I still enjoy using Apple products and prefer them over alternatives, but in recent years, an increasing number of small bugs has made using them less pleasant. No single bug is fatal, but they add up. I wanted to document them to make it more likely that they will be fixed.
There are two main topics:
Student: Xavier Weber
Mentors: Vladimir Tyan & Yida Wang
Student on the same project: Fanny Monori
Link to accomplished work:
| # (Variant #4 for exercise 16.2 on EPI (Elements of Programming Interviews)) (September 2018 edition) | |
| # The core idea is calculate the levenshtein distance, while taking into account the special cases of the regex expression | |
| # *, +, ? and . were taken into account for the regex expression. Expression blocks are not supported | |
| # This algorithm uses recursion with memoization (could be transposed to a DP solution), yielding a O(mn) time complexity, O(mn) auxiliary space for cache and O(max(m,n)) function call stack | |
| # (m and n are the lengths of regex and target strings respectively) | |
| # | |
| # Version using dynamic programming: https://gist.github.com/lopespm/2362a77e7bd230a4622a43709c195826 | |
| def regex_dist(regex: str, target: str): | |
| def regex_dist_aux(r_i, t_i): |
| # Cache sudo password | |
| sudo -v | |
| # Enable installation of 32-bit packages | |
| sudo dpkg --add-architecture i386 | |
| # Enable the Ubuntu partner repository (this doesn't seem to be necessary for 20.04) | |
| if [ $(lsb_release -r | awk '{print $2}') == '18.04' ]; then | |
| sudo sed -i 's/^# deb http:\/\/archive.canonical.com\/ubuntu/deb http:\/\/archive.canonical.com\/ubuntu/' /etc/apt/sources.list | |
| sudo apt update |
Mermaid is a JavaScript based parsing engine that allows you to embed blocks of graph markup into a page. Markdown Monster supports two different approaches using either code block or HTML tag syntax to embed the mermaid blocks.
Note the MM internal preview can't properly display or refresh all chart types, but you can preview in your system browser via
Shift-F12to see proper representation.
You can use a mermaid code block:
| import cv2 | |
| import numpy as np | |
| def deskew(im, max_skew=10): | |
| height, width = im.shape | |
| # Create a grayscale image and denoise it | |
| im_gs = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) | |
| im_gs = cv2.fastNlMeansDenoising(im_gs, h=3) |
You would think it would be easy to find this information, but none of the Github or Gandi documentation is clear so I have recorded the required steps here.
Create the following A records:
@ 1800 IN A 185.199.108.153
@ 1800 IN A 185.199.109.153
@ 1800 IN A 185.199.110.153
| # use ImageMagick convert | |
| # the order is important. the density argument applies to input.pdf and resize and rotate to output.pdf | |
| convert -density 90 input.pdf -rotate 0.5 -attenuate 0.2 +noise Multiplicative -colorspace Gray output.pdf |
| #include "InternetButton.h" | |
| #include "math.h" | |
| /** | |
| * Sensor calibration data: | |
| * Wet = 1520 | |
| * Dry = 3120 | |
| * (Calibrate this for yourself!) | |
| */ |