Getting Started

Graphical Interface

That’s how the GUI looks like:


The main plot can display data points, model that is to be fitted to the data and individual functions in the model. Use can configure what is displayed and how (through GUI ‣ Configure or context menu).

The helper plot shows how well the model fits the data. You may have one, two or no helper plots (GUI ‣ Show). By default, the plot shows the difference between the data and the model. It can also show weighted or cumulative difference, and a couple of other things.

The helper plot is also handy for zooming – with left and middle mouse buttons. Selecting a horizontal span with the left button zooms into this span. The middle button goes back to the whole dataset (the same as Zoom All in the toolbar).

The sidebar is for switching between datasets, inspecting functions, and for changing function parameters. It also provides quick access to a few properties of the main plot, such as the size of data points.

On the main plot, the meaning of the left and right mouse button depends on the current mouse mode. Mouse modes are switched using toolbar buttons:

  • Normal Mode normal mode – the left button zooms in and the right button shows pop-up menu,
  • Data-Range Mode data-range mode – for activating and de-activating data, i.e. for selecting regions of interest,
  • Baseline Mode baseline mode – manual baseline subtraction (in may never need it),
  • Add-Peak Mode add-peak mode – for placing peaks and other functions.

The status bar shows a hint what the mouse does in the current mode.

Finally, the input field and the output window provide alternative, console-like way of interacting with the program. Also, the GUI operations that change the state of the program (data, model, non-visual settings) are translated into textual commands and printed in the output window.


To save configuration of the GUI (visible windows, colors, etc.) for next session use GUI ‣ Save current config.

Minimal Example

Let us analyze a diffraction pattern of NaCl. Our goal is to determine the position of the center of the highest peak. It is needed for calculating the pressure under which the sample was measured, but this later detail in the processing is irrelevent for the time being.

The data file used in this example is distributed with the program and can be found in the samples directory.

Textual commands that correspond to performed operations are shown in this section in CLI boxes.

First load data from the nacl01.dat file. Select Data ‣ Load File from the menu (or Load Data from the toolbar) and choose the file.


@0 < nacl01.dat

You can zoom-in to the biggest peak using the left mouse button on the residual (helper) plot. To zoom out, press Zoom All on the toolbar.

Now all data points are active. Only the biggest peak is of our interest, so we want to deactivate the remaining points. Change to the range mode (toolbar: Data-Range Mode) and deactivate not needed points with the right mouse button.


A = (x > 23.0 and x < 26.0)

As our example data has no background to worry about, our next step is to define a peak with reasonable initial values and fit it to the data. We will use Gaussian. To see its formula, type: info Gaussian (or i Gaussian) or look for it in the section Built-In Functions.

Select Gaussian from the list of functions on the toolbar and press Auto Add.


guess Gaussian

Automatic peak detection works in this case, but if it wouldn’t, you may set the initial peak position, height and width manually. Either with mouse in the add-peak mode, or with a command.


F += Gaussian(~60000, ~24.6, ~0.2)

Parameters of an existing function can be changed in the sideber, or by dragging that little square handle attached to each function (you should see a handle at the top of your Gaussian).

If the peaks/functions are not named explicitely (like in this example), they get automatic names %_1, %_2, etc.

Now let us fit the function. Select Fit ‣ Run from the menu or press Fit.




Fitting minimizes the weighted sum of squared residuals (see Nonlinear Optimization). The default weights of points are not equal.

Now you can check the peak position together with other parameters on the sidebar. Alternatively, right click the peak handle and select Show Info from the context menu.


info prop %_1

That’s it!

By the way, you can save all the issued commands to a file (Session ‣ Save History)


info history >

and later use it as a macro (Session ‣ Execute script).



Command Line

Fityk comes with a small domain-specific language (DSL). All operations in Fityk are driven by commands of this language. Commands can be typed in the input box in the GUI, but if all you want to do is to type commands, the program has a separate CLI version (cfityk) for this.

Do not worry

you do not need to learn these commands. It is possible to use menus and dialogs in the GUI and completely avoid typing commands.

When you use the GUI and perform an action using the menu, you can see the corresponding command in the output window. Fityk has less than 30 commands. Each performs a single actions, such as loading data from file, adding function, assigning variable, fitting, or writing results to a file.

A sequence of commands written down in a file makes a script (macro), which can automate common tasks. Complex tasks may need to be programmed in a general-purpose language. That is why Fityk has embedded Lua interpreter (Lua is a lightweight programming language). It is also possible to use Fityk library from a program in Python, C, C++, Java, Ruby or Perl, and possibly from other languages supported by SWIG.

Now a quick glimpse at the syntax. The =-> prompt below marks an input:

=-> print pi
=-> # this is a comment -- from `#' to the end of line
=-> p '2+3=', 2+3  # p stands for print
2+3 = 5
=-> set numeric_format='%.9f'  # show 9 digits after dot
=-> pr pi, pi^2, pi^3  # pr, pri and prin also stand for print
3.141592654 9.869604401 31.006276680

Usually, one line has one command, but if it is really needed, two or more commands can be put in one line:

=-> $a = 3; $b = 5  # two commands separated with `;'

or a backslash can be used to continue a command in the next line:

=-> print \
... 'this'

If the user works simultaneously with multiple datasets, she can refer to a dataset using its number: the first dataset is @0, the second – @1, etc:

=-> fit # perform fitting of the default dataset (the first one)
=-> @2: fit # fit the third dataset (@2)
=-> @2 @3: fit # fit the third dataset (@2) and then the fourth one (@3)
=-> @*: fit # fit all datasets, one by one

Settings in the program are changed with the command set:

set key = value

For example:

=-> set logfile = 'C:\' # log all commands to this file
=-> set verbosity = 1 # make output from the program more verbose
=-> set epsilon = 1e-14

The last example changes the ε value, which is used to test floating-point numbers a and b for equality (it is well known that due to rounding errors the equality test for two numbers should have some tolerance, and the tolerance should be tailored to the application): |a−b| < ε.

To run a single command with different settings, add with key=value before the command:

=-> print pi == 3.14  # default epsilon = 10^-12
=-> with epsilon = 0.1 print pi == 3.14  # abusing epsilon

Putting it all together, a line typically has a single command, often prefixed with datasets+:, sometimes prefixed with with. In general it is:

[[@...:] [with ...] command [";" command]...] [#comment]

All the commands are described in next chapters.