semvec/pypi-packages huggingface dataset:
private-lecture-automation 0.1.1
Automation tools for private lecture management
License: MIT
Requires Python: >=3.8

## Private Lecture Automation

This repository contains tools that make managing private lectures easier.

These tools include:
- Sending out an introduction email to new potential clients (`send_introduction_email`)
- Creating a calendar event for the next lecture for a client (`send_calendar_event`)

## Usage
The package heavily relies on the `config.ini` file.
This file contains all the necessary information such as where specific files are located.

The file is structured as follows:
```ini
[calendar_event]
student_data_path = data/students.json  ; Path to the student data file

[introduction_email]
email_path = data/introduction.html  ; Path to the email template
logo_path = data/logo.png  ; Path to the logo in the email (optional)

[email]
email_address = your@email.com  ; Your email address
email_password = your_password  ; The password of your email address
host = smtp.gmail.com  ; The host of your email provider
port = 465  ; The port for the SSL connection (https://developers.google.com/gmail/imap/imap-smtp)
```

### Send introduction email
Upon request, the script sends out an email containing the most up-to-date information about the lectures.
For this an `html` template file must be created. This file will be used as the body of the email.
The template file can contain placeholders that will be replaced when the email is sent.


It is also possible to include a logo in the email, but this is not required.

Running the script:
```python
from private_lecture_automation import send_introduction_email

send_introduction_email(
    recipient_email="aranyb@gmail.com",  # The email address of the recipient
    values_to_replace={"NAME": "Arany Barna"},  # The values to replace in the email template
)
```


### Create calendar event for the next lesson
When called, sends out the calendar event of the next lecture for the client.
For this a `json` file must be created that contains the information about the students.

The file is structured as follows:
```json
{
    "Arany Barna": {
        "name": "Arany Barna",  // The name of the student
        "email": "aranyb@gmail.com",  // The email address of the student
        "content_link": "https://www.pythonvilag.hu",  // The link to where lesson specific content can be found (usually a Google Drive folder)
        "day": "2",  // The day of the week when the lecture takes place (0: Monday, 1: Tuesday, ..., 6: Sunday)
        "time": "1630",  // The time of the lecture in the format HHMM
        "duration": "90",  // The duration of the lecture in minutes
        "occasion_number": "3"  // The number of the next lecture
    },
    ...
}
```

Running the script:

```python
from private_lecture_automation import send_calendar_event

send_calendar_event(
    student_name="Arany Barna",  # The name of the student, must match the key in the json file
    **kwargs: {time: "1700"}  # For temporal changes the content of the json file can be overwritten
)
```
quickdraw 1.0.0
An API for downloading and reading the google quickdraw data.
License: MIT

# quickdraw

[Google Quick, Draw!](https://quickdraw.withgoogle.com/) is a game which is 
training a neural network to recognise doodles.

`quickdraw` is an API for using the Google Quick, Draw! data 
[quickdraw.withgoogle.com/data](https://quickdraw.withgoogle.com/data), 
downloading the data files as and when needed, caching them locally and 
allowing them to be used.

## Getting started

+ Windows

```bash
pip install quickdraw
```

+ macOS

```bash
pip3 install quickdraw
```

+ Linux / Raspberry Pi

```bash
sudo pip3 install quickdraw
```

## Use

Open the Quick Draw data, pull back an **anvil** drawing and save it.

```python
    from quickdraw import QuickDrawData
    qd = QuickDrawData()
    anvil = qd.get_drawing("anvil")
    anvil.image.save("my_anvil.gif")
```

## Documentation

[quickdraw.readthedocs.io](https://quickdraw.readthedocs.io)