Diff for "PJJung/CAGMonEtude"

⇤ ← Revision 33 as of 2021-01-26 13:39:30 →

Size: 9734

Editor: PJJung

Comment:

← Revision 128 as of 2021-07-28 08:43:57 → ⇥

Size: 3

Editor: PJJung

Comment:

Deletions are marked like this.

Additions are marked like this.

{{{
,-----. ,---. ,----. ,--. ,--. ,--. ,--.
' .--./ / O \ ' .-./ | `.' | ,---. ,--,--, ,---. ,-' '-.,--.,--. ,-| | ,---.
| | | .-. || | .---.| |'.'| || .-. || \ | .-. :'-. .-'| || |' .-. || .-. :
' '--'\| | | |' '--' || | | |' '-' '| || | \ --. | | ' '' '\ `-' |\ --.
`-----'`--' `--' `------' `--' `--' `---' `--''--' `----' `--' `----' `---' `----'
}}}

== Description ==

The CAGMon etude is a study version of CAGMon that evaluates the dependence between the primary and auxiliary channels.

== Project goal ==

The goal of this project is to find a systematic way of identifying the abnormal glitches in the gravitational-wave data using various methods of correlation analysis. Usually, the community such as LIGO, Virgo, and KAGRA uses a conventional way of finding glitches in auxiliary channels of the detector - Klein-Welle, Omicron, Ordered Veto Lists, etc. However, some different ways can be possible to find and monitor them in a (quasi-) realtime. Also, the method can point out which channel is responsible for the found glitch. In this project, we study its possible to apply three different correlation methods - maximal information coefficient, Pearson's correlation coefficient, and Kendall's tau coefficient - in the gravitational wave data from the KAGRA detector.

== Participants ==

* John.J Oh (NIMS)
* Young-Min Kim (UNIST)
* Pil-Jong Jung (NIMS)

== Methods and Frameworks ==

==== Maximal Information Coefficient (MIC) ====

the Maximal Information coefficient(MIC) of a set D of two-variable data with sample size n and grid less than B(n) is given by

\[
MIC(D)=\underset{xy<B(n)}{\max}{\left\{ \frac{I^{*}(D,x,y)}{\log \min \left\{x,y \right\}} \right \}}
\],

where \[\omega(1)<B(n)\le O(n^{1-\epsilon}) \] for some \[ 0<\epsilon<1 \]
==== Pearson's Correlation Coefficient (PCC) ====

Pearson Correlation Coefficient(PCC) is a statistic that explains the amount of variance accounted for in the relationship between two (or more) variables by
\[
R={{\sum_{i=1}^{n} (X_i - \overline{X})(Y_i - \overline{Y})} \over {\sqrt{\sum_{i=1}^{n} (X_i - \overline{X}) \sum_{i=1}^{n} (Y_i - \overline{Y})}}}
\],

where \[ \overline{X} \] and \[ \overline{Y} \] are the mean of X and Y, respectively

==== Kendall's tau Coefficient ====

Kendall’s tau with a random samples n of observations from two variables measures the strength of the relationship between two ordinal level variables by

\[
\tau =\frac{c-d}{{n \choose 2}}
\],

where c is the number of concordant pairs, and d is the number of discordant pairs

==== Flow chart ====

== Code development ==

==== GitHub ====
[[TBA]]

==== Code versions ====
1. CAGMon Etude Alpha
   * for the basic test and evaluation of the LASSO regression method developed by LIGO
   * reproduced original CAGMon methods and idea
2. CAGMon Etude Beta
  * added coefficient trend plots with LASSO beta, coherence, MIC, PCC, and Kendall's tau
3. CAGMon Etude Delta
  * fixed a critical problem that sucked enormous memory when it used the matplotlib module
4. CAGMon Etude Eta
  * fixed minor issues
  * added the range limitation of stride
5. CAGMon Etude Flat (current version)
  * fixed minor issues and optimized scripts
  * added the script of HTML summary page
  * added coefficient distribution plots
6. CAGMon Etude Octave (development version)
  * remove some processes that make Time-series and Scatter plots. Even though it required tremendous memory, this information is not useful
  * adjust HTML code
  * fixed minor issues and optimized scripts

==== Series of scripts ====
* Agrement.py
  * the script gathered functions the model required
* Melody.py
  * the script to calcutate each coefficient and to save trend data as csv
* Conchord.py
  * the script to make plots
* Echo.py
  * the script to save the result as HTML web page
* CAGMonEtude{Version}.py
  * the script to run each script

==== User guide ====
* [[/userguide | How to use CAGMon]]

==== Needs of code development ====
* Fundamental critarian or guideline of the stride and its data-size
* Daily running on KAGRA

