pycharm中下载pyqt5的三个安装包

# Sample bindings example This example showcases how to generate Python bindings for a non-Qt C++ library. The example defines a CMake project that builds two libraries: * `libuniverse` - a sample library with two C++ classes. * `Universe` - the generated Python extension module that contains bindings to the library above. The project file is structured in such a way that a user can copy-paste in into their own project, and be able to build it with a minimal amount of modifications. ## Description The libuniverse library declares two classes: `Icecream` and `Truck`. `Icecream` objects have a flavor, and an accessor for returning the flavor. `Truck` instances store a vector of `Icecream` objects, and have various methods for adding new flavors, printing available flavors, delivering icecream, etc. From a C++ perspective, `Icecream` instances are treated as **object types** (pointer semantics) because the class declares virtual methods. In contrast `Truck` does not define virtual methods and is treated as a **value type** (copy semantics). Because `Truck` is a value type and it stores a vector of `Icecream` pointers, the rule of three has to be taken into account (implement the copy constructor, assignment operator, destructor). And due to `Icecream` objects being copyable, the type has to define an implementation of the *clone()* method, to avoid type slicing issues. Both of these types and their methods will be exposed to Python by generating CPython code. The code is generated by **shiboken** and placed in separate ".cpp" files named after each C++ type. The code is then compiled and linked into a shared library. The shared library is a CPython extension module, which is loaded by the Python interpreter. Beacuse the C++ language has different semantics to Python, shiboken needs help in figuring out how to generate the bindings code. This is done by specifying a special XML file called a typesystem file. In the typesystem file you specify things like: * which C++ primitive types should have bindings (int, bool, float) * which C++ classes should have bindings (Icecream) and what kind of semantics (value / object) * Ownership rules (who deletes the C++ objects, C++ or Python) * Code injection (for various special cases that shiboken doesn't know about) * Package name (name of package as imported from Python) In this example we declare `bool` and `std::string` as primitive types, `Icecream` as an object type, `Truck` as a value type, and the `clone()` and `addIcecreamFlavor(Icecream*)` need additional info about who owns the parameter objects when passing them across language boundaries (in this case C++ will delete the objects). The `Truck` has getters and setters for the string `arrivalMessage`. In the type system file, we declare this to be a property in Python: ``` <property type="std::string" name="arrivalMessage" get="getArrivalMessage" set="setArrivalMessage"/> ``` It can then be used in a more pythonic way: ``` special_truck.arrivalMessage = "A new SPECIAL icecream truck has arrived!\n" ``` After shiboken generates the C++ code and CMake makes an extension module from the code, the types can be accessed in Python simply by importing them using the original C++ names. ``` from Universe import Icecream, Truck ``` Constructing C++ wrapped objects is the same as in Python ``` icecream = Icecream("vanilla") truck = Truck() ``` And actual C++ constructors are mapped to the Python `__init__` method. ``` class VanillaChocolateIcecream(Icecream): def __init__(self, flavor=""): super(VanillaChocolateIcecream, self).__init__(flavor) ``` C++ methods can be accessed as regular Python methods using the C++ names ``` truck.addIcecreamFlavor(icecream) ``` Inheritance works as with regular Python classes, and virtual C++ methods can be overridden simply by definining a method with the same name as in the C++ class. ``` class VanillaChocolateIcecream(Icecream): # ... def getFlavor(self): return "vanilla sprinked with chocolate" ``` The `main.py` script demonstrates usages of these types. The CMake project file contains many comments explaining all the build rules for those interested in the build process. ## Building the project This example can only be built using **CMake**. The following requirements need to be met: * A PySide2 package is installed into the current active Python environment (system or virtualenv) * A new enough version of CMake (**3.1+**). For Windows you will also need: * a Visual Studio environment to be active in your terminal * Correct visual studio architecture chosen (32 vs 64 bit) * Make sure that your Python intepreter and bindings project build configuration is the same (all Release, which is more likely, or all Debug). The build uses the `pyside2_config.py` file to configure the project using the current PySide2/Shiboken2 installation. ### Using CMake You can build and run this example by executing the following commands (slightly adapted to your file system layout) in a terminal: On macOS/Linux: ```bash cd ~/pyside-setup/examples/samplebinding mkdir build cd build cmake -H.. -B. -G "Unix Makefiles" -DCMAKE_BUILD_TYPE=Release make make install python ../main.py ``` On Windows: ```bash cd C:\pyside-setup\examples\samplebinding mkdir build cd build cmake -H.. -B. -G "NMake Makefiles" -DCMAKE_BUILD_TYPE=Release # or if you have jom available # cmake -H.. -B. -G "NMake Makefiles JOM" -DCMAKE_BUILD_TYPE=Release nmake # or jom nmake install # or jom install python ..\main.py ``` #### Windows troubleshooting It is possible that **CMake** can pick up the wrong compiler for a different architecture, but it can be addressed explicitly using the -G option: ```bash cmake -H.. -B. -G "Visual Studio 14 Win64" ``` If the `-G "Visual Studio 14 Win64"` option is used, a `sln` file will be generated, and can be used with `MSBuild` instead of `nmake/jom`. The easiest way to both build and install in this case, is to use the cmake executable: ```bash cmake --build . --target install --config Release ``` Note that using the "NMake Makefiles JOM" generator is preferred to the MSBuild one, because the MSBuild one generates configs for both Debug and Release, and this might lead to building errors if you accidentally build the wrong config at least once. ## Virtualenv Support If the python application is started from a terminal with an activated python virtual environment, that environment's packages will be used for the python module import process. In this case, make sure that the bindings were built while the `virtualenv` was active, so that the build system picks up the correct python shared library and PySide2 / shiboken package. ## Linux Shared Libraries Notes For this example's purpose, we link against the absolute path of the dependent shared library `libshiboken` because the installation of the library is done via a wheel, and there is no clean solution to include symbolic links in a wheel package (so that passing -lshiboken to the linker would work). ## Windows Notes The build config of the bindings (Debug or Release) should match the PySide2 build config, otherwise the application will not properly work. In practice this means the only supported configurations are: 1. release config build of the bindings + PySide2 `setup.py` without `--debug` flag + `python.exe` for the PySide2 build process + `python36.dll` for the linked in shared library. 2. debug config build of the application + PySide2 `setup.py` **with** `--debug` flag + `python_d.exe` for the PySide2 build process + `python36_d.dll` for the linked in shared library. This is necessary because all the shared libraries in question have to link t