Notebook Format

Otter’s notebook format groups prompts, solutions, and tests together into questions. Autograder tests are specified as cells in the notebook and their output is used as the expected output of the autograder when generating tests. Each question has metadata, expressed in raw YAML config cell when the question is declared.

Note that the major difference between v0 format and v1 format is the use of raw notebook cells as delimiters. Each boundary cell denotes the start or end of a block and contains valid YAML syntax. First-line comments are used in these YAML raw cells to denote what type of block is being entered or ended.

In the v1 format, Python and R notebooks follow the same structure. There are some features available in Python that are not available in R, and these are noted below, but otherwise the formats are the same.

Assignment Config

In addition to various command line arguments discussed below, Otter Assign also allows you to specify various assignment generation arguments in an assignment config cell. These are very similar to the question config cells described in the next section. Assignment config, included by convention as the first cell of the notebook, places YAML-formatted configurations in a raw cell that begins with the comment # ASSIGNMENT CONFIG.

init_cell: false
export_cell: true
generate: true
# etc.

This cell is removed from both output notebooks. These configurations can be overwritten by their command line counterparts (if present). The options, their defaults, and descriptions are listed below. Any unspecified keys will keep their default values. For more information about many of these arguments, see Usage and Output. Any keys that map to sub-dictionaries (e.g. export_cell, generate) can have their behaviors turned off by changing their value to false. The only one that defaults to true (with the specified sub-key defaults) is export_cell.

name: null                     # a name for the assignment (to validate that students submit to the correct autograder)
requirements: null             # the path to a requirements.txt file or a list of packages
overwrite_requirements: false  # whether to overwrite Otter's default requirement.txt in Otter Generate
environment: null              # the path to a conda environment.yml file
run_tests: true                # whether to run the assignment tests against the autograder notebook
solutions_pdf: false           # whether to generate a PDF of the solutions notebook
template_pdf: false            # whether to generate a filtered Gradescope assignment template PDF
init_cell: true                # whether to include an Otter initialization cell in the output notebooks
check_all_cell: false          # whether to include an Otter check-all cell in the output notebooks
export_cell:                   # whether to include an Otter export cell in the output notebooks
  instructions: ''             # additional submission instructions to include in the export cell
  pdf: true                    # whether to include a PDF of the notebook in the generated zip file
  filtering: true              # whether the generated PDF should be filtered
  force_save: false            # whether to force-save the notebook with JavaScript (only works in classic notebook)
  run_tests: true              # whether to run student submissions against local tests during export
seed:                          # intercell seeding configurations
  variable: null               # a variable name to override with the autograder seed during grading
  autograder_value: null       # the value of the autograder seed
  student_value: null          # the value of the student seed
generate: false                # grading configurations to be passed to Otter Generate as an otter_config.json; if false, Otter Generate is disabled
save_environment: false        # whether to save the student's environment in the log
variables: null                # a mapping of variable names to type strings for serializing environments
ignore_modules: []             # a list of modules to ignore variables from during environment serialization
files: []                      # a list of other files to include in the output directories and autograder
autograder_files: []           # a list of other files only to include in the autograder
plugins: []                    # a list of plugin names and configurations
tests:                         # information about the structure and storage of tests
  files: false                 # whether to store tests in separate files, instead of the notebook metadata
  ok_format: true              # whether the test cases are in OK-format (instead of the exception-based format)
  url_prefix: null             # a URL prefix for where test files can be found for student use
show_question_points: false    # whether to add the question point values to the last cell of each question
runs_on: default               # the interpreter this notebook will be run on if different from the default interpreter (one of {'default', 'colab', 'jupyterlite'})

All paths specified in the configuration should be relative to the directory containing the master notebook. If, for example, you were running Otter Assign on the lab00.ipynb notebook in the structure below:

├── lab
│   └── lab00
│       ├── data
│       │   └── data.csv
│       ├── lab00.ipynb
│       └──
└── requirements.txt

and you wanted your requirements from dev/requirements.txt to be included, your configuration would look something like this:

requirements: ../../requirements.txt
    - data/data.csv

The requirements key of the assignment config can also be formatted as a list of package names in lieu of a path to a requirements.txt file; for exmaple:

    - pandas
    - numpy
    - scipy

This structure is also compatible with the overwrite_requirements key.

A note about Otter Generate: the generate key of the assignment config has two forms. If you just want to generate and require no additional arguments, set generate: true in the YAML and Otter Assign will simply run otter generate from the autograder directory (this will also include any files passed to files, whose paths should be relative to the directory containing the notebook, not to the directory of execution). If you require additional arguments, e.g. points or show_stdout, then set generate to a nested dictionary of these parameters and their values:

    seed: 42
    show_stdout: true
    show_hidden: true

You can also set the autograder up to automatically upload PDFs to student submissions to another Gradescope assignment by setting the necessary keys in the pdfs subkey of generate:

    token: ''
    course_id: 1234        # required
    assignment_id: 5678    # required
    filtering: true        # true is the default

If you don’t specify a token, you will be prompted for your username and password when you run Otter Assign; optionally, you can specify these via the command line with the --username and --password flags. You can also run the following to retrieve your token:

from otter.generate.token import APIClient

Any configurations in your generate key will be put into an otter_config.json and used when running Otter Generate.

If you are grading from the log or would like to store students’ environments in the log, use the save_environment key. If this key is set to true, Otter will serialize the stuednt’s environment whenever a check is run, as described in Logging. To restrict the serialization of variables to specific names and types, use the variables key, which maps variable names to fully-qualified type strings. The ignore_modules key is used to ignore functions from specific modules. To turn on grading from the log on Gradescope, set generate[grade_from_log] to true. The configuration below turns on the serialization of environments, storing only variables of the name df that are pandas dataframes.

save_environment: true
    df: pandas.core.frame.DataFrame

As an example, the following assignment config includes an export cell but no filtering, no init cell, and passes the configurations points and seed to Otter Generate via the otter_config.json.

    filtering: false
init_cell: false
    points: 3
    seed: 0

You can also configure assignments created with Otter Assign to ensure that students submit to the correct assignment by setting the name key in the assignment config. When this is set, Otter Assign adds the provided name to the notebook metadata and the autograder configuration zip file; this configures the autograder to fail if the student uploads a notebook with a different assignment name in the metadata.

name: hw01

You can find more information about how Otter performs assignment name verification here.

Intercell Seeding

Python assignments support intercell seeding, and there are two flavors of this. The first involves the use of a seed variable, and is configured in the assignment config; this allows you to use tools like np.random.default_rng instead of just np.random.seed. The second flavor involves comments in code cells, and is described below.

To use a seed variable, specify the name of the variable, the autograder seed value, and the student seed value in your assignment config.

    variable: rng_seed
    autograder_value: 42
    student_value: 713

With this type of seeding, you do not need to specify the seed inside the generate key; this automatically taken care of by Otter Assign.

Then, in a cell of your notebook, define the seed variable with the autograder value. This value needs to be defined in a separate cell from any of its uses and the variable name cannot be used for anything other than seeding RNGs. This is because it the variable will be redefined in the student’s submission at the top of every cell. We recommend defining it in, for example, your imports cell.

import numpy as np
rng_seed = 42

To use the seed, just use the variable as normal:

rng = np.random.default_rng(rng_seed)
rvs = [rng.random() for _ in range(1000)] # SOLUTION

Or, in R:


If you use this method of intercell seeding, the solutions notebook will contain the original value of the seed, but the student notebook will contain the student value:

# from the student notebook
import numpy as np
rng_seed = 713

When you do this, Otter Generate will be configured to overwrite the seed variable in each submission, allowing intercell seeding to function as normal.

Remember that the student seed is different from the autograder seed, so any public tests cannot be deterministic otherwise they will fail on the student’s machine. Also note that only one seed is available, so each RNG must use the same seed.

You can find more information about intercell seeding here.

Autograded Questions

Here is an example question in an Otter Assign-formatted question:

Note the use of the delimiting raw cells and the placement of question config in the # BEGIN QUESTION cell. The question config can contain the following fields (in any order):

name: null        # (required) the path to a requirements.txt file
manual: false     # whether this is a manually-graded question
points: null      # how many points this question is worth; defaults to 1 internally
check_cell: true  # whether to include a check cell after this question (for autograded questions only)
export: false     # whether to force-include this question in the exported PDF

As an example, the question config below indicates an autograded question q1 that should be included in the filtered PDF.

name: q1
export: true

Solution Removal

Solution cells contain code formatted in such a way that the assign parser replaces lines or portions of lines with prespecified prompts. Otter uses the same solution replacement rules as jAssign. From the jAssign docs:

  • A line ending in # SOLUTION will be replaced by ... (or NULL # YOUR CODE HERE in R), properly indented. If that line is an assignment statement, then only the expression(s) after the = symbol (or the <- symbol in R) will be replaced.

  • A line ending in # SOLUTION NO PROMPT or # SEED will be removed.

  • A line # BEGIN SOLUTION or # BEGIN SOLUTION NO PROMPT must be paired with a later line # END SOLUTION. All lines in between are replaced with ... (or # YOUR CODE HERE in R) or removed completely in the case of NO PROMPT.

  • A line """ # BEGIN PROMPT must be paired with a later line """ # END PROMPT. The contents of this multiline string (excluding the # BEGIN PROMPT) appears in the student cell. Single or double quotes are allowed. Optionally, a semicolon can be used to suppress output: """; # END PROMPT

def square(x):
    y = x * x # SOLUTION NO PROMPT
    return y # SOLUTION

nine = square(3) # SOLUTION

would be presented to students as

def square(x):

nine = ...


pi = 3.14
if True:
    radius = 3
    area = radius * pi * pi
    print('A circle with radius', radius, 'has area', area)

def circumference(r):
    return 2 * pi * r
    """ # BEGIN PROMPT
    # Next, define a circumference function.
    """; # END PROMPT

would be presented to students as

pi = 3.14
if True:
    print('A circle with radius', radius, 'has area', area)

def circumference(r):
    # Next, define a circumference function.

For R,

square <- function(x) {
    return(x ^ 2)
x2 <- square(25)

would be presented to students as

x2 <- square(25)

Test Cells

Any cells within the # BEGIN TESTS and # END TESTS boundary cells are considered test cells. Each test cell corresponds to a single test case. There are two types of tests: public and hidden tests. Tests are public by default but can be hidden by adding the # HIDDEN comment as the first line of the cell. A hidden test is not distributed to students, but is used for scoring their work.

Test cells also support test case-level metadata. If your test requires metadata beyond whether the test is hidden or not, specify the test by including a mutliline string at the top of the cell that includes YAML-formatted test config. For example,

points: 1
success_message: Good job!
...  # your test goes here

The test config supports the following keys with the defaults specified below:

hidden: false          # whether the test is hidden
points: null           # the point value of the test
success_message: null  # a messsge to show to the student when the test case passes
failure_message: null  # a messsge to show to the student when the test case fails

Because points can be specified at the question level and at the test case level, Otter will resolve the point value of each test case as described here.

If a question has no solution cell provided, the question will either be removed from the output notebook entirely if it has only hidden tests or will be replaced with an unprompted Notebook.check cell that runs those tests. In either case, the test files are written, but this provides a way of defining additional test cases that do not have public versions. Note, however, that the lack of a Notebook.check cell for questions with only hidden tests means that the tests are run at the end of execution, and therefore are not robust to variable name collisions.

Because Otter supports two different types of test files, test cells can be written in two different ways.

OK-Formatted Test Cells

To use OK-formatted tests, which are the default for Otter Assign, you can write the test code in a test cell; Otter Assign will parse the output of the cell to write a doctest for the question, which will be used for the test case. Make sure that only the last line of the cell produces any output, otherwise the test will fail.

Exception-Based Test Cells

To use Otter’s exception-based tests, you must set tests: ok_format: false in your assignment config. Your test cells should define a test case function as described here. You can run the test in the master notebook by calling the function, but you should make sure that this call is “ignored” by Otter Assign so that it’s not included in the test file by appending # IGNORE to the end of line. You should not add the test_case decorator; Otter Assign will do this for you.

For example,

points: 0.5
def test_validity(arr):
    assert len(arr) == 10
    assert (0 <= arr <= 1).all()

test_validity(arr)  # IGNORE

It is important to note that the exception-based test files are executed before the student’s global environment is provided, so no work should be performed outside the test case function that relies on student code, and any libraries or other variables declared in the student’s environment must be passed in as arguments, otherwise the test will fail.

For example,

def test_values(arr):
    assert np.allclose(arr, [1.2, 3.4, 5.6])  # this will fail, because np is not in the test file

def test_values(np, arr):
    assert np.allclose(arr, [1.2, 3.4, 5.6])  # this works

def test_values(env):
    assert env["np"].allclose(env["arr"], [1.2, 3.4, 5.6])  # this also works

R Test Cells

Test cells in R notebooks are like a cross between exception-based test cells and OK-formatted test cells: the checks in the cell do not need to be wrapped in a function, but the passing or failing of the test is determined by whether it raises an error, not by checking the output. For example,

hidden: true
points: 1
testthat::expect_equal(sieve(3), c(2, 3))

Intercell Seeding

The second flavor of intercell seeding involves writing a line that ends with # SEED; when Otter Assign runs, this line will be removed from the student version of the notebook. This allows instructors to write code with deterministic output, with which hidden tests can be generated.

For example, the first line of the cell below would be removed in the student version of the notebook.

np.random.seed(42) # SEED
rvs = [np.random.random() for _ in range(1000)] # SOLUTION

The same caveats apply for this type of seeding as above.

R Example

Here is an example autograded question for R:

Manually-Graded Questions

Otter Assign also supports manually-graded questions using a similar specification to the one described above. To indicate a manually-graded question, set manual: true in the question config.

A manually-graded question can have an optional prompt block and a required solution block. If the solution has any code cells, they will have their syntax transformed by the solution removal rules listed above.

If there is a prompt for manually-graded questions, then this prompt is included unchanged in the output. If none is present, Otter Assign automatically adds a Markdown cell with the contents _Type your answer here, replacing this text._ if the solution block has any Markdown cells in it.

Here is an example of a manually-graded code question:

Manually graded questions are automatically enclosed in <!-- BEGIN QUESTION --> and <!-- END QUESTION --> tags by Otter Assign so that only these questions are exported to the PDF when filtering is turned on (the default). In the autograder notebook, this includes the question cell, prompt cell, and solution cell. In the student notebook, this includes only the question and prompt cells. The <!-- END QUESTION --> tag is automatically inserted at the top of the next cell if it is a Markdown cell or in a new Markdown cell before the next cell if it is not.

Ignoring Cells

For any cells that you don’t want to be included in either of the output notebooks that are present in the master notebook, include a line at the top of the cell with the ## Ignore ## comment (case insensitive) just like with test cells. Note that this also works for Markdown cells with the same syntax.

## Ignore ##
print("This cell won't appear in the output.")

Student-Facing Plugins

Otter supports student-facing plugin events via the otter.Notebook.run_plugin method. To include a student-facing plugin call in the resulting versions of your master notebook, add a multiline plugin config string to a code cell of your choosing. The plugin config should be YAML-formatted as a mutliline comment-delimited string, similar to the solution and prompt blocks above. The comments # BEGIN PLUGIN and # END PLUGIN should be used on the lines with the triple-quotes to delimit the YAML’s boundaries. There is one required configuration: the plugin name, which should be a fully-qualified importable string that evaluates to a plugin that inherits from otter.plugins.AbstractOtterPlugin.

There are two optional configurations: args and kwargs. args should be a list of additional arguments to pass to the plugin. These will be left unquoted as-is, so you can pass variables in the notebook to the plugin just by listing them. kwargs should be a dictionary that mappins keyword argument names to values; thse will also be added to the call in key=value format.

Here is an example of plugin replacement in Otter Assign:

Note that student-facing plugins are not supported with R assignments.

Running on Non-standard Python Environments

For non-standard Python notebook environments (which use their own interpreters, such as Colab or Jupyterlite), some Otter features are disabled and the the notebooks that are produced for running on those environments are slightly different. To indicate that the notebook produce by Otter Assign is going to be run in such an environment, use the runs_on assignment configuration. It currently supports these values:

  • default, indicating a normal IPython environment (the default value)

  • colab, indicating that the notebook will be used on Google Colab

  • jupyterlite, indicating that the notebook will be used on Jupyterlite (or any environment using the Pyolite kernel)

Sample Notebook

You can find a sample Python notebook here.