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 0x7f88bf18f3d0>
Or by string concatenation, directly as:
entry.isoscan(scan=90).show(yname='ekin')

Out:
<matplotlib.collections.QuadMesh object at 0x7f88bff3cdd0>
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 85.4002 59.1 90.0002
92.0000 87.4002 58.6 92.0002
94.0000 89.3999 59.4 93.9999
96.0000 91.3999 58.3 95.9999
98.0000 93.4010 57.1 98.0010
100.0000 95.4004 59.3 100.0004
102.0000 97.4003 58.6 102.0003
104.0000 99.4007 58.2 104.0007
106.0000 101.4005 58.7 106.0005
108.0000 103.4001 57.8 108.0001
110.0000 105.4000 58.9 110.0000
112.0000 107.4004 59.0 112.0004
114.0000 109.4004 59.4 114.0004
116.0000 111.4007 57.0 116.0007
118.0000 113.4007 58.0 118.0007
120.0000 115.3997 60.7 119.9997
122.0000 117.4001 59.6 122.0001
124.0000 119.4002 58.8 124.0002
126.0000 121.3999 59.4 125.9999
128.0000 123.4003 58.0 128.0003
130.0000 125.4008 57.6 130.0008
132.0000 127.4000 59.0 132.0000
134.0000 129.4007 58.7 134.0007
136.0000 131.4003 58.8 136.0003
138.0000 133.4005 59.1 138.0005
140.0000 135.4001 59.3 140.0001
142.0000 137.3999 60.4 141.9999
144.0000 139.4001 59.1 144.0001
146.0000 141.4004 58.0 146.0004
148.0000 143.3999 59.5 147.9999
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 = 90.00020111984664 eV, E_F = 85 eV, Res = 59 meV
hv = 92.00021531733567 eV, E_F = 87 eV, Res = 59 meV
hv = 93.99992288529215 eV, E_F = 89 eV, Res = 59 meV
hv = 95.99992708456995 eV, E_F = 91 eV, Res = 58 meV
hv = 98.00095379279354 eV, E_F = 93 eV, Res = 57 meV
hv = 100.00039070510493 eV, E_F = 95 eV, Res = 59 meV
hv = 102.00025876040007 eV, E_F = 97 eV, Res = 59 meV
hv = 104.00070771313781 eV, E_F = 99 eV, Res = 58 meV
hv = 106.00049817478725 eV, E_F = 101 eV, Res = 59 meV
hv = 108.00010710778888 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 0x7f88bf974810>
Plotting iso-energetic cut at ekin = efermi¶
entry.isoenergy(0).show()

Out:
<matplotlib.collections.QuadMesh object at 0x7f88bf95e250>
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 0x7f88bfe65590>
sphinx_gallery_thumbnail_number = 17
entry.isoenergy(0).show(cmap='cividis')

Out:
<matplotlib.collections.QuadMesh object at 0x7f88bf9f5950>
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 6.015 seconds)