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 [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 0x1a215f4470>






    

In [15]:

    

data = pd.read_feather("/Users/weilu/Research/server/may_2018/second/rerun_4_09_May_225324.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])
rerun4 = 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[15]:





<seaborn.axisgrid.FacetGrid at 0x1a4241d0f0>






    

In [18]:

    

data = pd.read_feather("/Users/weilu/Research/server/may_2018/second/rerun_5_09_May_225324.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])
rerun5 = data
t = a.query("Temp < 600").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[18]:





<seaborn.axisgrid.FacetGrid at 0x1a20f44470>






    

In [22]:

    

rerun5.query("Temp == 373").plot.hexbin("DisReal", "Qw", cmap="seismic", sharex=False)


    

    
Out[22]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a14360ba8>






    

In [66]:

    

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


    

    
Out[66]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a177c0208>






    

In [6]:

    

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


    

    
Out[6]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a0de51f28>






    

In [7]:

    

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


    

    
Out[7]:





<matplotlib.axes._subplots.AxesSubplot at 0x103a5aef0>






    

In [17]:

    

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


    

    
Out[17]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a0ce5fc50>






    

In [16]:

    

rerun4.query("Temp == 335").plot.hexbin("z_h6", "Qw", cmap="seismic", sharex=False)


    

    
Out[16]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a143df898>






    

In [9]:

    

rerun5.query("Temp == 335").plot.hexbin("z_h6", "Qw", cmap="seismic", sharex=False)


    

    
Out[9]:





<matplotlib.axes._subplots.AxesSubplot at 0x103a5afd0>






    

In [10]:

    

rerun5.query("Temp == 335 and Qw < 0.5").plot.hexbin("z_h6", "Qw", cmap="seismic", sharex=False)


    

    
Out[10]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a1324aba8>






    

In [8]:

    

rerun5.query("Temp == 335 and z_h6 < -10").shape


    

    
Out[8]:





(23374, 44)

In [30]:

    

pre = "/Users/weilu/Research/server/may_2018/01_week"
temp = 340
location = pre + "/second_rerun2/_280-350/2d_z_qw/quick_force_0.05/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), block=[-15,-10,0.1,0.2],end=(20,20),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 0x1a0918fef0>






    
Out[30]:





[<matplotlib.lines.Line2D at 0x1a15718128>]






    













    













    













    

In [69]:

    

pre = "/Users/weilu/Research/server/may_2018/01_week"
temp = 330
location = pre + "/second_rerun2/_280-350/2d_z_qw/quick_force_0.1/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), end=(20,20),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 0x1a0918fef0>






    
Out[69]:





[<matplotlib.lines.Line2D at 0x1a162674e0>]






    













    













    













    

In [65]:

    

pre = "/Users/weilu/Research/server/may_2018/01_week"
temp = 310
location = pre + "/second_rerun2/_280-350/2d_z_qw/force_0.1/"
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 0x1a0918fef0>






    
Out[65]:





[<matplotlib.lines.Line2D at 0x1a182f37b8>]






    













    













    













    

In [62]:

    

pre = "/Users/weilu/Research/server/may_2018/01_week"
temp = 300
location = pre + "/second_rerun2/_280-350/2d_z_qw/force_0.15/"
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 0x1a0918fef0>






    
Out[62]:





[<matplotlib.lines.Line2D at 0x1a17011c18>]






    













    













    













    

In [54]:

    

pre = "/Users/weilu/Research/server/may_2018/01_week"
temp = 290
location = pre + "/second_rerun2/_280-350/2d_z_qw/force_0.2/"
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 0x1a0918fef0>






    
Out[54]:





[<matplotlib.lines.Line2D at 0x1a17043978>]






    













    













    













    

In [27]:

    

pre = "/Users/weilu/Research/server/may_2018/01_week"
temp = 340
location = pre + "/second_rerun2/_280-350/2d_z_qw/quick/"
location2 = location + f"pmf-{temp}.dat"
path, f = shortest_path(location2, start=(1, 10), end=(20,20),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 0x1a0918fef0>






    
Out[27]:





[<matplotlib.lines.Line2D at 0x1a3cd23a20>]






    













    













    













    

In [14]:

    

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=(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 0x1a0918fef0>






    
Out[14]:





[<matplotlib.lines.Line2D at 0x1a1445b4a8>]






    













    













    













    

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