/
test_queries.py
259 lines (232 loc) · 8.57 KB
/
test_queries.py
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from datetime import datetime, timedelta
import numpy as np
import pandas as pd
import pytest
import pytz
import timely_beliefs as tb
from flexmeasures.data.models.assets import Asset, Power
from flexmeasures.data.models.data_sources import DataSource
from flexmeasures.data.models.time_series import Sensor, TimedBelief
from flexmeasures.data.queries.utils import (
multiply_dataframe_with_deterministic_beliefs,
simplify_index,
)
@pytest.mark.parametrize(
"query_start, query_end, num_values",
[
(
datetime(2015, 1, 1, tzinfo=pytz.utc),
datetime(2015, 1, 2, tzinfo=pytz.utc),
96,
),
(datetime(2015, 1, 1, tzinfo=pytz.utc), None, 96),
(None, datetime(2015, 1, 2, tzinfo=pytz.utc), 96),
(None, None, 96),
(
datetime(2015, 1, 1, tzinfo=pytz.utc),
datetime(2015, 1, 1, 12, tzinfo=pytz.utc),
48,
),
(None, datetime(2015, 1, 1, 12, tzinfo=pytz.utc), 48),
# (
# datetime(1957, 1, 1, tzinfo=pytz.utc),
# datetime(1957, 1, 2, tzinfo=pytz.utc),
# 0,
# ), # test empty BeliefsDataFrame # todo: uncomment when this if fixed: https://github.com/pandas-dev/pandas/issues/30517
],
)
def test_collect_power(db, app, query_start, query_end, num_values):
wind_device_1 = Asset.query.filter_by(name="wind-asset-1").one_or_none()
data = Power.query.filter(Power.asset_id == wind_device_1.id).all()
print(data)
bdf: tb.BeliefsDataFrame = Power.collect(
wind_device_1.name, (query_start, query_end)
)
print(bdf)
assert (
bdf.index.names[0] == "event_start"
) # first index level of collect function should be event_start, so that df.loc[] refers to event_start
assert pd.api.types.is_timedelta64_dtype(
bdf.index.get_level_values("belief_horizon")
) # dtype of belief_horizon is timedelta64[ns], so the minimum horizon on an empty BeliefsDataFrame is NaT instead of NaN
assert len(bdf) == num_values
for v1, v2 in zip(bdf.values, data):
assert abs(v1[0] - v2.value) < 10 ** -6
@pytest.mark.parametrize(
"query_start, query_end, resolution, num_values",
[
(
datetime(2015, 1, 1, tzinfo=pytz.utc),
datetime(2015, 1, 2, tzinfo=pytz.utc),
timedelta(minutes=15),
96,
),
(
datetime(2015, 1, 1, tzinfo=pytz.utc),
datetime(2015, 1, 2, tzinfo=pytz.utc),
timedelta(minutes=30),
48,
),
(
datetime(2015, 1, 1, tzinfo=pytz.utc),
datetime(2015, 1, 2, tzinfo=pytz.utc),
"30min",
48,
),
(
datetime(2015, 1, 1, tzinfo=pytz.utc),
datetime(2015, 1, 2, tzinfo=pytz.utc),
"PT45M",
32,
),
],
)
def test_collect_power_resampled(
db, app, query_start, query_end, resolution, num_values
):
wind_device_1 = Asset.query.filter_by(name="wind-asset-1").one_or_none()
bdf: tb.BeliefsDataFrame = Power.collect(
wind_device_1.name, (query_start, query_end), resolution=resolution
)
print(bdf)
assert len(bdf) == num_values
def test_multiplication():
df1 = pd.DataFrame(
[[30.0, timedelta(hours=3)]],
index=pd.date_range(
"2000-01-01 10:00", "2000-01-01 15:00", freq="1h", closed="left"
),
columns=["event_value", "belief_horizon"],
)
df2 = pd.DataFrame(
[[10.0, timedelta(hours=1)]],
index=pd.date_range(
"2000-01-01 13:00", "2000-01-01 18:00", freq="1h", closed="left"
),
columns=["event_value", "belief_horizon"],
)
df = multiply_dataframe_with_deterministic_beliefs(df1, df2)
df_compare = pd.concat(
[
pd.DataFrame(
[[np.nan, timedelta(hours=3)]],
index=pd.date_range(
"2000-01-01 10:00", "2000-01-01 13:00", freq="1h", closed="left"
),
columns=["event_value", "belief_horizon"],
),
pd.DataFrame(
[[300.0, timedelta(hours=1)]],
index=pd.date_range(
"2000-01-01 13:00", "2000-01-01 15:00", freq="1h", closed="left"
),
columns=["event_value", "belief_horizon"],
),
pd.DataFrame(
[[np.nan, timedelta(hours=1)]],
index=pd.date_range(
"2000-01-01 15:00", "2000-01-01 18:00", freq="1h", closed="left"
),
columns=["event_value", "belief_horizon"],
),
],
axis=0,
)
pd.testing.assert_frame_equal(df, df_compare)
def test_multiplication_with_one_empty_dataframe():
df1 = pd.DataFrame(
[],
columns=["event_value", "belief_horizon"],
)
# set correct types
df1["event_value"] = pd.to_numeric(df1["event_value"])
df1["belief_horizon"] = pd.to_timedelta(df1["belief_horizon"])
df2 = pd.DataFrame(
[[10.0, timedelta(hours=1)]],
index=pd.date_range(
"2000-01-01 13:00", "2000-01-01 18:00", freq="1h", closed="left"
),
columns=["event_value", "belief_horizon"],
)
df_compare = pd.DataFrame(
[[np.nan, timedelta(hours=1)]],
index=pd.date_range(
"2000-01-01 13:00", "2000-01-01 18:00", freq="1h", closed="left"
),
columns=["event_value", "belief_horizon"],
)
# set correct types
df_compare["event_value"] = pd.to_numeric(df_compare["event_value"])
df_compare["belief_horizon"] = pd.to_timedelta(df_compare["belief_horizon"])
df = multiply_dataframe_with_deterministic_beliefs(df1, df2)
pd.testing.assert_frame_equal(df, df_compare)
def test_multiplication_with_both_empty_dataframe():
df1 = pd.DataFrame(
[],
columns=["event_value", "belief_horizon"],
)
# set correct types
df1["event_value"] = pd.to_numeric(df1["event_value"])
df1["belief_horizon"] = pd.to_timedelta(df1["belief_horizon"])
df2 = pd.DataFrame(
[],
columns=["event_value", "belief_horizon"],
)
# set correct types
df2["event_value"] = pd.to_numeric(df2["event_value"])
df2["belief_horizon"] = pd.to_timedelta(df2["belief_horizon"])
df_compare = pd.DataFrame(
[],
columns=["event_value", "belief_horizon"],
)
# set correct types
df_compare["event_value"] = pd.to_numeric(df_compare["event_value"])
df_compare["belief_horizon"] = pd.to_timedelta(df_compare["belief_horizon"])
df = multiply_dataframe_with_deterministic_beliefs(df1, df2)
pd.testing.assert_frame_equal(df, df_compare)
def test_simplify_index():
"""Check whether simplify_index retains the event resolution."""
wind_device_1 = Asset.query.filter_by(name="wind-asset-1").one_or_none()
bdf: tb.BeliefsDataFrame = Power.collect(
wind_device_1.name,
(
datetime(2015, 1, 1, tzinfo=pytz.utc),
datetime(2015, 1, 2, tzinfo=pytz.utc),
),
resolution=timedelta(minutes=15),
)
df = simplify_index(bdf)
assert df.event_resolution == timedelta(minutes=15)
def test_query_beliefs(setup_beliefs):
"""Check various ways of querying for beliefs."""
sensor = Sensor.query.filter_by(name="epex_da").one_or_none()
source = DataSource.query.filter_by(name="Seita").one_or_none()
bdfs = [
TimedBelief.search(sensor, source=source),
sensor.search_beliefs(source=source),
tb.BeliefsDataFrame(sensor.beliefs), # doesn't allow filtering
]
for bdf in bdfs:
assert sensor.event_resolution == timedelta(
hours=0
) # todo change to 1 after migrating Markets to Sensors
assert bdf.event_resolution == timedelta(
hours=0
) # todo change to 1 after migrating Markets to Sensors
assert len(bdf) == setup_beliefs
def test_persist_beliefs(setup_beliefs):
"""Check whether persisting beliefs works.
We load the already set up beliefs, and form new beliefs an hour later.
"""
sensor = Sensor.query.filter_by(name="epex_da").one_or_none()
bdf: tb.BeliefsDataFrame = TimedBelief.search(sensor)
# Form new beliefs
df = bdf.reset_index()
df["belief_time"] = df["belief_time"] + timedelta(hours=1)
df["event_value"] = df["event_value"] * 10
bdf = df.set_index(
["event_start", "belief_time", "source", "cumulative_probability"]
)
TimedBelief.add(bdf)
bdf: tb.BeliefsDataFrame = TimedBelief.search(sensor)
assert len(bdf) == setup_beliefs * 2