V, Vg, Ag, Vs, Vs_, Ah, Gamma, Gamma_, CL, CL_, CD, CD_ = d
Mach = aero.tas2mach(V, alts[:-1])
Vcas = aero.tas2cas(V, alts[:-1])
if sf:
count += 1
CD0_set.append(CD0)
CLCD_max_set.append(CLCD_max)
if count > MAX_DATA_SAMPLE:
break
if STEP_FLAG:
# get the airport
airport = utils.get_closest_airport(data[0][1], data[0][2])
plt.suptitle("%s at %s [Take-Off]" % (icao, airport['name']))
plt.subplot(331)
plt.xlabel('time (s)')
plt.ylabel('altitude (m)')
plt.plot(ts, alts, '.-', ms=5, color='steelblue')
plt.legend(loc=4, ncol=2)
plt.subplot(332)
plt.xlabel('time (s)')
plt.ylabel('speeds (m/s)')
plt.plot(ts, Vg, '.-', ms=5, color='g', label="Vg_ / Vtas^")
plt.plot(ts[:-1], Vcas, '.-', ms=5, color='y', label="Vcas^")
plt.legend(loc=2, prop={'size': 10})
def extract():
mongo_client = MongoClient('localhost', PORT)
mcollin = mongo_client[DB][COLL]
res = mcollin.find().skip(2000)
dataset = []
for r in res:
data = r['data']
icao = r['icao']
# skip data starting too low
if data[0][3] > ALT_LOW: # ft
continue
# get the takeoff data chunk (h=0)
chunk = []
for d in data:
if d[3] < ALT_LOW:
continue
elif d[3] > ALT_LOW and d[3] < ALT_HIGH:
chunk.append(d)
else:
break
# ignore insufficient chunk size
if len(chunk) < 10:
continue
if chunk[-1][3] < ALT_HIGH * 0.8:
continue
chunkNP = np.asarray(chunk)
ts = chunkNP[:, 0].astype(int)
ts = ts - ts[0]
alts = chunkNP[:, 3].astype(int)
spds = (chunkNP[:, 4]).astype(int)
hdgs = chunkNP[:, 5].astype(int)
dhdg = hdgs.max() - hdgs.min()
if dhdg < 90 or dhdg > 270:
continue
p0 = [spds[0], 0, hdgs[0]]
plsq = optimize.leastsq(residuals, p0,
args=([spds, hdgs]),
Dfun=dfunc)
va, vw, xw = plsq[0]
vcas = aero.tas2cas(va, (ALT_LOW+ALT_HIGH)/2 * aero.ft)
print plsq
print ''
# pkdata = {'speeds': spds, 'angles': hdgs}
# pickle.dump(pkdata, open("temp.pkl", "wb"))
airport = utils.get_closest_airport(data[0][1], data[0][2])
plt.suptitle("%s at %s" % (icao, airport['name']))
plt.subplot(3, 2, 1)
plt.xlabel('time (s)')
plt.ylabel('ground speeds (kts)')
plt.plot(ts, spds, '.-', ms=5, color='g', label="Ground Speed")
plt.ylim([0, 600])
plt.grid()
plt.subplot(3, 2, 3)
plt.xlabel('time (s)')
plt.ylabel('altitude (ft)')
plt.plot(ts, alts, '.-', ms=5, color='b')
plt.ylim([0, 40000])
plt.grid()
plt.subplot(3, 2, 5)
plt.xlabel('time (s)')
plt.ylabel('track angle (deg)')
plt.plot(ts, hdgs, '.-', ms=5, color='y', label="Track Angle")
plt.ylim([0, 360])
plt.grid()
plt.subplot(3, 2, 2)
plt.xlabel('time (s)')
plt.ylabel('TAS (kts)')
plt.plot(ts, np.ones(ts.shape)*va, '-', lw=3, color='b', label="TAS")
plt.plot(ts, np.ones(ts.shape)*vcas, '-', lw=3, color='g', label="CAS")
plt.ylim([0, 600])
plt.legend()
plt.grid()
plt.subplot(3, 2, 4)
plt.xlabel('time (s)')
plt.ylabel('Wind Speed (kts)')
plt.plot(ts, np.ones(ts.shape)*vw, '-', lw=3, color='b')
plt.grid()
plt.subplot(3, 2, 6)
plt.xlabel('time (s)')
plt.ylabel('Wind Angle (deg)')
plt.plot(ts, np.ones(ts.shape)*xw, '-', lw=3, color='b')
plt.ylim([-10, 360])
plt.grid()
plt.draw()
plt.waitforbuttonpress(-1)
plt.clf()
return dataset