Python cm.get_cmap函数代码示例

  def plot (self, xres, yres, plain, spectra, nchan, freq, bw, tsamp, nsamps):
    self.openPlot(xres, yres, plain)

    tmin = 0
    tmax = nsamps * tsamp

    fmin = freq - (bw/2)
    fmax = freq + (bw/2)

    if numpy.amax(spectra) == numpy.amin(spectra):
      spectra[0][0] = 1

    if self.zap:
      spectra[0,:] = 0

    if self.log:
      vmax = numpy.log(numpy.amax(spectra))
      self.ax.imshow(spectra, extent=(tmin, tmax, fmin, fmax), aspect='auto', 
                     origin='lower', interpolation='nearest', norm=LogNorm(vmin=0.0001,vmax=vmax), 
                     cmap=cm.get_cmap('gray'))
    else:
      self.ax.imshow(spectra, extent=(tmin, tmax, fmin, fmax), aspect='auto', 
                     origin='lower', interpolation='nearest', 
                     cmap=cm.get_cmap('gray'))

    self.closePlot()

    def _create_palette(self):
        '''Create a palette based on Matplotlib colormap name

        default number of color palette is 250.
        it means 6 colors are possible to use for other purposes.
        the last palette (255) is white and the second last (254) is black.

        Modifies
        --------
        self.palette : numpy array (uint8)

        '''
        # test if given colormap name is in builtin or added colormaps
        try:
            cmap = cm.get_cmap(self.cmapName)
        except:
            self.logger.error('%s is not a valid colormap' % self.cmapName)
            self.cmapName = self._cmapName

        # get colormap by name
        cmap = cm.get_cmap(self.cmapName)

        # get colormap look-up
        cmapLUT = np.uint8(cmap(range(self.numOfColor)) * 255)
        # replace all last colors to black and...
        lut = np.zeros((3, 256), 'uint8')
        lut[:, :self.numOfColor] = cmapLUT.T[:3]
        # ...and the most last color to white
        lut[:, -1] = 255

        # set palette to be used by PIL
        self.palette = lut.T.flatten().astype(np.uint8)

def main(months=None):
    """
    Note: if you want to compare more than 2 simulations at the same time
    make sure that the differences are plotted correctly
    :param months:
    """
    matplotlib.rc("font", size=20)
    # List of simulations to compare
    label_to_path = OrderedDict([
        ("(1) Without interflow", "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5"),
        ("(2) With interflow-a", "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_ITFS.hdf5"),
        # ("With interflow (b)",
        # "/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl-intfl_ITFS_avoid_truncation1979-1989.hdf5")
    ])

    nsims = len(label_to_path)
    width_ratios = nsims * [1.0, ] + [0.05, ] + [1.3, 0.05]
    gs = GridSpec(1, nsims + 3, width_ratios=width_ratios, wspace=0.25)

    cdelta = 0.05
    clevels = np.arange(-1.0, 1.0 + cdelta, cdelta)
    cnorm = BoundaryNorm(clevels, len(clevels) - 1)
    cmap = cm.get_cmap("RdBu_r", len(clevels) - 1)

    start_year = 1980
    end_year = 1989

    keys = [k.replace(" ", "-") for k in label_to_path]
    keys.extend([str(i) for i in (start_year, end_year)])
    keys.append("-".join(str(m) for m in months))
    img_path = "corr-SM-LH_{}_{}_{}-{}_{}.png".format(*keys)

    fig = plt.figure()
    assert isinstance(fig, Figure)
    fig.set_figwidth(fig.get_figwidth() * 3)

    im = None
    sim_label_to_corr = OrderedDict()
    for col, (sim_label, sim_path) in enumerate(label_to_path.items()):
        ax = fig.add_subplot(gs[0, col])
        im, corr = plot_for_simulation(axis=ax, sim_path=sim_path, cmap=cmap, cnorm=cnorm,
                                       start_year=start_year, end_year=end_year, months=months)
        ax.set_title(sim_label)
        sim_label_to_corr[sim_label] = corr

    plt.colorbar(im, cax=fig.add_subplot(gs[0, nsims]))

