docs: repoint plot docs at the functional autofit.plot API

docs/api/plot.rst

@@ -1,27 +1,54 @@
 ========
-Plotters
+Plotting
 ========
 
-Create figures and subplots of non-linear search specific visualization of every search algorithm supported
+Create figures and subplots of the non-linear search specific visualization of every search algorithm supported
 by **PyAutoFit**.
 
+The plotting API is **functional**: import ``autofit.plot`` and call a plot function directly, passing it the
+``Samples`` object of a completed fit. There are no ``Plotter`` classes.
+
+.. code-block:: python
+
+   import autofit.plot as aplt
+
+   aplt.corner_cornerpy(samples=result.samples)
+
 **Examples / Tutorials:**
 
 - `readthedocs: non-linear search example <https://pyautofit.readthedocs.io/en/latest/cookbooks/search.html>`_
 - `autofit_workspace: plot tutorials <https://github.com/PyAutoLabs/autofit_workspace/tree/main/notebooks/guides/plot>`_
 - `HowToFit: tutorial lectures (detailed step-by-step examples) <https://github.com/PyAutoLabs/HowToFit>`_
 
---------
-Plotters
---------
+Search Plot Functions [aplt]
+----------------------------
 
 .. currentmodule:: autofit.plot
 
+**Posterior Corner Plots:**
+
+.. autosummary::
+   :toctree: _autosummary
+
+   corner_cornerpy
+   corner_anesthetic
+
+**Sampling Trace Plots:**
+
+.. autosummary::
+   :toctree: _autosummary
+
+   subplot_parameters
+   log_likelihood_vs_iteration
+
+Figure Output [aplt]
+--------------------
+
+Every plot function above takes ``path``, ``filename`` and ``format`` arguments controlling where the figure goes:
+``format="show"`` (the default) displays it interactively, whereas ``"png"`` or ``"pdf"`` writes it to
+``path/filename.format``. The shared helper that performs this is also public:
+
 .. autosummary::
    :toctree: _autosummary
-   :template: custom-class-template.rst
-   :recursive:
 
-   NestPlotter
-   MCMCPlotter
-   MLEPlotter
+   output_figure

docs/cookbooks/samples.md

@@ -289,12 +289,11 @@ Sigma =  10.001029545296722
 
 ## Search Plots
 
-The Probability Density Functions (PDF's) of the results can be plotted using the Emcee's visualization
-tool `corner.py`, which is wrapped via the `EmceePlotter` object.
+The Probability Density Functions (PDF's) of the results can be plotted using the visualization
+tool `corner.py`, which is wrapped via the `corner_cornerpy` function.
 
 ```python
-plotter = aplt.MCMCPlotter(samples=result.samples)
-plotter.corner()
+aplt.corner_cornerpy(samples=result.samples)
 ```
 
 This plot appears as follows:

docs/cookbooks/search.md

@@ -152,22 +152,18 @@ search = af.Emcee(number_of_cores=4)
 
 ## Plots
 
-Every non-linear search supported by **PyAutoFit** has a dedicated `plotter` class that allows the results of the
-model-fit to be plotted and inspected.
+The results of a model-fit are plotted and inspected via the plot functions of `autofit.plot`, which wrap the
+in-built visualization libraries of the searches. These are fully described in the `plot` package of the workspace.
 
-This uses that search's in-built visualization libraries, which are fully described in the `plot` package of the
-workspace.
-
-For example, `Emcee` has a corresponding `EmceePlotter`, which is used as follows.
+For example, `corner_cornerpy` wraps `corner.py` and is used as follows.
 
 Checkout the `plot` package for a complete description of the plots that can be made for a given search.
 
 ```python
 samples = result.samples
 
-plotter = aplt.MCMCPlotter(samples=samples)
-
-plotter.corner(
+aplt.corner_cornerpy(
+    samples=samples,
     bins=20,
     range=None,
     color="k",
@@ -256,8 +252,8 @@ Information about Emcee can be found at the following links:
 
 The following workspace example shows examples of fitting data with Emcee and plotting the results.
 
-- `autofit_workspace/notebooks/searches/mcmc/Emcee.ipynb`
-- `autofit_workspace/notebooks/plot/EmceePlotter.ipynb`
+- `autofit_workspace/notebooks/searches/mcmc.ipynb`
+- `autofit_workspace/notebooks/plot/emcee_plotter.ipynb`
 
 The following code shows how to use Emcee with all available options.
 

docs/overview/the_basics.md

@@ -539,8 +539,7 @@ algorithm (e.g. the internal dynesty samples).
 Below we use the samples to plot the probability density function cornerplot of the results.
 
 ```python
-plotter = aplt.NestPlotter(samples=result.samples)
-plotter.corner_anesthetic()
+aplt.corner_anesthetic(samples=result.samples)
 ```
 
 The plot appears as follows:

docs/sphinx_warning_baseline.txt

@@ -1 +1 @@
-67
+31