Skip to main content

​
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 Quickstart.

​
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

​
Setup dev environment

conda create -n dev_env python=3.10
conda activate dev_env

​
Install ATAI Python Library

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

​
Clone Cookbook Repository

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

​
Quick Start

Analyze time-series sensor data to classify machine states using few-shot learning with example patterns.

​
Running the Demo

From the cookbook root directory with your conda environment activated: 
cd command-line-demos/machine-state
pip install -r requirements.txt
python quickstart.py
Sample CSV files are provided in the sample-files/ directory, including a data file and example focus files for testing the demo.

​
Interactive Prompts

  1. API Key: Your API key
  2. Data File: Path to CSV file containing sensor data to analyze (drag & drop supported)
  3. Focus Files: Paths to example CSV files for each state you want to classify
  4. Window Configuration: Size and step parameters for analysis windows

​
Example Session

=== Machine State Analysis ===

Enter your ArchetypeAI API key: your-key-here

Enter path to data file: /path/to/sensor_data.csv

How many focus files? (examples of different states): 2

Focus file 1 (e.g., healthy.csv): /path/to/healthy.csv
Focus file 2 (e.g., broken.csv): /path/to/broken.csv

Window size (16-1024, default: auto): [Enter]
Step size (default: same as window): [Enter]

--- Configuration Summary ---
Data file: sensor_data.csv
Focus files:
  - healthy.csv (class: healthy)
  - broken.csv (class: broken)
Window size: 256
Step size: 256

Press Enter to start analysis...

🔍 Analyzing time series data...

Results:
Window 1: healthy (confidence: 0.92)
Window 2: healthy (confidence: 0.88)
Window 3: broken (confidence: 0.95)

​
Sample Data

The demo includes sample files in the sample_files/ directory:
  • data.csv - Sensor data to analyze
  • focus/healthy.csv - Example of healthy machine state
  • focus/broken.csv - Example of broken machine state

​
Classification Tips

​
Naming Convention

Class names are automatically derived from focus file names:
  • healthy.csv creates class “healthy”
  • broken.csv creates class “broken”
  • normal_operation.csv creates class “normal_operation”

​
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

​
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.

​
Constraints

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