Python pyplot.switch_backend函数代码示例

def test_asc():
    mean1 = [0.5, 0.5]
    mean2 = [3.0, 0.5]
    conv1 = [[1, 0], [0, 1]]
    conv2 = [[1.5, 0], [0, 1.2]]
    samples = 2000
    class1_data = np.random.multivariate_normal(mean1, conv1, samples)
    class1_label = np.ones(samples, dtype=np.int32) * 0
    class2_data = np.random.multivariate_normal(mean2, conv2, samples)
    class2_label = np.ones(samples, dtype=np.int32) * 1
    train_data = np.concatenate((class1_data, class2_data))
    train_label = np.concatenate((class1_label, class2_label))

    prototypes, labels = asc(train_data, train_label, 20, 20, 3)
    plt.switch_backend('Qt4Agg')
    plt.figure(1)
    plt.hold(True)
    plt.plot(class1_data[:, 0], class1_data[:, 1], 'r*')
    plt.plot(class2_data[:, 0], class2_data[:, 1], 'b.')
    plt.figure(2)
    plt.hold(True)
    for i in xrange(labels.shape[0]):
        if labels[i] == 0:
            plt.plot(prototypes[i, 0], prototypes[i, 1], 'r*')
        else:
            plt.plot(prototypes[i, 0], prototypes[i, 1], 'b.')
    plt.hold(False)
    plt.show()

    def switch_backend(backend, sloppy=True):
        """
        Switch matplotlib backend.

        :type backend: str
        :param backend: Name of matplotlib backend to switch to.
        :type sloppy: bool
        :param sloppy: If ``True``, only uses
            :func:`matplotlib.pyplot.switch_backend` and no warning will be
            shown if the backend was not switched successfully. If ``False``,
            additionally tries to use :func:`matplotlib.use` first and also
            shows a warning if the backend was not switched successfully.
        """
        import matplotlib
        # sloppy. only do a `plt.switch_backend(..)`
        if sloppy:
            import matplotlib.pyplot as plt
            plt.switch_backend(backend)
        else:
            # check if `matplotlib.use(..)` is emitting a warning
            try:
                with warnings.catch_warnings(record=True):
                    warnings.simplefilter("error", UserWarning)
                    matplotlib.use(backend)
            # if that's the case, follow up with `plt.switch_backend(..)`
            except UserWarning:
                import matplotlib.pyplot as plt
                plt.switch_backend(backend)
            # finally check if the switch was successful,
            # show a warning if not
            if matplotlib.get_backend().upper() != backend.upper():
                msg = "Unable to change matplotlib backend to '%s'" % backend
                warnings.warn(msg)

def has_matplotlib(version=None, op=">="):
    """
    True if matplotlib_ is installed.
    If version is None, the result of matplotlib.__version__ `op` version is returned.
    """
    try:
        import matplotlib
        # have_display = "DISPLAY" in os.environ
    except ImportError:
        print("Skipping matplotlib test")
        return False

    matplotlib.use("Agg")
    #matplotlib.use("Agg", force=True)  # Use non-graphical display backend during test.
    import matplotlib.pyplot as plt
    # http://stackoverflow.com/questions/21884271/warning-about-too-many-open-figures
    plt.close("all")

    backend = matplotlib.get_backend()
    if backend.lower() != "agg":
        #raise RuntimeError("matplotlib backend now is %s" % backend)
        #matplotlib.use("Agg", warn=True, force=False)
        # Switch the default backend.
        # This feature is experimental, and is only expected to work switching to an image backend.
        plt.switch_backend("Agg")

    if version is None: return True
    return cmp_version(matplotlib.__version__, version, op=op)

    def plot(self, filedir=None, file_format='pdf'):
        if filedir is None:
            filedir = self.workdir
        import matplotlib.pyplot as plt
        plt.switch_backend('agg')

        plt.figure(figsize=(8, 6))
        plt.subplots_adjust(left=0.1, bottom=0.08, right=0.95, top=0.95, wspace=None, hspace=None)
        forces = np.array(self.output['forces'])
        maxforce = [np.max(np.apply_along_axis(np.linalg.norm, 1, x)) for x in forces]
        avgforce = [np.mean(np.apply_along_axis(np.linalg.norm, 1, x)) for x in forces]

        if np.max(maxforce) > 0.0 and np.max(avgforce) > 0.0:
            plt.semilogy(maxforce, 'b.-', label='Max force')
            plt.semilogy(avgforce, 'r.-', label='Mean force')
        else:
            plt.plot(maxforce, 'b.-', label='Max force')
            plt.plot(avgforce, 'r.-', label='Mean force')
        plt.xlabel('Ion movement iteration')
        plt.ylabel('Max Force')
        plt.savefig(filedir + os.sep + 'forces.' + file_format)
        plt.clf()

        plt.figure(figsize=(8, 6))
        plt.subplots_adjust(left=0.1, bottom=0.08, right=0.95, top=0.95, wspace=None, hspace=None)
        stress = np.array(self.output['stress'])
        diag_stress = [np.trace(np.abs(x)) for x in stress]
        offdiag_stress = [np.sum(np.abs(np.triu(x, 1).flatten())) for x in stress]
        plt.semilogy(diag_stress, 'b.-', label='diagonal')
        plt.semilogy(offdiag_stress, 'r.-', label='off-diagonal')
        plt.legend()
        plt.xlabel('Ion movement iteration')
        plt.ylabel(r'$\sum |stress|$ (diag, off-diag)')
        plt.savefig(filedir + os.sep + 'stress.' + file_format)

def createFFT(data, cols, currentAnalysis):
    plt.switch_backend('agg')
    fg, ax = plt.subplots()
    plt.title('FFT - ' + cols[0])
    plotting.fft(data.data, cols[0], ax)
    createDirForPlot(currentAnalysis)
    plt.savefig(STATIC_DIR + '/plots/' + currentAnalysis.title +'/fft.png')

def ecg2rri(x):
    global rri, t, ax2, pos_c
    pos_c = []
    plt.switch_backend('qt4Agg')
    plt.ion()
    msg = "RRi Detection Parameters"
    title = "Parameters Dialog"
    fieldNames = ["Threshold", "Refractory Period", "Low Cuttof Freq.",
                  "Upper Cuttof Freq.", "Sampling Frequency"]
    fieldValues = ["0.5", "200", "5", "40", "1000"]
    fieldValues = multenterbox(msg, title, fieldNames, fieldValues)
    fieldValues = [float(k) for k in fieldValues]
    thr = fieldValues[0]
    Fs = fieldValues[4]
    lC = fieldValues[2] / (0.5 * Fs)  #normalized lower cutoff frequency.
    uC = fieldValues[3] / (0.5 * Fs)  #normalized upper cutoff frequency.
    B, A = butter(4, [lC, uC], 'band')
    xf = filtfilt(B, A, x)  #filtered ecg.
    xd = diff(xf)  #first derivative of the ecg.
    peaks = array([peaks + 1 for peaks in xrange(len(xd)) if xd[peaks] > 0 and
        xd[peaks + 1] < 0 and xf[peaks] > thr or xd[peaks] == 0])  #find RR peaks above threshold.

