#!/usr/bin/env python
# coding: utf-8

# ## [HW4] 
# MAT 201A Winter | Jing  Yan | theuniqueeye@gmail.com 

# #### Description:  
# Step1 : To three audio files, I compute the DFTs of the entire files and then identify the indexs of the most prominent peaks in the magnitude spectrums. 
# 
# Step2: Take the sample rate as 44100Hz, I Calculate the frequencies (in Hz) correspond to the bins with the most prominent peaks.

# In[33]:


# import libraries
get_ipython().run_line_magic('pylab', 'inline')
from __future__ import print_function
from __future__ import division


# In[34]:


# read three audio files 
from scipy.io import wavfile
glockenspiel = wavfile.read('glockenspiel.wav')
piano = wavfile.read('piano.wav')
tom = wavfile.read('tom.wav')


# In[35]:


# print the information of three wave files
print("glockenspiel:", glockenspiel)
print("piano:", piano)
print("tom:", tom)


# In[36]:


# diaplay of each audio
from IPython.display import Audio
Audio(glockenspiel[1],rate=44100)


# In[37]:


Audio(piano[1],rate=44100)


# In[38]:


Audio(tom[1],rate=44100)


# In[85]:


# do DFT to each entire file
a = fft.rfft(glockenspiel[1]) 
b = fft.rfft(piano[1])
c = fft.rfft(tom[1])
# print("a:", a, "b:", b, "c:", c) 
# the output is a complex ndarray


# In[150]:


# draw the magnitude spectrum of the DFT results
rcParams['figure.figsize'] = (12, 12) 

subplot(3,1,1)
plot(abs(a))
xlabel("frequency",size = 8)
ylabel("magnitude",size = 8)
title("the magnitude spectrum of glockenspiel")

subplot(3,1,2)
plot(abs(b))
xlabel("frequency",size = 8)
ylabel("magnitude",size = 8)
title("the magnitude spectrum of piano")

subplot(3,1,3)
plot(abs(c))
xlabel("frequency",size = 8)
ylabel("magnitude",size = 8)
title("the magnitude spectrum of tom")


# In[67]:


# compute the index of the most prominent peak
index_a = argmax(abs(a))
index_b = argmax(abs(b))
index_c = argmax(abs(c))
print(index_a, index_b, index_c)


# In[103]:


max(abs(c))


# #### Thus, the indexs of the most prominent peak in those magnitude spectrum is 1158  for glockenspiel, 58  for piano, 81 for tom.

# In[151]:


# show the position of maximum magnitude with red dot.

subplot(3,1,1)
xlim(0,2000)
plot(abs(a))
xlabel("frequency",size = 8)
ylabel("magnitude",size = 8)
title("zoom in view of the magnitude spectrum of glockenspiel")
scatter(index_a, max(abs(a)), 50, color ='red')

subplot(3,1,2)
xlim(0,200)
plot(abs(b))
xlabel("frequency",size = 8)
ylabel("magnitude",size = 8)
title("zoom in view of the magnitude spectrum of piano")
scatter(index_b, max(abs(b)), 50, color ='red')

subplot(3,1,3)
xlim(0,200)
plot(abs(c))
xlabel("frequency",size = 8)
ylabel("magnitude",size = 8)
title("zoom in view of the magnitude spectrum of tom")
scatter(index_c, max(abs(c)), 50, color ='red')


# In[80]:


sr = 44100

freq_a = index_a / len(abs(a)) * (sr/2)
freq_b = index_b / len(abs(b)) * (sr/2)
freq_c = index_c / len(abs(c)) * (sr/2)
print(freq_a,freq_b, freq_c)


# #### Thus, the frequencies (in Hz) correspond to the bins with the most prominent peaks is 1323.27Hz for glockenspiel, 78.05Hz for piano, and 92.56Hz for tom.
#