NAVARP(1)

NAME
navarp - navarp Documentation

ABSTRACT

    Navigation tools for Angle Resolved Photoemission spectroscopy data, i.e.:

        •   a companion app during ARPES data acquitision (as in beamtime);

        •   a set of dedicated libs helping to get high quality figures for publication.

By Federico Bisti, University of L'Aquila, Italy. [image]

CONTENTS

  Installation

Download and install the last Anaconda distribution for Python 3.x from here, it can be installed with or without admin privilege (just remind the chosen option for later).

After installation, few commands must be run in the proper command line interface before being able to use the NavARP package, and this command line interface depends on the operating system.

The command line interface corresponds to the Anaconda Prompt on Windows (that can be found in the Start menu after searching Anaconda Prompt or after opening the Anaconda Navigator), and to the terminal on macOS and Linux.

Regarding macOS, it might happen that after Anaconda installation the default Python version accessible from the terminal is still the default one from macOS (so not the one related to the Anaconda distribution). To check if it is the case, type and run python --version in the terminal. If the word Anaconda is not the printed lines, then Python is not of Anaconda and the installation cannot continue. To fix it, you can change the python environment by typing conda activate. Or (at your risk!), you can try to change your .bash_profile making sure that the Anaconda directory is the first one in the line beginning with export PATH=... .

Therefore, launch the proper command line interface and run the following command to install igor (necessary for opening ibw and pxt files) and colorcet (it gives additional perceptually uniform colormap):

        conda install --channel conda-forge igor colorcet

Otherwise, add conda-forge channel first and then install the packages:

        conda config --append channels conda-forge
        conda install igor colorcet

After this step, NavARP must be installed as a package using pip. To do it with admin privilege (depending on the Anaconda installation), run the following command for the last stable version:

        pip install https://gitlab.com/fbisti/navarp/-/archive/master/navarp-master.zip

Without administrator privilege run instead:

        pip install --user https://gitlab.com/fbisti/navarp/-/archive/master/navarp-master.zip

If you are brave enough you can also install the version still under development by using one of the two commands:

        pip install https://gitlab.com/fbisti/navarp/-/archive/develop/navarp-develop.zip
        pip install --user https://gitlab.com/fbisti/navarp/-/archive/develop/navarp-develop.zip

After this steps NavARP should run directly by typing in the command line interface the following command:

        navarp

Instead, for getting familiar with the libraries, launch Jupyter Notebook from the Anaconda Navigator (or in proper command line interface run the command jupyter notebook) and open some examples which you can find in the example folder:

Finally, if you are familiar with conda you can also create a dedicated enviroment (for example navarp-env) and install only the basic packages using the following commands:

        conda create --name navarp-env numpy scipy matplotlib colorcet h5py pyqt=5 jupyter pyyaml click
        conda activate navarp-env
        conda install -c conda-forge igor

And then you can install it directly with:

        pip install https://gitlab.com/fbisti/navarp/-/archive/master/navarp-master.zip

Or maybe you can also have it under control with git, then do:

        conda install -c conda-forge git
        git clone https://gitlab.com/fbisti/navarp
        pip install -e .

The version under development will be installed and any modification in the local directory will affect the installed program.

  Update

To update the NavARP project to the last version, run the following command for the last stable version:

        pip install https://gitlab.com/fbisti/navarp/-/archive/master/navarp-master.zip

Or for the last version still under development run:

        pip install https://gitlab.com/fbisti/navarp/-/archive/develop/navarp-develop.zip

  Usage

The following documentation is covering only the basic commands in the NavARP GUI (navarp.py). The independent usage of the navarp.utils libraries is instead reported as Jupyter notebooks in the example folder.

  Loading data

NavARP can open the following data types:

    •   NXarpes file from LOREA/ALBA(ES) and I05/Diamond(GB);

    •   HDF5 file from SXARPES-ADRESS/PSI(CH);

    •   NEXUS file from Antares/Soleil(FR) (only deflector scan);

    •   folder with txt-files from Cassiopee/Soleil(FR);

    •   txt-file from MBS A1Soft program;

    •   zip- or txt-file from Scienta-Omicron SES program;

    •   sp2 file from Specs program;

    •   pxt, ibw and itx file of Igor-pro as saved by Scienta-Omicron SES program;

    •   txt file in itx format of Igor-pro considering the order (energy, angle, scan);

    •   yaml file with dictionary to load files (txt, sp2 or pxt) in a folder or to only add metadata to a single file.

To open the file click on the button on the top left and select the single file or, in the case collection of txt-files from Cassiopee/Soleil(FR), select a ROI-file inside the folder. To open a folder with a collection of txt-files from MBS A1Soft, sp2-files from Specs program, pxt- or txt-files from Scienta-Omicron SES program, write first a yaml-file inside that folder with the instruction for opening the file. If for example the folder contains "file_name_001.txt, file_name_002.txt, file_name_003.txt, etc." the related yaml-file inside that folder (called for example file_name.yaml) can be:

        # ----------------------------------------------------------------
        # Required parameters
        # ----------------------------------------------------------------

        # file_path, the * must to be exactly where the variable number is
        file_path: 'file_name_*.txt'

        # scans can be start and step (as below), or start and stop (replace step with stop)
        # in the case below, it starts from 20 with a step of 0.5
        scans:
          start: 20
          step: 0.5

        # scan_type can be 'tilt', 'polar', 'azimuth', 'deflector' or 'hv'
        scan_type: 'azimuth'

        # ----------------------------------------------------------------
        # Optional parameters
        # ----------------------------------------------------------------

        # photon energy, it can be specified and other value from the files will be discarded
        hv: 60

        # analyzer, this define the analyzer geometry, if not specified default values will be used
        analyzer:
          tht_ap: 50
          phi_ap: 0
          work_fun: 4.5
          deflector: False

  Navigate through the data

To navigate the data use the Navigation panel at the top right or mouse right-click on the top (bottom) graph to change the energy (scan) value.

The mouse action can be only a single right-click-and-release in a particular region or the right-click can be kept pressed for a smooth movement to a final release point.

Important, the right-click mouse navigation works properly if the "pan/zoom" or "zoom rect" are not selected in the navigation toolbar at the bottom of the figure (the GUI start with neither of the two options selected).

With the mouse cursor on top of a graph (either top or bottom), use the mouse scroll to change the integration region for the iso-value (energy or angle), each scroll step is doubling or halving such region.

  Colormap scale

Color scale setting can be modified in the Color scale parameters tab on the right (click on the tab name if not already selected).

  Fermi level

Fermi level can be determined in the Fermi level alignment tab (click on the tab name if not already selected).

The "No Fermi level alignment" option keep the data in kinetic energy (E_{kin}) or, in the case of photon energy scan, in binding energy (E_{bin}) as obtained from E_{bin} = E_{kin} - (h\nu - \Phi), where h\nu is the photon energy and \Phi is the analyzer work function.

"Use Fermi level at" uses the Fermi level inserted in the box below. "Find Fermi level using" looks for the Fermi level following the set-up selected below where: "Energy range" can be all the available one (selecting the radio button on full) or within the horizontal lines in the top panel (selecting cursor instead); the Fermi level can be the same for all the scan image (selecting "Scan value" all) or different values for each scan image, option particularly useful in photon energy scan ("Scan value" radio button on each).

  Transformation in the k-space

In the k-space transformation tab it is possible to set up all the parameters used for the transformation from angle to momentum scale (the method is based on the kinetic energy of the electrons and so it is independent from the Fermi level alignment).

The from cursor button auto-fills the "Point in angles" with the actual cursor position in the figure.

The set \Gamma just put zeros in k_x and k_y. The inner potential (V0) is 10 by default, it is used only for the photon energy scan and so it must be properly modified only in that case. Use photons = yes to include the photon momentum for the determination of the initial electron momentum. In this case, the "Analyzer" parameters must be properly filled (important, at the present version, the "Analyzer" parameters, and so the photon momentum, are correctly defined only for beamlines LOREA/ALBA(ES) and SXARPES-ADRESS/PSI(CH)). Once everything is properly set, it is possible to click the Iso-E (k) blue button in the "Plot mode".

  File information

File information tab is the selected one by default after starting the GUI and it is reporting the information extracted from the data file.

  Credits

  Development Lead

    •   Federico Bisti <federico.bisti@univaq.it>

  Contributors

None yet. Why not be the first?

  Contributing

Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.

You can contribute in many ways:

  Types of Contributions

  Report Bugs

Report bugs at Issue tracker.

If you are reporting a bug, please include:

    •   Your operating system name and version.

    •   Any details about your local setup that might be helpful in troubleshooting.

    •   Detailed steps to reproduce the bug.

  Fix Bugs

Look through the Issue tracker for bugs. Anything tagged with "bug" and "help wanted" is open to whoever wants to implement it.

  Implement Features

Look through the Issue tracker for features. Anything tagged with "enhancement" and "help wanted" is open to whoever wants to implement it.

  Write Documentation

NavARP could always use more documentation, whether as part of the official NavARP docs, in docstrings, or even on the web in blog posts, articles, and such.

  Submit Feedback

The best way to send feedback is to file an issue at Issue tracker .

If you are proposing a feature:

    •   Explain in detail how it would work.

    •   Keep the scope as narrow as possible, to make it easier to implement.

    •   Remember that this is a volunteer-driven project, and that contributions are welcome :)

  Get Started!

Ready to contribute? Here's how to set up navarp for local development.

    1.  Fork the navarp repo on GitLab.

    2.  Clone your fork locally:

            $ git clone git@gitlab.com:your_name_here/navarp.git

    3.  Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development:

            $ mkvirtualenv navarp
            $ cd navarp/
            $ python setup.py develop

    4.  Create a branch for local development:

            $ git checkout -b name-of-your-bugfix-or-feature

    Now you can make your changes locally.

    5.  When you're done making changes, check the code by using navarp.py and/or running the examples.

    6.  Commit your changes and push your branch to GitLab:

            $ git add .
            $ git commit -m "Your detailed description of your changes."
            $ git push origin name-of-your-bugfix-or-feature

    7.  Submit a pull request through the GitLab website.

  Pull Request Guidelines

Before you submit a pull request, check that it meets these guidelines:

    1.  The pull request should include tests.

    2.  If the pull request adds functionality, the docs should be updated. Put your new functionality into a function with a docstring, and add the feature to the list in README.rst.

  Deploying

A reminder for the maintainers on how to deploy. Make sure all your changes are committed (including an entry in CHANGELOG.rst). Then run:

        $ bump2version patch # possible: major / minor / patch
        $ git push
        $ git push --tags

  Changelog

  1.0.0: 2021/04/11

This is one of the most important release of NavARP, and the project is approaching to a more stable stage. This new version adds very important methods to the NavEntry class which are nicely shown in the now-available examples gallery! In addition, the loading speed has been improved for many text-based files input, and now the krx-MBS format is also supported. Below a list of the main changes divided by modules:

    •   navarp_gui.py:

        •   Added more colorscale maps;

        •   After closing the program, its window size and position are saved (as a file called .navarp inside the user home directory) and they will be used to restore the program the next time it will be opened;

    •   utils.navfile:

        •   Added support for krx-MBS file format;

        •   Added support for ibw file format as saved by Scienta-Omicron SES program;

        •   Added support for txt-Scienta file format with 3 dimensions;

        •   Added support for igor-pro text file format (saved as .itx or .txt);

        •   Loading speed of about x3 faster for txt-based file input (txt from Scienta and MBS, sp2 from Specs);

        •   Added set_efermi method to NavEntry to set the efermi of the entry object;

        •   Added autoset_efermi method to NavEntry to automatically found the efermi by curve fitting;

        •   Added plt_efermi_fit method to NavEntry to show the autoset_efermi results;

        •   Added set_tht_an method to NavEntry to set the tht_an angle of the the entry object for k-space tranformation;

        •   Added set_kspace method to NavEntry to set tht_an, phi_an, scans_0 and phi used in the k-space tranformation;

        •   Added isoscan, isoenergy, isoangle and isok methods in the NavEntry calling the respectively classes in isomclass.

        •   Extended dictionary in yaml-file, if loaded overwrite any attribute and efermi and tht_an can be also directly assigned.

    •   utils.isomclass (new file):

        •   Added IsoScan class with the methods to show in a plot, export as NXdata or Igor-pro text file, postprocessing as interpolation, second derivative and curvature;

        •   Added IsoEnergy class with the methods to show in a plot, export as NXdata or Igor-pro text file, postprocessing as interpolation, second derivative and curvature;

        •   Added IsoAngle class with the methods to show in a plot, postprocessing as second derivative and curvature;

        •   Added IsoK class with the methods to show in a plot, export as NXdata or Igor-pro text file, postprocessing as second derivative and curvature;

    •   utils.fermilevel:

        •   Added fit procedure without using lmfit, lmfit is no longer required in navarp;

        •   Removed all the previous functions based on lmfit.

    •   utils.ktransf:

        •   Removed all the deprecated functions for k-space transformation.

    •   examples directory:

        •   The old example directory now is called examples and contains the python script to get the gallery.

    •   extras.simulation:

        •   Added functions to simulate the graphene band structure probed by deflector and hv scans.

    •   requirements.txt and setup.py:

        •   Removed lmfit from the required packages.

  0.18.0: 2020/04/11

This version gives the possibility to open four additional file formats.

    •   navarp.py (now renamed as navarp_gui.py):

        •   Added click command;

        •   Import navarp.utils, so now navarp requires installation with pip before usage;

        •   Renamed as navarp_gui.py so to avoid importing conflicts.

    •   utils.navfile:

        •   Added loading MBS A1Soft text-files;

        •   Added case scan_type=deflector for Lorea by reading defl_angles.

    •   setup.py:

        •   Added click command.

  0.17.0: 2020/08/14

This version gives the possibility to open four additional file formats.

    •   navarp.py:

        •   Added .navarp configuration file saved in the local user home

        •   Set the initial default value of k-transf to be without photons contribution

    •   utils.navfile:

        •   Added loading Scienta-Omicron SES zip and text files;

        •   Added loading Specs Prodigy sp2 files;

        •   Added loading Igor-pro pxt files as saved by Scienta-Omicron SES;

        •   Added loading folder with txt, sp2 or pxt files using instructions in a yaml file.

    •   setup.py:

        •   Improved requirements avoiding to install PyQt5 if in conda it is already present.

  0.16.0: 2020/08/05

This version adds the sphinx-doc integrated in GitLab pages and utils.kinterp.

    •   navarp.py:

        •   Added azimuth scan_type

        •   Added arctan2 value for sample alignment

        •   Fixed Antares data including the case of MBSAcquisition_1

    •   utils:

        •   Added kinterp

EXAMPLES

  Examples gallery

Below is a gallery of examples

  Simulated cone basic analysis

Simple workflow for analyzing a deflector scan data. The same workflow can be applied in the case of manipulator angular scans.

Import the "fundamental" python libraries for a generic data analysis:

        import numpy as np

Import the navarp libraries:

        from navarp.utils import navfile

Load the data from a file:

        file_name = r"nxarpes_simulated_cone.nxs"
        entry = navfile.load(file_name)

Out:

        instrument_name = simulated

Plot a single slice Ex: scan = 0.5

        scan = 0.5
        entry.isoscan(scan).show()
[image: plot basic commands] [image]

Out:

        <matplotlib.collections.QuadMesh object at 0x7f3cea3d9160>

Fermi level determination

        entry.autoset_efermi(energy_range=[93.8, 94.3])
        entry.plt_efermi_fit()

        print("Fermi level = {:.3f} meV".format(entry.efermi))
        print("Energy resolution = {:.0f} meV".format(entry.efermi_fwhm*1000))
        print("hv = {:g} eV".format(np.squeeze(entry.hv)))
[image: plot basic commands] [image]

Out:

        /build/navarp-uNOccK/navarp-1.0.0/navarp/utils/fermilevel.py:67: RuntimeWarning: divide by zero encountered in true_divide
          ddata_s_denergies = ddata_s_denergies/np.abs(data_sum)
        /build/navarp-uNOccK/navarp-1.0.0/navarp/utils/fermilevel.py:67: RuntimeWarning: invalid value encountered in true_divide
          ddata_s_denergies = ddata_s_denergies/np.abs(data_sum)
        Fermi level at 93.8881 eV
        Energy resolution = 67.2 meV (i.e. FWHM of the Gaussian shape which, convoluted with a step function, fits the Fermi edge)
        Photon energy is now set to 98.4881 eV (instead of 100.0000 eV)
        Fermi level = 93.888 meV
        Energy resolution = 67 meV
        hv = 98.4881 eV

Since now the Fermi level is known, the same plot is automatically aligned Ex: scan = 0.5

        scan = 0.5
        entry.isoscan(scan).show()
[image: plot basic commands] [image]

Out:

        <matplotlib.collections.QuadMesh object at 0x7f3ce9894d00>

Plotting iso-energetic cut Ex: isoenergy cut at ekin = efermi

        ebin = 0
        debin = 0.005
        entry.isoenergy(ebin, debin).show()
[image: plot basic commands] [image]

Out:

        <matplotlib.collections.QuadMesh object at 0x7f3ce9893790>

Total running time of the script: ( 0 minutes 3.123 seconds)

  Export isoenergy at the Fermi level

Simple workflow for exporting the isoenergy at the Fermi level. The data are a deflector scan on graphene as simulated from a third nearest neighbor tight binding model. The same workflow can be applied to any tilt-, polar-, deflector- or hv-scan.