    rri = diff(peaks)  # RRi in miliseconds
    t = cumsum(rri) / 1000.0
    t_ecg = arange(0, len(xf)) / Fs
    fig = plt.figure()
    ax1 = fig.add_subplot(2, 1, 1)
    ax2 = fig.add_subplot(2, 1, 2)
    ax1.plot(t_ecg, xf)
    ax1.plot(t_ecg[peaks], xf[peaks], 'g.-')
    ax2.plot(t, rri, 'k.-')
    fig.canvas.mpl_connect('button_press_event', onclick)
    return t, rri

    def testPlot(self):
        """Test plotting of spectrum

        Not easy to test the actual result, but we can test that the API hasn't
        been broken.
        """
        import matplotlib.pyplot as plt
        plt.switch_backend("agg")  # In case someone has loaded a different backend that will cause trouble
        ext = ".png"  # Extension to use for plot filenames
        spectrum = self.makeSpectrum()
        # Write directly to file
        with lsst.utils.tests.getTempFilePath(ext) as filename:
            spectrum.plot(numRows=4, doBackground=True, doReferenceLines=True, filename=filename)
        # Check return values
        with lsst.utils.tests.getTempFilePath(ext) as filename:
            numRows = 4  # Must be > 1 for len(axes) to work
            figure, axes = spectrum.plot(numRows=numRows)
            self.assertEqual(len(axes), numRows)
            figure.savefig(filename)
        # Test one row, write directly to file
        with lsst.utils.tests.getTempFilePath(ext) as filename:
            figure, axes = spectrum.plot(numRows=1, filename=filename)
        # Test one row, check return values
        with lsst.utils.tests.getTempFilePath(ext) as filename:
            figure, axes = spectrum.plot(numRows=1)
            with self.assertRaises(TypeError):
                axes[0]
            figure.savefig(filename)

 def test_chinese_restaurant_process(self):
     print sys.path
     from matplotlib import pyplot
     import matplotlib
     from scipy import stats
     alpha = 20
     test_size = 1000
     tests = 1000
     data = [0]
     for j in range(0, tests):
         cr = ChineseRestaurant(alpha, Numbers())
         for i in range(0, test_size):
             new_sample = cr.draw()
             if new_sample >= len(data):
                 data.append(0)
             data[new_sample] += 1
         assert cr.heap[1] == test_size
     pyplot.switch_backend('Qt5Agg')
     #data=sorted(data, reverse=True)
     print len(data)
     actual_plot, = pyplot.plot(range(1,len(data)), data[1:], label='actual avg')
     expected = [0]
     remain = test_size * tests
     for i in range(1, len(data)):
         break_ = stats.beta.mean(1.0, float(alpha)) * remain
         expected.append(break_)
         remain -= break_
     #print est
     expected_plot, = pyplot.plot(range(1,len(data)), expected[1:], 'r', linewidth=1, label='expected')
     matplotlib.interactive(True)
     pyplot.ylabel("People at Table")
     pyplot.xlabel("Table Number")
     pyplot.title("Chinese Restaurant Process Unit Test")
     pyplot.legend()
     pyplot.show(block=True)

 def test_QueueNetwork_animate(self):
     if not HAS_MATPLOTLIB:
         with mock.patch('queueing_tool.network.queue_network.plt.show'):
             self.qn.animate(frames=5)
     else:
         plt.switch_backend('Agg')
         self.qn.animate(frames=5)

    def movie(self,di=10, coord='latlon',land="nice", heatmap=False):
        curr_backend = plt.get_backend()
        plt.switch_backend('Agg')

        FFMpegWriter = animation.writers['ffmpeg']
        metadata = dict(title='%s %s' % (self.projname, self.casename),
                        artist='pytraj',
                        comment='https://github.com/TRACMASS/pytraj')
        writer = FFMpegWriter(fps=15, metadata=metadata)

        fig = plt.figure()
        with writer.saving(fig, "traj_test.mp4", 200):
            for part in self.partvec:
                self.load(part=part)
                jdvec = np.sort(self.jdvec)                
                for jd in jdvec:
                    print part, jdvec[-1] - jd, len(self.jd[self.jd==jd])
                    if len(self.jd[self.jd==jd]) <= 1: continue
                    if jd/di == float(jd)/di:
                        if heatmap == True:
                            self.heatmap(log=True, jd=jd)
                        else:
                            self.scatter(jd=jd, coord=coord, land=land)
                        writer.grab_frame()
        plt.switch_backend(curr_backend)

