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lingtypology's Issues

Default location for app-specific user data

I've seen you are looking in .lingtypology_data for user-supplied Glottolog data. There's a python package to determine locations for such data in a fairly cross-platform way: appdirs. So in this case, rather than in ~/.lingtypology_data one would look in appdirs.user_data_dir('lingtypology').

stroke_features delete normal features

The following code, taken from the documentation, does not work as intended:

import lingtypology
languages = ["Adyghe", "Kabardian", "Polish", "Russian", "Bulgarian"]
features = ["Agglutinative", "Agglutinative", "Inflected", "Inflected", "Analytic"]
stroke_features = ['Ergative', 'Ergative', 'Accusative', 'Accusative', 'Accusative']
m = lingtypology.LingMap(languages)
m.add_features(features)
m.add_stroke_features(stroke_features)
m.create_map()

While there are distinct inner and outer colors, both legends only show the values Ergative and Accusative; the features list is ignored and only appears when no stroke_features are added. Downgrading to earlier versions did not fix this; I wonder why it appears to work in the documentation?

Access glottolog data from github repos fork

Hi there, just came across this repos and I think what I noted for the R repos applies here as well:
ropensci/lingtypology#1

Basically,whenever you provide access to glottolog data through your tools, you should

  • either state explicitly and visibly which version of glottolog data you are using
  • or (recommended) give users control over which version of glottolog data they want to use your tools with.

Let me know if I can help with anything!

Kernel-density Estimation

A feature request from myself. Unfortunately, I am not familiar with this thing. It would be appreciated, if somebody could give me an example of how to calculate it.

I have coordinates (example below). I need to perform Kernel-destiny Estimation and draw a plot with matplotlib. Then I can implement it into lingtypology.

coordinates = [
    (53.2186, -7.6150899999999995),
    (58.55, 25.82),
    (59.92, 10.71),
    (46.3913, 24.2256),
    (51.8439, 18.6255),
    (43.0464, 12.6489),
    (39.8667, 32.8667),
    (48.0, 2.0), (53.0, -1.0),
    (40.4414, -1.11788)
]

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