Metadata-Version: 2.1
Name: mockseries
Version: 0.1.4
Summary: Easy and intuitive generation of synthetic timeseries.
Home-page: http://mockseries.catheu.tech/
Author: Cyril de Catheu
Author-email: cdecatheu@hey.com
License: BSD
Project-URL: Bug Tracker, https://github.com/cyrilou242/mockseries/issues
Project-URL: Documentation, http://mockseries.catheu.tech/
Project-URL: Source Code, https://github.com/cyrilou242/mockseries
Description: <p align="center">
          <img src="https://github.com/cyrilou242/mockseries/blob/main/website/static/img/mockingbird200.png">
        </p>
        
        # mockseries
        **mockseries** is and easy to use and intuitive  Python package that helps generate synthetic (**mock**) time**series**.
        
        [-> Documentation website](https://mockseries.catheu.tech/docs/intro).
        
        
        ## Installation  
            
            #python >=3.6.6 
            pip install mockseries
            
        ## Contributing
        Contributions are welcome!   
        Standards, objectives and process not defined yet.
            
        ## Quick Run
        
        #### Define a timeseries
        
        ```python
        from datetime import timedelta
        from mockseries.trend import LinearTrend
        from mockseries.seasonality import SinusoidalSeasonality
        from mockseries.noise import RedNoise
        
        trend = LinearTrend(coefficient=2, time_unit=timedelta(days=4), flat_base=100)
        seasonality = SinusoidalSeasonality(amplitude=20, period=timedelta(days=7)) \
                      + SinusoidalSeasonality(amplitude=4, period=timedelta(days=1))
        noise = RedNoise(mean=0, std=3, correlation=0.5)
        
        timeseries = trend + seasonality + noise
        ```
        
        #### Generate values
        
        ``` 
        from datetime import datetime
        from mockseries.utils import datetime_range
        
        ts_index = datetime_range(
            granularity=timedelta(hours=1),
            start_time=datetime(2021, 5, 31),
            end_time=datetime(2021, 8, 30),
        )
        ts_values = timeseries.generate(ts_index)
        ```
        
        #### Plot or write to csv
        ```python
        from mockseries.utils import plot_timeseries, write_csv
        
        print(ts_index, ts_values)
        plot_timeseries(ts_index, ts_values, save_path="hello_mockseries.png")
        write_csv(ts_index, ts_values, "hello_mockseries.csv")
        ```
        
        ### References
        - J. R. Maat, A. Malali, and P. Protopapas, “TimeSynth: A Multipurpose Library for Synthetic Time Series in Python,” 2017. [Online]. Available: http://github.com/TimeSynth/TimeSynth.
        - TStimulus. Available: https://github.com/cetic/TSimulus.
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Typing :: Typed
Classifier: Operating System :: OS Independent
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Requires-Python: >=3.6
Description-Content-Type: text/markdown
