Iver Jordal iver56

## aes-256-cbc.js
'use strict';

const crypto = require('crypto');
const ENC_KEY = "bf3c199c2470cb477d907b1e0917c17b"; // set random encryption key
const IV = "5183666c72eec9e4"; // set random initialisation vector
// ENC_KEY and IV can be generated as crypto.randomBytes(32).toString('hex');

const phrase = "who let the dogs out";

var encrypt = ((val) => {

## gitBash_windows.md

      
              1 file
            
          
              81 forks
            
          
              87 comments
            
          
              576 stars
            
          
                evanwill
                / gitBash_windows.md
            
            
              Last active
              April 26, 2024 03:58
            
              
                how to add more utilities to git bash for windows, wget, make
              
          
    How to add more to Git Bash on Windows

Git for Windows comes bundled with the "Git Bash" terminal which is incredibly handy for unix-like commands on a windows machine.
It is missing a few standard linux utilities, but it is easy to add ones that have a windows binary available.
The basic idea is that C:\Program Files\Git\mingw64\ is your / directory according to Git Bash (note: depending on how you installed it, the directory might be different. from the start menu, right click on the Git Bash icon and open file location. It might be something like C:\Users\name\AppData\Local\Programs\Git, the mingw64 in this directory is your root. Find it by using pwd -W).
If you go to that directory, you will find the typical linux root folder structure (bin, etc, lib and so on).
If you are missing a utility, such as wget, track down a binary for windows and copy the files to the corresponding directories.
Sometimes the windows binary have funny prefixes, so

  
## base64ArrayBuffer.js
// Converts an ArrayBuffer directly to base64, without any intermediate 'convert to string then
// use window.btoa' step. According to my tests, this appears to be a faster approach:
// http://jsperf.com/encoding-xhr-image-data/5

/*
MIT LICENSE

Copyright 2011 Jon Leighton

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

## wiener_deconvolution_example.py
#!/usr/bin/env python

# Simple example of Wiener deconvolution in Python.
# We use a fixed SNR across all frequencies in this example.
#
# Written 2015 by Dan Stowell. Public domain.

import numpy as np
from numpy.fft import fft, ifft, ifftshift

## elastic_transform.py
import numpy as np
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter

def elastic_transform(image, alpha, sigma, random_state=None):
    """Elastic deformation of images as described in [Simard2003]_.
    .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
       Convolutional Neural Networks applied to Visual Document Analysis", in
       Proc. of the International Conference on Document Analysis and
       Recognition, 2003.

## pyximport_numpy.py
import pyximport
import numpy as np
pyximport.install(setup_args={'include_dirs': np.get_include()})

## xor.py
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD
import numpy as np

X = np.array([[0,0],[0,1],[1,0],[1,1]])
y = np.array([[0],[1],[1],[0]])

model = Sequential()
model.add(Dense(8, input_dim=2))

## resample_benchmark.ipynb

      
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                mogwai
                / resample_benchmark.ipynb
            
            
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              January 11, 2023 03:42
            
              
                Resampling Benchmarks
              
          
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## getusermedia_picture.html
<html>
<body>
	<video id="v" width="300" height="300"></video>
	<input id="b" type="button" disabled="true" value="Take Picture"></input>
	<canvas id="c" style="display:none;" width="300" height="300"></canvas>
</body>
<script>
	navigator.getUserMedia({video: true}, function(stream) {
		var video = document.getElementById("v");
		var canvas = document.getElementById("c");

## overlaps.py
from __future__ import division, print_function, absolute_import
import numpy as np
import scipy
from spectral import check_COLA  # https://github.com/scipy/scipy/pull/6058

windows = np.unique(scipy.signal.windows._win_equiv.values())
windows = sorted(windows, key=lambda x: x.__name__)

for window in windows:
    try:
	'use strict';

	const crypto = require('crypto');
	const ENC_KEY = "bf3c199c2470cb477d907b1e0917c17b"; // set random encryption key
	const IV = "5183666c72eec9e4"; // set random initialisation vector
	// ENC_KEY and IV can be generated as crypto.randomBytes(32).toString('hex');

	const phrase = "who let the dogs out";

	var encrypt = ((val) => {
	// Converts an ArrayBuffer directly to base64, without any intermediate 'convert to string then
	// use window.btoa' step. According to my tests, this appears to be a faster approach:
	// http://jsperf.com/encoding-xhr-image-data/5

	/*
	MIT LICENSE

	Copyright 2011 Jon Leighton

	Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
	#!/usr/bin/env python

	# Simple example of Wiener deconvolution in Python.
	# We use a fixed SNR across all frequencies in this example.
	#
	# Written 2015 by Dan Stowell. Public domain.

	import numpy as np
	from numpy.fft import fft, ifft, ifftshift
	import numpy as np
	from scipy.ndimage.interpolation import map_coordinates
	from scipy.ndimage.filters import gaussian_filter

	def elastic_transform(image, alpha, sigma, random_state=None):
	"""Elastic deformation of images as described in [Simard2003]_.
	.. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
	Convolutional Neural Networks applied to Visual Document Analysis", in
	Proc. of the International Conference on Document Analysis and
	Recognition, 2003.
	import pyximport
	import numpy as np
	pyximport.install(setup_args={'include_dirs': np.get_include()})
	from keras.models import Sequential
	from keras.layers.core import Dense, Dropout, Activation
	from keras.optimizers import SGD
	import numpy as np

	X = np.array([[0,0],[0,1],[1,0],[1,1]])
	y = np.array([[0],[1],[1],[0]])

	model = Sequential()
	model.add(Dense(8, input_dim=2))
	<html>
	<body>
	<video id="v" width="300" height="300"></video>
	<input id="b" type="button" disabled="true" value="Take Picture"></input>
	<canvas id="c" style="display:none;" width="300" height="300"></canvas>
	</body>
	<script>
	navigator.getUserMedia({video: true}, function(stream) {
	var video = document.getElementById("v");
	var canvas = document.getElementById("c");
	from __future__ import division, print_function, absolute_import
	import numpy as np
	import scipy
	from spectral import check_COLA # https://github.com/scipy/scipy/pull/6058

	windows = np.unique(scipy.signal.windows._win_equiv.values())
	windows = sorted(windows, key=lambda x: x.__name__)

	for window in windows:
	try: