Python API Function Summary

plottoolbox.plottoolbox.autocorrelation([...])	[time index, 1 column] Autocorrelation plot.
plottoolbox.plottoolbox.bar([input_ts, ...])	[category index, N columns] Bar plot, sometimes called a "column" plot.
plottoolbox.plottoolbox.barh([input_ts, ...])	[category index, N columns] Bar plot
plottoolbox.plottoolbox.barh_stacked([...])	[category index, N columns] Horizontal stacked bar plot.
plottoolbox.plottoolbox.bar_stacked([...])	[category index, N columns] Stacked vertical bar, sometimes called a stacked column plot.
plottoolbox.plottoolbox.bootstrap([...])	[1 column] Bootstrap plots a randomly selected subset of the input series.
plottoolbox.plottoolbox.boxplot([input_ts, ...])	[N columns] Box and whiskers plot.
plottoolbox.plottoolbox.double_mass([...])	[x1, y1, x2, y2, x3, y3, ...] Double mass curve - cumulative sum of x against cumulative sum of y.
plottoolbox.plottoolbox.handh([input_ts, ...])	[time index, Q, P] Hydrograph and hyetograph time-series plot.
plottoolbox.plottoolbox.heatmap([input_ts, ...])	[time index(day), 1 column] 2D heatmap of daily data.
plottoolbox.plottoolbox.hexbin([input_ts, ...])	[x, y, optional third data column] Hexbin plot.
plottoolbox.plottoolbox.histogram([...])	[N columns] Histogram.
plottoolbox.plottoolbox.kde([input_ts, ...])	[N columns] Kernel density estimation of probability density function.
plottoolbox.plottoolbox.kde_time([input_ts, ...])	[time index, N columns] A time-series plot with a kernel density estimation (KDE) plot.
plottoolbox.plottoolbox.lag_plot([input_ts, ...])	[time index, 1 column] Lag plot.
plottoolbox.plottoolbox.lognorm_xaxis([...])	[N columns] Log-normal x-axis.
plottoolbox.plottoolbox.lognorm_yaxis([...])	[N columns] Log-normal y-axis.
plottoolbox.plottoolbox.norm_xaxis([...])	[N columns] Normal x-axis.
plottoolbox.plottoolbox.norm_yaxis([...])	[N columns] Normal y-axis.
plottoolbox.plottoolbox.probability_density([...])	[N columns] Probability plot.
plottoolbox.plottoolbox.scatter_matrix([...])	[N columns] Plots all columns against each other in matrix of plots.
plottoolbox.plottoolbox.target([input_ts, ...])	[obs column, sim N columns] Creates a "target" diagram to plot goodness of fit.
plottoolbox.plottoolbox.taylor([input_ts, ...])	[obs columns, sim N columns] Taylor diagram to plot goodness of fit.
plottoolbox.plottoolbox.time([input_ts, ...])	[time index, N columns] Time-series plot.
plottoolbox.plottoolbox.waterfall([...])	[time index, N columns] Watefall plot.
plottoolbox.plottoolbox.weibull_xaxis([...])	[N columns] Weibull x-axis.
plottoolbox.plottoolbox.weibull_yaxis([...])	[N columns] Weibull y-axis.
plottoolbox.plottoolbox.xy([input_ts, ...])	[x1, y1, x2, y2, x3, y3, ...] Creates an 'x,y' plot, also known as a scatter plot.