Merge branch 'type-hints' into wbfit

src/pint/fitter.py

@@ -2491,7 +2491,13 @@ def scaled_all_sigma(self):
                 scaled_sigmas_no_unit.append(scaled_sigma)
         return np.hstack(scaled_sigmas_no_unit)
 
-    def fit_toas(self, maxiter=1, threshold=0, full_cov=False, debug=False):
+    def fit_toas(
+        self,
+        maxiter: int = 1,
+        threshold: float = 0.0,
+        full_cov: bool = False,
+        debug: bool = False,
+    ):
         """Carry out a generalized least-squares fitting procedure.
 
         The algorithm here is essentially the same as used in
@@ -2671,15 +2677,15 @@ def fit_toas(self, maxiter=1, threshold=0, full_cov=False, debug=False):
 class LMFitter(Fitter):
     def fit_toas(
         self,
-        maxiter=50,
+        maxiter: int = 50,
         *,
-        min_chi2_decrease=1e-3,
-        lambda_factor_decrease=2,
-        lambda_factor_increase=3,
-        lambda_factor_invalid=10,
-        threshold=1e-14,
-        min_lambda=0.5,
-        debug=False,
+        min_chi2_decrease: float = 1e-3,
+        lambda_factor_decrease: float = 2.0,
+        lambda_factor_increase: float = 3.0,
+        lambda_factor_invalid: float = 10.0,
+        threshold: float = 1e-14,
+        min_lambda: float = 0.5,
+        debug: bool = False,
     ):
         current_state = self.create_state()
         try:
@@ -2800,7 +2806,14 @@ class WidebandLMFitter(LMFitter):
     Unfortunately it doesn't.
     """
 
-    def __init__(self, toas, model, track_mode=None, residuals=None, add_args=None):
+    def __init__(
+        self,
+        toas: TOAs,
+        model: TimingModel,
+        track_mode: Optional[Literal["use_pulse_numbers", "nearest"]] = None,
+        residuals: WidebandTOAResiduals = None,
+        add_args: Optional[dict] = None,
+    ):
         self.method = "downhill_wideband"
         self.full_cov = False
         self.threshold = 0
@@ -2810,9 +2823,12 @@ def __init__(self, toas, model, track_mode=None, residuals=None, add_args=None):
         )
         self.is_wideband = True
 
+        self.model: TimingModel
+        self.toas: TOAs
         self.resids: WidebandTOAResiduals
+        self.parameter_covariance_matrix: CovarianceMatrix
 
-    def make_resids(self, model):
+    def make_resids(self, model: TimingModel):
         return WidebandTOAResiduals(
             self.toas,
             model,
@@ -2825,12 +2841,14 @@ def create_state(self):
             self, self.model, full_cov=self.full_cov, threshold=self.threshold
         )
 
-    def fit_toas(self, maxiter=50, full_cov=False, debug=False, **kwargs):
+    def fit_toas(
+        self, maxiter: int = 50, full_cov: bool = False, debug: bool = False, **kwargs
+    ):
         self.full_cov = full_cov
         # FIXME: set up noise residuals et cetera
         return super().fit_toas(maxiter=maxiter, debug=debug, **kwargs)
 
-    def update_from_state(self, state, debug=False):
+    def update_from_state(self, state: WidebandState, debug: bool = False):
         # Nicer not to keep this if we have a choice, it introduces reference cycles
         self.current_state = state
         self.model = state.model