Python pyplot.tight_layout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 tight_layout [as 别名]
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控制音调变化尺度
    y_ts = librosa.effects.time_stretch(y, rate=1.2)   # rate控制时间维度的变换尺度
    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() 

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

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import tight_layout [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 tight_layout [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 tight_layout [as 别名]
def _show_plot(x_values, y_values, x_labels=None, y_labels=None):
    try:
        import matplotlib.pyplot as plt
    except ImportError:
        raise ImportError('The plot function requires matplotlib to be installed.'
                         'See http://matplotlib.org/')

    plt.locator_params(axis='y', nbins=3)
    axes = plt.axes()
    axes.yaxis.grid()
    plt.plot(x_values, y_values, 'ro', color='red')
    plt.ylim(ymin=-1.2, ymax=1.2)
    plt.tight_layout(pad=5)
    if x_labels:
        plt.xticks(x_values, x_labels, rotation='vertical')
    if y_labels:
        plt.yticks([-1, 0, 1], y_labels, rotation='horizontal')
    # Pad margins so that markers are not clipped by the axes
    plt.margins(0.2)
    plt.show()

#////////////////////////////////////////////////////////////
#{ Parsing and conversion functions
#//////////////////////////////////////////////////////////// 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import tight_layout [as 别名]
def plot_command(self, ns):
        import matplotlib.pyplot as plt

        results = pd.read_csv(ns.file)

        orientation = COLSROWS[ns.orientation]
        size = ns.size if ns.size else DEFAULT_SIZES[ns.orientation]

        fig, axes = plt.subplots(**orientation)
        fig.set_size_inches(*size)

        plot(results, *axes)

        fig.suptitle("")
        plt.tight_layout()
        if ns.out is None:
            print(f"Showing plot for data stored in '{ns.file.name}'...")
            fig.canvas.set_window_title(f"{self.parser.prog} - {ns.file.name}")
            plt.show()
        else:
            print(
                f"Storing plot for data in '{ns.file.name}' -> '{ns.out}'...")
            plt.savefig(ns.out)
            print("DONE!") 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import tight_layout [as 别名]
def plot_command(self, ns):
        import matplotlib.pyplot as plt

        results = pd.read_csv(ns.file)

        size = ns.size if ns.size else DEFAULT_SIZE

        fig, ax = plt.subplots()
        fig.set_size_inches(*size)

        plot(results, ax)

        fig.suptitle("")
        plt.tight_layout()
        if ns.out is None:
            print(f"Showing plot for data stored in '{ns.file.name}'...")
            fig.canvas.set_window_title(f"{self.parser.prog} - {ns.file.name}")
            plt.show()
        else:
            print(
                f"Storing plot for data in '{ns.file.name}' -> '{ns.out}'...")
            plt.savefig(ns.out)
            print("DONE!") 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import tight_layout [as 别名]
def plotNNFilter(units, figure_id, interp='bilinear', colormap=cm.jet, colormap_lim=None):
    plt.ion()
    filters = units.shape[2]
    n_columns = round(math.sqrt(filters))
    n_rows = math.ceil(filters / n_columns) + 1
    fig = plt.figure(figure_id, figsize=(n_rows*3,n_columns*3))
    fig.clf()

    for i in range(filters):
        ax1 = plt.subplot(n_rows, n_columns, i+1)
        plt.imshow(units[:,:,i].T, interpolation=interp, cmap=colormap)
        plt.axis('on')
        ax1.set_xticklabels([])
        ax1.set_yticklabels([])
        plt.colorbar()
        if colormap_lim:
            plt.clim(colormap_lim[0],colormap_lim[1])

    plt.subplots_adjust(wspace=0, hspace=0)
    plt.tight_layout()

# Epochs 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import tight_layout [as 别名]
def plotNNFilter(units, figure_id, interp='bilinear', colormap=cm.jet, colormap_lim=None):
    plt.ion()
    filters = units.shape[2]
    n_columns = round(math.sqrt(filters))
    n_rows = math.ceil(filters / n_columns) + 1
    fig = plt.figure(figure_id, figsize=(n_rows*3,n_columns*3))
    fig.clf()

    for i in range(filters):
        ax1 = plt.subplot(n_rows, n_columns, i+1)
        plt.imshow(units[:,:,i].T, interpolation=interp, cmap=colormap)
        plt.axis('on')
        ax1.set_xticklabels([])
        ax1.set_yticklabels([])
        plt.colorbar()
        if colormap_lim:
            plt.clim(colormap_lim[0],colormap_lim[1])

    plt.subplots_adjust(wspace=0, hspace=0)
    plt.tight_layout()

# Load options 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import tight_layout [as 别名]
def plotNNFilter(units, figure_id, interp='bilinear', colormap=cm.jet, colormap_lim=None, title=''):
    plt.ion()
    filters = units.shape[2]
    n_columns = round(math.sqrt(filters))
    n_rows = math.ceil(filters / n_columns) + 1
    fig = plt.figure(figure_id, figsize=(n_rows*3,n_columns*3))
    fig.clf()

    for i in range(filters):
        ax1 = plt.subplot(n_rows, n_columns, i+1)
        plt.imshow(units[:,:,i].T, interpolation=interp, cmap=colormap)
        plt.axis('on')
        ax1.set_xticklabels([])
        ax1.set_yticklabels([])
        plt.colorbar()
        if colormap_lim:
            plt.clim(colormap_lim[0],colormap_lim[1])

    plt.subplots_adjust(wspace=0, hspace=0)
    plt.tight_layout()
    plt.suptitle(title) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import tight_layout [as 别名]
def plotNNFilterOverlay(input_im, units, figure_id, interp='bilinear',
                        colormap=cm.jet, colormap_lim=None, title='', alpha=0.8):
    plt.ion()
    filters = units.shape[2]
    fig = plt.figure(figure_id, figsize=(5,5))
    fig.clf()

    for i in range(filters):
        plt.imshow(input_im[:,:,0], interpolation=interp, cmap='gray')
        plt.imshow(units[:,:,i], interpolation=interp, cmap=colormap, alpha=alpha)
        plt.axis('off')
        plt.colorbar()
        plt.title(title, fontsize='small')
        if colormap_lim:
            plt.clim(colormap_lim[0],colormap_lim[1])

    plt.subplots_adjust(wspace=0, hspace=0)
    plt.tight_layout()

    # plt.savefig('{}/{}.png'.format(dir_name,time.time()))




## Load options 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import tight_layout [as 别名]
def imshow(data, which, levels):
    """
        Display order book data as an image, where order book data is either of
        `df_price` or `df_volume` returned by `load_hdf5` or `load_postgres`.
    """

    if which == 'prices':
        idx = ['askprc.' + str(i) for i in range(levels, 0, -1)]
        idx.extend(['bidprc.' + str(i) for i in range(1, levels + 1, 1)])
    elif which == 'volumes':
        idx = ['askvol.' + str(i) for i in range(levels, 0, -1)]
        idx.extend(['bidvol.' + str(i) for i in range(1, levels + 1, 1)])
    plt.imshow(data.loc[:, idx].T, interpolation='nearest', aspect='auto')
    plt.yticks(range(0, levels * 2, 1), idx)
    plt.colorbar()
    plt.tight_layout()
    plt.show() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import tight_layout [as 别名]
def pearson_filter(projectPath, featuresDf, del_corr_status, del_corr_threshold, del_corr_plot_status):
    print('Reducing features. Correlation threshold: ' + str(del_corr_threshold))
    col_corr = set()
    corr_matrix = featuresDf.corr()
    for i in range(len(corr_matrix.columns)):
        for j in range(i):
            if (corr_matrix.iloc[i, j] >= del_corr_threshold) and (corr_matrix.columns[j] not in col_corr):
                colname = corr_matrix.columns[i]
                col_corr.add(colname)
                if colname in featuresDf.columns:
                    del featuresDf[colname]
    if del_corr_plot_status == 'yes':
        print('Creating feature correlation heatmap...')
        dateTime = datetime.now().strftime('%Y%m%d%H%M%S')
        plt.matshow(featuresDf.corr())
        plt.tight_layout()
        plt.savefig(os.path.join(projectPath, 'logs', 'Feature_correlations_' + dateTime + '.png'), dpi=300)
        plt.close('all')
        print('Feature correlation heatmap .png saved in project_folder/logs directory')

    return featuresDf 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import tight_layout [as 别名]
def plot_path_hist(results, labels, tols, figsize, ylim=None):
    configure_plt()
    sns.set_palette('colorblind')
    n_competitors = len(results)
    fig, ax = plt.subplots(figsize=figsize)
    width = 1. / (n_competitors + 1)
    ind = np.arange(len(tols))
    b = (1 - n_competitors) / 2.
    for i in range(n_competitors):
        plt.bar(ind + (i + b) * width, results[i], width,
                label=labels[i])
    ax.set_ylabel('path computation time (s)')
    ax.set_xticks(ind + width / 2)
    plt.xticks(range(len(tols)), ["%.0e" % tol for tol in tols])
    if ylim is not None:
        plt.ylim(ylim)

    ax.set_xlabel(r"$\epsilon$")
    plt.legend(loc='upper left')
    plt.tight_layout()
    plt.show(block=False)
    return fig 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import tight_layout [as 别名]
def test_if_scatterplot_colorbars_are_next_to_parent_axes(self):
        import matplotlib.pyplot as plt
        random_array = np.random.random((1000, 3))
        df = pd.DataFrame(random_array,
                          columns=['A label', 'B label', 'C label'])

        fig, axes = plt.subplots(1, 2)
        df.plot.scatter('A label', 'B label', c='C label', ax=axes[0])
        df.plot.scatter('A label', 'B label', c='C label', ax=axes[1])
        plt.tight_layout()

        points = np.array([ax.get_position().get_points()
                           for ax in fig.axes])
        axes_x_coords = points[:, :, 0]
        parent_distance = axes_x_coords[1, :] - axes_x_coords[0, :]
        colorbar_distance = axes_x_coords[3, :] - axes_x_coords[2, :]
        assert np.isclose(parent_distance,
                          colorbar_distance, atol=1e-7).all()