ionics 0.2.3

This package contains various ionization cross section models.

Installation

Requirements

If you are on Unix then all dependencies are installed automatically upon installation. If you are using Windows then the numpy and scipy dependencies can’t be installed automatically as they require precompiled binaries for the specific operating system. Scipy doesn’t support binaries for Windows officially. Please consider the scipy installation instructions about scientific Python distributions and inofficial binaries.

The GUI uses PyQt4 which must be installed separately (the GUI also supports PyQt5 which can be installed similarly to the PyQt4 installation instructions below). You can use the package of course without the GUI in your scripts by simply importing it. However if you want to use the GUI please consider the following resources:

• Unix - You can downloaded the latest release from here. Please follow the installation instructions on this website. Usually you have to build it from source which is fairly simple though.

• Windows - Download the correct PyQt4 version from this website; make sure that you match your Python and OS version. Then open Powershell (press the Windows key and type “Powershell” in the search field; we recommend to use the x86 version) and navigate to the folder where you downloaded the .whl file to (usually Downloads). Then install the package via pip install <version>.whl where you replace <version>.whl with the name of the file you downloaded (you can use tab completion in Powershell).

The GUI also uses matplotlib; please visit their website for the installation instructions.

Installing the package

You can install the package from pip by running pip install ionics.

Note: If you are using Windows then Python must have been added to your path in order for the above command to succeed.

Usage

The GUI

To use the graphical user interface navigate to the directory where the package has been installed. You can find it by running python -c "import ionics; print ionics.__file__". Within that directory just run python start_gui.py.

Browsing cross section

On the left side you find a file browser exposing the cross section files that are contained by the ionics package. You can get a list of available cross sections by either clicking on a directory (all cross section within that directory) or a module itself (all cross sections within that module). The cross sections are shown in the window below the file browser. By clicking on a cross section you can obtain information about them.

Plotting cross sections

To plot a specific cross section drag and drop it onto a plot canvas on the right side. You are prompted to enter the cross section’s specific parameters as well as the plot range. You can also specify the scale for which the data should be generated (linear means the data is evenly distributed, log means the data is exponentially distributed so it will be evenly distributed when using a log-scale).

Also:

• You can stack multiple single-differential cross sections by dropping them onto the same canvas.

• You can add a new canvas by clicking on the “add” button on the very right.

• You can change the scale for each axis at the bottom of the corresponding plot.

Usage within your applications

This package contains various ionization cross sections as well as related auxiliary functions (such as random sampling). Two kinds of ionization cross sections are provided:

• Single differential ionization cross sections (SDCS); see ionics.ddcs.

• Double differential ionization cross sections (DDCS); see ionics.sdcs.

Required parameters for cross sections must be specified in their __init__ methods. A cross section can be evaluated by calling it (via __call__) (for the signature see the help text of one of the cross sections). SDCS require the kinetic energy of the ionized particle as an argument while DDCS also require the polar scattering angle (in addition).

Random sampling is available for double differential cross sections. Two general methods are available which are meant to work with any two-dimensional distribution:

• Inverse transform sampling

• Rejection sampling

Please consider ionics.ddcs.random_sampling for more information.

Examples

Using a double differential cross section:

>>> from ionics.ddcs.voitkiv import VoitkivDDCS
>>> ddcs = VoitkivDDCS(4.0e12, 1, 'H')  # 4 TeV protons on Hydrogen.
>>> ddcs(10, pi/2)  # Kinetic energy 10eV, transverse scattering.
0.00043127346990368256