In [2]:

    

%matplotlib inline

from matplotlib import pyplot as plt
import numpy
import csv


    

In [37]:

    

data = open('../data/data.csv', 'r').readlines()
fieldnames = ['x', 'y', 'z', 'unmasked', 'synapses']
reader = csv.reader(data)
reader.next()

rows = [[int(col) for col in row] for row in reader]


    

    




ERROR! Session/line number was not unique in database. History logging moved to new session 51

In [38]:

    

x = list([r[0] for r in rows])
y = list([r[1] for r in rows])
z = list([r[2] for r in rows])

sorted_x = sorted(list(set([r[0] for r in rows])))
sorted_y = sorted(list(set([r[1] for r in rows])))
sorted_z = sorted(list(set([r[2] for r in rows])))

print len(sorted_x)
print len(sorted_y)
print len(sorted_z)

print sorted_x
print sorted_y
print sorted_z

print min(sorted_x)
print max(sorted_x)
print min(sorted_z)
print max(sorted_z)


print len(x)
#print len(y)
#print len(z)

#print x
#print y
#print z


    

    




108
52
11
[19, 58, 97, 136, 175, 214, 253, 292, 331, 370, 409, 448, 487, 526, 565, 604, 643, 682, 721, 760, 799, 838, 877, 916, 955, 994, 1033, 1072, 1111, 1150, 1189, 1228, 1267, 1306, 1345, 1384, 1423, 1462, 1501, 1540, 1579, 1618, 1657, 1696, 1735, 1774, 1813, 1852, 1891, 1930, 1969, 2008, 2047, 2086, 2125, 2164, 2203, 2242, 2281, 2320, 2359, 2398, 2437, 2476, 2515, 2554, 2593, 2632, 2671, 2710, 2749, 2788, 2827, 2866, 2905, 2944, 2983, 3022, 3061, 3100, 3139, 3178, 3217, 3256, 3295, 3334, 3373, 3412, 3451, 3490, 3529, 3568, 3607, 3646, 3685, 3724, 3763, 3802, 3841, 3880, 3919, 3958, 3997, 4036, 4075, 4114, 4153, 4192]
[1369, 1408, 1447, 1486, 1525, 1564, 1603, 1642, 1681, 1720, 1759, 1798, 1837, 1876, 1915, 1954, 1993, 2032, 2071, 2110, 2149, 2188, 2227, 2266, 2305, 2344, 2383, 2422, 2461, 2500, 2539, 2578, 2617, 2656, 2695, 2734, 2773, 2812, 2851, 2890, 2929, 2968, 3007, 3046, 3085, 3124, 3163, 3202, 3241, 3280, 3319, 3358]
[55, 166, 277, 388, 499, 610, 721, 832, 943, 1054, 1165]
19
4192
55
1165
61776

In [39]:

    

volume = numpy.ndarray((len(x), len(y)))

for row in rows:
    volume[row[0], row[1]] += row[-1]


    

In [13]:

    

#numpy.set_printoptions(threshold='nan')
print volume[19]
print len(volume)
print len(volume[19])


    

    




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   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
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   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   4.   0.   0.   0.   0.
   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
   0.   0.   0.   0.  11.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  10.   0.
   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
   0.   0.   0.   0.   0.   0.   0.   8.   0.   0.   0.   0.   0.   0.   0.
   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
   0.  14.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  14.   0.   0.   0.   0.
   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
   0.   0.   0.   0.   7.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   9.   0.
   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
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   0.   8.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
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   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.
   0.   0.   0.   0.   0.   0.]
61776
61776

In [5]:

    

plt.imshow(volume[0:500])
plt.show()


    

In [6]:

    

xaxis = numpy.ndarray((len(x)))
yaxis = numpy.ndarray((len(x)))
zaxis = numpy.ndarray((len(x)))

for row in rows:
    xaxis[row[0]] += row[-1]
    yaxis[row[1]] += row[-1]
    zaxis[row[2]] += row[-1]


    

In [10]:

    

numpy.set_printoptions(threshold='nan')
print xaxis

plt.plot(xaxis)
plt.show()


    

    




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In [40]:

    

volume = numpy.ndarray((max(sorted_x)+1, max(sorted_z)+1))

for row in rows:
    if row[-2] != 0:
        volume[row[0], row[2]] += float(row[-1])
    
    
count = 0
for i in range(0,max(sorted_x)):  #to iterate between 10 to 20
    for j in range(0,max(sorted_z)):
        if volume[i ,j] != 0:
            print i, j, volume[i ,j]


    

    




19 55 236.0
19 610 130.0
19 832 3.0
19 1054 2.0
58 55 411.0
58 610 275.0
58 721 4.0
58 832 13.0
97 55 651.0
97 166 10.0
97 610 382.0
97 721 4.0
97 832 11.0
136 55 1047.0
136 166 17.0
136 277 3.0
136 610 424.0
136 721 14.0
136 832 12.0
136 943 1.0
136 1054 1.0
175 55 1747.0
175 166 241.0
175 277 10.0
175 388 60.0
175 610 613.0
175 721 26.0
175 832 11.0
175 943 1.0
175 1054 19.0
214 55 3405.0
214 166 1438.0
214 277 124.0
214 388 283.0
214 499 7.0
214 610 653.0
214 721 108.0
214 832 67.0
214 943 24.0
214 1054 101.0
253 55 4765.0
253 166 3463.0
253 277 1395.0
253 388 1126.0
253 499 229.0
253 610 1050.0
253 721 633.0
253 832 280.0
253 943 130.0
253 1054 366.0
292 55 5250.0
292 166 6286.0
292 277 3731.0
292 388 2713.0
292 499 1750.0
292 610 2543.0
292 721 1734.0
292 832 1535.0
292 943 734.0
292 1054 1797.0
331 55 5283.0
331 166 5828.0
331 277 5647.0
331 388 4331.0
331 499 5487.0
331 610 5285.0
331 721 4637.0
331 832 5145.0
331 943 4833.0
331 1054 4880.0
370 55 6079.0
370 166 5739.0
370 277 6734.0
370 388 5771.0
370 499 8685.0
370 610 5927.0
370 721 7130.0
370 832 7358.0
370 943 8509.0
370 1054 6522.0
409 55 6379.0
409 166 6354.0
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409 388 6538.0
409 499 8825.0
409 610 6074.0
409 721 7300.0
409 832 8489.0
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448 55 7111.0
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448 499 8473.0
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526 55 7359.0
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526 499 10477.0
526 610 7329.0
526 721 6315.0
526 832 7477.0
526 943 6913.0
526 1054 8440.0
565 55 6841.0
565 166 5850.0
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565 499 10727.0
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565 832 7414.0
565 943 7040.0
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604 55 6814.0
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604 943 6598.0
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643 55 6848.0
643 166 5439.0
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643 388 6902.0
643 499 9807.0
643 610 7368.0
643 721 7510.0
643 832 8762.0
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643 1054 8020.0
682 55 7402.0
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682 499 9221.0
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682 721 8115.0
682 832 9096.0
682 943 6955.0
682 1054 8765.0
721 55 7133.0
721 166 6578.0
721 277 6818.0
721 388 6356.0
721 499 9049.0
721 610 7862.0
721 721 8880.0
721 832 9957.0
721 943 6446.0
721 1054 7980.0
760 55 6614.0
760 166 6443.0
760 277 7248.0
760 388 7141.0
760 499 9768.0
760 610 8126.0
760 721 8787.0
760 832 9360.0
760 943 5554.0
760 1054 7309.0
799 55 6873.0
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799 610 7800.0
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838 55 6965.0
838 166 6455.0
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955 55 6619.0
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994 55 7098.0
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994 499 9617.0
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1033 55 7559.0
1033 166 6308.0
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1033 388 6969.0
1033 499 9256.0
1033 610 7425.0
1033 721 7375.0
1033 832 9589.0
1033 943 8185.0
1033 1054 8166.0
1072 55 6790.0
1072 166 6481.0
1072 277 7593.0
1072 388 7187.0
1072 499 9041.0
1072 610 7813.0
1072 721 7891.0
1072 832 9140.0
1072 943 7993.0
1072 1054 7644.0
1111 55 6183.0
1111 166 6033.0
1111 277 7846.0
1111 388 7139.0
1111 499 7879.0
1111 610 7002.0
1111 721 7820.0
1111 832 9486.0
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1111 1054 8541.0
1150 55 7136.0
1150 166 5935.0
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1228 55 7355.0
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1267 55 6915.0
1267 166 5735.0
1267 277 6625.0
1267 388 7511.0
1267 499 8644.0
1267 610 7509.0
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1267 943 6975.0
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1306 55 6205.0
1306 166 5627.0
1306 277 6796.0
1306 388 7390.0
1306 499 8431.0
1306 610 6807.0
1306 721 7819.0
1306 832 9175.0
1306 943 7948.0
1306 1054 7213.0
1345 55 6267.0
1345 166 5693.0
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1345 388 7770.0
1345 499 8842.0
1345 610 6622.0
1345 721 7527.0
1345 832 9312.0
1345 943 7720.0
1345 1054 7769.0
1384 55 7103.0
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1384 721 6851.0
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1384 943 7358.0
1384 1054 7857.0
1423 55 6937.0
1423 166 6389.0
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1540 55 5825.0
1540 166 5433.0
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1540 1054 8359.0
1579 55 6153.0
1579 166 6240.0
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1579 1054 8198.0
1618 55 6806.0
1618 166 6273.0
1618 277 7722.0
1618 388 8103.0
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1618 943 7452.0
1618 1054 8575.0
1657 55 7230.0
1657 166 6732.0
1657 277 6713.0
1657 388 7684.0
1657 499 9204.0
1657 610 7252.0
1657 721 6900.0
1657 832 7029.0
1657 943 7004.0
1657 1054 7659.0
1696 55 6807.0
1696 166 6219.0
1696 277 6769.0
1696 388 7334.0
1696 499 9106.0
1696 610 6711.0
1696 721 7075.0
1696 832 6852.0
1696 943 6428.0
1696 1054 6834.0
1735 55 6380.0
1735 166 5932.0
1735 277 6470.0
1735 388 7147.0
1735 499 9665.0
1735 610 7956.0
1735 721 7371.0
1735 832 7411.0
1735 943 7120.0
1735 1054 8148.0
1774 55 6032.0
1774 166 5039.0
1774 277 6260.0
1774 388 6622.0
1774 499 9080.0
1774 610 7898.0
1774 721 7929.0
1774 832 7794.0
1774 943 7854.0
1774 1054 8594.0
1813 55 6513.0
1813 166 6301.0
1813 277 7428.0
1813 388 7463.0
1813 499 9019.0
1813 610 7340.0
1813 721 7758.0
1813 832 8683.0
1813 943 7876.0
1813 1054 7890.0
1852 55 6550.0
1852 166 6819.0
1852 277 7573.0
