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67 lines
1.6 KiB
67 lines
1.6 KiB
6 years ago
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import bisect
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import numpy as np
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from scipy.interpolate import interp1d
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def deep_interp_0_fast(dx, x, y):
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FIX = False
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if len(y.shape) == 1:
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y = y[:, None]
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FIX = True
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ret = np.zeros((dx.shape[0], y.shape[1]))
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index = list(x)
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for i in range(dx.shape[0]):
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idx = bisect.bisect_left(index, dx[i])
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if idx == x.shape[0]:
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idx = x.shape[0] - 1
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ret[i] = y[idx]
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if FIX:
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return ret[:, 0]
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else:
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return ret
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def running_mean(x, N):
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cumsum = np.cumsum(np.insert(x, [0]*(int(N/2)) + [-1]*(N-int(N/2)), [x[0]]*int(N/2) + [x[-1]]*(N-int(N/2))))
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return (cumsum[N:] - cumsum[:-N]) / N
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def deep_interp_np(x, xp, fp):
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x = np.atleast_1d(x)
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xp = np.array(xp)
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if len(xp) < 2:
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return np.repeat(fp, len(x), axis=0)
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if min(np.diff(xp)) < 0:
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raise RuntimeError('Bad x array for interpolation')
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j = np.searchsorted(xp, x) - 1
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j = np.clip(j, 0, len(xp)-2)
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d = np.divide(x - xp[j], xp[j + 1] - xp[j], out=np.ones_like(x, dtype=np.float64), where=xp[j + 1] - xp[j] != 0)
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vals_interp = (fp[j].T*(1 - d)).T + (fp[j + 1].T*d).T
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if len(vals_interp) == 1:
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return vals_interp[0]
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else:
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return vals_interp
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def clipping_deep_interp(x, xp, fp):
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if len(xp) < 2:
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return deep_interp_np(x, xp, fp)
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bad_idx = np.where(np.diff(xp) < 0)[0]
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if len(bad_idx) > 0:
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if bad_idx[0] ==1:
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return np.zeros([] + list(fp.shape[1:]))
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return deep_interp_np(x, xp[:bad_idx[0]], fp[:bad_idx[0]])
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else:
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return deep_interp_np(x, xp, fp)
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def deep_interp(dx, x, y, kind="slinear"):
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return interp1d(
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x, y,
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axis=0,
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kind=kind,
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bounds_error=False,
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fill_value="extrapolate",
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assume_sorted=True)(dx)
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