semvec/pypi-packages huggingface dataset:
imptools 1.3.0
Tools for improving Python imports.

[![PyPI](https://img.shields.io/pypi/v/imptools.svg)](https://pypi.python.org/pypi/imptools/#history)

# imptools

Tools for improving Python imports.

## Installation

```sh
pip3 install imptools
```

## Docs

### `import_path()`

Import a module from any path on the filesystem.

Usually, this would be achieved by adding the parent directory of the module to
`sys.path` or the `PYTHONPATH` environment variable and then importing it
normally. However, this pollutes Python's import path, which can lead to
accidentally importing the wrong modules. The function `import_path()` avoids
this problem by importing a package from a path on the filesystem without
modifying the Python import path.

The module can be either a directory containing `__init__.py` or a single file.

Relative paths are resolved relative to the directory of the source file that
calls `import_path()`.

```python
import imptools

my_module = imptools.import_path(
    '../path/to/my_module',  # Path to a module directory or single file.
    notfound='error',        # Raise 'error' or 'ignore' if not found.
    reload=False,            # Whether to import if already available.
)

import my_module  # Import statement also works.
```

### `enable_relative()`

Enable relative imports for scripts that are not executed as module.

Usually, scripts that are part of a module and use relative imports must be run
as `python3 -m module.script`. However, this requires being in the correct
working directory and can be annoying. The `enable_relative()` function allows
to execute those scripts normally as `python3 script.py`.

Since PEP 366, this can be achieved by setting the `__package__` variable in
the script and importing the package or making it available on the Pyhton
import path. The `enable_relative()` function hides this behind a simple
function that can be imported and called inside the script, before any relative
imports.

```python
import imptools

imptools.enable_relative()

# Relative imports...
```

## Tests

```
python3 -m pytest tests
```
OpenContest-Client 2.1.0
A very simple OpenContest command line client written in Python

# OpenContest Client

A very simple [OpenContest](https://github.com/LadueCS/OpenContest) command line client written in Python. The only dependency is the [requests](https://docs.python-requests.org/en/master/index.html) library. Tested with Linux only. Please report bugs on our [issues page](https://github.com/LadueCS/OpenContest-Client/).


## Usage

You can get started with 
```
pip install OpenContest-Client
```

Now register an account on an OpenContest server with
```
occ register -s homeserver.com -N name -E email -U username -P password
```

You can save your username, homeserver (the server you registered your account on), and password with
```
occ save -U username -H homeserver.com -P pasword
```

Query information about an OpenContest server with
```
occ about -s server.com
```

You can view the contests on the server with
```
occ contests -s server.com
```

Check the contest info with
```
occ info -s server.com -c contest
```

Save a problem statement with
```
occ problems -s server.com -c contest -p problem
```

Now you can submit with
```
occ submit -s server.com -c contest -p problem -c code_file
```

And check your status with
```
occ status -s server.com -c contest
```

You can query your submission history for a contest
```
occ submissions -s server.com -c contest
```

And examine a particular submission with
```
occ code -s server.com -c contest -n submission_number
```

View the number of solves of each problem with
```
occ solves -s server.com -c contest
```
cranet 0.2.0
A tiny library for deep learning using numpy.
Requires Python: >=3.6

# CraNet

CraNet is a python library for Deep Learning.

## Installation

There are two ways to install CraNet

### From pip

```
pip install cranet
```

### From source

For now, This way is only available for linux.

Git clone repository

```
git clone https://github.com/shizuku/cranet.git
```

Build locally

```
cd cranet
./scripts/build_local_install.sh
```

## Basic Information

| Module | Description |
| --- | --- |
| cranet.autograd | tensor computation library based on `numpy`，集成了自动梯度的功能，支持张量的微分操作，所有需要微分的地方都用到了该库(包括梯度下降) |
| cranet.nn | 基于tensor神经网络库，支持灵活地自定义神经网络结构(自定义层数，神经元个数，计算方式) |
| cranet.optim | 优化器库，支持梯度下降神经网络参数 |
| cranet.data | 数据集库，以不同的方式处理各式各样的数据，以统一的方式将这些数据交接给神经网络，进行训练 |
| cranet.utils | 杂项，支持神经网络的储存与读入，辅助地显示一些计算过程 |

## Release

## Credits

+ [pytorch/pytorch](https://github.com/pytorch/pytorch)
+ [joelgrus/autograd](https://github.com/joelgrus/autograd)

## License