update matlab type codes to be explicit

Author: jverswijver

datajoint/blob.py

@@ -38,7 +38,23 @@
     )
 )
 
-rev_class_id = {dtype: i for i, dtype in enumerate(scalar_id.values())}
+# Matlab numeric codes
+matlab_scalar_mapping = {
+    np.dtype("bool"): 3,  # LOGICAL
+    np.dtype("c"): 4,  # CHAR
+    np.dtype("O"): 5,  # VOID
+    np.dtype("float64"): 6,  # DOUBLE
+    np.dtype("float32"): 7,  # SINGLE
+    np.dtype("int8"): 8,  # INT8
+    np.dtype("uint8"): 9,  # UINT8
+    np.dtype("int16"): 10,  # INT16
+    np.dtype("uint16"): 11,  # UINT16
+    np.dtype("int32"): 12,  # INT32
+    np.dtype("uint32"): 13,  # UINT32
+    np.dtype("int64"): 14,  # INT64
+    np.dtype("uint64"): 15,  # UINT64
+}
+
 dtype_list = list(scalar_id.values())
 type_names = list(scalar_id)
 
@@ -256,9 +272,13 @@ def pack_array(self, array):
         if is_complex:
             array, imaginary = np.real(array), np.imag(array)
         type_id = (
-            rev_class_id[array.dtype]
-            if array.dtype.char != "U"
-            else rev_class_id[np.dtype("O")]
+            matlab_scalar_mapping[np.dtype("O")]
+            if array.dtype not in matlab_scalar_mapping
+            else (
+                matlab_scalar_mapping[array.dtype]
+                if array.dtype.char != "U"
+                else matlab_scalar_mapping[np.dtype("O")]
+            )
         )
         if dtype_list[type_id] is None:
             raise DataJointError("Type %s is ambiguous or unknown" % array.dtype)

tests/test_blob.py

@@ -233,13 +233,13 @@ def test_datetime_serialization_speed():
     # np arrays of np.datetime64 types is now slower than regular arrays of datetime64
 
     np_array_dt_exe_time = timeit.timeit(
-        setup='myarr=pack(np.array([np.datetime64(f"{x}") for x in range(1900, 2000)]))',
+        setup="myarr=pack(np.array([np.datetime64(f'{x}') for x in range(1900, 2000)]))",
         stmt="unpack(myarr)",
         number=10,
         globals=globals(),
     )
     python_array_dt_exe_time = timeit.timeit(
-        setup='myarr2=pack([np.datetime64(f"{x}") for x in range(1900, 2000)])',
+        setup="myarr2=pack([datetime.now() for x in range (1900, 2000)])",
         stmt="unpack(myarr2)",
         number=10,
         globals=globals(),