Merge branch 'master' of https://github.com/muscbridge/PyDesigner

Author: gonzoBlackMamba

.github/workflows-disabled/release.yml

@@ -46,4 +46,4 @@ jobs:
         uses: actions/upload-artifact@v4
         with:
           name: wheels-${{ matrix.os }}
-          path: dist/*.whl
+          path: dist/*.whl

.github/workflows/build-wheels.yml

@@ -40,4 +40,4 @@ jobs:
         uses: actions/upload-artifact@v5
         with:
           name: distributions
-          path: dist/*
+          path: dist/*

pydesigner/__init__.py

@@ -6,4 +6,4 @@
     try:
         __version__ = version("pydesigner")
     except PackageNotFoundError:
-        __version__ = "0+unknown"
+        __version__ = "0+unknown"

pydesigner/fitting/dwipy.py

@@ -1367,7 +1367,7 @@ def __costCalculator(grid, BT, GT, b0, IMG, iDT, iaDT, zeta, shB, Pl0, g2l_fa_R_
             cost_fn : array_like(dype=float)
                 Cost values for input grid
             """
-            
+
             if grid.ndim > 1:
                 raise Exception("Grid needs to be a flattened 1D vector")
             ndir = [len(x) for x in GT]
@@ -2083,7 +2083,7 @@ def fbi_helper(
             fbwm_SH2 = fbwm_SH2[:, harmonics]
             dt, kt = self.tensorReorder("dki")
             dt = vectorize(dt, self.mask)
-  
+
             (
                 zeta,
                 faa,
@@ -2915,7 +2915,7 @@ def outlierHelper(dwi, bmat, sigma, b, b0_pos, maxiter=25, convcrit=1e-3, levera
 
             #     with np.errstate(divide="ignore", invalid="ignore", over="ignore"):
             #         dwi_hat = highprecisionexp(np.matmul(bmat_i, dt_i))
-                
+
             #     dwi_hat = np.nan_to_num(dwi_hat, nan=0.0, posinf=0.0, neginf=0.0)
 
             #     dwi_hat[dwi_hat < 1] = 1

pydesigner/tractography/odf.py

@@ -978,7 +978,7 @@ def odf_conversion(odf, target="tournier"):
     vol_table = np.array([0.5 * (x + 1) * (x + 2) for x in l_max_table], dtype=int)
     vols = odf.shape[0]
     l_max = l_max_table[np.where(vol_table == vols)[0]]
-    
+
     l_max = np.asarray(l_max)
     if l_max.size != 1:
         raise ValueError(f"l_max must be scalar, got shape {l_max.shape}")