View

(Source code, svg)

class View(graph, pixelsize=None, figure=None, figure_kw=None, axes=[], plot_kw=None, axes_kw=None, bar_kw=None, pie_kw=None, polygon_kw=None, polygoncollection_kw=None, contour_kw=None, step_kw=None, clabel_kw=None, scatter_kw=None, text_kw=None, legend_kw=None, colorbar_kw=None, add_legend=True, square_axes=False, **kwargs)

Create the figure.

Parameters:

graphGraph, Drawable or GridLayout

An object to draw.

pixelsize2-tuple of int

The requested size in pixels (width, height).

figurematplotlib.figure.Figure

The figure to draw on.

figure_kwdict, optional

Passed on to matplotlib.pyplot.figure kwargs

axesmatplotlib.axes.Axes

The axes to draw on.

plot_kwdict, optional

Used when drawing Curve drawables Passed on as matplotlib.axes.Axes.plot kwargs

axes_kwdict, optional

Passed on to matplotlib.figure.Figure.add_subplot kwargs

bar_kwdict, optional

Used when drawing BarPlot drawables Passed on to matplotlib.pyplot.bar kwargs

pie_kwdict, optional

Used when drawing Pie drawables Passed on to matplotlib.pyplot.pie kwargs

polygon_kwdict, optional

Used when drawing Polygon drawables Passed on to matplotlib.patches.Polygon kwargs

polygoncollection_kwdict, optional

Used when drawing PolygonArray drawables Passed on to matplotlib.collection.PolygonCollection kwargs

contour_kwdict, optional

Used when drawing Contour drawables Passed on to matplotlib.pyplot.contour kwargs

clabel_kwdict, optional

Used when drawing Contour drawables Passed on to matplotlib.pyplot.clabel kwargs

scatter_kwdict, optional

Used when drawing Cloud drawables Passed on to matplotlib.pyplot.scatter kwargs

step_kwdict, optional

Used when drawing Staircase drawables Passed on to matplotlib.pyplot.step kwargs

text_kwdict, optional

Used when drawing Pairs, Text drawables Passed on to matplotlib.axes.Axes.text kwargs

legend_kwdict, optional

Passed on to matplotlib.axes.Axes.legend kwargs

colorbar_kwdict, optional

Passed on to matplotlib.figure.Figure.colorbar kwargs

add_legendbool, optional

Adds a legend if True. Default is True.

square_axesbool, optional

Forces the axes to share the same scale if True. Default is False.

Methods

ShowAll(**kwargs)	Display all graphs.
close()	Close the figure.
getAxes()	Get the matrix of Axes objects if the graph is a GridLayout, the list of Axes objects otherwise.
getContourSets()	Get the list of QuadContourSet objects.
getFigure()	Accessor to the underlying figure object.
getSubviews()	Get the matrix of View objects if the graph is GridLayout, None otherwise.
save(fname, **kwargs)	Save the graph as file.
show(**kwargs)	Display the graph.

Examples

>>> import openturns as ot
>>> from openturns.viewer import View
>>> graph = ot.Normal().drawPDF()
>>> view = View(graph, plot_kw={'color':'blue'})
>>> view.save('graph.png', dpi=100)
>>> view.show()

__init__(graph, pixelsize=None, figure=None, figure_kw=None, axes=[], plot_kw=None, axes_kw=None, bar_kw=None, pie_kw=None, polygon_kw=None, polygoncollection_kw=None, contour_kw=None, step_kw=None, clabel_kw=None, scatter_kw=None, text_kw=None, legend_kw=None, colorbar_kw=None, add_legend=True, square_axes=False, **kwargs)

static ShowAll(**kwargs)

Display all graphs.

Examples

>>> import openturns as ot
>>> import openturns.viewer as otv
>>> n = ot.Normal()
>>> graph = n.drawPDF()
>>> view = otv.View(graph)
>>> u = ot.Uniform()
>>> graph = u.drawPDF()
>>> view = otv.View(graph)
>>> otv.View.ShowAll()

close()

Close the figure.

Examples

>>> import openturns as ot
>>> import openturns.viewer as otv
>>> n = ot.Normal()
>>> graph = n.drawPDF()
>>> view = otv.View(graph)
>>> view.close()

getAxes()

Get the matrix of Axes objects if the graph is a GridLayout, the list of Axes objects otherwise.

See matplotlib.axes.Axes for further information.

Examples

>>> import openturns as ot
>>> import openturns.viewer as otv
>>> n = ot.Normal()
>>> graph = n.drawPDF()
>>> view = otv.View(graph)
>>> axes = view.getAxes()
>>> _ = axes[0].set_ylim(-0.1, 1.0);

getContourSets()

Get the list of QuadContourSet objects.

See matplotlib.contour.QuadContourSet for further information.

Examples

>>> import openturns as ot
>>> import openturns.viewer as otv
>>> f = ot.SymbolicFunction(['x', 'y'], ['exp(-sin(cos(y)^2*x^2+sin(x)^2*y^2))'])
>>> view = otv.View(f.draw([0.,0.],[10.,10.],[50]*2))
>>> contoursets = view.getContourSets()
>>> colorbar = view.getFigure().colorbar(contoursets[0]);

getFigure()

Accessor to the underlying figure object.

See matplotlib.figure.Figure for further information.

Examples

>>> import openturns as ot
>>> from openturns.viewer import View
>>> graph = ot.Normal().drawPDF()
>>> view = View(graph)
>>> fig = view.getFigure()
>>> _ = fig.suptitle("The suptitle");

getSubviews()

Get the matrix of View objects if the graph is GridLayout, None otherwise.

Examples

>>> import openturns as ot
>>> import openturns.viewer as otv
>>> f = ot.SymbolicFunction(['x', 'y'], ['exp(-sin(cos(y)^2*x^2+sin(x)^2*y^2))'])
>>> grid = ot.GridLayout(1, 2)
>>> grid.setGraphCollection(ot.graph._GraphCollection([f.draw(0, 0, [0., 0.], 0., 10., 50), f.draw([0., 0.], [10., 10.], [50]*2)]))
>>> view = otv.View(grid)
>>> colorbar = view.getFigure().colorbar(view.getSubviews()[0][1].getContourSets()[0])

save(fname, **kwargs)

Save the graph as file.

Parameters:

fnamebool, optional

A string containing a path to a filename from which file format is deduced.

kwargsdict

See matplotlib.figure.Figure.savefig documentation for valid keyword arguments.

Examples

>>> import openturns as ot
>>> from openturns.viewer import View
>>> graph = ot.Normal().drawPDF()
>>> view = View(graph)
>>> view.save('graph.png', dpi=100)

show(**kwargs)

Display the graph.

See matplotlib.figure.Figure.show

Examples

>>> import openturns as ot
>>> import openturns.viewer as otv
>>> n = ot.Normal()
>>> graph = n.drawPDF()
>>> view = otv.View(graph)
>>> view.show()

Examples using the class

Customize your Metropolis-Hastings algorithm

Customize your Metropolis-Hastings algorithm

Bayesian calibration of a computer code

Bayesian calibration of a computer code

Bayesian calibration of the flooding model

Bayesian calibration of the flooding model

Gibbs sampling of the posterior distribution

Gibbs sampling of the posterior distribution

Linear Regression with interval-censored observations

Linear Regression with interval-censored observations

Bayesian calibration of hierarchical fission gas release models

Bayesian calibration of hierarchical fission gas release models

Sampling from an unscaled probability density

Sampling from an unscaled probability density

Posterior sampling using a PythonDistribution

Posterior sampling using a PythonDistribution

Calibration of the Chaboche mechanical model

Calibration of the Chaboche mechanical model

Calibration of the deflection of a tube

Calibration of the deflection of a tube

Calibration of the flooding model

Calibration of the flooding model

Calibration of the logistic model

Calibration of the logistic model

Calibrate a parametric model: a quick-start guide to calibration

Calibrate a parametric model: a quick-start guide to calibration

Calibration without observed inputs

Calibration without observed inputs

Generate observations of the Chaboche mechanical model

Generate observations of the Chaboche mechanical model

Generate flooding model observations

Generate flooding model observations

Get the asymptotic distribution of the estimators

Get the asymptotic distribution of the estimators

Estimate a conditional quantile

Estimate a conditional quantile

Estimate a GEV on the Fremantle sea-levels data

Estimate a GEV on the Fremantle sea-levels data

Estimate a GEV on the Port Pirie sea-levels data

Estimate a GEV on the Port Pirie sea-levels data

Estimate a GEV on race times data

Estimate a GEV on race times data

Estimate a GEV on the Venice sea-levels data

Estimate a GEV on the Venice sea-levels data

Estimate a GPD on the Dow Jones Index data

Estimate a GPD on the Dow Jones Index data

Estimate a GPD on the daily rainfall data

Estimate a GPD on the daily rainfall data

Estimate a GPD on the Wooster temperature data

Estimate a GPD on the Wooster temperature data

Fit a non parametric distribution

Fit a non parametric distribution

Fit a parametric distribution

Fit a parametric distribution

Fit an extreme value distribution

Fit an extreme value distribution

Model a singular multivariate distribution

Model a singular multivariate distribution

Define a distribution from quantiles

Define a distribution from quantiles

Bandwidth sensitivity in kernel smoothing

Bandwidth sensitivity in kernel smoothing

Fit a parametric copula

Fit a parametric copula

Estimate tail dependence coefficients on the wave-surge data

Estimate tail dependence coefficients on the wave-surge data

Estimate tail dependence coefficients on the wind data

Estimate tail dependence coefficients on the wind data

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 sensitivity

Visualize sensitivity

Visualize clouds

Visualize clouds

Visualize pairs between two samples

Visualize pairs between two samples

Visualize pairs

Visualize pairs

Build and validate a linear model

Build and validate a linear model

Estimate correlation coefficients

Estimate correlation coefficients

Compare unconditional and conditional histograms

Compare unconditional and conditional histograms

Draw a survival function

Draw a survival function

Compute squared SRC indices confidence intervals

Compute squared SRC indices confidence intervals

Draw the empirical CDF

Draw the empirical CDF

Draw an histogram

Draw an histogram

Select fitted distributions

Select fitted distributions

Kolmogorov-Smirnov : get the statistics distribution

Kolmogorov-Smirnov : get the statistics distribution

Kolmogorov-Smirnov : understand the p-value

Kolmogorov-Smirnov : understand the p-value

Kolmogorov-Smirnov : understand the statistics

Kolmogorov-Smirnov : understand the statistics

Draw the QQ-Plot

Draw the QQ-Plot

Test identical distributions

Test identical distributions

Test the copula

Test the copula

Test Normality

Test Normality

Function manipulation

Function manipulation

Logistic growth model

Logistic growth model

Create a process sample from a sample

Create a process sample from a sample

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

Create univariate functions

Create univariate functions

Create a quadratic function

Create a quadratic function

Create a symbolic function

Create a symbolic function

Getting started

Getting started

Gaussian Process Regression vs KrigingAlgorithm

Gaussian Process Regression vs KrigingAlgorithm

A quick start guide to graphs

A quick start guide to graphs

A quick start guide to contours

A quick start guide to contours

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

Metamodel of a field function

Metamodel of a field function

Validation of a Karhunen-Loeve decomposition

Validation of a Karhunen-Loeve decomposition

Viscous free fall: metamodel of a field function

Viscous free fall: metamodel of a field function

Create a linear least squares model

Create a linear least squares model

Distribution of estimators in linear regression

Distribution of estimators in linear regression

Mixture of experts

Mixture of experts

Create a general linear model metamodel

Create a general linear model metamodel

Create a linear model

Create a linear model

Over-fitting and model selection

Over-fitting and model selection

Perform stepwise regression

Perform stepwise regression

Taylor approximations

Taylor approximations

Kriging : draw covariance models

Kriging : draw covariance models

Gaussian Process Regression: multiple input dimensions

Gaussian Process Regression: multiple input dimensions

Gaussian Process Regression : quick-start

Gaussian Process Regression : quick-start

Gaussian Process-based active learning for reliability

Gaussian Process-based active learning for reliability

Advanced Gaussian process regression

Advanced Gaussian process regression

Gaussian Process Regression: choose a polynomial trend on the beam model

Gaussian Process Regression: choose a polynomial trend on the beam model

Gaussian Process Regression : cantilever beam model

Gaussian Process Regression : cantilever beam model

Gaussian Process Regression: choose a polynomial trend

Gaussian Process Regression: choose a polynomial trend

Gaussian process regression: draw the likelihood

Gaussian process regression: draw the likelihood

Gaussian Process Regression : generate trajectories from the metamodel

Gaussian Process Regression : generate trajectories from the metamodel

Gaussian Process Regression: metamodel of the Branin-Hoo function

Gaussian Process Regression: metamodel of the Branin-Hoo function

Example of multi output Gaussian Process Regression on the fire satellite model

Example of multi output Gaussian Process Regression on the fire satellite model

Sequentially adding new points to a Gaussian Process metamodel

Sequentially adding new points to a Gaussian Process metamodel

Gaussian Process Regression: propagate uncertainties

Gaussian Process Regression: propagate uncertainties

Polynomial chaos is sensitive to the degree

Polynomial chaos is sensitive to the degree

Create a polynomial chaos metamodel by integration on the cantilever beam

Create a polynomial chaos metamodel by integration on the cantilever beam

Create a sparse chaos by integration

Create a sparse chaos by integration

Conditional expectation of a polynomial chaos expansion

Conditional expectation of a polynomial chaos expansion

Polynomial chaos expansion cross-validation

Polynomial chaos expansion cross-validation

Validate a polynomial chaos

Validate a polynomial chaos

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

Compute Sobol’ indices confidence intervals

Compute Sobol' indices confidence intervals

Plot enumeration rules

Plot enumeration rules

Create a polynomial chaos metamodel from a data set

Create a polynomial chaos metamodel from a data set

Polynomial chaos graphs

Polynomial chaos graphs

Integrate a function with Gauss-Kronrod algorithm

Integrate a function with Gauss-Kronrod algorithm

Estimate a multivariate integral with IteratedQuadrature

Estimate a multivariate integral with IteratedQuadrature

Iterated Functions System

Iterated Functions System

Compute leave-one-out error of a polynomial chaos expansion

Compute leave-one-out error of a polynomial chaos expansion

Compute confidence intervals of a regression model from data

Compute confidence intervals of a regression model from data

Compute confidence intervals of a univariate noisy function

Compute confidence intervals of a univariate noisy function

Estimate extrema iteratively

Estimate extrema iteratively

Estimate moments iteratively

Estimate moments iteratively

Estimate threshold exceedance iteratively

Estimate threshold exceedance iteratively

EfficientGlobalOptimization examples

EfficientGlobalOptimization examples

Optimization with constraints

Optimization with constraints

Optimization using dlib

Optimization using dlib

Optimization using NLopt

Optimization using NLopt

Multi-objective optimization using Pagmo

Multi-objective optimization using Pagmo

Optimization of the Rastrigin test function

Optimization of the Rastrigin test function

Quick start guide to optimization

Quick start guide to optimization

Create the ordinal sum of copulas

Create the ordinal sum of copulas

Create a Joint by Conditioning distribution

Create a Joint by Conditioning distribution

Create and draw scalar distributions

Create and draw scalar distributions

Create and draw multivariate distributions

Create and draw multivariate distributions

Create an extreme value distribution

Create an extreme value distribution

Create a random mixture

Create a random mixture

Create your own distribution given its quantile function

Create your own distribution given its quantile function

Create a deconditioned distribution

Create a deconditioned distribution

Create a deconditioned random vector

Create a deconditioned random vector

Distribution manipulation

Distribution manipulation

Transform a distribution

Transform a distribution

Generate random variates by inverting the CDF

Generate random variates by inverting the CDF

Draw minimum volume level sets

Draw minimum volume level sets

Create a mixture of distributions

Create a mixture of distributions

Overview of univariate distribution management

Overview of univariate distribution management

Create a Point Conditional Distribution

Create a Point Conditional Distribution

Compare frequentist and Bayesian estimation

Compare frequentist and Bayesian estimation

Create a customized distribution or copula

Create a customized distribution or copula

Quick start guide to distributions

Quick start guide to distributions

Truncate a distribution

Truncate a distribution

Create a maximum entropy order statistics distribution

Create a maximum entropy order statistics distribution

Create the distribution of the maximum of distributions

Create the distribution of the maximum of distributions

Compute the joint distribution of order statistics

Compute the joint distribution of order statistics

Use the Ratio of Uniforms algorithm to sample a distribution

Use the Ratio of Uniforms algorithm to sample a distribution

Add a trend to a process

Add a trend to a process

Aggregate processes

Aggregate processes

Use the Box-Cox transformation

Use the Box-Cox transformation

Create and manipulate an ARMA process

Create and manipulate an ARMA process

Create a mesh

Create a mesh

Create a Gaussian process

Create a Gaussian process

Create a discrete Markov chain process

Create a discrete Markov chain process

Export a field to VTK

Export a field to VTK

Draw a field

Draw a field

Create a functional basis process

Create a functional basis process

Compare covariance models

Compare covariance models

Sample trajectories from a Gaussian Process with correlated outputs

Sample trajectories from a Gaussian Process with correlated outputs

Create a process from random vectors and processes

Create a process from random vectors and processes

Manipulate stochastic processes

Manipulate stochastic processes

Create a random walk process

Create a random walk process

Manipulate a time series

Manipulate a time series

Create a custom covariance model

Create a custom covariance model

Create a spectral model

Create a spectral model

Create a white noise process

Create a white noise process

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

Evaluate the mean of a random vector by simulations

Evaluate the mean of a random vector by simulations

Create a composite design of experiments

Create a composite design of experiments

Create a deterministic design of experiments

Create a deterministic design of experiments

Create a random design of experiments

Create a random design 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 Gauss product design

Create a Gauss product design

LOLA-Voronoi sequential design of experiment

LOLA-Voronoi sequential design of experiment

Generate low discrepancy sequences

Generate low discrepancy sequences

Create mixed deterministic and probabilistic designs of experiments

Create mixed deterministic and probabilistic designs of experiments

Create a Monte Carlo design of experiments

Create a Monte Carlo design of experiments

Optimize an LHS design of experiments

Optimize an LHS design of experiments

Probabilistic design of experiments

Probabilistic design of experiments

Plot the Smolyak quadrature

Plot the Smolyak quadrature

Plot Smolyak multi-indices

Plot Smolyak multi-indices

Merge nodes in Smolyak quadrature

Merge nodes in Smolyak quadrature

Use the Smolyak quadrature

Use the Smolyak quadrature

Axial stressed beam : comparing different methods to estimate a probability

Axial stressed beam : comparing different methods to estimate a probability

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

Create a domain event

Create a domain event

Create a threshold event

Create a threshold event

Cross Entropy Importance Sampling

Cross Entropy Importance Sampling

Use the Directional Sampling Algorithm

Use the Directional Sampling Algorithm

Use the FORM - SORM algorithms

Use the FORM - SORM algorithms

Use the Importance Sampling algorithm

Use the Importance Sampling algorithm

Create unions or intersections of events

Create unions or intersections of events

Estimate a flooding probability

Estimate a flooding probability

An illustrated example of a FORM probability estimate

An illustrated example of a FORM probability estimate

Estimate a probability using Line Sampling

Estimate a probability using Line Sampling

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)

Time variant system reliability problem

Time variant system reliability problem

Exploitation of simulation algorithm results

Exploitation of simulation algorithm results

Estimate a buckling probability

Estimate a buckling probability

Subset Sampling

Subset Sampling

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

Sobol’ sensitivity indices from chaos

Sobol' sensitivity indices from chaos

The HSIC sensitivity indices: the Ishigami model

The HSIC sensitivity indices: the Ishigami model

Use the ANCOVA indices

Use the ANCOVA indices

FAST sensitivity indices

FAST sensitivity indices

Parallel coordinates graph as sensitivity tool

Parallel coordinates graph as sensitivity tool

Sobol’ sensitivity indices using rank-based algorithm

Sobol' sensitivity indices using rank-based algorithm

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

Estimate Sobol’ indices for a function with multivariate output

Estimate Sobol' indices for a function with multivariate output

Estimate Sobol’ indices for the beam by simulation algorithm

Estimate Sobol' indices for the beam by simulation algorithm

Example of sensitivity analyses on the wing weight model

Example of sensitivity analyses on the wing weight model