# siyucosmo/ML-Recon

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Latest commit 501af91 Mar 15, 2019
Type Name Latest commit message Commit time
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Unet Sep 9, 2018
configs Sep 5, 2018
model Sep 5, 2018
.gitignore May 31, 2018
data_utils.py Sep 9, 2018
plot.py Sep 9, 2018
reconLPT2Nbody_uNet.py Sep 23, 2018

# ML-Recon

## Objective:

ML project to predict Nbody simulation output from initial condition. Both input and output are particle displacement fields.

## File descriptions:

• reconLPT2Nbody_uNet.py : main excute files
• periodic_padding.py : code to fulfill periodic boundary padding
• data_utils.py : how to load data + test/analysis
• model/BestModel.pt : Best trained model
• configs/config_unet.json : most of the hyperparameters
• Unet/uNet.py : architecture
• plot.py : plot the result

## To run the code:

python reconLPT2Nbody_uNet.py --config_file_path configs/config_unet.json

or

./reconLPT2Nbody_uNet.py -c configs/config_unet.json

## Instruction:

1. Input raw data should be in the format of x_y.npy (y is in range of (0,1000,1) and x is controled by lIndex and hIndex in configs/config_unet.json e.g. 0_0.npy, 1_999.npy). The shape of the data in each file should be (32,32,32,10), where the first coloumn is density, the second to forth coloumn is (\phi_x, \phi_y,\phi_z) for ZA, the fifth to seventh column is for 2LPT, and the eighth to tenth is for fastPM. (Yu provides simulation files and each file contains 1000 simulations. I stored the 1000 simulations in each file into separate files. The reason why I did this is because GPU doesn't have enough memory to store all the files. Thus I only provide the name and the path to each files.)

2. The output of the model is in the shape of (6,32,32,32) where (0:3,32,32,32) stores the predicted fastPM simulations from uNet model and (3:6,32,32,32) stores the corresponding real simulations.

3. The best trained model is stored in model/BestModel.pt. All the tests (pancake, cosmology, etc) should be tested on this model. You should only change the following parameters in configs/config_unet.json to do different tests:

• base_data_path: tell where the input (LPT/ZA) is stored.
• output_path: where do you want to store the output
4. The ZA/2LPT/fastPM data Yu provides are all stored in the following directory on Nersc: /global/homes/y/yfeng1/m3035/yfeng1/siyu-ml/

5. I have wrote code plot.py to do all the plots. You can use it as a reference.

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