small updates.

Author: jverswijver

CHANGELOG.md

@@ -1,7 +1,7 @@
 ## Release notes
 
-### 0.13.7 -- TBD
-* Bugfix - Fix networkx incompatable change by version pinning to 2.8.3 PR #1036 (#1035)
+### 0.13.7 -- Jul 13, 2022
+* Bugfix - Fix networkx incompatable change by version pinning to 2.6.3 PR #1036 (#1035)
 * Add - Support for serializing numpy datetime64 types PR #1036 (#1022)
 * Update - Add traceback to default logging PR #1036
 

datajoint/blob.py

@@ -32,19 +32,19 @@
     14: {"dtype": np.dtype("int64"), "scalar_type": "INT64"},
     15: {"dtype": np.dtype("uint64"), "scalar_type": "UINT64"},
     16: {"dtype": None, "scalar_type": "FUNCTION"},
-    65536: {"dtype": np.dtype("datetime64[Y]"), "scalar_type": "DATETIME64[Y]"},
-    65537: {"dtype": np.dtype("datetime64[M]"), "scalar_type": "DATETIME64[M]"},
-    65538: {"dtype": np.dtype("datetime64[W]"), "scalar_type": "DATETIME64[W]"},
-    65539: {"dtype": np.dtype("datetime64[D]"), "scalar_type": "DATETIME64[D]"},
-    65540: {"dtype": np.dtype("datetime64[h]"), "scalar_type": "DATETIME64[h]"},
-    65541: {"dtype": np.dtype("datetime64[m]"), "scalar_type": "DATETIME64[m]"},
-    65542: {"dtype": np.dtype("datetime64[s]"), "scalar_type": "DATETIME64[s]"},
-    65543: {"dtype": np.dtype("datetime64[ms]"), "scalar_type": "DATETIME64[ms]"},
-    65544: {"dtype": np.dtype("datetime64[us]"), "scalar_type": "DATETIME64[us]"},
-    65545: {"dtype": np.dtype("datetime64[ns]"), "scalar_type": "DATETIME64[ns]"},
-    65546: {"dtype": np.dtype("datetime64[ps]"), "scalar_type": "DATETIME64[ps]"},
-    65547: {"dtype": np.dtype("datetime64[fs]"), "scalar_type": "DATETIME64[fs]"},
-    65548: {"dtype": np.dtype("datetime64[as]"), "scalar_type": "DATETIME64[as]"},
+    65_536: {"dtype": np.dtype("datetime64[Y]"), "scalar_type": "DATETIME64[Y]"},
+    65_537: {"dtype": np.dtype("datetime64[M]"), "scalar_type": "DATETIME64[M]"},
+    65_538: {"dtype": np.dtype("datetime64[W]"), "scalar_type": "DATETIME64[W]"},
+    65_539: {"dtype": np.dtype("datetime64[D]"), "scalar_type": "DATETIME64[D]"},
+    65_540: {"dtype": np.dtype("datetime64[h]"), "scalar_type": "DATETIME64[h]"},
+    65_541: {"dtype": np.dtype("datetime64[m]"), "scalar_type": "DATETIME64[m]"},
+    65_542: {"dtype": np.dtype("datetime64[s]"), "scalar_type": "DATETIME64[s]"},
+    65_543: {"dtype": np.dtype("datetime64[ms]"), "scalar_type": "DATETIME64[ms]"},
+    65_544: {"dtype": np.dtype("datetime64[us]"), "scalar_type": "DATETIME64[us]"},
+    65_545: {"dtype": np.dtype("datetime64[ns]"), "scalar_type": "DATETIME64[ns]"},
+    65_546: {"dtype": np.dtype("datetime64[ps]"), "scalar_type": "DATETIME64[ps]"},
+    65_547: {"dtype": np.dtype("datetime64[fs]"), "scalar_type": "DATETIME64[fs]"},
+    65_548: {"dtype": np.dtype("datetime64[as]"), "scalar_type": "DATETIME64[as]"},
 }
 serialize_lookup = {
     v["dtype"]: {"type_id": k, "scalar_type": v["scalar_type"]}
@@ -269,14 +269,13 @@ def pack_array(self, array):
         try:
             type_id = serialize_lookup[array.dtype]["type_id"]
         except KeyError:
+            # U is for unicode string
             if array.dtype.char == "U":
                 type_id = serialize_lookup[np.dtype("O")]["type_id"]
-                pass
             else:
-                raise DataJointError("Type %s is ambiguous or unknown" % array.dtype)
+                raise DataJointError(f"Type {array.dtype} is ambiguous or unknown")
 
         blob += np.array([type_id, is_complex], dtype=np.uint32).tobytes()
-        # array of dtype('O'), U is for unicode string
         if (
             array.dtype.char == "U"
             or serialize_lookup[array.dtype]["scalar_type"] == "VOID"
@@ -286,7 +285,6 @@ def pack_array(self, array):
                 for it in (self.pack_blob(e) for e in array.flatten(order="F"))
             )
             self.set_dj0()  # not supported by original mym
-        # array of dtype('c')
         elif serialize_lookup[array.dtype]["scalar_type"] == "CHAR":
             blob += (
                 array.view(np.uint8).astype(np.uint16).tobytes()

docs-parts/intro/Releases_lang1.rst

@@ -1,6 +1,6 @@
-0.13.7 -- TBD
---------------
-* Bugfix - Fix networkx incompatable change by version pinning to 2.8.3 PR #1036 (#1035)
+0.13.7 -- Jul 13, 2022
+----------------------
+* Bugfix - Fix networkx incompatable change by version pinning to 2.6.3 PR #1036 (#1035)
 * Add - Support for serializing numpy datetime64 types PR #1036 (#1022)
 * Update - Add traceback to default logging PR #1036
 

tests/test_blob.py

@@ -2,6 +2,7 @@
 import timeit
 import numpy as np
 import uuid
+from faker import Faker
 from . import schema
 from decimal import Decimal
 from datetime import datetime
@@ -232,17 +233,17 @@ def test_datetime_serialization_speed():
     # If this fails that means for some reason deserializing/serializing
     # 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)]))",
+    optimized_exe_time = timeit.timeit(
+        setup="myarr=pack(np.array([np.datetime64('2022-10-13 03:03:13') for _ in range(0, 10000)]))",
         stmt="unpack(myarr)",
         number=10,
         globals=globals(),
     )
-    python_array_dt_exe_time = timeit.timeit(
-        setup="myarr2=pack([datetime.now() for x in range (1900, 2000)])",
+    baseline_exe_time = timeit.timeit(
+        setup="myarr2=pack(np.array([datetime(2022,10,13,3,3,13) for _ in range (0, 10000)]))",
         stmt="unpack(myarr2)",
         number=10,
         globals=globals(),
     )
 
-    assert np_array_dt_exe_time < python_array_dt_exe_time
+    assert optimized_exe_time * 1000 < baseline_exe_time