Go the lower path.

four helix out is more favored than 2 out. (at force 0.1)



In [1]:

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

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

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



In [28]:

    
data = pd.read_feather("/Users/weilu/Research/server/may_2018/second/rerun_2_07_May_154957.feather")
dic = {"T0":300, "T1":335, "T2":373, "T3":417, "T4":465, "T5":519, "T6":579, "T7":645, "T8":720, "T9":803, "T10":896, "T11":1000}
a = data
a["Temp"] = a["Temp"].apply(lambda x: dic[x])
rerun2 = 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[28]:





<seaborn.axisgrid.FacetGrid at 0x1a2b6c64a8>



In [4]:

    
data = pd.read_feather("/Users/weilu/Research/server/may_2018/second/rerun_3_07_May_154957.feather")
dic = {"T0":300, "T1":335, "T2":373, "T3":417, "T4":465, "T5":519, "T6":579, "T7":645, "T8":720, "T9":803, "T10":896, "T11":1000}
a = data
a["Temp"] = a["Temp"].apply(lambda x: dic[x])
rerun3 = 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[4]:





<seaborn.axisgrid.FacetGrid at 0x1a1539cfd0>



In [16]:

    
rerun3.query("Temp == 373").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[16]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a17d3c7b8>



In [5]:

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









    Out[5]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a45de2438>



In [44]:

    
rerun2.query("Temp == 335").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[44]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a173ebd30>



In [32]:

    
rerun3.query("Temp == 335").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[32]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a184782e8>



In [7]:

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









    Out[7]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a1cdf15c0>



In [29]:

    
rerun2.query("Temp == 335").plot.hexbin("z_h6", "Qw", cmap="seismic", sharex=False)









    Out[29]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a1d2cb4a8>



In [8]:

    
rerun3.query("Temp == 335").plot.hexbin("z_h6", "Qw", cmap="seismic", sharex=False)









    Out[8]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a17779550>



In [30]:

    
rerun2.query("Temp == 335 and z_h6 < -10").shape









    Out[30]:





(17984, 44)



In [31]:

    
rerun3.query("Temp == 335 and z_h6 < -10").shape









    Out[31]:





(18724, 44)



In [18]:

    
pre = "/Users/weilu/Research/server/may_2018/01_week"
temp = 335
location = pre + "/second_rerun1/_280-350/2d_z_qw/quick/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(10, 10), end=(20,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=20,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 0x1a1216df28>






    Out[18]:





[<matplotlib.lines.Line2D at 0x1a174b5d68>]



In [19]:

    
pre = "/Users/weilu/Research/server/may_2018/01_week"
temp = 335
location = pre + "/second_rerun1/_280-350/2d_z_qw/quick_force_0.1/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(10, 10), end=(20,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=20,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 0x1a1216df28>






    Out[19]:





[<matplotlib.lines.Line2D at 0x1a1773aeb8>]



In [ ]:



In [27]:

    
pre = "/Users/weilu/Research/server/may_2018/01_week"
temp = 330
location = pre + "/second_rerun1/_280-350/2d_z_qw/force_0.1/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 8), end=(20,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=20,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 0x1a1216df28>






    Out[27]:





[<matplotlib.lines.Line2D at 0x1a1d3e24e0>]



In [26]:

    
pre = "/Users/weilu/Research/server/may_2018/01_week"
temp = 330
location = pre + "/second_rerun1/_280-350/2d_z_qw/force_0.1/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 8), end=(20,20),block=[-11,-10,0.1,0.3],save=False, xlabel="z_H6", ylabel="Qw", zmax=20,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 0x1a1216df28>






    Out[26]:





[<matplotlib.lines.Line2D at 0x1a1fe2cc88>]



In [42]:

    
x = np.linspace(0.05, 0.2, 100)
y = np.log(19/12)/np.log(1+x/1.04)



In [43]:

    
plt.plot(x,y+2017)









    Out[43]:





[<matplotlib.lines.Line2D at 0x1a173eb6d8>]



In [ ]: