semvec/pypi-packages huggingface dataset:
Xlassify 1.0.0
An alignment-free deep-learning model trained to classify human gut bacteria
Requires Python: >=3.7.1
Keywords: Xlassify,genome,microbiome,taxonomic,bacteria

# Xlassify

Fast and accurate taxonomic classification of bacteria genomes is a key step in human gut microbiome analysis. Here we propose Xlassify, an alignment-free deep-learning model that is specifically trained to classify human gut bacteria.

Xlassify demonstrated 98% accuracy in UHGG genomes dataset and \~90% accuracy on an independent testset of 76 gut bacterial genomes isolated from healthy Chinese individuals. Better than alignment-based methods such as GTDBTk, Xlassify requires only <4GB of memory and reaches thirty-second-per-genome speed on a single CPU.


### Architecture

16S model:
![16s_model](https://raw.githubusercontent.com/SenseTime-Knowledge-Mining/Xlassify/main/docs/images/16s_model.png)

genome model:
![genome_model](https://github.com/SenseTime-Knowledge-Mining/Xlassify/raw/main/docs/images/genome_model.png)


### Installation

We provide three ways to install Xlassify locally via pip, conda or Docker.

From pip:

```bash
pip install Xlassify
```

From conda:
```bash
conda install -c ai4drug Xlassify
```

From Docker:
```bash
docker pull SenseTime-Knowledge-Mining/Xlassify
```


### Usage
```
usage: xlassify [-h] [-m MODEL_NAME] [-i INPUT_PATH]
                [-f INPUT_FILE_LST [INPUT_FILE_LST ...]] [-s SAVE_PATH]
                [-r SAVE_FILE] [--save_kmer SAVE_KMER] [-b BATCH] [-k K]
                [--nproc NPROC]

optional arguments:
  -h, --help            show this help message and exit
  -m MODEL_NAME, --model_name MODEL_NAME
                        Choose a model from {compute_kmer, species_genome,
                        genus_full, species_full}. Default: species_genome
  -i INPUT_PATH, --input_path INPUT_PATH
                        The path of input fasta file. Using testing data as
                        default.
  -f INPUT_FILE_LST [INPUT_FILE_LST ...], --input_file_lst INPUT_FILE_LST [INPUT_FILE_LST ...]
                        The list of input file.
  -s SAVE_PATH, --save_path SAVE_PATH
                        The path of save file. Default: ./Xlassify_results
  -r SAVE_FILE, --save_file SAVE_FILE
                        The path of results file. Default: res.csv
  --save_kmer SAVE_KMER
                        Save kmer or not {0,1}. Default: 1
  -b BATCH, --batch BATCH
                        The batch of prediction.
  -k K                  The k of kmer. Default: 7
  --nproc NPROC         The number of CPUs to use. Default: 1
```
django-bulk-saving 1.0.0
Save multiple model instances using one SQL query.

# Django Bulk Saving

![Test package](https://github.com/innovationinit/django-bulk-saving/actions/workflows/test-package.yml/badge.svg?branch=main)
[![Coverage Status](https://coveralls.io/repos/github/innovationinit/django-bulk-saving/badge.svg)](https://coveralls.io/github/innovationinit/django-bulk-saving)


## About

This package provides utility for saving multiple Django model instances using one SQL query.

## Install

```bash
pip install django-bulk-saving
```

## Usage

Inherit from _BulkSavableModel_:

```python
from bulk_saving.models import BulkSavableModel
from django.db import models


class Product(BulkSavableModel):
    name = models.CharField(max_length=30)
```

Use as follows:

```python
with Product.bulk_saving():
    for idx, product in enumerate(Product.objects.all(), 1):
        product.name = 'Product nr %s' % idx
        product.save_later()
```

Products will be saved after exit from context.

## License
The Django Wicked Historian package is licensed under the [FreeBSD
License](https://opensource.org/licenses/BSD-2-Clause).
simple-coverage 0.3.2
Simple Coverage measurements for Python
Requires Python: >=3.6

## simple-coverage

Very basic coverage tool for educational purposes. 

### Installation
```bash 
pip install simple-coverage
```

### Usage 

Just import the package and add the @print_coverage decorator above functions you want to inspect.
```python
from simple_coverage.coverage import coverage

@print_coverage
def demo(x, y) -> int:
    """
    Demo function
    """
	product = x * y 
	if product < 10:
		return product * 2
	else: 
	return product

if __name__ == "__main__":
	demo(3,5)
```

When simply runnning the Python-file, this will create the following output:
```bash
Function: demo(3, 5)

CALLED line 9:      product = x * y
CALLED line 10:     if product < 10:
MISSED line 11:         return product * 2
IGNORE line 12:     else:
CALLED line 13:         return product

Instruction coverage: 75.0 %
Branch coverage: 50.0 %
```
### Doctests

If you want to use it with doctests, use the meta wrapper `@doctest_wrapper` and the `log_coverage` decorator. This will create a `simple-coverage.json` file in the current working directory since writing into the console would collide with the doctests.
```python
from simple_coverage.coverage import log_coverage, doctest_wrapper

@doctest_wrapper(log_coverage)
def demo(x,y) -> int:
	...
```
Look at the `demo.py` file for reference. To start the doctest use pytest as usual.
```bash
pytest --doctest-modules demo.py
```
Afterwards you can create the report:
```bash
python3 -m simple_coverage.report
```
Note that all runs will be saved for the next report. To start over and delete to report run:
```bash
python3 -m simple_coverage.clean
```
freqtools 0.1.0
Tools for phase and frequency data
Requires Python: >=3.5

# Tools for phase and frequency data

## Installation

```
git clone https://github.com/bleykauf/freqtools
cd freqtools
pip install -e .
```

The option `-e` installs `freqtools` in editable mode, i.e. changes made to the files
will automatically be applied.

## Features

The main feature are simple conversion between power and amplitude spectral densities as shown in this diagram.

![svg](./docs/representations.svg)

The other features are shown in the Example section below.

## Examples

An example notebook can be found [here](examples/Examples.ipynb).
eval-hj3415 0.6.4
Evaluating and scoring financial data
Requires Python: >=3.6

eval-hj3415
==========

eval-hj3415 evaluate naver financial data.


Quick start
------------

from eval_hj3415 import eval

for all eval dictionary..
   eval.Eval(<<code:str>>).get_all()
or..
   eval.Score(<<code:str>>).eval_dict

for get score..
   s = eval.Score(<<code:str>>)
   s.red()
   s.mil()
   s.blue()