semvec/pypi-packages huggingface dataset:
heifang-2011-part3 2022.6.1.0
黑客防线2011合订本（下）第一部分
Requires Python: >=3.6

# 黑客防线2011合订本（下）第一部分

## 下载

### Docker

```
docker pull apachecn0/heifang-2011-part3
docker run -tid -p <port>:80 apachecn0/heifang-2011-part3
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install heifang-2011-part3
heifang-2011-part3 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g heifang-erlingyiyi-part3
heifang-erlingyiyi-part3 <port>
# 访问 http://localhost:{port} 查看文档
```
gmsh4mrst 1.0.0
Gmsh backend integration for MRST
Requires Python: >=3.8

# gmsh4mrst

Automatically create Gmsh meshes for use in MRST.

`gmsh4mrst` is created to bridge the gap between Python package Gmsh and MATLAB module MRST, and was developed as part of my Bachelor's thesis during spring 2022.

## Citation
If you end up using `gmsh4mrst` in any research, I would love to hear about it. Feel free to cite the bachelor thesis introducing the software as well:
Berg, Andreas B. (2022). _Combining Gmsh and MRST - Developing software for more efficient grid creation in two dimensions_

The thesis can be found [here](https://github.com/BollaBerg/MA2002-Bachelor-project/blob/main/Berg%2C%20Andreas%20Bjelland%20(2022)%20-%20Combining%20Gmsh%20and%20MRST.pdf).

## Installation
The Python part of the module is available on [PyPi](https://pypi.org/project/gmsh4mrst/).

The MATLAB part of the module must be manually added to your MATLAB Path.

## Requirements
In order to use the Python part of the module, simply install the package (`pip install gmsh4mrst`).

In order to use the MATLAB part of the module, ensure `MRST 2022a` is on your MATLAB Path, with module `gmsh` active, and install the Python part of the module. Ensure that your default Python (i.e. the Python that comes up when using the command `python`) has `gmsh4mrst` installed, and is one of the Python versions compitable with MATLAB (i.e. `Python 3.8` or `Python 3.9` when using MATLAB 2022a).

## Common issues
### MATLAB
#### SEGFAULT
This sometimes happens when calling Python from MATLAB. One thing that has previously worked to fix this is ensuring Python is run Out-of-process, rather than In-process. This can be ensured by running `pyenv("ExecutionMode","OutOfProcess")` from within MATLAB. Note that this must be done before running any Python code from MATLAB, so may require a restart of MATLAB.
heifang-2011-part2 2022.6.1.0
黑客防线2011合订本（上）第二部分
Requires Python: >=3.6

# 黑客防线2011合订本（上）第二部分

## 下载

### Docker

```
docker pull apachecn0/heifang-2011-part2
docker run -tid -p <port>:80 apachecn0/heifang-2011-part2
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install heifang-2011-part2
heifang-2011-part2 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g heifang-erlingyiyi-part2
heifang-erlingyiyi-part2 <port>
# 访问 http://localhost:{port} 查看文档
```
heifang-2011-part1 2022.6.1.0
黑客防线2011合订本（上）第一部分
Requires Python: >=3.6

# 黑客防线2011合订本（上）第一部分

## 下载

### Docker

```
docker pull apachecn0/heifang-2011-part1
docker run -tid -p <port>:80 apachecn0/heifang-2011-part1
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install heifang-2011-part1
heifang-2011-part1 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g heifang-erlingyiyi-part1
heifang-erlingyiyi-part1 <port>
# 访问 http://localhost:{port} 查看文档
```
python-ioc 1.13.0
Enter a description for this Unimatrix package

# Python Inversion-of-Control framework

## Synopsis

The `ioc` module provides a framework to dynamically inject dependencies
in Python applications. This intends to reduce coupling between application
and infrastructure, and application and third-party libraries.

## Usage

### Basic
In it's most basic usage, dependencies can be declared and invoked using a few simple
calls to functions in the `ioc` library.

```
>>> import ioc
>>>
>>> req = ioc.require('MY_REQUIREMENT')
>>> ioc.provide('MY_REQUIREMENT', 'foo')
>>> print(req)
'foo'
```

The procedure is simple: state your requirement by a symbolic name and call to `ioc.require()`, and then provide the requirement using `ioc.provide()`.

When an application fails to provide a dependency for a requirement, an exception is raised upon invocation:

```
>>> import ioc
>>>
>>> req = ioc.require('MY_REQUIREMENT')
>>> print(req)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "requirement.py", line 11, in inner
    self._setup()
  File "ioc/requirement.py", line 58, in _setup
    self._injected = self._provider.resolve(self._names[0])
  File "ioc/provider.py", line 33, in resolve
    raise UnsatisfiedDependency(name)
ioc.exc.UnsatisfiedDependency: MY_REQUIREMENT
```

### Class properties
A call to `ioc.require()` will not return the actual dependency (it might not be resolved yet) but a wrapper that mimics the behavior of the (to-be) injected dependency.

```
>>> import ioc
>>>
>>> repr(ioc.require('not_resolved'))
'<ioc.requirement.DeclaredRequirement object at 0x101db7ac8>'
```

This might not always be a desirable property, for example with Python C-extensions that enforce datatypes on their input parameters. To solve this problem, the `ioc` module provides the `ioc.class_property()` decorator. This adds a descriptor to a class that will always return the dependency as it was injected.

```
>>> import ioc
>>>
>>> class Foo:
...     bar = ioc.class_property('bar')
...
>>>
>>> ioc.provide('bar', 'baz')
>>>
>>> repr(Foo.bar)
'<property object at 0x101ce5ae8>'
>>>
>>> foo = Foo()
>>> repr(foo.bar)
"'baz'"
```

## Advanced usage

## Changelog

### 1.3

- Append ``symbol`` dependencies to an existing iterable.
- Load literal dependencies from environment variables.
- Fix parser loading literal dependencies as symbols.
- Allow default arguments in signatures with `ArgumentDependencyInjector`.
- `marshmallow` 3.0.0b11 compatibility.

### 1.2

- Add support for `copy.copy()` and `copy.deepcopy()`.
- Allow `default` argument with `ioc.class_property()`.
- Make `ioc.class_property()` a real class property.
- Allow overriding dependencies at runtime.
heifang-2011-jinghua-part2 2022.6.1.0
黑客防线2011精华本（下）
Requires Python: >=3.6

# 黑客防线2011精华本（下）

## 下载

### Docker

```
docker pull apachecn0/heifang-2011-jinghua-part2
docker run -tid -p <port>:80 apachecn0/heifang-2011-jinghua-part2
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install heifang-2011-jinghua-part2
heifang-2011-jinghua-part2 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g heifang-erlingyiyi-jinghua-part2
heifang-erlingyiyi-jinghua-part2 <port>
# 访问 http://localhost:{port} 查看文档
```
heifang-2011-jinghua-part1 2022.6.1.0
黑客防线2011精华本（上）
Requires Python: >=3.6

# 黑客防线2011精华本（上）

## 下载

### Docker

```
docker pull apachecn0/heifang-2011-jinghua-part1
docker run -tid -p <port>:80 apachecn0/heifang-2011-jinghua-part1
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install heifang-2011-jinghua-part1
heifang-2011-jinghua-part1 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g heifang-erlingyiyi-jinghua-part1
heifang-erlingyiyi-jinghua-part1 <port>
# 访问 http://localhost:{port} 查看文档
```