def gen_basis(tessellation, gradient):
file1 = '%dvectors.txt' % tessellation
file2 = '%dvectors.txt' % gradient
data, dsize = gen_1fib(file1, file2)
filename = 's_t%d_g%d_f1.flt' % (tessellation, gradient)
flt_utils.write_flt_file(filename, data, dsize)
return data, dsize, filename
def gen_basis(tessellation,gradient):
file1 = '%dvectors.txt'%tessellation
file2 = '%dvectors.txt'%gradient
data, dsize = gen_1fib(file1, file2)
filename = 's_t%d_g%d_f1.flt'%(tessellation,gradient)
flt_utils.write_flt_file(filename,data,dsize)
return data,dsize,filename
def add_rician_noise_to_file(filename, stds, shuffle=False):
#stds = numpy.arange(0.01,0.1,0.01)
flt_filename = filename + '.flt'
dsize, data = flt_utils.read_flt_file(flt_filename)
inds = numpy.arange(data.shape[1])
for std in stds:
s_noise = add_rician_noise(data, std)
k, n = s_noise.shape
if shuffle:
numpy.random.shuffle(inds)
output_filename = '%s_sd%03d.flt' % (filename, std * 100)
txt_filename = '%s_sd%03d.txt' % (filename, std * 100)
flt_utils.write_flt_file(output_filename, s_noise[:, inds], dsize)
f = open(txt_filename, 'w')
f.write('\n'.join([str(ind) for ind in inds]))
f.close()
def add_rician_noise_to_file(filename, stds, shuffle = False):
#stds = numpy.arange(0.01,0.1,0.01)
flt_filename = filename + '.flt'
dsize, data = flt_utils.read_flt_file(flt_filename)
inds = numpy.arange(data.shape[1])
for std in stds:
s_noise = add_rician_noise(data, std)
k,n = s_noise.shape
if shuffle:
numpy.random.shuffle(inds)
output_filename = '%s_sd%03d.flt'%(filename,std*100)
txt_filename = '%s_sd%03d.txt'%(filename,std*100)
flt_utils.write_flt_file(output_filename,s_noise[:,inds],dsize)
f = open(txt_filename,'w')
f.write('\n'.join([str(ind) for ind in inds]))
f.close()