Fix Numpy 1.20 warnings.

awkward0/array/base.py

@@ -375,11 +375,11 @@ def VirtualArray(self):
 
     @classmethod
     def _util_isinteger(cls, x):
-        return isinstance(x, (numbers.Integral, cls.numpy.integer)) and not isinstance(x, (bool, cls.numpy.bool_, cls.numpy.bool))
+        return isinstance(x, (numbers.Integral, cls.numpy.integer)) and not isinstance(x, (bool, cls.numpy.bool_))
 
     @classmethod
     def _util_isintegertype(cls, x):
-        return issubclass(x, cls.numpy.integer) and not issubclass(x, (cls.numpy.bool_, cls.numpy.bool))
+        return issubclass(x, cls.numpy.integer) and not issubclass(x, (bool, cls.numpy.bool_))
 
     @classmethod
     def _util_toarray(cls, value, defaultdtype, passthrough=None):
@@ -621,7 +621,7 @@ def _util_isstringslice(cls, where):
             return False
         elif isinstance(where, (cls.numpy.ndarray, AwkwardArray)) and issubclass(where.dtype.type, (numpy.str, numpy.str_)):
             return True
-        elif isinstance(where, (cls.numpy.ndarray, AwkwardArray)) and issubclass(where.dtype.type, (numpy.object, numpy.object_)) and not issubclass(where.dtype.type, (numpy.bool, numpy.bool_)):
+        elif isinstance(where, (cls.numpy.ndarray, AwkwardArray)) and issubclass(where.dtype.type, (numpy.object, numpy.object_)) and not issubclass(where.dtype.type, (bool, numpy.bool_)):
             return len(where) > 0 and all(isinstance(x, awkward0.util.string) for x in where)
         elif isinstance(where, (cls.numpy.ndarray, AwkwardArray)):
             return False

awkward0/array/chunked.py

@@ -479,7 +479,7 @@ def __getitem__(self, where):
                 else:
                     raise NotImplementedError
 
-            elif len(head.shape) == 1 and issubclass(head.dtype.type, (self.numpy.bool, self.numpy.bool_)):
+            elif len(head.shape) == 1 and issubclass(head.dtype.type, (bool, self.numpy.bool_)):
                 if len(self) != len(head):
                     raise IndexError("boolean index did not match indexed array along dimension 0; dimension is {0} but corresponding boolean dimension is {1}".format(len(self), len(head)))
 
@@ -735,7 +735,7 @@ def _prepare(self, ufunc, identity, dtype):
             if self._chunksizes[chunkid] > 0:
                 this = chunk[:self._chunksizes[chunkid]]
                 if out is None:
-                    if dtype is None and issubclass(this.dtype.type, (self.numpy.bool_, self.numpy.bool)):
+                    if dtype is None and issubclass(this.dtype.type, (bool, self.numpy.bool_)):
                         dtype = self.numpy.dtype(type(identity))
                     if dtype is None:
                         dtype = this.dtype

awkward0/array/indexed.py

@@ -570,7 +570,7 @@ def __getitem__(self, where):
 
             return self.copy(length=length, index=index, content=content)[tail]
 
-        elif isinstance(head, SparseArray) and len(head.shape) == 1 and issubclass(head.dtype.type, (self.numpy.bool, self.numpy.bool_)):
+        elif isinstance(head, SparseArray) and len(head.shape) == 1 and issubclass(head.dtype.type, (bool, self.numpy.bool_)):
             head._valid()
             if self._length != head._length:
                 raise IndexError("boolean index did not match indexed array along dimension 0; dimension is {0} but corresponding boolean dimension is {1}".format(self._length, head._length))
@@ -595,7 +595,7 @@ def __getitem__(self, where):
 
         else:
             head = self._util_toarray(head, self.INDEXTYPE)
-            if len(head.shape) == 1 and issubclass(head.dtype.type, (self.numpy.bool, self.numpy.bool_)):
+            if len(head.shape) == 1 and issubclass(head.dtype.type, (bool, self.numpy.bool_)):
                 if self._length != len(head):
                     raise IndexError("boolean index did not match indexed array along dimension 0; dimension is {0} but corresponding boolean dimension is {1}".format(self._length, len(head)))
 

awkward0/array/jagged.py

@@ -559,7 +559,7 @@ def __getitem__(self, where):
 
                 return self.copy(starts=head._starts, stops=head._stops, content=self._content[indexes])
 
-            elif len(head.shape) == 1 and issubclass(head._content.dtype.type, (self.numpy.bool, self.numpy.bool_)):
+            elif len(head.shape) == 1 and issubclass(head._content.dtype.type, (bool, self.numpy.bool_)):
                 try:
                     offsets = self.offsets
                     thyself = self
@@ -588,7 +588,7 @@ def __getitem__(self, where):
 
                 return self.copy(starts=offsets[:-1].reshape(intheadsum.shape), stops=offsets[1:].reshape(intheadsum.shape), content=thyself._content[headcontent])
 
-            elif head.shape == self.shape and issubclass(head._content.dtype.type, (self.numpy.bool, self.numpy.bool_)):
+            elif head.shape == self.shape and issubclass(head._content.dtype.type, (bool, self.numpy.bool_)):
                 index = self.localindex + self.starts
                 flatindex = index.flatten()[head.flatten()]
                 return type(self).fromcounts(head.sum(), self._content[flatindex])
@@ -747,7 +747,7 @@ def __getitem__(self, where):
                         index += node._starts
                         node = node._content[index]
 
-                elif len(head.shape) == 1 and issubclass(head.dtype.type, (self.numpy.bool, self.numpy.bool_)):
+                elif len(head.shape) == 1 and issubclass(head.dtype.type, (bool, self.numpy.bool_)):
                     if wasslice:
                         stack = []
                         for x in range(nslices):
@@ -826,7 +826,7 @@ def __setitem__(self, where, what):
 
                 self._content[indexes] = what
 
-            elif issubclass(where._content.dtype.type, (self.numpy.bool, self.numpy.bool_)):
+            elif issubclass(where._content.dtype.type, (bool, self.numpy.bool_)):
                 index = self.localindex + self.starts
                 flatindex = index.flatten()[where.flatten()]
                 self._content[flatindex] = what
@@ -982,7 +982,7 @@ def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
                 except TypeError:
                     pass
                 else:
-                    if "first" not in locals() or isinstance(first, (numbers.Number, self.numpy.bool_, self.numpy.bool, self.numpy.number)):
+                    if "first" not in locals() or isinstance(first, (numbers.Number, bool, self.numpy.bool_, self.numpy.number)):
                         inputs[i] = self.numpy.array(inputs[i], copy=False)
                     else:
                         inputs[i] = self.JaggedArray.fromiter(inputs[i])
@@ -1527,21 +1527,21 @@ def _reduce(self, ufunc, identity, dtype):
                 content = content.copy()
                 content[mask] = identity
 
-        if dtype is None and issubclass(content.dtype.type, (self.numpy.bool_, self.numpy.bool)):
+        if dtype is None and issubclass(content.dtype.type, (bool, self.numpy.bool_)):
             dtype = self.numpy.dtype(type(identity))
         if dtype is None:
             dtype = content.dtype
         else:
             content = content.astype(dtype)
 
         if identity == self.numpy.inf:
-            if issubclass(dtype.type, (self.numpy.bool_, self.numpy.bool)):
+            if issubclass(dtype.type, (bool, self.numpy.bool_)):
                 identity = True
             elif self._util_isintegertype(dtype.type):
                 identity = self.numpy.iinfo(dtype.type).max
 
