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_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_make_graph_warning_duplicate_endogenous_variable():
with assert_warns(Warning) as cm:
FSIC.optimise.order.make_graph([
'self.H_s[period] = self.H_s[period-1] + self.G_d[period] - self.T_d[period]',
'self.H_h[period] = self.H_h[period-1] + self.YD[period] - self.C_d[period]',
'self.H_h[period] = self.H_s[period]'
])
assert str(
cm.warning
) == 'An endogenous variable appears as the left hand-side variable in more than one equation'
def assert_warns(exception=Warning,
*args,
glob=None,
regex=None,
match_case=None,
**kwargs):
if glob is None and regex is None:
return tools.assert_warns(exception, *args, **kwargs)
pattern = get_pattern(glob, regex, match_case)
return tools.assert_warns_regex(exception, pattern, *args, **kwargs)
def test_warn_unsafe_type():
with n.assert_warns(UserWarning):
parse({'one', 'two', 'three'})
def test_warn_unsafe_type():
with n.assert_warns(UserWarning):
parse({'one','two','three'})
def test_surface():
volume = np.random.uniform(10, 100)
tau = np.random.uniform(.5, 5.)
x = np.atleast_2d([tau, 0., 0.])
sigma = np.atleast_2d([volume/tau, 0., 0.])
v = np.zeros((1, 2))
surf = Surface(x, sigma, v)
assert surf.boost_invariant, 'Surface should be boost-invariant.'
assert_almost_equal(
surf.volume, volume, delta=1e-12,
msg='incorrect volume'
)
ymax = np.random.uniform(.5, 2.)
surf = Surface(x, sigma, v, ymax=ymax)
assert_almost_equal(
surf.volume, 2*ymax*volume, delta=1e-12,
msg='incorrect volume'
)
x = np.random.uniform(0, 10, size=(1, 4))
sigma = np.random.uniform(0, 10, size=(1, 4))
v = np.random.uniform(-.5, .5, size=(1, 3))
surf = Surface(x, sigma, v)
assert not surf.boost_invariant, 'Surface should not be boost-invariant.'
gamma = 1/np.sqrt(1 - (v*v).sum())
u_ = np.empty(4)
u_[0] = gamma
u_[1:] = -gamma*v
volume = np.inner(sigma, u_)
assert_almost_equal(
surf.volume, volume, delta=1e-12,
msg='incorrect volume'
)
with assert_warns(Warning):
Surface(x, sigma, v, ymax=1.)
with assert_raises(ValueError):
Surface(
np.ones((1, 1)),
np.ones((1, 1)),
np.ones((1, 1)),
)
with assert_raises(ValueError):
Surface(
np.ones((1, 4)),
np.ones((1, 3)),
np.ones((1, 2)),
)
with assert_raises(ValueError):
Surface(
np.ones((2, 4)),
np.ones((2, 4)),
np.ones((3, 3)),
)