t
understands `CensoredData` is the ``fit`` method.  An instance of
`CensoredData` can not be passed to methods such as ``pdf`` and
``cdf``.

An observation is said to be *censored* when the precise value is unknown,
but it has a known upper and/or lower bound.  The conventional terminology
is:

* left-censored: an observation is below a certain value but it is
  unknown by how much.
* right-censored: an observation is above a certain value but it is
  unknown by how much.
* interval-censored: an observation lies somewhere on an interval between
  two values.

Left-, right-, and interval-censored data can be represented by
`CensoredData`.

For convenience, the class methods ``left_censored`` and
``right_censored`` are provided to create a `CensoredData`
instance from a single one-dimensional array of measurements
and a corresponding boolean array to indicate which measurements
are censored.  The class method ``interval_censored`` accepts two
one-dimensional arrays that hold the lower and upper bounds of the
intervals.

Parameters
----------
uncensored : array_like, 1D
    Uncensored observations.
left : array_like, 1D
    Left-censored observations.
right : array_like, 1D
    Right-censored observations.
interval : array_like, 2D, with shape (m, 2)
    Interval-censored observations.  Each row ``interval[k, :]``
    represents the interval for the kth interval-censored observation.

Notes
-----
In the input array `interval`, the lower bound of the interval may
be ``-inf``, and the upper bound may be ``inf``, but at least one must be
finite. When the lower bound is ``-inf``, the row represents a left-
censored observation, and when the upper bound is ``inf``, the row
represents a right-censored observation.  If the length of an interval
is 0 (i.e. ``interval[k, 0] == interval[k, 1]``, the observation is
treated as uncensored.  So one can represent all the types of censored
and uncensored data in ``interval``, but it is generally more convenient
to use `uncensored`, `left` and `right` for uncensored, left-censored and
right-censored observations, respectively.

Examples
--------
In the most general case, a censored data set may contain values that
are left-censored, right-censored, interval-censored, and uncensored.
For example, here we create a data set with five observations.  Two
are uncensored (values 1 and 1.5), one is a left-censored observation
of 0, one is a right-censored observation of 10 and one is
interval-censored in the interval [2, 3].

>>> import numpy as np
>>> from scipy.stats import CensoredData
>>> data = CensoredData(uncensored=[1, 1.5], left=[0], right=[10],
...                     interval=[[2, 3]])
>>> print(data)
CensoredData(5 values: 2 not censored, 1 left-censored,
1 right-censored, 1 interval-censored)

Equivalently,

>>> data = CensoredData(interval=[[1, 1],
...                               [1.5, 1.5],
...                               [-np.inf, 0],
...                               [10, np.inf],
...                               [2, 3]])
>>> print(data)
CensoredData(5 values: 2 not censored, 1 left-censored,
1 right-censored, 1 interval-censored)

A common case is to have a mix of uncensored observations and censored
observations that are all right-censored (or all left-censored). For
example, consider an experiment in which six devices are started at
various times and left running until they fail.  Assume that time is
measured in hours, and the experiment is stopped after 30 hours, even
if all the devices have not failed by that time.  We might end up with
data such as this::

    Device  Start-time  Fail-time  Time-to-failure
       1         0         13           13
       2         2         24           22
       3         5         22           17
       4         8         23           15
       5        10        ***          >20
       6        12        ***          >18

Two of the devices had not failed when the experiment was stopped;
the observations of the time-to-failure for these two devices are
right-censored.  We can represent this data with

>>> data = CensoredData(uncensored=[13, 22, 17, 15], right=[20, 18])
>>> print(data)
CensoredData(6 values: 4 not censored, 2 right-censored)

Alternatively, we can use the method `CensoredData.right_censored` to
create a representation of this data.  The time-to-failure observations
are put the list ``ttf``.  The ``censored`` list indicates which values
in ``ttf`` are censored.

>>> ttf = [13, 22, 17, 15, 20, 18]
>>> censored = [False, False, False, False, True, True]

Pass these lists to `CensoredData.right_censored` to create an
instance of `CensoredData`.

>>> data = CensoredData.right_censored(ttf, censored)
>>> print(data)
CensoredData(6 values: 4 not censored, 2 right-censored)

If the input data is interval censored and already stored in two
arrays, one holding the low end of the intervals and another
holding the high ends, the class method ``interval_censored`` can
be used to create the `CensoredData` instance.

This example creates an instance with four interval-censored values.
The intervals are [10, 11], [0.5, 1], [2, 3], and [12.5, 13.5].

>>> a = [10, 0.5, 2, 12.5]  # Low ends of the intervals
>>> b = [11, 1.0, 3, 13.5]  # High ends of the intervals
>>> data = CensoredData.interval_censored(low=a, high=b)
>>> print(data)
CensoredData(4 values: 0 not censored, 4 interval-censored)

Finally, we create and censor some data from the `weibull_min`
distribution, and then fit `weibull_min` to that data. We'll assume
that the location parameter is known to be 0.

>>> from scipy.stats import weibull_min
>>> rng = np.random.default_rng()

Create the random data set.

>>> x = weibull_min.rvs(2.5, loc=0, scale=30, size=250, random_state=rng)
>>> x[x > 40] = 40  # Right-censor values greater or equal to 40.

Create the `CensoredData` instance with the `right_censored` method.
The censored values are those where the value is 40.

>>> data = CensoredData.right_censored(x, x == 40)
>>> print(data)
CensoredData(250 values: 215 not censored, 35 right-censored)

35 values have been right-censored.

Fit `weibull_min` to the censored data.  We expect to shape and scale
to be approximately 2.5 and 30, respectively.

>>> weibull_min.fit(data, floc=0)
(2.3575922823897315, 0, 30.40650074451254)

NŠ