semvec/pypi-packages huggingface dataset:
tds-django 4.2.2
Django backend for SQL Server using tds
Requires Python: >=3.8

# SQL Server backend for Django >=3.2
- django 4
- django 3.2 (pypi package version 0.1)
- tested and used with SQL Server 2017 and for version 4.2 with python 3.11 only

## Warning
- There is an official package supported by microsoft, microsoft/mssql-django.
- This package does not need pyodbc nor the microsoft odbc driver, only pytds.
- This passes about 15000 tests from the django test suite, but I personally use the django ORM in a basic way and
don't use most of the features.

## Requirements
- [python-tds](https://github.com/denisenkom/pytds)

- optional:
  - bitarray, recommended by python-tds for performance
  - for regex support you need to compile `clr/django_clr.cs` and install the resulting assembly or read and then run
the `tds_django/sql/clr.sql` script.
  - for date "math" as well as bit-shift operations you need to read and run the `tds_django/sql/init.sql` script.
    
## Unsupported
- JSON
- foreign keys to a nullable field (limitation of SQL Server)
- feel free to read `tds_django/features.py` for more details.
- queryset iterator with chunk size

## Warning If you have used another backend before
- this one uses `uniqueidentifier` field for UUIDField while others may have used nvarchar.

# Installation
For django 4.2
`pip install bitarray python-tds tds_django~=4.2`

For django 4.1
`pip install bitarray python-tds tds_django==4.1.0`

For django 4.0
`pip install bitarray python-tds tds_django==4.0.0`

For django 3.2
`pip install bitarray python-tds tds_django==0.1`

# settings.DATABASES

```python
DATABASES = {
    'default': {
        'ENGINE': 'tds_django',
        'HOST': 'localhost',
        'PORT': '1433',
        'NAME': '<db_name>',
        'USER': '<db_user>',
        'PASSWORD': '<db_password>',
    }, 
}
```
otree-vcee-payment 0.9.2
VCEE Payment App for oTree

==========================
VCEE Payment App for oTree
==========================

Quick start
-----------


#. Install the package

   .. code-block:: console

     $ pip install otree-vcee-payment

#. Add the app to the end of your session config, e.g.:

   .. code-block:: python

      SESSION_CONFIGS = [
        dict(
            name="experiment",
            display_name="Some Experiment",
            num_demo_participants=1,
            app_sequence=["experiment", "result", "vceepayment"],
        ),
      ]

#. Download the payment file from the otree admin interface.

Options
-------

You can add the options to the session config, e.g.:

   .. code-block:: python

      SESSION_CONFIGS = [
        dict(
            name="experiment",
            display_name="Some Experiment",
            num_demo_participants=1,
            encrypt_payment_file=True,
            disable_waiting_for_others=True,
            app_sequence=["experiment", "result", "vceepayment"],
        ),
      ]

disable_waiting_for_others
^^^^^^^^^^^^^^^^^^^^^^^^^^

default: True

The default behavior is that participants are directly able to fill out their payment information as soon as they have completed everything else. Careful, this means only the amount earned up until that point in time is displayed and saved.
Setting disable_waiting_for_others to false forces every participant to wait for the last participant to complete everything before starting the payment app.
For more control of when a subject should be shown the payment app, set disable_waiting_for_others=True and add a WaitPage into the last app before vceepayment with the desired options.


encrypt_payment_file
^^^^^^^^^^^^^^^^^^^^

default: False

Encrypts the payment file. Only use this if you know what you are doing.

Remarks
-------
- Currently, the app requires a persistent file system to be run on. I.e., it does not work with an ephemeral filesystem (like Heroku).

- The payment file persists between sessions. This means you only need to download the payment file after the last session.
dactim-mri 0.0.20
A tool to preprocess MRI data
Requires Python: >=3.7

# Dactim_MRI

[![Windows](https://svgshare.com/i/ZhY.svg)](https://svgshare.com/i/ZhY.svg)
[![Python 3.7](https://img.shields.io/badge/python-3.7-blue.svg)](https://www.python.org/downloads/release/python-370/)
[![PyPI version](https://img.shields.io/pypi/v/dactim_mri?label=PyPI%20version&logo=python&logoColor=white)](https://pypi.org/project/dactim-mri/)
[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/PierreFayolle/Dactim_MRI/blob/master/tests/transformation.ipynb)
[![Doc](https://img.shields.io/readthedocs/dactim_mri?label=Docs&logo=Read%20the%20Docs)](https://dactim-mri.readthedocs.io/en/latest/)


## Description

> **Warning**
: Currently in development

A MRI preprocessing tool which provides a dozen of functions easy to use made from some of the most popular Python libraries 🚀

It includes :
  - Transformations : skull stripping, resampling, registration, histogram matching, bias field correction, normalization and so on
  - Conversion : Dicom to Nifti, Nifti to Dicom
  - Sorting : an elegant way to organize your Dicom files 
  - Anonymization : Dicom files can be anonymized according to your own rules
  - Visualization : 2D and 3D light viewer
  - Spectroscopy : generation of mask of the MRS voxel/slab

## Installation

Requires at least Python 3.7 or above. Also requires [Pytorch](https://github.com/pytorch/pytorch), [Torchio](https://github.com/fepegar/torchio), [Numpy](https://github.com/numpy/numpy), [Nibabel](https://github.com/nipy/nibabel), [SimpleITK](https://github.com/SimpleITK/SimpleITK), [Matplotlib](https://github.com/matplotlib/matplotlib), [Pydicom](https://github.com/pydicom/pydicom), [Dicom2nifti](https://github.com/icometrix/dicom2nifti).

Can be quickly installed with pip :

```
pip install dactim-mri
```