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Merge branch 'develop' of https://github.com/stdlib-js/stdlib into develop
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‎docs/migration-guides/numpy/README.md‎

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| Filter an array according to a predicate function | `x[np.vectorize(predicate)(x)]` | [`filter(x, predicate)`][@stdlib/ndarray/filter] |
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| Find the index of the first element which equals a specified value | `np.argmax(x == v, axis=dim)` | [`indexOf(x, v, {dim: dim})`][@stdlib/blas/ext/index-of] |
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| Find the index of the last element which equals a specified value | `x.shape[dim]-1-np.argmax(np.flip(x, axis=dim) == v, axis=dim)` | [`lastIndexOf(x, v, {dim: dim})`][@stdlib/blas/ext/last-index-of] |
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| Flatten an array to a desired depth | `np.reshape(x, newshape)`] | [`flatten(x, {depth: depth})`][@stdlib/ndarray/flatten] |
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| Flatten an array to a desired depth | `np.reshape(x, newshape)` | [`flatten(x, {depth: depth})`][@stdlib/ndarray/flatten] |
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| Flatten an array starting from a specific dimension | `np.reshape(x, x.shape[:dim] + (-1,))` | [`flattenFrom(x, dim)`][@stdlib/ndarray/flatten-from] |
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| Prepend a specified number of singleton dimensions | `np.reshape(x, (1,)*n + x.shape)` | [`prependSingletonDimensions(x, n)`][@stdlib/ndarray/prepend-singleton-dimensions] |
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| Prepend a zero-filled array of the same shape along a specified dimension | `np.concat((np.zeros_like(x), x), axis=dim)` | [`concat([zerosLike(x), x], {dim: dim})`][@stdlib/ndarray/concat] |

‎lib/node_modules/@stdlib/blas/base/ndarray/caxpy/README.md‎

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The function has the following parameters:
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- **arrays**: array-like object containing the following ndarrays in order:
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- **arrays**: array-like object containing the following ndarrays:
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- input ndarray
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- output ndarray
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- zero-dimensional ndarray containing a scalar constant
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- a one-dimensional input ndarray.
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- a one-dimensional output ndarray.
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- a zero-dimensional ndarray containing a scalar constant.
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</section>
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‎lib/node_modules/@stdlib/blas/base/ndarray/caxpy/docs/repl.txt‎

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Parameters
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----------
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arrays: ArrayLikeObject<ndarray>
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Array-like object containing an input ndarray, an output ndarray, and
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a zero-dimensional ndarray containing a scalar constant `alpha`.
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Array-like object containing the following ndarrays:
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- a one-dimensional input ndarray.
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- a one-dimensional output ndarray.
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- a zero-dimensional ndarray containing a scalar constant `alpha`.
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Returns
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-------

‎lib/node_modules/@stdlib/blas/base/ndarray/caxpy/docs/types/index.d.ts‎

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/**
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* Multiplies a one-dimensional single-precision complex floating-point ndarray `x` by a constant `alpha` and adds the result to a one-dimensional single-precision complex floating-point ndarray `y`.
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*
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* @param arrays - array-like object containing an input ndarray, an output ndarray, and a zero-dimensional ndarray containing a scalar constant
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* ## Notes
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*
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* - The function expects the following ndarrays:
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*
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* - a one-dimensional input ndarray.
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* - a one-dimensional output ndarray.
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* - a zero-dimensional ndarray containing a scalar constant.
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*
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* @param arrays - array-like object containing ndarrays
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* @returns output ndarray
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*
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* @example

‎lib/node_modules/@stdlib/blas/base/ndarray/caxpy/lib/main.js‎

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/**
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* Multiplies a one-dimensional single-precision complex floating-point ndarray `x` by a constant `alpha` and adds the result to a one-dimensional single-precision complex floating-point ndarray `y`.
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*
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* @param {ArrayLikeObject<Object>} arrays - array-like object containing an input ndarray, an output ndarray, and a zero-dimensional ndarray containing a scalar constant
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* ## Notes
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*
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* - The function expects the following ndarrays:
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*
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* - a one-dimensional input ndarray.
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* - a one-dimensional output ndarray.
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* - a zero-dimensional ndarray containing a scalar constant.
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*
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* @param {ArrayLikeObject<Object>} arrays - array-like object containing ndarrays
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* @returns {Object} output ndarray
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*
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* @example

‎lib/node_modules/@stdlib/blas/base/ndarray/ccopy/README.md‎

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The function has the following parameters:
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- **arrays**: array-like object containing the following ndarrays in order:
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- **arrays**: array-like object containing the following ndarrays:
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- input ndarray
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- output ndarray
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- a one-dimensional input ndarray.
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- a one-dimensional output ndarray.
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</section>
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‎lib/node_modules/@stdlib/blas/base/ndarray/ccopy/docs/repl.txt‎

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Parameters
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----------
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arrays: ArrayLikeObject<ndarray>
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Array-like object containing an input ndarray and an output ndarray.
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Array-like object containing the following ndarrays:
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- a one-dimensional input ndarray.
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- a one-dimensional output ndarray.
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-------

‎lib/node_modules/@stdlib/blas/base/ndarray/ccopy/docs/types/index.d.ts‎

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/**
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* Copies values from a one-dimensional single-precision complex floating-point ndarray `x` into a one-dimensional single-precision complex floating-point ndarray `y`.
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*
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* @param arrays - array-like object containing an input ndarray and an output ndarray
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* ## Notes
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*
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* - The function expects the following ndarrays:
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*
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* - a one-dimensional input ndarray.
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* - a one-dimensional output ndarray.
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*
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* @param arrays - array-like object containing ndarrays
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* @returns output ndarray
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*
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* @example

‎lib/node_modules/@stdlib/blas/base/ndarray/ccopy/lib/main.js‎

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/**
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* Copies values from a one-dimensional single-precision complex floating-point ndarray `x` into a one-dimensional single-precision complex floating-point ndarray `y`.
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*
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* @param {ArrayLikeObject<Object>} arrays - array-like object containing an input ndarray and an output ndarray
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* ## Notes
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*
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* - The function expects the following ndarrays:
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*
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* - a one-dimensional input ndarray.
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* - a one-dimensional output ndarray.
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*
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* @param {ArrayLikeObject<Object>} arrays - array-like object containing ndarrays
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* @returns {Object} output ndarray
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*
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* @example

‎lib/node_modules/@stdlib/blas/base/ndarray/dasum/docs/types/index.d.ts‎

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* Computes the sum of absolute values for all elements in a one-dimensional double-precision floating-point ndarray.
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*
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* @param arrays - array-like object containing a one-dimensional input ndarray
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* ## Notes
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*
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* - The function expects the following ndarrays:
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*
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* - a one-dimensional input ndarray.
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*
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* @param arrays - array-like object containing ndarrays
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* @returns sum
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* @example

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