3346.sagews

import numpy
import matplotlib.pyplot as plt
from pylab import *

data = numpy.array([
[2, 0, 1.74, 0.5507338, 50, 0, 0.1293335, 0.04733296, 0 ],
[3, 0, 2.94, 0.6446815, 33.33333, 0, 0.2140511, 0.07572497, 1.626587E-06 ],
[4, 0, 3.34, 0.6123136, 25, 0, 0.3842335, 0.07589734, 0 ],
[5, 0, 4.5, 0.7278761, 20, 0, 0.269679, 0.07596686, 8.132935E-07 ],
[6, 0, 4.920001, 0.7643754, 16.66667, 0, 0.1848826, 0.09637531, 1.626587E-06 ],
[7, 0, 5.42, 0.7693202, 14.28572, 0, 0.1525535, 0.06223192, 1.075886E-06 ],
[8, 2.206327, 5.9, 1.041942, 12.5, 1.349027, 0.5999979, 0.09793357, 5.750853E-07 ],
[9, 0.2597403, 6.579999, 1.063269, 11.11111, 0.4953036, 0.2442048, 0.0891977, 5.750853E-07 ],
[10, 1.171583, 6.7, 1.152188, 10, 1.403493, 0.4221145, 0.1237744, 7.043328E-07 ],
[11, 1.768442, 7.179999, 1.171377, 9.09091, 2.065561, 0.3432455, 0.1696715, 1.075886E-06 ],
[12, 3.637994, 7.1, 1.377672, 8.333334, 2.797255, 0.2891986, 0.08050866, 8.132935E-07 ],
[13, 3.5499, 7.34, 1.471425, 7.692307, 1.718624, 0.311009, 0.05810517, 4.546449E-07 ],
[14, 3.984817, 7.460001, 1.447319, 7.142858, 2.531988, 0.2304139, 0.1681354, 7.043328E-07 ],
[15, 5.803256, 7.48, 1.613384, 6.666667, 2.365893, 0.1944683, 0.1393566, 4.546449E-07 ],
[16, 7.339121, 7.4, 1.742477, 6.25, 2.783654, 0.3247366, 0.1439819, 4.546449E-07 ],
[17, 6.605962, 7.82, 1.702466, 5.882353, 1.979973, 0.3269685, 0.1155895, 7.043328E-07 ],
[18, 12.48771, 7.6, 1.734841, 5.555556, 1.970876, 0.3954269, 0.1374908, 4.546449E-07 ],
[19, 12.91758, 7.920001, 1.959919, 5.263157, 3.078428, 0.348502, 0.1392864, 6.743473E-07 ],
[20, 10.38415, 7.72, 1.757898, 5, 3.75649, 0.3587847, 0.1282542, 2.875427E-07 ],
[21, 11.92945, 7.760001, 2.04784, 4.761905, 1.529305, 0.2736944, 0.0963183, 4.066467E-07 ],
[22, 17.14606, 7.54, 2.107716, 4.545455, 3.558665, 0.3648151, 0.1524849, 2.033234E-07 ],
[23, 13.63389, 7.54, 2.068578, 4.347826, 2.452156, 0.420388, 0.0925362, 5.750853E-07 ],
[24, 15.25905, 7.56, 2.038462, 4.166667, 1.183028, 0.3335746, 0.1227202, 3.521664E-07 ],
[25, 17.4309, 7.54, 2.186424, 4, 2.622391, 0.4116471, 0.1073515, 5.474649E-07 ],
[26, 19.01536, 7.14, 2.158948, 3.846154, 3.542428, 0.3335746, 0.0786586, 4.066467E-07 ],
[27, 17.26961, 7.22, 2.228806, 3.703704, 2.187476, 0.3432454, 0.0282723, 3.665464E-07 ],
[28, 24.15737, 7.62, 2.40896, 3.571429, 4.844326, 0.2288302, 0.1479188, 3.93734E-07 ],
[29, 21.19352, 7.26, 2.381809, 3.448276, 2.065455, 0.2802586, 0.1527738, 1.760832E-07 ],
[30, 22.10594, 7.32, 2.383116, 3.333333, 2.807373, 0.2723626, 0.1224707, 2.273224E-07 ],
[31, 26.30661, 7.4, 2.372016, 3.225807, 4.591747, 0.4670978, 0.2004746, 4.43133E-07 ],
[32, 21.01702, 6.98, 2.33823, 3.125, 2.38178, 0.3379068, 0.07856874, 2.490193E-07 ],
[33, 26.80709, 7.4, 2.471628, 3.030303, 2.096406, 0.3247366, 0.08100424, 0 ],
[34, 24.30853, 7.16, 2.434504, 2.941176, 3.193152, 0.3443032, 0.1118601, 4.31314E-07 ],
[35, 24.82792, 6.72, 2.549253, 2.857143, 3.18208, 0.2036032, 0.1635871, 3.04985E-07 ],
[36, 29.2878, 6.820001, 2.59973, 2.777778, 1.496673, 0.1111916, 0.1180152, 3.521664E-07 ]
])

data2 = numpy.array([
[2, 0, 1.64, 0.5572441, 50, 0, 0.1963293, 0.03713929, 0 ],
[3, 0, 2.72, 0.6078846, 33.33333, 0, 0.3536808, 0.05200816, 1.626587E-06 ],
[4, 0, 3.18, 0.6659194, 25, 0, 0.2366423, 0.08670345, 8.132935E-07 ],
[5, 0.4761905, 3.96, 0.7376153, 20, 0.9080566, 0.2736943, 0.08953401, 1.626587E-06 ],
[6, 0, 5.02, 0.7489271, 16.66667, 0, 0.1111914, 0.03197602, 0 ],
[7, 0.3636364, 5.04, 0.9077262, 14.28572, 0.6934251, 0.3335746, 0.1129452, 1.075886E-06 ],
[8, 0.611578, 5.76, 0.9215946, 12.5, 0.7150333, 0.1768404, 0.09193859, 5.750853E-07 ],
[9, 2.75641, 6.28, 1.217712, 11.11111, 2.787762, 0.4447661, 0.1735578, 0 ],
[10, 3.23064, 6.88, 1.338866, 10, 1.761084, 0.2917026, 0.08727212, 5.750853E-07 ],
[11, 4.727174, 6.64, 1.208684, 9.090909, 1.86567, 0.2929465, 0.1424686, 5.750853E-07 ],
[12, 4.252491, 7.52, 1.450052, 8.333333, 1.714447, 0.3379069, 0.1332322, 4.066467E-07 ],
[13, 7.52662, 7.54, 1.619539, 7.692307, 2.09167, 0.437346, 0.106318, 2.875427E-07 ],
[14, 8.660913, 7, 1.544113, 7.142858, 2.668238, 0.1348394, 0.1438754, 3.521664E-07 ],
[15, 9.818837, 7.78, 1.69295, 6.666667, 2.181964, 0.3039129, 0.1151709, 4.546449E-07 ],
[16, 15.94798, 7.44, 1.832737, 6.25, 4.619504, 0.1662412, 0.184368, 3.521664E-07 ],
[17, 14.77901, 7.94, 1.882387, 5.882353, 5.335073, 0.1768404, 0.1012135, 4.980385E-07 ],
[18, 15.87411, 7.460001, 1.868855, 5.555556, 4.298868, 0.3981763, 0.1029558, 4.066467E-07 ],
[19, 17.89647, 7.739999, 1.854986, 5.263158, 2.597454, 0.2223832, 0.1340423, 5.379431E-07 ],
[20, 19.60849, 7.82, 2.038498, 5, 1.723606, 0.4281022, 0.09557257, 4.980385E-07 ],
[21, 21.36985, 7.64, 1.878754, 4.761905, 8.132299, 0.6167343, 0.2297976, 3.521664E-07 ],
[22, 19.9662, 7.82, 1.969891, 4.545455, 2.654711, 0.6964824, 0.144751, 6.429649E-07 ],
[23, 23.34717, 7.64, 2.048495, 4.347826, 3.037118, 0.1293335, 0.1658382, 5.379431E-07 ],
[24, 21.54832, 7.76, 2.008299, 4.166667, 5.419007, 0.305107, 0.06396197, 5.750853E-07 ],
[25, 26.32268, 7.920001, 2.177679, 4, 2.914673, 0.3269684, 0.3273838, 3.665464E-07 ],
[26, 25.97418, 7.84, 2.147439, 3.846154, 2.601257, 0.2529815, 0.1505293, 5.083084E-07 ],
[27, 25.43676, 7.78, 2.042865, 3.703704, 5.469237, 0.3156513, 0.1608661, 4.875523E-07 ],
[28, 27.63406, 7.7, 2.284563, 3.571428, 3.124546, 0.3133388, 0.166422, 3.214825E-07 ],
[29, 27.35211, 7.579999, 2.163847, 3.448276, 4.999136, 0.6131864, 0.08975177, 4.066467E-07 ],
[30, 29.79042, 7.26, 2.337183, 3.333333, 1.418153, 0.3110091, 0.1607994, 1.760832E-07 ],
[31, 31.72563, 7.4, 2.347872, 3.225806, 1.634279, 0.6660582, 0.1183433, 4.31314E-07 ],
[32, 32.03634, 7.8, 2.503875, 3.125, 3.825308, 0.3015102, 0.2218757, 5.660283E-07 ],
[33, 29.68907, 7.26, 2.301215, 3.030303, 2.34023, 0.2736943, 0.167478, 2.033234E-07 ],
[34, 35.91091, 7.84, 2.635725, 2.941176, 2.827178, 0.4160405, 0.1426133, 3.521664E-07 ],
[35, 32.41558, 7.5, 2.437679, 2.857143, 3.007363, 0.3618124, 0.09424997, 2.875427E-07 ],
[36, 36.84351, 7.46, 2.453087, 2.777778, 3.544791, 0.4616162, 0.1139183, 2.033234E-07 ],
[37, 33.76235, 7.24, 2.411247, 2.702703, 3.652099, 0.4732847, 0.09659044, 2.875427E-07 ],
[38, 34.81205, 7.08, 2.578168, 2.631579, 3.797431, 0.3485021, 0.2411115, 3.665464E-07 ],
[39, 39.73793, 7.46, 2.541428, 2.564103, 3.231926, 0.2802588, 0.04920825, 1.760832E-07 ],
[40, 35.90194, 7.12, 2.631221, 2.5, 3.967272, 0.2917025, 0.188751, 3.214825E-07 ]
])

data3 = numpy.array([
[2, 0, 1.94, 0.2356872, 50, 0, 0.2669686, 0.01596603, 0 ],
[3, 0, 2.78, 0.2733634, 33.33333, 0, 0.265603, 0.02233885, 3.253174E-06 ],
[4, 0, 3.92, 0.3322178, 25, 0, 0.2656031, 0.02140444, 8.132935E-07 ],
[5, 0, 4.86, 0.3916525, 20, 0, 0.4116473, 0.03669752, 8.132935E-07 ],
[6, 0, 5.54, 0.4528818, 16.66667, 0, 0.3051071, 0.04218561, 1.818579E-06 ],
[7, 0, 6.62, 0.5839855, 14.28572, 0, 0.2207419, 0.06243502, 9.092897E-07 ],
[8, 0.5095748, 7.42, 0.704171, 12.5, 0.5950733, 0.2854015, 0.06990166, 5.750853E-07 ],
[9, 0, 8.04, 0.765749, 11.11111, 0, 0.2381742, 0.01972485, 7.043328E-07 ],
[10, 0.4444445, 8.82, 0.8388354, 10, 0.8475195, 0.4238339, 0.09240504, 5.750853E-07 ],
[11, 0, 9.48, 1.007394, 9.09091, 0, 0.4323285, 0.07243685, 9.96077E-07 ],
[12, 1.200782, 9.839999, 1.202758, 8.333333, 0.3630155, 0.2304141, 0.0889782, 8.132935E-07 ],
[13, 1.69628, 10.2, 1.332454, 7.692307, 1.642742, 0.3765863, 0.2343763, 8.383237E-07 ],
[14, 2.243779, 10.48, 1.507937, 7.142857, 0.7266983, 0.2207418, 0.09215449, 5.379431E-07 ],
[15, 3.882436, 10.76, 1.641259, 6.666667, 1.793363, 0.07627672, 0.08863232, 4.546449E-07 ],
[16, 4.832792, 10.9, 1.827682, 6.25, 2.248968, 0.1348396, 0.2131216, 7.330928E-07 ],
[17, 7.297272, 11.04, 1.832298, 5.882352, 4.297935, 0.07627704, 0.1286823, 4.980385E-07 ],
[18, 7.195118, 11.06, 1.976856, 5.555555, 1.258776, 0.2140511, 0.1826203, 3.521664E-07 ],
[19, 11.54357, 11.08, 2.082212, 5.263158, 3.180464, 0.2207418, 0.2037104, 6.429649E-07 ],
[20, 13.0826, 11.12, 2.309076, 5.000001, 1.65099, 0.1944684, 0.07249351, 8.132935E-07 ],
[21, 14.48151, 11.06, 2.489634, 4.761905, 1.809909, 0.2456893, 0.1205721, 6.429649E-07 ],
[22, 21.21513, 11.12, 2.720679, 4.545455, 5.254843, 0.2123456, 0.1691073, 5.750853E-07 ],
[23, 23.82773, 11.28, 2.643145, 4.347826, 3.148007, 0.1747719, 0.1169451, 4.980385E-07 ],
[24, 22.22767, 11.22, 2.691088, 4.166667, 4.049544, 0.2288301, 0.1374274, 7.330928E-07 ],
[25, 24.58049, 11.22, 2.869664, 4, 1.220894, 0.1401292, 0.09237938, 6.509524E-07 ],
[26, 30.92018, 11.32, 2.990379, 3.846154, 3.988613, 0.1640393, 0.1764534, 3.803832E-07 ],
[27, 32.5946, 11.28, 3.132079, 3.703704, 0.9712218, 0.174772, 0.1012211, 8.321367E-07 ],
[28, 32.77276, 11.34, 3.111738, 3.571428, 2.43774, 0.2053815, 0.1712693, 8.132935E-07 ],
[29, 36.5183, 11.28, 3.186089, 3.448276, 2.855409, 0.1640393, 0.1203573, 1.026733E-06 ],
[30, 39.02151, 11.26, 3.282049, 3.333334, 2.720871, 0.1293335, 0.09503042, 8.920776E-07 ],
[31, 39.18984, 11.06, 3.312398, 3.225806, 2.708153, 0.222383, 0.1708625, 8.566165E-07 ],
[32, 39.48735, 11.2, 3.353913, 3.125, 3.534204, 0.1348394, 0.1133504, 7.116318E-07 ],
[33, 41.35386, 11.16, 3.450663, 3.030304, 2.46832, 0.2381741, 0.06105586, 2.875427E-07 ],
[34, 45.64016, 11.22, 3.434446, 2.941177, 2.383476, 0.1264906, 0.07883425, 5.750853E-07 ],
[35, 44.84463, 11.36, 3.500546, 2.857143, 3.053186, 0.154919, 0.1558096, 5.183749E-07 ],
[36, 46.72078, 11.38, 3.509177, 2.777778, 2.208599, 0.1848824, 0.09545515, 5.56824E-07 ],
[37, 48.17866, 11.18, 3.572021, 2.702703, 2.658405, 0.2207418, 0.09223574, 6.666402E-07 ],
[38, 49.80432, 11.24, 3.650869, 2.631579, 1.338319, 0.1963294, 0.1210623, 4.31314E-07 ],
[39, 49.48249, 11.12, 3.744555, 2.564103, 2.481492, 0.1944684, 0.1481144, 5.56824E-07 ],
[40, 52.08429, 11.3, 3.678046, 2.5, 2.484952, 0.190692, 0.1086535, 6.509524E-07 ],
[41, 51.43584, 11.34, 3.666216, 2.439025, 2.558499, 0.1293333, 0.0164267, 4.658723E-07 ],
[42, 53.13995, 11.26, 3.7845, 2.380952, 1.932991, 0.1427006, 0.1497527, 5.474649E-07 ],
[43, 55.63354, 11.16, 3.77788, 2.325582, 0.8550937, 0.1427009, 0.09966832, 3.803832E-07 ],
[44, 56.07171, 11.18, 3.759054, 2.272727, 2.171164, 0.1944682, 0.09917584, 4.43133E-07 ],
[45, 57.22124, 11.22, 3.869528, 2.222223, 2.011166, 0.1401292, 0.05396182, 4.191619E-07 ],
[46, 57.51528, 11.32, 3.887252, 2.173913, 1.984938, 0.1111915, 0.1061983, 6.099701E-07 ],
[47, 60.50383, 11.16, 3.889413, 2.12766, 1.369774, 0.1662413, 0.05699088, 4.066467E-07 ],
[48, 59.90145, 11.14, 3.959037, 2.083334, 1.685656, 0.1293336, 0.0680174, 1.437713E-07 ],
[49, 59.88967, 11.18, 3.885535, 2.040816, 1.181574, 0.2036033, 0.05651147, 6.743473E-07 ],
[50, 59.63591, 11.34, 3.889618, 2, 0.8320795, 0.1549188, 0.1425526, 5.183749E-07 ],
[51, 61.58869, 11.38, 4.013744, 1.960785, 1.232071, 0.265603, 0.1074953, 5.183749E-07 ],
[52, 62.42444, 10.94, 4.127634, 1.923077, 1.782434, 0.1293337, 0.134772, 6.266847E-07 ],
[53, 64.06718, 11.26, 3.980768, 1.886793, 2.271329, 0.097234, 0.09139827, 6.58843E-07 ],
[54, 63.84888, 11.14, 4.047785, 1.851852, 1.278272, 0.1427006, 0.03929812, 5.884095E-07 ],
[55, 64.55423, 11.18, 4.087347, 1.818182, 1.185025, 0.09341958, 0.03451621, 6.120844E-07 ],
[56, 65.87489, 11.16, 4.15561, 1.785714, 1.177372, 0.09723414, 0.09890864, 7.260097E-07 ],
[57, 66.38548, 11.14, 4.1422, 1.754386, 1.204954, 0.2669688, 0.1332069, 5.90601E-07 ],
[58, 66.87743, 11.2, 4.18091, 1.724138, 0.6633688, 0.1595442, 0.1021075, 3.09192E-07 ],
[59, 68.38908, 11.1, 4.21808, 1.694915, 0.4149787, 0.1206042, 0.0630243, 3.970016E-07 ],
[60, 67.15519, 11.24, 4.205814, 1.666667, 0.5818477, 0.1549186, 0.1054971, 3.333201E-07 ],
[61, 68.83672, 11.2, 4.200865, 1.639344, 1.078836, 0.1809063, 0.08117937, 1.344858E-07 ],
[62, 69.18694, 11.14, 4.202991, 1.612904, 0.7565907, 0.1427009, 0.03218839, 5.057605E-07 ],
[63, 68.51573, 11.24, 4.137588, 1.587302, 0.4723157, 0.1868391, 0.1214023, 7.043328E-07 ],
[64, 69.51827, 11.28, 4.173837, 1.5625, 0.5989783, 0.1111913, 0.1163758, 4.489258E-07 ],
[65, 69.7549, 11.3, 4.178141, 1.538461, 1.509948, 0.3015105, 0.06528847, 4.795372E-07 ],
[66, 69.90067, 11.22, 4.22351, 1.515152, 1.196096, 0.1111913, 0.09217971, 4.546449E-07 ],
[67, 71.42152, 11.12, 4.281083, 1.492537, 0.6897618, 0.2123456, 0.07204706, 3.803832E-07 ]
])

