Cleanup type hints + spelling, source API links

CHANGES.md

@@ -27,7 +27,7 @@
 **Bug fixes**
 
 * Correct dependency name for jupyter-notebook
-* Move jupyter-notebook to base install dependecy
+* Move jupyter-notebook to base install dependency
 
 **New features**
 

docs/source/tutorials.rst

@@ -5,7 +5,7 @@ The tutorials are written as Jupyter Notebooks which are available in the `GitHu
 The two tutorials use the `WaveBot`_ WEC, which is a single-body WEC developed at Sandia.
 The tutorials are meant to be sequential.
 The first tutorial solves only the inner optimization loop, and serves as an introduction to WecOptTool.
-The second tutorial builds on the first to solve a design opotimization problem, using both the inner and outer optimization loops, and is more reflective of the optimization problems WecOptTool is designed to solve.
+The second tutorial builds on the first to solve a design optimization problem, using both the inner and outer optimization loops, and is more reflective of the optimization problems WecOptTool is designed to solve.
 
 - :doc:`_examples/tutorial_1_wavebot`: Simple example of the *inner loop* using a single-body WEC moving in one degree of freedom in regular waves. The example finds the optimal control strategy for a fixed WEC design. See :cite:`Coe2020Initial` for further discussion of this example.
 - :doc:`_examples/tutorial_2_wavebot_optimization`: Simple example of a design optimization problem (*outer* and *inner* optimization loops). The example optimizes the WEC geometry (outer loop) while finding the optimal control strategy for each design considered (inner loop).

wecopttool/core.py

@@ -22,7 +22,7 @@
 from autograd import grad, jacobian
 import xarray as xr
 import capytaine as cpy
-from scipy.optimize import minimize
+from scipy.optimize import minimize, OptimizeResult
 from scipy.linalg import block_diag
 import matplotlib.pyplot as plt
 import matplotlib as mpl
@@ -68,15 +68,15 @@ def __init__(self, fb: cpy.FloatingBody, mass: np.ndarray,
         mass: np.ndarray
             Mass matrix shape of (``ndof`` x ``ndof``).
         hydrostatic_stiffness: np.ndarray
-            Hydrstatic stiffness matrix matrix of shape
+            Hydrostatic stiffness matrix matrix of shape
             (``ndof`` x ``ndof``).
         f0: float
             Initial frequency (in Hz) for frequency array.
             Frequency array given as [``f0``, 2 ``f0``, ..., ``nfreq f0``].
         nfreq: int
             Number of frequencies in frequency array. See ``f0``.
         dissipation: np.ndarray
-            Additional dissipiation for the impedance calculation in
+            Additional dissipation for the impedance calculation in
             ``capytaine.post_pro.impedance``. Shape:
             (``ndof`` x ``ndof`` x ``1``) or (``ndof`` x ``ndof`` x ``nfreq``).
         stiffness: np.ndarray
@@ -586,7 +586,7 @@ def solve(self,
               use_grad: bool = True,
               maximize: bool = False,
               ) -> tuple[xr.Dataset, xr.Dataset, np.ndarray, np.ndarray, float,
-                         optimize.optimize.OptimizeResult]:
+                         OptimizeResult]:
         """Solve the WEC co-design problem.
 
         Parameters
@@ -884,7 +884,7 @@ def scale_dofs(scale_list: list[float], ncomponents: int) -> np.ndarray:
 
 
 def complex_xarray_from_netcdf(fpath: str | Path) -> xr.Dataset:
-    """Read a NetCDF file with commplex entries as an xarray dataSet.
+    """Read a NetCDF file with complex entries as an xarray dataSet.
     """
     with xr.open_dataset(fpath) as ds:
         ds.load()
@@ -1226,7 +1226,7 @@ def delaxes(axs, idof, jdof, ndof):
 
 
 def post_process_continuous_time(results: xr.DataArray
-                                 ) -> Callable[float, float]:
+                                 ) -> Callable[[float, float]]:
     """Create a continuous function from the results in an xarray
     DataArray.