sweetiepy 1.0.1

Type 1 Diabetes Data Analysis from DIY Loop system

SweetiePy - Type 1 Diabetes Data Analysis

A Python package for accessing and analyzing Type 1 diabetes data from a DIY Loop system stored in MongoDB Atlas. Get insights from your CGM data, correlate pump settings with glucose outcomes, and optimize your diabetes management with data-driven analysis.

⚡ Quick Start (5 minutes)

1. Install SweetiePy

Option A: Using uv (recommended)

# Install uv if you don't have it
curl -LsSf https://astral.sh/uv/install.sh | sh

# Create project and install sweetiepy
mkdir my-diabetes-analysis && cd my-diabetes-analysis
uv init --name my-diabetes-analysis
uv add sweetiepy

Option B: Using pip

mkdir my-diabetes-analysis && cd my-diabetes-analysis
pip install sweetiepy

2. Configure Database

Create a .env file with your MongoDB Atlas credentials:

# Get these from your MongoDB Atlas dashboard
MONGODB_USERNAME=your_username
MONGODB_PW=your_password

# Change 'yourcluster' to your actual cluster name
MONGODB_URI=mongodb+srv://<username>:<password>@cluster0.yourcluster.mongodb.net/?retryWrites=true&w=majority

# Database name (usually this for Loop systems)
MONGODB_DATABASE=myCGMitc

⚠️ Important: Keep <username> and <password> in the URI exactly as shown!

3. Test & Analyze

from sweetiepy.data.cgm import CGMDataAccess

# Test connection and get glucose data
with CGMDataAccess() as cgm:
    df = cgm.get_dataframe_for_period('last_week')
    
    print(f"📊 {len(df)} glucose readings analyzed")
    print(f"📅 Date range: {df['datetime'].min()} to {df['datetime'].max()}")
    print(f"🩸 Average glucose: {df['sgv'].mean():.1f} mg/dL")
    
    # Calculate time in range manually (safe approach)
    total_readings = len(df)
    in_range = ((df['sgv'] >= 70) & (df['sgv'] <= 180)).sum()
    high = (df['sgv'] > 180).sum() 
    low = (df['sgv'] < 70).sum()
    
    print(f"🎯 Time in range (70-180): {in_range/total_readings*100:.1f}%")
    print(f"📈 Time high (>180): {high/total_readings*100:.1f}%")
    print(f"📉 Time low (<70): {low/total_readings*100:.1f}%")

4. Advanced: Correlate CGM + Pump Settings

from sweetiepy.data.merged import MergedDataAccess

# Analyze how pump settings affect glucose outcomes
with MergedDataAccess() as merged:
    df = merged.get_merged_cgm_and_settings(days=7)
    correlations = merged.analyze_settings_correlation(df)
    
    print("🔗 How settings correlate with glucose:")
    for setting, correlation in correlations['correlations'].items():
        direction = "higher" if correlation > 0 else "lower"
        print(f"   {setting}: {correlation:+.3f} ({direction} setting → higher glucose)")

🎉 That's it! You're now analyzing your diabetes data with Python.

---

Features

• 📊 CGM Data Analysis - Access and analyze continuous glucose monitor data
• 💉 Pump Data Integration - Query insulin doses, basal rates, and treatment data
• 🔗 Merged Data Analysis - Key Innovation: Synchronize CGM readings with active pump settings at each timestamp
• 📈 Time-Series Analysis - Built-in time-in-range calculations and statistics
• 🎯 Settings Correlation - Analyze how basal rates, carb ratios, and ISF affect glucose outcomes
• 🔍 Flexible Queries - Predefined periods or custom date ranges
• 🚀 High Performance - PyArrow-backed DataFrames for efficient processing
• 🔐 Secure - Environment-based configuration for credentials

🔧 Troubleshooting

Connection Issues

"Connection failed" or "Authentication failed"

1. Double-check credentials: Verify username/password in MongoDB Atlas dashboard
2. Check cluster name: Make sure cluster name in MONGODB_URI matches your Atlas cluster
3. Test with MongoDB Compass: Try connecting with the GUI first to verify credentials
4. Database permissions: Ensure your user has read access to myCGMitc database

Import Errors

"Module not found" errors

• Make sure you installed: pip install sweetiepy or uv add sweetiepy
• Try in a fresh virtual environment
• Check Python version: requires 3.12+

No Data Found

"No recent readings" or empty DataFrames

• This is normal if your CGM hasn't uploaded data recently
• Try a longer time period: get_dataframe_for_period('last_month')
• Check your database name matches your Loop system

Code/Formatting Errors

"Format specifier missing precision" or syntax errors

• When copying from README, watch for line breaks in f-strings
• Use the safe manual calculation approach if analyze_dataframe has issues:

  # Safe approach - always works
  total = len(df)
  in_range = ((df['sgv'] >= 70) & (df['sgv'] <= 180)).sum()
  print(f"Time in range: {in_range/total*100:.1f}%")
  

Still having issues?

Run the connection test:

python -m sweetiepy.connection.mongodb

Expected output:

✓ Connected to MongoDB database: myCGMitc
Available databases: ['myCGMitc', ...]
Collections in myCGMitc: ['entries', 'treatments', ...]

---

📚 Complete Usage Examples

Basic CGM Analysis

from sweetiepy.data.cgm import CGMDataAccess

# Context manager handles connection automatically
with CGMDataAccess() as cgm:
    # Get last week's data
    df = cgm.get_dataframe_for_period('last_week')
    analysis = cgm.analyze_dataframe(df)
    
    print(f"📊 Analyzed {len(df)} glucose readings")
    print(f"📅 Date range: {df['datetime'].min()} to {df['datetime'].max()}")
    print(f"🩸 Average: {analysis['basic_stats']['avg_glucose']:.1f} mg/dL")
    print(f"🎯 Time in range (70-180): {analysis['time_in_range']['normal_percent']:.1f}%")
    print(f"📈 Time high (>180): {analysis['time_in_range']['high_percent']:.1f}%")
    print(f"📉 Time low (<70): {analysis['time_in_range']['low_percent']:.1f}%")

Advanced: Settings Correlation Analysis

from sweetiepy.data.merged import MergedDataAccess

# Correlate CGM readings with active pump settings
with MergedDataAccess() as merged:
    # Get CGM data with pump settings active at each timestamp
    df = merged.get_merged_cgm_and_settings(days=7)
    
    # Analyze how settings affect glucose outcomes  
    correlations = merged.analyze_settings_correlation(df)
    
    print("🔗 Settings impact on glucose:")
    for setting, correlation in correlations['correlations'].items():
        impact = "raises" if correlation > 0 else "lowers"
        strength = "strong" if abs(correlation) > 0.5 else "moderate" if abs(correlation) > 0.3 else "weak"
        print(f"   📈 {setting}: {correlation:+.3f} ({strength} - higher setting {impact} glucose)")

Time Pattern Analysis

from sweetiepy.data.cgm import CGMDataAccess
import matplotlib.pyplot as plt

with CGMDataAccess() as cgm:
    df = cgm.get_dataframe_for_period('last_month')
    
    # Hourly patterns
    hourly_avg = df.groupby(df['datetime'].dt.hour)['sgv'].mean()
    
    # Find problematic hours
    high_hours = hourly_avg[hourly_avg > 180]
    print(f"⚠️  Hours with average glucose > 180: {list(high_hours.index)}")
    
    # Daily patterns
    daily_avg = df.groupby(df['datetime'].dt.day_name())['sgv'].mean()
    print(f"📅 Average glucose by day:\n{daily_avg.round(1)}")

🗃️ Data Overview

SweetiePy works with DIY Loop diabetes data stored in MongoDB Atlas. Your database typically contains:

