def mms_load_edp(trange=['2015-10-16', '2015-10-17'],
probe='1',
data_rate='fast',
level='l2',
datatype='dce',
varformat=None,
varnames=[],
get_support_data=False,
suffix='',
time_clip=True,
no_update=False,
available=False,
notplot=False,
latest_version=False,
major_version=False,
min_version=None,
cdf_version=None,
spdf=False,
always_prompt=False):
"""
This function loads EDP data into tplot variables
Parameters:
trange : list of str
time range of interest [starttime, endtime] with the format
'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day
['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss']
probe : str or list of str
list of probes, valid values for MMS probes are ['1','2','3','4'].
data_rate : str or list of str
instrument data rates for EDP include ['brst', 'fast', 'slow', 'srvy']. The
default is 'fast'.
level : str
indicates level of data processing. the default if no level is specified is 'l2'
datatype : str or list of str
Valid datatypes for EDP are: ['dce', 'dcv', 'ace', 'hmfe']; default is 'dce'
get_support_data: bool
Data with an attribute "VAR_TYPE" with a value of "support_data"
will be loaded into tplot. By default, only loads in data with a
"VAR_TYPE" attribute of "data".
time_clip: bool
Data will be clipped to the exact trange specified by the trange keyword.
varformat: str
The file variable formats to load into tplot. Wildcard character
"*" is accepted. By default, all variables are loaded in.
varnames: list of str
List of variable names to load (if not specified,
all data variables are loaded)
suffix: str
The tplot variable names will be given this suffix. By default,
no suffix is added.
notplot: bool
If True, then data are returned in a hash table instead of
being stored in tplot variables (useful for debugging, and
access to multi-dimensional data products)
available: bool
If True, simply return the available data files (without downloading)
for the requested paramters
no_update: bool
Set this flag to preserve the original data. if not set and newer
data is found the existing data will be overwritten
cdf_version: str
Specify a specific CDF version # to load (e.g., cdf_version='4.3.0')
min_version: str
Specify a minimum CDF version # to load
latest_version: bool
Only grab the latest CDF version in the requested time interval
major_version: bool
Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval
always_prompt: bool
Set this keyword to always prompt for the user's username and password;
useful if you accidently save an incorrect password, or if your SDC password has changed
spdf: bool
If True, download the data from the SPDF instead of the SDC
Returns:
List of tplot variables created.
"""
tvars = mms_load_data(trange=trange,
notplot=notplot,
probe=probe,
data_rate=data_rate,
level=level,
instrument='edp',
datatype=datatype,
varformat=varformat,
varnames=varnames,
get_support_data=get_support_data,
suffix=suffix,
time_clip=time_clip,
no_update=no_update,
available=available,
latest_version=latest_version,
major_version=major_version,
min_version=min_version,
cdf_version=cdf_version,
spdf=spdf,
always_prompt=always_prompt)
if tvars == None or available or notplot or CONFIG['download_only']:
return tvars
mms_edp_set_metadata(probe, data_rate, level, suffix=suffix)
return tvars
def mms_load_scm(trange=['2015-10-16', '2015-10-17'],
probe='1',
data_rate='srvy',
level='l2',
datatype='',
varformat=None,
varnames=[],
suffix='',
get_support_data=False,
time_clip=True,
no_update=False,
available=False,
notplot=False,
latest_version=False,
major_version=False,
min_version=None,
cdf_version=None,
spdf=False,
always_prompt=False):
"""
This function loads SCM data into tplot variables
Parameters:
trange : list of str
time range of interest [starttime, endtime] with the format
'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day
['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss']
probe : str or list of str
list of probes, valid values for MMS probes are ['1','2','3','4'].
data_rate : str or list of str
instrument data rates for SCM include ['brst' 'fast' 'slow' 'srvy']. The
default is 'srvy'.
level : str
indicates level of data processing. the default if no level is specified is 'l2'
datatype : str or list of str
Valid datatypes for SCM are: ['scsrvy', 'cal', 'scb', 'scf', 'schb', 'scm', 'scs']
If no value is given the default is scsrvy for srvy data, and scb for brst data.
get_support_data: bool
Data with an attribute "VAR_TYPE" with a value of "support_data"
will be loaded into tplot. By default, only loads in data with a
"VAR_TYPE" attribute of "data".
time_clip: bool
Data will be clipped to the exact trange specified by the trange keyword.
varformat: str
The file variable formats to load into tplot. Wildcard character
"*" is accepted. By default, all variables are loaded in.
varnames: list of str
List of variable names to load (if not specified,
all data variables are loaded)
suffix: str
The tplot variable names will be given this suffix. By default,
no suffix is added.
notplot: bool
If True, then data are returned in a hash table instead of
being stored in tplot variables (useful for debugging, and
access to multi-dimensional data products)
available: bool
If True, simply return the available data files (without downloading)
for the requested paramters
no_update: bool
Set this flag to preserve the original data. if not set and newer
data is found the existing data will be overwritten
cdf_version: str
Specify a specific CDF version # to load (e.g., cdf_version='4.3.0')
min_version: str
Specify a minimum CDF version # to load
latest_version: bool
Only grab the latest CDF version in the requested time interval
major_version: bool
Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval
always_prompt: bool
Set this keyword to always prompt for the user's username and password;
useful if you accidently save an incorrect password, or if your SDC password has changed
spdf: bool
If True, download the data from the SPDF instead of the SDC
Returns:
List of tplot variables created.
"""
if not isinstance(data_rate, list):
data_rate = list([data_rate])
if isinstance(datatype, str) and datatype == '':
# guess from data_rate
datatype = list()
for dr in data_rate:
if dr == 'srvy':
datatype.append('scsrvy')
if dr == 'brst':
datatype.extend(['scb', 'schb'])
datatype = list(set(datatype)) # make it unique
else:
if not isinstance(datatype, list):
datatype = list([datatype])
# ensure datatype does not contain empty string
datatype = list(set([dt.strip() for dt in datatype]))
if '' in datatype:
datatype.remove('')
tvars = mms_load_data(trange=trange,
notplot=notplot,
probe=probe,
data_rate=data_rate,
level=level,
instrument='scm',
datatype=datatype,
varformat=varformat,
varnames=varnames,
suffix=suffix,
get_support_data=get_support_data,
time_clip=time_clip,
no_update=no_update,
available=available,
latest_version=latest_version,
major_version=major_version,
min_version=min_version,
cdf_version=cdf_version,
spdf=spdf,
always_prompt=always_prompt)
if tvars == None or available or notplot or CONFIG['download_only']:
return tvars
coord = ''
if level == 'l1a':
coord = '123'
elif level == 'l1b':
coord = 'scm123'
elif level == 'l2':
coord = 'gse'
if not isinstance(probe, list):
probe = [probe]
if not isinstance(datatype, list):
datatype = [datatype]
probe = [str(p) for p in probe]
for p in probe:
for dtype in datatype:
mms_scm_set_metadata(tvars, p, dtype, coord, suffix=suffix)
return tvars
def mms_load_feeps(trange=['2015-10-16', '2015-10-17'],
probe='1',
data_rate='srvy',
level='l2',
datatype='electron',
varformat=None,
varnames=[],
get_support_data=True,
suffix='',
time_clip=False,
no_update=False,
available=False,
notplot=False,
no_flatfield_corrections=False,
data_units=['count_rate', 'intensity'],
latest_version=False,
major_version=False,
min_version=None,
cdf_version=None,
spdf=False,
always_prompt=False):
"""
This function loads FEEPS data into tplot variables
Parameters
----------
trange : list of str
time range of interest [starttime, endtime] with the format
'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day
['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss']
probe : str or list of str
list of probes, valid values for MMS probes are ['1','2','3','4'].
data_rate : str or list of str
instrument data rates for FEEPS include ['brst', 'srvy']. The
default is 'srvy'.
level : str
indicates level of data processing. the default if no level is specified is 'l2'
datatype : str or list of str
Valid datatypes for FEEPS are:
L2, L1b: ['electron', 'ion']
L1a: ['electron-bottom', 'electron-top', 'ion-bottom', 'ion-top']
get_support_data: bool
Data with an attribute "VAR_TYPE" with a value of "support_data"
will be loaded into tplot. By default, only loads in data with a
"VAR_TYPE" attribute of "data".
time_clip: bool
Data will be clipped to the exact trange specified by the trange keyword.
varformat: str
The file variable formats to load into tplot. Wildcard character
"*" is accepted. By default, all variables are loaded in.
varnames: list of str
List of variable names to load (if not specified,
all data variables are loaded)
suffix: str
The tplot variable names will be given this suffix. By default,
no suffix is added.
notplot: bool
If True, then data are returned in a hash table instead of
being stored in tplot variables (useful for debugging, and
access to multi-dimensional data products)
available: bool
If True, simply return the available data files (without downloading)
for the requested paramters
no_update: bool
Set this flag to preserve the original data. if not set and newer
data is found the existing data will be overwritten
cdf_version: str
Specify a specific CDF version # to load (e.g., cdf_version='4.3.0')
min_version: str
Specify a minimum CDF version # to load
latest_version: bool
Only grab the latest CDF version in the requested time interval
major_version: bool
Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval
always_prompt: bool
Set this keyword to always prompt for the user's username and password;
useful if you accidently save an incorrect password, or if your SDC password has changed
spdf: bool
If True, download the data from the SPDF instead of the SDC
Returns:
List of tplot variables created.
"""
tvars = mms_load_data(trange=trange,
notplot=notplot,
probe=probe,
data_rate=data_rate,
level=level,
instrument='feeps',
datatype=datatype,
varformat=varformat,
varnames=varnames,
get_support_data=get_support_data,
suffix=suffix,
time_clip=time_clip,
no_update=no_update,
available=available,
latest_version=latest_version,
major_version=major_version,
min_version=min_version,
cdf_version=cdf_version,
spdf=spdf,
always_prompt=always_prompt)
if tvars == [] or available or notplot or CONFIG['download_only']:
return tvars
probes = probe if isinstance(probe, list) else [probe]
data_rates = data_rate if isinstance(data_rate, list) else [data_rate]
levels = level if isinstance(level, list) else [level]
datatypes = datatype if isinstance(datatype, list) else [datatype]
data_units = data_units if isinstance(data_units, list) else [data_units]
probes = [str(p) for p in probes]
mms_feeps_correct_energies(probes, data_rate, level=level, suffix=suffix)
if not no_flatfield_corrections:
mms_feeps_flat_field_corrections(probes=probes,
data_rate=data_rate,
suffix=suffix)
for probe in probes:
for lvl in levels:
for drate in data_rates:
for datatype in datatypes:
mms_feeps_remove_bad_data(probe=probe,
data_rate=drate,
datatype=datatype,
level=lvl,
suffix=suffix)
for data_unit in data_units:
eyes = mms_feeps_active_eyes(trange, probe, drate,
datatype, lvl)
split_vars = mms_feeps_split_integral_ch(
data_unit,
datatype,
probe,
suffix=suffix,
data_rate=drate,
level=lvl,
sensor_eyes=eyes)
sun_removed_vars = mms_feeps_remove_sun(
eyes,
trange,
probe=probe,
datatype=datatype,
data_units=data_unit,
data_rate=drate,
level=lvl,
suffix=suffix)
omni_vars = mms_feeps_omni(eyes,
probe=probe,
datatype=datatype,
data_units=data_unit,
data_rate=drate,
level=lvl,
suffix=suffix)
tvars = tvars + split_vars + sun_removed_vars + omni_vars
tvars.append(
mms_feeps_spin_avg(probe=probe,
data_units=data_unit,
datatype=datatype,
data_rate=drate,
level=lvl,
suffix=suffix))
return tvars
def mms_load_fpi(trange=['2015-10-16', '2015-10-17'],
probe='1',
data_rate='fast',
level='l2',
datatype='*',
varformat=None,
varnames=[],
suffix='',
get_support_data=False,
time_clip=False,
no_update=False,
center_measurement=False,
available=False,
notplot=False,
latest_version=False,
major_version=False,
min_version=None,
cdf_version=None,
spdf=False,
always_prompt=False):
"""
This function loads FPI data into tplot variables
Parameters
----------
trange : list of str
time range of interest [starttime, endtime] with the format
'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day
['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss']
probe : str or list of str
list of probes, valid values for MMS probes are ['1','2','3','4'].
