fix ModelResult.eval_uncertainty() for complex model

lmfit/model.py

@@ -1585,18 +1585,20 @@ def eval_uncertainty(self, params=None, sigma=1, **kwargs):
 
         nvarys = self.nvarys
         # ensure fjac and df2 are correct size if independent var updated by kwargs
-        ndata = self.model.eval(params, **userkws).size
+        feval = self.model.eval(params, **userkws)
+        ndata = len(feval.view('float64'))        # allows feval to be complex
         covar = self.covar
         if any(p.stderr is None for p in params.values()):
             return np.zeros(ndata)
 
-        fjac = {'0': np.zeros((nvarys, ndata))}  # '0' signify 'Full', an invalid prefix
-        df2 = {'0': np.zeros(ndata)}
+        # '0' would be an invalid prefix, here signifying 'Full'
+        fjac = {'0': np.zeros((nvarys, ndata), dtype='float64')}
+        df2 = {'0': np.zeros(ndata, dtype='float64')}
 
         for comp in self.components:
             label = comp.prefix if len(comp.prefix) > 1 else comp._name
-            fjac[label] = np.zeros((nvarys, ndata))
-            df2[label] = np.zeros(ndata)
+            fjac[label] = np.zeros((nvarys, ndata), dtype='float64')
+            df2[label] = np.zeros(ndata, dtype='float64')
 
         # find derivative by hand!
         pars = params.copy()
@@ -1614,7 +1616,8 @@ def eval_uncertainty(self, params=None, sigma=1, **kwargs):
 
             pars[pname].value = val0
             for key in fjac:
-                fjac[key][i] = (res1[key] - res2[key]) / (2*dval)
+                fjac[key][i] = (res1[key].view('float64')
+                                - res2[key].view('float64')) / (2*dval)
 
         for i in range(nvarys):
             for j in range(nvarys):
@@ -1627,6 +1630,12 @@ def eval_uncertainty(self, params=None, sigma=1, **kwargs):
             prob = erf(sigma/np.sqrt(2))
 
         scale = t.ppf((prob+1)/2.0, self.ndata-nvarys)
+
+        # for complex data, convert back to real/imag pairs
+        if feval.dtype in ('complex64', 'complex128'):
+            for key in fjac:
+                df2[key] = df2[key][0::2] + 1j * df2[key][1::2]
+
         self.dely = scale * np.sqrt(df2.pop('0'))
 
         self.dely_comps = {}