add first draft of correlogram #253
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esda/correlogram.py
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| attrs = [] | ||
| all_attrs = list(dict(vars(autocorr)).keys()) | ||
| for attribute in all_attrs: | ||
| attrs.append(getattr(autocorr, str(attribute))) |
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I wonder if this hits any attributes that are not scalar values, and what that may do?
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i went through a few drafts first (despite the PRs name :P). It does grab a few, (in particular some of the attributes on the original Moran etc object are arrays, so they end up as lists in each row. not ideal). So I filter them out down in line 142 (and also drop a few constants like n and permutations)
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(there's an example of the table it generates in here)
martinfleis
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Can you add tests, please? :)
esda/correlogram.py
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| n_jobs: int = -1, | ||
| backend: str = "loky", | ||
| ): | ||
| """Generate a spatial correlogram |
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Could you add a sentence explaining what a spatial correlogram is?
esda/correlogram.py
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| additional keyword arguments passed to the libpysal.weights.W class | ||
| stat_kwargs : dict | ||
| additional keyword arguments passed to the `esda` autocorrelation statistic class. | ||
| For example for faster results with no statistical inference, set the nuumber |
esda/correlogram.py
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| n_jobs : int | ||
| number of jobs to pass to joblib. If -1 (default), all cores will be used | ||
| backend : str | ||
| backend parameter passed to joblb |
esda/correlogram.py
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| Parameters | ||
| ---------- | ||
| gdf : gpd.GeoDataFrame |
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While not critical for this PR (or code functionality itself), we should strive for proper spelling, grammar, and punctuation within our docstrings. English may not be the first language of many beginner users of the PySAL ecosystem so having clearly written documentation could likely help them get over the hump for using (and possibly contributing to) PySAL.
notebooks/correlogram.ipynb
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| @@ -0,0 +1,893 @@ | |||
| { | |||
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Going to piggyback on Martin's comment and request a title a brief explanation be added in the notebook.
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