19121091639

Committed 06 Nov 2025 12:49AM UTC coverage: 87.696% (-0.03%) from 87.724%

Build # 19121091639

Build Type

Pull #292

github

Committed by

web-flow

Commit Message

Merge dc3a4ebb7 into 5813eaa20

Pull Request Pull Request #292: Create a mechanism to generate and test on reference results

Run Details

23 of 36 new or added lines in 2 files covered. (63.89%)

2 existing lines in 1 file now uncovered.

14476 of 16507 relevant lines covered (87.7%)

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51.85

/redback/test_utils/ref_data_utils.py

"""A helper function to generate reference data for a given model."""

import numpy as np
from astropy.table import Table
from itertools import product


def make_ref_data(model, param_grid, other_kwargs, filename):
    """Generate reference data for a given model.

    Parameters
    ----------
    model : callable
        The model function to generate data for.
    required_args : dict
        A list of the names of the required arguments for the model function.
    param_grid : dict
        A dictionary where keys are parameter names and values are lists of parameter values to sample.
    other_kwargs : dict
        Additional keyword arguments to pass to the model function.
    filename : str
        The name of the file to save the reference data to (ECSV format).
    """
    # Create a list of all combinations of parameters
    keys, values = zip(*param_grid.items())
    param_combinations = [dict(zip(keys, v)) for v in product(*values)]

    # Generate a table of all inputs to the function and the results.
    all_data = {k: [] for k in keys}
    all_data.update({k: [] for k in other_kwargs.keys()})
    all_data["results"] = []

    for current_params in param_combinations:
        # Save these sets of keyword arguments.
        current_kwargs = {**other_kwargs, **current_params}
        for k, v in current_kwargs.items():
            all_data[k].append(v)

        # Evaluate the model and save the results.
        result = model(**current_kwargs)
        all_data["results"].append(result)

    # Convert to Table and save as an ECSV file.
    table = Table(all_data)
    table.write(filename, format='ascii.ecsv', overwrite=True)


def assert_matches_ref_data(model, filename):
    """Test a model against reference data.

    Parameters
    ----------
    model : callable
        The model function to test.
    filename : str
        The name of the file containing the reference data (ECSV format).
    other_kwargs : dict
        Additional keyword arguments to pass to the model function.
    """
    # Load the reference data
    table = Table.read(filename, format='ascii.ecsv')

    # Extract parameters and results
    param_names = [col for col in table.colnames if col != "results"]
    params = table[param_names].as_array()
    reference_results = [np.array(x) for x in table["results"]]

    # Test the model against the reference results
    for idx in range(len(table)):
        current_params = {name: params[idx][name] for name in param_names}
        ref_result = reference_results[idx]
        model_result = model(**current_params)
        assert np.allclose(model_result, ref_result), f"Model output does not match reference data for parameters: {current_params}"

1	"""A helper function to generate reference data for a given model."""
2
3	import numpy as np	1✔
4	from astropy.table import Table	1✔
5	from itertools import product	1✔
6
7
8	def make_ref_data(model, param_grid, other_kwargs, filename):	1✔
9	"""Generate reference data for a given model.
10
11	Parameters
12	----------
13	model : callable
14	The model function to generate data for.
15	required_args : dict
16	A list of the names of the required arguments for the model function.
17	param_grid : dict
18	A dictionary where keys are parameter names and values are lists of parameter values to sample.
19	other_kwargs : dict
20	Additional keyword arguments to pass to the model function.
21	filename : str
22	The name of the file to save the reference data to (ECSV format).
23	"""
24	# Create a list of all combinations of parameters
NEW 25	keys, values = zip(*param_grid.items())	×
NEW 26	param_combinations = [dict(zip(keys, v)) for v in product(*values)]	×
27
28	# Generate a table of all inputs to the function and the results.
NEW 29	all_data = {k: [] for k in keys}	×
NEW 30	all_data.update({k: [] for k in other_kwargs.keys()})	×
NEW 31	all_data["results"] = []	×
32
NEW 33	for current_params in param_combinations:	×
34	# Save these sets of keyword arguments.
NEW 35	current_kwargs = {other_kwargs, current_params}	×
NEW 36	for k, v in current_kwargs.items():	×
NEW 37	all_data[k].append(v)	×
38
39	# Evaluate the model and save the results.
NEW 40	result = model(**current_kwargs)	×
NEW 41	all_data["results"].append(result)	×
42
43	# Convert to Table and save as an ECSV file.
NEW 44	table = Table(all_data)	×
NEW 45	table.write(filename, format='ascii.ecsv', overwrite=True)	×
46
47
48	def assert_matches_ref_data(model, filename):	1✔
49	"""Test a model against reference data.
50
51	Parameters
52	----------
53	model : callable
54	The model function to test.
55	filename : str
56	The name of the file containing the reference data (ECSV format).
57	other_kwargs : dict
58	Additional keyword arguments to pass to the model function.
59	"""
60	# Load the reference data
61	table = Table.read(filename, format='ascii.ecsv')	1✔
62
63	# Extract parameters and results
64	param_names = [col for col in table.colnames if col != "results"]	1✔
65	params = table[param_names].as_array()	1✔
66	reference_results = [np.array(x) for x in table["results"]]	1✔
67
68	# Test the model against the reference results
69	for idx in range(len(table)):	1✔
70	current_params = {name: params[idx][name] for name in param_names}	1✔
71	ref_result = reference_results[idx]	1✔
72	model_result = model(**current_params)	1✔
73	assert np.allclose(model_result, ref_result), f"Model output does not match reference data for parameters: {current_params}"	1✔

nikhil-sarin / redback / 19121091639

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