def yclip(names, ymin, ymax, flag=None, new_names=None, suffix=None,
overwrite=None):
"""
Clip data values.
Parameters
----------
names: str/list of str
List of pytplot names.
ymin: float
Minimum value to clip.
ymax: float
Maximum value to clip.
flag: float, optional
Values outside (ymin, ymax) are replaced with flag.
Default is float('nan').
new_names: str/list of str, optional
List of new_names for pytplot variables.
If not given, then a suffix is applied.
suffix: str, optional
A suffix to apply. Default is '-clip'.
overwrite: bool, optional
Replace the existing tplot name.
Returns
-------
None.
"""
old_names = pyspedas.tnames(names)
if len(old_names) < 1:
print('yclip error: No pytplot names were provided.')
return
if suffix is None:
suffix = '-clip'
if flag is None:
flag = np.nan
if overwrite is not None:
n_names = old_names
elif new_names is None:
n_names = [s + suffix for s in old_names]
else:
n_names = new_names
if isinstance(n_names, str):
n_names = [n_names]
if len(n_names) != len(old_names):
n_names = [s + suffix for s in old_names]
for i, old in enumerate(old_names):
new = n_names[i]
data = pytplot.clip(old, ymin, ymax, new)
print('yclip was applied to: ' + new)
def pwe_hfa(trange=['2017-04-01', '2017-04-02'],
datatype='spec',
mode='low',
level='l2',
suffix='',
get_support_data=False,
varformat=None,
varnames=[],
downloadonly=False,
notplot=False,
no_update=False,
uname=None,
passwd=None,
time_clip=False,
ror=True):
"""
This function loads data from the PWE experiment from the Arase mission
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:
level: str
Data level; Valid options:
suffix: str
The tplot variable names will be given this suffix. By default,
no suffix is added.
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)
downloadonly: bool
Set this flag to download the CDF files, but not load them into
tplot variables
notplot: bool
Return the data in hash tables instead of creating tplot variables
no_update: bool
If set, only load data from your local cache
time_clip: bool
Time clip the variables to exactly the range specified in the trange keyword
ror: bool
If set, print PI info and rules of the road
Returns:
List of tplot variables created.
"""
initial_notplot_flag = False
if notplot:
initial_notplot_flag = True
file_res = 3600. * 24
if level == 'l2':
prefix = 'erg_pwe_hfa_' + level + '_' + mode + '_'
if level == 'l2':
pathformat = 'satellite/erg/pwe/hfa/'+level+'/'+datatype+'/'+mode + \
'/%Y/%m/erg_pwe_hfa_'+level+'_'+datatype+'_'+mode+'_%Y%m%d_v??_??.cdf'
elif level == 'l3':
prefix = 'erg_pwe_hfa_' + level + '_1min_'
pathformat = 'satellite/erg/pwe/hfa/'+level + \
'/%Y/%m/erg_pwe_hfa_'+level+'_1min_%Y%m%d_v??_??.cdf'
loaded_data = load(pathformat=pathformat,
trange=trange,
level=level,
datatype=datatype,
file_res=file_res,
prefix=prefix,
suffix=suffix,
get_support_data=get_support_data,
varformat=varformat,
varnames=varnames,
downloadonly=downloadonly,
notplot=notplot,
time_clip=time_clip,
no_update=no_update,
uname=uname,
passwd=passwd)
if (len(loaded_data) > 0) and ror:
try:
if isinstance(loaded_data, list):
if downloadonly:
cdf_file = cdflib.CDF(loaded_data[-1])
gatt = cdf_file.globalattsget()
else:
gatt = get_data(loaded_data[-1],
metadata=True)['CDF']['GATT']
elif isinstance(loaded_data, dict):
gatt = loaded_data[list(loaded_data.keys())[-1]]['CDF']['GATT']
# --- print PI info and rules of the road
print(' ')
print(' ')
print(
'**************************************************************************'
)
print(gatt["LOGICAL_SOURCE_DESCRIPTION"])
print('')
print('Information about ERG PWE HFA')
print('')
print('PI: ', gatt['PI_NAME'])
print("Affiliation: " + gatt["PI_AFFILIATION"])
print('')
print(
'RoR of ERG project common: https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en'
)
print(
'RoR of PWE/HFA: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Pwe/Hfa'
)
print('')
print('Contact: erg_pwe_info at isee.nagoya-u.ac.jp')
print(
'**************************************************************************'
)
except:
print('printing PI info and rules of the road was failed')
if initial_notplot_flag or downloadonly:
return loaded_data
if (level == 'l2') and (mode == 'low') and (not notplot):
# set spectrogram plot option
options(prefix + 'spectra_eu' + suffix, 'Spec', 1)
options(prefix + 'spectra_ev' + suffix, 'Spec', 1)
options(prefix + 'spectra_bgamma' + suffix, 'Spec', 1)
options(prefix + 'spectra_esum' + suffix, 'Spec', 1)
options(prefix + 'spectra_er' + suffix, 'Spec', 1)
options(prefix + 'spectra_el' + suffix, 'Spec', 1)
options(prefix + 'spectra_e_mix' + suffix, 'Spec', 1)
options(prefix + 'spectra_e_ar' + suffix, 'Spec', 1)
if prefix + 'spectra_er' + suffix in loaded_data:
# remove minus values in y array
clip(prefix + 'spectra_er' + suffix, 0., 5000.)
if prefix + 'spectra_el' + suffix in loaded_data:
# remove minus values in y array
clip(prefix + 'spectra_el' + suffix, 0., 5000.)
if prefix + 'spectra_eu' + suffix in loaded_data:
# remove minus values in y array
clip(prefix + 'spectra_eu' + suffix, 0., 5000.)
# set ylim
ylim(prefix + 'spectra_eu' + suffix, 2.0, 10000.0)
# set zlim
zlim(prefix + 'spectra_eu' + suffix, 1e-10, 1e-3)
if prefix + 'spectra_ev' + suffix in loaded_data:
# remove minus values in y array
clip(prefix + 'spectra_ev' + suffix, 0., 5000.)
# set ylim
ylim(prefix + 'spectra_ev' + suffix, 2.0, 10000.0)
# set zlim
zlim(prefix + 'spectra_ev' + suffix, 1e-10, 1e-3)
if prefix + 'spectra_bgamma' + suffix in loaded_data:
# set ylim
ylim(prefix + 'spectra_bgamma' + suffix, 2.0, 200.0)
# set zlim
zlim(prefix + 'spectra_bgamma' + suffix, 1e-4, 1e+2)
if prefix + 'spectra_esum' + suffix in loaded_data:
# set ylim
ylim(prefix + 'spectra_esum' + suffix, 2.0, 10000.0)
# set zlim
zlim(prefix + 'spectra_esum' + suffix, 1e-10, 1e-3)
if prefix + 'spectra_e_ar' + suffix in loaded_data:
# set ylim
ylim(prefix + 'spectra_e_ar' + suffix, 2.0, 10000.0)
# set zlim
zlim(prefix + 'spectra_e_ar' + suffix, -1, 1)
# set y axis to logscale
options(prefix + 'spectra_eu' + suffix, 'ylog', 1)
options(prefix + 'spectra_ev' + suffix, 'ylog', 1)
options(prefix + 'spectra_bgamma' + suffix, 'ylog', 1)
options(prefix + 'spectra_esum' + suffix, 'ylog', 1)
options(prefix + 'spectra_er' + suffix, 'ylog', 1)
options(prefix + 'spectra_el' + suffix, 'ylog', 1)
options(prefix + 'spectra_e_mix' + suffix, 'ylog', 1)
options(prefix + 'spectra_e_ar' + suffix, 'ylog', 1)
# set z axis to logscale
options(prefix + 'spectra_eu' + suffix, 'zlog', 1)
options(prefix + 'spectra_ev' + suffix, 'zlog', 1)
options(prefix + 'spectra_bgamma' + suffix, 'zlog', 1)
options(prefix + 'spectra_esum' + suffix, 'zlog', 1)
options(prefix + 'spectra_er' + suffix, 'zlog', 1)
options(prefix + 'spectra_el' + suffix, 'zlog', 1)
options(prefix + 'spectra_e_mix' + suffix, 'zlog', 1)
# set ytitle
options(prefix + 'spectra_eu' + suffix, 'ytitle', 'ERG PWE/HFA (EU)')
options(prefix + 'spectra_ev' + suffix, 'ytitle', 'ERG PWE/HFA (EV)')
options(prefix + 'spectra_esum' + suffix, 'ytitle',
'ERG PWE/HFA (ESUM)')
options(prefix + 'spectra_e_ar' + suffix, 'ytitle',
'ERG PWE/HFA (E_AR)')
options(prefix + 'spectra_bgamma' + suffix, 'ytitle',
'ERG PWE/HFA (BGAMMA)')
# set ysubtitle
options(prefix + 'spectra_eu' + suffix, 'ysubtitle', 'frequency [Hz]')
options(prefix + 'spectra_ev' + suffix, 'ysubtitle', 'frequency [Hz]')
options(prefix + 'spectra_esum' + suffix, 'ysubtitle',
'frequency [Hz]')
options(prefix + 'spectra_e_ar' + suffix, 'ysubtitle',
'frequency [Hz]')
options(prefix + 'spectra_bgamma' + suffix, 'ysubtitle',
'frequency [Hz]')
# set ztitle
options(prefix + 'spectra_eu' + suffix, 'ztitle', 'mV^2/m^2/Hz')
options(prefix + 'spectra_ev' + suffix, 'ztitle', 'mV^2/m^2/Hz')
options(prefix + 'spectra_esum' + suffix, 'ztitle', 'mV^2/m^2/Hz')
options(prefix + 'spectra_e_ar' + suffix, 'ztitle', 'LH:-1/RH:+1')
options(prefix + 'spectra_bgamma' + suffix, 'ztitle', 'pT^2/Hz')
# change colormap option
options(prefix + 'spectra_eu' + suffix, 'Colormap', 'jet')
options(prefix + 'spectra_ev' + suffix, 'Colormap', 'jet')
options(prefix + 'spectra_bgamma' + suffix, 'Colormap', 'jet')
options(prefix + 'spectra_esum' + suffix, 'Colormap', 'jet')
options(prefix + 'spectra_er' + suffix, 'Colormap', 'jet')
options(prefix + 'spectra_el' + suffix, 'Colormap', 'jet')
options(prefix + 'spectra_e_mix' + suffix, 'Colormap', 'jet')
options(prefix + 'spectra_e_ar' + suffix, 'Colormap', 'jet')
elif level == 'l3':
# set ytitle
options(prefix + 'Fuhr' + suffix, 'ytitle', 'UHR frequency [Mhz]')
options(prefix + 'ne_mgf' + suffix, 'ytitle',
'eletctorn density [/cc]')
# set y axis to logscale
options(prefix + 'Fuhr' + suffix, 'ylog', 1)
options(prefix + 'ne_mgf' + suffix, 'ylog', 1)
return loaded_data
def lepi(trange=['2017-07-01', '2017-07-02'],
datatype='omniflux',
level='l2',
suffix='',
get_support_data=False,
varformat=None,
varnames=[],
downloadonly=False,
notplot=False,
no_update=False,
uname=None,
passwd=None,
time_clip=False,
ror=True,
version=None):
"""
This function loads data from the LEP-i experiment from the Arase mission
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:
level: str
Data level; Valid options:
suffix: str
The tplot variable names will be given this suffix. By default,
no suffix is added.
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)
downloadonly: bool
Set this flag to download the CDF files, but not load them into
tplot variables
notplot: bool
Return the data in hash tables instead of creating tplot variables
no_update: bool
If set, only load data from your local cache
time_clip: bool
Time clip the variables to exactly the range specified in the trange keyword
ror: bool
If set, print PI info and rules of the road
version: str
Set this value to specify the version of cdf files (such as "v03_00")
Returns:
List of tplot variables created.
"""
initial_notplot_flag = False
if notplot:
initial_notplot_flag = True
if level == 'l3':
datatype = 'pa'
file_res = 3600. * 24
prefix = 'erg_lepi_' + level + '_' + datatype + '_'
pathformat = 'satellite/erg/lepi/'+level+'/'+datatype + \
'/%Y/%m/erg_lepi_'+level+'_'+datatype+'_%Y%m%d_'
if version is None:
pathformat += 'v??_??.cdf'
else:
pathformat += version + '.cdf'
loaded_data = load(pathformat=pathformat,
trange=trange,
file_res=file_res,
prefix=prefix,
suffix=suffix,
get_support_data=get_support_data,
varformat=varformat,
varnames=varnames,
downloadonly=downloadonly,
notplot=notplot,
time_clip=time_clip,
no_update=no_update,
uname=uname,
passwd=passwd)
if (len(loaded_data) > 0) and ror:
try:
if isinstance(loaded_data, list):
if downloadonly:
cdf_file = cdflib.CDF(loaded_data[-1])
gatt = cdf_file.globalattsget()
else:
gatt = get_data(loaded_data[-1],
metadata=True)['CDF']['GATT']
elif isinstance(loaded_data, dict):
gatt = loaded_data[list(loaded_data.keys())[-1]]['CDF']['GATT']
# --- print PI info and rules of the road
print(' ')
print(
'**************************************************************************'
)
print(gatt["LOGICAL_SOURCE_DESCRIPTION"])
print('')
print('Information about ERG LEPi')
print('')
print('PI: ', gatt['PI_NAME'])
print("Affiliation: " + gatt["PI_AFFILIATION"])
print('')
print(
'RoR of ERG project common: https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en'
)
print(
'RoR of LEPi L2: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Lepi'
)
print(
'RoR of ERG/LEPi: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Lepi#Rules_of_the_Road'
)
print('')
print('Contact: erg_lepi_info at isee.nagoya-u.ac.jp')
print(
'**************************************************************************'
)
except:
print('printing PI info and rules of the road was failed')
if initial_notplot_flag or downloadonly:
return loaded_data
if (datatype == 'omniflux') and (level == 'l2'):
tplot_variables = []
if prefix + 'FPDO' + suffix in loaded_data:
store_data(prefix + 'FPDO' + suffix,
newname=prefix + 'FPDO_raw' + suffix)
loaded_data.append(prefix + 'FPDO_raw' + suffix)
get_data_vars = get_data(prefix + 'FPDO_raw' + suffix)
flag_FPDO = store_data(prefix + 'FPDO' + suffix,
data={
'x': get_data_vars[0],
'y': get_data_vars[1][:, :-2],
'v': get_data_vars[2][:-2]
})
tplot_variables.append(prefix + 'FPDO' + suffix)
if prefix + 'FHEDO' + suffix in loaded_data:
store_data(prefix + 'FHEDO' + suffix,
newname=prefix + 'FHEDO_raw' + suffix)
loaded_data.append(prefix + 'FHEDO_raw' + suffix)
get_data_vars = get_data(prefix + 'FHEDO_raw' + suffix)
store_data(prefix + 'FHEDO' + suffix,
data={
'x': get_data_vars[0],
'y': get_data_vars[1][:, :-2],
'v': get_data_vars[2][:-2]
})
tplot_variables.append(prefix + 'FHEDO' + suffix)
if prefix + 'FODO' + suffix in loaded_data:
store_data(prefix + 'FODO' + suffix,
newname=prefix + 'FODO_raw' + suffix)
loaded_data.append(prefix + 'FODO_raw' + suffix)
get_data_vars = get_data(prefix + 'FODO_raw' + suffix)
store_data(prefix + 'FODO' + suffix,
data={
'x': get_data_vars[0],
'y': get_data_vars[1][:, :-2],
'v': get_data_vars[2][:-2]
})
tplot_variables.append(prefix + 'FODO' + suffix)
# remove minus valuse of y array
if flag_FPDO:
clip(prefix + 'FPDO' + suffix, 0., 2.e+16)
# set ytitle
options(tplot_variables, 'ytitle', 'LEPi\nomniflux\nLv2\nEnergy')
# set ysubtitle
options(tplot_variables, 'ysubtitle', '[keV/q]')
# set spectrogram plot option
options(tplot_variables, 'Spec', 1)
# set y axis to logscale
options(tplot_variables, 'ylog', 1)
for i in range(len(tplot_variables)):
# set ylim
ylim(tplot_variables[i], 0.01, 25.0)
# set zlim
zlim(tplot_variables[i], 1e+1, 1e+9)
# set ztitle
options(tplot_variables, 'ztitle', '[/cm^2-str-s-keV]')
# set z axis to logscale
options(tplot_variables, 'zlog', 1)
# change colormap option
options(tplot_variables, 'Colormap', 'jet')
elif (datatype == '3dflux') and (level == 'l2') and (not notplot):
vns_fidu = [
'FPDU', 'FPDU_sub', 'FHEDU', 'FHEDU_sub', 'FODU', 'FODU_sub'
]
vns_fiedu = [
'FPEDU', 'FPEDU_sub', 'FHEEDU', 'FHEEDU_sub', 'FOEDU', 'FOEDU_sub'
]
vns_cnt = [
'FPDU_COUNT_RAW', 'FPDU_COUNT_RAW_sub', 'FHEDU_COUNT_RAW',
'FHEDU_COUNT_RAW_sub', 'FODU_COUNT_RAW', 'FODU_COUNT_RAW_sub'
]
vns_list = vns_fidu + vns_fiedu + vns_cnt
v2_array_for_not_sub = np.arange(8)
v2_array_for_sub = np.arange(7) + 8
v3_array = np.arange(16)
for vns_pattarn in vns_list:
t_plot_name = prefix + vns_pattarn + suffix
if t_plot_name in loaded_data:
tplot_copy(t_plot_name, t_plot_name + '_raw')
get_data_vars_temporal = get_data(t_plot_name)
meta_data_in = get_data(t_plot_name, metadata=True)
if 'sub' in t_plot_name:
store_data(t_plot_name,
data={
'x': get_data_vars_temporal[0],
'y': get_data_vars_temporal[1][:,
0:30, :, :],
'v1': get_data_vars_temporal[2][0:30],
'v2': v2_array_for_sub,
'v3': v3_array
},
attr_dict=meta_data_in)
else:
store_data(t_plot_name,
data={
'x': get_data_vars_temporal[0],
'y': get_data_vars_temporal[1][:,
0:30, :, :],
'v1': get_data_vars_temporal[2][0:30],
'v2': v2_array_for_not_sub,
'v3': v3_array
},
attr_dict=meta_data_in)
ylim(t_plot_name, 0.01, 30.)
options(t_plot_name, 'ylog', 1)
options(t_plot_name, 'zlog', 1)
"""comment out in order to match the result of part_product of IDL.
if prefix + 'FPDU' + suffix in loaded_data:
clip(prefix + 'FPDU' + suffix, -1.0e+10, 1.0e+10)
if prefix + 'FHEDU' + suffix in loaded_data:
clip(prefix + 'FHEDU' + suffix, -1.0e+10, 1.0e+10)
if prefix + 'FODU' + suffix in loaded_data:
clip(prefix + 'FODU' + suffix, -1.0e+10, 1.0e+10)"""
elif level == 'l3':
tplot_variables = []
if prefix + 'FPDU' + suffix in loaded_data:
tplot_variables.append(prefix + 'FPDU' + suffix)
get_data_vars = get_data(prefix + 'FPDU' + suffix)
ylim(prefix + 'FPDU' + suffix, 0, 180)
zlim(prefix + 'FPDU' + suffix, 1e2, 1e5)
options(prefix + 'FPDU' + suffix, 'spec', 1)
ytitle_keV_array = np.round(np.nan_to_num(get_data_vars[2]), 2)
for i in range(get_data_vars[1].shape[1]):
tplot_name = prefix + 'pabin_' + \
str(i).zfill(2) + '_FPDU' + suffix
store_data(tplot_name,
data={
'x': get_data_vars[0],
'y': get_data_vars[1][:, i, :],
'v': get_data_vars[3]
})
options(tplot_name, 'spec', 1)
ylim(tplot_name, 0, 180)
zlim(tplot_name, 1e2, 1e5)
options(
tplot_name, 'ytitle', 'ERG LEP-i P\n' +
str(ytitle_keV_array[i]) + ' keV\nPitch angle')
tplot_variables.append(tplot_name)
loaded_data += tplot_variables[1:]
tplot_variables_length = len(tplot_variables)
if prefix + 'FHEDU' + suffix in loaded_data:
tplot_variables.append(prefix + 'FHEDU' + suffix)
get_data_vars = get_data(prefix + 'FHEDU' + suffix)
options(prefix + 'FHEDU' + suffix, 'spec', 1)
ylim(prefix + 'FHEDU' + suffix, 0, 180)
zlim(prefix + 'FHEDU' + suffix, 1e2, 1e5)
ytitle_keV_array = np.round(np.nan_to_num(get_data_vars[2]), 2)
for i in range(get_data_vars[1].shape[1]):
tplot_name = prefix + 'pabin_' + \
str(i).zfill(2) + '_FHEDU' + suffix
store_data(tplot_name,
data={
'x': get_data_vars[0],
'y': get_data_vars[1][:, i, :],
'v': get_data_vars[3]
})
options(tplot_name, 'spec', 1)
ylim(tplot_name, 0, 180)
zlim(tplot_name, 1e2, 1e5)
options(
tplot_name, 'ytitle', 'ERG LEP-i P\n' +
str(ytitle_keV_array[i]) + ' keV\nPitch angle')
tplot_variables.append(tplot_name)
loaded_data += tplot_variables[tplot_variables_length + 1:]
tplot_variables_length = len(tplot_variables)
if prefix + 'FODU' + suffix in loaded_data:
tplot_variables.append(prefix + 'FODU' + suffix)
get_data_vars = get_data(prefix + 'FODU' + suffix)
options(prefix + 'FODU' + suffix, 'spec', 1)
ylim(prefix + 'FODU' + suffix, 0, 180)
zlim(prefix + 'FODU' + suffix, 1e2, 1e5)
ytitle_keV_array = np.round(np.nan_to_num(get_data_vars[2]), 2)
for i in range(get_data_vars[1].shape[1]):
tplot_name = prefix + 'pabin_' + \
str(i).zfill(2) + '_FODU' + suffix
store_data(tplot_name,
data={
'x': get_data_vars[0],
'y': get_data_vars[1][:, i, :],
'v': get_data_vars[3]
})
options(tplot_name, 'spec', 1)
ylim(tplot_name, 0, 180)
zlim(tplot_name, 1e2, 1e5)
options(
tplot_name, 'ytitle', 'ERG LEP-i P\n' +
str(ytitle_keV_array[i]) + ' keV\nPitch angle')
tplot_variables.append(tplot_name)
loaded_data += tplot_variables[tplot_variables_length + 1:]
options(tplot_variables, 'zlog', 1)
options(tplot_variables, 'ysubtitle', 'PA [deg]')
options(tplot_variables, 'colormap', 'jet')
options(tplot_variables, 'ztitle', '[/s-cm^{2}-sr-keV/q]')
return loaded_data
def hep(trange=['2017-03-27', '2017-03-28'],
datatype='omniflux',
level='l2',
suffix='',
get_support_data=False,
varformat=None,
varnames=[],
downloadonly=False,
notplot=False,
no_update=False,
uname=None,
passwd=None,
time_clip=False,
ror=True,
version=None):
"""
This function loads data from the HEP experiment from the Arase mission
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:
level: str
Data level; Valid options:
suffix: str
The tplot variable names will be given this suffix. By default,
no suffix is added.
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)
downloadonly: bool
Set this flag to download the CDF files, but not load them into
tplot variables
notplot: bool
Return the data in hash tables instead of creating tplot variables
no_update: bool
If set, only load data from your local cache
time_clip: bool
Time clip the variables to exactly the range specified in the trange keyword
ror: bool
If set, print PI info and rules of the road
version: str
Set this value to specify the version of cdf files (such as "v01_02", "v01_03", ...)
Returns:
List of tplot variables created.
"""
file_res = 3600. * 24
prefix = 'erg_hep_'+level+'_'
if level == 'l2':
pathformat = 'satellite/erg/hep/'+level+'/'+datatype + \
'/%Y/%m/erg_hep_'+level+'_'+datatype + '_%Y%m%d_'
if version is None:
pathformat += 'v??_??.cdf'
else:
pathformat += version + '.cdf'
if level == 'l3':
pathformat = 'satellite/erg/hep/'+level + \
'/pa/%Y/%m/erg_hep_'+level+'_pa_%Y%m%d_'
if version is None:
pathformat += 'v??_??.cdf'
else:
pathformat += version + '.cdf'
initial_notplot_flag = False
if notplot:
initial_notplot_flag = True
if ((level == 'l2') and (datatype == 'omniflux')) or (datatype == '3dflux') or (level == 'l3'):
# to avoid failure of creation plot variables (at store_data.py) of hep
notplot = True
loaded_data = load(pathformat=pathformat, trange=trange, level=level, datatype=datatype, file_res=file_res, prefix=prefix, suffix=suffix, get_support_data=get_support_data,
varformat=varformat, varnames=varnames, downloadonly=downloadonly, notplot=notplot, time_clip=time_clip, no_update=no_update, uname=uname, passwd=passwd, version=version)
if (len(loaded_data) > 0) and ror:
try:
if isinstance(loaded_data, list):
if downloadonly:
cdf_file = cdflib.CDF(loaded_data[-1])
gatt = cdf_file.globalattsget()
else:
gatt = get_data(loaded_data[-1], metadata=True)['CDF']['GATT']
elif isinstance(loaded_data, dict):
gatt = loaded_data[list(loaded_data.keys())[-1]]['CDF']['GATT']
# --- print PI info and rules of the road
print(' ')
print(
'**************************************************************************')
print(gatt["LOGICAL_SOURCE_DESCRIPTION"])
print('')
print('PI: ', gatt['PI_NAME'])
print("Affiliation: "+gatt["PI_AFFILIATION"])
print('')
print('- The rules of the road (RoR) common to the ERG project:')
print(
' https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en')
print(
'- RoR for HEP data: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Hep')
if level == 'l3':
print(
'- RoR for MGF data: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Mgf')
print('')
print('Contact: erg_hep_info at isee.nagoya-u.ac.jp')
print(
'**************************************************************************')
except:
print('printing PI info and rules of the road was failed')
if initial_notplot_flag or downloadonly:
return loaded_data
if isinstance(loaded_data, dict):
if (level == 'l2') and (datatype == 'omniflux'):
tplot_variables = []
if prefix + 'FEDO_L' + suffix in loaded_data:
v_vars_min = loaded_data[prefix + 'FEDO_L' + suffix]['v'][0]
v_vars_max = loaded_data[prefix + 'FEDO_L' + suffix]['v'][1]
# log average of energy bins
v_vars = np.power(
10., (np.log10(v_vars_min) + np.log10(v_vars_max)) / 2.)
store_data(prefix + 'FEDO_L' + suffix, data={'x': loaded_data[prefix + 'FEDO_L' + suffix]['x'],
'y': loaded_data[prefix + 'FEDO_L' + suffix]['y'],
'v': v_vars},
attr_dict={'CDF':loaded_data[prefix + 'FEDO_L' + suffix]['CDF']})
tplot_variables.append(prefix + 'FEDO_L' + suffix)
if prefix + 'FEDO_H' + suffix in loaded_data:
v_vars_min = loaded_data[prefix + 'FEDO_H' + suffix]['v'][0]
v_vars_max = loaded_data[prefix + 'FEDO_H' + suffix]['v'][1]
# log average of energy bins
v_vars = np.power(
10., (np.log10(v_vars_min) + np.log10(v_vars_max)) / 2.)
store_data(prefix + 'FEDO_H' + suffix, data={'x': loaded_data[prefix + 'FEDO_H' + suffix]['x'],
'y': loaded_data[prefix + 'FEDO_H' + suffix]['y'],
'v': v_vars},
attr_dict={'CDF':loaded_data[prefix + 'FEDO_H' + suffix]['CDF']})
tplot_variables.append(prefix + 'FEDO_H' + suffix)
# remove minus valuse of y array
if prefix + 'FEDO_L' + suffix in tplot_variables:
clip(prefix + 'FEDO_L' + suffix, 0., 1.0e+10)
if prefix + 'FEDO_H' + suffix in tplot_variables:
clip(prefix + 'FEDO_H' + suffix, 0., 1.0e+10)
# set spectrogram plot option
options(prefix + 'FEDO_L' + suffix, 'Spec', 1)
options(prefix + 'FEDO_H' + suffix, 'Spec', 1)
# set y axis to logscale
options(prefix + 'FEDO_L' + suffix, 'ylog', 1)
options(prefix + 'FEDO_H' + suffix, 'ylog', 1)
# set yrange
options(prefix + 'FEDO_L' + suffix, 'yrange', [3.0e+01, 2.0e+03])
options(prefix + 'FEDO_H' + suffix, 'yrange', [7.0e+01, 2.0e+03])
# set ytitle
options(prefix + 'FEDO_L' + suffix, 'ytitle',
'HEP-L\nomniflux\nLv2\nEnergy')
options(prefix + 'FEDO_H' + suffix, 'ytitle',
'HEP-H\nomniflux\nLv2\nEnergy')
# set ysubtitle
options(prefix + 'FEDO_L' + suffix, 'ysubtitle', '[keV]')
options(prefix + 'FEDO_H' + suffix, 'ysubtitle', '[keV]')
# set ylim
if prefix + 'FEDO_L' + suffix in tplot_variables:
ylim(prefix + 'FEDO_L' + suffix, 30, 1800)
if prefix + 'FEDO_H' + suffix in tplot_variables:
ylim(prefix + 'FEDO_H' + suffix, 500, 2048)
# set z axis to logscale
options(prefix + 'FEDO_L' + suffix, 'zlog', 1)
options(prefix + 'FEDO_H' + suffix, 'zlog', 1)
# set zrange
options(prefix + 'FEDO_L' + suffix, 'zrange', [1.0e-15, 1.0e+06])
options(prefix + 'FEDO_H' + suffix, 'zrange', [1.0e-10, 1.0e+5])
# set ztitle
options(prefix + 'FEDO_L' + suffix,
'ztitle', '[/cm^{2}-str-s-keV]')
options(prefix + 'FEDO_H' + suffix,
'ztitle', '[/cm^{2}-str-s-keV]')
# set zlim
if prefix + 'FEDO_L' + suffix in tplot_variables:
zlim(prefix + 'FEDO_L' + suffix, 1e+0, 1e+5)
if prefix + 'FEDO_H' + suffix in tplot_variables:
zlim(prefix + 'FEDO_H' + suffix, 1e+0, 1e+5)
# change colormap option
options(prefix + 'FEDO_L' + suffix, 'Colormap', 'jet')
options(prefix + 'FEDO_H' + suffix, 'Colormap', 'jet')
return tplot_variables
if (level == 'l2') and (datatype == '3dflux'):
tplot_variables = []
v2_array = [i for i in range(15)]
if prefix + 'FEDU_L' + suffix in loaded_data:
store_data(prefix + 'FEDU_L' + suffix, data={'x': loaded_data[prefix + 'FEDU_L' + suffix]['x'],
'y': loaded_data[prefix + 'FEDU_L' + suffix]['y'],
'v1': np.sqrt(loaded_data[prefix + 'FEDU_L' + suffix]['v'][0, :] *
loaded_data[prefix + 'FEDU_L' + suffix]['v'][1, :]), # geometric mean for 'v1'
'v2': v2_array},
attr_dict={'CDF':loaded_data[prefix + 'FEDU_L' + suffix]['CDF']})
tplot_variables.append(prefix + 'FEDU_L' + suffix)
clip(prefix + 'FEDU_L' + suffix, -1.0e+10, 1.0e+10)
if prefix + 'FEDU_H' + suffix in loaded_data:
store_data(prefix + 'FEDU_H' + suffix, data={'x': loaded_data[prefix + 'FEDU_H' + suffix]['x'],
'y': loaded_data[prefix + 'FEDU_H' + suffix]['y'],
'v1': np.sqrt(loaded_data[prefix + 'FEDU_H' + suffix]['v'][0, :] *
loaded_data[prefix + 'FEDU_H' + suffix]['v'][1, :]), # geometric mean for 'v1'
'v2': v2_array},
attr_dict={'CDF':loaded_data[prefix + 'FEDU_H' + suffix]['CDF']})
tplot_variables.append(prefix + 'FEDU_H' + suffix)
clip(prefix + 'FEDU_H' + suffix, -1.0e+10, 1.0e+10)
return tplot_variables
if level == 'l3': # implementation for level = 'l3'
tplot_variables = []
if prefix + 'FEDU_L' + suffix in loaded_data:
L_energy_array_ave = np.sqrt(loaded_data[prefix + 'FEDU_L' + suffix]['v1'][0, :] *
loaded_data[prefix + 'FEDU_L' + suffix]['v1'][1, :]) # geometric mean for 'v1'
# get energy [keV] array for ytitle options
L_energy_array = np.trunc(L_energy_array_ave).astype(int)
non_negative_y_array = np.where(
loaded_data[prefix + 'FEDU_L' + suffix]['y'] < 0., np.nan, loaded_data[prefix + 'FEDU_L' + suffix]['y'])
store_data(prefix + 'FEDU_L' + suffix, data={'x': loaded_data[prefix + 'FEDU_L' + suffix]['x'],
'y': non_negative_y_array,
'v1': L_energy_array_ave,
'v2': loaded_data[prefix + 'FEDU_L' + suffix]['v2']},
attr_dict={'CDF':loaded_data[prefix + 'FEDU_L' + suffix]['CDF']})
options(prefix + 'FEDU_L' + suffix, 'spec', 1)
# set ylim
ylim(prefix + 'FEDU_L' + suffix, 0, 180)
# set zlim
zlim(prefix + 'FEDU_L' + suffix, 1e+2, 1e+6)
tplot_variables.append(prefix + 'FEDU_L' + suffix)
# make Tplot Variables of erg_hep_l3_FEDU_L_paspec_ene?? (??: 00, 01, 02, ..., 15)
for i in range(loaded_data[prefix + 'FEDU_L' + suffix]['y'].shape[1]):
tplot_name = prefix + 'FEDU_L_paspec_ene' + \
str(i).zfill(2) + suffix
store_data(tplot_name, data={'x': loaded_data[prefix + 'FEDU_L' + suffix]['x'],
'y': non_negative_y_array[:, i, :],
'v': loaded_data[prefix + 'FEDU_L' + suffix]['v2']},
attr_dict={'CDF':loaded_data[prefix + 'FEDU_L' + suffix]['CDF']})
options(tplot_name, 'spec', 1)
# set ylim
ylim(tplot_name, 0, 180)
# set zlim
zlim(tplot_name, 1e+2, 1e+6)
# set ytitle
options(
tplot_name, 'ytitle', f'HEP-L\nEne{str(i).zfill(2)}\n{L_energy_array[i]} keV')
tplot_variables.append(tplot_name)
if prefix + 'FEDU_H' + suffix in loaded_data:
H_energy_array_ave = np.sqrt(loaded_data[prefix + 'FEDU_H' + suffix]['v1'][0, :] *
loaded_data[prefix + 'FEDU_H' + suffix]['v1'][1, :]) # geometric mean for 'v1'
# get energy [keV] array for ytitle options
H_energy_array = np.trunc(H_energy_array_ave).astype(int)
non_negative_y_array = np.where(
loaded_data[prefix + 'FEDU_H' + suffix]['y'] < 0., np.nan, loaded_data[prefix + 'FEDU_H' + suffix]['y'])
store_data(prefix + 'FEDU_H' + suffix, data={'x': loaded_data[prefix + 'FEDU_H' + suffix]['x'],
'y': non_negative_y_array,
'v1': H_energy_array_ave,
'v2': loaded_data[prefix + 'FEDU_H' + suffix]['v2']},
attr_dict={'CDF':loaded_data[prefix + 'FEDU_H' + suffix]['CDF']})
options(prefix + 'FEDU_H' + suffix, 'spec', 1)
# set ylim
ylim(prefix + 'FEDU_H' + suffix, 0, 180)
# set zlim
zlim(prefix + 'FEDU_H' + suffix, 1e+1, 1e+4)
tplot_variables.append(prefix + 'FEDU_H' + suffix)
# make Tplot Variables of erg_hep_l3_FEDU_H_paspec_ene?? (??: 00, 01, 02, ..., 10)
for i in range(loaded_data[prefix + 'FEDU_H' + suffix]['y'].shape[1]):
tplot_name = prefix + 'FEDU_H_paspec_ene' + \
str(i).zfill(2) + suffix
store_data(tplot_name, data={'x': loaded_data[prefix + 'FEDU_H' + suffix]['x'],
'y': non_negative_y_array[:, i, :],
'v': loaded_data[prefix + 'FEDU_H' + suffix]['v2']},
attr_dict={'CDF':loaded_data[prefix + 'FEDU_H' + suffix]['CDF']})
options(tplot_name, 'spec', 1)
# set ylim
ylim(tplot_name, 0, 180)
# set zlim
zlim(tplot_name, 1e+1, 1e+4)
# set ytitle
options(
tplot_name, 'ytitle', f'HEP-H\nEne{str(i).zfill(2)}\n{H_energy_array[i]} keV')
tplot_variables.append(tplot_name)
# set z axis to logscale
options(tplot_variables, 'zlog', 1)
# change colormap option
options(tplot_variables, 'colormap', 'jet')
# set ysubtitle
options(tplot_variables, 'ysubtitle', 'PA [deg]')
# set ztitle
options(tplot_variables, 'ztitle', '[/keV/cm^{2}/sr/s]')
return tplot_variables
return loaded_data
def xep(trange=['2017-06-01', '2017-06-02'],
datatype='omniflux',
level='l2',
suffix='',
get_support_data=False,
varformat=None,
varnames=[],
downloadonly=False,
notplot=False,
no_update=False,
uname=None,
passwd=None,
time_clip=False,
ror=True):
"""
This function loads data from the XEP-e experiment from the Arase mission
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:
level: str
Data level; Valid options:
suffix: str
The tplot variable names will be given this suffix. By default,
no suffix is added.
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)
downloadonly: bool
Set this flag to download the CDF files, but not load them into
tplot variables
notplot: bool
Return the data in hash tables instead of creating tplot variables
no_update: bool
If set, only load data from your local cache
time_clip: bool
Time clip the variables to exactly the range specified in the trange keyword
ror: bool
If set, print PI info and rules of the road
Returns:
List of tplot variables created.
"""
initial_notplot_flag = False
if notplot:
initial_notplot_flag = True
if (datatype == 'omniflux') or (datatype == '2dflux'):
# to avoid failure of creation Tplot variables (at store_data.py) of xep
notplot = True
file_res = 3600. * 24
prefix = 'erg_xep_'+level+'_'
pathformat = 'satellite/erg/xep/'+level+'/'+datatype + \
'/%Y/%m/erg_xep_'+level+'_'+datatype+'_%Y%m%d_v??_??.cdf'
loaded_data = load(pathformat=pathformat, trange=trange, level=level, datatype=datatype, file_res=file_res, prefix=prefix, suffix=suffix, get_support_data=get_support_data,
varformat=varformat, varnames=varnames, downloadonly=downloadonly, notplot=notplot, time_clip=time_clip, no_update=no_update, uname=uname, passwd=passwd)
if (len(loaded_data) > 0) and ror:
try:
if isinstance(loaded_data, list):
if downloadonly:
cdf_file = cdflib.CDF(loaded_data[-1])
gatt = cdf_file.globalattsget()
else:
gatt = get_data(loaded_data[-1], metadata=True)['CDF']['GATT']
elif isinstance(loaded_data, dict):
gatt = loaded_data[list(loaded_data.keys())[-1]]['CDF']['GATT']
# --- print PI info and rules of the road
print(' ')
print(
'**************************************************************************')
print(gatt["LOGICAL_SOURCE_DESCRIPTION"])
print('')
print('Information about ERG XEP')
print('')
print('PI: ', gatt['PI_NAME'])
print("Affiliation: "+gatt["PI_AFFILIATION"])
print('')
print('RoR of ERG project common: https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en')
print('RoR of XEP: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Xep')
print('')
print('Contact: erg_xep_info at isee.nagoya-u.ac.jp')
print(
'**************************************************************************')
except:
print('printing PI info and rules of the road was failed')
if initial_notplot_flag or downloadonly:
return loaded_data
if isinstance(loaded_data, dict):
if datatype == 'omniflux':
tplot_variables = []
if prefix + 'FEDO_SSD' + suffix in loaded_data:
v_vars_min = loaded_data[prefix + 'FEDO_SSD' + suffix]['v'][0]
v_vars_max = loaded_data[prefix + 'FEDO_SSD' + suffix]['v'][1]
v_vars = np.sqrt(v_vars_min * v_vars_max) # Geometric mean
store_data(prefix + 'FEDO_SSD' + suffix, data={'x': loaded_data[prefix + 'FEDO_SSD' + suffix]['x'],
'y': loaded_data[prefix + 'FEDO_SSD' + suffix]['y'],
'v': v_vars},
attr_dict={'CDF':loaded_data[prefix + 'FEDO_SSD' + suffix]['CDF']})
tplot_variables.append(prefix + 'FEDO_SSD' + suffix)
if prefix + 'FEDO_SSD' + suffix in tplot_variables:
# remove minus valuse of y array
clip(prefix + 'FEDO_SSD' + suffix, 0., 5000.)
# set spectrogram plot option
options(prefix + 'FEDO_SSD' + suffix, 'Spec', 1)
# set y axis to logscale
options(prefix + 'FEDO_SSD' + suffix, 'ylog', 1)
# set yrange
options(prefix + 'FEDO_SSD' + suffix,
'yrange', [4.0e+02, 4.5e+03])
# set z axis to logscale
options(prefix + 'FEDO_SSD' + suffix, 'zlog', 1)
# set zrange
options(prefix + 'FEDO_SSD' + suffix,
'zrange', [1.0e-01, 1.0e+3])
# change colormap option
options(prefix + 'FEDO_SSD' + suffix, 'Colormap', 'jet')
# set ztitle
options(prefix + 'FEDO_SSD' + suffix,
'ztitle', '[/cm^{2}-str-s-keV]')
# set ytitle
options(prefix + 'FEDO_SSD' + suffix,
'ytitle', 'XEP\nomniflux\nLv2\nEnergy')
# set ysubtitle
options(prefix + 'FEDO_SSD' + suffix, 'ysubtitle', '[keV]')
ylim(prefix + 'FEDO_SSD' + suffix, 4.0e+02, 4.5e+03)
zlim(prefix + 'FEDO_SSD' + suffix, 1.0e-01, 1.0e+3)
return tplot_variables
if datatype == '2dflux':
tplot_variables = []
if prefix + 'FEDU_SSD' + suffix in loaded_data:
store_data(prefix + 'FEDU_SSD' + suffix,
data={'x': loaded_data[prefix + 'FEDU_SSD' + suffix]['x'],
'y': loaded_data[prefix + 'FEDU_SSD' + suffix]['y'],
'v1': np.sqrt(loaded_data[prefix + 'FEDU_SSD' + suffix]['v'][:, 0]
* loaded_data[prefix + 'FEDU_SSD' + suffix]['v'][:, 1]), # Geometric mean of 'v'
'v2': [i for i in range(16)]}, # [0, 1, 2, .., 15]
attr_dict={'CDF':loaded_data[prefix + 'FEDU_SSD' + suffix]['CDF']})
tplot_variables.append(prefix + 'FEDU_SSD' + suffix)
if prefix + 'FEDU_SSD' + suffix in tplot_variables:
clip(prefix + 'FEDU_SSD' + suffix, -1.0e+10, 1.0e+10)
return tplot_variables
return loaded_data
def orb(trange=['2017-03-27', '2017-03-28'],
datatype='def',
level='l2',
model="op",
suffix='',
get_support_data=False,
varformat=None,
varnames=[],
downloadonly=False,
notplot=False,
no_update=False,
uname=None,
passwd=None,
time_clip=False,
version=None,
ror=True):
"""
This function loads orbit data from the Arase mission
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:
level: str
Data level; Valid options:
suffix: str
The tplot variable names will be given this suffix. By default,
no suffix is added.
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)
downloadonly: bool
Set this flag to download the CDF files, but not load them into
tplot variables
notplot: bool
Return the data in hash tables instead of creating tplot variables
no_update: bool
If set, only load data from your local cache
time_clip: bool
Time clip the variables to exactly the range specified in the trange keyword
version: str
Set this value to specify the version of cdf files (such as "v03")
Returns:
List of tplot variables created.
"""
initial_notplot_flag = False
if notplot:
initial_notplot_flag = True
file_res = 3600. * 24
prefix = 'erg_orb_' + level + '_'
if (datatype in ["pre", "spre", "mpre", "lpre"]) and (level == 'l2'):
prefix = 'erg_orb_' + datatype + '_' + level + '_'
if level == 'l3':
if model == 'op':
pathformat = 'satellite/erg/orb/'+level + \
'/opq/%Y/%m/erg_orb_'+level+'_op_%Y%m%d_'
else:
pathformat = 'satellite/erg/orb/'+level+'/'+model + \
'/%Y/%m/erg_orb_'+level+'_'+model+'_%Y%m%d_'
elif level == 'l2':
if datatype == 'def':
pathformat = 'satellite/erg/orb/' + datatype + '/%Y/erg_orb_' + level + '_%Y%m%d_'
else:
pathformat = 'satellite/erg/orb/' + datatype + \
'/%Y/erg_orb_' + datatype + '_'+level+'_%Y%m%d_'
if version is None:
pathformat += 'v??.cdf'
else:
pathformat += version + '.cdf'
loaded_data = load(pathformat=pathformat,
trange=trange,
level=level,
datatype=datatype,
file_res=file_res,
prefix=prefix,
suffix=suffix,
get_support_data=get_support_data,
varformat=varformat,
varnames=varnames,
downloadonly=downloadonly,
notplot=notplot,
time_clip=time_clip,
no_update=no_update,
uname=uname,
passwd=passwd,
version=version)
if (len(loaded_data) > 0) and ror:
try:
if isinstance(loaded_data, list):
if downloadonly:
cdf_file = cdflib.CDF(loaded_data[-1])
gatt = cdf_file.globalattsget()
else:
gatt = get_data(loaded_data[-1],
metadata=True)['CDF']['GATT']
elif isinstance(loaded_data, dict):
gatt = loaded_data[list(loaded_data.keys())[-1]]['CDF']['GATT']
# --- print PI info and rules of the road
print(' ')
print(
'**************************************************************************'
)
print(gatt["LOGICAL_SOURCE_DESCRIPTION"])
print('')
if level == 'l3':
print('Information about ERG L3 orbit')
elif level == 'l2':
print('Information about ERG orbit')
print('')
# print('PI: ', gatt['PI_NAME']) # not need?
# print("Affiliation: "+gatt["PI_AFFILIATION"]) # not need?
print('')
print(
'RoR of ERG project common: https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en'
)
print('')
print('Contact: erg-sc-core at isee.nagoya-u.ac.jp')
print(
'**************************************************************************'
)
except:
print('printing PI info and rules of the road was failed')
if initial_notplot_flag or downloadonly:
return loaded_data
remove_duplicated_tframe(tnames(prefix + '*pos*'))
if (level == 'l2') and (datatype == 'def'):
# remove -1.0e+30
if prefix + 'pos_Lm' + suffix in loaded_data:
clip(prefix + 'pos_Lm' + suffix, -1e+6, 1e6)
_, bdata = get_data(prefix + 'pos_Lm' + suffix)
ylim(prefix + 'pos_Lm' + suffix, np.nanmin(bdata),
np.nanmax(bdata))
# set labels
options(prefix + 'pos_gse' + suffix, 'legend_names', ['X', 'Y', 'Z'])
options(prefix + 'pos_gsm' + suffix, 'legend_names', ['X', 'Y', 'Z'])
options(prefix + 'pos_sm' + suffix, 'legend_names', ['X', 'Y', 'Z'])
options(prefix + 'pos_rmlatmlt' + suffix, 'legend_names',
['Re', 'MLAT', 'MLT'])
options(prefix + 'pos_eq' + suffix, 'legend_names', ['Req', 'MLT'])
options(prefix + 'pos_iono_north' + suffix, 'legend_names',
['GLAT', 'GLON'])
options(prefix + 'pos_iono_south' + suffix, 'legend_names',
['GLAT', 'GLON'])
options(prefix + 'pos_blocal' + suffix, 'legend_names',
['X', 'Y', 'Z'])
options(prefix + 'pos_blocal_mag' + suffix, 'legend_names',
['B(model)_at_ERG'])
# options(prefix + 'pos_blocal_mag' + suffix, 'legend_names', ['B(model)\n_at_ERG']) # Can't break?
options(prefix + 'pos_beq' + suffix, 'legend_names', ['X', 'Y', 'Z'])
options(prefix + 'pos_Lm' + suffix, 'legend_names',
['90deg', '60deg', '30deg'])
options(prefix + 'vel_gse' + suffix, 'legend_names',
['X[km/s]', 'Y[km/s]', 'Z[km/s]'])
options(prefix + 'vel_gsm' + suffix, 'legend_names',
['X[km/s]', 'Y[km/s]', 'Z[km/s]'])
options(prefix + 'vel_sm' + suffix, 'legend_names',
['X[km/s]', 'Y[km/s]', 'Z[km/s]'])
# set color
options(prefix + 'pos_gse' + suffix, 'Color', ['b', 'g', 'r'])
options(prefix + 'pos_gsm' + suffix, 'Color', ['b', 'g', 'r'])
options(prefix + 'pos_sm' + suffix, 'Color', ['b', 'g', 'r'])
options(prefix + 'pos_rmlatmlt' + suffix, 'Color', ['b', 'g', 'r'])
options(prefix + 'pos_blocal' + suffix, 'Color', ['b', 'g', 'r'])
options(prefix + 'pos_beq' + suffix, 'Color', ['b', 'g', 'r'])
options(prefix + 'pos_Lm' + suffix, 'Color', ['b', 'g', 'r'])
options(prefix + 'vel_gse' + suffix, 'Color', ['b', 'g', 'r'])
options(prefix + 'vel_gsm' + suffix, 'Color', ['b', 'g', 'r'])
options(prefix + 'vel_sm' + suffix, 'Color', ['b', 'g', 'r'])
# set y axis to logscale
options(prefix + 'pos_blocal_mag' + suffix, 'ylog', 1)
options(prefix + 'pos_beq' + suffix, 'ylog', 1)
elif (datatype in ["pre", "spre", "mpre", "lpre"]) and (level == 'l2'):
# set labels
options(prefix + 'pos_gse' + suffix, 'legend_names', ['X', 'Y', 'Z'])
options(prefix + 'pos_gsm' + suffix, 'legend_names', ['X', 'Y', 'Z'])
options(prefix + 'pos_sm' + suffix, 'legend_names', ['X', 'Y', 'Z'])
options(prefix + 'pos_rmlatmlt' + suffix, 'legend_names',
['Re', 'MLAT', 'MLT'])
options(prefix + 'pos_eq' + suffix, 'legend_names', ['Req', 'MLT'])
options(prefix + 'pos_iono_north' + suffix, 'legend_names',
['GLAT', 'GLON'])
options(prefix + 'pos_iono_south' + suffix, 'legend_names',
['GLAT', 'GLON'])
options(prefix + 'pos_blocal' + suffix, 'legend_names',
['X', 'Y', 'Z'])
options(prefix + 'pos_blocal_mag' + suffix, 'legend_names',
'B(' + datatype + ')\n_at ERG')
options(prefix + 'pos_beq' + suffix, 'legend_names', ['X', 'Y', 'Z'])
options(prefix + 'pos_Lm' + suffix, 'legend_names',
['90deg', '60deg', '30deg'])
options(prefix + 'vel_gse' + suffix, 'legend_names',
['X[km/s]', 'Y[km/s]', 'Z[km/s]'])
options(prefix + 'vel_gsm' + suffix, 'legend_names',
['X[km/s]', 'Y[km/s]', 'Z[km/s]'])
options(prefix + 'vel_sm' + suffix, 'legend_names',
['X[km/s]', 'Y[km/s]', 'Z[km/s]'])
# set color
options(prefix + 'pos_gse' + suffix, 'Color', ['b', 'g', 'r'])
options(prefix + 'pos_gsm' + suffix, 'Color', ['b', 'g', 'r'])
options(prefix + 'pos_sm' + suffix, 'Color', ['b', 'g', 'r'])
options(prefix + 'pos_rmlatmlt' + suffix, 'Color', ['b', 'g', 'r'])
options(prefix + 'pos_blocal' + suffix, 'Color', ['b', 'g', 'r'])
options(prefix + 'pos_beq' + suffix, 'Color', ['b', 'g', 'r'])
options(prefix + 'pos_Lm' + suffix, 'Color', ['b', 'g', 'r'])
options(prefix + 'vel_gse' + suffix, 'Color', ['b', 'g', 'r'])
options(prefix + 'vel_gsm' + suffix, 'Color', ['b', 'g', 'r'])
options(prefix + 'vel_sm' + suffix, 'Color', ['b', 'g', 'r'])
# set y axis to logscale
options(prefix + 'pos_blocal_mag' + suffix, 'ylog', 1)
options(prefix + 'pos_beq' + suffix, 'ylog', 1)
elif level == 'l3':
# remove -1.0e+30
for i in range(len(loaded_data)):
get_data_vars = get_data(loaded_data[i])
if len(get_data_vars) < 3:
if np.nanmin(get_data_vars[1]) < -1.0e+29:
clip(loaded_data[i], -1e+6, 1e6)
_, bdata = get_data(loaded_data[i])
ylim(loaded_data[i], np.nanmin(bdata), np.nanmax(bdata))
if model in ["op", "t89", "ts04"]:
if model == "ts04":
model = model.upper()
# set ytitle
options(prefix + 'pos_lmc_' + model + suffix, 'ytitle',
f'Lmc ({model})')
options(prefix + 'pos_lstar_' + model + suffix, 'ytitle',
f'Lstar ({model})')
options(prefix + 'pos_I_' + model + suffix, 'ytitle',
f'I ({model})')
options(prefix + 'pos_blocal_' + model + suffix, 'ytitle',
f'Blocal ({model})')
options(prefix + 'pos_beq_' + model + suffix, 'ytitle',
f'Beq ({model})')
options(prefix + 'pos_eq_' + model + suffix, 'ytitle',
f'Eq_pos ({model})')
options(prefix + 'pos_iono_north_' + model + suffix, 'ytitle',
f'footprint_north ({model})')
options(prefix + 'pos_iono_south_' + model + suffix, 'ytitle',
f'footprint_south ({model})')
# set ysubtitle
options(prefix + 'pos_lmc_' + model + suffix, 'ysubtitle',
'[dimensionless]')
options(prefix + 'pos_lstar_' + model + suffix, 'ysubtitle',
'[dimensionless]')
options(prefix + 'pos_I_' + model + suffix, 'ysubtitle', '[Re]')
options(prefix + 'pos_blocal_' + model + suffix, 'ysubtitle',
'[nT]')
options(prefix + 'pos_beq_' + model + suffix, 'ysubtitle', '[nT]')
options(prefix + 'pos_eq_' + model + suffix, 'ysubtitle',
'[Re Hour]')
options(prefix + 'pos_iono_north_' + model + suffix, 'ysubtitle',
'[deg. deg.]')
options(prefix + 'pos_iono_south_' + model + suffix, 'ysubtitle',
'[deg. deg.]')
# set ylabels
options(prefix + 'pos_lmc_' + model + suffix, 'legend_names', [
'90deg', '80deg', '70deg', '60deg', '50deg', '40deg', '30deg',
'20deg', '10deg'
])
options(prefix + 'pos_lstar_' + model + suffix, 'legend_names', [
'90deg', '80deg', '70deg', '60deg', '50deg', '40deg', '30deg',
'20deg', '10deg'
])
options(prefix + 'pos_I_' + model + suffix, 'legend_names', [
'90deg', '80deg', '70deg', '60deg', '50deg', '40deg', '30deg',
'20deg', '10deg'
])
options(prefix + 'pos_blocal_' + model + suffix, 'legend_names',
'|B|')
options(prefix + 'pos_beq_' + model + suffix, 'legend_names',
'|B|')
options(prefix + 'pos_eq_' + model + suffix, 'legend_names',
['Re', 'MLT'])
options(prefix + 'pos_iono_north_' + model + suffix,
'legend_names', ['GLAT', 'GLON'])
options(prefix + 'pos_iono_south_' + model + suffix,
'legend_names', ['GLAT', 'GLON'])
# set y axis to logscale
options(prefix + 'pos_blocal_' + model + suffix, 'ylog', 1)
options(prefix + 'pos_beq_' + model + suffix, 'ylog', 1)
return loaded_data
def mgf(trange=['2017-03-27', '2017-03-28'],
datatype='8sec',
level='l2',
suffix='',
get_support_data=False,
varformat=None,
varnames=[],
downloadonly=False,
notplot=False,
no_update=False,
uname=None,
passwd=None,
time_clip=False,
ror=True,
coord='dsi',
version=None):
"""
This function loads data from the MGF experiment from the Arase mission
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:
level: str
Data level; Valid options:
suffix: str
The tplot variable names will be given this suffix. By default,
no suffix is added.
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.
downloadonly: bool
Set this flag to download the CDF files, but not load them into
tplot variables
notplot: bool
Return the data in hash tables instead of creating tplot variables
no_update: bool
If set, only load data from your local cache
time_clip: bool
Time clip the variables to exactly the range specified in the trange keyword
ror: bool
If set, print PI info and rules of the road
coord: str
"sm", "dsi", "gse", "gsm", "sgi"
version: str
Set this value to specify the version of cdf files (such as "v03.03", "v03.04", ...)
Returns:
List of tplot variables created.
"""
initial_notplot_flag = False
if notplot:
initial_notplot_flag = True
if datatype == '8s' or datatype == '8':
datatype = '8sec'
elif datatype == '64':
datatype = '64hz'
elif datatype == '128':
datatype = '128hz'
elif datatype == '256':
datatype = '256hz'
prefix = 'erg_mgf_'+level+'_'
if datatype == '8sec':
file_res = 3600. * 24
pathformat = 'satellite/erg/mgf/'+level+'/'+datatype + \
'/%Y/%m/erg_mgf_'+level+'_'+datatype+'_%Y%m%d_'
else:
file_res = 3600.
pathformat = 'satellite/erg/mgf/'+level+'/'+datatype + \
'/%Y/%m/erg_mgf_'+level+'_'+datatype+'_' + coord + '_%Y%m%d%H_'
if version is None:
pathformat += 'v??.??.cdf'
else:
pathformat += version + '.cdf'
loaded_data = load(pathformat=pathformat, file_res=file_res, trange=trange, level=level, datatype=datatype, prefix=prefix, suffix=suffix, get_support_data=get_support_data,
varformat=varformat, downloadonly=downloadonly, notplot=notplot, time_clip=time_clip, no_update=no_update, uname=uname, passwd=passwd)
if (loaded_data is None) or (loaded_data == []):
return loaded_data
if (len(loaded_data) > 0) and ror:
try:
if isinstance(loaded_data, list):
if downloadonly:
cdf_file = cdflib.CDF(loaded_data[-1])
gatt = cdf_file.globalattsget()
else:
gatt = get_data(loaded_data[-1], metadata=True)['CDF']['GATT']
elif isinstance(loaded_data, dict):
gatt = loaded_data[list(loaded_data.keys())[-1]]['CDF']['GATT']
# --- print PI info and rules of the road
print(' ')
print(
'**************************************************************************')
print(gatt["LOGICAL_SOURCE_DESCRIPTION"])
print('')
print('Information about ERG MGF')
print('')
print('PI: ', gatt['PI_NAME'])
print("Affiliation: "+gatt["PI_AFFILIATION"])
print('')
print('RoR of ERG project common: https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en')
print(
'RoR of MGF L2: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Mgf')
print('Contact: erg_mgf_info at isee.nagoya-u.ac.jp')
print(
'**************************************************************************')
except:
print('printing PI info and rules of the road was failed')
if initial_notplot_flag or downloadonly:
return loaded_data
if datatype == '8sec':
# remove -1.0e+30
clip(prefix + 'mag_'+datatype+'_dsi'+suffix, -1e+6, 1e6)
clip(prefix + 'mag_'+datatype+'_gse'+suffix, -1e+6, 1e6)
clip(prefix + 'mag_'+datatype+'_gsm'+suffix, -1e+6, 1e6)
clip(prefix + 'mag_'+datatype+'_sm'+suffix, -1e+6, 1e6)
clip(prefix + 'magt_'+datatype+suffix, -1e+6, 1e6)
clip(prefix + 'rmsd_'+datatype+'_dsi'+suffix, -1e+6, +1e+6)
clip(prefix + 'rmsd_'+datatype+'_gse'+suffix, -1e+6, +1e+6)
clip(prefix + 'rmsd_'+datatype+'_gsm'+suffix, -1e+6, +1e+6)
clip(prefix + 'rmsd_'+datatype+'_sm'+suffix, -1e+6, +1e+6)
clip(prefix + 'rmsd_'+datatype+suffix, 0., 80.)
clip(prefix + 'dyn_rng_'+datatype+suffix, -120., +1e+6)
clip(prefix + 'igrf_'+datatype+'_dsi'+suffix, -1e+6, +1e+6)
clip(prefix + 'igrf_'+datatype+'_gse'+suffix, -1e+6, +1e+6)
clip(prefix + 'igrf_'+datatype+'_gsm'+suffix, -1e+6, +1e+6)
clip(prefix + 'igrf_'+datatype+'_sm'+suffix, -1e+6, +1e+6)
# set yrange
_, bdata = get_data(prefix + 'mag_'+datatype+'_dsi'+suffix)
ylim(prefix + 'mag_'+datatype+'_dsi'+suffix,
np.nanmin(bdata), np.nanmax(bdata))
_, bdata = get_data(prefix + 'mag_'+datatype+'_gse'+suffix)
ylim(prefix + 'mag_'+datatype+'_gse'+suffix,
np.nanmin(bdata), np.nanmax(bdata))
_, bdata = get_data(prefix + 'mag_'+datatype+'_gsm'+suffix)
ylim(prefix + 'mag_'+datatype+'_gsm'+suffix,
np.nanmin(bdata), np.nanmax(bdata))
_, bdata = get_data(prefix + 'mag_'+datatype+'_sm'+suffix)
ylim(prefix + 'mag_'+datatype+'_sm'+suffix,
np.nanmin(bdata), np.nanmax(bdata))
_, bdata = get_data(prefix + 'magt_'+datatype+suffix)
ylim(prefix + 'magt_'+datatype+suffix,
np.nanmin(bdata), np.nanmax(bdata))
_, bdata = get_data(prefix + 'rmsd_'+datatype+suffix,)
ylim(prefix + 'rmsd_'+datatype+suffix,
np.nanmin(bdata), np.nanmax(bdata))
_, bdata = get_data(prefix + 'rmsd_'+datatype+'_dsi'+suffix)
ylim(prefix + 'rmsd_'+datatype+'_dsi'+suffix,
np.nanmin(bdata), np.nanmax(bdata))
_, bdata = get_data(prefix + 'rmsd_'+datatype+'_gse'+suffix)
ylim(prefix + 'rmsd_'+datatype+'_gse'+suffix,
np.nanmin(bdata), np.nanmax(bdata))
_, bdata = get_data(prefix + 'rmsd_'+datatype+'_gsm'+suffix)
ylim(prefix + 'rmsd_'+datatype+'_gsm'+suffix,
np.nanmin(bdata), np.nanmax(bdata))
_, bdata = get_data(prefix + 'rmsd_'+datatype+'_sm'+suffix)
ylim(prefix + 'rmsd_'+datatype+'_sm'+suffix,
np.nanmin(bdata), np.nanmax(bdata))
_, bdata = get_data(prefix + 'rmsd_'+datatype+suffix)
ylim(prefix + 'rmsd_'+datatype+suffix,
np.nanmin(bdata), np.nanmax(bdata))
_, bdata = get_data(prefix + 'quality_'+datatype+suffix)
ylim(prefix + 'quality_'+datatype+suffix,
np.nanmin(bdata), np.nanmax(bdata))
_, bdata = get_data(prefix + 'quality_'+datatype+'_gc'+suffix)
ylim(prefix + 'quality_'+datatype+'_gc' +
suffix, np.nanmin(bdata), np.nanmax(bdata))
# set labels
options(prefix + 'mag_'+datatype+'_dsi'+suffix,
'legend_names', ['Bx', 'By', 'Bz'])
options(prefix + 'mag_'+datatype+'_gse'+suffix,
'legend_names', ['Bx', 'By', 'Bz'])
options(prefix + 'mag_'+datatype+'_gsm'+suffix,
'legend_names', ['Bx', 'By', 'Bz'])
options(prefix + 'mag_'+datatype+'_sm'+suffix,
'legend_names', ['Bx', 'By', 'Bz'])
options(prefix + 'rmsd_'+datatype+'_dsi'+suffix,
'legend_names', ['Bx', 'By', 'Bz'])
options(prefix + 'rmsd_'+datatype+'_gse'+suffix,
'legend_names', ['Bx', 'By', 'Bz'])
options(prefix + 'rmsd_'+datatype+'_gsm'+suffix,
'legend_names', ['Bx', 'By', 'Bz'])
options(prefix + 'rmsd_'+datatype+'_sm'+suffix,
'legend_names', ['Bx', 'By', 'Bz'])
options(prefix + 'igrf_'+datatype+'_dsi'+suffix,
'legend_names', ['Bx', 'By', 'Bz'])
options(prefix + 'igrf_'+datatype+'_gse'+suffix,
'legend_names', ['Bx', 'By', 'Bz'])
options(prefix + 'igrf_'+datatype+'_gsm'+suffix,
'legend_names', ['Bx', 'By', 'Bz'])
options(prefix + 'igrf_'+datatype+'_sm'+suffix,
'legend_names', ['Bx', 'By', 'Bz'])
# set color of the labels
options(prefix + 'mag_'+datatype+'_dsi' +
suffix, 'Color', ['b', 'g', 'r'])
options(prefix + 'mag_'+datatype+'_gse' +
suffix, 'Color', ['b', 'g', 'r'])
options(prefix + 'mag_'+datatype+'_gsm' +
suffix, 'Color', ['b', 'g', 'r'])
options(prefix + 'mag_'+datatype+'_sm' +
suffix, 'Color', ['b', 'g', 'r'])
options(prefix + 'rmsd_'+datatype+'_dsi' +
suffix, 'Color', ['b', 'g', 'r'])
options(prefix + 'rmsd_'+datatype+'_gse' +
suffix, 'Color', ['b', 'g', 'r'])
options(prefix + 'rmsd_'+datatype+'_gsm' +
suffix, 'Color', ['b', 'g', 'r'])
options(prefix + 'rmsd_'+datatype+'_sm' +
suffix, 'Color', ['b', 'g', 'r'])
options(prefix + 'quality_'+datatype+suffix, 'Color', ['r', 'g', 'b'])
options(prefix + 'igrf_'+datatype+'_dsi' +
suffix, 'Color', ['b', 'g', 'r'])
options(prefix + 'igrf_'+datatype+'_gse' +
suffix, 'Color', ['b', 'g', 'r'])
options(prefix + 'igrf_'+datatype+'_gsm' +
suffix, 'Color', ['b', 'g', 'r'])
options(prefix + 'igrf_'+datatype+'_sm' +
suffix, 'Color', ['b', 'g', 'r'])
else:
# remove -1.0e+30
clip(prefix + 'mag_'+datatype+'_' + coord + suffix, -1e+6, 1e6)
# set yrange
_, bdata = get_data(prefix + 'mag_'+datatype+'_' + coord + suffix)
ylim(prefix + 'mag_'+datatype+'_' + coord +
suffix, np.nanmin(bdata), np.nanmax(bdata))
# set labels
options(prefix + 'mag_'+datatype+'_' + coord +
suffix, 'legend_names', ['Bx', 'By', 'Bz'])
# set color of the labels
options(prefix + 'mag_'+datatype+'_' + coord +
suffix, 'Color', ['b', 'g', 'r'])
return loaded_data
def lepe(trange=['2017-04-04', '2017-04-05'],
datatype='omniflux',
level='l2',
suffix='',
get_support_data=False,
varformat=None,
varnames=[],
downloadonly=False,
notplot=False,
no_update=False,
uname=None,
passwd=None,
time_clip=False,
ror=True,
version=None,
only_fedu=False,
et_diagram=False):
"""
This function loads data from the LEP-e experiment from the Arase mission
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:
level: str
Data level; Valid options:
suffix: str
The tplot variable names will be given this suffix. By default,
no suffix is added.
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)
downloadonly: bool
Set this flag to download the CDF files, but not load them into
tplot variables
notplot: bool
Return the data in hash tables instead of creating tplot variables
no_update: bool
If set, only load data from your local cache
time_clip: bool
Time clip the variables to exactly the range specified in the trange keyword
ror: bool
If set, print PI info and rules of the road
version: str
Set this value to specify the version of cdf files (such as "v02_02")
only_fedu: bool
If set, not make erg_lepe_l3_pa_enech_??(??:01,01,..32)_FEDU Tplot Variables
et_diagram: bool
If set, make erg_lepe_l3_pa_pabin_??(??:01,01,..16)_FEDU Tplot Variables
Returns:
List of tplot variables created.
"""
initial_notplot_flag = False
if notplot:
initial_notplot_flag = True
if level == 'l3':
datatype = 'pa'
if ((level == 'l2') and (datatype == 'omniflux')) or \
((level == 'l2') and (datatype == '3dflux')) or \
(level == 'l3'):
# to avoid failure of creation plot variables (at store_data.py) of lepe
notplot = True
file_res = 3600. * 24
prefix = 'erg_lepe_'+level+'_' + datatype + '_'
pathformat = 'satellite/erg/lepe/'+level+'/'+datatype + \
'/%Y/%m/erg_lepe_'+level+'_'+datatype+'_%Y%m%d_'
if version is None:
pathformat += 'v??_??.cdf'
else:
pathformat += version + '.cdf'
loaded_data = load(pathformat=pathformat, trange=trange, level=level, datatype=datatype, file_res=file_res, prefix=prefix, suffix=suffix, get_support_data=get_support_data,
varformat=varformat, varnames=varnames, downloadonly=downloadonly, notplot=notplot, time_clip=time_clip, no_update=no_update, uname=uname, passwd=passwd)
if (len(loaded_data) > 0) and ror:
try:
if isinstance(loaded_data, list):
if downloadonly:
cdf_file = cdflib.CDF(loaded_data[-1])
gatt = cdf_file.globalattsget()
else:
gatt = get_data(loaded_data[-1], metadata=True)['CDF']['GATT']
elif isinstance(loaded_data, dict):
gatt = loaded_data[list(loaded_data.keys())[-1]]['CDF']['GATT']
# --- print PI info and rules of the road
print(' ')
print(
'**************************************************************************')
print(gatt["LOGICAL_SOURCE_DESCRIPTION"])
print('')
print('Information about ERG LEPe')
print('')
print('PI: ', gatt['PI_NAME'])
print("Affiliation: "+gatt["PI_AFFILIATION"])
print('')
print('RoR of ERG project common: https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en')
if level == 'l2':
print(
'RoR of LEPe L2: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Lepe')
if level == 'l3':
print(
'RoR of LEPe L3: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Lepe')
print(
'RoR of MGF L2: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Mgf')
print('')
print('Contact: erg_lepe_info at isee.nagoya-u.ac.jp')
print(
'**************************************************************************')
except:
print('printing PI info and rules of the road was failed')
if initial_notplot_flag or downloadonly:
return loaded_data
if (isinstance(loaded_data, dict)) and (len(loaded_data) > 0):
if (level == 'l2') and (datatype == 'omniflux'):
tplot_variables = []
v_array = (loaded_data[prefix + 'FEDO' + suffix]['v'][:, 0, :] +
loaded_data[prefix + 'FEDO' + suffix]['v'][:, 1, :]) / 2.
# change minus values to NaN
v_array = np.where(v_array < 0., np.nan, v_array)
all_nan_v_indices_array = np.where(
np.all(np.isnan(v_array), axis=1))[0]
store_data(prefix + 'FEDO' + suffix,
data={'x': np.delete(loaded_data[prefix + 'FEDO' + suffix]['x'], all_nan_v_indices_array, axis=0),
'y': np.delete(loaded_data[prefix + 'FEDO' + suffix]['y'], all_nan_v_indices_array, axis=0),
'v': np.delete(v_array, all_nan_v_indices_array, 0)},
attr_dict={'CDF':loaded_data[prefix + 'FEDO' + suffix]['CDF']})
tplot_variables.append(prefix + 'FEDO' + suffix)
# set spectrogram plot option
options(prefix + 'FEDO' + suffix, 'Spec', 1)
# change minus values to NaN in y array
clip(prefix + 'FEDO' + suffix, 0.,
np.nanmax(loaded_data[prefix + 'FEDO' + suffix]['y']))
# set y axis to logscale
options(prefix + 'FEDO' + suffix, 'ylog', 1)
# set ylim
ylim(prefix + 'FEDO' + suffix, 19, 21*1e3)
# set ytitle
options(prefix + 'FEDO' + suffix, 'ytitle',
'ERG\nLEP-e\nFEDO\nEnergy')
# set ysubtitle
options(prefix + 'FEDO' + suffix, 'ysubtitle', '[eV]')
# set z axis to logscale
options(prefix + 'FEDO' + suffix, 'zlog', 1)
# set zlim
zlim(prefix + 'FEDO' + suffix, 1, 1e6)
# set ztitle
options(prefix + 'FEDO' + suffix, 'ztitle', '[/cm^{2}-str-s-eV]')
# change colormap option
options(prefix + 'FEDO' + suffix, 'Colormap', 'jet')
return tplot_variables
if (level == 'l2') and (datatype == '3dflux'):
tplot_variables = []
other_variables_dict = {}
if prefix + 'FEDU' + suffix in loaded_data:
store_data(prefix + 'FEDU' + suffix,
data={'x': loaded_data[prefix + 'FEDU' + suffix]['x'],
'y': loaded_data[prefix + 'FEDU' + suffix]['y'],
'v1': np.sqrt(loaded_data[prefix + 'FEDU' + suffix]['v'][:, 0, :]
* loaded_data[prefix + 'FEDU' + suffix]['v'][:, 1, :]), # geometric mean
'v2': ['01', '02', '03', '04', '05', 'A', 'B', '18', '19', '20', '21', '22'],
'v3': [i for i in range(16)]},
attr_dict={'CDF':loaded_data[prefix + 'FEDU' + suffix]['CDF']})
tplot_variables.append(prefix + 'FEDU' + suffix)
options(prefix + 'FEDU' + suffix, 'spec', 1)
if prefix + 'FEDU' + suffix in tplot_variables:
clip(prefix + 'FEDU' + suffix, 0,
np.nanmax(loaded_data[prefix + 'FEDU' + suffix]['y']))
ylim(prefix + 'FEDU' + suffix, 19, 21*1e3)
zlim(prefix + 'FEDU' + suffix, 1, 1e6)
options(prefix + 'FEDU' + suffix, 'zlog', 1)
options(prefix + 'FEDU' + suffix, 'ylog', 1)
options(prefix + 'FEDU' + suffix, 'ysubtitle', '[eV]')
other_variables_dict[prefix + 'Count_Rate' +
suffix] = loaded_data[prefix + 'Count_Rate' + suffix]
other_variables_dict[prefix + 'Count_Rate_BG' +
suffix] = loaded_data[prefix + 'Count_Rate_BG' + suffix]
tplot_variables.append(other_variables_dict)
return tplot_variables
if level == 'l3':
tplot_variables = []
if prefix + 'FEDU' + suffix in loaded_data:
store_data(prefix + 'FEDU' + suffix,
data={'x': loaded_data[prefix + 'FEDU' + suffix]['x'],
'y': loaded_data[prefix + 'FEDU' + suffix]['y'],
'v1': (loaded_data[prefix + 'FEDU' + suffix]['v1'][:, 0, :]
+ loaded_data[prefix + 'FEDU' + suffix]['v1'][:, 1, :]) / 2., # arithmetic mean
'v2': loaded_data[prefix + 'FEDU' + suffix]['v2']},
attr_dict={'CDF':loaded_data[prefix + 'FEDU' + suffix]['CDF']})
tplot_variables.append(prefix + 'FEDU' + suffix)
options(prefix + 'FEDU' + suffix, 'spec', 1)
if prefix + 'FEDU' + suffix in tplot_variables:
clip(prefix + 'FEDU' + suffix, 0,
np.nanmax(loaded_data[prefix + 'FEDU' + suffix]['y']))
ylim(prefix + 'FEDU' + suffix, 19, 21*1e3)
zlim(prefix + 'FEDU' + suffix, 1, 1e6)
options(prefix + 'FEDU' + suffix, 'zlog', 1)
options(prefix + 'FEDU' + suffix, 'ylog', 1)
options(prefix + 'FEDU' + suffix, 'ysubtitle', '[eV]')
FEDU_get_data = get_data(prefix + 'FEDU' + suffix)
FEDU_CDF_data = loaded_data[prefix + 'FEDU' + suffix]['CDF']
if not only_fedu:
ytitle_eV_array = np.round(
np.nan_to_num(FEDU_get_data[2][0, :]), 2)
# processing for erg_lepe_l3_pa_enech_??(??:01,01,..32)_FEDU
for i in range(FEDU_get_data[1].shape[1]):
tplot_name = prefix + 'enech_' + \
str(i + 1).zfill(2) + '_FEDU' + suffix
store_data(tplot_name, data={'x': FEDU_get_data[0],
'y': FEDU_get_data[1][:, i, :],
'v': FEDU_get_data[3]},
attr_dict={'CDF':FEDU_CDF_data})
options(tplot_name, 'spec', 1)
ylim(tplot_name, 0, 180)
zlim(tplot_name, 1, 1e6)
options(tplot_name, 'ytitle', 'ERG LEP-e\n' +
str(ytitle_eV_array[i]) + ' eV\nPitch angle')
tplot_variables.append(tplot_name)
options(tplot_variables[1:], 'zlog', 1)
options(tplot_variables[1:], 'ysubtitle', '[deg]')
options(tplot_variables[1:], 'yrange', [0, 180])
options(tplot_variables[1:], 'colormap', 'jet')
options(tplot_variables[1:],
'ztitle', '[/s-cm^{2}-sr-keV/q]')
if et_diagram:
ytitle_deg_array = np.round(
np.nan_to_num(FEDU_get_data[3]), 3)
all_nan_v_indices_array = np.where(
np.all(np.isnan(FEDU_get_data[2]), axis=1))[0]
x_all_nan_deleted_array = np.delete(
FEDU_get_data[0], all_nan_v_indices_array, axis=0)
y_all_nan_deleted_array = np.delete(
FEDU_get_data[1], all_nan_v_indices_array, axis=0)
v_all_nan_deleted_array = np.delete(
FEDU_get_data[2], all_nan_v_indices_array, axis=0)
# processing for erg_lepe_l3_pa_pabin_??(??:01,01,..16)_FEDU
for i in range(FEDU_get_data[1].shape[2]):
tplot_name = prefix + 'pabin_' + \
str(i + 1).zfill(2) + '_FEDU' + suffix
store_data(tplot_name, data={'x': x_all_nan_deleted_array,
'y': y_all_nan_deleted_array[:, :, i],
'v': v_all_nan_deleted_array},
attr_dict={'CDF':FEDU_CDF_data})
options(tplot_name, 'spec', 1)
ylim(tplot_name, 19, 21*1e3)
zlim(tplot_name, 1, 1e6)
options(tplot_name, 'ytitle', 'ERG LEP-e\n' +
str(ytitle_deg_array[i]) + ' deg\nEnergy')
tplot_variables.append(tplot_name)
options(
tplot_variables[-FEDU_get_data[1].shape[2]:], 'ysubtitle', '[eV]')
options(
tplot_variables[-FEDU_get_data[1].shape[2]:], 'zlog', 1)
options(
tplot_variables[-FEDU_get_data[1].shape[2]:], 'ylog', 1)
options(
tplot_variables[-FEDU_get_data[1].shape[2]:], 'colormap', 'jet')
options(
tplot_variables[-FEDU_get_data[1].shape[2]:], 'ztitle', '[/s-cm^{2}-sr-eV]')
return tplot_variables
return loaded_data
def mgf(trange=['2017-03-27', '2017-03-28'],
datatype='8sec',
level='l2',
suffix='',
get_support_data=False,
varformat=None,
downloadonly=False,
notplot=False,
no_update=False,
uname=None,
passwd=None,
time_clip=False):
"""
This function loads data from the MGF experiment from the Arase mission
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:
level: str
Data level; Valid options:
suffix: str
The tplot variable names will be given this suffix. By default,
no suffix is added.
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.
downloadonly: bool
Set this flag to download the CDF files, but not load them into
tplot variables
notplot: bool
Return the data in hash tables instead of creating tplot variables
no_update: bool
If set, only load data from your local cache
time_clip: bool
Time clip the variables to exactly the range specified in the trange keyword
Returns:
List of tplot variables created.
"""
if datatype == '8s' or datatype == '8':
datatype = '8sec'
elif datatype == '64':
datatype = '64hz'
elif datatype == '128':
datatype = '128hz'
elif datatype == '256':
datatype = '256hz'
loaded_data = load(instrument='mgf',
trange=trange,
level=level,
datatype=datatype,
suffix=suffix,
get_support_data=get_support_data,
varformat=varformat,
downloadonly=downloadonly,
notplot=notplot,
time_clip=time_clip,
no_update=no_update,
uname=uname,
passwd=passwd)
if loaded_data == None or loaded_data == [] or notplot or downloadonly:
return loaded_data
clip('erg_mgf_' + level + '_mag_' + datatype + '_dsi' + suffix, -1e+6, 1e6)
clip('erg_mgf_' + level + '_mag_' + datatype + '_gse' + suffix, -1e+6, 1e6)
clip('erg_mgf_' + level + '_mag_' + datatype + '_gsm' + suffix, -1e+6, 1e6)
clip('erg_mgf_' + level + '_mag_' + datatype + '_sm' + suffix, -1e+6, 1e6)
# set yrange
times, bdata = get_data('erg_mgf_' + level + '_mag_' + datatype + '_dsi' +
suffix)
ylim('erg_mgf_' + level + '_mag_' + datatype + '_dsi' + suffix,
np.nanmin(bdata), np.nanmax(bdata))
times, bdata = get_data('erg_mgf_' + level + '_mag_' + datatype + '_gse' +
suffix)
ylim('erg_mgf_' + level + '_mag_' + datatype + '_gse' + suffix,
np.nanmin(bdata), np.nanmax(bdata))
times, bdata = get_data('erg_mgf_' + level + '_mag_' + datatype + '_gsm' +
suffix)
ylim('erg_mgf_' + level + '_mag_' + datatype + '_gsm' + suffix,
np.nanmin(bdata), np.nanmax(bdata))
times, bdata = get_data('erg_mgf_' + level + '_mag_' + datatype + '_sm' +
suffix)
ylim('erg_mgf_' + level + '_mag_' + datatype + '_sm' + suffix,
np.nanmin(bdata), np.nanmax(bdata))
# set labels
options('erg_mgf_' + level + '_mag_' + datatype + '_dsi' + suffix,
'legend_names', ['Bx', 'By', 'Bz'])
options('erg_mgf_' + level + '_mag_' + datatype + '_gse' + suffix,
'legend_names', ['Bx', 'By', 'Bz'])
options('erg_mgf_' + level + '_mag_' + datatype + '_gsm' + suffix,
'legend_names', ['Bx', 'By', 'Bz'])
options('erg_mgf_' + level + '_mag_' + datatype + '_sm' + suffix,
'legend_names', ['Bx', 'By', 'Bz'])
# set color of the labels
options('erg_mgf_' + level + '_mag_' + datatype + '_dsi' + suffix, 'Color',
['b', 'g', 'r'])
options('erg_mgf_' + level + '_mag_' + datatype + '_gse' + suffix, 'Color',
['b', 'g', 'r'])
options('erg_mgf_' + level + '_mag_' + datatype + '_gsm' + suffix, 'Color',
['b', 'g', 'r'])
options('erg_mgf_' + level + '_mag_' + datatype + '_sm' + suffix, 'Color',
['b', 'g', 'r'])
return loaded_data
print('add')
pytplot.add('a', 'c', 'a+c')
print(pytplot.data_quants['a+c'].data)
print('avg_res_data')
pytplot.avg_res_data('d', 2, 'd2res')
print(pytplot.data_quants['d'].data)
print(pytplot.data_quants['d2res'].data)
pytplot.avg_res_data('h', 7, 'h7res')
print(pytplot.data_quants['h'].data)
print(pytplot.data_quants['h'].spec_bins)
print(pytplot.data_quants['h7res'].data)
print(pytplot.data_quants['h7res'].spec_bins)
print('clip')
pytplot.clip('d', 2, 6, 'd_clip')
print(pytplot.data_quants['d_clip'].data)
pytplot.clip('h', 2, 6, 'h_clip')
print(pytplot.data_quants['h_clip'].data)
print(pytplot.data_quants['h_clip'].spec_bins)
print('crop')
pytplot.crop('a', 'b')
print('deflag')
pytplot.deflag('d', [100, 90, 7, 2, 57], 'd_deflag')
print(pytplot.data_quants['d'].data)
print(pytplot.data_quants['d_deflag'].data)
pytplot.deflag('h', [100, 90, 7, 2, 57], 'h_deflag')
print(pytplot.data_quants['h_deflag'].data)
print(pytplot.data_quants['h_deflag'].spec_bins)
