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Getting Started

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.

Install Tada

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

Install Tada with pip

$ pip install tada-predict
---> 100%
Successfully installed 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.

Clone the Github Repository

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

$ git clone
---> 100%
Successfully cloned tada

Install Dependencies

You can install dependencies through either poetry or pipenv with Tada; you would first need to install either of these dependency management tool on your local machine and then install the dependencies like the following:

$ pip install poetry
---> 100%
Successfully installed poetry
$ poetry install --no-dev
---> 100%
Successfully installed tada-predict and dependencies from lock file
$ pip install pipenv
---> 100%
Successfully installed pipenv
$ pipenv install
---> 100%
Successfully installed dependencies from Pipfile.lock

You can activate the shell with one of the following commands:

poetry shell
pipenv shell

Use Tada

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 with the dependency management tool (or within the activated shell) you previously installed like this:

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

poetry add <library-name>
pipenv install <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 --directory . --module speedsurprises.lists.sorting --function insertion_sort --types hypothesis --schema speedsurprises/jsonschema/single_int_list.json

        Tada!: auTomAtic orDer-of-growth Analysis!

      For Help Information Type: python -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


Tada provides more features and customization on argument parameters for you to choose. The tool adopts Hypothesis and Hypothesis-jsonschema to generate random data for your provided Python function, so that you can easily conduct a doubling experiment with a dynamic range of data by simply providing a JSON schema that describes the type and constraints of your function arguments. Besides, Tada also has a set of built-in data generation functions that would support most primary types. You can even write your own customized data generation function for more specific constraints the function might expect. You can also fine-tune your experiment through the aforementioned CLI arguments from starting size of the experiment --startsize to --position that determines which parameter(s) to double within your function. You can also make use of our --backfill and --indicator checks to accelerate your experiment process. Please be sure to check out Using Tada to find out more details about these features.


Tada comes with an extensive test suite. In order to run the tests, you will need to clone the git repository and set up the development environment for Tada using either pipenv or poetry

See Test Tada for more details and information.

Last update: 2020-12-06