Use Remedian

This example exists to demonstrate the basic usage of remedian.Remedian.

# Authors: Stefan Appelhoff <stefan.appelhoff@mailbox.org>
# License: MIT

First we import what we need for this example.

import matplotlib.pyplot as plt
import numpy as np

from remedian import Remedian

Now we make up some data SHAPE reflecting the shape of the data that we want to calculate the Remedian on. We can have data of any shape … e.g., 3D:

data_shape = (2, 3, 4)

Now we have to decide how many data observations we want to load into memory at a time before computing a first intermediate median from it

n_obs = 100

Pick some example number … assume we have t arrays of shape data_shape that we want to summarize with Remedian

t = 500

Initialize the object

r = Remedian(data_shape, n_obs, t)

Feed it the data … for now, we just generate the data randomly on the go … also save the actual data for comparison with true median

my_data = []
for obs_i in range(t):
    obs = np.random.random(data_shape)  # We just generate some random data
    r.add_obs(obs)
    my_data.append(obs)  # save random data for later (for example purposes)

Now we have the Remedian in r.remedian Let’s summarize the results

x = np.median(np.asarray(my_data).squeeze(), axis=0)  # the *true* median
y = r.remedian  # the *remedian*
xydiff = x-y

Now let’s make a plot! We force the 3D data to 2D using numpy.reshape().

# For colorbar scaling
vmax = np.max([np.abs(x), np.abs(y), np.abs(xydiff)])
vmin = -vmax


fig, (ax1, ax2, ax3) = plt.subplots(1, 3)

for data_to_plot, ax, name in zip([x, y, xydiff], [ax1, ax2, ax3],
                                  ['True median', 'Remedian', 'Difference']):
    im = ax.imshow(data_to_plot.reshape(1, -1), aspect='auto', cmap='bwr',
                   vmin=vmin, vmax=vmax)
    ax.set_xticks([])
    ax.set_yticks([])
    ax.set_title(name)

cbar = plt.colorbar(im, ax=ax)