corrected seq_multi_fourparam_... by implementing a stretch factor

SineTools.py

@@ -894,7 +894,7 @@ def seq_multi_threeparsinefit(f,y,t,periods=1, D_fr=None, abc0=None, progressive
     return f_ab_c, y0, y-y0  # coefficients, fit signal, residuals
 
 
-def seq_multi_fourparam_Dmatrix(f,t, abc=None, periods=1, progressive=True):
+def seq_multi_fourparam_Dmatrix(f,t, delta, abc=None, periods=1, progressive=True):
     """
     Fit a multi-sinus-signal in slices in one go.
 
@@ -904,6 +904,8 @@ def seq_multi_fourparam_Dmatrix(f,t, abc=None, periods=1, progressive=True):
         list of (adapted) frequencies in the signal 
     t : numpy.array of floats
         timestamps of y (typically seconds) 
+    delta: float (scalar)
+         common stretch factor for the time-scale or shrink for the frequency
     abc :numpy.array of floats
          parameters from last fit-iteration 
     periods : float, optional
@@ -923,7 +925,7 @@ def seq_multi_fourparam_Dmatrix(f,t, abc=None, periods=1, progressive=True):
 
     ci = []  # initialise column indices
     ri = [a for a in range(Nt)]*(2*Nf+2) # row indices
-    data = [0.0]*len(ri)  # initialise column indices
+    data = [0.0]*len(ri)  # initialise data
     fr = []
     col_a =[]  # extra part -A*t*sin(om t)
     col_b=[]   # extra part  B*t*cos(om t)
@@ -936,7 +938,7 @@ def seq_multi_fourparam_Dmatrix(f,t, abc=None, periods=1, progressive=True):
     for fi, omi in zip(f,2*np.pi*f):
         col_a =[]  # extra part -A*t*sin(om t)
         col_b=[]   # extra part  B*t*cos(om t)
-        om_t = omi*t        # omega*t
+        om_t = omi*t*delta        # omega*t
         co = np.cos(om_t)
         si = np.sin(om_t)
         data[f_count*Nt:(f_count+1)*Nt] = co.tolist() # sparse matrix entries
@@ -950,6 +952,7 @@ def seq_multi_fourparam_Dmatrix(f,t, abc=None, periods=1, progressive=True):
         N_samp = int(tau//np.mean(np.diff(t)))  # samples/rows per slice
         N_slices = len(t)//N_samp               # slices for 
         
+        
         slic_inds = np.array_split(np.arange(len(t)), N_slices) # r-indices in the slices
         if len(slic_inds[-1] < N_samp):  # if last slice too short
             slic_inds[-2] = np.concatenate([slic_inds[-2],slic_inds[-1]])  # merge last two slices
@@ -968,7 +971,7 @@ def seq_multi_fourparam_Dmatrix(f,t, abc=None, periods=1, progressive=True):
             for i,s in enumerate(slic_inds):
                 col_a = col_a + [-abc[2*i]]*len(s)  # list of A coeffs of cos
                 col_b = col_b + [abc[2*i+1]]*len(s) # list of B coeffs of sine
-            last_col = last_col + t*omi*(np.array(col_b)*co + np.array(col_a)*si) # add up 4 param column
+            last_col = last_col + om_t*(np.array(col_b)*co + np.array(col_a)*si) # add up 4 param column
         # else last column is zero as initialised
             
         c_count += 2*len(slic_inds)                        
@@ -1027,19 +1030,22 @@ def seq_multi_fourparam_sineFit(f,y,t,periods=1, progressive=True, tol=1.0e-9, n
     abc = (f_abc[0:-1,1:3]).flatten()  # a,b,a,b,a,b,
     abc = np.hstack([abc, f_abc[-2,-2],0.0])  # ababab, c, dw
     
+    delta = 1.0 # frequency/time stretch factor
     i = 0
     
     while True: # do-while emulation 
-        fr, D = seq_multi_fourparam_Dmatrix(f,t, abc=abc, periods=periods, progressive=progressive)  # calculate the design matrix (as sparse matrix)
+        print("%d. call to solver" % (i+1))
+        fr, D = seq_multi_fourparam_Dmatrix(f,t,delta, abc=abc, periods=periods, progressive=progressive)  # calculate the design matrix (as sparse matrix)
         param = sp_lsqr(D,y,x0=abc, atol=1.0e-10, btol=1.0e-10)
-        #print("abc: %s" % str(param))
-        f = f*(1+param[0][-1]) # korrekt the frequencies by the factor \Delta\omega
         abc = param[0] 
-        print("\delta\Omega: %g" % param[0][-1])
-        print("f= %s" % str(f))
+        delta = delta*(1+abc[-1]) # korrekt the timestamps by the factor 1 over \Delta\omega
+        print("\delta\Omega: %g" % abc[-1])
+        print("delta= %s" % str(delta))
+        print("corrected f: %s" % str(delta*f))
         
-        if (param[0][-1]/f[0]<tol) :
-            print("fit arrived at tolerance")
+        if (np.abs(abc[-1])<tol) :
+            print("fit arrived at tolerance after %d iterations" % (i+1))
+            print("last correction: %g" % abc[-1])
             break 
         
         if (i>n_max):
@@ -1048,7 +1054,7 @@ def seq_multi_fourparam_sineFit(f,y,t,periods=1, progressive=True, tol=1.0e-9, n
         i += 1
     
     y0 = D.dot(param[0])
-    fr = fr*(1+param[0][-1])
+    fr = fr*delta
     
     f_ab_c =[]
     k=0