Metadata-Version: 2.1
Name: py-pal
Version: 1.1.2
Summary: Estimate Asymptotic Runtime Complexity from Bytecode executions
Home-page: https://gitlab.lukasjung.de/root/py-pal
Author: Lukas Jung
Author-email: mail@lukasjung.de
License: MIT
Project-URL: Documentation, https://py-pal.readthedocs.io/en/latest/
Description: ========
        Overview
        ========
        
        
        
        The *Python Performance Analysis Library* (*py-pal*) is a profiling tool for the Python programming language. With
        *py-pal* one can approximate the time complexity (big O notation) of Python functions in an empirical way. The arguments
        of the function and the executed opcodes serve as a basis for the analysis.
        
        To the `docs <https://py-pal.readthedocs.io>`_.
        
        
        Installation
        ============
        
        Requirements
        ------------
        - An installation of the CPython implementation of the Python programming language of version greater or equal to 3.7
            - For instance: https://www.python.org/ftp/python/3.7.9/python-3.7.9-amd64.exe
        - A compiler for the C/C++ programming language:
            - On Microsoft Windows, we use the *Buildtools für Visual Studio 2019*:
                https://visualstudio.microsoft.com/de/thank-you-downloading-visual-studio/?sku=BuildTools&rel=16
            - On Linux, any C compiler supported by Cython e.g. g++
        
        Install py-pal via pip by running:
        ----------------------------------
        This project requires CPython and a C compiler to run. Install CPython >= 3.7, then install py-pal by running:
        
        
            pip install py-pal
            
        or
        
            python -m pip install py-pal
        
            
        Command line usage of the py-pal module
        =======================================
        
            python -m py_pal <target-module/file>
        
        or
        
            py-pal <target-module/file>
        
        There are multiple aliases to the same command: `py-pal`, `py_pal` and `pypal`. If py-pal is executed this way, all
        functions called in the code are captured and analyzed. The output is in the form of a pandas data frame.
            
        See the help message:
        
            py-pal -h
        
        Programmatic usage of the py-pal module
        =======================================
        
        To profile a single function and get the complexity estimate there is *profile_function*.
        
        .. sourcecode:: python
        
            from py_pal.core import profile_function
            from py_pal.data_collection.opcode_metric import OpcodeMetric
            from py_pal.datagen import gen_random_growing_lists
            from algorithms.sort import bubble_sort
        
            profile_function(OpcodeMetric(), gen_random_growing_lists(), bubble_sort)
        
        
        The *profile* decorator:
        
        .. sourcecode:: python
        
            from py_pal.core import profile, DecoratorStore
        
            @profile
            def test():
                pass
        
            # Must be called at some point
            test()
        
            estimator = AllArgumentEstimator(DecoratorStore.get_call_stats(), DecoratorStore.get_opcode_stats())
            res = estimator.export()
        
        
        By using the *profile* decorator, it is possible to annotate Python functions such that only the annotated Python
        functions will be profiled. It acts similar to a whitelist filter.
        
        Another possibility is to use the context-manager protocol:
        
        .. sourcecode:: python
        
            from py_pal.analysis.estimator import AllArgumentEstimator
            from py_pal.data_collection.tracer import Tracer
        
            with Tracer() as t:
                pass
        
            estimator = AllArgumentEstimator(t.get_call_stats(), t.get_opcode_stats())
            res = estimator.export()
        
            # Do something with the resulting DataFrame
            print(res)
        
        
        The most verbose way to use the *py-pal* API:
        
        .. sourcecode:: python
        
            from py_pal.analysis.estimator import AllArgumentEstimator
            from py_pal.data_collection.tracer import Tracer
        
        
            t = Tracer()
            t.trace()
        
            # Your function
            pass
        
            t.stop()
            estimator = AllArgumentEstimator(t.get_call_stats(), t.get_opcode_stats())
            res = estimator.export()
        
            # Do something with the resulting DataFrame
            print(res)
        
        All examples instantiate a tracer object that is responsible for collecting the data. After execution, the collected
        data is passed to the analysis module. Finally, an estimate of the asymptotic runtime of the functions contained in the
        code is obtained.
        
        Modes
        -----
        In the current version py-pal offers only the **profiling mode**. Although ``py_pal.datagen`` offers some functions for
        generating inputs, py-pal must be combined with appropriate test cases to realize a **performance testing mode**. An
        automatic detection and generation of appropriate test inputs does not exist at the moment.
        
        Limitations
        -----------
        The profiling approach implemented by the py-pal modules does not distinguish between different threads executing a
        Python function. Actually it is a major problem to profile a Python script which makes use of threads. The bytecode
        counting strategy will increase all counters of Python functions on the current call stack no matter what threads is
        executing it. Thus, the data points will not be accurate to what really happened during the profiled execution of the
        script.
        
        Licensing Notes
        ===============
        This work integrates some code from the `big_O <https://github.com/pberkes/big_O>`_ project. More specifically, most
        code in ``py_pal.analysis.complexity``, ``py_pal.datagen`` and ``py_pal.analysis.estimator.Estimator.infer_complexity``
        is adapted from bigO.
        
        Changelog
        =========
        
        What's New in Py-PAL 1.1.0
        --------------------------
        
        - Improved Data Collection: The heuristic for determining the size of function arguments has been improved.
        - More tests
        - More documentation
        - More argument generation functions in `py_pal.datagen`
        - Replaced command line option --debug with --log-level for more configurable log output
        
        Refactoring
        """""""""""
        Project structure changes, overall CLI interface is unchanged.
        API changes:
        
        - `py_pal.tracer` moved to `py_pal.data_collection.tracer`
        - `py_pal.complexity` and `py_pal.estimator` moved to the `py_pal.analysis` package.
        - `py_pal.analysis.estimator.Estimator` now takes call and opcode stats as arguments.
        
        Py-PAL 1.0.0
        ------------
        
        - More thorough testing from different combinations of requirements and Python versions.
        - Bug fixes
        
        Py-PAL 0.2.1
        ------------
        
        Refactoring
        """""""""""
        
        The `estimator` module was refactored which introduces a slight change to the API.
        Classes inheriting from `Estimator` now only specify how to transform the collected data with respect to the arguments
        of the function.
        
        Instead of `ComplexityEstimator` you should use the `AllArgumentEstimator` class. Additionally there is the `SeparateArgumentEstimator` which is experimental.
        
        Py-PAL 0.1.6
        ------------
        
        More accurate Data Collection
        """""""""""""""""""""""""""""
        
        The `Tracer` is enhanced by measuring builtin function calls with `AdvancedOpcodeMetric`.
        
        Opcodes resembling a function call .e.g `FUNCTION_CALL` are filtered for built in function calls.
        If the called function is found in the complexity mapping a synthetic Opcode weight gets assigned.
        A builtin function call is evaluated using its argument and a pre-defined runtime complexity e.g. O(n log n) for
        `sort()`.
        
        - The feature is enabled by default
        - The calculation produces a performance overhead and can be disabled by providing a `OpcodeMetric` instance to the `Tracer`
        - The `AdvancedOpcodeMetric` instance assigned to the `Tracer` provides statistics about how many builtin function calls were observed and how many were found in the complexity map
        
        Bugfixes
        """"""""
        - Cleaning data after normalization introduced wrong data points
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: Unix
Classifier: Operating System :: POSIX
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Utilities
Classifier: Topic :: Software Development :: Debuggers
Requires-Python: >=3.7
Description-Content-Type: text/x-rst
