def tt89(pos_var_gsm, iopt=3, suffix='', igrf_only=False):
"""
tplot wrapper for the functional interface to Sheng Tian's implementation
of the Tsyganenko 96 and IGRF model:
https://github.com/tsssss/geopack
Input
------
pos_gsm_tvar: str
tplot variable containing the position data (km) in GSM coordinates
Parameters
-----------
iopt: int
Specifies the ground disturbance level:
iopt= 1 2 3 4 5 6 7
correspond to:
kp= 0,0+ 1-,1,1+ 2-,2,2+ 3-,3,3+ 4-,4,4+ 5-,5,5+ > =6-
suffix: str
Suffix to append to the tplot output variable
Returns
--------
Name of the tplot variable containing the model data
"""
pos_data = get_data(pos_var_gsm)
if pos_data is None:
print('Variable not found: ' + pos_var_gsm)
return
b0gsm = np.zeros((len(pos_data.times), 3))
dbgsm = np.zeros((len(pos_data.times), 3))
# convert to Re
pos_re = pos_data.y/6371.2
for idx, time in enumerate(pos_data.times):
tilt = geopack.recalc(time)
# dipole B in GSM
b0gsm[idx, 0], b0gsm[idx, 1], b0gsm[idx, 2] = geopack.dip(pos_re[idx, 0], pos_re[idx, 1], pos_re[idx, 2])
# T89 dB in GSM
dbgsm[idx, 0], dbgsm[idx, 1], dbgsm[idx, 2] = t89.t89(iopt, tilt, pos_re[idx, 0], pos_re[idx, 1], pos_re[idx, 2])
if igrf_only:
bgsm = b0gsm
else:
bgsm = b0gsm + dbgsm
saved = store_data(pos_var_gsm + '_bt89' + suffix, data={'x': pos_data.times, 'y': bgsm})
if saved:
return pos_var_gsm + '_bt89' + suffix
def b(self, xvec):
# x,y,z can be an array or list
try:
x, y, z = xvec
except:
x, y, z = xvec.T
# we need to call recalc since common block may be shared between instances
# of geopack-2008 and T89,T96,T01,T04
self.ps = geopack.recalc(self.dt_seconds)
x = np.array([x])
y = np.array([y])
z = np.array([z])
x = x.flatten()
y = y.flatten()
z = z.flatten()
nx = len(x)
ny = len(y)
nz = len(z)
nn = min([nx, ny, nz])
bx_out = np.zeros(nn, dtype=float)
by_out = np.zeros(nn, dtype=float)
bz_out = np.zeros(nn, dtype=float)
for ix in range(nn):
rr = np.sqrt(x[ix]**2 + y[ix]**2 + z[ix]**2)
if (rr > 0.000001):
bx_, by_, bz_ = geopack.t04.t04(self.parmod, self.ps, x[ix],
y[ix], z[ix])
if self.use_igrf:
bx0, by0, bz0 = geopack.igrf_gsm(x[ix], y[ix], z[ix])
else:
bx0, by0, bz0 = geopack.dip(x[ix], y[ix], z[ix])
bx_out[ix] = bx_ + bx0
by_out[ix] = by_ + by0
bz_out[ix] = bz_ + bz0
else:
bx_out[ix] = np.nan
by_out[ix] = np.nan
bz_out[ix] = np.nan
return (np.column_stack((bx_out, by_out, bz_out)))
def plot_field_slice(max_L):
'''
Need to convert L, MLAT, MLON into a coordinate system that meshes with geopack:
-- GSM (in RE)
-- GSW
Nah for now just plot in GSM around xy plane (z = 0) up to max_L in R_E
'''
from geopack import geopack
ds = 0.1
x_gsm = np.arange(-max_L, max_L, ds)
y_gsm = np.arange(-max_L, max_L, ds)
z_gsm = 0.0
r_lim = 3.0
B_out = np.zeros((x_gsm.shape[0], y_gsm.shape[0], 3), dtype=float)
for ii in range(x_gsm.shape[0]):
for jj in range(y_gsm.shape[0]):
r_gsm = np.sqrt(x_gsm[ii]**2 + y_gsm[jj]**2)
if r_gsm < r_lim:
B_out[ii, jj] = np.ones(3) * np.nan
else:
B_out[ii, jj] = geopack.dip(x_gsm[ii], y_gsm[jj], z_gsm)
B0 = np.sqrt(B_out[:, :, 0]**2 + B_out[:, :, 1]**2 + B_out[:, :, 2]**2)
# Normalize
B_out[:, :,
0] = B_out[:, :, 0] / np.sqrt(B_out[:, :, 0]**2 + B_out[:, :, 1]**2)
B_out[:, :,
1] = B_out[:, :, 1] / np.sqrt(B_out[:, :, 0]**2 + B_out[:, :, 1]**2)
plt.ioff()
fig, ax = plt.subplots()
im1 = ax.quiver(x_gsm, y_gsm, B_out[:, :, 0].T, B_out[:, :, 1].T, B0)
ax.set_xlabel('X (RE, GSM)')
ax.set_ylabel('Y (RE, GSM)')
fig.colorbar(im1).set_label('|B| (nT)')
plt.show()
return x_gsm, y_gsm, B0
def tt01(pos_var_gsm, parmod=None, suffix=''):
"""
tplot wrapper for the functional interface to Sheng Tian's implementation of the Tsyganenko 2001 and IGRF model:
https://github.com/tsssss/geopack
Input
------
pos_gsm_tvar: str
tplot variable containing the position data (km) in GSM coordinates
Parameters
-----------
parmod: ndarray
10-element array (vs. time), but only the first 6 elements are used
(1) solar wind pressure pdyn (nanopascals),
(2) dst (nanotesla)
(3) byimf (nanotesla)
(4) bzimf (nanotesla)
(5) g1-index
(6) g2-index (see Tsyganenko [2001] for an exact definition of these two indices)
suffix: str
Suffix to append to the tplot output variable
Returns
--------
Name of the tplot variable containing the model data
"""
pos_data = get_data(pos_var_gsm)
if pos_data is None:
print('Variable not found: ' + pos_var_gsm)
return
b0gsm = np.zeros((len(pos_data.times), 3))
dbgsm = np.zeros((len(pos_data.times), 3))
# convert to Re
pos_re = pos_data.y/6371.2
if parmod is not None:
par = get_data(parmod)
if par is not None:
par = par.y
else:
print('parmod keyword required.')
return
for idx, time in enumerate(pos_data.times):
tilt = geopack.recalc(time)
# dipole B in GSM
b0gsm[idx, 0], b0gsm[idx, 1], b0gsm[idx, 2] = geopack.dip(pos_re[idx, 0], pos_re[idx, 1], pos_re[idx, 2])
# T96 dB in GSM
dbgsm[idx, 0], dbgsm[idx, 1], dbgsm[idx, 2] = t01.t01(par[idx, :], tilt, pos_re[idx, 0], pos_re[idx, 1], pos_re[idx, 2])
bgsm = b0gsm + dbgsm
saved = store_data(pos_var_gsm + '_bt01' + suffix, data={'x': pos_data.times, 'y': bgsm})
if saved:
return pos_var_gsm + '_bt01' + suffix
def tts04(pos_var_gsm, parmod=None, suffix=''):
"""
tplot wrapper for the functional interface to Sheng Tian's implementation of the
Tsyganenko-Sitnov (2004) storm-time geomagnetic field model
https://github.com/tsssss/geopack
Input
------
pos_gsm_tvar: str
tplot variable containing the position data (km) in GSM coordinates
Parameters
-----------
parmod: ndarray
10-element array (vs. time):
(1) solar wind pressure pdyn (nanopascals),
(2) dst (nanotesla),
(3) byimf,
(4) bzimf (nanotesla)
(5-10) indices w1 - w6, calculated as time integrals from the beginning of a storm
see the reference (3) below, for a detailed definition of those variables
suffix: str
Suffix to append to the tplot output variable
Returns
--------
Name of the tplot variable containing the model data
"""
pos_data = get_data(pos_var_gsm)
if pos_data is None:
print('Variable not found: ' + pos_var_gsm)
return
b0gsm = np.zeros((len(pos_data.times), 3))
dbgsm = np.zeros((len(pos_data.times), 3))
# convert to Re
pos_re = pos_data.y / 6371.2
if parmod is not None:
par = get_data(parmod)
if par is not None:
par = par.y
else:
print('parmod keyword required.')
return
for idx, time in enumerate(pos_data.times):
tilt = geopack.recalc(time)
if not np.isfinite(par[idx, :]).all():
# skip if there are any NaNs in the input
continue
# dipole B in GSM
b0gsm[idx,
0], b0gsm[idx,
1], b0gsm[idx,
2] = geopack.dip(pos_re[idx, 0],
pos_re[idx, 1], pos_re[idx,
2])
# T96 dB in GSM
dbgsm[idx,
0], dbgsm[idx,
1], dbgsm[idx,
2] = t04.t04(par[idx, :], tilt, pos_re[idx,
0],
pos_re[idx, 1], pos_re[idx, 2])
bgsm = b0gsm + dbgsm
saved = store_data(pos_var_gsm + '_bts04' + suffix,
data={
'x': pos_data.times,
'y': bgsm
})
if saved:
return pos_var_gsm + '_bts04' + suffix
def test_to_known_values(self):
"""geopack should give known result with known input"""
