Metadata-Version: 1.1
Name: tstoolbox
Version: 0.9.4
Summary: Command line script to manipulate time series files.
Home-page: http://pypi.python.org/pypi/tstoolbox
Author: Tim Cera, P.E.
Author-email: tim@cerazone.net
License: UNKNOWN
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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 won't have a problem.
        
        Usage - Command Line
        --------------------
        Just run 'tstoolbox --help' to get a list of subcommands
        
          accumulate
                    Calculates accumulating statistics.
          
          add_trend
                    Adds a trend.
          
          aggregate
                    Takes a time series and aggregates to specified
                    frequency, outputs to 'ISO-8601date,value' format.
          
          calculate_fdc
                    Returns the frequency distribution curve. DOES NOT
                    return a time-series.
          
          clip
                    Returns a time-series with values limited to [a_min,
                    a_max]
          
          convert
                    Converts values of a time series by applying a factor
                    and offset. See the 'equation' subcommand for a
                    generalized form of this command.
          
          date_slice
                    Prints out data to the screen between start_date and
                    end_date
          
          describe
                    Prints out statistics for the time-series.
          
          dtw
                    Dynamic Time Warping (beta)
          
          equation
                    Applies <equation> to the time series data. The
                    <equation> argument is a string contained in single
                    quotes with 'x' used as the variable representing the
                    input. For example, '(1 - x)*sin(x)'.
          
          fill
                    Fills missing values (NaN) with different methods.
                    Missing values can occur because of NaN, or because
                    the time series is sparse. The 'interval' option can
                    insert NaNs to create a dense time series.
          
          filter
                    Apply different filters to the time-series.
          
          normalization
                      Returns the normalization of the time series.
            
          pca
                    Returns the principal components analysis of the time
                    series. Does not return a time-series. (beta)
          
          peak_detection
                    Peak and valley detection.
          
          pick
                    Will pick a column or list of columns from input.
                    Start with 1.
          
          plot
                    Plots.
          
          read
                    Collect time series from a list of pickle or csv files
                    then print in the tstoolbox standard format.
          
          remove_trend
                    Removes a 'trend'.
          
          rolling_window
                    Calculates a rolling window statistic.
          
          stack
                    Returns the stack of the input table.
          
          stdtozrxp
                    Prints out data to the screen in a WISKI ZRXP format.
          
          tstopickle
                    Pickles the data into a Python pickled file. Can be
                    brought back into Python with 'pickle.load' or
                    'numpy.load'. See also 'tstoolbox read'.
          
          unstack
                    Returns the unstack of the input table.
          
        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 :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development :: Libraries :: Python Modules
