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add info about topographic maps
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@ausgerechnet
ausgerechnet committed Jun 27, 2024
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23 changes: 23 additions & 0 deletions README.md
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```

## Topographic Maps

**New since version 0.3**: You can use `association_measures.grid.topography` to create a dataframe for visualising association measures in terms of topographic maps. It yields a lograthmically scaled grid from `N1` to `N2` with values of all association measures at resaonable sampling points of all combinations of `f1` and `f2`.
```python3
>>> from association_measures.grids import topography
>>> topography(N1=10e6, N2=10e6)
O11 O12 O21 O22 R1 R2 C1 C2 N E11 ... dice log_ratio conservative_log_ratio mutual_information local_mutual_information ipm ipm_reference ipm_expected clr_normal log_ratio_hardie
index ...
0 0 10000000.0 0 10000000.0 10000000.0 10000000.0 0 20000000.0 20000000.0 0.0 ... 0.000000 0.000000 0.000000 inf NaN 0.0 0.0 0.00 0.000000 0.000000
1 0 10000000.0 1 9999999.0 10000000.0 10000000.0 1 19999999.0 20000000.0 0.5 ... 0.000000 -9.967226 0.000000 -2.698970 0.000000 0.0 0.1 0.05 0.000000 -9.965784
2 0 10000000.0 2 9999998.0 10000000.0 10000000.0 2 19999998.0 20000000.0 1.0 ... 0.000000 -10.966505 0.000000 -3.000000 0.000000 0.0 0.2 0.10 0.000000 -10.965784
3 0 10000000.0 3 9999997.0 10000000.0 10000000.0 3 19999997.0 20000000.0 1.5 ... 0.000000 -11.551228 0.000000 -3.176091 -0.000000 0.0 0.3 0.15 0.000000 -11.550747
4 0 10000000.0 4 9999996.0 10000000.0 10000000.0 4 19999996.0 20000000.0 2.0 ... 0.000000 -11.966145 0.000000 -3.301030 -0.000000 0.0 0.4 0.20 0.000000 -11.965784
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
39995 10000000 0.0 7205937 2794063.0 10000000.0 10000000.0 17205937 2794063.0 20000000.0 8602968.5 ... 0.735134 0.472742 0.468813 0.065352 653516.672773 1000000.0 720593.7 860296.85 0.471159 0.472742
39996 10000000 0.0 7821100 2178900.0 10000000.0 10000000.0 17821100 2178900.0 20000000.0 8910550.0 ... 0.718879 0.354557 0.350718 0.050095 500954.884892 1000000.0 782110.0 891055.00 0.353215 0.354557
39997 10000000 0.0 8488779 1511221.0 10000000.0 10000000.0 18488779 1511221.0 20000000.0 9244389.5 ... 0.702031 0.236371 0.232619 0.034122 341217.643897 1000000.0 848877.9 924438.95 0.235298 0.236371
39998 10000000 0.0 9213457 786543.0 10000000.0 10000000.0 19213457 786543.0 20000000.0 9606728.5 ... 0.684616 0.118186 0.114514 0.017424 174244.829132 1000000.0 921345.7 960672.85 0.117443 0.118186
39999 10000000 0.0 10000000 0.0 10000000.0 10000000.0 20000000 0.0 20000000.0 10000000.0 ... 0.666667 0.000000 0.000000 0.000000 0.000000 1000000.0 1000000.0 1000000.00 0.000000 0.000000

[40000 rows x 29 columns]
```

# Development

The package is tested using pylint and pytest.
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6 changes: 6 additions & 0 deletions tests/test_readme.py
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import association_measures.frequencies as fq
import association_measures.measures as am
from association_measures.grids import topography


def test_frequencies(ucs_dataframe):
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df_ams = am.score(df, measures=['log_likelihood'], signed=False, freq=False)
print(df_ams.head())


def test_topography():

print(topography())

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