Implementing basic RQA functionality#15
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zeyus merged 14 commits intoNexusDynamic:devfrom Mar 8, 2024
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zeyus
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Mar 7, 2024
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Minor things but this is awesome 😎
| self._metadata[k] = v | ||
| else: | ||
| # Metadata entry for an existing key: append values to the list | ||
| self._metadata[k] += v |
| v_sum = d_line_dist[lmin:].sum() | ||
| avg_vert_length = (v_line_dist[lmin:] * np.arange(msize+1)[lmin:]).sum() / v_sum if v_sum > 0 else 0 | ||
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| d_line_dist[lmin:] > 0 |
| d_probs /= d_probs.sum() | ||
| d_entropy = -(d_probs * np.log(d_probs)).sum() | ||
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| v_line_dist[lmin:] > 0 |
| return x.interpolate(method="linear") | ||
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| def calc_rqa(x: np.array, y: np.array, dim: int = 1, tau: int = 1, threshold: float = 0.1, lmin: int = 2): |
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This could maybe go into mopipe.core somewhere?
zeyus
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Mar 7, 2024
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I'll have to revisit the documentation generation, now that we are getting to the meat of the code it should break it out a bit more for the API documentation (like "stuff you want to use as a user of mopipe" / " base structures and internal stuff you can build from in mopipe") but we can discuss what makes the most sense...I think it should be as automated as possible, but a bit more structured than the huge list of classes there now. |
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This is the implementation of a basic RQA analysis fitting the Mopipe architecture.
New Segments:
Bugfixes: