def ex_spectra():
# Delete any existing pytplot variables
pytplot.del_data()
# Download THEMIS data for 2015-12-31
pyspedas.load_data('themis', ['2015-12-31'], ['tha'], 'sst', 'l2')
# Specify options
pytplot.tplot_options('title', 'tha_psif_en_eflux 2015-12-31')
pytplot.ylim('tha_psif_en_eflux', 10000.0, 10000000.0)
pytplot.options('tha_psif_en_eflux', 'colormap', 'viridis')
# Plot spectrogram
pytplot.tplot(['tha_psif_en_eflux'])
def ex_omni():
# Print the installed version of pyspedas
pyspedas.version()
# Delete any existing pytplot variables
pytplot.del_data()
# Download OMNI data for 2015-12-31
pyspedas.load_data('omni', ['2015-12-31 00:00:00', '2016-01-01 23:59:59'],
'', '', '1min')
# Plot
pytplot.tplot_options('title', 'OMNI flow_speed 2015-12-31 to 2016-01-01')
pytplot.tplot(['flow_speed'])
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 ex_basic():
# Delete any existing pytplot variables
pytplot.del_data()
# Download THEMIS state data for 2015-12-31
time_range = ['2015-12-31 00:00:00', '2016-01-01 12:00:00']
pyspedas.load_data('themis', time_range, ['tha'], 'state', 'l1')
# Get data into python variables
alldata = pytplot.get_data("tha_pos")
time = alldata[0]
data = alldata[1]
# Store a new pytplot variable
pytplot.store_data("tha_position", data={'x': time, 'y': data})
# Define the y-axis limits
pytplot.ylim('tha_pos', -23000.0, 81000.0)
pytplot.ylim('tha_position', -23000.0, 81000.0)
pytplot.ylim('tha_vel', -8.0, 12.0)
# Plot position and velocity using the pyqtgraph library (default)
pytplot.tplot(["tha_pos", "tha_position", "tha_vel"])
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)
# and the version of the installed pyspedas package, using the function version().
version()
####################################################################################
# Delete any existing pytplot variables
del_data()
####################################################################################
# Define a time range. Here, we pick a time range that spans one and a half day.
time_range = ['2015-12-31 00:00:00', '2016-01-01 12:00:00']
####################################################################################
# Download THEMIS state data and store it into the pytplot object.
# This following function downloads all the necessary files, loads data,
# and time-clips data to the specified time range.
load_data('themis', time_range, ['tha'], 'state', 'l1')
####################################################################################
# Get data from pytplot object into python variables.
# This is useful when we want to work on the data using standard python libraries.
alldata = get_data("tha_vel")
time = alldata[0]
data = alldata[1]
####################################################################################
# After working with the data, we can store a new pytplot variable.
# We can store any data in the pytplot object.
store_data("tha_new_vel", data={'x': time, 'y': data})
####################################################################################
# Preparing for the plots, we define the y-axis limits.