In [1]:
import numpy as np
import matplotlib.pyplot as plt
from IPython.display import display
import riip
ri = riip.RiiDataFrame()
plt.style.use('seaborn-notebook')
plot_params = {
'figure.figsize': [8.0, 8.0],
'axes.labelsize': 'xx-large',
'xtick.labelsize': 'xx-large',
'ytick.labelsize': 'xx-large',
'legend.fontsize': 'xx-large',
# 'legend.handlelength': 2.0,
}
plt.rcParams.update(plot_params)
props = plt.rcParams['axes.prop_cycle']
In [2]:
ri.search('Ag')
Out[2]:
shelf
book
page
formula
tabulated
wl_min
wl_max
id
0
main
Ag (Experimental data)
Johnson
0
nk
0.187900
1.937000
1
main
Ag (Experimental data)
McPeak
0
nk
0.300000
1.700000
2
main
Ag (Experimental data)
Babar
0
nk
0.206600
12.400000
3
main
Ag (Experimental data)
Werner
0
nk
0.017586
2.479684
4
main
Ag (Experimental data)
Stahrenberg
0
nk
0.127820
0.495940
5
main
Ag (Experimental data)
Windt
0
nk
0.002360
0.121570
6
main
Ag (Experimental data)
Hagemann
0
nk
0.000002
248.000000
7
main
Ag (Models and simulations)
Rakic-BB
0
nk
0.247970
12.398000
8
main
Ag (Models and simulations)
Rakic-LD
0
nk
0.247970
12.398000
9
main
Ag (Models and simulations)
Werner-DFT
0
nk
0.017586
2.479684
88
main
AgBr
Schröter
8
f
0.495000
0.670000
126
main
AgCl
Tilton
4
f
0.578000
20.600000
422
main
Ag3AsS3
Hulme-o
4
f
0.630000
4.600000
423
main
Ag3AsS3
Hulme-e
4
f
0.590000
4.600000
424
main
AgGaS2
Takaoka-o
4
f
0.580000
10.590000
425
main
AgGaS2
Takaoka-e
4
f
0.580000
10.590000
426
main
AgGaS2
Kato-o
4
f
0.540000
12.900000
427
main
AgGaS2
Kato-e
4
f
0.540000
12.900000
428
main
AgGaS2
Boyd-o
2
f
0.490000
12.000000
429
main
AgGaS2
Boyd-e
2
f
0.490000
12.000000
483
main
AgGaSe2
Harasaki-o
4
f
0.810000
16.000000
484
main
AgGaSe2
Harasaki-e
4
f
0.810000
16.000000
485
main
AgGaSe2
Boyd-o
2
f
0.725000
13.500000
486
main
AgGaSe2
Boyd-e
2
f
0.725000
13.500000
1660
other
Au-Ag
Rioux-Au100Ag0
0
nk
0.270000
1.200000
1661
other
Au-Ag
Rioux-Au90Ag10
0
nk
0.270000
1.200000
1662
other
Au-Ag
Rioux-Au80Ag20
0
nk
0.270000
1.200000
1663
other
Au-Ag
Rioux-Au70Ag30
0
nk
0.270000
1.200000
1664
other
Au-Ag
Rioux-Au60Ag40
0
nk
0.270000
1.200000
1665
other
Au-Ag
Rioux-Au50Ag50
0
nk
0.270000
1.200000
1666
other
Au-Ag
Rioux-Au40Ag60
0
nk
0.270000
1.200000
1667
other
Au-Ag
Rioux-Au30Ag70
0
nk
0.270000
1.200000
1668
other
Au-Ag
Rioux-Au20Ag80
0
nk
0.270000
1.200000
1669
other
Au-Ag
Rioux-Au10Ag90
0
nk
0.270000
1.200000
1670
other
Au-Ag
Rioux-Au0Ag100
0
nk
0.270000
1.200000
1778
DL
Ag
Rakic
21
f
0.206600
12.400000
1779
DL
Ag
Vial
21
f
0.400000
1.000000
1780
DL
Ag
Lee
21
f
0.250000
1.000000
1793
BB
Ag
Rakic
22
f
0.206600
12.400000
In [3]:
Ag_id_list = [0, 1779]
ri.show(Ag_id_list)
Out[3]:
shelf
book
page
formula
tabulated
wl_min
wl_max
id
0
main
Ag (Experimental data)
Johnson
0
nk
0.1879
1.937
1779
DL
Ag
Vial
21
f
0.4000
1.000
In [4]:
wls = np.linspace(0.4, 1.0, 200)
for idx in Ag_id_list:
Ag = ri.material(idx)
Ag.plot(wls, 'eps', alpha=0.6)
plt.ylim(-50, 5)
plt.show()
In [5]:
ri.search('Au')
Out[5]:
shelf
book
page
formula
tabulated
wl_min
wl_max
id
50
main
Au (Experimental data)
Johnson
0
nk
0.187900
1.937000
51
main
Au (Experimental data)
McPeak
0
nk
0.300000
1.700000
52
main
Au (Experimental data)
Babar
0
nk
0.206600
12.400000
53
main
Au (Experimental data)
Lemarchand
0
nk
0.350000
1.800000
54
main
Au (Experimental data)
Lemarchand
0
nk
0.350000
1.800000
55
main
Au (Experimental data)
Lemarchand
0
nk
0.350000
1.800000
56
main
Au (Experimental data)
Lemarchand
0
nk
0.350000
1.800000
57
main
Au (Experimental data)
Olmon-ev
0
nk
0.300000
24.930000
58
main
Au (Experimental data)
Olmon-sc
0
nk
0.300000
24.930000
59
main
Au (Experimental data)
Olmon-ts
0
nk
0.300000
24.930000
60
main
Au (Experimental data)
Werner
0
nk
0.017586
2.479684
61
main
Au (Experimental data)
Windt
0
nk
0.002360
0.121570
62
main
Au (Experimental data)
Ordal
0
nk
0.667000
286.000000
63
main
Au (Experimental data)
Hagemann
0
nk
0.000008
248.000000
64
main
Au (Experimental data)
Hagemann-2
0
nk
0.003542
0.826600
65
main
Au (Models and simulations)
Rakic-BB
0
nk
0.247970
6.199200
66
main
Au (Models and simulations)
Rakic-LD
0
nk
0.247970
6.199200
67
main
Au (Models and simulations)
Werner-DFT
0
nk
0.017586
2.479684
1660
other
Au-Ag
Rioux-Au100Ag0
0
nk
0.270000
1.200000
1661
other
Au-Ag
Rioux-Au90Ag10
0
nk
0.270000
1.200000
1662
other
Au-Ag
Rioux-Au80Ag20
0
nk
0.270000
1.200000
1663
other
Au-Ag
Rioux-Au70Ag30
0
nk
0.270000
1.200000
1664
other
Au-Ag
Rioux-Au60Ag40
0
nk
0.270000
1.200000
1665
other
Au-Ag
Rioux-Au50Ag50
0
nk
0.270000
1.200000
1666
other
Au-Ag
Rioux-Au40Ag60
0
nk
0.270000
1.200000
1667
other
Au-Ag
Rioux-Au30Ag70
0
nk
0.270000
1.200000
1668
other
Au-Ag
Rioux-Au20Ag80
0
nk
0.270000
1.200000
1669
other
Au-Ag
Rioux-Au10Ag90
0
nk
0.270000
1.200000
1670
other
Au-Ag
Rioux-Au0Ag100
0
nk
0.270000
1.200000
1782
DL
Au
Rakic
21
f
0.206600
12.400000
1783
DL
Au
Stewart
21
f
0.400000
2.000000
1784
DL
Au
Vial
21
f
0.500000
1.000000
1795
BB
Au
Rakic
22
f
0.206600
12.400000
1804
Drude
Au
Vial
21
f
0.500000
1.000000
In [6]:
Au_id_list = [50, 51, 1783, 1784, 1804]
ri.show(Au_id_list)
Out[6]:
shelf
book
page
formula
tabulated
wl_min
