def plotAngleEstimationError(X, Y, U_AoA, V_AoA, U_AoU, V_AoU, ERR):
# Cosmetics
# plt.title("Bearing error")
plt.xlabel("x [m]")
plt.ylabel("y [m]")
plt.grid(linestyle=':', linewidth=1, color='gainsboro')
plt.axis('equal')
plt.axis([0, 650, 0, 500])
plot.plot_scenario()
plt.quiver(X, Y, U_AoA, V_AoA, zorder=3, alpha=0.2)
# Plot terminal
opt = {
"marker": "*",
"markeredgewidth": 2,
"markersize": 20,
"color": 'white',
"markeredgecolor": 'black'
}
plot.plot_terminals([TERMINALS[USER]], bsOpt=opt)
# Weakest-user
plot.plot_heatmap(X,
Y,
map(abs, ERR),
np.ones(len(X)) * STEP,
np.ones(len(X)) * STEP,
legend="Bearing error [$^\circ$]")
def main(csvPath, resultDir):
time, sim, drone, users, rss = readData(csvPath)
df = pd.read_csv(DB_FILE, index_col=0)
df['rss'] = (df[['re',
'im']].apply(lambda row: getRss(row['re'], row['im']),
axis=1))
df = df.groupby('id').apply(criterion)
# Cosmetics
# plt.title("Flight trajectory")
plt.xlabel("x [m]")
plt.ylabel("y [m]")
plt.grid(linestyle=':', linewidth=1, color='gainsboro')
plt.axis('equal')
plt.axis([0, 650, 0, 500])
# Plot
plot.plot_scenario()
plot.plot_flight(drone,
insideOpt={
"color": "black",
"markerfacecolor": 'black',
"markeredgecolor": 'black'
})
# Plot terminals
bsOpt = {
"markeredgewidth": 2,
"markersize": 15,
"color": 'white',
"markeredgecolor": 'black'
}
userOpt = {
"markeredgewidth": 2,
"markersize": 20,
"color": 'white',
"markeredgecolor": 'black'
}
plot.plot_terminals(users, bsOpt=bsOpt, userOpt=userOpt)
# Plot the ground-truth
plot.plot_heatmap(df['x'].values,
df['y'].values,
df['rss'].values,
np.ones(len(df['x'])) * STEP,
np.ones(len(df['x'])) * STEP,
legend='RSS [dBm]')
figureName = os.path.join(resultDir, "flight.png")
plt.savefig(figureName, bbox_inches='tight', dpi=300)
def plotQuantile(initJob, df, X):
quantile = df['rss'].quantile(1 - X / 100)
# print quantile
d = {}
for i in range(0, 101):
d[i] = df['rss'].quantile(i / 100)
# print(d)
minRss = min(df['rss'])
def isHigherQuantile(row):
return row['rss'] if row['rss'] >= quantile else minRss
df['rss_quantile'] = df.apply(isHigherQuantile, axis=1)
# Figure
plt.title("Top " + str(X) + " % positions")
plt.xlabel("x [m]")
plt.ylabel("y [m]")
plt.grid(linestyle=':', linewidth=1, color='gainsboro')
plt.axis('equal')
plt.axis([0, 650, 0, 500])
# Plot the city
plot.plot_scenario()
# Plot terminals
bsOpt = {
"markeredgewidth": 2,
"markersize": 15,
"color": 'white',
"markeredgecolor": 'black'
}
userOpt = {
"markeredgewidth": 2,
"markersize": 20,
"color": 'white',
"markeredgecolor": 'black'
}
plot.plot_terminals(initJob['terminals'], bsOpt=bsOpt, userOpt=userOpt)
# Plot the ground-truth
plot.plot_heatmap(
df['x'].values,
df['y'].values,
df['rss_quantile'].values,
np.ones(len(df['x'])) * STEP,
np.ones(len(df['x'])) * STEP,
legend='RSS [dBm]'
)
def plotWeakestUser(initJob, df):
# Figure
# plt.title("Weakest user")
plt.xlabel("x [m]")
plt.ylabel("y [m]")
plt.grid(linestyle=':', linewidth=1, color='gainsboro')
plt.axis('equal')
plt.axis([0, 650, 0, 500])
# Plot the city
plot.plot_scenario()
# Plot terminals
bsOpt = {
"marker": "o",
"markeredgewidth": 1,
"markersize": 15,
"color": 'white',
"markeredgecolor": 'black'
}
userOpt = {
"marker": "*",
"markeredgewidth": 1,
"markersize": 20,
"color": 'white',
"markeredgecolor": 'black'
}
for idx, t in enumerate(initJob['terminals']):
opt = bsOpt if idx == 0 else userOpt
color = matplotlib.colors.to_hex(
plot.CMAP(float(idx) / (len(initJob['terminals']) - 1))
)
opt["color"] = color
# opt["markeredgecolor"] = color
plot.plot_terminals([t], bsOpt=opt, userOpt={})
# Plot the weakest-user
plot.plot_heatmap(
df['x'].values,
df['y'].values,
df['user'].values,
np.ones(len(df['x'])) * STEP,
np.ones(len(df['x'])) * STEP,
)
def plotGroundTruth(initJob, df):
# Figure
# plt.title("Ground truth")
plt.xlabel("x [m]")
plt.ylabel("y [m]")
plt.grid(linestyle=':', linewidth=1, color='gainsboro')
plt.axis('equal')
plt.axis([0, 650, 0, 500])
# Plot the city
plot.plot_scenario()
# Plot terminals
bsOpt = {
"markeredgewidth": 2,
"markersize": 15,
"color": 'white',
"markeredgecolor": 'black'
}
userOpt = {
"markeredgewidth": 2,
"markersize": 20,
"color": 'white',
"markeredgecolor": 'black'
}
plot.plot_terminals(initJob['terminals'], bsOpt=bsOpt, userOpt=userOpt)
# Plot the ground-truth
plot.plot_heatmap(
df['x'].values,
df['y'].values,
df['rss'].values,
np.ones(len(df['x'])) * STEP,
np.ones(len(df['x'])) * STEP,
legend='RSS [dBm]'
)
def main():
exp = utils.readJson(INITIAL_CONF)
simFiles = [os.path.join(OUTPUT_DIR, f) for f in os.listdir(OUTPUT_DIR)]
heatmap = np.zeros((MAP_X // STEP, MAP_Y // STEP))
initRss, finalRss = [], []
nSucceed = np.zeros(len(QUANTILES))
N = 0
for simFile in simFiles:
N += 1
df_sim = pd.read_csv(simFile, names=COLS)
posA = df_sim.iloc[-1]
posB = df_sim.iloc[-2]
posC = df_sim.iloc[1]
posAMinRss = utils.nat2db(
float(min(posA[['ant.0', 'ant.1', 'ant.2', 'ant.3']])))
posBMinRss = utils.nat2db(
float(min(posB[['ant.0', 'ant.1', 'ant.2', 'ant.3']])))
posCMinRss = utils.nat2db(
float(min(posC[['ant.0', 'ant.1', 'ant.2', 'ant.3']])))
for i in range(len(QUANTILES)):
# if posBMinRss >= QUANTILE or posAMinRss >= QUANTILE:
if posAMinRss >= QUANTILES[i]:
nSucceed[i] += 1
finalRss.append(posAMinRss)
initRss.append(posCMinRss)
i = int(float(posA['drone.x']) // STEP)
j = int(float(posA['drone.y']) // STEP)
# TODO fix this
if i == 65:
i -= 1
if j == 50:
j -= 1
# plt.plot([i*10], [j*10], 'og')
heatmap[i, j] += 1
# print("{:.2f}% of simulation endup in top {:d}% positions"
# .format(float(nSucceed) / N * 100, TOP))
# OMG Really ? xD
# TODO learn meshgrid
X, Y, Z = [], [], []
for x in range(0, 650, STEP):
for y in range(0, 500, STEP):
i = x // 10
j = y // 10
X.append(x)
Y.append(y)
Z.append(heatmap[i, j] * 100 / N)
# Plot
# Cumulative sum
plt.figure()
plotCdfRss(finalRss)
plotCdfRss(initRss)
# Cosmetics
# plt.title("RSS Empirical CDF for d = " + d)
plt.xlabel("RSS [dBm]")
plt.ylabel("Percentage [%]")
plt.grid(linestyle=':', linewidth=1, color='gainsboro')
figureName = os.path.join(os.getcwd(), "cdf-" + d + ".png")
plt.savefig(figureName, bbox_inches='tight', dpi=300)
plt.close()
# Cosmetics
# plt.title("Arrival density - d = " + d + " (" + str(N) + " simulations)")
plt.xlabel("x [m]")
plt.ylabel("y [m]")
plt.grid(linestyle=':', linewidth=1, color='gainsboro')
plt.axis('equal')
plt.axis([0, 650, 0, 500])
# Plot map
plot.plot_scenario(edge='gainsboro', face='whitesmoke')
# Plot terminals
bsOpt = {
"markeredgewidth": 2,
"markersize": 15,
"color": 'white',
"markeredgecolor": 'black'
}
userOpt = {
"markeredgewidth": 2,
"markersize": 20,
"color": 'white',
"markeredgecolor": 'black'
}
plot.plot_terminals(exp['terminals'], bsOpt=bsOpt, userOpt=userOpt)
# Plot heatmap of arrival
plot.plot_heatmap(X,
Y,
Z,
np.ones(len(X)) * STEP,
np.ones(len(X)) * STEP,
legend="Arrival density [%]")
figureName = os.path.join(os.getcwd(), "monteCarlo-" + d + ".png")
plt.savefig(figureName, bbox_inches='tight', dpi=300)
plt.close()