{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 🗎 Оценка качества"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<div class=\"admonition note\">\n",
       "<p class=\"admonition-title\">О среде выполнения </p>\n",
       "<p> \n",
       "    Данная расчётно-графическая работа выполняется в <a href=i_sreda.html>программной среде Jupyter Notebook</a>.\n",
       "Если у Вас на компьютере нет установленной вычислительной среды, см. \n",
       "  <a href=i_sredaonline.html>Выполнение расчетно-графических работ онлайн</a>.\n",
       "</p>\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, Markdown\n",
    "display(Markdown(open('_prew.md', 'r', encoding='utf-8').read()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Задание**: представить отчет о качестве выполнения нескольких заданий, выполненных по инструкции. Качество оценивается в 1.0 (100%), если выполнение полностью соответствовало инструкции, и меньше 1.0, если есть несоответствия."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Предлагается использовать данные из теста \"[Ускорение нажатий](w_data2)\", в инструкции к которому можно выделить несколько критериев.\n",
    "\n",
    "Альтернативно это могут быть описания товаров, услуг, выступлений и т.п. но при этом должны выполняться условия:\n",
    "\n",
    "-   Должны быть даны оценки качества от 0 до 1 по как минимум трём критериям;\n",
    "-   Итоговое качество как произведение всех частных оценок соответствия критериям;\n",
    "-   Условное качество как взвешенная сумма частных оценок соответствия критериям;\n",
    "-   Расчет среднего качества по группе объектов;\n",
    "-   Вывод о качестве группы в целом."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "При использовании собственных данных необходимо подробно описать требования к качеству.\n",
    "При проблемах с определением критериев качества - проконсультируйтесь с преподавателем."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Те, кто использует данные из теста \"Ускорение нажатий\", могут использовать шаблон отчета <a href=d/qual_Familia.ipynb>qual_Familia.ipynb</a>.\n",
    "\n",
    "Для заданий на ускорение нажатий можно использовать критерии:\n",
    "\n",
    " -  начальная частота нажатий - примерно 1 раз в с;\n",
    " -  конечная частота нажатий - вдвое быстрее;\n",
    " -  полных паттернов не менее 6;\n",
    " -  ошибки паттернов не превышают 10%.\n",
    " \n",
    "\n",
    "Далее даны пошаговые инструкции для выполнения работы по этому шаблону."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Начало работы\n",
    "\n",
    "Переименуйте блокнот шаблона qual_Familia.ipynb, заменив \"Familia\" в названии на свою фамилию, чтобы не путаться при отправке и проверке. \n",
    "\n",
    "[](rename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выполните импорт библиотек."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "from ipywidgets import interact\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Загрузка данных\n",
    "\n",
    "Укажите источник данных. \n",
    "Данные теста \"Ускорение нажатий\" представляют собой маркеры - целочисленные коды событий в течение теста. Все события снабжены отметками времени.\n",
    "\n",
    "Если у Вас есть доступ к Сети, то удобнее скачать данные с сайта http://uskor.stireac.com\n",
    "\n",
    "Чтобы найти ссылку на свои данные - прилогиньтесь (вводится только адрес почты, потому что данные открыты, и логин нужен только для перехода к своим данным). Кликните на свой адрес и в выпадающем меню выберите пункт `Результаты`. Откроется список тестирований в виде таблицы, у которой в правой колонке есть кнопки для просмотра информации по каждому тестированию. \n",
    "\n",
    "На странице отчета помимо разнообразной информации есть две кнопки в конце для скачивания результатов. Первая из них ![](i/tsv_save_button.png) позволяет скачать таблицу маркеров в формате `*.tsv`. Если вызывать контекстное меню на этой картинке и скопировать ссылку, то эту же таблицу мы можем загрузить по этой ссылке автоматически.\n",
    "Подставьте скопированную ссылку в код. \n",
    "\n",
    "Если Сеть недоступна, то можно открыть таблицу из скачанного файла, помещенного в текущую папку. Для этого надо изменить переменную `u` с Интернет-ссылки на имя файла (см. пример в закомментированной второй строке). Пример файла прилагается.\n",
    "\n",
    "В конце ячейки мы выводим информацию о созданной таблице.\n",
    "Проверьте, что данные загрузились в таблицу с двумя колонками.\n",
    "\n",
    "Сделайте краткий комментарий об успешности загрузки данных.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "u = 'm__178.155.4.92__3219165950000190320.tsv'\n",
    "M = pd.read_table(u)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Подготовка сводной таблицы\n",
    "\n",
    "Основа данной работы - сбор сведений по каждому этапу с последующей интеграцией в виде оценок качества.\n",
    "\n",
    "Вспомогательную информацию о тесте мы оформляем в виде мини-словарей `scancode_hand`, `hand_color`. \n",
    "Это соответствия сканкодов и обозначений рук (L - левая, R - правая) и соответствия рук и цветов на рисунках (потенциальных).\n",
    "\n",
    "\n",
    "Посмотрите код создания сводной таблицы `S` с помощью библиотеки Pandas. Одну колонку с уникальными значениями мы создаем при вызове метода `DataFrame()`. Другие колонки мы создаем позже, присваивая одинаковое значение во всех строках, или в зависимости от позиции.\n",
    "Всего у нас шесть строк по количеству этапов. \n",
    "Для удобства автоматический индекс, начинающийся с 0, мы переименовываем и увеличиваем на 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Паттерн</th>\n",
       "      <th>Условие</th>\n",
       "      <th>L</th>\n",
       "      <th>R</th>\n",
       "      <th>Скорость в начале, Гц</th>\n",
       "      <th>Скорость в конце, Гц</th>\n",
       "      <th>Ошибки</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Этап</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>одна рука</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>одна рука</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01</td>\n",
       "      <td>поочередно</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>поочередно</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0001</td>\n",
       "      <td>паттерн</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1000</td>\n",
       "      <td>паттерн</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Паттерн     Условие  L  R  Скорость в начале, Гц  Скорость в конце, Гц  \\\n",
       "Этап                                                                          \n",
       "1          0   одна рука  0  0                    1.0                   5.0   \n",
       "2          1   одна рука  0  0                    1.0                   5.0   \n",
       "3         01  поочередно  0  0                    1.0                   5.0   \n",
       "4         10  поочередно  0  0                    1.0                   5.0   \n",
       "5       0001     паттерн  0  0                    1.0                   5.0   \n",
       "6       1000     паттерн  0  0                    1.0                   5.0   \n",
       "\n",
       "      Ошибки  \n",
       "Этап          \n",
       "1          0  \n",
       "2          0  \n",
       "3          0  \n",
       "4          0  \n",
       "5          0  \n",
       "6          0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scancode_hand = {70:'L', 74:'R'}\n",
    "hand_color = {'L':'red', 'R':'blue'}\n",
    "\n",
    "S = pd.DataFrame([\n",
    "    '0',\n",
    "    '1',\n",
    "    '01',\n",
    "    '10',\n",
    "    '0001',\n",
    "    '1000',\n",
    "         ], columns=['Паттерн'])\n",
    "S.index+=1\n",
    "S.index.name = 'Этап'\n",
    "S['Условие'] = list(map(lambda n: ((n<=2) and 'одна рука') or ((n<=4) and 'поочередно') or 'паттерн', S.index ))\n",
    "\n",
    "# нулевые значения для последующих подсчётов\n",
    "S['L']=0\n",
    "S['R']=0\n",
    "S['Скорость в начале, Гц'] = 1.0\n",
    "S['Скорость в конце, Гц'] = 5.0\n",
    "S['Ошибки'] = 0\n",
    "\n",
    "S"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Сбор сведений\n",
    "\n",
    "В последующих блоках мы заносим добытые сведения в таблицу. \n",
    "Смотрите примеры внесения данных.\n",
    "\n",
    "Образцы расчетов см. в блокнотах с образцами кода <a href=d/qual_Familia.ipynb>Task_qual_uskor.ipynb</a> и \n",
    "<a href=d/Task_seq_uskor.ipynb>Task_seq_uskor.ipynb</a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Качество этапа 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Отберем данные для первого этапа. Логика отбора моментов нажатий в пределах одного этапа состоит в вычислении моментов времени начала и конца этапа. Затем делаем копию массива данных между этими отметками времени и очищаем от ненужных маркеров (нам нужно только нажатия, отжатия и служебные коды не нужны)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Коды для первого этапа 1001 и до 1002 (начала второго этапа)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74    94\n",
       "13     1\n",
       "Name: v, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coab, coad = 1001, 1002\n",
    "tab = M.t[M.v==coab].iloc[0]\n",
    "tad = M.t[(M.t>tab) & (M.v==coad)].iloc[0]\n",
    "R = M[(M.t>=tab)&(M.t<tad)].copy()\n",
    "R = R[R.v.abs()<100]\n",
    "R = R[R.v > 0]\n",
    "R.v.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На первом этапе были нажатия правой рукой. В разных случаях на первом этапе может быть или правая или левая рука. Но коду клавиши можно проверить какой рукой были сделаны нажатия с этим кодом."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 74)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ord('F'), ord('J'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оценим скоростные характеристики в начале и конце теста. Поскольку сколько длительность \"начала\" и \"конца\" не определена, то проще оценить скорость \"на глаз\", глядя на рисунок."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": [
    "tt = R[R.v.isin([70,74])].t\n",
    "iri = tt.diff()\n",
    "plot(tt, 1/iri, 'o');\n",
    "grid(True)\n",
    "xlabel('Время, с')\n",
    "ylabel('Частота, Гц');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Когда вся информация перед глазами, внесем сведения для первого этапа. Для удобства заведем переменную для обозначения строки. Такой код легко будет приспособить для повторного использования на следующих этапах."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Паттерн                          0\n",
       "Условие                  одна рука\n",
       "L                                0\n",
       "R                               94\n",
       "Скорость в начале, Гц          1.0\n",
       "Скорость в конце, Гц           7.0\n",
       "Ошибки                           0\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ietap = 1\n",
    "S.loc[ietap, 'Скорость в начале, Гц'] = 1\n",
    "S.loc[ietap, 'Скорость в конце, Гц'] = 7\n",
    "S.loc[ietap, 'R'] = 94\n",
    "\n",
    "S.loc[ietap]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ошибок паттернов на первых двух этапах быть не может, поскольку нажатия производятся только одной рукой."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Качество этапа 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Зададим номер этапа с самого начала, чтобы использовать эту переменную повторно."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70    63\n",
       "Name: v, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ietap = 2\n",
    "\n",
    "coab, coad = 1000+ietap, 1000+ietap+1\n",
    "tab = M.t[M.v==coab].iloc[0]\n",
    "tad = M.t[(M.t>tab) & (M.v==coad)].iloc[0]\n",
    "R = M[(M.t>=tab)&(M.t<tad)].copy()\n",
    "R = R[R.v.abs()<100]\n",
    "R = R[R.v > 20]\n",
    "N = R.v.value_counts()\n",
    "N"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Динамику частоты нажатий оцениваем так же, как для первого этапа."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Еще одно улучшение - сохранение счета нажатий для двух рук в переменной `N`. После этого количество нажатий в таблицу заносим без ручного ввода числа."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Паттерн                          1\n",
       "Условие                  одна рука\n",
       "L                               63\n",
       "R                                0\n",
       "Скорость в начале, Гц          1.0\n",
       "Скорость в конце, Гц           7.0\n",
       "Ошибки                           0\n",
       "Name: 2, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S.loc[ietap, 'Скорость в начале, Гц'] = 1\n",
    "S.loc[ietap, 'Скорость в конце, Гц'] = 7\n",
    "S.loc[ietap, 'L'] = N.iloc[0]\n",
    "S.loc[ietap]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Качество этапа 3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70    31\n",
       "74    31\n",
       "Name: v, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ietap = 3\n",
    "\n",
    "coab, coad = 1000+ietap, 1000+ietap+1\n",
    "tab = M.t[M.v==coab].iloc[0]\n",
    "tad = M.t[(M.t>tab) & (M.v==coad)].iloc[0]\n",
    "R = M[(M.t>=tab)&(M.t<tad)].copy()\n",
    "R = R[R.v.abs()<100]\n",
    "R = R[R.v > 20]\n",
    "N = R.v.value_counts()\n",
    "N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 15.0)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEGCAYAAABiq/5QAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAdX0lEQVR4nO3df5RcdZnn8fdD0yEdGmgEbEyjBmfHsBjQJj3MDnF3umEkzMJKNjgqjgqje7Jn94zDzmrGRGfXVY6HjBnXcXfmHE92ZYI/lp6RhOiCS0SSlh1AZtMESBAiDiBDB+Vnqw0d0kme/aNuJ9XdVV23bt176/74vM7pk6pv1b33eejmqW997/d+r7k7IiJSHse1OwAREUmXCr+ISMmo8IuIlIwKv4hIyajwi4iUzPHtDiCM008/3ZcsWTKn/ZVXXuHEE09MP6AEFS2nouUDyqlVj/3sV0wdPjKnvbPjOM4586RYjqHfUcXo6OgL7n7G7PZcFP4lS5awa9euOe0jIyMMDg6mH1CCipZT0fIB5dSqs9fdTq1J5Abs2nB5LMfQ76jCzH5aq11DPSKSqsU9XU21S/xU+EUkVWtXLqWrs2NGW1dnB2tXLm1TROWTi6EeESmOVf19AGzcvo/945Ms7uli7cqlR9sleSr8IpK6Vf19KvRtpKEeEZGSUeEXESkZFX4RkZJR4RcRKRkVfhGRklHhFxEpmcQKv5ndaGbPmdneGq99wszczE5P6vgiIlJbkj3+zcBlsxvN7I3Au4CnEzy2iIjUkVjhd/e7gZdqvPQl4E+g5jpNIiKSMEvyZutmtgS4zd2XBc/fDVzi7teZ2VPAgLu/UGfbNcAagN7e3uXDw8Nz3jMxMUF3d3dC0bdH0XIqWj6gnPKgaPlAtJyGhoZG3X1gzgvuntgPsATYGzxeBNwPnBI8fwo4Pcx+li9f7rXs3LmzZnueFS2nouXjrpzyoGj5uEfLCdjlNWpqmrN6fg04G3go6O2fBTxgZmemGIOISOmltkibu+8BXj/9vNFQj4iIJCPJ6Zw3A/cBS83sGTP7aFLHEhGR8BLr8bv71Q1eX5LUsUVEpD5duSsiUjIq/CIiJaPCLyJSMrr1oogUyrbdY1w/8iov3XG77udbhwq/iBTGtt1jrN+6h8mpyooEY+OTrN+6B0DFv4qGekSkMDZu38fk1OEZbZNTh9m4fV+bIsomFX4RKYz945NNtZeVCr+IFMbinq6m2stKhV9ECmPtyqV0dXbMaOvq7GDtyqVtiiibdHJXRApj+gTu9d9+iJcOuGb11KHCLyKFsqq/j55fPM7g4GC7Q8ksDfWIiJSMCr+ISMmo8IuIlIwKv4hIyejkrogU0rbdY2zcvo/945Oa3TOLCr+IFM69+6f4+l17ji7foDV7ZtJQj4gUzpYfT2nNnnmo8ItI4bx4wGu2a82eChV+ESmc0xZazXat2VORWOE3sxvN7Dkz21vVttHMHjOzh83sVjPrSer4IlJeV721U2v2zCPJHv9m4LJZbXcCy9z9fODHwPoEjy8iJXXR4k5uWH0efT1dGNDX08UNq8/Tid1AYrN63P1uM1syq+17VU9/CLwnqeOLSLmt6u9Toa/D3GufBIll55XCf5u7L6vx2v8G/sbdv1Fn2zXAGoDe3t7lw8PDc94zMTFBd3d3rDG3W9FyKlo+oJzyoGj5QLSchoaGRt19YM4L7p7YD7AE2Fuj/dPArQQfPI1+li9f7rXs3LmzZnueFS2nouXjrpzyoGj5uEfLCdjlNWpq6hdwmdk1wBXAJUFgIiKSolQLv5ldBnwS+G13fzXNY4uISEWS0zlvBu4DlprZM2b2UeAvgZOAO83sQTP7SlLHFxGR2pKc1XN1jeavJnU8EREJR1fuioiUjAq/iEjJqPCLiJSMCr+ISMmo8IuIlIwKv4hIyajwi4iUjAq/iEjJqPCLiJSMCr+ISMmo8IuIlEzqyzKLiEhj23aPsXH7PvaPT7K4p4vL33SYwZj2rR6/iEjGbNs9xvqtexgbn8SBsfFJNu89yLbdY7HsX4VfRCRjNm7fx+TU4RltB49U2uOgwi8ikjH7xyebam+WCr+ISMYs7ulqqr1ZKvwiIhmzduVSujo7ZrQtOK7SHgfN6hERyZhV/X0Ac2b1TLe3SoVfRCSDVvX3zSj0IyMjse1bQz0iIiWTWOE3sxvN7Dkz21vV9jozu9PMHg/+PTWp44uISG1J9vg3A5fNalsH3OXuvw7cFTwXEZEUJVb43f1u4KVZzVcCNwWPbwJWJXV8ERGpzdw9uZ2bLQFuc/dlwfNxd++pev1ld6853GNma4A1AL29vcuHh4fnvGdiYoLu7u4EIm+fouVUtHxAOeVB0fKBaDkNDQ2NuvvA7PbMzupx903AJoCBgQEfHByc856RkRFqtedZ0XIqWj6gnNI2e7GytSuXNpzWmOV8ooozp7QL/8/N7A3u/qyZvQF4LuXji0iMohTlZve/fuueo+vWjI1Psn7rHuDYXPdaMfTEFkExpT2d8zvANcHja4Bvp3x8EYlJrRUk12/dE9sKklB7sbLJqcNHFyurF8O9+6dii6GIkpzOeTNwH7DUzJ4xs48CG4B3mdnjwLuC5yKSQ42KchwaLVZWL4YtP1bhn09iQz3ufnWdly5J6pgikp6kV5CEyqJkYzX2N71YWb1jvXgguUkrRZDZk7sikm2NinJU1WP2p3R10tlhTB0+Vsi7OjuOLlZWL4bTFlpLMRRd5KEeM3u9mb3JzE6KMyARyYdaK0hWF+UoZo/Zj09OgcOpizoxoK+nixtWn3f0xG69GK56a2fkGMogVI/fzD5co/lTwL3AFuD2OIMSkeyrtYJkq7N6ao3ZTx1xFi04nt3/+dLQMfT84vHIMZRB2KGe36jR1u3uH4kzGBHJl9krSDbSaPpnlPMGtWIYGVHhn0+owu/uH5vdZmbviD0aESmsMHPykzpvMDuOJK89yINWpnPqtLmIhBZm+mcS5w2qpXHtQR6EHeP/78ws9Aa8JZGIRKSQ6g3XjI1PsmLDjqM98KuW97Hzsefr9sij9Nint6n1bWL6w6dMvf6wY/y7QraJiNRUbxjH4Gj72PgkW0bHZszcqRZmuKjRNrXEee1BHsw71GNmZwK4+021ftIJUUSKoNYwjjF3zHi+q3+jXC1ca5vZ4jyHkAeNevzfBS5IIxARKbZaUy9rfQOA5mf3zNdjb9Sb7+rsYOicM2YMNxX9hG+jwq/L30QkNrOnXq7YsKOpWTxRZv3M9wHT19PF0DlnsGV0rKnho7xrNKtHM3dEJDHNzuKJMuun3jZ/8b53cM+6i9n52POJLzaXNY16/GeZ2X+r96K7/1HM8YhIiTR79W+Uq4UbbZPGYnNZ06jwr00lChHJvagXRjV79W+z72+0TRoXjWXNvIVfM3dEJIwo0yyzYu3KpXOme8Z50VgWpX0HLhEpoDRuypKUVf193LD6PPp6uo6uAHrV8j42bt/H2etuZ8WGHYW7slfr8YtIy/I+Tl49FJTnby9hqccvIi2bb/pl3uT520tYkQq/mf17M3ufmekbg4gkvrhamuZbU6goQz9Re/wGvBPYGmMsIpJTtcbJ6623k3XzfUspyoqekXrs7v5XrRzUzP4Y+DdU/jvuAf7A3Q+0sk8Raa8o0yyzqNYsn9lqreiZp3X+Qxd+M7sceBuwcLrN3T/X7AHNrA/4I+Bcd580s78F3g9sbnZfIiJxm33BV73lC6qHhPJ2QjjUUI+ZfQV4H/AxKsM8vwe8uYXjHg90BecIFgH7W9iXiEisVvX3cc+6i3lyw+X0hThxnbcTwubeeDkeM3vY3c+v+rcb2Oruc+9+HOagZtcBnwcmge+5++/XeM8aYA1Ab2/v8uHh4Tn7mZiYoLu7O0oImVW0nIqWDyinPIgzn3v3T7F570EOHjnW1gF0dcLEFJy20HjxQP06uvmyE2OJI0pOQ0NDo+4+MLs97FDP9HeaV81sMfAicHZTEQTM7FTgymD7ceBbZvZBd/9G9fvcfROwCWBgYMAHBwfn7GtkZIRa7XlWtJyKlg8opzyIM59B4Nyq8ftTujp55eAhJqYqxf7FA17zvgJQOckdVxxx5hS28N9mZj3ARuABKjn+j4jH/B3gSXd/HsDMtgIXAd+YdysRkTapPnG9YsMOxienZrzuzL2pTJans4Yq/O5+ffBwi5ndBix0919EPObTwD8zs0VUvklcgm7jKCI5UW+ev1Pp4RdmVo+ZPeDuFwC4+2vAa1EP6O73m9ktVL45HAJ2EwzpiEg+5GnqYtzqrebZ19PFPesubkNEzQt7AVesd+Jy98+4+znuvszdPxR8mIhIDkxPXRwLpjoW4YKmZhThKuWwY/xLzezhqucGuLufn0BMIpJh801dLEOvP8rNYLImbOF/EvhXSQYiIvmQ95U445D3q5TDFv6D7v7TRCMRkVwo4x2riibsGP/HEo1CRHKjCGPcZRe2x7/HzL4E/PPg+Q+Az7UwpVNEcmT2LJ6rlvex87HnQ41xl3kGUFaFLfw3AnuB9wbPPwT8NbA6iaBEJDtqLUC2ZXQs1LLLeVu8rCzCDvX8WjAF84ng57PAW5IMTESyoZUFyPK2eFlZhC38k2b2zuknZraCY+v3iEiBtTKLRzOAsinsUM+/A24ys1OC5y8D1yQTkohkSSuzeDQDKJvC9vh/5u5vB84Hznf3fnd/uNFGIpJ/rczi0QygY7btHmPFhh2ZuG9v2B7/d4EL3P2XSQYjItnTypWqRbjKNQ5ZO8kd6Z67IlIurVypmverXOOQtWUuwhb+882surc/vVbPyQnEJCJSKFk7yR12jH+Pu59c9XOSir6ISDj1Tma36yR32MIvIiIR1TvJPXTOGW054Rt2qOeqRKMQESmwWie5h845gy2jY2054Ru28H/GzK5z93E4esP0L7r7RxKLTEQkx2qtUVR9h64VG3a07YRv6JO700UfwN1fNrP+ZEISEcm3MNM323nCN+wY/3FBLx8AM3sdmgoqIlJTmDWK2nnCN2zh/yJwr5ldb2bXA/cCX0guLBGR/ArTm691wrfzOOPVg4cSP9kbqvC7+9eA9wA/B54DVrv716Me1Mx6zOwWM3vMzB41s9+Kui8RkawJ05tf1d/HDavPo6+nCwN6ujrB4OVXpxK/iX3o6Zzu/gjwt8C3gQkze1MLx/0ycIe7nwO8HXi0hX2JiGRK2DWKVvX3cc+6i3lyw+WceMLxTB32Ga8ntYR1qHF6M3s3leGexVR6/G+mUqzf1uwBzexk4F8A1wK4+0HgYLP7ERHJqihrFKV5stfcvfGbzB4CLga+7+79ZjYEXO3ua5o+oNk7gE3Aj6j09keB69z9lVnvWwOsAejt7V0+PDw8Z18TExN0d3c3G0KmFS2nouUDyikP8pjPx0de5cUDc+vxaQuNLw4uipTT0NDQqLsPzG4PW/h3uftA8AHQ7+5HzOzv3f3CpqKo7GsA+CGwwt3vN7MvA7909/9Ub5uBgQHftWvXnPaRkREGBwebDSHTipZT0fIB5ZQHecxn9hRQqAwPTd/iMkpOZlaz8IedkjluZt3A3cA3zew54FBTERzzDPCMu98fPL8FWBdxXyIihZDmEtZhC/+VwAHgj4HfB04BPhflgO7+MzP7RzNb6u77gEuoDPuIiJRaWktYhyr8s8bfb4rhuB+j8s1hAfAE8Acx7FNEREKYt/Cb2ZNA3ZMA7v6WKAd19weBOeNOIiKSvEY9/uribMAOYCi5cEREJGnzFn53f7H6uZkdmt0mIiL5EvrKXTN7C5Vev4iI5FijMf49VMb4TwAWAf82jaBERCQ5jcb4rwj+PeDuP086GBERSV6jMf6fphWIiIikQzdbFxEpGRV+EZGSUeEXESkZFX4RkZJR4RcRKRkVfhGRkgm7LLOIlMi23WOprAsv7aHCLyIzzL4T1Nj4JOu37gFQ8S8IDfWIyAwbt++bcfs/gMmpw2zcvq9NEUncVPhFZIb945NNtUv+qPCLyAyLe7qaapf8UeEXkRnWrlxKV2fHjLauzg7Wrlzapogkbjq5KyIzTJ/A1aye4lLhF5E5VvX3qdAXWNsKv5l1ALuAMXe/otH7RSQ6zcuXau3s8V8HPAqc3MYYRApP8/Jltrac3DWzs4DLgf/ZjuOLlEmjefnbdo/x8ZFXOXvd7azYsINtu8faEaakyNw9/YOa3QLcAJwEfKLWUI+ZrQHWAPT29i4fHh6es5+JiQm6u7sTjjZdRcupaPlA/nK69o5X6r625vwFbN57kINHjrUtOA6uXbaAixZ3phBdMvL2OwojSk5DQ0Oj7j4wuz31oR4zuwJ4zt1HzWyw3vvcfROwCWBgYMAHB+e+dWRkhFrteVa0nIqWD+Qvp74f7mCsxsVXfT1d3P40M4o+VJ7f/nQHn/rAYDoBJiBvv6Mw4sypHUM9K4B3m9lTwDBwsZl9ow1xiJTCfPPydZVuOaVe+N19vbuf5e5LgPcDO9z9g2nHIZI323aPsWLDjqbH4lf193HD6vPo6+nCqPT0b1h9Hqv6+3SVbklpHr9IDrQ6M6fevPy1K5fO2C/oKt0yaOuSDe4+ojn8Io0ltWLm9LeB0xbanG8DUlzq8YvkQJJj8av6++j5xeOFOxkq9WmRNpEc0Fi8xEmFXyQHwqyYGfXkr5SPhnpEcqDRiplalkGaocIvkhPzrZg538lfFX6ZTUM9IgWgC7GkGerxi2RI1OWTF/d01VyWQSd/pRb1+EUyYnqcfmx8EufYOH2Yk7S6XaI0Qz1+kZQ06s23Mk6v2yVKM1T4RVIQZtZNq+P0ul2ihKWhHpEUhFlyQRdpSVpU+EVSEKY3r3F6SYsKv0gKwvTm51s+WSROGuMXSUHY5Y81Ti9pUOEXSYFm3UiWqPCLpES9eckKjfGLiJSMCr+ISMloqEekRVHX1xFpFxV+kRZoHXzJo9QLv5m9EfgacCZwBNjk7l+O+zjqhUkafwNaB1/yqB09/kPAx939ATM7CRg1szvd/UdxHUC9MEnrb0Dr4EsepX5y192fdfcHgse/Ah4FYq3GYdZFkXxp9n6yrfwNNHMsra8jeWTu3r6Dmy0B7gaWufsvZ722BlgD0Nvbu3x4eHjO9hMTE3R3d89pv/aOV+oec/NlJ7YUc9Lq5ZRXceRz7/4pNu89yMEjx9oWHAfXLlvARYs7a24T9W8gzLGqc4oSWxbp7y77ouQ0NDQ06u4Ds9vbVvjNrBv4AfB5d98633sHBgZ8165dc9pHRkYYHByc075iw46adyPq6+ninnUXRw05FfVyyqvZ+UQZd4/y+4z6NxBmuzhyypqi/90VQZSczKxm4W/LrB4z6wS2AN9sVPSjCLsuynyK8D9z1kQdd48yjh71byDKsXRFruRNO2b1GPBV4FF3/69JHKPVdVGKenK43R9mUWfARLmfbNS/Ad27VsqgHT3+FcCHgD1m9mDQ9il3/26cB2mlF5bFKXqtFu0sfJhFnQETtfce5W8gjm+LIlmXeuF3978DLO3jNiPuKXpZKNpxfphFzSdqbzrNlS21iqaUga7crSHOr/tZKdpxfZi1kk8rvek0x9E1Zi9Fp0XaaojzFnhxXFMQR9GOa755K/noDlMi2aAefw1xft2Pq2i3+g0krrHrVvNRb1qk/VT464irQGWlaMf1YaZZLyL5p8KfsCwV7Tg+zDTrRST/VPgTlqWiHQfNehHJPxX+FGSlaMelaPmIlI1m9YiIlIwKv4hIyajwi4iUjAq/iEjJqPCLiJSMCr+ISMmo8IuIlIwKv4hIyajwi4iUjAq/iEjJqPCLiJSMCr+ISMmo8IuIlExbCr+ZXWZm+8zsJ2a2rh0xiIiUVeqF38w6gL8Cfhc4F7jazM5NOw4RkbJqR4//QuAn7v6Eux8EhoEr2xCHiEgpteNGLH3AP1Y9fwb4zdlvMrM1wJrg6YSZ7auxr9OBF2KPsL2KllPR8gHllAdFywei5fTmWo3tKPxWo83nNLhvAjbNuyOzXe4+EFdgWVC0nIqWDyinPChaPhBvTu0Y6nkGeGPV87OA/W2IQ0SklNpR+P8f8OtmdraZLQDeD3ynDXGIiJRS6kM97n7IzP4Q2A50ADe6+yMRdzfvUFBOFS2nouUDyikPipYPxJiTuc8ZXhcRkQLTlbsiIiWjwi8iUjKZLvxm9kYz22lmj5rZI2Z2XdD+e8HzI2Y2UPX+d5nZqJntCf69uH3Rz9VsPlXbvcnMJszsE+lHPb8oOZnZ+WZ2X/D6HjNb2J7o54rwN9dpZjcFeTxqZuvbF31t8+S00cweM7OHzexWM+up2mZ9sKTKPjNb2bbga2g2n6zXBYj2Owpej1Yb3D2zP8AbgAuCxycBP6ayzMM/BZYCI8BA1fv7gcXB42XAWLtzaCWfqu22AN8CPtHuHGL4HR0PPAy8PXh+GtDR7jxayOcDwHDweBHwFLCk3XmEzOlS4Pig/c+APwsenws8BJwAnA38Q05+R/XyyXRdiJJT1XaRakM7LuAKzd2fBZ4NHv/KzB4F+tz9TgAzm/3+3VVPHwEWmtkJ7v5aSiHPq9l8grZVwBPAK+lFGl6EnC4FHnb3h4JtXkwx3IYi5OPAiWZ2PNAFHAR+mV7Ejc2T0/eq3vZD4D3B4yupfJi9BjxpZj+hstTKfSmGXVez+WS9LkCk31FLtSHTQz3VzGwJlU/u+0NuchWwO0u/3Gph8jGzE4FPAp9NKayWhPwdvRVwM9tuZg+Y2Z+kElwEIfO5hcr/eM8CTwN/7u4vJR9dNPPk9BHg/wSPay2r0pd4cBGEzKdapusChMup1dqQ6R7/NDPrpvKV5j+4e8PelJm9jcrXokuTji2KJvL5LPAld5+o9W0gS5rI6XjgncBvAK8Cd5nZqLvflUKYoTWRz4XAYWAxcCrwf83s++7+RAphNqVeTmb2aeAQ8M3pphqbZ27edxP5TLdnui5AUzm1VBsyX/jNrJPKf4hvuvvWEO8/C7gV+LC7/0PS8TWryXx+E3iPmX0B6AGOmNkBd//LhMNsSpM5PQP8wN1fCLb9LnABkJnC32Q+HwDucPcp4DkzuwcYoPIVPDPq5WRm1wBXAJd4MGhMDpZVaTKfzNcFaDqn1mpDu09qNDjhYcDXgL+o8/oIM0+09VA5KXVVu2OPI59Zr/0Xsnlyt9nf0anAA1ROhB4PfB+4vN15tJDPJ4G/DrY7EfgRcH678wiTE3BZEO8Zs9rfxsyTu0+QrZO7zeaT6boQJadZ72m6NrQ94Qb/Md5J5Svmw8CDwc+/BP41lV7Ja8DPge3B+/+Uynjrg1U/r293HlHzafWXm9WcgA9SOcm2F/hCu3No8W+um8qsikeC/0HXtjuHJnL6CZWx/Om2r1Rt82kqs3n2Ab/b7hxaySfrdSHq76hq26Zrg5ZsEBEpmdzM6hERkXio8IuIlIwKv4hIyajwi4iUjAq/iEjJqPBLaZjZYTN70MweCpaLuKjdMYm0g6ZzSmmY2YS7dwePVwKfcvffbnNYIqlTj1/K6mTgZQAzGzSzu4P1zn9kZl8xs+OC1y61yr0DHjCzbwVrqWBmT5nZzdM7M7O/MbOngscLgn3tDdaAf6pRMGb24WDN9YfM7OsJ5CtyVObX6hGJUZeZPQgspLL+efUNOS6ksv75T4E7gNVmNkLlqs/fcfdXzOyTwH8EPhdss9jMTqVyuf2ZVftaCXS6+zIzOx3YNV9QweJhnwZWuPsLZva61tIUmZ8Kv5TJpLu/A8DMfgv4mpktC177ew9W1Ax68u8EDlD5MLgnWAFxATPXpL+ZyiJtBvwvYPruW4eBRWbWETKui4FbPFi4zjO8rLMUgwq/lJK73xf0xs+Ybpr9FioF/U53v7rObr7DsQXaruVY4f8esBp4HhgLEY7VOL5IYjTGL6VkZucAHcD0HcAuNLOzg7H99wF/R+WORyvM7J8E2ywys7dW7eZg8J77gscAuPshYBJYCwxVHbPPzGotP30X8F4zOy14n4Z6JFHq8UuZTI/xQ6WXfY27Hw6Gce4DNgDnAXcDt7r7ETO7FrjZzE4ItvtTKvdDBcDdPwMQfHsgePxe4GR3/2p1O5XzCodmB+Xuj5jZ54EfmNlhYDeVbxAiidB0Tik9MxuksqztFQkf5w+Bp939O0keR6QR9fhFUuIZu3OalJd6/CIiJaOTuyIiJaPCLyJSMir8IiIlo8IvIlIyKvwiIiXz/wFHp/mz9dMTHgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tt = R[R.v.isin([70,74])].t\n",
    "iri = tt.diff()\n",
    "plot(tt, 1/iri, 'o');\n",
    "grid(True)\n",
    "xlabel('Время, с')\n",
    "ylabel('Частота, Гц');\n",
    "ylim(0, 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На третьем этапе появляется возможность ошибок. Ошибка как правило возникают при переходе на быстрый темп нажатий."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "rr = R.v.values\n",
    "rr = (rr - rr.min())/(rr.max()-rr.min())\n",
    "plot(rr);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В конце видно одно нарушение зубчатого рисунка. Можно внести к таблицу количество ошибок - 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Количество \"левых\" и \"правых\" нажатий вносим одной командой."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Паттерн                          01\n",
       "Условие                  поочередно\n",
       "L                                31\n",
       "R                                31\n",
       "Скорость в начале, Гц           1.9\n",
       "Скорость в конце, Гц            9.0\n",
       "Ошибки                            1\n",
       "Name: 3, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S.loc[ietap, 'Скорость в начале, Гц'] = 1.9\n",
    "S.loc[ietap, 'Скорость в конце, Гц'] = 9\n",
    "S.loc[ietap, ['L','R']] = N.loc[70], N.loc[74]\n",
    "S.loc[ietap, 'Ошибки'] = 1\n",
    "S.loc[ietap]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Качество этапа 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74    39\n",
       "70    38\n",
       "Name: v, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ietap = 4\n",
    "\n",
    "coab, coad = 1000+ietap, 1000+ietap+1\n",
    "tab = M.t[M.v==coab].iloc[0]\n",
    "tad = M.t[(M.t>tab) & (M.v==coad)].iloc[0]\n",
    "R = M[(M.t>=tab)&(M.t<tad)].copy()\n",
    "R = R[R.v.abs()<100]\n",
    "R = R[R.v > 20]\n",
    "N = R.v.value_counts()\n",
    "N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 15.0)"
      ]
     },
     "execution_count": 17,
     "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": [
    "tt = R[R.v.isin([70,74])].t\n",
    "iri = tt.diff()\n",
    "plot(tt, 1/iri, 'o');\n",
    "grid(True)\n",
    "xlabel('Время, с')\n",
    "ylabel('Частота, Гц');\n",
    "ylim(0, 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": [
    "rr = R.v.values\n",
    "rr = (rr - rr.min())/(rr.max()-rr.min())\n",
    "plot(rr);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Паттерн                          10\n",
       "Условие                  поочередно\n",
       "L                                38\n",
       "R                                39\n",
       "Скорость в начале, Гц           2.0\n",
       "Скорость в конце, Гц            8.0\n",
       "Ошибки                            2\n",
       "Name: 4, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S.loc[ietap, 'Скорость в начале, Гц'] = 2\n",
    "S.loc[ietap, 'Скорость в конце, Гц'] = 8\n",
    "S.loc[ietap, ['L','R']] = N.loc[70], N.loc[74]\n",
    "S.loc[ietap, 'Ошибки'] = 2\n",
    "S.loc[ietap]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Качество этапа 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70    50\n",
       "74    17\n",
       "Name: v, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ietap = 5\n",
    "\n",
    "coab, coad = 1000+ietap, 1000+ietap+1\n",
    "tab = M.t[M.v==coab].iloc[0]\n",
    "tad = M.t[(M.t>tab) & (M.v==coad)].iloc[0]\n",
    "R = M[(M.t>=tab)&(M.t<tad)].copy()\n",
    "R = R[R.v.abs()<100]\n",
    "R = R[R.v > 20]\n",
    "N = R.v.value_counts()\n",
    "N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 10.0)"
      ]
     },
     "execution_count": 21,
     "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": [
    "tt = R[R.v.isin([70,74])].t\n",
    "iri = tt.diff()\n",
    "plot(tt, 1/iri, 'o');\n",
    "grid(True)\n",
    "xlabel('Время, с')\n",
    "ylabel('Частота, Гц');\n",
    "ylim(0, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "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": [
    "rr = R.v.values\n",
    "rr = (rr - rr.min())/(rr.max()-rr.min())\n",
    "plot(rr);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На пятом и шестом этапах \"гребенка\" для проверки ошибок отличается, но сбой в паттерне нажатий видно также четко."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Паттерн                     0001\n",
       "Условие                  паттерн\n",
       "L                             50\n",
       "R                             17\n",
       "Скорость в начале, Гц        1.4\n",
       "Скорость в конце, Гц         5.0\n",
       "Ошибки                         1\n",
       "Name: 5, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S.loc[ietap, 'Скорость в начале, Гц'] = 1.4\n",
    "S.loc[ietap, 'Скорость в конце, Гц'] = 5\n",
    "S.loc[ietap, ['L','R']] = N.loc[70], N.loc[74]\n",
    "S.loc[ietap, 'Ошибки'] = 1\n",
    "S.loc[ietap]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Качество этапа 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для 6-го этапа, после которого нет других этапов, время окончания находим по времени первого нажатия клавиши \"Enter\" после начала. Этот шаблон можно применять на всех этапах - все этапы начинаются с кода `1000+ietap` и заканчиваются кодом 13 (\"Enter\")."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74    59\n",
       "70    20\n",
       "Name: v, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ietap = 6\n",
    "\n",
    "coab = 1000+ietap\n",
    "coad = 13\n",
    "tab = M.t[M.v==coab].iloc[0]\n",
    "tad = M.t[(M.t>tab) & (M.v==coad)].iloc[0]\n",
    "R = M[(M.t>=tab)&(M.t<tad)].copy()\n",
    "R = R[R.v.abs()<100]\n",
    "R = R[R.v > 20]\n",
    "N = R.v.value_counts()\n",
    "N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 10.0)"
      ]
     },
     "execution_count": 25,
     "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": [
    "tt = R[R.v.isin([70,74])].t\n",
    "iri = tt.diff()\n",
    "plot(tt, 1/iri, 'o');\n",
    "grid(True)\n",
    "xlabel('Время, с')\n",
    "ylabel('Частота, Гц');\n",
    "ylim(0, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "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": [
    "rr = R.v.values\n",
    "rr = (rr - rr.min())/(rr.max()-rr.min())\n",
    "plot(rr);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Паттерн                     1000\n",
       "Условие                  паттерн\n",
       "L                             20\n",
       "R                             59\n",
       "Скорость в начале, Гц        3.0\n",
       "Скорость в конце, Гц         6.0\n",
       "Ошибки                         2\n",
       "Name: 6, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S.loc[ietap, 'Скорость в начале, Гц'] = 3\n",
    "S.loc[ietap, 'Скорость в конце, Гц'] = 6\n",
    "S.loc[ietap, ['L','R']] = N.loc[70], N.loc[74]\n",
    "S.loc[ietap, 'Ошибки'] = 2\n",
    "S.loc[ietap]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Альтернативные источники сведений для заполнения таблицы - отчеты на сайте тестирования.\n",
    "Если Вы берете сведения оттуда, а не из своих рисунков, надо это указать."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Количество паттернов посчитаем по формуле..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Паттерн</th>\n",
       "      <th>Условие</th>\n",
       "      <th>L</th>\n",
       "      <th>R</th>\n",
       "      <th>Скорость в начале, Гц</th>\n",
       "      <th>Скорость в конце, Гц</th>\n",
       "      <th>Ошибки</th>\n",
       "      <th>Паттернов</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Этап</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>одна рука</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>94.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>одна рука</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>63.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01</td>\n",
       "      <td>поочередно</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>1.9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "      <td>31.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>поочередно</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2</td>\n",
       "      <td>38.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0001</td>\n",
       "      <td>паттерн</td>\n",
       "      <td>50</td>\n",
       "      <td>17</td>\n",
       "      <td>1.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>16.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1000</td>\n",
       "      <td>паттерн</td>\n",
       "      <td>20</td>\n",
       "      <td>59</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "      <td>19.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Паттерн     Условие   L   R  Скорость в начале, Гц  Скорость в конце, Гц  \\\n",
       "Этап                                                                            \n",
       "1          0   одна рука   0  94                    1.0                   7.0   \n",
       "2          1   одна рука  63   0                    1.0                   7.0   \n",
       "3         01  поочередно  31  31                    1.9                   9.0   \n",
       "4         10  поочередно  38  39                    2.0                   8.0   \n",
       "5       0001     паттерн  50  17                    1.4                   5.0   \n",
       "6       1000     паттерн  20  59                    3.0                   6.0   \n",
       "\n",
       "      Ошибки  Паттернов  \n",
       "Этап                     \n",
       "1          0      94.00  \n",
       "2          0      63.00  \n",
       "3          1      31.00  \n",
       "4          2      38.50  \n",
       "5          1      16.75  \n",
       "6          2      19.75  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S['Паттернов'] = (S['L']+S['R']) / S['Паттерн'].str.len()\n",
    "S"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Итак, мы заполнили таблицу. Перед тем, как двигаться дальше, проверим, что внесенные значения соответствуют методике. На первых двух этапах нажатия были только одной рукой, и отсутствовали ошибки. Количество нажатий в колонках \"L\" и \"R\" в 3 и 4 этапах примерно равны, а на 5 и 6 этапах в соотношении 1 к 3."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Расчет качества\n",
    "\n",
    "> Создайте новые колонки для каждого критерия качества.\n",
    "\n",
    "Создаем три критерия Q1, Q2, ...  со значениями от 0 до 1.\n",
    "\n",
    "- Q1 Медленное начало, примерно раз в секунду.\n",
    "- Q2 Быстрое окончание.\n",
    "- Q3 Количество патернов не меньше 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "S['Q1'] = ((S['Скорость в начале, Гц'] > 0.5) & (S['Скорость в начале, Гц'] < 1.5)) + 0.0\n",
    "S['Q2'] = (S['Скорость в конце, Гц'] > 2.) + 0.0\n",
    "S['Q3'] = (S['Паттернов'] > 5) + 0.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Затем создайте колонки с интегральными оценками. Их можно считать, производя операции целыми колонками."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Этап\n",
       "1    1.0\n",
       "2    1.0\n",
       "3    0.0\n",
       "4    0.0\n",
       "5    1.0\n",
       "6    0.0\n",
       "Name: Q, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S['Q'] = S['Q1'] * S['Q2'] * S['Q3']\n",
    "S.Q"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "При произведении частных критериев комплексная оценка обнуляется любым нулевым критерием."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Сумма всех коэффициентов не должна быть больше 1\n",
    "k1 = 0.5\n",
    "k2 = 0.2\n",
    "k3 = 1 - (k1 + k2)\n",
    "\n",
    "S['K'] = k1*S ['Q1'] + k2*S['Q2'] + k3*S['Q3'] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Обоснуйте выбор весовых коэффициентов. Почему одни критерии весомее других?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q</th>\n",
       "      <th>K</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Этап</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Q    K\n",
       "Этап          \n",
       "1     1.0  1.0\n",
       "2     1.0  1.0\n",
       "3     0.0  0.5\n",
       "4     0.0  0.5\n",
       "5     1.0  1.0\n",
       "6     0.0  0.5"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S[['Q','K']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Которая из комплексных оценок лучше согласуется с Вашей личной \"экспертной\" оценкой?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Среднее качество по всем этапам."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Q    0.50\n",
       "K    0.75\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S[['Q','K']].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Вывод\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "В конце отчета по расчётно-графической работе обязательно сделайте вывод.\n",
       "\n",
       "1. Основной результат. Прежде всего в выводе должен прозвучать ответ на поставленный вопрос.\n",
       "Что было целью работы, то и должно быть указано в выводе с конкретным числовым результатом.\n",
       "\n",
       "2. Ограничения данной работы. Если в ходе работе были выявлены новые обстоятельства, которые привели к изменению хода работы, к ошибкам, к необходимости переосмысления возможности ее выполнения, то эти соображения надо изложить во вторую очередь. \n",
       "\n",
       "3. Педагогические и эмоциональные замечания. Поскольку эта работа учебная, то можно поделиться впечатлениями от самого процесса решения задачи. Что было сложным, что особенно понравилось, что оказалось удивительным и неожиданным."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, Markdown\n",
    "display(Markdown(open('_postw.md', 'r', encoding='utf-8').read()))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
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