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import matplotlib as mpl
import matplotlib.pyplot as plt
from scipy.interpolate import griddata
from scipy.interpolate import interp2d
import sys
import argparse
import os
import numpy as np
import pandas as pd
from numpy.random import uniform
import glob
%matplotlib inline
import re
# %matplotlib notebook
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location = "/Users/weilu/Research/server/oct_2017/30oct/strengthen_helix_1_baseline_without_strengthen/"
folder_list = glob.glob(pathname=location + "*_")
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location = folder_list[0] + "/simulation/0/0/"
i = 0
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def read(location):
file = "lipid.dat"
lipid = pd.read_csv(location+file)
lipid.columns = lipid.columns.str.strip()
file = "energy.dat"
energy = pd.read_csv(location+file)
energy.columns = energy.columns.str.strip()
file = "addforce.dat"
dis = pd.read_csv(location+file)
dis.columns = dis.columns.str.strip()
# remove_columns = ['AddedForce', 'Dis12', 'Dis34', 'Dis56']
file = "rgs.dat"
rgs = pd.read_csv(location+file)
rgs.columns = rgs.columns.str.strip()
file = "wham.dat"
wham = pd.read_csv(location+file)
wham.columns = wham.columns.str.strip()
remove_columns = ['Rg', 'Tc']
wham = wham.drop(remove_columns, axis=1)
data = wham.merge(rgs, how='inner', left_on=["Steps"], right_on=["Steps"]).\
merge(dis, how='inner', left_on=["Steps"], right_on=["Steps"]).\
merge(energy, how='inner', left_on=["Steps"], right_on=["Steps"]).\
merge(lipid, how='inner', left_on=["Steps"], right_on=["Steps"])
data = data.assign(TotalE = data.Energy + data.Lipid)
return data
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location = "/Users/weilu/Research/server/jan_2018/week_of_jan29/pulling/pressure_0.6_/simulation/0/0/"
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location = "/Users/weilu/Research/server/jan_2018/week_of_jan29/pulling/pressure_0.6_"
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glob.glob(location+"/simulation/*")
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location = "/Users/weilu/Research/server/jan_2018/week_of_jan29/pulling"
test = "pre"
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os.path.join(location, test)
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glob.glob("/Users/weilu/Research/server/jan_2018/week_of_jan29/pulling/*_")
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read(location)
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pre = "/Users/weilu/Research/server/nov_2017/20nov/force_ramp/"
glob.glob(pre+"*_")
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test = "/Users/weilu/Research/server/nov_2017/20nov/force_ramp/rg_0.4_memb_k_1_"
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test.split("/")[-1].split("_")
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print(re.findall(r'\d+', "11"))
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os.path.join("/Users/weilu/Research/server/nov_2017/20nov/force_ramp/rg_0.4_memb_k_2_", "test")
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os.listdir(path)
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pre
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location = "/Users/weilu/Research/server/nov_2017/20nov/force_ramp/rg_0.0_memb_k_1_/simulation"
glob.glob(location + "[0-9]")
run_list = [f for f in os.listdir(location) if re.search(r'^\d+$', f)]
for f in res:
print(f)
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def read_data(pre):
folder_list = glob.glob(pre+"*_")
all_data_list = []
for folder in folder_list:
print(folder)
location = os.path.join(folder, "simulation")
run_list = [f for f in os.listdir(location) if re.search(r'^\d+$', f)]
for i in run_list:
data = read(folder + "/simulation/{}/0/".format(i))
tmp = folder.split("/")[-1]
_,rg,_,_,memb,_ = tmp.split("_")
data = data.assign(Run = i, folder=tmp, rg=rg, memb=memb)
all_data_list.append(data)
data = pd.concat(all_data_list)
data.reset_index(drop=True).to_feather("/Users/weilu/Research/data/pulling/nov23.feather")
