semvec/pypi-packages huggingface dataset:
ksif 20210409.post1
Quantitative investment tools for KSIF
Requires Python: >=3.5
Keywords: ksif,portfolio,backtest,finance

# ksif
Data download and back testing tools for KSIF
# KSIF Preprocessing
Data preprocessing tool for KSIF library

## Usage
Set the root directory as your working directory like 'C:\Users\willbe\PycharmProjects\ksif_preprocessing'.
Pass a parameter indicating a file name in data folder like '180730'.
After running 'core/__init__.py', the result files are saved in the root directory.

## 여러가지 feature 추가 작업 재시작, daily, semi-month frequency 추가
swagger-markdown 0.9.16
A Python Markdown extension to include Swagger informations in your documentation
Requires Python: >=3.6

# swagger-markdown

A Python Markdown extension to include Swagger Definitions and Paths in your markdown documentation.
This extension works with Swagger 2.0 JSON files.

For now this extension supports only Definitions and Paths and can do those things:

 * For Definitions it can create description tables.
 * For Paths it can create request parameters table, request examples, response tables, and responses examples.


To install:

```bash
pip install swagger-markdown
```

## How to use with python-markdown

```python
import markdown
from swaggermarkdown import SwaggerExtension

md = markdown.Markdown(extensions=[SwaggerExtension()])
text = ':swg-def: FirstDefinition'
converted = md.convert(text)
```

Or more simply

```python
import markdown

markdown.markdown('some markdown', extensions=['swaggermarkdown']))
```

## Configuration

```python
SwaggerExtension(
  definitionsUrlRoot='/types',    # add an url in front of definition links (only if not present in current page)
  file='tests/test_swagger.json'  # redefine the default file (default: swagger.json)
)
```

## How to use with MkDocs

```yaml
markdown_extensions:
  - swaggermarkdown
```

You have the option to define some configuration for the extension as well here:

```yaml
markdown_extensions:
  - swaggermarkdown:
      file: swagger.json
      definitionsUrlRoot: '/types'
```
## How to use in your markdown files

```markdown
## My Pet Api

### Endpoint /pet/findByTags

:swg-path: /pet/findByTags

### Pet definition

:swg-def: Pet
```

You should get a table similar to this

 <img src="https://raw.githubusercontent.com/batiste/swagger-markdown/main/swaggermarkdown.png" width="700">

## Individual configuration for Paths and Definition

You can decide with more precision what you want to show by defining a YAML configuration
for each Path and Definition. The YAML definition must be indented with 4 spaces:

```markdown
:swg-path: /pet/{petId}/uploadImage
    verbs:
      - post
      - get
    sections:
      parametersTable: true
      requestExamples: true
      requestCodeExamples: true
      responseTable: true
      responseExamples: true


:swg-def: Pet
    properties:
      photoUrls:
        hide: true
      id:
        description: "New description"
```

For now only the options presented above are supported
basacommons 0.0.16
Basic project utilities
Requires Python: >=3.7

This project joins some useful scripts designed to speed up project development.

Main scripts are:

## configuration.py

This script generates a configparser.configuration instance.

Configuration load from different sources:
### AWS Systems manager parameter store
* Given that AWS CLI is configured and credentials loaded
```python
configuration = load_config('ssm',environment)
```
SSM variable key must have format: 
/$environment/$section 
and value in json format

Key | Value
--- | ---
/pre/ddbb | {"host":"localhost","port":3306,"username":"root","password":"changeit"}
#### Local file
```python
configuration = load_config('file',environment, file_name)
```
File expected in standard INI file structure. 

```
[ddbb]
host=localhost
port=3306
username=root
password=changeit
```
#### Environment variables.
```python
configuration = load_config('env',environment, projectname)
```
Variables should have the following format:
$projectname_$environemnt_$sectionname

```
MYPROJECT_PRO_DDBB_HOST=localhost
MYPROJECT_PRO_DDBB_PORT=3306
MYPROJECT_PRO_DDBB_USERNAME=root
MYPROJECT_PRO_DDBB_PASSWORD=changeit
```
### Database.py
Database connection and query execution
```python
database = Database.ofConfiguration(configuration)
```
To execute query, use method *Database.run_query*
```python
records = database.run_query('SELECT * FROM table WHERE mycolumn = %s AND myothercolumn = '%s',('value1', 'value2' ))
```
Pagination is supported using class *database.Page*:
```python
page_number = 1
page_size = 50
page = Database.Page(page_number, page_size)
records = database.run_query('SELECT * FROM table WHERE mycolumn = %s AND myothercolumn = '%s', ('value1', 'value2' ), page)
```
### alerts.py
Alert sending, via AWS simple email service.
gh-tools-gradient 0.0.7
Gradient Health training tools
Requires Python: >=3.6

# Gradient Health Tools
[![Python Package](https://github.com/gradienthealth/gh-tools/actions/workflows/main.yml/badge.svg)](https://github.com/gradienthealth/gh-tools/actions/workflows/main.yml)

Tools to be used primarily in colab training environment and using wasabi storage for logging/data.
Also includes tools for common dicom preprocessing steps.

# Callbacks

### gh_tools.callbacks.keras_storage
Used for syncing models/logs into s3 file system

### gh_tools.callbacks.log_code
Used to snapshot notebooks into s3 file system

# Utils

### gh_tools.utils.image
Used for various 2D image transforms see (projection matrix)[https://en.wikipedia.org/wiki/Transformation_matrix]

### gh_tools.utils.colab
Used for checking and saving notebooks in colab programmatically

### gh_tools.utils.version_service
Bundles callbacks related to versioning and logging.

### gh_tools.layers
Custom Keras Layers Useful for wrapping models.

# Contributing and Issues
Contributions are welcome in the form of pull requests.
For issues, please provide a minimal example of code which results in an error.
 
## Software License
Copyright 2020 Ouwen Huang, Gradient Health Inc.
pyparasol 1.0b3
Web-based visualization tool for hyper-parameters based on Parasol (parallel coordinates)
Requires Python: >=3.6

# Parasol
Parasol is a JavaScript library for visualization of multi-objective optimization problems 
such as hyper-parameter tuning and is based on parallel coordinates.

Parallel coordinates make it easy to visualize different parameter configurations

For an interactive example, check out the original authors site for the javascript library:

https://parasoljs.github.io/demo/lrgv.html
 
# PyParasol

PyParasol is a Command line interface and API written in python that makes it easy to use Parasol 
without the need to write javascript code. It creates all HTML and javascript automatically and 
includes a HTTP server to let you view your data with ease.


# Quickstart:

PyParasol requires a python version of at least 3.6. Download python at https://www.python.org/downloads/ 

Install pyparasol
`pip install --user pyparasol`

Use command line interface to view a csv file
```shell script
$ pyparasol data\lrgv.csv  # replace with path to your csv file(s)

 * Serving Flask app "lrgv" (lazy loading)
 * Environment: production
   WARNING: This is a development server. Do not use it in a production deployment.
   Use a production WSGI server instead.
 * Debug mode: off
 * Running on http://localhost:5000/ (Press CTRL+C to quit)
```
Now navigate to http://localhost:5000/

# API usage example:
For an example how to use the API, have a look at the [example.py](https://github.com/dominikandreas/pyparasol/blob/master/example.py) script.

## Acknowledgements
The original implementation PyParasol which this fork based on and Parasol itself were created by the
 Kasprzyk Research Group at the University of Colorado Boulder.
 
For more information about parasol, refer to https://github.com/ParasolJS/parasol-es