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test_mrdmd.py
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test_mrdmd.py
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from __future__ import division
from past.utils import old_div
from unittest import TestCase
from pydmd.mrdmd import MrDMD
import matplotlib.pyplot as plt
import numpy as np
def create_data():
x = np.linspace(-10, 10, 80)
t = np.linspace(0, 20, 1600)
Xm, Tm = np.meshgrid(x, t)
D = np.exp(-np.power(old_div(Xm, 2), 2)) * np.exp(0.8j * Tm)
D += np.sin(0.9 * Xm) * np.exp(1j * Tm)
D += np.cos(1.1 * Xm) * np.exp(2j * Tm)
D += 0.6 * np.sin(1.2 * Xm) * np.exp(3j * Tm)
D += 0.6 * np.cos(1.3 * Xm) * np.exp(4j * Tm)
D += 0.2 * np.sin(2.0 * Xm) * np.exp(6j * Tm)
D += 0.2 * np.cos(2.1 * Xm) * np.exp(8j * Tm)
D += 0.1 * np.sin(5.7 * Xm) * np.exp(10j * Tm)
D += 0.1 * np.cos(5.9 * Xm) * np.exp(12j * Tm)
D += 0.1 * np.random.randn(*Xm.shape)
D += 0.03 * np.random.randn(*Xm.shape)
D += 5 * np.exp(-np.power(old_div((Xm + 5), 5), 2)) * np.exp(-np.power(
old_div((Tm - 5), 5), 2))
D[:800, 40:] += 2
D[200:600, 50:70] -= 3
D[800:, :40] -= 2
D[1000:1400, 10:30] += 3
D[1000:1080, 50:70] += 2
D[1160:1240, 50:70] += 2
D[1320:1400, 50:70] += 2
return D.T
sample_data = create_data()
class TestMrDmd(TestCase):
def test_max_level_threshold(self):
level = 10
dmd = MrDMD(svd_rank=1, max_level=level, max_cycles=2)
dmd.fit(X=sample_data)
lvl_threshold = int(np.log(sample_data.shape[1]/4.)/np.log(2.)) + 1
assert lvl_threshold == dmd.max_level
def test_max_level_threshold2(self):
level = 10
dmd = MrDMD(svd_rank=1, max_level=level, max_cycles=2)
dmd.fit(X=sample_data)
assert dmd._steps[-1] == 1
def test_index_list(self):
level = 5
dmd = MrDMD(svd_rank=1, max_level=level, max_cycles=2)
assert dmd._index_list(3, 0) == 7
def test_index_list2(self):
level = 5
dmd = MrDMD(svd_rank=1, max_level=level, max_cycles=2)
with self.assertRaises(ValueError):
dmd._index_list(3, 10)
def test_index_list_reversed(self):
level = 3
dmd = MrDMD(svd_rank=1, max_level=level, max_cycles=2)
assert dmd._index_list_reversed(6) == (2, 3)
def test_index_list_reversed2(self):
level = 3
dmd = MrDMD(svd_rank=1, max_level=level, max_cycles=2)
with self.assertRaises(ValueError):
dmd._index_list_reversed(7)
def test_partial_time_interval(self):
level = 4
dmd = MrDMD(svd_rank=1, max_level=level, max_cycles=2)
dmd.original_time = {'t0': 0, 'tend': 8, 'dt': 1}
ans = {'t0': 6.0, 'tend': 7.0, 'dt': 1.0}
assert dmd.partial_time_interval(3, 6) == ans
def test_partial_time_interval2(self):
level = 4
dmd = MrDMD(svd_rank=1, max_level=level, max_cycles=2)
dmd.original_time = {'t0': 0, 'tend': 8, 'dt': 1}
with self.assertRaises(ValueError):
dmd.partial_time_interval(4, 0)
def test_partial_time_interval3(self):
level = 4
dmd = MrDMD(svd_rank=1, max_level=level, max_cycles=2)
dmd.original_time = {'t0': 0, 'tend': 8, 'dt': 1}
with self.assertRaises(ValueError):
dmd.partial_time_interval(3, 8)
def test_time_window_bins(self):
level = 4
dmd = MrDMD(svd_rank=1, max_level=level, max_cycles=2)
dmd.original_time = {'t0': 0, 'tend': 9, 'dt': 1}
comparison = dmd.time_window_bins(0, 9) == np.arange(2**level-1)
assert comparison.all()
def test_time_window_bins2(self):
level = 3
dmd = MrDMD(svd_rank=1, max_level=level, max_cycles=2)
dmd.original_time = {'t0': 0, 'tend': 4, 'dt': 1}
comparison = dmd.time_window_bins(1, 2) == np.array([0, 1, 4])
assert comparison.all()
def test_time_window_bins3(self):
level = 3
dmd = MrDMD(svd_rank=1, max_level=level, max_cycles=2)
dmd.original_time = {'t0': 0, 'tend': 4, 'dt': 1}
comparison = dmd.time_window_bins(0, 3) == np.arange(6)
assert comparison.all()
def test_time_window_bins4(self):
level = 3
dmd = MrDMD(svd_rank=1, max_level=level, max_cycles=2)
dmd.original_time = {'t0': 0, 'tend': 4, 'dt': 1}
comparison = dmd.time_window_bins(1, 3) == np.array([0, 1, 2, 4, 5])
assert comparison.all()
def test_time_window_eigs(self):
level = 3
dmd = MrDMD(svd_rank=1, max_level=level, max_cycles=2)
dmd.fit(X=sample_data)
assert len(dmd.time_window_eigs(0, dmd._snapshots.shape[1])) == 7
def test_time_window_frequency(self):
level = 3
dmd = MrDMD(svd_rank=1, max_level=level, max_cycles=2)
dmd.fit(X=sample_data)
assert len(dmd.time_window_frequency(0, dmd._snapshots.shape[1])) == 7
def test_time_window_growth_rate(self):
level = 3
dmd = MrDMD(svd_rank=1, max_level=level, max_cycles=2)
dmd.fit(X=sample_data)
assert len(dmd.time_window_growth_rate(0, dmd._snapshots.shape[1])) == 7
def test_time_window_amplitudes(self):
level = 3
dmd = MrDMD(svd_rank=1, max_level=level, max_cycles=2)
dmd.fit(X=sample_data)
assert len(dmd.time_window_amplitudes(0, dmd._snapshots.shape[1])) == 7
def test_shape_modes(self):
level = 5
dmd = MrDMD(svd_rank=1, max_level=level, max_cycles=2)
dmd.fit(X=sample_data)
assert dmd.modes.shape == (sample_data.shape[0], 2**level - 1)
def test_shape_dynamics(self):
level = 5
dmd = MrDMD(svd_rank=1, max_level=level, max_cycles=2)
dmd.fit(X=sample_data)
assert dmd.dynamics.shape == (2**level - 1, sample_data.shape[1])
def test_reconstructed_data(self):
dmd = MrDMD(svd_rank=0, max_level=6, max_cycles=2, exact=True)
dmd.fit(X=sample_data)
dmd_data = dmd.reconstructed_data
norm_err = (old_div(
np.linalg.norm(sample_data - dmd_data),
np.linalg.norm(sample_data)))
assert norm_err < 1
def test_partial_modes1(self):
max_level = 5
level = 2
rank = 2
dmd = MrDMD(svd_rank=rank, max_level=max_level, max_cycles=2)
dmd.fit(X=sample_data)
pmodes = dmd.partial_modes(level)
assert pmodes.shape == (sample_data.shape[0], 2**level * rank)
def test_partial_modes2(self):
max_level = 5
level = 2
rank = 2
dmd = MrDMD(svd_rank=rank, max_level=max_level, max_cycles=2)
dmd.fit(X=sample_data)
pmodes = dmd.partial_modes(level, 3)
assert pmodes.shape == (sample_data.shape[0], rank)
def test_partial_dynamics1(self):
max_level = 5
level = 2
rank = 2
dmd = MrDMD(svd_rank=rank, max_level=max_level, max_cycles=2)
dmd.fit(X=sample_data)
pdynamics = dmd.partial_dynamics(level)
assert pdynamics.shape == (2**level * rank, sample_data.shape[1])
def test_partial_dynamics2(self):
max_level = 5
level = 2
rank = 2
dmd = MrDMD(svd_rank=rank, max_level=max_level, max_cycles=2)
dmd.fit(X=sample_data)
pdynamics = dmd.partial_dynamics(level, 3)
assert pdynamics.shape == (rank, old_div(sample_data.shape[1], 2**level)
)
def test_eigs2(self):
max_level = 5
level = 2
rank = -1
dmd = MrDMD(svd_rank=rank, max_level=max_level, max_cycles=2)
dmd.fit(X=sample_data)
assert dmd.eigs.ndim == 1
def test_partial_eigs1(self):
max_level = 5
level = 2
rank = 2
dmd = MrDMD(svd_rank=rank, max_level=max_level, max_cycles=2)
dmd.fit(X=sample_data)
peigs = dmd.partial_eigs(level)
assert peigs.shape == (rank * 2**level, )
def test_partial_eigs2(self):
max_level = 5
level = 2
rank = 2
dmd = MrDMD(svd_rank=rank, max_level=max_level, max_cycles=2)
dmd.fit(X=sample_data)
peigs = dmd.partial_eigs(level, 3)
assert peigs.shape == (rank, )
def test_partial_reconstructed1(self):
max_level = 5
level = 2
rank = 2
dmd = MrDMD(svd_rank=rank, max_level=max_level, max_cycles=2)
dmd.fit(X=sample_data)
pdata = dmd.partial_reconstructed_data(level)
assert pdata.shape == sample_data.shape
def test_partial_reconstructed2(self):
max_level = 5
level = 2
rank = 2
dmd = MrDMD(svd_rank=rank, max_level=max_level, max_cycles=2)
dmd.fit(X=sample_data)
pdata = dmd.partial_reconstructed_data(level, 3)
assert pdata.shape == (sample_data.shape[0], old_div(
sample_data.shape[1], 2**level))
def test_wrong_partial_reconstructed(self):
max_level = 5
level = 2
rank = 2
dmd = MrDMD(svd_rank=rank, max_level=max_level, max_cycles=2)
dmd.fit(X=sample_data)
with self.assertRaises(ValueError):
pdata = dmd.partial_reconstructed_data(max_level, 2)
def test_wrong_level(self):
max_level = 5
dmd = MrDMD(max_level=max_level)
dmd.fit(sample_data)
with self.assertRaises(ValueError):
dmd.partial_modes(max_level + 1)
def test_wrong_bin(self):
max_level = 5
level = 2
dmd = MrDMD(max_level=max_level)
dmd.fit(sample_data)
with self.assertRaises(ValueError):
dmd.partial_modes(level=level, node=2**level)
def test_wrong_plot_eig1(self):
dmd = MrDMD(svd_rank=-1, max_level=7, max_cycles=1)
dmd.fit(X=sample_data)
with self.assertRaises(ValueError):
dmd.plot_eigs(
show_axes=True, show_unit_circle=True, figsize=(8, 8), level=7)
def test_wrong_plot_eig2(self):
dmd = MrDMD(svd_rank=1, max_level=7, max_cycles=1)
with self.assertRaises(ValueError):
dmd.plot_eigs()
def test_plot_eig1(self):
dmd = MrDMD(svd_rank=-1, max_level=7, max_cycles=1)
dmd.fit(X=sample_data)
dmd.plot_eigs(show_axes=True, show_unit_circle=True, figsize=(8, 8))
plt.close()
def test_plot_eig2(self):
dmd = MrDMD(svd_rank=-1, max_level=7, max_cycles=1)
dmd.fit(X=sample_data)
dmd.plot_eigs(show_axes=True, show_unit_circle=False, title='Title')
plt.close()
def test_plot_eig3(self):
dmd = MrDMD(svd_rank=-1, max_level=7, max_cycles=1)
dmd.fit(X=sample_data)
dmd.plot_eigs(show_axes=False, show_unit_circle=False, level=1, node=0)
plt.close()