Note
Click here to download the full example code
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 0x7f750c6c03d0>
Or by string concatenation, directly as:
entry.isoscan(scan=90).show(yname='ekin')

Out:
<matplotlib.collections.QuadMesh object at 0x7f750c62be80>
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 85.3998 59.4 89.9998
92.0000 87.4002 58.9 92.0002
94.0000 89.4001 58.1 94.0001
96.0000 91.4001 59.1 96.0001
98.0000 93.4002 59.1 98.0002
100.0000 95.4000 59.0 100.0000
102.0000 97.4004 58.4 102.0004
104.0000 99.4009 57.4 104.0009
106.0000 101.4003 58.4 106.0003
108.0000 103.4003 58.4 108.0003
110.0000 105.4003 59.6 110.0003
112.0000 107.4004 59.2 112.0004
114.0000 109.4002 58.5 114.0002
116.0000 111.4004 58.4 116.0004
118.0000 113.4004 58.7 118.0004
120.0000 115.4006 58.1 120.0006
122.0000 117.4005 58.1 122.0005
124.0000 119.4000 58.9 124.0000
126.0000 121.4006 58.5 126.0006
128.0000 123.4007 57.9 128.0007
130.0000 125.4000 59.8 130.0000
132.0000 127.4003 58.9 132.0003
134.0000 129.3997 60.2 133.9997
136.0000 131.4004 58.7 136.0004
138.0000 133.4003 59.0 138.0003
140.0000 135.3997 60.8 139.9997
142.0000 137.4002 59.5 142.0002
144.0000 139.4000 58.7 144.0000
146.0000 141.4006 57.8 146.0006
148.0000 143.4002 59.2 148.0002
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)
Out:
hv = 89.99983261915811 eV, E_F = 85 eV, Res = 59 meV
hv = 92.00024905844872 eV, E_F = 87 eV, Res = 59 meV
hv = 94.00008301988478 eV, E_F = 89 eV, Res = 58 meV
hv = 96.0001286240135 eV, E_F = 91 eV, Res = 59 meV
hv = 98.00019431692235 eV, E_F = 93 eV, Res = 59 meV
hv = 100.00001779788433 eV, E_F = 95 eV, Res = 59 meV
hv = 102.00044424253366 eV, E_F = 97 eV, Res = 58 meV
hv = 104.0009166329101 eV, E_F = 99 eV, Res = 57 meV
hv = 106.00031664427812 eV, E_F = 101 eV, Res = 58 meV
hv = 108.0002899714147 eV, E_F = 103 eV, Res = 58 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 0x7f750c0e0100>
Plotting iso-energetic cut at ekin = efermi¶
entry.isoenergy(0).show()

Out:
<matplotlib.collections.QuadMesh object at 0x7f750c621f70>
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 0x7f750c3d25e0>
sphinx_gallery_thumbnail_number = 17
entry.isoenergy(0).show(cmap='cividis')

Out:
<matplotlib.collections.QuadMesh object at 0x7f750c422580>
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()

Total running time of the script: ( 0 minutes 4.843 seconds)