tag v0.7

CHANGELOG.md

@@ -1,6 +1,6 @@
 # Changelog
 
-## [0.7] Development version
+## [0.7] - 2023-05-20
 
 - **Breaking changes in the command line parser**
     - The previously required options __N__ and __angular_block_size__ are now optional.
@@ -24,7 +24,7 @@
 - New online documentation available at http://nlsam.readthedocs.io/ for the current (and future) versions.
 - The dictionary learning part of the algorithm now respects **--cores** instead of ignoring it and always using all available processors.
 - joblib is now used for parallel processing.
-    - For now it means we lose the frozen executable until they fix it.
+    - The frozen executable is now using dask and performs a bit slower than the normal version until joblib.loky is fixed to work with pyinstaller.
     - Binary wheels are now available for all platforms instead.
 - A new option to estimate automatically the noise distribution (sigma and N) is now available by passing **auto** to both N and **--noise_est**.
     - This option is also the new default now.

docs/conf.py

@@ -51,17 +51,17 @@
 
 # General information about the project.
 project = 'NLSAM'
-copyright = '2020, Samuel St-Jean'
+copyright = '2023, Samuel St-Jean'
 author = 'Samuel St-Jean'
 
 # The version info for the project you're documenting, acts as replacement for
 # |version| and |release|, also used in various other places throughout the
 # built documents.
 #
 # The short X.Y version.
-version = '0.6.1'
+version = '0.7'
 # The full version, including alpha/beta/rc tags.
-release = '0.6.1'
+release = '0.7'
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.

example/README.md

@@ -47,16 +47,16 @@ There are 4 required command line inputs (their order is important) and a switch
 
 + The input dataset (dwi.nii.gz)
 + The output dataset (dwi_nlsam.nii.gz)
-+ The b-values file for our input dataset (bvals)
-+ The b-vectors file for our input dataset (bvecs)
++ The bvalues file for our input dataset (bvals)
++ The bvectors file for our input dataset (bvecs)
 + -m or --mask mask.nii.gz for the brain mask
 
 The bvals/bvecs files are needed for identifying the angular neighbors and we need to choose how many we want to denoise at once (the default is now 5).
 
 Using a larger number could mean more blurring if we mix q-space points which are too far part.
 
 For a multishell acquisition, only the direction (as opposed to the norm)
-of the b-vector is taken into account, so you can freely mix dwi from different
+of the bvector is taken into account, so you can freely mix dwi from different
 shells to favor picking radial decay in the denoising.
 
 There is also a new option called **--split_shell** to process each shell by itself separately and **--split_b0s** to process the b0s separately in each block.

nlsam/smoothing.py

@@ -23,7 +23,7 @@ def sh_smooth(data, bvals, bvecs, sh_order=4, b0_threshold=1.0, similarity_thres
     sh_order : int, default 8
         Order of the spherical harmonics to fit.
     similarity_threshold : int, default 50
-        All b-values such that |b_1 - b_2| < similarity_threshold
+        All bvalues such that |b_1 - b_2| < similarity_threshold
         will be considered as identical for smoothing purpose.
         Must be lower than 200.
     regul : float, default 0.006
@@ -56,7 +56,7 @@ def sh_smooth(data, bvals, bvecs, sh_order=4, b0_threshold=1.0, similarity_thres
         idx = np.abs(unique_bval - bvals) < similarity_threshold
         rounded_bvals[idx] = unique_bval
 
-    # process each b-value separately
+    # process each bvalue separately
     for unique_bval in np.unique(rounded_bvals):
         idx = rounded_bvals == unique_bval
 

pyproject.toml

@@ -9,10 +9,8 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "nlsam"
-version = '0.6.1'
-authors = [
-    {name = "Samuel St-Jean"},
-]
+version = '0.7'
+authors = [{name = "Samuel St-Jean"}]
 description='Implementation of "Non Local Spatial and Angular Matching : Enabling higher spatial resolution diffusion MRI datasets through adaptive denoising"'
 readme = "README.md"
 requires-python = ">=3.7"

scripts/nlsam_denoising

@@ -86,14 +86,14 @@ def buildArgsParser():
                            'This is now a required input to prevent sampling (and reconstructing) background noise instead of the data.')
 
     optionals.add_argument('--b0_threshold', metavar='int', default=10, type=int,
-                           help='Lowest b-value to be considered as a b0. Default 10')
+                           help='Lowest bvalue to be considered as a b0. Default 10')
 
     optionals.add_argument('--split_b0s', action='store_true',
                            help='If set and multiple b0s are present, they are split amongst the '
                            'training data.')
 
     optionals.add_argument('--split_shell', action='store_true',
-                           help='If set, each shell/b-value is processed separately by itself.')
+                           help='If set, each shell/bvalue is processed separately by itself.')
 
     optionals.add_argument('--bval_threshold', metavar='int', default=25, type=int,
                            help='Any bvalue within += bval_threshold of each others will be considered on the same shell (e.g. b=990 and b=1000 are on the same shell). Default 25')