Use `np.full`

[Daisuke Oyama]

See QuantEcon/lecture-python.myst#165

[Shu Hu]

Thanks @oyamad .

Hi @oyamad and @jstac , next I will

1. identify the pattern np.ones * alpha through all advanced lectures,
2. list related lectures and
3. create a PR to modify this pattern to the np.full version.

The pattern np.ones * alpha appears in the following lectures (call the following list 1):

• amss
• amss3
• arellano
• calvo
• rosen_schooling_model
• smoothing_tax
• smoothing

Except those above, I also identify the np.ones pattern in the following lectures and do you think we should modify these as well (call the following list 2)?

• additive_functionals: nx1 = np.ones(nx)
• amss2: c1, c2 = fsolve(equations, np.ones(2), args=(Φ))
• discrete_dp: # data = np.ones(L)
• dyn_stack: βs = np.ones(n)
• lucas_model: f = np.ones_like(grid) # Initial guess of f
• opt_tax_recur: tax_seq = SequentialLS(CRRAutility(), g=np.array([0.15]), π=np.ones((1, 1)))

[Daisuke Oyama]

There are a few more in some .py files:

• https://github.com/QuantEcon/lecture-python-advanced.myst/blob/main/lectures/_static/lecture_specific/amss2/log_utility.py
• https://github.com/QuantEcon/lecture-python-advanced.myst/blob/main/lectures/_static/lecture_specific/amss2/crra_utility.py
• https://github.com/QuantEcon/lecture-python-advanced.myst/blob/main/lectures/_static/lecture_specific/opt_tax_recur/sequential_allocation.py
• https://github.com/QuantEcon/lecture-python-advanced.myst/blob/main/lectures/_static/lecture_specific/amss2/sequential_allocation.py
• https://github.com/QuantEcon/lecture-python-advanced.myst/blob/main/lectures/_static/lecture_specific/opt_tax_recur/recursive_allocation.py
• https://github.com/QuantEcon/lecture-python-advanced.myst/blob/main/lectures/_static/lecture_specific/amss2/recursive_allocation.py