def snr_ls_fit(data, bvals, mask, unique_b):
"""
Fits a first order least squares solution to the SNR data versus b value
Parameters
----------
data: 4 dimensional array
Diffusion MRI data
bvals: 1 dimensional array
All b values
mask: 3 dimensional array
Brain mask of the data
unique_b: 1 dimensional array
Array of all the unique b values found
Returns
-------
ls_fit_bsnr: 1 dimensional array
An array with the results from the least squares fit to the SNR data
"""
if 0 in unique_b:
unique_b = sort(unique_b)[1:]
data, mask = obtain_data(data, mask)
all_bsnr = list()
for bi in np.arange(len(unique_b)):
all_bsnr.append(snr.b_snr(data, bvals, unique_b[bi], mask)[np.where(mask)])
ls_fit_bsnr = ls_fit_b(all_bsnr, unique_b)
return ls_fit_bsnr
def test_b_snr():
idx_mask_t = (np.array([0, 0, 1, 1]), np.array([0, 1, 0, 1]), np.array([1, 1, 1, 1]))
test_data = data_t[idx_mask_t]
snr_unbiased_t = snr.calculate_snr(test_data[:,np.array([0,1,2])],test_data[:,np.array([3,6])])
bsnr_t = np.zeros([2,2,2])
bsnr_t[idx_mask_t] = np.squeeze(snr_unbiased_t)
bsnr_t[np.where(~np.isfinite(bsnr_t))] = 0
npt.assert_equal(bsnr_t, snr.b_snr(data_t, bvals_t, 2, mask_t))
def scat_prop_snr(log_prop, data, bvals, mask):
"""
Displays a scatter density plot of SNR versus the slope of the desired property
Parameters
----------
log_prop: list
List of all the log of the desired property values
data: 4 dimensional array
Diffusion MRI data
bvals: 1 dimensional array
All b values
mask: 3 dimensional array
Brain mask of the data
"""
bval_list, bval_ind, unique_b, _ = ozu.separate_bvals(bvals)
if 0 in unique_b:
unique_b = unique_b[1:]
bsnr = snr.b_snr(data, bvals, unique_b[0], mask)[np.where(mask)]
ls_fit_prop = ls_fit_b(log_prop, unique_b)
mpl.scatter_density(bsnr, ls_fit_prop[0,:])
