typos + add plotting tracks with selection of good track in datacard files

Python/README

 scripts in this folder : 
-viewtfmap.py (+mynormalize) : reading and f\plotting from t-f map 
+viewtfmap.py (+mynormalize) : reading and plotting from t-f map 
 (or t-ra) fits files written by visiavg
 
 cmp_simddat.py : to plot fitted and input tracks (visi vs time at some freq) used in the fitting of pointing & phase with satellites (and sources) 

Python/cmp_simddat.py

@@ -7,7 +7,7 @@ from matplotlib import pyplot as plt
 # data and fitted results from trkacxfit
 # this will also plot the difference (in gray, scale on the right)
 # unused tracks are alsor plotted
-#numero_antenne = 0,1,2,3 pour 1H, 2H,...
+#numero_antenne = 1,2,.. pour 1H, 2H,...
 
 # make_fig_scan_xc(filename,cross,nombre_de_traces)
 # plots of all cross-cor visi comparision between 
@@ -15,10 +15,11 @@ from matplotlib import pyplot as plt
 # this will also plot the difference (in gray, scale on the right)
 # for the real part 
 # unused tracks are alsor plotted
-# cross = 0,1,...,5 pour 1Hx2H, 1Hx3H, ...,3Hx4H
-
+# cross = 1,...,6 pour 1Hx2H, 1Hx3H, ...,3Hx4H
 
 
+names_ac=['1H','2H','3H','4H']
+names_xc=['1Hx2H','1Hx3H','1Hx4H','2Hx3H','2Hx4H','3Hx4H']
 #cx_1_check_gal.fits
 keynam='HIERARCH NOM_OBJET'
 
@@ -114,7 +115,7 @@ def make_fig_scan(ffnam,ant,ntot):
     #plt.figure(figsize=(20,17))
     f, axarr = plt.subplots(nrows,ncols,figsize=(ncols*4,nrows*4))
     axarf=axarr.flatten()
-    f.suptitle('Antenna '+str(ant))
+    f.suptitle('Auto '+names_ac[ant-1])
     for ip in np.arange(ntot):
         i=get_num(ant,ip+1,ntot)
         
@@ -147,7 +148,8 @@ def make_fig_scan_xc(ffnam,cross,ntot):
 
     f, axarr = plt.subplots(nrows,ncols,figsize=(ncols*4,nrows*4))
     axarf=axarr.flatten()
-    f.suptitle('CrossCor '+str(cross))
+    
+    f.suptitle('CrossCor '+names_xc[cross-1])
     for ip in np.arange(ntot):
 
         i=get_num_cx(cross,ip+1,ntot)
@@ -179,4 +181,133 @@ def make_fig_scan_xc(ffnam,cross,ntot):
         
