samuroi.event package¶
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class
samuroi.event.biexponential.BiExponentialParameters(tau1, tau2)[source]¶ Bases:
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amplitude¶
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baseline¶
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kernel(x=None)[source]¶ Create a kernel for the given parameters. x may be a numpy array to be used as support. tau1 is slow, tau2 is fast. i.e. tau1 >= tau2 If x is None, the support is chosen automatically such that the difference of the last value of the kernel and the baseline is less than 1%.
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tau1¶
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tau2¶
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class
samuroi.event.template_matching.ClementsBekkersResult(indices, crit, s, c, threshold, kernel)[source]¶ Bases:
objectCollection of results of least squares optimization for template matching.
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c= None¶ the vector of optimal offset parameters
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crit= None¶ the criterion vector used for comparison with the threshold
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indices= None¶ the indices of detected events, since zero padding is used for convolution, all indices need to get shifted by N/2 where N is the length of the kernel
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kernel= None¶ the kernel that was used for matching
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s= None¶ the vector of optimal scaling parameters
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threshold= None¶ the threshold used for detection
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samuroi.event.template_matching.template_matching(data, kernel, threshold)[source]¶ Note
Threshold values usually should be in the range 1 to 5 for reasonable results.
Input \(\mathbf{y}\) and \(\mathbf{e}\) are two vectors, the normalized(todo: what means normalized?) template that should be used for matching and the data vector. Some intermediate values are:
\(\overline{e} = \frac{1}{K}\sum_k e_k\)
\(\overline{y_n} = \frac{1}{K}\sum_k y_{n+k}\)
The goal is to minimize the least squares distance:
\(\chi_n^2(S,C)=\sum_K\left[y_{n+k} - (S e_k +C)\right]^2\)
W.r.t. the variables \(S\) and \(C\). According to (ClementsBekkers, Appendix I)[1] the result is:
\(S_n = \frac{\sum_k e_k y_{n+k}-1/K \sum_k e_k \sum_k y_{n+k}}{\sum e_k^2-1/K \sum_k e_k \sum_k e_k} = \frac{\sum_k e_k y_{n+k}-K\overline{e}\ \overline{y_n}}{\sum e_k^2-K\overline{e}^2}\)
and
\(C_n = \overline{y_n} -S_n \overline{e}\)
Parameters: - data – 1D numpy array with the timeseries to analyze, above denoted as \(\mathbf{y}\)
- kernel – 1D numpy array with the template to use, above denoted as \(\mathbf{e}\)
- threshold – scalar value usually between 4 to 5.
Returns: A result object
samuroi.event.template_matching.ClementsBekkersResult