Python pyplot.title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 title [as 別名]
def demo_plot():
    audio = './data/esc10/audio/Dog/1-30226-A.ogg'
    y, sr = librosa.load(audio, sr=44100)
    y_ps = librosa.effects.pitch_shift(y, sr, n_steps=6)   # n_steps控製音調變化尺度
    y_ts = librosa.effects.time_stretch(y, rate=1.2)   # rate控製時間維度的變換尺度
    plt.subplot(311)
    plt.plot(y)
    plt.title('Original waveform')
    plt.axis([0, 200000, -0.4, 0.4])
    # plt.axis([88000, 94000, -0.4, 0.4])
    plt.subplot(312)
    plt.plot(y_ts)
    plt.title('Time Stretch transformed waveform')
    plt.axis([0, 200000, -0.4, 0.4])
    plt.subplot(313)
    plt.plot(y_ps)
    plt.title('Pitch Shift transformed waveform')
    plt.axis([0, 200000, -0.4, 0.4])
    # plt.axis([88000, 94000, -0.4, 0.4])
    plt.tight_layout()
    plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import title [as 別名]
def plot_confusion_matrix(y_true, y_pred, size=None, normalize=False):
    """plot_confusion_matrix."""
    cm = confusion_matrix(y_true, y_pred)
    fmt = "%d"
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        fmt = "%.2f"
    xticklabels = list(sorted(set(y_pred)))
    yticklabels = list(sorted(set(y_true)))
    if size is not None:
        plt.figure(figsize=(size, size))
    heatmap(cm, xlabel='Predicted label', ylabel='True label',
            xticklabels=xticklabels, yticklabels=yticklabels,
            cmap=plt.cm.Blues, fmt=fmt)
    if normalize:
        plt.title("Confusion matrix (norm.)")
    else:
        plt.title("Confusion matrix")
    plt.gca().invert_yaxis() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import title [as 別名]
def __init__(self, title, varieties, data_points, attrs,
                 anim=False, data_func=None, is_headless=False):
        global anim_func

        plt.close()
        self.legend = ["Type"]
        self.title = title
        # self.anim = anim
        # self.data_func = data_func
        for i in varieties:
            data_points = len(varieties[i]["data"])
            break
        self.headless = is_headless
        self.draw_graph(data_points, varieties, attrs)

        # if anim and not self.headless:
        #     anim_func = animation.FuncAnimation(self.fig,
        #                                         self.update_plot,
        #                                         frames=1000,
        #                                         interval=500,
        #                                         blit=False) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import title [as 別名]
def __init__(self, title, varieties, data_points,
                 anim=False, data_func=None, is_headless=False, legend_pos=4):
        global anim_func

        self.title = title
        self.anim = anim
        self.data_func = data_func
        for i in varieties:
            data_points = len(varieties[i]["data"])
            break
        self.draw_graph(data_points, varieties)
        self.headless = is_headless

        if anim and not self.headless:
            anim_func = animation.FuncAnimation(self.fig,
                                    self.update_plot,
                                    frames=1000,
                                    interval=500,
                                    blit=False) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import title [as 別名]
def plot_roc_curve(y_true, y_score, size=None):
    """plot_roc_curve."""
    false_positive_rate, true_positive_rate, thresholds = roc_curve(
        y_true, y_score)
    if size is not None:
        plt.figure(figsize=(size, size))
        plt.axis('equal')
    plt.plot(false_positive_rate, true_positive_rate, lw=2, color='navy')
    plt.plot([0, 1], [0, 1], color='gray', lw=1, linestyle='--')
    plt.xlabel('False positive rate')
    plt.ylabel('True positive rate')
    plt.ylim([-0.05, 1.05])
    plt.xlim([-0.05, 1.05])
    plt.grid()
    plt.title('Receiver operating characteristic AUC={0:0.2f}'.format(
        roc_auc_score(y_true, y_score))) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import title [as 別名]
def update(self, xPhys, u, title=None):
        """Plot to screen"""
        self.im.set_array(-xPhys.reshape((self.nelx, self.nely)).T)
        stress = self.stress_calculator.calculate_stress(xPhys, u, self.nu)
        # self.stress_calculator.calculate_fdiff_stress(xPhys, u, self.nu)
        self.myColorMap.set_norm(colors.Normalize(vmin=0, vmax=max(stress)))
        stress_rgba = self.myColorMap.to_rgba(stress)
        stress_rgba[:, :, 3] = xPhys.reshape(-1, 1)
        self.stress_im.set_array(np.swapaxes(
            stress_rgba.reshape((self.nelx, self.nely, 4)), 0, 1))
        self.fig.canvas.draw()
        self.fig.canvas.flush_events()
        if title is not None:
            plt.title(title)
        else:
            plt.xlabel("Max stress = {:.2f}".format(max(stress)[0]))
        plt.pause(0.01) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import title [as 別名]
def plot_alignment(alignment, gs, dir=hp.logdir):
    """Plots the alignment.

    Args:
      alignment: A numpy array with shape of (encoder_steps, decoder_steps)
      gs: (int) global step.
      dir: Output path.
    """
    if not os.path.exists(dir): os.mkdir(dir)

    fig, ax = plt.subplots()
    im = ax.imshow(alignment)

    fig.colorbar(im)
    plt.title('{} Steps'.format(gs))
    plt.savefig('{}/alignment_{}.png'.format(dir, gs), format='png')
    plt.close(fig) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import title [as 別名]
def compute_roc(y_true, y_pred, plot=False):
    """
    TODO
    :param y_true: ground truth
    :param y_pred: predictions
    :param plot:
    :return:
    """
    fpr, tpr, _ = roc_curve(y_true, y_pred)
    auc_score = auc(fpr, tpr)
    if plot:
        plt.figure(figsize=(7, 6))
        plt.plot(fpr, tpr, color='blue',
                 label='ROC (AUC = %0.4f)' % auc_score)
        plt.legend(loc='lower right')
        plt.title("ROC Curve")
        plt.xlabel("FPR")
        plt.ylabel("TPR")
        plt.show()

    return fpr, tpr, auc_score 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import title [as 別名]
def compute_roc_rfeinman(probs_neg, probs_pos, plot=False):
    """
    TODO
    :param probs_neg:
    :param probs_pos:
    :param plot:
    :return:
    """
    probs = np.concatenate((probs_neg, probs_pos))
    labels = np.concatenate((np.zeros_like(probs_neg), np.ones_like(probs_pos)))
    fpr, tpr, _ = roc_curve(labels, probs)
    auc_score = auc(fpr, tpr)
    if plot:
        plt.figure(figsize=(7, 6))
        plt.plot(fpr, tpr, color='blue',
                 label='ROC (AUC = %0.4f)' % auc_score)
        plt.legend(loc='lower right')
        plt.title("ROC Curve")
        plt.xlabel("FPR")
        plt.ylabel("TPR")
        plt.show()

    return fpr, tpr, auc_score 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import title [as 別名]
def data_stat():
    """data statistic"""
    audio_path = './data/esc10/audio/'
    class_list = [os.path.basename(i) for i in glob(audio_path + '*')]
    nums_each_class = [len(glob(audio_path + cl + '/*.ogg')) for cl in class_list]
    rects = plt.bar(range(len(nums_each_class)), nums_each_class)

    index = list(range(len(nums_each_class)))
    plt.title('Numbers of each class for ESC-10 dataset')
    plt.ylim(ymax=60, ymin=0)
    plt.xticks(index, class_list, rotation=45)
    plt.ylabel("numbers")

    for rect in rects:
        height = rect.get_height()
        plt.text(rect.get_x() + rect.get_width() / 2, height, str(height), ha='center', va='bottom')

    plt.tight_layout()
    plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import title [as 別名]
def visualize_sampling(self,permutations):
        max_length = len(permutations[0])
        grid = np.zeros([max_length,max_length]) # initialize heatmap grid to 0
        transposed_permutations = np.transpose(permutations)
        for t, cities_t in enumerate(transposed_permutations): # step t, cities chosen at step t
            city_indices, counts = np.unique(cities_t,return_counts=True,axis=0)
            for u,v in zip(city_indices, counts):
                grid[t][u]+=v # update grid with counts from the batch of permutations
        # plot heatmap
        fig = plt.figure()
        rcParams.update({'font.size': 22})
        ax = fig.add_subplot(1,1,1)
        ax.set_aspect('equal')
        plt.imshow(grid, interpolation='nearest', cmap='gray')
        plt.colorbar()
        plt.title('Sampled permutations')
        plt.ylabel('Time t')
        plt.xlabel('City i')
        plt.show()

