Python cm.jet方法代码示例

# 需要导入模块: from matplotlib import cm [as 别名]
# 或者: from matplotlib.cm import jet [as 别名]
def plotNNFilter(units, figure_id, interp='bilinear', colormap=cm.jet, colormap_lim=None):
    plt.ion()
    filters = units.shape[2]
    n_columns = round(math.sqrt(filters))
    n_rows = math.ceil(filters / n_columns) + 1
    fig = plt.figure(figure_id, figsize=(n_rows*3,n_columns*3))
    fig.clf()

    for i in range(filters):
        ax1 = plt.subplot(n_rows, n_columns, i+1)
        plt.imshow(units[:,:,i].T, interpolation=interp, cmap=colormap)
        plt.axis('on')
        ax1.set_xticklabels([])
        ax1.set_yticklabels([])
        plt.colorbar()
        if colormap_lim:
            plt.clim(colormap_lim[0],colormap_lim[1])

    plt.subplots_adjust(wspace=0, hspace=0)
    plt.tight_layout()

# Epochs 

# 需要导入模块: from matplotlib import cm [as 别名]
# 或者: from matplotlib.cm import jet [as 别名]
def plotNNFilter(units, figure_id, interp='bilinear', colormap=cm.jet, colormap_lim=None):
    plt.ion()
    filters = units.shape[2]
    n_columns = round(math.sqrt(filters))
    n_rows = math.ceil(filters / n_columns) + 1
    fig = plt.figure(figure_id, figsize=(n_rows*3,n_columns*3))
    fig.clf()

    for i in range(filters):
        ax1 = plt.subplot(n_rows, n_columns, i+1)
        plt.imshow(units[:,:,i].T, interpolation=interp, cmap=colormap)
        plt.axis('on')
        ax1.set_xticklabels([])
        ax1.set_yticklabels([])
        plt.colorbar()
        if colormap_lim:
            plt.clim(colormap_lim[0],colormap_lim[1])

    plt.subplots_adjust(wspace=0, hspace=0)
    plt.tight_layout()

# Load options 

# 需要导入模块: from matplotlib import cm [as 别名]
# 或者: from matplotlib.cm import jet [as 别名]
def plotNNFilter(units, figure_id, interp='bilinear', colormap=cm.jet, colormap_lim=None, title=''):
    plt.ion()
    filters = units.shape[2]
    n_columns = round(math.sqrt(filters))
    n_rows = math.ceil(filters / n_columns) + 1
    fig = plt.figure(figure_id, figsize=(n_rows*3,n_columns*3))
    fig.clf()

    for i in range(filters):
        ax1 = plt.subplot(n_rows, n_columns, i+1)
        plt.imshow(units[:,:,i].T, interpolation=interp, cmap=colormap)
        plt.axis('on')
        ax1.set_xticklabels([])
        ax1.set_yticklabels([])
        plt.colorbar()
        if colormap_lim:
            plt.clim(colormap_lim[0],colormap_lim[1])

    plt.subplots_adjust(wspace=0, hspace=0)
    plt.tight_layout()
    plt.suptitle(title) 

# 需要导入模块: from matplotlib import cm [as 别名]
# 或者: from matplotlib.cm import jet [as 别名]
def plotNNFilterOverlay(input_im, units, figure_id, interp='bilinear',
                        colormap=cm.jet, colormap_lim=None, title='', alpha=0.8):
    plt.ion()
    filters = units.shape[2]
    fig = plt.figure(figure_id, figsize=(5,5))
    fig.clf()

    for i in range(filters):
        plt.imshow(input_im[:,:,0], interpolation=interp, cmap='gray')
        plt.imshow(units[:,:,i], interpolation=interp, cmap=colormap, alpha=alpha)
        plt.axis('off')
        plt.colorbar()
        plt.title(title, fontsize='small')
        if colormap_lim:
            plt.clim(colormap_lim[0],colormap_lim[1])

    plt.subplots_adjust(wspace=0, hspace=0)
    plt.tight_layout()

    # plt.savefig('{}/{}.png'.format(dir_name,time.time()))




## Load options 

# 需要导入模块: from matplotlib import cm [as 别名]
# 或者: from matplotlib.cm import jet [as 别名]
def test_kde_colors(self):
        _skip_if_no_scipy_gaussian_kde()

        from matplotlib import cm

        custom_colors = 'rgcby'
        df = DataFrame(rand(5, 5))

        ax = df.plot.kde(color=custom_colors)
        self._check_colors(ax.get_lines(), linecolors=custom_colors)
        tm.close()

        ax = df.plot.kde(colormap='jet')
        rgba_colors = lmap(cm.jet, np.linspace(0, 1, len(df)))
        self._check_colors(ax.get_lines(), linecolors=rgba_colors)
        tm.close()

        ax = df.plot.kde(colormap=cm.jet)
        rgba_colors = lmap(cm.jet, np.linspace(0, 1, len(df)))
        self._check_colors(ax.get_lines(), linecolors=rgba_colors) 

# 需要导入模块: from matplotlib import cm [as 别名]
# 或者: from matplotlib.cm import jet [as 别名]
def plot_stacked_power_generation(results, ax=None, kind='bar', legend=False):
    if ax is None:
        fig, axs = plt.subplots(1, 1, figsize=(16, 10))
        ax = axs

    df = results.power_generated
    cols = (df - results.unit_commitment*results.maximum_power_output).std().sort_values().index
    df = df[[c for c in cols]]

    df.plot(kind=kind, stacked=True, ax=ax, colormap=cm.jet, alpha=0.5, legend=legend)

    df = results.unit_commitment * results.maximum_power_output

    df = df[[c for c in cols]]

    df.plot.area(stacked=True, ax=ax, alpha=0.125/2,  colormap=cm.jet, legend=None)

    ax.set_ylabel('Dispatch and Committed Capacity (MW)')
    ax.set_xlabel('Time (h)')
    return ax 

# 需要导入模块: from matplotlib import cm [as 别名]
# 或者: from matplotlib.cm import jet [as 别名]
def plot3D_data(data, x, y):
    X,Y = meshgrid(x,y)
    fig = plt.figure()
    ax = Axes3D(fig)
    #ax = fig.add_subplot(111, projection = "3d")
    ax.plot_surface(X, Y, data, rstride=1, cstride=1, cmap=cm.jet)

#def conditions_emitt_spread(screen):
#    if screen.ne ==1 and (screen.nx and screen.ny):
#        effect = 1
#    elif screen.ne ==1 and (screen.nx==1 and screen.ny):
#        effect = 2
#    elif screen.ne ==1 and (screen.nx and screen.ny == 1):
#        effect = 3
#    elif screen.ne >1 and (screen.nx == 1 and screen.ny == 1):
#        effect = 4
#    else:
#        effect = 0
#    return effect 

# 需要导入模块: from matplotlib import cm [as 别名]
# 或者: from matplotlib.cm import jet [as 别名]
def test_kde_colors(self):
        _skip_if_no_scipy_gaussian_kde()
        if not self.mpl_ge_1_5_0:
            pytest.skip("mpl is not supported")

        from matplotlib import cm

        custom_colors = 'rgcby'
        df = DataFrame(rand(5, 5))

        ax = df.plot.kde(color=custom_colors)
        self._check_colors(ax.get_lines(), linecolors=custom_colors)
        tm.close()

        ax = df.plot.kde(colormap='jet')
        rgba_colors = lmap(cm.jet, np.linspace(0, 1, len(df)))
        self._check_colors(ax.get_lines(), linecolors=rgba_colors)
        tm.close()

        ax = df.plot.kde(colormap=cm.jet)
        rgba_colors = lmap(cm.jet, np.linspace(0, 1, len(df)))
        self._check_colors(ax.get_lines(), linecolors=rgba_colors) 

# 需要导入模块: from matplotlib import cm [as 别名]
# 或者: from matplotlib.cm import jet [as 别名]
def plot4():
    # Density 1
    Z = gen_gaussian_plot_vals(x_hat, Σ)
    cs1 = ax.contour(X, Y, Z, 6, colors="black")
    ax.clabel(cs1, inline=1, fontsize=10)
    # Density 2
    M = Σ * G.T * linalg.inv(G * Σ * G.T + R)
    x_hat_F = x_hat + M * (y - G * x_hat)
    Σ_F = Σ - M * G * Σ
    Z_F = gen_gaussian_plot_vals(x_hat_F, Σ_F)
    cs2 = ax.contour(X, Y, Z_F, 6, colors="black")
    ax.clabel(cs2, inline=1, fontsize=10)
    # Density 3
    new_x_hat = A * x_hat_F
    new_Σ = A * Σ_F * A.T + Q
    new_Z = gen_gaussian_plot_vals(new_x_hat, new_Σ)
    cs3 = ax.contour(X, Y, new_Z, 6, colors="black")
    ax.clabel(cs3, inline=1, fontsize=10)
    ax.contourf(X, Y, new_Z, 6, alpha=0.6, cmap=cm.jet)
    ax.text(float(y[0]), float(y[1]), r"$y$", fontsize=20, color="black")

# == Choose a plot to generate == # 

# 需要导入模块: from matplotlib import cm [as 别名]
# 或者: from matplotlib.cm import jet [as 别名]
def add_polar_bar():
    ax = fig.add_axes([0.025, 0.075, 0.2, 0.85], projection='polar')

    ax.patch.set_alpha(axalpha)
    ax.set_axisbelow(True)
    N = 7
    arc = 2. * np.pi
    theta = np.arange(0.0, arc, arc/N)
    radii = 10 * np.array([0.2, 0.6, 0.8, 0.7, 0.4, 0.5, 0.8])
    width = np.pi / 4 * np.array([0.4, 0.4, 0.6, 0.8, 0.2, 0.5, 0.3])
    bars = ax.bar(theta, radii, width=width, bottom=0.0)
    for r, bar in zip(radii, bars):
        bar.set_facecolor(cm.jet(r/10.))
        bar.set_alpha(0.6)

    ax.tick_params(labelbottom=False, labeltop=False,
                   labelleft=False, labelright=False)

    ax.grid(lw=0.8, alpha=0.9, ls='-', color='0.5')

    ax.set_yticks(np.arange(1, 9, 2))
    ax.set_rmax(9) 

# 需要导入模块: from matplotlib import cm [as 别名]
# 或者: from matplotlib.cm import jet [as 别名]
def estimate_density_map(img_root,gt_dmap_root,model_param_path,index):
    '''
    Show one estimated density-map.
    img_root: the root of test image data.
    gt_dmap_root: the root of test ground truth density-map data.
    model_param_path: the path of specific mcnn parameters.
    index: the order of the test image in test dataset.
    '''
    device=torch.device("cuda")
    model=CANNet().to(device)
    model.load_state_dict(torch.load(model_param_path))
    dataset=CrowdDataset(img_root,gt_dmap_root,8,phase='test')
    dataloader=torch.utils.data.DataLoader(dataset,batch_size=1,shuffle=False)
    model.eval()
    for i,(img,gt_dmap) in enumerate(dataloader):
        if i==index:
            img=img.to(device)
            gt_dmap=gt_dmap.to(device)
            # forward propagation
            et_dmap=model(img).detach()
            et_dmap=et_dmap.squeeze(0).squeeze(0).cpu().numpy()
            print(et_dmap.shape)
            plt.imshow(et_dmap,cmap=CM.jet)
            break 

# 需要导入模块: from matplotlib import cm [as 别名]
# 或者: from matplotlib.cm import jet [as 别名]
def init_fig(*args, **kwargs):
            '''Initialize figures.'''
            fig = tfmpl.create_figure(figsize=(8,6))
            ax = fig.add_subplot(111, projection='3d', elev=50, azim=-30)
            ax.w_xaxis.set_pane_color((1.0,1.0,1.0,1.0))
            ax.w_yaxis.set_pane_color((1.0,1.0,1.0,1.0))
            ax.w_zaxis.set_pane_color((1.0,1.0,1.0,1.0))
            ax.set_title('Gradient descent on Beale surface')
            ax.set_xlabel('$x$')
            ax.set_ylabel('$y$')
            ax.set_zlabel('beale($x$,$y$)')
        
            xx, yy = np.meshgrid(np.linspace(-4.5, 4.5, 40), np.linspace(-4.5, 4.5, 40))
            zz = beale(xx, yy)
            ax.plot_surface(xx, yy, zz, norm=LogNorm(), rstride=1, cstride=1, edgecolor='none', alpha=.8, cmap=cm.jet)
            ax.plot([3], [.5], [beale(3, .5)], 'k*', markersize=5)
            
            for o in optimizers:
                path, = ax.plot([],[],[], label=o[1])
                paths.append(path)

            ax.legend(loc='upper left')
            fig.tight_layout()

            return fig, paths 

# 需要导入模块: from matplotlib import cm [as 别名]
# 或者: from matplotlib.cm import jet [as 别名]
def __init__(self, nelx, nely, stress_calculator, nu, title=""):
        """Initialize plot and plot the initial design"""
        super(StressGUI, self).__init__(nelx, nely, title)
        self.stress_im = self.ax.imshow(
            np.swapaxes(np.zeros((nelx, nely, 4)), 0, 1),
            norm=colors.Normalize(vmin=0, vmax=1), cmap='jet')
        self.fig.colorbar(self.stress_im)
        self.stress_calculator = stress_calculator
        self.nu = nu
        self.myColorMap = colormaps.ScalarMappable(
            norm=colors.Normalize(vmin=0, vmax=1), cmap=colormaps.jet) 

# 需要导入模块: from matplotlib import cm [as 别名]
# 或者: from matplotlib.cm import jet [as 别名]
def test_radviz(self, iris):
        from pandas.plotting import radviz
        from matplotlib import cm

        df = iris
        _check_plot_works(radviz, frame=df, class_column='Name')

        rgba = ('#556270', '#4ECDC4', '#C7F464')
        ax = _check_plot_works(
            radviz, frame=df, class_column='Name', color=rgba)
        # skip Circle drawn as ticks
        patches = [p for p in ax.patches[:20] if p.get_label() != '']
        self._check_colors(
            patches[:10], facecolors=rgba, mapping=df['Name'][:10])

        cnames = ['dodgerblue', 'aquamarine', 'seagreen']
        _check_plot_works(radviz, frame=df, class_column='Name', color=cnames)
        patches = [p for p in ax.patches[:20] if p.get_label() != '']
        self._check_colors(patches, facecolors=cnames, mapping=df['Name'][:10])

        _check_plot_works(radviz, frame=df,
                          class_column='Name', colormap=cm.jet)
        cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique()))
        patches = [p for p in ax.patches[:20] if p.get_label() != '']
        self._check_colors(patches, facecolors=cmaps, mapping=df['Name'][:10])

        colors = [[0., 0., 1., 1.],
                  [0., 0.5, 1., 1.],
                  [1., 0., 0., 1.]]
        df = DataFrame({"A": [1, 2, 3],
                        "B": [2, 1, 3],
                        "C": [3, 2, 1],
                        "Name": ['b', 'g', 'r']})
        ax = radviz(df, 'Name', color=colors)
        handles, labels = ax.get_legend_handles_labels()
        self._check_colors(handles, facecolors=colors) 

# 需要导入模块: from matplotlib import cm [as 别名]
# 或者: from matplotlib.cm import jet [as 别名]
def test_bar_colors(self):
        import matplotlib.pyplot as plt
        default_colors = self._unpack_cycler(plt.rcParams)

        df = DataFrame(randn(5, 5))
        ax = df.plot.bar()
        self._check_colors(ax.patches[::5], facecolors=default_colors[:5])
        tm.close()

        custom_colors = 'rgcby'
        ax = df.plot.bar(color=custom_colors)
        self._check_colors(ax.patches[::5], facecolors=custom_colors)
        tm.close()

        from matplotlib import cm
        # Test str -> colormap functionality
        ax = df.plot.bar(colormap='jet')
        rgba_colors = lmap(cm.jet, np.linspace(0, 1, 5))
        self._check_colors(ax.patches[::5], facecolors=rgba_colors)
        tm.close()

        # Test colormap functionality
        ax = df.plot.bar(colormap=cm.jet)
        rgba_colors = lmap(cm.jet, np.linspace(0, 1, 5))
        self._check_colors(ax.patches[::5], facecolors=rgba_colors)
        tm.close()

        ax = df.loc[:, [0]].plot.bar(color='DodgerBlue')
        self._check_colors([ax.patches[0]], facecolors=['DodgerBlue'])
        tm.close()

        ax = df.plot(kind='bar', color='green')
        self._check_colors(ax.patches[::5], facecolors=['green'] * 5)
        tm.close()