def test_naccounts():
remove_clutter()
session = Session(location=TEST_CON)
assert isinstance(session, Session)
assert isinstance(session.dataset, pd.DataFrame)
na_ = session.get(dataset="na").dataset
assert isinstance(na_, dict)
assert len(na_) == 6
remove_clutter()
def test_edge():
remove_clutter()
session = Session(location=TEST_DIR)
assert isinstance(session, Session)
assert isinstance(session.dataset, pd.DataFrame)
session.get(dataset="cpi", update=False, save=False)
with pytest.raises(ValueError):
session.get(dataset="wrong")
with pytest.raises(ValueError):
session.get_frequent(dataset="wrong")
remove_clutter()
session = Session(location="new_dir")
assert path.isdir("new_dir")
shutil.rmtree(session.location)
Session(location=TEST_DIR).get_frequent(dataset="price_measures",
update=False,
save=False)
remove_clutter()
def upload_private_datasets():
repo_details = alphacast.repository.create(
"Uruguay Macro (econuy private repo)",
repo_description=PRIVATE_REPO_DESCRIPTION,
slug="private-repo",
privacy="Private",
returnIdIfExists=True,
)
repo_id = repo_details["id"]
s = Session(location=eng, download=False)
custom_only_uruguay = [
x for x in datasets.custom().keys()
if not any([y in x for y in ["global", "regional", "lin_gdp"]])
]
s.get(custom_only_uruguay)
for name, dataset in s.datasets.items():
upload_dataset(dataset, name, repo_id)
return
def test_wages():
remove_clutter()
session = Session(location=TEST_CON)
assert isinstance(session, Session)
assert isinstance(session.dataset, pd.DataFrame)
full_wages = session.get_frequent(dataset="real_wages",
seas_adj="trend").dataset
wages_tfm = full_wages.iloc[:, [0, 1, 2]]
remove_clutter()
wages_ = session.get(dataset="wages").dataset
session.only_get = True
compare = session.get(dataset="wages").dataset
assert wages_.round(4).equals(compare.round(4))
session.only_get = False
remove_clutter()
wages_trend, wages_sa = transform.decompose(wages_,
outlier=False,
trading=True)
wages_trend = transform.base_index(wages_trend, start_date="2008-07-31")
wages_trend.columns = wages_tfm.columns
assert wages_trend.equals(wages_tfm)
remove_clutter()
full_wages = session.get_frequent(dataset="wages", seas_adj="seas").dataset
wages_tfm = full_wages.iloc[:, [0, 1, 2]]
wages_sa = transform.base_index(wages_sa, start_date="2008-07-31")
wages_sa.columns = wages_tfm.columns
assert wages_sa.equals(wages_tfm)
remove_clutter()
full_wages = session.get_frequent(dataset="wages", seas_adj=None).dataset
real_wages = full_wages.iloc[:, [3, 4, 5]]
compare = transform.convert_real(wages_)
compare = transform.base_index(compare, start_date="2008-07-31")
compare.columns = real_wages.columns
assert real_wages.equals(compare)
remove_clutter()
with pytest.raises(ValueError):
session.get_frequent(dataset="wages", seas_adj="wrong")
def test_prices_inflation():
remove_clutter()
session = Session(location=TEST_CON)
assert isinstance(session, Session)
assert isinstance(session.dataset, pd.DataFrame)
measures = session.get_frequent(dataset="price_measures").dataset
remove_clutter()
prices = session.get(dataset="cpi").dataset
prices = prices.loc[prices.index >= "1997-03-31"]
prices = transform.base_index(prices,
start_date="2010-12-01",
end_date="2010-12-31")
compare = measures.iloc[:, [0]]
compare.columns = prices.columns
assert compare.equals(prices)
def test_labor():
remove_clutter()
session = Session(location=TEST_CON)
assert isinstance(session, Session)
assert isinstance(session.dataset, pd.DataFrame)
labor_tfm = session.get_frequent(dataset="labor", seas_adj="trend").dataset
labor_tfm = labor_tfm.iloc[:, [0, 1, 2]]
remove_clutter()
labor_ = session.get(dataset="labor").dataset.iloc[:, [0, 3, 6]]
session.only_get = True
compare = session.get(dataset="labor").dataset.iloc[:, [0, 3, 6]]
compare = transform.decompose(compare, flavor="trend")
compare.columns = labor_tfm.columns
assert labor_tfm.round(4).equals(compare.round(4))
remove_clutter()
session.only_get = False
labor_trend, labor_sa = transform.decompose(labor_,
outlier=True,
trading=True)
labor_trend.columns = labor_tfm.columns
assert labor_trend.equals(labor_tfm)
remove_clutter()
labor_tfm = session.get_frequent(dataset="labor", seas_adj="seas").dataset
labor_tfm = labor_tfm.iloc[:, [0, 1, 2]]
labor_sa.columns = labor_tfm.columns
assert labor_sa.equals(labor_tfm)
remove_clutter()
labor_tfm = session.get_frequent(dataset="labor", seas_adj=None).dataset
compare = labor_.iloc[:, 0].div(labor_.iloc[:, 1]).round(4)
compare_2 = labor_tfm.iloc[:, 3].div(labor_tfm.iloc[:, 4]).round(4)
assert compare.equals(compare_2)
compare = labor_tfm.iloc[:, 3].mul(labor_.iloc[:, 2]).div(100).round(4)
assert compare.equals(labor_tfm.iloc[:, 5].round(4))
remove_clutter()
with pytest.raises(ValueError):
session.get_frequent(dataset="labor", seas_adj="wrong")
def test_trade():
remove_clutter()
session = Session(location=TEST_CON)
assert isinstance(session, Session)
assert isinstance(session.dataset, pd.DataFrame)
tb_ = session.get(dataset="trade").dataset
assert isinstance(tb_, dict)
assert len(tb_) == 12
remove_clutter()
net = session.get_frequent(dataset="net_trade").dataset
compare = (
tb_["tb_x_dest_val"].rename(columns={"Total exportaciones": "Total"}) -
tb_["tb_m_orig_val"].rename(columns={"Total importaciones": "Total"}))
compare.columns = net.columns
assert net.equals(compare)
remove_clutter()
def test_tot():
remove_clutter()
session = Session(location=TEST_CON)
assert isinstance(session, Session)
assert isinstance(session.dataset, pd.DataFrame)
tb_ = session.get(dataset="trade").dataset
assert isinstance(tb_, dict)
assert len(tb_) == 12
remove_clutter()
net = session.get_frequent(dataset="tot").dataset
compare = (
tb_["tb_x_dest_pri"].rename(columns={"Total exportaciones": "Total"}) /
tb_["tb_m_orig_pri"].rename(columns={"Total importaciones": "Total"}))
compare = compare.loc[:, ["Total"]]
compare = transform.base_index(compare,
start_date="2005-01-01",
end_date="2005-12-31")
compare.columns = net.columns
assert net.equals(compare)
remove_clutter()
def test_fiscal():
remove_clutter()
session = Session(location=TEST_CON)
assert isinstance(session, Session)
assert isinstance(session.dataset, pd.DataFrame)
fiscal_tfm = session.get_frequent(dataset="fiscal",
aggregation="nfps",
fss=True,
unit="gdp").dataset
remove_clutter()
fiscal_ = session.get(dataset="fiscal").dataset
nfps = fiscal_["nfps"]