array([ 5., 10., 15., 20., 25., 30., 35., 40., 45., 50., 55., 60., 65., 70.])

xcoords = data[::1, 0]

ycoordsFracDropped = data[::1, 1]
ycoordsChannelGoodput = data[::1, 2]
ycoordsPktDelay = data[::1,3]
ycoordsFairness = data[::1,4]

fracDroppedError = data[::1, 5]
channelGoodputError = data[::1, 6]
pktDelayError = data[::1, 7]
fairnessError = data[::1, 8]

xcoords2 = data2[::1, 0]

ycoordsFracDropped2 = data2[::1, 1]
ycoordsChannelGoodput2 = data2[::1, 2]
ycoordsPktDelay2 = data2[::1,3]
ycoordsFairness2 = data2[::1,4]

fracDroppedError2 = data2[::1, 5]
channelGoodputError2 = data2[::1, 6]
pktDelayError2 = data2[::1, 7]
fairnessError2 = data2[::1, 8]

xcoords3 = data3[::1, 0]

ycoordsFracDropped3 = data3[::1, 1]
ycoordsChannelGoodput3 = data3[::1, 2]
ycoordsPktDelay3 = data3[::1,3]
ycoordsFairness3 = data3[::1,4]

fracDroppedError3 = data3[::1, 5]
channelGoodputError3 = data3[::1, 6]
pktDelayError3 = data3[::1, 7]
fairnessError3 = data3[::1, 8]

plt.figure()
plt.errorbar(xcoords, ycoordsChannelGoodput , yerr=channelGoodputError, label="CSMA Linear")
plt.errorbar(xcoords2, ycoordsChannelGoodput2 , yerr=channelGoodputError2, label="CSMA Exponential" )
plt.errorbar(xcoords3, ycoordsChannelGoodput3 , yerr=channelGoodputError3, label="TDMA")
plt.title("Channel Goodput vs # Nodes")
plt.xlabel("Number of Nodes")
plt.ylabel("Channel Goodput (pkts/s)")
plt.legend(loc="lower right")
savefig('wsn_channelGoodput.png')

plt.figure()
plt.errorbar(xcoords, ycoordsFracDropped , yerr=fracDroppedError, label="CSMA Linear")
plt.errorbar(xcoords2, ycoordsFracDropped2 , yerr=fracDroppedError2, label="CSMA Exponential" )
plt.errorbar(xcoords3, ycoordsFracDropped3 , yerr=fracDroppedError3, label="TDMA")
plt.title("Fraction Dropped Packets vs # Nodes")
plt.xlabel("Number of Nodes")
plt.ylabel("Fraction Dropped Packets")
plt.legend(loc="lower right")
savefig('wsn_fracDroppedPckts.png')

plt.figure()
plt.errorbar(xcoords, ycoordsPktDelay, yerr=pktDelayError, label="CSMA Linear")
plt.errorbar(xcoords2, ycoordsPktDelay2, yerr=pktDelayError2, label="CSMA Exponential")
plt.errorbar(xcoords3, ycoordsPktDelay3, yerr=pktDelayError3, label="TDMA" )
plt.title("Packet Delay vs # Nodes")
plt.xlabel("Number of Nodes")
plt.ylabel("Packet Delay (seconds)")
plt.legend(loc="lower right")
savefig('wsn_pcktDelays.png')

plt.figure()
plt.errorbar(xcoords, ycoordsFairness , yerr=fairnessError, label="CSMA Linear")
plt.errorbar(xcoords2, ycoordsFairness2, yerr=fairnessError2, label="CSMA Exponential")
plt.errorbar(xcoords3, ycoordsFairness3, yerr=fairnessError3, label="TDMA" )
plt.title("Fairness per Node vs # Nodes")
plt.xlabel("Number of Nodes")
plt.ylabel("Fairness per Node (%)")
plt.legend(loc="upper right")
savefig('wsn_fairnessPerNode.png')