semvec/pypi-packages huggingface dataset:
HLTV 0.2.0
A python library that functions as a HLTV API to use in your code
Requires Python: >=3.6

This python library functions as an HLTV API to extract, parse, and format data from [HLTV.org](https://www.hltv.org/). This library allows for real time data extraction to use in your code. This library is still in its beta stages and is being rapidly updated/altered with new features still being added/adjusted.

# Installation 

    pip install HLTV

# Dependencies

```
pip install bs4

pip install lxml
```

# Usage

**Importing**

```python
from HLTV import *
```

**FUNCTIONS**

    get_live_matches()
        returns a list of all LIVE matches on HLTV along with the maps being played and the star ratings
    
    get_upcoming_matches()
        returns a list of all upcoming matches on HLTV
    	
    get_important_upcoming_matches(star_rating=1)
        returns a list of all upcoming matches on HLTV with the star rating argument (should be between 0 and 5)
    
    get_match_results(offset=0)
        returns a list of results from past 100 matches on HLTV starting from the offset param
    
    get_important_match_results(star_rating=1, offset=0)
        returns a list of 100 past matches on HLTV with or above the star rating param starting from the offset
    
    get_top_teams()
        returns a list of the top 30 teams
    	
    get_best_players(time_filter=30)
        returns a list of the top players within a certain time frame (ex: 30 days ago until today)

# Uninstallation

    pip uninstall HLTV
semantic-nutrition 0.0.2
Tools for semantic nutrition
Requires Python: >=3.6

# Semantic Nutrition
This repo demonstrates how our basic semantic nutrition API works. We submitted this process as a clinical abtract to MLHC 2020 ([poster](Poster_Semantic_Nutrition.pdf), [abstract](Abstract_Semantic_Nutrition.pdf)). 

This is a base version of what was presented at the MLHC, and operates on a limited dataset. The dataset we used for training and publication is not ours to share. If you're interested in using your own nutrition dataset with this API, you can designate a different (local or hosted) URL when you initialize the class. The USDA provides an excellent [starting point](https://fdc.nal.usda.gov) to build a dataset.

## Installation

```
pip install nutritiontools
```

## Usage

```
from nutritiontools import Nutrition

n_obj = Nutrition() 

match = n_obj.semantic_search("I ate an apple") 

print(match)
```

## Connecting to mobile assistants, placing the API in an online server, and managing a database

For testing purposes, we used [Siri Shortcuts](https://support.apple.com/en-us/HT209055) (iOS) and [Google Assistant / IFTTT webhooks](https://ifttt.com/google_assistant) (Android) to send voice transcriptions to [Google Firebase](https://firebase.google.com/docs/storage/web/start). Once received, the incoming data would trigger a [Firebase Cloud Function](https://firebase.google.com/docs/functions). The Firebase Function relayed the voice transcription to our [Semantic Nutrition API](semantic.py)(hosted online with [Flask](https://flask.palletsprojects.com/en/1.1.x/quickstart/) and [Google App Engine](https://cloud.google.com/appengine/docs/standard/python3/building-app)), which responded with estimates for nutrition data. The nutrition data was then logged in Google Firebase.

While useful for testing and demonstrations, this method has been depecrated in favor of a native mobile application and private server / database. Please stay tuned for future developments, and feel free to use our API locally in the meantime!

*updated on 7/27/2020*
helloworldsyasya 0.0.1
Say hello!

# Hello World 

This is an example peoject demonstrating how to publish a python module to PyPi.

```python
pip install helloworld
```
## Usage

```python 
from helloworldsyasya import say_hello
# Generate "Hello, World!"
say_hello()

#Generate "Hello, Everybody!"
say_hello("Everybody")
```
semanticjoshuadrc 0.0.1
Tools for semantic nutrition
Requires Python: >=3.6

# Semantic Nutrition
This repo demonstrates how our basic semantic nutrition API works. We submitted this process as a clinical abtract to MLHC 2020 ([poster](Poster_Semantic_Nutrition.pdf), [abstract](Abstract_Semantic_Nutrition.pdf)). 

This is a base version of what was presented at the MLHC, and operates on a limited dataset. The dataset we used for training and publication is not ours to share. If you're interested in using your own nutrition dataset with this API, you can designate a different (local or hosted) URL when you initialize the class. The USDA provides an excellent [starting point](https://fdc.nal.usda.gov) to build a dataset.

## Installation

```
pip install nutritiontools
```

## Usage

```
from nutritiontools import Nutrition

n_obj = Nutrition() 

match = n_obj.semantic_search("I ate an apple") 

print(match)
```

## Connecting to mobile assistants, placing the API in an online server, and managing a database

For testing purposes, we used [Siri Shortcuts](https://support.apple.com/en-us/HT209055) (iOS) and [Google Assistant / IFTTT webhooks](https://ifttt.com/google_assistant) (Android) to send voice transcriptions to [Google Firebase](https://firebase.google.com/docs/storage/web/start). Once received, the incoming data would trigger a [Firebase Cloud Function](https://firebase.google.com/docs/functions). The Firebase Function relayed the voice transcription to our [Semantic Nutrition API](semantic.py)(hosted online with [Flask](https://flask.palletsprojects.com/en/1.1.x/quickstart/) and [Google App Engine](https://cloud.google.com/appengine/docs/standard/python3/building-app)), which responded with estimates for nutrition data. The nutrition data was then logged in Google Firebase.

While useful for testing and demonstrations, this method has been depecrated in favor of a native mobile application and private server / database. Please stay tuned for future developments, and feel free to use our API locally in the meantime!

*updated on 7/27/2020*
har-dl 0.1.1
Utility for downloading content from .HAR files
Requires Python: >=3.6

# HAR-DL (.HAR Downloader)

Utility for downloading content from .HAR files.

## Installation

Python >=3.6 is required.

```
$ pip3 install har-dl
```

## Usage (CLI)

```
$ har-dl <har_path> <output_path> [-h] [-p] [-q]
```

### Options

`-p`, `--proxy`: Enable proxy
`-q`, `--quiet`: Disable console output

## Usage (API)

```py
from har_dl import Downloader

dl = Downloader("path/to/file.har")
dl.download(proxy=True)
```

## License

[MIT](./LICENSE)
PDFDiagramming 1.0.8
Draw UML diagrams in PDF format

The documentation is [here](https://hasii2011.github.io/pdfdiagrams/pdfdiagrams/index.html).



Sample Snippets



Create a basic class

```python
diagram: Diagram = Diagram(fileName='basicClass.pdf', dpi=75)
classDef: ClassDefinition = ClassDefinition(name='BasicClass', size=Size(width=100, height=100))

diagram.drawClass(classDef)
diagram.write()
```



Create a class with a method

```python
diagram: Diagram = Diagram(fileName=f'Test-BasicMethod.pdf', dpi=75)

position: Position = Position(107, 30)
size:     Size     = Size(width=266, height=100)

car: ClassDefinition = ClassDefinition(name='Car', position=position, size=size)

initMethodDef: MethodDefinition = MethodDefinition(name='__init__', visibility=DefinitionType.Public)

initParam: ParameterDefinition = ParameterDefinition(name='make', parameterType='str', defaultValue='')
initMethodDef.parameters = [initParam]
car.methods = [initMethodDef]

diagram.drawClass(car)

diagram.write()

```



Create inheritance diagram



```python
diagram: Diagram = Diagram(fileName='MinimalInheritance.pdf', dpi=75)

cat:  ClassDefinition = ClassDefinition(name='Gato', position=Position(536, 19), size=Size(height=74, width=113))
opie: ClassDefinition = ClassDefinition(name='Opie', position=Position(495, 208), size=Size(width=216, height=87))

diagram.drawClass(classDefinition=cat)
diagram.drawClass(classDefinition=opie)

opieToCat: UmlLineDefinition = UmlLineDefinition(lineType=LineType.Inheritance, source=Position(600, 208), destination=Position(600, 93))

diagram.drawUmlLine(lineDefinition=opieToCat)
diagram.write()
```