def density_lookup(year, month, day, hour, altitude, glat, glon):
"""
Function: density_lookup
Gets atmospheric density using MSISE-00 atmospheric model.
Must have https://pypi.org/project/msise00/ library installed.
Inputs:
year
month
day
hour
altitude (km)
glat: geodetic latitude
glon: geodetic longitude
Outputs:
rho: atmospheric density (kg/m^3)
"""
atmos = msise00.run(time=datetime(year, month, day, hour),
altkm=altitude,
glat=glat,
glon=glon)
rho = atmos.Total.values[0].item()
return rho
def msise00_model(alt, datetime_obj): atmos = msise00.run(time=datetime_obj, altkm=alt/1000, glat=65., glon=-148.) r = D(atmos['Total'].values[0,0,0,0]) # Denisty (kg/m^3) T = D(atmos['Tn'].values[0,0,0,0]) # Temperature (K) p = r*D(0.2869)*T # Pressure (kPa) return [T-D(273.15), p, r] # Return C, kPa, kg/m^3
def test_forecast():
t = datetime(2023, 3, 31, 12)
altkm = 150.0
glat = 65.0
glon = -148.0
atmos = msise00.run(t, altkm, glat, glon)
assert atmos["He"].ndim == 4
assert atmos["He"].size == 1
dims = list(atmos.dims)
assert ["alt_km", "lat", "lon", "time"] == dims
assert atmos["He"].item() == approx(4.3926901e12)
assert atmos["O"].item() == approx(4.5728936e15)
assert atmos["N2"].item() == approx(3.7001117e16)
assert atmos["O2"].item() == approx(8.1215314e15)
assert atmos["Ar"].item() == approx(3.3594181e14)
assert atmos["Total"].item() == approx(2.2950717e-09)
assert atmos["N"].item() == approx(2.155302e12)
assert atmos["AnomalousO"].item() == approx(8.7590428e-16)
assert atmos.species == [
"He", "O", "N2", "O2", "Ar", "Total", "H", "N", "AnomalousO"
]
assert atmos["Tn"].item() == approx(667.06, abs=0.01)
assert atmos["Texo"].item() == approx(922.28, abs=0.01)
def test_ccmc():
t = datetime(2001, 2, 2, 8, 0, 0)
glat = 60.0
glon = -70.0
altkm = 400.0
indices = {"f107s": 163.6666, "f107": 146.7, "Ap": 7}
with importlib.resources.path(__package__, "ccmc.log") as fn:
A = np.loadtxt(fn, skiprows=25)
atmos = msise00.run(t, altkm, glat, glon, indices)
assert atmos.f107s == approx(indices["f107s"])
assert atmos.f107 == approx(indices["f107"])
assert atmos.Ap == indices["Ap"]
assert A[0] == approx(altkm)
assert A[1] == approx(atmos["O"].item() / 1e6, rel=0.01)
assert A[2] == approx(atmos["N2"].item() / 1e6, rel=0.01)
assert A[3] == approx(atmos["O2"].item() / 1e6, rel=0.01)
assert A[7] == approx(atmos["He"].item() / 1e6, rel=0.01)
assert A[8] == approx(atmos["Ar"].item() / 1e6, rel=0.01)
assert A[9] == approx(atmos["H"].item() / 1e6, rel=0.01)
assert A[10] == approx(atmos["N"].item() / 1e6, rel=0.01)
assert A[11] == approx(atmos["AnomalousO"].item() / 1e6, rel=0.01)
assert A[5] == approx(atmos["Tn"].item(), rel=0.001)
assert A[6] == approx(atmos["Texo"].item(), rel=0.001)
def test_past():
t = datetime(2013, 3, 31, 12)
altkm = 150.0
glat = 65.0
glon = -148.0
try:
atmos = msise00.run(t, altkm, glat, glon)
except ConnectionError:
pytest.skip("unable to download RecentIndices.txt")
assert atmos["He"].ndim == 4
assert atmos["He"].size == 1
dims = list(atmos.dims)
assert ["alt_km", "lat", "lon", "time"] == dims
