Loop Gaussian functions over a data set

[Tommy88Participant]

Hello everyone,

I would like to ask you if, given a two column data set, where in the first column is the a progressive index and the second the associated energy value, it is possible to center for each point a Gaussian function so to have a resulting plot as (that is, to plot the Density of States of a molecule):

The data used to generate the blue plot above are the following two columns listed here:
<p style=”text-align: left;”>band      E0[eV]
400     0.546252
401      1.549338
402      1.637316
403      1.645290
404     1.671798
405     2.825250
406    2.849823
407    2.895508
408    2.956904
409    4.173942
410     4.237125</p>
Is it possible to loop over the data with a Gaussian function?

Thanks in advance for the help!

[dgteamModerator]

Do you mean you want to fit the data at these locations?  Or do you want to construct a graph like this using just this input?

DataGraph has a peak fitting option in the Beta version –  Fit command..

If you want to construct the graph, it seems like you would also need some deviation or scale parameter. Or do you have an assumption about these?

[Tommy88Participant]

Hello!

Yes, actually I fix the deviation to a generic value of 0.1, which can be modified accordingly. The idea come from the “def plotDOS” function as reported in the Westpy code @ https://github.com/west-code-development/westpy/blob/master/westpy/electronicStructure.py

I was wondering if something similar to that function can be done in DataGraph.

Meanwhile I will check the Fit command in the Beta version.

 

Thanks!

[dgteamModerator]

We uploaded a new example file for you to test out.  See File > Online Examples – GAUSSIAN CURVES.

It is set up so you input a, b, c, in a table format and the curves get added.  Let us know if this is what you were interested in doing.

[Tommy88Participant]

Hello,

amazing, thanks a lot!!! I will check it out asap and let you know if it what I was looking for…but seems quite promising!

Thanks again for the help!

Tom

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