notebook.community

Edit and run



In [2]:

    
import os
import sys
import random
import time
from random import seed, randint
import argparse
import platform
from datetime import datetime
import imp
import numpy as np
import fileinput
from itertools import product
import pandas as pd
from scipy.interpolate import griddata
from scipy.interpolate import interp2d
import seaborn as sns
from os import listdir

import matplotlib.pyplot as plt
import seaborn as sns
from scipy.interpolate import griddata
import matplotlib as mpl
sys.path.insert(0,'..')
from notebookFunctions import *
# from .. import notebookFunctions

%matplotlib inline
plt.rcParams['figure.figsize'] = (10,6.180)    #golden ratio
# %matplotlib notebook
%load_ext autoreload
%autoreload 2



In [8]:

    
pre = "/Users/weilu/Research/server/apr_2018/02_week"
temp = 260
location = pre + "/sixth_expectedEnergy/_280-350/2d_z_qw/force_0.2/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(4, 14), end=(28,24), save=False, xlabel="z_H6", ylabel="Qw")
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location2 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location2, zmin=-500, zmax=-300)
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 0x1a0d1fe198>






    Out[8]:





[<matplotlib.lines.Line2D at 0x1a191e6fd0>]



In [12]:

    
pre = "/Users/weilu/Research/server/apr_2018/02_week"
temp = 260
location = pre + "/sixth_expectedEnergy/_280-350/2d_z_qw/force_0.2/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(4, 14), end=(28,24), save=False, xlabel="z_H6", ylabel="Qw")
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location2 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location2, zmin=-30, zmax=0, z=4)
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 0x1a0d1fe198>






    Out[12]:





[<matplotlib.lines.Line2D at 0x1a192b5080>]



In [17]:

    
pre = "/Users/weilu/Research/server/apr_2018/02_week"
temp = 260
location = pre + "/sixth_expectedEnergy/_280-350/2d_z_qw/force_0.2/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(4, 14), end=(28,24), save=False, xlabel="z_H6", ylabel="Qw")
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location2 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location2, zmin=-45, zmax=-10, z=5)
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 0x1a0d1fe198>






    Out[17]:





[<matplotlib.lines.Line2D at 0x1a1a773470>]



In [23]:

    
pre = "/Users/weilu/Research/server/apr_2018/02_week"
temp = 260
location = pre + "/sixth_expectedEnergy/_280-350/2d_z_qw/force_0.2/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(4, 14), end=(28,24), save=False, xlabel="z_H6", ylabel="Qw")
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location2 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location2, zmin=-5, zmax=20, z=6)
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 0x1a0d1fe198>






    Out[23]:





[<matplotlib.lines.Line2D at 0x1a19003eb8>]



In [3]:

    
data = pd.read_feather("/Users/weilu/Research/server/apr_2018/second/force_0.02_rg_0.1_lipid_1.0_mem_1_go_0.8/rerun_1_10_Apr_225016.feather")
dic = {"T0":280, "T1":290, "T2":300, "T3":310, "T4":320, "T5":335, "T6":350, "T7":365, "T8":380, "T9":410, "T10":440, "T11":470}
a = data
a["Temp"] = a["Temp"].apply(lambda x: dic[x])
rerun1 = data
t = a.query("Temp < 400").groupby(["BiasTo","Temp"])[["DisReal","Run"]].mean().reset_index()
t["Diff"] = t["DisReal"]-t["BiasTo"].apply(pd.to_numeric)
t["BiasTo"] = t["BiasTo"].apply(pd.to_numeric)
fg = sns.FacetGrid(data=t, hue='Temp', size=8, aspect=1.61)
fg.map(plt.scatter, 'BiasTo', 'Diff').add_legend()









    Out[3]:





<seaborn.axisgrid.FacetGrid at 0x1a2b52a470>



In [9]:

    
rerun1.query("Temp == 300").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[9]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a1c849ac8>



In [20]:

    
rerun1.query("Temp == 300 and z_h6 < -10").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[20]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a1acfcdd8>



In [10]:

    
rerun1.query("Temp == 300 and DisReal < 55").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[10]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a1c862048>



In [ ]:

    
rerun1.query("Temp == 300 and DisReal < 55").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)



In [22]:

    
t = rerun1.query("Temp == 300 and DisReal < 55 and z_h6 < -10")
t.groupby(["BiasTo", "Run"])["Qw"].describe().query("count > 300")



In [11]:

    
rerun1.query("Temp == 300 and DisReal < 55").plot.hexbin("z_h6", "z_h3", cmap="seismic", sharex=False)









    Out[11]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a1c62cef0>



In [6]:

    
rerun1.query("Temp == 300").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[6]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a19cd6240>



In [4]:

    
rerun1.query("Temp == 300 and z_h5 < -20 and DisReal < 50").plot.hexbin("DisReal", "z_h5", cmap="seismic", sharex=False)
t = rerun1.query("Temp == 300 and z_h5 < -20 and DisReal < 50")
t.groupby(["BiasTo", "Run"])["DisReal"].describe().query("count > 300")



In [75]:

    
pre = "/Users/weilu/Research/server/apr_2018/01_week"
temp = 280
location = pre + "/second_freeEnergy_1/_280-350/2d_z_qw/force_0.0/"
location2 = location + f"pmf-{temp}.dat"
# , block=[-15,-10,0.5,0.9]
path, f = shortest_path(location2, start=(3, 10), plot1d=True, save=False)
# 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 0x1a16d648d0>






    Out[75]:





[<matplotlib.lines.Line2D at 0x1a2c122be0>]



In [29]:

    
t = rerun1.query("Temp < 350 and DisReal < 60 and z_h6 < -10 and Qw  < 0.4 and Qw > 0.2")
t.groupby(["BiasTo", "Run"])["DisReal"].describe()



In [41]:

    
pre = "/Users/weilu/Research/server/apr_2018/01_week"
temp = 290
location = pre + "/first_freeEnergy_1/_280-350/2d_z_qw/force_0.1/"
location2 = location + f"pmf-{temp}.dat"
# , block=[-15,-10,0.5,0.9]
path, f = shortest_path(location2, start=(3, 10), plot1d=True, save=False)
# 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 0x1a16d648d0>






    Out[41]:





[<matplotlib.lines.Line2D at 0x1a26fe0438>]



In [36]:

    
pre = "/Users/weilu/Research/server/apr_2018/01_week"
temp = 290
location = pre + "/first_freeEnergy_1/_280-350/2d_z_qw/remove_34_46_force_0.1/"
location2 = location + f"pmf-{temp}.dat"
# , block=[-15,-10,0.5,0.9]
path, f = shortest_path(location2, start=(3, 10), plot1d=True, save=False)
# 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=100)
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 0x1a16d648d0>






    Out[36]:





[<matplotlib.lines.Line2D at 0x1a275829e8>]



In [28]:

    
pre = "/Users/weilu/Research/server/apr_2018/01_week"
temp = 300
location = pre + "/first_freeEnergy_1/_280-350/2d_z_qw/remove_34/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(3, 10), plot1d=True, save=False)
# 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=100)
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 0x1a16d648d0>






    Out[28]:





[<matplotlib.lines.Line2D at 0x1a26c0b2e8>]



In [13]:

    
pre = "/Users/weilu/Research/server/apr_2018/01_week"
temp = 300
location = pre + "/first_freeEnergy_1/_280-350/2d_z_qw/quick/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(3, 10), plot1d=True, save=False)
# 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=100)
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 0x1a16d648d0>






    Out[13]:





[<matplotlib.lines.Line2D at 0x1a2475b9b0>]



In [151]:

    
pre = "/Users/weilu/Research/server/apr_2018/01_week"
temp = 320
location = pre + "/tenth_freeEnergy_5/_280-350/2d_z_qw/remove_52_and_30/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), block=[-15,-10,0.5,0.9], plot1d=True, save=False)
# 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=100)
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 0x1157877f0>






    Out[151]:





[<matplotlib.lines.Line2D at 0x1a5eb03630>]



In [147]:

    
pre = "/Users/weilu/Research/server/apr_2018/01_week"
temp = 310
location = pre + "/tenth_freeEnergy_5/_280-350/2d_z_qw/quick/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), block=[-15,-10,0.5,0.9], plot1d=True, save=False)
# 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=100)
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 0x1157877f0>






    Out[147]:





[<matplotlib.lines.Line2D at 0x1a60100860>]



In [126]:

    
pre = "/Users/weilu/Research/server/apr_2018/01_week"
temp = 270
location = pre + "/tenth_freeEnergy_3/_280-350/2d_z_qw/force_0.0/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), block=[-15,-10,0.5,0.9], plot1d=True, save=False)
# 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=100)
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 0x1157877f0>






    Out[126]:





[<matplotlib.lines.Line2D at 0x1a83c78630>]



In [55]:

    
pre = "/Users/weilu/Research/server/apr_2018/01_week"
temp = 280
location = pre + "/tenth_freeEnergy_3_less_temp/_280-350/2d_z_qw/force_0.0/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), block=[-15,-10,0.5,0.9], plot1d=True, save=False)
# 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=100)
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 0x1157877f0>






    Out[55]:





[<matplotlib.lines.Line2D at 0x1a2beb58d0>]



In [42]:



In [43]:



In [44]:

    
data = pd.read_feather("/Users/weilu/Research/server/apr_2018/second/force_0.04_rg_0.15_lipid_1.0_mem_1_go_0.8/rerun_1_09_Apr_154731.feather")
dic = {"T0":280, "T1":290, "T2":300, "T3":310, "T4":320, "T5":335, "T6":350, "T7":365, "T8":380, "T9":410, "T10":440, "T11":470}
a = data
a["Temp"] = a["Temp"].apply(lambda x: dic[x])
rerun1 = data
t = a.query("Temp < 400").groupby(["BiasTo","Temp"])[["DisReal","Run"]].mean().reset_index()
t["Diff"] = t["DisReal"]-t["BiasTo"].apply(pd.to_numeric)
t["BiasTo"] = t["BiasTo"].apply(pd.to_numeric)
fg = sns.FacetGrid(data=t, hue='Temp', size=8, aspect=1.61)
fg.map(plt.scatter, 'BiasTo', 'Diff').add_legend()









    Out[44]:





<seaborn.axisgrid.FacetGrid at 0x1a29fa96d8>



In [5]:

    
t = a.query("Temp < 400").groupby(["BiasTo","Temp"])[["DisReal","Run"]].mean().reset_index()
t["Diff"] = t["DisReal"]-t["BiasTo"].apply(pd.to_numeric)
t["BiasTo"] = t["BiasTo"].apply(pd.to_numeric)
fg = sns.FacetGrid(data=t, hue='Temp', size=8, aspect=1.61)
fg.map(plt.scatter, 'BiasTo', 'Diff').add_legend()









    Out[5]:





<seaborn.axisgrid.FacetGrid at 0x1a22a918d0>



In [50]:

    
rerun1.query("Temp == 280").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[50]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a2547dc88>



In [65]:

    
rerun1.query("Temp == 300").plot.hexbin("DisReal", "z_h6", cmap="seismic", sharex=False)









    Out[65]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a54c2af60>



In [66]:

    
rerun1.query("Temp == 300 and z_h5 < -20 and DisReal < 50").plot.hexbin("DisReal", "z_h5", cmap="seismic", sharex=False)
t = rerun1.query("Temp == 300 and z_h5 < -20 and DisReal < 50")
t.groupby(["BiasTo", "Run"])["DisReal"].describe().query("count > 300")









    Out[66]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a275a98d0>



In [68]:



In [ ]:

    
rerun1.query("Temp == 280").plot.hexbin("DisReal", "z_h5", cmap="seismic", sharex=False)



In [6]:

    
rerun1.query("Temp == 300").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[6]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a2276e978>



In [31]:

    
rerun3.query("Temp == 300").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[31]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a29293f98>



In [108]:

    
rerun5.query("Temp == 300").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[108]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a60154dd8>



In [9]:

    
rerun1.query("Temp == 300 and DisReal > 60 and z_h6 > -10").plot.hexbin("z_h6", "Qw", cmap="seismic", sharex=False)

rerun1.query("Temp == 300 and DisReal > 60 and z_h6 > -10").shape









    Out[9]:





(44424, 43)



In [158]:

    
rerun3.query("Temp == 300 and DisReal > 60 and z_h6 > -10").plot.hexbin("z_h6", "Qw", cmap="seismic", sharex=False)

rerun3.query("Temp == 300 and DisReal > 60 and z_h6 > -10").shape









    Out[158]:





(17082, 43)



In [157]:

    
rerun5.query("Temp == 300 and DisReal > 60 and z_h6 > -10").plot.hexbin("z_h6", "Qw", cmap="seismic", sharex=False)

rerun5.query("Temp == 300 and DisReal > 60 and z_h6 > -10").shape









    Out[157]:





(10766, 43)



In [161]:

    
rerun5.query("Temp == 300 and DisReal > 60 and z_h6 > -10 and Lipid1 > -5").plot.hexbin("z_h6", "Qw", cmap="seismic", sharex=False)

rerun5.query("Temp == 300 and DisReal > 60 and z_h6 > -10 and Lipid1 > -5").shape









    Out[161]:





(1410, 43)



In [162]:

    
rerun5.query("Temp == 300 and DisReal > 60 and z_h6 > -10 and Lipid1 < -5").plot.hexbin("z_h6", "Lipid1", cmap="seismic", sharex=False)

rerun5.query("Temp == 300 and DisReal > 60 and z_h6 > -10 and Lipid1 < -5").shape









    Out[162]:





(9356, 43)

The problem is that convergence is too slow I think.



In [165]:

    
rerun5.query("Temp == 300 and DisReal > 60 and z_h6 > -10 and Lipid1 < -5").groupby(["BiasTo", "Run"])["DisReal"].describe().query("count > 100")



In [133]:

    
rerun3.query("Temp == 300 and DisReal < 60 and z_h6 < -10").groupby(["BiasTo", "Run"])["DisReal"].describe()



In [129]:

    
rerun3.query("Temp == 300 and DisReal < 60 and z_h6 < -10").shape









    Out[129]:





(1280, 43)



In [17]:

    
rerun1.query("Temp == 300 and DisReal > 60 and z_h6 < -10").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[17]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a2509a748>



In [76]:

    
rerun3.query("Temp == 300 and DisReal > 60").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[76]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a549e3588>



In [116]:

    
rerun5.query("Temp == 300 and DisReal > 60").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[116]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a2be30b70>



In [117]:

    
rerun5.query("Temp == 300 and DisReal > 60").plot.hexbin("z_h6", "Qw", cmap="seismic", sharex=False)









    Out[117]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a2da9a828>



In [118]:

    
rerun5.query("Temp == 300 and DisReal > 60 and z_h6 > -10").plot.hexbin("z_h6", "Qw", cmap="seismic", sharex=False)









    Out[118]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a58a7a5f8>



In [ ]:

what is in the low Qw region(~0.1)



In [78]:

    
rerun3.query("Temp == 300 and DisReal > 60 and Qw < 0.18").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[78]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a55837be0>



In [89]:

    
t = rerun3.query("Temp == 300 and DisReal > 60 and Qw < 0.18")
t.plot.hexbin("z_h6", "Qw", cmap="seismic", sharex=False)









    Out[89]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a29863438>



In [97]:

    
t = rerun3.query("Temp == 300 and DisReal > 60 and Qw < 0.18")
t.plot.hexbin("z_h6", "TotalE", cmap="seismic", sharex=False)









    Out[97]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a20138ba8>



In [98]:

    
t = rerun3.query("Temp == 300 and DisReal > 60 and Qw < 0.18").query("z_h6 < -10")
t.plot.hexbin("z_h6", "DisReal", cmap="seismic", sharex=False)









    Out[98]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a2bf1dd68>



In [99]:

    
t.groupby(["BiasTo", "Run"])["DisReal"].describe().query("count > 100")



In [104]:

    
t = rerun3.query("Temp == 300 and DisReal > 60 and Qw < 0.18").query("z_h6 > -10")
t.plot.hexbin("z_h6", "DisReal", cmap="seismic", sharex=False)









    Out[104]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a2daa2da0>



In [105]:

    
t.groupby(["BiasTo", "Run"])["DisReal"].describe().query("count > 100")



In [32]:

    
rerun1.query("(z_h6 < -10 or DisReal > 60) and Temp == 300").plot.hexbin("DisReal", "z_h6", cmap="seismic", sharex=False)









    Out[32]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a29467cf8>



In [21]:

    
rerun3.query("(z_h6 < -10 or DisReal > 60) and Temp == 300").plot.hexbin("DisReal", "z_h6", cmap="seismic", sharex=False)









    Out[21]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a20d548d0>



In [33]:

    
rerun3.query("Temp == 300").plot.hexbin("Qw", "z_h6", cmap="seismic", sharex=False)









    Out[33]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a2a976898>



In [7]:

    
data.query("Temp == 300").hist("Lipid1", bins=50)









    Out[7]:





array([[<matplotlib.axes._subplots.AxesSubplot object at 0x110b0b4a8>]], dtype=object)



In [27]:

    
rerun3.query("Temp == 300 and DisReal < 60").hist("Lipid1", bins=50)









    Out[27]:





array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a1ed000b8>]], dtype=object)

Rerun 7



In [122]:

    
data = pd.read_feather("/Users/weilu/Research/server/mar_2018/ninth/force_0.06_rg_0.15_lipid_1.0_mem_1_go_0.8/rerun_7_04_Apr_231330.feather")
dic = {"T0":280, "T1":290, "T2":300, "T3":310, "T4":320, "T5":335, "T6":350, "T7":365, "T8":380, "T9":410, "T10":440, "T11":470}
a = data
a["Temp"] = a["Temp"].apply(lambda x: dic[x])



In [123]:

    
rerun7 = data
# data["BiasTo"] = data["BiasTo"].apply(pd.to_numeric)



In [124]:

    
data.query("Temp == 300").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[124]:





<matplotlib.axes._subplots.AxesSubplot at 0x10cb1fa90>



In [154]:

    
data.query("Temp == 300 and DisReal > 60").shape









    Out[154]:





(28983, 43)



In [125]:

    
data.query("Temp == 300").plot.hexbin("TotalE", "Qw", cmap="seismic", sharex=False)









    Out[125]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a22c04a90>



In [139]:

    
data.query("Temp == 300 and Qw > 0.6").plot.hexbin("TotalE", "DisReal", cmap="seismic", sharex=False)









    Out[139]:





<matplotlib.axes._subplots.AxesSubplot at 0x10cea14a8>



In [134]:

    
data.query("Temp == 300 and Qw > 0.6")["TotalE"].hist(bins=50)
data.query("Temp == 300 and Qw > 0.6")["TotalE"].mean()









    Out[134]:





-908.9993475062817



In [148]:

    
data.query("Temp == 300 and z_h6 < -10 and Qw > 0.3").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[148]:





<matplotlib.axes._subplots.AxesSubplot at 0x10cbd5940>



In [164]:

    
data.query("Temp == 300 and Qw < 0.6")["z_h6"].hist(bins=50)









    Out[164]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a20134da0>



In [203]:

    
data.query("Temp == 300 and Qw > 0.3 and z_h6 < -10 and DisReal > 60")["TotalE"].hist(bins=50)
print(data.query("Temp == 300 and Qw > 0.3 and z_h6 < -10 and DisReal > 60")["TotalE"].mean())
data.query("Temp == 300 and Qw > 0.3 and z_h6 < -10 and DisReal > 60").shape









    



-858.5170866171323






    Out[203]:





(7175, 43)

Two part here,

One with DisReal > 60



In [204]:

    
data.query("Temp == 300 and z_h6 > -10 and Qw > 0.3 and DisReal > 60")["TotalE"].hist(bins=50)
print(data.query("Temp == 300 and z_h6 > -10 and Qw > 0.3 and DisReal > 60").shape)
data.query("Temp == 300 and z_h6 > -10 and Qw > 0.3 and DisReal > 60")["TotalE"].mean()









    



(6221, 43)






    Out[204]:





-875.0166693690952



In [195]:

    
t = data.query("Temp == 300 and z_h6 > -10 and Qw > 0.3 and DisReal > 60")
t.groupby(["BiasTo", "Run"])["Qw"].describe().query("count > 100")

DisReal < 60



In [190]:

    
data.query("Temp == 300 and z_h6 > -10 and Qw < 0.6 and DisReal < 60")["TotalE"].hist(bins=50)
data.query("Temp == 300 and z_h6 > -10 and Qw < 0.6 and DisReal < 60")["TotalE"].mean()









    Out[190]:





-896.6218664373583



In [206]:

    
t = data.query("Temp == 300 and z_h6 > -10 and Qw < 0.6 and DisReal < 60")
t.groupby(["BiasTo", "Run"])["TotalE"].describe().query("count > 100")









    Out[206]:







  
    
      
      
      count
      mean
      std
      min
      25%
      50%
      75%
      max
    
    
      BiasTo
      Run
      
      
      
      
      
      
      
      
    
  
  
    
      30.0
      0
      394.0
      -890.251209
      19.283854
      -947.544810
      -904.379298
      -888.767989
      -877.038622
      -838.763927
    
    
      3
      620.0
      -901.879032
      18.444902
      -961.730918
      -914.355321
      -901.868031
      -888.899866
      -845.904847
    
    
      5
      114.0
      -889.103539
      17.625567
      -935.345576
      -901.511543
      -888.432567
      -878.658842
      -850.719708
    
    
      7
      332.0
      -885.430943
      17.821426
      -932.626242
      -897.484021
      -885.885062
      -873.832352
      -835.073611
    
    
      8
      588.0
      -899.772309
      18.880098
      -957.719562
      -912.416068
      -900.452462
      -888.016278
      -823.522148
    
    
      32.0
      3
      632.0
      -899.654913
      18.584577
      -951.211078
      -912.247703
      -899.309120
      -887.062842
      -843.420836
    
    
      6
      194.0
      -883.791805
      17.641747
      -927.904285
      -896.698596
      -883.547391
      -872.018513
      -829.492370
    
    
      9
      558.0
      -901.007225
      19.587102
      -947.474355
      -913.153758
      -901.538200
      -888.388642
      -837.419089
    
    
      10
      157.0
      -887.753531
      19.533920
      -945.888816
      -900.551720
      -887.865169
      -873.062984
      -839.553259
    
    
      34.0
      0
      303.0
      -901.857320
      18.758789
      -956.152877
      -914.247053
      -900.549990
      -888.930325
      -857.552025
    
    
      5
      206.0
      -881.787809
      17.559785
      -935.028185
      -893.783233
      -884.337537
      -868.700505
      -817.040406
    
    
      6
      499.0
      -886.639411
      19.204203
      -956.004474
      -898.319677
      -886.362851
      -873.585164
      -831.227083
    
    
      8
      439.0
      -886.771852
      18.936983
      -935.404168
      -900.759851
      -888.298555
      -873.989461
      -819.988017
    
    
      9
      166.0
      -875.682184
      18.760039
      -920.377910
      -887.859687
      -874.233890
      -864.202902
      -833.039203
    
    
      10
      468.0
      -889.802760
      19.740321
      -940.281849
      -903.157731
      -890.244557
      -876.323514
      -839.329975
    
    
      11
      390.0
      -889.543798
      20.667989
      -945.690371
      -902.701796
      -889.173510
      -876.046579
      -827.609107
    
    
      36.0
      6
      745.0
      -898.672030
      17.722600
      -944.592028
      -911.162059
      -899.124975
      -887.156187
      -838.921809
    
    
      9
      178.0
      -887.958891
      19.361262
      -933.458611
      -901.874268
      -887.386495
      -874.446654
      -839.795967
    
    
      10
      751.0
      -899.442786
      18.264277
      -948.574290
      -911.876551
      -899.434582
      -887.730034
      -842.397713
    
    
      38.0
      0
      190.0
      -887.070169
      19.997046
      -932.513527
      -899.400700
      -887.331952
      -873.157369
      -837.380043
    
    
      1
      803.0
      -897.982131
      18.899288
      -948.417071
      -910.692875
      -898.353706
      -884.968253
      -845.413398
    
    
      2
      292.0
      -884.828799
      18.548164
      -929.632641
      -895.771273
      -886.483368
      -872.085254
      -822.687180
    
    
      3
      284.0
      -888.637338
      17.777921
      -935.713625
      -900.609155
      -888.930495
      -875.283016
      -841.905732
    
    
      9
      202.0
      -888.994218
      18.454132
      -940.793974
      -900.077167
      -890.945484
      -876.725354
      -840.839724
    
    
      40.0
      0
      332.0
      -881.066871
      18.798838
      -931.016997
      -894.962503
      -880.563670
      -868.173569
      -824.507881
    
    
      1
      421.0
      -884.327925
      18.959000
      -934.258436
      -897.606460
      -886.138082
      -871.902673
      -829.521160
    
    
      2
      433.0
      -886.459083
      18.767784
      -937.516092
      -899.308611
      -886.358190
      -874.860867
      -837.242895
    
    
      3
      415.0
      -886.970365
      19.547075
      -932.519441
      -900.623635
      -888.358786
      -873.599376
      -823.503081
    
    
      6
      266.0
      -874.557970
      18.951226
      -920.341500
      -886.050496
      -874.460368
      -862.144591
      -811.120793
    
    
      8
      303.0
      -885.330511
      17.744929
      -934.041834
      -897.705181
      -884.941002
      -874.706651
      -834.326246
    
    
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    
    
      66.0
      0
      121.0
      -901.122000
      18.884814
      -950.234870
      -914.512399
      -900.147314
      -891.314805
      -847.123991
    
    
      1
      116.0
      -895.673453
      20.593193
      -943.158672
      -909.286231
      -892.700447
      -882.176605
      -843.592094
    
    
      2
      122.0
      -893.386943
      16.639179
      -940.563389
      -903.356458
      -890.835211
      -882.714439
      -860.893218
    
    
      5
      146.0
      -892.489449
      18.084460
      -939.473970
      -903.558047
      -892.728915
      -881.652826
      -829.790894
    
    
      9
      113.0
      -900.106981
      19.429993
      -947.721683
      -913.266159
      -901.885025
      -886.696900
      -849.944626
    
    
      68.0
      2
      114.0
      -900.485431
      18.094734
      -955.734272
      -911.996865
      -900.974327
      -892.588273
      -840.315608
    
    
      4
      117.0
      -899.122680
      18.390827
      -937.082189
      -912.868173
      -897.244308
      -887.730809
      -849.170993
    
    
      5
      122.0
      -899.202206
      18.096502
      -942.277956
      -912.256825
      -899.429781
      -886.356515
      -849.526101
    
    
      6
      108.0
      -899.350525
      19.895123
      -948.184033
      -912.749381
      -900.136850
      -887.966586
      -852.250856
    
    
      70.0
      0
      128.0
      -894.078248
      19.151928
      -932.680240
      -906.530740
      -893.622555
      -882.169923
      -845.602397
    
    
      1
      116.0
      -892.546498
      19.361662
      -941.066566
      -905.893757
      -890.558962
      -878.897157
      -854.799927
    
    
      2
      102.0
      -886.921617
      19.749254
      -931.882107
      -899.941331
      -886.186594
      -874.562656
      -839.546481
    
    
      4
      117.0
      -900.586853
      15.987925
      -931.885593
      -911.162284
      -900.690096
      -891.125473
      -843.020882
    
    
      6
      151.0
      -897.635438
      19.035434
      -948.544722
      -910.327936
      -898.369543
      -884.971073
      -850.162537
    
    
      9
      144.0
      -899.479657
      18.821801
      -961.859476
      -910.614437
      -899.454667
      -887.326860
      -836.591334
    
    
      11
      129.0
      -896.937018
      18.231441
      -931.164155
      -912.345856
      -898.066799
      -882.549609
      -854.551788
    
    
      72.0
      5
      843.0
      -889.735359
      18.385546
      -945.165975
      -901.767226
      -889.610041
      -876.941197
      -834.454650
    
    
      7
      887.0
      -890.804077
      19.030764
      -950.500121
      -903.870195
      -890.725187
      -878.413063
      -825.277069
    
    
      74.0
      2
      563.0
      -889.161729
      19.215481
      -945.370722
      -901.770235
      -888.700659
      -876.424557
      -820.053293
    
    
      4
      486.0
      -891.617002
      18.427406
      -942.722054
      -903.735952
      -891.787370
      -879.126522
      -821.591513
    
    
      9
      260.0
      -876.031653
      18.858182
      -933.126162
      -889.470288
      -876.422794
      -862.308188
      -799.717821
    
    
      10
      573.0
      -892.888059
      20.179539
      -966.404766
      -907.757438
      -893.599110
      -879.050420
      -830.427500
    
    
      76.0
      0
      177.0
      -899.930496
      17.860655
      -951.523665
      -911.374667
      -899.719720
      -887.991498
      -849.665731
    
    
      1
      172.0
      -898.798099
      16.600835
      -936.725827
      -909.445908
      -901.499495
      -887.709274
      -857.098197
    
    
      4
      258.0
      -889.555351
      19.880130
      -946.994701
      -902.343378
      -888.851546
      -875.051645
      -841.475881
    
    
      5
      120.0
      -898.296793
      20.845806
      -947.423702
      -911.565177
      -899.877191
      -883.328435
      -841.984704
    
    
      9
      261.0
      -888.241465
      18.550261
      -947.615076
      -901.440237
      -889.035516
      -877.595380
      -836.922891
    
    
      78.0
      8
      986.0
      -889.553024
      19.162510
      -943.750324
      -902.323468
      -889.685562
      -875.594160
      -832.961716
    
    
      82.0
      5
      109.0
      -875.688598
      23.427553
      -934.032782
      -892.162279
      -874.180832
      -858.468330
      -823.612029
    
    
      84.0
      0
      131.0
      -881.062538
      20.784376
      -922.191985
      -896.345220
      -881.796198
      -868.651078
      -825.547022
    
  

119 rows × 8 columns



In [205]:

    
t = data.query("Temp == 300 and z_h6 > -10 and Qw < 0.6 and DisReal < 60")
t.groupby(["BiasTo", "Run"])["Qw"].describe().query("count > 100")









    Out[205]:







  
    
      
      
      count
      mean
      std
      min
      25%
      50%
      75%
      max
    
    
      BiasTo
      Run
      
      
      
      
      
      
      
      
    
  
  
    
      30.0
      0
      394.0
      0.489531
      0.043844
      0.358303
      0.461404
      0.492923
      0.520311
      0.594439
    
    
      3
      620.0
      0.485340
      0.029477
      0.408019
      0.466635
      0.484666
      0.505224
      0.578709
    
    
      5
      114.0
      0.444789
      0.047450
      0.344071
      0.408037
      0.429610
      0.479605
      0.575462
    
    
      7
      332.0
      0.524273
      0.028805
      0.454118
      0.505165
      0.523628
      0.544429
      0.598160
    
    
      8
      588.0
      0.488453
      0.031958
      0.389779
      0.464817
      0.488286
      0.510258
      0.579697
    
    
      32.0
      3
      632.0
      0.487750
      0.030249
      0.410464
      0.467053
      0.487285
      0.507899
      0.585787
    
    
      6
      194.0
      0.521501
      0.028685
      0.441429
      0.500648
      0.521954
      0.539422
      0.589675
    
    
      9
      558.0
      0.486652
      0.027890
      0.405111
      0.468676
      0.486566
      0.504700
      0.576786
    
    
      10
      157.0
      0.522495
      0.031685
      0.437480
      0.504383
      0.519569
      0.541603
      0.595622
    
    
      34.0
      0
      303.0
      0.488812
      0.028451
      0.418062
      0.468822
      0.487874
      0.509373
      0.565705
    
    
      5
      206.0
      0.443994
      0.035986
      0.322582
      0.427907
      0.446196
      0.465365
      0.543804
    
    
      6
      499.0
      0.522046
      0.029040
      0.428436
      0.503733
      0.523642
      0.541547
      0.588953
    
    
      8
      439.0
      0.524791
      0.029219
      0.430359
      0.505522
      0.526952
      0.545704
      0.594427
    
    
      9
      166.0
      0.496579
      0.026986
      0.408038
      0.480445
      0.496830
      0.514039
      0.578228
    
    
      10
      468.0
      0.413349
      0.022430
      0.344875
      0.399962
      0.413567
      0.427569
      0.482350
    
    
      11
      390.0
      0.410743
      0.022368
      0.350805
      0.396504
      0.408812
      0.424746
      0.482739
    
    
      36.0
      6
      745.0
      0.483690
      0.030461
      0.394670
      0.463188
      0.482566
      0.503314
      0.585620
    
    
      9
      178.0
      0.414196
      0.020174
      0.364020
      0.401110
      0.413120
      0.428199
      0.467040
    
    
      10
      751.0
      0.484260
      0.030414
      0.404256
      0.463737
      0.483324
      0.504644
      0.582029
    
    
      38.0
      0
      190.0
      0.408572
      0.024124
      0.338022
      0.391332
      0.406393
      0.424663
      0.472623
    
    
      1
      803.0
      0.481138
      0.030067
      0.398346
      0.460458
      0.480804
      0.501786
      0.571299
    
    
      2
      292.0
      0.520303
      0.028539
      0.445324
      0.500611
      0.520745
      0.542394
      0.587540
    
    
      3
      284.0
      0.423729
      0.041731
      0.363189
      0.396241
      0.413268
      0.431222
      0.575946
    
    
      9
      202.0
      0.415782
      0.022594
      0.363667
      0.400491
      0.415146
      0.429986
      0.500137
    
    
      40.0
      0
      332.0
      0.442155
      0.023612
      0.370792
      0.426444
      0.444496
      0.456481
      0.507021
    
    
      1
      421.0
      0.518245
      0.030308
      0.421739
      0.499133
      0.519452
      0.538511
      0.591126
    
    
      2
      433.0
      0.407968
      0.022324
      0.351707
      0.392262
      0.407573
      0.422140
      0.473064
    
    
      3
      415.0
      0.411648
      0.021136
      0.323997
      0.398344
      0.410570
      0.425364
      0.472014
    
    
      6
      266.0
      0.351914
      0.017503
      0.300588
      0.341483
      0.352147
      0.361913
      0.418239
    
    
      8
      303.0
      0.443511
      0.021982
      0.363102
      0.428339
      0.443220
      0.459870
      0.503529
    
    
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    
    
      66.0
      0
      121.0
      0.575636
      0.017451
      0.524251
      0.560832
      0.577432
      0.591042
      0.599985
    
    
      1
      116.0
      0.577491
      0.021303
      0.495074
      0.569036
      0.584397
      0.592174
      0.599894
    
    
      2
      122.0
      0.467920
      0.031502
      0.386007
      0.445745
      0.470909
      0.490302
      0.548944
    
    
      5
      146.0
      0.467205
      0.025945
      0.404986
      0.451272
      0.465407
      0.485017
      0.535565
    
    
      9
      113.0
      0.579406
      0.016709
      0.511979
      0.570995
      0.583537
      0.590994
      0.599611
    
    
      68.0
      2
      114.0
      0.576348
      0.019013
      0.507565
      0.564166
      0.581423
      0.591033
      0.599923
    
    
      4
      117.0
      0.580757
      0.017576
      0.521178
      0.572045
      0.586170
      0.594763
      0.599066
    
    
      5
      122.0
      0.574987
      0.018805
      0.529303
      0.564137
      0.578293
      0.591018
      0.599944
    
    
      6
      108.0
      0.580065
      0.015938
      0.539403
      0.571373
      0.582882
      0.592905
      0.599633
    
    
      70.0
      0
      128.0
      0.462869
      0.026190
      0.395824
      0.447547
      0.460828
      0.479186
      0.534200
    
    
      1
      116.0
      0.466758
      0.026348
      0.388508
      0.450535
      0.467621
      0.483546
      0.525900
    
    
      2
      102.0
      0.464029
      0.027526
      0.391007
      0.447468
      0.463903
      0.481800
      0.542475
    
    
      4
      117.0
      0.575161
      0.021442
      0.482658
      0.565996
      0.579825
      0.591280
      0.599993
    
    
      6
      151.0
      0.577447
      0.017979
      0.519017
      0.565755
      0.581196
      0.592861
      0.599645
    
    
      9
      144.0
      0.576912
      0.019614
      0.518649
      0.567711
      0.582269
      0.592874
      0.599916
    
    
      11
      129.0
      0.580298
      0.017659
      0.505153
      0.572408
      0.586535
      0.592519
      0.599641
    
    
      72.0
      5
      843.0
      0.464094
      0.025547
      0.389147
      0.446118
      0.463628
      0.481530
      0.543629
    
    
      7
      887.0
      0.467385
      0.027932
      0.393338
      0.448422
      0.466138
      0.485082
      0.568665
    
    
      74.0
      2
      563.0
      0.466495
      0.026990
      0.401119
      0.449493
      0.464952
      0.483591
      0.561554
    
    
      4
      486.0
      0.466071
      0.028482
      0.375480
      0.447966
      0.466290
      0.483575
      0.550493
    
    
      9
      260.0
      0.496669
      0.028378
      0.404756
      0.478535
      0.496790
      0.515455
      0.580669
    
    
      10
      573.0
      0.470901
      0.028174
      0.383592
      0.452267
      0.470268
      0.488985
      0.579701
    
    
      76.0
      0
      177.0
      0.577569
      0.020088
      0.503613
      0.564750
      0.583074
      0.593591
      0.599979
    
    
      1
      172.0
      0.576932
      0.018177
      0.516327
      0.566479
      0.579116
      0.592593
      0.599804
    
    
      4
      258.0
      0.461957
      0.085142
      0.345313
      0.394413
      0.419904
      0.569953
      0.599947
    
    
      5
      120.0
      0.579387
      0.015364
      0.531898
      0.570654
      0.582667
      0.591257
      0.599300
    
    
      9
      261.0
      0.465503
      0.026522
      0.393174
      0.449805
      0.465494
      0.483787
      0.533802
    
    
      78.0
      8
      986.0
      0.463921
      0.027725
      0.365707
      0.444712
      0.463058
      0.482697
      0.557890
    
    
      82.0
      5
      109.0
      0.422596
      0.086911
      0.280349
      0.363041
      0.388452
      0.430845
      0.597187
    
    
      84.0
      0
      131.0
      0.425814
      0.070791
      0.274786
      0.387266
      0.447998
      0.476494
      0.558342
    
  

119 rows × 8 columns



In [197]:

    
data.query("Temp == 300 and z_h6 > -10 and Qw > 0.3 and DisReal < 60")["Qw"].hist(bins=50)
data.query("Temp == 300 and z_h6 > -10 and Qw > 0.3 and DisReal < 60")["Qw"].mean()









    Out[197]:





0.5200406120854402



In [ ]:

    
t = data.query("Temp == 300 and z_h6 < -10 and Qw > 0.3")
t.groupby(["BiasTo", "Run"])["Qw"].describe().query("count > 100")



In [149]:

    
t = data.query("Temp == 300 and z_h6 < -10 and Qw > 0.3")
t.groupby(["BiasTo", "Run"])["Qw"].describe().query("count > 100")



In [145]:

    
data.query("Temp == 300 and z_h6 < -10 and Qw > 0.3").plot.hexbin("z_h4", "Qw", cmap="seismic", sharex=False)









    Out[145]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a191c06a0>



In [6]:

    
t = data.query("Temp < 350 and DisReal > 60 and Qw > 0.18")
t.hist("Lipid1")









    Out[6]:





array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a21e912e8>]], dtype=object)



In [9]:

    
data.query("Temp < 350").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[9]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a21e913c8>



In [20]:

    
rerun7.query("Temp < 350")["Step"].count()









    Out[20]:





540000



In [17]:

    
t = rerun5.query("Temp < 350 and DisReal > 60 and Qw > 0.18 and Lipid1 > -1")
t["Step"].count()









    Out[17]:





53939



In [18]:

    
t = rerun7.query("Temp < 350 and DisReal > 60 and Qw > 0.18 and Lipid1 > -1")
t["Step"].count()









    Out[18]:





52305



In [10]:

    
t = data.query("Temp < 350 and DisReal > 60 and Qw > 0.18 and Lipid1 > -1")
t.plot.hexbin("Lipid1", "z_h1", cmap="seismic", sharex=False)









    Out[10]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a215447b8>



In [283]:

    
data.query("Temp < 350 and DisReal > 60 and Qw > 0.18").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[283]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a59b3e198>



In [277]:

    
data.query("Qw > 0.58").groupby("Temp")["DisReal"].describe()



In [220]:

    
t = data.query("z_h6 < -20 and Qw > 0.25 and Qw < 0.35 and DisReal > 60")
t.groupby(["BiasTo", "Run"])["DisReal"].describe().query("count > 100")



In [216]:

    
data.query("z_h6 < -20 and Qw > 0.25 and Qw < 0.35 and DisReal > 60").plot.hexbin("z_h4", "TotalE", cmap="seismic", sharex=False)









    Out[216]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a4b46f390>



In [196]:

    
data.query("z_h6 < -20 and Qw > 0.25 and Qw < 0.35").hist("DisReal", bins=50)









    Out[196]:





array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a4a99ada0>]], dtype=object)



In [193]:

    
data.query("z_h6 < -20 and Qw > 0.25 and Qw < 0.35").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[193]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a4a625cc0>



In [40]:

    
data.query("Temp == 300").plot.hexbin("z_h3", "z_h6", cmap="seismic", sharex=False)









    Out[40]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a3ca9f828>



In [42]:

    
a = data.query("(z_h3 < -10) or (z_h6 < -10)")
a.query("Temp == 300").plot.hexbin("z_h3", "z_h6", cmap="seismic", sharex=False)









    Out[42]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a299f2588>



In [66]:

    
data.columns









    Out[66]:





Index(['Step', 'Run', 'Temp', 'Qw', 'Energy', 'DisReal', 'z_average',
       'abs_z_average', 'z_h1', 'z_h2', 'z_h3', 'z_h4', 'z_h5', 'z_h6',
       'Distance', 'AMH-Go', 'Membrane', 'Rg', 'rg1', 'rg2', 'rg3', 'rg4',
       'rg5', 'rg6', 'rg_all', 'Lipid', 'Lipid1', 'Lipid2', 'Lipid3', 'Lipid4',
       'Lipid5', 'Lipid6', 'Lipid7', 'Lipid8', 'Lipid9', 'Lipid10', 'Lipid11',
       'Lipid12', 'Lipid13', 'Lipid14', 'Lipid15', 'TotalE', 'BiasTo'],
      dtype='object')



In [88]:

    
data.query("Temp <= 300").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[88]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a43e3fd30>



In [87]:

    
data.query("Temp <= 300 and DisReal > 60").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[87]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a259706d8>



In [91]:

    
data.columns









    Out[91]:





Index(['Step', 'Run', 'Temp', 'Qw', 'Energy', 'DisReal', 'z_average',
       'abs_z_average', 'z_h1', 'z_h2', 'z_h3', 'z_h4', 'z_h5', 'z_h6',
       'Distance', 'AMH-Go', 'Membrane', 'Rg', 'rg1', 'rg2', 'rg3', 'rg4',
       'rg5', 'rg6', 'rg_all', 'Lipid', 'Lipid1', 'Lipid2', 'Lipid3', 'Lipid4',
       'Lipid5', 'Lipid6', 'Lipid7', 'Lipid8', 'Lipid9', 'Lipid10', 'Lipid11',
       'Lipid12', 'Lipid13', 'Lipid14', 'Lipid15', 'TotalE', 'BiasTo'],
      dtype='object')



In [94]:

    
data.query("Temp == 300 and DisReal > 60").plot.hexbin("Lipid1", "Qw", cmap="seismic", sharex=False)









    Out[94]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a431b42b0>



In [96]:

    
data.query("Temp == 300").plot.hexbin("abs_z_average", "z_h6", cmap="seismic", sharex=False)









    Out[96]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a435ea860>



In [126]:

    
data.query("Temp == 300 and Qw < 0.6 and DisReal > 60 and Lipid1 > -1").plot.hexbin("abs_z_average", "Qw", cmap="seismic", sharex=False)









    Out[126]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a25a95b38>



In [130]:

    
data.query("Temp == 300 and Qw < 0.6 and DisReal > 60").shape









    Out[130]:





(35124, 43)



In [129]:

    
t = data.query("Temp == 300 and Qw < 0.6 and DisReal > 60 and Lipid1 > -1")
print(t.shape)
t.groupby(["BiasTo", "Run"])["DisReal"].describe().query("count > 500")









    



(11439, 43)






    Out[129]:







  
    
      
      
      count
      mean
      std
      min
      25%
      50%
      75%
      max
    
    
      BiasTo
      Run
      
      
      
      
      
      
      
      
    
  
  
    
      72.0
      8
      538.0
      77.631154
      4.978481
      64.034331
      74.265102
      77.679578
      81.148599
      92.402795
    
    
      74.0
      5
      671.0
      79.352226
      4.992393
      64.103251
      75.905704
      79.439485
      82.716725
      94.980508
    
    
      8
      597.0
      80.370943
      4.771834
      63.743749
      77.312220
      80.329319
      83.590274
      94.733364
    
    
      76.0
      2
      609.0
      82.491103
      4.934288
      66.209328
      79.096101
      82.395038
      85.988688
      95.370163
    
    
      80.0
      7
      570.0
      84.330678
      4.942327
      65.585395
      81.343990
      84.503898
      87.671476
      98.049908
    
    
      8
      583.0
      84.765906
      4.744009
      71.252220
      81.699702
      84.902951
      87.689288
      99.617263
    
    
      82.0
      7
      591.0
      86.381836
      4.886817
      74.129515
      82.791748
      86.400281
      89.636735
      101.037585
    
    
      9
      515.0
      86.697468
      4.841471
      71.817455
      83.436906
      86.703616
      90.212749
      101.873019
    
    
      88.0
      1
      503.0
      91.928638
      4.759968
      75.885403
      88.872399
      91.672946
      95.171128
      105.171102
    
    
      90.0
      11
      817.0
      92.899936
      4.619934
      79.678197
      89.964819
      92.984239
      95.992677
      107.310590
    
    
      98.0
      10
      540.0
      98.684912
      4.785347
      85.058600
      95.322567
      98.759484
      102.196297
      111.325873



In [128]:

    
t = data.query("Temp == 300 and Qw < 0.6 and DisReal > 60 and z_h6 < -10")
print(t.shape)
t.groupby(["BiasTo", "Run"])["DisReal"].describe().query("count > 500")









    



(15583, 43)






    Out[128]:







  
    
      
      
      count
      mean
      std
      min
      25%
      50%
      75%
      max
    
    
      BiasTo
      Run
      
      
      
      
      
      
      
      
    
  
  
    
      100.0
      11
      544.0
      84.293570
      3.425672
      75.519237
      81.961637
      84.397217
      86.622628
      94.611569
    
    
      72.0
      5
      548.0
      73.933853
      4.387406
      61.049616
      71.126700
      74.101592
      77.014725
      86.079380
    
    
      6
      570.0
      77.566640
      4.805078
      63.593634
      74.509323
      77.478254
      80.691643
      92.106523
    
    
      78.0
      5
      520.0
      82.549008
      4.721425
      70.541042
      79.102200
      82.721582
      85.779293
      95.670568
    
    
      10
      540.0
      82.675814
      4.635266
      68.840841
      79.520502
      82.781726
      85.958293
      96.702022
    
    
      80.0
      0
      568.0
      79.695028
      4.399605
      67.366476
      76.710162
      79.807395
      82.590351
      96.141058
    
    
      84.0
      8
      530.0
      83.894069
      5.306648
      70.917908
      80.118754
      83.161541
      87.110482
      100.301417
    
    
      11
      553.0
      86.657368
      4.707773
      73.011489
      83.747428
      86.888678
      89.551823
      100.057218
    
    
      86.0
      11
      531.0
      87.787863
      4.444919
      76.416774
      84.737965
      87.608177
      90.635270
      102.053831
    
    
      88.0
      2
      595.0
      84.620395
      4.761268
      70.773094
      81.346685
      84.557121
      87.582304
      98.051481
    
    
      92.0
      2
      564.0
      91.000057
      5.468401
      78.150078
      87.060316
      90.448845
      94.965484
      108.250962
    
    
      96.0
      7
      963.0
      95.072311
      5.525327
      77.822069
      91.490362
      94.993429
      98.830402
      110.993673



In [125]:

    
data.query("Temp == 300 and Qw < 0.6 and DisReal > 60 and z_h6 < -10").plot.hexbin("abs_z_average", "Qw", cmap="seismic", sharex=False)









    Out[125]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a43fc2908>



In [124]:

    
data.query("Temp == 300 and Qw < 0.6 and DisReal > 60").plot.hexbin("abs_z_average", "Qw", cmap="seismic", sharex=False)









    Out[124]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a44fa2fd0>



In [120]:

    
t = data.query("Temp == 300 and Qw < 0.6 and Lipid1 < -1 and DisReal < 60")
t.groupby(["BiasTo", "Run"])["DisReal"].describe().query("count > 500")



In [114]:

    
data.query("Temp == 300 and Qw < 0.6 and Lipid1 < -1 and DisReal < 60").plot.hexbin("z_h1", "Qw", cmap="seismic", sharex=False)









    Out[114]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a4573e7b8>



In [85]:

    
data.query("Temp <= 300 and DisReal > 60").plot.hexbin("abs_z_average", "Qw", cmap="seismic", sharex=False)









    Out[85]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a4631c438>



In [64]:

    
data.query("Temp == 300").plot.hexbin("z_h6", "Qw", cmap="seismic", sharex=False)









    Out[64]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a471194e0>



In [34]:

    
a = data.query("(z_h6 > -10 and z_h1 < -10) or (z_h6 < -10 and z_h1 > -10)")



In [ ]:



In [35]:

    
a.query("Temp == 300").plot.hexbin("z_h1", "z_h6", cmap="seismic", sharex=False)









    Out[35]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a22079908>

I hope we don't see this, for rerun 3.

(h6 inside, h1 outside)



In [59]:

    
rerun1.query("Temp <= 300 and z_h1 < -10")["DisReal"].count()









    Out[59]:





23027



In [60]:

    
rerun1.query("Temp <= 300 and z_h1 < -10").plot.hexbin("DisReal", "z_h6", cmap="seismic", sharex=False)









    Out[60]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a44ad06d8>



In [300]:

    
rerun5.query("Temp <= 300 and z_h1 < -10").plot.hexbin("DisReal", "z_h6", cmap="seismic", sharex=False)









    Out[300]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a4c006d30>



In [7]:

    
data.query("Temp <= 300 and z_h1 < -10").plot.hexbin("DisReal", "z_h6", cmap="seismic", sharex=False)









    Out[7]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a5239eb70>

Good, decrease in number



In [61]:

    
data.query("Temp <= 300")["DisReal"].count()









    Out[61]:





270000



In [62]:

    
data.query("Temp <= 300 and z_h1 < -10")["DisReal"].describe()









    Out[62]:





count    15664.000000
mean        86.338072
std         16.724329
min         23.835159
25%         84.224392
50%         91.396990
75%         96.274063
max        115.018263
Name: DisReal, dtype: float64



In [63]:

    
data.query("Temp <= 300 and z_h1 < -10").plot.hexbin("DisReal", "z_h6", cmap="seismic", sharex=False)









    Out[63]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a42dd2e10>

enhance h1, h2 connection.

check if it increase the unfolding force, and if the helix 5,6 unfold first. (how strong h1,h2 bond needs to be)



In [69]:

    
data = pd.read_feather("/Users/weilu/Research/server/apr_2018/01_week/unfold_strengthen_h1_h2/07_Apr_135640.feather")



In [74]:

    
data["Run"] = data["Run"].apply(pd.to_numeric)



In [29]:

    
fg = sns.FacetGrid(data=data.query("Steps < 4e7 and Qw > 0.1 and Folder=='force_6_' and Run ==0"), hue='Run', size=8, aspect=1.61)
fg.map(plt.scatter, 'Steps', 'Qw').add_legend()









    Out[29]:





<seaborn.axisgrid.FacetGrid at 0x1a2f93b128>



In [71]:

    
fg = sns.FacetGrid(data=data.query("Steps < 4e7 and Qw > 0.1"), hue='Folder', size=8, aspect=1.61)
fg.map(plt.scatter, 'Steps', 'Qw').add_legend()









    Out[71]:





<seaborn.axisgrid.FacetGrid at 0x1a2ad2e198>



In [72]:

    
a = data.query("(z_h6 > -10 and z_h1 < -10) or (z_h6 < -10 and z_h1 > -10)")



In [75]:

    
a.query("Folder == 'force_8_'").plot.hexbin("z_h1", "z_h6", cmap="cool", sharex=False)









    Out[75]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a4e26bb38>



In [15]:

    
data["Folder"].unique()









    Out[15]:





array(['force_2_mem_1.0_', 'force_1_mem_1.0_', 'force_3_',
       'force_2_mem_0.8_', 'force_1_mem_0.8_', 'force_4_'], dtype=object)



In [ ]:



In [ ]:



In [ ]:



In [ ]:



In [ ]:



In [ ]:



In [ ]:

		count	mean	std	min	25%	50%	75%	max
BiasTo	Run
32.0	0	408.0	0.207630	0.022516	0.132922	0.192521	0.206902	0.223214	0.274679
32.0	3	646.0	0.295860	0.022529	0.233403	0.279708	0.297560	0.311383	0.358921
34.0	1	350.0	0.305950	0.022710	0.248184	0.288942	0.306891	0.319656	0.364268
	2	348.0	0.302553	0.023916	0.234047	0.285842	0.301970	0.318966	0.387317
	5	359.0	0.301079	0.023499	0.217187	0.286635	0.300756	0.317220	0.375491
	6	348.0	0.303509	0.024971	0.231530	0.285589	0.302540	0.321343	0.374513
	8	382.0	0.302625	0.024186	0.241498	0.287331	0.299426	0.319425	0.367993
	11	366.0	0.305291	0.024236	0.243061	0.290411	0.306185	0.320459	0.373608
36.0	1	322.0	0.302475	0.026488	0.224957	0.285036	0.303037	0.319929	0.373673
	5	405.0	0.302817	0.024464	0.240123	0.286364	0.302248	0.318334	0.388778
	7	355.0	0.297774	0.025603	0.224926	0.282275	0.297205	0.316055	0.371603
	9	328.0	0.300768	0.023364	0.232542	0.285630	0.301278	0.315238	0.369415
38.0	1	395.0	0.297909	0.024286	0.232771	0.282431	0.295211	0.314988	0.383321
	2	410.0	0.296901	0.028159	0.178959	0.281087	0.297486	0.314929	0.376961
	6	469.0	0.299374	0.027444	0.224790	0.280788	0.298169	0.317680	0.400986
	8	482.0	0.300439	0.024015	0.236319	0.283514	0.300758	0.315697	0.378431
40.0	0	416.0	0.298752	0.026795	0.225444	0.282230	0.299181	0.315989	0.375265
	2	427.0	0.300652	0.023760	0.234078	0.284049	0.301228	0.316109	0.359218
	5	346.0	0.298074	0.024923	0.206725	0.281198	0.296238	0.315597	0.371299
	8	420.0	0.298774	0.024466	0.230950	0.281093	0.298708	0.315803	0.373220
	11	301.0	0.293686	0.026253	0.205075	0.279027	0.294424	0.308322	0.378695
42.0	10	550.0	0.290338	0.022129	0.222033	0.276959	0.290725	0.306280	0.350626
44.0	3	550.0	0.295638	0.026440	0.225975	0.278204	0.296254	0.311611	0.396165
	5	509.0	0.296504	0.023392	0.228345	0.282192	0.295529	0.311465	0.359904
	6	435.0	0.286894	0.033103	0.192652	0.273506	0.290211	0.310525	0.358367
46.0	0	444.0	0.293566	0.024803	0.232870	0.278118	0.292692	0.309750	0.367420
	1	345.0	0.286133	0.032047	0.182090	0.267036	0.289887	0.308355	0.369342
	3	402.0	0.297618	0.025631	0.232307	0.279979	0.296635	0.314210	0.377506
	6	435.0	0.294881	0.024803	0.229723	0.277771	0.292972	0.311775	0.358860
	7	411.0	0.292408	0.023057	0.231480	0.276307	0.292923	0.308867	0.364187
48.0	0	489.0	0.293111	0.025162	0.215243	0.276252	0.293997	0.311107	0.366028
48.0	4	565.0	0.290827	0.027069	0.217511	0.271691	0.289908	0.309596	0.364749
50.0	3	494.0	0.288745	0.023065	0.191465	0.275506	0.288599	0.302867	0.363905
50.0	9	581.0	0.290759	0.024030	0.219821	0.274833	0.291045	0.305578	0.371194
52.0	2	363.0	0.268315	0.040295	0.178919	0.231616	0.277232	0.299423	0.348623
52.0	10	413.0	0.281107	0.040133	0.180007	0.263837	0.281966	0.300981	0.409555

		count	mean	std	min	25%	50%	75%	max
BiasTo	Run
32.0	0	320.0	30.600703	5.089213	15.659740	27.326947	30.489386	33.714216	44.501580
34.0	1	350.0	30.749763	4.732271	18.487694	27.274469	30.553602	34.192073	46.378855
	2	348.0	30.650012	4.117803	17.571337	27.831502	30.550741	33.816862	42.919044
	5	359.0	30.807649	4.353649	17.893820	27.842581	30.641248	33.576568	43.620992
	6	348.0	31.442497	4.381820	16.930372	28.709032	31.525766	34.092051	43.718876
	8	381.0	30.479198	4.430799	18.242114	27.447752	30.364277	33.386729	42.698286
	11	366.0	30.568140	4.407249	17.242213	27.597367	30.392294	33.368946	44.107742
36.0	1	322.0	31.610923	4.554115	16.442470	28.427412	31.573663	34.545290	44.808560
	5	405.0	31.792096	4.493576	18.300224	28.460105	31.698835	34.753392	44.955090
	7	355.0	31.980624	4.481062	21.353281	28.712778	31.962010	34.949697	45.396338
	9	328.0	32.199280	4.542215	18.414032	29.015070	32.377358	35.266697	47.370807
38.0	1	395.0	33.391924	4.498415	20.176224	30.137547	32.982613	36.261027	49.030869
	2	409.0	33.418138	4.895244	21.236112	30.355348	33.120916	36.226249	48.269350
	6	469.0	32.976916	4.515556	19.647481	30.150142	32.845168	35.890070	47.124393
	8	482.0	33.562322	4.440339	21.268113	30.464877	33.625195	36.647264	45.060788
40.0	0	415.0	34.353749	4.534940	21.406551	31.142116	34.353984	37.214856	48.393647
	2	427.0	35.083661	4.455114	24.662640	31.960539	35.017102	38.320103	48.110716
	5	345.0	34.669044	4.678243	23.348292	31.418841	34.344363	37.967166	48.171741
	8	420.0	34.599973	4.674275	24.366743	31.179520	34.462123	38.009348	47.569496
	11	301.0	35.036074	4.570229	22.793838	31.476969	34.915843	38.027037	48.141857
44.0	3	547.0	37.474685	4.265686	21.851974	34.733777	37.459143	40.336012	49.011460
	5	507.0	37.582231	4.619758	24.645794	34.234549	37.425842	40.770112	49.832555
	6	418.0	37.873560	5.034221	24.196811	34.484058	37.597240	40.968960	49.900429
46.0	0	437.0	39.060455	4.710545	25.717187	35.899471	39.002991	42.436197	49.770467
	1	329.0	38.755429	4.619595	26.082116	35.258550	38.899663	42.176299	49.861196
	3	398.0	38.881637	4.622304	25.074804	35.940105	39.048328	41.980755	49.131202
	6	433.0	38.864495	4.458226	22.741744	35.736110	39.067321	42.224370	48.493042
	7	407.0	38.341217	4.533874	25.624146	35.230563	38.689933	41.435068	49.012736
48.0	0	483.0	40.219442	4.316037	26.694145	37.225257	40.330396	43.417476	49.972263
48.0	4	545.0	40.040957	4.451745	28.092196	36.775364	40.043420	43.182294	49.859849
50.0	3	469.0	41.368375	4.203223	27.445262	38.755971	41.666380	44.530420	49.819812
50.0	9	553.0	41.025948	4.445371	28.651777	37.817299	41.223216	44.201592	49.903475
52.0	10	302.0	43.083734	3.849976	31.860405	40.514810	43.565921	46.080353	49.825383

		count	mean	std	min	25%	50%	75%	max
BiasTo	Run
30.0	4	6.0	41.006222	5.041650	33.110139	38.101547	42.197567	45.108890	45.716518
34.0	2	43.0	39.949554	5.164750	31.090879	35.533119	39.389264	42.208664	51.736888
	7	11.0	36.640621	6.045258	27.088338	32.449004	36.995894	41.051114	46.113603
	8	1.0	27.806744	NaN	27.806744	27.806744	27.806744	27.806744	27.806744
	10	872.0	32.181324	4.841434	16.764560	28.929615	31.956359	35.396399	52.786074
36.0	4	7.0	42.909455	3.404116	38.716239	40.446675	43.216252	44.552141	48.436060
40.0	3	1.0	43.163209	NaN	43.163209	43.163209	43.163209	43.163209	43.163209
	8	31.0	44.539105	6.060073	31.727845	40.723865	43.374401	49.418043	57.363641
	10	2.0	36.089678	2.913776	34.029327	35.059502	36.089678	37.119853	38.150028
	11	1.0	29.796479	NaN	29.796479	29.796479	29.796479	29.796479	29.796479
42.0	9	6.0	49.784714	5.276936	40.146452	49.330081	50.559151	52.184937	55.796271
46.0	0	299.0	49.906283	4.735988	36.525933	46.657418	50.020159	53.264903	59.924340
	2	32.0	52.443186	3.907864	45.366041	49.494201	53.068668	55.311558	59.252062
	6	19.0	51.484882	5.341404	41.507961	47.895421	53.528449	56.152731	59.363497
	7	202.0	44.307198	6.869292	29.087520	39.205512	43.352594	49.349331	59.337085
	9	5.0	51.091808	4.004346	46.997042	49.711992	50.107722	50.882936	57.759348
48.0	7	9.0	51.232610	6.215824	42.474518	47.804201	50.764755	57.736108	58.496795
48.0	9	13.0	50.710592	3.304929	45.868227	47.873837	50.794927	53.009640	56.656699
54.0	0	3.0	57.694482	3.391918	53.778368	56.687414	59.596460	59.652539	59.708618
	6	18.0	55.960376	1.513157	53.573642	54.935420	55.518765	56.681682	59.945366
	9	3.0	40.110784	1.508486	38.567418	39.375283	40.183147	40.882466	41.581785
	11	1.0	48.324070	NaN	48.324070	48.324070	48.324070	48.324070	48.324070
68.0	3	2.0	56.373507	2.118943	54.875188	55.624347	56.373507	57.122666	57.871826
70.0	4	10.0	56.336501	2.864167	52.458914	53.612847	56.374691	58.880549	59.707680
70.0	9	32.0	58.232478	1.438579	55.363267	57.162003	58.346373	59.482124	59.925406

		count	mean	std	min	25%	50%	75%	max
BiasTo	Run
30.0	0	304.0	29.979023	4.790765	13.705478	26.875790	29.877991	32.810311	44.084832
	1	321.0	29.644018	4.517274	17.891105	26.533124	29.738254	32.575121	44.005556
	5	344.0	29.754010	4.491797	16.993181	27.042696	29.681701	32.712464	41.226739
	8	330.0	30.441045	4.424125	14.388906	27.534684	30.250623	33.173724	43.364021
	9	328.0	29.931905	4.240580	18.450473	26.948791	29.885532	32.737185	40.656914
34.0	1	394.0	32.462580	4.382931	19.792071	29.657475	32.524224	35.446231	45.513583
	2	325.0	32.752681	4.535961	19.289740	29.819029	32.740985	36.009455	44.359873
	3	302.0	32.822112	4.524936	22.141380	29.733919	32.774411	35.804096	44.384750
	4	398.0	32.389971	4.290879	20.187013	29.591531	32.353272	34.881566	44.043286
36.0	0	367.0	33.305430	4.317637	21.629140	29.993074	33.260484	36.211089	47.261113
	1	400.0	34.364454	4.530151	21.893598	31.345396	34.349174	37.183175	46.116171
	7	384.0	33.642128	4.769815	20.561968	30.336517	33.418239	37.024575	47.247295
	11	370.0	34.424845	4.451086	22.233200	31.482867	34.240181	37.722697	48.325648
38.0	4	579.0	36.880259	5.490024	22.359146	33.040469	36.612923	40.685759	49.986397
	5	481.0	35.314404	4.391869	21.529822	32.257253	35.310916	38.186951	48.220139
	8	443.0	35.378696	4.496303	22.874511	32.218225	35.239426	38.481410	49.785316
40.0	2	308.0	36.649061	4.995315	22.611220	33.235333	36.567645	39.884125	49.654730
	3	310.0	36.177240	4.647701	24.580325	32.858440	35.998416	39.346971	47.881250
	4	398.0	36.443190	4.514258	22.690500	33.498606	36.639893	39.630461	48.037086
	9	429.0	37.048543	4.840340	23.310791	33.899419	37.161046	40.706441	48.557046
42.0	5	406.0	37.732604	4.493181	24.132447	34.611803	37.718531	40.872639	49.668550
42.0	10	446.0	37.731362	4.330579	25.725483	34.454804	37.593847	40.716017	49.816359
44.0	1	339.0	39.602261	4.530057	28.195416	36.635874	39.662952	42.958963	49.923268
	5	324.0	39.733014	4.525915	27.884550	36.743964	39.615360	42.669845	49.920130
	7	463.0	39.515229	4.427439	27.344606	36.686821	39.868041	42.796974	49.858770
	10	422.0	39.163437	4.720635	21.410014	35.986818	39.069022	42.610669	49.568808
	11	347.0	39.248348	4.542191	27.348084	36.236094	39.262928	42.483500	49.961964
46.0	0	418.0	40.619876	4.354686	27.843686	37.836525	40.692501	43.744947	49.946566
	3	459.0	40.919039	4.371154	28.977153	38.082099	41.117773	44.152462	49.972463
	6	453.0	40.742784	4.336766	29.864420	37.664342	40.944588	44.035930	49.904709
	9	483.0	40.294067	4.329095	28.732254	37.261039	40.479785	43.206031	49.884505
48.0	0	350.0	41.421598	4.382525	25.362666	38.507405	41.465709	44.954527	49.766820
	2	364.0	42.079499	4.138902	30.335834	39.435982	42.134848	44.813583	49.882612
	11	418.0	41.920377	4.206719	29.919265	39.250647	42.530749	44.982215	49.997791
52.0	5	371.0	43.742936	3.581647	30.745418	41.322944	43.881151	46.466176	49.917294

		count	mean	std	min	25%	50%	75%	max
BiasTo	Run
100.0	4	291.0	101.290983	4.914689	86.579550	97.997594	101.602563	104.648987	113.736413
80.0	9	160.0	80.056665	4.733235	69.595291	76.925908	79.666362	83.291439	93.704545
84.0	0	154.0	86.847791	5.733815	75.881057	82.671942	86.352351	91.036747	105.955720
	1	150.0	83.663390	4.831534	72.431718	79.776429	83.866325	86.548257	96.760654
	7	109.0	66.520216	6.384462	60.021062	61.344709	63.221418	71.311561	81.221194
	9	182.0	80.644752	4.641475	66.935735	77.478708	80.518190	83.822827	95.109196
88.0	0	416.0	88.691537	5.686923	73.481455	84.382588	88.868544	92.483306	103.152485
	1	395.0	84.072939	5.126198	63.522197	80.824414	84.401033	87.382173	99.204221
	2	120.0	93.013277	4.938997	78.700207	89.525464	92.927306	96.223770	110.960913
	3	526.0	85.880574	4.941370	70.467115	82.511596	85.923398	89.076483	104.171858
	9	178.0	90.929513	5.342915	74.163396	87.247793	90.370119	94.217393	104.348811
	10	116.0	91.904287	5.454988	76.575644	88.624011	91.657609	96.406329	104.028057
	11	213.0	86.409209	5.013692	74.382506	83.111880	86.086568	89.393202	104.485684
90.0	3	177.0	92.022529	6.496569	74.044918	87.811923	92.602703	95.800873	107.293983
	4	124.0	92.680477	5.175468	77.225293	89.009148	92.756547	97.018139	104.639917
	5	144.0	88.600461	5.398619	76.846153	84.725755	88.356667	92.344662	103.148131
	8	260.0	88.191447	4.884069	76.657861	84.779059	88.025785	91.480807	104.350145
	9	144.0	92.761811	5.176547	78.058877	89.941068	92.917139	95.985245	104.965785
92.0	1	163.0	95.032807	5.439215	75.579980	91.546305	94.887129	98.358832	109.956456
	2	218.0	87.742949	4.868243	76.067328	84.316909	87.946001	90.743216	101.191623
	4	126.0	94.748303	5.013327	83.802866	91.334321	94.383913	97.923619	106.351076
	5	224.0	94.529308	5.673440	77.623815	91.000031	94.504562	98.344721	108.717923
	8	240.0	93.287250	5.448400	79.897277	89.447653	92.947305	96.752970	108.378930
	9	190.0	86.878637	5.129757	75.520161	83.486481	86.398769	90.083818	104.813487
94.0	0	599.0	90.452648	5.018139	73.192655	86.793777	90.305607	93.761666	104.710011
	4	154.0	91.322481	5.369170	79.386582	87.711463	91.113092	94.228464	112.906898
	5	214.0	95.329540	4.978814	82.674000	92.222113	95.608548	98.696176	107.752551
	10	308.0	92.584228	9.994715	60.825264	89.701218	95.330814	98.930434	113.695503
96.0	1	271.0	97.138030	5.382518	83.537617	93.521440	96.907231	100.534041	113.967276
	2	160.0	98.726043	5.535312	84.045245	94.662148	98.494065	102.944248	110.989851
	7	191.0	98.313458	5.349487	84.984262	94.949656	98.692672	102.293149	113.560510
	8	356.0	97.912529	5.360339	85.289507	93.890598	97.818150	101.898636	115.164603
98.0	9	448.0	99.577290	5.500899	81.445258	95.804577	99.850925	103.204493	113.819815

		count	mean	std	min	25%	50%	75%	max
BiasTo	Run
30.0	5	198.0	29.336068	3.950605	19.336090	26.903153	29.186558	31.891191	41.489075
30.0	6	276.0	29.636263	4.360028	19.859353	26.500580	29.388270	32.715810	43.859331
32.0	8	328.0	30.973013	4.532931	19.527018	27.872598	30.884822	33.807393	46.069122
32.0	10	30.0	32.144568	4.226787	24.855882	28.795477	31.613792	34.753016	42.810558
36.0	10	18.0	34.693258	3.546499	25.427479	32.634434	35.001718	36.928215	40.005021
48.0	0	10.0	45.752164	5.198689	36.677268	42.916804	46.584106	48.765530	52.269899
48.0	3	2.0	47.033096	3.602952	44.485424	45.759260	47.033096	48.306932	49.580768
50.0	1	6.0	55.148826	4.149208	49.991137	52.239366	54.663588	58.877874	59.860716
	2	81.0	52.621320	5.223459	36.729027	49.800749	53.284565	56.531006	59.992958
	3	3.0	53.325541	1.396782	51.743503	52.794139	53.844776	54.116560	54.388345
	4	90.0	50.157335	5.215401	37.003271	46.413763	49.488309	54.522507	59.738818
	5	8.0	55.825293	2.656410	51.689690	54.367031	57.228660	57.576248	58.010585
	6	6.0	55.729481	2.181204	52.023106	54.801028	56.694051	56.875744	57.859350
	7	122.0	54.810251	3.574890	44.419525	52.276434	55.394273	57.743415	59.974125
52.0	1	20.0	55.443303	3.453660	47.710008	54.028646	55.538788	57.918754	59.958714
	7	26.0	52.919640	3.398741	46.065557	50.947097	53.057708	55.145351	59.174719
	8	12.0	55.810775	3.714312	47.287634	53.730993	56.812499	58.635310	59.440824
54.0	11	5.0	54.355347	2.856183	50.113638	53.285473	54.488018	56.762857	57.126752
58.0	0	1.0	59.780390	NaN	59.780390	59.780390	59.780390	59.780390	59.780390
58.0	7	16.0	56.420526	3.541696	46.886228	55.137791	57.320794	58.912627	59.886564
60.0	6	3.0	57.093266	1.685845	55.200260	56.423501	57.646741	58.039768	58.432795
62.0	4	18.0	57.172135	2.918663	49.742723	57.029016	58.270282	59.239834	59.933577
86.0	11	1.0	59.543311	NaN	59.543311	59.543311	59.543311	59.543311	59.543311

		count	mean	std	min	25%	50%	75%	max
BiasTo	Run
100.0	3	343.0	101.419737	5.344603	88.471741	97.948046	101.193647	105.224594	116.639743
	5	133.0	101.743107	5.215276	89.262132	98.154284	101.732000	105.369159	117.949514
	8	237.0	101.403051	5.389449	88.268174	97.258334	101.131563	105.215454	115.846037
	10	271.0	102.294095	5.322607	84.519699	98.983044	102.220950	106.238675	115.483940
	11	190.0	102.240759	5.173857	87.112626	98.689538	102.592647	105.600489	116.393328
80.0	1	295.0	83.725518	5.320322	71.601636	79.449579	83.800767	87.463644	98.414480
	3	184.0	84.322601	6.032452	68.884681	80.134650	84.463636	88.162564	100.143565
	5	238.0	82.250431	5.323923	66.376455	79.014000	82.117065	85.466534	97.980040
	6	239.0	83.959596	5.283500	69.065391	80.471425	84.460431	87.018960	99.419883
	9	370.0	83.464110	5.184569	68.024194	80.065255	83.194071	86.826692	99.624269
	10	150.0	83.591743	5.479064	72.777700	79.593404	83.597017	87.637008	96.069277
84.0	1	144.0	86.620354	5.845277	69.958706	82.286564	86.721995	90.106481	101.845931
	8	158.0	86.305667	4.921131	71.949944	82.860028	87.076810	89.414453	99.457320
	10	146.0	86.746866	5.729380	72.995224	82.324080	86.265695	90.347880	100.849729
	11	147.0	86.818128	5.195763	72.925778	83.445519	87.181429	90.135563	99.213723
88.0	0	122.0	91.495361	4.552710	78.015671	88.278897	91.350692	94.303603	104.367169
	1	156.0	90.619782	5.092408	77.400641	86.644256	90.898990	93.994523	106.082967
	2	157.0	90.519874	5.007912	78.645786	87.381845	90.433571	94.089745	104.368808
	4	118.0	88.162467	6.004869	75.220847	83.562134	87.616361	93.076441	102.811417
	6	161.0	90.223707	5.301429	77.193535	86.577150	90.610569	93.295657	106.617391
	7	154.0	91.054202	5.066067	75.218310	88.020922	91.353845	94.741519	101.490822
	8	147.0	91.019100	5.353024	77.322312	87.204732	91.342619	94.679690	105.731032
	11	312.0	90.746532	5.516535	72.802829	87.341199	90.963266	94.386680	103.411247
90.0	2	227.0	92.477460	5.362277	78.167770	89.232440	92.793720	96.536465	106.534644
	3	151.0	92.069256	5.170890	80.429869	88.175550	91.385073	95.578193	107.327533
	4	210.0	92.148623	5.503215	76.057310	88.533815	91.884827	95.279972	106.152031
	8	110.0	91.235474	4.825832	79.876495	87.556499	91.698293	93.993410	102.969739
	10	125.0	91.523449	5.239083	77.679926	87.943114	91.937501	94.936225	105.611906
92.0	3	152.0	94.427903	5.578285	83.000003	90.313785	94.191581	97.810216	108.375412
	8	288.0	94.651978	5.249958	81.261607	90.686894	94.634416	98.063185	109.326186
	9	180.0	93.604335	5.265653	78.810665	90.073513	93.454337	97.004841	107.684861
	11	107.0	95.811854	5.709908	84.858141	92.187122	95.480859	99.858725	112.476407
94.0	0	223.0	97.000770	5.070741	84.975848	93.202979	97.562265	100.572276	113.135545
	1	115.0	96.787236	5.886649	81.948847	93.231287	96.537970	100.195595	112.412212
	2	164.0	96.548006	4.853964	82.458984	92.809060	96.833227	100.315487	112.083958
	4	173.0	96.525051	5.541299	83.775962	92.797773	96.257867	100.139837	110.602757
	6	136.0	95.696398	5.473483	82.625543	92.479375	95.425257	99.008615	110.357449
	9	199.0	96.663555	5.123588	85.116593	92.958629	96.785625	99.634166	110.654366
	10	222.0	96.253284	5.257964	82.307029	92.854815	96.005613	99.482751	108.893141
	11	144.0	95.012932	5.279298	83.130842	91.753912	95.069447	98.213289	107.339766
96.0	0	135.0	99.193167	4.814695	85.831601	96.082552	99.289575	102.443407	115.480518
	4	151.0	97.202784	4.762319	83.666895	94.222227	97.238807	100.232032	110.823329
	5	123.0	97.766732	5.192767	84.791851	93.951835	98.236856	101.413631	108.357424
	8	143.0	98.391997	5.033282	84.603583	94.744820	98.656281	101.261500	111.328596
	11	193.0	98.464471	5.058766	87.812560	94.644367	98.573577	102.112389	113.575824
98.0	0	206.0	99.467782	5.481900	81.509614	96.167807	99.586660	103.036994	113.936211
	7	254.0	99.985547	5.379235	86.040609	96.225558	100.224658	103.758493	114.780268
	9	269.0	99.922524	4.870982	84.277430	96.794690	99.601445	103.055479	115.807165
	10	236.0	99.425080	5.041973	85.731022	96.224602	99.256099	102.834661	112.692029

		count	mean	std	min	25%	50%	75%	max
BiasTo	Run
72.0	8	251.0	0.331380	0.016914	0.300520	0.318481	0.329844	0.343734	0.402180
74.0	8	115.0	0.350842	0.024570	0.300538	0.334939	0.349419	0.361610	0.435940
74.0	11	184.0	0.352030	0.025777	0.300973	0.334212	0.350647	0.364934	0.442350
78.0	2	582.0	0.350725	0.021947	0.301000	0.335213	0.349560	0.365497	0.418287
80.0	1	383.0	0.347422	0.021195	0.303433	0.331633	0.346739	0.360955	0.411149
	7	478.0	0.349377	0.022634	0.300982	0.332810	0.347720	0.363692	0.418313
	8	382.0	0.346878	0.021583	0.300855	0.330785	0.346393	0.360646	0.421669
	9	396.0	0.349728	0.021193	0.301145	0.333405	0.349832	0.365705	0.411630
84.0	7	361.0	0.347062	0.020163	0.300202	0.332871	0.345651	0.360076	0.416170
88.0	1	489.0	0.387654	0.031921	0.300672	0.365318	0.389423	0.410119	0.472375
88.0	10	459.0	0.399617	0.030593	0.318703	0.381806	0.400317	0.419547	0.487651
90.0	9	204.0	0.344613	0.020983	0.300634	0.329445	0.344976	0.358986	0.404646
90.0	10	225.0	0.398894	0.027372	0.316385	0.383034	0.398304	0.415516	0.468483
92.0	8	471.0	0.372714	0.032957	0.302890	0.347729	0.370922	0.396583	0.470442
92.0	11	198.0	0.341023	0.020253	0.304259	0.325441	0.339567	0.353536	0.428579
96.0	5	383.0	0.339173	0.020122	0.300796	0.324488	0.338162	0.350960	0.411800
96.0	9	393.0	0.340088	0.019831	0.302228	0.325773	0.339094	0.352400	0.399826

	count	mean	std	min	25%	50%	75%	max
Temp
280	34395.0	42.940793	5.531507	24.547152	38.811300	42.836657	47.382027	59.594937
290	22489.0	42.784274	5.330718	21.744802	38.877496	42.851639	46.927034	57.014944
300	11801.0	43.579822	5.151529	25.652358	39.889707	44.021430	47.580846	58.152470
310	5845.0	44.908710	4.777508	27.114228	41.968282	45.678484	48.428579	55.947778
320	2639.0	45.515907	4.648256	26.487897	42.868365	46.234237	48.820234	56.778925
335	574.0	45.595006	4.176323	31.826684	42.863389	46.208786	48.586064	54.507667
350	82.0	45.239074	4.337481	32.754204	42.069960	46.152603	48.677832	52.897284
365	5.0	46.504712	3.444580	41.733659	45.157007	46.514402	48.106523	51.011969

		count	mean	std	min	25%	50%	75%	max
BiasTo	Run
60.0	5	113.0	66.512154	3.886832	60.147610	63.580878	66.285957	69.405557	77.179416
68.0	11	180.0	73.756180	6.057438	60.749281	69.398390	73.617739	77.737753	92.431294
70.0	0	469.0	74.191685	5.318893	60.414137	70.405310	74.056265	78.122878	89.513138
74.0	2	101.0	76.827775	5.457357	66.320898	72.989188	76.996523	80.230168	89.134214
74.0	11	144.0	75.486415	4.963238	62.432900	71.845102	74.953984	79.007245	87.323786
76.0	3	235.0	78.447992	5.253855	66.259977	74.608759	78.511404	81.494020	95.362849
80.0	5	217.0	79.799922	4.855510	67.250980	76.633111	80.259033	83.007123	96.753831
80.0	11	198.0	79.955361	5.238624	65.716158	76.483398	80.330906	83.365813	93.809286
82.0	3	114.0	82.243958	3.904281	72.643975	79.513151	81.941932	85.159661	91.372623
84.0	6	416.0	85.558974	4.732438	70.555359	82.467007	85.292550	88.945509	98.381981
84.0	8	415.0	86.876651	6.218563	70.161543	82.480690	87.035292	91.161424	104.109995
86.0	6	199.0	91.192025	5.494117	75.331115	88.039038	91.072395	94.250562	106.096975
88.0	2	262.0	90.216668	5.130045	75.261177	86.876216	90.352033	93.509445	104.125687
88.0	5	351.0	87.891460	5.725054	73.764376	83.802720	87.646519	91.993305	105.204918
92.0	2	542.0	89.927684	5.273472	70.467923	86.323840	89.760363	93.542206	105.406497
94.0	1	106.0	90.242444	6.574085	77.091854	84.989310	90.004269	95.004842	106.691579
96.0	7	812.0	96.576466	5.142513	79.397742	93.199597	96.335172	100.062148	110.993673
98.0	1	818.0	97.598809	4.302016	86.262144	94.911896	97.646100	100.322980	112.100967
98.0	6	441.0	97.337894	5.101396	81.527405	94.271081	97.383584	100.831160	109.210235

		count	mean	std	min	25%	50%	75%	max
BiasTo	Run
72.0	8	538.0	77.631154	4.978481	64.034331	74.265102	77.679578	81.148599	92.402795
74.0	5	671.0	79.352226	4.992393	64.103251	75.905704	79.439485	82.716725	94.980508
74.0	8	597.0	80.370943	4.771834	63.743749	77.312220	80.329319	83.590274	94.733364
76.0	2	609.0	82.491103	4.934288	66.209328	79.096101	82.395038	85.988688	95.370163
80.0	7	570.0	84.330678	4.942327	65.585395	81.343990	84.503898	87.671476	98.049908
80.0	8	583.0	84.765906	4.744009	71.252220	81.699702	84.902951	87.689288	99.617263
82.0	7	591.0	86.381836	4.886817	74.129515	82.791748	86.400281	89.636735	101.037585
82.0	9	515.0	86.697468	4.841471	71.817455	83.436906	86.703616	90.212749	101.873019
88.0	1	503.0	91.928638	4.759968	75.885403	88.872399	91.672946	95.171128	105.171102
90.0	11	817.0	92.899936	4.619934	79.678197	89.964819	92.984239	95.992677	107.310590
98.0	10	540.0	98.684912	4.785347	85.058600	95.322567	98.759484	102.196297	111.325873

		count	mean	std	min	25%	50%	75%	max
BiasTo	Run
100.0	11	544.0	84.293570	3.425672	75.519237	81.961637	84.397217	86.622628	94.611569
72.0	5	548.0	73.933853	4.387406	61.049616	71.126700	74.101592	77.014725	86.079380
72.0	6	570.0	77.566640	4.805078	63.593634	74.509323	77.478254	80.691643	92.106523
78.0	5	520.0	82.549008	4.721425	70.541042	79.102200	82.721582	85.779293	95.670568
78.0	10	540.0	82.675814	4.635266	68.840841	79.520502	82.781726	85.958293	96.702022
80.0	0	568.0	79.695028	4.399605	67.366476	76.710162	79.807395	82.590351	96.141058
84.0	8	530.0	83.894069	5.306648	70.917908	80.118754	83.161541	87.110482	100.301417
84.0	11	553.0	86.657368	4.707773	73.011489	83.747428	86.888678	89.551823	100.057218
86.0	11	531.0	87.787863	4.444919	76.416774	84.737965	87.608177	90.635270	102.053831
88.0	2	595.0	84.620395	4.761268	70.773094	81.346685	84.557121	87.582304	98.051481
92.0	2	564.0	91.000057	5.468401	78.150078	87.060316	90.448845	94.965484	108.250962
96.0	7	963.0	95.072311	5.525327	77.822069	91.490362	94.993429	98.830402	110.993673

		count	mean	std	min	25%	50%	75%	max
BiasTo	Run
30.0	11	501.0	34.550022	3.497956	24.170195	32.341842	34.733842	36.927544	44.118947
32.0	8	523.0	35.867688	3.378665	24.100050	33.797883	35.808496	38.302579	45.959715
32.0	10	645.0	36.086853	2.985883	26.805858	34.255239	35.990810	38.118236	47.008506
34.0	6	514.0	32.001355	3.933645	19.487454	29.375800	32.185595	34.763219	43.226268
36.0	6	683.0	36.538337	3.075776	26.712718	34.600427	36.581137	38.632251	45.696680
	9	535.0	37.053009	3.487852	27.163052	34.778089	36.952146	39.300564	46.858792
	10	599.0	36.493984	3.050589	28.673133	34.313042	36.475634	38.632653	45.187989
38.0	0	542.0	38.292865	3.117689	29.507186	36.509181	38.429570	40.372452	47.251375
	1	714.0	37.156266	3.051513	27.119173	35.145635	37.288075	39.339564	46.587213
	2	646.0	38.042212	2.957526	29.438891	36.105246	37.955290	40.029292	47.525100
42.0	6	707.0	38.366364	2.976499	28.001329	36.458438	38.554256	40.285623	47.530492
42.0	9	636.0	38.318101	2.830133	30.080318	36.520623	38.279709	40.229305	45.076937
44.0	7	653.0	39.187825	2.832088	31.375651	37.427158	39.179752	41.124606	47.085678
46.0	5	582.0	39.461132	2.802785	29.656917	37.685658	39.756264	41.339765	46.331141
46.0	9	592.0	39.515688	2.656214	31.390946	37.743350	39.514984	41.435002	46.551716
48.0	2	532.0	39.997772	2.763595	32.714515	38.160923	40.078110	41.975311	47.424742
48.0	5	617.0	40.078830	2.917347	30.190290	38.052028	40.208077	42.158398	49.065847
50.0	4	572.0	40.516251	2.894581	29.218862	38.590035	40.625680	42.319545	49.911833
50.0	6	579.0	40.452561	2.723055	31.306359	38.851076	40.420034	42.158544	47.852737
52.0	6	585.0	42.891324	3.309122	34.838188	40.494285	42.791672	45.103617	53.407546
54.0	3	593.0	43.652043	3.418296	34.941781	41.392058	43.590167	45.989911	55.849975
54.0	8	504.0	43.468905	3.307635	33.128995	41.161186	43.479570	45.611239	52.045254
56.0	1	557.0	44.201995	3.411782	33.753458	41.816051	44.090526	46.363673	54.841648
56.0	7	542.0	44.164520	3.429523	32.235866	41.796897	44.247860	46.500655	55.349855
58.0	7	824.0	42.481098	2.637412	33.582897	40.764434	42.498365	44.173776	50.799169
60.0	8	608.0	42.638195	2.560345	34.202688	40.911645	42.578285	44.345535	49.555813
60.0	10	543.0	42.848207	2.621652	34.645227	41.066412	42.700465	44.606472	51.479163
62.0	0	597.0	43.277374	2.653459	32.995607	41.460755	43.142450	45.150519	53.672116
	7	634.0	43.225784	2.627363	35.509930	41.493669	43.174808	45.023711	50.970356
	9	684.0	43.219135	2.749727	34.961009	41.375080	43.101129	44.931003	53.977729
64.0	7	666.0	47.337949	2.991526	37.048099	45.439008	47.615525	49.520250	54.931101
64.0	10	588.0	46.540005	3.096186	38.317320	44.321463	46.640451	48.617557	54.942772
68.0	4	590.0	44.379569	2.656873	36.506250	42.625571	44.344091	46.021600	53.099925
70.0	1	678.0	45.143000	2.616832	36.997997	43.303478	45.142127	46.904454	53.147709
70.0	11	679.0	45.274321	2.628822	36.256916	43.455996	45.276506	47.127284	53.834286
74.0	1	549.0	50.903293	3.274476	40.958378	48.939727	50.828384	53.000226	59.523403
76.0	0	522.0	51.331612	3.403327	42.337286	49.037508	51.416541	53.997385	59.633690
76.0	4	526.0	51.618093	3.490510	40.795565	49.258330	51.623358	53.998894	59.458026