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In [23]:



    


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import datetime
import os
# from small_script.myFunctions import *



%matplotlib inline
%load_ext autoreload
%autoreload 2



    











In [24]:



    


plt.rcParams['figure.figsize'] = [16.18033, 10]
def get_data(pre, pdb_list, simType="all_simulations"):
    # to get last 20 frame of each run
    _all = []
    for p in pdb_list:
        name = p.lower()[:4]
        for i in range(30):
            for ii in range(1):
                location = pre + f"{simType}/{name}/simulation/{i}/{ii}/wham.dat"
                try:
                    tmp = pd.read_csv(location).tail(50).reset_index()
                    tmp.columns = tmp.columns.str.strip()
                    _all.append(tmp.assign(Run=i, Name=name, Rerun=ii))
                except Exception as e: 
                    print(e)
    data = pd.concat(_all)
    data["Run"] = "Run" + data["Run"].astype(str)
    return data



    











In [25]:



    


dataset = {"old":("1R69, 1UTG, 3ICB, 256BA, 4CPV, 1CCR, 2MHR, 1MBA, 2FHA".split(", "), 40),
            "new":("1FC2C, 1ENH, 2GB1, 2CRO, 1CTF, 4ICB".split(", "), 80),
            "test":(['1A2J', '1A3H', '1A5Y', '1A8Q', '1AGY', '1AKZ', '1AUZ', '1B1A', '1B31', '1B8X', '1BCO', '1BN6', '1BOH', '1BOI'], 80)}
# pdb_list, steps = dataset["old"]



    











In [30]:



    


pre = "/Users/weilu/Research/server/march_2019/frag_memory_explore_2/"
simulationType = "top20"
today = datetime.datetime.today().strftime('%m-%d')
# pdb_list, steps = dataset["test"]
pdb_list, steps = dataset["new"]
# pdb_list = "1FC2C, 1ENH, 2GB1, 2CRO, 1CTF, 4ICB".split(", ")
data = get_data(pre, pdb_list, simType=simulationType)
data.reset_index(drop=True).to_csv(f"/Users/weilu/Research/data/optimization/{simulationType}_{today}.csv")
# data.reset_index(drop=True).to_csv(f"/Users/weilu/Research/data/optimization/withoutContact_{today}.csv")
sns.boxplot("Name", "Qw", data=data)



    


















    
Out[30]:








<matplotlib.axes._subplots.AxesSubplot at 0x1a17f67828>










    
























In [28]:



    


pre = "/Users/weilu/Research/server/march_2019/frag_memory_explore_2/"
simulationType = "frag"
today = datetime.datetime.today().strftime('%m-%d')
# pdb_list, steps = dataset["test"]
pdb_list, steps = dataset["new"]
# pdb_list = "1FC2C, 1ENH, 2GB1, 2CRO, 1CTF, 4ICB".split(", ")
data = get_data(pre, pdb_list, simType=simulationType)
data.reset_index(drop=True).to_csv(f"/Users/weilu/Research/data/optimization/{simulationType}_{today}.csv")
# data.reset_index(drop=True).to_csv(f"/Users/weilu/Research/data/optimization/withoutContact_{today}.csv")
sns.boxplot("Name", "Qw", data=data)



    


















    
Out[28]:








<matplotlib.axes._subplots.AxesSubplot at 0x1a177200f0>










    
























In [26]:



    


pre = "/Users/weilu/Research/server/march_2019/frag_memory_explore/"
simulationType = "top3"
today = datetime.datetime.today().strftime('%m-%d')
# pdb_list, steps = dataset["test"]
pdb_list, steps = dataset["new"]
# pdb_list = "1FC2C, 1ENH, 2GB1, 2CRO, 1CTF, 4ICB".split(", ")
data = get_data(pre, pdb_list, simType=simulationType)
data.reset_index(drop=True).to_csv(f"/Users/weilu/Research/data/optimization/{simulationType}_{today}.csv")
# data.reset_index(drop=True).to_csv(f"/Users/weilu/Research/data/optimization/withoutContact_{today}.csv")
sns.boxplot("Name", "Qw", data=data)



    


















    
Out[26]:








<matplotlib.axes._subplots.AxesSubplot at 0x102af0cf8>










    
























In [4]:



    


pre = "/Users/weilu/Research/server/march_2019/frag_memory_explore/"
simulationType = "top3"
today = datetime.datetime.today().strftime('%m-%d')
# pdb_list, steps = dataset["test"]
pdb_list, steps = dataset["new"]
# pdb_list = "1FC2C, 1ENH, 2GB1, 2CRO, 1CTF, 4ICB".split(", ")
data = get_data(pre, pdb_list, simType=simulationType)
data.reset_index(drop=True).to_csv(f"/Users/weilu/Research/data/optimization/{simulationType}_{today}.csv")
# data.reset_index(drop=True).to_csv(f"/Users/weilu/Research/data/optimization/withoutContact_{today}.csv")
sns.boxplot("Name", "Qw", data=data)



