Changeset c7c8143 in sasview for src/sas/sascalc/dataloader/readers/cansas_reader_HDF5.py

Timestamp:

Nov 28, 2018 10:01:20 AM (5 years ago)

Author:

Jeff Krzywon <jkrzywon@…>

Branches:

master, magnetic_scatt, release-4.2.2, ticket-1009, ticket-1094-headless, ticket-1242-2d-resolution, ticket-1243, ticket-1249

Children:

109afbd

Parents:

9220e89c

Message:

Load and save mask from NXcanSAS properly.

File:

• src/sas/sascalc/dataloader/readers/cansas_reader_HDF5.py (modified) (2 diffs)

src/sas/sascalc/dataloader/readers/cansas_reader_HDF5.py

@@ -567 +567 @@
         # Type cast data arrays to float64 and find min/max as appropriate
         for dataset in self.data2d:
-            zeros = np.ones(dataset.data.size, dtype=bool)
-            try:
-                for i in range(0, dataset.mask.size - 1):
-                    zeros[i] = dataset.mask[i]
-            except:
-                self.errors.append(sys.exc_value)
-            dataset.mask = zeros
             # Calculate the actual Q matrix
             try:
@@ -585 +578 @@
 
             if dataset.data.ndim == 2:
-                (n_rows, n_cols) = dataset.data.shape
-                flat_qy = dataset.qy_data[0::n_cols].flatten()
-                # For 2D arrays of Qx and Qy, the Q value should be constant
-                # along each row -OR- each column. The direction is not
-                # specified in the NXcanSAS standard.
-                if flat_qy[0] == flat_qy[1]:
-                    flat_qy = np.transpose(dataset.qy_data)[0::n_cols].flatten()
-                dataset.y_bins = np.unique(flat_qy)
-                flat_qx = dataset.qx_data[0::n_rows].flatten()
-                # For 2D arrays of Qx and Qy, the Q value should be constant
-                # along each row -OR- each column. The direction is not
-                # specified in the NXcanSAS standard.
-                if flat_qx[0] == flat_qx[1]:
-                    flat_qx = np.transpose(dataset.qx_data)[0::n_rows].flatten()
-                dataset.x_bins = np.unique(flat_qx)
+                dataset.y_bins = np.unique(dataset.qy_data.flatten())
+                dataset.x_bins = np.unique(dataset.qx_data.flatten())
                 dataset.data = dataset.data.flatten()
                 dataset.qx_data = dataset.qx_data.flatten()
                 dataset.qy_data = dataset.qy_data.flatten()
+
+            try:
+                iter(dataset.mask)
+                dataset.mask = np.invert(np.asarray(dataset.mask, dtype=bool))
+            except TypeError:
+                dataset.mask = np.ones(dataset.data.shape, dtype=bool)
             self.current_dataset = dataset
             self.send_to_output()