Import the "fundamental" python libraries for a generic data analysis:

        import numpy as np

Instead of loading the file as for example:

        # from navarp.utils import navfile
        # file_name = r"nxarpes_simulated_cone.nxs"
        # entry = navfile.load(file_name)

Here we build the simulated graphene signal with a dedicated function defined just for this purpose:

        from navarp.extras.simulation import get_tbgraphene_deflector

        entry = get_tbgraphene_deflector(
            scans=np.linspace(-5., 20., 91),
            angles=np.linspace(-25, 6, 400),
            ebins=np.linspace(-13, 0.4, 700),
            tht_an=-18,
            phi_an=0,
            hv=120,
            gamma=0.05
        )

  Fermi level autoset

        entry.autoset_efermi(scan_range=[-5, 5], energy_range=[115.2, 115.8])
        print("Energy of the Fermi level = {:.0f} eV".format(entry.efermi))
        print("Energy resolution = {:.0f} meV".format(entry.efermi_fwhm*1000))

        entry.plt_efermi_fit()
[image: plot export isoenergy as nxs or itx] [image]

Out:

        Fermi level at 115.4091 eV
        Energy resolution = 138.1 meV (i.e. FWHM of the Gaussian shape which, convoluted with a step function, fits the Fermi edge)
        Photon energy is now set to 120.0091 eV (instead of 120.0000 eV)
        Energy of the Fermi level = 115 eV
        Energy resolution = 138 meV

  Set the k-space for the transformation

        entry.set_kspace(
            tht_p=0.1,
            k_along_slit_p=1.7,
            scan_p=0,
            ks_p=0,
            e_kin_p=114.3,
        )

Out:

        tht_an = -17.979
        scan_type =  deflector
        inn_pot = 14.000
        scans_0 = 0.000
        phi_an = 0.000
        kspace transformation ready

  Export the Fermi surface:

First of all let's show it:

        entry.isoenergy(0, 0.02).show()
[image: plot export isoenergy as nxs or itx] [image]

Out:

        <matplotlib.collections.QuadMesh object at 0x7f3ce9439d60>

Then to be exported it mush be interpolated in a uniform grid. This can be done by defining kbins in the isoenergy definition, which is the number of points the momentum along the analyzer slit and the scan. In this case the number will be [1000, 800] and we will call such isoenergy object as isoatfermi. sphinx_gallery_thumbnail_number = 3

        isoatfermi = entry.isoenergy(0, 0.02, kbins=[1000, 800])

To show what we are going to save, the method is always the same:

        isoatfermi.show()
[image: plot export isoenergy as nxs or itx] [image]

Out:

        <matplotlib.collections.QuadMesh object at 0x7f3ce93d71f0>

To export it as NXdata class of the nexus format uncomment this line:

        # isoatfermi.export_as_nxs('fermimap.nxs')

To export it as igor-pro text file (itx) uncomment this line:

        # isoatfermi.export_as_itx('fermimap.itx')

Total running time of the script: ( 0 minutes 20.597 seconds)

  Export isoenergy at the Fermi level

Simple workflow for exporting the isoenergy at the Fermi level. The data are a deflector scan on graphene as simulated from a third nearest neighbor tight binding model. The same workflow can be applied to any tilt-, polar-, deflector- or hv-scan.

Import the "fundamental" python libraries for a generic data analysis:

        import numpy as np

Instead of loading the file as for example:

        # from navarp.utils import navfile
        # file_name = r"nxarpes_simulated_cone.nxs"
        # entry = navfile.load(file_name)

Here we build the simulated graphene signal with a dedicated function defined just for this purpose:

        from navarp.extras.simulation import get_tbgraphene_deflector

        entry = get_tbgraphene_deflector(
            scans=np.linspace(-0.1, 0.1, 3),
            angles=np.linspace(-25, 6, 400),
            ebins=np.linspace(-13, 0.4, 700),
            tht_an=-18,
            phi_an=0,
            hv=120,
            gamma=0.05
        )

  Fermi level autoset

        entry.autoset_efermi(scan_range=[-2, 2], energy_range=[115.2, 115.8])
        print("Energy of the Fermi level = {:.0f} eV".format(entry.efermi))
        print("Energy resolution = {:.0f} meV".format(entry.efermi_fwhm*1000))

        entry.plt_efermi_fit()
[image: plot postprocessing isoscan] [image]

Out:

        Fermi level at 115.4065 eV
        Energy resolution = 143.7 meV (i.e. FWHM of the Gaussian shape which, convoluted with a step function, fits the Fermi edge)
        Photon energy is now set to 120.0065 eV (instead of 120.0000 eV)
        Energy of the Fermi level = 115 eV
        Energy resolution = 144 meV

  Set the k-space for the transformation

        entry.set_kspace(
            tht_p=0.1,
            k_along_slit_p=1.7,
            scan_p=0,
            ks_p=0,
            e_kin_p=114.3,
        )

Out:

        tht_an = -17.979
        scan_type =  deflector
        inn_pot = 14.000
        scans_0 = 0.000
        phi_an = 0.000
        kspace transformation ready

  Post processing on the isoscan:

First of all let's show it:

        entry.isoscan(0).show()
[image: plot postprocessing isoscan] [image]

Out:

        <matplotlib.collections.QuadMesh object at 0x7f3ceb81dfa0>

The second derivative can be obtained using sigma in the definition, which define the extension in points of the Gaussian filter used to then get the second derivative. In this case the sigma is different from zero only on the second element, meaning that the derivative will be performed only along the energy axis: sphinx_gallery_thumbnail_number = 3

        entry.isoscan(0, sigma=[0, 5]).show()
[image: plot postprocessing isoscan] [image]

Out:

        <matplotlib.collections.QuadMesh object at 0x7f3ce9418070>

Only the Gaussian filtered image can be obtained using again sigma but also specifying the order=0, which by default is equal to 2 giving the second derivative as before.:

        entry.isoscan(0, sigma=[3, 5], order=0).show()
[image: plot postprocessing isoscan] [image]

Out:

        <matplotlib.collections.QuadMesh object at 0x7f3ce944a370>

To export it as NXdata class of the nexus format uncomment this line:

        # entry.isoscan(0, 0, sigma=[3, 5], order=0).export_as_nxs('fermimap.nxs')

To export it as igor-pro text file (itx) uncomment this line:

        # entry.isoscan(0, 0, sigma=[3, 5], order=0).export_as_itx('fermimap.itx')

Total running time of the script: ( 0 minutes 1.881 seconds)

  Interpolation on Graphene deflector scan

Simple workflow for the interpolation of data along a generic path in the k-space from its isoenergy cuts. The data are a deflector scan on graphene as simulated from a third nearest neighbor tight binding model. The same workflow can be applied to any tilt-, polar-, deflector- or hv-scan.

Import the "fundamental" python libraries for a generic data analysis:

        import numpy as np
        import matplotlib.pyplot as plt

Instead of loading the file as for example:

        # from navarp.utils import navfile
        # file_name = r"nxarpes_simulated_cone.nxs"
        # entry = navfile.load(file_name)

Here we build the simulated graphene signal with a dedicated function defined just for this purpose:

        from navarp.extras.simulation import get_tbgraphene_deflector

        entry = get_tbgraphene_deflector(
            scans=np.linspace(-5., 20., 91),
            angles=np.linspace(-25, 6, 400),
            ebins=np.linspace(-13, 0.4, 700),
            tht_an=-18,
            phi_an=0,
            hv=120,
            gamma=0.05
        )

  Fermi level autoset

        entry.autoset_efermi(scan_range=[-5, 5], energy_range=[115.2, 115.8])
        print("Energy of the Fermi level = {:.0f} eV".format(entry.efermi))
        print("Energy resolution = {:.0f} meV".format(entry.efermi_fwhm*1000))

        entry.plt_efermi_fit()
[image: plot interpolation gr deflector scan] [image]

Out:

        Fermi level at 115.4094 eV
        Energy resolution = 137.5 meV (i.e. FWHM of the Gaussian shape which, convoluted with a step function, fits the Fermi edge)
        Photon energy is now set to 120.0094 eV (instead of 120.0000 eV)
        Energy of the Fermi level = 115 eV
        Energy resolution = 138 meV

  Check for the Fermi level alignment

        entry.isoscan(scan=0, dscan=0).show(yname='eef')
[image: plot interpolation gr deflector scan] [image]

Out:

        <matplotlib.collections.QuadMesh object at 0x7f3ce9495d90>

  Plotting iso-energetic cut at ekin = efermi

        entry.isoenergy(0, 0.02).show()
[image: plot interpolation gr deflector scan] [image]

Out:

        <matplotlib.collections.QuadMesh object at 0x7f3ce9f502e0>

  Set the k-space for the transformation

        entry.set_kspace(
            tht_p=0.1,
            k_along_slit_p=1.7,
            scan_p=0,
            ks_p=0,
            e_kin_p=114.3,
        )

Out:

        tht_an = -17.979
        scan_type =  deflector
        inn_pot = 14.000
        scans_0 = 0.000
        phi_an = 0.000
        kspace transformation ready

and check the isoenergy at the Fermi level:

        entry.isoenergy(0, 0.02).show()
[image: plot interpolation gr deflector scan] [image]

Out:

        <matplotlib.collections.QuadMesh object at 0x7f3ce9349c70>

  Define the interpolation path points

        kbins = 900
        k_GK = 1.702
        k_pts_xy = np.array([
            [0, 0],
            [k_GK, 0],
            [k_GK*np.cos(np.pi/3), k_GK*np.sin(np.pi/3)+0.05],
            [0, 0]
        ])
        kx_pts = k_pts_xy[:, 0]
        ky_pts = k_pts_xy[:, 1]

        klabels = [
            r'$\Gamma$',
            r'$\mathrm{K}$',
            r'$\mathrm{K}^{\prime}$',
            r'$\Gamma$'
        ]

and show them on the isoenergy at the Dirac point energy:

        entry.isoenergy(-1.1, 0.02).show()
        plt.plot(kx_pts, ky_pts, '-+')
[image: plot interpolation gr deflector scan] [image]

Out:

        [<matplotlib.lines.Line2D object at 0x7f3ce94a5f70>]

Run the interpolation defining an isok:

        isok = entry.isok(kx_pts, ky_pts, klabels)

Show the final results with the executed path on the isoenergy: sphinx_gallery_thumbnail_number = 6

        fig, axs = plt.subplots(1, 2)

        entry.isoenergy(0, 0.02).show(ax=axs[0])

        isok.path_show(axs[0], 'k', 'k', xytext=(8, 8))

        qmesh = isok.show(ax=axs[1])

        fig.tight_layout()
        fig.colorbar(qmesh)
[image: plot interpolation gr deflector scan] [image]

Out:

        <matplotlib.colorbar.Colorbar object at 0x7f3ce943a190>

Total running time of the script: ( 0 minutes 21.724 seconds)

  The old way analysis

Old workflow for analyzing a deflector scan data. This workflow use the all the function in the most explicit way without using any entry method. This is not a recommended workflow but it can help on understanding what it is behind the entry methods.

Import the "fundamental" python libraries for a generic data analysis:

        import numpy as np
        import matplotlib.pyplot as plt

Import the navarp libraries:

        from navarp.utils import navfile, fermilevel, navplt, ktransf, isocut

Load the data from a file:

        file_name = r"nxarpes_simulated_cone.nxs"
        entry = navfile.load(file_name)

Out:

        instrument_name = simulated

Plot a single slice Ex: scan = 0.5

        scan = 0.5
        scan_ind = np.argmin(abs(entry.scans-scan))
        isoscan = isocut.maps_sum(scan, 0, entry.scans, entry.data)

        qmisoscan = navplt.pimage(
            entry.angles, entry.energies, isoscan, cmap='binary', style='tht_ekin')
[image: plot cone old way] [image]

Fermi level determination

        energy_range = [93.8, 94.3]
        data_sum = np.sum(entry.data, axis=tuple([0, 1]))
        popt = fermilevel.fit_efermi(entry.energies, data_sum, energy_range)
        efermi, res = popt[0], popt[1]*4

        fig, axfit = plt.subplots(1)
        axfit.axvline(popt[0])
        axfit.plot(entry.energies, data_sum, '+')
        axfit.plot(entry.energies, fermilevel.fermi_fun(entry.energies, *popt), 'r-')
        axfit.set_xlabel(r'Kinetic Energy (eV)')
        axfit.set_xlim(energy_range)
        dvis = data_sum[
            (entry.energies >= energy_range[0]) &
            (entry.energies <= energy_range[1])
        ]
        dvis_min = dvis.min()
        dvis_max = dvis.max()
        dvis_delta = dvis_max - dvis_min
        axfit.set_ylim(
            dvis_min - dvis_delta*0.05,
            dvis_max + dvis_delta*0.05
        )

        # Overwrite hv from the derived fermi level and the known work function
        hv_from_file = np.copy(entry.hv)
        entry.hv = np.array([efermi + entry.analyzer.work_fun])
        print(
            "hv = {:g} eV (from the file was {:g})".format(
                np.squeeze(entry.hv), np.squeeze(hv_from_file))
        )
        print("Energy resolution = {:.0f} meV".format(res*1000))
[image: plot cone old way] [image]

Out:

        /build/navarp-uNOccK/navarp-1.0.0/navarp/utils/fermilevel.py:67: RuntimeWarning: divide by zero encountered in true_divide
          ddata_s_denergies = ddata_s_denergies/np.abs(data_sum)
        /build/navarp-uNOccK/navarp-1.0.0/navarp/utils/fermilevel.py:67: RuntimeWarning: invalid value encountered in true_divide
          ddata_s_denergies = ddata_s_denergies/np.abs(data_sum)
        hv = 98.4881 eV (from the file was 100)
        Energy resolution = 67 meV

Plot a single slice with fermi level alignment Ex: scan = 0.5

        scan = 0.5
        scan_ind = np.argmin(abs(entry.scans-scan))
        isoscan = isocut.maps_sum(scan, 0, entry.scans, entry.data)

        qmisoscan = navplt.pimage(
            entry.angles, entry.energies-efermi, isoscan,
            cmap='magma_r', style='tht_eef')
[image: plot cone old way] [image]

Plotting iso-energetic cut Ex: isoenergy cut at ekin = efermi

        ekin = efermi
        dekin = 0.005
        isoev = isocut.maps_sum(ekin, dekin, entry.energies, entry.data)
        qmisoev = navplt.pimage(
            entry.angles, entry.scans, isoev,
            cmap='magma_r', style='tht_phi', cmapscale='linear')
[image: plot cone old way] [image]

Total running time of the script: ( 0 minutes 1.861 seconds)

  Graphene deflector scan

Simple workflow for analyzing a deflector scan data of graphene as simulated from a third nearest neighbor tight binding model. The same workflow can be applied to any tilt-, polar- or deflector-scan.

Import the "fundamental" python libraries for a generic data analysis:

        import numpy as np
        import matplotlib.pyplot as plt

Instead of loading the file as for example:

        # from navarp.utils import navfile
        # file_name = r"nxarpes_simulated_cone.nxs"
        # entry = navfile.load(file_name)

Here we build the simulated graphene signal with a dedicated function defined just for this purpose:

        from navarp.extras.simulation import get_tbgraphene_deflector

        entry = get_tbgraphene_deflector(
            scans=np.linspace(-5., 5., 51),
            angles=np.linspace(-7, 7, 300),
            ebins=np.linspace(-3.3, 0.4, 450),
            tht_an=-18,
            phi_an=0,
            hv=120
        )

  Plot a single analyzer image at scan = 0

First I have to extract the isoscan from the entry, so I use the isoscan method of entry:

        iso0 = entry.isoscan(scan=0, dscan=0)

Then to plot it using the 'show' method of the extracted iso0:

        iso0.show(yname='ekin')
[image: plot gr deflector scan] [image]

Out:

        <matplotlib.collections.QuadMesh object at 0x7f3ce94162b0>

Or by string concatenation, directly as:

        entry.isoscan(scan=0, dscan=0).show(yname='ekin')
[image: plot gr deflector scan] [image]

Out:

        <matplotlib.collections.QuadMesh object at 0x7f3ce917f160>

  Fermi level determination

The initial guess for the binding energy is: ebins = ekins - (hv - work_fun). However, the better way is to proper set the Fermi level first and then derives everything form it. The Fermi level can be given directly as a value using:

        entry.set_efermi(114.8)

Or it can be detected from a fit using the method autoset_efermi. In both cases the binding energy and the photon energy will be updated consistently. Note that the work function depends on the beamline or laboratory. If not specified is 4.5 eV.

        entry.autoset_efermi(scan_range=[-5, 5], energy_range=[115.2, 115.8])
        print("Energy of the Fermi level = {:.0f} eV".format(entry.efermi))
        print("Energy resolution = {:.0f} meV".format(entry.efermi_fwhm*1000))

        entry.plt_efermi_fit()
[image: plot gr deflector scan] [image]

Out:

        Fermi level at 115.4016 eV
        Energy resolution = 56.6 meV (i.e. FWHM of the Gaussian shape which, convoluted with a step function, fits the Fermi edge)
        Photon energy is now set to 120.0016 eV (instead of 120.0000 eV)
        Energy of the Fermi level = 115 eV
        Energy resolution = 57 meV

  Plot a single analyzer image at scan = 0 with the Fermi level aligned

        entry.isoscan(scan=0, dscan=0).show(yname='eef')
[image: plot gr deflector scan] [image]

Out:

        <matplotlib.collections.QuadMesh object at 0x7f3ce936e100>

  Plotting iso-energetic cut at ekin = efermi

        entry.isoenergy(0).show()
[image: plot gr deflector scan] [image]

Out:

        <matplotlib.collections.QuadMesh object at 0x7f3ce93f4d00>

  Plotting in the reciprocal space (k-space)

I have to define first the reference point to be used for the transformation. Meaning a point in the angular space which I know it correspond to a particular point in the k-space. In this case the graphene Dirac-point which is at ekin = 114.3 eV, in angle is at (tht_p, phi_p) = (0.1, 0) and in the k-space has to correspond to (kx, ky) = (1.7, 0).

        entry.set_kspace(
            tht_p=0.1,
            k_along_slit_p=1.7,
            scan_p=0,
            ks_p=0,
            e_kin_p=114.3,
        )

Out:

        tht_an = -17.979
        scan_type =  deflector
        inn_pot = 14.000
        scans_0 = 0.000
        phi_an = 0.000
        kspace transformation ready

Once it is set, all the isoscan or isoenergy extracted from the entry will now get their proper k-space scales:

        entry.isoscan(0).show()
[image: plot gr deflector scan] [image]

Out:

        <matplotlib.collections.QuadMesh object at 0x7f3ce944da30>

        entry.isoenergy(0).show()
[image: plot gr deflector scan] [image]

Out:

        <matplotlib.collections.QuadMesh object at 0x7f3ce92c0c40>

I can also place together in a single figure different images: sphinx_gallery_thumbnail_number = 5

        fig, axs = plt.subplots(1, 2)

        entry.isoscan(0).show(ax=axs[0])
        entry.isoenergy(0).show(ax=axs[1])

        plt.tight_layout()
[image: plot gr deflector scan] [image]

  Many other options:

        fig, axs = plt.subplots(2, 2)

        scan = 0.8
        dscan = 0.05
        ebin = -0.4
        debin = 0.05

        entry.isoscan(scan, dscan).show(ax=axs[0][0], xname='tht', yname='ekin')
        entry.isoscan(scan, dscan).show(ax=axs[0][1], cmap='binary')

        axs[0][0].axhline(ebin-debin+entry.efermi)
        axs[0][0].axhline(ebin+debin+entry.efermi)

        axs[0][1].axhline(ebin-debin)
        axs[0][1].axhline(ebin+debin)

        entry.isoenergy(ebin, debin).show(
            ax=axs[1][0], xname='tht', yname='phi', cmap='cividis')
        entry.isoenergy(ebin, debin).show(
            ax=axs[1][1], cmap='magma', cmapscale='log')

        axs[1][0].axhline(scan, color='w')

        x_note = 0.05
        y_note = 0.98

        for ax in axs[0][:]:
            ax.annotate(
                "$scan \: = \: {} \pm {} \; ^\circ$".format(scan, dscan),
                (x_note, y_note),
                xycoords='axes fraction',
                size=8, rotation=0, ha="left", va="top",
                bbox=dict(
                    boxstyle="round", fc='w', alpha=0.65, edgecolor='None', pad=0.05
                )
            )

        for ax in axs[1][:]:
            ax.annotate(
                "$E-E_F \: = \: {} \pm {} \; eV$".format(ebin, debin),
                (x_note, y_note),
                xycoords='axes fraction',
                size=8, rotation=0, ha="left", va="top",
                bbox=dict(
                    boxstyle="round", fc='w', alpha=0.65, edgecolor='None', pad=0.05
                )
            )

        plt.tight_layout()
[image: plot gr deflector scan] [image]

Total running time of the script: ( 0 minutes 5.859 seconds)

  Graphene hv scan

Simple workflow for analyzing a photon energy scan data of graphene as simulated from a third nearest neighbor tight binding model. The same workflow can be applied to any photon energy scan.

Import the "fundamental" python libraries for a generic data analysis:

        import numpy as np
        import matplotlib.pyplot as plt

Instead of loading the file as for example:

        # from navarp.utils import navfile
        # file_name = r"nxarpes_simulated_cone.nxs"
        # entry = navfile.load(file_name)

Here we build the simulated graphene signal with a dedicated function defined just for this purpose:

        from navarp.extras.simulation import get_tbgraphene_hv

        entry = get_tbgraphene_hv(
            scans=np.arange(90, 150, 2),
            angles=np.linspace(-7, 7, 300),
            ebins=np.linspace(-3.3, 0.4, 450),
            tht_an=-18,
        )

  Plot a single analyzer image at scan = 90

First I have to extract the isoscan from the entry, so I use the isoscan method of entry:

        iso0 = entry.isoscan(scan=90)

Then to plot it using the 'show' method of the extracted iso0:

        iso0.show(yname='ekin')
[image: plot gr hv scan] [image]

Out:

        <matplotlib.collections.QuadMesh object at 0x7f3ce8f78be0>

Or by string concatenation, directly as:

        entry.isoscan(scan=90).show(yname='ekin')
[image: plot gr hv scan] [image]

Out:

        <matplotlib.collections.QuadMesh object at 0x7f3ce9164be0>

  Fermi level determination

The initial guess for the binding energy is: ebins = ekins - (hv - work_fun). However, the better way is to proper set the Fermi level first and then derives everything form it. In this case the Fermi level kinetic energy is changing along the scan since it is a photon energy scan. So to set the Fermi level I have to give an array of values corresponding to each photon energy. By definition I can give:

        efermis = entry.hv - entry.analyzer.work_fun
        entry.set_efermi(efermis)

Or I can use a method for its detection, but in this case, it is important to give a proper energy range for each photon energy. For example for each photon a good range is within 0.4 eV around the photon energy minus the analyzer work function:

        energy_range = (
            (entry.hv[:, None] - entry.analyzer.work_fun) +
            np.array([-0.4, 0.4])[None, :])

        entry.autoset_efermi(energy_range=energy_range)

Out:

        scan(eV)  efermi(eV)  FWHM(meV)  new hv(eV)
        90.0000  85.4000  58.5  90.0000
        92.0000  87.3999  58.8  91.9999
        94.0000  89.3995  59.6  93.9995
        96.0000  91.4000  58.7  96.0000
        98.0000  93.4004  58.5  98.0004
        100.0000  95.3997  59.6  99.9997
        102.0000  97.4000  59.1  102.0000
        104.0000  99.3997  60.1  103.9997
        106.0000  101.4007  58.5  106.0007
        108.0000  103.4006  58.5  108.0006
        110.0000  105.4002  59.4  110.0002
        112.0000  107.4006  57.4  112.0006
        114.0000  109.4006  58.9  114.0006
        116.0000  111.4004  58.9  116.0004
        118.0000  113.3998  59.5  117.9998
        120.0000  115.4002  59.4  120.0002
        122.0000  117.4009  57.7  122.0009
        124.0000  119.4004  58.0  124.0004
        126.0000  121.4001  58.9  126.0001
        128.0000  123.4002  59.0  128.0002
        130.0000  125.4004  58.6  130.0004
        132.0000  127.4006  58.9  132.0006
        134.0000  129.4005  57.3  134.0005
        136.0000  131.4002  59.1  136.0002
        138.0000  133.4001  58.7  138.0001
        140.0000  135.4000  58.1  140.0000
        142.0000  137.4007  57.6  142.0007
        144.0000  139.4005  58.5  144.0005
        146.0000  141.4004  57.8  146.0004
        148.0000  143.4001  60.0  148.0001

In both cases the binding energy and the photon energy will be updated consistently. Note that the work function depends on the beamline or laboratory. If not specified is 4.5 eV.

To check the Fermi level detection I can have a look on each photon energy. Here I show only the first 10 photon energies:

        for scan_i in range(10):
            print("hv = {} eV,  E_F = {:.0f} eV,  Res = {:.0f} meV".format(
                entry.hv[scan_i],
                entry.efermi[scan_i],
                entry.efermi_fwhm[scan_i]*1000
            ))
            entry.plt_efermi_fit(scan_i=scan_i)

    •   [image: plot gr hv scan] [image]

    •   [image: plot gr hv scan] [image]

    •   [image: plot gr hv scan] [image]

    •   [image: plot gr hv scan] [image]

    •   [image: plot gr hv scan] [image]

    •   [image: plot gr hv scan] [image]

    •   [image: plot gr hv scan] [image]

    •   [image: plot gr hv scan] [image]

    •   [image: plot gr hv scan] [image]

    •   [image: plot gr hv scan] [image]

Out:

        hv = 89.99998283633579 eV,  E_F = 85 eV,  Res = 59 meV
        hv = 91.9998627668236 eV,  E_F = 87 eV,  Res = 59 meV
        hv = 93.99949668680733 eV,  E_F = 89 eV,  Res = 60 meV
        hv = 96.00002470385328 eV,  E_F = 91 eV,  Res = 59 meV
        hv = 98.00040009140567 eV,  E_F = 93 eV,  Res = 59 meV
        hv = 99.99973916893491 eV,  E_F = 95 eV,  Res = 60 meV
        hv = 101.99995912743783 eV,  E_F = 97 eV,  Res = 59 meV
        hv = 103.99974209235835 eV,  E_F = 99 eV,  Res = 60 meV
        hv = 106.00073875670226 eV,  E_F = 101 eV,  Res = 58 meV
        hv = 108.0006350982113 eV,  E_F = 103 eV,  Res = 59 meV

  Plot a single analyzer image at scan = 110 with the Fermi level aligned

        entry.isoscan(scan=110).show(yname='eef')
[image: plot gr hv scan] [image]

Out:

        <matplotlib.collections.QuadMesh object at 0x7f3ce910a3a0>

  Plotting iso-energetic cut at ekin = efermi

        entry.isoenergy(0).show()
[image: plot gr hv scan] [image]

Out:

        <matplotlib.collections.QuadMesh object at 0x7f3ce92906d0>

  Plotting in the reciprocal space (k-space)

I have to define first the reference point to be used for the transformation. Meaning a point in the angular space which I know it correspond to a particular point in the k-space. In this case the graphene Dirac-point is for hv = 120 is at ekin = 114.3 eV and tht_p = -0.6 (see the figure below), which in the k-space has to correspond to kx = 1.7.

        hv_p = 120

        entry.isoscan(scan=hv_p, dscan=0).show(yname='ekin', cmap='cividis')

        tht_p = -0.6
        e_kin_p = 114.3
        plt.axvline(tht_p, color='w')
        plt.axhline(e_kin_p, color='w')

        entry.set_kspace(
            tht_p=tht_p,
            k_along_slit_p=1.7,
            scan_p=0,
            ks_p=0,
            e_kin_p=e_kin_p,
            inn_pot=14,
            p_hv=True,
            hv_p=hv_p,
        )
[image: plot gr hv scan] [image]

Out:

        tht_an = -18.040
        scan_type =  hv
        inn_pot = 14.000
        phi_an = 0.000
        k_perp_slit_for_kz = 0.000
        kspace transformation ready

Once it is set, all the isoscan or iscoenergy extracted from the entry will now get their proper k-space scales:

        entry.isoscan(120).show()
[image: plot gr hv scan] [image]

Out:

        <matplotlib.collections.QuadMesh object at 0x7f3ce9244340>

sphinx_gallery_thumbnail_number = 17

        entry.isoenergy(0).show(cmap='cividis')
[image: plot gr hv scan] [image]

Out:

        <matplotlib.collections.QuadMesh object at 0x7f3ce9324b20>

I can also place together in a single figure different images:

        fig, axs = plt.subplots(1, 2)

        entry.isoscan(120).show(ax=axs[0])
        entry.isoenergy(-0.9).show(ax=axs[1])

        plt.tight_layout()
[image: plot gr hv scan] [image]

  Many other options:

        fig, axs = plt.subplots(2, 2)

        scan = 110
        dscan = 0
        ebin = -0.9
        debin = 0.01

        entry.isoscan(scan, dscan).show(ax=axs[0][0], xname='tht', yname='ekin')
        entry.isoscan(scan, dscan).show(ax=axs[0][1], cmap='binary')

        axs[0][1].axhline(ebin-debin)
        axs[0][1].axhline(ebin+debin)

        entry.isoenergy(ebin, debin).show(
            ax=axs[1][0], xname='tht', yname='phi', cmap='cividis')
        entry.isoenergy(ebin, debin).show(
            ax=axs[1][1], cmap='magma', cmapscale='log')

        axs[1][0].axhline(scan, color='w', ls='--')
        axs[0][1].axvline(1.7, color='r', ls='--')
        axs[1][1].axvline(1.7, color='r', ls='--')

        x_note = 0.05
        y_note = 0.98

        for ax in axs[0][:]:
            ax.annotate(
                "$scan \: = \: {} eV$".format(scan, dscan),
                (x_note, y_note),
                xycoords='axes fraction',
                size=8, rotation=0, ha="left", va="top",
                bbox=dict(
                    boxstyle="round", fc='w', alpha=0.65, edgecolor='None', pad=0.05
                )
            )

        for ax in axs[1][:]:
            ax.annotate(
                "$E-E_F \: = \: {} \pm {} \; eV$".format(ebin, debin),
                (x_note, y_note),
                xycoords='axes fraction',
                size=8, rotation=0, ha="left", va="top",
                bbox=dict(
                    boxstyle="round", fc='w', alpha=0.65, edgecolor='None', pad=0.05
                )
            )

        plt.tight_layout()
[image: plot gr hv scan] [image]

Total running time of the script: ( 0 minutes 7.611 seconds)

API REFERENCE

    •   genindex

    •   modindex

    •   search

PROJECT ON GITLAB

    •   Source repository

    •   Issue tracker

AUTHOR
Federico Bisti

COPYRIGHT
2021, Federico Bisti