# Melissa Trainer

**ALL FILES ON THIS PAGE WERE MISSING FROM ORIGINAL WIKI**

Online site for IGOR-based routines used to analyze atmospheric QMS
data - contributed by Melissa Trainer.

## Recommended Analysis Procedure using these IGOR routines 

[Media:MGT_IGOR_Analysis_Procedure.pdf‎](Media:MGT_IGOR_Analysis_Procedure.pdf‎ "wikilink")

## Igor scripts for SAM data anaylsis 

**Deadtime Correction**

[Media:Deadtime_mgt.ipf](Media:Deadtime_mgt.ipf "wikilink")

This IGOR routine is used to perform a deadtime correction on any SAM
data wave. There is an option to select the electron multiplier used in
the experiment, since that determines which deadtime coefficients are
most relevant. The deadtime coefficients are parsed as discussed in the
following file from Heather Franz:
[Media:Franz_SAM_deadtime_coeff_20120808.pdf](Media:Franz_SAM_deadtime_coeff_20120808.pdf "wikilink")

To use these IGOR routines (and the ones on [ Mike Wong\'s
page](Mikewong "wikilink")) you need to use python scripts to export SAM
data into a delimited text file. *For instructions on how to do this
(using gcms.py) for a range of m/z values, see the tutorial below.* This
data is then loaded into IGOR through the Data \--\> Load Waves \--\>
Load Delimited Text menu item. If the header information is removed from
the file exported via gcms.py, then IGOR will find the column headings
and automatically name the waves.

TUTORIAL: Exporting data using python

Run through the Command terminal window.

1\. In HOME directory, access data folder of interest. It will probably
be located in `<home>`/SAM/gse/data

2\. To export chromatograms and save as text file \[where \"filename\"
is your chosen file name\], either for a single value (X) or a range of
m/z values (X-Y), type:

\"gcms.py X \> filename.txt\" OR \"gcms.py {X..Y} \> filename.txt\"

3\. This will place .txt file in the data folder (use different
extension, such as .xls, to suit needs). The data will be in time (s)
and counts (cps) per m/z charge. It is tab delimited.

4\. To export bands, either a single (B) or range of bands (B-C), and
save as text file type:

\"gcms.py -b B \> filename.txt\" or \"gcms.py -b {B..C} \>
filename.txt\"

\[Note for windows, the \"{X..Y}\" will not work. Either list the m/z
values you want one by one, or export all by just running gcms.py.\]

------------------------------------------------------------------------

**Calculate Average Ratio Fit and Error**

[Media:Calculate_average_fit.ipf](Media:Calculate_average_fit.ipf "wikilink")

This IGOR routine is used after the makeratio.ipf to get the count ratio
of two m/z values (see [ Mike Wong\'s page](Mikewong "wikilink")). This
routine will calculate the average ratio, and will use a fitting mask to
only include certain points in the fit (ex// exclude background
regions). To use this routine you need to have a fitting mask wave
prepared, with the same number of points as the ratio wave. Fitting mask
values should be \"1\" for points you want to include in the fit, and
\"0\" for points to exclude. The routine results will print to a new
table, giving the average value, standard deviation, standard error of
the mean, and number of fit points.

------------------------------------------------------------------------

**Load and average Fractional Mass Spectra**

This is an IGOR routine that will take data exported from gcms.py (must
include fractional scan points) and generate averaged mass spectra over
specific time regions. Can be used to create \"Background\" and
\"Sample\" mass spectra for analysis. There is a how-to pdf included in
addition to the IGOR procedure file:

[Media:AtmTeam_Fractional_Spectra_HowTo.pdf](Media:AtmTeam_Fractional_Spectra_HowTo.pdf "wikilink")

[Media:Fractional Load and Average.ipf](Media:Fractional_Load_and_Average.ipf "wikilink")

**Fractional Mass NEW VERSION:**

(2012-09-06 \--mikewong) In addition to calculating a wave for the
average spectrum, it also calculates waves for the standard deviation at
each fractional mass point, and the number of samples used in the
average. **Note:** Standard deviation is not necessarily the same as
uncertainty, unless the signal is constant in time and all the variation
is due to noise.

[Media:Fractional_Stats.ipf](Media:Fractional_Stats.ipf "wikilink")

[![Mhw_25008_fracs_16_24.png](http://wiki2.xina.io/uploads/images/gallery/2023-03/scaled-1680-/mhw-25008-fracs-16-24.png)](http://wiki2.xina.io/uploads/images/gallery/2023-03/mhw-25008-fracs-16-24.png)