data_rate : str or list of str
instrument data rates for FPI include 'brst', 'fast'. The
default is 'srvy'.
level : str
indicates level of data processing. the default if no level is specified is 'l2'
datatype : str or list of str
Valid datatypes for FPI are:
'des-moms', 'dis-moms' (default)
'des-dist', 'dis-dist'
get_support_data: bool
Data with an attribute "VAR_TYPE" with a value of "support_data"
will be loaded into tplot. By default, only loads in data with a
"VAR_TYPE" attribute of "data".
time_clip: bool
Data will be clipped to the exact trange specified by the trange keyword.
varformat: str
The file variable formats to load into tplot. Wildcard character
"*" is accepted. By default, all variables are loaded in.
varnames: list of str
List of variable names to load (if not specified,
all data variables are loaded)
suffix: str
The tplot variable names will be given this suffix. By default,
no suffix is added.
center_measurement: bool
If True, the CDF epoch variables are time-shifted to the middle
of the accumulation interval by their DELTA_PLUS_VAR and
DELTA_MINUS_VAR variable attributes
notplot: bool
If True, then data are returned in a hash table instead of
being stored in tplot variables (useful for debugging, and
access to multi-dimensional data products)
available: bool
If True, simply return the available data files (without downloading)
for the requested paramters
no_update: bool
Set this flag to preserve the original data. if not set and newer
data is found the existing data will be overwritten
cdf_version: str
Specify a specific CDF version # to load (e.g., cdf_version='4.3.0')
min_version: str
Specify a minimum CDF version # to load
latest_version: bool
Only grab the latest CDF version in the requested time interval
major_version: bool
Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval
always_prompt: bool
Set this keyword to always prompt for the user's username and password;
useful if you accidently save an incorrect password, or if your SDC password has changed
spdf: bool
If True, download the data from the SPDF instead of the SDC
Returns:
List of tplot variables created.
"""
# different datatypes for burst mode files
if data_rate.lower() == 'brst':
if isinstance(datatype, str):
if (datatype == '*' or datatype == '') and level.lower() != 'ql':
datatype = ['des-dist', 'dis-dist', 'dis-moms', 'des-moms']
else:
if isinstance(datatype, str):
if (datatype == '*' or datatype == '') and level.lower() == 'ql':
datatype = ['des', 'dis']
if (datatype == '*' or datatype == '') and level.lower() != 'ql':
datatype = ['des-dist', 'dis-dist', 'dis-moms', 'des-moms']
# kludge for level = 'sitl' -> datatype shouldn't be defined for sitl data.
if level.lower() == 'sitl' or level.lower() == 'trig':
datatype = ''
if varformat is None and varnames == []:
get_support_data = True
tvars = mms_load_data(trange=trange,
probe=probe,
data_rate=data_rate,
level=level,
instrument='fpi',
datatype=datatype,
varformat=varformat,
varnames=varnames,
suffix=suffix,
get_support_data=get_support_data,
time_clip=time_clip,
no_update=no_update,
center_measurement=center_measurement,
available=available,
notplot=notplot,
latest_version=latest_version,
major_version=major_version,
min_version=min_version,
cdf_version=cdf_version,
spdf=spdf,
always_prompt=always_prompt)
if tvars is None or available or notplot or CONFIG['download_only']:
return tvars
mms_fpi_set_metadata(probe, data_rate, datatype, level, suffix=suffix)
if not isinstance(probe, list): probe = [probe]
if not isinstance(data_rate, list): data_rate = [data_rate]
if not isinstance(datatype, list): datatype = [datatype]
if not isinstance(level, list): level = [level]
# the following kludge is due to the errorflags variable in the dist and moments files having the
# same variable name, so loading d?s-dist and d?s-moms files at the same time will overwrite
# one of the vars containing errorflags
if 'des-dist' in datatype and 'des-moms' in datatype:
# delete the old vars first
del_data('*_des_errorflags_*')
del_data('*_des_compressionloss_*')
# load the bars with a suffix applied
tplotnames_errflags_emom = mms_load_data(
trange=trange,
probe=probe,
data_rate=data_rate,
level=level,
instrument='fpi',
datatype='des-moms',
varformat='*errorflags*|*compressionloss*',
varnames=varnames,
suffix=suffix + '_moms',
get_support_data=0,
time_clip=time_clip,
no_update=no_update,
center_measurement=center_measurement,
available=available,
notplot=notplot,
latest_version=latest_version,
major_version=major_version,
min_version=min_version,
cdf_version=cdf_version,
spdf=spdf,
always_prompt=always_prompt)
tplotnames_errflags_edist = mms_load_data(
trange=trange,
probe=probe,
data_rate=data_rate,
level=level,
instrument='fpi',
datatype='des-dist',
varformat='*errorflags*|*compressionloss*',
varnames=varnames,
suffix=suffix + '_dist',
get_support_data=0,
time_clip=time_clip,
no_update=no_update,
center_measurement=center_measurement,
available=available,
notplot=notplot,
latest_version=latest_version,
major_version=major_version,
min_version=min_version,
cdf_version=cdf_version,
spdf=spdf,
always_prompt=always_prompt)
tvars.extend(tplotnames_errflags_emom)
tvars.extend(tplotnames_errflags_edist)
else:
# user didn't request both dist and moments, so no variables should have been clobbered
# but we still need to append _dist, _moms to the end of the names
for prb in probe:
for drate in data_rate:
this_probe = str(prb)
if 'des-dist' in datatype:
tplot_rename(
'mms' + this_probe + '_des_errorflags_' + drate +
suffix, 'mms' + this_probe + '_des_errorflags_' +
drate + suffix + '_dist')
tplot_rename(
'mms' + this_probe + '_des_compressionloss_' + drate +
suffix, 'mms' + this_probe + '_des_compressionloss_' +
drate + suffix + '_dist')
tvars.append('mms' + this_probe + '_des_errorflags_' +
drate + suffix + '_dist')
tvars.append('mms' + this_probe + '_des_compressionloss_' +
drate + suffix + '_dist')
if 'des-moms' in datatype or 'des' in datatype:
tplot_rename(
'mms' + this_probe + '_des_errorflags_' + drate +
suffix, 'mms' + this_probe + '_des_errorflags_' +
drate + suffix + '_moms')
tplot_rename(
'mms' + this_probe + '_des_compressionloss_' + drate +
suffix, 'mms' + this_probe + '_des_compressionloss_' +
drate + suffix + '_moms')
tvars.append('mms' + this_probe + '_des_errorflags_' +
drate + suffix + '_moms')
tvars.append('mms' + this_probe + '_des_compressionloss_' +
drate + suffix + '_moms')
# same kludge as above, but for ions
if 'dis-dist' in datatype and 'dis-moms' in datatype:
# delete the old vars first
del_data('*_dis_errorflags_*')
del_data('*_dis_compressionloss_*')
# load the bars with a suffix applied
tplotnames_errflags_imom = mms_load_data(
trange=trange,
probe=probe,
data_rate=data_rate,
level=level,
instrument='fpi',
datatype='dis-moms',
varformat='*errorflags*|*compressionloss*',
varnames=varnames,
suffix=suffix + '_moms',
get_support_data=0,
time_clip=time_clip,
no_update=no_update,
center_measurement=center_measurement,
available=available,
notplot=notplot,
latest_version=latest_version,
major_version=major_version,
min_version=min_version,
cdf_version=cdf_version,
spdf=spdf,
always_prompt=always_prompt)
tplotnames_errflags_idist = mms_load_data(
trange=trange,
probe=probe,
data_rate=data_rate,
level=level,
instrument='fpi',
datatype='dis-dist',
varformat='*errorflags*|*compressionloss*',
varnames=varnames,
suffix=suffix + '_dist',
get_support_data=0,
time_clip=time_clip,
no_update=no_update,
center_measurement=center_measurement,
available=available,
notplot=notplot,
latest_version=latest_version,
major_version=major_version,
min_version=min_version,
cdf_version=cdf_version,
spdf=spdf,
always_prompt=always_prompt)
tvars.extend(tplotnames_errflags_imom)
tvars.extend(tplotnames_errflags_idist)
else:
# user didn't request both dist and moments, so no variables should have been clobbered
# but we still need to append _dist, _moms to the end of the names
for prb in probe:
for drate in data_rate:
this_probe = str(prb)
if 'dis-dist' in datatype:
tplot_rename(
'mms' + this_probe + '_dis_errorflags_' + drate +
suffix, 'mms' + this_probe + '_dis_errorflags_' +
drate + suffix + '_dist')
tplot_rename(
'mms' + this_probe + '_dis_compressionloss_' + drate +
suffix, 'mms' + this_probe + '_dis_compressionloss_' +
drate + suffix + '_dist')
tvars.append('mms' + this_probe + '_dis_errorflags_' +
drate + suffix + '_dist')
tvars.append('mms' + this_probe + '_dis_compressionloss_' +
drate + suffix + '_dist')
if 'dis-moms' in datatype or 'dis' in datatype:
tplot_rename(
'mms' + this_probe + '_dis_errorflags_' + drate +
suffix, 'mms' + this_probe + '_dis_errorflags_' +
drate + suffix + '_moms')
tplot_rename(
'mms' + this_probe + '_dis_compressionloss_' + drate +
suffix, 'mms' + this_probe + '_dis_compressionloss_' +
drate + suffix + '_moms')
tvars.append('mms' + this_probe + '_dis_errorflags_' +
drate + suffix + '_moms')
tvars.append('mms' + this_probe + '_dis_compressionloss_' +
drate + suffix + '_moms')
for prb in probe:
for drate in data_rate:
for dtype in datatype:
for lvl in level:
out_var = mms_load_fpi_calc_pad(probe=prb,
level=lvl,
datatype=dtype,
data_rate=drate,
suffix=suffix,
autoscale=True)
if out_var:
tvars.extend(out_var)
return tvars
def mms_load_eis(trange=['2015-10-16', '2015-10-17'],
probe='1',
data_rate='srvy',
level='l2',
datatype='extof',
varformat=None,
varnames=[],
get_support_data=True,
suffix='',
time_clip=False,
no_update=False,
available=False,
notplot=False,
latest_version=False,
major_version=False,
min_version=None,
cdf_version=None,
spdf=False,
always_prompt=False):
"""
This function loads EIS data into tplot variables
Parameters
----------
trange : list of str
time range of interest [starttime, endtime] with the format
'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day
['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss']
probe : str or list of str
list of probes, valid values for MMS probes are ['1','2','3','4'].
data_rate : str or list of str
instrument data rates for EIS include ['brst', 'srvy']. The
default is 'srvy'.
level : str
indicates level of data processing. the default if no level is specified is 'l2'
datatype : str or list of str
Valid datatypes for EIS are: ['extof', 'phxtof', and 'electronenergy']; default is 'extof'
get_support_data: bool
Data with an attribute "VAR_TYPE" with a value of "support_data"
will be loaded into tplot. By default, only loads in data with a
"VAR_TYPE" attribute of "data".
time_clip: bool
Data will be clipped to the exact trange specified by the trange keyword.
varformat: str
The file variable formats to load into tplot. Wildcard character
"*" is accepted. By default, all variables are loaded in.
varnames: list of str
List of variable names to load (if not specified,
all data variables are loaded)
suffix: str
The tplot variable names will be given this suffix. By default,
no suffix is added.
notplot: bool
If True, then data are returned in a hash table instead of
being stored in tplot variables (useful for debugging, and
access to multi-dimensional data products)
available: bool
If True, simply return the available data files (without downloading)
for the requested paramters
no_update: bool
Set this flag to preserve the original data. if not set and newer
data is found the existing data will be overwritten
cdf_version: str
Specify a specific CDF version # to load (e.g., cdf_version='4.3.0')
min_version: str
Specify a minimum CDF version # to load
latest_version: bool
Only grab the latest CDF version in the requested time interval
major_version: bool
Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval
always_prompt: bool
Set this keyword to always prompt for the user's username and password;
useful if you accidently save an incorrect password, or if your SDC password has changed
spdf: bool
If True, download the data from the SPDF instead of the SDC
Returns:
List of tplot variables created.
"""
tvars = mms_load_data(trange=trange,
notplot=notplot,
probe=probe,
data_rate=data_rate,
level=level,
instrument='epd-eis',
datatype=datatype,
varformat=varformat,
varnames=varnames,
get_support_data=get_support_data,
prefix='',
suffix=suffix,
time_clip=time_clip,
no_update=no_update,
available=available,
latest_version=latest_version,
major_version=major_version,
min_version=min_version,
cdf_version=cdf_version,
spdf=spdf,
always_prompt=always_prompt)
if tvars == [] or available or notplot or CONFIG['download_only']:
return tvars
if not isinstance(probe, list): probe = [probe]
if not isinstance(data_rate, list): data_rate = [data_rate]
if not isinstance(level, list): level = [level]
if not isinstance(datatype, list): datatype = [datatype]
# the probes will need to be strings beyond this point
if isinstance(probe, list):
probe = [str(p) for p in probe]
for probe_id in probe:
for datatype_id in datatype:
for level_id in level:
for data_rate_id in data_rate:
if datatype_id == 'electronenergy':
e_spin_avg_var = mms_eis_spin_avg(
probe=probe_id,
species='electron',
datatype=datatype_id,
data_rate=data_rate_id,
level=level_id,
suffix=suffix)
# create non-spin averaged omni-directional spectra
e_omni_spectra = mms_eis_omni(probe_id,
species='electron',
data_rate=data_rate_id,
level=level_id,
datatype=datatype_id)
# create spin averaged omni-directional spectra
e_omni_spectra_spin = mms_eis_omni(
probe_id,
species='electron',
data_rate=data_rate_id,
level=level_id,
datatype=datatype_id,
suffix=suffix + '_spin')
# add the vars to the output
if e_spin_avg_var is not None:
for tvar in e_spin_avg_var:
tvars.append(tvar)
if e_omni_spectra is not None:
tvars.append(e_omni_spectra)
if e_omni_spectra_spin is not None:
tvars.append(e_omni_spectra_spin)
elif datatype_id == 'extof':
# 9Feb2021, egrimes added 'helium' species for updates coming soon to the CDFs
p_spin_avg_var = mms_eis_spin_avg(
probe=probe_id,
species='proton',
datatype=datatype_id,
data_rate=data_rate_id,
level=level_id,
suffix=suffix)
o_spin_avg_var = mms_eis_spin_avg(
probe=probe_id,
species='oxygen',
datatype=datatype_id,
data_rate=data_rate_id,
level=level_id,
suffix=suffix)
a_spin_avg_var = mms_eis_spin_avg(
probe=probe_id,
species='alpha',
datatype=datatype_id,
data_rate=data_rate_id,
level=level_id,
suffix=suffix)
h_spin_avg_var = mms_eis_spin_avg(
probe=probe_id,
species='helium',
datatype=datatype_id,
data_rate=data_rate_id,
level=level_id,
suffix=suffix)
# create non-spin averaged omni-directional spectra
p_omni_spectra = mms_eis_omni(probe_id,
species='proton',
data_rate=data_rate_id,
level=level_id,
datatype=datatype_id,
suffix=suffix)
o_omni_spectra = mms_eis_omni(probe_id,
species='oxygen',
data_rate=data_rate_id,
level=level_id,
datatype=datatype_id,
suffix=suffix)
a_omni_spectra = mms_eis_omni(probe_id,
species='alpha',
data_rate=data_rate_id,
level=level_id,
datatype=datatype_id,
suffix=suffix)
h_omni_spectra = mms_eis_omni(probe_id,
species='helium',
data_rate=data_rate_id,
level=level_id,
datatype=datatype_id,
suffix=suffix)
# create spin averaged omni-directional spectra
p_omni_spectra_spin = mms_eis_omni(
probe_id,
species='proton',
data_rate=data_rate_id,
level=level_id,
datatype=datatype_id,
suffix=suffix + '_spin')
o_omni_spectra_spin = mms_eis_omni(
probe_id,
species='oxygen',
data_rate=data_rate_id,
level=level_id,
datatype=datatype_id,
suffix=suffix + '_spin')
a_omni_spectra_spin = mms_eis_omni(
probe_id,
species='alpha',
data_rate=data_rate_id,
level=level_id,
datatype=datatype_id,
suffix=suffix + '_spin')
h_omni_spectra_spin = mms_eis_omni(
probe_id,
species='helium',
data_rate=data_rate_id,
level=level_id,
datatype=datatype_id,
suffix=suffix + '_spin')
# add the vars to the output
if p_spin_avg_var is not None:
for tvar in p_spin_avg_var:
tvars.append(tvar)
if o_spin_avg_var is not None:
for tvar in o_spin_avg_var:
tvars.append(tvar)
if a_spin_avg_var is not None:
for tvar in a_spin_avg_var:
tvars.append(tvar)
if h_spin_avg_var is not None:
for tvar in h_spin_avg_var:
tvars.append(tvar)
if p_omni_spectra is not None:
tvars.append(p_omni_spectra)
if o_omni_spectra is not None:
tvars.append(o_omni_spectra)
if a_omni_spectra is not None:
tvars.append(a_omni_spectra)
if h_omni_spectra is not None:
tvars.append(h_omni_spectra)
if p_omni_spectra_spin is not None:
tvars.append(p_omni_spectra_spin)
if o_omni_spectra_spin is not None:
tvars.append(o_omni_spectra_spin)
if a_omni_spectra_spin is not None:
tvars.append(a_omni_spectra_spin)
if h_omni_spectra_spin is not None:
tvars.append(h_omni_spectra_spin)
elif datatype_id == 'phxtof':
# 9Feb2021, egrimes commented out oxygen calculations to match IDL updates
p_spin_avg_var = mms_eis_spin_avg(
probe=probe_id,
species='proton',
datatype=datatype_id,
data_rate=data_rate_id,
level=level_id,
suffix=suffix)
# o_spin_avg_var = mms_eis_spin_avg(probe=probe_id, species='oxygen', datatype=datatype_id, level=level_id, data_rate=data_rate_id, suffix=suffix)
# create non-spin averaged omni-directional spectra
p_omni_spectra = mms_eis_omni(probe_id,
species='proton',
data_rate=data_rate_id,
datatype=datatype_id,
level=level_id,
suffix=suffix)
# o_omni_spectra = mms_eis_omni(probe_id, species='oxygen', data_rate=data_rate_id, level=level_id, datatype=datatype_id, suffix=suffix)
# create spin averaged omni-directional spectra
p_omni_spectra_spin = mms_eis_omni(
probe_id,
species='proton',
data_rate=data_rate_id,
datatype=datatype_id,
level=level_id,
suffix=suffix + '_spin')
# o_omni_spectra_spin = mms_eis_omni(probe_id, species='oxygen', data_rate=data_rate_id, level=level_id, datatype=datatype_id, suffix=suffix+'_spin')
# add the vars to the output
if p_spin_avg_var is not None:
for tvar in p_spin_avg_var:
tvars.append(tvar)
# if o_spin_avg_var is not None:
# for tvar in o_spin_avg_var:
# tvars.append(tvar)
if p_omni_spectra is not None:
tvars.append(p_omni_spectra)
# if o_omni_spectra is not None:
# tvars.append(o_omni_spectra)
if p_omni_spectra_spin is not None:
tvars.append(p_omni_spectra_spin)
# if o_omni_spectra_spin is not None:
# tvars.append(o_omni_spectra_spin)
mms_eis_set_metadata(tnames(tvars),
data_rate=data_rate_id,
datatype=datatype_id,
suffix=suffix)
return tnames(tvars)
def mms_load_fgm(trange=['2015-10-16', '2015-10-17'], probe='1', data_rate='srvy',
level='l2', instrument='fgm', datatype='', varformat=None, varnames=[], suffix='',
keep_flagged=False, get_support_data=True, time_clip=False, no_update=False,
available=False, notplot=False, latest_version=False, major_version=False,
min_version=None, cdf_version=None, spdf=False, always_prompt=False):
"""
This function loads FGM data into tplot variables
Parameters:
trange : list of str
time range of interest [starttime, endtime] with the format
'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day
['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss']
probe : str or list of str
list of probes, valid values for MMS probes are ['1','2','3','4'].
data_rate : str or list of str
instrument data rates for FGM include 'brst' 'fast' 'slow' 'srvy'. The
default is 'srvy'.
level : str
indicates level of data processing. the default if no level is specified is 'l2'
datatype : str or list of str
no datatype for FGM instrument (all science data are loaded)
get_support_data: bool
Data with an attribute "VAR_TYPE" with a value of "support_data"
will be loaded into tplot. By default, only loads in data with a
"VAR_TYPE" attribute of "data".
time_clip: bool
Data will be clipped to the exact trange specified by the trange keyword.
varformat: str
The file variable formats to load into tplot. Wildcard character
"*" is accepted. By default, all variables are loaded in.
varnames: list of str
List of variable names to load (if not specified,
all data variables are loaded)
suffix: str
The tplot variable names will be given this suffix. By default,
no suffix is added.
notplot: bool
If True, then data are returned in a hash table instead of
being stored in tplot variables (useful for debugging, and
access to multi-dimensional data products)
available: bool
If True, simply return the available data files (without downloading)
for the requested paramters
no_update: bool
Set this flag to preserve the original data. if not set and newer
data is found the existing data will be overwritten
cdf_version: str
Specify a specific CDF version # to load (e.g., cdf_version='4.3.0')
min_version: str
Specify a minimum CDF version # to load
latest_version: bool
Only grab the latest CDF version in the requested time interval
major_version: bool
Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval
keep_flagged: bool
If True, don't remove flagged data (flagged data are set to NaNs by
default, this keyword turns this off)
always_prompt: bool
Set this keyword to always prompt for the user's username and password;
useful if you accidently save an incorrect password, or if your SDC password has changed
spdf: bool
If True, download the data from the SPDF instead of the SDC
Returns:
List of tplot variables created.
"""
if (varformat is not None) and (not keep_flagged) and (not available) and (not notplot):
varformat_fetch = varformat+'|*_flag_*'
else:
varformat_fetch = varformat
tvars = mms_load_data(trange=trange, notplot=notplot, probe=probe, data_rate=data_rate, level=level, instrument=instrument,
datatype=datatype, varformat=varformat_fetch, varnames=varnames, suffix=suffix, get_support_data=get_support_data,
time_clip=time_clip, no_update=no_update, available=available, latest_version=latest_version, major_version=major_version,
min_version=min_version, cdf_version=cdf_version, spdf=spdf, always_prompt=always_prompt)
if tvars == None or available or notplot or CONFIG['download_only']:
return tvars
# the probes will need to be strings beyond this point
if isinstance(probe, list):
probe = [str(p) for p in probe]
# remove flagged data
if not keep_flagged:
mms_fgm_remove_flags(probe, data_rate, level, instrument, suffix=suffix)
# Delete the flags variable if it was not originally requested
if varformat is not None:
regex = re.compile(varformat.replace("*", ".*"))
tvars_to_delete = [tvar for tvar in tvars if not re.match(regex, tvar)]
for tvar in tvars_to_delete:
del_data(tvar)
tvars.remove(tvar)
mms_fgm_set_metadata(probe, data_rate, level, instrument, suffix=suffix)
return tvars
def mms_load_hpca(trange=['2015-10-16', '2015-10-17'],
probe='1',
data_rate='srvy',
level='l2',
datatype='moments',
get_support_data=None,
time_clip=False,
no_update=False,
varformat=None,
varnames=[],
suffix='',
center_measurement=False,
available=False,
notplot=False,
latest_version=False,
major_version=False,
min_version=None,
cdf_version=None,
spdf=False,
always_prompt=False):
"""
This function loads HPCA data into tplot variables
Parameters
----------
trange : list of str
time range of interest [starttime, endtime] with the format
'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day
['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss']
probe : str or list of str
list of probes, valid values for MMS probes are ['1','2','3','4'].
data_rate : str or list of str
instrument data rates for HPCA include 'brst', 'srvy'. The
default is 'srvy'.
level : str
indicates level of data processing. the default if no level is specified is 'l2'
datatype : str or list of str
Valid datatypes for HPCA are 'moments' and 'ion'; the default is 'moments'
get_support_data: bool
Data with an attribute "VAR_TYPE" with a value of "support_data"
will be loaded into tplot. By default, only loads in data with a
"VAR_TYPE" attribute of "data".
varformat: str
The file variable formats to load into tplot. Wildcard character
"*" is accepted. By default, all variables are loaded in.
varnames: list of str
List of variable names to load (if not specified,
all data variables are loaded)
time_clip: bool
Data will be clipped to the exact trange specified by the trange keyword.
suffix: str
The tplot variable names will be given this suffix. By default,
no suffix is added.
center_measurement: bool
If True, the CDF epoch variables are time-shifted to the middle
of the accumulation interval by their DELTA_PLUS_VAR and
DELTA_MINUS_VAR variable attributes
notplot: bool
If True, then data are returned in a hash table instead of
being stored in tplot variables (useful for debugging, and
access to multi-dimensional data products)
available: bool
If True, simply return the available data files (without downloading)
for the requested paramters
no_update: bool
Set this flag to preserve the original data. if not set and newer
data is found the existing data will be overwritten
cdf_version: str
Specify a specific CDF version # to load (e.g., cdf_version='4.3.0')
min_version: str
Specify a minimum CDF version # to load
latest_version: bool
Only grab the latest CDF version in the requested time interval
major_version: bool
Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval
always_prompt: bool
Set this keyword to always prompt for the user's username and password;
useful if you accidently save an incorrect password, or if your SDC password has changed
spdf: bool
If True, download the data from the SPDF instead of the SDC
Returns:
List of tplot variables created.
"""
if level.lower() != 'l2':
if varformat is None:
if level.lower() != 'l1a':
if datatype.lower() == 'ion':
varformat = '*'
elif datatype.lower() == 'combined':
varformat = '*'
elif datatype.lower() == 'rf_corr':
varformat = '*_RF_corrected'
elif datatype.lower() == 'count_rate':
varformat = '*_count_rate'
elif datatype.lower() == 'flux':
varformat = '*_flux'
elif datatype.lower() == 'vel_dist':
varformat = '*_vel_dist_fn'
elif datatype.lower() == 'bkgd_corr':
varformat = '*_bkgd_corrected'
elif datatype.lower() == 'moments':
varformat = '*'
else:
varformat = '*_RF_corrected'
if varformat != None and varformat != '*':
datatype = 'ion'
if level.lower() == 'sitl':
varformat = '*'
else:
if get_support_data is None:
get_support_data = True
if isinstance(datatype, str):
if datatype not in ['ion', 'moments']:
logging.warning(
"Unknown datatype: " + datatype +
"for L2 HPCA data; expected 'ion' or 'moments', loading 'ion'"
)
datatype = 'ion'
tvars = mms_load_data(trange=trange,
notplot=notplot,
probe=probe,
data_rate=data_rate,
level=level,
instrument='hpca',
datatype=datatype,
varformat=varformat,
varnames=varnames,
suffix=suffix,
get_support_data=get_support_data,
time_clip=time_clip,
no_update=no_update,
center_measurement=center_measurement,
available=available,
latest_version=latest_version,
major_version=major_version,
min_version=min_version,
cdf_version=cdf_version,
spdf=spdf,
always_prompt=always_prompt)
if tvars is None or available or notplot or CONFIG['download_only']:
return tvars
mms_hpca_set_metadata(probe=probe, suffix=suffix)
if not isinstance(datatype, list):
datatype = [datatype]
if not isinstance(probe, list):
probe = [probe]
# Replace supplementary fields in 3D distribution variables with actual
# values from supplementary tplot variables (theta).
for dtype in datatype:
if dtype == 'moments':
continue
for prb in probe:
prb = str(prb)
for tvar in tvars:
df_var = re.search(
'^mms' + prb +
'_hpca_[^_]+plus(_(phase_space_density|count_rate|flux)| ?)'
+ suffix + '$', tvar)
if df_var:
df_data = get_data(tvar)
df_metadata = get_data(tvar, metadata=True)
theta_data = get_data('mms' + prb +
'_hpca_centroid_elevation_angle' +
suffix)
if theta_data is None:
info = mms_get_hpca_info()
theta = info['elevation']
else:
theta = theta_data
store_data(tvar,
data={
'x': df_data.times,
'y': df_data.y,
'v1': theta,
'v2': df_data.v2
},
attr_dict=df_metadata)
# check if energy table contains all 0s
zerocheck = np.argwhere(df_data.v2 == 0.0)
if len(zerocheck) == 63:
# energy table is all 0s, using hard coded table
energy_table = mms_hpca_energies()
logging.warning(
'Found energy table with all 0s: ' + tvar +
'; using hard-coded energy table instead')
store_data(tvar,
data={
'x': df_data.times,
'y': df_data.y,
'v1': theta,
'v2': energy_table
},
attr_dict=df_metadata)
return tvars
def mms_load_hpca(trange=['2015-10-16', '2015-10-17'],
probe='1',
data_rate='srvy',
level='l2',
datatype='moments',
get_support_data=None,
time_clip=False,
no_update=False,
varformat=None,
varnames=[],
suffix='',
center_measurement=False,
available=False,
notplot=False,
latest_version=False,
major_version=False,
min_version=None,
cdf_version=None,
spdf=False,
always_prompt=False):
"""
This function loads HPCA data into tplot variables
Parameters:
trange : list of str
time range of interest [starttime, endtime] with the format
'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day
['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss']
probe : str or list of str
list of probes, valid values for MMS probes are ['1','2','3','4'].
data_rate : str or list of str
instrument data rates for HPCA include 'brst', 'srvy'. The
default is 'srvy'.
level : str
indicates level of data processing. the default if no level is specified is 'l2'
datatype : str or list of str
Valid datatypes for HPCA are 'moments' and 'ion'; the default is 'moments'
get_support_data: bool
Data with an attribute "VAR_TYPE" with a value of "support_data"
will be loaded into tplot. By default, only loads in data with a
"VAR_TYPE" attribute of "data".
varformat: str
The file variable formats to load into tplot. Wildcard character
"*" is accepted. By default, all variables are loaded in.
varnames: list of str
List of variable names to load (if not specified,
all data variables are loaded)
time_clip: bool
Data will be clipped to the exact trange specified by the trange keyword.
suffix: str
The tplot variable names will be given this suffix. By default,
no suffix is added.
center_measurement: bool
If True, the CDF epoch variables are time-shifted to the middle
of the accumulation interval by their DELTA_PLUS_VAR and
DELTA_MINUS_VAR variable attributes
notplot: bool
If True, then data are returned in a hash table instead of
being stored in tplot variables (useful for debugging, and
access to multi-dimensional data products)
available: bool
If True, simply return the available data files (without downloading)
for the requested paramters
no_update: bool
Set this flag to preserve the original data. if not set and newer
data is found the existing data will be overwritten
cdf_version: str
Specify a specific CDF version # to load (e.g., cdf_version='4.3.0')
min_version: str
Specify a minimum CDF version # to load
latest_version: bool
Only grab the latest CDF version in the requested time interval
major_version: bool
Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval
always_prompt: bool
Set this keyword to always prompt for the user's username and password;
useful if you accidently save an incorrect password, or if your SDC password has changed
spdf: bool
If True, download the data from the SPDF instead of the SDC
Returns:
List of tplot variables created.
"""
if level.lower() != 'l2':
if varformat == None:
if level.lower() != 'l1a':
if datatype.lower() == 'ion':
varformat = '*'
elif datatype.lower() == 'combined':
varformat = '*'
elif datatype.lower() == 'rf_corr':
varformat = '*_RF_corrected'
elif datatype.lower() == 'count_rate':
varformat = '*_count_rate'
elif datatype.lower() == 'flux':
varformat = '*_flux'
elif datatype.lower() == 'vel_dist':
varformat = '*_vel_dist_fn'
elif datatype.lower() == 'bkgd_corr':
varformat = '*_bkgd_corrected'
elif datatype.lower() == 'moments':
varformat = '*'
else:
varformat = '*_RF_corrected'
if varformat != None and varformat != '*':
datatype = 'ion'
if level.lower() == 'sitl':
varformat = '*'
else:
if get_support_data is None:
get_support_data = True
if isinstance(datatype, str):
if datatype not in ['ion', 'moments']:
logging.warning(
"Unknown datatype: " + datatype +
"for L2 HPCA data; expected 'ion' or 'moments', loading 'ion'"
)
datatype = 'ion'
tvars = mms_load_data(trange=trange,
notplot=notplot,
probe=probe,
data_rate=data_rate,
level=level,
instrument='hpca',
datatype=datatype,
varformat=varformat,
varnames=varnames,
suffix=suffix,
get_support_data=get_support_data,
time_clip=time_clip,
no_update=no_update,
center_measurement=center_measurement,
available=available,
latest_version=latest_version,
major_version=major_version,
min_version=min_version,
cdf_version=cdf_version,
spdf=spdf,
always_prompt=always_prompt)
if tvars == None or available or notplot or CONFIG['download_only']:
return tvars
mms_hpca_set_metadata(probe=probe, suffix=suffix)
return tvars