Scientific Computing and Data

Arraymancer: N-dimensional tensors, linear algebra, neural networks (CPU/CUDA/OpenCL), NumPy-like slicing:

Datamancer (DataFrames), ggplotnim (plotting with a grammar of graphics), numericalnim (ODEs, integration, optimization), manu (linear algebra), neo, nimblas/nimlapack, SciNim organization projects (units with unchained, measuremancer for uncertainties).

Python interop for the scientific ecosystem: nimpy (let np = pyImport("numpy")), and build fast Python extensions in Nim with nimporter.

Jupyter: jupyternim kernel. Notebooks via nimib (literate programming and reports).

import arraymancer
let a = [[1.0, 2.0], [3.0, 4.0]].toTensor
let b = a * a                 # matrix multiply
echo b, a[_, 0], a.transpose, a.mean