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python_coupling [2020/02/18 11:00] argemiro |
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| ====== Dinamica EGO and Python Coupling ====== | ====== Dinamica EGO and Python Coupling ====== | ||
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| - | Python support is present in the "Python" branch in the repositories (on top of the "Tasks" branch). To compile the branch, it is necessary to have Python dependencies in dff_dependencies_windows. The version containing the dependencies can be downloaded at [[http://csr.ufmg.br/~romulo/dff_dependencies_windows_python.7z]]. For execution, it is necessary to have the folder "PyEnvironment" inside the Dinamica folder, the PyEnvironment can also be obtained in [[http://csr.ufmg.br/~romulo/PyEnvironment.7z]]. | ||
| === Example: Calculate Python Expression === | === Example: Calculate Python Expression === | ||
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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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| + | See the documentation about [[Calculate Python Expression]] for further information about to use Python together with Dinamica EGO. | ||
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