Data Viewers

2D DataViewer

This viewer allows for processing two-dimensional energy vs. momentum datasets that are basic detector’s images acquired during ARPES experiment.

The window consists of two main objects: DataViewer2D and UtilitiesPanel, responsible for slicing/displaying updated plots and manipulation on the data, respectively. Its layout and components are described below:

a	Main data plot. Horizontal and vertical axes correspond to the energy and angle (momentum) directions, respectively.
b	Horizontal and vertical slices taken at the positions of the sliders.
c	Utilities panel containing all image processing and analysis features, to large extent shared between 2D, 3D Viewer and 4D Viewer. Detailed description of all its options can be found below.
d	close - close this window save - save current dataset to a pickle file. MDC fitter - open current dataset in MDC Fitter EDC fitter - open current dataset in EDC Fitter open in PIT - open current dataset with data-slicer’s PIT. Refer to the note below for additional information.

Note

save button saves applied energy corrections and k-space conversion but does not keep information on performed image processing like smoothing or curvature analysis.

Note

PIT enables users to extract arbitrary cuts from multidimensional datasets. It is, however, part of an external package, data_slicer. While the core functionality is self-contained, deprecation warnings or compatibility issues may arise when used with newer versions of Python or third-party packages.

For any critical issues, please contact the development team—preferably by opening an issue on GitHub.

3D DataViewer

During the measurement, energy vs. momentum images are very often acquired as a function of a third parameter, e.g. rotation angle or photon energy. 3D Viewer allows for inspection of such datasets. Window’s layout helps to simultaneously and intuitively present slices across all three perpendicular directions.

a	Main image plot. Horizontal and vertical axes correspond to scanned dimension and analyzer (usually angle/momentum) axis, respectively.
b	Main energy slider. The white and red curves represent the integrated and single-point energy distribution curves, respectively — the latter corresponding to the crossing point of the sliders in the main image plot. The slider allows you to change the energy slice displayed in the main image plot.
c	Horizontal and vertical cuts taken along the sliders in main image plot. Vertical axes correspond to energy, horizontal to corresponing axes of main image plot.
d	Slices taken along energy sliders of horizontal/vertical cuts.
e	Utilities panel containing all image processing and analysis features, to large extent shared between 2D, 3D Viewer and 4D Viewer. Detailed description of all its options can be found below.
f	See description (d) here.

4D DataViewer

Micro- and nano-ARPES are methods interested in examining physically small systems, like microscopic crystallographic domains, nano-structures or fabricated devices. They use special focusing optics to minimize beam spot size and obtain information about how band structure varies between different regions of the sample. 4D Viewer is a convenient tool for visualizing such datasets, acquired by rastering the beam over the sample’s surface and spatially resolving electronic dispersion.

a	Raster scan plot. Horizontal and vertical axes correspond to the translation of the manipulator. Each data point represents spectrum acquired at the given position. Details on the representation can be found in update_raster_data()
b	Band map plot. Horizontal and vertical axes correspond to the energy and angle (momentum) directions, respectively.
c	Horizontal and vertical slices taken at the positions of the sliders.
d	Utilities panel containing all image processing and analysis features, to large extent shared between 2D, 3D Viewer and 4D Viewer. Detailed description of all its options can be found below.
e	See description here.

The Utilities Panel

The top panel of the DataViewers, consists of different tabs, which give access to piva’s functionalities. Utilities Panels of 2D, 3D Viewers and 4D Viewers share a lot of similarities and, therefore, are discussed on example of dv2D.p and dv3D.p windows.

Volume tab

Gives additional control over the sliders and allows to integrate data. Helps to better orient on both levels: data matrix and experimental coordinates.

Functionality	Description
positions	Set manually the location (in pixels) of the Energy and Momentum sliders.
binning options	Apply integration window by enabling the respective binning checkbox and setting the size of the window in units of pixels.
linking options	Link different DataViewers (of the same type) to control their sliders simultaneously. Functionality operates in a parent - child system, where one (parent) window controls all the others. The parent window is set as the window from which Link button was clicked. All other windows receive status child. Windows added to existing linked combination will also receive status child.

Note

To use the linking feature, two or more windows of the same dimensionality must be open. Upon clicking the dropdown menu, a list of all compatible viewers (i.e., those with the same dimensionality, excluding the native viewer) will appear. The user can then select one or more viewers to link together.

Image tab

Controls visual aspects of displayed images, from simple colormap selection and color scaling to more advanced processing methods.

Functionality	Description
colors	Select matplotlib colormap, invert its colors, apply power-law normalization according to \(x \rightarrow x^{1/\gamma}\).
normalization options	Apply normalization along selected direction.
smoothing options	Perform smoothing using uniform square kernel. box size determines size of the square in pixels, recursion number of smoothing iterations.
curvature method	Perform selected curvature method. Helps to enhance dispersive features hidden in a spectra. See curvature_2d() and curvature_1d() for more details.
Brillouin zone contour	Overlay a Brillouin zone contour centered at (0, 0) with a specified symmetry. Requires conversion to momentum space first.
open in 2D Viewer	Open the horizontal or vertical cut in a new 2D Viewer for closer inspection.

Axes tab

Allows to apply corrections, change scale of the energy axis and perform transformation to momentum space.

Functionality	Description
Energy correction	Apply manually energy corrections for Fermi level and change scale between kinetic and binding.
k-space conversion	Perform conversion from angle (photon energy) to momentum space. Note: Position of the Brillouin zone center \(\Gamma_{x0}\) (and \(\Gamma_{y0}\)) must be given in pixels, while angle offset in 2D case should be in degrees. Tick the kz box if converted dataset is along out-of-plane direction. a and c correspond to in- and out-of-plane lattice constants. Conversion with default values (\(\pi\)) gives axes in units of inverse angstroms. Copy from ‘Orient’ button allows to use the values for the location of \(\Gamma\) found in the Orient tab. Conversion algorithm follows procedure described by Ishida et al.

Note

k-space conversion of 3D datasets will create new Dataset with rescaled axes. New axes span between lowest and highest values, with the smallest step in new momentum coordinates. Such approach is applied to plot data as a regular rectangular image instead of pcolormesh object, which is incredibly slow.

See rescale_data() for more details.

Orient tab

Contains tools useful for sample alignment at the beginning of the experiment.

Functionality	Description
find \(\Gamma\)	Automated image processing routine for finding highest symmetry point in displayed image. Follows method described by Junck et al.
rotatable lines	Append rotatable lines to the plot to identify the sample azimuthal rotation.
Beamlines’ orientations	Display window showing relative dependency between some beamlines’ geometries and convention used in piva.

File tab

File related options. Allows to edit and browse through metadata. Gives a quick, one-click solution for jumping to the JupyterLab to perform more sophisticated analysis.

Functionality	Description
data provenance and metadata	Display windows showing data provenance entries and available metadata.
edit metadata	Edit (add/update or remove) metadata entries of loaded file. name indicates an attribute of the Dataset object.
summing options	Sum current data with a given data file. Method will compare available metadata and return detected conflicts. See sum_datasets for more details.
open in JupyterLab	Create a new notebook file (touch button) with exported details for easier plotting and analysis of the currently displayed image. (See Data Ingestion and Analysis for more information.) start JL session opens a file browser window for selecting the root directory where the new jupyter-lab session should be launched, and then starts the session.
create experimental logbook	Create new notebook file with an automated experimental logbook for selected beamline.

Note

Any changes in the metadata will only be saved to the pickle file generated/updated by hitting the save button in the top-right.