def testIRTAM(self):
# Top level manager config
managerConfig = ConfigValues.TICSConfig()
# Tell the datacontroller to use giro and roam to use irtam
managerConfig.dataControllerConfig.sources = ['giro']
managerConfig.roamConfig.backgroundModel = ['irtam']
manager = Manager(managerConfig)
manager.queryIonoState(37.2321, 256.2323, datetime(2015, 10, 20))
def testRIPE(self):
# Top level manager config
managerConfig = ConfigValues.TICSConfig()
# Tell the datacontroller to use noaa and roam to use ripe
managerConfig.dataControllerConfig.sources = ['noaa']
managerConfig.roamConfig.backgroundModel = ['ripe']
manager = Manager(managerConfig)
manager.queryIonoState(37.2321, 256.2323, datetime(2015, 10, 20))
def test_CalcIonoState_IRTAM(self):
import IonoModelEngine.Config.ConfigValues as ConfigValues
from numpy.testing import assert_approx_equal
logger.info("test_CalcIonoState_IRTAM")
directory = os.path.dirname(
os.path.realpath(__file__)) + "/UnitTestData/"
# Setup the input into ROAM
listOfFiles = {}
listOfFiles['giro'] = [directory + 'IRTAM_foF2_COEFFS_20151020_0000.ASC', \
directory + 'IRTAM_hmF2_COEFFS_20151020_0000.ASC', \
directory + 'IRTAM_B0_COEFFS_20151020_0000.ASC', \
directory + 'IRTAM_B1_COEFFS_20151020_0000.ASC']
listOfFiles['noaa'] = []
listOfFiles['localDigisonde'] = []
# First construct a config file for ROAM
self.config = ConfigValues.ROAMConfig()
# Tell roam to use IRTAM
self.config.backgroundModel = 'irtam'
# instantiate ROAM
self.roam = ROAM(self.config, self.irtamHandle, self.pharlapHandle)
# G10 receiver location
lat = 32.4824
lon = -106.3809
UT = numpy.array([2015, 10, 20, 0, 0])
DT = datetime(UT[0], UT[1], UT[2], UT[3], UT[4], 0)
#self.roam.background_model = 2 # force use of IRTAM
#self.roam.noaaConfig = [] # force use of IRTAM
state = self.roam.CalcIonoState(lat, lon, DT, listOfFiles)
logger.info(
"ROAM foF2={:.4f} hmF2={:.4f} foF1={:.4f} foE={:.4f} hmE={:.4f} B0={:.4f} B1={:.4f} foEs={:.4f} hmEs={:.4f} beta_lat={:.4f} beta_lon={:.4f}". \
format(state[0], state[1], state[2], state[3], state[4], state[5], \
state[6], state[7], state[8], state[9], state[10]))
# Output from ROAM.py running IRTAM
# [ROAM.py:176] IRTAM data: foF2=8.7484 hmF2=252.7329 B0=59.5018 B1=3.1669
# [ROAM.py:734] ROAM foF2=8.7484 hmF2=252.7329 foF1=-1.0000 foE=1.8131 hmE=110.0000 B0=59.5018 B1=3.1669 ...
# foEs=0.0000 hmEs=110.0000 beta_lat=-0.0085 beta_lon=-0.0116
assert_approx_equal(state[0], 8.7484, significant=3) # foF2
assert_approx_equal(state[1], 252.7329, significant=3) # hmF2
assert_approx_equal(state[2], -1.0, significant=3) # foF1
assert_approx_equal(state[3], 1.8131, significant=3) # foE
assert_approx_equal(state[5], 59.5018, significant=3) # B0
assert_approx_equal(state[6], 3.1669, significant=3) # B1
assert_approx_equal(state[9], -0.0085, significant=3) # gradLat
assert_approx_equal(state[10], -0.0116, significant=3) # gradLon
return
def test_CalcIonoState_RIPE(self):
import IonoModelEngine.Config.ConfigValues as ConfigValues
from numpy.testing import assert_approx_equal
logger.info("test_CalcIonoState_RIPE")
# Location of test files
directory = os.path.dirname(os.path.realpath(__file__)) + "/../RIPE"
# Setup the input into ROAM
listOfFiles = {}
listOfFiles['noaa'] = [directory + '/TestFiles/AU930_NOAA.TXT', \
directory + '/TestFiles/BC840_NOAA.TXT', \
directory + '/TestFiles/EG931_NOAA.TXT']
listOfFiles['giro'] = []
listOfFiles['localDigisonde'] = []
# First construct a config file for ROAM
self.config = ConfigValues.ROAMConfig()
# Tell roam to use RIPE
self.config.backgroundModel = 'ripe'
# instantiate ROAM
self.roam = ROAM(self.config, self.irtamHandle, self.pharlapHandle)
self.roam.refreshIRI = 0
# G10 receiver location
lat = 32.4824
lon = -106.3809
UT = numpy.array([2015, 10, 20, 0, 0])
DT = datetime(UT[0], UT[1], UT[2], UT[3], UT[4], 0)
state = self.roam.CalcIonoState(lat, lon, DT, listOfFiles)
logger.info(
"ROAM foF2={:.4f} hmF2={:.4f} foF1={:.4f} foE={:.4f} hmE={:.4f} B0={:.4f} B1={:.4f} foEs={:.4f} hmEs={:.4f} beta_lat={:.4f} beta_lon={:.4f}". \
format(state[0], state[1], state[2], state[3], state[4], state[5], \
state[6], state[7], state[8], state[9], state[10]))
# Output from Matlab run of roam_using_ripe_testfiles_20151020.m followed by roam_using_ripe.m
# YEAR MON DAY HR MIN foF2 hmF2 foF1 foE B0 B1 gradLat gradLon ... Ref_lat Ref_lon AoAErr
# 2015 10 20 00 00 9.2822 257.5859 -1.0000 1.8131 61.7588 2.5061 -0.0144 -0.0038 32.4824 -106.3809 0.0000
assert_approx_equal(state[0], 9.2822, significant=3) # foF2
assert_approx_equal(state[1], 257.5859, significant=3) # hmF2
assert_approx_equal(state[2], -1.0, significant=3) # foF1
assert_approx_equal(state[3], 1.8131, significant=3) # foE
assert_approx_equal(state[5], 61.7588, significant=1) # B0
assert_approx_equal(state[6], 2.5061, significant=2) # B1
assert_approx_equal(state[9], -0.0144, significant=3) # gradLat
assert_approx_equal(state[10], -0.0038, significant=3) # gradLon
return
def test(self):
# Setup the configurations
config = ConfigValues.GIROConfig()
# Remove the test dir to reset
# if os.path.isdir(config.giroStore):
# shutil.rmtree(config.giroStore)
# Need to stand this up
ionoPyToCHandle = IonoPyIface.Pharlap()
giroManager = GIRODataManager(config, ionoPyToCHandle)
# Perform test donwload
listOfOutputFiles = giroManager.process(datetime(2015, 10, 20))
self.assertEqual(len(listOfOutputFiles),4)
def testNOAA(self):
# Setup the configuration for the DataController
dataControllerConfig = ConfigValues.DataControllerConfig()
# Use only noaa
dataControllerConfig.sources = ['noaa']
# Need to stand this up, usually the Manager has it
saoReaderHandle = SaoPyIface()
pharlapHandle = Pharlap()
# Create an instance of DataController and let it go
dataController = DataController(dataControllerConfig,
saoReaderHandle=saoReaderHandle,
pharlapHandle=pharlapHandle)
listOfOutputFiles = dataController.process(datetime(2015, 10, 20))
self.assertEqual(len(listOfOutputFiles['noaa']), 4)
def testGIRO(self):
# Setup the configuration for the DataController
dataControllerConfig = ConfigValues.DataControllerConfig()
# Use only giro
dataControllerConfig.sources = ['giro']
# Need to stand this up, usually the Manager has it
pharlapHandle = Pharlap()
irtamHandle = IrtamPyIface()
# Create an instance of DataController and let it go
dataController = DataController(dataControllerConfig,
irtamHandle=irtamHandle,
pharlapHandle=pharlapHandle)
listOfOutputFiles = dataController.process(datetime(2015, 10, 20))
self.assertEqual(len(listOfOutputFiles['giro']), 4)
def test_CalcIonoState_RIPEwithIRTAM(self):
import IonoModelEngine.Config.ConfigValues as ConfigValues
from numpy.testing import assert_approx_equal
logger.info("test_CalcIonoState_RIPEwithIRTAM")
# Location of test files
directory = os.path.dirname(os.path.realpath(__file__)) + '/'
# Setup the input into ROAM
listOfFiles = {}
listOfFiles['noaa'] = [directory + '../RIPE/TestFiles/AU930_NOAA.TXT', \
directory + '../RIPE/TestFiles/BC840_NOAA.TXT', \
directory + '../RIPE/TestFiles/EG931_NOAA.TXT']
listOfFiles['giro'] = [directory + '../IRTAM/UnitTestData/IRTAM_foF2_COEFFS_20151020_0000.ASC', \
directory + '../IRTAM/UnitTestData/IRTAM_hmF2_COEFFS_20151020_0000.ASC', \
directory + '../IRTAM/UnitTestData/IRTAM_B0_COEFFS_20151020_0000.ASC', \
directory + '../IRTAM/UnitTestData/IRTAM_B1_COEFFS_20151020_0000.ASC']
listOfFiles['localDigisonde'] = []
# First construct a config file for ROAM
self.config = ConfigValues.ROAMConfig()
# Tell roam to use RIPE with IRTAM
self.config.backgroundModel = 'ripe-irtam'
#self.config.synopticTilt = 0
# instantiate ROAM
self.roam = ROAM(self.config, self.irtamHandle, self.pharlapHandle)
self.roam.refreshIRI = 0
# G10 receiver location
lat = 32.4824
lon = -106.3809
UT = numpy.array([2015, 10, 20, 0, 0])
DT = datetime(UT[0], UT[1], UT[2], UT[3], UT[4], 0)
state = self.roam.CalcIonoState(lat, lon, DT, listOfFiles)
logger.info(
"ROAM foF2={:.4f} hmF2={:.4f} foF1={:.4f} foE={:.4f} hmE={:.4f} B0={:.4f} B1={:.4f} foEs={:.4f} hmEs={:.4f} beta_lat={:.5f} beta_lon={:.5f}". \
format(state[0], state[1], state[2], state[3], state[4], state[5], \
state[6], state[7], state[8], state[9], state[10]))
# Output from Matlab run of roam_using_ripe_testfiles_20151020.m followed by roam_using_ripe.m
# YEAR MON DAY HR MIN foF2 hmF2 foF1 foE B0 B1 gradLat gradLon ... Ref_lat Ref_lon AoAErr
# 2015 10 20 00 00 9.2822 257.5859 -1.0000 1.8131 61.7588 2.5061 -0.0144 -0.0038 32.4824 -106.3809 0.0000
# foF2, hmF2, B0, B1 in this unit test are from RIPE.py using ripe-irtam option
# [RIPE.py: 666] RIPE foF2 = 9.5167 hmF2 = 259.2598 B0 = 69.4209 B1 = 2.3416
# [ROAM.py:759] ROAM foF2 = 9.5167 hmF2 = 259.2598 foF1 = -1.0000 foE = 1.8131 hmE = 110.0000 B0 = 69.4209 B1 = 2.3416 \
# foEs = 0.0000 hmEs = 110.0000 beta_lat=-0.01840 beta_lon=-0.00578
assert_approx_equal(state[0], 9.5167, significant=3) # foF2
assert_approx_equal(state[1], 259.2598, significant=3) # hmF2
assert_approx_equal(state[2], -1.0, significant=3) # foF1
assert_approx_equal(state[3], 1.8131, significant=3) # foE
assert_approx_equal(state[5], 69.4209, significant=1) # B0
assert_approx_equal(state[6], 2.3416, significant=2) # B1
assert_approx_equal(state[9], -0.01840, significant=3) # gradLat
assert_approx_equal(state[10], -0.00578, significant=3) # gradLon
return
def PharlapRayProcessorConfig(pharlap=None):
pharlapRayProcessorConfig = ConfigDef.PharlapRayProcessorConfig()
# The footprints of the voxel grids are squares with an incribed circle with this radius. (Meter)
pharlapRayProcessorConfig.grids.radiusMeter = 900e3
# The electron density grid's increment in latitude (degrees)
pharlapRayProcessorConfig.grids.latInc = numpy.deg2rad(0.10048)
# The electron density grid's increment in longitude (degrees)
pharlapRayProcessorConfig.grids.lonInc = numpy.deg2rad(0.10048)
# The electron density grid's lowest height (km)
pharlapRayProcessorConfig.grids.htStart = 60e3
# The electron density grid's increment in height (km)
pharlapRayProcessorConfig.grids.htInc = 2e3
# The electron density grid's number of voxels in height
pharlapRayProcessorConfig.grids.numHt = 201
# The geomagnetic field grid's increment in latitude (degrees)
pharlapRayProcessorConfig.grids.bLatInc = numpy.deg2rad(0.2)
# The geomagnetic field grid's increment in longitude (degrees)
pharlapRayProcessorConfig.grids.bLonInc = numpy.deg2rad(0.2)
# The geomagnetic field grid's lowest height (km)
pharlapRayProcessorConfig.grids.bHtStart = 60e3
# The geomagnetic field grid's increment in height (km)
pharlapRayProcessorConfig.grids.bHtInc = 10e3
# The geomagnetic field grid's number of voxels in height
pharlapRayProcessorConfig.grids.bNumHt = 41
# Parameters of PHaRLAP
pharlapRayProcessorConfig.pharlap.tolPharlapMin = numpy.array(
[1e-7, 0.01, 1])
# Root solver for ray homing
elLimits = numpy.array([0, 90.0])
elSpan = 180.0
azSpan = 30.0
# 1D Ray Shooting
options1d = {"fatol": 1e-2, "maxNumIter": 10, "nfev": 50}
elLimits = pharlapRayProcessorConfig.elLimits
pharlapRayProcessorConfig.elMaxSolver = RayData.genRootSolver(
"rayShooting1d", option=options1d, elLimits=elLimits)
#pharlapRayProcessorConfig.solver = RayData.genRootSolver("minimize")
#pharlapRayProcessorConfig.solver = RayData.genRootSolver("krylov", options={"fatol": 1.0e-3})
# 2D Bisectioning
#solverOptions = {"fatol": 20, "maxNumIter": 100, "func1d": pharlapRayProcessorConfig.elMaxSolver}
# pharlapRayProcessorConfig.solver = RayData.genRootSolver("bisect2d", options=solverOptions,
# elLimits=elLimits, elSpan=elSpan, azSpan=azSpan)
# 2D Ray Shooting
solverOptions = {
"fatol": 10000,
"maxNumIter": 50,
"nfev": 100,
"nInit": 200
}
pharlapRayProcessorConfig.solver = RayData.genRootSolver(
"rayShooting2d",
options=solverOptions,
elLimits=elLimits,
elSpan=elSpan,
azSpan=azSpan)
# If no Pharlap object is provided, construct a new one.
if not isinstance(pharlap, Pharlap):
pharlap = Pharlap()
# Construct a ROAM object
roamConfig = IonoConfig.ROAMConfig()
roam = ROAM(roamConfig, pharlapHandle=pharlap)
# Set the model to IRI
roam.background_model = 0
pharlapRayProcessorConfig.raytrace_3d = pharlap.raytrace_3d
pharlapRayProcessorConfig.genGeoMagGrid = GeoMagGrid.genGridIGRF12
pharlapRayProcessorConfig.genElectronDensityGridROAM = roam.genElectronDensityGridROAM
# Number of days as the threshold to trigger the regeneration of geomagnetic fields.
pharlapRayProcessorConfig.deltaDaysRegenGeomagGrid = datetime.timedelta(
90
) # Regenerate the geomagnetic fields if it is outdated by more than 90 days.
return pharlapRayProcessorConfig