• 📊 entries - CGM glucose readings (primary data - usually 200K+ readings)
• 💉 treatments - Insulin doses, carbs, basal changes
• ⚙️ profile - Pump settings (basal rates, carb ratios, ISF)
• 📱 devicestatus - Loop system status and connectivity
• 🍎 food - Food logs and carbohydrate entries
• 🏃 activity - Exercise and activity logs

Key Data Points

• Glucose readings: Every 1-5 minutes from your CGM
• Insulin treatments: Bolus doses, temporary basals
• Pump settings: Time-scheduled basal rates, carb ratios, insulin sensitivity factors
• Treatment context: Recent insulin/carbs for each glucose reading

📊 Available Analysis Types

• Time-in-Range Analysis: Calculate % time in target, high, low ranges
• Time Pattern Analysis: Identify hourly/daily glucose patterns
• Settings Correlation: How basal rates, carb ratios, ISF affect glucose
• Treatment Analysis: Insulin doses, carb entries, pump adjustments
• Custom Queries: Flexible date ranges and time periods

📋 Installation Options

For End Users

Option A: Using uv (recommended)

# Install uv if you don't have it
curl -LsSf https://astral.sh/uv/install.sh | sh

# Create project and install sweetiepy
mkdir my-diabetes-analysis && cd my-diabetes-analysis
uv init --name my-diabetes-analysis
uv add sweetiepy

Option B: Using pip

pip install sweetiepy

For Developers

# Clone and install from source
git clone <repository-url>
cd sweetiepy
uv sync
uv pip install -e .

📚 API Reference

CGMDataAccess - Glucose Data

from sweetiepy.data.cgm import CGMDataAccess

with CGMDataAccess() as cgm:
    # Standard periods: 'last_24h', 'last_week', 'last_month', 'last_3_months'
    df = cgm.get_dataframe_for_period('last_week')
    analysis = cgm.analyze_dataframe(df)

PumpDataAccess - Treatment Data

from sweetiepy.data.pump import PumpDataAccess

with PumpDataAccess() as pump:
    boluses = pump.get_bolus_data(days=7)      # Insulin doses
    carbs = pump.get_carb_data(days=7)         # Carb entries  
    basals = pump.get_basal_data(days=7)       # Temp basals
    profile = pump.get_basal_profile()         # Basal schedule

MergedDataAccess - Settings Correlation

from sweetiepy.data.merged import MergedDataAccess

with MergedDataAccess() as merged:
    # CGM data + active pump settings at each timestamp
    df = merged.get_merged_cgm_and_settings(days=7)
    correlations = merged.analyze_settings_correlation(df)

---

💼 Package Information

• 📦 PyPI: sweetiepy
• 📜 License: MIT
• 🐍 Python: 3.12+
• 📦 Dependencies: pymongo, pandas, pyarrow, python-dotenv, python-dateutil, matplotlib, plotly

🔒 Security & Privacy

• 🔐 Secure: Never commit .env files to version control
• 📝 Read-only: Package only reads your data, never modifies it
• 🏠 Local: All analysis runs on your computer - no data transmitted
• 🔒 Encrypted: Uses MongoDB Atlas encrypted connections

🤝 Contributing

SweetiePy is open source and community-driven! We welcome:

• 🐛 Bug reports and feature requests
• 📝 Documentation improvements
• 📊 Analysis examples and tutorials
• 🧠 Ideas for new analysis types

This project exists to help the Type 1 diabetes community make data-driven decisions about their health.

🎆 What's Next?

Once you're up and running with SweetiePy:

1. 🔍 Explore patterns - Find your optimal times and settings
2. 📈 Create visualizations - Use matplotlib/plotly for charts
3. 📊 Share insights - Help others in the diabetes community
4. 🚀 Build more - Contribute new analysis features

Happy analyzing! 🩸📊