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Python API Function Summary

tstoolbox.tstoolbox.about()

Display version number and system information.

tstoolbox.tstoolbox.accumulate([input_ts, …])

Calculate accumulating statistics.

tstoolbox.tstoolbox.add_trend(start_offset, …)

Add a trend.

tstoolbox.tstoolbox.aggregate([input_ts, …])

Take a time series and aggregate to specified frequency.

tstoolbox.tstoolbox.calculate_fdc([…])

Return the frequency distribution curve.

tstoolbox.tstoolbox.calculate_kde([…])

Return the kernel density estimation (KDE) curve.

tstoolbox.tstoolbox.clip([input_ts, …])

Return a time-series with values limited to [a_min, a_max].

tstoolbox.tstoolbox.convert([input_ts, …])

Convert values of a time series by applying a factor and offset.

tstoolbox.tstoolbox.convert_index(to[, …])

Convert datetime to/from Julian dates from different epochs.

tstoolbox.tstoolbox.convert_index_to_julian([…])

DEPRECATED: Use convert_index instead.

tstoolbox.tstoolbox.converttz(fromtz, totz)

Convert the time zone of the index.

tstoolbox.tstoolbox.correlation(lags[, …])

Develop a correlation between time-series and potentially lags.

tstoolbox.tstoolbox.createts([freq, …])

Create empty time series, optionally fill with a value.

tstoolbox.tstoolbox.date_offset(intervals, …)

Apply an offset to a time-series.

tstoolbox.tstoolbox.date_slice([input_ts, …])

Print out data to the screen between start_date and end_date.

tstoolbox.tstoolbox.describe([input_ts, …])

Print out statistics for the time-series.

tstoolbox.tstoolbox.dtw([input_ts, columns, …])

Dynamic Time Warping.

tstoolbox.tstoolbox.equation(equation_str[, …])

Apply <equation_str> to the time series data.

tstoolbox.tstoolbox.ewm_window([input_ts, …])

Calculate exponential weighted functions.

tstoolbox.tstoolbox.expanding_window([…])

Calculate an expanding window statistic.

tstoolbox.tstoolbox.fill([input_ts, method, …])

Fill missing values (NaN) with different methods.

tstoolbox.tstoolbox.filter(filter_type[, …])

Apply different filters to the time-series.

tstoolbox.tstoolbox.gof([input_ts, stats, …])

Will calculate goodness of fit statistics between two time-series.

tstoolbox.tstoolbox.lag(lags[, input_ts, …])

Create a series of lagged time-series.

tstoolbox.tstoolbox.normalization([…])

Return the normalization of the time series.

tstoolbox.tstoolbox.pca([input_ts, columns, …])

Return the principal components analysis of the time series.

tstoolbox.tstoolbox.pct_change([input_ts, …])

Return the percent change between times.

tstoolbox.tstoolbox.peak_detection([…])

Peak and valley detection.

tstoolbox.tstoolbox.pick(columns[, …])

Will pick a column or list of columns from input.

tstoolbox.tstoolbox.plot([input_ts, …])

Plot data.

tstoolbox.tstoolbox.rank([input_ts, …])

Compute numerical data ranks (1 through n) along axis.

tstoolbox.tstoolbox.read(filenames[, …])

Collect time series from a list of pickle or csv files.

tstoolbox.tstoolbox.remove_trend([input_ts, …])

Remove a ‘trend’.

tstoolbox.tstoolbox.replace(from_values, …)

Return a time-series replacing values with others.

tstoolbox.tstoolbox.rolling_window(statistic)

Calculate a rolling window statistic.

tstoolbox.tstoolbox.stack([input_ts, …])

Return the stack of the input table.

tstoolbox.tstoolbox.stdtozrxp([input_ts, …])

Print out data to the screen in a WISKI ZRXP format.

tstoolbox.tstoolbox.tstopickle(filename[, …])

Pickle the data into a Python pickled file.

tstoolbox.tstoolbox.unstack(column_names[, …])

Return the unstack of the input table.