def test_hwm():
t = datetime(2013, 3, 31, 12)
glat = 65
glon = -148
altkm = 150
f107a = 100
f107 = 100
ap = 4
wind = hwm93.run(t, altkm, glat, glon, f107a, f107, ap)
assert wind['meridional'] == approx(-110.16133881,
rel=1e-4) # gfortran 4.6 vs 5.2
assert wind['zonal'] == approx(-12.40071201,
rel=1e-4) # gfortran 4.6 vs 5.2
def main():
p = ArgumentParser(description='calls HWM93 from Python, a basic demo')
p.add_argument('simtime',
help='yyyy-mm-ddTHH:MM:SS time of sim',
nargs='?',
default='2016-01-01T12')
p.add_argument('-a',
'--altkm',
help='altitude (km) (start,stop,step)',
type=float,
nargs='+',
default=(60, 1000, 5))
p.add_argument('-c',
'--latlon',
help='geodetic latitude (deg)',
type=float,
default=(65, -148))
p.add_argument('f107a',
help=' 81 day AVERAGE OF F10.7 FLUX (centered on day DDD)',
type=float,
nargs='?',
default=150)
p.add_argument('f107',
help='DAILY F10.7 FLUX FOR PREVIOUS DAY',
type=float,
nargs='?',
default=150)
p.add_argument('ap', help='daily ap', type=int, nargs='?', default=4)
p.add_argument('-o', '--outfn', help='write NetCDF (HDF5) of data')
p = p.parse_args()
altkm = arange(p.altkm[0], p.altkm[1], p.altkm[2])
glat, glon = p.latlon
T = parse(p.simtime)
winds = hwm93.run(T, altkm, glat, glon, p.f107a, p.f107, p.ap)
if p.outfn:
outfn = Path(p.outfn).expanduser()
print('writing', outfn)
winds.to_netcdf(outfn)
plothwm(winds)
show()
def main():
p = ArgumentParser(description="calls HWM93 from Python, a basic demo")
p.add_argument("simtime",
help="yyyy-mm-ddTHH:MM:SS time of sim",
nargs="?",
default="2016-01-01T12")
p.add_argument("-a",
"--altkm",
help="altitude (km) (start,stop,step)",
type=float,
nargs="+",
default=(60, 1000, 5))
p.add_argument("-c",
"--latlon",
help="geodetic latitude (deg)",
type=float,
default=(65, -148))
p.add_argument("f107a",
help=" 81 day AVERAGE OF F10.7 FLUX (centered on day DDD)",
type=float,
nargs="?",
default=150)
p.add_argument("f107",
help="DAILY F10.7 FLUX FOR PREVIOUS DAY",
type=float,
nargs="?",
default=150)
p.add_argument("ap", help="daily ap", type=int, nargs="?", default=4)
p.add_argument("-o", "--outfn", help="write NetCDF (HDF5) of data")
p = p.parse_args()
altkm = arange(p.altkm[0], p.altkm[1], p.altkm[2])
glat, glon = p.latlon
T = parse(p.simtime)
winds = hwm93.run(T, altkm, glat, glon, p.f107a, p.f107, p.ap)
if p.outfn:
outfn = Path(p.outfn).expanduser()
print("writing", outfn)
winds.to_netcdf(outfn)
plothwm(winds)
show()
def getHWM(dt, lat, lon, elev): """ Gets the zonal and meridional winds at a specifc time and space dt - datetime obj lat, lon, elev - floats of position given in deg, deg, km """ if MODE == 93: winds = hwm93.run(dt, altkm=elev, glat=lat, glon=lon, f107a=150, f107=150, ap=4) u, v = winds.zonal.values[0], winds.meridional.values[0] else: dec_ut = dt.hour + dt.minute/60 + dt.second/3600 doy = dt.timetuple().tm_yday winds = HWM14(alt=elev, glat=lat, glon=lon, ut=dec_ut, \ year=dt.year, day=doy) print(winds) return u, v
def air_drag_par(df, filename, sw_data, gcrs_filename, date_i):
""" This function is designed to calculate the wind speed for the satellite.
Args:
df (dask.datafram): dataframe containing different information
filename (str): the output file name
"""
# extract year month date hour minute second
year = np.int(filename[-11:-7])
year = np.dot(np.ones(df.__len__()), year).astype(np.int)
month = np.int(filename[-6:-4])
month = np.dot(np.ones(df.__len__()), month).astype(np.int)
utc_time = df.utc_time.compute().to_numpy()
xpos = df.xpos.compute().to_numpy()
ypos = df.ypos.compute().to_numpy()
zpos = df.zpos.compute().to_numpy()
date = np.floor((utc_time - utc_time[0]) / 86400.).astype(np.int) + date_i
hour = np.floor((utc_time - utc_time[0] - (date - date_i) * 86400.) /
3600.).astype(np.int)
minute = np.floor((utc_time - utc_time[0] - hour * 3600.0 -
(date - date_i) * 86400.) / 60.).astype(np.int)
second = np.mod((utc_time - utc_time[0] - hour * 3600.0 -
(date - date_i) * 86400. - minute * 60.0),
60.).astype(np.int)
# using astropy transform ecef to geodetic
pos_el = el.from_geocentric(x=xpos, y=ypos, z=zpos, unit='km')
lla_el = pos_el.to_geodetic()
lat = np.asarray(lla_el.lat)
lon = np.asarray(lla_el.lon)
alt = np.asarray(lla_el.height)
# wind speed
winds = np.zeros([df.__len__(), 2])
air_density = np.zeros(df.__len__())
for i, y in enumerate(year):
y = np.int(y)
h = np.int(hour[i])
mon = np.int(month[i])
d = np.int(date[i])
minu = np.int(minute[i])
sec = np.int(second[i])
date_time = datetime.datetime(y, mon, d, h, minu, sec)
para_input, para_output = nrlmsise00(date_time, lat[i], lon[i], alt[i],
sw_data)
air_density[i] = para_output['Density'][
'RHO[kg/m^3]'] # unit = km / m^(-3)
reg = hwm93.run(date_time,
altkm=alt[i],
glat=lat[i],
glon=lon[i],
f107=para_input['f107Daily[10^-22 W/m^2/Hz]'],
f107a=para_input['f107Average[10^-22 W/m^2/Hz]'],
ap=para_input['ApDaily'])
winds[i, 0] = reg.meridional.values
winds[i, 1] = reg.zonal.values
# cartesian 2 spherical
slat, slon = transform.cartesian_to_spherical(xpos,
ypos,
zpos,
with_radius=False)
# lat
ns_morethan_0 = np.where(winds[:, 0] >= 0)
ns_lessthan_0 = np.where(winds[:, 0] < 0)
slat[ns_morethan_0] = np.abs(slat[ns_morethan_0] - np.pi / 2.0)
slat[ns_lessthan_0] = -np.abs(slat[ns_lessthan_0] - np.pi / 2.0)
ns_x, ns_y, ns_z = transform.spherical_to_cartesian(
winds[:, 0], slat, slon)
# lon
lon_lessthan_0 = np.where(slon < 0)
slon[lon_lessthan_0] = 2 * np.pi + slon[lon_lessthan_0]
slon[ns_morethan_0] = slon[ns_morethan_0] + np.pi / 2.0
slon[ns_lessthan_0] = slon[ns_lessthan_0] - np.pi / 2.0
lon_lessthan_0 = np.where(slon < 0)
slon[lon_lessthan_0] = 2 * np.pi + slon[lon_lessthan_0]
lon_morethan_2 = np.where(slon >= 2 * np.pi)
slon[lon_morethan_2] = slon[lon_morethan_2] - 2 * np.pi
lon_morethan_p = np.where(slon > np.pi)
slon[lon_morethan_p] = slon[lon_morethan_p] - 2 * np.pi
ew_x, ew_y, ew_z = transform.spherical_to_cartesian(
winds[:, 1], np.zeros(slon.__len__()), slon)
winds_x = ew_x + ns_x
winds_y = ew_y + ns_y
winds_z = ew_z + ns_z
winds_vector = np.c_[winds_x, winds_y, winds_z]
angular_vector = np.asarray([0.0, 0.0, 7.2921158553e-5])
spos = np.c_[xpos, ypos, zpos]
svel = np.c_[df.xvel.compute().to_numpy(),
df.yvel.compute().to_numpy(),
df.zvel.compute().to_numpy()]
relative_velocity = (svel * 1000. - winds_vector -
np.cross(angular_vector, spos * 1000.)) / 1000.
itrs2gcrs(year, month, date, hour, minute, second, spos, relative_velocity,
utc_time, gcrs_filename)
return relative_velocity, air_density