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

# In[33]:


get_ipython().run_line_magic('matplotlib', 'inline')
import matplotlib.pyplot as plt
import numpy as np
import scipy
import scipy.io.wavfile
import IPython

def setup_graph(title='', x_label='', y_label='', fig_size=None):
    fig = plt.figure()
    if fig_size != None:
        fig.set_size_inches(fig_size[0], fig_size[1])
    ax = fig.add_subplot(111)
    ax.set_title(title)
    ax.set_xlabel(x_label)
    ax.set_ylabel(y_label)


# # Seeing sound!

# In[34]:


# NOTE: This is only works with 1 channel (mono).  To record a mono audio sample,
# you can use this command: rec -r 44100 -c 1 -b 16 test.wav
(sample_rate, input_signal) = scipy.io.wavfile.read("audio_files/vowel_ah.wav")
time_array = np.arange(0, len(input_signal)/sample_rate, 1/sample_rate)


# In[35]:


setup_graph(title='Ah vowel sound', x_label='time (in seconds)', y_label='amplitude', fig_size=(14,7))
_ = plt.plot(time_array[0:4000], input_signal[0:4000])


# In[36]:


#IPython.display.Audio("audio_files/vowel_ah.wav")


# In[37]:


fft_out = np.fft.rfft(input_signal)
fft_mag = [np.sqrt(i.real**2 + i.imag**2)/len(fft_out) for i in fft_out]
num_samples = len(input_signal)
rfreqs = [(i*1.0/num_samples)*sample_rate for i in range(num_samples//2+1)]

setup_graph(title='FFT of Ah Vowel (first 5000)', x_label='FFT Bins', y_label='magnitude', fig_size=(14,7))
_ = plt.plot(rfreqs[0:5000], fft_mag[0:5000])


# ## Few notes about this
# 
# * Ratio of harmonics = **timbre** = This is what makes different people's voices sound different (and different from violins)
#     - Even if they are singing the same note, and the same volume
# * Possible application: synthesizing new sounds (with different harmonic profiles)
#     - Example: If you changed the ratio of the harmonics, you could make your voice sound like something else (Darth Vader?)

# # Spectrogram (FFT over time)
# 
# ### Axes
# 
# * x-axis: time
# * y-axis: frequency
# * z-axis (color): strength of each frequency
# 
# ### See the Harmonics!

# In[38]:


(doremi_sample_rate, doremi) = scipy.io.wavfile.read("audio_files/do-re-mi.wav")


# In[39]:


setup_graph(title='Spectrogram of diatonic scale (44100Hz sample rate)', x_label='time (in seconds)', y_label='frequency', fig_size=(14,8))
_ = plt.specgram(doremi, Fs=doremi_sample_rate)


# In[40]:


doremi_8000hz = [doremi[i] for i in range(0, len(doremi), 44100//8000)]
setup_graph(title='Spectrogram (8000Hz sample rate)', x_label='time (in seconds)', y_label='frequency', fig_size=(14,7))
_ = plt.specgram(doremi_8000hz, Fs=8000)


# In[41]:


doremi_4000hz = [doremi[i] for i in range(0, len(doremi), 44100//4000)]
setup_graph(title='Spectrogram (4000Hz sample rate)', x_label='time (in seconds)', y_label='frequency', fig_size=(14,7))
_ = plt.specgram(doremi_4000hz, Fs=4000)


# ## A few things to note
# 
# * Something that sounds like a single note actually is made up of a bunch of harmonics
# * Harmonics are integer multiples of the *fundamental frequency*
# * Notice that the spacing between the harmonics of the first note is about double of the spacing between the harmonics in the last note (1 octave difference)

# In[42]:


# Cleanup to reduce notebook size
del input_signal, time_array, rfreqs, doremi, fft_out, fft_mag, doremi_4000hz, doremi_8000hz, _


# In[ ]: