diff --git a/Turbulence_Sim_v1_python/v1_sim_example1.py b/Turbulence_Sim_v1_python/v1_sim_example1.py
deleted file mode 100644
index 9f71aa4..0000000
--- a/Turbulence_Sim_v1_python/v1_sim_example1.py
+++ /dev/null
@@ -1,39 +0,0 @@
-from matplotlib import pyplot as plt
-import numpy as np
-import utilities as util
-
-'''
-This script shows an example case of a the "version 1" simulator [see: https://arxiv.org/pdf/2004.11210.pdf]. The 
-tilts are drawn per pixel, however, the blurs are drawn independently over patches (see Sec 4.6). The 
-'''
-
-
-def rgb2gray(rgb):
-    return np.dot(rgb[...,:3], [0.2989, 0.5870, 0.1140])
-
-
-img = rgb2gray(plt.imread('chart.png'))
-N = 225 # size of the image -- assumed to be square (pixels)
-D = 0.2 # length of aperture diameter (meters)
-L = 7000 # length of propagation (meters)
-r0 = 0.05 # the Fried parameter r0. The value of D/r0 is critically important! (See associated paper)
-wvl = 0.525e-6 # the mean wavelength -- typically somewhere suitably in the middle of the spectrum will be sufficient
-obj_size = 2.06*1 # the size of the object in the object plane (meters). Can be different the Nyquist sampling, scaling
-                  # will be done automatically.
-
-param_obj = util.p_obj(N, D, L, r0, wvl, obj_size) # generating the parameter object, some other things are computed within this
-                                                   # function, see the def for details
-print(param_obj['N'] * param_obj['delta0'], param_obj['smax'], param_obj['scaling'])
-#print('hi')
-#print(param_obj['spacing'])
-#print('hi')
-S = util.gen_PSD(param_obj) # finding the PSD, see the def for details
-param_obj['S'] = S # appending the PSD to the parameter object for convenience
-
-for i in range(100):
-    img_tilt, _ = util.genTiltImg(img, param_obj) # generating the tilt-only image
-    img_blur = util.genBlurImage(param_obj, img_tilt)
-    plt.imshow(img_tilt,cmap='gray',vmin=0,vmax=1)
-    plt.show()
-    plt.imshow(img_blur, cmap='gray', vmin=0, vmax=1)
-    plt.show()