semvec/pypi-packages huggingface dataset:
pyembeds-sssa 0.0.7
A small package for Introduction to Programming and Machine Learning Course
Requires Python: >=3.7

# pyEmbedsSSSA Package

Package for Introduction to Programming and Machine Learning in Python

## Installing pyEmbedsSSSA
`pip install pyembeds-sssa`

## USAGE
-  AUTOTESTING ASSIGNMENT
```python
from pyembeds.testing.auto_testing import run_and_test
# Simple Test
def asgn01_01Hello_world():
    print('Ciao, mondo!')
run_and_test([], ["Ciao, mondo!"], asgn01_01Hello_world)
```
- TESTING DOWNLOAD FILE FUNCTION
```python
from pyembeds.data.load_data import donwload_file
url = 'https://raw.githubusercontent.com/EMbeDS-education/SNS-IProML2022/main/jupyter/jupyterNotebooks/data/WBCD.csv'
donwload_file(url)
```
-  LOAD CAPYTHONE ASSIGNMENT DATA
```python
from pyembeds.data.load_data import load_capythone_data
data = load_capythone_data()
reviews = data['reviews']
positive_word = data['positive_word']
negative_word = data['negative_word']
```
- LOAD WORDCLOUD ASSIGNMENT DATA
```python
from pyembeds.data.load_data import load_wordcloud_ita_data
data = load_wordcloud_ita_data()
text = data['manzoni']
stop_words = data['stopwords_italian']
```
- VISUALIZATION FUNCTION
```python
from pyembeds.vis.utils import draw_euclidean_distance
draw_euclidean_distance((20, 10), (5, 0), (5, 20))
```

## Course responsible:
- [Andrea Vandin](https://www.santannapisa.it/en/andrea-vandin), andrea.vandin@santannapisa.it 
- [Daniele Licari](https://www.linkedin.com/in/daniele-licari/), daniele.licari@santannapisa.it 

## Description:
The course introduces students to programming and data analysis, using python as a reference
language.
 - **Module 1** 
Introduces students to the fundamental principles of structured programming with basic applications to data processing

 - **Module 2**
Introduces the students to the components of typical data analysis processes and machine learning pipelines.
tdsql 0.0.5
Minimum test flamework for sql
Requires Python: >=3.10,<3.11

# Test Driven SQL
Tdsql is a minimum test flamework for sql.
You can replace any part of sql and check if the result is as expected.
You can define test cases as yaml file.

## Install
Currently, only bigquery is supported.

```bash
pip install 'tdsql[bigquery]'
```

## Authentication
Run either of these commands.

```bash
gcloud auth application-default login

# If you want to use service account
# export GOOGLE_APPLICATION_CREDENTIALS=/path/to/keyfile.json
```

## Quick start
Save these files in your working directory.

```yaml
# ./tdsql.yaml
database: bigquery
tests:
  - filepath: ./hello-world.sql
    replace:
      data: |
        SELECT * FROM UNNEST([
          STRUCT('2020-01-01' AS dt, 100 AS id),
          STRUCT('2020-01-01', 100),
          STRUCT('2020-01-01', 200)
        ])
      master: |
        FROM (
          SELECT 100 AS id, 1 AS category
        )
    expected: |
      SELECT * FROM UNNEST([
        STRUCT('2020-01-01' AS dt, 1 AS category, 2 AS cnt),
        STRUCT('2020-01-01', NULL, 1)
      ])
```

```sql
-- ./hello-world.sql
WITH data AS (
  -- tdsql-start: data
  SELECT dt, id
  FROM `data_table`
  -- tdsql-end: data
), master AS (
  SELECT id, category
  FROM `master_table` -- tdsql-line: master
)
SELECT
  dt,
  category,
  COUNT(*) AS cnt
FROM data INNER JOIN master USING(id)
GROUP BY 1, 2
```

Then, run this command.
You'll see an error message.

```sh
tdsql
```

Fix `hello-world.sql` and run `tdsql` again,
you won't see any error message this time.

```diff
- FROM data INNER JOIN master USING(id)
+ FROM data LEFT JOIN master USING(id)
```

Quite simple, isn't it?

