.. currentmodule:: pygeode
PyGeode :class:`Var` objects can be grouped together into :class:`Dataset` objects. This is convenient for dealing with, for instance, the contents of a large NetCDF file which defines multiple variables, but datasets can be used to perform a number of bulk operations on all (or some) of the variables they contain, which can also be quite powerful.
As a simple example, let's return to the second dataset in the tutorial module, and select a single timestep:
.. ipython:: In [0]: import pygeode as pyg, numpy as np In [0]: from pygeode.tutorial import t2 In [2]: print(t2(time='1 Sep 2010'))
As you can see this returns a new dataset with the appropriate selection from each variable contained in the dataset. Many operations defined for single variables have equivalent versions that act on whole datasets:
.. ipython::
In [0]: import pygeode as pyg
In [0]: from pygeode.tutorial import t2
In [2]: print(t2.mean('time'))
In [2]: print(t2.transpose('time', 'lon', 'lat', 'pres'))
In [2]: print(t2.extend(0, pyg.NamedAxis(name = 'member', values=np.arange(5))))
If you have a custom operation you need to perform, or perhaps a more complicated set of operations, this can also be done. Write a function that takes as its first argument a variable from the dataset, and returns a new, modified variable, then carry out this operation using :meth:`Dataset.map`. As a simple example, consider the following
.. ipython::
In [2]: def sel(v, lat=0):
...: return v(s_lat=lat).rename(v.name + '_' + v.lat.formatvalue(lat, '%dN'))
In [5]: t_eq = t2.map(sel); print(t_eq)
# You can also pass additional arguments, either by keyword
In [5]: t_5s = t2.map(sel, lat=-5); print(t_5s)
# or as a positional argument
In [5]: t_5n = t2.map(sel, 5); print(t_5n)
In more complicated datasets, this can be very useful for operating only on a subset of the variables (for instance, only those)
One can then combine these datasets
.. ipython:: In [6]: print(t_5s + t_5n) In [7]: print(t_5n.rename_vars(Temp_5N = 'T_5n'))