Django-Geo-SPaaS - GeoDjango framework for Satellite Data Management

First of all we need to initialize Django to work. Let's do some 'magic'



In [1]:

    
import os, sys
os.environ['DJANGO_SETTINGS_MODULE'] = 'geospaas_project.settings'
sys.path.insert(0, '/vagrant/shared/course_vm/geospaas_project/')

import django
django.setup()

from django.conf import settings

Now we can import our models



In [2]:

    
from geospaas.catalog.models import Dataset
from geospaas.catalog.models import DatasetURI

Now we can use the model Dataset to search for datasets



In [3]:

    
# find all images
datasets = Dataset.objects.all()

What is happening:

A SQL query is generated
The query is sent to the database (local database driven by SpatiaLite)
The query is executed by the database engine
The result is sent back to Python
The results is wrapped into a QuerySet object



In [4]:

    
print datasets.count()

# print info about each image
for ds in datasets:
    print ds









    



5
ENVISAT/MERIS/2012-03-03T09:38:10.423969+00:00
ENVISAT/ASAR/2008-11-10T20:56:23.207430+00:00
AQUA/MODIS/2015-05-01T11:36:49+00:00
AQUA/MODIS/2015-05-02T12:20:09+00:00
SENTINEL-1A/SAR/2015-07-02T17:29:54.066281+00:00

Use the complex structure of the catalog models



In [12]:

    
# get just one Dataset
ds0 = datasets.first()
print ds0.time_coverage_start









    



2012-03-03 09:38:10.423969+00:00






    Out[12]:





datetime.datetime(2008, 11, 10, 20, 56, 23, 207430, tzinfo=<UTC>)



In [6]:

    
# print joined fields (Foreign key)
for ds in datasets:
    print ds.source.instrument.short_name,
    print ds.source.platform.short_name









    



MERIS ENVISAT
ASAR ENVISAT
MODIS AQUA
MODIS AQUA
SAR SENTINEL-1A



In [7]:

    
# get infromation from Foreign key in the opposite direction
print ds0.dataseturi_set.first().uri









    



file://localhost/vagrant/shared/test_data/meris_l1/MER_FRS_1PNPDK20120303_093810_000000333112_00180_52349_3561.N1

Search for data



In [15]:

    
# search by time
ds = Dataset.objects.filter(time_coverage_start='2012-03-03 09:38:10.423969')
print ds

ds = Dataset.objects.filter(time_coverage_start__gte='1900-03-01')
print ds









    



[<Dataset: ENVISAT/MERIS/2012-03-03T09:38:10.423969+00:00>]
[<Dataset: ENVISAT/MERIS/2012-03-03T09:38:10.423969+00:00>, <Dataset: ENVISAT/ASAR/2008-11-10T20:56:23.207430+00:00>, <Dataset: AQUA/MODIS/2015-05-01T11:36:49+00:00>, <Dataset: AQUA/MODIS/2015-05-02T12:20:09+00:00>, <Dataset: SENTINEL-1A/SAR/2015-07-02T17:29:54.066281+00:00>]



In [16]:

    
# search by instrument
ds = Dataset.objects.filter(source__instrument__short_name='MODIS')
print ds









    



[<Dataset: AQUA/MODIS/2015-05-01T11:36:49+00:00>, <Dataset: AQUA/MODIS/2015-05-02T12:20:09+00:00>]



In [25]:

    
# search by spatial location
ds0 = Dataset.objects.first()
ds0_geom = ds0.geographic_location.geometry

ds_ovlp = Dataset.objects.filter(
    geographic_location__geometry__intersects=ds0_geom,
    time_coverage_start__gte='2015-05-02',
    source__platform__short_name='AQUA')
print ds_ovlp









    



[<Dataset: AQUA/MODIS/2015-05-02T12:20:09+00:00>]

Finally, get data



In [26]:

    
dsovlp0 = ds_ovlp.first()



In [31]:

    
uri0 = dsovlp0.dataseturi_set.first().uri
print uri0









    



file://localhost/vagrant/shared/test_data/obpg_l2/A2015122122000.L2_LAC.NorthNorwegianSeas.hdf



In [33]:

    
from nansat import Nansat
n = Nansat(uri0.replace('file://localhost', ''))
print n[1]









    



[[ 22.20515633  22.15754128  22.10977554 ...,  -2.4479444   -2.48379612
   -2.51980472]
 [ 22.20428276  22.15644073  22.10901642 ...,  -2.45618773  -2.49206471
   -2.52809858]
 [ 22.2034626   22.15546989  22.10785866 ...,  -2.46444297  -2.50034523
   -2.53640437]
 ..., 
 [ 19.98286629  19.89513779  19.80788994 ..., -20.16187286 -20.20990181
  -20.25743484]
 [ 19.98246765  19.8946743   19.8073597  ..., -20.18548775 -20.23342133
  -20.28373528]
 [ 19.98209     19.89422989  19.80684853 ..., -20.21025085 -20.25760651
  -20.30633354]]



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