Convert CNR Wave Data to NetCDF DSG (CF-1.6)

From Davide Bonaldo at CNR-ISMAR : here's a time series of wave data from Jesolo.

Columns 1 to 6: date (y m d h m s)
Column 7: Significant wave height (m)
Column 8: Mean period (s)
Column 9: Mean direction (deg)
Column 10: Sea surface elevation (m)



In [ ]:

    
import numpy as np
import urllib
%matplotlib inline



In [17]:

    
url='https://www.dropbox.com/s/0epy3vsjgl1h8ld/ONDE_Jesolo.txt?dl=1'
local_file = '/usgs/data2/notebook/data/ONDE_Jesolo.txt'



In [18]:

    
urllib.urlretrieve (url,local_file)









    Out[18]:





('/usgs/data2/notebook/data/ONDE_Jesolo.txt',
 <httplib.HTTPMessage instance at 0x7f602ce65998>)



In [19]:

    
from datetime import datetime

import pandas as pd

def date_parser(year, month, day, hour, minute, second):
    var = year, month, day, hour, minute, second
    var = [int(float(x)) for x in var]
    return datetime(*var)

df = pd.read_csv(local_file, header=None,
                 delim_whitespace=True, index_col='datetime',
                 parse_dates={'datetime': [0, 1, 2, 3, 4, 5]},
                 date_parser=date_parser)



In [20]:

    
df.columns=['Hsig','Twave','Dwave','Wlevel']



In [21]:

    
df[['Hsig','Wlevel']].plot()









    Out[21]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f602cc0e650>



In [41]:

    
import calendar
times     = [ calendar.timegm(x.timetuple()) for x in df.index ]
times=np.asarray(times, dtype=np.int64)



In [49]:

    
def pd_to_secs(df):
    import calendar
    """
    convert a pandas datetime index to seconds since 1970
    """
    return np.asarray([ calendar.timegm(x.timetuple()) for x in df.index ], dtype=np.int64)



In [50]:

    
secs = pd_to_secs(df)



In [51]:

    
# z is positive down, will generate ADCP if all z is not the same, simple time series otherwise
z = np.zeros_like(secs)



In [52]:

    
values = df['Hsig'].values



In [53]:

    
from pytools.netcdf.sensors import create,ncml,merge,crawl



In [54]:

    
sensor_urn='urn:it.cnr.ismar.ve:sensor:wave_height'
station_urn='urn:it.cnr.ismar.ve:station:onda'



In [55]:

    
attributes={'units':'m'}



In [56]:

    
create.create_timeseries_file(output_directory='/usgs/data2/notebook/data',
                              latitude=41.5,
                              longitude=-69.1,
                              full_station_urn=station_urn,
                              full_sensor_urn=sensor_urn,
                              sensor_vertical_datum=0.0,
                              times=secs,
                              verticals=z, 
                              values=values,
                              attributes=attributes,
                              global_attributes={},
                              output_filename='wave_data.nc')



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