cadence-edu 0.2.12

Cadence — live student progress dashboards for Jupyter teaching

Cadence — live student progress dashboards for Jupyter

cadence-edu is the Jupyter-side half of Cadence: a tiny set of magics and helpers that let a teacher register checkpoints in a notebook, students answer them inline from their own notebooks with check("id", value), and a teacher dashboard shows live solve counts, attempts-to-first-correct, and the most common wrong answers in real time.

No grader. No autograder pipeline. No login required for quick one-off lessons. Just pip install, %load_ext cadence, and a join code on the projector.

pip install cadence-edu

---

Quickstart (5 minutes)

The fastest path: let the CLI mint both notebooks for you, then edit in place.

pip install cadence-edu

cadence-cli new teacher --name "Fibonacci warm-up"   # writes ./teacher-setup.ipynb
cadence-cli new student --name "Fibonacci warm-up"   # writes ./student.ipynb

Each starter has the right magics pre-wired — %load_ext cadence, %cadence_create_lesson / %cadence_session, a YAML registration block, an example check(...) — so you can jupyter notebook straight into editing real content.

If you'd rather copy a cell by hand:

Teacher notebook (run once per lesson, keep it private):

%load_ext cadence
%cadence_create_lesson "Fibonacci warm-up"
# → prints a join code (e.g. soup-river-42) and the dashboard URL

%cadence_register fib-10 --comparator numeric --expected '{"value": 55}'
%cadence_register greet  --comparator exact   --expected '"hello"'

%cadence_self_test            # verifies your expected answers parse correctly
%cadence_show_join            # big-text join code for the projector

Student notebook (distributed to the class — reusable every term):

%load_ext cadence
%cadence_session soup-river-42 "Alice Smith"

from cadence import check
check("greet", "hello")
check("fib-10", fib(10))

The teacher's dashboard updates live as students submit. That's the whole product.

Discovering commands as you go: type %cadence_<Tab> for autocompletion, %cadence_register? for argparse-style help on any magic, or %cadence_help for a one-page cheatsheet of every Cadence magic with its exact syntax. After typing %cadence_lesson (or %cadence_course) the names cached in ~/.cadence/lessons.yaml tab-complete too.

---

Concepts

Term	What it is
Lesson	One notebook's worth of checkpoints. Created with %cadence_create_lesson. Has its own join code + teacher token. Anyone can create one — no account required.
Course	A named group of notebook lessons (e.g. "Fall 2026"). Created with %cadence_create_course. Requires %cadence_login. Students join the course with one code, then pick a notebook.
Checkpoint	One expected answer in a lesson. Has an ID, a comparator (exact/numeric/set/regex/manual), an optional hint, and an optional worked solution. Registered with %cadence_register.
Session	A student's enrollment in a lesson or course. Started with %cadence_session <code> "<name>".

Markers vs magics — the discipline

Cadence has two kinds of syntax. The rule is simple and worth holding in mind because every other detail follows from it.

	Form	Purpose	Examples
Markers	Comments — # cadence:foo in code cells, <!-- cadence:foo --> in markdown cells	Static metadata. They label what a cell IS. They never do anything when you run the cell — they sit there waiting for tools to read them.	# cadence:checkpoint setup.arange # cadence:solution # cadence:hint: try ... <!-- cadence:task setup.arange -->
Magics	Jupyter line/cell magics — %cadence_foo / %%cadence_foo	Actions. They DO something now: talk to the server, read your notebook, write a file.	%cadence_create_lesson "..." %cadence_autoregister %cadence_scaffold %%cadence_register_yaml

So when you see # cadence:checkpoint mean.basic, it doesn't talk to any server — it just labels the cell. When you see %cadence_autoregister, it's doing work in real time and might prompt you.

---

Teacher workflow

The recommended path: %cadence_autoregister

You already have (or are writing) a notebook with worked solutions. The fastest path from "I have a teaching notebook" to "I have a live class" is one magic and one prompt-walkthrough — %cadence_autoregister.

Concretely: in your existing teaching notebook, run all cells end-to-end so your answer variables exist in the kernel, then in a new cell at the bottom run:

%load_ext cadence
%cadence_autoregister

It walks you through three prompts:

1. Reveal solutions to students after N attempts? Empty → no reveals.
2. Sign in to track this lesson under your account? (Only asked if you aren't already signed in.) Required for courses, optional otherwise.
3. Add this lesson to a course? Only asked if you're signed in. Empty for standalone; a course name to attach to an existing one; or new: <name> to create a new course.
4. How many days to keep each student's data? Empty → 7 days for a standalone lesson, 90 days for a course.

It writes <your_notebook>_registered.ipynb. Open it, do "Run All", and the final cell — also generated for you — calls %cadence_scaffold, which writes <your_notebook>_registered_student.ipynb. That's the file you share with students.

Two ways to tell autoregister which cells are exercises, and you can mix them:

• Explicit (recommended for control): put # cadence:checkpoint <id> at the top of each solution cell.
• Implicit (works on a vanilla notebook): if you have no checkpoint markers, autoregister pairs every markdown-heading cell with the code cell that follows it as an exercise. The id is slugified from the heading. Pass --all to force this even when manual markers exist.

In either case, the answer value is read from the kernel namespace, the comparator is inferred from its type, and a %cadence_register … line gets injected at the top of the exercise cell — so when you scroll past an exercise the registration sits right above the code that produced it. No giant YAML block at the top to maintain.

Comparator inference:

Value	Comparator	Notes
int, float, numpy scalar	numeric (tolerance 0.001)
str	exact	text answers — punctuation matters
bool	exact
list, tuple, numpy array	set (order-insensitive)	override per-exercise with # cadence:checkpoint <id> exact for ordered match
set	set
(override)	manual	# cadence:checkpoint <id> manual — free-text reflection, no value extracted, student self-attests with mark_done

How autoregister decides — the rules

Two questions per cell: is this an exercise? and what's the answer? Stated explicitly so you can tell at a glance whether your notebook is going to autoregister cleanly or whether you need explicit markers.

Is this cell an exercise?

• Explicit mode (any cell has # cadence:checkpoint): exercise iff the cell has that marker. Every other code cell is treated as setup and copied verbatim. The markdown cell directly above an exercise becomes its task description (if there is one).
• Auto mode (no checkpoint markers in the notebook): exercise iff (a) a markdown cell with a heading sits above it, claimed by no earlier code cell, and (b) its extracted value is a "primitive answer type" (int, float, str, bool, list/tuple/set of those, numpy scalar / array). If either fails, the cell is treated as setup and copied verbatim.
• Pure-import cells and magic-only cells are always treated as setup regardless of mode.
• Pass --all to autoregister to force auto mode even when manual markers exist (useful for a quick first pass on a notebook you're about to add markers to).

What's the answer?

Autoregister parses the cell's AST and looks at the last statement:

Last statement	What gets used as the answer
A bare expression (Jupyter display style) — e.g. arr.mean() on its own line	The value of that expression, looked up in the live kernel namespace.
An assignment to a single name — e.g. mean_value = arr.mean()	The current value of mean_value in the kernel namespace.
An augmented assignment — total += 1	The current value of total in the kernel.
Anything else (an if/for/def/import-only ending, multi-target assignment, etc.)	Autoregister errors with a teacher-friendly message asking you to end the cell with the answer on its own line.

In all cases the value comes from the live kernel — you must have run the cell (and the cells before it) end-to-end before invoking %cadence_autoregister. If a referenced variable isn't defined, you get a clear error pointing at which cell and which variable.

Common notebook shapes and what happens:

Shape	Outcome
Heading → markdown explanation → solution cell → heading → solution cell ...	Clean pairing in auto mode; one exercise per heading.
Multiple solution cells under one heading	Only the first code cell under each heading becomes the exercise; the rest are treated as setup (copied verbatim). Add explicit # cadence:checkpoint markers if you want each one to be a separate checkpoint.
Code cells without any preceding heading	Treated as setup, copied verbatim. Most often this is imports, helper functions, or data loading — exactly the right outcome.
Lots of cells with no clear heading structure	Auto mode will find few or no exercises and emit "⚠ Found no exercise cells." Drop # cadence:checkpoint <id> markers on the cells you want, and re-run. Manual mode doesn't need any heading at all.
A "Setup" or "Imports" section under a heading	Auto-mode silently treats it as setup because the value isn't a primitive (a Generator / DataFrame / module). No special syntax needed.
Exercise that should be free-text (a reflection)	Mark explicitly: # cadence:checkpoint reflect manual. Autoregister skips value extraction and registers as manual.

Validating the result. The card autoregister prints lists every detected checkpoint with its inferred id / comparator / expected. Read it before opening the registered notebook — if anything looks wrong (an id slugged oddly, a comparator inferred the wrong way), the fix is usually one explicit # cadence:checkpoint <id> [<comparator>] marker on the offending cell. Re-running autoregister is cheap and idempotent.

The marker toolkit

All markers are comments (so they're inert at runtime — Cadence just reads them when scaffolding). They label what a cell is for tooling; magics (%cadence_*) are what do something. The set you can reach for:

Marker	Where	What it does
# cadence:checkpoint <id> [<comparator>]	Top of a code cell	Marks this cell as exercise <id>. Optional second word overrides the inferred comparator (e.g. manual, exact).
<!-- cadence:task [<id>] -->	Markdown cell	Marks the markdown as task prose. If an id is given, the next code cell becomes the exercise stub for that id (and # cadence:checkpoint isn't needed).
# cadence:hint: <text>	Inside an exercise cell	Becomes the hint for that checkpoint. Markdown allowed — backticks, code fences, **bold**.
# cadence:starter … # cadence:end	Inside an exercise code cell	The region between becomes the student stub body (instead of # Your code here). Good for multi-step problems. Anything outside the markers is treated as the teacher's reference and stripped.
# cadence:solution	Top of a code cell	Copy this code cell verbatim into the student notebook. Use for shared setup, an explainer snippet, or a worked solution you want students to see.
# cadence:hide … # cadence:end	Inside a code cell	The region between is stripped from both the registered teacher notebook and the student notebook — purely teacher-side authoring notes.
<!-- cadence:hide --> … <!-- cadence:end -->	Inside a markdown cell	Same — strips a private aside from inside an otherwise-public markdown cell (e.g. "this trips up students because…" inside an exercise description).

The "ad hoc content for students" cases all fall out of this toolkit:

• Extra code only the student sees → put it in its own cell, top-tag it # cadence:solution.
• Extra prose only the student sees → markdown cell with <!-- cadence:task --> (no id needed) or just a heading.
• Stuff only the teacher sees → wrap in cadence:hide (in either code or markdown).
• Starter code inside an exercise stub → wrap your scaffolded structure in cadence:starter / cadence:end.

Alternative registration paths

%cadence_autoregister is the recommended path. The original mechanisms all still work, in case one of them fits your workflow better:

• YAML in a cell — %%cadence_register_yaml followed by a YAML list of checkpoints. Good when you want to type expected values explicitly rather than have them inferred from kernel state, or when you have no obvious "answer variable" to extract:

  %%cadence_register_yaml
  - id: setup.mean-value
    comparator: numeric
    expected: {value: 49.5, tolerance: 0.001}
    hint: "average of 0..99"
  - id: discovery.higgs-peak
    comparator: exact
    expected: 125
    reveal_after: 3
    solution_code: |
      bin_edges = np.arange(100, 151)
      counts, _ = np.histogram(m_gg, bins=bin_edges)
      int(bin_edges[np.argmax(counts)])
    allow_submissions: true
  

• YAML in a file — %cadence_register_yaml_file checkpoints/week3.yaml. Use when checkpoint definitions belong in version control alongside the notebook (PR-reviewable rubric changes; many notebooks sharing one rubric).

• Inline per-checkpoint — %cadence_register <id> --comparator … --expected …. Useful for quick fixes mid-class or one-checkpoint demos. The full flag set:

  %cadence_register fib-10 \
      --comparator numeric \
      --expected '{"value": 55, "tolerance": 0.001}' \
      --hint "Remember: fib(0)=0, fib(1)=1." \
      --reveal-after 3 \
      --solution-value "55" \
      --order 2
  

• Python API — cadence.api.CadenceAPI().register_checkpoint(...). Use when generating checkpoints programmatically (per-student randomized variants; CI lesson-prep scripts).

Comparators across all forms:

Comparator	--expected / expected:	Match rule
exact	'"hello"' or {value: "hello"}	str(submitted).strip() == str(value).strip()
numeric	{value: 55} or {value: 3.14, tolerance: 0.001}	abs(submitted - value) <= tolerance
set	{value: [1, 2, 3]}	set(submitted) == set(value) (order-independent)
regex	{pattern: "^[A-Z].*"}	re.match(pattern, str(submitted))
manual	(none)	Student self-attests with mark_done("id")

Creating courses

%cadence_autoregister's third prompt covers the common case (attach this lesson to a course, or create a new one). If you'd rather do it explicitly:

%load_ext cadence
%cadence_login                    # required for courses

%cadence_create_course "Fall 2026 Statistics"
%cadence_add_notebook "Week 1 — Variables"
%cadence_add_notebook "Week 2 — Distributions"

%cadence_add_notebook creates a fresh lesson inside the active course and inherits the course's retention. To pull in a lesson you already created standalone, use %cadence_attach_lesson "My Lesson" --to "Fall 2026 Statistics".

