def test_Headbanging_Triples(self):
ht = sm.Headbanging_Triples(self.d, self.w)
self.assertEquals(len(ht.triples), len(self.d))
ht2 = sm.Headbanging_Triples(self.d, self.w, edgecor=True)
self.assertTrue(hasattr(ht2, 'extra'))
self.assertEquals(len(ht2.triples), len(self.d))
htr = sm.Headbanging_Median_Rate(self.e, self.b, ht2, iteration=5)
self.assertEquals(len(htr.r), len(self.e))
for i in htr.r:
self.assertTrue(i is not None)
def test_Headbanging_Median_Rate(self):
s_ht = sm.Headbanging_Triples(self.d, self.w, k=5)
sids_hb_r = sm.Headbanging_Median_Rate(self.e, self.b, s_ht)
np.testing.assert_array_almost_equal(self.sids_hb_rr5, sids_hb_r.r[:5])
sids_hb_r2 = sm.Headbanging_Median_Rate(self.e,
self.b,
s_ht,
iteration=5)
np.testing.assert_array_almost_equal(self.sids_hb_r2r5,
sids_hb_r2.r[:5])
sids_hb_r3 = sm.Headbanging_Median_Rate(self.e,
self.b,
s_ht,
aw=self.b)
np.testing.assert_array_almost_equal(self.sids_hb_r3r5,
sids_hb_r3.r[:5])
def test_Headbanging_Median_Rate(self):
sids_d = np.array([i.centroid for i in self.sids])
sids_w = pysal.knnW(sids_d, k=5)
if not sids_w.id_order_set:
sids_w.id_order = sids_w.id_order
s_ht = sm.Headbanging_Triples(sids_d, sids_w, k=5)
sids_db = pysal.open(pysal.examples.get_path('sids2.dbf'), 'r')
s_e, s_b = np.array(sids_db[:, 9]), np.array(sids_db[:, 8])
sids_hb_r = sm.Headbanging_Median_Rate(s_e, s_b, s_ht)
sids_hb_rr5 = np.array([0.00075586, 0.,
0.0008285, 0.0018315, 0.00498891])
np.testing.assert_array_almost_equal(sids_hb_rr5, sids_hb_r.r[:5])
sids_hb_r2 = sm.Headbanging_Median_Rate(s_e, s_b, s_ht, iteration=5)
sids_hb_r2r5 = np.array([0.0008285, 0.00084331,
0.00086896, 0.0018315, 0.00498891])
np.testing.assert_array_almost_equal(sids_hb_r2r5, sids_hb_r2.r[:5])
sids_hb_r3 = sm.Headbanging_Median_Rate(s_e, s_b, s_ht, aw=s_b)
sids_hb_r3r5 = np.array([0.00091659, 0.,
0.00156838, 0.0018315, 0.00498891])
np.testing.assert_array_almost_equal(sids_hb_r3r5, sids_hb_r3.r[:5])
def test_Headbanging_Median_Rate_tabular(self):
# test that dataframe columns are treated correctly
s_ht = sm.Headbanging_Triples(self.d, self.w, k=5)
sids_hb_r = sm.Headbanging_Median_Rate(self.df[self.ename],
self.df[self.bname], s_ht)
self.assertIsInstance(sids_hb_r.r, np.ndarray)
np.testing.assert_array_almost_equal(self.sids_hb_rr5, sids_hb_r.r[:5])
sids_hb_r2 = sm.Headbanging_Median_Rate(self.df[self.ename],
self.df[self.bname],
s_ht,
iteration=5)
self.assertIsInstance(sids_hb_r2.r, np.ndarray)
np.testing.assert_array_almost_equal(self.sids_hb_r2r5,
sids_hb_r2.r[:5])
sids_hb_r3 = sm.Headbanging_Median_Rate(self.df[self.ename],
self.df[self.bname],
s_ht,
aw=self.df[self.bname])
self.assertIsInstance(sids_hb_r3.r, np.ndarray)
np.testing.assert_array_almost_equal(self.sids_hb_r3r5,
sids_hb_r3.r[:5])
#test that the by col on multiple names works correctly
sids_hr_df = sm.Headbanging_Median_Rate.by_col(self.df,
self.enames,
self.bnames,
w=self.w)
outcols = [
'{}-{}_headbanging_median_rate'.format(e, b)
for e, b in zip(self.enames, self.bnames)
]
for col, answer in zip(outcols, [self.sids_hb_rr5, self.sids79r]):
this_col = sids_hr_df[col].values
self.assertIsInstance(this_col, np.ndarray)
np.testing.assert_allclose(sids_hr_df[col][:5],
answer,
rtol=RTOL,
atol=ATOL * 10)