semvec/pypi-packages huggingface dataset:
tunein-python 0.0.1
An unofficial, highly experimental wrapper around TuneIn.
Requires Python: >=3.8

# tunein-python
An unofficial, highly experimental wrapper around TuneIn.
# pip?
Later. 
# How to use
Simple! Import `tunein` and you have 3 functions for interfacing with TuneIn:
1. `get_popular_stations()` - gets the popular stations in your area
query: str - the GuideId for the stations. Still working out what does this mean.
Returns a list with a dict for each radio station
keys: 
- name: the name of the station
- id  : the id you can use later in the library. scraped from GuideId of station
- logo: an URL to station's image.
3. `get_station_metadata(id)` - Get stations data: logo, streaming urls, description, subtitle, frequency, language and if it's explicit
    id:str - a GuideId you obtained earlier

    returns a dict with all of the data mentioned above
    keys:
- streams: a list of dicts looking like `{"reliability": int, "stream_url": str, "media_type": str, "bitrate": int}` (`{"reliability": 99, "stream_url": "http://www.rmfon.pl/tunein/tunein.p[...]", "media_type": "mp3", "bitrate": 128}`)
- name: a str with the station name (RMF FM)
- subtitle: in API called a slogan, a str with the station's subtitle (Najlepsza Muzyka / The Best Music)
- frequency: if applicable, a float containing most likely FM frequency of the station. May be None (96.0)
- description: a str with a long description of the station (Rozgłośnia nadaje program muzyczno-informacyjny, emitowany 24 godz...)
- language: an str of the station's language (Polish)
- lang: an str of a short-code of the language (pl)
4. `search_for_stations()` - Gets stations under the query provided
query: str - keywords for the stations

for returns, look at get_popular_stations
neural-net-drawer 0.0.4
Plugin for visual display of a neural network.
Requires Python: >=3.7
Keywords: neural network display visual

<div class="cell code" collapsed="false">

``` python
# Install the neural-net-drawer package via pip
pip install neural-net-drawer

```

</div>

<div class="cell code" collapsed="false">

``` python
# Import necessary libraries
import matplotlib.pyplot as plt
from neural_net_drawer.drawer import NeuralNetworkDiagram

```

</div>

<div class="cell code" execution_count="5"
ExecuteTime="{&quot;end_time&quot;:&quot;2024-02-07T08:56:55.425789900Z&quot;,&quot;start_time&quot;:&quot;2024-02-07T08:56:55.153634700Z&quot;}"
collapsed="false">

``` python
# Example usage
fig = plt.figure(figsize=(9, 9))
ax = fig.gca()
ax.axis('off')

# Create an instance of the NeuralNetworkDiagram class
nn_diagram = NeuralNetworkDiagram()

# Call the method to draw the neural network diagram
nn_diagram.draw_neural_net(ax, [7, 5, 4, 3, 4, 2, 1])

plt.show()

```

<div class="output display_data">

![](https://raw.githubusercontent.com/EvgeniBondarev/Neural-Network-Drawer/master/img/1.png)

</div>

</div>

<div class="cell code" execution_count="13"
ExecuteTime="{&quot;end_time&quot;:&quot;2024-02-07T09:03:39.420467900Z&quot;,&quot;start_time&quot;:&quot;2024-02-07T09:03:39.268470Z&quot;}"
collapsed="false">

``` python
# Example usage with customizations
fig = plt.figure(figsize=(9, 9))
ax = fig.gca()
ax.axis('off')

# Create an instance of the NeuralNetworkDiagram class
nn_diagram = NeuralNetworkDiagram()

# Set maximum number of layers to display
nn_diagram.max_n_layers_size = 3

# Set maximum number of neurons per layer to display
nn_diagram.max_layer_size = 10

# Call the method to draw the neural network diagram
nn_diagram.draw_neural_net(ax, [7, 5, 4, 3, 4, 2, 1])

plt.show()
```

<div class="output display_data">

![](https://raw.githubusercontent.com/EvgeniBondarev/Neural-Network-Drawer/master/img/2.png)

</div>

</div>

<div class="cell code" execution_count="12"
ExecuteTime="{&quot;end_time&quot;:&quot;2024-02-07T09:01:57.191557900Z&quot;,&quot;start_time&quot;:&quot;2024-02-07T09:01:56.936069200Z&quot;}"
collapsed="false">

``` python
# Example usage with further customizations
fig = plt.figure(figsize=(9, 9))
ax = fig.gca()
ax.axis('off')

# Create an instance of the NeuralNetworkDiagram class
nn_diagram = NeuralNetworkDiagram()

# Set maximum number of layers to display
nn_diagram.max_n_layers_size = 3

# Hide neuron numbers
nn_diagram.show_neuron_numbers = False

# Call the method to draw the neural network diagram
nn_diagram.draw_neural_net(ax, [7, 5, 4, 3, 4, 2, 1])

plt.show()
```

<div class="output display_data">

![](https://raw.githubusercontent.com/EvgeniBondarev/Neural-Network-Drawer/master/img/3.png)

</div>

</div>
dls-toolkit 0.1.5
A set of my common tools.
Requires Python: >=3.10,<4.0

# DLS Toolkit

[![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)

## Description

A set of my common tools.


## Installation

`pip install dls-toolkit`

## Environment Variables

```

# Google Cloud

GOOGLE_APPLICATION_CREDENTIALS - Path to the Google Cloud Service Account JSON file.
    Needs access to:
        - Google Drive

# AWS

AWS_ACCESS_KEY_ID - AWS Access Key
AWS_SECRET_ACCESS_KEY - AWS Secret Access Key
AWS_REGION - AWS Region
    Needs access to:
        - Billing

```

## License

This project is licensed under the [MIT License](LICENSE).

## Contact

- [GitHub](https://github.com/your-username)