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.. _sphx_glr_auto_examples_grp1_RME_gettingstarted_plot_e04_common_grid_handling.py:


Common Grid Handling
====================

A common problem is when multiple inputs into a workflow
share a common dimension like frequency, but those frequency grids aren't
aligned properly. This often results in having to manually
trim down arrays based on a known frequency list.

The RME offers an automatic common grid handling feature to do that for you.
It acts on any RMEMeas input into a propagating function.

.. GENERATED FROM PYTHON SOURCE LINES 15-19

Creating an Objects
-------------------
First lets create 2 RMEMeas objects with slightly different
coordinates on dimension 'd1'.

.. GENERATED FROM PYTHON SOURCE LINES 19-52

.. code-block:: Python


    from rmellipse.uobjects import RMEMeas
    from rmellipse.propagators import RMEProp
    import xarray as xr
    import numpy as np


    def make_measurement(d1_coords):
        """Make a sample measurement with length 4 coordinate set."""
        nom = xr.DataArray(
            np.zeros((4, 2)),
            dims=('d1', 'd2'),
            coords={'d1': d1_coords, 'd2': np.arange(2)},
        )

        meas = RMEMeas.from_nom(name='meas', nom=nom)

        meas.add_umech(
            name='mymechanisms',
            value=meas.nom + np.ones(meas.nom.shape) * 0.01,
            dof=np.inf,
            category={'Type': 'B', 'Origin': 'Data Sheet'},
            add_uid=True,
        )

        for i in range(100):
            meas.add_mc_sample(meas.nom + np.random.rand(*meas.nom.shape) * 0.01)
        return meas


    m1 = make_measurement([0, 1, 2, 3])
    m2 = make_measurement([0, 1.1, 2, 2.9])


.. GENERATED FROM PYTHON SOURCE LINES 53-68

Setting up the Propagator
-------------------------

Next, let's create a propagator. We are going to define the common grid
as 'd1' and tell it to interpolate that grid to a set of common values. We
tell it what those common values are with the common_coords argument. The
verbose argument will print some information about the propagation as we go.
See the :func:`rmellipse.propagators.RME.handle_common_grid` function for
what methods are available for the propagator.

When we wrap our add function in the propagator and print the d1 coordinate,
we see that the d1 coordinate of x and y are now the same because the RME
interpolated them down to supplied values. Note how the print statement
happens twice, because a vectorized
propagator calls the function on the`RMEMeas.cov`attribute then on the`RMEMeas.mc` attribute.

.. GENERATED FROM PYTHON SOURCE LINES 68-88

.. code-block:: Python


    myprop = RMEProp(
        sensitivity=True,
        montecarlo_sims=100,
        common_grid='d1',
        handle_common_grid_method='interp_common',
        common_coords={'d1': [0, 0.5, 1.5, 2.5]},
        vectorize=True,
        verbose=True,
    )


    @myprop.propagate
    def add(x, y):
        """Add two data sets."""
        print(x.d1.values, y.d1.values)
        return x + y


    m3 = add(m1, m2)


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