{ "metadata": { "name": "", "signature": "sha256:f75c22f4a39c2e4e18e4d3be8f6d11cfaf395c67f0f026634707968abf09833e" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from holoviews import *\n", "from holoviews.operation import contours, threshold, gradient\n", "import numpy as np\n", "\n", "%load_ext holoviews.ipython" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "def sine(x, phase=0, freq=100):\n", " return np.sin((freq * x + phase))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "phases = np.linspace(0,2*np.pi,11) # Explored phases\n", "freqs = np.linspace(50,150,5) # Explored frequencies" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "dist = np.linspace(-0.5,0.5,202) # Linear spatial sampling\n", "x,y = np.meshgrid(dist, dist)\n", "grid = (x**2+y**2) # 2D spatial sampling" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "freq1 = Image(sine(grid, freq=50)) + Curve(zip(dist, sine(dist**2, freq=50)))\n", "freq2 = Image(sine(grid, freq=200)) + Curve(zip(dist, sine(dist**2, freq=200)))\n", "(freq1 + freq2).cols(2)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "