NMRPipe Script Generator Command Help
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Table of Contents
Enter parameter string / Load Parameters
Number of Data Points
Zero Filling
Acquisition Type
Time Domain Convolution (Solvent Suppression)
Correct First Point
Linear Prediction
First Apodization (Window Function)
Second Apodization
Reverse Matrix
Fourier Transformation
Baseline Correction
Referencing Parameters
Cut Acquisition Dimension in Half
Additional File Formats

Introduction: (top)
The NMRPipe Script generator is designed to aid in the creation of NMRPipe scripts for the processing of Varian 2- and 3-dimensional NMR data collected using States, or States-TPPI for quadrature detection. It also handles sensitivity-enhanced data without any prior manipulation of the data. In principle, the script generator should work with Bruker data sets with only minor adjustments.

The script generator is still under development. While I have attempted to eliminate as many bugs as possible I can not test all possible scenerios. If you find any bugs in the script generator please let me know and I will attempt to fix them as soon as possible. The program at this point does some very basic error checking when executed. If any of the parameters you have selected are incompatible an error message should appear with suggestions on how to fix the problem. However, the checks are not rigorous and there are sure to be cases where incorrectly entered parameters may not be caught by the error checking. If you find any additional error checks you would like included please let me know.

At this point the script generator still lacks some features that I hope to incorporate when I get time. At some point I plan on adding features to perform 4-dimensional transforms, to add the maximum entropy reconstruction as an alternative to linear prediction, and additional baseline correction features, add additional window function choices, and any other features that people suggest. At this point I have written a script that generates pictures for each window function you are using. This script can be run by selecting the view window function link from the NMR Tools page. Shortly, it will be incorporated as a button on the script generator page itself.

Many of the comments made here come from the book "NMR Data Processing" by Jefferey C. Hoch and Alan S. Stern (1996).

Usage: (top)
After creating a script using the nmrPipe script generator copy the script to your favorite text editor. Save the file to any name you like (here I will use process.com). Make the file executable by typing chmod a+x process.com, or whatever you called the script. Then simply execute the script by typing ./process.com, or whatever name you used.

Note that the nmrPipe script generator creates a conversion script, a processing script, and another script for converting to additional file formats if you chose that option. Each of these scripts may be executed together or each section may be copied into individual files and executed separately.

I have assumed that all processing will be performed inside of the Varian directory structure. If you want to be located somewhere else make sure that you change all files names to include the directory path that you are using.
Enter parameter string / Load Parameters: (top)
The Enter parameter string text box allows one to type in any parameter directly to set its value. For example if you want to set the number of cpu's to 4 you can type nproc=4 in the text box and hit the "Load Parameter" button. You can enter multiple values to be set at once by separating them with a comma, but with no spaces. For example "nproc=2,machine=V,proc_type2=ft" will set the number of cpus to 2, the machine type to Varian, and the t1 processing type to FT.

Of course this method is probably more laborious than simply filling out the form directly. However, I cave created macros/scripts that will generate an input string which may be copied and pasted into the text box to rapidly fill out the form.

For Varian instruments I have created a macro called varian2sbtools and a minor modification to the BPsvf macro which calls varian2sbtools upon a save. These macros create a file called sbtools.input which is the parameter string to be copied into the text box.

For Bruker data sets I have created a perl script called bruker2sbtools.prl which is run from the directory of the saved data. Likewise this script creates a file called sbtools.input which is the parameter string to be copied into the text box.

All of the scripts are available from the downloads page. These macros / scripts also create additional files, macros, etc. to aid in data processing. See the scripts themselves for more details.

Dimension: (top)
Toggle to switch between 2-dimensional and 3-dimensional data. If the spectra is 2-dimensional only the first two columns need to be filled in (direct dimension and f1:first indirect), for 3-dimensional data all three columns need to be entered (direct dimension, f1:first indirect, and f2:second indirect).
Input: (top)
Name of the nmrPipe formatted data before fourier transformation. When the script is executed the fid file in the Varian file structure will be converted to a file name that is entered here with a .pipe extension. The Varian fid file is preserved. The Varian data file is a directory structure with four files; fid (the actual NMR data), procpar (the NMR parameters), log and text. When using scripts generated from this page it is assumed that you will be running from inside the Varian directory structure, that is, the programs expects the files fid, procpar, text, and log to be in the current directory when nmrPipe is invoked.

Input Format - For security reasons only letters, numbers and underscore are allowed in valid filenames. Also, at this point only lower case letters are valid. This will change in the future. If you need to use other character types simply use a text editor to rename them after the script is generated.
Output: (top)
Name of the final transformed data set. For 2 dimensional data sets the output file will be named output.ft and will be located in the current directory. For 3 dimensional data sets the output file will be called output.ft3 and will be located inside a newly created directory called ft or lp (if linear prediction was used).

Output Format - For security reasons only letters, numbers and underscore are allowed in valid filenames. Also, at this point only lower case letters are valid. This will change in the future. If you need to use other character types simply use a text editor to rename them after the script is generated.
Number of Data Points: (top)
np - Number of points in each free induction decay (fid). Each fid consists of 1/2 real and 1/2 imaginary points. For example, if np = 1024 then there are 512 real and 512 imaginary points. Window functions should be applied to the number of real points or less (<= 1/2*np and not np).

ni - Number of increments collected in the second dimension. ni is listed as a complex number. Therefore, if ni = 128 there are 128 real and 128 imaginary points in the second dimension. In this example there would be 256 fids to process in the f1 (D2) dimension.

ni2 - Number of increments collected in the third dimension. Only used when processing 3-dimensional data sets. ni2, like ni, is listed as a complex number.
Zero-Filling: (top)
Zero filling extends an fid by appending zeros to the end. This causes a slight increase in the digital resolution of the frequency domain data after fourier transformation and allows imaginary data to be reconstructed using a Hilbert fourier transformation. In multidimensional NMR data sets the imaginary data is often discarded to save space after phasing. In order to rephase at a later time the imaginary data will need to be regenerated. It can be shown mathematically that this can only be done properly if the data was zero-filled once. A single zero fill will double the number of points in a fid by appending zeros at the end. It is typical to apply a single zero-fill when transforming NMR data. Any additional zero-filling will generally not improve the resolution any but may have some cosmetic appeal (smoother looking data). The zero-fill command should be applied after application of a window function or if used before apodization care must be taken to ensure that the window function is applied only to the actual data points that were collected and not to any of the zeros added by zero-filling.

xN - The size, in real points, of the acquisition dimension (f3) after fourier transformation. The value must be a fourier number and must be at least equal to the number of real points (1/2*np). The one exception to this is if the acquisition dimension is cut in half (see below). In this case xN can be as small as 1/2 the number of real points (1/4*np).

xN Zero filling - Zero filling is determined by the size of xN relative to the number of real points in the fid (1/2*np). If xN is set larger than the number of real data points the remainder of the points are padded with zeros before fourier transformation. The amount of zero-filling that is performed is illustrated with the following examples. If np = 1024, xN = 1024 and half = 'n' then there would be 512 real points and a single zero fill would be performed to pad the fid with 512 zeros to a final size of 1024 set by xN. Using the above example except half = 'y', two zero fills would be performed. The 512 real points would be padded with 1,536 points to bring the total of real points to 2048. After fourier transformation the 1024 points on the right half of the processed data set will be deleted leaving 1024 points, which is equal to xN.

yN - The size, in real points, of the second dimension (f1) after fourier transformation. The value must be a fourier number and must be at least equal to the total number of points in the yN dimension (real + imaginary). The total number of points is equal to 2*ni. Therefore, if ni = 128, there are 128 real points + 128 imaginary points for a total of 256 points and yN must be at least 256.

yN Zero filling - Zero filling in yN (f1) is determined by the value of yN relative to ni. If yN = 2*ni, its minimum value, then a single zero fill will be performed assuming no linear prediction (see below). If yN = 4*ni then two zero fills will be performed. Note that it is impossible to not perform at least a single zero fill in yN (f1) unless you are performing linear prediction (see below).

zN - The size, in real points, of the third dimension (f2) after fourier transformation. This value only needs to be set for 3-dimensional experiments. The value must be a fourier number and must be at least equal to the total number of points in the zN dimension (real + imaginary). The total number of points is equal to 2*ni2. Therefore, if ni2 = 32, there are 32 real points + 32 imaginary points for a total of 64 points and zN must be at least 64.

zN Zero filling - Zero filling in zN (f2) is determined by the value of zN relative to ni2. If zN = 2*ni2, its minimum value, then a single zero fill will be performed assuming no linear prediction (see below). If zN = 4*ni2 then two zero fills will be performed. Note that it is impossible to not perform at least a single zero fill in zN (f2) unless you are performing linear prediction (see below).

Output Size - The final output size can be determined for a 2-dimensional experiment by multiplying xN*yN*4, and for a 3-dimensional experiment by multiplying xN*yN*zN*4. While it is a good idea to perform zero filling to increase the digital resolution of 3-dimensional experiments there are limits determined by the final data set size that you will want to work with. It is typical to try and keep the final data sets to around 64 Megabytes in size to make analysis easier (too large a file will slow down screen drawing considerably). Some experiments, such as the 13C-edited noesy-hsqc and the HCCH-TOCSY are typically processed to have a 128 Megabyte final size. Note that nmrPipe often creates many intermediate files that can take up huge amounts of space. It is a good idea to delete these files after processing to free up space. Remember that if you keep the original data and the processing script it is easy to regenerate any files you may need later.
Acquisition Type: (top)
Here you make selections for what type of data you have collected in each dimension. Currently for the acquisition dimension the only choice is complex. For the two indirect dimensions the current choices are States, States-TPPI, and sensitivity enhanced data. There is no reason to rearrange sensitivity enhanced data before processing with nmrPipe.
Time Domain Convolution: (top)
The time domain convolution is a very effective method to remove huge solvent signals, such as residual water, from your spectra. In order for this to work the solvent signal to be removed must be on-resonance (the center of the spectra), because the frequency at the center of the spectra is zero. Note that is is possible to remove solvent or buffer signals that are not at the center of the spectra as well. To do this you will need to perform a circular shift of the data set to place the signal you want removed at the center, remove the signal, and then circular shift the data back to its original location. See me if this is something you are interested in.

Mode - Currently there are three different modes that can be used to subtract the solvent. I have not fully explored the effectiveness of each of these and therefore do not know how to suggest the best function. The initial values are the default values that nmrPipe uses.

Filter Length - Filter length is only used in Low Pass mode. The filter length is an empirical value that is dependent on the linewidth of the signal you are removing and the number of points in the fid. The smaller the value the greater the amount of signal that is subtracted and the faster the calculation time. For experiments where no signals overlap the solvent signal you generally want to use small values for size, such as 20. For experiments where you have closely spaced resonances to the solvent signal it is generally best to try larger values (~60) and to test several different window sizes to get the best results of subtracting solvent and leaving your signals alone.

Filter Shape - Filter shape is only used for Low Pass mode. At this point I do not know which shape works best. All I can say is to try each one and see for yourself.

Spline Noise and Smooth Factor - These settings are used for Spline mode. I am unsure what good values are for this mode. All I can suggest is to try and see.

Head/Tail Extrapolation - Because the fid has a finite size, and the window width is greater than a single point, data points before and after the fid must be determined. The choices presented in this section determine how the points are extrapolated. Like most of these settings I have not used them enough to know which ones work best.
Correct First Point: (top)
This setting allows you to correct the first point of the fid by multiplying it by a scale factor between 0 and 1 or to replace the initial point(s) by backward linear prediction. The linear prediction is only a choice in the acquisition dimension.

