In [1]:

    

import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime
import glob


    

In [2]:

    

%matplotlib inline


    

In [3]:

    

pd.set_option("display.max_rows", 15)


    

In [4]:

    

files = glob.glob('/Users/schriste/Dropbox/heroes_flares_lc_*.csv')


    

In [5]:

    

files


    

    
Out[5]:





['/Users/schriste/Dropbox/heroes_flares_lc_20130921_170000.csv',
 '/Users/schriste/Dropbox/heroes_flares_lc_20130921_190000.csv',
 '/Users/schriste/Dropbox/heroes_flares_lc_20130921_201000.csv']

In [51]:

    

time_ranges = ((datetime(2013,9,21,17,15,0), datetime(2013,9,21,18,0,0)),
               (datetime(2013,9,21,19,15,0), datetime(2013,9,21,21,30,0)),
               (datetime(2013,9,21,20,20,0), datetime(2013,9,21,21,10,0)))


    

In [52]:

    

data = pd.read_csv(files[0], parse_dates=True, header=0, index_col=0)


    

In [53]:

    

data = data.truncate(before=time_ranges[0][0], after = time_ranges[0][1])


    

In [61]:

    

plt.figure()
data.plot()
plt.title('Original data')
plt.show()


    

    






<matplotlib.figure.Figure at 0x1074f7550>






    

In [60]:

    

resample_rate = '16S'
for file, time_range in zip(files, time_ranges):
    data = pd.read_csv(file, parse_dates=True, header=0, index_col=0)
    data = data.truncate(before=time_range[0], after=time_range[1])
    plt.figure()
    max = 0
    for col in data.columns:
        data[col].resample(resample_rate, how='sum').plot()
        if max < data[col].resample(resample_rate, how='sum').max():
            max = data[col].resample(resample_rate, how='sum').max()
    plt.vlines(datetime(2013,9,21,17,25,0), 0, max, linestyle = 'dashed', label='start')
    plt.vlines(datetime(2013,9,21,17,45,0), 0, max, linestyle = 'dashed', label='end')
    plt.title(file)
    plt.ylim((-100,max*1.1))
    plt.legend(loc='upper left')
    plt.show()