The Cengal library adheres to an API design approach used in frameworks such as Qt. For those familiar with the C++ language, I will draw comparisons between the approaches of Qt and the C++ Standard Template Library (STL). The API provided by the STL was designed to significantly reduce the burden on programmers who develop the STL. This decision was logical from the standpoint of marketing the STL among compiler creators. However, this led to the usability of the STL for the user not being great. This is evident in the fact that the STL provides the most minimal possible API, and any conveniences must be programmed anew by each programmer every time - constantly reinventing the wheel. In contrast, Qt uses the opposite approach to API construction: classes have many methods whose purposes are similar, but are aimed at different usage models. This simplifies the use of Qt for users, speeds up the writing of the final code, and avoids many errors that we usually make when we write our own 'bicycles' for the same actions each time (not because the we are not smart, but because we are humans and therefore prone to make mistakes from time to time).

• Target platforms: Win32, Linux, macOS, Android, iOS, Emscripten
• Target architectures: x64, x86, ARM
• Target interpreters: CPython, PyPy
• Recommended Python versions: 3.8+

To get started with Cengal, you can easily install it along with all the mandatory dependencies by running pip install cengal. This command not only installs Cengal and its required dependencies but also takes care of fetching and installing prebuilt wheels tailored for the Cengal library. These wheels are compatible with both Windows and Linux systems and work seamlessly with both CPython and PyPy interpreters.

If you prefer to install Cengal without its dependencies, you can opt for the 'cengal-light' package, which serves as the backend for the 'cengal' package. Simply run pip install cengal-light to get the lightweight version of Cengal.

To cater to varying needs and streamline the installation process, I've introduced stand-alone packages for select Cengal modules. These packages are designed to offer users the ability to install specific Cengal functionality without the burden of the library's full set of dependencies.

The core of this approach lies in our 'cengal-light' package, which houses both Python and compiled Cengal modules. The 'cengal' package itself serves as a lightweight shell, devoid of its own modules, but dependent on 'cengal-light[full]' for a complete Cengal library installation with all required dependencies.

For users seeking individual Cengal features or looking to minimize dependencies, our stand-alone packages provide a convenient solution. Each stand-alone package is dedicated to a specific Cengal module and relies on 'cengal-light' as its sole dependency.

Automatic hackable build system for your package which supports Python modules made with different languages: Cython (including Python to Cython automatic compilation), C/C++, ObjectiveC, Go, Nim. Other languages support is in progress.

Compiles your code, gather binary artifacts and puts them into your wheel.

• Compilable Golang module
• Compilable Nim module
• Pure Python module auto-compiled with Cython

blazing-fast inter-process communication through shared memory, enabling Python objects to be shared across processes with exceptional efficiency. By minimizing the need for frequent serialization-deserialization, it enhances overall speed and responsiveness. The package offers a comprehensive suite of functionalities designed to support a diverse array of Python types and facilitate asynchronous IPC, optimizing performance for demanding applications.

from cengal.hardware.memory.shared_memory import *
from cengal.parallel_execution.asyncio.ashared_memory_manager import *

• Shared Memory Communication:

  • Enables sharing of Python objects directly between processes using shared memory.
  • Utilizes a linked list of global messages to inform connected processes about new shared objects.

• Lock-Free Synchronization:

  • Uses memory barriers for efficient communication, avoiding slow syscalls.
  • Ensures each process can access and modify shared memory without contention.

• Supported Python Types:

  • Handles various Python data structures including:
    • Basic types: None, bool, 64-bit int, large int (arbitrary precision integers), float, complex, bytes, bytearray, str.
    • Standard types: Decimal, slice, datetime, timedelta, timezone, date, time
    • Containers: tuple, list, classes inherited from: AbstractSet (frozenset), MutableSet (set), Mapping and MutableMapping (dict).
    • Pickable classes instancess: custom classes including dataclass
  • Allows mutable containers (lists, sets, mappings) to save basic types (None, bool, 64 bit int, float) internally, optimizing memory use and speed.

• NumPy and Torch Support:

  • Supports numpy arrays by creating shared bytes objects coupled with independent arrays.
  • Supports torch tensors by coupling them with shared numpy arrays.

• Custom Class Support:

  • Projects pickable custom classes instancess (including dataclasses) onto shared dictionaries in shared memory.
  • Modifies the class instance to override attribute access methods, managing data fields within the shared dictionary.
  • supports classes with or without __dict__ attr
  • supports classes with or without __slots__ attr

• Asyncio Compatibility:

  • Provides a wrapper module for async-await functionality, integrating seamlessly with asyncio.
  • Ensures asynchronous operations work smoothly with the package's lock-free approach.

Readme.md

• An async examples (with asyncio):
  • shared_objects__example__sender.py
  • shared_objects__example__receiver.py
  • shared_objects__types.py

• Connect more than two processes
• Use third-party fast hashing implementations instead of or in addition to built in hash() call
• Continuous performance improvements

Cengal offers a unique and powerful feature that allows you to execute a diverse set of tasks concurrently on a single thread, effectively managing CPU-bound and GUI-related operations without introducing the complexity of multithreading or multiprocessing. Notably, Cengal can convert blocking CPU-bound functions into proper asynchronous coroutines, preventing them from blocking the thread for extended periods.

In this example, an application concurrently (at the same time) executes all of the following components within a single thread:

• own blocking CPU-bound function
• third-party blocking CPU-bound function
• Tkinter application
• CustomTkinter application
• asyncio-based file reading task.

• rich_example.py
• third_party_cpu_bound.py

• Decorator which converts blocking code to concurrent code

An example of usage (unit test of the module):

• test__db.py

Developer can observe their logs in runtime using cengal.parallel_execution.coroutines.coro_tools.loop_administration.admin_tk module (made with Async Tkinter GUI):

• admin_tk.py

An example of usage of the admin_tk:

• admin_test.py

Alternatively, developer can load logs in off-line mode using Log Viewer application (made with async Tkinter GUI):

• log_viewer.py

• tkinter_0.py
• customtkinter_0.py

• async_wxpython_example.py

• pyside6__coro_slot_example_0.py

• hello_world_app_autoexit.py
• hello_world.py

• netty_benchmarks.py

• Target OS: Windows 11, Windows 10, Windows 8, Windows 7, Windows Vista.
• Target frameworks: PySide, PyQt, Kivy, PyWebView

,

• Target OS: Windows 11, Windows 10, Windows 8, Windows 7, Windows Vista.
• Target frameworks: CustomTkinter, Tkinter, ttkbootstrap, ...

Observe loop performance, services state and coroutines list with details. Use an async interactive console in order to interact with your application from inside.

admin_test.py

• "parallel_execution"
  • "coroutines" - asynchronous loop with almost preemptive multitasking within the single thread. Brings an async approach to an unmodified Tkinter, Qt, Kivy, etc. Unlike asyncio/trio/curio, it uses microkernel (services-based) approach which makes it highly- and easily-expandable. Can be executed both independently (asyncio/uvloop loop will be injected within the Cengal-coroutine when needed) and within already executed asyncio/uvloop loop. Can be used from the PyScript for the Web app creation.
    • "coro_standard_services" - set of standard services. You can replace standard service by yours for your app or for third-party module without code changes: by registering your own alias.
      • "loop_yield" - automatically kinda yield from your loops from time to time (priority based). Can be used to make a proper coroutine (which will not hangs on its endless loops) even from the long-running CPU-hungry third-party function (by function's bytecode modification made in runtime).
      • "tkinter" - make your Tkninter app async easily. Run any number of asynchronous Tkinter apps in single thread.
      • "db" - async wrapper around LMDB which provides an appropriate async API
      • "asyncio_loop" - use asyncio-based code directly from your async Cengal-coroutine neither Trio nor Curio able to to do this
      • "wait_coro" - 'put_atomic' request is an analogue of Trio's Nurseries for list of one or more coroutines; 'put_fastest' - returns when at least N of coroutines from the list were done successfully; etc.
      • "read_write_locker" - sync primitive usefull for DB creation (was made for a TagDB)
      • "remote_nodes" - in progress - connect to any opened listener/port of the node (TCP/UDP/Unix_Socket - doesn't matter), and identify your receiver by name (defined once - during the connection creation process). Uses improved version of the asyncio.streams as a backend in order to have a back pressure and an improved performance (see "efficient_streams" module description below).
    • "coro_tools" - tools
      • "await_coro" - await Cengal-coroutine or await for a call to the Cengal-service from your asyncio code
      • "low_latency" - use standard json module from your coroutines without hangs on huge Json-data (which usually hung even fast json implementation like orjson)
    • "integrations" -
      • "Qt" - wrapper around an unmodified Qt (supports: PySide, PySide2, PySide6, PyQt4, PyQt5, PyQt6). Adds asynchronous behavior to Slots. Doesn't require total reimplementation of your Qt app unlike other suggestions and competitors.
      • "customtkinter" - wrapper around an unmodified customtkinter. Implements an additional call, Customtkinter async apps needs to be executed for a proper work
      • "nicegui" - wrapper around an unmodified NiceGUI. Execute nicegui instance from within your code (administrative page for example). Build your pages in an asynchronous way in order to improve your server latency (NiceGUI makes it in a sync way).
      • "uvicorn" - wrapper around an unmodified uvicorn. Run uvicorn as a usual asyncio coroutine.
      • "uvloop" - an easy-install for a uvloop (if awailable).
      • "PyTermGUI" - wrapper around an unmodified PyTermGUI. Adds asynchronous behavior. No competitors currently.
  • "asyncio" - tools for an asyncio
    • "efficient_streams" - more efficient remake of an asyncion.streams. Better awailable traffic limits utilisation. Less kerner-calls number. Back pressure. Unlike asyncio, UDP version is planned but is not ready yet.
• "code_flow_control" -
  • "python_bytecode_manipulator" - modify your or third-party Python function's code in runtime easily
  • "chained_flow" - easy to use monad. Execute your your code if all/none/some of steps were completed withot an exceptions. Use all/none/some resutls of your steps at the final part of monad execution.
  • "multiinterface_essence" - Make your model and add different interfaces to it easily. Can be used for example in games: create "chair", "ball", "person" models and add to them your library of general interfaces like "touch", "push", "sit", "shot", "burn", "wet", etc.
• "hardware" - hardware related
  • "memory" - RAM related
    • "barriers" - fast full memory barriers for both x86/x64 and ARM (Windows, Linux, OS X, iOS, Android).
• "time_management" -
  • "high_precision_sync_sleep" - provides an ability to put your thread into legetimate sleep for at least 10x smaller time period than time.sleep() from the Python's Standard Library able to do on same Operating System: uses nanosleep() on Linux and periodic SwitchToThread() on Windows.
  • "cpu_clock_cycles" - Returnes value of RDTSCP on x86/x86_64 or CNTVCT_EL0 on ARM. Fast implementation: 6-12 times faster than all other competitors on Github. Note: CPU Time Stamp Counter (TSC) is not depends on actual current CPU frequency in modern CPUs (starting from around year 2007) so can be safely used as a high precision clock (see time_management.cpu_clock module). Windows, Linux and other Operating Systems are using it internaly.
  • "cpu_clock" - like perf_counter() but 25% faster. Supports both x86/x86_64 and ARM. cpu_clock is slightly faster than cpu_clock_cycles because double (float in Python terms) transfered from C-code to Python code more efficiently than 64-bit int (which needs an addition internal logic inside the Python itself for conversion). Highest-precision possible since it is CPU Time Stamp Counter based which is not depends on actual current CPU frequency in modern CPUs (starting from around year 2007) so can be safely used as a high precision clock (and Windows, Linux and other Operating Systems are using it internaly in this way). Benchmark: cpu_clock_test.py

• "parallel_execution"
  • "coroutines" -
    • "coro_tools" - tools
      • "wait_coro" - decorate your coroutine in order to be able to execute it from the plain sunc code as a sync function
      • "run_in_loop" - serves the same purpose as an asyncio.run() call
      • "prepare_loop" - creates and returns loop. You may use it later
  • "asyncio" - tools for an asyncio
    • "run_loop" - similar to asyncio.run() but ends only when all background tasks will be finished (main coro can be finished long before this moment).
    • "timed_yield" - simple (dum-dum but faster) version of the "loop_yield" (see above) but made directly for an asyncio.
• "bulk_pip_actions" - install lists of required modules. Lists can be different for a different operating systems
• "code_inspection" -
  • "auto_line_tracer" - smart and easy to use line logger (current func name, file, lines numbers, surrounding code)
  • "line_tracer" - - easy to use line logger (current func name, file, line number)
  • "line_profiling" - confinient work with a line_profiler if awailable
• "data_containers" - usefull data containers like stack, fast fifo, etc. Some of them are highly optimized for speed
• "data_manipulation" -
  • "conversion" -
    • "bit_cast_like" - similar to std::bit_cast from C++
    • "reinterpret_cast" - similar to reinterpret_cast from C++. You have a third-party object and you want to change its type (and behavior) in runtime.
  • "serialization" - automatically choose a fastest appropriate serializer for your type and structure of data (json, simplejson, ujson, ojson, msgpack, cbor, cbor2, marshal, pickle, cloudpickle, ...)
  • "tree_traversal" - both recrsive and nonrecursive tree traversal algorithms
• "ctypes_tools" - ctypes code and structures used by Cengal.
  • "tools" - ctypes tools usefull for your code
• "file_system" - normalized relative path, etc.
  • "app_fs_structure" - unified list of the default app directories (data, cache, temp, etc.) recommended by OS (Linux, Windows, Mac OS X) in a runtime for a given application name or a service name. Results are cached. Cache size can be modified in runtime.
• "hardware" - hardware related
  • "info" - hardware info
    • "cpu" - normalized results from cpuinfo extended with an info from psutil.
• "introspection" -
  • "inspect" - find out function parameters, entity owners list (method -> subclass -> class -> module), entitie's own properties (excluding parent's properties), etc.
  • "third_party" -
    • "ctypes" - provice an instance of ctypes Structure and take a dict with all internals of this structure. Good for inspecting/logging/printing values of a given structure with all values of all its substructures.
• "io" -
  • "used_ports" - database of known TCP/UDP ports. Updates from an appropriate Wikipedia page once per Cengal release but you can update if for your self anytime if you want to.
  • "serve_free_ports" - provide ports range with an interested port types set and receive number of the first open appropriate port on your machine within given port range.
  • "named_connections_manager" - base for the "remote_nodes" (see above) and similar entities
  • "net_io" - an experimental networking library with an expandable architecture. Has implemented modules for epoll and select.
• "math" -
  • "algebra" -
    • "fast_algorithms" - Fast inverse square root (the one from Quake III) implemented efficiently
  • "geometry" -
    • "ellipse" - ellipse related. Also several algorithms for precisely or efficiently compute an ellipse perimeter
    • "point" - numpy (if awailable) or python implementation of points (1D, 2D, 3D, nD)
    • "vector" - numpy (if awailable) or python algotithms on vectors (1D, 2D, 3D, nD). Implements CoordinateVectorNd, VectorNd, DirectedGraphNd
• "modules_management" - reload module, import drop-in-replacement module if an original is not awailable
• "statistics" -
  • "normal_distribution" - compute the normal distribution of your data. Booth count or use a formula. 99, 95, 68; standard_deviation: diff_mean, sqrt(variance), max_deviation, min_deviation.
• "text_processing" - text parsing, patching, detect BOM and encoding
• "time_management" -
  • "timer" - timer for any synchronous code
  • "sleep_tools" - sleep for a production code. Using usual sleep you may get not wat you want if you are not really into your target OS internals (Windows/Linux)
  • "repeat_for_a_time" - measures code/function executions per second. But it smart and eficiently repeats target code/function not N times but up to a T seconds. Results to a high precision measurements for even smallest and fastest pieces of code.
  • "relative_time" - time related module for a business purposes (calendars, payments, etc.)
• "unittest" -
  • "patcher" - set of context manager for monkey patching builtins or other entities
• "user_interface" -
  • "gui" -
    • "nt" -
      • "blur_behind" - Turn on Aero Glass backgrownd in Winndows 7, 10, 11 using documented or undocumented API (which one is awailable)
      • "dpi_awareness" - Turn on DPI awareness
• "web_tools" -
  • "detect_browsers_host_device_type" -
    • "by_http_user_agent" - detects is it mobile or tablet device by analizing its http user_agent string

• Can be found in examples dir
• Each module has a development folder. Examples are usually placed there
• Some of old examples can be fined inside the tests dir.

• General idea, greenlet main Cengal.coro
• General idea, async main Cengal.coro
• Transparent interconnection between Cengal.coroutines and asyncio

Ensures and updates copyright (with dates) in each Cengal's source file

Linux, macOS:

git clone https://github.com/FI-Mihej/Cengal.git
cd ./Cengal
./prepare__setup.sh
./install_in_dev_mode.sh

Windows:

git clone https://github.com/FI-Mihej/Cengal.git
cd .\Cengal
.\prepare__setup.cmd
.\install_in_dev_mode.cmd

Installation process requires compilation. So ensure that:

• GCC/Clang is installed in your Linux/WSL (sudo apt-get --yes install build-essential for Ubuntu. And ./before_install_on_wsl.sh for Ubuntu under WSL for UI like Tkinter or Qt if you are using some kind of XServer on your host Windows)
• At least Visual Studio Community - Build Tools are installed on your Windows and you are installing Cengal from within its Developer Command Prompt for an appropriate target CPU architecture (x64 Native Tools Command Prompt for VS 2022 for example). Make sure that you have compatible version of Visual Studio for your target CPython interpreter (see python -VV command output. For example Python 3.9.11 (tags/v3.9.11:2de452f, Mar 16 2022, 14:33:45) [MSC v.1929 64 bit (AMD64)]: this python interpreter requires Visual Studio 2019 version 16.11.2+ according to 1929 word search in Wikipedia page)
• Nim installed (Optional)
• Go installed (Optional)