The first few points of the acquisition fid are often responsible for causing baseline rolls after fourier transformation. This is a problem because the initial points of an fid are often collected incorrectly and have large amounts of noise as a result of collecting data usually only a few usec after applying high power RF pulses to the probe coil and other imperfections in the probe itself. These points can be corrected by backward linear prediction or by suppressing the initial points contribution by multiplying it by a scaling factor such as 0.5. Another way to deal with the problem is to FT the fid without any first point correction thus causing a baseline roll. This roll can then be removed by performing a baseline correction. The baseline is modeled, usually to a low order polynominal function, and then the model is subtracted from the fid. This method has the advantage that each fid has no vertical offset as discussed below.

By its very nature applying a fourier transform to a decaying signal will cause a constant vertical offset. In multidimensional data sets the amount of offset from fid to fid will not be the same. This will cause additional frequency components in the indirect dimensions and lead to t1-noise ridges. Because the vertical scale is determined by the initial point of the fid the t1 ridges can be suppressed by suppressing the intensity of the first point of the fid. Another approach would be to apply a baseline correction to each of the acquisition fids before fourier transforming any of the indirect dimensions. This will cause the baselines to be zero and eliminate the t1 ridges. Note, that the baseline correction in this case must be applyed before transforming the f1 dimension.

Scale - If Scale first point is selected then the value scale is multiplied by the initial point of the fid. This applies for all dimensions.

Linear Predict - If linear predict first point(s) is selected then the initial point or points are predicted. This replaces the existing points, it does not add additional points to the beginning of the fid. The value "predict" selects the number of points to predict. Generally a value of 1 is chosen. The value "points" selects the number of points to use in the prediction and the value "coef" selects the number of coefficients to use in calculation. Coef should not exceed 50% of "points". Linear prediction is only a choice for the acquisition dimension.

DC Offset - Often the tail end of an fid does not equal zero because the entire fid is either shifted up or down slightly from the zero point. This is referred to as a DC offset. If an fid with a DC offset is fourier transformed a spike at zero frequency will appear. Worse, if the fid is zero-filled, the appended zeros will not extend from the last point for the fid but rather will be offset from the last point. This will be interpreted as a truncation artifact when performing a fourier transformation and cause wiggles at the base of peaks. Both of these adverse effects can be removed by simply adjusting the fid up or down so that the center of the fid is near zero. This feature can be turned on and off by adjusting the DC Offset switch.

Linear Prediction: (top)
Linear prediction extrapolates additional data points to time-domain data (fid). Linear predicting can be an effective way to increase the number of data points, and hence resolution, for data sets that are truncated. Often in 3-dimensional data sets you set ni and ni2 small to save acquisition time even though the signal has not decayed away to zero. Using linear prediction to extend the fid in these cases can improve the resolution considerably.

Linear prediction should only be used when the signal you are trying to predict has not decayed completely away to zero. If the signal has already decayed to zero then linear predicting further data points will generally add noise and not improve resolution. It is therefore best to always process data without any linear prediction and then compare the spectra to one with linear prediction. It is also best to try different linear prediction parameters and compare them to see which works the best. Remember processing parameters can have huge effects on the quality of the data.

For experiments that have dimensions that were collected with constant time evolution it is generally best to use mirror-image linear prediction. See the readme file of the pulse sequence or ask me if you are unsure if any of the dimensions were collected with linear prediction. In general, the mirror-image linear prediction algorithm will give superior results and is much faster to perform.

Linear prediction works best when the signal is strong, truncated, and there are as few peaks as possible to predict. Because of this last feature it is best in 3-dimensional data sets where both the f1 (yN) and f2 (zN) are to be linear predicted to fourier transform the acquisition dimension (xN) first, then transform the f1 (yN) dimension without linear prediction and then process the f2 (zN) dimension with linear prediction. Afterwards the f1 (yN) dimension can be inverse fourier transformed, linear predicted, and then re-transformed. All of this is built into the script generator and takes no extra work on your part.

Linear Prediction Type - The choices are none, forward, backward, forward-backward, mirror-image (0,0) and mirror-image (90, -180). In general backward should not be used to linear predict forward data points. For dimensions without constant time use either forward or forward-backward linear prediction. The forward-backward gives better results but takes twice as much time. For cases where the dimension was collected with constant time evolution it is best to use mirror-image linear prediction. However, forward or forward-backward linear prediction may be used on constant time data. For mirror image linear prediction use the (90, -180) type when the inital point was collected at half dwell time. This is generally set by the f1180 and f2180 flags in Varian pulse sequences.

Last - Is the number of the last data point to predict. This value should generally be around 1.5*ni or 2*ni. The larger the value of last the better the resolution will be but at the expense of extra noise. Like most processing parameters it is best to try different values to see which one works the best. For cases where the signal is weak or the truncation effect is minimal it is best to use a smaller value for last and for cases where there is plenty of signal and the truncation effect is large use a larger value for last. Note that for protein work we generally do not make last greater than 2*ni, but for small molecule or peptide work you may be able to make last quite a bit larger with beneficial results.

Coefficients - The number of coefficients determines the effectiveness of the linear prediction algorithm. The greater the number of coefficients the better the results (50% of the number of points is the maximum value), however, as the number of coefficients increases the processing time increases quite dramatically. I have found it best to use around 0.25*ni to 0.33*ni for yN and 0.25*ni2 to 0.33*ni2 for zN for best results.
First Apodization (Window Functions): (top)
Rarely does fourier transformation of the fid give rise to good quality spectra. There are often problems with the final result such as truncation artifacts, low signal to noise or limited resolution. Apodization is the process where the spectra is convoluted to achieve a more satisfactory lineshape. This is done by multiplying the fid by a time domain filter function. Two common functions are the sinebell and gaussian functions. The idea is to multiply the fid by a function so that it always decays away to zero at the end. This will remove truncation artifacts that will give rise to wiggles along the baseline near peaks. For fids that are severely truncated this can lead to noise that stretches across the entire spectra. Depending on the strength of the signal will determine the type of function that you will want to apply. Typically one tries to increase the resolution as far as possible while keeping noise to a minimum. For some spectra that are very noisy the only thing that can be done is to decrease the noise at the expense of resolution. The initial few points of an fid are responsible for most of the signal to noise that you get. The stronger the initial part of the fid the weaker the noise will appear. The longer the fid "rings out" the higher the resolution will be. Therefore, increasing the initial part of the fid will lead to good signal to noise but poor resolution while enhancing the late parts of the fid will give better resolution but add noise. It is up to you to try and decide which function will give the most desirable effects. Often it is good to have two processed spectra, one with good signal to noise and one with good resolution. That way you can have the best of both worlds and will not have to compromise too much.

The script generator has a button that allows each of the window functions to be viewed. It is a good idea that you always view the function that you are using to make sure you know what you are doing to your data. This is especially true of gaussian functions where minor adjustments of the parameters can lead to huge changes in the shape of the function. No single setting when processing the NMR data will have a greater effect on the quality of your spectra than window functions. It is therefore important that you try several different functions to find the one that gives the best possible results.

Function - The functions to choose from include none, gaussian, and sinebell. In the case of sinebell the power can be adjusted with a power of two being the most common. I suggest trying each of them initially to find which ones give the best results. Sinebell squared functions are quite common, but gaussian functions are also used quite a bit, especially for the acquisition dimensions of NOESY spectra. Other functions can be added later if you like. The none options should generally only be used when you are applying EM with the second apodization.

Shift - Shift is used to determine the shift of the sinebell functions. It is not used for the gaussian functions and can be ignored. The shift is entered in degrees. A shift of 90 gives a pure cosine function and a shift of 0 gives a pure sinebell function. Small values give increased resolution at the expense of extra noise and large values give good signal to noise at the expense of resolution.

Power - Power determines the power of the sinebell functions. It is not used for the gaussian functions and can be ignored. Generally values of 1 or 2 are used, but feel free to try others as well.

lb - gaussian peaks have narrower line widths than lorentzian lineshapes, especially near the base of the peak. However, NMR signals have lorentzian lineshapes. The gaussian window functions try to convert the lorentzian line into a gaussian line by multiplying the signal by a exponential to cancel the decay of the fid followed by applying a decreasing gaussian function to introduce a gaussian decay and hence a gaussian lineshape. A negative lb value is used otherwise the decaying fid will be decayed further rather than removed. The value for lb is very important and dramatically determines the shape of the gaussian function. lb is only used with a gaussian window function and can be ignored for sinebell functions.

gc - gc is the gaussian decay coefficient. Typically a value of 0.2 is used with a negative lb to give the resonances a gaussian lineshape. Both lb and gc are dependent on the sweep width and the number of points in the fid. Because of this it is important to view the gaussian function first before applying it to make sure that it doing what you think it is. gc is only used with a gaussian window function and can be ignored for sinebell or sinebell squared functions.

Size - Size represents the number of points that the window function will be applied to in the direct dimension (xN). Typically you apply the window function to all of the real points (1/2*np). However, in cases where np was set too high the size variable allows you to only select part of the fid for transformation. Lets say that np = 2048, giving 1024 real and 1024 imaginary points. When viewing the 1024 real points of the fid you realize that the signal has decayed away by point 256. If you process the data using all 1024 real points you get a large amount of noise. If you chop the fid off after real point 512 and transform it you will get a reduction in the noise level and not diminish resolution significantly, as long as the signal truly has decayed away before the point in which you chopped the data. There is no size value for either of the indirect dimensions because only in bizarre cases would you not want to use all of the points in the transformation. For the indirect dimension size is set automatically to ni (ni2), or in the case of linear prediction it is set equal to last.

Viewing Window Functions from the Macro Generator - Soon there will be a view button located from within the script generator form page that will display the window function for each dimension based on the selected parameters. To do this now go to the View Window Function page located on the NMRTools page, fill in the proper information, and select View at the bottom of the page. Sorry for the hassle of filling out the form a second time to do this, but hopefully the problem will be resolved soon.
Second Apodization: (top)
This allows the fid to be multiplied by a second window function. Currently the only choice is exponential multiplication (em). EM multiplies the data in the work space by an exponential window. This apodization function is used to reduce noise at the expense of spectral resolution. EM may be used alone (by setting the 1st apodization to none) or in conjunction with other window functions. EM is dependent on the sweep width and number of points in the fid. Because of this always view the window function before transformation to be sure you know what you are applying. For instance if you are applying an em 5 to a fid with 1024 points it will be significantly different then applying em 5 to a fid with 256 points. Typically it is not beneficial to apply em unless the spectra is very noisy. In these cases it can be used quite effectively to help locate weak peaks hidden under the noise. However, this is done at the expense of resolution.

EM - No exponential multiplication is applied when em is set to 0. The larger the value of em the faster the exponential decay that is applied giving reduced noise but poorer resolution.
Reverse Matrix: (top)
Often the indirect dimensions of 2-dimensional and 3-dimensional NMR experiments are reversed. This can be fixed by changing the phase of the receiver during detection, but it is easier to reverse the fid during processing. This is done by taking the complex conjugate of the fid before fourier transformation. The complex conjugate negates the imaginary part of the data in the fid causing a reflection the spectrum about zero frequency. If checked that particular dimension will be reversed during processing.
Fourier Transformation: (top)
When this box is checked a complex fast fourier transform will be applied to the time domain data (fids). By not fourier transforming a given dimension it is possible to load 1D vectors through the untransformed dimension allowing you to view the fids in either of the indirect dimensions. This can be useful for deciding if the signal in either the f1 (yN) or f2 (zN) dimension is truncated and can therefore be linear predicted. It is also useful for troubleshooting processing problems and for deciding if any hardware or temperature problems occurred during acquisition.
Phasing: (top)
Applies a phase correction of the frequency domain data. If both the zero order and first order phase correction values are zero no phase correction will be performed. It is typical to process the data with no phase corrections, phase the data in nmrDraw and then reprocess the data with the phase correction applied. To speed things up first process the data without linear prediction to extend data sets and without other fancy processing parameters like baseline correction. After you have the phases determined then go ahead and add all the extra bells and whistles to the processing script. Note that things such as window functions and linear prediction to extend data sets will not affect phase parameters.
Baseline Correction: (top)
Often in NMR spectra the baseline is not flat, but rather curved with wavy rolls. This complicates the analysis of the NMR spectra by adding errors to the quantification of peak volumes and more importantly by making it very difficult to locate peaks that are in wells or are obstructed by t1 noise ridges, which result from baseline rolls in the acquisition dimension. The baseline rolls are generally due to the first few points of the fid being sampled incorrectly as discussed in the correct first point section above. The baseline correction function used in the script is the nmrPipe POLY -auto function. At this point I do not have a good understanding of all the baseline correction options so I have chosen to just go with the -auto option. This option also appears to be quite robust. I have included options to perform baseline correction in each of the three dimensions, however, baseline correction is generally only performed in the acquisition dimension. In the acquisition dimension you have the choice to perform the baseline correction before the f1 dimension is FT, in which case it will help in the suppression of t1 noise ridges, or after the F1 FT in which case it will just help clean up the spectra. In the future I plan on adding a greater number of options for baseline correction. If you have any suggestions please E-mail me and let me know.
Referencing Parameters: (top)
Referencing information can be found from a print out of the acquisition parameters from the NMR spectrometer or by running the perl script procpar.prl on bambam.

Sweep Width - sw, sw1 and sw2 are the sweep widths for each of the three dimensions. They are needed to properly conver the Varian data to nmrPipe format and for processing functions like em and gaussian window functions.

Spectrometer Frequency sfrq1, sfrq2, and sfrq3 are the frequencies used in each of the three dimensions. The frequency used should be the frequency at zero ppm. This can be determine by running the macro setcar on the NMR host computer or from the procpar.prl script on bambam. These values are needed to properly convert the Varian data to nmrPipe format.

Reference ref1, ref2, and ref3 are the reference ppm at the center of each of the three dimensions. This program assumes that the reference value is set to the center of the spectra. Let me know if you would like to be able to select a reference point manually and I can edit the script generator.

Nucleus The nucleus that is detected in each of the three dimensions. These values are for display purposes only and do not affect the referencing in any way.
Cut Dimension in Half? (top)
For 15N edited spectra it is typical that only the amide resonances appear in the direct dimension (xN). Since all of the amide resonances are downfield of water, which is typically the center of the spectra, there is no need to keep the right half of the spectra. For these cases it is best to cut the direct dimension in half. This saves disk space by 50%, decreases processing time 4 fold for 3-dimensional spectra, and allows for faster screen drawing during analysis. Updating of the referencing is handled by the program. At this point the cut dimension in half switch will only save the left half of the spectra. If there is a need I can edit the generator to allow selection of the right half of the spectra or allow the user to define a particular region to save. Let me know if either choice would be beneficial.
Additional File Formats (top)
In addition to the nmrPipe file format, which can be viewed with the program nmrDraw, the data can also be converted to Xeasy, Felix, or nmrView format. In each case an additional file conversion script is added to the end of the processing script. This script can be run together with the nmrPipe script or copied out and run separately. For the Felix conversion the program pipe2flx needs to be installed, which currently only runs under IRIX. For Xeasy the program spscan must be installed.