1852 388 7495.0
1852 499 8718.0
1852 610 6963.0
1852 721 7682.0
1852 832 8071.0
1852 943 7841.0
1852 1054 8725.0
1891 55 6258.0
1891 166 6124.0
1891 277 6767.0
1891 388 6724.0
1891 499 8361.0
1891 610 7291.0
1891 721 7004.0
1891 832 7817.0
1891 943 8388.0
1891 1054 9493.0
1930 55 5893.0
1930 166 5364.0
1930 277 6101.0
1930 388 6462.0
1930 499 8494.0
1930 610 6345.0
1930 721 6387.0
1930 832 8203.0
1930 943 8842.0
1930 1054 8810.0
1969 55 5847.0
1969 166 5443.0
1969 277 5698.0
1969 388 7040.0
1969 499 8770.0
1969 610 6614.0
1969 721 6240.0
1969 832 8316.0
1969 943 8289.0
1969 1054 8062.0
2008 55 6282.0
2008 166 5639.0
2008 277 6322.0
2008 388 6638.0
2008 499 8990.0
2008 610 6838.0
2008 721 7019.0
2008 832 8185.0
2008 943 7818.0
2008 1054 7750.0
2047 55 6653.0
2047 166 6090.0
2047 277 7069.0
2047 388 7001.0
2047 499 8369.0
2047 610 6182.0
2047 721 6750.0
2047 832 7256.0
2047 943 6650.0
2047 1054 8277.0
2086 55 7279.0
2086 166 6392.0
2086 277 7109.0
2086 388 6554.0
2086 499 7620.0
2086 610 5849.0
2086 721 6474.0
2086 832 7740.0
2086 943 7703.0
2086 1054 8360.0
2125 55 7153.0
2125 166 5932.0
2125 277 6567.0
2125 388 6521.0
2125 499 7427.0
2125 610 6279.0
2125 721 7131.0
2125 832 8640.0
2125 943 8388.0
2125 1054 8233.0
2164 55 6302.0
2164 166 5680.0
2164 277 6626.0
2164 388 6408.0
2164 499 8596.0
2164 610 6898.0
2164 721 7118.0
2164 832 8322.0
2164 943 7711.0
2164 1054 7931.0
2203 55 5900.0
2203 166 5225.0
2203 277 6354.0
2203 388 6940.0
2203 499 9148.0
2203 610 7067.0
2203 721 7357.0
2203 832 7845.0
2203 943 7665.0
2203 1054 8614.0
2242 55 5785.0
2242 166 4937.0
2242 277 6649.0
2242 388 7413.0
2242 499 10097.0
2242 610 7334.0
2242 721 7344.0
2242 832 8077.0
2242 943 7800.0
2242 1054 8165.0
2281 55 6050.0
2281 166 5373.0
2281 277 6583.0
2281 388 7131.0
2281 499 9425.0
2281 610 6891.0
2281 721 6954.0
2281 832 7690.0
2281 943 7336.0
2281 1054 8362.0
2320 55 6999.0
2320 166 5732.0
2320 277 7025.0
2320 388 7327.0
2320 499 9013.0
2320 610 6625.0
2320 721 7023.0
2320 832 8077.0
2320 943 7421.0
2320 1054 8404.0
2359 55 7616.0
2359 166 6386.0
2359 277 7014.0
2359 388 7287.0
2359 499 8899.0
2359 610 6829.0
2359 721 6693.0
2359 832 7992.0
2359 943 7591.0
2359 1054 8742.0
2398 55 6614.0
2398 166 6735.0
2398 277 6763.0
2398 388 6687.0
2398 499 8163.0
2398 610 6439.0
2398 721 6951.0
2398 832 8706.0
2398 943 8298.0
2398 1054 9487.0
2437 55 6556.0
2437 166 6313.0
2437 277 7899.0
2437 388 7428.0
2437 499 8631.0
2437 610 6863.0
2437 721 7132.0
2437 832 8170.0
2437 943 8009.0
2437 1054 8362.0
2476 55 6684.0
2476 166 6331.0
2476 277 7675.0
2476 388 7333.0
2476 499 8329.0
2476 610 7439.0
2476 721 7701.0
2476 832 8346.0
2476 943 7676.0
2476 1054 7998.0
2515 55 7298.0
2515 166 6120.0
2515 277 6982.0
2515 388 6886.0
2515 499 8037.0
2515 610 5883.0
2515 721 6542.0
2515 832 7590.0
2515 943 7610.0
2515 1054 7707.0
2554 55 7752.0
2554 166 7041.0
2554 277 7329.0
2554 388 7109.0
2554 499 8591.0
2554 610 5795.0
2554 721 6543.0
2554 832 7904.0
2554 943 7531.0
2554 1054 8163.0
2593 55 7015.0
2593 166 6493.0
2593 277 8378.0
2593 388 7628.0
2593 499 7962.0
2593 610 6320.0
2593 721 7284.0
2593 832 8638.0
2593 943 7787.0
2593 1054 8370.0
2632 55 7347.0
2632 166 6439.0
2632 277 7865.0
2632 388 7803.0
2632 499 7810.0
2632 610 6114.0
2632 721 7150.0
2632 832 7817.0
2632 943 7138.0
2632 1054 7374.0
2671 55 7262.0
2671 166 6274.0
2671 277 7435.0
2671 388 8350.0
2671 499 9227.0
2671 610 6568.0
2671 721 6660.0
2671 832 8318.0
2671 943 8063.0
2671 1054 7500.0
2710 55 7043.0
2710 166 6083.0
2710 277 7024.0
2710 388 8490.0
2710 499 10040.0
2710 610 7002.0
2710 721 7428.0
2710 832 8833.0
2710 943 8451.0
2710 1054 7885.0
2749 55 7354.0
2749 166 6007.0
2749 277 7252.0
2749 388 8624.0
2749 499 9409.0
2749 610 7084.0
2749 721 8112.0
2749 832 9157.0
2749 943 8447.0
2749 1054 8806.0
2788 55 6629.0
2788 166 6090.0
2788 277 7655.0
2788 388 7993.0
2788 499 7939.0
2788 610 6388.0
2788 721 7339.0
2788 832 8395.0
2788 943 8192.0
2788 1054 8415.0
2827 55 7820.0
2827 166 6728.0
2827 277 8155.0
2827 388 8541.0
2827 499 8823.0
2827 610 6182.0
2827 721 7250.0
2827 832 8485.0
2827 943 7849.0
2827 1054 7888.0
2866 55 8030.0
2866 166 6971.0
2866 277 7992.0
2866 388 8370.0
2866 499 8995.0
2866 610 5776.0
2866 721 7790.0
2866 832 9354.0
2866 943 8654.0
2866 1054 7954.0
2905 55 8199.0
2905 166 6329.0
2905 277 6892.0
2905 388 8076.0
2905 499 9051.0
2905 610 6637.0
2905 721 8095.0
2905 832 8785.0
2905 943 8429.0
2905 1054 7978.0
2944 55 8608.0
2944 166 7188.0
2944 277 7546.0
2944 388 7856.0
2944 499 9786.0
2944 610 7168.0
2944 721 7430.0
2944 832 9056.0
2944 943 8860.0
2944 1054 8422.0
2983 55 8241.0
2983 166 6956.0
2983 277 8664.0
2983 388 8915.0
2983 499 10123.0
2983 610 7422.0
2983 721 6750.0
2983 832 9125.0
2983 943 9143.0
2983 1054 8158.0
3022 55 6906.0
3022 166 6761.0
3022 277 8511.0
3022 388 9172.0
3022 499 10425.0
3022 610 7693.0
3022 721 7932.0
3022 832 9623.0
3022 943 8701.0
3022 1054 7674.0
3061 55 6582.0
3061 166 5625.0
3061 277 7143.0
3061 388 7956.0
3061 499 10184.0
3061 610 7450.0
3061 721 7935.0
3061 832 9999.0
3061 943 8345.0
3061 1054 7906.0
3100 55 6685.0
3100 166 5649.0
3100 277 6647.0
3100 388 7755.0
3100 499 9084.0
3100 610 6629.0
3100 721 7569.0
3100 832 9524.0
3100 943 7815.0
3100 1054 8268.0
3139 55 6603.0
3139 166 6099.0
3139 277 7449.0
3139 388 7978.0
3139 499 8891.0
3139 610 6286.0
3139 721 7763.0
3139 832 9579.0
3139 943 7470.0
3139 1054 8678.0
3178 55 6786.0
3178 166 5948.0
3178 277 7710.0
3178 388 8057.0
3178 499 9173.0
3178 610 7528.0
3178 721 8050.0
3178 832 9129.0
3178 943 7893.0
3178 1054 8836.0
3217 55 6441.0
3217 166 5339.0
3217 277 8004.0
3217 388 8500.0
3217 499 9385.0
3217 610 7194.0
3217 721 7603.0
3217 832 8308.0
3217 943 8015.0
3217 1054 8840.0
3256 55 6095.0
3256 166 5675.0
3256 277 8054.0
3256 388 8870.0
3256 499 8881.0
3256 610 6688.0
3256 721 7860.0
3256 832 8132.0
3256 943 6863.0
3256 1054 8872.0
3295 55 6135.0
3295 166 5709.0
3295 277 8147.0
3295 388 8633.0
3295 499 9341.0
3295 610 7432.0
3295 721 8305.0
3295 832 9548.0
3295 943 7619.0
3295 1054 9043.0
3334 55 5839.0
3334 166 5624.0
3334 277 7498.0
3334 388 8737.0
3334 499 9385.0
3334 610 6598.0
3334 721 7689.0
3334 832 9385.0
3334 943 8472.0
3334 1054 9246.0
3373 55 7123.0
3373 166 5972.0
3373 277 7591.0
3373 388 8686.0
3373 499 9628.0
3373 610 6823.0
3373 721 7775.0
3373 832 9605.0
3373 943 8710.0
3373 1054 9460.0
3412 55 7081.0
3412 166 6408.0
3412 277 8353.0
3412 388 8375.0
3412 499 10505.0
3412 610 7463.0
3412 721 7725.0
3412 832 9542.0
3412 943 8458.0
3412 1054 9320.0
3451 55 6852.0
3451 166 6006.0
3451 277 7495.0
3451 388 7900.0
3451 499 9920.0
3451 610 7501.0
3451 721 8284.0
3451 832 8805.0
3451 943 8272.0
3451 1054 8883.0
3490 55 6298.0
3490 166 5718.0
3490 277 7790.0
3490 388 8036.0
3490 499 8618.0
3490 610 6877.0
3490 721 7662.0
3490 832 9297.0
3490 943 7197.0
3490 1054 7935.0
3529 55 6938.0
3529 166 6682.0
3529 277 8671.0
3529 388 7744.0
3529 499 8677.0
3529 610 7177.0
3529 721 8079.0
3529 832 9762.0
3529 943 7147.0
3529 1054 7345.0
3568 55 7005.0
3568 166 7248.0
3568 277 8276.0
3568 388 7219.0
3568 499 8980.0
3568 610 7841.0
3568 721 8703.0
3568 832 10096.0
3568 943 8021.0
3568 1054 7978.0
3607 55 7126.0
3607 166 6905.0
3607 277 8377.0
3607 388 7847.0
3607 499 8706.0
3607 610 6764.0
3607 721 8586.0
3607 832 10235.0
3607 943 8626.0
3607 1054 8807.0
3646 55 6971.0
3646 166 6640.0
3646 277 8533.0
3646 388 9062.0
3646 499 8378.0
3646 610 6199.0
3646 721 7580.0
3646 832 8533.0
3646 943 8132.0
3646 1054 7812.0
3685 55 7208.0
3685 166 6829.0
3685 277 8560.0
3685 388 8750.0
3685 499 8269.0
3685 610 6434.0
3685 721 7925.0
3685 832 7908.0
3685 943 7514.0
3685 1054 6907.0
3724 55 6561.0
3724 166 6419.0
3724 277 8129.0
3724 388 8195.0
3724 499 8652.0
3724 610 7119.0
3724 721 8615.0
3724 832 9060.0
3724 943 6725.0
3724 1054 6068.0
3763 55 6060.0
3763 166 6271.0
3763 277 8309.0
3763 388 8707.0
3763 499 9735.0
3763 610 7166.0
3763 721 8695.0
3763 832 8725.0
3763 943 6721.0
3763 1054 5846.0
3802 55 6512.0
3802 166 6500.0
3802 277 8778.0
3802 388 8141.0
3802 499 8422.0
3802 610 6806.0
3802 721 8484.0
3802 832 9046.0
3802 943 7176.0
3802 1054 5800.0
3841 55 5417.0
3841 166 4429.0
3841 277 6226.0
3841 388 6107.0
3841 499 7381.0
3841 610 6081.0
3841 721 9199.0
3841 832 8127.0
3841 943 5792.0
3841 1054 5700.0
3880 55 4405.0
3880 166 2047.0
3880 277 2492.0
3880 388 4588.0
3880 499 6012.0
3880 610 3331.0
3880 721 5441.0
3880 832 6236.0
3880 943 4421.0
3880 1054 4450.0
3919 55 3330.0
3919 166 806.0
3919 277 266.0
3919 388 3258.0
3919 499 4168.0
3919 610 2230.0
3919 721 1082.0
3919 832 2869.0
3919 943 1498.0
3919 1054 2047.0
3958 55 2300.0
3958 166 322.0
3958 277 85.0
3958 388 1721.0
3958 499 1757.0
3958 610 1653.0
3958 721 638.0
3958 832 760.0
3958 943 140.0
3958 1054 1374.0
3997 55 1575.0
3997 166 234.0
3997 277 10.0
3997 388 674.0
3997 499 527.0
3997 610 740.0
3997 721 633.0
3997 832 412.0
3997 943 18.0
3997 1054 622.0
4036 55 936.0
4036 166 103.0
4036 277 5.0
4036 388 420.0
4036 499 127.0
4036 610 484.0
4036 721 526.0
4036 832 373.0
4036 1054 99.0
4075 55 731.0
4075 166 50.0
4075 277 7.0
4075 388 465.0
4075 610 337.0
4075 721 12.0
4075 832 311.0
4114 55 627.0
4114 166 48.0
4114 277 1.0
4114 388 404.0
4114 610 417.0
4114 721 4.0
4114 832 250.0
4153 55 613.0
4153 166 36.0
4153 388 207.0
4153 610 366.0
4153 721 12.0
4153 832 284.0

