This simple script will take a picture of a whiteboard and use parts of the ImageMagick library with sane defaults to clean it up tremendously.
The script is here:
#!/bin/bash
convert "$1" -morphology Convolve DoG:15,100,0 -negate -normalize -blur 0x1 -channel RBG -level 60%,91%,0.1 "$2"
| alienino 3511c0997339cfcccccca39f214322eb22e8fc43 | |
| all'equipaggio 14555555ec8dcf1400db9b375c5b8ca836362d8b | |
| Anacyclus 182c40fc4df5b4d997feeeeee22c4dbf059a95d4 | |
| bajar 1352a687f6840df8801aaaaaadf71de84b816f86 | |
| bandwagon's f6e6deb71111110839bc14dd9cbab6eb7b16f09c | |
| barbihecho bc45de24f03f2a086666668e2a0812a5f270c8cb | |
| calcitrant 86d4ffffff9aae00ace440e93c1d87bb4ec8b56c | |
| cornetti 000000f636f0d7cbc963a62f3a1bc87c9c985a04 | |
| crépir a21303cfa9b7c6f0cccccc19cc59556a188ccac7 | |
| cyclosporin's b5baaaaaa744f480586a905f692cdec2fa0a1919 |
Often it is useful to have access to the median value for fields of a data stream since they are more robust with respect to outliers. The median is defined as the value of a dataset such that, when sorted, 50% of the data is smaller than the value and 50% of the data is larger then the value. Ordinarily this is difficult to calculate on a stream because it requires the collection and sorting of all data.
The median of a data stream can be approximated with a technique called stochastic averaging. To approximate the median value of a data stream one could use the following approach:
Given the current estimate of the median M. If the next observed value in the stream is larger than M, increase the current estimate by r (= the learning rate). If it is smaller, decrease the estimate by r. When M is close to the median, it increases as often as it decreases, and therefore it stabilizes.
This approach was taken from the book "Real-time Analytics -
This is an example for computing running statistics with Lua backed by a hash in Redis. We support counting, average (with and without exponential smoothing), stddev, variance, min, max, sum of observed values. An example for approximating a running median can be found here: https://gist.github.com/thomasdarimont/fff68191d45a001b2d84
We use a hash for storing various statistic value under the key "stats_value" in redis.
Note: If you need a specific alpha value for smoothing the average, then set the desired alpha -> e.g. alpha 0.7.
If alpha is 0.0 then no smoothing is applied.
| perf stat ./client | |
| Error: Read timed out | |
| Performance counter stats for './client': | |
| 0.963859 task-clock (msec) # 0.081 CPUs utilized | |
| 3 context-switches # 0.003 M/sec | |
| 0 cpu-migrations # 0.000 K/sec | |
| 236 page-faults # 0.245 M/sec | |
| <not supported> cycles |
| INSTRUMENTS = [ | |
| 'Acoustic Grand Piano', | |
| 'Bright Acoustic Piano', | |
| 'Electric Grand Piano', | |
| 'Honky-tonk Piano', | |
| 'Electric Piano 1', | |
| 'Electric Piano 2', | |
| 'Harpsichord', | |
| 'Clavi', | |
| 'Celesta', |
| set -o pipefail | |
| set -o errtrace | |
| set -o nounset | |
| set -o errexit | |
| set -a | |
| # Scratch mount is the device which will be mounted on /mnt | |
| # and generally used for logs, core dumps etc. | |
| if ! $(mount | grep -q /mnt) ; then | |
| # Detected NVME drives |
(Scraped from the Internet Wayback Machine. Original content by Eran Hammer / hueniverse.com July 26, 2012)
They say the road to hell is paved with good intentions. Well, that’s OAuth 2.0.
Last month I reached the painful conclusion that I can no longer be associated with the OAuth 2.0 standard. I resigned my role as lead author and editor, withdraw my name from the specification, and left the working group. Removing my name from a document I have painstakingly labored over for three years and over two dozen drafts was not easy. Deciding to move on from an effort I have led for over five years was agonizing.
There wasn’t a single problem or incident I can point to in order to explain such an extreme move. This is a case of death by a thousand cuts, and as the work was winding down, I’ve found myself reflecting more and more on what we actually accomplished. At the end, I reached the conclusion that OAuth 2.0 is a bad
