GrabberProject

Overview

This project leverages ARTIQ (Advanced Real-Time Infrastructure for Quantum physics) to improve trapped ion measurement efficiency at the Duke Quantum Center. The primary goal is to reduce measurement times by 30% through faster data transmission speeds via the Camera Link Frame Grabber.

Project Goals

• Optimize Data Transmission: Increase data transmission speeds through the Camera Link Frame Grabber
• Reduce Measurement Times: Shorten trapped ion measurement times by ~30%
• Identify and Localize Ions: Automatically detect regions of interest (ROIs) where ions are located
• Visualize Camera Data: Generate clear visualizations of Camera Link output for measurement and orientation

Technologies & Libraries

• ARTIQ: Advanced Real-Time Infrastructure for Quantum physics control and measurement
• NumPy: Numerical computing and array operations
• Pandas: Data analysis and manipulation
• Matplotlib: Data visualization and plotting
• OpenCV (cv2): Computer vision for image processing and bounding box detection
• SciPy & scikit-image: Scientific computing and image processing algorithms
• Seaborn: Statistical data visualization
• tifffile: TIFF image reading and writing

Project Structure

GrabberProject/
├── ROI.py                    # Main ion region of interest detection module
├── Data/                     # Data files and measurements
│   ├── GrabberData.csv
│   ├── Region.csv
│   └── USBImage.csv
├── Scripts/                  # Processing and utility scripts
│   ├── BoundingBox.py        # Draw bounding boxes around detected regions
│   ├── FlipImage.py          # Image flipping utilities
│   ├── ImageCreation.py      # Convert CSV data to heatmaps and visualizations
│   └── ToTextFile.py         # Convert image data to text format
├── Outputs/                  # Output results and processed images
├── Region/                   # Region data and processed regions
├── IntImageAndor/            # Andor camera image data
├── img-ion-9/                # Ion imaging data set 1
├── img-ion-10/               # Ion imaging data set 2
├── 6-ion-chain/              # 6-ion chain experimental data
└── ColorMaps/                # Color map resources

Key Features

1. Region of Interest (ROI) Detection (ROI.py)

• Automatically detects ion positions in camera images
• Uses Gaussian blur for image smoothing
• Implements peak detection to locate local maxima (ion positions)
• Generates bounding boxes around detected ions
• Configurable parameters: number of ions, Manhattan distance threshold

2. Bounding Box Generation (BoundingBox.py)

• Identifies and draws bounding boxes around regions of interest
• Uses binary thresholding to segment regions
• Filters by region size to avoid noise
• Outputs annotated images with detected regions

3. Data Visualization (ImageCreation.py)

• Converts CSV data to heatmaps for visual analysis
• Generates pixelated images from tabular data
• Supports multiple color mapping schemes
• Useful for analyzing Camera Link data

Usage

Detecting Ions in Images

from ROI import roi

# Detect ions in a TIFF image
roi(image="path/to/image.tif", ion_number=6, Manhattan_distance=5)

Drawing Bounding Boxes

from Scripts.BoundingBox import draw_bb

# Generate bounding box visualization
draw_bb("./Region/GrabberRegionGray.png")

Creating Heatmap Visualizations

from Scripts.ImageCreation import csv_to_heatmap, csv_to_pixelated_image

# Convert CSV to heatmap
csv_to_heatmap("Data/Region.csv", "Outputs/heatmap.png")

# Convert CSV to pixelated image
csv_to_pixelated_image("Data/Region.csv", "Outputs/pixelated.png")

Data Files

• GrabberData.csv: Main sensor/measurement data
• Region.csv: Region-specific measurements
• USBImage.csv: USB camera image data
• Metadata files: Display settings and imaging parameters for each ion set

Output

Processed images and analysis results are saved to the Outputs/ directory, including:

• Detected ion positions and bounding boxes
• Heatmap visualizations
• Processed camera images