semvec/pypi-packages huggingface dataset:
det3d 0.0.7
A package of 3D object detection models

# 3D Detection Collection
        
        This repository collects/re-implements 3D object detection models, including training and evaluation processes.
        
        
        ## Environment
        
        It's tested in the followinng environment:
        
        - CUDA Toolkit: 10.2
        - python: 3.8
        - numpy: 1.23.1
        - pytorch: 1.8.2
ip2location-io 1.1.0
This SDK provides users with the ability to perform IP geolocation lookup, domain WHOIS lookup and hosted domain information lookup through a web service.

IP2Location.io Python SDK
========================
![PyPI - Version](https://img.shields.io/pypi/v/ip2location-io)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/ip2location-io)

This Python module enables user to query for an enriched data set, such as country, region, city, latitude & longitude, ZIP code, time zone, ASN, ISP, domain, net speed, IDD code, area code, weather station data, MNC, MCC, mobile brand, elevation, usage type, address type, advertisement category, fraud score and proxy data with an IP address. It supports both IPv4 and IPv6 address lookup.

In addition, this module provides WHOIS lookup api that helps users to obtain domain information, WHOIS record, by using a domain name. The WHOIS API returns a comprehensive WHOIS data such as creation date, updated date, expiration date, domain age, the contact information of the registrant, mailing address, phone number, email address, nameservers the domain is using and much more.

There is also a Hosted Domain API that allowing users to get the list of hosted domain names by IP address in real time. The REST API supports both IPv4 and IPv6 address lookup.

This module requires API key to function. You may sign up for a free API key at https://www.ip2location.io/pricing.

Developer Documentation
=====================

To learn more about installation, usage, and code examples, please visit the developer documentation at [https://ip2location-io-python.readthedocs.io/en/latest/index.html](https://ip2location-io-python.readthedocs.io/en/latest/index.html).


LICENCE
=====================
See the LICENSE file.
aiidalab-qe-wannier90 0.1.6
Web GUI for Quantum ESPRESSO and Wannier90 calculations in AiiDA.
Requires Python: >=3.9
Keywords: aiida, workflows, core-level, spectroscopy

# aiidalab-qe-wannier90

A plugin for running Wannier calculations inside the AiiDAlab Quantum ESPRESSO App.

## Features

- **Band structure comparison** with DFT bands.
- With predefined protocols (`fast`, `balanced`, `stringent`)
- **Optional real-space Wannier functions** (3D visualization) with the atomic structure.

<img src="docs/source/_static/images/overview.png"  width="800px"/>


<!-- <img src="docs/source/_static/images/qeapp-wannier90-wf.gif"  width="100%"/> -->


## Usage
Run Wannier calculations via the AiiDAlab QE App GUI.


## Compile Wanntier90

This plugin requires the Wannier90 code from the latest source code from the [Wannier90 GitHub repository](https://github.com/wannier-developers/wannier90).

To compile Wannier90, follow the instructions below:

```bash
git clone https://github.com/wannier-developers/wannier90.git
cd wannier90
sudo apt update
sudo apt install gfortran libblas-dev liblapack-dev
cp config/make.inc.gfort make.inc
make wannier
```


validate the installation by running the following command:

```bash
./wannier90.x -h
```



## Isosurface

I used PythonJob to calculate the isosurface of the wannier function, and save the mesh data as AiiDA output node, then visualize the isosurface using the `weas-widget`. This avoids the need to download and save the large density file.


### Set up PythonJob code
To setup the `python` code for isosurface calculation, you need to install the following packages in the Python environment:

```
cloudpickle
scikit-image
ase
```

Then one can use `verdi` command create the `pythonjob` code. Here is an example configuration file to setup the `pythonjob` code:

```yaml
---
label: python3
description: python3.9.10 at Merlin 7
default_calc_job_plugin: pythonjob.pythonjob
filepath_executable: /opt/psi/Programming/Python/3.9.10/bin/python
prepend_text: |
    module purge
    module load Python/3.9.10
append_text: ''
```
Run the fowlling command to create the `pythonjob` code in your AiiDA profile:

```
verdi code create core.code.installed --config pythonjob-code.yaml
```
jcompoundmapper-pywrapper 0.0.3
Python wrapper for jCompoundMapper molecular fingerprints
Keywords: jCompoundMapper, molecular fingerprints, cheminformatics, toxicoinformatics, QSAR

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

# Python wrapper for jCompoundMapper molecular fingerprints

Python wrapper to ease the calculation of jCompoundMApper molecular fingerprints.

## Installation

From source:

    git clone https://github.com/OlivierBeq/jcompoundmapper_pywrapper.git
    pip install ./jcompoundmapper_pywrapper

with pip:

```bash
pip install jcompoundmapper-pywrapper
```

### Get started

```python
from jcompoundmapper_pywrapper import JCompoundMapper
from rdkit import Chem

smiles_list = [
  # erlotinib
  "n1cnc(c2cc(c(cc12)OCCOC)OCCOC)Nc1cc(ccc1)C#C",
  # midecamycin
  "CCC(=O)O[C@@H]1CC(=O)O[C@@H](C/C=C/C=C/[C@@H]([C@@H](C[C@@H]([C@@H]([C@H]1OC)O[C@H]2[C@@H]([C@H]([C@@H]([C@H](O2)C)O[C@H]3C[C@@]([C@H]([C@@H](O3)C)OC(=O)CC)(C)O)N(C)C)O)CC=O)C)O)C",
  # selenofolate
  "C1=CC(=CC=C1C(=O)NC(CCC(=O)OCC[Se]C#N)C(=O)O)NCC2=CN=C3C(=N2)C(=O)NC(=N3)N",
  # cisplatin
  "N.N.Cl[Pt]Cl"
]
mols = [Chem.MolFromSmiles(smiles) for smiles in smiles_list]

jcm = JCompoundMapper()
print(jcm.calculate(mols))
```

One can use any of the following fingerprints: DFS, ASP, AP2D, AT2D, AP3D, AT3D, CATS2D, CATS3D, PHAP2POINT2D, PHAP3POINT2D, PHAP2POINT3D, PHAP3POINT3D,
ECFP, ECFPVariant, LSTAR, SHED, RAD2D, RAD3D or MACCS.

By default, 1024 bits fingerprints are generated, except for the 166 bits MACCS.

```python
jcm = JCompoundMapper('ECFP')
print(jcm.calculate(mols, 2048))

# or

from jCompoundMapper_pywrapper import Fingerprint

jcm = JCompoundMapper(Fingerprint.DFS)
print(jcm.calculate(mols, 2048))
```

:warning: Molecules with 3D conformers must be provided to calculate 3D fingerprints (i.e. CATS3D, PHAP2POINT3D, PHAP3POINT3D and RAD3D).

One can specify advanced parameters to the fingerprinter:

```python
from jCompoundMapper_pywrapper import DEFAULT_FP_PARAMETERS

custom_ecfp_params = DEFAULT_FP_PARAMETERS['ECFP']
custom_ecfp_params.depth = 6

jcm = JCompoundMapper('ECFP', custom_ecfp_params)
print(jcm.calculate(mols))
```
## Documentation

```python
def calculate(mols, nbits=1024, show_banner=True, njobs=1, chunksize=1000):
```

Default method to calculate jCompoundMapper fingerprints.

Parameters:

- ***mols  : Iterable[Chem.Mol]***  
  RDKit molecule objects for which to obtain jCompoundMapper fingerprints.
- ***nbits  : Union[int, List[int]]***  
  Size of the fingerprints.
- ***show_banner  : bool***  
  Displays default notice about jCompoundMapper.
- ***njobs  : int***  
  Maximum number of simultaneous processes.
- ***chunksize  : int***  
  Maximum number of molecules each process is in charge of.
latlon-and-rowcol 0.1.3
用于风云卫星数据的经纬度和坐标互转
Keywords: 经纬度, 坐标

# 经纬度行列号转换说明

- [latlon_and_rowcol](#service-rcTOlatlonEQ)
  - [说明]
    - [CONFIG.yml](#configyml)
    - [main.py](#mainpy)
    - [check_range.py](#check_rangepy)
    - [calcu_row_lat.py](#calcu_row_latpy)
  - [调用方式]
  
输入row和col得到经纬度:get_latlon(row,col,res,sat)

反查；输入经纬度得到row和col:get_rowcol(lat,lon,res,sat)
## 说明
### CONFIG.yml
存放的yml是FY4A/FY4B成像仪标称上行列号和经纬度的互相转换公式的参数说明，其中
·4A支持500m、1000m、2000m、4000m分辨率
·4B支持250m、500m、1000m、2000m、4000m分辨率

### main.py
程序入口
main.py

### check_range.py
检查行列号的范围
检查经纬度的范围

### calcu_row_lat.py
根据输入行列号，计算得到经纬度的公式
根据输入经纬度，计算得到行列号的公式

## 调用方式
1.运行python main.py启动服务

2.输入：
（1）行列号 to 经纬度 get_latlon(row,col,res,sat)\
"col": 列\
"row": 行\
"res": 分辨率\
"sat": 卫星

（2）经纬度 to 行列号 get_rowcol(lat,lon,res,sat)\
"lat": 纬度\
"lon": 经度\
"res": 分辨率\
"sat": 卫星

3.输出：
（1）行列号 to 经纬度\
"latitude": 纬度\
"longitude": 经度

（2）经纬度 to 行列号\
"column": 列 \
"row": 行

### 示例
根据输入行列号，计算得到经纬度\
"row": 621\
"col": 2764\
"res": 2000\
"sat": FY4A
get_latlon(621, 2764, 2000, 'FY4A')

输出：
{
    "latitude": 45.12023588,
    "longitude": 102.7335505
}

根据输入经纬度，计算得到行列号

"lat": 30,\
"lon": 156,\
"res": 2000,\
"sat": "B"\
get_rowcol(30,156,2000,'FY4B')\

输出：
{
    "column": 3788,
    "row": 1223
}
evaluate-post 0.1.0
A package for evaluating PDFs with text, image, and fact-checking modules.
Requires Python: >=3.7

# Evaluate Post

Evaluate Post is a Python package for evaluating PDFs using text, image, and fact-checking modules. It leverages Azure OpenAI and other tools to provide comprehensive analysis.

## Features
- Text content evaluation
- Image relevance analysis
- Fact-checking with external sources

## Installation

```bash
pip install evaluate_post
```

## Usage

```python
from evaluate_module import evaluate
from langchain_openai import AzureChatOpenAI

# Initialize models and evaluate a PDF
# Example usage here
```

## License

This project is licensed under the MIT License.