Python LineCollection.set_dashes方法代码示例

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_dashes [as 别名]

#.........这里部分代码省略.........
    # For the plotting, we will use the current colorcycle
    color_cycle = ax[0]._get_lines.color_cycle

    for i in range(nModels):
        # Max alpha for the plots
        max_alpha = 0.7

        # Cited from http://matplotlib.1069221.n5.nabble.com/Varying-alpha-in-ListedColormap-not-working-td39950.html

        x = x_plot
        y = model_predictions[i,:]
        scaling = model_weights[i,:]

        points = np.array([x, y]).T.reshape(-1, 1, 2)
        segments = np.concatenate([points[:-1],points[1:]], axis=1)

        # Values to scale by
        smin = 0
        smax = 1
        
        # Inline function to convert scaling value to alpha value in [0,1]
        alpha = lambda s0:(s0-smin)/(smax-smin)

        # Create a (rgba) color description for each line segment
        cmap = []

        # Get the next color in the colorcycle
        color = next(color_cycle)
        converter = cl.ColorConverter()
        # Get the rgb color tuple and convert it to a list
        color_rgba = list(converter.to_rgb(color))
        # Add placeholder alpha to get color_rgba
        color_rgba.append(0.0)

        for a in segments:
            # The x-value for the segment
            x0 = a.mean(0)[0]
            # With scaling value of
            s0 = np.interp(x0, x, scaling)
            # And alpha value of 
            a0 = alpha(s0)

            # Pop the previous alpha and add the current alpha
            color_rgba[3] = a0*max_alpha
            
            # Add the rgba entry to the colormap. Make sure to copy the list
            # Using list slicing.
            cmap.append(color_rgba[:])
        
        # Create the line collection objecft
        lc = LineCollection(segments)
        lc.set_color(cmap)

        # Set line properties. 
        # Dashed line
        lc.set_dashes('--')
        # line width 3.0
        lc.set_linewidth(3.0)
            
        ax[0].add_collection(lc)

        """
        # Normal plot
        #p0_line = ax[0].plot(x_plot, model_predictions[i,:])
        # Set line properties for miniplot
        #plt.setp(p0_line, linewidth=3.0, alpha=model_weights[i,:], linestyle='--',
        #         dash_capstyle='round')
        """

    # Now plot scatter of observations
    for i in range(nModels):
        f = bma.quadratic_models[i][0]
        ax[0].scatter(X[0,i],f,s=100.0, c="black", alpha=0.5)

    # Plot bma predictions
    pred_line = ax[0].plot(x_plot,Z)
    
    # Line properties
    plt.setp(pred_line, color="black", alpha=0.3, linewidth=3.0)

    # Set the plot range
    ax[0].set_xlim(x_min, x_max)
    ax[0].set_ylim(y_min, y_max)

    plt.savefig("figures/"+str(k_fig)+"_bma_model_breakdown.png")

    fig3, ax = plt.subplots(2, sharex=True)
    model_weights, errors, N_eff = bma.estimate_model_weights(x_grid, return_errors=True)
    
    ax[0].plot(x_plot, N_eff)

    ### Plot of errors
    for i in range(nModels):
        p1_line = ax[1].plot(x_plot, errors[i,:])
        plt.setp(p1_line, linewidth=3.0, alpha=1.0, linestyle='-',
                 dash_capstyle='round')
        
    # Set log scale
    ax[1].set_yscale('log')
    plt.savefig("figures/"+str(k_fig)+"_bma_error_breakdown.png")

# 需要导入模块: from matplotlib.collections import LineCollection [as 别名]
# 或者: from matplotlib.collections.LineCollection import set_dashes [as 别名]

#.........这里部分代码省略.........

        # Create a (rgba) color description for each line segment
        cmap = []

        # Get the next color in the colorcycle
        color = next(color_cycle)
        converter = cl.ColorConverter()
        # Get the rgb color tuple and convert it to a list
        color_rgba = list(converter.to_rgb(color))
        # Add placeholder alpha to get color_rgba
        color_rgba.append(0.0)

        for a in segments:
            # The x-value for the segment
            x0 = a.mean(0)[0]
            # With scaling value of
            s0 = np.interp(x0, x, scaling)
            # And alpha value of 
            a0 = alpha(s0)

            # Pop the previous alpha and add the current alpha
            color_rgba[3] = a0*max_alpha
            
            # Add the rgba entry to the colormap. Make sure to copy the list
            # Using list slicing.
            cmap.append(color_rgba[:])
        
        # Create the line collection objecft
        lc = LineCollection(segments)
        lc.set_color(cmap)

        # Set line properties. 
        # Dashed line
        lc.set_dashes('-')
        # line width 3.0
        lc.set_linewidth(3.0)
            
        ax[0].add_collection(lc)

        """
        # Normal plot
        #p0_line = ax[0].plot(x_plot, model_predictions[i,:])
        # Set line properties for miniplot
        #plt.setp(p0_line, linewidth=3.0, alpha=model_weights[i,:], linestyle='--',
        #         dash_capstyle='round')
        """

    # Now plot scatter of observations
    for i in range(nModels):
        f = bma.quadratic_models[i].get_y()
        ax[0].scatter(X[0,i],f,s=100.0, c="black", alpha=0.5)

    # Plot bma predictions
    pred_line = ax[0].plot(x_plot,Z)
    
    # Line properties
    plt.setp(pred_line, color="black", alpha=0.3, linewidth=3.0)

    # Set the plot range
    ax[0].set_xlim(x_min, x_max)
    ax[0].set_ylim(y_min, y_max)

    # set x axis
    ax[4].set_xlabel("x",fontsize=16)

    plt.savefig("figures/"+str(k_fig)+"_bma_model_breakdown.png", dpi=dpi)