In [2]:

    

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


    

In [23]:

    

def read_temper(n=4, location="."):
    all_lipid_list = []
    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="Temp", var_name="Run")
    temper["Run"] = temper["Run"].str[1:].astype(int)
    temper["Temp"] = "T" + temper["Temp"].astype(str) 
    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 [24]:

    

n= 12
location = "/Users/weilu/Research/server/oct_2017/week_oct09_two/more_higher_temp/simulation/dis_30.0/0/"
data= read_temper(location=location, n=n)


    

In [27]:

    

# folder_list = [
#    '/Users/weilu/Research/server/oct_2017/week_oct09_two/more_higher_temp/'
# ]
folder_list = [
   '/Users/weilu/Research/server/oct_2017/week_oct09_two/more_higher_temp_topology/'
]
dis_list = np.linspace(30, 130, 51)
dic = {"T0":350, "T1":400, "T2":450, "T3":500, "T4":550, "T5":600, "T6":650, "T7":700, "T8":750, "T9":800, "T10":900, "T11":1000}
for folder in folder_list:
    for dis in dis_list:
        location = folder + "/simulation/dis_{}/0/".format(dis)
        data = read_temper(location=location, n=12)
        temps = list(dic.keys())
        for temp in temps:
            tmp = data.query('Temp=="{}"& Step > 1e7 & Step <= 2.6e7'.format(temp))
            tmp.to_csv(location+"t{}.dat".format(dic[temp]), sep=' ', index=False, header=False)


    

In [28]:

    

folder_list = [
   '/Users/weilu/Research/server/oct_2017/week_oct09_two/more_higher_temp/'
]
# folder_list = [
#    '/Users/weilu/Research/server/oct_2017/week_oct09_two/more_higher_temp_topology/'
# ]
dis_list = np.linspace(30, 130, 51)
dic = {"T0":350, "T1":400, "T2":450, "T3":500, "T4":550, "T5":600, "T6":650, "T7":700, "T8":750, "T9":800, "T10":900, "T11":1000}
for folder in folder_list:
    for dis in dis_list:
        location = folder + "/simulation/dis_{}/0/".format(dis)
        data = read_temper(location=location, n=12)
        temps = list(dic.keys())
        for temp in temps:
            tmp = data.query('Temp=="{}"& Step > 1e7 & Step <= 2.6e7'.format(temp))
            tmp.to_csv(location+"t{}.dat".format(dic[temp]), sep=' ', index=False, header=False)


    

In [ ]:

    

folder_list = [
   '/Users/weilu/Research/server/oct_2017/week_oct09_two/more_higher_temp/'
]
# folder_list = [
#    '/Users/weilu/Research/server/oct_2017/week_oct09_two/more_higher_temp_topology/'
# ]
dis_list = np.linspace(30, 130, 51)
dic = {"T0":350, "T1":400, "T2":450, "T3":500, "T4":550, "T5":600, "T6":650, "T7":700, "T8":750, "T9":800, "T10":900, "T11":1000}
for folder in folder_list:
    for dis in dis_list:
        location = folder + "/simulation/dis_{}/0/".format(dis)
        data = read_temper(location=location, n=12)
        temps = list(dic.keys())
        for temp in temps:
            tmp = data.query('Temp=="{}"& Step > 1e7 & Step <= 2.6e7'.format(temp))
            tmp.to_csv(location+"t{}.dat".format(dic[temp]), sep=' ', index=False, header=False)


    

In [30]:

    

def read_temper2(n=4, location="."):
    all_lipid_list = []
    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")
#     temper["Run"] = temper["Run"].str[1:].astype(int)
#     temper["Temp"] = "T" + temper["Temp"].astype(str) 
    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 [31]:

    

dis_list = ["test"]
dic = {"T0":350, "T1":400, "T2":450, "T3":500, "T4":550, "T5":600, "T6":650, "T7":700, "T8":750, "T9":800, "T10":900, "T11":1000}
for folder in folder_list:
    for dis in dis_list:
        location = folder + "/simulation/{}/0/".format(dis)
        data = read_temper2(location=location, n=12)
        temps = list(dic.keys())
        for temp in temps:
            tmp = data.query('Temp=="{}"& Step > 1e7 & Step <= 2.6e7'.format(temp))
            tmp.to_csv(location+"t{}.dat".format(dic[temp]), sep=' ', index=False, header=False)


    

In [25]:

    

n= 12
location = "/Users/weilu/Research/server/oct_2017/week_oct09_two/more_higher_temp/simulation/dis_80.0/0/"
data= read_temper(location=location, n=n)


    

In [32]:

    

file = "../log.lammps"
location = "/Users/weilu/Research/server/oct_2017/week_oct09_two/more_higher_temp/simulation/dis_80.0/0/"
temper = pd.read_table(location+file, skiprows=2, sep=' ')
temper2 = temper.melt(id_vars=['Step'], value_vars=['T' + str(i) for i in range(n)], value_name="Temp", var_name="Run")


    

In [37]:

    

temper.tail()


    

    
Out[37]:







  

    

      

      
Step
      
T0
      
T1
      
T2
      
T3
      
T4
      
T5
      
T6
      
T7
      
T8
      
T9
      
T10
      
T11
    
  
  

    

      
7282
      
29128000
      
10
      
8
      
5
      
1
      
9
      
3
      
7
      
6
      
11
      
4
      
0
      
2
    
    

      
7283
      
29132000
      
10
      
8
      
5
      
1
      
9
      
3
      
7
      
6
      
11
      
4
      
0
      
2
    
    

      
7284
      
29136000
      
10
      
8
      
5
      
1
      
9
      
3
      
7
      
6
      
11
      
4
      
0
      
2
    
    

      
7285
      
29140000
      
10
      
8
      
5
      
1
      
9
      
3
      
7
      
6
      
11
      
4
      
0
      
2
    
    

      
7286
      
29144000
      
10
      
8
      
5
      
1
      
9
      
3
      
7
      
6
      
11
      
4
      
0
      
2
    
  

In [38]:

    

temper2.tail()


    

    
Out[38]:







  

    

      

      
Step
      
Run
      
Temp
    
  
  

    

      
87439
      
29128000
      
T11
      
2
    
    

      
87440
      
29132000
      
T11
      
2
    
    

      
87441
      
29136000
      
T11
      
2
    
    

      
87442
      
29140000
      
T11
      
2
    
    

      
87443
      
29144000
      
T11
      
2
    
  

In [39]:

    

file = "../log.lammps"
location = "/Users/weilu/Research/server/oct_2017/week_oct09_two/more_higher_temp/simulation/dis_80.0/0/"
temper = pd.read_table(location+file, skiprows=2, sep=' ')
temper3 = temper.melt(id_vars=['Step'], value_vars=['T' + str(i) for i in range(n)], value_name="Run", var_name="Temp")


    

In [40]:

    

temper3.tail()


    

    
Out[40]:







  

    

      

      
Step
      
Temp
      
Run
    
  
  

    

      
87439
      
29128000
      
T11
      
2
    
    

      
87440
      
29132000
      
T11
      
2
    
    

      
87441
      
29136000
      
T11
      
2
    
    

      
87442
      
29140000
      
T11
      
2
    
    

      
87443
      
29144000
      
T11
      
2
    
  

In [ ]:

    

for i in range(12):
    fig, axs = plt.subplots(ncols=2, nrows=1, figsize=(10, 4))
    tmp = data.query('Run=={}'.format(i))
    dic = {"T0":350, "T1":400, "T2":450, "T3":500, "T4":550, "T5":600, "T6":650, "T7":700, "T8":750, "T9":800, "T10":900, "T11":1000}
    tmp = tmp.assign(myT = tmp['Temp'].map(dic))
    tmp.plot('Step', 'myT', subplots=True, ax=axs[1])
    tmp.plot('Step', 'Qw', subplots=True, ax=axs[0])


    

In [26]:

    

for i in range(12):
    fig, axs = plt.subplots(ncols=2, nrows=1, figsize=(10, 4))
    tmp = data.query('Run=={}'.format(i))
    dic = {"T0":350, "T1":400, "T2":450, "T3":500, "T4":550, "T5":600, "T6":650, "T7":700, "T8":750, "T9":800, "T10":900, "T11":1000}
    tmp = tmp.assign(myT = tmp['Temp'].map(dic))
    tmp.plot('Step', 'myT', subplots=True, ax=axs[1])
    tmp.plot('Step', 'Qw', subplots=True, ax=axs[0])


    

In [10]:

    

for i in range(12):
    tmp = data.query('Run=={}'.format(i))
    dic = {"T0":350, "T1":400, "T2":450, "T3":500, "T4":550, "T5":600, "T6":650, "T7":700, "T8":750, "T9":800, "T10":900, "T11":1000}
    tmp = tmp.assign(myT = tmp['Temp'].map(dic))
    tmp.plot('Step', 'myT')


    

In [32]:

    

for i in range(12):
    fig, axs = plt.subplots(ncols=2, nrows=1, figsize=(10, 4))
    tmp = data.query('Run=={}'.format(i))
    dic = {"T0":350, "T1":400, "T2":450, "T3":500, "T4":550, "T5":600, "T6":650, "T7":700, "T8":750, "T9":800, "T10":900, "T11":1000}
    tmp = tmp.assign(myT = tmp['Temp'].map(dic))
    tmp.plot('Step', 'myT', subplots=True, ax=axs[1])
    tmp.plot('Step', 'Qw', subplots=True, ax=axs[0])


    

In [41]:

    

list(dic.keys())


    

    
Out[41]:





['T0', 'T1', 'T2', 'T3', 'T4', 'T5', 'T6', 'T7', 'T8', 'T9', 'T10', 'T11']

In [43]:

    

for i in list(dic.keys()):
    fig, axs = plt.subplots(ncols=2, nrows=1, figsize=(10, 4))
    tmp = data.query('Temp=="{}"'.format(i))
#     dic = {"T0":350, "T1":400, "T2":450, "T3":500, "T4":550, "T5":600, "T6":650, "T7":700, "T8":750, "T9":800, "T10":900, "T11":1000}
#     tmp = tmp.assign(myT = tmp['Temp'].map(dic))
    tmp.plot('Step', 'Run', subplots=True, ax=axs[1])
    tmp.plot('Step', 'Qw', subplots=True, ax=axs[0])


    

In [11]:

    

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


    

    
Out[11]:





<matplotlib.axes._subplots.AxesSubplot at 0x122204198>






    













    













    

In [55]:

    

test = {k_list=[1],
                  force_ramp_rate_list=[1],
                  memb_k_list=[1],
                  force_list=["ramp"],
                  rg_list=[0.08],
                  pressure_list=[0.1],
                  repeat=1,
                  mode_list=[2],
                  commons=0,
                  temperature_list=[300],
                  start_from_list=["native"],
                  simulation_model_list=["go"]}


    

    




  File "<ipython-input-55-1875d7e4a094>", line 1
    mylist = [k_list=[1],
                    ^
SyntaxError: invalid syntax

In [60]:

    

test = {"k_list":[1], "force":[1]}


    

In [63]:

    

for key, value in test.items():
    print(key, value)


    

    




k_list [1]
force [1]

In [69]:

    

list(range(10))


    

    
Out[69]:





[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

In [74]:

    

[list(range(10))]


    

    
Out[74]:





[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]]

In [ ]: