Ruhshan

import itertools


def all_perm(word):
    # prints all permutation to a given word
    for p in list(itertools.permutations(word)):
        print ''.join(p)

all_perm("abcde")

Ruhshan

# -*- coding: UTF-8 -*-

def upside_down(inp):
    #print upside-down of a given alphabetic string
    rev_dict = {'z': u'z', 'y': u'ʎ', 'x': u'x',
                'w': u'ʍ', 'v': u'ʌ', 'u': u'n',
                't': u'ʇ', 's': u's', 'r': u'ɹ',
                'q': u'b', 'p': u'd', 'o': u'o',
                'n': u'u', 'm': u'ɯ', 'l': u'\u0285',
                'k': u'ʞ', 'j': u'ɾ', 'i': u'ᴉ',

Ruhshan

import glob
import os, time

time_and_file=dict()
folder_name=raw_input()
ct=1
#assuming folder at the same directory as script
for filename in glob.glob(folder_name+"/*.*"):
	name=filename.split('.')[0]
	try:

Ruhshan

#___________myproject/settings.py__________
INSTALLED_APPS = [
    'django.contrib.admin',
    'django.contrib.auth',
    'django.contrib.contenttypes',
    'django.contrib.sessions',
    'django.contrib.messages',
    'django.contrib.staticfiles',
	'anotherapp',
	'activitystream'

Ruhshan

{"test_name":"TCHO","level_1_lower_range":10,"level_1_upper_range":20,"level_2_lower_range":20,"level_2_upper_range":30,"level_3_lower_range":30,"level_3_upper_range":40}

Ruhshan

library(Peptides)
library(seqinr)

get_comp <- function(fasta){
  s=getSequence(fasta, as.string = TRUE);
  sname=getName(fasta);
  
  comp = aaComp(s);
  
  r<-c(name=sname,

Ruhshan

with tf.Session() as sess:
	sess.run(tf.global_variables_initializer())

	for epoch in range(n_epochs):
		epoch_loss = 0
		i=0
		while i < len(train_x):
			start = i
			end = i+batch_size
			batch_x = np.array(train_x[start:end])

Ruhshan

def get_train_data_batch(n):
    features = []
    with open('data.csv') as f:
        count=0
        for line in f:
            splitted = line.split(';')
            featureset = [int(float(x)) for x in splitted[:len(splitted)-1]]
            label = int(float(splitted[-1].rstrip()))
            count+=1
            if count % n==0:

Ruhshan

with tf.Session() as sess:
	sess.run(tf.global_variables_initializer())
	for epoch in range(n_epochs):
		train_data_batch_gen = get_train_data_batch(batch_size)
		epoch_loss = 0
		i=0
		while i < total_lines:
			train_x, train_y = next(train_data_batch_gen)
			
			batch_x = np.array(train_x)

Ruhshan

findcommunities <- function(mygraph,minsize)
  {
     # Function to find network communities in a graph
     # Load up the igraph library:
     require("igraph")
     # Set the counter for the number of communities:
     cnt <- 0
     # First find the connected components in the graph:
     myconnectedcomponents <- connectedComp(mygraph)
     # For each connected component, find the communities within that connected component: