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--- python_coupling [2020/02/17 23:51]
argemiro created
+++ python_coupling [2026/07/18 02:29] (current)
hermann
@@ Line 1: / Line 1: @@
+{{ :logo_logo.png?400 |}}
 ====== Dinamica EGO and Python Coupling ======
-O suporte ao Python (atualmente na versão 3.7) está presente no ramo "Python" nos repositórios (em cima do ramo "Tasks"). Para compilar o ramo, é necessário ter as dependências do Python no dff_dependencies_windows. A versão contendo as dependências pode ser baixada em [[http://csr.ufmg.br/~romulo/dff_dependencies_windows_python.7z]]. Para execução, é necessário ter a pasta "PyEnvironment" dentro da pasta do Dinamica, o PyEnvironment também pode ser obtido em [[http://csr.ufmg.br/~romulo/PyEnvironment.7z]].
+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]].
-=== Exemplo: Calculate Python Expression ===
+===== Example =====
-Uma expressão que pode ser usada:
+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.
-<code>
-dinamica.package("numpy")
-a = numpy.arange(15).reshape(3, 5)
+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.
-print(a)
-print(dinamica.inputs)
+Install ''numpy'' and load the ''Area'' column into an array, skipping the header row:
-for row in dinamica.inputs["t1"]:
- print(row)
-for row in dinamica.inputs["t2"]:
+<code>
- print(row)
+dinamica.package("numpy")
-dinamica.outputs["teste"] = 2
+areas = numpy.array([row[1] for row in dinamica.inputs["t1"][1:]])
-dinamica.outputs["teste2"] = 2.5
-dinamica.outputs["teste3"] = 'a'
-dinamica.outputs["outraSaida"] = "yoyo"
-dinamica.outputs["tabela"] = dinamica.prepareTable(dinamica.inputs["t1"], 3)
-dinamica.outputs["lut"] = dinamica.prepareLookupTable(dinamica.inputs["t2"])
 </code>
-onde:
+Compute the mean and standard deviation of patch area:
 <code>
-dinamica.package("numpy")
+meanArea = float(numpy.mean(areas))
+stdArea = float(numpy.std(areas))
 </code>
-Pede ao PIP que instale o pacote numpy e faça o import.
-\\
+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>
-Imprime o vetor com todas as entradas passadas pelo Dinamica.
-\\
+Return the two summary statistics as scalar outputs:
 <code>
-dinamica.outputs["teste2"] = 2.5
+dinamica.outputs["meanArea"] = meanArea
+dinamica.outputs["stdArea"] = stdArea
 </code>
-Coloca uma saída na struct com nome "teste2", contendo uma double com valor 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>
-Coloca uma saída na struct com nome "tabela", contendo uma tabela com 3 colunas de chave. Essa função não é necessária se a tabela já estiver com os '*' nos nomes da coluna (portanto o usuário poderia fazer apenas dinamica.outputs["teste2"] = dinamica.inputs["t1"]). Toda tabela no Python é tratada como uma lista de listas, onde cada lista interna corresponde a uma **linha** da tabela.
-\\
+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>
-Coloca uma saída na struct com nome "lut", contendo uma LookupTable (Não existe outra forma de passar uma LookupTable de volta).
+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.
 ----
-=== Calculate Python Utilities ===
+===Congratulations, you have successfully completed this lesson!===
+☞[[lesson_21|Next Lesson]]
+☞[[:guidebook_start| Back to Guidebook Start]]
-^ Utility Name ^ Description ^ Parameters ^
-| 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 |