def test_time_double(self):
"""Test time_double function."""
t0 = time_float()
t1 = time_float_one()
t2 = time_float(1450181243)
print("Time_double functions test:", t0, t1, t2)
self.assertTrue(time_double('2015-12-15 12:07:23.767000')
== 1450181243.767)
self.assertTrue(time_double(['2015-12-15 12:07:23.767000',
'2015-12-15 12:07:43.767000'])
== [1450181243.767, 1450181263.767])
def time_datetime(time=None, tz=None):
"""Find python datetime.
Transform a list of float daytime values to a list of pythonic
'datetime.datetime' values.
Parameters
----------
time: float/list of floats or str/list of str, optional
Input time.
The default is None, which returns the time now.
Returns
-------
list of datetime.datetime
Datetimes as `datetime.datetime`.
"""
if tz == None:
tz = timezone.utc
if time is None:
return datetime.now()
if isinstance(time, str):
return time_datetime(time_float(time))
if isinstance(time, (int, float)):
return datetime.fromtimestamp(time, tz=tz)
return [time_datetime(_time) for _time in time]
def ex_create(plot=True):
"""Show how to create and plot pytplot variables."""
# Delete any existing pytplot variables
pytplot.del_data()
# Create a sin wave plot
a = list(range(0, 101))
b = [2.0 / 100.0 * numpy.pi * s for s in a]
c = time_float('2017-01-01')
x = list()
y = list()
for i in range(len(b)):
x.append(c + 60.0 / (2 * numpy.pi) * 60.0 * b[i])
y.append(1000.0 * numpy.sin(b[i]))
# Store data
pytplot.store_data('sinx', data={'x': x, 'y': y})
# Apply yclip
pyspedas.yclip('sinx', -800.0, 800.0)
# Remove NaN values
pyspedas.tdeflag('sinx-clip')
# Interpolate
pyspedas.tinterpol(['sinx-clip-deflag'], 'sinx', 'quadratic')
# Plot
pytplot.ylim('sinx', -1100.0, 1100.0)
pytplot.ylim('sinx-clip', -1100.0, 1100.0)
pytplot.ylim('sinx-clip-deflag', -1100.0, 1100.0)
pytplot.ylim('sinx-clip-deflag-itrp', -1100.0, 1100.0)
pytplot.tplot_options('title', 'Interpolation example')
if plot:
pytplot.tplot(
['sinx', 'sinx-clip', 'sinx-clip-deflag', 'sinx-clip-deflag-itrp'])
# Return 1 as indication that the example finished without problems.
return 1
def pwe_wfc(trange=['2017-04-01/12:00:00', '2017-04-01/13:00:00'],
datatype='waveform',
mode='65khz',
level='l2',
suffix='',
coord='sgi',
component='all',
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.
if level == 'l2':
prefix = 'erg_pwe_wfc_' + level + '_' + mode + '_'
loaded_data = []
if level == 'l2':
if datatype == 'waveform':
tplot_name_list = []
if component == 'all':
component_list = ['e', 'b']
elif (component == 'e') or (component == 'b'):
component_list = [component]
for com in component_list:
prefix = 'erg_pwe_wfc_' + level + '_' + com + '_' + mode + '_'
pathformat = 'satellite/erg/pwe/wfc/'+level+'/'+datatype+'/%Y/%m/erg_pwe_wfc_' + \
level+'_'+com+'_'+datatype+'_'+mode+'_'+coord+'_%Y%m%d%H_v??_??.cdf'
loaded_data.append(
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 com == 'e':
tplot_name_list += [
prefix + 'Ex_waveform', prefix + 'Ey_waveform'
]
elif com == 'b':
tplot_name_list += [
prefix + 'Bx_waveform', prefix + 'By_waveform',
prefix + 'Bz_waveform'
]
elif datatype == 'spec':
prefix_list = []
component_suffix_list = []
if component == 'all':
component_list = ['e', 'b']
elif (component == 'e') or (component == 'b'):
component_list = [component]
for com in component_list:
prefix = 'erg_pwe_wfc_' + level + '_' + com + '_' + mode + '_'
pathformat = 'satellite/erg/pwe/wfc/'+level+'/'+datatype+'/%Y/%m/erg_pwe_wfc_' + \
level+'_'+com+'_'+datatype+'_'+mode+'_%Y%m%d%H_v??_??.cdf'
loaded_data.append(
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))
prefix_list.append(prefix)
component_suffix_list.append(com.upper() + '_spectra')