# daily resolution
assert atmos["He"].item() == approx(1.2523275e13)
assert atmos["O"].item() == approx(1.3222577e16)
assert atmos["N2"].item() == approx(2.9318694e16)
assert atmos["O2"].item() == approx(2.6164412e15)
assert atmos["Ar"].item() == approx(5.9933693e13)
assert atmos["Total"].item() == approx(1.8571854e-9)
assert atmos["N"].item() == approx(9.1038102e12)
assert atmos["AnomalousO"].item() == approx(4.503e-14)
assert atmos["Tn"].item() == approx(655.79, abs=0.01)
assert atmos["Texo"].item() == approx(875.18, abs=0.01)
assert atmos.species == [
"He", "O", "N2", "O2", "Ar", "Total", "H", "N", "AnomalousO"
]
def test_forecast():
t = datetime(2023, 3, 31, 12)
altkm = 150.0
glat = 65.0
glon = -148.0
atmos = msise00.run(t, altkm, glat, glon)
assert atmos["He"].ndim == 4
assert atmos["He"].size == 1
dims = list(atmos.dims)
assert ["alt_km", "lat", "lon", "time"] == dims
assert atmos["He"].item() == approx(1240641440000.0)
assert atmos["O"].item() == approx(3047968100000000.0)
assert atmos["N2"].item() == approx(3.597249e16)
assert atmos["O2"].item() == approx(7343665540000000.0)
assert atmos["Ar"].item() == approx(334703983000000.0)
assert atmos["Total"].item() == approx(2.16534546e-09)
assert atmos["N"].item() == approx(2576280450000.0)
assert atmos["AnomalousO"].item() == approx(8.75042368e-16)
assert atmos.species == [
"He", "O", "N2", "O2", "Ar", "Total", "H", "N", "AnomalousO"
]
assert atmos["Tn"].item() == approx(671.513, abs=0.01)
assert atmos["Texo"].item() == approx(883.342, abs=0.01)
def test_ccmc():
t = datetime(2001, 2, 2, 8, 0, 0)
glat = 60.0
glon = -70.0
altkm = 400.0
indices = {"f107s": 163.6666, "f107": 146.7, "Ap": 7}
A = np.loadtxt(reffn, skiprows=25)
atmos = msise00.run(t, altkm, glat, glon, indices)
assert atmos.f107s == approx(163.6666)
assert atmos.f107 == approx(146.7)
assert atmos.Ap == approx(7)
assert A[0] == approx(altkm)
assert A[1] == approx(atmos["O"].item() / 1e6, rel=0.05)
assert A[2] == approx(atmos["N2"].item() / 1e6, rel=0.3)
assert A[3] == approx(atmos["O2"].item() / 1e6, rel=0.35)
assert A[7] == approx(atmos["He"].item() / 1e6, rel=0.2)
assert A[8] == approx(atmos["Ar"].item() / 1e6, rel=0.6)
assert A[9] == approx(atmos["H"].item() / 1e6, rel=0.1)
assert A[10] == approx(atmos["N"].item() / 1e6, rel=0.3)
assert A[11] == approx(atmos["AnomalousO"].item() / 1e6, rel=0.3)
assert A[5] == approx(atmos["Tn"].item(), rel=0.1)
assert A[6] == approx(atmos["Texo"].item(), rel=0.1)
def test_one_loc_one_time(altkm, reffn):
pytest.importorskip("netCDF4")
ref = xarray.open_dataset(R / reffn)
try:
dat_mod = msise00.run(time, altkm, lat, lon).squeeze()
except ConnectionError:
pytest.skip("unable to download RecentIndices.txt")
xarray.tests.assert_allclose(ref, dat_mod)
def test_multiple_time():
pytest.importorskip("netCDF4")
ref = xarray.open_dataset(R / "ref4.nc")
lat, lon = msise00.worldgrid.latlonworldgrid(90, 90)
try:
dat_mod = msise00.run(time, altkm, lat, lon).squeeze()
except ConnectionError:
pytest.skip("unable to download RecentIndices.txt")
xarray.tests.assert_allclose(ref, dat_mod)
def test_one_alt_one_time():
pytest.importorskip("netCDF4")
ref = xarray.open_dataset(R / "ref3.nc")
lat, lon = msise00.worldgrid.latlonworldgrid(30, 60)
indices = {"f107": 79.3, "f107s": 76.6802469, "Ap": 39}
try:
dat_mod = msise00.run(time, altkm, lat, lon, indices).squeeze()
except ConnectionError:
pytest.skip("unable to download RecentIndices.txt")
xarray.tests.assert_allclose(ref, dat_mod)
def test_one_alt_one_time():
pytest.importorskip("netCDF4")
with importlib.resources.path(__package__, "ref3.nc") as fn:
ref = xarray.open_dataset(fn)
lat, lon = msise00.worldgrid.latlonworldgrid(30, 60)
try:
dat_mod = msise00.run(time, altkm, lat, lon).squeeze()
except ConnectionError:
pytest.skip("unable to download RecentIndices.txt")
xarray.tests.assert_allclose(ref, dat_mod)
def test_ccmc2():
t = datetime(2018, 5, 17, 21, 0, 0)
glat = 55.0
glon = 120.0
altkm = 300.0
indices = {"f107s": 72.6, "f107": 71.5, "Ap": 9.5}
atmos = msise00.run(t, altkm, glat, glon, indices)
assert 4.874e7 == approx(atmos["N2"].item() / 1e6, rel=0.2)
assert 1.622e6 == approx(atmos["O2"].item() / 1e6, rel=0.1)
assert 794.1 == approx(atmos["Tn"].item(), rel=0.1)
assert 800.0 == approx(atmos["Texo"].item(), rel=0.1)
def test_past():
t = datetime(2013, 3, 31, 12)
altkm = 150.0
glat = 65.0
glon = -148.0
try:
atmos = msise00.run(t, altkm, glat, glon)
except ConnectionError:
pytest.skip("unable to download RecentIndices.txt")
assert atmos["He"].ndim == 4
assert atmos["He"].size == 1
dims = list(atmos.dims)
assert ["alt_km", "lat", "lon", "time"] == dims
try: # daily resolution
assert atmos["He"].item() == approx(4365665440000.0)
assert atmos["O"].item() == approx(9612878760000000.0)
assert atmos["N2"].item() == approx(3.15052301e16)
assert atmos["O2"].item() == approx(2312921490000000.0)
assert atmos["Ar"].item() == approx(71974164400000.0)
assert atmos["Total"].item() == approx(1.84757176e-09)
assert atmos["N"].item() == approx(9424203680000.0)
assert atmos["AnomalousO"].item() == approx(1.17317104e-14)
assert atmos["Tn"].item() == approx(682.538, abs=0.01)
assert atmos["Texo"].item() == approx(948.350, abs=0.01)
except AssertionError: # monthly resolution
assert atmos["He"].item() == approx(4200180480000.0)
assert atmos["O"].item() == approx(9338048100000000.0)
assert atmos["N2"].item() == approx(3.23984781e16)
assert atmos["O2"].item() == approx(2413811350000000.0)
assert atmos["Ar"].item() == approx(81071685200000.0)
assert atmos["Total"].item() == approx(1.88774951e-09)
assert atmos["N"].item() == approx(9310465690000.0)
assert atmos["AnomalousO"].item() == approx(5.3806201e-15)
assert atmos["Tn"].item() == approx(699.021, abs=0.01)
assert atmos["Texo"].item() == approx(1000.513, abs=0.01)
assert atmos.species == [
"He", "O", "N2", "O2", "Ar", "Total", "H", "N", "AnomalousO"
]
def get_atmos(time, lat, lon, altitude=(0, 140, 1), apindex=35):
"""
Retrieve atmospheric conditions (temperature and wind) from MSISE-00
and HWM2014 models.
"""
alt_vector = np.arange(*altitude)
Tn = msise00.run(time, altkm=alt_vector, glat=lat,
glon=lon).Tn.data.ravel()
wind = HWM14(altlim=[alt_vector[0], alt_vector[-1]],
altstp=altitude[2],
glat=lat,
glon=lon,
ap=[-1, apindex],
ut=time.hour,
day=time.julday,
year=time.year,
verbose=0)
W_zonal, W_meridional = np.array([wind.Uwind, wind.Vwind])
return alt_vector, Tn, W_zonal, W_meridional
mOxygen = 2.6567e-26;
mNitrogen = 2.3259e-26;
mHelium = 6.646477e-27;
mArgon = 6.6335e-26;
mHydrogen = 1.674e-27;
mOxygen2 = mOxygen * 2.;
mNitrogen2= mNitrogen * 2.;
# Construct altitude array
altitudeArr = np.linspace(lowerAlt,
upperAlt,
int((upperAlt+altitudeStepSize)/altitudeStepSize));
# Call MSIS model
atmos = msise00.run(time=time,
altkm=altitudeArr,
glat=g_lat_lon[0],
glon=g_lat_lon[1])
# If program gets any arguments it'll plot the profiles
if len(sys.argv) > 1:
import matplotlib.pyplot as plt
fig = plt.figure();
ax1 = fig.add_subplot(131)
for var in atmos.species:
if var is not 'Total':
atmos[var].plot(y='alt_km', label=var, ax=ax1)
plt.xscale('log'); plt.legend(fontsize=8);
plt.xlabel('Species number density [m$^{-3}$]')
plt.ylabel('Altitude [km]');
plt.title('Number Density Profile')
plt.grid();
def main():
p = ArgumentParser(
description='calls MSISE-00 from Python, save to NetCDF4 and/or plot')
p.add_argument(
'-t',
'--time',
help=
'time: (single time or START STOP (1 hour time step) or list of times)',
nargs='+',
required=True)
p.add_argument(
'-a',
'--altkm',
help='altitude (km). scalar, or (start,stop,step) or list of alts.',
type=float,
nargs='+',
required=True)
p.add_argument('-c',
'--latlon',
help='geodetic latitude/longitude (deg)',
metavar=('lat', 'lon'),
type=float,
nargs=2)
p.add_argument('-o', '--odir', help='directory to write plots to')
p.add_argument('-w', help='NetCDF4 .nc filename to write')
p.add_argument('-gs',
help='geographic grid spacing (lat, lon)',
nargs=2,
type=float,
default=(10, 10))
p.add_argument('-q',
'--quiet',
help='disable plotting',
action='store_true')
P = p.parse_args()
# %% altitude
if len(P.altkm) == 1:
altkm = P.altkm[0]
elif len(P.altkm) == 3:
altkm = np.arange(*P.altkm)
else:
altkm = P.altkm
# %% latlon
if P.latlon is not None:
glat, glon = P.latlon
else:
glat, glon = latlonworldgrid(*P.gs)
# %% run
atmos = msise00.run(P.time, altkm, glat, glon)
# %% save
if P.w:
ncfn = Path(P.w).expanduser()
print('saving', ncfn)
# NOTE: .squeeze() avoids ValueError: unsupported dtype for netCDF4 variable: datetime64[ns]
atmos.squeeze().to_netcdf(ncfn)
# %% plot
if msplots is not None and not P.quiet:
msplots.plotgtd(atmos, P.odir)
show()
else:
print('skipped plots')
#!/usr/bin/env python """ Time profile example """ import numpy as np from datetime import datetime, timedelta from matplotlib.pyplot import show import msise00.plots as msplots import msise00 glat = 65.1 glon = -147.5 alt_km = 200 t0 = datetime(2015, 12, 13, 10, 0, 0) t1 = datetime(2015, 12, 14, 10, 0, 0) times = np.arange(t0, t1, timedelta(hours=1)) atmos = msise00.run(times, alt_km, glat, glon) msplots.plotgtd(atmos) show()
#%% altitude 1-D mode
if p.latlon is not None:
print('entering single location mode')
if p.altkm is None:
p.altkm = (60., 1000, 5)
altkm = np.arange(*p.altkm)
glat, glon = p.latlon
#%% lat/lon grid mode at constant altitude
else: # len(p.altkm)==1:
print(
'lat/lon not specified, entering auto whole-world grid mode at first altitude'
)
if p.altkm is None:
altkm = 200.
else:
altkm = p.altkm[0]
glat, glon = latlonworldgrid()
altkm = np.atleast_1d(altkm)
print(f'using altitudes from {altkm[0]:.1f} to {altkm[-1]:.1f} km')
# %% run
atmos = msise00.run(time, altkm, glat, glon)
#%%
if p.w:
ncfn = Path(p.w).expanduser()
print('saving', ncfn)
atmos.to_netcdf(ncfn)
plotgtd(atmos, p.odir)
show()
