Fix typing annotations for autofit methods

Author: PierreRaybaut

plotpy/tests/features/test_fit.py

@@ -21,8 +21,8 @@ def fit(x, params):
         a, b = params
         return np.cos(b * x) + a
 
-    a = FitParam("Offset", 1.0, 0.0, 2.0)
-    b = FitParam("Frequency", 2.0, 1.0, 10.0, logscale=True)
+    a = FitParam("Offset", 0.7, -1.0, 1.0)
+    b = FitParam("Frequency", 1.2, 0.3, 3.0, logscale=True)
     params = [a, b]
     values = guifit(x, y, fit, params, xlabel="Time (s)", ylabel="Power (a.u.)")
 

plotpy/widgets/fit.py

@@ -100,7 +100,7 @@ class AutoFitParam(DataSet):
     err_norm = StringItem(
         "enorm",
         default=2.0,
-        help=_("for simplex, powel, cg and bfgs norm used " "by the error function"),
+        help=_("for simplex, powel, cg and bfgs norm used by the error function"),
     )
     xtol = FloatItem(
         "xtol", default=0.0001, help=_("for simplex, powel, least squares")
@@ -724,7 +724,7 @@ def func(params):
 
         return func
 
-    def autofit_simplex(self, x0: float) -> np.ndarray:
+    def autofit_simplex(self, x0: np.ndarray) -> np.ndarray:
         """Autofit using simplex
 
         Args:
@@ -738,7 +738,7 @@ def autofit_simplex(self, x0: float) -> np.ndarray:
         x = fmin(self.get_norm_func(), x0, xtol=prm.xtol, ftol=prm.ftol)
         return x
 
-    def autofit_powel(self, x0: float) -> np.ndarray:
+    def autofit_powel(self, x0: np.ndarray) -> np.ndarray:
         """Autofit using Powell
 
         Args:
@@ -752,7 +752,7 @@ def autofit_powel(self, x0: float) -> np.ndarray:
         x = fmin_powell(self.get_norm_func(), x0, xtol=prm.xtol, ftol=prm.ftol)
         return x
 
-    def autofit_bfgs(self, x0: float) -> np.ndarray:
+    def autofit_bfgs(self, x0: np.ndarray) -> np.ndarray:
         """Autofit using BFGS
 
         Args:
@@ -766,7 +766,7 @@ def autofit_bfgs(self, x0: float) -> np.ndarray:
         x = fmin_bfgs(self.get_norm_func(), x0, gtol=prm.gtol, norm=eval(prm.norm))
         return x
 
-    def autofit_l_bfgs(self, x0: float) -> np.ndarray:
+    def autofit_l_bfgs(self, x0: np.ndarray) -> np.ndarray:
         """Autofit using L-BFGS-B
 
         Args:
@@ -783,7 +783,7 @@ def autofit_l_bfgs(self, x0: float) -> np.ndarray:
         )
         return x
 
-    def autofit_cg(self, x0: float) -> np.ndarray:
+    def autofit_cg(self, x0: np.ndarray) -> np.ndarray:
         """Autofit using conjugate gradient
 
         Args:
@@ -797,7 +797,7 @@ def autofit_cg(self, x0: float) -> np.ndarray:
         x = fmin_cg(self.get_norm_func(), x0, gtol=prm.gtol, norm=eval(prm.norm))
         return x
 
-    def autofit_lq(self, x0: float) -> np.ndarray:
+    def autofit_lq(self, x0: np.ndarray) -> np.ndarray:
         """Autofit using leastsq
 
         Args: