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a1 = subplot(121);
loglog(channels(:,1),channels(:,2),'o')
xlabel('CH0')
ylabel('CH1')
a2 = subplot(122);
loglog(channels(:,3),channels(:,4),'o')
xlabel('CH2')
ylabel('CH3')

import matplotlib.pyplot as plt
import numpy as np

a1 = plt.subplot(221)
plt.loglog(channels[:,0],channels[:,1], 'o' )
plt.xlabel('CH0')
plt.ylabel('CH1')
a2 = plt.subplot(222)
plt.loglog(channels[:,2],channels[:,3], 'o' )
plt.xlabel('CH2')
plt.ylabel('CH3')

plt.hold(True)

aa = plt.gca()

aa.set_xcolor = 'k'

aa.set_ycolor = 'k'

limslista = plt.ginput(2)

# list: (x0,y0),(x1,y1)

plt.axes(a2)

plt.hold(True)

bb = plt.gca()

bb.set_xcolor = 'k'

bb.set_ycolor = 'k'

limslistb = plt.ginput(2)

# list: (x2,y2),(x3,y3)

limsa = np.array(limslista)

# \[ x0

   y0
# x1   y1 \]

limsb = np.array(limslistb)

# \[ x2

   y2
# x3   y3 \]

lims = np.hstack( [limsa, limsb

] )
# \[ x0  y0  x2  y2       =    \[ ch0  ch1  ch2  ch3 \]
# x1  y1  x3  y3 \]
# now each column corresponds to one channel.

print "limits array : ", lims

# fbool is an array of event indices where these conditions are met.
fbools0 = (channels[:,0]>lims[:,0].min())&(channels[:,0]<lims[:,0].max())

fbools1 = (channels[:,1]>lims[:,1].min())&(channels[:,1]<lims[:,1].max())

fbools2 = (channels[:,2]>lims[:,2].min())&(channels[:,2]<lims[:,2].max())

fbools3 = (channels[:,3]>lims[:,3].min())&(channels[:,3]<lims[:,3].max())