Merge pull request #176 from jonwright/master

ready for v2

ImageD11/__init__.py

@@ -18,7 +18,7 @@
 Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
 """
 
-__version__ = "1.9.9"
+__version__ = "2.0.0"
 __author__ = 'Jon Wright',
 __author_email__ = '[email protected]'
 

ImageD11/saintraw.py

@@ -294,7 +294,7 @@ def tocolumnfile(self):
         """
         cof = columnfile.newcolumnfile( self.alltitles )
         dlist = [ self.data[t] for t in self.alltitles ]
-        cof.bigarray = numpy.array( dlist, numpy.float )
+        cof.bigarray = numpy.array( dlist, float )
         cof.nrows = len( self.data[ self.alltitles[0] ] )
         cof.ncols = len( self.alltitles ) 
         cof.set_attributes()

ImageD11/scale.py

@@ -55,8 +55,8 @@ def __init__( self, im1, threshold = None):
         im1 = a * im2 + b
         returns a, b
         """
-        lsqmat = numpy.zeros((2, 2), numpy.float)
-        dyda   = numpy.ravel(im1).astype(numpy.float)
+        lsqmat = numpy.zeros((2, 2), float)
+        dyda   = numpy.ravel(im1).astype(float)
         self.threshold = threshold
         if threshold is None:
             self.indices = None
@@ -102,14 +102,14 @@ def scale(self, im2):
         ...use scale image for that
         """
         if self.indices is None:
-            rhs0 = numpy.sum(self.dyda * numpy.ravel(im2).astype(numpy.float))
-            rhs1 = numpy.sum(numpy.ravel(im2).astype(numpy.float))
+            rhs0 = numpy.sum(self.dyda * numpy.ravel(im2).astype(float))
+            rhs1 = numpy.sum(numpy.ravel(im2).astype(float))
             ans = numpy.dot(self.inverse, [rhs0, rhs1])
             return ans[0], ans[1]
         else:
             usedata = numpy.take(numpy.ravel(im2) , self.indices)
-            rhs0 = numpy.sum(self.dyda * usedata.astype(numpy.float))
-            rhs1 = numpy.sum(usedata.astype(numpy.float))
+            rhs0 = numpy.sum(self.dyda * usedata.astype(float))
+            rhs1 = numpy.sum(usedata.astype(float))
             ans = numpy.dot(self.inverse, [rhs0, rhs1])
             return ans[0], ans[1]
 
@@ -121,7 +121,7 @@ def scaleseries( target, stem, first, last,
     Scale a series of [bruker] images to the target
     TODO - make it work with fabio file series
     """
-    # d0 = numpy.ravel(target.data.astype(numpy.float))
+    # d0 = numpy.ravel(target.data.astype(float))
     scaler = scale(target.data, thresh)
     print("# Scaling with respect to:", sys.argv[1])
     if thresh is not None:

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/sinograms/properties.py

@@ -643,7 +643,7 @@ def pks_table_from_scan( sparsefilename, ds, row ):
     This is probably not threadsafe 
     """ 
     sps = ImageD11.sparseframe.SparseScan( sparsefilename, ds.scans[row] )
-    sps.motors['omega'] = ds.omega[i] 
+    sps.motors['omega'] = ds.omega[row]
     peaks, pairs = ImageD11.sinograms.properties.props( sps, row )
     # which frame/peak is which in the peaks array
     # For the 3D merging 

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:

ImageD11/tkGui/plot3d.py

@@ -97,7 +97,7 @@ def __init__(self,parent,data=None,lines=None,
         Tk.Label(self,text="Red=[1,0,0] Green=[0,1,0] Blue=[0,0,1]").pack(
             side=Tk.BOTTOM,expand=Tk.NO,fill=Tk.X)
         self.ubis=ubis
-        self.color=numpy.ones((xyz.shape[0],3),numpy.float)
+        self.color=numpy.ones((xyz.shape[0],3),float)
         print(self.color.shape)
         self.tex=False
         if ubis is not None:
@@ -253,10 +253,10 @@ def redraw(self,o):
 
         if self.ubis is not None and len(self.ubis)==1:
             hkl = numpy.dot(numpy.linalg.inv(self.ubis[0]), 
-                            numpy.identity(3,numpy.float)).T
+                            numpy.identity(3,float)).T
             # print hkl
         else:
-            hkl = numpy.identity(3,numpy.float)
+            hkl = numpy.identity(3,float)
             # print hkl
 
         GL.glBegin(GL.GL_LINE_LOOP)

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/qtcam.py

@@ -57,8 +57,8 @@ def find_offset( c ):
 def register_image( im1, im2 ):
     """ We take the current and place it onto reference """
 
