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#!/usr/bin/env python
#
# Author: Mike McKerns (mmckerns @caltech and @uqfoundation)
# Copyright (c) 2022-2024 The Uncertainty Quantification Foundation.
# License: 3-clause BSD. The full license text is available at:
# - https://github.com/uqfoundation/mystic/blob/master/LICENSE
"""
plot the sampled data (and cumulative average) from xrd_design*.py
"""
def plot_db(archive):
'plot cumulative average for sampled data in archive'
import mystic.cache as mc
db = mc.archive.read(archive, type=mc.archive.file_archive)
xx = list(db.values())
xtype = type(xx[-1])
multi = hasattr(xtype, '__len__')
import numpy as np
xs = np.array(xx)
var = xtype(np.nanvar(xs, axis=0)) if multi else np.nanvar(xs)
if xs.ndim == 1: xs = np.atleast_2d(xs).T
ys = np.arange(1, xs.shape[0]+1)
y_ = ys - np.cumsum(np.isnan(xs), axis=0).T # nan-adjusted
xs = (np.nancumsum(xs, axis=0).T/y_).T.tolist()
del y_; ave = xtype(xs[-1]) if multi else xs[-1][0]
print("%s: len = %s, ave = %s, var = %s" % (archive, len(db), ave, var))
import matplotlib.pyplot as plt
if type(ave) is tuple:
fig, axs = plt.subplots(len(ave))
for i,ax in enumerate(axs):
ax.plot(np.array(xs).T[i])
ax.plot(np.array(xx).T[i], 'x')
else:
fig, ax = plt.subplots(1)
ax.plot(np.array(xs).reshape(-1))
ax.plot(np.array(xx).reshape(-1), 'x')
plt.title(archive)
plt.show()
if __name__ == '__main__':
import os
archives = ('ave.db', 'min.db', 'max.db')
for archive in archives:
a = None
if os.path.exists(archive):
try:
plot_db(archive)
except Exception:
pass
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