semvec/pypi-packages huggingface dataset:
cfgrammar 0.1.2.2
Context-free grammars and parsing
Requires Python: >=3.10

# cfgrammar : Context-free grammars, parsing and semantic #

## Table of Contents

- [Main Features](#main-features)
- [Example](#example)
- [API overall graph](#API-overall-graph)
- [Parsing example](#Parsing-example)
- [Abstract Syntax Tree](#Abstract-Syntax-Tree)
## Main features

* Grammar properties
  * accessible and productive rules / variables
  * ε-productive rules / variables
  * reduced grammar
  * "First" and "Follow" sets
* LL1 parsing
  * computes and displays LL(1) table
  * constructs parser
* LR parsing
  * computes and displays LR(0) automaton
  * constructs LR(0), SLR(1), LALR(1) tables and parsers
* Semantic
  * parsing with semantic actions
* Abstract Syntax Tree
  * predefined semantic classes to produce AST
    * Graphviz / dot output 
    * Latex + Tikz output
  
## Example
Code :
```python
from cfgrammar import Grammar

g = Grammar.from_string('S -> ( S ) S | a')

print(g)
print('productives variables : ', g.productive.vars)
print('Follow sets : ', g.follow)
print(g.tableLL1().to_markdown())
```
Output:
```
Grammar(
     terminals : ( ) a
     variables : S
     axiom : S
     rules : ['S → ( S ) S', 'S → a']
    )
productives variables :  {'S'}
Follow sets :  {'S': {')', '#'}}
|    | S           |
|:---|:------------|
| (  | S → ( S ) S |
| )  |             |
| a  | S → a       |
| #  |             |
```
## API overall graph

![schema](https://gitlab.univ-lille.fr/bruno.bogaert/cfgrammar/-/raw/e7ce707148b95087c8ab35ed446bbd13cbe6d8de/images/doc.svg)


## Parsing example

```python
from cfgrammar.builder import ParserBuilder

parser = ParserBuilder.from_dict({
    'E -> E + T ': lambda p,*_ : p.E + p.T,
    'E -> T' : lambda p,*_: p.T,
    'T -> T * F ': lambda p,*_ : p.T * p.F,
    'T -> F' : lambda p,*_: p.F,
    'F -> ( E )' : lambda p,*_ : p.E,
    'F -> 0|1|2|3|4|5|6|7|8|9' : lambda p,*_ : int(p[0])
})
print('Value: ',parser.parse('2*(4+5*(1+1))'))
print(parser.grammar)
print(parser.method)
```
output :
```
Value:  28
GrammarBase(
     terminals : ( ) * + 0 1 2 3 4 5 6 7 8 9
     variables : F E T
     axiom : E
     rules : ['E → E + T', 'E → T', 'T → T * F', 'T → F', 'F → ( E )', 'F → 0', 'F → 1', 'F → 2', 'F → 3', 'F → 4', 'F → 5', 'F → 6', 'F → 7', 'F → 8', 'F → 9']
    )
LRMethod.LALR1
```
## Abstract Syntax Tree
### Graphviz AST
Predefined semantic producing graphviz (dot) text.
```python
from cfgrammar.semantic import GraphvizAST
display_ast  = GraphvizAST()
dot_source = parser.parse('2*(4+5*(1+1))',display_ast)

from graphviz import Source
Source(dot_source).view()
```
![example_ast](https://gitlab.univ-lille.fr/bruno.bogaert/cfgrammar/-/raw/89bfba77280ae6e0fd40d61cc0e08146cdd0c344/images/example_ast.svg)


### Latex AST

LatexForestAST produces latex source (needs forest latex package)

```python
from cfgrammar.semantic import LatexForestAST
display_latex_ast  = LatexForestAST()
latex_source = parser.parse('2*(4+5*(1+1))',display_latex_ast)
```
funROI 1.0.0.post1
A Python package for functional ROI analyses of fMRI data
Keywords: fMRI, ROI, neuroimaging, data analysis

funROI
========================

|docs|

.. |docs| image:: https://readthedocs.org/projects/funroi/badge
    :alt: Documentation Status
    :target: https://funroi.readthedocs.io/en/latest/?badge=latest

The **funROI** (FUNctional Region Of Interest) toolbox is designed to provide robust analytic methods for fMRI data analyses that accommodate inter-subject variability in the precise locations of functional activations. 

.. image:: https://github.com/GT-LIT-Lab/funROI/raw/main/doc/source/funROI-collage.png
   :width: 800px
   :align: center

Features
--------

- **Parcel generation:** generates parcels (brain masks) based on individual activation maps, which can serve as a spatial constraint for subsequent subject-level analyses. (This step can be skipped if you already have parcels of interest).

- **fROI definition:** defines functional regions of interest (fROIs) by selecting a subset of functionally responsive voxels within predefined parcels.

- **Effect estimation:** extracts average effect sizes for each subject-specific fROI.

- **Spatial correlation estimation:** quantifies the similarity of within-subject activation patterns across conditions (within either a parcel or an fROI).

- **Spatial overlap estimation:** calculates the overlap between parcels and/or fROIs from different subjects or definitions.

Installation
------------
Install funROI via pip:

.. code-block:: bash

   pip install funROI

Usage
-------------
For more details and examples, please refer to the full documentation at:
https://funroi.readthedocs.io/en/latest/

Citation
--------
If you use funROI in your work, please cite it as follows:

   Gao, R., & Ivanova, A. A. (2025). *funROI: A Python package for functional ROI analyses of fMRI data* (Version 1). Figshare. https://doi.org/10.6084/m9.figshare.28120967.v1

Acknowledgements
----------------

This toolbox implements the parcel definition, fROI definition, and fROI effect size estimation methods described in `Fedorenko et al. (2010) <https://pmc.ncbi.nlm.nih.gov/articles/PMC2934923/>`_. It builds heavily on the `spm_ss <https://github.com/alfnie/spm_ss>`_ toolbox, which provides a Matlab-based implementation for fROI analyses. We thank Alfonso Nieto-Castañon and Ev Fedorenko for developing these methods.
jmobile-location-mcp-server 0.1.1
一个提供手机号码（段）所属归属地查询功能的模型上下文协议（Model Context Protocol）服务器。该服务器使大型语言模型（LLMs）能够获取手机号码段归属地信息，如：省份、城市、运营商。
Requires Python: >=3.10

# Juhe Mobile Phone Location MCP Server

一个提供手机号码（段）所属归属地查询功能的模型上下文协议（Model Context Protocol）服务器。该服务器使大型语言模型（LLMs）能够获取手机号码段归属地信息，如：省份、城市、运营商。


## Components

### Tools

服务器实现了一个工具:

- get_mobile_phone_location: 根据手机号码查询其所属地信息。
  - 需要传入 "phone"（手机号码）作为必须的字符串参数。
```
async def get_mobile_location(phone: str = "") -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]:
```


## Install
This server requires Python 3.10 or higher. Install dependencies using uv (recommended) or pip

### Using uv (recommended)
When using [uv](https://docs.astral.sh/uv/) no specific installation is needed. We will use [uvx](https://docs.astral.sh/uv/guides/tools/) to directly run jmobile-location-mcp-server.

```bash
uvx jmobile-location-mcp-server
```

### Using PIP
Alternatively you can install jmobile-location-mcp-server via pip:
```
pip install jmobile-location-mcp-server
```
After installation, you can run it as a script using:
```
python -m jmobile_location_mcp_server
```

### Configuration

#### Environment Variables
`JUHE_MOBILE_LOCATION_API_KEY`: 聚合数据的手机归属地查询API密钥。获取：[https://www.juhe.cn/docs/api/id/11](https://www.juhe.cn/docs/api/id/11)
```
JUHE_MOBILE_LOCATION_API_KEY=your_api_key
```

#### Claude Desktop

- On MacOS: `~/Library/Application\ Support/Claude/claude_desktop_config.json`
- On Windows: `%APPDATA%/Claude/claude_desktop_config.json`

<details>
  <summary>Using uvx</summary>

  ```
  "mcpServers": {
    "jmobile-location-mcp-server": {
      "command": "uvx",
      "args": [
        "jmobile-location-mcp-server"
      ],
      "env": {
        "JUHE_MOBILE_LOCATION_API_KEY": "your_api_key"
      }
    }
  }
  ```
</details>

<details>
  <summary>Using pip installation</summary>

  ```
  "mcpServers": {
    "jmobile-location-mcp-server": {
      "command": "python",
      "args": [
        "-m",
        "jmobile_location_mcp_server"
      ],
      "env": {
        "JUHE_MOBILE_LOCATION_API_KEY": "your_api_key"
      }
    }
  }
  ```
</details>

## Debugging
You can use the MCP inspector to debug the server. For uvx installations:

```bash
npx @modelcontextprotocol/inspector uvx jmobile-location-mcp-server 
```

Or if you've installed the package in a specific directory or are developing on it:

```bash
cd path/to/servers/src/jmobile-location-mcp-server
npx @modelcontextprotocol/inspector uv run jmobile-location-mcp-server
```

## Examples of Questions for Cline
1. "查询下这个手机号码的归属地信息 18912341234"
streamlit-interactive-gantt-job-shop 0.0.6
Streamlit component for custom Gantt chart
Requires Python: >=3.7

# streamlit-custom-component

Streamlit component that allows you to do X

## Installation instructions

```sh
pip install streamlit_interactive_gantt_job_shop
```

## Usage instructions

```python
import streamlit as st

from streamlit_interactive_gantt_job_shop import gantt_component

value = gantt_component()

st.write(value)
```