Cleaner notebook with python.

.gitignore

@@ -5,4 +5,6 @@ fig/
 .ipynb_checkpoints/
 data/
 __pycache__/
-*.mat
+*.mat
+
+!T2_CPMG.mat

cfl.py

@@ -1,114 +0,0 @@
-# Copyright 2013-2015. The Regents of the University of California.
-# Copyright 2021. Uecker Lab. University Center Göttingen.
-# All rights reserved. Use of this source code is governed by
-# a BSD-style license which can be found in the LICENSE file.
-#
-# Authors:
-# 2013 Martin Uecker <uecker@eecs.berkeley.edu>
-# 2015 Jonathan Tamir <jtamir@eecs.berkeley.edu>
-
-from __future__ import print_function
-from __future__ import with_statement
-
-import numpy as np
-import mmap
-import os
-
-
-def readcfl(name):
-    # get dims from .hdr
-    with open(name + ".hdr", "rt") as h:
-        h.readline() # skip
-        l = h.readline()
-    dims = [int(i) for i in l.split()]
-
-    # remove singleton dimensions from the end
-    n = np.prod(dims)
-    dims_prod = np.cumprod(dims)
-    dims = dims[:np.searchsorted(dims_prod, n)+1]
-
-    # load data and reshape into dims
-    with open(name + ".cfl", "rb") as d:
-        a = np.fromfile(d, dtype=np.complex64, count=n);
-    return a.reshape(dims, order='F') # column-major
-
-def readmulticfl(name):
-    # get dims from .hdr
-    with open(name + ".hdr", "rt") as h:
-        lines = h.read().splitlines()
-
-    index_dim = 1 + lines.index('# Dimensions')
-    total_size = int(lines[index_dim])
-    index_sizes = 1 + lines.index('# SizesDimensions')
-    sizes = [int(i) for i in lines[index_sizes].split()]
-    index_dims = 1 + lines.index('# MultiDimensions')
-
-    with open(name + ".cfl", "rb") as d:
-        a = np.fromfile(d, dtype=np.complex64, count=total_size)
-
-    offset = 0
-    result = []
-    for i in range(len(sizes)):
-        dims = ([int(i) for i in lines[index_dims + i].split()])
-        n = np.prod(dims)
-        result.append(a[offset:offset+n].reshape(dims, order='F'))
-        offset += n
-
-    if total_size != offset:
-        print("Error")
-
-    return result
-
-
-def writecfl(name, array):
-    with open(name + ".hdr", "wt") as h:
-        h.write('# Dimensions\n')
-        for i in (array.shape):
-                h.write("%d " % i)
-        h.write('\n')
-
-    size = np.prod(array.shape) * np.dtype(np.complex64).itemsize
-
-    with open(name + ".cfl", "a+b") as d:
-        os.ftruncate(d.fileno(), size)
-        mm = mmap.mmap(d.fileno(), size, flags=mmap.MAP_SHARED, prot=mmap.PROT_WRITE)
-        if array.dtype != np.complex64:
-            array = array.astype(np.complex64)
-        mm.write(np.ascontiguousarray(array.T))
-        mm.close()
-        #with mmap.mmap(d.fileno(), size, flags=mmap.MAP_SHARED, prot=mmap.PROT_WRITE) as mm:
-        #    mm.write(array.astype(np.complex64).tobytes(order='F'))
-
-def writemulticfl(name, arrays):
-    size = 0
-    dims = []
-
-    for array in arrays:
-        size += array.size
-        dims.append(array.shape)
-
-    with open(name + ".hdr", "wt") as h:
-        h.write('# Dimensions\n')
-        h.write("%d\n" % size)
-
-        h.write('# SizesDimensions\n')
-        for dim in dims:
-            h.write("%d " % len(dim))
-        h.write('\n')
-
-        h.write('# MultiDimensions\n')
-        for dim in dims:
-            for i in dim:
-                h.write("%d " % i)
-            h.write('\n')
-            
-    size = size * np.dtype(np.complex64).itemsize
-
-    with open(name + ".cfl", "a+b") as d:
-        os.ftruncate(d.fileno(), size)
-        mm = mmap.mmap(d.fileno(), size, flags=mmap.MAP_SHARED, prot=mmap.PROT_WRITE)
-        for array in arrays:
-            if array.dtype != np.complex64:
-                array = array.astype(np.complex64)
-            mm.write(np.ascontiguousarray(array.T))
-        mm.close()