## Examples
Heavily documented sample codes are [here](./sample).

## Feedback
If you find any bugs, please feel free to create an issue.
nonebot-plugin-heisi 0.3.1
nonebot2 plugin heisi
Keywords: pip,nonebot2,nonebot,heisi,nonebot_plugin

<p align="center">
  <a href="https://v2.nonebot.dev/"><img src="https://v2.nonebot.dev/logo.png" width="200" height="200" alt="nonebot"></a>
</p>


<div align="center">

   
**你的star是我的动力**  
**↓**  
<img src="https://img.shields.io/github/stars/yzyyz1387/nonebot_plugin_heisi.svg?style=social">  
# Hei Si

_✨ NoneBot2 黑丝 （备考中，可能会更新）✨_

  **由于jsd在国内无法访问的问题，国内服务器或无法使用，pypi暂未更新，可食用github最新版**  
  **见[commit155e479](https://github.com/yzyyz1387/nonebot_plugin_heisi/commit/155e4796c883d4448a85647ed3afadd4403375b7)**

</div>


用于发送色色图片，内置CD

请在 `.env.dev`中做出如下配置：
```

heisi_group=["237161342"] #黑丝白名单
heisi_cd=30 #黑丝cd时间（s）

```

## 安装💿
`pip install nonebot-plugin-heisi`


## 导入📲
在**bot.py** 导入，语句：
`nonebot.load_plugin("nonebot_plugin_heisi")`



## 指令💻
```
.黑丝
.丝袜
```


**给个star吧~**

其他插件  
[简易群管](https://github.com/yzyyz1387/nonebot_plugin_admin)  
[在线运行代码](https://github.com/yzyyz1387/nonebot_plugin_code)  
[it咨讯（垃圾插件）](https://github.com/yzyyz1387/nonebot_plugin_itnews "it资讯")  
[工作性价比（还没更新beta不能用）](https://github.com/yzyyz1387/nonebot_plugin_workscore)  
[黑丝插件（jsdelivr问题国内服务器不能用）](https://github.com/yzyyz1387/nonebot_plugin_heisi)

## 截图🖼

没有
brainbox 0.0.3
Simplifying the life of a computational neuroscientist.
Requires Python: >=3.8

![](BrainBox.png?raw=true)

<h4 align="center">Simplifying the life of a computational neuroscientist.</h4>

<p align="center">
  <a href="https://badge.fury.io/js/electron-markdownify">
    <img src="https://anaconda.org/anaconda/conda-build/badges/installer/conda.svg">
  </a>
  <a href="https://badge.fury.io/js/electron-markdownify">
    <img src="https://img.shields.io/badge/License-MIT-blue.svg">
  </a>
</p>

<p align="center">
  <a href="#key-features">Key features</a> •
  <a href="#installation">Installation</a> •
  <a href="#getting-started">Getting started</a> •
<a href="#roadmap">Roadmap</a> •
  <a href="#license">License</a>
</p>

## Key features

* Model construction (bookkeeping, snn layers)
* Model training (different flavours, bookkeeping, customisability/flexibility)
* Model validation (GPU optimised metrics e.g. cc, ccnorm, rdm, deviation)
* Model inspection (virtual e-phys)
* Datasets (plug and play format)


## Installation

```bash
# Clone this repository
$ git clone https://github.com/webstorms/BrainBox.git
```

## Getting started

* [Building and training a model](https://github.com/webstorms/BrainBox/blob/main/notebooks/Predicting%20neural%20responses.ipynb)
* [Estimating model RFs and fitting gabors](https://github.com/webstorms/BrainBox/blob/main/notebooks/Receptive%20field%20analysis.ipynb)

## Roadmap

- Create conda build
- Make all datasets publicly available (and including more datasets)
- More notebook tutorials
- Finish e-phys pipeline

## License

BrainBox has a MIT license, as found in the LICENSE file.
OrfPP 1.0
An ORF predictor from SNPs

OrfPP is a tool that can predict open reading frames (ORFs) including sORFs from populational genomic variants.

OrfPP requires three compulsory inputs: the sequence of reference genome (in fasta format), the genome annotation file (in gtf format) and the nucleotide diversity calculated from genomic variants (in vcf format)

There are five options

--start OrfPP use AUG as the start codon by default. As many non-canonical start codons have been identified in many studies. The users can select other triples as start codons. Codons should be seperated by comma if more than one is used. For example, AUG,UUG,CUG,GUG

--cov The minimum coverage of diversified nucleotides for a canidate ORF. This value should be set between 0-1.

--nCores The number of multiprocesssors. The identification of ORFs could be speed up by using more processors simultaneously.

--outdir The output directory of results.

--prefix The prefix of output files.
pyseing 0.1.8
SEING is a C/C++ package for fingerprint calculations suitable for machine learning studies of molecular systems.

SEING was developed in the Clancy Group (https://clancygroup.wse.jhu.edu/). Fingerprints (in this context) are numerical representations of chemical environments designed to be invariant under property-perseving operations such as permutation of atoms of the same nature, geometric rotation, etc. For more information on fingerprints in general and the ones currently implemented in SEING, please see the official documentation and user-guide.