modules unannotated. The more you annotate, the more useful mypy will be, but even a little annotation coverage is useful. Write annotations as you go Consider adding something like these in your code style increase annotation coverage in your codebase without much effort. Automate annotation of legacy code There are tools for automatically adding draft annotations based on simple static analysis or on type profiles com/dropbox/pyannotate]. A simple approach is to collect types from test runs. This may work well if your test coverage is good (and if your tests aren’t very slow). Another approach is to enable type collection for

modules unannotated. The more you annotate, the more useful mypy will be, but even a little annotation coverage is useful. Write annotations as you go Consider adding something like these in your code style increase annotation coverage in your codebase without much effort. Automate annotation of legacy code There are tools for automatically adding draft annotations based on simple static analysis or on type profiles com/dropbox/pyannotate]. A simple approach is to collect types from test runs. This may work well if your test coverage is good (and if your tests aren’t very slow). Another approach is to enable type collection for

Tutorials Language Reference Abstract definitions Built-ins Coinduction Copatterns Core language Coverage Checking Cubical Cubical compatible Cumulativity Data Types Flat Modality Foreign Function Interface empty vector, since there is no possible index of type Fin 0. For more details, see the section on coverage checking. Agda as a Proof Assistant: Proving Associativity of Addition In this section we state Lexer Parser Concrete Syntax Nice Concrete Syntax Abstract Syntax Internal Syntax Treeless Syntax Coverage Checking Single match on a non-indexed datatype Matching on multiple arguments Copattern matching

Tutorials Language Reference Abstract definitions Built-ins Coinduction Copatterns Core language Coverage Checking Cubical Cumulativity Data Types Flat Modality Foreign Function Interface Function Definitions empty vector, since there is no possible index of type Fin 0. For more details, see the section on coverage checking. Agda as a Proof Assistant: Proving Associativity of Addition In this section we state Lexer Parser Concrete Syntax Nice Concrete Syntax Abstract Syntax Internal Syntax Treeless Syntax Coverage Checking Single match on a non-indexed datatype Matching on multiple arguments Copattern matching

Tutorials Language Reference Abstract definitions Built-ins Coinduction Copatterns Core language Coverage Checking Cubical Cumulativity Data Types Flat Modality Foreign Function Interface Function Definitions empty vector, since there is no possible index of type Fin 0. For more details, see the section on coverage checking. Agda as a Proof Assistant: Proving Associativity of Addition In this section we state Lexer Parser Concrete Syntax Nice Concrete Syntax Abstract Syntax Internal Syntax Treeless Syntax Coverage Checking Single match on a non-indexed datatype Matching on multiple arguments Copattern matching

Tutorials Language Reference Abstract definitions Built-ins Coinduction Copatterns Core language Coverage Checking Cubical Cumulativity Data Types Flat Modality Foreign Function Interface Function Definitions empty vector, since there is no possible index of type Fin 0. For more details, see the section on coverage checking. Agda as a Proof Assistant: Proving Associativity of Addition In this section we state Lexer Parser Concrete Syntax Nice Concrete Syntax Abstract Syntax Internal Syntax Treeless Syntax Coverage Checking Single match on a non-indexed datatype Matching on multiple arguments Copattern matching

possible, including matplotlib figures and HTML tables (as used, for example, in the pandas data analysis package). This is known as IPython’s rich display capability. See also Rich Output [https://nbviewer -e .[test] To run the Python tests, use: nosetests If you want coverage statistics as well, you can run: nosetests --with-coverage --cover-package=notebook notebook JavaScript Tests To run the JavaScript

possible, including matplotlib figures and HTML tables (as used, for example, in the pandas data analysis package). This is known as IPython’s rich display capability. See also Rich Output [https://nbviewer -e .[test] To run the Python tests, use: nosetests If you want coverage statistics as well, you can run: nosetests --with-coverage --cover-package=notebook notebook JavaScript Tests To run the JavaScript

possible, including matplotlib figures and HTML tables (as used, for example, in the pandas data analysis package). This is known as IPython’s rich display capability. See also Rich Output [https://nbviewer -e .[test] To run the Python tests, use: nosetests If you want coverage statistics as well, you can run: nosetests --with-coverage --cover-package=notebook notebook JavaScript Tests To run the JavaScript

possible, including matplotlib figures and HTML tables (as used, for example, in the pandas data analysis package). This is known as IPython’s rich display capability. See also Rich Output [https://nbviewer -e .[test] To run the Python tests, use: nosetests If you want coverage statistics as well, you can run: nosetests --with-coverage --cover-package=notebook notebook JavaScript Tests To run the JavaScript