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

- 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’.
- replace
- Return a time-series replacing values with others.
- 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)

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