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can_cast

Check whether a cast between data types is possible according to the given casting rule. 
function can_cast(
  from_: ArrayLike | DType,
  to: DType,
  casting?: 'no' | 'equiv' | 'safe' | 'same_kind' | 'unsafe'
): boolean
ParameterTypeDefaultDescription
from_ArrayLike | DType—Source value or data type.
toDType—Target data type.
casting'no' | 'equiv' | 'safe' | 'same_kind' | 'unsafe''safe'Casting rule. 'no' allows no casting, 'equiv' allows byte-order changes, 'safe' allows only casts that preserve values, 'same_kind' allows safe casts and casts within a kind (e.g. float to float), 'unsafe' allows all casts.
Returns: boolean — true if the cast is allowed under the given rule. 
import { can_cast } from 'numpy-ts';

can_cast('int32', 'float64');                // true  (safe)
can_cast('float64', 'int32');                // false (not safe)
can_cast('float64', 'int32', 'unsafe');      // true
can_cast('float32', 'float64', 'same_kind'); // true
can_cast('int32', 'int32', 'no');            // true

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common_type

Return the common data type that can represent all input arrays without loss of precision. 
function common_type(...arrays: ArrayLike[]): DType
ParameterTypeDefaultDescription
...arraysArrayLike[]—One or more input arrays.
Returns: DType — The common type that can hold values from all input arrays. 
import { array, common_type } from 'numpy-ts';

const a = array([1, 2], 'float32');
const b = array([3, 4], 'int32');

common_type(a, b);    // 'float64'
common_type(a);       // 'float32'

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result_type

Determine the result data type from a set of arrays and/or dtypes, applying NumPy’s type promotion rules. 
function result_type(...arrays_and_dtypes: (ArrayLike | DType)[]): DType
ParameterTypeDefaultDescription
...arrays_and_dtypes(ArrayLike | DType)[]—Arrays and/or dtype strings.
Returns: DType — The dtype that would result from an operation on the given inputs. 
import { array, result_type } from 'numpy-ts';

result_type('int32', 'float32');           // 'float64'
result_type('float32', 'float32');         // 'float32'
result_type(array([1, 2], 'int16'), 'int32'); // 'int32'

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min_scalar_type

Return the minimum-size dtype that can hold the given scalar value without overflow or loss of precision. 
function min_scalar_type(val: number | bigint | boolean): DType
ParameterTypeDefaultDescription
valnumber | bigint | boolean—A scalar value.
Returns: DType — The smallest dtype capable of representing the value. 
import { min_scalar_type } from 'numpy-ts';

min_scalar_type(0);       // 'uint8'
min_scalar_type(255);     // 'uint8'
min_scalar_type(256);     // 'uint16'
min_scalar_type(-1);      // 'int8'
min_scalar_type(3.14);    // 'float64'

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issubdtype

Check whether the first dtype is a subtype of the second. This follows NumPy’s type hierarchy where, for example, all integer types are subtypes of 'integer' and all floating types are subtypes of 'floating'. 
function issubdtype(dtype1: ArrayLike | DType, dtype2: DType | string): boolean
ParameterTypeDefaultDescription
dtype1ArrayLike | DType—The dtype (or array-like value) to test.
dtype2DType | string—The dtype or abstract type category to test against (e.g. 'integer', 'floating', 'signedinteger', 'unsignedinteger', 'number').
Returns: boolean — true if arg1 is a subtype of arg2. 
import { issubdtype } from 'numpy-ts';

issubdtype('int32', 'integer');           // true
issubdtype('float64', 'floating');        // true
issubdtype('int32', 'floating');          // false
issubdtype('uint8', 'unsignedinteger');   // true
issubdtype('float32', 'number');          // true
issubdtype('bool', 'integer');            // true

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typename

Return a human-readable type name for a numeric type code. 
function typename(dtype: DType): string
ParameterTypeDefaultDescription
dtypeDType—Dtype code to resolve.
Returns: string — A descriptive string for the type code. 
import { typename } from 'numpy-ts';

typename(1);   // 'int8'
typename(5);   // 'float32'
typename(12);  // 'float64'

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mintypecode

Return the minimum-size type character from the given type characters that can hold all of them. Optionally restricted to a given set of types. 
function mintypecode(
  typechars: string,
  typeset?: string,
  default_?: string
): string
ParameterTypeDefaultDescription
typecharsstring—String of type character codes.
typesetstring'GDFgdf'Set of allowed type characters.
default_string'd'Default type character returned when typechars is empty.
Returns: string — The minimum type character that encompasses all inputs. 
import { mintypecode } from 'numpy-ts';

mintypecode(['f', 'd']);    // 'd'  (float64 needed for float32 + float64)
mintypecode(['i', 'f']);    // 'd'  (float64 covers int + float32)
mintypecode([]);            // 'd'  (default)