semvec/pypi-packages huggingface dataset:
batchimage 0.3.2
Batch Image Converter
Requires Python: >=3.6,<4.0
Keywords: image,cli,batch image,converter,opencv,multithreaded,pictures,compress

# Batch Image Processing


A Python Package for Fast Parallel Processing of Images. It's Simple, easy and extremely fast since it uses, all the cores in your CPU. batchimage uses opencv-python as its main dependency and few python core modules.

![PyPI - Python Version](https://img.shields.io/pypi/pyversions/batchimage?style=for-the-badge)

### Python Package Index Badges

[![PyPI](https://img.shields.io/pypi/v/batchimage?style=for-the-badge&color=gree&logo=pypi)](https://pypi.org/project/batchimage/)
![PyPI - Downloads](https://img.shields.io/pypi/dm/batchimage?label=Downloads&style=for-the-badge)
![PyPI - Status](https://img.shields.io/pypi/status/batchimage?label=Status&style=for-the-badge)
![PyPI - Format](https://img.shields.io/pypi/format/batchimage?label=Format&style=for-the-badge)


### Github Badges

![GitHub last commit](https://img.shields.io/github/last-commit/insumanth/batchimage?style=for-the-badge)
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## Python Package Index Install 

```
pip install batchimage
```

### Note :
This is a pre-alpha version of the package, the api will change without any notice. Please use this package with caution.

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### Timeline for Development

| Present |                              September 2022                               |                              October 2022                               | November'22                                                                  |
|:---:|:-------------------------------------------------------------------------:|:-----------------------------------------------------------------------:|------------------------------------------------------------------------------|
| ![Pre-Alpha](https://img.shields.io/badge/0-PRE--ALPHA-red?style=for-the-badge) | ![Alpha](https://img.shields.io/badge/1-ALPHA-orange?style=for-the-badge) | ![Beta](https://img.shields.io/badge/2-BETA-yellow?style=for-the-badge) | ![Release](https://img.shields.io/badge/3-RELEASE-green?style=for-the-badge) |



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![Made With Python](https://forthebadge.com/images/badges/made-with-python.svg)
![forthebadge](https://forthebadge.com/images/badges/built-with-love.svg)

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AgamPrimer 0.5.5
A small package to store some functions for the the AgamPrimer notebook, to improve readability.
Requires Python: >=3.6

[<img src="https://github.com/sanjaynagi/AgamPrimer/blob/main/graphics/AgamPrimer_logo.png?raw=True" width="400"/>](https://github.com/sanjaynagi/AgamPrimer/blob/main/graphics/AgamPrimer_logo.png?raw=True)    
[![DOI](https://zenodo.org/badge/503315581.svg)](https://zenodo.org/badge/latestdoi/503315581)


Primer Design in *Anopheles gambiae s.l* taking into account SNP variation in primer binding sites, using primer3-py and malariagen_data. Supports:

- genomic DNA primers
- quantitative PCR primers (cDNA, designed to span exon-exon junctions)
- probe design

------------>------------>------------>------------>   [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sanjaynagi/AgamPrimer/blob/main/Primer-Design-in-Anopheles-gambiae.ipynb)    <------------<------------<------------<------------

#### Release notes

- 0.4.0 - plot_primer_ag3_frequencies() now uses plotly to make an interactive plot, where one can hover over primer bases, which returns the exact frequency for each base.
- 0.3.4 - Fix bug in get_gDNA_sequence() and in plot_primer_locs() 
- 0.3.3 - Introduced feature to enable a sample query (e.g subset to a specific species within a cohort)
- 0.3.2 - minor fix to remove printing of variable in `plot_primer_ag3_frequencies()`
- 0.3.1 - minor fix to bug introduced in 0.3.0 to `plot_primer()` function 
- 0.3.0 - Support for probe design added, functions restructured to accommodate this
lie-conv 10.7.8
lie-conv of lie-conv of lie-conv

# Installation

To enable importing of modules, from the parent directory execute:

`pip install -e .`


To install requirements: `python -m pip install requirements.txt`

To save requirements: `python -m pip list --format=freeze --exclude-editable -f https://download.pytorch.org/whl/torch_stable.html > requirements.txt`

* Note we use Python 3.9.4 for our experiments

The code in augerino_lib is an extension (with our modification) from the original Augerino code https://github.com/g-benton/learning-invariances. The code in data_augmentation/my_training.py is a modification of the pytorch example in https://github.com/pytorch/examples/tree/master/imagenet.

# Running the code 

For the experiments of Section 2:

To train the model update the appropriate `result_dir` (folder where to save the results) and `data` (your path to ImageNet data) in `config_test_ddp_local.yaml` and run `python hydra_app_local.py`.
To test the model update the appropriate `result_dir` (folder where the trained models are) and `job_id` (id of the folder for a specific trained model you want to test) in `config_test_local.yaml` and run `python hydra_test_local.py`.


For remaining experiments: 

Navigate to the corresponding directory, then execute: `python run.py -m` with the corresponding `config.yaml` file (which stores experiment configs).

# License

Consult License.md