Add common usage explanations and code examples to qiskit.visualizati…

…on module API page (Qiskit#8569)

* add draft

* revert unintended changes

* revert unintended changes

* add example usage

* add common keyword arguments section

* add sections to apis

* fix internal links

* remove generated hist figure

* lint

* use matplotlib instead of Matplotlib in class reference

* change to a valid denisty matrix in examples

* import from qiskit.visualization instead of qiskit.tools.visualization

* remove unintended changes

* split counts and state visualizations

* remove overview and prerequisite headings

* move common kwargs sections to the top level

* remove link to api table

* remove apis headings

* remove extra blank lines

* remove extra blank lines

* minor twig on the counts to make them 1000 shots in total

* add an intro sentence before the example for common kwargs

Co-authored-by: Luciano Bello <[email protected]>
Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>

qiskit/visualization/__init__.py

@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2017, 2018.
+# (C) Copyright IBM 2017, 2022.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -17,20 +17,144 @@
 
 .. currentmodule:: qiskit.visualization
 
-Counts and State Visualizations
-===============================
+The visualization module contain functions that visualizes measurement outcome counts, quantum
+states, circuits, pulses, devices and more.
+
+To use visualization functions, you are required to install visualization optionals to your
+development environment:
+
+.. code-block:: bash
+
+   pip install 'qiskit[visualization]'
+
+Common Keyword Arguments
+========================
+
+Many of the figures created by visualization functions in this module are created by `Matplotlib
+<https://matplotlib.org/>`_ and accept a subset of the following common arguments. Consult the
+individual documentation for exact details.
+
+* ``title`` (``str``): a text string to use for the plot title.
+* ``legend`` (``list``): a list of strings to use for labels of the data.
+* ``figsize`` (``tuple``): figure size in inches .
+* ``color`` (``list``): a list of strings for plotting.
+* ``ax`` (`matplotlib.axes.Axes <https://matplotlib.org/stable/api/axes_api.html>`_): An optional
+  ``Axes`` object to be used for the visualization output. If none is specified a new
+  `matplotlib.figure.Figure <https://matplotlib.org/stable/api/figure_api.html>`_ will be created
+  and used. Additionally, if specified there will be no returned ``Figure`` since it is redundant.
+* ``filename`` (``str``): file path to save image to.
+
+The following example demonstrates the common usage of these arguments:
+
+.. jupyter-execute::
+
+   from qiskit.visualization import plot_histogram
+
+   counts1 = {'00': 499, '11': 501}
+   counts2 = {'00': 511, '11': 489}
+
+   data = [counts1, counts2]
+   plot_histogram(data)
+
+You can specify ``legend``, ``title``, ``figsize`` and ``color`` by passing to the kwargs.
+
+.. jupyter-execute::
+
+   legend = ['First execution', 'Second execution']
+   title = 'New histogram'
+   figsize = (10,10)
+   color=['crimson','midnightblue']
+   plot_histogram(data, legend=legend, title=title, figsize=figsize, color=color)
+
+You can save the figure to file either by passing the file name to ``filename`` kwarg or use
+`matplotlib.figure.Figure.savefig
+<https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure.savefig>`_ method.
+
+.. jupyter-execute::
+
+   plot_histogram(data, filename='new_hist.png')
+
+   hist = plot_histogram(data)
+   hist.savefig('new_hist.png')
+
+Counts Visualizations
+=====================
+
+This section contains functions that visualize measurement outcome counts.
 
 .. autosummary::
    :toctree: ../stubs/
 
    plot_histogram
+
+Example Usage
+-------------
+
+Here is an example of using :func:`plot_histogram` to visualize measurement outcome counts:
+
+.. jupyter-execute::
+
+   from qiskit.visualization import plot_histogram
+
+   counts = {"00": 501, "11": 499}
+   plot_histogram(counts)
+
+The data can be a dictionary with bit string as key and counts as value, or more commonly a
+:class:`~qiskit.result.Counts` object obtained from :meth:`~qiskit.result.Result.get_counts`.
+
+State Visualizations
+====================
+
+This section contains functions that visualize quantum states.
+
+.. autosummary::
+   :toctree: ../stubs/
+
    plot_bloch_vector
    plot_bloch_multivector
    plot_state_city
    plot_state_hinton
    plot_state_paulivec
    plot_state_qsphere
 
+Example Usage
+-------------
+
+Here is an example of using :func:`plot_state_city` to visualize a quantum state:
+
+.. jupyter-execute::
+
+   from qiskit.visualization import plot_state_city
+
+   state = [[ 0.75  , 0.433j],
+            [-0.433j, 0.25  ]]
+   plot_state_city(state)
+
+The state can be array-like list of lists, ``numpy.array``, or more commonly
+:class:`~qiskit.quantum_info.Statevector` or :class:`~qiskit.quantum_info.DensityMatrix` objects
+obtained from a :class:`~qiskit.circuit.QuantumCircuit`:
+
+.. jupyter-execute::
+
+   from qiskit import QuantumCircuit
+   from qiskit.quantum_info import Statevector, DensityMatrix
+
+   qc = QuantumCircuit(2)
+   qc.h(0)
+   qc.cx(0,1)
+
+   # plot using a Statevector
+   state = Statevector(qc)
+   plot_state_city(state)
+
+.. jupyter-execute::
+
+   # plot using a DensityMatrix
+   state = DensityMatrix(qc)
+   plot_state_city(state)
+
+You can find code examples for each visualization functions on the individual function API page.
+
 Device Visualizations
 =====================