Archive for the ‘This and That’ Category

What I hate about Pulseaudio is its crappy default configuration. After fresh installation of SUSE Leap 42.2 I encountered an issue that subwoofer didn’t work with Amarok… again((( Lucky me, the first link that google provided me with was link to my own(!!!) thread that I started about 6 years ago (sick!!!). In 2011 I encountered the same issue, asked for help, but solved on my own and answered for my own request. And now future me was able to find that long forgotten thread of mine. That’s so sweet to recieve a messege to miself )))

This time I will put the answer here to remember it better 😉

The problem solved, tnx to this post.
In /et/pulse/daemon.conf I’ve made following changes:

Code:
 
enable-lfe-remixing = yes
default-channel-map = front-left,front-right,rear-left,rear-right,front-center,lfe
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A satisfied customer

Posted: November 9, 2015 in GIS, This and That
Tags: ,

Recently I did a QGIS scripting job and here is the feedback from an extremely satisfied customer:

It was fantastic to work with Yury. He provided an excellent product, that went far beyond what I was expecting. I will certainly be contacting Yury in the future for any jobs that relate to GIS and python scripting. Communication was excellent and his ability to understand the job requirement was very impressive. A+++

Guys, if you are in need of geoprocessing tool for your project – don’t hesitate to contact me 😉

A Dialogue With Bot

Posted: January 29, 2015 in This and That
Tags: , , , ,

Back in the days when ICQ was popular spam bots added you to their contact lists dozens times a day. But they were brute and ugly. At first they spew their spam links at your face when you added them, then they simply started to insert spam links in the body of the invitation. ICQ is long dead and spam-bots never bothered me in modern protocols… until some time ago. (more…)

Can you guess the projection that I used to display a world map at the picture below (EPSG code is left there because I doubt someone would recall it anyway)?

It is the World Bonne projection in QGIS (original shp’s CRS is EPSG:4326). I already had some fun with Bonne in this post. The world got torn apart at scale 1:75 000 000. At bigger scales map renders normally:

 

Fortunately this issue can be easily resolved by disabling “feature simplification by default for newly added layers” under Settings -> Options -> Rendering.

Donetsk Label at OpenStreetMap

Posted: August 31, 2014 in This and That
Tags:

Now, when Donetsk became the hot spot of the civil war at the Ukraine I decided to take a glance at the map of the war zone. Of course I used OSM for this. But despite I knew the approximate location of the city I couldn’t find it for several minuets. I was confused… The reason was that “Donetsk” label (label of the city which is the administrative center of the Donetskaya region BTW) at the scales smaller than 10 km is suppressed by the label of adjusted town “Makeevka”. WTF?!

“Donetsk” (“Донецьк”) label at OSM is suppressed by the label of adjusted town

Introduction

Here is an interesting post about 6.81 patch influence on heroes win rate in Dota 2. One point caught my attention: a drastic change in Axe win rate: from 52% to 48%; see image below.
Axe’s win rate before and after 6.81 patch (29-th of April)
The only change Axe get in this patch was implementation of the preudo random approach for the determination of Counter Helix skill triggering instead of random. As was guessed in the article:  

“…where you manage to Berserker’s Call several enemies, you only have 3.2 seconds of being unconditionally hit by the enemy. In this time frame, the amount of successful Counter Helixes that use PRD is probably lower than with a true random. Hence the decrease in damage output and Win Rate.” 

 Ok, nice guess! Lets test it!

Create a simulation to test the hypothesis

Import needed modules
import random
import numpy
from tabulate import tabulate
import collections

<div>
<div style="text-align: justify;">
Lets create functions to calculate number of Counter Helix PROCs for given number of hits using random and pseudo random approaches. First one will be using random approach (notice that random.randrange() actually generates pseudo random set of values)</div>
<br />

def randHelixProc(hits):
  '''
  checks if Counter Helix PROCs (chance = 17%) using random approach
  @param hits: number of times Axe was hit
  @return procs: number of times Counter Helix PROCs
  '''
  procs = 0

  for i in xrange(hits):
    chance = random.randrange(1, 101)
    if chance < 18:
      procs += 1
    else:
      continue

  return procs

I don't know how exactly pseudo random PROCs are designed for Axe or for Dota 2 in general, but they say that the probability of the pseudo random event in Dota 2 in it initial state has lower chance to proc than the stated probability for the event and each time the event is not triggered when it could the chance for this event to occur is increased. Lets say some event triggers with the 50% chance. Suggested pseudo random approach can be realised with 3 consecutive checks that have different chances: 0%, 50% and 100% (average is 50%). When event finally triggers it reset chance for the event to initial value (0% in this case) and it all starts over again.
def pseudoRandHelixProc(hits):
  '''
  checks if Counter Helix PROCs (chance = 17%) using pseudo random approach
  @param hits: number of times Axe was hit
  @return procs: number of times Counter Helix PROCs
  '''
  treshold = 0
  prob_list = [2, 5, 9, 14, 23, 47] # ~17% on average
  procs = 0
  for i in xrange(hits):
    chance = random.randrange(1, 101)
    try:
      success = prob_list[treshold]
    except:
      success = prob_list[5]
    if chance >= success:
      treshold += 1
    else:
      procs += 1
      treshold = 0
  return procs
Check if the chances for PROCs are the same for the both functions. We should have 1700 procs of Counter Helix for 10000 attacs on Axe. Launch 100 simulations of 10000 hits for each function and compute average procs for random and pseudo random approaches.
rhelix_list = []
for i in xrange(100):
  value = randHelixProc(10000)
  rhelix_list.append(value)
numpy.mean(rhelix_list)

>>>1702.79

p_rhelix_list = []
for i in xrange(100):
  value = pseudoRandHelixProc(10000)
  p_rhelix_list.append(value)
numpy.mean(p_rhelix_list)

>>>1702.3

Output difference is negligible for random and pseudo random implementations.
Now its time to create a simulation function.

def simulation(times, hits):
  '''
  Computes average number of PROCs for given hits for random and pseudo random
  approaches and difference between them.

  @param times: number of times simulation will run
  @param hits: number of hits to simulate
  @return: average difference in pocs between random and pseudo random approaches;
           table of simul results
  '''
  # create lists of results
  diff_list = []
  rand_mean_list = []
  p_rand_mean_list = []

  # run simulation
  for hit in xrange(1, hits + 1):

    rand_list = []
    pseudo_rand_list = []

    for t in xrange(times):
      rand = randHelixProc(hit)
      p_rand = pseudoRandHelixProc(hit)
      rand_list.append(rand)
      pseudo_rand_list.append(p_rand)

    # compute statistics and populate lists of results
    rand_mean = numpy.mean(rand_list)
    rand_mean_list.append(rand_mean)
    p_rand_mean = numpy.mean(pseudo_rand_list)
    p_rand_mean_list.append(p_rand_mean)
    diff = rand_mean - p_rand_mean
    diff = round(diff, 2)
    diff_list.append(diff)

  # print average difference in PROCs
  total_diff = sum(diff_list)
  l = len(diff_list)
  print 'average difference:', total_diff/l
  print '#######################################################################################################'

  # create table output for simulation results
  out_dict = {}
  out_dict['(1) cumulative hits'] = range(1, l + 1)
  out_dict['(2) random mean PROCs'] = rand_mean_list
  out_dict['(3) pseudo random mean PROCs'] = p_rand_mean_list
  out_dict['(4) difference'] = diff_list
  out_dict = collections.OrderedDict(sorted(out_dict.items()))
  print tabulate(out_dict, headers = 'keys', tablefmt="orgtbl")

Lets run 100 simulations of 100 consecutive hits. Of course it is possible to run 10000 hits but it is unnecessary since every time the Counter Helix is triggered we jump back to the first row from the table below (result of the simulation). As was already mentioned:

"[if] ...you manage to Berserker's Call several enemies, you only have 3.2 seconds of being unconditionally hit by the enemy"

If Axe was able to catch 3 enemies, together they will hit him about 9-12 times. This means that with the pseudo random approach he will spin 0.3-0.4 times less than with random approach. It will cause him to deal ~(205*0.35)*3 = 215,25 (or 71.75 per hero) less damage in engagement.

So the hypothesis was true - pseudo random approach caused the decrease in damage output on the short time interval even if total number of PROCs during the game stayed the same.

average difference: 0.34
#######################################################################################################
|   (1) cumulative hits |   (2) random mean PROCs |   (3) pseudo random mean PROCs |   (4) difference |
|-----------------------+-------------------------+--------------------------------+------------------|
|                     1 |                    0.17 |                           0    |             0.17 |
|                     2 |                    0.45 |                           0.09 |             0.36 |
|                     3 |                    0.56 |                           0.12 |             0.44 |
|                     4 |                    0.71 |                           0.26 |             0.45 |
|                     5 |                    0.81 |                           0.36 |             0.45 |
|                     6 |                    1.06 |                           0.75 |             0.31 |
|                     7 |                    1.37 |                           0.88 |             0.49 |
|                     8 |                    1.35 |                           0.97 |             0.38 |
|                     9 |                    1.47 |                           1.22 |             0.25 |
|                    10 |                    1.64 |                           1.33 |             0.31 |
|                    11 |                    1.95 |                           1.65 |             0.3  |
|                    12 |                    2.1  |                           1.68 |             0.42 |
|                    13 |                    2.36 |                           1.79 |             0.57 |
|                    14 |                    2.56 |                           2.16 |             0.4  |
|                    15 |                    2.61 |                           2.26 |             0.35 |
|                    16 |                    2.61 |                           2.37 |             0.24 |
|                    17 |                    2.87 |                           2.43 |             0.44 |
|                    18 |                    2.77 |                           2.83 |            -0.06 |
|                    19 |                    3.22 |                           2.84 |             0.38 |
|                    20 |                    3.03 |                           2.84 |             0.19 |
|                    21 |                    3.6  |                           3.22 |             0.38 |
|                    22 |                    3.71 |                           3.32 |             0.39 |
|                    23 |                    3.68 |                           3.68 |             0    |
|                    24 |                    3.93 |                           3.72 |             0.21 |
|                    25 |                    4.54 |                           3.92 |             0.62 |
|                    26 |                    4.65 |                           4.04 |             0.61 |
|                    27 |                    4.47 |                           4.27 |             0.2  |
|                    28 |                    4.83 |                           4.39 |             0.44 |
|                    29 |                    4.78 |                           4.48 |             0.3  |
|                    30 |                    4.93 |                           4.8  |             0.13 |
|                    31 |                    5.3  |                           4.92 |             0.38 |
|                    32 |                    5.1  |                           5.04 |             0.06 |
|                    33 |                    5.79 |                           5.34 |             0.45 |
|                    34 |                    5.82 |                           5.54 |             0.28 |
|                    35 |                    6.04 |                           5.52 |             0.52 |
|                    36 |                    5.67 |                           5.7  |            -0.03 |
|                    37 |                    6.64 |                           5.98 |             0.66 |
|                    38 |                    6.4  |                           6.03 |             0.37 |
|                    39 |                    6.72 |                           6.41 |             0.31 |
|                    40 |                    7.07 |                           6.46 |             0.61 |
|                    41 |                    7.04 |                           6.59 |             0.45 |
|                    42 |                    7.18 |                           6.81 |             0.37 |
|                    43 |                    7.08 |                           6.9  |             0.18 |
|                    44 |                    7.61 |                           7.09 |             0.52 |
|                    45 |                    7.72 |                           7.21 |             0.51 |
|                    46 |                    7.73 |                           7.66 |             0.07 |
|                    47 |                    8    |                           7.68 |             0.32 |
|                    48 |                    8.41 |                           7.7  |             0.71 |
|                    49 |                    8.57 |                           7.93 |             0.64 |
|                    50 |                    8.54 |                           8.11 |             0.43 |
|                    51 |                    8.16 |                           8.31 |            -0.15 |
|                    52 |                    9    |                           8.4  |             0.6  |
|                    53 |                    9.01 |                           8.79 |             0.22 |
|                    54 |                    8.98 |                           8.88 |             0.1  |
|                    55 |                    9.54 |                           8.99 |             0.55 |
|                    56 |                    9.4  |                           9.13 |             0.27 |
|                    57 |                    9.75 |                           9.08 |             0.67 |
|                    58 |                   10.1  |                           9.42 |             0.68 |
|                    59 |                    9.71 |                           9.64 |             0.07 |
|                    60 |                   10.31 |                           9.87 |             0.44 |
|                    61 |                   10.19 |                          10.31 |            -0.12 |
|                    62 |                   10.29 |                          10.21 |             0.08 |
|                    63 |                   10.76 |                          10.55 |             0.21 |
|                    64 |                   10.82 |                          10.48 |             0.34 |
|                    65 |                   10.7  |                          10.77 |            -0.07 |
|                    66 |                   11.27 |                          10.94 |             0.33 |
|                    67 |                   11.81 |                          11.06 |             0.75 |
|                    68 |                   11.54 |                          11.34 |             0.2  |
|                    69 |                   11.98 |                          11.26 |             0.72 |
|                    70 |                   12.26 |                          11.66 |             0.6  |
|                    71 |                   11.35 |                          12.01 |            -0.66 |
|                    72 |                   12.03 |                          11.64 |             0.39 |
|                    73 |                   12.37 |                          11.94 |             0.43 |
|                    74 |                   12.74 |                          12.16 |             0.58 |
|                    75 |                   13.16 |                          12.66 |             0.5  |
|                    76 |                   12.77 |                          12.59 |             0.18 |
|                    77 |                   13.1  |                          12.65 |             0.45 |
|                    78 |                   13.2  |                          12.95 |             0.25 |
|                    79 |                   13.59 |                          13.06 |             0.53 |
|                    80 |                   13.32 |                          13.27 |             0.05 |
|                    81 |                   13.74 |                          13.25 |             0.49 |
|                    82 |                   13.98 |                          13.47 |             0.51 |
|                    83 |                   14.86 |                          13.74 |             1.12 |
|                    84 |                   14.53 |                          13.8  |             0.73 |
|                    85 |                   14.54 |                          14.29 |             0.25 |
|                    86 |                   14.49 |                          14.22 |             0.27 |
|                    87 |                   15.03 |                          14.46 |             0.57 |
|                    88 |                   15.49 |                          14.55 |             0.94 |
|                    89 |                   15.51 |                          14.8  |             0.71 |
|                    90 |                   15.58 |                          15    |             0.58 |
|                    91 |                   16.17 |                          15.01 |             1.16 |
|                    92 |                   14.89 |                          15.43 |            -0.54 |
|                    93 |                   15.73 |                          15.4  |             0.33 |
|                    94 |                   16.16 |                          15.67 |             0.49 |
|                    95 |                   16.11 |                          16.17 |            -0.06 |
|                    96 |                   16.03 |                          16.17 |            -0.14 |
|                    97 |                   16.41 |                          16.07 |             0.34 |
|                    98 |                   17.26 |                          16.27 |             0.99 |
|                    99 |                   15.65 |                          16.7  |            -1.05 |
|                   100 |                   16.15 |                          16.96 |            -0.81 |
It’s been a while since I analysed something with R. I decided to pick into Dota 2 statistics. This game is the real eSport now with players fighting for respectable prise pools. And the main Dota 2 event is The International where best teams compete each other in astonishing battles.
So I grabbed some stats on The International 3 from here and started to think what can I do with it… The one major aspect of the game that was actively discussed prior the Tournament was the Dire side has an advantage over the Radiant via having Roshan Pit in their possession. Many people still think that Dire side is preferable in competitive games (they say that Roshan is killed by the Dire in 70% of cases). In fact The Alliance (TI3 winner) played like 90% of their games on the Dire side at the tournament. But is it a valid proof of the Dire side advantage? – I doubt. I think that in contrary – the Radiant side has an advantage over Dire, but I will add my arguments after I prove I’m right.
Ok, here is my hypothesis. There is no time limitation for the game. The match in Dota 2 lasts until the main building of the one of the sides is destroyed (or one of the team will give up). So if one of the sides (all the other things being equal) has an advantage it will cause the median time that was used to win the game for this side to be lower than to win for the other (and vice versa for time to loose). So lets take a look at the boxplot below:
dota 2 the international 3 winning time comparrison by side
Dire Vs Radiant winning time comparisson

Code:

library(ggplot2)

TI3 <- read.csv("~/R/Dota2_Stats/The_International-3")

# create winning time per side plot
p <- ggplot(TI3, aes(TI3[,5], TI3[,8])) +
  geom_boxplot(aes(fill = TI3[,5]))+
  labs(title = 'Winning time per side at TI-3',
       x = 'Side',
       y = 'Time, min.',
       fill = 'Side')

print(p)
Clearly, the Radiant side generally wins slightly quickly then Dire (and have higher number of wins: 82 against 76). This means that not the Dire, but the Radiant team has an advantage in game. But why? (You may skip the rest of the paragraph if you never played Dota). There are several reasons. Radiant advantages are: easier rune control, ability to deny creeps at the hard lane, camera angle (it is not parallel to the terrain surface and facing north towards the Dire side). Camera angle was never discussed as the advantage/disadvantage because most people just got used to it, but Radiant has slight, but sure vision advantage. Seems Roshan accessibility and a safer ancient camp does not help that much to the Dire. 
What else can we do with time analysis? We can compare win and loss times for all the teams that competed at TI3:
Teams winning and loosing time comparison
Code:

# get the list of teams
Teams <- unique(TI3[,3])

# create dataset to store data about winning/loosing times
A_time <- data.frame('Team'= character(),
                     'Result' = character(),
                     'Time' = numeric(),
                     stringsAsFactors = FALSE)

# extract time data and write it to the A_time data frame
for (team in Teams) {
  A <- subset(TI3, TI3[,3] == team | TI3[,4] == team)

  for (i in 1:nrow(A)) {
    winner <- A[i,][5]
    dire <- A[i,][4]
    radiant <- A[i,][3]
    time <- A[i,][8]
    if ( (winner == 'DIRE' & dire == team) | (winner == 'RADIANT' & radiant == team) ) {
     result <- paste(team, 'WIN')
    }
    else {
     result <- paste(team, 'LOSS') 
    }
    A_time[(nrow(A_time)+1),] <- c(team,result, time)
  }
}

# create plot for winning time per team
p <- ggplot(A_time, aes(A_time[,2], A_time[,3])) +
     geom_boxplot(aes(fill = A_time[,1]))+
     theme(axis.text.x = element_text(angle=90, hjust = 0),
          axis.title = element_text(face = 'bold', size = 14),
          title = element_text(face = 'bold', size = 16),
          legend.position = 'right'
          ) +
     labs(title = 'Win and loss times at The International 3',
          x = 'Results',
          y = 'Time, min.',
          fill = 'Teams')

print(p)

Generally my assumption was correct: better teams wins quicker. Alliance and NaVi  (1-st and 2-nd places) is a nice conformation to it (DK and IG (TI-2 champion) have the similar pattern as well despite shared 5-th place). But Orange and TongFu (3-rd and 4-th place) tends to loose quicker than win. This could be explained by the general playstile of this two Asian teams which often aims at the late game. This infamous style with prolonged no action farming stage is often referred as ‘Chinese Dota’. But DK and IG are Chinese teams too.   Seems that both TongFu and Orange were able overcame the odds and jumped over their heads in the given tournament. They took places that DK and IG should have get (DK and IG were more favourable teams than Orange and TongFu before the tournament).

About Questions and Answers

Posted: March 14, 2013 in This and That
Tags:
That is very interesting how people perceive and assess questions and answers. How do we assess quality of the question? The first and the main criteria is whether topic-starter did his homework searching for the answer himself. That’s why “Do your homework” is the first advise on “how to ask the question” page on every software forum (and there is one on gis.stackexchange). The second criteria is sufficiency of information provided by the topic-starter. No one likes to play clairvoyant and guess what is the actual situation (but with experience the ability to foresight omitted details is being developed) and waste their time asking for more information. (more…)

GRUB2 Recovery

Posted: March 9, 2013 in This and That
Tags: ,
Encountered an unpleasant issue today. Suddenly without any reason GRUB2 at my dual boot laptop halted with the “GRUB” string on the black screen and refused to proceed any further. Seems that is a known issue for the openSUSE 12.2 (GRUB2 became a bootloader for this version).
GRUB recovery usually an easy procedure and I did it quite often having several machines with a windows as a spare OS (it has an annoying habit deleting GRUB during own installation). I just used openSUSE live USB to load, and in Yast in Bootloader settings asked to propose the configuration for GRUB and then just saved it to the /boot partition.
This time it didn’t work at all… So I’ve spent all the day looking for solution. This two sources helped me a lot: Re-install Grub2 from DVD Rescue and openSUSE Help and Troubleshooting. Here you are the steps that helped me with my dualboot needs:
  1. Boot to Rescue System from the openSUSE installation DVD (login is root).
  2. Run fdisk -l to locate root partition (/dev/sda6 for me)
  3. Mount all that is needed (including partitions with other OSs so they will be scanned and included in GRUB configuration):
    • mount /dev/sda6 /mnt 
    • mount --bind /dev /mnt/dev 
    • chroot /mnt 
    • mount /proc 
    • mount /sys 
    • mount -a
  4. If the configuration file for GRUB is lost (like in my case):
    • grub2-mkconfig -o /boot/grub2/grub.cfg
  5. Write GRUB2 configuration to disc:
    • grub2-install /dev/sda
  6. Unmount and reboot:
    • umount -a
    • exit
    • reboot
 

Lost entire day fighting this ridicules crash during export of raster created in R to GeoTiff (other “rgdal” functions worked fine). This option worked well several months ago when I last time used it, so I wondered what happened. I searched for the only error message I was able to catch: “Failure during IO”, but wasn’t able to find anything useful.

After some time I started to suspect GDAL itself in abnormal behaviour since its latest updates caused some troubles already. I was wrong but the idea itself turned to be useful – I recalled that I had about a dozen of GDAL updates since “rgdal” package was installed. Despite the package was “up to date” I’ve reinstalled it. It solved the problem)))