Get data from cache

Example:
```python
import os
import pandas
import shutil
from executorlib import Executor
from executorlib.standalone.hdf import get_cache_data

cache_directory = "./cache"
with Executor(backend="local", cache_directory=cache_directory) as exe:
    future_lst = [exe.submit(sum, [i, i]) for i in range(1, 4)]
    print([f.result() for f in future_lst])

df = pandas.DataFrame(get_cache_data(cache_directory=cache_directory))
df
```

executorlib/standalone/hdf.py

@@ -1,10 +1,21 @@
-from typing import Optional, Tuple
+import os
+from typing import Optional, Tuple, List
 
 import cloudpickle
 import h5py
 import numpy as np
 
 
+group_dict = {
+    "fn": "function",
+    "args": "input_args",
+    "kwargs": "input_kwargs",
+    "output": "output",
+    "runtime": "runtime",
+    "queue_id": "queue_id",
+}
+
+
 def dump(file_name: str, data_dict: dict) -> None:
     """
     Dump data dictionary into HDF5 file
@@ -13,14 +24,6 @@ def dump(file_name: str, data_dict: dict) -> None:
         file_name (str): file name of the HDF5 file as absolute path
         data_dict (dict): dictionary containing the python function to be executed {"fn": ..., "args": (), "kwargs": {}}
     """
-    group_dict = {
-        "fn": "function",
-        "args": "input_args",
-        "kwargs": "input_kwargs",
-        "output": "output",
-        "runtime": "runtime",
-        "queue_id": "queue_id",
-    }
     with h5py.File(file_name, "a") as fname:
         for data_key, data_value in data_dict.items():
             if data_key in group_dict.keys():
@@ -97,3 +100,16 @@ def get_queue_id(file_name: str) -> Optional[int]:
             return cloudpickle.loads(np.void(hdf["/queue_id"]))
         else:
             return None
+
+
+def get_cache_data(cache_directory: str) -> List[dict]:
+    file_lst = []
+    for file_name in os.listdir(cache_directory):
+        with h5py.File(os.path.join(cache_directory, file_name), "r") as hdf:
+            file_content_dict = {
+                key: cloudpickle.loads(np.void(hdf["/" + key]))
+                for key in group_dict.values() if key in hdf
+            }
+        file_content_dict["filename"] = file_name
+        file_lst.append(file_content_dict)
+    return file_lst

tests/test_cache_executor_interactive.py

@@ -0,0 +1,32 @@
+import os
+import shutil
+import unittest
+
+from executorlib import Executor
+
+try:
+
+    from executorlib.standalone.hdf import get_cache_data
+
+    skip_h5py_test = False
+except ImportError:
+    skip_h5py_test = True
+
+
+@unittest.skipIf(
+    skip_h5py_test, "h5py is not installed, so the h5io tests are skipped."
+)
+class TestCacheFunctions(unittest.TestCase):
+    def test_cache_data(self):
+        cache_directory = "./cache"
+        with Executor(backend="local", cache_directory=cache_directory) as exe:
+            future_lst = [exe.submit(sum, [i, i]) for i in range(1, 4)]
+            result_lst = [f.result() for f in future_lst]
+
+        cache_lst = get_cache_data(cache_directory=cache_directory)
+        self.assertEqual(sum([c["output"] for c in cache_lst]), sum(result_lst))
+        self.assertEqual(sum([sum(c["input_args"][0]) for c in cache_lst]), sum(result_lst))
+
+    def tearDown(self):
+        if os.path.exists("cache"):
+            shutil.rmtree("cache")