juliaplots.py

# coding: utf-8

# # Julia Set Plotting Extension
# 
# Load module for a JuliaSet that conforms to the specified interface.
# 
# It is wise to run the test suite in `test_juliaset.py` with `nosetests` prior to attempting to plot here.

# In[1]:


from juliaset import JuliaSet


# Load additional libraries needed for plotting and profiling.

# In[2]:


# Math libraries
import numpy as np
from math import sqrt

# Matplotlib plotting libraries
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')

# Bokeh plotting libraries
import bokeh.plotting as blt
blt.output_notebook()


# Extend JuliaSet class with additional functionality.

# In[3]:


class JuliaSetPlot(JuliaSet):
    """Extend JuliaSet to add plotting functionality"""
    
    def __init__(self, *args, **kwargs):
        # Invoke constructor for JuliaSet first, unaltered
        super(JuliaSetPlot, self).__init__(*args, **kwargs)
        # Add one more attribute: a rendered image array
        self.img = np.array([])
    
    def get_dim(self):
        # get what should be an attribute
        return int(4.0 / self._d)
    
    def render(self):
        if not self.set: self.generate()
        # Convert inefficient list to efficient numpy array
        self.img = np.array(self.set)
        dim = self.get_dim()
        # Reshape array into a 2d complex plane
        self.img = np.reshape(self.img, (dim,dim)).T
        
    def show(self):
        if not self.img.size: self.render()
        # Specify complex plane axes efficiently
        xy = np.linspace(-2,2,self.get_dim())
        # Use matplotlib to plot image as an efficient mesh
        plt.figure(1, figsize=(12,9))
        plt.pcolormesh(xy,xy,self.img, cmap=plt.cm.hot)
        plt.colorbar()
        plt.show()
        
    def interact(self):
        from matplotlib.colors import rgb2hex
        if not self.img.size: self.render()
        # Mimic matplotlib "hot" color palette
        colormap = plt.cm.get_cmap("hot")
        bokehpalette = [rgb2hex(m) for m in colormap(np.arange(colormap.N))]
        # Use bokeh to plot an interactive image
        f = blt.figure(x_range=[-2,2], y_range=[-2,2], plot_width=600, plot_height=600)
        f.image(image=[j.img], x=[-2,2], y=[-2,2], dw=[4], dh=[4], palette=bokehpalette)
        blt.show(f)


# Visualize a Julia set using matplotlib.

# In[4]:


j = JuliaSetPlot(-1.037 + 0.17j)
get_ipython().run_line_magic('time', 'j.set_spacing(0.006)')
get_ipython().run_line_magic('time', 'j.show()')


# Visualize a different Julia set using Bokeh as an interactive Javascript plot.

# In[5]:


j = JuliaSetPlot(-0.624 + 0.435j)
get_ipython().run_line_magic('time', 'j.set_spacing(0.006)')
get_ipython().run_line_magic('time', 'j.interact()')


# In[6]:


get_ipython().run_line_magic('prun', 'j.generate()')


# In[7]:


get_ipython().run_line_magic('load_ext', 'line_profiler')
get_ipython().run_line_magic('lprun', '-f j.generate j.generate()')