gakhov

    Alias /munin /var/cache/munin/www
    <Directory /var/cache/munin/www>
        # Rewrite rules to serve traffic from the root instead of /munin-cgi
        RewriteEngine On
        
        # Static files
        RewriteRule ^/favicon.ico /var/cache/munin/www/static/favicon.ico [L]
        RewriteRule ^/static/(.*) /var/cache/munin/www/static/$1          [L]

        # HTML

gakhov

from scipy.signal import argrelextrema

def get_intervals(local_min, local_max, total):
    """Calculate growing and lowering intervals based on
    local max and min. From the definition, growing intervals
    start from local minimum, and lowering intervals start from
    local maximum.
    """
    growing = []
    lowering = []

gakhov

import heapq


def get_fft(signal, f_s=1.0):
    """Calculate FFT of the signal
    """
    fft = np.abs(np.fft.fft(signal))
    freq = np.fft.fftfreq(fft.size, 1.0 / f_s)
 
    half_n = np.ceil(fft.size / 2.0)

gakhov

#!/bin/bash
# script for backuping ES index to the filesystem
#                                                                               
# This script gets all records indexed on the specified day
# on SERVER/INDEXNAME and saves them on the filesystem                          
# as a json file INDEXNAME/BACKUPDIR/BACKUPFILE                                 
# Script also check if mapping and index settings are                           
# already saved and dump them if not. 
#
# Script accept one parameter in format YYYY-MM-DD,

gakhov

#!/usr/bin/env python
from __future__ import division
import os
import sys
import time
import re

DS18B20_ID = "28-0000055f1c88"
DS18B20_TEMP_RE = re.compile(r't=(?P<temperature>\d+)', re.M)

gakhov

1. General Background and Overview

• Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
• Models and Issues in Data Stream Systems
• Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
• Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
• [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

gakhov

# Find potential outliers in values array
# and visualize them on a plot

def is_outlier(value, p25, p75):
    """Check if value is an outlier
    """
    lower = p25 - 1.5 * (p75 - p25)
    upper = p75 + 1.5 * (p75 - p25)
    return value <= lower or value >= upper
 

gakhov

import json

from psda.cardinality.hyperloglog import HyperLogLog

hll = HyperLogLog(precision=10)  # 2^{10} = 1024 counters

with open('visitors.txt') as f:
    for line in f:
        ip = json.loads(line)['ip']
        hll.add(ip)

gakhov

from rdp import rdp

def angle(directions):
    """Return the angle between vectors
    """
    vec2 = directions[1:]
    vec1 = directions[:-1]

    norm1 = np.sqrt((vec1 ** 2).sum(axis=1))
    norm2 = np.sqrt((vec2 ** 2).sum(axis=1))