Python 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 genreating tests. Each question has metadata, expressed in a code block in YAML format when the question is declared. Tests generated by Otter Assign follow the Otter- compliant OK format.

Note: Otter Assign is also backwards-compatible with jAssign-formatted notebooks. For more information about formatting notebooks for jAssign, see its documentation.

Assignment Metadata

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

init_cell: false
export_cell: true

This cell is removed from both output notebooks. These configurations, listed in the YAML snippet below, can be overwritten by their command line counterparts (e.g. init_cell: true is overwritten by the --no-init-cell flag). 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.

requirements: null             # the path to a requirements.txt file
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: true           # 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: false             # whether to run student submissions against local tests during export
seed: null                     # a seed for intercell seeding
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: {}                  # a mapping of variable names to type strings for serlizing 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
test_files: true               # whether to store tests in separate .py files rather than in the notebook metadata
runs_on: default               # the interpreter this notebook will be run on if different from the default IPython 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, I was running Otter Assign on the lab00.ipynb notebook in the structure below:

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

and I wanted my requirements from dev/requirements.txt to be include, my configuration would look something like this:

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

A note about Otter Generate: the generate key of the assignment metadata 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 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 metadata 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

Autograded Questions

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

For code questions, a question is a description Markdown cell, followed by a solution code cell and zero or more test code cells. The description cell must contain a code block (enclosed in triple backticks ```) that begins with BEGIN QUESTION on its own line, followed by YAML that defines metadata associated with the question.

The rest of the code block within the description cell must be YAML-formatted with 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)

As an example, the question metadata below indicates an autograded question q1 worth 1 point.

name: q1
manual: false

Question Points

The points key of the question metadata defines how many points each autograded question is worth. Note that the value specified here will be divided evenly among each test case you define for the question. Test cases are defined by the test cells you create (one test cell is one test case). So if you have three test cells and the question is worth 1 point (the default), each test case is worth 1/3 point and students will earn partial credit on the question by according to the proportion of test cases they pass.

Note that you can also define a point value for each individual test case by setting points to a dictionary with a single key, each:

    each: 1

or by setting points to a list of point values. The length of this list must equal the number of test cases, public and hidden, that correspond to this test case.

    - 0
    - 1
    - 0.5
    # etc.

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 ..., properly indented. If that line is an assignment statement, then only the expression(s) after the = symbol 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 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.

Test Cells

There are two ways to format test cells. The test cells are any code cells following the solution cell that begin with the comment ## Test ## or ## Hidden Test ## (case insensitive). A Test is distributed to students so that they can validate their work. 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 metadata. For example,

points: 1
success_message: Good job!

The test metadata 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.

Note: Currently, the conversion to OK format does not handle multi-line tests if any line but the last one generates output. So, if you want to print twice, make two separate test cells instead of a single cell with:


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.

Intercell Seeding

Otter Assign maintains support for intercell seeding by allowing seeds to be set in solution cells. To add a seed, write a line that ends with # SEED; when Otter 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.

Note that seed cells are removed in student outputs, so any results in that notebook may be different from the provided tests. However, when grading, seeds are executed between each cell, so if you are using seeds, make sure to use the same seed every time to ensure that seeding before every cell won’t affect your tests. You will also be required to set this seed as a configuration of the generate key of the assignment metadata if using Otter Generate with Otter Assign.

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 metadata. A manually-graded question is defined by three parts:

  • a question cell with metadata

  • (optionally) a prompt cell

  • a solution cell

Manually-graded solution cells have two formats:

  • If a code cell, they can be delimited by solution removal syntax as above.

  • If a Markdown cell, the start of at least one line must match the regex (<strong>|\*{2})solution:?(<\/strong>|\*{2}).

The latter means that as long as one of the lines in the cell starts with SOLUTION (case insensitive, with or without a colon :) in boldface, the cell is considered a solution cell. If there is a prompt cell for manually-graded questions (i.e. a cell between the question cell and solution cell), then this prompt is included in the output. If none is present, Otter Assign automatically adds a Markdown cell with the contents _Type your answer here, replacing this text._.

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.

An example of a manually-graded code question:

An example of a manually-graded written question (with no prompt):

An example of a manuall-graded written question with a custom prompt:

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: