NumCpp: A Templatized Header Only C++ Implementation of the Python NumPy Library

Author: David Pilger dpilger26@gmail.com

Version: 2.1.0

License

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files(the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Testing

C++ Standards:
C++14
C++17
C++2a

Compilers:
Visual Studio: 2017, 2019
GNU: 6.5, 7.5, 8.4, 9.3, 10.1
Clang: 6, 7, 8, 9, 10

Boost Versions:
1.68, 1.70, 1.72, and 1.73

Documentation

GitHub

Installation

Building

Release Notes

Version 2.1.0

Improved installation and usage with CMake find_package support
Various minor improvements

Version 2.0.0

Dropped support of C++11, now requires a C++14 or higher compiler
Added support for std::complex<T>, closing Issue #58
Added more NdArray constructors for STL containers including std::vector<std::vector<T>>, closing Issue #59
Added polyfit routine inline with Numpy polyfit, closing Issue #61
Added ability to use NdArray as container for generic structs
Non-linear least squares fitting using Gauss-Newton
Root finding routines
Numerical integration routines
lu_decomposition and pivotLU_decomposition added to Linalg namespace
New STL iterators added to NdArray
- iterator
- const_iterator
- reverse_iterator
- const_reverse_iterator
- column_iterator
- const_column_iterator
- reverse_column_iterator
- const_reverse_column_iterator
Added rodriguesRotation and wahbasProblem to Rotations namespace
Various efficiency and/or bug fixes

From NumPy To NumCpp – A Quick Start Guide

This quick start guide is meant as a very brief overview of some of the things that can be done with NumCpp. For a full breakdown of everything available in the NumCpp library please visit the Full Documentation.

CONTAINERS

The main data structure in NumCpp is the NdArray. It is inherently a 2D array class, with 1D arrays being implemented as 1xN arrays. There is also a DataCube class that is provided as a convenience container for storing an array of 2D NdArrays, but it has limited usefulness past a simple container.

NumPy	NumCpp
`a = np.array([[1, 2], [3, 4], [5, 6]])`	`nc::NdArray<int> a = { {1, 2}, {3, 4}, {5, 6} }`
`a.reshape([2, 3])`	`a.reshape(2, 3)`
`a.astype(np.double)`	`a.astype<double>()`

INITIALIZERS

Many initializer functions are provided that return NdArrays for common needs.

NumPy	NumCpp
`np.linspace(1, 10, 5)`	`nc::linspace<dtype>(1, 10, 5)`
`np.arange(3, 7)`	`nc::arange<dtype>(3, 7)`
`np.eye(4)`	`nc::eye<dtype>(4)`
`np.zeros([3, 4])`	`nc::zeros<dtype>(3, 4)`
	`nc::NdArray<dtype>(3, 4) a = 0`
`np.ones([3, 4])`	`nc::ones<dtype>(3, 4)`
	`nc::NdArray<dtype>(3, 4) a = 1`
`np.nans([3, 4])`	`nc::nans(3, 4)`
	`nc::NdArray<double>(3, 4) a = nc::constants::nan`
`np.empty([3, 4])`	`nc::empty<dtype>(3, 4)`
	`nc::NdArray<dtype>(3, 4) a`

SLICING/BROADCASTING

NumCpp offers NumPy style slicing and broadcasting.

NumPy	NumCpp
`a[2, 3]`	`a(2, 3)`
`a[2:5, 5:8]`	`a(nc::Slice(2, 5), nc::Slice(5, 8))`
	`a({2, 5}, {5, 8})`
`a[:, 7]`	`a(a.rSlice(), 7)`
`a[a > 5]`	`a[a > 50]`
`a[a > 5] = 0`	`a.putMask(a > 50, 666)`

RANDOM

The random module provides simple ways to create random arrays.

NumPy	NumCpp
`np.random.seed(666)`	`nc::random::seed(666)`
`np.random.randn(3, 4)`	`nc::random::randn<double>(nc::Shape(3,4))`
	`nc::random::randn<double>({3, 4})`
`np.random.randint(0, 10, [3, 4])`	`nc::random::randInt<int>(nc::Shape(3,4),0,10)`
	`nc::random::randInt<int>({3, 4},0,10)`
`np.random.rand(3, 4)`	`nc::random::rand<double>(nc::Shape(3,4))`
	`nc::random::rand<double>({3, 4})`
`np.random.choice(a, 3)`	`nc::random::choice(a, 3)`

CONCATENATION

Many ways to concatenate NdArray are available.

NumPy	NumCpp
`np.stack([a, b, c], axis=0)`	`nc::stack({a, b, c}, nc::Axis::ROW)`
`np.vstack([a, b, c])`	`nc::vstack({a, b, c})`
`np.hstack([a, b, c])`	`nc::hstack({a, b, c})`
`np.append(a, b, axis=1)`	`nc::append(a, b, nc::Axis::COL)`

DIAGONAL, TRIANGULAR, AND FLIP

The following return new NdArrays.

NumPy	NumCpp
`np.diagonal(a)`	`nc::diagonal(a)`
`np.triu(a)`	`nc::triu(a)`
`np.tril(a)`	`nc::tril(a)`
`np.flip(a, axis=0)`	`nc::flip(a, nc::Axis::ROW)`
`np.flipud(a)`	`nc::flipud(a)`
`np.fliplr(a)`	`nc::fliplr(a)`

ITERATION

NumCpp follows the idioms of the C++ STL providing iterator pairs to iterate on arrays in different fashions.

