In [4]:
import matplotlib.pyplot as plt
import numpy as np
from copy import deepcopy
Volumes = [[0.0130705, 0.0230351, 0.0459612, 0.0917046, 0.182975, 0.365082,
0.728435, 1.45342, 2.89995, 5.78617, 11.5449, 23.0351, 45.9612,
91.7046], [0.0129317, 0.0227905, 0.045473, 0.0907305, 0.181031,
0.361205, 0.720698, 1.43798, 2.86915, 5.72471, 11.4223, 22.7905,
45.473, 90.7305], [0.0127682, 0.0225024, 0.0448982, 0.0895837,
0.178743, 0.356639, 0.711589, 1.41981, 2.83288, 5.65235, 11.2779,
22.5024, 44.8982, 89.5837], [0.0125805, 0.0221715, 0.044238,
0.0882664, 0.176115, 0.351395, 0.701125, 1.39893, 2.79123, 5.56923,
11.1121, 22.1715, 44.238, 88.2664], [0.0123688, 0.0217984,
0.0434935, 0.086781, 0.173151, 0.345481, 0.689326, 1.37539, 2.74426,
5.47551, 10.9251, 21.7984, 43.4935, 86.781], [0.0121335, 0.0213838,
0.0426663, 0.0851305, 0.169858, 0.338911, 0.676216, 1.34923,
2.69206, 5.37137, 10.7173, 21.3838, 42.6663, 85.1305], [0.0118752,
0.0209285, 0.0417579, 0.0833179, 0.166241, 0.331695, 0.661818,
1.3205, 2.63474, 5.257, 10.4891, 20.9285, 41.7579,
83.3179], [0.0115942, 0.0204334, 0.0407699, 0.0813467, 0.162308,
0.323847, 0.64616, 1.28926, 2.57241, 5.13263, 10.2409, 20.4334,
40.7699, 81.3467]]
Density = np.array([[3509.02,3386.86,3463.04,7094.49,3536.56,5026.91,2619.75,3636.14,0,0,659.631,808.578,1009.2,2.96997],
[1806.88,1774.4,1973.73,2495.78,2227.39,982.267,1824.64,1236.56,151.02,0,0,333.787,39.0793,13.9813],
[498.246,500.227,542.332,486.406,840.018,272.937,1183.46,478.65,1253.68,240.276,0,313.679,535.046,3.98433],
[284.361,375.393,530.052,335.993,388.931,757.639,221.358,1486.74,474.114,499.432,139.454,0,651.971,6.17692],
[403.731,0,0,343.008,140.303,77.3169,223.946,830.782,361.218,937.194,0,3.896,850.521,1.33007],
[102.216,13.8589,2.73515,92.9978,85.0742,0,241.176,234.738,434.828,378.97,411.298,0,893.818,7.71594],
[0,17.2049,77.6222,0,15.1037,109.527,0,309.346,165.754,533.685,0,192.642,705.879,5.6529],
[0,82.9729,14.048,15.1386,0,174.735,0,99.7931,377.167,135.385,541.105,0,543.908,23.4899]])
plt.imshow(Density[:,0:13], cmap="binary", extent=[16, 22.5, 7,0])
plt.show()
In [2]:
Density = np.array([[0,507.705,571.141,834.445,597.625,2056.37,1325.49,2059.8,2992.97,807.117,3777.19,0,3673.97,0,9.63822],
[1,308.56,366.673,147.695,1591.24,0,2049.1,1793.42,2678.89,1590.95,3432.81,2346.49,3138.99,599.18,11.7028],
[3,298.73,232.398,41.7534,1180.98,364.152,1450.44,1328.66,2345.74,1766.99,2274.7,1314.34,2212.32,1544.5,8.46079],
[4,77.0447,102.595,389.865,67.8707,1140.61,18.3627,1952.32,350.257,2666.39,51.9299,3071.66,0,2464.24,0.0411486],
[5,288.752,17.2775,0,651.52,71.5222,732.505,717.929,1389.37,637.245,1931.31,734.304,1468.86,1425.37,0.728988],
[6,28.1518,132.789,56.2275,439.347,0,1157.07,124.287,1391.5,441.688,2095.07,0,2242.36,946.491,0.327455],
[7,48.456,104.498,0,377.628,0,769.763,480.456,693.302,1229.48,1219.8,874.846,581.087,1712.5,14.2831]])
plt.imshow(Density[:,1:13], cmap="binary", extent=[16, 22.5, 7,0])
plt.show()
In [3]:
Density = np.array([[0,507.396,548.635,802.156,315.281,969.184,563.915,1702.87,877.593,1727.36,2172.27,3232.85,3043.99,0,8.5824],
[2,106.306,144.564,282.532,136.161,541.444,514.246,564.915,974.118,647.499,1721.99,989.552,1956.3,1626.57,6.43335],
[3,0,187.7,284.878,0,464.46,605.869,0,1734.99,10.3244,1605.01,1760.14,532.25,2159.43,4.41317],
[4,0,100.346,215.125,0,477.684,427.296,342.516,699.655,1107.31,635.077,1497.03,1177.06,1688.01,7.52288],
[5,0,63.4819,291.462,0,440.155,360.708,497.998,760.396,1188.94,634.655,1876.02,213.754,2488.67,6.68116],
[6,34.1598,50.9408,150.976,68.173,455.849,255.438,480.575,707.444,883.788,575.43,2033.81,101.651,2089.96,3.68135],
[7,0,87.0134,157.389,0,354.356,190.78,391.167,748.257,484.644,1014.36,1398.92,0,2084.58,9.20064]])
plt.imshow(Density[:,1:13], cmap="binary", extent=[16, 22.5, 7,0])
plt.show()
In [18]:
Gauss = [[0.000146222,0.00152518,0.0104476,0.0470463,0.139381,0.271853,0.343851,0.169326,0.0163913,0.000275136,0.000000749684],
[0.000146292,0.00152567,0.0104498,0.0470513,0.139386,0.271852,0.343844,0.169322,0.016391,0.00027513,0.000000749667],
[0.00014647,0.00152694,0.0104552,0.0470641,0.139399,0.271849,0.343826,0.169312,0.01639,0.000275115,0.000000749625],
[0.000146993,0.00153067,0.0104711,0.0471017,0.139438,0.271838,0.34377,0.169284,0.0163873,0.000275069,0.000000749499],
[0.000148947,0.00154456,0.0105301,0.0472411,0.139583,0.271799,0.343566,0.169179,0.0163771,0.000274897,0.000000749033],
[0.000158861,0.00161417,0.0108227,0.0479266,0.140287,0.271603,0.342566,0.168663,0.0163271,0.000274059,0.000000746749],
[0.000240385,0.0021425,0.0129031,0.0525479,0.144804,0.270141,0.335936,0.165252,0.015997,0.000268518,0.000000731649],
[0.00202997,0.00913824,0.0314354,0.0826496,0.166109,0.255227,0.294742,0.144323,0.013971,0.00023451,0.000000638985],
[0.0124662,0.0306631,0.0642212,0.114533,0.173935,0.224931,0.243223,0.118616,0.0114824,0.000192738,0.000000525168],
[0.0145973,0.0339889,0.0680515,0.117162,0.173455,0.220825,0.237361,0.115715,0.0112016,0.000188025,0.000000512324],
[0.0146547,0.0340756,0.0681486,0.117226,0.17344,0.220719,0.237212,0.115641,0.0111945,0.000187905,0.000000511997],
[0.0146552,0.0340765,0.0681495,0.117226,0.173439,0.220718,0.23721,0.11564,0.0111944,0.000187903,0.000000511994],
[0.0146552,0.0340765,0.0681495,0.117226,0.173439,0.220718,0.23721,0.11564,0.0111944,0.000187903,0.000000511994]]
total = [0.0 for i in range(len(Gauss))]
for i in Gauss:
print(sum(i))
plt.imshow(Gauss, cmap="binary", extent=[16, 22.5, 7,0])
plt.colorbar()
plt.show()
In [49]:
def flipUD(H):
for i in range(int((len(H) - len(H) % 2)/2)):
temp = deepcopy(H[i])
H[i] = deepcopy(H[len(H) - 1 - i])
H[len(H) - 1 - i] = deepcopy(temp)
return H
f = open("../../SummerREUDiskWork/public_html/RaeHelmreich2018/Density_to_StarCounts/OutputOrganized/StartingData/pencilBeams/g10B3010875L11125.csv")
f.readline()
Data = flipUD([list(map(float, i.split(","))) for i in f])
f.close()
f = open("../../SummerREUDiskWork/public_html/RaeHelmreich2018/Density_to_StarCounts/OutputOrganized/StartingData/pencilBeams/g30B1010875L11125.csv")
f.readline()
Data+=flipUD([list(map(float, i.split(","))) for i in f])
f.close()
plt.figure(figsize=(30,5))
plt.imshow(Data, cmap="binary", extent=[0, 59, -30,30])
plt.colorbar()
plt.show()
In [8]:
Sigmoid = [0.940193, 0.940185, 0.940167, 0.940125, 0.940032, 0.939824, 0.939356, 0.938307, 0.935962, 0.930739, 0.919227, 0.894395, 0.843261]
NewbyCompleteness = [0.986088,0.972016,0.957948,0.944999,0.934449,0.927228,0.922852,0.917635,0.902512,0.861945,0.777409,0.640076,0.471073]
TotalCompleteness = [0.927113, 0.913875, 0.900631, 0.888417, 0.878412, 0.871431, 0.866886, 0.861023, 0.844717, 0.802246, 0.714615, 0.572481, 0.397237]
plt.plot(np.arange(16, 22.5, 0.5), Sigmoid, 'ko')
plt.plot(np.arange(16, 22.5, 0.5), NewbyCompleteness, 'bo')
plt.plot(np.arange(16, 22.5, 0.5), TotalCompleteness, 'ro')
plt.show()
In [11]:
#Convolution = [0, 7.96611e-07, 0.000278424, 0.0164108, 0.169163, 0.342566, 0.270141, 0.166109, 0.115194, 0.0685756, 0.0347314, 0.0169331, 0]
#Convolution = [0.428904, 0.288875, 0.141076, 0.0471693, 0.0105291, 0.00161417, 0.000240385, 0, 0, 0, 0, 0, 0] # data[1] = 1
#Convolution = [0, 0, 0, 0, 0, 0, 7.31649e-07, 0.00023451, 0.0115486, 0.116606, 0.241778, 0.255026, 0.276061]# data[11]=1
#Convolution = [0, 0, 0, 0, 0, 0, 0, 6.38985e-07, 0.00019385, 0.0112879, 0.117867, 0.274081, 0.351314]# data[12]=1
Convolution = [0, 7.96611e-07, 0.000279182, 0.0166863, 0.185538, 0.511229, 0.606077, 0.421336, 0.290132, 0.18664, 0.104192, 0.0563064, 0.0233265]
interpConvolution = [0, 0.000139212, 0.000278424, 0.0847206, 0.169163, 0.219652, 0.270141, 0.192667, 0.115194, 0.0749626, 0.0347314, 0.0173657, 0]
print(sum(Convolution)/2.)
plt.plot(np.arange(16, 22.5, 0.5), Convolution, 'ko')
plt.show()
In [ ]: