readying release

ImageD11/sinograms/lima_segmenter.py

@@ -110,7 +110,11 @@ def save(self, h5name, h5group):
 import numba
 # pip install ImageD11 --no-deps # if you do not have it yet:
 from ImageD11 import sparseframe, cImageD11
-from bslz4_to_sparse import chunk2sparse
+
+try:
+    from bslz4_to_sparse import chunk2sparse
+except:
+    chunk2sparse = None
 
 @numba.njit
 def select(img, mask, row, col, val, cut):
@@ -217,7 +221,8 @@ def reader(frms, mask, cut):
     iterator to read chunks or frames and segment them
     returns sparseframes
     """
-    if '32008' in frms._filters and not frms.is_virtual and OPTIONS.bg is None:
+    if (chunk2sparse is not None) and ('32008' in frms._filters) and (
+        not frms.is_virtual) and (OPTIONS.bg is None):
         print('# reading compressed chunks')
         fun = chunk2sparse( mask, dtype = frms.dtype )
         for i in range(frms.shape[0]):
@@ -228,7 +233,9 @@ def reader(frms, mask, cut):
     else:
         fun = frmtosparse( mask, frms.dtype )
         for i in range(frms.shape[0]):
-            frm = frms[i].astype(np.float32) - OPTIONS.bg
+            frm = frms[i]
+            if OPTIONS.bg is not None:
+                frm = frm.astype(np.float32) - OPTIONS.bg
             npx, row, col, val = fun( frm, cut )
             spf = clean( npx, row, col, val )
             yield spf

ImageD11/sparseframe.py

@@ -113,7 +113,7 @@ def to_dense(self, data=None, out=None):
             if len(ks)==1:
                 data = self.pixels[ks[0]] # default for only one
             else:
-                data = np.ones( self.nnz, np.bool ) # give a mask
+                data = np.ones( self.nnz, bool ) # give a mask
         if out is None:
             out = np.zeros( self.shape, data.dtype )
         else:

sandbox/Index1DSearch.py

@@ -25,7 +25,7 @@
 
 uc = unitcell.unitcell( [7.89, 8.910, 9.1011, 92, 97, 99], "P")
 
-orient = Rotation.from_euler("XYZ",(10,20,31)).as_dcm()
+orient = Rotation.from_euler("XYZ",(10,20,31)).as_matrix()
 ub = np.dot( orient, uc.B )
 ubi = np.linalg.inv( ub )
 
@@ -56,7 +56,7 @@
 print(vec, ang)
 direc = vec / np.sqrt( np.dot(vec,vec))
 
-u0 = Rotation.from_rotvec( ang * direc).as_dcm()
+u0 = Rotation.from_rotvec( ang * direc).as_matrix()
 ub0 = np.dot( u0, uc.B)
 gtest = np.dot( ub0, (h0,k0,l0))
 print(gtest, gobs)
@@ -71,8 +71,8 @@
 unitG = gtest / np.sqrt( np.dot(gtest,gtest)  )
 angles_to_test = np.linspace( -np.pi, np.pi, 360 )
 
-testmats = [ np.dot( ubi0, Rotation.from_rotvec( unitG*x ).as_dcm() ) for x in angles_to_test ]
-rmats    = [ Rotation.from_rotvec( unitG*x ).as_dcm() for x in angles_to_test ]
+testmats = [ np.dot( ubi0, Rotation.from_rotvec( unitG*x ).as_matrix() ) for x in angles_to_test ]
+rmats    = [ Rotation.from_rotvec( unitG*x ).as_matrix() for x in angles_to_test ]
 print(np.dot(testmats[0],gtest))
 
 gobs = gcalc.T.copy()

sandbox/art.py

@@ -50,7 +50,7 @@ def update_wtd( recon, proj, angle, msk, dbg=True ):
     cImageD11.put_incr( calc_proj, idx_lo, r*wt_lo )
     cImageD11.put_incr( calc_proj, idx_hi, r*wt_hi )
     error = np.zeros( calc_proj.shape, np.float32 )
-    start = (len(calc_proj)- len(proj))/2
+    start = int((len(calc_proj)- len(proj))/2)
     error[ start:start+len(proj) ] = proj
     error = error - calc_proj
     npcalc_proj = np.zeros( (mx-mn), np.float32 )
@@ -124,7 +124,7 @@ def test_u_n():
     for j in range(3):
         for proj, angle in zip(projections, theta):
             print(j, angle, end=' ')
-            start = time.clock()
+            start = time.time()
             update = update_wtd( recon, proj, angle, msk, dbg=False)
             recon = recon + update*0.9
             # clip to zero - constructing positive intensity
@@ -136,7 +136,7 @@ def test_u_n():
                 pl.imshow(recon)
                 pl.colorbar()
             #    pl.show()
-            end = time.clock()
+            end = time.time()
             sumtime += end-start
             ncalc +=1
             # print err.sum()

sandbox/bgfilter_grab.py

@@ -57,7 +57,7 @@ def open_sub_dark( names, darkfilename, DARK_N=1 ):
 @bench("mask")
 def do_mask( imgs, maskfilename ):
     """ applies a mask to the data """
-    msk = fabio.open(maskfilename).data.astype(np.bool)
+    msk = fabio.open(maskfilename).data.astype(bool)
     for img in imgs:
         img.data[msk] = 0
         yield img

sandbox/buffered_series.py

@@ -10,7 +10,7 @@ def buffered( names, bufsize ):
     buffer = []
     # init
     while len( buffer ) < bufsize:
-        name = names.next()
+        name = next(names)
         buffer.append( name )
 #        print("buffer", name )
     for i in range(bufsize//2+1):

sandbox/collect_peak_pixels.py

@@ -65,7 +65,7 @@ def to_coo( data, mask, name ):
     Convert an image (2D, probably float) and mask (0 versus !0)
     to a sparse array (could be more efficient...)
     """
-    m = mask.astype(np.bool)
+    m = mask.astype(bool)
     j = np.outer(np.ones(  data.shape[0], dtype=np.uint16),
                  np.arange(data.shape[1], dtype=np.uint16)  )
     ind0, ind1 = j.T[m], j[m]

sandbox/ringselect.py

@@ -27,7 +27,7 @@
 dsmax = 2*np.sin(1.03*tthmax*np.pi/360)/w
 u.makerings(dsmax)
 
-mask = np.zeros( c.nrows, dtype=np.bool )
+mask = np.zeros( c.nrows, dtype=bool )
 for d in u.ringds:
     tthc = np.arcsin(w*d/2)*360/np.pi
     M = len(u.ringhkls[d])