def moran_scatterplt(moran, bivariate=False, disease=''):
if bivariate:
fig, ax = moran_scatterplot(moran, p=0.05)
ax.set_ylabel('Spatial lag (' + disease + ')')
else:
fig, ax = moran_scatterplot(moran, aspect_equal=True)
ax.set_ylabel('Spatial lag de Suicídios')
ax.set_xlabel('Suicídios')
st.pyplot(fig)
def test_moran_scatterplot():
# Load data and apply statistical analysis
link_to_data = examples.get_path('Guerry.shp')
gdf = gpd.read_file(link_to_data)
y = gdf['Donatns'].values
w = lp.Queen.from_dataframe(gdf)
w.transform = 'r'
# Calc Global Moran
w = lp.Queen.from_dataframe(gdf)
moran = Moran(y, w)
# plot
fig, _ = moran_scatterplot(moran)
plt.close(fig)
# customize
fig, _ = moran_scatterplot(moran, zstandard=False,
fitline_kwds=dict(color='#4393c3'))
plt.close(fig)
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)