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Week 24 · Build: Make the project reproducible

Session 2 of 4 · Demonstrate and defend · Phase 6

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 someone else understand, reproduce and challenge your engineering conclusions? This session focuses on make the project reproducible.

Before you start

The previous week’s recorded baseline and Week 23, 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

Make the project reproducible

A portfolio README should let a future reader find requirements, setup, configuration, code, map, data, plots and conclusions without opening every file. Include exact firmware/dependency versions and the command used to run analysis. Avoid committing credentials, personal information or third-party code without permission. The public course does not require making a student repository public. Export a reviewed report or demonstration link instead if desired. A clean clone and successful desktop replay are practical checks of the handoff.

Worked example — illustrative values

A defensible claim is: “Version v1.0 arrived in 8 of 10 held-out trials on our surveyed mat; arrived endpoint error averaged 6 cm, with two hazard stops.” It names version, denominator, conditions and a conditional metric. “The robot always navigates accurately” is unsupported by that evidence. These figures are fictional examples, not course results.

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/w24.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 claim_summary(version, attempts, arrivals, conditions):
    if attempts <= 0 or not 0 <= arrivals <= attempts:
        raise ValueError("invalid trial counts")
    return (f"{version}: {arrivals}/{attempts} arrivals under {conditions}. "
            "See raw trials for errors, stops and limitations.")

print(claim_summary("example-v1", 10, 8, "fictional surveyed-mat tests"))

Run the example on desktop Python before adapting it. Change one valid input and check the result; keep device-only calls in a separate adapter. 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

The summary function checks that counts are logically possible before forming a claim. It deliberately leaves detailed errors and stop reasons in the linked raw record, because a one-line summary cannot establish every result. Replace its fictional version and conditions with the exact archived build and evaluated envelope. Then audit the prose: can each number be reconstructed, and are failed attempts represented? A peer should locate the dataset and repeat a calculation without asking you for an undocumented file. An honest “not tested” is stronger than a confident statement unsupported by evidence.

Practical instructions

  1. Create the portfolio README using the handbook/report checklist.
  2. Run analysis from a clean student checkout and verify relative data paths.
  3. Collect a supervised demonstration and independent endpoint measurement using the frozen version.
  4. Review any shareable files for personal information; publication is optional and separately chosen.

Experiment

Attempt a clean-clone desktop replay; record missing files, setup ambiguities and fixes before the final demonstration.

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

A reproducible private portfolio and a logged demonstration from the frozen version.

Save notebook/w24-s2.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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