MeteoApp.ipynb

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    "# Meteorological Data Visualization Tool / Инструмент визуализации метеорологических данных\n",
    "\n",
    "This notebook can extract meteorological data stored as binary files in a server belonging to the Institute of Monitoring of Climatic and Ecological Systems of the Siberian Branch of the Russian Academy of Sciences (IMCES SB RAS). Simply click the Run button above, and a user interface will appear in the output of the first cell. However, be careful about requesting more than a few days of data at once, since most regulars computers don't have enough RAM to store that much information.\n",
    "\n",
    "Эта программа может извлекать метеорологические данные, хранящиеся в виде двоичных файлов на сервере, принадлежащем Институту мониторинга климатических и экологических систем Сибирского отделения Российской академии наук (ИМКЭС СО РАН). Просто нажмите кнопку «Выполнить» выше, и пользовательский интерфейс появится на выходе первой ячейки. Тем не менее, будьте осторожны с запросом данных более чем за несколько дней, поскольку у большинства обычных компьютеров недостаточно ОЗУ для хранения такого большого количества информации."
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      "       Temperature, °C / Температура, °C  \\\n",
      "count                       3.514259e+06   \n",
      "mean                        2.055339e+01   \n",
      "std                         1.563756e+00   \n",
      "min                         1.769000e+01   \n",
      "25%                         1.912000e+01   \n",
      "50%                         2.056000e+01   \n",
      "75%                         2.202000e+01   \n",
      "max                         2.498000e+01   \n",
      "\n",
      "       Southern Wind Speed, m/s / Скорость южного ветра, м/с  \\\n",
      "count                                       3.514259e+06       \n",
      "mean                                       -4.307011e-01       \n",
      "std                                         8.897929e-01       \n",
      "min                                        -7.310000e+00       \n",
      "25%                                        -8.900000e-01       \n",
      "50%                                        -2.600000e-01       \n",
      "75%                                         1.800000e-01       \n",
      "max                                         2.890000e+00       \n",
      "\n",
      "       Eastern Wind Speed, m/s / Скорость восточного ветра, м/с  \\\n",
      "count                                       3.514259e+06          \n",
      "mean                                        5.445182e-01          \n",
      "std                                         7.734559e-01          \n",
      "min                                        -3.750000e+00          \n",
      "25%                                         3.000000e-02          \n",
      "50%                                         5.100000e-01          \n",
      "75%                                         1.020000e+00          \n",
      "max                                         6.090000e+00          \n",
      "\n",
      "       Vertical Wind Speed, m/s / Вертикальная скорость ветра, м/с  \\\n",
      "count                                       3.514259e+06             \n",
      "mean                                        9.107008e-02             \n",
      "std                                         4.607748e-01             \n",
      "min                                        -3.390000e+00             \n",
      "25%                                        -1.500000e-01             \n",
      "50%                                         7.000000e-02             \n",
      "75%                                         3.400000e-01             \n",
      "max                                         4.380000e+00             \n",
      "\n",
      "       Atmospheric Pressure, mmHg / Атмосферное давление, мм рт. ст  \\\n",
      "count                                       3.514259e+06              \n",
      "mean                                        7.355377e+02              \n",
      "std                                         1.130392e+00              \n",
      "min                                         7.334000e+02              \n",
      "25%                                         7.345000e+02              \n",
      "50%                                         7.353000e+02              \n",
      "75%                                         7.368000e+02              \n",
      "max                                         7.378000e+02              \n",
      "\n",
      "       Relative Air Humidity, % / Относительная влажность воздуха, %  \\\n",
      "count                                       3.514259e+06               \n",
      "mean                                        6.381142e+01               \n",
      "std                                         1.353424e+01               \n",
      "min                                         4.204000e+01               \n",
      "25%                                         4.877000e+01               \n",
      "50%                                         6.616000e+01               \n",
      "75%                                         7.420000e+01               \n",
      "max                                         8.593000e+01               \n",
      "\n",
      "        Error (0-9)  \n",
      "count  3.514259e+06  \n",
      "mean   1.095537e-04  \n",
      "std    2.253705e-02  \n",
      "min    0.000000e+00  \n",
      "25%    0.000000e+00  \n",
      "50%    0.000000e+00  \n",
      "75%    0.000000e+00  \n",
      "max    5.000000e+00  \n"
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      "                                                    Temperature, °C / Температура, °C  \\\n",
      "Temperature, °C / Температура, °C                                            1.000000   \n",
      "Southern Wind Speed, m/s / Скорость южного ветр...                           0.312280   \n",
      "Eastern Wind Speed, m/s / Скорость восточного в...                          -0.024344   \n",
      "Vertical Wind Speed, m/s / Вертикальная скорост...                          -0.024209   \n",
      "Atmospheric Pressure, mmHg / Атмосферное давлен...                           0.531681   \n",
      "Relative Air Humidity, % / Относительная влажно...                          -0.875451   \n",
      "Error (0-9)                                                                 -0.003101   \n",
      "\n",
      "                                                    Southern Wind Speed, m/s / Скорость южного ветра, м/с  \\\n",
      "Temperature, °C / Температура, °C                                                            0.312280       \n",
      "Southern Wind Speed, m/s / Скорость южного ветр...                                           1.000000       \n",
      "Eastern Wind Speed, m/s / Скорость восточного в...                                          -0.104630       \n",
      "Vertical Wind Speed, m/s / Вертикальная скорост...                                          -0.174200       \n",
      "Atmospheric Pressure, mmHg / Атмосферное давлен...                                           0.467040       \n",
      "Relative Air Humidity, % / Относительная влажно...                                          -0.211189       \n",
      "Error (0-9)                                                                                 -0.001475       \n",
      "\n",
      "                                                    Eastern Wind Speed, m/s / Скорость восточного ветра, м/с  \\\n",
      "Temperature, °C / Температура, °C                                                           -0.024344          \n",
      "Southern Wind Speed, m/s / Скорость южного ветр...                                          -0.104630          \n",
      "Eastern Wind Speed, m/s / Скорость восточного в...                                           1.000000          \n",
      "Vertical Wind Speed, m/s / Вертикальная скорост...                                          -0.443215          \n",
      "Atmospheric Pressure, mmHg / Атмосферное давлен...                                          -0.226846          \n",
      "Relative Air Humidity, % / Относительная влажно...                                          -0.037470          \n",
      "Error (0-9)                                                                                  0.002304          \n",
      "\n",
      "                                                    Vertical Wind Speed, m/s / Вертикальная скорость ветра, м/с  \\\n",
      "Temperature, °C / Температура, °C                                                           -0.024209             \n",
      "Southern Wind Speed, m/s / Скорость южного ветр...                                          -0.174200             \n",
      "Eastern Wind Speed, m/s / Скорость восточного в...                                          -0.443215             \n",
      "Vertical Wind Speed, m/s / Вертикальная скорост...                                           1.000000             \n",
      "Atmospheric Pressure, mmHg / Атмосферное давлен...                                          -0.050724             \n",
      "Relative Air Humidity, % / Относительная влажно...                                           0.042389             \n",
      "Error (0-9)                                                                                 -0.000388             \n",
      "\n",
      "                                                    Atmospheric Pressure, mmHg / Атмосферное давление, мм рт. ст  \\\n",
      "Temperature, °C / Температура, °C                                                            0.531681              \n",
      "Southern Wind Speed, m/s / Скорость южного ветр...                                           0.467040              \n",
      "Eastern Wind Speed, m/s / Скорость восточного в...                                          -0.226846              \n",
      "Vertical Wind Speed, m/s / Вертикальная скорост...                                          -0.050724              \n",
      "Atmospheric Pressure, mmHg / Атмосферное давлен...                                           1.000000              \n",
      "Relative Air Humidity, % / Относительная влажно...                                          -0.315659              \n",
      "Error (0-9)                                                                                 -0.003737              \n",
      "\n",
      "                                                    Relative Air Humidity, % / Относительная влажность воздуха, %  \\\n",
      "Temperature, °C / Температура, °C                                                           -0.875451               \n",
      "Southern Wind Speed, m/s / Скорость южного ветр...                                          -0.211189               \n",
      "Eastern Wind Speed, m/s / Скорость восточного в...                                          -0.037470               \n",
      "Vertical Wind Speed, m/s / Вертикальная скорост...                                           0.042389               \n",
      "Atmospheric Pressure, mmHg / Атмосферное давлен...                                          -0.315659               \n",
      "Relative Air Humidity, % / Относительная влажно...                                           1.000000               \n",
      "Error (0-9)                                                                                  0.003828               \n",
      "\n",
      "                                                    Error (0-9)  \n",
      "Temperature, °C / Температура, °C                     -0.003101  \n",
      "Southern Wind Speed, m/s / Скорость южного ветр...    -0.001475  \n",
      "Eastern Wind Speed, m/s / Скорость восточного в...     0.002304  \n",
      "Vertical Wind Speed, m/s / Вертикальная скорост...    -0.000388  \n",
      "Atmospheric Pressure, mmHg / Атмосферное давлен...    -0.003737  \n",
      "Relative Air Humidity, % / Относительная влажно...     0.003828  \n",
      "Error (0-9)                                            1.000000  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       ____________________\n",
      "                        характеристики воздуха:\n",
      "                    \n",
      "    20.553394875001647 <= T - средняя температура, градусы Цельсия;\n",
      "    1.5637564277966511 <= RMS(T) - стандартное отклонение температуры, градусы Цельсия;\n",
      "    735.537719738809 <= P - атмосферное давление, мм.рт.ст.;\n",
      "    63.811417035602304 <= r - относительная влажность воздуха, %;\n",
      "    4.209312421986002 <= e - упругость (давление) водяного пара, гПа;\n",
      "    2.3871771415956773 <= Ed - дефицит влажности, гПа;\n",
      "    81.13595613775627 <= ro - плотность воздуха, кг/м^3;\n",
      "                       ____________________\n",
      "                       характеристики ветра:\n",
      "                   \n",
      "    125.39165170364736 <= V - средняя скорость горизонтального ветра, м/с;\n",
      "    0.6942647463225065 <= D - среднее направление горизонтального ветра, градусы;\n",
      "    0.901581837767498 <= w - средняя скорость вертикального ветра, м/с;\n",
      "    0.0910700776465966 <= RMS(w) - стандартное отклонение скорости вертикального ветра, м/с;\n",
      "    0.21231339567026028 <= Vm - модуль среднего вектора скорости ветра, м/с;\n",
      "    0.7002123228198797 <= Vs - среднее значение южной компоненты горизонтального ветра, м/с;\n",
      "    -0.4307010866298994 <= Ve - среднее значение восточной компоненты горизонтального ветра, м/с.\n",
      "\n",
      "                       ________________________\n",
      "                       ПАРАМЕТРЫ ТУРБУЛЕНТНОСТИ:\n",
      "\n",
      "    0.544518238410871 <= Ev - полная энергия турбулентных движений, (м^2)/(с^2);\n",
      "    0.6625907022789297 <= Iv - относительная интенсивность флуктуаций скорости ветра;\n",
      "    1.3514059863152659 <= Et - энергия температурных флуктуаций, град^2\n",
      "    1.2226670827376716 <= <u*w> - момент потока импульса, (м^2)/(с^2);\n",
      "    -0.09312598882726943 <= <w*T> - момент потока тепла, (м^2)/(с^2);\n",
      "    -0.01744380984135258 <= Tau - вертикальный поток импульса;\n",
      "    11.677221555620022 <= Cd - коэффициент сопротивления потоку;\n",
      "    0.1899377977231579 <= T* - масштаб температуры, град;\n",
      "    0.01744380984135258 <= L* - масштаб Монина-Обухова, м;\n",
      "    0.8188508854667628 <= Ct2 - структ. постоянная флукт. температуры, град^2/(см^2/3);\n",
      "    -26.065360330745822 <= Cv2 - структ. постоянная флуктуаций ветра, (м/с)^2/(см^2/3);\n",
      "    -3.702446453316866e-15 <= Cn2a - структ. пост.для акустич. показат. преломл., 1/(м^2/3);\n",
      "    -7.554153557350715e-05 <= Cn2o - структ. пост. для оптич. показат. преломл., 1/(см^2/3);\n",
      "    -5.057307267744357e-07 <= Локальный градиент скорости ветра;\n",
      "    3923.5986631975356 <= Локальный градиент потенциальной температуры;\n",
      "    223.9060975649098 <= Локальный градиент температуры;\n",
      "    223.8962975649098 <= Коэффициент турбулентного обмена количества движения;\n",
      "    2.3734840594368136e-05 <= Коэффициент турбулентного обмена тепла;\n",
      "    2.3774471503947746e-05 <= Внешний масштаб турбулентности;\n",
      "    7.777694324578886e-05 <= Скорость диссипации энергии ветровых флуктуаций;\n",
      "    365.38646451987154 <= Скорость диссипации энергии температурных;\n",
      "    365.99910832126125 <= Градиентное число Ричардсона\n",
      "    \n"
     ]
    },
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       "RadioButtons(description='Choose the field you want / Выберите поле, которое вы хотите:', layout=Layout(width=…"
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       "Button(description='Show graphs / Показать графики', layout=Layout(height='60px', width='40%'), style=ButtonSt…"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First record / Первая запись:\n",
      "2019-08-05 11:44:28.578000\n",
      "Last record / Последняя запись:\n",
      "2019-08-05 23:55:46.140500\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4bb80923b38b433dac4e290c6478879b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Export all data to CSV / Экспортировать все данные в CSV', layout=Layout(height='60px', wi…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2b0a0f1cfa6d493cad8ed33966bec1b2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Export resampled data to CSV / Экспортировать передискретизированые данные в CSV', layout=…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#HIDDEN\n",
    "%matplotlib inline\n",
    "\n",
    "from bs4 import BeautifulSoup\n",
    "import requests\n",
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from io import BytesIO\n",
    "import datetime\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import widgets as wd\n",
    "from IPython.display import display, clear_output, Markdown\n",
    "from traitlets import traitlets\n",
    "\n",
    "from scipy import constants\n",
    "import math\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pywt\n",
    "from statsmodels.robust import mad\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set(style='darkgrid')\n",
    "output = widgets.Output()\n",
    "interval_dict = {'1 second / 1 секунда': 'S', '1 minute / 1 минута': 'T', '10 minutes / 10 минут': '10T',\n",
    "                 '30 minutes / 30 минут': '30T', '1 hour / 1 час': 'H', '12 hours / 12 часов': '12H',\n",
    "                 '1 day / 1 день': 'D', '1 week / 1 неделя': 'W', '1 month / 1 месяц': 'M', \n",
    "                 '3 months / 3 месяца': 'Q', '6 months / 6 месяцев': '2Q', '1 year / 1 год': 'A'}\n",
    "\n",
    "#Progress bar widget, from https://github.com/kuk/log-progress\n",
    "def log_progress(sequence, every=None, size=None, name='Items'):\n",
    "    from ipywidgets import IntProgress, HTML, VBox\n",
    "    from IPython.display import display\n",
    "\n",
    "    is_iterator = False\n",
    "    if size is None:\n",
    "        try:\n",
    "            size = len(sequence)\n",
    "        except TypeError:\n",
    "            is_iterator = True\n",
    "    if size is not None:\n",
    "        if every is None:\n",
    "            if size <= 200:\n",
    "                every = 1\n",
    "            else:\n",
    "                every = int(size / 200)     # every 0.5%\n",
    "    else:\n",
    "        assert every is not None, 'sequence is iterator, set every'\n",
    "\n",
    "    if is_iterator:\n",
    "        progress = IntProgress(min=0, max=1, value=1)\n",
    "        progress.bar_style = 'info'\n",
    "    else:\n",
    "        progress = IntProgress(min=0, max=size, value=0)\n",
    "    label = HTML()\n",
    "    box = VBox(children=[label, progress])\n",
    "    display(box)\n",
    "\n",
    "    index = 0\n",
    "    try:\n",
    "        for index, record in enumerate(sequence, 1):\n",
    "            if index == 1 or index % every == 0:\n",
    "                if is_iterator:\n",
    "                    label.value = '{name}: {index} / ?'.format(\n",
    "                        name=name,\n",
    "                        index=index\n",
    "                    )\n",
    "                else:\n",
    "                    progress.value = index\n",
    "                    label.value = u'{name}: {index} / {size}'.format(\n",
    "                        name=name,\n",
    "                        index=index,\n",
    "                        size=size\n",
    "                    )\n",
    "            yield record\n",
    "    except:\n",
    "        progress.bar_style = 'danger'\n",
    "        raise\n",
    "    else:\n",
    "        progress.bar_style = 'success'\n",
    "        progress.value = index\n",
    "        label.value = \"{name}: {index}\".format(\n",
    "            name=name,\n",
    "            index=str(index or '?')\n",
    "        )\n",
    "\n",
    "#Special button class with variable \n",
    "#from https://stackoverflow.com/questions/45188464/return-output-of-the-function-executed-on-click\n",
    "class LoadedButton(wd.Button):\n",
    "    \"\"\"A button that can holds a value as a attribute.\"\"\"\n",
    "\n",
    "    def __init__(self, value=None, *args, **kwargs):\n",
    "        super(LoadedButton, self).__init__(*args, **kwargs)\n",
    "        # Create the value attribute.\n",
    "        self.add_traits(value=traitlets.Any(value))\n",
    "\n",
    "#URL for the main directory:\n",
    "url = 'http://amk030.imces.ru/meteodata/AMK_030_BIN/'\n",
    "\n",
    "#Create a data type to extract the timestamp:\n",
    "timestamp_type = np.dtype([('year', 'i2'),\n",
    "                           ('month', 'i2'),\n",
    "                           ('day', 'i2'),\n",
    "                           ('hour', 'i2'),\n",
    "                           ('minute', 'i2'),\n",
    "                           ('second', 'i2'),\n",
    "                           ('millisecond', 'i2')])\n",
    "\n",
    "#Create a data type with the binary data format and the desired column names:\n",
    "raw_data_type = np.dtype([('Temperature, °C / Температура, °C', 'i2'),\n",
    "                          ('Southern Wind Speed, m/s / Скорость южного ветра, м/с', 'i2'),\n",
    "                          ('Eastern Wind Speed, m/s / Скорость восточного ветра, м/с', 'i2'),\n",
    "                          ('Vertical Wind Speed, m/s / Вертикальная скорость ветра, м/с', 'i2'),\n",
    "                          ('Atmospheric Pressure, mmHg / Атмосферное давление, мм рт. ст', 'i2'),\n",
    "                          ('Relative Air Humidity, % / Относительная влажность воздуха, %', 'i2'),\n",
    "                          ('Error (0-9)', 'i1')])\n",
    "\n",
    "#Function for creation the relevant links available in the main directory:\n",
    "def MainDirectory(url):\n",
    "    page = requests.get(url).text\n",
    "    soup = BeautifulSoup(page, 'html.parser')\n",
    "    return [node.get('href').rstrip('/') \n",
    "            for node in soup.find_all('a') \n",
    "            if re.search(r'[0-9]/$|\\)/$', node.get('href'))]\n",
    "\n",
    "#Function for retrieving links to binary files,\n",
    "#checking for the 'B' at the end:\n",
    "def FileDirectory(url):\n",
    "    page = requests.get(url).text\n",
    "    soup = BeautifulSoup(page, 'html.parser')\n",
    "    return [url + '/' + node.get('href') \n",
    "            for node in soup.find_all('a') \n",
    "            if node.get('href').endswith('B')]\n",
    "\n",
    "#Function for timestamps:\n",
    "def Timestamps_Function(file, length):\n",
    "    try:\n",
    "        #raw_timestamp = np.frombuffer(file, dtype=timestamp_type, count=1)\n",
    "        raw_timestamp = np.frombuffer(file[:14], dtype=timestamp_type)\n",
    "        timestamp_elements = list(raw_timestamp[0].tolist())\n",
    "        timestamp_elements[6] = timestamp_elements[6]*1000\n",
    "        initial_date = datetime.datetime(*map(int, timestamp_elements))\n",
    "        timestamps.append(initial_date)\n",
    "        for i in range(length-1): \n",
    "            timestamps.append(timestamps[-1] + datetime.timedelta(milliseconds=12.5))\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "#Function for retrieving full data of each file:\n",
    "def Data_Array(url):\n",
    "    try:\n",
    "        r = requests.get(url)\n",
    "        file = r.content\n",
    "        raw_data = file[17:-14]\n",
    "        subarray = np.frombuffer(raw_data, dtype=raw_data_type)\n",
    "        Timestamps_Function(file, len(subarray))\n",
    "        return subarray\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "#Function for Wavelet Filtration, \n",
    "#from https://www.kaggle.com/jeffreyegan/vsb-power-line-fault-detection-approach/data\n",
    "\n",
    "def waveletSmooth(x, wavelet=\"db4\", level=10, title=None):\n",
    "    # calculate the wavelet coefficients\n",
    "    coeff = pywt.wavedec(x, wavelet, mode=\"per\" )\n",
    "    # calculate a threshold\n",
    "    sigma = mad(coeff[-level])\n",
    "    # changing this threshold also changes the behavior,\n",
    "    # but I have not played with this very much\n",
    "    uthresh = sigma * np.sqrt(2*np.log(len(x)))\n",
    "    coeff[1:] = (pywt.threshold(i, value=uthresh, mode=\"soft\" ) for i in coeff[1:] )\n",
    "    # reconstruct the signal using the thresholded coefficients\n",
    "    y = pywt.waverec( coeff, wavelet, mode=\"per\" )\n",
    "    f, ax = plt.subplots()\n",
    "    plt.plot( x, color=\"b\", alpha=0.5 )\n",
    "    plt.plot( y, color=\"r\" )\n",
    "    if title:\n",
    "        ax.set_title(title)\n",
    "    ax.set_xlim((0,len(y)))\n",
    "\n",
    "#Create the lists:\n",
    "available_years = MainDirectory(url)\n",
    "selected_years = []\n",
    "available_months = []\n",
    "selected_months = []\n",
    "file_directory = []\n",
    "days_list = []\n",
    "date_selection = []\n",
    "timestamps = []\n",
    "df = pd.DataFrame()\n",
    "\n",
    "\n",
    "start_button = widgets.Button(description='Check available years / Проверить доступные годы',\n",
    "                             layout=widgets.Layout(width='40%', height='60px'))\n",
    "years_button = widgets.Button(description='Confirm years / Подтвердить годы',\n",
    "                             layout=widgets.Layout(width='40%', height='60px'))\n",
    "months_button = LoadedButton(description='Confirm months / Подтвердить месяцы',\n",
    "                             layout=widgets.Layout(width='40%', height='60px'))\n",
    "range_button = widgets.Button(description='Confirm range / Подтвердить диапазон времени',\n",
    "                             layout=widgets.Layout(width='40%', height='60px'))\n",
    "interval = widgets.RadioButtons(\n",
    "    options=['1 second / 1 секунда', '1 minute / 1 минута', \n",
    "             '10 minutes / 10 минут', '30 minutes / 30 минут', \n",
    "             '1 hour / 1 час', '12 hours / 12 часов', '1 day / 1 день', \n",
    "             '1 week / 1 неделя', '1 month / 1 месяц', '3 months / 3 месяца', \n",
    "             '6 months / 6 месяцев', '1 year / 1 год'],\n",
    "    value='10 minutes / 10 минут',\n",
    "    description='Choose the data interval (how to group the data) / Выберите интервал данных (как сгруппировать данные):',\n",
    "    layout={'width': 'initial'},\n",
    "    style = {'description_width': 'initial'},\n",
    "    disabled=False)\n",
    "interval_button = LoadedButton(description='Confirm interval / Подтвердить интервал',\n",
    "                             layout=widgets.Layout(width='40%', height='60px'))\n",
    "fields = widgets.RadioButtons(\n",
    "    options=['Temperature, °C / Температура, °C', \n",
    "             'Southern Wind Speed, m/s / Скорость южного ветра, м/с', \n",
    "             'Eastern Wind Speed, m/s / Скорость восточного ветра, м/с',\n",
    "             'Vertical Wind Speed, m/s / Вертикальная скорость ветра, м/с', \n",
    "             'Atmospheric Pressure, mmHg / Атмосферное давление, мм рт. ст', \n",
    "             'Relative Air Humidity, % / Относительная влажность воздуха, %'],\n",
    "    value='Temperature, °C / Температура, °C',\n",
    "    description='Choose the field you want / Выберите поле, которое вы хотите:',\n",
    "    layout={'width': 'initial'},\n",
    "    style = {'description_width': 'initial'},\n",
    "    disabled=False)\n",
    "graph1_button = widgets.Button(description='Show graphs / Показать графики', \n",
    "                               layout=widgets.Layout(width='40%', height='60px'))\n",
    "csv_button1 = widgets.Button(description='Export all data to CSV / Экспортировать все данные в CSV', \n",
    "                               layout=widgets.Layout(width='70%', height='60px'))\n",
    "csv_button2 = widgets.Button(description='Export resampled data to CSV / Экспортировать передискретизированые данные в CSV', \n",
    "                               layout=widgets.Layout(width='70%', height='60px'))\n",
    "\n",
    "\n",
    "def start_button_clicked(_):\n",
    "    for i in log_progress(range(len(available_years)), every=1):\n",
    "        selected_years.append(widgets.Checkbox(description = available_years[i]))\n",
    "        display(selected_years[i])\n",
    "    display(years_button)\n",
    "\n",
    "def years_button_clicked(_):\n",
    "    for i in log_progress(range(len(available_years))):\n",
    "        if selected_years[i].value == True:\n",
    "            available_months.extend(MainDirectory(url + available_years[i] + '/'))\n",
    "    for i in log_progress(range(len(available_months))):\n",
    "        selected_months.append(widgets.Checkbox(description = available_months[i]))\n",
    "        display(selected_months[i])\n",
    "    display(months_button)\n",
    "        \n",
    "def months_button_clicked(button):\n",
    "    for i in log_progress(range(len(available_months))):\n",
    "        if selected_months[i].value == True:\n",
    "            file_directory.extend(\n",
    "                FileDirectory(\n",
    "                    url + re.search(r'\\d{4}', available_months[i]).group(0) \n",
    "                    + '/' + available_months[i] + '/'))\n",
    "    days_aux = [datetime.date(int('20' + file_directory[i][-3:-1]),\n",
    "                              int(file_directory[i][-12:-10]),\n",
    "                              int(file_directory[i][-10:-8]))\n",
    "                for i in log_progress(range(len(file_directory)))]\n",
    "    date_selection = widgets.SelectionRangeSlider(\n",
    "        options=days_aux,\n",
    "        index=(0, len(days_aux)-1),\n",
    "        #description='Select range / Выбрать диапазон времени:',\n",
    "        disabled=False,\n",
    "        layout={'width': 'initial'})\n",
    "    days_list.extend(days_aux)    \n",
    "    display(Markdown('Select range / Выбрать диапазон времени:'))\n",
    "    display(date_selection)\n",
    "    display(range_button)\n",
    "    button.value = date_selection\n",
    "    \n",
    "def range_button_clicked(_):\n",
    "    display(interval)\n",
    "    display(interval_button)\n",
    "\n",
    "    \n",
    "def interval_button_clicked(button):\n",
    "    date_selection = months_button.value.value\n",
    "    frames = [Data_Array(i) for i in log_progress(file_directory[days_list.index(date_selection[0]):\n",
    "                len(days_list) - 1 - days_list[::-1].index(date_selection[1])])]\n",
    "    data = np.concatenate(frames)\n",
    "    df = pd.DataFrame(data, columns=data.dtype.names)\n",
    "    df['Temperature, °C / Температура, °C'] = df[\n",
    "        'Temperature, °C / Температура, °C'].apply(lambda x: x/100)\n",
    "    df['Southern Wind Speed, m/s / Скорость южного ветра, м/с'] = df[\n",
    "        'Southern Wind Speed, m/s / Скорость южного ветра, м/с'].apply(lambda x: x/100)\n",
    "    df['Eastern Wind Speed, m/s / Скорость восточного ветра, м/с'] = df[\n",
    "        'Eastern Wind Speed, m/s / Скорость восточного ветра, м/с'].apply(lambda x: x/100)\n",
    "    df['Vertical Wind Speed, m/s / Вертикальная скорость ветра, м/с'] = df[\n",
    "        'Vertical Wind Speed, m/s / Вертикальная скорость ветра, м/с'].apply(lambda x: x/100)\n",
    "    df['Atmospheric Pressure, mmHg / Атмосферное давление, мм рт. ст'] = df[\n",
    "        'Atmospheric Pressure, mmHg / Атмосферное давление, мм рт. ст'].apply(lambda x: x/10)\n",
    "    df['Relative Air Humidity, % / Относительная влажность воздуха, %'] = df[\n",
    "        'Relative Air Humidity, % / Относительная влажность воздуха, %'].apply(lambda x: x/100)\n",
    "    df['Timestamp / Временная метка'] = timestamps\n",
    "    print(df.describe())\n",
    "    display(Markdown('Correlation matrix / Корреляционная матрица:'))\n",
    "    print(df.corr())\n",
    "    plt.matshow(df.corr())\n",
    "    plt.show()\n",
    "    # <T>\n",
    "    temperature_average = df['Temperature, °C / Температура, °C'].mean()\n",
    "    # <P>\n",
    "    pressure_average = df['Atmospheric Pressure, mmHg / Атмосферное давление, мм рт. ст'].mean()\n",
    "    # <w>\n",
    "    vertical_average = df['Vertical Wind Speed, m/s / Вертикальная скорость ветра, м/с'].mean()\n",
    "    # <vs>\n",
    "    southern_average = df['Southern Wind Speed, m/s / Скорость южного ветра, м/с'].mean()\n",
    "    # <ve>\n",
    "    eastern_average = df['Eastern Wind Speed, m/s / Скорость восточного ветра, м/с'].mean()\n",
    "    # <V>\n",
    "    average_wind = np.sqrt(southern_average ** 2 + eastern_average ** 2 + vertical_average ** 2)\n",
    "    # <Vh>\n",
    "    average_horizontal = np.sqrt(southern_average ** 2 + eastern_average ** 2)\n",
    "    # ψ\n",
    "    angle_inclination = np.arccos(average_horizontal / average_wind)\n",
    "    # <D>\n",
    "    f = np.arctan(-1 * eastern_average / southern_average)\n",
    "    d = np.select([\n",
    "        (southern_average > .01), \n",
    "        (southern_average < -.01) and (eastern_average >= 0), \n",
    "        (southern_average < -.01) and (eastern_average < 0), \n",
    "        (abs(southern_average) <= .01) and (eastern_average >= 0), \n",
    "        (abs(southern_average) <= .01) and (eastern_average < 0)], \n",
    "        [(constants.pi + f), (f), (2 * constants.pi + f), (constants.pi / 2), (3 * constants.pi / 2)])\n",
    "    # ew\n",
    "    df['Saturated vapor pressure / Давление насыщенного пара'] = (\n",
    "        (6.112 * np.exp((17.62 * df['Temperature, °C / Температура, °C']) / (243.12 * df['Temperature, °C / Температура, °C']))) * (\n",
    "        1.0016 + 3.15 * 10**(-6) * df['Atmospheric Pressure, mmHg / Атмосферное давление, мм рт. ст'] - 0.074 * df['Atmospheric Pressure, mmHg / Атмосферное давление, мм рт. ст']**(-1)))\n",
    "    # e\n",
    "    df['Elasticity (pressure) of water vapor / Упругость (давление) водяного пара'] = (\n",
    "        df['Saturated vapor pressure / Давление насыщенного пара'] * df[\n",
    "            'Relative Air Humidity, % / Относительная влажность воздуха, %']) / 100\n",
    "    # Ed\n",
    "    df['Moisture deficit / Дефицит влажности'] = df['Saturated vapor pressure / Давление насыщенного пара'] - df['Elasticity (pressure) of water vapor / Упругость (давление) водяного пара']\n",
    "    # q\n",
    "    df['Absolute air humidity / Абсолютная влажность воздуха'] = (df[\n",
    "        'Relative Air Humidity, % / Относительная влажность воздуха, %'] * df[\n",
    "        'Atmospheric Pressure, mmHg / Атмосферное давление, мм рт. ст'] * 28.9647) / (constants.R * df[\n",
    "        'Temperature, °C / Температура, °C'] * 100)\n",
    "    # p\n",
    "    df['Air density / Плотность воздуха'] = (df[\n",
    "        'Atmospheric Pressure, mmHg / Атмосферное давление, мм рт. ст'] * 28.9647) / (constants.R * df[\n",
    "        'Temperature, °C / Температура, °C'])\n",
    "    # T'\n",
    "    df['Temperature pulsations / Температурные пульсации'] = df[\n",
    "        'Temperature, °C / Температура, °C'] - temperature_average\n",
    "    # w'\n",
    "    df['Vertical components / Вертикальные компоненты'] = df[\n",
    "        'Vertical Wind Speed, m/s / Вертикальная скорость ветра, м/с'] - vertical_average\n",
    "    # u'\n",
    "    df['Longitudinal components / Продольные компоненты'] = (southern_average * (df[\n",
    "        'Southern Wind Speed, m/s / Скорость южного ветра, м/с'] - southern_average) + eastern_average * (df[\n",
    "        'Eastern Wind Speed, m/s / Скорость восточного ветра, м/с'] - eastern_average) / average_horizontal)\n",
    "    # v'\n",
    "    df['Transversal components / Поперечные компоненты'] = ((southern_average * -1) * (df[\n",
    "        'Eastern Wind Speed, m/s / Скорость восточного ветра, м/с'] - eastern_average) + eastern_average * (df[\n",
    "        'Southern Wind Speed, m/s / Скорость южного ветра, м/с'] - southern_average) / average_horizontal)\n",
    "    # σ_u^2, σ_v^2 , σ_w^2, σ_t^2\n",
    "    longitudinal_var = df['Longitudinal components / Продольные компоненты'].var()\n",
    "    transversal_var = df['Transversal components / Поперечные компоненты'].var()\n",
    "    vertical_var = df['Vertical components / Вертикальные компоненты'].var()\n",
    "    temperature_var = df['Temperature pulsations / Температурные пульсации'].var()\n",
    "    # 〈T' * w'〉\n",
    "    momentum_heat = (df['Temperature pulsations / Температурные пульсации'] * df[\n",
    "        'Vertical components / Вертикальные компоненты']).mean()\n",
    "    # 〈u' * w'〉\n",
    "    momentum_pulse = (df['Longitudinal components / Продольные компоненты'] * df[\n",
    "        'Vertical components / Вертикальные компоненты']).mean()\n",
    "    # Ev\n",
    "    ev = (longitudinal_var + transversal_var + vertical_var) / 2\n",
    "    # lv\n",
    "    lv = ev / (average_wind ** 2)\n",
    "    # Et\n",
    "    et = temperature_var / 2\n",
    "    # τ\n",
    "    df['Vertical impulse flow / Вертикальный поток импульса'] = -1 * df[\n",
    "        'Air density / Плотность воздуха'] * momentum_pulse\n",
    "    # v*\n",
    "    friction_velocity = math.sqrt(abs(momentum_pulse))\n",
    "    # T*\n",
    "    temperature_scale = -1 * momentum_heat\n",
    "    # L*\n",
    "    mo_scale = temperature_average * (friction_velocity ** 2) / 0.4 * 9.81 * temperature_scale\n",
    "    # other variables\n",
    "    average_turbotemp = df['Temperature pulsations / Температурные пульсации'].mean()\n",
    "    average_longcomp = df['Longitudinal components / Продольные компоненты'].mean()\n",
    "    # Constants:\n",
    "    cd = (friction_velocity / average_wind) ** 2\n",
    "    ct2 = ((average_turbotemp - df['Temperature pulsations / Температурные пульсации'] - temperature_average) *\n",
    "          (average_wind ** (-2/3))).mean()\n",
    "    cv2 = ((average_longcomp - ((southern_average * (df[\n",
    "        'Southern Wind Speed, m/s / Скорость южного ветра, м/с'] - southern_average) + eastern_average * (df[\n",
    "        'Eastern Wind Speed, m/s / Скорость восточного ветра, м/с'] - eastern_average)) / d)) * (average_wind ** (-2/3))).mean()\n",
    "    cna2 = (ct2 / (4 *((temperature_average + 273.15) ** 2))) + (cv2 / (343 ** 2))\n",
    "    cno2 = ((8 * (10**-5) * pressure_average / (temperature_average**2))**2) * ct2\n",
    "    height = 27\n",
    "    dimensionless_height = height / mo_scale\n",
    "    #φv\n",
    "    fv = (1 + 4.7 * dimensionless_height) if dimensionless_height > 0 else (1 - 15 * dimensionless_height) ** .25\n",
    "    #φt\n",
    "    ft = (0.74 + 4.7 * dimensionless_height) if dimensionless_height > 0 else (1 - 15 * dimensionless_height) ** .25\n",
    "    dudz = (friction_velocity / 0.4) * fv * (height / mo_scale)\n",
    "    dodz = (temperature_scale / 0.4) * ft * (height / mo_scale)\n",
    "    dtdz = dodz - .0098\n",
    "    km = ((friction_velocity)**2) / dudz\n",
    "    kh = (friction_velocity * temperature_scale) / dodz\n",
    "    lo = (km / abs(dudz))**0.5\n",
    "    energy_dissipation_wind = (km * (dudz**2)) - (9.81 / temperature_average) * kh * dodz\n",
    "    energy_dissipation_temperature = kh * (dudz**2)\n",
    "    richardson = (9.81 / temperature_average) * dodz / dudz\n",
    "    df = df.dropna(how='any')\n",
    "    output = \"\"\"                       ____________________\n",
    "                        характеристики воздуха:\n",
    "                    \n",
    "    {} <= T - средняя температура, градусы Цельсия;\n",
    "    {} <= RMS(T) - стандартное отклонение температуры, градусы Цельсия;\n",
    "    {} <= P - атмосферное давление, мм.рт.ст.;\n",
    "    {} <= r - относительная влажность воздуха, %;\n",
    "    {} <= e - упругость (давление) водяного пара, гПа;\n",
    "    {} <= Ed - дефицит влажности, гПа;\n",
    "    {} <= ro - плотность воздуха, кг/м^3;\n",
    "                       ____________________\n",
    "                       характеристики ветра:\n",
    "                   \n",
    "    {} <= V - средняя скорость горизонтального ветра, м/с;\n",
    "    {} <= D - среднее направление горизонтального ветра, градусы;\n",
    "    {} <= w - средняя скорость вертикального ветра, м/с;\n",
    "    {} <= RMS(w) - стандартное отклонение скорости вертикального ветра, м/с;\n",
    "    {} <= Vm - модуль среднего вектора скорости ветра, м/с;\n",
    "    {} <= Vs - среднее значение южной компоненты горизонтального ветра, м/с;\n",
    "    {} <= Ve - среднее значение восточной компоненты горизонтального ветра, м/с.\n",
    "\n",
    "                       ________________________\n",
    "                       ПАРАМЕТРЫ ТУРБУЛЕНТНОСТИ:\n",
    "\n",
    "    {} <= Ev - полная энергия турбулентных движений, (м^2)/(с^2);\n",
    "    {} <= Iv - относительная интенсивность флуктуаций скорости ветра;\n",
    "    {} <= Et - энергия температурных флуктуаций, град^2\n",
    "    {} <= <u*w> - момент потока импульса, (м^2)/(с^2);\n",
    "    {} <= <w*T> - момент потока тепла, (м^2)/(с^2);\n",
    "    {} <= Tau - вертикальный поток импульса;\n",
    "    {} <= Cd - коэффициент сопротивления потоку;\n",
    "    {} <= T* - масштаб температуры, град;\n",
    "    {} <= L* - масштаб Монина-Обухова, м;\n",
    "    {} <= Ct2 - структ. постоянная флукт. температуры, град^2/(см^2/3);\n",
    "    {} <= Cv2 - структ. постоянная флуктуаций ветра, (м/с)^2/(см^2/3);\n",
    "    {} <= Cn2a - структ. пост.для акустич. показат. преломл., 1/(м^2/3);\n",
    "    {} <= Cn2o - структ. пост. для оптич. показат. преломл., 1/(см^2/3);\n",
    "    {} <= Локальный градиент скорости ветра;\n",
    "    {} <= Локальный градиент потенциальной температуры;\n",
    "    {} <= Локальный градиент температуры;\n",
    "    {} <= Коэффициент турбулентного обмена количества движения;\n",
    "    {} <= Коэффициент турбулентного обмена тепла;\n",
    "    {} <= Внешний масштаб турбулентности;\n",
    "    {} <= Скорость диссипации энергии ветровых флуктуаций;\n",
    "    {} <= Скорость диссипации энергии температурных;\n",
    "    {} <= Градиентное число Ричардсона\n",
    "    \"\"\".format(temperature_average, \n",
    "               math.sqrt(temperature_var), \n",
    "               pressure_average, \n",
    "               df['Relative Air Humidity, % / Относительная влажность воздуха, %'].mean(), \n",
    "               df['Elasticity (pressure) of water vapor / Упругость (давление) водяного пара'].mean(), \n",
    "               df['Moisture deficit / Дефицит влажности'].mean(),\n",
    "               df['Absolute air humidity / Абсолютная влажность воздуха'].mean(),\n",
    "               df['Air density / Плотность воздуха'].mean(),\n",
    "               average_horizontal,\n",
    "               d,\n",
    "               vertical_average,\n",
    "               df['Vertical Wind Speed, m/s / Вертикальная скорость ветра, м/с'].var(),\n",
    "               average_wind,\n",
    "               southern_average,\n",
    "               eastern_average,\n",
    "               ev,\n",
    "               lv,\n",
    "               et,\n",
    "               momentum_pulse,\n",
    "               momentum_heat,\n",
    "               df['Vertical impulse flow / Вертикальный поток импульса'].mean(),\n",
    "               cd,\n",
    "               temperature_scale,\n",
    "               mo_scale,\n",
    "               ct2,\n",
    "               cv2,\n",
    "               cna2,\n",
    "               cno2,\n",
    "               dudz,\n",
    "               dodz,\n",
    "               dtdz,\n",
    "               km,\n",
    "               kh,\n",
    "               lo,\n",
    "               energy_dissipation_wind,\n",
    "               energy_dissipation_temperature,\n",
    "               richardson\n",
    "              )\n",
    "    print(output)\n",
    "    display(fields)\n",
    "    display(graph1_button)\n",
    "    button.value = df\n",
    "\n",
    "def graph1_button_clicked(_):\n",
    "    df = interval_button.value\n",
    "    copy = df.copy()\n",
    "    waveletSmooth(df[fields.value], title=fields.value +\n",
    "                           ',\\nFull Data with Daubechies 4 Wavelet Filtration /' +\n",
    "                           '\\nПолные данные с вейвлет-фильтрацией Daubechies 4')\n",
    "    plt.show()\n",
    "    copy.set_index('Timestamp / Временная метка', inplace = True)\n",
    "    copy[fields.value].resample(\n",
    "        interval_dict[interval.value]).mean().dropna().plot(title='Resampled Data / Передискретизация данных')\n",
    "    plt.show()\n",
    "    print('First record / Первая запись:')\n",
    "    print(copy.index[0])\n",
    "    print('Last record / Последняя запись:')\n",
    "    print(copy.index[-1])\n",
    "    display(csv_button1)\n",
    "    display(csv_button2)\n",
    "\n",
    "def csv_button1_clicked(_):\n",
    "    print('Exporting... / Подождите, пожалуйста...')\n",
    "    df = interval_button.value\n",
    "    copy = df.copy()\n",
    "    copy['Dir_S'] = np.where(copy['Southern Wind Speed, m/s / Скорость южного ветра, м/с'] >= 0, 1, 0)\n",
    "    copy['Dir_E'] = np.where(copy['Eastern Wind Speed, m/s / Скорость восточного ветра, м/с'] >= 0, 1, 0)\n",
    "    copy['Dir_V'] = np.where(copy['Vertical Wind Speed, m/s / Вертикальная скорость ветра, м/с'] >= 0, 1, 0)\n",
    "    copy['Southern Wind Speed, m/s / Скорость южного ветра, м/с'] = copy[\n",
    "        'Southern Wind Speed, m/s / Скорость южного ветра, м/с'].abs()\n",
    "    copy['Eastern Wind Speed, m/s / Скорость восточного ветра, м/с'] = copy[\n",
    "        'Eastern Wind Speed, m/s / Скорость восточного ветра, м/с'].abs()\n",
    "    copy['Vertical Wind Speed, m/s / Вертикальная скорость ветра, м/с'] = copy[\n",
    "        'Vertical Wind Speed, m/s / Вертикальная скорость ветра, м/с'].abs()\n",
    "    copy.set_index('Timestamp / Временная метка', inplace = True)\n",
    "    a = datetime.datetime.today().strftime('%Y.%m.%d_%H.%M.%S')\n",
    "    b = copy.index[0].strftime('%d.%m.%Y_%H.%M.%S')\n",
    "    c = copy.index[-1].strftime('%d.%m.%Y_%H.%M.%S')\n",
    "    copy.reset_index(inplace = True)\n",
    "    copy.index = copy.index + 1\n",
    "    copy['Date_n'] = copy['Timestamp / Временная метка'].dt.strftime('%Y%m%d')\n",
    "    copy['Time_n'] = copy['Timestamp / Временная метка'].dt.strftime('%H%M%S')\n",
    "    copy['Date'] = copy['Timestamp / Временная метка'].dt.strftime('%d.%m.%Y')\n",
    "    copy['Time'] = copy['Timestamp / Временная метка'].dt.time\n",
    "    copy.drop(['Saturated vapor pressure / Давление насыщенного пара',\n",
    "               'Elasticity (pressure) of water vapor / Упругость (давление) водяного пара',\n",
    "               'Moisture deficit / Дефицит влажности',\n",
    "               'Absolute air humidity / Абсолютная влажность воздуха',\n",
    "               'Air density / Плотность воздуха',\n",
    "               'Temperature pulsations / Температурные пульсации',\n",
    "               'Vertical components / Вертикальные компоненты',\n",
    "               'Longitudinal components / Продольные компоненты',\n",
    "               'Transversal components / Поперечные компоненты',\n",
    "               'Vertical impulse flow / Вертикальный поток импульса',], axis=1, inplace=True)\n",
    "    cols = ['Timestamp / Временная метка',\n",
    "            'Date_n',  \n",
    "            'Time_n',\n",
    "            'Date',\n",
    "            'Time',\n",
    "            'Temperature, °C / Температура, °C',\n",
    "            'Southern Wind Speed, m/s / Скорость южного ветра, м/с',\n",
    "            'Dir_S',\n",
    "            'Eastern Wind Speed, m/s / Скорость восточного ветра, м/с',\n",
    "            'Dir_E',\n",
    "            'Vertical Wind Speed, m/s / Вертикальная скорость ветра, м/с',\n",
    "            'Dir_V',\n",
    "            'Atmospheric Pressure, mmHg / Атмосферное давление, мм рт. ст',\n",
    "            'Relative Air Humidity, % / Относительная влажность воздуха, %',\n",
    "            'Error (0-9)']\n",
    "    copy = copy[cols]\n",
    "    copy.columns = ['Timestamp',\n",
    "            'Date_n',\n",
    "            'Time_n',\n",
    "            'Date',\n",
    "            'Time',\n",
    "            'Temperature, °C',\n",
    "            'Southern Wind Speed, m/s',\n",
    "            'Dir_S',\n",
    "            'Eastern Wind Speed, m/s',\n",
    "            'Dir_E',\n",
    "            'Vertical Wind Speed, m/s',\n",
    "            'Dir_V',\n",
    "            'Atmospheric Pressure, mmHg',\n",
    "            'Relative Air Humidity, %',\n",
    "            'Error (0-9)']\n",
    "    copy = copy.round(2)\n",
    "    copy.index.name='ID'\n",
    "    filename = 'AMK_030--' + a + '--' + b + '--' + c + '12.5 milliseconds.dat'\n",
    "    copy.to_csv(filename, encoding='utf-8-sig', sep = ';')\n",
    "    print('Done! Find a CSV file in the same folder as this program called '+ filename + '.')\n",
    "    print('Готово! Найдите файл CSV в той же папке, что и эта программа с именем '+ filename + '.')\n",
    "    \n",
    "def csv_button2_clicked(_):\n",
    "    print('Exporting... / Подождите, пожалуйста...')\n",
    "    df = interval_button.value\n",
    "    copy = df.copy()\n",
    "    copy.set_index('Timestamp / Временная метка', inplace = True)\n",
    "    copy = copy.resample(interval_dict[interval.value]).mean().dropna()\n",
    "    a = datetime.datetime.today().strftime('%Y.%m.%d_%H.%M.%S')\n",
    "    b = copy.index[0].strftime('%d.%m.%Y_%H.%M.%S')\n",
    "    c = copy.index[-1].strftime('%d.%m.%Y_%H.%M.%S')\n",
    "    copy.reset_index(inplace = True)\n",
    "    copy.index = copy.index + 1\n",
    "    copy['Dir_S'] = np.where(copy['Southern Wind Speed, m/s / Скорость южного ветра, м/с'] >= 0, 1, 0)\n",
    "    copy['Dir_E'] = np.where(copy['Eastern Wind Speed, m/s / Скорость восточного ветра, м/с'] >= 0, 1, 0)\n",
    "    copy['Dir_V'] = np.where(copy['Vertical Wind Speed, m/s / Вертикальная скорость ветра, м/с'] >= 0, 1, 0)\n",
    "    copy['Southern Wind Speed, m/s / Скорость южного ветра, м/с'] = copy[\n",
    "        'Southern Wind Speed, m/s / Скорость южного ветра, м/с'].abs()\n",
    "    copy['Eastern Wind Speed, m/s / Скорость восточного ветра, м/с'] = copy[\n",
    "        'Eastern Wind Speed, m/s / Скорость восточного ветра, м/с'].abs()\n",
    "    copy['Vertical Wind Speed, m/s / Вертикальная скорость ветра, м/с'] = copy[\n",
    "        'Vertical Wind Speed, m/s / Вертикальная скорость ветра, м/с'].abs()\n",
    "    copy['Date_n'] = copy['Timestamp / Временная метка'].dt.strftime('%Y%m%d')\n",
    "    copy['Time_n'] = copy['Timestamp / Временная метка'].dt.strftime('%H%M%S')\n",
    "    copy['Date'] = copy['Timestamp / Временная метка'].dt.strftime('%d.%m.%Y')\n",
    "    copy['Time'] = copy['Timestamp / Временная метка'].dt.time\n",
    "    copy.drop(['Saturated vapor pressure / Давление насыщенного пара',\n",
    "               'Elasticity (pressure) of water vapor / Упругость (давление) водяного пара',\n",
    "               'Moisture deficit / Дефицит влажности',\n",
    "               'Absolute air humidity / Абсолютная влажность воздуха',\n",
    "               'Air density / Плотность воздуха',\n",
    "               'Temperature pulsations / Температурные пульсации',\n",
    "               'Vertical components / Вертикальные компоненты',\n",
    "               'Longitudinal components / Продольные компоненты',\n",
    "               'Transversal components / Поперечные компоненты',\n",
    "               'Vertical impulse flow / Вертикальный поток импульса',], axis=1, inplace=True)\n",
    "    cols = ['Timestamp / Временная метка',\n",
    "            'Date_n',\n",
    "            'Time_n',\n",
    "            'Date',\n",
    "            'Time',\n",
    "            'Temperature, °C / Температура, °C',\n",
    "            'Southern Wind Speed, m/s / Скорость южного ветра, м/с',\n",
    "            'Dir_S',\n",
    "            'Eastern Wind Speed, m/s / Скорость восточного ветра, м/с',\n",
    "            'Dir_E',\n",
    "            'Vertical Wind Speed, m/s / Вертикальная скорость ветра, м/с',\n",
    "            'Dir_V',\n",
    "            'Atmospheric Pressure, mmHg / Атмосферное давление, мм рт. ст',\n",
    "            'Relative Air Humidity, % / Относительная влажность воздуха, %',\n",
    "            'Error (0-9)']\n",
    "    copy = copy[cols]\n",
    "    copy.columns = ['Timestamp',\n",
    "            'Date_n',\n",
    "            'Time_n',\n",
    "            'Date',\n",
    "            'Time',\n",
    "            'Temperature, °C',\n",
    "            'Southern Wind Speed, m/s',\n",
    "            'Dir_S',\n",
    "            'Eastern Wind Speed, m/s',\n",
    "            'Dir_E',\n",
    "            'Vertical Wind Speed, m/s',\n",
    "            'Dir_V',\n",
    "            'Atmospheric Pressure, mmHg',\n",
    "            'Relative Air Humidity, %',\n",
    "            'Error (0-9)']\n",
    "    copy = copy.round(2)\n",
    "    copy.index.name='ID'\n",
    "    filename = 'AMK_030--' + a + '--' + b + '--' + c + '--' + interval.value.replace('/', '-') + '.dat'\n",
    "    copy.to_csv(filename, sep = ';', encoding='utf-8-sig')\n",
    "    print('Done! Find a CSV file in the same folder as this program called '+ filename + '.')\n",
    "    print('Готово! Найдите файл CSV в той же папке, что и эта программа с именем '+ filename + '.')\n",
    "\n",
    "\n",
    "start_button.on_click(start_button_clicked)\n",
    "display(start_button)\n",
    "\n",
    "years_button.on_click(years_button_clicked)\n",
    "months_button.on_click(months_button_clicked)\n",
    "interval_button.on_click(interval_button_clicked)\n",
    "range_button.on_click(range_button_clicked)\n",
    "graph1_button.on_click(graph1_button_clicked)\n",
    "csv_button1.on_click(csv_button1_clicked)\n",
    "csv_button2.on_click(csv_button2_clicked)"
   ]
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