Files
EOS/tests/test_dataabc.py
Bobby Noelte b397b5d43e fix: automatic optimization (#596)
This fix implements the long term goal to have the EOS server run optimization (or
energy management) on regular intervals automatically. Thus clients can request
the current energy management plan at any time and it is updated on regular
intervals without interaction by the client.

This fix started out to "only" make automatic optimization (or energy management)
runs working. It turned out there are several endpoints that in some way
update predictions or run the optimization. To lock against such concurrent attempts
the code had to be refactored to allow control of execution. During refactoring it
became clear that some classes and files are named without a proper reference
to their usage. Thus not only refactoring but also renaming became necessary.
The names are still not the best, but I hope they are more intuitive.

The fix includes several bug fixes that are not directly related to the automatic optimization
but are necessary to keep EOS running properly to do the automatic optimization and
to test and document the changes.

This is a breaking change as the configuration structure changed once again and
the server API was also enhanced and streamlined. The server API that is used by
Andreas and Jörg in their videos has not changed.

* fix: automatic optimization

  Allow optimization to automatically run on configured intervals gathering all
  optimization parameters from configuration and predictions. The automatic run
  can be configured to only run prediction updates skipping the optimization.
  Extend documentaion to also cover automatic optimization. Lock automatic runs
  against runs initiated by the /optimize or other endpoints. Provide new
  endpoints to retrieve the energy management plan and the genetic solution
  of the latest automatic optimization run. Offload energy management to thread
  pool executor to keep the app more responsive during the CPU heavy optimization
  run.

* fix: EOS servers recognize environment variables on startup

  Force initialisation of EOS configuration on server startup to assure
  all sources of EOS configuration are properly set up and read. Adapt
  server tests and configuration tests to also test for environment
  variable configuration.

* fix: Remove 0.0.0.0 to localhost translation under Windows

  EOS imposed a 0.0.0.0 to localhost translation under Windows for
  convenience. This caused some trouble in user configurations. Now, as the
  default IP address configuration is 127.0.0.1, the user is responsible
  for to set up the correct Windows compliant IP address.

* fix: allow names for hosts additional to IP addresses

* fix: access pydantic model fields by class

  Access by instance is deprecated.

* fix: down sampling key_to_array

* fix: make cache clear endpoint clear all cache files

  Make /v1/admin/cache/clear clear all cache files. Before it only cleared
  expired cache files by default. Add new endpoint /v1/admin/clear-expired
  to only clear expired cache files.

* fix: timezonefinder returns Europe/Paris instead of Europe/Berlin

  timezonefinder 8.10 got more inaccurate for timezones in europe as there is
  a common timezone. Use new package tzfpy instead which is still returning
  Europe/Berlin if you are in Germany. tzfpy also claims to be faster than
  timezonefinder.

* fix: provider settings configuration

  Provider configuration used to be a union holding the settings for several
  providers. Pydantic union handling does not always find the correct type
  for a provider setting. This led to exceptions in specific configurations.
  Now provider settings are explicit comfiguration items for each possible
  provider. This is a breaking change as the configuration structure was
  changed.

* fix: ClearOutside weather prediction irradiance calculation

  Pvlib needs a pandas time index. Convert time index.

* fix: test config file priority

  Do not use config_eos fixture as this fixture already creates a config file.

* fix: optimization sample request documentation

  Provide all data in documentation of optimization sample request.

* fix: gitlint blocking pip dependency resolution

  Replace gitlint by commitizen. Gitlint is not actively maintained anymore.
  Gitlint dependencies blocked pip from dependency resolution.

* fix: sync pre-commit config to actual dependency requirements

  .pre-commit-config.yaml was out of sync, also requirements-dev.txt.

* fix: missing babel in requirements.txt

  Add babel to requirements.txt

* feat: setup default device configuration for automatic optimization

  In case the parameters for automatic optimization are not fully defined a
  default configuration is setup to allow the automatic energy management
  run. The default configuration may help the user to correctly define
  the device configuration.

* feat: allow configuration of genetic algorithm parameters

  The genetic algorithm parameters for number of individuals, number of
  generations, the seed and penalty function parameters are now avaliable
  as configuration options.

* feat: allow configuration of home appliance time windows

  The time windows a home appliance is allowed to run are now configurable
  by the configuration (for /v1 API) and also by the home appliance parameters
  (for the classic /optimize API). If there is no such configuration the
  time window defaults to optimization hours, which was the standard before
  the change. Documentation on how to configure time windows is added.

* feat: standardize mesaurement keys for battery/ ev SoC measurements

  The standardized measurement keys to report battery SoC to the device
  simulations can now be retrieved from the device configuration as a
  read-only config option.

* feat: feed in tariff prediction

  Add feed in tarif predictions needed for automatic optimization. The feed in
  tariff can be retrieved as fixed feed in tarif or can be imported. Also add
  tests for the different feed in tariff providers. Extend documentation to
  cover the feed in tariff providers.

* feat: add energy management plan based on S2 standard instructions

  EOS can generate an energy management plan as a list of simple instructions.
  May be retrieved by the /v1/energy-management/plan endpoint. The instructions
  loosely follow the S2 energy management standard.

* feat: make measurement keys configurable by EOS configuration.

  The fixed measurement keys are replaced by configurable measurement keys.

* feat: make pendulum DateTime, Date, Duration types usable for pydantic models

  Use pydantic_extra_types.pendulum_dt to get pydantic pendulum types. Types are
  added to the datetimeutil utility. Remove custom made pendulum adaptations
  from EOS pydantic module. Make EOS modules use the pydantic pendulum types
  managed by the datetimeutil module instead of the core pendulum types.

* feat: Add Time, TimeWindow, TimeWindowSequence and to_time to datetimeutil.

  The time windows are are added to support home appliance time window
  configuration. All time classes are also pydantic models. Time is the base
  class for time definition derived from pendulum.Time.

* feat: Extend DataRecord by configurable field like data.

  Configurable field like data was added to support the configuration of
  measurement records.

* feat: Add additional information to health information

  Version information is added to the health endpoints of eos and eosDash.
  The start time of the last optimization and the latest run time of the energy
  management is added to the EOS health information.

* feat: add pydantic merge model tests

* feat: add plan tab to EOSdash

  The plan tab displays the current energy management instructions.

* feat: add predictions tab to EOSdash

  The predictions tab displays the current predictions.

* feat: add cache management to EOSdash admin tab

  The admin tab is extended by a section for cache management. It allows to
  clear the cache.

* feat: add about tab to EOSdash

  The about tab resembles the former hello tab and provides extra information.

* feat: Adapt changelog and prepare for release management

  Release management using commitizen is added. The changelog file is adapted and
  teh changelog and a description for release management is added in the
  documentation.

* feat(doc): Improve install and devlopment documentation

  Provide a more concise installation description in Readme.md and add extra
  installation page and development page to documentation.

* chore: Use memory cache for interpolation instead of dict in inverter

  Decorate calculate_self_consumption() with @cachemethod_until_update to cache
  results in memory during an energy management/ optimization run. Replacement
  of dict type caching in inverter is now possible because all optimization
  runs are properly locked and the memory cache CacheUntilUpdateStore is properly
  cleared at the start of any energy management/ optimization operation.

* chore: refactor genetic

  Refactor the genetic algorithm modules for enhanced module structure and better
  readability. Removed unnecessary and overcomplex devices singleton. Also
  split devices configuration from genetic algorithm parameters to allow further
  development independently from genetic algorithm parameter format. Move
  charge rates configuration for electric vehicles from optimization to devices
  configuration to allow to have different charge rates for different cars in
  the future.

* chore: Rename memory cache to CacheEnergyManagementStore

  The name better resembles the task of the cache to chache function and method
  results for an energy management run. Also the decorator functions are renamed
  accordingly: cachemethod_energy_management, cache_energy_management

* chore: use class properties for config/ems/prediction mixin classes

* chore: skip debug logs from mathplotlib

  Mathplotlib is very noisy in debug mode.

* chore: automatically sync bokeh js to bokeh python package

  bokeh was updated to 3.8.0, make JS CDN automatically follow the package version.

* chore: rename hello.py to about.py

  Make hello.py the adapted EOSdash about page.

* chore: remove demo page from EOSdash

  As no the plan and prediction pages are working without configuration, the demo
  page is no longer necessary

* chore: split test_server.py for system test

  Split test_server.py to create explicit test_system.py for system tests.

* chore: move doc utils to generate_config_md.py

  The doc utils are only used in scripts/generate_config_md.py. Move it there to
  attribute for strong cohesion.

* chore: improve pydantic merge model documentation

* chore: remove pendulum warning from readme

* chore: remove GitHub discussions from contributing documentation

  Github discussions is to be replaced by Akkudoktor.net.

* chore(release): bump version to 0.1.0+dev for development

* build(deps): bump fastapi[standard] from 0.115.14 to 0.117.1

  bump fastapi and make coverage version (for pytest-cov) explicit to avoid pip break.

* build(deps): bump uvicorn from 0.36.0 to 0.37.0

BREAKING CHANGE: EOS configuration changed. V1 API changed.

  - The available_charge_rates_percent configuration is removed from optimization.
    Use the new charge_rate configuration for the electric vehicle
  - Optimization configuration parameter hours renamed to horizon_hours
  - Device configuration now has to provide the number of devices and device
    properties per device.
  - Specific prediction provider configuration to be provided by explicit
    configuration item (no union for all providers).
  - Measurement keys to be provided as a list.
  - New feed in tariff providers have to be configured.
  - /v1/measurement/loadxxx endpoints are removed. Use generic mesaurement endpoints.
  - /v1/admin/cache/clear now clears all cache files. Use
    /v1/admin/cache/clear-expired to only clear all expired cache files.

Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
2025-10-28 02:50:31 +01:00

1163 lines
48 KiB
Python

from datetime import datetime, timezone
from typing import Any, ClassVar, List, Optional, Union
import numpy as np
import pandas as pd
import pendulum
import pytest
from pydantic import Field, ValidationError
from akkudoktoreos.config.configabc import SettingsBaseModel
from akkudoktoreos.core.dataabc import (
DataBase,
DataContainer,
DataImportProvider,
DataProvider,
DataRecord,
DataSequence,
)
from akkudoktoreos.core.ems import get_ems
from akkudoktoreos.utils.datetimeutil import compare_datetimes, to_datetime, to_duration
# Derived classes for testing
# ---------------------------
class DerivedConfig(SettingsBaseModel):
env_var: Optional[int] = Field(default=None, description="Test config by environment var")
instance_field: Optional[str] = Field(default=None, description="Test config by instance field")
class_constant: Optional[int] = Field(default=None, description="Test config by class constant")
class DerivedBase(DataBase):
instance_field: Optional[str] = Field(default=None, description="Field Value")
class_constant: ClassVar[int] = 30
class DerivedRecord(DataRecord):
"""Date Record derived from base class DataRecord.
The derived data record got the
- `data_value` field and the
- `dish_washer_emr`, `solar_power`, `temp` configurable field like data.
"""
data_value: Optional[float] = Field(default=None, description="Data Value")
@classmethod
def configured_data_keys(cls) -> Optional[list[str]]:
return ["dish_washer_emr", "solar_power", "temp"]
class DerivedSequence(DataSequence):
# overload
records: List[DerivedRecord] = Field(
default_factory=list, description="List of DerivedRecord records"
)
@classmethod
def record_class(cls) -> Any:
return DerivedRecord
class DerivedDataProvider(DataProvider):
"""A concrete subclass of DataProvider for testing purposes."""
# overload
records: List[DerivedRecord] = Field(
default_factory=list, description="List of DerivedRecord records"
)
provider_enabled: ClassVar[bool] = False
provider_updated: ClassVar[bool] = False
@classmethod
def record_class(cls) -> Any:
return DerivedRecord
# Implement abstract methods for test purposes
def provider_id(self) -> str:
return "DerivedDataProvider"
def enabled(self) -> bool:
return self.provider_enabled
def _update_data(self, force_update: Optional[bool] = False) -> None:
# Simulate update logic
DerivedDataProvider.provider_updated = True
class DerivedDataImportProvider(DataImportProvider):
"""A concrete subclass of DataImportProvider for testing purposes."""
# overload
records: List[DerivedRecord] = Field(
default_factory=list, description="List of DerivedRecord records"
)
provider_enabled: ClassVar[bool] = False
provider_updated: ClassVar[bool] = False
@classmethod
def record_class(cls) -> Any:
return DerivedRecord
# Implement abstract methods for test purposes
def provider_id(self) -> str:
return "DerivedDataImportProvider"
def enabled(self) -> bool:
return self.provider_enabled
def _update_data(self, force_update: Optional[bool] = False) -> None:
# Simulate update logic
DerivedDataImportProvider.provider_updated = True
class DerivedDataContainer(DataContainer):
providers: List[Union[DerivedDataProvider, DataProvider]] = Field(
default_factory=list, description="List of data providers"
)
# Tests
# ----------
class TestDataBase:
@pytest.fixture
def base(self):
# Provide default values for configuration
derived = DerivedBase()
return derived
def test_get_config_value_key_error(self, base):
with pytest.raises(AttributeError):
base.config.non_existent_key
class TestDataRecord:
def create_test_record(self, date, value):
"""Helper function to create a test DataRecord."""
return DerivedRecord(date_time=date, data_value=value)
@pytest.fixture
def record(self):
"""Fixture to create a sample DerivedDataRecord with some data set."""
rec = DerivedRecord(date_time=None, data_value=10.0)
rec.configured_data = {"dish_washer_emr": 123.0, "solar_power": 456.0}
return rec
def test_getitem(self):
record = self.create_test_record(datetime(2024, 1, 3, tzinfo=timezone.utc), 10.0)
assert record["data_value"] == 10.0
def test_setitem(self):
record = self.create_test_record(datetime(2024, 1, 3, tzinfo=timezone.utc), 10.0)
record["data_value"] = 20.0
assert record.data_value == 20.0
def test_delitem(self):
record = self.create_test_record(datetime(2024, 1, 3, tzinfo=timezone.utc), 10.0)
record.data_value = 20.0
del record["data_value"]
assert record.data_value is None
def test_len(self):
record = self.create_test_record(datetime(2024, 1, 3, tzinfo=timezone.utc), 10.0)
record.date_time = None
record.data_value = 20.0
assert len(record) == 5 # 2 regular fields + 3 configured data "fields"
def test_to_dict(self):
record = self.create_test_record(datetime(2024, 1, 3, tzinfo=timezone.utc), 10.0)
record.data_value = 20.0
record_dict = record.to_dict()
assert "data_value" in record_dict
assert record_dict["data_value"] == 20.0
record2 = DerivedRecord.from_dict(record_dict)
assert record2.model_dump() == record.model_dump()
def test_to_json(self):
record = self.create_test_record(datetime(2024, 1, 3, tzinfo=timezone.utc), 10.0)
record.data_value = 20.0
json_str = record.to_json()
assert "data_value" in json_str
assert "20.0" in json_str
record2 = DerivedRecord.from_json(json_str)
assert record2.model_dump() == record.model_dump()
def test_record_keys_includes_configured_data_keys(self, record):
"""Ensure record_keys includes all configured configured data keys."""
assert set(record.record_keys()) >= set(record.configured_data_keys())
def test_record_keys_writable_includes_configured_data_keys(self, record):
"""Ensure record_keys_writable includes all configured configured data keys."""
assert set(record.record_keys_writable()) >= set(record.configured_data_keys())
def test_getitem_existing_field(self, record):
"""Test that __getitem__ returns correct value for existing native field."""
record.date_time = "2024-01-01T00:00:00+00:00"
assert record["date_time"] is not None
def test_getitem_existing_configured_data(self, record):
"""Test that __getitem__ retrieves existing configured data values."""
assert record["dish_washer_emr"] == 123.0
assert record["solar_power"] == 456.0
def test_getitem_missing_configured_data_returns_none(self, record):
"""Test that __getitem__ returns None for missing but known configured data keys."""
assert record["temp"] is None
def test_getitem_raises_keyerror(self, record):
"""Test that __getitem__ raises KeyError for completely unknown keys."""
with pytest.raises(KeyError):
_ = record["nonexistent"]
def test_setitem_field(self, record):
"""Test setting a native field using __setitem__."""
record["date_time"] = "2025-01-01T12:00:00+00:00"
assert str(record.date_time).startswith("2025-01-01")
def test_setitem_configured_data(self, record):
"""Test setting a known configured data key using __setitem__."""
record["temp"] = 25.5
assert record.configured_data["temp"] == 25.5
def test_setitem_invalid_key_raises(self, record):
"""Test that __setitem__ raises KeyError for unknown keys."""
with pytest.raises(KeyError):
record["unknown_key"] = 123
def test_delitem_field(self, record):
"""Test deleting a native field using __delitem__."""
record["date_time"] = "2025-01-01T12:00:00+00:00"
del record["date_time"]
assert record.date_time is None
def test_delitem_configured_data(self, record):
"""Test deleting a known configured data key using __delitem__."""
del record["solar_power"]
assert "solar_power" not in record.configured_data
def test_delitem_unknown_raises(self, record):
"""Test that __delitem__ raises KeyError for unknown keys."""
with pytest.raises(KeyError):
del record["nonexistent"]
def test_attribute_get_existing_field(self, record):
"""Test accessing a native field via attribute."""
record.date_time = "2025-01-01T12:00:00+00:00"
assert record.date_time is not None
def test_attribute_get_existing_configured_data(self, record):
"""Test accessing an existing configured data via attribute."""
assert record.dish_washer_emr == 123.0
def test_attribute_get_missing_configured_data(self, record):
"""Test accessing a missing but known configured data returns None."""
assert record.temp is None
def test_attribute_get_invalid_raises(self, record):
"""Test accessing an unknown attribute raises AttributeError."""
with pytest.raises(AttributeError):
_ = record.nonexistent
def test_attribute_set_existing_field(self, record):
"""Test setting a native field via attribute."""
record.date_time = "2025-06-25T12:00:00+00:00"
assert record.date_time is not None
def test_attribute_set_existing_configured_data(self, record):
"""Test setting a known configured data key via attribute."""
record.temp = 99.9
assert record.configured_data["temp"] == 99.9
def test_attribute_set_invalid_raises(self, record):
"""Test setting an unknown attribute raises AttributeError."""
with pytest.raises(AttributeError):
record.invalid = 123
def test_delattr_field(self, record):
"""Test deleting a native field via attribute."""
record.date_time = "2025-06-25T12:00:00+00:00"
del record.date_time
assert record.date_time is None
def test_delattr_configured_data(self, record):
"""Test deleting a known configured data key via attribute."""
record.temp = 88.0
del record.temp
assert "temp" not in record.configured_data
def test_delattr_ignored_missing_configured_data_key(self, record):
"""Test deleting a known configured data key that was never set is a no-op."""
del record.temp
assert "temp" not in record.configured_data
def test_len_and_iter(self, record):
"""Test that __len__ and __iter__ behave as expected."""
keys = list(iter(record))
assert set(record.record_keys_writable()) == set(keys)
assert len(record) == len(keys)
def test_in_operator_includes_configured_data(self, record):
"""Test that 'in' operator includes configured data keys."""
assert "dish_washer_emr" in record
assert "temp" in record # known key, even if not yet set
assert "nonexistent" not in record
def test_hasattr_behavior(self, record):
"""Test that hasattr returns True for fields and known configured dataWs."""
assert hasattr(record, "date_time")
assert hasattr(record, "dish_washer_emr")
assert hasattr(record, "temp") # allowed, even if not yet set
assert not hasattr(record, "nonexistent")
def test_model_validate_roundtrip(self, record):
"""Test that MeasurementDataRecord can be serialized and revalidated."""
dumped = record.model_dump()
restored = DerivedRecord.model_validate(dumped)
assert restored.dish_washer_emr == 123.0
assert restored.solar_power == 456.0
assert restored.temp is None # not set
def test_copy_preserves_configured_data(self, record):
"""Test that copying preserves configured data values."""
record.temp = 22.2
copied = record.model_copy()
assert copied.dish_washer_emr == 123.0
assert copied.temp == 22.2
assert copied is not record
def test_equality_includes_configured_data(self, record):
"""Test that equality includes the `configured data` content."""
other = record.model_copy()
assert record == other
def test_inequality_differs_with_configured_data(self, record):
"""Test that records with different configured datas are not equal."""
other = record.model_copy(deep=True)
# Modify one configured data value in the copy
other.configured_data["dish_washer_emr"] = 999.9
assert record != other
def test_in_operator_for_configured_data_and_fields(self, record):
"""Ensure 'in' works for both fields and configured configured data keys."""
assert "dish_washer_emr" in record
assert "solar_power" in record
assert "date_time" in record # standard field
assert "temp" in record # allowed but not yet set
assert "unknown" not in record
def test_hasattr_equivalence_to_getattr(self, record):
"""hasattr should return True for all valid keys/configured datas."""
assert hasattr(record, "dish_washer_emr")
assert hasattr(record, "temp")
assert hasattr(record, "date_time")
assert not hasattr(record, "nonexistent")
def test_dir_includes_configured_data_keys(self, record):
"""`dir(record)` should include configured data keys for introspection.
It shall not include the internal 'configured datas' attribute.
"""
keys = dir(record)
assert "configured datas" not in keys
for key in record.configured_data_keys():
assert key in keys
def test_init_configured_field_like_data_applies_before_model_init(self):
"""Test that keys listed in `_configured_data_keys` are moved to `configured_data` at init time."""
record = DerivedRecord(
date_time="2024-01-03T00:00:00+00:00",
data_value=42.0,
dish_washer_emr=111.1,
solar_power=222.2,
temp=333.3 # assume `temp` is also a valid configured key
)
assert record.data_value == 42.0
assert record.configured_data == {
"dish_washer_emr": 111.1,
"solar_power": 222.2,
"temp": 333.3,
}
class TestDataSequence:
@pytest.fixture
def sequence(self):
sequence0 = DerivedSequence()
assert len(sequence0) == 0
return sequence0
@pytest.fixture
def sequence2(self):
sequence = DerivedSequence()
record1 = self.create_test_record(datetime(1970, 1, 1), 1970)
record2 = self.create_test_record(datetime(1971, 1, 1), 1971)
sequence.append(record1)
sequence.append(record2)
assert len(sequence) == 2
return sequence
def create_test_record(self, date, value):
"""Helper function to create a test DataRecord."""
return DerivedRecord(date_time=date, data_value=value)
# Test cases
def test_getitem(self, sequence):
assert len(sequence) == 0
record = self.create_test_record("2024-01-01 00:00:00", 0)
sequence.insert_by_datetime(record)
assert isinstance(sequence[0], DerivedRecord)
def test_setitem(self, sequence2):
new_record = self.create_test_record(datetime(2024, 1, 3, tzinfo=timezone.utc), 1)
sequence2[0] = new_record
assert sequence2[0].date_time == datetime(2024, 1, 3, tzinfo=timezone.utc)
def test_set_record_at_index(self, sequence2):
record1 = self.create_test_record(datetime(2024, 1, 3, tzinfo=timezone.utc), 1)
record2 = self.create_test_record(datetime(2023, 11, 5), 0.8)
sequence2[1] = record1
assert sequence2[1].date_time == datetime(2024, 1, 3, tzinfo=timezone.utc)
sequence2[0] = record2
assert len(sequence2) == 2
assert sequence2[0] == record2
def test_insert_duplicate_date_record(self, sequence):
record1 = self.create_test_record(datetime(2023, 11, 5), 0.8)
record2 = self.create_test_record(datetime(2023, 11, 5), 0.9) # Duplicate date
sequence.insert_by_datetime(record1)
sequence.insert_by_datetime(record2)
assert len(sequence) == 1
assert sequence[0].data_value == 0.9 # Record should have merged with new value
def test_sort_by_datetime_ascending(self, sequence):
"""Test sorting records in ascending order by date_time."""
records = [
self.create_test_record(pendulum.datetime(2024, 11, 1), 0.7),
self.create_test_record(pendulum.datetime(2024, 10, 1), 0.8),
self.create_test_record(pendulum.datetime(2024, 12, 1), 0.9),
]
for i, record in enumerate(records):
sequence.insert(i, record)
sequence.sort_by_datetime()
sorted_dates = [record.date_time for record in sequence.records]
for i, expected_date in enumerate(
[
pendulum.datetime(2024, 10, 1),
pendulum.datetime(2024, 11, 1),
pendulum.datetime(2024, 12, 1),
]
):
assert compare_datetimes(sorted_dates[i], expected_date).equal
def test_sort_by_datetime_descending(self, sequence):
"""Test sorting records in descending order by date_time."""
records = [
self.create_test_record(pendulum.datetime(2024, 11, 1), 0.7),
self.create_test_record(pendulum.datetime(2024, 10, 1), 0.8),
self.create_test_record(pendulum.datetime(2024, 12, 1), 0.9),
]
for i, record in enumerate(records):
sequence.insert(i, record)
sequence.sort_by_datetime(reverse=True)
sorted_dates = [record.date_time for record in sequence.records]
for i, expected_date in enumerate(
[
pendulum.datetime(2024, 12, 1),
pendulum.datetime(2024, 11, 1),
pendulum.datetime(2024, 10, 1),
]
):
assert compare_datetimes(sorted_dates[i], expected_date).equal
def test_sort_by_datetime_with_none(self, sequence):
"""Test sorting records when some date_time values are None."""
records = [
self.create_test_record(pendulum.datetime(2024, 11, 1), 0.7),
self.create_test_record(pendulum.datetime(2024, 10, 1), 0.8),
self.create_test_record(pendulum.datetime(2024, 12, 1), 0.9),
]
for i, record in enumerate(records):
sequence.insert(i, record)
sequence.records[2].date_time = None
assert sequence.records[2].date_time is None
sequence.sort_by_datetime()
sorted_dates = [record.date_time for record in sequence.records]
for i, expected_date in enumerate(
[
None, # None values should come first
pendulum.datetime(2024, 10, 1),
pendulum.datetime(2024, 11, 1),
]
):
if expected_date is None:
assert sorted_dates[i] is None
else:
assert compare_datetimes(sorted_dates[i], expected_date).equal
def test_sort_by_datetime_error_on_uncomparable(self, sequence):
"""Test error is raised when date_time contains uncomparable values."""
records = [
self.create_test_record(pendulum.datetime(2024, 11, 1), 0.7),
self.create_test_record(pendulum.datetime(2024, 12, 1), 0.9),
self.create_test_record(pendulum.datetime(2024, 10, 1), 0.8),
]
for i, record in enumerate(records):
sequence.insert(i, record)
with pytest.raises(
ValidationError, match="Date string not_a_datetime does not match any known formats."
):
sequence.records[2].date_time = "not_a_datetime" # Invalid date_time
sequence.sort_by_datetime()
def test_key_to_series(self, sequence):
record = self.create_test_record(datetime(2023, 11, 6), 0.8)
sequence.append(record)
series = sequence.key_to_series("data_value")
assert isinstance(series, pd.Series)
assert series[to_datetime(datetime(2023, 11, 6))] == 0.8
def test_key_from_series(self, sequence):
series = pd.Series(
data=[0.8, 0.9], index=pd.to_datetime([datetime(2023, 11, 5), datetime(2023, 11, 6)])
)
sequence.key_from_series("data_value", series)
assert len(sequence) == 2
assert sequence[0].data_value == 0.8
assert sequence[1].data_value == 0.9
def test_key_to_array(self, sequence):
interval = to_duration("1 day")
start_datetime = to_datetime("2023-11-6")
last_datetime = to_datetime("2023-11-8")
end_datetime = to_datetime("2023-11-9")
record = self.create_test_record(start_datetime, float(start_datetime.day))
sequence.insert_by_datetime(record)
record = self.create_test_record(last_datetime, float(last_datetime.day))
sequence.insert_by_datetime(record)
assert sequence[0].data_value == 6.0
assert sequence[1].data_value == 8.0
series = sequence.key_to_series(
key="data_value", start_datetime=start_datetime, end_datetime=end_datetime
)
assert len(series) == 2
assert series[to_datetime("2023-11-6")] == 6
assert series[to_datetime("2023-11-8")] == 8
array = sequence.key_to_array(
key="data_value",
start_datetime=start_datetime,
end_datetime=end_datetime,
interval=interval,
)
assert isinstance(array, np.ndarray)
assert len(array) == 3
assert array[0] == start_datetime.day
assert array[1] == 7
assert array[2] == last_datetime.day
def test_key_to_array_linear_interpolation(self, sequence):
"""Test key_to_array with linear interpolation for numeric data."""
interval = to_duration("1 hour")
record1 = self.create_test_record(pendulum.datetime(2023, 11, 6, 0), 0.8)
record2 = self.create_test_record(pendulum.datetime(2023, 11, 6, 2), 1.0) # Gap of 2 hours
sequence.insert_by_datetime(record1)
sequence.insert_by_datetime(record2)
array = sequence.key_to_array(
key="data_value",
start_datetime=pendulum.datetime(2023, 11, 6),
end_datetime=pendulum.datetime(2023, 11, 6, 3),
interval=interval,
fill_method="linear",
)
assert len(array) == 3
assert array[0] == 0.8
assert array[1] == 0.9 # Interpolated value
assert array[2] == 1.0
def test_key_to_array_ffill(self, sequence):
"""Test key_to_array with forward filling for missing values."""
interval = to_duration("1 hour")
record1 = self.create_test_record(pendulum.datetime(2023, 11, 6, 0), 0.8)
record2 = self.create_test_record(pendulum.datetime(2023, 11, 6, 2), 1.0)
sequence.insert_by_datetime(record1)
sequence.insert_by_datetime(record2)
array = sequence.key_to_array(
key="data_value",
start_datetime=pendulum.datetime(2023, 11, 6),
end_datetime=pendulum.datetime(2023, 11, 6, 3),
interval=interval,
fill_method="ffill",
)
assert len(array) == 3
assert array[0] == 0.8
assert array[1] == 0.8 # Forward-filled value
assert array[2] == 1.0
def test_key_to_array_ffill_one_value(self, sequence):
"""Test key_to_array with forward filling for missing values and only one value at end available."""
interval = to_duration("1 hour")
record1 = self.create_test_record(pendulum.datetime(2023, 11, 6, 2), 1.0)
sequence.insert_by_datetime(record1)
array = sequence.key_to_array(
key="data_value",
start_datetime=pendulum.datetime(2023, 11, 6),
