A binary fieldmap batch conversion and trimming script is added. the...

A binary fieldmap batch conversion and trimming script is added. the conversion is now available also in TraceWin, but the rimming is still useful, specially if we add the other axes.

examples/Binary_fieldmap/Batch_Convert_and_Cut_Binary_Fieldmap.py

+'''
+Now that TraceWin can also convery binary fieldmaps to ASCII the main usage is reduced to trimming fieldmaps
+-- M. Eshraqi 2018 July 09
+
+
+
+The field map file syntax is the following in the BINARY format:
+- Dimension 1 :
+    nz (integer 4 bytes) zmax (double 8 bytes) Norm (double 8 bytes)
+    for k=0 to nz
+        Fz(k.zmax/nz) (float 4 bytes)
+
+- Dimension 2 :
+    nz (integer 4 bytes) zmax (double 8 bytes) nx (integer 4 bytes) xmax (double 8 bytes) Norm (double 8 bytes)
+    for k=0 to nz
+        for i=0 to nr
+            Fz(k*zmax/nz, i*rmax/nr) (float 4 bytes)
+
+- Dimension 3 :
+(Be careful to the dimention order)
+    nz (integer 4 bytes) zmax (double 8 bytes)
+    nx (integer 4 bytes) xmin (double 8 bytes) xmax (double 8 bytes) 
+    ny (integer 4 bytes) ymin (double 8 bytes) ymax (double 8 bytes) 
+    Norm (double 8 bytes)
+    for k=0 to nz
+        for j=0 to ny
+            for i=0 to nx
+                Fz(k*zmax/nz, ymin+j*(ymax-ymin)/ny, xmin+i*(xmax-xmin)/nx) (float 4 bytes) 
+Warning: The lattice has to be regular.
+The normalization factor is equal to ke/Norm or kb/Norm.
+Fz are in MV/m for electric field or in T for magnetic field. The dimensions are in meter.
+'''
+
+import os
+import numpy as np
+#import pandas as pd
+
+def read_binary_fieldmap(path_plus_file, fieldmap_dim):
+    '''
+    takes a filename for a binary fieldmap and dimension of the fieldmap, 
+    1D = 1, 2D = 2, 3D = 3
+    and returns a numpy array, with the same format as the TraceWin fieldmap, without the header in ASCII
+    '''
+    
+    File = open(os.path.join(mypath, f),'r')
+    #print(f)
+    
+    if fieldmap_dim == 3:
+        Header_Type = np.dtype([('Nz', 'int32'), ('Zmax', 'float64'), ('Nx','int32'), ('Xmin', 'float64'), ('Xmax', 'float64'), 
+                     ('Ny','int32'), ('Ymin','float64'), ('Ymax','float64'), ('Norm', 'float64')])
+        THeader = np.fromfile(File, dtype=Header_Type, count=1)
+        Nz = THeader['Nz'][0]
+        Zmax = THeader['Zmax'][0]
+        Nx = THeader['Nx'][0]
+        Xmin = THeader['Xmin'][0]
+        Xmax = THeader['Xmax'][0]
+        Ny = THeader['Ny'][0]
+        Ymin = THeader['Ymin'][0]
+        Ymax = THeader['Ymax'][0]
+        Norm = THeader['Norm'][0]
+        Header = np.zeros(len(THeader))
+        Header = np.array([Nz, Zmax, Nx, Xmin, Xmax, Ny, Ymin, Ymax, Norm])
+    elif fieldmap_dim == 2:
+        Header_Type = np.dtype([('Nz', 'int32'), ('Zmax', 'float64'), ('Nx','int32'), ('Xmax', 'float64'), 
+                     ('Norm', 'float64')])
+        Header = np.fromfile(File, dtype=Header_Type, count=1)
+        Nz = THeader['Nz'][0]
+        Zmax = THeader['Zmax'][0]
+        Nx = THeader['Nx'][0]
+        Xmax = THeader['Xmax'][0]
+        Norm = THeader['Norm'][0]
+        Header = np.zeros(len(THeader))
+    elif fieldmap_dim == 1:
+        Header_Type = np.dtype([('Nz', 'int32'), ('Zmax', 'float64'), ('Norm', 'float32')])
+        Header = np.fromfile(File, dtype=Header_Type, count=1)
+        Nz = THeader['Nz'][0]
+        Zmax = THeader['Zmax'][0]
+        Norm = THeader['Norm'][0]
+        Header = np.zeros(len(THeader))
+
+    Field_Type = np.dtype([('F_val','float32')])  
+    
+    Nf = int(Nz+1)
+    if fieldmap_dim == 2:
+        Nf *= int(Nx+1)
+    elif fieldmap_dim == 3:
+        Nf *= (int(Ny+1)*int(Nx+1))
+    #print(Nf)
+
+    Field = np.fromfile(File, dtype=Field_Type, count=Nf)
+
+    #print(Field)
+    #plot(Field)
+    return Header, Field
+
+def cut_fieldmap_length(Field, Header, fieldmap_dim, entrance_rows_cut, exit_rows_cut):
+    '''
+    Takes a Field (ASCII numpy array with TraceWin fieldmap data format),
+    dimesion of the fieldmap,
+    number of data rows to be removed from array from the entrance side of the field, and
+    number of data rows to be removed from array from the exit side of the field
+    and returns a numpy array which contains only the remaining rows.
+    '''
+    #print(Header[0])
+    lowx = np.arange(0,int(entrance_rows_cut))
+    #print('lowx', lowx)
+    highx = np.arange(int(Header[0]+1-exit_rows_cut), int(Header[0]+1))
+    #print('highx', highx)
+    newNz = (Header[0]+1)-entrance_rows_cut-exit_rows_cut
+    stepZ = Header[1] / Header[0]
+
+    if fieldmap_dim == 1:
+        newlength = newNz
+    elif fieldmap_dim == 2:
+        Fieldmapmatrix = np.reshape(Field, (int(Header[0]+1), int(Header[2]+1)))
+        newlength = newNz*(Header[2]+1)
+    elif fieldmap_dim == 3:
+        Fieldmapmatrix = np.reshape(Field, (int(Header[0]+1), int((Header[2]+1)*(Header[5]+1))))
+        #print(Fieldmapmatrix.shape)
+        newlength = newNz*(Header[2]+1)*(Header[5]+1)
+
+    if (entrance_rows_cut>0 and exit_rows_cut>0):
+        x = np.delete(Fieldmapmatrix,(lowx, highx), axis=0)
+    elif (entrance_rows_cut ==0):
+        x = np.delete(Fieldmapmatrix,(highx), axis=0)
+    elif exit_rows_cut ==0:
+        x = np.delete(Fieldmapmatrix,(lowx), axis=0)
+        
+    ShortenedMatrix = np.reshape(x, (int(newlength), 1))
+    
+    Header[0] = newNz-1
+    Header[1] = stepZ*(newNz-1)
+
+    return Header, ShortenedMatrix
+       
+'''
+The parameters in the following six rows define the path to your fieldmap, no name is needed, this script 
+scans the folder and converts all files, assumes all are binary fieldmaps, to ASCII fieldmaps.
+The dimension of the field is also needed.
+If you need to cut the field, the n_enter and n_exit should be adjusted.
+
+#  - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
+#                                                                              #
+#            C h a n g e   t h e   f o l l o w i n g    v a l u e s            #
+#                                                                              #
+#  - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
+'''
+
+mypath = 'Data/FM/'          # where the fieldmaps are stored
+fieldmap_dim = 3
+#fieldmap_dim = 2
+#fieldmap_dim = 1
+n_enter = 0                 #Number of lines to be removed from entrance of fieldmap
+n_exit = 0                  #Number of lines to be removed from exit of fieldmap
+
+onlyfiles = [f for f in os.listdir(mypath) if os.path.isfile(os.path.join(mypath, f))]
+
+for f in onlyfiles:
+    if f[0]!=".":
+        Header, Field = read_binary_fieldmap(f, fieldmap_dim)
+
+        if (n_enter+n_exit>0):
+            Header, Field = cut_fieldmap_length(Field, Header, fieldmap_dim, n_enter, n_exit)
+            
+        with open(os.path.join(mypath, 'ASCII', f), 'w') as out_file:
+
+            stepZ = Header[1] / Header[0]   #stepZ = Zmax / Nz
+
+            out_file.write(' '.join(str(hval) for hval in Header[0:2]))
+            out_file.write('\n')
+            if fieldmap_dim == 1:
+                #Norm = Header[2]
+
+                #out_file.write(' '.join(str(hval) for hval in Header[0:2]))
+                #out_file.write('\n')
+                out_file.write(' '.join(str(Header[2])))
+                out_file.write('\n')
+
+            elif fieldmap_dim == 2:
+                #Norm = Header[4]
+                stepX = Header[3]/Header[2]  #stepX = Xmax / Nx
+
+                #out_file.write(' '.join(str(hval) for hval in Header[0:2]))
+                #out_file.write('\n')
+                out_file.write(' '.join(str(hval) for hval in Header[2:4]))
+                out_file.write('\n')
+                out_file.write(' '.join(str(Header[4])))
+                out_file.write('\n')
+
+            elif fieldmap_dim == 3:
+                #Norm = Header[8]
+                stepX = (Header[4]-Header[3])/Header[2]  #stepX = (Xmax - Xmin) / Nx
+                stepY = (Header[7]-Header[6])/Header[5]  #stepY = (Ymax - Ymin) / Ny
+                
+                #out_file.write((' '.join(str(hval) for hval in Header[0:2])))
+                #out_file.write('\n')
+                out_file.write((' '.join(str(hval) for hval in Header[2:5])))
+                out_file.write('\n')
+                out_file.write((' '.join(str(hval) for hval in Header[5:8])))
+                out_file.write('\n')
+                out_file.write(''.join(str(Header[8])))
+                out_file.write('\n')
+     
+            for fval in Field:
+                out_file.write(str(fval[0][0]))
+                out_file.write('\n')#print(fval)
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