In [1]:
import os
import sys
import random
import time
from random import seed, randint
import argparse
import platform
from datetime import datetime
import imp
import numpy as np
import fileinput
from itertools import product
import pandas as pd
from scipy.interpolate import griddata
from scipy.interpolate import interp2d
import seaborn as sns
from os import listdir
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.interpolate import griddata
import matplotlib as mpl
sys.path.insert(0,'..')
from notebookFunctions import *
# from .. import notebookFunctions
%matplotlib inline
plt.rcParams['figure.figsize'] = (10,6.180) #golden ratio
# %matplotlib notebook
%load_ext autoreload
%autoreload 2
In [2]:
pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 370
location = pre + "/second_2/_280-350/2d_z_qw/more_temp_force_0.05/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 5), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)
<matplotlib.colors.LinearSegmentedColormap object at 0x1a08622ef0>
Out[2]:
[<matplotlib.lines.Line2D at 0x1a13e9a438>]
In [4]:
data = pd.read_feather("/Users/weilu/Research/server/may_2018/03_week/all_data_folder/second_start_extended_combined_may19.feather")
In [5]:
data.columns
Out[5]:
Index(['level_0', 'AMH', 'AMH-Go', 'AMH_3H', 'AMH_4H', 'BiasTo', 'DisReal',
'Dis_h56', 'Distance', 'Energy', 'Lipid', 'Lipid1', 'Lipid10',
'Lipid11', 'Lipid12', 'Lipid13', 'Lipid14', 'Lipid15', 'Lipid2',
'Lipid3', 'Lipid4', 'Lipid5', 'Lipid6', 'Lipid7', 'Lipid8', 'Lipid9',
'Membrane', 'Qw', 'Rg', 'Run', 'Step', 'Temp', 'TempT', 'TotalE',
'abs_z_average', 'index', 'rg1', 'rg2', 'rg3', 'rg4', 'rg5', 'rg6',
'rg_all', 'z_average', 'z_h1', 'z_h2', 'z_h3', 'z_h4', 'z_h5', 'z_h6'],
dtype='object')
In [7]:
t = data.query("TempT != 417 and DisReal > 51 \
and DisReal < 68 and z_average < -2 and z_average > -4")
In [11]:
t.hist("TotalE", bins=50)
Out[11]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a1432d358>]], dtype=object)
In [12]:
t373 = data.query("TempT == 373 and DisReal > 51 \
and DisReal < 68 and z_average < -2 and z_average > -4")
In [13]:
t373.hist("TotalE", bins=50)
Out[13]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a156cff28>]], dtype=object)
In [15]:
t373
Out[15]:
level_0
AMH
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AMH_3H
AMH_4H
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DisReal
Dis_h56
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Energy
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-8.920357
-6.222738
-5.070952
1369877
689877
-178.978954
-442.752594
-251.041686
-310.428351
58.0
57.372251
24.101972
17.117281
-776.186836
...
0.659232
1.388759
6.917900
-2.973023
-1.793542
-8.572124
-4.241207
-9.666051
-6.467283
-4.315183
1369881
689881
-180.042621
-438.069343
-249.521539
-306.802724
58.0
54.353183
21.930127
22.203907
-746.464074
...
0.689242
1.189123
8.812953
-2.811149
1.535911
-8.719435
-4.547737
-8.770011
-7.760791
-3.996591
1369897
689897
-175.491874
-436.549102
-245.704789
-307.160820
58.0
61.493837
24.983383
30.374334
-695.119857
...
1.061845
0.666535
5.954982
-2.356332
-1.761333
-6.591892
-3.830463
-8.876343
-6.461126
-5.000607
1369901
689901
-180.921826
-426.096778
-243.806223
-304.583260
58.0
65.467562
28.888631
26.532383
-655.491230
...
1.523927
0.576469
6.220829
-2.503380
-0.825682
-5.970642
-6.143692
-9.438208
-7.290208
-5.828049
1369905
689905
-177.214453
-415.670048
-241.089175
-293.190364
58.0
61.558249
21.352841
29.267518
-729.847428
...
0.882144
0.507857
5.510611
-2.573738
0.886495
-8.164106
-5.841094
-9.640226
-6.933722
-7.022550
1369909
689909
-184.989266
-421.026069
-248.254989
-304.092217
58.0
65.458591
19.869668
33.800344
-750.402341
...
0.884753
1.009770
6.179620
-2.434685
-0.699694
-6.053573
-6.945642
-10.590377
-3.724284
-5.724874
1369949
689949
-182.867201
-425.713621
-243.044299
-301.830884
58.0
60.753187
24.211236
32.745059
-708.737646
...
0.833724
1.567063
7.799867
-2.788074
0.158322
-10.072163
-2.016233
-9.945332
-10.334942
-4.894326
1369954
689954
-169.512144
-403.562528
-230.771951
-287.110965
58.0
58.385926
27.295161
58.019990
-665.754320
...
0.785803
0.898672
5.095570
-2.982549
-0.893082
-7.931155
-3.882855
-11.553770
-12.181218
-5.930881
1369958
689958
-168.248809
-408.656332
-228.646463
-288.215618
58.0
64.031782
26.298688
62.938943
-663.531977
...
0.523751
1.705066
8.443129
-2.247711
-0.747645
-8.366167
-3.652110
-10.107664
-10.017884
-4.773109
1369962
689962
-171.976391
-408.642097
-232.038082
-290.700566
58.0
59.272057
22.207873
59.010882
-691.452630
...
0.551707
1.083122
6.955481
-2.469045
0.890776
-8.923173
-4.899560
-10.272681
-9.278195
-5.283235
1369966
689966
-174.179444
-411.980266
-233.579556
-290.616702
58.0
53.961207
24.993706
52.876213
-695.549873
...
0.662805
1.862573
8.032325
-2.301522
-0.745049
-8.856118
-3.408631
-10.801352
-8.330045
-4.157258
1369974
689974
-178.716410
-418.673108
-242.045290
-298.791496
58.0
57.180724
27.149435
57.146770
-725.623393
...
0.690497
1.609234
7.266111
-2.648950
0.146495
-10.198642
-4.619366
-11.769098
-4.279254
-4.680950
1369982
689982
-174.178094
-418.339331
-232.609560
-289.983967
58.0
52.460293
21.292009
51.368217
-668.379985
...
0.634531
1.439340
8.347666
-2.974189
-0.603243
-10.578511
-3.882821
-8.888806
-7.950381
-4.231240
1369994
689994
-173.461338
-404.421160
-231.095617
-288.105001
58.0
54.023554
29.649051
53.137897
-702.681155
...
1.124590
1.303048
7.098627
-2.276091
1.198180
-8.571212
-5.444549
-9.652026
-6.670493
-5.032806
10095 rows × 50 columns
In [16]:
select(t373)
Out[16]:
count
mean
std
min
25%
50%
75%
max
BiasTo
Run
58.0
0
164.0
58.959353
4.383157
51.355846
55.218969
58.884139
62.420320
67.677721
2
179.0
58.896697
4.020753
51.051358
55.189069
59.006478
61.734849
67.877750
3
116.0
57.058870
4.000349
51.008300
53.677881
56.968076
59.529415
66.651877
60.0
0
247.0
58.921311
4.381339
51.086533
55.835819
58.771662
62.687725
67.731834
1
235.0
58.307482
4.081437
51.057232
54.888686
58.505485
61.022229
67.051867
62.0
0
301.0
59.884104
4.317887
51.135551
56.654802
59.827492
63.185373
67.970691
1
337.0
60.372263
4.030364
51.328884
57.406279
60.314684
63.725682
67.978842
2
122.0
58.825727
4.286660
51.440458
55.248349
58.379409
62.176576
67.405243
64.0
3
919.0
60.938503
4.148961
51.134149
58.118766
61.309105
64.278665
67.979501
5
548.0
61.341829
4.033957
51.099927
58.396224
61.686396
64.590414
67.965499
66.0
2
1000.0
61.117293
3.885831
51.014952
58.440880
61.301114
64.181268
67.938474
3
817.0
61.555133
3.881837
51.100628
59.046084
61.875465
64.619151
67.976028
5
315.0
60.927723
3.886924
51.380712
57.989457
61.260867
64.136502
67.882567
68.0
1
618.0
62.941671
3.577061
51.749130
60.422742
63.820359
65.968065
67.977633
2
360.0
62.709692
3.637080
51.117616
60.306652
63.318171
65.434900
67.986133
5
266.0
63.118088
3.512747
52.771601
60.618468
64.137759
65.909719
67.872947
70.0
1
190.0
63.187152
3.607405
52.241291
60.502627
64.044288
66.126573
67.998812
2
185.0
63.542112
3.309253
53.391605
61.866484
63.990347
66.363856
67.980271
5
628.0
63.849886
3.122292
51.826737
62.030705
64.432112
66.274656
67.988242
72.0
0
252.0
64.117716
3.363203
52.665611
62.512478
64.929230
66.885838
67.967183
2
244.0
63.958771
3.169318
52.539484
62.312326
64.657964
66.388173
67.975302
3
149.0
63.357248
3.810560
51.357889
61.708642
64.146320
66.265454
67.996480
4
133.0
63.995667
3.300765
52.265336
62.308285
65.138471
66.360343
67.798774
74.0
2
179.0
64.269876
2.871412
55.788808
63.163288
65.038223
66.594729
67.897062
7
215.0
64.799677
2.714039
54.839356
63.215404
65.608561
66.926641
67.988947
76.0
0
367.0
64.604183
2.566398
53.718974
63.180710
65.057184
66.633061
67.981778
4
105.0
64.869686
2.600159
55.287647
63.568517
65.393527
66.794506
67.945785
In [24]:
np.arange(8) // 2
Out[24]:
array([0, 0, 1, 1, 2, 2, 3, 3])
In [25]:
np.arange(8)
Out[25]:
array([0, 1, 2, 3, 4, 5, 6, 7])
In [26]:
t373_narrow = data.query("TempT == 373 and DisReal > 58 \
and DisReal < 65 and z_average < -2.5 and z_average > -3.5")
In [27]:
t373_narrow
Out[27]:
level_0
AMH
AMH-Go
AMH_3H
AMH_4H
BiasTo
DisReal
Dis_h56
Distance
Energy
...
rg5
rg6
rg_all
z_average
z_h1
z_h2
z_h3
z_h4
z_h5
z_h6
690879
10879
-188.979138
-390.081625
-249.115825
-289.262546
84.0
63.661735
20.669687
17.378090
-701.147517
...
0.650621
0.843067
4.545966
-3.066561
-1.748113
-6.430219
-8.190092
-9.252894
-8.895051
-6.098870
690899
10899
-183.186433
-392.944653
-248.231963
-290.903219
84.0
62.765665
20.238180
4.321019
-722.852166
...
2.083610
1.751864
8.232831
-2.584027
-0.334311
-4.870930
-4.314226
-7.357321
-12.419326
-3.192265
690951
10951
-198.371330
-418.571828
-262.922682
-306.794288
84.0
63.962096
24.762010
2.996987
-701.801432
...
1.135109
1.204688
6.678409
-3.242610
-3.586887
-8.136387
-6.533465
-8.381541
-7.687451
-5.183540
690963
10963
-203.890243
-410.824289
-263.026350
-303.628052
84.0
62.684794
26.680936
8.449340
-726.347395
...
1.423125
1.197325
6.840901
-3.244232
-5.257919
-7.654789
-8.554059
-9.237917
-2.725016
-2.810763
690983
10983
-204.946719
-424.061741
-266.330756
-309.658773
84.0
64.637249
24.843682
23.029081
-695.724410
...
1.021736
2.023428
8.815642
-3.377480
-4.444977
-7.599188
-7.986603
-8.800486
-7.926119
-5.178974
698563
18563
-200.476719
-406.283196
-254.381890
-298.644399
84.0
63.655067
21.874139
-20.290518
-743.007050
...
0.734103
1.320845
6.492530
-3.407151
0.666020
-5.851761
-11.573996
-11.560990
-7.626134
-7.147509
700136
20136
-198.617280
-403.437657
-256.952363
-302.751807
76.0
62.154514
20.276552
57.604418
-671.685930
...
0.825889
1.288798
6.359311
-2.564478
-2.033408
-8.763024
-8.968270
-9.715095
-2.393667
-4.623440
700168
20168
-204.006668
-404.623421
-266.981801
-310.144555
76.0
61.471915
22.424513
47.543739
-705.466515
...
1.003719
0.931413
7.588703
-2.781509
-3.820230
-8.485840
-9.287819
-9.172349
-3.685424
-3.974061
700212
20212
-201.402359
-399.899319
-257.784101
-299.495533
76.0
60.809113
24.815046
60.198799
-670.265642
...
0.710009
1.259152
5.545050
-2.753711
-3.074379
-9.317066
-7.355665
-8.175493
-4.717142
-3.300249
700276
20276
-205.320118
-413.030582
-267.203097
-306.812533
76.0
61.589629
15.407709
61.199536
-707.336586
...
0.799255
0.998693
6.362230
-2.644398
-2.460090
-9.528058
-5.657994
-6.338014
-6.228574
-2.725754
700368
20368
-178.281697
-376.366721
-237.920124
-282.115608
76.0
62.458367
21.742777
60.658867
-678.643748
...
0.892182
2.258068
8.675508
-2.931965
-1.060059
-9.247739
-11.683983
-10.090947
-6.260215
-1.603898
700420
20420
-183.816519
-382.630495
-237.802464
-285.334908
76.0
64.826625
22.343382
64.606152
-674.654894
...
0.775517
1.243357
6.269449
-2.732949
-1.934275
-9.097793
-6.550405
-7.561553
-5.098176
-4.888200
700468
20468
-178.883857
-382.595805
-241.597731
-284.935977
76.0
59.233940
17.803061
58.321568
-677.582467
...
1.175440
2.489691
9.451321
-3.242794
-2.487269
-9.405203
-8.207869
-11.912090
-2.094131
-6.141665
700480
20480
-182.098996
-383.596331
-240.888981
-279.891828
76.0
63.709435
19.934453
63.689453
-681.232302
...
0.664851
1.057470
7.756744
-2.517139
-2.955325
-10.482131
-6.265160
-6.966603
-5.933934
-2.154303
700856
20856
-190.593595
-381.792578
-249.956885
-284.321823
76.0
64.882616
25.082910
59.854211
-629.475287
...
0.643007
1.468188
6.761151
-3.347961
-3.165812
-8.298235
-8.384750
-7.930444
-8.327036
-2.489761
701040
21040
-192.501763
-396.777689
-256.917741
-297.257231
76.0
63.876424
18.262090
60.119692
-687.645318
...
0.902084
1.573500
7.929596
-2.891285
-1.656422
-6.029935
-9.494978
-8.515965
-5.847921
-2.994475
701044
21044
-186.817382
-384.768943
-249.155596
-285.446440
76.0
62.912696
25.874808
62.743467
-704.606274
...
0.765338
0.958489
6.955008
-2.730942
-2.162102
-6.211948
-8.597890
-8.595427
-5.751583
-4.693169
701120
21120
-205.672390
-405.665475
-265.144647
-303.513149
76.0
59.118626
18.761977
59.019030
-736.470916
...
1.580592
1.107690
6.606527
-2.867801
-2.758255
-8.365926
-9.588418
-6.454264
-4.806064
-2.829348
701124
21124
-199.724862
-407.073971
-264.479602
-304.586851
76.0
62.061784
20.991611
61.964846
-731.888874
...
1.204239
0.657606
6.559105
-3.050972
-4.354171
-8.348043
-8.444871
-7.364612
-3.183824
-3.659724
701182
21182
-203.031963
-409.457162
-263.489526
-305.137466
76.0
60.114533
25.969850
57.396877
-748.906300
...
1.145960
1.643953
6.771216
-2.769939
-3.430384
-9.706465
-8.892401
-7.016079
1.900854
-3.852103
701186
21186
-209.133908
-418.951106
-270.329612
-313.703091
76.0
62.014634
25.631543
59.764704
-795.730168
...
0.813192
0.650584
5.075778
-2.723255
-4.000245
-7.756103
-8.994198
-5.593929
-0.685517
-3.082913
701190
21190
-206.298143
-414.647769
-262.622324
-305.999107
76.0
63.376093
22.426479
61.111409
-706.696457
...
0.782928
1.787070
6.482941
-2.745467
-2.380926
-7.192482
-8.157692
-8.402699
-3.874107
-2.416835
701276
21276
-199.321702
-404.268693
-259.845269
-303.878295
76.0
62.883368
13.898133
62.723456
-688.334453
...
0.648381
2.660175
8.138812
-3.013106
-1.382305
-7.085366
-7.674150
-9.222306
-8.643840
-4.594032
701296
21296
-179.347609
-383.243626
-238.575135
-280.384450
76.0
64.165014
20.017145
63.686432
-721.606750
...
0.784921
1.679485
6.799706
-3.006630
-1.846675
-9.927289
-5.851953
-8.893977
-9.136661
-6.239752
701368
21368
-192.458934
-392.447008
-247.879018
-289.635008
76.0
61.886092
17.251761
61.447822
-683.297323
...
0.654819
1.696571
7.362176
-2.704251
-0.990646
-10.015917
-4.681644
-8.239166
-5.716358
-4.997329
701372
21372
-200.195008
-397.619030
-255.569261
-300.036854
76.0
63.789770
19.492147
62.679185
-659.478752
...
0.705595
1.343419
7.306642
-2.573645
-2.939448
-9.694644
-3.890476
-6.369296
-5.974685
-0.938720
701476
21476
-201.866091
-393.521296
-263.739594
-305.039912
76.0
61.263742
14.333677
60.656736
-699.154275
...
0.667946
0.943300
5.665992
-2.986017
-1.733400
-7.803968
-7.067322
-7.863725
-8.757457
-2.709796
701536
21536
-199.942152
-389.249038
-255.400849
-294.693822
76.0
63.728507
21.903112
62.615284
-700.473678
...
1.063016
0.827648
6.030357
-2.718377
-1.874737
-8.485858
-9.477464
-8.208474
-6.441917
-4.083910
701732
21732
-183.159453
-386.190922
-235.676824
-279.781222
76.0
62.736953
25.320034
62.305945
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2516 rows × 50 columns
In [47]:
t373_narrow.to_csv("/Users/weilu/Research/data/t373_narrow.csv")
In [49]:
pd.read_csv("/Users/weilu/Research/data/t373_narrow.csv", index_col=0)
Out[49]:
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AMH-Go
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76.0
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...
0.648381
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...
0.784921
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701368
21368
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76.0
61.886092
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...
0.654819
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701372
21372
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76.0
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...
0.705595
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701476
21476
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14.333677
60.656736
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...
0.667946
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701536
21536
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...
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21732
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701744
21744
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76.0
61.607309
24.374316
59.861844
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...
1.035286
1.554074
7.615217
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-6.082994
-6.736689
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
1368073
688073
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58.0
63.557769
26.553924
5.229743
-724.999601
...
0.755565
1.757790
8.589588
-2.780659
-3.636463
-9.032954
-3.538948
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-2.969545
1368149
688149
-174.210903
-434.951971
-238.312441
-300.856227
58.0
58.979148
26.441338
24.012457
-728.586715
...
1.032995
0.899696
6.416219
-3.131277
2.143593
-8.866949
-4.497864
-10.766226
-8.539579
-5.671882
1368217
688217
-184.901362
-415.909713
-250.051629
-299.849268
58.0
60.818516
31.461736
23.315544
-757.815327
...
0.864376
1.608958
8.831331
-2.721330
-1.497604
-7.684517
-1.215743
-11.005548
-10.589483
-3.704199
1368221
688221
-182.385335
-416.747796
-244.370078
-299.504787
58.0
59.587487
27.152610
19.635615
-712.058941
...
0.721974
1.263238
7.328826
-2.564413
-0.377317
-8.762928
-3.326583
-9.949359
-9.546802
-5.798570
1368245
688245
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-244.788796
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58.0
59.265955
25.231700
-6.671719
-732.918851
...
0.845738
1.517929
8.651643
-2.616635
-0.433013
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1368285
688285
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58.0
60.640554
26.592739
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...
0.794170
0.935052
6.529880
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1.725538
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-10.853723
-8.537626
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1368325
688325
-180.278907
-417.338236
-244.706023
-300.526281
58.0
61.095038
13.585796
-19.009305
-655.934411
...
1.283974
0.831167
5.932357
-3.074031
0.309547
-6.813591
-3.404757
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-6.107439
1368361
688361
-183.606638
-415.098958
-244.664187
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58.0
60.770273
27.241040
-21.828712
-747.726212
...