end_datetime=pendulum.datetime(2023, 11, 6, 4),
interval=interval,
fill_method="ffill",
)
assert len(array) == 4
assert array[0] == 1.0 # Backward-filled value
assert array[1] == 1.0 # Backward-filled value
assert array[2] == 1.0
assert array[2] == 1.0 # Forward-filled value
def test_key_to_array_bfill(self, sequence):
"""Test key_to_array with backward filling for missing values."""
interval = to_duration("1 hour")
record1 = self.create_test_record(pendulum.datetime(2023, 11, 6, 0), 0.8)
record2 = self.create_test_record(pendulum.datetime(2023, 11, 6, 2), 1.0)
sequence.insert_by_datetime(record1)
sequence.insert_by_datetime(record2)
array = sequence.key_to_array(
key="data_value",
start_datetime=pendulum.datetime(2023, 11, 6),
end_datetime=pendulum.datetime(2023, 11, 6, 3),
interval=interval,
fill_method="bfill",
)
assert len(array) == 3
assert array[0] == 0.8
assert array[1] == 1.0 # Backward-filled value
assert array[2] == 1.0
def test_key_to_array_with_truncation(self, sequence):
"""Test truncation behavior in key_to_array."""
interval = to_duration("1 hour")
record1 = self.create_test_record(pendulum.datetime(2023, 11, 5, 23), 0.8)
record2 = self.create_test_record(pendulum.datetime(2023, 11, 6, 1), 1.0)
sequence.insert_by_datetime(record1)
sequence.insert_by_datetime(record2)
array = sequence.key_to_array(
key="data_value",
start_datetime=pendulum.datetime(2023, 11, 6),
end_datetime=pendulum.datetime(2023, 11, 6, 2),
interval=interval,
)
assert len(array) == 2
assert array[0] == 0.9 # Interpolated from previous day
assert array[1] == 1.0
def test_key_to_array_with_none(self, sequence):
"""Test handling of empty series in key_to_array."""
interval = to_duration("1 hour")
array = sequence.key_to_array(
key="data_value",
start_datetime=pendulum.datetime(2023, 11, 6),
end_datetime=pendulum.datetime(2023, 11, 6, 3),
interval=interval,
)
assert isinstance(array, np.ndarray)
assert np.all(array == None)
def test_key_to_array_with_one(self, sequence):
"""Test handling of one element series in key_to_array."""
interval = to_duration("1 hour")
record1 = self.create_test_record(pendulum.datetime(2023, 11, 5, 23), 0.8)
sequence.insert_by_datetime(record1)
array = sequence.key_to_array(
key="data_value",
start_datetime=pendulum.datetime(2023, 11, 6),
end_datetime=pendulum.datetime(2023, 11, 6, 2),
interval=interval,
)
assert len(array) == 2
assert array[0] == 0.8 # Interpolated from previous day
assert array[1] == 0.8
def test_key_to_array_invalid_fill_method(self, sequence):
"""Test invalid fill_method raises an error."""
interval = to_duration("1 hour")
record1 = self.create_test_record(pendulum.datetime(2023, 11, 6, 0), 0.8)
sequence.insert_by_datetime(record1)
with pytest.raises(ValueError, match="Unsupported fill method: invalid"):
sequence.key_to_array(
key="data_value",
start_datetime=pendulum.datetime(2023, 11, 6),
end_datetime=pendulum.datetime(2023, 11, 6, 1),
interval=interval,
fill_method="invalid",
)
def test_to_datetimeindex(self, sequence2):
record1 = self.create_test_record(datetime(2023, 11, 5), 0.8)
record2 = self.create_test_record(datetime(2023, 11, 6), 0.9)
sequence2.insert(0, record1)
sequence2.insert(1, record2)
dt_index = sequence2.to_datetimeindex()
assert isinstance(dt_index, pd.DatetimeIndex)
assert dt_index[0] == to_datetime(datetime(2023, 11, 5))
assert dt_index[1] == to_datetime(datetime(2023, 11, 6))
def test_delete_by_datetime_range(self, sequence):
record1 = self.create_test_record(datetime(2023, 11, 5), 0.8)
record2 = self.create_test_record(datetime(2023, 11, 6), 0.9)
record3 = self.create_test_record(datetime(2023, 11, 7), 1.0)
sequence.append(record1)
sequence.append(record2)
sequence.append(record3)
assert len(sequence) == 3
sequence.delete_by_datetime(
start_datetime=datetime(2023, 11, 6), end_datetime=datetime(2023, 11, 7)
)
assert len(sequence) == 2
assert sequence[0].date_time == to_datetime(datetime(2023, 11, 5))
assert sequence[1].date_time == to_datetime(datetime(2023, 11, 7))
def test_delete_by_datetime_start(self, sequence):
record1 = self.create_test_record(datetime(2023, 11, 5), 0.8)
record2 = self.create_test_record(datetime(2023, 11, 6), 0.9)
sequence.append(record1)
sequence.append(record2)
assert len(sequence) == 2
sequence.delete_by_datetime(start_datetime=datetime(2023, 11, 6))
assert len(sequence) == 1
assert sequence[0].date_time == to_datetime(datetime(2023, 11, 5))
def test_delete_by_datetime_end(self, sequence):
record1 = self.create_test_record(datetime(2023, 11, 5), 0.8)
record2 = self.create_test_record(datetime(2023, 11, 6), 0.9)
sequence.append(record1)
sequence.append(record2)
assert len(sequence) == 2
sequence.delete_by_datetime(end_datetime=datetime(2023, 11, 6))
assert len(sequence) == 1
assert sequence[0].date_time == to_datetime(datetime(2023, 11, 6))
def test_filter_by_datetime(self, sequence):
record1 = self.create_test_record(datetime(2023, 11, 5), 0.8)
record2 = self.create_test_record(datetime(2023, 11, 6), 0.9)
sequence.append(record1)
sequence.append(record2)
filtered_sequence = sequence.filter_by_datetime(start_datetime=datetime(2023, 11, 6))
assert len(filtered_sequence) == 1
assert filtered_sequence[0].date_time == to_datetime(datetime(2023, 11, 6))
def test_to_dict(self, sequence):
record = self.create_test_record(datetime(2023, 11, 6), 0.8)
sequence.append(record)
data_dict = sequence.to_dict()
assert isinstance(data_dict, dict)
sequence_other = sequence.from_dict(data_dict)
assert sequence_other.model_dump() == sequence.model_dump()
def test_to_json(self, sequence):
record = self.create_test_record(datetime(2023, 11, 6), 0.8)
sequence.append(record)
json_str = sequence.to_json()
assert isinstance(json_str, str)
assert "2023-11-06" in json_str
assert ": 0.8" in json_str
def test_from_json(self, sequence, sequence2):
json_str = sequence2.to_json()
sequence = sequence.from_json(json_str)
assert len(sequence) == len(sequence2)
assert sequence[0].date_time == sequence2[0].date_time
assert sequence[0].data_value == sequence2[0].data_value
def test_key_to_value_exact_match(self, sequence):
"""Test key_to_value returns exact match when datetime matches a record."""
dt = datetime(2023, 11, 5)
record = self.create_test_record(dt, 0.75)
sequence.append(record)
result = sequence.key_to_value("data_value", dt)
assert result == 0.75
def test_key_to_value_nearest(self, sequence):
"""Test key_to_value returns value closest in time to the given datetime."""
record1 = self.create_test_record(datetime(2023, 11, 5, 12), 0.6)
record2 = self.create_test_record(datetime(2023, 11, 6, 12), 0.9)
sequence.append(record1)
sequence.append(record2)
dt = datetime(2023, 11, 6, 10) # closer to record2
result = sequence.key_to_value("data_value", dt)
assert result == 0.9
def test_key_to_value_nearest_after(self, sequence):
"""Test key_to_value returns value nearest after the given datetime."""
record1 = self.create_test_record(datetime(2023, 11, 5, 10), 0.7)
record2 = self.create_test_record(datetime(2023, 11, 5, 15), 0.8)
sequence.append(record1)
sequence.append(record2)
dt = datetime(2023, 11, 5, 14) # closer to record2
result = sequence.key_to_value("data_value", dt)
assert result == 0.8
def test_key_to_value_empty_sequence(self, sequence):
"""Test key_to_value returns None when sequence is empty."""
result = sequence.key_to_value("data_value", datetime(2023, 11, 5))
assert result is None
def test_key_to_value_missing_key(self, sequence):
"""Test key_to_value returns None when key is missing in records."""
record = self.create_test_record(datetime(2023, 11, 5), None)
sequence.append(record)
result = sequence.key_to_value("data_value", datetime(2023, 11, 5))
assert result is None
def test_key_to_value_multiple_records_with_none(self, sequence):
"""Test key_to_value skips records with None values."""
r1 = self.create_test_record(datetime(2023, 11, 5), None)
r2 = self.create_test_record(datetime(2023, 11, 6), 1.0)
sequence.append(r1)
sequence.append(r2)
result = sequence.key_to_value("data_value", datetime(2023, 11, 5, 12))
assert result == 1.0
def test_key_to_dict(self, sequence):
record1 = self.create_test_record(datetime(2023, 11, 5), 0.8)
record2 = self.create_test_record(datetime(2023, 11, 6), 0.9)
sequence.append(record1)
sequence.append(record2)
data_dict = sequence.key_to_dict("data_value")
assert isinstance(data_dict, dict)
assert data_dict[to_datetime(datetime(2023, 11, 5), as_string=True)] == 0.8
assert data_dict[to_datetime(datetime(2023, 11, 6), as_string=True)] == 0.9
def test_key_to_lists(self, sequence):
record1 = self.create_test_record(datetime(2023, 11, 5), 0.8)
record2 = self.create_test_record(datetime(2023, 11, 6), 0.9)
sequence.append(record1)
sequence.append(record2)
dates, values = sequence.key_to_lists("data_value")
assert dates == [to_datetime(datetime(2023, 11, 5)), to_datetime(datetime(2023, 11, 6))]
assert values == [0.8, 0.9]
def test_to_dataframe_full_data(self, sequence):
"""Test conversion of all records to a DataFrame without filtering."""
record1 = self.create_test_record("2024-01-01T12:00:00Z", 10)
record2 = self.create_test_record("2024-01-01T13:00:00Z", 20)
record3 = self.create_test_record("2024-01-01T14:00:00Z", 30)
sequence.append(record1)
sequence.append(record2)
sequence.append(record3)
df = sequence.to_dataframe()
# Validate DataFrame structure
assert isinstance(df, pd.DataFrame)
assert not df.empty
assert len(df) == 3 # All records should be included
assert "data_value" in df.columns
def test_to_dataframe_with_filter(self, sequence):
"""Test filtering records by datetime range."""
record1 = self.create_test_record("2024-01-01T12:00:00Z", 10)
record2 = self.create_test_record("2024-01-01T13:00:00Z", 20)
record3 = self.create_test_record("2024-01-01T14:00:00Z", 30)
sequence.append(record1)
sequence.append(record2)
sequence.append(record3)
start = to_datetime("2024-01-01T12:30:00Z")
end = to_datetime("2024-01-01T14:00:00Z")
df = sequence.to_dataframe(start_datetime=start, end_datetime=end)
assert isinstance(df, pd.DataFrame)
assert not df.empty
assert len(df) == 1 # Only one record should match the range
assert df.index[0] == pd.Timestamp("2024-01-01T13:00:00Z")
def test_to_dataframe_no_matching_records(self, sequence):
"""Test when no records match the given datetime filter."""
record1 = self.create_test_record("2024-01-01T12:00:00Z", 10)
record2 = self.create_test_record("2024-01-01T13:00:00Z", 20)
sequence.append(record1)
sequence.append(record2)
start = to_datetime("2024-01-01T14:00:00Z") # Start time after all records
end = to_datetime("2024-01-01T15:00:00Z")
df = sequence.to_dataframe(start_datetime=start, end_datetime=end)
assert isinstance(df, pd.DataFrame)
assert df.empty # No records should match
def test_to_dataframe_empty_sequence(self, sequence):
"""Test when DataSequence has no records."""
sequence = DataSequence(records=[])
df = sequence.to_dataframe()
assert isinstance(df, pd.DataFrame)
assert df.empty # Should return an empty DataFrame
def test_to_dataframe_no_start_datetime(self, sequence):
"""Test when only end_datetime is given (all past records should be included)."""
record1 = self.create_test_record("2024-01-01T12:00:00Z", 10)
record2 = self.create_test_record("2024-01-01T13:00:00Z", 20)
record3 = self.create_test_record("2024-01-01T14:00:00Z", 30)
sequence.append(record1)
sequence.append(record2)
sequence.append(record3)
end = to_datetime("2024-01-01T13:00:00Z") # Include only first record
df = sequence.to_dataframe(end_datetime=end)
assert isinstance(df, pd.DataFrame)
assert not df.empty
assert len(df) == 1
assert df.index[0] == pd.Timestamp("2024-01-01T12:00:00Z")
def test_to_dataframe_no_end_datetime(self, sequence):
"""Test when only start_datetime is given (all future records should be included)."""
record1 = self.create_test_record("2024-01-01T12:00:00Z", 10)
record2 = self.create_test_record("2024-01-01T13:00:00Z", 20)
record3 = self.create_test_record("2024-01-01T14:00:00Z", 30)
sequence.append(record1)
sequence.append(record2)
sequence.append(record3)
start = to_datetime("2024-01-01T13:00:00Z") # Include last two records
df = sequence.to_dataframe(start_datetime=start)
assert isinstance(df, pd.DataFrame)
assert not df.empty
assert len(df) == 2
assert df.index[0] == pd.Timestamp("2024-01-01T13:00:00Z")
class TestDataProvider:
# Fixtures and helper functions
@pytest.fixture
def provider(self):
"""Fixture to provide an instance of TestDataProvider for testing."""
DerivedDataProvider.provider_enabled = True
DerivedDataProvider.provider_updated = False
return DerivedDataProvider()
@pytest.fixture
def sample_start_datetime(self):
"""Fixture for a sample start datetime."""
return to_datetime(datetime(2024, 11, 1, 12, 0))
def create_test_record(self, date, value):
"""Helper function to create a test DataRecord."""
return DerivedRecord(date_time=date, data_value=value)
# Tests
def test_singleton_behavior(self, provider):
"""Test that DataProvider enforces singleton behavior."""
instance1 = provider
instance2 = DerivedDataProvider()
assert instance1 is instance2, (
"Singleton pattern is not enforced; instances are not the same."
)
def test_update_method_with_defaults(self, provider, sample_start_datetime, monkeypatch):
"""Test the `update` method with default parameters."""
ems_eos = get_ems()
ems_eos.set_start_datetime(sample_start_datetime)
provider.update_data()
assert provider.ems_start_datetime == sample_start_datetime
def test_update_method_force_enable(self, provider, monkeypatch):
"""Test that `update` executes when `force_enable` is True, even if `enabled` is False."""
# Override enabled to return False for this test
DerivedDataProvider.provider_enabled = False
DerivedDataProvider.provider_updated = False
provider.update_data(force_enable=True)
assert provider.enabled() is False, "Provider should be disabled, but enabled() is True."
assert DerivedDataProvider.provider_updated is True, (
"Provider should have been executed, but was not."
)
def test_delete_by_datetime(self, provider, sample_start_datetime):
"""Test `delete_by_datetime` method for removing records by datetime range."""
# Add records to the provider for deletion testing
provider.records = [
self.create_test_record(sample_start_datetime - to_duration("3 hours"), 1),
self.create_test_record(sample_start_datetime - to_duration("1 hour"), 2),
self.create_test_record(sample_start_datetime + to_duration("1 hour"), 3),
]
provider.delete_by_datetime(
start_datetime=sample_start_datetime - to_duration("2 hours"),
end_datetime=sample_start_datetime + to_duration("2 hours"),
)
assert len(provider.records) == 1, (
"Only one record should remain after deletion by datetime."
)
assert provider.records[0].date_time == sample_start_datetime - to_duration("3 hours"), (
"Unexpected record remains."
)
class TestDataImportProvider:
# Fixtures and helper functions
@pytest.fixture
def provider(self):
"""Fixture to provide an instance of DerivedDataImportProvider for testing."""
DerivedDataImportProvider.provider_enabled = True
DerivedDataImportProvider.provider_updated = False
return DerivedDataImportProvider()
@pytest.mark.parametrize(
"start_datetime, value_count, expected_mapping_count",
[
("2024-11-10 00:00:00", 24, 24), # No DST in Germany
("2024-08-10 00:00:00", 24, 24), # DST in Germany
("2024-03-31 00:00:00", 24, 23), # DST change in Germany (23 hours/ day)
("2024-10-27 00:00:00", 24, 25), # DST change in Germany (25 hours/ day)
],
)
def test_import_datetimes(self, provider, start_datetime, value_count, expected_mapping_count):
start_datetime = to_datetime(start_datetime, in_timezone="Europe/Berlin")
value_datetime_mapping = provider.import_datetimes(start_datetime, value_count)
assert len(value_datetime_mapping) == expected_mapping_count
@pytest.mark.parametrize(
"start_datetime, value_count, expected_mapping_count",
[
("2024-11-10 00:00:00", 24, 24), # No DST in Germany
("2024-08-10 00:00:00", 24, 24), # DST in Germany
("2024-03-31 00:00:00", 24, 23), # DST change in Germany (23 hours/ day)
("2024-10-27 00:00:00", 24, 25), # DST change in Germany (25 hours/ day)
],
)
def test_import_datetimes_utc(
self, set_other_timezone, provider, start_datetime, value_count, expected_mapping_count
):
original_tz = set_other_timezone("Etc/UTC")
start_datetime = to_datetime(start_datetime, in_timezone="Europe/Berlin")
assert start_datetime.timezone.name == "Europe/Berlin"
value_datetime_mapping = provider.import_datetimes(start_datetime, value_count)
assert len(value_datetime_mapping) == expected_mapping_count
class TestDataContainer:
# Fixture and helpers
@pytest.fixture
def container(self):
container = DerivedDataContainer()
return container
@pytest.fixture
def container_with_providers(self):
record1 = self.create_test_record(datetime(2023, 11, 5), 1)
record2 = self.create_test_record(datetime(2023, 11, 6), 2)
record3 = self.create_test_record(datetime(2023, 11, 7), 3)
provider = DerivedDataProvider()
provider.clear()
assert len(provider) == 0
provider.append(record1)
provider.append(record2)
provider.append(record3)
assert len(provider) == 3
container = DerivedDataContainer()
container.providers.clear()
assert len(container.providers) == 0
container.providers.append(provider)
assert len(container.providers) == 1
return container
def create_test_record(self, date, value):
"""Helper function to create a test DataRecord."""
return DerivedRecord(date_time=date, data_value=value)
def test_append_provider(self, container):
assert len(container.providers) == 0
container.providers.append(DerivedDataProvider())
assert len(container.providers) == 1
assert isinstance(container.providers[0], DerivedDataProvider)
@pytest.mark.skip(reason="type check not implemented")
def test_append_provider_invalid_type(self, container):
with pytest.raises(ValueError, match="must be an instance of DataProvider"):
container.providers.append("not_a_provider")
def test_getitem_existing_key(self, container_with_providers):
assert len(container_with_providers.providers) == 1
# check all keys are available (don't care for position)
for key in ["data_value", "date_time"]:
assert key in list(container_with_providers.keys())
series = container_with_providers["data_value"]
assert isinstance(series, pd.Series)
assert series.name == "data_value"
assert series.tolist() == [1.0, 2.0, 3.0]
def test_getitem_non_existing_key(self, container_with_providers):
with pytest.raises(KeyError, match="No data found for key 'non_existent_key'"):
container_with_providers["non_existent_key"]
def test_setitem_existing_key(self, container_with_providers):
new_series = container_with_providers["data_value"]
new_series[:] = [4, 5, 6]
container_with_providers["data_value"] = new_series
series = container_with_providers["data_value"]
assert series.name == "data_value"
assert series.tolist() == [4, 5, 6]
def test_setitem_invalid_value(self, container_with_providers):
with pytest.raises(ValueError, match="Value must be an instance of pd.Series"):
container_with_providers["test_key"] = "not_a_series"
def test_setitem_non_existing_key(self, container_with_providers):
new_series = pd.Series([4, 5, 6], name="non_existent_key")
with pytest.raises(KeyError, match="Key 'non_existent_key' not found"):
container_with_providers["non_existent_key"] = new_series
def test_delitem_existing_key(self, container_with_providers):
del container_with_providers["data_value"]
series = container_with_providers["data_value"]
assert series.name == "data_value"
assert series.tolist() == []
def test_delitem_non_existing_key(self, container_with_providers):
with pytest.raises(KeyError, match="Key 'non_existent_key' not found"):
del container_with_providers["non_existent_key"]
def test_len(self, container_with_providers):
assert len(container_with_providers) == 5
def test_repr(self, container_with_providers):
representation = repr(container_with_providers)
assert representation.startswith("DerivedDataContainer(")
assert "DerivedDataProvider" in representation
def test_to_json(self, container_with_providers):
json_str = container_with_providers.to_json()
container_other = DerivedDataContainer.from_json(json_str)
assert container_other == container_with_providers
def test_from_json(self, container_with_providers):
json_str = container_with_providers.to_json()
container = DerivedDataContainer.from_json(json_str)
assert isinstance(container, DerivedDataContainer)
assert len(container.providers) == 1
assert container.providers[0] == container_with_providers.providers[0]
def test_provider_by_id(self, container_with_providers):
provider = container_with_providers.provider_by_id("DerivedDataProvider")
assert isinstance(provider, DerivedDataProvider)