def test_plot_spatial_weights():
# get data
gdf = gpd.read_file(examples.get_path('43MUE250GC_SIR.shp'))
gdf.head()
# calculate weights
weights = Queen.from_dataframe(gdf)
# plot weights
fig, _ = plot_spatial_weights(weights, gdf)
plt.close(fig)
# calculate nonplanar_joins
wnp = libpysal.weights.util.nonplanar_neighbors(weights, gdf)
# plot new joins
fig2, _ = plot_spatial_weights(wnp, gdf)
plt.close(fig2)
#customize
fig3, _ = plot_spatial_weights(wnp,
gdf,
nonplanar_edge_kws=dict(color='#4393c3'))
plt.close(fig3)
# plot in existing figure
fig4, axs = plt.subplots(1, 3)
plot_spatial_weights(wnp, gdf, ax=axs[0])
plt.close(fig4)
# uses a column as the index for spatial weights object
weights_index = Queen.from_dataframe(gdf, idVariable="CD_GEOCMU")
fig, _ = plot_spatial_weights(weights_index, gdf, indexed_on="CD_GEOCMU")
plt.close(fig)
def test_plot_moran_simulation():
# Load data and apply statistical analysis
gdf = _test_data()
y = gdf['Donatns'].values
w = Queen.from_dataframe(gdf)
w.transform = 'r'
# Calc Global Moran
w = Queen.from_dataframe(gdf)
moran = Moran(y, w)
# plot
fig, _ = plot_moran_simulation(moran)
plt.close(fig)
# customize
fig, _ = plot_moran_simulation(moran,
fitline_kwds=dict(color='#4393c3'))
plt.close(fig)
def test_plot_local_autocorrelation():
columbus = examples.load_example('Columbus')
link_to_data = columbus.get_path('columbus.shp')
df = gpd.read_file(link_to_data)
y = df['HOVAL'].values
w = Queen.from_dataframe(df)
w.transform = 'r'
moran_loc = Moran_Local(y, w)
fig, _ = plot_local_autocorrelation(moran_loc, df, 'HOVAL', p=0.05)
plt.close(fig)
# also test with quadrant and mask
fig, _ = plot_local_autocorrelation(moran_loc, df, 'HOVAL', p=0.05,
region_column='POLYID',
aspect_equal=False,
mask=['1', '2', '3'], quadrant=1)
plt.close(fig)
# also test with quadrant and mask
assert_raises(ValueError, plot_local_autocorrelation, moran_loc,
df, 'HOVAL', p=0.05, region_column='POLYID',
mask=['100', '200', '300'], quadrant=1)
def test_moran_loc_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 Univariate and Bivariate Moran
moran_loc = Moran_Local(y, w)
moran_loc_bv = Moran_Local_BV(x, y, w)
# try with p value so points are colored
fig, _ = _moran_loc_bv_scatterplot(moran_loc_bv)
plt.close(fig)
# try with p value and different figure size
fig, _ = _moran_loc_bv_scatterplot(moran_loc_bv,
p=0.05,
aspect_equal=False)
plt.close(fig)
assert_raises(ValueError, _moran_loc_bv_scatterplot, moran_loc, p=0.5)
assert_warns(UserWarning,
_moran_loc_bv_scatterplot,
moran_loc_bv,
p=0.5,
scatter_kwds=dict(c='r'))
def test_plot_moran_simulation():
# 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 = Queen.from_dataframe(gdf)
w.transform = 'r'
# Calc Global Moran
w = Queen.from_dataframe(gdf)
moran = Moran(y, w)
# plot
fig, _ = plot_moran_simulation(moran)
plt.close(fig)
# customize
fig, _ = plot_moran_simulation(moran, fitline_kwds=dict(color='#4393c3'))
plt.close(fig)
def _test_calc_moran_loc(gdf, var='HOVAL'):
y = gdf[var].values
w = Queen.from_dataframe(gdf)
w.transform = 'r'
moran_loc = Moran_Local(y, w)
return moran_loc
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_global_scatterplot():
# Load data and apply statistical analysis
gdf = _test_data()
y = gdf['Donatns'].values
w = Queen.from_dataframe(gdf)
w.transform = 'r'
# Calc Global Moran
w = Queen.from_dataframe(gdf)
moran = Moran(y, w)
# plot
fig, _ = _moran_global_scatterplot(moran)
plt.close(fig)
# customize
fig, _ = _moran_global_scatterplot(moran, zstandard=False,
aspect_equal=False,
fitline_kwds=dict(color='#4393c3'))
plt.close(fig)
def test_plot_local_autocorrelation():
link = examples.get_path("columbus.shp")
df = gpd.read_file(link)
y = df["HOVAL"].values
w = Queen.from_dataframe(df)
w.transform = "r"
moran_loc = esda.moran.Moran_Local(y, w)
fig = plot_local_autocorrelation(moran_loc, df, "HOVAL")
def test_moran_scatterplot():
link = examples.get_path("columbus.shp")
df = gpd.read_file(link)
y = df["HOVAL"].values
w = Queen.from_dataframe(df)
w.transform = "r"
moran_loc = esda.moran.Moran_Local(y, w)
fig = moran_scatterplot(moran_loc, p=0.05)
def test_plot_choropleth():
link = examples.get_path("columbus.shp")
df = gpd.read_file(link)
w = Queen.from_dataframe(df)
w.transform = "r"
TOOLS = "tap,help"
fig = plot_choropleth(
df, "HOVAL", title="columbus", reverse_colors=True, tools=TOOLS
)
def test_moran_global_scatterplot():
# Load data and apply statistical analysis
guerry = examples.load_example('Guerry')
link_to_data = guerry.get_path('Guerry.shp')
gdf = gpd.read_file(link_to_data)
y = gdf['Donatns'].values
w = Queen.from_dataframe(gdf)
w.transform = 'r'
# Calc Global Moran
w = Queen.from_dataframe(gdf)
moran = Moran(y, w)
# plot
fig, _ = _moran_global_scatterplot(moran)
plt.close(fig)
# customize
fig, _ = _moran_global_scatterplot(moran,
zstandard=False,
aspect_equal=False,
fitline_kwds=dict(color='#4393c3'))
plt.close(fig)
def test_lisa_cluster():
link = examples.get_path("columbus.shp")
df = gpd.read_file(link)
y = df["HOVAL"].values
w = Queen.from_dataframe(df)
w.transform = "r"
moran_loc = esda.moran.Moran_Local(y, w)
TOOLS = "tap,reset,help"
fig = lisa_cluster(moran_loc, df, p=0.05, tools=TOOLS)
def test_lisa_cluster():
link = examples.get_path('columbus.shp')
df = gpd.read_file(link)
y = df['HOVAL'].values
w = Queen.from_dataframe(df)
w.transform = 'r'
moran_loc = Moran_Local(y, w)
fig, _ = lisa_cluster(moran_loc, df)
plt.close(fig)
def test_plot_choropleth():
link = examples.get_path('columbus.shp')
df = gpd.read_file(link)
w = Queen.from_dataframe(df)
w.transform = 'r'
TOOLS = "tap,help"
fig = plot_choropleth(df,
'HOVAL',
title='columbus',
reverse_colors=True,
tools=TOOLS)
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_moran_loc_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_loc_bv = Moran_Local_BV(x, y, w)
# try with p value so points are colored
fig, _ = _moran_loc_bv_scatterplot(moran_loc_bv)
plt.close(fig)
# try with p value and different figure size
fig, _ = _moran_loc_bv_scatterplot(moran_loc_bv, p=0.05)
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():
link = examples.get_path('columbus.shp')
df = gpd.read_file(link)
y = df['HOVAL'].values
w = Queen.from_dataframe(df)
w.transform = 'r'
moran_loc = Moran_Local(y, w)
# try with p value so points are colored
fig, _ = _moran_loc_scatterplot(moran_loc, p=0.05)
plt.close(fig)
# try with p value and different figure size
fig, _ = _moran_loc_scatterplot(moran_loc,
p=0.05,
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_plot_spatial_weights():
# get data
gdf = gpd.read_file(examples.get_path('43MUE250GC_SIR.shp'))
gdf.head()
# calculate weights
weights = Queen.from_dataframe(gdf)
# plot weights
fig, _ = plot_spatial_weights(weights, gdf)
plt.close(fig)
# calculate nonplanar_joins
wnp = libpysal.weights.util.nonplanar_neighbors(weights, gdf)
# plot new joins
fig2, _ = plot_spatial_weights(wnp, gdf)
plt.close(fig2)
#customize
fig3, _ = plot_spatial_weights(wnp,
gdf,
nonplanar_edge_kws=dict(color='#4393c3'))
plt.close(fig3)
def _data_generation():
# get csv and shp
shp_link = examples.get_path('us48.shp')
df = gpd.read_file(shp_link)
income_table = pd.read_csv(examples.get_path("usjoin.csv"))
# calculate relative values
for year in range(1969, 2010):
income_table[str(year) + '_rel'] = (
income_table[str(year)] / income_table[str(year)].mean())
# merge
gdf = df.merge(income_table,left_on='STATE_NAME',right_on='Name')
# retrieve spatial weights and data for two points in time
w = Queen.from_dataframe(gdf)
w.transform = 'r'
y1 = gdf['1969_rel'].values
y2 = gdf['2000_rel'].values
#calculate rose Object
Y = np.array([y1, y2]).T
rose = Rose(Y, w, k=5)
return gdf, y1, rose
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_loc_bv_scatterplot():
gdf = _test_data()
x = gdf['Suicids'].values
y = gdf['Donatns'].values
w = Queen.from_dataframe(gdf)
w.transform = 'r'
# Calculate Univariate and Bivariate Moran
moran_loc = Moran_Local(y, w)
moran_loc_bv = Moran_Local_BV(x, y, w)
# try with p value so points are colored
fig, _ = _moran_loc_bv_scatterplot(moran_loc_bv)
plt.close(fig)
# try with p value and different figure size
fig, _ = _moran_loc_bv_scatterplot(moran_loc_bv, p=0.05,
aspect_equal=False)
plt.close(fig)
raises(ValueError, _moran_loc_bv_scatterplot, moran_loc, p=0.5)
warns(UserWarning, _moran_loc_bv_scatterplot, moran_loc_bv, p=0.5,
scatter_kwds=dict(c='r'))
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)
def get_neighbors_dict(self):
neighborhoods = gpd.read_file("data/chicago/neighborhoods/geo_export_265e79d9-e1f1-47fc-856d-1c753cbbe120.shp")
weights = Queen.from_dataframe(neighborhoods) # Try with Rook later
wnp = libpysal.weights.util.nonplanar_neighbors(weights, neighborhoods)
wdf = weights.to_adjlist(remove_symmetric=True)
list_edges = []
dict_idx_edges = {}
for rowid, row in wdf.iterrows():
tidx1 = int(row["focal"])
tidx2 = int(row["neighbor"])
idx1 = np.minimum(tidx1, tidx2)
idx2 = np.maximum(tidx1, tidx2)
list_edges.append((idx1, idx2))
dict_idx_edges[idx1] = dict_idx_edges.get(idx1, []) + [idx2]
dict_idx_edges[idx2] = dict_idx_edges.get(idx2, []) + [idx1]
return dict_idx_edges
def test_plot_local_autocorrelation():
link = examples.get_path('columbus.shp')
df = gpd.read_file(link)
y = df['HOVAL'].values
w = Queen.from_dataframe(df)
w.transform = 'r'
moran_loc = Moran_Local(y, w)
fig, _ = plot_local_autocorrelation(moran_loc, df, 'HOVAL', p=0.05)
plt.close(fig)
# also test with quadrant and mask
fig, _ = plot_local_autocorrelation(moran_loc,
df,
'HOVAL',
p=0.05,
region_column='POLYID',
mask=['1', '2', '3'],
quadrant=1)
plt.close(fig)
def _data_generation():
from giddy.directional import Rose
# get csv and shp
shp_link = examples.get_path("us48.shp")
df = gpd.read_file(shp_link)
income_table = pd.read_csv(examples.get_path("usjoin.csv"))
# calculate relative values
for year in range(1969, 2010):
income_table[str(year) + "_rel"] = (income_table[str(year)] /
income_table[str(year)].mean())
# merge
gdf = df.merge(income_table, left_on="STATE_NAME", right_on="Name")
# retrieve spatial weights and data for two points in time
w = Queen.from_dataframe(gdf)
w.transform = "r"
y1 = gdf["1969_rel"].values
y2 = gdf["2000_rel"].values
# calculate rose Object
Y = np.array([y1, y2]).T
rose = Rose(Y, w, k=5)
return gdf, y1, rose
# -*- coding: utf-8 -*- # @Time : 2019/3/29 14:35 # @Author : Crsboom # @Email : [email protected] # @File : pysal_test.py # @Software: PyCharm from libpysal.weights.contiguity import Queen import libpysal from libpysal import examples import matplotlib.pyplot as plt import geopandas as gpd import random from pysal.viz.splot.libpysal import plot_spatial_weights gdf = gpd.read_file(r'C:\Users\RSGG\Desktop\123\city\Anhui.shp') gdf.head() weights = Queen.from_dataframe(gdf) plot_spatial_weights(weights, gdf) #plt.show() x = [random.randint(0, 100) for i in range(17)] gdf['test'] = x fig, ax = plt.subplots(figsize=(12, 10), subplot_kw={'aspect': 'equal'}) gdf.plot(column='test', scheme='Quantiles', k=5, cmap='GnBu', legend=True, ax=ax)
