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Tada!: auTomAtic orDer-of-growth Analysis

Build Status made-with-python

Tada is a tool that systematically runs a doubling experiment to ascertain the likely worst-case order-of-growth function for an arbitrary Python function. This documentation provides a brief overview about how to run the tool, its provided test suite, and more.


Install Tada

  • Operating system: Linux · macOS/OS X · Windows
  • Python version: Python 3.6+
  • Dependency Management: Pipenv · Poetry

Install Tada with pip

Install Tada with pip:

pip install tada-predict

Install through Github Repo

Alternatively, you can also install Tada manually by cloning the repository and installing the dependencies through either Pipenv or Poetry. This is also the common way if you want to make changes to the code base.

First, you can clone this repository with the following command:

git clone


If you would like to install dependencies through poetry, you would first need to install poetry on your local machine like this:

pip install poetry

Once you have installed poetry, you can then install the dependencies for Tada with the following command.

poetry install

You can also activate the poetry shell by running this command:

poetry shell


Similarly, you can run the following command to install pipenv on your local machine:

pip install pipenv

To install dependencies with pipenv, you can just run:

pipenv install --no-dev

You can activate the pipenv shell with this command:

pipenv shell

Run Command

To run Tada, you can just type the following command with the arguments into the terminal window within your preferred virtual environment:

tada [-h] --directory DIRECTORY --module MODULE --function FUNCTION --types TYPES [TYPES ...]

You can learn about Tada's checks and defaults by typing tada -h in your terminal window and then reviewing the following output.

usage: tada [-h] --directory DIRECTORY [DIRECTORY ...]
            --module [MODULE [MODULE ...]
            --function FUNCTION [FUNCTION ...]
            --types TYPES [TYPES ...]
            [--data_directory DATA_DIRECTORY]
            [--data_module DATA_MODULE]
            [--data_function DATA_FUNCTION] [--schema SCHEMA]
            [--startsize STARTSIZE] [--steps STEPS]
            [--runningtime RUNNINGTIME] [--expect EXPECT]
            [--backfill] [--indicator INDICATOR]
            [--maxsize MAXSIZE] [--sorted] [--log] [--md]
            [--contrast] [--level LEVEL]
            [--position] POSITION [POSITION ...]]

optional arguments:
  -h, --help
        show this help message and exit
  --directory DIRECTORY [DIRECTORY ...]
        Path to the package directory with functions to
        analyze (default: None)
  --module MODULE [MODULE ...]
        Module name with functions to analyze (default: None)
  --function FUNCTION [FUNCTION ...]
        Name of the function to analyze (default: None)
  --types TYPES [TYPES ...]
        Data generation type: hypothesis or parameter types
        of the function (default: None)
  --data_directory DATA_DIRECTORY
        Path to the package directory with function to
        generate data (default: None)
  --data_module DATA_MODULE
        Module name with functions to generate data
        (default: None)
  --data_function DATA_FUNCTION
        Name of the data generation function (default: None)
  --schema SCHEMA
        The path to the JSON schema that describes the data
        format (default: None)
  --startsize STARTSIZE
        Starting size of the doubling experiment (default: 1)
  --steps STEPS
        Maximum rounds of the doubling experiment
        (default: 10)
  --runningtime RUNNINGTIME
        Maximum running time of the doubling experiment
        (default: 200)
  --expect EXPECT
        Expected Growth Ratio: O(1) | O(logn) | O(n) |
        O(nlogn) | O(n^2) | O(n^3) | O(c^n). By using this
        argument, the experiment result will be stored in a
        csv file (default: None)
        Enable backfill to shrink experiments size according
        to the Predicted True Value (default: False)
  --indicator INDICATOR
        Indicator value (default: 0.1)
  --maxsize MAXSIZE
        Maximum size of the doubling experiment
        (default: 1500)
        Enable input data to be sorted (default: False)
        Show log/debug/diagnostic output (default: False)
        Show results table in markdown format (default: False)
        Show contrast result table. Only works with multiple
        experiments (default: False)
        Visualize a simple graph for the result
        (default: False)
  --level LEVEL
        The level of nested data structure to apply doubling
        experiment (default: 1)
  --position POSITION [POSITION ...]
        The position of input data to double in the
        multivariable doubling experiment. Must be the last
        argument (default: [0])

Sample usage:
  tada --directory /path/to/project_directory
       --module module_name.file_name --function function_name
       --types hypothesis

Running within Tada Repo

If you are running within the Tada repository, then you could also easily run Tada within the shell activated by the dependency management tool you previously installed like this:

python tada/ [-h] --directory DIRECTORY --module MODULE \
      --function FUNCTION --types TYPES [TYPES ...]

It is worth noting that when the provided experiment function is relied on an external Python library, it is likely that Tada might not have this dependency, and thus, it might cause an error when running the experiment. You can simply resolve this issue by installing the required dependencies through your chosen dependency management tool like this:

  • With pipenv: pipenv install <library-name>
  • With poetry: poetry add <library-name>

Quick Start Example

We have provided some code examples in Speed-Surprises for you to run Tada in conjunction and experience how Tada automatically suggests the likely worst-case order-of-growth function for various types of Python function. You can follow the instructions in Speed-Surprises to clone the repository and install the dependencies.

After successfully setting up the repository on your local machine, you can then run the following command to conduct an experiment for insertion_sort within the speed-surprises repository:

tada --directory . --module speedsurprises.lists.sorting \
     --function insertion_sort --types hypothesis \
     --schema speedsurprises/jsonschema/single_int_list.json

Within a minute or so, you will be able to inspect an output similar to the following with a results table provided at the end of the experiment.

        Tada!: auTomAtic orDer-of-growth Analysis!

        For Help Information Type: python tada -h

Start running experiment insertion_sort for size 1 →

→ Done running experiment insertion_sort for size 1
→ Done running experiment insertion_sort for size 64

|             insertion_sort: O(n) linear or O(nlogn) linearithmic            |
| Size |          Mean          |         Median         |       Ratio        |
|  1   | 4.882118635177613e-07  | 4.6806960487365676e-07 |         0          |
|  2   | 7.456634746551513e-07  | 7.133920059204101e-07  | 1.527335835885569  |
|  4   |  9.27755012257894e-07  | 9.209306488037112e-07  | 1.2442006934655812 |
|  8   | 1.3545460286458332e-06 | 1.3353490028381343e-06 | 1.4600255571233727 |
|  16  | 2.2379635269165037e-06 | 2.2146971740722657e-06 | 1.6521871384125948 |
|  32  | 3.9610248652140306e-06 | 3.913619827270508e-06  | 1.7699237800678478 |
|  64  | 7.2769234293619794e-06 | 7.211799896240237e-06  | 1.837131468996415  |
O(n) linear or O(nlogn) linearithmic

You can find more information about Tada usage, including experiment data generation and using different flags and arguments to configure your Tada experiment through our documentation here.

Add New Features to Tada

You can follow these steps to add a new feature if you are already a collaborator on the project. First, you should create and publish your new branch via the following command. Substitute the name of your feature/branch for the word feature-name.

  • git checkout -b feature-name
  • git checkout master
  • git push -u origin feature-name

To install development dependencies, type the following commands in the terminal:

poetry install

You can activate the shell with the following command:

poetry shell

Finally, you should open a pull request on the GitHub repository for the new branch that you have created. This pull request should describe the new feature that you are adding and give examples of how to run it on the command line. Of course, if you are not a collaborator on this project, then you will need to fork the repository, add your new feature, document and test it as appropriate, and then create a pull request similarly.

We highly recommend you to provide tests along with the feature that you implemented and you should not break the existing test cases or features.

Test Tada

To run the test suite for Tada's functions within the shell by typing the following in your terminal window:

pytest tests

If you want to collect the coverage of the provided test suite, then you can run:

pytest --cov-config pytest.cov --cov

If you want to collect the coverage of the provided test suite and see what lines of code are not covered, then you can run:

pytest --cov-config pytest.cov --cov --cov-report term-missing

Problems or Praise

If you have any problems with installing or using the Tada or its provided test suite, then please create an issue associated with this Git repository using the Issues link at the top of this site. The contributors to Tada will do all that they can to resolve your issue and ensure that all of its features and the test suite work well in your development environment.