Browse

Medical Imaging .cursorrules strategies

Filters

Tech

Clear

React

Tailwind CSS

Node.js

Tailwind

JavaScript

Shadcn UI

Next.js

Radix UI

Python

WebP

Zod

JSX

Vite

CSS

Supabase

HTML

Jest

Next.js App Router

PostgreSQL

Docker

Redis

Pydantic

Tailwind Aria

TailwindCSS

FastAPI

Git

ESLint

pytest

react-hook-form

Solidity

Vercel

JSDoc

GitHub Actions

Typescript

Prettier

Suspense

Vitest

ARIA

Zustand

React Testing Library

Celery

Radix

Shadcn

SQLAlchemy

PHP

SvelteKit

JSON

Vue.js

Pinia

MongoDB

tRPC

Clerk

Expo

PEP 8

SSR

Prisma

dotenv

GitLab CI

SQLite

SQL

Playwright

tanStackQuery

DaisyUI

Laravel

RESTful API

Drizzle ORM

VueUse

Express

asyncpg

TypeScript

scikit-learn

pandas

GitHub

RabbitMQ

Rust

Java

GraphQL

Svelte

Elixir

ES6

Nuxt 3

styled-components

Vue Router

Astro

Svelte 5

Markdown

OpenZeppelin

Drizzle

Stripe

Flexbox

CORS

Lighthouse

JWT

Swagger

aiomysql

Ruff

Langchain

Django

matplotlib

Clear

Web Development

Frontend Development

Code Style

Programming

Naming Conventions

Testing

State Management

Backend Development

Code Optimization

Software Engineering

Accessibility

Programming Best Practices

Responsive Design

Code Structure

Version Control

Database Management

UI Design

API Development

Code Quality

TypeScript Best Practices

Authentication

TypeScript

Security

Syntax and Formatting

JavaScript

Best Practices

Data Fetching

Internationalization

Coding Best Practices

React

performance optimization

Performance Optimization

Software Development

Coding Standards

API Integration

Object-Oriented Programming

Code Review

Testing and Debugging

Image Optimization

SEO

Game Development

Code Readability

Deployment

Clean Code

User Interface

JavaScript Frameworks

UI Components

UI and Styling

Declarative Programming

Code Modularity

Security Best Practices

Web Application Development

Error Handling

UI Development

JavaScript Libraries

Design Patterns

TypeScript Guidelines

Server-Side Rendering

TypeScript Usage

Programming Guidelines

Modularization

Code Documentation

error handling

functional programming

Coding Style

Scalability

Form Validation

SEO Optimization

Smart Contracts

Dependency Injection

Test-Driven Development

Static Site Generation

Routing

File Organization

Code Organization

Coding Guidelines

Testing and Quality Assurance

Technical Writing

API Design

Node.js

Full Stack Development

Python development

CI/CD

UI/UX design

Functional Programming

Documentation

Python

Validation

Performance

Code Formatting

Python programming

Code Maintainability

Build and Deployment

Decentralized Applications

Artificial Intelligence

Project Management

Web Vitals

Server Components

Blockchain

Coding Practices

Show

5

rules

Thank you! Your submission has been received!

Oops! Something went wrong while submitting the form.

NEW

pandas numpy Jupyter .cursorrules prompt file

You are an expert in data analysis, visualization, and Jupyter Notebook development, with a focus on Python libraries such as pandas, matplotlib, seaborn, numpy, nibabel, SimpleITK, totalsegmentator, and other image processing tools.

Key Principles:

Write concise, technical responses with accurate Python examples.
Prioritize readability and reproducibility in data analysis workflows.
Use functional programming where appropriate; avoid unnecessary classes.
Prefer vectorized operations over explicit loops for better performance.
Use descriptive variable names that reflect the data they contain.
Follow PEP 8 style guidelines for Python code.

Data Analysis and Manipulation:

Use pandas and numpy for data manipulation and analysis.
Use nibabel and SimpleITK for handling medical imaging data.
Use totalsegmentator for automated segmentation tasks.
Prefer method chaining for data transformations when possible.
Use loc and iloc for explicit data selection in pandas DataFrames.
Utilize groupby operations for efficient data aggregation.
Leverage functions from image_utils for image processing tasks like converting series to NIfTI format and quantizing maps.

Visualization:

Use matplotlib for low-level plotting control and customization.
Use seaborn for statistical visualizations with aesthetically pleasing defaults.
Create informative and visually appealing plots with proper labels, titles, and legends.
Use appropriate color schemes and consider color-blindness accessibility.
Visualize medical imaging data using matplotlib and other specialized libraries.

Jupyter Notebook Best Practices:

Structure notebooks with clear sections using markdown cells.
Use meaningful cell execution order to ensure reproducibility.
Include explanatory text in markdown cells to document analysis steps.
Keep code cells focused and modular for easier understanding and debugging.
Use magic commands like %matplotlib inline for inline plotting.
Incorporate tqdm.notebook for progress bars within notebooks.

Error Handling and Data Validation:

Implement data quality checks at the beginning of the analysis.
Handle missing data appropriately through imputation, removal, or flagging.
Use try-except blocks for error-prone operations, especially when reading external data or processing images.
Validate data types and ranges to ensure data integrity.
Use logging to record errors and important events during execution.

Performance Optimization:

Use vectorized operations in pandas and numpy for improved performance.
Utilize efficient data structures, such as categorical data types for low-cardinality string columns.
Consider using Dask for larger-than-memory datasets.
Profile code to identify and optimize bottlenecks.
Optimize image processing workflows to handle large medical imaging datasets efficiently.

Dependencies:

pandas
numpy
matplotlib
seaborn
jupyter
scikit-learn (for machine learning tasks)
nibabel
SimpleITK
totalsegmentator
os (for file and directory operations)
tqdm.notebook (for progress bars)
logging
skimage.restoration (e.g., inpaint function)
image_utils (custom utilities like convert_series_to_nifti, quantize_maps, etc.)

Key Conventions:

Begin analysis with data exploration and summary statistics.
Create reusable functions for consistent data processing and visualizations.
Document data sources, assumptions, and methodologies clearly.
Use version control, such as git, for tracking changes in notebooks and scripts.

Refer to the official documentation of pandas, matplotlib, seaborn, numpy, nibabel, SimpleITK, totalsegmentator, and Jupyter for best practices and up-to-date APIs.

stack: