def test_reserves():
remove_clutter()
session = Session(location=TEST_DIR)
reserves = session.get(dataset="reserves").dataset
assert len(reserves.columns) == 6
assert isinstance(reserves.index[0], pd._libs.tslibs.timestamps.Timestamp)
remove_clutter()
def test_nxr_monthly():
remove_clutter()
session = Session(location=TEST_DIR)
nxr = session.get(dataset="nxr_monthly").dataset
assert len(nxr.columns) == 2
assert isinstance(nxr.index[0], pd._libs.tslibs.timestamps.Timestamp)
remove_clutter()
def test_rxr_official():
remove_clutter()
session = Session(location=TEST_DIR)
tcr = session.get(dataset="rxr_official").dataset
assert isinstance(tcr, pd.DataFrame)
assert tcr.index[0] == dt.date(2000, 1, 31)
session.only_get = True
compare = session.get(dataset="rxr_official").dataset
assert tcr.round(4).equals(compare.round(4))
remove_clutter()
def test_chg():
data_m = dummy_df(freq="M")
session = Session(location=TEST_CON, dataset=data_m, inplace=True)
trf_last = session.chg_diff(operation="chg", period_op="last").dataset
trf_last.columns = data_m.columns
assert trf_last.equals(data_m.pct_change(periods=1).multiply(100))
data_m = dummy_df(freq="M")
session = Session(location=TEST_CON, dataset=data_m, inplace=True)
trf_last = session.chg_diff(operation="chg", period_op="last").dataset
trf_last.columns = data_m.columns
assert trf_last.equals(data_m.pct_change(periods=1).multiply(100))
data_q1 = dummy_df(freq="Q-DEC")
data_q2 = dummy_df(freq="Q-DEC")
data_dict = {"data_q1": data_q1, "data_q2": data_q2}
session = Session(location=TEST_CON, dataset=data_dict)
trf_inter = session.chg_diff(operation="chg", period_op="inter").dataset
trf_inter["data_q1"].columns = trf_inter[
"data_q2"].columns = data_q1.columns
assert trf_inter["data_q1"].equals(data_q1.pct_change(periods=4).
multiply(100))
assert trf_inter["data_q2"].equals(data_q2.pct_change(periods=4).
multiply(100))
data_a = dummy_df(freq="A", ts_type="Flow")
trf_annual = transform.chg_diff(data_a, operation="chg", period_op="last")
trf_annual.columns = data_a.columns
assert trf_annual.equals(data_a.pct_change(periods=1).multiply(100))
data_q_annual = dummy_df(freq="Q-DEC", ts_type="Flujo")
trf_q_annual = transform.chg_diff(data_q_annual, operation="chg",
period_op="annual")
trf_q_annual.columns = data_q_annual.columns
assert trf_q_annual.equals(data_q_annual.
rolling(window=4, min_periods=4).
sum().
pct_change(periods=4).multiply(100))
def test_comm_index():
remove_clutter()
session = Session(location=TEST_DIR)
comm = session.get_custom(dataset="commodity_index").dataset
assert isinstance(comm, pd.DataFrame)
assert comm.index[0] == dt.date(2002, 1, 31)
assert comm.iloc[0][0] == 100
session.only_get = True
compare = session.get_custom(dataset="commodity_index",
only_get_prices=True).dataset
assert compare.round(4).equals(comm.round(4))
remove_clutter()
def test_rxr_custom():
remove_clutter()
session = Session(location=TEST_DIR)
tcr = session.get_custom(dataset="rxr_custom").dataset
assert isinstance(tcr, pd.DataFrame)
assert tcr.index[0] == dt.date(1979, 12, 31)
assert len(tcr.columns) == 4
avs = tcr.loc[(tcr.index >= "2010-01-01")
& (tcr.index <= "2010-12-31")].mean().values.round(1)
arr = np.array([100] * 4, dtype="float64")
assert np.all(avs == arr)
session.only_get = True
compare = session.get_custom(dataset="rxr_custom").dataset
assert tcr.round(4).equals(compare.round(4))
remove_clutter()
def test_reserves_changes():
remove_clutter()
session = Session(location=TEST_CON)
previous_data = pd.read_csv(path.join(TEST_DIR, "reserves_changes.csv"),
index_col=0,
header=list(range(9)),
float_precision="high")
metadata._set(previous_data)
res = session.get(dataset="reserves_changes").dataset
previous_data.index = pd.to_datetime(previous_data.index)
compare = res.loc[previous_data.index].round(3)
compare.columns = previous_data.columns
assert compare.equals(previous_data.round(3))
session.only_get = True
compare = session.get(dataset="reserves_changes").dataset
assert res.round(3).equals(compare.round(3))
remove_clutter()
def test_diff():
data_m = dummy_df(freq="M")
session = Session(location=TEST_CON, dataset=data_m)
trf_last = session.chg_diff(operation="diff", period_op="last").dataset
trf_last.columns = data_m.columns
assert trf_last.equals(data_m.diff(periods=1))
data_q1 = dummy_df(freq="Q-DEC")
data_q2 = dummy_df(freq="Q-DEC")
data_dict = {"data_q1": data_q1, "data_q2": data_q2}
session = Session(location=TEST_CON, dataset=data_dict, inplace=True)
trf_inter = session.chg_diff(operation="diff", period_op="inter").dataset
trf_inter["data_q1"].columns = trf_inter[
"data_q2"].columns = data_q1.columns
assert trf_inter["data_q1"].equals(data_q1.diff(periods=4))
assert trf_inter["data_q2"].equals(data_q2.diff(periods=4))
data_a = dummy_df(freq="A", ts_type="Flow")
trf_annual = transform.chg_diff(data_a, operation="diff", period_op="last")
trf_annual.columns = data_a.columns
assert trf_annual.equals(data_a.diff(periods=1))
data_q_annual = dummy_df(freq="Q-DEC", ts_type="Flujo")
trf_q_annual = transform.chg_diff(data_q_annual, operation="diff",
period_op="annual")
trf_q_annual.columns = data_q_annual.columns
assert trf_q_annual.equals(data_q_annual.
rolling(window=4, min_periods=4).
sum().
diff(periods=4))
data_q_annual = dummy_df(freq="Q-DEC", ts_type="Stock")
trf_q_annual = transform.chg_diff(data_q_annual, operation="diff",
period_op="annual")
trf_q_annual.columns = data_q_annual.columns
assert trf_q_annual.equals(data_q_annual.diff(periods=4))
with pytest.raises(ValueError):
data_wrong = data_m.iloc[np.random.randint(0, 200, 100)]
transform.chg_diff(data_wrong)
def build_expimp(start, end):
s = Session(location=db.engine, download=False)
s.get(["trade_x_prod_val", "trade_m_sect_val"])
s.convert(flavor="gdp")
s.concat(concat_name="expimp")
data = s.datasets["concat_expimp"]
data.columns = data.columns.get_level_values(0)
expimp_plot = build_chart(
data,
title="Exportaciones e importaciones",
subtitle="% del PBI",
kind="line",
start=start,
end=end,
y=["Total exportaciones", "Total importaciones: CIF "],
)
return expimp_plot
def test_nxr_daily():
remove_clutter()
previous_data = pd.read_csv(path.join(TEST_DIR, "nxr_daily.csv"),
index_col=0,
header=list(range(9)))
metadata._set(previous_data)
previous_data.index = pd.to_datetime(previous_data.index)
session = Session(location=TEST_DIR)
nxr = session.get(dataset="nxr_daily").dataset
compare = nxr.loc[previous_data.index].round(4)
compare.columns = previous_data.columns
assert compare.equals(previous_data.round(4))
session.only_get = True
compare = session.get(dataset="nxr_daily").dataset
assert compare.round(4).equals(nxr.round(4))
session.only_get = False
remove_clutter()
def test_rolling():
data_m = dummy_df(freq="M", ts_type="Flujo")
session = Session(location=TEST_CON, dataset=data_m, inplace=True)
trf_none = session.rolling(operation="sum").dataset
trf_none.columns = data_m.columns
assert trf_none.equals(data_m.rolling(window=12, min_periods=12).sum())
data_q1 = dummy_df(freq="M", ts_type="Flujo")
data_q2 = dummy_df(freq="M", ts_type="Flujo")
data_dict = {"data_q1": data_q1, "data_q2": data_q2}
session = Session(location=TEST_CON, dataset=data_dict)
trf_inter = session.rolling(operation="sum").dataset
trf_inter["data_q1"].columns = trf_inter[
"data_q2"].columns = data_q1.columns
assert trf_inter["data_q1"].equals(data_q1.rolling(window=12,
min_periods=12).sum())
assert trf_inter["data_q2"].equals(data_q2.rolling(window=12,
min_periods=12).sum())
with pytest.warns(UserWarning):
data_wrong = dummy_df(freq="M", ts_type="Stock")
transform.rolling(data_wrong, periods=4, operation="average")
def test_resample():
data_m = dummy_df(freq="M", periods=204, ts_type="Flujo", cumperiods=2)
session = Session(location=TEST_CON, dataset=data_m)
trf_none = session.resample(target="Q-DEC", operation="sum").dataset
trf_none.columns = data_m.columns
assert trf_none.equals(data_m.resample("Q-DEC").sum())
data_q1 = dummy_df(freq="Q", ts_type="Flujo")
data_q2 = dummy_df(freq="Q", ts_type="Flujo")
data_dict = {"data_q1": data_q1, "data_q2": data_q2}
session = Session(location=TEST_CON, dataset=data_dict, inplace=True)
trf_inter = session.resample(target="A-DEC", operation="average").dataset
trf_inter["data_q1"].columns = trf_inter[
"data_q2"].columns = data_q1.columns
assert trf_inter["data_q1"].equals(data_q1.resample("A-DEC").mean())
assert trf_inter["data_q2"].equals(data_q2.resample("A-DEC").mean())
data_m = dummy_df(freq="Q-DEC")
trf_none = transform.resample(data_m, target="M", operation="upsample")
trf_none.columns = data_m.columns
assert trf_none.equals(data_m.resample("M").interpolate("linear"))
data_m = dummy_df(freq="Q-DEC")
trf_none = transform.resample(data_m, target="A-DEC", operation="end")
trf_none.columns = data_m.columns
assert trf_none.equals(data_m.asfreq(freq="A-DEC"))
data_m = dummy_df(freq="Q-DEC")
data_m.columns.set_levels(["-"], level=2, inplace=True)
trf_none = transform.resample(data_m, target="M", operation="upsample")
trf_none.columns = data_m.columns
assert trf_none.equals(data_m.resample("M").interpolate("linear"))
data_m = dummy_df(freq="Q-DEC")
data_m.columns.set_levels(["-"], level=2, inplace=True)
trf_none = transform.resample(data_m, target="A-DEC", operation="end")
trf_none.columns = data_m.columns
assert trf_none.equals(data_m.asfreq(freq="A-DEC"))
with pytest.raises(ValueError):
data_m = dummy_df(freq="M", periods=204, ts_type="Flujo")
transform.resample(data_m, target="Q-DEC", operation="wrong")
import sys
from econuy.session import Session
from econuy.utils import sqlutil
from econuy_web import db, create_app
if __name__ == "__main__":
app = create_app()
app.app_context().push()
s = Session(location=db.engine)
if len(sys.argv) == 1:
s.get_bulk("all")
s.get("_lin_gdp")
s.decompose(
component="trend", method="x13", force_x13=True, select="labor_rates_people"
)
sqlutil.df_to_sql(
s.datasets["labor_rates_people"],
name="labor_rates_people_seas",
con=db.engine,
)
elif sys.argv[1] == "labor_seas":
s.get("labor_rates_people")
s.decompose(
component="trend", method="x13", force_x13=True, select="labor_rates_people"
)
sqlutil.df_to_sql(
s.datasets["labor_rates_people"],
name="labor_rates_people_seas",
con=db.engine,
def test_convert():
data = dummy_df(freq="M", ts_type="Stock")
session = Session(location=TEST_CON, dataset=data)
usd = session.convert(flavor="usd").dataset
usd.columns = data.columns
assert np.all(abs(usd) <= abs(data))
data = dummy_df(freq="M", ts_type="Flujo")
session = Session(location=TEST_CON, dataset={
"data1": data, "data2": data})
usd = session.convert(flavor="usd").dataset["data1"]
usd.columns = data.columns
assert np.all(abs(usd) <= abs(data))
data = dummy_df(freq="M")
session = Session(location=TEST_CON, dataset=data)
real = session.convert(flavor="real", start_date="2000-01-31").dataset
real.columns = data.columns
assert np.all(abs(real.iloc[1:]) <= abs(data.iloc[1:]))
data = dummy_df(freq="M")
session = Session(location=TEST_CON, dataset={
"data1": data, "data2": data})
real = session.convert(flavor="real").dataset["data1"]
real.columns = data.columns
assert np.all(abs(real) <= abs(data))
data = dummy_df(freq="A-DEC", periods=10, currency="USD")
session = Session(location=TEST_CON, dataset=data, inplace=True)
pcgdp = session.convert(flavor="pcgdp").dataset
pcgdp.columns = data.columns
assert np.all(abs(pcgdp) <= abs(data))
data = dummy_df(freq="Q-DEC", periods=40)
session = Session(location=TEST_CON, dataset={
"data1": data, "data2": data})
pcgdp = session.convert(flavor="pcgdp").dataset["data1"]
pcgdp.columns = data.columns
assert np.all(abs(pcgdp) <= abs(data))
with pytest.raises(ValueError):
data = dummy_df(freq="Q-DEC", periods=40, currency="USD")
session = Session(location=TEST_CON, dataset=data)
session.convert(flavor="wrong")
with pytest.raises(ValueError):
data = dummy_df(freq="Q-DEC", periods=40, currency="USD")
session = Session(location=TEST_CON,
dataset={"data1": data, "data2": data})
session.convert(flavor="wrong")
with pytest.raises(ValueError):
data = dummy_df(freq="H", periods=200)
session = Session(location=TEST_CON,
dataset=data)
session.convert(flavor="gdp")
data_d = dummy_df(freq="D", periods=600, ts_type="Flujo")
session = Session(location=TEST_CON, dataset=data_d)
real = session.convert(flavor="real", start_date="2000-01-31")
pcgdp = session.convert(flavor="pcgdp")
usd = session.convert(flavor="usd")
data_d = dummy_df(freq="D", periods=600, ts_type="Stock", currency="USD")
session = Session(location=TEST_CON, dataset=data_d)
real = session.convert(flavor="real", start_date="2000-01-31",
end_date="2000-12-31")
pcgdp = session.convert(flavor="pcgdp")
usd = session.convert(flavor="usd")
real_2 = session.convert(flavor="real", start_date="2000-01-31",
end_date="2000-12-31")
assert real.dataset.equals(real_2.dataset)
pcgdp_2 = session.convert(flavor="pcgdp")
assert pcgdp.dataset.equals(pcgdp_2.dataset)
usd_2 = session.convert(flavor="usd")
assert usd.dataset.equals(usd_2.dataset)
data_m = dummy_df(freq="M", ts_type="Flujo")
session = Session(location=TEST_CON, dataset=data_m)
pcgdp = session.convert(flavor="pcgdp")
data_q = dummy_df(freq="Q-DEC", ts_type="Flujo")
session = Session(location=TEST_CON, dataset=data_q)
pcgdp = session.convert(flavor="pcgdp")
def test_base_index():
data = dummy_df(freq="M")
session = Session(location=TEST_CON, dataset=data)
base = session.base_index(start_date="2000-01-31").final()
assert np.all(base.loc["2000-01-31"].values == np.array([100] * 3))
chg = data.pct_change(periods=1).multiply(100)
session = Session(location=TEST_CON, dataset=data, inplace=True)
comp = session.chg_diff(operation="chg", period_op="last").dataset
chg.columns = comp.columns
assert chg.equals(comp)
data = dummy_df(freq="Q-DEC")
session = Session(location=TEST_CON, dataset={
"data1": data, "data2": data})
base = session.base_index(start_date="2000-03-31").final()
assert np.all(
base["data1"].loc["2000-03-31"].values == np.array([100] * 3))
chg = data.pct_change(periods=1).multiply(100)
session = Session(location=TEST_CON, dataset={
"data1": data, "data2": data})
comp = session.chg_diff(operation="chg", period_op="last").dataset["data1"]
chg.columns = comp.columns
assert chg.equals(comp)
data = dummy_df(freq="M")
session = Session(location=TEST_CON, dataset=data)
base = session.base_index(start_date="2000-01-31",
end_date="2000-12-31").dataset
assert np.all(base["2000-01-31":"2000-12-31"].mean().round(4).values ==
np.array([100] * 3, dtype="float64"))
def test_decompose():
df = pd.DataFrame(index=pd.date_range("2000-01-01", periods=100,
freq="Q-DEC"),
data=np.random.exponential(2, 100).cumsum(),
columns=["Exponential"])
df["Real"] = df["Exponential"]
df.loc[df.index.month == 12,
"Real"] = (df.loc[df.index.month == 12, "Real"].
multiply(np.random.uniform(1.06, 1.14)))
df.loc[df.index.month == 6,
"Real"] = (df.loc[df.index.month == 6, "Real"].
multiply(np.random.uniform(0.94, 0.96)))
df.loc[df.index.month == 3,
"Real"] = (df.loc[df.index.month == 3, "Real"].
multiply(np.random.uniform(1.04, 1.06)))
noise = np.random.normal(0, 1, 100)
df["Real"] = df["Real"] + noise
session = Session(location=TEST_CON, dataset=df[["Real"]])
trend, seas = session.decompose(flavor="both", trading=True,
outlier=True, fallback="loess",
ignore_warnings=False).dataset
trend.columns, seas.columns = ["Trend"], ["Seas"]
out = pd.concat([df, trend, seas], axis=1)
std = out.std()
assert std["Real"] >= std["Seas"]
assert std["Real"] >= std["Trend"]
session = Session(location=TEST_CON, dataset=df[["Real"]], inplace=True)
trend, seas = session.decompose(flavor="both", trading=False,
outlier=True, fallback="loess").dataset
trend.columns, seas.columns = ["Trend"], ["Seas"]
out = pd.concat([df, trend, seas], axis=1)
std = out.std()
assert std["Real"] >= std["Seas"]
assert std["Real"] >= std["Trend"]
session = Session(location=TEST_CON, dataset=df[["Real"]])
trend, seas = session.decompose(flavor="both", trading=False,
outlier=False, fallback="ma",
ignore_warnings=False).dataset
trend.columns, seas.columns = ["Trend"], ["Seas"]
out = pd.concat([df, trend, seas], axis=1)
std = out.std()
assert std["Real"] >= std["Seas"]
assert std["Real"] >= std["Trend"]
session = Session(location=TEST_CON, dataset=df[["Real"]])
trend, seas = session.decompose(flavor="both", trading=True,
outlier=False, fallback="ma").dataset
trend.columns, seas.columns = ["Trend"], ["Seas"]
out = pd.concat([df, trend, seas], axis=1)
std = out.std()
assert std["Real"] >= std["Seas"]
assert std["Real"] >= std["Trend"]
session = Session(location=TEST_CON, dataset=df[["Real"]])
trend = session.decompose(flavor="trend", trading=True,
outlier=False, force_x13=True).dataset
seas = session.decompose(flavor="seas", trading=True,
outlier=False, force_x13=True,
ignore_warnings=False).dataset
trend.columns, seas.columns = ["Trend"], ["Seas"]
out = pd.concat([df, trend, seas], axis=1)
std = out.std()
assert std["Real"] >= std["Seas"]
assert std["Real"] >= std["Trend"]
session = Session(location=TEST_CON, dataset={"data1": df[["Real"]],
"data2": df[["Real"]]})
trend, seas = session.decompose(flavor="both", trading=True,
outlier=False).dataset["data1"]
trend.columns, seas.columns = ["Trend"], ["Seas"]
out = pd.concat([df, trend, seas], axis=1)
std = out.std()
assert std["Real"] >= std["Seas"]
assert std["Real"] >= std["Trend"]
with pytest.raises(ValueError):
session = Session(location=TEST_CON, dataset=df[["Real"]])
session.decompose(flavor="both", trading=True,
outlier=False, x13_binary="wrong")
session = Session(location=TEST_CON, dataset=df[["Real"]])
trend = session.decompose(flavor="trend", method="loess").dataset
seas = session.decompose(flavor="seas", method="ma").dataset
trend.columns, seas.columns = ["Trend"], ["Seas"]
out = pd.concat([df, trend, seas], axis=1)
std = out.std()
assert std["Real"] >= std["Seas"]
assert std["Real"] >= std["Trend"]
with pytest.raises(ValueError):
trend = session.decompose(flavor="trend", method="wrong").dataset
with pytest.raises(ValueError):
trend = session.decompose(flavor="trend", method="x13",
fallback="wrong").dataset