def perform_computation(self, df, ameco_df):
uvgdh, uvgdh_1, knp = 'UVGDH', 'UVGDH.1.0.0.0', 'KNP.1.0.212.0'
series_meta = self.get_meta(uvgdh)
splicer = Splicer()
try:
series_data = self.get_data(ameco_df, uvgdh_1)
series_data = splicer.ratio_splice(series_data,
self.get_data(df, uvgdh_1),
type='forward')
except KeyError:
series_data = self.get_data(df, uvgdh)
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series, ignore_index=True, sort=True)
series_meta = self.get_meta(knp)
series_data = self.get_data(ameco_df, knp)
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series, ignore_index=True, sort=True)
self.result.set_index(['Country Ameco', 'Variable Code'],
drop=True,
inplace=True)
self.apply_scale()
export_to_excel(self.result, step=6, country=self.country)
return self.result
def perform_computation(self, output_gap_df):
variables = [
'ZNAWRU.1.0.0.0', 'AVGDGP.1.0.0.0', 'AVGDGT.1.0.0.0',
'OVGDP.1.0.0.0', 'OVGDT.1.0.0.0'
]
for variable in variables:
series_meta = self.get_meta(variable)
series_data = self.get_data(output_gap_df, variable)
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
variable = 'OVGDP.6.0.0.0'
series_meta = self.get_meta(variable)
series_data = self.get_data(self.result,
'OVGDP.1.0.0.0').pct_change() * 100
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series, ignore_index=True, sort=True)
self.result.set_index(['Country Ameco', 'Variable Code'],
drop=True,
inplace=True)
self.apply_scale()
export_to_excel(self.result, step=9, country=self.country)
return self.result
def perform_computation(self, df):
'''splice AMECO Historical data with forecast data and calculate percent change, and GNI (GDP deflator)'''
zcpih, zcpih_6, zcpin = 'ZCPIH', 'ZCPIH.6.0.0.0', 'ZCPIN'
# series_meta = self.get_meta(zcpin)
# series_meta['Variable Code'] = zcpih_6
series_meta = self.get_meta(zcpih_6)
try:
series_data = self.get_data(df, zcpih)
except KeyError:
series_data = self.get_data(df, zcpin)
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series, ignore_index=True, sort=True)
operators = Operators()
for variable in PD:
variable_u1 = re.sub('^P', 'U',
re.sub('.3.1.0.0', '.1.0.0.0', variable))
variable_o1 = re.sub('^P', 'O',
re.sub('.3.1.0.0', '.1.0.0.0', variable))
# series_meta = self.get_meta(variable_o1)
# series_meta['Variable Code'] = variable
series_meta = self.get_meta(variable)
series_data = operators.rebase(
self.get_data(df, variable_u1) /
self.get_data(df, variable_o1), BASE_PERIOD)
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
# GNI (GDP deflator)
variable = 'OVGN.1.0.0.0'
variable_6 = 'OVGN.6.0.0.0'
gross_income = 'UVGN.1.0.0.0'
gross_domestic_product = 'PVGD.3.1.0.0'
series_meta = self.get_meta(variable)
series_data = self.get_data(df, gross_income) / self.get_data(
self.result, gross_domestic_product) * 100
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series, ignore_index=True, sort=True)
series_meta = self.get_meta(variable_6)
series_data = series_data.copy().pct_change() * 100
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series, ignore_index=True, sort=True)
self.result.set_index(['Country Ameco', 'Variable Code'],
drop=True,
inplace=True)
self.apply_scale()
export_to_excel(self.result, step=7, country=self.country)
return self.result
def perform_computation(self, df, ameco_db_df, ovgd1):
addends = {
'UVGN.1.0.0.0': ['UVGD.1.0.0.0', 'UBRA.1.0.0.0'],
'UOGD.1.0.0.0': [
'UVGD.1.0.0.0', 'UYVG.1.0.0.0', 'UYEU.1.0.0.0',
'-UWCD.1.0.0.0', '-UTVG.1.0.0.0', '-UTEU.1.0.0.0'
],
'UTVNBP.1.0.0.0': ['UTVTBP.1.0.0.0', '-UYVTBP.1.0.0.0'],
'UVGE.1.0.0.0': ['UVGD.1.0.0.0', '-UTVNBP.1.0.0.0'],
'UWSC.1.0.0.0': ['UWCD.1.0.0.0', '-UWWD'],
}
self._sum_and_splice(addends, df, df, splice=False)
variable = 'UWCDA.1.0.0.0'
total_employment = 'NETD.1.0.0.0'
compensation = 'UWCD.1.0.0.0'
real_compensation = 'NWTD.1.0.0.0'
series_meta = self.get_meta(variable)
series_data = (self.get_data(df, total_employment) *
self.get_data(df, compensation) /
self.get_data(df, real_compensation))
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series, ignore_index=True, sort=True)
for variable in NA_IS_VA:
variable_1 = variable + '.1.0.0.0'
variable_cc = re.sub('^U', 'CC', variable_1)
series_meta = self.get_meta(variable_cc)
try:
pch = self.get_data(self.result, variable_1) / ovgd1
except (IndexError, KeyError):
pch = self.get_data(df, variable_1) / ovgd1
pch = pch.pct_change() * 100
try:
series_data = self.get_data(self.result,
variable_1) / self.get_data(
df, 'UVGD.1.0.0.0') * pch
except (IndexError, KeyError):
series_data = self.get_data(df, variable_1) / self.get_data(
df, 'UVGD.1.0.0.0') * pch
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
self.result.set_index(['Country Ameco', 'Variable Code'],
drop=True,
inplace=True)
self.apply_scale()
export_to_excel(self.result, step=5, country=self.country)
return self.result
def perform_computation(self, df, ameco_df):
operators = Operators()
splicer = Splicer()
variables = ['FETD9.1.0.0.0', 'FWTD9.1.0.0.0']
if self.country in FCRIF:
try:
fetd9 = self.get_data(df, 'FETD.1.0.0.0')
fwtd9 = self.get_data(df, 'FWTD.1.0.0.0')
except KeyError:
fetd9 = self.get_data(df, 'NETD.1.0.0.0')
fwtd9 = self.get_data(df, 'NWTD.1.0.0.0')
series_meta = self.get_meta(variables[0])
series_data = fetd9.copy()
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
series_meta = self.get_meta(variables[1])
series_data = fwtd9.copy()
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
else:
series_meta = self.get_meta(variables[0])
if self.country == 'US':
fetd9 = self.get_data(df, 'NETD.1.0.0.0')
fwtd9 = self.get_data(df, 'NWTD.1.0.0.0')
else:
fetd9 = splicer.ratio_splice(self.get_data(
ameco_df, variables[0]),
self.get_data(df, 'NETD'),
kind='forward')
fwtd9 = splicer.ratio_splice(self.get_data(
ameco_df, variables[0]),
self.get_data(df, 'NWTD'),
kind='forward')
series_data = fetd9.copy()
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
series_meta = self.get_meta(variables[1])
series_data = fwtd9.copy()
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
variables = ['UWCD', 'UWWD', 'UWSC']
variables_1 = [variable + '.1.0.0.0' for variable in variables]
variables_h1 = [
re.sub('^U', 'H', variable) + 'W.1.0.0.0' for variable in variables
]
compensation = 'FWTD9.1.0.0.0'
private_consumption_u = 'UCPH.1.0.0.0'
private_consumption_o = 'OCPH.1.0.0.0'
variables_r1 = [
re.sub('^U', 'R', variable) + 'C.3.1.0.0' for variable in variables
]
services = ['UMSN', 'UXSN', 'UMSN.1.0.0.0', 'UXSN.1.0.0.0']
for index, variable in enumerate(variables):
series_meta = self.get_meta(variables_h1[index])
series_data = self.get_data(df, variables_1[index]) / fwtd9
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
series_meta = self.get_meta(variables_r1[index])
series_data = operators.rebase(
self.get_data(df, variables_1[index]) / fwtd9 /
self.get_data(df, private_consumption_u) /
self.get_data(df, private_consumption_o),
base_period=BASE_PERIOD)
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
variables = [
'RVGDE.1.0.0.0', 'RVGEW.1.0.0.0', 'RVGEW.1.0.0.0', 'ZATN9.1.0.0.0',
'ZETN9.1.0.0.0', 'ZUTN9.1.0.0.0'
]
numerators = [
'OVGD.1.0.0.0', 'OVGE.1.0.0.0', 'OVGD.1.0.0.0', 'NLTN.1.0.0.0',
'NETN.1.0.0.0', 'NUTN.1.0.0.0'
]
denominators = [
'FETD9.1.0.0.0', 'FETD9.1.0.0.0', 'NETD.1.0.0.0', 'NPAN1.1.0.0.0',
'NPAN1.1.0.0.0', 'NLTN.1.0.0.0'
]
for index, variable in enumerate(variables):
series_meta = self.get_meta(variable)
if denominators[index] == 'FETD9.1.0.0.0':
denominator_series = fetd9
else:
denominator_series = self.get_data(df, denominators[index])
series_data = self.get_data(df,
numerators[index]) / denominator_series
if variable in ['ZATN9.1.0.0.0', 'ZETN9.1.0.0.0', 'ZUTN9.1.0.0.0']:
series_data = series_data * 100
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
variable = 'FETD9.6.0.0.0'
series_meta = self.get_meta(variable)
series_data = fetd9.pct_change() * 100
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series, ignore_index=True, sort=True)
variable = 'ZUTN.1.0.0.0'
if self.country in EU:
# ZUTN based on NUTN.1.0.0.0 and NETN.1.0.0.0 (18/01/2017) is commented out in FDMS+
last_observation = self.get_data(ameco_df,
variable).last_valid_index()
series_meta = self.get_meta(variable)
series_data = round(
self.get_data(df, 'NUTN') /
(self.get_data(df, 'NUTN') + self.get_data(df, 'NETN')) * 100,
1) + round(
self.get_data(ameco_df, 'NUTN.1.0.0.0')[last_observation] -
self.get_data(df, 'NUTN') /
(self.get_data(df, 'NUTN')[last_observation] +
self.get_data(df, 'NETN')[last_observation]), 1)
series_data = splicer.butt_splice(
self.get_data(ameco_df, variable),
self.get_data(ameco_df, variable),
kind='forward')
else:
try:
netn1 = self.get_data(df, 'NETN.1.0.0.0')
except KeyError:
netn1 = self.get_data(df, 'NETN')
series_data = splicer.level_splice(
self.get_data(ameco_df, variable),
self.get_data(df, 'NUTN.1.0.0.0') /
(self.get_data(df, 'NUTN.1.0.0.0') + self.get_data(df, netn1))
* 100)
# NUTN ratiospliced (18/01/2017) is commented out in FDMS+
plcd3 = 'plcd3_series'
variables = ['PLCD.3.1.0.0', 'QLCD.3.1.0.0']
numerators = ['HWCDW.1.0.0.0', 'PLCD.3.1.0.0']
denominators = ['RVGDE.1.0.0.0', 'PVGD.3.1.0.0']
for index, variable in enumerate(variables):
series_meta = self.get_meta(variable)
if denominators[index] == 'PVGD.3.1.0.0':
denominator_series = self.get_data(df, denominators[index])
else:
denominator_series = self.get_data(self.result,
denominators[index])
series_data = operators.rebase(
self.get_data(self.result, numerators[index]) /
denominator_series,
base_period=BASE_PERIOD)
if index == 0:
plcd3 = series_data.copy()
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
variables = [
'RWCDC.3.1.0.0', 'PLCD.3.1.0.0', 'QLCD.3.1.0.0', 'HWCDW.1.0.0.0',
'HWSCW.1.0.0.0', 'HWWDW.1.0.0.0', 'RVGDE.1.0.0.0', 'RVGEW.1.0.0.0'
]
variables_6 = [
re.sub('.....0.0$', '.6.0.0.0', variable) for variable in variables
]
for index, variable in enumerate(variables):
series_meta = self.get_meta(variables_6[index])
series_data = self.get_data(self.result,
variable).pct_change() * 100
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
self.result.set_index(['Country Ameco', 'Variable Code'],
drop=True,
inplace=True)
self.apply_scale()
export_to_excel(self.result, step=11, country=self.country)
return self.result
def perform_computation(self, result_1, result_7, ameco_h_df):
# TODO: Check the scales of the output variables
splicer = Splicer()
operators = Operators()
# First we will calculate ASGH.1.0.0.0 and OVGHA.3.0.0.0, and then we will use the _sum_and_splice method
# From SumAndSpliceMixin to calculate all the rest
addends = {'UYOH.1.0.0.0': ['UOGH.1.0.0.0', 'UYNH.1.0.0.0']}
self._sum_and_splice(addends, result_1, ameco_h_df, splice=False)
new_input_df = self.result.set_index(
['Country Ameco', 'Variable Code'], drop=True)
new_input_df = pd.concat([new_input_df, result_1], sort=True)
addends = {
'UVGH.1.0.0.0': [
'UWCH.1.0.0.0', 'UYOH.1.0.0.0', 'UCTRH.1.0.0.0',
'-UTYH.1.0.0.0', '-UCTPH.1.0.0.0'
]
}
self._sum_and_splice(addends, new_input_df, ameco_h_df, splice=False)
new_input_df = self.result.set_index(
['Country Ameco', 'Variable Code'], drop=True)
new_input_df = pd.concat([new_input_df, result_1], sort=True)
addends = {'UVGHA.1.0.0.0': ['UVGH.1.0.0.0', 'UEHH.1.0.0.0']}
self._sum_and_splice(addends, new_input_df, ameco_h_df, splice=False)
addends = {
'USGH.1.0.0.0': [
'UWCH.1.0.0.0', 'UOGH.1.0.0.0', 'UYNH.1.0.0.0',
'UCTRH.1.0.0.0', '-UTYH.1.0.0.0', '-UCTPH.1.0.0.0',
'UEHH.1.0.0.0', '-UCPH0.1.0.0.0'
]
}
self._sum_and_splice(addends, new_input_df, ameco_h_df, splice=False)
new_input_df = self.result.set_index(
['Country Ameco', 'Variable Code'], drop=True)
new_input_df = pd.concat([new_input_df, result_1], sort=True)
# Since this formula is using *ignoremissingsubtract* instead of *ignoremissingsum*, we change the sign of all
# but the first variables in the list
addends = {
'UBLH.1.0.0.0': ['USGH.1.0.0.0', '-UITH.1.0.0.0', '-UKOH.1.0.0.0']
}
self._sum_and_splice(addends, new_input_df, ameco_h_df, splice=False)
uvgha_data = self.get_data(new_input_df, 'UVGHA.1.0.0.0')
pcph_data = self.get_data(result_7, 'PCPH.3.1.0.0')
uvgha_base_period = uvgha_data.loc[BASE_PERIOD]
ovgha_data = operators.rebase(uvgha_data / pcph_data,
BASE_PERIOD) / 100 * uvgha_base_period
series_meta = self.get_meta('OVGHA.3.0.0.0')
series = pd.Series(series_meta)
series = series.append(ovgha_data)
self.result = self.result.append(series, ignore_index=True, sort=True)
usgh_data = self.get_data(new_input_df, 'USGH.1.0.0.0')
uvgha_data = self.get_data(new_input_df, 'UVGHA.1.0.0.0')
asgh_ameco_h = self.get_data(ameco_h_df, 'ASGH.1.0.0.0')
asgh_data = splicer.butt_splice(asgh_ameco_h,
usgh_data / uvgha_data * 100)
series_meta = self.get_meta('ASGH.1.0.0.0')
new_series = pd.Series(series_meta)
new_series = new_series.append(asgh_data)
self.result = self.result.append(new_series,
ignore_index=True,
sort=True)
self.result.set_index(['Country Ameco', 'Variable Code'],
drop=True,
inplace=True)
self.apply_scale()
export_to_excel(self.result, step=14, country=self.country)
return self.result
def test_country_calculation_BE(self):
# STEP 2
step_2 = Population(scales=self.scales, country=self.country)
step_2_vars = [
'NUTN.1.0.0.0', 'NETN.1.0.0.0', 'NWTD.1.0.0.0', 'NETD.1.0.0.0',
'NPAN1.1.0.0.0', 'NETN', 'NLHA.1.0.0.0'
]
# NECN.1.0.0.0 is calculated and used in step_2
step_2_df = self.result_1.loc[self.result_1.index.isin(
step_2_vars, level='Variable Code')].copy()
result_2 = step_2.perform_computation(step_2_df, self.ameco_df)
variables = [
'NLTN.1.0.0.0', 'NETD.1.0.414.0', 'NECN.1.0.0.0', 'NLHT.1.0.0.0',
'NLHT9.1.0.0.0', 'NLCN.1.0.0.0', 'NSTD.1.0.0.0'
]
missing_vars = [
v for v in variables
if v not in list(result_2.loc[self.country].index)
]
self.assertFalse(missing_vars)
# STEP 3
step_3 = GDPComponents(scales=self.scales, country=self.country)
step_3_vars = [
'UMGN', 'UMSN', 'UXGN', 'UXSN', 'UMGN', 'UMSN', 'UXGS', 'UMGS',
'UIGG0', 'UIGT', 'UIGG', 'UIGCO', 'UIGDW', 'UCPH', 'UCTG', 'UIGT',
'UIST'
]
step_3_additional_vars = [
'UMGN.1.0.0.0', 'UMSN.1.0.0.0', 'UXGN.1.0.0.0', 'UXSN.1.0.0.0',
'UMGN.1.0.0.0', 'UMSN.1.0.0.0', 'UXGS.1.0.0.0', 'UMGS.1.0.0.0',
'UIGG0.1.0.0.0', 'UIGT.1.0.0.0', 'UIGG.1.0.0.0', 'UIGCO.1.0.0.0',
'UIGDW.1.0.0.0', 'UCPH.1.0.0.0', 'UCTG.1.0.0.0', 'UIGT.1.0.0.0',
'UIST.1.0.0.0', 'UXGN', 'UMGN'
]
result_3 = step_3.perform_computation(self.result_1, self.ameco_df)
variables = [
'UMGS', 'UXGS', 'UBGN', 'UBSN', 'UBGS', 'UIGG', 'UIGP', 'UIGNR',
'UUNF', 'UUNT', 'UUTT', 'UITT', 'UMGS.1.0.0.0', 'UXGS.1.0.0.0',
'UBGN.1.0.0.0', 'UBSN.1.0.0.0', 'UBGS.1.0.0.0', 'UIGG.1.0.0.0',
'UIGP.1.0.0.0', 'UIGNR.1.0.0.0', 'UUNF.1.0.0.0', 'UUNT.1.0.0.0',
'UUTT.1.0.0.0', 'UITT.1.0.0.0'
]
missing_vars = [
v for v in variables
if v not in list(result_3.loc[self.country].index)
]
self.assertFalse(missing_vars)
# STEP 4
step_4 = NationalAccountsVolume(scales=self.scales,
country=self.country)
# These variables have been calculated and are needed later
calculated = [
'UMGS', 'UXGS', 'UBGN', 'UBSN', 'UBGS', 'UIGG', 'UIGP', 'UIGNR',
'UUNF', 'UUNT', 'UUTT', 'UUITT'
]
step_4_src_vars = list(NA_VO)
step_4_src_vars.extend(calculated)
step_4_src_vars.extend(['OMGS', 'OVGE', 'OVGD'])
step_4_1000vars = [
variable + '.1.0.0.0' for variable in step_4_src_vars
]
step_4_uvars = [
re.sub('^.', 'U', variable) for variable in step_4_src_vars
]
step_4_1100vars = [
variable + '.1.1.0.0' for variable in step_4_src_vars
]
# Per capita GDP: RVGDP.1.1.0.0
step_4_1000vars.append('OVGD.1.0.0.0')
step_4_1100vars.append('RVGDP.1.1.0.0')
df_input = self.df.copy()
df_input[1993] = pd.np.nan
df_input[1994] = pd.np.nan
df_input[1995] = pd.np.nan
step_4_df = pd.concat([df_input, self.result_1, result_3], sort=True)
# result_4, ovgd1 = step_4.perform_computation(step_4_df, ameco_df)
result_4, ovgd1 = step_4.perform_computation(step_4_df, self.ameco_df)
# missing_vars = [v for v in step_4_1000vars if v not in list(result_4.loc[self.country].index)]
# self.assertFalse(missing_vars)
# STEP 5
step_5 = NationalAccountsValue(scales=self.scales,
country=self.country)
step_5_df = self.result_1.copy()
result_5 = step_5.perform_computation(step_5_df, self.ameco_db_df,
ovgd1)
variables = [
'UVGN.1.0.0.0', 'UVGN.1.0.0.0', 'UOGD.1.0.0.0', 'UOGD.1.0.0.0',
'UTVNBP.1.0.0.0', 'UTVNBP.1.0.0.0', 'UVGE.1.0.0.0', 'UVGE.1.0.0.0',
'UWCDA.1.0.0.0', 'UWCDA.1.0.0.0', 'UWSC.1.0.0.0', 'UWSC.1.0.0.0'
]
missing_vars = [
v for v in variables
if v not in list(result_5.loc[self.country].index)
]
self.assertFalse(missing_vars)
PD = [
'PCPH.3.1.0.0', 'PCTG.3.1.0.0', 'PIGT.3.1.0.0', 'PIGCO.3.1.0.0',
'PIGDW.3.1.0.0', 'PIGNR.3.1.0.0', 'PIGEQ.3.1.0.0', 'PIGOT.3.1.0.0',
'PUNF.3.1.0.0', 'PUNT.3.1.0.0', 'PUTT.3.1.0.0', 'PVGD.3.1.0.0',
'PXGS.3.1.0.0', 'PMGS.3.1.0.0', 'PXGN.3.1.0.0', 'PXSN.3.1.0.0',
'PMGN.3.1.0.0', 'PMSN.3.1.0.0', 'PIGP.3.1.0.0', 'PIST.3.1.0.0',
'PVGE.3.1.0.0'
]
PD_O = [
'OCPH.1.0.0.0', 'OCTG.1.0.0.0', 'OIGT.1.0.0.0', 'OIGCO.1.0.0.0',
'OIGDW.1.0.0.0', 'OIGNR.1.0.0.0', 'OIGEQ.1.0.0.0', 'OIGOT.1.0.0.0',
'OUNF.1.0.0.0', 'OUNT.1.0.0.0', 'OUTT.1.0.0.0', 'OVGD.1.0.0.0',
'OXGS.1.0.0.0', 'OMGS.1.0.0.0', 'OXGN.1.0.0.0', 'OXSN.1.0.0.0',
'OMGN.1.0.0.0', 'OMSN.1.0.0.0', 'OIGP.1.0.0.0', 'OIST.1.0.0.0',
'OVGE.1.0.0.0'
]
PD_U = [
'UCPH.1.0.0.0', 'UCTG.1.0.0.0', 'UIGT.1.0.0.0', 'UIGCO.1.0.0.0',
'UIGDW.1.0.0.0', 'UIGNR.1.0.0.0', 'UIGEQ.1.0.0.0', 'UIGOT.1.0.0.0',
'UUNF.1.0.0.0', 'UUNT.1.0.0.0', 'UUTT.1.0.0.0', 'UVGD.1.0.0.0',
'UXGS.1.0.0.0', 'UMGS.1.0.0.0', 'UXGN.1.0.0.0', 'UXSN.1.0.0.0',
'UMGN.1.0.0.0', 'UMSN.1.0.0.0', 'UIGP.1.0.0.0', 'UIST.1.0.0.0',
'UVGE.1.0.0.0'
]
# STEP 6
ameco_vars = ['UVGDH.1.0.0.0', 'KNP.1.0.212.0']
ameco_df = self._get_ameco_df(ameco_vars)
step_6 = RecalculateUvgdh(scales=self.scales, country=self.country)
result_6 = step_6.perform_computation(self.df, ameco_df)
# STEP 7
step_7 = Prices(scales=self.scales, country=self.country)
step_7_df = pd.concat([self.result_1, result_3, result_4, result_5],
sort=True)
result_7 = step_7.perform_computation(step_7_df)
variables = list(PD)
missing_vars = [
v for v in variables
if v not in list(result_7.loc[self.country].index)
]
self.assertFalse(missing_vars)
# STEP 8
step_8 = CapitalStock(scales=self.scales, country=self.country)
step_8_df = pd.concat(
[self.result_1, result_2, result_3, result_4, result_5], sort=True)
result_8 = step_8.perform_computation(step_8_df, self.ameco_df,
self.ameco_db_df_all_data)
# variables = list(PD)
# missing_vars = [v for v in variables if v not in list(result_8.loc[self.country].index)]
# self.assertFalse(missing_vars)
# STEP 9
step_9 = OutputGap(scales=self.scales, country=self.country)
result_9 = step_9.perform_computation(read_output_gap_xls())
# variables = list(PD)
# missing_vars = [v for v in variables if v not in list(result_9.loc[self.country].index)]
# self.assertFalse(missing_vars)
# STEP 10
step_10 = ExchangeRates(scales=self.scales, country=self.country)
result_10 = step_10.perform_computation(self.ameco_db_df,
read_xr_ir_xls(),
read_ameco_xne_us_xls())
# variables = list(PD)
# missing_vars = [v for v in variables if v not in list(result_10.loc[self.country].index)]
# self.assertFalse(missing_vars)
# STEP 11
step_11 = LabourMarket(scales=self.scales, country=self.country)
step_11_df = pd.concat(
[self.result_1, result_2, result_4, result_5, result_7], sort=True)
result_11 = step_11.perform_computation(step_11_df, self.ameco_df)
variables = [
'FETD9.1.0.0.0', 'FWTD9.1.0.0.0', 'HWCDW.1.0.0.0', 'RWCDC.3.1.0.0',
'HWWDW.1.0.0.0', 'RWWDC.3.1.0.0', 'HWSCW.1.0.0.0', 'RWSCC.3.1.0.0',
'RVGDE.1.0.0.0', 'RVGEW.1.0.0.0', 'RVGEW.1.0.0.0', 'ZATN9.1.0.0.0',
'ZETN9.1.0.0.0', 'ZUTN9.1.0.0.0', 'FETD9.6.0.0.0', 'PLCD.3.1.0.0',
'QLCD.3.1.0.0', 'RWCDC.6.0.0.0', 'PLCD.6.0.0.0', 'QLCD.6.0.0.0',
'HWCDW.6.0.0.0', 'HWSCW.6.0.0.0', 'HWWDW.6.0.0.0', 'RVGDE.6.0.0.0',
'RVGEW.6.0.0.0'
]
missing_vars = [
v for v in variables
if v not in list(result_11.loc[self.country].index)
]
self.assertFalse(missing_vars)
# STEP 12
step_12 = FiscalSector(scales=self.scales, country=self.country)
result_12 = step_12.perform_computation(self.result_1, self.ameco_df)
# variables = list(PD)
# missing_vars = [v for v in variables if v not in list(result_12.loc[self.country].index)]
# self.assertFalse(missing_vars)
# STEP 13
step_13 = CorporateSector(scales=self.scales, country=self.country)
result_13 = step_13.perform_computation(self.result_1, self.ameco_df)
variables = ['USGC.1.0.0.0', 'UOGC.1.0.0.0']
missing_vars = [
v for v in variables
if v not in list(result_13.loc[self.country].index)
]
self.assertFalse(missing_vars)
# STEP 14
step_14 = HouseholdSector(scales=self.scales, country=self.country)
result_14 = step_14.perform_computation(self.result_1, result_7,
self.ameco_df)
variables = [
'UYOH.1.0.0.0', 'UVGH.1.0.0.0', 'UVGHA.1.0.0.0', 'OVGHA.3.0.0.0',
'USGH.1.0.0.0', 'ASGH.1.0.0.0', 'UBLH.1.0.0.0'
]
missing_vars = [
v for v in variables
if v not in list(result_14.loc[self.country].index)
]
self.assertFalse(missing_vars)
# TODO: Fix all scales
result = pd.concat([
self.result_1, result_2, result_3, result_4, result_5, result_6,
result_7, result_8, result_9, result_10, result_11, result_12,
result_13, result_14
],
sort=True)
result = remove_duplicates(result)
fix_scales(result, self.country)
export_to_excel(result, 'output/{}/outputall.txt'.format(self.country),
'output/{}/outputall.xlsx'.format(self.country))
# res = result.drop(columns=['Scale'])
res = result.copy()
# res.loc[:, YEARS] = res.loc[:, YEARS].round(decimals=4)
columns = res.columns
rows = result.index.tolist()
self.dfexp['Frequency'] = 'Annual'
exp = self.dfexp[columns].reindex(rows)
# exp.loc[:, YEARS] = exp.loc[:, YEARS].round(decimals=4)
diff = (exp == res) | (exp != exp) & (res != res)
diff_series = diff.all(axis=1)
wrong_series = set()
for i in range(1, res.shape[0]):
series, expected = res.iloc[i], exp.iloc[i]
for year in YEARS:
p, q = series[year], expected[year]
if not all([pd.isna(p), pd.isna(q)]):
if abs(p - q) < 5e-6:
wrong_series.add(series.name)
# if res.iloc[i].name[1] == 'OBSN.1.0.0.0':
# # TODO: Fix - This value is wrong, probably due to a previous calculation
# # res[i][2019] = -1.294279652
# continue
wrong_names = [name for name in wrong_series]
res_wrong, exp_wrong = res.loc[wrong_names].copy(
), exp.loc[wrong_names].copy()
report_diff(res_wrong, exp_wrong, country=self.country)
def perform_computation(self, df, ameco_df):
for variable in NA_VO:
new_variable = variable + '.1.0.0.0'
u_variable = re.sub('^.', 'U', variable)
variable11 = variable + '.1.1.0.0'
if self.country in FCWVACP:
try:
new_data = self.splicer.ratio_splice(
self.get_data(ameco_df, u_variable),
self.get_data(df, variable),
kind='forward')
except KeyError:
logger.error(
'Failed to calculate {} (national accounts volume).'.
format(variable))
continue
new_meta = pd.Series(self.get_meta(new_variable))
new_series = new_meta.append(new_data)
self.result = self.result.append(new_series, ignore_index=True)
else:
try:
series = self.get_data(df, variable)
u_series = self.get_data(df, u_variable)
except KeyError:
logger.error(
'Failed to calculate {} (national accounts volume).'.
format(variable))
continue
try:
series11 = self.get_data(ameco_df, variable11)
series11[2019] = pd.np.nan
except KeyError:
logger.warning(
'Missing Ameco data for variable {} (national accounts volume). Using data '
'from country desk forecast'.format(variable11))
splice_series = (series / u_series.shift(1) - 1) * 100
# RatioSplice(base, level(series)) = base * (1 + 0,01 * series)
new_data = self.splicer.splice_and_level_forward(
series11, splice_series)
new_meta = pd.Series(self.get_meta(new_variable))
new_series = new_meta.append(new_data)
self.result = self.result.append(new_series, ignore_index=True)
# Imports / exports of goods and services
omgs, oxgs, obgn, obsn, oigp = 'OMGS.1.0.0.0', 'OXGS.1.0.0.0', 'OBGN.1.0.0.0', 'OBSN.1.0.0.0', 'OIGP.1.0.0.0'
variables = {
omgs: {
'ameco': 'OMGS.1.1.0.0',
'goods': 'OMGN',
'services': 'OMSN',
'u_goods': 'UMGN',
'u_services': 'UMSN'
}
}
variables[oxgs] = {
'ameco': 'OXGS.1.1.0.0',
'goods': 'OXGN',
'services': 'OXSN',
'u_goods': 'UXGN',
'u_services': 'UXSN'
}
variables[obgn] = {
'exports': 'OXGN.1.1.0.0',
'imports': 'OMGN.1.1.0.0',
'new_exports': 'OXGN',
'u_exports': 'UXGN',
'new_imports': 'OMGN',
'u_imports': 'UMGN'
}
variables[obsn] = {
'exports': 'OXSN.1.1.0.0',
'imports': 'OMSN.1.1.0.0',
'new_exports': 'OXSN',
'u_exports': 'UXSN',
'new_imports': 'OMGN',
'u_imports': 'UMGN'
}
variables[oigp] = {
'exports': 'OIGT.1.1.0.0',
'imports': 'OIGG.1.1.0.0',
'new_exports': 'OIGG',
'u_exports': 'UIGG',
'new_imports': 'OIGG',
'u_imports': 'UIGG'
}
for variable in variables:
base_series = None
try:
base_series, splice_series_1, splice_series_2 = self._get_data(
variable, variables[variable], df, ameco_df)
except TypeError:
logger.error(
'Missing data for variable {} in national accounts volume'.
format(variable))
# if variable == obsn:
# import code;code.interact(local=locals())
self._update_result(variable, base_series, splice_series_1,
splice_series_2)
# Net exports goods and services
var = 'OBGS.1.0.0.0'
ameco_exports = 'OXGS.1.1.0.0'
ameco_imports = 'OMGS.1.1.0.0'
goods_exports = 'OXGN'
services_exports = 'OXSN'
goods_imports = 'OMGN'
services_imports = 'OMSN'
u_goods_exports = 'UXGN'
u_services_exports = 'UXSN'
u_goods_imports = 'UMGN'
u_services_imports = 'UMSN'
export_series = self.get_data(df, goods_exports) + self.get_data(
df, services_exports)
import_series = self.get_data(df, goods_imports) + self.get_data(
df, services_imports)
u_exports = self.get_data(df, u_goods_exports) + self.get_data(
df, u_services_exports)
u_imports = self.get_data(df, u_goods_imports) + self.get_data(
df, u_services_imports)
base_series = self.get_data(ameco_df, ameco_exports) - self.get_data(
ameco_df, ameco_imports)
splice_series_1 = export_series - import_series
splice_series_2 = ((export_series - import_series) /
(u_exports - u_imports).shift(1) - 1) * 100
self._update_result(var, base_series, splice_series_1, splice_series_2)
# Investments
var = 'OIGNR.1.0.0.0'
ameco_1 = 'OIGCO.1.1.0.0'
ameco_2 = 'OIGDW.1.1.0.0'
investments_1 = 'OIGCO'
investments_2 = 'OIGDW'
u_investments_1 = 'UIGCO'
u_investments_2 = 'UIGDW'
net_series = self.get_data(df, investments_1) - self.get_data(
df, investments_2)
u_net_series = self.get_data(df, u_investments_1) - self.get_data(
df, u_investments_2)
base_series = self.get_data(ameco_df, ameco_1) - self.get_data(
ameco_df, ameco_2)
splice_series_1 = net_series.copy()
splice_series_2 = (net_series / u_net_series.shift(1) - 1) * 100
self._update_result(var, base_series, splice_series_1, splice_series_2)
# Domestic demand
var = 'OUNF.1.0.0.0'
private_consumption = 'OCPH.1.1.0.0'
government_consumption = 'OCTG.1.1.0.0'
use_ameco = 'OIGT.1.1.0.0'
new_private_consumption = 'OCPH'
new_government_consumption = 'OCTG'
new_use = 'OIGT'
u_new_private_consumption = 'UCPH'
u_new_government_consumption = 'UCTG'
u_new_use = 'UIGT'
u_series = self.get_data(
df, u_new_private_consumption) + self.get_data(
df, u_new_government_consumption) + self.get_data(
df, u_new_use)
base_series = self.get_data(
ameco_df, private_consumption) + self.get_data(
ameco_df, government_consumption) + self.get_data(
ameco_df, use_ameco)
splice_series_1 = self.get_data(
df, new_private_consumption) + self.get_data(
df, new_government_consumption) + self.get_data(df, new_use)
splice_series_2 = (splice_series_1 / u_series.shift(1) - 1) * 100
self._update_result(var, base_series, splice_series_1, splice_series_2)
# Domestic demand
variables = {
'OUNT.1.0.0.0': ['OUNT.1.1.0.0', 'OCPH', 'OCTG', 'OIGT', 'OIST'],
'OUTT.1.0.0.0':
['OUTT.1.1.0.0', 'OCPH', 'OCTG', 'OIGT', 'OIST', 'OXGN', 'OXSN'],
'OITT.1.0.0.0': ['OITT.1.0.0.0', 'OIGT', 'OIST']
}
for var, new_vars in variables.items():
base_series = None
splice_series_1 = sum([self.get_data(df, v) for v in new_vars[1:]])
try:
base_series = self.get_data(df, new_vars[0])
except KeyError:
logger.warning(
'No historical data for {} to level_splice, country {}, using country forecast '
'data.'.format(new_vars[0], self.country))
splice_series_2 = None
if self.country not in FCWVACP:
u_new_vars = [re.sub('^.', 'U', v) for v in new_vars[1:]]
try:
sum_u_series = sum(
self.get_data(df, v) for v in new_vars[1:])
splice_series_2 = splice_series_1.copy(
) / sum_u_series.shift(1) - 1 * 100
self._update_result(var, base_series, splice_series_1,
splice_series_2)
except KeyError:
logger.error(
'Missing data for variable {} in national accounts volume (172)'
.format(new_variable))
else:
self._update_result(var, base_series, splice_series_1, None)
# Volume, rebase to baseperiod, percent change, contribution to percent change in GDP
for var in NA_VO:
new_variable = var + '.1.0.0.0'
u1_variable = re.sub('^.', 'U', var) + '.1.0.0.0'
# TODO: Review this
new_vars = ['OXGS.1.0.0.0', 'OVGE.1.0.0.0']
if new_variable in self.result['Variable Code'].values.tolist(
) + new_vars:
if new_variable not in new_vars:
result_series_index = self.get_index(new_variable)
series_orig = self.result.loc[result_series_index]
data_orig = pd.to_numeric(
series_orig.filter(regex=r'[0-9]{4}'), errors='coerce')
else:
logger.error(
'Missing data for variable {} in national accounts volume'
.format(u1_variable))
# Rebase to baseperiod
if u1_variable in df.index.get_level_values('Variable Code'):
series_meta = self.get_meta(new_variable)
u1_series = self.get_data(df, u1_variable)
value_to_rebase = data_orig[BASE_PERIOD] / u1_series[
BASE_PERIOD]
series_data = data_orig * value_to_rebase
series = pd.Series(series_meta)
series = series.append(series_data)
self.result.iloc[result_series_index] = series
else:
logger.error(
'Missing data for variable {} in national accounts volume'
.format(u1_variable))
# Percent change
variable_6 = var + '.6.0.0.0'
series_meta = self.get_meta(variable_6)
series_data = data_orig.pct_change() * 100
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
# Contribution to percent change in GDP
variable_c1 = re.sub('^.', 'C', var) + '.1.0.0.0'
variable_x = new_variable if self.country in [
'MT', 'TR'
] else u1_variable
series_6_index = self.get_index(variable_6)
data_6 = self.get_data(self.result, variable_6)
# series_6 = self.result.loc[result_series_index]
# data_6 = pd.to_numeric(series_6.filter(regex=r'[0-9]{4}'), errors='coerce')
xvgd = 'OVGD.1.0.0.0' if self.country in ['MT', 'TR'
] else 'UVGD.1.0.0.0'
series_meta = self.get_meta(variable_c1)
data_x = self.get_data(df, variable_x).shift(1)
data_xvgd = self.get_data(df, xvgd).shift(1)
if variable_c1 not in ['CBGN.1.0.0.0']:
try:
data_x[1996] = self.get_data(ameco_df,
variable_x)[1996]
except KeyError:
pass
try:
data_x[1996] = self.get_data(ameco_df, xvgd)[1996]
except KeyError:
pass
try:
series_data = data_6 * data_x / data_xvgd
except KeyError:
logger.error(
'Missing data for variable {} in national accounts volume'
.format(new_variable))
continue
series = pd.Series(series_meta)
series = series.append(series_data)
# if variable_c1 == 'CMGS.1.0.0.0':
# import code;code.interact(local=locals())
self.result = self.result.append(series,
ignore_index=True,
sort=True)
else:
logger.error(
'Missing data for variable {} in national accounts volume'.
format(new_variable))
r = self.result.copy()
if new_variable == 'OVGD.1.0.0.0':
ovgd1 = self.get_data(self.result, 'OVGD.1.0.0.0')
# if variable_c1 == 'CMGS.1.0.0.0':
# import code;code.interact(local=locals())
# Contribution to percent change in GDP (calculation for additional variables)
var = 'CMGS.1.0.0.0'
series_meta = self.get_meta(var)
series_data = -self.get_data(self.result, var)
index = self.get_index(var)
series = pd.Series(series_meta)
series = series.append(series_data)
self.result.iloc[index] = series
var = 'CBGS.1.0.0.0'
exports = 'CXGS.1.0.0.0'
imports = 'CMGS.1.0.0.0'
series_meta = self.get_meta(var)
series_meta['Variable Code'] = var
series_data = self.get_data(self.result, exports) + self.get_data(
self.result, imports)
index = self.get_index(var)
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series, ignore_index=True, sort=True)
# TODO: If Country in group 'Forecast: Countries with volumes at constant prices' line 202 country calc
# Per-capita GDP
# TODO: fix scale, frequency and country everywhere
# TODO: fix this
new_variable = 'RVGDP.1.0.0.0'
ameco_variable = 'RVGDP.1.1.0.0'
variable_6 = re.sub('.1.0.0.0', '.6.0.0.0', new_variable)
total_population = 'NPTD.1.0.0.0'
potential_gdp = 'OVGD.1.0.0.0'
series_meta = self.get_meta(new_variable)
series_6_meta = self.get_meta(variable_6)
ameco_series = self.get_data(ameco_df, ameco_variable)
splice_series = ovgd1 / self.get_data(df, total_population)
splicer = Splicer()
series_data = splicer.ratio_splice(ameco_series,
splice_series,
kind='forward')
series_6_data = series_data.pct_change() * 100
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series, ignore_index=True, sort=True)
series_6 = pd.Series(series_6_meta)
series_6 = series_6.append(series_6_data)
self.result = self.result.append(series_6,
ignore_index=True,
sort=True)
# TODO: Do not add series if they're alreade there, i.e. df.loc['BE','UMGS'] is repeated
# Terms of trade
variables = ['APGN.3.0.0.0', 'APSN.3.0.0.0', 'APGS.3.0.0.0']
exports_1 = ['UXGN.1.0.0.0', 'UXSN.1.0.0.0', 'UXGS.1.0.0.0']
exports_2 = ['OXGN.1.0.0.0', 'OXSN.1.0.0.0', 'OXGS.1.0.0.0']
imports_1 = ['UMGN.1.0.0.0', 'UMSN.1.0.0.0', 'UMGS.1.0.0.0']
imports_2 = ['OMGN.1.0.0.0', 'OMSN.1.0.0.0', 'OMGS.1.0.0.0']
for index, variable in enumerate(variables):
series_meta = self.get_meta(variable)
series_data = (
self.get_data(df, exports_1[index]) /
self.get_data(self.result, exports_2[index]) /
(self.get_data(df, imports_1[index]) /
self.get_data(self.result, imports_2[index]))) * 100
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
variable_6 = re.sub('3', '6', variable)
series_meta = self.get_meta(variable_6)
series_data = series_data.pct_change() * 100
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
# Set up OVGD.6.1.212.0 for World GDP volume table
variable = 'OVGD.6.1.212.0'
series_meta = self.get_meta(variable)
series_data = self.get_data(self.result, 'OVGD.6.0.0.0')
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series, ignore_index=True, sort=True)
# Convert percent change of trade variables (volume) from national currency to USD
for variable in T_VO:
new_variable = variable + '.6.0.30.0'
variable_6 = variable + '.6.0.0.0'
series_meta = self.get_meta(new_variable)
series_data = self.get_data(self.result, variable_6)
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
series_meta = self.get_meta('OVGD.1.0.0.0')
series = pd.Series(series_meta)
# TODO: This shouldn't be needed... Check what's going on
series = series.append(ovgd1)
self.result = self.result.append(series, ignore_index=True, sort=True)
self.result.set_index(['Country Ameco', 'Variable Code'],
drop=True,
inplace=True)
self.apply_scale()
export_to_excel(self.result, step=4, country=self.country)
return self.result, ovgd1
def perform_computation(self, ameco_db_df, xr_df, ameco_xne_us_df):
splicer = Splicer()
variable = 'XNE.1.0.99.0'
series_data = self.get_data(ameco_db_df, variable)
try:
xr_data = self.get_data(xr_df, variable)
except KeyError:
pass
else:
last_valid = xr_data.first_valid_index()
for year in range(last_valid + 1, LAST_YEAR + 1):
series_data[year] = pd.np.nan
series_data = splicer.ratio_splice(series_data.copy(),
xr_data,
kind='forward')
series_meta = self.get_meta(variable)
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series, ignore_index=True, sort=True)
variables = ['ILN.1.0.0.0', 'ISN.1.0.0.0']
sources = ['ILN.1.1.0.0', 'ISN.1.1.0.0']
null_dates = list(
range(int(datetime.datetime.now().year) - 1, LAST_YEAR))
for index, variable in enumerate(variables):
series_meta = self.get_meta(variable)
series_data = self.get_data(ameco_db_df,
sources[index],
null_dates=null_dates)
series_data = splicer.butt_splice(series_data,
self.get_data(
xr_df, sources[index]),
kind='forward')
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
if self.country in EA:
membership_date = get_membership_date(self.country)
variable = 'XNE.1.0.99.0'
for year in range(membership_date, LAST_YEAR + 1):
self.result.loc[self.result['Variable Code'] == 'XNE.1.0.99.0',
year] = 1
variable = 'XNEF.1.0.99.0'
series_meta = self.get_meta(variable)
series_data = self.get_data(ameco_db_df, 'XNE.1.0.99.0')
last_valid = series_data.last_valid_index()
if last_valid < LAST_YEAR:
for index in range(last_valid + 1, LAST_YEAR + 1):
series_data[index] = series_data[last_valid]
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
variable = 'XNEB.1.0.99.0'
series_meta = self.get_meta(variable)
series_data = self.get_data(self.result,
'XNE.1.0.99.0') * self.get_data(
self.result, 'XNEF.1.0.99.0')
for year in range(membership_date, LAST_YEAR + 1):
self.result.loc[self.result['Variable Code'] ==
'XNEF.1.0.99.0', year] = pd.np.nan
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
else:
variable = 'XNEB.1.0.99.0'
series_meta = self.get_meta(variable)
series_data = self.get_data(self.result, 'XNE.1.0.99.0').copy()
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
variable = 'XNU.1.0.30.0'
xne_us = self.get_data(xr_df, 'XNE.1.0.99.0', country='US')
last_observation = xne_us.first_valid_index()
new_xne_us = self.get_data(ameco_xne_us_df,
'XNE.1.0.99.0',
country='US')
for year in range(last_observation + 1, LAST_YEAR + 1):
new_xne_us[year] = pd.np.nan
series_meta = self.get_meta(variable)
series_data = splicer.ratio_splice(new_xne_us, xne_us, kind='forward')
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series, ignore_index=True, sort=True)
# Effective exchange rates and relative unit labour costs, currently not calculated in FDMS+
variables = [
'PLCDQ.3.0.0.437', 'PLCDQ.3.0.30.437', 'XUNNQ.3.0.30.437',
'XUNRQ.3.0.30.437', 'PLCDQ.3.0.0.414', 'PLCDQ.3.0.0.415',
'PLCDQ.3.0.0.417', 'PLCDQ.3.0.0.424', 'PLCDQ.3.0.0.427',
'PLCDQ.3.0.0.435', 'PLCDQ.3.0.0.436', 'PLCDQ.3.0.30.414',
'PLCDQ.3.0.30.415', 'PLCDQ.3.0.30.417', 'PLCDQ.3.0.30.424',
'PLCDQ.3.0.30.427', 'PLCDQ.3.0.30.435', 'PLCDQ.3.0.30.436',
'XUNNQ.3.0.30.414', 'XUNNQ.3.0.30.415', 'XUNNQ.3.0.30.417',
'XUNNQ.3.0.30.423', 'XUNNQ.3.0.30.424', 'XUNNQ.3.0.30.427',
'XUNNQ.3.0.30.435', 'XUNNQ.3.0.30.436', 'XUNNQ.3.0.30.441',
'XUNRQ.3.0.30.414', 'XUNRQ.3.0.30.415', 'XUNRQ.3.0.30.417',
'XUNRQ.3.0.30.424', 'XUNRQ.3.0.30.427', 'XUNRQ.3.0.30.435',
'XUNRQ.3.0.30.436'
]
missing_vars = []
for variable in variables:
series_meta = self.get_meta(variable)
try:
series_data = self.get_data(ameco_db_df, variable)
except KeyError:
missing_vars.append(variable)
else:
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
variables = ['PLCDQ.6.0.0.437', 'PLCDQ.6.0.0.435', 'PLCDQ.6.0.0.436']
sources = ['PLCDQ.3.0.0.437', 'PLCDQ.3.0.0.435', 'PLCDQ.3.0.0.436']
for index, variable in enumerate(variables):
series_meta = self.get_meta(variable)
try:
series_data = self.get_data(
self.result, sources[index]).copy().pct_change() * 100
except (KeyError, IndexError):
missing_vars.append(variable)
else:
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
variables = [
'XUNNQ.6.0.30.437', 'XUNRQ.6.0.30.437', 'XUNNQ.6.0.30.435',
'XUNNQ.6.0.30.436', 'XUNRQ.6.0.30.435', 'XUNRQ.6.0.30.436'
]
sources = [
'XUNNQ.3.0.30.437', 'XUNRQ.3.0.30.437', 'XUNNQ.3.0.30.435',
'XUNNQ.3.0.30.436', 'XUNRQ.3.0.30.435', 'XUNRQ.3.0.30.436'
]
for index, variable in enumerate(variables):
series_meta = self.get_meta(variable)
try:
series_data = self.get_data(
self.result, sources[index]).copy().pct_change() * 100
except (KeyError, IndexError):
missing_vars.append(variable)
else:
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
# TODO: is it OK? these are missing in ameco_db: PLCDQ.3.0.0.414 PLCDQ.3.0.0.435 PLCDQ.3.0.0.436
# PLCDQ.3.0.30.414 PLCDQ.3.0.30.435 PLCDQ.3.0.30.436 XUNNQ.3.0.30.414 XUNNQ.3.0.30.423 XUNNQ.3.0.30.435
# XUNNQ.3.0.30.436 XUNNQ.3.0.30.441 XUNRQ.3.0.30.414 XUNRQ.3.0.30.435 XUNRQ.3.0.30.436 PLCDQ.6.0.0.435
# PLCDQ.6.0.0.436
with open('errors_step_10.txt', 'w') as f:
f.write('\n'.join(missing_vars))
self.result.set_index(['Country Ameco', 'Variable Code'],
drop=True,
inplace=True)
self.apply_scale()
export_to_excel(self.result, step=10, country=self.country)
return self.result
def perform_computation(self, df, ameco_h_df):
splicer = Splicer()
addends = {
'UTOG.1.0.0.0': ['UROG.1.0.0.0', 'UPOMN.1.0.0.0'],
'UUCG.1.0.0.0': [
'UWCG.1.0.0.0', 'UYTGH.1.0.0.0', 'UYIG.1.0.0.0',
'UYVG.1.0.0.0', 'UUOG.1.0.0.0', 'UCTGI.1.0.0.0',
'UYTGM.1.0.0.0'
],
'URCG.1.0.0.0':
['UTVG.1.0.0.0', 'UTYG.1.0.0.0', 'UTSG.1.0.0.0', 'UTOG.1.0.0.0'],
'UUTG.1.0.0.0': ['UUCG.1.0.0.0', 'UIGG0.1.0.0.0', 'UKOG.1.0.0.0'],
'URTG.1.0.0.0': ['URCG.1.0.0.0', 'UKTTG.1.0.0.0'],
'UBLG.1.0.0.0': ['URTG.1.0.0.0', '-UUTG.1.0.0.0'],
}
# if country == JP: addends['UUCG.1.0.0.0'][0] = 'UCTG.1.0.0.0'; del(addends['UTOG.1.0.0.0'])
if self.country == 'JP':
addends['UUCG.1.0.0.0'][0] = 'UCTG.1.0.0.0'
del addends['UTOG.1.0.0.0']
self._sum_and_splice(addends, df, ameco_h_df)
# variable = 'UBLG.1.0.0.0'
# sources = {variable: ['URTG.1.0.0.0', 'UUTG.1.0.0.0']}
# series_meta = self.get_meta(variable)
# splice_series = self.get_data(df, sources[variable][0]).subtract(self.get_data(
# df, sources[variable][1], fill_value=0))
# if self.country == 'JP':
# series_data = splice_series.copy()
# else:
# base_series = self.get_data(ameco_h_df, variable)
# series_data = splicer.butt_splice(base_series, splice_series, kind='forward')
# series = pd.Series(series_meta)
# series = series.append(series_data)
# self.result = self.result.append(series, ignore_index=True, sort=True)
if self.country not in EU:
if self.country != 'MK':
variable = 'UBLGE.1.0.0.0'
series_meta = self.get_meta(variable)
series_data = self.get_data(self.result, 'UBLG.1.0.0.0')
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
variable = 'UYIGE.1.0.0.0'
series_meta = self.get_meta(variable)
series_data = self.get_data(df, 'UYIG.1.0.0.0')
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
addends = {
'UBLGI.1.0.0.0': ['UBLG.1.0.0.0', 'UYIG.1.0.0.0'],
'UBLGIE.1.0.0.0': ['UBLGE.1.0.0.0', 'UYIGE.1.0.0.0'],
'UTAT.1.0.0.0': [
'UTVG.1.0.0.0', 'UTYG.1.0.0.0', 'UTAG.1.0.0.0', 'UTKG.1.0.0.0',
'UTEU.1.0.0.0'
],
'UOOMS.1.0.0.0': ['UOOMSR.1.0.0.0', 'UOOMSE.1.0.0.0'],
'UTTG.1.0.0.0': ['UTVG.1.0.0.0', 'UTEU.1.0.0.0'],
'UDGGL.1.0.0.0': [
'UDGG.1.0.0.0',
]
}
self._sum_and_splice(addends, df, ameco_h_df)
variable = 'UDGG.1.0.0.0'
series_meta = self.get_meta(variable)
series_data = self.get_data(self.result, 'UDGGL.1.0.0.0')
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series, ignore_index=True, sort=True)
# TODO: copy EATTG, EATYG and EATSG from cyclical adjustment database
# for variable in ['EATTG', 'EATYG', 'EATSG']:
# series_meta = self.get_meta(variable)
# series_data = self.get_data(self.result, 'UDGGL.1.0.0.0')
# series = pd.Series(series_meta)
# series = series.append(series_data)
# self.result = self.result.append(series, ignore_index=True, sort=True)
self.result.set_index(['Country Ameco', 'Variable Code'],
drop=True,
inplace=True)
self.apply_scale()
export_to_excel(self.result, step=12, country=self.country)
return self.result
def perform_computation(self, df, ameco_df):
for index, row in df.iterrows():
variable = index[1]
if variable in TM:
# Convert all transfer matrix variables to 1.0.0.0 (except National Account (volume)) and splice in
# country desk forecast
if variable not in NA_VO:
splicer = Splicer()
operators = Operators()
meta = self.get_meta(variable)
new_variable = variable + '.1.0.0.0'
meta1000 = self.get_meta(new_variable)
meta['Variable Code'] = variable
meta1000['Variable Code'] = new_variable
splice_series = self.get_data(df, variable)
base_series = None
try:
base_series = self.get_data(ameco_df, new_variable)
except KeyError:
logger.warning(
'Missing Ameco data for variable {} (transfer matrix)'
.format(new_variable))
orig_series = splice_series.copy()
orig_series.name = None
new_meta = pd.Series(meta)
orig_series = new_meta.append(orig_series)
if variable in TM_TBBO:
new_series = splicer.butt_splice(base_series,
splice_series,
kind='forward')
new_series.name = None
new_meta = pd.Series(meta1000)
new_series = new_meta.append(new_series)
self.result = self.result.append(new_series,
ignore_index=True)
elif variable in TM_TBM:
df_to_be_merged = pd.DataFrame(
[splice_series, base_series])
new_series = operators.merge(df_to_be_merged)
new_series.name = None
new_meta = pd.Series(meta1000)
new_series = new_meta.append(new_series)
self.result = self.result.append(new_series,
ignore_index=True)
else:
new_series = splicer.butt_splice(splicer.ratio_splice(
base_series, splice_series, kind='forward'),
splice_series,
kind='forward')
new_series.name = None
new_meta = pd.Series(meta1000)
new_series = new_meta.append(new_series)
self.result = self.result.append(new_series,
ignore_index=True)
self.result = self.result.append(orig_series,
ignore_index=True)
self.result.set_index(['Country Ameco', 'Variable Code'],
drop=True,
inplace=True)
self.apply_scale()
export_to_excel(self.result, step=1, country=self.country)
return self.result
def perform_computation(self, df, ameco_df, ameco_db_df):
'''Capital Stock and Total Factor Productivity'''
# ameco_db_df should have data till 1960
variables = ['OIGT.1.0.0.0', 'OVGD.1.0.0.0', 'UIGT.1.0.0.0']
splicer = Splicer()
for variable in variables:
try:
series_data = self.get_data(df, variable)
except KeyError:
logger.warning(
'Missing data for variable {} (Capital Stock)'.format(
variable))
continue
if series_data is not None:
series_data = splicer.ratio_splice(series_data,
self.get_data(
ameco_db_df, variable),
kind='backward',
variable=variable)[YEARS]
series_meta = self.get_meta(variable)
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
# TODO: The AMECO_H.TXT only has data till 2017, we might need to update it
variable = 'UKCT.1.0.0.0'
try:
ameco_data = self.get_data(ameco_df, variable)
except KeyError:
series_data = self.get_data(ameco_db_df, variable)[YEARS]
else:
series_data = splicer.ratio_splice(ameco_data,
self.get_data(
ameco_db_df,
variable)[YEARS],
kind='backward')
series_meta = self.get_meta(variable)
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series, ignore_index=True, sort=True)
variable = 'OKCT.1.0.0.0'
series_meta = self.get_meta(variable)
series_data = self.get_data(
self.result, 'UKCT.1.0.0.0') / (self.get_data(df, 'UIGT.1.0.0.0') /
self.get_data(df, 'OIGT.1.0.0.0'))
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series, ignore_index=True, sort=True)
variable = 'OINT.1.0.0.0'
series_meta = self.get_meta(variable)
series_data = self.get_data(df, 'OIGT.1.0.0.0') - self.get_data(
self.result, 'OKCT.1.0.0.0')
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series, ignore_index=True, sort=True)
variable = 'OKND.1.0.0.0'
series_meta = self.get_meta(variable)
series_1 = self.get_data(ameco_db_df, 'OVGD.1.0.0.0')
series_2 = self.get_data(ameco_db_df, 'OIGT.1.0.0.0')
if series_1.first_valid_index() + 1 < series_2.first_valid_index():
last_observation = series_2.first_valid_index() - 1
else:
last_observation = series_1.first_valid_index()
new_series = pd.Series(series_meta)
oint_1 = self.get_data(ameco_db_df, 'OINT.1.0.0.0').copy()
oigt_1 = self.get_data(self.result, 'OIGT.1.0.0.0').copy()
new_data = pd.Series({
year: pd.np.nan
for year in range(last_observation, LAST_YEAR + 1)
})
new_data[last_observation] = 3 * series_1[last_observation]
for year in range(last_observation + 1, LAST_YEAR):
new_data[year] = new_data[year - 1] + oint_1[year]
last_observation = self.result[
self.result['Variable Code'] ==
'OKCT.1.0.0.0'].iloc[-1].last_valid_index()
if type(last_observation) != int:
last_observation = 1993
# Up until now we were discarding data before 1993, however here we need it if we want the same results
# We need to pass all_data=True to read_ameco_db_xls and get the right ameco_db_df
for year in range(last_observation + 1, LAST_YEAR + 1):
self.result.loc[self.result['Variable Code'] == 'OKCT.1.0.0.0',
[year]] = (new_data[year - 1] * self.result.loc[
self.result['Variable Code'] == 'OKCT.1.0.0.0',
[year - 1]] / new_data[year - 2]).iloc[0, 0]
new_data[year] = (
new_data[year - 1] + oigt_1[year] -
self.result.loc[self.result['Variable Code'] == 'OKCT.1.0.0.0',
[year]]).iloc[0, 0]
self.result.loc[self.result['Variable Code'] == 'OINT.1.0.0.0',
[year]] = (oigt_1[year] - self.result.loc[
self.result['Variable Code'] == 'OKCT.1.0.0.0',
[year]]).iloc[0, 0]
self.result.loc[
self.result['Variable Code'] == 'UKCT.1.0.0.0',
[year]] = (self.result.loc[self.result['Variable Code'] ==
'OKCT.1.0.0.0', [year]] *
self.get_data(self.result, 'UIGT.1.0.0.0')[year] /
oigt_1[year]).iloc[0, 0]
new_series = new_series.append(new_data[YEARS].copy())
self.result = self.result.append(new_series,
ignore_index=True,
sort=True)
# TODO: Fix this one, we get -6.897824 instead of -2.41 but it's because NLHT9.1.0.0.0 scale is wrong
variable = 'ZVGDFA3.3.0.0.0'
series_meta = self.get_meta(variable)
series_3 = self.get_data(df, 'NLHT9.1.0.0.0')
ovgd_1 = self.get_data(self.result, 'OVGD.1.0.0.0')
series_data = pd.np.log(
ovgd_1 / (pow(series_3 * 1000, 0.65) * pow(new_data, 0.35)))
series = pd.Series(series_meta)
series = series.append(series_data[YEARS].copy())
self.result = self.result.append(series, ignore_index=True, sort=True)
self.result.set_index(['Country Ameco', 'Variable Code'],
drop=True,
inplace=True)
self.apply_scale()
export_to_excel(self.result, step=8, country=self.country)
return self.result
def perform_computation(self, df, ameco_h_df):
splicer = Splicer()
# Imports and exports of goods and services at current prices (National accounts)
variables = ['UMGS', 'UXGS', 'UMGS.1.0.0.0', 'UXGS.1.0.0.0']
goods = ['UMGN', 'UXGN', 'UMGN.1.0.0.0', 'UXGN.1.0.0.0']
services = ['UMSN', 'UXSN', 'UMSN.1.0.0.0', 'UXSN.1.0.0.0']
country = 'BE'
for index, variable in enumerate(variables):
series_meta = self.get_meta(variable)
series_data = self.get_data(df, goods[index]) + self.get_data(
df, services[index])
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
# Gross fixed capital formation at current prices: general government
variables = ['UIGG', 'UIGG.1.0.0.0']
grossfcf = ['UIGG0', 'UIGG0.1.0.0.0']
country = 'BE'
for index, variable in enumerate(variables):
series_meta = self.get_meta(variable)
series_data = self.get_data(df, grossfcf[index])
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
# Net exports of goods, services, and goods & services at current prices (National accounts)
# TODO: Check that the 4th variable is correct in exports_goods_and_services and imports_goods_and_services
variables = [
'UBGN', 'UBSN', 'UBGS', 'UBGN.1.0.0.0', 'UBSN.1.0.0.0',
'UBGS.1.0.0.0', 'UIGP', 'UIGNR', 'UIGP.1.0.0.0', 'UIGNR.1.0.0.0'
]
exports_goods_and_services = [
'UXGN', 'UXSN', 'UXGS', 'UXGN', 'UXSN.1.0.0.0', 'UXGS.1.0.0.0',
'UIGT', 'UIGCO', 'UIGT.1.0.0.0', 'UIGCO.1.0.0.0'
]
imports_goods_and_services = [
'UMGN', 'UMSN', 'UMGS', 'UMGN', 'UMSN.1.0.0.0', 'UMGS.1.0.0.0',
'UIGG', 'UIGDW', 'UIGG.1.0.0.0', 'UIGDW.1.0.0.0'
]
country = 'BE'
for index, variable in enumerate(variables):
series_meta = self.get_meta(variable)
exports_data = self.get_data(df, exports_goods_and_services[index])
imports_data = self.get_data(df, imports_goods_and_services[index])
if not isinstance(exports_data.name, type(imports_data.name)):
imports_data.name = None
series_data = exports_data - imports_data
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
# Domestic demand excluding stocks at current prices
variables = ['UUNF', 'UUNF.1.0.0.0']
private_consumption = ['UCPH', 'UCPH.1.0.0.0']
government = ['UCTG', 'UCTG.1.0.0.0']
total = ['UIGT', 'UIGT.1.0.0.0']
country = 'BE'
for index, variable in enumerate(variables):
series_meta = self.get_meta(variable)
series_data = self.get_data(
df, private_consumption[index]) + self.get_data(
df, total[index]) + self.get_data(df, government[index])
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
# Domestic demand including stocks at current prices
variables = ['UUNT', 'UUNT.1.0.0.0']
private_consumption = ['UCPH', 'UCPH.1.0.0.0']
government = ['UCTG', 'UCTG.1.0.0.0']
total = ['UIGT', 'UIGT.1.0.0.0']
changes = ['UIST', 'UIST.1.0.0.0']
country = 'BE'
for index, variable in enumerate(variables):
series_meta = self.get_meta(variable)
series_data = self.get_data(
df, private_consumption[index]) + self.get_data(
df, total[index]) + self.get_data(
df, government[index]) + self.get_data(
df, changes[index])
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
# Final demand at current prices
variables = ['UUTT', 'UUTT.1.0.0.0']
private_consumption = ['UCPH', 'UCPH.1.0.0.0']
government = ['UCTG', 'UCTG.1.0.0.0']
total = ['UIGT', 'UIGT.1.0.0.0']
changes = ['UIST', 'UIST.1.0.0.0']
export_goods = ['UXGN', 'UXGN.1.0.0.0']
export_services = ['UXSN', 'UXSN.1.0.0.0']
country = 'BE'
for index, variable in enumerate(variables):
series_meta = self.get_meta(variable)
series_data = self.get_data(
df, private_consumption[index]) + self.get_data(
df, total[index]) + self.get_data(
df, government[index]) + self.get_data(
df, changes[index]) + self.get_data(
df, export_goods[index]) + self.get_data(
df, export_services[index])
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
# Gross capital formation at current prices: total economy
variables = ['UITT', 'UITT.1.0.0.0']
total = ['UIGT', 'UIGT.1.0.0.0']
changes = ['UIST', 'UIST.1.0.0.0']
country = 'BE'
for index, variable in enumerate(variables):
series_meta = self.get_meta(variable)
series_data = self.get_data(df, total[index]) + self.get_data(
df, changes[index])
series = pd.Series(series_meta)
series = series.append(series_data)
self.result = self.result.append(series,
ignore_index=True,
sort=True)
self.result.set_index(['Country Ameco', 'Variable Code'],
drop=True,
inplace=True)
self.apply_scale()
export_to_excel(self.result, step=3, country=self.country)
return self.result
def perform_computation(self, df, ameco_df):
splicer = Splicer()
# Total labour force (unemployed + employed)
variable = 'NLTN.1.0.0.0'
unemployed = 'NUTN.1.0.0.0'
employed = 'NETN.1.0.0.0'
base_series = self.get_data(ameco_df, variable)
splice_series = self.get_data(df, unemployed) + self.get_data(
df, employed)
NLTN1000_meta = self.get_meta(variable)
NLTN1000_data = splicer.ratio_splice(base_series,
splice_series,
kind='forward')
NLTN1000 = pd.Series(NLTN1000_meta)
NLTN1000 = NLTN1000.append(NLTN1000_data)
self.result = self.result.append(NLTN1000, ignore_index=True)
# Self employed (employed - wage and salary earners)
variable = 'NSTD.1.0.0.0'
employed = 'NETN.1.0.0.0'
salary_earners = 'NWTD.1.0.0.0'
base_series = None
try:
base_series = self.get_data(ameco_df, variable)
except KeyError:
logger.warning(
'Missing Ameco data for variable {} (population). Using data '
'from country desk forecast'.format(variable))
splice_series = self.get_data(df, employed) - self.get_data(
df, salary_earners)
NSTD1000_meta = self.get_meta(variable)
NSTD1000_data = splicer.ratio_splice(base_series,
splice_series,
kind='forward',
variable=variable)
NSTD1000 = pd.Series(NSTD1000_meta)
NSTD1000 = NSTD1000.append(NSTD1000_data)
self.result = self.result.append(NSTD1000, ignore_index=True)
# Percentage employed (total employed / population of working age (15-64)
variable = 'NETD.1.0.414.0'
employed = 'NETD.1.0.0.0'
working_age = 'NPAN1.1.0.0.0'
NETD104140_meta = self.get_meta(variable)
NETD104140_data = self.get_data(df, employed) / self.get_data(
df, working_age) * 100
NETD104140 = pd.Series(NETD104140_meta)
NETD104140 = NETD104140.append(NETD104140_data)
self.result = self.result.append(NETD104140, ignore_index=True)
# Civilian employment
variable = 'NECN.1.0.0.0'
employed = 'NETN'
NECN1000_meta = self.get_meta(variable)
NECN1000_data = splicer.ratio_splice(self.get_data(ameco_df, variable),
self.get_data(df, employed),
kind='forward')
NECN1000 = pd.Series(NECN1000_meta)
NECN1000 = NECN1000.append(NECN1000_data)
self.result = self.result.append(NECN1000, ignore_index=True)
# Total annual hours worked
variable = 'NLHT.1.0.0.0'
average_hours = 'NLHA.1.0.0.0'
employed = 'NETD.1.0.0.0'
total_hours_data = self.get_data(df, employed) * self.get_data(
df, average_hours)
NLHT1000_meta = self.get_meta(variable)
NLHT1000_data = splicer.ratio_splice(self.get_data(ameco_df, variable),
total_hours_data,
kind='forward')
NLHT1000 = pd.Series(NLHT1000_meta)
NLHT1000 = NLHT1000.append(NLHT1000_data)
self.result = self.result.append(NLHT1000, ignore_index=True)
# Total annual hours worked; total economy. for internal use only
variable = 'NLHT9.1.0.0.0'
average_hours = 'NLHA.1.0.0.0'
employed = 'NETD.1.0.0.0'
total_hours_data = self.get_data(df, employed) * self.get_data(
df, average_hours)
NLHT91000_meta = self.get_meta(variable)
NLHT91000_data = splicer.ratio_splice(self.get_data(
ameco_df, variable),
total_hours_data,
kind='forward')
NLHT91000 = pd.Series(NLHT91000_meta)
NLHT91000 = NLHT91000.append(NLHT91000_data)
self.result = self.result.append(NLHT91000, ignore_index=True)
# Civilian labour force
variable = 'NLCN.1.0.0.0'
civilian_employment = 'NECN.1.0.0.0'
unemployed = 'NUTN.1.0.0.0'
NLCN1000_meta = self.get_meta(variable)
try:
base_series = self.get_data(ameco_df, variable)
except KeyError:
logger.warning(
'Missing Ameco data for variable {} (population). Using data '
'from country desk forecast'.format(variable))
NLCN1000_data = splicer.ratio_splice(base_series,
NECN1000_data +
self.get_data(df, unemployed),
kind='forward',
variable=variable)
NLCN1000 = pd.Series(NLCN1000_meta)
NLCN1000 = NLCN1000.append(NLCN1000_data)
self.result = self.result.append(NLCN1000, ignore_index=True)
self.result.set_index(['Country Ameco', 'Variable Code'],
drop=True,
inplace=True)
self.apply_scale()
export_to_excel(self.result, step=2, country=self.country)
return self.result
