def run(table_type):
"""
For each entry in validation table, compute sTEC
Validation run takes a few minutes to complete so results are saved to file.
:param table_type: (str)
:return:
"""
print "Validating against", table_type, "Solar Activity data table"
# Hard coded solar indices
if table_type == "High":
BX = GalileoBroadcast(236.831641, -0.39362878, 0.00402826613)
elif table_type == "Medium":
BX = GalileoBroadcast(121.129893, 0.351254133, 0.0134635348)
elif table_type == "Low":
BX = GalileoBroadcast(2.580271, 0.127628236, 0.0252748384)
else:
raise ValueError(
'table_type argument must be either "High", "Medium" or "Low"')
# output variables
sTECs_expected = []
sTECs_computed = []
with open(os.path.join('Validation',
table_type + '_reference.dat')) as infile:
with file(os.path.join('Validation', table_type + '_output.dat'),
'w') as outfile:
writer = csv.writer(outfile, delimiter=' ')
reader = csv.reader(infile, delimiter=' ')
for row in reader:
row = map(float, row)
mth = int(row[0])
UT = int(row[1])
lon1 = row[2]
lat1 = row[3]
h1 = row[4] / 1000 # change m to km
lon2 = row[5]
lat2 = row[6]
h2 = row[7] / 1000 # change m to km
time = NEQTime(mth, UT)
NEQ_global = NequickG_global(time, BX)
ray = Ray(h1, lat1, lon1, h2, lat2, lon2)
stec = NEQ_global.sTEC(ray) / 10**16
sTECs_computed.append(stec)
sTECs_expected.append(row[8])
row.append(stec)
writer.writerow(row)
print "Input: ", row[:7]
print "---Expected: ", row[8], "---Obtained: ", row[9]
return sTECs_expected, sTECs_computed
import matplotlib.pyplot as plt import numpy as np from NequickG import NEQTime, Position, GalileoBroadcast from NequickG_global import NequickG_global """ Input Parameters here """ mth = 4 UT = 12 lat = 40 lon = 100 Az = 64 """ Processing Below """ # Create input objects TX = NEQTime(mth,UT) BX = GalileoBroadcast(Az,0,0) RX = Position(lat,lon) # Create Nequick models NEQ_global = NequickG_global(TX,BX) NEQ, Para = NEQ_global.get_Nequick_local(RX) # Extract information from model hmin = 100 hmax = 1000 hs = np.arange(hmin, hmax) N = NEQ.electrondensity(hs) Azr = Para.Azr # Plotting
"""This task demonstrates how to obtain vertical total electron count and the ratio of total electrons in bottomside to that in topside""" from NequickG import NEQTime, Position, GalileoBroadcast from NequickG_global import NequickG_global # Input objects TX = NEQTime(4, 12) RX = Position(40, 0) BX = GalileoBroadcast(236.831, 0, 0) # Nequick objects NEQ_global = NequickG_global(TX, BX) NEQ, Para = NEQ_global.get_Nequick_local(RX) # vTEC quantities of interest print(NEQ.vTEC(100, 2000)) print(NEQ.vTEC_ratio())
"""This task demonstrates how to compute slant total electron count""" from NequickG import NEQTime, Position, GalileoBroadcast from NequickG_global import NequickG_global, Ray # Input objects TX = NEQTime(10,12) BX = GalileoBroadcast(80,0,0) # Nequick objects NEQ_global = NequickG_global(TX, BX) ray = Ray(100,40,0,1000,40,0) print NEQ_global.sTEC(ray) print NEQ_global.sTEC2(ray) NEQ_global.slant_table(91,ray, 'slant_ray.dat', ex_attr=['foF1', 'foF2', 'foE'])
cs = mapp.contourf(xx, yy, outs[i])
plt.title(attrs[i])
mapp.colorbar(cs)
if not os.path.exists(path):
os.makedirs(path)
plt.savefig(os.path.join(path, attrs[i] + '.png'))
plt.close()
# plot parameters for Validation test scenarios
solar = 'Medium' # or 'Medium' or 'Low'
if solar == 'Low':
BX = GalileoBroadcast(2.580271, 0.127628236,
0.0252748384) # Low solar activity
elif solar == 'Medium':
BX = GalileoBroadcast(121.129893, 0.351254133,
0.0134635348) # Medium solar activity
elif solar == 'High':
BX = GalileoBroadcast(236.831641, -0.39362878,
0.00402826613) # High solar activity
else:
raise ValueError
for time in [0, 4, 8, 12, 16, 20]:
TX = NEQTime(4, time)
plotparameters(TX, BX, os.path.join('maps', solar, str(time)))
if not os.path.exists('foF2/' + str(mth) + '/'):
os.makedirs('foF2/' + str(mth) + '/')
if not os.path.exists('M3000F2/' + str(mth) + '/'):
os.makedirs('M3000F2/' + str(mth) + '/')
for UT in range(0,23):
foF2map_high = np.ones([res, res], dtype=np.float32)
foF2map_low = np.ones([res, res], dtype=np.float32)
M3000map_low = np.ones([res, res], dtype=np.float32)
M3000map_high = np.ones([res, res], dtype=np.float32)
for i in range(res):
for j in range(res):
lat = latlat[j, i]
lon = lonlon[j, i]
pos = Position(lat, lon)
time = NEQTime(mth, UT)
ccir = CCIR_Unpacker(pos, time)
foF2map_low[j, i], M3000map_low[j, i] = ccir.compute('low')
foF2map_high[j, i], M3000map_high[j, i] = ccir.compute('high')
path = os.path.join('foF2', str(mth), "high" + '{:02d}'.format(UT) + '.png')
plotmap(lonlon, latlat, foF2map_high, 'foF2map: mth= ' + str(mth) + " UT=" + str(UT),path)
path = os.path.join('foF2', str(mth), "low" + '{:02d}'.format(UT) + '.png')
plotmap(lonlon, latlat, foF2map_low, 'foF2map: mth= ' + str(mth) + " UT=" + str(UT), path)
path = os.path.join('M3000F2', str(mth), 'high' + '{:02d}'.format(UT) + '.png')
plotmap(lonlon, latlat, M3000map_high, 'M3000F2map: mth= ' + str(mth) + " UT=" + str(UT),path)
path = os.path.join('M3000F2', str(mth), 'low' + '{:02d}'.format(UT) + '.png')
plotmap(lonlon, latlat, M3000map_low, 'M3000F2map: mth=' + str(mth) + " UT=" + str(UT), path)