Envelope of signal + how to generate nice smooth 2d lines animation

Question

I'm trying to set the envelope of a signal with the function obspy.signal.filter.envelope. So far, my program is generating 2 lines: the red one is the raw signal. The blue one is the signal filtered with a bandpass filter (filter_sinc function).

Now, I would like the envelope of the red signal. However, when I run the code, the green line which is suppose to be the envelope of the red signal is not and I don't understand why.

Also I would like to make a smooth animation of these lines to show them generating over time, and I know that this is not a very good result. How could I make it run nicer? Like showing the lines generating smoothly?

Here is my test.dat file: file

And the code:

from __future__ import division
import matplotlib.pyplot as plt
import matplotlib.animation as anim
import numpy as np
import obspy.signal as sign

def init():
    im.set_data([], [])
    im2.set_data([], [])
    im3.set_data([], [])
    return im,im2,

def filter_sinc(x=None,n=None):
    f=1 / n
    nf=2 * np.fix(2 / f)
    o=(np.arange(1,nf+1)) - nf / 2
    filt=0 * o + 2 * f    
    temp = np.sin([2 * np.pi * f * np.concatenate((o[0:nf/2-1],o[nf/2:]),1)]) / np.concatenate((o[0:nf/2-1],o[nf/2:]),1) / np.pi
    filt[0:nf/2-2] = temp[:,0:nf/2-2]
    filt[nf/2:] = temp[:,nf/2-1:]   
    hamm=0.54 - 0.46 * np.cos(2 * np.pi * (np.arange(1,nf+1)) / nf)
    filt=filt*hamm
    xf = np.convolve(x,filt,mode='same')
    return xf


fig = plt.figure()
ax2 = fig.add_subplot(1,1,1)    

filtre_x = []
filtre_y = []
mesure = []
temps = []
hilb = []

i=0
n=30
zoom=100

fichier_dat = np.genfromtxt("test.dat", usecols = (0,1,2,3,4,5,6), comments = "#")
mesure = fichier_dat[:,6]
temps = fichier_dat[:,2]*86400 + fichier_dat[:,3]*3600 + fichier_dat[:,4]*60 + fichier_dat[:,5]

hilb = sign.filter.envelope(mesure)

while i < np.size(temps,0)-1200:
    filtre_x = np.hstack([filtre_x,temps[np.arange(i+n,i+(1200-n))]])
    filtre_y = np.hstack([filtre_y,filter_sinc(mesure[np.arange(i,i+1200)],n*2)[np.arange(n,1200-n)]])
    i = i+1200

filtre_x = filtre_x/20
temps = temps/20

ax2.set_xlim([filtre_x[0],filtre_x[0]+zoom])
ax2.set_ylim([0,6])

im, = ax2.plot([], [], color=(1,0,0))
im2, = ax2.plot([], [], color=(0,0,1))
im3, = ax2.plot([], [], color=(0,1,0))


def animate(k):

    im.set_xdata(temps)
    im.set_ydata(mesure)

    im2.set_xdata(filtre_x)
    im2.set_ydata(filtre_y)

    im3.set_xdata(temps)
    im3.set_ydata(hilb)

    ax2.set_xlim(k+filtre_x[0], k+filtre_x[0]+zoom)


ani = anim.FuncAnimation(fig, animate, init_func=init, frames=np.size(filtre_x), interval=30, blit=False)
plt.show()