In [3]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import glob
import jedi
%matplotlib inline
In [13]:
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_energy_list = []
for i in range(n):
file = "energy.{}.dat".format(i)
energy = pd.read_csv(location+file).assign(Run = i)
energy.columns = energy.columns.str.strip()
energy = energy[["Steps", "AMH-Go", "Membrane", "Rg", "Run"]]
all_energy_list.append(energy)
energy = pd.concat(all_energy_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"
file = "../log0/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)
t5 = t4.merge(energy, how='inner', left_on=["Step", "Run"], right_on=["Steps", "Run"]
).sort_values('Step').drop('Steps', axis=1)
t5 = t5.assign(TotalE = t5.Energy + t5.Lipid)
t6 = t5.assign(TotalE_perturb_mem_p = t5.TotalE + 0.05*t5.Membrane)
t6 = t6.assign(TotalE_perturb_mem_m = t6.TotalE - 0.05*t6.Membrane)
t6 = t6.assign(TotalE_perturb_lipid_p = t6.TotalE + 0.05*t6.Lipid)
t6 = t6.assign(TotalE_perturb_lipid_m = t6.TotalE - 0.05*t6.Lipid)
return t6
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# folder_list = [
# '/Users/weilu/Research/server/oct_2017/23oct/rgWidth_memb_3_rg_0.1_lipid_1_topology/',
# '/Users/weilu/Research/server/oct_2017/23oct/rgWidth_memb_3_rg_0.1_lipid_1_extended/'
# ]
pre = "/Users/weilu/Research/server/oct_2017/23oct/"
folder_list = [
'memb_3_rg_0.1_lipid_1_extended',
'memb_3_rg_0.1_lipid_1_topology'
]
dis_list = np.linspace(30, 130, 51)
# dis_list = np.linspace(30, 230, 101)
# dis_list = np.linspace(132, 232, 51)
# dis_list = [30.0]
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:
print(dis)
location = pre + 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'.format(temp))
tmp.to_csv(location+"t{}_new.dat".format(dic[temp]), sep=' ', index=False, header=False)
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