TinkerModellor can perform multiple analysis methods, including calculating atomic distances, angles, and root mean square deviation (RMSD). We aim to implement more functions in TinkerModellor. Therefore, the output data storage is crucial.
Here, we introduce a subfunction called CSVMaker, which stores data in .csv files. It is worth noting that .csv files can be utilized by the Pandas package. Hence, we must design an object to convert the output into .csv format to take advantage of Pandas' superb functionality.
Currently, CSVMaker is a preliminary module available at CSVMaker module. It is used in main.py to generate the .csv file.
The target descriptions are as follows:
- Test for any bugs in CSVMaker and fix them if found.
- Add analysis functions using Pandas, such as average value, standard deviation (SD), and standard error (SE).
TinkerModellor can perform multiple analysis methods, including calculating atomic distances, angles, and root mean square deviation (RMSD). We aim to implement more functions in TinkerModellor. Therefore, the output data storage is crucial.
Here, we introduce a subfunction called CSVMaker, which stores data in .csv files. It is worth noting that .csv files can be utilized by the Pandas package. Hence, we must design an object to convert the output into .csv format to take advantage of Pandas' superb functionality.
Currently, CSVMaker is a preliminary module available at CSVMaker module. It is used in main.py to generate the .csv file.
The target descriptions are as follows: