from .load import load
from pyspedas.utilities.datasets import find_datasets
[docs]
def ephemeris(trange=['2018-11-5', '2018-11-6'],
probe='1',
datatype='or',
suffix='',
get_support_data=False,
varformat=None,
varnames=[],
downloadonly=False,
notplot=False,
no_update=False,
time_clip=False):
"""
This function loads TWINS ephemeris data
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: ['2018-11-5', '2018-11-6']
probe: str or list of str
Probe to load. Valid options: '1', '2'
Default: '1'
datatype: str
Data type; Valid options:
Default: 'or'
suffix: str
The tplot variable names will be given this suffix.
Default: ''
get_support_data: bool
If True, data with an attribute "VAR_TYPE" with a value of "support_data"
will be loaded into tplot.
Default: False
varformat: str
The file variable formats to load into tplot. Wildcard character
"*" is accepted.
Default: '' (all variables will be loaded)
varnames: list of str
List of variable names to load
Default: [] (all variables will be 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.
Examples
--------
>>> import pyspedas
>>> from pytplot import tplot
>>> # Note: variables have the same names for both probes, so only load one at a time
>>> ephem_vars = pyspedas.twins.ephemeris(probe=['1'],trange=['2008-04-01','2008-04-02'])
>>> tplot('FEQUATORIALGSM')
"""
return load(instrument='ephemeris', trange=trange, probe=probe, 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 lad(trange=['2018-11-5', '2018-11-6'],
probe='1',
datatype='',
suffix='',
get_support_data=False,
varformat=None,
varnames=[],
downloadonly=False,
notplot=False,
no_update=False,
time_clip=False):
"""
This function loads data from the LAD instrument
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: ['2018-11-5', '2018-11-6']
datatype: str
Data type; Valid options: ''
Default: ''
suffix: str
The tplot variable names will be given this suffix.
Default: ''
get_support_data: bool
If True, data with an attribute "VAR_TYPE" with a value of "support_data"
will be loaded into tplot.
Default: False
varformat: str
The file variable formats to load into tplot. Wildcard character
"*" is accepted.
Default: '' (all variables loaded)
varnames: list of str
List of variable names to load
Default: [] (all variables 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.
Examples
--------
>>> import pyspedas
>>> from pytplot import tplot
>>> lad_vars = pyspedas.twins.lad(trange=['2018-11-5/6:00', '2018-11-5/6:20'], time_clip=True)
>>> tplot(['lad1_data', 'lad2_data'])
"""
return load(instrument='lad', trange=trange, probe=probe, 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 imager(trange=['2018-11-5', '2018-11-6'],
probe='1',
datatype='',
suffix='',
get_support_data=False,
varformat=None,
varnames=[],
downloadonly=False,
notplot=False,
no_update=False,
time_clip=False):
"""
This function loads TWINS imager data
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']
datatype: str
Data type; Valid options: ''
Default: ''
suffix: str
The tplot variable names will be given this suffix.
Default: ''
get_support_data: bool
If True, data with an attribute "VAR_TYPE" with a value of "support_data"
will be loaded into tplot.
Default: False
varformat: str
The file variable formats to load into tplot. Wildcard character
"*" is accepted.
Default: '' (all variables loaded)
varnames: list of str
List of variable names to load
Default: [] (all variables will be 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.
Examples
--------
>>> import pyspedas
>>> from pytplot import tplot
>>> img_vars = pyspedas.twins.imager(trange=['2018-11-5', '2018-11-6'])
>>> tplot('smooth_image_val')
"""
return load(instrument='imager', trange=trange, probe=probe, 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='TWINS', instrument=instrument, label=label)