1383 lines
63 KiB
Matlab
1383 lines
63 KiB
Matlab
%% How to analyse ISS XPS and NEXAFS data using Matlab
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%
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%% Introduction: Concept
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%
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% NEXAFS and XPS data from beam times is imported into a MATLAB table. The
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% table allows to add meta data such as sample temperature, cell pressure,
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% or Scienta settings (pass energy, etc). Further, data from several
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% beam times can be imported and then filtered - for example to process all
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% gas-phase CO2 PE from the last 5 years; or all ice NEXAFS at -10° to
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% -12°C. Functions and the table are organised such that one can always go
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% back to raw data to re-do the analysis or continue with processed data.
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%
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% The basic idea is that a set of MATLAB functions that import the data of
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% different format and from various sources and that can be used to perform
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% basic XPS and NEXAFS data processing are used, shared, and developed by
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% all group members. This requires common data formats and naming. The
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% scripts/live scripts/functions that are used to call the common MATLAB
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% functions are written, stored, named individually by each user.
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%
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%
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% <html>
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% <img src="../Bildschirmfoto 2022-03-24 um 07.17.54.png" width="800">
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% </html>
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%% Introduction: Function calls
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%
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% Most functions need the MATLAB data table as input. The output can be
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% stored in the same data table adding data to the original data table, or
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% in a new one - thus creating a copy. Some functions such as those used
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% for plotting do not write data.
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%%
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%
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% InputTable = nappFunctionAbc(InputTable);
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%
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%%
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% This command runs the function nappFunctionAbc on the data table
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% _InputTable_ and writes the results into the same table (with the name
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% _InputTable_.
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%
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% The _InputTable_ may hold different data types such as XPS data, NEXAFS,
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% and pictures. Mostly, the functions require one and the same data type as
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% input. *The _InputTable_ needs to be filtered when passed to a function*
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% (see below). Exceptions from this are the |nappGenerateSpectra| and
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% |nappPlot| functions that can be called with some limitations with
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% mixed data as input (including pictures, that are ignored).
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%
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%%
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%
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% OutputTable = nappFunctionAbc(InputTable);
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%
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%%
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%
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% This command runs the same function on the data in the table _InputTable and
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% writes the results into the data table _OutputTable_ which is a full copy
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% of _InputTable_ plus the results of the function nappFunctionAbc.
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%
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%
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% Most functions allow to set a number of optional input parameters, some
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% require additional mandatory input. Please check the documentation of
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% each function for further help:
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%
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%%
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%
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% help nappFunctionAbc
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%
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%%
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%
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% The mandatory input comes first in each function call. In the following
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% example the data of the _InputTable_ is passed to the function as well as
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% the variables _x_ and _y_. The optional function input is given just like
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% in any MATLAB function using parameter descriptions: 'option',setting.
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% Setting can be a text string in quotes, or a variable (array, logical,
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% table, date number, etc). Most common are:
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%
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%%
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%
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% OutputTable = nappFunctionAbc(InputTable,x,y,'tableSelection',tmpDataSelection,'...
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% 'tableVariable','TableVariable', energyScale','kinetic','mode','mean');
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%
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%%
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% * 'tableSelection': allows to filter the table and run the function only
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% on selected table lines. tmpDataSelection is a logical array, see section
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% Work with data: Filter table in this document.
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% * 'tableVariable': allows to choose the data of the data table that the
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% function should work with. See Introduction: Data structure section in
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% this document.
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% * 'energyScale': allows to use kinetic or binding energy scale to plot or
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% perform calculation.
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% * 'mode': allows to select options for functions such as using mean or
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% sum for calculations, etc.
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% * 'showFigures': allows to view or view not figures and function output.
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%
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% Please consult the help files of each function for a complete list of
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% options.
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%
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%% Introduction: Data structure
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%
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% It starts with importing the multi-dimensional IgorPro files that the SES
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% software saves or with the h5 file structure that is saved for the NEXAFS
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% data.
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%
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% * Upon import of the SES data, region iterations are averaged and
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% counts are normalised to 1 s.
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% * NEXAFS data...
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%
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% The data are then saved in a data table, each file in one row and the
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% data in columns. Columns are called table variables in MATLAB and in the
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% nappFunctions. The data columns are:
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%
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% * *image*: PE data: Intensity in counts-per-second vs. kinetic energy and sequence
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% iteration. Functions that work with image data, overwrite this column.
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% * *imageRoh*: a copy of image at the time of data import.
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% * *spectrum_countsPerSecond* and *spectrum_counts*: Hold the spectra that
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% are computed from the image. These data columns are modified by
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% nappFunctions that work with spectrum data.
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% * *spectrumRoh_countsPerSecond* and *spectrumRoh_counts*: a copy of the
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% spectrum data at time of generation.
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%
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% In other words, the data in the columns spectrum_countsPerSecond,
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% spectrum_counts, image always hold the processed data and are modified by
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% each function call. The Roh data allows to go back and re-do the
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% processing. Next to this the data table stores analyser settings such as
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% scientaEkin and sample parameters such as temperature and pressure. View
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% the data table in the data viewer or command window of MATLAB to see all
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% columns. This data structure allows to process all data and then still plot
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% images, unprocessed data, or processed data of selected experiments.
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%% ToDo
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%
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% * add code for intensity vs. time plotting
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% * add code for average and plot iterations.
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% * get rid of peakfind and replace by gauss fitting
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% * use gauss fitting function within functions?
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% * Tidy html output, and get Yanisha's feedback
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% * Add section on how to modify figures after plotting
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% * add section on how to access data in table
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% * add section on how to merge data tables
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% * Work on code and add section how to remove XPS from NEXAFS
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% * Work on code and add section on I0 including 2nd order light
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% * Add code to add combined spectra to data table as row.
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% * Update help files for NEXAFS functions and NEXAFS options in general
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% functions.
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% * Set-up folders for Yanisha
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%% Prepare: Initialise MATLAB
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%
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% You need to have access to the folder holding the functions and helper
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% files. These are shared via switch drive.
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%
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% *Add the paths to the initializeMatlab function at the end of this
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% script* This function needs to be added to the end of each of your
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% scripts or live scripts. Below is an example.
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%
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%%
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%
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% function [myColorMap, PsiFarbe] = initializeMatlab
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% %initializeMatlab Sets NappIce specific settings
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% % Adds Napp files to path and defines file locations for mac, windows,
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% % and Matlab online (which runs on unix)
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%
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% % change working directory
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% path = matlab.desktop.editor.getActiveFilename;
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% [filepath,~,~] = fileparts(path);
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% cd(filepath);
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%
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% % The fitting routines writes quite a lot of feedback and warnings. I shut them off.
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% warning('off','all')
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% warning off backtrace
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% warning off verbose
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%
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% % Functions and helper files path:
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% if ismac
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% % add Napp files to path
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% addpath('/Users/bartels-rausch/SwitchDrive/PSI_Matlab/Add-Ons');
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% addpath('/Users/bartels-rausch/SwitchDrive/PSI_Matlab/nappIce');
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% elseif ispc
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% % add Napp files to path
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% addpath('/Users/bartels-rausch/SwitchDrive/PSI_Matlab/Add-Ons');
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% addpath('/Users/bartels-rausch/SwitchDrive/PSI_Matlab/nappIce');
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% elseif isunix
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% % add Napp files to path
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% addpath('/Users/bartels-rausch/SwitchDrive/PSI_Matlab/Add-Ons');
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% addpath('/Users/bartels-rausch/SwitchDrive/PSI_Matlab/nappIce');
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% end
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%
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% % Color maps
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% myColorMap.red = colormap(autumn(7));
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% myColorMap.blue = colormap(winter(7));
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%
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% % define PSI cooperate identity colours
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% PsiFarbe.hellgrau = [226 226 226]/255;
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% PsiFarbe.mittelgrau = [184 184 184]/255;
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% PsiFarbe.dunkelgrau = [78 78 77]/255;
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% PsiFarbe.blaugrau = [236 236 243]/255; % This is actually not a PSI colour
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% PsiFarbe.gelb = [239 200 37]/255;
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% PsiFarbe.hellorange = [227 164 31]/255;
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% PsiFarbe.dunkelorange = [207 92 22]/255;
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% PsiFarbe.hellrot = [164 36 23]/255;
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% PsiFarbe.dunkelbraun = [114 85 65]/255;
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% PsiFarbe.hellbraun = [127 119 47]/255;
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% PsiFarbe.mittelgruen = [135 150 59]/255;
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% PsiFarbe.hellgruen = [195 214 47]/255;
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% PsiFarbe.dunkelgruen = [72 130 33]/255;
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% PsiFarbe.dunkelrot = [104 47 86]/255;
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% PsiFarbe.dunkelblau = [37 65 108]/255;
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% PsiFarbe.mittelgruenblau = [61 164 168]/255;
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% PsiFarbe.hellblau = [58 162 190]/255;
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% PsiFarbe.mittelblau = [50 118 158]/255;
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%
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% end
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%%
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% The path might be different
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% on your computer depending how you have set up SwitchDrive:
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%
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%%
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%
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% addpath('C:\Users\bletry_c\switchdrive\PSI_Matlab\nappIce');
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% addpath('C:\Users\bletry_c\switchdrive\PSI_Matlab\Add-Ons');
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%
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%%
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% then run the initialiseMatlab function:
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%%
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% clearvars
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% [myColorMap, PsiFarbe] = initializeMatlab;
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% close all
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% clc
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%% Prepare: ISS data structure and path to local copy of ISS data
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%
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% Data needs to be organised in beam time folders that can be located on the
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% Napp drive or locally on your computer. The data folder naming and
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% structure within the beam time folder (orange on figure) is strict and
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% there must not be any subfolders in the data folders:
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%
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% <html>
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% <img src="../Bildschirmfoto 2022-03-23 um 08.19.22.png" width="400">
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% </html>
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%%
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% * NEXAFS holds *.h5 XAS data
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% * Photos holds *.bmp pictures
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% * Pressure holds the *.dat LabView files
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% * SES holds the *.ibw PE data.
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% * To run the scripts, there *needs to be at
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% least ONE *.ibw file in the SES folder*
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%
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%
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%
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% *beam time folder location*
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%
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% * The beam time folder can be a subfolder of any number of folders.
