Scanning 3DXRD Strain refinement

ImageD11/fetch_data.py

@@ -17,14 +17,16 @@
 
 # what Zenodo folder can these be found in?
 dataset_base_urls = {
-    'Si_cube_S3DXRD_nt_moves_dty': "https://sandbox.zenodo.org/records/90518/files/",
+    'Si_cube_S3DXRD_nt_moves_dty': "https://sandbox.zenodo.org/records/118843/files/",
 }
 
 # What are the names of the files in the Zenodo folder?
 dataset_filenames = {
     'Si_cube_S3DXRD_nt_moves_dty': {
         'sparsefile': 'Si_cube_S3DXRD_nt_moves_dty_sparse.h5',
-        'parfile': 'Si_refined.par',
+        'parfile': 'pars.json',
+        'geomfile': 'geometry.par',
+        'phasefile0': 'Si_refined.par',
         'e2dxfile': 'e2dx_E-08-0144_20240205.edf',
         'e2dyfile': 'e2dy_E-08-0144_20240205.edf'
     }
@@ -123,25 +125,25 @@ def _get_dataset(test_dataset_name, dest_folder, allow_download):
         for filetype, filename in dataset_filenames[test_dataset_name].items():
             file_url = dataset_base_urls[test_dataset_name] + filename
             # is it a file that could be used as an attribute?
-            if filetype in id11dset.DataSet.ATTRNAMES:
-                if hasattr(ds, filetype):
-                    if getattr(ds, filetype) is None:
-                        # this is an attribute, but we don't have a path for it
-                        # put it in processed data root
-                        filepath = os.path.join(processed_data_root_dir, filename)
-                        download_url(file_url, filepath)
-                        setattr(ds, filetype, filepath)
-                    else:
-                        # the dataset has a path for this filetype already
-                        filepath = getattr(ds, filetype)
-                        download_url(file_url, filepath)
-                else:
-                    # probably a spatial or a parfile
-                    # chuck it in processed_data_root_dir
-                    # set the attribute
+            # if filetype in id11dset.DataSet.ATTRNAMES:
+            if hasattr(ds, filetype):
+                if getattr(ds, filetype) is None:
+                    # this is an attribute, but we don't have a path for it
+                    # put it in processed data root
                     filepath = os.path.join(processed_data_root_dir, filename)
                     download_url(file_url, filepath)
                     setattr(ds, filetype, filepath)
+                else:
+                    # the dataset has a path for this filetype already
+                    filepath = getattr(ds, filetype)
+                    download_url(file_url, filepath)
+            else:
+                # probably a spatial or a parfile
+                # chuck it in processed_data_root_dir
+                # set the attribute
+                filepath = os.path.join(processed_data_root_dir, filename)
+                download_url(file_url, filepath)
+                setattr(ds, filetype, filepath)
 
         ds.import_from_sparse(ds.sparsefile)
         ds.save()

ImageD11/nbGui/S3DXRD/0_S3DXRD_segment_and_label.ipynb → ImageD11/nbGui/S3DXRD/0_segment_and_label.ipynb

@@ -74,6 +74,9 @@
     "\n",
     "if len(dataroot)==0:\n",
     "    print(\"Please fix in the dataroot and analysisroot folder names above!!\")\n",
+    "\n",
+    "analysisroot = os.path.join(analysisroot, 'James', '20241009')\n",
+    "    \n",
     "print('dataroot =',repr(dataroot))\n",
     "print('analysisroot =',repr(analysisroot))"
    ]
@@ -101,7 +104,7 @@
    "outputs": [],
    "source": [
     "# USER: Decide which sample\n",
-    "sample = 'WAu'"
+    "sample = 'FeAu_0p5_tR_nscope'"
    ]
   },
   {
@@ -127,7 +130,7 @@
    "outputs": [],
    "source": [
     "# USER: Decide which dataset\n",
-    "dataset = \"siliconAttrz25\""
+    "dataset = \"top_100um\""
    ]
   },
   {
@@ -243,6 +246,20 @@
     "ImageD11.sinograms.properties.main(ds.dsfile, options={'algorithm': 'lmlabel', 'wtmax': 70000, 'save_overlaps': False})"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "3db1c3e1-e812-4098-a3ab-a7a10b7cab4c",
+   "metadata": {},
+   "source": [
+    "# Finished segmenting!\n",
+    "\n",
+    "You can now choose between two different indexing routes: tomographic (tomo) and point-by-point (pbp).  \n",
+    "Tomo gives you better grain shapes, but can't handle highly deformed samples.  \n",
+    "Point-by-point can only give you convex grain shapes (less accurate) but can handle high levels of deformation.  \n",
+    "Both techniques will join back together during the strain refinement stage (notebook 3).  \n",
+    "Therefore notebooks 4 and onwards should work from either the tomo or pbp route."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -269,9 +286,9 @@
     "    \"FeAu_0p5_tR_nscope\": [\"top_-50um\", \"top_-100um\"]\n",
     "}\n",
     "\n",
-    "dset_prefix = \"m\"  # some common string in the names of the datasets (*?)\n",
+    "dset_prefix = \"top_\"  # some common string in the names of the datasets (*?)\n",
     "\n",
-    "sample_list = [\"Klegs\"]\n",
+    "sample_list = [\"FeAu_0p5_tR_nscope\"]\n",
     "    \n",
     "samples_dict = utils.find_datasets_to_process(dataroot, skips_dict, dset_prefix, sample_list)\n",
     "\n",