In [1]:

    

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import glob
import jedi
%matplotlib inline


    

In [ ]:

    

pre = "/Users/weilu/Research/server/oct_2017/week_of_oct02/freeEnergy"
folder_list = glob.glob(pre+"/*")


    

In [ ]:

    

folder_list = [
   '/Users/weilu/Research/server/oct_2017/week_of_oct02/freeEnergy/rg_0.4_lipid_2_extended',
    '/Users/weilu/Research/server/oct_2017/week_of_oct02/freeEnergy/rg_0.4_lipid_2_topology'
]
dis_list = np.linspace(30, 130, 51)


    

In [ ]:

    

pre = '/Users/weilu/Research/server/oct_2017/week_of_oct02/freeEnergy/rg_0.4_lipid_2_topology'


    

In [ ]:

    

def process_data(folder, dis):
    location = folder + "/simulation/dis_{}/0/".format(dis)
    #     print(location)
    all_lipid_list = []
    for i in range(4):
        file = "lipid.{}.dat".format(i)
        lipid = pd.read_csv(location+file).assign(Run = i)
        lipid.columns = lipid.columns.str.strip()
        lipid = lipid[["Steps","Lipid","Run"]]
        all_lipid_list.append(lipid)
    lipid = pd.concat(all_lipid_list)

    all_dis_list = []
    for i in range(4):
        file = "addforce.{}.dat".format(i)
        dis = pd.read_csv(location+file).assign(Run = i)
        dis.columns = dis.columns.str.strip()
        remove_columns = ['AddedForce', 'Dis12', 'Dis34', 'Dis56']
        dis.drop(remove_columns, axis=1,inplace=True)
        all_dis_list.append(dis)
    dis = pd.concat(all_dis_list)

    all_wham_list = []
    for i in range(4):
        file = "wham.{}.dat".format(i)
        wham = pd.read_csv(location+file).assign(Run = i)
        wham.columns = wham.columns.str.strip()
        remove_columns = ['Rg', 'Tc']
        wham = wham.drop(remove_columns, axis=1)
        all_wham_list.append(wham)
    wham = pd.concat(all_wham_list)

    file = "../log.lammps"
    temper = pd.read_table(location+file, skiprows=2, sep=' ')
    temper = temper.melt(id_vars=['Step'], value_vars=['T0', 'T1', 'T2', 'T3'], value_name="Run", var_name="Temp")

    t2 = temper.merge(wham, how='inner', left_on=["Step", "Run"], right_on=["Steps", "Run"]
                     ).sort_values('Step').drop('Steps', axis=1)
    t3 = t2.merge(dis, how='inner', left_on=["Step", "Run"], right_on=["Steps", "Run"]
                     ).sort_values('Step').drop('Steps', axis=1)
    t4 = t3.merge(lipid, how='inner', left_on=["Step", "Run"], right_on=["Steps", "Run"]
                             ).sort_values('Step').drop('Steps', axis=1)
    t4 = t4.assign(TotalE = t4.Energy + t4.Lipid)
    t350 = t4.query('Temp=="T0" & Step > 1e7')
    t350.to_csv(location+"t350.dat", sep=' ', index=False, header=False)
    t400 = t4.query('Temp=="T1" & Step > 1e7')
    t400.to_csv(location+"t400.dat", sep=' ', index=False, header=False)
    t450 = t4.query('Temp=="T2" & Step > 1e7')
    t450.to_csv(location+"t450.dat", sep=' ', index=False, header=False)
    t4.query('Temp=="T3" & Step > 1e7').to_csv(location+"t500.dat", sep=' ', index=False, header=False)


    

In [ ]:

    

process_data(folder, dis)


    

In [ ]:

    

for folder in folder_list:
    for dis in dis_list:
        process_data(folder, dis)


    

In [ ]:

    

t350.plot('Step', 'Run')


    

In [ ]:

    

t350.plot('Step', 'Qw')


    

In [ ]:

    

t350.plot('Step', 'Energy')


    

In [2]:

    




    

In [7]:

    

import IPython
print(IPython.sys_info())


    

    




{'commit_hash': 'd86648c5d',
 'commit_source': 'installation',
 'default_encoding': 'UTF-8',
 'ipython_path': '/Users/weilu/anaconda/lib/python3.6/site-packages/IPython',
 'ipython_version': '6.1.0',
 'os_name': 'posix',
 'platform': 'Darwin-16.7.0-x86_64-i386-64bit',
 'sys_executable': '/Users/weilu/anaconda/bin/python',
 'sys_platform': 'darwin',
 'sys_version': '3.6.1 |Anaconda custom (x86_64)| (default, May 11 2017, '
                '13:04:09) \n'
                '[GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.57)]'}

In [9]:

    

folder = '/Users/weilu/Research/server/oct_2017/week_of_oct02/freeEnergy/rg_0.4_lipid_2_extended'
dis_list = np.linspace(30, 130, 51)
all_dis = []
for dis in dis_list:
    location = folder + "/simulation/dis_{}/0/".format(dis)
    #     print(location)
    all_lipid_list = []
    for i in range(4):
        file = "lipid.{}.dat".format(i)
        lipid = pd.read_csv(location+file).assign(Run = i)
        lipid.columns = lipid.columns.str.strip()
        lipid = lipid[["Steps","Lipid","Run"]]
        all_lipid_list.append(lipid)
    lipid = pd.concat(all_lipid_list)

    all_dis_list = []
    for i in range(4):
        file = "addforce.{}.dat".format(i)
        dis = pd.read_csv(location+file).assign(Run = i)
        dis.columns = dis.columns.str.strip()
        remove_columns = ['AddedForce', 'Dis12', 'Dis34', 'Dis56']
        dis.drop(remove_columns, axis=1,inplace=True)
        all_dis_list.append(dis)
    dis = pd.concat(all_dis_list)

    all_wham_list = []
    for i in range(4):
        file = "wham.{}.dat".format(i)
        wham = pd.read_csv(location+file).assign(Run = i)
        wham.columns = wham.columns.str.strip()
        remove_columns = ['Rg', 'Tc']
        wham = wham.drop(remove_columns, axis=1)
        all_wham_list.append(wham)
    wham = pd.concat(all_wham_list)

    file = "../log.lammps"
    temper = pd.read_table(location+file, skiprows=2, sep=' ')
    temper = temper.melt(id_vars=['Step'], value_vars=['T0', 'T1', 'T2', 'T3'], value_name="Run", var_name="Temp")

    t2 = temper.merge(wham, how='inner', left_on=["Step", "Run"], right_on=["Steps", "Run"]
                     ).sort_values('Step').drop('Steps', axis=1)
    t3 = t2.merge(dis, how='inner', left_on=["Step", "Run"], right_on=["Steps", "Run"]
                     ).sort_values('Step').drop('Steps', axis=1)
    t4 = t3.merge(lipid, how='inner', left_on=["Step", "Run"], right_on=["Steps", "Run"]
                             ).sort_values('Step').drop('Steps', axis=1)
    t4 = t4.assign(TotalE = t4.Energy + t4.Lipid)
    all_dis.append(t4)
data = pd.concat(all_dis)


    

In [12]:

    

data.to_feather("/Users/weilu/Research/data/test_data/test.feather")


    

    




---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
~/anaconda/lib/python3.6/site-packages/pandas/io/feather_format.py in _try_import()
     12     try:
---> 13         import feather
     14     except ImportError:

ModuleNotFoundError: No module named 'feather'

During handling of the above exception, another exception occurred:

ImportError                               Traceback (most recent call last)
<ipython-input-12-745126e06070> in <module>()
----> 1 data.to_feather("/Users/weilu/Research/data/test_data/test.feather")

~/anaconda/lib/python3.6/site-packages/pandas/core/frame.py in to_feather(self, fname)
   1512         """
   1513         from pandas.io.feather_format import to_feather
-> 1514         to_feather(self, fname)
   1515 
   1516     @Substitution(header='Write out column names. If a list of string is given, \

