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

Activate your conda environment: conda activate <env-name>
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.

License

moler-group/ssm_analyze

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