Python rope is a tool designed for Python developers to enhance their Integrated Development Environment with intelligent features like code refactoring, code completion, and more. It helps improve the overall development experience, making it easier and faster to write Python code.
Rope is a Python IDE that offers a wide range of features, including auto-completion, refactorings, content assistance, and outlines. Refactoring is a fundamental task in modern programming, especially in the Java community, and is considered one of the core practices in agile programming methodologies like Extreme Programming. While some IDEs support basic refactorings like 'PyDev' (which uses bicycle repair man), their sets of refactorings are limited and may fail with relatively large projects that require knowledge of the types of objects in the source code. Rope aims to provide a rich set of refactorings that require type inferencing, which is explained later.
Auto completion is one of the fundamental features of modern IDEs, and while some Python IDEs have auto-completion support, their support is limited since deducing the types of many variables from simple source code analysis is difficult. This makes it challenging to auto-complete methods and attributes of an object since the IDE must know the object's type, which is not easy in dynamic languages. Rope addresses this issue by leveraging Type Inferencing algorithms to solve the problem.
Dynamic languages like Python make it challenging to determine the type of variables from the program's source code. This makes it essential to know their types to offer many of the refactorings and auto-completions. Rope leverages type inferencing to overcome this issue. Although static type inferencing tries to guess the object's type from the program source code, type inferencing for Python is tough. There have been attempts to address this, like psycho, which only supports str and int types, StarKiller, which was never released, and ShedSkin, which is good but limited. These attempts were not designed for building IDEs, but to speed up Python programs by transforming code to other typed languages. These algorithms can still be useful.
Another approach towards type inferencing is the analysis of running programs. This approach records the types variables are assigned to during program execution. While this is a lot easier to implement than the alternative, it is limited since only the parts of the program that are executed are analyzed. Nonetheless, developers can use unit tests and test-driven development to make this approach work efficiently.
Auto completion is one of the fundamental features of modern IDEs, and while some Python IDEs have auto-completion support, their support is limited since deducing the types of many variables from simple source code analysis is difficult. This makes it challenging to auto-complete methods and attributes of an object since the IDE must know the object's type, which is not easy in dynamic languages. Rope addresses this issue by leveraging Type Inferencing algorithms to solve the problem.
Dynamic languages like Python make it challenging to determine the type of variables from the program's source code. This makes it essential to know their types to offer many of the refactorings and auto-completions. Rope leverages type inferencing to overcome this issue. Although static type inferencing tries to guess the object's type from the program source code, type inferencing for Python is tough. There have been attempts to address this, like psycho, which only supports str and int types, StarKiller, which was never released, and ShedSkin, which is good but limited. These attempts were not designed for building IDEs, but to speed up Python programs by transforming code to other typed languages. These algorithms can still be useful.
Another approach towards type inferencing is the analysis of running programs. This approach records the types variables are assigned to during program execution. While this is a lot easier to implement than the alternative, it is limited since only the parts of the program that are executed are analyzed. Nonetheless, developers can use unit tests and test-driven development to make this approach work efficiently.
What's New
Version 0.9.2: N/A