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

# ![alt text](https://github.com/callysto/callysto-sample-notebooks/blob/master/notebooks/images/Callysto_Notebook-Banner_Top_06.06.18.jpg?raw=true)
# 
# <center> Sound Demo 2 </center>
# <p>
# <center> Michael Lamoureux </center>

# In[1]:


get_ipython().run_line_magic('matplotlib', 'inline')
from matplotlib.pyplot import *
from numpy import *    ## for numerical functions
from IPython.display import Audio  ## to output audio


# ## What is sound?
# 
# Rapid changes, or **vibrations** in air pressure are heard in our ears as sound. 
# 
# A computer creates sound by sending a list of numbers to a speaker, which then **vibrates**.

# ## Random numbers
# 
# A list of random numbers, will create a sound. 
# 
# What do you think is will sound like?
# 
# 

# In[2]:


Fs = 44100
random_sd = random.randn(Fs)


# In[4]:


## Random sound

Audio(data=random_sd, rate=Fs)


# We can plot a few of those numbers, using a plotting command. 

# In[5]:


plot(random_sd[0:200]);


# ## Periodic vibrations
# 
# A vibration that has a periodic vibration will sound like a tone to us. 
# 
# The sine function has a periodic cycle, and can be used to represent such a tone. 
# 

# In[6]:


Fs, Len, f1 = 44100, 3, 440  ## sample rate, length in second, frequency
t = linspace(0,Len,Fs*Len)  ## time variable


# In[7]:


sine_sd = sin(2*pi*440*t) 

Audio(data=sine_sd, rate=Fs)


# We can plot this sine sound, using a plotting command.

# In[8]:


plot(sine_sd[0:500]);


# By clipping the tops of the sine wave, we get a square wave.
# 
# 

# In[9]:


square_sd = minimum(.1, maximum(-.1,sine_sd))
plot(square_sd[0:500]);


# In[11]:


## What does a square wave sound like?

Audio(data=square_sd, rate=Fs)


# The Sine and Square waves have the same **pitch** (frequency) but different **timbres**.

# ## Beats
# 
# Playing two tones at similar frequencies results in **beats**. 
# 
# This is useful for tuning instruments!

# In[12]:


sine2_sd = sin(2*pi*440*t) - sin(2*pi*442*t)

Audio(data=sine2_sd, rate=Fs)


# We can plot to see the beats, or variations in amplitude. 
# 
# Note we plot the whole 3 seconds of the sound. Two beats per second. 

# In[11]:


plot(t,sine2_sd);


# ## A chirp
# 
# Bats use a chirp, or tones that rapidly increase in frequency, to use as sonar.
# 
# We can use a chirp to explore the range of frequencies that we can hear.
# 
# Humans (with very good ears) can hear from 20 Hz to 20,000 Hz. 

# In[14]:


f = 20000/(2*Len)
chirp_sd = sin(2*pi*f*t**2) ## t**2 creates the increasing freq's.

Audio(data=chirp_sd, rate=Fs)


# ## Count the pulses
# 
# We test how well you hear, by testing various frequencies.
# 
# 

# In[73]:


freq = 10000

n = random.randint(1,5)
test_sd = sin(2*pi*freq*t)*sin(pi*n*t/Len)
Audio(data=test_sd,rate=Fs,autoplay=True)


# In[74]:


n


# ## More on chirps.
# 
# We can vary the parameters, to vary the sounds

# In[13]:


Fs = 44100
Len = 3
t = linspace(0,Len,Fs*Len)
fmax = 15000
chirp2_sd = sin(pi*(fmax/Len)*t**2) 

Audio(data=chirp2_sd, rate=Fs,autoplay=True)


# <center> Thank you for your attention. </center>
# 
# ![alt text](https://github.com/callysto/callysto-sample-notebooks/blob/master/notebooks/images/Callysto_Notebook-Banners_Bottom_06.06.18.jpg?raw=true)