semvec/pypi-packages huggingface dataset:
zesty.zbs-api-k8s 1.0.2023.9.5.1693917041
Zesty Disk API

## Description
Zesty Disk API

## Installation
pip install zesty.zbs-api

## Usage
from zesty.zbs-api import *

## Alembic and Postgres Data Models.
For instructions on how to manage the Postgres data model as well as using Alembic to automatically prepare database migrations, please refer to the `README.md` in the `alembic` folder which is located at the root of this repository.

## SQLAlchemy Dependency
Some of the models within this package require SQLAlchemy to be imported. To keep this package light as it's used across many places in our code base, we do not install SQLAlchemy with this package. Thus, if you need to use any of the models that depend on SQLAlchemy, please install the Postgres-Utils package with your requirements.txt.
citext 0.1.0
PostgreSQL CITEXT integration for Django.
Requires Python: >=3.10
Keywords: PostgreSQL,Django,CITEXT,Case Insensitive,postgres

# Django CITEXT

PostgreSQL CITEXT integration for Django.

[![PyPi Version](https://img.shields.io/pypi/v/citext.svg)](https://pypi.python.org/pypi/citext/)
[![Test Coverage](https://codecov.io/gh/voiio/citext/branch/main/graph/badge.svg)](https://codecov.io/gh/voiio/citext)
[![GitHub License](https://img.shields.io/github/license/voiio/citext)](https://raw.githubusercontent.com/voiio/citext/master/LICENSE)

## Setup

```ShellSession
python3 -m pip install django-citext
```

```python
# settings.py
INSTALLED_APPS = [
    'citext',
    # ...
]
```

## Usage

```python
# myapp/models.py
from django.db import models
from citext import CITextField, CIEmailField


class MyModel(models.Model):
    name = CITextField()
    email = CIEmailField(unique=True)
```

```python
# myapp/views.py
from django.http import HttpResponse, HttpResponseNotFound

from . import models


def my_view(request, email):
    try:
        my_model = models.MyModel.objects.get(email=email)
    except models.MyModel.DoesNotExist:
        return HttpResponseNotFound()
    return HttpResponse(my_model.name)
```

## Credits

Project is based on the Django's own CITEXT implementation,
which was removed in Django 5.0. Big thanks to the Django contributors
for their excellent work.
scratch-websocket-api 0.1.7
A API for scratch with websocket
Requires Python: >=3.7


# Scratch Websocket API #  
## Overview
このライブラリは、Websocketを通じてScratch（またはTurbowarp）に接続し、そのクラウド変数を変更するために作成されました。  
制作者: [yosshi---](https://scratch.mit.edu/users/yosshi---)
## Installation 
Python3が必須となります。Python2は使えないと思います。
```bash  
pip install scratch-websocket-api websocket-client requests
```  
websocket-clientとrequestsはこのライブラリで必要なのでインストールしてください。
## Usage  
### Connect  
Websocketを使用してサーバーに接続します  
```python  
import scratch_api.scratch_ws_api as scratch3  
conn = scratch3.login("USERNAME", "PASSWORD").connect(ProjectID, max_connections, isScratch)  
```  
USERNAME: (string) あなたのユーザー名  
  
PASSWORD: (string) あなたのパスワード  
  
ProjectID: (int) あなたのプロジェクトのID ( https://scratch.mit.edu/projects/{projectid} ) この部分がプロジェクトIDです  
  
max_connections: (int) 最大同時送信数、この数の分、同時に変数を**変更**できます  
  
isScratch: (boolean or string) TrueならScratch、FalseならTurbowarp、URLを入力すると特定のサーバーに接続できます。  
### Get Cloud Variable 
```python
conn.get(var_NAME)  
```
var_NAME: **list** 取得するクラウド変数の名前（例: `["Sample1", "Sample2"]`）
戻り値: **list** 取得できなかった場合、Noneが返ります。 （例: `[200, None]`
#### 補足　クラウド変数の取得のみの場合には、最大接続数は0でもOKです
### Set Cloud Valiable
```python
conn.set(var_NAME, var_VALUE)
```
var_NAME: **list** 変更するクラウド変数の名前（例: `["Sample1", "Sample2"]`）
var_VALUE: **list** 変更する数値 (intまたはstr)（例: `["20", 1304]`）
## Sample
```python
import random  
import scratch_api.scratch_ws_api as scratch3 

conn = scratch3.login("yosshi---", "PASSWORD").connect(846708650, 2, True)
conn.send(["1", "2"], [random.randint(1, 10), random.randint(1, 10)])  
print(conn.get(["1","2"]))
```
ランダムに変数を変更するだけの単純なプログラムです

[こちらがそのプロジェクトです](https://scratch.mit.edu/projects/846708650)
dbt-argo 0.2.0a4
A dbt pipeline converter to Argo Workflows
Requires Python: >=3.9,<3.12.0

# dbt-argo

![PyPI - Version](https://img.shields.io/pypi/v/dbt-argo?logo=pypi&color=blue&link=https%3A%2F%2Fpypi.org%2Fproject%2Fdbt-argo%2F)


A simple Python package to parse dbt dependencies to create an Argo Workflow job. It can also submit Argo files directly to the server.

