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 [8]:

    
data = pd.read_feather("/Users/weilu/Research/server/may_2018/second_start_topology/rerun_5_14_May_143016.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 < 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[8]:





<seaborn.axisgrid.FacetGrid at 0x1a32bff198>



In [5]:

    
data = pd.read_feather("/Users/weilu/Research/server/may_2018/second_start_topology/rerun_3_14_May_000912.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[5]:





<seaborn.axisgrid.FacetGrid at 0x1a2f72fe48>



In [6]:

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









    Out[6]:





<matplotlib.axes._subplots.AxesSubplot at 0x1198df7f0>



In [44]:

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









    Out[44]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a32b042e8>



In [45]:

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









    Out[45]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a7bd00a20>



In [15]:

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









    Out[15]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a21e2e278>



In [49]:

    
t = rerun5.query("Temp == 335 and z_h6 < -10 and Qw > 0.3")
t.plot.hexbin("z_h6", "Qw", cmap="seismic", sharex=False)









    Out[49]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a7c7d1be0>



In [50]:

    
select(t)



In [26]:

    
t = rerun5.query("Temp == 335 and Qw < 0.5 and Qw > 0.3")
t.plot.hexbin("z_h6", "Qw", cmap="seismic", sharex=False)









    Out[26]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a219847f0>



In [41]:

    
t = rerun5.query("Temp == 335 and Qw < 0.5 and Qw > 0.3")
t.plot.hexbin("z_h6", "AMH", cmap="seismic", sharex=False)









    Out[41]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a2ed17198>



In [29]:

    
t.columns









    Out[29]:





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', '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 [39]:

    
t.query("z_h6 < -10").mean() - t.query("z_h6 > -10").mean()









    Out[39]:





Step            -223392.612380
Run                   0.464941
Temp                  0.000000
Qw                    0.028944
Energy                8.607668
DisReal               2.983515
Dis_h56              37.559262
z_average            -4.282559
abs_z_average         1.793494
z_h1                 -1.001620
z_h2                  0.840928
z_h3                 -1.276398
z_h4                 -0.557522
z_h5                 -8.476437
z_h6                -15.686500
AMH                 -28.557913
Distance             -1.928730
AMH-Go                5.685854
Membrane              3.166002
Rg                   -0.794932
rg1                  -0.216657
rg2                  -0.485125
rg3                   1.054752
rg4                  -0.601068
rg5                   0.524981
rg6                  -1.071815
rg_all               -0.794932
Lipid                 6.167467
Lipid1               -1.584031
Lipid2               -0.214806
Lipid3                0.587414
Lipid4                0.161079
Lipid5               -0.004109
Lipid6               -0.113974
Lipid7                0.034872
Lipid8                1.645485
Lipid9                0.106180
Lipid10              -0.219162
Lipid11              -0.289506
Lipid12               2.048641
Lipid13               1.477813
Lipid14               2.106511
Lipid15               0.425061
TotalE               14.775135
dtype: float64



In [37]:

    
t.plot.hexbin("z_h6", "Lipid1", cmap="seismic", sharex=False)









    Out[37]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a2e577f28>



In [35]:

    
t.plot.hexbin("z_h6", "Lipid", cmap="seismic", sharex=False)









    Out[35]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a2e244710>



In [7]:

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









    Out[7]:





<matplotlib.axes._subplots.AxesSubplot at 0x11bd41208>



In [2]:

    
data = pd.read_feather("/Users/weilu/Research/server/may_2018/second_start_topology/rerun_1_11_May_133146.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[2]:





<seaborn.axisgrid.FacetGrid at 0x10ecc27b8>



In [9]:

    
rerun5.columns









    Out[9]:





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', '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]:

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









    Out[10]:





<matplotlib.axes._subplots.AxesSubplot at 0x11d2b35f8>



In [14]:

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









    Out[14]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a21e346a0>



In [3]:

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









    Out[3]:





<matplotlib.axes._subplots.AxesSubplot at 0x1172c9d30>



In [4]:

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









    Out[4]:





<matplotlib.axes._subplots.AxesSubplot at 0x11be690f0>



In [ ]:

		count	mean	std	min	25%	50%	75%	max
BiasTo	Run
84.0	1	1423.0	81.393215	5.564351	60.871722	77.711340	81.343503	85.089099	99.247940
84.0	7	116.0	83.205295	5.751692	67.081221	79.243822	83.199307	87.014939	99.618052
88.0	0	1803.0	84.729612	5.492301	64.903350	81.037497	84.657217	88.371127	102.454786
92.0	4	2025.0	88.302857	5.334048	68.278444	84.739558	88.399934	92.051578	105.008583
94.0	7	1618.0	89.476024	5.080354	72.981274	85.975856	89.511800	93.126893	105.117783
98.0	3	254.0	93.141554	4.401653	79.081327	90.490400	93.210370	96.022300	105.878986