### Frontend Setup and Development Server

Source: https://github.com/markusneusinger/pyplots/blob/main/docs/development.md

Navigates to the 'app' directory, installs frontend dependencies using Yarn, and starts the React development server. Also includes the command for building the production version.

```bash
cd app
yarn install
yarn dev
# → http://localhost:3000

yarn build
```

--------------------------------

### Backend Setup and API Server

Source: https://github.com/markusneusinger/pyplots/blob/main/docs/development.md

Clones the Pyplots repository, installs backend dependencies using 'uv sync', configures the database connection via '.env', applies migrations, and starts the FastAPI development server.

```bash
git clone https://github.com/MarkusNeusinger/pyplots.git
cd pyplots
uv sync --all-extras
cp .env.example .env
# Edit .env with your DATABASE_URL:
# DATABASE_URL=postgresql+asyncpg://user:pass@host:5432/pyplots
uv run alembic upgrade head
uv run uvicorn api.main:app --reload
# → http://localhost:8000/docs
```

--------------------------------

### Install Frontend Dependencies and Start Development Server

Source: https://context7.com/markusneusinger/pyplots/llms.txt

Installs frontend dependencies using Yarn and starts the development server for the frontend application. This allows for real-time development and testing of the user interface.

```bash
# Frontend development
cd app
yarn install
yarn dev                             # Development server
yarn build                           # Production build
```

--------------------------------

### Install Dependencies and Run Backend API (Bash)

Source: https://github.com/markusneusinger/pyplots/blob/main/CLAUDE.md

Installs project dependencies using 'uv' and starts the backend API server using 'uvicorn'. Ensure you have 'uv' and 'uvicorn' installed.

```bash
# Install dependencies (uses uv - fast Python package manager)
uv sync --all-extras

# Start backend API
uv run uvicorn api.main:app --reload --port 8000
```

--------------------------------

### Install Project Dependencies with uv

Source: https://context7.com/markusneusinger/pyplots/llms.txt

Installs project dependencies using the 'uv' package manager. The '--all-extras' flag ensures that all optional dependencies are also installed, providing a complete development environment.

```bash
# Install dependencies with uv (fast Python package manager)
uv sync --all-extras
```

--------------------------------

### Frontend Development Commands (Bash)

Source: https://github.com/markusneusinger/pyplots/blob/main/CLAUDE.md

Navigates to the frontend directory, installs dependencies using Yarn, and starts the development server or builds for production.

```bash
cd app
yarn install
yarn dev          # Development server
yarn build        # Production build
```

--------------------------------

### Seaborn Datasets for Realistic Examples

Source: https://github.com/markusneusinger/pyplots/blob/main/prompts/plot-generator.md

Illustrates loading realistic, domain-specific datasets using Seaborn. The example shows how to load the 'tips' dataset, which is suitable for demonstrations involving real-world patterns.

```python
import seaborn as sns

df = sns.load_dataset('tips')  # Restaurant tipping data
```

--------------------------------

### SQL Query Optimization Example

Source: https://github.com/markusneusinger/pyplots/blob/main/docs/reference/database.md

Illustrates how to use `EXPLAIN ANALYZE` in SQL to analyze and optimize the performance of slow queries. This example shows analyzing a query that joins `specs` and `impls` tables.

```sql
EXPLAIN ANALYZE
SELECT s.*, COUNT(i.id) as impl_count
FROM specs s
LEFT JOIN impls i ON s.id = i.spec_id
GROUP BY s.id;
```

--------------------------------

### Plot Implementation Structure Example

Source: https://context7.com/markusneusinger/pyplots/llms.txt

Provides an example of the expected structure for plot implementation scripts. It highlights the header comment format containing metadata and demonstrates basic data generation using NumPy for a scatter plot.

```python
""" pyplots.ai
scatter-basic: Basic Scatter Plot
Library: matplotlib 3.10.0 | Python 3.13
Quality: 92/100 | Created: 2025-01-10
"""

import matplotlib.pyplot as plt
import numpy as np

# Data - study hours vs exam scores
np.random.seed(42)
study_hours = np.random.uniform(1, 12, 120)
exam_scores = 45 + study_hours * 4.5 + np.random.randn(120) * 8
exam_scores = np.clip(exam_scores, 0, 100)


```

--------------------------------

### Start Backend API with Uvicorn

Source: https://context7.com/markusneusinger/pyplots/llms.txt

Starts the backend API server using Uvicorn. The '--reload' flag enables hot-reloading for development, automatically restarting the server when code changes are detected. The API runs on port 8000.

```bash
# Start backend API
uv run uvicorn api.main:app --reload --port 8000
```

--------------------------------

### Set Up Environment Variables

Source: https://context7.com/markusneusinger/pyplots/llms.txt

Copies an example environment file to a new file named '.env'. This is a common practice to set up configuration variables for the application, such as database URLs and API keys.

```bash
# Set up environment variables
cp .env.example .env
# Edit .env with your DATABASE_URL and other settings
```

--------------------------------

### Install uv Package Manager

Source: https://github.com/markusneusinger/pyplots/blob/main/docs/development.md

Installs the 'uv' package manager, a fast alternative to pip and poetry. This command fetches and executes an installation script from a remote URL.

```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```

--------------------------------

### Test Python Implementation

Source: https://github.com/markusneusinger/pyplots/blob/main/prompts/workflow-prompts/impl-repair-claude.md

This snippet demonstrates how to activate a virtual environment, navigate to the implementation directory, and run a Python script using a specific backend. It's used to test the implementation after making fixes.

