{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pythonの基礎\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UhfzISPjmJEe"
   },
   "source": [
    "---\n",
    "\n",
    "[実践計算物理学 ―物理を理解するためのPython活用法―](https://www.kyoritsu-pub.co.jp/book/b10025215.html)\n",
    "\n",
    "より抜粋して説明します。\n",
    "\n",
    "書籍内のコードが一部公開されておりますので参照下さい。\n",
    "\n",
    " - 1_Pythonプログラミングへの準備\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/comp-phys-kyoritsu/codes/blob/main/1_Python%E3%83%95%E3%82%9A%E3%83%AD%E3%82%AF%E3%82%99%E3%83%A9%E3%83%9F%E3%83%B3%E3%82%AF%E3%82%99%E3%81%B8%E3%81%AE%E6%BA%96%E5%82%99.ipynb)\n",
    "\n",
    " - 2_Pythonの基礎 - 基本的なルール\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/comp-phys-kyoritsu/codes/blob/main/2_Python%E3%81%AE%E5%9F%BA%E7%A4%8E%20-%20%E5%9F%BA%E6%9C%AC%E7%9A%84%E3%81%AA%E3%83%AB%E3%83%BC%E3%83%AB.ipynb)\n",
    "\n",
    " - 3_Pythonの基礎 - ライブラリ\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/comp-phys-kyoritsu/codes/blob/main/3_Python%E3%81%AE%E5%9F%BA%E7%A4%8E%20-%20%E3%83%A9%E3%82%A4%E3%83%95%E3%82%99%E3%83%A9%E3%83%AA.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UhfzISPjmJEe"
   },
   "source": [
    "---\n",
    "\n",
    "## インデント\n",
    "\n",
    " - Pythonではインデントの揃った部分が1つのブロックとみなされる。\n",
    " - 異なるブロックはキーワード+コロン:で始まる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 136
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "error",
     "timestamp": 1682313422938,
     "user": {
      "displayName": "ryota nishiguchi",
      "userId": "17176417616353695325"
     },
     "user_tz": -540
    },
    "id": "iW8Q_DT7mIze",
    "outputId": "fbf8dc07-8597-4e95-9584-63d0ae9e66e4"
   },
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "unexpected indent (<ipython-input-3-08b837f97b05>, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-3-08b837f97b05>\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m    print(\"Hello world\")\u001b[0m\n\u001b[1;37m    ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m unexpected indent\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello world\")\n",
    "    print(\"Hello world\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 355,
     "status": "ok",
     "timestamp": 1682313461579,
     "user": {
      "displayName": "ryota nishiguchi",
      "userId": "17176417616353695325"
     },
     "user_tz": -540
    },
    "id": "igUfWSjZsesi",
    "outputId": "dd35c9e1-12d0-42ae-e404-66714e2c83d2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "# シャープ(#)以下の文章は無視される(コメントアウト)\n",
    "# コードの説明や補足, 不要な文を無効化したいときなどに用いる\n",
    "for i in range(10):\n",
    "    # rangeは連番を取得する関数. この例では0,1,...,9を取得し,\n",
    "    # 順番にiに代入することで繰り返し処理をする.\n",
    "    x = i + 1\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Sug-NAdWsorO"
   },
   "source": [
    "---\n",
    "\n",
    "## データ型\n",
    "\n",
    " - Pythonで取り扱うことができるデータは、数値、真偽値、文字列の3種類\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "vp8RqHvys7TR"
   },
   "outputs": [],
   "source": [
    "x = 12   # xを整数(int)型変数として定義.\n",
    "         # 12.あるいは12.0とすると浮動小数点(float)型,\n",
    "         # 12+0jとすると複素数(complex)型となる(jは虚数単位).\n",
    "         # 数値型はint, float, complexの3種類.\n",
    "y = True # yを真偽値(bool)型変数として定義.\n",
    "         # bool型はint型のサブクラスでTrue=1, False=0と同じ.\n",
    "z = \"PC\" # zを文字列(str)型変数として定義.\n",
    "         # \" \"や’ ’で囲うとstr型として認識される"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9xmUW4cJspHl"
   },
   "source": [
    "---\n",
    "\n",
    "## データ型：複合的な型\n",
    "\n",
    " - Pythonのデータ型には、上記の基本的なデータ型を要素とする複合的なデータ型（コンテナ型）が存在する。\n",
    " - リスト、タプル、集合、辞書など "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rfLFputHumdt"
   },
   "source": [
    "### リスト"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "Z8mELwKbuZsq"
   },
   "outputs": [],
   "source": [
    "x = [1, 2, 3]\n",
    "y = [\"Hello\", \" \", \"world\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 275,
     "status": "ok",
     "timestamp": 1682314040944,
     "user": {
      "displayName": "ryota nishiguchi",
      "userId": "17176417616353695325"
     },
     "user_tz": -540
    },
    "id": "TknWT4Rgut2j",
    "outputId": "07872944-3418-4559-fff7-25fd129a1657"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a= A\n",
      "a= 2\n",
      "a= 3\n",
      "a= X\n"
     ]
    }
   ],
   "source": [
    "x + y             # リストの結合→ [1,2,3,\"Hello\",\" \",\"world\"]\n",
    "len(x)            # リストの長さの取得→ 3\n",
    "2 in x            # リストに要素が含まれるか→ True\n",
    "x[0] = \"A\"        # xの0番目の要素を\"A\"に書き換える\n",
    "x.append(\"X\")     # xの最後に\"X\"を追加\n",
    "for a in x:       # リストの要素で繰り返し処理をする\n",
    "    print(\"a=\", a)  # → a=A, a=2, a=3, a=Xと連続して出力される"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6kxmK1z8uzQW"
   },
   "source": [
    "---\n",
    "\n",
    "### 辞書"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1682314205123,
     "user": {
      "displayName": "ryota nishiguchi",
      "userId": "17176417616353695325"
     },
     "user_tz": -540
    },
    "id": "ZO4sVw8su2wz",
    "outputId": "f5854b29-6f5c-4929-c4b3-569440f01844"
   },
   "outputs": [],
   "source": [
    "d = {\"h\":6.626e-34, \"c\":0} # {\"key1\":value1, · · · }のように書く\n",
    "print(d[\"h\"])              # Key=\"h\"のValueの取得\n",
    "d[\"c\"] = 2.998e+8          # Key=\"c\"のValueを変更\n",
    "d[\"e\"] = 1.602e-19         # {\"e\":1.602e-19}の要素を追加\n",
    "for key, value in d.items():\n",
    "    # itemsメソッドはkeyとvalueの組を取得. 反復処理に用いられる\n",
    "    print(key, value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "z-jQHrqPspXu"
   },
   "source": [
    "---\n",
    "\n",
    "## 関数:def"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "wYQ8yJv3uk26"
   },
   "outputs": [],
   "source": [
    "def user_sum(x, y):\n",
    "    ans = x+y\n",
    "    return ans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "bOYwzTnfv-nd"
   },
   "outputs": [],
   "source": [
    "user_sum(2, 3) # → 5\n",
    "ans = user_sum(1, 7) # ansに8を代入"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JtYl5npGwSFQ"
   },
   "source": [
    " - 引数の型を指定しないため、関数を引数にすることもできる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 271,
     "status": "ok",
     "timestamp": 1682314396792,
     "user": {
      "displayName": "ryota nishiguchi",
      "userId": "17176417616353695325"
     },
     "user_tz": -540
    },
    "id": "ubz65uWAwHfZ",
    "outputId": "d592cac2-4448-4bf6-d2b1-1295900c8aa5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def ope(func, vals):\n",
    "    return func(vals[0], vals[1])\n",
    "ope(user_sum, [3,4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4IDXwfGMwk-V"
   },
   "source": [
    " - デフォルト値を設定することができる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 136
    },
    "executionInfo": {
     "elapsed": 295,
     "status": "error",
     "timestamp": 1682314501953,
     "user": {
      "displayName": "ryota nishiguchi",
      "userId": "17176417616353695325"
     },
     "user_tz": -540
    },
    "id": "CXbhCYTXweF-",
    "outputId": "ddb7075c-ccf2-4c98-9c42-ce4eaec341fd"
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "positional argument follows keyword argument (<ipython-input-7-a58389f95d0f>, line 7)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-7-a58389f95d0f>\"\u001b[1;36m, line \u001b[1;32m7\u001b[0m\n\u001b[1;33m    ans = func(3, y=2, 1)   # エラー\u001b[0m\n\u001b[1;37m                      ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m positional argument follows keyword argument\n"
     ]
    }
   ],
   "source": [
    "def func(x, y=1, z=2):\n",
    "    return z * (x + y)\n",
    "ans = func(3, 4, 5)     # ansに5*(3+4)を代入\n",
    "ans = func(3)           # ansに2*(3+1)を代入\n",
    "ans = func(3, z=-1)     # ansに-(3+1)を代入\n",
    "ans = func(3, z=1, y=2) # ansに1*(3+2)を代入. 順番は変更可\n",
    "ans = func(3, y=2, 1)   # エラー\n",
    "# 一度引数を指定したら, その後の引数はすべて指定しなければならない"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EzJGWKg-spiS"
   },
   "source": [
    "---\n",
    "\n",
    "## 関数：lambda\n",
    "\n",
    " - 1行のみの関数を定義したい場合はlambda式を用いる方法がある。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "3SZe5-GgxLOe"
   },
   "outputs": [],
   "source": [
    "func = lambda x, y=1, z=2: z*(x+y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 285,
     "status": "ok",
     "timestamp": 1682314832771,
     "user": {
      "displayName": "ryota nishiguchi",
      "userId": "17176417616353695325"
     },
     "user_tz": -540
    },
    "id": "QgywnrE_xp0r",
    "outputId": "9d5659ca-a23c-44b6-fa6b-c676e0f0e62d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22\n",
      "11\n"
     ]
    }
   ],
   "source": [
    "print(func(10))\n",
    "print(func(10,1,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z3MAvY2vspqu"
   },
   "source": [
    "---\n",
    "\n",
    "## 条件分岐と繰り返し処理\n",
    "\n",
    " - 条件分岐のif文、繰り返し処理のfor文は以下のとおり"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 266,
     "status": "ok",
     "timestamp": 1682314941529,
     "user": {
      "displayName": "ryota nishiguchi",
      "userId": "17176417616353695325"
     },
     "user_tz": -540
    },
    "id": "ln27zIUFyJuY",
    "outputId": "e00eb3c8-9913-4ed5-c755-acb970e841b4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 even number\n",
      "1 odd number\n",
      "2 even number\n",
      "3 odd number\n",
      "4 even number\n",
      "5 odd number\n",
      "6 even number\n",
      "7 odd number\n",
      "8 even number\n",
      "9 odd number\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    if i % 2 == 0: # if 条件式:の形で用いる. Trueなら以下を実行\n",
    "        print(i, \"even number\")\n",
    "    else:          # Falseなら以下を実行. elif文で複数分岐も可能\n",
    "        print(i, \"odd number\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ni-i1q_7spy7"
   },
   "source": [
    "---\n",
    "\n",
    "## リスト内包表記\n",
    "\n",
    " - for文、if文を用いたリスト作成方法と以下のような表記が用いられる。\n",
    " - このような書き方はリスト内包表記と呼ばれる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "vNrRQosvysVs"
   },
   "outputs": [],
   "source": [
    "a = [i for i in range(3)]              # リスト[0,1,2]を作成\n",
    "a = [i**2 for i in range(5) if i%2==0] # if文と組み合わせも可能"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "u5rUqBUcsqTB"
   },
   "source": [
    "---\n",
    "\n",
    "## ファイル操作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bQ0xVlrDzcRz"
   },
   "source": [
    " - ファイルの開き方"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "vhGvrv4PzWwF"
   },
   "outputs": [],
   "source": [
    "f = open(\"filename\",\"w\") # 書き込み用に開く\n",
    "f = open(\"filename\",\"r\") # 読み込み用に開く\n",
    "f = open(\"filename\",\"a\") # 追記用に開く."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gKlJDPxVzo9F"
   },
   "source": [
    " - ファイルへの書き込み"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "mgmWpEsuzoPV"
   },
   "outputs": [],
   "source": [
    "f = open(\"test.txt\",\"w\")\n",
    "f.write(\"Hello World\\n\") # 文字列を書き込む. \\nは改行を表す\n",
    "f.write(\"Hello {0}, {1}\\n\".format(\"Tokyo\",\"Japan\"))\n",
    "f.close() # openしたファイルは最後にcloseで閉じる必要がある"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Fah6XdNxzwE6"
   },
   "source": [
    " - 読み込み"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 291,
     "status": "ok",
     "timestamp": 1682315373662,
     "user": {
      "displayName": "ryota nishiguchi",
      "userId": "17176417616353695325"
     },
     "user_tz": -540
    },
    "id": "qQu2aftFzvdn",
    "outputId": "8ab6901a-053c-42d0-96da-1bc474a67baf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World\n",
      "Hello Tokyo, Japan\n"
     ]
    }
   ],
   "source": [
    "with open(\"test.txt\") as f:  # open関数のデフォルトは\"r\"\n",
    "    for line in f:           # 1行ずつ読み込む\n",
    "        print(line.rstrip()) # rstrip()で文字列の右端を削除"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lXc5MS5s0B7B"
   },
   "source": [
    "---\n",
    "\n",
    "## クラスの基礎\n",
    "\n",
    " - クラスは奥が深いので詳しく理解する必要は無いが、以下の構文程度は感覚的に知っておいたほうが良い。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "7DHQwfHo0NWg"
   },
   "outputs": [],
   "source": [
    "class Person:                 # Personというクラスを定義\n",
    "    def __init__(self, name): # インスタンス化の際に実行される\n",
    "        self.name = name\n",
    "    def __repr__(self):       # print文での出力結果を指定\n",
    "        return \"Name : \"+str(self.name)+\", Age : \"+str(self.age)\n",
    "    def set_age(self, age):   # age変数を設定するメソッド\n",
    "        self.age = age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 273,
     "status": "ok",
     "timestamp": 1682315486874,
     "user": {
      "displayName": "ryota nishiguchi",
      "userId": "17176417616353695325"
     },
     "user_tz": -540
    },
    "id": "N3mKZq6m0RpL",
    "outputId": "e2046afb-795c-4fe8-9966-16335e916f99"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name : PhysicsTarou, Age : 30\n",
      "PhysicsTarou 30\n"
     ]
    }
   ],
   "source": [
    "TP = Person(\"PhysicsTarou\")   # クラスのインスタンス化.\n",
    "TP.set_age(30)                # メソッドの呼び出し\n",
    "print(TP)                     # __repr__で指定された出力をする\n",
    "print(TP.name, TP.age)        # メンバ変数を直接呼び出す"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gBRcO1BU1FS9"
   },
   "source": [
    "---\n",
    "\n",
    "## ライブラリの基礎\n",
    "\n",
    " - Pythonは、数多くのライブラリ（パッケージと呼ぶ）が公開されいる。この膨大なパッケージの存在が、Pythonが多くのユーザーに利用される理由である。\n",
    " - Pythonを使う上でパッケージの利用は必要不可欠である。\n",
    " - パッケージの呼び出し方法は次のとおりとなっている。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "5o4AuRgB1K7X"
   },
   "outputs": [],
   "source": [
    "import numpy       # numpyパッケージを読み込む\n",
    "import numpy as np # numpyパッケージをnpという名前で読み込む\n",
    "from numpy import random\n",
    "    # numpyパッケージの中のrandomモジュールを読み込む\n",
    "import numpy.random\n",
    "    # 同じモジュールをnumpy.randomという名前で読み込む\n",
    "from numpy import * # numpyパッケージの全モジュールを読み込む"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yTA3B7Nj3Hzc"
   },
   "source": [
    "---\n",
    "\n",
    "### numpy\n",
    "\n",
    " - 数値計算を効率的に行なうための必須ライブラリ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vd_RTDKA4u2y"
   },
   "source": [
    " - 下に示すnumpy用の配列（ndarrayと呼ばれる）はベクトル、行列などの多次元配列を示すことができ、数値計算では必要不可欠"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "U4XTI_G-3mMX"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "a = np.array([1, 2, 3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XGHUWeZM5UL7"
   },
   "source": [
    " - ndarrayの操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "HQnM0bBG5Uva"
   },
   "outputs": [],
   "source": [
    "a = np.array([1, 2, 2, 4])\n",
    "b = np.array([5, 6, 7, 8])\n",
    "c = a[:3] # 0-2番目の要素を取り出す(スライス)\n",
    "          # スライスには様々な使い方がある\n",
    "          # a[1:3], a[::2], a[-1], a[::-1]なども確認してみよう\n",
    "d = np.append(a, b)     # 2つの配列の結合\n",
    "e = a.reshape(2, 2)     # 配列の形状を任意に変換\n",
    "f = e.flatten()         # 多次元配列を1次元配列に変換\n",
    "g = d.shape             # ndarrayの形状をタプルで取得 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AtEshw_q50Aa"
   },
   "source": [
    " - ndarrayの演算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "Nq-Y6iQT50gr"
   },
   "outputs": [],
   "source": [
    "m = 2.0*a              # 全ての要素に係数をかける.  \n",
    "n = a*b                # 要素ごとの積\n",
    "o = a**3               # 各要素のべき乗  \n",
    "p = np.dot(a,b)        # ベクトルの内積\n",
    "q = a + 1j*b           # 要素ごとの和. 複素数のndarrayに対して, \n",
    "  # np.conj(q)=a-1j*b, np.real(q)=a, np.imag(q)=bなどとなる\n",
    "r = a[:,None] + b[None,:]\n",
    "  # s_ij = a_i + b_jを要素とする2次元配列を所得する\n",
    "s = 2 + a \n",
    "  # スカラー量との加減算は2 => 2*np.ones(a.shape)と解釈される"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RzElAJ5y6DVE"
   },
   "source": [
    "---\n",
    "\n",
    "### matplotlib\n",
    "\n",
    " - Python用の可視化ツール\n",
    " - 詳細は[公式](https://matplotlib.org/)を参照"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 430
    },
    "executionInfo": {
     "elapsed": 1357,
     "status": "ok",
     "timestamp": 1682317163656,
     "user": {
      "displayName": "ryota nishiguchi",
      "userId": "17176417616353695325"
     },
     "user_tz": -540
    },
    "id": "vIuLDykx6q77",
    "outputId": "51c46d40-7900-4630-f535-484c899d5475"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "x = np.linspace(0, 2*np.pi, 10)\n",
    "y = np.sin(x)             # プロットしたい1次元配列を準備\n",
    "plt.plot(x, y, color=\"red\", linestyle=\"dashed\", label=\"sin\")\n",
    "  # x, y座標のデータの他、色やスタイル、凡例の指定ができる\n",
    "plt.scatter(x, y, s=30, c=\"blue\")\n",
    "  # 散布図プロットにはscatterを用いる\n",
    "plt.legend()              # 凡例の表示\n",
    "plt.show()                # 描画"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 433
    },
    "executionInfo": {
     "elapsed": 1051,
     "status": "ok",
     "timestamp": 1682317172902,
     "user": {
      "displayName": "ryota nishiguchi",
      "userId": "17176417616353695325"
     },
     "user_tz": -540
    },
    "id": "5rCp_iME6tIk",
    "outputId": "3a40af02-3576-446f-b829-63a2b9fc983e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = np.linspace(0, 4*np.pi, 100)\n",
    "x, y = np.meshgrid(a, a)       # 2次元メッシュの生成\n",
    "z = np.sin(x) + 0.2*np.cos(y)  # プロットしたい配列の準備\n",
    "plt.imshow(z)\n",
    "plt.colorbar()                 # カラーバーを表示\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyPSiCwpTqnN4JsGt1/gMYUo",
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