harmonic

akvo/gui/akvoGUI.py

@@ -165,7 +165,8 @@ class ApplicationWindow(QtWidgets.QMainWindow):
         self.ui.gateIntegrateGO.pressed.connect( self.gateIntegrate )
         self.ui.calcQGO.pressed.connect( self.calcQ )
         self.ui.FDSmartStackGO.pressed.connect( self.FDSmartStack )
-        
+        self.ui.harmonicGO.pressed.connect( self.harmonicModel )       
+ 
         self.ui.plotQD.setEnabled(False) 
         self.ui.plotQD.pressed.connect( self.plotQD )
         
@@ -1061,6 +1062,12 @@ class ApplicationWindow(QtWidgets.QMainWindow):
                 (self.ui.CentralVSpinBox.value(), \
                 self.ui.mplwidget))
 
+    def harmonicModel(self):
+        self.lock("harmonic noise modelling")
+        thread.start_new_thread(self.RAWDataProc.harmonicModel, \
+                (self.ui.f0Spin.value(), \
+                self.ui.mplwidget))
+
     def FDSmartStack(self):
 
         if "TD stack" not in self.YamlNode.Processing.keys():

akvo/gui/main.ui

@@ -978,6 +978,35 @@ background: dark grey;
               <property name="checked">
                <bool>false</bool>
               </property>
+              <widget class="QPushButton" name="harmonicGO">
+               <property name="geometry">
+                <rect>
+                 <x>380</x>
+                 <y>120</y>
+                 <width>88</width>
+                 <height>34</height>
+                </rect>
+               </property>
+               <property name="text">
+                <string>PushButton</string>
+               </property>
+              </widget>
+              <widget class="QDoubleSpinBox" name="f0Spin">
+               <property name="geometry">
+                <rect>
+                 <x>160</x>
+                 <y>120</y>
+                 <width>71</width>
+                 <height>32</height>
+                </rect>
+               </property>
+               <property name="singleStep">
+                <double>0.100000000000000</double>
+               </property>
+               <property name="value">
+                <double>60.000000000000000</double>
+               </property>
+              </widget>
              </widget>
             </item>
             <item>
@@ -2196,8 +2225,8 @@ background: dark grey;
               <rect>
                <x>0</x>
                <y>0</y>
-               <width>100</width>
-               <height>30</height>
+               <width>96</width>
+               <height>26</height>
               </rect>
              </property>
              <attribute name="label">

akvo/tressel/harmonic.py

@@ -1,12 +1,55 @@
 import numpy as np 
 from scipy.optimize import least_squares 
+from scipy.optimize import minimize
+from scipy.linalg import lstsq as sclstsq
 
-def harmonic ( sN, f0, fs, nK, t  ): 
+def harmonic2 ( f1, f2, sN, fs, nK, t ): 
     """
-    Performs inverse calculation of harmonics contaminating a signal. 
+    Performs inverse calculation of two harmonics contaminating a signal. 
     Args:
+        f01 = base frequency of the first sinusoidal noise 
+        f02 = base frequency of the second sinusoidal noise 
         sN = signal containing noise 
+        fs = sampling frequency
+        nK = number of harmonics to calculate 
+        t = time samples 
+    """
+    print("building matrix ")
+    A = np.zeros( (len(t),  4*nK) )
+    #f1 = f1MHz * 1e-3
+    #f2 = f2MHz * 1e-3
+    for irow, tt in enumerate(t): 
+        A[irow, 0:2*nK:2] = np.cos( np.arange(nK)*2*np.pi*(f1/fs)*irow )
+        A[irow, 1:2*nK:2] = np.sin( np.arange(nK)*2*np.pi*(f1/fs)*irow )
+        A[irow, 2*nK::2] = np.cos( np.arange(nK)*2*np.pi*(f2/fs)*irow )
+        A[irow, 2*nK+1::2] = np.sin( np.arange(nK)*2*np.pi*(f2/fs)*irow )
+
+    v = np.linalg.lstsq(A, sN, rcond=1e-8)
+    #v = sclstsq(A, sN) #, rcond=1e-6)
+
+    alpha = v[0][0:2*nK:2]
+    beta  = v[0][1:2*nK:2]
+    amp = np.sqrt( alpha**2 + beta**2 )
+    phase = np.arctan(- beta/alpha)
+    
+    alpha2 = v[0][2*nK::2]
+    beta2  = v[0][2*nK+1::2]
+    amp2 = np.sqrt( alpha2**2 + beta2**2 )
+    phase2 = np.arctan(- beta2/alpha2)
+
+    h = np.zeros(len(t))
+    for ik in range(nK):
+        h += np.sqrt(alpha[ik]**2 + beta[ik]**2) * np.cos( 2.*np.pi*ik * (f1/fs) * np.arange(0, len(t), 1 )  + phase[ik] ) \
+           + np.sqrt(alpha2[ik]**2 + beta2[ik]**2) * np.cos( 2.*np.pi*ik * (f2/fs) * np.arange(0, len(t), 1 )  + phase2[ik] )
+
+    return sN-h
+
+def harmonic ( f0, sN, fs, nK, t ): 
+    """
+    Performs inverse calculation of harmonics contaminating a signal. 
+    Args:
         f0 = base frequency of the sinusoidal noise 
+        sN = signal containing noise 
         fs = sampling frequency
         nK = number of harmonics to calculate 
         t = time samples 
@@ -16,10 +59,6 @@ def harmonic ( sN, f0, fs, nK, t  ):
     for irow, tt in enumerate(t): 
         A[irow, 0::2] = np.cos( np.arange(nK)*2*np.pi*(f0/fs)*irow )
         A[irow, 1::2] = np.sin( np.arange(nK)*2*np.pi*(f0/fs)*irow )
-        # brutal 
-        #for k, ik in enumerate( np.arange(0, 2*nK, 2) ):
-        #    A[irow, ik  ] = np.cos( k*2*np.pi*(f0/fs)*irow )
-        #    A[irow, ik+1] = np.sin( k*2*np.pi*(f0/fs)*irow )
 
     v = np.linalg.lstsq(A, sN, rcond=None) #, rcond=1e-8)
 
@@ -29,9 +68,11 @@ def harmonic ( sN, f0, fs, nK, t  ):
     amp = np.sqrt( alpha**2 + beta**2 )
     phase = np.arctan(- beta/alpha)
 
+    #print("amp:", amp, " phase", phase)
+
     h = np.zeros(len(t))
     for ik in range(nK):
-        h +=  np.sqrt(alpha[ik]**2 + beta[ik]**2) * np.cos( 2.*np.pi*ik * (f0/fs) * np.arange(0, len(t), 1 )  + phase[ik] )
+        h += np.sqrt(alpha[ik]**2 + beta[ik]**2) * np.cos( 2.*np.pi*ik * (f0/fs) * np.arange(0, len(t), 1 )  + phase[ik] )
 
     #plt.matshow(A, aspect='auto')
     #plt.colorbar()
@@ -44,36 +85,99 @@ def harmonic ( sN, f0, fs, nK, t  ):
     #plt.figure()
     #plt.plot(h)
     #plt.title("modelled noise")
+    return sN-h
+
+def jacobian( f0, sN, fs, nK, t):
+    print("building Jacobian matrix ")
+    A = np.zeros( (len(t),  2*nK) )
+    for irow, tt in enumerate(t): 
+        #A[irow, 0::2] = np.cos( np.arange(nK)*2*np.pi*(f0/fs)*irow )
+        #A[irow, 1::2] = np.sin( np.arange(nK)*2*np.pi*(f0/fs)*irow )
+        # brutal 
+        for k, ik in enumerate( np.arange(0, 2*nK, 2) ):
+            #A[irow, ik  ] = np.cos( k*2*np.pi*(f0/fs)*irow )
+            #A[irow, ik+1] = np.sin( k*2*np.pi*(f0/fs)*irow )    
+            A[irow, ik  ] = - (2.*np.pi*k*irow * sin((2.*np.pi*irow*f0)/fs)) / fs
+            A[irow, ik+1] =   (2.*np.pi*k*irow * cos((2.*np.pi*irow*f0)/fs)) / fs
+
+
+def harmonicNorm ( f0, sN, fs, nK, t ): 
+    return np.linalg.norm( harmonic(f0, sN, fs, nK, t))
 
-    return h
+def harmonic2Norm ( f0, sN, fs, nK, t ): 
+    return np.linalg.norm(harmonic2(f0[0], f0[1], sN, fs, nK, t))
+
+def minHarmonic(f0, sN, fs, nK, t):
+    f02 = guessf0(sN, fs)
+    print("minHarmonic", f0, fs, nK, " guess=", f02)
+    res = minimize( harmonicNorm, np.array((f0)), args=(sN, fs, nK, t)) #, method='Nelder-Mead' )# jac=None, hess=None, bounds=None )
+    print(res)
+    return harmonic(res.x[0], sN, fs, nK, t)
+
+def minHarmonic2(f1, f2, sN, fs, nK, t):
+    #f02 = guessf0(sN, fs)
+    #print("minHarmonic2", f0, fs, nK, " guess=", f02)
+    #methods with bounds, L-BFGS-B, TNC, SLSQP
+    res = minimize( harmonic2Norm, np.array((f1,f2)), args=(sN, fs, nK, t)) #, bounds=((f1-1.,f1+1.0),(f2-1.0,f2+1.0)), method='SLSQP' )
+    print(res)
+    return harmonic2(res.x[0], res.x[1], sN, fs, nK, t) 
+
+def guessf0( sN, fs ):
+    S = np.fft.fft(sN)
+    w = np.fft.fftfreq( len(sN), 1/fs )
+    imax = np.argmax( np.abs(S) )
+    #plt.plot( w, np.abs(S) )
+    #plt.show()
+    #print(w)
+    #print ( w[imax], w[imax+1] )
+    return abs(w[imax])
 
 if __name__ == "__main__":
+    
     import matplotlib.pyplot as plt 
 
     f0 = 60      # Hz
-    delta = np.random.rand()
-    fs = 50000  #1e4    
+    f1 = 60      # Hz
+    delta  = 0 #np.random.rand() 
+    delta2 = 0 #np.random.rand() 
+    print("delta", delta)
+    fs = 10000   # GMR 
     t = np.arange(0, 1, 1/fs)
-    phi = .234
-    A = 1.0
-    nK = 20
-    sN = A * np.sin( (delta+f0)*2*np.pi*t + phi ) + np.random.normal(0,.1,len(t)) 
-    sNc = A * np.sin( (delta+f0)*2*np.pi*t + phi ) 
-    h = harmonic(sN, f0, fs, nK, t)
+    phi = 0 #np.random.rand() 
+    phi2 = 0 # np.random.rand() 
+    A =  1.0
+    A2 = 0.0 
+    nK = 10
+    T2 = .200
+    sN  = A * np.sin( ( 1*(delta  +f0))*2*np.pi*t + phi ) + \
+          A2* np.sin( ( 1*(delta2 +f1))*2*np.pi*t + phi2 ) + \
+              np.random.normal(0,.1,len(t)) + \
+              + np.exp( -t/T2  ) 
+
+    sNc = A * np.sin(  (1*(delta +f0))*2*np.pi*t + phi ) + \
+          A2* np.sin(  (1*(delta2+f1))*2*np.pi*t + phi2 ) + \
+              + np.exp( -t/T2  ) 
+
+
+    guessf0(sN, fs)
+
+    #h = harmonic( f0, sN, fs, nK, t) 
+    #h = minHarmonic2( f0, f1, sN, fs, nK, t) 
+    h = harmonic2( f0, f1, sN, fs, nK, t) 
 
     plt.figure()
     plt.plot(t, sN, label="sN")
-    plt.plot(t, sN-h, label="sN-h")
+    #plt.plot(t, sN-h, label="sN-h")
     plt.plot(t, h, label='h')
-    plt.title("true noise")
+    plt.title("harmonic")
     plt.legend()
 
     plt.figure()
     plt.plot(t, sN-sNc, label='true noise')
-    plt.plot(t, sN-h, label='harmonic removal')
+    plt.plot(t, h, label='harmonic removal')
+    plt.plot(t, np.exp(-t/T2), label="nmr")
     plt.legend()
     plt.title("true noise")
     
     plt.show()
 
-    print("hello")

akvo/tressel/mrsurvey.py

@@ -25,6 +25,7 @@ import akvo.tressel.decay as decay
 import akvo.tressel.pca as pca
 import akvo.tressel.rotate as rotate
 import akvo.tressel.cmaps as cmaps
+import akvo.tressel.harmonic as harmonic
 
 import cmocean # colormaps for geophysical data 
 plt.register_cmap(name='viridis', cmap=cmaps.viridis)
@@ -516,6 +517,73 @@ class GMRDataProcessor(SNMRDataProcessor):
         canvas.draw()
         self.doneTrigger.emit() 
 
