Total Complexity | 3 |
Total Lines | 33 |
Duplicated Lines | 0 % |
Changes | 0 |
1 | import numpy as np |
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2 | from numpy import log10 |
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3 | from math import log10 as mlog10 |
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4 | import scipy.interpolate |
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5 | |||
6 | |||
7 | class LogLogInterpolator(object): # pragma: no cover |
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8 | |||
9 | def __init__(self, x, y, k=2): |
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10 | |||
11 | y = y.astype(np.float64) |
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12 | y = np.clip(y, 2 * np.finfo(np.float64).tiny, None) |
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13 | |||
14 | logx = log10(x) |
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15 | logy = log10(y) |
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16 | |||
17 | self._interp = scipy.interpolate.InterpolatedUnivariateSpline(logx, logy, k=k) |
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18 | |||
19 | def __call__(self, x): |
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20 | |||
21 | return 10**self._interp(log10(x)) |
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22 | |||
23 | def integral(self, a, b, n_points=100, k=1): |
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24 | |||
25 | # Make a second interpolator |
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26 | xx = np.logspace(mlog10(a), mlog10(b), n_points) |
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27 | |||
28 | yy = self.__call__(xx) |
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29 | |||
30 | int_interp = scipy.interpolate.InterpolatedUnivariateSpline(xx, yy, k=k) |
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31 | |||
32 | return int_interp.integral(a, b) |
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