Merge branch 'FABLE-3DXRD:master' into master

ImageD11/sinograms/dataset.py

@@ -246,7 +246,7 @@ def import_all(self, scans=None, shape=None):
         except:
             logging.info("nnz not available. Segmentation done?")
 
-    def import_from_sparse(self, hname, scans=None):
+    def import_from_sparse(self, hname, scans=None, shape=None):
         """
         hname = hdf5 file containing sparse pixels (and motors)
         dataset = a dataset instance to import into
@@ -260,12 +260,26 @@ def import_from_sparse(self, hname, scans=None):
                 scans = list(hin["/"])
                 order = np.argsort([float(v) for v in scans if v.endswith(".1")])
                 self.scans = [scans[i] for i in order]
+        else:
+            self.scans = scans
         self.masterfile = hname  # hacky, motors come from the sparsefile
         self.import_nnz_from_sparse()  # must exist
         self.import_motors_from_master()
-        self.shape = self.nnz.shape
+        # self.guess_shape() # fails with sparse
+        if shape is not None:
+            self.shape = shape
+            self.nnz = np.array(self.nnz).reshape(self.shape)
+        else:
+            self.shape = self.nnz.shape
         self.omega = np.array(self.omega).reshape(self.shape)
         self.dty = np.array(self.dty).reshape(self.shape)
+        if len(scans) == 1 and self.shape[0]>1:
+            file_nums = np.arange(self.shape[0]*self.shape[1]).reshape(self.shape)
+            self.scans = [
+                "%s::[%d:%d]" % (self.scans[0], row[0], row[-1] + 1)
+                for row in file_nums
+            ]
+
         self.guessbins()
 
     def import_scans(self, scans=None, hname=None):
@@ -525,7 +539,7 @@ def get_monitor(self, name="fpico6"):
                     lo, hi = [int(v) for v in slc[1:-1].split(":")]
                     mon = hin[snum]["measurement"][name][lo:hi]
                 else:
-                    mon = hin[snum]["measurement"][name][:]
+                    mon = hin[scan]["measurement"][name][:]
                 monitor.append(mon)
         self.monitor = np.concatenate(monitor).reshape(self.shape)
         return self.monitor

ImageD11/sinograms/roi_iradon.py

@@ -526,8 +526,13 @@ def correct_recon_central_zingers(recon, radius=25):
 
 
 def run_iradon(sino, angles, pad=20, shift=0,
-               workers=1, mask=None,
-               apply_halfmask=False, mask_central_zingers=False, central_mask_radius=25):
+               workers=1,
+               mask=None,
+               apply_halfmask=False,
+               mask_central_zingers=False,
+               central_mask_radius=25,
+               filter_name='hamming',
+               ):
     """Applies an iradon to a sinogram, with an optional pad
        Calculates scaled-up output size from pad value
        Applies projection shifts of shift
@@ -550,7 +555,7 @@ def run_iradon(sino, angles, pad=20, shift=0,
                    mask=mask,
                    output_size=outsize,
                    projection_shifts=np.full(sino.shape, shift),
-                   filter_name='hamming',
+                   filter_name=filter_name,
                    interpolation='linear',
                    workers=workers)
 

ImageD11/sinograms/sinogram.py

@@ -160,6 +160,8 @@ def build_sinogram(self, columns=('omega',)):
         # intensity weighted sums
         angs = {}
         for name in columns:
+            if name == 'sum_intensity':
+                continue
             angs[name] = np.zeros((npks, NY), 'f')
             ImageD11.cImageD11.put_incr64(angs[name], adr, self.cf_for_sino[name] * sig )
         sinoangles = angs['omega'] .sum(axis=1) / sino.sum(axis=1)
@@ -172,7 +174,13 @@ def build_sinogram(self, columns=('omega',)):
         self.ssino = self.sino[order].T
         self.proj_scale = self.proj_scale[order]
         if len(columns)>1:
-            self.angle_wt_sinos = { name : angs[name][order].T for name in columns }
+            self.angle_wt_sinos = { name : angs[name][order].T
+                                    for name in columns
+                                    if name != 'sum_intensity'
+                                   }
+            if 'sum_intensity' in columns:
+                self.angle_wt_sinos['sum_intensity'] = sino[order].T
+        self.hits = hits
         self.hkle = pkindices[:, order]  # dims are [ (h,k,l,sign(eta)) , nprojections ]
 
     def update_lab_position_from_peaks(self, cf_4d, grain_label):
@@ -252,7 +260,10 @@ def recon(self,
 
            iradon -> ImageD11.sinograms.roi_iradon.run_iradon
            { pad=20, shift=0, workers=1, mask=None,
-                      apply_halfmask=False, mask_central_zingers=False, central_mask_radius=25 }
+                      apply_halfmask=False,
+                      mask_central_zingers=False,
+                      central_mask_radius=25,
+                      filter_name='hamming'}
            mlem : ImageD11.sinograms.roi_iradon.run_mlem
               { mask=None, pad=20, shift=0, workers=1, niter=20, apply_halfmask=False,
              mask_central_zingers=False, central_mask_radius=25 }