Update types

doc/conf.py

@@ -27,7 +27,7 @@
 # -- General configuration ---------------------------------------------------
 
 # mock import for autodoc
-autodoc_mock_imports = ["baseclasses", "scipy", "numpy", "sqlitedict"]
+autodoc_mock_imports = ["baseclasses", "scipy", "sqlitedict"]
 
 # logo
 html_logo = "_static/pyOptSparse_logo.svg"

doc/install.rst

@@ -29,7 +29,7 @@ Requirements
 ~~~~~~~~~~~~
 pyOptSparse has the following dependencies:
 
-* Python 3
+* Python 3.10+
 * C and Fortran compilers.
   We recommend ``gcc`` and ``gfortran`` which can be installed via the package manager for your operating system.
 

doc/requirements.txt

@@ -1,4 +1,3 @@
 sphinx_mdolab_theme>=1.2
 sphinx-codeautolink
-# temporary pin from https://github.com/executablebooks/sphinx-tabs/issues/212
-docutils==0.21.2
+numpy

pyoptsparse/pyOpt_constraint.py

@@ -1,7 +1,7 @@
 # Standard Python modules
 from collections import OrderedDict
+from collections.abc import Iterable
 import copy
-from typing import Dict, Iterable, List, Optional, Union
 
 # External modules
 import numpy as np
@@ -18,7 +18,7 @@ def __init__(
         name: str,
         nCon: int,
         linear: bool,
-        wrt: Union[None, str, Iterable[str]],
+        wrt: str | Iterable[str] | None,
         jac: Dict1DType,
         lower,
         upper,
@@ -36,10 +36,10 @@ def __init__(
         self.linear = linear
         self.wrt = wrt
         self.jac = jac
-        self.partialReturnOk: Optional[bool] = None
+        self.partialReturnOk: bool | None = None
         self.scale = scale
-        self.rs: Optional[int] = None
-        self.re: Optional[int] = None
+        self.rs: int | None = None
+        self.re: int | None = None
         # Before we can do the processing below we need to have lower
         # and upper arguments expanded:
 
@@ -67,12 +67,12 @@ def __init__(
         # automatically.
 
         # This keeps track of the equality constraints:
-        equalityConstraints: Dict[str, List] = {"value": [], "ind": [], "fact": []}
+        equalityConstraints: dict[str, list] = {"value": [], "ind": [], "fact": []}
 
         # All (inequality) constraints get added to
         # "twoSidedConstraints". This will be used in optimizers that
         # can do two-sided constraints properly
-        twoSidedConstraints: Dict[str, List] = {"lower": [], "upper": [], "ind": [], "fact": []}
+        twoSidedConstraints: dict[str, list] = {"lower": [], "upper": [], "ind": [], "fact": []}
 
         # All (inequality) constraints are also added to
         # "oneSidedConstraints". These are processed such that the
@@ -82,7 +82,7 @@ def __init__(
         # defined which is precisely 1.0 or -1.0. The -1.0 appears
         # when a greater-than-constraint is turned into a
         # less-than-constraint.
-        oneSidedConstraints: Dict[str, List] = {"lower": [], "upper": [], "ind": [], "fact": []}
+        oneSidedConstraints: dict[str, list] = {"lower": [], "upper": [], "ind": [], "fact": []}
 
         for icon in range(self.ncon):
             # Check for equality constraint:
@@ -167,7 +167,7 @@ def __init__(
         self.oneSidedConstraints = oneSidedConstraints
         self.twoSidedConstraints = twoSidedConstraints
 
-    def finalize(self, variables: OrderedDict, dvOffset, index: int):
+    def finalize(self, variables: OrderedDict, dvOffset, index: int) -> None:
         """
         After the design variables have been finalized and the order
         is known we can check the constraint for consistency.

pyoptsparse/pyOpt_gradient.py

@@ -1,6 +1,3 @@
-# Standard Python modules
-from typing import Tuple, Union
-
 # External modules
 import numpy as np
 from numpy import ndarray
@@ -37,7 +34,7 @@ def __init__(self, optProb: Optimization, sensType: str, sensStep: float = None,
         """
         self.optProb = optProb
         self.sensType = sensType
-        self.sensStep: Union[float, complex]
+        self.sensStep: float | complex
         if sensStep is None:
             if self.sensType in ["fd", "fdr"]:
                 self.sensStep = 1e-6
@@ -58,7 +55,7 @@ def __init__(self, optProb: Optimization, sensType: str, sensStep: float = None,
         else:
             self.mydvs = list(range(ndvs))
 
-    def _eval_func(self, x: ndarray) -> Tuple[ndarray, ndarray, bool]:
+    def _eval_func(self, x: ndarray) -> tuple[ndarray, ndarray, bool]:
         """Internal method to call function and extract obj, con"""
 
         xCall = self.optProb.processXtoDict(x)
@@ -76,7 +73,7 @@ def _eval_func(self, x: ndarray) -> Tuple[ndarray, ndarray, bool]:
 
         return fobj, fcon, fail
 
-    def __call__(self, x: Dict1DType, funcs: Dict1DType) -> Tuple[Dict2DType, bool]:
+    def __call__(self, x: Dict1DType, funcs: Dict1DType) -> tuple[Dict2DType, bool]:
         """
         We need to make this object "look" the same as a user supplied
         function handle. That way, the optimizers need not care how

pyoptsparse/pyOpt_history.py

@@ -57,7 +57,7 @@ def __init__(self, fileName, optProb=None, temp=False, flag="r"):
         self.temp = temp
         self.fileName = fileName
 
-    def close(self):
+    def close(self) -> None:
         """
         Close the underlying database.
         This should only be used in write mode. In read mode, we close the db
@@ -68,7 +68,7 @@ def close(self):
             if self.temp:
                 os.remove(self.fileName)
 
-    def write(self, callCounter, data):
+    def write(self, callCounter, data) -> None:
         """
         This is the main to write data. Basically, we just pass in
         the callCounter, the integer forming the key, and a dictionary
@@ -95,7 +95,7 @@ def write(self, callCounter, data):
         self.db.sync()
         self.keys = list(self.db.keys())
 
-    def writeData(self, key, data):
+    def writeData(self, key, data) -> None:
         """
         Write arbitrary `key:data` value to db.
 
@@ -111,7 +111,7 @@ def writeData(self, key, data):
         self.db.commit()
         self.keys = list(self.db.keys())
 
-    def pointExists(self, callCounter):
+    def pointExists(self, callCounter) -> bool:
         """
         Determine if callCounter is in the database
 
@@ -152,7 +152,7 @@ def read(self, key):
         except KeyError:
             return None
 
-    def _searchCallCounter(self, x, last=None):
+    def _searchCallCounter(self, x, last=None) -> int | None:
         """
         Searches through existing callCounters, and finds the one corresponding
         to an evaluation at the design vector `x`.
@@ -186,7 +186,7 @@ def _searchCallCounter(self, x, last=None):
                 break
         return callCounter
 
-    def _processDB(self):
+    def _processDB(self) -> None:
         """
         Pre-processes the DB file and store various values into class attributes.
         These will be used later when calling self.getXX functions.
@@ -735,7 +735,7 @@ def _readValidCallCounter(self, i, user_specified_callCounter, allowSens, major)
             # end if - ("funcs" in val.keys()
         # end if - pointExists
 
-    def __del__(self):
+    def __del__(self) -> None:
         try:
             self.db.close()
             if self.temp:

pyoptsparse/pyOpt_objective.py

@@ -25,7 +25,7 @@ def __init__(self, name, scale=1.0):
         self.value = 0.0
         self.scale = scale
 
-    def __str__(self):
+    def __str__(self) -> str:
         """
         Structured Print of Objective
         """