2_intro_to_numpy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "af850385",
   "metadata": {},
   "outputs": [],
   "source": [
    "def section(text):\n",
    "    print('\\033[1m{}\\033[0m'.format(text))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e7385682",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d477e2e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mBasic manipulations\u001b[0m\n",
      "1D Array\n",
      "Simplest initialization [0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5]\n",
      "Attribue (10,) float64\n",
      "Changing data type [0 1 2 3 4 5 6 7 8 9]\n",
      "Attribue (10,) int16\n",
      "Indexing 4 5\n",
      "Slicing 1 [4 5 6] [0 1 2] [7 8 9]\n",
      "Slicing 2 9 [9 8 7 6] [1 3 5 7 9]\n"
     ]
    }
   ],
   "source": [
    "section('Basic manipulations')\n",
    "print('1D Array')\n",
    "array = np.array([0.5,1.5,2.5,3.5,4.5, 5.5, 6.5, 7.5, 8.5, 9.5])\n",
    "print('Simplest initialization', array)\n",
    "print('Attribue', array.shape, array.dtype)\n",
    "\n",
    "array = np.array([0.5,1.5,2.5,3.5,4.5, 5.5, 6.5, 7.5, 8.5, 9.5],\n",
    "                 dtype=np.int16)\n",
    "print('Changing data type', array)\n",
    "print('Attribue', array.shape, array.dtype)\n",
    "\n",
    "print('Indexing', array[4], array[5])\n",
    "print('Slicing 1', array[4:7], array[:3], array[7:])\n",
    "print('Slicing 2', array[-1], array[:5:-1], array[1::2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "104d0e65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mBasic operation\u001b[0m\n",
      "ones [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n",
      "modified [0.5 0.5 0.5 0.5 0.5 1.  1.  1.  1.  1. ]\n",
      "multiply [ 8.5  8.5  8.5  8.5  8.5 10.  10.  10.  10.  10. ]\n",
      "random [0.85158415 0.41192805 0.87582097 0.07236214 0.82809069]\n",
      "arange [0 1 2 3 4]\n",
      "arange [10. 15. 20. 25.]\n"
     ]
    }
   ],
   "source": [
    "section('Basic operation')\n",
    "array = np.ones((10,))\n",
    "print('ones', array)\n",
    "array[0:5] = 0.5\n",
    "print('modified', array)\n",
    "array = array*3 + 7\n",
    "print('multiply', array)\n",
    "\n",
    "print('random', np.random.random((5,)))\n",
    "print('arange', np.arange(5))\n",
    "print('arange', np.linspace(10, 25, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6d5c0e4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mPlotting 1D data\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "section('Plotting 1D data')\n",
    "\n",
    "x = np.arange(100)\n",
    "y = np.arange(100)\n",
    "plt.plot(x,y)\n",
    "plt.plot(x,(0.1*y)**2)\n",
    "plt.plot(x, np.log((y+1)**3))\n",
    "plt.show()\n",
    "\n",
    "y = np.linspace(-2*np.pi, 2*np.pi, 100)\n",
    "plt.plot(x,np.sin(y))\n",
    "plt.plot(x,np.cos(y))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d0984d1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m2D manipulations\u001b[0m\n",
      "2D Array\n",
      "Initialization\n",
      "[[1 2 3]\n",
      " [4 5 6]\n",
      " [7 8 9]]\n",
      "Attribue (3, 3) int64\n",
      "Indexing [7 8 9] [3 6 9]\n"
     ]
    }
   ],
   "source": [
    "section('2D manipulations')\n",
    "print('2D Array')\n",
    "array = np.array([[1,2,3], [4,5,6], [7,8,9]])\n",
    "print('Initialization')\n",
    "print(array)\n",
    "\n",
    "print('Attribue', array.shape, array.dtype)\n",
    "print('Indexing', array[2, :], array[:, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "98aa2c7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m2D operations\u001b[0m\n",
      "identity (2x)\n",
      "[[2. 0. 0.]\n",
      " [0. 2. 0.]\n",
      " [0. 0. 2.]]\n",
      "array_1 [1 2 3]\n",
      "array_2 [2 2 2]\n",
      "[[ 2.  4.  6.]\n",
      " [ 8. 10. 12.]\n",
      " [14. 16. 18.]] [2 4 6] False\n"
     ]
    }
   ],
   "source": [
    "section('2D operations')\n",
    "identity = np.eye(3)*2\n",
    "\n",
    "print('identity (2x)')\n",
    "print(identity)\n",
    "\n",
    "array_1 = np.arange(1,4)\n",
    "array_2 = np.array([2,2,2])\n",
    "print('array_1', array_1)\n",
    "print('array_2', array_2)\n",
    "\n",
    "print(np.dot(array, identity), array_1*array_2,\n",
    "      np.all(np.equal(np.dot(array, identity), array_1*array_2)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e23a799f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mPlotting 2D data\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "section('Plotting 2D data')\n",
    "fig, axs = plt.subplots(1, 2, figsize=(15, 8))\n",
    "axs[0].imshow(np.eye(10))\n",
    "axs[1].imshow(np.random.random((10,10)))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1f35203f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mContrast\u001b[0m\n",
      "0 min 0.0047135507111326636 max 0.9975372111300082 avg 0.4483106777026933 std 0.3052492626490378\n",
      "1 min 0.0047135507111326636 max 1.053590394077137 avg 0.4583106777026933 std 0.30851694714435074\n",
      "2 min 0.0047135507111326636 max 3.053590394077137 avg 0.4783106777026933 std 0.3982384905182419\n",
      "3 min 0.0047135507111326636 max 6.053590394077137 avg 0.5083106777026933 std 0.6342008185998458\n",
      "4 min 0.0047135507111326636 max 10.053590394077137 avg 0.5483106777026933 std 1.002114292694536\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x400 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "section('Contrast')\n",
    "# Matplotlib automatically determines the contrast based on MIN/MAX\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "fig, axs = plt.subplots(1, 5, figsize=(20, 4))\n",
    "array = np.random.random((10,10))\n",
    "for i, ax in enumerate(axs):\n",
    "    array[6,8] += 1*i\n",
    "    print(i, 'min', np.min(array), 'max', np.max(array), 'avg', np.average(array), 'std', np.std(array))\n",
    "    im = ax.imshow(array)\n",
    "    divider = make_axes_locatable(ax)\n",
    "    cax = divider.append_axes('right', size='5%', pad=0.05)\n",
    "    fig.colorbar(im, cax=cax, orientation='vertical')\n",
    "    \n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2b1e2972",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x400 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "section('Masking')\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "fig, axs = plt.subplots(1, 5, figsize=(20, 4))\n",
    "array = np.random.random((5, 10,10))\n",
    "array[0, :, 5] = 0\n",
    "array[1, 4, :] = 0\n",
    "array[2, 2:7, 2:5] = 0\n",
    "array[3, :, :] = np.tril(array[3, :, :])\n",
    "array[4, :, :] = np.triu(array[4, :, :])\n",
    "for i, ax in enumerate(axs):\n",
    "    im = ax.imshow(array[i])\n",
    "    divider = make_axes_locatable(ax)\n",
    "    cax = divider.append_axes('right', size='5%', pad=0.05)\n",
    "    fig.colorbar(im, cax=cax, orientation='vertical')\n",
    "    \n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "864396ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mThresholding\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x400 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "section('Thresholding')\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "fig, axs = plt.subplots(1, 5, figsize=(20, 4))\n",
    "array = np.random.random((10,10))\n",
    "\n",
    "for i, ax in enumerate(axs):\n",
    "    tmp = array.copy()\n",
    "    tmp[array > 0.2*(i+1)] = 0\n",
    "    im = ax.imshow(tmp, vmin=0, vmax=1)\n",
    "    divider = make_axes_locatable(ax)\n",
    "    cax = divider.append_axes('right', size='5%', pad=0.05)\n",
    "    fig.colorbar(im, cax=cax, orientation='vertical')\n",
    "    \n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5115348e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}