== Exemplary results ==

1. Earthquake effects during O3GK
* Datetime: 19 April 2020 20:39 UTC
* Purpose
  * Test to run CAGMon algorithm with a remarkable event
  * To figure out the cause of lock-loss in KAGRA
* Results
  * stride 5 seconds [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/2020-04-19_K1:CAL-CS_PROC_C00_STRAIN_DBL_DQ_1271363358-1271364078(5)/ | Summary page]]
  * stride 20 seconds [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/2020-04-19_K1:CAL-CS_PROC_C00_STRAIN_DBL_DQ_1271363358-1271364078/ | Summary page]]
  * stride 30 seconds [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/2020-04-19_K1:CAL-CS_PROC_C00_STRAIN_DBL_DQ_1271363358-1271364078(30)/ | Summary page]]

2. Skim through all obs-segments of O3GK
* Purpose
  * Test for calculation time and required resources with all observation segments during O3GK
  * To figure out trigger events or abnormal behaviors
* Results
  * April 7, 1270287158 - 1270328032
  * April 8, 1270339218 - 1270425618
   * Full Data is unavailable in the KISTI cluster
  * April 9, 1270425618 - 1270510167
  * April 10, 1270513160 - 1270596544
  * April 11, 1270598418 - 1270683904
  * April 12, 1270684818 - 1270762046
  * April 14, 1270909686 - 1270937768
  * April 15, 1270945288 - 1271017582
   * Event: GRB200415 (08:48:05 UTC)　
   * Full Data is unavailable in the KISTI cluster
  * April 16, 1271030433 - 1271112809
  * April 17, 1271119833 - 1271186507
  * April 18, 1271227441 - 1271288128
  * April 19, 1271289618 - 1271364033
  * April 20, 1271377409 - 1271460608
   * Event: GRB200420A (2:32:58 UTC)
   * Full Data is unavailable in the KISTI cluster

3. With iKAGRA hardware injection data
* Event
  * Phenomenon: the strain channel and seismometer channels in iKAGRA had a high correlation during the hardware injection test
  * Cause: still unknown
  * Hypothesis: the glitches have relatively the same behavior as the vacuum rotary pump
  * More detail analysis: [[https://www.dropbox.com/s/950vjc807sgz24u/hveto%20brief%20Report%20for%20K1.pdf?dl=0 | hveto brief Report for K1]] and [[https://www.dropbox.com/s/hb7rx93an8yluiq/PilJong%2C%20KGWG%20Face-to-Face%20Meeting.pdf?dl=0 | KGWG Face-to-Face Meeting]]
* Purpose
  * To verify whether this model senses injected signals and abnormal glitches
  * To test noise resistance and data-size limitation
*Results
  || stride || sample sata || data size || dada length || summary page link ||
  || 10s || 512Hz || about 5,000 || about 12m || [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145624200-1145624936%5b5000%5d/ | summary page]] ||
  || 10s || 1024Hz || about 10,000 || about 12m || [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145624200-1145624936%5b10000%5d/ | summary page]] ||
  || 10s || 2048Hz || about 20,000 || about 12m || [[ https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145624200-1145624936%5b20000%5d/| summary page]] ||
  || 10s || 3072Hz || about 30,000 || about 12m || [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145624200-1145624936%5b30000%5d/ | summary page]] ||
  || 10s || 4096Hz || about 40,000 || about 12m || [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145624200-1145624936%5b40000%5d/ | summary page]] ||
  || 2s || 4096Hz || about 8,000 || about 12m || [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145624200-1145624936%5b2s%5d/| summary page]] ||
  || 5s || 4096Hz || about 20,000 || about 12m || [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145624200-1145624936%5b5s%5d/| summary page]] ||
  || 60s || 128Hz || about 7,500 || whole iKAGRA data || [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145621548-1145670954%5b60s%5d/ | summary page]] ||
  || 150s || 64Hz || about 10,000 || whole iKAGRA data || [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145621548-1145670954%5b150s%5d/ | summary page]] ||
  || 300s || 64Hz || about 20,000 || whole iKAGRA data || [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145621548-1145670954%5b300s%5d/ | summary page]] ||
  || 600s || 16Hz || about 10,000 || whole iKAGRA data || [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145621548-1145670954%5b600s%5d/ | summary page]] ||

== Beyond ==

== References ==

==== Presentation materials ====
[[https://gwdoc.icrr.u-tokyo.ac.jp/cgi-bin/private/DocDB/ShowDocument?docid=12481|JGW-G2112481-v1]]

==== Papers ====
[[https://science.sciencemag.org/content/334/6062/1518 | Science.1518; Detecting Novel Associations in Large Data Sets]]

-  ⇤ ← Revision 33 as of 2021-01-26 13:39:30 → 
  Size: 9734
  Editor: PJJung
  Comment:
+   ← Revision 128 as of 2021-07-28 08:43:57 → ⇥
  Size: 3
  Editor: PJJung
  Comment:
-Deletions are marked like this.
+Additions are marked like this.
 Line 1:
-{{{                                                                                           
 ,-----.  ,---.   ,----.   ,--.   ,--.                            ,--.             ,--.        
'  .--./ /  O  \ '  .-./   |   `.'   | ,---. ,--,--,      ,---. ,-'  '-.,--.,--. ,-|  | ,---.  
|  |    |  .-.  ||  | .---.|  |'.'|  || .-. ||      \    | .-. :'-.  .-'|  ||  |' .-. || .-. : 
'  '--'\|  | |  |'  '--'  ||  |   |  |' '-' '|  ||  |    \   --.  |  |  '  ''  '\ `-' |\   --. 
 `-----'`--' `--' `------' `--'   `--' `---' `--''--'     `----'  `--'   `----'  `---'  `----'                                                                                             
}}}


== Description ==

The CAGMon etude is a study version of CAGMon that evaluates the dependence between the primary and auxiliary channels.


== Project goal ==

The goal of this project is to find a systematic way of identifying the abnormal glitches in the gravitational-wave data using various methods of correlation analysis. Usually, the community such as LIGO, Virgo, and KAGRA uses a conventional way of finding glitches in auxiliary channels of the detector - Klein-Welle, Omicron, Ordered Veto Lists, etc. However, some different ways can be possible to find and monitor them in a (quasi-) realtime. Also, the method can point out which channel is responsible for the found glitch. In this project, we study its possible to apply three different correlation methods - maximal information coefficient, Pearson's correlation coefficient, and Kendall's tau coefficient - in the gravitational wave data from the KAGRA detector.


== Participants ==

 * John.J Oh (NIMS)
 * Young-Min Kim (UNIST)
 * Pil-Jong Jung (NIMS)

== Methods and Frameworks ==

==== Maximal Information Coefficient (MIC) ====

the Maximal Information coefficient(MIC) of a set D of two-variable data with sample size n and grid less than B(n) is given by

\[
MIC(D)=\underset{xy<B(n)}{\max}{\left\{ \frac{I^{*}(D,x,y)}{\log \min \left\{x,y \right\}} \right \}}
\],

where \[\omega(1)<B(n)\le O(n^{1-\epsilon}) \] for some \[ 0<\epsilon<1 \]
==== Pearson's Correlation Coefficient (PCC) ====

Pearson Correlation Coefficient(PCC) is a statistic that explains the amount of variance accounted for in the relationship between two (or more) variables by
\[
R={{\sum_{i=1}^{n} (X_i - \overline{X})(Y_i - \overline{Y})} \over {\sqrt{\sum_{i=1}^{n} (X_i - \overline{X}) \sum_{i=1}^{n} (Y_i - \overline{Y})}}}
\],

where \[ \overline{X} \] and  \[ \overline{Y} \]  are the mean of X and Y, respectively 


==== Kendall's tau Coefficient ====

Kendall’s tau with a random samples n of observations from two variables measures the strength of the relationship between two ordinal level variables by

\[
\tau =\frac{c-d}{{n \choose 2}}
\],

where c is the number of concordant pairs, and  d is the number of discordant pairs

==== Flow chart ====



== Code development ==

==== GitHub ====
[[TBA]]

==== Code versions ====
 1. CAGMon Etude Alpha
   * for the basic test and evaluation of the LASSO regression method developed by LIGO
   * reproduced original CAGMon methods and idea
 2. CAGMon Etude Beta
  * added coefficient trend plots with LASSO beta, coherence, MIC, PCC, and Kendall's tau
 3. CAGMon Etude Delta
  * fixed a critical problem that sucked enormous memory when it used the matplotlib module
 4. CAGMon Etude Eta
  * fixed minor issues
  * added the range limitation of stride
 5. CAGMon Etude Flat (current version)
  * fixed minor issues and optimized scripts
  * added the script of HTML summary page
  * added coefficient distribution plots
 6. CAGMon Etude Octave (development version)
  * remove some processes that make Time-series and Scatter plots. Even though it required tremendous memory, this information is not useful
  * adjust HTML code
  * fixed minor issues and optimized scripts
-Line 86:
+Line 2:
-==== Series of scripts ====
 * Agrement.py
  * the script gathered functions the model required
 * Melody.py
  * the script to calcutate each coefficient and to save trend data as csv 
 * Conchord.py
  * the script to make plots
 * Echo.py
  * the script to save the result as HTML web page
 * CAGMonEtude{Version}.py
  * the script to run each script

==== User guide ====
 * [[/userguide | How to use CAGMon]]

==== Needs of code development ====
 * Fundamental critarian or guideline of the stride and its data-size 
 * Daily running on KAGRA


