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')

Out:

<matplotlib.collections.QuadMesh object at 0x7f9c59707500>

Or by string concatenation, directly as:

entry.isoscan(scan=90).show(yname='ekin')

Out:

<matplotlib.collections.QuadMesh object at 0x7f9c59358080>

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  85.4004  58.5  90.0004
92.0000  87.4003  59.1  92.0003
94.0000  89.4006  58.4  94.0006
96.0000  91.4006  56.9  96.0006
98.0000  93.4000  58.8  98.0000
100.0000  95.4003  58.8  100.0003
102.0000  97.4003  58.3  102.0003
104.0000  99.4005  58.7  104.0005
106.0000  101.4003  58.6  106.0003
108.0000  103.4006  59.1  108.0006
110.0000  105.4002  58.3  110.0002
112.0000  107.4002  58.2  112.0002
114.0000  109.4006  57.4  114.0006
116.0000  111.4001  59.8  116.0001
118.0000  113.4004  58.8  118.0004
120.0000  115.4003  58.6  120.0003
122.0000  117.4005  58.8  122.0005
124.0000  119.4000  57.9  124.0000
126.0000  121.3995  59.6  125.9995
128.0000  123.4003  58.8  128.0003
130.0000  125.4003  58.9  130.0003
132.0000  127.4002  59.2  132.0002
134.0000  129.4008  57.3  134.0008
136.0000  131.4003  58.9  136.0003
138.0000  133.4004  58.5  138.0004
140.0000  135.3998  60.1  139.9998
142.0000  137.4002  58.1  142.0002
144.0000  139.4004  58.9  144.0004
146.0000  141.4004  57.9  146.0004
148.0000  143.4008  58.6  148.0008

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)

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Out:

hv = 90.00038659513478 eV,  E_F = 85 eV,  Res = 59 meV
hv = 92.00031533731607 eV,  E_F = 87 eV,  Res = 59 meV
hv = 94.00057600000557 eV,  E_F = 89 eV,  Res = 58 meV
hv = 96.00059645032866 eV,  E_F = 91 eV,  Res = 57 meV
hv = 97.99999550491164 eV,  E_F = 93 eV,  Res = 59 meV
hv = 100.00031261347864 eV,  E_F = 95 eV,  Res = 59 meV
hv = 102.00026348727914 eV,  E_F = 97 eV,  Res = 58 meV
hv = 104.00050758074667 eV,  E_F = 99 eV,  Res = 59 meV
hv = 106.00028708268954 eV,  E_F = 101 eV,  Res = 59 meV
hv = 108.00059887401729 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')

Out:

<matplotlib.collections.QuadMesh object at 0x7f9c58f22ff0>

Plotting iso-energetic cut at ekin = efermi

entry.isoenergy(0).show()

Out:

<matplotlib.collections.QuadMesh object at 0x7f9c58cd8dd0>

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,
)

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()

Out:

<matplotlib.collections.QuadMesh object at 0x7f9c58da9c70>

sphinx_gallery_thumbnail_number = 17

entry.isoenergy(0).show(cmap='cividis')

Out:

<matplotlib.collections.QuadMesh object at 0x7f9c58c39d30>

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()

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()

Out:

/build/navarp-FXpRAX/navarp-1.6.0/examples/plot_gr_hv_scan.py:29: SyntaxWarning: invalid escape sequence '\:'
  entry = get_tbgraphene_hv(
/build/navarp-FXpRAX/navarp-1.6.0/examples/plot_gr_hv_scan.py:40: SyntaxWarning: invalid escape sequence '\:'
  # method of entry: