affo

#!/usr/bin/python
import os
import logging
logging.basicConfig(filename='clean.log', filemode='w', level=logging.DEBUG, format='%(asctime)s:%(levelname)s:%(message)s')

wd = os.getcwd()
alll = os.listdir(wd)

files = []
dirs = []

affo

from functools import wraps

def logger_without_wrap(fn):
	def _wrapper(*args, **kwargs):
		print '# begin: ' + fn.__name__
		fn(*args, **kwargs)
		print '# end: ' + fn.__name__
	return _wrapper

def logger_with_wrap(fn):

affo

import ConfigParser as cp

CONFIG_FILE = 'project.conf'
_CONF = cp.ConfigParser()
_CONF.readfp(open(CONFIG_FILE))

def get(opt, section='DEFAULT'):
    return _CONF.get(section, opt)

affo

#!/bin/bash
if [[ $# -eq 0 ]]; then
    echo "Usage:"
    echo "  templater key=value ... inputfile"
    echo
    echo "Sample input file:"
    echo "  Hello \${name}, enjoy templater!"
    echo
    echo "Sample output:"
    echo "  $ ./templater name=Affo input"

affo

*.pdf
*.log
*.aux
*.fdb_latexmk
*.fls
*.toc
*.out

affo

#include <stdio.h>
#include <stdlib.h>
#include <time.h>

#define N 5
#define MAX 10

int f (int m[][N]) {
    int a[N * N], cont = 0, in;
    int i, j, k;

affo

import random
import time
from rx.subjects import Subject

#### The System

# The entrypoint
s = Subject()
def produce(word):
    s.on_next(word)

affo

import rx

class StatefulOperator(object):
    def default(self, tup):
        msg = 'Default method called with ' + str(tup)
        return dict(message=msg)

    def __call__(self, tup):
        method_name = tup.pop('method_name', 'default')
        return getattr(self, method_name)(tup)

affo

import numpy as np
import lxmls.readers.simple_sequence as ssr
simple = ssr.SimpleSequence()

print simple.train, type(simple.train.seq_list)
print simple.test, type(simple.test.seq_list)

for s in simple.train.seq_list:
    print s.x, s.y

affo

import random
import numpy as np

# Ok, the point is now to create a naive
# bayes model and stop without accounting
# for the lab...

# We want to model for example the food.
# The food can be terrific, bad, not bad, good
# or awesome: