import xlrd
def getDataFromFile(fileName):
with xlrd.open_workbook(fileName) as wb:
# we are using the first sheet here
worksheet = wb.sheet_by_index(0)
# getting number or rows and setting current row as 0 -e.g first
num_rows, curr_row = worksheet.nrows - 1, 0
# retrieving keys values(first row values)
# -*- coding: utf-8 -*-
from xml.dom import minidom
from core.utils import asciize
from signin import sign_file
import urllib2
import os, decimal
import settings
from nfe.exceptions import NoContratorOrIncompleteInformation
from nfe.utils import dict2XML
import os, sys
# sys.path.insert(0, '../')
import libxml2
import xmlsec
def start():
View html_equipaments.html
<!DOCTYPE html>
<html lang="en">
<meta charset="utf-8" />
<meta http-equiv="x-ua-compatible" content="ie=edge" />
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1" />
<link rel="shortcut icon" href="{{ STATIC_URL }}img/favicon.ico" type="image/x-icon" />
<title>Genomika Labs</title>
<!--[if lt IE 9]>
from __future__ import absolute_import
from celery import shared_task
# import the logging library
import logging
# Get an instance of a logger
logger = logging.getLogger(__name__)
#-*- coding: utf-8 -*-
import celery
import hashlib
import datetime
from django.db import models
from django.conf import settings
from django.utils.html import escape
from django.utils.translation import ugettext_lazy as _
View sequence.pyx
String object representing biological sequences with alphabets.
View node.js
$("#send").click(function(e) {
// convert canvas to data url
var img = canvas.toDataURL();
// make request to server
$.post("/", {img: img, n: n}, function() {
// when request is finished, redirect to homepage
return false;
import os
from PIL import Image
import numpy as np
files = [f for f in os.listdir("handwriting/numbers/")]
files = ["handwriting/numbers/" + f for f in files]
STANDARD_SIZE = (50, 50)
def get_image_data(filename):
img =
from sklearn.decomposition import RandomizedPCA
from sklearn.preprocessing import StandardScaler
pca = RandomizedPCA(n_components=10)
std_scaler = StandardScaler()
X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.1)
X_train = pca.fit_transform(X_train)
X_test = pca.transform(X_test)