Version 3.2 of QSoas is out ! It is mostly a bug-fix release, fixing the computation mistake found in the eecr-relay wave shape fit, see the correction to our initial article in JACS. We strongly encourage all the users of the eecr-relay wave shape fit to upgrade, and, unfortunately, refit previously fitted data as the results might change. The other wave shape fits are not affected by the issue. New features In addition to this important bug fix, new possibilities have been added, including a way to make fits with partially global parameters using the new define-indexed-fit command, to pick the best parameters dataset-by-dataset within fit trajectories, but also a parameter space explorer trying all possible permutations of one or more sets of parameters, and the possibility to save the results of a command to a global ruby variable. There are a lot of other new features, improvements and so on, look for the full list there. About QSoas
QSoas is a powerful open source data analysis program that focuses on flexibility and powerful fitting capacities. It is released under the GNU General Public License. It is described in Fourmond, Anal. Chem., 2016, 88 (10), pp 5050 5052. Current version is 3.2. You can download for free its source code or precompiled versions for MacOS and Windows there. Alternatively, you can clone from the GitHub repository.

The purpose of this post is to demonstrate a first approach to the analysis of multiwavelength kinetic data, like those obtained using stopped-flow data. To practice, we will use data that were acquired during the stopped flow practicals of the MetBio summer school from the FrenchBIC. During the practicals, the student monitored the reaction of myoglobin (in its Fe(III) state) with azide, which yields a fast and strong change in the absorbance spectrum of the protein, which was monitored using a diode array. The data is publicly available on zenodo. Aims of this tutorial The purpose of this tutorial is to teach you to use the free softwareQSoas to run a simple, multiwavelength exponential fit on the data, and to look at the results. This is not a kinetics lecture, so that it will not go in depth about the use of the exponential fit and its meaning. Getting started: loading the file First, make sure you have a working version of QSoas, you can download them (for free) there. Then download the data files from zenodo. We will work only on the data file Azide-1.25mm_001.dat, but of course, the purpose of this tutorial is to enable you to work on all of them. The data files contain the time evolution of the absorbance for all wavelengths, in a matrix format, in which each row correpond to a time point and each column to a wavelength. Start QSoas, and launch the command:

QSoas> load /comments='"'

Then, choose the Azide-1.25mm_001.dat data file. This should bring up a horizontal red line at the bottom of the data display, with X values between about 0 and 2.5. If you zoom on the red line with the mouse wheel, you'll realize it is data. The /comments='"' part is very important since it allows the extraction of the wavelength from the data. We will look at what it means another day. At this stage, you can look at the loaded data using the command:

QSoas> edit

You should have a window looking like this: The rows each correspond to a data point displayed on the window below. The first column correspond to the X values, the second the Y values, and all the other ones to extra Y columns (they are not displayed by default). What is especially interesting is the first row, which contains a nan as the X value and what is obviously the wavelength for all the Y values. To tell that QSoas should take this line as the wavelength (which will be the perpendicular coordinate, the coordinate of the other direction of the matrix), first close the edit window and run:

QSoas> set-perp /from-row=0

Splitting and fitting Now, we have a single dataset containing a lot of Y columns. We want to fit all of them simultaneously with a (mono) exponential fit. For that, we first need to split the big matrix into a series of X,Y datasets (because fitting only works on the first Y). This is possible by running:

QSoas> expand /style=red-to-blue /flags=kinetics

Your screen should now look like this: You're looking at the kinetics at all wavelengths at the same time (this may take some time to display on your computer, it is after all a rather large number of data points). The /style=red-to-blue is not strictly necessary, but it gives the red to blue color gradient which makes things easier to look at (and cooler !). The /flags=kinetics is there to attach a label (a flag) to the newly created datasets so we can easily manipulate all of them at the same time. Then it's time to fit, with the following command:

QSoas> mfit-exponential-decay flagged:kinetics

This should bring up a new window. After resizing it, you should have something that looks like this: The bottom of the fit window is taken by the parameters, each with two checkboxes on the right to set them fixed (i.e. not determined by the fitting mechanism) and/or global (i.e. with a single value for all the datasets, here all the wavelengths). The top shows the current dataset along with the corresponding fit (in green), and, below, the residuals. You can change the dataset by clicking on the horizontal arrows or using Ctrl+PgUp or Ctrl+PgDown (keep holding it to scan fast). See the Z = 728.15 showing that QSoas has recognized that the currently displayed dataset corresponds to the wavelength 728.15. The equation fitted to the data is: $$y(x) = A_\infty + A_1 \times \exp -(x - x_0)/\tau_1$$ In this case, while the \(A_1\) and \(A_\infty\) parameters clearly depend on the wavelength, the time constant of evolution should be independent of wavelength (the process happens at a certain rate regardless of the wavelength we're analyzing), so that the \(\tau_1\) parameter should be common for all the datasets/wavelengths. Just click on the global checkbox at the right of the tau_1 parameter, make sure it is checked, and hit the Fit button... The fit should not take long (less than a minute), and then you end up with the results of the fits: all the parameters. The best way to look at the non global parameters like \(A_1\) and \(A_\infty\) is to use the Show Parameters item from the Parameters menu. Using it and clicking on A_inf too should give you a display like this one: The A_inf parameter corresponds to the spectum at infinite time (of azide-bound heme), while the A_1 parameter corresponds to the difference spectrum between the initial (azide-free) and final (azide-bound) states. Now, the fit is finished, you can save the parameters if you want to reload them in a later fit by using the Parameters/Save menu item or export them in a form more suitable for plotting using Parameters/Export (although QSoas can also display and the parameters saved using Save). This concludes this first approach to fitting the data. What you can do is

• look at the depence of the tau_1 parameter as a function of the azide concentration;
• try fitting more than one exponential, using for instance:

  QSoas> mfit-exponential-decay /exponentials=2 flagged:kinetics
  

How to read the code above All the lines starting by QSoas> in the code areas above are meant to be typed into the QSoas command line (at the bottom of the window), and started by pressing enter at the end. You must remove the QSoas> bit. The other lines (when applicable) show you the response of QSoas, in the terminal just above the command-line. You may want to play with the QSoas tutorial to learn more about how to interact with QSoas. About QSoas QSoas is a powerful open source data analysis program that focuses on flexibility and powerful fitting capacities. It is released under the GNU General Public License. It is described in Fourmond, Anal. Chem., 2016, 88 (10), pp 5050 5052. Current version is 3.1. You can freely (and at no cost) download its source code or precompiled versions for MacOS and Windows there. Alternatively, you can clone from the GitHub repository.
Contact: find my email address there, or contact me on LinkedIn.

The new version of QSoas has just been released ! It brings in a host of new features, as the releases before, but maybe the most important change is the following... Binary images now freely available ! Starting from now, all the binary images for the new versions of QSoas will freely available from the download page. You can download the precompiled versions of QSoas for MacOS or windows. So now, you have no reason anymore not to try !
My aim with making the binaries freely available is also to simplify the release process for me and therefore increase the rate at which new versions are released. Improvements to the fit interface Some work went into improving the fit interface, in particular for the handling of fit trajectories when doing parameter space exploration, for difficult fits with many parameters and many local minima. The fit window now features real menus, along with tab a way to display the terminal (see the menus and the tabs selection on the image).
Individual fits have also been improved, with, among others, the possibility to easily simulate voltammograms with the kinetic-system fits, and the handling of Marcus-Hush-Chidsey (or Marcus "distribution of states") kinetics for electron transfers. Column and row names This release greatly improves the handling of column and row names, including commands to easily modify them, the possibility to use Ruby formulas to change them, and a much better way read and write them to data files. Mastering the use of column names (and to a lesser extent, row names) can greatly simplify data handling, especially when dealing with files with a large number of columns. Complex numbers Version 3.1 brings in support for formulas handling complex numbers. Although it is not possible to store complex numbers directly into datasets, it is easy to separate them in real and imaginary parts to your liking. Scripting improvement Two important improvements for scripting are included in version 3.1. The first is the possibility to define virtual files inside a script file, which makes it easy to define subfunctions to run using commands like run-for-each. The second is the possibility to define variables to be reused later (like the script arguments) using the new command let. There are a lot of other new features, improvements and so on, look for the full list there. About QSoas
QSoas is a powerful open source data analysis program that focuses on flexibility and powerful fitting capacities. It is released under the GNU General Public License. It is described in Fourmond, Anal. Chem., 2016, 88 (10), pp 5050 5052. Current version is 3.1. You can download its source code or precompiled versions for MacOS and Windows there. Alternatively, you can clone from the GitHub repository.

For the past years, most of the development has happened behind the scene in a private repository, and the code has appeared in the public repository only a couple of months before the release, in the release branch. I have now decided to publish the current code of QSoas in the github repository (in the public branch). This way, you can follow and use all the good things that were developed since the last release, and also verify whether any bug you have is still present in the currently developed version !

Upcoming features
This is the occasion to write a bit about the some of the features that have been added since the publication of the 3.0 release. Not all of them are polished nor documented yet, but here are a few teasers. The current version in github has:

• a comprehensive handling of column/row names, which makes it much easier to work with files with named columns (like the output files QSoas produces !);
• better handling of lists of meta-data, when there is one value of the meta for each segment or each Y column;
• handling of complex numbers in apply-formula;
• defining fits using external python code;
• a command for linear least squares (which has the huge advantage of being exact and not needing any initial parameters);
• commands to pause in a script or sort datasets in the stack;
• improvements over previous commands, in particular with eval;
• ... and more...

Check out the github repository if you want to know more about the new features !

As of now, no official date is planned for the 3.1 release, but this could happen during fall.

About QSoas
QSoas is a powerful open source data analysis program that focuses on flexibility and powerful fitting capacities. It is released under the GNU General Public License. It is described in Fourmond, Anal. Chem., 2016, 88 (10), pp 5050 5052. Current version is 3.0. You can download its source code there (or clone from the GitHub repository) and compile it yourself, or buy precompiled versions for MacOS and Windows there.

This post describes two similar solutions to the Quiz #2, using the data files found there. The two solutions described here rely on split-on-values. The first solution is the one that came naturally to me, and is by far the most general and extensible, but the second one is shorter, and doesn't require external script files.
Solution #1 The key to both solution is to separate the original data into a series of datasets that only contain data at a fixed value of x (which corresponds here to a fixed pH), and then process each dataset one by one to extract the average and standard deviation. This first step is done thus:

QSoas> load kcat-vs-ph.dat
QSoas> split-on-values pH x /flags=data

After these commands, the stacks contains a series of datasets bearing the data flag, that each contain a single column of data, as can be seen from the beginnings of a show-stack command:

QSoas> k
Normal stack:
	 F  C	Rows	Segs	Name	
#0	(*) 1	43	1	'kcat-vs-ph_subset_22.dat'
#1	(*) 1	44	1	'kcat-vs-ph_subset_21.dat'
#2	(*) 1	43	1	'kcat-vs-ph_subset_20.dat'
...

Each of these datasets have a meta-data named pH whose value is the original x value from kcat-vs-ph.dat. Now, the idea is to run a stats command on the resulting datasets, extracting the average value of x and its standard deviation, together with the value of the meta pH. The most natural and general way to do this is to use run-for-datasets, using the following script file (named process-one.cmds):

stats /meta=pH /output=true /stats=x_average,x_stddev

So the command looks like:

QSoas> run-for-datasets process-one.cmds flagged:data

This command produces an output file containing, for each flagged dataset, a line containing x_average, x_stddev, and pH. Then, it is just a matter of loading the output file and shuffling the columns in the right order to get the data in the form asked. Overall, this looks like this:

l kcat-vs-ph.dat
split-on-values pH x /flags=data
output result.dat /overwrite=true
run-for-datasets process-one.cmds flagged:data
l result.dat
apply-formula tmp=y2;y2=y;y=x;x=tmp
dataset-options /yerrors=y2

The slight improvement over what is described above is the use of the output command to write the output to a dedicated file (here result.dat), instead of out.dat and ensuring it is overwritten, so that no data remains from previous runs.

Solution #2 The second solution is almost the same as the first one, with two improvements:

• the stats command can work with datasets other than the current one, by supplying them to the /buffers= option, so that it is not necessary to use run-for-datasets;
• the use of the output file can by replaced by the use of the accumulator.

This yields the following, smaller, solution:

l kcat-vs-ph.dat
split-on-values pH x /flags=data
stats /meta=pH /accumulate=* /stats=x_average,x_stddev /buffers=flagged:data
pop
apply-formula tmp=y2;y2=y;y=x;x=tmp
dataset-options /yerrors=y2

About QSoas QSoas is a powerful open source data analysis program that focuses on flexibility and powerful fitting capacities. It is released under the GNU General Public License. It is described in Fourmond, Anal. Chem., 2016, 88 (10), pp 5050 5052. Current version is 3.0. You can download its source code there (or clone from the GitHub repository) and compile it yourself, or buy precompiled versions for MacOS and Windows there.

