semvec/pypi-packages huggingface dataset:
pyappbundler 0.1.9
automate bundle a Python application into a single package
Requires Python: >=3.11

Automate bundle a Python application and all its dependencies into a single package with [PyInstaller](https://pyinstaller.org/en/stable). And then (for Windows), put it all into a setup file with [Inno Setup](https://jrsoftware.org/isdl.php#stable).
math-genie-calc 0.1.8
math genie calculator
Requires Python: >=3.10

# math\_genie\_calc

`math_genie_calc`是一款专注于科学计算的 Python 应用，为用户提供便捷、准确的各类数学运算功能，涵盖了基础运算到复杂三角函数等多种计算需求，适合学生、科研人员以及任何需要进行数学计算的场景使用。

## 主要功能

### 基础运算



*   **加法**：实现两个及多个数字的相加运算，支持整数、浮点数等多种数值类型。

*   **减法**：进行数字间的减法操作，可处理正数与负数的运算。

*   **乘法**：完成数字的乘法计算，包括多位数相乘。

*   **除法**：支持除法运算，同时会对除数为 0 的情况进行处理，避免程序错误。

### 三角函数计算



*   提供正弦（sin）、余弦（cos）、正切（tan）函数的计算，输入参数为弧度值。

*   支持余切（cot）、正割（sec）、余割（csc）函数的计算，通过相应三角函数的倒数推导得出，同样以弧度为输入单位。

### 其他运算



*   **幂运算**：能够计算一个数的任意次幂，包括整数幂、小数幂等。

*   **平方根**：可对非负数字进行平方根计算，返回精确的结果。

*   **阶乘**：计算非负整数的阶乘，0 的阶乘为 1，对于较大的整数也能准确处理。

## 安装方法

你可以通过 PyPI 来安装`math_genie_calc`，使用以下命令：


```
pip install math_genie_calc
```

## 使用示例

```
npx @modelcontextprotocol/inspector uvx math_genie_calc
```

## MCP 服务器配置

```
{
  "mcpServers": {
    "math_genie_calc": {
      "command": "uvx",
      "args": [
        "math_genie_calc@latest"
      ]
    }
  }
}
```
cauliflow 0.2.0
A simple workflow tool designed to work with EPICS PVs
Requires Python: >=3.11

# CauliFlow

[![image](https://img.shields.io/pypi/v/cauliflow)](https://pypi.org/project/cauliflow)
[![image](https://img.shields.io/pypi/l/cauliflow)](https://github.com/sasaki77/cauliflow/blob/master/LICENSE)
[![mage](https://img.shields.io/pypi/pyversions/cauliflow)](https://pypi.org/project/cauliflow)



CauliFlow allows you to construct and run workflows by connecting multiple nodes in a YAML-format file.
CauliFlow enables the creation of flexible, reusable workflows by utilising three types of variable: Flowdata, Blackboard and Macros.
Although CauliFlow is primarily designed to work with EPICS PVs, it can also be used for other purposes.
For more details, please refer to [Documentation](https://sasaki77.github.io/cauliflow)

## Installation

CauliFlow requires Python 3.11 or higher.

To install CauliFlow with `pip`, run the following command:

```bash
pip install cauliflow
```

To confirm that the installation was successful, run:

```bash
cauliflow --version
```

You should see an output like the following:

```bash
cauliflow 0.1.0
```

## Usage

CauliFlow loads workflows from a YAML-format file.

Create a file named `increment.yml`, write the following content into it.

```yaml
---
sequential:
  flows:
    - name: "init"
      flow:
        - message:
            name: "increment"
            msg: 0
            out_bb: yes
    - name: "main"
      flow:
        - interval:
            name: "interval"
            interval: "{{ macro.interval | float }}"
        - message:
            name: "add"
            msg: "{{ bb.increment + 1 }}"
            out_bb: yes
            out_field: "increment"
        - message:
            name: "out_msg"
            msg: "{{ 'Count: ' + bb.increment | str }}"
        - stdout:
            name: "out"
            src: "{{ fd.out_msg }}"
macros:
  interval: "1"
```

In the above flows, two flows are executed sequentially.
The `init` flow runs first and sets the value of the `increment` key in the blackboard to `0`.
After that, the `main` flow is executed at regular intervals, controlled by an `interval` node. Each time it runs, the `add` node increments the value of `increment` key in the blackboard and updates the value. Next, the `out_msg` node creates a message string and writes it to the flowdata. This string is then passed and printed to the standard output by the `out` node.

The `interval` parameter of the `interval` node is configured using a macro.
Since the macro value is passed as a string, the filter is used to convert it to a float.

To run the flows, use the cauliflow run command.
the incremented value is showed every second.

```bash
$ cauliflow run increment.yml
Count: 1
Count: 2
Count: 3
Count: 4
Count: 5
```

You can change the interval value by specifying the macro at runtime.

```bash
$ cauliflow run -m interval 5 increment.yml
Count: 1
Count: 2
Count: 3
Count: 4
Count: 5
```
analyst-intern 0.0.5
An analyst intern in your terminal

# Intern CLI

## Installation

1. `pip install analyst-intern`
2. Install ffmpeg with homebrew.
3. Set your `OPENAI_API_KEY` in your `.bashrc` or `.vimrc` or `.zshrc`. Can also support Gemini and Anthropic.

## Usage

Run `intern --help`:

```
Usage: intern [OPTIONS] COMMAND [ARGS]...

  Intern CLI.

Options:
  --help  Show this message and exit.

Commands:
  read      Given a file, read it out and save the speech to an mp3 file.
  research  Visit provided links, apply the prompt, summarize the results.
```

### `intern read`

Given a txt file, md file, or string, use OpenAI's TTS to save an mp3 file. 

```
Usage: intern read [OPTIONS]

  Given a file, read it out and save the speech to an mp3 file.

Options:
  --output TEXT  Output file.  [required]
  --text TEXT    txt or md file or string.  [required]
  --voice TEXT   Voice to use.  [required]
  --model TEXT   Which model.  [required]
  --help         Show this message and exit.
```

### `intern research`

```
Usage: intern research [OPTIONS] PROMPT

  Visit provided urls, apply the prompt, summarize the results.

Options:
  --url TEXT                      List of urls to visit.  [required]
  --headless                      Whether or not to run the browser headless.
  --model [openai|anthropic|gemini]
                                  Which model to use.
  --output TEXT                   Output file to save the results to.
  --help                          Show this message and exit.
```
pod5-random-access 0.6.0
Add your description here
Requires Python: >=3.10

# pod5-random-access

## Introduction

A high-performance Python library for efficient random access to nanopore sequencing signals stored in POD5 files. This library creates optimized indexes that enable fast retrieval of specific reads without loading entire files into memory.

## Installation

Install from PyPI:

```bash
pip install pod5-random-access
```

Requirements:

- Python 3.10+

## Usage

### Basic workflow: Build index → Load reader → Fetch signals

```python
# 1. Build index from POD5 files
from pod5_random_access import build_pod5_index
from pathlib import Path

build_pod5_index(
    input_pod5_dir=Path("path/to/pod5/files"),
    output_index_dir=Path("path/to/index/output")
)

# 2. Load reader with index settings
from pod5_random_access import Pod5RandomAccessReader, IndexSettings

settings = IndexSettings.from_yaml(Path("path/to/index/output/index_settings.yaml"))
reader = Pod5RandomAccessReader()
reader.add_pod5_index_settings(settings)

# 3. Fetch signal data
signal_data = reader.fetch_signal("filename.pod5", "read_uuid") # np.array of int16
offset, scaling = reader.get_calibration("filename.pod5", "read_uuid")
pA_signal = (signal_data + offset) * scaling # converted to pA values
```