diff --git a/ess/__init__.py b/ess/__init__.py
index 429da9d3034d036aa0cb242e80ae438ca3b55c2e..a6d9497803bc95223f3c361e266a10e69306d94d 100644
--- a/ess/__init__.py
+++ b/ess/__init__.py
@@ -1,5 +1,5 @@
-__all__ = ["TraceWin", "installed", "lib_tw"]
-
+__all__ = ["fieldmap", "installed", "lib_tw", "SP_Relativity", "TraceWin" "TTF"]
from . import TraceWin
from . import installed
from . import lib_tw
+from . import fieldmap
diff --git a/ess/fieldmapy.py b/ess/fieldmap.py
similarity index 67%
rename from ess/fieldmapy.py
rename to ess/fieldmap.py
index 8ee1805b22a6a544ec18d63d93f7e113ef0ea2b6..f8e727ffb18665baa95b164a855b27a947f868b4 100644
--- a/ess/fieldmapy.py
+++ b/ess/fieldmap.py
@@ -28,7 +28,7 @@ v0.4:
The field map file syntax is the following in the BINARY format:
- Dimension 1 :
- nz (integer 4 bytes) zmax (double 8 bytes)
+ nz (integer 4 bytes) zmax (double 8 bytes)
Norm (double 8 bytes)
# Nz Zmax
@@ -38,8 +38,8 @@ v0.4:
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)
+ nz (integer 4 bytes) zmax (double 8 bytes)
+ nx (integer 4 bytes) xmax (double 8 bytes)
Norm (double 8 bytes)
# Nz Zmax
@@ -53,8 +53,8 @@ v0.4:
- 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)
+ 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)
# Nz Zmax
# Nx Xmin Xmax
@@ -64,7 +64,7 @@ v0.4:
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)
+ 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.
@@ -75,24 +75,24 @@ v0.4:
---------------------------------------------------------------------------------------------------------
'''
-__all__ = ['read_binary_fieldmap', 'read_ascii_fieldmap', 'cut_fieldmap_length', 'field_on_axis_from_file'
+__all__ = ['read_fieldmap', 'cut_fieldmap_length', 'field_on_axis', 'field_on_axis_from_file'
'save_to_ascii', 'batch_convert_to_ascii', 'unify_grid_size', 'batch_cut_fieldmap',
- 'uniform_mesh', 'create_TW_fieldmap']
+ 'uniform_mesh', 'create_tw_fieldmap']
#class FieldMapy:
#'''
#class contains functions needed for manipulating fieldmaps for TraceWin.
#'''
-#__version__ = "0.4"
+__version__ = "0.4"
import os
import numpy as np
-def read_binary_fieldmap(path_plus_file: str, fieldmap_dim: int):
+def _read_binary_fieldmap(filepath: str, fieldmap_dim: int):
'''
parameters
----------
- path_plus_file: string
+ filepath: string
path to the binary fieldmap file including the path, file name and extension
fieldmap_dim: int
dimension of the fieldmap, 1, 2 and 3 for 1D, 2D and 3D TraceWin formatted fieldmap
@@ -100,14 +100,14 @@ def read_binary_fieldmap(path_plus_file: str, fieldmap_dim: int):
returns
-------
a list containing Header and Field
- Header: 1D array of size 3, 5, 8 (for 1D, 2D and 3D fieldmaps)
+ Header: 1D array of size 3, 5, 9 (for 1D, 2D and 3D fieldmaps)
TraceWin fieldmap header info
Field: 1D numpy array containing the nD field data
'''
- field_File = open(path_plus_file,'r')
+ field_File = open(filepath,'r')
if fieldmap_dim == 3:
- Header_Type = np.dtype([('Nz', 'int32'), ('Zmax', 'float64'), ('Nx','int32'), ('Xmin', 'float64'), ('Xmax', 'float64'),
+ 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(field_File, dtype=Header_Type, count=1)
Nz = THeader['Nz'][0]
@@ -123,7 +123,7 @@ def read_binary_fieldmap(path_plus_file: str, fieldmap_dim: int):
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'),
+ Header_Type = np.dtype([('Nz', 'int32'), ('Zmax', 'float64'), ('Nx','int32'), ('Xmax', 'float64'),
('Norm', 'float64')])
THeader = np.fromfile(field_File, dtype=Header_Type, count=1)
Nz = THeader['Nz'][0]
@@ -142,7 +142,7 @@ def read_binary_fieldmap(path_plus_file: str, fieldmap_dim: int):
Norm = THeader['Norm'][0]
Header = np.zeros(len(THeader))
- Field_Type = np.dtype([('F_val','float32')])
+ Field_Type = np.dtype('float32')
Nf = int(Nz+1)
if fieldmap_dim == 2:
@@ -154,71 +154,71 @@ def read_binary_fieldmap(path_plus_file: str, fieldmap_dim: int):
return Header, Field
-def read_ascii_fieldmap(path_plus_file, fieldmap_dim: int):
+def _read_ascii_fieldmap(filepath, interpolate=None):
'''
parameters
----------
- path_plus_file: string
+ filepath: string
path to the ASCII fieldmap file including the path, file name and extension
- fieldmap_dim: int
- dimension of the fieldmap, 1, 2 and 3 for 1D, 2D and 3D TraceWin formatted fieldmap
returns
-------
a list containing Header and Field
- Header: 1D array of size 3, 5, 8 (for 1D, 2D and 3D fieldmaps)
+ Header: 1D array of size 3, 5, 9 (for 1D, 2D and 3D fieldmaps)
TraceWin fieldmap header info
Field: 1D numpy array containing the nD field data
'''
+ from ess import TraceWin
- from itertools import islice
- import numpy as np
+ fmap = TraceWin.field_map(filepath)
- items_per_row = np.array([[2, 1, 0, 0],
- [2, 2, 1, 0],
- [2, 3, 3, 1]]) # for 1D, 2D and 3D fields in an ASCII fieldmap in TraceWin format
- l_header = np.sum(items_per_row[fieldmap_dim-1,:], axis=0) # getting the length of the header from the items
- Header = np.zeros(l_header)
+ if interpolate:
+ fmap.interpolate(interpolate)
+ Fieldmap=np.array(fmap.get_flat_fieldmap())
+ Header=np.array(fmap.header)
+ return Header, Fieldmap
- j = 0
- with open(path_plus_file, 'r') as f:
- for i in range(0, fieldmap_dim+1):
- header_line = list(islice(f, 1))
- fl = header_line[0]
- fl = fl.rstrip('\n')
- fl = fl.split(' ')
- l_header_line = items_per_row[int(fieldmap_dim)-1, i]
- Header[j:j+l_header_line] = fl[:]
- j+=l_header_line
-
- Nf = int(Header[0]+1)
- if fieldmap_dim == 2:
- Nf *= int(Header[2]+1)
- elif fieldmap_dim == 3:
- Nf *= int(Header[2]+1)*int(Header[5]+1)
+def read_fieldmap(filepath: str, fieldmap_dim: int, file_type: str):
+ '''
+ parameters
+ ----------
+ filepath: string
+ path to the binary fieldmap file including the path, file name and extension
+ fieldmap_dim: int
+ dimension of the fieldmap, 1, 2 and 3 for 1D, 2D and 3D TraceWin formatted fieldmap
+ file_type: str
+ 'b' for binary and 'a' for ascii fieldmap files.
+
+ returns
+ -------
+ a list containing Header and Field
+ Header: 1D array of size 3, 5, 9 (for 1D, 2D and 3D fieldmaps)
+ TraceWin fieldmap header info
+ Field: 1D numpy array containing the nD field data
+ '''
- fieldmap = np.zeros(int(Nf))
+ if file_type == 'b':
+ header, field = _read_binary_fieldmap(filepath, fieldmap_dim)
+ elif file_type == 'a':
+ header, field = _read_ascii_fieldmap(filepath)
+ else:
+ raise ValueError('file_type should be a for ascii or b for binary')
- i = 0
- for line in f:
- eorb = line.rstrip('\n')
- fieldmap[i] = eorb[:]
- i+=1
+ return header, field
- return Header, fieldmap
-def cut_fieldmap_length(path_plus_file: str, fieldmap_dim: int, entrance_rows_cut: int,
+def cut_fieldmap_length(filepath: str, fieldmap_dim: int, entrance_rows_cut: int,
exit_rows_cut: int, file_type: str):
'''
parameters
----------
- path_plus_file: string
+ filepath: string
path to the fieldmap file (ASCII or binary) including the path, file name and extension
fieldmap_dim: int
dimension of the fieldmap, 1, 2 and 3 for 1D, 2D and 3D TraceWin formatted fieldmap
entrance_rows_cut: int
number of data rows to be removed from array from the entrance side of the field
- exit_rows_cut: int
+ exit_rows_cut: int
number of data rows to be removed from array from the exit side of the field
file_type: str
'b' for binary and 'a' for ascii fieldmap files.
@@ -226,7 +226,7 @@ def cut_fieldmap_length(path_plus_file: str, fieldmap_dim: int, entrance_rows_cu
returns
-------
a list containing Header and Field
- Header: 1D array of size 3, 5, 8 (for 1D, 2D and 3D fieldmaps)
+ Header: 1D array of size 3, 5, 9 (for 1D, 2D and 3D fieldmaps)
TraceWin fieldmap header info
Field: 1D numpy array containing the nD field data with only the remaining rows
@@ -237,15 +237,10 @@ def cut_fieldmap_length(path_plus_file: str, fieldmap_dim: int, entrance_rows_cu
missing
-------
- cutting the field in other directions
+ cutting the field in other directions
'''
- if file_type == 'a':
- Field, Header = read_ascii_fieldmap(path_plus_file, fieldmap_dim)
- elif file_type == 'b':
- Field, Header = read_binary_fieldmap(path_plus_file, fieldmap_dim)
- else:
- print('file_type should be a for ascii or b for binary')
+ Header, Field = read_fieldmap(filepath, fieldmap_dim, file_type)
lowx = np.arange(0,int(entrance_rows_cut)) #list of rows to be removed from the entrance side
highx = np.arange(int(Header[0]+1-exit_rows_cut), int(Header[0]+1)) # list of rows to be removed from the exit side
@@ -261,7 +256,6 @@ def cut_fieldmap_length(path_plus_file: str, fieldmap_dim: int, entrance_rows_cu
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):
@@ -272,46 +266,40 @@ def cut_fieldmap_length(path_plus_file: str, fieldmap_dim: int, entrance_rows_cu
x = np.delete(Fieldmapmatrix,(lowx), axis=0)
# reshaping the array back to a 1D array
- ShortenedMatrix = np.reshape(x, (int(newlength), 1))
+ ShortenedMatrix = np.reshape(x, int(newlength))
- # creating new header data (if other directions are added to the cut fuction the header for those have
+ # creating new header data (if other directions are added to the cut fuction the header for those have
# to be rewritten too)
Header[0] = newNz-1
Header[1] = stepZ*(newNz-1)
return Header, ShortenedMatrix
-def field_on_axis_from_file(path_plus_file, fieldmap_dim: int, file_type: str):
+def field_on_axis(Header, Field, fieldmap_dim: int):
'''
parameters
----------
- path_plus_file: string
- name of the fieldmap file including its path and extension
+ Header: 1D numpy array of size 3, 5, 9 (for 1D, 2D and 3D fieldmaps)
+ TraceWin fieldmap header info
+ Field: 1D numpy array containing the nD field data
fieldmap_dim: int
dimesion of the fieldmap, 1, 2, 3 for 1D, 2D and 3D fieldmaps in TraceWin format
- file_type: string
- 'b' for binary and 'a' for ascii fieldmap files.
returns
-------
returns a 2D array containing the z coordinates and the field on axis in the z direction
- Take a 2D or 3D fieldmap as a 1D numpy array (as from TraceWin) in MV/m,
- dimension of the fieldmap,
+ Take a 2D or 3D fieldmap as a 1D numpy array (as from TraceWin) in MV/m,
+ dimension of the fieldmap,
returns the field on-axis in the z direction
example:
field_on_axis_from_file('Data/FM/HB_W_coupler.edz', 3, 'b')
'''
- from numpy import zeros as npzeros
+ import numpy
- if file_type == 'b':
- Header, Field = read_binary_fieldmap(path_plus_file, fieldmap_dim)
- elif file_type == 'a':
- Header, Field = read_ascii_fieldmap(path_plus_file, fieldmap_dim)
- else:
- print('file_type should be a for ascii or b for binary')
-
- if fieldmap_dim == 2:
+ if fieldmap_dim == 1:
+ return Field
+ elif fieldmap_dim == 2:
Nz, Nrx = Header[0], Header[2]
Fieldmapmatrix = np.reshape(Field, (int(Nz+1), int(Nrx+1)))
midpoint = 0
@@ -321,27 +309,49 @@ def field_on_axis_from_file(path_plus_file, fieldmap_dim: int, file_type: str):
midpoint = int(Nrx*Ny/2)
dz = Header[1]/Nz
- field_on_axis = npzeros((2,int(Nz)+1))
+ field_on_axis = numpy.zeros((2,int(Nz)+1))
for i in range(int(Nz+1)):
field_on_axis[0,i] = i*dz
field_on_axis[1,:] = Fieldmapmatrix[:, midpoint]
return field_on_axis
-def save_to_ascii(Header, Field, fieldmap_dim: int, path_to_out_file: str, out_file_name: str):
+def field_on_axis_from_file(filepath, fieldmap_dim: int, file_type: str):
+ '''
+ parameters
+ ----------
+ filepath: string
+ name of the fieldmap file including its path and extension
+ fieldmap_dim: int
+ dimesion of the fieldmap, 1, 2, 3 for 1D, 2D and 3D fieldmaps in TraceWin format
+ file_type: string
+ 'b' for binary and 'a' for ascii fieldmap files.
+
+ returns
+ -------
+ returns a 2D array containing the z coordinates and the field on axis in the z direction
+
+ Take a 2D or 3D fieldmap as a 1D numpy array (as from TraceWin) in MV/m,
+ dimension of the fieldmap,
+ returns the field on-axis in the z direction
+ example:
+ field_on_axis_from_file('Data/FM/HB_W_coupler.edz', 3, 'b')
+ '''
+
+ Header, Field = read_fieldmap(filepath, fieldmap_dim, file_type)
+
+ return field_on_axis(Header, Field, fieldmap_dim)
+
+def save_to_ascii(Header, Field, output_filepath: str):
'''
Parameters
----------
Header: array like
- 1D array of size (3, 5 or 8) for 1D, 2D or 3D fieldmap header info, TraceWin format
+ 1D array of size (3, 5 or 9) for 1D, 2D or 3D fieldmap header info, TraceWin format
Field: array like
1D array of size Nz fieldmap containing field data
- fieldmap_dim: int
- dimesion of the fieldmap, 1, 2, 3 for 1D, 2D and 3D fieldmaps in TraceWin format
- path_to_out_file: string
- path to the output file folder
- out_file_name: string
- name of the outputfile, including the extension
+ output_filepath: string
+ path to the output file
Returns
-------
@@ -351,57 +361,38 @@ def save_to_ascii(Header, Field, fieldmap_dim: int, path_to_out_file: str, out_f
---------------
Saves a TraceWin readable ASCII file
'''
- with open(os.path.join(path_to_out_file, out_file_name), 'w') as out_file:
+
+ # The dimension is defined from the size of the Header:
+ if len(Header)==3:
+ fieldmap_dim=1
+ elif len(Header)==5:
+ fieldmap_dim=2
+ elif len(Header)==9:
+ fieldmap_dim=3
+ else:
+ raise ValueError("Wrong length of header, cannot write {}".format(output_file_path))
+
+
+ with open(output_file_path, 'w') as out_file:
stepZ = Header[1] / Header[0] #stepZ = Zmax / Nz
- out_file.write(str(int(Header[0])))
- out_file.write(' ')
- out_file.write(str(Header[1]))
- #out_file.write(' '.join(str(hval) for hval in Header[0:2]))
- out_file.write('\n')
+ out_file.write('{0[0]:.0f} {0[1]}\n'.format(Header))
if fieldmap_dim == 1:
- #Norm = Header[2]
- out_file.write((str(Header[2])))
- out_file.write('\n')
+ out_file.write('{}\n'.format(Header[2]))
elif fieldmap_dim == 2:
- #Norm = Header[4]
- stepX = Header[3]/Header[2] #stepX = Xmax / Nx
-
- out_file.write(str(int(Header[2])))
- out_file.write(' ')
- out_file.write(str(int(Header[3])))
- #out_file.write(' '.join(str(hval) for hval in Header[2:4]))
- out_file.write('\n')
- out_file.write(str(Header[4]))
- out_file.write('\n')
+ out_file.write('{0[2]:.0f} {0[3]}\n'.format(Header))
+ out_file.write('{}\n'.format(Header[4]))
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(str(int(Header[2])))
- out_file.write(' ')
- out_file.write((' '.join(str(hval) for hval in Header[3:5])))
- out_file.write('\n')
- out_file.write(str(int(Header[5])))
- out_file.write(' ')
- out_file.write((' '.join(str(hval) for hval in Header[6:8])))
- out_file.write('\n')
- out_file.write(''.join(str(Header[8])))
- out_file.write('\n')
+ out_file.write('{0[2]:.0f} {0[3]} {0[4]}\n'.format(Header))
+ out_file.write('{0[5]:.0f} {0[6]} {0[7]}\n'.format(Header))
+ out_file.write('{}\n'.format(Header[8]))
for fval in Field:
- try:
- out_file.write(str(fval[0]))
- out_file.write('\n')#print(fval)
- except IndexError:
- out_file.write(str(fval))
- out_file.write('\n')#print(fval)
+ out_file.write('{:g}\n'.format(fval))
- return
def batch_convert_to_ascii(binary_field_path: str, fieldmap_dim: int):
'''
@@ -417,21 +408,21 @@ def batch_convert_to_ascii(binary_field_path: str, fieldmap_dim: int):
for f in onlyfiles:
if f[0]!=".":
- Header, Field = read_binary_fieldmap(binary_field_path+f, fieldmap_dim)
- save_to_ascii(Header, Field, fieldmap_dim, binary_field_path+'/ASCII/', f)
+ Header, Field = _read_binary_fieldmap( os.path.join( binary_field_path, f), fieldmap_dim)
+ save_to_ascii(Header, Field, fieldmap_dim, os.path.join( binary_field_path, 'ASCII', f))
return
def unify_grid_size(old_x: list, old_y: list, new_x: list, **kwargs):
'''
parameters:
- old_x: list,
- old_y: list,
+ old_x: list,
+ old_y: list,
new_x: list
**kwargs: kind
- ‘linear’,
- ‘nearest’,
+ ‘linear’,
+ ‘nearest’,
‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’: refer to a spline interpolation of zeroth, first, second or third order
‘previous’, ‘next’: return the previous or next value of the point
An integer specifying the order of the spline interpolator to use. Default is ‘linear’.
@@ -443,7 +434,7 @@ def unify_grid_size(old_x: list, old_y: list, new_x: list, **kwargs):
Takes a mesh as x values and the corresponding values of a function.
creates a spline interpolation for those data sets.
Using a new_x it returns the correcposnding new_y.
- Could be used to convert an irregular mesh to a regular mesh or
+ Could be used to convert an irregular mesh to a regular mesh or
to increase the number of mesh points
'''
kind = kwargs.get('kind', 'linear')
@@ -460,7 +451,7 @@ def unify_grid_size(old_x: list, old_y: list, new_x: list, **kwargs):
return new_y
-def batch_cut_fieldmap(fieldmaps_path: str, fieldmap_dim: int, entrance_rows_cut: int,
+def batch_cut_fieldmap(fieldmaps_path: str, fieldmap_dim: int, entrance_rows_cut: int,
exit_rows_cut: int, file_type: str):
'''
parameters
@@ -471,7 +462,7 @@ def batch_cut_fieldmap(fieldmaps_path: str, fieldmap_dim: int, entrance_rows_cut
dimension of the fieldmap, 1, 2 and 3 for 1D, 2D and 3D TraceWin formatted fieldmap
entrance_rows_cut: int
number of data rows to be removed from array from the entrance side of the field
- exit_rows_cut: int
+ exit_rows_cut: int
number of data rows to be removed from array from the exit side of the field
file_type: str
'b' for binary and 'a' for ascii fieldmap files.
@@ -491,14 +482,14 @@ def batch_cut_fieldmap(fieldmaps_path: str, fieldmap_dim: int, entrance_rows_cut
if (entrance_rows_cut+exit_rows_cut>0):
Header, Field = cut_fieldmap_length(f, fieldmap_dim, entrance_rows_cut, exit_rows_cut, file_type)
- save_to_ascii(Header, Field, fieldmap_dim, fieldmaps_path+'/ASCII/', f)
+ save_to_ascii(Header, Field, fieldmap_dim, fieldmaps_path+'/ASCII/', f)
def uniform_mesh(mesh: list):
'''
parameters: list
a 1D array which is the coordinate values of the field
returns: Tuple
- a tuple containing a boolean (if the mesh is uniform or not), mesh_size (average to reduce the effect of
+ a tuple containing a boolean (if the mesh is uniform or not), mesh_size (average to reduce the effect of
floating point error), and the number of steps from min to max (n numbers would result in n-1)
'''
unique = np.unique(mesh, return_counts=False)
@@ -513,12 +504,12 @@ def uniform_mesh(mesh: list):
else:
return True, np.average(mesh_check), sum(mesh_size)
-def create_TW_fieldmap(fieldmaps_path: str, tabular_file_name: str, fieldmap_dim: int = 1, n_col: int = 1, header: bool = True, **kwargs):
+def create_tw_fieldmap(fieldmaps_path: str, tabular_file_name: str, fieldmap_dim: int = 1, n_col: int = 1, header: bool = True, **kwargs):
'''
paramters
---------
fieldmaps_path: str
- path to the fieldmap
+ path to the fieldmap
tabular_file_with_path: file name
tabular data with coordinates z, x and y or z and r or only z depending on dimension as first 1-3 cols.
n_col is the number of data columns except coordinate ones (1-6)
@@ -550,7 +541,7 @@ def create_TW_fieldmap(fieldmaps_path: str, tabular_file_name: str, fieldmap_dim
print('xyz array is shorter than the number of dimensions')
return
- tabular_file_with_path = fieldmaps_path + tabular_file_name
+ tabular_file_with_path = os.path.join(fieldmaps_path, tabular_file_name)
field_File = open(tabular_file_with_path,'r')
if header==True:
@@ -559,7 +550,7 @@ def create_TW_fieldmap(fieldmaps_path: str, tabular_file_name: str, fieldmap_dim
field_array = np.genfromtxt(field_File, delimiter=delim, skip_header=0)
field_File.close()
- items_per_row = np.array([[2, 1, 0, 0],
+ items_per_row = np.array([[2, 1, 0, 0],
[2, 2, 1, 0],
[2, 3, 3, 1]]) # for 1D, 2D and 3D fields in an ASCII fieldmap in TraceWin format
l_header = np.sum(items_per_row[fieldmap_dim-1,:], axis=0) # getting the length of the header from the items
@@ -604,50 +595,8 @@ def create_TW_fieldmap(fieldmaps_path: str, tabular_file_name: str, fieldmap_dim
ordr = np.argsort(coor, kind = 'mergesort')
field_array = field_array[ordr, :]
- #print('\n', field_array[:,:3])
- #print(Header)
filename_wo_ext = os.path.splitext(tabular_file_name)[0]
for i in range(fieldmap_dim, fieldmap_dim+n_col):
- #print(len(field_array[:,i]))
- save_to_ascii(Header, field_array[:,i], fieldmap_dim, fieldmaps_path, filename_wo_ext+"."+extensions[i-fieldmap_dim])
-
-# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
-# #
-# C h a n g e t h e f o l l o w i n g v a l u e s #
-# #
-# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
-
-def example_func():
- '''
- Example
-
- 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.
- '''
- 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]!=".":
-
- if (n_enter+n_exit>0):
- Header, Field = cut_fieldmap_length(f, fieldmap_dim, n_enter, n_exit, 'b')
- else:
- Header, Field = read_binary_fieldmap(mypath+f, fieldmap_dim)
+ save_to_ascii(Header, field_array[:,i], fieldmap_dim, os.path.join(fieldmaps_path, filename_wo_ext+"."+extensions[i-fieldmap_dim]))
- save_to_ascii(Header, Field, fieldmap_dim, 'Data/FM/ASCII/', f)
- test = False
- #%pylab inline
- if test == True:
- #field = field_on_axis_from_file('Data/FM/MB_W_coupler.edz', 3, 'b')
- field = field_on_axis_from_file('Data/FM/ASCII/Spoke_W_coupler.edz', 3, 'a')
- plot(field[0,:], field[1,:])
diff --git a/examples/fieldmap/example.py b/examples/fieldmap/example.py
index 8fe860d2fe09796129dbafa80329d83e5b3774ce..3b4a410e14081c91eebd822deb04576cf76af497 100644
--- a/examples/fieldmap/example.py
+++ b/examples/fieldmap/example.py
@@ -1,4 +1,5 @@
from ess import fieldmap
+import os
'''
Example
@@ -8,28 +9,61 @@ scans the folder and converts all files, assumes all are binary fieldmaps, to AS
The dimension of the field is also needed.
If you need to cut the field, the n_enter and n_exit should be adjusted.
'''
-mypath = 'Data/FM/' # where the fieldmaps are stored
-fieldmap_dim = 3
-#fieldmap_dim = 2
-#fieldmap_dim = 1
+plot = False # Make comparison plots
+mypath = 'maps' # where the fieldmaps are stored
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]!=".":
+if plot:
+ from matplotlib import pyplot as plt
- if (n_enter+n_exit>0):
- Header, Field = fieldmap.cut_fieldmap_length(f, fieldmap_dim, n_enter, n_exit, 'b')
- else:
- Header, Field = fieldmap.read_binary_fieldmap(mypath+f, fieldmap_dim)
+for f in os.listdir(mypath):
+
+ print(f)
+ fullpath=os.path.join(mypath,f)
+
+ ftype=''
+ if 'ascii' in f:
+ ftype='a'
+ elif 'binary' in f:
+ ftype='b'
+ fdim=0
+ if '1D' in f:
+ fdim=1
+ elif '2D' in f:
+ fdim=2
+ elif '3D' in f:
+ fdim=3
- save_to_ascii(Header, Field, fieldmap.fieldmap_dim, 'Data/FM/ASCII/', f)
+ if (n_enter or n_exit):
+ Header, Field = fieldmap.cut_fieldmap_length(fullpath, fdim, n_enter, n_exit, ftype)
+ else:
+ Header, Field = fieldmap.read_fieldmap(fullpath, fdim, ftype)
+
+ fieldmap.save_to_ascii(Header, Field, 'new_'+f)
+ oldfield = fieldmap.field_on_axis(Header,Field, fdim)
+ origfield = fieldmap.field_on_axis_from_file(fullpath, fdim, 'a')
+ newfield = fieldmap.field_on_axis_from_file('new_'+f, fdim, 'a')
+ if plot:
+ # Plot for comparison
+ plt.figure()
+ plt.title(f)
+ if fdim==1:
+ plt.plot(oldfield,label='old')
+ plt.plot(newfield,label='new')
+ plt.plot(origfield,label='orig')
+ else:
+ plt.plot(oldfield[0,:], oldfield[1,:], label='old')
+ plt.plot(newfield[0,:], newfield[1,:], label='new')
+ plt.plot(origfield[0,:], origfield[1,:],label='orig')
+ plt.legend()
+ else:
+ if fdim==1:
+ avg_diff=sum(newfield-oldfield)/len(newfield)
+ else:
+ avg_diff=sum(newfield[1,:]-oldfield[1,:])/len(newfield[1,:])
+ print("Average diff",avg_diff)
-test = False
-#%pylab inline
-if test == True:
- #field = field_on_axis_from_file('Data/FM/MB_W_coupler.edz', 3, 'b')
- field = field_on_axis_from_file('Data/FM/ASCII/Spoke_W_coupler.edz', 3, 'a')
- plot(field[0,:], field[1,:])
+if plot:
+ plt.show()
diff --git a/examples/fieldmap/maps/HB_W_coupler_1D_ascii.edz b/examples/fieldmap/maps/HB_W_coupler_1D_ascii.edz
new file mode 100644
index 0000000000000000000000000000000000000000..f729acfba9a21702ae765620580da050ddab46b1
--- /dev/null
+++ b/examples/fieldmap/maps/HB_W_coupler_1D_ascii.edz
@@ -0,0 +1,10002 @@
+9999 1.500
+1
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diff --git a/examples/fieldmap/maps/HB_W_coupler_3D_ascii_50x15x15.edz b/examples/fieldmap/maps/HB_W_coupler_3D_ascii_50x15x15.edz
new file mode 100644
index 0000000000000000000000000000000000000000..8bc317c0c0333c404c8af2904000b1ce7c27b56b
--- /dev/null
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diff --git a/examples/fieldmap/maps/sef2_545_4mm_2D_ascii.bsz b/examples/fieldmap/maps/sef2_545_4mm_2D_ascii.bsz
new file mode 100644
index 0000000000000000000000000000000000000000..e00d8123dde434815cfd29057486c00615464986
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