     plt.tight_layout()
     plt.subplots_adjust(top=0.9)
+
+def get_good_xctrks(datac):
+    flist = open(datac ,"r")
+    ntrk =0
+    ngtrk=0
+    goods=[]
+    for line in flist :
+        iok=0
+        if "@trk" in line :
+            print 'found track '
+            ntrk=ntrk+1
+            if "NOCX" in line :
+                goods.append(iok)
+                continue
+            if "NOACX" in line :
+                goods.append(iok)
+                continue
+            ngtrk=ngtrk+1
+            goods.append(1  )
+
+    return ntrk,ngtrk,goods
+def get_good_actrks(datac):
+    flist = open(datac ,"r")
+    ntrk =0
+    ngtrk=0
+    goods=[]
+    for line in flist :
+        iok=0
+        if "@trk" in line :
+            print 'found track '
+            ntrk=ntrk+1
+            if "NOAC" in line :
+                goods.append(iok)
+                continue
+            if "NOACX" in line :
+                goods.append(iok)
+                continue
+            ngtrk=ngtrk+1
+            goods.append(1  )
+
+    return ntrk,ngtrk,goods
+
+def make_fig_scan_xc_sel(ffnam,cross,datac):
+    hda=fits.open(ffnam)
+
+    ntot,nok,goods=get_good_xctrks(datac)
+    print ntot,nok,goods
+    nrows=np.int(np.sqrt(nok))
+    ncols=nok/ nrows
+    ncols=ncols+nok-ncols*nrows
+    print str(nrows)+ "  " +str(ncols)
+
+    f, axarr = plt.subplots(nrows,ncols,figsize=(ncols*4,nrows*4))
+    axarf=axarr.flatten()
+    f.suptitle('CrossCor '+names_xc[cross-1])
+    ipl=0
+    for ip in np.arange(ntot):
+        if goods[ip]==0 :
+            continue
+        i=get_num_cx(cross,ip+1,ntot)
+        print hda[i].header[keynam]
+        print hda[i+1].header[keynam]
+        #axarf[ip ].plot(hda[i].data,label='Signal')
+        #axarf[ip ].plot(hda[i+1].data,label='Simu')
+        #axarf[ip ].legend()
+        axarf[ipl].plot(hda[i].data,label='Real')
+        axarf[ipl].plot(hda[i+1].data,label='Imag')
+        axarf[ipl].plot(hda[i+2].data,label='simu(real)',linestyle=":")
+        axarf[ipl].plot(hda[i+3].data,label='simu(imag)',linestyle=":")
+        axarf[ipl].legend()
+
+        vmaxr=np.max(hda[i].data)*1.2
+        vminr=np.min(hda[i].data)*1.2
+        vmaxi=np.max(hda[i+1].data)*1.2
+        vmini=np.min(hda[i+1].data)*1.2
+        vmax=np.max((vmaxr,vmaxi))
+        vmin=np.min((vminr,vmini))
+        
+        axarf[ipl].set_ylim((vmin,vmax))
+        axarf[ipl].set_title('Track '+str(ip))
+        axarf[ipl].set(xlabel='time (mn)', ylabel='signal (AU)')
+        ax2 = axarf[ipl].twinx()
+        ax2.plot((hda[i].data-hda[i+2].data)/(vmax-vmin),label='Data-Simu (real)',color='gray')
+        ax2.set_ylim((-1,1))
+        ax2.legend(loc=3)
+        ipl=ipl+1
+    plt.tight_layout()
+    plt.subplots_adjust(top=0.9)
+    
+ 
+def make_fig_scan_sel(ffnam,ant, datac   ):
+    ntot,nok,goods=get_good_actrks(datac)
+    print ntot,nok,goods
+    hda=fits.open(ffnam)
+    nrows=np.int(np.sqrt(nok))
+    ncols=nok/ nrows
+    ncols=ncols+nok-ncols*nrows
+    print str(nrows)+ "  " +str(ncols)
+    #plt.figure(figsize=(20,17))
+    f, axarr = plt.subplots(nrows,ncols,figsize=(ncols*4,nrows*4))
+    axarf=axarr.flatten()
     
+    f.suptitle('Auto '+names_ac[ant-1])
+    ipl=0
+    for ip in np.arange(ntot):
+        if goods[ip]==0 :
+            continue
+        i=get_num(ant,ip+1,ntot)
+        
+        print hda[i].header[keynam]
+        print hda[i+1].header[keynam]
+        axarf[ipl ].plot(hda[i].data,label='Signal')
+        axarf[ipl ].plot(hda[i+1].data,label='Simu')        
+        axarf[ipl ].legend()
+        vscale=np.max(hda[i].data)-1.
+        vmin=1-0.2*vscale
+        vmax=(1.+vscale)*1.1
+        
+        axarf[ipl ].set_ylim((vmin,vmax))
+        axarf[ipl].set_title('Track '+str(ip))
+        axarf[ipl].set(xlabel='time (mn)', ylabel='signal (AU)')
+        ax2 = axarf[ipl].twinx()
+        ax2.plot(hda[i].data-hda[i+1].data,label='Data-Simu',color='gray')
+        ax2.set_ylim((-1,9))
+        ax2.legend(loc=2)
+        ipl=ipl+1
+
+    plt.tight_layout()
+    plt.subplots_adjust(top=0.9)
+

Python/viewtfmap.py

@@ -29,8 +29,8 @@ names = ['1H','2H','3H','4H','1V','2V','3V','4V','1Hx2H','1Hx3H','1Hx4H','2Hx3H'
 # the for the cross-correlations - the TF amplitudes are complex : in each case one HDU with real part, one with imaginary part averages
 # and two for their respective variances
 # NB the variance computation is optional 
-# at the end of the fits files, acddition HDUs contain 1D data (in reverse order) : times (center of bins), frequencies and RA
-# (omitted in the case od RA-freq maps 
+# at the end of the fits files, acddition HDUs contain 1D data (in reverse order) : times (center of bins, in  seconds),
+# frequencies (MHz) and RA (hours, omitted in the case of RA-freq maps 
 # 
 
 # --------------------------------