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====== Dinamica EGO and Python Coupling ======

Dinamica EGO can run arbitrary Python code as part of a model through the [[Calculate Python Expression]] functor. This lesson walks through a complete example; for the full reference — including the ''dinamica.inputs''/''dinamica.outputs'' mechanism, all available utilities, and how to retrieve the results back into the model — see [[Calculate Python Expression]].

===== Example =====

The example below installs ''numpy'' and uses it to compute area statistics for a table of land cover patches, then returns both summary values and a table of outlier patches — something considerably more convenient with ''numpy'' than with plain Python loops.

The input, ''t1'', is a table of land cover patches with columns ''PatchId*'' and ''Area''. Inside the ''CalculatePythonExpression'' container, ''t1'' must be provided by a [[Number Table]] hook connected to the actual table functor — this is what makes the table available to the expression as ''dinamica.inputs["t1"]''. See [[calculate_python_expression#expression_inputs|Expression inputs]] for the full hook mechanism.

Install ''numpy'' and load the ''Area'' column into an array, skipping the header row:

<code>
dinamica.package("numpy")

areas = numpy.array([row[1] for row in dinamica.inputs["t1"][1:]])
</code>

Compute the mean and standard deviation of patch area:

<code>
meanArea = float(numpy.mean(areas))
stdArea = float(numpy.std(areas))
</code>

Flag patches whose area lies more than two standard deviations from the mean, using ''numpy'''s vectorized comparison instead of a manual loop:

<code>
isOutlier = numpy.abs(areas - meanArea) > 2 * stdArea
</code>

Return the two summary statistics as scalar outputs:

<code>
dinamica.outputs["meanArea"] = meanArea
dinamica.outputs["stdArea"] = stdArea
</code>

Build and return a table containing only the outlier patches:

<code>
header = dinamica.inputs["t1"][0]
outlierRows = [row for row, flagged in zip(dinamica.inputs["t1"][1:], isOutlier) if flagged]
outlierTable = [header] + outlierRows

dinamica.outputs["outlierPatches"] = dinamica.prepareTable(outlierTable, 1)
</code>

The complete expression:

<code>
dinamica.package("numpy")

areas = numpy.array([row[1] for row in dinamica.inputs["t1"][1:]])

meanArea = float(numpy.mean(areas))
stdArea = float(numpy.std(areas))

isOutlier = numpy.abs(areas - meanArea) > 2 * stdArea

dinamica.outputs["meanArea"] = meanArea
dinamica.outputs["stdArea"] = stdArea

header = dinamica.inputs["t1"][0]
outlierRows = [row for row, flagged in zip(dinamica.inputs["t1"][1:], isOutlier) if flagged]
outlierTable = [header] + outlierRows

dinamica.outputs["outlierPatches"] = dinamica.prepareTable(outlierTable, 1)
</code>

Back in the model, ''meanArea'', ''stdArea'', and ''outlierPatches'' are retrieved from the returned ''Struct'' using the ''ExtractStruct*'' family of functors — see [[calculate_python_expression#retrieving_outputs|Retrieving outputs]] for details.

===== Reference =====

The full set of Python utilities available inside the expression — ''dinamica.package()'', ''dinamica.prepareTable()'', ''dinamica.prepareLookupTable()'', and ''dinamica.toTable()'' — along with their parameters and additional examples of installing packages with specific versions or from custom sources, is documented on the [[calculate_python_expression#utilities|Calculate Python Expression]] page.

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