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utils.draw.plot_distribution_curves

Function ยท Source

mdnc.utils.draw.plot_distribution_curves(
    gen, method='mean', level=3, outlier=0.1,
    xlabel=None, ylabel='value', y_log=False,
    figure_size=(6, 5.5), legend_loc=None, legend_col=None,
    fig=None, ax=None
)

Plot lines with multi-level distribution for multiple data groups. Each group is given by:

  • one 1D array and one 2D array, representing the x axis (assuming to have a shape of (N,)) and a stack of data samples (with a shape of (N, M)). Each time point corresponds to M samples in the same distribution.
  • or a 2D array. In this case, we only have the (N, M) data stack.
Tip

This function has similar meaning of plot_error_curves. It is used for compressing the time-series histograms. Its output is similar to tensorboard.distribution.

Arguments

Requries

Argument Type Description
gen object A generator callable object (function), each yield returns a sample. It allows users to provide an extra kwargs dict for each iteration (see Examples). For each iteration, it returns 4 1D arrays, or 2 2D arrays, or 2 1D arrays, or a 4D array, or a 2D array, or a 1D array.
method str The method for calculating curves, could be 'mean' or 'middle', representing the mean value and the median value respectively.
level int The histogram level.
outlier float Outlier proportion (should be 0~1), the part marked as outliers would be thrown away when drawing the figures.
xlabel str The x axis label.
ylabel str The y axis label.
y_log bool A flag. Whether to convert the y axis into the logarithmic format.
figure_size (float, float) A tuple with two values representing the (width, height) of the output figure. The unit is inch.
legend_loc str or
int or
(float, float)		 The localtion of the legend, see matplotlib.pyplot.legend to view details. (The legend only works when passing label to each iteration).
legend_col int The number of columns of the legend, see matplotlib.pyplot.legend to view details. (The legend only works when passing label to each iteration).
fig object A matplotlib figure instance. If not given, would use plt.gcf() for instead.
ax object A matplotlib subplot instance. If not given, would use plt.gca() for instead.

Examples

Example
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import numpy as np
import matplotlib.pyplot as plt
import mdnc

@mdnc.utils.draw.setFigure(style='classic', font_size=16)
def test_distribution():
    def func_gen():
        size = 100
        x = np.arange(start=0, stop=size)
        for i in range(1):
            begin = 1 + 99.0 * np.random.rand()
            end = 2 + 10 * np.random.rand()
            exp_v = np.square((x - size) / size) - 1.0
            exp_vnoise = np.random.normal(0.0, np.expand_dims((size - x) / (10 * size), axis=-1), (size, 50))
            v = begin * np.exp((np.expand_dims(exp_v, axis=-1) + exp_vnoise) * end)
            yield x, v, {'label': r'$x_{' + str(i + 1) + r'}$'}
    mdnc.utils.draw.plot_distribution_curves(func_gen(), method='mean', level=5, outlier=0.05,
                                             xlabel='Step', ylabel=r'$\mathcal{L}$', y_log=True)
    plt.show()

test_distribution()

Last update: March 14, 2021

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