tstoolbox 15.38.19.18

Command line script to manipulate time series files.

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]

{fill,about,createts,filter,read,date_slice,describe,peak_detection,convert,equation,pick,stdtozrxp,tstopickle,accumulate,rolling_window,aggregate,replace,clip,add_trend,remove_trend,calculate_fdc,stack,unstack,plot,dtw,pca,normalization,converttz,convert_index_to_julian,pct_change,rank,date_offset} …

about

Display version number and system information.

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.

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_to_julian

Convert date/time index to Julian dates from different epochs.

converttz

Convert the time zone of the index.

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.

fill

Fill missing values (NaN) with different methods.

filter

Apply different filters to the 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.

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)