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fixes to docstrings
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@OriolAbril
OriolAbril committed Jan 2, 2023
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12 changes: 6 additions & 6 deletions arviz/plots/autocorrplot.py
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Expand Up @@ -23,9 +23,9 @@ def plot_autocorr(
backend_kwargs=None,
show=None,
):
"""Bar plot of the autocorrelation function (ACF) for a sequence of data.
r"""Bar plot of the autocorrelation function (ACF) for a sequence of data.
The ACF plots are helpful as a convergence diagnostic for posteriors from MCMC
The ACF plots are helpful as a convergence diagnostic for posteriors from MCMC
samples which display autocorrelation.
Parameters
Expand All @@ -42,7 +42,7 @@ def plot_autocorr(
interpret `var_names` as regular expressions on the real variables names. See
the :ref:`this section <common_filter_vars>` for usage examples.
max_lag : int, optional
Maximum lag to calculate autocorrelation. By Default, the plot displays the
Maximum lag to calculate autocorrelation. By Default, the plot displays the
first 100 lag or the total number of draws, whichever is smaller.
combined : bool, default False
Flag for combining multiple chains into a single chain. If False, chains will be
Expand All @@ -62,13 +62,13 @@ def plot_autocorr(
ax : 2D array-like of matplotlib_axes or bokeh_figure, optional
A 2D array of locations into which to plot the densities. If not supplied, Arviz will create
its own array of plot areas (and return it).
backend : str, optional
Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib".
backend : {"matplotlib", "bokeh"}, default "matplotlib"
Select plotting backend.
backend_config : dict, optional
Currently specifies the bounds to use for bokeh axes. Defaults to value set in ``rcParams``.
backend_kwargs : dict, optional
These are kwargs specific to the backend being used, passed to
:func:`matplotlib.pyplot.subplots` or :func:`bokeh.plotting.figure`.
:func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`.
For additional documentation check the plotting method of the backend.
show : bool, optional
Call backend show function.
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48 changes: 25 additions & 23 deletions arviz/plots/bpvplot.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,8 +36,7 @@ def plot_bpv(
group="posterior",
show=None,
):
"""
Plot Bayesian p-value for observed data and Posterior/Prior predictive.
r"""Plot Bayesian p-value for observed data and Posterior/Prior predictive.
Parameters
----------
Expand All @@ -47,28 +46,31 @@ def plot_bpv(
kind : {"u_value", "p_value", "t_stat"}, default "u_value"
Specify the kind of plot:
* The ``kind="p_value"`` computes :math:`p := p(y* \leq y | y)`. This is the probability of the
data y being larger or equal than the predicted data y*. The ideal value is 0.5
(half the predictions below and half above the data).
* The ``kind="u_value"`` argument computes :math:`p_i := p(y_i* \leq y_i | y)`. i.e. like a p_value
but per observation :math:`y_i`. This is also known as marginal p_value. The ideal distribution
is uniform. This is similar to the LOO-pit calculation/plot, the difference is than in
LOO-pit plot we compute :math:`pi = p(y_i* r \leq y_i | y_{-i} )`, where :math:`y_{-i}`, is all
other data except :math:`y_i`.
* The ``kind="t_stat"`` argument computes :math:`:= p(T(y)* \leq T(y) | y)` where T is any test statistic.
See ``t_stat`` argument below for details of available options.
* The ``kind="p_value"`` computes :math:`p := p(y* \leq y | y)`.
This is the probability of the data y being larger or equal than the predicted data y*.
The ideal value is 0.5 (half the predictions below and half above the data).
* The ``kind="u_value"`` argument computes :math:`p_i := p(y_i* \leq y_i | y)`.
i.e. like a p_value but per observation :math:`y_i`. This is also known as marginal
p_value. The ideal distribution is uniform. This is similar to the LOO-PIT
calculation/plot, the difference is than in LOO-pit plot we compute
:math:`pi = p(y_i* r \leq y_i | y_{-i} )`, where :math:`y_{-i}`,
is all other data except :math:`y_i`.
* The ``kind="t_stat"`` argument computes :math:`:= p(T(y)* \leq T(y) | y)`
where T is any test statistic. See ``t_stat`` argument below for details
of available options.
t_stat : str, float, or callable, default "median"
Test statistics to compute from the observations and predictive distributions. Allowed strings are
“mean”, “median” or “std”. Alternative a quantile can be passed as a float (or str) in the interval
(0, 1). Finally a user defined function is also acepted, see examples section for details.
Test statistics to compute from the observations and predictive distributions.
Allowed strings are “mean”, “median” or “std”. Alternative a quantile can be passed
as a float (or str) in the interval (0, 1). Finally a user defined function is also
acepted, see examples section for details.
bpv : bool, default True
If True add the Bayesian p_value to the legend when ``kind = t_stat``.
plot_mean : bool, default True
Whether or not to plot the mean test statistic.
reference : {"analytical", "samples", None}, default "analytical"
How to compute the distributions used as reference for ``kind=u_values`` or ``kind=p_values``.
Use `None` to do not plot any reference.
How to compute the distributions used as reference for ``kind=u_values``
or ``kind=p_values``. Use `None` to not plot any reference.
mse : bool, default False
Show scaled mean square error between uniform distribution and marginal p_value
distribution.
Expand Down Expand Up @@ -102,8 +104,8 @@ def plot_bpv(
Class providing the method ``make_pp_label`` to generate the labels in the plot titles.
Read the :ref:`label_guide` for more details and usage examples.
var_names : list of str, optional
Variables to be plotted. If `None` all variable are plotted. Prefix the variables by ``~``
when you want to exclude them from the plot. See the :ref:`this section <common_var_names>`
Variables to be plotted. If `None` all variable are plotted. Prefix the variables by ``~``
when you want to exclude them from the plot. See the :ref:`this section <common_var_names>`
for usage examples.
filter_vars : {None, "like", "regex"}, default None
If `None` (default), interpret `var_names` as the real variables names. If "like",
Expand Down Expand Up @@ -137,11 +139,11 @@ def plot_bpv(
and ``reference=analytical``).
backend_kwargs : bool, optional
These are kwargs specific to the backend being used, passed to
:func:`matplotlib.pyplot.subplots` or :func:`bokeh.plotting.figure`.
:func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`.
For additional documentation check the plotting method of the backend.
group : {"posterior", "prior"}, default "posterior"
Specifies which InferenceData group should be plotted. If "posterior", then the values
in `posterior_predictive` group are compared to the ones in `observed_data`, if "prior" then
Specifies which InferenceData group should be plotted. If "posterior", then the values
in `posterior_predictive` group are compared to the ones in `observed_data`, if "prior" then
the same comparison happens, but with the values in `prior_predictive` group.
show : bool, optional
Call backend show function.
Expand Down
28 changes: 14 additions & 14 deletions arviz/plots/essplot.py
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Original file line number Diff line number Diff line change
Expand Up @@ -37,8 +37,8 @@ def plot_ess(
show=None,
**kwargs,
):
"""Generate quantile, local, or evolution ESS plots.
r"""Generate quantile, local, or evolution ESS plots.
The local and the quantile ESS plots are recommended for checking
that there are enough samples for all the explored regions of the
parameter space. Checking local and quantile ESS is particularly
Expand All @@ -47,7 +47,7 @@ def plot_ess(
Parameters
----------
idata : obj
idata : InferenceData
Any object that can be converted to an :class:`arviz.InferenceData` object
Refer to documentation of :func:`arviz.convert_to_dataset` for details
var_names : list of str, optional
Expand All @@ -61,13 +61,13 @@ def plot_ess(
kind : {"local", "quantile", "evolution"}, default "local"
Specify the kind of plot:
* The ``kind="local"`` argument generates the ESS' local efficiency for
estimating quantiles of a desired posterior.
* The ``kind="quantile"`` argument generates the ESS' local efficiency
for estimating small-interval probability of a desired posterior.
* The ``kind="evolution"`` argument generates the estimated ESS'
with incrised number of iterations of a desired posterior.
* The ``kind="local"`` argument generates the ESS' local efficiency for
estimating quantiles of a desired posterior.
* The ``kind="quantile"`` argument generates the ESS' local efficiency
for estimating small-interval probability of a desired posterior.
* The ``kind="evolution"`` argument generates the estimated ESS'
with incrised number of iterations of a desired posterior.
relative : bool, default False
Show relative ess in plot ``ress = ess / N``.
coords : dict, optional
Expand All @@ -81,7 +81,7 @@ def plot_ess(
Text size scaling factor for labels, titles and lines. If None it will be autoscaled based
on `figsize`.
rug : bool, default False
Add a `rug plot <https://en.wikipedia.org/wiki/Rug_plot>` for a specific subset of values.
Add a `rug plot <https://en.wikipedia.org/wiki/Rug_plot>`_ for a specific subset of values.
rug_kind : str, default "diverging"
Variable in sample stats to use as rug mask. Must be a boolean variable.
n_points : int, default 20
Expand Down Expand Up @@ -113,11 +113,11 @@ def plot_ess(
:func:`~matplotlib.axes.Axes.plot` or to :class:`~bokeh.plotting.figure.line`
rug_kwargs : dict
kwargs passed to rug plot.
backend : str, optional
Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib".
backend : {"matplotlib", "bokeh"}, default "matplotlib"
Select plotting backend.
backend_kwargs : dict, optional
These are kwargs specific to the backend being used, passed to
:func:`matplotlib.pyplot.subplots` or :func:`bokeh.plotting.figure`.
:func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`.
For additional documentation check the plotting method of the backend.
show : bool, optional
Call backend show function.
Expand Down

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