wl_max
id
50
main
Au (Experimental data)
Johnson
0
nk
0.1879
1.937
51
main
Au (Experimental data)
McPeak
0
nk
0.3000
1.700
1783
DL
Au
Stewart
21
f
0.4000
2.000
1784
DL
Au
Vial
21
f
0.5000
1.000
1804
Drude
Au
Vial
21
f
0.5000
1.000
In [7]:
wls = np.linspace(0.5, 1.0, 200)
for idx in Au_id_list:
Au = ri.material(idx)
Au.plot(wls, 'eps', alpha=0.6)
plt.show()
In [2]:
ri.search('Al')
Out[2]:
shelf
book
page
formula
tabulated
wl_min
wl_max
id
10
main
Al (Experimental data)
Rakic
0
nk
0.000124
200.00000
11
main
Al (Experimental data)
McPeak
0
nk
0.150000
1.70000
12
main
Al (Experimental data)
Larruquert
0
nk
0.077000
0.11350
13
main
Al (Experimental data)
Ordal
0
nk
0.667000
200.00000
14
main
Al (Experimental data)
Hagemann
0
nk
0.000010
1240.00000
15
main
Al (Models and simulations)
Rakic-BB
0
nk
0.061992
247.97000
16
main
Al (Models and simulations)
Rakic-LD
0
nk
0.061992
247.97000
17
main
MgAl2O4
Tropf
1
f
0.350000
5.50000
18
main
Y3Al5O12
Zelmon
2
f
0.400000
5.00000
36
main
AlAs (Experimental data)
Fern
1
f
0.560000
2.20000
37
main
AlAs (Models and simulations)
Rakic
0
nk
0.221400
2.47970
81
main
LuAl3(BO3)4
Fang-o
4
f
0.266000
1.33800
82
main
LuAl3(BO3)4
Fang-e
4
f
0.266000
1.33800
168
main
LiCaAlF6
Woods-o
4
f
0.400000
1.00000
169
main
LiCaAlF6
Woods-e
4
f
0.400000
1.00000
262
main
AlN
Kischkat
0
nk
1.538460
14.28571
263
main
AlN
Pastrnak-o
1
f
0.220000
5.00000
264
main
AlN
Pastrnak-e
1
f
0.220000
5.00000
296
main
Al2O3 (Crystal)
Malitson-o
1
f
0.200000
5.00000
297
main
Al2O3 (Crystal)
Malitson-e
1
f
0.200000
5.00000
298
main
Al2O3 (Crystal)
Querry-o
0
nk
0.210000
55.55560
299
main
Al2O3 (Crystal)
Querry-e
0
nk
0.210000
55.55560
300
main
Al2O3 (Crystal)
Malitson
1
f
0.265200
5.57700
301
main
Al2O3 (Thin film)
Hagemann
0
nk
0.000775
0.20660
302
main
Al2O3 (Thin film)
Boidin
0
n
0.300000
18.00300
303
main
Al2O3 (Thin film)
Kischkat
0
nk
1.539410
14.28571
304
main
Al2O3 (Thin film)
Querry
0
nk
0.210000
12.50000
458
main
AlSb (Experimental data)
Zollner
0
nk
0.213800
0.88560
459
main
AlSb (Models and simulations)
Adachi
0
nk
0.206640
12.39800
460
main
AlSb (Models and simulations)
Djurisic
0
nk
0.206640
12.39800
1684
other
AlAs-GaAs
Aspnes-0
0
nk
0.206600
0.82660
1685
other
AlAs-GaAs
Aspnes-9.9
0
nk
0.206600
0.82660
1686
other
AlAs-GaAs
Aspnes-19.8
0
nk
0.206600
0.82660
1687
other
AlAs-GaAs
Aspnes-31.5
0
nk
0.206600
0.82660
1688
other
AlAs-GaAs
Aspnes-41.9
0
nk
0.206600
0.82660
1689
other
AlAs-GaAs
Aspnes-49.1
0
nk
0.206600
0.82660
1690
other
AlAs-GaAs
Aspnes-59.0
0
nk
0.206600
0.82660
1691
other
AlAs-GaAs
Aspnes-70.0
0
nk
0.206600
0.82660
1692
other
AlAs-GaAs
Aspnes-80.4
0
nk
0.206600
0.82660
1693
other
AlSb-GaSb
Ferrini-0
0
nk
0.207000
2.48000
1694
other
AlSb-GaSb
Ferrini-10
0
nk
0.207000
2.48000
1695
other
AlSb-GaSb
Ferrini-30
0
nk
0.207000
2.48000
1696
other
AlSb-GaSb
Ferrini-50
0
nk
0.207000
2.48000
1697
other
AlN-Al2O3
Hartnett-6.69
1
f
0.240000
5.60000
1698
other
AlN-Al2O3
Hartnett-5.88
0
nk
0.240000
5.60000
1699
other
AlN-Al2O3
Hartnett-6.53
0
nk
0.240000
5.60000
1700
other
AlN-Al2O3
Hartnett-7.17
0
nk
0.240000
5.60000
1722
other
Al:ZnO
Treharne
0
nk
0.300000
0.90000
1782
DL
Al
Rakic
21
f
0.206600
12.40000
1795
BB
Al
Rakic
22
f
0.206600
12.40000
In [9]:
Al_id_list = [10, 11, 1781, 1794]
ri.show(Al_id_list)
Out[9]:
shelf
book
page
formula
tabulated
wl_min
wl_max
id
10
main
Al (Experimental data)
Rakic
0
nk
0.000124
200.0
11
main
Al (Experimental data)
McPeak
0
nk
0.150000
1.7
1781
DL
Al
Rakic
21
f
0.206600
12.4
1794
BB
Al
Rakic
22
f
0.206600
12.4
In [10]:
wls = np.linspace(0.3, 1.0, 200)
for idx in Al_id_list:
Al = ri.material(idx)
Al.plot(wls, 'eps', alpha=0.6)
plt.show()
In [11]:
ri.search('H2O')
Out[11]:
shelf
book
page
formula
tabulated
wl_min
wl_max
id
316
main
H2O (Liquid water, H2O)
Hale
0
nk
0.2000
200.000
317
main
H2O (Liquid water, H2O)
Kedenburg
2
k
0.5000
1.600
318
main
H2O (Liquid water, H2O)
Daimon-19.0C
2
f
0.1820
1.129
319
main
H2O (Liquid water, H2O)
Daimon-20.0C
2
f
0.1820
1.129
320
main
H2O (Liquid water, H2O)
Daimon-21.5C
2
f
0.1820
1.129
321
main
H2O (Liquid water, H2O)
Daimon-24.0C
2
f
0.1820
1.129
322
main
H2O (Liquid water, H2O)
Asfar-H2O
0
nk
22.2200
1733.000
323
main
H2O (Water ice)
Warren
0
nk
0.0443
167.000
324
main
H2O (Heavy water, D2O)
Kedenburg-D2O
2
k
0.5000
1.600
325
main
H2O (Heavy water, D2O)
Asfar-D2O
0
nk
250.0000
2000.000
In [12]:
water_id_list = [318, 319, 320, 321]
alpha = 0.6
wls = np.linspace(0.38, 0.75, 200)
waters = [ri.material(idx) for idx in water_id_list]
for water in waters:
water.plot(wls, 'n')
plt.ylim(1.325, 1.35)
plt.show()
In [13]:
wls = np.linspace(0.38, 0.75, 200)
water = ri.material(318)
np.average(water.n(wls))
Out[13]:
1.3352825266061357
In [14]:
water = ri.material(283)
print(water.ID)
print(water.catalog)
print(display(ri.show([water.ID])))
wls = np.linspace(0.5, 1.6, 200)
water.plot(wls, 'k', alpha=0.6)
plt.show()
283
shelf main
shelf_name MAIN - simple inorganic materials
division Nb - Niobium and niobates
book KNbO3
book_name KNbO<sub>3</sub> (Potassium niobate)
page Umemura-γ
path /home/mnishida/usr/lib/site-packages/RII_Panda...
formula 4
tabulated f
num_n 0
num_k 0
wl_n_min 0.4
wl_n_max 5.3
wl_k_min 0.4
wl_k_max 5.3
wl_min 0.4
wl_max 5.3
Name: 283, dtype: object
shelf
book
page
formula
tabulated
wl_min
wl_max
id
283
main
KNbO3
Umemura-γ
4
f
0.4
5.3
None
In [15]:
id_list = ri.select({'wl_min': 0.39, 'wl_max': 0.41, 'n_min': 2.5, 'k_max': 0.1})
print(len(id_list))
ri.show(id_list)
11
Out[15]:
shelf
book
page
formula
tabulated
wl_min
wl_max
id
265
main
GaN (Experimental data)
Barker-o
1
f
0.350000
10.000000
267
main
GaN (Experimental data)
Lin-wurtzite
0
n
0.368124
0.991745
268
main
GaN (Experimental data)
Lin-zincblende
0
n
0.385447
0.960283
283
main
KNbO3
Umemura-γ
4
f
0.400000
5.300000
335
main
Nb2O5
Lemarchand
0
nk
0.250000
2.500000
353
main
TeO2
Uchida-e
1
f
0.400000
1.000000
358
main
TiO2 (Nanoparticles)
Bodurov
1
f
0.405000
0.635000
452
main
ZnS (Experimental data)
Debenham
4
f
0.405000
13.000000
454
main
ZnS (Models and simulations)
Ozaki
0
nk
0.221400
1.033200
553
main
BaTiO3
Wemple-o
1
f
0.400000
0.700000
554
main
BaTiO3
Wemple-e
1
f
0.400000
0.700000
In [16]:
gd = ri.load_grid_data()
id_list = gd[(gd['wl'] >= 0.39) & (gd['wl']<=0.41) &
(gd['n']>=2.5) & ((gd['k']<=0.1) | (gd['k']!=gd['k']))].index.unique()
print(len(id_list))
ri.show(id_list)
11
Out[16]:
shelf
book
page
formula
tabulated
wl_min
wl_max
id
265
main
GaN (Experimental data)
Barker-o
1
f
0.350000
10.000000
267
main
GaN (Experimental data)
Lin-wurtzite
0
n
0.368124
0.991745
268
main
GaN (Experimental data)
Lin-zincblende
0
n
0.385447
0.960283
283
main
KNbO3
Umemura-γ
4
f
0.400000
5.300000
335
main
Nb2O5
Lemarchand
0
nk
0.250000
2.500000
353
main
TeO2
Uchida-e
1
f
0.400000
1.000000
358
main
TiO2 (Nanoparticles)
Bodurov
1
f
0.405000
0.635000
452
main
ZnS (Experimental data)
Debenham
4
f
0.405000
13.000000
454
main
ZnS (Models and simulations)
Ozaki
0
nk
0.221400
1.033200
553
main
BaTiO3
Wemple-o
1
f
0.400000
0.700000
554
main
BaTiO3
Wemple-e
1
f
0.400000
0.700000
In [17]:
ri.search('KNbO3')
Out[17]:
shelf
book
page
formula
tabulated
wl_min
wl_max
id
281
main
KNbO3
Umemura-α
4
f
0.4
5.3
282
main
KNbO3
Umemura-β
4
f
0.4
5.3
283
main
KNbO3
Umemura-γ
4
f
0.4
5.3
In [18]:
ri = riip.RiiDataFrame()
KNbO3_alpha = ri.material(281)
KNbO3_beta = ri.material(282)
KNbO3_gamma = ri.material(283)
print(display(ri.show([KNbO3_alpha.ID, KNbO3_beta.ID, KNbO3_gamma.ID])))
wls = np.linspace(0.5, 1.6, 200)
KNbO3_alpha.plot(wls, 'n', '-')
KNbO3_beta.plot(wls, 'n', '-')
KNbO3_gamma.plot(wls, 'n', '-')
plt.show()
shelf
book
page
formula
tabulated
wl_min
wl_max
id
281
main
KNbO3
Umemura-α
4
f
0.4
5.3
282
main
KNbO3
Umemura-β
4
f
0.4
5.3
283
main
KNbO3
Umemura-γ
4
f
0.4
5.3
None
In [19]:
ri.search('CH3OH')
Out[19]:
shelf
book
page
formula
tabulated
wl_min
wl_max
id
622
organic
methanol
El-Kashef
5
f
0.40
0.8000
623
organic
methanol
Moutzouris
3
f
0.45
1.5510
624
organic
methanol
Kozma
5
f
0.23
0.6407
In [20]:
ri.search('Methanol')
Out[20]:
shelf
book
page
formula
tabulated
wl_min
wl_max
id
622
organic
methanol
El-Kashef
5
f
0.40
0.8000
623
organic
methanol
Moutzouris
3
f
0.45
1.5510
624
organic
methanol
Kozma
5
f
0.23
0.6407
In [21]:
ri.search('methanol')
Out[21]:
shelf
book
page
formula
tabulated
wl_min
wl_max
id
622
organic
methanol
El-Kashef
5
f
0.40
0.8000
623
organic
methanol
Moutzouris
3
f
0.45
1.5510
624
organic
methanol
Kozma
5
f
0.23
0.6407
In [ ]:
Content source: mnishida/RII_Pandas
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