reco.py

@@ -1,78 +0,0 @@
-import scipy.io as sio
-import matplotlib.pyplot as plt
-import numpy as np
-from scipy.optimize import curve_fit
-import sys,os
-from cfl import writecfl
-
-os.environ['TOOLBOX_PATH'] = '/home/jpfitzer/bart-0.9.00'
-os.environ['BART_TOOLBOX_PATH'] = '/home/jpfitzer/bart-0.9.00'
-sys.path.append('/home/jpfitzer/bart-0.9.00/bart/python')
-
-rawName = 'T2_CPMG.mat'
-mat_data_0=sio.loadmat(rawName)
-
-# Format: sampled, # kSpace [kRd, kPh, kSl, kSpace_echo_1, kSpace_echo_2, ..., kSpace_echo_nETL] (102400, 23)
-# So the first three are the coordinates of the kspace, and the rest are the echoes
-print(mat_data_0['kSpaces3D'].shape)
-
-kSpaces3D = mat_data_0['kSpaces3D']
-# self.mapVals['sampled'] = np.concatenate((kRD, kPH, kSL, dataAll_sampled), axis=1)
-
-# nReadout, nPhase, nSlice
-nPoints = (80, 80, 16)
-
-echo_train_length = mat_data_0['kSpaces3D'].shape[1] - 3 # Because the first 3 are kRD, kPH, kSL -> should give 20
-print(f"Echo train length: {echo_train_length}")
-
-echo_spacing = mat_data_0['echoSpacing'][0][0]
-print(f"Echo spacing: {echo_spacing}")
-
-k_readout = kSpaces3D[:, 0]
-k_phase = kSpaces3D[:, 1]
-k_slice = kSpaces3D[:, 2]
-
-# The rest of the data is the echoes
-echos = kSpaces3D[:, 3:]
-
-# Reshape the kspace data for bart
-kSpace = echos.reshape(nPoints[2], nPoints[1], nPoints[0], echo_train_length)
-
-
-
-print(kSpace.shape)
-
-# cfl = writecfl('kSpace', kSpace)
-
-# Create the image with bart fft -i 7 kSpace fft -> three dimensional
-# Put the echos on the fifth dimension:
-# bart transpose 3 5
-# Put the slices on the correct dimension
-# bart transpose 0 2
-
-# traj = writecfl('traj', np.stack((k_readout, k_phase, k_slice), axis=1))
-
-# Echo times with echo spacing
-TE = np.linspace(echo_spacing, echo_spacing * echo_train_length, echo_train_length, endpoint=True)
-print("TE: ", TE)
-
-# Create the echotimes file:
-# bart vec ... echo_times
-# bart scale 0.001 echo_times echo_times_scaled
-# Move the echo_times to the correct dimension:
-# bart transpose 0 5 echo_times_scaled echo_times_final
-
-# Fit the model:
-# bart mobafit -T echo_times_final fft_transposed fit
-
-# Now some values will be very large so we can apply a threshold to obtain a mask
-# bart threshold -M 1000 reco/fit reco/mask
-
-# Multiply the fit with the mask
-# bart fmac reco/fit reco/fit reco/fit_mask
-
-# Select slice
-# bart slice 6 1 reco/fit_mask reco/R2_map
-
-# Invert the data to get T2
-# bart invert reco/R2_map reco/T2_map