def test_year_quarter_to_datetime():
""" Test year and quarter to datetime: """
# Date Year Q
test_data = [['2005-03-31', '05', '1'], ['2011-12-31', '11', '4'],
['2015-09-30', '15', '3']]
for [date_str, year, quarter] in test_data:
from_time_tools = time_tools.year_quarter_to_datetime(year, quarter)
expected = np.datetime64(date_str)
nose.tools.eq_(
from_time_tools, expected, 'Wrong datetime detected: %s -> %s' %
(str(from_time_tools), str(expected)))
def test_year_quarter_to_datetime():
""" Test year and quarter to datetime: """
# Date Year Q
test_data = [['2005-03-31', '05', '1'],
['2011-12-31', '11', '4'],
['2015-09-30', '15', '3']]
for [date_str, year, quarter] in test_data:
from_time_tools = time_tools.year_quarter_to_datetime(year, quarter)
expected = np.datetime64(date_str)
nose.tools.eq_(from_time_tools, expected,
'Wrong datetime detected: %s -> %s'%(
str(from_time_tools), str(expected)))
def remap_to_relative_time(self,
prediction_data,
actual_data,
prediction_horizon=16):
""" Convert the data to columns of how old the prediction is.
"""
# Create a new empty DataFrame, and add the CPI column.
index = self.raw_data.index
slength = len(index)
pred_relative = pd.DataFrame(index=index, data=None)
colname_prefix = actual_data.name
# Create the column for relative predictions, and fill with NaN.
pred_relative_col_names = []
# TODO(Camilla): Hvis språkbruken er "ett kvartal frem" for første
# prediksjon i banen, bytt om til range(1, prediction_horizon+1).
for dq in range(0, prediction_horizon):
col_name = colname_prefix + '_dQ' + str(dq)
pred_relative_col_names.append(col_name)
ser = pd.Series(np.empty(slength), index=index)
pred_relative[col_name] = ser
pred_relative[col_name] = np.NaN
# Okay now we have an empty frame. Let's fill it up...
# Loop through all the PPR columns in CPI predictions.
for col_name in prediction_data:
col_info = decode_column_name.decode(col_name)
ppr_date = time_tools.year_quarter_to_datetime(
col_info['year'], col_info['quarter'])
# Loop through each prediction in this PPR.
for date in prediction_data.index:
if date is pd.NaT:
continue
prediction = prediction_data.loc[date, col_name]
if not math.isnan(prediction):
# How old is the prediction, in quarters?
dq = time_tools.time_diff_in_quarters(ppr_date, date)
n_col_name = pred_relative_col_names[dq]
# Insert prediction, in the new dataframe.
pred_relative.loc[date, n_col_name] = (
prediction_data.loc[date, col_name])
#print 'Put prediction from ', col_name,
# ' for ', date.strftime('%Y-%m-%d'),
# ' in ', n_col_name
return pred_relative
def remap_to_relative_time(self, prediction_data, actual_data,
prediction_horizon=16):
""" Convert the data to columns of how old the prediction is.
"""
# Create a new empty DataFrame, and add the CPI column.
index = self.raw_data.index
slength = len(index)
pred_relative = pd.DataFrame(index=index, data=None)
colname_prefix = actual_data.name
# Create the column for relative predictions, and fill with NaN.
pred_relative_col_names = []
# TODO(Camilla): Hvis språkbruken er "ett kvartal frem" for første
# prediksjon i banen, bytt om til range(1, prediction_horizon+1).
for dq in range(0, prediction_horizon):
col_name = colname_prefix + '_dQ' + str(dq)
pred_relative_col_names.append(col_name)
ser = pd.Series(np.empty(slength), index=index)
pred_relative[col_name] = ser
pred_relative[col_name] = np.NaN
# Okay now we have an empty frame. Let's fill it up...
# Loop through all the PPR columns in CPI predictions.
for col_name in prediction_data:
col_info = decode_column_name.decode(col_name)
ppr_date = time_tools.year_quarter_to_datetime(
col_info['year'], col_info['quarter'])
# Loop through each prediction in this PPR.
for date in prediction_data.index:
if date is pd.NaT:
continue
prediction = prediction_data.loc[date, col_name]
if not math.isnan(prediction):
# How old is the prediction, in quarters?
dq = time_tools.time_diff_in_quarters(ppr_date, date)
n_col_name = pred_relative_col_names[dq]
# Insert prediction, in the new dataframe.
pred_relative.loc[date, n_col_name] = (
prediction_data.loc[date, col_name])
#print 'Put prediction from ', col_name,
# ' for ', date.strftime('%Y-%m-%d'),
# ' in ', n_col_name
return pred_relative
