def testTransformationHandle():
np.random.seed(10)
n=200
data = np.random.randint(100,size=(n,1))
dt = np.arange(n)
hndl_Trns = EMF_Transformation_Handle('None')
assert np.all(hndl_Trns.transform_data(data) == data)
assert str(hndl_Trns) == 'raw'
# Test Past
hndl_Trns = EMF_Transformation_Handle('Past_Lvl')
hndl_Trns.set_extra_parameter(PERIODS_AWAY, 10)
assert hndl_Trns.transform_data(data).shape == (190,1)
assert np.all(hndl_Trns.transform_time(dt) == np.arange(10,n))
assert str(hndl_Trns) == 'PastLvl.10'
# Test Future
hndl_Trns = EMF_Transformation_Handle('Futr_Change')
hndl_Trns.set_extra_parameter(FIRST_ORDER_DIFF_TIME, 20)
assert hndl_Trns.transform_data(data).shape == (180,1)
assert np.all(hndl_Trns.transform_time(dt) == np.arange(n-20))
assert str(hndl_Trns) == 'FutrDiff.20'
def select_resp_words_all_permutations(self):
log.info('WORDSELECT: Response Words: Choosing All Data Tickers')
if self._resp_data_tickers is None:
self.__add_resp_data_tickers()
log.info('WORDSELECT: Response Words: Choosing All Transformations')
trns_list = {}
for trns in self.resp_trns_ptrns:
for (k, v_list) in self.resp_trns_kwargs.iteritems():
for v in v_list:
hndl_Trns = EMF_Transformation_Handle(trns)
hndl_Trns.set_extra_parameter(k, v)
trns_list[str(hndl_Trns)] = hndl_Trns
trns_list = trns_list.values()
log.info('WORDSELECT: Response Words: Created {0} Transformations'.format(len(trns_list)))
self._resp_words = []
count = 0
for ticker in self.resp_data_tickers:
hndl_Data = EMF_DataSeries_Handle(self.hndl_DB, ticker=ticker)
hndl_Data.save_series_local()
for hndl_Trns in trns_list:
hndl_Word = EMF_WordSeries_Handle(self.hndl_DB, hndl_Data, hndl_Trns)
self._resp_words.append(hndl_Word)
count += 1
log.info('WORDSELECT: Response Words: Created {0} Response Words'.format(count))
