cgoliver

A 5 3 2 2 2 -12 -12 -9 -1 -2 0 -8 -7 -7 -7 -5 -4 -6 -6 -3 -5 -3 -4
Y 5 2 3 2 -15 -10 -10 -1 -2 -1 -8 -8 -7 -7 -5 -6 -7 -7 -3 -5 -3 -4
B 5 2 2 -12 -10 -10 1 -2 -2 -7 -7 -6 -6 -5 -4 -6 -5 -2 -5 -3 -4
C 5 1 -11 -9 -9 -1 1 -1 -8 -7 -7 -6 -5 -5 -6 -6 -3 -5 -3 -4
D 5 -10 -9 -9 1 0 1 -6 -5 -5 -5 -4 -1 -4 -4 -1 -4 -2 -3
E 6 1 2 -8 -9 -8 -2 -1 -4 -4 -8 -6 -3 -4 -6 -6 -7 -3
F 6 0 -6 -7 -7 1 -1 -3 -3 -6 -5 -2 -4 -4 -4 -5 -2
H 6 -5 -6 -6 -1 2 -3 -2 -6 -4 0 -3 -4 -2 -4 -2
G 7 0 -1 -4 -4 -3 -3 -3 -1 -2 -1 2 -2 1 -2
I 9 3 -4 -3 -4 -4 -2 2 -3 -3 -1 -2 -1 -2

cgoliver

import numpy as np
import matplotlib.pyplot as plt
from sklearn.neighbors import NearestNeighbors

def get_eps_nei(neigh, X, i):
    """ Wrap the unnecessary output from sklearn.
    Returns the indices of the k nearest neigbhors of a point,
    excluding the query point.
    """
    e = list(neigh.radius_neighbors(X[i].reshape(1, -1),

cgoliver

from mycsv import * 

if __name__ == "__main__":
    #creating parser object
    #these are default values anyway
    #passing them explicitly just to show funcionatity
    parser = MyCSV(delimiter=",", header=True)

    #parsing test file
    data = parser.parse("test.csv")

cgoliver

## Nussinov RNA folding algorithm + recursive backtrack. Implemented by Carlos G. Oliver ##

import numpy as np

min_loop_length = 4

def ss_to_bp(ss):
    pairs = []
    stack = []
    for i, b in enumerate(ss):

cgoliver

"set nocompatible              " required
filetype plugin on	      " required

" set the runtime path to include Vundle and initialize
set rtp+=~/.vim/bundle/Vundle.vim
call vundle#begin()
"
" " alternatively, pass a path where Vundle should install plugins
" "call vundle#begin('~/some/path/here')
"

cgoliver

0x8F31Ac74713f91f0A1b096b2731E925871AEba2E