| from numpy.fft import fft, ifft, fft2, ifft2, fftshift | |
| import numpy as np | |
| def fft_convolve2d(x,y): | |
| """ 2D convolution, using FFT""" | |
| fr = fft2(x) | |
| fr2 = fft2(np.flipud(np.fliplr(y))) | |
| m,n = fr.shape | |
| cc = np.real(ifft2(fr*fr2)) | |
| cc = np.roll(cc, -m/2+1,axis=0) |
| #!/usr/bin/env python | |
| # -*- coding: utf-8 -*- | |
| # ----------------------------------------------------------------------------- | |
| # Copyright INRIA | |
| # Contributors: Nicolas P. Rougier (Nicolas.Rougier@inria.fr) | |
| # | |
| # DANA is a computing framework for the simulation of distributed, | |
| # asynchronous, numerical and adaptive models. | |
| # | |
| # This software is governed by the CeCILL license under French law and abiding |
| %% Datos iniciales | |
| % | |
| % Script para analizar el cambio de los esfuerzos normal y cortante | |
| % en un plano oblicuo bajo carga axial | |
| A_0 = 1; % Area de la seccion transversal | |
| P = 1; % Fuerza externa | |
| % La variacion en theta desde un corte transversal (0º - 0 rad) | |
| % a un corte longitudinal (90º - pi/2 rad) |
| theta = 60 | |
| beta = 18.02 | |
| // Omega en radianes | |
| w = 1500*2*%pi/60 | |
| // Analisis de velocidades | |
| // | |
| // V_A = V_B + V_A/B |
| # This Python file uses the following encoding: utf-8 | |
| import re, subprocess, os, shlex | |
| from string import Template | |
| class REMatcher(object): | |
| def __init__(self, matchstring): | |
| self.matchstring = matchstring | |
| def match(self,regexp): |
| import csv | |
| with open('papers.csv','r') as f: | |
| sheet = csv.reader(f) | |
| papers = [] | |
| for row in sheet: | |
| # Every row is a list, thus I concatenate all to form a single list | |
| papers = papers + row | |
| # remove blank spaces | |
| papers = filter(None,papers) |
| #!/usr/bin/python | |
| from datetime import date | |
| import sys | |
| now = date.today() | |
| inputDate= sys.argv[1].split() | |
| day = int(inputDate[0]) | |
| month = int(inputDate[1]) |
| """ | |
| This code takes two screenshots from the camera roll combines them into one image and saves the new image to the camera roll. | |
| This is adapted from Federico Viticci's blog post at: | |
| http://www.macstories.net/stories/automating-ios-how-pythonista-changed-my-workflow/ | |
| It removes the option to change which picture is where, automatically assigning the first one chosen as the leftmost image. | |
| It also removes the necessity to copy the images to the clipboard outside of Pythonista using the new photos library in version 1.3. Finally, it removes the clipboard output. | |
| """ | |
| import photos |
| Andress-MacBook-Pro:cv Andre$ brew install https://gist.githubusercontent.com/steakknife/60a39a32ae84e12238a2/raw/openmotif.rb | |
| ######################################################################## 100.0% | |
| ==> Installing openmotif dependency: libpng | |
| ==> Downloading https://homebrew.bintray.com/bottles/libpng-1.6.17.yosemite.bott | |
| ######################################################################## 100.0% | |
| ==> Pouring libpng-1.6.17.yosemite.bottle.tar.gz | |
| 🍺 /usr/local/Cellar/libpng/1.6.17: 17 files, 1.2M | |
| ==> Installing openmotif | |
| ==> Downloading https://downloads.sourceforge.net/project/motif/Motif%202.3.4%20 | |
| ==> Downloading from http://colocrossing.dl.sourceforge.net/project/motif/Motif% |
Python version of the MATLAB code in this Stack Overflow post: http://stackoverflow.com/a/18648210/97160
The example shows how to determine the best-fit plane/surface (1st or higher order polynomial) over a set of three-dimensional points.
Implemented in Python + NumPy + SciPy + matplotlib.