-    ref = normalise(numpy.asarray(im1).sum(axis=2, dtype=numpy.float))
-    cur = normalise(numpy.asarray(im2).sum(axis=2, dtype=numpy.float))
+    ref = normalise(numpy.asarray(im1).sum(axis=2, dtype=float))
+    cur = normalise(numpy.asarray(im2).sum(axis=2, dtype=float))
 
     fftshape = ( ref.shape[0] + cur.shape[0] ,
                  ref.shape[1] + cur.shape[1]  )

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])

sandbox/sigma_3_matrices.py

@@ -0,0 +1,42 @@
+
+import ImageD11.grain
+import ImageD11.grid_index_parallel
+import scipy.spatial.transform
+import numpy as np
+
+
+def make_sigma3_mats( ):
+    """ Rotations of 60 degrees on 111 """
+    v = 60/np.sqrt( 3 )
+    S3_matrices = [ scipy.spatial.transform.Rotation.from_rotvec( (x,y,z), degrees=True ).as_matrix()
+            for x in (-v,v)
+            for y in (-v,v)
+            for z in (-v,) ]  # only need 1 z as -60 == 60
+    return S3_matrices
+
+
+def applytwinmat( g, mat ):
+    """ U.B -> U.M'.B """
+    ubinew = np.linalg.inv( g.U.dot( mat.T ).dot( g.B ) )
+    return ImageD11.grain.grain( ubinew, g.translation )
+
+
+def twin_check( ind, matrices, symmetry='cubic', poserr=1000, angerr=0.5 ):
+    gl = ImageD11.grid_index_parallel.uniq_grain_list( symmetry, poserr, angerr )
+    gl.add( [ ImageD11.grain.grain( ubi, np.array((0,0,0)) ) for ubi in ind.ubis ] )
+    for ubi in ind.ubis:
+        g = ImageD11.grain.grain( ubi )
+        sg1 = ImageD11.cImageD11.score( ubi, ind.gvflat, ind.hkl_tol )
+        print("%4d   "%(sg1), end='')
+        for mat in matrices:
+            gnew = applytwinmat( g, mat )
+            s1 = ImageD11.cImageD11.score( gnew.ubi, ind.gvflat, ind.hkl_tol )
+            if s1 > ind.minpks:  # should be accepted assuming uniq
+                b4 = len(gl.uniqgrains)
+            gl.add( [gnew,] )
+            if len(gl.uniqgrains) == b4:
+                e = 'r'
+            else:
+                e = '!'
+            print("%4d "%(s1), end=e)
+        print() 

test/testconnectedpixels/testconnectedpixels.py

@@ -13,7 +13,7 @@ def setUp(self):
 
     def test_1(self):
         for t in [np.uint8, np.int8, np.uint16, np.int16,
-                  np.int32, np.uint32, np.float32, np.float]:
+                  np.int32, np.uint32, np.float32, float]:
             data = np.array( [[ 0, 0, 0, 0, 0, 0, 0],
                              [ 0, 0, 0, 1, 0, 0, 0],
                              [ 0, 0, 0, 0, 0, 0, 0],
@@ -28,7 +28,7 @@ def test_1(self):
 
     def test_1_shape(self):
         for t in [np.uint8, np.int8, np.uint16, np.int16,
-                  np.int32, np.uint32, np.float32, np.float]:
+                  np.int32, np.uint32, np.float32, float]:
             data = np.array( [[ 1, 0, 1, 0, 1, 0, 1],
                              [ 1, 0, 1, 0, 1, 0, 1],
                              [ 1, 0, 1, 0, 0, 1, 0],
@@ -43,7 +43,7 @@ def test_1_shape(self):
 
     def test_2_shapes(self):
         for t in [np.uint8, np.int8, np.uint16, np.int16,
-                  np.int32, np.uint32, np.float32, np.float]:
+                  np.int32, np.uint32, np.float32, float]:
             data = np.array( [[ 1, 0, 1, 0, 2, 0, 2],
                              [ 1, 0, 1, 0, 2, 0, 2],
                              [ 1, 0, 1, 0, 0, 2, 0],
@@ -109,7 +109,7 @@ def test_prop_names(self):
 
     def test_find_max(self):
         for t in [np.uint8, np.int8, np.uint16, np.int16,
-                  np.int32, np.uint32, np.float32, np.float]:
+                  np.int32, np.uint32, np.float32, float]:
             data = np.array( [[ 1, 0, 1],
                              [ 1, 0, 1],
                              [ 1, 8, 1],

test/testscale.py

@@ -8,7 +8,7 @@ class testscale(unittest.TestCase):
     """old testcase """
     def testscaleimage(self):
         """check the object works """
-        im1 = numpy.ones((10, 10), numpy.float)
+        im1 = numpy.ones((10, 10), float)
         im1[2:8, 2:8] *= 2
         im1[4:6, 4:6] *= 2
         im2 = im1 * 2. + 3.