start adding links to api docs (#2081)

* start adding links to api docs

* addressed review comments

* added python to intersphinx

* added space formatting in line 72 and 77

* space formatting in line 60,71,73,75

* space formatting changes

* space foramtting changes

* space formatting changes were added to links

* modified line 62

* modified autocorrplot

* modified line 59

* modified line 63

* changed the parameter type to docstring format

* removed the redundant word object in InferenceData object

* changed line 65 from type hints to docstring

* included space in the before an argument type

* added space after an argument name just before the colon

* included space after argument name just before the colon

* added space after aegument name and before argument type

* rectified the chnages suggested im autcorrplot

* made changes to 86,101 and 127

* updated the compareplot

* updated densityplot

* distcomaprisonplot updated

* add two additional elelements to to numpydocs aliases

* changed labeller arguement's capitalisation

* edited lumpyplot and mcseplot

* edited kdeplot,khatplot and implot by adding necessary formatting and edits yo argument descriptions

* made links clickable in forestplot

* edited labeller description and addressed space formatting in essplot

* added the 'ax' description in dotplot

* edited an 'ax' description

* effected suggested changes

* effected suggested changes

* fix conf.py and a handful of parameters

* remove type-hints from function signature

* black

* update bokeh figure alias

Co-authored-by: Oriol (ZBook) <oriol.abril.pla@gmail.com>

arviz/plots/autocorrplot.py

@@ -29,53 +29,53 @@ def plot_autocorr(
 
     Parameters
     ----------
-    data: obj
+    data : 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 variable names, optional
+    var_names : list of str, optional
         Variables to be plotted, if None all variables are plotted. Prefix the
         variables by ``~`` when you want to exclude them from the plot. Vector-value
         stochastics are handled automatically.
-    filter_vars: {None, "like", "regex"}, optional, default=None
+    filter_vars : {None, "like", "regex"},  default=None
         If `None` (default), interpret var_names as the real variables names. If "like",
         interpret var_names as substrings of the real variables names. If "regex",
         interpret var_names as regular expressions on the real variables names. A la
         ``pandas.filter``.
-    max_lag: int, optional
+    max_lag : int, optional
         Maximum lag to calculate autocorrelation. Defaults to 100 or num draws,
         whichever is smaller.
-    combined: bool, default=False
+    combined : bool, default=False
         Flag for combining multiple chains into a single chain. If False, chains will be
         plotted separately.
     grid : tuple
         Number of rows and columns. Defaults to None, the rows and columns are
         automatically inferred.
-    figsize: tuple
+    figsize : (float, float), optional
         Figure size. If None it will be defined automatically.
         Note this is not used if ``ax`` is supplied.
-    textsize: float
+    textsize : float
         Text size scaling factor for labels, titles and lines. If None it will be autoscaled based
         on ``figsize``.
-    labeller : labeller instance, optional
+    labeller : Labeller, optional
         Class providing the method ``make_label_vert`` to generate the labels in the plot titles.
         Read the :ref:`label_guide` for more details and usage examples.
-    ax: numpy array-like of matplotlib axes or bokeh figures, optional
+    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
+    backend : str, optional
         Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib".
-    backend_config: dict, optional
+    backend_config : dict, optional
         Currently specifies the bounds to use for bokeh axes. Defaults to value set in ``rcParams``.
-    backend_kwargs: dict, optional
+    backend_kwargs : dict, optional
         These are kwargs specific to the backend being used, passed to
         :func:`matplotlib.pyplot.subplots` or
         :func:`bokeh.plotting.figure`.
-    show: bool, optional
+    show : bool, optional
         Call backend show function.
 
     Returns
     -------
-    axes: matplotlib axes or bokeh figures
+    axes : matplotlib_axes or bokeh_figures
 
     See Also
     --------

arviz/plots/bpvplot.py

@@ -41,11 +41,11 @@ def plot_bpv(
 
     Parameters
     ----------
-    data : az.InferenceData object
+    data : InferenceData
         :class:`arviz.InferenceData` object containing the observed and
         posterior/prior predictive data.
-    kind : str
-        Type of plot to display ("p_value", "u_value", "t_stat"). Defaults to u_value.
+    kind : str, default "u_value"
+        Type of plot to display ("p_value", "u_value", "t_stat").
         For "p_value" we compute p := p(y* ≤ 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).
@@ -55,40 +55,40 @@ def plot_bpv(
         pi = p(yi* r ≤ yi | y-i ), where y-i, is all other data except yi.
         For "t_stat" we compute := p(T(y)* ≤ 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
+    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". Defaults to "median".
+        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
-        If True (default) add the Bayesian p_value to the legend when ``kind = t_stat``.
-    plot_mean : bool
-        Whether or not to plot the mean test statistic. Defaults to True.
-    reference : str
+    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 : str, default "analytical"
         How to compute the distributions used as reference for u_values or p_values. Allowed values
-        are "analytical" (default) and "samples". Use `None` to do not plot any reference.
+        are "analytical" and "samples". Use `None` to do not plot any reference.
         Defaults to "samples".
-    mse :bool
+    mse : bool, default False
         Show scaled mean square error between uniform distribution and marginal p_value
-        distribution. Defaults to False.
-    n_ref : int, optional
-        Number of reference distributions to sample when ``reference=samples``. Defaults to 100.
-    hdi_prob: float, optional
+        distribution.
+    n_ref : int, default 100
+        Number of reference distributions to sample when ``reference=samples``.
+    hdi_prob : float, optional
         Probability for the highest density interval for the analytical reference distribution when
-        computing u_values. Should be in the interval (0, 1]. Defaults to
-        0.94.
-    color : str
+        computing u_values. Should be in the interval (0, 1]. Defaults to the
+        rcParam ``stats.hdi_prob``.
+    color : str, optional
         Matplotlib color
-    grid : tuple
+    grid : tuple, optional
         Number of rows and columns. Defaults to None, the rows and columns are
         automatically inferred.
-    figsize : tuple
+    figsize : (float, float), optional
         Figure size. If None it will be defined automatically.
-    textsize : float
+    textsize : float, optional
         Text size scaling factor for labels, titles and lines. If None it will be
         autoscaled based on ``figsize``.
-    data_pairs : dict
+    data_pairs : dict, optional
         Dictionary containing relations between observed data and posterior/prior predictive data.
         Dictionary structure:
 
@@ -98,7 +98,7 @@ def plot_bpv(
         For example, ``data_pairs = {'y' : 'y_hat'}``
         If None, it will assume that the observed data and the posterior/prior
         predictive data have the same variable name.
-    labeller : labeller instance, optional
+    Labeller : Labeller, optional
         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 variable names
@@ -109,22 +109,22 @@ def plot_bpv(
         interpret var_names as substrings of the real variables names. If "regex",
         interpret var_names as regular expressions on the real variables names. A la
         ``pandas.filter``.
-    coords : dict
+    coords : dict, optional
         Dictionary mapping dimensions to selected coordinates to be plotted.
         Dimensions without a mapping specified will include all coordinates for
         that dimension. Defaults to including all coordinates for all
         dimensions if None.
-    flatten : list
+    flatten : list, optional
         List of dimensions to flatten in observed_data. Only flattens across the coordinates
         specified in the coords argument. Defaults to flattening all of the dimensions.
-    flatten_pp : list
+    flatten_pp : list, optional
         List of dimensions to flatten in posterior_predictive/prior_predictive. Only flattens
         across the coordinates specified in the coords argument. Defaults to flattening all
         of the dimensions. Dimensions should match flatten excluding dimensions for data_pairs
         parameters. If flatten is defined and flatten_pp is None, then ``flatten_pp=flatten``.
-    legend : bool
-        Add legend to figure. By default True.
-    ax : numpy array-like of matplotlib axes or bokeh figures, optional
+    legend : bool, default True
+        Add legend to figure.
+    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
@@ -147,7 +147,7 @@ def plot_bpv(
 
     Returns
     -------
-    axes: matplotlib axes or bokeh figures
+    axes : 2D ndarray of matplotlib_axes or bokeh_figure
 
     See Also
     --------

arviz/plots/compareplot.py

@@ -38,7 +38,7 @@ def plot_compare(
 
     Parameters
     ----------
-    comp_df : pd.DataFrame
+    comp_df : pandas.DataFrame
         Result of the :func:`arviz.compare` method
     insample_dev : bool, optional
         Plot in-sample ELPD, that is the value of the information criteria without the
@@ -51,28 +51,28 @@ def plot_compare(
         and the top-ranked model. Defaults to True
     order_by_rank : bool
         If True (default) ensure the best model is used as reference.
-    legend : bool
-        Add legend to figure. By default True.
-    figsize : tuple, optional
+    legend : bool, default True
+        Add legend to figure.
+    figsize : (float, float), optional
         If None, size is (6, num of models) inches
-    title : bool:
+    title : bool
         Show a tittle with a description of how to interpret the plot. Defaults to True.
-    textsize: float
+    textsize : float
         Text size scaling factor for labels, titles and lines. If None it will be autoscaled based
         on ``figsize``.
-    labeller : labeller instance, optional
+    labeller : Labeller, optional
         Class providing the method ``model_name_to_str`` to generate the labels in
         the plot.
         Read the :ref:`label_guide` for more details and usage examples.
     plot_kwargs : dict, optional
         Optional arguments for plot elements. Currently accepts 'color_ic',
         'marker_ic', 'color_insample_dev', 'marker_insample_dev', 'color_dse',
         'marker_dse', 'ls_min_ic' 'color_ls_min_ic',  'fontsize'
-    ax: axes, optional
-        Matplotlib axes or bokeh figures.
-    backend: str, optional
+    ax : matplotlib_axes or bokeh_figure, optional
+        Matplotlib axes or bokeh figure.
+    backend : str, optional
         Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib".
-    backend_kwargs: bool, optional
+    backend_kwargs : bool, optional
         These are kwargs specific to the backend being used, passed to
         :func:`matplotlib.pyplot.subplots` or
         :func:`bokeh.plotting.figure`.
@@ -81,7 +81,7 @@ def plot_compare(
 
     Returns
     -------
-    axes : matplotlib axes or bokeh figures
+    axes : matplotlib_axes or bokeh_figure
 
     See Also
     --------

arviz/plots/densityplot.py

@@ -44,23 +44,23 @@ def plot_density(
 
     Parameters
     ----------
-    data : Union[Object, Iterator[Object]]
+    data : object or iterable of object
         Any object that can be converted to an :class:`arviz.InferenceData` object, or an Iterator
         returning a sequence of such objects.
         Refer to documentation of :func:`arviz.convert_to_dataset` for details about such objects.
-    group: Optional[str]
+    group : str, optional
         Specifies which :class:`arviz.InferenceData` group should be plotted.
         Defaults to 'posterior'.
         Alternative values include 'prior' and any other strings used as dataset keys in the
         :class:`arviz.InferenceData`.
-    data_labels : Optional[List[str]]
+    data_labels : list of str, optional
         List with names for the datasets passed as "data." Useful when plotting more than one
         dataset.  Must be the same shape as the data parameter.  Defaults to None.
-    var_names: Optional[List[str]]
+    var_names : list of str, optional
         List of variables to plot.  If multiple datasets are supplied and var_names is not None,
         will print the same set of variables for each dataset.  Defaults to None, which results in
         all the variables being plotted.
-    filter_vars: {None, "like", "regex"}, optional, default=None
+    filter_vars : {None, "like", "regex"}, default=None
         If `None` (default), interpret var_names as the real variables names. If "like",
         interpret var_names as substrings of the real variables names. If "regex",
         interpret var_names as regular expressions on the real variables names. A la
@@ -73,10 +73,10 @@ def plot_density(
     hdi_prob : float
         Probability for the highest density interval. Should be in the interval (0, 1].
         Defaults to 0.94.
-    point_estimate : Optional[str]
+    point_estimate : str, optional
         Plot point estimate per variable. Values should be 'mean', 'median', 'mode' or None.
         Defaults to 'auto' i.e. it falls back to default set in ``rcParams``.
-    colors : Optional[Union[List[str],str]]
+    colors : str or list of str, optional
         List with valid matplotlib colors, one color per model. Alternative a string can be passed.
         If the string is `cycle`, it will automatically choose a color per model from matplotlib's
         cycle. If a single color is passed, e.g. 'k', 'C2' or 'red' this color will be used for all
@@ -86,36 +86,36 @@ def plot_density(
     hdi_markers : str
         A valid `matplotlib.markers` like 'v', used to indicate the limits of the highest density
         interval. Defaults to empty string (no marker).
-    shade : Optional[float]
+    shade : float, optional
         Alpha blending value for the shaded area under the curve, between 0 (no shade) and 1
         (opaque). Defaults to 0.
-    bw: Optional[float or str]
+    bw : float or str, optional
         If numeric, indicates the bandwidth and must be positive.
         If str, indicates the method to estimate the bandwidth and must be
         one of "scott", "silverman", "isj" or "experimental" when `circular` is False
         and "taylor" (for now) when `circular` is True.
         Defaults to "default" which means "experimental" when variable is not circular
         and "taylor" when it is.
-    circular: Optional[bool]
+    circular : bool, optional
         If True, it interprets the values passed are from a circular variable measured in radians
         and a circular KDE is used. Only valid for 1D KDE. Defaults to False.
     grid : tuple
         Number of rows and columns. Defaults to None, the rows and columns are
         automatically inferred.
-    figsize : Optional[Tuple[int, int]]
+    figsize : (float, float), optional
         Figure size. If None it will be defined automatically.
-    textsize: Optional[float]
+    textsize : float, optional
         Text size scaling factor for labels, titles and lines. If None it will be autoscaled based
         on ``figsize``.
-    labeller : labeller instance, optional
+    labeller : Labeller, optional
         Class providing the method ``make_label_vert`` to generate the labels in the plot titles.
         Read the :ref:`label_guide` for more details and usage examples.
-    ax: numpy array-like of matplotlib axes or bokeh figures, optional
+    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
+    backend : str, optional
         Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib".
-    backend_kwargs: bool, optional
+    backend_kwargs : dict, optional
         These are kwargs specific to the backend being used, passed to
         :func:`matplotlib.pyplot.subplots` or :func:`bokeh.plotting.figure`.
         For additional documentation check the plotting method of the backend.
@@ -124,7 +124,7 @@ def plot_density(
 
     Returns
     -------
-    axes : matplotlib axes or bokeh figures
+    axes : 2D ndarray of matplotlib_axes or bokeh_figure
 
     See Also
     --------