Metadata-Version: 1.2
Name: tstoolbox
Version: 100.4.6
Summary: Command line script to manipulate time series files.
Home-page: http://timcera.bitbucket.io/tstoolbox/docsrc/index.html
Author: Tim Cera, P.E.
Author-email: tim@cerazone.net
License: BSD
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        .. image:: https://img.shields.io/pypi/v/tstoolbox.svg
            :alt: Latest release
            :target: https://pypi.python.org/pypi/tstoolbox
        
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            :alt: tstoolbox license
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        TSToolbox - Quick Guide
        =======================
        The tstoolbox is a Python script to manipulate time-series on the command line
        or by function calls within Python.  Uses pandas (http://pandas.pydata.org/)
        or numpy (http://numpy.scipy.org) for any heavy lifting.
        
        Requirements
        ------------
        * pandas - on Windows this is part scientific Python distributions like
          Python(x,y), Anaconda, or Enthought.
        
        * mando - command line parser
        
        Installation
        ------------
        Should be as easy as running ``pip install tstoolbox`` or ``easy_install
        tstoolbox`` at any command line.  Not sure on Windows whether this will bring
        in pandas, but as mentioned above, if you start with scientific Python
        distribution then you shouldn't have a problem.
        
        Usage - Command Line
        --------------------
        Just run 'tstoolbox --help' to get a list of subcommands::
        
        
            usage: tstoolbox [-h]
                             {accumulate, add_trend, aggregate, calculate_fdc,
                             calculate_kde, clip, convert, convert_index,
                             convert_index_to_julian, converttz, lag, correlation,
                             createts, date_offset, date_slice, describe, dtw,
                             equation, ewm_window, expanding_window, fill, filter, gof,
                             normalization, pca, pct_change, peak_detection, pick,
                             plot, rank, read, remove_trend, replace, rolling_window,
                             stack, stdtozrxp, tstopickle, unstack, about} ...
            
            positional arguments:
              {accumulate, add_trend, aggregate, calculate_fdc, calculate_kde, clip,
              convert, convert_index, convert_index_to_julian, converttz, lag,
              correlation, createts, date_offset, date_slice, describe, dtw, equation,
              ewm_window, expanding_window, fill, filter, gof, normalization, pca,
              pct_change, peak_detection, pick, plot, rank, read, remove_trend,
              replace, rolling_window, stack, stdtozrxp, tstopickle, unstack, about}
        
            accumulate          
                Calculate accumulating statistics.
            add_trend           
                Add a trend.
            aggregate           
                Take a time series and aggregate to specified frequency.
            calculate_fdc       
                Return the frequency distribution curve.
            calculate_kde       
                Return the kernel density estimation (KDE) curve.
            clip                
                Return a time-series with values limited to [a_min, a_max].
            convert             
                Convert values of a time series by applying a factor and offset.
            convert_index       
                Convert datetime to/from Julian dates from different epochs.
            convert_index_to_julian
                DEPRECATED: Use convert_index instead.
            converttz           
                Convert the time zone of the index.
            lag                 
                Create a series of lagged time-series.
            correlation         
                Develop a correlation between time-series and potentially lags.
            createts            
                Create empty time series, optionally fill with a value.
            date_offset         
                Apply an offset to a time-series.
            date_slice          
                Print out data to the screen between start_date and end_date.
            describe            
                Print out statistics for the time-series.
            dtw                 
                Dynamic Time Warping.
            equation            
                Apply <equation_str> to the time series data.
            ewm_window          
                Calculate exponential weighted functions.
            expanding_window    
                Calculate an expanding window statistic.
            fill                
                Fill missing values (NaN) with different methods.
            filter              
                Apply different filters to the time-series.
            gof                 
                Will calculate goodness of fit statistics between two time-series.
            normalization       
                Return the normalization of the time series.
            pca                 
                Return the principal components analysis of the time series.
            pct_change          
                Return the percent change between times.
            peak_detection      
                Peak and valley detection.
            pick                
                Will pick a column or list of columns from input.
            plot                
                Plot data.
            rank                
                Compute numerical data ranks (1 through n) along axis.
            read                
                Collect time series from a list of pickle or csv files.
            remove_trend        
                Remove a 'trend'.
            replace             
                Return a time-series replacing values with others.
            rolling_window      
                Calculate a rolling window statistic.
            stack               
                Return the stack of the input table.
            stdtozrxp           
                Print out data to the screen in a WISKI ZRXP format.
            tstopickle          
                Pickle the data into a Python pickled file.
            unstack             
                Return the unstack of the input table.
            about               
                Display version number and system information.
        
            optional arguments:
                -h, --help            show this help message and exit
        
        The default for all of the subcommands is to accept data from stdin (typically
        a pipe).  If a subcommand accepts an input file for an argument, you can use
        "--input_ts=input_file_name.csv", or to explicitly specify from stdin (the
        default) "--input_ts='-'".
        
        For the subcommands that output data it is printed to the screen and you can
        then redirect to a file.
        
        Usage - API
        -----------
        You can use all of the command line subcommands as functions.  The function
        signature is identical to the command line subcommands.  The return is always
        a PANDAS DataFrame.  Input can be a CSV or TAB separated file, or a PANDAS
        DataFrame and is supplied to the function via the 'input_ts' keyword.
        
        Simply import tstoolbox::
        
            from tstoolbox import tstoolbox
        
            # Then you could call the functions
            ntsd = tstoolbox.fill(method='linear', input_ts='tests/test_fill_01.csv')
        
            # Once you have a PANDAS DataFrame you can use that as input to other 
            # tstoolbox functions.
            ntsd = tstoolbox.aggregate(statistic='mean', agg_interval='daily', input_ts=ntsd)
        
        
Keywords: time_series
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Developers
Classifier: Environment :: Console
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.6
