def ex_gmag(plot=True):
"""Demonstrate how to use gmag functions."""
# Delete any existing pytplot variables
pytplot.del_data()
# Define a time rage as a list
trange = ['2015-12-31', '2015-12-31']
# Get a list of EPO gmag stations
sites = pyspedas.themis.ground.gmag.gmag_list('epo')
# Download gmag files and load data into pytplot variables
pyspedas.themis.gmag(sites=sites, trange=trange)
# Get a list of loaded sites
sites_loaded = pyspedas.tnames()
# Subtract mean values
pyspedas.subtract_average(sites_loaded, '')
# Download AE index data
# pyspedas.load_data('gmag', time_list, ['idx'], '', '')
pyspedas.themis.gmag(sites='idx', trange=trange)
# Plot
sites_loaded = pytplot.tplot_names()
pytplot.tplot_options('title', 'EPO GMAG 2015-12-31')
if plot:
pytplot.tplot(sites_loaded)
# Return 1 as indication that the example finished without problems.
return 1
def ex_avg(plot=True):
"""Load GMAG data and average over 5 min intervals."""
# Delete any existing pytplot variables.
pytplot.del_data()
# Define a time rage as a list
trange = ['2007-03-23', '2007-03-23']
# Download gmag files and load data into pytplot variables.
sites = ['ccnv']
var = 'thg_mag_ccnv'
pyspedas.themis.gmag(sites=sites, trange=trange, varnames=[var])
pytplot.tplot_options('title', 'GMAG data, thg_mag_ccnv 2007-03-23')
pyspedas.subtract_average(var, median=1)
var += '-m'
# Five minute average using time dt.
avg_data(var, dt=5 * 60.)
# Five minute average using width (number of measurements).
# Each measurement is 0.5 sec.
avg_data(var, width=5 * 60. * 2., new_names=var + '-avg2')
# Plot.
if plot:
pytplot.tplot([var, var + '-avg', var + '-avg2'])
# Return 1 as indication that the example finished without problems.
return 1
def ex_deriv(plot=True):
"""Find the derivative of a GMAG variable."""
# Derivative of data
pytplot.del_data()
# Define a time rage as a list
trange = ['2007-03-23', '2007-03-23']
# Download gmag files and load data into pytplot variables
sites = ['ccnv']
var = 'thg_mag_ccnv'
pyspedas.themis.gmag(sites=sites, trange=trange, varnames=[var])
# pytplot.tplot_options('title', 'GMAG data, thg_mag_ccnv 2007-03-23')
pyspedas.subtract_average(var, median=1)
var += '-m'
# Five minute average
deriv_data(var)
# pytplot.options(var, 'ytitle', var)
# pytplot.options(var + '-der', 'ytitle', var + '-der')
if plot:
pytplot.tplot([var, var + '-der'])
# Return 1 as indication that the example finished without problems.
return 1
def ex_analysis():
# Print the installed version of pyspedas
pyspedas.version()
# Delete any existing pytplot variables
pytplot.del_data()
# Download THEMIS state data for 2015-12-31
pyspedas.load_data('themis', '2015-12-31', ['tha'], 'state', 'l1')
# Use some analysis functions on tplot variables
pyspedas.subtract_average('tha_pos')
pyspedas.subtract_median('tha_pos')
# Plot
pytplot.tplot(["tha_pos", "tha_pos-d", "tha_pos-m"])
def test_subtract_median(self):
"""Test subtract_median."""
subtract_median('aaabbbccc') # Test non-existent name
subtract_median('test')
d = get_data('test-m')
self.assertTrue(d[1].tolist() == [-3.5, -1.5, 1.5, 8.5, 13.5, -5.5])
dn = [[3., 5., 8.], [15., 20., 1.], [3., 5., 8.], [15., 20., 1.],
[23., 15., 28.], [15., 20., float('nan')]]
store_data('test1', data={'x': [1., 2., 3., 4., 5., 6.], 'y': dn})
subtract_median('aaabbbcc')
subtract_median('test1', new_names='aabb')
d = get_data('aabb')
subtract_median(['test', 'aabb'], new_names='aaabbb')
subtract_median('test1', overwrite=1)
subtract_average('test', new_names="testtest")
subtract_average(['test-m', 'test'], new_names="testtest2")
self.assertTrue(len(d[1]) == 6)
def ex_analysis():
# Delete any existing pytplot variables
pytplot.del_data()
# Download THEMIS state data for 2015-12-31
time_range = ['2015-12-31 00:00:00', '2015-12-31 23:59:59']
pyspedas.themis.state(probe='a', trange=time_range)
# Use some analysis functions on tplot variables
pyspedas.subtract_average('tha_pos')
pyspedas.subtract_median('tha_pos')
# Plot
pytplot.tplot(["tha_pos", "tha_pos-d", "tha_pos-m"])
# Return 1 as indication that the example finished without problems.
return 1
def ex_gmag():
# Delete any existing pytplot variables
pytplot.del_data()
# Define list of dates
time_list = ['2015-12-31']
# Get a list of EPO gmag stations
sites = pyspedas.gmag_list(group='epo')
# Download gmag files and load data into pytplot variables
pyspedas.load_data('gmag', time_list, sites, '', '')
# Get a list of loaded sites
sites_loaded = pyspedas.tplot_names()
# Subtact mean values
pyspedas.subtract_average(sites_loaded, '')
# Download AE index data
pyspedas.load_data('gmag', time_list, ['idx'], '', '')
# Plot
sites_loaded = pyspedas.tplot_names()
pytplot.tplot_options('title', 'EPO GMAG 2015-12-31')
pytplot.tplot(sites_loaded)
def ex_avg(plot=False):
"""Load GMAG data and average over 5 min intervals."""
# Delete any existing pytplot variables
pytplot.del_data()
# Define a time rage as a list
trange = ['2007-03-23', '2007-03-23']
# Download gmag files and load data into pytplot variables
sites = ['ccnv']
var = 'thg_mag_ccnv'
pyspedas.themis.gmag(sites=sites, trange=trange, varnames=[var])
pytplot.tplot_options('title', 'GMAG data, thg_mag_ccnv 2007-03-23')
pyspedas.subtract_average(var, median=1)
var += '-m'
# Five minute average
avg_data(var, width=5 * 60)
if plot:
pytplot.tplot([var, var + '-avg'])
# Return 1 as indication that the example finished without problems.
return 1
# set the y-axis to log scale
options('mms4_des_numberdensity_brst', 'ylog', True)
options('mms4_hpca_hplus_number_density', 'ylog', True)
tplot(['mms4_des_numberdensity_brst', 'mms4_hpca_hplus_number_density'])
''' ==========================================================================
Analysis Tools
========================================================================== '''
# subtract_average
from pyspedas import subtract_average
subtract_average('mms4_fgm_b_gsm_brst_l2')
tplot(['mms4_fgm_b_gsm_brst_l2', 'mms4_fgm_b_gsm_brst_l2-d'])
# subtract_median
from pyspedas import subtract_median
subtract_median('mms4_fgm_b_gsm_brst_l2')
tplot([
'mms4_fgm_b_gsm_brst_l2', 'mms4_fgm_b_gsm_brst_l2-d',
'mms4_fgm_b_gsm_brst_l2-m'
])
# time clip
from pyspedas import time_clip
####################################################################################
# Download cdf files for all EPO GMAG stations and load data into pytplot variables.
#
# Some GMAG stations may not have any data files for the specified time interval.
# In that case, we will get an error message that the remote file does not exist
# for that GMAG station.
load_data('gmag', time_range, sites, '', '')
####################################################################################
# Print the names of the loaded GMAG sites.
sites_loaded = tplot_names()
####################################################################################
# Subtract the average values for these sites.
subtract_average(sites_loaded, '')
####################################################################################
# Download AE index data.
load_data('gmag', time_range, ['idx'], '', '')
####################################################################################
# Get a list of all the loaded GMAG sites plus the AE index data.
sites_loaded = tplot_names()
####################################################################################
# Plot GMAG and AE index data.
# Use the bokeh library - the plots will appear in the web browser.
tplot_options('title', 'EPO GMAG 2015-12-31')
tplot(sites_loaded, bokeh=True)
def test_subtract_average(self):
subtract_average('test')
d = get_data('test-d')
self.assertTrue(
(np.round(d[1].tolist()) == [-6., -4., -1., 6., 11., -8.]).all())
def test_subtract_average(self):
"""Test subtract_average."""
subtract_average('aaabbbccc') # Test non-existent name
subtract_average('test')
d = get_data('test-d')
self.assertTrue(
(np.round(d[1].tolist()) == [-6., -4., -1., 6., 11., -8.]).all())
dn = [[3., 5., 8.], [15., 20., 1.], [3., 5., 8.], [15., 20., 1.],
[23., 15., 28.], [15., 20., float('nan')]]
store_data('test1', data={'x': [1., 2., 3., 4., 5., 6.], 'y': dn})
subtract_average('aaabbbcc')
subtract_average('test1', new_names='aabb')
d = get_data('aabb')
subtract_average(['test', 'aabb'], new_names='aaabbb')
subtract_average('test1', overwrite=1)
subtract_average('test1', new_names="testtest")
subtract_average(['test1', 'test'], new_names="testtest2")
self.assertTrue(len(d[1]) == 6)
