C Sharp Logs

Thursday, 13 August 2009

TightVNC and UltraVNC

seems like ultra vnc http://uvnc.com/ is being more actively developed and has more features than tightvnc, but tightvnc has released a new version after a long time http://www.tightvnc.com/

Friday, 31 July 2009

read and write data

   1:  using System;

   2:  using System.Collections.Generic;

   3:  using System.IO;

   4:  using System.Text;

   5:  using System.Windows.Forms;

   6:  namespace ReadData

   7:  {

   8:      public partial class Form1 : Form

   9:      {

  10:          static readonly StringBuilder outData = new StringBuilder();

  11:          readonly List<double> a = new List<double>(); // the 2 input TSs

  12:          readonly List<double> b = new List<double>();

  13:          public Form1()

  14:          {

  15:              InitializeComponent();

  16:          }

  17:          void Form1_Load(object sender, EventArgs e)

  18:          {

  19:              LoadData();

  20:              int totalFound = 0;

  21:              double lastDiff = 0;

  22:              bool haveEntry = false;

  23:              const int window = 100; // how many values we use to estimate mean and stdDev

  24:              for (int i = window; i <= a.Count; i++)

  25:              //for (int i = 144; i < 145; i++) // one sample only, for testing

  26:              {

  27:                  double meanA = GetMean(a, i - window, i);

  28:                  double meanB = GetMean(b, i - window, i);

  29:                  double stdA = GetStd(a, i - window, i, meanA);

  30:                  double stdB = GetStd(b, i - window, i, meanB);

  31:                  double aNorm = (a[i - 1] - meanA) / stdA;

  32:                  double bNorm = (b[i - 1] - meanB) / stdB;

  33:                  double diff = aNorm - bNorm;

  34:                  double absDiff = Math.Abs(diff);

  35:                  if (haveEntry &amp;&amp; ((lastDiff > 1 &amp;&amp; diff <> -1)))

  36:                  {

  37:                      Console.WriteLine("==== Exit index " + i);

  38:                      haveEntry = false;

  39:                  }

  40:                  //outData.AppendLine(i + ", " + diff); // for writing to file

  41:                  // outData.AppendLine(diff.ToString()); // for writing to file

  42:                  outData.AppendLine(string.Format("{0:d4}", i-window+1) + " " + diff); // for writing to file

  43:                  if (absDiff > 15)

  44:                  {

  45:                      totalFound++;

  46:                      Console.WriteLine("Found at index " + i + " diff " + diff);

  47:                      if (absDiff < Math.Abs(lastDiff) &amp;&amp; !haveEntry)

  48:                      {

  49:                          Console.WriteLine("==== Entry index " + i);

  50:                          haveEntry = true;

  51:                      }

  52:                  }

  53:                  lastDiff = diff;

  54:              }

  55:              Console.WriteLine("total found " + totalFound);

  56:              WriteData();

  57:          }

  58:          static void WriteData()

  59:          {

  60:              const string fileout = "out.csv";

  61:              if (File.Exists(fileout))

  62:              {

  63:                  File.Delete(fileout);

  64:              }

  65:              using (StreamWriter sw = new StreamWriter(fileout))

  66:              {

  67:                  sw.Write(outData.ToString());

  68:              }

  69:          }

  70:          static double GetStd(IList<double> array, int start, int limit, double mean)

  71:          {

  72:              double sum = 0;

  73:              for (int i = start; i < limit; i++)

  74:              {

  75:                  sum += (array[i] - mean) * (array[i] - mean);

  76:              }

  77:              sum = Math.Sqrt(sum);

  78:              sum /= (limit - start);

  79:              return sum;

  80:          }

  81:          static double GetMean(IList<double> array, int start, int limit)

  82:          {

  83:              double sum = 0;

  84:              for (int i = start; i < limit; i++)

  85:              {

  86:                  sum += array[i];

  87:              }

  88:              sum /= (limit - start);

  89:              return sum;

  90:          }

  91:          /// <summary>

  92:          /// Assume input file has 2 columns, one for each TS we want to track.

  93:          /// </summary>

  94:          void LoadData()

  95:          {

  96:              using (StreamReader sr = new StreamReader("5m-db.csv"))

  97:              {

  98:                  string newData;

  99:                  a.Clear();

 100:                  b.Clear();

 101:                  while (sr.Peek() != -1)

 102:                  {

 103:                      newData = sr.ReadLine();

 104:                      string[] line = newData.Split(',');

 105:                      a.Add(double.Parse(line[0]));

 106:                      b.Add(double.Parse(line[1]));

 107:                  }

 108:                  Console.WriteLine("Loaded " + a.Count + " data points.");

 109:              }

 110:          }

 111:      }

free drive

http://www.freedrive.com/folder/23026

Wednesday, 29 July 2009

R stuff for normal distribution fitting

Look at this site for more: http://www.bigre.ulb.ac.be/Users/jvanheld/statistics_bioinformatics/practicals/microarray_fitting_solutions.html

1:

   2:  # data loading

   3:  filepath <- system.file("data", "morley.tab" , package="datasets")

   4:  mm <- read.table(filepath)

   5:  m <- mm[,1]

6:

   7:  hist(m)

8:

   9:  ## install

  10:  library(fBasics)

11:

  12:  skewness(m)

13:

  14:  kurtosis(m)

15:

  16:  plot(density(m))

17:

  18:  plot(ecdf(m))

19:

  20:  qqnorm(m)

  21:  abline(0,1)

22:

  23:  gal <- m

24:

  25:  ## Calculate estimators

  26:  m <- mean(gal,na.rm=T)

  27:  s <- sd(gal,na.rm=T)

28:

  29:  ## Draw the density histogram of the galactose microarray values

  30:  h <- hist(gal,breaks=100,col='#CCCCFF',border='#CCCCFF',freq=F)

31:

  32:  ## On the histogram, draw vertical bars at the following values :

  33:  ## mean, mean + 1*sd, mean -1*sd, mean +2*sd, mean -2*sd

  34:  abline(v=c(m,m-s,m-2*s,m+s,m+2*s),col="#000088",lwd=1)

35:

  36:  ## Superimpose the theoretical distribution

  37:  lines(h$mids,dnorm(h$mids,m,s), type="l", lwd=2,col="red")

FITTING DISTRIBUTIONS WITH R

http://www.google.co.uk/search?hl=en&q=r+normal+distribution+fit&meta=

Thursday, 23 July 2009

Very nice SQL articles and some tools

http://www.sommarskog.se/

SQL List indexes in db with fragmentation > 30 %

   1:  SELECT

   2:      OBJECT_NAME(object_id) ObjectName,

   3:      index_id,

   4:      index_type_desc,

   5:      avg_fragmentation_in_percent

   6:  FROM sys.dm_db_index_physical_stats

   7:      (DB_ID('AdventureWorks'),NULL, NULL, NULL, 'LIMITED')

   8:  WHERE

   9:      avg_fragmentation_in_percent > 30

  10:  ORDER BY

  11:      OBJECT_NAME(object_id)