How to set Axes limits on OpenTurns Viewer?

Question

I'm using openturns to find the best fit distribution for my data. I got to plot it alright, but the X limit is far bigger than I'd like. My code is:

import statsmodels.api as sm
import openturns as ot
import openturns.viewer as otv

data = in_seconds

sample = ot.Sample(data, 1)
tested_factories = ot.DistributionFactory.GetContinuousUniVariateFactories()
best_model, best_bic = ot.FittingTest.BestModelBIC(sample, tested_factories)
print(best_model)


graph = ot.HistogramFactory().build(sample).drawPDF()
bestPDF = best_model.drawPDF()
bestPDF.setColors(["blue"])
graph.add(bestPDF)

name = best_model.getImplementation().getClassName()
graph.setLegends(["Histogram",name])
graph.setXTitle("Latências (segundos)")
graph.setYTitle("Frequência")


otv.View(graph)

I'd like to set X limits as something like "graph.setXLim", as we'd do in matplotlib, but I'm stuck with it as I'm new to OpenTurns.

Thanks in advance.

Answer 1

Any OpenTURNS graph has a getBoundingBox method which returns the bounding box as a dimension 2 Interval. We can get/set the lower and upper bounds of this interval with getLowerBound and getUpperBound. Each of these bounds is a Point with dimension 2. Hence, we can set the bounds of the graphics prior to the use of the View class.

In the following example, I create a simple graph containing the PDF of the gaussian distribution.

import openturns as ot
import openturns.viewer as otv
n = ot.Normal()
graph = n.drawPDF()
_ = otv.View(graph)

Suppose that I want to set the lower X axis to -1. The script:

boundingBox = graph.getBoundingBox()
lb = boundingBox.getLowerBound()
print(lb)

produces:

[-4.10428,-0.0195499]

The first value in the Point is the X lower bound and the second is the Y lower bound. The following script sets the first component of the lower bound to -1, wraps the lower bound into the bounding box and sets the bounding box into the graph.

lb[0] = -1.0
boundingBox.setLowerBound(lb)
graph.setBoundingBox(boundingBox)
_ = otv.View(graph)

This produces the following graph.

The advantage of these methods is that they configure the graphics from the library, before the rendering is done by Matplotlib. The drawback is that they are a little more verbose than the Matplotlib counterpart.

Answer 2

Here is a minimal example adapted from openTURNS examples (see http://openturns.github.io/openturns/latest/examples/graphs/graphs_basics.html) to set the x range (initially from [-4,4] to [-2,2]) :

import openturns as ot
import openturns.viewer as viewer
from matplotlib import pylab as plt

n = ot.Normal()

# To configure the look of the plot, we can first observe the type
# of graphics returned by the `drawPDF` method returns: it is a `Graph`.
graph = n.drawPDF()

# The `Graph` class provides several methods to configure the legends,
# the title and the colors. Since a graphics  can contain several sub-graphics,
# the `setColors` takes a list of colors as inputs argument: each item of
# the list corresponds to the sub-graphics.

graph.setXTitle("N")
graph.setYTitle("PDF")
graph.setTitle("Probability density function of the standard gaussian distribution")
graph.setLegends(["N"])
graph.setColors(["blue"])

# Combine several graphics
# In order to combine several graphics, we can use the `add` method.

# Let us create an empirical histogram from a sample.
sample = n.getSample(100)

histo = ot.HistogramFactory().build(sample).drawPDF()

# Then we add the histogram to the `graph` with the `add` method.
# The `graph` then contains two plots.
graph.add(histo)

# Using openturns.viewer
view = viewer.View(graph)

# Get the matplotlib.axes.Axes member with getAxes()
# Similarly, there is a getFigure() method as well
axes = view.getAxes()  # axes is a matplotlib object
_ = axes[0].set_xlim(-2.0, 2.0)

plt.show()

You can read the definition of the View object here :

https://github.com/openturns/openturns/blob/master/python/src/viewer.py

As you will see, the View class contains matplotlib objects such as axes and figure. Once accessed by the getAxes (or getFigure) you can use the matplotlib methods.