def IRI(time, altkmrange, glat, glon, ap=None, f107=None, ssn=None, var=None):
if isinstance(time, str):
time = parse(time)
assert len(altkmrange) == 3, "altitude (km) min, max, step"
# doy = squeeze(TimeUtilities().CalcDOY(year, month, dom))
# IRI options
jf = Switches()
# additional "input parameters" (necessary to scale the empirical model results
# to measurements)
# addinp = -np.ones(12)
# ------------------------------------------------------------------------------
#
# if time.year < 1958:
#
# addinp[10 - 1] = ssn # RZ12 (This switches 'jf[17 - 1]' to '0' and
# # uses correlation function to estimate IG12)
#
# jf[25 - 1] = 1 # 25 F107D from APF107.DAT F107D user input (oarr(41)) 1
# jf[27 - 1] = 1 # 27 IG12 from file IG12 - user 1
# jf[32 - 1] = 1 # 32 F10.7_81 from file PF10.7_81 - user input (oarr(46)) 1
#
# else: # case for solar and geomagnetic indices from files
#
# jf[17 - 1] = 1 # 17 Rz12 from file Rz12 - user input 1
# jf[26 - 1] = 1 # 26 foF2 storm model no storm updating 1
# jf[35 - 1] = 1 # 35 foE storm model no foE storm updating 0
# #
# #------------------------------------------------------------------------------
mmdd = 100 * time.month + time.day # month and dom (MMDD)
# hour + 25 denotes UTC time
dhour = (time.hour + 25) + time.minute / 60.0
# %% more inputs
jmag = 0 # 0: geographic; 1: geomagnetic
# iut = 0 # 0: for LT; 1: for UT
# height = 300. # in km
# h_tec_max = 2000 # 0: no TEC; otherwise: upper boundary for integral
# ivar = var # 1: altitude; 2: latitude; 3: longitude; ...
# Ionosphere (IRI)
# a, b = iriwebg(jmag, jf, glat, glon, int(time.year), mmdd, iut, time.hour,
# height, h_tec_max, ivar, ivbeg, ivend, ivstp, addinp, self.iriDataFolder)
altkm = np.arange(*altkmrange)
if altkm.size < 10:
raise ValueError(
"Altitude grid must have enough points to compute quantities of interest"
)
outf, oarr = iri16.iri_sub(jf, jmag, glat, glon, time.year, mmdd, dhour,
altkmrange[0], altkmrange[1], altkmrange[2],
str(proot / "data/"))
outf = outf[:, :altkm.size]
# %% collect output
dsf = {
k:
(("time", "alt_km", "lat", "lon"), np.atleast_2d(v[None, :, None,
None]))
for (k, v) in zip(simout, outf[:11, :])
}
dsf.update({"NmF2": (("time", "lat", "lon"), np.atleast_3d(oarr[0]))})
dsf.update({"hmF2": (("time", "lat", "lon"), np.atleast_3d(oarr[1]))})
dsf.update({"NmF1": (("time", "lat", "lon"), np.atleast_3d(oarr[2]))})
dsf.update({"hmF1": (("time", "lat", "lon"), np.atleast_3d(oarr[3]))})
dsf.update({"NmE": (("time", "lat", "lon"), np.atleast_3d(oarr[4]))})
dsf.update({"hmE": (("time", "lat", "lon"), np.atleast_3d(oarr[5]))})
dsf.update({"B0": (("time", "lat", "lon"), np.atleast_3d(oarr[9]))})
iri = xarray.Dataset(
dsf,
coords={
"time": [time],
"alt_km": altkm,
"lat": [glat],
"lon": [glon]
},
attrs={
"f107": oarr[40],
"ap": oarr[50],
"glat": glat,
"glon": glon,
"time": time
},
)
return iri
def IRI(time, altkm, glat, glon, ap=None, f107=None, ssn=None, var=None):
if isinstance(time, str):
time = parse(time)
altkm = np.atleast_1d(altkm)
# doy = squeeze(TimeUtilities().CalcDOY(year, month, dom))
# IRI options
jf = Switches()
# additional "input parameters" (necessary to scale the empirical model results
# to measurements)
# addinp = -np.ones(12)
# ------------------------------------------------------------------------------
#
# if time.year < 1958:
#
# addinp[10 - 1] = ssn # RZ12 (This switches 'jf[17 - 1]' to '0' and
# # uses correlation function to estimate IG12)
#
# jf[25 - 1] = 1 # 25 F107D from APF107.DAT F107D user input (oarr(41)) 1
# jf[27 - 1] = 1 # 27 IG12 from file IG12 - user 1
# jf[32 - 1] = 1 # 32 F10.7_81 from file PF10.7_81 - user input (oarr(46)) 1
#
# else: # case for solar and geomagnetic indices from files
#
# jf[17 - 1] = 1 # 17 Rz12 from file Rz12 - user input 1
# jf[26 - 1] = 1 # 26 foF2 storm model no storm updating 1
# jf[35 - 1] = 1 # 35 foE storm model no foE storm updating 0
# #
# #------------------------------------------------------------------------------
mmdd = 100*time.month + time.day # month and dom (MMDD)
# hour + 25 denotes UTC time
dhour = (time.hour + 25) + time.minute/60.
# %% more inputs
jmag = 0 # 0: geographic; 1: geomagnetic
# iut = 0 # 0: for LT; 1: for UT
# height = 300. # in km
# h_tec_max = 2000 # 0: no TEC; otherwise: upper boundary for integral
# ivar = var # 1: altitude; 2: latitude; 3: longitude; ...
# Ionosphere (IRI)
# a, b = iriwebg(jmag, jf, glat, glon, int(time.year), mmdd, iut, time.hour,
# height, h_tec_max, ivar, ivbeg, ivend, ivstp, addinp, self.iriDataFolder)
outf, oarr = iri16.iri_sub(jf, jmag, glat, glon,
time.year, mmdd, dhour, altkm,
proot/'data/')
# %% collect output
dsf = {k: (('time', 'alt_km', 'lat', 'lon'), np.atleast_2d(v[None, :, None, None])) for (k, v) in zip(simout, outf[:9, :])}
dsf.update({'NmF2': (('time', 'lat', 'lon'), np.atleast_3d(oarr[0]))})
dsf.update({'hmF2': (('time', 'lat', 'lon'), np.atleast_3d(oarr[1]))})
dsf.update({'NmF1': (('time', 'lat', 'lon'), np.atleast_3d(oarr[2]))})
dsf.update({'hmF1': (('time', 'lat', 'lon'), np.atleast_3d(oarr[3]))})
dsf.update({'NmE': (('time', 'lat', 'lon'), np.atleast_3d(oarr[4]))})
dsf.update({'hmE': (('time', 'lat', 'lon'), np.atleast_3d(oarr[5]))})
dsf.update({'B0': (('time', 'lat', 'lon'), np.atleast_3d(oarr[9]))})
iri = xarray.Dataset(dsf,
coords={'time': [time], 'alt_km': altkm, 'lat': [glat], 'lon': [glon]},
attrs={'f107': oarr[40], 'ap': oarr[50],
'glat': glat, 'glon': glon, 'time': time,
})
# FIRI Ne (in m-3)
# iri_ne_firi = self._RmNeg(a[13 - 1, :])[0]
# Ionic density (NO+, O2+, O+, H+, He+, N+, Cluster Ions)
# iri = {'ne' : neIRI, 'te' : teIRI, 'ti' : tiIRI, 'neFIRI' : iri_ne_firi,
# 'oplus' : oplusIRI, 'o2plus' : o2plusIRI, 'noplus' : noplusIRI,
# 'hplus' : hplusIRI, 'heplus' : heplusIRI, 'nplus' : nplusIRI}
# iriadd = { 'NmF2' : b[1 - 1, :][0], 'hmF2' : b[2 - 1, :][0],
# 'B0' : b[10 - 1, :][0] }
return iri