from .load import load
from pyspedas.utilities.datasets import find_datasets
[docs]
def dcb(trange=['2001-09-05', '2001-09-06'],
datatype='',
level='k0',
suffix='',
get_support_data=False,
varformat=None,
varnames=[],
downloadonly=False,
notplot=False,
no_update=False,
time_clip=False):
"""
This function loads data from the Fluxgate Magnetometer
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']
Default: ['2001-09-05', '2001-09-06']
datatype: str
Data type; Unused for dcb
Default: ''
level: str
Data level, valid levels: 'k0', 'l2'
Default: 'k0'
suffix: str
The tplot variable names will be given this suffix.
Default: No suffix
get_support_data: bool
Data with an attribute "VAR_TYPE" with a value of "support_data"
will be loaded into tplot.
Default: False, Loads in data with a "VAR_TYPE" attribute of "data".
varformat: str
The file variable formats to load into tplot. Wildcard character
"*" is accepted.
Default: None, all variables are loaded.
varnames: list of str
List of variable names to load
Default: Empty list; all data variables are loaded
downloadonly: bool
Set this flag to download the CDF files, but not load them into
tplot variables
Default: False
notplot: bool
Return the data in hash tables instead of creating tplot variables
Default: False
no_update: bool
If set, only load data from your local cache
Default: False
time_clip: bool
Time clip the variables to exactly the range specified in the trange keyword
Default: False
Returns
----------
List of str
List of tplot variables created
Empty list if no data
Examples
--------
import pyspedas
from pytplot import tplot
dcb_vars = pyspedas.fast.dcb(trange=['2001-09-05', '2001-09-06'])
tplot(['EX','EZ','BX','BY','BZ'])
"""
return load(instrument='dcb', trange=trange, level=level, datatype=datatype, suffix=suffix, get_support_data=get_support_data, varformat=varformat, varnames=varnames, downloadonly=downloadonly, notplot=notplot, time_clip=time_clip, no_update=no_update)
[docs]
def acb(trange=['1998-01-05', '1998-01-06'],
datatype='',
level='k0',
suffix='',
get_support_data=False,
varformat=None,
varnames=[],
downloadonly=False,
notplot=False,
no_update=False,
time_clip=False):
"""
This function loads data from the Search-coil Magnetometer
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']
Default: ['1998-01-05', '1998-01-06']
datatype: str
Data type, Unused for acb data
Default: ''
level: str
Data level, valid levels: 'k0'
Default: 'k0'
suffix: str
The tplot variable names will be given this suffix.
Default: No suffix
get_support_data: bool
Data with an attribute "VAR_TYPE" with a value of "support_data"
will be loaded into tplot.
Default: False, Loads in data with a "VAR_TYPE" attribute of "data".
varformat: str
The file variable formats to load into tplot. Wildcard character
"*" is accepted.
Default: None, all variables are loaded
varnames: list of str
List of variable names to load
Default: Empty list; all data variables are loaded
downloadonly: bool
Set this flag to download the CDF files, but not load them into
tplot variables
Default: False
notplot: bool
Return the data in hash tables instead of creating tplot variables
Default: False
no_update: bool
If set, only load data from your local cache
Default: False
time_clip: bool
Time clip the variables to exactly the range specified in the trange keyword
Defauly: False
Returns
----------
List of str
List of tplot variables created
Empty list fr no data
Example
-------
import pyspedas
from pytplot import tplot
acb_vars = pyspedas.fast.acb()
tplot('HF_E_SPEC')
"""
return load(instrument='acb', trange=trange, level=level, datatype=datatype, suffix=suffix, get_support_data=get_support_data, varformat=varformat, varnames=varnames, downloadonly=downloadonly, notplot=notplot, time_clip=time_clip, no_update=no_update)
def esa(trange=['1998-09-05', '1998-09-06'],
datatype='ies',
level='l2',
suffix='',
get_support_data=False,
varformat=None,
varnames=[],
downloadonly=False,
notplot=False,
no_update=False,
time_clip=False):
"""
This function loads data from the Electrostatic Analyzers (ESA)
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']
Default: ['1998-09-05', '1998-09-06']
datatype: str
Data type; Valid options: 'ies' (ion survey data)
'ees' (electron survey data)
'ieb' (ion burst data)
'eeb' (electron burst data)
Default: 'ies'
level: str
Data level, valid levels: 'l2'
Default: 'l2'
suffix: str
The tplot variable names will be given this suffix.
Default: No suffix
get_support_data: bool
Data with an attribute "VAR_TYPE" with a value of "support_data"
will be loaded into tplot.
Default: False, Loads in data with a "VAR_TYPE" attribute of "data".
varformat: str
The file variable formats to load into tplot. Wildcard character
"*" is accepted.
Default: None, all variables are loaded.
varnames: list of str
List of variable names to load
Default: Empty list; all data variables are loaded
downloadonly: bool
Set this flag to download the CDF files, but not load them into
tplot variables
Default: False
notplot: bool
Return the data in hash tables instead of creating tplot variables
Default: False
no_update: bool
If set, only load data from your local cache
Default: False
time_clip: bool
Time clip the variables to exactly the range specified in the trange keyword
Default: False
Returns
----------
List of str
List of tplot variables created
Empty list if no data
Examples
--------
import pyspedas
from pytplot import tplot
esa_vars = pyspedas.fast.esa()
"""
return load(instrument='esa', trange=trange, level=level, datatype=datatype, suffix=suffix, get_support_data=get_support_data, varformat=varformat, varnames=varnames, downloadonly=downloadonly, notplot=notplot, time_clip=time_clip, no_update=no_update)
[docs]
def teams(trange=['1998-09-05', '1998-09-06'],
datatype='',
level='k0',
suffix='',
get_support_data=False,
varformat=None,
varnames=[],
downloadonly=False,
notplot=False,
no_update=False,
time_clip=False):
"""
This function loads data from the Time-of-flight Energy Angle Mass Spectrograph (TEAMS)
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']
Default: ['1998-09-05', '1998-09-06']
datatype: str
Data type; Unused for TEAMS
Default: ''
level:
Data level: valid levels: 'k0'
Default: 'k0'
suffix: str
The tplot variable names will be given this suffix.
Default: No suffix
get_support_data: bool
Data with an attribute "VAR_TYPE" with a value of "support_data"
will be loaded into tplot.
Default: False, Loads in data with a "VAR_TYPE" attribute of "data".
varformat: str
The file variable formats to load into tplot. Wildcard character
"*" is accepted.
Default: None, all variables are loaded.
varnames: list of str
List of variable names to load
Default: Empty list; all data variables are loaded
downloadonly: bool
Set this flag to download the CDF files, but not load them into
tplot variables
Default: False
notplot: bool
Return the data in hash tables instead of creating tplot variables
Default: False
no_update: bool
If set, only load data from your local cache
Default: False
time_clip: bool
Time clip the variables to exactly the range specified in the trange keyword
Default: False
Returns
----------
List of str
List of tplot variables created
Empty list if no data
Examples
--------
import pyspedas
from pytplot import tplot
teams_vars = pyspedas.fast.teams(['1998-09-05', '1998-09-06'])
tplot(['H+', 'H+_low', 'H+_high'])
"""
return load(instrument='teams', trange=trange, level=level, datatype=datatype, suffix=suffix, get_support_data=get_support_data, varformat=varformat, varnames=varnames, downloadonly=downloadonly, notplot=notplot, time_clip=time_clip, no_update=no_update)
def datasets(instrument=None, label=True):
return find_datasets(mission='FAST', instrument=instrument, label=label)