Python matplotlib.colorbar() Examples

def plotTZ(filename=None):
    cmap = cm.get_cmap('cool')
    fig, (ax1, ax2) = plt.subplots(1, 2, gridspec_kw = {'width_ratios':[19, 1]})
    ax1.add_patch(pl.Rectangle(xy=[0, 0], width=0.5, height=0.5, facecolor=cmap(0.0), linewidth='2.0'))
    ax1.add_patch(pl.Rectangle(xy=[0.5, 0.5], width=0.5, height=0.5, facecolor=cmap(0.0), linewidth='2.0'))
    ax1.add_patch(pl.Rectangle(xy=[0, 0.5], width=0.5, height=0.5, facecolor=cmap(1.0), linewidth='2.0'))
    ax1.add_patch(pl.Rectangle(xy=[0.5, 0], width=0.5, height=0.5, facecolor=cmap(1.0), linewidth='2.0'))
    ax1.set_xlabel('x1', size=22)
    ax1.set_ylabel('x2', size=22)
    ax1.set_title('True Data', size=28)
    colorbar.ColorbarBase(ax2, cmap=cmap, format='%.1f')
    ax2.set_ylabel('Output y', size=22)
    plt.show()
    if not filename is None:
        plt.savefig(filename, format="pdf", bbox_inches="tight")
        plt.close() 

def plotTZ(filename=None):
    t = np.linspace(0, 1, 101)
    z = 0.25 + 0.5 / (1 + np.exp(- 20 * (t - 0.5))) + 0.05 * np.cos(t * 2 * np.pi)
    cmap = cm.get_cmap('cool')
    fig, (ax1, ax2) = plt.subplots(1, 2, gridspec_kw = {'width_ratios':[19, 1]})
    poly1 = [[0, 0]]
    poly1.extend([[t[i], z[i]] for i in range(t.size)])
    poly1.extend([[1, 0], [0, 0]])
    poly2 = [[0, 1]]
    poly2.extend([[t[i], z[i]] for i in range(t.size)])
    poly2.extend([[1, 1], [0, 1]])
    poly1 = plt.Polygon(poly1,fc=cmap(0.0))
    poly2 = plt.Polygon(poly2,fc=cmap(1.0))
    ax1.add_patch(poly1)
    ax1.add_patch(poly2)
    ax1.set_xlabel('x1', size=22)
    ax1.set_ylabel('x2', size=22)
    ax1.set_title('True Data', size=28)
    colorbar.ColorbarBase(ax2, cmap=cmap, format='%.1f')
    ax2.set_ylabel('Output y', size=22)
    plt.show()
    if not filename is None:
        plt.savefig(filename, format="pdf", bbox_inches="tight")
        plt.close() 

def _plot_colorbar(self, color_legend_ax: Axes, normalize):
        """
        Plots a horizontal colorbar given the ax an normalize values

        Parameters
        ----------
        color_legend_ax
        normalize

        Returns
        -------
        None, updates color_legend_ax

        """
        cmap = pl.get_cmap(self.cmap)
        import matplotlib.colorbar

        matplotlib.colorbar.ColorbarBase(
            color_legend_ax, orientation='horizontal', cmap=cmap, norm=normalize
        )

        color_legend_ax.set_title(self.color_legend_title, fontsize='small')

        color_legend_ax.xaxis.set_tick_params(labelsize='small') 

def test_colorbar_extension_shape():
    '''Test rectangular colorbar extensions.'''
    # Create figures for uniform and proportionally spaced colorbars.
    fig1 = _colorbar_extension_shape('uniform')
    fig2 = _colorbar_extension_shape('proportional') 

def colorbar(mappable=None, cax=None, ax=None, **kw):
    if mappable is None:
        mappable = gci()
        if mappable is None:
            raise RuntimeError('No mappable was found to use for colorbar '
                               'creation. First define a mappable such as '
                               'an image (with imshow) or a contour set ('
                               'with contourf).')
    if ax is None:
        ax = gca()

    ret = gcf().colorbar(mappable, cax = cax, ax=ax, **kw)
    draw_if_interactive()
    return ret 

def colorbar(mappable=None, cax=None, ax=None, **kw):
    if mappable is None:
        mappable = gci()
        if mappable is None:
            raise RuntimeError('No mappable was found to use for colorbar '
                               'creation. First define a mappable such as '
                               'an image (with imshow) or a contour set ('
                               'with contourf).')
    if ax is None:
        ax = gca()

    ret = gcf().colorbar(mappable, cax = cax, ax=ax, **kw)
    return ret 

def colorbar(mappable=None, cax=None, ax=None, **kw):
    if mappable is None:
        mappable = gci()
        if mappable is None:
            raise RuntimeError('No mappable was found to use for colorbar '
                               'creation. First define a mappable such as '
                               'an image (with imshow) or a contour set ('
                               'with contourf).')
    if ax is None:
        ax = gca()

    ret = gcf().colorbar(mappable, cax = cax, ax=ax, **kw)
    return ret 

def test_SymLogNorm_colorbar():
    """
    Test un-called SymLogNorm in a colorbar.
    """
    norm = mcolors.SymLogNorm(0.1, vmin=-1, vmax=1, linscale=1)
    fig = plt.figure()
    cbar = mcolorbar.ColorbarBase(fig.add_subplot(111), norm=norm)
    plt.close(fig) 

def test_cmap_and_norm_from_levels_and_colors():
    data = np.linspace(-2, 4, 49).reshape(7, 7)
    levels = [-1, 2, 2.5, 3]
    colors = ['red', 'green', 'blue', 'yellow', 'black']
    extend = 'both'
    cmap, norm = mcolors.from_levels_and_colors(levels, colors, extend=extend)

    ax = plt.axes()
    m = plt.pcolormesh(data, cmap=cmap, norm=norm)
    plt.colorbar(m)

    # Hide the axes labels (but not the colorbar ones, as they are useful)
    ax.tick_params(labelleft=False, labelbottom=False) 

def colorbar(mappable=None, cax=None, ax=None, **kw):
    if mappable is None:
        mappable = gci()
        if mappable is None:
            raise RuntimeError('No mappable was found to use for colorbar '
                               'creation. First define a mappable such as '
                               'an image (with imshow) or a contour set ('
                               'with contourf).')
    if ax is None:
        ax = gca()

    ret = gcf().colorbar(mappable, cax = cax, ax=ax, **kw)
    return ret 

def spect(tr,fmin = 0.1,fmax = None,wlen=10,title=None):
    import matplotlib as plt
    if fmax is None:
        fmax = tr.stats.sampling_rate/2
    fig = plt.figure()
    ax1 = fig.add_axes([0.1, 0.75, 0.7, 0.2]) #[left bottom width height]
    ax2 = fig.add_axes([0.1, 0.1, 0.7, 0.60], sharex=ax1)
    ax3 = fig.add_axes([0.83, 0.1, 0.03, 0.6])

    #make time vector
    t = np.arange(tr.stats.npts) / tr.stats.sampling_rate

    #plot waveform (top subfigure)    
    ax1.plot(t, tr.data, 'k')

    #plot spectrogram (bottom subfigure)
    tr2 = tr.copy()
    fig = tr2.spectrogram(per_lap=0.9,wlen=wlen,show=False, axes=ax2)
    mappable = ax2.images[0]
    plt.colorbar(mappable=mappable, cax=ax3)
    ax2.set_ylim(fmin, fmax)
    ax2.set_xlabel('Time [s]')
    ax2.set_ylabel('Frequency [Hz]')
    if title:
        plt.suptitle(title)
    else:
        plt.suptitle('{}.{}.{} {}'.format(tr.stats.network,tr.stats.station,
                  tr.stats.channel,tr.stats.starttime))
    plt.show() 

def test_SymLogNorm_colorbar():
    """
    Test un-called SymLogNorm in a colorbar.
    """
    norm = mcolors.SymLogNorm(0.1, vmin=-1, vmax=1, linscale=1)
    fig = plt.figure()
    cbar = mcolorbar.ColorbarBase(fig.add_subplot(111), norm=norm)
    plt.close(fig) 