    


















    
Out[4]:








<matplotlib.axes._subplots.AxesSubplot at 0x115a24e80>










    
























In [9]:



    


45.655321*20



    


















    
Out[9]:








913.1064200000001

















In [17]:



    


16.8/0.8



    


















    
Out[17]:








21.0

















In [18]:



    


22.4/0.8



    


















    
Out[18]:








27.999999999999996

















In [22]:



    


(28+7)*0.8



    


















    
Out[22]:








28.0

















In [23]:



    


(28+14)*0.8



    


















    
Out[23]:








33.6

















In [16]:



    


263/0.8/54



    


















    
Out[16]:








6.087962962962963

















In [14]:



    


21*0.8*60



    


















    
Out[14]:








1008.0

















In [29]:



    


# data = pd.read_csv("/Users/weilu/Research/data/optimization/iter1_2_03-01.csv", index_col=0)
# data = pd.read_csv("/Users/weilu/Research/data/optimization/iter1_with_bias_98percent_03-04.csv", index_col=0)
# data = pd.read_csv("/Users/weilu/Research/data/optimization/new_iter1_combined_on_B_03-07.csv", index_col=0)
# data = pd.read_csv("/Users/weilu/Research/data/optimization/top3_03-08.csv", index_col=0)

data = pd.read_csv("/Users/weilu/Research/data/optimization/top3_03-09.csv", index_col=0)
data2 = pd.read_csv("/Users/weilu/Research/data/optimization/frag_03-11.csv", index_col=0)
# data3 = pd.read_csv("/Users/weilu/Research/data/optimization/newContact_02-20.csv", index_col=0)
# data4 = pd.read_csv("/Users/weilu/Research/data/optimization/new_iter1_96_03-06.csv", index_col=0)
# data5 = pd.read_csv("/Users/weilu/Research/data/optimization/newContactWithBurial_02-23.csv", index_col=0)
# data6 = pd.read_csv("/Users/weilu/Research/data/optimization/filtered_gamma_iter1_02-13.csv", index_col=0)
# data7 = pd.read_csv("/Users/weilu/Research/data/optimization/iter2_02-15.csv", index_col=0)
# d = pd.concat([data.assign(Scheme="single_memory"), data4.assign(Scheme="No contact")
#               , data2.assign(Scheme="fragMemory"), data3.assign(Scheme="newContact"), 
#                data5.assign(Scheme="iter1")
#               ])
d = pd.concat([
#                 data.assign(Scheme="new_iter1_98"), 
                data.assign(Scheme="top3"), 
                   data2.assign(Scheme="frag"), 
#                  data3.assign(Scheme="newContact"), 
#                 data5.assign(Scheme="newContactWithBurial"),
#                data4.assign(Scheme="new iter1_96"),
#                data.assign(Scheme="old_iter1_98"), 
#                data6.assign(Scheme="filtered_iter1"),
#                data7.assign(Scheme="iter2")
              ])
sns.boxplot("Name", "Qw", hue="Scheme", data=d)



    


















    
Out[29]:








<matplotlib.axes._subplots.AxesSubplot at 0x1a175aba20>










    
























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In [2]:



    


def test_var_args(f_arg, *argv):
    print("first normal arg:", f_arg)
    print(argv)
    for arg in argv:
        print("another arg through *argv:", arg)

test_var_args('yasoob', 'python', 'eggs', 'test')



    


















    






first normal arg: yasoob
('python', 'eggs', 'test')
another arg through *argv: python
another arg through *argv: eggs
another arg through *argv: test

















In [13]:



    


def test_a(a,b, **kwargs):
    print(kwargs)
    return a*b

def test_var_kwargs(farg, **kwargs):
    print("formal arg:", farg)
    print(kwargs)
    print(test_a(**kwargs))
    for key in kwargs:
        print("another keyword arg: %s: %s" % (key, kwargs[key]))

test_var_kwargs(farg=1, myarg2="two", myarg3=3, a=10, b=2)



    


















    






formal arg: 1
{'myarg2': 'two', 'myarg3': 3, 'a': 10, 'b': 2}
{'myarg2': 'two', 'myarg3': 3}
20
another keyword arg: myarg2: two
another keyword arg: myarg3: 3
another keyword arg: a: 10
another keyword arg: b: 2

















In [14]:



    


import numpy as np



    











In [15]:



    


a = np.zeros((2,10))



    











In [16]:



    


b = np.ones((4,10))



    











In [18]:



    


np.concatenate((a,b))



    


















    
Out[18]:








array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]])

















In [21]:



    


np.concatenate([a])



    


















    
Out[21]:








array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])

















In [ ]:

Content source: luwei0917/awsemmd_script

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