# Equivalent of numpy.gradient in Julia

**URL:** <https://discourse.julialang.org/t/equivalent-of-numpy-gradient-in-julia/93172>\
**Category:** General Usage\
**Tags:** question, package\
**Created:** [January 18, 2023, 5:12pm UTC](https://discourse.julialang.org/t/equivalent-of-numpy-gradient-in-julia/93172 "2023-01-18T17:12:37Z")\
**Posts on this page:** 1\
**Showing post:** 11

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**Author:** ![Mattriks](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/mattriks/32/351_2.png) [@Mattriks](https://discourse.julialang.org/u/Mattriks)\
**Post date:** [January 24, 2023, 7:25pm UTC](https://discourse.julialang.org/t/equivalent-of-numpy-gradient-in-julia/93172/11 "2023-01-24T19:25:33Z")

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I’m going to add a “Theory” page to the CoupledFields documentation, but for now there is a brief explanation here: [Smooth delta functions to compute Jacobian and Hessian from samples - #10 by Mattriks](https://discourse.julialang.org/t/smooth-delta-functions-to-compute-jacobian-and-hessian-from-samples/45282/10)

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