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

# In[1]:


get_ipython().run_line_magic('matplotlib', 'notebook')
from matplotlib.pyplot import *
from numpy import *
from audio.coders import *
from bitstream import *


# In[2]:


N = 16000


# ### Random Exponential (Independent) Values

# [exponential distributions](https://docs.scipy.org/doc/numpy-1.12.0/reference/generated/numpy.random.exponential.html)

# In[3]:


data = random.exponential(scale=30.0, size=N).astype(uint16)


# In[4]:


figure()
plot(data)


# In[5]:


figure()
hist(data, bins=r_[0:256], density=True)
grid()


# In[6]:


BitStream(0, unary)


# In[7]:


BitStream(100, unary)


# In[8]:


N * 16


# In[9]:


stream = BitStream(data, unary)


# In[10]:


len(stream) / (N * 16.0) * 100.0


# In[11]:


BitStream(0, rice(b = 8, signed=False))


# In[12]:


BitStream(1, rice(b = 8, signed=False))


# In[13]:


BitStream(255, rice(b = 8, signed=False))


# In[14]:


BitStream([0,1,255], rice(b = 8, signed=False))


# In[15]:


n = 1024+512+256 + 42
display(BitStream(n, uint16))
display(BitStream(n, rice(b = 8, signed=False)))


# In[16]:


r = rice.from_frame(data, signed=False)


# In[17]:


r = rice(b=6, signed=False)
stream = BitStream(data, r)
len(stream) / (16.0 * N) * 100.0


# ### Pure Tone

# In[18]:


df = 44100
dt = 1.0 / df
t = r_[0:N] * dt
f= 440
data = sin(2*pi*f*t)


# In[19]:


import audio.wave as wave
from IPython.display import *
wave.write(data, "A4.wav", df=df)
Audio("A4.wav")


# In[20]:


figure()
data = wave.read("A4.wav", scale=False)[0]
plot(t, data)


# In[21]:


figure()
hist(data, density=True)


# In[22]:


r = rice.from_frame(data, signed=True)
r


# In[23]:


stream = BitStream(data, r)
len(stream) / (16.0 * N) * 100


# In[24]:


len(data), len(diff(data))


# In[25]:


ddata = r_[data[0], diff(data)]


# In[26]:


ddata2 = diff(r_[0, data])
all(ddata2 == ddata)


# In[27]:


figure()
hist(ddata, density=True)


# In[28]:


stream = BitStream(ddata, rice.from_frame(ddata, signed=True))
len(stream)/ (16.0 * N) * 100


# In[29]:


all(cumsum(ddata) == data)


# In[30]:


dddata = diff(r_[0, ddata])
r = rice.from_frame(dddata, signed=True)
#display(r)
#r = rice(b=12, signed=True)
stream = BitStream(dddata, r)
len(stream)/ (16.0 * N) * 100


# In[31]:


from audio.index import *
import nltk
nltk.download("timit")


# In[32]:


wave.write(search("circle")[0].parent.audio, "circle.wav", df=16000)


# In[33]:


Audio("circle.wav")