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

# This notebook shows how to use R BoolNet package from python notebooks to generate random Boolean networks, and load them either with `biolqm` or with `minibn`.
# 
# Two different methods are used: the first with a pure python approach; the second using Jupyter to mix both python and R code.
# 
# The documentation for BoolNet is available here: https://cran.r-project.org/web/packages/BoolNet/BoolNet.pdf

# In[1]:


import biolqm
from colomoto import minibn


# ### Method 1: pure python interface
# 
# We use `rpy2` to provide an interface to the `BoolNet` R package:

# In[2]:


from rpy2.robjects.packages import importr
boolnet = importr("BoolNet")


# `boolnet` is a python object offering a bridge to the R package.
# Then, BoolNet functions can be directly invoked as if `boolnet` was a python module:

# In[3]:


net = boolnet.generateRandomNKNetwork(10, 10, topology="scale_free", gamma=2, 
                                      linkage="uniform", functionGeneration="uniform")


# We save the network to a temporary file:

# In[4]:


netfile = "/tmp/random.bnet"
boolnet.saveNetwork(net, netfile)
netfile


# We can directly load it with `minibn` and show its influence graph:

# In[5]:


bn = minibn.BooleanNetwork.load(netfile)
bn.influence_graph()


# ... or with `biolqm` for further processing and translation to other analysis tools:

# In[6]:


lqm = biolqm.load(netfile)
lqm


# ### Method 2: Mixing python and R in Jupyter notebooks
# 
# We first load the R extension offered by `rpy2`.

# In[7]:


get_ipython().run_line_magic('load_ext', 'rpy2.ipython')


# R commands should be prefixed with `%R`.
# 
# First, let us load the `BoolNet` package:

# In[8]:


get_ipython().run_line_magic('R', 'require(BoolNet)')


# Multiline R code can be written in cells starting with `%%R` line, with optional input and output variables to translate to/from python:

# In[9]:


get_ipython().run_cell_magic('R', '-o netfiles', 'n <- generateRandomNKNetwork(n=10, k=10, topology="scale_free",\n                             linkage="uniform", functionGeneration="uniform")\nnetfiles <- "/tmp/rn.bnet"\nsaveNetwork(n, netfiles)\n')


# Then, similarly to the first method:

# In[10]:


bn = minibn.BooleanNetwork.load(netfiles[0])


# and so on.

# In[ ]: