ds_arpes_plugin package

Submodules

ds_arpes_plugin.ds_arpes_plugin module

exception ds_arpes_plugin.ds_arpes_plugin.DatasetError

Bases: Exception

Error raised when the type of data found does not conform to our expectations.

class ds_arpes_plugin.ds_arpes_plugin.ARPES_Plugin(*args, **kwargs)

Bases: data_slicer.plugin.Plugin

A plugin which connects the analysis functionalities of the aprys module with PIT.

filename = '<missing filename>'

load_data(filename)

Load a set of ARPES data and bring it into PIT-friendly form. Also return the arpys data Namespace for inspection.

load(filename)

Load a set of ARPES data and bring it into PIT-friendly form. Also return the arpys data Namespace for inspection.

This is a convenience alias for load_data.

open(filename)

Load a set of ARPES data and bring it into PIT-friendly form. Also return the arpys data Namespace for inspection.

This is a convenience alias for load_data.

store(filename, force=False)

Store the data Namespace D in a pickle file filename. This can severely reduce loading times for certain filetypes.

dump(filename, force=False)

Store the data Namespace D in a pickle file filename. This can severely reduce loading times for certain filetypes.

This is a convenience alias for store.

a2k(alpha_axis, beta_axis=None, dalpha=0, dbeta=0, orientation='horizontal', work_func=4, units=0, hv=None, store=True)

Convert the axes from angles to k-space. This updates the selected axes in the pit.axes and makes the change visible in the main plot. Notice that there will be no error message or anything if you happen to select nonsensical axes for alpha_axis and beta_axis, so check carefully if your result makes sense. The calculated KX and KY meshes (in the specified units) are stored in self.D, so the result can be retained when storing the data with store.

Parameters

alpha_axis	int; index of the axis containing the angles along the analyser slit. In PIT, 0 corresponds to the horizontal axis of the main plot, 1 to its vertical axis and 2 to the remaining 3rd axis.
beta_axis	int or None; index of the axis containing the angles perpendicular to the analyser slit. Can be left out (i.e. set to None) to only transform 1 axis. A constant value for the respective angle can then be specified with dbeta.
dalpha	float; angular offset along alpha.
dbeta	float; angular offset along beta or, if beta_axis is None, the value of beta.
orientation	str, must start with ‘h’ or ‘v’; specifies the analyzer slit geometry (horizontal or vertical).
work_func	float; work function in eV.
units	float; toggle what units to use. - 0 corresponds to inverse Angstrom; - any nonzero value corresponds to units of pi/units (this is useful, e.g. to convert to units of pi/lattice_constant).
hv	float; used photon energy in eV. If not given, this will use the value stored in self.D (which is accurate in most cases, but not all, e.g. in photon-energy scans)
store	boolean; set to False to suppress storing the found KX and KY meshes directly in the data object D.

Returns

KX	array of shape (nkx, nky); mesh of k values in parallel direction in units of inverse Angstrom.
KY	array of shape (nkx, nky); mesh of k values in perpendicular direction in units of inverse Angstrom.

shift_axis(shift, dim=0, store=True)

Apply a linear shift to the axis along dim, both on PIT’s data and in the D data object.

main_plot_normalize_per_segment(dim=0, min=False)

Apply the arpys function normalize_per_segment to the data in the main_plot and visualize the result.

Note

This result is not stored, does not affect other plots (like cut_plot and the x- and y-plots) and is lost the next time the main_plot is updated by any means. To create a more persisting result, see normalize_per_segment

cut_plot_normalize_per_segment(dim=0, min=False)

Apply the arpys function normalize_per_segment to the data in the cut_plot and visualize the result.

Note

This result is not stored, does not affect other plots (like cut_plot and the x- and y-plots) and is lost the next time the cut_plot is updated by any means. To create a more persisting result, see normalize_per_segment

normalize_per_segment(dim=0, min=False)

Apply the arpys function normalize_per_segment to every slice along z.

Note

The result of this operation is stored, i.e. the dataset is updated. If you just want to have a quick look at what this operation might look like without applying it to the whole dataset, confer main_plot_normalize_per_segment or cut_plot_normalize_per_segment

apply_model(model=<function ARPES_Plugin.<lambda>>, eps=0.1)

Work in progress.

Module contents