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# See http://docs.python.org/tutorial/modules.html#packages

import os

import pyximport;
pyximport.install()

os.sys.path.extend([os.path.abspath('./PyGraphStat/code/'),
                 os.path.abspath('./MR-connectome/mrcap/')])

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def embed_graph(ccfn, roiRootName, fgfn, embedfn, dim=10):
    """
    ccfn - connected components file with numpy arrray
    roiRootName - roi files root name
    fgfn - mat file with the fibergraph
    embedfn - full file name of output file to be saved
    dim - desired dimension for the embedding
    """
    
    vcc = lcc.ConnectedComponent(fn =ccfn)

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# Dependencies cython
# zindex.pyx - https://github.com/openconnectome/MR-connectome/blob/master/mrcap/zindex.pyx
# setup.py - https://github.com/openconnectome/MR-connectome/blob/master/mrcap/setup.py
# Build zindex & install it first - from terminal: sudo python setup.py install 

import zindex 

def xyzToz(xyz):  # xyz is a tuple [x,y,z]
  return XYZMorton() # returns z-index as int

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import networkx as nx
from math import factorial as fact
import sys

def test_tri_count(num_nodes):
  g = nx.complete_graph(num_nodes)
  tri = nx.triangles(g)
  num_tri = sum(tri.values())/3 # Div by 3 because of triple counting
  comb = int(fact(num_nodes)/(fact(3)*fact(num_nodes-3)))

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require(Matrix)
    require(igraph)
    require(gtools)

    args <- commandArgs(trailingOnly = TRUE)

    n <- 10^6
    m <- 10^2
    (KA <- n/m)
    rho <- c(0.02,0.5,0.15,0.08,0.25)

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require(Matrix)
require(igraph)

createc3 <- function()
{
  set.seed(12345)
  n <- 10^6
  m <- 10^2
  (KA <- n/m)
  rho <- c(0.02,0.5,0.15,0.08,0.25)

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import networkx as nx
import numpy as np

p = 0.3 # Alter as necessary
n = 1000 # Alter as necessary
fn = "graph%d"%n # Alter as necessary

g = nx.erdos_renyi_graph(n, p,False)
gsp = nx.to_scipy_sparse_matrix(g)

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require(Matrix)
require(igraph)
require(argparse)

parser <- ArgumentParser(description="Process some integers")
parser$add_argument("gfn", help="The graph file name")
parser$add_argument("nev", type="integer", help="Number of eigenvectors to compute")
parser$add_argument("ncv", type="integer",help="Number of Lanczos vectors to compute")
parser$add_argument("maxiter", type="integer",help="Max number of iterations to compute eigenvectors")

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require(igraph)
dat <- read.table("./fly.csv", header=TRUE, sep=",")
dat <- dat[c("presynaptic",  "postsynaptic" , "pre.x", "post.x", "pre.y", "post.y", "pre.z", "post.z","proofreading.details")]
g <- graph.data.frame(dat, directed=TRUE)
g <- set.graph.attribute(g, "source", value="http://www.nature.com/nature/journal/v500/n7461/full/nature12450.html")
g <- set.graph.attribute(g, "info", value="source=presynaptic, targe=postsynaptic")
write.graph(g, "drosophila_retina.graphml", format="graphml")

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require(igraph)

g <- read.graph("./web-Google.txt") # Change fn
cl <- clusters(g, "strong") # "weak"
unique.clusters <-  unique(cl$csize)
for (i in 1: length(unique.clusters) ) { 
  cat(unique.clusters[i], length(which(cl$csize == unique.clusters[i])),"\n")
}