def test_moran_loc_scatterplot():
df = _test_data_columbus()
x = df['INC'].values
y = df['HOVAL'].values
w = Queen.from_dataframe(df)
w.transform = 'r'
moran_loc = Moran_Local(y, w)
moran_bv = Moran_BV(x, y, w)
# try without p value
fig, _ = _moran_loc_scatterplot(moran_loc)
plt.close(fig)
# try with p value and different figure size
fig, _ = _moran_loc_scatterplot(moran_loc, p=0.05,
aspect_equal=False,
fitline_kwds=dict(color='#4393c3'))
plt.close(fig)
# try with p value and zstandard=False
fig, _ = _moran_loc_scatterplot(moran_loc, p=0.05, zstandard=False,
fitline_kwds=dict(color='#4393c3'))
plt.close(fig)
# try without p value and zstandard=False
fig, _ = _moran_loc_scatterplot(moran_loc, zstandard=False,
fitline_kwds=dict(color='#4393c3'))
plt.close(fig)
raises(ValueError, _moran_loc_scatterplot, moran_bv, p=0.5)
warns(UserWarning, _moran_loc_scatterplot, moran_loc, p=0.5,
scatter_kwds=dict(c='#4393c3'))
def test_moran_bv_scatterplot():
gdf = _test_data()
x = gdf['Suicids'].values
y = gdf['Donatns'].values
w = Queen.from_dataframe(gdf)
w.transform = 'r'
# Calculate Bivariate Moran
moran_bv = Moran_BV(x, y, w)
# plot
fig, _ = _moran_bv_scatterplot(moran_bv)
plt.close(fig)
# customize plot
fig, _ = _moran_bv_scatterplot(moran_bv, aspect_equal=False,
fitline_kwds=dict(color='#4393c3'))
plt.close(fig)
def test_moran_bv_scatterplot():
link_to_data = examples.get_path('Guerry.shp')
gdf = gpd.read_file(link_to_data)
x = gdf['Suicids'].values
y = gdf['Donatns'].values
w = Queen.from_dataframe(gdf)
w.transform = 'r'
# Calculate Bivariate Moran
moran_bv = Moran_BV(x, y, w)
# plot
fig, _ = _moran_bv_scatterplot(moran_bv)
plt.close(fig)
# customize plot
fig, _ = _moran_bv_scatterplot(moran_bv,
fitline_kwds=dict(color='#4393c3'))
plt.close(fig)
def test_plot_moran_bv():
# Load data and calculate weights
link_to_data = examples.get_path('Guerry.shp')
gdf = gpd.read_file(link_to_data)
x = gdf['Suicids'].values
y = gdf['Donatns'].values
w = Queen.from_dataframe(gdf)
w.transform = 'r'
# Calculate Bivariate Moran
moran_bv = Moran_BV(x, y, w)
# plot
fig, _ = plot_moran_bv(moran_bv)
plt.close(fig)
# customize plot
fig, _ = plot_moran_bv(moran_bv,
aspect_equal=False,
fitline_kwds=dict(color='#4393c3'))
plt.close(fig)
def test_moran_loc_scatterplot():
columbus = examples.load_example('Columbus')
link_to_data = columbus.get_path('columbus.shp')
df = gpd.read_file(link_to_data)
x = df['INC'].values
y = df['HOVAL'].values
w = Queen.from_dataframe(df)
w.transform = 'r'
moran_loc = Moran_Local(y, w)
moran_bv = Moran_BV(x, y, w)
# try without p value
fig, _ = _moran_loc_scatterplot(moran_loc)
plt.close(fig)
# try with p value and different figure size
fig, _ = _moran_loc_scatterplot(moran_loc,
p=0.05,
aspect_equal=False,
fitline_kwds=dict(color='#4393c3'))
plt.close(fig)
# try with p value and zstandard=False
fig, _ = _moran_loc_scatterplot(moran_loc,
p=0.05,
zstandard=False,
fitline_kwds=dict(color='#4393c3'))
plt.close(fig)
# try without p value and zstandard=False
fig, _ = _moran_loc_scatterplot(moran_loc,
zstandard=False,
fitline_kwds=dict(color='#4393c3'))
plt.close(fig)
assert_raises(ValueError, _moran_loc_scatterplot, moran_bv, p=0.5)
assert_warns(UserWarning,
_moran_loc_scatterplot,
moran_loc,
p=0.5,
scatter_kwds=dict(c='#4393c3'))
def test_moran_scatterplot():
gdf = _test_data()
x = gdf['Suicids'].values
y = gdf['Donatns'].values
w = Queen.from_dataframe(gdf)
w.transform = 'r'
# Calculate `esda.moran` Objects
moran = Moran(y, w)
moran_bv = Moran_BV(y, x, w)
moran_loc = Moran_Local(y, w)
moran_loc_bv = Moran_Local_BV(y, x, w)
# try with p value so points are colored or warnings apply
fig, _ = moran_scatterplot(moran, p=0.05, aspect_equal=False)
plt.close(fig)
fig, _ = moran_scatterplot(moran_loc, p=0.05)
plt.close(fig)
fig, _ = moran_scatterplot(moran_bv, p=0.05)
plt.close(fig)
fig, _ = moran_scatterplot(moran_loc_bv, p=0.05)
plt.close(fig)
def test_moran_scatterplot():
link_to_data = examples.get_path('Guerry.shp')
gdf = gpd.read_file(link_to_data)
x = gdf['Suicids'].values
y = gdf['Donatns'].values
w = Queen.from_dataframe(gdf)
w.transform = 'r'
# Calculate `esda.moran` Objects
moran = Moran(y, w)
moran_bv = Moran_BV(y, x, w)
moran_loc = Moran_Local(y, w)
moran_loc_bv = Moran_Local_BV(y, x, w)
# try with p value so points are colored or warnings apply
fig, _ = moran_scatterplot(moran, p=0.05)
plt.close(fig)
fig, _ = moran_scatterplot(moran_loc, p=0.05)
plt.close(fig)
fig, _ = moran_scatterplot(moran_bv, p=0.05)
plt.close(fig)
fig, _ = moran_scatterplot(moran_loc_bv, p=0.05)
plt.close(fig)
