def test_radardisplay_init():
    # test that a display object can be created with and without
    radar = pyart.io.read_cfradial(pyart.testing.CFRADIAL_PPI_FILE)
    radar.antenna_transition = {'data': np.zeros((40, ))}
    display = pyart.graph.RadarDisplay(radar)
    assert display.antenna_transition is not None
    plt.close()

def scree_plot(pca_obj, fname=None): 
    '''
    Scree plot for variance & cumulative variance by component from PCA. 

    Arguments: 
        - pca_obj: a fitted sklearn PCA instance
        - fname: path to write plot to file

    Output: 
        - scree plot 
    '''   
    components = pca_obj.n_components_ 
    variance = pca.explained_variance_ratio_
    plt.figure()
    plt.plot(np.arange(1, components + 1), np.cumsum(variance), label='Cumulative Variance')
    plt.plot(np.arange(1, components + 1), variance, label='Variance')
    plt.xlim([0.8, components]); plt.ylim([0.0, 1.01])
    plt.xlabel('No. Components', labelpad=11); plt.ylabel('Variance Explained', labelpad=11)
    plt.legend(loc='best') 
    plt.tight_layout() 
    if fname is not None:
        plt.savefig(fname)
        plt.close() 
    else:
        plt.show() 
    return 

def display_d3(fig=None, closefig=True, d3_url=None):
    """Display figure in IPython notebook via the HTML display hook

    Parameters
    ----------
    fig : matplotlib figure
        The figure to display (grabs current figure if missing)
    closefig : boolean (default: True)
        If true, close the figure so that the IPython matplotlib mode will not
        display the png version of the figure.
    d3_url : string (optional)
        The URL of the d3 library.  If not specified, a standard web path
        will be used.

    Returns
    -------
    fig_d3 : IPython.display.HTML object
        the IPython HTML rich display of the figure.

    See Also
    --------
    show_d3 : show a figure in a new browser window, notebook not required.
    enable_notebook : automatically embed figures in the IPython notebook
    """
    # import here, in case users don't have requirements installed
    from IPython.display import HTML
    import matplotlib.pyplot as plt
    if fig is None:
        fig = plt.gcf()
    if closefig:
        plt.close(fig)
    return HTML(fig_to_d3(fig, d3_url=d3_url))

def graph_view(request):
    try:
        fig = plot.figure()
        ax = fig.add_subplot(111)
        plot_data(request, ax, 0)
        plot_data(request, ax, 1)
        ax.set_xlabel("Time")
        ax.set_ylabel(u"Temperature (°C)")
        fig.autofmt_xdate()
        imgdata = StringIO.StringIO()
        fig.savefig(imgdata, format='svg')
        return Response(imgdata.getvalue(), content_type='image/svg+xml')
    except DBAPIError:
        conn_err_msg = """\
<?xml version="1.0" standalone="no"?>
<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN"
  "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
<svg width="10cm" height="10cm" viewBox="0 0 100 300"
     xmlns="http://www.w3.org/2000/svg" version="1.1">
  <desc>Database connection error message</desc>
  <text x="0" y="0" fill="red">Database error.</text>
  </svg>"""
        return Response(conn_err_msg, content_type='image/svg+xml', status_int=500)
    finally:
        plot.close('all')

def make_iso_visual_panel(fn, img_compo, img_map, contours1, contours3, z, pixsize, legend_suffix, name, title_compo, title_map, label_cbar):
    fig, (ax0, ax1, ax_cb) = get_figure_grids(n_panel=2)

    # plot composit
    ax_imshow(fig, ax0, img_compo, origin='upper', tocolorbar=False, tosetlim=True)

    nx, ny = img_compo.shape[:2]
    overplot_ruler(ax0, z, pixsize=pixsize, rlength_arcsec=10., nx=nx, ny=ny)

    ax0.text(5, 12, name, color='white', fontsize=12)
    ax0.text(nx-35, 12, '$z={}$'.format('%.2f'%z), color='white', fontsize=10)
    ax0.set_title(title_compo)
    ax0.title.set_position([.5, 1.03])

    # plot line map
    im = ax_imshow(fig, ax1, img_map, vmin=-1, vmax=8, origin='lower', tocolorbar=False, tosetlim=True)
    overplot_contours(ax1, contours3, lw=1.)
    overplot_contours(ax1, contours1, lw=0.2)

    make_legend_isophotes(ax1, lw=2, suffix=legend_suffix)

    ax1.set_title(title_map)
    ax1.title.set_position([.5, 1.03])

    # plot color bar
    cbar = fig.colorbar(im, cax=ax_cb, label=label_cbar, format='%i')
    ax_cb.set_aspect(20)

    # set ticks off
    for ax in [ax0, ax1]: 
        ax.axis('off')

    # saving
    fig.savefig(fn, format='pdf')
    plt.close()

    def plot_cc(self):
        with PdfPages("CCplot.pdf") as pdf:
            for each_frame in self.frame_list:
                fig, axs = plt.subplots(5,5, sharex=True, sharey=True, squeeze=True, facecolor='w', edgecolor='k')
                fig.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.1, hspace = 0.5, wspace=0.1)

                fig.text(0.5, 0.04, 'CCweak', ha='center')
                fig.text(0.04, 0.5, 'CCall', va='center', rotation='vertical')
                fig.set_size_inches(8,11)

                axs = axs.flatten()

                for i in range(len(each_frame)):
                   ccall, ccweak = get_CCs(each_frame[i])
                   if len(ccweak) > len(ccall):
                     diff = len(ccweak) - len(ccall)
                     axs[i].plot(ccweak[0:(len(ccweak)-diff)], ccall, 'o', linewidth=1, rasterized=True)
                   elif len(ccweak) < len(ccall):
                      diff = len(ccall) - len(ccweak)
                      axs[i].plot(ccweak, ccall[0:(len(ccall)-diff)], 'o', linewidth=1, rasterized=True)
                   else:
                      axs[i].plot(ccweak, ccall, 'o', linewidth=1, rasterized=True)

                   titles = each_frame[i].strip('-shelxd.log')
                   axs[i].set_title(titles, fontsize=8)
                pdf.savefig(fig)
                plt.close()

def export(data, F, k):
    '''Write data to a png image
    
    Arguments
    ---------
    data : numpy.ndarray
        array containing the data to be written as png image
    F : float
        feed rate of the current configuration
    k : float
        rate constant of the current configuration
    '''
        
    figsize = tuple(s / 72.0 for s in data.shape)
    fig = plt.figure(figsize=figsize, dpi=72.0, facecolor='white')
    fig.add_axes([0, 0, 1, 1], frameon=False)
    plt.xticks([])
    plt.yticks([])

    plt.imshow(data, cmap=plt.cm.RdBu_r, interpolation='bicubic')
    plt.gci().set_clim(0, 1)

    filename = './study/F{:03d}-k{:03d}.png'.format(int(1000*F), int(1000*k))
    plt.savefig(filename, dpi=72.0)
    plt.close()

def test_singleton_ax_dim():
    for axis, direction in enumerate("xyz"):
        shape = [5, 6, 7]
        shape[axis] = 1
        img = nibabel.Nifti1Image(np.ones(shape), np.eye(4))
        plot_stat_map(img, None, display_mode=direction)
        plt.close()

def test_plot_anat():
    img = _generate_img()

    # Test saving with empty plot
    z_slicer = plot_anat(anat_img=False, display_mode='z')
    filename = tempfile.mktemp(suffix='.png')
    try:
        z_slicer.savefig(filename)
    finally:
        os.remove(filename)

    z_slicer = plot_anat(display_mode='z')
    filename = tempfile.mktemp(suffix='.png')
    try:
        z_slicer.savefig(filename)
    finally:
        os.remove(filename)

    ortho_slicer = plot_anat(img, dim='auto')
    filename = tempfile.mktemp(suffix='.png')
    try:
        ortho_slicer.savefig(filename)
    finally:
        os.remove(filename)

    # Save execution time and memory
    plt.close()

def test_radardisplay_get_colorbar_label():
    radar = pyart.io.read_cfradial(pyart.testing.CFRADIAL_PPI_FILE)
    display = pyart.graph.RadarDisplay(radar)

    # default is to base the label on the standard_name key
    assert (display._get_colorbar_label('reflectivity_horizontal') ==
            'equivalent reflectivity factor (dBZ)')

    # next is to look at the long_name
    del display.fields['reflectivity_horizontal']['standard_name']
    assert (display._get_colorbar_label('reflectivity_horizontal') ==
            'Reflectivity (dBZ)')

    # use the field if standard_name and long_name missing
    del display.fields['reflectivity_horizontal']['long_name']
    print(display._get_colorbar_label('reflectivity_horizontal'))
    assert (display._get_colorbar_label('reflectivity_horizontal') ==
            'reflectivity horizontal (dBZ)')

    # no units if key is missing
    del display.fields['reflectivity_horizontal']['units']
    print(display._get_colorbar_label('reflectivity_horizontal'))
    assert (display._get_colorbar_label('reflectivity_horizontal') ==
            'reflectivity horizontal (?)')
    plt.close()

def test_plot_stat_map():
    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)

    # Save execution time and memory
    plt.close()

def test_radardisplay_misc():
    # misc methods which are not tested above
    radar = pyart.io.read_cfradial(pyart.testing.CFRADIAL_PPI_FILE)
    display = pyart.graph.RadarDisplay(radar)
    fig = plt.figure()
    ax = fig.add_subplot(111)

    # _set_vpt_title with a title
    display._set_vpt_title('foo_field', 'title_string', ax)
    assert ax.get_title() == 'title_string'

    # _generate_field_name method
    fn = pyart.graph.common.generate_field_name(
        radar, 'reflectivity_horizontal')
    assert fn == 'Equivalent reflectivity factor'

    display.fields['reflectivity_horizontal'].pop('standard_name')
    fn = pyart.graph.common.generate_field_name(
        radar, 'reflectivity_horizontal')
    assert fn == 'Reflectivity'

    display.fields['reflectivity_horizontal'].pop('long_name')
    fn = pyart.graph.common.generate_field_name(
        radar, 'reflectivity_horizontal')
    assert fn == 'Reflectivity horizontal'

    plt.close()

def test_radardisplay_user_specified_labels():
    # test that labels are set when a user specifies them.
    radar = pyart.io.read_cfradial(pyart.testing.CFRADIAL_PPI_FILE)
    display = pyart.graph.RadarDisplay(radar)
    fig = plt.figure()
    ax = fig.add_subplot(111)

    display._set_ray_title('field', 0, 'foo', ax)
    assert ax.get_title() == 'foo'

    display._label_axes_ppi(('foo', 'bar'), ax)
    assert ax.get_xlabel() == 'foo'
    assert ax.get_ylabel() == 'bar'

    display._label_axes_rhi(('spam', 'eggs'), ax)
    assert ax.get_xlabel() == 'spam'
    assert ax.get_ylabel() == 'eggs'

    display._label_axes_ray(('baz', 'qux'), 'field', ax)
    assert ax.get_xlabel() == 'baz'
    assert ax.get_ylabel() == 'qux'

    display._label_axes_vpt(('nick', 'nock'), False, ax)
    assert ax.get_xlabel() == 'nick'
    assert ax.get_ylabel() == 'nock'
    plt.close()

def test_radardisplay_plot_rhi_reverse():
    radar = pyart.io.read_cfradial(pyart.testing.CFRADIAL_RHI_FILE)
    display = pyart.graph.RadarDisplay(radar)
    fig = plt.figure()
    ax = fig.add_subplot(111)
    display.plot('reflectivity_horizontal', 0, ax=ax, reverse_xaxis=True)
    plt.close()

    def test_heatmap_ticklabel_rotation(self):

        f, ax = plt.subplots(figsize=(2, 2))
        mat.heatmap(self.df_norm, ax=ax)

        for t in ax.get_xticklabels():
            nt.assert_equal(t.get_rotation(), 0)

        for t in ax.get_yticklabels():
            nt.assert_equal(t.get_rotation(), 90)

        plt.close(f)

        df = self.df_norm.copy()
        df.columns = [str(c) * 10 for c in df.columns]
        df.index = [i * 10 for i in df.index]

        f, ax = plt.subplots(figsize=(2, 2))
        mat.heatmap(df, ax=ax)

        for t in ax.get_xticklabels():
            nt.assert_equal(t.get_rotation(), 90)

        for t in ax.get_yticklabels():
            nt.assert_equal(t.get_rotation(), 0)

        plt.close(f)

def make_fish(zoom=False):
    plt.close(1)
    plt.figure(1, figsize=(6, 4))
    plt.plot(plot_limits['pitch'], plot_limits['rolldev'], '-g', lw=3)
    plt.plot(plot_limits['pitch'], -plot_limits['rolldev'], '-g', lw=3)
    plt.plot(pitch.midvals, roll.midvals, '.b', ms=1, alpha=0.7)

    p, r = make_ellipse()  # pitch, off nominal roll
    plt.plot(p, r, '-c', lw=2)

    gf = -0.08  # Fudge on pitch value for illustrative purposes
    plt.plot(greta['pitch'] + gf, -greta['roll'], '.r', ms=1, alpha=0.7)
    plt.plot(greta['pitch'][-1] + gf, -greta['roll'][-1], 'xr', ms=10, mew=2)

    if zoom:
        plt.xlim(46.3, 56.1)
        plt.ylim(4.1, 7.3)
    else:
        plt.ylim(-22, 22)
        plt.xlim(40, 180)
    plt.xlabel('Sun pitch angle (deg)')
    plt.ylabel('Sun off-nominal roll angle (deg)')
    plt.title('Mission off-nominal roll vs. pitch (5 minute samples)')
    plt.grid()
    plt.tight_layout()
    plt.savefig('fish{}.png'.format('_zoom' if zoom else ''))

    def test_savefig(self):
        # Not sure if this is the right way to test....
        cm = mat.ClusterGrid(self.df_norm, **self.default_kws)
        cm.plot(**self.default_plot_kws)
        cm.savefig(tempfile.NamedTemporaryFile(), format='png')

        plt.close('all')

def plot_dpi_dpr_distribution(args, dpis, dprs, diagnoses):
    print log.INFO, 'Plotting estimate distributions...'
    diagnoses = np.array(diagnoses)
    diagnoses[(0.25 <= diagnoses) & (diagnoses <= 0.75)] = 0.5

