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 [75]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 370
location = pre + "/second_2/_280-350/2d_z_qw/force_0.05/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 5), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1a08de65c0>






    
Out[75]:





[<matplotlib.lines.Line2D at 0x1a224c69b0>]






    













    













    













    

In [74]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 370
location = pre + "/second_2/_280-350/2d_z_qw/more_temp_force_0.05/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 5), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1a08de65c0>






    
Out[74]:





[<matplotlib.lines.Line2D at 0x1a3d506e80>]






    













    













    













    

In [62]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 370
location = pre + "/second_2/_280-350/2d_z_qw/force_0.1/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 5), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1a08de65c0>






    
Out[62]:





[<matplotlib.lines.Line2D at 0x1a22c475f8>]






    













    













    













    

In [73]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 370
location = pre + "/second_2/_280-350/2d_z_qw/real_force_0.0/"
location2 = location + f"perturbation-1-pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 5), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1a08de65c0>






    
Out[73]:





[<matplotlib.lines.Line2D at 0x1a23e66630>]






    













    













    













    

In [66]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 380
location = pre + "/second_2/_280-350/2d_z_qw/real_force_0.0/"
location2 = location + f"perturbation-1-pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 5), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1a08de65c0>






    
Out[66]:





[<matplotlib.lines.Line2D at 0x1a227acd68>]






    













    













    













    

In [53]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 380
location = pre + "/second_2/_280-350/2d_z_qw/force_0.05/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1a08de65c0>






    
Out[53]:





[<matplotlib.lines.Line2D at 0x1a2368db00>]






    













    













    













    

In [42]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 380
location = pre + "/second_2/_280-350/2d_z_qw/high_temp_with_perturb_force_0.05/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 5), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1a08de65c0>






    
Out[42]:





[<matplotlib.lines.Line2D at 0x1a1887a748>]






    













    













    













    

In [36]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 380
location = pre + "/second_2/_280-350/2d_z_qw/high_temp_with_perturb_force_0.05/"
location2 = location + f"perturbation-1-pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 5), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1a08de65c0>






    
Out[36]:





[<matplotlib.lines.Line2D at 0x1a210e9390>]






    













    













    













    

In [34]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 380
location = pre + "/second_2/_280-350/2d_z_qw/high_temp_with_perturb_quicker/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 5), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1a08de65c0>






    
Out[34]:





[<matplotlib.lines.Line2D at 0x1a20069780>]






    













    













    













    

In [33]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 380
location = pre + "/second_2/_280-350/2d_z_qw/high_temp_with_perturb/"
location2 = location + f"perturbation-1-pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 5), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1a08de65c0>






    
Out[33]:





[<matplotlib.lines.Line2D at 0x1a221529b0>]






    













    













    













    

In [18]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 380
location = pre + "/second_2/_280-350/2d_z_qw/high_temp_with_perturb/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 5), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1a08de65c0>






    
Out[18]:





[<matplotlib.lines.Line2D at 0x1a231a07f0>]






    













    













    













    

In [14]:

    

data.columns


    

    
Out[14]:





Index(['Step', 'Run', 'Temp', 'Qw', 'Energy', 'DisReal', 'Dis_h56',
       'z_average', 'abs_z_average', 'z_h1', 'z_h2', 'z_h3', 'z_h4', 'z_h5',
       'z_h6', 'AMH', 'AMH_3H', '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', 'TempT'],
      dtype='object')

In [12]:

    

rerun7 = pd.read_feather("/Users/weilu/Research/server/may_2018/second/rerun_7_17_May_211310.feather")
rerun6 = pd.read_feather("/Users/weilu/Research/server/may_2018/second/rerun_6_17_May_211310.feather")
data = pd.concat([rerun6, rerun7])
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}
data["TempT"] = data["Temp"].apply(lambda x: dic[x])
chosen = data.query("TempT < 400")
chosen.reset_index().to_feather("/Users/weilu/Research/server/may_2018/02_week/all_data_folder/second_may17.feather")


    

In [3]:

    

data = pd.read_feather("/Users/weilu/Research/server/may_2018/second/rerun_7_17_May_211310.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])
rerun7 = 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 0x10503b198>






    

In [5]:

    

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


    

    
Out[5]:





<matplotlib.axes._subplots.AxesSubplot at 0x1035996d8>






    

In [7]:

    

data.columns


    

    
Out[7]:





Index(['Step', 'Run', 'Temp', 'Qw', 'Energy', 'DisReal', 'Dis_h56',
       'z_average', 'abs_z_average', 'z_h1', 'z_h2', 'z_h3', 'z_h4', 'z_h5',
       'z_h6', 'AMH', 'AMH_3H', '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 [10]:

    

data.query("Temp == 373 and z_h6 < -10").plot.hexbin("AMH_3H", "Qw", cmap="seismic", sharex=False)


    

    
Out[10]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a0efc9588>






    

In [9]:

    

data.query("Temp == 335").plot.hexbin("AMH_3H", "Qw", cmap="seismic", sharex=False)


    

    
Out[9]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a0ccd8a20>






    

In [8]:

    

data.query("Temp == 335 and z_h6 < -10").plot.hexbin("AMH_3H", "Qw", cmap="seismic", sharex=False)


    

    
Out[8]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a0c8266a0>






    

In [169]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 330
location = pre + "/second/_280-350/2d_z_qw/middle_temp/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 5), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1155c3ef0>






    
Out[169]:





[<matplotlib.lines.Line2D at 0x1a2da5e320>]






    













    













    













    

In [171]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 360
location = pre + "/second/_280-350/2d_z_qw/high_temp_force_0.05/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 5), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1155c3ef0>






    
Out[171]:





[<matplotlib.lines.Line2D at 0x1a2da76c18>]






    













    













    













    

In [140]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 360
location = pre + "/second/_280-350/2d_z_qw/high_temp_force_0.1/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 5), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1155c3ef0>






    
Out[140]:





[<matplotlib.lines.Line2D at 0x1a2d8b1d30>]






    













    













    













    

In [197]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 370
location = pre + "/second/_280-350/2d_z_qw/high_temp/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), end=(28,20),block=[-15,-10,0.1,0.3],save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1155c3ef0>






    
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[<matplotlib.lines.Line2D at 0x1a2e502f28>]






    













    













    













    

In [195]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 380
location = pre + "/second/_280-350/2d_z_qw/high_temp/"
location2 = location + f"perturbation-2-pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 5), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1155c3ef0>






    
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[<matplotlib.lines.Line2D at 0x1a2f01d2b0>]






    













    













    













    

In [118]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 300
location = pre + "/second/_280-350/2d_z_qw/perturb/"
location2 = location + f"perturbation-1-pmf-{temp}.dat"
path, f = shortest_path(location2, start=(10, 5), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1155c3ef0>






    
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[<matplotlib.lines.Line2D at 0x1a2b43ee10>]






    













    













    













    

In [107]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 300
location = pre + "/second/_280-350/2d_z_qw/perturb/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(13, 5), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1155c3ef0>






    
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[<matplotlib.lines.Line2D at 0x1a28b41048>]






    













    













    













    

In [81]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 300
location = pre + "/second/_280-350/2d_z_qw/quick/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(13, 5), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1155c3ef0>






    
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[<matplotlib.lines.Line2D at 0x1a25d5b320>]






    













    













    













    

In [86]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 280
location = pre + "/second/_280-350/2d_z_qw/quick/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(13, 5), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1155c3ef0>






    
Out[86]:





[<matplotlib.lines.Line2D at 0x1a24157eb8>]






    













    













    













    

In [85]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 280
location = pre + "/second/_280-350/2d_z_qw/zero_energy/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1155c3ef0>






    
Out[85]:





[<matplotlib.lines.Line2D at 0x1a22fddf60>]






    













    













    













    

In [84]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 300
location = pre + "/second/_280-350/2d_z_qw/zero_energy/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1155c3ef0>






    
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[<matplotlib.lines.Line2D at 0x1a2890b828>]






    













    













    













    

In [95]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 270
location = pre + "/second/_280-350/2d_z_qw/amh_energy/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(10, 5), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1155c3ef0>






    
Out[95]:





[<matplotlib.lines.Line2D at 0x1a29bb9be0>]






    













    













    













    

In [58]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 290
location = pre + "/second/_280-350/2d_z_qw/amh_energy_x10/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1155c3ef0>






    
Out[58]:





[<matplotlib.lines.Line2D at 0x1a261e0128>]






    













    













    













    

In [14]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 310
location = pre + "/second/_280-350/2d_z_qw/force_0.13/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1155c3ef0>






    
Out[14]:





[<matplotlib.lines.Line2D at 0x1a21e9a7b8>]






    













    













    













    

In [83]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 310
location = pre + "/second/_280-350/2d_z_qw/force_0.15/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 5), end=(28,20),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 0x1155c3ef0>






    
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[<matplotlib.lines.Line2D at 0x1a28725f98>]






    













    













    













    

In [104]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 320
location = pre + "/second/_280-350/2d_z_qw/force_0.13/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 5), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1155c3ef0>






    
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[<matplotlib.lines.Line2D at 0x1a2a48ea20>]






    













    













    













    

In [ ]:

    

### pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 300
location = pre + "/second/_280-350/2d_z_qw/force_0.15/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 5), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

In [74]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 300
location = pre + "/second/_280-350/2d_z_qw/force_0.15/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 5), end=(28,20),block=[-15,-10,0.3,0.5],save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1155c3ef0>






    
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[<matplotlib.lines.Line2D at 0x1a26ce6390>]






    













    













    













    

In [106]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 270
location = pre + "/second/_280-350/2d_z_qw/perturb/"
location2 = location + f"perturbation-1-pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 5), end=(25,25),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1155c3ef0>






    
Out[106]:





[<matplotlib.lines.Line2D at 0x1a284f0a58>]






    













    













    













    

In [105]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 270
location = pre + "/second/_280-350/2d_z_qw/perturb/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 5), end=(25,25),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1155c3ef0>






    
Out[105]:





[<matplotlib.lines.Line2D at 0x1a25cc49e8>]






    













    













    













    

In [92]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 300
location = pre + "/second/_280-350/2d_z_qw/remove_test2/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 5), end=(25,25),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1155c3ef0>






    
Out[92]:





[<matplotlib.lines.Line2D at 0x1a2425dda0>]






    













    













    













    

In [80]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 300
location = pre + "/second/_280-350/2d_z_qw/remove_test/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 5), end=(25,25),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




<matplotlib.colors.LinearSegmentedColormap object at 0x1155c3ef0>






    
Out[80]:





[<matplotlib.lines.Line2D at 0x1a27d04f28>]






    













    













    













    

In [165]:

    

pre = "/Users/weilu/Research/server/may_2018/02_week"
temp = 380
location = pre + "/second/_280-350/2d_z_qw/distance_high_temp/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 5), end=(28,20),save=False, xlabel="z_H6", ylabel="Qw", zmax=25,res=30)
# plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
# plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
location3 = location + f"evpb-{temp}.dat"
(xi,yi,zi) = plot2d(location3, zmax=120)
plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
# plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)
plt.figure()
f_on_path = [zi[tuple(p)] for p in reversed(path)]
plt.plot(f_on_path)
# plt.savefig("/Users/weilu/papers/figures/shortest_path_expected_dis.png", dpi=300)


    

    




---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
<ipython-input-165-95c2a2a1be15> in <module>()
      7 # plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)
      8 location3 = location + f"evpb-{temp}.dat"
----> 9 (xi,yi,zi) = plot2d(location3, zmax=120)
     10 plt.plot(xi[path[:,1]], yi[path[:,0]], 'r.-')
     11 # plt.savefig("/Users/weilu/papers/figures/2d_expected_dis.png", dpi=300)

~/opt/notebook/notebookFunctions.py in plot2d(location, temp, res, zmin, zmax, z, xlabel, ylabel, title, outname)
    191 def plot2d(location, temp="450", res=30, zmin=0, zmax=30, z=3, xlabel="xlabel", ylabel="ylabel", title="", outname=None):
    192     titlefontsize = 28
--> 193     data = np.loadtxt(location)
    194     xi, yi, zi = getxyz(data, zmin=zmin, zmax=zmax,res=res, z=z)
    195     # plt.contour(xi, yi, zi, 50, linewidths=0.25,colors='k')

~/anaconda3/lib/python3.6/site-packages/numpy/lib/npyio.py in loadtxt(fname, dtype, comments, delimiter, converters, skiprows, usecols, unpack, ndmin)
    896                 fh = iter(open(fname, 'U'))
    897             else:
--> 898                 fh = iter(open(fname))
    899         else:
    900             fh = iter(fname)

FileNotFoundError: [Errno 2] No such file or directory: '/Users/weilu/Research/server/may_2018/02_week/second/_280-350/2d_z_qw/distance_high_temp/evpb-380.dat'





    













    

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