Note

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# Postprocessing on isoscan

Example of isoscan postprocessing procedures. 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()

    Fermi level at 115.4071 eV
    Energy resolution = 142.0 meV (i.e. FWHM of the Gaussian shape which, convoluted with a step function, fits the Fermi edge)
    Photon energy is now set to 120.0071 eV (instead of 120.0000 eV)
    Energy of the Fermi level = 115 eV
    Energy resolution = 142 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,
    )

    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()

    <matplotlib.collections.QuadMesh object at 0x780946e4a210>

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=[3, 5]).show()

    <matplotlib.collections.QuadMesh object at 0x780946f18410>

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=[10, 10], order=0).show()

    <matplotlib.collections.QuadMesh object at 0x78094700a710>

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 1.021 seconds)

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