Data Types and Data Storage

This section describes the data types and data storage modes of the KML_NUMPY library functions.

Type	Description	Remarks
bool__	Boolean data type (true or false)	A NumPy data type is actually an instance of the dtype object and can correspond to a unique character, for example, numpy.bool__, numpy.int32, or numpy.float64.
int__	Default integer type (similar to long, int32, or int64 in C language)
intc	Same as the int type of C, generally int32 or int64
intp	Integer type used for indexing (similar to ssize_t of C, which is still int32 or int64 in general)
int8	Byte (-128 to 127)
int16	Integer (-32768 to 32767)
int32	Integer (-2147483648 to 2147483647)
int64	Integer (-9223372036854775808 to 9223372036854775807)
uint8	Unsigned integer (0 to 255)
uint16	Unsigned integer (0 to 65535)
uint32	Unsigned integer (0 to 4294967295)
uint64	Unsigned integer (0 to 18446744073709551615)
float__	Abbreviation of the float64 type
float16	Half-precision floating point number, including one sign bit, five exponent bits, and 10 mantissa bits
float32	Single-precision floating point number, including one sign bit, eight exponent bits, and 23 mantissa bits
float64	Double-precision floating point number, including 1 sign bit, 11 exponent bits, and 52 mantissa bits
complex__	Abbreviation of the complex128 type, that is, 128-bit complex number
complex64	Complex number, represented by two 32-bit floats (real and imaginary parts)
complex128	Complex number, represented by two 64-bit floats (real and imaginary parts)

array_like can be the first argument that passes numpy.array() to create any content of an array.
Optional: ndarray: multi-dimensional array that stores data of a single type. You can use indexes (starting from 0) to access elements in an ndarray object. Any array_like object can be converted to an ndarray object using the numpy.array() function.
scalar: A scalar may be any data type listed in NumPy Data Types.

Parent topic: KML_NUMPY Library Functions