# test recalc, which returns dipole tilt angle in rad.
self.assertEqual(self.test['recalc'],
geopack.recalc(self.test['ut'], *self.test['v_gse']))
# test sun, which returns 5 angles in rad.
self.assertEqual(self.test['sun'], geopack.sun(self.test['ut']))
# test internal models.
self.assertTrue(
approx_eq(self.test['igrf'],
geopack.igrf_gsm(*self.test['r_gsm'])))
self.assertTrue(
approx_eq(self.test['dip'], geopack.dip(*self.test['r_gsm'])))
self.assertTrue(
approx_eq(self.test['igrf_gsw'],
geopack.igrf_gsw(*self.test['r_gsw']), 1e-3))
self.assertTrue(
approx_eq(self.test['dip_gsw'],
geopack.dip_gsw(*self.test['r_gsw']), 1e-3))
# test external models.
self.assertTrue(
approx_eq(self.test['t89'], t89.t89(*self.test['par1'])))
self.assertTrue(
approx_eq(self.test['t96'], t96.t96(*self.test['par2'])))
self.assertTrue(
approx_eq(self.test['t01'], t01.t01(*self.test['par2'])))
self.assertTrue(
approx_eq(self.test['t04'], t04.t04(*self.test['par2'])))
# test coord transform, which returns B in nT.
self.assertTrue(
approx_eq(self.test['r_mag'],
geopack.geomag(*self.test['r_geo'], 1)))
self.assertTrue(
approx_eq(self.test['r_geo'],
geopack.geomag(*self.test['r_mag'], -1)))
self.assertTrue(
approx_eq(self.test['r_geo'],
geopack.geigeo(*self.test['r_gei'], 1)))
self.assertTrue(
approx_eq(self.test['r_gei'],
geopack.geigeo(*self.test['r_geo'], -1)))
self.assertTrue(
approx_eq(self.test['r_sm'], geopack.magsm(*self.test['r_mag'],
1)))
self.assertTrue(
approx_eq(self.test['r_mag'],
geopack.magsm(*self.test['r_sm'], -1)))
self.assertTrue(
approx_eq(self.test['r_gse'],
geopack.gsmgse(*self.test['r_gsm'], 1)))
self.assertTrue(
approx_eq(self.test['r_gsm'],
geopack.gsmgse(*self.test['r_gse'], -1)))
self.assertTrue(
approx_eq(self.test['r_gsm'], geopack.smgsm(*self.test['r_sm'],
1)))
self.assertTrue(
approx_eq(self.test['r_sm'], geopack.smgsm(*self.test['r_gsm'],
-1)))
self.assertTrue(
approx_eq(self.test['r_gsm'],
geopack.geogsm(*self.test['r_geo'], 1)))
self.assertTrue(
approx_eq(self.test['r_geo'],
geopack.geogsm(*self.test['r_gsm'], -1)))
self.assertTrue(
approx_eq(self.test['r_gsw'],
geopack.gswgsm(*self.test['r_gsm'], -1)))
self.assertTrue(
approx_eq(self.test['r_gsm'],
geopack.gswgsm(*self.test['r_gsw'], 1)))
self.assertTrue(
approx_eq(self.test['geo'], geopack.geodgeo(*self.test['geod'], 1),
1e-3))
self.assertTrue(
approx_eq(self.test['geod2'],
geopack.geodgeo(*self.test['geo2'], -1), 1e-3))
# test trace.
self.assertTrue(
approx_eq(
self.test['trace_t89_igrf'],
geopack.trace(*self.test['r_gsm'], *self.test['trace_setting'],
self.test['par1'][0], 't89', 'igrf')))
self.assertTrue(
approx_eq(
self.test['trace_t96_igrf'],
geopack.trace(*self.test['r_gsm'], *self.test['trace_setting'],
self.test['par2'][0], 't96', 'igrf')))
self.assertTrue(
approx_eq(
self.test['trace_t01_igrf'],
geopack.trace(*self.test['r_gsm'], *self.test['trace_setting'],
self.test['par2'][0], 't01', 'igrf')))
self.assertTrue(
approx_eq(
self.test['trace_t04_igrf'],
geopack.trace(*self.test['r_gsm'], *self.test['trace_setting'],
self.test['par2'][0], 't04', 'igrf')))
# test magnetopaus model.
self.assertTrue(
approx_eq(
self.test['mgnp_t96'],
geopack.t96_mgnp(*self.test['mgnp_t96_par'],
*self.test['r_gsm'])))
self.assertTrue(
approx_eq(
self.test['mgnp_shu'],
geopack.shuetal_mgnp(*self.test['mgnp_shu_par'],
*self.test['r_gsm'])))