+    def harmonicModel(self, f0, canvas):
+        print("harmonic modelling...", f0)
+        plot = True
+        if plot:
+            canvas.reAx2()
+            canvas.ax1.tick_params(axis='both', which='major', labelsize=8)
+            canvas.ax1.ticklabel_format(style='sci', scilimits=(0,0), axis='y')  
+            canvas.ax2.tick_params(axis='both', which='major', labelsize=8)
+            canvas.ax2.ticklabel_format(style='sci', scilimits=(0,0), axis='y')  
+
+        # Data
+        iFID = 0
+        for pulse in self.DATADICT["PULSES"]:
+            self.DATADICT[pulse]["TIMES"] =  self.DATADICT[pulse]["TIMES"]
+            for ipm in range(self.DATADICT["nPulseMoments"]):
+                for istack in self.DATADICT["stacks"]:
+                    canvas.ax1.clear()
+                    canvas.ax2.clear()
+                    #for ichan in np.append(self.DATADICT[pulse]["chan"], self.DATADICT[pulse]["rchan"]):
+                    for ichan in self.DATADICT[pulse]["rchan"]:
+                        
+                        if plot:
+                            canvas.ax1.plot( self.DATADICT[pulse]["TIMES"], 1e9*self.DATADICT[pulse][ichan][ipm][istack], \
+                                label = "orig " +  pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " rchan="  + str(ichan))
+
+                        #self.DATADICT[pulse][ichan][ipm][istack] = harmonic.minHarmonic( f0, self.DATADICT[pulse][ichan][ipm][istack], self.samp, 40, self.DATADICT[pulse]["TIMES"] ) 
+                        #self.DATADICT[pulse][ichan][ipm][istack] = harmonic.minHarmonic2( f0-.25, f0+.25, self.DATADICT[pulse][ichan][ipm][istack], self.samp, 20, self.DATADICT[pulse]["TIMES"] ) 
+
+                        # plot
+                        #if plot:
+                        #    canvas.ax1.plot( self.DATADICT[pulse]["TIMES"], 1e9*self.DATADICT[pulse][ichan][ipm][istack], \
+                        #        label = pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " rchan="  + str(ichan))
+
+                    for ichan in self.DATADICT[pulse]["chan"]:
+                        
+                        if plot:
+                            canvas.ax2.plot( self.DATADICT[pulse]["TIMES"], 1e9*self.DATADICT[pulse][ichan][ipm][istack], \
+                                label = "orig " +  pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " chan="  + str(ichan))
+                        
+                        self.DATADICT[pulse][ichan][ipm][istack] = harmonic.minHarmonic( f0, self.DATADICT[pulse][ichan][ipm][istack], self.samp, 40, self.DATADICT[pulse]["TIMES"] ) 
+                        #self.DATADICT[pulse][ichan][ipm][istack] = harmonic.minHarmonic2( f0-.25, f0+.25, self.DATADICT[pulse][ichan][ipm][istack], self.samp, 20, self.DATADICT[pulse]["TIMES"] ) 
+                        #self.DATADICT[pulse][ichan][ipm][istack] = harmonic.harmonic( f0, self.DATADICT[pulse][ichan][ipm][istack], self.samp, 50, self.DATADICT[pulse]["TIMES"] ) 
+               
+                        # plot
+                        if plot:
+                            canvas.ax2.plot( self.DATADICT[pulse]["TIMES"], 1e9*self.DATADICT[pulse][ichan][ipm][istack], \
+                                label = "data " + pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " chan="  + str(ichan))
+
+                    if plot:
+                        canvas.ax1.set_xlabel(r"time [s]", fontsize=8)
+                        canvas.ax1.set_ylabel(r"signal [nV]", fontsize=8)
+                        canvas.ax2.set_xlabel(r"time [s]", fontsize=8)
+                        canvas.ax2.set_ylabel(r"signal [nV]", fontsize=8)
+                        canvas.ax1.legend(prop={'size':6})
+                        canvas.ax2.legend(prop={'size':6})
+                        canvas.draw() 
+                    
+                percent = (int)(1e2*((float)(iFID*self.DATADICT["nPulseMoments"]+(ipm))/(len(self.DATADICT["PULSES"])*self.nPulseMoments)))
+                self.progressTrigger.emit(percent)  
+            iFID += 1
+        self.doneTrigger.emit() 
+        self.updateProcTrigger.emit()  
+
+
+        self.doneTrigger.emit() 
+        
+    
     def FDSmartStack(self, outlierTest, MADcutoff, canvas):
         
         print("FFT stuff")