add np.datetime64 serialization and tests

CHANGELOG.md

@@ -1,5 +1,10 @@
 ## Release notes
 
+### 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
+
 ### 0.13.6 -- Jun 13, 2022
 * Add - Config option to set threshold for when to stop using checksums for filepath stores. PR #1025
 * Add - Unified package level logger for package (#667) PR #1031

datajoint/blob.py

@@ -14,32 +14,44 @@
 from .settings import config
 
 
-mxClassID = dict(
-    (
-        # see http://www.mathworks.com/help/techdoc/apiref/mxclassid.html
-        ("mxUNKNOWN_CLASS", None),
-        ("mxCELL_CLASS", None),
-        ("mxSTRUCT_CLASS", None),
-        ("mxLOGICAL_CLASS", np.dtype("bool")),
-        ("mxCHAR_CLASS", np.dtype("c")),
-        ("mxVOID_CLASS", np.dtype("O")),
-        ("mxDOUBLE_CLASS", np.dtype("float64")),
-        ("mxSINGLE_CLASS", np.dtype("float32")),
-        ("mxINT8_CLASS", np.dtype("int8")),
-        ("mxUINT8_CLASS", np.dtype("uint8")),
-        ("mxINT16_CLASS", np.dtype("int16")),
-        ("mxUINT16_CLASS", np.dtype("uint16")),
-        ("mxINT32_CLASS", np.dtype("int32")),
-        ("mxUINT32_CLASS", np.dtype("uint32")),
-        ("mxINT64_CLASS", np.dtype("int64")),
-        ("mxUINT64_CLASS", np.dtype("uint64")),
-        ("mxFUNCTION_CLASS", None),
-    )
-)
-
-rev_class_id = {dtype: i for i, dtype in enumerate(mxClassID.values())}
-dtype_list = list(mxClassID.values())
-type_names = list(mxClassID)
+deserialize_lookup = {
+    0: {"dtype": None, "scalar_type": "UNKNOWN"},
+    1: {"dtype": None, "scalar_type": "CELL"},
+    2: {"dtype": None, "scalar_type": "STRUCT"},
+    3: {"dtype": np.dtype("bool"), "scalar_type": "LOGICAL"},
+    4: {"dtype": np.dtype("c"), "scalar_type": "CHAR"},
+    5: {"dtype": np.dtype("O"), "scalar_type": "VOID"},
+    6: {"dtype": np.dtype("float64"), "scalar_type": "DOUBLE"},
+    7: {"dtype": np.dtype("float32"), "scalar_type": "SINGLE"},
+    8: {"dtype": np.dtype("int8"), "scalar_type": "INT8"},
+    9: {"dtype": np.dtype("uint8"), "scalar_type": "UINT8"},
+    10: {"dtype": np.dtype("int16"), "scalar_type": "INT16"},
+    11: {"dtype": np.dtype("uint16"), "scalar_type": "UINT16"},
+    12: {"dtype": np.dtype("int32"), "scalar_type": "INT32"},
+    13: {"dtype": np.dtype("uint32"), "scalar_type": "UINT32"},
+    14: {"dtype": np.dtype("int64"), "scalar_type": "INT64"},
+    15: {"dtype": np.dtype("uint64"), "scalar_type": "UINT64"},
+    16: {"dtype": None, "scalar_type": "FUNCTION"},
+    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"]}
+    for k, v in deserialize_lookup.items()
+    if v["dtype"] is not None
+}
+
 
 compression = {b"ZL123\0": zlib.decompress}
 
@@ -176,7 +188,7 @@ def pack_blob(self, obj):
                 return self.pack_float(obj)
         if isinstance(obj, np.ndarray) and obj.dtype.fields:
             return self.pack_recarray(np.array(obj))
-        if isinstance(obj, np.number):
+        if isinstance(obj, (np.number, np.datetime64)):
             return self.pack_array(np.array(obj))
         if isinstance(obj, (bool, np.bool_)):
             return self.pack_array(np.array(obj))
@@ -211,14 +223,18 @@ def read_array(self):
         shape = self.read_value(count=n_dims)
         n_elem = np.prod(shape, dtype=int)
         dtype_id, is_complex = self.read_value("uint32", 2)
-        dtype = dtype_list[dtype_id]
 
-        if type_names[dtype_id] == "mxVOID_CLASS":
+        # Get dtype from type id
+        dtype = deserialize_lookup[dtype_id]["dtype"]
+
+        # Check if name is void
+        if deserialize_lookup[dtype_id]["scalar_type"] == "VOID":
             data = np.array(
                 list(self.read_blob(self.read_value()) for _ in range(n_elem)),
                 dtype=np.dtype("O"),
             )
-        elif type_names[dtype_id] == "mxCHAR_CLASS":
+        # Check if name is char
+        elif deserialize_lookup[dtype_id]["scalar_type"] == "CHAR":
             # compensate for MATLAB packing of char arrays
             data = self.read_value(dtype, count=2 * n_elem)
             data = data[::2].astype("U1")
@@ -240,6 +256,8 @@ def pack_array(self, array):
         """
         Serialize an np.ndarray into bytes.  Scalars are encoded with ndim=0.
         """
+        if "datetime64" in array.dtype.name:
+            self.set_dj0()
         blob = (
             b"A"
             + np.uint64(array.ndim).tobytes()
@@ -248,22 +266,26 @@ def pack_array(self, array):
         is_complex = np.iscomplexobj(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")]
-        )
-        if dtype_list[type_id] is None:
-            raise DataJointError("Type %s is ambiguous or unknown" % array.dtype)
+        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"]
+            else:
+                raise DataJointError(f"Type {array.dtype} is ambiguous or unknown")
 
         blob += np.array([type_id, is_complex], dtype=np.uint32).tobytes()
-        if type_names[type_id] == "mxVOID_CLASS":  # array of dtype('O')
+        if (
+            array.dtype.char == "U"
+            or serialize_lookup[array.dtype]["scalar_type"] == "VOID"
+        ):
             blob += b"".join(
                 len_u64(it) + it
                 for it in (self.pack_blob(e) for e in array.flatten(order="F"))
             )
             self.set_dj0()  # not supported by original mym
-        elif type_names[type_id] == "mxCHAR_CLASS":  # array of dtype('c')
+        elif serialize_lookup[array.dtype]["scalar_type"] == "CHAR":
             blob += (
                 array.view(np.uint8).astype(np.uint16).tobytes()
             )  # convert to 16-bit chars for MATLAB

datajoint/logging.py

@@ -21,12 +21,7 @@ def excepthook(exc_type, exc_value, exc_traceback):
         sys.__excepthook__(exc_type, exc_value, exc_traceback)
         return
 
-    if logger.getEffectiveLevel() == 10:
-        logger.debug(
-            "Uncaught exception", exc_info=(exc_type, exc_value, exc_traceback)
-        )
-    else:
-        logger.error(f"Uncaught exception: {exc_value}")
+    logger.error("Uncaught exception", exc_info=(exc_type, exc_value, exc_traceback))
 
 
 sys.excepthook = excepthook

datajoint/version.py

@@ -1,3 +1,3 @@
-__version__ = "0.13.6"
+__version__ = "0.13.7"
 
 assert len(__version__) <= 10  # The log table limits version to the 10 characters

docs-parts/intro/Releases_lang1.rst

@@ -1,3 +1,9 @@
+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
+
 0.13.6 -- Jun 13, 2022
 ----------------------
 * Add - Config option to set threshold for when to stop using checksums for filepath stores. PR #1025

requirements.txt

@@ -4,7 +4,7 @@ pyparsing
 ipython
 pandas
 tqdm
-networkx
+networkx<=2.6.3
 pydot
 minio>=7.0.0
 matplotlib

tests/test_blob.py

@@ -1,4 +1,5 @@
 import datajoint as dj
+import timeit
 import numpy as np
 import uuid
 from . import schema
@@ -149,6 +150,9 @@ def test_pack():
         x == unpack(pack(x)), "Numpy string array object did not pack/unpack correctly"
     )
 
+    x = np.datetime64("1998").astype("datetime64[us]")
+    assert_true(x == unpack(pack(x)))
+
 
 def test_recarrays():
     x = np.array([(1.0, 2), (3.0, 4)], dtype=[("x", float), ("y", int)])
@@ -222,3 +226,25 @@ def test_insert_longblob():
     }
     (schema.Longblob & "id=1").delete()
     dj.blob.use_32bit_dims = False
+
+
+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
+
+    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(),
+    )
+    print(f"np time {optimized_exe_time}")
+    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(),
+    )
+    print(f"python time {baseline_exe_time}")
+
+    assert optimized_exe_time * 1000 < baseline_exe_time