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Conditioned Fields
==================

Kriged fields tend to approach the field mean outside the area of observations.
To generate random fields, that coincide with given observations, but are still
random according to a given covariance model away from the observations proximity,
we provide the generation of conditioned random fields.

The idea behind conditioned random fields builds up on kriging.
First we generate a field with a kriging method, then we generate a random field,
with 0 as mean and 1 as variance that will be multiplied with the kriging
standard deviation.

To do so, you can instantiate a :any:`CondSRF` class with a configured
:any:`Krige` class.

The setup of the a conditioned random field should be as follows:

.. code-block:: python

    krige = gs.Krige(model, cond_pos, cond_val)
    cond_srf = gs.CondSRF(krige)
    field = cond_srf(grid)

Examples
--------


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    <div class="sphx-glr-thumbcontainer" tooltip="Here we use ordinary kriging in 1D (for plotting reasons) with 5 given observations/conditions, to generate an ensemble of conditioned random fields.">

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  .. image:: /examples/06_conditioned_fields/images/thumb/sphx_glr_00_condition_ensemble_thumb.png
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  :doc:`/examples/06_conditioned_fields/00_condition_ensemble`

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      <div class="sphx-glr-thumbnail-title">Conditioning with Ordinary Kriging</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Let&#x27;s create an ensemble of conditioned random fields in 2D.">

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  :doc:`/examples/06_conditioned_fields/01_2D_condition_ensemble`

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      <div class="sphx-glr-thumbnail-title">Creating an Ensemble of conditioned 2D Fields</div>
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.. toctree::
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   /examples/06_conditioned_fields/00_condition_ensemble
   /examples/06_conditioned_fields/01_2D_condition_ensemble



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