Python code annotation manual#

We recommend a three-step workflow including automatic cell annotation (wherever possible), manual cell annotation and verification.

Python code annotation how to#

This tutorial focuses on how to interpret these data to identify cell types, states and other biologically relevant patterns with the objective of creating an annotated map of cells. Standard experimental protocols and analysis workflows have been developed to create single-cell transcriptomic maps from tissues. To address these cases, Pyre will give precedence to type stub files with a *.pyi extension over source files when these are specified in the search path in the project configuration or if they are located next to the implementation file.Single-cell transcriptomics can profile thousands of cells in a single experiment and identify novel cell types, states and dynamics in a wide variety of tissues and organisms.

Python code annotation code#

Other times, we may be working with Python code that is just too dynamic to be reasonably typed. builtins is compiled native code, and other libraries may be using Cython.

We do not always have access to all the source code that contributes type information to our project: e.g. It doesn’t make sense to use a TypeVar on a function or method when it’s only used in its parameters (just use the most general known supertype instead).Īnother story is the difference between TypeVars’ invariance, variance and contravariance, which is covered in here. linking the parameters and return types of a function, or class attribute types with its method signatures. one line/function call/operation) they remember what type they were. Like object, they accept all types, but within a single typecheck (i.e. TypeVars are somewhat interesting beasts, as in a vacuum, they can behave both like Any and like object. Note that such checks are valid only for a short time, see here. Thanks to Python’s runtime reflection, the original type can be recovered, e.g. This can be useful everywhere we need type erasure, like (de)serialization or generic heterogeneous containers where there is no obvious common supertype. The advantage of using object over Any is that while any type can be “put inside” it, it is an error to use it as any type other than object. However, the general TL DR is that object is an opaque superclass of all types, while TypeVar is for preserving a type across one or more function calls.

As it turns out, it is not at all obvious which of them should be used where. We have two main methods for annotating generic code: object and TypeVar. It is not, therefore, a good idea to use it in generic code. The main problem with Any is that it unifies with every type - which effectively hides all potential type errors that could stem from incorrect usage of anything annotated with Any.

But while it is a convenient utility for quickly annotating untyped code, it has no place in a strict-mode codebase. It may be tempting to annotate a generic function parameter with Any. How to move away from Any What if I want to use Any? To opt individual files out of strict mode, use # pyre-unsafe in place of # pyre-strict. Strict mode can also be set as the default in a project configuration. As you can see in the example, Any can still sneak into modules that are strict, but increasing strict coverage and fixing the surfaced errors will gradually eliminate them.