def returnTEC(data, sv, navfile, yamlfile, timelim=None, el_mask=30, leap_seconds=18,
alt=300, sattype='G', el=False, lla=False, vertical=False, svbias=False, fN=0):
obstimes = np.array((data.major_axis))
obstimes = pandas.to_datetime(obstimes) - datetime.timedelta(seconds=leap_seconds)
stream = yaml.load(open(yamlfile, 'r'))
rx_xyz = stream.get('APPROX POSITION XYZ')
if timelim is not None:
idt = np.where( (obstimes>timelim[0]) & (obstimes<timelim[1]) ) [0]
# print (idt)
t = obstimes[idt]
# print (t.shape)
# print (sv)
L1 = np.array(data['L1', sv, :, 'data'])
L2 = np.array(data['L2', sv, :, 'data'])
C1 = np.array(data['C1', sv, :, 'data'])
C2 = np.array(data['P2', sv, :, 'data'])
L1 = L1[idt]
L2 = L2[idt]
C1 = C1[idt]
C2 = C2[idt]
if sattype == 'R':
aer = pyGps.getIonosphericPiercingPoints(rx_xyz, sv-32, t, alt, navfile, cs='aer', sattype=sattype)
llt = pyGps.getIonosphericPiercingPoints(rx_xyz, sv-32, t, alt, navfile, cs='wsg84', sattype=sattype)
else:
aer = pyGps.getIonosphericPiercingPoints(rx_xyz, sv, t, alt, navfile, cs='aer', sattype='G')
llt = pyGps.getIonosphericPiercingPoints(rx_xyz, sv, t, alt, navfile, cs='wsg84', sattype='G')
## filter by Elevation angle
idel = np.where((aer[1] > el_mask))[0]
if vertical == False:
tec = pyGps.getPhaseCorrTEC(L1[idel],L2[idel], C1[idel], C2[idel])
else:
if svbias:
bstream = yaml.load(open('/media/smrak/Eclipse2017/Eclipse/jplg2330.yaml', 'r'))
bias = float(bstream.get(sv))
tec = pyGps.getPhaseCorrTEC(L1[idel],L2[idel],C1[idel],C2[idel])
tec = pyGps.getVerticalTEC(tec+bias, aer[1][idel], alt)
else:
tec = pyGps.getPhaseCorrTEC(L1[idel],L2[idel],C1[idel],C2[idel])
tec = pyGps.getVerticalTEC(tec, aer[1][idel], alt)
t = t[idel]
#Return time, TEC and respective third argumnet masked by an elavation mask angle
if el==False and lla==False:
return t, tec
elif el==True and lla==False:
return t, tec, aer[:,idel]
elif el==False and lla ==True:
return t, tec, llt[:,idel]
else:
return t, tec, aer[:,idel]
else:
L1 = np.array(data['L1', sv, :, 'data'])
L2 = np.array(data['L2', sv, :, 'data'])
C1 = np.array(data['C1', sv, :, 'data'])
C2 = np.array(data['P2', sv, :, 'data'])
def plotObservationDay():
# Green keogram
ipp_aer = pyGps.getIonosphericPiercingPoints(rx_xyz,
sv,
obstimes,
ipp_alt,
navfname,
cs='aer')
ipp = pyGps.getIonosphericPiercingPoints(rx_xyz,
sv,
obstimes,
ipp_alt,
navfname,
cs='wsg84')
t, el, i, el_out4 = asi.getASIKeogramIPP(asi_folder3,
ipp_aer[0],
ipp_alt,
timelim,
'558',
obstimes=obstimes,
elevation=ipp[1])
# Magnetic Field
mag_folder = '/home/smrak/Documents/TheMahali/magnetometer/poker_2015_10_07.csv'
mag = magnetometer.readMag(mag_folder, '2015-10-07')
#mag_start = np.datetime64('2015-10-07T00:00:00')
#mag_stop = np.datetime64('2015-10-07T22:00:00')
#idx = np.where( (mag[0] > mag_start) & (mag[0] < mag_stop) )
mag_xyz = magnetometer.magHDZ2XYZ(mag, angle=True)
plotting.plot2subplotK_xy(t,
el,
i / 1000,
mag[0][8000:],
mag_xyz[0][8000:] / 1000,
obstimes=obstimes,
ipp=ipp_aer[1],
xlabel='UT',
ylim1=[45, 85],
ylabel1='Elevation[deg]',
ylabel2='Bx [uT]',
ytick1=[45, 55, 65, 75, 85],
ylim2=[12.2, 13.1],
ytick2=[12, 12.2, 12.4, 12.6, 12.8, 13],
pcolorbar=True,
cbartick=[0, 2, 4, 6, 8],
cbartitle='558nm [kR]',
xlim=[
datetime.datetime(2015, 10, 7, 6, 10, 0),
datetime.datetime(2015, 10, 7, 6, 40, 0)
])
def p2kg():
#Get keograms
ipp = pyGps.getIonosphericPiercingPoints(rx_xyz,
sv,
obstimes,
ipp_alt,
navfname,
cs='aer')
t5, el5, i5, el_out4 = asi.getASIKeogramIPP(asi_folder3,
ipp[0],
ipp_alt,
timelim,
'558',
obstimes=obstimes,
elevation=ipp[1])
t4, el4, i4, el_out5 = asi.getASIKeogramIPP(asi_folder3,
ipp[0],
ipp_alt,
timelim,
'428',
obstimes=obstimes,
elevation=ipp[1])
t6, el6, i6, el_out6 = asi.getASIKeogramIPP(asi_folder3,
ipp[0],
ipp_alt,
timelim,
'630',
obstimes=obstimes,
elevation=ipp[1])
plotoptics.plot2Keogram(t5,
t6,
el5,
el6,
i5 / 1000,
i6 / i4,
ylim=[45, 85],
pcolorbar=True,
ytick2=[45, 55, 65, 75],
ytick1=[45, 55, 65, 75, 85],
cmap='viridis',
cbartick1=[0, 2, 4, 6, 8],
cbartick2=[0, 0.4, 0.8, 1.2, 1.6, 2],
cbartitle1='558nm [kR]',
cbartitle2='red/blue',
xtick=[
datetime.datetime(2015, 10, 7, 6, 10, 0),
datetime.datetime(2015, 10, 7, 6, 15, 0),
datetime.datetime(2015, 10, 7, 6, 20, 0),
datetime.datetime(2015, 10, 7, 6, 25, 0),
datetime.datetime(2015, 10, 7, 6, 30, 0),
datetime.datetime(2015, 10, 7, 6, 35, 0),
datetime.datetime(2015, 10, 7, 6, 40, 0)
],
xlim=[
datetime.datetime(2015, 10, 7, 6, 10, 0),
datetime.datetime(2015, 10, 7, 6, 40, 0)
])
def pkg():
#Get keograms
ipp = pyGps.getIonosphericPiercingPoints(rx_xyz,
sv,
obstimes,
ipp_alt,
navfname,
cs='aer')
t5, el5, i5, el_out4 = asi.getASIKeogramIPP(asi_folder3,
ipp[0],
ipp_alt,
timelim,
'558',
obstimes=obstimes,
elevation=ipp[1])
t4, el4, i4, el_out5 = asi.getASIKeogramIPP(asi_folder3,
ipp[0],
ipp_alt,
timelim,
'428',
obstimes=obstimes,
elevation=ipp[1])
t6, el6, i6, el_out6 = asi.getASIKeogramIPP(asi_folder3,
ipp[0],
ipp_alt,
timelim,
'630',
obstimes=obstimes,
elevation=ipp[1])
plotoptics.plotKeogram(t5,
el5,
i5 / 1E3,
ylim=[45, 85],
title='Green line intensity',
pcolorbar=True,
cmap='viridis',
cbartick=[0, 2, 4, 6, 8],
cbartitle='kR',
ytick=[45, 55, 65, 75, 85],
xtick=[
datetime.datetime(2015, 10, 7, 6, 10, 0),
datetime.datetime(2015, 10, 7, 6, 15, 0),
datetime.datetime(2015, 10, 7, 6, 20, 0),
datetime.datetime(2015, 10, 7, 6, 25, 0),
datetime.datetime(2015, 10, 7, 6, 30, 0),
datetime.datetime(2015, 10, 7, 6, 35, 0),
datetime.datetime(2015, 10, 7, 6, 40, 0)
],
xlim=[
datetime.datetime(2015, 10, 7, 6, 10, 0),
datetime.datetime(2015, 10, 7, 6, 40, 0)
])
def pInt():
ipp = pyGps.getIonosphericPiercingPoints(rx_xyz, sv, obstimes, ipp_alt,
navfname)
t, d = asi.getAllSkyIntensity(asi_folder3,
ipp[0],
ipp[1],
120,
timelim,
'558',
obstimes=obstimes)
t4, d4 = asi.getAllSkyIntensity(asi_folder3,
ipp[0],
ipp[1],
90,
timelim,
'428',
obstimes=obstimes)
t6, d6 = asi.getAllSkyIntensity(asi_folder3,
ipp[0],
ipp[1],
250,
timelim,
'630',
obstimes=obstimes)
plotoptics.plotIntensity(t,
d,
ylabel='Intensity [R]',
xlabel='UT',
label1='558nm',
t2=t4,
y2=d4,
t3=t6,
y3=d6,
label2='428nm',
label3='630nm',
color1='g',
color2='b',
color3='r',
title='All-sky imager intensity',
ylim=[300, 2500],
legend=True,
xlim=[
datetime.datetime(2015, 10, 7, 6, 10, 0),
datetime.datetime(2015, 10, 7, 6, 40, 0)
])
def plotGlobalMap():
start = datetime.datetime(2015, 10, 7, 6, 10, 0)
stop = datetime.datetime(2015, 10, 7, 6, 40, 0)
idx = np.where((obstimes >= start) & (obstimes <= stop))[0]
ipp = pyGps.getIonosphericPiercingPoints(rx_xyz,
sv,
obstimes,
ipp_alt,
navfname,
cs='wsg84')
#print (obstimes[idx])
plotSatellite.plotSatelliteGlobalMap(rx_xyz,
sv,
obstimes[idx],
width=5000000,
height=3000000,
center=[-130, 60])
plotSatellite.plotIPPGlobalMap(ipp[0][idx],
ipp[1][idx],
width=5000000,
height=3000000,
center=[-130, 60])
plotSatellite.plotSatelliteGlobalMap(rx_xyz,
23,
obstimes[0:6000],
color='green',
lw=2,
width=5000000,
height=3000000,
center=[-130, 60])
plotSatellite.plotSatelliteGlobalMap(rx_xyz,
23,
obstimes[idx],
color='red',
lw=3,
width=5000000,
height=3000000,
center=[-130, 60])
def tec_keogram_intensity5():
N_roti = 100
aer = pyGps.getSatellitePosition(np.asarray(rx_xyz),
sv,
obstimes,
navfname,
cs='aer')
L1 = np.array(data['L1', sv, :, 'data'])
L2 = np.array(data['L2', sv, :, 'data'])
P1 = np.array(data['C1', sv, :, 'data'])
P2 = np.array(data['P2', sv, :, 'data'])
l2_lli = np.array(data['L2', sv, :, 'lli'])
#Phase corrected TEC
sTECp = pyGps.getPhaseCorrTEC(L1, L2, P1, P2)
vTECp = pyGps.getVerticalTEC(sTECp, aer[1], 130)
tec_norm = vTECp - vTECp[np.isfinite(vTECp[0:1000])].min()
# Rate of TEC Index
roti = pyGps.getROTI(vTECp, N_roti)
roti_t = obstimes[0:-N_roti]
#ASI Intensity data
# Piercing point for all-sky data
aer = pyGps.getIonosphericPiercingPoints(rx_xyz,
sv,
obstimes,
ipp_alt,
navfname,
cs='aer')
#ipp = pyGps.getIonosphericPiercingPoints(rx_xyz, sv, obstimes, ipp_alt, navfname, cs='wsg84')
#Asi intensity:
t5, intensity5 = asi.getAllSkyIntensityAER(asi_folder3,
aer[0],
aer[1],
ipp_alt,
timelim,
'558',
obstimes=obstimes)
t6, intensity6 = asi.getAllSkyIntensityAER(asi_folder3,
aer[0],
aer[1],
ipp_alt,
timelim,
'630',
obstimes=obstimes)
t4, intensity4 = asi.getAllSkyIntensityAER(asi_folder3,
aer[0],
aer[1],
ipp_alt,
timelim,
'428',
obstimes=obstimes)
# Asi Keogram
ipp_aer = pyGps.getIonosphericPiercingPoints(rx_xyz,
sv,
obstimes,
ipp_alt,
navfname,
cs='aer')
t, el, i, el_out4 = asi.getASIKeogramIPP(asi_folder3,
ipp_aer[0],
ipp_alt,
timelim,
'558',
obstimes=obstimes,
elevation=aer[1])
# Magnetometer
mag_folder = '/home/smrak/Documents/TheMahali/magnetometer/poker_2015_10_07.csv'
mag = magnetometer.readMag(mag_folder, '2015-10-07')
b_t = mag[0][5000:]
mag_xyz = magnetometer.magHDZ2XYZ(mag, angle=True)
plotting.plot5subplot(t,
t5,
obstimes,
roti_t,
i / 1E3,
np.array(intensity5) / 1E3,
tec_norm,
roti,
el1=el,
ylabel1='Elevation [deg]',
ylabel3='vTEC [TECU]',
ylabel2='Intensity [kR]',
ylabel4='ROTI[TECU/10s]',
xlabel='UT',
ylim1=[45, 85],
color5='b',
ytick1=[45, 55, 65, 75, 85],
pcolorbar=True,
cbartick=[0, 2, 4, 6, 8],
cbartitle='Intensity [kR]',
t31=t6,
t32=t4,
y31=np.array(intensity6) / 1E3,
y32=np.array(intensity4) / 1E3,
color2='g',
color3='r',
color4='b',
legend1=True,
label2='558nm',
label3='630nm',
label4='428nm',
lli1=l2_lli,
ylim2=[0, 3.5],
ylim4=[0, 3.5],
ylim3=[0, 18],
ipp_elevation=aer[1],
ytick2=[0, 1, 2, 3],
ytick3=[0, 4, 8, 12, 16],
ytick4=[0, 0.5, 1, 1.5, 2, 2.5, 3],
Bt=b_t,
Bx=mag_xyz[0][5000:] / 1000,
ylabel5='Bx [uT]',
cbx='b',
ylim5=[12.2, 13],
ytick5=[12.2, 12.4, 12.6, 12.8],
xlim=[
datetime.datetime(2015, 10, 7, 6, 10, 0),
datetime.datetime(2015, 10, 7, 6, 40, 0)
])
def tec_keogram_intensity3():
aer = pyGps.getSatellitePosition(np.asarray(rx_xyz),
sv,
obstimes,
navfname,
cs='aer')
L1 = np.array(data['L1', sv, :, 'data'])
L2 = np.array(data['L2', sv, :, 'data'])
P1 = np.array(data['C1', sv, :, 'data'])
P2 = np.array(data['P2', sv, :, 'data'])
l2_lli = np.array(data['L2', sv, :, 'lli'])
#Phase corrected TEC
sTECp = pyGps.getPhaseCorrTEC(L1, L2, P1, P2)
vTECp = pyGps.getVerticalTEC(sTECp, aer[1], 130)
tec_norm = vTECp - vTECp[np.isfinite(vTECp[0:1000])].min()
# Rate of TEC Index
roti = pyGps.getROTI(vTECp, 10)
roti_t = obstimes[0:-10]
#ASI Intensity data
# Piercing point for all-sky data
aer = pyGps.getIonosphericPiercingPoints(rx_xyz,
sv,
obstimes,
ipp_alt,
navfname,
cs='aer')
ipp = pyGps.getIonosphericPiercingPoints(rx_xyz,
sv,
obstimes,
ipp_alt,
navfname,
cs='wsg84')
#Asi intensity:
t5, intensity5 = asi.getAllSkyIntensityAER(asi_folder3,
aer[0],
aer[1],
ipp_alt,
timelim,
'558',
obstimes=obstimes)
t5a, intensity5a = asi.getAllSkyIntensity(asi_folder3,
ipp[0],
ipp[1],
ipp_alt,
timelim,
'558',
obstimes=obstimes)
# Asi Keogram
ipp_aer = pyGps.getIonosphericPiercingPoints(rx_xyz,
sv,
obstimes,
ipp_alt,
navfname,
cs='aer')
t, el, i, el_out4 = asi.getASIKeogramIPP(asi_folder3,
ipp_aer[0],
ipp_alt,
timelim,
'558',
obstimes=obstimes,
elevation=ipp[1])
plotting.plot4subplot(t,
obstimes,
t5,
roti_t,
i / 1E3,
tec_norm,
np.array(intensity5) / 1E3,
roti,
el1=el,
ylabel1='Elevation [deg]',
ylabel2='vTEC [TECU]',
ylabel3='Intensity [kR]',
ylabel4='ROTI[TECU/10s]',
xlabel='UT',
ylim1=[45, 85],
color5='b',
title1='Rx8 - PRN23, Poker Flat 10/07/2015',
ytick1=[45, 55, 65, 75, 85],
pcolorbar=True,
cbartick=[0, 2, 4, 6, 8],
cbartitle='Intensity [kR]',
t31=t5a,
y31=np.array(intensity5a) / 1E3,
color2='g',
color3='r',
legend2=True,
label2='org',
label3='int',
label4='630nm',
lli1=l2_lli,
ylim3=[0, 3.5],
ylim4=[0, 3.5],
ylim2=[0, 18],
ipp_elevation=aer[1],
ytick3=[0, 1, 2, 3],
ytick2=[0, 4, 8, 12, 16],
ytick4=[0, 0.5, 1, 1.5, 2, 2.5, 3],
xlim=[
datetime.datetime(2015, 10, 7, 6, 10, 0),
datetime.datetime(2015, 10, 7, 6, 40, 0)
])
def tec_vs_asi():
aer = pyGps.getSatellitePosition(np.asarray(rx_xyz),
sv,
obstimes,
navfname,
cs='aer')
L1 = np.array(data['L1', sv, :, 'data'])
L2 = np.array(data['L2', sv, :, 'data'])
P1 = np.array(data['C1', sv, :, 'data'])
P2 = np.array(data['P2', sv, :, 'data'])
l2_lli = np.array(data['L2', sv, :, 'lli'])
#Phase corrected TEC
sTECp = pyGps.getPhaseCorrTEC(L1, L2, P1, P2)
vTECp = pyGps.getVerticalTEC(sTECp, aer[1], 130)
tec_norm = vTECp - vTECp[np.isfinite(vTECp[0:1000])].min()
#ASI Intensity data
# Piercing point for all-sky data
# aer = pyGps.getIonosphericPiercingPoints(rx_xyz, sv, obstimes, ipp_alt,
# navfname, cs='aer')
ipp = pyGps.getIonosphericPiercingPoints(rx_xyz,
sv,
obstimes,
130E3,
navfname,
cs='wsg84')
#Asi intensity:
t5, intensity5 = asi.getAllSkyIntensity(asi_folder3,
ipp[0],
ipp[1],
130,
timelim,
'558',
obstimes=obstimes)
t4, intensity4 = asi.getAllSkyIntensity(asi_folder3,
ipp[0],
ipp[1],
130,
timelim,
'428',
obstimes=obstimes)
t6, intensity6 = asi.getAllSkyIntensity(asi_folder3,
ipp[0],
ipp[1],
130,
timelim,
'630',
obstimes=obstimes)
#Plot 2 subplots
plotting.plot2subplot(obstimes,
t5,
tec_norm, (np.array(intensity5) / 1E3),
ylabel1='vTEC [TECU]',
ylabel2='Intensity [kR]',
xlabel='UT',
lli1=l2_lli,
ylim2=[0.2, 2.6],
lm='line',
color2='g',
lli2=l2_lli,
t21=t4,
y21=(np.array(intensity4) / 1E3),
t22=t6,
y22=(np.array(intensity6) / 1E3),
label4='630nm',
color4='r',
color3='b',
label2='558nm',
label3='428nm',
legend2=True,
xlim=[
datetime.datetime(2015, 10, 7, 6, 10, 0),
datetime.datetime(2015, 10, 7, 6, 40, 0)
])
def singleStation(day, el_mask, state, station=''):
#Find HDF5 filename
if station == '':
wlstr = '*' + str(day) + '*.h5'
else:
wlstr = state + station + str(day) + '*.h5'
filestr = os.path.join(obsfolder + state + '/' + str(day) + '/', wlstr)
flistHDF = glob.glob(filestr)
#Find NAV filename
wlstr = '*' + str(day) + '*.17n'
filestr = os.path.join(navfolder, wlstr)
flistNAV = glob.glob(filestr)
#Find YAML filename
if station == '':
wlstr = '*' + str(day) + '*.yaml'
else:
wlstr = state + station + str(day) + '*.yaml'
filestr = os.path.join(obsfolder + state + '/' + str(day) + '/', wlstr)
flistYML = glob.glob(filestr)
#filenames
hdffn = flistHDF[0]
navfn = flistNAV[0]
ymlfn = flistYML[0]
# Processing per station
# YAML import header
stream = yaml.load(open(ymlfn, 'r'))
rx_xyz = stream.get('APPROX POSITION XYZ')
# HDF Observations
data = read_hdf(hdffn)
obstimes = np.array((data.major_axis))
obstimes = pandas.to_datetime(obstimes) - datetime.timedelta(seconds=18)
dumb = data['L1', :, 1, 'data']
svlist = dumb.axes[0]
fig1 = plt.figure(figsize=(12, 12))
formatter = mdates.DateFormatter('%H:%M')
ax1 = fig1.add_subplot(311)
#fig2 = plt.figure(figsize=(12,4))
ax2 = fig1.add_subplot(312, sharex=ax1)
#fig3 = plt.figure(figsize=(12,4))
ax3 = fig1.add_subplot(313, sharex=ax1)
for sv in svlist:
# GPS only svnum <=32
if sv <= 32:
C1 = np.array(data['C1', sv, :, 'data'])
C2 = np.array(data['P2', sv, :, 'data'])
L1 = np.array(data['L1', sv, :, 'data'])
L2 = np.array(data['L2', sv, :, 'data'])
aer = pyGps.getIonosphericPiercingPoints(rx_xyz,
sv,
obstimes,
120,
navfn,
cs='aer')
#filter by Elevation angle
idel = np.where((aer[1] > el_mask))[0]
t = obstimes[idel]
#Get TEC
# Calc only if there is more than 30min of data
if (np.isfinite(L1[idel]).shape[0] > 1000):
tec, intervals = pyGps.getPhaseCorrTEC(L1[idel],
L2[idel],
C1[idel],
C2[idel],
intervals=True)
l = []
for ran in intervals:
l.append(ran[1] - ran[0])
idl = np.where(np.array(l) < 1000)[0]
# print (idl)
if len(idl) > 0:
idLL = intervals[idl[0]]
tec[idLL[0]:idLL[1]] = np.nan
# Detrend data without NaNs
if np.sum(np.isfinite(tec)) > 100:
idf = np.isfinite(tec)
tec_recovery = np.nan * np.zeros(tec.shape[0])
# techpf_recovery = np.nan * np.zeros(tec.shape[0])
diff_tec = pyGps.phaseDetrend(tec[idf], order=3)
# sigma_phi = pyGps.phaseScintillation(C1[idel], polyfit_order=3, skip=40)
tec_recovery[idf] = diff_tec
# Plottings
ax2.plot(t, tec_recovery, label='sv' + str(sv))
ax3.plot(t[:-1], np.diff(tec), label='sv' + str(sv))
ax1.plot(t, C1[idel], label='sv' + str(sv))
ax3.set_ylim([-1, 1])
ax1.set_xlim([obstimes[0], obstimes[-1]])
ax2.set_ylabel('Detr. tec')
ax3.set_ylabel('Delta tec')
ax1.set_ylabel('C1')
ax3.set_xlabel('time [UTC]')
ax1.set_title('Missouri, day ' + str(day) + ' Rx: ' + hdffn[-11:-7])
plt.setp(ax1.get_xticklabels(), visible=False)
plt.setp(ax2.get_xticklabels(), visible=False)
#ax3.legend(bbox_to_anchor=(1.1, 1.1))
ax1.xaxis.set(major_formatter=formatter)
ax2.xaxis.set(major_formatter=formatter)
ax3.xaxis.set(major_formatter=formatter)
plt.savefig('/home/smrak/Documents/eclipse/plots/mo/' + hdffn[-11:-3] +
'.png',
dpi=300)
def runSateListRaw(day, el_mask, state):
#Find HDF5 filename
wlstr = '*' + str(day) + '*.h5'
filestr = os.path.join(obsfolder + state + '/' + str(day) + '/', wlstr)
flistHDF = glob.glob(filestr)
#Find NAV filename
wlstr = '*' + str(day) + '*.17n'
filestr = os.path.join(navfolder, wlstr)
flistNAV = glob.glob(filestr)
#Find YAML filenamE
wlstr = '*' + str(day) + '*.yaml'
filestr = os.path.join(obsfolder + state + '/' + str(day) + '/', wlstr)
flistYML = glob.glob(filestr)
for i in range(len(flistHDF)):
#filenames
hdffn = flistHDF[i]
navfn = flistNAV[0]
ymlfn = flistYML[i]
# Processing per station
# YAML import header
stream = yaml.load(open(ymlfn, 'r'))
rx_xyz = stream.get('APPROX POSITION XYZ')
# HDF Observations
data = read_hdf(hdffn)
obstimes = np.array((data.major_axis))
obstimes = pandas.to_datetime(obstimes) - datetime.timedelta(
seconds=18)
dumb = data['L1', :, 1, 'data']
svlist = dumb.axes[0]
fig1 = plt.figure(figsize=(12, 12))
formatter = mdates.DateFormatter('%H:%M')
ax1 = fig1.add_subplot(411)
#fig2 = plt.figure(figsize=(12,4))
ax2 = fig1.add_subplot(412, sharex=ax1)
#fig3 = plt.figure(figsize=(12,4))
ax3 = fig1.add_subplot(413, sharex=ax1)
ax4 = fig1.add_subplot(414, sharex=ax1)
for sv in svlist:
# GPS only svnum <=32
if sv <= 32:
C1 = np.array(data['C1', sv, :, 'data'])
C2 = np.array(data['P2', sv, :, 'data'])
L1 = np.array(data['L1', sv, :, 'data'])
L2 = np.array(data['L2', sv, :, 'data'])
aer = pyGps.getIonosphericPiercingPoints(rx_xyz,
sv,
obstimes,
120,
navfn,
cs='aer')
#filter by Elevation angle
idel = np.where((aer[1] > el_mask))[0]
t = obstimes[idel]
#Get TEC
ax1.plot(t, C1[idel], label='sv' + str(sv))
ax2.plot(t, C2[idel], label='sv' + str(sv))
ax3.plot(t, L1[idel], label='sv' + str(sv))
ax4.plot(t, L2[idel], label='sv' + str(sv))
ax1.set_xlim([obstimes[0], obstimes[-1]])
ax1.set_ylabel('C1 [m]')
ax2.set_ylabel('P2 [m]')
ax3.set_ylabel('L1 [cycle]')
ax4.set_ylabel('L2 [cycle]')
ax3.set_xlabel('time [UTC]')
ax1.set_title(state + ', day ' + str(day) + ' Rx: ' + hdffn[-11:-7])
plt.setp(ax1.get_xticklabels(), visible=False)
plt.setp(ax2.get_xticklabels(), visible=False)
plt.setp(ax3.get_xticklabels(), visible=False)
#ax3.legend(bbox_to_anchor=(1.1, 1.1))
# ax1.xaxis.set(major_formatter = formatter)
# ax2.xaxis.set(major_formatter = formatter)
ax4.xaxis.set(major_formatter=formatter)
plt.savefig('/home/smrak/Documents/eclipse/plots/' + state + '/' +
hdffn[-11:-3] + '.png',
dpi=300)
def saveTrajectories2HDF(day='184',
state='tn',
el_mask=30,
altitude=100,
hdffilename='trajectories_tn.h5',
loop=True):
hdffolder = '/home/smrak/sharedrive/cors/'
navfolder = '/home/smrak/sharedrive/cors/nav/'
ymlfolder = '/home/smrak/sharedrive/cors/'
#Find HDF5 filename
wlstr = '*' + day + '*.h5'
filestr = os.path.join(hdffolder + state + '/' + day + '/', wlstr)
flistHDF = glob.glob(filestr)
#Find NAV filename
wlstr = '*' + str(day) + '*.17n'
filestr = os.path.join(navfolder, wlstr)
flistNAV = glob.glob(filestr)
#Find YAML filenamE
wlstr = '*' + str(day) + '*.yaml'
filestr = os.path.join(ymlfolder + state + '/' + day + '/', wlstr)
flistYML = glob.glob(filestr)
h5file = h5py.File(hdffilename, 'w')
gr = h5file.create_group(day)
h5state = gr.create_group(state)
if loop:
for i in range(len(flistHDF)):
hdffn = flistHDF[i]
navfn = flistNAV[0]
ymlfn = flistYML[i]
# YAML import header
stream = yaml.load(open(ymlfn, 'r'))
rx_xyz = stream.get('APPROX POSITION XYZ')
rx_llt = ecef2geodetic(rx_xyz[0], rx_xyz[1], rx_xyz[2])
#Timelim
timelim = [
datetime.datetime(2017, 7, 3, 15, 30, 0),
datetime.datetime(2017, 7, 3, 19, 30, 0)
]
h5rx = h5state.create_group(hdffn[-11:-7])
h5rx.create_dataset('rxpos', data=rx_llt)
# HDF Observations
try:
data = read_hdf(hdffn)
obstimes = np.array((data.major_axis))
obstimes = pandas.to_datetime(
obstimes[::10]) - datetime.timedelta(seconds=18)
idt = np.where((obstimes > timelim[0])
& (obstimes < timelim[1]))
obstimes = obstimes[idt]
#
h5rx.create_dataset('time',
data=pyGps.datetime2posix(obstimes))
#
dumb = data['L1', :, 1, 'data']
svlist = dumb.axes[0]
for sv in svlist:
# GPS only svnum <=32
az = np.nan * np.zeros(obstimes.shape[0])
el = np.nan * np.zeros(obstimes.shape[0])
lat = np.nan * np.zeros(obstimes.shape[0])
lon = np.nan * np.zeros(obstimes.shape[0])
if sv <= 32:
h5sv = h5rx.create_group('sv' + str(sv))
aer = pyGps.getIonosphericPiercingPoints(rx_xyz,
sv,
obstimes,
altitude,
navfn,
cs='aer')
llt = pyGps.getIonosphericPiercingPoints(rx_xyz,
sv,
obstimes,
altitude,
navfn,
cs='wsg84')
idel = np.where(aer[1] > el_mask)[0]
az[idel] = aer[0][idel]
el[idel] = aer[1][idel]
lat[idel] = llt[0][idel]
lon[idel] = llt[1][idel]
h5sv.create_dataset('az', data=az)
h5sv.create_dataset('el', data=el)
h5sv.create_dataset('lat', data=lat)
h5sv.create_dataset('lon', data=lon)
print('Saving data for sat: ' + str(sv))
else:
break
except:
raise ValueError
h5file.close()