semvec/pypi-packages huggingface dataset:
SolarNet-Lib 0.1.0
Deep Learning for Solar Physics Prediction
License: MIT
Requires Python: >=3.6

# SolarNet

> Deep Learning for Solar Physics Prediction

The SolarNet library permits to use the different Pytorch models, datasets, preprocessing, and others utilities
developed during the SolarNet project. It also makes straightforward the download, loading, and finetuning of the big
pretrained SSL models. With SolarNet, anyone can access the datasets (at this time, only SDO-Benchmark is available
through the library) and finetune the powerful models. The library is compatible with Pytorch-Lightning, but the models
are also pure Pytorch Module and the training loop can be written from scratch.

Find the docs on [jdonzallaz.gitlab.io/solarnet](https://jdonzallaz.gitlab.io/solarnet/).

## Installation

Use pip to install:

```
pip install solarnet-lib
```

Python 3.6+ is required.

## Data

Two datasets are supported: SDO-Dataset and SDO-Benchmark. SDO-Dataset needs to be downloaded from the Stanford's servers.
The SDO-Benchmark dataset can be downloaded using the CLI:

```
solarnet download sdo-benchmark data/
```

## Dataset

```python
path = Path("data") / "sdo-benchmark" / "train"
dataset = SDOBenchmarkDataset(path)
```

Also available as a pytorch-lightning datamodule.

```py
path = Path("data") / "sdo-benchmark"
datamodule = SDOBenchmarkDatamodule(path)
```

## Model

```py
model = ImageClassification.from_pretrained("solarnet-ssl-bz-ft-sdo-benchmark")
```

## Finetuning

```py
trainer = pl.Trainer(
    max_epochs=10,
    gpus=0,
)
trainer.fit(model, datamodule=datamodule)
```

## Deployment

```bash
python setup.py sdist bdist_wheel
twine check dist/*
twine upload dist/*
```

## Author

SolarNet is a deep learning research toolbox for solar physics. It was developed during a Master thesis
by [Jonathan Donzallaz](mailto:jonathan.donzallaz@hefr.ch).
chanjet-openapi-python-sdk 1.0.2
畅捷通开放平台python版sdk
License: MIT

# chanjet-open-sdk

欢迎使用 Chanjet Open SDK for Python

## 环境要求
1. Chanjet Open SDK for Python 需要配合`Python 3.8`或其以上版本。

2. 使用 Chanjet Open SDK for Python 之前 ，您需要先前往[畅捷通-开发者中心](https://dev.chanjet.com)完成开发者接入的一些准备工作，包括创建应用、获取应用的appKey和appSecret。

## 安装依赖
推荐通过pip下载
```text
pip install chanjet-openapi-python-sdk
```
## 快速使用

```python
import logging

from chanjet_openapi_python_sdk.chanjet_client import ChanjetClient
from chanjet_openapi_python_sdk.request.tplus.query_inventory_request import QueryInventoryRequest
from chanjet_openapi_python_sdk.response.tplus.query_inventory_response import QueryInventoryResponse
from chanjet_openapi_python_sdk.content.tplus.query_inventory_content import QueryInventoryContent
from chanjet_openapi_python_sdk.utils.chanjet_logger import ChanjetLogger


def query_inventory_demo():
    req = QueryInventoryRequest()
    req.request_uri = '/tplus/api/v2/inventory/Query'
    # 推荐使用字典传值方式，直接传实体实例会有一定的性能损耗
    # req.content = {'param': {'Code': '123'}}
    req.content = QueryInventoryContent('124', '123', False, True)
    # 支持将返回值解析成实体类属性，如果不传入实体类名，则按照json的格式进行转换成字典、列表、字符串等
    result = client.execute(req, QueryInventoryResponse)
    return result

if __name__ == '__main__':
    # 设置自定义日志配置
    ChanjetLogger(logging.INFO)

    client = ChanjetClient('https://openapi.chanjet.com')
    # 请填入您从开放平台申请下来的appKey
    client.app_key = 'A******8'
    # 请填入您从开放平台申请下来的appSecret
    client.app_secret = '9****************8'
    # 请填入您的开放平台openToken
    client.open_token = """e****************************************************************************k"""

    # 查询存货
    res = query_inventory_demo()
```