         elif identity == -self.numpy.inf:
-            if issubclass(dtype.type, (self.numpy.bool_, self.numpy.bool)):
+            if issubclass(dtype.type, (bool, self.numpy.bool_)):
                 identity = False
             elif self._util_isintegertype(dtype.type):
                 identity = self.numpy.iinfo(dtype.type).min
@@ -1802,13 +1802,13 @@ def ready(x):
                     columns1[n] = x._content
                 elif isinstance(x, Iterable):
                     columns1[n] = first.tojagged(x)._content
-                elif isinstance(x, (numbers.Number, numpy.number, numpy.bool, numpy.bool_)):
+                elif isinstance(x, (numbers.Number, numpy.number, bool, numpy.bool_)):
                     columns1[n] = JaggedArray(first._starts, first._stops, numpy.full(first._stops.max(), columns1, dtype=type(columns1)))._content
                 else:
                     raise TypeError("unrecognized type for JaggedArray.zip: {0}".format(type(x)))
         elif isinstance(columns1, Iterable):
             columns1 = first.tojagged(columns1)._content
-        elif isinstance(columns1, (numbers.Number, numpy.number, numpy.bool, numpy.bool_)):
+        elif isinstance(columns1, (numbers.Number, numpy.number, bool, numpy.bool_)):
             columns1 = JaggedArray(first._starts, first._stops, numpy.full(first._stops.max(), columns1, dtype=type(columns1)))._content
         else:
             raise TypeError("unrecognized type for JaggedArray.zip: {0}".format(type(columns1)))
@@ -1819,7 +1819,7 @@ def ready(x):
                 columns2[i] = x._content
             elif not isinstance(x, dict) and isinstance(x, Iterable):
                 columns2[i] = first.tojagged(x)._content
-            elif isinstance(x, (numbers.Number, numpy.number, numpy.bool, numpy.bool_)):
+            elif isinstance(x, (numbers.Number, numpy.number, bool, numpy.bool_)):
                 columns2[i] = JaggedArray(first._starts, first._stops, numpy.full(first._stops.max(), x, dtype=type(x)))._content
             else:
                 raise TypeError("unrecognized type for JaggedArray.zip: {0}".format(type(x)))
@@ -1830,7 +1830,7 @@ def ready(x):
                 columns3[n] = x._content
             elif not isinstance(x, dict) and isinstance(x, Iterable):
                 columns3[n] = first.tojagged(x)._content
-            elif isinstance(x, (numbers.Number, numpy.number, numpy.bool, numpy.bool_)):
+            elif isinstance(x, (numbers.Number, numpy.number, bool, numpy.bool_)):
                 columns3[n] = JaggedArray(first._starts, first._stops, numpy.full(first._stops.max(), x, dtype=type(x)))._content
             else:
                 raise TypeError("unrecognized type for JaggedArray.zip: {0}".format(type(x)))

awkward0/array/masked.py

@@ -94,7 +94,7 @@ def mask(self, value):
         if self.check_prop_valid:
             if len(value.shape) != 1:
                 raise ValueError("mask must have 1-dimensional shape")
-        if not issubclass(value.dtype.type, (self.numpy.bool_, self.numpy.bool)):
+        if not issubclass(value.dtype.type, (bool, self.numpy.bool_)):
             value = (value != 0)
         self._mask = value
         self._isvalid = False
@@ -344,7 +344,7 @@ def _prepare(self, ufunc, identity, dtype):
             return out
 
         if isinstance(self._content, self.numpy.ndarray):
-            if dtype is None and issubclass(self._content.dtype.type, (self.numpy.bool_, self.numpy.bool)):
+            if dtype is None and issubclass(self._content.dtype.type, (bool, self.numpy.bool_)):
                 dtype = self.numpy.dtype(type(identity))
             if ufunc is None:
                 content = self.numpy.zeros(self._content.shape, dtype=self.numpy.float32)
@@ -370,12 +370,12 @@ def _prepare(self, ufunc, identity, dtype):
             dtype = content.dtype
 
             if identity == self.numpy.inf:
-                if issubclass(dtype.type, (self.numpy.bool_, self.numpy.bool)):
+                if issubclass(dtype.type, (bool, self.numpy.bool_)):
                     identity = True
                 elif self._util_isintegertype(dtype.type):
                     identity = self.numpy.iinfo(dtype.type).max
             elif identity == -self.numpy.inf:
-                if issubclass(dtype.type, (self.numpy.bool_, self.numpy.bool)):
+                if issubclass(dtype.type, (bool, self.numpy.bool_)):
                     identity = False
                 elif self._util_isintegertype(dtype.type):
                     identity = self.numpy.iinfo(dtype.type).min
@@ -503,7 +503,7 @@ def bool2bit(cls, boolmask, lsborder=False):
         boolmask = cls._util_toarray(boolmask, cls.MaskedArray.fget(None).MASKTYPE, cls.numpy.ndarray)
         if len(boolmask.shape) != 1:
             raise ValueError("boolmask must have 1-dimensional shape")
-        if not issubclass(boolmask.dtype.type, (cls.numpy.bool_, cls.numpy.bool)):
+        if not issubclass(boolmask.dtype.type, (bool, cls.numpy.bool_)):
             boolmask = (boolmask != 0)
 
         if lsborder:
@@ -627,7 +627,7 @@ def _maskwhere(self, where):
                 self.numpy.bitwise_and(bitmasks, self._mask[byteposes], bitmasks)
                 return bitmasks.astype(self.numpy.bool_)
 
-            elif len(where.shape) == 1 and issubclass(where.dtype.type, (self.numpy.bool, self.numpy.bool_)):
+            elif len(where.shape) == 1 and issubclass(where.dtype.type, (bool, self.numpy.bool_)):
                 # scales with the size of the mask anyway, so go ahead and unpack the whole mask
                 unpacked = self.numpy.unpackbits(self._mask).view(self.MASKTYPE)
 
@@ -868,7 +868,7 @@ def _prepare(self, ufunc, identity, dtype):
             return out
 
         if isinstance(self._content, self.numpy.ndarray):
-            if dtype is None and issubclass(self._content.dtype.type, (self.numpy.bool_, self.numpy.bool)):
+            if dtype is None and issubclass(self._content.dtype.type, (bool, self.numpy.bool_)):
                 dtype = self.numpy.dtype(type(identity))
             if dtype is None:
                 content = self._content
@@ -878,13 +878,13 @@ def _prepare(self, ufunc, identity, dtype):
             content = self._content._prepare(ufunc, identity, dtype)
 
         if identity == self.numpy.inf:
-            if issubclass(dtype.type, (self.numpy.bool_, self.numpy.bool)):
+            if issubclass(dtype.type, (bool, self.numpy.bool_)):
                 identity = True
             elif self._util_isintegertype(dtype.type):
                 identity = self.numpy.iinfo(dtype.type).max
 
         elif identity == -self.numpy.inf:
-            if issubclass(dtype.type, (self.numpy.bool_, self.numpy.bool)):
+            if issubclass(dtype.type, (bool, self.numpy.bool_)):
                 identity = False
             elif self._util_isintegertype(dtype.type):
                 identity = self.numpy.iinfo(dtype.type).min

awkward0/array/table.py

@@ -541,7 +541,7 @@ def _newslice(self, head):
                 else:
                     return self._view[head]
 
-            elif issubclass(head.dtype.type, (self.numpy.bool, self.numpy.bool_)):
+            elif issubclass(head.dtype.type, (bool, self.numpy.bool_)):
                 length = self._length()
                 if len(head) != length:
                     raise IndexError("boolean index of length {0} does not fit array of length {1}".format(len(head), length))
@@ -713,7 +713,7 @@ def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
             out = None
             for x in newcolumns.values():
                 assert isinstance(x, self.numpy.ndarray)
-                assert issubclass(x.dtype.type, (self.numpy.bool_, self.numpy.bool))
+                assert issubclass(x.dtype.type, (bool, self.numpy.bool_))
                 if out is None:
                     out = x
                 else:
@@ -856,7 +856,7 @@ def _prepare(self, ufunc, identity, dtype):
         out = self.copy(contents={})
         for n, x in self._contents.items():
             if isinstance(x, self.numpy.ndarray):
-                if dtype is None and issubclass(x.dtype.type, (self.numpy.bool_, self.numpy.bool)):
+                if dtype is None and issubclass(x.dtype.type, (bool, self.numpy.bool_)):
                     dtype = self.numpy.dtype(type(identity))
                 if dtype is not None:
                     x = x.astype(dtype)

awkward0/array/union.py

@@ -163,7 +163,7 @@ def contents(self, value):
 
     @classmethod
     def uniondtype(cls, arrays):
-        if all(issubclass(x.dtype.type, (cls.numpy.bool_, cls.numpy.bool)) for x in arrays):
+        if all(issubclass(x.dtype.type, (bool, cls.numpy.bool_)) for x in arrays):
             return cls.numpy.dtype(cls.numpy.bool_)
 
         elif all(issubclass(x.dtype.type, (cls.numpy.int8)) for x in arrays):
@@ -501,7 +501,7 @@ def _reduce(self, ufunc, identity, dtype):
         return ufunc.reduce(prepared)
 
     def _prepare(self, ufunc, identity, dtype):
-        if dtype is None and issubclass(self.dtype.type, (self.numpy.bool_, self.numpy.bool)):
+        if dtype is None and issubclass(self.dtype.type, (bool, self.numpy.bool_)):
             dtype = self.numpy.dtype(type(identity))
         if dtype is None:
             dtype = self.dtype

awkward0/array/virtual.py

@@ -205,7 +205,7 @@ def persistvirtual(self):
     @persistvirtual.setter
     def persistvirtual(self, value):
         if self.check_prop_valid:
-            if not isinstance(value, (bool, self.numpy.bool_, self.numpy.bool)):
+            if not isinstance(value, (bool, self.numpy.bool_)):
                 raise TypeError("persistvirtual must be boolean")
         self._persistvirtual = bool(value)
 
@@ -454,7 +454,7 @@ def _reduce(self, ufunc, identity, dtype):
     def _prepare(self, ufunc, identity, dtype):
         array = self.array
         if isinstance(array, self.numpy.ndarray):
-            if dtype is None and issubclass(array.dtype.type, (self.numpy.bool_, self.numpy.bool)):
+            if dtype is None and issubclass(array.dtype.type, (bool, self.numpy.bool_)):
                 dtype = self.numpy.dtype(type(identity))
             if dtype is None:
                 return array

awkward0/generate.py

@@ -19,7 +19,7 @@ def typeof(obj):
     if obj is None:
         return None
 
-    elif isinstance(obj, (bool, numpy.bool_, numpy.bool)):
+    elif isinstance(obj, (bool, numpy.bool_)):
         return BoolFillable
     elif isinstance(obj, (numbers.Number, awkward0.numpy.number)):
         return NumberFillable

awkward0/persist.py

@@ -24,7 +24,7 @@
 import awkward0.version
 
 compression = [
-        {"minsize": 8192, "types": [numpy.bool_, numpy.bool, numpy.integer], "contexts": "*", "pair": (zlib.compress, ("zlib", "decompress"))},
+        {"minsize": 8192, "types": [bool, numpy.bool_, numpy.integer], "contexts": "*", "pair": (zlib.compress, ("zlib", "decompress"))},
     ]
 
 whitelist = [
@@ -231,7 +231,7 @@ def jsonable(obj):
     elif isinstance(obj, str):
         return str(obj)
 
-    elif isinstance(obj, (bool, numpy.bool_, numpy.bool)):
+    elif isinstance(obj, (bool, numpy.bool_)):
         return bool(obj)      # policy: eliminate Numpy types
 
     elif isinstance(obj, (numbers.Integral, numpy.integer)):

awkward0/version.py

@@ -4,7 +4,7 @@
 
 import re
 
-__version__ = "0.15.4"
+__version__ = "0.15.5"
 version = __version__
 version_info = tuple(re.split(r"[-\.]", __version__))