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def read_data(pre):
folder_list = glob.glob(pre+"*_")
all_data_list = []
for folder in folder_list:
print(folder)
location = os.path.join(folder, "simulation")
run_list = [f for f in os.listdir(location) if re.search(r'^\d+$', f)]
for i in run_list:
data = read(folder + "/simulation/{}/0/".format(i))
tmp = folder.split("/")[-1]
_,rg,_,memb,_ = tmp.split("_")
data = data.assign(Run = i, folder=tmp, rg=rg, memb=memb)
all_data_list.append(data)
data = pd.concat(all_data_list)
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pre = "/Users/weilu/Research/server/nov_2017/20nov/slower_ramp/"
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read_data(pre)
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data.columns
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location = "/Users/weilu/Research/server/nov_2017/20nov/force_ramp/rg_0.0_memb_k_0_/simulation/0/0/"
data = read(location)
data
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location = "/Users/weilu/Research/server/nov_2017/06nov/my_configue/study/recompute_offset_0/"
data = read(location)
data.reset_index().to_feather("/Users/weilu/Research/data/pulling/nov08_2.feather")
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all_data_list = []
location_list = ["next_gen_lipid_distance"]
pre = "/Users/weilu/Research/server/nov_2017/06nov/"
for location in location_list:
folder_list = glob.glob(pathname=pre + location + "/*_")
for folder in folder_list:
print(folder)
for i in range(5):
data = read(folder + "/simulation/{}/0/".format(i))
tmp = folder.split("/")[-1]
# _,temp,_,memb,_,rg, _ = tmp.split("_")
data = data.assign(Run = i, folder=tmp)
all_data_list.append(data)
data = pd.concat(all_data_list)
data.reset_index().to_feather("/Users/weilu/Research/data/pulling/nov10_lipid_distance.feather")
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all_data_list = []
location_list = ["next_gen_lipid_distance"]
pre = "/Users/weilu/Research/server/nov_2017/06nov/"
for location in location_list:
folder_list = glob.glob(pathname=pre + location + "/rg_*_")
for folder in folder_list:
print(folder)
for i in range(5):
data = read(folder + "/simulation/{}/0/".format(i))
tmp = folder.split("/")[-1]
# _,temp,_,memb,_,rg, _ = tmp.split("_")
data = data.assign(Run = i, folder=tmp)
all_data_list.append(data)
data = pd.concat(all_data_list)
data.reset_index().to_feather("/Users/weilu/Research/data/pulling/nov10_lipid_distance_rg.feather")
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all_data_list = []
location_list = ["next_gen_lipid_distance"]
pre = "/Users/weilu/Research/server/nov_2017/06nov/"
for location in location_list:
folder_list = glob.glob(pathname=pre + location + "/pressure_*_")
for folder in folder_list:
print(folder)
for i in range(5):
data = read(folder + "/simulation/{}/0/".format(i))
tmp = folder.split("/")[-1]
pressure = tmp.split("_")[1]
rg = tmp.split("_")[3]
# _,temp,_,memb,_,rg, _ = tmp.split("_")
data = data.assign(Run = i, folder=tmp, pressure=pressure, rgsize=rg)
all_data_list.append(data)
data = pd.concat(all_data_list)
data.reset_index().to_feather("/Users/weilu/Research/data/pulling/nov10_lipid_distance_pressure.feather")
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all_data_list = []
location_list = ["next_gen_lipid_distance"]
pre = "/Users/weilu/Research/server/nov_2017/06nov/"
for location in location_list:
folder_list = glob.glob(pathname=pre + location + "/tes*")
for folder in folder_list:
print(folder)
for i in range(2):
data = read(folder + "/recompute_offset_{}/".format(i))
# _,temp,_,memb,_,rg, _ = tmp.split("_")
data = data.assign(Run = i, folder=tmp)
all_data_list.append(data)
data = pd.concat(all_data_list)
data.reset_index().to_feather("/Users/weilu/Research/data/pulling/nov11_lipid_distance.feather")
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all_data_list = []
location_list = ["strengthen_helix_1", "strengthen_helix_1_baseline_without_strengthen"]
pre = "/Users/weilu/Research/server/oct_2017/30oct/"