NumPy	NumCpp
`for value in a`	`for(auto it = a.begin(); it < a.end(); ++it)`
	`for(auto& value : a)`

LOGICAL

Logical FUNCTIONS in NumCpp behave the same as NumPy.

NumPy	NumCpp
`np.where(a > 5, a, b)`	`nc::where(a > 5, a, b)`
`np.any(a)`	`nc::any(a)`
`np.all(a)`	`nc::all(a)`
`np.logical_and(a, b)`	`nc::logical_and(a, b)`
`np.logical_or(a, b)`	`nc::logical_or(a, b)`
`np.isclose(a, b)`	`nc::isclose(a, b)`
`np.allclose(a, b)`	`nc::allclose(a, b)`

COMPARISONS

NumPy	NumCpp
`np.equal(a, b)`	`nc::equal(a, b)`
	`a == b`
`np.not_equal(a, b)`	`nc::not_equal(a, b)`
	`a != b`
`rows, cols = np.nonzero(a)`	`auto [rows, cols] = nc::nonzero(a)`

MINIMUM, MAXIMUM, SORTING

NumPy	NumCpp
`np.min(a)`	`nc::min(a)`
`np.max(a)`	`nc::max(a)`
`np.argmin(a)`	`nc::argmin(a)`
`np.argmax(a)`	`nc::argmax(a)`
`np.sort(a, axis=0)`	`nc::sort(a, nc::Axis::ROW)`
`np.argsort(a, axis=1)`	`nc::argsort(a, nc::Axis::COL)`
`np.unique(a)`	`nc::unique(a)`
`np.setdiff1d(a, b)`	`nc::setdiff1d(a, b)`
`np.diff(a)`	`nc::diff(a)`

REDUCERS

Reducers accumulate values of NdArrays along specified axes. When no axis is specified, values are accumulated along all axes.

NumPy	NumCpp
`np.sum(a)`	`nc::sum(a)`
`np.sum(a, axis=0)`	`nc::sum(a, nc::Axis::ROW)`
`np.prod(a)`	`nc::prod(a)`
`np.prod(a, axis=0)`	`nc::prod(a, nc::Axis::ROW)`
`np.mean(a)`	`nc::mean(a)`
`np.mean(a, axis=0)`	`nc::mean(a, nc::Axis::ROW)`
`np.count_nonzero(a)`	`nc::count_nonzero(a)`
`np.count_nonzero(a, axis=0)`	`nc::count_nonzero(a, nc::Axis::ROW)`

I/O

Print and file output methods. All NumCpp classes support a print() method and << stream operators.

NumPy	NumCpp
`print(a)`	`a.print()`
	`std::cout << a`
`a.tofile(filename, sep=’\n’)`	`a.tofile(filename, "\n")`
`np.fromfile(filename, sep=’\n’)`	`nc::fromfile<dtype>(filename, "\n")`
`np.dump(a, filename)`	`nc::dump(a, filename)`
`np.load(filename)`	`nc::load<dtype>(filename)`

MATHEMATICAL FUNCTIONS

NumCpp universal functions are provided for a large set number of mathematical functions.

BASIC FUNCTIONS

NumPy	NumCpp
`np.abs(a)`	`nc::abs(a)`
`np.sign(a)`	`nc::sign(a)`
`np.remainder(a, b)`	`nc::remainder(a, b)`
`np.clip(a, 3, 8)`	`nc::clip(a, 3, 8)`
`np.interp(x, xp, fp)`	`nc::interp(x, xp, fp)`

EXPONENTIAL FUNCTIONS

NumPy	NumCpp
`np.exp(a)`	`nc::exp(a)`
`np.expm1(a)`	`nc::expm1(a)`
`np.log(a)`	`nc::log(a)`
`np.log1p(a)`	`nc::log1p(a)`

POWER FUNCTIONS

NumPy	NumCpp
`np.power(a, 4)`	`nc::power(a, 4)`
`np.sqrt(a)`	`nc::sqrt(a)`
`np.square(a)`	`nc::square(a)`
`np.cbrt(a)`	`nc::cbrt(a)`

TRIGONOMETRIC FUNCTIONS

NumPy	NumCpp
`np.sin(a)`	`nc::sin(a)`
`np.cos(a)`	`nc::cos(a)`
`np.tan(a)`	`nc::tan(a)`

HYPERBOLIC FUNCTIONS

NumPy	NumCpp
`np.sinh(a)`	`nc::sinh(a)`
`np.cosh(a)`	`nc::cosh(a)`
`np.tanh(a)`	`nc::tanh(a)`

CLASSIFICATION FUNCTIONS

NumPy	NumCpp
`np.isnan(a)`	`nc::isnan(a)`
`np.isinf(a)`	`nc::isinf(a)`

LINEAR ALGEBRA

NumPy	NumCpp
`np.linalg.norm(a)`	`nc::norm(a)`
`np.dot(a, b)`	`nc::dot(a, b)`
`np.linalg.det(a)`	`nc::linalg::det(a)`
`np.linalg.inv(a)`	`nc::linalg::inv(a)`
`np.linalg.lstsq(a, b)`	`nc::linalg::lstsq(a, b)`
`np.linalg.matrix_power(a, 3)`	`nc::linalg::matrix_power(a, 3)`
`Np.linalg.multi_dot(a, b, c)`	`nc::linalg::multi_dot({a, b, c})`
`np.linalg.svd(a)`	`nc::linalg::svd(a)`

femperor/NumCpp