semvec/pypi-packages huggingface dataset:
bingads 13.0.26
A library to make working with the Bing Ads APIs and bulk services easy
License: MIT
Keywords: bingads

=================== 
Bing Ads Python SDK 
=================== 
 .. image:: https://img.shields.io/pypi/v/bingads.svg :target: https://pypi.python.org/pypi/bingads 
 
 
The Bing Ads Python Software Development Kit (SDK) Version 13.0 enhances the experience of developing Bing Ads applications 
with the Python programming language. 
The SDK includes proxy classes for all Bing Ads API web services and abstracts the low level details of authentication with OAuth. 
You can also read and write bulk files with the SDK BulkFileReader and BulkFileWriter, 
and use the high level BulkServiceManager interface to abstract and execute operations in the low level Bulk API. For more information, see `Bing Ads Client Libraries <https://docs.microsoft.com/en-us/bingads/guides/client-libraries>`_. 
 
Getting Started 
--------------- 
 
To get started developing Bing Ads applications with Python, install the SDK and either start with the `examples <https://github.com/BingAds/BingAds-Python-SDK/tree/master/examples>`_ or follow one of the application walkthroughs. For more information, see `Getting Started Using Python with Bing Ads Services <https://docs.microsoft.com/en-us/bingads/guides/get-started-python>`_. 
 
External Dependencies 
--------------------- 
 - `suds-community <https://pypi.org/pypi/suds-community>`_ 
 - `requests <http://pypi.python.org/pypi/requests>`_ 
 - `enum34 <http://pypi.python.org/pypi/enum34>`_
Functions-d 1.28
包含数据处理、Hive交互、企业微信消息发送、Excel操作等功能的工具类库
License: MIT
Requires Python: >=3.7

# 说明
这是一个函数封装工具

## 安装
使用pip安装：
bash
pip install Functions_d
 
### 联系我们
如果你有任何问题或建议，请联系我649898871@qq.com
Microlensify 1.0.8
Microlensing Single Lens Classification – Atousa Kalantari & Somayeh Khakpash
License: MIT
Requires Python: >=3.9

# Microlensify



Microlensify is a Physics-Informed Transformer-Based Variational Autoencode that detects single lens microlensing events in light curves. It works with light curves from any telescope, either via URLs (e.g., MAST FITS files) or your own data files.

Microlensify splits your light curve into chunks and downsamples them to a fixed length for prediction. Predictions are made for each chunk individually and also for the whole light curve.

---

## Installation

```bash
pip install Microlensify
```

## Usage
```bash
Microlensify <input_file> <compute_stats> <n_cores>
```

## Arguments

#### `<input_file>`

- **URL input:** a file containing URLs of light curves and their flux & time column names.  
- **Local files:** a file containing paths to your files and their flux & time column names.

#### `<compute_stats>`

- `yes` — compute flux statistics (min, max, median, std, std/(max-min)) from raw flux.  
- `no` — flux is already normalized (like TESS QLP pipeline flux), so the model uses fixed values from the training set for these statistics.

#### `<n_cores>`

- Number of CPU cores to use for parallel processing.

## Input File Format

- Tab-separated file.

- **Column 1:** time  
- **Column 2:** flux

- **URL input:** list the URLs and the flux/time column names.  
- **Local files:** list file paths and the flux/time column names.

- Example input files are provided in the `Microlensify_Input_Examples` folder.

## Output

All results are saved to `prediction_results.csv` with the following columns:

| Column | Description |
|--------|-------------|
| `Source` | URL or file path of the light curve |
| `Class` | 1 if probability > 0.99, else 0 |
| `Probability` | Model probability of microlensing |
| `Real_4FWHM_days` | Predicted 4FWHM duration (trustworthy if light curve has 940 points over 27.4 days) |
| `Latent_Space` | 20 latent space values from the model (can be used for reconstruction) |
| `Points` | Number of points in the chunk |
| `Chunk_Description` | Description of the chunk used |

---

## Additional Project Codes:
All additional scripts and notebooks used in this work are provided in the `Project_Codes` folder.


## Citation

If you use Microlensify in your research, please cite it appropriately.

---

## Contact

For questions or support, please open an issue or contact atousakalantari99@gmail.com.