Add week 7 manuscript

docs/02advanced/03simulation/_samples/draw_circle.ipynb

@@ -0,0 +1,92 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {},
+ "cells": [
+  {
+   "metadata": {},
+   "source": [
+    "!pip install ita"
+   ],
+   "cell_type": "code",
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Defaulting to user installation because normal site-packages is not writeable\n",
+      "Requirement already satisfied: ita in /home/w/.local/lib/python3.10/site-packages (0.2.12)\n",
+      "Requirement already satisfied: numpy in /home/w/.local/lib/python3.10/site-packages (from ita) (1.23.3)\n",
+      "Requirement already satisfied: matplotlib in /home/w/.local/lib/python3.10/site-packages (from ita) (3.6.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in /home/w/.local/lib/python3.10/site-packages (from matplotlib->ita) (0.11.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/w/.local/lib/python3.10/site-packages (from matplotlib->ita) (1.4.4)\n",
+      "Requirement already satisfied: pyparsing>=2.2.1 in /usr/lib/python3/dist-packages (from matplotlib->ita) (2.4.7)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in /home/w/.local/lib/python3.10/site-packages (from matplotlib->ita) (1.0.5)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /home/w/.local/lib/python3.10/site-packages (from matplotlib->ita) (4.37.4)\n",
+      "Requirement already satisfied: pillow>=6.2.0 in /usr/lib/python3/dist-packages (from matplotlib->ita) (9.0.1)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in /home/w/.local/lib/python3.10/site-packages (from matplotlib->ita) (2.8.2)\n",
+      "Requirement already satisfied: packaging>=20.0 in /home/w/.local/lib/python3.10/site-packages (from matplotlib->ita) (21.3)\n",
+      "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib->ita) (1.16.0)\n"
+     ]
+    }
+   ],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "source": [
+    "import ita\n",
+    "\n",
+    "%matplotlib inline"
+   ],
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "source": [
+    "def draw_circle(x, y, r):\n",
+    "    image = ita.array.make2d(100, 100)\n",
+    "    y = len(image) - 1 - y\n",
+    "    for i in range(y - r, y + r + 1):\n",
+    "        for j in range(x - r, x + r + 1):\n",
+    "            if (\n",
+    "                0 <= i < len(image)\n",
+    "                and 0 <= j < len(image[i])\n",
+    "                and (x - j) ** 2 + (y - i) ** 2 < r**2\n",
+    "            ):\n",
+    "                image[i][j] = 1\n",
+    "    return image\n",
+    "\n",
+    "\n",
+    "image = draw_circle(10, 10, 10)\n",
+    "ita.plot.image_show(image)"
+   ],
+   "cell_type": "code",
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f866d3da260>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    }
+   ],
+   "execution_count": null
+  }
+ ]
+}

docs/02advanced/03simulation/_samples/uniform_linear_motion.ipynb

@@ -0,0 +1,39 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {},
+ "cells": [
+  {
+   "metadata": {},
+   "source": [
+    "def uniform_linear_motion_step(x, v0, dt):\n",
+    "    return x + v0 * dt\n",
+    "\n",
+    "\n",
+    "def uniform_linear_motion(x0, v0, t, dt):\n",
+    "    x = x0\n",
+    "    n = int(t / dt)\n",
+    "    for i in range(n):\n",
+    "        x = uniform_linear_motion_step(x, v0, dt)\n",
+    "    return x\n",
+    "\n",
+    "\n",
+    "uniform_linear_motion(0, 10, 10, 0.1)"
+   ],
+   "cell_type": "code",
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "100.0"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "execution_count": null
+  }
+ ]
+}

docs/02advanced/03simulation/_samples/uniform_linear_motion_analytical.ipynb

@@ -0,0 +1,31 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {},
+ "cells": [
+  {
+   "metadata": {},
+   "source": [
+    "def uniform_linear_motion(x0, v0, t):\n",
+    "    return x0 + v0 * t\n",
+    "\n",
+    "\n",
+    "uniform_linear_motion(0, 10, 10)"
+   ],
+   "cell_type": "code",
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "100"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 2
+    }
+   ],
+   "execution_count": null
+  }
+ ]
+}

docs/02advanced/03simulation/_samples/uniform_linear_motion_return_array.ipynb

@@ -0,0 +1,37 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {},
+ "cells": [
+  {
+   "metadata": {},
+   "source": [
+    "def uniform_linear_motion_step(x, v0, dt):\n",
+    "    return x + v0 * dt\n",
+    "\n",
+    "\n",
+    "def uniform_linear_motion(x0, v0, t, dt):\n",
+    "    x = []\n",
+    "    x.append(x0)\n",
+    "    n = int(t / dt)\n",
+    "    for i in range(n):\n",
+    "        x.append(uniform_linear_motion_step(x[i], v0, dt))\n",
+    "    return x\n",
+    "\n",
+    "\n",
+    "print(uniform_linear_motion(0, 10, 10, 0.1))"
+   ],
+   "cell_type": "code",
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0, 70.0, 71.0, 72.0, 73.0, 74.0, 75.0, 76.0, 77.0, 78.0, 79.0, 80.0, 81.0, 82.0, 83.0, 84.0, 85.0, 86.0, 87.0, 88.0, 89.0, 90.0, 91.0, 92.0, 93.0, 94.0, 95.0, 96.0, 97.0, 98.0, 99.0, 100.0]\n"
+     ]
+    }
+   ],
+   "execution_count": null
+  }
+ ]
+}