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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)
Raw data
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Uses pandas (http://pandas.pydata.org/)\nor numpy (http://numpy.scipy.org) for any heavy lifting.\n\nInstallation\n------------\nShould be as easy as running ``pip install tstoolbox`` or\n``easy_install tstoolbox`` at any command line. Not sure on Windows whether\nthis will bring in pandas, but as mentioned above, if you start with scientific\nPython distribution then you shouldn't have a problem.\n\nUsage - Command Line\n--------------------\nJust run 'tstoolbox --help' to get a list of subcommands::\n\n\n usage: tstoolbox [-h]\n {accumulate, add_trend, aggregate, calculate_fdc,\n calculate_kde, clip, convert, convert_index,\n convert_index_to_julian, converttz, lag, correlation,\n createts, date_offset, date_slice, describe, dtw,\n equation, ewm_window, expanding_window, fill, filter, fit,\n read, gof, normalization, pca, pct_change, peak_detection,\n pick, plot, rank, regression, remove_trend, replace,\n rolling_window, stack, stdtozrxp, tstopickle, unstack,\n about} ...\n\n positional arguments:\n {accumulate, add_trend, aggregate, calculate_fdc, calculate_kde, clip,\n convert, convert_index, convert_index_to_julian, converttz, lag,\n correlation, createts, date_offset, date_slice, describe, dtw, equation,\n ewm_window, expanding_window, fill, filter, fit, read, gof,\n normalization, pca, pct_change, peak_detection, pick, plot, rank,\n regression, remove_trend, replace, rolling_window, stack, stdtozrxp,\n tstopickle, unstack, about}\n\n accumulate\n Calculate accumulating statistics.\n add_trend\n Add a trend.\n aggregate\n Take a time series and aggregate to specified frequency.\n calculate_fdc\n Return the frequency distribution curve.\n calculate_kde\n Return the kernel density estimation (KDE) curve.\n clip\n Return a time-series with values limited to [a_min, a_max].\n convert\n Convert values of a time series by applying a factor and offset.\n convert_index\n Convert datetime to/from Julian dates from different epochs.\n convert_index_to_julian\n DEPRECATED: Use convert_index instead.\n converttz\n Convert the time zone of the index.\n lag\n Create a series of lagged time-series.\n correlation\n Develop a correlation between time-series and potentially lags.\n createts\n Create empty time series, optionally fill with a value.\n date_offset\n Apply a date offset to a time-series index.\n date_slice\n Print out data to the screen between start_date and end_date.\n describe\n Print out statistics for the time-series.\n dtw\n Dynamic Time Warping.\n equation\n Apply <equation_str> cto the time series data.\n ewm_window\n Calculate exponential weighted functions.\n expanding_window\n Calculate an expanding window statistic.\n fill\n Fill missing values (NaN) with different methods.\n filter\n Apply different filters to the time-series.\n fit\n Fit model to data.\n read\n Combines time-series from a list of pickle or csv files.\n gof\n Will calculate goodness of fit statistics between two time-series.\n normalization\n Return the normalization of the time series.\n pca\n Return the principal components analysis of the time series.\n pct_change\n Return the percent change between times.\n peak_detection\n Peak and valley detection.\n pick\n DEPRECATED: Will pick a column or list of columns from input\n plot\n Plot data.\n rank\n Compute numerical data ranks (1 through n) along axis.\n regression\n Regression of one or more time-series or indices to a time-series.\n remove_trend\n Remove a 'trend'.\n replace\n Return a time-series replacing values with others.\n rolling_window\n Calculate a rolling window statistic.\n stack\n Return the stack of the input table.\n stdtozrxp\n Print out data to the screen in a WISKI ZRXP format.\n tstopickle\n Pickle the data into a Python pickled file.\n unstack\n Return the unstack of the input table.\n about\n Display version number and system information.\n\n optional arguments:\n -h, --help show this help message and exit\n\nThe default for all of the subcommands is to accept data from stdin (typically\na pipe). 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