def test_sort_files():
fgm_brst_files = ['mms/data/mms1/fgm/srvy/l2/2017/11/mms1_fgm_srvy_l2_20171128_v5.115.0.cdf',
'mms/data/mms1/fgm/srvy/l2/2017/11/mms1_fgm_srvy_l2_20171127_v5.113.0.cdf',
'mms/data/mms1/fgm/srvy/l2/2017/11/mms1_fgm_srvy_l2_20171126_v5.113.0.cdf',
'mms/data/mms1/fgm/srvy/l2/2017/11/mms1_fgm_srvy_l2_20171125_v5.114.0.cdf',
'mms/data/mms1/fgm/srvy/l2/2017/11/mms1_fgm_srvy_l2_20171124_v5.113.0.cdf']
fgm_srvy_files = ['mms1_fgm_brst_l2_20171124231933_v5.113.0.cdf',
'mms1_fgm_brst_l2_20171124020733_v5.113.0.cdf',
'mms1_fgm_brst_l2_20171124020503_v5.113.0.cdf',
'mms1_fgm_brst_l2_20171124020233_v5.113.0.cdf',
'mms1_fgm_brst_l2_20171124020003_v5.113.0.cdf']
edp_files = ['mms1_edp_brst_l2_dce_20171124014733_v3.0.0.cdf',
'mms1_edp_brst_l2_dce_20171124014503_v3.0.0.cdf',
'mms1_edp_brst_l2_dce_20171124014233_v3.0.0.cdf',
'mms1_edp_brst_l2_dce_20171124014003_v3.0.0.cdf',
'mms1_edp_brst_l2_dce_20171124013733_v3.0.0.cdf',
'mms1_edp_brst_l2_dce_20171124013503_v3.0.0.cdf',
'mms1_edp_brst_l2_dce_20171124013233_v3.0.0.cdf',
'mms2_edp_brst_l2_dce_20171124012733_v3.0.0.cdf',
'mms2_edp_brst_l2_dce_20171124012503_v3.0.0.cdf',
'mms2_edp_brst_l2_dce_20171124012233_v3.0.0.cdf',
'mms2_edp_brst_l2_dce_20171124012003_v3.0.0.cdf',
'mms2_edp_brst_l2_dce_20171124011733_v3.0.0.cdf',
'mms2_edp_fast_l2_dce_20171125_v3.0.0.cdf',
'mms2_edp_fast_l2_dce_20171124_v3.0.0.cdf']
assert sdc.sort_files(fgm_brst_files)[0] == fgm_brst_files[::-1]
assert sdc.sort_files(fgm_srvy_files)[0] == fgm_srvy_files[::-1]
edp_sorted = sdc.sort_files(edp_files)
for file_type in edp_sorted:
ref_parts = file_type[0].split('_')
try:
tref = datetime.datetime.strptime(ref_parts[5], '%Y%m%d%H%M%S')
except ValueError:
tref = datetime.datetime.strptime(ref_parts[5], '%Y%m%d')
for file in file_type:
fparts = file.split('_')
try:
ftime = datetime.datetime.strptime(fparts[5], '%Y%m%d%H%M%S')
except ValueError:
ftime = datetime.datetime.strptime(fparts[5], '%Y%m%d')
assert ((fparts[0:5] == ref_parts[0:5]) and
(tref <= ftime)
)
def load_data(sc, mode, start_date, end_date, level='l2', coords='gse'):
"""
Load EDP spacecraft potential data.
Parameters
----------
sc : str
Spacecraft ID: ('mms1', 'mms2', 'mms3', 'mms4')
mode : str
Instrument mode: ('fast', 'brst'). If 'srvy' is given, it is
automatically changed to 'fast'.
start_date, end_date : `datetime.datetime`
Start and end of the data interval.
Returns
-------
scpot : `metaarray.metaarray`
Spacecraft potential.
"""
# Check the inputs
check_spacecraft(sc)
mode = check_mode(mode)
# File and variable name parameters
instr = 'edp'
optdesc = 'dce'
t_vname = '_'.join((sc, instr, 'epoch', mode, level))
e_vname = '_'.join((sc, instr, optdesc, coords, mode, level))
# Download the data
sdc = api.MrMMS_SDC_API(sc,
instr,
mode,
level,
optdesc=optdesc,
start_date=start_date,
end_date=end_date)
edp_files = sdc.download_files()
edp_files = api.sort_files(edp_files)[0]
# Concatenate data along the records (time) dimension, which
# should be equivalent to the DEPEND_0 variable name of the
# magnetic field variable.
edp_data = []
for file in edp_files:
edp_data.append(util.cdf_to_ds(file, e_vname))
edp_data = xr.concat(edp_data, dim=edp_data[0][e_vname].dims[0])
edp_data = edp_data.rename({t_vname: 'time', e_vname: 'E'})
edp_data = edp_data.sel(time=slice(start_date, end_date))
edp_data.attrs['files'] = edp_files
return edp_data
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
def load_data(sc,
mode,
start_date,
end_date,
instr='fgm',
level='l2',
coords='gse',
pd=False):
"""
Load FPI distribution function data.
Parameters
----------
sc : str
Spacecraft ID: ('mms1', 'mms2', 'mms3', 'mms4')
mode : str
Instrument mode: ('fast', 'brst'). If 'srvy' is given, it is
automatically changed to 'fast'.
start_date, end_date : `datetime.datetime`
Start and end of the data interval.
Returns
-------
dist : `metaarray.metaarray`
Particle distribution function.
"""
# Check the inputs
check_spacecraft(sc)
mode = check_mode(mode)
check_level(level, instr=instr)
check_coords(coords)
# File and variable name parameters
t_vname = 'Epoch'
b_vname = '_'.join((sc, instr, 'b', coords, mode, level))
b_labl_vname = '_'.join(('label', 'b', coords))
# Download the data
sdc = api.MrMMS_SDC_API(sc,
instr,
mode,
level,
start_date=start_date,
end_date=end_date)
fgm_files = sdc.download_files()
fgm_files = api.sort_files(fgm_files)[0]
# Read the data from files
if pd:
fgm_data = util.cdf_to_df(fgm_files, b_vname)
util.rename_df_cols(fgm_data, b_vname, ('Bx', 'By', 'Bz', '|B|'))
else:
# Concatenate data along the records (time) dimension, which
# should be equivalent to the DEPEND_0 variable name of the
# magnetic field variable.
fgm_data = []
for file in fgm_files:
fgm_data.append(util.cdf_to_ds(file, b_vname))
fgm_data = xr.concat(fgm_data, dim=fgm_data[0][b_vname].dims[0])
fgm_data = fgm_data.rename({
t_vname: 'time',
b_vname: 'B',
b_labl_vname: 'B_index'
})
fgm_data = fgm_data.assign_coords(B_index=['Bx', 'By', 'Bz', '|B|'])
fgm_data = fgm_data.sel(time=slice(start_date, end_date))
return fgm_data
def load_data(sc='mms1',
instr='fgm',
mode='srvy',
level='l2',
optdesc=None,
start_date=None,
end_date=None,
offline=False,
record_dim='Epoch',
team_site=False,
data_type='science',
**kwargs):
"""
Load MMS data.
Empty files are silently skipped. NoVariablesInFileError is raised only
if all files in time interval are empty.
Parameters
----------
sc : str
Spacecraft ID: ('mms1', 'mms2', 'mms3', 'mms4')
instr : str
Instrument ID
mode : str
Instrument mode: ('slow', 'fast', 'srvy', 'brst').
optdesc : str
Optional descriptor for dataset
start_date, end_date : `datetime.datetime`
Start and end of the data interval.
offline : bool
If True, search only for local files
record_dim : str
Name of the record varying dimension. This is the dimension
along which the data from different files will be concatenated.
If *None*, the name of the leading dimension of the first data
variable will be used.
team_site : bool
If True, search the password-protected team site
data_type : str
Type of data to download. ('science', 'hk', 'ancillary')
\*\*kwargs : dict
Keywords passed to *cdf_to_ds*
Returns
-------
data : `xarray.DataArray` or list
The requested data. If data from all files can be concatenated
successfully, a Dataset is returned. If not, a list of Datasets
is returned, where each dataset is the data from a single file.
"""
if start_date is None:
start_date = np.datetime64('2015-10-16T13:06:04')
if end_date is None:
end_date = np.datetime64('2015-10-16T13:07:20')
site = 'public'
if team_site:
site = 'private'
# Download the data
sdc = api.MrMMS_SDC_API(sc,
instr,
mode,
level,
optdesc=optdesc,
start_date=start_date,
end_date=end_date,
data_type=data_type,
offline=offline)
# The data level parameter will automatically set the site keyword.
# If the user specifies the site, set it after instantiation.
sdc.site = site
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:
try:
data.append(cdf_to_ds(file, **kwargs))
except NoVariablesInFileError:
pass
if len(data) == 0:
raise NoVariablesInFileError('All {0} files were empty.'.format(
len(files)))
# Determine the name of the record varying dimension. This should be the
# value of the DEPEND_0 attribute of a data variable.
if record_dim is None:
varnames = [name for name in data[0].data_vars]
rec_vname = data[0][varnames[0]].dims[0]
else:
rec_vname = record_dim
# Notes:
# 1. Concatenation can fail if, e.g., a variable does not have a
# coordinate assigned along a given dimension. Instead of crashing,
# return the list of datasets so that they can be corrected and
# concatenated externally.
#
# 2. If data variables in the dataset do not have the dimension
# identified by rec_vname, a new dimension is added. If the dataset is
# large, this can cause xarray/python to use all available ram and
# crash. A fix would be to 1) find all DEPEND_0 variables, 2) use the
# data_vars='minimal' option to concat for each one, 3) combine the
# resulting datasets back together.
#
# 3. If there is only one dataset in the list and that dataset is empty
# then xr.concat will return the dataset even if the dim=rec_vname is
# not present.
try:
data = xr.concat(data, dim=rec_vname)
except (MemoryError, Exception) as E:
return data
# cdf_to_df loads all of the data from the file. Now we need to trim to
# the time interval of interest
try:
data = data.sel(indexers={rec_vname: slice(start_date, end_date)})
except KeyError:
warnings.warn('{0} out unordered; cannot slice.'.format(rec_vname))
# Keep information about the data
data.attrs['sc'] = sc
data.attrs['instr'] = instr
data.attrs['mode'] = mode
data.attrs['level'] = level
data.attrs['optdesc'] = optdesc
data.attrs['files'] = files
return data
def plot_burst_selections(sc, start_date, end_date, figsize=(5.5, 7)):
mode = 'srvy'
level = 'l2'
# FGM
b_vname = '_'.join((sc, 'fgm', 'b', 'gse', mode, level))
api = sdc.MrMMS_SDC_API(sc,
'fgm',
mode,
level,
start_date=start_date,
end_date=end_date)
files = api.download_files()
files = sdc.sort_files(files)[0]
fgm_data = metaarray.from_pycdf(files,
b_vname,
tstart=start_date,
tend=end_date)
# FPI DIS
fpi_mode = 'fast'
ni_vname = '_'.join((sc, 'dis', 'numberdensity', fpi_mode))
espec_i_vname = '_'.join((sc, 'dis', 'energyspectr', 'omni', fpi_mode))
api = sdc.MrMMS_SDC_API(sc,
'fpi',
fpi_mode,
level,
optdesc='dis-moms',
start_date=start_date,
end_date=end_date)
files = api.download_files()
files = sdc.sort_files(files)[0]
ni_data = metaarray.from_pycdf(files,
ni_vname,
tstart=start_date,
tend=end_date)
especi_data = metaarray.from_pycdf(files,
espec_i_vname,
tstart=start_date,
tend=end_date)
# FPI DES
ne_vname = '_'.join((sc, 'des', 'numberdensity', fpi_mode))
espec_e_vname = '_'.join((sc, 'des', 'energyspectr', 'omni', fpi_mode))
api = sdc.MrMMS_SDC_API(sc,
'fpi',
fpi_mode,
level,
optdesc='des-moms',
start_date=start_date,
end_date=end_date)
files = api.download_files()
files = sdc.sort_files(files)[0]
ne_data = metaarray.from_pycdf(files,
ne_vname,
tstart=start_date,
tend=end_date)
espece_data = metaarray.from_pycdf(files,
espec_e_vname,
tstart=start_date,
tend=end_date)
# Grab selections
# abs_files = sdc.sitl_selections('abs_selections',
# start_date=start_date, end_date=end_date)
# gls_files = sdc.sitl_selections('gls_selections', gls_type='mp-dl-unh',
# start_date=start_date, end_date=end_date)
# Read the files
abs_data = sel.read_csv(
(pathlib.Path(pymms.config['dropbox_root']) / 'selections' /
'abs_selections_all_20150901_000000.csv'),
start_time=start_date,
stop_time=end_date)
sitl_data = sel.read_csv(
(pathlib.Path(pymms.config['dropbox_root']) / 'selections' /
'sitl_selections_all_20150901_000000.csv'),
start_time=start_date,
stop_time=end_date)
gls_data = sel.read_csv(
(pathlib.Path(pymms.config['dropbox_root']) / 'selections' /
'gls_selections_all_20191016_000000.csv'),
start_time=start_date,
stop_time=end_date)
# SITL data time series
t_abs = []
x_abs = []
for selection in abs_data:
t_abs.extend([
selection.start_time, selection.start_time, selection.stop_time,
selection.stop_time
])
x_abs.extend([0, selection.fom, selection.fom, 0])
if len(abs_data) == 0:
t_abs = [start_date, end_date]
x_abs = [0, 0]
abs = metaarray.MetaArray(x_abs, x0=metatime.MetaTime(t_abs))
t_sitl = []
x_sitl = []
for selection in sitl_data:
t_sitl.extend([
selection.start_time, selection.start_time, selection.stop_time,
selection.stop_time
])
x_sitl.extend([0, selection.fom, selection.fom, 0])
if len(sitl_data) == 0:
t_sitl = [start_date, end_date]
x_sitl = [0, 0]
sitl = metaarray.MetaArray(x_sitl, x0=metatime.MetaTime(t_sitl))
t_gls = []
x_gls = []
for selection in gls_data:
t_gls.extend([
selection.start_time, selection.start_time, selection.stop_time,
selection.stop_time
])
x_gls.extend([0, selection.fom, selection.fom, 0])
if len(gls_data) == 0:
t_gls = [start_date, end_date]
x_gls = [0, 0]
gls = metaarray.MetaArray(x_gls, x0=metatime.MetaTime(t_gls))
# Set attributes to make plot pretty
especi_data.plot_title = sc.upper()
especi_data.title = 'DEF'
especi_data.x1.title = '$E_{ion}$\n(eV)'
espece_data.title = 'DEF\n(keV/(cm^2 s sr keV))'
espece_data.x1.title = '$E_{e-}$\n(eV)'
fgm_data.title = 'B\n(nT)'
fgm_data.label = ['Bx', 'By', 'Bz', '|B|']
ni_data.title = 'N\n($cm^{-3}$)'
ne_data.title = 'N\n($cm^{-3}$)'
abs.title = 'ABS'
gls.title = 'GLS'
gls.lim = (0, 200)
sitl.title = 'SITL'
# Plot
fig, axes = metabase.MetaCache.plot(
(especi_data, espece_data, fgm_data, ni_data, abs, gls, sitl),
figsize=figsize)
plt.subplots_adjust(left=0.15, right=0.85, top=0.93)
return fig, axes
def load_data(sc='mms1',
instr='fgm',
mode='srvy',
level='l2',
optdesc=None,
start_date=None,
end_date=None,
offline=False,
record_dim=None,
team_site=False,
**kwargs):
"""
Load MMS data.
Parameters
----------
sc : str
Spacecraft ID: ('mms1', 'mms2', 'mms3', 'mms4')
instr : str
Instrument ID
mode : str
Instrument mode: ('slow', 'fast', 'srvy', 'brst').
optdesc : str
Optional descriptor for dataset
start_date, end_date : `datetime.datetime`
Start and end of the data interval.
offline : bool
If True, search only for local files
record_dim : str
Name of the record varying dimension. This is the dimension
along which the data from different files will be concatenated.
If *None*, the name of the leading dimension of the first data
variable will be used.
team_site : bool
If True, search the password-protected team site
\*\*kwargs : dict
Keywords passed to *cdf_to_ds*
Returns
-------
data : `xarray.DataArray` or list
The requested data. If data from all files can be concatenated
successfully, a Dataset is returned. If not, a list of Datasets
is returned, where each dataset is the data from a single file.
"""
if start_date is None:
start_date = np.datetime64('2015-10-16T13:06:04')
if end_date is None:
end_date = np.datetime64('2015-10-16T13:07:20')
site = 'public'
if team_site:
site = 'private'
# Download the data
sdc = api.MrMMS_SDC_API(sc,
instr,
mode,
level,
optdesc=optdesc,
start_date=start_date,
end_date=end_date,
offline=offline)
# The data level parameter will automatically set the site keyword.
# If the user specifies the site, set it after instantiation.
sdc.site = site
files = sdc.download_files()
try:
files = api.sort_files(files)[0]
except IndexError:
raise IndexError('No files found: {0}'.format(sdc))
# Concatenate data along the records (time) dimension, which
# should be equivalent to the DEPEND_0 variable name of the
# density variable.
data = []
for file in files:
data.append(cdf_to_ds(file, **kwargs))
# Concatenate all datasets along the time dimension. If not given,
# assume that the time dimension is the leading dimension of the data
# variables.
if record_dim is None:
varnames = [name for name in data[0].data_vars]
rec_vname = data[0].data_vars[varnames[0]].dims[0]
else:
rec_vname = record_dim
# Notes:
# 1. Concatenation can fail if, e.g., a variable does not have a
# coordinate assigned along a given dimension. Instead of crashing,
# return the list of datasets so that they can be corrected and
# concatenated externally.
#
# 2. If data variables in the dataset do not have the dimension
# identified by rec_vname, a new dimension is added. If the dataset is
# large, this can cause xarray/python to use all available ram and
# crash. A fix would be to 1) find all DEPEND_0 variables, 2) use the
# data_vars='minimal' option to concat for each one, 3) combine the
# resulting datasets back together.
try:
data = xr.concat(data, dim=rec_vname)
except Exception as E:
return data
# Keep information about the data
data.attrs['sc'] = sc
data.attrs['instr'] = instr
data.attrs['mode'] = mode
data.attrs['level'] = level
data.attrs['optdesc'] = optdesc
data.attrs['files'] = files
return data
def load_data(sc='mms1', instr='fgm', mode='srvy', level='l2',
optdesc=None, start_date=None, end_date=None,
offline=False, record_dim='Epoch', team_site=False,
**kwargs):
"""
Load FPI distribution function data.
Parameters
----------
sc : str
Spacecraft ID: ('mms1', 'mms2', 'mms3', 'mms4')
instr : str
Instrument ID
mode : str
Instrument mode: ('slow', 'fast', 'srvy', 'brst').
optdesc : str
Optional descriptor for dataset
start_date, end_date : `datetime.datetime`
Start and end of the data interval.
offline : bool
If True, search only for local files
record_dim : str
Name of the record varying dimension. This is the dimension
along which the data from different files will be concatenated.
If *None*, the name of the leading dimension of the first data
variable will be used.
team_site : bool
If True, search the password-protected team site
\*\*kwargs : dict
Keywords passed to *cdf_to_ds*
Returns
-------
data : `xarray.DataArray`
The requested data.
"""
if start_date is None:
start_date = np.datetime64('2015-10-16T13:06:04')
if end_date is None:
end_date = np.datetime64('2015-10-16T13:07:20')
site = 'public'
if team_site:
site = 'private'
# Download the data
sdc = api.MrMMS_SDC_API(sc, instr, mode, level,
optdesc=optdesc,
start_date=start_date,
end_date=end_date,
offline=offline,
site=site)
files = sdc.download_files()
files = api.sort_files(files)[0]
# Concatenate data along the records (time) dimension, which
# should be equivalent to the DEPEND_0 variable name of the
# density variable.
data = []
for file in files:
data.append(cdf_to_ds(file, **kwargs))
# Concatenate all datasets along the time dimension. If not given,
# assume that the time dimension is the leading dimension of the data
# variables.
if record_dim is None:
varnames = [name for name in data[0].data_vars]
rec_vname = data[0].data_vars[varnames[0]].dims[0]
else:
rec_vname = record_dim
data = xr.concat(data, dim=rec_vname)
data = data.sel({rec_vname: slice(start_date, end_date)})
# Keep information about the data
data.attrs['sc'] = sc
data.attrs['instr'] = instr
data.attrs['mode'] = mode
data.attrs['level'] = level
data.attrs['optdesc'] = optdesc
data.attrs['files'] = files
return data
def plot_burst_selections(sc, start_date, end_date,
figsize=(5.5, 7)):
mode = 'srvy'
level = 'l2'
# FGM
b_vname = '_'.join((sc, 'fgm', 'b', 'gse', mode, level))
mms = api.MrMMS_SDC_API(sc, 'fgm', mode, level,
start_date=start_date, end_date=end_date)
files = mms.download_files()
files = api.sort_files(files)[0]
fgm_data = from_cdflib(files, b_vname,
start_date, end_date)
fgm_data[fgm_data['LABL_PTR_1']]['data'] = ['Bx', 'By', 'Bz', '|B|']
# FPI DIS
fpi_mode = 'fast'
ni_vname = '_'.join((sc, 'dis', 'numberdensity', fpi_mode))
espec_i_vname = '_'.join((sc, 'dis', 'energyspectr', 'omni', fpi_mode))
mms = api.MrMMS_SDC_API(sc, 'fpi', fpi_mode, level,
optdesc='dis-moms',
start_date=start_date, end_date=end_date)
files = mms.download_files()
files = api.sort_files(files)[0]
ni_data = from_cdflib(files, ni_vname,
start_date, end_date)
especi_data = from_cdflib(files, espec_i_vname,
start_date, end_date)
# FPI DES
ne_vname = '_'.join((sc, 'des', 'numberdensity', fpi_mode))
espec_e_vname = '_'.join((sc, 'des', 'energyspectr', 'omni', fpi_mode))
mms = api.MrMMS_SDC_API(sc, 'fpi', fpi_mode, level,
optdesc='des-moms',
start_date=start_date, end_date=end_date)
files = mms.download_files()
files = api.sort_files(files)[0]
ne_data = from_cdflib(files, ne_vname,
start_date, end_date)
espece_data = from_cdflib(files, espec_e_vname,
start_date, end_date)
# Grab selections
abs_data = sel.selections('abs', start_date, end_date)
sitl_data = sel.selections('sitl+back', start_date, end_date)
gls_data = sel.selections('mp-dl-unh', start_date, end_date)
# SITL data time series
t_abs = []
x_abs = []
for selection in abs_data:
t_abs.extend([selection.tstart, selection.tstart,
selection.tstop, selection.tstop])
x_abs.extend([0, selection.fom, selection.fom, 0])
if len(abs_data) == 0:
t_abs = [start_date, end_date]
x_abs = [0, 0]
abs = {'data': x_abs,
'DEPEND_0': 't',
't': {'data': t_abs}}
t_sitl = []
x_sitl = []
for selection in sitl_data:
t_sitl.extend([selection.tstart, selection.tstart,
selection.tstop, selection.tstop])
x_sitl.extend([0, selection.fom, selection.fom, 0])
if len(sitl_data) == 0:
t_sitl = [start_date, end_date]
x_sitl = [0, 0]
sitl = {'data': x_sitl,
'DEPEND_0': 't',
't': {'data': t_sitl}}
t_gls = []
x_gls = []
for selection in gls_data:
t_gls.extend([selection.tstart, selection.tstart,
selection.tstop, selection.tstop])
x_gls.extend([0, selection.fom, selection.fom, 0])
if len(gls_data) == 0:
t_gls = [start_date, end_date]
x_gls = [0, 0]
gls = {'data': x_gls,
'DEPEND_0': 't',
't': {'data': t_gls}}
# Setup plot
nrows = 7
ncols = 1
fig, axes = plt.subplots(nrows=nrows, ncols=ncols,
figsize=figsize, squeeze=False)
locator = mdates.AutoDateLocator()
formatter = mdates.ConciseDateFormatter(locator)
# Plot FGM
plot_2D(especi_data, axes[0,0])
axes[0,0].set_title(sc.upper())
fig.axes[-1].set_label('DEF')
axes[0,0].set_ylabel('$E_{ion}$\n(eV)')
axes[0,0].set_xticks([])
axes[0,0].set_xlabel('')
plot_2D(espece_data, axes[1,0])
fig.axes[-1].set_label('DEF\nLog_{10}(keV/(cm^2 s sr keV))')
axes[1,0].set_ylabel('$E_{e-}$\n(eV)')
axes[1,0].set_xticks([])
axes[1,0].set_xlabel('')
axes[1,0].set_title('')
plot_1D(fgm_data, axes[2,0])
axes[2,0].set_ylabel('B\n(nT)')
axes[2,0].set_xticks([])
axes[2,0].set_xlabel('')
axes[2,0].set_title('')
plot_1D(ni_data, axes[3,0])
axes[3,0].set_ylabel('$N_{i}$\n($cm^{-3}$)')
axes[3,0].set_xticks([])
axes[3,0].set_xlabel('')
axes[3,0].set_title('')
plot_1D(abs, axes[4,0])
axes[4,0].set_ylabel('ABS')
axes[4,0].set_xticks([])
axes[4,0].set_xlabel('')
axes[4,0].set_title('')
plot_1D(gls, axes[5,0])
axes[5,0].set_ylabel('GLS')
axes[5,0].set_ylim(0, 200)
axes[5,0].set_xticks([])
axes[5,0].set_xlabel('')
axes[5,0].set_title('')
plot_1D(sitl, axes[6,0])
axes[6,0].set_ylabel('SITL')
axes[6,0].set_title('')
axes[6,0].xaxis.set_major_locator(locator)
axes[6,0].xaxis.set_major_formatter(formatter)
for tick in axes[6,0].get_xticklabels():
tick.set_rotation(45)
# Set a common time range
plt.setp(axes, xlim=mdates.date2num([start_date, end_date]))
plt.subplots_adjust(left=0.15, right=0.85, top=0.93)
return fig, axes