== Exemplary results ==

1. Earthquake effects during O3GK
 * Datetime: 19 April 2020 20:39 UTC
 * Purpose
  * Test to run CAGMon algorithm with a remarkable event
  * To figure out the cause of lock-loss in KAGRA
 * Results
  * stride 5 seconds [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/2020-04-19_K1:CAL-CS_PROC_C00_STRAIN_DBL_DQ_1271363358-1271364078(5)/ | Summary page]]
  * stride 20 seconds [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/2020-04-19_K1:CAL-CS_PROC_C00_STRAIN_DBL_DQ_1271363358-1271364078/ | Summary page]]
  * stride 30 seconds [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/2020-04-19_K1:CAL-CS_PROC_C00_STRAIN_DBL_DQ_1271363358-1271364078(30)/ | Summary page]]

2. Skim through all obs-segments of O3GK 
 * Purpose
  * Test for calculation time and required resources with all observation segments during O3GK
  * To figure out trigger events or abnormal behaviors
 * Results
  * April 7, 1270287158 - 1270328032
  * April 8, 1270339218 - 1270425618
   * Full Data is unavailable in the KISTI cluster
  * April 9, 1270425618 - 1270510167
  * April 10, 1270513160 - 1270596544
  * April 11, 1270598418 - 1270683904
  * April 12, 1270684818 - 1270762046
  * April 14, 1270909686 - 1270937768
  * April 15, 1270945288 - 1271017582
   * Event: GRB200415 (08:48:05 UTC)　
   * Full Data is unavailable in the KISTI cluster
  * April 16, 1271030433 - 1271112809
  * April 17, 1271119833 - 1271186507
  * April 18, 1271227441 - 1271288128
  * April 19, 1271289618 - 1271364033
  * April 20, 1271377409 - 1271460608
   * Event: GRB200420A (2:32:58 UTC)
   * Full Data is unavailable in the KISTI cluster

3. With iKAGRA hardware injection data
 * Event
  * Phenomenon: the strain channel and seismometer channels in iKAGRA had a high correlation during the hardware injection test
  * Cause: still unknown
  * Hypothesis: the glitches have relatively the same behavior as the vacuum rotary pump
  * More detail analysis: [[https://www.dropbox.com/s/950vjc807sgz24u/hveto%20brief%20Report%20for%20K1.pdf?dl=0 | hveto brief Report for K1]] and [[https://www.dropbox.com/s/hb7rx93an8yluiq/PilJong%2C%20KGWG%20Face-to-Face%20Meeting.pdf?dl=0 | KGWG Face-to-Face Meeting]]
 * Purpose
  * To verify whether this model senses injected signals and abnormal glitches 
  * To test noise resistance and data-size limitation
 *Results
  || stride || sample sata || data size || dada length || summary page link ||
  || 10s || 512Hz || about 5,000 || about 12m || [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145624200-1145624936%5b5000%5d/ | summary page]] ||
  ||  10s ||  1024Hz || about 10,000  || about 12m  || [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145624200-1145624936%5b10000%5d/ | summary page]]  ||
  ||  10s ||  2048Hz || about 20,000 || about 12m  || [[ https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145624200-1145624936%5b20000%5d/| summary page]]  ||
  ||  10s ||  3072Hz || about 30,000 || about 12m  ||  [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145624200-1145624936%5b30000%5d/ | summary page]] ||
  ||  10s ||  4096Hz || about 40,000 || about 12m  || [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145624200-1145624936%5b40000%5d/ | summary page]]  ||
  ||  2s ||  4096Hz || about 8,000  || about 12m  || [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145624200-1145624936%5b2s%5d/| summary page]]  ||
  ||  5s ||  4096Hz || about 20,000 || about 12m  ||  [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145624200-1145624936%5b5s%5d/| summary page]]  ||
  ||  60s ||  128Hz || about 7,500  || whole iKAGRA data  ||  [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145621548-1145670954%5b60s%5d/ | summary page]] ||
  ||  150s ||  64Hz || about 10,000  || whole iKAGRA data  ||  [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145621548-1145670954%5b150s%5d/ | summary page]] ||
  ||  300s ||  64Hz || about 20,000 || whole iKAGRA data  || [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145621548-1145670954%5b300s%5d/ | summary page]]  ||
  ||  600s ||  16Hz || about 10,000 || whole iKAGRA data  || [[https://ldas-jobs.ligo.caltech.edu/~pil-jong.jung/CAGMon/iKAGRA/2016-04-25_K1:LSC-MICH_CTRL_CAL_OUT_DQ_1145621548-1145670954%5b600s%5d/ | summary page]]  ||


== Beyond ==

== References ==

==== Presentation materials ====
[[https://gwdoc.icrr.u-tokyo.ac.jp/cgi-bin/private/DocDB/ShowDocument?docid=12481|JGW-G2112481-v1]]

==== Papers ====
[[https://science.sciencemag.org/content/334/6062/1518 | Science.1518; Detecting Novel Associations in Large Data Sets]]