GridLayout¶
(Source code
, png
)
- class GridLayout(nbRows=0, nbColumns=0)¶
Organize subgraphs in a grid.
- Parameters:
- nb_rowsint
Number of grid rows
- nb_colsint
Number of grid columns
Examples
>>> import openturns as ot >>> grid = ot.GridLayout(2, 3) >>> for j in range(grid.getNbColumns()): ... beta = 1.0 + j ... grid.setGraph(0, j, ot.Gumbel(beta, 0.0).drawPDF()) ... grid.setGraph(1, j, ot.Gumbel(beta, 0.0).drawCDF())
Methods
Accessor to the object's name.
getGraph
(i, j)Subgraph accessor (grid layout only).
Accessor to the collection of graphs.
getName
()Accessor to the object's name.
Column count accessor (grid layout only).
Row count accessor (grid layout only).
getTitle
()Accessor to the title.
hasName
()Test if the object is named.
setAxes
(showAxes)Accessor to the indication of axes' presence on the Graph.
setGraph
(i, j, elt)Subgraph accessor (grid layout only).
setGraphCollection
(coll)Accessor to the collection of graphs.
setLayout
(nbRows, nbColumns)Accessor to the layout.
setLegendPosition
(position)Accessor to the legend's position of the subgraphs.
setName
(name)Accessor to the object's name.
setTitle
(title)Accessor to the title.
- __init__(nbRows=0, nbColumns=0)¶
- getClassName()¶
Accessor to the object’s name.
- Returns:
- class_namestr
The object class name (object.__class__.__name__).
- getGraph(i, j)¶
Subgraph accessor (grid layout only).
- Parameters:
- iint
Row index
- jint
Column index
- Returns:
- graph
Graph
Subgraph at (i, j).
- graph
- getGraphCollection()¶
Accessor to the collection of graphs.
- Returns:
- collCollection of
Graph
The graphs stored into the
GridLayout
.
- collCollection of
- getName()¶
Accessor to the object’s name.
- Returns:
- namestr
The name of the object.
- getNbColumns()¶
Column count accessor (grid layout only).
- Returns:
- nb_rowsint
Number of grid columns.
- getNbRows()¶
Row count accessor (grid layout only).
- Returns:
- nb_rowsint
Number of grid rows.
- getTitle()¶
Accessor to the title.
- Returns:
- titlestr
Graph title.
- hasName()¶
Test if the object is named.
- Returns:
- hasNamebool
True if the name is not empty.
- setAxes(showAxes)¶
Accessor to the indication of axes’ presence on the Graph.
- Parameters:
- axesbool
True to draw the axes, False to hide the axes.
- setGraph(i, j, elt)¶
Subgraph accessor (grid layout only).
- Parameters:
- iint
Row index
- jint
Column index
- graph
Graph
Subgraph at (i, j).
- setGraphCollection(coll)¶
Accessor to the collection of graphs.
- Parameters:
- collsequence of
Graph
The graphs to store into the
GridLayout
. The collection must have at most elements.
- collsequence of
- setLayout(nbRows, nbColumns)¶
Accessor to the layout.
- Parameters:
- nbRowsint
The new number of rows.
- nbColumnsint
The new number of columns.
Notes
If the new layout contains fewer graphs than the old layout, the remaining graphs are removed from the layout.
- setLegendPosition(position)¶
Accessor to the legend’s position of the subgraphs.
- Parameters:
- positionstr
Legend’s position used for the subgraphs contained inside the Graph.
- setName(name)¶
Accessor to the object’s name.
- Parameters:
- namestr
The name of the object.
- setTitle(title)¶
Accessor to the title.
- Parameters:
- titlestr
Graph title.
Examples using the class¶
Estimate a GEV on the Venice sea-levels data
Bandwidth sensitivity in kernel smoothing
Estimate a GPD on the Wooster temperature data
Estimate a GPD on the Dow Jones Index data
Estimate a GPD on the daily rainfall data
Estimate a GEV on the Fremantle sea-levels data
Estimate tail dependence coefficients on the wave-surge data
Estimate tail dependence coefficients on the wind data
Quick start guide to distributions
Over-fitting and model selection
Create a polynomial chaos metamodel by integration on the cantilever beam
Create a polynomial chaos for the Ishigami function: a quick start guide to polynomial chaos
Kriging : cantilever beam model
Kriging the cantilever beam model using HMAT
Kriging: choose a polynomial trend on the beam model
Example of sensitivity analyses on the wing weight model
Calibrate a parametric model: a quick-start guide to calibration
Calibration of the logistic model
Calibration of the deflection of a tube
Calibration of the flooding model
Calibration of the Chaboche mechanical model
Posterior sampling using a PythonDistribution
Bayesian calibration of a computer code
Bayesian calibration of the flooding model
Linear Regression with interval-censored observations