1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
|
"""Test the Kramers-Kronig transform module (qmix.mathfn.kktrans).
This module contains functions that can be used to calculate the
Kramers-Kronig transform. This is used to generate the real component
of the response function from a DC I-V curve.
Note: Future warnings (FutureWarning) are raised by the SciPy module. I ignore
these using the decorators seen below.
"""
import numpy as np
import pytest
from qmix.mathfn.ivcurve_models import polynomial
from qmix.mathfn.kktrans import kk_trans, kk_trans_trapz
@pytest.mark.filterwarnings("ignore::FutureWarning")
def test_compare_kk_techniques():
"""This module contains two different functions for calculating the
Kramers-Kronig transform. Make sure that they (roughly) produce the same
results."""
# Generate DC I-V curve
voltage = np.linspace(-5, 5, 10001)
current = polynomial(voltage, 50)
# Find KK transform with technique #1 (uses an FFT)
ikk1 = kk_trans(voltage, current)
# Find KK transform with technique #2 (uses a trapezoidal integration)
ikk2 = kk_trans_trapz(voltage, current)
# Don't test very limits (these are off but that's okay)
mask = (-4.5 < voltage) & (voltage < 4.5)
# Compare the two techniques
np.testing.assert_almost_equal(ikk1[mask], ikk2[mask], decimal=3)
@pytest.mark.filterwarnings("ignore::FutureWarning")
def test_uneven_voltage_array():
"""Both techniques should raise errors if the x data is not evenly spaced.
Make sure that this works."""
x = np.array([0, 0.1, 0.5, 1., 1.5])
y = polynomial(x, 50)
with pytest.raises(AssertionError):
kk_trans(x, y)
with pytest.raises(AssertionError):
kk_trans_trapz(x, y)
|
