In [123]:
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 *
import scipy
# from .. import notebookFunctions
%matplotlib inline
plt.rcParams['figure.figsize'] = (10,6.180) #golden ratio
# %matplotlib notebook
%load_ext autoreload
%autoreload 2
The autoreload extension is already loaded. To reload it, use:
%reload_ext autoreload
In [125]:
pre = "/Users/weilu/Research/server_backup/may_2018/03_week"
temp = 370
location = pre + "/second_start_extended_combined_2/_280-350/2d_zAverage_dis/more_bins/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin = shortest_path_2(location2, start=(18, 30), end=(29,1),save=False, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
In [128]:
pre = "/Users/weilu/Research/server_backup/may_2018/03_week"
temp = 370
location = pre + "/enhance_go/_280-350/2d_zAverage_dis/force_0.25/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin = shortest_path_2(location2, start=(18, 30), end=(29,1),save=False, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
In [126]:
pre = "/Users/weilu/Research/server_backup/may_2018/03_week"
temp = 370
location = pre + "/enhance_go/_280-350/2d_zAverage_dis/force_0.2/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin = shortest_path_2(location2, start=(18, 30), end=(29,1),save=False, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
In [2]:
pre = "/Users/weilu/Research/server/may_2018/03_week"
temp = 370
location = pre + "/second_start_extended_combined_2/_280-350/2d_zAverage_dis/more_bins/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin = shortest_path_2(location2, start=(18, 30), end=(29,1),save=False, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
In [11]:
pre = "/Users/weilu/Research/server/may_2018/03_week"
temp = 370
location = pre + "/second_start_extended_combined_2/_280-350/2d_zAverage_dis/even_more_bins/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
zmax=25
res=50
path_origin, f_origin = shortest_path_2(location2, start=(18, 40), end=(29,1),save=False, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
In [76]:
pre = "/Users/weilu/Research/server/may_2018/03_week"
temp = 370
location = pre + "/second_start_extended_combined_2/_280-350/2d_zAverage_dis/small_temp_change/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_370 = shortest_path_2(location2, start=(18, 30), end=(29,1),save=False, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
In [77]:
pre = "/Users/weilu/Research/server/may_2018/03_week"
temp = 376
location = pre + "/second_start_extended_combined_2/_280-350/2d_zAverage_dis/small_temp_change/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_376 = shortest_path_2(location2, start=(18, 30), end=(29,1),save=False, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
In [78]:
pre = "/Users/weilu/Research/server/may_2018/03_week"
temp = 373
location = pre + "/second_start_extended_combined_2/_280-350/2d_zAverage_dis/small_temp_change/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_373 = shortest_path_2(location2, start=(18, 30), end=(29,1),save=False, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
In [80]:
plt.plot(f_370, label="370")
plt.plot(f_373, label="373")
plt.plot(f_376, label="376")
plt.legend()
Out[80]:
<matplotlib.legend.Legend at 0x1a22a91f60>
In [113]:
pre = "/Users/weilu/Research/server/may_2018/03_week"
temp = 370
location = pre + "/second_start_extended_combined_2/_280-350/2d_zAverage_dis/expected_energys/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin = shortest_path_2(location2, start=(18, 30), end=(29,1),save=True, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
In [70]:
pre = "/Users/weilu/Research/server/may_2018/03_week"
temp = 370
location = pre + "/second_start_extended_combined_2/_280-350/2d_zAverage_dis/expected_energys/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin = shortest_path_2(location2, start=(18, 30), end=(29,1),save=False, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
In [118]:
location3 = location + f"perturbation-2-evpb-{temp}.dat"
path = path_origin
outname = "/Users/weilu/Dropbox/GlpG_paper_2018/figures/2d_expected_energy_go.png"
f = plot2d(location3, path, zmin=-800, zmax=120,
res=res, xlabel="Distance", ylabel="AverageZ",
xmin=xmin,xmax=xmax,ymin=ymin,ymax=ymax, outname=outname)
x = np.arange(len(f))
x_smooth = np.linspace(x.min(), x.max(), 200)
spl1 = scipy.interpolate.interp1d(x, f_origin, kind="cubic")
# plt.plot(x_smooth1, spl1(x_smooth1))
spl = scipy.interpolate.interp1d(x, f, kind="cubic")
# plt.plot(x_smooth, spl(x_smooth))
# Create axes
fig, ax = plt.subplots()
ax1, ax2 = two_scales(ax, x_smooth, spl1(x_smooth), spl(x_smooth), 'r', 'b')
color_y_axis(ax1, 'r')
color_y_axis(ax2, 'b')
# plt.show()
plt.savefig("/Users/weilu/Dropbox/GlpG_paper_2018/figures/1d_expected_energy_go.png")
In [114]:
location3 = location + f"perturbation-2-evpb-{temp}.dat"
path = path_origin
outname = "/Users/weilu/Dropbox/GlpG_paper_2018/figures/2d_expected_energy_go.png"
f = plot2d(location3, path, zmin=-800, zmax=120,
res=res, xlabel="Distance", ylabel="AverageZ",
xmin=xmin,xmax=xmax,ymin=ymin,ymax=ymax, outname=outname)
# plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
plt.figure()
fig, ax = plt.subplots()
ax1, ax2 = two_scales(ax, np.arange(len(f)), f_origin, f, 'r', 'b')
<matplotlib.colors.LinearSegmentedColormap object at 0x1a153161d0>
<matplotlib.figure.Figure at 0x1a21df3390>
In [119]:
location3 = location + f"perturbation-2-evpb-{temp}.dat"
path = path_origin
name = "lipid"
outname = f"/Users/weilu/Dropbox/GlpG_paper_2018/figures/2d_expected_energy_{name}.png"
f = plot2d(location3, path, zmin=-800, zmax=120, z=4,
res=res, xlabel="Distance", ylabel="AverageZ",
xmin=xmin,xmax=xmax,ymin=ymin,ymax=ymax, outname=outname)
x = np.arange(len(f))
x_smooth = np.linspace(x.min(), x.max(), 200)
spl1 = scipy.interpolate.interp1d(x, f_origin, kind="cubic")
# plt.plot(x_smooth1, spl1(x_smooth1))
spl = scipy.interpolate.interp1d(x, f, kind="cubic")
# plt.plot(x_smooth, spl(x_smooth))
# Create axes
fig, ax = plt.subplots()
ax1, ax2 = two_scales(ax, x_smooth, spl1(x_smooth), spl(x_smooth), 'r', 'b')
color_y_axis(ax1, 'r')
color_y_axis(ax2, 'b')
# plt.show()
plt.savefig(f"/Users/weilu/Dropbox/GlpG_paper_2018/figures/1d_expected_energy_{name}.png")
<matplotlib.colors.LinearSegmentedColormap object at 0x1a153161d0>
In [72]:
location3 = location + f"perturbation-2-evpb-{temp}.dat"
path = path_origin
f = plot2d(location3, path, zmin=-800, zmax=120, z=4,
res=res, xlabel="Distance", ylabel="AverageZ",
xmin=xmin,xmax=xmax,ymin=ymin,ymax=ymax)
# plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
plt.figure()
fig, ax = plt.subplots()
ax1, ax2 = two_scales(ax, np.arange(len(f)), f_origin, f, 'r', 'b')
<matplotlib.colors.LinearSegmentedColormap object at 0x1a153161d0>
<matplotlib.figure.Figure at 0x1a240e12b0>
In [120]:
location3 = location + f"perturbation-2-evpb-{temp}.dat"
path = path_origin
name = "membrane"
outname = f"/Users/weilu/Dropbox/GlpG_paper_2018/figures/2d_expected_energy_{name}.png"
f = plot2d(location3, path, zmin=-800, zmax=120, z=5,
res=res, xlabel="Distance", ylabel="AverageZ",
xmin=xmin,xmax=xmax,ymin=ymin,ymax=ymax, outname=outname)
x = np.arange(len(f))
x_smooth = np.linspace(x.min(), x.max(), 200)
spl1 = scipy.interpolate.interp1d(x, f_origin, kind="cubic")
# plt.plot(x_smooth1, spl1(x_smooth1))
spl = scipy.interpolate.interp1d(x, f, kind="cubic")
# plt.plot(x_smooth, spl(x_smooth))
# Create axes
fig, ax = plt.subplots()
ax1, ax2 = two_scales(ax, x_smooth, spl1(x_smooth), spl(x_smooth), 'r', 'b')
color_y_axis(ax1, 'r')
color_y_axis(ax2, 'b')
# plt.show()
plt.savefig(f"/Users/weilu/Dropbox/GlpG_paper_2018/figures/1d_expected_energy_{name}.png")
<matplotlib.colors.LinearSegmentedColormap object at 0x1a153161d0>
In [73]:
location3 = location + f"perturbation-2-evpb-{temp}.dat"
path = path_origin
f = plot2d(location3, path, zmin=-800, zmax=120, z=5,
res=res, xlabel="Distance", ylabel="AverageZ",
xmin=xmin,xmax=xmax,ymin=ymin,ymax=ymax)
# plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
plt.figure()
fig, ax = plt.subplots()
ax1, ax2 = two_scales(ax, np.arange(len(f)), f_origin, f, 'r', 'b')
<matplotlib.colors.LinearSegmentedColormap object at 0x1a153161d0>
<matplotlib.figure.Figure at 0x1a2099dc18>
In [121]:
location3 = location + f"perturbation-2-evpb-{temp}.dat"
path = path_origin
name = "rg"
outname = f"/Users/weilu/Dropbox/GlpG_paper_2018/figures/2d_expected_energy_{name}.png"
f = plot2d(location3, path, zmin=-800, zmax=120, z=6,
res=res, xlabel="Distance", ylabel="AverageZ",
xmin=xmin,xmax=xmax,ymin=ymin,ymax=ymax, outname=outname)
x = np.arange(len(f))
x_smooth = np.linspace(x.min(), x.max(), 200)
spl1 = scipy.interpolate.interp1d(x, f_origin, kind="cubic")
# plt.plot(x_smooth1, spl1(x_smooth1))
spl = scipy.interpolate.interp1d(x, f, kind="cubic")
# plt.plot(x_smooth, spl(x_smooth))
# Create axes
fig, ax = plt.subplots()
ax1, ax2 = two_scales(ax, x_smooth, spl1(x_smooth), spl(x_smooth), 'r', 'b')
color_y_axis(ax1, 'r')
color_y_axis(ax2, 'b')
# plt.show()
plt.savefig(f"/Users/weilu/Dropbox/GlpG_paper_2018/figures/1d_expected_energy_{name}.png")
<matplotlib.colors.LinearSegmentedColormap object at 0x1a153161d0>
In [74]:
location3 = location + f"perturbation-2-evpb-{temp}.dat"
path = path_origin
f = plot2d(location3, path, zmin=-800, zmax=120, z=6,
res=res, xlabel="Distance", ylabel="AverageZ",
xmin=xmin,xmax=xmax,ymin=ymin,ymax=ymax)
# plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
plt.figure()
fig, ax = plt.subplots()
ax1, ax2 = two_scales(ax, np.arange(len(f)), f_origin, f, 'r', 'b')
<matplotlib.colors.LinearSegmentedColormap object at 0x1a153161d0>
<matplotlib.figure.Figure at 0x1a2404d5c0>
In [94]:
pre = "/Users/weilu/Research/server/may_2018/03_week"
temp = 370
location = pre + "/second_start_extended_combined_2/_280-350/2d_zAverage_dis/lipid_change/"
location2 = location + f"perturbation-3-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin = shortest_path_2(location2, start=(18, 30), end=(29,1),save=False, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
In [95]:
perturbation_table = {3:"Decrease 10% Lipid",
4:"Increase 10% Lipid",
5:"Decrease 20% Lipid",
6:"Increase 20% Lipid",
7:"Decrease 30% Lipid",
8:"Increase 30% Lipid",
9:"Decrease 50% Lipid",
10:"Increase 50% Lipid",}
all_path = {}
all_f = {}
all_location = {}
In [96]:
for i in perturbation_table:
# print(i,perturbation_table[i])
location_i = location + f"perturbation-{i}-pmf-{temp}.dat"
path, f = plot_shortest_path(location_i, path, save=False,
xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res,
xmin=xmin,xmax=xmax,ymin=ymin,ymax=ymax, plot2d=False)
all_path[i] = path
all_f[i] = f
all_location[i] = location_i
In [102]:
i = 3
j = 4
# title = "Go"
plot2d_side_by_side(all_location[i], all_location[j], title1=perturbation_table[i], title2=perturbation_table[j])
# plt.savefig(f"/Users/weilu/Dropbox/GlpG_paper_2018/figures/{title}_compare.png", dpi=300)
plt.figure()
plt.plot(f_origin, label="original")
plt.plot(all_f[i], label=perturbation_table[i])
plt.plot(all_f[j], label=perturbation_table[j])
plt.ylim([0,17.5])
plt.legend(prop={'size': 20})
# plt.savefig(f"/Users/weilu/Dropbox/GlpG_paper_2018/figures/{title}_1d.png", dpi=300)
Out[102]:
<matplotlib.legend.Legend at 0x1a225126d8>
In [99]:
i = 5
j = 6
# title = "Go"
plot2d_side_by_side(all_location[i], all_location[j], title1=perturbation_table[i], title2=perturbation_table[j])
# plt.savefig(f"/Users/weilu/Dropbox/GlpG_paper_2018/figures/{title}_compare.png", dpi=300)
plt.figure()
plt.plot(f_origin, label="original")
plt.plot(all_f[i], label=perturbation_table[i])
plt.plot(all_f[j], label=perturbation_table[j])
plt.ylim([0,17.5])
plt.legend(prop={'size': 20})
# plt.savefig(f"/Users/weilu/Dropbox/GlpG_paper_2018/figures/{title}_1d.png", dpi=300)
Out[99]:
<matplotlib.legend.Legend at 0x1a241dac18>
In [100]:
i = 7
j = 8
# title = "Go"
plot2d_side_by_side(all_location[i], all_location[j], title1=perturbation_table[i], title2=perturbation_table[j])
# plt.savefig(f"/Users/weilu/Dropbox/GlpG_paper_2018/figures/{title}_compare.png", dpi=300)
plt.figure()
plt.plot(f_origin, label="original")
plt.plot(all_f[i], label=perturbation_table[i])
plt.plot(all_f[j], label=perturbation_table[j])
plt.ylim([0,17.5])
plt.legend(prop={'size': 20})
# plt.savefig(f"/Users/weilu/Dropbox/GlpG_paper_2018/figures/{title}_1d.png", dpi=300)
Out[100]:
<matplotlib.legend.Legend at 0x1a20f7add8>
In [105]:
i = 9
j = 10
# title = "Go"
plot2d_side_by_side(all_location[i], all_location[j], title1=perturbation_table[i], title2=perturbation_table[j])
# plt.savefig(f"/Users/weilu/Dropbox/GlpG_paper_2018/figures/{title}_compare.png", dpi=300)
plt.figure()
plt.plot(f_origin, label="original")
plt.plot(all_f[i], label=perturbation_table[i])
plt.plot(all_f[j], label=perturbation_table[j])
plt.ylim([0,17.5])
plt.legend(prop={'size': 20})
# plt.savefig(f"/Users/weilu/Dropbox/GlpG_paper_2018/figures/{title}_1d.png", dpi=300)
Out[105]:
<matplotlib.legend.Legend at 0x1a2101b128>
In [90]:
pre = "/Users/weilu/Research/server/may_2018/03_week"
temp = 370
location = pre + "/second_start_extended_combined_2/_280-350/2d_zAverage_dis/higer_force_0.2/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
zmax=25
res=40
path_origin, f_origin = shortest_path_2(location2, start=(10, 35), end=(28,1),save=False, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
In [108]:
pre = "/Users/weilu/Research/server/may_2018/03_week"
temp = 370
location = pre + "/second_start_extended_combined_2/_280-350/2d_zAverage_dis/higer_force_0.25/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
zmax=35
res=40
path_origin, f_origin = shortest_path_2(location2, start=(10, 35), end=(28,1),save=False, xlabel="Distance", ylabel="AverageZ", zmax=zmax,res=res)
# print(getBound(location2, res=res, zmax=zmax))
xmin,xmax,ymin,ymax = getBound(location2, res=res, zmax=zmax)
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
AMH-Go
AMH_3H
AMH_4H
BiasTo
DisReal
Dis_h56
Distance
Energy
...
rg5
rg6
rg_all
z_average
z_h1
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-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
-717.663661
...
0.759227
1.514345
5.729742
-2.773871
0.626748
-9.051280
-9.519281
-9.054574
-6.101329
-5.240148
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
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
1368073
688073
-185.566725
-433.548999
-249.879598
-307.708963
58.0
63.557769
26.553924
5.229743
-724.999601
...
0.755565
1.757790
8.589588
-2.780659
-3.636463
-9.032954
-3.538948
-7.427103
-7.288845
-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
-177.887323
-428.014053
-244.788796
-302.143592
58.0
59.265955
25.231700
-6.671719
-732.918851
...
0.845738
1.517929
8.651643
-2.616635
-0.433013
-9.111535
-3.449159
-10.857177
-7.044298
-5.581970
1368285
688285
-184.210201
-430.548018
-250.086784
-305.998335
58.0
60.640554
26.592739
-5.221510
-727.315327
...
0.794170
0.935052
6.529880
-2.665478
1.725538
-8.744802
-5.221135
-10.853723
-8.537626
-6.591871
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
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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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58.0
63.557769
26.553924
5.229743
-724.999601
...
0.755565
1.757790
8.589588
-2.780659
-3.636463
-9.032954
-3.538948
-7.427103
-7.288845
-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
-177.887323
-428.014053
-244.788796
-302.143592
58.0
59.265955
25.231700
-6.671719
-732.918851
...
0.845738
1.517929
8.651643
-2.616635
-0.433013
-9.111535
-3.449159
-10.857177
-7.044298
-5.581970
1368285
688285
-184.210201
-430.548018
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58.0
60.640554
26.592739
-5.221510
-727.315327
...
0.794170
0.935052
6.529880
-2.665478
1.725538
-8.744802
-5.221135
-10.853723
-8.537626
-6.591871
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
-10.631560
-12.007895
-6.107439
1368361
688361
-183.606638
-415.098958
-244.664187
-300.696670
58.0
60.770273
27.241040
-21.828712
-747.726212
...
0.869720
1.079696
6.470778
-2.783935
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-8.395565
-3.589856
-10.334181
-10.820362
-5.422851
1368365
688365
-170.485342
-410.446007
-235.935563
-290.030268
58.0
58.441573
25.915303
-25.488909
-704.633895
...
0.596922
1.227126
6.685990
-2.924974
-2.254350
-10.311079
-3.971000
-9.888635
-10.048325
-4.535247
1368405
688405
-171.017655
-417.268400
-233.666533
-291.802412
58.0
59.227292
24.564113
5.064778
-722.212197
...
1.072983
1.198726
7.022781
-2.654217
-2.779565
-9.138782
-6.440966
-9.872572
-5.586225
-4.953839
1368421
688421
-174.129100
-417.718375
-237.096229
-294.843969
58.0
61.994997
26.401146
10.573494
-721.641583
...
0.973994
1.347110
6.480872
-2.809157
2.713803
-6.655543
-5.198070
-10.890147
-8.842316
-5.834637
1368746
688746
-179.121831
-417.353552
-243.942584
-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
-436.256822
-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
-4.141764
-9.007082
-6.944422
-3.195684
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
-298.556609
58.0
58.456522
27.621790
47.294456
-684.129366
...
1.111318
1.476957
6.769122
-2.655099
-1.873088
-8.552198
-4.006576
-10.618220
-7.687263
-4.993024
1369102
689102
-168.319223
-417.837647
-231.479184
-290.313380
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
-9.603307
-5.175364
-11.635361
-7.566449
-7.385186
1369198
689198
-178.243939
-419.998665
-235.984567
-290.399571
58.0
58.508104
25.556783
55.111334
-708.657623
...
1.208107
0.556888
6.169614
-2.737007
2.171651
-8.647830
-6.081449
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1369202
689202
-178.609499
-416.061316
-238.913211
-296.585357
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
58.858789
27.640180
46.522631
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...
0.969568
0.846451
5.765233
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0.170344
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1369234
689234
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58.0
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22.240237
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
62.934132
26.216408
58.354414
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...
0.716470
0.913194
5.907705
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1369258
689258
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58.0
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29.289282
62.555680
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...
0.496093
1.300823
7.333361
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689294
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58.0
62.205866
28.057959
62.151074
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...
0.943809
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1369394
689394
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58.0
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...
1.067481
0.725100
6.398078
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1369817
689817
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58.0
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41.713169
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0.898382
1.258251
8.202878
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1369905
689905
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21.352841
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0.882144
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0.886495
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1369949
689949
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58.0
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24.211236
32.745059
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...
0.833724
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0.158322
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1369954
689954
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58.0
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27.295161
58.019990
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...
0.785803
0.898672
5.095570
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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
-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
16
16
-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
17
17
-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
18
18
-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
19
19
-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
20
20
-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
21
21
-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
22
22
-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
23
23
-198.103094
-408.174967
-256.059433
-299.686255
76.0
62.369909
18.123410
20.131948
-704.187926
...
0.809389
1.222921
7.130008
-3.492088
-2.068909
-9.216425
-13.613160
-6.471469
-8.147841
-2.197857
24
24
-199.025804
-412.025040
-267.941874
-313.322494
76.0
61.496671
16.571651
53.412272
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...
1.195463
1.603197
8.203108
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25
25
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76.0
62.893771
20.045472
52.172994
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...
1.401528
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26
26
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60.267215
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...
0.594485
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27
27
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76.0
62.875795
20.662846
54.749443
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...
1.017907
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6.806332
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28
28
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76.0
61.811020
13.226111
60.401425
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...
0.640460
2.199162
7.204001
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29
29
-209.344681
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76.0
61.323506
23.279207
60.654290
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...
1.127948
1.217919
6.943568
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...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
1093
1093
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58.0
61.143547
29.842580
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...
1.219156
1.293550
7.596216
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1094
1094
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58.0
61.185343
26.234047
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...
0.863806
1.043906
6.171285
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0.412182
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1095
1095
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58.0
60.773128
27.196709
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...
1.056415
1.290780
7.399700
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1096
1096
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58.0
60.651985
25.353754
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...
0.660203
0.883399
6.025683
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1097
1097
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58.0
61.645265
26.388548
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...
0.493353
2.460976
9.731254
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1098
1098
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58.0
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26.108806
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0.063740
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1099
1099
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58.0
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25.280646
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...
0.687493
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6.950195
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0.102693
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1100
1100
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58.0
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17.828335
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...
0.640597
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6.020941
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1101
1101
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58.0
60.591313
22.127825
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...
0.758276
1.070956
7.091750
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1102
1102
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58.0
61.447386
27.762346
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...
0.800377
0.948902
6.409108
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1103
1103
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58.0
60.967074
24.897062
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...
0.955982
0.923271
6.630029
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1.893512
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1104
1104
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58.0
62.424577
23.195869
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-721.036621
...
0.649406
1.374586
6.979247
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0.177224
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1105
1105
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58.0
61.084242
24.844553
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...
0.497966
1.306762
6.545507
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1106
1106
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58.0
61.348253
25.255532
6.533869
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...
0.706337
1.100097
6.864341
-2.988014
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1107
1107
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58.0
62.920589
23.912972
13.444316
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...
0.534341
1.608464
6.854060
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-2.564418
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1108
1108
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58.0
60.818516
31.461736
23.315544
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...
0.864376
1.608958
8.831331
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1109
1109
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58.0
60.640554
26.592739
-5.221510
-727.315327
...
0.794170
0.935052
6.529880
-2.665478
1.725538
-8.744802
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-8.537626
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1110
1110
-180.278907
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-300.526281
58.0
61.095038
13.585796
-19.009305
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...
1.283974
0.831167
5.932357
-3.074031
0.309547
-6.813591
-3.404757
-10.631560
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-6.107439
1111
1111
-183.606638
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58.0
60.770273
27.241040
-21.828712
-747.726212
...
0.869720
1.079696
6.470778
-2.783935
-1.973842
-8.395565
-3.589856
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1112
1112
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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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-8.842316
-5.834637
1113
1113
-180.626124
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58.0
62.615036
24.593743
58.388955
-703.422838
...
0.852610
1.152187
7.960150
-2.995607
-0.275691
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-6.944422
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1114
1114
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58.0
61.568734
28.438961
45.158228
-726.176637
...
0.738207
1.361105
7.062270
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1115
1115
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58.0
62.324539
25.954151
50.169640
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...
0.662318
0.795600
7.451465
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1116
1116
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58.0
60.326950
27.636576
51.779129
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...
0.665393
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4.989163
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1.763269
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1117
1117
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58.0
60.780064
22.240237
56.533185
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...
0.529463
1.487205
7.263872
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0.400207
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1118
1118
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58.0
62.934132
26.216408
58.354414
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...
0.716470
0.913194
5.907705
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1119
1119
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58.0
62.205866
28.057959
62.151074
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0.943809
1.396559
7.720693
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1120
1120
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58.0
61.161116
22.387550
55.973068
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...
1.067481
0.725100
6.398078
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1121
1121
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58.0
61.558249
21.352841
29.267518
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...
0.882144
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5.510611
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0.886495
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1122
1122
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58.0
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24.211236
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0.833724
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0.158322
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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
-179.816118
-436.125312
-245.541039
-305.695423
68.0
64.995160
26.508368
57.145418
-717.405939
...
1.520013
7.289126
-2.577021
-1.175693
-7.954141
-2.995597
-8.741945
-8.811388
-4.013220
-788.845781
546
221108
-201.469940
-407.854973
-266.116236
-308.453982
66.0
64.987222
24.206840
42.046223
-681.002588
...
1.198128
8.335522
-3.220784
-6.182657
-8.109057
-7.768108
-12.207467
-1.276039
-7.417777
-748.609091
532
220916
-203.006755
-414.495915
-261.079897
-304.069182
66.0
64.987207
24.529796
38.266241
-730.848119
...
1.328753
7.506422
-2.659843
-0.392917
-6.738003
-6.311969
-8.566641
-4.399358
-6.305643
-795.856419
351
131056
-175.660512
-399.364832
-241.721499
-297.085204
62.0
64.975605
33.820984
64.870234
-689.285927
...
1.030485
7.311189
-3.075666
-1.779076
-7.365427
-5.637872
-10.007806
-9.452039
-6.223536
-760.507156
2248
661197
-169.672965
-426.494537
-235.575945
-293.426529
64.0
64.974094
25.550759
-58.916889
-707.111669
...
0.918407
6.452709
-2.650790
-1.800819
-8.767094
-3.838908
-10.032964
-4.690880
-5.962602
-777.374805
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
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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
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DisReal
Dis_h56
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...
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rg6
rg_all
z_average
z_h1
z_h2
z_h3
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z_h5
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10899
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20276
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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
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-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
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39850
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72.0
52.241362
28.935426
44.313962
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...
0.892460
1.538403
6.691036
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23.419343
36.659341
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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
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72.0
50.107109
22.905990
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-872.040575
...
0.892242
1.314513
6.642784
-2.864897
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32123
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72.0
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25.995547
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...
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6.655265
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32363
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1.086510
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32415
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72.0
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713571
33571
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...
0.759659
1.707409
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33615
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72.0
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...
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33863
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714019
34019
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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
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DisReal
Dis_h56
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Energy
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rg_all
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z_h1
z_h2
z_h3
z_h4
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z_h6
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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)
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Content source: luwei0917/awsemmd_script
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