def plot_variance_raw()

in reproduce/plot_transform_locking.py [0:0]

• 40 lines of code
• 6 McCabe index (conditional complexity)

def plot_variance_raw(plot_name, data_files, labels):
    ylabel = "SVRG Variance"

    xvals = [0.7, 0.7] # Position of in-plot labels along the x axis

    epochs = []

    plt.cla()
    fig = plt.figure(figsize=(3.2,2))
    ax = fig.add_subplot(111)
    ax.set_prop_cycle("color", colors)


    for fname, label in zip(data_files, labels):
        print("(ALL) processing file ", fname)
        with open(fname, 'rb') as fdata:
            rd = pickle.load(fdata)
            #pdb.set_trace()
            if 'batch_indices' in rd:
                print("Has batch indices")
                # Calculate x axis for plotting
                batch_indices = np.array(rd["batch_indices"])
                nk = len(batch_indices)
                if max(batch_indices) == min(batch_indices):
                    eval_points = np.array(range(nk))/nk
                else:
                    eval_points = batch_indices/max(batch_indices)

                epochs.append(rd["epoch"])
                #pdb.set_trace()

                var_points = rd["vr_step_variances"]
                #pdb.set_trace()

                ax.plot(eval_points, var_points, label=label)

    # Only compared data from the same epoch
    if len(set(epochs)) > 1:
        print("More than one epoch encountered: {}".format(epochs))


    print("Finalizing plot")
    plt.xlabel('Progress within epoch')
    plt.ylabel(ylabel)
    plt.ylim([0, 0.7])
    plt.xlim([0.0, 1.0])

    # Format x axis as percentage
    def myfunc(x, pos=0):
     return '%1.0f%%'%(100*x)
    ax.xaxis.set_major_formatter(FuncFormatter(myfunc))


    ax.grid(False)
    ax.xaxis.set_tick_params(direction='in')
    ax.yaxis.set_tick_params(direction='in', right="on")
    labelLines(plt.gca().get_lines(), align=False, fontsize=label_fontsize, xvals=xvals)
    figname = "{}/{}.pdf".format(plot_dir, plot_name)

    fig.savefig(figname, bbox_inches='tight', pad_inches=0)
    print("saved", figname)