fblas.TestCswap) ... ok
test_x_bad_size (test_fblas.TestCswap) ... ok
test_x_stride (test_fblas.TestCswap) ... ok
test_y_bad_size (test_fblas.TestCswap) ... ok
test_y_stride (test_fblas.TestCswap) ... ok
test_default_a (test_fblas.TestDaxpy) ... ok
test_simple (test_fblas.TestDaxpy) ... ok
test_x_and_y_stride (test_fblas.TestDaxpy) ... ok
test_x_bad_size (test_fblas.TestDaxpy) ... ok
test_x_stride (test_fblas.TestDaxpy) ... ok
test_y_bad_size (test_fblas.TestDaxpy) ... ok
test_y_stride (test_fblas.TestDaxpy) ... ok
test_simple (test_fblas.TestDcopy) ... ok
test_x_and_y_stride (test_fblas.TestDcopy) ... ok
test_x_bad_size (test_fblas.TestDcopy) ... ok
test_x_stride (test_fblas.TestDcopy) ... ok
test_y_bad_size (test_fblas.TestDcopy) ... ok
test_y_stride (test_fblas.TestDcopy) ... ok
test_default_beta_y (test_fblas.TestDgemv) ... ok
test_simple (test_fblas.TestDgemv) ... ok
test_simple_transpose (test_fblas.TestDgemv) ... ok
test_simple_transpose_conj (test_fblas.TestDgemv) ... ok
test_x_stride (test_fblas.TestDgemv) ... ok
test_x_stride_assert (test_fblas.TestDgemv) ... ok
test_x_stride_transpose (test_fblas.TestDgemv) ... ok
test_y_stride (test_fblas.TestDgemv) ... ok
test_y_stride_assert (test_fblas.TestDgemv) ... ok
test_y_stride_transpose (test_fblas.TestDgemv) ... ok
test_simple (test_fblas.TestDscal) ... ok
test_x_bad_size (test_fblas.TestDscal) ... ok
test_x_stride (test_fblas.TestDscal) ... ok
test_simple (test_fblas.TestDswap) ... ok
test_x_and_y_stride (test_fblas.TestDswap) ... ok
test_x_bad_size (test_fblas.TestDswap) ... ok
test_x_stride (test_fblas.TestDswap) ... ok
test_y_bad_size (test_fblas.TestDswap) ... ok
test_y_stride (test_fblas.TestDswap) ... ok
test_default_a (test_fblas.TestSaxpy) ... ok
test_simple (test_fblas.TestSaxpy) ... ok
test_x_and_y_stride (test_fblas.TestSaxpy) ... ok
test_x_bad_size (test_fblas.TestSaxpy) ... ok
test_x_stride (test_fblas.TestSaxpy) ... ok
test_y_bad_size (test_fblas.TestSaxpy) ... ok
test_y_stride (test_fblas.TestSaxpy) ... ok
test_simple (test_fblas.TestScopy) ... ok
test_x_and_y_stride (test_fblas.TestScopy) ... ok
test_x_bad_size (test_fblas.TestScopy) ... ok
test_x_stride (test_fblas.TestScopy) ... ok
test_y_bad_size (test_fblas.TestScopy) ... ok
test_y_stride (test_fblas.TestScopy) ... ok
test_default_beta_y (test_fblas.TestSgemv) ... ok
test_simple (test_fblas.TestSgemv) ... ok
test_simple_transpose (test_fblas.TestSgemv) ... ok
test_simple_transpose_conj (test_fblas.TestSgemv) ... ok
test_x_stride (test_fblas.TestSgemv) ... ok
test_x_stride_assert (test_fblas.TestSgemv) ... ok
test_x_stride_transpose (test_fblas.TestSgemv) ... ok
test_y_stride (test_fblas.TestSgemv) ... ok
test_y_stride_assert (test_fblas.TestSgemv) ... ok
test_y_stride_transpose (test_fblas.TestSgemv) ... ok
test_simple (test_fblas.TestSscal) ... ok
test_x_bad_size (test_fblas.TestSscal) ... ok
test_x_stride (test_fblas.TestSscal) ... ok
test_simple (test_fblas.TestSswap) ... ok
test_x_and_y_stride (test_fblas.TestSswap) ... ok
test_x_bad_size (test_fblas.TestSswap) ... ok
test_x_stride (test_fblas.TestSswap) ... ok
test_y_bad_size (test_fblas.TestSswap) ... ok
test_y_stride (test_fblas.TestSswap) ... ok
test_default_a (test_fblas.TestZaxpy) ... ok
test_simple (test_fblas.TestZaxpy) ... ok
test_x_and_y_stride (test_fblas.TestZaxpy) ... ok
test_x_bad_size (test_fblas.TestZaxpy) ... ok
test_x_stride (test_fblas.TestZaxpy) ... ok
test_y_bad_size (test_fblas.TestZaxpy) ... ok
test_y_stride (test_fblas.TestZaxpy) ... ok
test_simple (test_fblas.TestZcopy) ... ok
test_x_and_y_stride (test_fblas.TestZcopy) ... ok
test_x_bad_size (test_fblas.TestZcopy) ... ok
test_x_stride (test_fblas.TestZcopy) ... ok
test_y_bad_size (test_fblas.TestZcopy) ... ok
test_y_stride (test_fblas.TestZcopy) ... ok
test_default_beta_y (test_fblas.TestZgemv) ... ok
test_simple (test_fblas.TestZgemv) ... ok
test_simple_transpose (test_fblas.TestZgemv) ... ok
test_simple_transpose_conj (test_fblas.TestZgemv) ... ok
test_x_stride (test_fblas.TestZgemv) ... ok
test_x_stride_assert (test_fblas.TestZgemv) ... ok
test_x_stride_transpose (test_fblas.TestZgemv) ... ok
test_y_stride (test_fblas.TestZgemv) ... ok
test_y_stride_assert (test_fblas.TestZgemv) ... ok
test_y_stride_transpose (test_fblas.TestZgemv) ... ok
test_simple (test_fblas.TestZscal) ... ok
test_x_bad_size (test_fblas.TestZscal) ... ok
test_x_stride (test_fblas.TestZscal) ... ok
test_simple (test_fblas.TestZswap) ... ok
test_x_and_y_stride (test_fblas.TestZswap) ... ok
test_x_bad_size (test_fblas.TestZswap) ... ok
test_x_stride (test_fblas.TestZswap) ... ok
test_y_bad_size (test_fblas.TestZswap) ... ok
test_y_stride (test_fblas.TestZswap) ... ok
test_clapack_dsyev (test_esv.TestEsv) ... SKIP: Skipping test: test_clapack_dsyev
Clapack empty, skip clapack test
test_clapack_dsyevr (test_esv.TestEsv) ... SKIP: Skipping test: test_clapack_dsyevr
Clapack empty, skip clapack test
test_clapack_dsyevr_ranges (test_esv.TestEsv) ... SKIP: Skipping test: test_clapack_dsyevr_ranges
Clapack empty, skip clapack test
test_clapack_ssyev (test_esv.TestEsv) ... SKIP: Skipping test: test_clapack_ssyev
Clapack empty, skip clapack test
test_clapack_ssyevr (test_esv.TestEsv) ... SKIP: Skipping test: test_clapack_ssyevr
Clapack empty, skip clapack test
test_clapack_ssyevr_ranges (test_esv.TestEsv) ... SKIP: Skipping test: test_clapack_ssyevr_ranges
Clapack empty, skip clapack test
test_dsyev (test_esv.TestEsv) ... ok
test_dsyevr (test_esv.TestEsv) ... ok
test_dsyevr_ranges (test_esv.TestEsv) ... ok
test_ssyev (test_esv.TestEsv) ... ok
test_ssyevr (test_esv.TestEsv) ... ok
test_ssyevr_ranges (test_esv.TestEsv) ... ok
test_clapack_dsygv_1 (test_gesv.TestSygv) ... SKIP: Skipping test: test_clapack_dsygv_1
Clapack empty, skip flapack test
test_clapack_dsygv_2 (test_gesv.TestSygv) ... SKIP: Skipping test: test_clapack_dsygv_2
Clapack empty, skip flapack test
test_clapack_dsygv_3 (test_gesv.TestSygv) ... SKIP: Skipping test: test_clapack_dsygv_3
Clapack empty, skip flapack test
test_clapack_ssygv_1 (test_gesv.TestSygv) ... SKIP: Skipping test: test_clapack_ssygv_1
Clapack empty, skip flapack test
test_clapack_ssygv_2 (test_gesv.TestSygv) ... SKIP: Skipping test: test_clapack_ssygv_2
Clapack empty, skip flapack test
test_clapack_ssygv_3 (test_gesv.TestSygv) ... SKIP: Skipping test: test_clapack_ssygv_3
Clapack empty, skip flapack test
test_dsygv_1 (test_gesv.TestSygv) ... ok
test_dsygv_2 (test_gesv.TestSygv) ... ok
test_dsygv_3 (test_gesv.TestSygv) ... ok
test_ssygv_1 (test_gesv.TestSygv) ... ok
test_ssygv_2 (test_gesv.TestSygv) ... ok
test_ssygv_3 (test_gesv.TestSygv) ... ok
test_clapack_dgebal (test_lapack.TestLapack) ... SKIP: Skipping test: test_clapack_dgebal
Clapack empty, skip flapack test
test_clapack_dgehrd (test_lapack.TestLapack) ... SKIP: Skipping test: test_clapack_dgehrd
Clapack empty, skip flapack test
test_clapack_sgebal (test_lapack.TestLapack) ... SKIP: Skipping test: test_clapack_sgebal
Clapack empty, skip flapack test
test_clapack_sgehrd (test_lapack.TestLapack) ... SKIP: Skipping test: test_clapack_sgehrd
Clapack empty, skip flapack test
test_dgebal (test_lapack.TestLapack) ... ok
test_dgehrd (test_lapack.TestLapack) ... ok
test_sgebal (test_lapack.TestLapack) ... ok
test_sgehrd (test_lapack.TestLapack) ... ok
test_random (test_basic.TestDet) ... ok
test_random_complex (test_basic.TestDet) ... ok
test_simple (test_basic.TestDet) ... ok
test_simple_complex (test_basic.TestDet) ... ok
test_random (test_basic.TestInv) ... ok
test_random_complex (test_basic.TestInv) ... ok
test_simple (test_basic.TestInv) ... ok
test_simple_complex (test_basic.TestInv) ... ok
test_random_complex_exact (test_basic.TestLstsq) ... ok
test_random_complex_overdet (test_basic.TestLstsq) ... ok
test_random_exact (test_basic.TestLstsq) ... ok
test_random_overdet (test_basic.TestLstsq) ... ok
test_random_overdet_large (test_basic.TestLstsq) ... ok
test_simple_exact (test_basic.TestLstsq) ... ok
test_simple_overdet (test_basic.TestLstsq) ... ok
test_simple_overdet_complex (test_basic.TestLstsq) ... ok
test_simple_underdet (test_basic.TestLstsq) ... ok
test_basic.TestNorm.test_zero_norm ... ok
test_simple (test_basic.TestPinv) ... ok
test_simple_0det (test_basic.TestPinv) ... ok
test_simple_cols (test_basic.TestPinv) ... ok
test_simple_rows (test_basic.TestPinv) ... ok
test_20Feb04_bug (test_basic.TestSolve) ... ok
test_nils_20Feb04 (test_basic.TestSolve) ... ok
test_random (test_basic.TestSolve) ... ok
test_random_complex (test_basic.TestSolve) ... ok
test_random_sym (test_basic.TestSolve) ... ok
test_random_sym_complex (test_basic.TestSolve) ... ok
test_simple (test_basic.TestSolve) ... ok
test_simple_complex (test_basic.TestSolve) ... ok
test_simple_sym (test_basic.TestSolve) ... ok
test_simple_sym_complex (test_basic.TestSolve) ... ok
test_bad_shape (test_basic.TestSolveBanded) ... ok
test_complex (test_basic.TestSolveBanded) ... ok
test_real (test_basic.TestSolveBanded) ... ok
test_01_complex (test_basic.TestSolveHBanded) ... ok
test_01_float32 (test_basic.TestSolveHBanded) ... ok
test_01_lower (test_basic.TestSolveHBanded) ... ok
test_01_upper (test_basic.TestSolveHBanded) ... ok
test_02_complex (test_basic.TestSolveHBanded) ... ok
test_02_float32 (test_basic.TestSolveHBanded) ... ok
test_02_lower (test_basic.TestSolveHBanded) ... ok
test_02_upper (test_basic.TestSolveHBanded) ... ok
test_03_upper (test_basic.TestSolveHBanded) ... ok
test_bad_shapes (test_basic.TestSolveHBanded) ... ok
solve_triangular on a simple 2x2 matrix. ... ok
solve_triangular on a simple 2x2 complex matrix ... ok
test_axpy (test_blas.TestCBLAS1Simple) ... ok
test_amax (test_blas.TestFBLAS1Simple) ... ok
test_asum (test_blas.TestFBLAS1Simple) ... ok
test_axpy (test_blas.TestFBLAS1Simple) ... ok
test_complex_dotc (test_blas.TestFBLAS1Simple) ... ok
test_complex_dotu (test_blas.TestFBLAS1Simple) ... ok
test_copy (test_blas.TestFBLAS1Simple) ... ok
test_dot (test_blas.TestFBLAS1Simple) ... ok
test_nrm2 (test_blas.TestFBLAS1Simple) ... ok
test_scal (test_blas.TestFBLAS1Simple) ... ok
test_swap (test_blas.TestFBLAS1Simple) ... ok
test_gemv (test_blas.TestFBLAS2Simple) ... ok
test_ger (test_blas.TestFBLAS2Simple) ... ok
test_gemm (test_blas.TestFBLAS3Simple) ... ok
test_lapack (test_build.TestF77Mismatch) ... SKIP: Skipping test: test_lapack
Skipping fortran compiler mismatch on non Linux platform
test_datanotshared (test_decomp.TestDataNotShared) ... ok
test_simple (test_decomp.TestDiagSVD) ... ok
test_decomp.TestEig.test_bad_geneig ... ok
Test matrices giving some Nan generalized eigen values. ... ok
Check that passing a non-square array raises a ValueError. ... ok
Check that passing arrays of with different shapes raises a ValueError. ... ok
test_decomp.TestEig.test_simple ... ok
test_decomp.TestEig.test_simple_complex ... ok
test_decomp.TestEig.test_simple_complex_eig ... ok
Test singular pair ... ok
Compare dgbtrf LU factorisation with the LU factorisation result ... ok
Compare dgbtrs solutions for linear equation system A*x = b ... ok
Compare dsbev eigenvalues and eigenvectors with ... ok
Compare dsbevd eigenvalues and eigenvectors with ... ok
Compare dsbevx eigenvalues and eigenvectors ... ok
Compare eigenvalues and eigenvectors of eig_banded ... ok
Compare eigenvalues of eigvals_banded with those of linalg.eig. ... ok
Compare zgbtrf LU factorisation with the LU factorisation result ... ok
Compare zgbtrs solutions for linear equation system A*x = b ... ok
Compare zhbevd eigenvalues and eigenvectors ... ok
Compare zhbevx eigenvalues and eigenvectors ... ok
test_simple (test_decomp.TestEigVals) ... ok
test_simple_complex (test_decomp.TestEigVals) ... ok
test_simple_tr (test_decomp.TestEigVals) ... ok
test_random (test_decomp.TestHessenberg) ... ok
test_random_complex (test_decomp.TestHessenberg) ... ok
test_simple (test_decomp.TestHessenberg) ... ok
test_simple2 (test_decomp.TestHessenberg) ... ok
test_simple_complex (test_decomp.TestHessenberg) ... ok
test_hrectangular (test_decomp.TestLU) ... ok
test_hrectangular_complex (test_decomp.TestLU) ... ok
Check lu decomposition on medium size, rectangular matrix. ... ok
Check lu decomposition on medium size, rectangular matrix. ... ok
test_simple (test_decomp.TestLU) ... ok
test_simple2 (test_decomp.TestLU) ... ok
test_simple2_complex (test_decomp.TestLU) ... ok
test_simple_complex (test_decomp.TestLU) ... ok
test_vrectangular (test_decomp.TestLU) ... ok
test_vrectangular_complex (test_decomp.TestLU) ... ok
test_hrectangular (test_decomp.TestLUSingle) ... ok
test_hrectangular_complex (test_decomp.TestLUSingle) ... ok
Check lu decomposition on medium size, rectangular matrix. ... ok
Check lu decomposition on medium size, rectangular matrix. ... ok
test_simple (test_decomp.TestLUSingle) ... ok
test_simple2 (test_decomp.TestLUSingle) ... ok
test_simple2_complex (test_decomp.TestLUSingle) ... ok
test_simple_complex (test_decomp.TestLUSingle) ... ok
test_vrectangular (test_decomp.TestLUSingle) ... ok
test_vrectangular_complex (test_decomp.TestLUSingle) ... ok
test_lu (test_decomp.TestLUSolve) ... ok
test_random (test_decomp.TestQR) ... ok
test_random_complex (test_decomp.TestQR) ... ok
test_random_tall (test_decomp.TestQR) ... ok
test_random_tall_e (test_decomp.TestQR) ... ok
test_random_trap (test_decomp.TestQR) ... ok
test_simple (test_decomp.TestQR) ... ok
test_simple_complex (test_decomp.TestQR) ... ok
test_simple_tall (test_decomp.TestQR) ... ok
test_simple_tall_e (test_decomp.TestQR) ... ok
test_simple_trap (test_decomp.TestQR) ... ok
test_random (test_decomp.TestRQ) ... ok
test_simple (test_decomp.TestRQ) ... ok
test_random (test_decomp.TestSVD) ... ok
test_random_complex (test_decomp.TestSVD) ... ok
test_simple (test_decomp.TestSVD) ... ok
test_simple_complex (test_decomp.TestSVD) ... ok
test_simple_overdet (test_decomp.TestSVD) ... ok
test_simple_singular (test_decomp.TestSVD) ... ok
test_simple_underdet (test_decomp.TestSVD) ... ok
test_simple (test_decomp.TestSVDVals) ... ok
test_simple_complex (test_decomp.TestSVDVals) ... ok
test_simple_overdet (test_decomp.TestSVDVals) ... ok
test_simple_overdet_complex (test_decomp.TestSVDVals) ... ok
test_simple_underdet (test_decomp.TestSVDVals) ... ok
test_simple_underdet_complex (test_decomp.TestSVDVals) ... ok
test_simple (test_decomp.TestSchur) ... ok
test_decomp.test_eigh('ordinary', 6, 'f', True, True, True, None) ... ok
test_decomp.test_eigh('general ', 6, 'f', True, True, True, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'f', True, False, True, None) ... ok
test_decomp.test_eigh('general ', 6, 'f', True, False, True, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'f', True, True, True, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'f', True, True, True, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'f', True, False, True, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'f', True, False, True, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'f', True, True, False, None) ... ok
test_decomp.test_eigh('general ', 6, 'f', True, True, False, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'f', True, False, False, None) ... ok
test_decomp.test_eigh('general ', 6, 'f', True, False, False, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'f', True, True, False, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'f', True, True, False, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'f', True, False, False, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'f', True, False, False, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'f', False, True, True, None) ... ok
test_decomp.test_eigh('general ', 6, 'f', False, True, True, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'f', False, False, True, None) ... ok
test_decomp.test_eigh('general ', 6, 'f', False, False, True, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'f', False, True, True, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'f', False, True, True, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'f', False, False, True, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'f', False, False, True, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'f', False, True, False, None) ... ok
test_decomp.test_eigh('general ', 6, 'f', False, True, False, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'f', False, False, False, None) ... ok
test_decomp.test_eigh('general ', 6, 'f', False, False, False, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'f', False, True, False, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'f', False, True, False, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'f', False, False, False, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'f', False, False, False, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'd', True, True, True, None) ... ok
test_decomp.test_eigh('general ', 6, 'd', True, True, True, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'd', True, False, True, None) ... ok
test_decomp.test_eigh('general ', 6, 'd', True, False, True, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'd', True, True, True, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'd', True, True, True, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'd', True, False, True, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'd', True, False, True, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'd', True, True, False, None) ... ok
test_decomp.test_eigh('general ', 6, 'd', True, True, False, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'd', True, False, False, None) ... ok
test_decomp.test_eigh('general ', 6, 'd', True, False, False, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'd', True, True, False, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'd', True, True, False, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'd', True, False, False, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'd', True, False, False, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'd', False, True, True, None) ... ok
test_decomp.test_eigh('general ', 6, 'd', False, True, True, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'd', False, False, True, None) ... ok
test_decomp.test_eigh('general ', 6, 'd', False, False, True, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'd', False, True, True, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'd', False, True, True, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'd', False, False, True, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'd', False, False, True, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'd', False, True, False, None) ... ok
test_decomp.test_eigh('general ', 6, 'd', False, True, False, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'd', False, False, False, None) ... ok
test_decomp.test_eigh('general ', 6, 'd', False, False, False, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'd', False, True, False, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'd', False, True, False, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'd', False, False, False, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'd', False, False, False, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'F', True, True, True, None) ... ok
test_decomp.test_eigh('general ', 6, 'F', True, True, True, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'F', True, False, True, None) ... ok
test_decomp.test_eigh('general ', 6, 'F', True, False, True, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'F', True, True, True, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'F', True, True, True, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'F', True, False, True, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'F', True, False, True, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'F', True, True, False, None) ... ok
test_decomp.test_eigh('general ', 6, 'F', True, True, False, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'F', True, False, False, None) ... ok
test_decomp.test_eigh('general ', 6, 'F', True, False, False, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'F', True, True, False, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'F', True, True, False, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'F', True, False, False, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'F', True, False, False, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'F', False, True, True, None) ... ok
test_decomp.test_eigh('general ', 6, 'F', False, True, True, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'F', False, False, True, None) ... ok
test_decomp.test_eigh('general ', 6, 'F', False, False, True, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'F', False, True, True, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'F', False, True, True, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'F', False, False, True, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'F', False, False, True, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'F', False, True, False, None) ... ok
test_decomp.test_eigh('general ', 6, 'F', False, True, False, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'F', False, False, False, None) ... ok
test_decomp.test_eigh('general ', 6, 'F', False, False, False, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'F', False, True, False, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'F', False, True, False, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'F', False, False, False, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'F', False, False, False, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'D', True, True, True, None) ... ok
test_decomp.test_eigh('general ', 6, 'D', True, True, True, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'D', True, False, True, None) ... ok
test_decomp.test_eigh('general ', 6, 'D', True, False, True, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'D', True, True, True, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'D', True, True, True, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'D', True, False, True, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'D', True, False, True, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'D', True, True, False, None) ... ok
test_decomp.test_eigh('general ', 6, 'D', True, True, False, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'D', True, False, False, None) ... ok
test_decomp.test_eigh('general ', 6, 'D', True, False, False, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'D', True, True, False, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'D', True, True, False, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'D', True, False, False, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'D', True, False, False, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'D', False, True, True, None) ... ok
test_decomp.test_eigh('general ', 6, 'D', False, True, True, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'D', False, False, True, None) ... ok
test_decomp.test_eigh('general ', 6, 'D', False, False, True, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'D', False, True, True, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'D', False, True, True, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'D', False, False, True, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'D', False, False, True, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'D', False, True, False, None) ... ok
test_decomp.test_eigh('general ', 6, 'D', False, True, False, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'D', False, False, False, None) ... ok
test_decomp.test_eigh('general ', 6, 'D', False, False, False, None) ... ok
test_decomp.test_eigh('ordinary', 6, 'D', False, True, False, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'D', False, True, False, (2, 4)) ... ok
test_decomp.test_eigh('ordinary', 6, 'D', False, False, False, (2, 4)) ... ok
test_decomp.test_eigh('general ', 6, 'D', False, False, False, (2, 4)) ... ok
test_decomp.test_eigh_integer ... ok
Check linalg works with non-aligned memory ... ok
Check linalg works with non-aligned memory ... ok
Check that complex objects don't need to be completely aligned ... ok
test_decomp.test_lapack_misaligned ... KNOWNFAIL: Ticket #1152, triggers a segfault in rare cases.
test_random (test_decomp_cholesky.TestCholesky) ... ok
test_random_complex (test_decomp_cholesky.TestCholesky) ... ok
test_simple (test_decomp_cholesky.TestCholesky) ... ok
test_simple_complex (test_decomp_cholesky.TestCholesky) ... ok
test_lower_complex (test_decomp_cholesky.TestCholeskyBanded) ... ok
test_lower_real (test_decomp_cholesky.TestCholeskyBanded) ... ok
test_upper_complex (test_decomp_cholesky.TestCholeskyBanded) ... ok
test_upper_real (test_decomp_cholesky.TestCholeskyBanded) ... ok
test_default_a (test_fblas.TestCaxpy) ... ok
test_simple (test_fblas.TestCaxpy) ... ok
test_x_and_y_stride (test_fblas.TestCaxpy) ... ok
test_x_bad_size (test_fblas.TestCaxpy) ... ok
test_x_stride (test_fblas.TestCaxpy) ... ok
test_y_bad_size (test_fblas.TestCaxpy) ... ok
test_y_stride (test_fblas.TestCaxpy) ... ok
test_simple (test_fblas.TestCcopy) ... ok
test_x_and_y_stride (test_fblas.TestCcopy) ... ok
test_x_bad_size (test_fblas.TestCcopy) ... ok
test_x_stride (test_fblas.TestCcopy) ... ok
test_y_bad_size (test_fblas.TestCcopy) ... ok
test_y_stride (test_fblas.TestCcopy) ... ok
test_default_beta_y (test_fblas.TestCgemv) ... ok
test_simple (test_fblas.TestCgemv) ... ok
test_simple_transpose (test_fblas.TestCgemv) ... ok
test_simple_transpose_conj (test_fblas.TestCgemv) ... ok
test_x_stride (test_fblas.TestCgemv) ... ok
test_x_stride_assert (test_fblas.TestCgemv) ... ok
test_x_stride_transpose (test_fblas.TestCgemv) ... ok
test_y_stride (test_fblas.TestCgemv) ... ok
test_y_stride_assert (test_fblas.TestCgemv) ... ok
test_y_stride_transpose (test_fblas.TestCgemv) ... ok
test_simple (test_fblas.TestCscal) ... ok
test_x_bad_size (test_fblas.TestCscal) ... ok
test_x_stride (test_fblas.TestCscal) ... ok
test_simple (test_fblas.TestCswap) ... ok
test_x_and_y_stride (test_fblas.TestCswap) ... ok
test_x_bad_size (test_fblas.TestCswap) ... ok
test_x_stride (test_fblas.TestCswap) ... ok
test_y_bad_size (test_fblas.TestCswap) ... ok
test_y_stride (test_fblas.TestCswap) ... ok
test_default_a (test_fblas.TestDaxpy) ... ok
test_simple (test_fblas.TestDaxpy) ... ok
test_x_and_y_stride (test_fblas.TestDaxpy) ... ok
test_x_bad_size (test_fblas.TestDaxpy) ... ok
test_x_stride (test_fblas.TestDaxpy) ... ok
test_y_bad_size (test_fblas.TestDaxpy) ... ok
test_y_stride (test_fblas.TestDaxpy) ... ok
test_simple (test_fblas.TestDcopy) ... ok
test_x_and_y_stride (test_fblas.TestDcopy) ... ok
test_x_bad_size (test_fblas.TestDcopy) ... ok
test_x_stride (test_fblas.TestDcopy) ... ok
test_y_bad_size (test_fblas.TestDcopy) ... ok
test_y_stride (test_fblas.TestDcopy) ... ok
test_default_beta_y (test_fblas.TestDgemv) ... ok
test_simple (test_fblas.TestDgemv) ... ok
test_simple_transpose (test_fblas.TestDgemv) ... ok
test_simple_transpose_conj (test_fblas.TestDgemv) ... ok
test_x_stride (test_fblas.TestDgemv) ... ok
test_x_stride_assert (test_fblas.TestDgemv) ... ok
test_x_stride_transpose (test_fblas.TestDgemv) ... ok
test_y_stride (test_fblas.TestDgemv) ... ok
test_y_stride_assert (test_fblas.TestDgemv) ... ok
test_y_stride_transpose (test_fblas.TestDgemv) ... ok
test_simple (test_fblas.TestDscal) ... ok
test_x_bad_size (test_fblas.TestDscal) ... ok
test_x_stride (test_fblas.TestDscal) ... ok
test_simple (test_fblas.TestDswap) ... ok
test_x_and_y_stride (test_fblas.TestDswap) ... ok
test_x_bad_size (test_fblas.TestDswap) ... ok
test_x_stride (test_fblas.TestDswap) ... ok
test_y_bad_size (test_fblas.TestDswap) ... ok
test_y_stride (test_fblas.TestDswap) ... ok
test_default_a (test_fblas.TestSaxpy) ... ok
test_simple (test_fblas.TestSaxpy) ... ok
test_x_and_y_stride (test_fblas.TestSaxpy) ... ok
test_x_bad_size (test_fblas.TestSaxpy) ... ok
test_x_stride (test_fblas.TestSaxpy) ... ok
test_y_bad_size (test_fblas.TestSaxpy) ... ok
test_y_stride (test_fblas.TestSaxpy) ... ok
test_simple (test_fblas.TestScopy) ... ok
test_x_and_y_stride (test_fblas.TestScopy) ... ok
test_x_bad_size (test_fblas.TestScopy) ... ok
test_x_stride (test_fblas.TestScopy) ... ok
test_y_bad_size (test_fblas.TestScopy) ... ok
test_y_stride (test_fblas.TestScopy) ... ok
test_default_beta_y (test_fblas.TestSgemv) ... ok
test_simple (test_fblas.TestSgemv) ... ok
test_simple_transpose (test_fblas.TestSgemv) ... ok
test_simple_transpose_conj (test_fblas.TestSgemv) ... ok
test_x_stride (test_fblas.TestSgemv) ... ok
test_x_stride_assert (test_fblas.TestSgemv) ... ok
test_x_stride_transpose (test_fblas.TestSgemv) ... ok
test_y_stride (test_fblas.TestSgemv) ... ok
test_y_stride_assert (test_fblas.TestSgemv) ... ok
test_y_stride_transpose (test_fblas.TestSgemv) ... ok
test_simple (test_fblas.TestSscal) ... ok
test_x_bad_size (test_fblas.TestSscal) ... ok
test_x_stride (test_fblas.TestSscal) ... ok
test_simple (test_fblas.TestSswap) ... ok
test_x_and_y_stride (test_fblas.TestSswap) ... ok
test_x_bad_size (test_fblas.TestSswap) ... ok
test_x_stride (test_fblas.TestSswap) ... ok
test_y_bad_size (test_fblas.TestSswap) ... ok
test_y_stride (test_fblas.TestSswap) ... ok
test_default_a (test_fblas.TestZaxpy) ... ok
test_simple (test_fblas.TestZaxpy) ... ok
test_x_and_y_stride (test_fblas.TestZaxpy) ... ok
test_x_bad_size (test_fblas.TestZaxpy) ... ok
test_x_stride (test_fblas.TestZaxpy) ... ok
test_y_bad_size (test_fblas.TestZaxpy) ... ok
test_y_stride (test_fblas.TestZaxpy) ... ok
test_simple (test_fblas.TestZcopy) ... ok
test_x_and_y_stride (test_fblas.TestZcopy) ... ok
test_x_bad_size (test_fblas.TestZcopy) ... ok
test_x_stride (test_fblas.TestZcopy) ... ok
test_y_bad_size (test_fblas.TestZcopy) ... ok
test_y_stride (test_fblas.TestZcopy) ... ok
test_default_beta_y (test_fblas.TestZgemv) ... ok
test_simple (test_fblas.TestZgemv) ... ok
test_simple_transpose (test_fblas.TestZgemv) ... ok
test_simple_transpose_conj (test_fblas.TestZgemv) ... ok
test_x_stride (test_fblas.TestZgemv) ... ok
test_x_stride_assert (test_fblas.TestZgemv) ... ok
test_x_stride_transpose (test_fblas.TestZgemv) ... ok
test_y_stride (test_fblas.TestZgemv) ... ok
test_y_stride_assert (test_fblas.TestZgemv) ... ok
test_y_stride_transpose (test_fblas.TestZgemv) ... ok
test_simple (test_fblas.TestZscal) ... ok
test_x_bad_size (test_fblas.TestZscal) ... ok
test_x_stride (test_fblas.TestZscal) ... ok
test_simple (test_fblas.TestZswap) ... ok
test_x_and_y_stride (test_fblas.TestZswap) ... ok
test_x_bad_size (test_fblas.TestZswap) ... ok
test_x_stride (test_fblas.TestZswap) ... ok
test_y_bad_size (test_fblas.TestZswap) ... ok
test_y_stride (test_fblas.TestZswap) ... ok
test_gebal (test_lapack.TestFlapackSimple) ... ok
test_gehrd (test_lapack.TestFlapackSimple) ... ok
test_clapack (test_lapack.TestLapack) ... ok
test_flapack (test_lapack.TestLapack) ... ok
test_consistency (test_matfuncs.TestExpM) ... ok
test_zero (test_matfuncs.TestExpM) ... ok
test_nils (test_matfuncs.TestLogM) ... ok
test_defective1 (test_matfuncs.TestSignM) ... ok
test_defective2 (test_matfuncs.TestSignM) ... ok
test_defective3 (test_matfuncs.TestSignM) ... ok
test_nils (test_matfuncs.TestSignM) ... ok
test_bad (test_matfuncs.TestSqrtM) ... ok
test_special_matrices.TestBlockDiag.test_bad_arg ... ok
test_special_matrices.TestBlockDiag.test_basic ... ok
test_special_matrices.TestBlockDiag.test_dtype ... ok
test_special_matrices.TestBlockDiag.test_no_args ... ok
test_special_matrices.TestBlockDiag.test_scalar_and_1d_args ... ok
test_basic (test_special_matrices.TestCirculant) ... ok
test_bad_shapes (test_special_matrices.TestCompanion) ... ok
test_basic (test_special_matrices.TestCompanion) ... ok
test_basic (test_special_matrices.TestHadamard) ... ok
test_basic (test_special_matrices.TestHankel) ... ok
test_special_matrices.TestKron.test_basic ... ok
test_bad_shapes (test_special_matrices.TestLeslie) ... ok
test_basic (test_special_matrices.TestLeslie) ... ok
test_basic (test_special_matrices.TestToeplitz) ... ok
test_complex_01 (test_special_matrices.TestToeplitz) ... ok
Scalar arguments still produce a 2D array. ... ok
test_scalar_01 (test_special_matrices.TestToeplitz) ... ok
test_scalar_02 (test_special_matrices.TestToeplitz) ... ok
test_scalar_03 (test_special_matrices.TestToeplitz) ... ok
test_scalar_04 (test_special_matrices.TestToeplitz) ... ok
test_2d (test_special_matrices.TestTri) ... ok
test_basic (test_special_matrices.TestTri) ... ok
test_diag (test_special_matrices.TestTri) ... ok
test_diag2d (test_special_matrices.TestTri) ... ok
test_basic (test_special_matrices.TestTril) ... ok
test_diag (test_special_matrices.TestTril) ... ok
test_basic (test_special_matrices.TestTriu) ... ok
test_diag (test_special_matrices.TestTriu) ... ok
test_logsumexp (test_maxentropy.TestMaxentropy) ... ok
test_doccer.test_unindent('Another test\n with some indent', 'Another test\n with some indent') ... ok
test_doccer.test_unindent('Another test, one line', 'Another test, one line') ... ok
test_doccer.test_unindent('Another test\n with some indent', 'Another test\n with some indent') ... ok
test_doccer.test_unindent_dict('Another test\n with some indent', 'Another test\n with some indent') ... ok
test_doccer.test_unindent_dict('Another test, one line', 'Another test, one line') ... ok
test_doccer.test_unindent_dict('Another test\n with some indent', 'Another test\n with some indent') ... ok
test_doccer.test_docformat('Docstring\n Another test\n with some indent\n Another test, one line\n Another test\n with some indent\n', 'Docstring\n Another test\n with some indent\n Another test, one line\n Another test\n with some indent\n') ... ok
test_doccer.test_docformat('Single line doc Another test\n with some indent', 'Single line doc Another test\n with some indent') ... ok
test_doccer.test_decorator(' Docstring\n Another test\n with some indent\n ', ' Docstring\n Another test\n with some indent\n ') ... ok
test_doccer.test_decorator(' Docstring\n Another test\n with some indent\n ', ' Docstring\n Another test\n with some indent\n ') ... ok
test_bytescale (test_pilutil.TestPILUtil) ... SKIP: Skipping test: test_bytescale
Need to import PIL for this test
test_imresize (test_pilutil.TestPILUtil) ... SKIP: Skipping test: test_imresize
Need to import PIL for this test
test_imresize2 (test_pilutil.TestPILUtil) ... SKIP: Skipping test: test_imresize2
Need to import PIL for this test
test_imresize3 (test_pilutil.TestPILUtil) ... SKIP: Skipping test: test_imresize3
Need to import PIL for this test
Failure: SkipTest (Skipping test: test_fromimage
Need to import PIL for this test) ... SKIP: Skipping test: test_fromimage
Need to import PIL for this test
test_filters.test_ticket_701 ... ok
test_filters.test_orders_gauss(0, array([ 0.])) ... ok
test_filters.test_orders_gauss(0, array([ 0.])) ... ok
test_filters.test_orders_gauss(<type 'exceptions.ValueError'>, <function gaussian_filter at 0x33b5570>, array([ 0.]), 1, -1) ... ok
test_filters.test_orders_gauss(<type 'exceptions.ValueError'>, <function gaussian_filter at 0x33b5570>, array([ 0.]), 1, 4) ... ok
test_filters.test_orders_gauss(0, array([ 0.])) ... ok
test_filters.test_orders_gauss(0, array([ 0.])) ... ok
test_filters.test_orders_gauss(<type 'exceptions.ValueError'>, <function gaussian_filter1d at 0x33b5530>, array([ 0.]), 1, -1, -1) ... ok
test_filters.test_orders_gauss(<type 'exceptions.ValueError'>, <function gaussian_filter1d at 0x33b5530>, array([ 0.]), 1, -1, 4) ... ok
test_io.test_imread ... SKIP: Skipping test: test_imread
The Python Image Library could not be found.
test_basic (test_measurements.Test_measurements_select) ... ok
test_a (test_measurements.Test_measurements_stats) ... ok
test_a_centered (test_measurements.Test_measurements_stats) ... ok
test_b (test_measurements.Test_measurements_stats) ... ok
test_b_centered (test_measurements.Test_measurements_stats) ... ok
test_nonint_labels (test_measurements.Test_measurements_stats) ... ok
label 1 ... ok
label 2 ... ok
label 3 ... ok
label 4 ... ok
label 5 ... ok
label 6 ... ok
label 7 ... ok
label 8 ... ok
label 9 ... ok
label 10 ... ok
label 11 ... ok
label 12 ... ok
label 13 ... ok
find_objects 1 ... ok
find_objects 2 ... ok
find_objects 3 ... ok
find_objects 4 ... ok
find_objects 5 ... ok
find_objects 6 ... ok
find_objects 7 ... ok
find_objects 8 ... ok
find_objects 9 ... ok
sum 1 ... ok
sum 2 ... ok
sum 3 ... ok
sum 4 ... ok
sum 5 ... ok
sum 6 ... ok
sum 7 ... ok
sum 8 ... ok
sum 9 ... ok
sum 10 ... ok
sum 11 ... ok
sum 12 ... ok
mean 1 ... ok
mean 2 ... ok
mean 3 ... ok
mean 4 ... ok
minimum 1 ... ok
minimum 2 ... ok
minimum 3 ... ok
minimum 4 ... ok
maximum 1 ... ok
maximum 2 ... ok
maximum 3 ... ok
maximum 4 ... ok
Ticket #501 ... ok
variance 1 ... ok
variance 2 ... ok
variance 3 ... ok
variance 4 ... ok
variance 5 ... ok
variance 6 ... ok
standard deviation 1 ... ok
standard deviation 2 ... ok
standard deviation 3 ... ok
standard deviation 4 ... ok
standard deviation 5 ... ok
standard deviation 6 ... ok
standard deviation 7 ... ok
minimum position 1 ... ok
minimum position 2 ... ok
minimum position 3 ... ok
minimum position 4 ... ok
minimum position 5 ... ok
minimum position 6 ... ok
minimum position 7 ... ok
maximum position 1 ... ok
maximum position 2 ... ok
maximum position 3 ... ok
maximum position 4 ... ok
maximum position 5 ... ok
maximum position 6 ... ok
extrema 1 ... ok
extrema 2 ... ok
extrema 3 ... ok
extrema 4 ... ok
center of mass 1 ... ok
center of mass 2 ... ok
center of mass 3 ... ok
center of mass 4 ... ok
center of mass 5 ... ok
center of mass 6 ... ok
center of mass 7 ... ok
center of mass 8 ... ok
center of mass 9 ... ok
histogram 1 ... ok
histogram 2 ... ok
histogram 3 ... ok
affine_transform 1 ... ok
affine transform 2 ... ok
affine transform 3 ... ok
affine transform 4 ... ok
affine transform 5 ... ok
affine transform 6 ... ok
affine transform 7 ... ok
affine transform 8 ... ok
affine transform 9 ... ok
affine transform 10 ... ok
affine transform 11 ... ok
affine transform 12 ... ok
affine transform 13 ... ok
affine transform 14 ... ok
affine transform 15 ... ok
affine transform 16 ... ok
affine transform 17 ... ok
affine transform 18 ... ok
affine transform 19 ... ok
affine transform 20 ... ok
affine transform 21 ... ok
binary closing 1 ... ok
binary closing 2 ... ok
binary dilation 1 ... ok
binary dilation 2 ... ok
binary dilation 3 ... ok
binary dilation 4 ... ok
binary dilation 5 ... ok
binary dilation 6 ... ok
binary dilation 7 ... ok
binary dilation 8 ... ok
binary dilation 9 ... ok
binary dilation 10 ... ok
binary dilation 11 ... ok
binary dilation 12 ... ok
binary dilation 13 ... ok
binary dilation 14 ... ok
binary dilation 15 ... ok
binary dilation 16 ... ok
binary dilation 17 ... ok
binary dilation 18 ... ok
binary dilation 19 ... ok
binary dilation 20 ... ok
binary dilation 21 ... ok
binary dilation 22 ... ok
binary dilation 23 ... ok
binary dilation 24 ... ok
binary dilation 25 ... ok
binary dilation 26 ... ok
binary dilation 27 ... ok
binary dilation 28 ... ok
binary dilation 29 ... ok
binary dilation 30 ... ok
binary dilation 31 ... ok
binary dilation 32 ... ok
binary dilation 33 ... ok
binary dilation 34 ... ok
binary dilation 35 ... ok
binary erosion 1 ... ok
binary erosion 2 ... ok
binary erosion 3 ... ok
binary erosion 4 ... ok
binary erosion 5 ... ok
binary erosion 6 ... ok
binary erosion 7 ... ok
binary erosion 8 ... ok
binary erosion 9 ... ok
binary erosion 10 ... ok
binary erosion 11 ... ok
binary erosion 12 ... ok
binary erosion 13 ... ok
binary erosion 14 ... ok
binary erosion 15 ... ok
binary erosion 16 ... ok
binary erosion 17 ... ok
binary erosion 18 ... ok
binary erosion 19 ... ok
binary erosion 20 ... ok
binary erosion 21 ... ok
binary erosion 22 ... ok
binary erosion 23 ... ok
binary erosion 24 ... ok
binary erosion 25 ... ok
binary erosion 26 ... ok
binary erosion 27 ... ok
binary erosion 28 ... ok
binary erosion 29 ... ok
binary erosion 30 ... ok
binary erosion 31 ... ok
binary erosion 32 ... ok
binary erosion 33 ... ok
binary erosion 34 ... ok
binary erosion 35 ... ok
binary erosion 36 ... ok
binary fill holes 1 ... ok
binary fill holes 2 ... ok
binary fill holes 3 ... ok
binary opening 1 ... ok
binary opening 2 ... ok
binary propagation 1 ... ok
binary propagation 2 ... ok
black tophat 1 ... ok
black tophat 2 ... ok
boundary modes ... ok
boundary modes 2 ... ok
correlation 1 ... ok
correlation 2 ... ok
correlation 3 ... ok
correlation 4 ... ok
correlation 5 ... ok
correlation 6 ... ok
correlation 7 ... ok
correlation 8 ... ok
correlation 9 ... ok
correlation 10 ... ok
correlation 11 ... ok
correlation 12 ... ok
correlation 13 ... ok
correlation 14 ... ok
correlation 15 ... ok
correlation 16 ... ok
correlation 17 ... ok
correlation 18 ... ok
correlation 19 ... ok
correlation 20 ... ok
correlation 21 ... ok
correlation 22 ... ok
correlation 23 ... ok
correlation 24 ... ok
correlation 25 ... ok
brute force distance transform 1 ... ok
brute force distance transform 2 ... ok
brute force distance transform 3 ... ok
brute force distance transform 4 ... ok
brute force distance transform 5 ... ok
brute force distance transform 6 ... ok
chamfer type distance transform 1 ... ok
chamfer type distance transform 2 ... ok
chamfer type distance transform 3 ... ok
euclidean distance transform 1 ... ok
euclidean distance transform 2 ... ok
euclidean distance transform 3 ... ok
euclidean distance transform 4 ... ok
line extension 1 ... ok
line extension 2 ... ok
line extension 3 ... ok
line extension 4 ... ok
line extension 5 ... ok
line extension 6 ... ok
line extension 7 ... ok
line extension 8 ... ok
line extension 9 ... ok
line extension 10 ... ok
ellipsoid fourier filter for complex transforms 1 ... ok
ellipsoid fourier filter for real transforms 1 ... ok
gaussian fourier filter for complex transforms 1 ... ok
gaussian fourier filter for real transforms 1 ... ok
shift filter for complex transforms 1 ... ok
shift filter for real transforms 1 ... ok
uniform fourier filter for complex transforms 1 ... ok
uniform fourier filter for real transforms 1 ... ok
gaussian filter 1 ... ok
gaussian filter 2 ... ok
gaussian filter 3 - single precision data ... ok
gaussian filter 4 ... ok
gaussian filter 5 ... ok
gaussian filter 6 ... ok
gaussian gradient magnitude filter 1 ... ok
gaussian gradient magnitude filter 2 ... ok
gaussian laplace filter 1 ... ok
gaussian laplace filter 2 ... ok
generation of a binary structure 1 ... ok
generation of a binary structure 2 ... ok
generation of a binary structure 3 ... ok
generation of a binary structure 4 ... ok
generic filter 1 ... ok
generic 1d filter 1 ... ok
generic gradient magnitude 1 ... ok
generic laplace filter 1 ... ok
geometric transform 1 ... ok
geometric transform 2 ... ok
geometric transform 3 ... ok
geometric transform 4 ... ok
geometric transform 5 ... ok
geometric transform 6 ... ok
geometric transform 7 ... ok
geometric transform 8 ... ok
geometric transform 10 ... ok
geometric transform 13 ... ok
geometric transform 14 ... ok
geometric transform 15 ... ok
geometric transform 16 ... ok
geometric transform 17 ... ok
geometric transform 18 ... ok
geometric transform 19 ... ok
geometric transform 20 ... ok
geometric transform 21 ... ok
geometric transform 22 ... ok
geometric transform 23 ... ok
geometric transform 24 ... ok
grey closing 1 ... ok
grey closing 2 ... ok
grey dilation 1 ... ok
grey dilation 2 ... ok
grey dilation 3 ... ok
grey erosion 1 ... ok
grey erosion 2 ... ok
grey erosion 3 ... ok
grey opening 1 ... ok
grey opening 2 ... ok
binary hit-or-miss transform 1 ... ok
binary hit-or-miss transform 2 ... ok
binary hit-or-miss transform 3 ... ok
iterating a structure 1 ... ok
iterating a structure 2 ... ok
iterating a structure 3 ... ok
laplace filter 1 ... ok
laplace filter 2 ... ok
map coordinates 1 ... ok
map coordinates 2 ... ok
maximum filter 1 ... ok
maximum filter 2 ... ok
maximum filter 3 ... ok
maximum filter 4 ... ok
maximum filter 5 ... ok
maximum filter 6 ... ok
maximum filter 7 ... ok
maximum filter 8 ... ok
maximum filter 9 ... ok
minimum filter 1 ... ok
minimum filter 2 ... ok
minimum filter 3 ... ok
minimum filter 4 ... ok
minimum filter 5 ... ok
minimum filter 6 ... ok
minimum filter 7 ... ok
minimum filter 8 ... ok
minimum filter 9 ... ok
morphological gradient 1 ... ok
morphological gradient 2 ... ok
morphological laplace 1 ... ok
morphological laplace 2 ... ok
prewitt filter 1 ... ok
prewitt filter 2 ... ok
prewitt filter 3 ... ok
prewitt filter 4 ... ok
rank filter 1 ... ok
rank filter 2 ... ok
rank filter 3 ... ok
rank filter 4 ... ok
rank filter 5 ... ok
rank filter 6 ... ok
rank filter 7 ... ok
median filter 8 ... ok
rank filter 9 ... ok
rank filter 10 ... ok
rank filter 11 ... ok
rank filter 12 ... ok
rank filter 13 ... ok
rank filter 14 ... ok
rotate 1 ... ok
rotate 2 ... ok
rotate 3 ... ok
rotate 4 ... ok
rotate 5 ... ok
rotate 6 ... ok
rotate 7 ... ok
rotate 8 ... ok
shift 1 ... ok
shift 2 ... ok
shift 3 ... ok
shift 4 ... ok
shift 5 ... ok
shift 6 ... ok
shift 7 ... ok
shift 8 ... ok
shift 9 ... ok
sobel filter 1 ... ok
sobel filter 2 ... ok
sobel filter 3 ... ok
sobel filter 4 ... ok
spline filter 1 ... ok
spline filter 2 ... ok
spline filter 3 ... ok
spline filter 4 ... ok
spline filter 5 ... ok
uniform filter 1 ... ok
uniform filter 2 ... ok
uniform filter 3 ... ok
uniform filter 4 ... ok
uniform filter 5 ... ok
uniform filter 6 ... ok
watershed_ift 1 ... ok
watershed_ift 2 ... ok
watershed_ift 3 ... ok
watershed_ift 4 ... ok
watershed_ift 5 ... ok
watershed_ift 6 ... ok
watershed_ift 7 ... ok
white tophat 1 ... ok
white tophat 2 ... ok
zoom 1 ... ok
zoom 2 ... ok
zoom by affine transformation 1 ... ok
Regression test for #413: median_filter does not handle bytes orders. ... ok
Ticket #643 ... ok
test_explicit (test_odr.TestODR) ... ok
test_implicit (test_odr.TestODR) ... ok
test_lorentz (test_odr.TestODR) ... ok
test_multi (test_odr.TestODR) ... ok
test_pearson (test_odr.TestODR) ... ok
test_ticket_1253 (test_odr.TestODR) ... ok
test_simple (test_cobyla.TestCobyla) ... ok
test_linesearch.TestLineSearch.test_armijo_terminate_1 ... ok
test_linesearch.TestLineSearch.test_line_search_armijo ... ok
test_linesearch.TestLineSearch.test_line_search_wolfe1 ... ok
test_linesearch.TestLineSearch.test_line_search_wolfe2 ... ok
test_linesearch.TestLineSearch.test_scalar_search_armijo ... ok
test_linesearch.TestLineSearch.test_scalar_search_wolfe1 ... ok
test_linesearch.TestLineSearch.test_scalar_search_wolfe2 ... ok
test_linesearch.TestLineSearch.test_wolfe_terminate ... ok
test_one_argument (test_minpack.TestCurveFit) ... ok
test_two_argument (test_minpack.TestCurveFit) ... ok
fsolve without gradient, equal pipes -> equal flows ... ok
fsolve with gradient, equal pipes -> equal flows ... ok
The callables 'func' and 'deriv_func' have no 'func_name' attribute. ... ok
test_minpack.TestFSolve.test_wrong_shape_fprime_function ... ok
The callable 'func' has no 'func_name' attribute. ... ok
test_minpack.TestFSolve.test_wrong_shape_func_function ... ok
f(x) = c * x**2; fixed point should be x=1/c ... ok
f(x) = c * x**0.5; fixed point should be x=c**2 ... ok
test_array_trivial (test_minpack.TestFixedPoint) ... ok
f(x) = x**2; x0=1.05; fixed point should be x=1 ... ok
f(x) = x**0.5; x0=1.05; fixed point should be x=1 ... ok
f(x) = 2x; fixed point should be x=0 ... ok
test_basic (test_minpack.TestLeastSq) ... ok
test_full_output (test_minpack.TestLeastSq) ... ok
test_input_untouched (test_minpack.TestLeastSq) ... ok
The callables 'func' and 'deriv_func' have no 'func_name' attribute. ... ok
test_wrong_shape_Dfun_function (test_minpack.TestLeastSq) ... ok
The callable 'func' has no 'func_name' attribute. ... ok
test_wrong_shape_func_function (test_minpack.TestLeastSq) ... ok
test_nnls (test_nnls.TestNNLS) ... ok
fsolve without gradient, equal pipes -> equal flows ... ok
fsolve with gradient, equal pipes -> equal flows ... ok
The callables 'func' and 'deriv_func' have no 'func_name' attribute. ... ok
test_nonlin.TestFSolve.test_wrong_shape_fprime_function ... ok
The callable 'func' has no 'func_name' attribute. ... ok
test_nonlin.TestFSolve.test_wrong_shape_func_function ... ok
test_nonlin.TestJacobianDotSolve.test_anderson ... ok
test_nonlin.TestJacobianDotSolve.test_broyden1 ... ok
test_nonlin.TestJacobianDotSolve.test_broyden2 ... ok
test_nonlin.TestJacobianDotSolve.test_diagbroyden ... ok
test_nonlin.TestJacobianDotSolve.test_excitingmixing ... ok
test_nonlin.TestJacobianDotSolve.test_krylov ... ok
test_nonlin.TestJacobianDotSolve.test_linearmixing ... ok
test_anderson (test_nonlin.TestLinear) ... ok
test_broyden1 (test_nonlin.TestLinear) ... ok
test_broyden2 (test_nonlin.TestLinear) ... ok
test_krylov (test_nonlin.TestLinear) ... ok
test_nonlin.TestNonlin.test_problem(<function F at 0x421ae70>, <function anderson at 0x33a6070>) ... ok
test_nonlin.TestNonlin.test_problem(<function F at 0x421ae70>, <function diagbroyden at 0x33a60f0>) ... ok
test_nonlin.TestNonlin.test_problem(<function F at 0x421ae70>, <function linearmixing at 0x33a60b0>) ... ok
test_nonlin.TestNonlin.test_problem(<function F at 0x421ae70>, <function excitingmixing at 0x33a6130>) ... ok
test_nonlin.TestNonlin.test_problem(<function F at 0x421ae70>, <function broyden1 at 0x33a3fb0>) ... ok
test_nonlin.TestNonlin.test_problem(<function F at 0x421ae70>, <function broyden2 at 0x33a3f70>) ... ok
test_nonlin.TestNonlin.test_problem(<function F at 0x421ae70>, <function newton_krylov at 0x33a6170>) ... ok
test_nonlin.TestNonlin.test_problem(<function F2 at 0x421aeb0>, <function anderson at 0x33a6070>) ... ok
test_nonlin.TestNonlin.test_problem(<function F2 at 0x421aeb0>, <function diagbroyden at 0x33a60f0>) ... ok
test_nonlin.TestNonlin.test_problem(<function F2 at 0x421aeb0>, <function broyden1 at 0x33a3fb0>) ... ok
test_nonlin.TestNonlin.test_problem(<function F2 at 0x421aeb0>, <function broyden2 at 0x33a3f70>) ... ok
test_nonlin.TestNonlin.test_problem(<function F2 at 0x421aeb0>, <function newton_krylov at 0x33a6170>) ... ok
test_nonlin.TestNonlin.test_problem(<function F3 at 0x421aef0>, <function anderson at 0x33a6070>) ... ok
test_nonlin.TestNonlin.test_problem(<function F3 at 0x421aef0>, <function diagbroyden at 0x33a60f0>) ... ok
test_nonlin.TestNonlin.test_problem(<function F3 at 0x421aef0>, <function linearmixing at 0x33a60b0>) ... ok
test_nonlin.TestNonlin.test_problem(<function F3 at 0x421aef0>, <function excitingmixing at 0x33a6130>) ... ok
test_nonlin.TestNonlin.test_problem(<function F3 at 0x421aef0>, <function broyden1 at 0x33a3fb0>) ... ok
test_nonlin.TestNonlin.test_problem(<function F3 at 0x421aef0>, <function broyden2 at 0x33a3f70>) ... ok
test_nonlin.TestNonlin.test_problem(<function F3 at 0x421aef0>, <function newton_krylov at 0x33a6170>) ... ok
test_nonlin.TestNonlin.test_problem(<function F4_powell at 0x421af30>, <function anderson at 0x33a6070>) ... ok
test_nonlin.TestNonlin.test_problem(<function F4_powell at 0x421af30>, <function broyden1 at 0x33a3fb0>) ... ok
test_nonlin.TestNonlin.test_problem(<function F4_powell at 0x421af30>, <function broyden2 at 0x33a3f70>) ... ok
test_nonlin.TestNonlin.test_problem(<function F4_powell at 0x421af30>, <function newton_krylov at 0x33a6170>) ... ok
test_nonlin.TestNonlin.test_problem(<function F5 at 0x421af70>, <function anderson at 0x33a6070>) ... ok
test_nonlin.TestNonlin.test_problem(<function F5 at 0x421af70>, <function broyden1 at 0x33a3fb0>) ... ok
test_nonlin.TestNonlin.test_problem(<function F5 at 0x421af70>, <function broyden2 at 0x33a3f70>) ... ok
test_nonlin.TestNonlin.test_problem(<function F5 at 0x421af70>, <function newton_krylov at 0x33a6170>) ... ok
test_nonlin.TestNonlin.test_problem(<function F6 at 0x421afb0>, <function anderson at 0x33a6070>) ... ok
test_nonlin.TestNonlin.test_problem(<function F6 at 0x421afb0>, <function broyden1 at 0x33a3fb0>) ... ok
test_nonlin.TestNonlin.test_problem(<function F6 at 0x421afb0>, <function broyden2 at 0x33a3f70>) ... ok
test_nonlin.TestNonlin.test_problem(<function F6 at 0x421afb0>, <function newton_krylov at 0x33a6170>) ... ok
test_anderson (test_nonlin.TestNonlinOldTests) ... ok
test_broyden1 (test_nonlin.TestNonlinOldTests) ... ok
test_broyden2 (test_nonlin.TestNonlinOldTests) ... ok
test_diagbroyden (test_nonlin.TestNonlinOldTests) ... ok
test_exciting (test_nonlin.TestNonlinOldTests) ... ok
test_linearmixing (test_nonlin.TestNonlinOldTests) ... ok
test_anderson (test_nonlin.TestSecant) ... ok
test_broyden1 (test_nonlin.TestSecant) ... ok
test_broyden1_update (test_nonlin.TestSecant) ... ok
test_broyden2 (test_nonlin.TestSecant) ... ok
test_broyden2_update (test_nonlin.TestSecant) ... ok
Broyden-Fletcher-Goldfarb-Shanno optimization routine ... ok
brent algorithm ... ok
conjugate gradient optimization routine ... ok
Test fminbound ... ok
test_fminbound_scalar (test_optimize.TestOptimize) ... ok
limited-memory bound-constrained BFGS algorithm ... ok
line-search Newton conjugate gradient optimization routine ... ok
Nelder-Mead simplex algorithm ... ok
Powell (direction set) optimization routine ... ok
Compare rosen_hess(x) times p with rosen_hess_prod(x,p) (ticket #1248) ... ok
test_tnc (test_optimize.TestTnc) ... ok
Ticket #1214 ... ok
Ticket #1074 ... ok
test_bound_approximated (test_slsqp.TestSLSQP) ... ok
test_bound_equality_given (test_slsqp.TestSLSQP) ... ok
test_bound_equality_inequality_given (test_slsqp.TestSLSQP) ... ok
test_unbounded_approximated (test_slsqp.TestSLSQP) ... ok
test_unbounded_given (test_slsqp.TestSLSQP) ... ok
test_bisect (test_zeros.TestBasic) ... ok
test_brenth (test_zeros.TestBasic) ... ok
test_brentq (test_zeros.TestBasic) ... ok
test_deriv_zero_warning (test_zeros.TestBasic) ... ok
test_ridder (test_zeros.TestBasic) ... ok
Regression test for #651: better handling of badly conditioned ... ok
test_simple (test_filter_design.TestTf2zpk) ... ok
Test that invalid cutoff argument raises ValueError. ... ok
test_bandpass (test_fir_filter_design.TestFirWinMore) ... ok
Test that attempt to create a highpass filter with an even number ... ok
test_highpass (test_fir_filter_design.TestFirWinMore) ... ok
test_lowpass (test_fir_filter_design.TestFirWinMore) ... ok
test_multi (test_fir_filter_design.TestFirWinMore) ... ok
Test the nyq keyword. ... ok
test_response (test_fir_filter_design.TestFirwin) ... ok
For one lowpass, bandpass, and highpass example filter, this test ... ok
test01 (test_fir_filter_design.TestFirwin2) ... ok
test02 (test_fir_filter_design.TestFirwin2) ... ok
test03 (test_fir_filter_design.TestFirwin2) ... ok
test_invalid_args (test_fir_filter_design.TestFirwin2) ... ok
test_nyq (test_fir_filter_design.TestFirwin2) ... ok
test_hilbert (test_fir_filter_design.TestRemez) ... ok
test_ltisys.TestSS2TF.test_basic(3, 3, 3) ... ok
test_ltisys.TestSS2TF.test_basic(1, 3, 3) ... ok
test_ltisys.TestSS2TF.test_basic(1, 1, 1) ... ok
test_ltisys.Test_impulse2.test_01 ... ok
Specify the desired time values for the output. ... ok
Specify an initial condition as a scalar. ... ok
Specify an initial condition as a list. ... ok
test_ltisys.Test_impulse2.test_05 ... ok
test_ltisys.Test_impulse2.test_06 ... ok
test_ltisys.Test_lsim2.test_01 ... ok
test_ltisys.Test_lsim2.test_02 ... ok
test_ltisys.Test_lsim2.test_03 ... ok
test_ltisys.Test_lsim2.test_04 ... ok
test_ltisys.Test_lsim2.test_05 ... ok
Test use of the default values of the arguments `T` and `U`. ... ok
test_ltisys.Test_step2.test_01 ... ok
Specify the desired time values for the output. ... ok
Specify an initial condition as a scalar. ... ok
Specify an initial condition as a list. ... ok
test_ltisys.Test_step2.test_05 ... ok
test_ltisys.Test_step2.test_06 ... ok
test_basic (test_signaltools.TestCSpline1DEval) ... ok
test_2d_arrays (test_signaltools.TestConvolve) ... ok
test_basic (test_signaltools.TestConvolve) ... ok
test_complex (test_signaltools.TestConvolve) ... ok
test_same_mode (test_signaltools.TestConvolve) ... ok
test_valid_mode (test_signaltools.TestConvolve) ... ok
test_zero_order (test_signaltools.TestConvolve) ... ok
test_rank1_full (test_signaltools.TestCorrelateComplex128) ... ok
test_rank1_same (test_signaltools.TestCorrelateComplex128) ... ok
test_rank1_valid (test_signaltools.TestCorrelateComplex128) ... ok
test_rank3 (test_signaltools.TestCorrelateComplex128) ... ok
test_rank1_full (test_signaltools.TestCorrelateComplex256) ... ok
test_rank1_same (test_signaltools.TestCorrelateComplex256) ... ok
test_rank1_valid (test_signaltools.TestCorrelateComplex256) ... ok
test_rank3 (test_signaltools.TestCorrelateComplex256) ... ok
test_rank1_full (test_signaltools.TestCorrelateComplex256) ... ok
test_rank1_same (test_signaltools.TestCorrelateComplex256) ... ok
test_rank1_valid (test_signaltools.TestCorrelateComplex256) ... ok
test_rank3 (test_signaltools.TestCorrelateComplex256) ... ok
test_rank1_full (test_signaltools.TestCorrelateComplex64) ... ok
test_rank1_same (test_signaltools.TestCorrelateComplex64) ... ok
test_rank1_valid (test_signaltools.TestCorrelateComplex64) ... ok
test_rank3 (test_signaltools.TestCorrelateComplex64) ... ok
test_rank1_full (test_signaltools.TestCorrelateDecimal) ... ok
test_rank1_same (test_signaltools.TestCorrelateDecimal) ... ok
test_rank1_valid (test_signaltools.TestCorrelateDecimal) ... ok
test_rank3_all (test_signaltools.TestCorrelateDecimal) ... ok
test_rank3_same (test_signaltools.TestCorrelateDecimal) ... ok
test_rank3_valid (test_signaltools.TestCorrelateDecimal) ... ok
test_rank1_full (test_signaltools.TestCorrelateFloat128) ... ok
test_rank1_same (test_signaltools.TestCorrelateFloat128) ... ok
test_rank1_valid (test_signaltools.TestCorrelateFloat128) ... ok
test_rank3_all (test_signaltools.TestCorrelateFloat128) ... ok
test_rank3_same (test_signaltools.TestCorrelateFloat128) ... ok
test_rank3_valid (test_signaltools.TestCorrelateFloat128) ... ok
test_rank1_full (test_signaltools.TestCorrelateFloat32) ... ok
test_rank1_same (test_signaltools.TestCorrelateFloat32) ... ok
test_rank1_valid (test_signaltools.TestCorrelateFloat32) ... ok
test_rank3_all (test_signaltools.TestCorrelateFloat32) ... ok
test_rank3_same (test_signaltools.TestCorrelateFloat32) ... ok
test_rank3_valid (test_signaltools.TestCorrelateFloat32) ... ok
test_rank1_full (test_signaltools.TestCorrelateFloat64) ... ok
test_rank1_same (test_signaltools.TestCorrelateFloat64) ... ok
test_rank1_valid (test_signaltools.TestCorrelateFloat64) ... ok
test_rank3_all (test_signaltools.TestCorrelateFloat64) ... ok
test_rank3_same (test_signaltools.TestCorrelateFloat64) ... ok
test_rank3_valid (test_signaltools.TestCorrelateFloat64) ... ok
test_rank1_full (test_signaltools.TestCorrelateInt) ... ok
test_rank1_same (test_signaltools.TestCorrelateInt) ... ok
test_rank1_valid (test_signaltools.TestCorrelateInt) ... ok
test_rank3_all (test_signaltools.TestCorrelateInt) ... ok
test_rank3_same (test_signaltools.TestCorrelateInt) ... ok
test_rank3_valid (test_signaltools.TestCorrelateInt) ... ok
test_rank1_full (test_signaltools.TestCorrelateInt16) ... ok
test_rank1_same (test_signaltools.TestCorrelateInt16) ... ok
test_rank1_valid (test_signaltools.TestCorrelateInt16) ... ok
test_rank3_all (test_signaltools.TestCorrelateInt16) ... ok
test_rank3_same (test_signaltools.TestCorrelateInt16) ... ok
test_rank3_valid (test_signaltools.TestCorrelateInt16) ... ok
test_rank1_full (test_signaltools.TestCorrelateInt8) ... ok
test_rank1_same (test_signaltools.TestCorrelateInt8) ... ok
test_rank1_valid (test_signaltools.TestCorrelateInt8) ... ok
test_rank3_all (test_signaltools.TestCorrelateInt8) ... ok
test_rank3_same (test_signaltools.TestCorrelateInt8) ... ok
test_rank3_valid (test_signaltools.TestCorrelateInt8) ... ok
test_rank1_full (test_signaltools.TestCorrelateUint16) ... ok
test_rank1_same (test_signaltools.TestCorrelateUint16) ... ok
test_rank1_valid (test_signaltools.TestCorrelateUint16) ... ok
test_rank3_all (test_signaltools.TestCorrelateUint16) ... ok
test_rank3_same (test_signaltools.TestCorrelateUint16) ... ok
test_rank3_valid (test_signaltools.TestCorrelateUint16) ... ok
test_rank1_full (test_signaltools.TestCorrelateUint32) ... ok
test_rank1_same (test_signaltools.TestCorrelateUint32) ... ok
test_rank1_valid (test_signaltools.TestCorrelateUint32) ... ok
test_rank3_all (test_signaltools.TestCorrelateUint32) ... ok
test_rank3_same (test_signaltools.TestCorrelateUint32) ... ok
test_rank3_valid (test_signaltools.TestCorrelateUint32) ... ok
test_rank1_full (test_signaltools.TestCorrelateUint64) ... ok
test_rank1_same (test_signaltools.TestCorrelateUint64) ... ok
test_rank1_valid (test_signaltools.TestCorrelateUint64) ... ok
test_rank3_all (test_signaltools.TestCorrelateUint64) ... ok
test_rank3_same (test_signaltools.TestCorrelateUint64) ... ok
test_rank3_valid (test_signaltools.TestCorrelateUint64) ... ok
test_rank1_full (test_signaltools.TestCorrelateUint8) ... ok
test_rank1_same (test_signaltools.TestCorrelateUint8) ... ok
test_rank1_valid (test_signaltools.TestCorrelateUint8) ... ok
test_rank3_all (test_signaltools.TestCorrelateUint8) ... ok
test_rank3_same (test_signaltools.TestCorrelateUint8) ... ok
test_rank3_valid (test_signaltools.TestCorrelateUint8) ... ok
test_signaltools.TestDecimate.test_basic ... ok
test_2d_complex_same (test_signaltools.TestFFTConvolve) ... ok
test_2d_real_same (test_signaltools.TestFFTConvolve) ... ok
test_complex (test_signaltools.TestFFTConvolve) ... ok
test_random_data (test_signaltools.TestFFTConvolve) ... ok
test_real (test_signaltools.TestFFTConvolve) ... ok
test_real_same_mode (test_signaltools.TestFFTConvolve) ... ok
test_real_valid_mode (test_signaltools.TestFFTConvolve) ... ok
test_zero_order (test_signaltools.TestFFTConvolve) ... ok
test_signaltools.TestFiltFilt.test_basic ... ok
test_signaltools.TestHilbert.test_hilbert_axisN(array([[ 0.+2.30940108j, 6.+2.30940108j, 12.+2.30940108j], ... ok
test_signaltools.TestHilbert.test_hilbert_axisN(array([ 0.+2.30940108j, 1.-1.15470054j, 2.-1.15470054j, 3.-1.15470054j, ... ok
test_signaltools.TestHilbert.test_hilbert_axisN((3, 20), [3, 20]) ... ok
test_signaltools.TestHilbert.test_hilbert_axisN((20, 3), [20, 3]) ... ok
test_signaltools.TestHilbert.test_hilbert_axisN(array([ 0.00000000e+00-1.7201583j , 1.00000000e+00-2.04779451j, ... ok
test_signaltools.TestHilbert.test_hilbert_theoretical ... ok
Regression test for #880: empty array for zi crashes. ... ok
test_rank1 (test_signaltools.TestLinearFilterComplex128) ... ok
test_rank2 (test_signaltools.TestLinearFilterComplex128) ... ok
test_rank2_init_cond_a0 (test_signaltools.TestLinearFilterComplex128) ... ok
test_rank2_init_cond_a1 (test_signaltools.TestLinearFilterComplex128) ... ok
test_rank3 (test_signaltools.TestLinearFilterComplex128) ... ok
Regression test for #880: empty array for zi crashes. ... ok
test_rank1 (test_signaltools.TestLinearFilterComplex64) ... ok
test_rank2 (test_signaltools.TestLinearFilterComplex64) ... ok
test_rank2_init_cond_a0 (test_signaltools.TestLinearFilterComplex64) ... ok
test_rank2_init_cond_a1 (test_signaltools.TestLinearFilterComplex64) ... ok
test_rank3 (test_signaltools.TestLinearFilterComplex64) ... ok
Regression test for #880: empty array for zi crashes. ... ok
test_rank1 (test_signaltools.TestLinearFilterComplexxxiExtended28) ... ok
test_rank2 (test_signaltools.TestLinearFilterComplexxxiExtended28) ... ok
test_rank2_init_cond_a0 (test_signaltools.TestLinearFilterComplexxxiExtended28) ... ok
test_rank2_init_cond_a1 (test_signaltools.TestLinearFilterComplexxxiExtended28) ... ok
test_rank3 (test_signaltools.TestLinearFilterComplexxxiExtended28) ... ok
Regression test for #880: empty array for zi crashes. ... ok
test_rank1 (test_signaltools.TestLinearFilterDecimal) ... ok
test_rank2 (test_signaltools.TestLinearFilterDecimal) ... ok
test_rank2_init_cond_a0 (test_signaltools.TestLinearFilterDecimal) ... ok
test_rank2_init_cond_a1 (test_signaltools.TestLinearFilterDecimal) ... ok
test_rank3 (test_signaltools.TestLinearFilterDecimal) ... ok
Regression test for #880: empty array for zi crashes. ... ok
test_rank1 (test_signaltools.TestLinearFilterFloat32) ... ok
test_rank2 (test_signaltools.TestLinearFilterFloat32) ... ok
test_rank2_init_cond_a0 (test_signaltools.TestLinearFilterFloat32) ... ok
test_rank2_init_cond_a1 (test_signaltools.TestLinearFilterFloat32) ... ok
test_rank3 (test_signaltools.TestLinearFilterFloat32) ... ok
Regression test for #880: empty array for zi crashes. ... ok
test_rank1 (test_signaltools.TestLinearFilterFloat64) ... ok
test_rank2 (test_signaltools.TestLinearFilterFloat64) ... ok
test_rank2_init_cond_a0 (test_signaltools.TestLinearFilterFloat64) ... ok
test_rank2_init_cond_a1 (test_signaltools.TestLinearFilterFloat64) ... ok
test_rank3 (test_signaltools.TestLinearFilterFloat64) ... ok
Regression test for #880: empty array for zi crashes. ... ok
test_rank1 (test_signaltools.TestLinearFilterFloatExtended) ... ok
test_rank2 (test_signaltools.TestLinearFilterFloatExtended) ... ok
test_rank2_init_cond_a0 (test_signaltools.TestLinearFilterFloatExtended) ... ok
test_rank2_init_cond_a1 (test_signaltools.TestLinearFilterFloatExtended) ... ok
test_rank3 (test_signaltools.TestLinearFilterFloatExtended) ... ok
test_basic (test_signaltools.TestMedFilt) ... ok
Ticket #1124. Ensure this does not segfault. ... ok
test_basic (test_signaltools.TestOrderFilt) ... ok
test_basic (test_signaltools.TestWiener) ... ok
test_hyperbolic_at_zero (test_waveforms.TestChirp) ... ok
test_hyperbolic_freq_01 (test_waveforms.TestChirp) ... ok
test_hyperbolic_freq_02 (test_waveforms.TestChirp) ... ok
test_hyperbolic_freq_03 (test_waveforms.TestChirp) ... ok
test_integer_all (test_waveforms.TestChirp) ... ok
test_integer_f0 (test_waveforms.TestChirp) ... ok
test_integer_f1 (test_waveforms.TestChirp) ... ok
test_integer_t1 (test_waveforms.TestChirp) ... ok
test_linear_at_zero (test_waveforms.TestChirp) ... ok
test_linear_freq_01 (test_waveforms.TestChirp) ... ok
test_linear_freq_02 (test_waveforms.TestChirp) ... ok
test_logarithmic_at_zero (test_waveforms.TestChirp) ... ok
test_logarithmic_freq_01 (test_waveforms.TestChirp) ... ok
test_logarithmic_freq_02 (test_waveforms.TestChirp) ... ok
test_logarithmic_freq_03 (test_waveforms.TestChirp) ... ok
test_quadratic_at_zero (test_waveforms.TestChirp) ... ok
test_quadratic_at_zero2 (test_waveforms.TestChirp) ... ok
test_quadratic_freq_01 (test_waveforms.TestChirp) ... ok
test_quadratic_freq_02 (test_waveforms.TestChirp) ... ok
test_unknown_method (test_waveforms.TestChirp) ... ok
test_integer_bw (test_waveforms.TestGaussPulse) ... ok
test_integer_bwr (test_waveforms.TestGaussPulse) ... ok
test_integer_fc (test_waveforms.TestGaussPulse) ... ok
test_integer_tpr (test_waveforms.TestGaussPulse) ... ok
test_sweep_poly_const (test_waveforms.TestSweepPoly) ... ok
test_sweep_poly_cubic (test_waveforms.TestSweepPoly) ... ok
Use an array of coefficients instead of a poly1d. ... ok
Use a list of coefficients instead of a poly1d. ... ok
test_sweep_poly_linear (test_waveforms.TestSweepPoly) ... ok
test_sweep_poly_quad1 (test_waveforms.TestSweepPoly) ... ok
test_sweep_poly_quad2 (test_waveforms.TestSweepPoly) ... ok
test_cascade (test_wavelets.TestWavelets) ... ok
test_daub (test_wavelets.TestWavelets) ... ok
test_morlet (test_wavelets.TestWavelets) ... ok
test_qmf (test_wavelets.TestWavelets) ... ok
test_windows.TestChebWin.test_cheb_even ... ok
test_windows.TestChebWin.test_cheb_odd ... ok
test_windows.TestGetWindow.test_boxcar ... ok
test_windows.TestGetWindow.test_cheb_even ... ok
test_windows.TestGetWindow.test_cheb_odd ... ok
Getting factors of complex matrix ... SKIP: Skipping test: test_complex_lu
UMFPACK appears not to be compiled
Getting factors of real matrix ... SKIP: Skipping test: test_real_lu
UMFPACK appears not to be compiled
Getting factors of complex matrix ... SKIP: Skipping test: test_complex_lu
UMFPACK appears not to be compiled
Getting factors of real matrix ... SKIP: Skipping test: test_real_lu
UMFPACK appears not to be compiled
Prefactorize (with UMFPACK) matrix for solving with multiple rhs ... SKIP: Skipping test: test_factorized_umfpack
UMFPACK appears not to be compiled
Prefactorize matrix for solving with multiple rhs ... SKIP: Skipping test: test_factorized_without_umfpack
UMFPACK appears not to be compiled
Solve with UMFPACK: double precision complex ... SKIP: Skipping test: test_solve_complex_umfpack
UMFPACK appears not to be compiled
Solve: single precision complex ... SKIP: Skipping test: test_solve_complex_without_umfpack
UMFPACK appears not to be compiled
Solve with UMFPACK: double precision, sparse rhs ... SKIP: Skipping test: test_solve_sparse_rhs
UMFPACK appears not to be compiled
Solve with UMFPACK: double precision ... SKIP: Skipping test: test_solve_umfpack
UMFPACK appears not to be compiled
Solve: single precision ... SKIP: Skipping test: test_solve_without_umfpack
UMFPACK appears not to be compiled
test_non_square (test_linsolve.TestLinsolve) ... ok
test_singular (test_linsolve.TestLinsolve) ... ok
test_smoketest (test_linsolve.TestLinsolve) ... ok
test_twodiags (test_linsolve.TestLinsolve) ... ok
test_linsolve.TestSplu.test_lu_refcount ... ok
test_linsolve.TestSplu.test_spilu_nnz0 ... ok
test_linsolve.TestSplu.test_spilu_smoketest ... ok
test_linsolve.TestSplu.test_splu_basic ... ok
test_linsolve.TestSplu.test_splu_nnz0 ... ok
test_linsolve.TestSplu.test_splu_perm ... ok
test_linsolve.TestSplu.test_splu_smoketest ... ok
test_complex_nonsymmetric_modes (test_arpack.TestEigenComplexNonSymmetric) ... ok
test_no_convergence (test_arpack.TestEigenComplexNonSymmetric) ... ok
test_complex_symmetric_modes (test_arpack.TestEigenComplexSymmetric) ... ok
test_no_convergence (test_arpack.TestEigenComplexSymmetric) ... ok
test_no_convergence (test_arpack.TestEigenNonSymmetric) ... ok
test_nonsymmetric_modes (test_arpack.TestEigenNonSymmetric) ... ok
test_starting_vector (test_arpack.TestEigenNonSymmetric) ... ok
test_no_convergence (test_arpack.TestEigenSymmetric) ... ok
test_starting_vector (test_arpack.TestEigenSymmetric) ... ok
test_symmetric_modes (test_arpack.TestEigenSymmetric) ... ok
test_simple_complex (test_arpack.TestSparseSvd) ... ok
test_simple_real (test_arpack.TestSparseSvd) ... ok
test_arpack.test_eigen_bad_shapes ... ok
test_arpack.test_eigs_operator ... ok
test (test_speigs.TestEigs) ... ok
test_lobpcg.test_Small ... ok
test_lobpcg.test_ElasticRod ... ok
test_lobpcg.test_MikotaPair ... ok
test_lobpcg.test_trivial ... ok
test_callback (test_iterative.TestGMRES) ... ok
test whether all methods converge ... ok
test whether maxiter is respected ... ok
test whether all methods accept a trivial preconditioner ... ok
Check that QMR works with left and right preconditioners ... ok
test_outer_v (test_lgmres.TestLGMRES) ... ok
test_preconditioner (test_lgmres.TestLGMRES) ... ok
test_lsqr.test_basic ... ok
test_utils.test_make_system_bad_shape ... ok
test_basic (test_interface.TestAsLinearOperator) ... ok
test_matvec (test_interface.TestLinearOperator) ... ok
test_iterative.test_gmres_basic ... ok
test_abs (test_base.TestBSR) ... ok
test_add (test_base.TestBSR) ... ok
adding a dense matrix to a sparse matrix ... ok
test_add_sub (test_base.TestBSR) ... ok
test_asfptype (test_base.TestBSR) ... ok
test_astype (test_base.TestBSR) ... ok
test_bsr_matvec (test_base.TestBSR) ... ok
test_bsr_matvecs (test_base.TestBSR) ... ok
check native BSR format constructor ... ok
construct from dense ... ok
Check whether the copy=True and copy=False keywords work ... ok
Does the matrix's .diagonal() method work? ... ok
test_elementwise_divide (test_base.TestBSR) ... ok
test_elementwise_multiply (test_base.TestBSR) ... ok
test_eliminate_zeros (test_base.TestBSR) ... ok
create empty matrices ... ok
Test manipulating empty matrices. Fails in SciPy SVN <= r1768 ... ok
test_from_array (test_base.TestBSR) ... ok
test_from_list (test_base.TestBSR) ... ok
test_from_matrix (test_base.TestBSR) ... ok
test_from_sparse (test_base.TestBSR) ... ok
test_getcol (test_base.TestBSR) ... ok
test_getrow (test_base.TestBSR) ... ok
test_idiv_scalar (test_base.TestBSR) ... ok
test_imag (test_base.TestBSR) ... ok
test_imul_scalar (test_base.TestBSR) ... ok
test_invalid_shapes (test_base.TestBSR) ... ok
test_matmat_dense (test_base.TestBSR) ... ok
test_matmat_sparse (test_base.TestBSR) ... ok
test_matvec (test_base.TestBSR) ... ok
Does the matrix's .mean(axis=...) method work? ... ok
test_mu (test_base.TestBSR) ... ok
test_mul_scalar (test_base.TestBSR) ... ok
test_neg (test_base.TestBSR) ... ok
test_nonzero (test_base.TestBSR) ... ok
test_pow (test_base.TestBSR) ... ok
test_radd (test_base.TestBSR) ... ok
test_real (test_base.TestBSR) ... ok
test_repr (test_base.TestBSR) ... ok
test_rmatvec (test_base.TestBSR) ... ok
test_rmul_scalar (test_base.TestBSR) ... ok
test_rsub (test_base.TestBSR) ... ok
test that A*x works for x with shape () (1,) and (1,1) ... ok
test_sparse_format_conversions (test_base.TestBSR) ... ok
test_str (test_base.TestBSR) ... ok
test_sub (test_base.TestBSR) ... ok
subtracting a dense matrix to/from a sparse matrix ... ok
Does the matrix's .sum(axis=...) method work? ... ok
test_toarray (test_base.TestBSR) ... ok
test_tobsr (test_base.TestBSR) ... ok
test_todense (test_base.TestBSR) ... ok
test_transpose (test_base.TestBSR) ... ok
test_abs (test_base.TestCOO) ... ok
test_add (test_base.TestCOO) ... ok
adding a dense matrix to a sparse matrix ... ok
test_asfptype (test_base.TestCOO) ... ok
test_astype (test_base.TestCOO) ... ok
unsorted triplet format ... ok
unsorted triplet format with duplicates (which are summed) ... ok
empty matrix ... ok
from dense matrix ... ok
Check whether the copy=True and copy=False keywords work ... ok
Does the matrix's .diagonal() method work? ... ok
test_elementwise_divide (test_base.TestCOO) ... ok
test_elementwise_multiply (test_base.TestCOO) ... ok
create empty matrices ... ok
Test manipulating empty matrices. Fails in SciPy SVN <= r1768 ... ok
test_from_array (test_base.TestCOO) ... ok
test_from_list (test_base.TestCOO) ... ok
test_from_matrix (test_base.TestCOO) ... ok
test_from_sparse (test_base.TestCOO) ... ok
test_getcol (test_base.TestCOO) ... ok
test_getrow (test_base.TestCOO) ... ok
test_imag (test_base.TestCOO) ... ok
test_invalid_shapes (test_base.TestCOO) ... ok
test_matmat_dense (test_base.TestCOO) ... ok
test_matmat_sparse (test_base.TestCOO) ... ok
test_matvec (test_base.TestCOO) ... ok
Does the matrix's .mean(axis=...) method work? ... ok
test_mul_scalar (test_base.TestCOO) ... ok
test_neg (test_base.TestCOO) ... ok
test_nonzero (test_base.TestCOO) ... ok
test_pow (test_base.TestCOO) ... ok
test_radd (test_base.TestCOO) ... ok
test_real (test_base.TestCOO) ... ok
test_repr (test_base.TestCOO) ... ok
test_rmatvec (test_base.TestCOO) ... ok
test_rmul_scalar (test_base.TestCOO) ... ok
test_rsub (test_base.TestCOO) ... ok
test that A*x works for x with shape () (1,) and (1,1) ... ok
test_sparse_format_conversions (test_base.TestCOO) ... ok
test_str (test_base.TestCOO) ... ok
test_sub (test_base.TestCOO) ... ok
subtracting a dense matrix to/from a sparse matrix ... ok
Does the matrix's .sum(axis=...) method work? ... ok
test_toarray (test_base.TestCOO) ... ok
test_tobsr (test_base.TestCOO) ... ok
test_todense (test_base.TestCOO) ... ok
test_transpose (test_base.TestCOO) ... ok
test_abs (test_base.TestCSC) ... ok
test_add (test_base.TestCSC) ... ok
adding a dense matrix to a sparse matrix ... ok
test_add_sub (test_base.TestCSC) ... ok
test_asfptype (test_base.TestCSC) ... ok
test_astype (test_base.TestCSC) ... ok
test_constructor1 (test_base.TestCSC) ... ok
test_constructor2 (test_base.TestCSC) ... ok
test_constructor3 (test_base.TestCSC) ... ok
using (data, ij) format ... ok
infer dimensions from arrays ... ok
Check whether the copy=True and copy=False keywords work ... ok
Does the matrix's .diagonal() method work? ... ok
test_elementwise_divide (test_base.TestCSC) ... ok
test_elementwise_multiply (test_base.TestCSC) ... ok
test_eliminate_zeros (test_base.TestCSC) ... ok
create empty matrices ... ok
Test manipulating empty matrices. Fails in SciPy SVN <= r1768 ... ok
test_fancy_indexing (test_base.TestCSC) ... ok
test_fancy_indexing_randomized (test_base.TestCSC) ... ok
test_fancy_indexing_set (test_base.TestCSC) ... KNOWNFAIL: Fancy indexing is known to be broken for CSC matrices
test_from_array (test_base.TestCSC) ... ok
test_from_list (test_base.TestCSC) ... ok
test_from_matrix (test_base.TestCSC) ... ok
test_from_sparse (test_base.TestCSC) ... ok
Test for new slice functionality (EJS) ... ok
test_get_slices (test_base.TestCSC) ... ok
Test for new slice functionality (EJS) ... ok
test_getcol (test_base.TestCSC) ... ok
test_getelement (test_base.TestCSC) ... ok
test_getrow (test_base.TestCSC) ... ok
test_idiv_scalar (test_base.TestCSC) ... ok
test_imag (test_base.TestCSC) ... ok
test_imul_scalar (test_base.TestCSC) ... ok
test_invalid_shapes (test_base.TestCSC) ... ok
test_matmat_dense (test_base.TestCSC) ... ok
test_matmat_sparse (test_base.TestCSC) ... ok
test_matvec (test_base.TestCSC) ... ok
Does the matrix's .mean(axis=...) method work? ... ok
test_mu (test_base.TestCSC) ... ok
test_mul_scalar (test_base.TestCSC) ... ok
test_neg (test_base.TestCSC) ... ok
test_nonzero (test_base.TestCSC) ... ok
test_pow (test_base.TestCSC) ... ok
test_radd (test_base.TestCSC) ... ok
test_real (test_base.TestCSC) ... ok
test_repr (test_base.TestCSC) ... ok
test_rmatvec (test_base.TestCSC) ... ok
test_rmul_scalar (test_base.TestCSC) ... ok
test_rsub (test_base.TestCSC) ... ok
test_setelement (test_base.TestCSC) ... ok
test that A*x works for x with shape () (1,) and (1,1) ... ok
Test whether the lu_solve command segfaults, as reported by Nils ... ok
test_sort_indices (test_base.TestCSC) ... ok
test_sparse_format_conversions (test_base.TestCSC) ... ok
test_str (test_base.TestCSC) ... ok
test_sub (test_base.TestCSC) ... ok
subtracting a dense matrix to/from a sparse matrix ... ok
Does the matrix's .sum(axis=...) method work? ... ok
test_toarray (test_base.TestCSC) ... ok
test_tobsr (test_base.TestCSC) ... ok
test_todense (test_base.TestCSC) ... ok
test_transpose (test_base.TestCSC) ... ok
test_unsorted_arithmetic (test_base.TestCSC) ... ok
test_abs (test_base.TestCSR) ... ok
test_add (test_base.TestCSR) ... ok
adding a dense matrix to a sparse matrix ... ok
test_add_sub (test_base.TestCSR) ... ok
test_asfptype (test_base.TestCSR) ... ok
test_astype (test_base.TestCSR) ... ok
test_constructor1 (test_base.TestCSR) ... ok
test_constructor2 (test_base.TestCSR) ... ok
test_constructor3 (test_base.TestCSR) ... ok
using (data, ij) format ... ok
infer dimensions from arrays ... ok
Check whether the copy=True and copy=False keywords work ... ok
Does the matrix's .diagonal() method work? ... ok
test_elementwise_divide (test_base.TestCSR) ... ok
test_elementwise_multiply (test_base.TestCSR) ... ok
test_eliminate_zeros (test_base.TestCSR) ... ok
create empty matrices ... ok
Test manipulating empty matrices. Fails in SciPy SVN <= r1768 ... ok
test_fancy_indexing (test_base.TestCSR) ... ok
test_fancy_indexing_randomized (test_base.TestCSR) ... ok
test_fancy_indexing_set (test_base.TestCSR) ... KNOWNFAIL: Fancy indexing is known to be broken for CSR matrices
test_from_array (test_base.TestCSR) ... ok
test_from_list (test_base.TestCSR) ... ok
test_from_matrix (test_base.TestCSR) ... ok
test_from_sparse (test_base.TestCSR) ... ok
Test for new slice functionality (EJS) ... ok
test_get_slices (test_base.TestCSR) ... ok
Test for new slice functionality (EJS) ... ok
test_getcol (test_base.TestCSR) ... ok
test_getelement (test_base.TestCSR) ... ok
test_getrow (test_base.TestCSR) ... ok
test_idiv_scalar (test_base.TestCSR) ... ok
test_imag (test_base.TestCSR) ... ok
test_imul_scalar (test_base.TestCSR) ... ok
test_invalid_shapes (test_base.TestCSR) ... ok
test_matmat_dense (test_base.TestCSR) ... ok
test_matmat_sparse (test_base.TestCSR) ... ok
test_matvec (test_base.TestCSR) ... ok
Does the matrix's .mean(axis=...) method work? ... ok
test_mu (test_base.TestCSR) ... ok
test_mul_scalar (test_base.TestCSR) ... ok
test_neg (test_base.TestCSR) ... ok
test_nonzero (test_base.TestCSR) ... ok
test_pow (test_base.TestCSR) ... ok
test_radd (test_base.TestCSR) ... ok
test_real (test_base.TestCSR) ... ok
test_repr (test_base.TestCSR) ... ok
test_rmatvec (test_base.TestCSR) ... ok
test_rmul_scalar (test_base.TestCSR) ... ok
test_rsub (test_base.TestCSR) ... ok
test_setelement (test_base.TestCSR) ... ok
test that A*x works for x with shape () (1,) and (1,1) ... ok
Test whether the lu_solve command segfaults, as reported by Nils ... ok
test_sort_indices (test_base.TestCSR) ... ok
test_sparse_format_conversions (test_base.TestCSR) ... ok
test_str (test_base.TestCSR) ... ok
test_sub (test_base.TestCSR) ... ok
subtracting a dense matrix to/from a sparse matrix ... ok
Does the matrix's .sum(axis=...) method work? ... ok
test_toarray (test_base.TestCSR) ... ok
test_tobsr (test_base.TestCSR) ... ok
test_todense (test_base.TestCSR) ... ok
test_transpose (test_base.TestCSR) ... ok
test_unsorted_arithmetic (test_base.TestCSR) ... ok
test_abs (test_base.TestDIA) ... ok
test_add (test_base.TestDIA) ... ok
adding a dense matrix to a sparse matrix ... ok
test_add_sub (test_base.TestDIA) ... ok
test_asfptype (test_base.TestDIA) ... ok
test_astype (test_base.TestDIA) ... ok
test_constructor1 (test_base.TestDIA) ... ok
Check whether the copy=True and copy=False keywords work ... ok
Does the matrix's .diagonal() method work? ... ok
test_elementwise_divide (test_base.TestDIA) ... ok
test_elementwise_multiply (test_base.TestDIA) ... ok
create empty matrices ... ok
Test manipulating empty matrices. Fails in SciPy SVN <= r1768 ... ok
test_from_array (test_base.TestDIA) ... ok
test_from_list (test_base.TestDIA) ... ok
test_from_matrix (test_base.TestDIA) ... ok
test_from_sparse (test_base.TestDIA) ... ok
test_getcol (test_base.TestDIA) ... ok
test_getrow (test_base.TestDIA) ... ok
test_imag (test_base.TestDIA) ... ok
test_invalid_shapes (test_base.TestDIA) ... ok
test_matmat_dense (test_base.TestDIA) ... ok
test_matmat_sparse (test_base.TestDIA) ... ok
test_matvec (test_base.TestDIA) ... ok
Does the matrix's .mean(axis=...) method work? ... ok
test_mu (test_base.TestDIA) ... ok
test_mul_scalar (test_base.TestDIA) ... ok
test_neg (test_base.TestDIA) ... ok
test_nonzero (test_base.TestDIA) ... ok
test_pow (test_base.TestDIA) ... ok
test_radd (test_base.TestDIA) ... ok
test_real (test_base.TestDIA) ... ok
test_repr (test_base.TestDIA) ... ok
test_rmatvec (test_base.TestDIA) ... ok
test_rmul_scalar (test_base.TestDIA) ... ok
test_rsub (test_base.TestDIA) ... ok
test that A*x works for x with shape () (1,) and (1,1) ... ok
test_sparse_format_conversions (test_base.TestDIA) ... ok
test_str (test_base.TestDIA) ... ok
test_sub (test_base.TestDIA) ... ok
subtracting a dense matrix to/from a sparse matrix ... ok
Does the matrix's .sum(axis=...) method work? ... ok
test_toarray (test_base.TestDIA) ... ok
test_tobsr (test_base.TestDIA) ... ok
test_todense (test_base.TestDIA) ... ok
test_transpose (test_base.TestDIA) ... ok
test_abs (test_base.TestDOK) ... ok
test_add (test_base.TestDOK) ... ok
adding a dense matrix to a sparse matrix ... ok
test_asfptype (test_base.TestDOK) ... ok
test_astype (test_base.TestDOK) ... ok
Test provided by Andrew Straw. Fails in SciPy <= r1477. ... ok
Check whether the copy=True and copy=False keywords work ... ok
test_ctor (test_base.TestDOK) ... ok
Does the matrix's .diagonal() method work? ... ok
test_elementwise_divide (test_base.TestDOK) ... ok
test_elementwise_multiply (test_base.TestDOK) ... ok
create empty matrices ... ok
Test manipulating empty matrices. Fails in SciPy SVN <= r1768 ... ok
test_from_array (test_base.TestDOK) ... ok
test_from_list (test_base.TestDOK) ... ok
test_from_matrix (test_base.TestDOK) ... ok
test_from_sparse (test_base.TestDOK) ... ok
test_getcol (test_base.TestDOK) ... ok
test_getelement (test_base.TestDOK) ... ok
test_getrow (test_base.TestDOK) ... ok
test_imag (test_base.TestDOK) ... ok
test_invalid_shapes (test_base.TestDOK) ... ok
test_matmat_dense (test_base.TestDOK) ... ok
test_matmat_sparse (test_base.TestDOK) ... ok
test_matvec (test_base.TestDOK) ... ok
Does the matrix's .mean(axis=...) method work? ... ok
test_mul_scalar (test_base.TestDOK) ... ok
test_mult (test_base.TestDOK) ... ok
test_neg (test_base.TestDOK) ... ok
test_nonzero (test_base.TestDOK) ... ok
test_pow (test_base.TestDOK) ... ok
test_radd (test_base.TestDOK) ... ok
test_real (test_base.TestDOK) ... ok
test_repr (test_base.TestDOK) ... ok
A couple basic tests of the resize() method. ... ok
test_rmatvec (test_base.TestDOK) ... ok
test_rmul_scalar (test_base.TestDOK) ... ok
test_rsub (test_base.TestDOK) ... ok
Test for slice functionality (EJS) ... ok
test_setelement (test_base.TestDOK) ... ok
test that A*x works for x with shape () (1,) and (1,1) ... ok
Test whether the lu_solve command segfaults, as reported by Nils ... ok
test_sparse_format_conversions (test_base.TestDOK) ... ok
test_str (test_base.TestDOK) ... ok
test_sub (test_base.TestDOK) ... ok
subtracting a dense matrix to/from a sparse matrix ... ok
Does the matrix's .sum(axis=...) method work? ... ok
Regression test for ticket #1160. ... ok
test_toarray (test_base.TestDOK) ... ok
test_tobsr (test_base.TestDOK) ... ok
test_todense (test_base.TestDOK) ... ok
test_transpose (test_base.TestDOK) ... ok
test_abs (test_base.TestLIL) ... ok
test_add (test_base.TestLIL) ... ok
adding a dense matrix to a sparse matrix ... ok
test_add_sub (test_base.TestLIL) ... ok
test_asfptype (test_base.TestLIL) ... ok
test_astype (test_base.TestLIL) ... ok
Check whether the copy=True and copy=False keywords work ... ok
Does the matrix's .diagonal() method work? ... ok
test_dot (test_base.TestLIL) ... ok
test_elementwise_divide (test_base.TestLIL) ... ok
test_elementwise_multiply (test_base.TestLIL) ... ok
create empty matrices ... ok
Test manipulating empty matrices. Fails in SciPy SVN <= r1768 ... ok
test_fancy_indexing (test_base.TestLIL) ... ok
test_fancy_indexing_randomized (test_base.TestLIL) ... KNOWNFAIL: Fancy indexing is known to be broken for LIL matrices
test_fancy_indexing_set (test_base.TestLIL) ... KNOWNFAIL: Fancy indexing is known to be broken for LIL matrices
test_from_array (test_base.TestLIL) ... ok
test_from_list (test_base.TestLIL) ... ok
test_from_matrix (test_base.TestLIL) ... ok
test_from_sparse (test_base.TestLIL) ... ok
Test for new slice functionality (EJS) ... ok
test_get_slices (test_base.TestLIL) ... ok
Test for new slice functionality (EJS) ... ok
test_getcol (test_base.TestLIL) ... ok
test_getelement (test_base.TestLIL) ... ok
test_getrow (test_base.TestLIL) ... ok
test_idiv_scalar (test_base.TestLIL) ... ok
test_imag (test_base.TestLIL) ... ok
test_imul_scalar (test_base.TestLIL) ... ok
test_inplace_ops (test_base.TestLIL) ... ok
test_invalid_shapes (test_base.TestLIL) ... ok
Tests whether a lil_matrix can be constructed from a ... ok
test_lil_iteration (test_base.TestLIL) ... ok
Tests whether a row of one lil_matrix can be assigned to ... ok
test_lil_sequence_assignment (test_base.TestLIL) ... ok
test_lil_slice_assignment (test_base.TestLIL) ... ok
test_matmat_dense (test_base.TestLIL) ... ok
test_matmat_sparse (test_base.TestLIL) ... ok
test_matvec (test_base.TestLIL) ... ok
Does the matrix's .mean(axis=...) method work? ... ok
test_mu (test_base.TestLIL) ... ok
test_mul_scalar (test_base.TestLIL) ... ok
test_neg (test_base.TestLIL) ... ok
test_nonzero (test_base.TestLIL) ... ok
test_point_wise_multiply (test_base.TestLIL) ... ok
test_pow (test_base.TestLIL) ... ok
test_radd (test_base.TestLIL) ... ok
test_real (test_base.TestLIL) ... ok
test_repr (test_base.TestLIL) ... ok
test_reshape (test_base.TestLIL) ... ok
test_rmatvec (test_base.TestLIL) ... ok
test_rmul_scalar (test_base.TestLIL) ... ok
test_rsub (test_base.TestLIL) ... ok
test_scalar_mul (test_base.TestLIL) ... ok
test_setelement (test_base.TestLIL) ... ok
test that A*x works for x with shape () (1,) and (1,1) ... ok
Test whether the lu_solve command segfaults, as reported by Nils ... ok
test_sparse_format_conversions (test_base.TestLIL) ... ok
test_str (test_base.TestLIL) ... ok
test_sub (test_base.TestLIL) ... ok
subtracting a dense matrix to/from a sparse matrix ... ok
Does the matrix's .sum(axis=...) method work? ... ok
test_toarray (test_base.TestLIL) ... ok
test_tobsr (test_base.TestLIL) ... ok
test_todense (test_base.TestLIL) ... ok
test_transpose (test_base.TestLIL) ... ok
test_bmat (test_construct.TestConstructUtils) ... ok
test_eye (test_construct.TestConstructUtils) ... ok
test_hstack (test_construct.TestConstructUtils) ... ok
test_identity (test_construct.TestConstructUtils) ... ok
test_kron (test_construct.TestConstructUtils) ... ok
test_kronsum (test_construct.TestConstructUtils) ... ok
test_rand (test_construct.TestConstructUtils) ... ok
test_spdiags (test_construct.TestConstructUtils) ... ok
test_vstack (test_construct.TestConstructUtils) ... ok
test_tril (test_extract.TestExtract) ... ok
test_triu (test_extract.TestExtract) ... ok
test_count_blocks (test_spfuncs.TestSparseFunctions) ... ok
test_cs_graph_components (test_spfuncs.TestSparseFunctions) ... ok
test_estimate_blocksize (test_spfuncs.TestSparseFunctions) ... ok
test_scale_rows_and_cols (test_spfuncs.TestSparseFunctions) ... ok
test_getdtype (test_sputils.TestSparseUtils) ... ok
test_isdense (test_sputils.TestSparseUtils) ... ok
test_isintlike (test_sputils.TestSparseUtils) ... ok
test_isscalarlike (test_sputils.TestSparseUtils) ... ok
test_issequence (test_sputils.TestSparseUtils) ... ok
test_isshape (test_sputils.TestSparseUtils) ... ok
test_upcast (test_sputils.TestSparseUtils) ... ok
Tests cdist(X, 'braycurtis') on random data. ... ok
Tests cdist(X, 'canberra') on random data. ... ok
Tests cdist(X, 'chebychev') on random data. ... ok
Tests cdist(X, 'cityblock') on random data. ... ok
Tests cdist(X, 'correlation') on random data. ... ok
Tests cdist(X, 'cosine') on random data. ... ok
Tests cdist(X, 'dice') on random data. ... ok
Tests cdist(X, 'euclidean') on random data. ... ok
Tests cdist(X, u'euclidean') using unicode metric string ... ok
Tests cdist(X, 'hamming') on random boolean data. ... ok
Tests cdist(X, 'hamming') on random data. ... ok
Tests cdist(X, 'jaccard') on random boolean data. ... ok
Tests cdist(X, 'jaccard') on random data. ... ok
Tests cdist(X, 'kulsinski') on random data. ... ok
Tests cdist(X, 'mahalanobis') on random data. ... ok
Tests cdist(X, 'matching') on random data. ... ok
Tests cdist(X, 'minkowski') on random data. (p=1.23) ... ok
Tests cdist(X, 'minkowski') on random data. (p=3.8) ... ok
Tests cdist(X, 'minkowski') on random data. (p=4.6) ... ok
Tests cdist(X, 'rogerstanimoto') on random data. ... ok
Tests cdist(X, 'russellrao') on random data. ... ok
Tests cdist(X, 'seuclidean') on random data. ... ok
Tests cdist(X, 'sokalmichener') on random data. ... ok
Tests cdist(X, 'sokalsneath') on random data. ... ok
Tests cdist(X, 'sqeuclidean') on random data. ... ok
Tests cdist(X, 'wminkowski') on random data. (p=1.23) ... ok
Tests cdist(X, 'wminkowski') on random data. (p=3.8) ... ok
Tests cdist(X, 'wminkowski') on random data. (p=4.6) ... ok
Tests cdist(X, 'yule') on random data. ... ok
Tests is_valid_dm(*) on an assymetric distance matrix. Exception expected. ... ok
Tests is_valid_dm(*) on an assymetric distance matrix. False expected. ... ok
Tests is_valid_dm(*) on a correct 1x1. True expected. ... ok
Tests is_valid_dm(*) on a correct 2x2. True expected. ... ok
Tests is_valid_dm(*) on a correct 3x3. True expected. ... ok
Tests is_valid_dm(*) on a correct 4x4. True expected. ... ok
Tests is_valid_dm(*) on a correct 5x5. True expected. ... ok
Tests is_valid_dm(*) on a 1D array. Exception expected. ... ok
Tests is_valid_dm(*) on a 1D array. False expected. ... ok
Tests is_valid_dm(*) on a 3D array. Exception expected. ... ok
Tests is_valid_dm(*) on a 3D array. False expected. ... ok
Tests is_valid_dm(*) on an int16 array. Exception expected. ... ok
Tests is_valid_dm(*) on an int16 array. False expected. ... ok
Tests is_valid_dm(*) on a distance matrix with a nonzero diagonal. Exception expected. ... ok
Tests is_valid_dm(*) on a distance matrix with a nonzero diagonal. False expected. ... ok
Tests is_valid_y(*) on 100 improper condensed distance matrices. Expecting exception. ... ok
Tests is_valid_y(*) on a correct 2x2 condensed. True expected. ... ok
Tests is_valid_y(*) on a correct 3x3 condensed. True expected. ... ok
Tests is_valid_y(*) on a correct 4x4 condensed. True expected. ... ok
Tests is_valid_y(*) on a correct 5x5 condensed. True expected. ... ok
Tests is_valid_y(*) on a 2D array. Exception expected. ... ok
Tests is_valid_y(*) on a 2D array. False expected. ... ok
Tests is_valid_y(*) on a 3D array. Exception expected. ... ok
Tests is_valid_y(*) on a 3D array. False expected. ... ok
Tests is_valid_y(*) on an int16 array. Exception expected. ... ok
Tests is_valid_y(*) on an int16 array. False expected. ... ok
Tests num_obs_dm(D) on a 0x0 distance matrix. Expecting exception. ... ok
Tests num_obs_dm(D) on a 1x1 distance matrix. ... ok
Tests num_obs_dm(D) on a 2x2 distance matrix. ... ok
Tests num_obs_dm(D) on a 3x3 distance matrix. ... ok
Tests num_obs_dm(D) on a 4x4 distance matrix. ... ok
Tests num_obs_dm with observation matrices of multiple sizes. ... ok
Tests num_obs_y(y) on a condensed distance matrix over 1 observations. Expecting exception. ... ok
Tests num_obs_y(y) on a condensed distance matrix over 2 observations. ... ok
Tests num_obs_y(y) on 100 improper condensed distance matrices. Expecting exception. ... ok
Tests num_obs_y(y) on a condensed distance matrix over 3 observations. ... ok
Tests num_obs_y(y) on a condensed distance matrix over 4 observations. ... ok
Tests num_obs_y(y) on a condensed distance matrix between 5 and 15 observations. ... ok
Tests num_obs_y with observation matrices of multiple sizes. ... ok
Tests pdist(X, 'canberra') to see if the two implementations match on the Iris data set. ... ok
Tests pdist(X, 'canberra') to see if Canberra gives the right result as reported in Scipy bug report 711. ... ok
Tests pdist(X, 'chebychev') on the Iris data set. ... ok
Tests pdist(X, 'chebychev') on the Iris data set. (float32) ... ok
Tests pdist(X, 'test_chebychev') [the non-C implementation] on the Iris data set. ... ok
Tests pdist(X, 'chebychev') on random data. ... ok
Tests pdist(X, 'chebychev') on random data. (float32) ... ok
Tests pdist(X, 'test_chebychev') [the non-C implementation] on random data. ... ok
Tests pdist(X, 'cityblock') on the Iris data set. ... ok
Tests pdist(X, 'cityblock') on the Iris data set. (float32) ... ok
Tests pdist(X, 'test_cityblock') [the non-C implementation] on the Iris data set. ... ok
Tests pdist(X, 'cityblock') on random data. ... ok
Tests pdist(X, 'cityblock') on random data. (float32) ... ok
Tests pdist(X, 'test_cityblock') [the non-C implementation] on random data. ... ok
Tests pdist(X, 'correlation') on the Iris data set. ... ok
Tests pdist(X, 'correlation') on the Iris data set. (float32) ... ok
Tests pdist(X, 'test_correlation') [the non-C implementation] on the Iris data set. ... ok
Tests pdist(X, 'correlation') on random data. ... ok
Tests pdist(X, 'correlation') on random data. (float32) ... ok
Tests pdist(X, 'test_correlation') [the non-C implementation] on random data. ... ok
Tests pdist(X, 'cosine') on the Iris data set. ... ok
Tests pdist(X, 'cosine') on the Iris data set. ... ok
Tests pdist(X, 'test_cosine') [the non-C implementation] on the Iris data set. ... ok
Tests pdist(X, 'cosine') on random data. ... ok
Tests pdist(X, 'cosine') on random data. (float32) ... ok
Tests pdist(X, 'test_cosine') [the non-C implementation] on random data. ... ok
Tests pdist(X, 'hamming') on random data. ... ok
Tests pdist(X, 'hamming') on random data. (float32) ... ok
Tests pdist(X, 'test_hamming') [the non-C implementation] on random data. ... ok
Tests pdist(X, 'dice') to see if the two implementations match on random double input data. ... ok
Tests dice(*,*) with mtica example #1. ... ok
Tests dice(*,*) with mtica example #2. ... ok
Tests pdist(X, 'jaccard') on random data. ... ok
Tests pdist(X, 'jaccard') on random data. (float32) ... ok
Tests pdist(X, 'test_jaccard') [the non-C implementation] on random data. ... ok
Tests pdist(X, 'euclidean') on the Iris data set. ... ok
Tests pdist(X, 'euclidean') on the Iris data set. (float32) ... ok
Tests pdist(X, 'test_euclidean') [the non-C implementation] on the Iris data set. ... ok
Tests pdist(X, 'euclidean') on random data. ... ok
Tests pdist(X, 'euclidean') on random data (float32). ... ok
Tests pdist(X, 'test_euclidean') [the non-C implementation] on random data. ... ok
Tests pdist(X, 'euclidean') with unicode metric string ... ok
Tests pdist(X, 'hamming') on random data. ... ok
Tests pdist(X, 'hamming') on random data. ... ok
Tests pdist(X, 'test_hamming') [the non-C implementation] on random data. ... ok
Tests pdist(X, 'jaccard') to see if the two implementations match on random double input data. ... ok
Tests jaccard(*,*) with mtica example #1. ... ok
Tests jaccard(*,*) with mtica example #2. ... ok
Tests pdist(X, 'jaccard') on random data. ... ok
Tests pdist(X, 'jaccard') on random data. (float32) ... ok
Tests pdist(X, 'test_jaccard') [the non-C implementation] on random data. ... ok
Tests pdist(X, 'kulsinski') to see if the two implementations match on random double input data. ... ok
Tests pdist(X, 'matching') to see if the two implementations match on random boolean input data. ... ok
Tests matching(*,*) with mtica example #1 (nums). ... ok
Tests matching(*,*) with mtica example #2. ... ok
Tests pdist(X, 'minkowski') on iris data. ... ok
Tests pdist(X, 'minkowski') on iris data. (float32) ... ok
Tests pdist(X, 'test_minkowski') [the non-C implementation] on iris data. ... ok
Tests pdist(X, 'minkowski') on iris data. ... ok
Tests pdist(X, 'minkowski') on iris data. (float32) ... ok
Tests pdist(X, 'test_minkowski') [the non-C implementation] on iris data. ... ok
Tests pdist(X, 'minkowski') on random data. ... ok
Tests pdist(X, 'minkowski') on random data. (float32) ... ok
Tests pdist(X, 'test_minkowski') [the non-C implementation] on random data. ... ok
Tests pdist(X, 'rogerstanimoto') to see if the two implementations match on random double input data. ... ok
Tests rogerstanimoto(*,*) with mtica example #1. ... ok
Tests rogerstanimoto(*,*) with mtica example #2. ... ok
Tests pdist(X, 'russellrao') to see if the two implementations match on random double input data. ... ok
Tests russellrao(*,*) with mtica example #1. ... ok
Tests russellrao(*,*) with mtica example #2. ... ok
Tests pdist(X, 'seuclidean') on the Iris data set. ... ok
Tests pdist(X, 'seuclidean') on the Iris data set (float32). ... ok
Tests pdist(X, 'test_seuclidean') [the non-C implementation] on the Iris data set. ... ok
Tests pdist(X, 'seuclidean') on random data. ... ok
Tests pdist(X, 'seuclidean') on random data (float32). ... ok
Tests pdist(X, 'test_sqeuclidean') [the non-C implementation] on random data. ... ok
Tests pdist(X, 'sokalmichener') to see if the two implementations match on random double input data. ... ok
Tests pdist(X, 'sokalsneath') to see if the two implementations match on random double input data. ... ok
Tests sokalsneath(*,*) with mtica example #1. ... ok
Tests sokalsneath(*,*) with mtica example #2. ... ok
test_pdist_wminkowski (test_distance.TestPdist) ... ok
Tests pdist(X, 'yule') to see if the two implementations match on random double input data. ... ok
Tests yule(*,*) with mtica example #1. ... ok
Tests yule(*,*) with mtica example #2. ... ok
Tests squareform on a 1x1 matrix. ... ok
Tests squareform on a 2x2 matrix. ... ok
Tests squareform on an empty matrix. ... ok
Tests squareform on an empty vector. ... ok
Tests squareform on a square matrices of multiple sizes. ... ok
Tests squareform on a 1-D array, length=1. ... ok
Loading test data files for the scipy.spatial.distance tests. ... ok
Regression test for ticket #876 ... ok
test_kdtree.test_count_neighbors.test_large_radius ... ok
test_kdtree.test_count_neighbors.test_multiple_radius ... ok
test_kdtree.test_count_neighbors.test_one_radius ... ok
test_kdtree.test_random.test_approx ... ok
test_kdtree.test_random.test_m_nearest ... ok
test_kdtree.test_random.test_nearest ... ok
test_kdtree.test_random.test_points_near ... ok
test_kdtree.test_random.test_points_near_l1 ... ok
test_kdtree.test_random.test_points_near_linf ... ok
test_kdtree.test_random_ball.test_found_all ... ok
test_kdtree.test_random_ball.test_in_ball ... ok
test_kdtree.test_random_ball_approx.test_found_all ... ok
test_kdtree.test_random_ball_approx.test_in_ball ... ok
test_kdtree.test_random_ball_far.test_found_all ... ok
test_kdtree.test_random_ball_far.test_in_ball ... ok
test_kdtree.test_random_ball_l1.test_found_all ... ok
test_kdtree.test_random_ball_l1.test_in_ball ... ok
test_kdtree.test_random_ball_linf.test_found_all ... ok
test_kdtree.test_random_ball_linf.test_in_ball ... ok
test_kdtree.test_random_compiled.test_approx ... ok
test_kdtree.test_random_compiled.test_m_nearest ... ok
test_kdtree.test_random_compiled.test_nearest ... ok
test_kdtree.test_random_compiled.test_points_near ... ok
test_kdtree.test_random_compiled.test_points_near_l1 ... ok
test_kdtree.test_random_compiled.test_points_near_linf ... ok
test_kdtree.test_random_far.test_approx ... ok
test_kdtree.test_random_far.test_m_nearest ... ok
test_kdtree.test_random_far.test_nearest ... ok
test_kdtree.test_random_far.test_points_near ... ok
test_kdtree.test_random_far.test_points_near_l1 ... ok
test_kdtree.test_random_far.test_points_near_linf ... ok
test_kdtree.test_random_far_compiled.test_approx ... ok
test_kdtree.test_random_far_compiled.test_m_nearest ... ok
test_kdtree.test_random_far_compiled.test_nearest ... ok
test_kdtree.test_random_far_compiled.test_points_near ... ok
test_kdtree.test_random_far_compiled.test_points_near_l1 ... ok
test_kdtree.test_random_far_compiled.test_points_near_linf ... ok
test_kdtree.test_rectangle.test_max_inside ... ok
test_kdtree.test_rectangle.test_max_one_side ... ok
test_kdtree.test_rectangle.test_max_two_sides ... ok
test_kdtree.test_rectangle.test_min_inside ... ok
test_kdtree.test_rectangle.test_min_one_side ... ok
test_kdtree.test_rectangle.test_min_two_sides ... ok
test_kdtree.test_rectangle.test_split ... ok
test_kdtree.test_small.test_approx ... ok
test_kdtree.test_small.test_m_nearest ... ok
test_kdtree.test_small.test_nearest ... ok
test_kdtree.test_small.test_nearest_two ... ok
test_kdtree.test_small.test_points_near ... ok
test_kdtree.test_small.test_points_near_l1 ... ok
test_kdtree.test_small.test_points_near_linf ... ok
test_kdtree.test_small_compiled.test_approx ... ok
test_kdtree.test_small_compiled.test_m_nearest ... ok
test_kdtree.test_small_compiled.test_nearest ... ok
test_kdtree.test_small_compiled.test_nearest_two ... ok
test_kdtree.test_small_compiled.test_points_near ... ok
test_kdtree.test_small_compiled.test_points_near_l1 ... ok
test_kdtree.test_small_compiled.test_points_near_linf ... ok
test_kdtree.test_small_nonleaf.test_approx ... ok
test_kdtree.test_small_nonleaf.test_m_nearest ... ok
test_kdtree.test_small_nonleaf.test_nearest ... ok
test_kdtree.test_small_nonleaf.test_nearest_two ... ok
test_kdtree.test_small_nonleaf.test_points_near ... ok
test_kdtree.test_small_nonleaf.test_points_near_l1 ... ok
test_kdtree.test_small_nonleaf.test_points_near_linf ... ok
test_kdtree.test_small_nonleaf_compiled.test_approx ... ok
test_kdtree.test_small_nonleaf_compiled.test_m_nearest ... ok
test_kdtree.test_small_nonleaf_compiled.test_nearest ... ok
test_kdtree.test_small_nonleaf_compiled.test_nearest_two ... ok
test_kdtree.test_small_nonleaf_compiled.test_points_near ... ok
test_kdtree.test_small_nonleaf_compiled.test_points_near_l1 ... ok
test_kdtree.test_small_nonleaf_compiled.test_points_near_linf ... ok
test_kdtree.test_sparse_distance_matrix.test_consistency_with_neighbors ... ok
test_kdtree.test_sparse_distance_matrix.test_zero_distance ... ok
test_kdtree.test_two_random_trees.test_all_in_ball ... ok
test_kdtree.test_two_random_trees.test_found_all ... ok
test_kdtree.test_two_random_trees_far.test_all_in_ball ... ok
test_kdtree.test_two_random_trees_far.test_found_all ... ok
test_kdtree.test_two_random_trees_linf.test_all_in_ball ... ok
test_kdtree.test_two_random_trees_linf.test_found_all ... ok
test_kdtree.test_vectorization.test_single_query ... ok
test_kdtree.test_vectorization.test_single_query_all_neighbors ... ok
test_kdtree.test_vectorization.test_single_query_multiple_neighbors ... ok
test_kdtree.test_vectorization.test_vectorized_query ... ok
test_kdtree.test_vectorization.test_vectorized_query_all_neighbors ... ok
test_kdtree.test_vectorization.test_vectorized_query_multiple_neighbors ... ok
test_kdtree.test_vectorization_compiled.test_single_query ... ok
test_kdtree.test_vectorization_compiled.test_single_query_multiple_neighbors ... ok
test_kdtree.test_vectorization_compiled.test_vectorized_query ... ok
test_kdtree.test_vectorization_compiled.test_vectorized_query_multiple_neighbors ... ok
test_kdtree.test_vectorization_compiled.test_vectorized_query_noncontiguous_values ... ok
test_kdtree.test_random_ball_vectorized ... ok
test_kdtree.test_distance_l2 ... ok
test_kdtree.test_distance_l1 ... ok
test_kdtree.test_distance_linf ... ok
test_kdtree.test_distance_vectorization ... ok
test_kdtree.test_distance_matrix ... ok
test_kdtree.test_distance_matrix_looping ... ok
test_kdtree.test_onetree_query(<scipy.spatial.kdtree.KDTree object at 0x47164b0>, 0.10000000000000001) ... ok
test_kdtree.test_onetree_query(<scipy.spatial.kdtree.KDTree object at 0x471b290>, 0.10000000000000001) ... ok
test_kdtree.test_onetree_query(<scipy.spatial.kdtree.KDTree object at 0x471b290>, 0.001) ... ok
test_kdtree.test_onetree_query(<scipy.spatial.kdtree.KDTree object at 0x471b290>, 1.0000000000000001e-05) ... ok
test_kdtree.test_onetree_query(<scipy.spatial.kdtree.KDTree object at 0x471b290>, 9.9999999999999995e-07) ... ok
test_kdtree.test_query_pairs_single_node ... ok
test_qhull.TestRidgeIter2D.test_complicated ... ok
test_qhull.TestRidgeIter2D.test_rectangle ... ok
test_qhull.TestRidgeIter2D.test_triangle ... ok
test_qhull.TestTriangulation.test_2d_square ... ok
test_qhull.TestTriangulation.test_duplicate_points ... ok
test_qhull.TestTriangulation.test_nd_simplex ... ok
test_qhull.TestTriangulation.test_pathological ... ok
test_qhull.TestUtilities.test_convex_hull ... ok
test_qhull.TestUtilities.test_find_simplex ... ok
test_qhull.TestUtilities.test_plane_distance ... ok
test_ai_zeros (test_basic.TestAiry) ... ok
test_airy (test_basic.TestAiry) ... ok
test_airye (test_basic.TestAiry) ... ok
test_bi_zeros (test_basic.TestAiry) ... ok
test_assoc_laguerre (test_basic.TestAssocLaguerre) ... ok
test_bernoulli (test_basic.TestBernoulli) ... ok
test_i0 (test_basic.TestBessel) ... ok
test_i0_series (test_basic.TestBessel) ... ok
test_i0e (test_basic.TestBessel) ... ok
test_i1 (test_basic.TestBessel) ... ok
test_i1_series (test_basic.TestBessel) ... ok
test_i1e (test_basic.TestBessel) ... ok
test_it2i0k0 (test_basic.TestBessel) ... ok
test_it2j0y0 (test_basic.TestBessel) ... ok
test_iti0k0 (test_basic.TestBessel) ... ok
test_itj0y0 (test_basic.TestBessel) ... ok
test_iv (test_basic.TestBessel) ... ok
test_iv_cephes_vs_amos (test_basic.TestBessel) ... ok
test_iv_hyperg_poles (test_basic.TestBessel) ... ok
test_iv_series (test_basic.TestBessel) ... ok
test_ive (test_basic.TestBessel) ... ok
test_ivp (test_basic.TestBessel) ... ok
test_ivp0 (test_basic.TestBessel) ... ok
test_j0 (test_basic.TestBessel) ... ok
test_j1 (test_basic.TestBessel) ... ok
test_jacobi (test_basic.TestBessel) ... ok
test_jn (test_basic.TestBessel) ... ok
test_jn_zeros (test_basic.TestBessel) ... ok
test_jn_zeros_slow (test_basic.TestBessel) ... ok
test_jnjnp_zeros (test_basic.TestBessel) ... ok
test_jnp_zeros (test_basic.TestBessel) ... ok
test_jnyn_zeros (test_basic.TestBessel) ... ok
test_jv (test_basic.TestBessel) ... ok
test_jv_cephes_vs_amos (test_basic.TestBessel) ... ok
test_jve (test_basic.TestBessel) ... ok
test_jvp (test_basic.TestBessel) ... ok
test_k0 (test_basic.TestBessel) ... ok
test_k0e (test_basic.TestBessel) ... ok
test_k1 (test_basic.TestBessel) ... ok
test_k1e (test_basic.TestBessel) ... ok
test_kn (test_basic.TestBessel) ... ok
test_kv0 (test_basic.TestBessel) ... ok
test_kv1 (test_basic.TestBessel) ... ok
test_kv2 (test_basic.TestBessel) ... ok
test_kv_cephes_vs_amos (test_basic.TestBessel) ... ok
test_kve (test_basic.TestBessel) ... ok
test_kvp_n1 (test_basic.TestBessel) ... ok
test_kvp_n2 (test_basic.TestBessel) ... ok
test_kvp_v0n1 (test_basic.TestBessel) ... ok
test_negv_iv (test_basic.TestBessel) ... ok
test_negv_ive (test_basic.TestBessel) ... ok
test_negv_jv (test_basic.TestBessel) ... ok
test_negv_jve (test_basic.TestBessel) ... ok
test_negv_kv (test_basic.TestBessel) ... ok
test_negv_kve (test_basic.TestBessel) ... ok
test_negv_yv (test_basic.TestBessel) ... ok
test_negv_yve (test_basic.TestBessel) ... ok
Real-valued Bessel I overflow ... ok
test_ticket_623 (test_basic.TestBessel) ... ok
Negative-order Bessels ... ok
Real-valued Bessel domains ... ok
test_y0 (test_basic.TestBessel) ... ok
test_y0_zeros (test_basic.TestBessel) ... ok
test_y1 (test_basic.TestBessel) ... ok
test_y1_zeros (test_basic.TestBessel) ... ok
test_y1p_zeros (test_basic.TestBessel) ... ok
test_yn (test_basic.TestBessel) ... ok
test_yn_zeros (test_basic.TestBessel) ... ok
test_ynp_zeros (test_basic.TestBessel) ... ok
test_ynp_zeros_large_order (test_basic.TestBessel) ... KNOWNFAIL: cephes/yv is not eps accurate for large orders on all platforms, and has nan/inf issues
test_yv (test_basic.TestBessel) ... ok
test_yv_cephes_vs_amos (test_basic.TestBessel) ... KNOWNFAIL: cephes/yv is not eps accurate for large orders on all platforms, and has nan/inf issues
test_yv_cephes_vs_amos_only_small_orders (test_basic.TestBessel) ... ok
test_yve (test_basic.TestBessel) ... ok
test_yvp (test_basic.TestBessel) ... ok
test_besselpoly (test_basic.TestBesselpoly) ... ok
test_beta (test_basic.TestBeta) ... ok
test_betainc (test_basic.TestBeta) ... ok
test_betaincinv (test_basic.TestBeta) ... ok
test_betaln (test_basic.TestBeta) ... ok
test_airy (test_basic.TestCephes) ... ok
test_airye (test_basic.TestCephes) ... ok
test_bdtr (test_basic.TestCephes) ... ok
test_bdtrc (test_basic.TestCephes) ... ok
test_bdtri (test_basic.TestCephes) ... ok
test_bdtrik (test_basic.TestCephes) ... ok
test_bdtrin (test_basic.TestCephes) ... ok
test_bei (test_basic.TestCephes) ... ok
test_beip (test_basic.TestCephes) ... ok
test_ber (test_basic.TestCephes) ... ok
test_berp (test_basic.TestCephes) ... ok
test_besselpoly (test_basic.TestCephes) ... ok
test_beta (test_basic.TestCephes) ... ok
test_betainc (test_basic.TestCephes) ... ok
test_betaincinv (test_basic.TestCephes) ... ok
test_betaln (test_basic.TestCephes) ... ok
test_btdtr (test_basic.TestCephes) ... ok
test_btdtri (test_basic.TestCephes) ... ok
test_btdtria (test_basic.TestCephes) ... ok
test_btdtrib (test_basic.TestCephes) ... ok
test_cbrt (test_basic.TestCephes) ... ok
test_chdtr (test_basic.TestCephes) ... ok
test_chdtrc (test_basic.TestCephes) ... ok
test_chdtri (test_basic.TestCephes) ... ok
test_chdtriv (test_basic.TestCephes) ... ok
test_chndtr (test_basic.TestCephes) ... ok
test_chndtridf (test_basic.TestCephes) ... ok
test_chndtrinc (test_basic.TestCephes) ... ok
test_chndtrix (test_basic.TestCephes) ... ok
test_cosdg (test_basic.TestCephes) ... ok
test_cosm1 (test_basic.TestCephes) ... ok
test_cotdg (test_basic.TestCephes) ... ok
test_dawsn (test_basic.TestCephes) ... ok
test_ellipe (test_basic.TestCephes) ... ok
test_ellipeinc (test_basic.TestCephes) ... ok
test_ellipj (test_basic.TestCephes) ... ok
test_ellipk (test_basic.TestCephes) ... ok
test_ellipkinc (test_basic.TestCephes) ... ok
test_erf (test_basic.TestCephes) ... ok
test_erfc (test_basic.TestCephes) ... ok
test_exp1 (test_basic.TestCephes) ... ok
test_exp10 (test_basic.TestCephes) ... ok
test_exp1_reg (test_basic.TestCephes) ... ok
test_exp2 (test_basic.TestCephes) ... ok
test_expi (test_basic.TestCephes) ... ok
test_expm1 (test_basic.TestCephes) ... ok
test_expn (test_basic.TestCephes) ... ok
test_fdtr (test_basic.TestCephes) ... ok
test_fdtrc (test_basic.TestCephes) ... ok
test_fdtri (test_basic.TestCephes) ... ok
test_fdtridfd (test_basic.TestCephes) ... ok
test_fresnel (test_basic.TestCephes) ... ok
test_gamma (test_basic.TestCephes) ... ok
test_gammainc (test_basic.TestCephes) ... ok
test_gammaincc (test_basic.TestCephes) ... ok
test_gammainccinv (test_basic.TestCephes) ... ok
test_gammaln (test_basic.TestCephes) ... ok
test_gdtr (test_basic.TestCephes) ... ok
test_gdtrc (test_basic.TestCephes) ... ok
test_gdtria (test_basic.TestCephes) ... ok
test_gdtrib (test_basic.TestCephes) ... ok
test_gdtrix (test_basic.TestCephes) ... ok
test_hankel1 (test_basic.TestCephes) ... ok
test_hankel1e (test_basic.TestCephes) ... ok
test_hankel2 (test_basic.TestCephes) ... ok
test_hankel2e (test_basic.TestCephes) ... ok
test_hyp1f1 (test_basic.TestCephes) ... ok
test_hyp1f2 (test_basic.TestCephes) ... ok
test_hyp2f0 (test_basic.TestCephes) ... ok
test_hyp2f1 (test_basic.TestCephes) ... ok
test_hyp3f0 (test_basic.TestCephes) ... ok
test_hyperu (test_basic.TestCephes) ... ok
test_i0 (test_basic.TestCephes) ... ok
test_i0e (test_basic.TestCephes) ... ok
test_i1 (test_basic.TestCephes) ... ok
test_i1e (test_basic.TestCephes) ... ok
test_it2i0k0 (test_basic.TestCephes) ... ok
test_it2j0y0 (test_basic.TestCephes) ... ok
test_it2struve0 (test_basic.TestCephes) ... ok
test_itairy (test_basic.TestCephes) ... ok
test_iti0k0 (test_basic.TestCephes) ... ok
test_itj0y0 (test_basic.TestCephes) ... ok
test_itmodstruve0 (test_basic.TestCephes) ... ok
test_itstruve0 (test_basic.TestCephes) ... ok
test_iv (test_basic.TestCephes) ... ok
test_j0 (test_basic.TestCephes) ... ok
test_j1 (test_basic.TestCephes) ... ok
test_jn (test_basic.TestCephes) ... ok
test_jv (test_basic.TestCephes) ... ok
test_k0 (test_basic.TestCephes) ... ok
test_k0e (test_basic.TestCephes) ... ok
test_k1 (test_basic.TestCephes) ... ok
test_k1e (test_basic.TestCephes) ... ok
test_kei (test_basic.TestCephes) ... ok
test_keip (test_basic.TestCephes) ... ok
test_ker (test_basic.TestCephes) ... ok
test_kerp (test_basic.TestCephes) ... ok
test_kn (test_basic.TestCephes) ... ok
test_kolmogi (test_basic.TestCephes) ... ok
test_kolmogorov (test_basic.TestCephes) ... ok
test_log1p (test_basic.TestCephes) ... ok
test_lpmv (test_basic.TestCephes) ... ok
test_mathieu_a (test_basic.TestCephes) ... ok
test_mathieu_b (test_basic.TestCephes) ... ok
test_mathieu_cem (test_basic.TestCephes) ... ok
test_mathieu_modcem1 (test_basic.TestCephes) ... ok
test_mathieu_modcem2 (test_basic.TestCephes) ... ok
test_mathieu_modsem1 (test_basic.TestCephes) ... ok
test_mathieu_modsem2 (test_basic.TestCephes) ... ok
test_mathieu_sem (test_basic.TestCephes) ... ok
test_modfresnelm (test_basic.TestCephes) ... ok
test_modfresnelp (test_basic.TestCephes) ... ok
test_nbdtr (test_basic.TestCephes) ... ok
test_nbdtrc (test_basic.TestCephes) ... ok
test_nbdtri (test_basic.TestCephes) ... ok
test_nbdtrin (test_basic.TestCephes) ... ok
test_ncfdtr (test_basic.TestCephes) ... ok
test_ncfdtri (test_basic.TestCephes) ... ok
test_ncfdtridfd (test_basic.TestCephes) ... ok
test_nctdtr (test_basic.TestCephes) ... ok
test_nctdtrinc (test_basic.TestCephes) ... ok
test_nctdtrit (test_basic.TestCephes) ... ok
test_ndtr (test_basic.TestCephes) ... ok
test_ndtri (test_basic.TestCephes) ... ok
test_nrdtrimn (test_basic.TestCephes) ... ok
test_nrdtrisd (test_basic.TestCephes) ... ok
test_obl_ang1 (test_basic.TestCephes) ... ok
test_obl_ang1_cv (test_basic.TestCephes) ... ok
test_obl_rad1 (test_basic.TestCephes) ... ok
test_obl_rad1_cv (test_basic.TestCephes) ... ok
test_obl_rad2 (test_basic.TestCephes) ... ok
test_obl_rad2_cv (test_basic.TestCephes) ... ok
test_pbdv (test_basic.TestCephes) ... ok
test_pbvv (test_basic.TestCephes) ... ok
test_pbwa (test_basic.TestCephes) ... ok
test_pdtr (test_basic.TestCephes) ... ok
test_pdtrc (test_basic.TestCephes) ... ok
test_pdtri (test_basic.TestCephes) ... ok
test_pdtrik (test_basic.TestCephes) ... ok
test_pro_ang1 (test_basic.TestCephes) ... ok
test_pro_ang1_cv (test_basic.TestCephes) ... ok
test_pro_rad1 (test_basic.TestCephes) ... ok
test_pro_rad1_cv (test_basic.TestCephes) ... ok
test_pro_rad2 (test_basic.TestCephes) ... ok
test_pro_rad2_cv (test_basic.TestCephes) ... ok
test_psi (test_basic.TestCephes) ... ok
test_radian (test_basic.TestCephes) ... ok
test_rgamma (test_basic.TestCephes) ... ok
test_round (test_basic.TestCephes) ... ok
test_shichi (test_basic.TestCephes) ... ok
test_sici (test_basic.TestCephes) ... ok
test_sindg (test_basic.TestCephes) ... ok
test_smirnov (test_basic.TestCephes) ... ok
test_smirnovi (test_basic.TestCephes) ... ok
test_spence (test_basic.TestCephes) ... ok
test_stdtr (test_basic.TestCephes) ... ok
test_stdtridf (test_basic.TestCephes) ... ok
test_stdtrit (test_basic.TestCephes) ... ok
test_struve (test_basic.TestCephes) ... ok
test_tandg (test_basic.TestCephes) ... ok
test_tklmbda (test_basic.TestCephes) ... ok
test_wofz (test_basic.TestCephes) ... ok
test_y0 (test_basic.TestCephes) ... ok
test_y1 (test_basic.TestCephes) ... ok
test_yn (test_basic.TestCephes) ... ok
test_yv (test_basic.TestCephes) ... ok
test_zeta (test_basic.TestCephes) ... ok
test_zetac (test_basic.TestCephes) ... ok
test_ellipe (test_basic.TestEllip) ... ok
test_ellipeinc (test_basic.TestEllip) ... ok
test_ellipj (test_basic.TestEllip) ... ok
Regression test for #912. ... ok
test_ellipk (test_basic.TestEllip) ... ok
test_ellipkinc (test_basic.TestEllip) ... ok
test_erf (test_basic.TestErf) ... ok
test_erf_zeros (test_basic.TestErf) ... ok
test_erfcinv (test_basic.TestErf) ... ok
test_erfinv (test_basic.TestErf) ... ok
test_errprint (test_basic.TestErf) ... ok
test_euler (test_basic.TestEuler) ... ok
test_exp10 (test_basic.TestExp) ... ok
test_exp10more (test_basic.TestExp) ... ok
test_exp2 (test_basic.TestExp) ... ok
test_exp2more (test_basic.TestExp) ... ok
test_expm1 (test_basic.TestExp) ... ok
test_expm1more (test_basic.TestExp) ... ok
test_fresnel (test_basic.TestFresnel) ... ok
test_fresnel_zeros (test_basic.TestFresnel) ... ok
test_fresnelc_zeros (test_basic.TestFresnel) ... ok
test_fresnels_zeros (test_basic.TestFresnel) ... ok
test_modfresnelm (test_basic.TestFresnelIntegral) ... ok
test_modfresnelp (test_basic.TestFresnelIntegral) ... ok
test_975 (test_basic.TestGamma) ... ok
test_gamma (test_basic.TestGamma) ... ok
test_gammainc (test_basic.TestGamma) ... ok
test_gammaincc (test_basic.TestGamma) ... ok
test_gammainccinv (test_basic.TestGamma) ... ok
test_gammaincinv (test_basic.TestGamma) ... ok
test_gammaln (test_basic.TestGamma) ... ok
test_rgamma (test_basic.TestGamma) ... ok
test_hankel1 (test_basic.TestHankel) ... ok
test_hankel1e (test_basic.TestHankel) ... ok
test_hankel2 (test_basic.TestHankel) ... ok
test_hankl2e (test_basic.TestHankel) ... ok
test_neg2e (test_basic.TestHankel) ... ok
test_negv1 (test_basic.TestHankel) ... ok
test_negv1e (test_basic.TestHankel) ... ok
test_negv2 (test_basic.TestHankel) ... ok
test_h1vp (test_basic.TestHyper) ... ok
test_h2vp (test_basic.TestHyper) ... ok
test_hyp0f1 (test_basic.TestHyper) ... ok
test_hyp1f1 (test_basic.TestHyper) ... ok
test_hyp1f2 (test_basic.TestHyper) ... ok
test_hyp2f0 (test_basic.TestHyper) ... ok
test_hyp2f1 (test_basic.TestHyper) ... ok
test_hyp3f0 (test_basic.TestHyper) ... ok
test_hyperu (test_basic.TestHyper) ... ok
test_bei (test_basic.TestKelvin) ... ok
test_bei_zeros (test_basic.TestKelvin) ... ok
test_beip (test_basic.TestKelvin) ... ok
test_beip_zeros (test_basic.TestKelvin) ... ok
test_ber (test_basic.TestKelvin) ... ok
test_ber_zeros (test_basic.TestKelvin) ... ok
test_berp (test_basic.TestKelvin) ... ok
test_berp_zeros (test_basic.TestKelvin) ... ok
test_kei (test_basic.TestKelvin) ... ok
test_kei_zeros (test_basic.TestKelvin) ... ok
test_keip (test_basic.TestKelvin) ... ok
test_keip_zeros (test_basic.TestKelvin) ... ok
test_kelvin (test_basic.TestKelvin) ... ok
test_kelvin_zeros (test_basic.TestKelvin) ... ok
test_ker (test_basic.TestKelvin) ... ok
test_ker_zeros (test_basic.TestKelvin) ... ok
test_kerp (test_basic.TestKelvin) ... ok
test_kerp_zeros (test_basic.TestKelvin) ... ok
test_genlaguerre (test_basic.TestLaguerre) ... ok
test_laguerre (test_basic.TestLaguerre) ... ok
test_lmbda (test_basic.TestLambda) ... ok
test_legendre (test_basic.TestLegendre) ... ok
test_lpmn (test_basic.TestLegendreFunctions) ... ok
test_lpmv (test_basic.TestLegendreFunctions) ... ok
test_lpn (test_basic.TestLegendreFunctions) ... ok
test_lqmn (test_basic.TestLegendreFunctions) ... ok
test_lqmn_shape (test_basic.TestLegendreFunctions) ... ok
test_lqn (test_basic.TestLegendreFunctions) ... ok
test_log1p (test_basic.TestLog1p) ... ok
test_log1pmore (test_basic.TestLog1p) ... ok
test_mathieu_a (test_basic.TestMathieu) ... ok
test_mathieu_even_coef (test_basic.TestMathieu) ... ok
test_mathieu_odd_coef (test_basic.TestMathieu) ... ok
test_obl_cv_seq (test_basic.TestOblCvSeq) ... ok
test_pbdn_seq (test_basic.TestParabolicCylinder) ... ok
test_pbdv (test_basic.TestParabolicCylinder) ... ok
test_pbdv_gradient (test_basic.TestParabolicCylinder) ... ok
test_pbdv_points (test_basic.TestParabolicCylinder) ... ok
test_pbdv_seq (test_basic.TestParabolicCylinder) ... ok
test_pbvv_gradient (test_basic.TestParabolicCylinder) ... ok
test_polygamma (test_basic.TestPolygamma) ... ok
test_pro_cv_seq (test_basic.TestProCvSeq) ... ok
test_psi (test_basic.TestPsi) ... ok
test_radian (test_basic.TestRadian) ... ok
test_radianmore (test_basic.TestRadian) ... ok
test_riccati_jn (test_basic.TestRiccati) ... ok
test_riccati_yn (test_basic.TestRiccati) ... ok
test_round (test_basic.TestRound) ... ok
test_sph_harm (test_basic.TestSpherical) ... ok
test_sph_in (test_basic.TestSpherical) ... ok
test_sph_inkn (test_basic.TestSpherical) ... ok
test_sph_jn (test_basic.TestSpherical) ... ok
test_sph_jnyn (test_basic.TestSpherical) ... ok
test_sph_kn (test_basic.TestSpherical) ... ok
test_sph_yn (test_basic.TestSpherical) ... ok
Regression test for #679 ... ok
test_basic.TestStruve.test_some_values ... ok
Check Struve function versus its power series ... ok
test_specialpoints (test_basic.TestTandg) ... ok
test_tandg (test_basic.TestTandg) ... ok
test_tandgmore (test_basic.TestTandg) ... ok
test_0 (test_basic.TestTrigonometric) ... ok
test_cbrt (test_basic.TestTrigonometric) ... ok
test_cbrtmore (test_basic.TestTrigonometric) ... ok
test_cosdg (test_basic.TestTrigonometric) ... ok
test_cosdgmore (test_basic.TestTrigonometric) ... ok
test_cosm1 (test_basic.TestTrigonometric) ... ok
test_cotdg (test_basic.TestTrigonometric) ... ok
test_cotdgmore (test_basic.TestTrigonometric) ... ok
test_sinc (test_basic.TestTrigonometric) ... ok
test_sindg (test_basic.TestTrigonometric) ... ok
test_sindgmore (test_basic.TestTrigonometric) ... ok
test_specialpoints (test_basic.TestTrigonometric) ... ok
test_basic.test_sph_harm(array((0.28209479177387814+0j)), 0.28209479177387814) ... ok
test_basic.test_sph_harm(array((0.19313710101159479+0j)), 0.19313710101159473) ... ok
test_basic.test_sph_harm(array((0.38627420202318968+0j)), 0.38627420202318957) ... ok
test_basic.test_sph_harm(array((0.38627420202318957-9.4606768423053307e-17j)), (0.38627420202318957-9.4606768423053307e-17j)) ... ok
test_basic.test_sph_harm(array((1.1521668490919394e-17+0.18816934037548774j)), (1.1521668490919398e-17+0.18816934037548777j)) ... ok
test_basic.test_sph_harm(array((1.6935260841945282e-18+0.027658293277811382j)), (1.6935260841945294e-18+0.027658293277811399j)) ... ok
test_basic.test_chi2_smalldf ... ok
test_basic.test_chi2c_smalldf ... ok
test_basic.test_chi2_inv_smalldf ... ok
test_data.test_boost(<Data for arccosh: acosh_data_ipp-acosh_data>,) ... ok
test_data.test_boost(<Data for arccosh (complex): acosh_data_ipp-acosh_data>,) ... ok
test_data.test_boost(<Data for arcsinh: asinh_data_ipp-asinh_data>,) ... ok
test_data.test_boost(<Data for arcsinh (complex): asinh_data_ipp-asinh_data>,) ... ok
test_data.test_boost(<Data for arctanh: atanh_data_ipp-atanh_data>,) ... ok
test_data.test_boost(<Data for arctanh (complex): atanh_data_ipp-atanh_data>,) ... ok
test_data.test_boost(<Data for assoc_legendre_p_boost_: assoc_legendre_p_ipp-assoc_legendre_p>,) ... ok
test_data.test_boost(<Data for legendre_p_via_assoc_: legendre_p_ipp-legendre_p>,) ... ok
test_data.test_boost(<Data for beta: beta_exp_data_ipp-beta_exp_data>,) ... ok
test_data.test_boost(<Data for beta: beta_exp_data_ipp-beta_exp_data>,) ... ok
test_data.test_boost(<Data for beta: beta_small_data_ipp-beta_small_data>,) ... ok
test_data.test_boost(<Data for cbrt: cbrt_data_ipp-cbrt_data>,) ... ok
test_data.test_boost(<Data for psi: digamma_data_ipp-digamma_data>,) ... ok
test_data.test_boost(<Data for psi (complex): digamma_data_ipp-digamma_data>,) ... ok
test_data.test_boost(<Data for psi: digamma_neg_data_ipp-digamma_neg_data>,) ... ok
test_data.test_boost(<Data for psi (complex): digamma_neg_data_ipp-digamma_neg_data>,) ... ok
test_data.test_boost(<Data for psi: digamma_root_data_ipp-digamma_root_data>,) ... ok
test_data.test_boost(<Data for psi (complex): digamma_root_data_ipp-digamma_root_data>,) ... ok
test_data.test_boost(<Data for psi: digamma_small_data_ipp-digamma_small_data>,) ... ok
test_data.test_boost(<Data for psi (complex): digamma_small_data_ipp-digamma_small_data>,) ... ok
test_data.test_boost(<Data for ellipk_: ellint_k_data_ipp-ellint_k_data>,) ... ok
test_data.test_boost(<Data for ellipe_: ellint_e_data_ipp-ellint_e_data>,) ... ok
test_data.test_boost(<Data for ellipeinc_: ellint_e2_data_ipp-ellint_e2_data>,) ... ok
test_data.test_boost(<Data for erf: erf_data_ipp-erf_data>,) ... ok
test_data.test_boost(<Data for erf (complex): erf_data_ipp-erf_data>,) ... ok
test_data.test_boost(<Data for erfc: erf_data_ipp-erf_data>,) ... ok
test_data.test_boost(<Data for erf: erf_large_data_ipp-erf_large_data>,) ... ok
test_data.test_boost(<Data for erf (complex): erf_large_data_ipp-erf_large_data>,) ... ok
test_data.test_boost(<Data for erfc: erf_large_data_ipp-erf_large_data>,) ... ok
test_data.test_boost(<Data for erf: erf_small_data_ipp-erf_small_data>,) ... ok
test_data.test_boost(<Data for erf (complex): erf_small_data_ipp-erf_small_data>,) ... ok
test_data.test_boost(<Data for erfc: erf_small_data_ipp-erf_small_data>,) ... ok
test_data.test_boost(<Data for erfinv: erf_inv_data_ipp-erf_inv_data>,) ... ok
test_data.test_boost(<Data for erfcinv: erfc_inv_data_ipp-erfc_inv_data>,) ... ok
test_data.test_boost(<Data for exp1: expint_1_data_ipp-expint_1_data>,) ... ok
test_data.test_boost(<Data for exp1 (complex): expint_1_data_ipp-expint_1_data>,) ... ok
test_data.test_boost(<Data for expi: expinti_data_ipp-expinti_data>,) ... ok
test_data.test_boost(<Data for expi: expinti_data_double_ipp-expinti_data_double>,) ... ok
test_data.test_boost(<Data for expn: expint_small_data_ipp-expint_small_data>,) ... ok
test_data.test_boost(<Data for expn: expint_data_ipp-expint_data>,) ... ok
test_data.test_boost(<Data for gamma: test_gamma_data_ipp-near_0>,) ... ok
test_data.test_boost(<Data for gamma: test_gamma_data_ipp-near_1>,) ... ok
test_data.test_boost(<Data for gamma: test_gamma_data_ipp-near_2>,) ... ok
test_data.test_boost(<Data for gamma: test_gamma_data_ipp-near_m10>,) ... ok
test_data.test_boost(<Data for gamma: test_gamma_data_ipp-near_m55>,) ... ok
test_data.test_boost(<Data for gamma (complex): test_gamma_data_ipp-near_0>,) ... ok
test_data.test_boost(<Data for gamma (complex): test_gamma_data_ipp-near_1>,) ... ok
test_data.test_boost(<Data for gamma (complex): test_gamma_data_ipp-near_2>,) ... ok
test_data.test_boost(<Data for gamma (complex): test_gamma_data_ipp-near_m10>,) ... ok
test_data.test_boost(<Data for gamma (complex): test_gamma_data_ipp-near_m55>,) ... ok
test_data.test_boost(<Data for gammaln: test_gamma_data_ipp-near_0>,) ... ok
test_data.test_boost(<Data for gammaln: test_gamma_data_ipp-near_1>,) ... ok
test_data.test_boost(<Data for gammaln: test_gamma_data_ipp-near_2>,) ... ok
test_data.test_boost(<Data for gammaln: test_gamma_data_ipp-near_m10>,) ... ok
test_data.test_boost(<Data for gammaln: test_gamma_data_ipp-near_m55>,) ... ok
test_data.test_boost(<Data for log1p: log1p_expm1_data_ipp-log1p_expm1_data>,) ... ok
test_data.test_boost(<Data for expm1: log1p_expm1_data_ipp-log1p_expm1_data>,) ... ok
test_data.test_boost(<Data for iv: bessel_i_data_ipp-bessel_i_data>,) ... ok
test_data.test_boost(<Data for iv (complex): bessel_i_data_ipp-bessel_i_data>,) ... ok
test_data.test_boost(<Data for iv: bessel_i_int_data_ipp-bessel_i_int_data>,) ... ok
test_data.test_boost(<Data for iv (complex): bessel_i_int_data_ipp-bessel_i_int_data>,) ... ok
test_data.test_boost(<Data for jv: bessel_j_int_data_ipp-bessel_j_int_data>,) ... ok
test_data.test_boost(<Data for jv (complex): bessel_j_int_data_ipp-bessel_j_int_data>,) ... ok
test_data.test_boost(<Data for jv: bessel_j_large_data_ipp-bessel_j_large_data>,) ... ok
test_data.test_boost(<Data for jv (complex): bessel_j_large_data_ipp-bessel_j_large_data>,) ... ok
test_data.test_boost(<Data for jv: bessel_j_int_data_ipp-bessel_j_int_data>,) ... ok
test_data.test_boost(<Data for jv (complex): bessel_j_int_data_ipp-bessel_j_int_data>,) ... ok
test_data.test_boost(<Data for jv: bessel_j_data_ipp-bessel_j_data>,) ... ok
test_data.test_boost(<Data for jv (complex): bessel_j_data_ipp-bessel_j_data>,) ... ok
test_data.test_boost(<Data for kn: bessel_k_int_data_ipp-bessel_k_int_data>,) ... KNOWNFAIL: Known bug in Cephes kn implementation
test_data.test_boost(<Data for kv: bessel_k_int_data_ipp-bessel_k_int_data>,) ... ok
test_data.test_boost(<Data for kv (complex): bessel_k_int_data_ipp-bessel_k_int_data>,) ... ok
test_data.test_boost(<Data for kv: bessel_k_data_ipp-bessel_k_data>,) ... ok
test_data.test_boost(<Data for kv (complex): bessel_k_data_ipp-bessel_k_data>,) ... ok
test_data.test_boost(<Data for yn: bessel_y01_data_ipp-bessel_y01_data>,) ... ok
test_data.test_boost(<Data for yn: bessel_yn_data_ipp-bessel_yn_data>,) ... ok
test_data.test_boost(<Data for yv: bessel_yn_data_ipp-bessel_yn_data>,) ... ok
test_data.test_boost(<Data for yv (complex): bessel_yn_data_ipp-bessel_yn_data>,) ... ok
test_data.test_boost(<Data for yv: bessel_yv_data_ipp-bessel_yv_data>,) ... KNOWNFAIL: Known bug in Cephes yv implementation
test_data.test_boost(<Data for yv (complex): bessel_yv_data_ipp-bessel_yv_data>,) ... ok
test_data.test_boost(<Data for zeta_: zeta_data_ipp-zeta_data>,) ... ok
test_data.test_boost(<Data for zeta_: zeta_neg_data_ipp-zeta_neg_data>,) ... ok
test_data.test_boost(<Data for zeta_: zeta_1_up_data_ipp-zeta_1_up_data>,) ... ok
test_data.test_boost(<Data for zeta_: zeta_1_below_data_ipp-zeta_1_below_data>,) ... ok
test_data.test_boost(<Data for gammaincinv: gamma_inv_data_ipp-gamma_inv_data>,) ... ok
test_data.test_boost(<Data for gammaincinv: gamma_inv_big_data_ipp-gamma_inv_big_data>,) ... ok
test_lambertw.test_values ... ok
test_lambertw.test_ufunc ... ok
test_mpmath.test_expi_complex ... SKIP: Skipping test: test_expi_complex
mpmath library is not present
test_mpmath.test_hyp2f1_strange_points ... SKIP: Skipping test: test_hyp2f1_strange_points
mpmath library is not present
test_mpmath.test_hyp2f1_real_some_points ... SKIP: Skipping test: test_hyp2f1_real_some_points
mpmath library is not present
test_mpmath.test_hyp2f1_some_points_2 ... SKIP: Skipping test: test_hyp2f1_some_points_2
mpmath library is not present
test_mpmath.test_hyp2f1_real_some ... SKIP: Skipping test: test_hyp2f1_real_some
mpmath library is not present
test_mpmath.test_erf_complex ... SKIP: Skipping test: test_erf_complex
mpmath library is not present
test_mpmath.test_lpmv ... SKIP: Skipping test: test_lpmv
mpmath library is not present
test_orthogonal.TestCall.test_call ... ok
test_chebyc (test_orthogonal.TestCheby) ... ok
test_chebys (test_orthogonal.TestCheby) ... ok
test_chebyt (test_orthogonal.TestCheby) ... ok
test_chebyu (test_orthogonal.TestCheby) ... ok
test_gegenbauer (test_orthogonal.TestGegenbauer) ... ok
test_hermite (test_orthogonal.TestHermite) ... ok
test_hermitenorm (test_orthogonal.TestHermite) ... ok
test_orthogonal_eval.TestPolys.test_chebyc ... ok
test_orthogonal_eval.TestPolys.test_chebys ... ok
test_orthogonal_eval.TestPolys.test_chebyt ... ok
test_orthogonal_eval.TestPolys.test_chebyu ... ok
test_orthogonal_eval.TestPolys.test_gegenbauer ... ok
test_orthogonal_eval.TestPolys.test_genlaguerre ... ok
test_orthogonal_eval.TestPolys.test_hermite ... ok
test_orthogonal_eval.TestPolys.test_hermitenorm ... ok
test_orthogonal_eval.TestPolys.test_jacobi ... ok
test_orthogonal_eval.TestPolys.test_laguerre ... ok
test_orthogonal_eval.TestPolys.test_legendre ... ok
test_orthogonal_eval.TestPolys.test_sh_chebyt ... ok
test_orthogonal_eval.TestPolys.test_sh_chebyu ... ok
test_orthogonal_eval.TestPolys.test_sh_jacobi ... ok
test_orthogonal_eval.TestPolys.test_sh_legendre ... ok
test_orthogonal_eval.test_eval_chebyt ... ok
test_orthogonal_eval.test_warnings ... ok
test1 (test_spfun_stats.TestMultiGammaLn) ... ok
test_ararg (test_spfun_stats.TestMultiGammaLn) ... ok
test_bararg (test_spfun_stats.TestMultiGammaLn) ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.alpha_gen object at 0x36d3090>, (3.5704770516650459,), array(inf), array(inf), 0.31772708039386671, 0.021186836778540902, 1000, 'alphasample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.alpha_gen object at 0x36d3090>, (3.5704770516650459,), array(inf), array(inf), 'alpha') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.alpha_gen object at 0x36d3090>, (3.5704770516650459,), 'alpha') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.alpha_gen object at 0x36d3090>, (3.5704770516650459,), 'alpha') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.alpha_gen object at 0x36d3090>, (3.5704770516650459,), 'alpha') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.alpha_gen object at 0x36d3090>, (3.5704770516650459,), 'alpha') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.alpha_gen object at 0x36d3090>, (3.5704770516650459,), 'alpha') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.alpha_gen object at 0x36d3090>, (3.5704770516650459,), 'alpha') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.anglit_gen object at 0x36d3130>, (), array(0.0), array(0.11685027506808487), 0.019485173966289539, 0.11461131582481687, 1000, 'anglitsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.anglit_gen object at 0x36d3130>, (), array(0.0), array(0.11685027506808487), 'anglit') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.anglit_gen object at 0x36d3130>, (), 'anglit') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.anglit_gen object at 0x36d3130>, (), 'anglit') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.anglit_gen object at 0x36d3130>, (), 'anglit') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.anglit_gen object at 0x36d3130>, (), 'anglit') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.anglit_gen object at 0x36d3130>, (), 'anglit') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.anglit_gen object at 0x36d3130>, (), 'anglit') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.arcsine_gen object at 0x36d31f0>, (), array(0.5), array(0.125), 0.51691545030819297, 0.12586663168201145, 1000, 'arcsinesample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.arcsine_gen object at 0x36d31f0>, (), array(0.5), array(0.125), 'arcsine') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.arcsine_gen object at 0x36d31f0>, (), 'arcsine') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.arcsine_gen object at 0x36d31f0>, (), 'arcsine') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.arcsine_gen object at 0x36d31f0>, (), 'arcsine') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.arcsine_gen object at 0x36d31f0>, (), 'arcsine') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.arcsine_gen object at 0x36d31f0>, (), 'arcsine') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.arcsine_gen object at 0x36d31f0>, (), 'arcsine') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.beta_gen object at 0x36d3270>, (2.3098496451481823, 0.62687954300963677), array(0.78653818488354665), array(0.04264856955583702), 0.78396526766379881, 0.045854302817002014, 1000, 'betasample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.beta_gen object at 0x36d3270>, (2.3098496451481823, 0.62687954300963677), array(0.78653818488354665), array(0.04264856955583702), 'beta') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.beta_gen object at 0x36d3270>, (2.3098496451481823, 0.62687954300963677), 'beta') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.beta_gen object at 0x36d3270>, (2.3098496451481823, 0.62687954300963677), 'beta') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.beta_gen object at 0x36d3270>, (2.3098496451481823, 0.62687954300963677), 'beta') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.beta_gen object at 0x36d3270>, (2.3098496451481823, 0.62687954300963677), 'beta') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.beta_gen object at 0x36d3270>, (2.3098496451481823, 0.62687954300963677), 'beta') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.beta_gen object at 0x36d3270>, (2.3098496451481823, 0.62687954300963677), 'beta') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.betaprime_gen object at 0x36d3370>, (5, 6), array(1.0), array(0.5), 0.9608076505807116, 0.47274240606696361, 1000, 'betaprimesample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.betaprime_gen object at 0x36d3370>, (5, 6), array(1.0), array(0.5), 'betaprime') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.betaprime_gen object at 0x36d3370>, (5, 6), 'betaprime') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.betaprime_gen object at 0x36d3370>, (5, 6), 'betaprime') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.betaprime_gen object at 0x36d3370>, (5, 6), 'betaprime') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.betaprime_gen object at 0x36d3370>, (5, 6), 'betaprime') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.betaprime_gen object at 0x36d3370>, (5, 6), 'betaprime') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.betaprime_gen object at 0x36d3370>, (5, 6), 'betaprime') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.bradford_gen object at 0x36d3410>, (0.29891359763170633,), array(0.47823078529291196), array(0.083238491922311225), 0.4933692442090703, 0.083341918695194112, 1000, 'bradfordsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.bradford_gen object at 0x36d3410>, (0.29891359763170633,), array(0.47823078529291196), array(0.083238491922311225), 'bradford') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.bradford_gen object at 0x36d3410>, (0.29891359763170633,), 'bradford') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.bradford_gen object at 0x36d3410>, (0.29891359763170633,), 'bradford') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.bradford_gen object at 0x36d3410>, (0.29891359763170633,), 'bradford') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.bradford_gen object at 0x36d3410>, (0.29891359763170633,), 'bradford') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.bradford_gen object at 0x36d3410>, (0.29891359763170633,), 'bradford') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.bradford_gen object at 0x36d3410>, (0.29891359763170633,), 'bradford') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.burr_gen object at 0x36d3510>, (10.5, 4.2999999999999998), array(1.2109372989617821), array(0.029148272765685535), 1.2204374750635194, 0.030007409783013278, 1000, 'burrsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.burr_gen object at 0x36d3510>, (10.5, 4.2999999999999998), array(1.2109372989617821), array(0.029148272765685535), 'burr') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.burr_gen object at 0x36d3510>, (10.5, 4.2999999999999998), 'burr') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.burr_gen object at 0x36d3510>, (10.5, 4.2999999999999998), 'burr') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.burr_gen object at 0x36d3510>, (10.5, 4.2999999999999998), 'burr') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.burr_gen object at 0x36d3510>, (10.5, 4.2999999999999998), 'burr') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.burr_gen object at 0x36d3510>, (10.5, 4.2999999999999998), 'burr') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.burr_gen object at 0x36d3510>, (10.5, 4.2999999999999998), 'burr') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.cauchy_gen object at 0x36d3630>, (), array(inf), array(inf), 1.552645887869553, 327.63988189221925, 1000, 'cauchysample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.cauchy_gen object at 0x36d3630>, (), array(inf), array(inf), 'cauchy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.cauchy_gen object at 0x36d3630>, (), 'cauchy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.cauchy_gen object at 0x36d3630>, (), 'cauchy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.cauchy_gen object at 0x36d3630>, (), 'cauchy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.cauchy_gen object at 0x36d3630>, (), 'cauchy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.cauchy_gen object at 0x36d3630>, (), 'cauchy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.cauchy_gen object at 0x36d3630>, (), 'cauchy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.chi_gen object at 0x36d3770>, (78,), array(8.8035000285242742), array(0.49838724777310972), 8.7666853364107826, 0.4613094602619911, 1000, 'chisample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.chi_gen object at 0x36d3770>, (78,), array(8.8035000285242742), array(0.49838724777310972), 'chi') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.chi_gen object at 0x36d3770>, (78,), 'chi') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.chi_gen object at 0x36d3770>, (78,), 'chi') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.chi_gen object at 0x36d3770>, (78,), 'chi') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.chi_gen object at 0x36d3770>, (78,), 'chi') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.chi_gen object at 0x36d3770>, (78,), 'chi') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.chi_gen object at 0x36d3770>, (78,), 'chi') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.chi2_gen object at 0x36d3830>, (55,), array(55.0), array(110.0), 54.443237765581728, 99.92590900706503, 1000, 'chi2sample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.chi2_gen object at 0x36d3830>, (55,), array(55.0), array(110.0), 'chi2') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.chi2_gen object at 0x36d3830>, (55,), 'chi2') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.chi2_gen object at 0x36d3830>, (55,), 'chi2') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.chi2_gen object at 0x36d3830>, (55,), 'chi2') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.chi2_gen object at 0x36d3830>, (55,), 'chi2') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.chi2_gen object at 0x36d3830>, (55,), 'chi2') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.chi2_gen object at 0x36d3830>, (55,), 'chi2') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.dgamma_gen object at 0x36d38d0>, (1.1023326088288166,), array(0.0), array(2.3174697893161609), 0.058649521093078708, 2.2265558396676179, 1000, 'dgammasample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.dgamma_gen object at 0x36d38d0>, (1.1023326088288166,), array(0.0), array(2.3174697893161609), 'dgamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.dgamma_gen object at 0x36d38d0>, (1.1023326088288166,), 'dgamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.dgamma_gen object at 0x36d38d0>, (1.1023326088288166,), 'dgamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.dgamma_gen object at 0x36d38d0>, (1.1023326088288166,), 'dgamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.dgamma_gen object at 0x36d38d0>, (1.1023326088288166,), 'dgamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.dgamma_gen object at 0x36d38d0>, (1.1023326088288166,), 'dgamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.dgamma_gen object at 0x36d38d0>, (1.1023326088288166,), 'dgamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.dweibull_gen object at 0x36d3a10>, (2.0685080649914673,), array(0.0), array(0.98644644671326842), 0.036400052129263775, 0.95809285003898315, 1000, 'dweibullsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.dweibull_gen object at 0x36d3a10>, (2.0685080649914673,), array(0.0), array(0.98644644671326842), 'dweibull') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.dweibull_gen object at 0x36d3a10>, (2.0685080649914673,), 'dweibull') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.dweibull_gen object at 0x36d3a10>, (2.0685080649914673,), 'dweibull') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.dweibull_gen object at 0x36d3a10>, (2.0685080649914673,), 'dweibull') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.dweibull_gen object at 0x36d3a10>, (2.0685080649914673,), 'dweibull') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.dweibull_gen object at 0x36d3a10>, (2.0685080649914673,), 'dweibull') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.dweibull_gen object at 0x36d3a10>, (2.0685080649914673,), 'dweibull') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.erlang_gen object at 0x36d3b10>, (20,), array(20.0), array(20.0), 19.75909577220186, 18.118858828066092, 1000, 'erlangsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.erlang_gen object at 0x36d3b10>, (20,), array(20.0), array(20.0), 'erlang') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.erlang_gen object at 0x36d3b10>, (20,), 'erlang') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.erlang_gen object at 0x36d3b10>, (20,), 'erlang') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.erlang_gen object at 0x36d3b10>, (20,), 'erlang') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.erlang_gen object at 0x36d3b10>, (20,), 'erlang') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.erlang_gen object at 0x36d3b10>, (20,), 'erlang') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.erlang_gen object at 0x36d3b10>, (20,), 'erlang') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.expon_gen object at 0x36d3bd0>, (), array(1.0), array(1.0), 1.0489197735650717, 1.063581407259818, 1000, 'exponsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.expon_gen object at 0x36d3bd0>, (), array(1.0), array(1.0), 'expon') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.expon_gen object at 0x36d3bd0>, (), 'expon') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.expon_gen object at 0x36d3bd0>, (), 'expon') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.expon_gen object at 0x36d3bd0>, (), 'expon') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.expon_gen object at 0x36d3bd0>, (), 'expon') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.expon_gen object at 0x36d3bd0>, (), 'expon') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.expon_gen object at 0x36d3bd0>, (), 'expon') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.exponpow_gen object at 0x36c5cb0>, (2.697119160358469,), array(0.76622330667382488), array(0.05900404926303171), 0.78088147091689752, 0.055982343857351249, 1000, 'exponpowsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.exponpow_gen object at 0x36c5cb0>, (2.697119160358469,), array(0.76622330667382488), array(0.05900404926303171), 'exponpow') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.exponpow_gen object at 0x36c5cb0>, (2.697119160358469,), 'exponpow') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.exponpow_gen object at 0x36c5cb0>, (2.697119160358469,), 'exponpow') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.exponpow_gen object at 0x36c5cb0>, (2.697119160358469,), 'exponpow') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.exponpow_gen object at 0x36c5cb0>, (2.697119160358469,), 'exponpow') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.exponpow_gen object at 0x36c5cb0>, (2.697119160358469,), 'exponpow') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.exponpow_gen object at 0x36c5cb0>, (2.697119160358469,), 'exponpow') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.exponweib_gen object at 0x36d3c90>, (2.8923945291034436, 1.9505288745913174), array(1.2873418037984079), array(0.18119174498960655), 1.3122933415946192, 0.18047748963723587, 1000, 'exponweibsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.exponweib_gen object at 0x36d3c90>, (2.8923945291034436, 1.9505288745913174), array(1.2873418037984079), array(0.18119174498960655), 'exponweib') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.exponweib_gen object at 0x36d3c90>, (2.8923945291034436, 1.9505288745913174), 'exponweib') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.exponweib_gen object at 0x36d3c90>, (2.8923945291034436, 1.9505288745913174), 'exponweib') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.exponweib_gen object at 0x36d3c90>, (2.8923945291034436, 1.9505288745913174), 'exponweib') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.exponweib_gen object at 0x36d3c90>, (2.8923945291034436, 1.9505288745913174), 'exponweib') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.exponweib_gen object at 0x36d3c90>, (2.8923945291034436, 1.9505288745913174), 'exponweib') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.exponweib_gen object at 0x36d3c90>, (2.8923945291034436, 1.9505288745913174), 'exponweib') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.f_gen object at 0x36d3f70>, (29, 18), array(1.125), array(0.2805572660098522), 1.1043635143445401, 0.2668974538642544, 1000, 'fsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.f_gen object at 0x36d3f70>, (29, 18), array(1.125), array(0.2805572660098522), 'f') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.f_gen object at 0x36d3f70>, (29, 18), 'f') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.f_gen object at 0x36d3f70>, (29, 18), 'f') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.f_gen object at 0x36d3f70>, (29, 18), 'f') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.f_gen object at 0x36d3f70>, (29, 18), 'f') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.f_gen object at 0x36d3f70>, (29, 18), 'f') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.f_gen object at 0x36d3f70>, (29, 18), 'f') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.fatiguelife_gen object at 0x36d3d30>, (29,), array(421.5), array(884942.25), 381.5231692182964, 659709.81429483229, 1000, 'fatiguelifesample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.fatiguelife_gen object at 0x36d3d30>, (29,), array(421.5), array(884942.25), 'fatiguelife') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.fatiguelife_gen object at 0x36d3d30>, (29,), 'fatiguelife') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.fatiguelife_gen object at 0x36d3d30>, (29,), 'fatiguelife') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.fatiguelife_gen object at 0x36d3d30>, (29,), 'fatiguelife') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.fatiguelife_gen object at 0x36d3d30>, (29,), 'fatiguelife') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.fatiguelife_gen object at 0x36d3d30>, (29,), 'fatiguelife') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.fatiguelife_gen object at 0x36d3d30>, (29,), 'fatiguelife') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.fisk_gen object at 0x36d35f0>, (3.0857548622253179,), array(1.19619763403311), array(0.84763509403100801), 1.2366804437236734, 0.81012589838120275, 1000, 'fisksample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.fisk_gen object at 0x36d35f0>, (3.0857548622253179,), array(1.19619763403311), array(0.84763509403100801), 'fisk') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.fisk_gen object at 0x36d35f0>, (3.0857548622253179,), 'fisk') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.fisk_gen object at 0x36d35f0>, (3.0857548622253179,), 'fisk') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.fisk_gen object at 0x36d35f0>, (3.0857548622253179,), 'fisk') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.fisk_gen object at 0x36d35f0>, (3.0857548622253179,), 'fisk') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.fisk_gen object at 0x36d35f0>, (3.0857548622253179,), 'fisk') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.fisk_gen object at 0x36d35f0>, (3.0857548622253179,), 'fisk') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.foldcauchy_gen object at 0x36d3eb0>, (4.7164673455831894,), array(inf), array(inf), 7.1128596673275473, 316.34888997897662, 1000, 'foldcauchysample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.foldcauchy_gen object at 0x36d3eb0>, (4.7164673455831894,), array(inf), array(inf), 'foldcauchy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.foldcauchy_gen object at 0x36d3eb0>, (4.7164673455831894,), 'foldcauchy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.foldcauchy_gen object at 0x36d3eb0>, (4.7164673455831894,), 'foldcauchy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.foldcauchy_gen object at 0x36d3eb0>, (4.7164673455831894,), 'foldcauchy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.foldcauchy_gen object at 0x36d3eb0>, (4.7164673455831894,), 'foldcauchy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.foldcauchy_gen object at 0x36d3eb0>, (4.7164673455831894,), 'foldcauchy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.foldcauchy_gen object at 0x36d3eb0>, (4.7164673455831894,), 'foldcauchy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.foldnorm_gen object at 0x36dc050>, (1.9521253373555869,), array(1.97141281966251), array(0.92432482721597609), 1.9485668609369076, 0.89445098899946973, 1000, 'foldnormsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.foldnorm_gen object at 0x36dc050>, (1.9521253373555869,), array(1.97141281966251), array(0.92432482721597609), 'foldnorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.foldnorm_gen object at 0x36dc050>, (1.9521253373555869,), 'foldnorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.foldnorm_gen object at 0x36dc050>, (1.9521253373555869,), 'foldnorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.foldnorm_gen object at 0x36dc050>, (1.9521253373555869,), 'foldnorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.foldnorm_gen object at 0x36dc050>, (1.9521253373555869,), 'foldnorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.foldnorm_gen object at 0x36dc050>, (1.9521253373555869,), 'foldnorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.foldnorm_gen object at 0x36dc050>, (1.9521253373555869,), 'foldnorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_l_gen object at 0x36dc270>, (3.6279911255583239,), array(-0.90148416697658329), array(0.076288054283963236), -0.88452420295607126, 0.07344496156832396, 1000, 'frechet_lsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_l_gen object at 0x36dc270>, (3.6279911255583239,), array(-0.90148416697658329), array(0.076288054283963236), 'frechet_l') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_l_gen object at 0x36dc270>, (3.6279911255583239,), 'frechet_l') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_l_gen object at 0x36dc270>, (3.6279911255583239,), 'frechet_l') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_l_gen object at 0x36dc270>, (3.6279911255583239,), 'frechet_l') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_l_gen object at 0x36dc270>, (3.6279911255583239,), 'frechet_l') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_l_gen object at 0x36dc270>, (3.6279911255583239,), 'frechet_l') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_l_gen object at 0x36dc270>, (3.6279911255583239,), 'frechet_l') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_r_gen object at 0x36dc0f0>, (1.8928171603534227,), array(0.88747270666698841), array(0.23766896745884436), 0.91443119143867235, 0.24138404811476438, 1000, 'frechet_rsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_r_gen object at 0x36dc0f0>, (1.8928171603534227,), array(0.88747270666698841), array(0.23766896745884436), 'frechet_r') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_r_gen object at 0x36dc0f0>, (1.8928171603534227,), 'frechet_r') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_r_gen object at 0x36dc0f0>, (1.8928171603534227,), 'frechet_r') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_r_gen object at 0x36dc0f0>, (1.8928171603534227,), 'frechet_r') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_r_gen object at 0x36dc0f0>, (1.8928171603534227,), 'frechet_r') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_r_gen object at 0x36dc0f0>, (1.8928171603534227,), 'frechet_r') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_r_gen object at 0x36dc0f0>, (1.8928171603534227,), 'frechet_r') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gamma_gen object at 0x36dc630>, (1.9932305483800778,), array(1.9932305483800778), array(1.9932305483800778), 1.9049475453784119, 1.683287232178736, 1000, 'gammasample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gamma_gen object at 0x36dc630>, (1.9932305483800778,), array(1.9932305483800778), array(1.9932305483800778), 'gamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gamma_gen object at 0x36dc630>, (1.9932305483800778,), 'gamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gamma_gen object at 0x36dc630>, (1.9932305483800778,), 'gamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gamma_gen object at 0x36dc630>, (1.9932305483800778,), 'gamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gamma_gen object at 0x36dc630>, (1.9932305483800778,), 'gamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gamma_gen object at 0x36dc630>, (1.9932305483800778,), 'gamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gamma_gen object at 0x36dc630>, (1.9932305483800778,), 'gamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genextreme_gen object at 0x36dc610>, (-0.10000000000000001,), array(0.68628702119319673), array(2.226241073208163), 0.7659800257484175, 2.3195034224827276, 1000, 'genextremesample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genextreme_gen object at 0x36dc610>, (-0.10000000000000001,), array(0.68628702119319673), array(2.226241073208163), 'genextreme') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genextreme_gen object at 0x36dc610>, (-0.10000000000000001,), 'genextreme') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genextreme_gen object at 0x36dc610>, (-0.10000000000000001,), 'genextreme') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genextreme_gen object at 0x36dc610>, (-0.10000000000000001,), 'genextreme') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genextreme_gen object at 0x36dc610>, (-0.10000000000000001,), 'genextreme') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genextreme_gen object at 0x36dc610>, (-0.10000000000000001,), 'genextreme') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genextreme_gen object at 0x36dc610>, (-0.10000000000000001,), 'genextreme') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gengamma_gen object at 0x36dc790>, (4.4162385429431925, 3.1193091679242761), array(1.5702162249275609), array(0.060317549758582167), 1.5854913681198324, 0.057829220024230694, 1000, 'gengammasample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gengamma_gen object at 0x36dc790>, (4.4162385429431925, 3.1193091679242761), array(1.5702162249275609), array(0.060317549758582167), 'gengamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gengamma_gen object at 0x36dc790>, (4.4162385429431925, 3.1193091679242761), 'gengamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gengamma_gen object at 0x36dc790>, (4.4162385429431925, 3.1193091679242761), 'gengamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gengamma_gen object at 0x36dc790>, (4.4162385429431925, 3.1193091679242761), 'gengamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gengamma_gen object at 0x36dc790>, (4.4162385429431925, 3.1193091679242761), 'gengamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gengamma_gen object at 0x36dc790>, (4.4162385429431925, 3.1193091679242761), 'gengamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gengamma_gen object at 0x36dc790>, (4.4162385429431925, 3.1193091679242761), 'gengamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genhalflogistic_gen object at 0x36dc850>, (0.77274727809929322,), array(0.70597656450848112), array(0.12459765121103794), 0.72486157273526908, 0.12193473451964035, 1000, 'genhalflogisticsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genhalflogistic_gen object at 0x36dc850>, (0.77274727809929322,), array(0.70597656450848112), array(0.12459765121103794), 'genhalflogistic') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genhalflogistic_gen object at 0x36dc850>, (0.77274727809929322,), 'genhalflogistic') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genhalflogistic_gen object at 0x36dc850>, (0.77274727809929322,), 'genhalflogistic') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genhalflogistic_gen object at 0x36dc850>, (0.77274727809929322,), 'genhalflogistic') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genhalflogistic_gen object at 0x36dc850>, (0.77274727809929322,), 'genhalflogistic') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genhalflogistic_gen object at 0x36dc850>, (0.77274727809929322,), 'genhalflogistic') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genhalflogistic_gen object at 0x36dc850>, (0.77274727809929322,), 'genhalflogistic') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genlogistic_gen object at 0x36dc3d0>, (0.41192440799679475,), array(-1.8996417990533176), array(8.5520107632574316), -1.6915023776411073, 7.2701839656308955, 1000, 'genlogisticsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genlogistic_gen object at 0x36dc3d0>, (0.41192440799679475,), array(-1.8996417990533176), array(8.5520107632574316), 'genlogistic') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genlogistic_gen object at 0x36dc3d0>, (0.41192440799679475,), 'genlogistic') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genlogistic_gen object at 0x36dc3d0>, (0.41192440799679475,), 'genlogistic') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genlogistic_gen object at 0x36dc3d0>, (0.41192440799679475,), 'genlogistic') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genlogistic_gen object at 0x36dc3d0>, (0.41192440799679475,), 'genlogistic') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genlogistic_gen object at 0x36dc3d0>, (0.41192440799679475,), 'genlogistic') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genlogistic_gen object at 0x36dc3d0>, (0.41192440799679475,), 'genlogistic') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genpareto_gen object at 0x36dc490>, (0.10000000000000001,), array(1.1111111111111116), array(1.543209876543199), 1.1693857515263717, 1.6532801760783784, 1000, 'genparetosample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genpareto_gen object at 0x36dc490>, (0.10000000000000001,), array(1.1111111111111116), array(1.543209876543199), 'genpareto') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genpareto_gen object at 0x36dc490>, (0.10000000000000001,), 'genpareto') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genpareto_gen object at 0x36dc490>, (0.10000000000000001,), 'genpareto') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genpareto_gen object at 0x36dc490>, (0.10000000000000001,), 'genpareto') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genpareto_gen object at 0x36dc490>, (0.10000000000000001,), 'genpareto') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genpareto_gen object at 0x36dc490>, (0.10000000000000001,), 'genpareto') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.genpareto_gen object at 0x36dc490>, (0.10000000000000001,), 'genpareto') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gilbrat_gen object at 0x36e59b0>, (), array(1.6487212707001282), array(4.670774270471604), 1.5542857947354762, 3.0177319205419764, 1000, 'gilbratsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gilbrat_gen object at 0x36e59b0>, (), array(1.6487212707001282), array(4.670774270471604), 'gilbrat') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gilbrat_gen object at 0x36e59b0>, (), 'gilbrat') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gilbrat_gen object at 0x36e59b0>, (), 'gilbrat') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gilbrat_gen object at 0x36e59b0>, (), 'gilbrat') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gilbrat_gen object at 0x36e59b0>, (), 'gilbrat') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gilbrat_gen object at 0x36e59b0>, (), 'gilbrat') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gilbrat_gen object at 0x36e59b0>, (), 'gilbrat') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gompertz_gen object at 0x36dc870>, (0.94743713075105251,), array(0.61842381762891141), array(0.18616258957403664), 0.64114216641328903, 0.19073684302906721, 1000, 'gompertzsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gompertz_gen object at 0x36dc870>, (0.94743713075105251,), array(0.61842381762891141), array(0.18616258957403664), 'gompertz') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gompertz_gen object at 0x36dc870>, (0.94743713075105251,), 'gompertz') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gompertz_gen object at 0x36dc870>, (0.94743713075105251,), 'gompertz') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gompertz_gen object at 0x36dc870>, (0.94743713075105251,), 'gompertz') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gompertz_gen object at 0x36dc870>, (0.94743713075105251,), 'gompertz') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gompertz_gen object at 0x36dc870>, (0.94743713075105251,), 'gompertz') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gompertz_gen object at 0x36dc870>, (0.94743713075105251,), 'gompertz') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gumbel_l_gen object at 0x36dca90>, (), array(-0.57721566490153287), array(1.6449340668482264), -0.48770832402349951, 1.403145247583542, 1000, 'gumbel_lsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gumbel_l_gen object at 0x36dca90>, (), array(-0.57721566490153287), array(1.6449340668482264), 'gumbel_l') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gumbel_l_gen object at 0x36dca90>, (), 'gumbel_l') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gumbel_l_gen object at 0x36dca90>, (), 'gumbel_l') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gumbel_l_gen object at 0x36dca90>, (), 'gumbel_l') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gumbel_l_gen object at 0x36dca90>, (), 'gumbel_l') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gumbel_l_gen object at 0x36dca90>, (), 'gumbel_l') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gumbel_l_gen object at 0x36dca90>, (), 'gumbel_l') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gumbel_r_gen object at 0x36dc9d0>, (), array(0.57721566490153287), array(1.6449340668482264), 0.64936088543269999, 1.6827982006295068, 1000, 'gumbel_rsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gumbel_r_gen object at 0x36dc9d0>, (), array(0.57721566490153287), array(1.6449340668482264), 'gumbel_r') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gumbel_r_gen object at 0x36dc9d0>, (), 'gumbel_r') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gumbel_r_gen object at 0x36dc9d0>, (), 'gumbel_r') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gumbel_r_gen object at 0x36dc9d0>, (), 'gumbel_r') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gumbel_r_gen object at 0x36dc9d0>, (), 'gumbel_r') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gumbel_r_gen object at 0x36dc9d0>, (), 'gumbel_r') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.gumbel_r_gen object at 0x36dc9d0>, (), 'gumbel_r') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.halfcauchy_gen object at 0x36dcb50>, (), array(inf), array(inf), 5.8851391398051538, 1194.9588343167102, 1000, 'halfcauchysample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.halfcauchy_gen object at 0x36dcb50>, (), array(inf), array(inf), 'halfcauchy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.halfcauchy_gen object at 0x36dcb50>, (), 'halfcauchy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.halfcauchy_gen object at 0x36dcb50>, (), 'halfcauchy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.halfcauchy_gen object at 0x36dcb50>, (), 'halfcauchy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.halfcauchy_gen object at 0x36dcb50>, (), 'halfcauchy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.halfcauchy_gen object at 0x36dcb50>, (), 'halfcauchy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.halfcauchy_gen object at 0x36dcb50>, (), 'halfcauchy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.halflogistic_gen object at 0x36dcc10>, (), array(1.3862943611198906), array(1.3680560780236473), 1.4456693604391833, 1.4370223442716139, 1000, 'halflogisticsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.halflogistic_gen object at 0x36dcc10>, (), array(1.3862943611198906), array(1.3680560780236473), 'halflogistic') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.halflogistic_gen object at 0x36dcc10>, (), 'halflogistic') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.halflogistic_gen object at 0x36dcc10>, (), 'halflogistic') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.halflogistic_gen object at 0x36dcc10>, (), 'halflogistic') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.halflogistic_gen object at 0x36dcc10>, (), 'halflogistic') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.halflogistic_gen object at 0x36dcc10>, (), 'halflogistic') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.halflogistic_gen object at 0x36dcc10>, (), 'halflogistic') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.halfnorm_gen object at 0x36dcc70>, (), array(0.79788456080286541), array(0.36338022763241862), 0.78953928239319027, 0.34253579133614653, 1000, 'halfnormsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.halfnorm_gen object at 0x36dcc70>, (), array(0.79788456080286541), array(0.36338022763241862), 'halfnorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.halfnorm_gen object at 0x36dcc70>, (), 'halfnorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.halfnorm_gen object at 0x36dcc70>, (), 'halfnorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.halfnorm_gen object at 0x36dcc70>, (), 'halfnorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.halfnorm_gen object at 0x36dcc70>, (), 'halfnorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.halfnorm_gen object at 0x36dcc70>, (), 'halfnorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.halfnorm_gen object at 0x36dcc70>, (), 'halfnorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.hypsecant_gen object at 0x36dcd30>, (), array(0.0), array(2.4674011002723395), 0.10651732466849634, 2.2821454925062783, 1000, 'hypsecantsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.hypsecant_gen object at 0x36dcd30>, (), array(0.0), array(2.4674011002723395), 'hypsecant') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.hypsecant_gen object at 0x36dcd30>, (), 'hypsecant') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.hypsecant_gen object at 0x36dcd30>, (), 'hypsecant') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.hypsecant_gen object at 0x36dcd30>, (), 'hypsecant') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.hypsecant_gen object at 0x36dcd30>, (), 'hypsecant') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.hypsecant_gen object at 0x36dcd30>, (), 'hypsecant') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.hypsecant_gen object at 0x36dcd30>, (), 'hypsecant') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.invgamma_gen object at 0x36dceb0>, (2.0668996136993067,), array(0.93729530609975309), array(13.131951625091871), 0.98003361245261933, 2.2163927217803892, 1000, 'invgammasample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.invgamma_gen object at 0x36dceb0>, (2.0668996136993067,), array(0.93729530609975309), array(13.131951625091871), 'invgamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.invgamma_gen object at 0x36dceb0>, (2.0668996136993067,), 'invgamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.invgamma_gen object at 0x36dceb0>, (2.0668996136993067,), 'invgamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.invgamma_gen object at 0x36dceb0>, (2.0668996136993067,), 'invgamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.invgamma_gen object at 0x36dceb0>, (2.0668996136993067,), 'invgamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.invgamma_gen object at 0x36dceb0>, (2.0668996136993067,), 'invgamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.invgamma_gen object at 0x36dceb0>, (2.0668996136993067,), 'invgamma') ... ok
test_continuous_basic.test_cont_basic('invgamma', (2.0668996136993067,), 0.01, array([ 0.211, 0.326, 0.295, 2.314, 0.359, 0.75 , 1.167, 0.806, 1.237, ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.invnorm_gen object at 0x36dcff0>, (0.14546264555347513,), array(0.14546264555347513), array(0.0030778995751055637), 0.14654162671350618, 0.0029042058229545743, 1000, 'invnormsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.invnorm_gen object at 0x36dcff0>, (0.14546264555347513,), array(0.14546264555347513), array(0.0030778995751055637), 'invnorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.invnorm_gen object at 0x36dcff0>, (0.14546264555347513,), 'invnorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.invnorm_gen object at 0x36dcff0>, (0.14546264555347513,), 'invnorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.invnorm_gen object at 0x36dcff0>, (0.14546264555347513,), 'invnorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.invnorm_gen object at 0x36dcff0>, (0.14546264555347513,), 'invnorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.invnorm_gen object at 0x36dcff0>, (0.14546264555347513,), 'invnorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.invnorm_gen object at 0x36dcff0>, (0.14546264555347513,), 'invnorm') ... ok
test_continuous_basic.test_cont_basic('invnorm', (0.14546264555347513,), 0.01, array([ 0.191, 0.239, 0.122, 0.175, 0.092, 0.082, 0.175, 0.121, 0.242, 0.124, ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.invgauss_gen object at 0x36e50b0>, (0.14546264555347513,), array(0.14546264555347513), array(0.0030778995751055637), 0.14654162671350618, 0.0029042058229545743, 1000, 'invgausssample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.invgauss_gen object at 0x36e50b0>, (0.14546264555347513,), array(0.14546264555347513), array(0.0030778995751055637), 'invgauss') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.invgauss_gen object at 0x36e50b0>, (0.14546264555347513,), 'invgauss') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.invgauss_gen object at 0x36e50b0>, (0.14546264555347513,), 'invgauss') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.invgauss_gen object at 0x36e50b0>, (0.14546264555347513,), 'invgauss') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.invgauss_gen object at 0x36e50b0>, (0.14546264555347513,), 'invgauss') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.invgauss_gen object at 0x36e50b0>, (0.14546264555347513,), 'invgauss') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.invgauss_gen object at 0x36e50b0>, (0.14546264555347513,), 'invgauss') ... ok
test_continuous_basic.test_cont_basic('invgauss', (0.14546264555347513,), 0.01, array([ 0.191, 0.239, 0.122, 0.175, 0.092, 0.082, 0.175, 0.121, 0.242, 0.124, ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.johnsonsb_gen object at 0x36e5170>, (4.3172675099141058, 3.1837781130785063), array(0.20952073643389132), array(0.0026608544463244121), 0.21254860453459329, 0.0026502849724359253, 1000, 'johnsonsbsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.johnsonsb_gen object at 0x36e5170>, (4.3172675099141058, 3.1837781130785063), array(0.20952073643389132), array(0.0026608544463244121), 'johnsonsb') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.johnsonsb_gen object at 0x36e5170>, (4.3172675099141058, 3.1837781130785063), 'johnsonsb') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.johnsonsb_gen object at 0x36e5170>, (4.3172675099141058, 3.1837781130785063), 'johnsonsb') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.johnsonsb_gen object at 0x36e5170>, (4.3172675099141058, 3.1837781130785063), 'johnsonsb') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.johnsonsb_gen object at 0x36e5170>, (4.3172675099141058, 3.1837781130785063), 'johnsonsb') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.johnsonsb_gen object at 0x36e5170>, (4.3172675099141058, 3.1837781130785063), 'johnsonsb') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.johnsonsb_gen object at 0x36e5170>, (4.3172675099141058, 3.1837781130785063), 'johnsonsb') ... ok
test_continuous_basic.test_cont_basic('johnsonsb', (4.3172675099141058, 3.1837781130785063), 0.01, array([ 0.135, 0.165, 0.158, 0.294, 0.172, 0.223, 0.252, 0.228, 0.256, 0.164, ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.laplace_gen object at 0x36e5310>, (), array(0.0), array(2.0), 0.098100813619852997, 1.8262500606297374, 1000, 'laplacesample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.laplace_gen object at 0x36e5310>, (), array(0.0), array(2.0), 'laplace') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.laplace_gen object at 0x36e5310>, (), 'laplace') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.laplace_gen object at 0x36e5310>, (), 'laplace') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.laplace_gen object at 0x36e5310>, (), 'laplace') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.laplace_gen object at 0x36e5310>, (), 'laplace') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.laplace_gen object at 0x36e5310>, (), 'laplace') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.laplace_gen object at 0x36e5310>, (), 'laplace') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.levy_gen object at 0x36e5450>, (), array(inf), array(inf), 1932.0205477129166, 578434680.15985334, 1000, 'levysample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.levy_gen object at 0x36e5450>, (), array(inf), array(inf), 'levy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.levy_gen object at 0x36e5450>, (), 'levy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.levy_gen object at 0x36e5450>, (), 'levy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.levy_gen object at 0x36e5450>, (), 'levy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.levy_gen object at 0x36e5450>, (), 'levy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.levy_gen object at 0x36e5450>, (), 'levy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.levy_gen object at 0x36e5450>, (), 'levy') ... ok
test_continuous_basic.test_cont_basic('levy', (), 0.01, array([ 2.704e-01, 6.147e-01, 5.045e-01, 1.637e+02, 7.518e-01, 4.449e+00, ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.levy_l_gen object at 0x36e5530>, (), array(inf), array(inf), -146.03264446610152, 5369822.6715053003, 1000, 'levy_lsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.levy_l_gen object at 0x36e5530>, (), array(inf), array(inf), 'levy_l') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.levy_l_gen object at 0x36e5530>, (), 'levy_l') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.levy_l_gen object at 0x36e5530>, (), 'levy_l') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.levy_l_gen object at 0x36e5530>, (), 'levy_l') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.levy_l_gen object at 0x36e5530>, (), 'levy_l') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.levy_l_gen object at 0x36e5530>, (), 'levy_l') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.levy_l_gen object at 0x36e5530>, (), 'levy_l') ... ok
test_continuous_basic.test_cont_basic('levy_l', (), 0.01, array([ -2.141e+02, -1.524e+01, -2.480e+01, -2.878e-01, -9.949e+00, -1.216e+00, ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.loggamma_gen object at 0x36e5770>, (0.41411931826052117,), array(-2.4617679388246936), array(6.8426502245961007), -2.4057296757832174, 5.9831621683265688, 1000, 'loggammasample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.loggamma_gen object at 0x36e5770>, (0.41411931826052117,), array(-2.4617679388246936), array(6.8426502245961007), 'loggamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.loggamma_gen object at 0x36e5770>, (0.41411931826052117,), 'loggamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.loggamma_gen object at 0x36e5770>, (0.41411931826052117,), 'loggamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.loggamma_gen object at 0x36e5770>, (0.41411931826052117,), 'loggamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.loggamma_gen object at 0x36e5770>, (0.41411931826052117,), 'loggamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.loggamma_gen object at 0x36e5770>, (0.41411931826052117,), 'loggamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.loggamma_gen object at 0x36e5770>, (0.41411931826052117,), 'loggamma') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.logistic_gen object at 0x36e56b0>, (), array(0.0), array(3.2898681336964528), 0.12005549995830994, 3.078985058384891, 1000, 'logisticsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.logistic_gen object at 0x36e56b0>, (), array(0.0), array(3.2898681336964528), 'logistic') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.logistic_gen object at 0x36e56b0>, (), 'logistic') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.logistic_gen object at 0x36e56b0>, (), 'logistic') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.logistic_gen object at 0x36e56b0>, (), 'logistic') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.logistic_gen object at 0x36e56b0>, (), 'logistic') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.logistic_gen object at 0x36e56b0>, (), 'logistic') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.logistic_gen object at 0x36e56b0>, (), 'logistic') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.loglaplace_gen object at 0x36e5830>, (3.2505926592051435,), array(1.1045330480739952), array(0.38917293304417666), 1.1334773520725614, 0.37349384621458165, 1000, 'loglaplacesample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.loglaplace_gen object at 0x36e5830>, (3.2505926592051435,), array(1.1045330480739952), array(0.38917293304417666), 'loglaplace') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.loglaplace_gen object at 0x36e5830>, (3.2505926592051435,), 'loglaplace') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.loglaplace_gen object at 0x36e5830>, (3.2505926592051435,), 'loglaplace') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.loglaplace_gen object at 0x36e5830>, (3.2505926592051435,), 'loglaplace') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.loglaplace_gen object at 0x36e5830>, (3.2505926592051435,), 'loglaplace') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.loglaplace_gen object at 0x36e5830>, (3.2505926592051435,), 'loglaplace') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.loglaplace_gen object at 0x36e5830>, (3.2505926592051435,), 'loglaplace') ... ok
test_continuous_basic.test_cont_basic('loglaplace', (3.2505926592051435,), 0.01, array([ 0.506, 0.757, 0.703, 1.898, 0.807, 1.102, 1.335, 1.134, 1.372, 0.743, ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.lognorm_gen object at 0x36e58f0>, (0.95368226960575331,), array(1.5757871665018548), array(3.6827062109137709), 1.4935980843433927, 2.4720855647743387, 1000, 'lognormsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.lognorm_gen object at 0x36e58f0>, (0.95368226960575331,), array(1.5757871665018548), array(3.6827062109137709), 'lognorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.lognorm_gen object at 0x36e58f0>, (0.95368226960575331,), 'lognorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.lognorm_gen object at 0x36e58f0>, (0.95368226960575331,), 'lognorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.lognorm_gen object at 0x36e58f0>, (0.95368226960575331,), 'lognorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.lognorm_gen object at 0x36e58f0>, (0.95368226960575331,), 'lognorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.lognorm_gen object at 0x36e58f0>, (0.95368226960575331,), 'lognorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.lognorm_gen object at 0x36e58f0>, (0.95368226960575331,), 'lognorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.lomax_gen object at 0x36ed0b0>, (1.8771398388773268,), array(1.1400690695795155), array(inf), 1.2046982570012683, 8.1205985026367333, 1000, 'lomaxsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.lomax_gen object at 0x36ed0b0>, (1.8771398388773268,), array(1.1400690695795155), array(inf), 'lomax') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.lomax_gen object at 0x36ed0b0>, (1.8771398388773268,), 'lomax') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.lomax_gen object at 0x36ed0b0>, (1.8771398388773268,), 'lomax') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.lomax_gen object at 0x36ed0b0>, (1.8771398388773268,), 'lomax') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.lomax_gen object at 0x36ed0b0>, (1.8771398388773268,), 'lomax') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.lomax_gen object at 0x36ed0b0>, (1.8771398388773268,), 'lomax') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.lomax_gen object at 0x36ed0b0>, (1.8771398388773268,), 'lomax') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.maxwell_gen object at 0x36e5a70>, (), array(1.5957691216057308), array(0.45352091052967447), 1.5580947217508414, 0.41026340192631527, 1000, 'maxwellsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.maxwell_gen object at 0x36e5a70>, (), array(1.5957691216057308), array(0.45352091052967447), 'maxwell') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.maxwell_gen object at 0x36e5a70>, (), 'maxwell') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.maxwell_gen object at 0x36e5a70>, (), 'maxwell') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.maxwell_gen object at 0x36e5a70>, (), 'maxwell') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.maxwell_gen object at 0x36e5a70>, (), 'maxwell') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.maxwell_gen object at 0x36e5a70>, (), 'maxwell') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.maxwell_gen object at 0x36e5a70>, (), 'maxwell') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.nakagami_gen object at 0x36e5bb0>, (4.9673794866666237,), array(0.97519075370169761), array(0.049002993894714963), 0.9885798461330112, 0.047891039771988025, 1000, 'nakagamisample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.nakagami_gen object at 0x36e5bb0>, (4.9673794866666237,), array(0.97519075370169761), array(0.049002993894714963), 'nakagami') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.nakagami_gen object at 0x36e5bb0>, (4.9673794866666237,), 'nakagami') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.nakagami_gen object at 0x36e5bb0>, (4.9673794866666237,), 'nakagami') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.nakagami_gen object at 0x36e5bb0>, (4.9673794866666237,), 'nakagami') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.nakagami_gen object at 0x36e5bb0>, (4.9673794866666237,), 'nakagami') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.nakagami_gen object at 0x36e5bb0>, (4.9673794866666237,), 'nakagami') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.nakagami_gen object at 0x36e5bb0>, (4.9673794866666237,), 'nakagami') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.ncf_gen object at 0x36e5c70>, (27, 27, 0.41578441799226107), array(1.0966313767196905), array(0.19835325341303081), 1.0759414510260812, 0.18247641865181716, 1000, 'ncfsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.ncf_gen object at 0x36e5c70>, (27, 27, 0.41578441799226107), array(1.0966313767196905), array(0.19835325341303081), 'ncf') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.ncf_gen object at 0x36e5c70>, (27, 27, 0.41578441799226107), 'ncf') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.ncf_gen object at 0x36e5c70>, (27, 27, 0.41578441799226107), 'ncf') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.ncf_gen object at 0x36e5c70>, (27, 27, 0.41578441799226107), 'ncf') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.ncf_gen object at 0x36e5c70>, (27, 27, 0.41578441799226107), 'ncf') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.ncf_gen object at 0x36e5c70>, (27, 27, 0.41578441799226107), 'ncf') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.ncf_gen object at 0x36e5c70>, (27, 27, 0.41578441799226107), 'ncf') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.nct_gen object at 0x36e5ef0>, (14, 0.24045031331198066), array(0.25436732738327772), array(1.1694163414603564), 0.23917507013451059, 1.071479548334112, 1000, 'nctsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.nct_gen object at 0x36e5ef0>, (14, 0.24045031331198066), array(0.25436732738327772), array(1.1694163414603564), 'nct') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.nct_gen object at 0x36e5ef0>, (14, 0.24045031331198066), 'nct') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.nct_gen object at 0x36e5ef0>, (14, 0.24045031331198066), 'nct') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.nct_gen object at 0x36e5ef0>, (14, 0.24045031331198066), 'nct') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.nct_gen object at 0x36e5ef0>, (14, 0.24045031331198066), 'nct') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.nct_gen object at 0x36e5ef0>, (14, 0.24045031331198066), 'nct') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.nct_gen object at 0x36e5ef0>, (14, 0.24045031331198066), 'nct') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.ncx2_gen object at 0x36e5c10>, (21, 1.0560465975116415), array(22.056046597511642), array(46.224186390046569), 22.092814053046368, 48.384580315939367, 1000, 'ncx2sample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.ncx2_gen object at 0x36e5c10>, (21, 1.0560465975116415), array(22.056046597511642), array(46.224186390046569), 'ncx2') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.ncx2_gen object at 0x36e5c10>, (21, 1.0560465975116415), 'ncx2') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.ncx2_gen object at 0x36e5c10>, (21, 1.0560465975116415), 'ncx2') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.ncx2_gen object at 0x36e5c10>, (21, 1.0560465975116415), 'ncx2') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.ncx2_gen object at 0x36e5c10>, (21, 1.0560465975116415), 'ncx2') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.ncx2_gen object at 0x36e5c10>, (21, 1.0560465975116415), 'ncx2') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.ncx2_gen object at 0x36e5c10>, (21, 1.0560465975116415), 'ncx2') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.norm_gen object at 0x36c5fb0>, (), array(0.0), array(1.0), -0.021857613430289052, 0.96543031451323214, 1000, 'normsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.norm_gen object at 0x36c5fb0>, (), array(0.0), array(1.0), 'norm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.norm_gen object at 0x36c5fb0>, (), 'norm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.norm_gen object at 0x36c5fb0>, (), 'norm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.norm_gen object at 0x36c5fb0>, (), 'norm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.norm_gen object at 0x36c5fb0>, (), 'norm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.norm_gen object at 0x36c5fb0>, (), 'norm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.norm_gen object at 0x36c5fb0>, (), 'norm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.pareto_gen object at 0x36e5fd0>, (2.621716532144454,), array(1.6166305764162521), array(1.6034057205287133), 1.6550137301062546, 1.2674974417903764, 1000, 'paretosample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.pareto_gen object at 0x36e5fd0>, (2.621716532144454,), array(1.6166305764162521), array(1.6034057205287133), 'pareto') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.pareto_gen object at 0x36e5fd0>, (2.621716532144454,), 'pareto') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.pareto_gen object at 0x36e5fd0>, (2.621716532144454,), 'pareto') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.pareto_gen object at 0x36e5fd0>, (2.621716532144454,), 'pareto') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.pareto_gen object at 0x36e5fd0>, (2.621716532144454,), 'pareto') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.pareto_gen object at 0x36e5fd0>, (2.621716532144454,), 'pareto') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.pareto_gen object at 0x36e5fd0>, (2.621716532144454,), 'pareto') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.powerlaw_gen object at 0x36ed190>, (1.6591133289905851,), array(0.62393479469353574), array(0.85857496135780687), 0.63812341708244613, 0.061503031152340903, 1000, 'powerlawsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.powerlaw_gen object at 0x36ed190>, (1.6591133289905851,), array(0.62393479469353574), array(0.85857496135780687), 'powerlaw') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.powerlaw_gen object at 0x36ed190>, (1.6591133289905851,), 'powerlaw') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.powerlaw_gen object at 0x36ed190>, (1.6591133289905851,), 'powerlaw') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.powerlaw_gen object at 0x36ed190>, (1.6591133289905851,), 'powerlaw') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.powerlaw_gen object at 0x36ed190>, (1.6591133289905851,), 'powerlaw') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.powerlaw_gen object at 0x36ed190>, (1.6591133289905851,), 'powerlaw') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.powerlaw_gen object at 0x36ed190>, (1.6591133289905851,), 'powerlaw') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.powernorm_gen object at 0x36ed310>, (4.4453652254590779,), array(-1.0934378551735171), array(0.46999722851193249), -1.0492605411034246, 0.4387505202446717, 1000, 'powernormsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.powernorm_gen object at 0x36ed310>, (4.4453652254590779,), array(-1.0934378551735171), array(0.46999722851193249), 'powernorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.powernorm_gen object at 0x36ed310>, (4.4453652254590779,), 'powernorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.powernorm_gen object at 0x36ed310>, (4.4453652254590779,), 'powernorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.powernorm_gen object at 0x36ed310>, (4.4453652254590779,), 'powernorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.powernorm_gen object at 0x36ed310>, (4.4453652254590779,), 'powernorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.powernorm_gen object at 0x36ed310>, (4.4453652254590779,), 'powernorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.powernorm_gen object at 0x36ed310>, (4.4453652254590779,), 'powernorm') ... ok
test_continuous_basic.test_cont_basic('powernorm', (4.4453652254590779,), 0.01, array([-2.241, -1.649, -1.771, -0.089, -1.536, -0.831, -0.503, -0.773, -0.465, -1.681, ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.rayleigh_gen object at 0x36ed490>, (), array(1.2533141373155001), array(0.42920367320510344), 1.2898620140336594, 0.43409553188317485, 1000, 'rayleighsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.rayleigh_gen object at 0x36ed490>, (), array(1.2533141373155001), array(0.42920367320510344), 'rayleigh') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.rayleigh_gen object at 0x36ed490>, (), 'rayleigh') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.rayleigh_gen object at 0x36ed490>, (), 'rayleigh') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.rayleigh_gen object at 0x36ed490>, (), 'rayleigh') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.rayleigh_gen object at 0x36ed490>, (), 'rayleigh') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.rayleigh_gen object at 0x36ed490>, (), 'rayleigh') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.rayleigh_gen object at 0x36ed490>, (), 'rayleigh') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.reciprocal_gen object at 0x36ed550>, (0.0062309367010521255, 1.0062309367010522), array(0.19667848552072986), array(0.060882307287375481), 0.20860644275950979, 0.064125334510458903, 1000, 'reciprocalsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.reciprocal_gen object at 0x36ed550>, (0.0062309367010521255, 1.0062309367010522), array(0.19667848552072986), array(0.060882307287375481), 'reciprocal') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.reciprocal_gen object at 0x36ed550>, (0.0062309367010521255, 1.0062309367010522), 'reciprocal') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.reciprocal_gen object at 0x36ed550>, (0.0062309367010521255, 1.0062309367010522), 'reciprocal') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.reciprocal_gen object at 0x36ed550>, (0.0062309367010521255, 1.0062309367010522), 'reciprocal') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.reciprocal_gen object at 0x36ed550>, (0.0062309367010521255, 1.0062309367010522), 'reciprocal') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.reciprocal_gen object at 0x36ed550>, (0.0062309367010521255, 1.0062309367010522), 'reciprocal') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.reciprocal_gen object at 0x36ed550>, (0.0062309367010521255, 1.0062309367010522), 'reciprocal') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.t_gen object at 0x36e5d70>, (2.7433514990818093,), array(0.0), array(3.6905172081719186), -0.027973201793274144, 3.5344407590010793, 1000, 'tsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.t_gen object at 0x36e5d70>, (2.7433514990818093,), array(0.0), array(3.6905172081719186), 't') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.t_gen object at 0x36e5d70>, (2.7433514990818093,), 't') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.t_gen object at 0x36e5d70>, (2.7433514990818093,), 't') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.t_gen object at 0x36e5d70>, (2.7433514990818093,), 't') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.t_gen object at 0x36e5d70>, (2.7433514990818093,), 't') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.t_gen object at 0x36e5d70>, (2.7433514990818093,), 't') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.t_gen object at 0x36e5d70>, (2.7433514990818093,), 't') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.triang_gen object at 0x36ed830>, (0.15785029824528218,), array(0.38595009941509401), array(0.048170356578380126), 0.3979467137648407, 0.048758189137974826, 1000, 'triangsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.triang_gen object at 0x36ed830>, (0.15785029824528218,), array(0.38595009941509401), array(0.048170356578380126), 'triang') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.triang_gen object at 0x36ed830>, (0.15785029824528218,), 'triang') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.triang_gen object at 0x36ed830>, (0.15785029824528218,), 'triang') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.triang_gen object at 0x36ed830>, (0.15785029824528218,), 'triang') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.triang_gen object at 0x36ed830>, (0.15785029824528218,), 'triang') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.triang_gen object at 0x36ed830>, (0.15785029824528218,), 'triang') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.triang_gen object at 0x36ed830>, (0.15785029824528218,), 'triang') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.truncexpon_gen object at 0x36ed8b0>, (4.6907725456810478,), array(0.95654169346841034), array(0.79425834443323384), 1.0006988174499489, 0.83089849698746843, 1000, 'truncexponsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.truncexpon_gen object at 0x36ed8b0>, (4.6907725456810478,), array(0.95654169346841034), array(0.79425834443323384), 'truncexpon') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.truncexpon_gen object at 0x36ed8b0>, (4.6907725456810478,), 'truncexpon') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.truncexpon_gen object at 0x36ed8b0>, (4.6907725456810478,), 'truncexpon') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.truncexpon_gen object at 0x36ed8b0>, (4.6907725456810478,), 'truncexpon') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.truncexpon_gen object at 0x36ed8b0>, (4.6907725456810478,), 'truncexpon') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.truncexpon_gen object at 0x36ed8b0>, (4.6907725456810478,), 'truncexpon') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.truncexpon_gen object at 0x36ed8b0>, (4.6907725456810478,), 'truncexpon') ... ok
test_continuous_basic.test_cont_basic('truncexpon', (4.6907725456810478,), 0.01, array([ 5.550e-02, 2.235e-01, 1.716e-01, 2.646e+00, 2.830e-01, 9.932e-01, ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.truncnorm_gen object at 0x36ed9d0>, (-1.0978730080013919, 2.7306754109031979), array(0.24256013977032281), array(0.63221524437974919), 0.28565694330796559, 0.63826177893087388, 1000, 'truncnormsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.truncnorm_gen object at 0x36ed9d0>, (-1.0978730080013919, 2.7306754109031979), array(0.24256013977032281), array(0.63221524437974919), 'truncnorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.truncnorm_gen object at 0x36ed9d0>, (-1.0978730080013919, 2.7306754109031979), 'truncnorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.truncnorm_gen object at 0x36ed9d0>, (-1.0978730080013919, 2.7306754109031979), 'truncnorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.truncnorm_gen object at 0x36ed9d0>, (-1.0978730080013919, 2.7306754109031979), 'truncnorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.truncnorm_gen object at 0x36ed9d0>, (-1.0978730080013919, 2.7306754109031979), 'truncnorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.truncnorm_gen object at 0x36ed9d0>, (-1.0978730080013919, 2.7306754109031979), 'truncnorm') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.truncnorm_gen object at 0x36ed9d0>, (-1.0978730080013919, 2.7306754109031979), 'truncnorm') ... ok
test_continuous_basic.test_cont_basic('truncnorm', (-1.0978730080013919, 2.7306754109031979), 0.01, array([ -9.039e-01, -4.955e-01, -6.034e-01, 1.582e+00, -3.846e-01, 4.763e-01, ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.tukeylambda_gen object at 0x36eda90>, (3.1321477856738267,), array(0.0), array(0.30476472279111871), 0.0090800061342219563, 0.026536393118696645, 1000, 'tukeylambdasample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.tukeylambda_gen object at 0x36eda90>, (3.1321477856738267,), array(0.0), array(0.30476472279111871), 'tukeylambda') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.tukeylambda_gen object at 0x36eda90>, (3.1321477856738267,), 'tukeylambda') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.tukeylambda_gen object at 0x36eda90>, (3.1321477856738267,), 'tukeylambda') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.tukeylambda_gen object at 0x36eda90>, (3.1321477856738267,), 'tukeylambda') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.tukeylambda_gen object at 0x36eda90>, (3.1321477856738267,), 'tukeylambda') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.tukeylambda_gen object at 0x36eda90>, (3.1321477856738267,), 'tukeylambda') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.tukeylambda_gen object at 0x36eda90>, (3.1321477856738267,), 'tukeylambda') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.uniform_gen object at 0x36edb30>, (), array(0.5), array(0.083333333333333329), 0.51516123993082641, 0.083002600178291724, 1000, 'uniformsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.uniform_gen object at 0x36edb30>, (), array(0.5), array(0.083333333333333329), 'uniform') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.uniform_gen object at 0x36edb30>, (), 'uniform') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.uniform_gen object at 0x36edb30>, (), 'uniform') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.uniform_gen object at 0x36edb30>, (), 'uniform') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.uniform_gen object at 0x36edb30>, (), 'uniform') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.uniform_gen object at 0x36edb30>, (), 'uniform') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.uniform_gen object at 0x36edb30>, (), 'uniform') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.wald_gen object at 0x36edcd0>, (), array(1.0), array(1.0), 1.0190852081914157, 1.0256627129688456, 1000, 'waldsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.wald_gen object at 0x36edcd0>, (), array(1.0), array(1.0), 'wald') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.wald_gen object at 0x36edcd0>, (), 'wald') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.wald_gen object at 0x36edcd0>, (), 'wald') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.wald_gen object at 0x36edcd0>, (), 'wald') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_l_gen object at 0x36dc330>, (2.8687961709100187,), array(-0.89129676887660936), array(0.11369305518071726), -0.87022627496962057, 0.10761318131749308, 1000, 'weibull_maxsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_l_gen object at 0x36dc330>, (2.8687961709100187,), array(-0.89129676887660936), array(0.11369305518071726), 'weibull_max') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_l_gen object at 0x36dc330>, (2.8687961709100187,), 'weibull_max') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_l_gen object at 0x36dc330>, (2.8687961709100187,), 'weibull_max') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_l_gen object at 0x36dc330>, (2.8687961709100187,), 'weibull_max') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_l_gen object at 0x36dc330>, (2.8687961709100187,), 'weibull_max') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_l_gen object at 0x36dc330>, (2.8687961709100187,), 'weibull_max') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_l_gen object at 0x36dc330>, (2.8687961709100187,), 'weibull_max') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_r_gen object at 0x36dc1d0>, (1.7866166930421596,), array(0.88961629797475072), array(0.26510662289002929), 0.9178211327965029, 0.27042300220730853, 1000, 'weibull_minsample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_r_gen object at 0x36dc1d0>, (1.7866166930421596,), array(0.88961629797475072), array(0.26510662289002929), 'weibull_min') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_r_gen object at 0x36dc1d0>, (1.7866166930421596,), 'weibull_min') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_r_gen object at 0x36dc1d0>, (1.7866166930421596,), 'weibull_min') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_r_gen object at 0x36dc1d0>, (1.7866166930421596,), 'weibull_min') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_r_gen object at 0x36dc1d0>, (1.7866166930421596,), 'weibull_min') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_r_gen object at 0x36dc1d0>, (1.7866166930421596,), 'weibull_min') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.frechet_r_gen object at 0x36dc1d0>, (1.7866166930421596,), 'weibull_min') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.wrapcauchy_gen object at 0x36edd90>, (0.031071279018614728,), array(3.1415926535897931), array(3.4151322438845), 3.2377370605003604, 3.4056398320138892, 1000, 'wrapcauchysample mean test') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.wrapcauchy_gen object at 0x36edd90>, (0.031071279018614728,), array(3.1415926535897931), array(3.4151322438845), 'wrapcauchy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.wrapcauchy_gen object at 0x36edd90>, (0.031071279018614728,), 'wrapcauchy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.wrapcauchy_gen object at 0x36edd90>, (0.031071279018614728,), 'wrapcauchy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.wrapcauchy_gen object at 0x36edd90>, (0.031071279018614728,), 'wrapcauchy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.wrapcauchy_gen object at 0x36edd90>, (0.031071279018614728,), 'wrapcauchy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.wrapcauchy_gen object at 0x36edd90>, (0.031071279018614728,), 'wrapcauchy') ... ok
test_continuous_basic.test_cont_basic(<scipy.stats.distributions.wrapcauchy_gen object at 0x36edd90>, (0.031071279018614728,), 'wrapcauchy') ... ok
test_continuous_basic.test_cont_basic('wrapcauchy', (0.031071279018614728,), 0.01, array([ 0.322, 1.211, 0.949, 5.915, 1.501, 4.04 , 5.113, 4.253, 5.215, 1.139, ... ok
test_continuous_extra.test_540_567 ... ok
test_discrete_basic.test_discrete_basic(0.29999999999999999, array(0.29999999999999999), 'bernoulli sample mean test') ... ok
test_discrete_basic.test_discrete_basic(0.20999999999999627, array(0.20999999999999999), 'bernoulli sample var test') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.bernoulli_gen object at 0x36edf30>, (0.29999999999999999,), 'bernoulli cdf_ppf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.bernoulli_gen object at 0x36edf30>, (0.29999999999999999,), array([0, 1]), 'bernoulli cdf_ppf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.bernoulli_gen object at 0x36edf30>, (0.29999999999999999,), 'bernoulli pmf_cdf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.bernoulli_gen object at 0x36edf30>, (0.29999999999999999,), 'bernoulli oth') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.bernoulli_gen object at 0x36edf30>, (0.29999999999999999,), -1.2380952380951449, 0.87287156094400487, 'bernoulli skew_kurt') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.bernoulli_gen object at 0x36edf30>, (0.29999999999999999,), array([0, 0, 0, ..., 1, 0, 0]), 0.01, 'bernoulli chisquare') ... ok
test_discrete_basic.test_discrete_basic(2.0015000000000001, array(2.0), 'binom sample mean test') ... ok
test_discrete_basic.test_discrete_basic(1.1854977500000026, array(1.2), 'binom sample var test') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.binom_gen object at 0x36ede70>, (5, 0.40000000000000002), 'binom cdf_ppf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.binom_gen object at 0x36ede70>, (5, 0.40000000000000002), array([0, 1, 2, 3, 4, 5]), 'binom cdf_ppf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.binom_gen object at 0x36ede70>, (5, 0.40000000000000002), 'binom pmf_cdf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.binom_gen object at 0x36ede70>, (5, 0.40000000000000002), 'binom oth') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.binom_gen object at 0x36ede70>, (5, 0.40000000000000002), -0.26248929225026352, 0.28057933666556623, 'binom skew_kurt') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.binom_gen object at 0x36ede70>, (5, 0.40000000000000002), array([2, 2, 2, ..., 4, 1, 3]), 0.01, 'binom chisquare') ... ok
test_discrete_basic.test_discrete_basic(0.32900000000000001, array(0.32731081784804011), 'boltzmann sample mean test') ... ok
test_discrete_basic.test_discrete_basic(0.43975900000001117, array(0.4344431884043245), 'boltzmann sample var test') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.boltzmann_gen object at 0x36f74f0>, (1.3999999999999999, 19), 'boltzmann cdf_ppf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.boltzmann_gen object at 0x36f74f0>, (1.3999999999999999, 19), array([0, 1, 2, 3, 4]), 'boltzmann cdf_ppf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.boltzmann_gen object at 0x36f74f0>, (1.3999999999999999, 19), 'boltzmann pmf_cdf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.boltzmann_gen object at 0x36f74f0>, (1.3999999999999999, 19), 'boltzmann oth') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.boltzmann_gen object at 0x36f74f0>, (1.3999999999999999, 19), 6.7133652484343216, 2.418691392797208, 'boltzmann skew_kurt') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.boltzmann_gen object at 0x36f74f0>, (1.3999999999999999, 19), array([0, 0, 0, ..., 2, 0, 0]), 0.01, 'boltzmann chisquare') ... ok
test_discrete_basic.test_discrete_basic(0.0070000000000000001, array(7.9181711188056743e-17), 'dlaplace sample mean test') ... ok
test_discrete_basic.test_discrete_basic(2.9319510000000588, array(2.9635341891843714), 'dlaplace sample var test') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.dlaplace_gen object at 0x36f7750>, (0.80000000000000004,), 'dlaplace cdf_ppf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.dlaplace_gen object at 0x36f7750>, (0.80000000000000004,), array([-8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7]), 'dlaplace cdf_ppf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.dlaplace_gen object at 0x36f7750>, (0.80000000000000004,), 'dlaplace pmf_cdf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.dlaplace_gen object at 0x36f7750>, (0.80000000000000004,), 'dlaplace oth') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.dlaplace_gen object at 0x36f7750>, (0.80000000000000004,), 3.0660776822072453, 0.021996158609059947, 'dlaplace skew_kurt') ... ok
test_discrete_basic.test_discrete_basic(1.9870000000000001, array(2.0), 'geom sample mean test') ... ok
test_discrete_basic.test_discrete_basic(2.0098310000000303, array(2.0), 'geom sample var test') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.geom_gen object at 0x36f7130>, (0.5,), 'geom cdf_ppf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.geom_gen object at 0x36f7130>, (0.5,), array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]), 'geom cdf_ppf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.geom_gen object at 0x36f7130>, (0.5,), 'geom pmf_cdf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.geom_gen object at 0x36f7130>, (0.5,), 'geom oth') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.geom_gen object at 0x36f7130>, (0.5,), 5.1935883716655766, 2.0476504362662378, 'geom skew_kurt') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.geom_gen object at 0x36f7130>, (0.5,), array([1, 1, 2, ..., 6, 1, 2]), 0.01, 'geom chisquare') ... ok
test_discrete_basic.test_discrete_basic(2.3860000000000001, array(2.4000000000000004), 'hypergeom sample mean test') ... ok
test_discrete_basic.test_discrete_basic(1.1500039999999776, array(1.1917241379310344), 'hypergeom sample var test') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.hypergeom_gen object at 0x36f7190>, (30, 12, 6), 'hypergeom cdf_ppf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.hypergeom_gen object at 0x36f7190>, (30, 12, 6), array([0, 1, 2, 3, 4, 5, 6]), 'hypergeom cdf_ppf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.hypergeom_gen object at 0x36f7190>, (30, 12, 6), 'hypergeom pmf_cdf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.hypergeom_gen object at 0x36f7190>, (30, 12, 6), 'hypergeom oth') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.hypergeom_gen object at 0x36f7190>, (30, 12, 6), -0.29686916362552029, 0.020906577365969316, 'hypergeom skew_kurt') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.hypergeom_gen object at 0x36f7190>, (30, 12, 6), array([1, 1, 4, ..., 3, 2, 2]), 0.01, 'hypergeom chisquare') ... ok
test_discrete_basic.test_discrete_basic(1.724, array(1.7142857142857142), 'hypergeom sample mean test') ... ok
test_discrete_basic.test_discrete_basic(0.65282400000000207, array(0.66122448979591841), 'hypergeom sample var test') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.hypergeom_gen object at 0x36f7190>, (21, 3, 12), 'hypergeom cdf_ppf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.hypergeom_gen object at 0x36f7190>, (21, 3, 12), array([0, 1, 2, 3]), 'hypergeom cdf_ppf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.hypergeom_gen object at 0x36f7190>, (21, 3, 12), 'hypergeom pmf_cdf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.hypergeom_gen object at 0x36f7190>, (21, 3, 12), 'hypergeom oth') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.hypergeom_gen object at 0x36f7190>, (21, 3, 12), -0.46243472564588117, -0.18093529905213196, 'hypergeom skew_kurt') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.hypergeom_gen object at 0x36f7190>, (21, 3, 12), array([2, 3, 2, ..., 2, 2, 1]), 0.01, 'hypergeom chisquare') ... ok
test_discrete_basic.test_discrete_basic(9.4184999999999999, array(9.4285714285714288), 'hypergeom sample mean test') ... ok
test_discrete_basic.test_discrete_basic(0.68435774999998678, array(0.67346938775510201), 'hypergeom sample var test') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.hypergeom_gen object at 0x36f7190>, (21, 18, 11), 'hypergeom cdf_ppf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.hypergeom_gen object at 0x36f7190>, (21, 18, 11), array([ 8, 9, 10, 11]), 'hypergeom cdf_ppf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.hypergeom_gen object at 0x36f7190>, (21, 18, 11), 'hypergeom pmf_cdf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.hypergeom_gen object at 0x36f7190>, (21, 18, 11), 'hypergeom oth') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.hypergeom_gen object at 0x36f7190>, (21, 18, 11), -0.53396352457617935, 0.093601755841816861, 'hypergeom skew_kurt') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.hypergeom_gen object at 0x36f7190>, (21, 18, 11), array([ 9, 8, 9, ..., 10, 10, 10]), 0.01, 'hypergeom chisquare') ... ok
test_discrete_basic.test_discrete_basic(1.635, array(1.637035001905937), 'logser sample mean test') ... ok
test_discrete_basic.test_discrete_basic(1.325775000000023, array(1.4127039072996714), 'logser sample var test') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.logser_gen object at 0x36f7270>, (0.59999999999999998,), 'logser cdf_ppf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.logser_gen object at 0x36f7270>, (0.59999999999999998,), array([1, 2, 3, 4, 5, 6, 7, 8, 9]), 'logser cdf_ppf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.logser_gen object at 0x36f7270>, (0.59999999999999998,), 'logser pmf_cdf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.logser_gen object at 0x36f7270>, (0.59999999999999998,), 'logser oth') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.logser_gen object at 0x36f7270>, (0.59999999999999998,), 7.559198377977479, 2.4947797038220592, 'logser skew_kurt') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.logser_gen object at 0x36f7270>, (0.59999999999999998,), array([1, 1, 1, ..., 1, 1, 4]), 0.01, 'logser chisquare') ... ok
test_discrete_basic.test_discrete_basic(4.9210000000000003, array(5.0), 'nbinom sample mean test') ... ok
test_discrete_basic.test_discrete_basic(9.4787590000000037, array(10.0), 'nbinom sample var test') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.nbinom_gen object at 0x36f7050>, (5, 0.5), 'nbinom cdf_ppf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.nbinom_gen object at 0x36f7050>, (5, 0.5), array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21]), 'nbinom cdf_ppf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.nbinom_gen object at 0x36f7050>, (5, 0.5), 'nbinom pmf_cdf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.nbinom_gen object at 0x36f7050>, (5, 0.5), 'nbinom oth') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.nbinom_gen object at 0x36f7050>, (5, 0.5), 1.5000586959708402, 0.97358518373019021, 'nbinom skew_kurt') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.nbinom_gen object at 0x36f7050>, (5, 0.5), array([0, 2, 6, ..., 3, 3, 3]), 0.01, 'nbinom chisquare') ... ok
test_discrete_basic.test_discrete_basic(0.58399999999999996, array(0.60000000000000009), 'nbinom sample mean test') ... ok
test_discrete_basic.test_discrete_basic(1.4729440000000598, array(1.5000000000000002), 'nbinom sample var test') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.nbinom_gen object at 0x36f7050>, (0.40000000000000002, 0.40000000000000002), 'nbinom cdf_ppf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.nbinom_gen object at 0x36f7050>, (0.40000000000000002, 0.40000000000000002), array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12]), 'nbinom cdf_ppf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.nbinom_gen object at 0x36f7050>, (0.40000000000000002, 0.40000000000000002), 'nbinom pmf_cdf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.nbinom_gen object at 0x36f7050>, (0.40000000000000002, 0.40000000000000002), 'nbinom oth') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.nbinom_gen object at 0x36f7050>, (0.40000000000000002, 0.40000000000000002), 13.929082276070467, 3.2071528858780165, 'nbinom skew_kurt') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.nbinom_gen object at 0x36f7050>, (0.40000000000000002, 0.40000000000000002), array([0, 0, 0, ..., 0, 0, 0]), 0.01, 'nbinom chisquare') ... ok
test_discrete_basic.test_discrete_basic(1.496, array(1.5031012098113492), 'planck sample mean test') ... ok
test_discrete_basic.test_discrete_basic(3.8119840000000167, array(3.7624144567476914), 'planck sample var test') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.planck_gen object at 0x36f7430>, (0.51000000000000001,), 'planck cdf_ppf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.planck_gen object at 0x36f7430>, (0.51000000000000001,), array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]), 'planck cdf_ppf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.planck_gen object at 0x36f7430>, (0.51000000000000001,), 'planck pmf_cdf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.planck_gen object at 0x36f7430>, (0.51000000000000001,), 'planck oth') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.planck_gen object at 0x36f7430>, (0.51000000000000001,), 5.0921201134828475, 1.9924056300476671, 'planck skew_kurt') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.planck_gen object at 0x36f7430>, (0.51000000000000001,), array([1, 1, 1, ..., 7, 0, 2]), 0.01, 'planck chisquare') ... ok
test_discrete_basic.test_discrete_basic(0.58550000000000002, array(0.59999999999999998), 'poisson sample mean test') ... ok
test_discrete_basic.test_discrete_basic(0.59768974999998681, array(0.59999999999999998), 'poisson sample var test') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.poisson_gen object at 0x36f7370>, (0.59999999999999998,), 'poisson cdf_ppf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.poisson_gen object at 0x36f7370>, (0.59999999999999998,), array([0, 1, 2, 3, 4, 5]), 'poisson cdf_ppf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.poisson_gen object at 0x36f7370>, (0.59999999999999998,), 'poisson pmf_cdf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.poisson_gen object at 0x36f7370>, (0.59999999999999998,), 'poisson oth') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.poisson_gen object at 0x36f7370>, (0.59999999999999998,), 1.9406814436782422, 1.3589585241917534, 'poisson skew_kurt') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.poisson_gen object at 0x36f7370>, (0.59999999999999998,), array([0, 0, 0, ..., 1, 0, 0]), 0.01, 'poisson chisquare') ... ok
test_discrete_basic.test_discrete_basic(18.4725, array(18.5), 'randint sample mean test') ... ok
test_discrete_basic.test_discrete_basic(48.800243749999929, array(47.916666666666664), 'randint sample var test') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.randint_gen object at 0x36f75b0>, (7, 31), 'randint cdf_ppf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.randint_gen object at 0x36f75b0>, (7, 31), array([ 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.randint_gen object at 0x36f75b0>, (7, 31), 'randint pmf_cdf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.randint_gen object at 0x36f75b0>, (7, 31), 'randint oth') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.randint_gen object at 0x36f75b0>, (7, 31), -1.2115060412211844, -0.025412774105826177, 'randint skew_kurt') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.randint_gen object at 0x36f75b0>, (7, 31), array([27, 10, 15, ..., 16, 9, 17]), 0.01, 'randint chisquare') ... ok
test_discrete_basic.test_discrete_basic(7.0019999999999998, array(7.0), 'skellam sample mean test') ... ok
test_discrete_basic.test_discrete_basic(22.550995999999991, array(23.0), 'skellam sample var test') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.skellam_gen object at 0x36f77f0>, (15, 8), 'skellam cdf_ppf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.skellam_gen object at 0x36f77f0>, (15, 8), array([-10, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.skellam_gen object at 0x36f77f0>, (15, 8), 'skellam pmf_cdf') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.skellam_gen object at 0x36f77f0>, (15, 8), 'skellam oth') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.skellam_gen object at 0x36f77f0>, (15, 8), 0.11554402415317133, 0.10806520422790773, 'skellam skew_kurt') ... ok
test_discrete_basic.test_discrete_basic(<scipy.stats.distributions.skellam_gen object at 0x36f77f0>, (15, 8), array([ 4, 6, 10, ..., 5, 14, 15]), 0.01, 'skellam chisquare') ... ok
Failure: SkipTest (Skipping test: test_discrete_privateTest skipped due to test condition) ... SKIP: Skipping test: test_discrete_privateTest skipped due to test condition
test_noexception (test_distributions.TestArrayArgument) ... ok
test_rvs (test_distributions.TestBernoulli) ... ok
test_rvs (test_distributions.TestBinom) ... ok
test_precision (test_distributions.TestChi2) ... ok
test_rvs (test_distributions.TestDLaplace) ... ok
See ticket #761 ... ok
See ticket #497 ... ok
test_beta (test_distributions.TestExpect) ... ok
test_hypergeom (test_distributions.TestExpect) ... ok
test_norm (test_distributions.TestExpect) ... ok
test_poisson (test_distributions.TestExpect) ... ok
test_tail (test_distributions.TestExpon) ... ok
test_zero (test_distributions.TestExpon) ... ok
test_tail (test_distributions.TestExponpow) ... ok
test_gamma (test_distributions.TestFrozen) ... ok
test_norm (test_distributions.TestFrozen) ... ok
Regression test for ticket #1293. ... ok
test_cdf_bounds (test_distributions.TestGenExpon) ... ok
test_pdf_unity_area (test_distributions.TestGenExpon) ... ok
test_cdf_sf (test_distributions.TestGeom) ... ok
test_pmf (test_distributions.TestGeom) ... ok
test_rvs (test_distributions.TestGeom) ... ok
test_precision (test_distributions.TestHypergeom) ... ok
test_rvs (test_distributions.TestLogser) ... ok
test_rvs (test_distributions.TestNBinom) ... ok
test_rvs (test_distributions.TestPoisson) ... ok
test_cdf (test_distributions.TestRandInt) ... ok
test_pdf (test_distributions.TestRandInt) ... ok
test_rvs (test_distributions.TestRandInt) ... ok
test_rvs (test_distributions.TestRvDiscrete) ... ok
test_cdf (test_distributions.TestSkellam) ... ok
test_pmf (test_distributions.TestSkellam) ... ok
test_rvs (test_distributions.TestZipf) ... ok
test_distributions.test_all_distributions('uniform', (), 0.01) ... ok
test_distributions.test_all_distributions('norm', (), 0.01) ... ok
test_distributions.test_all_distributions('lognorm', (1.5876170641754364,), 0.01) ... ok
test_distributions.test_all_distributions('expon', (), 0.01) ... ok
test_distributions.test_all_distributions('beta', (1.4449890262755161, 1.5962868615831063), 0.01) ... ok
test_distributions.test_all_distributions('powerlaw', (1.3849011459726603,), 0.01) ... ok
test_distributions.test_all_distributions('bradford', (1.5756510141648885,), 0.01) ... ok
test_distributions.test_all_distributions('burr', (1.2903295024027579, 1.1893913285543563), 0.01) ... ok
test_distributions.test_all_distributions('fisk', (1.186729528255555,), 0.01) ... ok
test_distributions.test_all_distributions('cauchy', (), 0.01) ... ok
test_distributions.test_all_distributions('halfcauchy', (), 0.01) ... ok
test_distributions.test_all_distributions('foldcauchy', (1.6127731798686067,), 0.01) ... ok
test_distributions.test_all_distributions('gamma', (1.6566593889896288,), 0.01) ... ok
test_distributions.test_all_distributions('gengamma', (1.4765309920093808, 1.0898243611955936), 0.01) ... ok
test_distributions.test_all_distributions('loggamma', (1.7576039219664368,), 0.01) ... ok
test_distributions.test_all_distributions('alpha', (1.8767703708227748,), 0.01) ... ok
test_distributions.test_all_distributions('anglit', (), 0.01) ... ok
test_distributions.test_all_distributions('arcsine', (), 0.01) ... ok
test_distributions.test_all_distributions('betaprime', (1.9233810159462807, 1.8424602231401823), 0.01) ... ok
test_distributions.test_all_distributions('erlang', (4, 0.89817312135787897, 0.92308243982017679), 0.01) ... ok
test_distributions.test_all_distributions('dgamma', (1.5405999249480544,), 0.01) ... ok
test_distributions.test_all_distributions('exponweib', (1.391296050234625, 1.7052833998544061), 0.01) ... ok
test_distributions.test_all_distributions('exponpow', (1.2756341213121272,), 0.01) ... ok
test_distributions.test_all_distributions('frechet_l', (1.8116287085078784,), 0.01) ... ok
test_distributions.test_all_distributions('frechet_r', (1.8494859651863671,), 0.01) ... ok
test_distributions.test_all_distributions('gilbrat', (), 0.01) ... ok
test_distributions.test_all_distributions('f', (1.8950389674266752, 1.5898011835311598), 0.01) ... ok
test_distributions.test_all_distributions('ncf', (1.9497648732321204, 1.5796950107456058, 1.4505631066311553), 0.01) ... ok
test_distributions.test_all_distributions('chi2', (1.660245378622389,), 0.01) ... ok
test_distributions.test_all_distributions('chi', (1.9962578393535728,), 0.01) ... ok
test_distributions.test_all_distributions('nakagami', (1.9169412179474561,), 0.01) ... ok
test_distributions.test_all_distributions('genpareto', (1.7933250841302242,), 0.01) ... ok
test_distributions.test_all_distributions('genextreme', (1.0823729881966475,), 0.01) ... ok
test_distributions.test_all_distributions('genhalflogistic', (1.6127831050407122,), 0.01) ... ok
test_distributions.test_all_distributions('pareto', (1.4864442019691668,), 0.01) ... ok
test_distributions.test_all_distributions('lomax', (1.6301473404114728,), 0.01) ... ok
test_distributions.test_all_distributions('halfnorm', (), 0.01) ... ok
test_distributions.test_all_distributions('halflogistic', (), 0.01) ... ok
test_distributions.test_all_distributions('fatiguelife', (1.8450775756715152,), 0.001) ... ok
test_distributions.test_all_distributions('foldnorm', (1.2430356220618561,), 0.01) ... ok
test_distributions.test_all_distributions('ncx2', (1.7314892207908477, 1.117134293208518), 0.01) ... ok
test_distributions.test_all_distributions('t', (1.2204605368678285,), 0.01) ... ok
test_distributions.test_all_distributions('nct', (1.7945829717105759, 1.3325361492196555), 0.01) ... ok
test_distributions.test_all_distributions('weibull_min', (1.8159130965336594,), 0.01) ... ok
test_distributions.test_all_distributions('weibull_max', (1.1006075202160961,), 0.01) ... ok
test_distributions.test_all_distributions('dweibull', (1.1463584889123037,), 0.01) ... ok
test_distributions.test_all_distributions('maxwell', (), 0.01) ... ok
test_distributions.test_all_distributions('rayleigh', (), 0.01) ... ok
test_distributions.test_all_distributions('genlogistic', (1.6976706401912387,), 0.01) ... ok
test_distributions.test_all_distributions('logistic', (), 0.01) ... ok
test_distributions.test_all_distributions('gumbel_l', (), 0.01) ... ok
test_distributions.test_all_distributions('gumbel_r', (), 0.01) ... ok
test_distributions.test_all_distributions('gompertz', (1.0452340678656125,), 0.01) ... ok
test_distributions.test_all_distributions('hypsecant', (), 0.01) ... ok
test_distributions.test_all_distributions('laplace', (), 0.01) ... ok
test_distributions.test_all_distributions('reciprocal', (0.57386603678916692, 1.573866036789167), 0.01) ... ok
test_distributions.test_all_distributions('triang', (0.53419796826072397,), 0.01) ... ok
test_distributions.test_all_distributions('tukeylambda', (1.6805891325622566,), 0.01) ... ok
test_distributions.test_all_distributions('vonmises', (10,), 0.01) ... ok
test_distributions.test_all_distributions('vonmises', (101,), 0.01) ... ok
test_distributions.test_all_distributions('vonmises', (1.0266967946622052,), 0.01) ... ok
test_distributions.test_vonmises_pdf_periodic(0.10000000000000001, 0, 1, 0) ... ok
test_distributions.test_vonmises_pdf_periodic(0.10000000000000001, 1, 1, 0) ... ok
test_distributions.test_vonmises_pdf_periodic(0.10000000000000001, 0, 10, 0) ... ok
test_distributions.test_vonmises_pdf_periodic(0.10000000000000001, 0, 1, 0) ... ok
test_distributions.test_vonmises_pdf_periodic(0.10000000000000001, 1, 1, 0) ... ok
test_distributions.test_vonmises_pdf_periodic(0.10000000000000001, 0, 10, 0) ... ok
test_distributions.test_vonmises_pdf_periodic(0.10000000000000001, 0, 1, 1) ... ok
test_distributions.test_vonmises_pdf_periodic(0.10000000000000001, 1, 1, 1) ... ok
test_distributions.test_vonmises_pdf_periodic(0.10000000000000001, 0, 10, 1) ... ok
test_distributions.test_vonmises_pdf_periodic(0.10000000000000001, 0, 1, 1) ... ok
test_distributions.test_vonmises_pdf_periodic(0.10000000000000001, 1, 1, 1) ... ok
test_distributions.test_vonmises_pdf_periodic(0.10000000000000001, 0, 10, 1) ... ok
test_distributions.test_vonmises_pdf_periodic(0.10000000000000001, 0, 1, 3.1415926535897931) ... ok
test_distributions.test_vonmises_pdf_periodic(0.10000000000000001, 1, 1, 3.1415926535897931) ... ok
test_distributions.test_vonmises_pdf_periodic(0.10000000000000001, 0, 10, 3.1415926535897931) ... ok
test_distributions.test_vonmises_pdf_periodic(0.10000000000000001, 0, 1, 3.1415926535897931) ... ok
test_distributions.test_vonmises_pdf_periodic(0.10000000000000001, 1, 1, 3.1415926535897931) ... ok
test_distributions.test_vonmises_pdf_periodic(0.10000000000000001, 0, 10, 3.1415926535897931) ... ok
test_distributions.test_vonmises_pdf_periodic(0.10000000000000001, 0, 1, 10) ... ok
test_distributions.test_vonmises_pdf_periodic(0.10000000000000001, 1, 1, 10) ... ok
test_distributions.test_vonmises_pdf_periodic(0.10000000000000001, 0, 10, 10) ... ok
test_distributions.test_vonmises_pdf_periodic(0.10000000000000001, 0, 1, 10) ... ok
test_distributions.test_vonmises_pdf_periodic(0.10000000000000001, 1, 1, 10) ... ok
test_distributions.test_vonmises_pdf_periodic(0.10000000000000001, 0, 10, 10) ... ok
test_distributions.test_vonmises_pdf_periodic(0.10000000000000001, 0, 1, 100) ... ok
test_distributions.test_vonmises_pdf_periodic(0.10000000000000001, 1, 1, 100) ... ok
test_distributions.test_vonmises_pdf_periodic(0.10000000000000001, 0, 10, 100) ... ok
test_distributions.test_vonmises_pdf_periodic(0.10000000000000001, 0, 1, 100) ... ok
test_distributions.test_vonmises_pdf_periodic(0.10000000000000001, 1, 1, 100) ... ok
test_distributions.test_vonmises_pdf_periodic(0.10000000000000001, 0, 10, 100) ... ok
test_distributions.test_vonmises_pdf_periodic(1, 0, 1, 0) ... ok
test_distributions.test_vonmises_pdf_periodic(1, 1, 1, 0) ... ok
test_distributions.test_vonmises_pdf_periodic(1, 0, 10, 0) ... ok
test_distributions.test_vonmises_pdf_periodic(1, 0, 1, 0) ... ok
test_distributions.test_vonmises_pdf_periodic(1, 1, 1, 0) ... ok
test_distributions.test_vonmises_pdf_periodic(1, 0, 10, 0) ... ok
test_distributions.test_vonmises_pdf_periodic(1, 0, 1, 1) ... ok
test_distributions.test_vonmises_pdf_periodic(1, 1, 1, 1) ... ok
test_distributions.test_vonmises_pdf_periodic(1, 0, 10, 1) ... ok
test_distributions.test_vonmises_pdf_periodic(1, 0, 1, 1) ... ok
test_distributions.test_vonmises_pdf_periodic(1, 1, 1, 1) ... ok
test_distributions.test_vonmises_pdf_periodic(1, 0, 10, 1) ... ok
test_distributions.test_vonmises_pdf_periodic(1, 0, 1, 3.1415926535897931) ... ok
test_distributions.test_vonmises_pdf_periodic(1, 1, 1, 3.1415926535897931) ... ok
test_distributions.test_vonmises_pdf_periodic(1, 0, 10, 3.1415926535897931) ... ok
test_distributions.test_vonmises_pdf_periodic(1, 0, 1, 3.1415926535897931) ... ok
test_distributions.test_vonmises_pdf_periodic(1, 1, 1, 3.1415926535897931) ... ok
test_distributions.test_vonmises_pdf_periodic(1, 0, 10, 3.1415926535897931) ... ok
test_distributions.test_vonmises_pdf_periodic(1, 0, 1, 10) ... ok
test_distributions.test_vonmises_pdf_periodic(1, 1, 1, 10) ... ok
test_distributions.test_vonmises_pdf_periodic(1, 0, 10, 10) ... ok
test_distributions.test_vonmises_pdf_periodic(1, 0, 1, 10) ... ok
test_distributions.test_vonmises_pdf_periodic(1, 1, 1, 10) ... ok
test_distributions.test_vonmises_pdf_periodic(1, 0, 10, 10) ... ok
test_distributions.test_vonmises_pdf_periodic(1, 0, 1, 100) ... ok
test_distributions.test_vonmises_pdf_periodic(1, 1, 1, 100) ... ok
test_distributions.test_vonmises_pdf_periodic(1, 0, 10, 100) ... ok
test_distributions.test_vonmises_pdf_periodic(1, 0, 1, 100) ... ok
test_distributions.test_vonmises_pdf_periodic(1, 1, 1, 100) ... ok
test_distributions.test_vonmises_pdf_periodic(1, 0, 10, 100) ... ok
test_distributions.test_vonmises_pdf_periodic(101, 0, 1, 0) ... ok
test_distributions.test_vonmises_pdf_periodic(101, 1, 1, 0) ... ok
test_distributions.test_vonmises_pdf_periodic(101, 0, 10, 0) ... ok
test_distributions.test_vonmises_pdf_periodic(101, 0, 1, 0) ... ok
test_distributions.test_vonmises_pdf_periodic(101, 1, 1, 0) ... ok
test_distributions.test_vonmises_pdf_periodic(101, 0, 10, 0) ... ok
test_distributions.test_vonmises_pdf_periodic(101, 0, 1, 1) ... ok
test_distributions.test_vonmises_pdf_periodic(101, 1, 1, 1) ... ok
test_distributions.test_vonmises_pdf_periodic(101, 0, 10, 1) ... ok
test_distributions.test_vonmises_pdf_periodic(101, 0, 1, 1) ... ok
test_distributions.test_vonmises_pdf_periodic(101, 1, 1, 1) ... ok
test_distributions.test_vonmises_pdf_periodic(101, 0, 10, 1) ... ok
test_distributions.test_vonmises_pdf_periodic(101, 0, 1, 3.1415926535897931) ... ok
test_distributions.test_vonmises_pdf_periodic(101, 1, 1, 3.1415926535897931) ... ok
test_distributions.test_vonmises_pdf_periodic(101, 0, 10, 3.1415926535897931) ... ok
test_distributions.test_vonmises_pdf_periodic(101, 0, 1, 3.1415926535897931) ... ok
test_distributions.test_vonmises_pdf_periodic(101, 1, 1, 3.1415926535897931) ... ok
test_distributions.test_vonmises_pdf_periodic(101, 0, 10, 3.1415926535897931) ... ok
test_distributions.test_vonmises_pdf_periodic(101, 0, 1, 10) ... ok
test_distributions.test_vonmises_pdf_periodic(101, 1, 1, 10) ... ok
test_distributions.test_vonmises_pdf_periodic(101, 0, 10, 10) ... ok
test_distributions.test_vonmises_pdf_periodic(101, 0, 1, 10) ... ok
test_distributions.test_vonmises_pdf_periodic(101, 1, 1, 10) ... ok
test_distributions.test_vonmises_pdf_periodic(101, 0, 10, 10) ... ok
test_distributions.test_vonmises_pdf_periodic(101, 0, 1, 100) ... ok
test_distributions.test_vonmises_pdf_periodic(101, 1, 1, 100) ... ok
test_distributions.test_vonmises_pdf_periodic(101, 0, 10, 100) ... ok
test_distributions.test_vonmises_pdf_periodic(101, 0, 1, 100) ... ok
test_distributions.test_vonmises_pdf_periodic(101, 1, 1, 100) ... ok
test_distributions.test_vonmises_pdf_periodic(101, 0, 10, 100) ... ok
Regression test for #1191 ... ok
test_distributions.TestArgsreduce ... ok
Regression test for ticket #1316. ... ok
Regression test for ticket #1326. ... ok
test_kdeoth.test_kde_1d ... ok
test_bad_arg (test_morestats.TestAnderson) ... ok
test_expon (test_morestats.TestAnderson) ... ok
test_normal (test_morestats.TestAnderson) ... ok
test_approx (test_morestats.TestAnsari) ... ok
test_bad_arg (test_morestats.TestAnsari) ... ok
test_exact (test_morestats.TestAnsari) ... ok
test_small (test_morestats.TestAnsari) ... ok
Too few args raises ValueError. ... ok
test_data (test_morestats.TestBartlett) ... ok
Length of x must be 1 or 2. ... ok
len(x) is 1, but n is invalid. ... ok
test_bad_p (test_morestats.TestBinomP) ... ok
test_data (test_morestats.TestBinomP) ... ok
test_basic (test_morestats.TestFindRepeats) ... ok
test_bad_center_value (test_morestats.TestFligner) ... ok
test_bad_keyword (test_morestats.TestFligner) ... ok
Too few args raises ValueError. ... ok
test_data (test_morestats.TestFligner) ... ok
Test that center='trimmed' gives the same result as center='mean' when proportiontocut=0. ... ok
test_trimmed2 (test_morestats.TestFligner) ... ok
test_bad_center_value (test_morestats.TestLevene) ... ok
test_bad_keyword (test_morestats.TestLevene) ... ok
test_data (test_morestats.TestLevene) ... ok
test_equal_mean_median (test_morestats.TestLevene) ... ok
test_too_few_args (test_morestats.TestLevene) ... ok
Test that center='trimmed' gives the same result as center='mean' when proportiontocut=0. ... ok
test_trimmed2 (test_morestats.TestLevene) ... ok
test_bad_arg (test_morestats.TestShapiro) ... ok
test_basic (test_morestats.TestShapiro) ... ok
test_morestats.test_mood ... ok
Raise ValueError when the sum of the lengths of the args is less than 3. ... ok
Raise ValueError is fewer than two args are given. ... ok
Raise ValueError when two args of different lengths are given. ... ok
Raise ValueError if fewer than two data points are given. ... ok
Raise ValueError if n > 4 or n > 1. ... ok
Raise ValueError is n is not 1 or 2. ... ok
Raise ValueError when given an invalid distribution. ... ok
Raise ValueError when given an invalid distribution. ... ok
Raise ValueError if any data value is negative. ... ok
Tests some computations of Kendall's tau ... ok
Tests the seasonal Kendall tau. ... ok
Tests some computations of Pearson's r ... ok
Tests point biserial ... ok
Tests some computations of Spearman's rho ... ok
test_1D (test_mstats_basic.TestGMean) ... ok
test_2D (test_mstats_basic.TestGMean) ... ok
test_1D (test_mstats_basic.TestHMean) ... ok
test_2D (test_mstats_basic.TestHMean) ... ok
Tests the Friedman Chi-square test ... ok
Tests the Kolmogorov-Smirnov 2 samples test ... ok
Tests Obrien transform ... ok
sum((testcase-mean(testcase,axis=0))**4,axis=0)/((sqrt(var(testcase)*3/4))**4)/4 ... ok
Tests the mode ... ok
mean((testcase-mean(testcase))**power,axis=0),axis=0))**power)) ... ok
sum((testmathworks-mean(testmathworks,axis=0))**3,axis=0)/((sqrt(var(testmathworks)*4/5))**3)/5 ... ok
variation = samplestd/mean ... ok
Ticket #867 ... ok
test_2D (test_mstats_basic.TestPercentile) ... ok
test_percentile (test_mstats_basic.TestPercentile) ... ok
test_ranking (test_mstats_basic.TestRanking) ... ok
Tests trimming ... ok
Tests trimming. ... ok
Tests the trimmed mean standard error. ... ok
Tests the trimmed mean. ... ok
Tests the Winsorization of the data. ... ok
this is not in R, so used ... ok
this is not in R, so used ... ok
not in R, so tested by using ... ok
not in R, so tested by using ... ok
Regress a line with sinusoidal noise. Test for #1273. ... ok
Regression test for #1256 ... ok
Tests ideal-fourths ... ok
Tests the Marits-Jarrett estimator ... ok
Tests the confidence intervals of the trimmed mean. ... ok
test_hdquantiles (test_mstats_extras.TestQuantiles) ... ok
test_tmeanX (test_stats.TestBasicStats) ... ok
test_tstdX (test_stats.TestBasicStats) ... ok
test_tvarX (test_stats.TestBasicStats) ... ok
test_basic (test_stats.TestCMedian) ... ok
test_pBIGBIG (test_stats.TestCorrPearsonr) ... ok
test_pBIGHUGE (test_stats.TestCorrPearsonr) ... ok
test_pBIGLITTLE (test_stats.TestCorrPearsonr) ... ok
test_pBIGROUND (test_stats.TestCorrPearsonr) ... ok
test_pBIGTINY (test_stats.TestCorrPearsonr) ... ok
test_pHUGEHUGE (test_stats.TestCorrPearsonr) ... ok
test_pHUGEROUND (test_stats.TestCorrPearsonr) ... ok
test_pHUGETINY (test_stats.TestCorrPearsonr) ... ok
test_pLITTLEHUGE (test_stats.TestCorrPearsonr) ... ok
test_pLITTLELITTLE (test_stats.TestCorrPearsonr) ... ok
test_pLITTLEROUND (test_stats.TestCorrPearsonr) ... ok
test_pLITTLETINY (test_stats.TestCorrPearsonr) ... ok
test_pROUNDROUND (test_stats.TestCorrPearsonr) ... ok
test_pTINYROUND (test_stats.TestCorrPearsonr) ... ok
test_pTINYTINY (test_stats.TestCorrPearsonr) ... ok
test_pXBIG (test_stats.TestCorrPearsonr) ... ok
test_pXHUGE (test_stats.TestCorrPearsonr) ... ok
test_pXLITTLE (test_stats.TestCorrPearsonr) ... ok
test_pXROUND (test_stats.TestCorrPearsonr) ... ok
test_pXTINY (test_stats.TestCorrPearsonr) ... ok
test_pXX (test_stats.TestCorrPearsonr) ... ok
test_r_exactly_neg1 (test_stats.TestCorrPearsonr) ... ok
test_r_exactly_pos1 (test_stats.TestCorrPearsonr) ... ok
test_sBIGBIG (test_stats.TestCorrSpearmanr) ... ok
test_sBIGHUGE (test_stats.TestCorrSpearmanr) ... ok
test_sBIGLITTLE (test_stats.TestCorrSpearmanr) ... ok
test_sBIGROUND (test_stats.TestCorrSpearmanr) ... ok
test_sBIGTINY (test_stats.TestCorrSpearmanr) ... ok
test_sHUGEHUGE (test_stats.TestCorrSpearmanr) ... ok
test_sHUGEROUND (test_stats.TestCorrSpearmanr) ... ok
test_sHUGETINY (test_stats.TestCorrSpearmanr) ... ok
test_sLITTLEHUGE (test_stats.TestCorrSpearmanr) ... ok
test_sLITTLELITTLE (test_stats.TestCorrSpearmanr) ... ok
test_sLITTLEROUND (test_stats.TestCorrSpearmanr) ... ok
test_sLITTLETINY (test_stats.TestCorrSpearmanr) ... ok
test_sROUNDROUND (test_stats.TestCorrSpearmanr) ... ok
test_sTINYROUND (test_stats.TestCorrSpearmanr) ... ok
test_sTINYTINY (test_stats.TestCorrSpearmanr) ... ok
test_sXBIG (test_stats.TestCorrSpearmanr) ... ok
test_sXHUGE (test_stats.TestCorrSpearmanr) ... ok
test_sXLITTLE (test_stats.TestCorrSpearmanr) ... ok
test_sXROUND (test_stats.TestCorrSpearmanr) ... ok
test_sXTINY (test_stats.TestCorrSpearmanr) ... ok
test_sXX (test_stats.TestCorrSpearmanr) ... ok
test_tie1 (test_stats.TestCorrSpearmanrTies) ... ok
A test of stats.f_oneway, with F=2. ... ok
A trivial test of stats.f_oneway, with F=0. ... ok
test_1D_array (test_stats.TestGMean) ... ok
test_1D_list (test_stats.TestGMean) ... ok
test_2D_array_default (test_stats.TestGMean) ... ok
test_2D_array_dim1 (test_stats.TestGMean) ... ok
test_large_values (test_stats.TestGMean) ... ok
Test a 1d array ... ok
Test a 1d array with zero element ... ok
Test a 1d list ... ok
Test a 1d list with zero element ... ok
Test a 1d masked array ... ok
Test a 1d masked array with zero element ... ok
Test a 1d masked array with negative element ... ok
Test a 1d masked array with a masked value ... ok
Test a 2d array ... ok
Test a 2d list with axis=0 ... ok
Test a 2d list with axis=1 ... ok
Test a 2d list ... ok
Test a 2d masked array ... ok
Test a 2d list with axis=1 ... ok
Test a 2d list with axis=0 ... ok
test_1D_array (test_stats.TestHMean) ... ok
test_1D_list (test_stats.TestHMean) ... ok
test_2D_array_default (test_stats.TestHMean) ... ok
test_2D_array_dim1 (test_stats.TestHMean) ... ok
Test a 1d array ... ok
Test a 1d list ... ok
Test a 1d masked array ... ok
Test a 1d masked array with a masked value ... ok
Test a 2d array ... ok
Test a 2d list with axis=0 ... ok
Test a 2d list with axis=1 ... ok
Test a 2d list ... ok
Test a 2d masked array ... ok
Test a 2d list with axis=1 ... ok
Test a 2d list with axis=0 ... ok
Tests that increasing the number of bins produces expected results ... ok
Tests that reducing the number of bins produces expected results ... ok
Tests that each of the tests works as expected with default params ... ok
Tests that weights give expected histograms ... ok
test_basic (test_stats.TestMode) ... ok
sum((testcase-mean(testcase,axis=0))**4,axis=0)/((sqrt(var(testcase)*3/4))**4)/4 ... ok
test_kurtosis_array_scalar (test_stats.TestMoments) ... ok
mean((testcase-mean(testcase))**power,axis=0),axis=0))**power)) ... ok
sum((testmathworks-mean(testmathworks,axis=0))**3,axis=0)/ ... ok
`skew` must return a scalar for 1-dim input ... ok
variation = samplestd/mean ... ok
Check nanmean when all values are nan. ... ok
Check nanmean when no values are nan. ... ok
Check nanmean when some values only are nan. ... ok
Check nanmedian when all values are nan. ... ok
Check nanmedian when no values are nan. ... ok
Check nanmedian for scalar inputs. See ticket #1098. ... ok
Check nanmedian when some values only are nan. ... ok
Check nanstd when all values are nan. ... ok
test_nanstd_negative_axis (test_stats.TestNanFunc) ... ok
Check nanstd when no values are nan. ... ok
Check nanstd when some values only are nan. ... ok
test_2D (test_stats.TestPercentile) ... ok
test_percentile (test_stats.TestPercentile) ... ok
compared with multivariate ols with pinv ... ok
W.II.F. Regress BIG on X. ... ok
W.IV.B. Regress X on X. ... ok
W.IV.D. Regress ZERO on X. ... ok
Check that a single input argument to linregress with wrong shape ... ok
Regress a line with sinusoidal noise. ... ok
Regress a line with sinusoidal noise, with a single input of shape ... ok
Regress a line with sinusoidal noise, with a single input of shape ... ok
W.II.A.0. Print ROUND with only one digit. ... ok
W.II.A.1. Y = INT(2.6*7 -0.2) (Y should be 18) ... ok
W.II.A.2. Y = 2-INT(EXP(LOG(SQR(2)*SQR(2)))) (Y should be 0) ... ok
W.II.A.3. Y = INT(3-EXP(LOG(SQR(2)*SQR(2)))) (Y should be 1) ... ok
test_stats.TestSigamClip.test_sigmaclip1 ... ok
test_stats.TestSigamClip.test_sigmaclip2 ... ok
test_stats.TestSigamClip.test_sigmaclip3 ... ok
test_onesample (test_stats.TestStudentTest) ... ok
test_basic (test_stats.TestThreshold) ... ok
this is not in R, so used ... ok
this is not in R, so used ... ok
not in R, so tested by using ... ok
not in R, so tested by using ... ok
test_stats.Test_Trim.test_trim1 ... ok
test_stats.Test_Trim.test_trim_mean ... ok
test_stats.Test_Trim.test_trimboth ... ok
Some tests to show that fisher_exact() works correctly. ... ok
Some tests for kendalltau. ... ok
test_stats.test_cumfreq ... ok
test_stats.test_relfreq ... ok
test_stats.test_scoreatpercentile ... ok
test_stats.test_percentileofscore(35.0, 35.0) ... ok
test_stats.test_percentileofscore(30.0, 30.0) ... ok
test_stats.test_percentileofscore(40.0, 40.0) ... ok
test_stats.test_percentileofscore(45.0, 45.0) ... ok
test_stats.test_percentileofscore(30.0, 30.0) ... ok
test_stats.test_percentileofscore(50.0, 50.0) ... ok
test_stats.test_percentileofscore(40.0, 40.0) ... ok
test_stats.test_percentileofscore(50.0, 50.0) ... ok
test_stats.test_percentileofscore(45.0, 45.0) ... ok
test_stats.test_percentileofscore(30.0, 30.0) ... ok
test_stats.test_percentileofscore(60.0, 60.0) ... ok
test_stats.test_percentileofscore(30.0, 30) ... ok
test_stats.test_percentileofscore(30.0, 30) ... ok
test_stats.test_percentileofscore(30.0, 30) ... ok
test_stats.test_percentileofscore(30.0, 30) ... ok
test_stats.test_percentileofscore(35.0, 35.0) ... ok
test_stats.test_percentileofscore(30.0, 30.0) ... ok
test_stats.test_percentileofscore(40.0, 40.0) ... ok
test_stats.test_percentileofscore(45.0, 45.0) ... ok
test_stats.test_percentileofscore(30.0, 30.0) ... ok
test_stats.test_percentileofscore(60.0, 60.0) ... ok
test_stats.test_percentileofscore(30.0, 30.0) ... ok
test_stats.test_percentileofscore(30.0, 30.0) ... ok
test_stats.test_percentileofscore(30.0, 30.0) ... ok
test_stats.test_percentileofscore(30.0, 30.0) ... ok
test_stats.test_percentileofscore(10.0, 10.0) ... ok
test_stats.test_percentileofscore(5.0, 5.0) ... ok
test_stats.test_percentileofscore(0.0, 0.0) ... ok
test_stats.test_percentileofscore(10.0, 10.0) ... ok
test_stats.test_percentileofscore(100.0, 100.0) ... ok
test_stats.test_percentileofscore(95.0, 95.0) ... ok
test_stats.test_percentileofscore(90.0, 90.0) ... ok
test_stats.test_percentileofscore(100.0, 100.0) ... ok
test_stats.test_percentileofscore(100.0, 100.0) ... ok
test_stats.test_percentileofscore(100.0, 100.0) ... ok
test_stats.test_percentileofscore(0.0, 0.0) ... ok
test_stats.test_friedmanchisquare ... ok
test_stats.test_kstest ... ok
test_stats.test_ks_2samp ... ok
test_stats.test_ttest_rel ... ok
test_stats.test_ttest_ind ... ok
test_stats.test_ttest_1samp_new ... ok
test_stats.test_describe ... ok
test_stats.test_normalitytests((3.9237191815818493, 0.14059672529747549), (3.92371918, 0.14059673)) ... ok
test_stats.test_normalitytests((1.9807882609087573, 0.047615023828432253), (1.98078826, 0.047615020000000001)) ... ok
test_stats.test_normalitytests((-0.014037344047597383, 0.98880018772590561), (-0.014037340000000001, 0.98880018999999997)) ... ok
test_stats.test_pointbiserial ... ok
test_stats.test_obrientransform ... ok
test_stats.test_binomtest ... ok
convert simple expr to blitz ... ok
convert fdtd equation to blitz. ... ok
convert simple expr to blitz ... ok
bad path should return same as default (and warn) ... ok
make sure it handles relative values. ... ok
default behavior is to return current directory ... ok
make sure it handles relative values ... ok
test_simple (test_build_tools.TestConfigureSysArgv) ... ok
bad path should return same as default (and warn) ... ok
make sure it handles relative values. ... ok
default behavior returns tempdir ... ok
make sure it handles relative values ... ok
There should always be a writable file -- even if it is in temp ... ok
test_add_function_ordered (test_catalog.TestCatalog) ... ok
Test persisting a function in the default catalog ... ok
MODULE in search path should be replaced by module_dir. ... ok
MODULE in search path should be removed if module_dir==None. ... ok
If MODULE is absent, module_dir shouldn't be in search path. ... ok
Make sure environment variable is getting used. ... ok
Be sure we get at least one file even without specifying the path. ... ok
Ignore bad paths in the path. ... ok
test_clear_module_directory (test_catalog.TestCatalog) ... ok
test_get_environ_path (test_catalog.TestCatalog) ... ok
Shouldn't get any files when temp doesn't exist and no path set. ... ok
Shouldn't get a single file from the temp dir. ... ok
test_set_module_directory (test_catalog.TestCatalog) ... ok
Check that we can create a file in the writable directory ... ok
Check that we can create a file in the writable directory ... ok
There should always be a writable file -- even if search paths contain ... ok
test_bad_path (test_catalog.TestCatalogPath) ... ok
test_current (test_catalog.TestCatalogPath) ... ok
test_default (test_catalog.TestCatalogPath) ... ok
test_module (test_catalog.TestCatalogPath) ... ok
test_path (test_catalog.TestCatalogPath) ... ok
test_user (test_catalog.TestCatalogPath) ... ok
test_is_writable (test_catalog.TestDefaultDir) ... ok
get_test_dir (test_catalog.TestGetCatalog) ... ok
test_create_catalog (test_catalog.TestGetCatalog) ... ok
test_nonexistent_catalog_is_none (test_catalog.TestGetCatalog) ... ok
test_assign_variable_types (test_ext_tools.TestAssignVariableTypes) ... ok
test_numpy_scalar_spec.setup_test_location ... ok
test_numpy_scalar_spec.teardown_test_location ... ok
test_error1 (test_size_check.TestBinaryOpSize) ... ok
test_error2 (test_size_check.TestBinaryOpSize) ... ok
test_scalar (test_size_check.TestBinaryOpSize) ... ok
test_x1 (test_size_check.TestBinaryOpSize) ... ok
test_x_y (test_size_check.TestBinaryOpSize) ... ok
test_x_y2 (test_size_check.TestBinaryOpSize) ... ok
test_x_y3 (test_size_check.TestBinaryOpSize) ... ok
test_x_y4 (test_size_check.TestBinaryOpSize) ... ok
test_x_y5 (test_size_check.TestBinaryOpSize) ... ok
test_x_y6 (test_size_check.TestBinaryOpSize) ... ok
test_x_y7 (test_size_check.TestBinaryOpSize) ... ok
test_y1 (test_size_check.TestBinaryOpSize) ... ok
test_error1 (test_size_check.TestDummyArray) ... ok
test_error2 (test_size_check.TestDummyArray) ... ok
test_scalar (test_size_check.TestDummyArray) ... ok
test_x1 (test_size_check.TestDummyArray) ... ok
test_x_y (test_size_check.TestDummyArray) ... ok
test_x_y2 (test_size_check.TestDummyArray) ... ok
test_x_y3 (test_size_check.TestDummyArray) ... ok
test_x_y4 (test_size_check.TestDummyArray) ... ok
test_x_y5 (test_size_check.TestDummyArray) ... ok
test_x_y6 (test_size_check.TestDummyArray) ... ok
test_x_y7 (test_size_check.TestDummyArray) ... ok
test_y1 (test_size_check.TestDummyArray) ... ok
test_1d_0 (test_size_check.TestDummyArrayIndexing) ... ok
test_1d_1 (test_size_check.TestDummyArrayIndexing) ... ok
test_1d_10 (test_size_check.TestDummyArrayIndexing) ... ok
test_1d_2 (test_size_check.TestDummyArrayIndexing) ... ok
test_1d_3 (test_size_check.TestDummyArrayIndexing) ... ok
test_1d_4 (test_size_check.TestDummyArrayIndexing) ... ok
test_1d_5 (test_size_check.TestDummyArrayIndexing) ... ok
test_1d_6 (test_size_check.TestDummyArrayIndexing) ... ok
test_1d_7 (test_size_check.TestDummyArrayIndexing) ... ok
test_1d_8 (test_size_check.TestDummyArrayIndexing) ... ok
test_1d_9 (test_size_check.TestDummyArrayIndexing) ... ok
test_1d_index_0 (test_size_check.TestDummyArrayIndexing) ... ok
test_1d_index_1 (test_size_check.TestDummyArrayIndexing) ... ok
test_1d_index_2 (test_size_check.TestDummyArrayIndexing) ... ok
test_1d_index_3 (test_size_check.TestDummyArrayIndexing) ... ok
test_1d_index_calculated (test_size_check.TestDummyArrayIndexing) ... ok
through a bunch of different indexes at it for good measure. ... ok
test_1d_stride_0 (test_size_check.TestDummyArrayIndexing) ... ok
test_1d_stride_1 (test_size_check.TestDummyArrayIndexing) ... ok
test_1d_stride_10 (test_size_check.TestDummyArrayIndexing) ... ok
test_1d_stride_11 (test_size_check.TestDummyArrayIndexing) ... ok
test_1d_stride_12 (test_size_check.TestDummyArrayIndexing) ... ok
test_1d_stride_2 (test_size_check.TestDummyArrayIndexing) ... ok
test_1d_stride_3 (test_size_check.TestDummyArrayIndexing) ... ok
test_1d_stride_4 (test_size_check.TestDummyArrayIndexing) ... ok
test_1d_stride_5 (test_size_check.TestDummyArrayIndexing) ... ok
test_1d_stride_6 (test_size_check.TestDummyArrayIndexing) ... ok
test_1d_stride_7 (test_size_check.TestDummyArrayIndexing) ... ok
test_1d_stride_8 (test_size_check.TestDummyArrayIndexing) ... ok
test_1d_stride_9 (test_size_check.TestDummyArrayIndexing) ... ok
test_2d_0 (test_size_check.TestDummyArrayIndexing) ... ok
test_2d_1 (test_size_check.TestDummyArrayIndexing) ... ok
test_2d_2 (test_size_check.TestDummyArrayIndexing) ... ok
through a bunch of different indexes at it for good measure. ... ok
through a bunch of different indexes at it for good measure. ... ok
test_calculated_index (test_size_check.TestExpressions) ... ok
test_calculated_index2 (test_size_check.TestExpressions) ... ok
test_generic_1d (test_size_check.TestExpressions) ... ok
test_single_index (test_size_check.TestExpressions) ... ok
test_scalar (test_size_check.TestMakeSameLength) ... ok
test_x_scalar (test_size_check.TestMakeSameLength) ... ok
test_x_short (test_size_check.TestMakeSameLength) ... ok
test_y_scalar (test_size_check.TestMakeSameLength) ... ok
test_y_short (test_size_check.TestMakeSameLength) ... ok
test_1d_0 (test_size_check.TestReduction) ... ok
test_2d_0 (test_size_check.TestReduction) ... ok
test_2d_1 (test_size_check.TestReduction) ... ok
test_3d_0 (test_size_check.TestReduction) ... ok
test_error0 (test_size_check.TestReduction) ... ok
test_error1 (test_size_check.TestReduction) ... ok
test_exclusive_end (test_slice_handler.TestBuildSliceAtom) ... ok
match slice from a[1:] ... ok
match slice from a[1::] ... ok
match slice from a[1:2] ... ok
match slice from a[1:2:] ... ok
match slice from a[1:2:3] ... ok
match slice from a[1::3] ... ok
match slice from a[:] ... ok
match slice from a[::] ... ok
match slice from a[:2] ... ok
match slice from a[:2:] ... ok
match slice from a[:2:3] ... ok
match slice from a[:1+i+2:] ... ok
match slice from a[0] ... ok
match slice from a[::3] ... ok
transform a[:] to slice notation ... ok
transform a[:,:] = b[:,1:1+2:3] *(c[1-2+i:,:] - c[:,:]) ... ok
test_type_match_array (test_standard_array_spec.TestArrayConverter) ... ok
test_type_match_int (test_standard_array_spec.TestArrayConverter) ... ok
test_type_match_string (test_standard_array_spec.TestArrayConverter) ... ok
----------------------------------------------------------------------
Ran 4733 tests in 322.886s
OK (KNOWNFAIL=12, SKIP=42)
<nose.result.TextTestResult run=4733 errors=0 failures=0>
>>>
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