def scat_prop_snrSlope(log_prop, data, bvals, mask):
"""
Displays a scatter density plot of the slopes of the log of the desired property
values versus the slopes of the first order fit through SNR.
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 = snr.separate_bvals(bvals)
ls_fit_bsnr = snr_ls_fit(data, bvals, mask, unique_b)
ls_fit_prop = ls_fit_b(log_prop, unique_b)
mpl.scatter_density(ls_fit_bsnr[0,:], ls_fit_prop[0,:])
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,:])
