英文:
Is there a numpy object containing all types?
问题
I'm writting a unit test of a function that only accepts numpy arrays of type numpy.uint8 and I wanted to test that I get the right exception from all the other types.
So I did a set like this:
self.supported_types = {np.uint8}self.not_supported_types = {np.bool_,np.int8,np.int16,np.uint16,np.int32,np.uint32,np.int64,np.uint64,np.longlong,np.ulonglong,np.float16,np.float32,np.float64,np.float128,np.complex64,np.complex128,np.complex256,np.object_,np.bytes_,np.str_,np.void,np.datetime64,np.timedelta64}
I was wondering if there's a way to get a list with all numpy's types without writing all of them as:
all_types = numpy.something() # does this exist?not_supported = all_types.remove(numpy.uint8)
英文:
I'm writting a unit test of a function that only accepts numpy arrays of type numpy.uint8 and I wanted to test that I get the right exception from all the other types.
So I did a set like this:
self.supported_types = {np.uint8}self.not_supported_types = {np.bool_,np.int8,np.int16,np.uint16,np.int32,np.uint32,np.int64,np.uint64,np.longlong,np.ulonglong,np.float16,np.float32,np.float64,np.float128,np.complex64,np.complex128,np.complex256,np.object_,np.bytes_,np.str_,np.void,np.datetime64,np.timedelta64}
I was wondering if there's a way to get a list with all numpy's types without writing all of them as:
all_types = numpy.something() # does this exist?not_supported = all_types.remove(numpy.uint8)
答案1
得分: 2
你可以尝试使用np.dtype.__subclasses__()：
[x.type for x in np.dtype.__subclasses__()]
输出：
[numpy.bool_,numpy.int8,numpy.uint8,numpy.int16,numpy.uint16,numpy.int32,numpy.uint32,numpy.int64,numpy.uint64,numpy.longlong,numpy.ulonglong,numpy.float32,numpy.float64,numpy.float128,numpy.complex64,numpy.complex128,numpy.complex256,numpy.object_,numpy.bytes_,numpy.str_,numpy.void,numpy.datetime64,numpy.timedelta64,numpy.float16,int,float,complex]
在你的情况下：
supported_types = {np.uint8}not_supported_types = ({x.type for x in np.dtype.__subclasses__()}- supported_types - {int, complex, float})
输出：
{numpy.bool_,numpy.bytes_,numpy.complex128,numpy.complex256,numpy.complex64,numpy.datetime64,numpy.float128,numpy.float16,numpy.float32,numpy.float64,numpy.int16,numpy.int32,numpy.int64,numpy.int8,numpy.longlong,numpy.object_,numpy.str_,numpy.timedelta64,numpy.uint16,numpy.uint32,numpy.uint64,numpy.ulonglong,numpy.void}
英文:
You can maybe use np.dtype.__subclasses__():
[x.type for x in np.dtype.__subclasses__()]
Output:
[numpy.bool_,numpy.int8,numpy.uint8,numpy.int16,numpy.uint16,numpy.int32,numpy.uint32,numpy.int64,numpy.uint64,numpy.longlong,numpy.ulonglong,numpy.float32,numpy.float64,numpy.float128,numpy.complex64,numpy.complex128,numpy.complex256,numpy.object_,numpy.bytes_,numpy.str_,numpy.void,numpy.datetime64,numpy.timedelta64,numpy.float16,int,float,complex]
In your case:
supported_types = {np.uint8}not_supported_types = ({x.type for x in np.dtype.__subclasses__()}- supported_types - {int, complex, float})
Output:
{numpy.bool_,numpy.bytes_,numpy.complex128,numpy.complex256,numpy.complex64,numpy.datetime64,numpy.float128,numpy.float16,numpy.float32,numpy.float64,numpy.int16,numpy.int32,numpy.int64,numpy.int8,numpy.longlong,numpy.object_,numpy.str_,numpy.timedelta64,numpy.uint16,numpy.uint32,numpy.uint64,numpy.ulonglong,numpy.void}
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