def test___init__(self):
import numpy as np
f = pysal.open(pysal.examples.get_path('usjoin.csv'))
pci = np.array([f.by_col[str(y)]
for y in range(1929, 2010)]).transpose()
w = pysal.open(pysal.examples.get_path("states48.gal")).read()
lm = pysal.LISA_Markov(pci, w)
obs = np.array([1, 2, 3, 4])
np.testing.assert_array_almost_equal(obs, lm.classes)
ss = np.matrix([[0.28561505], [0.14190226], [0.40493672],
[0.16754598]])
np.testing.assert_array_almost_equal(lm.steady_state, ss)
transitions = np.array(
[[1.08700000e+03, 4.40000000e+01, 4.00000000e+00, 3.40000000e+01],
[4.10000000e+01, 4.70000000e+02, 3.60000000e+01, 1.00000000e+00],
[5.00000000e+00, 3.40000000e+01, 1.42200000e+03, 3.90000000e+01],
[3.00000000e+01, 1.00000000e+00, 4.00000000e+01, 5.52000000e+02]])
np.testing.assert_array_almost_equal(lm.transitions, transitions)
p = np.matrix([[0.92985458, 0.03763901, 0.00342173, 0.02908469],
[0.07481752, 0.85766423, 0.06569343, 0.00182482],
[0.00333333, 0.02266667, 0.948, 0.026],
[0.04815409, 0.00160514, 0.06420546, 0.88603531]])
np.testing.assert_array_almost_equal(lm.p, p)
np.random.seed(10)
lm_random = pysal.LISA_Markov(pci, w, permutations=99)
expected = np.array(
[[1.12328098e+03, 1.15377356e+01, 3.47522158e-01, 3.38337644e+01],
[3.50272664e+00, 5.28473882e+02, 1.59178880e+01, 1.05503814e-01],
[1.53878082e-01, 2.32163556e+01, 1.46690710e+03, 9.72266513e+00],
[9.60775143e+00, 9.86856346e-02, 6.23537392e+00, 6.07058189e+02]])
np.testing.assert_allclose(lm_random.expected_t, expected, RTOL)
c = np.array([1058.207904, 0., 9.])
np.testing.assert_allclose(lm_random.chi_2, c, RTOL)
def computeMarkovLISA(self):
"""
precomputing LISA maps
"""
try:
self.id_groups = []
# computer the markov lisa and lisa maps
progress_dlg = wx.ProgressDialog(
"Progress",
"Pre-computing Markov LISA... ",
maximum=2,
parent=self,
style=wx.PD_APP_MODAL | wx.PD_AUTO_HIDE)
progress_dlg.CenterOnScreen()
progress_dlg.Update(1)
self.lisa_markov = pysal.LISA_Markov(
np.array(self.data_sel_values).transpose(), self.weight)
self.lisa_markov_mt = self.lisa_markov.move_types
self.lisa_markov_p = np.array(self.lisa_markov.p)
progress_dlg.Update(2)
progress_dlg.Destroy()
# precompute LISAs
self.moran_locals = self.precomputeLISA()
# filter out non-sig: all LISA p-values should be
# significant for one shape object
for i in range(self.t):
ml_p_sim = np.array(self.moran_locals[i][1]) # 1~p values
for j in range(self.n):
if ml_p_sim[j] > 0.05:
# then object j is not significant
self.lisa_markov_mt[j][-1] = 0
except:
raise Exception("compute Markov LISA error! ")
def test___init__(self):
import numpy as np
f = pysal.open(pysal.examples.get_path('usjoin.csv'))
pci = np.array([f.by_col[str(y)]
for y in range(1929, 2010)]).transpose()
w = pysal.open(pysal.examples.get_path("states48.gal")).read()
lm = pysal.LISA_Markov(pci, w)
obs = np.array([1, 2, 3, 4])
np.testing.assert_array_almost_equal(obs, lm.classes)
"""
def markov(observations, w=None, numQuints=5, method="regular"):
result = None
s = None
if method == "regular": # non spatial analysis
quintiles = np.array([pysal.Quantiles(y, k=numQuints).yb for y in observations]).transpose()
result = pysal.Markov(quintiles)
#s = result.steady_state
else:
observations = observations.transpose()
if method == "spatial":
# standardize observations for smoother calculations:
observations = observations / (observations.mean(axis=0))
result = pysal.Spatial_Markov(observations, w, fixed=True, k=numQuints)
#s = result.S
else: # method == lisa
result = pysal.LISA_Markov(observations, w)
#s = result.steady_state
return result.transitions, result.p, s, pysal.ergodic.fmpt(result.p)
print(m5.transitions)
print(m5.p)
print(m5.steady_state)
print(pysal.ergodic.fmpt(m5.p))
################################################################# Spatial Markov
fpci = pci.transpose() / (pci.transpose().mean(axis=0))
print(fpci)
w = pysal.open(
r'C:\Anaconda\Lib\site-packages\pysal\examples\us_income\states48.gal'
).read()
w.transform = 'r'
sm = pysal.Spatial_Markov(fpci, w, fixed=True, k=5)
for p in sm.p:
print(p)
for f in sm.F:
print(f)
################################################################# LISA Markov
lm = pysal.LISA_Markov(pci.transpose(), w)
print(lm.classes)
# the estimated transition probability matrix
print(lm.transitions)
print(lm.p)
print(lm.steady_state)
print(pysal.ergodic.fmpt(lm.p))