Graph

class Graph(*args)

Class Graph containing drawable elements and a graphical context.

Available constructors:

Graph(title=’’)

Graph(title, xTitle, yTitle, showAxes, legendPosition=’’, legendFontSize=1.0, logScale=ot.GraphImplementation.NONE)

Parameters:

titlestr

Title of the graph.

xTitlestr

Legend of the X axe.

yTitlestr

Legend of the Y axe.

showAxesbool

True to draw the axes. False to hide them.

legendPositionstr

Indication of the legend’s position. If legendPosition is not specified, the Graph has no legend. The valid strings are given by the GetValidLegendPositions() method.

legendFontSizefloat

Font size of the legend.

logScaleint

logScale indicates whether the logarithmic scale is used either for one or both axes:

• ot.GraphImplementation.NONE or 0: no log scale is used,

• ot.GraphImplementation.LOGX or 1: log scale is used only for horizontal data,

• ot.GraphImplementation.LOGY or 2: log scale is used only for vertical data,

• ot.GraphImplementation.LOGXY or 3: log scale is used for both data.

Methods

GetValidLegendPositions()	Accessor to the list of valid legend positions.
IsValidLegendPosition(position)	Test if the proposed legend position is valid or not.
add(*args)	Add drawable instances to the collection of drawables contained in Graph.
erase(i)	Erase a drawable instance from the collection of drawables contained in Graph.
getAutomaticBoundingBox()	Accessor to the indication of automatic bounding box.
getAxes()	Accessor to the indication of axes' presence on the Graph.
getBoundingBox()	Accessor to the bounding box of the whole plot.
getClassName()	Accessor to the object's name.
getColors()	Accessor to the colors of the Drawables included in the Graph.
getDrawable(index)	Accessor to a Drawable included in the Graph.
getDrawables()	Accessor to the Drawables included in the Graph.
getGrid()	Accessor to the indication of grid's presence on the Graph.
getGridColor()	Accessor to the indication of grid's color on the Graph.
getId()	Accessor to the object's id.
getImplementation()	Accessor to the underlying implementation.
getIntegerXTick()	Accessor to the integer x-axis ticks flag.
getIntegerYTick()	Accessor to the integer y-axis ticks flag.
getLegendFontSize()	Accessor to the legends' font size of the Drawables inside the Graph.
getLegendPosition()	Accessor to the legend's position of the Drawables inside the Graph.
getLegends()	Accessor to the legends of the Drawables inside the Graph.
getLogScale()	Accessor to the indication of axes' scale of the Graph.
getName()	Accessor to the object's name.
getTickLocation()	Accessor to the ticks location flag.
getTitle()	Accessor to the title of the Graph.
getXTitle()	Accessor to the title of the X axe.
getYTitle()	Accessor to the title of the Y axe.
setAutomaticBoundingBox(automaticBoundingBox)	Accessor to the indication of automatic bounding box.
setAxes(showAxes)	Accessor to the indication of axes' presence on the Graph.
setBoundingBox(boundingBox)	Accessor to the bounding box of the whole plot.
setColors(colors)	Update the colors of the Drawables inside the Graph.
setDefaultColors()	Assign colors to a default palette to all the drawables of the Graph.
setDrawable(drawable, index)	Accessor to a Drawable included in the Graph.
setDrawables(drawableCollection)	Accessor to the Drawables included in the Graph.
setGrid(showGrid)	Hide or shows grid of the Graph.
setGridColor(color)	Accessor to the indication of grid's color on the Graph.
setIntegerXTick(integerXTick)	Accessor to the integer x-axis ticks flag.
setIntegerYTick(integerYTick)	Accessor to the integer y-axis ticks flag.
setLegendFontSize(legendFontSize)	Accessor to the legend's font size of the Drawables inside the Graph.
setLegendPosition(position)	Accessor to the legend's position of the Drawables inside the Graph.
setLegends(legends)	Accessor to the legends of the Drawables inside the Graph.
setLogScale(logScale)	Accessor to the indication of axes' scale of the Graph.
setName(name)	Accessor to the object's name.
setTickLocation(tickLocation)	Accessor to the ticks location flag.
setTitle(title)	Accessor to the title of the Graph.
setXMargin(xMargin)	Accessor to the horizontal margin size.
setXTitle(title)	Accessor to the title of the X axe.
setYMargin(yMargin)	Accessor to the vertical margin size.
setYTitle(title)	Accessor to the title of the Y axe.

clean
draw
getRCommand

__init__(*args)

static GetValidLegendPositions()

Accessor to the list of valid legend positions.

Returns:

listPositionsDescription

All the valid legend positions.

Examples

>>> import openturns as ot
>>> print(ot.Graph.GetValidLegendPositions())
[,bottomright,bottom,bottomleft,left,topleft,top,topright,right,center]#10

static IsValidLegendPosition(position)

Test if the proposed legend position is valid or not.

Parameters:

positionstr

Proposed legend position of the Drawables inside the Graph.

Returns:

validitybool

True if the proposed legend position is valid, False if it is not.

See also

setLegendPosition, GetValidLegendPositions

Examples

>>> import openturns as ot
>>> print(ot.Graph.IsValidLegendPosition('lefttop'))
False
>>> print(ot.Graph.IsValidLegendPosition('topleft'))
True

add(*args)

Add drawable instances to the collection of drawables contained in Graph.

Available usages:

add(drawables)

add(aGraph)

Parameters:

drawablesDrawable or list of Drawable

Drawable to add in the Graph.

aGraphGraph

Graph to add in the Graph.

Notes

It adds the new drawables or graph inside the first one, with their legend. It keeps the graphical context of the first graph. Each drawable keeps its graphical context.

Warning

Different drawables might be colored the same…

erase(i)

Erase a drawable instance from the collection of drawables contained in Graph.

Parameters:

indexint

Index of the drawable instance to erase from the collection of drawables contained in Graph.

getAutomaticBoundingBox()

Accessor to the indication of automatic bounding box.

Returns:

autoBoundingBoxbool

Indicates if the bounding box is automatically created or not. The bounding box of the drawable element is a rectangle determined by its range along X and its range along Y.

getAxes()

Accessor to the indication of axes’ presence on the Graph.

Returns:

axesbool

True if the axes are drawn, False if they are hidden.

getBoundingBox()

Accessor to the bounding box of the whole plot.

Returns:

boundingBoxInterval of dimension 2

Bounding box of the drawable element, which is a rectangle determined by its range along X and its range along Y. This methods adds x/y margins according to the margin attributes.

getClassName()

Accessor to the object’s name.

Returns:

class_namestr

The object class name (object.__class__.__name__).

getColors()

Accessor to the colors of the Drawables included in the Graph.

Returns:

listColorsDescription

List of all the colors used for the Drawables contained inside the graph.

getDrawable(index)

Accessor to a Drawable included in the Graph.

Parameters:

indexpositive int

Position of the Drawable.

Returns:

drawableDrawable

Drawable included in the Graph at the index.

getDrawables()

Accessor to the Drawables included in the Graph.

Returns:

drawableslist of Drawable

Drawables included in the Graph.

getGrid()

Accessor to the indication of grid’s presence on the Graph.

Returns:

showGridbool

True to show the grid of the Graph, False to hide it. By default there is a gray grid.

getGridColor()

Accessor to the indication of grid’s color on the Graph.

Returns:

gridColorstr

Color of the grid. By default the grid is gray.

getId()

Accessor to the object’s id.

Returns:

idint

Internal unique identifier.

getImplementation()

Accessor to the underlying implementation.

Returns:

implImplementation

A copy of the underlying implementation object.

getIntegerXTick()

Accessor to the integer x-axis ticks flag.

Returns:

integerXTickbool

Whether to draw only integer ticks on the x-axis.

getIntegerYTick()

Accessor to the integer y-axis ticks flag.

Returns:

integerYTickbool

Whether to draw only integer ticks on the y-axis.

getLegendFontSize()

Accessor to the legends’ font size of the Drawables inside the Graph.

Returns:

fontSizefloat

Legends’ font size used for the drawables contained inside the Graph.

See also

setLegendFontSize

getLegendPosition()

Accessor to the legend’s position of the Drawables inside the Graph.

Returns:

positionstr

Legend’s position used for the drawables contained inside the Graph.

See also

setLegendPosition, GetValidLegendPositions

getLegends()

Accessor to the legends of the Drawables inside the Graph.

Returns:

legendsDescription

Legends used for the drawables contained inside the Graph.

getLogScale()

Accessor to the indication of axes’ scale of the Graph.

Returns:

scaleint

Indicates the type of the axes’s scale:

• 0: no log scale is used,

• 1: log scale is used only for horizontal data,

• 2: log scale is used only for vertical data,

• 3: log scale is used for both data.

getName()

Accessor to the object’s name.

Returns:

namestr

The name of the object.

getTickLocation()

Accessor to the ticks location flag.

Returns:

locint

Indicates the ticks location.

getTitle()

Accessor to the title of the Graph.

Returns:

titlestr

Title of the Graph.

getXTitle()

Accessor to the title of the X axe.

Returns:

Xtitlestr

Title of the X axe.

getYTitle()

Accessor to the title of the Y axe.

Returns:

Ytitlestr

Title of the Y axe.

setAutomaticBoundingBox(automaticBoundingBox)

Accessor to the indication of automatic bounding box.

Parameters:

autoBoundingBoxbool

Indicates if the bounding box is automatically created or not. The bounding box of the drawable element is a rectangle determined by its range along X and its range along Y.

setAxes(showAxes)

Accessor to the indication of axes’ presence on the Graph.

Parameters:

axesbool

True to draw the axes, False to hide the axes.

setBoundingBox(boundingBox)

Accessor to the bounding box of the whole plot.

Parameters:

boundingBoxInterval of dimension 2

Bounding box of the drawable element, which is a rectangle determined by its range along X and its range along Y.

setColors(colors)

Update the colors of the Drawables inside the Graph.

Parameters:

listColorssequence of str

List of the colors used for each Drawable of the Graph. If the listColors’s size is lower than the number of Drawables, the first colors of listColors are re-used. If it is greated than the number of Drawables, the last colors of the list are ignored.

The listColors argument can be the result of the static method BuildDefaultPalette() or BuildTableauPalette() of the Drawable object.

setDefaultColors()

Assign colors to a default palette to all the drawables of the Graph.

Notes

This method ensures that drawables of the Graph have different colors.

setDrawable(drawable, index)

Accessor to a Drawable included in the Graph.

Parameters:

drawableDrawable

Drawable included in the Graph.

indexint

Position of the Drawable.

setDrawables(drawableCollection)

Accessor to the Drawables included in the Graph.

Parameters:

drawableslist of Drawable

Drawables included in the Graph.

setGrid(showGrid)

Hide or shows grid of the Graph.

Parameters:

showGridbool

True to show the grid of the Graph, False to hide it.

setGridColor(color)

Accessor to the indication of grid’s color on the Graph.

Parameters:

gridColorstr

Color of the grid. By default the grid is gray.

setIntegerXTick(integerXTick)

Accessor to the integer x-axis ticks flag.

Parameters:

integerXTickbool

Whether to draw only integer ticks on the x-axis.

setIntegerYTick(integerYTick)

Accessor to the integer y-axis ticks flag.

Parameters:

integerYTickbool

Whether to draw only integer ticks on the y-axis.

setLegendFontSize(legendFontSize)

Accessor to the legend’s font size of the Drawables inside the Graph.

Parameters:

fontSizefloat

Legend’s font size used for the drawables contained inside the Graph.

Examples

>>> import openturns as ot
>>> fontSize = 1.0
>>> # Create an empty graph
>>> myGraph = ot.Graph('Some curves', 'x1', 'x2', True, 'topright', fontSize, 0)
>>> myGraph.setLegendFontSize(1.5)
>>> print(myGraph.getLegendFontSize())
1.5

setLegendPosition(position)

Accessor to the legend’s position of the Drawables inside the Graph.

Parameters:

positionstr

Legend’s position used for the drawables contained inside the Graph. The valid positions are given by the method GetValidLegendPositions().

Examples

>>> import openturns as ot
>>> position = 'topright'
>>> # Create an empty graph
>>> myGraph = ot.Graph('Some curves', 'x1', 'x2', True, position, 1.0, 0)
>>> myGraph.setLegendPosition('bottomleft')
>>> print(myGraph.getLegendPosition())
bottomleft

setLegends(legends)

Accessor to the legends of the Drawables inside the Graph.

Parameters:

legendssequence of str

Legends used for the drawables contained inside the Graph.

setLogScale(logScale)

Accessor to the indication of axes’ scale of the Graph.

Parameters:

scaleint

Indicates the type of the axes’s scale:

• ot.GraphImplementation.NONE or 0: no log scale is used,

• ot.GraphImplementation.LOGX or 1: log scale is used only for horizontal data,

• ot.GraphImplementation.LOGY or 2: log scale is used only for vertical data,

• ot.GraphImplementation.LOGXY or 3: log scale is used for both data.

setName(name)

Accessor to the object’s name.

Parameters:

namestr

The name of the object.

setTickLocation(tickLocation)

Accessor to the ticks location flag.

Parameters:

locint

Indicates the ticks location:

• ot.GraphImplementation.TICKNONE: no ticks,

• ot.GraphImplementation.TICKX: horizontal ticks,

• ot.GraphImplementation.TICKY: vertical ticks,

• ot.GraphImplementation.TICKXY: horizontal and vertical ticks.

setTitle(title)

Accessor to the title of the Graph.

Parameters:

titlestr

Title of the Graph.

setXMargin(xMargin)

Accessor to the horizontal margin size.

Parameters:

xMarginfloat

Horizontal margin ratio, defaults to 5% of the range on each side. In log-scale, it is interpreted as a power of 10; setting a value of 1 means a margin of one decade on each side. Defaults to Graph-DefaultHorizontalMargin map value.

setXTitle(title)

Accessor to the title of the X axe.

Parameters:

Xtitlestr

Title of the X axe.

setYMargin(yMargin)

Accessor to the vertical margin size.

Parameters:

yMarginfloat

Vertical margin ratio, defaults to 5% of the range on each side. In log-scale, it is interpreted as a power of 10; setting a value of 1 means a margin of one decade on each side. Defaults to Graph-DefaultVerticalMargin map value.

setYTitle(title)

Accessor to the title of the Y axe.

Parameters:

Ytitlestr

Title of the Y axe.

Examples using the class

Estimate Wilks and empirical quantile

Estimate Wilks and empirical quantile

Build and validate a linear model

Build and validate a linear model

Estimate correlation coefficients

Estimate correlation coefficients

Draw an histogram

Draw an histogram

Draw the empirical CDF

Draw the empirical CDF

Compare unconditional and conditional histograms

Compare unconditional and conditional histograms

Compute SRC indices confidence intervals

Compute SRC indices confidence intervals

Draw a survival function

Draw a survival function

Fit a parametric distribution

Fit a parametric distribution

Model a singular multivariate distribution

Model a singular multivariate distribution

Define a distribution from quantiles

Define a distribution from quantiles

Get the asymptotic distribution of the estimators

Get the asymptotic distribution of the estimators

Bandwidth sensitivity in kernel smoothing

Bandwidth sensitivity in kernel smoothing

Fit an extreme value distribution

Fit an extreme value distribution

Estimate a conditional quantile

Estimate a conditional quantile

Fit a non parametric distribution

Fit a non parametric distribution

Draw the QQ-Plot

Draw the QQ-Plot

Test identical distributions

Test identical distributions

Select fitted distributions

Select fitted distributions

Test Normality

Test Normality

Test the copula

Test the copula

Kolmogorov-Smirnov : understand the statistics

Kolmogorov-Smirnov : understand the statistics

Kolmogorov-Smirnov : understand the p-value

Kolmogorov-Smirnov : understand the p-value

Kolmogorov-Smirnov : get the statistics distribution

Kolmogorov-Smirnov : get the statistics distribution

Fit a parametric copula

Fit a parametric copula

Fit a non parametric copula

Fit a non parametric copula

Estimate a non stationary covariance function

Estimate a non stationary covariance function

Estimate a spectral density function

Estimate a spectral density function

Estimate a stationary covariance function

Estimate a stationary covariance function

Visualize clouds

Visualize clouds

Visualize sensitivity

Visualize sensitivity

Create the distribution of the maximum of independent distributions

Create the distribution of the maximum of independent distributions

Create a maximum entropy statistics distribution

Create a maximum entropy statistics distribution

Create a conditional distribution

Create a conditional distribution

Create your own distribution given its quantile function

Create your own distribution given its quantile function

Create a Bayes distribution

Create a Bayes distribution

Create a mixture of PDFs

Create a mixture of PDFs

Create an extreme value distribution

Create an extreme value distribution

Create and draw scalar distributions

Create and draw scalar distributions

Truncate a distribution

Truncate a distribution

Create a random mixture

Create a random mixture

Create and draw multivariate distributions

Create and draw multivariate distributions

Generate random variates by inverting the CDF

Generate random variates by inverting the CDF

Transform a distribution

Transform a distribution

Overview of univariate distribution management

Overview of univariate distribution management

Distribution manipulation

Distribution manipulation

Quick start guide

Quick start guide

Create a customized distribution or copula

Create a customized distribution or copula

Draw minimum volume level sets

Draw minimum volume level sets

Create the ordinal sum of copulas

Create the ordinal sum of copulas

Create a functional basis process

Create a functional basis process

Add a trend to a process

Add a trend to a process

Export a field to VTK

Export a field to VTK

Create a gaussian process from a cov. model using HMatrix

Create a gaussian process from a cov. model using HMatrix

Aggregate processes

Aggregate processes

Create a white noise process

Create a white noise process

Create a custom covariance model

Create a custom covariance model

Create a discrete Markov chain process

Create a discrete Markov chain process

Manipulate a time series

Manipulate a time series

Use the Box-Cox transformation

Use the Box-Cox transformation

Create a stationary covariance model

Create a stationary covariance model

Create a normal process

Create a normal process

Create a random walk process

Create a random walk process

Create a spectral model

Create a spectral model

Draw a field

Draw a field

Create a process from random vectors and processes

Create a process from random vectors and processes

Sample trajectories from a Gaussian Process with correlated outputs

Sample trajectories from a Gaussian Process with correlated outputs

Draw fields

Draw fields

Create and manipulate an ARMA process

Create and manipulate an ARMA process

Compare covariance models

Compare covariance models

Create a mesh

Create a mesh

Create a linear least squares model

Create a linear least squares model

Create a general linear model metamodel

Create a general linear model metamodel

Taylor approximations

Taylor approximations

Create a linear model

Create a linear model

Mixture of experts

Mixture of experts

Perfom stepwise regression

Perfom stepwise regression

Over-fitting and model selection

Over-fitting and model selection

Polynomial chaos graphs

Polynomial chaos graphs

Create a polynomial chaos metamodel

Create a polynomial chaos metamodel

Create a polynomial chaos for the Ishigami function: a quick start guide to polynomial chaos

Create a polynomial chaos for the Ishigami function: a quick start guide to polynomial chaos

Plot enumeration rules

Plot enumeration rules

Chaos cross-validation

Chaos cross-validation

Polynomial chaos is sensitive to the degree

Polynomial chaos is sensitive to the degree

Compute Sobol’ indices confidence intervals

Compute Sobol' indices confidence intervals

Kriging : propagate uncertainties

Kriging : propagate uncertainties

Kriging : multiple input dimensions

Kriging : multiple input dimensions

Kriging : draw the likelihood

Kriging : draw the likelihood

Kriging : cantilever beam model

Kriging : cantilever beam model

Example of multi output Kriging on the fire satellite model

Example of multi output Kriging on the fire satellite model

Kriging the cantilever beam model using HMAT

Kriging the cantilever beam model using HMAT

Kriging : generate trajectories from a metamodel

Kriging : generate trajectories from a metamodel

Choose the trend basis of a kriging metamodel

Choose the trend basis of a kriging metamodel

Kriging: metamodel of the Branin-Hoo function

Kriging: metamodel of the Branin-Hoo function

Kriging : quick-start

Kriging : quick-start

Sequentially adding new points to a kriging

Sequentially adding new points to a kriging

Advanced kriging

Advanced kriging

Kriging : choose a trend vector space

Kriging : choose a trend vector space

Kriging : draw covariance models

Kriging : draw covariance models

Viscous free fall: metamodel of a field function

Viscous free fall: metamodel of a field function

Metamodel of a field function

Metamodel of a field function

Evaluate the mean of a random vector by simulations

Evaluate the mean of a random vector by simulations

Analyse the central tendency of a cantilever beam

Analyse the central tendency of a cantilever beam

Estimate moments from Taylor expansions

Estimate moments from Taylor expansions

Use the Directional Sampling Algorithm

Use the Directional Sampling Algorithm

Estimate a flooding probability

Estimate a flooding probability

Use the Importance Sampling algorithm

Use the Importance Sampling algorithm

Create a threshold event

Create a threshold event

Estimate a probability with Monte-Carlo on axial stressed beam: a quick start guide to reliability

Estimate a probability with Monte-Carlo on axial stressed beam: a quick start guide to reliability

Exploitation of simulation algorithm results

Exploitation of simulation algorithm results

Use the FORM algorithm in case of several design points

Use the FORM algorithm in case of several design points

Non parametric Adaptive Importance Sampling (NAIS)

Non parametric Adaptive Importance Sampling (NAIS)

Create a domain event

Create a domain event

Use the FORM - SORM algorithms

Use the FORM - SORM algorithms

Subset Sampling

Subset Sampling

Time variant system reliability problem

Time variant system reliability problem

Axial stressed beam : comparing different methods to estimate a probability

Axial stressed beam : comparing different methods to estimate a probability

Create unions or intersections of events

Create unions or intersections of events

An illustrated example of a FORM probability estimate

An illustrated example of a FORM probability estimate

Estimate a process-based event probability

Estimate a process-based event probability

Estimate Sobol indices on a field to point function

Estimate Sobol indices on a field to point function

FAST sensitivity indices

FAST sensitivity indices

Parallel coordinates graph as sensitivity tool

Parallel coordinates graph as sensitivity tool

Estimate Sobol’ indices for a function with multivariate output

Estimate Sobol' indices for a function with multivariate output

Sobol’ sensitivity indices from chaos

Sobol' sensitivity indices from chaos

Use the ANCOVA indices

Use the ANCOVA indices

Estimate Sobol’ indices for the Ishigami function by a sampling method: a quick start guide to sensitivity analysis

Estimate Sobol' indices for the Ishigami function by a sampling method: a quick start guide to sensitivity analysis

The HSIC sensitivity indices: the Ishigami model

The HSIC sensitivity indices: the Ishigami model

Example of sensitivity analyses on the wing weight model

Example of sensitivity analyses on the wing weight model

Create a composite design of experiments

Create a composite design of experiments

Create a Monte Carlo design of experiments

Create a Monte Carlo design of experiments

Create a Gauss product design

Create a Gauss product design

Create a random design of experiments

Create a random design of experiments

Create mixed deterministic and probabilistic designs of experiments

Create mixed deterministic and probabilistic designs of experiments

Create a design of experiments with discrete and continuous variables

Create a design of experiments with discrete and continuous variables

Deterministic design of experiments

Deterministic design of experiments

Create a deterministic design of experiments

Create a deterministic design of experiments

Generate low discrepancy sequences

Generate low discrepancy sequences

Optimize an LHS design of experiments

Optimize an LHS design of experiments

Create a symbolic function

Create a symbolic function

Create a quadratic function

Create a quadratic function

Define a function with a field output: the viscous free fall example

Define a function with a field output: the viscous free fall example

Define a connection function with a field output

Define a connection function with a field output

Logistic growth model

Logistic growth model

Function manipulation

Function manipulation

Calibration without observed inputs

Calibration without observed inputs

Calibration of the logistic model

Calibration of the logistic model

Calibration of the Chaboche mechanical model

Calibration of the Chaboche mechanical model

Calibration of the flooding model

Calibration of the flooding model

Gibbs sampling of the posterior distribution

Gibbs sampling of the posterior distribution

Sampling from an unnormalized probability density

Sampling from an unnormalized probability density

Bayesian calibration of a computer code

Bayesian calibration of a computer code

Bayesian calibration of the flooding model

Bayesian calibration of the flooding model

Posterior sampling using a PythonDistribution

Posterior sampling using a PythonDistribution

Customize your Metropolis-Hastings algorithm

Customize your Metropolis-Hastings algorithm

Linear Regression with interval-censored observations

Linear Regression with interval-censored observations

Estimate an integral

Estimate an integral

Iterated Functions System

Iterated Functions System

Optimization with constraints

Optimization with constraints

Optimization using NLopt

Optimization using NLopt

Quick start guide to optimization

Quick start guide to optimization

Multi-objective optimization using Pagmo

Multi-objective optimization using Pagmo

Optimization of the Rastrigin test function

Optimization of the Rastrigin test function

Optimization using dlib

Optimization using dlib

EfficientGlobalOptimization examples

EfficientGlobalOptimization examples

Estimate moments iteratively

Estimate moments iteratively

Estimate extrema iteratively

Estimate extrema iteratively

Estimate threshold exceedance iteratively

Estimate threshold exceedance iteratively

How to fill an area

How to fill an area

Plot the log-likelihood contours of a distribution

Plot the log-likelihood contours of a distribution

A quick start guide to graphs

A quick start guide to graphs