Editing Code Guide (section)

===Extract data from database===
Extracting data from the database is simple. For example this
code will retrieve all the demand data for the SA1 region:


<code>
1 import h5py

2

3 with h5py.File(’STORPROJ_Database.h5’, ’r’) as f:

4 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;demand_data = f[’Regions’][’SA1’][’Demand’][...,0]
</code>


Extracting data over a specified time frame:


<code>
1 import h5py

2 from AUSP_DatetimeToSample import DatetimeToSample as d2s

3

4 start_date = d2s (’2016/09/01 00:00:00’)

5 end_date = d2s (’2017/09/01 00:00:00’)

6

7 with h5py.File(’STORPROJ_Database.h5’, ’r’) as f:

8 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;demand_data = f[’Regions’][’SA1’][’Demand’][start_date:end_date][...,0]
</code>


Extracting and summing several known datasets.
The following code will extract all the data from the generators
HDWF1 and HDWF2 and sum across each sample resulting in an
array of summed generator outputs.


<code>
1 import h5py

2 import numpy as np

3

4 with h5py.File(’STORPROJ_Database.h5’, ’r’) as f:

5 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;data = np.nansum(zip(f[’DUIDs’][’HDWF1’][...,0],f[’DUIDs’][’HDWF2’][...,0]),1)
</code>


Finally, extracting all data from a range of datasets that have an
attribute that is the same. This example retrieves all generators
that are in the SA1 region with a primary fuel source of "wind".
It also skips past any generators that have no attributes.


<code>
1 import h5py

2 import numpy as np

3

4 with h5py.File(’STORPROJ_Database.h5’,’r’) as f:

5 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;for generator in f[’DUIDs’]:

6 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;if f[’DUIDs’][generator].attrs.keys( ) != [ ]:

7 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;if f[’DUIDs’][generator].attrs[’Fuel Source - Primary’ ] == "Wind":

8 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;if f[’DUIDs’][generator].attrs[’Region’] == "SA1":

9 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;sa_wind = [np.nansum(x) for x in zip(sa_wind,f[’DUIDs’][generator][...,0])]
</code>