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% * The beam time folder needs to have the same name, same subfolders, and
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% folders above (path) locally and on the NAPP drive. This can be set by
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% the user when calling the import function. As standard, we use
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% "year:YYYY_MM__monthIssIceNapp"
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% * on Napp drive: the beam time folder (and folders above) needs to be
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% within the "SC Data" folder
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% * locally: the beam time folder (and folders above) needs to be within
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% your "MATLAB home folder:NAPP:SC Data" folder
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%
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%
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% The see all import options (few examples given below) check out the
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% nappFileList.m help (this works for all nappIce functions):
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%%
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%
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% help nappFileList.m
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%
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%% Helper functions
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%
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% * Napp_p_ice(T_current) states and plots the vapour pressure of ice between 200-270 K
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% * Napp_p_RH(T_current,RH) calculates the partial pressure of water for a given
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% temperature and relative humidity.
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% * Napp_RH_p(T_current,partial_pressure_water) calculates the relative humidity for a given
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% temperature and water pressure.
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%% Import ISS data: Complete beam time folder
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%
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%%
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%
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% FileList = nappFileList('2022/2022_02__FebIssIceNapp');
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%
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%%
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% This will create a new data table "FileList" overwriting any previous
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% versions of this list with data imported from the local copy of the beam
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% time folder with the name "2022_02__FebIssIceNapp" being a subfolder of
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% the folder "2022".
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%%
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clearvars
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%%
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[myColorMap, PsiFarbe] = initializeMatlab;
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close all
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clc
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%%
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if isfile('nappIceHowToData.mat')
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load('nappIceHowToData.mat')
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disp('data read from mat file')
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else
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FileList = nappFileList('2022/2022_02__FebIssIceNapp');
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% add photon energy to table
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FileList = addPhotonEnergy(FileList,[1,8:12,26],1000,2,2022);
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FileList = addPhotonEnergy(FileList,[2:3,5,7,13:14,16:18,20:25,27:31,33:35,37:38,40,42,44,46,47,49:51,54:56,60:62,65:66,68,70,73,74,76,77,82],900,2,2022);
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FileList = addPhotonEnergy(FileList,[4,6,15,19,58,59,64,67,72,80,81],650,2,2022);
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FileList = addPhotonEnergy(FileList,[32,36,39,41,43,45,48,57,63,69,71,75,78,83],1150,2,2022);
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FileList = addPhotonEnergy(FileList,79,NaN,2,2022);
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% add sample description
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FileList = addSampleNotes(FileList,[1,8:11],'Sample holder',2,2022);
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FileList = addSampleNotes(FileList,[2:7,35:36,56:60],'Ice with N2',2,2022);
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FileList = addSampleNotes(FileList,[12:34,54:55],'Ice',2,2022);
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FileList = addSampleNotes(FileList,[16,20],'Ice (bad data)',2,2022);
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FileList = addSampleNotes(FileList,[37:51,61:72],'Ice with N2 and phenol in Ar',2,2022);
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FileList = addSampleNotes(FileList,44,'Ice with N2 and phenol in Ar (bad data)',2,2022);
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FileList = addSampleNotes(FileList,73:76,'H2O gas-phase',2,2022);
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FileList = addSampleNotes(FileList,73,'H2O gas-phase (bad data)',2,2022);
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FileList = addSampleNotes(FileList,77:83,'CO2 gas-phase',2,2022);
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FileList = addSampleNotes(FileList,79,'CO2 gas-phase (bad data)',2,2022);
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% import data and add to data table
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FileList = nappFileList('2021/2021_10__OctIssIceNapp',FileList);
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FileList = nappFileList('2021/2021_08__AugIssIceNapp',FileList);
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% add photon energy to table
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FileList = addPhotonEnergy(FileList,1:10,1000,8,2021);
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FileList = addPhotonEnergy(FileList,36:42,1000,8,2021);
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FileList = addPhotonEnergy(FileList,51:72,1000,8,2021);
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FileList = addPhotonEnergy(FileList,78:120,1000,8,2021);
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FileList = addPhotonEnergy(FileList,1:6,1000,10,2021);
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FileList = addPhotonEnergy(FileList,8,500,10,2021);
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FileList = addPhotonEnergy(FileList,10,750,10,2021);
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FileList = addPhotonEnergy(FileList,13,500,10,2021);
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FileList = addPhotonEnergy(FileList,16,500,10,2021);
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FileList = addPhotonEnergy(FileList,17,750,10,2021);
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FileList = addPhotonEnergy(FileList,18:20,500,10,2021);
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FileList = addPhotonEnergy(FileList,21,750,10,2021);
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FileList = addPhotonEnergy(FileList,23,500,10,2021);
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FileList = addPhotonEnergy(FileList,26,600,10,2021);
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FileList = addPhotonEnergy(FileList,27,850,10,2021);
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FileList = addPhotonEnergy(FileList,28:31,600,10,2021);
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FileList = addPhotonEnergy(FileList,32:51,1000,10,2021);
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FileList = addPhotonEnergy(FileList,53:68,1000,10,2021);
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FileList = addPhotonEnergy(FileList,[74,76:85,87:91,93:94],1000,10,2021);
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% add sample description
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FileList = addSampleNotes(FileList,1:10,'MgO in N2',8,2021);
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FileList = addSampleNotes(FileList,36:39,'MgO in H2O',8,2021);
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FileList = addSampleNotes(FileList,40:44,'MgO in N2',8,2021);
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FileList = addSampleNotes(FileList,45:49,'MgO in Ar',8,2021);
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FileList = addSampleNotes(FileList,[51,53:55],'MgO in UHV',8,2021);
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FileList = addSampleNotes(FileList,56:60,'MgO ice (bad data)',8,2021);
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FileList = addSampleNotes(FileList,[61,66:72],'MgO ice in Ar HONO',8,2021);
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FileList = addSampleNotes(FileList,61,'MgO ice in Ar HONO (bad data)',8,2021);
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FileList = addSampleNotes(FileList,[68,69,70, 72],'MgO ice in Ar HONO on(bad data)',8,2021);
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FileList = addSampleNotes(FileList,78:80,'MgO in UHV',8,2021);
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FileList = addSampleNotes(FileList,81:82,'MgO in H2O',8,2021);
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FileList = addSampleNotes(FileList,83:85,'MgO ice',8,2021);
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FileList = addSampleNotes(FileList,86:94,'MgO ice AA in Ar',8,2021);
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FileList = addSampleNotes(FileList,[86 88],'MgO ice AA in Ar (bad data)',8,2021); % temporarily, Ar very dominating
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FileList = addSampleNotes(FileList,89,'MgO ice AA in Ar (bad data)',8,2021);
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FileList = addSampleNotes(FileList,98,'Ar',8,2021);
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FileList = addSampleNotes(FileList,99,'SiO2 in H2O',8,2021);
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FileList = addSampleNotes(FileList,100:103,'SiO2 ice',8,2021);
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FileList = addSampleNotes(FileList,104:110,'SiO2 ice AA in Ar',8,2021);
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FileList = addSampleNotes(FileList,111:112,'SiO2 ice',8,2021);
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FileList = addSampleNotes(FileList,113:118,'SiO2 ice AA in Ar',8,2021);
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FileList = addSampleNotes(FileList,119:120,'CO2',8,2021);
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FileList = addSampleNotes(FileList,1,'MgO in N2',10,2021);
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FileList = addSampleNotes(FileList,2,'MgO in N2, PE 50, Slits 1/1/1',10,2021);
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FileList = addSampleNotes(FileList,3,'MgO in N2, PE 50, Slits 1/1/1',10,2021);
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FileList = addSampleNotes(FileList,5,'MgO in N2, PE 50, Slits 1/0.5/0.5',10,2021);
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FileList = addSampleNotes(FileList,6,'MgO in N2, PE 50, Slits 1/1/1',10,2021);
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FileList = addSampleNotes(FileList,8,'MgO in N2, PE 20, Slits 1/1/1',10,2021);
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FileList = addSampleNotes(FileList,10,'MgO in N2, PE 20, Slits 1/1/1',10,2021);
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FileList = addSampleNotes(FileList,13,'MgO in N2, PE 20, Slits 1/1/1',10,2021);
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FileList = addSampleNotes(FileList,16,'MgO in N2, PE 20, Slits 1/0.8/0.8',10,2021);
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FileList = addSampleNotes(FileList,18,'MgO in N2, PE 20, Slits 1/0.8/0.8',10,2021);
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FileList = addSampleNotes(FileList,19,'MgO in N2, PE 20, Slits 0.5/1/1',10,2021);
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FileList = addSampleNotes(FileList,20,'MgO in N2, PE 20, Slits 0.5/1/1',10,2021);
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FileList = addSampleNotes(FileList,21,'MgO in N2, PE 20, Slits 0.5/1/1',10,2021);
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FileList = addSampleNotes(FileList,23,'MgO in N2, PE 20, Slits 0.5/1/1',10,2021);
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FileList = addSampleNotes(FileList,26,'MgO in N2, PE 50, Slits 0.5/1/1',10,2021);
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FileList = addSampleNotes(FileList,27,'MgO in N2, PE 20, Slits 0.5/1/1',10,2021);
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|
FileList = addSampleNotes(FileList,28,'MgO in N2, PE 50, Slits 0.5/0.5/0.5',10,2021);
|
|
FileList = addSampleNotes(FileList,29,'MgO in N2, PE 50, Slits 1/1/1',10,2021);
|
|
FileList = addSampleNotes(FileList,31,'MgO in N2, PE 20, Slits 1/1/1',10,2021);
|
|
FileList = addSampleNotes(FileList,32:33,'MgO in N2',10,2021);
|
|
FileList = addSampleNotes(FileList,34:36,'MgO ice',10,2021);
|
|
FileList = addSampleNotes(FileList,37:41,'MgO in H2O',10,2021);
|
|
FileList = addSampleNotes(FileList,42:51,'MgO ice',10,2021);
|
|
FileList = addSampleNotes(FileList,45,'MgO ice (bad data)',10,2021);
|
|
FileList = addSampleNotes(FileList,[53:68,70:71],'MgO ice AA in N2' ,10,2021);
|
|
FileList = addSampleNotes(FileList,54,'MgO ice AA in N2 (bad data)' ,10,2021);
|
|
FileList = addSampleNotes(FileList,68,'MgO ice AA in N2 (bad data)' ,10,2021);
|
|
FileList = addSampleNotes(FileList,63,'MgO ice AA in N2 (bad data)' ,10,2021);
|
|
FileList = addSampleNotes(FileList, [72,74],'MgO in H2O' ,10,2021);
|
|
FileList = addSampleNotes(FileList, [76,78:80],'MgO in N2 AA plus H2O' ,10,2021);
|
|
FileList = addSampleNotes(FileList,81:82,'SiO2 in N2',10,2021);
|
|
FileList = addSampleNotes(FileList,83:85,'SiO2 ice',10,2021);
|
|
FileList = addSampleNotes(FileList,87:90,'SiO2 in H2O',10,2021);
|
|
FileList = addSampleNotes(FileList,91,'SiO2 AA in N2',10,2021);
|
|
FileList = addSampleNotes(FileList,93:94,'SiO2 in H2O AA',10,2021);
|
|
% import
|
|
FileList = nappFileList('2020/202008AugustISS',FileList);
|
|
FileList = addSampleNotes(FileList,3,'diode upstream',8,2020);
|
|
FileList = addSampleNotes(FileList,4,'diode in-cell',8,2020);
|
|
FileList = addSampleNotes(FileList,8,'diode upstream',8,2020);
|
|
FileList = addSampleNotes(FileList,9,'diode in-cell',8,2020);
|
|
FileList = addSampleNotes(FileList,10,'diode in-cell',8,2020);
|
|
FileList = addSampleNotes(FileList,11,'diode upstream',8,2020);
|
|
FileList = addSampleNotes(FileList,13:23,'O_K-edge ice',8,2020);
|
|
FileList = addSampleNotes(FileList,13:18,'O_K-edge ice (bad S/N)',8,2020);
|
|
FileList = addSampleNotes(FileList,[15,19,20],'O_K-edge ice (no notes)',8,2020);
|
|
%ListNexafs = addSampleNotes(ListNexafs,21:23,'O_K-edge ice (ice unstable)',8,2020);
|
|
FileList = addSampleNotes(FileList,24,'diode (no notes)',8,2020);
|
|
FileList = addSampleNotes(FileList,25,'diode upstream',8,2020);
|
|
FileList = addSampleNotes(FileList,[26,28,30],'O_K-edge gold',8,2020);
|
|
FileList = addSampleNotes(FileList,[27,29,31],'O_K-edge gold (no notes)',8,2020);
|
|
FileList = addSampleNotes(FileList,[32,33],'(no notes)',8,2020);
|
|
FileList = addSampleNotes(FileList,[34,35,37,38],'diode in-cell',8,2020);
|
|
FileList = addSampleNotes(FileList,[36,39:42],'diode upstream',8,2020);
|
|
FileList = addSampleNotes(FileList,43:45,'O_K-edge gold',8,2020);
|
|
FileList = addSampleNotes(FileList,[48,50],'diode upstream',8,2020);
|
|
FileList = addSampleNotes(FileList,[49,51,54],'(no notes)',8,2020);
|
|
FileList = addSampleNotes(FileList,[43:45,52,53,55:59],'O_K-edge gold',8,2020);
|
|
FileList = addSampleNotes(FileList,53,'O_K-edge gas-phase (interference)',8,2020);
|
|
FileList = addSampleNotes(FileList,54,'O_K-edge (no data)',8,2020);
|
|
FileList = addSampleNotes(FileList,55,'O_K-edge gas-phase',8,2020);
|
|
FileList = addSampleNotes(FileList,60,'diode upstream',8,2020);
|
|
FileList = addSampleNotes(FileList,[61,63],'O_K-edge ice',8,2020);
|
|
% ListNexafs = addSampleNotes(ListNexafs,61,'O_K-edge ice (ice unstable)',8,2020);
|
|
FileList = addSampleNotes(FileList,62,'O_K-edge ice (no notes)',8,2020);
|
|
FileList = addSampleNotes(FileList,64,'diode upstream',8,2020);
|
|
FileList = addSampleNotes(FileList,65,'diode in-cell',8,2020);
|
|
FileList = addSampleNotes(FileList,66,'diode in-cell',8,2020);
|
|
save('nappIceHowToData.mat','FileList');
|
|
end
|
|
|
|
%% Import ISS data: Error messages
|
|
%
|
|
% If you get a message like this in the command window
|
|
%
|
|
% There appears to be no file in the SES folder. Add at least one file first.
|
|
% I quit now
|
|
%
|
|
%%
|
|
% and MATLAB quits running the script, please check that there is at least
|
|
% a *.ibw file in the SES folder
|
|
%%
|
|
% If you instead get a message like this
|
|
%
|
|
% There is something wrong with the path. Check for typo and that the drives are mounted
|
|
% I quit now
|
|
%
|
|
%%
|
|
% and MATLAB quits running the script, please check
|
|
%
|
|
% * that there are no typos in the path names
|
|
% * that the drives are mounted.
|
|
%% Import ISS data: Add data to existing data table
|
|
%
|
|
% This will import data from the beam time folder
|
|
% "2021/2021_08__AugIssIceNapp" on the NAPP drive adding the data to the data table
|
|
% "FileList" in the MATLAB workspace
|
|
%%
|
|
% FileList = nappFileList('2021/2021_08__AugIssIceNapp',FileList,'mode','nappDrive');
|
|
%% Import ISS data: Add new SES or NEXAFS files from local beam time folder
|
|
%
|
|
% Using the mode 'localNew' the content of the beam time folder will be
|
|
% compared to that of the FileList in the workspace and only those files
|
|
% not yet imported will be processed. This is a little faster and preserves
|
|
% any changes to the FileList. *To prevent data failure of running SES
|
|
% measurements, this only works on the local copy of the beam time folder*
|
|
%%
|
|
%
|
|
% FileList = nappFileList('2022/2022_02__FebIssIceNapp',FileList,'mode','localNew');
|
|
%
|
|
%% Import ISS data: Choose files manually
|
|
%
|
|
% This will open a windows file explorer window where you can choose as
|
|
% many data files to be imported as you want. In this example, a new
|
|
% FileList is written, overwriting any previous versions in the workspace.
|
|
%%
|
|
%
|
|
% FileList = nappFileList('2022/2022_02__FebIssIceNapp','mode','nappDrivePick');
|
|
%
|
|
%
|
|
% <html>
|
|
% <img src="../Bildschirmfoto 2022-03-23 um 11.14.44.png" width="400">
|
|
% </html>
|
|
%% Work with data: Excel
|
|
%
|
|
% The nappFileList.m function also writes a csv file to the beam time
|
|
% folder. Open it as xlsx and use Excels formatting and filtering tools to
|
|
% work with your data set. So, the xlsx file is edited manually and might
|
|
% contain additional information that you have added such as slit settings,
|
|
% comments, type of sample, scope of the measurement, etc. The csv file it
|
|
% automatically generated and might thus be frequently be over-written. Use
|
|
% copy/paste to update the xlsx file with new data, formatting will be
|
|
% automatically applied to new data then.
|
|
%%
|
|
%
|
|
%
|
|
% <html>
|
|
% <img src="../Bildschirmfoto 2019-05-08 um 09.58.47.png" width="800">
|
|
% </html>
|
|
%% Work with data: Add photon energy to table (XPS)
|
|
%
|
|
% ISS beamline does not log settings of SES data. Photon energy, which is
|
|
% needed to calculate the binding energy later, needs to be added manually.
|
|
% This will read the FileList table and add the photon energy "1000" to the
|
|
% SES data with file numbers 1,8 to 12, and 26 acquired in February (month
|
|
% "2") of the year "2022"). Note, this is only adding to XPS data, NEXAFS
|
|
% data are not touched:
|
|
%%
|
|
%
|
|
% FileList = addPhotonEnergy(FileList,[1,8:12,26],1000,2,2022);
|
|
%
|
|
|
|
%% Work with data: Add sample description
|
|
%
|
|
% Sample description to label figures and for filtering data can be added.
|
|
% It does not make sense to use temperature or pressure, as these data are
|
|
% written to the table anyway. It does make sense to use standard phrases
|
|
% to enable filtering all beam time data. This code example follow the same
|
|
% input structure as the addPhotonEnergy function. This function works on
|
|
% both NEXAFS and XPS data. One may limit and select either one
|
|
% specifically by adding either 'NEXAFS' or 'XPS' strings as last input
|
|
% parameter. This is optional, if not specified function runs on both data
|
|
% types:
|
|
%%
|
|
%
|
|
% FileList = addSampleNotes(FileList,[1,8:11],'Sample holder',2,2022,'XPS');
|
|
% FileList = addSampleNotes(FileList,[1,8:11],'Sample holder',2,2022,'NEXAFS');
|
|
% FileList = addSampleNotes(FileList,[2:7,35:36,56:60],'Ice with N2',2,2022);
|
|
% FileList = addSampleNotes(FileList,73:76,'H2O gas-phase',2,2022);
|
|
% FileList = addSampleNotes(FileList,73,'H2O gas-phase (bad data)',2,2022);
|
|
%% Work with data: Filter table
|
|
%
|
|
% An advantage of the data table is filtering. This works with standard
|
|
% MATLAB functions using logical operations. Main motivation of filtering
|
|
% is to work with selected subset of data which for example can be
|
|
% processed with the same settings, fitted with the same constrains, or
|
|
% shall be plotted in one graph for comparison.
|
|
%
|
|
% Further, the _InputTable_ may hold different data types such as XPS data,
|
|
% NEXAFS, and pictures. Mostly, the functions require one and the same data
|
|
% type as input. Therefore, *the _InputTable_ needs to be filtered when
|
|
% passed to most functions*. Exceptions from this are the
|
|
% |nappGenerateSpectra| and |nappPlot| functions that can be called with
|
|
% some limitations in the settings with mixed data as input (including
|
|
% pictures, that are ignored).
|
|
%
|
|
% The first example filter CO2 gas-phase C1s PE data that is not "(bad
|
|
% data)" and has a pass energy of 50 eV. In the second example, we pick a
|
|
% C1s or O1s PE data from the SES file #77 from February 2022. Any table
|
|
% column can be used for filtering. "&" means AND "|" is OR.
|
|
%
|
|
%%
|
|
% tmpDataSelection1 = matches(FileList.sample,'CO2 gas-phase') ...
|
|
% & ~contains(FileList.sample,'(bad data)')...
|
|
% & contains(FileList.name,'C1s')...
|
|
% & FileList.scientaPassEnergy_eV == 50 ;
|
|
%
|
|
% tmpDataSelection2 = (contains(FileList.name,'077_C1s') | contains(FileList.name,'077_O1s')) ...
|
|
% (month(FileList.lastModifiedDatenum) == 2) &...
|
|
% (year(FileList.lastModifiedDatenum) == 2022);
|
|
%%
|
|
tmpDataSelection1 = matches(FileList.sample,'CO2 gas-phase') ...
|
|
& ~contains(FileList.sample,'(bad data)')...
|
|
& contains(FileList.name,'C1s')...
|
|
& FileList.scientaPassEnergy_eV == 50 ;
|
|
|
|
|
|
tmpDataSelection2 = (contains(FileList.name,'077_C1s') | contains(FileList.name,'077_O1s'))& ...
|
|
(month(FileList.lastModifiedDatenum) == 2) &...
|
|
(year(FileList.lastModifiedDatenum) == 2022);
|
|
|
|
%% Work with data: Display filtered table and selected columns
|
|
%
|
|
% MATLAB tables allow to address the columns by name. To display only the
|
|
% rows that match the filter criteria and only specific columns, code like
|
|
% the examples below can be used:
|
|
%%
|
|
% disp(FileList(tmpDataSelection1,{'name','sample','xRayEkin_eV','scientaPassEnergy_eV','lastModifiedDatestr','sampleTemp_dC',...
|
|
% 'cellPressure_mbar' }))
|
|
% disp(FileList(tmpDataSelection2,{'name','scientaLensMode','xRayEkinRange_eV','scientaPassEnergy_eV'}))
|
|
%%
|
|
disp(FileList(tmpDataSelection1,{'name','sample','xRayEkin_eV','scientaPassEnergy_eV','lastModifiedDatestr','sampleTemp_dC',...
|
|
'cellPressure_mbar' }))
|
|
disp(FileList(tmpDataSelection2,{'name','scientaLensMode','xRayEkinRange_eV','scientaPassEnergy_eV'}))
|
|
clearvars tmp*
|
|
%%
|
|
%
|
|
%% Work with data: Modify data in table
|
|
%
|
|
% There might be bad sweeps (sequence iterations) either with too low
|
|
% signal to noise or because some setting was changed during the run. These
|
|
% can be removed by directly addressing the data in the images. Same
|
|
% procedure, first the data (table row) is filtered, then the image
|
|
% modified for the selected data.
|
|
%
|
|
% *remove one sweep*
|
|
%%
|
|
%
|
|
% tmpDataSelection = contains(FileList.name,'077_C1s')& ...
|
|
% (month(FileList.lastModifiedDatenum) == 2) &...
|
|
% (year(FileList.lastModifiedDatenum) == 2022);
|
|
% FileList(tmpDataSelection,:).image{:}(:,3)=[];
|
|
%
|
|
% tmpDataSelection = contains(FileList.name,'077_O1s')& ...
|
|
% (month(FileList.lastModifiedDatenum) == 2) &...
|
|
% (year(FileList.lastModifiedDatenum) == 2022);
|
|
% FileList(tmpDataSelection,:).image{:}(:,3)=[];
|
|
%
|
|
%%
|
|
% *remove several sweeps*
|
|
%
|
|
%%
|
|
%
|
|
% tmpDataSelection = contains(FileList.name,'059_C1s')& ...
|
|
% (month(FileList.lastModifiedDatenum) == 10) &...
|
|
% (year(FileList.lastModifiedDatenum) == 2021);
|
|
% FileList(tmpDataSelection,:).image{:}(:,5:15)=[];
|
|
%%
|
|
% *remove all data (complete table rows) outside a specific time window*
|
|
%
|
|
%%
|
|
% tmpTime.start = datenum('30-Apr-2019 02:00:00');
|
|
% tmpTime.end = datenum('30-Apr-2019 05:00:00');
|
|
% toDelete = Table_FileList.datenum <= tmpTime.start;
|
|
% Table_FileList(toDelete,:) = [];
|
|
% toDelete = Table_FileList.datenum >= tmpTime.end;
|
|
% Table_FileList(toDelete,:) = [];
|
|
%%
|
|
% *Correct beam line-offset in data*
|
|
%
|
|
% This will correct 1.6 from the X-ray energies, of particular importance
|
|
% for NEXAFS data. One can also do this on selected data.
|
|
%%
|
|
% beamlineOffset = 1.6;
|
|
% FileList.xRayEkin_eV = cellfun(@(x) x+beamlineOffset,FileList.xRayEkin_eV,'UniformOutput',false);
|
|
%% Add data (columns) to table
|
|
%
|
|
% Calculation results for each row can be added to data tables using
|
|
% standard Matlab functions. This example illustrates the use of
|
|
% anonymous functions, their call for each row using |cellfun|, and
|
|
% the generation of additional columns to which (selection of) the data are
|
|
% written.
|
|
%%
|
|
%
|
|
% fun1 =@(c) interp1(IzeroAugInCell(:,1),IzeroAugInCell(:,2), c);
|
|
% fun2 =@(c) interp1(IzeroAugInCell(:,1),IzeroAugInCell(:,3), c);
|
|
% tmpMatch = (month(ListNexafs.lastModifiedDatenum) == 8) &...
|
|
% (year(ListNexafs.lastModifiedDatenum) == 2020) |...
|
|
% (startsWith(ListNexafs.name, 'sum_Aug'));
|
|
% ListNexafs.uhvI0(tmpMatch) = cellfun(fun1, ListNexafs.xRayEkin_eV(tmpMatch) ,'uni' , 0);
|
|
% ListNexafs.uhvI0std(tmpMatch) = cellfun(fun2, ListNexafs.xRayEkin_eV(tmpMatch) ,'uni' , 0);
|
|
% clearvars tmp* fun*
|
|
|
|
%% Plot data: XPS images
|
|
%
|
|
% We use the nappPlot function to plot the images. For this, we specify
|
|
% the column of the data table that should be plotted:
|
|
% 'tableVarialbe','image' plots the image for all data in FileList that are
|
|
% selected by the logical array tmpDataSelection (See _Filtering table_
|
|
% above).
|
|
%%
|
|
%
|
|
% nappPlot(FileList,tmpDataSelection,'tableVariable','image');
|
|
%
|
|
%%
|
|
close all
|
|
tmpDataSelection = contains(FileList.name,'035_O1s') & FileList.xRayEkinRange_eV(:,1) == 900 ...
|
|
& FileList.scientaPassEnergy_eV == 50 ...
|
|
& (month(FileList.lastModifiedDatenum) == 2) ...
|
|
& (year(FileList.lastModifiedDatenum) == 2022) ...
|
|
& (day(FileList.lastModifiedDatenum) >= 27);
|
|
nappPlot(FileList,tmpDataSelection,'tableVariable','image');
|
|
%% Plot data: XPS and NEXAFS images
|
|
%
|
|
% This example filters for a XPS file name and a NEXAFS file name and shows
|
|
% two things. First, that the XPS file name is not unique and filtering
|
|
% thus needs to be combined with extra parameters such as date and second
|
|
% how NEXAS and XPS data can be simultaneously passed to the |nappPlot|
|
|
% function
|
|
%%
|
|
% tmpDataSelection = matches(FileList.name,'0067067_O1s_1000eV.ibw') | matches(FileList.name, '0063_20200824_042852_O_K_edge.h5');
|
|
% nappPlot(FileList,tmpDataSelection);
|
|
|
|
%%
|
|
tmpDataSelection = matches(FileList.name,'0067067_O1s_1000eV.ibw') | matches(FileList.name, '0063_20200824_042852_O_K_edge.h5');
|
|
nappPlot(FileList,tmpDataSelection);
|
|
%% Plot data: XPS spectra
|
|
%
|
|
% We use the nappPlot function to plot the spectra by selecting
|
|
% 'spectrum_countsPerSecond' as 'tableVariable'. Further options include
|
|
% the binding or kinetic energy scale, and each spectrum in a separate
|
|
% window, or all in one window by setting the 'mode'. This works, of
|
|
% course, only after the spectrum has been generated using the function
|
|
% nappGenerateSpectra.
|
|
%%
|
|
%
|
|
% nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsPerSecond',...
|
|
% 'mode','AllInOne','energyScale','kinetic');
|
|
%
|
|
%%
|
|
close all
|
|
tmpDataSelection = matches(FileList.sample,'Ice with N2')...
|
|
& ~contains(FileList.sample,'(bad data)')...
|
|
& contains(FileList.name,'O1s') & FileList.xRayEkinRange_eV(:,1) == 900 ...
|
|
& FileList.scientaPassEnergy_eV == 50 ...
|
|
& (month(FileList.lastModifiedDatenum) == 2) ...
|
|
& (year(FileList.lastModifiedDatenum) == 2022) ...
|
|
& (day(FileList.lastModifiedDatenum) >= 27);
|
|
FileList = nappGenerateSpectra(FileList,tmpDataSelection);
|
|
nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsPerSecond',...
|
|
'mode','AllInOne','energyScale','kinetic');
|
|
%% Plot data: NEXAFS spectra
|
|
%
|
|
% We use the same, one-and-only nappPlot function to plot the NEXAFS
|
|
% spectra by selecting 'spectrum_counts' as 'tableVariable' and
|
|
% 'energyScale' as 'xRay'. Further options include plotting each spectrum
|
|
% in a separate window, or all in one window by setting the 'mode'. This
|
|
% works, of course, only after the spectrum has been generated using the
|
|
% function nappGenerateSpectra.
|
|
%%
|
|
%
|
|
% nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_counts','energyScale','xRay','mode','OneEach')
|
|
%
|
|
%%
|
|
%
|
|
close all
|
|
clc
|
|
tmpDataSelection = contains(FileList.name, '0063_20200824_042852_O_K_edge');
|
|
% Sum Auger-Meitner Spectrum
|
|
FileList = nappGenerateSpectra(FileList,'tableSelection',tmpDataSelection,'mode','sum');
|
|
% Plot
|
|
nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_counts','energyScale','xRay','mode','OneEach');
|
|
%% Plot data: Figure layout
|
|
%
|
|
% The |nappPlot| function has 3 pre-defined figure layouts optimised for
|
|
% the computer screen, for powerpoint presentation or for word (and other
|
|
% text) files:
|
|
%%
|
|
%
|
|
% nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_counts','energyScale','xRay','mode','OneEach','figureMode','screen');
|
|
%
|
|
%%
|
|
close all
|
|
clc
|
|
tmpDataSelection = contains(FileList.name, '0063_20200824_042852_O_K_edge');
|
|
% Sum Auger-Meitner Spectrum
|
|
FileList = nappGenerateSpectra(FileList,'tableSelection',tmpDataSelection,'mode','sum');
|
|
% Plot
|
|
nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_counts','energyScale','xRay','mode','OneEach','figureMode','screen');
|
|
%%
|
|
%
|
|
% nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_counts','energyScale','xRay','mode','OneEach','figureMode','word');
|
|
%
|
|
%%
|
|
close all
|
|
clc
|
|
tmpDataSelection = contains(FileList.name, '0063_20200824_042852_O_K_edge');
|
|
% Sum Auger-Meitner Spectrum
|
|
FileList = nappGenerateSpectra(FileList,'tableSelection',tmpDataSelection,'mode','sum');
|
|
% Plot
|
|
nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_counts','energyScale','xRay','mode','OneEach','figureMode','word');
|
|
%%
|
|
%
|
|
% nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_counts','energyScale','xRay','mode','OneEach','figureMode','powerpoint');
|
|
%
|
|
%%
|
|
close all
|
|
clc
|
|
tmpDataSelection = contains(FileList.name, '0063_20200824_042852_O_K_edge');
|
|
% Sum Auger-Meitner Spectrum
|
|
FileList = nappGenerateSpectra(FileList,'tableSelection',tmpDataSelection,'mode','sum');
|
|
% Plot
|
|
nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_counts','energyScale','xRay','mode','OneEach','figureMode','powerpoint');
|
|
%% Plot data: Export the Figure (for use in powerpoint)
|
|
%
|
|
% Either, copy paste the figure, or use this command on the front most
|
|
% figure in Matlab:
|
|
%%
|
|
%
|
|
% set(gcf,'InvertHardcopy','on');
|
|
% print('NameForFigureFile, '-dpng', '-r220');
|
|
%
|
|
%% Plot data: Add region labels to survey
|
|
%
|
|
% Here is an example of how to mark regions of interest in survey plots.
|
|
% For this, a survey must be the topmost figure.
|
|
|
|
%%
|
|
% dataObjs = findobj(gcf,'-property','YData');
|
|
% tmpxShift = 5; % binding energy shift and mono offset
|
|
% tmpxRayEnergy = 900; % of this survey
|
|
% tmpyPosition = 0.5E5; % y position of labels, delta to signal height, in axis scale
|
|
% tmpyPositionTwo = 0.1E5; % optional, to shift neighouring labels up and dow
|
|
% % n
|
|
% % Au peaks
|
|
% nappAnnotateSurvey(tmpxRayEnergy-57-tmpxShift,tmpyPosition-tmpyPositionTwo,dataObjs(1),'Au5p3/2');
|
|
% nappAnnotateSurvey(tmpxRayEnergy-74-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Au5p5/2');
|
|
% nappAnnotateSurvey(tmpxRayEnergy-84-tmpxShift,tmpyPosition,dataObjs(1),'Au4f7/2');
|
|
% nappAnnotateSurvey(tmpxRayEnergy-88-tmpxShift,tmpyPosition-tmpyPositionTwo,dataObjs(1),'Au4f5/2');
|
|
% nappAnnotateSurvey(tmpxRayEnergy-107-tmpxShift,tmpyPosition,dataObjs(1),'Au5s');
|
|
% nappAnnotateSurvey(tmpxRayEnergy-335-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Au4d1/2');
|
|
% nappAnnotateSurvey(tmpxRayEnergy-353-tmpxShift,tmpyPosition,dataObjs(1),'Au4d3/2');
|
|
% nappAnnotateSurvey(tmpxRayEnergy-546-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Au4p3/2');
|
|
% nappAnnotateSurvey(tmpxRayEnergy-643-tmpxShift,tmpyPosition,dataObjs(1),'Au4p1/2');
|
|
% nappAnnotateSurvey(tmpxRayEnergy-762-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Au4s');
|
|
% nappAnnotateSurvey(tmpxRayEnergy-8-tmpxShift,tmpyPosition,dataObjs(1),'AuVB');
|
|
% % usual suspects
|
|
% nappAnnotateSurvey(tmpxRayEnergy-285-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'C1s');
|
|
% nappAnnotateSurvey(tmpxRayEnergy-534-tmpxShift,tmpyPosition,dataObjs(1),'O1s');
|
|
% nappAnnotateSurvey(tmpxRayEnergy-30-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'O2s');
|
|
% nappAnnotateSurvey(tmpxRayEnergy-405-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'N1s');
|
|
% nappAnnotateSurvey(tmpxRayEnergy-495-tmpxShift,tmpyPosition,dataObjs(1),'Sn3d');
|
|
% nappAnnotateSurvey(tmpxRayEnergy-691-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'F1s');
|
|
% nappAnnotateSurvey(tmpxRayEnergy-295-tmpxShift,tmpyPosition,dataObjs(1),'K2p');
|
|
% nappAnnotateSurvey(tmpxRayEnergy-380-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'K2s');
|
|
% nappAnnotateSurvey(tmpxRayEnergy-153-tmpxShift,tmpyPosition,dataObjs(1),'Si2s');
|
|
% nappAnnotateSurvey(tmpxRayEnergy-100-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Si2p');
|
|
% nappAnnotateSurvey(tmpxRayEnergy-197-tmpxShift,tmpyPosition,dataObjs(1),'Cl2p');
|
|
% nappAnnotateSurvey(tmpxRayEnergy-286-tmpxShift,tmpyPosition-tmpyPositionTwo,dataObjs(1),'Sn3p');
|
|
% % Some Auger
|
|
% nappAnnotateSurvey(360-tmpxShift,tmpyPosition-tmpyPositionTwo,dataObjs(1),'N KLL');
|
|
% nappAnnotateSurvey(500-tmpxShift,tmpyPosition-tmpyPositionTwo,dataObjs(1),'O KLL');
|
|
% % tid up
|
|
% clearvars tmp*
|
|
|
|
%%
|
|
close all
|
|
tmpDataSelection = contains(FileList.name,'007_Survey');
|
|
FileList = nappGenerateSpectra(FileList,tmpDataSelection);
|
|
nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsPerSecond',...
|
|
'mode','AllInOne','energyScale','kinetic');
|
|
% add text to Figure
|
|
dataObjs = findobj(gcf,'-property','YData');
|
|
tmpxShift = 6;
|
|
tmpxRayEnergy = 900;
|
|
tmpyPosition = 0.5E5;
|
|
tmpyPositionTwo = 0.1E5;
|
|
% Au peaks
|
|
nappAnnotateSurvey(tmpxRayEnergy-57-tmpxShift,tmpyPosition-tmpyPositionTwo,dataObjs(1),'Au5p3/2');
|
|
nappAnnotateSurvey(tmpxRayEnergy-74-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Au5p5/2');
|
|
nappAnnotateSurvey(tmpxRayEnergy-84-tmpxShift,tmpyPosition,dataObjs(1),'Au4f7/2');
|
|
nappAnnotateSurvey(tmpxRayEnergy-88-tmpxShift,tmpyPosition-tmpyPositionTwo,dataObjs(1),'Au4f5/2');
|
|
nappAnnotateSurvey(tmpxRayEnergy-107-tmpxShift,tmpyPosition,dataObjs(1),'Au5s');
|
|
nappAnnotateSurvey(tmpxRayEnergy-335-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Au4d1/2');
|
|
nappAnnotateSurvey(tmpxRayEnergy-353-tmpxShift,tmpyPosition,dataObjs(1),'Au4d3/2');
|
|
nappAnnotateSurvey(tmpxRayEnergy-546-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Au4p3/2');
|
|
nappAnnotateSurvey(tmpxRayEnergy-643-tmpxShift,tmpyPosition,dataObjs(1),'Au4p1/2');
|
|
nappAnnotateSurvey(tmpxRayEnergy-762-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Au4s');
|
|
nappAnnotateSurvey(tmpxRayEnergy-8-tmpxShift,tmpyPosition,dataObjs(1),'AuVB');
|
|
% usual suspects
|
|
nappAnnotateSurvey(tmpxRayEnergy-285-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'C1s');
|
|
nappAnnotateSurvey(tmpxRayEnergy-534-tmpxShift,tmpyPosition,dataObjs(1),'O1s');
|
|
nappAnnotateSurvey(tmpxRayEnergy-30-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'O2s');
|
|
nappAnnotateSurvey(tmpxRayEnergy-405-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'N1s');
|
|
nappAnnotateSurvey(tmpxRayEnergy-495-tmpxShift,tmpyPosition,dataObjs(1),'Sn3d');
|
|
nappAnnotateSurvey(tmpxRayEnergy-691-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'F1s');
|
|
nappAnnotateSurvey(tmpxRayEnergy-295-tmpxShift,tmpyPosition,dataObjs(1),'K2p');
|
|
nappAnnotateSurvey(tmpxRayEnergy-380-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'K2s');
|
|
nappAnnotateSurvey(tmpxRayEnergy-153-tmpxShift,tmpyPosition,dataObjs(1),'Si2s');
|
|
nappAnnotateSurvey(tmpxRayEnergy-100-tmpxShift,tmpyPosition+tmpyPositionTwo,dataObjs(1),'Si2p');
|
|
nappAnnotateSurvey(tmpxRayEnergy-197-tmpxShift,tmpyPosition,dataObjs(1),'Cl2p');
|
|
nappAnnotateSurvey(tmpxRayEnergy-286-tmpxShift,tmpyPosition-tmpyPositionTwo,dataObjs(1),'Sn3p');
|
|
% Some Auger
|
|
nappAnnotateSurvey(360-tmpxShift,tmpyPosition-tmpyPositionTwo,dataObjs(1),'N KLL');
|
|
nappAnnotateSurvey(500-tmpxShift,tmpyPosition-tmpyPositionTwo,dataObjs(1),'O KLL');
|
|
% tid up
|
|
clearvars tmp*
|
|
|
|
%% Process data: Align sequence iterations in images
|
|
%
|
|
% To shift and align the kinetic energy of each sequence iteration relative
|
|
% to each other, one can use
|
|
%
|
|
% # nappDetermineShiftXps: calculate the kinetic energy shift of each
|
|
% sequence iteration of an image relative to one sequence iteration (first
|
|
% is default)
|
|
% # nappCopyShift: optionally copy the kinetic energy shift to other data
|
|
% # nnappAlignImages: shift each sequence iterations of an image so that
|
|
% the peak maxima align.
|
|
%%
|
|
%
|
|
% % compile data
|
|
% tmpDataSelection = contains(FileList.name,'67_O1s') ...
|
|
% & (month(FileList.lastModifiedDatenum) == 8) ...
|
|
% & (year(FileList.lastModifiedDatenum) == 2021);
|
|
% % plot images
|
|
% nappPlot(FileList,tmpDataSelection,'tableVariable','image');
|
|
% % align images
|
|
% FileList = nappDetermineShiftXps(FileList,'tableSelection',tmpDataSelection,...
|
|
% 'showFigures','off','tableVariable','image');
|
|
% FileList = nappAlignImages(FileList,'tableSelection',tmpDataSelection,...
|
|
% 'tableVariable','image');
|
|
% % plot images
|
|
% nappPlot(FileList,tmpDataSelection,'tableVariable','image');
|
|
|
|
%%
|
|
close all
|
|
clc
|
|
% compile data
|
|
tmpDataSelection = contains(FileList.name,'67_O1s') ...
|
|
& (month(FileList.lastModifiedDatenum) == 8) ...
|
|
& (year(FileList.lastModifiedDatenum) == 2021);
|
|
% plot images
|
|
nappPlot(FileList,tmpDataSelection,'tableVariable','image');
|
|
% align images
|
|
FileList = nappDetermineShiftXps(FileList,'tableSelection',tmpDataSelection,...
|
|
'showFigures','off','tableVariable','image');
|
|
FileList = nappAlignImages(FileList,'tableSelection',tmpDataSelection,...
|
|
'tableVariable','image');
|
|
% plot images
|
|
nappPlot(FileList,tmpDataSelection,'tableVariable','image');
|
|
%% Process data: Generate XPS spectra
|
|
%
|
|
% All sequence iterations of an image are summed or the mean of all
|
|
% sequence iterations is calculated. The following command calculates the
|
|
% average of all sequence iterations from the filtered files in the
|
|
% FileList table. The spectrum based on the mean is saved in the column
|
|
% 'spectrum_countsPerSecond'.
|
|
%%
|
|
%
|
|
% FileList = nappGenerateSpectra(FileList,tmpDataSelection);
|
|
%
|
|
%% Process data: Generate NEXAFS spectra
|
|
%
|
|
% % All Auger spectra of an image are summed. Options include to sum the
|
|
% data with the energy interval of the aquisition, or to use the trapz
|
|
% function to normalise to 1 eV data spacing. The NEXAFS spectrua are
|
|
% always saved in the column 'spectrum_counts'.
|
|
%%
|
|
%
|
|
% FileList = nappGenerateSpectra(FileList,'tableSelection',tmpDataSelection,'mode','sum');
|
|
%
|
|
%%
|
|
close all
|
|
clc
|
|
tmpDataSelection = contains(FileList.name, '0063_20200824_042852_O_K_edge');
|
|
% Sum Auger-Meitner Spectrum
|
|
FileList = nappGenerateSpectra(FileList,'tableSelection',tmpDataSelection,'mode','sum');
|
|
% Plot
|
|
nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_counts','energyScale','xRay','mode','OneEach');
|
|
|
|
%%
|
|
%
|
|
% FileList = nappGenerateSpectra(FileList,tmpDataSelection);
|
|
%
|
|
%% Process data: Align XPS spectra to background
|
|
%
|
|
% To align the spectra to a background the function nappBacktround can
|
|
% be used. Options include to compute the mean or a linear regression.
|
|
% Here, we calculate the mean between 367 and 370 eV.
|
|
%%
|
|
%
|
|
% FileList = nappBackground(FileList,367,370,'tableSelection',tmpDataSelection,'...
|
|
% energyScale','kinetic','mode','mean');
|
|
%
|
|
%%
|
|
close all
|
|
clc
|
|
tmpDataSelection = matches(FileList.sample,'Ice with N2')...
|
|
& ~contains(FileList.sample,'(bad data)')...
|
|
& contains(FileList.name,'O1s') & FileList.xRayEkinRange_eV(:,1) == 900 ...
|
|
& FileList.scientaPassEnergy_eV == 50 ...
|
|
& (month(FileList.lastModifiedDatenum) == 2) ...
|
|
& (year(FileList.lastModifiedDatenum) == 2022) ...
|
|
& (day(FileList.lastModifiedDatenum) >= 27);
|
|
FileList = nappGenerateSpectra(FileList,tmpDataSelection);
|
|
FileList = nappBackground(FileList,367,370,'tableSelection',tmpDataSelection,'energyScale','kinetic',...
|
|
'mode','mean');
|
|
% plot spectra
|
|
nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsPerSecond',...
|
|
'mode','AllInOne','energyScale','kinetic');
|
|
|
|
%% Process data: Align NEXAFS spectra to background
|
|
%
|
|
% To align the spectra to a background the function nappBacktround can
|
|
% be used. Options include to compute the mean or a linear regression.
|
|
% Here, we calculate the mean between 522 and 525 eV xRay energy.
|
|
%%
|
|
%
|
|
% FileList = nappBackground(FileList,522,525,'tableSelection',tmpDataSelection,...
|
|
% 'tableVariable','spectrum_counts','energyScale','xRay','mode','mean');
|
|
%
|
|
%%
|
|
close all
|
|
clc
|
|
tmpDataSelection = contains(FileList.name, '0063_20200824_042852_O_K_edge');
|
|
% Sum Auger-Meitner Spectrum
|
|
FileList = nappGenerateSpectra(FileList,'tableSelection',tmpDataSelection,'mode','sum');
|
|
% Background
|
|
FileList = nappBackground(FileList,522,525,'tableSelection',tmpDataSelection,'tableVariable','spectrum_counts','energyScale','xRay',...
|
|
'mode','mean');
|
|
% Plot
|
|
nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_counts','energyScale','xRay','mode','OneEach');
|
|
|
|
%% Process data: Gauss peak fit
|
|
%
|
|
% Gauss fitting can be used to determine area, width, and position of PE
|
|
% peaks. nappFitGaussXps de-convolutes the PE signal using 4 features. The
|
|
% fits can be constrained by
|
|
%
|
|
% * 'widthPosition' (Default): With of all peaks is identical. Position of
|
|
% peaks 2-3 is set relative to peak 1.
|
|
% * 'position': Position of peaks 2-3 is set relative to peak 1.
|
|
% * 'width': all peaks have same width
|
|
% * 'unconstrained': Each gauss peak is fitted independent.
|
|
% * 'unconstrainedAndWidthPosition': One unconstrained peak #3 (for gas phase
|
|
% feature) and 4 peaks constrained in width and/or position
|
|
%
|
|
%
|
|
% The function needs fit parameters as input, such as min and max peak
|
|
% area, width, and position; initial values for fitting; and the spectrum
|
|
% range.
|
|
%
|
|
% * spectrum range: define the region of the PE spectrum used for fitting
|
|
% (plus and minus from peak 1 position).
|
|
% % position of peak 2-4 are relative to peak 1, that of peak 1 absolute
|
|
% (either in binding or kinetic energy scale)
|
|
% * If you are only interested in less than 4 peaks, choose small dummy
|
|
% constrains for those peaks you don't want to fit.
|
|
%
|
|
%%
|
|
% % initial fit parameters
|
|
% tmpFitParameters.peakArea = [1E4 5E3 0.1 0.1];
|
|
% tmpFitParameters.peakWidth = [2.0 2.0 0.1 0.1];
|
|
% tmpFitParameters.peakPos = [363 -1 -2 -3];
|
|
% % lower bonds
|
|
% tmpFitParameters.peakAreaLowerBond = [1E3 5E3 0.1 0.1];
|
|
% tmpFitParameters.peakWidthLowerBond = [2.0 2.0 0.1 0.1];
|
|
% tmpFitParameters.peakPosLowerBond = 361;
|
|
% % upper bonds
|
|
% tmpFitParameters.peakAreaUpperBond = [1E6 10E4 0.1 0.1];
|
|
% tmpFitParameters.peakWidthUpperBond = [3.0 2.0 0.1 0.1];
|
|
% tmpFitParameters.peakPosUpperBond = 365;
|
|
% % spectrum range
|
|
% tmpFitParameters.backgroundRange = [5 5];
|
|
%
|
|
% % „position“ mode
|
|
% tmpFitParameters.peakArea = [abs#1 abs#2 abs#3 abs#4];
|
|
% tmpFitParameters.peakWidth = [abs#1 abs#2 abs#3 abs#4];
|
|
% tmpFitParameters.peakPos = [abs#1 rel#2 rel#3 rel#4];
|
|
%
|
|
% tmpFitParameters.peakAreaUpperBond = [#1 #2 #3 #4];
|
|
% tmpFitParameters.peakWidthUpperBond = [#1 #2 #3 #4];
|
|
% tmpFitParameters.peakPosUpperBond = #1;
|
|
% tmpFitParameters.peakAreaLowerBond = [#1 #2 #3 #4];
|
|
% tmpFitParameters.peakWidthLowerBond = [#1 #2 #3 #4];
|
|
% tmpFitParameters.peakPosLowerBond = #1;
|
|
%
|
|
%
|
|
% % „unconstrained“ mode
|
|
% tmpFitParameters.peakArea = [abs#1 abs#2 abs#3 abs#4];
|
|
% tmpFitParameters.peakWidth = [abs#1 abs#2 abs#3 abs#4];
|
|
% tmpFitParameters.peakPos = [abs#1 abs#2 abs#3 abs#4];
|
|
%
|
|
% tmpFitParameters.peakAreaUpperBond = [#1 #2 #3 #4];
|
|
% tmpFitParameters.peakWidthUpperBond = [#1 #2 #3 #4];
|
|
% tmpFitParameters.peakPosUpperBond = [#1 #2 #3 #4];
|
|
% tmpFitParameters.peakAreaLowerBond = [#1 #2 #3 #4];
|
|
% tmpFitParameters.peakWidthLowerBond = [#1 #2 #3 #4];
|
|
% tmpFitParameters.peakPosLowerBond = [#1 #2 #3 #4];
|
|
%
|
|
%
|
|
% % „ widthPosition“ mode
|
|
% tmpFitParameters.peakArea = [abs#1 abs#2 abs#3 abs#4];
|
|
% tmpFitParameters.peakWidth = [abs#1-#4];
|
|
% tmpFitParameters.peakPos = [abs#1 rel#2 rel#3 rel#4];
|
|
%
|
|
% tmpFitParameters.peakAreaUpperBond = [#1 #2 #3 #4];
|
|
% tmpFitParameters.peakWidthUpperBond = #1-#4;
|
|
% tmpFitParameters.peakPosUpperBond = #1;
|
|
% tmpFitParameters.peakAreaLowerBond = [#1 #2 #3 #4];
|
|
% tmpFitParameters.peakWidthLowerBond = #1-#4;
|
|
% tmpFitParameters.peakPosLowerBond = #1;
|
|
%
|
|
% % „ width“ mode
|
|
% tmpFitParameters.peakArea = [abs#1 abs#2 abs#3 abs#4];
|
|
% tmpFitParameters.peakWidth = [abs#1-#4];
|
|
% tmpFitParameters.peakPos = [abs#1 abs#2 abs#3 abs#4];
|
|
%
|
|
% tmpFitParameters.peakAreaUpperBond = [#1 #2 #3 #4];
|
|
% tmpFitParameters.peakWidthUpperBond = #1-#4;
|
|
% tmpFitParameters.peakPosUpperBond = [#1 #2 #3 #4];
|
|
% tmpFitParameters.peakAreaLowerBond = [#1 #2 #3 #4];
|
|
% tmpFitParameters.peakWidthLowerBond = #1-#4;
|
|
% tmpFitParameters.peakPosLowerBond = [#1 #2 #3 #4];
|
|
%
|
|
%
|
|
% % „ unconstrainedAndWidthPosition" mode (novel: rel distance fit parameter)
|
|
% tmpFitParameters.peakArea = [abs#1 abs#2 abs#3 abs#4 abs#5];
|
|
% tmpFitParameters.peakWidth = [abs#1,2,4,5 abs#3];
|
|
% tmpFitParameters.peakPos = [rel#1 abs#2 abs#3 abs#4 abs#5];
|
|
%
|
|
% tmpFitParameters.peakAreaUpperBond = [#1 #2 #3 #4 #5];
|
|
% tmpFitParameters.peakWidthUpperBond = [#1,2,4,5 #3];
|
|
% tmpFitParameters.peakPosUpperBond = [#1 #2 #3 #4 #5];
|
|
% tmpFitParameters.peakAreaLowerBond = [#1 #2 #3 #4];
|
|
% tmpFitParameters.peakWidthLowerBond = [#1,2,4,5 #3];
|
|
% tmpFitParameters.peakPosLowerBond = [#1 #2 #3 #4];
|
|
%
|
|
%
|
|
% FileList = nappFitGaussXps(FileList,tmpDataSelection,tmpFitParameters,'tableSelection',tmpDataSelection,...
|
|
% 'energyScale','kinetic','mode','position','showFigures','on');
|
|
%%
|
|
% % The fit results are written to the data table. Peak position is only
|
|
% written to the table if in binding energy mode. Further, results are
|
|
% plotted to the command line and fits are shown in graphs:
|
|
%%
|
|
close all
|
|
tmpDataSelection = matches(FileList.sample,'Ice with N2')...
|
|
& ~contains(FileList.sample,'(bad data)')...
|
|
& contains(FileList.name,'O1s') & FileList.xRayEkinRange_eV(:,1) == 900 ...
|
|
& FileList.scientaPassEnergy_eV == 50 ...
|
|
& (month(FileList.lastModifiedDatenum) == 2) ...
|
|
& (year(FileList.lastModifiedDatenum) == 2022) ...
|
|
& (day(FileList.lastModifiedDatenum) >= 27);
|
|
FileList = nappBackground(FileList,367,370,'tableSelection',tmpDataSelection,'energyScale','kinetic',...
|
|
'mode','mean');
|
|
tmpFitParameters.peakArea = [1E4 5E3 0.1 0.1];
|
|
tmpFitParameters.peakWidth = [2.0 2.0 0.1 0.1];
|
|
tmpFitParameters.peakPos = [363 -1 -2 -3]; % position of peak 2-4 are relative to peak 1
|
|
|
|
tmpFitParameters.peakAreaLowerBond = [1E3 5E3 0.1 0.1];
|
|
tmpFitParameters.peakWidthLowerBond = [2.0 2.0 0.1 0.1];
|
|
tmpFitParameters.peakPosLowerBond = 361;
|
|
|
|
tmpFitParameters.peakAreaUpperBond = [1E6 10E4 0.1 0.1];
|
|
tmpFitParameters.peakWidthUpperBond = [3.0 2.0 0.1 0.1];
|
|
tmpFitParameters.peakPosUpperBond = 365;
|
|
|
|
tmpFitParameters.backgroundRange = [5 5];
|
|
FileList = nappFitGaussXps(FileList,tmpDataSelection,tmpFitParameters,'tableSelection',tmpDataSelection,...
|
|
'energyScale','kinetic','mode','position','showFigures','on');
|
|
|
|
%% Process data: Norm spectra
|
|
%
|
|
% To normalise spectra, nappScaleToArea can be used. A typical example is
|
|
% to normalise C1s to the O1s peak area to compensate for changes in the
|
|
% working distance. The function uses the peakArea table column, thus
|
|
% nappFitGaussXps needs to run first. Results are written to the table
|
|
% variable spectrum_countsNorm (as the unit is no longer countsPerSecond).
|
|
% To normalise C1s to the O1s peak area, select C1s spectra using a logical
|
|
% (tmpDataSelection), and choose C1s for regionOld and O1s for regionNew:
|
|
%
|
|
% * regionOld: C1s (default) or O1s - region that will be normalised
|
|
% * regionNew: O1s (default) or C1s - peak area that will be used to normalise
|
|
%
|
|
%%
|
|
%
|
|
% FileList = nappScale(FileList,tmpDataSelection,'regionOld','C1s','regionNew','O1s');
|
|
% nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsNorm');
|
|
%
|
|
%%
|
|
close all
|
|
clc
|
|
tmpDataSelection = matches(FileList.sample,'Ice with N2')...
|
|
& ~contains(FileList.sample,'(bad data)')...
|
|
& contains(FileList.name,'O1s') & FileList.xRayEkinRange_eV(:,1) == 900 ...
|
|
& FileList.scientaPassEnergy_eV == 50 ...
|
|
& (month(FileList.lastModifiedDatenum) == 2) ...
|
|
& (year(FileList.lastModifiedDatenum) == 2022) ...
|
|
& (day(FileList.lastModifiedDatenum) >= 27);
|
|
% plot spectra
|
|
nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsPerSecond',...
|
|
'mode','AllInOne','energyScale','kinetic');
|
|
% scale to area
|
|
FileList = nappScale(FileList,tmpDataSelection,'regionOld','O1s','regionNew','O1s');
|
|
FileList = nappBackground(FileList,367,370,'tableSelection',tmpDataSelection,'energyScale','kinetic',...
|
|
'mode','mean','tableVariable','spectrum_countsNorm');
|
|
% plot spectra
|
|
nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsNorm',...
|
|
'mode','AllInOne','energyScale','kinetic');
|
|
tmpDataSelection = matches(FileList.sample,'Ice with N2')...
|
|
& ~contains(FileList.sample,'(bad data)')...
|
|
& contains(FileList.name,'C1s') & FileList.xRayEkinRange_eV(:,1) == 900 ...
|
|
& FileList.scientaPassEnergy_eV == 50 ...
|
|
& (month(FileList.lastModifiedDatenum) == 2) ...
|
|
& (year(FileList.lastModifiedDatenum) == 2022) ...
|
|
& (day(FileList.lastModifiedDatenum) >= 27);
|
|
% Generate spectra
|
|
FileList = nappGenerateSpectra(FileList,tmpDataSelection);
|
|
% plot spectra
|
|
nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsPerSecond',...
|
|
'mode','AllInOne','energyScale','kinetic');
|
|
% scale to area
|
|
FileList = nappScale(FileList,tmpDataSelection,'regionOld','C1s','regionNew','O1s');
|
|
FileList = nappBackground(FileList,615,617,'tableSelection',tmpDataSelection,'energyScale','kinetic',...
|
|
'mode','mean','tableVariable','spectrum_countsNorm');
|
|
% plot spectra
|
|
nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsNorm');
|
|
%% Process data: Binding energy scale
|
|
%
|
|
% To calculate the binding energy, you need to
|
|
%
|
|
% # nappDetermineShiftXps: calculate the kinetic energy shift relative to a reference value
|
|
% # nappCopyShift: optionally copy the kinetic energy shift to other data
|
|
% # nappBindingEnergyXps: calculate the binding energy based on the kinetic
|
|
% energy shift either determined relative to a reference or copied to the
|
|
% individual data.
|
|
%
|
|
% The following example shows how the kinetic energy shift is calculated
|
|
% based on selected O1s spectra relative to 535 eV, copied to the C1s data
|
|
% with the same file number, and finally the binding energy of the C1s data is
|
|
% calculated (based on the observed shift of the O1s of the same sequence
|
|
% run) and plotted
|
|
%%
|
|
%
|
|
% close all
|
|
% % generate spectra for C1s and O1s
|
|
% tmpDataSelection = matches(FileList.sample,'Ice with N2')...
|
|
% & ~contains(FileList.sample,'(bad data)')...
|
|
% & (contains(FileList.name,'O1s') | contains(FileList.name,'C1s'))...
|
|
% & FileList.xRayEkinRange_eV(:,1) == 900 ...
|
|
% & FileList.scientaPassEnergy_eV == 50 ...
|
|
% & (month(FileList.lastModifiedDatenum) == 2) ...
|
|
% & (year(FileList.lastModifiedDatenum) == 2022) ...
|
|
% & (day(FileList.lastModifiedDatenum) >= 27);
|
|
% FileList = nappGenerateSpectra(FileList,tmpDataSelection);
|
|
% FileList = nappBackground(FileList,367,370,'tableSelection',tmpDataSelection,'energyScale','kinetic',...
|
|
% 'mode','mean');
|
|
% % determine shift of O1s
|
|
% tmpDataSelection = matches(FileList.sample,'Ice with N2')...
|
|
% & ~contains(FileList.sample,'(bad data)')...
|
|
% & contains(FileList.name,'O1s')...
|
|
% & FileList.xRayEkinRange_eV(:,1) == 900 ...
|
|
% & FileList.scientaPassEnergy_eV == 50 ...
|
|
% & (month(FileList.lastModifiedDatenum) == 2) ...
|
|
% & (year(FileList.lastModifiedDatenum) == 2022) ...
|
|
% & (day(FileList.lastModifiedDatenum) >= 27);
|
|
% FileList = nappDetermineShiftXps(FileList,'tableSelection',tmpDataSelection,...
|
|
% 'showFigures','off','tableVariable','spectrum_countsPerSecond','binEnReference',533);
|
|
% % copy shift from O1s to C1s
|
|
% FileList = nappCopyShift(FileList,tmpDataSelection,'energyScale','binding');
|
|
% % calculate binding energy of these C1s
|
|
% tmpDataSelection = matches(FileList.sample,'Ice with N2')...
|
|
% & ~contains(FileList.sample,'(bad data)')...
|
|
% & contains(FileList.name,'C1s') & FileList.xRayEkinRange_eV(:,1) == 900 ...
|
|
% & FileList.scientaPassEnergy_eV == 50 ...
|
|
% & (month(FileList.lastModifiedDatenum) == 2) ...
|
|
% & (year(FileList.lastModifiedDatenum) == 2022) ...
|
|
% & (day(FileList.lastModifiedDatenum) >= 27);
|
|
% FileList = nappBindingEnergyXps(FileList,'tableSelection',tmpDataSelection);
|
|
% % plot C1s spectra
|
|
% nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsPerSecond',...
|
|
% 'mode','AllInOne','energyScale','kinetic');
|
|
% nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsPerSecond',...
|
|
% 'mode','AllInOne','energyScale','binding');
|
|
%%
|
|
%
|
|
close all
|
|
tmpDataSelection = matches(FileList.sample,'Ice with N2')...
|
|
& ~contains(FileList.sample,'(bad data)')...
|
|
& (contains(FileList.name,'O1s') | contains(FileList.name,'C1s'))...
|
|
& FileList.xRayEkinRange_eV(:,1) == 900 ...
|
|
& FileList.scientaPassEnergy_eV == 50 ...
|
|
& (month(FileList.lastModifiedDatenum) == 2) ...
|
|
& (year(FileList.lastModifiedDatenum) == 2022) ...
|
|
& (day(FileList.lastModifiedDatenum) >= 27);
|
|
FileList = nappGenerateSpectra(FileList,tmpDataSelection);
|
|
FileList = nappBackground(FileList,367,370,'tableSelection',tmpDataSelection,'energyScale','kinetic',...
|
|
'mode','mean');
|
|
% determine shift of O1s
|
|
tmpDataSelection = matches(FileList.sample,'Ice with N2')...
|
|
& ~contains(FileList.sample,'(bad data)')...
|
|
& contains(FileList.name,'O1s')...
|
|
& FileList.xRayEkinRange_eV(:,1) == 900 ...
|
|
& FileList.scientaPassEnergy_eV == 50 ...
|
|
& (month(FileList.lastModifiedDatenum) == 2) ...
|
|
& (year(FileList.lastModifiedDatenum) == 2022) ...
|
|
& (day(FileList.lastModifiedDatenum) >= 27);
|
|
FileList = nappDetermineShiftXps(FileList,'tableSelection',tmpDataSelection,...
|
|
'showFigures','off','tableVariable','spectrum_countsPerSecond','binEnReference',533);
|
|
% copy shift from O1s to C1s
|
|
FileList = nappCopyShift(FileList,tmpDataSelection,'energyScale','binding');
|
|
% calculate binding energy of these C1s
|
|
tmpDataSelection = matches(FileList.sample,'Ice with N2')...
|
|
& ~contains(FileList.sample,'(bad data)')...
|
|
& contains(FileList.name,'C1s') & FileList.xRayEkinRange_eV(:,1) == 900 ...
|
|
& FileList.scientaPassEnergy_eV == 50 ...
|
|
& (month(FileList.lastModifiedDatenum) == 2) ...
|
|
& (year(FileList.lastModifiedDatenum) == 2022) ...
|
|
& (day(FileList.lastModifiedDatenum) >= 27);
|
|
FileList = nappBindingEnergyXps(FileList,'tableSelection',tmpDataSelection);
|
|
% plot C1s spectra
|
|
nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsPerSecond',...
|
|
'mode','AllInOne','energyScale','kinetic');
|
|
nappPlot(FileList,tmpDataSelection,'tableVariable','spectrum_countsPerSecond',...
|
|
'mode','AllInOne','energyScale','binding');
|
|
%% Process data: Combine spectra
|
|
%
|
|
% To get better statistics (signal-to-noise), repeated measurements can be
|
|
% added or averaged into one spectrum. The results are written into a
|
|
% matrix with energy in column 1, counts in column2, and standard deviation
|
|
% in column 3 (only for mean). So, the nappFunctions can no longer be used
|
|
% for this data format.
|
|
%%
|
|
%
|
|
% tmpDataSelection = contains(FileList.name,'C1s')...
|
|
% & contains(FileList.sample,'MgO ice AA')...
|
|
% & ~endsWith(FileList.sample,'(bad data)')...
|
|
% & FileList.sampleTemp_dC >= -26 & FileList.sampleTemp_dC <= -24;
|
|
% MgO_C1s_Warm = nappCombineSpectra(FileList,'tableRow',1,...
|
|
% 'tableSelection',tmpDataSelection,'tableVariable',...
|
|
% 'spectrum_countsPerSecond','EnergyScale','binding','mode','average');
|
|
|
|
%%
|
|
close all
|
|
tmpDataSelection = contains(FileList.name,'C1s')...
|
|
& contains(FileList.sample,'MgO ice AA')...
|
|
& ~endsWith(FileList.sample,'(bad data)')...
|
|
& FileList.sampleTemp_dC >= -26 & FileList.sampleTemp_dC <= -24;
|
|
FileList = nappGenerateSpectra(FileList,tmpDataSelection);
|
|
MgO_C1s_Warm = nappCombineSpectra(FileList,'tableRow',1,...
|
|
'tableSelection',tmpDataSelection,'tableVariable',...
|
|
'spectrum_countsPerSecond','EnergyScale','kinetic','mode','average');
|
|
figure
|
|
% axes('Parent',gcf,'XDir','reverse')
|
|
hold(gca,'on');
|
|
plot(MgO_C1s_Warm(:,1),MgO_C1s_Warm(:,2),'--','linewidth',2)
|
|
title('acetic acid adsorption')
|
|
legend({'ice on MgO, -25°C'},...
|
|
'location','SouthOutside','Orientation','horizontal')
|
|
xlim([704,714]);
|
|
xlabel('kinetic energy [eV]')
|
|
ylabel('PE [counts per second]')
|
|
|
|
%%
|
|
function [myColorMap, PsiFarbe] = initializeMatlab
|
|
%initializeMatlab Sets NappIce specific settings
|
|
% Adds Napp files to path and defines file locations for mac, windows,
|
|
% and Matlab online (which runs on unix)
|
|
|
|
% change working directory
|
|
path = matlab.desktop.editor.getActiveFilename;
|
|
[filepath,~,~] = fileparts(path);
|
|
cd(filepath);
|
|
|
|
% The fitting routines writes quite a lot of feedback and warnings. I shut them off.
|
|
warning('off','all')
|
|
warning off backtrace
|
|
warning off verbose
|
|
|
|
% Functions and helper files path:
|
|
if ismac
|
|
% add Napp files to path
|
|
addpath('/Users/bartels-rausch/SwitchDrive/PSI_Matlab/Add-Ons');
|
|
addpath('/Users/bartels-rausch/SwitchDrive/PSI_Matlab/nappIce');
|
|
elseif ispc
|
|
% add Napp files to path
|
|
addpath('/Users/bartels-rausch/SwitchDrive/PSI_Matlab/Add-Ons');
|
|
addpath('/Users/bartels-rausch/SwitchDrive/PSI_Matlab/nappIce');
|
|
elseif isunix
|
|
% add Napp files to path
|
|
addpath('/Users/bartels-rausch/SwitchDrive/PSI_Matlab/Add-Ons');
|
|
addpath('/Users/bartels-rausch/SwitchDrive/PSI_Matlab/nappIce');
|
|
end
|
|
|
|
% Color maps
|
|
myColorMap.red = colormap(autumn(7));
|
|
myColorMap.blue = colormap(winter(7));
|
|
|
|
% define PSI cooperate identity colours
|
|
PsiFarbe.hellgrau = [226 226 226]/255;
|
|
PsiFarbe.mittelgrau = [184 184 184]/255;
|
|
PsiFarbe.dunkelgrau = [78 78 77]/255;
|
|
PsiFarbe.blaugrau = [236 236 243]/255; % This is actually not a PSI colour
|
|
PsiFarbe.gelb = [239 200 37]/255;
|
|
PsiFarbe.hellorange = [227 164 31]/255;
|
|
PsiFarbe.dunkelorange = [207 92 22]/255;
|
|
PsiFarbe.hellrot = [164 36 23]/255;
|
|
PsiFarbe.dunkelbraun = [114 85 65]/255;
|
|
PsiFarbe.hellbraun = [127 119 47]/255;
|
|
PsiFarbe.mittelgruen = [135 150 59]/255;
|
|
PsiFarbe.hellgruen = [195 214 47]/255;
|
|
PsiFarbe.dunkelgruen = [72 130 33]/255;
|
|
PsiFarbe.dunkelrot = [104 47 86]/255;
|
|
PsiFarbe.dunkelblau = [37 65 108]/255;
|
|
PsiFarbe.mittelgruenblau = [61 164 168]/255;
|
|
PsiFarbe.hellblau = [58 162 190]/255;
|
|
PsiFarbe.mittelblau = [50 118 158]/255;
|
|
|
|
end
|
|
|
|
|