def load_amb_l1a(sc='mms1', mode='fast', level='l1a', optdesc='amb',
start_date=None, end_date=None, rename_vars=True,
**kwargs):
"""
Load EDI data.
CDF variable names are renamed to something easier to remember and
use. Original CDF variable names are kept as an attribute "cdf_name"
in each individual variable.
Parameters
----------
sc : str
Spacecraft ID: ('mms1', 'mms2', 'mms3', 'mms4')
mode : str
Instrument mode: ('slow', 'srvy', 'fast', 'brst').
level : str
Data quality level ('l1a', 'l2pre', 'l2')
optdesc : str
Optional descriptor. Options are: {'efield' | 'amb' | 'amb-pm2' |
'amb-alt-cc', 'amb-alt-oc', 'amb-alt-oob', 'amb-perp-c',
'amb-perp-ob'}
start_date, end_date : `datetime.datetime`
Start and end of the data interval.
rename_vars : bool
If true (default), rename the standard MMS variable names
to something more memorable and easier to use.
\*\*kwargs : dict
Any keyword accepted by *pymms.data.util.load_data*
Returns
-------
dist : `xarray.Dataset`
EDI data.
"""
# Download the data
sdc = api.MrMMS_SDC_API(sc, 'edi', mode, level,
optdesc=optdesc,
start_date=start_date,
end_date=end_date,
**kwargs)
files = sdc.download_files()
try:
files = api.sort_files(files)[0]
except IndexError:
raise IndexError('No files found: {0}'.format(sdc))
# Read all of the data files. Skip empty files unless all files are empty
data = []
for file in files:
data.append(util.cdf_to_ds(file, **kwargs))
# Variables must be concatenated based on their DEPEND_0 variable
rec_vnames = ['Epoch', 'epoch_angle', 'epoch_timetag']
out_data = []
for recname in rec_vnames:
# Remove all data not associated with the current record name
drop_vars = [varname
for varname in data[0]
if recname not in data[0][varname].coords]
drop_coords = [coord
for coord in data[0].coords
if coord != recname]
rec_data = [ds.drop(drop_vars + drop_coords) for ds in data]
# Concatenate remaining variables together
out = xr.concat(rec_data, dim=recname)
# Select the desired time range
out = out.sel(indexers={recname: slice(start_date, end_date)})
# All datasets will be merged back together, so keep track of them
out_data.append(out)
# Combine the datasets back together
data = xr.merge(out_data)
# Rename data variables to something simpler
if rename_vars:
data = rename(data, sc, mode, level, optdesc)
# Add data descriptors to attributes
data.attrs['sc'] = sc
data.attrs['instr'] = 'edi'
data.attrs['mode'] = mode
data.attrs['level'] = level
data.attrs['optdesc'] = optdesc
return data
r_gse_vname = '_'.join((sc, 'mec', 'r', 'gse'))
r_lbl_vname = '_'.join((sc, 'mec', 'r', 'gse', 'label'))
v_gse_vname = '_'.join((sc, 'mec', 'v', 'gse'))
v_lbl_vname = '_'.join((sc, 'mec', 'v', 'gse', 'label'))
mlt_vname = '_'.join((sc, 'mec', 'mlat'))
l_dip_vname = '_'.join((sc, 'mec', 'l', 'dipole'))
sdc.instr = 'mec'
sdc.mode = mode
sdc.optdesc = 'epht89d'
files = sdc.download()
mec_data = util.cdf_to_ds(files[0], [r_gse_vname, v_gse_vname, mlt_vname, l_dip_vname])
mec_data = mec_data.rename({r_gse_vname: 'R_sc', # convert R to polar
r_lbl_vname: 'R_sc_index',
v_gse_vname: 'V_sc',
v_lbl_vname: 'V_sc_index',
mlt_vname: 'MLT_sc',
l_dip_vname: 'L_sc',
'Epoch': 'time',
})
# Interpolate to EDP times
# - MEC velocity is smoothly and slowly varying so can be up sampled
# - FGM data is sampled the same or faster so can be sychronized/down sampled
mec_data = mec_data.interp_like(edi_data)
# Combine all into one data set
data = xr.merge([mec_data, edi_data])
optdesc='dce',
start_date=t0,
end_date=t1)
r_gse_vname = '_'.join((sc, 'mec', 'r', 'gse'))
r_lbl_vname = '_'.join((sc, 'mec', 'r', 'gse', 'label'))
v_gse_vname = '_'.join((sc, 'mec', 'v', 'gse'))
v_lbl_vname = '_'.join((sc, 'mec', 'v', 'gse', 'label'))
sdc.instr = 'mec'
sdc.mode = mode
sdc.optdesc = 'epht89d'
files = sdc.download()
mec_data = util.cdf_to_ds(files[0], [r_gse_vname, v_gse_vname])
mec_data = mec_data.rename({
r_gse_vname: 'R_sc',
r_lbl_vname: 'R_sc_index',
v_gse_vname: 'V_sc',
v_lbl_vname: 'V_sc_index',
'Epoch': 'time'
})
mec_data = mec_data.assign_coords(R_sc_index=['Rx', 'Ry', 'Rz'],
V_sc_index=['Vx', 'Vy', 'Vz'])
# EDP Fast Data
t_vname = '_'.join((sc, 'edp', 'epoch', 'fast', level))
e_fast_vname = '_'.join((sc, 'edp', 'dce', 'gse', 'fast', level))
e_labl_vname = '_'.join((sc, 'edp', 'label1', 'fast', level))
if tdelta.astype(int) < 5e8
else (t + np.timedelta64(1, 's') - tdelta)
for t, tdelta in zip(edi_time.data, edi_time.data - edi_time.data.astype('datetime64[s]'))
]
edi_data['time'] = edi_time
# FGM Data
fgm_data = fgm.load_data('mms1', 'fgm', 'srvy', 'l2', t0, t1)
# EDP Fast Data
t_vname = '_'.join((sc, 'edp', 'epoch', 'fast', level))
e_fast_vname = '_'.join((sc, 'edp', 'dce', 'gse', 'fast', level))
e_labl_vname = '_'.join((sc, 'edp', 'label1', 'fast', level))
sdc = api.MrMMS_SDC_API(sc, 'edp', 'fast', level, optdesc='dce', start_date=t0, end_date=t1)
files = sdc.download()
edp_fast_data = util.cdf_to_ds(files[0], e_fast_vname)
edp_fast_data = edp_fast_data.rename({t_vname: 'time',
e_fast_vname: 'E',
e_labl_vname: 'E_index'})
# EDP Slow Data
t_vname = '_'.join((sc, 'edp', 'epoch', 'slow', level))
e_slow_vname = '_'.join((sc, 'edp', 'dce', 'gse', 'slow', level))
e_labl_vname = '_'.join((sc, 'edp', 'label1', 'slow', level))
sdc.mode = 'slow'
files = sdc.download()
edp_slow_data = util.cdf_to_ds(files[0], e_slow_vname)
edp_slow_data = edp_slow_data.rename({t_vname: 'time',
e_slow_vname: 'E',
e_labl_vname: 'E_index'})