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

# In[1]:


get_ipython().run_line_magic('matplotlib', 'inline')
import numpy as np
import matplotlib.pyplot as plt, mpld3
from mpl_toolkits.mplot3d import Axes3D


# ## Signal and spectrogram

# In[2]:


from scipy.io import wavfile
rate, signal = wavfile.read("media/NST_Arpeggios_C_Audio.wav")
ns = signal.shape[0]
t = np.linspace(0, ns/rate, ns)


# In[3]:


fig, (ax1, ax2) = plt.subplots(1,2,figsize=(16,5))
ax1.plot(t, signal[:,0])
ax1.set_title('Raw audio signal')
# Spectrogram
Pxx, freqs, bins, im = ax2.specgram(signal[:,0], Fs=rate, cmap="RdBu")
ax2.set_ylim([0,max(freqs)])
ax2.set_title('Spectrogram');
bound = np.linspace(-40,40, 51)
cb = fig.colorbar(im, boundaries=bound, ticks=bound[::10])
cb.set_label('Intensity [dB]')
cb.set_clim(-40, 40)


# ## Embedded figures and JSON figures with mpld3

# In[4]:


plt.figure(figsize=(8,4))
x = np.linspace(0, 1, 1000)
y = np.sin(4 * np.pi * x) * np.exp(-5 * x)
plt.plot(x, y, lw=2)
plt.grid(True, lw=1, color='black', alpha=0.2)
plt.show()


# In[5]:


mpld3.enable_notebook()
plt.figure(figsize=(8,4))
x = np.linspace(0, 1, 1000)
y = np.sin(4 * np.pi * x) * np.exp(-5 * x)
plt.plot(x, y, lw=2)
plt.grid(True, lw=1, color='black', alpha=0.2)
plt.show()


# ### Contour plot with mpld3

# In[6]:


alpha = 0.7
phi_ext = 2 * np.pi * 0.5

def flux_qubit_potential(phi_m, phi_p):
    return 2 + alpha - 2 * np.cos(phi_p)*np.cos(phi_m) - alpha * np.cos(phi_ext - 2*phi_p)


# In[7]:


phi_m = np.linspace(0, 2*np.pi, 100)
phi_p = np.linspace(0, 2*np.pi, 100)
X,Y = np.meshgrid(phi_p, phi_m)
Z = flux_qubit_potential(X, Y).T


# In[8]:


fig, ax = plt.subplots()

im = ax.imshow(Z, cmap="RdBu", vmin=abs(Z).min(), vmax=abs(Z).max(), extent=[0, 1, 0, 1])
im.set_interpolation('bilinear')

cb = fig.colorbar(im, ax=ax)


# ## Plotly

# In[14]:


import plotly
print plotly.__version__            # version 1.9.x required


# In[15]:


plotly.offline.init_notebook_mode() # run at the start of every notebook


# In[17]:


plotly.offline.iplot({
    "data": [{
        "x": [1, 2, 3],
        "y": [4, 2, 5]
    }],
    "layout": {
        "title": "hello world"
    }
})


# In[18]:


x = np.linspace(0, 20, 100)
y = np.sin(x)
plotly.offline.iplot({
    "data": [{
        "x": x,
        "y": y
    }]
})


# In[19]:


from IPython.core.display import HTML
def css_styling():
    styles = open('./styles/custom_barba.css', 'r').read()
    return HTML(styles)
css_styling()


# In[ ]: