import pandas as pd
import pytest
import gridstatus
from gridstatus import NYISO, Markets
from gridstatus.tests.base_test_iso import BaseTestISO
from gridstatus.tests.decorators import with_markets
[docs]class TestNYISO(BaseTestISO):
iso = NYISO()
""""get_capacity_prices"""
[docs] def test_get_capacity_prices(self):
# test 2022, 2023, and today
df = self.iso.get_capacity_prices(date="Dec 1, 2022", verbose=True)
assert not df.empty, "DataFrame came back empty"
df = self.iso.get_capacity_prices(date="Jan 1, 2023", verbose=True)
assert not df.empty, "DataFrame came back empty"
# TODO: missing report: https://github.com/kmax12/gridstatus/issues/309
# df = self.iso.get_capacity_prices(date="today", verbose=True)
# assert not df.empty, "DataFrame came back empty"
"""get_fuel_mix"""
[docs] def test_get_fuel_mix_date_range(self):
df = self.iso.get_fuel_mix(start="Aug 1, 2022", end="Oct 22, 2022")
assert df.shape[0] >= 0
[docs] def test_range_two_days_across_month(self):
today = gridstatus.utils._handle_date("today", self.iso.default_timezone)
first_day_of_month = today.replace(day=1, hour=5, minute=0, second=0)
last_day_of_prev_month = first_day_of_month - pd.Timedelta(days=1)
df = self.iso.get_fuel_mix(start=last_day_of_prev_month, end=first_day_of_month)
# Midnight of the end date
assert df["Time"].max() == first_day_of_month.normalize() + pd.Timedelta(days=1)
# First 5 minute interval of the start date
assert df["Time"].min() == last_day_of_prev_month.normalize() + pd.Timedelta(
minutes=5,
)
assert df["Time"].dt.date.nunique() == 3 # 2 days in range + 1 day for midnight
self._check_fuel_mix(df)
[docs] def test_month_start_multiple_months(self):
start_date = pd.Timestamp("2022-01-01T06:00:00Z", tz=self.iso.default_timezone)
end_date = pd.Timestamp("2022-03-01T06:00:00Z", tz=self.iso.default_timezone)
df = self.iso.get_fuel_mix(start=start_date, end=end_date)
# Midnight of the end date
assert df["Time"].max() == end_date.replace(minute=0, hour=0) + pd.Timedelta(
days=1,
)
# First 5 minute interval of the start date
assert df["Time"].min() == start_date.replace(minute=5, hour=0)
assert (df["Time"].dt.month.unique() == [1, 2, 3]).all()
self._check_fuel_mix(df)
"""get_generators"""
[docs] def test_get_generators(self):
df = self.iso.get_generators()
columns = [
"Generator Name",
"PTID",
"Subzone",
"Zone",
"Latitude",
"Longitude",
]
assert set(df.columns).issuperset(set(columns))
assert df.shape[0] >= 0
"""get_load"""
[docs] def test_get_load_contains_zones(self):
df = self.iso.get_load("today")
nyiso_load_cols = [
"Time",
"Load",
"CAPITL",
"CENTRL",
"DUNWOD",
"GENESE",
"HUD VL",
"LONGIL",
"MHK VL",
"MILLWD",
"N.Y.C.",
"NORTH",
"WEST",
]
assert df.columns.tolist() == nyiso_load_cols
[docs] def test_get_load_month_range(self):
df = self.iso.get_load(start="2023-04-01", end="2023-05-16")
assert df.shape[0] >= 0
[docs] def test_get_load_historical(self):
# TODO: why does this not work more than 8 days in the past
super().test_get_load_historical(lookback_days=8)
"""get_lmp"""
@with_markets(
Markets.DAY_AHEAD_HOURLY,
)
[docs] def test_lmp_date_range(self, market):
super().test_lmp_date_range(market=market)
@with_markets(
Markets.DAY_AHEAD_HOURLY,
Markets.REAL_TIME_5_MIN,
# Markets.REAL_TIME_15_MIN, # Not supported
)
[docs] def test_get_lmp_historical(self, market):
super().test_get_lmp_historical(market=market)
