# Igor Code

The objective of this page is to serve a repository of useful Igor code that we keep needing for different applications. Feel free to add new code or to comment on existing code, but please don't delete old code w/o discussing with Jose.

There is a a "General Macros" igor procedure file that has a large suite of various Igor functions. It can be downloaded here. There is an Igor Help file (with clickable links) that helps to describe the various functions in this ipf that can be downloaded here. Some background: this ipf was created by Donna as a good-bye 'gift' to my NOAA friends. Two new versions of Igor have since been released, and several tools are now dated and/or obsolete. Still, I think it is a good reference for general programming techniques, and as a resource for a variety of tasks. - Donna

## Contents

- 1 JGGenTools
- 2 Multiplexed Experiment Tool (MPT) Code
- 3 Doing a Robust Linear Regression
- 4 Regridding a Wave into the Time Axis of Another Wave
- 5 Looking for an Object in Experiments in a Directory
- 6 Add experiment info to graph
- 7 Other Tricks
- 8 RK4 and Euler's Methods
- 9 A pretty flexible decile binning function
- 10 Bring to front all windows with a given wave

## JGGenTools

At a meeting on July 17, 2014 with Jose, Doug, Pedro, Donna, Harald, a new scheme for organizing code was instituted and debuted to the group on July 18, 2014. There is a google document with links and function names here.

## Multiplexed Experiment Tool (MPT) Code

This was also formerly known as the PAM tool, OFR tool and briefly, MET. This info can now be found at here.

Both ipfs are designed to work within a squirrel or pika experiment. For users who wish to use the code alone. If you wish to use the code not within a squirrel or pika experiments, some extra functions are needed to get the OFR code to compile. You can download a separate ipf with these functions here.

## Doing a Robust Linear Regression

Linear regression gets pretty complex when one starts thinking about the details. There are 3 important decisions, which depend on the data which you are trying to fit:

- (A) Error in one or both coordinates:
- (A1) Only one of the coordinates (Y) has error and the other does not (e.g. when fitting an aerosol measurement vs. time).
- (A2) Or do both coordinates have errors of the same relative order-of-magnitude? (e.g. as when comparing two instruments that measure the same quantity)

- (B) Assumptions about the relative magnitudes of the errors in different points
- (B1) All the points are assumed to have the same error
- (B2) The error varies point-by-point.

- (C) Standard vs. Robust regression
- (C1) The fit line is found by minimizing Chi^2, i.e. the sum of the squares of the deviations between each point and the regression line.
- (C2) The fit line is found by minimizing the sum of the absolute values of the deviations between each point and the regression line.

This gives rise to 2 x 2 x 2 = 8 options to choose from when fitting your data. Which to use depends on the nature of the data and the use of the fit, and should be chosen intelligently. In practice if there is a good relationship and the points are scattered around a line with few outliers, all the methods will produce similar results. However when there are outliers or significant scatter, there can be significant differences and some of the methods are plainly incorrect for some datasets.

To do regressions in Igor in each case:

- A1-B1-C1: this is the default assumption of the linear fit
- A2-B1-C1: an approximation to this fit can be done with the /ODR=2 option in Igor.
- A1-B1-C2: this can be done in Igor, but you need to define a new custom fit function (which can be just a line) and then set V_FitOptions = 2 in the command line. (Going back to V_FitOptions = 0 gets you back to the regular A1-B1-C1 fits)
- A2-B1-C2: this can be accomplished approximately by doing A1-B1-C2 for Y vs X, then for X vs Y, and averaging the fitting parameters appropriately (and the fitting parameter uncertainties in quadrature). There are some papers that show that this is approximately correct.
- For all the B2 options, choose the fitting weights in the Igor Curve Fitting tab

## Regridding a Wave into the Time Axis of Another Wave

To regrid a wave into another framework, use the following functions. For example you might have one curve, defined by a certain x- and y-wave (xwave1, ywave1). Let's say there are 10 points in these waves, and the maximum is 20 (and extreme case of length discrepancy). Another curve is defined by two other x- and y-waves, also with a maximum of 20, but with 20 points each (xwave2, ywave2). It is important that they have approximately the same bound values, or it will have a hard time interpolating between them.

If I want to look at the first curve in terms of the second x-wave, you have to regrid the first y-wave into the x-wave of the second. This can be done in two ways, and that is dependent on which inintial set of waves has more data points. If the wave you want to regrid into has more points than the original, then use the first of these functions (Interp_1). If the opposite is true (wave you want to regrid to has less points) use the second function (Interp_2). They can be run as is, after changing the wave names appropriately. The [p] refers to each point, and should stay with this syntax (replacing it with a number refers only to that specified cell).

Function Interp_1 ()

- Wave xwave1, ywave1, xwave2 //ywave2 is not used
- duplicate/o xwave2 ywave_regrid
- ywave_regrid = ywave1 [binarysearchinterp(xwave1, xwave2[p])]
- appendtotable ywave_regrid

End Function

Function Interp_2 ()

- Wave xwave1, ywave2, xwave2
- duplicate/o xwave1 ywave_regrid
- ywave_regrid = ywave1 [fAverageXY(xwave2, xwave1, xwave2[p-1], xwave2[p])]

End Function

Compiled by Qi Zhang and Alex Huffman, 2004

### March 2012 update

The routine Interp_3 below is a modification of interp_1, which can deal with gaps on the data series. Adapted by Anna Ripoll and Jose Jimenez in March 2012.

Function Interp_3 (xwave1, ywave1, xwave2, maxdur_secs) // Regridding a Wave with gaps into the Time Axis of Another Wave without gaps

- Variable maxdur_secs // We need to create a variable which define the maximum duration of the gap in seconds
- Wave xwave1, ywave1, xwave2 // xwave1 is the date/time of the initial wave, ywave1 is the data of the initial wave, xwave2 is the date/time of the final wave and ywave2 is not used
- variable i1,i2 // i1 is the point from xwave1 and i2 is the point from xwave2
- duplicate/o xwave2, ywave_regrid2 // ywave_regrid2 is the new data wave
- ywave_regrid2 = NaN // it just cleans the ywave_regrid2 in case there is anyone already
- ywave_regrid2 = ywave1 [binarysearchinterp(xwave1, xwave2[p])] // it regrid the ywave1 into the xwave2
- // Now we want to stop the regridding process when there is a gap, so we need to filter out
- for(i1=0; i1<NumPnts(xwave1)-1; i1 += 1) // For all points of the xwave1
- If ((xwave1[i1+1]-xwave1[i1])>=maxdur_secs) // If the xwave1 points are further from each other than the maximum duration gap we created
- for(i2=0; i2<NumPnts(xwave2)-1; i2 += 1) // For all the xwave2 points which are inside the gap
- If ((xwave2[i2]>(xwave1[i1]+maxdur_secs/2)) && (xwave2[i2]<(xwave1[i1+1]-maxdur_secs/2))) // If they are far from the bound (you can choose how far, in this case maxdur_secs/2)
- ywave_regrid2[i2]=NaN // then we decide don't have that data

- Endif

- If ((xwave2[i2]>(xwave1[i1]+maxdur_secs/2)) && (xwave2[i2]<(xwave1[i1+1]-maxdur_secs/2))) // If they are far from the bound (you can choose how far, in this case maxdur_secs/2)
- EndFor

- for(i2=0; i2<NumPnts(xwave2)-1; i2 += 1) // For all the xwave2 points which are inside the gap
- EndIf

- If ((xwave1[i1+1]-xwave1[i1])>=maxdur_secs) // If the xwave1 points are further from each other than the maximum duration gap we created
- endfor
- appendtotable ywave_regrid2

End Function

## Looking for an Object in Experiments in a Directory

Function findThisObjectInExpInPath(ObjectType, ObjectName, PathName, ThisSubFolder)

- //imu 3 Sept 2009
- // This function will help you look for lost objects in your experiments.
- // ObjectType must be "string", "variable", or "wave".
- // ObjectName must be the exact name of the object, and can be semicolon-separated list.
- // Before running this function you must create an Igor Path to the folder you want to search in.
- // Command line: NewPath ThisPathName "C:Program Files:WaveMetrics:Igor Pro Folder:"
- // or Misc -> New Path

- // Send the pathName as a string to the function, e.g, "ThisPathName"

- // ThisSubFolder refers to a subfolder to check in the experiments you're looking in.
- // To look in root: use "".
- // To look in all folders, use "allFolders". This is a cheat for selecting the /R flag for LoadData.
- // Note that if you look in allFolders, the complete directory structure of the opened file will be
- // created in the current experiment, even if the object is not found.

- //
- // You can further customize this function for overwriting old data and storing loaded data in a new dataFolder
- // by including the /O and /T flags, respectively, with LoadData

- string ObjectName, ObjectType, PathName, ThisSubFolder
- variable idex, LoadFlag
- // check that ObjectType is valid and set LoadFlag for LoadData
- // Note that LoadData could look for multiple objectTypes at once, but I'm not using it that way
- if(stringmatch(ObjectType, "string"))
- LoadFlag = 4

- elseif(stringmatch(ObjectType, "variable"))
- LoadFlag = 2

- elseif(stringmatch(ObjectType, "wave"))
- LoadFlag = 1

- else
- abort "ObjectType must be \"string\", \"variable\", or \"wave\"."

- endif
- // get ; -separated list of file names in PathName
- string ExperimentsInFolderList = indexedFile($PathName, -1, ".pxp")
- For(idex = 0; idex < ItemsInList(ExperimentsInFolderList); idex += 1)
- // Searching in a Subfolder or in root:
- if(strlen(ThisSubFolder) == 0) // root
- LoadData/J=objectName /L=(loadFlag)/P=$pathName StringFromList(idex,ExperimentsInFolderList)

- elseif(stringmatch(ThisSubFolder, "allFolders")) // recursive look in all subfolders
- LoadData/J=objectName/L=(loadFlag)/P=$pathName/R StringFromList(idex,ExperimentsInFolderList)

- else // subfolder provided
- LoadData/J=objectName /L=(loadFlag)/P=$pathName/S=ThisSubFolder StringFromList(idex,ExperimentsInFolderList)

- endif

- endfor

end

## Add experiment info to graph

Use this simple function to add experiment and graph info to any graph. It can be useful for talks to later find the igor experiment and graph that was used to make slides.

Function AddExpinfo2Graph()

pathinfo home

String message = "\\Z06Location:"+S_path

message +="\rname: \\{igorinfo(1)}"

message +="\rdate: \\{date()}, \\{time()}"

message +="\rgraphname: \\{winlist(\"*\",\"\",\"WIN:\")}"

TextBox/C/N=expinfo/F=0/A=RT/E=2 message

End Function

Added by Harald Stark, Oct 4th, 2013

## Other Tricks

- Making a radical in an axis label: \B\S\F'\F'Wingdings'l
- It is possible to place a subscript directly under another character in igor, here is the example for C*298: C\[0*\X0\B298\M (From Harald)

## RK4 and Euler's Methods

These functions are adapted from Numerical Recipes and allow the integration of a system of ordinary differential equations in time using the Euler method and the 4th Order Runge-Kutta Method. Adapted by Christoph Knote (NCAR) and edited by Jose-Luis Jimenez on Feb. 2013.

## A pretty flexible decile binning function

Download it here Note: Need to delete all NaNs first, and also need to be sure data is on the same time grid.

## Bring to front all windows with a given wave

Howard Rodstein - wavemetrics

Function DisplayGraphsAndTablesWithWave(w)

- Wave w
- String list = WinList("*", ";", "WIN:3") // List of all graphs and tables
- Variable numWindows = ItemsInList(list)
- Variable i
- for(i=0; i<numWindows; i+=1)
- String windowName = StringFromList(i, list)
- CheckDisplayed /W=$windowName w
- if (V_flag != 0)
- DoWindow /F $windowName

- endif

- endfor

End