PJ Trainor trainorpj

## websockets.md

      
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              90 stars
            
          
                jsomers
                / websockets.md
            
            
              Created
              September 27, 2018 12:50
            
              
                Using websockets to easily build GUIs for Python programs
              
          
    Using websockets to easily build GUIs for Python programs

I recently built a small agent-based model using Python and wanted to visualize the model in action. But as much as Python is an ideal tool for scientific computation (numpy, scipy, matplotlib), it's not as good for dynamic visualization (pygame?).
You know what's a very mature and flexible tool for drawing graphics? The DOM! For simple graphics you can use HTML and CSS; for more complicated stuff you can use Canvas, SVG, or WebGL. There are countless frameworks, libraries, and tutorials to help you draw exactly what you need. In my case, this was the animation I wanted:

(Each row represents a "worker" in my model, and each rectangle represents a "task.")

  
## classifier_from_little_data_script_3.py
'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

## app.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-

from flask import Flask, stream_with_context, request, Response, flash
from time import sleep

app = Flask(__name__)

def stream_template(template_name, **context):

## README-Template.md

      
              1 file
            
          
              4762 forks
            
          
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              13987 stars
            
          
                PurpleBooth
                / README-Template.md
            
            
              Last active
              April 22, 2024 11:45
            
              
                A template to make good README.md
              
          
    Project Title

One Paragraph of project description goes here
Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
Prerequisites


## README.md

      
              2 files
            
          
              263 forks
            
          
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              1237 stars
            
          
                roachhd
                / README.md
            
            
              Last active
              April 21, 2024 16:53
            
              
                EMOJI cheatsheet 😛😳😗😓🙉😸🙈🙊😽💀💢💥✨💏👫👄👃👀👛👛🗼🔮🔮🎄🎅👻
              
          
    EMOJI CHEAT SHEET
Emoji emoticons listed on this page are supported on Campfire, GitHub, Basecamp, Redbooth, Trac, Flowdock, Sprint.ly, Kandan, Textbox.io, Kippt, Redmine, JabbR, Trello, Hall, plug.dj, Qiita, Zendesk, Ruby China, Grove, Idobata, NodeBB Forums, Slack, Streamup, OrganisedMinds, Hackpad, Cryptbin, Kato, Reportedly, Cheerful Ghost, IRCCloud, Dashcube, MyVideoGameList, Subrosa, Sococo, Quip, And Bang, Bonusly, Discourse, Ello, and Twemoji Awesome.
However some of the emoji codes are not super easy to remember, so here is a little cheat sheet.
✈ Got flash enabled? Click the emoji code and it will be copied to your clipboard.
People

😄

  
## gist:7360908

      
              1 file
            
          
              3623 forks
            
          
              1003 comments
            
          
              16574 stars
            
          
                rxaviers
                / gist:7360908
            
            
              Last active
              April 25, 2024 11:15
            
              
                Complete list of github markdown emoji markup
              
          
    People


 :bowtie:
😄 :smile:
😆 :laughing:


😊 :blush:
😃 :smiley:
☺️ :relaxed:


😏 :smirk:
😍 :heart_eyes:
😘 :kissing_heart:


😚 :kissing_closed_eyes:
😳 :flushed:
😌 :relieved:


😆 :satisfied:
😁 :grin:
😉 :wink:


😜 :stuck_out_tongue_winking_eye:
😝 :stuck_out_tongue_closed_eyes:
😀 :grinning:


😗 :kissing:
😙 :kissing_smiling_eyes:
😛 :stuck_out_tongue:


## gist:5294248
function toArray(args) {
  return [].slice.call(args);
}

function autocurry(fn) {
  var len = fn.length;
  var args = [];
  return function next() {
    args = args.concat(toArray(arguments));
    return (args.length >= len) ?
	'''This script goes along the blog post
	"Building powerful image classification models using very little data"
	from blog.keras.io.
	It uses data that can be downloaded at:
	https://www.kaggle.com/c/dogs-vs-cats/data
	In our setup, we:
	- created a data/ folder
	- created train/ and validation/ subfolders inside data/
	- created cats/ and dogs/ subfolders inside train/ and validation/
	- put the cat pictures index 0-999 in data/train/cats
	#!/usr/bin/env python
	# -- coding: utf-8 --

	from flask import Flask, stream_with_context, request, Response, flash
	from time import sleep

	app = Flask(__name__)

	def stream_template(template_name, **context):
`:bowtie:`	😄 `:smile:`	😆 `:laughing:`
😊 `:blush:`	😃 `:smiley:`	☺️ `:relaxed:`
😏 `:smirk:`	😍 `:heart_eyes:`	😘 `:kissing_heart:`
😚 `:kissing_closed_eyes:`	😳 `:flushed:`	😌 `:relieved:`
😆 `:satisfied:`	😁 `:grin:`	😉 `:wink:`
😜 `:stuck_out_tongue_winking_eye:`	😝 `:stuck_out_tongue_closed_eyes:`	😀 `:grinning:`
😗 `:kissing:`	😙 `:kissing_smiling_eyes:`	😛 `:stuck_out_tongue:`
	function toArray(args) {
	return [].slice.call(args);
	}

	function autocurry(fn) {
	var len = fn.length;
	var args = [];
	return function next() {
	args = args.concat(toArray(arguments));
	return (args.length >= len) ?