def mock_scheme(tmp_path: Path) -> Scheme:
model_yml_str = """
megacomplex:
m1:
type: decay
k_matrix: []
dataset:
dataset1:
megacomplex: [m1]
"""
model_path = tmp_path / "model.yml"
model_path.write_text(model_yml_str)
parameter_path = tmp_path / "parameters.yml"
parameter_path.write_text("[1.0, 67.0]")
dataset_path = tmp_path / "dataset.nc"
xr.DataArray([[1, 2, 3]], coords=[
("spectral", [1]), ("time", [1, 2, 3])
]).to_dataset(name="data").to_netcdf(dataset_path)
scheme_yml_str = f"""
model: {model_path}
parameters: {parameter_path}
maximum_number_function_evaluations: 42
data:
dataset1: {dataset_path}
"""
scheme_path = tmp_path / "scheme.yml"
scheme_path.write_text(scheme_yml_str)
return load_scheme(scheme_path)
def test_scheme(tmpdir):
model_path = tmpdir.join("model.yml")
with open(model_path, "w") as f:
model = "type: kinetic-spectrum\ndataset:\n dataset1:\n megacomplex: []"
f.write(model)
parameter_path = tmpdir.join("parameters.yml")
with open(parameter_path, "w") as f:
parameter = "[1.0, 67.0]"
f.write(parameter)
dataset_path = tmpdir.join("dataset.nc")
xr.DataArray([[1, 2, 3]], coords=[
("e", [1]), ("c", [1, 2, 3])
]).to_dataset(name="data").to_netcdf(dataset_path)
scheme = f"""
model: {model_path}
parameters: {parameter_path}
non-negative-least-squares: True
maximum-number-function-evaluations: 42
data:
dataset1: {dataset_path}
saving:
level: minimal
data_filter: [a, b, c]
data_format: csv
parameter_format: yaml
report: false
"""
scheme_path = tmpdir.join("scheme.yml")
with open(scheme_path, "w") as f:
f.write(scheme)
scheme = load_scheme(scheme_path)
assert scheme.model is not None
assert scheme.model.model_type == "kinetic-spectrum"
assert scheme.parameters is not None
assert scheme.parameters.get("1") == 1.0
assert scheme.parameters.get("2") == 67.0
assert scheme.non_negative_least_squares
assert scheme.maximum_number_function_evaluations == 42
assert "dataset1" in scheme.data
assert scheme.data["dataset1"].data.shape == (1, 3)
assert scheme.saving.level == "minimal"
assert scheme.saving.data_filter == ["a", "b", "c"]
assert scheme.saving.data_format == "csv"
assert scheme.saving.parameter_format == "yaml"
assert not scheme.saving.report
def from_yaml_file(filename: str) -> Scheme:
"""Create :class:`Scheme` from specs in yaml file.
Warning
-------
Deprecated use ``glotaran.io.load_scheme(filename)`` instead.
Parameters
----------
filename : str
Path to the spec file.
Returns
-------
Scheme
Analysis schmeme
"""
return load_scheme(filename)
def test_save_scheme(tmp_path: Path):
save_model(MODEL, tmp_path / "m.yml")
save_parameters(PARAMETERS, tmp_path / "p.csv")
save_dataset(DATASET, tmp_path / "d.nc")
scheme = Scheme(
MODEL,
PARAMETERS,
{"dataset_1": DATASET},
)
scheme_path = tmp_path / "testscheme.yml"
save_scheme(file_name=scheme_path, format_name="yml", scheme=scheme)
assert scheme_path.is_file()
assert scheme_path.read_text() == want
loaded = load_scheme(scheme_path)
print(loaded.model.validate(loaded.parameters))
assert loaded.model.valid(loaded.parameters)
assert isinstance(scheme.data["dataset_1"], xr.Dataset)
def load_scheme_file(filename, verbose=False):
return _load_file(filename,
lambda file: load_scheme(file, format_name="yml"),
"scheme", verbose)