Py_lectures_04_Classes_3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python-in-the-lab: introduction to the classes III"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mainDir = \"/home/gf/src/Python/Python-in-the-lab/Bk\"\n",
    "filename = \"F64ac_0.01_S.dat\"\n",
    "filename = os.path.join(mainDir, filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Dist:\n",
    "    \"\"\"\n",
    "    This class load the data given a filename\n",
    "    \"\"\"\n",
    "    def __init__(self, filename, is_avoid_zeros=True):\n",
    "        # It is better to make general x,y arrays\n",
    "        self.x, self.y = np.loadtxt(filename, comments=\"#\", unpack=True)\n",
    "        if is_avoid_zeros:\n",
    "            s_len = len(self.x)\n",
    "            self.x, self.y = self.avoid_zeros()\n",
    "            print(\"%i lines deleted\" % (s_len - len(self.x)))\n",
    "    \n",
    "    def avoid_zeros(self):\n",
    "        is_not_zero = self.y != 0\n",
    "        x = self.x[is_not_zero]\n",
    "        y = self.y[is_not_zero]\n",
    "        return x, y\n",
    "\n",
    "    def plot(self, loglog=True):\n",
    "        fig = plt.figure()\n",
    "        ax = fig.add_subplot(111)\n",
    "        if loglog:\n",
    "            ax.loglog(self.x, self.y, 'o')\n",
    "        else:\n",
    "            ax.plot(self.x, self.y, 'o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Subclasses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Sizes(Dist):\n",
    "    def plot(self):\n",
    "        fig = plt.figure()\n",
    "        ax = fig.add_subplot(111)\n",
    "        ax.loglog(self.x, self.y, '*')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This class automatically import the method of the mother class Dist!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14 lines deleted\n"
     ]
    }
   ],
   "source": [
    "s0 = Sizes(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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muyXdImm2mQ1LetjdnzSz9ZJektQpaYe7HwsdKQDEqNpZA9LUxWLtdrYAewEBQBV5XSzGXkAAEFIRCsMkAACoogiFYRIAANTQ7oVhagAA0KTN/Uf09KEhrV7SnbnCcDM1gDhWAgNAW6osDOd9xTBDQAAQULsVhkkAABBQuxWGGQICgCZMFIYnDp0fzXEhmCIwAERg5Ow5rd/9hh5bdUOqPQIWggFAwvJ4lgBDQAAQQp5nBtEDAIAQ8jwziAQAACHkeWYQQ0AAEFJeZwYxCwgA2gizgAAADZEAACAGedgxlAQAADHIw7oAisAAEKE8rQugBwAAEcrTugASAABEKE/rAhgCAoCI5WVdAOsAAKCNsA4AANAQCQAACooEAAAJydriMBIAACQka4vDmAUEADHL6uIwegAAELOsLg4jAQBAzLK6OIwhIABIQBYXhyW2EMzMrpO0SdIV7n5XkOewEAwAmhP5QjAz22FmI2Z2tKJ9mZkdN7MTZrah3mu4+0l3XxvkegCA+AUdAtop6TFJT000mFmnpMclfUXSsKTDZrZXUqekRyqef6+7j4SOFgAQmUAJwN1fNbOeiuYlkk64+0lJMrM9kpa7+yOSvhFlkACA6IWZBTRP0qmy+8OltqrM7Eoze0LSDWa2sc7j1pnZoJkNjo6OhggPAFBPmFlAVqWtZkXZ3X8u6f5GL+ru2yVtl8aLwC1HBwCoK0wPYFjSNWX3r5Z0Olw4AICkhEkAhyXNN7NrzWy6pHsk7Y0mLABA3IJOA90t6aCkBWY2bGZr3f2CpPWSXpL0tqRn3P1YfKECAKIUdBbQyhrt+yTtizQiAEAi2AsIAAqKBAAABUUCAICCIgEAQEGRAACgoEgAAFBQJAAAKCgSAAAUFAkAAAqKBAAABUUCAICCIgEAQEGRAACgoEgAAFBQJAAASNHI2XO6e9tBjfzyXOLXJgEAQIq2/vRdHX7vjLa+/G7i1w5zKDwAoEULNu/X+Qtjk/d3DQxp18CQpneaFnd/Ro+tukFXzZwRawz0AAAgBQceXKo7F8/VjMvGP4ZnXNah5Yvn6o7fm5tYj4AeAACk4KpZMzSza5rOXxhT17QOnftkTM+/eXry6xM9gq5pHTq+5fZYYqAHAAAp+fCj81p94+fU/8AX9a0vzNNnr+i6pEdw4KGlsV2fHgAApGTbmt7J29+7e7E29R9R36Hxv/rPXxjTzK5psdYBSAAAkBETPYJVS7rVd2hIozFPDTV3j/UCYfT29vrg4GDaYQBAbpjZ6+7e2/iR1AAAoLBIAABQUCQAACgoEgAAFBQJAAAKigQAAAWV6WmgZjYq6f0qX7pC0i8CtNe7X+32xH9nS/qwhZBrxRXkMXmNPUhbvXjL2+KKPejPvFaslV/LW+yNbqcde9Lv9XqxNfp62rEH+f90vrtfEejV3D13/yRtD9Je736122X/HYwyrnaOPUhbvXiTiD3oz7zezznPsQd476Qae9Lv9TzHHuYzptq/vA4B/SRge7371W7Xet2ggjy/3WIP0tYo3rhjD/ozr2yr9X3kLfYgt1sRVexJv9eDvEZWYw/zGXOJTA8BpcXMBj3gSrqsIfZ0EHvy8hq3lJ3Y89oDiNv2tAMIgdjTQezJy2vcUkZipwcAAAVFDwAACooEAAAFRQIAgIIiAQRkZteZ2ZNm9mxF+6fM7HUz+0ZasTVSLXYzu97MnjCzZ83sO2nGV0+N2FeY2Q/M7Hkz+8M046ulRtxV30NZUyP2T5nZj0o/99VpxheEmS00s2fM7Ptmdlfa8TTDzLrNbK+Z7TCzDbFerNWFFHn6J2mHpBFJRyval0k6LumEpA0BX+vZivt/I+khSd/IW+yltg5JT+Y09s/EEXsCcV/SlvXYJa2RdEfp9j/FFX9U34Okv5R0c+n23jjjjSH2L0u6r3T7qTjjLcqRkDslPSbpqYkGM+uU9Likr0galnTYzPZK6pT0SMXz73X3kcoXNbMvS3pLUnyHdsYUe+l17pS0ofT6cdipmGIv2Vx6rajtVLxxx2mn4on9aklHSrcvRhxzpZ0K+T1I+rGkh0vv8StjjrfcToWP/Q1Jm8zs2xr/PmJTiATg7q+aWU9F8xJJJ9z9pCSZ2R5Jy939EUlBh3OWSvqUpIWS/s/M9rn7WDRRj4sxdrn7Xkl7zexFSX3RRDzl9WOJ3cxM0t9L2u/u/xldxOPi/JnHLcbYhzWeBN5UzEPHEX4Pf1r68H0urlgrRRG7mf2VpIdLr/WspB/GFW+RawDzJJ0quz9caqvKzK40syck3WBmGyXJ3Te5+19o/MPzB1F/+NcROnYzu8XMtprZNkn7Yo12qtCxS/ozjXeT7zKz+2OLdKoofubVvpckRPEzf07SH5nZ9xXNVgzNavZ76DGz7Rr/S/wfYo6tkaZil/Qvkv689Dt4L8a4itEDqMGqtNVcFefuP5dU9cPG3XdGFFNQoWN391ckvRJpVMFEEftWSVsjjquRKOKu+R6KWRSx/0rSH0ccVzOa/R7ek7Qutmia02zsRyUlUrgucg9gWNI1ZfevlnQ6pViaRezJy2vcUr5jn5Dn7yGzsRc5ARyWNN/MrjWz6ZLukbQ35ZiCIvbk5TVuKd+xT8jz95Dd2JOaHpXmP0m7Jf23pE80no3Xltq/JukdSf8laVPacRJ7Nv7lNe68x94O30PeYmczOAAoqCIPAQFAoZEAAKCgSAAAUFAkAAAoKBIAABQUCQAACooEAAAFRQIAgIIiAQBAQf0/jlw725PszxQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s0.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can even initialize them separately, with different inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Sizes(Dist):\n",
    "    def __init__(self, freq, mainDir, material=\"F64ac\"):\n",
    "        filename = \"%s_%s_S.dat\" % (material, freq)\n",
    "        filename = os.path.join(mainDir, filename)\n",
    "        print(filename)\n",
    "        #Dist.__init__(self, filename)\n",
    "        super().__init__(filename) # Works as the line above\n",
    "        \n",
    "    def plot(self):\n",
    "        fig = plt.figure()\n",
    "        ax = fig.add_subplot(111)\n",
    "        ax.loglog(self.x, self.y, '*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/gf/src/Python/Python-in-the-lab/Bk/F64ac_0.02_S.dat\n",
      "12 lines deleted\n"
     ]
    }
   ],
   "source": [
    "s002 = Sizes(\"0.02\", mainDir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "s002.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(mainDir):\n",
    "    print(\"Problems!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}