def build_debt(start, end):
p = Pipeline(location=db.engine, download=False)
p.get("net_public_debt")
p.convert(flavor="gdp")
debt = p.dataset
debt.columns = debt.columns.get_level_values(0)
debt_plot = build_chart(
debt,
title="Deuda neta del sector público global",
subtitle="% del PBI",
kind="area",
start=start,
end=end,
)
return debt_plot
def build_cpi(start, end):
p = Pipeline(location=db.engine, download=False)
p.get("cpi")
p.chg_diff(period="inter")
cpi = p.dataset
cpi.columns = cpi.columns.get_level_values(0)
cpi_plot = build_chart(
cpi,
title="IPC",
subtitle="Variación interanual",
kind="line",
start=start,
end=end,
)
return cpi_plot
def build_industrial(start, end):
p = Pipeline(location=db.engine, download=False)
p.get("core_industrial")
p.chg_diff(period="inter")
industrial = p.dataset
industrial.columns = industrial.columns.get_level_values(0)
industrial_plot = build_chart(
industrial,
title="Producción industrial",
subtitle="Variación interanual",
kind="line",
start=start,
end=end,
)
return industrial_plot
def build_gdp(start, end):
p = Pipeline(location=db.engine, download=False)
p.get("gdp_con_idx_sa_long")
p.chg_diff(period="last")
gdp = p.dataset
gdp.columns = gdp.columns.get_level_values(0)
gdp_plot = build_chart(
gdp,
title="PBI real",
subtitle="Desestacionalizado, variación trimestral",
kind="bar",
start=start,
end=end,
)
return gdp_plot
def build_rxr(start, end):
p = Pipeline(location=db.engine, download=False)
p.get("rxr_custom")
p.rebase(start_date=p.dataset.index.min(),
end_date=p.dataset.index.max())
rxr = p.dataset
rxr.columns = rxr.columns.get_level_values(0)
rxr_plot = build_chart(
rxr,
title=f"Tipo de cambio real",
subtitle=f"1980-{dt.date.today().year}=100",
kind="line",
start=start,
end=end,
)
return rxr_plot
def build_cpi_measures(start, end):
p = Pipeline(location=db.engine, download=False)
p.get("cpi_measures")
p.chg_diff(period="inter")
cpi_measures = p.dataset
cpi_measures.columns = cpi_measures.columns.get_level_values(0)
cpi_measures_plot = build_chart(
cpi_measures,
title="IPC transable, no transable y subyacente",
subtitle="Variación interanual",
kind="line",
y=cpi_measures.columns[:-2],
start=start,
end=end,
height=460,
)
return cpi_measures_plot
def build_fiscal_balance(start, end):
p = Pipeline(location=db.engine, download=False)
p.get("balance_summary")
p.convert(flavor="gdp")
balance = p.dataset
balance.columns = balance.columns.get_level_values(0)
balance_plot = build_chart(
balance,
title="Resultado fiscal del sector público consolidado",
subtitle="% del PBI",
kind="line",
start=start,
end=end,
y=[
"Resultado: Primario SPC ex FSS",
"Resultado: Primario SPC",
"Resultado: Global SPC ex FSS",
"Resultado: Global SPC",
],
)
balance_sectors_plot = build_chart(
balance,
title="Resultado global por sector",
subtitle="% del PBI",
kind="bar",
start=start,
end=end,
y=[
"Resultado: Global GC-BPS ex FSS",
"Resultado: Global EEPP",
"Resultado: Global intendencias",
"Resultado: Global BSE",
"Resultado: Global BCU",
],
)
return balance_plot, balance_sectors_plot
def store_transformed_data(
real_start,
real_end,
resample_freq,
resample_operation,
rolling_periods,
rolling_operation,
chg_diff_operation,
chg_diff_period,
rebase_start,
rebase_end,
rebase_base,
decompose_method,
decompose_component,
order,
query_data,
query_metadata,
):
if not order:
return query_data, query_metadata
if not query_data:
return {}, {}
if (
("resample" in order and (not resample_freq or not resample_operation))
or ("rolling" in order and (not rolling_periods or not rolling_operation))
or ("chg-diff" in order and (not chg_diff_operation or not chg_diff_period))
or ("rebase" in order and (not rebase_start or not rebase_base))
or (
"decompose" in order
and (not decompose_method or not decompose_component)
)
):
raise PreventUpdate
data = pd.DataFrame.from_records(query_data, coerce_float=True, index="index")
data.index = pd.to_datetime(data.index)
metadata = pd.DataFrame.from_records(query_metadata)
data.columns = pd.MultiIndex.from_frame(metadata)
p = Pipeline(location=db.engine, download=False)
transformations = {
"usd": lambda x: convert_usd(x, pipeline=p, errors="ignore"),
"real": lambda x: convert_real(
x, start_date=real_start, end_date=real_end, pipeline=p, errors="ignore"
),
"gdp": lambda x: convert_gdp(x, pipeline=p, errors="ignore"),
"resample": lambda x: resample(
x, rule=resample_freq, operation=resample_operation
),
"rolling": lambda x: rolling(
x, window=rolling_periods, operation=rolling_operation
),
"chg-diff": lambda x: chg_diff(
x, operation=chg_diff_operation, period=chg_diff_period
),
"rebase": lambda x: rebase(
x, start_date=rebase_start, end_date=rebase_end, base=rebase_base
),
"decompose": lambda x: decompose(
x,
component=decompose_component,
method=decompose_method,
force_x13=True,
errors="ignore",
),
}
transformed_data = data.copy()
for t in order:
transformed_data = transformations[t](transformed_data)
transformed_metadata = transformed_data.columns.to_frame()
transformed_data.columns = transformed_data.columns.get_level_values(0)
transformed_data.reset_index(inplace=True)
return transformed_data.to_dict("records"), transformed_metadata.to_dict(
"records"
)