def _setup():
    # The baseline images are created in this locale, so we should use
    # it during all of the tests.
    try:
        locale.setlocale(locale.LC_ALL, str('en_US.UTF-8'))
    except locale.Error:
        try:
            locale.setlocale(locale.LC_ALL, str('English_United States.1252'))
        except locale.Error:
            warnings.warn(
                "Could not set locale to English/United States. "
                "Some date-related tests may fail")

    plt.switch_backend('Agg')  # use Agg backend for these test
    if mpl.get_backend().lower() != "agg":
        msg = ("Using a wrong matplotlib backend ({0}), "
               "which will not produce proper images")
        raise Exception(msg.format(mpl.get_backend()))

    # These settings *must* be hardcoded for running the comparison
    # tests
    mpl.rcdefaults()  # Start with all defaults
    mpl.rcParams['text.hinting'] = True
    mpl.rcParams['text.antialiased'] = True
    mpl.rcParams['text.hinting_factor'] = 8

    # make sure we don't carry over bad plots from former tests
    msg = ("no of open figs: {} -> find the last test with ' "
           "python tests.py -v' and add a '@cleanup' decorator.")
    assert len(plt.get_fignums()) == 0, msg.format(plt.get_fignums())

def main():

    dataDir = sys.argv[1]
    dataType = "train2014"

    plt.switch_backend("TkAgg")
    pylab.rcParams['figure.figsize'] = (15.0, 10.0)

    ct = coco_text.COCO_Text(sys.argv[2])
    ct.info()

    # get all images containing at least one instance of legible text
    imgIds = ct.getImgIds(imgIds=ct.train, 
                                catIds=[('legibility','legible')])

    while True:
        # pick one at random
        img = ct.loadImgs(imgIds[np.random.randint(0,len(imgIds))])[0]

        I = io.imread('%s/images/%s/%s'%(dataDir,dataType,img['file_name']))
        print '/images/%s/%s'%(dataType,img['file_name'])
        plt.figure()
        annIds = ct.getAnnIds(imgIds=img['id'])
        anns = ct.loadAnns(annIds)
        ct.showAnns(anns)
        plt.imshow(I)
        plt.show()

def test_plot_connectome():
    import matplotlib.pyplot as plt
    plt.switch_backend('template')
    node_color = ['green', 'blue', 'k', 'cyan']
    # symmetric up to 1e-3 relative tolerance
    adjacency_matrix = np.array([[1., -2., 0.3, 0.],
                                 [-2.002, 1, 0., 0.],
                                 [0.3, 0., 1., 0.],
                                 [0., 0., 0., 1.]])
    node_coords = np.arange(3 * 4).reshape(4, 3)

    args = adjacency_matrix, node_coords
    kwargs = dict(edge_threshold=0.38,
                  title='threshold=0.38',
                  node_size=10, node_color=node_color)
    plot_connectome(*args, **kwargs)

    # used to speed-up tests for the next plots
    kwargs['display_mode'] = 'x'

    # node_coords not an array but a list of tuples
    plot_connectome(adjacency_matrix,
                    [tuple(each) for each in node_coords],
                    **kwargs)
    # saving to file
    with tempfile.NamedTemporaryFile(suffix='.png') as fp:
        display = plot_connectome(*args, output_file=fp.name,
                                  **kwargs)
        assert_true(display is None)
        assert_true(os.path.isfile(fp.name) and
                    os.path.getsize(fp.name) > 0)

    # with node_kwargs, edge_kwargs and edge_cmap arguments
    plot_connectome(*args,
                    edge_threshold='70%',
                    node_size=[10, 20, 30, 40],
                    node_color=np.zeros((4, 3)),
                    edge_cmap='RdBu',
                    node_kwargs={
                        'marker': 'v'},
                    edge_kwargs={
                        'linewidth': 4})

    # masked array support
    masked_adjacency_matrix = np.ma.masked_array(
        adjacency_matrix, np.abs(adjacency_matrix) < 0.5)
    plot_connectome(masked_adjacency_matrix, node_coords,
                    **kwargs)

    # sparse matrix support
    sparse_adjacency_matrix = sparse.coo_matrix(adjacency_matrix)
    plot_connectome(sparse_adjacency_matrix, node_coords,
                    **kwargs)

    # NaN matrix support
    nan_adjacency_matrix = np.array([[1., np.nan, 0.],
                                     [np.nan, 1., 2.],
                                     [np.nan, 2., 1.]])
    nan_node_coords = np.arange(3 * 3).reshape(3, 3)
    plot_connectome(nan_adjacency_matrix, nan_node_coords, **kwargs)

def test_plot_stat_map():
    mp.use('template', warn=False)
    import matplotlib.pyplot as plt
    plt.switch_backend('template')
    img = _generate_img()

    plot_stat_map(img, cut_coords=(80, -120, -60))

    # Smoke test coordinate finder, with and without mask
    masked_img = nibabel.Nifti1Image(
        np.ma.masked_equal(img.get_data(), 0),
        mni_affine)
    plot_stat_map(masked_img, display_mode='x')
    plot_stat_map(img, display_mode='y', cut_coords=2)

    # 'yx' display_mode
    plot_stat_map(img, display_mode='yx')

    # regression test #510
    data = np.zeros((91, 109, 91))
    aff = np.eye(4)
    new_img = nibabel.Nifti1Image(data, aff)
    plot_stat_map(new_img, threshold=1000, colorbar=True)

    rng = np.random.RandomState(42)
    data = rng.randn(91, 109, 91)
    new_img = nibabel.Nifti1Image(data, aff)
    plot_stat_map(new_img, threshold=1000, colorbar=True)

def main():
    parser = get_parser()
    args = parser.parse_args()
    root = os.path.splitext(os.path.basename(args.cgmap))[0]
    ctxstr = const_ctxstr(args.fasta)
    cgmap = const_cgmap(ctxstr, args.cgmap, args.depth)
    gtftree = const_gtftree(args.gtf)
    bulk = calc_bulk(ctxstr, cgmap)
    plt.switch_backend('Agg')
    bulk_ax = plot_bulkmean(bulk)
    fig = bulk_ax.get_figure()
    fig.savefig('{}.bulk.mean.png'.format(root), dpi=300)
    plt.close(fig)
    bulk_fig = plot_bulkhist(bulk)
    bulk_fig.savefig('{}.bulk.hist.png'.format(root), dpi=300)
    plt.close(fig)
    ign, cg_table, chg_table, chh_table = calc_mlevel(ctxstr, cgmap, gtftree, args.pmtsize)
    cg_table.to_csv('{}.feature.CG.txt'.format(root), sep='\t', float_format='%.3f')
    chg_table.to_csv('{}.feature.CHG.txt'.format(root), sep='\t', float_format='%.3f')
    chh_table.to_csv('{}.feature.CHH.txt'.format(root), sep='\t', float_format='%.3f')
    cg_ax, chg_ax, chh_ax = plot_feature_mlevel(bulk, ign, cg_table, chg_table, chh_table)
    fig = cg_ax.get_figure()
    fig.savefig('{}.feature.CG.png'.format(root), dpi=300)
    plt.close(fig)
    fig = chg_ax.get_figure()
    fig.savefig('{}.feature.CHG.png'.format(root), dpi=300)
    plt.close(fig)
    fig = chh_ax.get_figure()
    fig.savefig('{}.feature.CHH.png'.format(root), dpi=300)
    plt.close(fig)
    gpos, gmlevel = calc_genomewide(ctxstr, cgmap)
    gax = plot_genomewide(ctxstr, gpos, gmlevel)
    fig = gax.get_figure()
    fig.savefig('{}.genomewide.png'.format(root), dpi=300)
    plt.close(fig)