A UNet for recovering 21cm intensity information lost to "The Wedge".

• python version 3.5 or more recent
• numpy
• scipy
• matplotlib
• tensorflow
• tqdm
• scikit-image
• scikit-learn

• 2d_unet - Contains the 2D version of this code, as well as a file called img_maker.py. The files should be kept in the same directory as the /images/ and /masks/ folders. These folders should contain 2D slices of the images and their associated masks, named from 0.png to n.png. img_maker.py should be kept in a directory called /src/ which is parallel to the data directory /data/, which should be organized as specified at the end of the readme. The 2D version of the U-Net is no longer being maintained, and depends on keras.

• config.py - Contains the settings on variables relevant to the rest of the code. This should be the only file you need to edit to run the code.

• isensee2017.py - The definition of the 3D network model, based on that presented in Isensee (2017).

• isensee_train.py - Use this to train the 3D network from scratch or to continue training.

• isensee_validation.py - Use this to load an existing model and run validation on new data.

• utils.py - Miscellaneous utilities used in the code.

• concaveSitk.py - Defines watershed segmentation used in plot.py. This code was written by Yin Lin at the University of Chicago.

• plot.py - Various utilities used in the visualization of the network outputs.

• crossCorrelation.py - Edit the file locations within the file to tell it which data to cross-correlate, then run the script to generate a cross-correlation plot.

Simply executing isensee_train.py with appropriate keyword arguments will cause the network to train on whatever images are kept in the images/ and masks/ directories. For example,

python isensee_train.py 128 128 128 0.985 3 area1/ 100 5 3

will train a network on images with dimensions of 128x128x128 with a learn rate decay factor of 0.985, a batch size of 3, it will save all outputs in a directory ./area1/, the training will run for up to 100 epochs, and the network will be built with 5 levels of depth and three 3 segmentation layers for deep supervision.