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96 lines
3.1 KiB
96 lines
3.1 KiB
2 years ago
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from __future__ import print_function
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from scipy import interpolate
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import numpy as np
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def find_point(x, y, peakindex, val, direction="left", limits=None):
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"""
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Given a waveform defined by *x* and *y* arrays, return the first time
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at which the waveform crosses (y[peakindex] * val). The search begins at
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*peakindex* and proceeds in *direction*.
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Optionally, *limits* may specify a smaller search region in the form of
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(t0, t1, dt).
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"""
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# F = interpolate.UnivariateSpline(x, y, s=0) # declare function
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# To find x at y then do:
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istart = 0
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iend = len(y)
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if limits is not None:
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istart = int(limits[0] / limits[2])
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iend = int(limits[1] / limits[2])
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yToFind = y[peakindex] * val
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if direction == "left":
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yreduced = np.array(y[istart:peakindex]) - yToFind
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try:
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Fr = interpolate.UnivariateSpline(x[istart:peakindex], yreduced, s=0)
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except:
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print("find_point: insufficient time points for analysis")
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print("arg lengths:", len(x[istart:peakindex]), len(yreduced))
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print("istart, peakindex: ", istart, peakindex)
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print("ytofine: ", yToFind)
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raise
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res = float("nan")
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return res
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res = Fr.roots()
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if len(res) > 1:
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res = res[-1]
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else:
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yreduced = np.array(y[peakindex:iend]) - yToFind
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try:
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Fr = interpolate.UnivariateSpline(x[peakindex:iend], yreduced, s=0)
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except:
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print("find_point: insufficient time points for analysis?")
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print("arg lengths:", len(x[peakindex:iend]), len(yreduced))
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raise
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res = float("nan")
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return res
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res = Fr.roots()
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if len(res) > 1:
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res = res[0]
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# pdb.set_trace()
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try:
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res.pop()
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except:
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pass
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if not res: # tricky - an empty list is False, but does not evaluate to False
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res = float("nan") # replace with a NaN
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else:
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res = float(res) # make sure is just a simple number (no arrays)
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return res
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def find_crossing(data, start=0, direction=1, threshold=0):
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"""Return the index at which *data* crosses *threshold*, starting
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at index *start* and proceeding in *direction* (+/-1).
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The value returned is a float indicating the interpolated index
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position where the data crosses threshold, or NaN if the threshold was
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never crossed.
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"""
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# Note: this function is very similar in purpose to find_point, but was
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# added due to issues with interpolate.UnivariateSpline
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assert direction in (1, -1)
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cross_rising = data[start] < threshold
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def test(x):
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if cross_rising:
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return x > threshold
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else:
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return x < threshold
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while True:
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next_ind = start + direction
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if next_ind < 0 or next_ind >= len(data):
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return np.nan
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if test(data[next_ind]):
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# crossed; return interpolated value
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s1 = data[next_ind] - threshold
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s2 = threshold = data[start]
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return (next_ind * s2 + start * s1) / (s2 + s1)
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start = next_ind
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