Search Results for "output"

...Just give ic() a variable or expression and you're done. ic() returns its argument(s), so ic() can easily be inserted into pre-existing code. Additionally, ic()'s output can be entirely disabled, and later re-enabled, with ic.disable() and ic.enable() respectively. ic() continues to return its arguments when disabled, of course; no existing code with ic() breaks. To make ic() available in every file without needing to be imported in every file, you can install() it. ic() can also be imported in a manner that fails gracefully if IceCream isn't installed, like in production environments (i.e. not development).

...It supports multiple languages (C++, Python, TCL among others) via the Debug Adapter Protocol (DAP) model and provides an in-editor UI for viewing scopes, variables, stack frames, output windows and more. You configure it per-project via a .vimspector.json file (or per file type) specifying the adapter, launch configuration or attach process. Once configured you can run, pause, step into/out/over, set and clear breakpoints and inspect variables directly in Vim splits or floating windows. The UI is heavy compared to typical Vim plugins (multiple panes, layouts) but it fills the gap for users who want an integrated debug workflow without leaving the editor.

Existing libraries implement automatic differentiation by tracing a program's execution (at runtime, like PyTorch) or by staging out a dynamic data-flow graph and then differentiating the graph (ahead-of-time, like TensorFlow). In contrast, Tangent performs ahead-of-time autodiff on the Python source code itself, and produces Python source code as its output. Tangent fills a unique location in the space of machine learning tools. As a result, you can finally read your automatic derivative code just like the rest of your program. Tangent is useful to researchers and students who not only want to write their models in Python, but also read and debug automatically-generated derivative code without sacrificing speed and flexibility. ...

Modern software systems and application are commonly written in multiple languages, include scripting engines, and are frequently build on multiple specialized frameworks and middleware for a considerable diversity of runtime environments. The latest influencing update in development paradigm is the application of multicore processors. This projects is aimed to unify the required trace and logging output and integrate into debugging environments. The target is to provide general development, test, and production support of software environments based on multiple programming languages for distributed multicore environments.

Usually, coding a script involves writing the code and either debugging or running it several times, examining variables or output and fixing the code until the desired effect is achieved. With PyCodebug, you get to see the effects of the code during coding, which greatly reduces the time needed to write a script. ================================== Demo Video: http://youtu.be/a6nFo4y3kzo ================================== Requires: Python 2.7x PySide

A disassembler for the linux platform. Currently this supports x86 ELF files assumed to be written in C and output to intel- syntax assembly language; however the design is modular and replacements for any of these can be written.

A program to apply a link map to a Mac OS X crash log that came from a build without traceback tables. The output contains at least as much information as would the same crash log from a corresponding build with traceback tables.