Save to Ocean Data View file

Load a biofloat DataFrame, apply WOA calibrated gain factor, and save it as an ODV spreadsheet

Use the local cache file for float 5903891 that drifted around ocean station Papa. It's the file that was produced for compare_oxygen_calibrations.ipynb.



In [1]:

    
from biofloat import ArgoData, converters
from os.path import join, expanduser
ad = ArgoData(cache_file=join(expanduser('~'),'6881StnP_5903891.hdf'), verbosity=2)



In [2]:

    
wmo_list = ad.get_cache_file_all_wmo_list()
df = ad.get_float_dataframe(wmo_list)









    



INFO:root:Read all_wmo_list from cache
INFO:root:Using cache_file /home/mccann/6881StnP_5903891.hdf
INFO:root:Checking for updates at http://tds0.ifremer.fr/thredds/catalog/CORIOLIS-ARGO-GDAC-OBSaoml/5903891/profiles/catalog.xml
INFO:requests.packages.urllib3.connectionpool:Starting new HTTP connection (1): tds0.ifremer.fr

Show top 5 records.



In [3]:

    
df.head()









    Out[3]:






  
    
      
      
      
      
      
      
      TEMP_ADJUSTED
      PSAL_ADJUSTED
      DOXY_ADJUSTED
    
    
      wmo
      time
      lon
      lat
      profile
      pressure
      
      
      
    
  
  
    
      5903891
      2015-11-25 07:15:06.019200
      -135.904
      52.172
      196
      8.17
      9.7356
      32.296299
      240.571793
    
    
      11.70
      9.7349
      32.296501
      240.223999
    
    
      17.22
      9.7362
      32.296501
      239.519196
    
    
      22.12
      9.7371
      32.296600
      238.625793
    
    
      27.17
      9.7400
      32.296700
      237.367599

Remove NaNs and apply the gain factor from compare_oxygen_calibrations.ipynb.



In [4]:

    
corr_df = df.dropna().copy()
corr_df['DOXY_ADJUSTED'] *= 1.12
corr_df.head()









    Out[4]:






  
    
      
      
      
      
      
      
      TEMP_ADJUSTED
      PSAL_ADJUSTED
      DOXY_ADJUSTED
    
    
      wmo
      time
      lon
      lat
      profile
      pressure
      
      
      
    
  
  
    
      5903891
      2015-11-25 07:15:06.019200
      -135.904
      52.172
      196
      8.17
      9.7356
      32.296299
      269.440408
    
    
      11.70
      9.7349
      32.296501
      269.050879
    
    
      17.22
      9.7362
      32.296501
      268.261499
    
    
      22.12
      9.7371
      32.296600
      267.260889
    
    
      27.17
      9.7400
      32.296700
      265.851711

Convert to ODV format and save in a .txt file.



In [5]:

    
converters.to_odv(corr_df, '6881StnP_5903891.txt')

Import as an ODV Spreadsheet and use the tool.



In [6]:

    
from IPython.display import Image
Image('../doc/screenshots/Screen_Shot_2015-11-25_at_1.42.00_PM.png')









    Out[6]:

						TEMP_ADJUSTED	PSAL_ADJUSTED	DOXY_ADJUSTED
wmo	time	lon	lat	profile	pressure
5903891	2015-11-25 07:15:06.019200	-135.904	52.172	196	8.17	9.7356	32.296299	240.571793
					11.70	9.7349	32.296501	240.223999
					17.22	9.7362	32.296501	239.519196
					22.12	9.7371	32.296600	238.625793
					27.17	9.7400	32.296700	237.367599