Results for “pytdc”

Create Pydantic models following the multi-model pattern with Base, Create, Update, Response, and InDB variants for clean API contracts in Python applications using Pydantic v2.

Generate production-ready Python code for Dataverse SDK with advanced patterns including error handling, batch operations, OData optimization, and Pandas integration.

Python testing strategies using pytest, TDD methodology, fixtures, mocking, parametrization, and coverage requirements.

Use when designing a Digital Down Converter (DDC) using dsphdl System objects. Triggers on requests involving DDC design, frequency down-conversion for FPGA/ASIC, NCO + mixer + decimation filter chains, fractional/non-integer sample rate conversion, or HDL-optimized receiver front-end signal processing.

使用pytest的Python测试策略，包括TDD方法、夹具、模拟、参数化和覆盖率要求。

Run computational fluid dynamics simulations using Python, including Navier-Stokes equations, shallow water, and stratified flows with pseudospectral methods and HPC support.

Apply comprehensive Python testing strategies using pytest, including TDD methodology, fixtures, mocking, parametrization, and coverage requirements.

Biblioteca Python para trabalhar com arquivos DICOM (Digital Imaging and Communications in Medicine). Use essa skill ao ler, escrever ou modificar dados de imagens médicas em formato DICOM, extrair dados de pixel de imagens médicas (TC, RM, Raio-X, ultrassom), anonimizar arquivos DICOM, trabalhar com metadados e tags DICOM, converter imagens DICOM para outros formatos, processar dados DICOM comprimidos ou processar conjuntos de dados de imagens médicas. Aplica-se a tarefas envolvendo análise de imagens médicas, sistemas PACS, fluxos de trabalho de radiologia e aplicações de imagem médica.

Read, write, and modify DICOM medical imaging files, including pixel data extraction, anonymization, format conversion, and compression handling.

Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.

Modelagem Bayesiana com PyMC. Construa modelos hierárquicos, MCMC (NUTS), inferência variacional, comparação LOO/WAIC, verificações posteriores, para programação probabilística e inferência.

Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.

Fixes common PyTorch bugs including percentile calculations, LayerNorm for Conv1d, and buffer edge cases in reinforcement learning and neural network code.

PyTorch, TensorFlow, neural networks, CNNs, transformers, and deep learning for production

Use for PPC work when Codex should validate Meta catalog, feed, event, availability, and dynamic product ad readiness.

Write, review, debug, or test Python datetime, date, timedelta, timezone, and zoneinfo code, especially UTC conversion, DST gaps and folds, recurring local schedules, parsing, and interval boundaries.

High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.