Change bar style in matplotlib

Generate a code to analyze the frequency of the sound.

But I want to know how I can modify the style of the bars. That is, add effects of pyqt as setGraphicEffect(QT::DropShadowEffect) for example, and add this result within a frame and not in a separate window,

The purpose is to obtain this:

Here I leave the code and hope you can help me to add effect to the bars and remove it from the windows

import wave
import struct
import sys
import numpy as np
from math import sqrt
import matplotlib
matplotlib.use('Agg')
from matplotlib import pylab
import matplotlib.pyplot as plt

filename = sys.argv[1]

if __name__ == '__main__':
    # Open the wave file and get info
    wave_file = wave.open(filename, 'r')
    data_size = wave_file.getnframes()
    sample_rate = wave_file.getframerate()
    print "Sample rate: %s" % sample_rate
    sample_width = wave_file.getsampwidth()
    duration = data_size / float(sample_rate)

# Read in sample data
sound_data = wave_file.readframes(data_size)

# Close the file, as we don't need it any more
wave_file.close()

# Unpack the binary data into an array
unpack_fmt = '%dh' % (data_size)
sound_data = struct.unpack(unpack_fmt, sound_data)

# Process many samples
fouriers_per_second = 24 # Frames per second
fourier_spread = 1.0/fouriers_per_second
fourier_width = fourier_spread
fourier_width_index = fourier_width * float(sample_rate)

if len(sys.argv) < 3:
    length_to_process = int(duration)-1
else:
    length_to_process = float(sys.argv[2])

print "Fourier width: %s" % str(fourier_width)

total_transforms = int(round(length_to_process * fouriers_per_second))
fourier_spacing = round(fourier_spread * float(sample_rate))

print "Duration: %s" % duration
print "For Fourier width of "+str(fourier_width)+" need "+str(fourier_width_index)+" samples each FFT"
print "Doing "+str(fouriers_per_second)+" Fouriers per second"
print "Total " + str(total_transforms * fourier_spread)
print "Spacing: "+str(fourier_spacing)
print "Total transforms "+str(total_transforms)

lastpoint=int(round(length_to_process*float(sample_rate)+fourier_width_index))-1

sample_size = fourier_width_index
freq = sample_rate / sample_size * np.arange(sample_size)

x_axis = range(0, 12)

def getBandWidth():
    return (2.0/sample_size) * (sample_rate / 2.0)

def freqToIndex(f):
    # If f (frequency is lower than the bandwidth of spectrum[0]
    if f < getBandWidth()/2:
        return 0
    if f > (sample_rate / 2) - (getBandWidth() / 2):
        return sample_size -1
    fraction = float(f) / float(sample_rate)
    index = round(sample_size * fraction)
    return index

fft_averages = []

def average_fft_bands(fft_array):
    num_bands = 12 # The number of frequency bands (12 = 1 octave)
    del fft_averages[:]
    for band in range(0, num_bands):
        avg = 0.0

        if band == 0:
            lowFreq = int(0)
        else:
            lowFreq = int(int(sample_rate / 2) / float(2 ** (num_bands - band)))
        hiFreq = int((sample_rate / 2) / float(2 ** ((num_bands-1) - band)))
        lowBound = int(freqToIndex(lowFreq))
        hiBound = int(freqToIndex(hiFreq))
        for j in range(lowBound, hiBound):
            avg += fft_array[j]

        avg /= (hiBound - lowBound + 1)
        fft_averages.append(avg)

for offset in range(0, total_transforms):
    start = int(offset * sample_size)
    end = int((offset * sample_size) + sample_size -1)

    print "Processing sample %i of %i (%d seconds)" % (offset + 1, total_transforms, end/float(sample_rate))
    sample_range = sound_data[start:end]
    ## FFT the data
    fft_data = abs(np.fft.fft(sample_range))
    # Normalise the data a second time, to make numbers sensible
    fft_data *= ((2**.5)/sample_size)
    plt.ylim(0, 1000)
    average_fft_bands(fft_data)
    y_axis = fft_averages
    """Stuff for bar graph"""
    width = 0.35
    p1 = plt.bar(x_axis, y_axis, width, color='r')
    """End bar graph stuff"""
    filename = str('frame_%05d' % offset) + '.png'
    plt.savefig(filename, dpi=100)
    plt.close()
print "DONE!"