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.
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, fit,
read, gof, normalization, pca, pct_change, peak_detection,
pick, plot, rank, regression, 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, fit, read, gof,
normalization, pca, pct_change, peak_detection, pick, plot, rank,
regression, 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 a date offset to a time-series index.
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> cto 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.
fit
Fit model to data.
read
Combines time-series from a list of pickle or csv files.
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
DEPRECATED: Will pick a column or list of columns from input
plot
Plot data.
rank
Compute numerical data ranks (1 through n) along axis.
regression
Regression of one or more time-series or indices to a time-series.
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:
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', groupby='D', input_ts=ntsd)
1 maintainer
Metadata
1 maintainer
Release files for tstoolbox 108.2.6
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
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| File | Size | Uploaded | |
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| tstoolbox-108.2.6.tar.gz | 6.3 MB | Details |
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| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| tstoolbox-108.2.6-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 6.4 MB
Release files / tstoolbox-108.2.6.tar.gz
| Download URL | tstoolbox-108.2.6.tar.gz |
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| Tags | Source |
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