In [1]:
# import matplotlib as mpl
import matplotlib.pyplot as plt
from scipy.interpolate import griddata
from scipy.interpolate import interp2d
import sys
import argparse
import os
import numpy as np
from numpy.random import uniform
import pandas as pd
from itertools import product
import glob
import plotly.plotly as py
import plotly.graph_objs as go
import pandas as pd
%matplotlib inline
# %matplotlib notebook
# matplotlib.rcParams['figure.figsize'] = (10, 8)
In [15]:
%run ~/Research/opt_server/opt/small_script/myFunctions_helper.py
In [16]:
a = read_lammps("/Users/weilu/Research/server/jan_2018/rg_0.3_lipid_0.6_mem_1/simulation/dis_30.0/0/dump.lammpstrj.0")
In [30]:
z_list = []
for atoms in a:
b = np.array(atoms)
z_list.append(b.mean(axis=0)[2])
In [31]:
plt.plot(z_list)
Out[31]:
[<matplotlib.lines.Line2D at 0x11d605f28>]
In [3]:
a = [1,2,3]
In [6]:
test = data[:10]
In [10]:
test.groupby("BiasTo").count()
Out[10]:
Step
Run
Temp
Qw
Energy
Distance
Lipid
Lipid1
Lipid2
Lipid3
...
Membrane
Rg
rg1
rg2
rg3
rg4
rg5
rg6
rg_all
TotalE
BiasTo
86.0
10
10
10
10
10
10
10
10
10
10
...
10
10
10
10
10
10
10
10
10
10
1 rows × 33 columns
In [15]:
sample_range_mode = 0
if sample_range_mode == 0:
queryCmd = 'Step > 1e7 & Step <= 2e7'
elif sample_range_mode == 1:
queryCmd ='Step > 2e7 & Step <= 3e7'
elif sample_range_mode == 2:
queryCmd ='Step > 3e7 & Step <= 4e7'
tmp = data.query(queryCmd)
In [32]:
1e7/4000
Out[32]:
2500.0
In [23]:
dic = {"T0":350, "T1":400, "T2":450, "T3":500, "T4":550, "T5":600, "T6":650, "T7":700, "T8":750, "T9":800, "T10":900, "T11":1000}
temps = list(dic.values())
In [35]:
kmem=0.2
klipid=0.1
kgo=0.1
krg=0.2
In [26]:
dic["T0"]
Out[26]:
350
In [38]:
tmp
Out[38]:
Step
Run
Temp
Qw
Energy
Distance
Lipid
Lipid1
Lipid2
Lipid3
...
Rg
rg1
rg2
rg3
rg4
rg5
rg6
rg_all
TotalE
BiasTo
2070002
10004000
2
T0
0.280807
-828.622679
95.097724
0.003179
-0.001896
-3.067246e-06
-1.435432e-06
...
1.981866
0.104958
1.865722
0.000046
3.270879e-04
1.749809e-08
0.010813
1.981866
554.538343
100.0
2070015
10008000
2
T0
0.241949
-820.946682
92.085859
-0.011685
-0.001011
-2.385941e-06
-1.762214e-06
...
2.574357
0.023037
2.520015
0.000077
3.140997e-04
4.552749e-09
0.030914
2.574357
-552.578301
100.0
2070032
10012000
2
T0
0.289616
-831.120574
89.382463
0.004196
-0.000700
-1.591165e-06
-4.327326e-07
...
1.957082
0.007564
1.935920
0.000061
4.743287e-05
2.229813e-09
0.013490
1.957082
52.663447
100.0
2070042
10016000
2
T0
0.276164
-837.638974
87.175370
0.008105
-0.001097
-3.016379e-06
-1.070475e-06
...
2.171908
0.022962
2.117458
0.000332
3.243857e-04
2.518908e-09
0.030831
2.171908
-424.005007
100.0
2070059
10020000
2
T0
0.258317
-852.392087
89.419660
0.007872
-0.001066
-2.376344e-06
-1.702527e-06
...
2.129087
0.015999
2.046813
0.000075
8.437714e-04
7.656298e-09
0.065356
2.129087
-366.512967
100.0
2070069
10024000
2
T0
0.281347
-878.901771
95.729079
-0.000063
-0.004663
-8.941823e-06
-7.301624e-06
...
2.281742
0.518019
1.734976
0.000014
8.384061e-04
4.940997e-09
0.027895
2.281742
-732.919582
100.0
2070081
10028000
2
T0
0.273693
-864.299036
98.143204
0.002353
-0.004805
-1.061845e-05
-2.880777e-06
...
2.144361
0.357426
1.781956
0.000069
4.740358e-05
1.015580e-09
0.004862
2.144361
-675.965081
100.0
2070091
10032000
2
T0
0.294670
-837.192851
97.913422
0.007904
-0.001340
-2.378652e-06
-1.090359e-06
...
2.005027
0.045678
1.926878
0.000080
1.287484e-04
4.917504e-09
0.032261
2.005027
-479.514042
100.0
2070103
10036000
2
T0
0.258293
-848.607101
97.378846
0.005409
-0.000244
-5.352996e-07
-3.362953e-07
...
1.646520
0.000927
1.619524
0.000038
7.299746e-04
3.507701e-09
0.025300
1.646520
404.626220
100.0
2070111
10040000
2
T0
0.264011
-900.804873
103.861470
-0.003759
-0.000157
-2.676408e-07
-1.399249e-07
...
2.017078
0.000405
2.002695
0.000013
1.728702e-04
2.501549e-09
0.013793
2.017078
308.193517
100.0
2070123
10044000
2
T0
0.264111
-886.116573
96.940264
-0.005574
-0.000086
-2.205624e-07
-8.121570e-08
...
3.413019
0.000145
3.389989
0.000023
4.716771e-05
1.180023e-08
0.022815
3.413019
-474.217432
100.0
2070132
10048000
2
T0
0.290987
-858.877067
92.947561
0.001894
-0.000223
-9.220904e-07
-1.958215e-07
...
4.122275
0.000664
4.114014
0.000116
6.129720e-05
1.021856e-07
0.007420
4.122275
791.623088
100.0
2070147
10052000
2
T0
0.293219
-863.213910
96.273530
-0.016755
-0.002087
-1.714140e-05
-3.355435e-06
...
1.815108
0.161641
1.639265
0.003677
1.817888e-04
1.233003e-08
0.010344
1.815108
-535.681713
100.0
2070166
10056000
2
T0
0.271556
-877.336597
92.680086
0.002307
-0.004366
-5.432570e-05
-1.138370e-05
...
2.179433
0.357180
1.785654
0.000453
2.005247e-04
8.342630e-09
0.035945
2.179433
-631.623131
100.0
2070175
10060000
2
T0
0.262588
-878.790018
90.197453
0.004480
-0.001059
-7.971715e-06
-1.575284e-06
...
1.576918
0.024415
1.521309
0.010208
3.382776e-05
2.194339e-09
0.020952
1.576918
-150.525400
100.0
2070180
10064000
2
T0
0.239759
-847.448013
88.827176
0.007499
-0.001123
-1.177193e-05
-2.001472e-06
...
1.438423
0.013544
1.411050
0.000990
2.079835e-05
9.161279e-10
0.012818
1.438423
-495.360040
100.0
2070192
10068000
2
T0
0.292196
-877.744795
94.121497
0.013557
-0.006325
-8.874360e-05
-5.322351e-06
...
2.934803
0.771969
2.154811
0.001171
7.815609e-06
6.950938e-10
0.006845
2.934803
795.105805
100.0
2070208
10072000
2
T0
0.281996
-879.912079
97.205169
0.020104
-0.003825
-6.629481e-05
-7.159126e-06
...
1.919562
0.197471
1.664812
0.001590
6.777855e-05
2.438290e-09
0.055622
1.919562
-636.864504
100.0
2070223
10076000
2
T0
0.271448
-861.512180
93.009423
0.022200
-0.004437
-1.075250e-04
-7.268466e-06
...
2.298581
0.456399
1.823485
0.005945
5.190151e-05
3.743315e-09
0.012700
2.298581
127.574675
100.0
2070238
10080000
2
T0
0.259054
-902.405855
95.990514
0.025826
-0.006872
-2.177325e-04
-6.254952e-06
...
2.557898
0.995189
1.551846
0.005709
2.343173e-06
3.497833e-10
0.005152
2.557898
889.246832
100.0
2070243
10084000
2
T0
0.276094
-866.044029
99.468832
-0.010461
-0.001325
-2.765415e-05
-2.129147e-06
...
1.926163
0.019755
1.872060
0.024015
2.416031e-05
5.626796e-10
0.010308
1.926163
844.275088
100.0
2070255
10088000
2
T0
0.262880
-911.936946
92.201221
-0.008224
-0.000580
-8.560489e-06
-1.329102e-06
...
2.783579
0.010422
2.750547
0.001479
9.798287e-05
2.424943e-09
0.021033
2.783579
688.136707
100.0
2070269
10092000
2
T0
0.259813
-901.536159
97.033548
-0.044976
-0.000764
-6.573100e-06
-1.835527e-06
...
2.024969
0.012253
1.907766
0.000704
2.862056e-04
6.090422e-09
0.103959
2.024969
-232.378513
100.0
2070285
10096000
2
T0
0.245021
-884.704954
96.456969
-0.003517
-0.003910
-2.377674e-05
-4.456544e-06
...
1.828535
0.261010
1.526172
0.000647
8.068158e-05
7.893664e-10
0.040625
1.828535
-445.331655
100.0
2070292
10100000
2
T0
0.250206
-869.640611
95.488503
-0.003925
-0.003904
-4.481806e-05
-6.370413e-06
...
1.846899
0.336874
1.489072
0.000502
8.900035e-05
6.811789e-10
0.020362
1.846899
-485.750791
100.0
2070302
10104000
2
T0
0.301462
-867.000739
90.429262
0.011889
-0.002800
-3.005960e-05
-2.110053e-06
...
1.929319
0.183257
1.741436
0.000619
3.324118e-06
1.130038e-09
0.004003
1.929319
-515.898799
100.0
2070313
10108000
2
T0
0.290849
-875.918327
91.609422
0.013670
-0.002199
-2.390557e-05
-1.424606e-06
...
2.334396
0.059588
2.269884
0.000910
3.110603e-06
2.702390e-10
0.004011
2.334396
-457.616137
100.0
2070333
10112000
2
T0
0.258240
-875.699976
87.696438
0.007281
-0.001060
-1.077303e-05
-3.052834e-07
...
2.863450
0.059660
2.802642
0.000685
3.287320e-07
7.915593e-11
0.000462
2.863450
-321.124522
100.0
2070340
10116000
2
T0
0.285545
-871.004910
86.680978
0.006082
-0.001051
-1.033314e-05
-4.177733e-07
...
1.745222
0.062518
1.679689
0.001111
1.548031e-06
1.964475e-10
0.001902
1.745222
-183.856138
100.0
2070351
10120000
2
T0
0.291006
-878.718858
83.834321
0.010739
-0.001044
-1.967059e-05
-5.553298e-07
...
1.808741
0.038449
1.766443
0.002379
1.196788e-06
1.335475e-09
0.001469
1.808741
544.641155
100.0
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
2099645
19884000
8
T0
0.493733
-840.145177
77.972546
-32.391206
-6.209497
-6.453194e+00
-5.188259e+00
...
20.120077
1.951250
2.210894
5.356078
2.773537e+00
6.727858e+00
1.100460
20.120077
-394.990439
100.0
2099663
19888000
8
T0
0.499535
-850.557726
78.522437
-32.648184
-6.300870
-6.174191e+00
-4.889108e+00
...
18.549101
2.051146
2.896537
4.673224
2.576634e+00
4.524068e+00
1.827491
18.549101
372.698044
100.0
2099674
19892000
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0.512807
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...
15.875614
2.414944
2.517651
3.106878
2.494587e+00
4.957015e+00
0.384539
15.875614
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...
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...
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2.616107
2.847122
3.253868
4.085161e+00
4.357169e+00
0.882626
18.042052
324.180629
100.0
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19904000
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...
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...
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...
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21.838377
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19.734363
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19940000
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20.422617
639.473322
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19948000
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128.411991
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19952000
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...
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18.344306
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19956000
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0.480634
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...
16.253403
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2099888
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2099903
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0.491769
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2500 rows × 34 columns
In [37]:
biasName = "dis"
temp = "100"
for bias, oneBias in data.groupby("BiasTo"):
for tempSymbol, oneTempAndBias in oneBias.groupby("Temp"):
temp = dic[tempSymbol]
if float(temp) > 800:
continue
print(f"t_{temp}_{biasName}_{bias}.dat")
if sample_range_mode == 0:
queryCmd = 'Step > 1e7 & Step <= 2e7'
elif sample_range_mode == 1:
queryCmd ='Step > 2e7 & Step <= 3e7'
elif sample_range_mode == 2:
queryCmd ='Step > 3e7 & Step <= 4e7'
tmp = oneTempAndBias.query(queryCmd)
chosen_list = ["TotalE", "Qw", "Distance"]
chosen = tmp[chosen_list]
chosen = chosen.assign(TotalE_perturb_mem_p=tmp.TotalE + kmem*tmp.Membrane
,TotalE_perturb_mem_m=tmp.TotalE - kmem*tmp.Membrane
,TotalE_perturb_lipid_p=tmp.TotalE + klipid*tmp.Lipid
,TotalE_perturb_lipid_m=tmp.TotalE - klipid*tmp.Lipid
,TotalE_perturb_go_p=tmp.TotalE + kgo*tmp["AMH-Go"]
,TotalE_perturb_go_m=tmp.TotalE - kgo*tmp["AMH-Go"]
,TotalE_perturb_rg_p=tmp.TotalE + krg*tmp.Rg
,TotalE_perturb_rg_m=tmp.TotalE - krg*tmp.Rg)
# print(tmp.count())
# tmp.to_csv(freeEnergy_folder+folder+sub_mode_name+f"/data/t_{temp}_{biasName}_{bias}.dat", sep=' ', index=False, header=False)
# chosen
tmp
t_350_dis_100.0.dat
Out[37]:
TotalE
Qw
Distance
TotalE_perturb_go_m
TotalE_perturb_go_p
TotalE_perturb_lipid_m
TotalE_perturb_lipid_p
TotalE_perturb_mem_m
TotalE_perturb_mem_p
TotalE_perturb_rg_m
TotalE_perturb_rg_p
2070002
554.538343
0.280807
95.097724
607.423929
501.652757
554.538025
554.538661
557.792207
551.284479
554.141969
554.934716
2070015
-552.578301
0.241949
92.085859
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2070032
52.663447
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89.382463
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52.663027
52.663866
56.145764
49.181129
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53.054863
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404.626220
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2070243
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541.224786
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...
...
...
...
...
...
...
...
...
...
...
...
2099645
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2500 rows × 11 columns
In [39]:
data = pd.read_feather("/Users/weilu/Research/server/jan_2018/rg_0.3_lipid_0.6_mem_1/21_Jan_162805.feather")
In [41]:
a = [1,2,3]
In [43]:
a+["123"]
Out[43]:
[1, 2, 3, '123']
In [40]:
data
Out[40]:
Step
Run
Temp
Qw
Energy
AverageZ
Distance
Lipid
Lipid1
Lipid2
...
Rg
rg1
rg2
rg3
rg4
rg5
rg6
rg_all
TotalE
BiasTo
0
4000
0
T0
0.054497
-296.703350
-19.269009
261.903613
0.030251
-0.000200
-1.201127e-04
...
2.685203
0.341854
0.786050
0.081576
0.046778
1.370529e+00
0.058414
2.685203
-296.673099
86.0
1
4000
4
T5
0.051348
-4.347924
-19.073004
261.678422
0.024061
-0.000322
-2.410720e-04
...
4.170392
0.107406
0.938145
0.107614
0.181810
2.577284e+00
0.258131
4.170392
-4.323863
86.0
2
4000
11
T11
0.043486
398.833726
-18.794401
251.637817
0.020212
-0.000330
-2.363472e-04
...
6.064533
0.201165
2.390571
0.111203
0.295438
2.562292e+00
0.503865
6.064533
398.853938
86.0
3
4000
1
T1
0.059210
-218.717123
-19.225217
263.549594
0.029584
-0.000231
-1.807104e-04
...
3.605093
0.238323
0.833996
0.116581
0.186663
1.926612e+00
0.302919
3.605093
-218.687539
86.0
4
4000
5
T4
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2160000 rows × 35 columns
In [2]:
data = pd.read_feather("/Users/weilu/Research/server/jan_2018/rg_0.3_lipid_0.6_mem_1/complete.feather")
In [41]:
chosen = data[["Step", "Run"]]
In [8]:
data
Out[8]:
Step
Run
Temp
Qw
Energy
Distance
Lipid
Lipid1
Lipid2
Lipid3
...
Rg
rg1
rg2
rg3
rg4
rg5
rg6
rg_all
TotalE
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1.370529e+00
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4.170392
0.107406
0.938145
0.107614
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2160000 rows × 34 columns
In [34]:
location = "/Users/weilu/Research/server/jan_2018/rg_0.3_lipid_0.6_mem_1/simulation/dis_30.0/0/z_0.dat"
pd.read_table(location, header=None)
Out[34]:
0
0
-16.315357
1
-19.264572
2
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...
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5001 rows × 1 columns
In [32]:
a = read_lammps("/Users/weilu/Research/server/jan_2018/rg_0.3_lipid_0.6_mem_1/simulation/dis_30.0/0/dump.lammpstrj.10")
z_list = []
for atoms in a:
b = np.array(atoms)
z_list.append(b.mean(axis=0)[2])
plt.plot(z_list)
Out[32]:
[<matplotlib.lines.Line2D at 0x11a5ca588>]
In [23]:
atoms = a
b = np.array(a[0])
In [29]:
b.mean(axis=0)[2]
Out[29]:
-16.315356524637789
In [24]:
b.shape
Out[24]:
(181, 3)
In [17]:
first = a[0]
len(first)
Out[17]:
181
In [18]:
first
Out[18]:
[[156.66762503974203, 35.61035306724857, -16.392111729786457],
[153.74251556483804, 33.61691495714608, -17.640888102654685],
[150.07333183091086, 34.49813160983165, -17.326635526922637],
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[143.11664552583554, 38.13520977236943, -18.04333279461392],
[141.57322927867912, 36.91038960426643, -21.160138880775804],
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In [6]:
df2 = pd.DataFrame({ 'A' : 1.,
....: 'B' : pd.Timestamp('20130102'),
....: 'C' : pd.Series(1,index=list(range(4)),dtype='float32'),
....: 'D' : np.array([3] * 4,dtype='int32'),
....: 'E' : pd.Categorical(["test","train","test","train"]),
....: 'F' : 'foo' })
In [7]:
df2
Out[7]:
A
B
C
D
E
F
0
1.0
2013-01-02
1.0
3
test
foo
1
1.0
2013-01-02
1.0
3
train
foo
2
1.0
2013-01-02
1.0
3
test
foo
3
1.0
2013-01-02
1.0
3
train
foo
In [8]:
s= "Name1=Value1;Name2=Value2;Name3=Value3"
dict(item.split("=") for item in s.split(";"))
Out[8]:
{'Name1': 'Value1', 'Name2': 'Value2', 'Name3': 'Value3'}
In [44]:
df2.assign(Run=1,**{'pressure': 0.5, 'rg': 0.2, 'mem': 1.0})
Out[44]:
A
B
C
D
E
F
Run
mem
pressure
rg
0
1.0
2013-01-02
1.0
3
test
foo
1
1.0
0.5
0.2
1
1.0
2013-01-02
1.0
3
train
foo
1
1.0
0.5
0.2
2
1.0
2013-01-02
1.0
3
test
foo
1
1.0
0.5
0.2
3
1.0
2013-01-02
1.0
3
train
foo
1
1.0
0.5
0.2
In [28]:
splited
Out[28]:
['pressure', '0.5', 'rg', '0.2', 'mem', '1', '']
In [17]:
s.split("_")
Out[17]:
['pressure', '0.5', 'rg', '0.2', 'mem', '1', '']
In [31]:
splited[2*i+1]
Out[31]:
'0.5'
In [35]:
dict([splited[2*i],splited[2*i+1]])
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-35-434f75c6b6c6> in <module>()
----> 1 dict([splited[2*i],splited[2*i+1]])
ValueError: dictionary update sequence element #0 has length 8; 2 is required
In [38]:
dict([['pressure', '0.5']])
Out[38]:
{'pressure': '0.5'}
In [41]:
s= "pressure_0.5_rg_0.2_mem_1_"
splited = s.split("_")
variable_dic = {}
for i in range(len(s.split("_"))//2):
# print(i)
# print([splited[2*i],splited[2*i+1]])
tmp = dict([[splited[2*i],float(splited[2*i+1])]])
# print(tmp)
variable_dic.update(tmp)
variable_dic
Out[41]:
{'mem': 1.0, 'pressure': 0.5, 'rg': 0.2}
In [22]:
variable_dic = {}
In [24]:
variable_dic.update(dict(item.split("=") for item in s.split(";")))
In [25]:
variable_dic
Out[25]:
{'Name1': 'Value1', 'Name2': 'Value2', 'Name3': 'Value3'}
In [20]:
s= "Name1=Value1;Name2=Value2;Name3=Value3"
for item in s.split(";"):
print(item.split("="))
['Name1', 'Value1']
['Name2', 'Value2']
['Name3', 'Value3']
In [13]:
s= "pressure_0.5_rg_0.2_mem_1_"
dict(item.split("=") for item in s.split(";"))
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-13-5a4b2ef3b604> in <module>()
1 s= "pressure_0.5_rg_0.2_mem_1_"
----> 2 dict(item.split("=") for item in s.split(";"))
ValueError: dictionary update sequence element #0 has length 1; 2 is required
In [5]:
location = "/Users/weilu/Research/server/nov_2017/27nov/no_side_contraint_memb_3_rg_manual_lipid_0.6_extended/add_small_force/0/"
wham_file = location + "wham.0.dat"
wham = pd.read_csv(wham_file)
wham.columns = wham.columns.str.strip()
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
<ipython-input-5-597c6fdb89b7> in <module>()
1 location = "/Users/weilu/Research/server/nov_2017/27nov/no_side_contraint_memb_3_rg_manual_lipid_0.6_extended/add_small_force/0/"
2 wham_file = location + "wham.0.dat"
----> 3 wham = pd.read_csv(wham_file)
4 wham.columns = wham.columns.str.strip()
~/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)
653 skip_blank_lines=skip_blank_lines)
654
--> 655 return _read(filepath_or_buffer, kwds)
656
657 parser_f.__name__ = name
~/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
403
404 # Create the parser.
--> 405 parser = TextFileReader(filepath_or_buffer, **kwds)
406
407 if chunksize or iterator:
~/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py in __init__(self, f, engine, **kwds)
762 self.options['has_index_names'] = kwds['has_index_names']
763
--> 764 self._make_engine(self.engine)
765
766 def close(self):
~/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py in _make_engine(self, engine)
983 def _make_engine(self, engine='c'):
984 if engine == 'c':
--> 985 self._engine = CParserWrapper(self.f, **self.options)
986 else:
987 if engine == 'python':
~/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py in __init__(self, src, **kwds)
1603 kwds['allow_leading_cols'] = self.index_col is not False
1604
-> 1605 self._reader = parsers.TextReader(src, **kwds)
1606
1607 # XXX
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.__cinit__ (pandas/_libs/parsers.c:4209)()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._setup_parser_source (pandas/_libs/parsers.c:8873)()
FileNotFoundError: File b'/Users/weilu/Research/server/nov_2017/27nov/no_side_contraint_memb_3_rg_manual_lipid_0.6_extended/add_small_force/0/wham.0.dat' does not exist
In [ ]:
wham.Qw.hist(bins=200)
In [48]:
location = "/Users/weilu/Research/davinci/nov_2017/27nov/dec02_no_side_2/no_side_contraint_memb_3_rg_0.4_lipid_0.6_extended_350-550/2d_qw_dis/force_0.0/"
filename = location + "pmf-400.dat"
x = 1
y = 2
z = 3
xmin, xmax = 0, 1
ymin, ymax = 0, 150
zmin, zmax = 0, 30
xlabel, ylabel = "xlabel", "ylabel"
title = "title"
titlefontsize = 28
data = np.loadtxt(filename)
data = data[~np.isnan(data).any(axis=1)] # remove rows with nan
data = data[~(data[:,z] > zmax)] # remove rows of data for z not in [zmin zmax]
data = data[~(data[:,z] < zmin)]
xi = np.linspace(min(data[:,x]), max(data[:,x]), 20)
yi = np.linspace(min(data[:,y]), max(data[:,y]), 20)
zi = griddata((data[:,x], data[:,y]), data[:,z], (xi[None,:], yi[:,None]), method='linear')
# plt.contour(xi, yi, zi, 50, linewidths=0.25,colors='k')
jet = cm = plt.get_cmap('jet')
print(jet)
# plt.contourf(xi, yi, zi, 20, cmap='rainbow')
plt.figure()
plt.contourf(xi, yi, zi, 30, cmap='jet')
plt.xlim(xmin, xmax)
plt.clim(zmin, zmax)
plt.colorbar()
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(title, y=1.02, fontsize = titlefontsize)
#plt.tight_layout()
#plt.axis('equal')
#plt.axes().set_aspect('equal')
#plt.axes().set_aspect('scaled')
# plt.savefig(args.outname, dpi=args.dpi, bbox_inches='tight')
plt.show()
<matplotlib.colors.LinearSegmentedColormap object at 0x109aeb4a8>
In [56]:
file = "wham.0.dat"
b = pd.read_csv(location+file)
In [63]:
location = "/Users/weilu/Research/server/nov_2017/13nov/no_side_contraint_memb_3_rg_0.4_lipid_0.6_extended/simulation/dis_100.0/0/"
qc = pd.read_table(location+f"qc_{i}", names=["qc"])[1:].reset_index(drop=True)
qn = pd.read_table(location+f"qn_{i}", names=["qn"])[1:].reset_index(drop=True)
qc2 = pd.read_table(location+f"qc2_{i}", names=["qc2"])[1:].reset_index(drop=True)
In [65]:
pd.concat([a,b],axis=1)
Out[65]:
qc
Steps
Qw
Rg
Tc
Energy
0
0.098
4000
0.052376
96.576704
119
-230.463252
1
0.128
8000
0.062897
81.994732
121
-385.307319
2
0.166
12000
0.080449
69.938002
146
-465.731833
3
0.215
16000
0.104170
61.379717
151
-567.267901
4
0.221
20000
0.112049
56.330383
158
-601.194773
5
0.217
24000
0.113051
53.002372
152
-635.965014
6
0.219
28000
0.116622
51.394586
172
-690.651929
7
0.219
32000
0.119104
50.629356
169
-739.482429
8
0.213
36000
0.120307
50.940639
180
-777.368209
9
0.191
40000
0.118846
51.073804
170
-761.275873
10
0.190
44000
0.119369
49.965367
183
-757.431209
11
0.166
48000
0.110520
48.361202
181
-751.630463
12
0.172
52000
0.122541
46.040062
170
-737.560826
13
0.173
56000
0.113668
47.257738
173
-759.609979
14
0.198
60000
0.117068
47.240625
175
-740.406865
15
0.195
64000
0.110576
46.204786
163
-717.441438
16
0.203
68000
0.115101
46.668260
179
-688.299915
17
0.191
72000
0.117031
48.758690
178
-745.644330
18
0.180
76000
0.110990
50.058975
165
-776.474004
19
0.186
80000
0.102668
51.059310
170
-760.609357
20
0.204
84000
0.104236
50.689380
169
-726.023426
21
0.214
88000
0.119053
50.613297
180
-773.729312
22
0.209
92000
0.117483
49.373045
186
-786.775855
23
0.213
96000
0.116580
47.500473
167
-779.278770
24
0.194
100000
0.115479
45.500001
186
-763.188031
25
0.213
104000
0.109816
45.206320
167
-728.822255
26
0.187
108000
0.104053
45.202317
175
-722.587848
27
0.195
112000
0.108193
46.158852
182
-757.141725
28
0.202
116000
0.110481
47.102658
182
-713.114251
29
0.187
120000
0.110571
48.476007
179
-729.045676
...
...
...
...
...
...
...
4970
0.171
19884000
0.097729
43.833905
173
-411.508176
4971
0.171
19888000
0.101152
43.470590
167
-482.884087
4972
0.172
19892000
0.101059
42.189817
170
-479.982867
4973
0.182
19896000
0.100380
42.111450
151
-516.401361
4974
0.181
19900000
0.099469
40.960447
152
-455.042625
4975
0.169
19904000
0.100541
40.141068
149
-418.105405
4976
0.213
19908000
0.121238
39.318029
146
-404.577324
4977
0.161
19912000
0.098544
40.570618
163
-377.262558
4978
0.172
19916000
0.109954
43.024622
153
-436.428223
4979
0.165
19920000
0.098173
44.948550
160
-420.777441
4980
0.150
19924000
0.088031
47.127699
147
-399.119101
4981
0.177
19928000
0.108686
47.413757
177
-372.121535
4982
0.173
19932000
0.105737
47.969942
160
-369.460824
4983
0.151
19936000
0.095681
49.114456
165
-374.463392
4984
0.157
19940000
0.092545
50.497218
148
-385.387739
4985
0.182
19944000
0.101468
51.640140
153
-353.549776
4986
0.163
19948000
0.087176
53.255145
136
-372.979038
4987
0.161
19952000
0.086903
53.126812
140
-366.397326
4988
0.162
19956000
0.093185
52.912253
158
-350.071426
4989
0.123
19960000
0.073190
51.105345
136
-308.056577
4990
0.162
19964000
0.096270
49.243858
146
-263.716826
4991
0.150
19968000
0.095351
49.727551
148
-379.479529
4992
0.138
19972000
0.086461
50.262002
163
-290.552138
4993
0.159
19976000
0.092115
51.403897
154
-370.611215
4994
0.170
19980000
0.107046
51.318839
145
-332.800215
4995
0.166
19984000
0.091027
49.958547
147
-388.405190
4996
0.167
19988000
0.103496
48.483778
166
-299.223032
4997
0.187
19992000
0.098928
47.384029
155
-320.327021
4998
0.201
19996000
0.106586
47.129446
174
-384.791210
4999
0.199
20000000
0.109796
47.362726
149
-316.884116
5000 rows × 6 columns
In [71]:
location = "/Users/weilu/Research/davinci/dec_2017/all_data_folder/no_side_contraint_memb_3_rg_0.4_lipid_0.6_extended/"
a = pd.read_feather(location+"dis100.0.feather")
In [78]:
a["Qn"] = a["qn"]
a["Qc"] = a["qc"]
a["Qc2"] = a["qc2"]
a.drop(["qc","qn","qc2"], axis=1)
Out[78]:
index
Step
Run
Temp
Qw
Energy
Distance
Lipid
AMH-Go
Membrane
Rg
TotalE
Qc
Qn
Qc2
0
0
4000
0
T1
0.052376
-230.463252
260.697458
0.002846
-320.672985
-89.808640
20.289161
-230.460406
0.098
0.111
0.183
1
11
4000
8
T8
0.048493
44.765478
261.934545
0.039555
-273.643131
-94.959967
17.988436
44.805033
0.095
0.089
0.195
2
10
4000
7
T7
0.046195
99.783921
264.293987
0.020403
-269.317465
-98.032087
19.582566
99.804323
0.080
0.110
0.153
3
9
4000
3
T2
0.050422
-178.276749
259.472901
0.017870
-315.660777
-91.853011
15.716146
-178.258879
0.096
0.103
0.151
4
7
4000
2
T3
0.049041
-226.827483
266.022021
0.012576
-312.740668
-82.659715
13.856081
-226.814907
0.092
0.105
0.163
5
6
4000
6
T6
0.045750
11.781813
261.725221
0.024576
-287.356174
-87.152555
18.375624
11.806389
0.089
0.083
0.154
6
8
4000
10
T10
0.043264
285.365172
255.945450
0.015740
-242.037148
-100.941627
18.171787
285.380913
0.076
0.090
0.132
7
4
4000
5
T4
0.049164
-58.635146
261.994426
0.025020
-297.591449
-89.152319
21.201030
-58.610126
0.095
0.101
0.157
8
3
4000
9
T9
0.043879
173.111882
261.513616
0.032071
-263.504692
-95.854355
16.238814
173.143953
0.083
0.087
0.157
9
2
4000
1
T0
0.054363
-263.514703
261.605500
0.017367
-330.549656
-84.522104
12.297805
-263.497336
0.106
0.117
0.188
10
1
4000
11
T11
0.041352
380.616112
261.189069
0.019877
-246.831226
-94.003974
18.393798
380.635990
0.070
0.091
0.142
11
5
4000
4
T5
0.048073
-63.216458
264.321184
0.020545
-270.463340
-95.216605
14.883018
-63.195913
0.088
0.091
0.151
12
19
8000
3
T2
0.066254
-304.207262
186.115709
-0.074887
-331.555813
-84.996525
16.198299
-304.282149
0.116
0.156
0.233
13
23
8000
10
T10
0.045864
520.736957
179.061840
-0.057289
-224.424677
-72.102860
16.570245
520.679668
0.072
0.125
0.173
14
22
8000
9
T9
0.047327
360.827640
183.433458
-0.132879
-226.418196
-88.302150
15.720267
360.694761
0.075
0.128
0.209
15
21
8000
1
T0
0.062929
-488.576897
179.813131
-0.048813
-344.865534
-97.502099
25.789635
-488.625710
0.121
0.134
0.271
16
20
8000
7
T7
0.048678
208.057397
185.144411
-0.035643
-247.891787
-76.544446
15.752432
208.021754
0.073
0.138
0.147
17
18
8000
0
T1
0.062897
-385.307319
183.221037
-0.060766
-332.328076
-74.153887
22.604000
-385.368085
0.128
0.120
0.284
18
14
8000
6
T6
0.053647
94.858713
176.747966
-0.049707
-264.099856
-84.021040
14.497136
94.809006
0.090
0.137
0.214
19
16
8000
2
T3
0.058599
-257.270212
174.782471
-0.040857
-305.804757
-92.353686
21.074975
-257.311069
0.114
0.124
0.254
20
15
8000
8
T8
0.057165
221.393454
184.190539
-0.077251
-264.664221
-71.171399
16.404226
221.316202
0.105
0.129
0.284
21
13
8000
4
T5
0.059378
-77.052160
180.198201
-0.037302
-292.220362
-104.475042
26.400950
-77.089462
0.109
0.127
0.281
22
12
8000
5
T4
0.062652
-171.270179
185.870155
-0.028807
-305.948545
-76.968147
19.193998
-171.298986
0.111
0.146
0.231
23
17
8000
11
T11
0.046189
565.799119
174.046677
-0.069297
-200.366905
-88.077859
19.292606
565.729823
0.072
0.124
0.178
24
31
12000
8
T8
0.070688
172.832283
149.169041
-0.000171
-298.488788
-67.849840
11.055367
172.832113
0.118
0.180
0.274
25
35
12000
5
T4
0.087784
-273.605722
137.396714
-0.198979
-347.343224
-76.396274
13.140369
-273.804701
0.162
0.197
0.369
26
34
12000
0
T1
0.080449
-465.731833
138.952859
-0.002480
-377.215010
-67.103867
6.751241
-465.734313
0.166
0.159
0.364
27
33
12000
9
T9
0.067116
244.033512
138.680989
-0.026250
-241.170700
-87.307708
13.927827
244.007262
0.104
0.183
0.262
28
32
12000
7
T6
0.070652
105.469216
142.023264
-0.035487
-275.133562
-76.411618
10.879675
105.433729
0.120
0.174
0.265
29
30
12000
6
T7
0.063491
140.963431
131.112888
0.003554
-262.999687
-84.602052
13.331547
140.966985
0.119
0.144
0.221
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
119970
59968
39992000
10
T8
0.084644
125.225000
103.789374
0.090318
-321.185686
-125.695527
14.684923
125.315318
0.143
0.193
0.236
119971
59967
39992000
5
T1
0.349991
-875.756422
95.257083
-2.509045
-532.261303
-112.158109
17.440344
-878.265467
0.601
0.241
0.490
119972
59966
39992000
11
T9
0.090627
266.004836
89.849633
-3.504245
-254.283771
-133.853171
19.380148
262.500590
0.109
0.260
0.155
119973
59965
39992000
8
T3
0.152857
-660.319708
100.758117
-6.206477
-453.784150
-134.328173
12.607433
-666.526186
0.200
0.459
0.343
119974
59964
39992000
7
T0
0.169079
-912.040151
90.305251
-5.906491
-494.024593
-133.217904
13.670734
-917.946641
0.200
0.483
0.341
119975
59969
39992000
4
T4
0.114089
-444.277900
95.660271
-5.314091
-398.246881
-128.057087
9.608467
-449.591990
0.152
0.343
0.277
119976
59987
39996000
11
T9
0.074017
382.517185
91.639465
-4.724942
-228.640940
-133.677532
24.182730
377.792244
0.080
0.209
0.105
119977
59986
39996000
10
T8
0.063696
208.152815
100.148774
0.285182
-298.037024
-119.461887
15.373861
208.437997
0.126
0.108
0.208
119978
59985
39996000
0
T5
0.136838
-416.291851
96.437949
-4.623812
-453.438025
-109.864277
11.485843
-420.915663
0.174
0.400
0.305
119979
59983
39996000
3
T11
0.054917
756.580771
105.894603
-0.012843
-156.503404
-104.933674
15.775930
756.567928
0.093
0.122
0.122
119980
59982
39996000
6
T7
0.089424
122.986885
98.720483
-4.239269
-258.873243
-119.852487
15.178919
118.747616
0.116
0.217
0.164
119981
59984
39996000
5
T1
0.339551
-866.955251
96.224059
-3.020632
-534.135828
-112.357492
19.734441
-869.975883
0.614
0.215
0.505
119982
59980
39996000
7
T0
0.173838
-911.803888
92.408859
-6.877245
-502.691375
-132.888972
10.307421
-918.681134
0.222
0.462
0.367
119983
59979
39996000
1
T10
0.067316
404.458095
108.594429
0.375098
-222.586328
-122.517378
18.134658
404.833192
0.115
0.133
0.201
119984
59978
39996000
4
T4
0.121727
-505.135216
92.309613
-6.207101
-430.397193
-117.058347
10.813826
-511.342317
0.158
0.363
0.245
119985
59977
39996000
9
T2
0.156603
-660.139866
91.639243
-5.771314
-462.484791
-127.278876
10.799616
-665.911180
0.188
0.445
0.310
119986
59976
39996000
2
T6
0.090403
-108.878215
100.868756
-0.996695
-341.361686
-132.524702
11.953597
-109.874911
0.124
0.254
0.196
119987
59981
39996000
8
T3
0.159727
-644.308395
95.710298
-5.163244
-463.921253
-119.318109
12.246137
-649.471639
0.210
0.479
0.336
119988
59997
40000000
6
T7
0.121694
90.775558
97.820644
-6.382994
-296.725515
-146.721218
17.309919
84.392564
0.115
0.345
0.209
119989
59996
40000000
0
T5
0.117225
-398.106095
85.880769
-4.712317
-426.357958
-115.108330
11.240729
-402.818412
0.162
0.329
0.336
119990
59995
40000000
1
T10
0.064563
589.132379
98.088798
0.346817
-192.860089
-125.752621
19.455592
589.479197
0.110
0.137
0.180
119991
59994
40000000
3
T11
0.051702
906.766668
90.108483
-0.005943
-158.550245
-110.137634
17.307949
906.760725
0.114
0.075
0.198
119992
59993
40000000
2
T6
0.098242
-88.301639
92.525162
-1.773163
-348.588410
-119.259256
15.222263
-90.074802
0.143
0.266
0.215
119993
59990
40000000
9
T2
0.133199
-694.262202
86.425812
-6.205387
-472.458712
-132.501237
9.422770
-700.467590
0.181
0.350
0.322
119994
59991
40000000
7
T0
0.161546
-943.628702
92.865545
-5.851144
-492.677367
-137.029432
13.563649
-949.479846
0.200
0.446
0.359
119995
59989
40000000
5
T1
0.315990
-853.838423
100.449897
-2.131402
-532.164620
-110.294998
15.498966
-855.969825
0.560
0.215
0.543
119996
59988
40000000
10
T8
0.061048
199.429269
105.126176
0.711126
-271.350388
-117.233767
22.564685
200.140395
0.115
0.114
0.205
119997
59998
40000000
4
T4
0.123769
-510.229033
98.082233
-6.345745
-401.651487
-127.400773
11.718531
-516.574778
0.156
0.390
0.294
119998
59992
40000000
8
T3
0.141706
-580.866390
90.356360
-5.740184
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-128.659888
11.442731
-586.606573
0.199
0.404
0.333
119999
59999
40000000
11
T9
0.076814
318.402317
90.884389
-5.038724
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-132.436665
14.233994
313.363593
0.106
0.185
0.187
120000 rows × 15 columns
In [73]:
a.set_index('TotalE').reset_index()
Out[73]:
TotalE
index
Step
Run
Temp
Qw
Energy
qn
qc
qc2
Distance
Lipid
AMH-Go
Membrane
Rg
0
-230.460406
0
4000
0
T1
0.052376
-230.463252
0.111
0.098
0.183
260.697458
0.002846
-320.672985
-89.808640
20.289161
1
44.805033
11
4000
8
T8
0.048493
44.765478
0.089
0.095
0.195
261.934545
0.039555
-273.643131
-94.959967
17.988436
2
99.804323
10
4000
7
T7
0.046195
99.783921
0.110
0.080
0.153
264.293987
0.020403
-269.317465
-98.032087
19.582566
3
-178.258879
9
4000
3
T2
0.050422
-178.276749
0.103
0.096
0.151
259.472901
0.017870
-315.660777
-91.853011
15.716146
4
-226.814907
7
4000
2
T3
0.049041
-226.827483
0.105
0.092
0.163
266.022021
0.012576
-312.740668
-82.659715
13.856081
5
11.806389
6
4000
6
T6
0.045750
11.781813
0.083
0.089
0.154
261.725221
0.024576
-287.356174
-87.152555
18.375624
6
285.380913
8
4000
10
T10
0.043264
285.365172
0.090
0.076
0.132
255.945450
0.015740
-242.037148
-100.941627
18.171787
7
-58.610126
4
4000
5
T4
0.049164
-58.635146
0.101
0.095
0.157
261.994426
0.025020
-297.591449
-89.152319
21.201030
8
173.143953
3
4000
9
T9
0.043879
173.111882
0.087
0.083
0.157
261.513616
0.032071
-263.504692
-95.854355
16.238814
9
-263.497336
2
4000
1
T0
0.054363
-263.514703
0.117
0.106
0.188
261.605500
0.017367
-330.549656
-84.522104
12.297805
10
380.635990
1
4000
11
T11
0.041352
380.616112
0.091
0.070
0.142
261.189069
0.019877
-246.831226
-94.003974
18.393798
11
-63.195913
5
4000
4
T5
0.048073
-63.216458
0.091
0.088
0.151
264.321184
0.020545
-270.463340
-95.216605
14.883018
12
-304.282149
19
8000
3
T2
0.066254
-304.207262
0.156
0.116
0.233
186.115709
-0.074887
-331.555813
-84.996525
16.198299
13
520.679668
23
8000
10
T10
0.045864
520.736957
0.125
0.072
0.173
179.061840
-0.057289
-224.424677
-72.102860
16.570245
14
360.694761
22
8000
9
T9
0.047327
360.827640
0.128
0.075
0.209
183.433458
-0.132879
-226.418196
-88.302150
15.720267
15
-488.625710
21
8000
1
T0
0.062929
-488.576897
0.134
0.121
0.271
179.813131
-0.048813
-344.865534
-97.502099
25.789635
16
208.021754
20
8000
7
T7
0.048678
208.057397
0.138
0.073
0.147
185.144411
-0.035643
-247.891787
-76.544446
15.752432
17
-385.368085
18
8000
0
T1
0.062897
-385.307319
0.120
0.128
0.284
183.221037
-0.060766
-332.328076
-74.153887
22.604000
18
94.809006
14
8000
6
T6
0.053647
94.858713
0.137
0.090
0.214
176.747966
-0.049707
-264.099856
-84.021040
14.497136
19
-257.311069
16
8000
2
T3
0.058599
-257.270212
0.124
0.114
0.254
174.782471
-0.040857
-305.804757
-92.353686
21.074975
20
221.316202
15
8000
8
T8
0.057165
221.393454
0.129
0.105
0.284
184.190539
-0.077251
-264.664221
-71.171399
16.404226
21
-77.089462
13
8000
4
T5
0.059378
-77.052160
0.127
0.109
0.281
180.198201
-0.037302
-292.220362
-104.475042
26.400950
22
-171.298986
12
8000
5
T4
0.062652
-171.270179
0.146
0.111
0.231
185.870155
-0.028807
-305.948545
-76.968147
19.193998
23
565.729823
17
8000
11
T11
0.046189
565.799119
0.124
0.072
0.178
174.046677
-0.069297
-200.366905
-88.077859
19.292606
24
172.832113
31
12000
8
T8
0.070688
172.832283
0.180
0.118
0.274
149.169041
-0.000171
-298.488788
-67.849840
11.055367
25
-273.804701
35
12000
5
T4
0.087784
-273.605722
0.197
0.162
0.369
137.396714
-0.198979
-347.343224
-76.396274
13.140369
26
-465.734313
34
12000
0
T1
0.080449
-465.731833
0.159
0.166
0.364
138.952859
-0.002480
-377.215010
-67.103867
6.751241
27
244.007262
33
12000
9
T9
0.067116
244.033512
0.183
0.104
0.262
138.680989
-0.026250
-241.170700
-87.307708
13.927827
28
105.433729
32
12000
7
T6
0.070652
105.469216
0.174
0.120
0.265
142.023264
-0.035487
-275.133562
-76.411618
10.879675
29
140.966985
30
12000
6
T7
0.063491
140.963431
0.144
0.119
0.221
131.112888
0.003554
-262.999687
-84.602052
13.331547
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
119970
125.315318
59968
39992000
10
T8
0.084644
125.225000
0.193
0.143
0.236
103.789374
0.090318
-321.185686
-125.695527
14.684923
119971
-878.265467
59967
39992000
5
T1
0.349991
-875.756422
0.241
0.601
0.490
95.257083
-2.509045
-532.261303
-112.158109
17.440344
119972
262.500590
59966
39992000
11
T9
0.090627
266.004836
0.260
0.109
0.155
89.849633
-3.504245
-254.283771
-133.853171
19.380148
119973
-666.526186
59965
39992000
8
T3
0.152857
-660.319708
0.459
0.200
0.343
100.758117
-6.206477
-453.784150
-134.328173
12.607433
119974
-917.946641
59964
39992000
7
T0
0.169079
-912.040151
0.483
0.200
0.341
90.305251
-5.906491
-494.024593
-133.217904
13.670734
119975
-449.591990
59969
39992000
4
T4
0.114089
-444.277900
0.343
0.152
0.277
95.660271
-5.314091
-398.246881
-128.057087
9.608467
119976
377.792244
59987
39996000
11
T9
0.074017
382.517185
0.209
0.080
0.105
91.639465
-4.724942
-228.640940
-133.677532
24.182730
119977
208.437997
59986
39996000
10
T8
0.063696
208.152815
0.108
0.126
0.208
100.148774
0.285182
-298.037024
-119.461887
15.373861
119978
-420.915663
59985
39996000
0
T5
0.136838
-416.291851
0.400
0.174
0.305
96.437949
-4.623812
-453.438025
-109.864277
11.485843
119979
756.567928
59983
39996000
3
T11
0.054917
756.580771
0.122
0.093
0.122
105.894603
-0.012843
-156.503404
-104.933674
15.775930
119980
118.747616
59982
39996000
6
T7
0.089424
122.986885
0.217
0.116
0.164
98.720483
-4.239269
-258.873243
-119.852487
15.178919
119981
-869.975883
59984
39996000
5
T1
0.339551
-866.955251
0.215
0.614
0.505
96.224059
-3.020632
-534.135828
-112.357492
19.734441
119982
-918.681134
59980
39996000
7
T0
0.173838
-911.803888
0.462
0.222
0.367
92.408859
-6.877245
-502.691375
-132.888972
10.307421
119983
404.833192
59979
39996000
1
T10
0.067316
404.458095
0.133
0.115
0.201
108.594429
0.375098
-222.586328
-122.517378
18.134658
119984
-511.342317
59978
39996000
4
T4
0.121727
-505.135216
0.363
0.158
0.245
92.309613
-6.207101
-430.397193
-117.058347
10.813826
119985
-665.911180
59977
39996000
9
T2
0.156603
-660.139866
0.445
0.188
0.310
91.639243
-5.771314
-462.484791
-127.278876
10.799616
119986
-109.874911
59976
39996000
2
T6
0.090403
-108.878215
0.254
0.124
0.196
100.868756
-0.996695
-341.361686
-132.524702
11.953597
119987
-649.471639
59981
39996000
8
T3
0.159727
-644.308395
0.479
0.210
0.336
95.710298
-5.163244
-463.921253
-119.318109
12.246137
119988
84.392564
59997
40000000
6
T7
0.121694
90.775558
0.345
0.115
0.209
97.820644
-6.382994
-296.725515
-146.721218
17.309919
119989
-402.818412
59996
40000000
0
T5
0.117225
-398.106095
0.329
0.162
0.336
85.880769
-4.712317
-426.357958
-115.108330
11.240729
119990
589.479197
59995
40000000
1
T10
0.064563
589.132379
0.137
0.110
0.180
98.088798
0.346817
-192.860089
-125.752621
19.455592
119991
906.760725
59994
40000000
3
T11
0.051702
906.766668
0.075
0.114
0.198
90.108483
-0.005943
-158.550245
-110.137634
17.307949
119992
-90.074802
59993
40000000
2
T6
0.098242
-88.301639
0.266
0.143
0.215
92.525162
-1.773163
-348.588410
-119.259256
15.222263
119993
-700.467590
59990
40000000
9
T2
0.133199
-694.262202
0.350
0.181
0.322
86.425812
-6.205387
-472.458712
-132.501237
9.422770
119994
-949.479846
59991
40000000
7
T0
0.161546
-943.628702
0.446
0.200
0.359
92.865545
-5.851144
-492.677367
-137.029432
13.563649
119995
-855.969825
59989
40000000
5
T1
0.315990
-853.838423
0.215
0.560
0.543
100.449897
-2.131402
-532.164620
-110.294998
15.498966
119996
200.140395
59988
40000000
10
T8
0.061048
199.429269
0.114
0.115
0.205
105.126176
0.711126
-271.350388
-117.233767
22.564685
119997
-516.574778
59998
40000000
4
T4
0.123769
-510.229033
0.390
0.156
0.294
98.082233
-6.345745
-401.651487
-127.400773
11.718531
119998
-586.606573
59992
40000000
8
T3
0.141706
-580.866390
0.404
0.199
0.333
90.356360
-5.740184
-445.591781
-128.659888
11.442731
119999
313.363593
59999
40000000
11
T9
0.076814
318.402317
0.185
0.106
0.187
90.884389
-5.038724
-239.043224
-132.436665
14.233994
120000 rows × 15 columns
In [69]:
a["Energy"],a["TotalE"] = a["TotalE"], a["Energy"]
In [70]:
a
Out[70]:
index
Step
Run
Temp
Qw
Energy
qn
qc
qc2
Distance
Lipid
AMH-Go
Membrane
Rg
TotalE
0
0
4000
0
T1
0.052376
-230.460406
0.111
0.098
0.183
260.697458
0.002846
-320.672985
-89.808640
20.289161
-230.460406
1
11
4000
8
T8
0.048493
44.805033
0.089
0.095
0.195
261.934545
0.039555
-273.643131
-94.959967
17.988436
44.805033
2
10
4000
7
T7
0.046195
99.804323
0.110
0.080
0.153
264.293987
0.020403
-269.317465
-98.032087
19.582566
99.804323
3
9
4000
3
T2
0.050422
-178.258879
0.103
0.096
0.151
259.472901
0.017870
-315.660777
-91.853011
15.716146
-178.258879
4
7
4000
2
T3
0.049041
-226.814907
0.105
0.092
0.163
266.022021
0.012576
-312.740668
-82.659715
13.856081
-226.814907
5
6
4000
6
T6
0.045750
11.806389
0.083
0.089
0.154
261.725221
0.024576
-287.356174
-87.152555
18.375624
11.806389
6
8
4000
10
T10
0.043264
285.380913
0.090
0.076
0.132
255.945450
0.015740
-242.037148
-100.941627
18.171787
285.380913
7
4
4000
5
T4
0.049164
-58.610126
0.101
0.095
0.157
261.994426
0.025020
-297.591449
-89.152319
21.201030
-58.610126
8
3
4000
9
T9
0.043879
173.143953
0.087
0.083
0.157
261.513616
0.032071
-263.504692
-95.854355
16.238814
173.143953
9
2
4000
1
T0
0.054363
-263.497336
0.117
0.106
0.188
261.605500
0.017367
-330.549656
-84.522104
12.297805
-263.497336
10
1
4000
11
T11
0.041352
380.635990
0.091
0.070
0.142
261.189069
0.019877
-246.831226
-94.003974
18.393798
380.635990
11
5
4000
4
T5
0.048073
-63.195913
0.091
0.088
0.151
264.321184
0.020545
-270.463340
-95.216605
14.883018
-63.195913
12
19
8000
3
T2
0.066254
-304.282149
0.156
0.116
0.233
186.115709
-0.074887
-331.555813
-84.996525
16.198299
-304.282149
13
23
8000
10
T10
0.045864
520.679668
0.125
0.072
0.173
179.061840
-0.057289
-224.424677
-72.102860
16.570245
520.679668
14
22
8000
9
T9
0.047327
360.694761
0.128
0.075
0.209
183.433458
-0.132879
-226.418196
-88.302150
15.720267
360.694761
15
21
8000
1
T0
0.062929
-488.625710
0.134
0.121
0.271
179.813131
-0.048813
-344.865534
-97.502099
25.789635
-488.625710
16
20
8000
7
T7
0.048678
208.021754
0.138
0.073
0.147
185.144411
-0.035643
-247.891787
-76.544446
15.752432
208.021754
17
18
8000
0
T1
0.062897
-385.368085
0.120
0.128
0.284
183.221037
-0.060766
-332.328076
-74.153887
22.604000
-385.368085
18
14
8000
6
T6
0.053647
94.809006
0.137
0.090
0.214
176.747966
-0.049707
-264.099856
-84.021040
14.497136
94.809006
19
16
8000
2
T3
0.058599
-257.311069
0.124
0.114
0.254
174.782471
-0.040857
-305.804757
-92.353686
21.074975
-257.311069
20
15
8000
8
T8
0.057165
221.316202
0.129
0.105
0.284
184.190539
-0.077251
-264.664221
-71.171399
16.404226
221.316202
21
13
8000
4
T5
0.059378
-77.089462
0.127
0.109
0.281
180.198201
-0.037302
-292.220362
-104.475042
26.400950
-77.089462
22
12
8000
5
T4
0.062652
-171.298986
0.146
0.111
0.231
185.870155
-0.028807
-305.948545
-76.968147
19.193998
-171.298986
23
17
8000
11
T11
0.046189
565.729823
0.124
0.072
0.178
174.046677
-0.069297
-200.366905
-88.077859
19.292606
565.729823
24
31
12000
8
T8
0.070688
172.832113
0.180
0.118
0.274
149.169041
-0.000171
-298.488788
-67.849840
11.055367
172.832113
25
35
12000
5
T4
0.087784
-273.804701
0.197
0.162
0.369
137.396714
-0.198979
-347.343224
-76.396274
13.140369
-273.804701
26
34
12000
0
T1
0.080449
-465.734313
0.159
0.166
0.364
138.952859
-0.002480
-377.215010
-67.103867
6.751241
-465.734313
27
33
12000
9
T9
0.067116
244.007262
0.183
0.104
0.262
138.680989
-0.026250
-241.170700
-87.307708
13.927827
244.007262
28
32
12000
7
T6
0.070652
105.433729
0.174
0.120
0.265
142.023264
-0.035487
-275.133562
-76.411618
10.879675
105.433729
29
30
12000
6
T7
0.063491
140.966985
0.144
0.119
0.221
131.112888
0.003554
-262.999687
-84.602052
13.331547
140.966985
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
119970
59968
39992000
10
T8
0.084644
125.315318
0.193
0.143
0.236
103.789374
0.090318
-321.185686
-125.695527
14.684923
125.315318
119971
59967
39992000
5
T1
0.349991
-878.265467
0.241
0.601
0.490
95.257083
-2.509045
-532.261303
-112.158109
17.440344
-878.265467
119972
59966
39992000
11
T9
0.090627
262.500590
0.260
0.109
0.155
89.849633
-3.504245
-254.283771
-133.853171
19.380148
262.500590
119973
59965
39992000
8
T3
0.152857
-666.526186
0.459
0.200
0.343
100.758117
-6.206477
-453.784150
-134.328173
12.607433
-666.526186
119974
59964
39992000
7
T0
0.169079
-917.946641
0.483
0.200
0.341
90.305251
-5.906491
-494.024593
-133.217904
13.670734
-917.946641
119975
59969
39992000
4
T4
0.114089
-449.591990
0.343
0.152
0.277
95.660271
-5.314091
-398.246881
-128.057087
9.608467
-449.591990
119976
59987
39996000
11
T9
0.074017
377.792244
0.209
0.080
0.105
91.639465
-4.724942
-228.640940
-133.677532
24.182730
377.792244
119977
59986
39996000
10
T8
0.063696
208.437997
0.108
0.126
0.208
100.148774
0.285182
-298.037024
-119.461887
15.373861
208.437997
119978
59985
39996000
0
T5
0.136838
-420.915663
0.400
0.174
0.305
96.437949
-4.623812
-453.438025
-109.864277
11.485843
-420.915663
119979
59983
39996000
3
T11
0.054917
756.567928
0.122
0.093
0.122
105.894603
-0.012843
-156.503404
-104.933674
15.775930
756.567928
119980
59982
39996000
6
T7
0.089424
118.747616
0.217
0.116
0.164
98.720483
-4.239269
-258.873243
-119.852487
15.178919
118.747616
119981
59984
39996000
5
T1
0.339551
-869.975883
0.215
0.614
0.505
96.224059
-3.020632
-534.135828
-112.357492
19.734441
-869.975883
119982
59980
39996000
7
T0
0.173838
-918.681134
0.462
0.222
0.367
92.408859
-6.877245
-502.691375
-132.888972
10.307421
-918.681134
119983
59979
39996000
1
T10
0.067316
404.833192
0.133
0.115
0.201
108.594429
0.375098
-222.586328
-122.517378
18.134658
404.833192
119984
59978
39996000
4
T4
0.121727
-511.342317
0.363
0.158
0.245
92.309613
-6.207101
-430.397193
-117.058347
10.813826
-511.342317
119985
59977
39996000
9
T2
0.156603
-665.911180
0.445
0.188
0.310
91.639243
-5.771314
-462.484791
-127.278876
10.799616
-665.911180
119986
59976
39996000
2
T6
0.090403
-109.874911
0.254
0.124
0.196
100.868756
-0.996695
-341.361686
-132.524702
11.953597
-109.874911
119987
59981
39996000
8
T3
0.159727
-649.471639
0.479
0.210
0.336
95.710298
-5.163244
-463.921253
-119.318109
12.246137
-649.471639
119988
59997
40000000
6
T7
0.121694
84.392564
0.345
0.115
0.209
97.820644
-6.382994
-296.725515
-146.721218
17.309919
84.392564
119989
59996
40000000
0
T5
0.117225
-402.818412
0.329
0.162
0.336
85.880769
-4.712317
-426.357958
-115.108330
11.240729
-402.818412
119990
59995
40000000
1
T10
0.064563
589.479197
0.137
0.110
0.180
98.088798
0.346817
-192.860089
-125.752621
19.455592
589.479197
119991
59994
40000000
3
T11
0.051702
906.760725
0.075
0.114
0.198
90.108483
-0.005943
-158.550245
-110.137634
17.307949
906.760725
119992
59993
40000000
2
T6
0.098242
-90.074802
0.266
0.143
0.215
92.525162
-1.773163
-348.588410
-119.259256
15.222263
-90.074802
119993
59990
40000000
9
T2
0.133199
-700.467590
0.350
0.181
0.322
86.425812
-6.205387
-472.458712
-132.501237
9.422770
-700.467590
119994
59991
40000000
7
T0
0.161546
-949.479846
0.446
0.200
0.359
92.865545
-5.851144
-492.677367
-137.029432
13.563649
-949.479846
119995
59989
40000000
5
T1
0.315990
-855.969825
0.215
0.560
0.543
100.449897
-2.131402
-532.164620
-110.294998
15.498966
-855.969825
119996
59988
40000000
10
T8
0.061048
200.140395
0.114
0.115
0.205
105.126176
0.711126
-271.350388
-117.233767
22.564685
200.140395
119997
59998
40000000
4
T4
0.123769
-516.574778
0.390
0.156
0.294
98.082233
-6.345745
-401.651487
-127.400773
11.718531
-516.574778
119998
59992
40000000
8
T3
0.141706
-586.606573
0.404
0.199
0.333
90.356360
-5.740184
-445.591781
-128.659888
11.442731
-586.606573
119999
59999
40000000
11
T9
0.076814
313.363593
0.185
0.106
0.187
90.884389
-5.038724
-239.043224
-132.436665
14.233994
313.363593
120000 rows × 15 columns
In [37]:
data = pd.read_csv("/Users/weilu/Downloads/Default Dataset-2.csv", header=None, names=["x", "y"])
data
Out[37]:
x
y
0
0.081951
-0.368112
1
0.099926
-0.449932
2
0.153837
-0.667842
3
0.197925
-0.803559
4
0.250138
-0.883806
5
0.294199
-0.964428
6
0.361059
-0.988906
7
0.426181
-0.793079
8
0.476669
-0.680568
9
0.515664
-0.403295
10
0.577377
0.095406
11
0.619538
0.565661
12
0.663208
1.283920
13
0.689039
1.808522
14
0.722886
2.608974
15
0.742289
2.940443
16
0.772809
3.878484
17
0.795270
4.623317
18
0.811305
5.175017
19
0.830533
5.864605
20
0.851351
6.636911
In [38]:
data = pd.read_csv("/Users/weilu/Downloads/Default Dataset-3.csv", header=None, names=["x", "y"])
In [39]:
data
Out[39]:
x
y
0
0.042579
0.125948
1
0.078448
0.127595
2
0.106165
0.128867
3
0.156666
0.213831
4
0.184356
0.270198
5
0.220198
0.326940
6
0.259301
0.383832
7
0.291882
0.440425
8
0.316324
0.469095
9
0.355400
0.581082
10
0.381473
0.609827
11
0.422165
0.749437
12
0.467749
0.889271
13
0.518170
1.139520
14
0.545806
1.306078
15
0.579923
1.555578
16
0.614000
1.887721
17
0.665876
2.496164
18
0.686910
2.827708
19
0.720865
3.407779
20
0.762811
4.318796
21
0.788574
4.981136
22
0.812666
5.726044
23
0.831934
6.332989
24
0.847955
6.912237
25
0.862346
7.491410
In [40]:
x = data["x"].values
In [41]:
y = data["y"].values
In [42]:
order = 4
plt.figure()
p = np.poly1d(np.polyfit(x, y, order))
xp = np.linspace(0, 1, 1000)
_ = plt.plot(x, y, '.', xp, p(xp), '-')
In [34]:
np.vstack((xp, p(xp))).T
Out[34]:
array([[ 0.00000000e+00, 7.25512889e-02],
[ 1.00100100e-03, 6.61331844e-02],
[ 2.00200200e-03, 5.97415234e-02],
...,
[ 9.97997998e-01, 1.48552680e+01],
[ 9.98998999e-01, 1.49268326e+01],
[ 1.00000000e+00, 1.49986371e+01]])
In [23]:
np.concatenate(xp, p(xp)).shape
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-23-4dbd452ce7a7> in <module>()
----> 1 np.concatenate(xp, p(xp)).shape
TypeError: only integer scalar arrays can be converted to a scalar index
In [43]:
np.savetxt("/Users/weilu/Desktop/forYe_2.txt", np.vstack((xp, p(xp))).T)
In [26]:
xs = ["x" + str(i) for i in range(4)]
In [27]:
file_name = "/Users/weilu/Research/davinci/nov_2017/13nov/nov_18_all_freeEnergy_calculation_sample_range_mode_2/tiny/1d_dis/test/evpb-500.dat"
data = pd.read_table(file_name, sep="\s+", skiprows=1, names=["a", "row"] + xs)
In [28]:
data
Out[28]:
a
row
x0
x1
x2
x3
0
0
7.416
-47.994
-556.042
-123.379
25.934
1
1
10.594
-46.413
-511.629
-131.176
25.328
2
2
13.772
-45.912
-537.320
-124.032
21.976
3
3
16.950
-46.609
-525.053
-128.221
23.376
4
4
20.128
-46.699
-536.085
-126.515
23.981
5
5
23.306
-46.227
-533.611
-127.478
23.560
6
6
26.484
-46.499
-536.876
-127.584
23.816
7
7
29.662
-46.344
-537.512
-127.264
23.668
8
8
32.840
-45.969
-534.914
-127.464
23.668
9
9
36.018
-45.622
-532.993
-127.706
23.565
10
10
39.196
-44.831
-527.520
-128.307
23.571
11
11
42.374
-43.346
-519.088
-128.209
23.078
12
12
45.552
-38.318
-503.961
-128.945
23.026
13
13
48.730
-33.931
-484.439
-131.776
20.832
14
14
51.908
-34.038
-486.493
-130.452
20.768
15
15
55.086
-33.973
-488.881
-130.584
20.839
16
16
58.264
-34.064
-490.380
-130.336
21.056
17
17
61.442
-33.944
-489.637
-130.416
21.018
18
18
64.620
-33.821
-488.490
-130.625
20.795
19
19
67.798
-33.832
-487.646
-130.491
20.830
20
20
70.976
-33.606
-485.158
-130.588
20.763
21
21
74.154
-33.311
-482.975
-130.854
20.718
22
22
77.332
-33.219
-480.346
-130.802
20.839
23
23
80.510
-32.450
-478.988
-131.193
20.707
24
24
83.688
-30.148
-480.320
-130.246
20.823
25
25
86.866
-23.117
-468.846
-126.829
19.195
26
26
90.044
-10.410
-453.816
-123.397
14.668
27
27
93.222
-7.601
-449.360
-123.511
13.975
28
28
96.400
-6.724
-449.216
-123.739
13.804
29
29
99.578
-6.548
-449.829
-122.882
13.824
30
30
102.756
-6.284
-450.595
-123.391
13.879
31
31
105.934
-5.963
-447.360
-123.765
13.875
32
32
109.112
-5.911
-448.929
-123.075
13.533
33
33
112.290
-5.733
-446.972
-123.280
13.573
34
34
115.468
-5.832
-447.591
-123.226
13.815
35
35
118.646
-5.303
-445.784
-123.161
14.024
36
36
121.824
-4.844
-443.065
-122.454
13.988
37
37
125.002
-4.633
-442.670
-122.069
13.961
38
38
128.180
-4.494
-443.473
-121.084
13.902
39
39
131.358
-4.682
-443.466
-121.284
14.088
40
40
134.536
-4.627
-441.458
-121.614
14.114
41
41
137.714
-4.737
-441.957
-122.168
13.760
42
42
140.892
-4.783
-442.248
-122.861
13.723
43
43
144.070
-4.780
-443.675
-122.885
13.480
44
44
147.248
-4.295
-440.845
-123.261
13.856
45
45
150.426
-4.867
-446.190
-121.084
12.524
46
46
153.604
-3.991
-438.909
-124.392
14.087
47
47
156.782
-3.979
-438.342
-123.054
12.682
48
48
159.960
-3.862
-427.626
-122.390
12.970
In [29]:
data.plot("row")
Out[29]:
<matplotlib.axes._subplots.AxesSubplot at 0x1816e3ceb8>
In [18]:
file_name = "/Users/weilu/Research/davinci/nov_2017/13nov/all_data_folder/new_next_gen_native_based_memb_3_rg_0.4_lipid_0.6_extended/dis102.0.feather"
pd.read_feather(file_name)
Out[18]:
index
Step
Run
Temp
Qw
Energy
Distance
Lipid
AMH-Go
Membrane
Rg
TotalE
0
0
4000
0
T1
0.050231
-234.766568
265.647076
-0.006706
-307.914444
-91.507692
19.505292
-234.773274
1
11
4000
8
T8
0.045968
116.691673
259.980637
0.013824
-265.076280
-94.914409
18.454996
116.705497
2
10
4000
7
T7
0.043892
93.740996
261.769868
0.004286
-262.986419
-94.705882
21.454883
93.745282
3
9
4000
3
T3
0.051766
-189.528430
263.570988
0.022973
-322.935598
-92.257971
16.866771
-189.505457
4
7
4000
2
T2
0.048512
-229.777776
269.447277
0.004204
-305.492614
-87.394010
16.330156
-229.773571
5
6
4000
6
T6
0.047496
-5.218529
264.982003
0.016421
-283.891603
-95.155745
18.331384
-5.202108
6
8
4000
10
T10
0.041326
309.550477
259.120779
0.016156
-221.961107
-90.828335
22.227939
309.566633
7
4
4000
5
T5
0.047779
-15.438613
259.423772
0.014050
-281.433805
-96.976294
26.207688
-15.424562
8
3
4000
9
T9
0.046371
232.310776
256.865072
0.015319
-246.313400
-92.498208
19.954271
232.326095
9
2
4000
1
T0
0.055467
-256.314428
264.060065
0.005701
-321.907277
-89.737244
13.941821
-256.308727
10
1
4000
11
T11
0.043018
337.047892
263.160811
0.010790
-244.825356
-97.092587
23.117878
337.058681
11
5
4000
4
T4
0.048811
-109.367352
260.172062
0.008506
-285.362249
-104.802383
17.114985
-109.358846
12
19
8000
3
T3
0.062969
-272.646342
189.720595
-0.059241
-311.783470
-92.544651
22.689036
-272.705583
13
23
8000
10
T10
0.049768
498.937291
176.572801
-0.044437
-193.146466
-81.712693
20.753163
498.892853
14
22
8000
9
T9
0.053371
308.413157
179.893112
-0.023407
-231.113211
-87.735126
22.614856
308.389750
15
21
8000
1
T0
0.058895
-444.183383
186.423243
-0.119498
-324.237531
-98.652242
23.744580
-444.302882
16
20
8000
7
T8
0.050750
220.716851
179.934423
-0.061241
-216.718569
-89.218771
22.511368
220.655610
17
18
8000
0
T1
0.066869
-354.597857
185.111326
-0.080965
-324.318488
-89.634420
23.507561
-354.678822
18
14
8000
6
T6
0.054599
35.172071
190.388328
-0.016495
-276.656092
-88.902966
16.320541
35.155576
19
16
8000
2
T2
0.067242
-323.471752
192.825039
-0.026619
-329.733971
-99.782100
21.876145
-323.498371
20
15
8000
8
T7
0.055813
208.722619
182.691929
-0.043373
-251.014861
-94.494494
24.930775
208.679247
21
13
8000
4
T4
0.053429
-150.375662
180.107080
-0.086346
-295.812427
-107.865497
26.276475
-150.462008
22
12
8000
5
T5
0.063877
-62.011677
177.352764
-0.024557
-294.605184
-73.392384
20.775183
-62.036234
23
17
8000
11
T11
0.047850
644.524081
183.910158
-0.025173
-185.842671
-81.125388
15.811946
644.498908
24
31
12000
8
T7
0.070015
112.476297
141.452323
-0.032376
-288.462950
-102.135459
17.759276
112.443921
25
35
12000
5
T5
0.076522
-56.360121
142.905710
-0.015429
-308.616610
-78.517566
12.040642
-56.375550
26
34
12000
0
T1
0.080515
-429.504974
140.671601
-0.024788
-348.203680
-70.200165
7.954754
-429.529762
27
33
12000
9
T9
0.062101
405.788167
145.187588
0.005893
-183.516945
-90.214594
13.777486
405.794060
28
32
12000
7
T8
0.055516
188.267992
138.787821
-0.072939
-240.761991
-83.748272
9.280151
188.195054
29
30
12000
6
T6
0.072978
34.212218
148.074867
-0.007482
-299.899837
-75.443418
11.978723
34.204736
...
...
...
...
...
...
...
...
...
...
...
...
...
119970
59968
39992000
10
T0
0.473354
-964.767690
83.897199
-35.533529
-553.241111
-130.547448
22.148181
-1000.301219
119971
59967
39992000
5
T3
0.166380
-621.487662
104.213291
-5.717475
-449.137974
-132.039479
17.984726
-627.205137
119972
59966
39992000
11
T8
0.083826
229.409609
98.523540
1.210406
-251.318190
-121.771176
25.225163
230.620015
119973
59965
39992000
8
T9
0.087510
382.110346
98.018669
0.595350
-256.284460
-138.595736
30.396092
382.705696
119974
59964
39992000
7
T11
0.069946
860.479045
96.838165
-0.841994
-170.556001
-104.583387
26.700249
859.637051
119975
59969
39992000
4
T4
0.173980
-446.069262
95.188549
-8.510919
-442.237987
-137.905417
18.407810
-454.580181
119976
59987
39996000
11
T8
0.084809
167.875039
88.013006
0.562383
-294.050542
-135.239887
30.152353
168.437422
119977
59986
39996000
10
T0
0.435754
-939.697007
83.962555
-35.864223
-542.890361
-131.611827
23.512900
-975.561230
119978
59985
39996000
0
T7
0.099437
71.097040
88.899475
-0.228191
-322.799281
-112.925592
28.447192
70.868849
119979
59983
39996000
3
T1
0.396590
-889.625791
75.169962
-35.273809
-530.019088
-132.543463
24.763084
-924.899600
119980
59982
39996000
6
T5
0.113894
-267.101337
98.489769
-4.202295
-358.410965
-129.644436
23.818496
-271.303631
119981
59984
39996000
5
T3
0.150926
-605.083789
91.928745
-6.538834
-457.855480
-122.731267
14.793676
-611.622624
119982
59980
39996000
7
T11
0.058398
823.916215
97.822234
-0.093190
-157.167600
-112.648681
22.735316
823.823025
119983
59979
39996000
1
T6
0.098872
-87.364357
101.320148
-2.335267
-331.340907
-108.517130
15.680187
-89.699624
119984
59978
39996000
4
T4
0.144083
-506.515892
101.082405
-6.613090
-424.474999
-134.520153
15.677163
-513.128982
119985
59977
39996000
9
T10
0.069922
550.494311
95.018186
-0.095975
-170.799857
-119.137250
26.039205
550.398336
119986
59976
39996000
2
T2
0.149312
-736.906309
94.534059
-5.899493
-473.905877
-119.044270
12.616343
-742.805802
119987
59981
39996000
8
T9
0.076532
367.263997
94.992003
0.636725
-239.041510
-127.361796
34.866496
367.900722
119988
59997
40000000
6
T5
0.114523
-313.181798
93.844342
-4.601699
-383.824635
-129.015162
15.142883
-317.783497
119989
59996
40000000
0
T7
0.086224
110.899177
100.205209
0.061920
-299.256869
-111.238650
16.093093
110.961097
119990
59995
40000000
1
T6
0.087009
-68.032877
88.445756
-0.952790
-354.073710
-129.986228
16.022044
-68.985667
119991
59994
40000000
3
T1
0.436277
-903.796772
84.954701
-36.516806
-531.030466
-129.889202
22.639386
-940.313577
119992
59993
40000000
2
T2
0.144650
-752.760773
89.825377
-6.078300
-461.762772
-130.182392
16.563984
-758.839074
119993
59990
40000000
9
T10
0.073870
522.143489
92.908428
0.169944
-206.951254
-102.274775
26.885647
522.313433
119994
59991
40000000
7
T11
0.063499
740.924388
103.869403
0.064377
-172.224480
-96.325834
19.451740
740.988766
119995
59989
40000000
5
T3
0.152650
-563.227279
97.869168
-6.571973
-437.755236
-136.251628
13.900375
-569.799252
119996
59988
40000000
10
T0
0.445521
-932.555466
88.190758
-36.370036
-538.263297
-131.628756
26.054655
-968.925502
119997
59998
40000000
4
T4
0.157763
-474.064452
106.160885
-7.166546
-420.401375
-138.126175
18.982376
-481.230998
119998
59992
40000000
8
T9
0.068335
354.551394
96.722110
0.938624
-236.218463
-118.097264
33.895782
355.490017
119999
59999
40000000
11
T8
0.084654
169.065290
89.865639
-0.368532
-291.457858
-130.470839
25.581819
168.696758
120000 rows × 12 columns
In [2]:
z_data.as_matrix().shape
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-2-ffbea6f51752> in <module>()
----> 1 z_data.as_matrix().shape
NameError: name 'z_data' is not defined
In [19]:
x = np.array([5.05, 5.25, 5.91, 6.54, 7.11, 7.68, 8.26, 8.85, 9.44, 10.01, 10.62, 11.17, 11.75, 12.32, 12.89, 13.46, 14.05, 14.66, 15.28, 15.87, 16.52, 17.12, 17.75, 18.38, 19.04, 19.61, 20.33, 20.94, 21.60, 22.23, 22.91, 23.50, 24.12, 24.77, 25.43, 26.02, 26.68, 27.29, 27.95, 28.58, 29.22, 29.87, 30.53, 31.21, 31.86, 32.39, 32.97, 33.54, 34.09, 34.60, 35.03])
y = [-7.82, -7.78, -7.41, -7.04, -6.63, -6.14, -5.57, -4.96, -4.43, -3.94, -3.41, -2.88, -2.27, -1.70, -1.09, -0.52, -0.07, 0.41, 0.82, 1.23, 1.60, 1.85, 2.10, 2.31, 2.40, 2.56, 2.53, 2.58, 2.55, 2.52, 2.40, 2.25, 2.10, 1.90, 1.67, 1.48, 1.24, 0.97, 0.82, 0.58, 0.39, 0.24, 0.12, -0.03, -0.06, -0.05, -0.05, -0.08, -0.07, -0.07, -0.06]
x += 10
In [20]:
p = np.poly1d(np.polyfit(x, y, 5))
ynew = y - p(43.03356301)
p = np.poly1d(np.polyfit(x, ynew, 5))
d = np.polyder(p, m=1)
In [21]:
p(43.03356301)
Out[21]:
4.4053649617126212e-13
In [38]:
def compute_theta(z):
k_bin = 0.2
memb_b = 15
theta = 0.5*(np.tanh(k_bin*(z+memb_b))-np.tanh(k_bin*(z-memb_b)));
return theta
In [26]:
np.tanh(0.5)
Out[26]:
0.46211715726000974
In [29]:
d(14.33)
Out[29]:
0.000499517284119122
In [33]:
dis = np.linspace(15, 50, 100)
plt.plot(dis, p(dis))
Out[33]:
[<matplotlib.lines.Line2D at 0x181cd97198>]
In [39]:
z = np.linspace(-30,30, 100)
plt.plot(z, compute_theta(z))
Out[39]:
[<matplotlib.lines.Line2D at 0x181fa77400>]
In [50]:
n = 100
xv, yv = np.meshgrid(dis, z, sparse=False, indexing='ij')
f = np.zeros((n,n))
for i in range(n):
for j in range(n):
f[i][j] = p(xv[i,j]) * compute_theta(yv[i,j])
In [73]:
p(15)
Out[73]:
-7.6316250583217027
In [80]:
n = 100
f2 = np.zeros((n,n))
for i in range(n):
for j in range(n):
f2[i][j] = (p(xv[i,j]) -p(15)) * compute_theta(yv[i,j])
In [81]:
data = [
go.Surface(
x=dis,
y=z,
z=f2.T
)
]
layout = go.Layout(
title='Mt Bruno Elevation',
scene = dict(
xaxis = dict(
title='Dis'),
yaxis = dict(
title='Z'),
zaxis = dict(
title='f'),),
autosize=False,
width=1000,
height=1000,
margin=dict(
l=65,
r=50,
b=65,
t=90
)
)
fig = go.Figure(data=data, layout=layout)
py.iplot(fig, filename='elevations-3d-surface')
Out[81]:
In [83]:
data = [
go.Surface(
x=dis,
y=z,
z=f.T
)
]
layout = go.Layout(
title='Mt Bruno Elevation',
scene = dict(
xaxis = dict(
title='Dis'),
yaxis = dict(
title='Z'),
zaxis = dict(
title='f'),),
autosize=False,
width=1000,
height=1000,
margin=dict(
l=65,
r=50,
b=65,
t=90
)
)
fig2 = go.Figure(data=data, layout=layout)
py.iplot(fig2, filename='elevations-3d-surface')
Out[83]:
In [66]:
trace1 = go.Scatter(
x=[0, 1, 2, 3, 4, 5, 6, 7, 8],
y=[8, 7, 6, 5, 4, 3, 2, 1, 0]
)
trace2 = go.Scatter(
x=[0, 1, 2, 3, 4, 5, 6, 7, 8],
y=[0, 1, 2, 3, 4, 5, 6, 7, 8]
)
data = [trace1, trace2]
layout = go.Layout(
xaxis=dict(
title='AXIS TITLE',
titlefont=dict(
family='Arial, sans-serif',
size=18,
color='lightgrey'
),
showticklabels=True,
tickangle=45,
tickfont=dict(
family='Old Standard TT, serif',
size=14,
color='black'
),
exponentformat='e',
showexponent='All'
),
yaxis=dict(
title='AXIS sdfTITLE',
titlefont=dict(
family='Arial, sans-serif',
size=18,
color='lightgrey'
),
showticklabels=True,
tickangle=45,
tickfont=dict(
family='Old Standard TT, serif',
size=14,
color='black'
),
exponentformat='e',
showexponent='All'
)
)
fig = go.Figure(data=data, layout=layout)
py.iplot(fig, filename='axes-labels')
Out[66]:
In [8]:
def expand_grid(dictionary):
return pd.DataFrame([row for row in product(*dictionary.values())],
columns=dictionary.keys())
def variable_test2(k_list=[1],
force_ramp_rate_list=[1],
memb_k_list=[1],
force_list=["ramp"],
rg_list=[0.08],
pressure_list=[0.1],
repeat=1,
mode_list=[2],
commons=0,
temperature_list=[300],
start_from_list=["native"],
simulation_model_list=["go"]):
inputs = locals()
tmp = {}
for key,value in test.items():
if isinstance(value, list):
tmp[key] = value
return inputs
# all_inputs = expand_grid(inputs)
# for myInput in all_inputs:
# print(myInput)
In [9]:
start_from_list=["native"]
# start_from_list=["extended", "topology"]
mode_list = [3] # lipid mediated interaction
# pressure_list = [0, 0.1, 1.0]
pressure_list = [0]
force_ramp_rate_list=[10]
temperature_list=[500]
memb_k_list = [3]
rg_list = [0.1]
force_list = [0.4, 0.5, 0.6, 0.7, 0.8]
repeat = 2
test = variable_test2(temperature_list=temperature_list,
start_from_list=start_from_list,
rg_list=rg_list,
memb_k_list=memb_k_list,
mode_list=mode_list,
pressure_list=pressure_list,
force_ramp_rate_list=force_ramp_rate_list,
force_list=force_list,
repeat=repeat,
commons=1)
In [11]:
test
Out[11]:
{'commons': 1,
'force_list': [0.4, 0.5, 0.6, 0.7, 0.8],
'force_ramp_rate_list': [10],
'k_list': [1],
'memb_k_list': [3],
'mode_list': [3],
'pressure_list': [0],
'repeat': 2,
'rg_list': [0.1],
'simulation_model_list': ['go'],
'start_from_list': ['native'],
'temperature_list': [500]}
In [21]:
tmp = {}
for key,value in test.items():
if isinstance(value, list):
tmp[key] = value
print(tmp)
{'simulation_model_list': ['go'], 'start_from_list': ['native'], 'temperature_list': [500], 'mode_list': [3], 'pressure_list': [0], 'rg_list': [0.1], 'force_list': [0.4, 0.5, 0.6, 0.7, 0.8], 'memb_k_list': [3], 'force_ramp_rate_list': [10], 'k_list': [1]}
In [22]:
a = tmp['force_list']
In [24]:
len(a)
Out[24]:
5
In [25]:
length(a)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-25-e845004635c0> in <module>()
----> 1 length(a)
NameError: name 'length' is not defined
In [27]:
def expand_grid(dictionary):
return pd.DataFrame([row for row in product(*dictionary.values())],
columns=dictionary.keys())
atest = expand_grid(tmp)
In [30]:
for index, row in atest.iterrows():
print(row.columns)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-30-8a76c792c22b> in <module>()
1 for index, row in atest.iterrows():
----> 2 print(row.columns)
~/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py in __getattr__(self, name)
3075 if (name in self._internal_names_set or name in self._metadata or
3076 name in self._accessors):
-> 3077 return object.__getattribute__(self, name)
3078 else:
3079 if name in self._info_axis:
AttributeError: 'Series' object has no attribute 'columns'
In [33]:
a = atest.loc[0]
In [35]:
type(a)
Out[35]:
pandas.core.series.Series
In [40]:
for t in a.index:
exec(t.replace("_list", "")+"= '"+str(a[t]) + "'")
In [71]:
def move_data(data_folder, freeEnergy_folder, folder, kmem=0.2, klipid=0.1, kgo=0.1, krg=0.2):
print("move data")
os.system("mkdir -p "+freeEnergy_folder+folder+"/data")
dis_list = glob.glob(data_folder+folder+"/dis*.feather")
for dis_file in dis_list:
dis = dis_file.split("/")[-1].replace('dis', '').replace('.feather', '')
print(dis)
t6 = pd.read_feather(dis_file)
remove_columns = ['index']
t6 = t6.drop(remove_columns, axis=1)
t6 = t6.assign(TotalE_perturb_mem_p=t6.TotalE + kmem*t6.Membrane)
t6 = t6.assign(TotalE_perturb_mem_m=t6.TotalE - kmem*t6.Membrane)
t6 = t6.assign(TotalE_perturb_lipid_p=t6.TotalE + klipid*t6.Lipid)
t6 = t6.assign(TotalE_perturb_lipid_m=t6.TotalE - klipid*t6.Lipid)
t6 = t6.assign(TotalE_perturb_go_p=t6.TotalE + kgo*t6["AMH-Go"])
t6 = t6.assign(TotalE_perturb_go_m=t6.TotalE - kgo*t6["AMH-Go"])
t6 = t6.assign(TotalE_perturb_rg_p=t6.TotalE + krg*t6.Rg)
t6 = t6.assign(TotalE_perturb_rg_m=t6.TotalE - krg*t6.Rg)
dic = {"T0":350, "T1":400, "T2":450, "T3":500, "T4":550, "T5":600, "T6":650, "T7":700, "T8":750, "T9":800, "T10":900, "T11":1000}
temps = list(dic.values())
return t6
# def convert(x):
# return dic[x]
# t6["Temp"] = t6["Temp"].apply(convert)
# for temp in temps:
# if temp > 600:
# continue
# tmp = t6.query('Temp=="{}"& Step > 1e7'.format(temp))
# tmp.to_csv(freeEnergy_folder+folder+"/data/t_{}_dis_{}.dat".format(temp, dis), sep=' ', index=False, header=False)
In [72]:
pre = "/Users/weilu/Research/davinci/"
data_folder = pre + "all_data_folder/"
freeEnergy_folder = "all_freeEnergy_calculation_nov11/"
folder= "next_gen_native_based_memb_3_rg_0.2_lipid_0.6_extended"
test = move_data(data_folder, freeEnergy_folder, folder)
move data
93.0
In [90]:
pd.read_feather("/Users/weilu/Research/server/nov_2017/06nov/all_data_folder_nov15/memb_3_rg_0.1_lipid_1_extended/dis124.0.feather")
Out[90]:
index
Step
Run
Temp
Qw
Energy
Distance
Lipid
AMH-Go
Membrane
Rg
TotalE
0
0
4000
0
T0
0.054115
-351.880904
270.345832
0.016447
-329.338299
-116.860909
5.818426
-351.864458
1
11
4000
8
T8
0.045126
63.814883
269.423579
0.045335
-274.242233
-121.475216
5.531545
63.860218
2
10
4000
7
T7
0.048213
22.141554
266.712860
0.052354
-292.625826
-136.395339
6.386973
22.193909
3
9
4000
3
T3
0.048580
-199.175975
270.900760
0.009984
-304.747825
-120.918671
5.887292
-199.165991
4
7
4000
2
T2
0.047893
-204.223642
266.765199
0.016386
-296.750140
-118.157489
7.563550
-204.207256
5
6
4000
6
T6
0.044583
-53.954438
262.030362
0.109783
-278.444662
-125.019353
7.621083
-53.844655
6
8
4000
10
T10
0.041983
248.051582
268.038796
0.177896
-238.759180
-130.219741
6.710839
248.229478
7
4
4000
5
T5
0.047760
-90.384326
268.516017
-0.031774
-297.485620
-125.728921
5.936438
-90.416100
8
3
4000
9
T9
0.043942
149.467142
267.221176
0.034383
-270.782421
-122.545242
6.788831
149.501526
9
2
4000
1
T1
0.050061
-294.238044
266.284285
-0.037151
-328.196310
-117.636339
5.918008
-294.275195
10
1
4000
11
T11
0.039714
438.231528
264.466399
0.156438
-232.829569
-119.372722
6.187052
438.387966
11
5
4000
4
T4
0.045115
-170.191740
264.541605
0.018205
-278.986306
-128.210028
6.063303
-170.173535
12
19
8000
3
T3
0.056063
-281.982813
207.176806
-0.254685
-314.792467
-120.190724
5.805192
-282.237498
13
23
8000
10
T10
0.044685
349.429064
188.824875
-0.100695
-204.231006
-108.020079
7.827426
349.328368
14
22
8000
9
T9
0.048451
131.271724
193.684213
0.009991
-276.702520
-106.608127
6.662593
131.281716
15
21
8000
1
T1
0.061323
-512.890852
195.868418
-0.666855
-356.388488
-111.784327
6.942482
-513.557707
16
20
8000
7
T7
0.049664
83.746704
198.455709
-0.142284
-257.717059
-100.706217
6.278823
83.604421
17
18
8000
0
T0
0.063300
-547.420433
194.519214
-0.443692
-362.767802
-111.620459
5.595390
-547.864124
18
14
8000
6
T5
0.059013
-87.514848
195.679061
-0.741277
-293.567567
-132.606398
6.442564
-88.256125
19
16
8000
2
T2
0.050512
-409.231628
194.313956
-0.011977
-321.243062
-121.584912
8.655962
-409.243605
20
15
8000
8
T8
0.053989
119.067006
194.715393
-0.405259
-246.759407
-124.946293
5.699800
118.661747
21
13
8000
4
T4
0.056084
-177.429400
199.159235
-0.305182
-299.863260
-126.377275
6.309685
-177.734583
22
12
8000
5
T6
0.057187
-95.551317
197.904872
-0.933465
-303.754645
-112.163903
5.762946
-96.484782
23
17
8000
11
T11
0.044548
636.220711
195.273649
-0.180782
-196.589630
-113.896781
6.061343
636.039928
24
31
12000
8
T8
0.072416
204.284954
151.081026
-0.806811
-229.359302
-117.600561
4.716089
203.478143
25
35
12000
5
T6
0.083702
-144.217133
154.333882
-1.008831
-311.622546
-116.399633
5.152809
-145.225965
26
34
12000
0
T0
0.087851
-656.484421
149.547418
-0.841431
-394.202866
-117.036255
5.894072
-657.325852
27
33
12000
9
T9
0.061968
256.371561
163.013806
-0.069772
-257.962246
-117.448701
4.045138
256.301790
28
32
12000
7
T7
0.079746
24.320027
156.108126
-0.423707
-296.239456
-115.239595
4.256585
23.896320
29
30
12000
6
T5
0.072047
-174.429361
154.414573
-1.660969
-303.575072
-130.848113
4.975343
-176.090330
...
...
...
...
...
...
...
...
...
...
...
...
...
119970
59968
39992000
10
T0
0.372776
-946.355018
105.027309
-38.683553
-543.770262
-128.921567
6.492016
-985.038571
119971
59967
39992000
5
T4
0.333583
-577.311863
110.206760
-35.922282
-495.082302
-126.954363
6.787005
-613.234145
119972
59966
39992000
11
T9
0.087711
411.982265
128.692651
-1.656738
-231.833902
-146.700364
8.225935
410.325527
119973
59965
39992000
8
T10
0.082940
533.343975
117.966540
-3.635558
-229.885543
-132.330897
10.228751
529.708417
119974
59964
39992000
7
T2
0.357331
-787.254931
111.967013
-38.965602
-520.028145
-128.830151
5.953294
-826.220533
119975
59969
39992000
4
T1
0.352905
-880.521894
108.576482
-38.488676
-518.742353
-123.161860
5.959515
-919.010570
119976
59987
39996000
11
T9
0.081219
378.405563
120.826292
-3.520312
-226.827752
-131.259083
6.179524
374.885251
119977
59986
39996000
10
T0
0.397759
-1000.019018
109.243366
-37.651008
-567.599262
-130.170723
6.019018
-1037.670026
119978
59985
39996000
0
T11
0.068038
713.320150
110.518351
-0.304808
-162.821374
-117.716206
10.701913
713.015342
119979
59983
39996000
3
T3
0.346793
-659.251790
114.695598
-38.207975
-506.224122
-126.256116
6.931243
-697.459766
119980
59982
39996000
6
T6
0.287069
-309.086916
113.147057
-35.357858
-423.126299
-127.020498
9.231157
-344.444775
119981
59984
39996000
5
T4
0.338576
-584.582405
111.505568
-37.356876
-509.282236
-122.063831
6.812884
-621.939282
119982
59980
39996000
7
T2
0.389944
-785.855064
119.160400
-38.853646
-517.006719
-119.759688
8.053806
-824.708709
119983
59979
39996000
1
T7
0.099825
-70.625922
126.798717
0.298967
-314.819773
-135.926008
5.285914
-70.326954
119984
59978
39996000
4
T1
0.350104
-827.158192
116.993359
-37.011858
-528.662653
-120.763636
6.263044
-864.170050
119985
59977
39996000
9
T5
0.262148
-419.776716
109.302873
-33.330034
-442.376799
-127.541955
8.605727
-453.106749
119986
59976
39996000
2
T8
0.100737
10.000762
126.742081
-1.405365
-325.040883
-119.399064
4.642004
8.595397
119987
59981
39996000
8
T10
0.071909
534.302694
119.934157
-4.660159
-187.991066
-131.749643
10.532141
529.642535
119988
59997
40000000
6
T6
0.260731
-279.798216
117.232719
-33.837006
-413.065223
-116.444310
5.465174
-313.635222
119989
59996
40000000
0
T11
0.070635
750.103094
108.529912
-1.086546
-155.750902
-136.069970
5.983952
749.016548
119990
59995
40000000
1
T8
0.087493
88.083047
117.633546
0.273617
-280.331505
-142.587445
4.311181
88.356664
119991
59994
40000000
3
T3
0.329895
-648.362417
119.609117
-36.272957
-503.351198
-121.966109
5.639040
-684.635374
119992
59993
40000000
2
T7
0.093677
30.724765
107.400051
-0.003280
-291.188772
-133.065295
7.125238
30.721484
119993
59990
40000000
9
T5
0.248504
-371.329247
113.949242
-33.075371
-433.497181
-127.379459
6.986356
-404.404617
119994
59991
40000000
7
T2
0.346403
-776.278507
119.358464
-37.405684
-502.157548
-126.615712
6.967927
-813.684191
119995
59989
40000000
5
T4
0.300838
-540.931493
121.977246
-34.840728
-497.079326
-113.340417
4.252293
-575.772221
119996
59988
40000000
10
T0
0.384186
-972.969404
104.433060
-38.044366
-558.684165
-128.739303
5.641534
-1011.013770
119997
59998
40000000
4
T1
0.363469
-853.709084
112.512214
-36.558659
-522.895041
-118.983219
4.969266
-890.267744
119998
59992
40000000
8
T10
0.077688
508.479622
115.750870
-1.150006
-192.174294
-109.923069
5.581487
507.329616
119999
59999
40000000
11
T9
0.079237
359.050248
115.989939
-3.636335
-250.392017
-140.995752
7.170667
355.413913
120000 rows × 12 columns
In [ ]:
In [120]:
data2 = pd.read_table("/Users/weilu/Documents/yegao/gcmc/rho_vs_p_2.dat", sep="\s+", skiprows=2, names=["TimeStep", "rho", "p", "muex"])
In [106]:
data = pd.read_table("/Users/weilu/Documents/yegao/gcmc/rho_vs_p.dat", sep="\s+", skiprows=2, names=["TimeStep", "rho", "p", "muex"])
In [108]:
data.plot("rho", "muex", kind="scatter")
Out[108]:
<matplotlib.axes._subplots.AxesSubplot at 0x1824dd6dd8>
In [130]:
data2.plot("rho", "muex", kind="scatter")
Out[130]:
<matplotlib.axes._subplots.AxesSubplot at 0x182c67d2e8>
In [125]:
5.**(1/3)
Out[125]:
1.7099759466766968
In [114]:
0.53*5.**(1/3)
Out[114]:
0.9062872517386493
In [139]:
data3 = pd.read_table("/Users/weilu/Documents/yegao/gcmc/rho_vs_p_3.dat", sep="\s+", skiprows=2, names=["TimeStep", "rho", "p", "muex"])
data3.plot("rho", "muex", kind="scatter")
Out[139]:
<matplotlib.axes._subplots.AxesSubplot at 0x182dd81748>
In [136]:
rho_list = np.log(np.array([0.5, 1, 2, 3,4,5]))/(np.log(5))
In [138]:
rho_list*-1.25
Out[138]:
array([ 0.5383457 , -0. , -0.5383457 , -0.85325774, -1.0766914 ,
-1.25 ])
In [ ]:
Content source: luwei0917/awsemmd_script
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