refactor(src/openscm_calibration/type_hinting.py): remove :mod:`opens…

…cm_calibration.type_hinting`

This was a hack, it is much better using https://numpy.org/devdocs/reference/arrays.scalars.html#numpy.number

docs/source/api/openscm_calibration.rst

@@ -37,5 +37,4 @@
    openscm_calibration.scipy_plotting
    openscm_calibration.scmdata_utils
    openscm_calibration.store
-   openscm_calibration.type_hints
 

src/openscm_calibration/emcee_plotting.py

@@ -19,8 +19,6 @@
     import emcee
     import matplotlib
 
-    from openscm_calibration.type_hints import NPArrayFloatOrInt
-
 try:
     import corner
 
@@ -42,7 +40,9 @@ def plot_chains(  # noqa: PLR0913
     parameter_order: list[str],
     neg_log_likelihood_name: str,
     axes_d: dict[str, matplotlib.axes.Axes],
-    get_neg_log_likelihood_ylim: Callable[[NPArrayFloatOrInt], tuple[float, float]]
+    get_neg_log_likelihood_ylim: Callable[
+        [np.typing.NDArray[np.floating[Any] | np.integer[Any]]], tuple[float, float]
+    ]
     | None = None,
     **kwargs: Any,
 ) -> None:
@@ -106,7 +106,7 @@ def plot_chains(  # noqa: PLR0913
 
 def plot_parameter_chains(  # noqa: PLR0913
     ax: matplotlib.Axes.axes,
-    chain_values: NPArrayFloatOrInt,
+    chain_values: np.typing.NDArray[np.number[Any]],
     burnin: int,
     alpha_chain: float = 0.3,
     linewidth: float = 0.5,
@@ -173,7 +173,7 @@ def plot_parameter_chains(  # noqa: PLR0913
 
 
 def get_neg_log_likelihood_ylim_default(
-    neg_ll_values: NPArrayFloatOrInt,
+    neg_ll_values: np.typing.NDArray[np.floating[Any] | np.integer[Any]],
     median_scaling: float = 1.5,
     max_scaling: float = 2.0,
 ) -> tuple[float, float]:

src/openscm_calibration/emcee_utils.py

@@ -10,11 +10,11 @@
 if TYPE_CHECKING:
     # See here for explanation of this pattern and why we don't need quotes
     # below https://docs.python.org/3/library/typing.html#constant
+    from typing import Any
+
     import emcee.backends
     import numpy.typing as nptype
 
-    from openscm_calibration.type_hints import NPAnyFloat, NPArrayFloatOrInt
-
 
 def get_acceptance_fractions(
     chains: nptype.NDArray[np.float_],
@@ -53,7 +53,7 @@ def get_autocorrelation_info(
     thin: int = 1,
     autocorr_tol: int = 0,
     convergence_ratio: float = 50,
-) -> dict[str, float | int | bool | nptype.NDArray[NPAnyFloat]]:
+) -> dict[str, float | int | bool | nptype.NDArray[np.floating[Any]]]:
     """
     Get info about autocorrelation in chains
 
@@ -114,7 +114,7 @@ def get_labelled_chain_data(
     neg_log_likelihood_name: str | None = None,
     burnin: int = 0,
     thin: int = 0,
-) -> dict[str, NPArrayFloatOrInt]:
+) -> dict[str, np.typing.NDArray[np.floating[Any] | np.integer[Any]]]:
     """
     Get labelled chain data
 
@@ -135,6 +135,11 @@ def get_labelled_chain_data(
 
     thin
         Thinning to use when sampling the chains
+
+    Returns
+    -------
+        Chain data, labelled with parameter names and, if requested,
+        ``neg_log_likelihood_name``
     """
     all_samples = inp.get_chain(discard=burnin, thin=thin)
 

src/openscm_calibration/minimize.py

@@ -11,7 +11,7 @@
 from openscm_calibration.store import OptResStore
 
 if TYPE_CHECKING:
-    from openscm_calibration.type_hints import NPArrayFloatOrInt
+    from typing import Any
 
 
 class SupportsCostCalculation(Protocol):
@@ -41,7 +41,7 @@ class SupportsModelRun(Protocol):
 
     def run_model(
         self,
-        x: NPArrayFloatOrInt,
+        x: np.typing.NDArray[np.number[Any]],
     ) -> scmdata.run.BaseScmRun:
         """
         Calculate cost function
@@ -58,7 +58,7 @@ def run_model(
 
 
 def to_minimize_full(
-    x: NPArrayFloatOrInt,
+    x: np.typing.NDArray[np.number[Any]],
     cost_calculator: SupportsCostCalculation,
     model_runner: SupportsModelRun,
     store: OptResStore | None = None,

src/openscm_calibration/model_runner.py

@@ -20,8 +20,6 @@
     import pint
     import scmdata.run
 
-    from openscm_calibration.type_hints import NPAnyFloat, NPArrayFloatOrInt
-
 
 class XToNamedPintConvertor(Protocol):
     """
@@ -30,7 +28,7 @@ class XToNamedPintConvertor(Protocol):
 
     def __call__(
         self,
-        x: NPArrayFloatOrInt,
+        x: np.typing.NDArray[np.number[Any]],
     ) -> dict[str, pint.Quantity[np.float_] | np.float_]:
         """
         Convert x to pint quantities
@@ -136,7 +134,7 @@ def from_parameters(
 
     def run_model(
         self,
-        x: NPArrayFloatOrInt,
+        x: np.typing.NDArray[np.number[Any]],
     ) -> scmdata.run.BaseScmRun:
         """
         Run the model
@@ -160,10 +158,10 @@ def run_model(
 
 
 def x_and_parameters_to_named_with_units(
-    x: NPArrayFloatOrInt,
+    x: np.typing.NDArray[np.number[Any]],
     params: Iterable[tuple[str, str | pint.Unit | None]],
     get_unit_registry: Callable[[], pint.UnitRegistry] | None = None,
-) -> dict[str, pint.Quantity[NPAnyFloat] | NPAnyFloat]:
+) -> dict[str, pint.Quantity[np.floating[Any]] | np.floating[Any]]:
     """
     Convert x array and parameters to a dictionary and add units
 

src/openscm_calibration/scipy_plotting.py

@@ -29,7 +29,6 @@
     import tqdm
 
     from openscm_calibration.store import OptResStore
-    from openscm_calibration.type_hints import NPArrayFloatOrInt
 
 
 logger: logging.Logger = logging.getLogger(__name__)
@@ -43,7 +42,7 @@ class SupportsScipyOptCallback(Protocol):
 
     def callback_minimize(
         self,
-        xk: NPArrayFloatOrInt,
+        xk: np.typing.NDArray[np.number[Any]],
     ) -> None:
         """
         Get cost of parameter vector
@@ -58,7 +57,7 @@ def callback_minimize(
 
     def callback_differential_evolution(
         self,
-        xk: NPArrayFloatOrInt,
+        xk: np.typing.NDArray[np.number[Any]],
         convergence: float | None = None,
     ) -> None:
         """
@@ -290,7 +289,7 @@ class OptPlotter:
 
     def callback_minimize(
         self,
-        xk: NPArrayFloatOrInt,
+        xk: np.typing.NDArray[np.number[Any]],
     ) -> None:
         """
         Update the plots
@@ -307,7 +306,7 @@ def callback_minimize(
 
     def callback_differential_evolution(
         self,
-        xk: NPArrayFloatOrInt,
+        xk: np.typing.NDArray[np.number[Any]],
         convergence: float | None = None,
     ) -> None:
         """
@@ -607,7 +606,7 @@ def get_ymax_default(
 
 def plot_parameters(
     axes: dict[str, matplotlib.axes.Axes],
-    para_vals: dict[str, NPArrayFloatOrInt],
+    para_vals: dict[str, np.typing.NDArray[np.number[Any]]],
     alpha: float = 0.7,
     **kwargs: Any,
 ) -> None:
@@ -883,7 +882,7 @@ class CallbackProxy:
 
     def callback_minimize(
         self,
-        xk: NPArrayFloatOrInt,
+        xk: np.typing.NDArray[np.number[Any]],
     ) -> None:
         """
         Update the plots
@@ -901,7 +900,7 @@ def callback_minimize(
 
     def callback_differential_evolution(
         self,
-        xk: NPArrayFloatOrInt,
+        xk: np.typing.NDArray[np.number[Any]],
         convergence: float | None = None,
     ) -> None:
         """

src/openscm_calibration/store.py

@@ -19,8 +19,6 @@
     import attr
     import scmdata.run
 
-    from openscm_calibration.type_hints import NPArrayFloatOrInt
-
 
 class SupportsListLikeHandling(Protocol):
     """
@@ -146,7 +144,7 @@ class OptResStore:
     )
     """Costs of runs"""
 
-    x_samples: MutableSequence[None | NPArrayFloatOrInt] = field(
+    x_samples: MutableSequence[None | np.typing.NDArray[np.number[Any]]] = field(
         validator=[_all_none_to_start, _same_length_as_res]
     )
     """x vectors sampled"""
@@ -240,7 +238,7 @@ def set_result_cost_x(
         self,
         res: None | scmdata.run.BaseScmRun,
         cost: float,
-        x: NPArrayFloatOrInt,
+        x: np.typing.NDArray[np.number[Any]],
         idx: int,
     ) -> None:
         """
@@ -278,7 +276,7 @@ def append_result_cost_x(
         self,
         res: scmdata.run.BaseScmRun,
         cost: float,
-        x: NPArrayFloatOrInt,
+        x: np.typing.NDArray[np.number[Any]],
     ) -> None:
         """
         Append result, cost and x from a successful run to the results
@@ -308,7 +306,7 @@ def append_result_cost_x(
     def note_failed_run(
         self,
         cost: float,
-        x: NPArrayFloatOrInt,
+        x: np.typing.NDArray[np.number[Any]],
     ) -> None:
         """
         Note that a run failed
@@ -338,7 +336,7 @@ def get_costs_xsamples_res(
         self,
     ) -> tuple[
         tuple[float, ...],
-        tuple[NPArrayFloatOrInt, ...],
+        tuple[np.typing.NDArray[np.number[Any]], ...],
         tuple[scmdata.run.BaseScmRun, ...],
     ]:
         """
@@ -370,7 +368,7 @@ def get_costs_xsamples_res(
 
         # Help out type hinting
         costs: tuple[float, ...] = tmp[0]
-        xs_out: tuple[NPArrayFloatOrInt, ...] = tmp[1]
+        xs_out: tuple[np.typing.NDArray[np.number[Any]], ...] = tmp[1]
         ress: tuple[scmdata.run.BaseScmRun, ...] = tmp[2]
 
         out = (costs, xs_out, ress)
@@ -381,7 +379,7 @@ def get_costs_labelled_xsamples_res(
         self,
     ) -> tuple[
         tuple[float, ...],
-        dict[str, NPArrayFloatOrInt],
+        dict[str, np.typing.NDArray[np.number[Any]]],
         tuple[scmdata.run.BaseScmRun, ...],
     ]:
         """