Experimenting with GTS data from ERDDAP

In [1]:

    

import urllib2
import pandas as pd


    

In [2]:

    

# dictionary for valid parameter_name 
d={'sea_water_temperature':'ZTMP', 'sea_water_salinity':'ZSAL'}


    

In [3]:

    

# Specify variable to retrieve: 
param=d['sea_water_temperature']

# Get data shallower than this water depth:
depth_max = 10   

# Use ERDDAP's built-in relative time functionality to get last 48 hours:
start='now-24hours'
stop='now'
# or specify a specific period:
#start = '2013-05-06T00:00:00Z'
#stop =  '2013-05-07T00:00:00Z'


    

In [4]:

    

# Construct URL for large PNG:
url='http://osmc.noaa.gov:8180/erddap/tabledap/OSMC_PROFILERS\
.largePng?longitude,latitude,observation_value\
&time>=%s&time<=%s&parameter_name="%s"&observation_depth<=%d&.trim=5&\
.draw=markers&.marker=5|6&.color=0x000000&.colorBar=|||||' % (start,stop,param,depth_max)


    

In [5]:

    

# Read the image
im = imread(urllib2.urlopen(url),format='png')


    

In [6]:

    

# Display the image
figure(figsize=(12,8))
imshow(im)   
axis('off');


    

In [7]:

    

# Construct URL for CSV data:
url='http://osmc.noaa.gov:8180/erddap/tabledap/OSMC_PROFILERS\
.csv?time,longitude,latitude,observation_depth,observation_value\
&time>=%s&time<=%s&parameter_name="%s"&observation_depth<=%d' % (start,stop,param,depth_max)


    

In [8]:

    

# Load the CSV data directly into Pandas
df = pd.read_csv(url,index_col='time',parse_dates=True,skiprows=[1])  # skip the units row


    

In [9]:

    

# List last ten records
df.tail(10)


    

    
Out[9]:






  

    

      

      
longitude
      
latitude
      
observation_depth
      
observation_value
    
    

      
time
      

      

      

      

    
  
  

    

      
2013-05-09 00:34:00
      
  66.963
      
-15.239
      
 4
      
 27.03
    
    

      
2013-05-09 01:18:00
      
 -16.755
      
 46.913
      
 7
      
 12.94
    
    

      
2013-05-09 03:08:00
      
 151.490
      
  9.769
      
 4
      
 29.13
    
    

      
2013-05-09 04:24:00
      
 127.133
      
 10.454
      
 4
      
 29.99
    
    

      
2013-05-09 05:20:00
      
 124.187
      
 14.595
      
 3
      
 29.82
    
    

      
2013-05-09 05:20:00
      
 124.187
      
 14.595
      
 4
      
 29.82
    
    

      
2013-05-09 02:58:00
      
 153.961
      
-21.539
      
 8
      
 25.95
    
    

      
2013-05-09 01:56:00
      
  53.990
      
-15.413
      
 7
      
 26.64
    
    

      
2013-05-09 04:07:00
      
 110.261
      
-39.374
      
 4
      
 15.49
    
    

      
2013-05-09 05:20:00
      
 124.187
      
 14.595
      
 6
      
 29.85
    
  

In [11]:

    

df['longitude']


    

    
Out[11]:





time
2013-05-08 17:32:00    177.147
2013-05-08 20:22:00    162.065
2013-05-08 20:22:00    162.065
2013-05-08 12:38:00      6.598
2013-05-08 12:38:00      6.598
2013-05-08 22:02:00    127.302
2013-05-08 17:32:00    177.147
2013-05-08 17:32:00    177.147
2013-05-08 23:29:00    -21.663
2013-05-08 23:29:00    -21.663
2013-05-08 19:29:00   -170.083
2013-05-08 15:21:00    154.816
2013-05-08 22:47:00    139.343
2013-05-08 18:42:00   -139.713
2013-05-08 13:37:00   -127.738
...
2013-05-09 00:01:00      2.633
2013-05-09 00:01:00      2.633
2013-05-09 02:34:00   -159.244
2013-05-09 08:50:00   -178.964
2013-05-09 02:34:00   -159.244
2013-05-09 00:34:00     66.963
2013-05-09 01:18:00    -16.755
2013-05-09 03:08:00    151.490
2013-05-09 04:24:00    127.133
2013-05-09 05:20:00    124.187
2013-05-09 05:20:00    124.187
2013-05-09 02:58:00    153.961
2013-05-09 01:56:00     53.990
2013-05-09 04:07:00    110.261
2013-05-09 05:20:00    124.187
Name: longitude, Length: 435, dtype: float64

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