Igor Latkin igorcoding

## apache_log_parse.py

import re
from pprint import pprint

def parse_request(request_str):
    pattern = re.compile(r"""(?P<method>.*)\s     # method, space
			                 (?P<url>.*)\s	  # url, space
			                 (?P<protocol>.*)	  # protocol
			              """, re.VERBOSE)
    match = pattern.match(request_str)

## generate.py
import datetime
import json
import requests
from random_words import *
import random
import MySQLdb
import MySQLdb.cursors

users = []
forums = []

## index.html
<!doctype html>
<html>
<head>
    <title>Socket.IO chat</title>
    <style>
        * { margin: 0; padding: 0; box-sizing: border-box; }
        body { font: 13px Helvetica, Arial; }
        form { background: #000; padding: 3px; position: fixed; bottom: 0; width: 100%; }
        form input { border: 0; padding: 10px; width: 90%; margin-right: .5%; }
        form button { width: 9%; background: rgb(130, 224, 255); border: none; padding: 10px; }

## batch_rename.sh
#! /bin/bash

SRC=$1
DEST=$2
i=1
for ph in ${SRC}/*; do
	filename=$(basename "$ph")
	ext="${filename##*.}"
	new_filename="${i}.${ext}"
	#echo $new_filename

## series_analyser.py
from pprint import pprint
import math
from scipy.optimize import curve_fit
import numpy as np
import matplotlib.pyplot as plt
import pylab
import scipy.stats as stats
import sklearn.linear_model


## tcp_proxy.pl
use 5.010;
use strict;
use warnings;

use AnyEvent;
use AnyEvent::Handle;
use AnyEvent::Socket;
use AnyEvent::Log;

use Data::Dumper;

## screenshot.sh
#! /bin/sh

TIMESTAMP=`date +%Y-%m-%d-%H-%M-%S`
IMAGEPATH=$HOME/Dropbox/Public/Screenshots/Screenshot_$TIMESTAMP.png

gnome-screenshot $1 -f $IMAGEPATH
dropbox puburl $IMAGEPATH | xclip -selection clipboard

## Web.Config
<?xml version="1.0" encoding="UTF-8"?>
<configuration>
    <system.webServer>
        <handlers>
            <add name="httpplatformhandler" path="*" verb="*" modules="httpPlatformHandler" resourceType="Unspecified" />
        </handlers>
        <httpPlatform processPath="d:\home\site\wwwroot\go\bin\go.exe"
                      arguments="run d:\home\site\repository\app.go"
                      startupTimeLimit="60">
            <environmentVariables>

## tf_mnist_logicregression.py
import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data
import numpy as np

mnist = tensorflow.examples.tutorials.mnist.input_data.read_data_sets("MNIST_data/", one_hot=True)
image_size = 28
n_labels = 10


def create_w_b(n_prev_layer, n_next_layer):

## tf_mnist_nn.py
import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data
import numpy as np

mnist = tensorflow.examples.tutorials.mnist.input_data.read_data_sets("MNIST_data/", one_hot=True)
image_size = 28
n_labels = 10


def create_w_b(n_prev_layer, n_next_layer):

	import re
	from pprint import pprint

	def parse_request(request_str):
	pattern = re.compile(r"""(?P<method>.*)\s # method, space
	(?P<url>.*)\s # url, space
	(?P<protocol>.*) # protocol
	""", re.VERBOSE)
	match = pattern.match(request_str)
	import datetime
	import json
	import requests
	from random_words import *
	import random
	import MySQLdb
	import MySQLdb.cursors

	users = []
	forums = []
	<!doctype html>
	<html>
	<head>
	<title>Socket.IO chat</title>
	<style>
	* { margin: 0; padding: 0; box-sizing: border-box; }
	body { font: 13px Helvetica, Arial; }
	form { background: #000; padding: 3px; position: fixed; bottom: 0; width: 100%; }
	form input { border: 0; padding: 10px; width: 90%; margin-right: .5%; }
	form button { width: 9%; background: rgb(130, 224, 255); border: none; padding: 10px; }
	#! /bin/bash

	SRC=$1
	DEST=$2
	i=1
	for ph in ${SRC}/*; do
	filename=$(basename "$ph")
	ext="${filename##*.}"
	new_filename="${i}.${ext}"
	#echo $new_filename
	from pprint import pprint
	import math
	from scipy.optimize import curve_fit
	import numpy as np
	import matplotlib.pyplot as plt
	import pylab
	import scipy.stats as stats
	import sklearn.linear_model
	use 5.010;
	use strict;
	use warnings;

	use AnyEvent;
	use AnyEvent::Handle;
	use AnyEvent::Socket;
	use AnyEvent::Log;

	use Data::Dumper;
	#! /bin/sh

	TIMESTAMP=`date +%Y-%m-%d-%H-%M-%S`
	IMAGEPATH=$HOME/Dropbox/Public/Screenshots/Screenshot_$TIMESTAMP.png

	gnome-screenshot $1 -f $IMAGEPATH
	dropbox puburl $IMAGEPATH \| xclip -selection clipboard
	<?xml version="1.0" encoding="UTF-8"?>
	<configuration>
	<system.webServer>
	<handlers>
	<add name="httpplatformhandler" path="" verb="" modules="httpPlatformHandler" resourceType="Unspecified" />
	</handlers>
	<httpPlatform processPath="d:\home\site\wwwroot\go\bin\go.exe"
	arguments="run d:\home\site\repository\app.go"
	startupTimeLimit="60">
	<environmentVariables>
	import tensorflow as tf
	import tensorflow.examples.tutorials.mnist.input_data
	import numpy as np

	mnist = tensorflow.examples.tutorials.mnist.input_data.read_data_sets("MNIST_data/", one_hot=True)
	image_size = 28
	n_labels = 10


	def create_w_b(n_prev_layer, n_next_layer):