semvec/pypi-packages huggingface dataset:
hydroinform 0.1.17
A steady-state stream model and python access to DFS-files

# hydroinform
This package contains a steady-state stream model and some tools to access .dfs-files from DHI

# Usage
Convert .dfs, res1d and res11-files to netCDF:
```sh
#Import NetCDF and DFS from hydroinform
from hydroinform import NetCDF, DFS

#Saves "omr4_jag_3DSZ.dfs3 as "omr4_jag_3DSZ.nc"
NetCDF.save_as_NetCDF('omr4_jag_3DSZ.dfs3')

#Saves "omr4_jag_3DSZ.dfs3 as "newname.nc"
NetCDF.save_as_NetCDF('omr4_jag_3DSZ.dfs3','newname.nc')

#Saves only the first item and the second and fourth time step from "omr4_jag_3DSZ.dfs3""
d= DFS.DFSBase.open_file('omr4_jag_3DSZ.dfs3')
NetCDF.save_dfs_as_NetCDF(d, [0], [1,3], 'omr4_jag_3DSZ_one_time.nc')

#Saves the discharge data from "Storaa_HD_quasiStationary_20090701.res1d" as 'Storaa_HD_quasiStationary_20090701.nc'
NetCDF.save_as_NetCDF('Storaa_HD_quasiStationary_20090701.res1d')

```

Write a pump extraction file to be used with MikeZero:
```sh
#Import DFS from HydroInform
from hydroinform import DFS
import datetime

#The number of Items (In this case number of pumping wells)
numberofitems = 5;

#Now create the file.
_tso = DFS.DFS0.new_file(r'c:\temp\extraction.dfs0', numberofitems);

#Loop the items and set the units etc.
for itemCount in range (0, numberofitems):
    _tso.items[itemCount].value_type = DFS.DataValueType.MeanStepBackward
    _tso.items[itemCount].eum_item = DFS.EumItem.eumIPumpingRate
    _tso.items[itemCount].eum_unit = DFS.EumUnit.eumUm3PerYear
    _tso.items[itemCount].name = "Item number: " + str(itemCount)
      
#Loop the years where you have pumping data
tscount = 0;
for year in range(2010, 2016):
    #For every year append a new timestep
    _tso.append_time_step(datetime.datetime(year, 12, 31, 12))
    #Loop the items and set a value for this timestep
    for itemCount in range (0, numberofitems):
        #Sets the data. Note that timesteps count from 0 and Items count from 1
        _tso.set_data(tscount, itemCount+1, year * itemCount)
    tscount+=1
#Call dispose which will save and close the file.
_tso.dispose()
```
qp-prob 0.6.3
Quantile parametrization of probability distribution functions

# qp

Quantile parametrization for probability distribution functions.

[![Build Status](https://travis-ci.org/aimalz/qp.svg?branch=master)](https://travis-ci.org/aimalz/qp)[![Documentation Status](http://readthedocs.org/projects/qp/badge/?version=latest)](http://qp.readthedocs.io/en/latest/?badge=latest)


## Motivation
This repository exists for two reasons.

1. To be the home of `qp`, a python package for handling probability distributions using various parametrizations, including a set of quantiles;
2. To help us learn best practices in software carpentry.

## Examples

* **[Basic Demo](http://htmlpreview.github.io/?https://github.com/LSSTDESC/qp/blob/master/docs/demo.html)** [(raw notebook)](https://github.com/LSSTDESC/qp/blob/master/nb/demo.ipynb)

* **[Practical Example](http://htmlpreview.github.io/?https://github.com/LSSTDESC/qp/blob/master/docs/demo.html)** [(raw notebook)](https://github.com/LSSTDESC/qp/blob/master/nb/demo.ipynb)

Also: [Read the Docs](http://qp.readthedocs.io/)


## People

* [Alex Malz](https://github.com/LSSTDESC/qp/issues/new?body=@aimalz) (NYU)
* [Phil Marshall](https://github.com/LSSTDESC/qp/issues/new?body=@drphilmarshall) (SLAC)
* [Eric Charles](https://github.com/LSSTDESC/qp/issues/new?body=@eacharles) (SLAC)
* [Sam Schmidt](https://github.com/LSSTDESC/qp/issues/new?body=@sschmidt) (UC Davis)

## License, Contributing etc

The code in this repo is available for re-use under the MIT license, which means that you can do whatever you like with it, just don't blame us. If you end up using any of the code or ideas you find here in your academic research, please cite us as `Malz et al, ApJ 156 1 35`. If you are interested in this project, please do drop us a line via the hyperlinked contact names above, or by [writing us an issue](https://github.com/aimalz/qp/issues/new). To get started contributing to the `qp` project, just fork the repo - pull requests are always welcome!
alfabet 0.4.1
A library to estimate bond dissociation energies (BDEs) of organic molecules

![ALFABET logo](/docs/logo.svg)

[![PyPI version](https://badge.fury.io/py/alfabet.svg)](https://badge.fury.io/py/alfabet)
[![Build Status](https://travis-ci.com/NREL/alfabet.svg?branch=master)](https://travis-ci.com/NREL/alfabet)

# A machine-Learning derived, Fast, Accurate Bond dissociation Enthalpy Tool (ALFABET)

This library contains the trained graph neural network model for the prediction of homolytic bond dissociation energies (BDEs) of organic molecules with C, H, N, and O atoms. This package offers a command-line interface to the web-based model predictions at [bde.ml.nrel.gov](https://bde.ml.nrel.gov/).

The basic interface works as follows, where `predict` expects a list of SMILES strings of the target molecules
```python
>>> from alfabet import model
>>> model.predict(['CC', 'NCCO'])
```
```
  molecule  bond_index bond_type fragment1 fragment2  ...    bde_pred  is_valid
0       CC           0       C-C     [CH3]     [CH3]  ...   90.278282      True
1       CC           1       C-H       [H]    [CH2]C  ...   99.346184      True
2     NCCO           0       C-N   [CH2]CO     [NH2]  ...   89.988495      True
3     NCCO           1       C-C    [CH2]O    [CH2]N  ...   82.122429      True
4     NCCO           2       C-O   [CH2]CN      [OH]  ...   98.250961      True
5     NCCO           3       H-N       [H]   [NH]CCO  ...   99.134750      True
6     NCCO           5       C-H       [H]   N[CH]CO  ...   92.216087      True
7     NCCO           7       C-H       [H]   NC[CH]O  ...   92.562988      True
8     NCCO           9       H-O       [H]    NCC[O]  ...  105.120598      True
```

The model breaks all single, non-cyclic bonds in the input molecules and calculates their bond dissociation energies. Typical prediction errors are less than 1 kcal/mol. 
The model is based on Tensorflow (2.x), and makes heavy use of the [neural fingerprint](github.com/NREL/nfp) library (0.1.x).

For additional details, see the publication:
St. John, P. C., Guan, Y., Kim, Y., Kim, S., & Paton, R. S. (2020). Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature Communications, 11(1). doi:10.1038/s41467-020-16201-z

*Note:* For the exact model described in the text, install `alfabet` version 0.0.x. Versions >0.1 have been updated for tensorflow 2.

## Installation
Installation with `conda` is recommended, as [`rdkit`](https://github.com/rdkit/rdkit) can otherwise be difficult to install

```bash
$ conda create -n alfabet -c conda-forge python=3.7 rdkit
$ source activate alfabet
$ pip install alfabet
``
unboxapi 0.3.0
The official Python API library for Unbox: the Testing and Debugging Platform for AI
Requires Python: >=3.7
Keywords: MLOps,AI,Unbox

<div align="left">
  <img src="docs/source/_static/logo-blue-text.svg"><br>
</div>

# Unbox AI | Python API Library

[![PyPI Latest Release](https://img.shields.io/pypi/v/unboxapi.svg)](https://pypi.org/project/unboxapi/)
[![downloads](https://pepy.tech/badge/unboxapi)](https://pepy.tech/project/unboxapi)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/)

## What is it?

Unbox is a debugging workspace for ML & Data Science. Unbox combines and builds upon SOTA techniques in explainability, model and dataset versioning, synthetic data generation, data-centric testing and much more to form a powerful, **unified platform for model development**.

👉 [Join our Slack community!](https://l.linklyhq.com/l/1DG73) We'd love to meet you and help you get started with Unbox!

This is the official Python library for interacting with the Unbox platform. Navigate [here](https://docs.unbox.ai) for a quickstart guide and for in-depth tutorials.

## Main Features

This library's primary function is to enable you to easily package your models and datasets and add them to your Unbox account.

## Installation

Install with PyPI (pip)

```console
pip install --upgrade unboxapi
```

or install with Anaconda (conda)

```console
conda install unboxapi --channel conda-forge
```

## Documentation

The official documentation for this Python library can be found [here](https://reference.unbox.ai).

## Contributing

All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome! Just send us a message on [Slack](https://l.linklyhq.com/l/1DG73).
pyrcache 1.1.2
Python API for accessing RedisCache
Requires Python: >=3.8

## Pyrcache
#### Setup
1. Install Docker + Docker Desktop
2. Open a terminal and enter: 
    ```
        git clone github.com/yamaceay/rediscache
        cd rediscache
        docker-compose up --build
    ```
3. Now you are ready to go!

#### Get Started

```
    import rcache
    print(rcache.keys()) # prints all keys from the database
    if len(rcache.keys()) == 0:
        exit()
    key = rcache.keys()[0]
    print(rcache.rget(key)) # print the value of key
    value = "{\"name\": \"mike\", \"surname\": \"smith\"}"
    rcache.rset(key, value) # updates the key-value pair
```
bing-hashing-image-downloader 22.8.24.1326
Python library to download bulk images from Bing.com without repeated images
Keywords: bing,images,scraping,image download,bulk image downloader,sha1

![GitHub top language](https://img.shields.io/github/languages/top/korreckj328/bing_hashing_image_downloader)
![GitHub](https://img.shields.io/github/license/korreckj328/bing_hashing_image_downloader)
[![Hits](https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2Fkorreckj328%2Fbing_hashing_image_downloader&count_bg=%2379C83D&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false)](https://hits.seeyoufarm.com)

## Bing Hashing Image Downloader
<hr>

Python library to download bulk of images from Bing.com.
The downloader will obtain a sha1 hash of each image as it downloads and compare it to a hash of previous
downloads before saving it to ensure no duplicate images are saved.  
This package uses async url, which makes it very fast while downloading.<br/>


### Disclaimer<br />

This program lets you download tons of images from Bing.
Please do not download or use any image that violates its copyright terms. 

### Installation <br />

```bash
git clone https://github.com/korreckj328/bing_hashing_image_downloader
cd bing_hashing_image_downloader
pip install .
```

or 
```bash
pip install bing-hashing-image-downloader
```

### Usage <br />

```python
from bing_hashing_image_downloader import downloader

downloader.download(query_string, limit=100, output_dir='dataset', adult_filter_off=True,
                    timeout=60, size='none' verbose=True)
```

`query_string` : String to be searched.<br />
`limit` : (optional, default is 100) Number of images to download.<br />
`output_dir` : (optional, default is 'dataset') Name of output dir.<br />
`adult_filter_off` : (optional, default is True) Enable of disable adult filteration.<br />
`timeout` : (optional, default is 60) timeout for connection in seconds.<br />
`filter` : (optional, default is "") filter, choose from [line, photo, clipart, gif, transparent]<br />
`size` : (optional, default is 'none') or a tuple can be entered to resize the image.<br />
`verbose` : (optional, default is True) Enable downloaded message.<br />


You can also test the programm by runnning `test.py`


</br>


### Thanks
Thanks to the original author of this program.  It formed the base for these improvements that I made for my
own use.  You can see the original here: https://github.com/gurugaurav/bing_image_downloader <br />
autoscirpt 1.0.4
Daily Workflow Python Automation Script
Requires Python: >=3.6

# Package Name

Package function description

## Installation

`pip install autoscirpt`

## How to use it?

Function of the package

## License

copyright @ 2021 BDFD

This repository is licensed under the MIT license. See LICENSE for details.

### References

https://github.com/bdfd/Section5.4-Python-Automation-Script