Achilles Rasquinha achillesrasquinha

## responsive-align.less
.text-xs-left { text-align: left; }
.text-xs-right { text-align: right; }
.text-xs-center { text-align: center; }
.text-xs-justify { text-align: justify; }

@media (min-width: @screen-sm-min) {
    .text-sm-left { text-align: left; }
    .text-sm-right { text-align: right; }
    .text-sm-center { text-align: center; }
    .text-sm-justify { text-align: justify; }

## metainfo.py
"""Extract metadata from Project Gutenberg RDF catalog into a Python dict.

Based on https://bitbucket.org/c-w/gutenberg/

>>> md = readmetadata()
>>> md[123]
{'LCC': {'PS'},
 'author': u'Burroughs, Edgar Rice',
 'authoryearofbirth': 1875,
 'authoryearofdeath': 1950,

## index.html
<!DOCTYPE html>
<html lang="en">
  <head>
    <script type="module">
      import { h, text, app } from "https://unpkg.com/hyperapp"

      app({
        init: () => 0,
        view: state =>
          h("main", {}, [

## frappe-bench-frappe-default-worker.service
[Unit]
Description="frappe-bench-frappe-default-worker"
PartOf=frappe-bench-workers.target

[Service]
User=revant
Group=revant
Restart=always
ExecStart=/home/revant/.local/bin/bench worker --queue default
StandardOutput=file:/home/revant/frappe-bench/logs/worker.log

## simple_mlp_tensorflow.py
# Implementation of a simple MLP network with one hidden layer. Tested on the iris data set.
# Requires: numpy, sklearn>=0.18.1, tensorflow>=1.0

# NOTE: In order to make the code simple, we rewrite x * W_1 + b_1 = x' * W_1'
# where x' = [x | 1] and W_1' is the matrix W_1 appended with a new row with elements b_1's.
# Similarly, for h * W_2 + b_2
import tensorflow as tf
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

## ANSIColorFix.py
import os
import platform

if os.name == 'nt' and platform.release() == '10' and platform.version() >= '10.0.14393':
    # Fix ANSI color in Windows 10 version 10.0.14393 (Windows Anniversary Update)
    import ctypes
    kernel32 = ctypes.windll.kernel32
    kernel32.SetConsoleMode(kernel32.GetStdHandle(-11), 7)

## plot_decision_boundary.py
# Helper function to plot a decision boundary.
# If you don't fully understand this function don't worry, it just generates the contour plot below.
def plot_decision_boundary(pred_func):
    # Set min and max values and give it some padding
    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
    h = 0.01
    # Generate a grid of points with distance h between them
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
    # Predict the function value for the whole gid

## compare.py
#!/usr/bin/env python
"""Compare two aligned images of the same size.

Usage: python compare.py first-image second-image
"""

import sys

from scipy.misc import imread
from scipy.linalg import norm

## tweet_image_dumper.py
#!/usr/bin/env python
# encoding: utf-8

import tweepy #https://github.com/tweepy/tweepy
import csv
import sys

#Twitter API credentials
consumer_key = ""
consumer_secret = ""

## snippets.md

      
              1 file
            
          
              5 forks
            
          
              2 comments
            
          
              25 stars
            
          
                tomhodgins
                / snippets.md
            
            
              Last active
              August 8, 2022 14:27
            
              
                Snippets.md is my most often used HTML, CSS and JavaScript snippets for front-end web development
              
          
    This is a collection of my most often used lines of code, as well as a few other examples I want to have ready at a moment's notice. These snippets focus on building HTML from scratch, linking in good web fonts, and setting up responsive styles for your pages.
Table of Contents


HTML Snippets
Font Snippets
CSS Snippets
JavaScript Functions
Shell Commands
	.text-xs-left { text-align: left; }
	.text-xs-right { text-align: right; }
	.text-xs-center { text-align: center; }
	.text-xs-justify { text-align: justify; }

	@media (min-width: @screen-sm-min) {
	.text-sm-left { text-align: left; }
	.text-sm-right { text-align: right; }
	.text-sm-center { text-align: center; }
	.text-sm-justify { text-align: justify; }
	"""Extract metadata from Project Gutenberg RDF catalog into a Python dict.

	Based on https://bitbucket.org/c-w/gutenberg/

	>>> md = readmetadata()
	>>> md[123]
	{'LCC': {'PS'},
	'author': u'Burroughs, Edgar Rice',
	'authoryearofbirth': 1875,
	'authoryearofdeath': 1950,
	<!DOCTYPE html>
	<html lang="en">
	<head>
	<script type="module">
	import { h, text, app } from "https://unpkg.com/hyperapp"

	app({
	init: () => 0,
	view: state =>
	h("main", {}, [
	[Unit]
	Description="frappe-bench-frappe-default-worker"
	PartOf=frappe-bench-workers.target

	[Service]
	User=revant
	Group=revant
	Restart=always
	ExecStart=/home/revant/.local/bin/bench worker --queue default
	StandardOutput=file:/home/revant/frappe-bench/logs/worker.log
	# Implementation of a simple MLP network with one hidden layer. Tested on the iris data set.
	# Requires: numpy, sklearn>=0.18.1, tensorflow>=1.0

	# NOTE: In order to make the code simple, we rewrite x * W_1 + b_1 = x' * W_1'
	# where x' = [x \| 1] and W_1' is the matrix W_1 appended with a new row with elements b_1's.
	# Similarly, for h * W_2 + b_2
	import tensorflow as tf
	import numpy as np
	from sklearn import datasets
	from sklearn.model_selection import train_test_split
	import os
	import platform

	if os.name == 'nt' and platform.release() == '10' and platform.version() >= '10.0.14393':
	# Fix ANSI color in Windows 10 version 10.0.14393 (Windows Anniversary Update)
	import ctypes
	kernel32 = ctypes.windll.kernel32
	kernel32.SetConsoleMode(kernel32.GetStdHandle(-11), 7)
	# Helper function to plot a decision boundary.
	# If you don't fully understand this function don't worry, it just generates the contour plot below.
	def plot_decision_boundary(pred_func):
	# Set min and max values and give it some padding
	x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
	y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
	h = 0.01
	# Generate a grid of points with distance h between them
	xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
	# Predict the function value for the whole gid
	#!/usr/bin/env python
	"""Compare two aligned images of the same size.

	Usage: python compare.py first-image second-image
	"""

	import sys

	from scipy.misc import imread
	from scipy.linalg import norm
	#!/usr/bin/env python
	# encoding: utf-8

	import tweepy #https://github.com/tweepy/tweepy
	import csv
	import sys

	#Twitter API credentials
	consumer_key = ""
	consumer_secret = ""