In [1]:
%pylab inline
import seaborn as sns
sns.set_context("notebook", font_scale=1.5, rc={"lines.linewidth": 2.5})
cmap = sns.cubehelix_palette(light=1, as_cmap=True)
sns.palplot(sns.cubehelix_palette(light=1))
import pandas as pd
pd.set_option('display.max_columns', 100)
Populating the interactive namespace from numpy and matplotlib
In [55]:
df = pd.read_csv('../data/cln_20150206_cary5000.csv')
df.set_index('wavelength', inplace=True)
df.drop('empty_4_2', axis=1, inplace=True)
df.head()
Out[55]:
Baseline 100%T
empty_1
empty_2_1
empty_2_2
empty_2_3
empty_2_4
empty_2_5
empty_2_6
empty_2_7
empty_2_8
empty_2_9
empty_2_10
empty_2_11
empty_2_12
empty_2_13
empty_2_14
empty_2_15
empty_3_1
empty_3_2
empty_3_3
empty_3_4
empty_3_5
VG05_1_1
VG05_1_2
VG05_1_3
VG05_1_4
VG05_1_5
Baseline 100%T.1
empty_4_1
VG09-12_1_0
VG09-12_1_2
VG09-12_1_4
VG09-12_1_6
VG09-12_1_8
VG09-12_1_10
VG09-12_1_12
VG09-12_1_14
VG09-12_1_16
VG09-12_1_18
VG09-12_1_20
VG09-12_1_22
VG09-12_1_24
VG09-12_1_26
VG09-12_1_28
VG09-12_1_30
VG09-12_1_32
VG09-12_1_34
VG09-12_1_36
VG09-12_1_38
VG09-12_1_40
VG09-12_1_42
VG09-12_1_44
VG09-12_1_46
VG09-12_1_48
VG09-12_1_50
empty_5_1
empty_5_2
empty_5_3
empty_5_4
empty_5_5
wavelength
1775
0.054900
0.996738
0.994788
0.993000
0.992427
0.992727
0.992566
0.993178
0.993542
0.993699
0.993695
0.994273
0.994382
0.994913
0.995192
0.997182
0.996089
1.000317
1.000646
1.000757
1.001592
1.001691
0.531966
0.532362
0.532803
0.532895
0.533078
0.055321
1.000382
1.001493
0.874571
0.520288
0.513987
0.520696
0.522658
0.526750
0.526497
0.571637
0.535314
0.535065
0.534981
0.534620
0.535057
0.535745
0.536009
0.536586
0.536681
0.537011
0.537481
0.517272
0.491913
1.005398
1.008597
1.009794
1.010392
1.011241
1.011373
1.011569
1.011567
1.011826
1765
0.056587
0.996698
0.994949
0.993029
0.992323
0.992540
0.992392
0.993101
0.993253
0.993590
0.993916
0.994138
0.994222
0.994880
0.995069
0.997146
0.996170
1.000276
1.000489
1.000754
1.001109
1.001378
0.532075
0.532165
0.532466
0.532575
0.532848
0.057007
1.000621
1.001283
0.875052
0.519546
0.514007
0.520622
0.522183
0.526571
0.525914
0.571612
0.535326
0.534940
0.534768
0.534424
0.535212
0.536067
0.535856
0.536452
0.536578
0.537032
0.537514
0.517388
0.491351
1.005273
1.008588
1.010112
1.010448
1.011166
1.011300
1.011852
1.011722
1.012028
1755
0.058528
0.996616
0.994781
0.993856
0.992115
0.992431
0.992578
0.993081
0.993325
0.993445
0.993944
0.994041
0.994368
0.994741
0.995185
0.995766
0.996120
1.000049
1.000574
1.000839
1.001166
1.001465
0.531898
0.532115
0.532214
0.532562
0.532866
0.058966
1.000626
1.001497
0.875337
0.518847
0.513831
0.520083
0.521865
0.526216
0.525509
0.571603
0.535314
0.534978
0.534978
0.534248
0.535097
0.535835
0.535614
0.536186
0.536287
0.536813
0.537127
0.517123
0.491549
1.005627
1.008868
1.009983
1.010426
1.011105
1.011550
1.011774
1.011923
1.011949
1745
0.060380
0.996552
0.994699
0.993798
0.992180
0.992515
0.992373
0.992759
0.993148
0.993443
0.993975
0.994108