def test_cmap_and_norm_from_levels_and_colors():
    data = np.linspace(-2, 4, 49).reshape(7, 7)
    levels = [-1, 2, 2.5, 3]
    colors = ['red', 'green', 'blue', 'yellow', 'black']
    extend = 'both'
    cmap, norm = mcolors.from_levels_and_colors(levels, colors, extend=extend)

    ax = plt.axes()
    m = plt.pcolormesh(data, cmap=cmap, norm=norm)
    plt.colorbar(m)

    # Hide the axes labels (but not the colorbar ones, as they are useful)
    ax.tick_params(labelleft=False, labelbottom=False) 

def colorbar(mappable=None, cax=None, ax=None, **kw):
    if mappable is None:
        mappable = gci()
        if mappable is None:
            raise RuntimeError('No mappable was found to use for colorbar '
                               'creation. First define a mappable such as '
                               'an image (with imshow) or a contour set ('
                               'with contourf).')
    if ax is None:
        ax = gca()

    ret = gcf().colorbar(mappable, cax = cax, ax=ax, **kw)
    return ret 

def colorbar(mappable=None, cax=None, ax=None, **kw):
    if mappable is None:
        mappable = gci()
        if mappable is None:
            raise RuntimeError('No mappable was found to use for colorbar '
                               'creation. First define a mappable such as '
                               'an image (with imshow) or a contour set ('
                               'with contourf).')
    if ax is None:
        ax = gca()
    ret = gcf().colorbar(mappable, cax=cax, ax=ax, **kw)
    return ret 

def test_SymLogNorm_colorbar():
    """
    Test un-called SymLogNorm in a colorbar.
    """
    norm = mcolors.SymLogNorm(0.1, vmin=-1, vmax=1, linscale=1)
    fig = plt.figure()
    cbar = mcolorbar.ColorbarBase(fig.add_subplot(111), norm=norm)
    plt.close(fig) 

def test_cmap_and_norm_from_levels_and_colors():
    data = np.linspace(-2, 4, 49).reshape(7, 7)
    levels = [-1, 2, 2.5, 3]
    colors = ['red', 'green', 'blue', 'yellow', 'black']
    extend = 'both'
    cmap, norm = mcolors.from_levels_and_colors(levels, colors, extend=extend)

    ax = plt.axes()
    m = plt.pcolormesh(data, cmap=cmap, norm=norm)
    plt.colorbar(m)

    # Hide the axes labels (but not the colorbar ones, as they are useful)
    ax.tick_params(labelleft=False, labelbottom=False) 

def test_remove_from_figure_no_gridspec():
    """
    Make sure that `remove_from_figure` removes a colorbar that was created
    without modifying the gridspec
    """
    _test_remove_from_figure(False) 

def test_remove_from_figure_with_gridspec():
    """
    Make sure that `remove_from_figure` removes the colorbar and properly
    restores the gridspec
    """
    _test_remove_from_figure(True) 

def _test_remove_from_figure(use_gridspec):
    """
    Test `remove_from_figure` with the specified ``use_gridspec`` setting
    """
    fig = plt.figure()
    ax = fig.add_subplot(111)
    sc = ax.scatter([1, 2], [3, 4], cmap="spring")
    sc.set_array(np.array([5, 6]))
    pre_figbox = np.array(ax.figbox)
    cb = fig.colorbar(sc, use_gridspec=use_gridspec)
    fig.subplots_adjust()
    cb.remove()
    fig.subplots_adjust()
    post_figbox = np.array(ax.figbox)
    assert (pre_figbox == post_figbox).all() 

def test_colorbar_single_scatter():
    # Issue #2642: if a path collection has only one entry,
    # the norm scaling within the colorbar must ensure a
    # finite range, otherwise a zero denominator will occur in _locate.
    plt.figure()
    x = np.arange(4)
    y = x.copy()
    z = np.ma.masked_greater(np.arange(50, 54), 50)
    cmap = plt.get_cmap('jet', 16)
    cs = plt.scatter(x, y, z, c=z, cmap=cmap)
    plt.colorbar(cs) 

def test_colorbar_extension_length():
    '''Test variable length colorbar extensions.'''
    # Create figures for uniform and proportionally spaced colorbars.
    fig1 = _colorbar_extension_length('uniform')
    fig2 = _colorbar_extension_length('proportional') 

def _colorbar_extension_length(spacing):
    '''
    Produce 12 colorbars with variable length extensions for either
    uniform or proportional spacing.

    Helper function for test_colorbar_extension_length.
    '''
    # Get a colormap and appropriate norms for each extension type.
    cmap, norms = _get_cmap_norms()
    # Create a figure and adjust whitespace for subplots.
    fig = plt.figure()
    fig.subplots_adjust(hspace=.6)
    for i, extension_type in enumerate(('neither', 'min', 'max', 'both')):
        # Get the appropriate norm and use it to get colorbar boundaries.
        norm = norms[extension_type]
        boundaries = values = norm.boundaries
        for j, extendfrac in enumerate((None, 'auto', 0.1)):
            # Create a subplot.
            cax = fig.add_subplot(12, 1, i*3 + j + 1)
            # Turn off text and ticks.
            for item in cax.get_xticklabels() + cax.get_yticklabels() +\
                    cax.get_xticklines() + cax.get_yticklines():
                item.set_visible(False)
            # Generate the colorbar.
            cb = ColorbarBase(cax, cmap=cmap, norm=norm,
                    boundaries=boundaries, values=values,
                    extend=extension_type, extendfrac=extendfrac,
                    orientation='horizontal', spacing=spacing)
    # Return the figure to the caller.
    return fig 

def _colorbar_extension_shape(spacing):
    '''
    Produce 4 colorbars with rectangular extensions for either uniform
    or proportional spacing.

    Helper function for test_colorbar_extension_shape.
    '''
    # Get a colormap and appropriate norms for each extension type.
    cmap, norms = _get_cmap_norms()
    # Create a figure and adjust whitespace for subplots.
    fig = plt.figure()
    fig.subplots_adjust(hspace=4)
    for i, extension_type in enumerate(('neither', 'min', 'max', 'both')):
        # Get the appropriate norm and use it to get colorbar boundaries.
        norm = norms[extension_type]
        boundaries = values = norm.boundaries
        # Create a subplot.
        cax = fig.add_subplot(4, 1, i + 1)
        # Turn off text and ticks.
        for item in cax.get_xticklabels() + cax.get_yticklabels() +\
                cax.get_xticklines() + cax.get_yticklines():
            item.set_visible(False)
        # Generate the colorbar.
        cb = ColorbarBase(cax, cmap=cmap, norm=norm,
                boundaries=boundaries, values=values,
                extend=extension_type, extendrect=True,
                orientation='horizontal', spacing=spacing)
    # Return the figure to the caller.
    return fig 

def colorbar(mappable=None, cax=None, ax=None, **kw):
    if mappable is None:
        mappable = gci()
        if mappable is None:
            raise RuntimeError('No mappable was found to use for colorbar '
                               'creation. First define a mappable such as '
                               'an image (with imshow) or a contour set ('
                               'with contourf).')
    if ax is None:
        ax = gca()

    ret = gcf().colorbar(mappable, cax = cax, ax=ax, **kw)
    draw_if_interactive()
    return ret 

def colorbar(mappable=None, cax=None, ax=None, **kw):
    if mappable is None:
        mappable = gci()
        if mappable is None:
            raise RuntimeError('No mappable was found to use for colorbar '
                               'creation. First define a mappable such as '
                               'an image (with imshow) or a contour set ('
                               'with contourf).')
    if ax is None:
        ax = gca()

    ret = gcf().colorbar(mappable, cax = cax, ax=ax, **kw)
    draw_if_interactive()
    return ret 

def plotEachRule(mdl, X, d1, d2, alpha=0.8, filename='', rnum=-1, plot_line=[]):
    if rnum <= 0:
        rnum = len(mdl.rule_)
    else:
        rnum = min(len(mdl.rule_), rnum)
    m = rnum // 4
    if m * 4 < rnum:
        m += 1
    cmap = cm.get_cmap('cool')
    fig, ax = plt.subplots(m, 4 + 1, figsize=(4 * 4, 3 * m), gridspec_kw = {'width_ratios':[15, 15, 15, 15, 1]})
    idx = np.argsort(mdl.weight_[:rnum])
    for i in range(rnum):
        j = i // 4
        k = i - 4 * j
        r = mdl.rule_[idx[i]]
        box, vmin, vmax = __r2boxWithX(r, X)
        if mdl.modeltype_ == 'regression':
            c = cmap(mdl.pred_[idx[i]])
        elif mdl.modeltype_ == 'classification':
            r = mdl.pred_[idx[i]] / max(np.unique(mdl.pred_).size - 1, 1)
            c = cmap(r)
        ax[j, k].add_patch(pl.Rectangle(xy=[box[0, d1], box[0, d2]], width=(box[1, d1] - box[0, d1]), height=(box[1, d2] - box[0, d2]), facecolor=c, linewidth='2.0', alpha=alpha))
        if len(plot_line) > 0:
            for l in plot_line:
                ax[j, k].plot(l[0], l[1], 'k--')
        ax[j, k].set_xlim([0, 1])
        ax[j, k].set_ylim([0, 1])
        if k == 3:
            cbar = colorbar.ColorbarBase(ax[j, -1], cmap=cmap, format='%.1f', ticks=[0.0, 0.5, 1.0])
            cbar.ax.set_yticklabels([0.0, 0.5, 1.0])
            ax[j, -1].set_ylabel('Predictor y', size=12)
    plt.show()
    if not filename == '':
        plt.savefig(filename, format="pdf", bbox_inches="tight")
        plt.close() 

def plotRule(mdl, X, d1, d2, alpha=0.8, filename='', rnum=-1, plot_line=[]):
    cmap = cm.get_cmap('cool')
    fig, (ax1, ax2) = plt.subplots(1, 2, gridspec_kw = {'width_ratios':[19, 1]})
    if rnum <= 0:
        rnum = len(mdl.rule_)
    else:
        rnum = min(len(mdl.rule_), rnum)
    idx = np.argsort(mdl.weight_[:rnum])
    for i in range(rnum):
        r = mdl.rule_[idx[i]]
        box, vmin, vmax = __r2boxWithX(r, X)
        if mdl.modeltype_ == 'regression':
            c = cmap(mdl.pred_[idx[i]])
        elif mdl.modeltype_ == 'classification':
            r = mdl.pred_[idx[i]] / max(np.unique(mdl.pred_).size - 1, 1)
            c = cmap(r)
        ax1.add_patch(pl.Rectangle(xy=[box[0, d1], box[0, d2]], width=(box[1, d1] - box[0, d1]), height=(box[1, d2] - box[0, d2]), facecolor=c, linewidth='2.0', alpha=alpha))
    if len(plot_line) > 0:
        for l in plot_line:
            ax1.plot(l[0], l[1], 'k--')
    ax1.set_xlabel('x1', size=22)
    ax1.set_ylabel('x2', size=22)
    ax1.set_title('Simplified Model (K = %d)' % (rnum,), size=28)
    colorbar.ColorbarBase(ax2, cmap=cmap, format='%.1f')
    ax2.set_ylabel('Predictor y', size=22)
    plt.show()
    if not filename == '':
        plt.savefig(filename, format="pdf", bbox_inches="tight")
        plt.close() 

