def setUp(self):
data = south()
data['n_samples'] = 0
np.random.seed(TEST_SEED)
self.trace = Trace.from_csv(FULL_PATH + '/data/south_mvcm_5000',
multi=True)
self.single_trace = Trace.from_csv(FULL_PATH +
'/data/south_mvcm_5000_0.csv')
self.geweke_known = json.load(open(FULL_PATH + '/data/geweke.json'))
def setUp(self):
self.a = {chr(i + 97): list(range(10)) for i in range(5)}
self.t = Trace(**self.a)
self.mt = Trace(self.a, self.a, self.a)
self.real_mt = Trace.from_csv(FULL_PATH + r'/data/south_mvcm_5000',
multi=True)
self.real_singles = [
Trace.from_csv(FULL_PATH +
r'/data/south_mvcm_5000_{}.csv'.format(i))
for i in range(4)
]
def setUp(self):
super(Test_Lower_SE, self).build_self()
self.cls = lower.SE
del self.inputs["M"]
self.inputs['n_samples'] = 0
instance = self.cls(**self.inputs)
self.answer_trace = Trace.from_csv(FULL_PATH + '/data/lower_se.csv')
def setUp(self):
super(Test_Upper_SMA, self).build_self()
self.cls = upper.SMA
del self.inputs["W"]
self.inputs['n_samples'] = 0
instance = self.cls(**self.inputs)
self.answer_trace = Trace.from_csv(FULL_PATH + '/data/upper_sma.csv')
def setUp(self):
data = south()
data['n_samples'] = 0
np.random.seed(TEST_SEED)
test_methods = ['obm', 'bm', 'bartlett', 'hanning', 'tukey']
self.trace = Trace.from_csv(FULL_PATH + '/data/south_mvcm_5000',
multi=True)
self.single_trace = Trace.from_csv(FULL_PATH +
'/data/south_mvcm_5000_0.csv')
self.bm = json.load(open(FULL_PATH + '/data/mcse_bm.json', 'r'))
self.obm = json.load(open(FULL_PATH + '/data/mcse_obm.json', 'r'))
self.tukey = json.load(open(FULL_PATH + '/data/mcse_hanning.json',
'r'))
self.bartlett = json.load(
open(FULL_PATH + '/data/mcse_bartlett.json', 'r'))
self.hanning = self.tukey
def setUp(self):
super(Test_MVCM, self).build_self()
self.cls = M.MVCM
del self.inputs['M']
del self.inputs['W']
self.inputs['n_samples'] = 0
self.instance = self.cls(**self.inputs)
self.answer_trace = Trace.from_csv(FULL_PATH + '/data/mvcm.csv')
def setUp(self):
self.answer = Trace.from_csv(FULL_PATH + '/data/svc.csv')
self.inputs = dict()
baltim = ps.pdio.read_files(ps.examples.get_path('baltim.shp'))
Y = np.log(baltim.PRICE.values).reshape(-1, 1)
Yz = Y - Y.mean()
X = baltim[['AGE', 'LOTSZ', 'SQFT']].values
Xz = X - X.mean(axis=0)
coords = baltim[['X', 'Y']].values
self.inputs.update({'Y': Yz, 'X': Xz, 'coordinates': coords})
self.ignore_shape = True
self.test_trace = no_op
def setUp(self):
data = south()
data['n_samples'] = 0
with open(FULL_PATH + '/data/psrf_noburn.json', 'r') as noburn:
self.noburn = json.load(noburn)
with open(FULL_PATH + '/data/psrf_brooks.json', 'r') as brooks:
self.known_brooks = json.load(brooks)
with open(FULL_PATH + '/data/psrf_gr.json', 'r') as gr:
self.known_gr = json.load(gr)
np.random.seed(TEST_SEED)
self.trace = Trace.from_csv(FULL_PATH + '/data/south_mvcm_5000',
multi=True)
self.mockmodel = Hashmap(trace=self.trace)
def test_mvcm(self):
instance = self.cls(**self.inputs)
np.random.seed(TEST_SEED)
instance.draw()
other_answers = Trace.from_csv(FULL_PATH + '/data/mvcm.csv')
strip_out = [
col for col in instance.trace.varnames
if col not in other_answers.varnames
]
other_answers._assert_allclose(instance.trace.drop(*strip_out,
inplace=False),
rtol=RTOL,
atol=ATOL)
def test_from_csv(self):
self.t.to_csv('./test_from_csv.csv')
new_t = Trace.from_csv('./test_from_csv.csv')
assert self.t == new_t
os.remove('./test_from_csv.csv')
def setUp(self):
super(Test_SESE, self).build_self()
self.cls = M.SESE
self.inputs['n_samples'] = 0
self.instance = self.cls(**self.inputs)
self.answer_trace = Trace.from_csv(FULL_PATH + '/data/sese.csv')
def setUp(self):
data = south()
data['n_samples'] = 0
np.random.seed(TEST_SEED)
self.trace = Trace.from_csv(FULL_PATH + '/data/south_mvcm_5000',
multi=True)
def setUp(self):
super(Test_Generic, self).build_self()
self.cls = M.Generic
self.inputs['n_samples'] = 0
instance = self.cls(**self.inputs)
self.answer_trace = Trace.from_csv(FULL_PATH + '/data/generic.csv')
