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/*
* Copyright (c) 2022. JetBrains s.r.o.
* Use of this source code is governed by the MIT license that can be found in the LICENSE file.
*/
package org.jetbrains.letsPlot.bistro.qq
/**
* Produces a Q-Q plot (quantile-quantile plot).
*
* Supply the `sample` parameter to compare distribution of observations with a theoretical distribution
* ('normal' or as otherwise specified by the `distribution` parameter).
* Alternatively, supply `x` and `y` parameters to compare the distribution of `x` with the distribution of `y`.
*
* ## Examples
*
* - [qq_plots.ipynb](https://nbviewer.jupyter.org/github/JetBrains/lets-plot-kotlin/blob/master/docs/examples/jupyter-notebooks/qq_plots.ipynb)
*
* @param data The data to be displayed in this layer. If null, the default, the data
* is inherited from the plot data as specified in the call to [letsPlot][org.jetbrains.letsPlot.letsPlot].
* @param sample Name of variable.
* Specifies a vector of observations used for computing of "sample quantiles".
* Use this parameter to produce a "sample vs. theoretical" Q-Q plot.
* @param x Name of variable specifying a vector of observations used for computing of x "sample quantiles".
* Use `x` and `y` parameters to produce a "sample X vs. sample Y" Q-Q plot.
* @param y Name of variable specifying a vector of observations used for computing of y "sample quantiles".
* Use `x` and `y` parameters to produce a "sample X vs. sample Y" Q-Q plot.
* @param distribution default = "norm".
* Distribution function to use: "norm", "uniform", "t", "gamma", "exp", "chi2".
* Could be specified if `sample` is.
* @param dParams Additional parameters passed on to distribution function.
* Could be specified if `sample` is.
* - If `distribution` is "norm" then `dParams` is a pair (mean, std) (default = listOf(0.0, 1.0)).
* - If `distribution` is "uniform" then `dParams` is a pair (a, b) (default = listOf(0.0, 1.0)).
* - If `distribution` is "t" then `dParams` is an integer number (d) (default = listOf(1)).
* - If `distribution` is "gamma" then `dParams` is a pair (alpha, beta) (default = listOf(1.0, 1.0)).
* - If `distribution` is "exp" then `dParams` is a float number (lambda) (default = listOf(1.0)).
* - If `distribution` is "chi2" then `dParams` is an integer number (k) (default = listOf(1)).
* @param quantiles default = Pair(0.25, 0.75).
* Pair of quantiles to use when fitting the Q-Q line.
* @param group Grouping parameter.
* If it is specified and color-parameters isn't then different groups will have different colors.
* @param showLegend default = true.
* false - do not show legend for this layer.
* @param color Color of a points.
* String in the following formats:
* - RGB/RGBA (e.g. "rgb(0, 0, 255)")
* - HEX (e.g. "#0000FF")
* - color name (e.g. "red")
* - role name ("pen", "paper" or "brush")
*
* Or an instance of the `java.awt.Color` class.
* @param fill Color to paint shape's inner points.
* Is applied only to the points of shapes having inner points.
* @param alpha default = 0.5.
* Transparency level of a points. Understands numbers between 0 and 1.
* @param size default = 3.0.
* Size of the points.
* @param shape Shape of the points.
* @param lineColor default = "#FF0000".
* Color of the fitting line.
* @param lineSize default = 0.75.
* Width of the fitting line.
* @param linetype Int or String.
* Type of the fitting line.
* Codes and names: 0 = "blank", 1 = "solid", 2 = "dashed", 3 = "dotted", 4 = "dotdash",
* 5 = "longdash", 6 = "twodash"
*/
fun qqPlot(
data: Map<*, *>,
sample: String? = null,
x: String? = null,
y: String? = null,
distribution: String? = null,
dParams: List? = null,
quantiles: Pair? = null,
group: String? = null,
showLegend: Boolean = true,
color: String? = null,
fill: String? = null,
alpha: Number? = null,
size: Number? = null,
shape: Int? = null,
lineColor: String? = null,
lineSize: Number? = null,
linetype: Any? = null,
) = QQPlotBuilder(
data,
sample,
x,
y,
distribution,
dParams,
quantiles,
group,
showLegend,
color,
fill,
alpha,
size,
shape,
lineColor,
lineSize,
linetype
).build()
