add permissive to all mrcfile.open

Author: procyontao

bin/isonet.py

@@ -445,7 +445,7 @@ def resize(self, star_file:str, apix: float=15, out_folder="tomograms_resized"):
             tomo_name = item.rlnMicrographName
             zoom_factor = float(ori_apix)/apix
             new_tomo_name = "{}/{}".format(out_folder,os.path.basename(tomo_name))
-            with mrcfile.open(tomo_name) as mrc:
+            with mrcfile.open(tomo_name, permissive=True) as mrc:
                 data = mrc.data
             print("scaling: {}".format(tomo_name))
             new_data = zoom(data, zoom_factor,order=3, prefilter=False)

models/unet/data_sequence.py

@@ -22,8 +22,8 @@ def __getitem__(self, i):
         idx = slice(i*self.batch_size,(i+1)*self.batch_size)
         idx = self.perm[idx]
         # print('*******',self.x[-1],mrcfile.open(self.x[0]).data[:,:,:,np.newaxis].shape)
-        rx = np.array([mrcfile.open(self.x[j]).data[:,:,:,np.newaxis] for j in idx])
-        ry = np.array([mrcfile.open(self.y[j]).data[:,:,:,np.newaxis] for j in idx])
+        rx = np.array([mrcfile.open(self.x[j], permissive=True).data[:,:,:,np.newaxis] for j in idx])
+        ry = np.array([mrcfile.open(self.y[j], permissive=True).data[:,:,:,np.newaxis] for j in idx])
         # for j in idx:
         #     print(mrcfile.open(self.x[j]).data.shape,mrcfile.open(self.y[j]).data.shape)
         return rx,ry
@@ -52,8 +52,8 @@ def gen():
             for i in range(len(x_set)//batch_size):
                 idx = slice(i * batch_size,(i+1) * batch_size)
                 idx = all_idx[idx]
-                rx = np.array([mrcfile.open(x_set[j]).data[:,:,:,np.newaxis] for j in idx])
-                ry = np.array([mrcfile.open(y_set[j]).data[:,:,:,np.newaxis] for j in idx])
+                rx = np.array([mrcfile.open(x_set[j], permissive=True).data[:,:,:,np.newaxis] for j in idx])
+                ry = np.array([mrcfile.open(y_set[j], permissive=True).data[:,:,:,np.newaxis] for j in idx])
 
                 yield rx,ry
     return gen
@@ -67,6 +67,6 @@ def gen():
             for i in range(len(x_set)//batch_size):
                 idx = slice(i * batch_size,(i+1) * batch_size)
                 idx = all_idx[idx]
-                rx = np.array([mrcfile.open(x_set[j]).data[:,:,:,np.newaxis] for j in idx])
+                rx = np.array([mrcfile.open(x_set[j], permissive=True).data[:,:,:,np.newaxis] for j in idx])
                 yield rx
     return gen

models/unet/losses/wedge_power.py

@@ -47,7 +47,7 @@ def power(xz):
     mw = np.repeat(TwoDPsf(sidelen,sidelen).getMW()[:,np.newaxis,:],sidelen,axis=1)
     circle = np.repeat(TwoDPsf(sidelen,sidelen).circleMask()[:,np.newaxis,:],sidelen,axis=1)
     gain = (circle -mw)/2
-    with mrcfile.open('/storage/heng/mwrtest3D/t371_2/cuberesults/pp676-bin4-wbp_000073_iter15.mrc') as op:
+    with mrcfile.open('/storage/heng/mwrtest3D/t371_2/cuberesults/pp676-bin4-wbp_000073_iter15.mrc', permissive=True) as op:
         data = op.data
     a = tf.convert_to_tensor(mw[np.newaxis,:,:,:,np.newaxis])
     b = tf.convert_to_tensor(data[np.newaxis,:,:,:,np.newaxis])

models/unet/predict.py

@@ -28,7 +28,7 @@ def predict(settings):
     data = []
     for i,mrc in enumerate(list(settings.mrc_list) + list(settings.mrc_list[:append_number])):
         root_name = mrc.split('/')[-1].split('.')[0]
-        with mrcfile.open(mrc) as mrcData:
+        with mrcfile.open(mrc, permissive=True) as mrcData:
             real_data = mrcData.data.astype(np.float32)*-1
         real_data=normalize(real_data, percentile = settings.normalize_percentile)
 

preprocessing/cubes.py

@@ -156,7 +156,7 @@ def cubesX(self):
                     path_noise = sorted([self.noise_folder+'/'+f for f in os.listdir(self.noise_folder)])
                     path_index = np.random.permutation(len(path_noise))[0:self.__cubesX.shape[0]]
                     def read_vol(f):
-                        with mrcfile.open(f) as mf:
+                        with mrcfile.open(f, permissive=True) as mf:
                             res = mf.data
                         return res
                     noise_volume = np.array([read_vol(path_noise[j]) for j in path_index])

preprocessing/prepare.py

@@ -35,7 +35,7 @@ def extract_subtomos(settings):
             pixel_size = it.rlnPixelSize
             if settings.use_deconv_tomo and "rlnDeconvTomoName" in md.getLabels() and os.path.isfile(it.rlnDeconvTomoName):
                 logging.info("Extract from deconvolved tomogram {}".format(it.rlnDeconvTomoName))
-                with mrcfile.open(it.rlnDeconvTomoName) as mrcData:
+                with mrcfile.open(it.rlnDeconvTomoName, permissive=True) as mrcData:
                     orig_data = mrcData.data.astype(np.float32)
             else:        
                 print("Extract from origional tomogram {}".format(it.rlnMicrographName))
@@ -44,7 +44,7 @@ def extract_subtomos(settings):
 
 
             if "rlnMaskName" in md.getLabels() and it.rlnMaskName not in [None, "None"]:
-                with mrcfile.open(it.rlnMaskName) as m:
+                with mrcfile.open(it.rlnMaskName, permissive=True) as m:
                     mask_data = m.data
             else:
                 mask_data = None
@@ -89,7 +89,7 @@ def get_cubes_one(data_X, data_Y, settings, start = 0, mask = None, add_noise =
             path_noise = sorted([settings.noise_dir+'/'+f for f in os.listdir(settings.noise_dir)])
             path_index = np.random.randint(len(path_noise))
             def read_vol(f):
-                with mrcfile.open(f) as mf:
+                with mrcfile.open(f, permissive=True) as mf:
                     res = mf.data
                 return res
             noise_volume = read_vol(path_noise[path_index])
@@ -119,11 +119,11 @@ def get_cubes(inp,settings):
     mrc, start = inp
     root_name = mrc.split('/')[-1].split('.')[0]
     current_mrc = '{}/{}_iter{:0>2d}.mrc'.format(settings.result_dir,root_name,settings.iter_count-1)
-    with mrcfile.open(mrc) as mrcData:
+    with mrcfile.open(mrc, permissive=True) as mrcData:
         iw_data = mrcData.data.astype(np.float32)*-1
     iw_data = normalize(iw_data, percentile = settings.normalize_percentile)
 
-    with mrcfile.open(current_mrc) as mrcData:
+    with mrcfile.open(current_mrc, permissive=True) as mrcData:
         ow_data = mrcData.data.astype(np.float32)*-1
     ow_data = normalize(ow_data, percentile = settings.normalize_percentile)
 
@@ -212,7 +212,7 @@ def generate_first_iter_mrc(mrc,settings):
     '''
     root_name = mrc.split('/')[-1].split('.')[0]
     extension = mrc.split('/')[-1].split('.')[1]
-    with mrcfile.open(mrc) as mrcData:
+    with mrcfile.open(mrc, permissive=True) as mrcData:
         orig_data = normalize(mrcData.data.astype(np.float32)*-1, percentile = settings.normalize_percentile)
     orig_data = apply_wedge(orig_data, ld1=1, ld2=0)
     orig_data = normalize(orig_data, percentile = settings.normalize_percentile)

preprocessing/simulate.py

@@ -51,7 +51,7 @@ def apply_wedge_dcube(ori_data, mw2d, mw3d=None):
 
     else:
         import mrcfile
-        with mrcfile.open(mw3d, 'r') as mrc:
+        with mrcfile.open(mw3d, permissive=True) as mrc:
             mw = mrc.data
         mwshift = np.fft.fftshift(mw)
         data = np.zeros_like(ori_data)
@@ -105,7 +105,7 @@ def apply_wedge1(ori_data, ld1 = 1, ld2 =0, mw3d = None):
         return outData
     else:
         import mrcfile
-        with mrcfile.open(mw3d, 'r') as mrc:
+        with mrcfile.open(mw3d, permissive=True) as mrc:
             mw = mrc.data
         mw = np.fft.fftshift(mw)
         mw = mw * ld1 + (1-mw) * ld2

util/deconv_gpu.py

@@ -177,7 +177,7 @@ def restore(self,new_list):
     num_cpu = int(args[6])
     deconvstrength = 1
 
-    with mrcfile.open(mrcFile) as mrc:
+    with mrcfile.open(mrcFile, permissive=True) as mrc:
         vol = mrc.data
     c = Chunks(num=(1,4,4),overlap=0.25)
     chunks_list = c.get_chunks(vol)

util/deconvolution.py

@@ -45,7 +45,7 @@ def wiener1d(angpix, voltage, cs, defocus, snrfalloff, deconvstrength, highpassn
     return ctf, wiener
 
 def tom_deconv_tomo(vol_file, out_file,angpix, voltage, cs, defocus, snrfalloff, deconvstrength, highpassnyquist, phaseflipped, phaseshift, ncpu=8):
-    with mrcfile.open(vol_file) as f:
+    with mrcfile.open(vol_file, permissive=True) as f:
         header_in = f.header
         vol = f.data
         voxelsize = f.voxel_size
@@ -135,7 +135,7 @@ def __init__(self,chunk_size=200,overlap=0.25):
     def get_chunks(self,tomo_name):
         #side*(1-overlap)*(num-1)+side = sp + side*overlap -> side *(1-overlap) * num = side
         root_name = os.path.splitext(os.path.basename(tomo_name))[0]
-        with mrcfile.open(tomo_name) as f:
+        with mrcfile.open(tomo_name, permissive=True) as f:
             vol = f.data#.astype(np.float32)
         cropsize = int(self.chunk_size*(1+self.overlap))
         cubesize = self.chunk_size
@@ -169,7 +169,7 @@ def restore(self,new_file_list):
             for j in range(self._N[1]):
                 for k in range(self._N[2]):
                     one_chunk_file = new_file_list[i*self._N[1]*self._N[2]+j*self._N[2]+k]
-                    with mrcfile.open(one_chunk_file) as f:
+                    with mrcfile.open(one_chunk_file, permissive=True) as f:
                         one_chunk_data = f.data
                     new[i*cubesize:(i+1)*cubesize,j*cubesize:(j+1)*cubesize,k*cubesize:(k+1)*cubesize] \
                             = one_chunk_data[start:end,start:end,start:end]
@@ -205,7 +205,7 @@ def deconv_one(tomo, out_tomo, voltage=300.0, cs=2.7, defocus=1.0, pixel_size=1.
             chunks_deconv_list = list(p.map(partial_func,chunks_list))
         vol_restored = c.restore(chunks_deconv_list)
 
-        with mrcfile.open(tomo) as n:
+        with mrcfile.open(tomo, permissive=True) as n:
             header_input = n.header
             pixel_size = n.voxel_size
 

util/filter.py

@@ -111,7 +111,7 @@ def get_polygon(points):
     import sys
     import mrcfile
     args = sys.argv
-    with mrcfile.open(args[1]) as n:
+    with mrcfile.open(args[1], permissive=True) as n:
         tomo = n.data
     mask = stdmask_mpi(tomo,cubelen=20,cubesize=80,ncpu=20,if_rescale=True)
     with mrcfile.new(args[2],overwrite=True) as n: