Functional diversity #581
Functional diversity #581
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Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## main #581 +/- ##
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+ Coverage 97.4% 97.9% +0.6%
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Files 26 37 +11
Lines 4328 5807 +1479
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+ Hits 4214 5687 +1473
- Misses 114 120 +6
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momepy/functional/_diversity.py
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| from libpysal.graph import Graph | ||
| from numpy.typing import NDArray | ||
| from packaging.version import Version | ||
| from pandas import DataFrame, Series | ||
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| import momepy as mm |
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I would prefer to load just the functions you need. Any maybe check if we can njit them :)
momepy/functional/_diversity.py
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| @@ -176,6 +188,322 @@ def describe( | |||
| return _compute_stats(grouper, q, include_mode) | |||
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| def values_range( | |||
| data: DataFrame | Series, graph: Graph, rng: tuple | list = (0, 100), **kwargs | |||
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| data: DataFrame | Series, graph: Graph, rng: tuple | list = (0, 100), **kwargs | |
| y: DataFrame | Series | NDArray[np.float_], graph: Graph, q: tuple | list = (0, 100), **kwargs |
rng is now used by numpy for random-number-generator. Let's switch as we did in describe. And let's call the input array y as we do in desribe.
momepy/functional/_diversity.py
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| data: DataFrame | Series, graph: Graph, rng: tuple | list = (0, 100), **kwargs | ||
| ): | ||
| """Calculates the range of values within neighbours defined in ``graph``. | ||
| Uses ``scipy.stats.iqr`` under the hood. |
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Remove the note if scipy iqr to avoid the confusion of keywords as they are still using rng.
momepy/functional/_diversity.py
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| """ | ||
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| def _apply_range(values): | ||
| return sp.stats.iqr(values, rng=rng, **kwargs) |
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Might be actually easier to use numpy.percentile directly like we have in describe and pass the kwargs there for the sake of consistency. you can also njit that.
momepy/functional/_diversity.py
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| Parameters | ||
| ---------- | ||
| data : DataFrame | Series |
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| data : DataFrame | Series | |
| y : DataFrame | Series | NDArray[np.float_] |
momepy/functional/_diversity.py
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| def unique(data: DataFrame | Series, graph: Graph, dropna: bool = True): | ||
| """Calculates the number of unique values within neighbours defined in ``graph``. | ||
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| .. math:: |
| "min": 3789.0228732928035, | ||
| "max": 34510.77694161156, | ||
| } | ||
| print(np.mean(full_sw)) |
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| print(np.mean(full_sw)) |
| quan_sw, quan_sw_expected, self.df_tessellation, check_names=False | ||
| ) | ||
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| with pytest.raises(ValueError): |
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| with pytest.raises(ValueError): | |
| with pytest.raises(ValueError, match=""): |
check against the error to ensure we hit the correct one
| assert_result(cat, cat_expected, self.df_tessellation, check_names=False) | ||
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| def test_gini(self): | ||
| with pytest.raises(ValueError): |
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| with pytest.raises(ValueError): | |
| with pytest.raises(ValueError, match=""): |
same here. add expected error message
| assert_result(limit, limit_expected, self.df_tessellation, check_names=False) | ||
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| def test_shannon(self): | ||
| with pytest.raises(ValueError): |
Functional implementation of the distribution description functions. Code is same as OO version, with the difference that for loops are replaced by calls to libpysal.Graph.apply