Documentation Index
Fetch the complete documentation index at: https://numpyts.dev/llms.txt
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take
Take elements from an array along an axis.
function take(a: ArrayLike, indices: number[], axis?: number): NDArray
| Name | Type | Default | Description |
|---|
a | ArrayLike | — | Source array. |
indices | ArrayLike | — | Indices of the values to extract. |
axis | number | undefined | Axis along which to select values. When undefined, the input array is flattened before indexing. |
Returns: NDArray — Array of values taken from a at the specified indices.
import * as np from 'numpy-ts';
const a = np.array([10, 20, 30, 40, 50]);
np.take(a, [0, 2, 4]);
// array([10, 30, 50])
const mat = np.array([[1, 2], [3, 4], [5, 6]]);
np.take(mat, [0, 2], 0);
// array([[1, 2],
// [5, 6]])
put
Replace specified elements of an array with given values.
function put(
a: NDArray,
indices: number[],
values: ArrayLike
): void
| Name | Type | Default | Description |
|---|
a | NDArray | — | Target array. Modified in-place. |
indices | number[] | — | Flat indices of elements to replace. |
values | ArrayLike | — | Values to place at the target indices. If shorter than indices, values are repeated cyclically. |
Returns: void — The array a is modified in-place.
import * as np from 'numpy-ts';
const a = np.array([0, 0, 0, 0, 0]);
np.put(a, [1, 3], [10, 20]);
// a is now array([0, 10, 0, 20, 0])
choose
Construct an array from an index array and a list of arrays to choose from.
function choose(a: ArrayLike, choices: ArrayLike[]): NDArray
| Name | Type | Default | Description |
|---|
a | ArrayLike | — | Index array. Each value selects which array in choices to take the corresponding element from. |
choices | ArrayLike[] | — | List of arrays to choose from. All must be broadcastable to the same shape. |
Returns: NDArray — Array formed by picking elements from choices according to a.
import * as np from 'numpy-ts';
const choices = [
np.array([0, 1, 2, 3]),
np.array([10, 11, 12, 13]),
np.array([20, 21, 22, 23]),
];
const idx = np.array([0, 2, 1, 0]);
np.choose(idx, choices);
// array([0, 21, 12, 3])
compress
Return selected slices of an array along a given axis.
function compress(condition: ArrayLike, a: ArrayLike, axis?: number): NDArray
| Name | Type | Default | Description |
|---|
condition | ArrayLike | — | Boolean array selecting which entries to keep. Its length must match the size of a along the given axis. |
a | ArrayLike | — | Array from which to extract. |
axis | number | undefined | Axis along which to operate. When undefined, the array is flattened first. |
Returns: NDArray — Array containing only the slices of a where condition is true.
import * as np from 'numpy-ts';
const a = np.array([[1, 2], [3, 4], [5, 6]]);
np.compress([true, false, true], a, 0);
// array([[1, 2],
// [5, 6]])
np.compress([true, false], a, 1);
// array([[1],
// [3],
// [5]])
select
Return an array drawn from elements in choicelist depending on conditions.
function select(
condlist: ArrayLike[],
choicelist: ArrayLike[],
defaultVal?: number
): NDArray
| Name | Type | Default | Description |
|---|
condlist | ArrayLike[] | — | List of boolean arrays. The first condition that is true determines which choice is used. |
choicelist | ArrayLike[] | — | List of arrays from which output elements are taken. Must be the same length as condlist. |
defaultVal | number | 0 | Value used when no condition is satisfied. |
Returns: NDArray — Array where element i is taken from choicelist[j][i] for the first j where condlist[j][i] is true, or default_ if no condition is true.
import * as np from 'numpy-ts';
const x = np.arange(6);
const result = np.select(
[np.less(x, 2), np.greater(x, 3)],
[x, np.multiply(x, 10)],
-1
);
// array([0, 1, -1, -1, 40, 50])
place
Change elements of an array based on a boolean mask and replacement values.
function place(a: NDArray, mask: ArrayLike, vals: ArrayLike): void
| Name | Type | Default | Description |
|---|
a | NDArray | — | Array to modify in-place. |
mask | ArrayLike | — | Boolean mask. Must be broadcastable to a. |
vals | ArrayLike | — | Values to place into a where mask is true. Cycled if shorter than the number of true values. |
Returns: void — The array arr is modified in-place.
import * as np from 'numpy-ts';
const a = np.array([0, 1, 2, 3, 4]);
np.place(a, np.greater(a, 2), [10, 20]);
// a is now array([0, 1, 2, 10, 20])
putmask
Change elements of an array based on a boolean mask.
function putmask(a: NDArray, mask: ArrayLike, values: ArrayLike): void
| Name | Type | Default | Description |
|---|
a | NDArray | — | Target array. Modified in-place. |
mask | ArrayLike | — | Boolean mask. Where true, a is replaced by the corresponding element from values. |
values | ArrayLike | — | Replacement values. Cycled if shorter than the number of true entries in mask. |
Returns: void — The array a is modified in-place.
import * as np from 'numpy-ts';
const a = np.array([1, 2, 3, 4, 5]);
np.putmask(a, np.array([true, false, true, false, true]), [10, 20, 30]);
// a is now array([10, 2, 20, 4, 30])
take_along_axis
Take values from an array by matching indices along an axis.
function take_along_axis(arr: ArrayLike, indices: ArrayLike, axis: number): NDArray
| Name | Type | Default | Description |
|---|
arr | ArrayLike | — | Source array. |
indices | ArrayLike | — | Indices to take along each slice of arr. Must have the same number of dimensions as arr. |
axis | number | — | The axis to take along. |
Returns: NDArray — Array of values gathered from arr at the given indices along axis.
import * as np from 'numpy-ts';
const a = np.array([[10, 30, 20], [60, 40, 50]]);
const idx = np.argsort(a, 1);
np.take_along_axis(a, idx, 1);
// array([[10, 20, 30],
// [40, 50, 60]])
put_along_axis
Put values into an array by matching indices along an axis.
function put_along_axis(
arr: NDArray,
indices: ArrayLike,
values: ArrayLike,
axis: number
): void
| Name | Type | Default | Description |
|---|
arr | NDArray | — | Destination array. Modified in-place. |
indices | ArrayLike | — | Indices along axis where values are placed. |
values | ArrayLike | — | Values to insert. Must be broadcastable to indices. |
axis | number | — | The axis along which to put values. |
Returns: void — The array arr is modified in-place.
import * as np from 'numpy-ts';
const a = np.zeros([2, 3]);
const idx = np.array([[1], [2]]);
np.put_along_axis(a, idx, 99, 1);
// a is now array([[0, 99, 0],
// [0, 0, 99]])
copyto
Copy values from one array to another.
function copyto(dst: NDArray, src: ArrayLike | number | bigint): void
| Name | Type | Default | Description |
|---|
dst | NDArray | — | Destination array. Modified in-place. |
src | ArrayLike | number | bigint | — | Source array or scalar. Must be broadcastable to dst. |
Returns: void — The array dst is modified in-place.
import * as np from 'numpy-ts';
const dst = np.zeros([3]);
np.copyto(dst, np.array([1, 2, 3]));
// dst is now array([1, 2, 3])
broadcast_to
Broadcast an array to a new shape without copying data.
function broadcast_to(a: ArrayLike, shape: number[]): NDArray
| Name | Type | Default | Description |
|---|
a | ArrayLike | — | The array to broadcast. |
shape | number[] | — | Target shape. Must be compatible with the input shape under NumPy broadcasting rules. |
Returns: NDArray — A read-only view of the original array with the given shape.
import * as np from 'numpy-ts';
const a = np.array([1, 2, 3]);
const b = np.broadcast_to(a, [3, 3]);
// array([[1, 2, 3],
// [1, 2, 3],
// [1, 2, 3]])
broadcast_arrays
Broadcast any number of arrays against each other.
function broadcast_arrays(...arrays: ArrayLike[]): NDArray[]
| Name | Type | Default | Description |
|---|
...arrays | ArrayLike[] | — | Arrays to broadcast. |
Returns: NDArray[] — List of arrays with the same shape, broadcast from the inputs.
import * as np from 'numpy-ts';
const a = np.array([[1], [2], [3]]); // shape [3, 1]
const b = np.array([10, 20]); // shape [2]
const [ba, bb] = np.broadcast_arrays(a, b);
// ba: array([[1, 1], [2, 2], [3, 3]]) -- shape [3, 2]
// bb: array([[10, 20], [10, 20], [10, 20]]) -- shape [3, 2]
broadcast_shapes
Compute the shape resulting from broadcasting the given shapes together.
function broadcast_shapes(...shapes: number[][]): number[]
| Name | Type | Default | Description |
|---|
...shapes | number[][] | — | Array shapes to broadcast together. |
Returns: number[] — The broadcast shape.
Throws: Error if the shapes are not compatible for broadcasting.
import * as np from 'numpy-ts';
np.broadcast_shapes([3, 1], [1, 4]);
// [3, 4]
np.broadcast_shapes([5], [3, 5], [1, 3, 5]);
// [1, 3, 5]