```bash
source .venv/bin/activate
cd plots/{SPEC_ID}/implementations
MPLBACKEND=Agg python {LIBRARY}.py
```

--------------------------------

### Matplotlib API Compatibility for Boxplots (3.9+)

Source: https://github.com/markusneusinger/pyplots/blob/main/prompts/library/matplotlib.md

Highlights the change in matplotlib's API for setting labels in boxplots starting from version 3.9. It shows the deprecated method using `labels` and the correct, current method using `tick_labels`.

```python
# DEPRECATED: labels in boxplot
ax.boxplot(data, labels=group_names)  # Wrong

# CORRECT: use tick_labels
ax.boxplot(data, tick_labels=group_names)  # Right
```

--------------------------------

### Python API Function Documentation

Source: https://github.com/markusneusinger/pyplots/blob/main/CLAUDE.md

Example of a Python function with Google-style docstrings, including arguments, return values, and potential exceptions. This adheres to the project's Python style guide for API and core code.

```python
def get_spec_by_id(spec_id: str, db: Session) -> Spec:
    """
    Retrieve a spec by its ID.

    Args:
        spec_id: The unique spec identifier
        db: Database session

    Returns:
        Spec object if found

    Raises:
        NotFoundError: If spec doesn't exist
    """
    pass
```

--------------------------------

### Alembic Database Migration Commands and Python Code

Source: https://github.com/markusneusinger/pyplots/blob/main/docs/reference/database.md

Demonstrates the use of Alembic for managing database schema migrations. Includes commands for creating new migrations, applying them, and rolling back, along with a Python example of an initial schema migration script.

```bash
# Create new migration
alembic revision -m "add new column"

# Apply migrations
alembic upgrade head

# Rollback
alembic downgrade -1
```

```python
# migrations/versions/001_initial_schema.py
def upgrade():
    op.create_table('specs', ...)
    op.create_table('libraries', ...)
    op.create_table('impls', ...)

def downgrade():
    op.drop_table('impls')
    op.drop_table('libraries')
    op.drop_table('specs')
```

--------------------------------

### Markdown Data Section Format Example with Python

Source: https://github.com/markusneusinger/pyplots/blob/main/prompts/spec-validator.md

An example of the required format for the 'Data' section in a Markdown specification. It includes a list of required columns with their types and descriptions, and an optional Python code snippet for data example.

```markdown
## Data

**Required columns:**
- `column_name` (type) - description

**Example:** *(optional)*
```python
data = pd.DataFrame({...})
```
```

--------------------------------

### Markdown Tags Section Format Example

Source: https://github.com/markusneusinger/pyplots/blob/main/prompts/spec-validator.md

An example of the required format for the 'Tags' section in a Markdown specification. Tags should be comma-separated and at least two tags are required.

```markdown
## Tags

scatter, correlation, basic
```

--------------------------------

### Import Lets-Plot and Setup HTML

Source: https://github.com/markusneusinger/pyplots/blob/main/prompts/library/letsplot.md

Imports the necessary components from the lets_plot library and sets up HTML rendering, which is required for notebook environments or exporting plots.

```python
from lets_plot import *
LetsPlot.setup_html()  # Required for notebook/export
```

--------------------------------

### Python Dataframe Example for Data Section

Source: https://github.com/markusneusinger/pyplots/blob/main/prompts/spec-validator.md

A Python code snippet demonstrating how to create a Pandas DataFrame, which can be used as an example in the 'Data' section of the plot specification.

```python
import pandas as pd
data = pd.DataFrame({...})
```

--------------------------------

### Spec-Level Tags Example (YAML)

Source: https://github.com/markusneusinger/pyplots/blob/main/docs/reference/tagging-system.md

An example of Spec-Level tags in YAML format, illustrating how plot type, data type, domain, and features are defined for a scatter plot.

```yaml
# plots/scatter-basic/specification.yaml
tags:
  plot_type:
    - scatter
  data_type:
    - numeric
    - continuous
  domain:
    - statistics
    - general
  features:
    - basic
    - 2d
    - correlation

```

--------------------------------

### Local PostgreSQL Database Setup

Source: https://github.com/markusneusinger/pyplots/blob/main/CLAUDE.md

Commands to set up a local PostgreSQL database connection for the project. It includes copying an environment file, editing the database URL, and testing the connection using uv and Python.

```bash
# Copy environment template
cp .env.example .env

# Edit .env with your DATABASE_URL
DATABASE_URL=postgresql+asyncpg://user:pass@host:5432/pyplots

# Test connection
uv run python -c "from core.database import is_db_configured; print(is_db_configured())"
```

--------------------------------

### Markdown Description Format Example

Source: https://github.com/markusneusinger/pyplots/blob/main/prompts/spec-validator.md

An example of the required format for the plot specification's description section in Markdown. It should be 2-4 sentences explaining the plot's purpose, usage, and utility.

```markdown
## Description

2-4 sentences describing what the plot shows, when to use it,
and what makes it useful.
```

--------------------------------

### SQL Inserts for Supported Libraries

Source: https://github.com/markusneusinger/pyplots/blob/main/docs/reference/database.md

Provides example SQL INSERT statements to populate the 'libraries' table with common plotting libraries like Matplotlib, Seaborn, Plotly, and Altair.

```sql
INSERT INTO libraries (id, name, version, documentation_url) VALUES
('matplotlib', 'Matplotlib', '3.9.0', 'https://matplotlib.org'),
('seaborn', 'Seaborn', '0.13.0', 'https://seaborn.pydata.org'),
('plotly', 'Plotly', '5.18.0', 'https://plotly.com/python'),
('altair', 'Altair', '5.2.0', 'https://altair-viz.github.io');
```