def get_legend(ctx: ServiceContext,
               ds_id: str,
               var_name: str,
               params: RequestParams):
    cmap_name = params.get_query_argument('cbar', default=None)
    cmap_vmin = params.get_query_argument_float('vmin', default=None)
    cmap_vmax = params.get_query_argument_float('vmax', default=None)
    cmap_w = params.get_query_argument_int('width', default=None)
    cmap_h = params.get_query_argument_int('height', default=None)
    if cmap_name is None or cmap_vmin is None or cmap_vmax is None or cmap_w is None or cmap_h is None:
        default_cmap_cbar, (default_cmap_vmin, default_cmap_vmax) = ctx.get_color_mapping(ds_id, var_name)
        cmap_name = cmap_name or default_cmap_cbar
        cmap_vmin = cmap_vmin or default_cmap_vmin
        cmap_vmax = cmap_vmax or default_cmap_vmax
        cmap_w = cmap_w or DEFAULT_CMAP_WIDTH
        cmap_h = cmap_h or DEFAULT_CMAP_HEIGHT

    try:
        _, cmap = get_cmap(cmap_name)
    except ValueError:
        raise ServiceResourceNotFoundError(f"color bar {cmap_name!r} not found")

    fig = matplotlib.figure.Figure(figsize=(cmap_w, cmap_h))
    ax1 = fig.add_subplot(1, 1, 1)
    if '.cpd' in cmap_name:
        norm, ticks = get_norm(cmap_name)
    else:
        norm = matplotlib.colors.Normalize(vmin=cmap_vmin, vmax=cmap_vmax)
        ticks = None

    image_legend = matplotlib.colorbar.ColorbarBase(ax1,
                                                    format='%.1f',
                                                    ticks=ticks,
                                                    cmap=cmap,
                                                    norm=norm,
                                                    orientation='vertical')

    image_legend_label = ctx.get_legend_label(ds_id, var_name)
    if image_legend_label is not None:
        image_legend.set_label(image_legend_label)

    fig.patch.set_facecolor('white')
    fig.patch.set_alpha(0.0)
    fig.tight_layout()

    buffer = io.BytesIO()
    fig.savefig(buffer, format='png')

    return buffer.getvalue() 

def test_colorbar_positioning():
    data = np.arange(1200).reshape(30, 40)
    levels = [0, 200, 400, 600, 800, 1000, 1200]

    # -------------------
    plt.figure()
    plt.contourf(data, levels=levels)
    plt.colorbar(orientation='horizontal', use_gridspec=False)

    locations = ['left', 'right', 'top', 'bottom']
    plt.figure()
    for i, location in enumerate(locations):
        plt.subplot(2, 2, i + 1)
        plt.contourf(data, levels=levels)
        plt.colorbar(location=location, use_gridspec=False)

    # -------------------
    plt.figure()
    # make some other data (random integers)
    data_2nd = np.array([[2, 3, 2, 3], [1.5, 2, 2, 3], [2, 3, 3, 4]])
    # make the random data expand to the shape of the main data
    data_2nd = np.repeat(np.repeat(data_2nd, 10, axis=1), 10, axis=0)

    color_mappable = plt.contourf(data, levels=levels, extend='both')
    # test extend frac here
    hatch_mappable = plt.contourf(data_2nd, levels=[1, 2, 3], colors='none',
                                  hatches=['/', 'o', '+'], extend='max')
    plt.contour(hatch_mappable, colors='black')

    plt.colorbar(color_mappable, location='left', label='variable 1',
                 use_gridspec=False)
    plt.colorbar(hatch_mappable, location='right', label='variable 2',
                 use_gridspec=False)

    # -------------------
    plt.figure()
    ax1 = plt.subplot(211, anchor='NE', aspect='equal')
    plt.contourf(data, levels=levels)
    ax2 = plt.subplot(223)
    plt.contourf(data, levels=levels)
    ax3 = plt.subplot(224)
    plt.contourf(data, levels=levels)

    plt.colorbar(ax=[ax2, ax3, ax1], location='right', pad=0.0, shrink=0.5,
                 panchor=False, use_gridspec=False)
    plt.colorbar(ax=[ax2, ax3, ax1], location='left', shrink=0.5,
                 panchor=False, use_gridspec=False)
    plt.colorbar(ax=[ax1], location='bottom', panchor=False,
                 anchor=(0.8, 0.5), shrink=0.6, use_gridspec=False)