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--- python_coupling [2023/07/13 15:23]
admin
+++ python_coupling [2026/07/18 02:29] (current)
hermann
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 ====== Dinamica EGO and Python Coupling ======
-=== Example: Calculate Python Expression ===
+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]].
-An expression that can be used:
+===== Example =====
-<code>
-dinamica.package("numpy")
-a = numpy.arange(15).reshape(3, 5)
+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.
-print(a)
-print(dinamica.inputs)
+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.
-for row in dinamica.inputs["t1"]:
- print(row)
-for row in dinamica.inputs["t2"]:
+Install ''numpy'' and load the ''Area'' column into an array, skipping the header row:
- print(row)
-dinamica.outputs["teste"] = 2
+<code>
-dinamica.outputs["teste2"] = 2.5
+dinamica.package("numpy")
-dinamica.outputs["teste3"] = 'a'
-dinamica.outputs["outraSaida"] = "yoyo"
+areas = numpy.array([row[1] for row in dinamica.inputs["t1"][1:]])
-dinamica.outputs["tabela"] = dinamica.prepareTable(dinamica.inputs["t1"], 3)
-dinamica.outputs["lut"] = dinamica.prepareLookupTable(dinamica.inputs["t2"])
 </code>
-where:
+Compute the mean and standard deviation of patch area:
 <code>
-dinamica.package("numpy")
+meanArea = float(numpy.mean(areas))
+stdArea = float(numpy.std(areas))
 </code>
-Ask the PIP to install the numpy package and import it:
-\\
+Flag patches whose area lies more than two standard deviations from the mean, using ''numpy'''s vectorized comparison instead of a manual loop:
 <code>
-print(dinamica.inputs)
+isOutlier = numpy.abs(areas - meanArea) > 2 * stdArea
 </code>
-Prints the vector with all the entries passed by Dinamica:
-\\
+Return the two summary statistics as scalar outputs:
 <code>
-dinamica.outputs["teste2"] = 2.5
+dinamica.outputs["meanArea"] = meanArea
+dinamica.outputs["stdArea"] = stdArea
 </code>
-Place an output in the struct named "test2", containing a double with a value of 2.5:
-\\
+Build and return a table containing only the outlier patches:
 <code>
-dinamica.outputs["tabela"] = dinamica.prepareTable(dinamica.inputs["t1"], 3)
+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>
-Place an output in the struct named "table", containing a table with 3 key columns. This function is not necessary if the table already has '*' in the column names (so the user could only do dinamica.outputs ["teste2"] = dinamica.inputs ["t1"]). Every table in Python is treated as a list of lists, where each internal list corresponds to a ** row ** of the table:
-\\
+The complete expression:
 <code>
-dinamica.outputs["lut"] = dinamica.prepareLookupTable(dinamica.inputs["t2"])
+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>
-Put an output in the struct named "lut", containing a LookupTable (There is no other way to pass a LookupTable back):
-----
+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.
-=== Calculate Python Utilities ===
+===== Reference =====
-^ Utility Name ^ Description ^ Parameters ^
+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.
-| package | It imports the module requested. | str packageName, str installPath=None, str loadPath=None |
-| prepareTable | It returns the table prepared to output. Input Table has to be in the form [[[header1...headerN][line1]...[lineM]]] where the first list contains the headers of the table and all the other lists are lines containing the data. | list(list) inputTable, int numKeys |
-| prepareLookupTable | It returns the lookup table prepared to output. The lut has to be in the form [[[key,value][line1]...[lineM]]] where the first list contains the headers of the table and all the other lists are lines containing the data. | list(list) lut |
-| toTable | It returns a valid representation of dinamica table to output. Input Table can be: [[[header1...headerN][line1]...[lineM]]] where the first list contains the headers of the table and all the other lists are lines containing the data; {header1: [valuesOfColumn1], header2: [valuesOfColumn2]...} where the valuesOfComlumn# are all values of that column in table; [[(header1...headerN)(line)...(lineM)]] where the first tuple contains the headers of the table and all the other tuples are lines containing the data; [value1, value2, ..., valueN], those are the values for a lookup table with sequential key; pandas.Dataframe is a commom structure table used to manipulate CSVs; numpy.array is a commom structure for matrix, that can be tables as well. The first line of matrix needs to be the table header. | list(list);dict(list);list(tuple);list;pandas.DataFrame;numpy.array inputTable |
-See the documentation about [[Calculate Python Expression]] for further information about to use Python together with Dinamica EGO.
+----
-\\
-\\
 ===Congratulations, you have successfully completed this lesson!===
-\\
 ☞[[lesson_21|Next Lesson]]
-\\
 ☞[[:guidebook_start| Back to Guidebook Start]]