def initial_params(data, bvecs, bvals, model, mask=None, params_file='temp'):
"""
Determine the initial values for fitting the isotropic diffusion model.
This only works on the models that fit to the relative diffusion signal.
Parameters
----------
data: 4 dimensional array
Diffusion MRI data
bvecs: 2 dimensional array
All the b vectors
bvals: 1 dimensional array
All b values
model: str
Isotropic model
mask: 3 dimensional array
Mask of the data
params_file: obj or str
File handle of the param_files containing the tensor parameters.
Returns
-------
bounds: list
A list containing the bounds for each parameter for least squares
fitting.
initial: list
A list containing the initial values for each parameter for least
squares fitting.
"""
dti_mod = dti.TensorModel(data, bvecs, bvals, mask=mask,
params_file=params_file)
d = dti_mod.mean_diffusivity[np.where(mask)]
# Find initial noise floor
_, b_inds, _, _ = ozu.separate_bvals(bvals)
b0_data = data[np.where(mask)][:, b_inds[0]]
#nf = np.std(b0_data, -1)/np.mean(b0_data, -1)
nf = np.min(data[np.where(mask)], -1)
if model == single_exp_rs:
bounds = [(0, 4)]
initial = d
elif model == single_exp_nf_rs:
bounds = [(0, 10000), (0, 4)]
initial = np.concatenate([nf[..., None],
np.ones(d[...,None].shape)], -1)
elif model== bi_exp_rs:
bounds = [(0, 1), (0, 4), (0, 4)]
initial = np.concatenate([0.5*np.ones((len(d),1)), d[...,None],
d[...,None]], -1)
elif model== bi_exp_nf_rs:
bounds = [(0, 10000), (0, 1), (0, 4), (0, 4)]
initial = np.concatenate([nf[..., None], 0.5*np.ones((len(d),1)),
d[...,None], d[...,None]], -1)
return bounds, initial
def rm_ventricles(wm_data_file, bvals, bvecs, data, data_path):
"""
Removes the ventricles from the white matter mask and saves a new
white matter mask.
Parameters
----------
wm_data_file: obj
File handle for the white matter mask
bvals: 1 dimensional array
All b values
bvecs: 2 dimensional array
All the b vectors
data: 4 dimensional array
The diffusion data
data_path: str
Path to the data directory with all the sub files.
Returns
-------
new_wm_mask: 3 dimensional array
A new white matter mask with the ventricles removed.
"""
# Separate b-values and find the indices corresponding to the b0 and
# bNk measurements where N = the lowest b-value other than 0
wm_data = wm_data_file.get_data()
bval_list, b_inds, unique_b, bvals_scaled = ozu.separate_bvals(bvals)
all_b_idx = np.where(bvals_scaled != 0)
bNk_b0_inds = np.concatenate((b_inds[0], b_inds[1]))
# Fit a tensor model
tm = dti.TensorModel(data[..., bNk_b0_inds],
bvecs[:, bNk_b0_inds],
bvals[bNk_b0_inds],
mask=wm_data,
params_file="temp")
# Find the median, and 25th and 75th percentiles of mean diffusivities
md_median = np.median(tm.mean_diffusivity[np.where(wm_data)])
q1 = stats.scoreatpercentile(tm.mean_diffusivity[np.where(wm_data)], 25)
q3 = stats.scoreatpercentile(tm.mean_diffusivity[np.where(wm_data)], 75)
# Exclude voxels with MDs above median + 2*interquartile range
md_exclude = md_median + 2 * (q3 - q1)
md_include = np.where(tm.mean_diffusivity[np.where(wm_data)] < md_exclude)
new_wm_mask = np.zeros(wm_data.shape)
new_wm_mask[np.where(wm_data)[0][md_include],
np.where(wm_data)[1][md_include],
np.where(wm_data)[2][md_include]] = 1
wm = ni.Nifti1Image(new_wm_mask, wm_data_file.get_affine())
ni.save(wm, os.path.join(data_path, 'wm_mask_no_vent.nii.gz'))
return new_wm_mask
def sph_cc_ineq(cod_single_mod,
cod_multi_mod,
bvals,
single_thresh,
multi_thresh,
tol=0.1):
"""
Helper function to find the indices where inequalities occur between two given
input arrays and to separate the b values.
Parameters
----------
cod_single_mod: 1 dimensional array
Coefficient of determination at each voxel for the single fODF model
cod_multi_mod: 1 dimensional array
Coefficient of determination at each voxel for the multi fODF model
bvals: 1 dimensional array
All b values
single_thresh: int
Coefficient of determination threshold for the single fODF model
multi_thresh: int
Coefficient of determination threshold for the multi fODF model
tol: float
Tolerance
Returns
-------
inds: 1 dimensional array
Indices indicating the voxels within the COD inequality/equality
b_inds: list
List of indices corresponding to each b value
all_b_inds: 1 dimensional array
Indices corresponding to the non-zero b values
"""
bval_list, b_inds, unique_b, rounded_bvals = ozu.separate_bvals(bvals)
all_b_inds = np.where(rounded_bvals != 0)
if single_thresh > multi_thresh:
inds = np.where((cod_single_mod > single_thresh)
& (cod_multi_mod < multi_thresh)
& (cod_multi_mod > multi_thresh - tol))
elif single_thresh < multi_thresh:
inds = np.where((cod_single_mod < single_thresh)
& (cod_single_mod > single_thresh - tol)
& (cod_multi_mod > multi_thresh))
elif single_thresh == multi_thresh:
inds = np.where((cod_single_mod < single_thresh + tol / 2)
& (cod_single_mod > single_thresh - tol / 2)
& (cod_multi_mod < multi_thresh + tol / 2)
& (cod_multi_mod > multi_thresh - tol / 2))
return np.squeeze(inds), b_inds, all_b_inds
def rm_ventricles(wm_data_file, bvals, bvecs, data, data_path):
"""
Removes the ventricles from the white matter mask and saves a new
white matter mask.
Parameters
----------
wm_data_file: obj
File handle for the white matter mask
bvals: 1 dimensional array
All b values
bvecs: 2 dimensional array
All the b vectors
data: 4 dimensional array
The diffusion data
data_path: str
Path to the data directory with all the sub files.
Returns
-------
new_wm_mask: 3 dimensional array
A new white matter mask with the ventricles removed.
"""
# Separate b-values and find the indices corresponding to the b0 and
# bNk measurements where N = the lowest b-value other than 0
wm_data = wm_data_file.get_data()
bval_list, b_inds, unique_b, bvals_scaled = ozu.separate_bvals(bvals)
all_b_idx = np.where(bvals_scaled != 0)
bNk_b0_inds = np.concatenate((b_inds[0], b_inds[1]))
# Fit a tensor model
tm = dti.TensorModel(data[..., bNk_b0_inds], bvecs[:, bNk_b0_inds],
bvals[bNk_b0_inds], mask=wm_data,
params_file = "temp")
# Find the median, and 25th and 75th percentiles of mean diffusivities
md_median = np.median(tm.mean_diffusivity[np.where(wm_data)])
q1 = stats.scoreatpercentile(tm.mean_diffusivity[np.where(wm_data)],25)
q3 = stats.scoreatpercentile(tm.mean_diffusivity[np.where(wm_data)],75)
# Exclude voxels with MDs above median + 2*interquartile range
md_exclude = md_median + 2*(q3 - q1)
md_include = np.where(tm.mean_diffusivity[np.where(wm_data)] < md_exclude)
new_wm_mask = np.zeros(wm_data.shape)
new_wm_mask[np.where(wm_data)[0][md_include],
np.where(wm_data)[1][md_include],
np.where(wm_data)[2][md_include]] = 1
wm = ni.Nifti1Image(new_wm_mask, wm_data_file.get_affine())
ni.save(wm, os.path.join(data_path, 'wm_mask_no_vent.nii.gz'))
return new_wm_mask
def sph_cc_ineq(cod_single_mod, cod_multi_mod, bvals, single_thresh, multi_thresh, tol = 0.1):
"""
Helper function to find the indices where inequalities occur between two given
input arrays and to separate the b values.
Parameters
----------
cod_single_mod: 1 dimensional array
Coefficient of determination at each voxel for the single fODF model
cod_multi_mod: 1 dimensional array
Coefficient of determination at each voxel for the multi fODF model
bvals: 1 dimensional array
All b values
single_thresh: int
Coefficient of determination threshold for the single fODF model
multi_thresh: int
Coefficient of determination threshold for the multi fODF model
tol: float
Tolerance
Returns
-------
inds: 1 dimensional array
Indices indicating the voxels within the COD inequality/equality
b_inds: list
List of indices corresponding to each b value
all_b_inds: 1 dimensional array
Indices corresponding to the non-zero b values
"""
bval_list, b_inds, unique_b, rounded_bvals = ozu.separate_bvals(bvals)
all_b_inds = np.where(rounded_bvals != 0)
if single_thresh > multi_thresh:
inds = np.where((cod_single_mod > single_thresh) &
(cod_multi_mod < multi_thresh) &
(cod_multi_mod > multi_thresh - tol))
elif single_thresh < multi_thresh:
inds = np.where((cod_single_mod < single_thresh) &
(cod_single_mod > single_thresh - tol) &
(cod_multi_mod > multi_thresh))
elif single_thresh == multi_thresh:
inds = np.where((cod_single_mod < single_thresh + tol/2) &
(cod_single_mod > single_thresh - tol/2) &
(cod_multi_mod < multi_thresh + tol/2) &
(cod_multi_mod > multi_thresh - tol/2))
return np.squeeze(inds), b_inds, all_b_inds
def test_separate_bvals():
bvals_t = np.array([5, 5, 10, 2010, 1005, 950, 1950, 1000])
unique_b_t = np.array([0,1000,2000])
bvals_scaled_t = np.array([0, 0, 0, 2000, 1000, 1000, 2000, 1000])
bval_list_t = [(np.array([0, 0, 0]))]
bval_list_t.append(np.array([1000, 1000, 1000]))
bval_list_t.append(np.array([2000, 2000]))
bval_ind_t = [np.array([0,1,2]), np.array([4,5,7]), np.array([3,6])]
bval_list, bval_ind, unique_b, bvals_scaled = ozu.separate_bvals(bvals_t)
npt.assert_equal(unique_b, unique_b_t)
npt.assert_equal(bvals_scaled, bvals_scaled_t)
for i in np.arange(len(bval_list)):
npt.assert_equal(np.squeeze(bval_list)[i], bval_list_t[i])
npt.assert_equal(np.squeeze(bval_ind)[i], bval_ind_t[i])
def test_separate_bvals():
bvals_t = np.array([5, 5, 10, 2010, 1005, 950, 1950, 1000])
unique_b_t = np.array([0, 1000, 2000])
bvals_scaled_t = np.array([0, 0, 0, 2000, 1000, 1000, 2000, 1000])
bval_list_t = [(np.array([0, 0, 0]))]
bval_list_t.append(np.array([1000, 1000, 1000]))
bval_list_t.append(np.array([2000, 2000]))
bval_ind_t = [np.array([0, 1, 2]), np.array([4, 5, 7]), np.array([3, 6])]
bval_list, bval_ind, unique_b, bvals_scaled = ozu.separate_bvals(bvals_t)
npt.assert_equal(unique_b, unique_b_t)
npt.assert_equal(bvals_scaled, bvals_scaled_t)
for i in np.arange(len(bval_list)):
npt.assert_equal(np.squeeze(bval_list)[i], bval_list_t[i])
npt.assert_equal(np.squeeze(bval_ind)[i], bval_ind_t[i])
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,:])
def slope(data, bvals, bvecs, prop, mask = 'None', saved_file = 'yes'):
"""
Calculates and displays the slopes of a least squares solution fitted to either
the log of the fractional anisotropy data or mean diffusivity data of the tensor
model across the brain at different b values.
Parameters
----------
data: 4 dimensional array or Nifti1Image
Diffusion MRI data
bvals: 1 dimensional array
All b values
bvecs: 3 dimensional array
All the b vectors
prop: str
String indicating the property to analyzed
'FA': Fractional anisotropy
'MD': Mean diffusivity
mask: 3 dimensional array or Nifti1Image
Brain mask of the data
saved_file: 'str'
Indicate whether or not you want the function to create or use saved
parameter files
'no': Function will not create or use saved files
'yes': Function will create or use saved files
Returns
-------
slopeProp_all: 3 dimensional array
Slope of the desired property across b values at each voxel
"""
# Making sure inputs are all in the right format for calculations
data, mask = obtain_data(data, mask)
# Separate b values
bval_list, bval_ind, unique_b, _ = ozu.separate_bvals(bvals)
idx_array = np.arange(len(unique_b))
new_bval_list = []
for bi in idx_array:
new_bval_list.append(bvals[bval_ind[bi]])
# Add b = 0 values and indices to the other b values for tensor calculation
bval_ind_wb0, bvals_wb0 = include_b0vals(idx_array, bval_ind, new_bval_list)
# Find the property values for each grouped b values
idx_mask = np.where(mask)
log_prop = log_prop_vals(prop, saved_file, data, bvecs, idx_mask, idx_array, bval_ind_wb0, bvals_wb0, mask)
# Fit a first order least squares solution to the specified property versus
# b value to obtain slopes
ls_fit = ls_fit_b(log_prop, unique_b/1000)
# Display slopes of property on a mosaic
slopeProp_all = disp_slopes(mask, ls_fit, prop)
# Save all the values
np.save('slope{0}_all.npy'.format(prop), slopeProp_all)
np.save('log_{0}.npy'.format(prop), log_prop)
np.save('ls_fit_{0}.npy'.format(prop), ls_fit[0])
np.save('unique_b.npy', unique_b)
return slopeProp_all, log_prop, ls_fit, unique_b
def across_sph_cc(vol_b_list,
bvals,
bvecs,
mask,
cod_single_mod=None,
cod_multi_mod=None,
single_thresh=None,
multi_thresh=None,
idx=None,
vol_mp_single=None,
tol=0.1,
n=20):
"""
Calculates the spherical cross correlation at a certain index for all b values fit
together and b values fit separately.