0.869720
1.079696
6.470778
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-5.422851
1368365
688365
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-290.030268
58.0
58.441573
25.915303
-25.488909
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...
0.596922
1.227126
6.685990
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1368405
688405
-171.017655
-417.268400
-233.666533
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58.0
59.227292
24.564113
5.064778
-722.212197
...
1.072983
1.198726
7.022781
-2.654217
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-6.440966
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-5.586225
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1368421
688421
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58.0
61.994997
26.401146
10.573494
-721.641583
...
0.973994
1.347110
6.480872
-2.809157
2.713803
-6.655543
-5.198070
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-5.834637
1368746
688746
-179.121831
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-301.340221
58.0
63.485361
25.888623
50.526942
-725.928732
...
1.095397
2.815720
10.716150
-2.990654
0.340139
-10.486181
-3.211063
-9.472440
-8.844071
-3.137275
1368806
688806
-180.626124
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-251.007931
-312.815521
58.0
62.615036
24.593743
58.388955
-703.422838
...
0.852610
1.152187
7.960150
-2.995607
-0.275691
-8.259458
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-6.944422
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1368870
688870
-180.501256
-423.224316
-243.600950
-300.038990
58.0
58.627532
23.814134
50.773716
-756.535330
...
0.668297
2.118517
7.982507
-3.192017
1.695633
-11.731090
-5.610044
-10.959165
-7.712080
-5.165576
1369010
689010
-179.487590
-424.101373
-243.873436
-300.164283
58.0
61.568734
28.438961
45.158228
-726.176637
...
0.738207
1.361105
7.062270
-2.812035
-3.532588
-9.255291
-4.783310
-10.607113
-4.007548
-5.726850
1369030
689030
-168.919052
-419.221522
-236.213064
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58.0
58.456522
27.621790
47.294456
-684.129366
...
1.111318
1.476957
6.769122
-2.655099
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-4.006576
-10.618220
-7.687263
-4.993024
1369102
689102
-168.319223
-417.837647
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58.0
58.625259
25.475529
43.501368
-726.895572
...
1.016458
1.044342
6.975522
-2.766631
0.876065
-6.886716
-4.719075
-10.103571
-6.066721
-6.437804
1369126
689126
-179.690809
-430.921987
-244.620814
-301.194331
58.0
62.324539
25.954151
50.169640
-755.364416
...
0.662318
0.795600
7.451465
-3.112160
-1.584609
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-7.566449
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1369198
689198
-178.243939
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-290.399571
58.0
58.508104
25.556783
55.111334
-708.657623
...
1.208107
0.556888
6.169614
-2.737007
2.171651
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1369202
689202
-178.609499
-416.061316
-238.913211
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58.0
60.326950
27.636576
51.779129
-736.783370
...
0.665393
0.914440
4.989163
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1.763269
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1369222
689222
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58.0
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27.640180
46.522631
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...
0.969568
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0.170344
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689234
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58.0
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56.533185
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...
0.529463
1.487205
7.263872
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0.400207
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1369246
689246
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58.0
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26.216408
58.354414
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...
0.716470
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5.907705
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1369258
689258
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62.555680
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0.496093
1.300823
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689294
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58.0
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689394
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689817
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689905
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689949
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24.211236
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689954
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58.0
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58.019990
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2516 rows × 50 columns
In [50]:
t373_super_narrow = data.query("TempT == 373 and DisReal > 60 \
and DisReal < 63 and z_average < -2.5 and z_average > -3.5")
In [ ]:
In [62]:
t373_super_narrow.drop("level_0", axis=1).reset_index(drop=True).reset_index()
Out[62]:
level_0
AMH
AMH-Go
AMH_3H
AMH_4H
BiasTo
DisReal
Dis_h56
Distance
Energy
...
rg5
rg6
rg_all
z_average
z_h1
z_h2
z_h3
z_h4
z_h5
z_h6
0
0
-183.186433
-392.944653
-248.231963
-290.903219
84.0
62.765665
20.238180
4.321019
-722.852166
...
2.083610
1.751864
8.232831
-2.584027
-0.334311
-4.870930
-4.314226
-7.357321
-12.419326
-3.192265
1
1
-203.890243
-410.824289
-263.026350
-303.628052
84.0
62.684794
26.680936
8.449340
-726.347395
...
1.423125
1.197325
6.840901
-3.244232
-5.257919
-7.654789
-8.554059
-9.237917
-2.725016
-2.810763
2
2
-198.617280
-403.437657
-256.952363
-302.751807
76.0
62.154514
20.276552
57.604418
-671.685930
...
0.825889
1.288798
6.359311
-2.564478
-2.033408
-8.763024
-8.968270
-9.715095
-2.393667
-4.623440
3
3
-204.006668
-404.623421
-266.981801
-310.144555
76.0
61.471915
22.424513
47.543739
-705.466515
...
1.003719
0.931413
7.588703
-2.781509
-3.820230
-8.485840
-9.287819
-9.172349
-3.685424
-3.974061
4
4
-201.402359
-399.899319
-257.784101
-299.495533
76.0
60.809113
24.815046
60.198799
-670.265642
...
0.710009
1.259152
5.545050
-2.753711
-3.074379
-9.317066
-7.355665
-8.175493
-4.717142
-3.300249
5
5
-205.320118
-413.030582
-267.203097
-306.812533
76.0
61.589629
15.407709
61.199536
-707.336586
...
0.799255
0.998693
6.362230
-2.644398
-2.460090
-9.528058
-5.657994
-6.338014
-6.228574
-2.725754
6
6
-178.281697
-376.366721
-237.920124
-282.115608
76.0
62.458367
21.742777
60.658867
-678.643748
...
0.892182
2.258068
8.675508
-2.931965
-1.060059
-9.247739
-11.683983
-10.090947
-6.260215
-1.603898
7
7
-186.817382
-384.768943
-249.155596
-285.446440
76.0
62.912696
25.874808
62.743467
-704.606274
...
0.765338
0.958489
6.955008
-2.730942
-2.162102
-6.211948
-8.597890
-8.595427
-5.751583
-4.693169
8
8
-199.724862
-407.073971
-264.479602
-304.586851
76.0
62.061784
20.991611
61.964846
-731.888874
...
1.204239
0.657606
6.559105
-3.050972
-4.354171
-8.348043
-8.444871
-7.364612
-3.183824
-3.659724
9
9
-203.031963
-409.457162
-263.489526
-305.137466
76.0
60.114533
25.969850
57.396877
-748.906300
...
1.145960
1.643953
6.771216
-2.769939
-3.430384
-9.706465
-8.892401
-7.016079
1.900854
-3.852103
10
10
-209.133908
-418.951106
-270.329612
-313.703091
76.0
62.014634
25.631543
59.764704
-795.730168
...
0.813192
0.650584
5.075778
-2.723255
-4.000245
-7.756103
-8.994198
-5.593929
-0.685517
-3.082913
11
11
-199.321702
-404.268693
-259.845269
-303.878295
76.0
62.883368
13.898133
62.723456
-688.334453
...
0.648381
2.660175
8.138812
-3.013106
-1.382305
-7.085366
-7.674150
-9.222306
-8.643840
-4.594032
12
12
-192.458934
-392.447008
-247.879018
-289.635008
76.0
61.886092
17.251761
61.447822
-683.297323
...
0.654819
1.696571
7.362176
-2.704251
-0.990646
-10.015917
-4.681644
-8.239166
-5.716358
-4.997329
13
13
-201.866091
-393.521296
-263.739594
-305.039912
76.0
61.263742
14.333677
60.656736
-699.154275
...
0.667946
0.943300
5.665992
-2.986017
-1.733400
-7.803968
-7.067322
-7.863725
-8.757457
-2.709796
14
14
-183.159453
-386.190922
-235.676824
-279.781222
76.0
62.736953
25.320034
62.305945
-717.663661
...
0.759227
1.514345
5.729742
-2.773871
0.626748
-9.051280
-9.519281
-9.054574
-6.101329
-5.240148
15
15
-181.221021
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16
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17
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25
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28
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29
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...
...
...
...
...
...
...
...
...
...
...
...
...
...
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...
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...
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1093
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1110
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1111
1111
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1112
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1113
1113
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1123 rows × 50 columns
In [64]:
t373_super_narrow.to_csv("/Users/weilu/Research/data/t373_super_narrow.csv")
In [63]:
t373_super_narrow.drop("level_0", axis=1).reset_index(drop=True).reset_index().plot("level_0","Lipid1")
Out[63]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a13c8b6d8>
In [81]:
t373_super_narrow.drop("level_0", axis=1).reset_index(drop=True).reset_index().plot("level_0","Lipid7")
Out[81]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a1723dbe0>
In [75]:
t373_narrow = t373_narrow.reset_index(drop=True)
t373_narrow["BiasedEnergy"] = t373_narrow["TotalE"] + 0.2*t373_narrow["AMH_4H"]
In [77]:
t373_narrow.sort_values("BiasedEnergy").head()
Out[77]:
2186 0.002062
398 0.002075
20 -1.876488
988 0.002086
2261 0.002059
Name: Lipid1, dtype: float64
In [68]:
t373_super_narrow = t373_super_narrow.reset_index(drop=True)
t373_super_narrow["BiasedEnergy"] = t373_super_narrow["TotalE"] + 0.2*t373_super_narrow["AMH_4H"]
In [78]:
t373_super_narrow.query("Lipid1 < -0.5").sort_values("BiasedEnergy").head().to_csv("/Users/weilu/Research/data/selected.csv")
In [82]:
t373_super_narrow.query("Lipid1 < -0.5 and Lipid7 < -0.5").sort_values("BiasedEnergy").head()["Lipid1"]
Out[82]:
Series([], Name: Lipid1, dtype: float64)
In [84]:
t373_narrow.query("Lipid1 < -0.5 and Lipid7 < -0.5").to_csv("/Users/weilu/Research/data/selected2.csv")
In [89]:
t373_narrow.sort_values("DisReal", ascending=False).head()
Out[89]:
level_0
AMH
AMH-Go
AMH_3H
AMH_4H
BiasTo
DisReal
Dis_h56
Distance
Energy
...
rg6
rg_all
z_average
z_h1
z_h2
z_h3
z_h4
z_h5
z_h6
BiasedEnergy
946
286388
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-245.541039
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68.0
64.995160
26.508368
57.145418
-717.405939
...
1.520013
7.289126
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546
221108
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66.0
64.987222
24.206840
42.046223
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...
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8.335522
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532
220916
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24.529796
38.266241
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...
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351
131056
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62.0
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33.820984
64.870234
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...
1.030485
7.311189
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2248
661197
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64.0
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25.550759
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...
0.918407
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5 rows × 51 columns
In [85]:
t373_narrow.query("Lipid1 < -0.5 and Lipid7 < -0.5")
Out[85]:
level_0
AMH
AMH-Go
AMH_3H
AMH_4H
BiasTo
DisReal
Dis_h56
Distance
Energy
...
rg6
rg_all
z_average
z_h1
z_h2
z_h3
z_h4
z_h5
z_h6
BiasedEnergy
101
27117
-203.930506
-410.706077
-274.186428
-331.105482
76.0
64.897123
35.214455
37.688040
-685.391837
...
1.732591
9.798258
-2.963674
-0.805897
-7.900613
-0.682853
-9.747542
-6.584439
-14.803409
-756.022058
1184
307888
-195.630242
-435.155569
-256.611899
-309.100543
64.0
58.183612
19.117130
53.955815
-736.440977
...
1.423229
5.543619
-3.372721
-1.120181
-6.685898
-4.955295
-10.356982
-8.509524
-4.613997
-806.479888
1185
309158
-193.264620
-432.867410
-262.231255
-319.094199
64.0
58.600919
26.562725
-39.499154
-728.018780
...
2.024190
7.469871
-3.195907
-3.636113
-5.186055
-2.285889
-9.187340
-8.547995
-3.866942
-799.467778
1193
380116
-194.734815
-415.998650
-261.859307
-324.052138
76.0
64.182486
40.610641
41.699050
-727.524526
...
1.431345
6.697026
-3.216031
-1.079433
-7.731002
-5.865393
-9.799235
-5.988281
-16.944518
-798.934579
1194
380800
-184.085074
-395.333849
-253.502592
-304.337555
76.0
63.458593
43.401303
28.169537
-697.228949
...
2.309146
6.624908
-2.896678
-1.165570
-6.983405
-4.931913
-10.518770
-8.802494
-7.181036
-765.655801
1491
443746
-199.795756
-421.490782
-270.596935
-328.038519
80.0
64.888988
33.014588
22.832945
-756.243689
...
3.683204
9.512102
-3.225312
-1.372187
-4.244962
-1.137230
-7.634294
-9.893363
-12.793285
-826.553641
6 rows × 51 columns
In [86]:
t373_super_narrow.query("Lipid1 < -0.5").sort_values("BiasedEnergy").head()
Out[86]:
level_0
AMH
AMH-Go
AMH_3H
AMH_4H
BiasTo
DisReal
Dis_h56
Distance
Energy
...
rg6
rg_all
z_average
z_h1
z_h2
z_h3
z_h4
z_h5
z_h6
BiasedEnergy
10
21186
-209.133908
-418.951106
-270.329612
-313.703091
76.0
62.014634
25.631543
59.764704
-795.730168
...
0.650584
5.075778
-2.723255
-4.000245
-7.756103
-8.994198
-5.593929
-0.685517
-3.082913
-864.700700
783
581253
-201.116930
-413.832648
-258.702356
-304.689194
66.0
61.414392
26.005718
13.321524
-780.344135
...
1.375310
7.643183
-2.961339
-2.644498
-9.656189
-12.192673
-6.613459
-0.593869
-0.676587
-848.976545
304
226069
-203.865362
-414.742325
-264.843559
-309.038487
66.0
60.759228
25.615073
23.097784
-778.462800
...
1.499382
6.344030
-2.984441
-3.419661
-9.565722
-8.099691
-8.470239
-6.105927
-4.251687
-847.279984
227
221220
-200.730946
-410.319419
-261.402907
-306.431810
66.0
61.064203
18.522741
55.609889
-775.898991
...
1.371612
7.268464
-2.732144
-4.542499
-7.893796
-5.047760
-9.848075
-1.950217
-6.329883
-845.157923
871
587589
-194.713299
-408.114385
-258.357263
-303.285148
66.0
60.793929
25.228643
52.950716
-767.308158
...
1.764626
6.267082
-3.479249
-1.210820
-8.421729
-5.441395
-9.063197
-8.485487
-5.500987
-833.864010
5 rows × 51 columns
In [ ]:
data.query("TempT == 373 and DisReal > 60 \
and DisReal < 63 and z_average < -2.5 and z_average > -3.5").\
query("Lipid1 < -0.5")
In [91]:
data.query("TempT == 373 and DisReal > 50 \
and DisReal < 63 and z_average < -2.5 and z_average > -3.5").\
query("Lipid1 < -0.5")
Out[91]:
level_0
AMH
AMH-Go
AMH_3H
AMH_4H
BiasTo
DisReal
Dis_h56
Distance
Energy
...
rg5
rg6
rg_all
z_average
z_h1
z_h2
z_h3
z_h4
z_h5
z_h6
690899
10899
-183.186433
-392.944653
-248.231963
-290.903219
84.0
62.765665
20.238180
4.321019
-722.852166
...
2.083610
1.751864
8.232831
-2.584027
-0.334311
-4.870930
-4.314226
-7.357321
-12.419326
-3.192265
690963
10963
-203.890243
-410.824289
-263.026350
-303.628052
84.0
62.684794
26.680936
8.449340
-726.347395
...
1.423125
1.197325
6.840901
-3.244232
-5.257919
-7.654789
-8.554059
-9.237917
-2.725016
-2.810763
700136
20136
-198.617280
-403.437657
-256.952363
-302.751807
76.0
62.154514
20.276552
57.604418
-671.685930
...
0.825889
1.288798
6.359311
-2.564478
-2.033408
-8.763024
-8.968270
-9.715095
-2.393667
-4.623440
700168
20168
-204.006668
-404.623421
-266.981801
-310.144555
76.0
61.471915
22.424513
47.543739
-705.466515
...
1.003719
0.931413
7.588703
-2.781509
-3.820230
-8.485840
-9.287819
-9.172349
-3.685424
-3.974061
700212
20212
-201.402359
-399.899319
-257.784101
-299.495533
76.0
60.809113
24.815046
60.198799
-670.265642
...
0.710009
1.259152
5.545050
-2.753711
-3.074379
-9.317066
-7.355665
-8.175493
-4.717142
-3.300249
700276
20276
-205.320118
-413.030582
-267.203097
-306.812533
76.0
61.589629
15.407709
61.199536
-707.336586
...
0.799255
0.998693
6.362230
-2.644398
-2.460090
-9.528058
-5.657994
-6.338014
-6.228574
-2.725754
700368
20368
-178.281697
-376.366721
-237.920124
-282.115608
76.0
62.458367
21.742777
60.658867
-678.643748
...
0.892182
2.258068
8.675508
-2.931965
-1.060059
-9.247739
-11.683983
-10.090947
-6.260215
-1.603898
700468
20468
-178.883857
-382.595805
-241.597731
-284.935977
76.0
59.233940
17.803061
58.321568
-677.582467
...
1.175440
2.489691
9.451321
-3.242794
-2.487269
-9.405203
-8.207869
-11.912090
-2.094131
-6.141665
701044
21044
-186.817382
-384.768943
-249.155596
-285.446440
76.0
62.912696
25.874808
62.743467
-704.606274
...
0.765338
0.958489
6.955008
-2.730942
-2.162102
-6.211948
-8.597890
-8.595427
-5.751583
-4.693169
701120
21120
-205.672390
-405.665475
-265.144647
-303.513149
76.0
59.118626
18.761977
59.019030
-736.470916
...
1.580592
1.107690
6.606527
-2.867801
-2.758255
-8.365926
-9.588418
-6.454264
-4.806064
-2.829348
701124
21124
-199.724862
-407.073971
-264.479602
-304.586851
76.0
62.061784
20.991611
61.964846
-731.888874
...
1.204239
0.657606
6.559105
-3.050972
-4.354171
-8.348043
-8.444871
-7.364612
-3.183824
-3.659724
701182
21182
-203.031963
-409.457162
-263.489526
-305.137466
76.0
60.114533
25.969850
57.396877
-748.906300
...
1.145960
1.643953
6.771216
-2.769939
-3.430384
-9.706465
-8.892401
-7.016079
1.900854
-3.852103
701186
21186
-209.133908
-418.951106
-270.329612
-313.703091
76.0
62.014634
25.631543
59.764704
-795.730168
...
0.813192
0.650584
5.075778
-2.723255
-4.000245
-7.756103
-8.994198
-5.593929
-0.685517
-3.082913
701276
21276
-199.321702
-404.268693
-259.845269
-303.878295
76.0
62.883368
13.898133
62.723456
-688.334453
...
0.648381
2.660175
8.138812
-3.013106
-1.382305
-7.085366
-7.674150
-9.222306
-8.643840
-4.594032
701368
21368
-192.458934
-392.447008
-247.879018
-289.635008
76.0
61.886092
17.251761
61.447822
-683.297323
...
0.654819
1.696571
7.362176
-2.704251
-0.990646
-10.015917
-4.681644
-8.239166
-5.716358
-4.997329
701476
21476
-201.866091
-393.521296
-263.739594
-305.039912
76.0
61.263742
14.333677
60.656736
-699.154275
...
0.667946
0.943300
5.665992
-2.986017
-1.733400
-7.803968
-7.067322
-7.863725
-8.757457
-2.709796
701744
21744
-181.221021
-377.463607
-237.919629
-281.416346
76.0
61.607309
24.374316
59.861844
-681.319335
...
1.035286
1.554074
7.615217
-2.945745
-2.379840
-5.846920
-6.168532
-10.228673
-6.082994
-6.736689
701768
21768
-192.502843
-390.958450
-251.793742
-295.671782
76.0
53.718974
26.988017
47.728832
-705.437122
...
0.734432
1.513660
5.661005
-2.937127
-2.079911
-6.932423
-5.748674
-10.243032
-6.169006
-5.348224
701780
21780
-189.710193
-390.173220
-253.045609
-295.121123
76.0
61.631306
24.044134
60.144295
-679.540192
...
0.868744
0.511458
5.450385
-2.739204
-1.619467
-7.916267
-6.572121
-8.527189
-6.398939
-7.221901
701880
21880
-196.183966
-406.138688
-258.857467
-299.875884
76.0
61.946134
25.504089
61.291518
-719.405160
...
1.094599
1.312474
6.926905
-2.650496
-4.341284
-10.439736
-3.986843
-8.784379
-4.672728
-3.861032
701888
21888
-182.123524
-382.441557
-234.983425
-275.899654
76.0
61.295090
19.887148
52.833097
-699.013294
...
0.950484
1.048522
6.631513
-3.259112
-5.539501
-9.242057
-6.369288
-7.044428
-9.616248
-2.159119
701912
21912
-189.169792
-385.324471
-248.156855
-290.348566
76.0
60.681187
18.173458
46.942436
-696.476873
...
0.722515
1.190232
5.971119
-2.946683
-1.995478
-8.974955
-8.833224
-7.349788
-6.732135
-1.237385
701916
21916
-186.931038
-386.367808
-242.847558
-282.272994
76.0
58.652312
12.701777
49.232927
-692.828464
...
1.504041
1.630386
7.021528
-2.553763
-2.004846
-8.324360
-9.997737
-9.259703
-4.593541
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701948
21948
-196.316278
-400.143398
-255.116610
-298.578717
76.0
56.487222
18.780343
47.179682
-687.715042
...
1.017564
2.798181
9.120421
-2.690931
-1.519216
-7.825602
-7.605637
-7.611923
-4.665628
-1.864141
702108
22108
-190.220942
-402.813799
-249.167746
-295.647170
76.0
62.378067
21.391853
50.484732
-723.196455
...
0.587846
1.598440
7.352005
-3.249223
-2.029978
-6.738988
-7.185882
-10.995796
-8.571060
-4.540602
702582
22582
-197.277252
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-297.855477
76.0
57.613737
26.254727
36.994472
-730.957889
...
1.000809
1.481825
6.455283
-2.717015
-1.265193
-8.254415
-6.241128
-10.476718
-7.183740
-4.966733
703062
23062
-204.807537
-417.642170
-269.440318
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76.0
57.803567
26.887167
35.130313
-739.331052
...
0.756028
1.581873
8.001162
-2.853559
-3.947986
-7.351372
-8.160585
-9.203630
-6.901076
-2.597042
703180
23180
-196.827300
-393.983121
-252.648082
-293.313550
76.0
61.762469
15.618466
54.818228
-690.801136
...
1.028693
0.547849
5.459401
-3.155704
-1.160795
-9.622743
-14.384605
-6.285290
-4.718719
-5.475780
703488
23488
-208.609130
-418.099838
-268.313641
-310.499139
76.0
62.221925
13.688927
32.159859
-695.677421
...
1.050160
1.105813
9.495926
-3.234484
-0.733311
-6.274690
-14.411684
-7.791673
-4.620366
-5.713177
703790
23790
-181.355665
-403.939860
-242.282703
-290.700230
76.0
59.296378
24.179108
58.972964
-705.960324
...
1.141144
0.895286
7.219929
-3.193301
0.605638
-8.064535
-8.238747
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-6.125196
-7.465287
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
1268997
588997
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19.713229
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-707.077093
...
1.189504
2.014005
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1269001
589001
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66.0
62.130993
16.878213
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...
0.958035
1.874443
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1269017
589017
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24.915816
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...
0.642439
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1269021
589021
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66.0
60.179164
26.633738
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...
0.735274
1.869645
7.100545
-3.348017
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1269037
589037
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61.653446
26.620225
-49.370929
-713.904281
...
0.830634
2.338165
8.714490
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589269
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22.661676
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...
0.670657
2.206382
8.639000
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1269273
589273
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66.0
60.104471
23.849378
-54.434424
-742.807014
...
0.823515
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589296
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0.917824
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589304
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31.215324
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1.773059
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0.478851
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0.546153
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1269308
589308
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28.138494
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...
0.740696
1.489314
7.612840
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1145 rows × 50 columns
In [95]:
data.query("TempT == 373 and DisReal > 50 \
and DisReal < 63 and z_average < -2.5 and z_average > -3.5").\
query("Lipid1 < -0.5 and Lipid7 < -0.5")
Out[95]:
level_0
AMH
AMH-Go
AMH_3H
AMH_4H
BiasTo
DisReal
Dis_h56
Distance
Energy
...
rg5
rg6
rg_all
z_average
z_h1
z_h2
z_h3
z_h4
z_h5
z_h6
719790
39790
-200.165415
-449.409061
-271.433191
-332.108513
72.0
50.788637
22.805240
41.767110
-738.455973
...
0.773651
1.059144
6.847160
-2.845979
-2.202658
-5.662142
-2.826926
-9.916290
-7.535219
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719850
39850
-200.344851
-441.937429
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72.0
52.241362
28.935426
44.313962
-725.821803
...
0.892460
1.538403
6.691036
-3.025566
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72.0
51.357889
23.419343
36.659341
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540129
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540716
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544508
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544512
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544804
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546685
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546757
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546992
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547992
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548126
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166 rows × 50 columns
In [97]:
data.query("TempT != 417 and DisReal > 50 \
and DisReal < 63 and z_average < -2.5 and z_average > -3.5").\
query("Lipid1 < -0.5 and Lipid7 < -0.5")
Out[97]:
level_0
AMH
AMH-Go
AMH_3H
AMH_4H
BiasTo
DisReal
Dis_h56
Distance
Energy
...
rg5
rg6
rg_all
z_average
z_h1
z_h2
z_h3
z_h4
z_h5
z_h6
705501
25501
-216.112719
-442.122646
-291.026235
-347.501318
76.0
62.647961
44.540263
61.763450
-836.562429
...
1.415303
3.163537
10.881090
-2.839091
1.001305
-5.652217
-3.721500
-8.175074
-5.209776
-13.531687
712055
32055
-209.110431
-474.873349
-285.046768
-347.584255
72.0
50.107109
22.905990
-18.148255
-872.040575
...
0.892242
1.314513
6.642784
-2.864897
-2.255518
-6.794366
-4.231318
-9.941246
-4.001791
-4.464314
712123
32123
-201.882750
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-278.612081
-338.638506
72.0
50.713034
25.995547
-17.065924
-894.482517
...
0.668988
1.318091
6.655265
-2.562783
-2.618729
-6.824996
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712363
32363
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72.0
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26.092201
3.520444
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...
1.086510
1.262166
6.403256
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712383
32383
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32415
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...
0.888004
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32443
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32655
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712727
32727
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72.0
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...
0.863319
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...
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33439
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...
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713447
33447
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72.0
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...
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33543
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...
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713567
33567
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72.0
50.977163
29.323776
15.017174
-871.798472
...
0.819609
1.385356
6.579523
-2.889971
-0.089324
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713571
33571
-203.713041
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72.0
50.316337
28.729577
11.817938
-869.231801
...
