def test_by_col(self):
import pysal.contrib.pdio as pdio
df = pdio.read_files(pysal.examples.get_path('sids2.dbf'))
lm = moran.Moran_Local_Rate.by_col(df, ['SID79'], ['BIR79'], w=self.w,
outvals=['p_z_sim', 'z_sim'],
transformation='r', permutations=99)
self.assertAlmostEqual(lm['SID79-BIR79_z_sim'][0], -0.13699844503985936, 7)
self.assertAlmostEqual(lm['SID79-BIR79_p_z_sim'][0], 0.44551601210081715)
def test_by_col(self):
import pysal.contrib.pdio as pdio
df = pdio.read_files(pysal.examples.get_path('sids2.dbf'))
mi = moran.Moran_Rate.by_col(df, ['SID79'], ['BIR79'], w=self.w, two_tailed=False)
sidr = np.unique(mi["SID79-BIR79_moran_rate"].values)
pval = np.unique(mi["SID79-BIR79_p_sim"].values)
np.testing.assert_allclose(sidr, 0.16622343552567395, rtol=RTOL, atol=ATOL)
self.assertAlmostEquals(pval, 0.009)
def test_by_col(self):
import pysal.contrib.pdio as pdio
df = pdio.read_files(pysal.examples.get_path('sids2.dbf'))
lm = moran.Moran_Local_Rate.by_col(df, ['SID79'], ['BIR79'], w=self.w,
outvals=['p_z_sim', 'z_sim'],
transformation='r', permutations=99)
self.assertAlmostEquals(lm['SID79-BIR79_z_sim'][0], -0.13699844503985936, 7)
self.assertAlmostEquals(lm['SID79-BIR79_p_z_sim'][0], 0.44551601210081715)
def test_by_col(self):
import pysal.contrib.pdio as pdio
df = pdio.read_files(pysal.examples.get_path('sids2.dbf'))
mi = moran.Moran_Rate.by_col(df, ['SID79'], ['BIR79'], w=self.w, two_tailed=False)
sidr = np.unique(mi["SID79-BIR79_moran_rate"].values)
pval = np.unique(mi["SID79-BIR79_p_sim"].values)
np.testing.assert_allclose(sidr, 0.16622343552567395, rtol=RTOL, atol=ATOL)
self.assertAlmostEqual(pval, 0.009)
def test_by_col(self):
import pysal.contrib.pdio as pdio
df = pdio.read_files(pysal.examples.get_path('sids2.dbf'))
w = pysal.open(pysal.examples.get_path("sids2.gal")).read()
mi = moran.Moran.by_col(df, ['SIDR74'], w=w, two_tailed=False)
sidr = np.unique(mi.SIDR74_moran.values)
pval = np.unique(mi.SIDR74_p_sim.values)
np.testing.assert_allclose(sidr, 0.24772519320480135, atol=ATOL, rtol=RTOL)
self.assertAlmostEquals(pval, 0.001)
def test_by_col(self):
import pysal.contrib.pdio as pdio
df = pdio.read_files(pysal.examples.get_path('sids2.dbf'))
w = pysal.open(pysal.examples.get_path("sids2.gal")).read()
mi = moran.Moran.by_col(df, ['SIDR74'], w=w, two_tailed=False)
sidr = np.unique(mi.SIDR74_moran.values)
pval = np.unique(mi.SIDR74_p_sim.values)
np.testing.assert_allclose(sidr, 0.24772519320480135, atol=ATOL, rtol=RTOL)
self.assertAlmostEqual(pval, 0.001)
def setUp(self):
import pandas as pd
self.columbus = pdio.read_files(get_path('columbus.shp'))
grid = [Polygon([(0,0),(0,1),(1,1),(1,0)]),
Polygon([(0,1),(0,2),(1,2),(1,1)]),
Polygon([(1,2),(2,2),(2,1),(1,1)]),
Polygon([(1,1),(2,1),(2,0),(1,0)])]
regime = [0,0,1,1]
ids = range(4)
data = np.array((regime, ids)).T
self.exdf = pd.DataFrame(data, columns=['regime', 'ids'])
self.exdf['geometry'] = grid
def test_by_col(self):
import pysal.contrib.pdio as pdio
df = pdio.read_files(pysal.examples.get_path('sids2.dbf'))
np.random.seed(12345)
moran.Moran_Local_BV.by_col(df, ['SIDR74', 'SIDR79'], w=self.w,
inplace=True, outvals=['z_sim', 'p_z_sim'],
transformation='r', permutations=99)
bvstats = df['SIDR79-SIDR74_moran_local_bv'].values
bvz = df['SIDR79-SIDR74_z_sim'].values
bvzp = df['SIDR79-SIDR74_p_z_sim'].values
self.assertAlmostEquals(bvstats[0], 1.4649221250620736)
self.assertAlmostEquals(bvz[0], 1.657427, 5)
self.assertAlmostEquals(bvzp[0], 0.048717, 5)
def test_by_col(self):
import pysal.contrib.pdio as pdio
df = pdio.read_files(pysal.examples.get_path('sids2.dbf'))
np.random.seed(12345)
moran.Moran_Local_BV.by_col(df, ['SIDR74', 'SIDR79'], w=self.w,
inplace=True, outvals=['z_sim', 'p_z_sim'],
transformation='r', permutations=99)
bvstats = df['SIDR79-SIDR74_moran_local_bv'].values
bvz = df['SIDR79-SIDR74_z_sim'].values
bvzp = df['SIDR79-SIDR74_p_z_sim'].values
self.assertAlmostEqual(bvstats[0], 1.4649221250620736)
self.assertAlmostEqual(bvz[0], 1.657427, 5)
self.assertAlmostEqual(bvzp[0], 0.048717, 5)
def test_mplot():
link = ps.examples.get_path('columbus.shp')
db = read_files(link)
y = db['HOVAL'].values
w = ps.queen_from_shapefile(link)
w.transform = 'R'
m = ps.Moran(y, w)
fig = mplot(m, xlabel='Response', ylabel='Spatial Lag',
title='Moran Scatterplot', custom=(7,7))
plt.close(fig)
def setUp(self):
import pandas as pd
self.columbus = pdio.read_files(get_path('columbus.shp'))
grid = [
Polygon([(0, 0), (0, 1), (1, 1), (1, 0)]),
Polygon([(0, 1), (0, 2), (1, 2), (1, 1)]),
Polygon([(1, 2), (2, 2), (2, 1), (1, 1)]),
Polygon([(1, 1), (2, 1), (2, 0), (1, 0)])
]
regime = [0, 0, 1, 1]
ids = range(4)
data = np.array((regime, ids)).T
self.exdf = pd.DataFrame(data, columns=['regime', 'ids'])
self.exdf['geometry'] = grid
def test_mplot():
link = ps.examples.get_path('columbus.shp')
db = read_files(link)
y = db['HOVAL'].values
w = ps.queen_from_shapefile(link)
w.transform = 'R'
m = ps.Moran(y, w)
fig = mplot(m,
xlabel='Response',
ylabel='Spatial Lag',
title='Moran Scatterplot',
custom=(7, 7))
plt.close(fig)
