Data classes

This page will describe how the data is stored in the classes that handles the data. It will not be a complete manual. It will only deal with basic functionality which expert users might find handy to know. For more information the reader is refereed to in the source tree.

For storing the data two classes are implemented: The DataSet class which contains one data set and the DataList class which contains several DataSets.


The major use of the DataList is to make ordinary list methods available and at the same time making it possible to apply commands to entire data set or subset when working with the data from the GUI. Some of the list functionality include:

  • Extracting one element by list-like access

    dataset = datalist[2]
  • Using slicing to extract a subset of the datalist such as:

    subdatalist = datalist[0:5]
  • Iterations over the DataSet`s in the `DataList such as:

    for dataset in datalist:
    • Using the len function such as len(datalist)

The rest of the functions for the DataList is mainly of interest for GUI programmers and should not be necessary to dwell upon here, if you need to know use the source.


The DataSet class contains all the information about a certain data set.

General data handling

When data has been loaded into the data class it is loaded into the member variables x_raw, y_raw, error_raw, extra_data_raw then the commands as defined in x_command, y_command, error_command, extra_data_command is evaluated and stored in the variable names x, y, e and the keys in the extra_data identical to the raw data variables. This makes the evaluations of the data calculations/transformations independent of previous evaluations. The commands are always on the raw data. The result of these evaluations is then set to the member variables x, y, error, extra_data.

Important members

The members of the class that could be of interest are:

The x-values after the commands has been executed on the raw data.
The y-values after the commands has been executed on the raw data.
The error on the y-values after the commands has been executed on the raw data.
The simulation of the data as calculated from the Sim function in the model.
A dictionary of the extra data as defined b y the data loader plugin. This is also after the commands has been executed on the extra data if it is defined to be accessible from the data loader plugin.
A flag, boolean, that defines if the DataSet should be visible.
A flag, boolean, that defines if the DataSet should be used the FOM calculation.
A flag, boolean, that defines if the DataSet has errorbars that should be used.
The name of the DataSet. This is a string which can be non-unique.

There are also a number of member variables that defines plotting:

data_color = (0.0, 0.0, 1.0)
sim_color = (1.0, 0.0, 0.0)
data_symbol = 'o'
data_symbolsize = 4
data_linetype = '-'
data_linethickness = 2
sim_symbol = ''
sim_symbolsize = 1
sim_linetype = '-'
sim_linethickness = 2

And also the member variables that contains the raw data values as loaded from file and the commands applied to them.

x_raw = array([])
y_raw = array([])
error_raw = array([])
extra_data_raw = {}

extra_commands = {}
x_command = 'x'
y_command = 'y'
error_command = 'e'

Extra data

In order to operate on loaded extra_data as ordinary data it has to be added as an item to the dictionary extra_command. In doing so it will also be subjected to the same rigorous constraint as the x, y and error values. It has to an array of the same length as all the other and it has to possible to use it the commands for the data. Otherwise the data will just be present in the extra_data array to use. This can be handy for external conditions that is stored in the data file, for example magnetic field, temperature or pressure.

To create a new extra_data instance use the method set_extra_data(self, name, value, command = None) for example to add a temperature variable T to the DataSet dataset:

dataset.set_extra_data('T', 10)

If you on the other hand want to make it as an additional independent variable

dataset.set_extra_data('T', array([0, 1, 2, 3]), command = 'T')

To get extra data you can use the method get_extra_data(self, name), for example:


In the future there might be an implementation so that the extra dat can be directly accessed as dataset.T but that will not be implemented right now.