day1 (live colab session).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled1.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dX7zfEI0VKYp",
        "outputId": "cd3ce40b-3948-49d1-c8bb-4723a8788b7b"
      },
      "source": [
        "print(\"hello world\", 23, 4.5, 4e12)\n",
        "print(\"something else\")"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "hello world 23 4.5 4000000000000.0\n",
            "something else\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qzJaS0rVV5Yc"
      },
      "source": [
        "# Programming Tutorial, Day 1\n",
        "Part of the *Better With Scattering* Workshop by www.bam.de\n",
        "\n",
        "equation: $\\phi = \\sqrt{1 \\over 9}$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-1tAYjUHV-CG",
        "outputId": "e0c3e412-ad67-4cec-bc37-1e912477a529"
      },
      "source": [
        "type(\"hello\"), \"hello\".upper()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(str, 'HELLO')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IDgSk0laWzq0",
        "outputId": "b0fc15d8-dcad-4128-bae9-c975a7bbe25b"
      },
      "source": [
        "3+4"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6K8A6GnpXSUr",
        "outputId": "bded26e2-a7da-459c-8332-22a0c7e5d147"
      },
      "source": [
        "print(3**4)\n",
        "3+4*5"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "81\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rzsIdD1TXYOO",
        "outputId": "a5838609-7cab-479b-c2c0-900495285314"
      },
      "source": [
        "import math\n",
        "math.sqrt(81)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.0"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7tP9z73VX6h7",
        "outputId": "5cae7b11-7727-4ffa-e51a-a97b422a9cf3"
      },
      "source": [
        "import numpy\n",
        "numpy.sqrt(81)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.0"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "12gYgn8sYsp4"
      },
      "source": [
        "## Lists and Arrays"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "862z6pjJYUzn",
        "outputId": "8409a10f-4562-4706-abab-dede68483045"
      },
      "source": [
        "alist = [2, 3, 34, \"world\", 4.5]\n",
        "alist"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2, 3, 34, 'world', 4.5]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6w0raSmDZirX",
        "outputId": "771d9e77-d6df-40a5-a423-e42ab304eb4b"
      },
      "source": [
        "alist[1]"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8V9n3e-LZs2g",
        "outputId": "3f7af6eb-233a-4d6c-ce1f-668f83b4b9b8"
      },
      "source": [
        "alist[1] = 7\n",
        "alist"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2, 7, 34, 'world', 4.5]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F67Izg3NZHsW",
        "outputId": "4a2783d6-4869-4e67-a345-9c145430d966"
      },
      "source": [
        "atuple = (2, 3, 34, \"world\", 4.5)\n",
        "atuple"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2, 3, 34, 'world', 4.5)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5KzltLjwZxsP",
        "outputId": "047b9108-966b-47e1-dd85-7b0b6d896c95"
      },
      "source": [
        "atuple[1]"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "fOUonJQdZdZH",
        "outputId": "80f20237-7e31-4af6-b0d0-7fa8e76b9d80"
      },
      "source": [
        "arr1 = numpy.array(alist)\n",
        "arr1, arr1 + 2"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "error",
          "ename": "UFuncTypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mUFuncTypeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-26-dc195962c91e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0marr1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0marr1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marr1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mUFuncTypeError\u001b[0m: ufunc 'add' did not contain a loop with signature matching types (dtype('<U21'), dtype('<U21')) -> dtype('<U21')"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V1GZBThRaTWq",
        "outputId": "56a16d73-39a8-4782-de58-a4a363fbd876"
      },
      "source": [
        "arr1 = numpy.array([1, 2, 4.5, 7])\n",
        "arr1, arr1 + 2"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([1. , 2. , 4.5, 7. ]), array([3. , 4. , 6.5, 9. ]))"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Viv6BRL9aq75"
      },
      "source": [
        "## Dictionary (*dict*)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Af7AjpJ_aWzd",
        "outputId": "4dd96c18-86fe-4880-e507-da5e4286f0cb"
      },
      "source": [
        "d = {\"first\": 3, \"second\": 3.14, 23: 42}\n",
        "print(d[\"second\"])\n",
        "d"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3.14\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{23: 42, 'first': 3, 'second': 3.14}"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ORwVq768dAij"
      },
      "source": [
        "## Plotting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "lEFTITSCdB0Z",
        "outputId": "745ee525-3fd4-461e-8847-bb81e332ef03"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot([1,2,3], [6, 4, 8], marker='o', color='red')\n",
        "plt.grid()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZoG7kE0ad_Uc",
        "outputId": "4b5a6fc7-3b09-4f1f-d92d-a60f4ff33338"
      },
      "source": [
        "xpos = numpy.arange(1,10)\n",
        "ypos = numpy.linspace(4, 9, 9)\n",
        "xpos, ypos"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
              " array([4.   , 4.625, 5.25 , 5.875, 6.5  , 7.125, 7.75 , 8.375, 9.   ]))"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "l3uw8QWpejxk",
        "outputId": "024c0eab-8b9a-4ac1-e4a6-75c605cddf01"
      },
      "source": [
        "plt.plot(xpos, ypos, 'ro-', label=\"test data\")\n",
        "plt.plot([1,2,3], [6, 4, 8], label=\"fst plot\")\n",
        "plt.xlabel(\"x axis $\\phi$\")\n",
        "plt.ylabel(\"on y it is $\\psi$\")\n",
        "plt.legend(); plt.grid()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ShfWeuLgFi2"
      },
      "source": [
        "## Functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vjxNk0wBgHg0",
        "outputId": "927cbe78-036c-4462-8a2f-2965b4ad9721"
      },
      "source": [
        "def calcSquare(x):\n",
        "  return x**2\n",
        "a = calcSquare(41)\n",
        "xpos"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3, 4, 5, 6, 7, 8, 9])"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BCiYwMlzgt-_",
        "outputId": "0f0ed291-4eb1-452d-a4a6-ec42c0277b4b"
      },
      "source": [
        "calcSquare(xpos)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1,  4,  9, 16, 25, 36, 49, 64, 81])"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "TTJJA__ohYov",
        "outputId": "206a3d3f-30b4-47d6-9c47-7f964faa0705"
      },
      "source": [
        "plt.plot(xpos, calcSquare(xpos))\n",
        "plt.grid();"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5roheHVrw8MTTOal986AjyXGowEWEqmrndy8v569vr+bMfh24/2vDaJXWOOhYcgIqcJEGbn9ZJd+ZuZDXlm7jmtE9+NmEU0lNqc1pkqSuqMBFGrAtew4yaXoBy7fu5RcXDGTiGT2DjiQRUIGLNFAfbizhhhkFHCivYtq1pzNmQKegI0mEVOAiDdBLi7dw29ML6ZDelMcmjWRA55ZBR5IoqMBFGhB35/+9tYr/e+Ujhp3UhinXDKdDetOgY0mUVOAiDURZZRU/enYxz87fxIVDuvDbr5xGWuOUoGNJLajARRqA3fvLufGxAuatLea2cf359jl9CZ2gTpKYClyknlu5fR/XTy9g295D/OWKoXx5cJegI0mMqMBF6rF3VuzgG0/Mp2lqCnm5oxh6UtugI0kMqcBF6qnH5q7j5y8soV+ndKZOHE63tjosvr4Ju8DNLAUoADa5+wQz6wXkAe2BQuBqdy+PT0wRCVdVtfOrF5fyyHtrOefkTvz5iqGkN9W2Wn0UyfGytwLLjlj+LTDZ3ftSc8X6SbEMJiKR23eoghtmzOOR99Yy6fO9eOia4SrveiysAjezbsD5wNTQsgHnAM+EnjIDuCgeAUUkPBt2H+CSB+bw9oqd/Pp/svjphFNJ0QUY6rVwP5r/BPwQOHy4VnugxN0rQ8sbga4xziYiYSpcV8yNjxVQVlnNjOtG8Pl+ulp8Q2DufvwnmE0AvuTu3zCzMcD3gWuBuaHhE8ysOzDb3bOOsX4ukAuQkZGRnZeXF1XQ0tJS0tPTo1o3npQrMsoVmXByzd1cydSiMtqlGd8ZlkaX9PifSTCZ368g1DZXTk5OobsP/9QD7n7cG/Abaraw1wJbgQPAE8BOIDX0nNHAKyd6rezsbI9Wfn5+1OvGk3JFRrkic7xc1dXVPvm1j7zH7bP8qw/+23eXliVEriDV11xAgR+jU0/4Ue3uP3L3bu7eE7gceNPdvwbkA5eEnjYReD7qjxcRicihiipuzVvIn15fwSXZ3Xh80kjatmgSdCypY7WZnr4dyDOzXwELgIdjE0lEjmfHvjJyHytgwfoSbj/vZG46u7cOi2+gIipwd38LeCv0/WpgROwjichnWb51L5OmF7BrfxkPXjWM87Iyg44kAdIOoiJJIn/5dr715HzS01L5241nMKhb66AjScBU4CIJzt2Z/u+13D1rKad2acXUa06nc+u0oGNJAlCBiySwymrnzn8U8cT76zl3YAaTLxtC8yb6s5Ua+k0QSVB7DlQwufAQS3at56az+/DDcwfQSEdWyhFU4CIJ6IM1u/nuzIVs3VPN7y45jUuHdw86kiSg+B+yJSJhq6iq5g+vfsTlU+aQmmL8ZGSayls+kwpcJEGs27Wfrz44h7+8uZKLh3XjxVvOpHcbXbNSPpuGUEQC5u78ff4m7nq+iJRGxn1XDmXCabrsmZyYClwkQHsOVvCT5xYz68MtjOjVjsmXDaFrm2ZBx5IkoQIXCcgnE5V7D/GDcwdw09l9dP5uiYgKXKSOVVRV8+c3VnB//kq6t2vO328+gyHd2wQdS5KQClykDq3btZ9b8xaycEMJl2R34+cXDNQlzyRq+s0RqQPuzrPzN/Gz54topIlKiREVuEicaaJS4kUFLhJHmqiUeFKBi8SBJiqlLpywwM0sDXgbaBp6/jPufpeZ9QLyqLlCfSFwtbuXxzOsSDLQRKXUlXB+q8qAc9y91MwaA++a2WzgNmCyu+eZ2YPAJOCBOGYVSWiaqJS6dsICD10RuTS02Dh0c+Ac4MrQ/TOAn6MClwZqz8EK7vxHEf9ctJkRPdsx+XJNVEr8WU0/n+BJZinUDJP0Be4H/g+Y6+59Q493B2a7e9Yx1s0FcgEyMjKy8/LyogpaWlpKenp6VOvGk3JFpj7m+mh3FVM+LKO4zLmob2Mm9G5MoxhdZLg+vl/xVF9z5eTkFLr78E894O5h34A2QD7weWDlEfd3B4pOtH52drZHKz8/P+p140m5IlOfcpVXVvnvX1nuve6Y5Wf97k2fv253QuSqC8oVmdrmAgr8GJ0a6VXpS8wsHxgNtDGzVHevBLoBm6L+eBFJMpqolEQQzl4oHYGKUHk3A8YBv6VmS/wSavZEmQg8H8+gIonAj5qo/MsVQ/nyYE1USjDC2WTIBGaExsEbAU+7+ywzWwrkmdmvgAXAw3HMKRI4TVRKoglnL5QPgaHHuH81MCIeoUQSzZFHVH7/i/25eUxfHVEpgdOgnchxVIaOqLwvfyXd2jbnmZtGM/SktkHHEgFU4CKfad2u/Xxn5kIWrC/hK8O68YsLNVEpiUW/jSJH0USlJAsVuMgRjp6o/ONlg+nWtnnQsUSOSQUuEjJv7W6+k6eJSkkeKnBp8Cqrqnl2RTmzXpmjiUpJKipwadDmrNrFL/65hOVbKzRRKUlHv6nSIG0sPsBvXlrOi4u30K1tM749tCnfu3Rw0LFEIqIClwblUEUVD/5rFQ+8tQozuG1cf3LP6s3c994JOppIxFTg0iC4O7OLtvLrF5exqeQgE07L5EdfOkWHwktSU4FLvbd8615+8cJS5qzexcmdW5KXO4pRvdsHHUuk1lTgUm+VHChn8msf8/j762mZlsrdF2VxxendSU1pFHQ0kZhQgUu9U1XtPPXBev7w6kfsOVjBVaN6cNu4/rRp3iToaCIxpQKXeuWDNbv5+QtLWLplLyN7tePnFwzklMxWQccSiQsVuNQLm0sO8pvZy/nnos10aZ3GfVcO5fxBmViMrk0pkohU4JLUDlVUMfWd1dyfv4pqd24Z24+bz+5DsyYpQUcTibtwLqnWHXgUyAAcmOLu95pZO2Am0BNYC1zq7sXxiyryH+7Oq0u38asXl7Jh90HGZ3Xmx186he7tdOIpaTjC2QKvBL7n7vPNrCVQaGavAdcCb7j7PWZ2B3AHcHv8oorUWLl9H7/451LeWbGT/hnpPHnDSM7o2yHoWCJ1LpxLqm0BtoS+32dmy4CuwIXAmNDTZgBvoQKXONpzsIJ7X1/Bo3PW0rxJCj//8qlcNaqHdguUBsvcPfwnm/UE3gaygPXu3iZ0vwHFh5ePWicXyAXIyMjIzsvLiypoaWkp6enpUa0bT8oVmWhyVbvzzqZKnvm4nNJyOLt7Khf3a0KrJrGboKxP71ddUK7I1DZXTk5OobsP/9QD7h7WDUgHCoGLQ8slRz1efKLXyM7O9mjl5+dHvW48KVdkIs1VsHaXT/jzO97j9ll+yQPv+eKNJQmRq64oV2Tqay6gwI/RqWHthWJmjYG/A0+4+7Ohu7eZWaa7bzGzTGB71B8vIkfZtvcQ98xeznMLNtG5VRr3Xj6ECwZ30W6BIkcIZy8UAx4Glrn7H4946AVgInBP6OvzcUkoDUpZZRXT3l3LX95cQWWV862cvtw8pg8tdI5ukU8J56/ic8DVwGIzWxi678fUFPfTZjYJWAdcGp+I0hC4O28u387ds5aydtcBxp2awZ3nn0KP9i2CjiaSsMLZC+Vd4LP+bx0b2zjSEK3aUcrds5by1kc76NOxBY9eP4Kz+ncMOpZIwtP/pRKYfYcq+MubK5n27hqaNU7hpxNO5ZrRPWis3QJFwqIClzpXXe08u2AT98xezq79ZVya3Z0fnDeADulNg44mklRU4FKnVpdUMfmBf7NoQwnDTmrDtGuHc1q3Tx0+ICJhUIFLnVi0oYQpb6/mxcWH6NTS+eOlg7loSFcaNdJugSLRUoFL3FRX1+xZMuWd1XywZjct01L5cu/G/GbiGNK1W6BIremvSGLuUEUV/1iwiYfeWc2qHfvp2qYZP51wKped3p2COe+qvEViRH9JEjPF+8t54v11TP/3OnaWljGwSyvuvXwI5w/K1AmnROJABS61tn7XAR5+dzVPF2zkYEUVYwZ0JPfM3ozu016HvovEkQpconZ4YnJ20RZSGhkXDunK18/szYDOLYOOJtIgqMAlIseamMw9qw/Xfa4nGa3Sgo4n0qCowCUsx5uY1KSkSDD0lyfHVXKgnMfnamJSJBGpwOWYNuw+wMPvrmHmvA2amBRJUCpw+S+LNpQw5Z3VzF6siUmRRKcCF01MiiQpFXgDpolJkeQWziXVpgETgO3unhW6rx0wE+gJrAUudffi+MWUWPqsickvDcrUubhFkkg4m1nTgfuAR4+47w7gDXe/x8zuCC3fHvt4EkuamBSpX8K5pNrbZtbzqLsvBMaEvp8BvIUKPGFpYlKkfjJ3P/GTagp81hFDKCXu3ib0vQHFh5ePsW4ukAuQkZGRnZeXF1XQ0tJS0tPTo1o3nhI11959paw+mMbsNRV8VFxNs1TI6d6YcT1SaZsW3DBJor5fyhUZ5YpMbXPl5OQUuvvwTz3g7ie8UTPWXXTEcslRjxeH8zrZ2dkerfz8/KjXjadEy7Wx+IBPfWe1j/rli97j9lk++n9f94feXuX7DlUEHc3dE+/9Oky5IqNckaltLqDAj9Gp0e5qsM3MMt19i5llAtujfB2JgbU79zO7aCsvF21h0cY9APRs1Yh7Lx+siUmReizaAn8BmAjcE/r6fMwSSVhWbNvH7KKtzC7ayrItewE4rVtrfnjeAMZnZbKuaB5jhnQNOKWIxFM4uxE+Rc2EZQcz2wjcRU1xP21mk4B1wKXxDCk1Q11LNu/l5aKtzC7awqod+wEY3qMtd55/CudldaZb2+afPH9dUEFFpM6EsxfKFZ/x0NgYZ5GjuDsLN5SESnsr63cfoJHByF7tmXhGT84d2FlHSoo0YDrcLsFUVTsFa3czu2grryzZypY9h2icYpzRpwPfGNOHcadm0D69adAxRSQBqMATQEVVNe+v3s3soi28smQbO0vLaJLaiLP6deQH5w5g7CkZtG7WOOiYIpJgVOABKaus4r2VO5m9eCuvLdtGyYEKmjVO4ZyTO3FeVmdyTu6k85GIyHGpIerQwfIq/vXxDmYXbeHNZdvZV1ZJy6apjD2lE+MHZXJ2/46kNU4JOqaIJAkVeJyVllXy5vLtvFy0hfzlOzhYUUXb5o0ZP6gz47MyOaNve5qmqrRFJHIq8DjYc6CC15dtY3bRFt5esZPyymo6pDflK9ldGZ+Vyche7XQ5MhGpNRV4jOwqLePVpduYXbSVf6/cSWW106V1Gl8beRLjszLJ7tGWlEY645+IxI4KPErV1c7aXft5fV0FD06ZwwdrdlPt0KN9cyad2YvxWZkM7tZap2kVkbhRgYehsqqaVTv2U7RpD0Wb97Bk016WbtlLaVklAH07lfPNnL6Mz8rklMyWKm0RqRMq8KOUVVbx8dZSijbvoWjTHpZs3suyLXspq6wGoFnjFE7t0oqvDOvKwK6tqdy6gisnnB1wahFpiBp0gR8or2TZln0sCZV10aa9fLxtH5XVNedIb5mWSlaX1lwzugdZXVszsEtrenVo8V9j2W+9tSqo+CLSwDWYAt9zsIKlm/f+p6w372X1jlJCXU37Fk0Y2LU1YwZ0JKtra7K6tKZ7u2YaDhGRhFUvC3xXaRlFm/dStGkPSzfvpWjzHtbtOvDJ45mt0xjYpTUTTsskq0trsrq2JqNVU5W1iCSVpC5wd2fb3rJPJheLNtVsYW/Zc+iT5/Ro35yBXVpx6fDuoWGQVnTQyaBEpB5ImgJ3d9bvOvDJ5GLR5r0s2bSHXfvLATCDPh3TGdmr3Sfj1ad2aaWTQIlIvVWrAjez84B7gRRgqrvfE5NUR/nxc4t5rvAAB1/JByC1kdE/oyXnnNypZry6aytO7tyKFjr5k4g0IFE3npmlAPcD44CNwDwze8Hdl8Yq3GFd2zRjVGYqXzz9FLK6tKZ/53SdP0REGrzabLKOAFa6+2oAM8sDLgRiXuDfzOnLW7aRMSNOivVLi4gkLau5Yn0UK5pdApzn7jeElq8GRrr7t456Xi6QC5CRkZGdl5cX1c8rLS0lPT09qnXjSbkio1yRUa7I1NdcOTk5he4+/FMPuHtUN+ASasa9Dy9fDdx3vHWys7M9Wvn5+VGvG0/KFRnlioxyRaa+5gIK/BidWptzmm4Cuh+x3C10n4iI1IHaFPg8oJ+Z9TKzJsDlwAuxiSUiIicS9SSmu1ea2beAV6jZjXCauy+JWTIRETmuWu047e4vAS/FKIuIiERA1yDONz8AAARCSURBVPUSEUlSKnARkSQV9X7gUf0wsx3AuihX7wDsjGGcWFGuyChXZJQrMvU1Vw9373j0nXVa4LVhZgV+rB3ZA6ZckVGuyChXZBpaLg2hiIgkKRW4iEiSSqYCnxJ0gM+gXJFRrsgoV2QaVK6kGQMXEZH/lkxb4CIicgQVuIhIkkr4AjezaWa23cyKgs5yJDPrbmb5ZrbUzJaY2a1BZwIwszQz+8DMFoVy/SLoTIeZWYqZLTCzWUFnOZKZrTWzxWa20MwKgs5zmJm1MbNnzGy5mS0zs9EJkGlA6H06fNtrZt8JOheAmX039DtfZGZPmVla0JkAzOzWUKYlsX6vEn4M3MzOAkqBR909K+g8h5lZJpDp7vPNrCVQCFzkcbikXIS5DGjh7qVm1hh4F7jV3ecGmQvAzG4DhgOt3H1C0HkOM7O1wHB3T6gDQMxsBvCOu08NnfGzubuXBJ3rsNBlFTdRcyGXaA/Qi1WWrtT8rp/q7gfN7GngJXefHnCuLCCPmiuYlQMvAze5+8pYvH7Cb4G7+9vA7qBzHM3dt7j7/ND3+4BlQNdgU0Ho/O+locXGoVvgn9Jm1g04H5gadJZkYGatgbOAhwHcvTyRyjtkLLAq6PI+QirQzMxSgebA5oDzAJwCvO/uB9y9EvgXcHGsXjzhCzwZmFlPYCjwfrBJaoSGKhYC24HX3D0Rcv0J+CFQHXSQY3DgVTMrDF0CMBH0AnYAj4SGnaaaWYugQx3lcuCpoEMAuPsm4PfAemALsMfdXw02FQBFwJlm1t7MmgNf4r8vhFMrKvBaMrN04O/Ad9x9b9B5ANy9yt2HUHOVpBGhf+MCY2YTgO3uXhhkjuP4vLsPA8YD3wwN2wUtFRgGPODuQ4H9wB3BRvqP0JDOBcDfgs4CYGZtqbmoei+gC9DCzK4KNhW4+zLgt8Cr1AyfLASqYvX6KvBaCI0x/x14wt2fDTrP0UL/cucD5wUc5XPABaGx5jzgHDN7PNhI/xHaesPdtwPPUTNeGbSNwMYj/nt6hppCTxTjgfnuvi3oICFfANa4+w53rwCeBc4IOBMA7v6wu2e7+1lAMfBxrF5bBR6l0GThw8Ayd/9j0HkOM7OOZtYm9H0zYBywPMhM7v4jd+/m7j2p+bf7TXcPfOsIwMxahCahCQ1RfJGaf3sD5e5bgQ1mNiB011gg0Anyo1xBggyfhKwHRplZ89Df5lhq5qUCZ2adQl9Pomb8+8lYvXatrshTF8zsKWAM0MHMNgJ3ufvDwaYCarYqrwYWh8abAX4cukpRkDKBGaE9BBoBT7t7Qu22l2AygOdq/uZJBZ5095eDjfSJbwNPhIYrVgPXBZwH+OSDbhxwY9BZDnP3983sGWA+UAksIHEOq/+7mbUHKoBvxnIyOuF3IxQRkWPTEIqISJJSgYuIJCkVuIhIklKBi4gkKRW4iEiSUoGLiCQpFbiISJL6/194VRX3mDlsAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NfxGvQKEhipX"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}