/usr/include/openturns/swig/VisualTest_doc.i is in libopenturns-dev 1.5-7build2.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 | %feature("docstring") OT::VisualTest::DrawHenryLine
"Draw an Henry plot as an OpenTURNS :class:`~openturns.Graph`.
Parameters
----------
sample : :class:`~openturns.NumericalSample` or 2d array, list or tuple
Tested (univariate) sample.
normal_distribution : :class:`~openturns.Normal`, optional
Tested (univariate) normal distribution.
If not given, this will build a :class:`~openturns.Normal` distribution
from the given sample using the :class:`~openturns.NormalFactory`.
Notes
-----
The Henry plot is a visual fitting test for the normal distribution. It
opposes the sample quantiles to those of the standard normal distribution
(the one with zero mean and unit variance) by plotting the following points
could:
.. math::
\\\\left(x^{(i)},
\\\\Phi^{-1}\\\\left[\\\\widehat{F}\\\\left(x^{(i)}\\\\right)\\\\right]
\\\\right), \\\\quad i = 1, \\\\ldots, m
where :math:`\\\\widehat{F}` denotes the empirical CDF of the sample and
:math:`\\\\Phi^{-1}` denotes the quantile function of the standard normal
distribution.
If the given sample fits to the tested normal distribution (with mean
:math:`\\\\mu` and standard deviation :math:`\\\\sigma`), then the points should be
close to be aligned (up to the uncertainty associated with the estimation
of the empirical probabilities) on the **Henry line** whose equation reads:
.. math::
y = \\\\frac{x - \\\\mu}{\\\\sigma}, \\\\quad x \\\\in \\\\Rset
The Henry plot is a special case of the more general QQ-plot.
See Also
--------
VisualTest_DrawQQplot, FittingTest_Kolmogorov
Examples
--------
>>> import openturns as ot
>>> from openturns.viewer import View
Generate a random sample from a Normal distribution:
>>> ot.RandomGenerator.SetSeed(0)
>>> distribution = ot.Normal(2., .5)
>>> sample = distribution.getSample(30)
Draw an Henry plot against a given (wrong) Normal distribution:
>>> henry_graph = ot.VisualTest_DrawHenryLine(sample, distribution)
>>> henry_graph.setTitle('Henry plot against given %s' % ot.Normal(3., 1.))
>>> View(henry_graph).show()
Draw an Henry plot against an inferred Normal distribution:
>>> henry_graph = ot.VisualTest_DrawHenryLine(sample)
>>> henry_graph.setTitle('Henry plot against inferred Normal distribution')
>>> View(henry_graph).show()"
// ---------------------------------------------------------------------
%feature("docstring") OT::VisualTest::DrawQQplot
"Draw a QQ-plot as an OpenTURNS :class:`~openturns.Graph`.
Parameters
----------
sample : :class:`~openturns.NumericalSample`
Tested sample.
tested_quantity : :class:`~openturns.Distribution` or :class:`~openturns.NumericalSample`
Tested model or sample.
n_points : int, if `tested_quantity` is a :class:`~openturns.NumericalSample`
The number of points that is used for interpolating the empirical CDF of
the two samples (with possibly different sizes).
It will default to `DistributionImplementation-DefaultPointNumber` from
the :class:`~openturns.ResourceMap`.
Notes
-----
The QQ-plot is a visual fitting test for univariate distributions. It
opposes the sample quantiles to those of the tested quantity (either a
distribution or another sample) by plotting the following points could:
.. math::
\\\\left(x^{(i)},
\\\\bullet\\\\left[\\\\widehat{F}\\\\left(x^{(i)}\\\\right)\\\\right]
\\\\right), \\\\quad i = 1, \\\\ldots, m
where :math:`\\\\widehat{F}` denotes the empirical CDF of the (first) tested
sample and :math:`\\\\bullet` denotes either the quantile function of the tested
distribution or the empirical quantile function of the second tested sample.
If the given sample fits to the tested distribution or sample, then the points
should be close to be aligned (up to the uncertainty associated with the
estimation of the empirical probabilities) with the **first bissector** whose
equation reads:
.. math::
y = x, \\\\quad x \\\\in \\\\Rset
Examples
--------
>>> import openturns as ot
>>> from openturns.viewer import View
Generate a random sample from a Normal distribution:
>>> ot.RandomGenerator.SetSeed(0)
>>> distribution = ot.Weibull(2., .5)
>>> sample = distribution.getSample(30)
>>> sample.setDescription(['Sample'])
Draw a QQ-plot against a given (inferred) distribution:
>>> tested_distribution = ot.WeibullFactory().build(sample)
>>> QQ_plot = ot.VisualTest_DrawQQplot(sample, tested_distribution)
>>> View(QQ_plot).show()
Draw a two-sample QQ-plot:
>>> another_sample = distribution.getSample(50)
>>> another_sample.setDescription(['Another sample'])
>>> QQ_plot = ot.VisualTest_DrawQQplot(sample, another_sample)
>>> View(QQ_plot).show()"
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