    # plot differences in correlation
    cdelta = 0.05
    clevels = np.arange(-0.5, -cdelta, cdelta)
    clevels = clevels.tolist() + [-c for c in reversed(clevels)]
    cnorm = BoundaryNorm(clevels, len(clevels) - 1)
    cmap = cm.get_cmap("RdBu_r", len(clevels) - 1)

    im = plot_correlation_diff(sim_label_to_corr, file_for_basemap=list(label_to_path.values())[0],
                               ax=fig.add_subplot(gs[0, nsims + 1]), cnorm=cnorm, cmap=cmap)

    plt.colorbar(im, cax=fig.add_subplot(gs[0, nsims + 2]))
    # fig.tight_layout()
    fig.savefig(img_path)

def main():

    get_file = lambda x: "cru_ts3.23.1901.2014.{0}.100mean.pkl".format(x)

    tair_data = pickle.load(open(FILEPATH + get_file('tmp'), 'rb'))
    rain_data = pickle.load(open(FILEPATH + get_file('pre'), 'rb'))

    sav_patch = pickle.load(open(PATCHPATH, 'rb'))

    fig = plt.figure(figsize=(10, 6), frameon=False)
    fig.add_axes([0, 0, 1.0, 1.0])

    n_plots = 2
    grid = gridspec.GridSpec(n_plots, 1, hspace=0.3)
    subaxs = [plt.subplot(grid[i]) for i in range(n_plots)]

    # Mean Annual Temperature plot
    make_map(subaxs[0], tair_data, sav_patch['clip'], \
             cmap=get_cmap(MAPCOLOR), \
             levels=np.arange(15, 35, 0.5), \
             cticks=np.arange(15, 35, 2.5), \
             title="Global Savanna Bioregion \\\\ Mean Annual Temperature (1901 to 2013)")

    # Mean Annual Rainfall plot
    make_map(subaxs[1], rain_data, sav_patch['clip'], \
             cmap=get_cmap(MAPCOLOR), \
             levels=np.logspace(1, 3, 100), \
             cticks=[10, 50, 100, 200, 500, 1000], \
             norm=SymLogNorm(linthresh=0.3, linscale=0.03), \
             title="Global Savanna Bioregion \\\\ Mean Annual Precipitation (1901 - 2013)")

    plt.savefig(SAVEPATH)

    def __plot_storage(prefix, show_cb=False, row=0, col=0, plot_deviations=True):

        if plot_deviations:
            clevs = [0, 1e3, 1e4, 1e5, 1.0e6, 1e7, 1e8, 1.0e9]
            clevs = [-c for c in reversed(clevs)][:-1] + clevs
            cmap = cm.get_cmap("bwr", len(clevs) - 1)
        else:
            clevs = [0, 1e3, 1e4, 1e5, 1.0e6, 1e7, 1e8, 1.0e9]
            cmap = cm.get_cmap("YlGnBu", len(clevs) - 1)

        norm = BoundaryNorm(boundaries=clevs, ncolors=len(clevs) - 1)

        _storage = ds["{}_{}".format(prefix, storage_var_name)][:]
        ax = fig.add_subplot(gs[row, col])
        _storage = _storage.where(_storage > storage_lower_limit_m3)
        _storage = maskoceans(lons2d_, lats2d, _storage)
        _storage = np.ma.masked_where(np.isnan(_storage), _storage)


        if plot_deviations:
            cs = bmap.pcolormesh(xx, yy, _storage - bankfull_storage, norm=norm, cmap=cmap)
        else:
            cs = bmap.pcolormesh(xx, yy, _storage, norm=norm, cmap=cmap)


        ext = "both" if plot_deviations else "max"


        cb = bmap.colorbar(cs, location="bottom", format=FuncFormatter(__storage_cb_format_ticklabels), extend=ext)
        cb.ax.set_visible(show_cb)
        cb.ax.set_xlabel(r"${\rm m^3}$")
        ax.set_title(prefix)
        axes.append(ax)
        return _storage

def cmap_discretize(cmap, n_colors=10):
    """Return discretized colormap.

    Parameters
    ----------
    cmap : str or colormap object
        Colormap to discretize.
    n_colors : int
        Number of discrete colors to divide `cmap` into.

    Returns
    ----------
    disc_cmap : LinearSegmentedColormap
        Discretized colormap.
    """
    try:
        cmap = cm.get_cmap(cmap)
    except:
        cmap = cm.get_cmap(eval(cmap))
    colors_i = np.concatenate((np.linspace(0, 1., n_colors), (0., 0., 0., 0.)))
    colors_rgba = cmap(colors_i)
    indices = np.linspace(0, 1., n_colors + 1)
    cdict = {}
    for ki, key in enumerate(('red', 'green', 'blue')):
        cdict[key] = [(indices[i], colors_rgba[i - 1, ki], colors_rgba[i, ki])
                      for i in range(n_colors + 1)]

    return mpl.colors.LinearSegmentedColormap(cmap.name + "_%d" % n_colors,
                                              cdict, 1024)