0.994200
0.994774
0.994995
0.995585
0.996152
0.999933
1.000622
1.000973
1.001230
1.001656
0.531830
0.532072
0.532285
0.532506
0.532870
0.060837
1.000456
1.001119
0.875349
0.518504
0.513229
0.519425
0.521210
0.526331
0.525357
0.571421
0.534957
0.534713
0.534617
0.533917
0.534785
0.535579
0.535679
0.536352
0.536031
0.536497
0.537138
0.516903
0.491059
1.005182
1.008798
1.010065
1.010098
1.011155
1.011324
1.011586
1.011805
1.011863
1735
0.062304
0.996772
0.994961
0.993465
0.992449
0.992538
0.992429
0.993018
0.993122
0.993576
0.994022
0.994134
0.994595
0.994813
0.995260
0.995902
0.996312
1.000246
1.000612
1.000831
1.001297
1.002066
0.531885
0.532085
0.532417
0.532486
0.532722
0.062780
1.000677
1.001375
0.875580
0.518685
0.513199
0.518752
0.520695
0.526437
0.525202
0.571170
0.534776
0.534700
0.534697
0.534020
0.534712
0.535287
0.535903
0.535945
0.536309
0.536709
0.536990
0.516755
0.491418
1.005340
1.008855
1.010012
1.010301
1.011332
1.011411
1.011607
1.011826
1.011643
In [56]:
corr = pd.read_csv('../data/rebaseline_20150206_cary5000.csv', index_col=0)
corr.T
Out[56]:
Baseline 100%T
empty_1
empty_2_1
empty_2_2
empty_2_3
empty_2_4
empty_2_5
empty_2_6
empty_2_7
empty_2_8
empty_2_9
empty_2_10
empty_2_11
empty_2_12
empty_2_13
empty_2_14
empty_2_15
empty_3_1
empty_3_2
empty_3_3
empty_3_4
empty_3_5
VG05_1_1
VG05_1_2
VG05_1_3
VG05_1_4
VG05_1_5
Baseline 100%T.1
empty_4_1
VG09-12_1_0
VG09-12_1_2
VG09-12_1_4
VG09-12_1_6
VG09-12_1_8
VG09-12_1_10
VG09-12_1_12
VG09-12_1_14
VG09-12_1_16
VG09-12_1_18
VG09-12_1_20
VG09-12_1_22
VG09-12_1_24
VG09-12_1_26
VG09-12_1_28
VG09-12_1_30
VG09-12_1_32
VG09-12_1_34
VG09-12_1_36
VG09-12_1_38
VG09-12_1_40
VG09-12_1_42
VG09-12_1_44
VG09-12_1_46
VG09-12_1_48
VG09-12_1_50
empty_5_1
empty_5_2
empty_5_3
empty_5_4
empty_5_5
time
2015-02-06 13:45:42
2015-02-06 13:48:36
2015-02-06 13:50:53
2015-02-06 13:51:57
2015-02-06 13:52:58
2015-02-06 13:53:58
2015-02-06 13:54:59
2015-02-06 13:55:59
2015-02-06 13:57:00
2015-02-06 13:58:00
2015-02-06 13:59:01
2015-02-06 14:00:01
2015-02-06 14:01:01
2015-02-06 14:02:01
2015-02-06 14:03:02
2015-02-06 14:04:02
2015-02-06 14:05:03
2015-02-06 14:20:40
2015-02-06 14:21:41
2015-02-06 14:22:42
2015-02-06 14:23:42
2015-02-06 14:24:43
2015-02-06 14:27:37
2015-02-06 14:28:38
2015-02-06 14:29:39
2015-02-06 14:30:39
2015-02-06 14:31:40
2015-02-06 14:39:58
2015-02-06 14:41:17
2015-02-06 14:50:46
2015-02-06 14:51:43
2015-02-06 14:52:40
2015-02-06 14:53:37
2015-02-06 14:54:35
2015-02-06 14:55:32
2015-02-06 14:56:30
2015-02-06 14:57:27
2015-02-06 14:58:24
2015-02-06 14:59:22
2015-02-06 15:00:19
2015-02-06 15:01:17
2015-02-06 15:02:14
2015-02-06 15:03:12
2015-02-06 15:04:09
2015-02-06 15:05:06
2015-02-06 15:06:04
2015-02-06 15:07:01
2015-02-06 15:07:58
2015-02-06 15:08:56
2015-02-06 15:09:53
2015-02-06 15:10:50
2015-02-06 15:11:48
2015-02-06 15:12:45
2015-02-06 15:13:42
2015-02-06 15:14:40
2015-02-06 15:18:03
2015-02-06 15:19:05
2015-02-06 15:20:05
2015-02-06 15:21:06
2015-02-06 15:22:06
mean
0.1087186
0.9957872
0.9926498
0.9908314
0.9906155
0.9904822
0.9908665
0.9912781
0.9916097
0.991675
0.9919954
0.992257
0.992545
0.9928936
0.9934452
0.9939193
0.9941676
0.9978184
0.9979871
0.9984328
0.998799
0.9992173
0.5280796
0.5283065
0.5285396
0.5287853
0.5289416
0.1097686
1.005132
1.005681
0.8794889
0.5120914
0.5047801
0.5123605
0.5162238
0.5217098
0.5219908
0.5664048
0.534421
0.5340435
0.53402
0.5330458
0.5340295
0.5346758
0.5350179
0.5352743
0.5355544
0.5359295
0.5363229
0.5136548
0.4928453
1.009119
1.012637
1.014035
1.014353
1.015114
1.015346
1.015579
1.015728
1.015885
stddev
0.03012316
0.000618184
0.00164418
0.001584798
0.001256841
0.001314182
0.001264141
0.001367735
0.00138019
0.00146376
0.001553155
0.00156878
0.001540197
0.001684987
0.001548717
0.001740542
0.001793219
0.002767232
0.002900688
0.002739297
0.002767122
0.002787385
0.002919238
0.002918341
0.002909277
0.002906488
0.00297041
0.03014187
0.0002875276
0.0004298377
0.000952451
0.007618797
0.007591666
0.005078568
0.005340543
0.004817525
0.004523641
0.007110308
0.002309255
0.002301742
0.002319278
0.002619655
0.002424139
0.00238429
0.002301456
0.002366789
0.002320788
0.002333405
0.002355324
0.004050169
0.001377504
0.0007203449
0.0006533268
0.0006116508
0.0005912818
0.0006968267
0.0006525894
0.0006781845
0.0006851215
0.0006973577
rebaseline
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1.004817
1.004817
1.004817
1.004817
1.004817
1.004817
1.004817
1.004817
1.004817
1.004817
1.004817
1.004817
1.004817
1.004817
1.004817
1.004817
1.004817
1.004817
1.004817
1.004817
1.004817
1.004817
1.004817
1.004817
1.004817
1.004817
1.004817
1.004817
1.004817
1.004817
1.004817
1.004817
1.004817
time_num
1.42323e+18
1.423231e+18
1.423231e+18
1.423231e+18
1.423231e+18
1.423231e+18
1.423231e+18
1.423231e+18
1.423231e+18
1.423231e+18
1.423231e+18
1.423231e+18
1.423231e+18
1.423231e+18
1.423231e+18
1.423231e+18
1.423232e+18
1.423232e+18
1.423233e+18
1.423233e+18
1.423233e+18
1.423233e+18
1.423233e+18
1.423233e+18
1.423233e+18
1.423233e+18
1.423233e+18
1.423234e+18
1.423234e+18
1.423234e+18
1.423234e+18
1.423234e+18
1.423234e+18
1.423234e+18
1.423235e+18
1.423235e+18
1.423235e+18
1.423235e+18
1.423235e+18
1.423235e+18
1.423235e+18
1.423235e+18
1.423235e+18
1.423235e+18
1.423235e+18
1.423235e+18
1.423235e+18
1.423235e+18
1.423235e+18
1.423235e+18
1.423235e+18
1.423236e+18
1.423236e+18
1.423236e+18
1.423236e+18
1.423236e+18
1.423236e+18
1.423236e+18
1.423236e+18
1.423236e+18
est_mean
0.9882757
0.9891132
0.9897726
0.9900806
0.9903742
0.990663
0.9909566
0.9912454
0.991539
0.9918278
0.9921214
0.9924102
0.992699
0.9929878
0.9932814
0.9935702
0.9938638
0.9983737
0.9986673
0.9989609
0.9992497
0.9995433
1.000381
1.000674
1.000968
1.001257
1.00155
1.003947
1.004328
1.007066
1.007341
1.007615
1.007889
1.008169
1.008443
1.008722
1.008996
1.009271
1.00955
1.009824
1.010103
1.010378
1.010657
1.010931
1.011206
1.011485
1.011759
1.012033
1.012313
1.012587
1.012861
1.013141
1.013415
1.013689
1.013968
1.014945
1.015244
1.015533
1.015826
1.016115
In [57]:
corrT = corr.T
#corrT.loc['rebaseline']
The net output is:
$T_{corr} = T_{obs} \times b / m$
where $b$ is the baseline correction and
$m$ is the model drift
In [58]:
cspec = df*b/m
In [59]:
plt.plot([-99, -91],sns.xkcd_rgb['scarlet'], alpha=1.0, label='raw', );
plt.plot(df.index, df, sns.xkcd_rgb['scarlet'], alpha=0.13);
b = corrT.loc['rebaseline']
m = corrT.loc['est_mean']
plt.plot([-99, -91], sns.xkcd_rgb['ocean blue'], alpha=1.0, label='corrected')
plt.plot(df.index, cspec, sns.xkcd_rgb['ocean blue'], alpha=0.33)
plt.xlim(1250, 1780)
plt.ylim(0.98, 1.02)
plt.xlabel('$\lambda$ (nm)')
plt.ylabel('$T$')
plt.legend(loc='best')
plt.title('Correct mean-level of spectra')
Out[59]:
<matplotlib.text.Text at 0x11089aed0>
In [92]:
VG05_cols = [col_name[0:4] == 'VG05' for col_name in cspec.columns.values]
empty_3_cols = [col_name[0:7] == 'empty_3' for col_name in cspec.columns.values]
empty_3 = cspec[cspec.columns[empty_3_cols]].mean(axis=1)
cVG05 = cspec[cspec.columns[VG05_cols]].div(empty_3, axis=0)
cVG05_mean = cVG05.mean(axis=1)
cVG05_std = cVG05.std(axis=1)
In [99]:
plt.plot([-99, -91],sns.xkcd_rgb['scarlet'], alpha=1.0, label='raw', );
plt.plot(cspec.index, cspec[cspec.columns[VG05_cols]], sns.xkcd_rgb['scarlet'], alpha=0.53);
plt.plot([-99, -91], sns.xkcd_rgb['ocean blue'], alpha=1.0, label='corrected')
plt.plot(df.index, cVG05, sns.xkcd_rgb['ocean blue'], alpha=0.53)
plt.errorbar(df.index, cVG05_mean, yerr=cVG05_std, fmt='k.')
plt.xlim(1250, 1780)
plt.ylim(0.52, 0.535)
plt.xlabel('$\lambda$ (nm)')
plt.ylabel('$T$')
plt.legend(loc='best')
plt.title('Correct spectral shape of VG05')
Out[99]:
<matplotlib.text.Text at 0x111e28dd0>
In [127]:
cln_corr = pd.DataFrame()
cln_corr['VG05'] = cVG05_mean
cln_corr.set_index(df.index)
cln_corr['VG05_e'] = cVG05_std
cln_corr.head()
Out[127]:
VG05
VG05_e
wavelength
1775
0.531022
0.000221
1765
0.530933
0.000081
1755
0.530829
0.000148
1745
0.530777
0.000158
1735
0.530716
0.000099
In [128]:
cspec.head()
Out[128]:
Baseline 100%T
empty_1
empty_2_1
empty_2_2
empty_2_3
empty_2_4
empty_2_5
empty_2_6
empty_2_7
empty_2_8
empty_2_9
empty_2_10
empty_2_11
empty_2_12
empty_2_13
empty_2_14
empty_2_15
empty_3_1
empty_3_2
empty_3_3
empty_3_4
empty_3_5
VG05_1_1
VG05_1_2
VG05_1_3
VG05_1_4
VG05_1_5
Baseline 100%T.1
empty_4_1
VG09-12_1_0
VG09-12_1_2
VG09-12_1_4
VG09-12_1_6
VG09-12_1_8
VG09-12_1_10
VG09-12_1_12
VG09-12_1_14
VG09-12_1_16
VG09-12_1_18
VG09-12_1_20
VG09-12_1_22
VG09-12_1_24
VG09-12_1_26
VG09-12_1_28
VG09-12_1_30
VG09-12_1_32
VG09-12_1_34
VG09-12_1_36
VG09-12_1_38
VG09-12_1_40
VG09-12_1_42
VG09-12_1_44
VG09-12_1_46
VG09-12_1_48
VG09-12_1_50
empty_5_1
empty_5_2
empty_5_3
empty_5_4
empty_5_5
wavelength
1775
0.05555102
1.007709
1.005067
1.002948
1.002073
1.002084
1.001624
1.001949
1.00202
1.001886
1.001586
1.001877
1.001696
1.001939
1.001923
1.003635
1.002239
1.001946
1.001981
1.001798
1.002344
1.002149
0.5317637
0.5320036
0.5322881
0.5322264
0.5322529
0.05536917
1.00087
0.9992563
0.8723805
0.5188433
0.5124204
0.5189653
0.520779
0.5247108
0.5243166
0.569115
0.532805
0.5324116
0.5321815
0.5316778
0.5319658
0.532505
0.5326229
0.5330494
0.5329984
0.533182
0.5335017
0.5133027
0.4880062
0.997138
1.000041
1.000956
1.001273
1.00115
1.000986
1.000896
1.000605
1.000576
1765
0.05725822
1.007668
1.00523
1.002977
1.001968
1.001895
1.001448
1.001871
1.001728
1.001777
1.001809
1.001741
1.001534
1.001905
1.001799
1.003599
1.00232
1.001905
1.001824
1.001795
1.001861
1.001835
0.5318729
0.5318065
0.5319507
0.5319062
0.5320227
0.05705611
1.001108
0.9990469
0.8728604
0.5181034
0.5124407
0.5188918
0.520306
0.5245322
0.5237363
0.5690901
0.5328167
0.5322874
0.5319697
0.5314828
0.5321198
0.5328253
0.532471
0.5329158
0.5328969
0.5332025
0.533534
0.5134181
0.4874492
0.9970148
1.000031
1.001272
1.001329
1.001076
1.000914
1.001175
1.000758
1.000775
1755
0.05922239
1.007585
1.005061
1.003813
1.001757
1.001785
1.001636
1.001852
1.001801
1.001631
1.001837
1.001644
1.001682
1.001766
1.001916
1.00221
1.00227
1.001678
1.001909
1.00188
1.001918
1.001922
0.5316957
0.5317562
0.5316996
0.5318935
0.532041
0.05901726
1.001114
0.999261
0.8731446
0.5174065
0.5122651
0.5183538
0.5199886
0.5241794
0.5233323
0.5690809
0.532805
0.5323254
0.5321782
0.5313076
0.5320049
0.5325948
0.5322304
0.5326517
0.532607
0.5329856
0.5331505
0.513155
0.4876448
0.9973654
1.000309
1.001144
1.001307
1.001016
1.001161
1.001098
1.000957
1.000697
1745
0.06109606
1.007521
1.004978
1.003755
1.001823
1.001869
1.001429
1.001527
1.001623
1.001628
1.001869
1.001711
1.001512
1.001799
1.001725
1.002028
1.002303
1.001562
1.001957
1.002014
1.001982
1.002114
0.5316271
0.5317139
0.5317707
0.5318373
0.5320451
0.06088938
1.000944
0.9988838
0.8731559
0.5170642
0.5116649
0.517698
0.5193364
0.5242939
0.5231811
0.5688993
0.5324489
0.5320617
0.5318196
0.5309783
0.5316946
0.5323399
0.5322948
0.5328164
0.532353
0.5326719
0.5331606
0.5129363
0.4871587
0.996924
1.000239
1.001225
1.000982
1.001065
1.000938
1.000913
1.00084
1.000613
1735
0.06304293
1.007743
1.005242
1.003418
1.002095
1.001892
1.001486
1.001788
1.001597
1.001763
1.001915
1.001737
1.00191
1.001838
1.001992
1.002347
1.002463
1.001875
1.001947
1.001872
1.002049
1.002524
0.5316829
0.5317261
0.5319026
0.5318179
0.5318973
0.06283404
1.001165
0.9991389
0.8733867
0.5172453
0.5116343
0.5170272
0.518823
0.5243987
0.523027
0.5686497
0.5322686
0.5320488
0.531899
0.5310815
0.5316227
0.5320492
0.532517
0.5324118
0.5326293
0.5328823
0.5330139
0.5127898
0.487515
0.9970811
1.000297
1.001172
1.001183
1.00124
1.001024
1.000933
1.00086
1.000395
In [129]:
VG09_12_cols = [col_name[0:7] == 'VG09-12' for col_name in cspec.columns.values]
empty_5_cols = [col_name[0:7] == 'empty_5' for col_name in cspec.columns.values]
empty_5 = cspec[cspec.columns[empty_5_cols]].mean(axis=1)
cVG0912 = cspec[cspec.columns[VG09_12_cols]].div((cspec['empty_4_1']+empty_5)*0.5, axis=0)
cVG0912.head()
Out[129]:
VG09-12_1_0
VG09-12_1_2
VG09-12_1_4
VG09-12_1_6
VG09-12_1_8
VG09-12_1_10
VG09-12_1_12
VG09-12_1_14
VG09-12_1_16
VG09-12_1_18
VG09-12_1_20
VG09-12_1_22
VG09-12_1_24
VG09-12_1_26
VG09-12_1_28
VG09-12_1_30
VG09-12_1_32
VG09-12_1_34
VG09-12_1_36
VG09-12_1_38
VG09-12_1_40
VG09-12_1_42
VG09-12_1_44
VG09-12_1_46
VG09-12_1_48
VG09-12_1_50
wavelength
1775
0.9984016
0.8716343
0.5183996
0.5119821
0.5185214
0.5203335
0.524262
0.5238681
0.5686282
0.5323493
0.5319562
0.5317263
0.531223
0.5315108
0.5320495
0.5321674
0.5325935
0.5325425
0.5327259
0.5330454
0.5128637
0.4875888
0.9962851
0.9991854
1.0001
1.000416
1765
0.9980249
0.8719674
0.5175733
0.5119164
0.5183609
0.5197737
0.5239956
0.5232005
0.5685079
0.5322716
0.5317428
0.5314255
0.5309391
0.5315754
0.5322802
0.5319262
0.5323706
0.5323517
0.532657
0.5329882
0.5128928
0.4869505
0.9959948
0.9990083
1.000247
1.000304
1755
0.998213
0.8722288
0.5168638
0.5117278
0.5178101
0.5194432
0.5236297
0.5227835
0.5684841
0.5322463
0.5317671
0.5316201
0.5307504
0.5314469
0.5320362
0.5316722
0.5320931
0.5320484
0.5324266
0.5325914
0.5126168
0.4871334
0.9963194
0.9992598
1.000094
1.000257
1745
0.9979769
0.8723632
0.5165948
0.5112003
0.5172279
0.5188649
0.5238179
0.5227061
0.5683828
0.5319654
0.5315786
0.5313367
0.5304962
0.5312118
0.5318566
0.5318116
0.5323326
0.5318697
0.5321883
0.5326765
0.5124706
0.4867164
0.9960189
0.999331
1.000316
1.000073
1735
0.9981129
0.8724899
0.5167141
0.5111089
0.5164963
0.5182902
0.5238602
0.5224899
0.5680657
0.531722
0.5315024
0.5313528
0.5305361
0.5310768
0.5315029
0.5319702
0.5318651
0.5320823
0.5323351
0.5324666
0.5122632
0.4870144
0.9960572
0.9992694
1.000144
1.000155
In [130]:
plt.plot([-99, -91],sns.xkcd_rgb['scarlet'], alpha=1.0, label='raw', );
plt.plot(cspec.index, cspec[cspec.columns[VG09_12_cols]], sns.xkcd_rgb['scarlet'], alpha=0.53);
plt.plot([-99, -91], sns.xkcd_rgb['ocean blue'], alpha=1.0, label='corrected')
plt.plot(df.index, cVG0912, sns.xkcd_rgb['ocean blue'], alpha=0.53)
plt.errorbar(df.index, cVG05_mean, yerr=cVG05_std, fmt='k.', label='DSP')
plt.xlim(1250, 1780)
plt.ylim(0.52, 0.538)
#plt.ylim(0.45, 0.538)
plt.xlabel('$\lambda$ (nm)')
plt.ylabel('$T$')
plt.legend(loc='best')
plt.title('Spectral correction of VG09-12 is negligible')
Out[130]:
<matplotlib.text.Text at 0x1168edbd0>
In [136]:
VG09_12_gap = cVG0912.div(cln_corr.VG05, axis=0)
In [139]:
#plt.plot([-99, -91], sns.xkcd_rgb['ocean blue'], alpha=1.0, label='corrected')
plt.plot(df.index, VG09_12_gap, sns.xkcd_rgb['ocean blue'], alpha=0.53)
plt.xlim(1250, 1780)
plt.ylim(0.90, 1.01)
plt.xlabel('$\lambda$ (nm)')
plt.ylabel('$T$')
#plt.legend(loc='best')
plt.title('VG09-12 sample transport')
Out[139]:
<matplotlib.text.Text at 0x117216ad0>
In [140]:
VG09_12_gap.to_csv('../data/VG09_12_gap_20150206.csv')
In [ ]:
Content source: Echelle/AO_bonding_paper
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