Dev/tests (#86)

Adding tests to improve coverage.

README.md

@@ -1,9 +1,10 @@
 # DaCapo ![DaCapo](docs/source/_static/icon_dacapo.png) 
 
-[![tests](https://github.com/funkelab/dacapo/actions/workflows/tests.yaml/badge.svg)](https://github.com/funkelab/dacapo/actions/workflows/tests.yaml)
-[![black](https://github.com/funkelab/dacapo/actions/workflows/black.yaml/badge.svg)](https://github.com/funkelab/dacapo/actions/workflows/black.yaml)
-[![mypy](https://github.com/funkelab/dacapo/actions/workflows/mypy.yaml/badge.svg)](https://github.com/funkelab/dacapo/actions/workflows/mypy.yaml)
-[![docs](https://github.com/funkelab/dacapo/actions/workflows/docs.yaml/badge.svg)](https://funkelab.github.io/dacapo/)
+[![tests](https://github.com/janelia-cellmap/dacapo/actions/workflows/tests.yaml/badge.svg)](https://github.com/janelia-cellmap/dacapo/actions/workflows/tests.yaml)
+[![black](https://github.com/janelia-cellmap/dacapo/actions/workflows/black.yaml/badge.svg)](https://github.com/janelia-cellmap/dacapo/actions/workflows/black.yaml)
+[![mypy](https://github.com/janelia-cellmap/dacapo/actions/workflows/mypy.yaml/badge.svg)](https://github.com/janelia-cellmap/dacapo/actions/workflows/mypy.yaml)
+[![docs](https://github.com/janelia-cellmap/dacapo/actions/workflows/docs.yaml/badge.svg)](https://janelia-cellmap.github.io/dacapo/)
+[![codecov](https://codecov.io/gh/janelia-cellmap/dacapo/branch/main/graph/badge.svg)](https://codecov.io/gh/janelia-cellmap/dacapo)
 
 A framework for easy application of established machine learning techniques on large, multi-dimensional images.
 

dacapo/store/file_config_store.py

@@ -98,10 +98,12 @@ def __save_insert(self, collection, data, ignore=None):
 
         file_store = collection / name
         if not file_store.exists():
-            pickle.dump(dict(data), file_store.open("wb"))
+            with file_store.open("wb") as fd:
+                pickle.dump(dict(data), fd)
 
         else:
-            existing = pickle.load(file_store.open("rb"))
+            with file_store.open("rb") as fd:
+                existing = pickle.load(fd)
 
             if not self.__same_doc(existing, data, ignore):
                 raise DuplicateNameError(
@@ -113,7 +115,8 @@ def __save_insert(self, collection, data, ignore=None):
     def __load(self, collection, name):
         file_store = collection / name
         if file_store.exists():
-            return pickle.load(file_store.open("rb"))
+            with file_store.open("rb") as fd:
+                return pickle.load(fd)
         else:
             raise ValueError(f"No config with name: {name} in collection: {collection}")
 

dacapo/store/file_stats_store.py

@@ -88,12 +88,14 @@ def __store_training_stats(self, stats, begin, end, run_name):
 
         if docs:
             file_store = self.training_stats / run_name
-            pickle.dump(docs, file_store.open("wb"))
+            with file_store.open("wb") as fd:
+                pickle.dump(docs, fd)
 
     def __read_training_stats(self, run_name):
         file_store = self.training_stats / run_name
         if file_store.exists():
-            docs = pickle.load(file_store.open("rb"))
+            with file_store.open("rb") as fd:
+                docs = pickle.load(fd)
         else:
             docs = []
         stats = TrainingStats(converter.structure(docs, List[TrainingIterationStats]))
@@ -117,12 +119,14 @@ def __store_validation_iteration_scores(
 
         if docs:
             file_store = self.validation_scores / run_name
-            pickle.dump(docs, file_store.open("wb"))
+            with file_store.open("wb") as fd:
+                pickle.dump(docs, fd)
 
     def __read_validation_iteration_scores(self, run_name):
         file_store = self.validation_scores / run_name
         if file_store.exists():
-            docs = pickle.load(file_store.open("rb"))
+            with file_store.open("rb") as fd:
+                docs = pickle.load(fd)
         else:
             docs = []
         scores = converter.structure(docs, List[ValidationIterationScores])

dacapo/store/local_weights_store.py

@@ -103,11 +103,10 @@ def store_best(self, run: str, iteration: int, dataset: str, criterion: str):
     def retrieve_best(self, run: str, dataset: str, criterion: str) -> int:
         logger.info("Retrieving weights for run %s, criterion %s", run, criterion)
 
-        weights_info = json.loads(
-            (self.__get_weights_dir(run) / criterion / f"{dataset}.json")
-            .open("r")
-            .read()
-        )
+        with (self.__get_weights_dir(run) / criterion / f"{dataset}.json").open(
+            "r"
+        ) as fd:
+            weights_info = json.load(fd)
 
         return weights_info["iteration"]