intro_numpy.ipynb

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   "source": [
    "import numpy as np "
   ]
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   "source": [
    "Create vector\n",
    "\n",
    "In Matlab:\n",
    "`v = [1,2,3,4,5,6,7]`\n",
    "In numpy python:"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "[ 1.  2.  3.  4.  5.  6.  7.]\n"
     ]
    }
   ],
   "source": [
    "v = np.array([1.0,2.0,3.0,4.0,5.0,6.0,7.0])\n",
    "print(v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Access the first element\n",
    "\n",
    "In Matlab:\n",
    "`v(1)`\n",
    "In numpy python:"
   ]
  },
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   "source": [
    "v[0]"
   ]
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   "source": [
    "Access the last element\n",
    "\n",
    "In Matlab:\n",
    "`v(end)`\n",
    "In numpy python:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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       "7.0"
      ]
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     "execution_count": 7,
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   ],
   "source": [
    "v[-1]"
   ]
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   "metadata": {},
   "source": [
    "Access all elements up to (including) the third element \n",
    "\n",
    "`v(1:3)`\n",
    "\n",
    "In python numpy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2.,  3.])"
      ]
     },
     "execution_count": 8,
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   ],
   "source": [
    "v[0:3] #Syntax: v['first index':'last index (excluding)']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Access every second element\n",
    "\n",
    "In Matlab:\n",
    "`v(1:2:end)`\n",
    "\n",
    "In numpy python:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  3.,  5.,  7.])"
      ]
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     "execution_count": 10,
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   "source": [
    "v[0:7:2] # Syntax v['first index':'last index (excluding)':'increment']"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Access every second element up to index 4"
   ]
  },
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   "cell_type": "code",
   "execution_count": 11,
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   "outputs": [
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       "array([ 1.,  3.])"
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     "execution_count": 11,
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   "source": [
    "v[0:4:2]"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other ways of creating vectors in numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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     "text": [
      "[ 1.  2.  3.  4.]\n",
      "[[ 1.]\n",
      " [ 1.]\n",
      " [ 1.]\n",
      " [ 1.]]\n",
      "[[ 0.  0.  0.  0.]]\n",
      "[ 1.  1.  1.  1.]\n"
     ]
    }
   ],
   "source": [
    "v = np.array([1,2,3,4.0])\n",
    "print(v)\n",
    "v = np.ones([4,1])\n",
    "print(v)\n",
    "v = np.zeros([1,4])\n",
    "print(v)\n",
    "v = np.ones(4)\n",
    "print(v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2 dimensional arrays:\n",
    "\n",
    "In Matlab: \n",
    "\n",
    "`A = [1,2,3,4; 5,6,7,8;9,10,11,12]`"
   ]
  },
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   "cell_type": "code",
   "execution_count": 14,
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     "name": "stdout",
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     "text": [
      "[[ 1  2  3  4]\n",
      " [ 5  6  7  8]\n",
      " [ 9 10 11 12]]\n"
     ]
    }
   ],
   "source": [
    "A = np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12]]) \n",
    "print(A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Access first column:\n",
    "\n",
    "In Matlab: `A(:,1)`\n",
    "\n",
    "In python numpy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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     "data": {
      "text/plain": [
       "array([1, 5, 9])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "A[:,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Access first row:\n",
    "\n",
    "In Matlab: `A(1,:)`\n",
    "\n",
    "In python numpy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 16,
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   ],
   "source": [
    "A[0,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Access i-th column:\n",
    "\n",
    "In Matlab: `A(:,i)`\n",
    "\n",
    "In python numpy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9, 10, 11, 12])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
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   ],
   "source": [
    "i=2\n",
    "A[i,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Access i-th row?\n",
    "\n",
    "In Matlab: `A(2,:)`\n",
    "\n",
    "In python numpy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 7, 8])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[1,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Access last column: \n",
    "\n",
    "In Matlab: `A(:,end)`\n",
    "\n",
    "In python numpy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  7, 11])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[:,-2]"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
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   "source": []
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