from sunpy.timeseries import TimeSeries
###############################################################################
# Let's first create a TimeSeries from sample data.
goes_lc = TimeSeries(GOES_XRS_TIMESERIES).truncate('2011/06/07 06:10',
'2011/06/07 07:00')
###############################################################################
# Now we will extract data values from the TimeSeries and apply a BoxCar filter
# to get smooth data. Boxcar smoothing is equivalent to taking our signal and
# using it to make a new signal where each element is the average of w adjacent
# elements. Here we will use astropy's convolve function with a "boxcar" kernel
# of width w = 10.
goes_lc = goes_lc.add_column(
'xrsa_smoothed', convolve(goes_lc.quantity('xrsa'),
kernel=Box1DKernel(50)))
###############################################################################
# Plotting original and smoothed timeseries.
plt.figure()
plt.xlabel('Time')
plt.ylabel("Flux (Wm$^{-2}$")
plt.title('Smoothing of Time Series')
plt.plot(goes_lc.quantity('xrsa'), label='original')
plt.plot(goes_lc.quantity('xrsa_smoothed'), label='smoothed')
plt.legend()
plt.show()
"""
import matplotlib.pyplot as plt
from astropy.convolution import convolve, Box1DKernel
from sunpy.timeseries import TimeSeries
from sunpy.data.sample import NOAAINDICES_TIMESERIES as noaa_ind
###############################################################################
# Let's first create a TimeSeries from sample data
ts_noaa_ind = TimeSeries(noaa_ind, source='NOAAIndices')
###############################################################################
# Now we will extract data values from the TimeSeries and apply a BoxCar filter
# to get smooth data. Boxcar smoothing is equivalent to taking our signal and
# using it to make a new signal where each element is the average of w adjacent
# elements. Here we will use AstroPy’s convolve function with a “boxcar” kernel
# of width w = 10.
ts_noaa_ind = ts_noaa_ind.add_column(
'sunspot SWO Smoothed',
convolve(ts_noaa_ind.quantity('sunspot SWO'), kernel=Box1DKernel(10)))
###############################################################################
# Plotting original and smoothed timeseries
plt.ylabel('Sunspot Number')
plt.xlabel('Time')
plt.title('Smoothing of Time Series')
plt.plot(ts_noaa_ind.quantity('sunspot SWO'), label='original data')
plt.plot(ts_noaa_ind.quantity('sunspot SWO Smoothed'), label='smoothed')
plt.legend()
plt.show()