## Motivations

Simplify CI steps to create Argo workflow for dbt projects.

## Limitations

- This version only support Google Cloud Storage as backend to store **dbt artifacts** to be used by Argo as artifacts.
- This version only support `CronWorkflow` and `Workflow` generation

## Usage

By default, it assumes the `dbt_project.yml` file is located at the root folder, if not the `DBT_PROJECT_DIR` can be defined to locate the project path.

```
Usage: dbt-argo [OPTIONS] COMMAND [ARGS]...

  Test

Options:
  --help  Show this message and exit.

Commands:
  cp      copy local folder to GCS Bucket
  create  Create a dbt Argo Workflow
  cron    Manage cron workflow
  submit  dbt-argo deploy {FILE}
```

## Inspirations

- [Hera](https://github.com/argoproj-labs/hera) for Argo Python CLI + pydantic validation objects
- [couler](https://github.com/couler-proj/couler) for easy cluster submission (like `kubectl apply -f ...`)
- [dbt > 1.5](https://docs.getdbt.com/reference/programmatic-invocations) for easy manifest parsing directly in python scripts
- [Argo CLI](https://argoproj.github.io/argo-workflows/walk-through/argo-cli/) for cli options
- [kubectl](https://argoproj.github.io/argo-workflows/kubectl/) for simple CLI application
- [cloudpathlib](https://github.com/drivendataorg/cloudpathlib) to deal with cloud file like `pathlib`
torchcrepeV2 0.2.0
Yet another crepe (pitch tracking tool) in PyTorch.
Requires Python: >=3.7

## TorchCrepeV2

My own version of [crepe](https://github.com/marl/crepe) in PyTorch, SOTA pitch tracking tool. Releasing as pip package for ease of usage.

Only `full` version of model is supported.

Similar project: https://github.com/maxrmorrison/torchcrepe

## Usage

`pip install torchcrepeV2`

```python
from torchcrepeV2 import TorchCrepePredictor
import librosa
import numpy as np
import matplotlib.pyplot as plt

y, sr = librosa.load("some_audio.wav", sr=16000)
torch_crepe = TorchCrepePredictor(device="cuda")

# all arguments are aligned with original crepe implementation
f = torch_crepe.predict(audio=y, 
                        sr=sr,
                        viterbi=True, 
                        center=True, 
                        step_size=10)

# can add some post-processing
block_size = 100
length = y.shape[0] // 100
if f.shape[-1] != length:
    f = np.interp(
        np.linspace(0, 1, length, endpoint=False),
        np.linspace(0, 1, f.shape[-1], endpoint=False),
        f,
    )

plt.plot(f, label='torchcrepe')
plt.show()
```

## Results

Tracked pitch nearly identical as compared to `crepe`.

![](sample_1.png)

![](sample_2.png)

## TODO
- [ ] Add unit tests
towbintools-spsalmon 0.0.1
All the tools used by the Towbin Lab !
Requires Python: >=3.7

# This is still in early development, use it carefuly

## Setting up a Virtual Environment

Using a virtual environment isolates your package dependencies and settings from your system Python.
Here's how you can set one up:

1. First, create a folder where the venv will be stored:

   ```bash
   mkdir ~/env_directory

2. Create the venv:

    ```bash
    python3 -m venv ~/env_directory/towbintools

3. To activate the venv:

    ```bash
    source ~/env_directory/towbintools/bin/activate

4. Whenever you are done working, you can deactivate it with:

    ```bash
    deactivate

## How to add a python venv to Jupyter

1. First, make sure Jupyter and related packages are installed:

    ```bash
    pip3 install jupyter ipykernel

2. Add your venv to Jupyter:

    ```bash
    python3 -m ipykernel install --user --name=towbintools

3. If you're using VSCode, reload VSCode and you should be able to find the kernel. 

## Build the package and install it

1. First, make sure build is installed:

    ```bash
    pip3 install build

2. Go to the package directory, eg:

    ```bash
    cd ~/towbintools

3. Build the package:

    ```bash
    python3 -m build

4. Install the package you just built:

    ```bash
    pip3 install dist/*.whl

You're now all set!