semvec/pypi-packages huggingface dataset:
Web-Templates 0.0.0a0.post3
Standardized templates for Python's web frameworks

Web Templates
=============

This project hopes to cater for the situation that one is working through a tutorial for their selected web framework and reads "Provide a base template for your website". 
Your thinking that's probably important perhaps there is a standardized template only to realise that "The cake is not real".

This package provides skeletal HTML templates for the different Python web frameworks one has used and will gladly accept contributions for the ones you are using.
Hopefully, after a period of time this project might present the standard for such templates.

Background 
----------

A number of Python web frameworks and template engines exist none of which appear to offer a standardized base template.
As a result each extention to these frameworks provides its own variation of a "base" HTML template.

This package hopes to provide a standardized **skeleton** for such packages and indeed the frameworks themselves.

Purpose
-------

This package provides `skeleton` templates for other packages to extend.
`skeleton` was chosen since it is less likely to clash with more customary terms, `base` and `layout`, used within web framework packages and their extentions.
These `skeleton(s)` provide a standard structure, outlined by the package documentation, and are named and organized according to how they are processed ::

    BACKEND_FRAMEWORK[/EXTENTION(S)][/FRONTEND_FRAMEWORK][/EXTENTION(S)]/skeleton.TEMPLATE_ENGINE.html

.. rubric:: Example

A template for the `RESTful` package, which exists for both `Django` as `Django-RESTfull` and `Flask` as `Flask-ReSTful`, might exist under the following paths ::

    flask/flask-RESTful/skeleton.jinja2.html
    flask/flask-RESTful/skeleton.mako.html
    django/django-RESTful/skeleton.DTL.html
    django/django-RESTful/skeleton.jinja2.html

Installation
------------

The package can be installed directly through `pip` ::

    pip install Web-Templates

Bleeding edge installations may be done directly from the repository ::

    pip install git+https://gitlab.com/manaikan/web-templates.git

Documentation
-------------

Documentation are available at `Gitlab.io <https://manaikan.gitlab.io/web-templates>`_.
syncvtools 0.1.13
CV Tools related to Object Detection
Requires Python: >=3.0

# Synapse Computer Vision Tools
More details are provided in Notion page. 

## Requirements
- Python 3.5+
- OpenCV
- Pillow
- LXML
- For TF Records - TensorFlow

## Installation
Using PIP

    pip3 import syncvtools

### Visualize predictions from prod

```
from syncvtools.utils.draw_detections import DrawDetections
from syncvtools.utils.parsers import ProdDetections, TFRecords, TFObjDetAPIDetections

prod_dets = ProdDetections.parse_prod_detections(img_dir='IMAGE_DIRECTORY', predictions_dir='DETECTIONS_FILE_DIR')
drawer = DrawDetections(bbox_line_height=1, threshold=0.5)
for pro_det in prod_dets:
    vis_img = drawer.draw_imageleveldetections(img_dets=prod_dets[pro_det])
    cv2.imshow("predictions", vis_img)
    cv2.waitKey(0)
```


### Visualize predictions from TF training
```
from syncvtools.utils.draw_detections import DrawDetections
from syncvtools.utils.parsers import ProdDetections, TFRecords, TFObjDetAPIDetections

tf_inf = TFObjDetAPIDetections.parse_detections(detection_file='detections_and_losses.json')
tf_gts = TFRecords.parse(tfrecord_src='val.tfrecord')
#adding image info to predictions/gt
tf_inf += tf_gts
if label_map is not None:
    tf_inf.process_labelmap('path_to_label_map.pbtxt')

drawer = DrawDetections(bbox_line_height=1, threshold=0.5)


for pro_det in tf_inf:
    if not tf_inf[pro_det].detections and not tf_inf[pro_det].ground_truth:
        continue #no gt/detections here
    vis_img = drawer.draw_imageleveldetections(img_dets=tf_inf[pro_det])
    cv2.imshow("predictions/gt from TF inference", vis_img)
    cv2.waitKey(0)
```

# utils

```
from syncvtools.utils import file_tools as ft

ft.get_file_list_by_ext(dir='input_path', ext=('jpg','png'))
```

Returns a list of string with full path to files with `ext` extension.
apitester 0.0
A Test Generating Framework for RESTful APIs based on Open API Specification
Keywords: apitester testapis testgenerator

#apitester
- an api test generating tool by referencing Open API Documentation
- input - OpenAPI Spec Document (Formats Supported - .yaml & .json)
- output - pytest cases covering all the combinations of inputs as per Spec Document


###process
1. Read OpenAPI Spec
2. Generate Meta Information about the Complete API Spec
3. List out Test Suite for each URI
4. List out Input Variables for each API Request
5. List out Input Variables Test Values for Arguments
6. Generate the TestCases considering all Permutation n Combinations of the Input Values
7. Generate pytest suites for Each API
amdgpu-pptable 0.9.2
AMDGPU PowerPlay table parser
Requires Python: >=3.6
Keywords: amdgpu radeon powerplay

|Tests badge| |flake8 lint badge| |pypi badge|

AMDGPU PowerPlay table parser
=============================

A Python library that converts AMDGPU PowerPlay tables to ctypes structs.

Uses code generated from MIT-licensed AMDGPU Linux driver headers.

For a Qt GUI editor see `amdgpu-pptable-editor-qt <https://github.com/amezin/amdgpu-pptable-editor-qt>`_

Generating ctypes structs
-------------------------

Generated code is tracked in git, it is located in ``src/amdgpu_pptable/generated``.

To re-generate it (with, maybe, different kernel sources)::

$ tox -e generate-ctypes -- -k path/to/kernel/sources


.. |Tests badge| image:: https://github.com/amezin/amdgpu-pptable/workflows/Tests/badge.svg
   :target: https://github.com/amezin/amdgpu-pptable/actions?query=workflow%3ATests
.. |flake8 lint badge| image:: https://github.com/amezin/amdgpu-pptable/workflows/flake8%20lint/badge.svg
   :target: https://github.com/amezin/amdgpu-pptable/actions?query=workflow%3A%22flake8+lint%22
.. |pypi badge| image:: https://img.shields.io/pypi/v/amdgpu-pptable
   :target: https://pypi.org/project/amdgpu-pptable/