Python pylab.tight_layout方法代码示例

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import tight_layout [as 别名]
def plot_pr_curve(pr_curve_dml, pr_curve_base, title):
    """
      Function that plots the PR-curve.

      Args:
        pr_curve: the values of precision for each recall value
        title: the title of the plot
    """
    plt.figure(figsize=(16, 9))
    plt.plot(np.arange(0.0, 1.05, 0.05),
             pr_curve_base, color='r', marker='o', linewidth=3, markersize=10)
    plt.plot(np.arange(0.0, 1.05, 0.05),
             pr_curve_dml, color='b', marker='o', linewidth=3, markersize=10)
    plt.grid(True, linestyle='dotted')
    plt.xlabel('Recall', color='k', fontsize=27)
    plt.ylabel('Precision', color='k', fontsize=27)
    plt.yticks(color='k', fontsize=20)
    plt.xticks(color='k', fontsize=20)
    plt.ylim([0.0, 1.05])
    plt.xlim([0.0, 1.0])
    plt.title(title, color='k', fontsize=27)
    plt.tight_layout()
    plt.show() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import tight_layout [as 别名]
def plot_alignment_to_numpy(alignment, info=None):
    fig, ax = plt.subplots(figsize=(6, 4))
    im = ax.imshow(alignment, aspect='auto', origin='lower',
                   interpolation='none')
    fig.colorbar(im, ax=ax)
    xlabel = 'Decoder timestep'
    if info is not None:
        xlabel += '\n\n' + info
    plt.xlabel(xlabel)
    plt.ylabel('Encoder timestep')
    plt.tight_layout()

    fig.canvas.draw()
    data = save_figure_to_numpy(fig)
    plt.close()
    return data 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import tight_layout [as 别名]
def show_pred(images, predictions, ground_truth):
    # choose 10 indice from images and visualize them
    indice = [np.random.randint(0, len(images)) for i in range(40)]
    for i in range(0, 40):
        plt.figure()
        plt.subplot(1, 3, 1)
        plt.tight_layout()
        plt.title('deformed image')
        plt.imshow(images[indice[i]])
        plt.subplot(1, 3, 2)
        plt.tight_layout()
        plt.title('predicted mask')
        plt.imshow(predictions[indice[i]])
        plt.subplot(1, 3, 3)
        plt.tight_layout()
        plt.title('ground truth label')
        plt.imshow(ground_truth[indice[i]])
    plt.show()

# Load Data Science Bowl 2018 training dataset 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import tight_layout [as 别名]
def plot_gate_outputs_to_numpy(gate_targets, gate_outputs):
    fig, ax = plt.subplots(figsize=(12, 3))
    ax.scatter(
        range(len(gate_targets)), gate_targets, alpha=0.5, color='green', marker='+', s=1, label='target',
    )
    ax.scatter(
        range(len(gate_outputs)), gate_outputs, alpha=0.5, color='red', marker='.', s=1, label='predicted',
    )

    plt.xlabel("Frames (Green target, Red predicted)")
    plt.ylabel("Gate State")
    plt.tight_layout()

    fig.canvas.draw()
    data = save_figure_to_numpy(fig)
    plt.close()
    return data 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import tight_layout [as 别名]
def plot(params_dir):
    model_dirs = [name for name in os.listdir(params_dir)
                  if os.path.isdir(os.path.join(params_dir, name))]

    df = defaultdict(list)
    for model_dir in model_dirs:
        df[re.sub('_bin_scaled_mono_True_ratio', '', model_dir)] = [
            dd.io.load(path)['best_epoch']['validate_objective']
            for path in glob.glob(os.path.join(
                params_dir, model_dir) + '/*.h5')]

    df = pd.DataFrame(dict([(k, pd.Series(v)) for k, v in df.iteritems()]))
    df.to_csv(os.path.basename(os.path.normpath(params_dir)))
    plt.figure(figsize=(16, 4), dpi=300)
    g = sns.boxplot(df)
    g.set_xticklabels(df.columns, rotation=45)
    plt.tight_layout()
    plt.savefig('{}_errors_box_plot.png'.format(
        os.path.join(IMAGES_DIRECTORY,
                     os.path.basename(os.path.normpath(params_dir))))) 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import tight_layout [as 别名]
def plot_losses(losses_d, losses_g, filename):
    losses_d = np.array(losses_d)
    fig, axes = plt.subplots(3, 2, figsize=(8, 8))
    axes = axes.flatten()
    axes[0].plot(losses_d[:, 0])
    axes[1].plot(losses_d[:, 1])
    axes[2].plot(losses_d[:, 2])
    axes[3].plot(losses_d[:, 3])
    axes[4].plot(losses_g)
    axes[0].set_title("losses_d")
    axes[1].set_title("losses_d_real")
    axes[2].set_title("losses_d_fake")
    axes[3].set_title("losses_d_gp")
    axes[4].set_title("losses_g")
    plt.tight_layout()
    plt.savefig(filename)
    plt.close() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import tight_layout [as 别名]
def plot_spectrogram_to_numpy(spectrogram):
    spectrogram = spectrogram.transpose(1, 0)
    fig, ax = plt.subplots(figsize=(12, 3))
    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
                   interpolation='none')
    plt.colorbar(im, ax=ax)
    plt.xlabel("Frames")
    plt.ylabel("Channels")
    plt.tight_layout()

    fig.canvas.draw()
    data = _save_figure_to_numpy(fig)
    plt.close()
    return data


####################
# PLOT SPECTROGRAM #
#################### 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import tight_layout [as 别名]
def plot1D_mat(a, b, M, title=''):
    """ Plot matrix M  with the source and target 1D distribution

    Creates a subplot with the source distribution a on the left and
    target distribution b on the tot. The matrix M is shown in between.


    Parameters
    ----------
    a : ndarray, shape (na,)
        Source distribution
    b : ndarray, shape (nb,)
        Target distribution
    M : ndarray, shape (na, nb)
        Matrix to plot
    """
    na, nb = M.shape

    gs = gridspec.GridSpec(3, 3)

    xa = np.arange(na)
    xb = np.arange(nb)

    ax1 = pl.subplot(gs[0, 1:])
    pl.plot(xb, b, 'r', label='Target distribution')
    pl.yticks(())
    pl.title(title)

    ax2 = pl.subplot(gs[1:, 0])
    pl.plot(a, xa, 'b', label='Source distribution')
    pl.gca().invert_xaxis()
    pl.gca().invert_yaxis()
    pl.xticks(())

    pl.subplot(gs[1:, 1:], sharex=ax1, sharey=ax2)
    pl.imshow(M, interpolation='nearest')
    pl.axis('off')

    pl.xlim((0, nb))
    pl.tight_layout()
    pl.subplots_adjust(wspace=0., hspace=0.2) 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import tight_layout [as 别名]
def plot_spectrogram_to_numpy(spectrogram):
    fig, ax = plt.subplots(figsize=(12, 3))
    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
                   interpolation='none')
    plt.colorbar(im, ax=ax)
    plt.xlabel("Frames")
    plt.ylabel("Channels")
    plt.tight_layout()

    fig.canvas.draw()
    data = save_figure_to_numpy(fig)
    plt.close()
    return data

# from https://github.com/NVIDIA/vid2vid/blob/951a52bb38c2aa227533b3731b73f40cbd3843c4/models/networks.py#L17 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import tight_layout [as 别名]
def plot_spectrogram_to_numpy(spectrogram):
    fig, ax = plt.subplots(figsize=(12, 3))
    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
                   interpolation='none')
    plt.colorbar(im, ax=ax)
    plt.xlabel("Frames")
    plt.ylabel("Channels")
    plt.tight_layout()

    fig.canvas.draw()
    data = save_figure_to_numpy(fig)
    plt.close()
    return data 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import tight_layout [as 别名]
def plot_gate_outputs_to_numpy(gate_targets, gate_outputs):
    fig, ax = plt.subplots(figsize=(12, 3))
    ax.scatter(list(range(len(gate_targets))), gate_targets, alpha=0.5,
               color='green', marker='+', s=1, label='target')
    ax.scatter(list(range(len(gate_outputs))), gate_outputs, alpha=0.5,
               color='red', marker='.', s=1, label='predicted')

    plt.xlabel("Frames (Green target, Red predicted)")
    plt.ylabel("Gate State")
    plt.tight_layout()

    fig.canvas.draw()
    data = save_figure_to_numpy(fig)
    plt.close()
    return data 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import tight_layout [as 别名]
def show_batch(sample_batched):
    """Show image with landmarks for a batch of samples."""
    images_batch, masks_batch = sample_batched['image'].numpy().astype(np.uint8), sample_batched['mask'].numpy().astype(np.bool)
    batch_size = len(images_batch)
    for i in range(batch_size):
        plt.figure()
        plt.subplot(1, 2, 1)
        plt.tight_layout()
        plt.imshow(images_batch[i].transpose((1, 2, 0)))
        plt.subplot(1, 2, 2)
        plt.tight_layout()
        plt.imshow(np.squeeze(masks_batch[i].transpose((1, 2, 0)))) 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import tight_layout [as 别名]
def plot_waveform_to_numpy(waveform):
    fig, ax = plt.subplots(figsize=(12, 3))
    ax.plot()
    ax.plot(range(len(waveform)), waveform,
            linewidth=0.1, alpha=0.7, color='blue')

    plt.xlabel("Samples")
    plt.ylabel("Amplitude")
    plt.ylim(-1, 1)
    plt.tight_layout()

    fig.canvas.draw()
    data = save_figure_to_numpy(fig)
    plt.close()
    return data 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import tight_layout [as 别名]
def show_batch(sample_batched):
    """Show image with landmarks for a batch of samples."""
    images_batch, masks_batch = sample_batched['image'].numpy().astype(np.uint8), sample_batched['mask'].numpy().astype(np.bool)
    batch_size = len(images_batch)
    for i in range(batch_size):
        plt.figure()
        plt.subplot(1, 2, 1)
        plt.tight_layout()
        plt.imshow(images_batch[i].transpose((1, 2, 0)))
        plt.subplot(1, 2, 2)
        plt.tight_layout()
        plt.imshow(np.squeeze(masks_batch[i].transpose((1, 2, 0))))

# Load Data Science Bowl 2018 training dataset 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import tight_layout [as 别名]
def plot_alignment_to_numpy(alignment, info=None):
    fig, ax = plt.subplots(figsize=(6, 4))
    im = ax.imshow(alignment, aspect='auto', origin='lower', interpolation='none')
    fig.colorbar(im, ax=ax)
    xlabel = 'Decoder timestep'
    if info is not None:
        xlabel += '\n\n' + info
    plt.xlabel(xlabel)
    plt.ylabel('Encoder timestep')
    plt.tight_layout()

    fig.canvas.draw()
    data = save_figure_to_numpy(fig)
    plt.close()
    return data