semvec/pypi-packages huggingface dataset:
ykpstools 1.0.2.1
Tools & utilities associated with online logins of YKPS.
License: MIT
Requires Python: >=3

# YKPS Tools
YKPS Tools is a package containing tools &amp; utilities associated with online logins of YKPS. It is distributed under the [MIT License](/LICENSE). YKPS Tools requires a distribution of _Python 3.x_ installed. It is also published on [PyPI](https://pypi.org/project/ykpstools/).

## Features
YKPS Tools has the following tools:
- Authorize to school Wi-Fi
- Request to Powerschool Learning
- Request to Powerschool
- Request to Outlook

## Installation

### Dependencies
YKPS Tools depends on (See [Insights / Dependency graph](https://github.com/hanwenzhu/ykpstools/network/dependencies)):
- A distribution of **Python3.x** ([Python](https://www.python.org/downloads/), [Anaconda](https://www.anaconda.com/downloads/), etc.)
- [requests / **requests**](https://github.com/requests/requests)
- [waylan / **beautifulsoup**](https://github.com/waylan/beautifulsoup)
- [lxml / **lxml**](https://github.com/lxml/lxml)

### Installation on Python
YKPS Tools can be installed using `pip` in shell:
```sh
python3 -m pip install --upgrade ykpstools
```
Or, to get the newest version of YKPS Tools (recommended since it is still at an early stage of development):
```sh
python3 -m pip install --upgrade git+https://github.com/HanwenZhu/ykpstools.git
```
Or, with local installation:
```sh
git clone https://github.com/HanwenZhu/ykpstools.git
cd ykpstools
python3 -m pip install --upgrade -e .
```

## Demonstration

### Test
To test what the repository can do:
```sh
python3 -m ykpstools
```

### Example
In Python shell:
```python
>>> import ykpstools as yt
>>>
>>> # Login to Powerschool Learning
>>> page = yt.powerschool_learning(prompt=True)
>>> # Print html
>>> page.soup().find('div', id='navbarowner').get_text(strip=True)
*Your name should appear here*
```
umakit 0.1.1
Paket python buatan uma
License: MIT
Requires Python: >=3.6.0


![GitHub last commit](https://img.shields.io/github/last-commit/taruma/umakit.svg) ![PyPI](https://img.shields.io/pypi/v/umakit.svg)
![GitHub](https://img.shields.io/github/license/taruma/umakit.svg)

# umakit
Paket python pribadi

`umalog.logtool` untuk alat pencatat (logging)
ictrainer 0.2.7
cli tool for training your own image classifier with one line command!
License: MIT

<p align="center">
  <img width="auto" height="auto" src="https://github.com/koji/icTrainer/blob/master/top.png" style="border: none;
outline: none;">
</p>

[![PyPI version](https://badge.fury.io/py/pypi.svg)](https://pypi.org/project/ictrainer/)
### icTrainer is a python module which allows users to train image classifier easily

Basically, this module is for `python3`
## Install

```
$ pip install ictrainer
```
Also you can install manually.  
```
clone repo
$ git https://github.com/koji/icTrainer.git
$ cd icTrainer/ictrainer
$ python setup.py install
```

## How to Use
In this gude, we will create a dog/cat image classifier.
### 1.Collect Images
https://icrawler.readthedocs.io/en/latest/   

```
$ ictrainer --mode collect --keyword dog -n 250
$ ictrainer --mode collect --keyword cat -n 250
```
You'll have dogs & cats images under `dataset` folder.


### 2. Resize images
In this step, we will change all images size for training. The current input size must be `320 x 180`(required).
This step may be mess up images you collected, so you need to check all images manually. In the furture, there will be a function that save your time.  

```
$ ictrainer --mode resize --target dog
$ ictrainer --mode resize --target cat
```

For people want to use resize mode for other thing, you can use reize images with the following command.  
The folder structure should be the same the above.  
```
$ ictrainer --mode resize --target cat --image_width 480 --image_height 320
```

### 3.Create folders for classes
This step, we'll need to create folders and distribute images to `train` & `validation` folder.

#### 3-1. create folders
Create a couple of folders under dataset.   
This step will be automated in the future.  
```
 dataset
    ├── train
    │   ├── cat
    │   └── dog
    └── val
        ├── cat
        └── dog
```

#### 3-2. distribute images
Move images we got via `image collect mode`. In this case, probably we have 250 images for each other.
We will put 225 images for train and 25 images for validation so that `train/dog` has 225 images and `validation/dog` has 25 images. The cats should be the same.

### 4.Train Images
There are some options we need to put. The most important one is `--classes` which will be labels. In this case, we have dog & cat, so we need to put them as classes.
`--batch`: batch size default 16        
`--epoch`: epoch default 30       
`--mname`: output model name      
`--lr`: learning rate default 1e-3       
`momentum`: mementum default 0.9     

We will use default settings.

```
$ ictrainer --mode train --classes "cat" "dog" --mname "dogAndcat_"
```

## code
This code will be pushed soon. (cleaning up now)

## pre-train model
#### `smart device`   
https://github.com/koji/icTrainer/blob/master/model/smartdevice_epoch30.h5      
```
classes = ['echo', 'echoplus', 'echoshow', 'googlehome', 'googlehomemini', 'nest']   
```
JustWatch 0.5.1
A simple api for justwatch.com
License: MIT
Keywords: movies tv api

# JustWatchAPI

JustWatch.com Python 3 API

How To 
----------------------------------------------------------
search for an item
----------------------------------------------------------
from justwatchapi import JustWatch

just_watch = JustWatch()

results = just_watch.search_for_item(query='the matrix')
----------------------------------------------------------
or search for combination of genres
----------------------------------------------------------
just_watch = JustWatch(genres=['act', 'scf', 'hrr'])

results_by_genres = just_watch.search_for_item()
----------------------------------------------------------
or maybe search by provider
----------------------------------------------------------
just_watch = JustWatch()

results_by_providers = just_watch.search_for_item(providers=['nfx', 'stn'])
----------------------------------------------------------
or possibly a combination of the above 
----------------------------------------------------------
just_watch = JustWatch()

results_by_multiple = just_watch.search_for_item(
    providers=['nfx', 'stn'], 
    content_types=['movie'], 
    monetization_types=['free'])
----------------------------------------------------------
Read api_payload.txt for more information