Merge pull request #18 from samuelstjean/fix_memmap_bug

fix the numpy memmap keepdim bug

CHANGELOG.md

@@ -1,13 +1,17 @@
 # Changelog
 
+## [0.2.1] - 2016-04-26
+
+- Fixed a bug in the nlsam script where .nii would be memmaped and crash with an invalid dimension broadcast. Thansk to Jelle Veraart for reporting.
+
 ## [0.2] - 2016-04-19
 
 - stabilization script is now clipping values to zero (Bai et al. 2014) as used in the paper.
 The previous release was using the Koay et al. 2009 approach, which could produce negative values in really noisy cases.
 
 - More doc to the readme.
 - Added link to the synthetic and in vivo data used in the experiments.
-- Linux binaries now needs glibc >= 2.13 (Debian 7 and newer) instead of glibc >= 2.21 (Ubuntu 15.04 and newer).
+<!--- - Linux binaries now needs glibc >= 2.13 (Debian 7 and newer) instead of glibc >= 2.21 (Ubuntu 15.04 and newer). (debina is giving me trouble, might be jessie from now on or centos)-->
 - Removed the source archive and wheel files, as one can grab the source archive from github instead.
 
 ## [0.1] - 2016-03-22

scripts/nlsam

@@ -88,14 +88,15 @@ def main():
     print("Now denoising " + os.path.realpath(args.input))
 
     vol = nib.load(args.input)
-    data = vol.get_data()
+    data = np.array(vol.get_data())
     vol.uncache()
     affine = vol.get_affine()
 
     sigma = nib.load(args.sigma).get_data()**2
 
     greedy_subsampler = True
     implausible_signal_boost = True
+    b0_thresh = 10
     n_iter = args.iterations
 
     original_dtype = data.dtype
@@ -127,29 +128,8 @@ def main():
     if data.shape[:-1] != mask.shape:
         raise ValueError('data shape is {}, but mask shape {} is different!'.format(data.shape, mask.shape))
 
-
-
-    crop=False
-    if crop:
-        print("cropping data and mask")
-        ca = 51#20#80#23
-        cb = 56#170#170#150#86#27#data.shape[1]
-        # [90:, 88:96, 40:] [90:140, 90:160, 38:46]
-        # isbi : [:,22:28, :]
-        #mask_data=np.ones_like(data[...,0],dtype=np.bool)
-        data = data[:, ca:cb]#[40:129,40:129,...]#[90:150, :165, ...] #88:96, 40:]
-        original_shape = data.shape[:-1] + (original_shape[-1],)
-        # data_orig = data_orig[:, ca:cb]#[40:129,40:129,...]#[90:150, :165, ...]#88:96, 40:]
-        # mask_noise = mask_noise[:, ca:cb]#[40:129,40:129,...]#[90:150, :165, ...]#88:96, 40:]
-        mask = mask[:, ca:cb]#[40:129,40:129,...]#[90:150, :165, ...]#88:96, 40:]
-        # gold_standard = gold_standard[:, ca:cb]#[40:129,40:129,...]#[90:150, :165, ...]#88:96, 40:]
-        sigma = sigma[:, ca:cb]
-
-
-
     bvals, bvecs = read_bvals_bvecs(args.bvals, args.bvecs)
 
-    b0_thresh = 10
     b0_loc = tuple(np.where(bvals <= b0_thresh)[0])
     num_b0s = len(b0_loc)