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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##EE16A: Homework 1\n",
      "This notebook contains the code for Homework problem 1.1.\n",
      "\n",
      "###Problem 1.1\n",
      "\n",
      "We will give you this following function, which may come in useful:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import Image\n",
      "import numpy as np\n",
      "from matplotlib import pyplot as plt\n",
      "\n",
      "w,h = 6,6\n",
      "\n",
      "def show_black_pixel(row_start, row_end, col_start, col_end):\n",
      "    \"\"\" Generates an image with black pixels within given coordinates        \n",
      "        row_start: first row of black pixel region\n",
      "        row_end: last row after black pixel region\n",
      "        col_start: first column of black pixel region\n",
      "        col_end: last column after black pixel region\n",
      "        \n",
      "        Make sure row_end - row-start >= 1 (same with col_end and col_start)\n",
      "\n",
      "    \"\"\"\n",
      "    data = np.zeros( (w,h), dtype=np.uint8)\n",
      "    data.fill(1)\n",
      "    try:\n",
      "        data[row_start:row_end, col_start:col_end] = 0\n",
      "    except Exception:\n",
      "        print('Make sure your coordinates are in the right format and order!')\n",
      "    plt.imshow(data, interpolation='nearest', cmap='gray')\n",
      "    plt.show()\n",
      "    return data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Part (a)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Note that to generate a pixel at (0,0), the x-coordinates are (0,1) and the y coordinates are also (0,1)\n",
      "#grid = show_black_pixel(0, 1, 0, 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Part (b)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#grid = show_black_pixel()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Part (c)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Generate an image that has all black pixels in the top 2 rows\n",
      "#grid = show_black_pixel()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Part(d)\n",
      "The command [np.shape()](http://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.shape.html) will help you find the dimensions of the data grid. The command [np.reshape()](http://docs.scipy.org/doc/numpy/reference/generated/numpy.reshape.html) will be useful to convert the grid to a vector. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Problem 1.4\n",
      "\n",
      "The following function converts a vector to an image and displays it."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def display_image(vector):\n",
      "    data_grid=np.reshape(vector,(50,50))\n",
      "    plt.imshow(data_grid, cmap='gray')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Part(a)\n",
      "Run the cell below to load in the image vectors."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cal_vector = np.load('cal.npy')\n",
      "campanile_vector= np.load('campanile.npy')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Display the above images using the ``display_image`` function."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figure()\n",
      "#TODO: call display_image to show the cal image\n",
      "\n",
      "figure()\n",
      "#TODO: call display_image to show the campanile image\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Create a scaling matrix that scales each element in a vector by 0.5. The commands [np.eye()](http://docs.scipy.org/doc/numpy/reference/generated/numpy.eye.html) and [np.dot()](http://docs.scipy.org/doc/numpy/reference/generated/numpy.dot.html) might be useful."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#scaling_matrix = \n",
      "#result_vector = "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Part(b)\n",
      "Create the Campanile and Cal image scaling matrices separately"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scaling_matrix_cal = np.eye(50*50)\n",
      "#TODO:Change the scaling matrix here\n",
      "\n",
      "scaling_matrix_campanile = np.eye(50*50)\n",
      "#TODO:Change the scaling matrix here\n",
      "\n",
      "#result_vector =\n",
      "#display_image(result_vector)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    }
   ],
   "metadata": {}
  }
 ]
}