def featurePosPlot(featureList, name, k=1, save=None):
    font = {'family' : 'monospace',
        'weight' : 'normal',
        'size'   : '6'}
    plt.rc('font', **font)
    fig = plt.figure(k)
    fig.suptitle(name)
    ax = fig.add_subplot(221)
    sc = ax.scatter([2,2,3,3,3,2], [3,1,1,2,3,2], c=featureList['mav'], s=1000, cmap=cm.get_cmap('binary'))
    ax.set_title("mav")
    plt.colorbar(sc, ticks = featureList['mav'])
    ax.grid(True)
    ax = fig.add_subplot(224)
    sc = ax.scatter([2,2,3,3,3,2], [3,1,1,2,3,2], c=featureList['ni'], s=1000, cmap=cm.get_cmap('binary'))
    ax.set_title("ni")
    plt.colorbar(sc, ticks = featureList['ni'])
    ax.grid(True)
    
    ax = fig.add_subplot(222)
    sc = ax.scatter([2,2,3,3,3,2], [3,1,1,2,3,2], c=featureList['rmsd'], s=1000, cmap=cm.get_cmap('binary'))
    plt.colorbar(sc, ticks = featureList['rmsd'])
    ax.set_title("rmsd")
    ax.grid(True)
    ax = fig.add_subplot(223)
    sc = ax.scatter([2,2,3,3,3,2], [3,1,1,2,3,2], c=featureList['var'], s=1000, cmap=cm.get_cmap('binary'))
    ax.set_title("var")
    plt.colorbar(sc, ticks = featureList['var'])
    ax.grid(True)
    
    if save:
        save.savefig(k)
    else:
        plt.show()

    def __plot(self, spectrum):
        x, y, z = create_mesh(spectrum, True)
        self.parent.clear_plots()

        if self.settings.autoL:
            vmin, vmax = self.barBase.get_clim()
        else:
            zExtent = self.extent.get_l()
            vmin = zExtent[0]
            vmax = zExtent[1]
        if self.parent.settings.wireframe:
            self.parent.plot = \
                self.axes.plot_wireframe(x, y, z,
                                         rstride=1, cstride=1,
                                         linewidth=0.1,
                                         cmap=cm.get_cmap(self.settings.colourMap),
                                         gid='plot_line',
                                         antialiased=True,
                                         alpha=1)
        else:
            self.parent.plot = \
                self.axes.plot_surface(x, y, z,
                                       rstride=1, cstride=1,
                                       vmin=vmin, vmax=vmax,
                                       linewidth=0,
                                       cmap=cm.get_cmap(self.settings.colourMap),
                                       gid='plot_line',
                                       antialiased=True,
                                       alpha=1)

        return self.extent.get_peak_flt()

    def showMask(self, splot, mask):
        if mask != None:
            nr, nc = mask.shape
            extent = [-0.5, nc-0.5, -0.5, nr-0.5]

            if self.displayMaskFlag == self.MASK_SHOWN:
                labels = _N.unique(mask)
                for il, lab in enumerate(labels):
                    if lab != 0:
                        if self.maskCm!=None : cm = self.maskCm[lab]
                        else : cm = get_cmap('binary')
                        splot.contour((mask==lab).astype(int), 1,
                                      cmap=cm, linewidths=2., extent=extent,alpha=.7)
                                     #cmap=cm, linewidths=1.5, extent=extent,alpha=.7)
            if self.displayMaskFlag == self.MASK_ONLY:
                if self.maskLabel == 0:
                    labels = _N.unique(mask)
                    for il, lab in enumerate(labels):
                        if lab != 0:
                            if self.maskCm != None:
                                cm = self.maskCm[lab]
                            else:
                                cm = get_cmap('binary')
                            ml = (mask==lab).astype(int)
                            print 'contouf of mask label %d -> %d pos' \
                                %(lab, ml.sum())
                            splot.contourf(ml, 1,
                                           cmap=cm, linewidths=1., extent=extent)
                elif (mask==self.maskLabel).sum() > 0:
                    if self.maskCm != None:
                        cm = self.maskCm[_N.where(mask==self.maskLabel)[0]]
                    else:
                        cm = get_cmap('binary')
                    ax.contourf((mask==self.maskLabel).astype(int), 1,
                                cmap=cm, linewidths=1.5, extent=extent)

def plotKernel(xRange, yRange, kernel, label):
    fig = plt.figure()
    
    plt.subplot(1,2,1)
    
    # adding the Contour lines with labels
    kernelMax = np.max(kernel)
    kernelMin = np.min(kernel)
    kernelValueThreshold = 10**-1
    
    if np.abs(kernelMax - kernelMin) < kernelValueThreshold:
        print "kernel value range is < " + str(kernelValueThreshold) + ", therefore image will not contains contours"
    else:
        contourIncrement = np.round(np.abs((kernelMax - kernelMin)) * 0.1, 2)
        contourRange = np.arange(np.min(kernel), np.max(kernel) , contourIncrement )
    
        cset = contour(kernel,contourRange, linewidths=2,cmap=cm.get_cmap('set2'))
        plt.clabel(cset,inline=True,fmt='%1.1f',fontsize=10)

    # latex fashion title
#     title('$z=3*e^{-(x^2+y^2)}$')
    title(label)
    
    plt.imshow(kernel,cmap=cm.get_cmap('gray')) # drawing the function

    ax = fig.add_subplot(1, 2, 2, projection='3d')
    surf = ax.plot_surface(xRange, yRange, kernel, rstride=1, cstride=1, 
    cmap=cm.get_cmap('RdBu'),linewidth=0, antialiased=False)
    ax.zaxis.set_major_locator(LinearLocator(10))
    ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))
    
    plt.show()

def create_2_plots_v(mic1, mic2, title1, title2, xlabel1, ylabel1, xlabel2, ylabel2, colormap):

    fig, axes = plt.subplots(2, sharex=True)
    mic_array = array(mic1)
    dimensions = mic_array.shape
    nx = dimensions[0]
    ny = dimensions[1]
    axes[0].imshow(mic1, extent=(0, nx, ny, 0),cmap=cm.get_cmap(colormap))
    axes[0].set_title(title1)
    axes[0].set_xlabel(xlabel1)
    axes[0].set_ylabel(ylabel1)
    axes[0].set_ylim([0,ny])
    
    mic_array = array(mic2)
    dimensions = mic_array.shape
    nx = dimensions[0]
    ny = dimensions[1]
    print(nx, ny)
    axes[1].imshow(mic2, extent=(0, nx, ny, 0),cmap=cm.get_cmap(colormap))
    axes[1].set_title(title2)
    axes[1].set_xlabel(xlabel2)
    axes[1].set_ylabel(ylabel2)
    axes[1].set_ylim([0,ny])
    plt.axis('tight')
    plt.autoscale(enable=True, axis='y', tight=True)

    plt.show()

def save_seg_plot(outfile_path, title_str1, title_str2,
                  ref_intervals, ref_labels, est_intervals, est_labels,
                  cmap='Accent'):
    fig = plt.figure(figsize=(9, 3))

    ulabel, invind = np.unique(ref_labels, return_inverse=True)
    norm = mpl.colors.Normalize(vmin=np.min(invind), vmax=np.max(invind))
    cmap = cm.get_cmap(cmap)
    m = cm.ScalarMappable(norm=norm, cmap=cmap)

    plt.subplot(211)
    seg = [interval[0] for interval in ref_intervals[1:]]
    plt.vlines(seg, 0, 1, linewidth=1)
    for interval, i in zip(ref_intervals, invind):
        plt.fill_between([interval[0], interval[1]], 0, 1, color=m.to_rgba(i))
    plt.title('Ground truth segmentation - ' + title_str1)
    plt.xlim(0, ref_intervals[-1][1])
    plt.yticks([])
    plt.xticks([])

    ax = fig.add_subplot(212)
    ulabel, invind = np.unique(est_labels, return_inverse=True)
    norm = mpl.colors.Normalize(vmin=np.min(invind), vmax=np.max(invind))
    cmap = cm.get_cmap(cmap)
    m = cm.ScalarMappable(norm=norm, cmap=cmap)
    seg = [interval[0] for interval in est_intervals[1:]]
    plt.vlines(seg, 0, 1, linewidth=1)
    for interval, i in zip(est_intervals, invind):
        plt.fill_between([interval[0], interval[1]], 0, 1, color=m.to_rgba(i))
    plt.title('Detected segmentation - ' + title_str2)
    plt.xlim(0, est_intervals[-1][1])
    plt.yticks([])
    plt.xticks([])
    plt.tight_layout()
    plt.savefig(outfile_path)

def _plot_radiation_pattern_quiver(ax3d, ned_mt, type):
    """
    Private routine that plots the wave farfield into an
    :class:`~mpl_toolkits.mplot3d.axes3d.Axes3D` object

    :type ax3d: :class:`mpl_toolkits.mplot3d.axes3d.Axes3D`
    :param ax3d: matplotlib Axes3D object
    :param ned_mt: the 6 comp moment tensor in NED orientation
    :type type: str
    :param type: 'P' or 'S' (P or S wave).
    """
    import matplotlib.pyplot as plt
    if MATPLOTLIB_VERSION < [1, 4]:
        msg = ("Matplotlib 3D quiver plot needs matplotlib version >= 1.4.")
        raise ImportError(msg)

    type = type.upper()
    if type not in ("P", "S"):
        msg = ("type must be 'P' or 'S'")
        raise ValueError(msg)
    is_p_wave = type == "P"

    # precompute even spherical grid and directional cosine array
    points = _equalarea_spherical_grid(nlat=14)

    if is_p_wave:
        # get radiation pattern
        disp = farfield(ned_mt, points, type="P")
        # normalized magnitude:
        magn = np.sum(disp * points, axis=0)
        magn /= np.max(np.abs(magn))
        cmap = get_cmap('bwr')
    else:
        # get radiation pattern
        disp = farfield(ned_mt, points, type="S")
        # normalized magnitude (positive only):
        magn = np.sqrt(np.sum(disp * disp, axis=0))
        magn /= np.max(np.abs(magn))
        cmap = get_cmap('Greens')

    # plot
    # there is a mlab3d bug that quiver vector colors and lengths
    # can only be changed if we plot each arrow independently
    for loc, vec, mag in zip(points.T, disp.T, magn.T):
        norm = plt.Normalize(-1., 1.)
        color = cmap(norm(mag))
        if is_p_wave:
            loc *= (1. + mag / 2.)
            length = abs(mag) / 2.0
        else:
            length = abs(mag) / 5.0
        ax3d.quiver(loc[0], loc[1], loc[2], vec[0], vec[1], vec[2],
                    length=length, color=color)
    ax3d.set(xlim=(-1.5, 1.5), ylim=(-1.5, 1.5), zlim=(-1.5, 1.5),
             xticks=[-1, 1], yticks=[-1, 1], zticks=[-1, 1],
             xticklabels=['South', 'North'],
             yticklabels=['West', 'East'],
             zticklabels=['Up', 'Down'],
             title='{} wave farfield'.format(type))
    ax3d.view_init(elev=-110., azim=0.)

def mplcmap_to_palette(cmap, ncolors=None, categorical=False):
    """
    Converts a matplotlib colormap to palette of RGB hex strings."
    """
    from matplotlib.colors import Colormap, ListedColormap

    ncolors = ncolors or 256
    if not isinstance(cmap, Colormap):
        import matplotlib.cm as cm
        # Alias bokeh Category cmaps with mpl tab cmaps
        if cmap.startswith('Category'):
            cmap = cmap.replace('Category', 'tab')
        try:
            cmap = cm.get_cmap(cmap)
        except:
            cmap = cm.get_cmap(cmap.lower())
    if isinstance(cmap, ListedColormap):
        if categorical:
            palette = [rgb2hex(cmap.colors[i%cmap.N]) for i in range(ncolors)]
            return palette
        elif cmap.N > ncolors:
            palette = [rgb2hex(c) for c in cmap(np.arange(cmap.N))]
            if len(palette) != ncolors:
                palette = [palette[int(v)] for v in np.linspace(0, len(palette)-1, ncolors)]
            return palette
    return [rgb2hex(c) for c in cmap(np.linspace(0, 1, ncolors))]

    def ChangePlotType(self, event):
        
        self.figure.clear()
        
        # Create an image plot
        if event.GetId() == 100:
            self.subplot = self.figure.add_subplot(111)
            self.plot = self.subplot.imshow(self.z_values, cmap=cm.get_cmap('gnuplot2'), norm=None, aspect='equal',
                                            interpolation=None, alpha=None, vmin=self.minValue, vmax=self.maxValue,
                                            origin='lower')
            self.plot_type = constants.IMAGE

        # Create a 3D wire plot
        if event.GetId() == 101:
            self.subplot = self.figure.add_subplot(111, projection='3d')
            self.plot = self.subplot.plot_wireframe(self.x_values, self.y_values, self.z_values)
            self.plot_type = constants.WIREFRAME
            
        # Create a 3D surface plt    
        if event.GetId() == 102:
            self.subplot = self.figure.add_subplot(111, projection='3d')
            self.plot = self.subplot.plot_surface(self.x_values, self.y_values, self.z_values, rstride=1, cstride=1,
                                                  cmap=cm.get_cmap('cool'), linewidth=0, antialiased=False)
            self.plot_type = constants.SURFACE

        if event.GetId() == 103:
            self.subplot = self.figure.add_subplot(111)
            dataCopy = self.z_values.copy(order='K')
            x_values = np.arange(0, dataCopy.size)
            self.subplot.plot(x_values, dataCopy.flatten('A'))
            self.plot_type = constants.TWOD_PLOT
            
        self.canvas.draw()
        
        return