diff --git a/demo.py b/demo.py
deleted file mode 100644
index 3105f51..0000000
--- a/demo.py
+++ /dev/null
@@ -1,56 +0,0 @@
-'''
-Demo code for imaging through turbulence simulation
-
-Z. Mao, N. Chimitt, and S. H. Chan, "Accerlerating Atmospheric Turbulence 
-Simulation via Learned Phase-to-Space Transform", ICCV 2021
-
-Arxiv: https://arxiv.org/abs/2107.11627
-
-Zhiyuan Mao, Nicholas Chimitt, and Stanley H. Chan
-Copyright 2021
-Purdue University, West Lafayette, IN, USA
-'''
-
-from simulator import Simulator
-from turbStats import tilt_mat, corr_mat
-import matplotlib.pyplot as plt
-import torch
-
-# Select device.
-device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('CPU')
-
-
-'''
-The corr_mat function is used to generate spatial-temporal correlation matrix 
-for point spread functions. It may take over 10 minutes to finish. However, 
-for each correlation value, it only needs to be computed once and can be 
-used for all D/r0 values. You can also download the pre-generated correlation
-matrix from our website. 
-https://engineering.purdue.edu/ChanGroup/project_turbulence.html
-'''
-
-# Uncomment the following line to generate correlation matrix
-# corr_mat(-0.1,'./data/')
-
-# Load image, permute axis if color
-x = plt.imread('./images/color.png')
-
-if len(x.shape) == 3: 
-    x = x.transpose((2,0,1))
-x = torch.tensor(x, device = device, dtype=torch.float32)
-
-# Generate correlation matrix for tilt. Do this once for each different turbulence parameter. 
-tilt_mat(x.shape[1], 0.1, 0.05, 3000)
-
-# Simulate
-simulator = Simulator(2, 512).to(device, dtype=torch.float32)
-
-out = simulator(x).detach().cpu().numpy()
-
-if len(out.shape) == 3: 
-    out = out.transpose((1,2,0))
-
-# save image
-plt.imsave('./images/out.png',out)
-
-