kevinhowbrook

name: Deploy to heroku

on:
  push:
    branches: [ master ]

jobs:
  build:
    runs-on: ubuntu-latest

kevinhowbrook

add the following to test settings

# By default, Django uses a computationally difficult algorithm for passwords hashing.
# We don't need such a strong algorithm in tests, so use MD5
PASSWORD_HASHERS = [
    "django.contrib.auth.hashers.MD5PasswordHasher",
]

kevinhowbrook

from django.core.management.base import BaseCommand
from wagtail.core.models import Page
from django.core.files.images import ImageFile
from projects.models import ProjectPage
from home.models import HomePage
from images.models import CustomImage
from io import BytesIO
import requests
from django.core.files.images import ImageFile

kevinhowbrook

How to test a class method using mock:

You can patch the entire requests module with:

@mock.patch('requests.get')

But it's better to keep it specific as per:

@mock.patch('rca.shortcourses.access_planit.requests.get')

kevinhowbrook

Spotify playlist links generated by a bot from scraping https://hardwax.com/. Playlist are updated weekly at midnight (uk time)

• Latest/Top tracks from various sections - https://open.spotify.com/playlist/0VfCikT8DEpwTX1xe0kk29?si=zIjT9X6DS-2G_MKYZm05vw
• Basic Channel - https://open.spotify.com/playlist/0YnPd3o97urh9zkYUEnuDd?si=rwmQKhXkT5CK5xkgFozYyw
• Detroit - https://open.spotify.com/playlist/0KkY6uHqXXdlwXMp2oB2Y5?si=D7oPPp4NQJKdo2R8Mv8ejg
• Electro - https://open.spotify.com/playlist/28QWqmvMwPjWKuFOG9bn75?si=peNS5mTNSPCJu54zJnFqgw
• Techno vol1 - https://open.spotify.com/playlist/3GPNFeLs7Z4kFvofdMPcbT?si=aUJu_MS_TRGwQ2SY9wqgrw
• Techno vol2 - https://open.spotify.com/playlist/30gojfQ2mR4NYW8tl3FQdV?si=eLXBU-D9R8CNzf_3obyvvg
• Techno vol3 - https://open.spotify.com/playlist/7kZYJM8SnG89SbRe35usNQ?si=pUh2GxCNQL6tlyqnQSswEg
• Surgeon - https://open.spotify.com/playlist/1TzSNDnLpJMFD1yjkQvqEr?si=TQjFV-aGSHq6LYKUu2TOFA

kevinhowbrook

Some various tips on writing yaml for template tags.

Looping over a template tag

HTML

{% social_media_links as social_links %}
<ul class="footer__social-links">
    {% for link in social_links %}

kevinhowbrook

from django.db import models

from wagtail.core.models import Page
from wagtail.admin.edit_handlers import FieldPanel


class HomePage(Page):
    publication_date = models.DateTimeField(null=True, blank=True)
    updated_date = models.DateTimeField("Modified date", blank=True, null=True)

kevinhowbrook

pipenv for function/code completion

When using vagrant, vscode won't be able to map to the vm, well not yet anyway. A way around this is to checkout a branch called pipenv, run pipenv --python 3 to create a virtual environment. Then run pipenv install -r requirements.txt to install everything in the project.

After doing this, in vscode ctrl+p then search for >python: select interpreter. Select the virtual env python, it will look something like this: /home/yourname/.local/share/virtualenvs/project-foo-JbRjzqlX/bin/python.

Just don't commit the pipenv stuff.

Flake8

Place the following in .git/hooks/pre-commit

kevinhowbrook

j = 0 # vertical counter
k = 0 # horizontal counter
for i,v in enumerate(convert()):
        if i % squares_per_row == 0 and i != 0: # if i is a multiple of squares per row
            j += 1 # increments after the squares per row is hit like 0,0,0,1,1,1,2,2,2...
        if i % squares_per_row == 0 and i != 0:
            k = 0 # increments after the squares per row is hit like 0,1,2,0,1,2...
        points = ((k*s,j*s), (k*s, j*s+s),(k*s+s,j*s+s),(k*s+s, j*s)) #set points with incrementing values :/
        draw.polygon((points[0], points[1], points[2], points[3]), outline='black',  fill = (v[0],v[1],v[2])) # outline='red', fill='blue'
        #borders so redraw the same plots with white lines

kevinhowbrook

def calculate_m():
    # get a list of possible squared numbers, this give us the number of
    # rows and columns to use for the canvas, eg 4 = 2 x 2, 6 = 3 x 3...
    # Return: [] of squared numbers up to 10000 (hopefully we don't get that high!)
    m = []
    for i in range(1,10000):
        m.append(i*i)
    return(m)

def grid(n,m):