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feets characterizes and analyzes light-curves from astronomical photometric databases for modelling, classification, data cleaning, outlier detection and data analysis. It uses machine learning algorithms to determine the numerical descriptors that characterize and distinguish the different variability classes of light-curves; these range from basic statistical measures such as the mean or standard deviation to complex time-series characteristics such as the autocorrelation function. The library is not restricted to the astronomical field and could also be applied to any kind of time series. This project is a derivative work of FATS (ascl:1711.017).
Carpyncho, is a catalog browser that we hope will be reutilized to search for and characterize time variable data of the ~PiB size VVV/VVVx survey. Is being developed for the detection and classification of periodic variables. For this purpose the stacked pawprint data from the VDFS CASU v >= 1.3 catalogs have been crossed matched with the VDFS CASU v1.3 tile catalogs into a Parquet files.
The Carpyncho infrastructure https://carpyncho.gihub.io is being developed entirely in Python on top of a Custom-Framework for data processing.
Also, a companion Python library is developed to access the same dataset as a Pandas DataFrame (https://pandas.pydata.org/).