Homec4science
Diffusion tamaas (features/materials) Browse

tamaas/examples/statistics.py
b77ec286b333features/materials

Last Change
Raw File

statistics.py
View Options

#!/usr/bin/env python3
# @file
# @section LICENSE
#
# Copyright (©) 2016-19 EPFL (École Polytechnique Fédérale de Lausanne),
# Laboratory (LSMS - Laboratoire de Simulation en Mécanique des Solides)
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
import tamaas as tm
# Initialize threads and fftw
tm.initialize()
tm.set_log_level(tm.LogLevel.info) # Show progression of solver
# Surface size
n = 512
# Surface generator
sg = tm.SurfaceGeneratorFilter2D()
sg.setSizes([n, n])
sg.random_seed = 0
# Spectrum
spectrum = tm.Isopowerlaw2D()
sg.setFilter(spectrum)
# Parameters
spectrum.q0 = 16
spectrum.q1 = 16
spectrum.q2 = 64
spectrum.hurst = 0.8
# Generating surface
surface = sg.buildSurface()
# Computing PSD and shifting for plot
psd = tm.Statistics2D.computePowerSpectrum(surface)
psd = np.fft.fftshift(psd, axes=0)
plt.imshow(psd.real, norm=LogNorm())
plt.gca().set_title('Power Spectrum Density')
plt.gcf().tight_layout()
# Computing autocorrelation and shifting for plot
acf = tm.Statistics2D.computeAutocorrelation(surface)
acf = np.fft.fftshift(acf)
plt.figure()
plt.imshow(acf)
plt.gca().set_title('Autocorrelation')
plt.gcf().tight_layout()
plt.show()
c4science · Help