This second quiz may sound like the first one, but in fact, the approach used is completely different. The point is to gather some elementary statistics from a series of experiments performed under different conditions, but with several repeats at the same conditions.
Quiz You are given a file (which you can download there) that contains a series of pH value data: the X column is the pH, the Y column the result of the experiment at the given pH (let's say the measure of the catalytic rate of an enzyme). Your task is to take this data and produce a single dataset which contains, for each pH value, the pH, the average of the results at that pH and the standard deviation. The result should be identical to the following file, and should look like that: There are several ways to do this, but all ways must rely on stats, and the more natural way in QSoas is to take advantage of split-on-values, which is a very powerful command but somehow hard to master, which is the point of this Quiz.
By the way, the data file is purely synthetic, if you look in the GitHub repository, you'll see how it was generated.

About QSoas QSoas is a powerful open source data analysis program that focuses on flexibility and powerful fitting capacities. It is released under the GNU General Public License. It is described in Fourmond, Anal. Chem., 2016, 88 (10), pp 5050 5052. Current version is 3.0. You can download its source code there (or clone from the GitHub repository) and compile it yourself, or buy precompiled versions for MacOS and Windows there.

Redox-dependent inactivations are actually rather common in the field of metalloenzymes, and electrochemistry can be an extremely powerful tool to study them, providing one can analyze the data quantitatively. The point of this point is to teach the reader how to do so using QSoas. For more general information about redox inactivations and how to study them using electrochemical techniques, the reader is invited to read the review del Barrio and Fourmond, ChemElectroChem 2019. This post is a tutorial to learn the analysis of data coming from the study of the redox-dependent substrate inhibition of periplasmic nitrate reductase NapAB, which has the advantage of being relatively simple. The whole processed is discussed in Jacques et al, BBA, 2014. What you need to know in order to follow this tutorial is the following:

• the whole inactivation/reactivation process can be modelled by a simple reversible reaction: $$ \mathrm A \rightleftharpoons \mathrm I $$ A is the active form, I the inactive form;
• the forward rate constant is \(k_i(E)\) (dependent on potential) and the backward rate constant is \(k_a(E)\), also dependent on potential;
• the experiment is done in a series of 5 steps at 3 different potentials: \(E_0\) then \(E_1\) then \(E_2\) then \(E_1\) then, finally, \(E_0\);
• the enzyme is assumed to be fully active at the beginning of the first step;
• a single experiment is used to obtain the values of \(k_i\) and \(k_a\) for the three potentials (although not reliably for the value at \(E_0\)
• the current given by the active species depends on potential (and it is negative because the enzyme catalyzes a reduction), and the inactive species gives no current;
• in addition to the reversible reaction above, there is an irreversible, potential-dependent loss.

You can download the data files from the GitHub repository. Before fitting the data to determine the values of the rate constants at the potentials of the experiment, we will first subtract the background current, assuming that the respective contributions of faradaic and non-faradaic currents is additive. Start QSoas, go to the directory where you saved the files, and load both the data file and the blank file thus:

QSoas> cd
QSoas> load 27.oxw
QSoas> load 27-blanc.oxw
QSoas> S 1 0

(after the first command, you have to manually select the directory in which you downloaded the data files). The S 1 0 command just subtracts the dataset 1 (the first loaded) from the dataset 0 (the last loaded), see more there. blanc is the French for blank... Then, we remove a bit of the beginning and the end of the data, corresponding to one half of the steps at \(E_0\), which we don't exploit much here (they are essentially only used to make sure that the irreversible loss is taken care of properly). This is done using strip-if:

QSoas> strip-if x<30 x>300

Then, we can fit ! The fit used is called fit-linear-kinetic-system, which is used to fit kinetic models with only linear reactions (like here) and steps which change the values of the rate constants but do not instantly change the concentrations. The specific command to fit the data is:

QSoas> fit-linear-kinetic-system /species=2 /steps=0,1,2,1,0

The /species=2 indicates that there are two species (A and I). The /steps=0,1,2,1,0 indicates that there are 5 steps, with three different conditions (0 to 2) in order 0,1,2,1,0. This fits needs a bit of setup before getting started. The species are numbered, 1 and 2, and the conditions (potentials) are indicated by #0, #1 and #2 suffixes.

• The I_1 and I_2 are the currents for the species 1 and 2, so something for 1 (active form) and 0 for 2 (inactive form). Moreover, the parameters I_2_#0 (and _#1, _#2) should be fixed and not free (since we don't need to adjust a current for the inactive form).
• The k_11 and k_22 correspond to species-specific irreversible loss. It is generally best to leave them fixed to 0.
• k_12 is the formation of 2 (I) from 1 (A), and k_21 is the formation of A from I. Their values will be determined for the three conditions. The default values should work here.
• The k_loss parameters are the rates of irreversible loss that apply indiscriminately on all species (unlike k_11 and k_22). They are adjusted and ther default values should work too.
• alpha_1_0 and alpha_2_0 are the initial concentrations of species 1 and 2, so they should be fixed to 1 and 0.
• Last, the xstart_a and (_b, _c, _d and _e) correspond to the starting times for the steps, here, 0, 60, 120, 210 and 270.

For the sake of simplicity, you can also simply load the starting-parameters.params parameters to have all setup the correct way. Then, just hit Fit, enjoy this moment when QSoas works and you don't have to... The screen should now look like this: Now, it's done ! The fit is actually pretty good, and you can read the values of the inactivation and reactivation rate constants from the fit parameters. You can train also on the 21.oxw and 21-blanc.oxw files. Usually, re-loading the best fit parameters from other potentials as starting parameters work really well. Gathering the results of several fits into a real curve of rate constants as a function of potentials is left as an exercise for the reader (or maybe a later post), although you may find these series of posts useful in this context !
About QSoas QSoas is a powerful open source data analysis program that focuses on flexibility and powerful fitting capacities. It is released under the GNU General Public License. It is described in Fourmond, Anal. Chem., 2016, 88 (10), pp 5050 5052. Current version is 3.0. You can download its source code there (or clone from the GitHub repository) and compile it yourself, or buy precompiled versions for MacOS and Windows there.

I've decided to post regular summaries of all the articles written here about QSoas; this is the first post of this kind. All the articles related to QSoas can be found here also. The articles written here can be separated into several categories. Tutorials to analyze real data These are posts about how to reproduce the data analysis of published articles, including links to the original data so you can fully reproduce our results.

• how to determine the \(K_m\) of enzymes working with gaseous substrates with electrochemistry, as in Domnik et al, Angewandte Chemie 2017.

These posts all have the label tutorial. All about fits QSoas has a particularly powerful interface for non-linear least square minimisations (fits):

• See here to learn how you can take advantage of the fit interface to explore easily how a parameter influences the shape of a function.
• See here how you can produce smooth curves for a fit on jaggy data.

Meta-data Meta data describe the conditions in which experiments were performed.

• You can see here how to use them in data analysis, to plot for instance peak position as a function of meta-data;
• You can see here how to permanently store meta-data for files.

Quiz and their solutions Quiz are small problems that take some skill to solve; they can teach you a lot about how to work with QSoas.

• Quiz #1: computing the standard deviation of spectra, along with the solution. This quiz can teach you a lot about combining data from different datasets and manipulating data row-by-row.

Other tips and tricks

• See here how to find the 0s of experimental data.
• See here how one can save just the selected points in a baseline, and reuse them.
• See here to learn how to generate many datasets in one go from a mathematical formula.
• See here how to define a custom mathematical function using Ruby.
• See here how one can take advantage of Ruby, the underlying programming language to sum columns, extend the values of columns with lots of missing values and rename datasets using a pattern.
• See here how you can use QSoas without starting the graphical interface !

Release annoucements These have generally lot of general information about the possibilities in QSoas:

• initial release;
• version 2.0;
• version 2.1;
• version 2.2;
• and, finally, version 3.0.

About QSoas QSoas is a powerful open source data analysis program that focuses on flexibility and powerful fitting capacities. It is released under the GNU General Public License. It is described in Fourmond, Anal. Chem., 2016, 88 (10), pp 5050 5052. Current version is 3.0. You can download its source code there (or clone from the GitHub repository) and compile it yourself, or buy precompiled versions for MacOS and Windows there.

It is one thing to acquire and process data, but the data themselves are most often useless without the context, the conditions in which the experiments were made. These additional informations can be called meta-data. In a previous post, we have already described how one can set meta-data to data that are already loaded, and how one can make use of them. QSoas is already able to figure out some meta-data in the case of electrochemical data, most notably in the case of files acquired by GPES, ECLab or CHI potentiostats. However, only a small number of constructors are supported as of now^[1], and there are a number of experimental details that the software is never going to be able to figure out for you, such as the pH, the sample, what you were doing... The new version of QSoas provides a means to permanently store meta-data for experimental data files:

QSoas> record-meta pH 7 file.dat

This command uses record-meta to permanently store the information pH = 7 for the file file.dat. Any time QSoas loads the file again, either today or in one year, the meta-data will contain the value 7 for the field pH. Behind the scenes, QSoas creates a single small file, file.dat.qsm, in which the meta-data are stored (in the form of a JSON dictionnary). You can set the same meta-data to many files in one go, using wildcards (see load for more information). For instance, to set the pH=7 meta-data to all the .dat files in the current directory, you can use:

QSoas> record-meta pH 7 *.dat

You can only set one meta-data for each call to record-meta, but you can use it as many times as you like. Finally, you can use the /for-which option to load or browse to select only the files which have the meta you need:

QSoas> browse /for-which=$meta.pH<=7

This command browses the files in the current directory, showing only the ones that have a pH meta-data which is 7 or below.

[1] I'm always ready to implement the parsing of other file formats that could be useful for you. If you need parsing of special files, please contact me, sending the given files and the meta-data you'd expect to find in those. About QSoas QSoas is a powerful open source data analysis program that focuses on flexibility and powerful fitting capacities. It is released under the GNU General Public License. It is described in Fourmond, Anal. Chem., 2016, 88 (10), pp 5050 5052. Current version is 3.0. You can download its source code there (or clone from the GitHub repository) and compile it yourself, or buy precompiled versions for MacOS and Windows there.

First of all, let me all wish you a happy new year, with all my wishes of health and succes. I sincerely hope this year will be simpler for most people as last year ! For the first post of the year, I wanted to show you how to take advantage of Ruby, the programming language embedded in QSoas, to make various things, like:

• creating a column with the sum of Y values;
• extending values that are present only in a few lines;
• renaming datasets using a pattern.

Summing the values in a column When using commands that take formulas (Ruby code), like apply-formula, the code is run for every single point, for which all the values are updated. In particulier, the state of the previous point is not known. However, it is possible to store values in what is called global variables, whose name start with an $ sign. Using this, we can keep track of the previous values. For instance, to create a new column with the sum of the y values, one can use the following approach:

QSoas> eval $sum=0
QSoas> apply-formula /extra-columns=1 $sum+=y;y2=$sum

The first line initializes the variable to 0, before we start summing, and the code in the second line is run for each dataset row, in order. For the first row, for instance, $sum is initially 0 (from the eval line); after the execution of the code, it is now the first value of y. After the second row, the second value of y is added, and so on. The image below shows the resulting y2 when used on:

QSoas> generate-dataset -1 1 x

Extending values in a column Another use of the global variables is to add "missing" data. For instance, let's imagine that a files given the variation of current over time as the potential is changed, but the potential is only changed stepwise and only indicated when it changes:

## time	current	potential
0	0.1	0.5
1	0.2
2	0.3
3	0.2
4	1.2	0.6
5	1.3
...

If you need to have the values everywhere, for instance if you need to split on their values, you could also use a global variable, taking advantage of the fact that missing values are represented by QSoas using "Not A Number" values, which can be detected using the Ruby function nan?:

QSoas> apply-formula "if y2.nan?; then y2=$value; else $value=y2;end"

Note the need of quotes because there are spaces in the ruby code. If the value of y2 is NaN, that is it is missing, then it is taken from the global variable $value else $value is set the current value of y2. Hence, the values are propagated down:

## time	current	potential
0	0.1	0.5
1	0.2	0.5
2	0.3	0.5
3	0.2	0.5
4	1.2	0.6
5	1.3	0.6
...

Of course, this doesn't work if the first value of y2 is missing.

Renaming using a pattern The command save-datasets can be used to save a whole series of datasets to the disk. It can also rename them on the fly, and, using the /mode=rename option, does only the renaming part, without saving. You can make full use of meta-data (see also a first post here)for renaming. The full power is unlocked using the /expression= option. For instance, for renaming the last 5 datasets (so numbers 0 to 4) using a scheme based on the value of their pH meta-data, you can use the following code:

QSoas> save-datasets /mode=rename /expression='"dataset-# $meta.pH "' 0..4

The double quotes are cumbersome but necessary, since the outer quotes (') prevent the inner ones (") to be removed and the inner quotes are here to indicate to Ruby that we are dealing with text. The bit inside # ... is interpreted by Ruby as Ruby code; here it is $meta.pH, the value of the "pH" meta-data. Finally the 0..4 specifies the datasets to work with. So theses datasets will change name to become dataset-7 for pH 7, etc...

About QSoas QSoas is a powerful open source data analysis program that focuses on flexibility and powerful fitting capacities. It is released under the GNU General Public License. It is described in Fourmond, Anal. Chem., 2016, 88 (10), pp 5050 5052. Current version is 3.0. You can download its source code there (or clone from the GitHub repository) and compile it yourself, or buy precompiled versions for MacOS and Windows there.

After almost two years of development, version 3.0 of QSoas is finally out ! It brings in a number of new features. An expert mode for fitting Undoubtedly the most important feature in the new version is a complete upgrade of the fit system, which now features an expert mode, turned on by using the /expert=true option with the fit commands. The expert mode features a command prompt that looks like the normal command prompt, in which it is possible:

• to fix/unfix, set, reset and load ans save parameters;
• export parameters or computed datasets;
• launch the fit;
• select fit engines and configure them;
• list of all the fits that were run in the session with their starting and finishing parameters (see picture), and reuse them;
• run scripts;
• fit data completely non-interactively;
• and, last but not least, perform parameter space exploration, which consists in running (automatically) a number of fits with different (random) starting parameters to maximize the chances of finding out the best parameters for the fit, avoiding local minima.

The latter feature is very important when running fits with many parameters. In that case, there are a number of local minima, and it is necessary to try a number of different starting parameters to really find the best parameters. The new parameter space exploration feature makes it much easier than before, with an interface that allows easily finding the best parameters tried so far and reuse them. A new documentation system Another very important update is the inclusion of a new, offline, documentation system. The documentation features browsing via table of contents, a command index and text search. It also features the possibility to copy commands from the help to the command prompt, or even run them directly. To top it all, it comes with a series of startup tips that might teach you a thing or two about QSoas (try hitting Show random to learn new tricks !). Many other features For the full list of changes, please see the changelog. Apart from the changes described above, these are my favorites:

• running the command from the menu now gives a dialog box for selecting all the arguments and options, which is very useful to discover the possibilities of the various commands;
• features that make the statistics and meta-data much easier to use with commands like apply-formula, like automatic code completion;
• a new system for permanently storing meta-data using record-meta;
• a new fit for fitting implicit equations, in which one cannot express \(y = f(x)\), but only \(f(x,y)=0\);
• and, of course, a brand shiny new logo by Marta Meneghello !

To get the new version, you can just download the source code from the downloads page, where you can also purchase precompiled versions for Windows and MacOS. You can also clone the source from the GitHub repository. About QSoasQSoas is a powerful open source data analysis program that focuses on flexibility and powerful fitting capacities. It is released under the GNU General Public License. It is described in Fourmond, Anal. Chem., 2016, 88 (10), pp 5050 5052. Current version is 3.0. You can download its source code there (or clone from the GitHub repository) and compile it yourself, or buy precompiled versions for MacOS and Windows there.

By essence, QSoas works with \(y = f(x)\) datasets. However, in practice, when working with experimental data (or data generated from simulations), one has often more than one experimental parameter (\(x\)). For instance, one could record series of spectra (\(A = f(\lambda)\)) for different pH values, so that the absorbance is in fact a function of both the pH and \(\lambda\). QSoas has different ways to deal with such situations, and we'll describe one today, using meta-data. Setting meta-data Meta-data are simply series of name/values attached to a dataset. It can be numbers, dates or just text. Some of these are automatically detected from certain type of data files (but that is the topic for another day). The simplest way to set meta-data is to use the set-meta command:

QSoas> set-meta pH 7.5

This command sets the meta-data pH to the value 7.5. Keep in mind that QSoas does not know anything about the meaning of the meta-data^[1]. It can keep track of the meta-data you give, and manipulate them, but it will not interpret them for you. You can set several meta-data by repeating calls to set-meta, and you can display the meta-data attached to a dataset using the command show. Here is an example:

QSoas> generate-buffer 0 10
QSoas> set-meta pH 7.5
QSoas> set-meta sample "My sample"
QSoas> show 0
Dataset generated.dat: 2 cols, 1000 rows, 1 segments, #0
Flags: 
Meta-data:	pH =	 7.5	sample =	 My sample

Note here the use of quotes around My sample since there is a space inside the value. Using meta-data There are many ways to use meta-data in QSoas. In this post, we will discuss just one: using meta-data in the output file. The output file can collect data from several commands, like peak data, statistics and so on. For instance, each time the command 1 is run, a line with the information about the largest peak of the current dataset is written to the output file. It is possible to automatically add meta-data to those lines by using the /meta= option of the output command. Just listing the names of the meta-data will add them to each line of the output file. As a full example, we'll see how one can take advantage of meta-data to determine the position of the peak of the function \(x^2 \exp (-a\,x)\) depends on \(a\). For that, we first create a script that generates the function for a certain value of \(a\), sets the meta-data a to the corresponding value, and find the peak. Let's call this file do-one.cmds (all the script files can be found in the GitHub repository):

generate-buffer 0 20 x**2*exp(-x*$ 1 )
set-meta a $ 1 
1 

This script takes a single argument, the value of \(a\), generates the appropriate dataset, sets the meta-data a and writes the data about the largest (and only in this case) peak to the output file. Let's now run this script with 1 as an argument:

QSoas> @ do-one.cmds 1

This command generates a file out.dat containing the following data:

## buffer       what    x       y       index   width   left_width      right_width     area
generated.dat   max     2.002002002     0.541340590883  100     3.4034034034    1.24124124124   2.162162162161.99999908761

This gives various information about the peak found: the name of the dataset it was found in, whether it's a maximum or minimum, the x and y positions of the peak, the index in the file, the widths of the peak and its area. We are interested here mainly in the x position. Then, we just run this script for several values of \(a\) using run-for-each, and in particular the option /range-type=lin that makes it interpret values like 0.5..5:80 as 80 values evenly spread between 0.5 and 5. The script is called run-all.cmds:

output peaks.dat /overwrite=true /meta=a
run-for-each do-one.cmds /range-type=lin 0.5..5:80
V all /style=red-to-blue

The first line sets up the output to the output file peaks.dat. The option /meta=a makes sure the meta a is added to each line of the output file, and /overwrite=true make sure the file is overwritten just before the first data is written to it, in order to avoid accumulating the results of different runs of the script. The last line just displays all the curves with a color gradient. It looks like this: Running this script (with @ run-all.cmds) creates a new file peaks.dat, whose first line looks like this:

## buffer       what    x       y       index   width   left_width      right_width     area    a

The column x (the 3rd) contains the position of the peaks, and the column a (the 10th) contains the meta a (this column wasn't present in the output we described above, because we had not used yet the output /meta=a command). Therefore, to load the peak position as a function of a, one has just to run:

QSoas> load peaks.dat /columns=10,3

This looks like this: Et voil ! To train further, you can:

• improve the resolution in x;
• improve the resolution in y;
• plot the magnitude of the peak;
• extend the range;
• derive the analytical formula for the position of the peak and verify it !

