| def str2int(s): | |
| i = 0 | |
| for c in s: | |
| i = (i << 1) + (1 if c == '1' else 0) | |
| return i | |
| def xor(a, b): | |
| return ['1' if aa != bb else '0' for aa, bb in zip(a, b)] | |
| def inv(a): |
| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 29, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ |
notes about logging in python. Put together from reading the logging module source, PEP 282, the official documentation, as well as the blog entries mentioned in References.
Its just intended to collect some notes in one place...so it will (most likely) never comprehensive.
| """ simple LFSR generator | |
| Uses galois algorithmn to return next bit of LFSR. | |
| To initialize the generator pass the seed value (one | |
| value that the generator will generate, != 0) and | |
| the used polynom. | |
| Observe that for a n bit polynom, x^n has to be in | |
| the polynom, which should be the case anyhow therefore e.g.: | |
| x^4 + x^3 + 1 = 0b1100 |
After a discussion about the performance implications of dynamic_cast, I had some time on my hands and wanted to check it myself (at least to some degree). That prompted the measurements below, where dynamic_cast is compared against an implementation that uses class member that cary an unique identifier to check. The functionality of the implementation is not the same as with dynamic_cast, but it satisfies the application that was discussed:
class Base {
| """ simple script to convert image into c array | |
| Image is converted into a 4 color greyscale image | |
| and then exported as a space-optimized array. | |
| The exact format can be found at the end of the | |
| file as it is printed as a comment into the resulting | |
| C file. | |
| """ | |
| import matplotlib.pyplot as plt | |
| import matplotlib.image as mpimg |
| % version using the layout package | |
| \usepackage{layout} | |
| \printinunitsof{cm}\prntlen{\textwidth} \prntlen{\textheight} | |
| % Snippet by junanjo from www.latex-community.org (http://www.latex-community.org/forum/viewtopic.php?f=5&t=2712) | |
| \usepackage{fp} | |
| \newlength{\TOarg} \newlength{\TOunit} | |
| {\catcode`p=12 \catcode`t=12 \gdef\TOnum#1pt{#1}} |
| import numpy as np | |
| mu_0 = 4 * np.pi * 10e-7 | |
| def field_contribution(x, y, pos, I, r): | |
| v = np.asarray([x,y]) | |
| v = v - pos | |
| if np.abs(np.sum(v*v)) < r*r: | |
| return np.asarray([0,0]) | |
| factor = mu_0 / (2 * np.pi * np.dot(v,v)) |
| """ PyCalc | |
| A small "package" that makes it easier for engineers to use IPython as a calculator. | |
| The idea is to extend the python syntax to understand the SI prefixes that are | |
| customary in engineering, like "milli" or "Mega". The code below registers a | |
| TokenInputTransformer with IPython that will rewrite every python line before | |
| it is given to python itself. | |
| It also changes the IPython output routines so that the same prefixes are also |