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Add simple_efficiency tests for numpy array inputs #2661

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Add simple_efficiency tests for numpy array inputs
aman-coder03 Jan 21, 2026
7abb5e7
fix flake
aman-coder03 Jan 21, 2026
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40 changes: 40 additions & 0 deletions tests/test_transformer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,8 @@

from pvlib import transformer

import numpy as np


def test_simple_efficiency():

Expand Down Expand Up @@ -58,3 +60,41 @@ def test_simple_efficiency_known_values():
*args),
rating,
)


def test_simple_efficiency_numpy_array_inputs():
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@cwhanse cwhanse Feb 4, 2026

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@aman-coder03 I think it is premature to test with all inputs as np.array. In other discussions we don't have consensus to allow the parameters no_load_loss and load_loss to be anything other than float, and intend to edit the docstring accordingly.

If we agree in the future to allow vectors for these parameters, then this test would be needed.

I don't want to include it now so to prevent someone's AI from deciding we have missed a combination of types in other tests.

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@aman-coder03 aman-coder03 Feb 5, 2026

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Would you prefer that I remove the test that uses array inputs for no_load_loss and load_loss, and keep only the vectorized input_power case? Or should I drop the vectorized tests entirely for now?

input_power = np.array([100.0, 500.0, 1000.0])
no_load_loss = np.array([0.005, 0.005, 0.005])
load_loss = np.array([0.01, 0.01, 0.01])
rating = 1000.0

output_power = transformer.simple_efficiency(
input_power=input_power,
no_load_loss=no_load_loss,
load_loss=load_loss,
transformer_rating=rating
)

assert isinstance(output_power, np.ndarray)
assert output_power.shape == input_power.shape


def test_simple_efficiency_vector_equals_scalar():
input_power = np.array([200.0, 600.0, 900.0])
no_load_loss = 0.005
load_loss = 0.01
rating = 1000.0

vector_result = transformer.simple_efficiency(
input_power=input_power,
no_load_loss=no_load_loss,
load_loss=load_loss,
transformer_rating=rating
)

scalar_result = np.array([
transformer.simple_efficiency(p, no_load_loss, load_loss, rating)
for p in input_power
])

assert_allclose(vector_result, scalar_result)