semvec/pypi-packages huggingface dataset:
csvdbclass 0.0.2
Python Database Testing Tool
Requires Python: >=3.6

## CSV DB CLASS

A python package for easy python database usage through CSVs.

Usage:


```
>>> from csvdbclass import csvDB
>>> data = csvDB("data")
>>> data.show()
data
Empty DataFrame
Columns: []
Index: []
>>> data.db = pandas.DataFrame({
...     "Name" : ['joe', 'joe2', 'joe3']
... })
>>> data.show()
data
   Name
0   joe
1  joe2
2  joe3
```
pdfpop 0.5.0
Populate a PDF with data from Microsoft Excel
Requires Python: >=3.9
Keywords: pdf,excel,populate

<p align="center">
  <img src="assets/images/pdfpop-banner.png"
      alt="Automate PDF population with pdfpop"
      title="pdfpop" />
</p>

[![License](https://img.shields.io/github/license/iandinwoodie/pdfpop)](LICENSE.txt)
[![Release](https://img.shields.io/github/v/tag/iandinwoodie/pdfpop)](https://github.com/iandinwoodie/pdfpop/releases)
[![CI/CD Tests](https://github.com/iandinwoodie/pdfpop/actions/workflows/tests.yml/badge.svg)](https://github.com/iandinwoodie/pdfpop/actions/workflows/tests.yml)
[![Codecov](https://codecov.io/gh/iandinwoodie/pdfpop/branch/main/graph/badge.svg?token=ZNY5FIHA9U)](https://codecov.io/gh/iandinwoodie/pdfpop)
[![Size](https://img.shields.io/github/repo-size/iandinwoodie/pdfpop)](https://github.com/iandinwoodie/pdfpop)

---

Automate PDF population with pdfpop.

## Installation

You can install `pdfpop` with:

```bash
pip install pdfpop
```

## Usage

The `pdfpop` usage consists of two steps: (1) form configuration and (2)
execution.

### Step 1. Form Configuration

> ℹ️  This step only needs to be run once for each unique form.

This step generates a form-specific configuration file that allows you to inform
`pdfpop` how data should be routed from the data file to the PDF form. You can
generate this file with the `config` command:

```bash
# Usage: pdfpop config <form>
pdfpop config examples/example-form.pdf
```

This will output a `pdfpop-` prefixed JSON file in your current working
directory (e.g., `pdfpop-example-form.json`). By default, all fields will be
assigned a value of `null` and, therefore, will be ignored until the `null`
value is replaced with instructions on how to populate the field. An example of
an edited configuration file is available [here](examples/example-form.json).

### Step 2. Exectuion

Once you have a form configuration file you can populate your PDF form using the
`run` command:

```bash
# Usage: pdfpop run <config> <data>
pdfpop run examples/example-form.json examples/example-data.xlsx
```

This will generate a populated PDF form at the location prescribed by the values
of`<output_dir>/<output_name>` in the configuration file (e.g.,
`examples/pdfpop-example-form.pdf`).

# License

Copyright (C) 2022 Ian Dinwoodie

* Licensed under [GNU General Public License v3.0](LICENSE.txt).
* Exceptions:
    * Material covered by [Third Party Licenses](LICENSE-THIRD-PARTY.txt).
    * Logo icon: <a href="https://www.flaticon.com/free-icons/popsicle-stick" title="popsicle stick icons">Popsicle stick icons created by Freepik - Flaticon</a>
slack-oauth-store 0.0.4
Implementing Encrypted FileInstallationStore for Slack OAuth flow
Requires Python: >=3.6

# slack-OAuth-store
A wapper class of FileInstallationStore to support encryption at rest

## Example with Bolt
```
from slack_installation_store import EncryptedFileInstallationStore
from asyncio.log import logger
import os
import logging
from slack_bolt import App
from slack_bolt.oauth.oauth_settings import OAuthSettings
from slack_sdk.oauth.state_store import FileOAuthStateStore

logging.basicConfig(level=logging.DEBUG)

oauth_settings=OAuthSettings(
    client_id=os.environ.get("SLACK_CLIENT_ID"),
    client_secret=os.environ.get("SLACK_CLIENT_SECRET"),
    scopes=os.environ.get("SLACK_SCOPES","").split(','),
    user_scopes=[],
    install_path="/slack/install",\
    installation_store=EncryptedFileInstallationStore(base_dir="./data/installations", encryption_key=os.environ.get("SLACK_INSTALL_STORE_KEY")),
    state_store=FileOAuthStateStore(expiration_seconds=600, base_dir="./data/states")
)

# Initializes app with your bot oauth settings
app = App(
    signing_secret=os.environ["SLACK_SIGNING_SECRET"],
    oauth_settings=oauth_settings
)

@app.event("app_mention")
def event_test(body, say, logger):
    logger.info(body)
    say("Hello")

# Start your app
if __name__ == "__main__":
    app.start(port=int(os.environ.get("PORT", 3000)))

```

See https://github.com/slackapi/python-slack-sdk
Minio-hung 0.0.7.1
Makes you use Minio easier.
Requires Python: >=3.6


# Minio-hung: Print your buckets and .csv files from Minio
## Powered by Ly Duc Hung (FPT Infomation System)

### Bản update nho nhỏ bớt cục súc hơn & bớt mất dạy hơn (Còn nếu bạn chưa biết thì oke bỏ qua đi đừng quan tâm làm gì).

# How to install?
```
pip install Minio-hung
```
## or

Windows
```
py -m pip install Minio-hung
```
Linux/Mac OS
```
python3 -m pip install Minio-hung
```

# What's in package?
```
minio_hung
```

# How to use Minio-hung?

## You can use to one of codes below here:

## Print list buckets in Minio

ACCESS_KEY: Access key in your Services Account.

SECRET_KEY: Secret key in your Services Account.

BUCKET_NAME: Your Bucket name.

OBJECT_NAME: The Object name in your Bucket.

```
#1
import minio_hung

minio_hung.read_list(ACCESS_KEY, PRIVATE_KEY)



#2
from minio_hung import read_list

read_list(ACCESS_KEY, PRIVATE_KEY)

```


## Print .csv file from Minio

### Print .txt file

```
#1
import minio_hung

minio_hung.readtext(ACCESS_KEY, SECRET_KEY, BUCKET_NAME, OBJECT_NAME)



#2
from minio_hung import readtext

readtext(ACCESS_KEY, SECRET_KEY, BUCKET_NAME, OBJECT_NAME)

```

### Print .csv file

```
#1
import minio_hung

minio_hung.readcsv(ACCESS_KEY, SECRET_KEY, BUCKET_NAME, OBJECT_NAME)



#2
from minio_hung import readcsv

readcsv(ACCESS_KEY, SECRET_KEY, BUCKET_NAME, OBJECT_NAME)

```

### Print .xlsx file

```
#1
import minio_hung

minio_hung.readexcel(ACCESS_KEY, SECRET_KEY, BUCKET_NAME, OBJECT_NAME)



#2
from minio_hung import readexcel

readexcel(ACCESS_KEY, SECRET_KEY, BUCKET_NAME, OBJECT_NAME)

```

## Write file from Minio

ACCESS_KEY: Access key in your Services Account.

SECRET_KEY: Secret key in your Services Account.

BUCKET_NAME_IN: Your Bucket name (INPUT).

OBJECT_NAME_IN: The Object name in your Bucket (INPUT).

BUCKET_NAME_OUT: Your Bucket name (OUTPUT).

OBJECT_NAME_OUT: The Object name in your Bucket (OUTPUT).


### Write .txt file

```

#1
import minio_hung

minio_hung.writetext(ACCESS_KEY,PRIVATE_KEY,BUCKET_NAME_IN,OBJECT_NAME_IN,BUCKET_NAME_OUT,OBJECT_NAME_OUT)



#2
from minio_hung import writetext

writetext(ACCESS_KEY,PRIVATE_KEY,BUCKET_NAME_IN,OBJECT_NAME_IN,BUCKET_NAME_OUT,OBJECT_NAME_OUT)

```

### Write .csv file

```
#1
import minio_hung

minio_hung.writecsv(ACCESS_KEY,PRIVATE_KEY,BUCKET_NAME_IN,OBJECT_NAME_IN,BUCKET_NAME_OUT,OBJECT_NAME_OUT)



#2
from minio_hung import writecsv

writecsv(ACCESS_KEY,PRIVATE_KEY,BUCKET_NAME_IN,OBJECT_NAME_IN,BUCKET_NAME_OUT,OBJECT_NAME_OUT)

```

### Write .xlsx file

```
#1
import minio_hung

minio_hung.writeexcel(ACCESS_KEY,PRIVATE_KEY,BUCKET_NAME_IN,OBJECT_NAME_IN,BUCKET_NAME_OUT,OBJECT_NAME_OUT)



#2
from minio_hung import writeexcel

writeexcel(ACCESS_KEY,PRIVATE_KEY,BUCKET_NAME_IN,OBJECT_NAME_IN,BUCKET_NAME_OUT,OBJECT_NAME_OUT)

```
aleatora 0.2.1
Compose music with streams.
Requires Python: >=3.6

# Aleatora

Aleatora is a music composition framework, implemented as a Python library, built around the abstraction of lazy, effectful streams.

What does that mean? Like most audio synthesis frameworks, Aleatora lets you build up complex sounds by connecting generators in an audio graph (function composition + parallel composition). Unlike most, it also lets you build things up _horizontally_: streams can be composed sequentially, so the audio graph _change over time_ on its own (based on the computation described in the graph itself).

Additionally, streams may contain any kind of data type, not just samples. So you can use the same basic abstraction, and all the operations that it offers, to work with strings, events, arrays, MIDI data, etc., just as well as with individual audio samples.

## Installation

First, set up the environment:

    virtualenv venv -p python3  # or pypy3 for better performance
    source venv/bin/activate

Then, get the stable version of Aleatora:

    pip install aleatora  # for optional features, append a list like `[speech,foxdot]` (or `[all]`)

Or, get the latest version instead:

    pip install git+https://github.com/ijc8/aleatora.git

To ensure installation succeeded and that you can get sound out, try playing a sine tone:
```python
from aleatora import *
play(osc(440))
```

## [Documentation](https://aleatora.readthedocs.io/)
cometpy 0.1
Comet Domain Specific Compiler as Python package
Requires Python: >=3.6


# cometpy

Comet Domain Specific Compiler for NumPy frontends.

Latest Version : v0.1

Requirements:
1. python 3 and above
2. COMET Python package v0.1
3. OS support - Linux and macOS

# "COMET" / Domain specific COMpiler for Extreme Targets

The COMET compiler consists of a Domain Specific Language (DSL) for sparse and dense tensor algebra computations, a progressive lowering process to map high-level operations to low-level architectural resources, a series of optimizations performed in the lowering process, and various IR dialects to represent key concepts, operations, and types at each level of the multi-level IR. At each level of the IR stack, COMET performs different optimizations and code transformations. Domain-specific, hardware- agnostic optimizations that rely on high-level semantic information are applied at high-level IRs. These include reformulation of high-level operations in a form that is amenable for execution on heterogeneous devices (e.g., rewriting Tensor contraction operations as Transpose-Transpose-GEMM-Transpose) and automatic parallelization of high-level primitives (e.g., tiling for thread- and task-level parallelism).

More information about the COMET compiler can be found at: 
1. COMET's source code - https://github.com/pnnl/COMET
2. COMET’s documentation - https://pnnl-comet.readthedocs.io/en/latest/


# Installation and Testing:
1. Getting COMET pyhton package:
   - Install the COMET python package using pip (or) pip3 python package managers as:
    - For macOS users:
        python3 -m pip install cometpy
    - For Linux users:
        python3 -m pip install cometpy-lnx

2. Using COMET compiler as a backend to lower and execute test methods:
    -   Import the Comet Python package in your python script using - "import cometpy as comet"
    -   To compile a test method using COMET, add the decorator "@comet.compile(flags=...)"
        before the method.
    -   The "numpy" keyword in a target method needs to be replace with "comet"
        e.g. "comet.einsum" , "comet.multiply"
        
    -   Supported "numpy" methods:
        - <numpy>.einsum()
        - <numpy>.multiply()
    -   For the actual "einsum" computation, follow the convention - "comet.einsum()"