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,:])