Parameters
----------
vol_b_list: list
List of the model parameters for each voxel for an fODF fit to each b value.
bvals: 1 dimensional array
All b values
bvecs: 2 dimensional array
All the b vectors
mask: 3 dimensional array
Brain mask of the data
cod_single_mod: 1 dimensional array
Coefficient of determination at each voxel for the single fODF model
cod_multi_mod: 1 dimensional array
Coefficient of determination at each voxel for the multi fODF model
single_thresh: int
Coefficient of determination threshold for the single fODF model
multi_thresh: int
Coefficient of determination threshold for the multi fODF model
idx: int
Index into the indices indicating the voxels included in the COD (in)equality
vol_mp_single: 2 dimensional array
Model parameters from fitting a single fODF to each voxel
tol: float
Tolerance for the COD (in)equality
n: int
Integer indicating the number of directions to divide by for spherical
cross-correlation
Returns
-------
deg_list: list
List indicating the degrees included in spherical cross-correlation
cc_list: list
List with the cross-correlations between each combination of b values
idx: int
Index into the indices indicating the voxels included in the COD (in)equality
cod_s: float
Coefficient of determination of single fODF model
cod_m: float
Coefficient of determination of multi fODF model
"""
if (single_thresh != None) & (multi_thresh != None):
# Get the indices with a desired COD.
inds, b_inds, all_b_inds = sph_cc_ineq(cod_single_mod,
cod_multi_mod,
bvals,
single_thresh,
multi_thresh,
tol=tol)
else:
# With no COD threshold, just find all the indices.
inds = np.arange(int(np.sum(mask)))
bval_list, b_inds, unique_b, rounded_bvals = ozu.separate_bvals(bvals)
all_b_inds = np.where(rounded_bvals != 0)
if idx is None:
# If a specific index is not given, just find a random index.
if ri is None:
ri = np.random.randint(0, len(inds))
else:
ri = ri
idx = inds[ri]
data_list = []
bvecs_b_list = []
deg_list = []
cc_list = []
pool = np.arange(len(vol_b_list))
for ii in pool:
# Just get the data (model parameters) and bvecs within the chosen voxel (idx)
# for each b value and mirror them.
data_list.append(
np.concatenate((vol_b_list[ii][np.where(mask)][idx],
vol_b_list[ii][np.where(mask)][idx]), -1))
bvecs_b_list.append(
np.squeeze(
np.concatenate(
(bvecs[:, b_inds[ii + 1]], -1 * bvecs[:, b_inds[ii + 1]]),
-1)).T)
if vol_mp_single is None:
# Make combinations of b values for spherical cross correlation between b values
combos = list(itertools.combinations(pool, 2))
this_iter = np.arange(len(combos))
else:
# No need for combos since comparing between the the single fODF and the multi fODF
combos = None
this_iter = np.arange(len(vol_b_list))
bvecs_all = np.squeeze(
np.concatenate((bvecs[:, all_b_inds], -1 * bvecs[:, all_b_inds]),
-1)).T
data_all = np.concatenate((vol_mp_single[np.where(mask)][idx],
vol_mp_single[np.where(mask)][idx]), -1)
for itr in this_iter:
if vol_mp_single is None:
# Inputs are data and bvecs from two different b values that you want to find the
# spherical cross-correlation between
inputs = [
np.squeeze(data_list[combos[itr][0]]),
np.squeeze(data_list[combos[itr][1]]),
bvecs_b_list[combos[itr][0]], bvecs_b_list[combos[itr][1]]
]
else:
# Inputs are the data and bvecs from one b value and the single fODF
inputs = [
np.squeeze(data_all),
np.squeeze(data_list[itr]), bvecs_all, bvecs_b_list[itr]
]
# Put the inputs into the spherical cross-correlation function
deg, cc = ozu.sph_cc(*inputs, n=n)
deg_list.append(deg)
cc_list.append(cc)
if (single_thresh != None) & (multi_thresh != None):
# Because sometimes it's nice to know what the actual CODs are
cod_s = cod_single_mod[idx]
cod_m = cod_multi_mod[idx]
else:
cod_s = None
cod_m = None
return deg_list, cc_list, combos, idx, cod_s, cod_m
def kfold_xval_MD_mod(data,
bvals,
bvecs,
mask,
func,
n,
factor=1000,
initial="preset",
bounds="preset",
params_file='temp',
signal="relative_signal"):
"""
Finds the parameters of the given function to the given data
that minimizes the sum squared errors using kfold cross validation.
Parameters
----------
data: 4 dimensional array
Diffusion MRI data
bvals: 1 dimensional array
All b values
bvecs: 3 dimensional array
All the b vectors
mask: 3 dimensional array
Brain mask of the data
func: function handle
Mean model to perform kfold cross-validation on.
initial: tuple
Initial values for the parameters.
n: int
Integer indicating the percent of vertices that you want to predict
factor: int
Integer indicating the scaling factor for the b values
bounds: list
List containing tuples indicating the bounds for each parameter in
the mean model function.
Returns
-------
cod: 1 dimensional array
Coefficent of Determination between data and predicted values
predicted: 2 dimensional array
Predicted mean for the vertices left out of the fit
"""
if isinstance(func, str):
# Grab the function handle for the desired isotropic model
func = globals()[func]
# Get the initial values for the desired isotropic model
if (bounds == "preset") | (initial == "preset"):
all_params = initial_params(data,
bvecs,
bvals,
func,
mask=mask,
params_file=params_file)
if bounds == "preset":
bounds = all_params[0]
if initial == "preset":
func_initial = all_params[1]
else:
this_initial = initial
bval_list, b_inds, unique_b, rounded_bvals = ozu.separate_bvals(bvals)
all_b_idx, b0_inds = _diffusion_inds(bvals, b_inds, rounded_bvals)
b_scaled = bvals / factor
flat_data = data[np.where(mask)]
# Pre-allocate outputs
ss_err = np.zeros(int(np.sum(mask)))
predict_out = np.zeros((int(np.sum(mask)), len(all_b_idx)))
# Setting up for creating combinations of directions for kfold cross
# validation:
# Number of directions to leave out at a time.
num_choose = (n / 100.) * len(all_b_idx)
# Find the indices to all the non-b = 0 directions and shuffle them.
vec_pool = np.arange(len(all_b_idx))
np.random.shuffle(vec_pool)
all_inc_0 = np.arange(len(rounded_bvals))
# Start cross-validation
for combo_num in np.arange(np.floor(100. / n)):
(si, vec_combo, vec_combo_rm0, vec_pool_inds, these_bvecs, these_bvals,
this_data, these_inc0) = ozu.create_combos(bvecs, bvals, data,
all_b_idx,
np.arange(len(all_b_idx)),
all_b_idx, vec_pool,
num_choose, combo_num)
these_b = b_scaled[vec_combo] # b values to predict
for vox in np.arange(np.sum(mask)).astype(int):
s0 = np.mean(flat_data[vox, b0_inds], -1)
if initial == "preset":
this_initial = func_initial[vox]
input_signal = flat_data[vox, these_inc0] / s0
if signal == "log":
input_signal = np.log(input_signal)
# Fit mean model to part of the data
params, _ = opt.leastsq(err_func,
this_initial,
args=(b_scaled[these_inc0], input_signal,
func))
if bounds == None:
params, _ = opt.leastsq(err_func,
this_initial,
args=(b_scaled[these_inc0],
input_signal, func))
else:
lsq_b_out = lsq.leastsqbound(err_func,
this_initial,
args=(b_scaled[these_inc0],
input_signal, func),
bounds=bounds)
params = lsq_b_out[0]
predict_out[vox, vec_combo_rm0] = func(these_b, *params)
# Find the relative diffusion signal.
s0 = np.mean(flat_data[:, b0_inds], -1).astype(float)
input_signal = flat_data[:, all_b_idx] / s0[..., None]
if signal == "log":
input_signal = np.log(input_signal)
cod = ozu.coeff_of_determination(input_signal, predict_out, axis=-1)
return cod, predict_out
def place_files(file_names, mask_vox_num, expected_file_num, mask_data,
data, bvals, file_path=os.getcwd(), vol=False,
f_type="npy", save=False):
"""
Function to aggregate sub data files from parallelizing. Assumes that
the sub_files are in the format:
(file name)_(number of sub_file).(file_type)
Parameters
----------
file_names: list
List of strings indicating the base file names for each output data
aggregation
mask_vox_num: int
Number of voxels in each sub file
expected_file_num: int
Expected number of sub files
mask_data: 3 dimensional array
White matter mask
data: 4 dimensional array
The diffusion data
bvals: 1 dimensional array
The b-values used for the diffusion data
file_path: str
Path to the directory with all the sub files. Default is the current
directory
vol: str
String indicating whether or not the sub files are in volumes and
whether the output files are saved as volumes as well
f_type: str
String indicating the type of file the sub files are saved as
save: str
String indicating whether or not to save the output aggregation/volumes
num_dirs: int
Number of directions in each output aggregation/volume
Returns
-------
missing_files: 1 dimensional array
All the sub files that are missing in the aggregation
aggre_list: list
List with all the aggregations/volumes
"""
files = os.listdir(file_path)
# Get data and indices
mask_idx = np.where(mask_data)
bval_list, b_inds, unique_b, bvals_scaled = ozu.separate_bvals(bvals)
all_b_idx = np.where(bvals_scaled != 0)
# Remove voxels from the mask that contain zero signal values and turn the
# mask into linear form.
S0 = np.mean(data[..., b_inds[0]],-1)
pre_mask = np.array(mask_data, dtype=bool)
ravel_mask = np.ravel(pre_mask).astype(int)
ravel_mask[np.where(ravel_mask)[0][np.where(S0[pre_mask] == 0)]] = 2
ravel_mask = ravel_mask[np.where(ravel_mask != 0)]
ravel_mask[np.where(ravel_mask == 2)] = 0
ravel_mask = ravel_mask.astype(bool)
aggre_list = []
missing_files_list = []
for fn in file_names:
count = 0
# Keep track of files in case there are any missing ones
i_track = np.ones(expected_file_num)
# If you don't want to put the voxels back into a volume, just
# preallocate enough for each voxel included in the mask.
for f_idx in np.arange(len(files)):
this_file = files[f_idx]
if this_file[(len(this_file)-len(f_type)):len(this_file)] == f_type:
if f_type == "npy":
sub_data = np.load(os.path.join(file_path, this_file))
elif f_type == "nii.gz":
sub_data = ni.load(os.path.join(file_path,
this_file)).get_data()
# If the name of this file is equal to file name that you want
# to aggregate, load it and find the voxels corresponding to its
# location in the given mask.
if this_file[0:len(fn)] == fn:
if count == 0:
if len(sub_data.shape) == 1:
num_dirs = 1
else:
num_dirs = sub_data.shape[-1]
if vol is False:
aggre = np.squeeze(ozu.nans((int(np.sum(mask_data)),
) + (num_dirs,)))
else:
aggre = np.squeeze(ozu.nans((mask_data_file.shape
+ (num_dirs,))))
count = count + 1
# Find the location in this aggregate for this file piece.
i = int(this_file.split(".")[0][len(fn):])
low = i*mask_vox_num
high = np.min([(i+1) * mask_vox_num,
int(np.sum(mask_data))])
# If you don't have a volume input and don't want a volume
# output, output just an aggregate the output files.
if vol is False:
if sub_data.shape[0] > aggre[low:high][ravel_mask[
low:high]].shape[0]:
aggre[low:high][ravel_mask[low:high]] = np.squeeze(
sub_data)[ravel_mask[low:high]]
else:
aggre[low:high][ravel_mask[low:high]] = np.squeeze(
sub_data)
else:
mask = np.zeros(mask_data_file.shape)
mask[mask_idx[0][low:high][ravel_mask[low:high]],
mask_idx[1][low:high][ravel_mask[low:high]],
mask_idx[2][low:high]][ravel_mask[low:high]] = 1
aggre[np.where(mask)] = sub_data
# If the file is present, change its index within the
# tracking array to 0.
i_track[i] = 0
missing_files_list.append(np.squeeze(np.where(i_track)))
aggre_list.append(aggre)
if save is True:
if vol is False:
np.save("aggre_%s.npy"%fn, aggre)
else:
aff = mask_data_file.get_affine()
ni.Nifti1Image(aggre, aff).to_filename("vol_%s.nii.gz"%fn)
return missing_files_list, aggre_list
def isotropic_params(data,
bvals,
bvecs,
mask,
func,
factor=1000,
initial="preset",
bounds="preset",
params_file='temp',
signal="relative_signal"):
"""
Finds the parameters of the given function to the given data
that minimizes the sum squared errors.
Parameters
----------
data: 4 dimensional array
Diffusion MRI data
bvals: 1 dimensional array
All b values
mask: 3 dimensional array
Brain mask of the data
func: str or callable
String indicating the mean model function to perform kfold
cross-validation on.
initial: tuple
Initial values for the parameters.
factor: int
Integer indicating the scaling factor for the b values
bounds: list
List containing tuples indicating the bounds for each parameter in
the mean model function.
Returns
-------
param_out: 2 dimensional array
Parameters that minimize the residuals
fit_out: 2 dimensional array
Model fitted means
ss_err: 2 dimensional array
Sum squared error between the model fitted means and the actual means
"""
if isinstance(func, str):
# Grab the function handle for the desired mean model
func = globals()[func]
# Get the initial values for the desired mean model
if (bounds == "preset") | (initial == "preset"):
all_params = initial_params(data,
bvecs,
bvals,
func,
mask=mask,
params_file=params_file)
if bounds == "preset":
bounds = all_params[0]
if initial == "preset":
func_initial = all_params[1]
else:
this_initial = initial
# Separate b values and grab their indices
bval_list, b_inds, unique_b, rounded_bvals = ozu.separate_bvals(bvals)
all_b_idx, b0_inds = _diffusion_inds(bvals, b_inds, rounded_bvals)
# Divide the b values by a scaling factor first.
b = bvals[all_b_idx] / factor
flat_data = data[np.where(mask)]
# Get the number of inputs to the mean diffusivity function
param_num = len(inspect.getargspec(func)[0])
# Pre-allocate the outputs:
param_out = np.zeros((int(np.sum(mask)), param_num - 1))
cod = ozu.nans(np.sum(mask))
fit_out = ozu.nans(cod.shape + (len(all_b_idx), ))
prog_bar = ozu.ProgressBar(flat_data.shape[0])
for vox in np.arange(np.sum(mask)).astype(int):
prog_bar.animate(vox)
s0 = np.mean(flat_data[vox, b0_inds], -1)
if initial == "preset":
this_initial = func_initial[vox]
input_signal = flat_data[vox, all_b_idx] / s0
if signal == "log":
input_signal = np.log(input_signal)
if bounds == None:
params, _ = opt.leastsq(err_func,
this_initial,
args=(b, input_signal, func))
else:
lsq_b_out = lsq.leastsqbound(err_func,
this_initial,
args=(b, input_signal, func),
bounds=bounds)
params = lsq_b_out[0]
param_out[vox] = np.squeeze(params)
fit_out[vox] = func(b, *params)
cod[vox] = ozu.coeff_of_determination(input_signal, fit_out[vox])
return param_out, fit_out, cod
if __name__ == "__main__":
sid = "103414"
hcp_path = "/biac4/wandell/data/klchan13/hcp_data_q3"
data_path = os.path.join(hcp_path, "%s/T1w/Diffusion" % sid)
data_file = nib.load(os.path.join(data_path, "data.nii.gz"))
wm_data_file = nib.load(os.path.join(data_path, "wm_mask_no_vent.nii.gz"))
data = data_file.get_data()
wm_data = wm_data_file.get_data()
wm_idx = np.where(wm_data == 1)
bvals = np.loadtxt(os.path.join(data_path, "bvals"))
bvecs = np.loadtxt(os.path.join(data_path, "bvecs"))
bval_list, b_inds, unique_b, bvals_scaled = ozu.separate_bvals(bvals)
all_b_idx = np.where(bvals_scaled != 0)
ad_rd = np.loadtxt(os.path.join(data_path, "ad_rd_%s.txt" % sid))
ad = {1000: ad_rd[0, 0], 2000: ad_rd[0, 1], 3000: ad_rd[0, 2]}
rd = {1000: ad_rd[1, 0], 2000: ad_rd[1, 1], 3000: ad_rd[1, 2]}
b_inds_w0 = np.concatenate((b_inds[0], b_inds[3]))
actual, predicted = pn.kfold_xval_gen(
dti.TensorModel, data[..., b_inds_w0], bvecs[:, b_inds_w0], bvals[b_inds_w0], 10, mask=wm_data
)
cod = ozu.coeff_of_determination(actual, predicted)
np.save(os.path.join(data_path, "dtm_predict_b3k.npy"), predicted)
np.save(os.path.join(data_path, "dtm_cod_b3k.npy"), cod)
def isotropic_params(data, bvals, bvecs, mask, func, factor=1000,
initial="preset", bounds="preset", params_file='temp',
signal="relative_signal"):
"""
Finds the parameters of the given function to the given data
that minimizes the sum squared errors.
Parameters
----------
data: 4 dimensional array
Diffusion MRI data
bvals: 1 dimensional array
All b values
mask: 3 dimensional array
Brain mask of the data
func: str or callable
String indicating the mean model function to perform kfold
cross-validation on.
initial: tuple
Initial values for the parameters.
factor: int
Integer indicating the scaling factor for the b values
bounds: list
List containing tuples indicating the bounds for each parameter in
the mean model function.
Returns
-------
param_out: 2 dimensional array
Parameters that minimize the residuals
fit_out: 2 dimensional array
Model fitted means
ss_err: 2 dimensional array
Sum squared error between the model fitted means and the actual means
"""
if isinstance(func, str):
# Grab the function handle for the desired mean model
func = globals()[func]
# Get the initial values for the desired mean model
if (bounds == "preset") | (initial == "preset"):
all_params = initial_params(data, bvecs, bvals, func, mask=mask,
params_file=params_file)
if bounds == "preset":
bounds = all_params[0]
if initial == "preset":
func_initial = all_params[1]
else:
this_initial = initial
# Separate b values and grab their indices
bval_list, b_inds, unique_b, rounded_bvals = ozu.separate_bvals(bvals)
all_b_idx, b0_inds = _diffusion_inds(bvals, b_inds, rounded_bvals)
# Divide the b values by a scaling factor first.
b = bvals[all_b_idx]/factor
flat_data = data[np.where(mask)]
# Get the number of inputs to the mean diffusivity function
param_num = len(inspect.getargspec(func)[0])
# Pre-allocate the outputs:
param_out = np.zeros((int(np.sum(mask)), param_num - 1))
cod = ozu.nans(np.sum(mask))
fit_out = ozu.nans(cod.shape + (len(all_b_idx),))
for vox in np.arange(np.sum(mask)).astype(int):
s0 = np.mean(flat_data[vox, b0_inds], -1)
if initial == "preset":
this_initial = func_initial[vox]
input_signal = flat_data[vox, all_b_idx]/s0
if signal == "log":
input_signal = np.log(input_signal)
if bounds == None:
params, _ = opt.leastsq(err_func, this_initial, args=(b,
input_signal, func))
else:
lsq_b_out = lsq.leastsqbound(err_func, this_initial,
args=(b, input_signal, func),
bounds = bounds)
params = lsq_b_out[0]
param_out[vox] = np.squeeze(params)
fit_out[vox] = func(b, *params)
cod[vox] = ozu.coeff_of_determination(input_signal, fit_out[vox])
return param_out, fit_out, cod
low = i*2000
# Make sure not to go over the edge of the mask:
high = np.min([(i+1) * 2000, int(np.sum(wm_data))])
# Preserve memory by getting rid of this data:
del wm_data
# Now set the mask:
mask = np.zeros(wm_data_file.shape)
mask[wm_idx[0][low:high], wm_idx[1][low:high], wm_idx[2][low:high]] = 1
# Predict 10% (n = 10)
ad = {1000:1.6386920952169737, 2000:1.2919249903637751, 3000:0.99962593218241236}
rd = {1000:0.33450124887561905, 2000:0.28377379537043729, 3000:0.24611723207420028}
bval_list, b_inds, unique_b, rounded_bvals = ozu.separate_bvals(bvals)
_, b_inds_rm0, _, _ = ozu.separate_bvals(bvals, mode = "remove0")
all_b_idx = np.where(rounded_bvals != 0)
mod_full = sfm.SparseDeconvolutionModelMultiB(data, bvecs, bvals, mask = mask,
params_file = "temp", solver = "nnls",
mean = "mean_model", axial_diffusivity = ad,
radial_diffusivity = rd)
sig_out, new_params = mod_full.fit_flat_rel_sig_avg
for idx in np.arange(1, len(unique_b)):
these_b_inds = np.concatenate((b_inds[0], b_inds[idx]))
b_mod = sfm.SparseDeconvolutionModelMultiB(data[..., these_b_inds], bvecs[:, these_b_inds],
bvals[these_b_inds], mask = mask,
params_file = "temp", solver = "nnls",
mean = "mean_model", axial_diffusivity = ad,
radial_diffusivity = rd)
def place_files(file_names,
mask_vox_num,
expected_file_num,
mask_data,
data,
bvals,
file_path=os.getcwd(),
vol=False,
f_type="npy",
save=False):
"""
Function to aggregate sub data files from parallelizing. Assumes that
the sub_files are in the format:
(file name)_(number of sub_file).(file_type)
Parameters
----------
file_names: list
List of strings indicating the base file names for each output data
aggregation
mask_vox_num: int
Number of voxels in each sub file
expected_file_num: int
Expected number of sub files
mask_data: 3 dimensional array
White matter mask
data: 4 dimensional array
The diffusion data
bvals: 1 dimensional array
The b-values used for the diffusion data
file_path: str
Path to the directory with all the sub files. Default is the current
directory
vol: str
String indicating whether or not the sub files are in volumes and
whether the output files are saved as volumes as well
f_type: str
String indicating the type of file the sub files are saved as
save: str
String indicating whether or not to save the output aggregation/volumes
num_dirs: int
Number of directions in each output aggregation/volume
Returns
-------
missing_files: 1 dimensional array
All the sub files that are missing in the aggregation
aggre_list: list
List with all the aggregations/volumes
"""
files = os.listdir(file_path)
# Get data and indices
mask_idx = np.where(mask_data)
bval_list, b_inds, unique_b, bvals_scaled = ozu.separate_bvals(bvals)
all_b_idx = np.where(bvals_scaled != 0)
# Remove voxels from the mask that contain zero signal values and turn the
# mask into linear form.
S0 = np.mean(data[..., b_inds[0]], -1)
pre_mask = np.array(mask_data, dtype=bool)
ravel_mask = np.ravel(pre_mask).astype(int)
ravel_mask[np.where(ravel_mask)[0][np.where(S0[pre_mask] == 0)]] = 2
ravel_mask = ravel_mask[np.where(ravel_mask != 0)]
ravel_mask[np.where(ravel_mask == 2)] = 0
ravel_mask = ravel_mask.astype(bool)
aggre_list = []
missing_files_list = []
for fn in file_names:
count = 0
# Keep track of files in case there are any missing ones
i_track = np.ones(expected_file_num)
# If you don't want to put the voxels back into a volume, just
# preallocate enough for each voxel included in the mask.
for f_idx in np.arange(len(files)):
this_file = files[f_idx]
if this_file[(len(this_file) -
len(f_type)):len(this_file)] == f_type:
if f_type == "npy":
sub_data = np.load(os.path.join(file_path, this_file))
elif f_type == "nii.gz":
sub_data = ni.load(os.path.join(file_path,
this_file)).get_data()
# If the name of this file is equal to file name that you want
# to aggregate, load it and find the voxels corresponding to its
# location in the given mask.
if this_file[0:len(fn)] == fn:
if count == 0:
if len(sub_data.shape) == 1:
num_dirs = 1
else:
num_dirs = sub_data.shape[-1]
if vol is False:
aggre = np.squeeze(
ozu.nans((int(np.sum(mask_data)), ) +
(num_dirs, )))
else:
aggre = np.squeeze(
ozu.nans(
(mask_data_file.shape + (num_dirs, ))))
count = count + 1
# Find the location in this aggregate for this file piece.
i = int(this_file.split(".")[0][len(fn):])
low = i * mask_vox_num
high = np.min([(i + 1) * mask_vox_num,
int(np.sum(mask_data))])
# If you don't have a volume input and don't want a volume
# output, output just an aggregate the output files.
if vol is False:
if sub_data.shape[0] > aggre[low:high][
ravel_mask[low:high]].shape[0]:
aggre[low:high][ravel_mask[low:high]] = np.squeeze(
sub_data)[ravel_mask[low:high]]
else:
aggre[low:high][ravel_mask[low:high]] = np.squeeze(
sub_data)
else:
mask = np.zeros(mask_data_file.shape)
mask[mask_idx[0][low:high][ravel_mask[low:high]],
mask_idx[1][low:high][ravel_mask[low:high]],
mask_idx[2][low:high]][ravel_mask[low:high]] = 1
aggre[np.where(mask)] = sub_data
# If the file is present, change its index within the
# tracking array to 0.
i_track[i] = 0
missing_files_list.append(np.squeeze(np.where(i_track)))
aggre_list.append(aggre)
if save is True:
if vol is False:
np.save("aggre_%s.npy" % fn, aggre)
else:
aff = mask_data_file.get_affine()
ni.Nifti1Image(aggre, aff).to_filename("vol_%s.nii.gz" % fn)
return missing_files_list, aggre_list
def kfold_xval_MD_mod(data, bvals, bvecs, mask, func, n, factor = 1000,
initial="preset", bounds = "preset", params_file='temp',
signal="relative_signal"):
"""
Finds the parameters of the given function to the given data
that minimizes the sum squared errors using kfold cross validation.
Parameters
----------
data: 4 dimensional array
Diffusion MRI data
bvals: 1 dimensional array
All b values
bvecs: 3 dimensional array
All the b vectors
mask: 3 dimensional array
Brain mask of the data
func: function handle
Mean model to perform kfold cross-validation on.
initial: tuple
Initial values for the parameters.
n: int
Integer indicating the percent of vertices that you want to predict
factor: int
Integer indicating the scaling factor for the b values
bounds: list
List containing tuples indicating the bounds for each parameter in
the mean model function.
Returns
-------
cod: 1 dimensional array
Coefficent of Determination between data and predicted values
predicted: 2 dimensional array
Predicted mean for the vertices left out of the fit
"""
if isinstance(func, str):
# Grab the function handle for the desired mean model
func = globals()[func]
# Get the initial values for the desired mean model
if (bounds == "preset") | (initial == "preset"):
all_params = initial_params(data, bvecs, bvals, func, mask=mask,
params_file=params_file)
if bounds == "preset":
bounds = all_params[0]
if initial == "preset":
func_initial = all_params[1]
else:
this_initial = initial
bval_list, b_inds, unique_b, rounded_bvals = ozu.separate_bvals(bvals)
all_b_idx, b0_inds = _diffusion_inds(bvals, b_inds, rounded_bvals)
b_scaled = bvals/factor
flat_data = data[np.where(mask)]
# Pre-allocate outputs
ss_err = np.zeros(int(np.sum(mask)))
predict_out = np.zeros((int(np.sum(mask)),len(all_b_idx)))
# Setting up for creating combinations of directions for kfold cross
# validation:
# Number of directions to leave out at a time.
num_choose = (n/100.)*len(all_b_idx)
# Find the indices to all the non-b = 0 directions and shuffle them.
vec_pool = np.arange(len(all_b_idx))
np.random.shuffle(vec_pool)
all_inc_0 = np.arange(len(rounded_bvals))
# Start cross-validation
for combo_num in np.arange(np.floor(100./n)):
(si, vec_combo, vec_combo_rm0,
vec_pool_inds, these_bvecs, these_bvals,
this_data, these_inc0) = ozu.create_combos(bvecs, bvals, data,
all_b_idx,
np.arange(len(all_b_idx)),
all_b_idx, vec_pool,
num_choose, combo_num)
this_flat_data = this_data[np.where(mask)]
for vox in np.arange(np.sum(mask)).astype(int):
s0 = np.mean(flat_data[vox, b0_inds], -1)
these_b = b_scaled[vec_combo] # b values to predict
if initial == "preset":
this_initial = func_initial[vox]
input_signal = flat_data[vox, these_inc0]/s0
if signal == "log":
input_signal = np.log(input_signal)
# Fit mean model to part of the data
params, _ = opt.leastsq(err_func, this_initial,
args=(b_scaled[these_inc0],
input_signal, func))
if bounds == None:
params, _ = opt.leastsq(err_func, this_initial,
args=(b_scaled[these_inc0],
input_signal, func))
else:
lsq_b_out = lsq.leastsqbound(err_func, this_initial,
args = (b_scaled[these_inc0],
input_signal, func),
bounds = bounds)
params = lsq_b_out[0]
# Predict the mean values of the left out b values using the
# parameters from fitting to part of the b values.
predict = func(these_b, *params)
predict_out[vox, vec_combo_rm0] = func(these_b, *params)
# Find the relative diffusion signal.
s0 = np.mean(flat_data[:, b0_inds], -1).astype(float)
input_signal = flat_data[:, all_b_idx]/s0[..., None]
if signal == "log":
input_signal = np.log(input_signal)
cod = ozu.coeff_of_determination(input_signal, predict_out, axis=-1)
return cod, predict_out
if __name__ == "__main__":
sid = "103414"
hcp_path = '/biac4/wandell/data/klchan13/hcp_data_q3'
data_path = os.path.join(hcp_path, "%s/T1w/Diffusion" % sid)
data_file = nib.load(os.path.join(data_path, "data.nii.gz"))
wm_data_file = nib.load(os.path.join(data_path, "wm_mask_no_vent.nii.gz"))
data = data_file.get_data()
wm_data = wm_data_file.get_data()
wm_idx = np.where(wm_data == 1)
bvals = np.loadtxt(os.path.join(data_path, "bvals"))
bvecs = np.loadtxt(os.path.join(data_path, "bvecs"))
bval_list, b_inds, unique_b, bvals_scaled = ozu.separate_bvals(bvals)
all_b_idx = np.where(bvals_scaled != 0)
ad_rd = np.loadtxt(os.path.join(data_path, "ad_rd_%s.txt" % sid))
ad = {1000: ad_rd[0, 0], 2000: ad_rd[0, 1], 3000: ad_rd[0, 2]}
rd = {1000: ad_rd[1, 0], 2000: ad_rd[1, 1], 3000: ad_rd[1, 2]}
b_inds_w0 = np.concatenate((b_inds[0], b_inds[3]))
actual, predicted = pn.kfold_xval_gen(dti.TensorModel,
data[..., b_inds_w0],
bvecs[:, b_inds_w0],
bvals[b_inds_w0],
10,
mask=wm_data)
cod = ozu.coeff_of_determination(actual, predicted)
np.save(os.path.join(data_path, "dtm_predict_b3k.npy"), predicted)
mask = np.zeros(wm_data_file.shape)
mask[wm_idx[0][low:high], wm_idx[1][low:high], wm_idx[2][low:high]] = 1
# Predict 10% (n = 10)
ad = {
1000: 1.6386920952169737,
2000: 1.2919249903637751,
3000: 0.99962593218241236
}
rd = {
1000: 0.33450124887561905,
2000: 0.28377379537043729,
3000: 0.24611723207420028
}
bval_list, b_inds, unique_b, rounded_bvals = ozu.separate_bvals(bvals)
_, b_inds_rm0, _, _ = ozu.separate_bvals(bvals, mode="remove0")
all_b_idx = np.where(rounded_bvals != 0)
mod_full = sfm.SparseDeconvolutionModelMultiB(data,
bvecs,
bvals,
mask=mask,
params_file="temp",
solver="nnls",
mean="mean_model",
axial_diffusivity=ad,
radial_diffusivity=rd)
sig_out, new_params = mod_full.fit_flat_rel_sig_avg
for idx in np.arange(1, len(unique_b)):
these_b_inds = np.concatenate((b_inds[0], b_inds[idx]))
def across_sph_cc(vol_b_list, bvals, bvecs, mask, cod_single_mod = None, cod_multi_mod = None,
single_thresh = None, multi_thresh = None, idx = None, vol_mp_single = None,
tol = 0.1, n = 20):
"""
Calculates the spherical cross correlation at a certain index for all b values fit
together and b values fit separately.
Parameters
----------
vol_b_list: list
List of the model parameters for each voxel for an fODF fit to each b value.
bvals: 1 dimensional array
All b values
bvecs: 2 dimensional array
All the b vectors
mask: 3 dimensional array
Brain mask of the data
cod_single_mod: 1 dimensional array
Coefficient of determination at each voxel for the single fODF model
cod_multi_mod: 1 dimensional array
Coefficient of determination at each voxel for the multi fODF model
single_thresh: int
Coefficient of determination threshold for the single fODF model
multi_thresh: int
Coefficient of determination threshold for the multi fODF model
idx: int
Index into the indices indicating the voxels included in the COD (in)equality
vol_mp_single: 2 dimensional array
Model parameters from fitting a single fODF to each voxel
tol: float
Tolerance for the COD (in)equality
n: int
Integer indicating the number of directions to divide by for spherical
cross-correlation
Returns
-------
deg_list: list
List indicating the degrees included in spherical cross-correlation
cc_list: list
List with the cross-correlations between each combination of b values
idx: int
Index into the indices indicating the voxels included in the COD (in)equality
cod_s: float
Coefficient of determination of single fODF model
cod_m: float
Coefficient of determination of multi fODF model
"""
if (single_thresh != None) & (multi_thresh != None):
# Get the indices with a desired COD.
inds, b_inds, all_b_inds = sph_cc_ineq(cod_single_mod, cod_multi_mod, bvals,
single_thresh, multi_thresh, tol = tol)
else:
# With no COD threshold, just find all the indices.
inds = np.arange(int(np.sum(mask)))
bval_list, b_inds, unique_b, rounded_bvals = ozu.separate_bvals(bvals)
all_b_inds = np.where(rounded_bvals != 0)
if idx is None:
# If a specific index is not given, just find a random index.
if ri is None:
ri = np.random.randint(0, len(inds))
else:
ri = ri
idx = inds[ri]
data_list = []
bvecs_b_list = []
deg_list = []
cc_list = []
pool = np.arange(len(vol_b_list))
for ii in pool:
# Just get the data (model parameters) and bvecs within the chosen voxel (idx)
# for each b value and mirror them.
data_list.append(np.concatenate((vol_b_list[ii][np.where(mask)][idx],
vol_b_list[ii][np.where(mask)][idx]), -1))
bvecs_b_list.append(np.squeeze(np.concatenate((bvecs[:, b_inds[ii+1]],
-1*bvecs[:, b_inds[ii+1]]), -1)).T)
if vol_mp_single is None:
# Make combinations of b values for spherical cross correlation between b values
combos = list(itertools.combinations(pool, 2))
this_iter = np.arange(len(combos))
else:
# No need for combos since comparing between the the single fODF and the multi fODF
combos = None
this_iter = np.arange(len(vol_b_list))
bvecs_all = np.squeeze(np.concatenate((bvecs[:, all_b_inds],
-1*bvecs[:, all_b_inds]), -1)).T
data_all = np.concatenate((vol_mp_single[np.where(mask)][idx],
vol_mp_single[np.where(mask)][idx]), -1)
for itr in this_iter:
if vol_mp_single is None:
# Inputs are data and bvecs from two different b values that you want to find the
# spherical cross-correlation between
inputs = [np.squeeze(data_list[combos[itr][0]]), np.squeeze(data_list[combos[itr][1]]),
bvecs_b_list[combos[itr][0]], bvecs_b_list[combos[itr][1]]]
else:
# Inputs are the data and bvecs from one b value and the single fODF
inputs = [np.squeeze(data_all), np.squeeze(data_list[itr]), bvecs_all, bvecs_b_list[itr]]
# Put the inputs into the spherical cross-correlation function
deg, cc = ozu.sph_cc(*inputs, n = n)
deg_list.append(deg)
cc_list.append(cc)
if (single_thresh != None) & (multi_thresh != None):
# Because sometimes it's nice to know what the actual CODs are
cod_s = cod_single_mod[idx]
cod_m = cod_multi_mod[idx]
else:
cod_s = None
cod_m = None
return deg_list, cc_list, combos, idx, cod_s, cod_m
data_t[:,:,:,10:13] = np.squeeze(500 + abs(np.random.randn(2,2,2,3)*200))#For b=2
# Mock mask to be used in most tests
mask_t = np.zeros([2,2,2])
mask_t[:,:,1] = 1
ad = {1000:1.6386920952169737, 2000:1.2919249903637751, 3000:0.99962593218241236}
rd = {1000:0.33450124887561905, 2000:0.28377379537043729, 3000:0.24611723207420028}
data_path = os.path.join(osmosis.__path__[0], 'data')
data_pv = nib.load(os.path.join(data_path, "red_data.nii.gz")).get_data()
bvals_pv = np.loadtxt(os.path.join(data_path, "bvals"))
bvecs_pv = np.loadtxt(os.path.join(data_path, "bvecs"))
bval_list, b_inds, unique_b, rounded_bvals = ozu.separate_bvals(bvals_pv)
all_b_inds = np.where(rounded_bvals != 0)
mask_pv = np.zeros(data_pv.shape[0:3])
mask_pv[0, 0, 0:2] = 1
actual_all = np.squeeze(data_pv[np.where(mask_pv)][:, all_b_inds])
np.random.seed(1975)
def test_regressors():
full_mod_t = sfm.SparseDeconvolutionModelMultiB(data_t, bvecs_t, bvals_t,
mask = mask_t,
axial_diffusivity = ad,
radial_diffusivity = rd,
params_file = "temp")
def initial_params(data, bvecs, bvals, model, mask=None, params_file='temp'):
"""
Determine the initial values for fitting the isotropic diffusion model.
This only works on the models that fit to the relative diffusion signal.
Parameters
----------
data: 4 dimensional array
Diffusion MRI data
bvecs: 2 dimensional array
All the b vectors
bvals: 1 dimensional array
All b values
model: str
Isotropic model
mask: 3 dimensional array
Mask of the data
params_file: obj or str
File handle of the param_files containing the tensor parameters.
Returns
-------
bounds: list
A list containing the bounds for each parameter for least squares
fitting.
initial: list
A list containing the initial values for each parameter for least
squares fitting.
"""
dti_mod = dti.TensorModel(data,
bvecs,
bvals,
mask=mask,
params_file=params_file)
d = dti_mod.mean_diffusivity[np.where(mask)]
# Find initial noise floor
_, b_inds, _, _ = ozu.separate_bvals(bvals)
b0_data = data[np.where(mask)][:, b_inds[0]]
#nf = np.std(b0_data, -1)/np.mean(b0_data, -1)
nf = np.min(data[np.where(mask)], -1)
if model == single_exp_rs:
bounds = [(0, 4)]
initial = d
elif model == single_exp_nf_rs:
bounds = [(0, 10000), (0, 4)]
initial = np.concatenate(
[nf[..., None], np.ones(d[..., None].shape)], -1)
elif model == bi_exp_rs:
bounds = [(0, 1), (0, 4), (0, 4)]
initial = np.concatenate(
[0.5 * np.ones((len(d), 1)), d[..., None], d[..., None]], -1)
elif model == bi_exp_nf_rs:
bounds = [(0, 10000), (0, 1), (0, 4), (0, 4)]
initial = np.concatenate([
nf[..., None], 0.5 * np.ones(
(len(d), 1)), d[..., None], d[..., None]
], -1)
return bounds, initial