gc = fiscal_["gc-bps"]
proc = pd.DataFrame(index=nfps.index)
proc["Ingresos: SPNF-SPC"] = nfps["Ingresos: SPNF"]
proc["Egresos: Primarios SPNF-SPC"] = nfps["Egresos: Primarios SPNF"]
proc["Egresos: Inversiones SPNF-SPC"] = nfps["Egresos: Inversiones"]
proc["Intereses: SPNF"] = nfps["Intereses: Totales"]
proc["Egresos: Totales SPNF"] = (proc["Egresos: Primarios SPNF-SPC"] +
proc["Intereses: SPNF"])
proc["Resultado: Primario intendencias"] = nfps[
"Resultado: Primario intendencias"]
proc["Resultado: Primario BSE"] = nfps["Resultado: Primario BSE"]
proc["Resultado: Primario SPNF"] = nfps["Resultado: Primario SPNF"]
proc["Resultado: Global SPNF"] = nfps["Resultado: Global SPNF"]
proc["Ingresos: FSS"] = gc["Ingresos: FSS"]
proc["Intereses: FSS"] = gc["Intereses: BPS-FSS"]
proc["Ingresos: SPNF-SPC aj. FSS"] = (proc["Ingresos: SPNF-SPC"] -
proc["Ingresos: FSS"])
proc["Intereses: SPNF aj. FSS"] = (proc["Intereses: SPNF"] -
proc["Intereses: FSS"])
proc["Egresos: Totales SPNF aj. FSS"] = (proc["Egresos: Totales SPNF"] -
proc["Intereses: FSS"])
proc["Resultado: Primario SPNF aj. FSS"] = (
proc["Resultado: Primario SPNF"] - proc["Ingresos: FSS"])
proc["Resultado: Global SPNF aj. FSS"] = (proc["Resultado: Global SPNF"] -
proc["Ingresos: FSS"] +
proc["Intereses: FSS"])
cols = fiscal_metadata["nfps"][True]
compare = proc.loc[:, cols]
metadata._set(compare,
area="Cuentas fiscales y deuda",
currency="UYU",
inf_adj="No",
unit="No",
seas_adj="NSA",
ts_type="Flujo",
cumperiods=1)
compare_gdp = transform.rolling(compare, periods=12, operation="sum")
compare_gdp = transform.convert_gdp(compare_gdp)
compare_gdp.columns = fiscal_tfm.columns
assert compare_gdp.equals(fiscal_tfm)
remove_clutter()
fiscal_tfm = session.get_frequent(dataset="fiscal",
aggregation="nfps",
fss=True,
unit="usd").dataset
compare_usd = transform.convert_usd(compare)
compare_usd.columns = fiscal_tfm.columns
assert compare_usd.equals(fiscal_tfm)
remove_clutter()
fiscal_tfm = session.get_frequent(dataset="fiscal",
aggregation="nfps",
fss=True,
unit="real").dataset
compare_real = transform.convert_real(compare)
compare_real.columns = fiscal_tfm.columns
assert compare_real.equals(fiscal_tfm)
remove_clutter()
start_date = "2010-01-31"
end_date = "2010-12-31"
fiscal_tfm = session.get_frequent(dataset="fiscal",
aggregation="nfps",
fss=True,
unit="real_usd",
start_date=start_date,
end_date=end_date).dataset
compare_real_usd = transform.convert_real(compare,
start_date=start_date,
end_date=end_date)
xr = nxr.get_monthly(update_loc=None, save_loc=None)
compare_real_usd = compare_real_usd.divide(
xr[start_date:end_date].mean()[1])
compare_real_usd.columns = fiscal_tfm.columns
assert compare_real_usd.equals(fiscal_tfm)
remove_clutter()
with pytest.raises(ValueError):
session.get_frequent(dataset="fiscal",
aggregation="nfps",
unit="wrong")
with pytest.raises(ValueError):
session.get_frequent(dataset="fiscal", aggregation="wrong")
remove_clutter()
fiscal_ = session.get(dataset="fiscal").dataset
session.only_get = True
compare = session.get(dataset="fiscal").dataset
for v, v2 in zip(fiscal_.values(), compare.values()):
assert v.round(4).equals(v2.round(4))
remove_clutter()
session.only_get = False