if (len(loaded_data) > 0) and ror:
try:
if isinstance(loaded_data, list):
if downloadonly:
cdf_file = cdflib.CDF(loaded_data[-1][-1])
gatt = cdf_file.globalattsget()
elif notplot:
gatt = loaded_data[-1][list(
loaded_data[-1].keys())[-1]]['CDF']['GATT']
else:
gatt = get_data(loaded_data[-1][-1],
metadata=True)['CDF']['GATT']
# --- print PI info and rules of the road
print(' ')
print(' ')
print(
'**************************************************************************'
)
print(gatt["LOGICAL_SOURCE_DESCRIPTION"])
print('')
print('Information about ERG PWE WFC')
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/WFC: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Pwe/Wfc'
)
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 datatype == 'spec':
trange_in_float = time_float(trange)
for i in range(len(prefix_list)):
t_plot_name = prefix_list[i] + component_suffix_list[i]
options(t_plot_name, 'spec', 1)
options(t_plot_name, 'colormap', 'jet')
options(t_plot_name, 'ylog', 1)
options(t_plot_name, 'zlog', 1)
options(t_plot_name, 'ysubtitle', '[Hz]')
ylim(t_plot_name, 32., 2e4)
if 'E_spectra' in component_suffix_list[i]:
zlim(t_plot_name, 1e-9, 1e-2)
options(t_plot_name, 'ztitle', '[mV^2/m^2/Hz]')
options(t_plot_name, 'ytitle', 'E\nspectra')
elif 'B_spectra' in component_suffix_list[i]:
zlim(t_plot_name, 1e-4, 1e2)
options(t_plot_name, 'ztitle', '[pT^2/Hz]')
options(t_plot_name, 'ytitle', 'B\nspectra')
get_data_vars = get_data(t_plot_name)
time_array = get_data_vars[0]
if time_array[0] <= trange_in_float[0]:
t_ge_indices = np.where(time_array <= trange_in_float[0])
t_min_index = t_ge_indices[0][-1]
else:
t_min_index = 0
if trange_in_float[1] <= time_array[-1]:
t_le_indices = np.where(trange_in_float[1] <= time_array)
t_max_index = t_le_indices[0][0]
else:
t_max_index = -1
if t_min_index == t_max_index:
t_max_index = +1
if (t_min_index != 0) or (t_max_index != -1):
meta_data = get_data(t_plot_name, metadata=True)
store_data(t_plot_name,
newname=t_plot_name + '_all_loaded_time_range')
store_data(t_plot_name,
data={
'x': time_array[t_min_index:t_max_index],
'y': get_data_vars[1][t_min_index:t_max_index],
'v': get_data_vars[2]
},
attr_dict=meta_data)
options(t_plot_name, 'zlog', 1)
if 'E_spectra' in t_plot_name:
zlim(t_plot_name, 1e-9, 1e-2)
elif 'B_spectra' in t_plot_name:
zlim(t_plot_name, 1e-4, 1e2)
if datatype == 'waveform':
trange_in_float = time_float(trange)
yn = ''
all_time_range_flag = False
if trange_in_float[1] - trange_in_float[0] <= 0.:
yn = input('Invalid time range. Use full time range ?:[y/n] ')
if yn == 'y':
all_time_range_flag = True
t_min_index = 0
t_max_index = -1
else:
return
for t_plot_name in tplot_name_list:
get_data_vars = get_data(t_plot_name)
dl_in = get_data(t_plot_name, metadata=True)
time_array = get_data_vars[0]
if not all_time_range_flag:
if time_array[0] <= trange_in_float[0]:
t_ge_indices = np.where(time_array <= trange_in_float[0])
t_min_index = t_ge_indices[0][-1]
else:
t_min_index = 0
if trange_in_float[1] <= time_array[-1]:
t_le_indices = np.where(trange_in_float[1] <= time_array)
t_max_index = t_le_indices[0][0]
else:
t_max_index = -1
if t_min_index == t_max_index:
t_max_index = +1
data = np.where(get_data_vars[1] <= -1e+30, np.nan,
get_data_vars[1])
dt = get_data_vars[2]
ndt = dt.size
ndata = (t_max_index - t_min_index) * ndt
time_new = (np.tile(time_array[t_min_index:t_max_index],
(ndt, 1)).T + dt * 1e-3).reshape(ndata)
data_new = data[t_min_index:t_max_index].reshape(ndata)
store_data(t_plot_name,
data={
'x': time_new,
'y': data_new
},
attr_dict=dl_in)
options(t_plot_name, 'ytitle', '\n'.join(t_plot_name.split('_')))
# ylim settings because pytplot.timespan() doesn't affect in ylim.
# May be it will be no need in future.
if not all_time_range_flag:
if time_new[0] <= trange_in_float[0]:
t_min_index = np.where(
(time_new <= trange_in_float[0]))[0][-1]
else:
t_min_index = 0
if trange_in_float[1] <= time_new[-1]:
t_max_index = np.where(
trange_in_float[1] <= time_new)[0][0]
else:
t_max_index = -1
ylim_min = np.nanmin(data_new[t_min_index:t_max_index])
ylim_max = np.nanmax(data_new[t_min_index:t_max_index])
ylim(t_plot_name, ylim_min, ylim_max)
return loaded_data
def att(trange=['2017-04-01', '2017-04-02'],
level='l2',
downloadonly=False,
notplot=False,
no_update=False,
uname=None,
passwd=None):
"""
This function loads attitude 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']
level: str
Data level; Valid options:
downloadonly: bool
Set this flag to download the 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
Returns:
None
"""
file_res = 24*3600.
pathformat = 'satellite/erg/att/txt/erg_att_'+level+'_%Y%m%d_v??.txt'
out_files = load(pathformat=pathformat, trange=trange, file_res=file_res,
downloadonly=True, no_update=no_update, uname=uname, passwd=passwd)
if downloadonly:
return out_files
data_flame_list = []
for file in out_files:
raw_read_table = pd.read_table(file)
data_flame_list.append(
raw_read_table[10:][raw_read_table.keys()[0]].str.split(expand=True))
concat_frame_for_tplot = pd.concat(data_flame_list)
time_float_array = time_float(concat_frame_for_tplot.iloc[:, 0])
Omega_float_array = concat_frame_for_tplot.iloc[:, 1].astype(float)
Phase_float_array = concat_frame_for_tplot.iloc[:, 9].astype(float)
I_Alpha_float_array = concat_frame_for_tplot.iloc[:, 2].astype(float)
I_Delta_float_array = concat_frame_for_tplot.iloc[:, 3].astype(float)
GX_Alpha_float_array = concat_frame_for_tplot.iloc[:, 10].astype(float)
GX_Delta_float_array = concat_frame_for_tplot.iloc[:, 11].astype(float)
GZ_Alpha_float_array = concat_frame_for_tplot.iloc[:, 12].astype(float)
GZ_Delta_float_array = concat_frame_for_tplot.iloc[:, 13].astype(float)
if notplot:
output_dictionary = {}
output_dictionary['erg_att_sprate'] = {
'x': time_float_array, 'y': Omega_float_array}
output_dictionary['erg_att_spphase'] = {
'x': time_float_array, 'y': Phase_float_array}
output_dictionary['erg_att_izras'] = {
'x': time_float_array, 'y': I_Alpha_float_array}
output_dictionary['erg_att_izdec'] = {
'x': time_float_array, 'y': I_Delta_float_array}
output_dictionary['erg_att_gxras'] = {
'x': time_float_array, 'y': GX_Alpha_float_array}
output_dictionary['erg_att_gxdec'] = {
'x': time_float_array, 'y': GX_Delta_float_array}
output_dictionary['erg_att_gzras'] = {
'x': time_float_array, 'y': GZ_Alpha_float_array}
output_dictionary['erg_att_gzdec'] = {
'x': time_float_array, 'y': GZ_Delta_float_array}
return output_dictionary
else:
store_data('erg_att_sprate', data={
'x': time_float_array, 'y': Omega_float_array})
store_data('erg_att_spphase', data={
'x': time_float_array, 'y': Phase_float_array})
store_data('erg_att_izras', data={
'x': time_float_array, 'y': I_Alpha_float_array})
store_data('erg_att_izdec', data={
'x': time_float_array, 'y': I_Delta_float_array})
store_data('erg_att_gxras', data={
'x': time_float_array, 'y': GX_Alpha_float_array})
store_data('erg_att_gxdec', data={
'x': time_float_array, 'y': GX_Delta_float_array})
store_data('erg_att_gzras', data={
'x': time_float_array, 'y': GZ_Alpha_float_array})
store_data('erg_att_gzdec', data={
'x': time_float_array, 'y': GZ_Delta_float_array})
return None
def pwe_efd(trange=['2017-04-01', '2017-04-02'],
datatype='E_spin',
level='l2',
suffix='',
coord='dsi',
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
prefix = 'erg_pwe_efd_' + level + '_'
if ('64' in datatype) or ('256' in datatype):
if '64' in datatype:
mode = '64Hz'
elif '256' in datatype:
mode = '256Hz'
md = 'E' + mode
pathformat = 'satellite/erg/pwe/efd/'+level+'/'+md + \
'/%Y/%m/erg_pwe_efd_'+level+'_'+md+'_'+coord+'_%Y%m%d_v??_??.cdf'
prefix += md + '_' + coord + '_'
if coord == 'wpt':
component = ['Eu_waveform', 'Ev_waveform']
elif coord == 'dsi':
component = [
'Ex_waveform', 'Ey_waveform', 'Eu_offset' + '_' + mode,
'Ev_offset' + '_' + mode
]
else:
pathformat = 'satellite/erg/pwe/efd/'+level+'/'+datatype + \
'/%Y/%m/erg_pwe_efd_'+level+'_'+datatype+'_%Y%m%d_v??_??.cdf'
prefix += datatype + '_'
if 'spin' in datatype:
component = ['Eu', 'Ev', 'Eu1', 'Ev1', 'Eu2', 'Ev2']
labels = ['Ex', 'Ey']
if datatype == 'pot':
component = ['Vu1', 'Vu2', 'Vv1', 'Vv2']
if datatype == 'pot8Hz':
component = [
'Vu1_waveform_8Hz', 'Vu2_waveform_8Hz', 'Vv1_waveform_8Hz',
'Vv2_waveform_8Hz'
]
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 EFD')
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/EFD: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Pwe/Efd'
)
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
time_min_max = time_float(trange)
if 'spin' in datatype:
for elem in component:
t_plot_name = prefix + elem + '_dsi'
options(t_plot_name, 'ytitle', elem + ' vector in DSI')
options(t_plot_name, 'legend_names', labels)
# ylim settings because pytplot.timespan() doesn't affect in ylim.
# May be it will be no need in future.
get_data_vars = get_data(t_plot_name)
if get_data_vars[0][0] < time_min_max[0]:
min_time_index = np.where(
(get_data_vars[0] <= time_min_max[0]))[0][-1]
else:
min_time_index = 0
if time_min_max[1] < get_data_vars[0][-1]:
max_time_index = np.where(
time_min_max[1] <= get_data_vars[0])[0][0]
else:
max_time_index = -1
ylim_min = np.nanmin(
get_data_vars[1][min_time_index:max_time_index])
ylim_max = np.nanmax(
get_data_vars[1][min_time_index:max_time_index])
ylim(t_plot_name, ylim_min, ylim_max)
if ('64' in datatype) or ('256' in datatype) or (datatype == 'pot8Hz'):
for elem in component:
t_plot_name = prefix + elem
get_data_vars = get_data(t_plot_name)
dl_in = get_data(t_plot_name, metadata=True)
time1 = get_data_vars[0]
data = np.where(get_data_vars[1] <= -1e+30, np.nan,
get_data_vars[1])
dt = get_data_vars[2]
ndt = dt.size
ndata = data.size
time_new = (np.tile(time1, (ndt, 1)).T + dt * 1e-3).reshape(ndata)
data_new = data.reshape(ndata)
store_data(t_plot_name,
data={
'x': time_new,
'y': data_new
},
attr_dict=dl_in)
options(t_plot_name, 'ytitle', '\n'.join(t_plot_name.split('_')))
# ylim settings because pytplot.timespan() doesn't affect in ylim.
# May be it will be no need in future.
if time_new[0] < time_min_max[0]:
min_time_index = np.where((time_new <= time_min_max[0]))[0][-1]
else:
min_time_index = 0
if time_min_max[1] < time_new[-1]:
max_time_index = np.where(time_min_max[1] <= time_new)[0][0]
else:
max_time_index = -1
ylim_min = np.nanmin(data_new[min_time_index:max_time_index])
ylim_max = np.nanmax(data_new[min_time_index:max_time_index])
ylim(t_plot_name, ylim_min, ylim_max)
if datatype == 'pot':
for elem in component:
t_plot_name = prefix + elem
options(t_plot_name, 'ytitle', elem + ' potential')
if datatype == 'spec':
options(tnames(prefix + '*spectra*'), 'spec', 1)
options(tnames(prefix + '*spectra*'), 'colormap', 'jet')
options(tnames(prefix + '*spectra*'), 'zlog', 1)
ylim(prefix + 'spectra', 0, 100)
zlim(prefix + 'spectra', 1e-6, 1e-2)
options(tnames(prefix + '*spectra*'), 'ysubtitle', '[Hz]')
options(tnames(prefix + '*spectra*'), 'ztitle', '[mV^2/m^2/Hz]')
for t_plot_name in (tnames(prefix + '*spectra*')):
options(t_plot_name, 'ytitle', '\n'.join(t_plot_name.split('_')))
return loaded_data
