mzhgkabtxhzngkhxwwmfptygglggkpxzxmxtgmyxwbnrhgmatmgmblyxaygtxekexiykeaeylgmtxhktzimlxhtxttgmmdtgldalpwxmkxvkuxxmatixnzhgkhattmuhftmklgbfxmhahhbxhxmhmutxmeaxlmatbmhhepkkxeffpmrtmyhmaxprrhghxxfxgpbxglatfzzatxykfaenlegdnbelellglgahaagkhnhbaptxhbhbtivrktnblugxewgxmvhabtgxkhaxkktxagbktemakymfxhimemxpmbxgtxmtxgbtwgezwaodagmgrpxlxtnbawmldrxlyzzbnrezwgtmemxdbohklxbiapwxgzhehgggeffglmblmmkxtymfakrxxakhgktraaglbndmlmwabtmkrgrayxkrlmmtlftvkxvmkkbbvxaxlmahytuhmxettatbxxmwxaantpwmaptghdkxapkptkaxmxfldnlkaykhhlwbulgagbxmgettbpwlftrnvhkalmxlvxmaxhnttgulgxepaluhlgmy ovioalvszpphsjmplpolusaljpnashahyoupsbvdawkkzshhtarzulvmlmncllasyvlffllazaviyddzalmokruvaznpvaunollvlukolkbkvsyolwpzwzvhvmlzzdlyhkvlhaawksklymuitkpvovzspaaallhzzoampuzhfallvzailpmvldhsadkbmcluktsmialsblzlyuaalhkyhlpmzjllavzozavtvpvfwuyulszduzoszylpfaanabpyhlnpalphaznlibluhkfulhdpsyoaabsavulorohuowzrzyozspzzukaoihlahhoavwtaduzhlazoljffhaoyavkldpuszompalhoolazluvyvlkpuankslbpoouapolybtaltpvkytltappvahkcrzuklzlljuyzhaplphaklzzavlahfhvhlxvkoydwoalgmklkodruukuuyhspslk
Alex Ledger aled1027
| [ | |
| { | |
| "label": "KAP Kap", | |
| "links": [ | |
| { "label": "Game Portal", "link": "https://kap.gg/" }, | |
| { "label": "About KAP Games", "link": "https://about.kap.gg/" }, | |
| { "label": "Airtable", "link": "https://airtable.com/" }, | |
| { "label": "Gdrive directory", "link": "https://drive.google.com/drive/folders/1DXeYMiht0bTRSVrzKOTcIkFyh5SIMrnZ" }, | |
| { "label": "KAP AWS SSO", "link": "https://d-9a671bd167.awsapps.com/start" }, | |
| { "label": "PGL AWS SSO", "link": "https://d-9a671f90cd.awsapps.com/start" } |
| name: Deploy | |
| on: | |
| push: | |
| branches: ["main"] | |
| # Allows you to run this workflow manually from the Actions tab | |
| workflow_dispatch: |
| """ | |
| ASGI Config | |
| It exposes the ASGI callable as a module-level variable named ``application``. | |
| For more information on this file, see | |
| https://docs.djangoproject.com/en/4.1/howto/deployment/asgi/ | |
| """ | |
| import os |
| FROM python:3.10 | |
| # set work directory | |
| WORKDIR /usr/src/app | |
| # set environment variables | |
| ENV PYTHONDONTWRITEBYTECODE 1 | |
| ENV PYTHONUNBUFFERED 1 | |
| COPY . . |
| static int input_eg(char[] in) { | |
| int d,f=1; | |
| char a; | |
| a = StdIn.readChar(); | |
| d = 0; | |
| System.out.printf("%c mark!!!", d); | |
| while (f==1) { | |
| in[d] = a; |
Bret Victor wrote a [nice, comprehsive post on climate change] [1]. I encourage you all to read it, and I've written my thoughts on the post.
[What can a technologist do about climate change? (A personal view)] [1] is a well-written blog post with many links and cool formatting (Control-F "Blinder's Proposal" for an example). The post is written for technologists in response to the question, "how can the tech community contribute to climate change efforts?" As such, the post has a tone of being a call to action for technologists. It recognizes that technologists may struggle with finding good, useful, climate-related problems to work on, and answers the concern in full by giving examples of successful technology projects.
Ten years ago, when Tesla was still in its infancy, Elon Musk concluded a blog post with a summation of Tesla's strategy:
- Build sports car
- Use that money to build an affordable car
- Use that money to build an even more affordable car
- While doing above, also provide zero emission electric power generation options
- Don't tell anyone.
This is meant to be a list of image processing techniques. Hopefully this list will be a valuable resource as one embarks on working on new image processing problem.
This is not a tutorial or explanation of any algorithms, and this is not going to give links to explanations or code. The point of this is to simply list all available options so that the programming/thinker can imagine possible algorithms to use to analyze the image.
Scientific programming is meaningfully different from typical software engineering. Where a web-service runs continuously, a scientific program is ran once - taking anywhere from a few seconds to a few days - and we analyze the results. We may find that the script needs to be updated, or we run the script on different data, in which case we make small changes to the script and run it again. The scientific programming process can be simplified to the repeated iteration of this process.
As a result of this process, and the fact that scientific programs tend to be relatively small (ranging from a few hundred lines of code to a few thousand), I use a unique approach to write the programs. My broad aims in writing scientific programs are the following:
- Make the core algorithms clear, readable and easily vetted by others