account for possible angling in lmfit_gaussian

lib/imaging.py

@@ -299,8 +299,13 @@ def lmfit_gaussian(data, dA, k=None):
         sx = params["sx"]
         y0 = params["y0"]
         sy = params["sy"]
+        th = params["theta"]
         B = params["B"]
-        return A * np.exp(-((_X - x0) / sx)**2 - ((_Y - y0) / sy)**2) + B
+        _Xrel = _X - x0
+        _Yrel = _Y - y0
+        _Xrot = np.cos(th) * _Xrel + np.sin(th) * _Yrel
+        _Yrot = -np.sin(th) * _Xrel + np.cos(th) * _Yrel
+        return A * np.exp(-(_Xrot / sx)**2 - (_Yrot / sy)**2) + B
 
     def residual(params: lmfit.Parameters):
         m = model(params)
@@ -312,6 +317,7 @@ def lmfit_gaussian(data, dA, k=None):
     params.add("sx", value=(x.max() - x.min()) / 12)
     params.add("y0", value=y0)
     params.add("sy", value=(y.max() - y.min()) / 12)
+    params.add("theta", value=0.0, min=0.0, max=np.pi / 2)
     params.add("B", value=data.mean())
     fit = lmfit.minimize(residual, params)
     if not fit.success:
@@ -331,5 +337,6 @@ def lmfit_gaussian(data, dA, k=None):
         print(fit.params["sx"])
         print(fit.params["y0"])
         print(fit.params["sy"])
+        print(fit.params["theta"])
     return float(fit.params["sx"]), float(fit.params["sy"])