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Copyright 2020 David Pilger
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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.
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
std::complex<T>, closing Issue #58
NdArray constructors for STL containers including std::vector<std::vector<T>>, closing Issue #59
polyfit routine inline with Numpy polyfit, closing Issue #61
NdArray as container for generic structslu_decomposition and pivotLU_decomposition added to Linalg namespaceNdArray
iteratorconst_iteratorreverse_iteratorconst_reverse_iteratorcolumn_iteratorconst_column_iteratorreverse_column_iteratorconst_reverse_column_iteratorrodriguesRotation and wahbasProblem to Rotations namespaceThis 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.
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>() |
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 |
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) |
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) |
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) |
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) |
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 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) |
| 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) |
| 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 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) |
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) |
NumCpp universal functions are provided for a large set number of mathematical 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) |
| 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) |
| 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) |
| NumPy | NumCpp |
|---|---|
np.sin(a) |
nc::sin(a) |
np.cos(a) |
nc::cos(a) |
np.tan(a) |
nc::tan(a) |
| NumPy | NumCpp |
|---|---|
np.sinh(a) |
nc::sinh(a) |
np.cosh(a) |
nc::cosh(a) |
np.tanh(a) |
nc::tanh(a) |
| NumPy | NumCpp |
|---|---|
np.isnan(a) |
nc::isnan(a) |
np.isinf(a) |
nc::isinf(a) |
| 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) |
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