semvec/pypi-packages huggingface dataset:
kitx 1.0.15
kitx: A powerful toolbox; FamilyTools: kitx-snowflake (rs) build; 
Requires Python: >=3.7
Keywords: FileStorage, logger, snowflake

# kitx

[![PyPI - Version](https://img.shields.io/pypi/v/kitx.svg)](https://pypi.org/project/kitx)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/kitx.svg)](https://pypi.org/project/kitx)

-----

## Table of Contents

- [Installation](#installation)
- [Features](#kitx-features)
- [License](#license)

## Installation

```console
pip install kitx
```

## kitx-libs

|                        |               |
|------------------------|---------------|
| lib_async_multiprocess | 异步多进程(初始化和执行) |
| lib_file_storage       | cos oss minio |
| lib_utils              | 获取pid的内存占用    |
|                        |               |

## kitx-features

|                |                                                            |
|----------------|------------------------------------------------------------|
| kitx-snowflake | [kitx-snowflake](https://pypi.org/project/kitx-snowflake/) |
| kitx-pingo     | rs gateway coding...                                       |
|                |                                                            |

## lib env_prefix

`kitx_`

### example:

observe:

`kitx_observe_host`
`kitx_observe_port`
`kitx_observe_base_dir`
`kitx_observe_username`
`kitx_observe_password`
`kitx_observe_organization`
`kitx_observe_stream`

## License

`kitx` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
ctrlx-fbs 2.5.1
Python generated fbs
Requires Python: >=3.6

# Python generated fbs

Auto generated python files from all fbs files including the sdk

## Installation

Use the package manager [pip3](https://pip.pypa.io/en/stable/) to install ctrlx-fbs.

```bash
pip3 install ctrlx-fbs
```

## License

SPDX-FileCopyrightText: Bosch Rexroth AG
SPDX-License-Identifier: MIT

Python generated fbs is licensed under [T&C](https://dc-corp.resource.bosch.com/media/xc/boschrexroth_tac_delivery.pdf) or see 'boschrexroth_tac_delivery.txt' as part of this package.
eegpp2-beta 0.1.7
EEG Phrase Predictor ver 2
Requires Python: >=3.10

# EEG Phase Predictor ver 2

**Note: This is beta version, use for training with default dataset and inference only

## Setup

- Requirements: python >= 3.10
- Installing:

```aiignore
  pip install eegpp2-beta
```

## Train with default dataset

```aiignore
    python -m eegpp2 --mode "train" --model_type <mode_type> --lr <learning_rate> --batch_size <batch_size> --n_epochs <num_epochs> --n_splits <num_folds> --resume_checkpoint <resume_from_checkpoint>
```

ex: `python -m eegpp2 --mode "train" --model_type "stftcnn1dnc" --n_epochs 20 --n_splits 10 --resume_checkpoint False`

## Inference

1. Old version (**Recommend**)

```aiignore
    python -m eegpp2 -p <path_to_infer_config.yml>
```

ex: `python -m eegpp2 -p "data_config_infer.yml"`

2. New version

```aiignore
  python -m eegpp2 --mode "infer" --data_path <path_to_data_file> --infer_path <path_to_saving_file> --model_type <model_type>
```

ex:
`python -m eegpp2 --mode "infer" --data_path "./dump_eeg_1.pkl" --infer_path './inference_result.txt" --model_type "stftcnn1dnc"`

## Model type

- stftcnn1dnc: Multi-channels STFT-CNN
transformer-flows 0.0.13
Implementation of Transformer Flows (Apple ML) in JAX and Equinox.
Requires Python: >=3.11

<h1 align='center'>Transformer flows</h1>

Implementation of Apple ML's Transformer Flow (or TARFlow) from [Normalising flows are capable generative models](https://arxiv.org/pdf/2412.06329) in `jax` and `equinox`.

Features:
- `jax.vmap` & `jax.lax.scan` construction & forward-pass, for layers respectively for fast compilation and execution,
- multi-device training, inference and sampling,
- score-based denoising step (see paper),
- conditioning via class embedding (for discrete class labels) or adaptive layer-normalisation (for continuous variables, like in DiT),
- array-typed to-the-teeth for dependable execution with `jaxtyping` and `beartype`.

To implement:
- [x] Guidance
- [x] Denoising
- [x] Mixed precision
- [x] EMA
- [x] AdaLayerNorm
- [x] Class embedding
- [x] Hyperparameter/model saving
- [x] Uniform and Gaussian noise for dequantisation

#### Usage 

```
pip install transformer-flows
```

```
uv run --all-extras python examples/main.py
```

#### Samples

I haven't optimised anything here (the authors mention varying the variance of noise used to dequantise the images), nor have I trained for very long. You can see slight artifacts due to the dequantisation noise.

<p align="center">
  <picture>
    <img src="assets/mnist_warp.gif" alt="Your image description">
  </picture>
</p>

<p align="center">
  <picture>
    <img src="assets/cifar10_warp.gif" alt="Your image description">
  </picture>
</p>

#### Citation 

```bibtex
@misc{zhai2024normalizingflowscapablegenerative,
      title={Normalizing Flows are Capable Generative Models}, 
      author={Shuangfei Zhai and Ruixiang Zhang and Preetum Nakkiran and David Berthelot and Jiatao Gu and Huangjie Zheng and Tianrong Chen and Miguel Angel Bautista and Navdeep Jaitly and Josh Susskind},
      year={2024},
      eprint={2412.06329},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2412.06329}, 
}
```
sms-tools 1.0.1
Sound analysis/synthesis tools for music applications
Requires Python: >=3.9

sms-tools
========= 

Sound analysis/synthesis tools for music applications written in python.

The package includes the following sound analysis/synthesis models:

* dftModel.py: models based on the Discrete Fourier Transform
* stft.py: models based on the Short-Time Fourier Transform
* sineModel.py: models based on a Sinusoidal Model
* harmonicModel.py: models based on a Harmonic Model
* stochasticModel.py: models based on a Stochastic Model
* sprModel.py: models based on a Sinusoidal plus Residual Model
* spsModel.py: models based on a Sinusoidal plus Stochastic Model
* hprModel.py: models based on a Harmonic plus Residual Model
* hpsModel.py: models based on a Harmonic plus Stochastic Model


Installation
------------

Install using pip:

    pip install sms-tools

Binary packages are available for Linux, macOS (Intel & Apple Silicon) and Windows (64 bit) on all recent python versions.

To build and install the package locally you can use the python packaging tools:

    pip install build
    python -m build


Jupyter Notebooks
-------
We provide a separate repository of examples and teaching materials in the form of Jupyter notebooks.
You can find them at https://github.com/MTG/sms-tools-materials

License
-------

sms-tools is made available under the terms of the Affero GPL license (http://www.gnu.org/licenses/agpl-3.0.en.html).
fxbaogao-mcp 0.1.4
发现报告是专业研报搜索平台。全面覆盖行业分析、公司研究、宏观策略、财报、招股书等研究报告的相关搜索和阅读。
Requires Python: >=3.11

# 📊 发现报告（MCP Tool）

本工具集成于 MCP 协议，提供对发现报告网站研究报告的搜索与内容提取能力，适用于金融、产业研究、投资分析等场景。

---

## ✨ 工具一：`search_reports`

通过关键词、作者、机构名称、时间范围等条件检索研究报告列表。

### 🔧 参数说明

| 参数名        | 类型              | 必填 | 说明 |
|---------------|-------------------|------|------|
| `keywords`    | `str`             | 否   | 搜索关键词，支持中英文。 |
| `authors`     | `List[str]`       | 否   | 作者姓名列表，如 `["张三", "李四"]`。 |
| `org_names`   | `List[str]`       | 否   | 机构名称列表，如 `["中信证券", "华泰证券"]`。 |
| `start_time`  | `int`             | 否   | 起始时间，毫秒级时间戳，如 `1640995200000` 表示 2022-01-01 00:00:00。 |
| `end_time`    | `int` / `str`     | 否   | 结束时间，支持毫秒时间戳或相对时间字符串：<br>• `"last3day"`<br>• `"last7day"`<br>• `"last1mon"`<br>• `"last3mon"`<br>• `"last1year"` |
| `page_size`   | `int`             | 否   | 返回结果数量，默认 10，最大 100。 |

### 📥 使用示例

```python
# 按关键词搜索
search_reports(keywords="人工智能")

# 按机构搜索
search_reports(org_names=["中信证券"])

# 搜索最近一周某位作者的报告
search_reports(authors=["王磊"], end_time="last7day")

# 精确时间段搜索
search_reports(
    keywords="新能源",
    start_time=1748707200000,
    end_time=1749398399999
)
```

## ✨ 工具二：get_report_content

根据报告 ID（`doc_id`）获取研报的详细内容与总结信息。

---

### 🔧 参数说明

| 参数名   | 类型   | 必填 | 说明 |
|----------|--------|------|------|
| `doc_id` | `int`  | ✅ 是 | 研报文档 ID，来自 `search_reports` 返回结果中的 `docId` 字段。 |

---

### 📥 使用示例

```python
# 获取该研报的内容
content = await get_report_content(doc_id)
```

## 服务器配置：
```json
{
    "mcpServers": {
        "fxbaogao-mcp": {
            "command": "uv",
            "args": [
                "run",
                "server.py"
            ]
        }
    }
}
```

## 注意事项
本工具仅供学习和研究使用，请勿用于商业目的
请遵守必应的使用条款和相关法律法规