[WIP] Test code suggestions for PR5

.cruft.json

@@ -5,8 +5,8 @@
   "context": {
     "cookiecutter": {
       "full_name": "Anmol Bhatia",
-      "email": "[email protected]",
-      "github_username": "glotaran",
+      "email": "[email protected]",
+      "github_username": "anmolbhatia05",
       "project_name": "PyParamGUI",
       "project_slug": "pyparamgui",
       "project_slug_url": "pyparamgui",

.gitignore

@@ -104,3 +104,6 @@ ENV/
 
 # IDE settings
 .vscode/
+
+examples/
+!examples/*.ipynb

.pre-commit-config.yaml

@@ -74,13 +74,13 @@ repos:
         alias: flake8-docs
         args:
           - "--select=DOC"
-          - "--extend-ignore=DOC502"
+          - "--extend-ignore=DOC502,DOC601,DOC603,DOC101,DOC103,DOC201"
           - "--color=always"
           - "--require-return-section-when-returning-nothing=False"
           - "--allow-init-docstring=True"
           - "--skip-checking-short-docstrings=False"
         name: "flake8 lint docstrings"
-        exclude: "^(docs/|tests?/)"
+        exclude: "^(docs/|tests?/|pyparamgui/generator.py)"
         additional_dependencies: [pydoclint==0.5.3]
 
   - repo: https://github.com/econchick/interrogate
@@ -97,6 +97,7 @@ repos:
         types: [file]
         types_or: [python, pyi, markdown, rst, jupyter]
         args: [-L nnumber]
+        exclude: ^examples/
 
   - repo: https://github.com/rhysd/actionlint
     rev: "v1.7.1"

pyparamgui/__init__.py

@@ -3,5 +3,22 @@
 from __future__ import annotations
 
 __author__ = """Anmol Bhatia"""
-__email__ = "[email protected]"
+__email__ = "[email protected]"
 __version__ = "0.0.1"
+
+from pyparamgui.widget import _widget
+from pyparamgui.widget import setup_widget_observer
+
+__all__ = ["_widget", "setup_widget_observer"]
+"""
+Package Usage:
+    # considering that each command is run in a separate jupyter notebook cell
+
+    %env ANYWIDGET_HMR=1
+
+    from pyparamgui import widget, setup_widget_observer
+
+    widget
+
+    setup_widget_observer()
+"""

pyparamgui/generator.py

@@ -0,0 +1,278 @@
+"""The glotaran generator module."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+from typing import Any
+from typing import TypedDict
+from typing import cast
+
+from glotaran.builtin.io.yml.utils import write_dict
+from glotaran.builtin.megacomplexes.decay import DecayParallelMegacomplex
+from glotaran.builtin.megacomplexes.decay import DecaySequentialMegacomplex
+from glotaran.builtin.megacomplexes.spectral import SpectralMegacomplex
+from glotaran.model import Model
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+
+def _generate_decay_model(
+    *, nr_compartments: int, irf: bool, spectral: bool, decay_type: str
+) -> dict[str, Any]:
+    """Generate a decay model dictionary.
+
+    Parameters
+    ----------
+    nr_compartments : int
+        The number of compartments.
+    irf : bool
+        Whether to add a gaussian irf.
+    spectral : bool
+        Whether to add a spectral model.
+    decay_type : str
+        The type of the decay
+
+    Returns
+    -------
+    dict[str, Any]
+        The generated model dictionary.
+    """
+    compartments = [f"species_{i+1}" for i in range(nr_compartments)]
+    rates = [f"rates.species_{i+1}" for i in range(nr_compartments)]
+
+    model: dict[str, Any] = {
+        "megacomplex": {
+            f"megacomplex_{decay_type}_decay": {
+                "type": f"decay-{decay_type}",
+                "compartments": compartments,
+                "rates": rates,
+            },
+        },
+        "dataset": {"dataset_1": {"megacomplex": [f"megacomplex_{decay_type}_decay"]}},
+    }
+    if spectral:
+        model["megacomplex"] |= {
+            "megacomplex_spectral": {
+                "type": "spectral",
+                "shape": {
+                    compartment: f"shape_species_{i+1}"
+                    for i, compartment in enumerate(compartments)
+                },
+            }
+        }
+        model["shape"] = {
+            f"shape_species_{i+1}": {
+                "type": "skewed-gaussian",
+                "amplitude": f"shapes.species_{i+1}.amplitude",
+                "location": f"shapes.species_{i+1}.location",
+                "width": f"shapes.species_{i+1}.width",
+                "skewness": f"shapes.species_{i+1}.skewness",
+            }
+            for i in range(nr_compartments)
+        }
+        model["dataset"]["dataset_1"] |= {
+            "global_megacomplex": ["megacomplex_spectral"],
+            "spectral_axis_inverted": True,
+            "spectral_axis_scale": 1e7,
+        }
+    if irf:
+        model["dataset"]["dataset_1"] |= {"irf": "gaussian_irf"}
+        model["irf"] = {
+            "gaussian_irf": {"type": "gaussian", "center": "irf.center", "width": "irf.width"},
+        }
+    return model
+
+
+def generate_parallel_decay_model(
+    *, nr_compartments: int = 1, irf: bool = False
+) -> dict[str, Any]:
+    """Generate a parallel decay model dictionary.
+
+    Parameters
+    ----------
+    nr_compartments : int
+        The number of compartments.
+    irf : bool
+        Whether to add a gaussian irf.
+
+    Returns
+    -------
+    dict[str, Any]
+        The generated model dictionary.
+    """
+    return _generate_decay_model(
+        nr_compartments=nr_compartments, irf=irf, spectral=False, decay_type="parallel"
+    )
+
+
+def generate_parallel_spectral_decay_model(
+    *, nr_compartments: int = 1, irf: bool = False
+) -> dict[str, Any]:
+    """Generate a parallel spectral decay model dictionary.
+
+    Parameters
+    ----------
+    nr_compartments : int
+        The number of compartments.
+    irf : bool
+        Whether to add a gaussian irf.
+
+    Returns
+    -------
+    dict[str, Any]
+        The generated model dictionary.
+    """
+    return _generate_decay_model(
+        nr_compartments=nr_compartments, irf=irf, spectral=True, decay_type="parallel"
+    )
+
+
+def generate_sequential_decay_model(
+    *, nr_compartments: int = 1, irf: bool = False
+) -> dict[str, Any]:
+    """Generate a sequential decay model dictionary.
+
+    Parameters
+    ----------
+    nr_compartments : int
+        The number of compartments.
+    irf : bool
+        Whether to add a gaussian irf.
+
+    Returns
+    -------
+    dict[str, Any]
+        The generated model dictionary.
+    """
+    return _generate_decay_model(
+        nr_compartments=nr_compartments, irf=irf, spectral=False, decay_type="sequential"
+    )
+
+
+def generate_sequential_spectral_decay_model(
+    *, nr_compartments: int = 1, irf: bool = False
+) -> dict[str, Any]:
+    """Generate a sequential spectral decay model dictionary.
+
+    Parameters
+    ----------
+    nr_compartments : int
+        The number of compartments.
+    irf : bool
+        Whether to add a gaussian irf.
+
+    Returns
+    -------
+    dict[str, Any]
+        The generated model dictionary.
+    """
+    return _generate_decay_model(
+        nr_compartments=nr_compartments, irf=irf, spectral=True, decay_type="sequential"
+    )
+
+
+generators: dict[str, Callable] = {
+    "decay_parallel": generate_parallel_decay_model,
+    "spectral_decay_parallel": generate_parallel_spectral_decay_model,
+    "decay_sequential": generate_sequential_decay_model,
+    "spectral_decay_sequential": generate_sequential_spectral_decay_model,
+}
+
+available_generators: list[str] = list(generators.keys())
+
+
+class GeneratorArguments(TypedDict, total=False):
+    """Arguments used by ``generate_model`` and ``generate_model``.
+
+    Parameters
+    ----------
+    nr_compartments : int
+        The number of compartments.
+    irf : bool
+        Whether to add a gaussian irf.
+
+    See Also
+    --------
+    generate_model
+    generate_model_yml
+    """
+
+    nr_compartments: int
+    irf: bool
+
+
+def generate_model(*, generator_name: str, generator_arguments: GeneratorArguments) -> Model:
+    """Generate a model.
+
+    Parameters
+    ----------
+    generator_name : str
+        The generator to use.
+    generator_arguments : GeneratorArguments
+        Arguments for the generator.
+
+    Returns
+    -------
+    Model
+        The generated model
+
+    See Also
+    --------
+    generate_parallel_decay_model
+    generate_parallel_spectral_decay_model
+    generate_sequential_decay_model
+    generate_sequential_spectral_decay_model
+
+    Raises
+    ------
+    ValueError
+        Raised when an unknown generator is specified.
+    """
+    if generator_name not in generators:
+        msg = (
+            f"Unknown model generator '{generator_name}'. "
+            f"Known generators are: {list(generators.keys())}"
+        )
+        raise ValueError(msg)
+    model = generators[generator_name](**generator_arguments)
+    return Model.create_class_from_megacomplexes(
+        [DecayParallelMegacomplex, DecaySequentialMegacomplex, SpectralMegacomplex]
+    )(**model)
+
+
+def generate_model_yml(*, generator_name: str, generator_arguments: GeneratorArguments) -> str:
+    """Generate a model as yml string.
+
+    Parameters
+    ----------
+    generator_name : str
+        The generator to use.
+    generator_arguments : GeneratorArguments
+        Arguments for the generator.
+
+    Returns
+    -------
+    str
+        The generated model yml string.
+
+    See Also
+    --------
+    generate_parallel_decay_model
+    generate_parallel_spectral_decay_model
+    generate_sequential_decay_model
+    generate_sequential_spectral_decay_model
+
+    Raises
+    ------
+    ValueError
+        Raised when an unknown generator is specified.
+    """
+    if generator_name not in generators:
+        msg = (
+            f"Unknown model generator '{generator_name}'. "
+            f"Known generators are: {list(generators.keys())}"
+        )
+        raise ValueError(msg)
+    model = generators[generator_name](**generator_arguments)
+    return cast(str, write_dict(model))