semvec/pypi-packages huggingface dataset:
helpful-pys 0.2.3
A helper package to use common functions
License: MIT
Requires Python: >=3.10

# Helpful Helper
A (so far) little package that contains commonly used and useful functions. Good for team morale as they say.

# About
Contents include functions for connecting to redshift and using gspread, a py library for writing and reading from google sheets. For help with what functions are available, please run `get_help()` after importing helpful-pys.

[Official pypi](https://pypi.org/project/helpful-pys/)

# Installation
To install functional-functions, you can install it using pip:
````python
pip install helpful-pys
````

Within your jupyter lab/notebook setup:
````python
from helpful_pys import helper

helper.get_help()
````

## Before Using
Once the package is installed, your package will be found within your `site-packages` folder 
1. Rename your local creds file within the `helpful_pys`(rename creds.py.sample to creds.py)
3. Please authorize access to google sheets API and insert generated service account key into your local creds file, see Additional prerequisites for more details
4. Please fill in your redshift creds into the local creds file before using

# Additional Prequisites
The package will install `gspread` but will require further setup to get it running. I recommend using the [gspread documentation for Authentication](https://docs.gspread.org/en/v6.1.3/oauth2.html) which will allow you to authorize the google sheets API and generate a service account key.

Don't forget the client-email in your key, it will be constantly needed to give share access to sheets for usage with gspread.

I have included a creds.py.sample file in the package to allow the user to input their personal creds to use. If there are better access/security storage policies like AWS Secrets Manager, this repo can be updated to accommodate such.

# Usage
This package is intended for usage with local py setups and local jupyter notebook/lab setups. It is not intended for production script usage.

## Querying Redshift
````python
q = "select * from that_table limit 100"

result_df = helper.query_redshift(q)
````

## Using gspread
````python
gc = helper.gsheets_start()

workbook = gc.open_by_url('googlesheetsurlhere')

workbook.update_title("Really Cool and Helpful Workbook")

ws = test.worksheet('Sheet1')
ws.update_title("The coolest worksheet")


data_formatted = data[['id','helpful_id','cool_id']].copy()

ws.update([data_formatted.columns.values.tolist()] + data_formatted.values.tolist())
````

More [gspread example use cases](https://docs.gspread.org/en/v6.1.3/user-guide.html)
keycloak-jwt-checker 1.0.1
A little tool for debugging claims contained in JSON Web Tokens (JWT) issued by Keycloak confidential clients
License: MIT
Requires Python: <4.0,>=3.12

[![Poetry](https://img.shields.io/endpoint?url=https://python-poetry.org/badge/v0.json)](https://python-poetry.org/)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
[![codecov](https://codecov.io/gh/max-pfeiffer/keycloak-jwt-checker/graph/badge.svg?token=Cqp6pn58fB)](https://codecov.io/gh/max-pfeiffer/keycloak-jwt-checker)
![pipeline workflow](https://github.com/max-pfeiffer/keycloak-jwt-checker/actions/workflows/pipeline.yml/badge.svg)

# Keycloak JWT Checker
A little tool for debugging claims contained in JSON Web Tokens (JWT) issued by Keycloak confidential clients.

Keycloak configuration can be quite complex. So I found it useful to have a tool to look at the claims contained
in JWTs. You can see quickly if they contain the values you need for your use case.

The CLI tool takes the credentials of the client you configured in Keycloak and uses some user's credentials to issue
tokens for that user using OIDC endpoints. It then decodes the JWT and displays its claim content on stdout.

## Install
```shell
pip install keycloak-jwt-checker
```

## Usage
```shell
$ keycloak-jwt-checker --help
Usage: keycloak-jwt-checker [OPTIONS]

  Keycloak JWT Checker.

  A little tool for debugging claims contained in JSON Web Tokens (JWT) issued
  by Keycloak confidential clients.

Options:
  --server-url TEXT        URL of the Keycloak server  [required]
  --client-id TEXT         Client ID  [required]
  --client-secret TEXT     Client secret  [required]
  --realm TEXT             Realm  [required]
  --username TEXT          Username of a Keycloak user you configured for this
                           client  [required]
  --password TEXT          Password of a Keycloak user you configured for this
                           client  [required]
  --skip-tls-verification  Set this flag if the TLS verification should be
                           skipped on OIDC endpoints
  --help                   Show this message and exit.
```

## Environment Variables
If you are concerned about security or just don't want to use the CLI options for secrets or passwords, you can also use
the following environment variables to provide these values to Keycloak JWT Checker.
```shell
KEYCLOAK_JWT_CHECKER_SERVER_URL=http:localhost:8080
KEYCLOAK_JWT_CHECKER_CLIENT_ID=test
KEYCLOAK_JWT_CHECKER_CLIENT_SECRET=verysecretsecret
KEYCLOAK_JWT_CHECKER_REALM=myrealm
KEYCLOAK_JWT_CHECKER_USERNAME=testuser
KEYCLOAK_JWT_CHECKER_PASSWORD=testpassword
KEYCLOAK_JWT_CHECKER_SKIP_TLS_VERIFICATION=true
```
glacieroff 0.1.1
A Python package to process Sentinel-1 data for glacier velocity estimation.
License: MIT
Keywords: sentinel-1 glacier velocity offset tracking remote sensing

# Glacier Velocity Processing

This Python package processes Sentinel-1 SAR data to compute glacier velocity from offset tracking methods. It includes several preprocessing steps, including orbit file application, thermal noise removal, speckle filtering, and terrain correction, among others. This package is designed for users working with remote sensing data for glaciology, providing tools to handle and process raw Sentinel-1 data for velocity mapping.

## Features

- **Preprocessing**: The package includes functions for preprocessing Sentinel-1 SAR data, including:
  - **Orbit File Application**: Applies orbit file corrections to the raw data.
  - **Thermal Noise Removal**: Removes thermal noise from the raw data.
  - **Calibration**: Converts radar intensity values into physically meaningful measurements (e.g., backscatter coefficients).
  - **Speckle Filtering**: Applies refined Lee filtering for speckle reduction.
  - **Subset**: Crops the data to the region of interest (ROI).
  - **Terrain Correction**: Corrects for topographic effects and georeferences the data.
  - **Offset Tracking**: Computes glacier velocity based on offset tracking methods.

- **Flexible**: Customize parameters for the offset tracking procedure, such as azimuth spacing, range spacing, and correlation thresholds.

## Installation

You can install this package using pip (from TestPyPI or PyPI once it's uploaded).

To install from TestPyPI (for testing):

```bash
pip install --index-url https://test.pypi.org/simple/ --no-deps offsetglacier