[1] this is not exactly true. For instance, some commands like unwrap interpret the sr meta-data as a voltammetric scan rate if it is present. But this is the exception. About QSoasQSoas is a powerful open source data analysis program that focuses on flexibility and powerful fitting capacities. It is released under the GNU General Public License. It is described in Fourmond, Anal. Chem., 2016, 88 (10), pp 5050 5052. Current version is 2.2. You can download its source code there (or clone from the GitHub repository) and compile it yourself, or buy precompiled versions for MacOS and Windows there.

This post describes the solution to the Quiz #1, based on the files found there. The point is to produce both the average and the standard deviation of a series of spectra. Below is how the final averaged spectra shoud look like: I will present here two different solutions. Solution 1: using the definition of standard deviation There is a simple solution using the definition of the standard deviation: $$\sigma_y = \sqrt <y^2> - <y> ^2 $$ in which \(<y^2>\) is the average of \(y^2\) (and so on). So the simplest solution is to construct datasets with an additional column that would contain \(y^2\), average these columns, and replace the average with the above formula. For that, we need first a companion script that loads a single data file and adds a column with \(y^2\). Let's call this script load-one.cmds:

load $ 1 
apply-formula y2=y**2 /extra-columns=1
flag /flags=processed

When this script is run with the name of a spectrum file as argument, it loads it (replaces $ 1 by the first argument, the file name), adds a column y2 containing the square of the y column, and flag it with the processed flag. This is not absolutely necessary, but it makes it much easier to refer to all the spectra when they are processed. Then to process all the spectra, one just has to run the following commands:

run-for-each load-one.cmds Spectrum-1.dat Spectrum-2.dat Spectrum-3.dat
average flagged:processed
apply-formula y2=(y2-y**2)**0.5
dataset-options /yerrors=y2

The run-for-each command runs the load-one.cmds script for all the spectra (one could also have used Spectra-*.dat to not have to give all the file names). Then, the average averages the values of the columns over all the datasets. To be clear, it finds all the values that have the same X (or very close X values) and average them, column by column. The result of this command is therefore a dataset with the average of the original \(y\) data as y column and the average of the original \(y^2\) data as y2 column. So now, the only thing left to do is to use the above equation, which is done by the apply-formula code. The last command, dataset-options, is not absolutely necessary but it signals to QSoas that the standard error of the y column should be found in the y2 column. This is now available as script method-one.cmds in the git repository.

Solution 2: use QSoas's knowledge of standard deviation The other method is a little more involved but it demonstrates a good approach to problem solving with QSoas. The starting point is that, in apply-formula, the value $stats.y_stddev corresponds to the standard deviation of the whole y column... Loading the spectra yields just a series of x,y datasets. We can contract them into a single dataset with one x column and several y columns:

load Spectrum-*.dat /flags=spectra
contract flagged:spectra

After these commands, the current dataset contains data in the form of:

lambda1	a1_1	a1_2	a1_3
lambda2	a2_1	a2_2	a2_3
...

in which the ai_1 come from the first file, ai_2 the second and so on. We need to use transpose to transform that dataset into:

0	a1_1	a2_1	...
1	a1_2	a2_2	...
2	a1_3	a2_3	...

In this dataset, values of the absorbance for the same wavelength for each dataset is now stored in columns. The next step is just to use expand to obtain a series of datasets with the same x column and a single y column (each corresponding to a different wavelength in the original data). The game is now to replace these datasets with something that looks like:

0	a_average
1	a_stddev

For that, one takes advantage of the $stats.y_average and $stats.y_stddev values in apply-formula, together with the i special variable that represents the index of the point:

apply-formula "if i == 0; then y=$stats.y_average; end; if i == 1; then y=$stats.y_stddev; end"
strip-if i>1

Then, all that is left is to apply this to all the datasets created by expand, which can be just made using run-for-datasets, and then, we reverse the splitting by using contract and transpose ! In summary, this looks like this. We need two files. The first, process-one.cmds contains the following code:

apply-formula "if i == 0; then y=$stats.y_average; end; if i == 1; then y=$stats.y_stddev; end"
strip-if i>1
flag /flags=processed

The main file, method-two.cmds looks like this:

load Spectrum-*.dat /flags=spectra
contract flagged:spectra
transpose
expand /flags=tmp
run-for-datasets process-one.cmds flagged:tmp
contract flagged:processed
transpose
dataset-options /yerrors=y2

Note some of the code above can be greatly simplified using new features present in the upcoming 3.0 version, but that is the topic for another post. About QSoasQSoas is a powerful open source data analysis program that focuses on flexibility and powerful fitting capacities. It is released under the GNU General Public License. It is described in Fourmond, Anal. Chem., 2016, 88 (10), pp 5050 5052. Current version is 2.2. You can download its source code and compile it yourself or buy precompiled versions for MacOS and Windows there.

Often, one would want to generate smooth data from a fit over a small number of data points. For an example, take the data in the following file. It contains (fake) experimental data points that obey to Michaelis-Menten kinetics: $$v = \frac v_m 1 + K_m/s $$ in which \(v\) is the measured rate (the y values of the data), \(s\) the concentration of substrate (the x values of the data), \(v_m\) the maximal rate and \(K_m\) the Michaelis constant. To fit this equation to the data, just use the fit-arb fit:

QSoas> l michaelis.dat
QSoas> fit-arb vm/(1+km/x)

After running the fit, the window should look like this: Now, with the fit, we have reasonable values for \(v_m\) (vm) and \(K_m\) (km). But, for publication, one would want to generate "smooth" curve going through the lines... Saving the curve from "Data.../Save all" doesn't help, since the data has as many points as the original data and looks very "jaggy" (like on the screenshot above)... So one needs a curve with more data points. Maybe the most natural solution is simply to use generate-buffer together with apply-formula using the formula and the values of km and vm obtained from the fit, like:

QSoas> generate-buffer 0 20
QSoas> apply-formula y=3.51742/(1+3.69767/x)

By default, generate-buffer generate 1000 evenly spaced x values, but you can change their number using the /samples option. The two above commands can be combined to just one call to generate-buffer:

QSoas> generate-buffer 0 20 3.51742/(1+3.69767/x)

This works, but it is quite cumbersome and it is not going to work well for complex formulas or the results of differential equations or kinetic systems... This is why to each fit- command corresponds a sim- command that computes the result of the fit using a "saved parameters" file (here, michaelis.params, but you can also save it yourself) and buffers as "models" for X values:

QSoas> generate-buffer 0 20
QSoas> sim-arb vm/(1+km/x) michaelis.params 0

This strategy works with every single fit ! As an added benefit, you even get the fit parameters as meta-data, which are displayed by the show command:

QSoas> show 0
Dataset generated_fit_arb.dat: 2 cols, 1000 rows, 1 segments, #0
Flags: 
Meta-data:	commands =	 sim-arb vm/(1+km/x) michaelis.params 0	fit =	 arb (formula: vm/(1+km/x))	km =	 3.69767
	vm =	 3.5174

They also get saved as comments if you save the data. Important note: the sim-arb command will be available only in the 3.0 release, although you can already enjoy it if you use the github version. About QSoasQSoas is a powerful open source data analysis program that focuses on flexibility and powerful fitting capacities. It is released under the GNU General Public License. It is described in Fourmond, Anal. Chem., 2016, 88 (10), pp 5050 5052. Current version is 2.2. You can download its source code and compile it yourself or buy precompiled versions for MacOS and Windows there.

Here is the first QSoas quiz ! I recently measured several identical spectra in a row to evaluate the noise of the setup, and so I wanted to average all the spectra and also determine the standard deviation in the absorbances. Averaging the spectra can simply be done taking advantage of the average command:

QSoas> load Spectrum*.dat /flags=spectra
QSoas> average flagged:spectra

However, average does not provide means to make standard deviations, it just takes the average of all but the X column. I wanted to add this feature, but I realized there are already at least two distinct ways to do that...

Quiz Your task is to determine the average and standard deviations of the three spectra located there, (Spectrum-1.dat, Spectrum-2.dat and Spectrum-3.dat). There are at least two ways:

• One that relies simply on average and on apply-formula, and which requires that you remember how to compute standard deviations.
• One that is a little more involved, that requires more data manipulation (take a look at contract for instance) and relies on the fact that you can use statistics in apply-formula (and in particular you can use y_stddev to refer to the standard deviation of \(y\)), but which does not require you to know exactly how to compute standard deviations.

To help you, I've added the result in Average.dat. The figure below shows a zoom on the data superimposed to the average (bonus points to find how to display this light red area that corresponds to the standard deviation !). I will post the answer later. In the meantime, feel free to post your own solutions or attempts, hacks, and so on !

About QSoasQSoas is a powerful open source data analysis program that focuses on flexibility and powerful fitting capacities. It is released under the GNU General Public License. It is described in Fourmond, Anal. Chem., 2016, 88 (10), pp 5050 5052. Current version is 2.2. You can download its source code or buy precompiled versions for MacOS and Windows there.

This is the first post of a series in which we will provide the readers with simple tutorial approaches to reproduce the data analysis of some of our published papers. All our data analysis is performed using QSoas. Today, we will show you how to analyze the experiments we used to characterize the behaviour of an enzyme, the Nickel-Iron CO dehydrogenase IV from Carboxytothermus hydrogenoformans. The experiments we analyze here are described in much more details in the original publication, Domnik et al, Angewandte Chemie, 2017. The only things you need to know for now are the following:

• the data is the evolution of the current over time given by the absorbed enzyme;
• at a given point, a certain amount of CO (50 M) is injected, and its concentration decreases exponentially over time;
• the enzyme has a Michaelis-Menten behaviour with respect to CO.

This means that we expect a response of the type: $$i(t) = \frac i_m 1 + \frac K_m [\mathrm CO ](t) $$ in which $$[\mathrm CO ](t) = \begin cases 0, & \text for t < t_0 \\ C_0 \exp \frac t_0 - t \tau , & \text for t\geq t_0 %> \end cases $$ To begin this tutorial, first download the files from the github repository (direct links: data, parameter file and ruby script). Start QSoas, go to the directory where you saved the files, load the data file, and remove spikes in the data using the following commands:

QSoas> cd
QSoas> l Km-CODH-IV.dat
QSoas> R

First fitThen, to fit the above equation to the data, the simplest is to take advantage of the time-dependent parameters features of QSoas. Run simply:

QSoas> fit-arb im/(1+km/s) /with=s:1,exp

This simply launches the fit interface to fit the exact equations above. The im/(1+km/s) is simply the translation of the Michaelis-Menten equation above, and the /with=s:1,exp specifies that s is the result of the sum of 1 exponential like for the definition of above. Then, load the Km-CODH-IV.params parameter files (using the "Parameters.../Load from file" action at the bottom, or the Ctrl+L keyboard shortcut). Your window should now look like this: To fit the data, just hit the "Fit" button ! (or Ctrl+F). Including an offset The fit is not bad, but not perfect. In particular, it is easy to see why: the current predicted by the fit goes to 0 at large times, but the actual current is below 0. We need therefore to include an offset to take this into consideration. Close the fit window, and re-run a fit, but now with this command:

QSoas> fit-arb im/(1+km/s)+io /with=s:1,exp

Notice the +io bit that corresponds to the addition of an offset current. Load again the base parameters, run the fit again... Your fit window show now look like: See how the offset current is now much better taken into account. Let's talk a bit more about the parameters:

• im is \(i_m\), the maximal current, around 120 nA (which matches the magnitude of the original current).
• io is the offset current, around -3nA.
• km is the \(K_m\), expressed in the same units as s_1, the first "injected" value of s (we used 50 because the injection is 50 M CO). That means the value of \(K_m\) determined by this fit is around 9 nM ! (but see below).
• s_t_1 is the time of the injection of CO (it was in the parameter files you loaded).
• Finally, s_tau_1 is the time constant of departure of CO, noted \(\tau\) in the equations above.

