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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