Note
Go to the end 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')

<matplotlib.collections.QuadMesh object at 0x7904007df890>
Or by string concatenation, directly as:
entry.isoscan(scan=90).show(yname='ekin')

<matplotlib.collections.QuadMesh object at 0x7903ffd429c0>
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)
scan(eV) efermi(eV) FWHM(meV) new hv(eV)
90.0000 85.3999 58.9 89.9999
92.0000 87.3999 59.8 91.9999
94.0000 89.3996 60.3 93.9996
96.0000 91.3994 59.8 95.9994
98.0000 93.4000 59.8 98.0000
100.0000 95.4009 58.3 100.0009
102.0000 97.4003 59.0 102.0003
104.0000 99.4004 58.8 104.0004
106.0000 101.3999 58.4 105.9999
108.0000 103.4001 59.1 108.0001
110.0000 105.4000 59.4 110.0000
112.0000 107.4003 57.4 112.0003
114.0000 109.4005 58.5 114.0005
116.0000 111.3996 59.4 115.9996
118.0000 113.4007 58.2 118.0007
120.0000 115.4000 60.6 120.0000
122.0000 117.4009 57.8 122.0009
124.0000 119.4001 58.9 124.0001
126.0000 121.4009 56.8 126.0009
128.0000 123.4005 58.6 128.0005
130.0000 125.4000 59.7 130.0000
132.0000 127.4001 58.2 132.0001
134.0000 129.4002 59.6 134.0002
136.0000 131.4008 58.1 136.0008
138.0000 133.4009 57.3 138.0009
140.0000 135.4005 58.4 140.0005
142.0000 137.3998 60.4 141.9998
144.0000 139.4004 58.7 144.0004
146.0000 141.4002 59.8 146.0002
148.0000 143.4004 58.4 148.0004
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)
hv = 89.99987021446691 eV, E_F = 85 eV, Res = 59 meV
hv = 91.99993757671932 eV, E_F = 87 eV, Res = 60 meV
hv = 93.99958077616644 eV, E_F = 89 eV, Res = 60 meV
hv = 95.99936286034557 eV, E_F = 91 eV, Res = 60 meV
hv = 98.0000077076337 eV, E_F = 93 eV, Res = 60 meV
hv = 100.00089427679168 eV, E_F = 95 eV, Res = 58 meV
hv = 102.00032650103083 eV, E_F = 97 eV, Res = 59 meV
hv = 104.00040248890004 eV, E_F = 99 eV, Res = 59 meV
hv = 105.9999013504625 eV, E_F = 101 eV, Res = 58 meV
hv = 108.00007668122778 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')

<matplotlib.collections.QuadMesh object at 0x7903ffcab8c0>
Plotting iso-energetic cut at ekin = efermi¶
entry.isoenergy(0).show()

<matplotlib.collections.QuadMesh object at 0x7903ffa2a2d0>
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,
)

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()

<matplotlib.collections.QuadMesh object at 0x7903ffa2ac90>
sphinx_gallery_thumbnail_number = 17
entry.isoenergy(0).show(cmap='cividis')

<matplotlib.collections.QuadMesh object at 0x7903ff916ba0>
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()

/workspace/repo-tools/run-upstream-build/navarp-1.6.0/examples/plot_gr_hv_scan.py:29: SyntaxWarning: invalid escape sequence '\:'
entry = get_tbgraphene_hv(
/workspace/repo-tools/run-upstream-build/navarp-1.6.0/examples/plot_gr_hv_scan.py:40: SyntaxWarning: invalid escape sequence '\:'
# method of entry:
Total running time of the script: (0 minutes 2.158 seconds)