    # Setup plot
    fig, ax = plt.subplots()
    pt.setup_axes(plt, ax)

    biomarkers_str = args.method if args.biomarkers is None else ', '.join(args.biomarkers)
    ax.set_title('DP estimation using {0} at {1}'.format(biomarkers_str, ', '.join(args.visits)))
    ax.set_xlabel('DP')
    ax.set_ylabel('DPR')

    plt.scatter(dpis, dprs, c=diagnoses, edgecolor='none', s=25.0,
                vmin=0.0, vmax=1.0, cmap=pt.progression_cmap,
                alpha=0.5)

    # Plot legend
    # noinspection PyUnresolvedReferences
    rects = [mpl.patches.Rectangle((0, 0), 1, 1, fc=pt.color_cn + (0.5,), linewidth=0),
             mpl.patches.Rectangle((0, 0), 1, 1, fc=pt.color_mci + (0.5,), linewidth=0),
             mpl.patches.Rectangle((0, 0), 1, 1, fc=pt.color_ad + (0.5,), linewidth=0)]
    labels = ['CN', 'MCI', 'AD']
    legend = ax.legend(rects, labels, fontsize=10, ncol=len(rects), loc='upper center', framealpha=0.9)
    legend.get_frame().set_edgecolor((0.6, 0.6, 0.6))

    # Draw or save the plot
    plt.tight_layout()
    if args.plot_file is not None:
        plt.savefig(args.plot_file, transparent=True)
    else:
        plt.show()
    plt.close(fig)

    def key_press(event): 
        
        ax = event.inaxes 
        if ax == None: return 
        ax_title = ax.get_title()
        
        if event.key == 'q': plt.close('all')

        if event.key == 'm':
            click_x, click_y = event.xdata, event.ydata
            rr = np.arange((x2-2*dpix),(x2+2*dpix+1), dtype=np.int)
            #p = ip.gauss_fit(rr, row[rr], p0=[1, x2, 1])
            #a2.plot(rr, ip.gauss(rr, *p), 'r--')
            #mx = p[1]
            mx, mval = ip.find_features(rr, row[rr], gpix=dpix)
            mx, mval = mx[0], mval[0]
            a2.plot(mx, mval, 'ro')
            fig.canvas.draw()
            fwv = cwv[np.argmin(np.abs(cwv-wav[mx]))]
            strtmp = raw_input('input wavelength at (%d, %d) = %.7f:' % (mx, my, fwv))
            if strtmp == '': strtmp = fwv
            try:
                mwv = np.float(strtmp)
                lxx.append(mx)
                lyy.append(my)
                lwv.append(mwv)
                a1.plot(mx, my, 'ro')
                fig.canvas.draw()
                print '%.7f at (%d, %d)' % (mwv, mx, my)
            except:
                print 'No input, again...'

def plot_jacobian(A, name, cmap= plt.cm.coolwarm, normalize=True, precision=1e-6):

    """
    Customized visualization of jacobian matrices for observing
    sparsity patterns
    """
    
    plt.figure()
    fig, ax = plt.subplots()
    
    if normalize is True:
        plt.imshow(A, interpolation='none', cmap=cmap,
                   norm = mpl.colors.Normalize(vmin=-1.,vmax=1.))
    else:
        plt.imshow(A, interpolation='none', cmap=cmap)        
    plt.colorbar(format=ticker.FuncFormatter(fmt))
    
    ax.spy(A, marker='.', markersize=0,  precision=precision)
    
    ax.spines['right'].set_visible(True)
    ax.spines['bottom'].set_visible(True)
    ax.xaxis.set_ticks_position('top')
    ax.yaxis.set_ticks_position('left')

    xlabels = np.linspace(0, A.shape[0], 5, True, dtype=int)
    ylabels = np.linspace(0, A.shape[1], 5, True, dtype=int)

    plt.xticks(xlabels)
    plt.yticks(ylabels)

    plt.savefig(name, bbox_inches='tight', pad_inches=0.05)
    
    plt.close()

    return