gerritjandebruin

gerritjandebruin

gerritjandebruin

11m36s PowerPoint presentation -> .mp4 and then compressing in FFMPEG:

Size    | Vcodec    | CRF   | Preset    | Wall time | User time | Size  
1080p   | h264      | 23    | normal    | 5m        | 33m       | 87M
1080p   | libx265   | 28    | normal    | 20m       | 33m       | 40M
1080p   | h264      | 17    | normal    | 1m*       | 19m       | 188M
1080p   | libx265   | 23    | normal    | 21m       | 35m       | 47M
1080p   | h264      | 18    | normal    | 1m*       | 18m       | 166M
1080p   | h264      | 19    | normal    | 1m*       | 18m       | 145M
4k      | h264      | 23    | normal    | 5m*       | 137m      | 298M

gerritjandebruin

ffmpeg -framerate 30 -pattern_type glob -i "timelapse/*.JPG" -pix_fmt yuv420p -c:v libx265 -preset veryfast timelapse.mp4

gerritjandebruin

import joblib
from tqdm.auto import tqdm

class ProgressParallel(joblib.Parallel):
    def __init__(self, use_tqdm=True, total=None, desc=None, *args, **kwargs):
        self._use_tqdm = use_tqdm
        self._total = total
        self._desc = desc
        super().__init__(*args, **kwargs)

gerritjandebruin

html = hip.Experiment.from_iterable(df.to_dict(orient='records')).to_html()
soup = BeautifulSoup(html, 'html.parser')
tag1 = soup.new_tag('div', attrs={'margin-left': 'auto', 'margin-right': 'auto'})
tag1.append(BeautifulSoup(fig1.to_html(include_plotlyjs='cdn', full_html=False)))
soup.body.insert(0, tag1)
tag2 = soup.new_tag('div', attrs={'margin-left': 'auto', 'margin-right': 'auto'})
tag2.append(BeautifulSoup(fig2.to_html(include_plotlyjs='cdn', full_html=False)))
soup.body.insert(1, tag2)
with open('docs/run3.html', 'w') as file:
  file.write(str(soup))

gerritjandebruin

gerritjandebruin

% Define research questions: https://tex.stackexchange.com/questions/525921/ideas-for-presenting-the-research-question
\usepackage{enumitem}
\newlist{questions}{enumerate}{2}
\setlist[questions,1]{wide=0pt,leftmargin=\parindent,label=RQ\arabic*.,ref=RQ\arabic*}
\setlist[questions,2]{label=(\alph*),ref=\thequestionsi(\alph*)}
% In document

\begin{questions}
    \item \label{rq:events} How should event-based links, which may re-occur over time, be dealt with in order to accurately predict future links?
    \item \label{rq:availability} To what extent does the availability of temporal information improve the performance of link prediction approaches?

gerritjandebruin

for i in 'lightlevel' 'lumi_lumi' 'multisensor' 'rwl021' 'batterij' 'verbinding' 'herstart' 'notificatie' 'oplader' 'tradfri' 'water' 'wifi_signal' 'relais' 'on_off'
do
    influx -host xxx -username homeassistant -password xxx -port 8086 --execute 'show measurements' --database=homeassistant | grep $i | xargs -I{} influx -host xxx -username homeassistant -password xxx -port 8086 --execute 'drop measurement "{}"' --database=homeassistant
done

gerritjandebruin

import os, smtplib
from os.path import basename
from email.mime.application import MIMEApplication
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from email.utils import COMMASPACE, formatdate

def send_mail(
  send_from="bruingjde@liacs.leidenuniv.nl", 
  send_to: list = ["gerritjan.debruin@gmail.com"],