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Python Flask JSON Guide .cursorrules prompt file

This project is heavily reliant on our custom Drawscape Factorio python module.

Here is code examples of how to use the module:

```
from drawscape_factorio import create as createFactorio
from drawscape_factorio import importFUE5

Load the JSON file coming from the FUE5 MOD

with open('/path/to/exported-entities.json, 'r') as file:
json_data = json.load(file)

Parse the JSON data

data = importFUE5(json_data)

Call the create function with the parsed data and settings

result = createFactorio(data, {
'theme_name': 'default',
'color_scheme': 'main',
'show_layers': ['assets', 'belts', 'walls', 'rails', 'electrical', 'spaceship']
})

Save the SVG file

with open(output_file_name, 'w') as f:
f.write(result['svg_string'])
````

Here is my environment.yml that I'm running the project in

name: drawscape_api
channels:

conda-forge
dependencies:
python=3.12
pip
flask
flask-cors
svgwrite
numpy
python-dotenv
- drawscape-factorio
gunicorn
boto3
pip:
- flask[async]

stack:

Python Flask JSON Guide .cursorrules prompt file

Drawscape

0

NEW

Pandas Scikit-Learn Guide .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, and numpy.

  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 for data manipulation and analysis.
  - Prefer method chaining for data transformations when possible.
  - Use loc and iloc for explicit data selection.
  - Utilize groupby operations for efficient data aggregation.

  Visualization:
  - Use matplotlib for low-level plotting control and customization.
  - Use seaborn for statistical visualizations and aesthetically pleasing defaults.
  - Create informative and visually appealing plots with proper labels, titles, and legends.
  - Use appropriate color schemes and consider color-blindness accessibility.

  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.

  Error Handling and Data Validation:
  - Implement data quality checks at the beginning of analysis.
  - Handle missing data appropriately (imputation, removal, or flagging).
  - Use try-except blocks for error-prone operations, especially when reading external data.
  - Validate data types and ranges to ensure data integrity.

  Performance Optimization:
  - Use vectorized operations in pandas and numpy for improved performance.
  - Utilize efficient data structures (e.g., categorical data types for low-cardinality string columns).
  - Consider using dask for larger-than-memory datasets.
  - Profile code to identify and optimize bottlenecks.

  Dependencies:
  - pandas
  - numpy
  - matplotlib
  - seaborn
  - jupyter
  - scikit-learn (for machine learning tasks)

  Key Conventions:
  1. Begin analysis with data exploration and summary statistics.
  2. Create reusable plotting functions for consistent visualizations.
  3. Document data sources, assumptions, and methodologies clearly.
  4. Use version control (e.g., git) for tracking changes in notebooks and scripts.

Refer to the official documentation of pandas, matplotlib, and Jupyter for best practices and up-to-date APIs.

stack: