Python pyplot.subplots方法代码示例

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import subplots [as 别名]
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) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import subplots [as 别名]
def plot_n_image(X, n):
    """ plot first n images
    n has to be a square number
    """
    pic_size = int(np.sqrt(X.shape[1]))
    grid_size = int(np.sqrt(n))

    first_n_images = X[:n, :]

    fig, ax_array = plt.subplots(nrows=grid_size, ncols=grid_size,
                                    sharey=True, sharex=True, figsize=(8, 8))

    for r in range(grid_size):
        for c in range(grid_size):
            ax_array[r, c].imshow(first_n_images[grid_size * r + c].reshape((pic_size, pic_size)))
            plt.xticks(np.array([]))
            plt.yticks(np.array([])) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import subplots [as 别名]
def plot_bidimensional(model, test, recon_error, layer, title):
    bidimensional_data = model.deepfeatures(test, layer).cbind(recon_error).as_data_frame()

    cmap = cm.get_cmap('Spectral')

    fig, ax = plt.subplots()
    bidimensional_data.plot(kind='scatter',
                            x='DF.L{}.C1'.format(layer + 1),
                            y='DF.L{}.C2'.format(layer + 1),
                            s=500,
                            c='Reconstruction.MSE',
                            title=title,
                            ax=ax,
                            colormap=cmap)
    layer_column = 'DF.L{}.C'.format(layer + 1)
    columns = [layer_column + '1', layer_column + '2']
    for k, v in bidimensional_data[columns].iterrows():
        ax.annotate(k, v, size=20, verticalalignment='bottom', horizontalalignment='left')
    fig.canvas.draw()
    plt.show() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import subplots [as 别名]
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() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import subplots [as 别名]
def plot_images(imgs, targets, paths=None, fname='images.jpg'):
    # Plots training images overlaid with targets
    imgs = imgs.cpu().numpy()
    targets = targets.cpu().numpy()
    # targets = targets[targets[:, 1] == 21]  # plot only one class

    fig = plt.figure(figsize=(10, 10))
    bs, _, h, w = imgs.shape  # batch size, _, height, width
    bs = min(bs, 16)  # limit plot to 16 images
    ns = np.ceil(bs ** 0.5)  # number of subplots

    for i in range(bs):
        boxes = xywh2xyxy(targets[targets[:, 0] == i, 2:6]).T
        boxes[[0, 2]] *= w
        boxes[[1, 3]] *= h
        plt.subplot(ns, ns, i + 1).imshow(imgs[i].transpose(1, 2, 0))
        plt.plot(boxes[[0, 2, 2, 0, 0]], boxes[[1, 1, 3, 3, 1]], '.-')
        plt.axis('off')
        if paths is not None:
            s = Path(paths[i]).name
            plt.title(s[:min(len(s), 40)], fontdict={'size': 8})  # limit to 40 characters
    fig.tight_layout()
    fig.savefig(fname, dpi=200)
    plt.close() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import subplots [as 别名]
def plot_test_txt():  # from utils.utils import *; plot_test()
    # Plot test.txt histograms
    x = np.loadtxt('test.txt', dtype=np.float32)
    box = xyxy2xywh(x[:, :4])
    cx, cy = box[:, 0], box[:, 1]

    fig, ax = plt.subplots(1, 1, figsize=(6, 6))
    ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0)
    ax.set_aspect('equal')
    fig.tight_layout()
    plt.savefig('hist2d.jpg', dpi=300)

    fig, ax = plt.subplots(1, 2, figsize=(12, 6))
    ax[0].hist(cx, bins=600)
    ax[1].hist(cy, bins=600)
    fig.tight_layout()
    plt.savefig('hist1d.jpg', dpi=200) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import subplots [as 别名]
def plot_results(start=0, stop=0):  # from utils.utils import *; plot_results()
    # Plot training results files 'results*.txt'
    fig, ax = plt.subplots(2, 5, figsize=(14, 7))
    ax = ax.ravel()
    s = ['GIoU', 'Objectness', 'Classification', 'Precision', 'Recall',
         'val GIoU', 'val Objectness', 'val Classification', 'mAP', 'F1']
    for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')):
        results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
        n = results.shape[1]  # number of rows
        x = range(start, min(stop, n) if stop else n)
        for i in range(10):
            y = results[i, x]
            if i in [0, 1, 2, 5, 6, 7]:
                y[y == 0] = np.nan  # dont show zero loss values
            ax[i].plot(x, y, marker='.', label=f.replace('.txt', ''))
            ax[i].set_title(s[i])
            if i in [5, 6, 7]:  # share train and val loss y axes
                ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])

    fig.tight_layout()
    ax[1].legend()
    fig.savefig('results.png', dpi=200) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import subplots [as 别名]
def plot_results_overlay(start=0, stop=0):  # from utils.utils import *; plot_results_overlay()
    # Plot training results files 'results*.txt', overlaying train and val losses
    s = ['train', 'train', 'train', 'Precision', 'mAP', 'val', 'val', 'val', 'Recall', 'F1']  # legends
    t = ['GIoU', 'Objectness', 'Classification', 'P-R', 'mAP-F1']  # titles
    for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')):
        results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
        n = results.shape[1]  # number of rows
        x = range(start, min(stop, n) if stop else n)
        fig, ax = plt.subplots(1, 5, figsize=(14, 3.5))
        ax = ax.ravel()
        for i in range(5):
            for j in [i, i + 5]:
                y = results[j, x]
                if i in [0, 1, 2]:
                    y[y == 0] = np.nan  # dont show zero loss values
                ax[i].plot(x, y, marker='.', label=s[j])
            ax[i].set_title(t[i])
            ax[i].legend()
            ax[i].set_ylabel(f) if i == 0 else None  # add filename
        fig.tight_layout()
        fig.savefig(f.replace('.txt', '.png'), dpi=200) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import subplots [as 别名]
def figures(ext, show):

    for name, df in TablesRecorder.generate_dataframes('thames_output.h5'):
        df.columns = ['Very low', 'Low', 'Central', 'High', 'Very high']

        fig, (ax1, ax2) = plt.subplots(figsize=(12, 4), ncols=2, sharey='row',
                                       gridspec_kw={'width_ratios': [3, 1]})
        df['2100':'2125'].plot(ax=ax1)
        df.quantile(np.linspace(0, 1)).plot(ax=ax2)

        if name.startswith('reservoir'):
            ax1.set_ylabel('Volume [$Mm^3$]')
        else:
            ax1.set_ylabel('Flow [$Mm^3/day$]')

        for ax in (ax1, ax2):
            ax.set_title(name)
            ax.grid(True)
        plt.tight_layout()

        if ext is not None:
            fig.savefig(f'{name}.{ext}', dpi=300)

    if show:
        plt.show() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import subplots [as 别名]
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') 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import subplots [as 别名]
def plot_attention(sentences, attentions, labels, **kwargs):
    fig, ax = plt.subplots(**kwargs)
    im = ax.imshow(attentions, interpolation='nearest',
                   vmin=attentions.min(), vmax=attentions.max())
    plt.colorbar(im, shrink=0.5, ticks=[0, 1])
    plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
             rotation_mode="anchor")
    ax.set_yticks(range(len(labels)))
    ax.set_yticklabels(labels, fontproperties=getChineseFont())
    # Loop over data dimensions and create text annotations.
    for i in range(attentions.shape[0]):
        for j in range(attentions.shape[1]):
            text = ax.text(j, i, sentences[i][j],
                           ha="center", va="center", color="b", size=10,
                           fontproperties=getChineseFont())

    ax.set_title("Attention Visual")
    fig.tight_layout()
    plt.show() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import subplots [as 别名]
def analyze(context=None, results=None):
        
    f, (ax1, ax2, ax3) = plt.subplots(3, sharex = True)        
    ax1.plot(results.portfolio_value, linewidth = 2.0, label = 'porfolio')
    ax1.set_title('On-Line Moving Average Reversion')
    ax1.set_ylabel('Portfolio value (USD)')
    ax1.legend(loc=0)
    ax1.grid(True)
            
    ax2.plot(results['AAPL'], color = 'b', linestyle = '-', linewidth = 2.0, label = 'AAPL')
    ax2.plot(results['MSFT'], color = 'r', linestyle = '-', linewidth = 2.0, label = 'MSFT')
    ax2.set_ylabel('stock price (USD)')
    ax2.legend(loc=0)
    ax2.grid(True)
    
    ax3.semilogy(results['step_size'], color = 'b', linestyle = '-', linewidth = 2.0, label = 'step-size')
    ax3.semilogy(results['variability'], color = 'r', linestyle = '-', linewidth = 2.0, label = 'variability')
    ax3.legend(loc=0)
    ax3.grid(True)
    
    plt.show() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import subplots [as 别名]
def analyze(context=None, results=None, benchmark=None):
    
    hist_size = 300
        
    f, (ax1, ax2) = plt.subplots(2, sharex = True)        
    ax1.plot(results.portfolio_value[hist_size:], linewidth = 2.0, label = 'porfolio')
    ax1.set_title('Dual Moving Average Strategy')
    ax1.set_ylabel('Portfolio value (USD)')
    ax1.legend(loc=0)
    ax1.grid(True)
    
    ax2.plot(results['AAPL'][hist_size:], linewidth = 2.0, label = 'AAPL')
    ax2.plot(results['short_mavg'][hist_size:], color = 'r', linestyle = '-', linewidth = 2.0, label = 'short mavg')
    ax2.plot(results['long_mavg'][hist_size:], color = 'g', linestyle = '-', linewidth = 2.0, label = 'long mavg')
    ax2.set_ylabel('AAPL price (USD)')
    ax2.legend(loc=0)
    ax2.grid(True)

    plt.show() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import subplots [as 别名]
def visualize(problem, x, fig=None, ax=None, show=True, label=True):
    with plt.style.context('ggplot'):

        if fig is None or ax is None:
            fig, ax = plt.subplots()

        # plot cities using scatter plot
        ax.scatter(problem.cities[:, 0], problem.cities[:, 1], s=250)
        if label:
            # annotate cities
            for i, c in enumerate(problem.cities):
                ax.annotate(str(i), xy=c, fontsize=10, ha="center", va="center", color="white")

        # plot the line on the path
        for i in range(len(x)):
            current = x[i]
            next_ = x[(i + 1) % len(x)]
            ax.plot(problem.cities[[current, next_], 0], problem.cities[[current, next_], 1], 'r--')

        fig.suptitle("Route length: %.4f" % problem.get_route_length(x))

        if show:
            fig.show() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import subplots [as 别名]
def plot_contour(self, w=0.0):
    """
      Plot contour with poles of Green's function in the self-energy 
      SelfEnergy(w) = G(w+w')W(w')
      with respect to w' = Re(w')+Im(w')
      Poles of G(w+w') are located: w+w'-(E_n-Fermi)+i*eps sign(E_n-Fermi)==0 ==> 
      w'= (E_n-Fermi) - w -i eps sign(E_n-Fermi)
    """
    try :
      import matplotlib.pyplot as plt
      from matplotlib.patches import Arc, Arrow 
    except:
      print('no matplotlib?')
      return

    fig,ax = plt.subplots()
    fe = self.fermi_energy
    ee = self.mo_energy
    iee = 0.5-np.array(ee>fe)
    eew = ee-fe-w
    ax.plot(eew, iee, 'r.', ms=10.0)
    pp = list()
    pp.append(Arc((0,0),4,4,angle=0, linewidth=2, theta1=0, theta2=90, zorder=2, color='b'))
    pp.append(Arc((0,0),4,4,angle=0, linewidth=2, theta1=180, theta2=270, zorder=2, color='b'))
    pp.append(Arrow(0,2,0,-4,width=0.2, color='b', hatch='o'))
    pp.append(Arrow(-2,0,4,0,width=0.2, color='b', hatch='o'))
    for p in pp: ax.add_patch(p)
    ax.set_aspect('equal')
    ax.grid(True, which='both')
    ax.axhline(y=0, color='k')
    ax.axvline(x=0, color='k')
    plt.ylim(-3.0,3.0)
    plt.show()