QuantEcon / QuantEcon/lecture-python.myst
[kalman_2] Unclear purpose of single-worker plotting example code
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Description
Summary
I came across this block of code:
# We can generate a plot for only one of the workers:
T = 50
fig, ax = plt.subplots(figsize=(7, 7))
mu_0_1 = np.array([[1],
[100]])
mu_0_2 = np.array([[1],
[30]])
Sigma_0 = np.zeros((2,2))
uhat_0s = 100
αs = 0.5
βs = 0.3
worker = create_worker(uhat_0=uhat_0, α=α, β=β)
simulate_workers(worker, T, ax, mu_0=mu_0_1, Sigma_0=Sigma_0,
diff=False, name=r'Hard-working worker')
simulate_workers(worker, T, ax, mu_0=mu_0_2, Sigma_0=Sigma_0,
diff=False,
title='A hard-working worker and a less hard-working worker',
name=r'Normal worker')
ax.axhline(y=u_0, xmin=0, xmax=0, color='grey',
linestyle='dashed', label=r'$u_{i, 0}$')
ax.legend(bbox_to_anchor=(1, 0.5))
plt.show()
However, I find it a bit confusing:
-
The comment says "We can generate a plot for only one of the workers," but two workers are actually plotted here.
-
The variable uhat_0s looks like it should be a list but it's just a single scalar. Should this be looped over?
-
The call to ax.axhline(y=u_0, ...) seems unnecessary, as horizontal lines for true values are already plotted inside simulate_workers.
-
Also, u_0 is undefined in this context outside of the loop.
Request
Could we clarify whether this example is meant to plot just one worker or two?
Should the code be refactored to loop over multiple workers instead of hardcoding two calls to simulate_workers?
Should the redundant external ax.axhline be removed?
Any additional comments on how to clean up this example for clarity?
Thanks for the great work!
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Research direction
Start with the [kalman_2] example containing create_worker, simulate_workers, and the single-worker plotting block. Read the surrounding plotting code and definitions to determine whether the example demonstrates one worker or two and whether u_0 is in scope. Done means the example's comment, variables, plotting calls, and explanatory text consistently describe the intended behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data-visualization, documentation
- Issue type
- Refactor
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 38/100