semvec/pypi-packages huggingface dataset:
vault-psycopg2 0.1.11
Helper classes to integrate psycopg2 with Vault

[![Build Status](https://travis-ci.com/niranjan94/vault-psycopg2.svg?branch=master)](https://travis-ci.com/niranjan94/vault-psycopg2) [![PyPI version](https://badge.fury.io/py/vault-psycopg2.svg)](https://badge.fury.io/py/vault-psycopg2)

### vault-psycopg2

> Helper classes to integrate psycopg2 with Vault


#### Usage:


```bash
pip install vault-psycopg2
```

```python
from vault_psycopg2 import VaultPsycopg

vp = VaultPsycopg(
    database_config={
        'host': 'localhost',
        'dbname': 'name_of_database'
    },
    vault_config={
        'server': 'http://127.0.0.1:8200',
        'connection_mode': 'ec2',
        'nonce': '5daa3d21-4e21-4bd5-8978-fcb81e658d8b',
        'role': 'some-instance'
    }
)

# An instance of psycopg2.Connection that is properly authenticated
vp.connection
```

##### Other examples of vault config:

```python
vault_config={
    'server': 'http://127.0.0.1:8200',
    'connection_mode': 'token',
    'token': '5daa3d21-4e21-4bd5-8978-fcb81e658d8b'
}
```

```python
vault_config={
    'server': 'http://127.0.0.1:8200',
    'connection_mode': 'userpass',
    'username': 'john.doe',
    'password': 'xyzzyabc'
}
```
loop-calc 0.1.2
This program evaluates loop programs as described in the lecture 'Grundlagen der theoretischen Informatik 2'.

<h1 align="center">Welcome to loop-calc ��</h1>
<p>
  <img alt="PyPI" src="https://img.shields.io/pypi/v/loop-calc.svg">
  <img alt="PyPI - Python Version" src="https://img.shields.io/pypi/pyversions/loop-calc.svg">
  <img alt="PyPI - Wheel" src="https://img.shields.io/pypi/wheel/loop-calc.svg">
  <a href="https://github.com/fin-ger/loop-calc/blob/master/LICENSE">
    <img alt="PyPI - License" src="https://img.shields.io/pypi/l/loop-calc.svg">
  </a>
  <a href="http://makeapullrequest.com">
    <img alt="PRs Welcome" src="https://img.shields.io/badge/PRs-welcome-brightgreen.svg" target="_blank" />
  </a>
</p>

> This program evaluates loop programs as described in the lecture `Grundlagen der theoretischen Informatik 2`.

This software is written in python using pyparsing for language parsing.

## Install

```sh
$ pip install loop-calc
```

## Usage

To display all registers and steps of a loop program, run

```sh
$ loop-calc --debug ./path/to/program.loop
```

You can view all available options with

```sh
$ loop-calc --help
```

Have a look at the `/examples` folder for a Fibonacci implementation in loop.

## Author

�� **Fin Christensen**

* Twitter: [@fin_ger_github](https://twitter.com/fin_ger_github)
* Mastodon: [@fin_ger@mastodon.social](https://mastodon.social/@fin_ger)
* Github: [@fin-ger](https://github.com/fin-ger)

## �� Contributing

Contributions, issues and feature requests are welcome!<br />Feel free to check [issues page](https://github.com/fin-ger/loop-calc/issues).

## Show your support

Give a ⭐️ if this project helped you!

## �� License

Copyright © 2019 [Fin Christensen](https://github.com/fin-ger).<br />
This project is [GPL-2.0](https://github.com/fin-ger/loop-calc/blob/master/LICENSE) licensed.

***
_This README was generated with ❤️ by [readme-md-generator](https://github.com/kefranabg/readme-md-generator)_
sh-molli 0.0.55
Package to process (sh)MOLLI MRI data

# Process MOLLI and shMOLLI data

Reads a directory of shMOLLI DICOM images and processes the data to produce a look-locker corrected T1 map. Negative values are cropped to 0.

Two possible methods for processing the data are available.

1. Fast (default). Uses some numerical methods to solve exponential equations of the correct form. May be slightly less accurate, but significantly faster.

2. Slow. Uses scipy curve fitting algorithm to fit the exponential curve. More accurate, but much slower.

The difference between the two methods is very little as demonstrated in the figure below.

![fast_slow comparison](fast_slow.tif "Comparison of fast and slow fitting methods")

Any pixels/voxels where the max value is less than 100 is skipped. NB: For this version, data is processed top-left to bottom-right in the image. As images are often empty around the edges, this means the the processing is 'fast' to start with as each pixel is basically skipped, then slows down as the true data is processed, then speeds up again at the end.

## Usage

As module
```python
> pip3 install sh_molli
> process_sh_molli_series.py -i <inputfolder> -o <outputfilename> -p <plot_flag> -m <method>
```

In code
```python
> python3
> import sh_molli.sh_molli as sh
> im = sh.process_folder(dir,method='fast')
```

-i - input folder must be a path containing DICOM images only Can process data based on 'Inversion Time' being stored in dcm.TriggerTime, dcm.InversionTime and dcm.ImageComments

-o - output file name. Uses PIL for image writing, so supports all formats that PIL understands. Recommended using example.tiff to ensure that large values are not cropped

-p - plot flat (1, 0 or not present). If 1, the image will be displayed using matplotlib once the data is processed. Colorbar is cropped to 0-2000 range, sensible values for human tissue.

-m - method. ('fast' or 'slow'). See above.