In [42]:

    

import pandas as pd
import seaborn as sns
import numpy as np

volume = np.log(volume+1)
df = pd.DataFrame(volume)

print df
print df.get_value(4153, 832)


    

    




      0     1     2     3     4     5     6     7     8     9     ...   1156  \
0        0     0     0     0     0     0     0     0     0     0  ...      0   
1        0     0     0     0     0     0     0     0     0     0  ...      0   
2        0     0     0     0     0     0     0     0     0     0  ...      0   
3        0     0     0     0     0     0     0     0     0     0  ...      0   
4        0     0     0     0     0     0     0     0     0     0  ...      0   
5        0     0     0     0     0     0     0     0     0     0  ...      0   
6        0     0     0     0     0     0     0     0     0     0  ...      0   
7        0     0     0     0     0     0     0     0     0     0  ...      0   
8        0     0     0     0     0     0     0     0     0     0  ...      0   
9        0     0     0     0     0     0     0     0     0     0  ...      0   
10       0     0     0     0     0     0     0     0     0     0  ...      0   
11       0     0     0     0     0     0     0     0     0     0  ...      0   
12       0     0     0     0     0     0     0     0     0     0  ...      0   
13       0     0     0     0     0     0     0     0     0     0  ...      0   
14       0     0     0     0     0     0     0     0     0     0  ...      0   
15       0     0     0     0     0     0     0     0     0     0  ...      0   
16       0     0     0     0     0     0     0     0     0     0  ...      0   
17       0     0     0     0     0     0     0     0     0     0  ...      0   
18       0     0     0     0     0     0     0     0     0     0  ...      0   
19       0     0     0     0     0     0     0     0     0     0  ...      0   
20       0     0     0     0     0     0     0     0     0     0  ...      0   
21       0     0     0     0     0     0     0     0     0     0  ...      0   
22       0     0     0     0     0     0     0     0     0     0  ...      0   
23       0     0     0     0     0     0     0     0     0     0  ...      0   
24       0     0     0     0     0     0     0     0     0     0  ...      0   
25       0     0     0     0     0     0     0     0     0     0  ...      0   
26       0     0     0     0     0     0     0     0     0     0  ...      0   
27       0     0     0     0     0     0     0     0     0     0  ...      0   
28       0     0     0     0     0     0     0     0     0     0  ...      0   
29       0     0     0     0     0     0     0     0     0     0  ...      0   
...    ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...    ...   
4163     0     0     0     0     0     0     0     0     0     0  ...      0   
4164     0     0     0     0     0     0     0     0     0     0  ...      0   
4165     0     0     0     0     0     0     0     0     0     0  ...      0   
4166     0     0     0     0     0     0     0     0     0     0  ...      0   
4167     0     0     0     0     0     0     0     0     0     0  ...      0   
4168     0     0     0     0     0     0     0     0     0     0  ...      0   
4169     0     0     0     0     0     0     0     0     0     0  ...      0   
4170     0     0     0     0     0     0     0     0     0     0  ...      0   
4171     0     0     0     0     0     0     0     0     0     0  ...      0   
4172     0     0     0     0     0     0     0     0     0     0  ...      0   
4173     0     0     0     0     0     0     0     0     0     0  ...      0   
4174     0     0     0     0     0     0     0     0     0     0  ...      0   
4175     0     0     0     0     0     0     0     0     0     0  ...      0   
4176     0     0     0     0     0     0     0     0     0     0  ...      0   
4177     0     0     0     0     0     0     0     0     0     0  ...      0   
4178     0     0     0     0     0     0     0     0     0     0  ...      0   
4179     0     0     0     0     0     0     0     0     0     0  ...      0   
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...    ...   ...   ...   ...   ...   ...   ...   ...   ...  
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[4193 rows x 1166 columns]
5.65248918027

In [43]:

    

ax = sns.heatmap(df, yticklabels=False, xticklabels=False)


    

In [ ]: