Add symmetric st-neighbors in local knox #121
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Codecov Report
@@ Coverage Diff @@
## main #121 +/- ##
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+ Coverage 64.1% 69.4% +5.3%
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Files 12 12
Lines 1448 1769 +321
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+ Hits 928 1228 +300
- Misses 520 541 +21
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inference type in edge plots
@knaaptime, do you mean guidance on how to test the result of |
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Yeah. Looking at geopandas, like like you test for some known elements in the json output? |
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Yes. Folium can give you a string of the HTML it generates, and we just try to ensure that those bits we need are there in the number of occurrences we expect them. |
sjsrey
changed the title
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thinking about this just a little bit more, i wonder if we should set the default neighbor color to yellow or something instead of blue. If you're stuck in LISA mode, you might assume blue is some sort of coldspot |
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That sounds very reasonable. |
notebooks/spacetime.ipynb
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In cell 10, you can avoid apply.
sd_collisions["time_in_days"] = (sd_collisions.COLLISION_DATE - min_date).dt.days
| if dataframe.crs is None: | ||
| warn( | ||
| "There is no CRS set on the dataframe. The KDTree will assume coordinates " | ||
| "are stored in Euclidean distances" | ||
| ) |
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just fyi, GeoPandas does not warn in this case. We only warn when the crs is geographic. No CRS implies cartesian coordinates.
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we hit this with the burkitt data which is stored in planar coords but doesnt have a CRS, so figured i'd add a warning just in case. I dont think we can always assume no CRS necessarily implies cartesian coords here.
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I don't mind the warning, just wanted to clarify the assumptions geopandas makes in case we want to do the same.
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ah gotcha. I opted to warn here because the KDtree will still "work" (and thus the class will produce a result), but afaik there's no way to make it work correctly with cartesian coords, so its up to the user if they know what they're doing. Maybe it would be more conservative to fail instead?
pointpats/spacetime.py
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| """Create an integer valued column for time""" | ||
| time_series = pandas.to_datetime(df[column], format=fmt) | ||
| min_time = time_series.min() | ||
| time_int = time_series.appy(lambda x: x - min_time) |
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Here's a typo in apply. I also don't think you need apply here.
| time_int = time_series.appy(lambda x: x - min_time) | |
| time_int = time_series - min_time |
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we were going to try an introspect any datetime col if passed, but then punted and decided the time col needs to be given as integer from the user.
so this is just a vestige of that idea and can be removed
pointpats/spacetime.py
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| return s_coords, t_coords | ||
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| def _time_to_int(df, column, fmt="%Y%m%d"): |
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This is not used. Shall we remove it or plug it somewhere?
Co-authored-by: Martin Fleischmann <martin@martinfleischmann.net>
Co-authored-by: Martin Fleischmann <martin@martinfleischmann.net>
inference type in edge plots
No description provided.