~/anaconda/lib/python3.6/site-packages/pandas/io/feather_format.py in to_feather(df, path)
     47         raise ValueError("feather only support IO with DataFrames")
     48 
---> 49     feather = _try_import()
     50     valid_types = {'string', 'unicode'}
     51 

~/anaconda/lib/python3.6/site-packages/pandas/io/feather_format.py in _try_import()
     15 
     16         # give a nice error message
---> 17         raise ImportError("the feather-format library is not installed\n"
     18                           "you can install via conda\n"
     19                           "conda install feather-format -c conda-forge\n"

ImportError: the feather-format library is not installed
you can install via conda
conda install feather-format -c conda-forge
or via pip
pip install feather-format

In [15]:

    

data.plot.hexbin(x='Qw', y='Distance')


    

    
Out[15]:





<matplotlib.axes._subplots.AxesSubplot at 0x112d4aa58>






    

In [17]:

    

['T' + str(i) for i in range(5)]


    

    
Out[17]:





['T0', 'T1', 'T2', 'T3', 'T4']

In [19]:

    

def read_temper(n=4, location="."):
    for i in range(n):
        file = "lipid.{}.dat".format(i)
        lipid = pd.read_csv(location+file).assign(Run = i)
        lipid.columns = lipid.columns.str.strip()
        lipid = lipid[["Steps","Lipid","Run"]]
        all_lipid_list.append(lipid)
    lipid = pd.concat(all_lipid_list)

    all_dis_list = []
    for i in range(n):
        file = "addforce.{}.dat".format(i)
        dis = pd.read_csv(location+file).assign(Run = i)
        dis.columns = dis.columns.str.strip()
        remove_columns = ['AddedForce', 'Dis12', 'Dis34', 'Dis56']
        dis.drop(remove_columns, axis=1,inplace=True)
        all_dis_list.append(dis)
    dis = pd.concat(all_dis_list)

    all_wham_list = []
    for i in range(n):
        file = "wham.{}.dat".format(i)
        wham = pd.read_csv(location+file).assign(Run = i)
        wham.columns = wham.columns.str.strip()
        remove_columns = ['Rg', 'Tc']
        wham = wham.drop(remove_columns, axis=1)
        all_wham_list.append(wham)
    wham = pd.concat(all_wham_list)

    file = "../log.lammps"
    temper = pd.read_table(location+file, skiprows=2, sep=' ')
    temper = temper.melt(id_vars=['Step'], value_vars=['T' + str(i) for i in range(n)], value_name="Run", var_name="Temp")

    t2 = temper.merge(wham, how='inner', left_on=["Step", "Run"], right_on=["Steps", "Run"]
                     ).sort_values('Step').drop('Steps', axis=1)
    t3 = t2.merge(dis, how='inner', left_on=["Step", "Run"], right_on=["Steps", "Run"]
                     ).sort_values('Step').drop('Steps', axis=1)
    t4 = t3.merge(lipid, how='inner', left_on=["Step", "Run"], right_on=["Steps", "Run"]
                             ).sort_values('Step').drop('Steps', axis=1)
    t4 = t4.assign(TotalE = t4.Energy + t4.Lipid)
    return t4


    

In [21]:

    

n= 8
location = "/Users/weilu/Research/server/oct_2017/week_oct09_two/higher_temp/simulation/dis_30.0/0/"
data= read_temper(location=location, n=n)


    

In [67]:

    

t350 = data.query('Temp=="T0" & Step > 0e7')
t350.plot('Step', 'Run')
t350.plot('Step', 'Qw')
t350.plot('Step', 'Energy')


    

    
Out[67]:





<matplotlib.axes._subplots.AxesSubplot at 0x1295a3f28>






    













    













    

In [68]:

    

t350 = data.query('Temp=="T1" & Step > 0e7')
t350.plot('Step', 'Run')
t350.plot('Step', 'Qw')
t350.plot('Step', 'Energy')


    

    
Out[68]:





<matplotlib.axes._subplots.AxesSubplot at 0x129c4e278>






    













    













    

In [62]:

    

t350 = data.query('Temp=="T2" & Step > 0e7')
t350.plot('Step', 'Run')
t350.plot('Step', 'Qw')
t350.plot('Step', 'Energy')


    

    
Out[62]:





<matplotlib.axes._subplots.AxesSubplot at 0x128914ef0>






    













    













    

In [69]:

    

t350 = data.query('Temp=="T3" & Step > 0e7')
t350.plot('Step', 'Run')
t350.plot('Step', 'Qw')
t350.plot('Step', 'Energy')


    

    
Out[69]:





<matplotlib.axes._subplots.AxesSubplot at 0x128dd30f0>






    













    













    

In [61]:

    

t350 = data.query('Temp=="T4" & Step > 0e7')
t350.plot('Step', 'Run')
t350.plot('Step', 'Qw')
t350.plot('Step', 'Energy')


    

    
Out[61]:





<matplotlib.axes._subplots.AxesSubplot at 0x1250865c0>






    













    













    

In [60]:

    

t350 = data.query('Temp=="T5" & Step > 0e7')
t350.plot('Step', 'Run')
t350.plot('Step', 'Qw')
t350.plot('Step', 'Energy')


    

    
Out[60]:





<matplotlib.axes._subplots.AxesSubplot at 0x12175f630>






    













    













    

In [59]:

    




    

    
Out[59]:





<matplotlib.axes._subplots.AxesSubplot at 0x11fa3b2b0>






    













    













    

In [41]:

    

n= 8
location = "/Users/weilu/Research/server/oct_2017/week_oct09_two/higher_temp/simulation/dis_80.0/0/"
data2= read_temper(location=location, n=n)


    

In [58]:

    

t350 = data2.query('Temp=="T1" & Step > 0e7')
t350.plot('Step', 'Run')
t350.plot('Step', 'Qw')
t350.plot('Step', 'Energy')


    

    
Out[58]:





<matplotlib.axes._subplots.AxesSubplot at 0x1225c52b0>






    













    













    

In [ ]:

    

t350 = data2.query('Temp=="T1" & Step > 1e7')
t350.plot('Step', 'Run')
t350.plot('Step', 'Qw')
t350.plot('Step', 'Energy')


    

In [51]:

    

n= 4
location = "/Users/weilu/Research/server/oct_2017/week_of_oct02/freeEnergy/rg_0.4_lipid_2_extended/simulation/dis_30.0/0/"
data3= read_temper(location=location, n=n)


    

In [66]:

    

t350 = data3.query('Temp=="T0"')
t350.plot('Step', 'Run')
t350.plot('Step', 'Qw')
t350.plot('Step', 'Energy')


    

    
Out[66]:





<matplotlib.axes._subplots.AxesSubplot at 0x1225b05c0>






    













    













    

In [65]:

    

t350 = data3.query('Temp=="T1"')
t350.plot('Step', 'Run')
t350.plot('Step', 'Qw')
t350.plot('Step', 'Energy')


    

    
Out[65]:





<matplotlib.axes._subplots.AxesSubplot at 0x125b66278>






    













    













    

In [64]:

    

t350 = data3.query('Temp=="T2"')
t350.plot('Step', 'Run')
t350.plot('Step', 'Qw')
t350.plot('Step', 'Energy')


    

    
Out[64]:





<matplotlib.axes._subplots.AxesSubplot at 0x125a61ef0>






    













    













    

In [70]:

    

t350 = data3.query('Temp=="T3"')
t350.plot('Step', 'Run')
t350.plot('Step', 'Qw')
t350.plot('Step', 'Energy')


    

    
Out[70]:





<matplotlib.axes._subplots.AxesSubplot at 0x1288e9668>






    













    













    

In [74]:

    

t350 = data3.query('Run==0')
t350.plot('Step', 'Temp')
t350.plot('Step', 'Qw')
t350.plot('Step', 'Energy')


    

    




