otbenchmark
DrawEvent¶
- class DrawEvent(event, insideEventPointColor='lightsalmon3', outsideEventPointColor='darkseagreen3', insideEventFillColor='lightsalmon1', outsideEventFillColor='darkseagreen1')¶
Methods
buildCrossCutFunction(i, j)Create the cross-cut parametric function for projection (i,j).
draw(bounds[, sampleSize, nX, nY, ...])Draw the event, superimposing the graphics.
drawInputOutputSample(inputSample, outputSample)Draw the sample of an event.
drawLimitState(bounds[, nX, nY])Draw the limit state of an event.
drawLimitStateCrossCut(bounds[, i, j, nX, nY])Draw the cross-cut of the limit state of an event on a cross-cut.
drawSample(sampleSize)Draw the sample of an event.
drawSampleCrossCut(sampleSize[, i, j])Draw the sample of an event on a cross-cut.
fillEvent(bounds[, nX, nY])Draw the sample of an event.
fillEventCrossCut(bounds[, i, j, nX, nY])Fill the space inside an event with a color on a cross-cut.
- __init__(event, insideEventPointColor='lightsalmon3', outsideEventPointColor='darkseagreen3', insideEventFillColor='lightsalmon1', outsideEventFillColor='darkseagreen1')¶
Create an event with draw services.
The limit state function must be in dimension > 1 only.
- Parameters:
- eventan ot.Event
The event we want to draw.
- insideEventPointColora string
The color of the points inside the event. Suggested colors: “forestgreen”, “darkolivegreen”.
- outsideEventPointColora string
The color of the points outside of the event. Suggested colors: “orange”, “orangered”.
- insideEventFillColora string
The color of the filled domains inside the event.
- outsideEventFillColora string
The color of the filled domains outside of the event.
- buildCrossCutFunction(i, j)¶
Create the cross-cut parametric function for projection (i,j).
The parametric function is the event function where the only free variables are (X[i], X[j]) and other variables are set to the mean point.
We must have i < j, otherwise the function would be evaluated at the wrong input X.
- Parameters:
- iint
The index of the first marginal of the cross-cut.
- jint
The index of the second marginal of the cross-cut.
- Returns:
- crosscutFunctionot.Function
The cross-cut function.
- draw(bounds, sampleSize=1000, nX=50, nY=50, drawLimitState=True, drawSample=True, fillEvent=False)¶
Draw the event, superimposing the graphics.
- Parameters:
- sampleSize: int
The sample size.
- bounds: an ot.Interval
The lower and upper bounds of the cross-cut interval.
- nXint
The number of points in the X axis.
- nYint
The number of points in the Y axis.
- drawLimitStatebool
If True, draw the limit state surface.
- drawSamplebool
If True, draw the sample.
- fillEventbool
If True, fill the event.
- Returns:
- figMatplotlib.figure
The plot.
- drawInputOutputSample(inputSample, outputSample)¶
Draw the sample of an event. The points inside and outside the event are colored.
- Parameters:
- inputSample: an ot.Sample
The input 2D sample.
- outputSample: an ot.Sample
The output 1D sample.
- Returns
- ——-
- None.
- drawLimitState(bounds, nX=50, nY=50)¶
Draw the limit state of an event.
This produces a matrix of cross cuts.
- Parameters:
- bounds: an ot.Interval
The lower and upper bounds of the interval.
- nXan int
The number of points in the X axis.
- nYan int
The number of points in the Y axis.
- Returns:
- figmatplotlib.figure
The plot.
- drawLimitStateCrossCut(bounds, i=0, j=1, nX=50, nY=50)¶
Draw the cross-cut of the limit state of an event on a cross-cut.
- Parameters:
- bounds: an ot.Interval
The lower and upper bounds of the cross-cut interval.
- iint
The index of the first marginal of the cross-cut.
- jint
The index of the second marginal of the cross-cut.
- nXan int
The number of points in the X axis.
- nYan int
The number of points in the Y axis.
- Returns:
- graphot.Graph
The plot of the (i, j) cross cut.
- drawSample(sampleSize)¶
Draw the sample of an event.
The points inside and outside the event are colored.
- Parameters:
- sampleSize: int
The sample size.
- Returns:
- figMatplotlib.figure
The plot.
- drawSampleCrossCut(sampleSize, i=0, j=1)¶
Draw the sample of an event on a cross-cut.
The points inside and outside the event are colored.
The algorithm uses the getMarginal() method of the distribution in order to create the bivariate distribution. Then the sample is generated from this bivariate distribution. A more rigorous method would draw the conditional distribution, but this might reduce the performance in general. https://github.com/mbaudin47/otbenchmark/issues/47
- Parameters:
- sampleSize: int
The sample size.
- iint
The index of the first marginal of the cross-cut.
- jint
The index of the second marginal of the cross-cut.
- Returns:
- graphot.Graph
The plot.
- fillEvent(bounds, nX=50, nY=50)¶
Draw the sample of an event.
The points inside and outside the event are colored.
- Parameters:
- sampleSize: int
The sample size.
- Returns:
- figMatplotlib.figure
The plot.
- fillEventCrossCut(bounds, i=0, j=1, nX=50, nY=50)¶
Fill the space inside an event with a color on a cross-cut.
- Parameters:
- bounds: an ot.Interval
The lower and upper bounds of the cross-cut interval.
- iint
The index of the first marginal of the cross-cut.
- jint
The index of the second marginal of the cross-cut.
- nXint
The number of points in the X axis.
- nYint
The number of points in the Y axis.
- Returns:
- graphot.Graph
The plot.