Generating the student notebook (%cadence_scaffold)

If you used %cadence_autoregister, the generated registered notebook already has a %cadence_scaffold cell at the bottom — running it produces the student version. You can also run scaffold directly on any notebook with the marker toolkit applied:

%cadence_scaffold                    # auto-detects the current notebook
%cadence_scaffold teacher.ipynb      # or pass it explicitly

Or from the shell:

cadence-cli scaffold teacher.ipynb

Auto-detection works in VSCode's Jupyter extension and in JupyterLab / classic Notebook served by jupyter_server ≥ 2. If it can't figure out the path, you'll see a clear error asking you to pass it.

The output <teacher>_student.ipynb contains:

1. A boxed welcome panel summarising the student-side API (check, submit_image, mark_done, show_hint, show_solution).
2. A %load_ext cadence + %cadence_session <code> "your name" header, join code auto-filled from ~/.cadence/lessons.yaml.
3. All markdowns with <!-- cadence:task --> or a heading, copied through (so section structure is preserved).
4. One stub per exercise — placeholder body plus check("id", ...). Override the placeholder with a # cadence:starter block.
5. Any code cell tagged # cadence:solution, copied verbatim.
6. Nothing inside any cadence:hide block — those are teacher-only.

Pass --join-code <code> to override the lookup, --out path for a custom path, or --force to overwrite an existing student notebook.

Verifying before class

%cadence_self_test

Submits each checkpoint's own expected answer and prints a pass/fail table. Catches typos in --expected and bad tolerance bounds. Regex checkpoints are skipped (can't auto-synthesize a matching string).

Displaying the join code

%cadence_show_join

Big-text rendering of the active lesson or course's join code — designed for projector / screen share.

Managing existing lessons and courses

%cadence_lesson "Week 3: Fibonacci"           # reactivate a cached lesson
%cadence_course "Fall 2026 Statistics"        # reactivate a cached course

%cadence_clone_lesson "Fall 2026 Week 3" --as "Spring 2027 Week 3"
                                              # duplicate (fresh code + token)

%cadence_attach_lesson "Lab 1" --to "Fall 2026 Statistics"
%cadence_detach_lesson "Lab 1" --from "Fall 2026 Statistics"

%cadence_delete_lesson "Old test lesson" --yes    # wipes the lesson + ALL student data
%cadence_delete_course "Fall 2025" --yes          # wipes the course; attached lessons survive

Rotating a leaked teacher token

%cadence_rotate_token                         # mints a fresh teacher_token
%cadence_rotate_token --also-join-code        # full revocation: also reissues the join code
                                              # (existing student notebooks break)
%cadence_rotate_token --course                # rotate the active course's token

The local ~/.cadence/lessons.yaml is updated in place and a fresh dashboard URL is printed.

Code submissions (optional)

For checkpoints registered with --allow-submissions, students can use %%cadence_submit <id> to send the cell's source code to your dashboard for review:

%%cadence_submit fib-recursive
def fib(n):
    return n if n <= 1 else fib(n-1) + fib(n-2)
fib(10)

The cell still runs normally — the student sees their output. The source is sent alongside.

Retention controls

%cadence_set_retention --days 30              # active lesson
%cadence_set_retention --days 90 --course     # active course

Retention can only be shortened, never extended. To lengthen it you'd clone the lesson and migrate fresh students over — a design constraint, not a bug.

---

Student workflow

Joining

%load_ext cadence
%cadence_session soup-river-42 "Alice Smith"

If the join code belongs to a course, the magic prints the list of notebooks and reminds you to pick one:

%cadence_notebook "Week 1 — Variables"

Answering checkpoints

from cadence import check, show_hint, show_solution, mark_done, submit_image

result = check("fib-10", fib(10))       # returns a CheckResult
# result.is_correct, result.attempt_num, result.hint
# Also renders in the cell as a coloured ✅/❌ chip.

show_hint("fib-10")                     # fetches the teacher's hint
show_solution("fib-10")                 # fetches the worked solution if revealed
mark_done("manual-checkpoint")          # self-attest a manual checkpoint
submit_image("plot-1", fig)             # for `--allow-submissions` image checkpoints

Timed answers

%%cadence_time fib-10
def fib(n):
    return n if n <= 1 else fib(n-1) + fib(n-2)
fib(10)

Measures wall-clock time and submits the last-expression value as the answer. Only the first correct attempt's time contributes to the dashboard histogram, so re-running a known-good cell doesn't pollute the stats. If the cell raises, nothing is submitted.

Your data rights (GDPR)

cadence-edu ships explicit support for the three Article-15/17/20 rights:

%cadence_my_data                # Article 15 — see everything stored about this session
%cadence_export_my_data         # Article 20 — dump it as JSON
%cadence_export_my_data --path ~/my-cadence-data.json
%cadence_delete_my_data --yes   # Article 17 — wipe attempts, submissions, the session itself

%cadence_delete_my_data cannot be undone and clears the active session from the kernel afterwards.

---

CLI helpers

The package also installs a cadence-cli for two jobs from the shell:

Scaffold a starter notebook. Drops a pre-wired .ipynb in the current directory so you don't start from a blank cell:

cadence-cli new teacher --name "Week 3: Fibonacci"    # writes ./teacher-setup.ipynb
cadence-cli new student --name "Week 3: Fibonacci"    # writes ./student.ipynb
cadence-cli new teacher --out path/to/setup.ipynb --force   # custom location, overwrite

The teacher scaffold has %load_ext cadence → %cadence_login → %cadence_create_lesson → a YAML registration block → %cadence_self_test in order. The student scaffold has a placeholder %cadence_session line and one example check(...). Both are tiny on purpose — they're a launching pad, not a tutorial. The longer-form particle-physics demos live at cadence-dash.com/demo.

Generate the student notebook from a teacher notebook. Same logic as %cadence_scaffold (see above) but runnable from the shell — handy in CI or when prepping multiple lessons at once:

cadence-cli scaffold teacher.ipynb                     # writes ./teacher_student.ipynb
cadence-cli scaffold teacher.ipynb --out wk3.ipynb     # custom output path
cadence-cli scaffold teacher.ipynb --join-code abc-def # override the cached lookup
cadence-cli scaffold teacher.ipynb --force             # overwrite

Manage locally-cached teacher credentials — useful when the server-side lesson has been deleted but the local YAML is stale, or when you suspect a token leak:

cadence-cli lessons list                              # every cached lesson + course, tokens masked
cadence-cli lessons forget "Week 3: Fibonacci"        # drop a stale row (local only)
cadence-cli lessons forget "Week 3: Fibonacci" --yes  # skip the confirmation prompt
cadence-cli lessons rotate "Week 3: Fibonacci"        # mint a new teacher_token
cadence-cli lessons rotate "Spring 2026" --also-join-code   # full revocation

forget only touches the local YAML. rotate calls the backend and updates the cache in place.

---

Configuration

Files

Path	What it holds
~/.cadence/lessons.yaml	Cached teacher tokens + join codes for lessons and courses (mode 0600)
~/.cadence/credentials.yaml	Teacher JWT from %cadence_login (mode 0600)
~/.cadence/terms.yaml	Recorded ToS acceptance from %cadence_accept_terms

Environment variables

Variable	Default	Purpose
CADENCE_API_URL	https://api.cadence-dash.com	Backend API base URL. Set to http://localhost:8000 for local dev.
CADENCE_DASHBOARD_URL	(falls back to CADENCE_WEB_URL, then http://localhost:3000)	Where the teacher dashboard lives. Used to build the URLs printed by create-lesson.
CADENCE_WEB_URL	https://cadence-dash.com	Public web URL — used for legal-page links.

For self-hosted setups, set CADENCE_API_URL + CADENCE_DASHBOARD_URL to your own deployment.

---

Troubleshooting

"API not available" / SSL errors on first magic. The package defaults to the hosted backend at api.cadence-dash.com. If you're running locally, point it at your own instance:

export CADENCE_API_URL=http://localhost:8000
export CADENCE_DASHBOARD_URL=http://localhost:3000

Extension didn't load. Confirm the install with:

jupyter server extension list           # Notebook 7 / JupyterLab

Then re-run %load_ext cadence in the kernel.

Stale cached lesson. If you docker compose down -v'd your local backend, your ~/.cadence/lessons.yaml will reference lessons that no longer exist. Drop them with cadence-cli lessons forget "<name>".

Teacher token leaked. %cadence_rotate_token mints a new one and invalidates the old. Add --also-join-code if you also need to lock out existing students.

---

Other primitives

A few legacy helpers are still exposed for the original code-submission flow:

• CadenceAPI — direct API client (from cadence import CadenceAPI)
• ProblemNotebook / create_problem_notebook — pre-formatted problem-notebook templates with embedded metadata and test cases

These predate the live-progress flow and aren't on the happy path; use them only if you're integrating with an external grader.

---

Links

• Hosted dashboard & docs: https://cadence-dash.com
• Setup guide: https://cadence-dash.com/guide
• Source: https://github.com/livaage/cadence
• Issues: https://github.com/livaage/cadence/issues

License

MIT — see LICENSE.