---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-74-e60e2af1cad7> in <module>()
      1 t350 = data3.query('Run == 0')
----> 2 t350.plot('Step', 'Temp')
      3 t350.plot('Step', 'Qw')
      4 t350.plot('Step', 'Energy')

~/anaconda/lib/python3.6/site-packages/pandas/plotting/_core.py in __call__(self, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)
   2618                           fontsize=fontsize, colormap=colormap, table=table,
   2619                           yerr=yerr, xerr=xerr, secondary_y=secondary_y,
-> 2620                           sort_columns=sort_columns, **kwds)
   2621     __call__.__doc__ = plot_frame.__doc__
   2622 

~/anaconda/lib/python3.6/site-packages/pandas/plotting/_core.py in plot_frame(data, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)
   1855                  yerr=yerr, xerr=xerr,
   1856                  secondary_y=secondary_y, sort_columns=sort_columns,
-> 1857                  **kwds)
   1858 
   1859 

~/anaconda/lib/python3.6/site-packages/pandas/plotting/_core.py in _plot(data, x, y, subplots, ax, kind, **kwds)
   1680         plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds)
   1681 
-> 1682     plot_obj.generate()
   1683     plot_obj.draw()
   1684     return plot_obj.result

~/anaconda/lib/python3.6/site-packages/pandas/plotting/_core.py in generate(self)
    234     def generate(self):
    235         self._args_adjust()
--> 236         self._compute_plot_data()
    237         self._setup_subplots()
    238         self._make_plot()

~/anaconda/lib/python3.6/site-packages/pandas/plotting/_core.py in _compute_plot_data(self)
    343         if is_empty:
    344             raise TypeError('Empty {0!r}: no numeric data to '
--> 345                             'plot'.format(numeric_data.__class__.__name__))
    346 
    347         self.data = numeric_data

TypeError: Empty 'DataFrame': no numeric data to plot

In [72]:

    

data3


    

    
Out[72]:







  

    

      

      
Step
      
Temp
      
Run
      
Qw
      
Energy
      
Distance
      
Lipid
      
TotalE
    
  
  

    

      
0
      
4000
      
T0
      
1
      
0.051328
      
-180.561073
      
241.150081
      
-0.003150
      
-180.564223
    
    

      
15
      
4000
      
T3
      
3
      
0.051168
      
-52.272277
      
239.779323
      
0.257297
      
-52.014980
    
    

      
14
      
4000
      
T3
      
3
      
0.051168
      
-52.272277
      
239.779323
      
0.227489
      
-52.044788
    
    

      
13
      
4000
      
T3
      
3
      
0.051168
      
-52.272277
      
239.779323
      
0.289218
      
-51.983059
    
    

      
12
      
4000
      
T3
      
3
      
0.051168
      
-52.272277
      
239.779323
      
0.024245
      
-52.248032
    
    

      
10
      
4000
      
T1
      
0
      
0.051460
      
-145.509740
      
240.832594
      
0.102526
      
-145.407214
    
    

      
9
      
4000
      
T1
      
0
      
0.051460
      
-145.509740
      
240.832594
      
0.109315
      
-145.400426
    
    

      
8
      
4000
      
T1
      
0
      
0.051460
      
-145.509740
      
240.832594
      
0.013637
      
-145.496103
    
    

      
11
      
4000
      
T1
      
0
      
0.051460
      
-145.509740
      
240.832594
      
0.234867
      
-145.274873
    
    

      
6
      
4000
      
T2
      
2
      
0.054114
      
-178.377807
      
242.690315
      
0.255513
      
-178.122295
    
    

      
5
      
4000
      
T2
      
2
      
0.054114
      
-178.377807
      
242.690315
      
0.450095
      
-177.927713
    
    

      
4
      
4000
      
T2
      
2
      
0.054114
      
-178.377807
      
242.690315
      
0.070846
      
-178.306961
    
    

      
3
      
4000
      
T0
      
1
      
0.051328
      
-180.561073
      
241.150081
      
0.259122
      
-180.301951
    
    

      
2
      
4000
      
T0
      
1
      
0.051328
      
-180.561073
      
241.150081
      
0.110916
      
-180.450158
    
    

      
1
      
4000
      
T0
      
1
      
0.051328
      
-180.561073
      
241.150081
      
0.143941
      
-180.417132
    
    

      
7
      
4000
      
T2
      
2
      
0.054114
      
-178.377807
      
242.690315
      
0.230648
      
-178.147159
    
    

      
25
      
8000
      
T0
      
1
      
0.073783
      
-437.562864
      
143.866671
      
-0.345778
      
-437.908641
    
    

      
31
      
8000
      
T1
      
0
      
0.068913
      
-304.161400
      
132.744382
      
-0.137282
      
-304.298681
    
    

      
30
      
8000
      
T1
      
0
      
0.068913
      
-304.161400
      
132.744382
      
-1.060612
      
-305.222012
    
    

      
29
      
8000
      
T1
      
0
      
0.068913
      
-304.161400
      
132.744382
      
-0.136743
      
-304.298142
    
    

      
28
      
8000
      
T1
      
0
      
0.068913
      
-304.161400
      
132.744382
      
0.167048
      
-303.994352
    
    

      
27
      
8000
      
T0
      
1
      
0.073783
      
-437.562864
      
143.866671
      
-0.116031
      
-437.678895
    
    

      
26
      
8000
      
T0
      
1
      
0.073783
      
-437.562864
      
143.866671
      
-0.988660
      
-438.551524
    
    

      
24
      
8000
      
T0
      
1
      
0.073783
      
-437.562864
      
143.866671
      
-0.079496
      
-437.642360
    
    

      
19
      
8000
      
T3
      
3
      
0.072346
      
-145.424143
      
131.610711
      
-0.095539
      
-145.519682
    
    

      
22
      
8000
      
T2
      
2
      
0.071423
      
-300.009746
      
145.835932
      
-0.060098
      
-300.069844
    
    

      
21
      
8000
      
T2
      
2
      
0.071423
      
-300.009746
      
145.835932
      
-0.341059
      
-300.350804
    
    

      
20
      
8000
      
T2
      
2
      
0.071423
      
-300.009746
      
145.835932
      
-0.126099
      
-300.135845
    
    

      
18
      
8000
      
T3
      
3
      
0.072346
      
-145.424143
      
131.610711
      
-0.784124
      
-146.208267
    
    

      
17
      
8000
      
T3
      
3
      
0.072346
      
-145.424143
      
131.610711
      
0.028153
      
-145.395990
    
    

      
...
      
...
      
...
      
...
      
...
      
...
      
...
      
...
      
...
    
    

      
79981
      
19996000
      
T3
      
1
      
0.652191
      
-905.381943
      
26.901585
      
-18.459681
      
-923.841624
    
    

      
79980
      
19996000
      
T3
      
1
      
0.652191
      
-905.381943
      
26.901585
      
-63.980858
      
-969.362800
    
    

      
79978
      
19996000
      
T2
      
2
      
0.324935
      
-646.409437
      
13.227381
      
-77.295302
      
-723.704739
    
    

      
79977
      
19996000
      
T2
      
2
      
0.324935
      
-646.409437
      
13.227381
      
-104.755573
      
-751.165011
    
    

      
79976
      
19996000
      
T2
      
2
      
0.324935
      
-646.409437
      
13.227381
      
-80.888510
      
-727.297948
    
    

      
79979
      
19996000
      
T2
      
2
      
0.324935
      
-646.409437
      
13.227381
      
-115.024841
      
-761.434279
    
    

      
79974
      
19996000
      
T0
      
3
      
0.362070
      
-787.742135
      
12.027203
      
-76.153022
      
-863.895157
    
    

      
79973
      
19996000
      
T0
      
3
      
0.362070
      
-787.742135
      
12.027203
      
-90.608416
      
-878.350551
    
    

      
79972
      
19996000
      
T0
      
3
      
0.362070
      
-787.742135
      
12.027203
      
-79.371605
      
-867.113740
    
    

      
79971
      
19996000
      
T1
      
0
      
0.310672
      
-507.358561
      
27.283029
      
-98.308049
      
-605.666611
    
    

      
79970
      
19996000
      
T1
      
0
      
0.310672
      
-507.358561
      
27.283029
      
-71.183024
      
-578.541585
    
    

      
79969
      
19996000
      
T1
      
0
      
0.310672
      
-507.358561
      
27.283029
      
-110.215852
      
-617.574414
    
    

      
79968
      
19996000
      
T1
      
0
      
0.310672
      
-507.358561
      
27.283029
      
-75.155643
      
-582.514205
    
    

      
79975
      
19996000
      
T0
      
3
      
0.362070
      
-787.742135
      
12.027203
      
-115.935502
      
-903.677637
    
    

      
79997
      
20000000
      
T3
      
1
      
0.616985
      
-899.269416
      
23.835518
      
-18.306210
      
-917.575626
    
    

      
79996
      
20000000
      
T3
      
1
      
0.616985
      
-899.269416
      
23.835518
      
-61.815884
      
-961.085300
    
    

      
79995
      
20000000
      
T1
      
0
      
0.358867
      
-560.282896
      
18.402387
      
-105.725847
      
-666.008743
    
    

      
79994
      
20000000
      
T1
      
0
      
0.358867
      
-560.282896
      
18.402387
      
-68.904100
      
-629.186996
    
    

      
79993
      
20000000
      
T1
      
0
      
0.358867
      
-560.282896
      
18.402387
      
-106.093741
      
-666.376637
    
    

      
79992
      
20000000
      
T1
      
0
      
0.358867
      
-560.282896
      
18.402387
      
-76.526746
      
-636.809642
    
    

      
79991
      
20000000
      
T2
      
2
      
0.353911
      
-624.433682
      
19.300384
      
-117.319962
      
-741.753644
    
    

      
79987
      
20000000
      
T0
      
3
      
0.363336
      
-784.795841
      
13.186940
      
-117.778339
      
-902.574180
    
    

      
79989
      
20000000
      
T2
      
2
      
0.353911
      
-624.433682
      
19.300384
      
-110.248157
      
-734.681838
    
    

      
79988
      
20000000
      
T2
      
2
      
0.353911
      
-624.433682
      
19.300384
      
-78.414646
      
-702.848327
    
    

      
79986
      
20000000
      
T0
      
3
      
0.363336
      
-784.795841
      
13.186940
      
-78.180884
      
-862.976725
    
    

      
79985
      
20000000
      
T0
      
3
      
0.363336
      
-784.795841
      
13.186940
      
-97.246868
      
-882.042709
    
    

      
79984
      
20000000
      
T0
      
3
      
0.363336
      
-784.795841
      
13.186940
      
-76.516865
      
-861.312706
    
    

      
79998
      
20000000
      
T3
      
1
      
0.616985
      
-899.269416
      
23.835518
      
-107.755015
      
-1007.024430
    
    

      
79990
      
20000000
      
T2
      
2
      
0.353911
      
-624.433682
      
19.300384
      
-79.154152
      
-703.587834
    
    

      
79999
      
20000000
      
T3
      
1
      
0.616985
      
-899.269416
      
23.835518
      
-104.152052
      
-1003.421467
    
  

80000 rows × 8 columns

In [87]:

    

t350 = data3.query('Run==2')
dic = {"T0":350, "T1":400, "T2":450, "T3":500}
t350 = t350.assign(myT = t350['Temp'].map(dic))
t350.plot('Step', 'myT')


    

    
Out[87]:





<matplotlib.axes._subplots.AxesSubplot at 0x11850dac8>






    

In [90]:

    

for i in range(4):
    tmp = data3.query('Run=={}'.format(i))
    dic = {"T0":350, "T1":400, "T2":450, "T3":500}
    tmp = tmp.assign(myT = tmp['Temp'].map(dic))
    tmp.plot('Step', 'myT')


    

In [ ]: