numpy.testing.dec.skipif

Here are the examples of the python api numpy.testing.dec.skipif taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.

200 Examples 7

Example 1

Project: attention-lvcsr
Source File: test_complex.py
View license
    @dec.skipif(True, "Complex grads not enabled, see #178")
    def test_complex_grads(self):
        def f(m):
            c = complex(m[0], m[1])
            return .5 * real(c) + .9 * imag(c)

        rng = numpy.random.RandomState(9333)
        mval = numpy.asarray(rng.randn(2, 5))
        utt.verify_grad(f, [mval])

Example 2

Project: attention-lvcsr
Source File: test_complex.py
View license
    @dec.skipif(True, "Complex grads not enabled, see #178")
    def test_mul_mixed0(self):

        def f(a):
            ac = complex(a[0], a[1])
            return abs((ac)**2).sum()

        rng = numpy.random.RandomState(9333)
        aval = numpy.asarray(rng.randn(2, 5))
        try:
            utt.verify_grad(f, [aval])
        except utt.verify_grad.E_grad as e:
            print(e.num_grad.gf)
            print(e.analytic_grad)
            raise

Example 3

Project: attention-lvcsr
Source File: test_complex.py
View license
    @dec.skipif(True, "Complex grads not enabled, see #178")
    def test_mul_mixed1(self):

        def f(a):
            ac = complex(a[0], a[1])
            return abs(ac).sum()

        rng = numpy.random.RandomState(9333)
        aval = numpy.asarray(rng.randn(2, 5))
        try:
            utt.verify_grad(f, [aval])
        except utt.verify_grad.E_grad as e:
            print(e.num_grad.gf)
            print(e.analytic_grad)
            raise

Example 4

Project: attention-lvcsr
Source File: test_complex.py
View license
    @dec.skipif(True, "Complex grads not enabled, see #178")
    def test_mul_mixed(self):

        def f(a, b):
            ac = complex(a[0], a[1])
            return abs((ac*b)**2).sum()

        rng = numpy.random.RandomState(9333)
        aval = numpy.asarray(rng.randn(2, 5))
        bval = rng.randn(5)
        try:
            utt.verify_grad(f, [aval, bval])
        except utt.verify_grad.E_grad as e:
            print(e.num_grad.gf)
            print(e.analytic_grad)
            raise

Example 5

Project: attention-lvcsr
Source File: test_complex.py
View license
    @dec.skipif(True, "Complex grads not enabled, see #178")
    def test_polar_grads(self):
        def f(m):
            c = complex_from_polar(abs(m[0]), m[1])
            return .5 * real(c) + .9 * imag(c)

        rng = numpy.random.RandomState(9333)
        mval = numpy.asarray(rng.randn(2, 5))
        utt.verify_grad(f, [mval])

Example 6

Project: attention-lvcsr
Source File: test_complex.py
View license
    @dec.skipif(True, "Complex grads not enabled, see #178")
    def test_abs_grad(self):
        def f(m):
            c = complex(m[0], m[1])
            return .5 * abs(c)

        rng = numpy.random.RandomState(9333)
        mval = numpy.asarray(rng.randn(2, 5))
        utt.verify_grad(f, [mval])

Example 7

Project: scipy
Source File: test_quadpack.py
View license
    @dec.skipif(_ctypes_missing or _ctypes_multivariate_fail,
                msg="Compiled test functions not loaded")
    def setUp(self):
        self.lib = ctypes.CDLL(clib_test.__file__)
        restype = ctypes.c_double
        argtypes = (ctypes.c_int, ctypes.c_double)
        for name in ['_multivariate_typical', '_multivariate_indefinite',
                     '_multivariate_sin']:
            func = getattr(self.lib, name)
            func.restype = restype
            func.argtypes = argtypes

Example 8

Project: scipy
Source File: test_quadpack.py
View license
    @dec.skipif(_ctypes_missing or _ctypes_multivariate_fail,
                msg="Compiled test functions not loaded")
    def test_typical(self):
        # 1) Typical function with two extra arguments:
        assert_quad(quad(self.lib._multivariate_typical, 0, pi, (2, 1.8)),
                    0.30614353532540296487)

Example 9

Project: scipy
Source File: test_quadpack.py
View license
    @dec.skipif(_ctypes_missing or _ctypes_multivariate_fail,
                msg="Compiled test functions not loaded")
    def test_indefinite(self):
        # 2) Infinite integration limits --- Euler's constant
        assert_quad(quad(self.lib._multivariate_indefinite, 0, Inf),
                    0.577215664901532860606512)

Example 10

Project: scipy
Source File: test_quadpack.py
View license
    @dec.skipif(_ctypes_missing or _ctypes_multivariate_fail,
                msg="Compiled test functions not loaded")
    def test_threadsafety(self):
        # Ensure multivariate ctypes are threadsafe
        def threadsafety(y):
            return y + quad(self.lib._multivariate_sin, 0, 1)[0]
        assert_quad(quad(threadsafety, 0, 1), 0.9596976941318602)

Example 11

Project: scipy
Source File: test_quadpack.py
View license
    @dec.skipif(_ctypes_missing or _ctypes_multivariate_fail,
                msg="Compiled test functions not loaded")
    def test_improvement(self):
        def myfunc(x):           # Euler's constant integrand
            return -exp(-x)*log(x)
        import time
        start = time.time()
        for i in xrange(20):
            quad(self.lib._multivariate_indefinite, 0, 100)
        fast = time.time() - start
        start = time.time()
        for i in xrange(20):
            quad(myfunc, 0, 100)
        slow = time.time() - start
        # 2+ times faster speeds generated by nontrivial ctypes
        # function (single variable)
        assert_(fast < 0.5*slow, (fast, slow))

Example 12

Project: scipy
Source File: test_build.py
View license
    @dec.skipif(not(sys.platform[:5] == 'linux'),
                "Skipping fortran compiler mismatch on non Linux platform")
    def test_lapack(self):
        f = FindDependenciesLdd()
        deps = f.grep_dependencies(flapack.__file__,
                                   ['libg2c', 'libgfortran'])
        self.assertFalse(len(deps) > 1,
"""Both g77 and gfortran runtimes linked in scipy.linalg.flapack ! This is
likely to cause random crashes and wrong results. See numpy INSTALL.rst.txt for
more information.""")

Example 13

Project: scipy
Source File: test_io.py
View license
@dec.skipif(pil_missing, msg="The Python Image Library could not be found.")
def test_imread():
    lp = os.path.join(os.path.dirname(__file__), 'dots.png')
    with warnings.catch_warnings(record=True):  # Py3k ResourceWarning
        img = ndi.imread(lp, mode="RGB")
    assert_array_equal(img.shape, (300, 420, 3))

    with warnings.catch_warnings(record=True):  # PIL ResourceWarning
        img = ndi.imread(lp, flatten=True)
    assert_array_equal(img.shape, (300, 420))

    with open(lp, 'rb') as fobj:
        img = ndi.imread(fobj, mode="RGB")
        assert_array_equal(img.shape, (300, 420, 3))

Example 14

Project: scipy
Source File: test_sparsetools.py
View license
@dec.skipif(True, "64-bit indices in sparse matrices not available")
def test_csr_matmat_int64_overflow():
    n = 3037000500
    assert n**2 > np.iinfo(np.int64).max

    # the test would take crazy amounts of memory
    check_free_memory(n * (8*2 + 1) * 3 / 1e6)

    # int64 overflow
    data = np.ones((n,), dtype=np.int8)
    indptr = np.arange(n+1, dtype=np.int64)
    indices = np.zeros(n, dtype=np.int64)
    a = csr_matrix((data, indices, indptr))
    b = a.T

    assert_raises(RuntimeError, a.dot, b)

Example 15

Project: scipy
Source File: test__plotutils.py
View license
    @dec.skipif(not has_matplotlib, "Matplotlib not available")
    def test_delaunay(self):
        # Smoke test
        fig = plt.figure()
        obj = Delaunay(self.points)
        s_before = obj.simplices.copy()
        r = delaunay_plot_2d(obj, ax=fig.gca())
        assert_array_equal(obj.simplices, s_before)  # shouldn't modify
        assert_(r is fig)
        delaunay_plot_2d(obj, ax=fig.gca())

Example 16

Project: scipy
Source File: test__plotutils.py
View license
    @dec.skipif(not has_matplotlib, "Matplotlib not available")
    def test_voronoi(self):
        # Smoke test
        fig = plt.figure()
        obj = Voronoi(self.points)
        r = voronoi_plot_2d(obj, ax=fig.gca())
        assert_(r is fig)
        voronoi_plot_2d(obj)
        voronoi_plot_2d(obj, show_vertices=False)

Example 17

Project: scipy
Source File: test__plotutils.py
View license
    @dec.skipif(not has_matplotlib, "Matplotlib not available")
    def test_convex_hull(self):
        # Smoke test
        fig = plt.figure()
        tri = ConvexHull(self.points)
        r = convex_hull_plot_2d(tri, ax=fig.gca())
        assert_(r is fig)
        convex_hull_plot_2d(tri)

Example 18

Project: statsmodels
Source File: test_correlation.py
View license
@dec.skipif(not have_matplotlib)
def test_plot_corr():
    hie_data = randhie.load_pandas()
    corr_matrix = np.corrcoef(hie_data.data.values.T)

    fig = plot_corr(corr_matrix, xnames=hie_data.names)
    plt.close(fig)

    fig = plot_corr(corr_matrix, xnames=[], ynames=hie_data.names)
    plt.close(fig)

    fig = plot_corr(corr_matrix, normcolor=True, title='', cmap='jet')
    plt.close(fig)

Example 19

Project: statsmodels
Source File: test_correlation.py
View license
@dec.skipif(not have_matplotlib)
def test_plot_corr_grid():
    hie_data = randhie.load_pandas()
    corr_matrix = np.corrcoef(hie_data.data.values.T)

    fig = plot_corr_grid([corr_matrix] * 2, xnames=hie_data.names)
    plt.close(fig)

    fig = plot_corr_grid([corr_matrix] * 5, xnames=[], ynames=hie_data.names)
    plt.close(fig)

    fig = plot_corr_grid([corr_matrix] * 3, normcolor=True, titles='', cmap='jet')
    plt.close(fig)

Example 20

Project: statsmodels
Source File: test_gofplots.py
View license
    @dec.skipif(not have_matplotlib)
    def test_qqplot_pltkwargs(self):
        self.fig = self.prbplt.qqplot(ax=self.ax, line=self.line,
                                      marker='d',
                                      markerfacecolor='cornflowerblue',
                                      markeredgecolor='white',
                                      alpha=0.5)

Example 21

Project: statsmodels
Source File: test_gofplots.py
View license
    @dec.skipif(not have_matplotlib)
    def test_ppplot_pltkwargs(self):
        self.fig = self.prbplt.ppplot(ax=self.ax, line=self.line,
                                      marker='d',
                                      markerfacecolor='cornflowerblue',
                                      markeredgecolor='white',
                                      alpha=0.5)

Example 22

Project: statsmodels
Source File: test_gofplots.py
View license
    @dec.skipif(not have_matplotlib)
    def test_probplot_pltkwargs(self):
        self.fig = self.prbplt.probplot(ax=self.ax, line=self.line,
                                        marker='d',
                                        markerfacecolor='cornflowerblue',
                                        markeredgecolor='white',
                                        alpha=0.5)

Example 23

Project: statsmodels
Source File: test_gofplots.py
View license
    @dec.skipif(not have_matplotlib)
    def test_qqplot_2samples_ProbPlotObjects(self):
        # also tests all values for line
        for line in ['r', 'q', '45', 's']:
            # test with `ProbPlot` instances
            fig = sm.qqplot_2samples(self.prbplt, self.other_prbplot,
                                     line=line)
            plt.close('all')

Example 24

Project: statsmodels
Source File: test_gofplots.py
View license
    @dec.skipif(not have_matplotlib)
    def test_qqplot_2samples_arrays(self):
        # also tests all values for line
        for line in ['r', 'q', '45', 's']:
            # test with arrays
            fig = sm.qqplot_2samples(self.res, self.other_array, line=line)
            plt.close('all')

Example 25

Project: statsmodels
Source File: test_mosaicplot.py
View license
@dec.skipif(not have_matplotlib)
def test_axes_labeling():
    from numpy.random import rand
    key_set = (['male', 'female'], ['old', 'adult', 'young'],
               ['worker', 'unemployed'], ['yes', 'no'])
    # the cartesian product of all the categories is
    # the complete set of categories
    keys = list(product(*key_set))
    data = OrderedDict(zip(keys, rand(len(keys))))
    lab = lambda k: ''.join(s[0] for s in k)
    fig, (ax1, ax2) = pylab.subplots(1, 2, figsize=(16, 8))
    mosaic(data, ax=ax1, labelizer=lab, horizontal=True, label_rotation=45)
    mosaic(data, ax=ax2, labelizer=lab, horizontal=False,
        label_rotation=[0, 45, 90, 0])
    #fig.tight_layout()
    fig.suptitle("correct alignment of the axes labels")
    #pylab.show()
    pylab.close('all')

Example 26

Project: statsmodels
Source File: test_mosaicplot.py
View license
@dec.skipif(not have_matplotlib or pandas_old)
def test_default_arg_index():
    # 2116
    import pandas as pd
    df = pd.DataFrame({'size' : ['small', 'large', 'large', 'small', 'large',
                                 'small'],
                       'length' : ['long', 'short', 'short', 'long', 'long',
                                   'short']})
    assert_raises(ValueError, mosaic, data=df, title='foobar')
    pylab.close('all')

Example 27

Project: statsmodels
Source File: test_regressionplots.py
View license
    @dec.skipif(not have_matplotlib)
    def test_plot_fit(self):
        res = self.res

        fig = plot_fit(res, 0, y_true=None)

        x0 = res.model.exog[:, 0]
        yf = res.fittedvalues
        y = res.model.endog

        px1, px2 = fig.axes[0].get_lines()[0].get_data()
        np.testing.assert_equal(x0, px1)
        np.testing.assert_equal(y, px2)

        px1, px2 = fig.axes[0].get_lines()[1].get_data()
        np.testing.assert_equal(x0, px1)
        np.testing.assert_equal(yf, px2)

        close_or_save(pdf, fig)

Example 28

Project: statsmodels
Source File: test_regressionplots.py
View license
    @dec.skipif(not have_matplotlib)
    def test_one_column_exog(self):
        from statsmodels.formula.api import ols
        res = ols("y~var1-1", data=self.data).fit()
        plot_regress_exog(res, "var1")
        plt.close('all')
        res = ols("y~var1", data=self.data).fit()
        plot_regress_exog(res, "var1")
        plt.close('all')

Example 29

Project: statsmodels
Source File: test_regressionplots.py
View license
    @dec.skipif(not have_matplotlib)
    def test_abline_model(self):
        fig = abline_plot(model_results=self.mod)
        ax = fig.axes[0]
        ax.scatter(self.X[:,1], self.y)
        close_or_save(pdf, fig)

Example 30

Project: statsmodels
Source File: test_regressionplots.py
View license
    @dec.skipif(not have_matplotlib)
    def test_abline_model_ax(self):
        fig = plt.figure()
        ax = fig.add_subplot(111)
        ax.scatter(self.X[:,1], self.y)
        fig = abline_plot(model_results=self.mod, ax=ax)
        close_or_save(pdf, fig)

Example 31

Project: statsmodels
Source File: test_regressionplots.py
View license
    @dec.skipif(not have_matplotlib)
    def test_abline_ab(self):
        mod = self.mod
        intercept, slope = mod.params
        fig = abline_plot(intercept=intercept, slope=slope)
        close_or_save(pdf, fig)

Example 32

Project: statsmodels
Source File: test_regressionplots.py
View license
    @dec.skipif(not have_matplotlib)
    def test_abline_ab_ax(self):
        mod = self.mod
        intercept, slope = mod.params
        fig = plt.figure()
        ax = fig.add_subplot(111)
        ax.scatter(self.X[:,1], self.y)
        fig = abline_plot(intercept=intercept, slope=slope, ax=ax)
        close_or_save(pdf, fig)

Example 33

Project: statsmodels
Source File: test_tsaplots.py
View license
@dec.skipif(not have_matplotlib)
def test_plot_acf():
    # Just test that it runs.
    fig = plt.figure()
    ax = fig.add_subplot(111)

    ar = np.r_[1., -0.9]
    ma = np.r_[1., 0.9]
    armaprocess = tsp.ArmaProcess(ar, ma)
    rs = np.random.RandomState(1234)
    acf = armaprocess.generate_sample(100, distrvs=rs.standard_normal)
    plot_acf(acf, ax=ax, lags=10)
    plot_acf(acf, ax=ax)
    plot_acf(acf, ax=ax, alpha=None)

    plt.close(fig)

Example 34

Project: statsmodels
Source File: test_tsaplots.py
View license
@dec.skipif(not have_matplotlib)
def test_plot_acf_irregular():
    # Just test that it runs.
    fig = plt.figure()
    ax = fig.add_subplot(111)

    ar = np.r_[1., -0.9]
    ma = np.r_[1., 0.9]
    armaprocess = tsp.ArmaProcess(ar, ma)
    rs = np.random.RandomState(1234)
    acf = armaprocess.generate_sample(100, distrvs=rs.standard_normal)
    plot_acf(acf, ax=ax, lags=np.arange(1, 11))
    plot_acf(acf, ax=ax, lags=10, zero=False)
    plot_acf(acf, ax=ax, alpha=None, zero=False)

    plt.close(fig)

Example 35

Project: statsmodels
Source File: test_tsaplots.py
View license
@dec.skipif(not have_matplotlib)
def test_plot_pacf():
    # Just test that it runs.
    fig = plt.figure()
    ax = fig.add_subplot(111)

    ar = np.r_[1., -0.9]
    ma = np.r_[1., 0.9]
    armaprocess = tsp.ArmaProcess(ar, ma)
    rs = np.random.RandomState(1234)
    pacf = armaprocess.generate_sample(100, distrvs=rs.standard_normal)
    plot_pacf(pacf, ax=ax)
    plot_pacf(pacf, ax=ax, alpha=None)

    plt.close(fig)

Example 36

Project: statsmodels
Source File: test_tsaplots.py
View license
@dec.skipif(not have_matplotlib)
def test_plot_pacf_irregular():
    # Just test that it runs.
    fig = plt.figure()
    ax = fig.add_subplot(111)

    ar = np.r_[1., -0.9]
    ma = np.r_[1., 0.9]
    armaprocess = tsp.ArmaProcess(ar, ma)
    rs = np.random.RandomState(1234)
    pacf = armaprocess.generate_sample(100, distrvs=rs.standard_normal)
    plot_pacf(pacf, ax=ax, lags=np.arange(1, 11))
    plot_pacf(pacf, ax=ax, lags=10, zero=False)
    plot_pacf(pacf, ax=ax, alpha=None, zero=False)

    plt.close(fig)

Example 37

Project: statsmodels
Source File: test_tsaplots.py
View license
@dec.skipif(not have_matplotlib)
def test_seasonal_plot():
    rs = np.random.RandomState(1234)
    data = rs.randn(20,12)
    data += 6*np.sin(np.arange(12.0)/11*np.pi)[None,:]
    data = data.ravel()
    months = np.tile(np.arange(1,13),(20,1))
    months = months.ravel()
    df = pd.DataFrame([data,months],index=['data','months']).T
    grouped = df.groupby('months')['data']
    labels = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
    fig = seasonal_plot(grouped, labels)
    ax = fig.get_axes()[0]
    output = [tl.get_text() for tl in ax.get_xticklabels()]
    assert_equal(labels, output)
    plt.close('all')

Example 38

Project: statsmodels
Source File: test_mice.py
View license
    @dec.skipif(not have_matplotlib)
    def test_plot_missing_pattern(self):

        df = gendat()
        imp_data = mice.MICEData(df)

        for row_order in "pattern", "raw":
            for hide_complete_rows in False, True:
                for color_row_patterns in False, True:
                    plt.clf()
                    fig = imp_data.plot_missing_pattern(row_order=row_order,
                                      hide_complete_rows=hide_complete_rows,
                                      color_row_patterns=color_row_patterns)
                    close_or_save(pdf, fig)

Example 39

Project: statsmodels
Source File: test_mice.py
View license
    @dec.skipif(not have_matplotlib)
    def test_plot_bivariate(self):

        df = gendat()
        imp_data = mice.MICEData(df)
        imp_data.update_all()

        plt.clf()
        for plot_points in False, True:
            fig = imp_data.plot_bivariate('x2', 'x4', plot_points=plot_points)
            fig.get_axes()[0].set_title('plot_bivariate')
            close_or_save(pdf, fig)

Example 40

Project: statsmodels
Source File: test_mice.py
View license
    @dec.skipif(not have_matplotlib)
    def test_fit_obs(self):

        df = gendat()
        imp_data = mice.MICEData(df)
        imp_data.update_all()

        plt.clf()
        for plot_points in False, True:
            fig = imp_data.plot_fit_obs('x4', plot_points=plot_points)
            fig.get_axes()[0].set_title('plot_fit_scatterplot')
            close_or_save(pdf, fig)

Example 41

Project: statsmodels
Source File: test_mice.py
View license
    @dec.skipif(not have_matplotlib)
    def test_plot_imputed_hist(self):

        df = gendat()
        imp_data = mice.MICEData(df)
        imp_data.update_all()

        plt.clf()
        for plot_points in False, True:
            fig = imp_data.plot_imputed_hist('x4')
            fig.get_axes()[0].set_title('plot_imputed_hist')
            close_or_save(pdf, fig)

Example 42

Project: vispy
Source File: _testing.py
View license
def requires_application(backend=None, has=(), capable=()):
    """Return a decorator for tests that require an application"""
    good, msg = has_application(backend, has, capable)
    dec_backend = np.testing.dec.skipif(not good, "Skipping test: %s" % msg)
    try:
        import pytest
    except Exception:
        return dec_backend
    dec_app = pytest.mark.vispy_app_test
    return composed(dec_app, dec_backend)

Example 43

Project: vispy
Source File: _testing.py
View license
def requires_img_lib():
    """Decorator for tests that require an image library"""
    from ..io import _check_img_lib
    if sys.platform.startswith('win'):
        has_img_lib = False  # PIL breaks tests on windows (!)
    else:
        has_img_lib = not all(c is None for c in _check_img_lib())
    return np.testing.dec.skipif(not has_img_lib, 'imageio or PIL required')

Example 44

Project: sima
Source File: test_segment.py
View license
@dec.skipif(not cv2_available)
def test_PlaneNormalizedCuts():
    ds = ImagingDataset.load(example_data())[:, :, :, :50, :50]
    affinty_method = segment.BasicAffinityMatrix(num_pcs=5)
    method = segment.PlaneWiseSegmentation(
        segment.PlaneNormalizedCuts(affinty_method))
    ds.segment(method)

Example 45

Project: sima
Source File: test_segment.py
View license
@dec.skipif(not cv2_available)
def test_PlaneNormalizedCuts():
    ds = ImagingDataset.load(example_data())[:, :, :, :50, :50]
    affinty_method = segment.BasicAffinityMatrix(num_pcs=5)
    method = segment.PlaneWiseSegmentation(
        segment.PlaneNormalizedCuts(affinty_method))
    ds.segment(method)

Example 46

Project: sima
Source File: test_sequence.py
View license
    @dec.skipif(not h5py_available)
    def test_export_hdf5(self):
        self.tiff_seq.export(
            os.path.join(self.tmp_dir, 'test_export.h5'), fmt='HDF5',
            fill_gaps=False, channel_names=['Ch1', 'Ch2'], compression=None)

        with h5py.File(os.path.join(self.tmp_dir, 'test_export.h5'), 'r') as f:
            dims = [str(dim.label) for dim in f['imaging'].dims]
            channel_names = f['imaging'].attrs['channel_names']
            data = np.array(f['imaging'])

        assert_array_equal(['t', 'z', 'y', 'x', 'c'], dims)
        assert_array_equal(['Ch1', 'Ch2'], channel_names.astype('str'))
        assert_array_equal(np.array(self.tiff_seq), data)

Example 47

Project: sima
Source File: test_sequence.py
View license
    @dec.skipif(not h5py_available)
    def test_export_commpressed_hdf5(self):
        self.tiff_seq.export(
            os.path.join(self.tmp_dir, 'test_export_compressed.h5'),
            fmt='HDF5', channel_names=['Ch1', 'Ch2'], compression='gzip')

        with h5py.File(os.path.join(
                self.tmp_dir, 'test_export_compressed.h5'), 'r') as f:
            dims = [str(dim.label) for dim in f['imaging'].dims]
            channel_names = f['imaging'].attrs['channel_names']
            data = np.array(f['imaging'])

        assert_array_equal(['t', 'z', 'y', 'x', 'c'], dims)
        assert_array_equal(['Ch1', 'Ch2'], channel_names.astype('str'))
        assert_array_equal(np.array(self.tiff_seq), data)

Example 48

Project: sima
Source File: test_spikes.py
View license
    @dec.skipif(not _has_picos)
    def test_estimate_parameters(self):
        gamma_est, sigma_est = sima.spikes.estimate_parameters(
            [self.fluors_long], mode="correct")
        assert_(abs(gamma_est - self.gamma) < 0.01)
        assert_(abs(sigma_est - self.sigma) < 0.01)

Example 49

Project: sima
Source File: test_sequence.py
View license
    @dec.skipif(not h5py_available)
    def test_export_hdf5(self):
        self.tiff_seq.export(
            os.path.join(self.tmp_dir, 'test_export.h5'), fmt='HDF5',
            fill_gaps=False, channel_names=['Ch1', 'Ch2'], compression=None)

        with h5py.File(os.path.join(self.tmp_dir, 'test_export.h5'), 'r') as f:
            dims = [str(dim.label) for dim in f['imaging'].dims]
            channel_names = f['imaging'].attrs['channel_names']
            data = np.array(f['imaging'])

        assert_array_equal(['t', 'z', 'y', 'x', 'c'], dims)
        assert_array_equal(['Ch1', 'Ch2'], channel_names.astype('str'))
        assert_array_equal(np.array(self.tiff_seq), data)

Example 50

Project: sima
Source File: test_sequence.py
View license
    @dec.skipif(not h5py_available)
    def test_export_commpressed_hdf5(self):
        self.tiff_seq.export(
            os.path.join(self.tmp_dir, 'test_export_compressed.h5'),
            fmt='HDF5', channel_names=['Ch1', 'Ch2'], compression='gzip')

        with h5py.File(os.path.join(
                self.tmp_dir, 'test_export_compressed.h5'), 'r') as f:
            dims = [str(dim.label) for dim in f['imaging'].dims]
            channel_names = f['imaging'].attrs['channel_names']
            data = np.array(f['imaging'])

        assert_array_equal(['t', 'z', 'y', 'x', 'c'], dims)
        assert_array_equal(['Ch1', 'Ch2'], channel_names.astype('str'))
        assert_array_equal(np.array(self.tiff_seq), data)