merge conflicts

imgtools/modules/datagraph.py

@@ -342,9 +342,10 @@ def _form_agg(self):
         '''
         Form aggregates for easier parsing, gets the edge types for each study and aggregates as a string. This way one can do regex based on what type of subgraph the user wants
         '''
-        self.df_new = self.df_edges.groupby("study_x").agg({'edge_type':self.list_edges})
-        self.df_new.reset_index(level=0, inplace=True)
-        self.df_new["edge_type"] = self.df_new["edge_type"].astype(str)
+        self.df_edges['edge_type_str'] = self.df_edges['edge_type'].astype(str)
+        self.df_new = self.df_edges.groupby("study_x").agg({'edge_type_str':self.list_edges})
+        self.df_new.reset_index(level=0, inplace=True) 
+        self.df_new["edge_type"] = self.df_new["edge_type_str"]
 
     def _get_df(self,
                 df_edges_processed,

imgtools/modules/segmentation.py

@@ -110,6 +110,7 @@ def generate_sparse_mask(self, verbose=False) -> SparseMask:
 
         if verbose:
             voxels_with_overlap = set()
+
         if len(mask_arr.shape) == 4:
             for i in range(mask_arr.shape[0]):
                 slice = mask_arr[i, :, :, :]
@@ -125,9 +126,11 @@ def generate_sparse_mask(self, verbose=False) -> SparseMask:
             sparsemask_arr = mask_arr
 
         sparsemask = SparseMask(sparsemask_arr, roi_names)
+
         if verbose:
             if len(voxels_with_overlap) != 0:
                 raise Warning(f"{len(voxels_with_overlap)} voxels have overlapping contours.")
+
         return sparsemask
 
     def _max_adder(self, arr_1: np.ndarray, arr_2: np.ndarray) -> Tuple[np.ndarray, Set[Tuple[int, int, int]]]:

imgtools/utils/args.py

@@ -28,10 +28,10 @@ def parser():
     parser.add_argument("--nnunet_study_name", type=str, default=None,
                         help="Name of the study to be used for nn-Unet.")
 
-    parser.add_argument("train_size", type=float, default=1.0,
+    parser.add_argument("--train_size", type=float, default=1.0,
                         help="The proportion of data to be used for training, as a decimal.")
 
-    parser.add_argument("random_state", type=int, default=42,
+    parser.add_argument("--random_state", type=int, default=42,
                         help="The random state to be used for the train-test-split.")
 
     return parser.parse_known_args()[0]

imgtools/utils/crawl.py

@@ -115,7 +115,10 @@ def crawl(top,
     parent_imgtools = pathlib.Path(parent, ".imgtools").as_posix()
 
     if not os.path.exists(parent_imgtools):
-        os.makedirs(parent_imgtools)
+        try:
+            os.makedirs(parent_imgtools)
+        except:
+            pass
 
     # save as json
     with open(pathlib.Path(parent_imgtools, f'imgtools_{dataset}.json').as_posix(), 'w') as f:

setup.py

@@ -20,7 +20,7 @@
     extras_require={
         'debug': ['pyvis'],
     },
-    entry_points={'console_scripts': ['autopipe = imgtools.autopipeline:main']},
+    entry_points={'console_scripts': ['autopipe = imgtools.autopipeline:main', 'autotest = imgtools.autonew:main']},
     classifiers=[
         "Programming Language :: Python :: 3",
         "Operating System :: OS Independent",