Python pyplot.fill_between方法代碼示例

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import fill_between [as 別名]
def plot_3(data):
    x = data.Iteration.unique()
    y_mean = data.groupby('Iteration').mean()
    y_std = data.groupby('Iteration').std()
    
    sns.set(style="darkgrid", font_scale=1.5)
    value = 'AverageReturn'
    plt.plot(x, y_mean[value], label=data['Condition'].unique()[0] + '_train');
    plt.fill_between(x, y_mean[value] - y_std[value], y_mean[value] + y_std[value], alpha=0.2);
    value = 'ValAverageReturn'
    plt.plot(x, y_mean[value], label=data['Condition'].unique()[0] + '_test');
    plt.fill_between(x, y_mean[value] - y_std[value], y_mean[value] + y_std[value], alpha=0.2);
    
    plt.xlabel('Iteration')
    plt.ylabel('AverageReturn')
    plt.legend(loc='best') 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import fill_between [as 別名]
def dosplot (filename = None, data = None, fermi = None):
    if (filename is not None): data = np.loadtxt(filename)
    elif (data is not None): data = data

    import matplotlib.pyplot as plt
    from matplotlib import rc
    plt.rc('text', usetex=True)
    plt.rc('font', family='serif')
    plt.plot(data.T[0], data.T[1], label='MF Spin-UP', linestyle=':',color='r')
    plt.fill_between(data.T[0], 0, data.T[1], facecolor='r',alpha=0.1, interpolate=True)
    plt.plot(data.T[0], data.T[2], label='QP Spin-UP',color='r')
    plt.fill_between(data.T[0], 0, data.T[2], facecolor='r',alpha=0.5, interpolate=True)
    plt.plot(data.T[0],-data.T[3], label='MF Spin-DN', linestyle=':',color='b')
    plt.fill_between(data.T[0], 0, -data.T[3], facecolor='b',alpha=0.1, interpolate=True)
    plt.plot(data.T[0],-data.T[4], label='QP Spin-DN',color='b')
    plt.fill_between(data.T[0], 0, -data.T[4], facecolor='b',alpha=0.5, interpolate=True)
    if (fermi!=None): plt.axvline(x=fermi ,color='k', linestyle='--') #label='Fermi Energy'
    plt.axhline(y=0,color='k')
    plt.title('Total DOS', fontsize=20)
    plt.xlabel('Energy (eV)', fontsize=15) 
    plt.ylabel('Density of States (electron/eV)', fontsize=15)
    plt.legend()
    plt.savefig("dos_eigen.svg", dpi=900)
    plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import fill_between [as 別名]
def save_precision_recall_curve(eval_labels, pred_labels, average_precision, smell, config, out_folder, dim, method):
    fig = plt.figure()
    precision, recall, _ = precision_recall_curve(eval_labels, pred_labels)

    step_kwargs = ({'step': 'post'}
                   if 'step' in signature(plt.fill_between).parameters
                   else {})
    plt.step(recall, precision, color='b', alpha=0.2,
             where='post')
    plt.fill_between(recall, precision, alpha=0.2, color='b', **step_kwargs)

    plt.xlabel('Recall')
    plt.ylabel('Precision')
    plt.ylim([0.0, 1.05])
    plt.xlim([0.0, 1.0])
    if isinstance(config, cfg.CNN_config):
        title_str = smell + " (" + method + " - " + dim + ") - L=" + str(config.layers) + ", E=" + str(config.epochs) + ", F=" + str(config.filters) + \
                    ", K=" + str(config.kernel) + ", PW=" + str(config.pooling_window) + ", AP={0:0.2f}".format(average_precision)
    # plt.title(title_str)
    # plt.show()
    file_name = get_plot_file_name(smell, config, out_folder, dim, method, "_prc_")
    fig.savefig(file_name) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import fill_between [as 別名]
def plot_eigenval_estimates(estimates, label):
    """
    estimates = 2D array (num_trials x num_eigenvalues)

    x-axis = eigenvalue index
    y-axis = eigenvalue estimate
    """
    if len(estimates.shape) == 1:
        var = np.zeros_like(estimates)
    else:
        var = np.var(estimates, axis=0)
    y = np.mean(estimates, axis=0)
    x = list(range(len(y)))
    error = np.sqrt(var)
    plt.plot(x, y, label=label)
    plt.fill_between(x, y-error, y+error, alpha=.2) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import fill_between [as 別名]
def plot_eigenvec_errors(true, estimates, label):
    """
    plots error for all eigenvector estimates in L2 norm
    estimates = (num_trials x num_eigenvalues x num_params)
    true = (num_eigenvalues x num_params)
    """
    diffs = []
    num_eigenvals = true.shape[0]
    for i in range(num_eigenvals):
        cur_estimates = estimates[:, i, :]
        cur_eigenvec = true[i]
        diff = compute_eigenvec_cos_similarity(cur_eigenvec, cur_estimates)
        diffs.append(diff)
    diffs = np.array(diffs).T
    var = np.var(diffs, axis=0)
    y = np.mean(diffs, axis=0)
    x = list(range(len(y)))

    error = np.sqrt(var)
    plt.plot(x, y, label=label)
    plt.fill_between(x, y-error, y+error, alpha=.2) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import fill_between [as 別名]
def plot_gain(gain_his,name=None):
    #display data
    import matplotlib.pyplot as plt
    import pandas as pd
    import matplotlib as mpl

    gain_array = np.asarray(gain_his)
    df = pd.DataFrame(gain_his)

    mpl.style.use('seaborn')
    fig, ax = plt.subplots(figsize=(15,8))
    rolling_intv = 60
    df_roll=df.rolling(rolling_intv, min_periods=1).mean()
    if name != None:
        sio.savemat('./data/MUMT(%s)'%name,{'ratio':gain_his})

    plt.plot(np.arange(len(gain_array))+1, df_roll, 'b')
    plt.fill_between(np.arange(len(gain_array))+1, df.rolling(rolling_intv, min_periods=1).min()[0], df.rolling(rolling_intv, min_periods=1).max()[0], color = 'b', alpha = 0.2)
    plt.ylabel('Gain ratio')
    plt.xlabel('learning steps')
    plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import fill_between [as 別名]
def plot_total_dos(self, **kwargs):
        """
        Plots the total DOS

        Args:
            **kwargs: Variables for matplotlib.pylab.plot customization (linewidth, linestyle, etc.)

        Returns:
            matplotlib.pylab.plot
        """
        try:
            import matplotlib.pylab as plt
        except ImportError:
            import matplotlib.pyplot as plt
        fig = plt.figure(1, figsize=(6, 4))
        ax1 = fig.add_subplot(111)
        ax1.set_xlabel("E (eV)", fontsize=14)
        ax1.set_ylabel("DOS", fontsize=14)
        plt.fill_between(self.energies, self.t_dos, **kwargs)
        return plt 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import fill_between [as 別名]
def plotGPGO(gpgo, param, index, new=True):
    param_value = list(param.values())[0][1]
    x_test = np.linspace(param_value[0], param_value[1], 1000).reshape((1000, 1))
    y_hat, y_var = gpgo.GP.predict(x_test, return_std=True)
    std = np.sqrt(y_var)
    l, u = y_hat - 1.96 * std, y_hat + 1.96 * std
    if new:
        plt.figure()
        plt.subplot(5, 1, 1)
        plt.fill_between(x_test.flatten(), l, u, alpha=0.2)
        plt.plot(x_test.flatten(), y_hat)
    plt.subplot(5, 1, index)
    a = np.array([-gpgo._acqWrapper(np.atleast_1d(x)) for x in x_test]).flatten()
    plt.plot(x_test, a, color=colors[index - 2], label=acq_titles[index - 2])
    gpgo._optimizeAcq(method='L-BFGS-B', n_start=1000)
    plt.axvline(x=gpgo.best)
    plt.legend(loc=0) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import fill_between [as 別名]
def plotGPGO(gpgo, param):
    param_value = list(param.values())[0][1]
    x_test = np.linspace(param_value[0], param_value[1], 1000).reshape((1000, 1))
    hat = gpgo.GP.predict(x_test, return_std=True)
    y_hat, y_std = hat[0], np.sqrt(hat[1])
    l, u = y_hat - 1.96 * y_std, y_hat + 1.96 * y_std
    fig = plt.figure()
    r = fig.add_subplot(2, 1, 1)
    r.set_title('Fitted Gaussian process')
    plt.fill_between(x_test.flatten(), l, u, alpha=0.2)
    plt.plot(x_test.flatten(), y_hat, color='red', label='Posterior mean')
    plt.legend(loc=0)
    a = np.array([-gpgo._acqWrapper(np.atleast_1d(x)) for x in x_test]).flatten()
    r = fig.add_subplot(2, 1, 2)
    r.set_title('Acquisition function')
    plt.plot(x_test, a, color='green')
    gpgo._optimizeAcq(method='L-BFGS-B', n_start=1000)
    plt.axvline(x=gpgo.best, color='black', label='Found optima')
    plt.legend(loc=0)
    plt.tight_layout()
    plt.savefig(os.path.join(os.getcwd(), 'mthesis_text/figures/chapter3/sine/{}.pdf'.format(i)))
    plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import fill_between [as 別名]
def add_bg_graph(values, color):
    load_coefficient = 0
    time_ax = []
    load_ax = []
    initial_rate = values[0][2]
    for i in range(0, len(values)):
        if i == 0:
            load_coefficient = 0
            time_ax.extend([values[i][0], values[i][0]])
            load_ax.extend([0, initial_rate])
        else:
            time_ax.extend([values[i][0], values[i][0]])
            load_ax.append(initial_rate + values[i][2] * load_coefficient)
            load_coefficient += 1
            load_ax.append(initial_rate + values[i][2] * load_coefficient)
        if i == len(values) - 1:
            time_ax.extend([values[i][1], values[i][1]])
            load_ax.extend([initial_rate + values[i][2] * load_coefficient, 0])
    plt.fill_between(time_ax, load_ax, facecolor=color, alpha=0.4) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import fill_between [as 別名]
def add_graph(values, color):
    load_coefficient = 0
    time_ax = []
    load_ax = []
    spike_length = get_spike_length(values)
    initial_rate = values[0][2]
    for i in range(0, len(values)):
        if i % spike_length == 0:
            load_coefficient = 0
            time_ax.extend([values[i][0], values[i][0]])
            load_ax.extend([0, initial_rate])
        else:
            time_ax.extend([values[i][0], values[i][0]])
            load_ax.append(initial_rate + values[i][2] * load_coefficient)
            load_coefficient += 1
            load_ax.append(initial_rate + values[i][2] * load_coefficient)
        if (i + 1) % spike_length == 0:
            time_ax.extend([values[i][1], values[i][1]])
            load_ax.extend([initial_rate + values[i][2] * load_coefficient, 0])
    plt.fill_between(time_ax, load_ax, facecolor=color, alpha=0.4) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import fill_between [as 別名]
def viz_trap(f, a, b, n, N):
    h = (b - a) / float(n)
    midpoints = []
    for i in range(n + 1):
        midpoints.append(f(a + (i * h)))
    print midpoints
    data = np.zeros(n * N)
    for i in range(n):
        data[i * N:(i + 1) * N] = np.linspace(midpoints[i],
                                              midpoints[i + 1], N)

    x = np.linspace(a, b, n * N)
    plt.plot(x, f(x), linewidth=2, color=colorset[-1])
    plt.plot(x, data, color=colorset[-2], linestyle='--')
    for i in range(n):
        plt.plot([h * i, h * i], [0, data[i * N]],
                 color=colorset[-2], linestyle='--')

    plt.fill_between(x, f(x), data, color=colorset[1])
    plt.xlabel('x')
    plt.ylabel('f(x)')
    plt.title('%g segments' % n)
    plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import fill_between [as 別名]
def viz_rect(f, a, b, n, N):
    h = (b - a) / float(n)
    data = []
    for i in range(1, n + 1):
        for j in range(50):
            data.append(f(a + (i - 0.5) * h))

    x = np.linspace(a, b, n * N)
    plt.plot(x, f(x), linewidth=2, color=colorset[-1])
    plt.plot(x, data, color=colorset[-2], linestyle='--')
    for i in range(n):
        plt.plot([h * i, h * i], [0, data[i * N]],
                 color=colorset[-2], linestyle='--')

    plt.fill_between(x, f(x), data, color=colorset[1])
    plt.xlabel('x')
    plt.ylabel('f(x)')
    plt.title('%g segments' % n)
    plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import fill_between [as 別名]
def plot_loss(arr, window=50, figsize=(20, 10), name=None):
    def _rolling_window(a, window):
        shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
        strides = a.strides + (a.strides[-1],)
        return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)

    arr = np.asarray(arr)
    fig, ax = plt.subplots(figsize=figsize)
    rolling_mean = np.mean(_rolling_window(arr, 50), 1)
    rolling_std = np.std(_rolling_window(arr, 50), 1)
    plt.plot(range(len(rolling_mean)), rolling_mean, alpha=0.98, linewidth=0.9)
    plt.fill_between(
        range(len(rolling_std)),
        rolling_mean - rolling_std,
        rolling_mean + rolling_std,
        alpha=0.5,
    )
    plt.grid()
    plt.xlabel("Iteration #")
    plt.ylabel("Loss")
    if name is not None:
        if not os.path.exists("./plots/"):
            os.makedirs("./plots/")
        plt.savefig("./plots/{}.png".format(name), format="png", dpi=150)
    plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import fill_between [as 別名]
def __call__(self, args, env):

        import numpy as np
        import matplotlib.pyplot as plt
        from sklearn.metrics import average_precision_score
        from sklearn.metrics import precision_recall_curve
        from vergeml.plots import load_labels, load_predictions

        try:
            labels = load_labels(env)
        except FileNotFoundError:
            raise VergeMLError("Can't plot PR curve - not supported by model.")

        nclasses = len(labels)
        if args['class'] not in labels:
            raise VergeMLError("Unknown class: " + args['class'])

        try:
            y_test, y_score = load_predictions(env, nclasses)
        except FileNotFoundError:
            raise VergeMLError("Can't plot PR curve - not supported by model.")

        # From:
        # https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html#sphx-glr-auto-examples-model-selection-plot-precision-recall-py

        ix = labels.index(args['class'])
        y_test = y_test[:,ix].astype(np.int)
        y_score = y_score[:,ix]

        precision, recall, _ = precision_recall_curve(y_test, y_score)
        average_precision = average_precision_score(y_test, y_score)

        plt.step(recall, precision, color='b', alpha=0.2, where='post')
        plt.fill_between(recall, precision, alpha=0.2, color='b', step='post')

        plt.xlabel('Recall ({})'.format(args['class']))
        plt.ylabel('Precision ({})'.format(args['class']))
        plt.ylim([0.0, 1.05])
        plt.xlim([0.0, 1.0])
        plt.title('Precision-Recall curve for @{0}: AP={1:0.2f}'.format(args['@AI'], average_precision))
        plt.show()