rds simple.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x109ac21d0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQkAAAD+CAYAAAApiPBxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109998510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tw = 30\n",
    "th = 2*tw\n",
    "right = np.random.randint(2, size=[th,tw])\n",
    "divider = np.zeros((th, 3))\n",
    "left = right.copy()\n",
    "\n",
    "#add pop-out\n",
    "x, y, w, h = tw/4, th/4, tw/2, th/2\n",
    "shift = w/20 + 1\n",
    "print shift\n",
    "shading = 0\n",
    "right[y:y+h,x-shift:x+w-shift] = left[y:y+h,x:x+w] + shading\n",
    "left[y:y+h,x:x+w] += shading\n",
    "# left[y:y+h,x:x+w] +=1\n",
    "#  = np.ones((30,30)) #np.random.randint(2, size=[10,20])\n",
    "\n",
    "combo = np.concatenate((left, divider, right), axis = 1)\n",
    "plt.imshow(combo, cmap='Greys',  interpolation='nearest', aspect='equal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.0\n",
      "25\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x10c08fc90>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAC0CAYAAACE/NdTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c130350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tw = 50\n",
    "th = int(2*tw)\n",
    "\n",
    "panels = []\n",
    "\n",
    "bkg = np.random.randint(2, size=[th,tw])\n",
    "panels.append(bkg)\n",
    "# divider = np.zeros((th, 3))\n",
    "\n",
    "# lines = np.zeros_like(bkg)\n",
    "# for i in range(lines.shape[1]):\n",
    "#     lines[:,i] = i%2\n",
    "# bkg = lines \n",
    "\n",
    "#make depth mask\n",
    "fringe = th/3\n",
    "shift = tw/10\n",
    "shading = 1\n",
    "dmask = np.zeros( (bkg.shape[0],bkg.shape[0]*4) )\n",
    "dmask[fringe:-fringe,fringe:-fringe] =  -shift #( -1 * np.cumsum(np.ones((h,w)),axis = 0) / 5 ).astype(int)\n",
    "print np.max(np.abs(dmask))\n",
    "\n",
    "# #create pop out panels\n",
    "for i in range(4):\n",
    "    target = bkg.copy()\n",
    "    dmask_slice = dmask[:,i*tw:i*tw+tw]\n",
    "    for i in range(target.shape[1]):\n",
    "        row_i = ( np.cumsum(np.ones(target.shape[0])) - 1).astype(int)\n",
    "        col_i = ( i + dmask_slice[:,i] ).astype(int)\n",
    "        target[row_i,col_i] = bkg[:,i]\n",
    "    panels.append(target)\n",
    "\n",
    "combo = np.concatenate((panels), axis = 1)\n",
    "one_panel = np.zeros((th/10,tw))\n",
    "print one_panel.shape[1]/2\n",
    "fixation_square_width = one_panel.shape[1]/2 + np.array(range(-one_panel.shape[0]/8, one_panel.shape[0]/8))\n",
    "one_panel[:,fixation_square_width] = np.max(combo)\n",
    "legend = one_panel\n",
    "for i in range(len(panels) - 1):\n",
    "    legend = np.concatenate((legend,one_panel),axis=1)\n",
    "\n",
    "rds = np.concatenate((legend, combo), axis = 0)\n",
    "plt.imshow(rds, cmap='Greys',  interpolation='nearest', aspect='equal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a = np.array([[1,2],[3,4]])\n",
    "b = np.array([0,1])\n",
    "a[np.array([0,1]),np.array([0,1])] = 3\n",
    "print np.array([0,1]).shape\n",
    "print a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}