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Bobby NoelteandClaude Opus 5.5
faf37abfe7
An inverter profile describes the technical data of an inverter model: its nominal AC power, the DC→AC and AC→DC efficiency curves over the load fraction (AC power relative to the nominal power), the AC charge power limit of the model, whether that limit also limits PV charging, and an optional power derating over the ambient temperature. Curves are read linearly and clamped at their ends, so every lookup yields a value. The packaged profile generic-hybrid has typical hybrid inverter efficiencies, no nominal power (the inverter device has to provide it) and full power up to 45 degC. Inverter settings name their profile (inverter_profile, default generic-hybrid) and hold the differences of the unit in profile_adjustment: nominal_power_w (replaces the profile value, required for a generic profile), max_power_w and max_ac_charge_power_w (installation limits that can only lower; an AC charge limit of 0 disables AC charging). The effective parameters are resolved at the ambient temperature of the inverter, given by the new measurement key <device_id>-temperature-degc (45 degC without a fresh value); the power derating lowers the maximum power and the AC charge limit. Optimization parameters are always valid: the GENETIC and GENETIC0 parameter preparation resolve the inverter with resolve_current_operating_parameters_or_defaults, which never raises. An unusable profile gives the generic-hybrid profile, a missing nominal power the fallback of 10000 W, an inverter derated to zero power the parameters at 45 degC, and no usable profile at all the profile adjustment with constant efficiency; each is logged as error. The GENETIC inverter takes the AC→DC efficiency curve of the profile, solved exactly on each linear piece of the curve (about 2 % longer runs); GENETIC0 keeps constant efficiencies and no longer replaces the configured inverters with the demo inverter; a battery, electric vehicle or inverter that cannot be prepared is left out of the GENETIC0 run, never replaced by a demo device. Configurations migrate: max_power_w becomes profile_adjustment.nominal_power_w, max_ac_charge_power_w moves into the adjustment, an ac_to_dc_efficiency of 0 keeps AC charging disabled, and the efficiency fields are dropped with an error log, as for batteries, so the profile efficiencies apply. Home Assistant config paths follow. Additional changes - Move the curve algorithms of the battery profile into profilecurve, so battery and inverter profiles share them; the battery profile keeps its document format and classes. - Move ProfileBaseModel, the strict base of profile documents, into profilestore. - Serve inverter profiles through the profile REST API. Refuse to delete a profile that a configured device uses and that has no packaged profile to revert to (409). - Share the measurement access of the device settings in devicemeasurement. - Rename check_battery_profiles to check_device_profiles; the device profile check at configuration setup covers inverters, too. - Document inverter profiles, their configuration, defaults and sharing; regenerate the configuration and OpenAPI documentation. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Assisted-by: Claude Code (claude-opus-5-5)
93 lines
3.6 KiB
Python
93 lines
3.6 KiB
Python
"""Tests for the one-dimensional curves shared by the device profiles."""
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import numpy as np
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import pytest
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from akkudoktoreos.devices.profile.profilecurve import (
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CurveInterpolation,
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OutOfRangeBehavior,
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PrecomputedCurve,
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reference_curve_lookup,
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validate_curve_points,
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)
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X = (0.0, 0.2, 0.5, 1.0)
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Y = (0.80, 0.95, 0.97, 0.96)
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class TestValidateCurvePoints:
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def test_accepts_increasing_points(self):
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validate_curve_points(X, Y, "load_fraction", "efficiency")
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@pytest.mark.parametrize("x", [(0.0, 0.0, 1.0), (0.0, 0.5, 0.4)])
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def test_rejects_not_strictly_increasing(self, x):
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with pytest.raises(ValueError, match="load_fraction breakpoints must be strictly"):
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validate_curve_points(x, (1.0, 1.0, 1.0), "load_fraction", "efficiency")
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def test_rejects_mismatched_lengths(self):
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with pytest.raises(ValueError, match="same number of values"):
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validate_curve_points(X, Y[:-1], "load_fraction", "efficiency")
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class TestPrecomputedCurve:
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@pytest.mark.parametrize("interpolation", list(CurveInterpolation))
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@pytest.mark.parametrize("behavior", list(OutOfRangeBehavior))
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def test_lookup_matches_reference(self, interpolation, behavior):
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curve = PrecomputedCurve.from_points(X, Y, interpolation, behavior)
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queries = [-0.5, 0.0, 0.1, 0.2, 0.35, 0.5, 0.75, 1.0, 1.5, 0.6]
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for query in queries:
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expected = reference_curve_lookup(X, Y, interpolation, behavior, query)
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actual = curve.lookup(query)
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if expected is None:
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assert actual is None
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else:
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assert actual == pytest.approx(expected)
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def test_linear_interpolation(self):
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curve = PrecomputedCurve.from_points(X, Y)
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assert curve.lookup(0.1) == pytest.approx(0.875)
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assert curve.lookup(0.75) == pytest.approx(0.965)
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def test_clamps_by_default(self):
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curve = PrecomputedCurve.from_points(X, Y)
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assert curve.lookup(-1.0) == pytest.approx(0.80)
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assert curve.lookup(2.0) == pytest.approx(0.96)
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def test_nearest_tie_takes_lower_breakpoint(self):
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curve = PrecomputedCurve.from_points(X, Y, CurveInterpolation.NEAREST)
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assert curve.lookup(0.1) == pytest.approx(0.80)
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def test_none_interpolation_only_at_breakpoints(self):
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curve = PrecomputedCurve.from_points(X, Y, CurveInterpolation.NONE)
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assert curve.lookup(0.5) == pytest.approx(0.97)
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assert curve.lookup(0.4) is None
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def test_reject_out_of_range(self):
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curve = PrecomputedCurve.from_points(X, Y, out_of_range_behavior=OutOfRangeBehavior.REJECT)
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assert curve.lookup(-0.1) is None
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assert curve.lookup(1.1) is None
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def test_precomputes_coefficients(self):
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curve = PrecomputedCurve.from_points(X, Y)
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assert len(curve.slopes) == len(X) - 1
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assert curve.midpoints == pytest.approx((0.1, 0.35, 0.75))
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for i, slope in enumerate(curve.slopes):
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assert slope * X[i] + curve.intercepts[i] == pytest.approx(Y[i])
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def test_accepts_numpy_input(self):
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curve = PrecomputedCurve.from_points(np.array(X), np.array(Y))
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assert curve.x == X
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@pytest.mark.parametrize(
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"x, y",
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[((0.0,), (1.0,)), ((0.0, 0.0), (1.0, 1.0)), ((0.0, 1.0), (1.0,))],
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)
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def test_rejects_invalid_points(self, x, y):
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with pytest.raises(ValueError):
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PrecomputedCurve.from_points(x, y)
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def test_is_immutable(self):
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curve = PrecomputedCurve.from_points(X, Y)
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with pytest.raises(AttributeError):
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curve.x = (0.0, 1.0) # type: ignore[misc]
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