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

# # <div style="float: right;" markdown="1">     <img src="http://matplotlib.org/_static/logo2.png"> </div>  Matplotlib

# Matplotlib is a python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. matplotlib can be used in python scripts, the python and ipython shell, web application servers, and six graphical user interface toolkits.
# 
# 
# Matplotlib tries to make easy things easy and hard things possible. You can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc, with just a few lines of code. For a sampling, see the screenshots, thumbnail gallery, and examples directory
# 
# 
# For simple plotting the pyplot interface provides a MATLAB-like interface, particularly when combined with IPython. For the power user, you have full control of line styles, font properties, axes properties, etc, via an object oriented interface or via a set of functions familiar to MATLAB users.
# 
# 
# [**Official page**](http://matplotlib.org/)

# - - -
# 
# - [Basic Use](#Basic-Use) 
# - [Formating Figures](#Formating-Figures)
# - [Gallery](#Gallery)
# 
# - - -

# # Basic Use

# One of the most appealing advantages of Matplotlib is its versatility. You can use it in a very basic way as well as in a very customizable way, allowing a total control of what we want to sketch. We illustrate here some of the basic functions.

# In[1]:


#Importing numpy
import numpy as np
#Ignore me!! (for scripts)
get_ipython().run_line_magic('pylab', 'inline')
#Importing matplotlib
import matplotlib.pyplot as plt


# The line **%pylab inline** is always necessary when using a IPython notebook. If you are working on the interpreter or with scripts, always use the function **plt.show()** in order to display the resulting image.

# In[2]:


#Ploting a function
X = np.linspace( 0, 2*np.pi, 100 )
Y = np.sin( X )
plt.plot( X, Y )


# In[6]:


#Scatter of points
X = np.random.random(200)
Y = np.random.random(200)
plt.plot( X, Y, 'o' )


# In[7]:


#Multiple plots
#Ploting a function
X = np.linspace( 0, 2*np.pi, 100 )
Y1 = np.sin( X )
Y2 = np.cos( X )
plt.plot( X, Y1 )
plt.plot( X, Y2 )


# In[8]:


#Logaritmic axis Y
X = np.linspace( 1, 10, 100 )
Y = np.exp( X )
plt.semilogy( X, Y )


# In[9]:


#Logaritmic axis X
X = np.linspace( 1, 10, 100 )
Y = np.log( X )
plt.semilogx( X, Y )


# In[14]:


#Double Logaritmic axis
X = np.linspace( 0.1, 16.5*np.pi, 1000 )
Y = np.sin( X )**2
plt.loglog( X, Y )


# In[18]:


#Polar plots
r = np.arange( 0, 3.0, 0.01 )
theta = 2*np.pi*r

plt.subplot(111, polar=True)
plt.plot( theta, r )


# In[23]:


#Fill between
X = np.arange( 0, 2, 0.1 )
Y1 = X/2.
Y2 = X**2 + 1
plt.fill_between( X, Y1, Y2 )


# In[27]:


#Multiple figures
X = np.arange( 0, 10, 0.1 )
#First plot
plt.subplot( 1, 2, 1 )
plt.plot( X, X**2+1 )
#Second plot
plt.subplot( 1, 2, 2 )
plt.semilogy( X, np.exp(X-10) )


# In[33]:


#Histograms
X = np.random.random( 500 )
plt.hist( X )


# - - - 

# # Formating Figures

# Matplotlib supports complex formatting, even allowing LaTeX expressions. These features makes Matplotlib a very appealing package for making professional figures, for example for a scientific paper. Next it is shown an example (**script**) where you can see some of the capabilities of Matplotlib

# In[79]:


#! /usr/bin/python
#====================================================================
#Importing libraries
#====================================================================
import numpy as np
import matplotlib.pyplot as plt

#Arrays
X = np.linspace( 0, 10, 100 )
Y = X**0.5
Y1 = Y+0.5*(1+np.random.random(100))
Y2 = Y-0.5*(1+np.random.random(100))

#Setting the figure environment
plt.figure( figsize = (16,6) )

#====================================================================
#First plot
#====================================================================
plt.subplot( 1, 2, 1 )
plt.plot( X, Y )
plt.title( "This is an unformatted plot" )

#====================================================================
#Second plot
#====================================================================
plt.subplot( 1, 2, 2 )
#2D Plots
plt.plot( X, Y, linewidth=4, color="green", label="Function" )
plt.plot( X, Y1, linewidth=2, color="green" )
plt.plot( X, Y2, linewidth=2, color="green" )
#Error region
plt.fill_between( X, Y1, Y2, color="green", alpha=0.2 )
#Grid
plt.grid( True )
#X-axis limits
plt.xlim( (0, 10) )
#Y-axis limits
plt.ylim( (0, 5) )
#Label
plt.legend( loc="upper left", fontsize=16 )
#X label
plt.xlabel( "$x$ coordinate", fontsize=20 )
#Y label
plt.ylabel( "$\sqrt{x}$", fontsize=20 )
#Title
plt.title( "This is a formatted plot", fontsize=16 )

#====================================================================
#Showing
#====================================================================
#You can store this figure in any format, even in vectorial formats 
#like pdf
plt.savefig( "Figure.pdf" )
#Showing the figure on screen
plt.show()


# - - - 

# # Gallery

# This gallery comprehends some interesting advanced uses of matplotlib.

# In[70]:


from IPython.core.display import Image 
Image(filename='./figures/voids.png')


# In[77]:


Image(filename='./figures/VPHphase.png')


# In[78]:


Image(filename='./figures/simulation.png')


# In[75]:


Image(filename='./figures/voiddensity.png')


# In[76]:


Image(filename='./figures/halosfraction.png')


# - - -