numpy-pilot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "points = np.arange(-5, 5, 0.01) # 1000 equally spaced points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([[-5.  , -5.  , -5.  , ..., -5.  , -5.  , -5.  ],\n       [-4.99, -4.99, -4.99, ..., -4.99, -4.99, -4.99],\n       [-4.98, -4.98, -4.98, ..., -4.98, -4.98, -4.98],\n       ...,\n       [ 4.97,  4.97,  4.97, ...,  4.97,  4.97,  4.97],\n       [ 4.98,  4.98,  4.98, ...,  4.98,  4.98,  4.98],\n       [ 4.99,  4.99,  4.99, ...,  4.99,  4.99,  4.99]])"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "xs, ys = np.meshgrid(points, points)\n",
    "ys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([[7.07106781, 7.06400028, 7.05693985, ..., 7.04988652, 7.05693985,\n        7.06400028],\n       [7.06400028, 7.05692568, 7.04985815, ..., 7.04279774, 7.04985815,\n        7.05692568],\n       [7.05693985, 7.04985815, 7.04278354, ..., 7.03571603, 7.04278354,\n        7.04985815],\n       ...,\n       [7.04988652, 7.04279774, 7.03571603, ..., 7.0286414 , 7.03571603,\n        7.04279774],\n       [7.05693985, 7.04985815, 7.04278354, ..., 7.03571603, 7.04278354,\n        7.04985815],\n       [7.06400028, 7.05692568, 7.04985815, ..., 7.04279774, 7.04985815,\n        7.05692568]])"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "z = np.sqrt(xs ** 2 + ys ** 2)\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Image plot of $\\\\sqrt{x^2 + y^2}$ for a grid of values')"
     },
     "metadata": {},
     "execution_count": 5
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(z, cmap=plt.cm.gray); plt.colorbar\n",
    "plt.title(\"Image plot of $\\sqrt{x^2 + y^2}$ for a grid of values\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Image plot of $\\\\sqrt{x^2 + y^2}$ for a grid of values')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.4 64-bit ('pandas-sample-4shxcQQ7-py3.7': venv)",
   "language": "python",
   "name": "python37464bitpandassample4shxcqq7py37venv5d129e48a3204332a41b2c2d80da020e"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}