mirror of
https://github.com/Akkudoktor-EOS/EOS.git
synced 2025-04-17 07:55:15 +00:00
830af85fca
measurement:
- Add new measurement class to hold real world measurements.
- Handles load meter readings, grid import and export meter readings.
- Aggregates load meter readings aka. measurements to total load.
- Can import measurements from files, pandas datetime series,
pandas datetime dataframes, simple daetime arrays and
programmatically.
- Maybe expanded to other measurement values.
- Should be used for load prediction adaptions by real world
measurements.
core/coreabc:
- Add mixin class to access measurements
core/pydantic:
- Add pydantic models for pandas datetime series and dataframes.
- Add pydantic models for simple datetime array
core/dataabc:
- Provide DataImport mixin class for generic import handling.
Imports from JSON string and files. Imports from pandas datetime dataframes
and simple datetime arrays. Signature of import method changed to
allow import datetimes to be given programmatically and by data content.
- Use pydantic models for datetime series, dataframes, arrays
- Validate generic imports by pydantic models
- Provide new attributes min_datetime and max_datetime for DataSequence.
- Add parameter dropna to drop NAN/ None values when creating lists, pandas series
or numpy array from DataSequence.
config/config:
- Add common settings for the measurement module.
predictions/elecpriceakkudoktor:
- Use mean values of last 7 days to fill prediction values not provided by
akkudoktor.net (only provides 24 values).
prediction/loadabc:
- Extend the generic prediction keys by 'load_total_adjusted' for load predictions
that adjust the predicted total load by measured load values.
prediction/loadakkudoktor:
- Extend the Akkudoktor load prediction by load adjustment using measured load
values.
prediction/load_aggregator:
- Module removed. Load aggregation is now handled by the measurement module.
prediction/load_corrector:
- Module removed. Load correction (aka. adjustment of load prediction by
measured load energy) is handled by the LoadAkkudoktor prediction and
the generic 'load_mean_adjusted' prediction key.
prediction/load_forecast:
- Module removed. Functionality now completely handled by the LoadAkkudoktor
prediction.
utils/cacheutil:
- Use pydantic.
- Fix potential bug in ttl (time to live) duration handling.
utils/datetimeutil:
- Added missing handling of pendulum.DateTime and pendulum.Duration instances
as input. Handled before as datetime.datetime and datetime.timedelta.
utils/visualize:
- Move main to generate_example_report() for better testing support.
server/server:
- Added new configuration option server_fastapi_startup_server_fasthtml
to make startup of FastHTML server by FastAPI server conditional.
server/fastapi_server:
- Add APIs for measurements
- Improve APIs to provide or take pandas datetime series and
datetime dataframes controlled by pydantic model.
- Improve APIs to provide or take simple datetime data arrays
controlled by pydantic model.
- Move fastAPI server API to v1 for new APIs.
- Update pre v1 endpoints to use new prediction and measurement capabilities.
- Only start FastHTML server if 'server_fastapi_startup_server_fasthtml'
config option is set.
tests:
- Adapt import tests to changed import method signature
- Adapt server test to use the v1 API
- Extend the dataabc test to test for array generation from data
with several data interval scenarios.
- Extend the datetimeutil test to also test for correct handling
of to_datetime() providing now().
- Adapt LoadAkkudoktor test for new adjustment calculation.
- Adapt visualization test to use example report function instead of visualize.py
run as process.
- Removed test_load_aggregator. Functionality is now tested in test_measurement.
- Added tests for measurement module
docs:
- Remove sphinxcontrib-openapi as it prevents build of documentation.
"site-packages/sphinxcontrib/openapi/openapi31.py", line 305, in _get_type_from_schema
for t in schema["anyOf"]: KeyError: 'anyOf'"
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
117 lines
3.9 KiB
Python
117 lines
3.9 KiB
Python
from typing import Optional
|
|
|
|
import pandas as pd
|
|
import pendulum
|
|
import pytest
|
|
from pydantic import Field, ValidationError
|
|
|
|
from akkudoktoreos.core.pydantic import (
|
|
PydanticBaseModel,
|
|
PydanticDateTimeData,
|
|
PydanticDateTimeDataFrame,
|
|
PydanticDateTimeSeries,
|
|
)
|
|
from akkudoktoreos.utils.datetimeutil import compare_datetimes, to_datetime
|
|
|
|
|
|
class PydanticTestModel(PydanticBaseModel):
|
|
datetime_field: pendulum.DateTime = Field(
|
|
..., description="A datetime field with pendulum support."
|
|
)
|
|
optional_field: Optional[str] = Field(default=None, description="An optional field.")
|
|
|
|
|
|
class TestPydanticBaseModel:
|
|
def test_valid_pendulum_datetime(self):
|
|
dt = pendulum.now()
|
|
model = PydanticTestModel(datetime_field=dt)
|
|
assert model.datetime_field == dt
|
|
|
|
def test_invalid_datetime_string(self):
|
|
with pytest.raises(ValidationError, match="Input should be an instance of DateTime"):
|
|
PydanticTestModel(datetime_field="invalid_datetime")
|
|
|
|
def test_iso8601_serialization(self):
|
|
dt = pendulum.datetime(2024, 12, 21, 15, 0, 0)
|
|
model = PydanticTestModel(datetime_field=dt)
|
|
serialized = model.to_dict()
|
|
expected_dt = to_datetime(dt)
|
|
result_dt = to_datetime(serialized["datetime_field"])
|
|
assert compare_datetimes(result_dt, expected_dt)
|
|
|
|
def test_reset_to_defaults(self):
|
|
dt = pendulum.now()
|
|
model = PydanticTestModel(datetime_field=dt, optional_field="some value")
|
|
model.reset_to_defaults()
|
|
assert model.datetime_field == dt
|
|
assert model.optional_field is None
|
|
|
|
def test_from_dict_and_to_dict(self):
|
|
dt = pendulum.now()
|
|
model = PydanticTestModel(datetime_field=dt)
|
|
data = model.to_dict()
|
|
restored_model = PydanticTestModel.from_dict(data)
|
|
assert restored_model.datetime_field == dt
|
|
|
|
def test_to_json_and_from_json(self):
|
|
dt = pendulum.now()
|
|
model = PydanticTestModel(datetime_field=dt)
|
|
json_data = model.to_json()
|
|
restored_model = PydanticTestModel.from_json(json_data)
|
|
assert restored_model.datetime_field == dt
|
|
|
|
|
|
class TestPydanticDateTimeData:
|
|
def test_valid_list_lengths(self):
|
|
data = {
|
|
"timestamps": ["2024-12-21T15:00:00+00:00"],
|
|
"values": [100],
|
|
}
|
|
model = PydanticDateTimeData(root=data)
|
|
assert pendulum.parse(model.root["timestamps"][0]) == pendulum.parse(
|
|
"2024-12-21T15:00:00+00:00"
|
|
)
|
|
|
|
def test_invalid_list_lengths(self):
|
|
data = {
|
|
"timestamps": ["2024-12-21T15:00:00+00:00"],
|
|
"values": [100, 200],
|
|
}
|
|
with pytest.raises(
|
|
ValidationError, match="All lists in the dictionary must have the same length"
|
|
):
|
|
PydanticDateTimeData(root=data)
|
|
|
|
|
|
class TestPydanticDateTimeDataFrame:
|
|
def test_valid_dataframe(self):
|
|
df = pd.DataFrame(
|
|
{
|
|
"value": [100, 200],
|
|
},
|
|
index=pd.to_datetime(["2024-12-21", "2024-12-22"]),
|
|
)
|
|
model = PydanticDateTimeDataFrame.from_dataframe(df)
|
|
result = model.to_dataframe()
|
|
|
|
# Check index
|
|
assert len(result.index) == len(df.index)
|
|
for i, dt in enumerate(df.index):
|
|
expected_dt = to_datetime(dt)
|
|
result_dt = to_datetime(result.index[i])
|
|
assert compare_datetimes(result_dt, expected_dt).equal
|
|
|
|
|
|
class TestPydanticDateTimeSeries:
|
|
def test_valid_series(self):
|
|
series = pd.Series([100, 200], index=pd.to_datetime(["2024-12-21", "2024-12-22"]))
|
|
model = PydanticDateTimeSeries.from_series(series)
|
|
result = model.to_series()
|
|
|
|
# Check index
|
|
assert len(result.index) == len(series.index)
|
|
for i, dt in enumerate(series.index):
|
|
expected_dt = to_datetime(dt)
|
|
result_dt = to_datetime(result.index[i])
|
|
assert compare_datetimes(result_dt, expected_dt).equal
|