Results for “numpy”
16 skills
schattenspiegel numpyro-python
Write, debug, and test NumPyro probabilistic programs on JAX with correct shapes, PRNG keys, and inference choice.
0 · bundle
k-dense-ai dask
Scale pandas and NumPy workflows to larger-than-memory datasets using parallel and distributed computing.
30.2k · bundle
k-dense-ai zarr-python
Store and process large N-dimensional arrays with chunking, compression, and parallel I/O, integrating with NumPy, Dask, and Xarray for cloud-native scientific computing.
30.2k · bundle
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nvidia cupynumeric-migration-readiness
Assesses NumPy code for cuPyNumeric migration readiness by analyzing source code against an API support manifest and GPU-scaling idioms, producing a structured verdict with per-finding reasoning.
2.2k · bundle
lingxling sympy
Provides guidance for using SymPy for exact symbolic mathematics in Python, covering algebra, calculus, equation solving, linear algebra, physics, and code generation.
253 · bundle
antigravity arrowspace
Augments nearest-neighbour search with graph Laplacian features to retrieve items based on both semantic similarity and structural role.
42.4k
lucaspmarie-a11y sympy
Provides comprehensive guidance for performing symbolic algebra, calculus, linear algebra, equation solving, physics calculations, and code generation using the SymPy Python library.
5
nimoqup046-collab sympy
Provides comprehensive guidance for performing symbolic mathematics with SymPy, including algebra, calculus, equation solving, linear algebra, physics calculations, and code generation.
2
micsapp math-tools
Deterministic mathematical computation using SymPy. Use for ANY math operation requiring exact/verified results - basic arithmetic, algebra (simplify, expand, factor, solve equations), calculus (derivatives, integrals, limits, series), linear algebra (matrices, determinants, eigenvalues), trigonometry, number theory (primes, GCD/LCM, factorization), and statistics. Ensures mathematical accuracy by using symbolic computation rather than LLM estimation.
3 · bundle
lucaspmarie-a11y scanpy
Analyze single-cell RNA-seq data with Scanpy, covering quality control, normalization, clustering, marker gene identification, visualization, and trajectory analysis.
5
k-dense-ai optimize-for-gpu
GPU-accelerate Python code using CuPy, Numba CUDA, Warp, cuDF, cuML, cuGraph, and other RAPIDS libraries for dramatic speedups on numerical, data, ML, graph, and simulation workloads.
30.2k · bundle
jorcan sympy
Provides comprehensive guidance for symbolic mathematics in Python using SymPy, covering algebra, calculus, equation solving, linear algebra, physics, and code generation.
0 · bundle
phoroth sympy
Provides guidance for performing symbolic algebra, calculus, linear algebra, equation solving, physics calculations, and code generation using the SymPy Python library.
3
ecnu-icalk hsv
Implements a Python function that converts an RGB image to HSV color space and extracts a specified channel (H, S, or V), including handling to convert the image to uint8 type to avoid OpenCV depth errors.
559
schattenspiegel pymc-python
Use for writing, reviewing, debugging, testing, or diagnosing Python Bayesian models built directly with PyMC, including Model, coords/dims, Data, random variables, potentials, posterior sampling, prior/posterior predictive checks, and InferenceData output. Trigger on model geometry, shape errors, divergences, sampler choice, mutable prediction data, and probabilistic validation. Do not use for Bambi formula models, NumPyro/JAX programs, ArviZ-only analysis of existing draws, deterministic optimization, or general statistics without PyMC code.
0 · bundle
schattenspiegel arviz-python
Use for writing, reviewing, debugging, or testing Python analysis of Bayesian inference results with ArviZ, including 1.x DataTree groups, legacy InferenceData inputs, xarray dimensions and coordinates, conversion, summaries, R-hat/ESS/MCSE diagnostics, posterior predictive checks, PSIS-LOO, Pareto-k, and model comparison. Trigger on chain/draw shape errors, mislabeled groups, flattened samples, missing log likelihood, or misleading diagnostic claims. Do not use to construct or sample PyMC, NumPyro, or Bambi models, for generic plotting, or for deterministic statistics without Bayesian draws.
0 · bundle