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()
def bestResponseAsAFunctionOfDirectGain():
#formula to test: Bi = (1/c) - (Prx/hii), where c is constant, Prx= ct
#define a player
player = Player(10001, 25, 2, 1000.00, 1)
#player = Player(10001, 16, 17, 1000.00, 2)
#get min and max channel gains measured until 23 august
tmp = GainCalculations.getMinMaxGain(player.coordinator_id, player.tx_node.node_id, player.rx_node.node_id, year=2013, month=8, day=23)
#tmp = [min linear gain, maximum linear gain]
min_hii = 10.00*math.log10(tmp[0])
max_hii = 10.00*math.log10(tmp[1])
#define a hii array
hii = numpy.arange(min_hii, max_hii, 0.05)
#find max Prx_dBm in which Bi > -55 dBm
Prx_dBm = -100
while True:
tmp_bi = getBi(player.cost, math.pow(10.00, Prx_dBm/10.00)*0.001, min_hii)
if tmp_bi is None or tmp_bi<-55:
#return to the previous Prx_dBm
Prx_dBm-=0.0001
break
Prx_dBm+=0.0001
average_gain = GainCalculations.getAverageGain(player.coordinator_id, player.tx_node.node_id, player.rx_node.node_id, year=2013, month=8, day=23)
standard_deviation = GainCalculations.getStandardDeviation(player.coordinator_id, player.tx_node.node_id, player.rx_node.node_id, year=2013, month=8, day=23)
#now plot results
plot.ioff()
plot.clf()
plot.grid()
plot.title("B%d=f(h%d%d, I+N)" %(player.player_number, player.player_number, player.player_number))
plot.xlabel("h%d%d [dB]" %(player.player_number, player.player_number))
plot.ylabel("B%d [dBm]" %(player.player_number))
#change rc param
plot.rcParams.update({'font.size': 20})
#I just want to add a text to legend
plot.plot([0], [0], alpha = 0, label = "c%d=%d" %(player.player_number, player.cost))
#now plot bi
Prx_crt = Prx_dBm
Prx_step = 3
Prx_inf = Prx_crt - 10
max_bi = -float("inf")
min_bi = float("inf")
markers = [".", "*", "+", "h", "x", "_"]
marker_index = 0
while Prx_crt >= Prx_inf:
Bi = []
for i in hii:
best_response = getBi(player.cost, math.pow(10.00, Prx_crt/10.00)*0.001, i)
Bi.append(best_response)
if best_response > max_bi: max_bi = best_response
if best_response < min_bi: min_bi = best_response
plot.plot(hii, Bi, linewidth = 2.5, label = "Prx %.2f dBm" %(Prx_crt), marker = markers[marker_index], markersize = 5)
if marker_index<len(markers):
marker_index+=1
else:
marker_index = 0
Prx_crt-=Prx_step
#set axis limits
plot.axis([min(hii)-0.5, max(hii)+0.5, min_bi-2, max_bi+2])
#set ticks
plot.xticks(numpy.arange(min(hii),max(hii), 2))
plot.yticks(numpy.arange(min_bi, max_bi, 2))
#plot a vertical line with the average gain
plot.vlines(10.00*math.log10(average_gain), plot.axis()[2], plot.axis()[3], "red", label = "Mean gain=%.2f dB" %(10.00*math.log10(average_gain)), linestyle = "--", linewidth = 2)
#plot vertical lines with the +- standard deviation
max_std_hii = 10.00*math.log10(average_gain+standard_deviation)
min_std_hii = 10.00*math.log10(average_gain-standard_deviation)
#plot.vlines(min_std_hii, plot.axis()[2], plot.axis()[3], "black", linestyle = "--", linewidth = 1)
#plot.vlines(max_std_hii, plot.axis()[2], plot.axis()[3], "black", linestyle = "--", linewidth = 1)
#plot some spans for +- standard deviation
plot.axvspan(min_std_hii, max_std_hii, facecolor = "gray", alpha = 0.2)
#Plot horizontal arrows
#plot arrows for +- standard deviation
arrow_length = math.fabs(max_std_hii - min_std_hii)
plot.arrow(min_std_hii, min_bi + 1, arrow_length, 0, head_width = 0.02*(math.fabs(max(hii)-min(hii))), length_includes_head = True, color = "green",head_length = 0.02*(math.fabs(max(hii)-min(hii))), linewidth = 1.0)
plot.arrow(max_std_hii, min_bi + 1, -arrow_length, 0, head_width = 0.02*(math.fabs(max(hii)-min(hii))), length_includes_head = True, color = "green",head_length = 0.02*(math.fabs(max(hii)-min(hii))), linewidth = 1.0)
plot.text(min_std_hii+arrow_length/3, min_bi +1.1, "%.3f dB" %arrow_length, fontsize = 18, color = "green")
#plot horizontal lines for best response variation
#plot.hlines(getBi(player.cost, math.pow(10.00, Prx_dBm/10.00)*0.001, max_std_hii), min(hii), max(hii), color = "black", linestyle = "--", linewidth = 1)
#plot.hlines(getBi(player.cost, math.pow(10.00, Prx_dBm/10.00)*0.001, min_std_hii), min(hii), max(hii), color = "black", linestyle = "--", linewidth = 1)
#plot.axhspan(getBi(player.cost, math.pow(10.00, Prx_dBm/10.00)*0.001, min_std_hii), getBi(player.cost, math.pow(10.00, Prx_dBm/10.00)*0.001, max_std_hii), facecolor = "blue", alpha = 0.1)
#plot arrows for best response variation
#max_std_Bi = getBi(player.cost, math.pow(10.00, Prx_dBm/10.00)*0.001, max_std_hii)
#min_std_Bi = getBi(player.cost, math.pow(10.00, Prx_dBm/10.00)*0.001, min_std_hii)
#arrow_length = math.fabs(max_std_Bi - min_std_Bi)
#plot.arrow(min(hii) +0.5, max_std_Bi, 0, -arrow_length, head_width = 0.01*(math.fabs(max(hii)-min(hii))), length_includes_head = True, color = "blue", head_length = 0.01*(math.fabs(max(hii)-min(hii))), linewidth = 1.3)
#plot.arrow(min(hii) +0.5, min_std_Bi, 0, +arrow_length, head_width = 0.01*(math.fabs(max(hii)-min(hii))), length_includes_head = True, color = "blue", head_length = 0.01*(math.fabs(max(hii)-min(hii))), linewidth = 1.3)
#plot.text(min(hii) +0.5, max_std_Bi, "%.3f dBm" %arrow_length, fontsize = 18, weight = 500, color = "blue")
leg = plot.legend(loc = "center right", fontsize = 18, bbox_to_anchor=(1, 0.5))
leg.get_frame().set_alpha(0.6)
#maximize the window
mng = plot.get_current_fig_manager()
mng.resize(*mng.window.maxsize())
fig = plot.gcf()
fig.set_size_inches( (19, 11) )
#plot.savefig("/home/ciprian/Pictures/best response/%s%d.jpg" %("bi_f(hii)_player", player.player_number), dpi=250)
plot.show()
