semvec/pypi-packages huggingface dataset:
ec2-ssm-connect 0.1.5
An interactive command-line tool to list Amazon EC2 instances and establish secure connections using AWS Systems Manager (SSM) Session Manager.
Requires Python: >=3.9
Keywords: aws,ec2,ssm,systems manager,session manager

# ec2-ssm-connect

An interactive command-line tool to list Amazon EC2 instances and establish secure connections using AWS Systems Manager (SSM) Session Manager.


## 🚀 Installation

Before using this tool, ensure you have the AWS CLI installed. This tool depends on it. [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html)

``` bash
pip install ec2-ssm-connect
```

## 🛠 Usage

The application reads the AWS CLI credentials from the environment variables.
After installation, you can use the command:

``` bash
ec2-ssm-connect
```

To filter EC2 instances by name:

``` bash
ec2-ssm-connect --filter [INSTANCE_NAME]
```

Example:

``` bash
$ ec2-ssm-connect
1. ID: i-0bb72e6f61ef2s612   Name: acme-aws-int-keycloak-ec2
2. ID: i-0819d17cdf4ees213   Name: acme-aws-int-auth-proxy-ec2
3. ID: i-082949bfd9b12s7cf   Name: acme-aws-int-auth-app-ec2
4. ID: i-0caaf2ffbb132se5b   Name: acme-aws-int-auth-web-ec2
5. ID: i-0dd580f86932as157   Name: jenkins-int-instance
6. ID: i-001bfa8b72680s6d9   Name: vault-int-ecs-instance
7. ID: i-0295e01f6bc12se68   Name: server01-int-ecs-instance
Enter the number of the EC2 instance you want to connect to: [HERE YOU ENTER THE NUMBER OF THE INSTANCE YOU WANT TO CONNECT TO, IN THIS CASE 1 TO 7]

$ ec2-ssm-connect -f jenkins
1. ID: i-0dd580f86932as157   Name: jenkins-int-instance
Enter the number of the EC2 instance you want to connect to: [HERE YOU ENTER THE NUMBER OF THE INSTANCE YOU WANT TO CONNECT TO, IN THIS CASE ONLY 1 IS POSSIBLE]
```


## 📦 Dependencies

- **boto3**: For AWS SDK functionalities.
- **AWS CLI**: Required for establishing connections with EC2 instances.

## ✍️ Author

- **Ingo Marlos Batista de Sousa**

## 📜 License

This project is licensed under the MIT License.
gister-cli 0.0.6
A awesome cli project.
Requires Python: >=3.6

# Gister Cli

**Gister CLI** es una herramienta creada para simplificar tareas recurrentes en un entorno de desarrollo en especifico.

Por ahora estas son las features con las que cuenta:

- [x] Manejo de diferentes entornos de git (**GITEnv**)

> Nota: Se agregaran nuevas funcionalidades proximamente.

### GITEnv

Esta herramienta te permitira cambiar de entorno de manera rapida y sin complicadas configuraciones, podrás reducir tiempo y errores al cambiar entre entornos.

Para configurar la herramienta es necesario crear el archivo de configuración dentro de la home mediante:

```shell
$ cd ~
$ touch .gisterconfig
```
el contenido del archivo debe contener los entornos a configurar de la siguiente manera:

```shell
gitenv:
  env:
    work:
      username: TestUser
      email: testUser@email.com
      ssh: true
    me:
      username: TestUser2
      email: testUser2@email.com
      ssh: false
  active: me
```
#### Propiedades
* `gitenv`: Configuración de la herramienta **gitenv**
* `env`: Lista de entornos, es necesario que cuenten con los datos de **username**, **email**, **ssh**
  * `username`: Nombre de usuario registrado
  * `email`: Email registrado
  * `ssh`: Colocar en **true** si la cuenta trabaja con repositorios privados, por ejemplo la cuenta de usuario de una organización.
* `active`: Determina el entorno que se encuentra actualmente activo. Colocar el entorno preferido en la primera configuración posteriormente al usar el comando, este cambiará el entorno activo de forma automática.
#### Comandos

| Comando                           |                                 Descripción                                  |
| ----------:                       |:----------------------------------------------------------------------------:|
| `gister gitenv --env {entorno}`   | Cambia el entorno de ejecución dependiendo de <br> los entornos configurados |
|`gister --loglevel 10 gitenv --env work`|                      Ejecuta el comando en modo debug.                       |
funkeras 0.8.12
funkeras

# notekeras

[my blog](http://blog.notechats.cn/)

[attention](https://github.com/CyberZHG/keras-self-attention),
[transformer](https://github.com/CyberZHG/keras-transformer), 
[bert](https://github.com/CyberZHG/keras-bert) 等都是从大神[CyberZHG](https://github.com/CyberZHG/keras-transformer)学习来的，
主要是因为想学习源码并进行一些标注，所以进行了一些翻译、标注和整合


yolo 参考 [keras-yolo3](https://github.com/qqwweee/keras-yolo3), [keras-yolo3](https://github.com/SpikeKing/keras-yolo3-detection.git)

# Attention

## ScaledDotProductAttention
[参考论文](https://arxiv.org/pdf/1706.03762.pdf)

$$\text{Attention}(Q, K, V) = \text{softmax}(\frac{Q K^T}{\sqrt{d_k}}) V$$

## SeqSelfAttention
[参考论文](https://arxiv.org/pdf/1806.01264.pdf)

### multiplicative
$$e_{t, t'} = x_t^T W_a x_{t'} + b_a$$

$$a_{t} = \text{softmax}(e_t)$$

$$l_t = \sum_{t'} a_{t, t'} x_{t'}$$

### additive
$$h_{t, t'} = \tanh(x_t^T W_t + x_{t'}^T W_x + b_h)$$

$$e_{t, t'} = W_a h_{t, t'} + b_a$$

$$a_{t} = \text{softmax}(e_t)$$

$$l_t = \sum_{t'} a_{t, t'} x_{t'}$$


## SeqWeightedAttention
[参考论文](https://arxiv.org/pdf/1708.00524.pdf)

$$Y = \text{softmax}(XW + b) X$$


## MultiHeadAttention
[参考论文](https://arxiv.org/pdf/1706.03762.pdf)
farfun 0.1.7
farfun

- 👋 Hi, I’m @bingtaoo
- 👀 I’m interested in ...
- 🌱 I’m currently learning ...
- 💞️ I’m looking to collaborate on ...
- 📫 How to reach me ...

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thppred 1.1
A tool for prediction of therapeutic peptides
Requires Python: >=3.6

# ThpPred
A method for predicting therapeutic and non-therapeutic proteins

# Introduction
ThpPred is developed for predicting, designing and scanning therapeutic peptides. More information on ThpPred is available from its web server http://webs.iiitd.edu.in/raghava/thppred. This page provides information about standalone version of ThpPred.

**Minimum USAGE** 

To display the command line arguments, type the following command:
```
thppred -h
```

**Full Usage**: 

To run the code with your parameters, type the following command:
```
thppred  -i |Input_File|  -m |Model|  -t |Threshold|

```
Example Command:
```
thppred - i file.fasta -m 1 -t 0.5

```

**Input File**: It allow users to provide input in FASTA format. User should provide the file name along with its full path in case the input file is not in the same folder as the thppred_stand.py file.

**Model**: In this program, four models have been incorporated;  
  i) Model1 for predicting given input peptide/protein sequence as therapeutic and non-therapeutic peptide/proteins using XGBoost based on amino-acid composition of the peptide/proteins; 

  ii) Model2 for predicting given input peptide/protein sequence as therapeutic and non-therapeutic peptide/proteins using Hybrid approach, which is the ensemble of XGBoost + Motif Score. It combines the scores generated from machine learning (XGB), and motif occurence as Hybrid Score, and the prediction is based on Hybrid Score.
	
  iii) Model3 for predicting given input peptide/protein sequence as therapeutic and non-therapeutic peptide/proteins using random forest based on dipeptide composition of the peptide/proteins; 

  iv) Model4 for predicting given input peptide/protein sequence as therapeutic and non-therapeutic peptide/proteins using Hybrid approach, which is the ensemble of Random forest + Motif Score. It combines the scores generated from machine learning (RF), and motif occurence as Hybrid Score, and the prediction is based on Hybrid Score.
  
User must enter model number(1,2,3 or 4) in the command line, else default value will be considered i.e. 2.


**Threshold**: User should provide a threshold value that lies between 0 and 1, please note score is proportional to therapeutic potential of peptide.
 
User must enter a valid threshold value in the command line, else default value will be considered i.e. 0.5.