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        "author": "Brian O'Neill",
        "author_email": "twangist@gmail.com",
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        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: CPython",
            "Topic :: Software Development :: Code Generators",
            "Topic :: Software Development :: Libraries",
            "Topic :: System :: Logging",
            "Topic :: Utilities"
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        "description": "`log_calls` is a Python 3.3+ decorator that can print a lot of useful information\nabout calls to decorated functions, methods and properties. The decorator can\nwrite to ``stdout``, to another stream or file, or to a logger. `log_calls`\nprovides methods for writing your own debug messages, and for easily \"dumping\"\nvariables and expressions paired with their values. It can decorate individual\nfunctions, methods and properties; but it can also programmatically decorate\ncallable members of entire classes and class hierarchies, even of entire modules,\nwith just a single line \u2014 which can greatly expedite learning a new codebase.\n\nIn short, `log_calls` can save you from writing, rewriting, copying, pasting and\ntweaking a lot of ad hoc, debug-only, boilerplate code \u2014 and it can keep *your*\ncodebase free of that clutter.\n\nFor each call to a decorated function or method, `log_calls` can show you:\n\n* the caller (in fact, the complete call chain back to another `log_calls`-decorated\n  caller so there are no gaps in chains displayed)\n* the arguments passed to the function or method, and any default values used\n* nesting of calls, using indentation\n* the number of the call (whether it's the 1\\ :superscript:`st` call,\n  the 2\\ :superscript:`nd`, the 103\\ :superscript:`rd`, ...)\n* the return value\n* the time it took to execute\n* and more!\n\nThese and other features are optional and configurable settings, which can be\nspecified for each decorated callable via keyword parameters, as well as *en\nmasse* for a group of callables all sharing the same settings. You can examine\nand change these settings on the fly using attributes with the same names as\nthe keywords, or using a dict-like interface whose keys are the keywords.\n\n`log_calls` can also collect profiling data and statistics, accessible at runtime,\nsuch as:\n\n* the number of calls to a function\n* total time taken by the function\n* the function's entire call history (arguments, time elapsed, return values, callers,\n  and more), available as text in CSV format and, if `Pandas <http://pandas.pydata.org>`_\n  is installed, as a `DataFrame <http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe>`_.\n\nThe package contains two other decorators:\n\n* `record_history`, a stripped-down version of `log_calls`,\n  only collects call history and statistics, and outputs no messages;\n* `used_unused_keywords` lets a function or method easily determine, per-call,\n  which of its keyword parameters were actually supplied by the caller,\n  and which received their default values.\n\nNOTE: This package requires the CPython implementation, as it makes assumptions\nabout stack frame internals which may not be valid in other interpreters.\n\nSee the documentation online at http://www.pythonhosted.org/log_calls/,\nor in the log_calls/docs/ directory, for usage, details, examples and *tips und tricks*.",
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