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Analyze time-series sensor data and log results to Google Sheets for collaborative monitoring and visualization.

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How it works

  • Machine State Lens running in Archetype platform for time-series classification
  • CSV analysis with focus files sent to Newton for few-shot learning classification
  • Google Sheets integration for real-time results logging and collaborative monitoring
  • Two integration modes:
    1. File-to-Sheets: Run code in the terminal and log results in Sheets
    2. Spreadsheet-Driven: Control everything from Sheets

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Prerequisites

The instructions below assume you are following best practices and have downloaded this git repo into a parent directory at: ~/atai. If you have already installed the ATAI Python Library and cloned the cookbook repository,proceed directly to Setup.

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Install Conda

wget https://repo.anaconda.com/archive/Anaconda3-2022.05-Linux-x86_64.sh
bash Anaconda3-2022.05-Linux-x86_64.sh
source ~/.bashrc

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Setup dev environment

conda create -n dev_env python=3.10
conda activate dev_env

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Install ATAI Python Library

git clone [email protected]:archetypeai/python-client.git
cd python-client
python -m pip install .

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Clone Cookbook Repository

git clone https://github.com/archetypeai/archetypeai-cookbook.git

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Setup

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Google API Configuration

To authenticate with Google Sheets, you’ll need OAuth 2.0 credentials. Follow the Google Sheets API Credentials Setup Guide to configure your credentials.json file.

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Run Demos

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Demo 1: Command Line to Sheets

Analyze local CSV files and automatically log results to Google Sheets.

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Running the Demo

From the Cookbook root directory with your conda environment activated: 
cd spreadsheet-analysis/cl-to-sheets/
pip install -r requirements.txt
python app.py

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Interactive Prompts

  1. API Key: Your ArchetypeAI API key
  2. Google Sheets ID: Extract from your spreadsheet URL
  3. Data file: Path to CSV file to analyze (drag & drop supported) - you can use the files under command-line-demos/machine-state/sample-files
  4. Focus files: Example CSV files for each state class

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Example Session

=== Sensor Analysis with Google Sheets ===

Enter your Archetype AI API key: your-key-here
Enter your Google Sheets ID: 1ehByJmpvDBH357BRuDBmQ2EB12qjTjj4Y0Z1OXxmGro

Enter path to the file to analyze: /path/to/sensor_data.csv
✓ File loaded: 5000 rows

Enter path to focus CSV file: /path/to/healthy.csv
✓ Added: class 'healthy' from healthy.csv

Enter path to focus CSV file: /path/to/broken.csv  
✓ Added: class 'broken' from broken.csv

Enter path to focus CSV file: done

--- Configuration Summary ---
Data file: sensor_data.csv
Focus classes: healthy, broken

Press Enter to start analysis...

🔍 Analyzing sensor data...
✓ Window 1: healthy (92% confidence) - Logged to sheet
✓ Window 2: healthy (88% confidence) - Logged to sheet
✓ Window 3: broken (95% confidence) - Logged to sheet

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Demo 2: Spreadsheet-Driven

Control everything directly from Google Sheets - no need to interact with terminal once script is running.

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Setup Template

Create a pre-configured spreadsheet template: 
cd spreadsheet-analysis/spreadsheet-driven
pip install -r requirements.txt
python create_example_spreadsheet.py
Keep the spreadsheet ID for next step.

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Running the Monitor

Start the monitoring script: 
python app.py

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Triggering Analysis

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Prepare Your Data

  1. Open the Google Sheet created in the previous step
  2. Navigate to the Config sheet and add your API key in cell B1
  3. Import your sensor data:
    • Click on the Data sheet tab
    • Import your CSV data to analyze (File → Import → Upload)
    • Ensure headers match: timestamp, a1, a2, a3, a4

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Add Focus Examples

  1. Create sheets for each machine state you want to classify
  2. Import example data for each state:
    • Create/rename a sheet tab (e.g., “healthy”, “broken”)
    • Import CSV data representing that state
    • Repeat for each classification category

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Start Analysis

  1. Return to the Config sheet
  2. Type “RUN” in cell B10
  3. Monitor progress in cell B11 (status updates)
  4. View results in the automatically created Results sheet

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Real-time Monitoring

The script monitors cell B10 for triggers and updates status in B11:
  • “Ready” - Waiting for trigger
  • “TRIGGERED” - Analysis starting
  • “RUNNING - Processed X windows” - In progress
  • “COMPLETED - Analyzed X windows” - Finished
  • “ERROR - [details]” - Something went wrong

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Window Size Configuration (to be reviewed)

The window size parameter determines how many data points Newton analyzes in each inference cycle. This affects the granularity of state classification.
  • Default: Auto-calculated based on focus file lengths
  • High-frequency sensors (greater than 100Hz): 256-512 points
  • Low-frequency sensors (less than 10Hz): 64-128 points
  • Maximum: 1024 points

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How It Works

Each window represents a time segment of sensor data. The system compares these windows against your focus examples to classify the machine state. Non-overlapping windows (step size = window size) provide independent classifications, while overlapping windows offer smoother state transitions.

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Constraints

  • Window size cannot exceed the length of your shortest focus file
  • Minimum window size is 16 data points for meaningful analysis