diff --git a/advanced/advanced_numpy/index.rst b/advanced/advanced_numpy/index.rst index 80256289c..a2f90851e 100644 --- a/advanced/advanced_numpy/index.rst +++ b/advanced/advanced_numpy/index.rst @@ -1332,10 +1332,10 @@ Array siblings: :class:`chararray`, :class:`maskedarray`, :class:`matrix` -------------------------------------------------- >>> x = np.array(['a', ' bbb', ' ccc']).view(np.chararray) ->>> x.lstrip(' ') # doctest: +ELLIPSIS +>>> x.lstrip(' ') # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE chararray(['a', 'bbb', 'ccc'], dtype='...') ->>> x.upper() # doctest: +ELLIPSIS +>>> x.upper() # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE chararray(['A', ' BBB', ' CCC'], dtype='...') diff --git a/advanced/scipy_sparse/introduction.rst b/advanced/scipy_sparse/introduction.rst index 95c0891a3..9919bf888 100644 --- a/advanced/scipy_sparse/introduction.rst +++ b/advanced/scipy_sparse/introduction.rst @@ -32,9 +32,9 @@ Why Sparse Matrices? >>> plt.plot(x, 8.0 * (x**2) / 1e6, lw=5) # doctest: +ELLIPSIS [] >>> plt.xlabel('size n') # doctest: +ELLIPSIS - + Text(...,'size n') >>> plt.ylabel('memory [MB]') # doctest: +ELLIPSIS - + Text(...,'memory [MB]') Sparse Matrices vs. Sparse Matrix Storage Schemes ------------------------------------------------- diff --git a/environment.yml b/environment.yml index 2a0d1a01e..300d26489 100644 --- a/environment.yml +++ b/environment.yml @@ -3,12 +3,12 @@ # `./build_tools/requirements_to_environment.py` name: testenv dependencies: -- numpy=1.11 +- numpy=1.14 - scipy=0.19 -- scikit-learn=0.18 -- matplotlib=1.5 +- scikit-learn=0.19 +- matplotlib=2.2 - scikit-image=0.13 -- sympy=1.0 +- sympy=1.3 - statsmodels=0.8 - seaborn=0.7 - pandas=0.20 diff --git a/intro/numpy/elaborate_arrays.rst b/intro/numpy/elaborate_arrays.rst index 5b161cc3d..ef28b4b0d 100644 --- a/intro/numpy/elaborate_arrays.rst +++ b/intro/numpy/elaborate_arrays.rst @@ -193,9 +193,9 @@ Field access works by indexing with field names:: Multiple fields at once:: - >>> samples[['position', 'value']] - array([(1.0, 0.37), (1.0, 0.11), (1.0, 0.13), (1.5, 0.37), (3.0, 0.11), - (1.2, 0.13)], + >>> samples[['position', 'value']] # doctest: +NORMALIZE_WHITESPACE + array([( 1. , 0.37), ( 1. , 0.11), ( 1. , 0.13), ( 1.5, 0.37), + ( 3. , 0.11), ( 1.2, 0.13)], dtype=[('position', '>> plt.figure(figsize=(4, 3)) # doctest: +ELLIPSIS - +
>>> plt.plot(t, np.sqrt(mean_sq_distance), 'g.', t, np.sqrt(t), 'y-') # doctest: +ELLIPSIS [, ] >>> plt.xlabel(r"$t$") # doctest: +ELLIPSIS - + Text(...,'$t$') >>> plt.ylabel(r"$\sqrt{\langle (\delta x)^2 \rangle}$") # doctest: +ELLIPSIS - + Text(...,'$\\sqrt{\\langle (\\delta x)^2 \\rangle}$') >>> plt.tight_layout() # provide sufficient space for labels .. image:: auto_examples/images/sphx_glr_plot_randomwalk_001.png diff --git a/packages/scikit-image/index.rst b/packages/scikit-image/index.rst index 5c94ea314..c644dbe42 100644 --- a/packages/scikit-image/index.rst +++ b/packages/scikit-image/index.rst @@ -638,14 +638,14 @@ Some image processing operations:: Visualize binary result:: >>> plt.figure() # doctest: +ELLIPSIS - +
>>> plt.imshow(clean_border, cmap='gray') # doctest: +ELLIPSIS Visualize contour :: >>> plt.figure() # doctest: +ELLIPSIS - +
>>> plt.imshow(coins, cmap='gray') # doctest: +ELLIPSIS >>> plt.contour(clean_border, [0.5]) # doctest: +ELLIPSIS diff --git a/packages/scikit-learn/index.rst b/packages/scikit-learn/index.rst index ece1990bc..d0ecec2d6 100644 --- a/packages/scikit-learn/index.rst +++ b/packages/scikit-learn/index.rst @@ -747,7 +747,7 @@ others for our problem:: >>> for index, feature_name in enumerate(data.feature_names): ... plt.figure() ... plt.scatter(data.data[:, index], data.target) # doctest: +ELLIPSIS - >> sym.solveset(sym.exp(x) + 1, x) - {I*(2*n*pi + pi) | n in Integers()} + {I*(2*n*pi + pi) | n in Integers} .. topic:: **Systems of linear equations** diff --git a/requirements.txt b/requirements.txt index d926a33e1..a6eb16538 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,11 +1,11 @@ # This file is structured in a specific way please include all conda installable # libraries before the special comment marked `# For conda, pip installable:` -numpy==1.11 +numpy==1.14 scipy==0.19 -scikit-learn==0.18 -matplotlib==1.5 +scikit-learn==0.19 +matplotlib==2.2 scikit-image==0.13 -sympy==1.0 +sympy==1.3 statsmodels==0.8 seaborn==0.7 pandas==0.20