Scanning SQUID analysis

ssm_analyze is an analysis GUI for scanning SQUID microscopy datasets generated by the scanning-squid python package.

Installation

It is recommended to install ssm_analyze inside of a dedicated conda environment. ssm_analyze has been tested on Python 3.6, 3.7, 3.8 and 3.9. You can create a conda environment using the following commands:

conda create --name <env-name> python=<3.6, 3.7, 3.8, or 3.9>
conda activate <env-name>

After creating and activating your conda env, you can install ssm-analyze in one of two ways:

Install via pip

pip install ssm-analyze

Install from source

• Clone or download this repository
• Navigate to this directory and install ssm_analyze using pip:

  pip install -e .
  

Usage

• Start the GUI by running either of the below commands from the command line:

  ssm_analyze
  

  or

  python -m ssm_analyze.start_gui
  

• Use the Select directory button, File -> Select directory..., or ctrl+O (Windows)/cmd+O (Mac) to select the data directory. This base directory (for example sample_data/data in this repo) should contain dated subdirectories, which in turn contain single datasets (e.g. sample_data/data/2018-09-14/#016_scan_13-07-41).
• Click a specific dataset in the DataSet Browser to load its data. The dataset metadata and instrument settings can be examined with the Measurement Metadata and Microscope Snapshot widgets.
• The arrays contained in the dataset can be visualized/lightly processed in the DataSet Plotter.
• The current matplotlib figure can be exported using Plot -> Export matplotlib... or ctrl+P (Windows)/cmd+P (Mac).
• The currently displayed data (including any rotations, background subtraction, cross-sections, etc.) can be exported using File -> Export current data... or ctrl+S (Windows)/cmd+S (Mac). The available export formats are:
  • MATLAB .mat file: Each array is saved to a struct with field names 'array' and 'unit'.
  • HDF5 .h5 file: Can be read by h5py, MATLAB, or viewed with HDF5 View.
  • Python pickle: A dictionary of arrays is written directly to a file in binary form and can be loaded using:

    import pickle
    with open('filename.pickle', 'rb') as f:
        arrays = pickle.load(f)

• The built-in IPython console has access to the following:
  • matplotlib.pyplot: plt
  • numpy: np
  • a dict of the current arrays in the form of arrays of pint Quantities. For example, arrays['MAG'].magnitude will be the MAG array, with units arrays['MAG'].units
  • the current qcodes.data.data_set.DataSet: dataset

Notes

• If you wish to uninstall the program, run conda remove -n <env-name> --all to remove the conda environment, then delete this repository.
• If you find bugs/have suggestions for new features, please use the GitHub Issues feature.
• Contact: logan.bvh@gmail.com.
• Logo by Tom Shahar.