0.759659
1.707409
7.490420
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0.502036
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-8.948140
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713615
33615
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72.0
53.537870
25.201087
31.771235
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...
0.748774
1.893062
7.548450
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713863
33863
-206.922408
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72.0
51.656396
27.233337
49.756195
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...
0.581395
1.595338
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0.580777
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714019
34019
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72.0
55.765441
27.340233
48.295557
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...
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34023
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714135
34135
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...
0.676562
1.544641
6.417561
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714179
34179
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72.0
50.965717
24.394887
4.416452
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...
0.768449
1.606717
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714195
34195
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72.0
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...
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714207
34207
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...
0.802482
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963 rows × 50 columns
In [96]:
data.query("TempT == 373 and DisReal > 50 \
and DisReal < 63 and z_average < -2.5 and z_average > -3.5").shape
Out[96]:
(2764, 50)
In [98]:
data.query("TempT != 417 and DisReal > 50 \
and DisReal < 63 and z_average < -2.5 and z_average > -3.5").shape
Out[98]:
(6754, 50)
In [ ]:
data.query("TempT == 373 and DisReal > 60 \
and DisReal < 63 and z_average < -2.5 and z_average > -3.5").shape
In [104]:
t = data.query("TempT != 417 and DisReal > 50 \
and DisReal < 63 and z_average < -2.5 and z_average > -3.5").\
query("Lipid1 < -0.5")
t = t.reset_index(drop=True)
t["BiasedEnergy"] = t["TotalE"] + 0.2*t["AMH_4H"]
In [106]:
t.sort_values("BiasedEnergy").head().to_csv("/Users/weilu/Research/data/selected_all.csv")
In [112]:
tt = t.sort_values("BiasedEnergy").drop("level_0", axis=1).reset_index(drop=True).reset_index()
In [122]:
tt.query("Qw < 0.5 and Qw > 0.4").to_csv("/Users/weilu/Research/data/constrain_qw.csv")
In [ ]:
In [117]:
tt.plot.scatter("level_0", "Qw")
Out[117]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a175ece80>
In [126]:
tt.plot.hexbin("DisReal", "BiasedEnergy", cmap="seismic", sharex=False)
Out[126]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a17e236a0>
In [125]:
tt.plot.hexbin("Qw", "BiasedEnergy", cmap="seismic", sharex=False)
Out[125]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a17545278>
In [132]:
tt.query("TempT == 373").query("Qw < 0.5 and Qw > 0.4")\
.to_csv("/Users/weilu/Research/data/constrain_qw_temp.csv")
In [131]:
tt.query("TempT == 373").query("Qw < 0.5 and Qw > 0.4").sort_values("DisReal")
Out[131]:
level_0
AMH
AMH-Go
AMH_3H
AMH_4H
BiasTo
DisReal
Dis_h56
Distance
Energy
...
rg6
rg_all
z_average
z_h1
z_h2
z_h3
z_h4
z_h5
z_h6
BiasedEnergy
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...
1.726105
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30 rows × 51 columns
In [135]:
t.query("TempT == 373").plot.hexbin("Qw", "BiasedEnergy", cmap="seismic", sharex=False)
Out[135]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a18f5e5f8>
In [139]:
t.query("TempT == 373").plot.hexbin("z_average", "TotalE", cmap="seismic", sharex=False)
Out[139]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a1b0b8588>
In [138]:
t.query("TempT == 373").plot.hexbin("DisReal", "TotalE", cmap="seismic", sharex=False)
Out[138]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a18967ac8>
In [ ]:
data.query("TempT != 417 and DisReal > 50 \
and DisReal < 63 and z_average < -2.5 and z_average > -3.5").\
query("Lipid1 < -0.5").to_csv("/Users/weilu/Research/data/selected_all.csv")
In [99]:
data.query("TempT != 417 and DisReal > 50 \
and DisReal < 63 and z_average < -2.5 and z_average > -3.5").\
query("Lipid1 < -0.5").plot.hexbin("Lipid7", "TotalE", cmap="seismic", sharex=False)
Out[99]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a16a8e6d8>
In [101]:
data.query("TempT != 417 and DisReal > 50 \
and DisReal < 63 and z_average < -2.5 and z_average > -3.5").\
query("Lipid1 < -0.5 and Lipid7 < -0.5").hist("TotalE",bins=50)
Out[101]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a174053c8>]], dtype=object)
In [102]:
data.query("TempT != 417 and DisReal > 50 \
and DisReal < 63 and z_average < -2.5 and z_average > -3.5").\
query("Lipid1 < -0.5 and Lipid7 > -0.5").hist("TotalE",bins=50)
Out[102]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a197a4e48>]], dtype=object)
In [ ]:
In [ ]:
In [29]:
sample = t373_narrow.sample(5).reset_index(drop=True)
In [43]:
rerun = (sample["Step"] // 2e7).astype(int)
In [ ]:
((sample["Step"] - 2e7*rerun)/4000).astype("int")
In [46]:
(sample["Step"] % 2e7)/4000
Out[46]:
0 2009.0
1 105.0
2 4964.0
3 2068.0
4 4465.0
Name: Step, dtype: float64
In [32]:
sample["Step"]
Out[32]:
0 68036000
1 60420000
2 79856000
3 68272000
4 77860000
Name: Step, dtype: int64
In [30]:
sample["Run"]
Out[30]:
0 1
1 1
2 3
3 5
4 1
Name: Run, dtype: int64
In [ ]:
cmd_pre = "python2 ~/opt/script/BuildAllAtomsFromLammps.py"
location_pre = "/Users/weilu/Research/server/apr_2018/sixth/rg_0.15_lipid_1.0_mem_1_go_0.8/simulation"
# cmd = cmd_pre + " " + location + " structure_2 4080 -seq ~/opt/pulling/2xov.seq"
# tt = pd.read_csv("/Users/weilu/Research/server/barrier.csv", index_col=0)
# tt = pd.read_csv("/Users/weilu/Research/server/high_go.csv", index_col=0)
tt = pd.read_csv("/Users/weilu/Research/server/rerun3.csv", index_col=0)
# rerun = 1
sample = tt.sample(5).reset_index(drop=True)
# sample["Frame"] = ((sample["Step"] - 2e7*rerun)/4000).astype("int")
sample["rerun"] = (sample["Step"] // 2e7).astype(int)
sample["Frame"] = ((sample["Step"] % 2e7)/4000).astype("int")
for index, row in sample.iterrows():
BiasTo = row["BiasTo"]
Run = row["Run"]
Frame = row["Frame"]
rerun = row["rerun"]
print(BiasTo, Run, Frame)
location = location_pre + f"/dis_{BiasTo}/{rerun}/dump.lammpstrj.{int(Run)}"
cmd = cmd_pre + " " + location + f" structure_{index} {int(Frame)} -seq ~/opt/pulling/2xov.seq"
print(cmd)
do(cmd)
In [ ]:
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In [ ]:
In [ ]:
In [ ]:
In [ ]:
Content source: luwei0917/awsemmd_script
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