mirror of
https://github.com/Akkudoktor-EOS/EOS.git
synced 2025-04-19 08:55:15 +00:00
be26457563
* All config now nested.
- Use default config from model field default values. If providers
should be enabled by default, non-empty default config file could
be provided again.
- Environment variable support with EOS_ prefix and __ between levels,
e.g. EOS_SERVER__EOS_SERVER_PORT=8503 where all values are case
insensitive.
For more information see:
https://docs.pydantic.dev/latest/concepts/pydantic_settings/#parsing-environment-variable-values
- Use devices as registry for configured devices. DeviceBase as base
class with for now just initializion support (in the future expand
to operations during optimization).
- Strip down ConfigEOS to the only configuration instance. Reload
from file or reset to defaults is possible.
* Fix multi-initialization of derived SingletonMixin classes.
227 lines
8.1 KiB
Python
227 lines
8.1 KiB
Python
import numpy as np
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import pytest
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from pendulum import datetime, duration
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from akkudoktoreos.config.config import SettingsEOS
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from akkudoktoreos.measurement.measurement import (
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MeasurementCommonSettings,
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MeasurementDataRecord,
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get_measurement,
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)
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@pytest.fixture
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def measurement_eos():
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"""Fixture to create a Measurement instance."""
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measurement = get_measurement()
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measurement.records = [
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MeasurementDataRecord(
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date_time=datetime(2023, 1, 1, hour=0),
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measurement_load0_mr=100,
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measurement_load1_mr=200,
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),
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MeasurementDataRecord(
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date_time=datetime(2023, 1, 1, hour=1),
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measurement_load0_mr=150,
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measurement_load1_mr=250,
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),
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MeasurementDataRecord(
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date_time=datetime(2023, 1, 1, hour=2),
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measurement_load0_mr=200,
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measurement_load1_mr=300,
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),
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MeasurementDataRecord(
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date_time=datetime(2023, 1, 1, hour=3),
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measurement_load0_mr=250,
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measurement_load1_mr=350,
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),
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MeasurementDataRecord(
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date_time=datetime(2023, 1, 1, hour=4),
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measurement_load0_mr=300,
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measurement_load1_mr=400,
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),
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MeasurementDataRecord(
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date_time=datetime(2023, 1, 1, hour=5),
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measurement_load0_mr=350,
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measurement_load1_mr=450,
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),
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]
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return measurement
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def test_interval_count(measurement_eos):
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"""Test interval count calculation."""
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start = datetime(2023, 1, 1, 0)
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end = datetime(2023, 1, 1, 3)
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interval = duration(hours=1)
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assert measurement_eos._interval_count(start, end, interval) == 3
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def test_interval_count_invalid_end_before_start(measurement_eos):
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"""Test interval count raises ValueError when end_datetime is before start_datetime."""
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start = datetime(2023, 1, 1, 3)
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end = datetime(2023, 1, 1, 0)
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interval = duration(hours=1)
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with pytest.raises(ValueError, match="end_datetime must be after start_datetime"):
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measurement_eos._interval_count(start, end, interval)
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def test_interval_count_invalid_non_positive_interval(measurement_eos):
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"""Test interval count raises ValueError when interval is non-positive."""
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start = datetime(2023, 1, 1, 0)
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end = datetime(2023, 1, 1, 3)
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with pytest.raises(ValueError, match="interval must be positive"):
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measurement_eos._interval_count(start, end, duration(hours=0))
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def test_energy_from_meter_readings_valid_input(measurement_eos):
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"""Test _energy_from_meter_readings with valid inputs and proper alignment of load data."""
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key = "measurement_load0_mr"
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start_datetime = datetime(2023, 1, 1, 0)
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end_datetime = datetime(2023, 1, 1, 5)
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interval = duration(hours=1)
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load_array = measurement_eos._energy_from_meter_readings(
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key, start_datetime, end_datetime, interval
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)
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expected_load_array = np.array([50, 50, 50, 50, 50]) # Differences between consecutive readings
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np.testing.assert_array_equal(load_array, expected_load_array)
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def test_energy_from_meter_readings_empty_array(measurement_eos):
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"""Test _energy_from_meter_readings with no data (empty array)."""
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key = "measurement_load0_mr"
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start_datetime = datetime(2023, 1, 1, 0)
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end_datetime = datetime(2023, 1, 1, 5)
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interval = duration(hours=1)
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# Use empyt records array
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measurement_eos.records = []
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load_array = measurement_eos._energy_from_meter_readings(
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key, start_datetime, end_datetime, interval
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)
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# Expected: an array of zeros with one less than the number of intervals
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expected_size = (
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measurement_eos._interval_count(start_datetime, end_datetime + interval, interval) - 1
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)
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expected_load_array = np.zeros(expected_size)
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np.testing.assert_array_equal(load_array, expected_load_array)
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def test_energy_from_meter_readings_misaligned_array(measurement_eos):
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"""Test _energy_from_meter_readings with misaligned array size."""
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key = "measurement_load1_mr"
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start_datetime = measurement_eos.min_datetime
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end_datetime = measurement_eos.max_datetime
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interval = duration(hours=1)
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# Use misaligned array, latest interval set to 2 hours (instead of 1 hour)
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measurement_eos.records[-1].date_time = datetime(2023, 1, 1, 6)
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load_array = measurement_eos._energy_from_meter_readings(
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key, start_datetime, end_datetime, interval
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)
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expected_load_array = np.array([50, 50, 50, 50, 25]) # Differences between consecutive readings
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np.testing.assert_array_equal(load_array, expected_load_array)
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def test_energy_from_meter_readings_partial_data(measurement_eos, caplog):
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"""Test _energy_from_meter_readings with partial data (misaligned but empty array)."""
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key = "measurement_load2_mr"
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start_datetime = datetime(2023, 1, 1, 0)
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end_datetime = datetime(2023, 1, 1, 5)
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interval = duration(hours=1)
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with caplog.at_level("DEBUG"):
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load_array = measurement_eos._energy_from_meter_readings(
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key, start_datetime, end_datetime, interval
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)
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expected_size = (
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measurement_eos._interval_count(start_datetime, end_datetime + interval, interval) - 1
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)
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expected_load_array = np.zeros(expected_size)
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np.testing.assert_array_equal(load_array, expected_load_array)
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def test_energy_from_meter_readings_negative_interval(measurement_eos):
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"""Test _energy_from_meter_readings with a negative interval."""
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key = "measurement_load3_mr"
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start_datetime = datetime(2023, 1, 1, 0)
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end_datetime = datetime(2023, 1, 1, 5)
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interval = duration(hours=-1)
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with pytest.raises(ValueError, match="interval must be positive"):
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measurement_eos._energy_from_meter_readings(key, start_datetime, end_datetime, interval)
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def test_load_total(measurement_eos):
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"""Test total load calculation."""
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start = datetime(2023, 1, 1, 0)
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end = datetime(2023, 1, 1, 2)
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interval = duration(hours=1)
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result = measurement_eos.load_total(start_datetime=start, end_datetime=end, interval=interval)
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# Expected total load per interval
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expected = np.array([100, 100]) # Differences between consecutive meter readings
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np.testing.assert_array_equal(result, expected)
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def test_load_total_no_data(measurement_eos):
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"""Test total load calculation with no data."""
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measurement_eos.records = []
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start = datetime(2023, 1, 1, 0)
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end = datetime(2023, 1, 1, 3)
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interval = duration(hours=1)
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result = measurement_eos.load_total(start_datetime=start, end_datetime=end, interval=interval)
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expected = np.zeros(3) # No data, so all intervals are zero
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np.testing.assert_array_equal(result, expected)
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def test_name_to_key(measurement_eos):
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"""Test name_to_key functionality."""
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settings = SettingsEOS(
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measurement=MeasurementCommonSettings(
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measurement_load0_name="Household",
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measurement_load1_name="Heat Pump",
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)
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)
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measurement_eos.config.merge_settings(settings)
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assert measurement_eos.name_to_key("Household", "measurement_load") == "measurement_load0_mr"
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assert measurement_eos.name_to_key("Heat Pump", "measurement_load") == "measurement_load1_mr"
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assert measurement_eos.name_to_key("Unknown", "measurement_load") is None
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def test_name_to_key_invalid_topic(measurement_eos):
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"""Test name_to_key with an invalid topic."""
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settings = SettingsEOS(
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MeasurementCommonSettings(
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measurement_load0_name="Household",
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measurement_load1_name="Heat Pump",
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)
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)
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measurement_eos.config.merge_settings(settings)
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assert measurement_eos.name_to_key("Household", "invalid_topic") is None
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def test_load_total_partial_intervals(measurement_eos):
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"""Test total load calculation with partial intervals."""
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start = datetime(2023, 1, 1, 0, 30) # Start in the middle of an interval
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end = datetime(2023, 1, 1, 1, 30) # End in the middle of another interval
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interval = duration(hours=1)
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result = measurement_eos.load_total(start_datetime=start, end_datetime=end, interval=interval)
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expected = np.array([100]) # Only one complete interval covered
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np.testing.assert_array_equal(result, expected)
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