semvec/pypi-packages huggingface dataset:
Episode-DL 2020.5.4
(Podcast-)Episode)-D(own)L(oader). Download Podcast Episodes with Ease
License: MIT

# Episode Loader

__Download Podcast Episodes of Your Choice__

## What Does It Do
This is a simple package that does one thing really well:

## Dependencies
This package sits on the backs of giants. I would not really know how to do
this on my own.

- Requests
- Feedparser

**Given a Podcast RSS Feed it will:**

1. Give you a list of podcast episodes (thanks feedparser!)
2. Download the audio file associated with the requested episode (thanks
   requests)

Requirements:
* Python 3.8
mkdocs-word-filter 0.1.1
A MkDocs plugin to filter extra content that was to markdown documents for e.g. formatting docx files.
License: MIT
Requires Python: >=3.5
Keywords: mkdocs markdown word filter

# mkdocs-word-filter

Strips extra content from markdown files that was added e.g. for converting the files to docx using pandoc

Stripped content can include for example:

- pagebreaks:

  <https://github.com/pandocker/pandoc-docx-pagebreak-py>

  ```markdown
  \newpage
  \toc
  ```

## Install

```bash
python -m pip install mkdocs-word-filter
```

## Usage

Active the plugin in `mkdocs.yml`. You can specify the ignorable strings as a list under the `filter-lines-with`

```yaml
plugins:
    - search 
    - mkdocs-word-filter:
        filter-lines-with:
            - \toc
            - \newpage
```

> **Note:** If you have no `plugins` entry in your config file yet, you'll likely also want to add the `search` plugin. MkDocs enables it by default if there is no `plugins` entry set, but now you have to enable it explicitly.

More information about plugins in the [MkDocs documentation][mkdocs-plugins].

## See Also

More information about templates [here][mkdocs-template].

More information about blocks [here][mkdocs-block].

[mkdocs-block]: https://www.mkdocs.org/user-guide/styling-your-docs/#overriding-template-blocks
[mkdocs-plugins]: http://www.mkdocs.org/user-guide/plugins/
[mkdocs-template]: https://www.mkdocs.org/user-guide/custom-themes/#template-variables
Kami 0.4.3
Forecast sales with Entity Embedding LSTM
License: MIT
Requires Python: >= 3.6
Keywords: SALES,FORECAST,LSTM,EMBEDDING

# AM18_SPR20_LondonLAB

## Package Description

The package contains one object **Kami** with four methods in the order of execution:  
1. **Preprocess()**
2. **Analyse(n_sample)**
3. **Vis()**
4. **Forecast(store_list, product_list, start, end)**

To initiate the object **Kami**, the user is required to supply at least these three arguments:  
1. Path to the grouped product sales input data (***input_f_path***)
2. Path to an intermediary folder to store intermediary data (***cache_dir_path***)
3. Path to an output folder to store final predictions (***output_dir_path***)

While **Preprocess** and **Vis** methods are executed without any argument, **Analyse** method can be supplied with an optional argument *n_sample* which is the number of random samples drawn from the predefined training data.

**Forecast** method is required to be supplied with four arguments including:  
1. A list of stores whose sales are predicted (***store_list***)
2. A list of products whose sales are predicted (***product_list***)
3. The start date of the forecast (***start***)
4. The end date of the forecast (***end***)

## Typical Use Case

***
	from Kami import Kami

	obj = Kami(input_f_path = 'PATH_TO_SALES_DATA/SALES_DATA.csv',
			output_dir_path = 'OUTPUT_FOLDER/',
			cache_dir_path = 'CACHE_FOLDER/')
	obj.Preprocess()
	obj.Analyse()
	obj.Vis()
	obj.Forecast(store_list = ['STORE_A', 'STORE_B'],
			product_list = ['PRODUCT_A', 'PRODUCT_B'],
			start = 'MM/DD/YYYY',
			end = 'MM/DD/YYYY')
***