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python_coupling [2020/02/17 23:51] argemiro created |
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====== Dinamica EGO and Python Coupling ====== | ====== 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]]. | + | === Example: Calculate Python Expression === |
- | === Exemplo: Calculate Python Expression === | + | An expression that can be used: |
- | + | ||
- | Uma expressão que pode ser usada: | + | |
<code> | <code> | ||
dinamica.package("numpy") | dinamica.package("numpy") | ||
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</code> | </code> | ||
- | onde: | + | where: |
<code> | <code> | ||
dinamica.package("numpy") | dinamica.package("numpy") | ||
</code> | </code> | ||
- | Pede ao PIP que instale o pacote numpy e faça o import. | + | Ask the PIP to install the numpy package and import it: |
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<code> | <code> | ||
print(dinamica.inputs) | print(dinamica.inputs) | ||
</code> | </code> | ||
- | Imprime o vetor com todas as entradas passadas pelo Dinamica. | + | Prints the vector with all the entries passed by Dinamica: |
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<code> | <code> | ||
dinamica.outputs["teste2"] = 2.5 | dinamica.outputs["teste2"] = 2.5 | ||
</code> | </code> | ||
- | Coloca uma saída na struct com nome "teste2", contendo uma double com valor 2.5 | + | Place an output in the struct named "test2", containing a double with a value of 2.5: |
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<code> | <code> | ||
dinamica.outputs["tabela"] = dinamica.prepareTable(dinamica.inputs["t1"], 3) | dinamica.outputs["tabela"] = dinamica.prepareTable(dinamica.inputs["t1"], 3) | ||
</code> | </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. | + | 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: |
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<code> | <code> | ||
dinamica.outputs["lut"] = dinamica.prepareLookupTable(dinamica.inputs["t2"]) | dinamica.outputs["lut"] = dinamica.prepareLookupTable(dinamica.inputs["t2"]) | ||
</code> | </code> | ||
- | Coloca uma saída na struct com nome "lut", contendo uma LookupTable (Não existe outra forma de passar uma LookupTable de volta). | + | Put an output in the struct named "lut", containing a LookupTable (There is no other way to pass a LookupTable back): |
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| 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 | | | 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 | | | 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 | | ||
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+ | ===Congratulations, you have successfully completed this lesson!=== | ||
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+ | ☞[[lesson_21|Next Lesson]] | ||
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+ | ☞[[:guidebook_start| Back to Guidebook Start]] | ||
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