jupyter-notebooks/Arrays.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The original array is:\n",
      "[[ 0  1  2  3  4  5  6  7]\n",
      " [ 8  9 10 11 12 13 14 15]\n",
      " [16 17 18 19 20 21 22 23]\n",
      " [24 25 26 27 28 29 30 31]\n",
      " [32 33 34 35 36 37 38 39]\n",
      " [40 41 42 43 44 45 46 47]\n",
      " [48 49 50 51 52 53 54 55]]\n",
      "\n",
      "\n",
      "The transposed array is:\n",
      "[[ 0  8 16 24 32 40 48]\n",
      " [ 1  9 17 25 33 41 49]\n",
      " [ 2 10 18 26 34 42 50]\n",
      " [ 3 11 19 27 35 43 51]\n",
      " [ 4 12 20 28 36 44 52]\n",
      " [ 5 13 21 29 37 45 53]\n",
      " [ 6 14 22 30 38 46 54]\n",
      " [ 7 15 23 31 39 47 55]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "a = np.arange(56).reshape(7,8) \n",
    "\n",
    "print('The original array is:')\n",
    "print(a)\n",
    "print('\\n') \n",
    "\n",
    "print('The transposed array is:')\n",
    "print(np.transpose(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7, 8)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2D Arrays indexing\n",
    "# array[line, column]\n",
    "a[3,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7],\n",
       "       [24, 25, 26, 27, 28, 29, 30, 31],\n",
       "       [48, 49, 50, 51, 52, 53, 54, 55]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2D Arrays slicing\n",
    "# array[start:stop:step]\n",
    "a[::3] # each 3 lines from the first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11, 12, 13, 14, 15],\n",
       "       [16, 17, 18, 19, 20, 21, 22, 23],\n",
       "       [24, 25, 26, 27, 28, 29, 30, 31],\n",
       "       [32, 33, 34, 35, 36, 37, 38, 39],\n",
       "       [40, 41, 42, 43, 44, 45, 46, 47],\n",
       "       [48, 49, 50, 51, 52, 53, 54, 55]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0:7:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[24, 25, 26, 27, 28, 29, 30, 31],\n",
       "       [32, 33, 34, 35, 36, 37, 38, 39],\n",
       "       [40, 41, 42, 43, 44, 45, 46, 47],\n",
       "       [48, 49, 50, 51, 52, 53, 54, 55]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[3::]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.   , 0.125, 0.25 , 0.375, 0.5  , 0.625, 0.75 , 0.875, 1.   ,\n",
       "       1.125, 1.25 ])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clevs = np.arange(0,1.26,0.125)\n",
    "clevs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "A = np.ones((5,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "A[1:4,1:4] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1., 1.],\n",
       "       [1., 0., 0., 0., 1.],\n",
       "       [1., 0., 0., 0., 1.],\n",
       "       [1., 0., 0., 0., 1.],\n",
       "       [1., 1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.725 0.99  1.    0.87 ]\n",
      "[0.725 0.99  1.    0.87 ]\n",
      "[0.725 0.99  1.    0.87 ]\n"
     ]
    }
   ],
   "source": [
    "sampleArr = np.array([0.725, 0.39, 0.99, 1, 0.4, 0.223, 0.87])\n",
    "\n",
    "condition = (sampleArr > 0.5)\n",
    "extracted = np.extract(condition, sampleArr)  # returns [0.725 0.99]\n",
    "\n",
    "print(sampleArr[sampleArr > 0.5])\n",
    "print(sampleArr[condition])\n",
    "print(extracted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "z = np.random.random((5,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----Z-----\n",
      " [[0.98640279 0.05599376 0.88395648 0.58854191 0.04163174]\n",
      " [0.040764   0.10319411 0.89431422 0.13090256 0.77189185]\n",
      " [0.44030387 0.37432871 0.7907239  0.93497147 0.8616156 ]\n",
      " [0.57851542 0.78286221 0.08453555 0.01341801 0.70082027]\n",
      " [0.82497195 0.45224957 0.16597973 0.76979631 0.73428581]] \n",
      "\n",
      "-----X-----\n",
      " [5.  5.5 6.  6.5 7. ] \n",
      "\n",
      "-----Y-----\n",
      " [-2.   0.5  3.   5.5  8. ] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "ny, nx = z.shape\n",
    "x = np.linspace(5, 7, nx)\n",
    "y = np.linspace(-2, 8, ny)\n",
    "\n",
    "print ('-----Z-----\\n', z, '\\n')\n",
    "print ('-----X-----\\n', x, '\\n')\n",
    "print ('-----Y-----\\n', y, '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----XX----\n",
      " [[5.  5.  5.  5.  5. ]\n",
      " [5.5 5.5 5.5 5.5 5.5]\n",
      " [6.  6.  6.  6.  6. ]\n",
      " [6.5 6.5 6.5 6.5 6.5]\n",
      " [7.  7.  7.  7.  7. ]] \n",
      "\n",
      "-----YY----\n",
      " [[-2.   0.5  3.   5.5  8. ]\n",
      " [-2.   0.5  3.   5.5  8. ]\n",
      " [-2.   0.5  3.   5.5  8. ]\n",
      " [-2.   0.5  3.   5.5  8. ]\n",
      " [-2.   0.5  3.   5.5  8. ]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "yy, xx = np.meshgrid(y, x)\n",
    "\n",
    "print ('-----XX----\\n', xx, '\\n')\n",
    "print ('-----YY----\\n', yy, '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/ipykernel_launcher.py:12: MatplotlibDeprecationWarning: The bivariate_normal function was deprecated in Matplotlib 2.2 and will be removed in 3.1.\n",
      "  if sys.path[0] == '':\n",
      "/usr/local/lib/python3.5/dist-packages/ipykernel_launcher.py:13: MatplotlibDeprecationWarning: The bivariate_normal function was deprecated in Matplotlib 2.2 and will be removed in 3.1.\n",
      "  del sys.path[0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pylab import *\n",
    "%matplotlib inline\n",
    "\n",
    "# the random data\n",
    "x = np.random.randn(1000)\n",
    "y = np.random.randn(1000)\n",
    "\n",
    "fig = plt.figure(1, figsize=(5.5,5.5))\n",
    "\n",
    "X, Y = meshgrid(x, y)\n",
    "Z1 = bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)\n",
    "Z2 = bivariate_normal(X, Y, 1.5, 0.5, 1, 1)\n",
    "Z = 10 * (Z1 - Z2)\n",
    "\n",
    "origin = 'lower'\n",
    "CS = contourf(x, y, Z, 10, # [-1, -0.1, 0, 0.1],\n",
    "             cmap=cm.bone,\n",
    "             origin=origin)\n",
    "\n",
    "title('Nonsense')\n",
    "xlabel('x-stuff')\n",
    "ylabel('y-stuff')\n",
    "\n",
    "# the scatter plot:\n",
    "axScatter = plt.subplot(111)\n",
    "axScatter.scatter(x, y)\n",
    "\n",
    "# set axes range\n",
    "plt.xlim(-2, 2)\n",
    "plt.ylim(-2, 2)\n",
    "\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-6.         -5.98826979 -5.97653959 ...  5.97653959  5.98826979\n",
      "  6.        ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "X = np.linspace(-6, 6, 1024)\n",
    "print(X)\n",
    "plt.ylim(-.5, 1.5)\n",
    "plt.plot(X, np.sinc(X), c = 'k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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