REF and TEST: rank_size in inequality.py

[bktaha]

Based on the discussion in #519, refactor the method rank_size_plot in inequality.py so the output now is the data to be plotted instead of a pyplot Axes object. This is in line with the rest of the methods in this module. The method is renamed to rank_size to reflect this change.

Also added test for the new method, which checks that

• The x and y arrays are the same length.
• This length is (c x 100)% of the input, as specified.
• The output log-log plot is linear for a Pareto draw, and not otherwise.

Edited the docstring for the other two methods in the module for pep8 compliance.

This closes #519

[pep8speaks]

Hello @bktaha! Thanks for opening this PR. We checked the lines you've touched for PEP 8 issues, and found:

• In the file quantecon/__init__.py:

Line 30:1: W191 indentation contains tabs

• In the file quantecon/tests/test_inequality.py:

Line 134:1: W391 blank line at end of file

[mmcky]

thanks @bktaha.

As @jstac mentions in the issue we will need to coordinate updates for:

https://github.com/QuantEcon/lecture-python

source/rst/kesten_processes.rst:    qe.rank_size_plot(data, ax, c=0.01)
source/rst/wealth_dynamics.rst:If you like, you can use the function ``rank_size_plot`` from the ``quantecon`` library (documentation `here <https://quanteconpy.readthedocs.io/en/latest/tools/inequality.html#quantecon.inequality.rank_size_plot>`__). 
source/rst/wealth_dynamics.rst:    qe.rank_size_plot(ψ_star, ax, c=0.001)
source/rst/heavy_tails.rst:If you like you can use the function ``qe.rank_size_plot`` from the ``quantecon`` library to generate the plots.
source/rst/heavy_tails.rst:        qe.rank_size_plot(data, ax, label=label)

to support the new version of quantecon once this is merged and released.

[mmcky]

thanks @bktaha -- I have seen this draft PR to address the above

QuantEcon/lecture-python#231

[jstac]

Excellent job @bktaha. This is great. I'll leave the merge with @mmcky and look at your other PR now.

[mmcky]

thanks @bktaha I will merge this on Monday in coordination with QuantEcon/lecture-python#231