    # Heatmap of attention (x=cities; y=steps) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import title [as 別名]
def visualize_sampling(self, permutations):
        max_length = len(permutations[0])
        grid = np.zeros([max_length,max_length]) # initialize heatmap grid to 0

        transposed_permutations = np.transpose(permutations)
        for t, cities_t in enumerate(transposed_permutations): # step t, cities chosen at step t
            city_indices, counts = np.unique(cities_t,return_counts=True,axis=0)
            for u,v in zip(city_indices, counts):
                grid[t][u]+=v # update grid with counts from the batch of permutations

        # plot heatmap
        fig = plt.figure()
        rcParams.update({'font.size': 22})
        ax = fig.add_subplot(1,1,1)
        ax.set_aspect('equal')
        plt.imshow(grid, interpolation='nearest', cmap='gray')
        plt.colorbar()
        plt.title('Sampled permutations')
        plt.ylabel('Time t')
        plt.xlabel('City i')
        plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import title [as 別名]
def plot(PDF, figName, imgpath, show=False, save=True):
    # plot
    output = PDF.get_constraint_value()
    plt.plot(PDF.experimentalDistances,PDF.experimentalPDF, 'ro', label="experimental", markersize=7.5, markevery=1 )
    plt.plot(PDF.shellsCenter, output["pdf"], 'k', linewidth=3.0,  markevery=25, label="total" )

    styleIndex = 0
    for key in output:
        val = output[key]
        if key in ("pdf_total", "pdf"):
            continue
        elif "inter" in key:
            plt.plot(PDF.shellsCenter, val, STYLE[styleIndex], markevery=5, label=key.split('rdf_inter_')[1] )
            styleIndex+=1
    plt.legend(frameon=False, ncol=1)
    # set labels
    plt.title("$\\chi^{2}=%.6f$"%PDF.squaredDeviations, size=20)
    plt.xlabel("$r (\AA)$", size=20)
    plt.ylabel("$g(r)$", size=20)
    # show plot
    if save: plt.savefig(figName)
    if show: plt.show()
    plt.close() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import title [as 別名]
def visualize_anomaly(y_true, reconstruction_error, threshold):
    error_df = pd.DataFrame({'reconstruction_error': reconstruction_error,
                             'true_class': y_true})
    print(error_df.describe())

    groups = error_df.groupby('true_class')
    fig, ax = plt.subplots()

    for name, group in groups:
        ax.plot(group.index, group.reconstruction_error, marker='o', ms=3.5, linestyle='',
                label="Fraud" if name == 1 else "Normal")

    ax.hlines(threshold, ax.get_xlim()[0], ax.get_xlim()[1], colors="r", zorder=100, label='Threshold')
    ax.legend()
    plt.title("Reconstruction error for different classes")
    plt.ylabel("Reconstruction error")
    plt.xlabel("Data point index")
    plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import title [as 別名]
def plot_time_series(vals_bxtxn, bidx=None, n_to_plot=np.inf, scale=1.0,
                     color='r', title=None):

  if bidx is None:
    vals_txn = np.mean(vals_bxtxn, axis=0)
  else:
    vals_txn = vals_bxtxn[bidx,:,:]

  T, N = vals_txn.shape
  if n_to_plot > N:
    n_to_plot = N

  plt.plot(vals_txn[:,0:n_to_plot] + scale*np.array(range(n_to_plot)),
           color=color, lw=1.0)
  plt.axis('tight')
  if title:
    plt.title(title)