@with_markets(
Markets.DAY_AHEAD_HOURLY,
Markets.REAL_TIME_5_MIN,
Markets.REAL_TIME_15_MIN,
)
[docs] def test_get_lmp_today(self, market):
super().test_get_lmp_today(market=market)
@with_markets(
Markets.DAY_AHEAD_HOURLY,
Markets.REAL_TIME_5_MIN,
Markets.REAL_TIME_15_MIN,
)
[docs] def test_get_lmp_latest(self, market):
super().test_get_lmp_latest(market=market)
[docs] def test_get_lmp_real_time_5_and_15_min_today(self):
df_5 = self.iso.get_lmp("today", market=Markets.REAL_TIME_5_MIN)
df_15 = self.iso.get_lmp("today", market=Markets.REAL_TIME_15_MIN)
assert df_5["Interval End"].max() < df_15["Interval End"].min()
assert (
df_5["Interval End"] - df_5["Interval Start"] == pd.Timedelta(minutes=5)
).all()
assert (
df_15["Interval End"] - df_15["Interval Start"] == pd.Timedelta(minutes=15)
).all()
diffs_5 = df_5["Interval End"].diff()
# We can't check the min of diffs_5 is equal to 5 minutes because the intervals
# are not always exact
assert diffs_5[diffs_5 > pd.Timedelta(minutes=0)].min() <= pd.Timedelta(
minutes=5,
)
assert diffs_5.max() == pd.Timedelta(minutes=5)
diffs_15 = df_15["Interval End"].diff()
assert diffs_15[diffs_15 > pd.Timedelta(minutes=0)].min() == pd.Timedelta(
minutes=15,
)
assert diffs_15.max() == pd.Timedelta(minutes=15)
[docs] def test_get_lmp_real_time_5_and_15_min_latest(self):
df_5 = self.iso.get_lmp("latest", market=Markets.REAL_TIME_5_MIN)
df_15 = self.iso.get_lmp("latest", market=Markets.REAL_TIME_15_MIN)
assert df_5["Interval End"].max() < df_15["Interval End"].min()
assert (
df_5["Interval End"] - df_5["Interval Start"] == pd.Timedelta(minutes=5)
).all()
assert (
df_15["Interval End"] - df_15["Interval Start"] == pd.Timedelta(minutes=15)
).all()
diffs_5 = df_5["Interval End"].diff().dropna()
# There is only one interval, so the diff is 0
assert (diffs_5 == pd.Timedelta(minutes=0)).all()
diffs_15 = df_15["Interval End"].diff().dropna()
assert (diffs_15 == pd.Timedelta(minutes=0)).all()
[docs] def test_get_lmp_historical_with_range(self):
start = "2021-12-01"
end = "2022-02-02"
df = self.iso.get_lmp(
start=start,
end=end,
market=Markets.REAL_TIME_5_MIN,
)
assert df.shape[0] >= 0
[docs] def test_get_lmp_location_type_parameter(self):
date = "2022-06-09"
df_zone = self.iso.get_lmp(
date=date,
market=Markets.DAY_AHEAD_HOURLY,
location_type="zone",
)
assert (df_zone["Location Type"] == "Zone").all()
df_gen = self.iso.get_lmp(
date=date,
market=Markets.DAY_AHEAD_HOURLY,
location_type="generator",
)
assert (df_gen["Location Type"] == "Generator").all()
df_zone = self.iso.get_lmp(
date="today",
market=Markets.DAY_AHEAD_HOURLY,
location_type="zone",
)
assert (df_zone["Location Type"] == "Zone").all()
df_gen = self.iso.get_lmp(
date="today",
market=Markets.DAY_AHEAD_HOURLY,
location_type="generator",
)
assert (df_gen["Location Type"] == "Generator").all()
df_zone = self.iso.get_lmp(
date="latest",
market=Markets.DAY_AHEAD_HOURLY,
location_type="zone",
)
assert (df_zone["Location Type"] == "Zone").all()
df_gen = self.iso.get_lmp(
date="latest",
market=Markets.DAY_AHEAD_HOURLY,
location_type="generator",
)
assert (df_gen["Location Type"] == "Generator").all()
with pytest.raises(ValueError):
self.iso.get_lmp(
date="latest",
market=Markets.DAY_AHEAD_HOURLY,
location_type="dummy",
)
"""get_interconnection_queue"""
# This test is in addition to the base_test_iso test
[docs] def test_get_interconnection_queue_handles_new_file(self):
df = self.iso.get_interconnection_queue()
# There are a few missing values, but a small percentage
assert df["Interconnection Location"].isna().sum() < 0.01 * df.shape[0]
"""get_loads"""
[docs] def test_get_loads(self):
df = self.iso.get_loads()
columns = [
"Load Name",
"PTID",
"Subzone",
"Zone",
]
assert set(df.columns) == set(columns)
assert df.shape[0] >= 0
"""get_status"""
[docs] def test_get_status_historical_status(self):
date = "20220609"
status = self.iso.get_status(date)
self._check_status(status)
start = "2022-05-01"
end = "2022-10-02"
status = self.iso.get_status(start=start, end=end)
self._check_status(status)
"""get_storage"""
[docs] def test_get_storage_historical(self):
with pytest.raises(NotImplementedError):
super().test_get_storage_historical()
[docs] def test_get_storage_today(self):
with pytest.raises(NotImplementedError):
super().test_get_storage_today()
[docs] def test_various_edt_to_est(self):
# number of rows hardcoded based on when this test was written. should stay same
date = "Nov 7, 2021"
df = self.iso.get_status(date=date)
assert df.shape[0] >= 1
df = self.iso.get_fuel_mix(date=date)
assert df.shape[0] >= 307
df = self.iso.get_load_forecast(date=date)
assert df.shape[0] >= 145
df = self.iso.get_lmp(date=date, market=Markets.REAL_TIME_5_MIN)
assert df.shape[0] >= 4605
df = self.iso.get_lmp(date=date, market=Markets.DAY_AHEAD_HOURLY)
assert df.shape[0] >= 375
df = self.iso.get_load(date=date)
assert df.shape[0] >= 307
[docs] def test_various_est_to_edt(self):
# number of rows hardcoded based on when this test was written. should stay same
date = "March 14, 2021"
df = self.iso.get_status(date=date)
assert df.shape[0] >= 5
df = self.iso.get_lmp(date=date, market=Markets.REAL_TIME_5_MIN)
assert df.shape[0] >= 4215
df = self.iso.get_lmp(date=date, market=Markets.DAY_AHEAD_HOURLY)
assert df.shape[0] >= 345
df = self.iso.get_load_forecast(date=date)
assert df.shape[0] >= 143
df = self.iso.get_fuel_mix(date=date)
assert df.shape[0] >= 281
df = self.iso.get_load(date=date)
assert df.shape[0] >= 281
# test btm solar
[docs] def test_get_btm_solar(self):
# published ~8 hours after finish of previous day
two_days_ago = pd.Timestamp.now(tz="US/Eastern").date() - pd.Timedelta(days=2)
df = self.iso.get_btm_solar(
date=two_days_ago,
verbose=True,
)
columns = [
"Time",
"Interval Start",
"Interval End",
"SYSTEM",
"CAPITL",
"CENTRL",
"DUNWOD",
"GENESE",
"HUD VL",
"LONGIL",
"MHK VL",
"MILLWD",
"N.Y.C.",
"NORTH",
"WEST",
]
assert df.columns.tolist() == columns
assert df.shape[0] >= 0
# test range last month
start = "2023-04-30"
end = "2023-05-02"
df = self.iso.get_btm_solar(
start=start,
end=end,
verbose=True,
)
assert df["Time"].dt.date.nunique() == 3
[docs] def test_get_btm_solar_forecast(self):
df = self.iso.get_btm_solar_forecast(
date="today",
verbose=True,
)
columns = [
"Time",
"Interval Start",
"Interval End",
"SYSTEM",
"CAPITL",
"CENTRL",
"DUNWOD",
"GENESE",
"HUD VL",
"LONGIL",
"MHK VL",
"MILLWD",
"N.Y.C.",
"NORTH",
"WEST",
]
assert df.columns.tolist() == columns
assert df.shape[0] >= 0
# test range last month
start = "2023-04-30"
end = "2023-05-02"
df = self.iso.get_btm_solar_forecast(
start=start,
end=end,
verbose=True,
)
assert df["Time"].dt.date.nunique() == 3
@staticmethod
def _check_status(df):
assert set(df.columns) == set(
["Time", "Status", "Notes"],
)
