3.1. The project tree¶

The tree view on the right side of the main window reflects the structure of the project. All the data sets of one plot are shown together in the plot area left of the tree. The currently selected set is displayed in red, the others in black. You can select all sets of a plot by selecting the corresponding plot item or a subset of sets within one plot by pressing SHIFT/CTRL-keys while selecting. Sets can be hidden and will thus show up in the plot area only if they are part of the current selection. The tree supports drag&drop of plot and set items. CLick twice slowly on any tree item to rename its label. Right-ckling opens a context pop-up menu which allows for deattaching of models, transformations, weights from the current selection, deleting sets and entire plots.

3.2. The toolbar¶

Most toolbar buttons are self-explanatory. The log buttons / toggle the axis scale from linear to logarithmic (base 10). If a set has a model attached to it, use the button to show the single components of the model. The green autoscale button will automatically zoom the plot to the extension of the current model.

3.3. The data grid¶

The datagrid allows for direct data manipulation in a spreadsheet-like interfaceor using a python shell. Select ‘View->Data Grid’ in the menu to open the datagrid. To copy a set’s x/y-data to a new data grid, select ‘copy to data grid’ from the popup menu in the project tree. The data grid named exported fit paramters is of particular interest as it is the target when exporting the fitted parameters. In contrast to the normal data grid pages one row label is always painted in red. This is the target row for the new set of parameters. It will move downwards automatically after having recieved a new set of parameters, extending the datagrid when necessary. The target row can be placed manually by double-clicking on a row label.

Each data grid page contains a python shell window which can be used like an ordinary python shell. In addition, the following global symbols are available which allow for accessing and manipulating the data of the data grid:

_data        : the 2d array data (read-write)
_selection   : the selected area of the data grid, applicable as index for
               data, i.e. _data[_selection] (read-only)
_colN/rowN   : the N-th column/row vector (read-write)
_x/_y         : row/column vectors containing the column/row indices (read-only)
_name         : name of the grid (read-write)

Arrays and vectors refer to instances of the ndarray object provided by the python numpy extension (http://numpy.scipy.org). In order to manipulate them a basic understanding of numpy is necessary.

The _data/_colN/_rowN attributes can be read and written. When assigning to _colN and _rowN, the new value has to have the correct shape. This is best illustrated in a small example:

>>> import numpy as np
>>> _data = np.zeros((50,3))
>>> _col0 = y
>>> _col1 = _y**2+2*_x-2
>>> _col2 = 1/_col1
>>>