edgemodelkit 0.1.0

edgemodelkit: A Python library for seamless sensor data acquisition and logging.

EdgeModelKit: Sensor Data Acquisition and Logging Library

EdgeModelKit is a Python library developed by EdgeNeuron, designed for seamless sensor data acquisition, real-time processing, and efficient logging. This library bridges the gap between IoT devices and data processing pipelines, making it ideal for edge computing and machine learning applications.

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Features

• Serial Communication: Supports data acquisition over serial ports.
• Flexible Data Handling: Retrieve sensor data as Python lists or NumPy arrays.
• Real-Time Logging: Log sensor data into CSV files with optional timestamps and counters.
• Machine Learning Ready: Easily integrate with TensorFlow for on-device or server-side inference.

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Installation

pip install edgemodelkit

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Quick Start

1. Initialize the DataFetcher

from edgemodelkit import DataFetcher

# Initialize the DataFetcher with your serial port and baud rate
fetcher = DataFetcher(serial_port="/dev/ttyUSB0", serial_baud=9600)

2. Fetch Sensor Data

# Get data as a Python list
sensor_data = fetcher.get_data()
print("Sensor Data:", sensor_data)

# Get data as a NumPy array
sensor_data_numpy = fetcher.get_data(return_numpy=True)
print("Sensor Data (NumPy):", sensor_data_numpy)

3. Log Data to CSV

# Log 10 samples to a CSV file with timestamp and count columns
fetcher.log_data(samples=10, include_timestamp=True, include_count=True)

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Example: Real-Time Data Processing

from edgemodelkit import DataFetcher

fetcher = DataFetcher(serial_port="/dev/ttyUSB0", serial_baud=9600)

while True:
    # Get data as NumPy array
    sensor_data = fetcher.get_data(return_numpy=True)
    print("Received Data:", sensor_data)

    # Perform some processing (e.g., apply a TensorFlow model)
    # prediction = model.predict(sensor_data)
    # print("Prediction:", prediction)

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CSV Logging Details

The CSV file is named based on the sensor name (e.g., IMU_data_log.csv) and contains:

• Timestamp: (Optional) Current time when the data was logged.
• Count: (Optional) Incremental counter for the sample.
• Data Columns: Each element in the sensor data array is stored in separate columns (e.g., data_1, data_2, ...).

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Dependencies

EdgeStream requires the following Python packages:

• numpy
• pandas
• pyserial
• tensorflow (optional for machine learning integrations)

Install dependencies with:

pip install numpy pandas pyserial tensorflow

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Contributing

We welcome contributions to EdgeStream! Feel free to submit bug reports, feature requests, or pull requests on our GitHub repository.

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License

EdgeStream is licensed under the MIT License. See the LICENSE file for details.

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Support

For support and inquiries, reach out to us at [e-mail] or visit our GitHub repository.

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About EdgeNeuron

EdgeNeuron is a leader in edge computing solutions, empowering developers to create intelligent IoT applications with cutting-edge tools and libraries. Learn more at edgeneuron.ai.