def getAFewParameters():
#From the measured gain, you can get a few results
#If you just want to get a list with measured gains:
print GainCalculations.getFileResults(10001, 25, 2)
#You will find informations about: calculated gain, received RSSI, Noise power, transmitted power, date
#if you just want the average gain:
print GainCalculations.getAverageGain(10001, 25, 2)
print "%.3f dB" %(10.00*math.log10(GainCalculations.getAverageGain(10001, 25, 2)))
#if you just want to find info about the noise power
GainCalculations.getAverageNoise(10001, 25, 2)
GainCalculations.getMinMaxNoise(10001, 25, 2)
def bestResponseAsAfunctionOfCostAndPrx():
#plot best response variation as function of I+N and cost
player = Player(10001, 25, 2, 1000.00, player_number=1, game_Type=0)
#give the node id which cause interference to player
tx2_node_id = 16
#I want to determine the maximum level of interference
#average direct gain
hii = GainCalculations.getAverageGain(player.coordinator_id, player.tx_node.node_id, player.rx_node.node_id, year=2013, month=8, day=23)
#maximum cross gain
hji = GainCalculations.getMinMaxGain(10001, tx2_node_id, player.rx_node.node_id, year=2013, month=8, day=24)
#get average noise
noise = GainCalculations.getAverageNoise(player.coordinator_id, player.tx_node.node_id, player.rx_node.node_id, year=2013, month=8, day=23)
max_interference_and_noise = 0.001*hji[1] + noise
max_interference_and_noise = 10.00*math.log10(max_interference_and_noise/0.001)
interference_and_noise = numpy.arange(max_interference_and_noise, max_interference_and_noise-10, -2)
cost = numpy.arange(100, 10000, 0.1)
#now plot results
plot.ioff()
plot.clf()
plot.grid()
plot.title("B%d (c%d, I+N)" %(player.player_number, player.player_number))
plot.xlabel("c%d" %(player.player_number))
plot.ylabel("B%d [dBm]" %(player.player_number))
for i in interference_and_noise:
tmp_list = []
tmp_cost = []
for c in cost:
tmp_bi = getBi(c, math.pow(10.00, i/10.00)*0.001, 10.00*math.log10(hii))
if tmp_bi!=None:
tmp_list.append(tmp_bi)
tmp_cost.append(c)
plot.plot(tmp_cost, tmp_list, label = "I+N=%.1f dBm" %i)
plot.plot([],[],label = "h%d%d = %.3f dB" %(player.player_number, player.player_number, 10.00*math.log10(hii)))
plot.axhspan(-55, 0, alpha = 0.1)
plot.legend(bbox_to_anchor=(1.05, 1.05))
plot.show()
#bestResponseAsAfunctionOfCostAndPrx()
#bestResponseAsAFunctionOfDirectGain()
#bestResponseAsAFunctionOfRx()