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readstringtoplot.py
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readstringtoplot.py
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import matplotlib.pyplot as plt
import os,sys
#author: Jing jiw2026@med.cornell.edu
#function: plot string
#time: Sept. 16th 2018
def readstringtoplot():
'''
sx= "0.25 0.75 1.25 1.75 2.25 2.75 3.25 3.75 4.25 4.75 5.25 5.75 6.25 6.75 7.25 7.75 8.25 8.75 9.25 9.75 10.25 10.75 11.25 11.75 12.25 12.75 13.25 13.75 14.25 14.75 15.25 15.75 16.25 16.75 17.25 17.75 18.25 18.75 19.25 19.75 20.25 20.75 21.25 21.75 22.25 22.75 23.25 23.75 24.25 24.75 25.25 25.75 26.25 26.75 27.25 27.75 28.25 28.75 29.25 29.75 30.25 30.75 31.25 31.75 32.25 32.75 33.25 33.75 34.25 34.75 35.25 35.75 36.25 36.75 37.25 37.75 38.25 38.75 39.25 39.75 40.25 40.75 41.25 41.75 42.25 42.75 43.25 43.75 44.25 44.75 45.25 45.75 46.25 46.75 47.25 47.75 48.25 48.75 49.25 49.75 50.25 50.75 51.25 51.75 52.25 52.75 53.25 53.75 54.25 54.75 55.25 55.75 56.25 56.75 57.25 57.75 58.25 58.75 59.25 59.75 60.25 60.75 61.25 61.75 62.25 62.75 63.25 63.75 64.25 64.75 65.25 65.75 66.25 66.75 67.25 67.75 68.25 68.75 69.25 69.75 70.25 70.75 71.25 71.75 72.25 72.75 73.25 73.75 74.25 74.75 75.25 75.75 76.25 76.75 77.25 77.75 78.25 78.75 79.25 79.75 80.25 80.75 81.25 81.75 82.25 82.75 83.25 83.75 84.25 84.75 85.25 85.75 86.25 86.75 87.25 87.75 88.25 88.75 89.25 89.75 90.25 90.75 91.25 91.75 92.25 92.75 93.25 93.75 94.25 94.75 95.25 95.75 96.25 96.75 97.25 97.75 98.25 98.75 99.25 99.75 100.25 100.75 101.25 101.75 102.25 102.75 103.25 103.75 104.25 104.75 105.25 105.75 106.25 106.75 107.25 107.75 108.25 108.75 109.25 109.75 110.25 110.75 111.25 111.75 112.25 112.75 113.25 113.75 114.25 114.75 115.25 115.75 116.25 116.75 117.25 117.75 118.25 118.75 119.25 119.75 120.25 120.75 121.25 121.75 122.25 122.75 123.25 123.75 124.25 124.75 125.25 125.75 126.25 126.75 127.25 127.75 128.25 128.75 129.25 129.75 130.25 130.75 131.25 131.75"
sy = "0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0"
'''
sx="0.25 0.75 1.25 1.75 2.25 2.75 3.25 3.75 4.25 4.75 5.25 5.75 6.25 6.75 7.25 7.75 8.25 8.75 9.25 9.75 10.25 10.75 11.25 11.75 12.25 12.75 13.25 13.75 14.25 14.75 15.25 15.75 16.25 16.75 17.25 17.75 18.25 18.75 19.25 19.75 20.25 20.75 21.25 21.75 22.25 22.75 23.25 23.75 24.25 24.75 25.25 25.75 26.25 26.75 27.25 27.75 28.25 28.75 29.25 29.75 30.25 30.75 31.25 31.75 32.25 32.75 33.25 33.75 34.25 34.75 35.25 35.75 36.25 36.75 37.25 37.75 38.25 38.75 39.25 39.75 40.25 40.75 41.25 41.75 42.25 42.75 43.25 43.75 44.25 44.75 45.25 45.75 46.25 46.75 47.25 47.75 48.25 48.75 49.25 49.75 50.25 50.75 51.25 51.75 52.25 52.75 53.25 53.75 54.25 54.75 55.25 55.75 56.25 56.75 57.25 57.75 58.25 58.75 59.25 59.75 60.25 60.75 61.25 61.75 62.25 62.75 63.25 63.75 64.25 64.75 65.25 65.75 66.25 66.75 67.25 67.75 68.25 68.75 69.25 69.75 70.25 70.75 71.25 71.75 72.25 72.75 73.25 73.75 74.25 74.75 75.25 75.75 76.25 76.75 77.25 77.75 78.25 78.75 79.25 79.75 80.25 80.75 81.25 81.75 82.25 82.75 83.25 83.75 84.25 84.75 85.25 85.75 86.25 86.75 87.25 87.75 88.25 88.75 89.25 89.75 90.25 90.75 91.25 91.75 92.25 92.75 93.25 93.75 94.25 94.75 95.25 95.75 96.25 96.75 97.25 97.75 98.25 98.75 99.25 99.75 100.25 100.75 101.25 101.75 102.25 102.75 103.25 103.75 104.25 104.75 105.25 105.75 106.25 106.75 107.25 107.75 108.25 108.75 109.25 109.75 110.25 110.75 111.25 111.75 112.25 112.75 113.25 113.75 114.25 114.75 115.25 115.75 116.25 116.75 117.25 117.75 118.25 118.75 119.25 119.75 120.25 120.75 121.25 121.75 122.25 122.75 123.25 123.75 124.25 124.75 125.25 125.75 126.25 126.75 127.25 127.75 128.25 128.75 129.25 129.75 130.25 130.75 131.25 131.75"
sy="0 0 0 0 0 0 0 0 0 1.63825e-011 3.23248e-010 3.93592e-010 6.89671e-011 2.61013e-010 4.07319e-010 2.81527e-010 1.54745e-010 1.28832e-010 2.44532e-010 1.45036e-010 3.17652e-010 1.68299e-010 5.49897e-010 3.81125e-010 6.31019e-010 1.11503e-010 2.89007e-010 9.52927e-012 6.65145e-011 6.27191e-010 3.88695e-010 3.98583e-010 4.97176e-011 2.01519e-010 6.90419e-010 9.95776e-010 1.12192e-009 1.76653e-010 5.94221e-010 2.07839e-010 3.39964e-010 1.82501e-010 1.01064e-009 6.50973e-011 4.70285e-010 7.11324e-011 4.47838e-011 7.93959e-010 5.18609e-011 2.54712e-010 5.35837e-010 4.34185e-010 2.58047e-011 2.70242e-010 1.93069e-010 1.38101e-010 4.52617e-010 4.02679e-011 1.40976e-010 1.02851e-012 7.86908e-011 3.86441e-010 2.25787e-010 2.40988e-010 8.63607e-011 1.35856e-010 2.91246e-010 1.81319e-010 1.95531e-010 1.61076e-010 2.02578e-010 2.39668e-010 1.52951e-010 2.07915e-010 2.48438e-010 3.0224e-010 6.4535e-011 6.2185e-012 4.28759e-011 2.27769e-010 3.78893e-010 1.32222e-010 3.62652e-011 1.20034e-010 9.02762e-011 1.17533e-011 2.13186e-011 7.04539e-011 1.28222e-010 2.73657e-011 7.88226e-012 1.70412e-010 6.0204e-011 5.55396e-011 1.8264e-010 5.45457e-011 1.21785e-010 6.97874e-011 2.7491e-011 1.84143e-010 2.22196e-011 1.39296e-011 7.42801e-011 5.84906e-011 1.20295e-011 2.58261e-011 4.00057e-011 1.55485e-011 9.0234e-011 3.19239e-011 4.55101e-010 2.07369e-011 2.43228e-011 7.01013e-011 1.34218e-011 8.54793e-011 9.257e-011 2.45851e-011 2.94814e-010 1.25091e-011 2.63145e-010 1.19129e-010 1.88634e-010 2.50353e-011 5.57213e-013 1.12688e-010 5.50698e-011 2.15253e-010 6.67084e-011 2.42464e-010 1.38159e-010 9.74421e-012 1.03687e-011 7.22095e-012 7.7263e-011 3.48868e-011 2.16726e-012 1.64295e-011 1.74517e-010 1.08632e-010 2.55085e-012 1.71277e-011 4.22746e-011 1.02968e-010 1.27174e-010 1.28107e-010 2.01137e-010 1.01711e-010 1.65556e-011 5.9406e-011 5.96325e-012 1.98555e-010 2.0898e-011 8.98837e-012 6.97664e-011 1.50679e-011 1.42331e-011 1.45948e-010 5.18368e-011 8.36727e-011 9.53676e-011 2.77373e-011 6.23297e-011 3.08396e-013 4.34808e-011 9.54206e-011 2.25564e-010 1.20586e-010 5.63301e-011 7.48779e-011 1.66766e-011 5.42819e-011 1.32609e-010 2.06385e-010 5.81695e-011 5.25982e-013 2.10646e-011 7.86971e-012 1.37392e-011 2.25446e-011 3.37241e-012 5.1612e-011 1.46372e-012 2.24727e-012 5.59526e-011 1.52346e-011 1.74492e-011 1.33415e-010 1.41206e-010 4.3908e-011 1.46541e-011 2.37023e-010 4.91491e-011 1.21044e-010 1.30769e-010 8.29115e-012 3.65219e-011 2.73479e-011 0.174084 0.107179 0.182115 0.133081 0.0400524 0.0300011 0.0816753 0.111229 0.0791212 0.0857478 0.0662091 0.0756869 0.106075 0.177203 0.0496542 0.063157 0.134441 0.0929291 0.0657078 0.0646251 0.0608053 0.0570491 0.0553677 0.0464856 0.00383196 0.0888056 0.087491 0.0774077 0.122346 0.137537 0.0740824 0.0584203 0.0734034 0.0324283 0.0957575 0.122825 0.142039 0.197855 0.172284 0.124005 0.124275 0.136894 0.048674 0.0560998 0.0435432 5.9778e-010 0.0123799 0.0526434 0.197659 0.268558 0.300622 0.184787 0.136722 0.116591 0.0998779 0.0832039 0.173071 0.279924 0.311711 0.292929 0.114312 0.235782 0.239228 0.150751 0.158186 0.283962"
x = map(float, ''.join(sx).split())
y = map(float, ''.join(sy).split())
plt.figure(1)
plt.plot(x,y)
plt.show()
if __name__ == "__main__":
readstringtoplot()