Command line script to manipulate time series files.

## Project description

## 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)

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