Rename acquisition to gradient

scilpy/acquisition/gen_gradient_sampling.py → scilpy/gradients/gen_gradient_sampling.py

@@ -13,7 +13,7 @@
 import numpy as np
 from scipy import optimize
 
-from scilpy.acquisition.utils import random_uniform_on_sphere
+from scilpy.gradients.utils import random_uniform_on_sphere
 
 
 def generate_gradient_sampling(nb_samples_per_shell, verbose=1):

scilpy/gradientsampling/optimize_gradient_sampling.py

@@ -297,7 +297,7 @@ def compute_peak_power(q_scheme, ker_size=10):
     Parameters
     ------
     q_scheme: nd.array
-        Scheme of acquisition.
+        Scheme of gradients.
     ker_size: int
         Kernel size (default=10).
 

scilpy/preprocessing/distortion_correction.py

@@ -59,7 +59,7 @@ def create_multi_topup_index(bvals, mean, n_rev, b0_thr=0):
     """
     Create index of bvals for Eddy in cases where Topup ran on more
     than one b0 volume in both phase directions. The volumes must be
-    ordered such as all forward phase acquisition are followed by all
+    ordered such as all forward phase gradients are followed by all
     reverse phase ones (In the case of AP-PA, PA_1, PA_2, ..., PA_N,
     AP_1, AP_2, ..., AP_N).
 

scilpy/reconst/b_tensor_utils.py

@@ -55,7 +55,7 @@ def generate_btensor_input(in_dwis, in_bvals, in_bvecs,
         Powder-averaged diffusion data.
     gtab_infos : np.ndarray (if `do_pa_signals`)
         Contains information about the gtab, such as the unique bvals, the
-        encoding types, the number of directions and the acquisition index.
+        encoding types, the number of directions and the gradients index.
     """
     data_full = np.empty(0)
     bvals_full = np.empty(0)

scilpy/reconst/divide_fit.py

@@ -36,7 +36,7 @@ def random_p0(signal, gtab_infos, lb, ub, weight, n_iter):
         Diffusion data of a single voxel.
     gtab_infos : np.ndarray
         Contains information about the gtab, such as the unique bvals, the
-        encoding types, the number of directions and the acquisition index.
+        encoding types, the number of directions and the gradients index.
         Obtained as output of the function
         `reconst.b_tensor_utils.generate_btensor_input`.
     lb : np.ndarray of floats
@@ -79,7 +79,7 @@ def gamma_data2fit(signal, gtab_infos, fit_iters=1, random_iters=50,
         Diffusion data of a single voxel.
     gtab_infos : np.ndarray
         Contains information about the gtab, such as the unique bvals, the
-        encoding types, the number of directions and the acquisition index.
+        encoding types, the number of directions and the gradients index.
         Obtained as output of the function
         `reconst.b_tensor_utils.generate_btensor_input`.
     fit_iters : int, optional
@@ -187,7 +187,7 @@ def gamma_fit2data(gtab_infos, params):
     ----------
     gtab_infos : np.ndarray
         Contains information about the gtab, such as the unique bvals, the
-        encoding types, the number of directions and the acquisition index.
+        encoding types, the number of directions and the gradients index.
         Obtained as output of the function
         `reconst.b_tensor_utils.generate_btensor_input`.
     params : np.array

scilpy/reconst/multi_processes.py

@@ -626,7 +626,7 @@ def fit_gamma(data, gtab_infos, mask=None, fit_iters=1, random_iters=50,
         `reconst.b_tensor_utils.generate_powder_averaged_data`.
     gtab_infos : np.ndarray
         Contains information about the gtab, such as the unique bvals, the
-        encoding types, the number of directions and the acquisition index.
+        encoding types, the number of directions and the gradients index.
         Obtained as output of the function
         `reconst.b_tensor_utils.generate_powder_averaged_data`.
     mask : np.ndarray, optional

scripts/scil_compute_ihMT_maps.py

@@ -12,7 +12,7 @@
 myelin content in white matter of the brain.
 
 Different contrasts can be done with an off-resonance pulse prior to image
-acquisition (a prepulse), saturating the protons on non-aqueous molecules,
+gradients (a prepulse), saturating the protons on non-aqueous molecules,
 by applying different frequency irradiation. The two MT maps and two ihMT maps
 are obtained using five contrasts: single frequency positive or negative and
 dual frequency with an alternation of both positive and negative frequency

scripts/scil_generate_gradient_sampling.py

@@ -3,7 +3,7 @@
 
 """
 Generate multi-shell gradient sampling with various processing options. Helps
-accelerate acquisition, optimize duty cycle and avoid artefacts.
+accelerate gradients, optimize duty cycle and avoid artefacts.
 
 Multi-shell gradient sampling is generated as in [1]. The bvecs are then
 flipped to maximize spread for eddy current correction, b0s are interleaved at
@@ -18,7 +18,7 @@
 
 from scilpy.io.utils import (
     add_overwrite_arg, add_verbose_arg, assert_outputs_exist)
-from scilpy.acquisition.gen_gradient_sampling import (
+from scilpy.gradients.gen_gradient_sampling import (
     generate_gradient_sampling)
 from scilpy.gradientsampling.optimize_gradient_sampling import (
     add_b0s, add_bvalue_b0, compute_bvalue_lin_b, compute_bvalue_lin_q,

scripts/scil_prepare_eddy_command.py

@@ -30,7 +30,7 @@ def _build_arg_parser():
 
     p.add_argument('in_dwi',
                    help='Input DWI Nifti image. If using multiple '
-                        'acquisition and/or opposite phase directions, please '
+                        'gradients and/or opposite phase directions, please '
                         'merge in the same order as for prepare_topup using '
                         'scil_concatenate_dwi.py.')
 
@@ -172,7 +172,7 @@ def main():
                     bvals, 'cluster', n_rev, b0_threshold)
             else:
                 raise ValueError('Could not determine a valid index file '
-                                 'from the provided acquisition parameters '
+                                 'from the provided gradients parameters '
                                  'file: {}'.format(args.topup_params))
     else:
         acqparams = create_acqparams(args.readout, args.encoding_direction,

scripts/scil_prepare_topup_command.py

@@ -51,7 +51,7 @@ def _build_arg_parser():
                    help='Prefix of the topup results [%(default)s].')
 
     p.add_argument('--out_params', default='acqparams.txt',
-                   help='Filename for the acquisition '
+                   help='Filename for the gradients '
                         'parameters file [%(default)s].')
 
     p.add_argument('--out_script', action='store_true',

scripts/scil_snr_in_roi.py

@@ -19,7 +19,7 @@
 It is heavily dependent on the ROI and its quality.
 
 We highly recommend using a noise_map if you can acquire one.
-See refs [1, 2] that describe the noise map acquisition.
+See refs [1, 2] that describe the noise map gradients.
 [1] St-Jean, et al (2016). Non Local Spatial and Angular Matching... https://doi.org/10.1016/j.media.2016.02.010
 [2] Reymbaut, et al (2021). Magic DIAMOND... https://doi.org/10.1016/j.media.2021.101988