How to use the momepy.Simpson function in momepy
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martinfleis / momepy / tests / test_diversity.py View on Github 
self.df_tessellation, "area", self.sw, "uID", gini_simpson=True
).series
assert gs[0] == 1 - 0.385
inv = mm.Simpson(
self.df_tessellation, "area", self.sw, "uID", inverse=True
).series
assert inv[0] == 1 / 0.385
self.df_tessellation["cat"] = list(range(8)) * 18
cat = mm.Simpson(
self.df_tessellation, "cat", self.sw, "uID", categorical=True
).series
assert cat[0] == pytest.approx(0.15)
cat2 = mm.Simpson(
self.df_tessellation,
"cat",
self.sw,
"uID",
categorical=True,
categories=range(15),
).series
assert cat2[0] == pytest.approx(0.15)self.sw,
"uID",
binning="quantiles",
k=3,
).series
assert quan_sw[0] == 0.395
with pytest.raises(ValueError):
ht_sw = mm.Simpson(
self.df_tessellation, "area", self.sw, "uID", binning="nonexistent"
)
assert (
mm.Simpson(self.df_tessellation, "area", self.sw_drop, "uID")
.series.isna()
.any()
)
gs = mm.Simpson(
self.df_tessellation, "area", self.sw, "uID", gini_simpson=True
).series
assert gs[0] == 1 - 0.385
inv = mm.Simpson(
self.df_tessellation, "area", self.sw, "uID", inverse=True
).series
assert inv[0] == 1 / 0.385
self.df_tessellation["cat"] = list(range(8)) * 18
cat = mm.Simpson(
self.df_tessellation, "cat", self.sw, "uID", categorical=True
).series
assert cat[0] == pytest.approx(0.15)
cat2 = mm.Simpson(def test_Simpson(self):
ht_sw = mm.Simpson(self.df_tessellation, "area", self.sw, "uID").series
assert ht_sw[0] == 0.385
quan_sw = mm.Simpson(
self.df_tessellation,
self.df_tessellation.area,
self.sw,
"uID",
binning="quantiles",
k=3,
).series
assert quan_sw[0] == 0.395
with pytest.raises(ValueError):
ht_sw = mm.Simpson(
self.df_tessellation, "area", self.sw, "uID", binning="nonexistent"
)
assert (
mm.Simpson(self.df_tessellation, "area", self.sw_drop, "uID")mm.Simpson(self.df_tessellation, "area", self.sw_drop, "uID")
.series.isna()
.any()
)
gs = mm.Simpson(
self.df_tessellation, "area", self.sw, "uID", gini_simpson=True
).series
assert gs[0] == 1 - 0.385
inv = mm.Simpson(
self.df_tessellation, "area", self.sw, "uID", inverse=True
).series
assert inv[0] == 1 / 0.385
self.df_tessellation["cat"] = list(range(8)) * 18
cat = mm.Simpson(
self.df_tessellation, "cat", self.sw, "uID", categorical=True
).series
assert cat[0] == pytest.approx(0.15)
cat2 = mm.Simpson(
self.df_tessellation,
"cat",
self.sw,
"uID",
categorical=True,
categories=range(15),
).series
assert cat2[0] == pytest.approx(0.15)def test_Simpson(self):
ht_sw = mm.Simpson(self.df_tessellation, "area", self.sw, "uID").series
assert ht_sw[0] == 0.385
quan_sw = mm.Simpson(
self.df_tessellation,
self.df_tessellation.area,
self.sw,
"uID",
binning="quantiles",
k=3,
).series
assert quan_sw[0] == 0.395
with pytest.raises(ValueError):
ht_sw = mm.Simpson(
self.df_tessellation, "area", self.sw, "uID", binning="nonexistent"
)
assert (
mm.Simpson(self.df_tessellation, "area", self.sw_drop, "uID")
.series.isna()
.any()assert quan_sw[0] == 0.395
with pytest.raises(ValueError):
ht_sw = mm.Simpson(
self.df_tessellation, "area", self.sw, "uID", binning="nonexistent"
)
assert (
mm.Simpson(self.df_tessellation, "area", self.sw_drop, "uID")
.series.isna()
.any()
)
gs = mm.Simpson(
self.df_tessellation, "area", self.sw, "uID", gini_simpson=True
).series
assert gs[0] == 1 - 0.385
inv = mm.Simpson(
self.df_tessellation, "area", self.sw, "uID", inverse=True
).series
assert inv[0] == 1 / 0.385
self.df_tessellation["cat"] = list(range(8)) * 18
cat = mm.Simpson(
self.df_tessellation, "cat", self.sw, "uID", categorical=True
).series
assert cat[0] == pytest.approx(0.15)
cat2 = mm.Simpson(
self.df_tessellation,
"cat",
self.sw,
"uID",
categorical=True,def test_Simpson(self):
ht_sw = mm.Simpson(self.df_tessellation, "area", self.sw, "uID").series
assert ht_sw[0] == 0.385
quan_sw = mm.Simpson(
self.df_tessellation,
self.df_tessellation.area,
self.sw,
"uID",
binning="quantiles",
k=3,
).series
assert quan_sw[0] == 0.395
with pytest.raises(ValueError):
ht_sw = mm.Simpson(
self.df_tessellation, "area", self.sw, "uID", binning="nonexistent"
)
assert (
mm.Simpson(self.df_tessellation, "area", self.sw_drop, "uID")
.series.isna()
.any()
)
gs = mm.Simpson(
self.df_tessellation, "area", self.sw, "uID", gini_simpson=True
).series
assert gs[0] == 1 - 0.385
inv = mm.Simpson(
self.df_tessellation, "area", self.sw, "uID", inverse=True
).series
assert inv[0] == 1 / 0.385assert ht_sw[0] == 0.385
quan_sw = mm.Simpson(
self.df_tessellation,
self.df_tessellation.area,
self.sw,
"uID",
binning="quantiles",
k=3,
).series
assert quan_sw[0] == 0.395
with pytest.raises(ValueError):
ht_sw = mm.Simpson(
self.df_tessellation, "area", self.sw, "uID", binning="nonexistent"
)
assert (
mm.Simpson(self.df_tessellation, "area", self.sw_drop, "uID")
.series.isna()
.any()
)
gs = mm.Simpson(
self.df_tessellation, "area", self.sw, "uID", gini_simpson=True
).series
assert gs[0] == 1 - 0.385
inv = mm.Simpson(
self.df_tessellation, "area", self.sw, "uID", inverse=True
).series
assert inv[0] == 1 / 0.385
self.df_tessellation["cat"] = list(range(8)) * 18
cat = mm.Simpson(
self.df_tessellation, "cat", self.sw, "uID", categorical=True
Popular momepy functions
- momepy.Area
- momepy.AreaRatio
- momepy.AverageCharacter
- momepy.betweenness_centrality
- momepy.BlocksCount
- momepy.buffered_limit
- momepy.CourtyardArea
- momepy.datasets
- momepy.datasets.get_path
- momepy.FloorArea
- momepy.gdf_to_nx
- momepy.get_network_id
- momepy.nx_to_gdf
- momepy.Orientation
- momepy.Shannon
- momepy.SharedWallsRatio
- momepy.Simpson
- momepy.StreetAlignment
- momepy.sw_high
- momepy.Volume