import pandas as pd
import pytest
import gridstatus
from gridstatus import Markets, NotSupported
[docs]def test_get_fuel_mix_central_time():
iso = gridstatus.SPP()
fm = iso.get_fuel_mix(date="latest")
assert fm.time.tz.zone == iso.default_timezone
@pytest.mark.parametrize(
"market,location_type",
[
(Markets.REAL_TIME_5_MIN, "Hub"),
(Markets.REAL_TIME_5_MIN, "Interface"),
],
)
[docs]def test_get_lmp_latest(market, location_type):
iso = gridstatus.SPP()
df = iso.get_lmp(
date="latest",
market=market,
location_type=location_type,
)
cols = [
"Time",
"Market",
"Location",
"Location Type",
"LMP",
"Energy",
"Congestion",
"Loss",
]
assert df.shape[0] >= 0
assert df.columns.tolist() == cols
markets = df["Market"].unique()
assert len(markets) == 1
assert markets[0] == market.value
location_types = df["Location Type"].unique()
assert len(location_types) == 1
assert location_types[0] == location_type
[docs]def test_get_lmp_latest_settlement_type_returns_three_location_types():
iso = gridstatus.SPP()
df = iso.get_lmp(
date="latest",
market=Markets.REAL_TIME_5_MIN,
location_type="SETTLEMENT_LOCATION",
)
cols = [
"Time",
"Market",
"Location",
"Location Type",
"LMP",
"Energy",
"Congestion",
"Loss",
]
assert df.shape[0] >= 0
assert df.columns.tolist() == cols
markets = df["Market"].unique()
assert len(markets) == 1
assert markets[0] == Markets.REAL_TIME_5_MIN.value
assert set(df["Location Type"]) == {
"Interface",
"Hub",
"Settlement Location",
}
@pytest.mark.slow
@pytest.mark.parametrize(
"market,location_type",
[
(Markets.DAY_AHEAD_HOURLY, "Hub"),
(Markets.REAL_TIME_5_MIN, "Hub"),
],
)
[docs]def test_get_lmp_today(market, location_type):
iso = gridstatus.SPP()
df = iso.get_lmp(
date="today",
market=market,
location_type=location_type,
)
cols = [
"Time",
"Market",
"Location",
"Location Type",
"LMP",
"Energy",
"Congestion",
"Loss",
]
assert df.shape[0] >= 0
assert df.columns.tolist() == cols
markets = df["Market"].unique()
assert len(markets) == 1
assert markets[0] == market.value
location_types = df["Location Type"].unique()
assert len(location_types) == 1
assert location_types[0] == location_type
@pytest.mark.parametrize(
"date,market,location_type",
[
("latest", Markets.REAL_TIME_15_MIN, "Interface"),
(
pd.Timestamp.now() - pd.Timedelta(days=2),
Markets.REAL_TIME_15_MIN,
"Interface",
),
],
)
[docs]def test_get_lmp_unsupported_raises_not_supported(date, market, location_type):
iso = gridstatus.SPP()
with pytest.raises(NotSupported):
iso.get_lmp(
date=date,
market=market,
location_type=location_type,
)
@pytest.mark.parametrize(
"date,market,location_type",
[
("latest", Markets.DAY_AHEAD_HOURLY, "Hub"),
("latest", Markets.DAY_AHEAD_HOURLY, "Interface"),
],
)
[docs]def test_get_lmp_day_ahead_cannot_have_latest(date, market, location_type):
iso = gridstatus.SPP()
with pytest.raises(ValueError):
iso.get_lmp(
date=date,
market=market,
location_type=location_type,
)
[docs]def test_parse_gmt_interval_end():
df = pd.DataFrame(
[
{
"ExpectedTime": pd.Timestamp(
"2022-12-26 18:45:00-0600",
tz="US/Central",
),
"GMTIntervalEnd": 1672102200000,
},
],
)
df["ActualTime"] = gridstatus.SPP._parse_gmt_interval_end(
df,
interval_duration=pd.Timedelta(minutes=5),
timezone="US/Central",
)
assert df["ActualTime"].tolist() == df["ExpectedTime"].tolist()
[docs]def test_parse_gmt_interval_end_daylight_savings_time():
df = pd.DataFrame(
[
{
"ExpectedTime": pd.Timestamp(
"2022-03-15 13:00:00-0500",
tz="US/Central",
),
# 2022-03-15 13:05:00 CDT
"GMTIntervalEnd": 1647367500000,
},
],
)
df["ActualTime"] = gridstatus.SPP._parse_gmt_interval_end(
df,
interval_duration=pd.Timedelta(minutes=5),
timezone="US/Central",
)
assert df["ActualTime"].tolist() == df["ExpectedTime"].tolist()
[docs]def test_parse_day_ahead_hour_end():
df = pd.DataFrame(
[
{
"ExpectedTime": pd.Timestamp(
"2022-12-26 08:00:00-0600",
tz="US/Central",
),
"DA_HOUREND": "12/26/2022 9:00:00 AM",
},
],
)
df["ActualTime"] = gridstatus.SPP._parse_day_ahead_hour_end(
df,
timezone="US/Central",
)
assert df["ActualTime"].tolist() == df["ExpectedTime"].tolist()
[docs]def test_parse_day_ahead_hour_end_daylight_savings_time():
df = pd.DataFrame(
[
{
"ExpectedTime": pd.Timestamp(
"2022-03-15 13:00:00-0500",
tz="US/Central",
),
"DA_HOUREND": "03/15/2022 2:00:00 PM",
},
],
)
df["ActualTime"] = gridstatus.SPP._parse_day_ahead_hour_end(
df,
timezone="US/Central",
)
assert df["ActualTime"].tolist() == df["ExpectedTime"].tolist()
