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# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file.
"""
Testing the decorator for computation functions.
This test checks:
- The decorator can be applied to a function
- The function can be called with and without DataSet parameters
- The metadata is correctly set and can be introspected
"""
from __future__ import annotations
import guidata.dataset as gds
import numpy as np
import pytest
from sigima.objects import ImageObj, SignalObj, create_image, create_signal
from sigima.proc.base import dst_1_to_1
from sigima.proc.decorator import (
computation_function,
get_computation_metadata,
is_computation_function,
)
from sigima.tests.helpers import check_array_result
def this_is_not_a_computation_function() -> None:
"""A dummy function that is not a computation function."""
def test_non_computation_function_marker() -> None:
"""Test that a non-computation function is not marked as such."""
assert not is_computation_function(this_is_not_a_computation_function)
with pytest.raises(ValueError):
get_computation_metadata(this_is_not_a_computation_function)
class DummySignalParam(gds.DataSet):
"""Dummy DataSet for testing purposes"""
a = gds.FloatItem("X value", default=1.0)
b = gds.FloatItem("Y value", default=5.0)
methods = (("linear", "Linear"), ("quadratic", "Quadratic"))
method = gds.ChoiceItem("Method", choices=methods, default="linear")
SCF_NAME = "dummy_signal_func"
SCF_DESCRIPTION = "A dummy signal function"
@computation_function(name=SCF_NAME, description=SCF_DESCRIPTION)
def dummy_signal_func(src: SignalObj, p: DummySignalParam) -> SignalObj:
"""A dummy function that adds two parameters from a DataSet.
Args:
src: The source SignalObj.
param: The parameters from the DummySignalParam DataSet.
Returns:
The signal with the operation applied.
"""
dst = dst_1_to_1(src, SCF_NAME, f"x={p.a:.3f}, y={p.b:.3f}")
if p.method == "linear":
dst.y = src.y + src.x * p.a + p.b
else: # Quadratic method
dst.y = src.y + src.x**2 * p.a + p.b
return dst
class DummyImageParam(gds.DataSet):
"""Dummy DataSet for testing purposes"""
alpha = gds.FloatItem("Alpha value", default=0.5)
ICF_NAME = "dummy_image_func"
ICF_DESCRIPTION = "A dummy image function"
@computation_function(name=ICF_NAME, description=ICF_DESCRIPTION)
def dummy_image_func(src: ImageObj, param: DummyImageParam) -> ImageObj:
"""A dummy function that applies a simple operation based on a DataSet parameter.
Args:
src: The source ImageObj.
param: The parameters from the DummyImageParam DataSet.
Returns:
The image with the operation applied.
"""
dst = dst_1_to_1(src, ICF_NAME, f"sigma={param.alpha:.3f}")
dst.data = src.data * param.alpha # Simplified operation for testing
return dst
def test_signal_decorator_marker() -> None:
"""Test the computation function decorator marker for signals"""
# Check if the function is marked as a computation function
assert is_computation_function(dummy_signal_func)
def test_signal_decorator_metadata() -> None:
"""Test the computation function decorator metadata for signals"""
# Check if the metadata is correctly set
metadata = get_computation_metadata(dummy_signal_func)
assert metadata.name == SCF_NAME
assert metadata.description == SCF_DESCRIPTION
def test_signal_decorator_signature() -> None:
"""Test the computation function decorator signature for signals"""
x = np.linspace(0, 10, 100)
orig = create_signal("test_signal", x=x, y=x)
# Call the function with a DataSet parameter
p = DummySignalParam.create(a=3.0, b=4.0, method="quadratic")
res_ds = dummy_signal_func(orig, p)
name = "Signal[DataSet parameter]"
check_array_result(f"{name} x", res_ds.x, orig.x)
check_array_result(f"{name} y", res_ds.y, orig.y + orig.x**2 * 3.0 + 4.0)
# Call the function with keyword arguments
# pylint: disable=no-value-for-parameter
res_kw = dummy_signal_func(orig, a=3.0, b=4.0)
name = "Signal[keyword arguments]"
check_array_result(f"{name} x", res_kw.x, orig.x)
check_array_result(f"{name} y", res_kw.y, orig.y + orig.x * 3.0 + 4.0)
# Call the function with both DataSet and keyword arguments
# The DataSet should take precedence, kwargs for DataSet items are ignored
res_both = dummy_signal_func(orig, p, a=100.0, b=200.0, method="linear")
# The result should match the DataSet values, not the conflicting kwargs
check_array_result(
"Signal[DataSet param + kwargs: DataSet wins] x", res_both.x, orig.x
)
check_array_result(
"Signal[DataSet param + kwargs: DataSet wins] y",
res_both.y,
orig.y + orig.x**2 * 3.0 + 4.0,
)
def test_image_decorator_marker() -> None:
"""Test the computation function decorator marker for images"""
# Check if the function is marked as a computation function
assert is_computation_function(dummy_image_func)
def test_image_decorator_metadata() -> None:
"""Test the computation function decorator metadata for images"""
# Check if the metadata is correctly set
metadata = get_computation_metadata(dummy_image_func)
assert metadata.name == ICF_NAME
assert metadata.description == ICF_DESCRIPTION
def test_image_decorator_signature() -> None:
"""Test the computation function decorator signature for images"""
orig = create_image("test_image", data=np.random.rand(64, 64))
# Call the function with an ImageObj and DummyImageParam
p = DummyImageParam.create(alpha=0.8)
res_ds = dummy_image_func(orig, p)
check_array_result("Image data", res_ds.data, orig.data * p.alpha)
# Call the function with keyword arguments
# pylint: disable=no-value-for-parameter
res_kw = dummy_image_func(orig, alpha=0.8)
check_array_result("Image data", res_kw.data, orig.data * 0.8)
# Call the function with both DataSet and keyword arguments
# The DataSet should take precedence, kwargs for DataSet items are ignored
res_both = dummy_image_func(orig, p, alpha=0.4)
check_array_result(
"Image data [DataSet param + kwargs: DataSet wins]",
res_both.data,
orig.data * p.alpha,
)
if __name__ == "__main__":
test_signal_decorator_marker()
test_signal_decorator_metadata()
test_signal_decorator_signature()
test_image_decorator_marker()
test_image_decorator_metadata()
test_image_decorator_signature()
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