Taking into account mass-transport limitations However, the fit is still unsatisfactory: the predicted curve fails to reproduce the curvature at the beginning and at the end of the decrease. This is due to issues linked to mass-transport limitations, which are discussed in details in Merrouch et al, Electrochimica Acta, 2017. In short, what you need to do is to close the fit window again, load the transport.rb Ruby file that contains the definition of the itrpt function, and re-launch the fit window using:

QSoas> ruby-run transport.rb
QSoas> fit-arb itrprt(s,km,nFAm,nFAmu)+io /with=s:1,exp

Load again the parameter file... but this time you'll have to play a bit more with the starting parameters for QSoas to find the right values when you fit. Here are some tips:

• the curve predicted with the current parameters (use "Update" to update the display) should "look like" the data;
• apart from io, no parameter should be negative;
• there may be hints about the correct values in the papers...

A successful fit should look like this: Here you are ! I hope you enjoyed analyzing our data, and that it will help you analyze yours ! Feel free to comment and ask for clarifications.

About QSoasQSoas is a powerful open source data analysis program that focuses on flexibility and powerful fitting capacities. It is released under the GNU General Public License. It is described in Fourmond, Anal. Chem., 2016, 88 (10), pp 5050 5052. Current version is 2.2. You can download its source code or buy precompiled versions for MacOS and Windows there.

QSoas can read and execute Ruby code directly, while reading command files, or even at the command prompt. For that, just write plain Ruby code inside a ruby...ruby end block. Probably the most useful possibility is to define elaborated functions directly from within QSoas, or, preferable, from within a script; this is an alternative to defining a function in a completely separated Ruby-only file using ruby-run. For instance, you can define a function for plain Michaelis-Menten kinetics with a file containing:

ruby
def my_func(x, vm, km)
  return vm/(1 + km/x)
end
ruby end

This defines the function my_func with three parameters, , (vm) and (km), with the formula:

You can then test that the function has been correctly defined running for instance:

QSoas> eval my_func(1.0,1.0,1.0)
 => 0.5
QSoas> eval my_func(1e4,1.0,1.0)
 => 0.999900009999

This yields the correct answer: the first command evaluates the function with x = 1.0, vm = 1.0 and km = 1.0. For , the result is (here 0.5). For , the result is almost . You can use the newly defined my_func in any place you would use any ruby code, such as in the optional argument to generate-buffer, or for arbitrary fits:

QSoas> generate-buffer 0 10 my_func(x,3.0,0.6)
QSoas> fit-arb my_func(x,vm,km)

To redefine my_func, just run the ruby code again with a new definition, such as:

ruby
def my_func(x, vm, km)
  return vm/(1 + km/x**2)
end
ruby end

The previous version is just erased, and all new uses of my_func will refer to your new definition.


See for yourselfThe code for this example can be found there. Browse the qsoas-goodies github repository for more goodies !

About QSoasQSoas is a powerful open source data analysis program that focuses on flexibility and powerful fitting capacities. It is released under the GNU General Public License. It is described in Fourmond, Anal. Chem., 2016, 88 (10), pp 5050 5052. Current version is 2.1. You can download its source code or buy precompiled versions for MacOS and Windows there.

The new release of QSoas is finally ready ! It brings in a lot of new features and improvements, notably greatly improved memory use for massive multifits, a fit for linear (in)activation processes (the one we used in Fourmond et al, Nature Chemistry 2014), a new way to transform "numbers" like peak position or stats into new datasets and even SVG output ! Following popular demand, it also finally brings back the peak area output in the find-peaks command (and the other, related commands) ! You can browse the full list of changes there.

The new release can be downloaded from the downloads page.
Freely available binary images for QSoas 1.0In addition to the new release, we are now releasing the binary images for MacOS and Windows for the release 1.0. They are also freely available for download from the downloads page.

About QSoasQSoas is a powerful open source data analysis program that focuses on flexibility and powerful fitting capacities. It is released under the GNU General Public License. It is described in Fourmond, Anal. Chem., 2016, 88 (10), pp 5050 5052. Current version is 2.2. You can download its source code or buy precompiled versions for MacOS and Windows there.

QSoas can read and execute Ruby code directly, while reading command files, or even at the command prompt. For that, just write plain Ruby code inside a ruby...ruby end block. Probably the most useful possibility is to define elaborated functions directly from within QSoas, or, preferable, from within a script; this is an alternative to defining a function in a completely separated Ruby-only file using ruby-run. For instance, you can define a function for plain Michaelis-Menten kinetics with a file containing:

ruby
def my_func(x, vm, km)
  return vm/(1 + km/x)
end
ruby end

This defines the function my_func with three parameters, , (vm) and (km), with the formula:

You can then test that the function has been correctly defined running for instance:

QSoas> eval my_func(1.0,1.0,1.0)
 => 0.5
QSoas> eval my_func(1e4,1.0,1.0)
 => 0.999900009999

This yields the correct answer: the first command evaluates the function with x = 1.0, vm = 1.0 and km = 1.0. For , the result is (here 0.5). For , the result is almost . You can use the newly defined my_func in any place you would use any ruby code, such as in the optional argument to generate-buffer, or for arbitrary fits:

QSoas> generate-buffer 0 10 my_func(x,3.0,0.6)
QSoas> fit-arb my_func(x,vm,km)

To redefine my_func, just run the ruby code again with a new definition, such as:

ruby
def my_func(x, vm, km)
  return vm/(1 + km/x**2)
end
ruby end

The previous version is just erased, and all new uses of my_func will refer to your new definition.


See for yourselfThe code for this example can be found there. Browse the qsoas-goodies github repository for more goodies !

About QSoasQSoas is a powerful open source data analysis program that focuses on flexibility and powerful fitting capacities. It is released under the GNU General Public License. It is described in Fourmond, Anal. Chem., 2016, 88 (10), pp 5050 5052. Current version is 2.1. You can download its source code or buy precompiled versions for MacOS and Windows there.

I was recently looking for a way to extract many attachments from a series of emails. I first had a look at the AttachmentExtractor thunderbird plugin, but it seems very old and not maintained anymore. So I've come up with another very simple solution that also works with any other mail client.Just copy all the mails you want to extract attachments from to a single (temporary) mail folder, find out which file holds the mail folder and use ripmime on that file (ripmime is packaged for Debian). For my case, it looked like:

~ ripmime -i .icedove/XXXXXXX.default/Mail/pop.xxxx/tmp -d target-directory

Simple solution, but it saved me quite some time. Hope it helps !