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test_pandas_reporter.py
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test_pandas_reporter.py
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import pytest
from datetime import datetime, timedelta
from pytz import utc
import pandas as pd
from flexmeasures.data.models.reporting.pandas_reporter import PandasReporter
from flexmeasures.data.models.generic_assets import GenericAsset, GenericAssetType
from flexmeasures.data.models.data_sources import DataSource
from flexmeasures.data.models.time_series import Sensor, TimedBelief
@pytest.fixture(scope="module")
def setup_dummy_data(db, app):
"""
Create Sensors 2, 1 Asset and 1 AssetType
"""
dummy_asset_type = GenericAssetType(name="DummyGenericAssetType")
report_asset_type = GenericAssetType(name="ReportAssetType")
db.session.add_all([dummy_asset_type, report_asset_type])
dummy_asset = GenericAsset(
name="DummyGenericAsset", generic_asset_type=dummy_asset_type
)
pandas_report = GenericAsset(
name="PandasReport", generic_asset_type=report_asset_type
)
db.session.add_all([dummy_asset, pandas_report])
sensor1 = Sensor("sensor 1", generic_asset=dummy_asset, event_resolution="1h")
db.session.add(sensor1)
sensor2 = Sensor("sensor 2", generic_asset=dummy_asset, event_resolution="1h")
db.session.add(sensor2)
report_sensor = Sensor(
"report sensor", generic_asset=pandas_report, event_resolution="1h"
)
db.session.add(report_sensor)
"""
Create 2 DataSources
"""
source1 = DataSource("source1")
source2 = DataSource("source2")
"""
Create TimedBeliefs
"""
beliefs = []
for sensor in [sensor1, sensor2]:
for si, source in enumerate([source1, source2]):
for t in range(10):
print(si)
beliefs.append(
TimedBelief(
event_start=datetime(2023, 4, 10, tzinfo=utc)
+ timedelta(hours=t + si),
belief_horizon=timedelta(hours=24),
event_value=t,
sensor=sensor,
source=source,
)
)
db.session.add_all(beliefs)
db.session.commit()
yield sensor1, sensor2, report_sensor
db.session.delete(sensor1)
db.session.delete(sensor2)
for b in beliefs:
db.session.delete(b)
db.session.delete(dummy_asset)
db.session.delete(dummy_asset_type)
db.session.commit()
def test_reporter(setup_dummy_data):
s1, s2, reporter_sensor = setup_dummy_data
reporter_config_raw = dict(
tb_query_config=[dict(sensor=s1.id), dict(sensor=s2.id)],
transformations=[
dict(
df_input="sensor_1",
df_output="sensor_1_source_1",
method="xs",
args=["@source_1"],
kwargs=dict(level=2),
),
dict(
df_input="sensor_2",
df_output="sensor_2_source_1",
method="xs",
args=["@source_1"],
kwargs=dict(level=2),
),
dict(
df_output="df_merge",
df_input="sensor_1_source_1",
method="merge",
args=["@sensor_2_source_1"],
kwargs=dict(on="event_start", suffixes=("_sensor1", "_sensor2")),
),
dict(method="resample", args=["2h"]),
dict(method="mean"),
dict(method="sum", kwargs=dict(axis=1)),
],
final_df_output="df_merge",
)
reporter = PandasReporter(reporter_sensor, reporter_config_raw=reporter_config_raw)
start = datetime(2023, 4, 10, tzinfo=utc)
end = datetime(2023, 4, 10, 10, tzinfo=utc)
report1 = reporter.compute(start, end)
assert len(report1) == 5
assert str(report1.index[0]) == "2023-04-10 00:00:00+00:00"
assert (
report1.sensor == reporter_sensor
) # check that the output sensor is effectively assigned.
# check that calling compute with different parameters changes the result
report3 = reporter.compute(start=datetime(2023, 4, 10, 3, tzinfo=utc), end=end)
assert len(report3) == 4
assert str(report3.index[0]) == "2023-04-10 02:00:00+00:00"
def test_reporter_repeated(setup_dummy_data):
"""check that calling compute doesn't change the result"""
s1, s2, reporter_sensor = setup_dummy_data
reporter_config_raw = dict(
tb_query_config=[
dict(
sensor=s1.id,
event_starts_after="2023-04-10T00:00:00 00:00",
event_ends_before="2023-04-10T10:00:00 00:00",
),
dict(
sensor=s2.id,
event_starts_after="2023-04-10T00:00:00 00:00",
event_ends_before="2023-04-10T10:00:00 00:00",
),
],
transformations=[
dict(
df_input="sensor_1",
df_output="sensor_1_source_1",
method="xs",
args=["@source_1"],
kwargs=dict(level=2),
),
dict(
df_input="sensor_2",
df_output="sensor_2_source_1",
method="xs",
args=["@source_1"],
kwargs=dict(level=2),
),
dict(
df_output="df_merge",
df_input="sensor_1_source_1",
method="merge",
args=["@sensor_2_source_1"],
kwargs=dict(on="event_start", suffixes=("_sensor1", "_sensor2")),
),
dict(method="resample", args=["2h"]),
dict(method="mean"),
dict(method="sum", kwargs=dict(axis=1)),
],
final_df_output="df_merge",
)
reporter = PandasReporter(reporter_sensor, reporter_config_raw=reporter_config_raw)
start = datetime(2023, 4, 10, tzinfo=utc)
end = datetime(2023, 4, 10, 10, tzinfo=utc)
report1 = reporter.compute(start=start, end=end)
report2 = reporter.compute(start=start, end=end)
pd.testing.assert_series_equal(report1, report2)