NumCpp  1.0
A C++ implementation of the Python Numpy library
NumCpp Documentation

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

Author: David Pilger dpilg.nosp@m.er26.nosp@m.@gmai.nosp@m.l.co.nosp@m.m

Version: 2.0.0

License MIT license

Copyright 2020 David Pilger

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, and C++2a Compilers: VS 2017/2019, GCC 6.5.0/7.5.0/8.4.0/9.3.0, and Clang 8.0/9.0/10.0
Boost Versions: 1.68, 1.70, 1.72, and 1.73

Documentation

GitHub

Installation

Release Notes

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
  • 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)