from __future__ import annotations
from pathlib import Path
from typing import TYPE_CHECKING
from glotaran.builtin.io.yml.utils import load_dict
from glotaran.builtin.io.yml.utils import write_dict
from glotaran.deprecation.modules.builtin_io_yml import model_spec_deprecations
from glotaran.deprecation.modules.builtin_io_yml import scheme_spec_deprecations
from glotaran.io import SAVING_OPTIONS_DEFAULT
from glotaran.io import ProjectIoInterface
from glotaran.io import SavingOptions
from glotaran.io import register_project_io
from glotaran.io import save_model
from glotaran.io import save_result
from glotaran.io import save_scheme
from glotaran.model import Model
from glotaran.parameter import ParameterGroup
from glotaran.project.dataclass_helpers import asdict
from glotaran.project.dataclass_helpers import fromdict
from glotaran.project.project import Result
from glotaran.project.scheme import Scheme
from glotaran.utils.sanitize import sanitize_yaml
if TYPE_CHECKING:
from typing import Any
[docs]@register_project_io(["yml", "yaml", "yml_str"])
class YmlProjectIo(ProjectIoInterface):
[docs] def load_model(self, file_name: str) -> Model:
"""parse_yaml_file reads the given file and parses its content as YML.
Parameters
----------
filename : str
filename is the of the file to parse.
Returns
-------
Model
The content of the file as dictionary.
"""
spec = self._load_yml(file_name)
model_spec_deprecations(spec)
spec = sanitize_yaml(spec)
default_megacomplex = spec.get("default_megacomplex")
if default_megacomplex is None and any(
"type" not in m for m in spec["megacomplex"].values()
):
raise ValueError(
"Default megacomplex is not defined in model and "
"at least one megacomplex does not have a type."
)
if "megacomplex" not in spec:
raise ValueError("No megacomplex defined in model")
return Model.from_dict(spec, megacomplex_types=None, default_megacomplex_type=None)
[docs] def save_model(self, model: Model, file_name: str):
"""Save a Model instance to a spec file.
Parameters
----------
model: Model
Model instance to save to specs file.
file_name : str
File to write the model specs to.
"""
model_dict = model.as_dict()
# We replace tuples with strings
for items in model_dict.values():
if not isinstance(items, (list, dict)):
continue
item_iterator = items if isinstance(items, list) else items.values()
for item in item_iterator:
for prop_name, prop in item.items():
if isinstance(prop, dict) and any(isinstance(k, tuple) for k in prop):
keys = [f"({k[0]}, {k[1]})" for k in prop]
item[prop_name] = {f"{k}": v for k, v in zip(keys, prop.values())}
write_dict(model_dict, file_name=file_name)
[docs] def load_parameters(self, file_name: str) -> ParameterGroup:
"""Create a ParameterGroup instance from the specs defined in a file.
Parameters
----------
file_name : str
File containing the parameter specs.
Returns
-------
ParameterGroup
ParameterGroup instance created from the file.
"""
spec = self._load_yml(file_name)
if isinstance(spec, list):
return ParameterGroup.from_list(spec)
else:
return ParameterGroup.from_dict(spec)
[docs] def load_scheme(self, file_name: str) -> Scheme:
spec = self._load_yml(file_name)
scheme_spec_deprecations(spec)
return fromdict(Scheme, spec, folder=Path(file_name).parent)
[docs] def save_scheme(self, scheme: Scheme, file_name: str):
scheme_dict = asdict(scheme, folder=Path(file_name).parent)
write_dict(scheme_dict, file_name=file_name)
[docs] def load_result(self, result_path: str) -> Result:
"""Create a :class:`Result` instance from the specs defined in a file.
Parameters
----------
result_path : str | PathLike[str]
Path containing the result data.
Returns
-------
Result
:class:`Result` instance created from the saved format.
"""
spec = self._load_yml(result_path)
return fromdict(Result, spec, folder=Path(result_path).parent)
[docs] def save_result(
self,
result: Result,
result_path: str,
saving_options: SavingOptions = SAVING_OPTIONS_DEFAULT,
) -> list[str]:
"""Write a :class:`Result` instance to a spec file and data files.
Returns a list with paths of all saved items.
The following files are saved if not configured otherwise:
* ``result.md``: The result with the model formatted as markdown text.
* ``result.yml``: Yaml spec file of the result
* ``model.yml``: Model spec file.
* ``scheme.yml``: Scheme spec file.
* ``initial_parameters.csv``: Initially used parameters.
* ``optimized_parameters.csv``: The optimized parameter as csv file.
* ``parameter_history.csv``: Parameter changes over the optimization
* ``{dataset_label}.nc``: The result data for each dataset as NetCDF file.
Parameters
----------
result : Result
:class:`Result` instance to write.
result_path : str | PathLike[str]
Path to write the result data to.
saving_options : SavingOptions
Options for saving the the result.
Returns
-------
list[str]
List of file paths which were created.
"""
result_folder = Path(result_path).parent
paths = save_result(
result,
result_folder,
format_name="folder",
saving_options=saving_options,
allow_overwrite=True,
used_inside_of_plugin=True,
)
model_path = result_folder / "model.yml"
save_model(result.scheme.model, model_path, allow_overwrite=True)
paths.append(model_path.as_posix())
scheme_path = result_folder / "scheme.yml"
save_scheme(result.scheme, scheme_path, allow_overwrite=True)
paths.append(scheme_path.as_posix())
result_dict = asdict(result, folder=result_folder)
write_dict(result_dict, file_name=result_path)
paths.append(result_path)
return paths
def _load_yml(self, file_name: str) -> dict[str, Any]:
return load_dict(file_name, self.format != "yml_str")