def main():
lobj = lyra.lyra('2011/08/09')
lobj.download()
lobj.load()
#lobj.plot()
# 300 seconds worth of data summed up into 4 second bins
s = lyra.subthensum(lobj,'2011/08/09 08:30',3600,binsize = 4.0)
channel = lyra.channel(s,4)
analyse = lyra_gi.lyra_gi(channel.data, channel.dt)
analyse.dj = 0.125
analyse.neupert()
plt.figure(1)
plt.plot(channel.time,channel.data)
lyra_deriv = analyse.wave.real[0,:]
plt.figure(2)
plt.plot(channel.time,lyra_deriv)
plt.xlabel = 'time (s)'
plt.ylabel = 'time derivative of LYRA flux'
def zzz(u,t,Clyra,lyra_deriv): #defines the system of odes
return (lyra_deriv-Clyra*u) #the derivatives of u
Clyra = 1.0
u0 = 1.0
hxr = odeint(zzz,u0,channel.time,args=(Clyra,lyra_deriv))
plt.figure(3)
plt.plot(channel.time,hxr)
plt.xlabel = 'time (s)'
plt.ylabel = 'recovered hard x-ray flux'
"""
Test code that makes use of the lyra and kPyWavelet code.
"""
import lyra,lyra_gi
import matplotlib.pyplot as plt
import numpy as np
from scipy.integrate import odeint
import scipy
lobj = lyra.lyra('2011/08/09')
lobj.download()
lobj.load()
# 300 seconds worth of data summed up into 4 second bins
s = lyra.sub(lobj,'2011/08/09 08:30',600)
channel = lyra.channel(s,4)
analyse = lyra_gi.lyra_gi(channel.data, channel.dt)
analyse.dj = 0.125
analyse.neupert()
plt.figure(1)
plt.plot(channel.time,channel.data)
this = 20
print 'Smoothing scale = ',analyse.scale[this]
lyra_deriv = analyse.wave.real[this,:]
plt.figure(2)
plt.plot(channel.time,lyra_deriv)
plt.xlabel = 'time (s)'
plt.ylabel = 'time derivative of LYRA flux'
"""
Test code that makes use of the lyra and kPyWavelet code.
"""
import lyra
import kPyWavelet as wvt
#import matplotlib.pyplot as plt
import pylab
import numpy as np
lobj = lyra.lyra('2011/06/29')
lobj.download()
lobj.load()
#ss = lyra.subthensum(lyra,'2011/06/29 20:00',300,binsize = 4.0)
# get 300 seconds worth of data
s = lyra.sub(lobj,'2011/06/29 20:00',300)
choose = 4
units = s.data.columns[choose].name + ' ('+s.data.columns[choose].unit+')'
ch = s.data.field(choose)[:]
title = s.data.columns[choose].name + ' ('+s.data.columns[choose].unit+')' + str(lobj.tstart) + ' - ' + str(lobj.tend)
label = 'normalized'
xlabel = s.data.columns[0].name + ' ('+s.data.columns[0].unit+')'
avg1, avg2 = (2, 8) # Range of periods to average
slevel = 0.95 # Significance level
std = ch.std() # Standard deviation