for location in location_list:
folder_list = glob.glob(pathname=pre + location + "/*_")
for folder in folder_list:
print(folder)
for i in range(10):
data = read(folder + "/simulation/{}/0/".format(i), i)
tmp = folder.split("/")[-1]
_,temp,_,memb,_,rg, _ = tmp.split("_")
data = data.assign(Run = i, temp = temp, memb = memb, rg = rg, Location=location)
all_data_list.append(data)
data = pd.concat(all_data_list)
data.reset_index().to_feather("/Users/weilu/Research/data/pulling/nov01_strengthen")
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all_data_list = []
location_list = ["strengthen_helix_1", "strengthen_helix_1_baseline_without_strengthen"]
pre = "/Users/weilu/Research/server/oct_2017/30oct/"
location = pre + "strengthen_helix_1_baseline_without_strengthen/temp_350_memb_2_rg_0.1_/simulation/0"
for i in range(-10, 15, 1):
myLocation = location + "/recompute_offset_{}/".format(i)
file = "lipid.dat"
lipid = pd.read_csv(myLocation+file)
lipid.columns = lipid.columns.str.strip()
lipid = lipid.assign(Run = i)
all_data_list.append(lipid)
data = pd.concat(all_data_list).reset_index()
tmp = data.query('Steps < 1e6')
results = tmp.filter(items=["Steps", "Run"] +["Lipid"+str(i) for i in range(1,16)]).groupby("Run").mean()
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results
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record = []
labels = []
for label, group in results.groupby('Run'):
helix1 = 0
for i in range(1,6):
helix1 += group["Lipid" +str(i)]
helix6 = 0
ii = 0
for i in range(5,0,-1):
ii = ii + i
# print(ii)
helix6 += group["Lipid" +str(ii)]
print(float(helix1 - helix6))
record.append(helix1 - helix6)
labels.append(label)
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record = [float(i) for i in record]
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plt.plot(labels, record)
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helix1 = 0
for i in range(1,6):
helix1 += results["Lipid" +str(i)]
helix6 = 0
ii = 0
for i in range(5,0,-1):
ii = ii + i
print(ii)
helix6 += results["Lipid" +str(ii)]
helix1 - helix6
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all_data_list = []
location_list = ["strengthen_helix_1", "strengthen_helix_1_baseline_without_strengthen"]
pre = "/Users/weilu/Research/server/oct_2017/30oct/"
for location in location_list:
folder_list = glob.glob(pathname=pre + location + "/*_")
for folder in folder_list:
print(folder)
for i in range(10):
myLocation = folder + "/simulation/{}/recompute_offset_0/".format(i)
file = "lipid.dat"
lipid = pd.read_csv(myLocation+file)
lipid.columns = lipid.columns.str.strip()
tmp = folder.split("/")[-1]
_,temp,_,memb,_,rg, _ = tmp.split("_")
lipid = lipid.assign(Run = i, temp = temp, memb = memb, rg = rg, Location=location)
all_data_list.append(lipid)
data = pd.concat(all_data_list).reset_index()
tmp = data.query('Location=="strengthen_helix_1_baseline_without_strengthen"').query('Steps < 1e6')
results = tmp.filter(items=["Steps"] +["Lipid"+str(i) for i in range(1,16)]).mean()
helix1 = 0
for i in range(1,6):
helix1 += results["Lipid" +str(i)]
helix6 = 0
ii = 0
for i in range(5,0,-1):
ii = ii + i
print(ii)
helix6 += results["Lipid" +str(ii)]
helix1 - helix6
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tmp = data.query('Location=="strengthen_helix_1_baseline_without_strengthen"').query('Steps < 1e6').query('temp=="350"')
tmp =tmp.query('rg=="0.1"').query('memb=="2"')
results = tmp.filter(items=["Steps"] +["Lipid"+str(i) for i in range(1,16)]).mean()
helix1 = 0
for i in range(1,6):
helix1 += results["Lipid" +str(i)]
helix6 = 0
ii = 0
for i in range(5,0,-1):
ii = ii + i
print(ii)
helix6 += results["Lipid" +str(ii)]
helix1 - helix6
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# offset -5
helix1 - helix6
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# offset -2
helix1 - helix6
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# offset -1
helix1 - helix6
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# offset 0
helix1 - helix6
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