notebook.community

Edit and run



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 [204]:

    
data = pd.read_feather("/Users/weilu/Research/server/apr_2018/sixth/rg_0.15_lipid_1.0_mem_1_go_0.8/rerun_3_18_Apr_220421.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])
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[204]:





<seaborn.axisgrid.FacetGrid at 0x1a229a40f0>



In [36]:

    
data = pd.read_feather("/Users/weilu/Research/server/may_2018/second/rerun_0_05_May_155022.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])
rerun0 = 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[36]:





<seaborn.axisgrid.FacetGrid at 0x1a151f0908>



In [5]:

    
data = pd.read_feather("/Users/weilu/Research/server/may_2018/second/rerun_1_05_May_155022.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])
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[5]:





<seaborn.axisgrid.FacetGrid at 0x1a0e2d1d30>



In [11]:

    
rerun1.query("Temp == 417").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[11]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a1349b860>



In [12]:

    
rerun1.query("Temp == 373").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[12]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a0efec5f8>



In [179]:

    
rerun1.query("Temp == 335").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[179]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a2c2b0358>



In [203]:

    
rerun1.query("Temp == 335").plot.hexbin("z_h6", "Qw", cmap="seismic", sharex=False)









    Out[203]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a2bc8c4e0>



In [6]:

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









    Out[6]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a195d74a8>



In [82]:

    
t = rerun1.query("Temp == 300 and DisReal < 45 and Qw < 0.55")
select(t)



In [75]:

    
rerun0.query("Temp == 300 and DisReal < 120").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)









    Out[75]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a0f582748>



In [7]:

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









    Out[7]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a0fd97f28>

Temperature exchange.

Not so good. Since each trajectory do not experience high and low temperature. But I guess it is still okish~



In [39]:

    
all_data = pd.concat([rerun0,rerun1])



In [69]:

    
tmp = temperature_exchange_table(all_data.query("BiasTo == '92.0'"))
tmp



In [70]:

    
tmp = temperature_exchange_table(all_data.query("BiasTo == '40.0'"))
tmp



In [57]:

    
a = all_data.groupby("BiasTo").apply(summarise_temperature_exchange_table)
a.index = a.index.astype("float")



In [68]:

    
a.sort_index().plot()









    Out[68]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a0f6fc550>



In [71]:

    
a.describe()

Some small Bias distance didn't have high Q.



In [83]:

    
pre = "/Users/weilu/Research/server/may_2018/01_week"
temp = 300
location = pre + "/second/_280-350/2d_z_qw/quick/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(10, 10), end=(25,24),save=False, xlabel="z_H6", ylabel="Qw", zmax=30)
# 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, 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 0x1a0b088e10>






    Out[83]:





[<matplotlib.lines.Line2D at 0x1a11c7a4a8>]



In [88]:

    
pre = "/Users/weilu/Research/server/may_2018/01_week"
temp = 290
location = pre + "/second/_280-350/2d_z_qw/remove_I_know/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(10, 10), end=(25,24),save=False, xlabel="z_H6", ylabel="Qw", zmax=30)
# 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, 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 0x1a0b088e10>






    Out[88]:





[<matplotlib.lines.Line2D at 0x1a1a1e69e8>]



In [151]:

    
location = location2
data = np.loadtxt(location)
res = 40
zmin = 0
zmax = 20
xi, yi, zi = getxyz(data, res=res, zmin=zmin, zmax=zmax)
zi = np.where(np.isnan(zi), 20, zi)



In [153]:

    
def raw_2d_plot(location):
    data = np.loadtxt(location)
    x = data[:,1]
    y = data[:,2]
    z = data[:,3]
    plt.scatter(x,y, c=z, cmap='rainbow')
    # plt.gray()
    plt.colorbar()
    return data



In [155]:

    
data = raw_2d_plot(location)



In [170]:

    
data.shape[0]









    Out[170]:





700



In [180]:

    
np.ones((res**2, 4))*np.nan









    Out[180]:





array([[ nan,  nan,  nan,  nan],
       [ nan,  nan,  nan,  nan],
       [ nan,  nan,  nan,  nan],
       ..., 
       [ nan,  nan,  nan,  nan],
       [ nan,  nan,  nan,  nan],
       [ nan,  nan,  nan,  nan]])

Assign zmax around for each point if it is empty before.



In [194]:

    
# construct complete res*res array
tmp = np.ones((res**2, 4))*np.nan
pos = 0
count = 0
x = 1
y = 2
z = 3
xi = np.linspace(min(data[:,x]), max(data[:,x]), res)
yi = np.linspace(min(data[:,y]), max(data[:,y]), res)
for i in range(res):
    for j in range(res):
        tmp[pos,0] = pos
        tmp[pos,1] = xi[i]
        tmp[pos,2] = yi[j]
        if count < data.shape[0] and pos == int(data[count, 0]):
#             tmp[pos,1] = data[count, 1]
#             tmp[pos,2] = data[count, 2]
            tmp[pos,3] = data[count, z]
            count += 1
        pos += 1

# Assign zmax around for each point if it is nan before.
new_tmp = tmp.copy()
pos = 0
count = 0
for i in range(res):
    for j in range(res):
        if not np.isnan(tmp[pos,3]):
            new_tmp[pos] = tmp[pos]
            right_neighbour = i*res + (j+1)
            if j < res-1 and np.isnan(tmp[right_neighbour,3]):
                new_tmp[right_neighbour, 3] = zmax
            left_neighbour = i*res + (j-1)
            if j>0 and np.isnan(tmp[left_neighbour,3]):
                new_tmp[left_neighbour, 3] = zmax
            up_neighbour = (i+1)*res + j
            if i < res-1 and np.isnan(tmp[up_neighbour,3]):
                new_tmp[up_neighbour, 3] = zmax
            down_neighbour = (i-1)*res + j
            if i > 0 and np.isnan(tmp[down_neighbour,3]):
                new_tmp[down_neighbour, 3] = zmax
        pos += 1
tmp = new_tmp[~np.isnan(new_tmp).any(axis=1)]



In [195]:

    
tmp









    Out[195]:





array([[  2.00000000e+00,  -3.96280000e+01,   1.54871795e-01,
          2.00000000e+01],
       [  3.00000000e+00,  -3.96280000e+01,   1.72307692e-01,
          1.30470000e+01],
       [  4.00000000e+00,  -3.96280000e+01,   1.89743590e-01,
          2.00000000e+01],
       ..., 
       [  1.56200000e+03,   8.31700000e+00,   1.54871795e-01,
          2.00000000e+01],
       [  1.56300000e+03,   8.31700000e+00,   1.72307692e-01,
          1.47630000e+01],
       [  1.56400000e+03,   8.31700000e+00,   1.89743590e-01,
          2.00000000e+01]])



In [173]:

    
# fill in those nan with zmax
tmp = np.ones((res**2, 4))*zmax
pos = 0
count = 0
x = 1
y = 2
z = 3
xi = np.linspace(min(data[:,x]), max(data[:,x]), res)
yi = np.linspace(min(data[:,y]), max(data[:,y]), res)
for i in range(res):
    for j in range(res):
        tmp[pos,0] = pos
        tmp[pos,1] = xi[i]
        tmp[pos,2] = yi[j]
        if count < data.shape[0] and pos == int(data[count, 0]):
#             tmp[pos,1] = data[count, 1]
#             tmp[pos,2] = data[count, 2]
            tmp[pos,3] = data[count, z]
            count += 1
        pos += 1



In [196]:

    
x = tmp[:,1]
y = tmp[:,2]
z = tmp[:,3]
plt.scatter(x,y, c=z, cmap='rainbow')
# plt.gray()
plt.colorbar()









    Out[196]:





<matplotlib.colorbar.Colorbar at 0x1a2c04ed68>



In [174]:

    
x = tmp[:,1]
y = tmp[:,2]
z = tmp[:,3]
plt.scatter(x,y, c=z, cmap='rainbow')
# plt.gray()
plt.colorbar()









    Out[174]:





<matplotlib.colorbar.Colorbar at 0x1a11860ba8>



In [ ]:

    
# fill in those nan with zmax
tmp = np.ones((res**2, 4)) *zmax
count = 0
for i in range(res*res):
    tmp[i,0] = i
    if i == data[count, 1]:
        tmp[i,1] = data[count, 1]
        tmp[i,2] = data[count, 2]
        tmp[i,3] = data[count, 3]



In [148]:

    
plt.scatter(x,y, c=z, cmap='rainbow')
# plt.gray()
plt.colorbar()









    Out[148]:





<matplotlib.colorbar.Colorbar at 0x1a174c13c8>



In [197]:

    
pre = "/Users/weilu/Research/server/may_2018/01_week"
temp = 310
location = pre + "/second/_280-350/2d_z_qw/remove_I_know_force_0.1_more_bins/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(15, 15), end=(30,30),save=False, xlabel="z_H6", ylabel="Qw", zmax=20,res=40)
# 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 0x1a0b088e10>






    



---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-197-933ddad5c516> in <module>()
      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)
     12 plt.figure()

IndexError: index 30 is out of bounds for axis 1 with size 30



In [178]:

    
pre = "/Users/weilu/Research/server/may_2018/01_week"
temp = 310
location = pre + "/second/_280-350/2d_z_qw/remove_I_know_force_0.1_more_bins/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(15, 15), end=(30,30),save=False, xlabel="z_H6", ylabel="Qw", zmax=20,res=40)
# 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 0x1a0b088e10>






    



---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-178-933ddad5c516> in <module>()
      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)
     12 plt.figure()

IndexError: index 30 is out of bounds for axis 1 with size 30



In [133]:

    
pre = "/Users/weilu/Research/server/may_2018/01_week"
temp = 310
location = pre + "/second/_280-350/2d_z_qw/remove_I_know_force_0.1_more_bins/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(15, 15), end=(30,30),save=False, xlabel="z_H6", ylabel="Qw", zmax=20,res=40)
# 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 0x1a0b088e10>






    



---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-133-933ddad5c516> in <module>()
      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)
     12 plt.figure()

IndexError: index 30 is out of bounds for axis 1 with size 30



In [110]:

    
pre = "/Users/weilu/Research/server/may_2018/01_week"
temp = 310
location = pre + "/second/_280-350/2d_z_qw/remove_I_know_force_0.1_more_bins/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(12, 12), end=(25,24),save=False, xlabel="z_H6", ylabel="Qw", zmax=20)
# 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 0x1a0b088e10>






    Out[110]:





[<matplotlib.lines.Line2D at 0x1a1a316748>]



In [106]:

    
pre = "/Users/weilu/Research/server/may_2018/01_week"
temp = 310
location = pre + "/second/_280-350/2d_z_qw/remove_I_know_force_0.1_more_bins/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(10, 10), end=(25,24),block=(-20,-10,0.1,0.4),save=False, xlabel="z_H6", ylabel="Qw", zmax=20)
# 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, 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 0x1a0b088e10>






    Out[106]:





[<matplotlib.lines.Line2D at 0x1a133d6b38>]



In [104]:

    
pre = "/Users/weilu/Research/server/may_2018/01_week"
temp = 300
location = pre + "/second/_280-350/2d_z_qw/remove_I_know_force_0.1/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(10, 10), end=(25,24),save=False, xlabel="z_H6", ylabel="Qw", zmax=20)
# 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, 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 0x1a0b088e10>






    Out[104]:





[<matplotlib.lines.Line2D at 0x1a18dd8940>]



In [96]:

    
pre = "/Users/weilu/Research/server/may_2018/01_week"
temp = 310
location = pre + "/second/_280-350/2d_z_qw/remove_I_know_force_0.1/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(10, 10), end=(25,24),block=(-20,-10,0.1,0.4),save=False, xlabel="z_H6", ylabel="Qw", zmax=20)
# 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, 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 0x1a0b088e10>






    Out[96]:





[<matplotlib.lines.Line2D at 0x1a228492b0>]



In [199]:

    
pre = "/Users/weilu/Research/server/may_2018/01_week"
temp = 310
location = pre + "/second/_280-350/2d_z_qw/remove_I_know_force_0.1/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(10, 10), end=(25,24),block=(-20,-10,0.1,0.4),save=False, xlabel="z_H6", ylabel="Qw", zmax=20)
# 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, 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-199-c42a566851a7> in <module>()
      3 location = pre + "/second/_280-350/2d_z_qw/remove_I_know_force_0.1/"
      4 location2 = location + f"pmf-{temp}.dat"
----> 5 path, f = shortest_path(location2, start=(10, 10), end=(25,24),block=(-20,-10,0.1,0.4),save=False, xlabel="z_H6", ylabel="Qw", zmax=20)
      6 # plt.savefig("/Users/weilu/papers/figures/2d_z6_qw.png", dpi=300)
      7 # plt.savefig("/Users/weilu/papers/figures/shortest_path.png", dpi=300)

~/opt/notebook/notebookFunctions.py in shortest_path(location, temp, start, end, block, res, zmin, zmax, xlabel, ylabel, title, save, plot1d, plot2d)
    255 
    256 def shortest_path(location, temp="450", start=(4,5), end=-1, block=-1, res=30, zmin=0, zmax=30, xlabel="xlabel", ylabel="ylabel", title="", save=False, plot1d=True, plot2d=True):
--> 257     data = np.loadtxt(location)
    258     xi, yi, zi = getxyz_3(data, res=res, zmin=zmin, zmax=zmax)
    259     zi = np.where(np.isnan(zi), 50, zi)

~/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_2018/01_week/second/_280-350/2d_z_qw/remove_I_know_force_0.1/pmf-320.dat'



In [ ]:

		count	mean	std	min	25%	50%	75%	max
BiasTo	Run
40.0	0	573.0	31.684179	4.559896	18.082627	28.798987	31.413864	34.900028	42.865238
	1	487.0	35.010985	3.939312	25.361349	32.361609	34.799120	37.479427	44.893315
	3	554.0	34.402951	4.334561	24.168642	31.278684	33.904398	37.037829	44.964855
42.0	1	149.0	34.239528	2.716602	26.139341	32.653188	34.411445	36.032290	42.219263
46.0	0	137.0	40.806560	3.310182	29.230923	38.389591	41.898474	43.400666	44.960475
	3	933.0	37.329527	2.726133	29.750523	35.388018	37.302610	39.161815	44.690105
	5	888.0	35.453258	2.976437	20.807314	33.437213	35.554432	37.493720	43.104372
48.0	2	1911.0	37.756046	2.743566	28.040668	35.988553	37.837937	39.645801	44.876064
48.0	6	559.0	37.806057	2.769640	29.423852	36.029382	37.832777	39.643835	44.699863
54.0	4	155.0	38.038134	3.743593	27.455228	35.078298	38.338157	40.808957	44.982499
58.0	0	519.0	40.108739	2.452462	30.250536	38.517247	40.211232	41.700486	44.965996

Temp	300	335	373	417	465	519	579	645	720	803	896	1000
0	1090.0	879.0	349.0	710.0	1672.0	300.0	NaN	NaN	NaN	NaN	NaN	NaN
1	40.0	695.0	1525.0	1221.0	781.0	738.0	NaN	NaN	NaN	NaN	NaN	NaN
2	NaN	NaN	18.0	63.0	155.0	23.0	1259.0	1755.0	1435.0	184.0	108.0	NaN
3	2344.0	1044.0	790.0	608.0	214.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	706.0	1338.0	632.0	686.0	320.0	163.0	1081.0	74.0	NaN	NaN	NaN	NaN
5	404.0	893.0	1053.0	973.0	1111.0	566.0	NaN	NaN	NaN	NaN	NaN	NaN
6	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0	1447.0	1183.0	1329.0	1040.0
7	NaN	6.0	612.0	508.0	304.0	507.0	1591.0	1472.0	NaN	NaN	NaN	NaN
8	416.0	145.0	21.0	231.0	443.0	2703.0	995.0	45.0	1.0	NaN	NaN	NaN
9	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	228.0	2735.0	1025.0	1012.0
10	NaN	NaN	NaN	NaN	NaN	NaN	74.0	1653.0	895.0	375.0	1125.0	878.0
11	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	994.0	523.0	1413.0	2070.0

Temp	300	335	373	417	465	519	579	645	720	803	896	1000
0	2488.0	1187.0	1103.0	222.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	816.0	1322.0	2102.0	594.0	96.0	70.0	NaN	NaN	NaN	NaN	NaN	NaN
2	NaN	66.0	166.0	459.0	497.0	1875.0	1745.0	192.0	NaN	NaN	NaN	NaN
3	1696.0	2101.0	969.0	234.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	NaN	NaN	238.0	1693.0	2255.0	372.0	442.0	NaN	NaN	NaN	NaN	NaN
5	NaN	324.0	422.0	1548.0	1012.0	847.0	47.0	800.0	NaN	NaN	NaN	NaN
6	NaN	NaN	NaN	250.0	442.0	1687.0	2325.0	296.0	NaN	NaN	NaN	NaN
7	NaN	NaN	NaN	NaN	698.0	149.0	441.0	3712.0	NaN	NaN	NaN	NaN
8	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2148.0	1613.0	763.0	476.0
9	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	344.0	868.0	1758.0	2030.0
10	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2220.0	1268.0	850.0	662.0
11	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	288.0	1251.0	1629.0	1832.0

	0	1	2	3	4	5	6	7	8	9	10	11
count	36.000000	36.000000	36.000000	36.000000	36.000000	36.000000	36.000000	36.000000	36.000000	36.000000	36.000000	36.000000
mean	5.305556	5.472222	5.388889	6.055556	5.805556	5.722222	5.750000	5.777778	5.166667	4.222222	4.777778	5.027778
std	2.081475	1.576363	1.777282	1.970416	1.410561	1.161553	1.872737	1.837873	1.539944	0.796819	1.396140	2.090606
min	2.000000	2.000000	3.000000	3.000000	4.000000	3.000000	2.000000	2.000000	3.000000	3.000000	3.000000	3.000000
25%	4.000000	4.000000	4.000000	5.000000	5.000000	5.000000	5.000000	5.000000	4.000000	4.000000	4.000000	4.000000
50%	5.000000	6.000000	5.000000	6.000000	5.000000	6.000000	5.500000	5.000000	5.000000	4.000000	4.500000	4.000000
75%	6.250000	7.000000	6.000000	7.250000	6.000000	6.000000	7.000000	7.000000	6.000000	5.000000	5.000000	5.250000
max	12.000000	8.000000	10.000000	11.000000	10.000000	8.000000	10.000000	11.000000	9.000000	6.000000	9.000000	11.000000