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Week 2 · Improve: Small functions and clear failures

Session 4 of 4 · Python with a purpose · Phase 1

Plan about 15 minutes for explanation, 30 minutes for practical work and 10–15 minutes for documentation. A longer build may continue into the next session: stop safely, commit the current state and record the next check. Desktop simulations count as software evidence; label them clearly and record physical validation separately.

Engineering challenge

Can your code express a repeatable plan while refusing invalid inputs? This session focuses on small functions and clear failures.

Before you start

The previous week’s recorded baseline and Week 1, Improve. For later sessions this week, retain the preceding session’s files and predictions.

Equipment: Desktop Python, editor, paper and ruler; for physical work, the configured 3pi+ 2040, clear floor mat and hardware checklist. Week 3 additionally uses the separate low-voltage LED circuit described in its procedure.

For any motion, verify the stop button, short time limit and clear floor area first. Keep the wheels raised for a new device program until its commands and stop behaviour are checked. A hazard or uncertain input is a reason to stop and document, not to force the trial to finish.

Theory and mathematics

Small functions and clear failures

When a program grows, separate calculating a value from sending a hardware command. A calculation function can be tested on a laptop; a device adapter contains the firmware-specific API. This is a practical boundary, not a reason to build a large class hierarchy. Make one change at a time and rerun the same cases. A failed assertion identifies a violated expectation. Do not catch every exception and silently continue: a robot that ignores errors can move with stale instructions. A readable error message often saves more time than a clever shortcut.

Worked example — illustrative values

If distance_m = 0.6 and duration_s = 3.0, speed is 0.2 m/s. Three 0.5-second movements take 1.5 seconds of commanded motion. A printed stop after each movement is an instruction trace; it is not a measurement of actual stopping distance.

Write the calculation in your notebook before running code. State which values you measured, which you assumed and which the program calculates. A correct numerical calculation cannot rescue an incorrect physical assumption.

Run and explain the model

The following is desktop Python, not a ready-to-run motor program. Download this week’s example, save it in your student repository and run python3 code/w02.py from the repository root. The same small model is reused across the week so you can learn it, build with it, test it and revise it.

# Desktop Python teaching example. Numerical inputs are illustrative.
def speed(distance_m, duration_s):
    if duration_s <= 0:
        raise ValueError("duration_s must be positive")
    return distance_m / duration_s

for trial in range(3):
    print(trial + 1, "forward", speed(0.6, 3.0), "m/s")
    print("stop")
assert abs(speed(0.6, 3.0) - 0.2) < 1e-9

Compare the unchanged baseline and your proposed change on the same inputs. Keep the original files so another reader can reproduce the comparison. If an exception appears, read its final line, identify the input or assumption that caused it and make the smallest explained correction. Do not delete validation merely to obtain output.

Understanding the model and its limits

Read the function from top to bottom. def speed(...) names two inputs; the indented body rejects a nonpositive time before division. return sends the numerical result back to its caller. The three-pass loop supplies trial indices 0, 1 and 2; printing trial + 1 gives human-readable labels 1, 2 and 3. The final assertion compares an approximate float within a tiny tolerance instead of requiring exact binary equality. Change one argument at a time. This example computes a speed and prints a plan; it does not measure elapsed time or communicate with motors.

Practical instructions

  1. Separate calculation, plan validation and printing into small functions.
  2. Replace duplicated values with named constants carrying units.
  3. Introduce one deliberate misspelling or indentation error, capture the message and repair it.
  4. Rerun all session-3 cases and commit only the explained final program.

Experiment

Compare the old and refactored programs on the same six inputs. Match valid outputs and deliberate rejection behaviour.

Before testing, record your prediction, changed factor, measured response, fixed conditions and stopping rule. Save every attempted run, including failures, with a condition and source version. If hardware is unavailable, use an explicitly labelled synthetic/replay dataset and list the physical question it cannot answer. Do not invent completed trials.

Deliverable

Equivalent valid results after refactoring, clearer failure messages and a reviewed Git diff.

Save notebook/w02-s4.md, the relevant code revision, raw CSV or test-case records, and one labelled diagram/plot/table. Link the files relatively from your notebook. Use the entry template and report guide.

Completion criteria

A documented failed prediction can meet the learning criteria. A missing physical trial must remain marked untested; software success alone does not validate the robot.

Reading and video

Reflection and next step

Which assumption most affected your result? Point to one observation that supports your explanation and one alternative explanation the evidence has not ruled out. Write a specific next test with a changed factor and measurable outcome, then proceed through the week’s Learn → Build → Experiment → Improve cycle.

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