Quantum Dot Image Analysis

To install dependencies on bascottie for MSE 150 students:

    $ wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
    $ bash Miniconda3-latest-Linux-x86_64.sh # accept license, default options
    $ source ~/.bashrc
    $ pip install -r requirements.txt

To create a conda environment (maybe OSX-specific?) (Not needed on bascottie right now)

    $ conda env create -f requirements.yaml

Activate the qd conda environment and open the jupyter notebooks

    $ conda activate qd
    $ jupyter notebook

10 December 2020: The extract_qd_from_afm jupyter notebooks contain software for identifying quantum dots in AFM(Atomic Force Microscopy) images and calculating their radial distribution functions. All of the AFM images come from the 86, 106, 107, and 87 samples from the Simmonds lab google drive, and tifs have been renamed with a convention Un.tif, where U is an integer representing the number of microns across each of the square images is, and n is a letter index if there are multiple images of the same size. The jupyter notebooks currently encapsulate code for:

• Gaussian filtering of the initial images
• Peakfinding of the smoothed images
• Optional visualization of the peaks over a version of the original image.

Key parameters to get\_dots2 are size, which specifies the number in pixels of the smallest quantum dots to identify, and spacing which controls a minimum spacing threshold between dots.

get_dots returns the x-y coordinates of the dots which are then used in analysis of the RDF and the areal density b.

Opening this up to MSE150 on March 22nd.