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 0x7f26fb91dad0>
Or by string concatenation, directly as:
entry.isoscan(scan=90).show(yname='ekin')

Out:
<matplotlib.collections.QuadMesh object at 0x7f26fba41c90>
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 85.4001 58.5 90.0001
92.0000 87.4002 58.8 92.0002
94.0000 89.4007 57.5 94.0007
96.0000 91.4003 58.2 96.0003
98.0000 93.4006 58.7 98.0006
100.0000 95.4000 59.3 100.0000
102.0000 97.4002 58.4 102.0002
104.0000 99.4003 59.0 104.0003
106.0000 101.4001 59.8 106.0001
108.0000 103.4003 58.2 108.0003
110.0000 105.3996 59.8 109.9996
112.0000 107.4007 57.5 112.0007
114.0000 109.4003 59.1 114.0003
116.0000 111.4001 60.0 116.0001
118.0000 113.4002 59.3 118.0002
120.0000 115.4009 56.4 120.0009
122.0000 117.4008 58.6 122.0008
124.0000 119.4003 59.7 124.0003
126.0000 121.4007 57.4 126.0007
128.0000 123.4002 58.9 128.0002
130.0000 125.4005 59.5 130.0005
132.0000 127.4002 58.4 132.0002
134.0000 129.4001 58.6 134.0001
136.0000 131.4004 58.3 136.0004
138.0000 133.4007 58.7 138.0007
140.0000 135.4001 59.3 140.0001
142.0000 137.4001 60.0 142.0001
144.0000 139.4007 57.5 144.0007
146.0000 141.4005 58.0 146.0005
148.0000 143.4001 57.8 148.0001
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)
Out:
hv = 90.00009398730614 eV, E_F = 85 eV, Res = 59 meV
hv = 92.00017109713805 eV, E_F = 87 eV, Res = 59 meV
hv = 94.0006961951023 eV, E_F = 89 eV, Res = 58 meV
hv = 96.0002643721981 eV, E_F = 91 eV, Res = 58 meV
hv = 98.00061764174058 eV, E_F = 93 eV, Res = 59 meV
hv = 100.00003812165316 eV, E_F = 95 eV, Res = 59 meV
hv = 102.0002487530854 eV, E_F = 97 eV, Res = 58 meV
hv = 104.00029090971142 eV, E_F = 99 eV, Res = 59 meV
hv = 106.0001249151925 eV, E_F = 101 eV, Res = 60 meV
hv = 108.00028568702929 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 0x7f26fb502c90>
Plotting iso-energetic cut at ekin = efermi¶
entry.isoenergy(0).show()

Out:
<matplotlib.collections.QuadMesh object at 0x7f26fb840a50>
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 0x7f26fb2fad90>
sphinx_gallery_thumbnail_number = 17
entry.isoenergy(0).show(cmap='cividis')

Out:
<matplotlib.collections.QuadMesh object at 0x7f26fb15b7d0>
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 3.078 seconds)