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reduced random blank lines
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de0cb1a4e3
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@ -201,6 +201,7 @@ def basic_stats(data):
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@jit(forceobj=True)
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@jit(forceobj=True)
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def z_score(point, mean, stdev):
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def z_score(point, mean, stdev):
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score = (point - mean) / stdev
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score = (point - mean) / stdev
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return score
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return score
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# expects 2d array, normalizes across all axes
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# expects 2d array, normalizes across all axes
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@ -208,9 +209,7 @@ def z_score(point, mean, stdev):
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def z_normalize(array, *args):
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def z_normalize(array, *args):
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array = np.array(array)
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array = np.array(array)
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for arg in args:
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for arg in args:
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array = preprocessing.normalize(array, axis = arg)
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array = preprocessing.normalize(array, axis = arg)
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return array
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return array
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@ -220,13 +219,9 @@ def z_normalize(array, *args):
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def histo_analysis(hist_data):
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def histo_analysis(hist_data):
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hist_data = np.array(hist_data)
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hist_data = np.array(hist_data)
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derivative = np.array(len(hist_data) - 1, dtype = float)
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derivative = np.array(len(hist_data) - 1, dtype = float)
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t = np.diff(hist_data)
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t = np.diff(hist_data)
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derivative = t[1] / t[0]
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derivative = t[1] / t[0]
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np.sort(derivative)
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np.sort(derivative)
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return basic_stats(derivative)[0], basic_stats(derivative)[3]
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return basic_stats(derivative)[0], basic_stats(derivative)[3]
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