reduced search for nls

akvo/tressel/decay.py

     if x0=="None":
         x0 = np.array( [1., 0., 0., .2] )
         res_lsq = least_squares(fun, x0, args=(tt, np.concatenate((X, Y))), loss='cauchy', f_scale=1.0,\
-            bounds=( [1., 0, -13, .005] , [1000., 2*np.pi, 13, .800] ))
+            bounds=( [1., -np.pi, -5, .005] , [1000., np.pi, 5, .800] ))
     else:
         res_lsq = least_squares(fun, x0, args=(tt, np.concatenate((X, Y))), loss='cauchy', f_scale=1.0,\
-            bounds=( [1., 0, -13, .005] , [1000., 2*np.pi, 13, .800] ))
+            bounds=( [1., -np.pi, -5, .005] , [1000., np.pi, 5, .800] ))
 
         #bounds=( [0., 0, -20, .0] , [1., np.pi, 20, .6] ))
 

akvo/tressel/rotate.py

         # decimate
     # blind decimation
     # 1 instead of T 
-    irsamp = (T) * int(  (1./vL) / dt) # real 
+    irsamp = int(T) * int(  (1./vL) / dt) # real 
     iisamp =       int(  ((1./vL)/ dt) * ( .5*np.pi / (2.*np.pi) ) ) # imaginary
    
 
     #############################################################
     ## In-phase 
     #2*np.cos(wL*t)  
-    dw = -2.*np.pi*2
+    dw = 0 # -2.*np.pi*2
     Q = signal.filtfilt(b, a, xn*2*np.cos((wL+dw)*t))  # X
     I = signal.filtfilt(b, a, xn*2*np.sin((wL+dw)*t))  # Y