In [30]:

    

import h5py
import matplotlib.pyplot as plt
import os
import numpy as np


    

In [17]:

    

%matplotlib inline


    

In [8]:

    

data_path = '/Volumes/wjlee_apl_2/ooi_zplsc_all_h5/'
fname = 'CE04OSPS_201708.h5'


    

In [9]:

    

f = h5py.File(os.path.join(data_path,fname),'r')


    

In [11]:

    

f['Sv'].shape


    

    
Out[11]:





(3, 1072, 526829)

In [ ]:

    

idx_jump = 1000
ping_idx = range(0,10000,idx_jump)


    

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In [58]:

    

plt.figure(figsize=(12,6))
plt.imshow(f['Sv'][0,:,range(10000)],aspect='auto',vmin=-90,vmax=-30)


    

    
Out[58]:





<matplotlib.image.AxesImage at 0x11e39ffd0>






    

In [37]:

    

plt.plot(f['data_times'][idx])


    

    
Out[37]:





[<matplotlib.lines.Line2D at 0x112afa110>]






    

In [25]:

    

from matplotlib.dates import date2num, num2date


    

In [36]:

    

num2date(f['data_times'][0:20])


    

    
Out[36]:





[datetime.datetime(2017, 8, 10, 20, 52, 55, 497504, tzinfo=<matplotlib.dates._UTC object at 0x10ae4c190>),
 datetime.datetime(2017, 8, 10, 20, 52, 57, 507511, tzinfo=<matplotlib.dates._UTC object at 0x10ae4c190>),
 datetime.datetime(2017, 8, 10, 20, 52, 58, 510503, tzinfo=<matplotlib.dates._UTC object at 0x10ae4c190>),
 datetime.datetime(2017, 8, 10, 20, 52, 59, 513505, tzinfo=<matplotlib.dates._UTC object at 0x10ae4c190>),
 datetime.datetime(2017, 8, 10, 20, 53, 0, 516507, tzinfo=<matplotlib.dates._UTC object at 0x10ae4c190>),
 datetime.datetime(2017, 8, 10, 20, 53, 1, 520505, tzinfo=<matplotlib.dates._UTC object at 0x10ae4c190>),
 datetime.datetime(2017, 8, 10, 20, 53, 2, 524503, tzinfo=<matplotlib.dates._UTC object at 0x10ae4c190>),
 datetime.datetime(2017, 8, 10, 20, 53, 3, 526509, tzinfo=<matplotlib.dates._UTC object at 0x10ae4c190>),
 datetime.datetime(2017, 8, 10, 20, 53, 4, 529511, tzinfo=<matplotlib.dates._UTC object at 0x10ae4c190>),
 datetime.datetime(2017, 8, 10, 20, 53, 5, 532503, tzinfo=<matplotlib.dates._UTC object at 0x10ae4c190>),
 datetime.datetime(2017, 8, 10, 20, 53, 6, 536501, tzinfo=<matplotlib.dates._UTC object at 0x10ae4c190>),
 datetime.datetime(2017, 8, 10, 20, 53, 7, 538507, tzinfo=<matplotlib.dates._UTC object at 0x10ae4c190>),
 datetime.datetime(2017, 8, 10, 20, 53, 8, 542505, tzinfo=<matplotlib.dates._UTC object at 0x10ae4c190>),
 datetime.datetime(2017, 8, 10, 20, 53, 9, 544511, tzinfo=<matplotlib.dates._UTC object at 0x10ae4c190>),
 datetime.datetime(2017, 8, 10, 20, 53, 10, 547503, tzinfo=<matplotlib.dates._UTC object at 0x10ae4c190>),
 datetime.datetime(2017, 8, 10, 20, 53, 11, 551511, tzinfo=<matplotlib.dates._UTC object at 0x10ae4c190>),
 datetime.datetime(2017, 8, 10, 20, 53, 12, 553507, tzinfo=<matplotlib.dates._UTC object at 0x10ae4c190>),
 datetime.datetime(2017, 8, 10, 20, 53, 13, 556509, tzinfo=<matplotlib.dates._UTC object at 0x10ae4c190>),
 datetime.datetime(2017, 8, 10, 20, 53, 14, 560507, tzinfo=<matplotlib.dates._UTC object at 0x10ae4c190>),
 datetime.datetime(2017, 8, 10, 20, 53, 15, 563509, tzinfo=<matplotlib.dates._UTC object at 0x10ae4c190>)]

In [39]:

    

[num2date(f['data_times'][x])-num2date(f['data_times'][x-1]) for x in range(1,10)]


    

    
Out[39]:





[datetime.timedelta(0, 2, 10007),
 datetime.timedelta(0, 1, 2992),
 datetime.timedelta(0, 1, 3002),
 datetime.timedelta(0, 1, 3002),
 datetime.timedelta(0, 1, 3998),
 datetime.timedelta(0, 1, 3998),
 datetime.timedelta(0, 1, 2006),
 datetime.timedelta(0, 1, 3002),
 datetime.timedelta(0, 1, 2992)]

In [51]:

    

a=num2date(f['data_times'][1])-num2date(f['data_times'][0])
a


    

    
Out[51]:





datetime.timedelta(0, 2, 10007)

In [54]:

    

(a.days,a.seconds,a.microseconds)


    

    
Out[54]:





(0, 2, 10007)

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