tmiyama/read_pumice_csv.ipynb Secret

## read_pumice_csv.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "21d4033e-bdbe-4a59-b127-22c9febf0579",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6b9b45b0-bc2f-4844-83aa-432c0cae3999",
   "metadata": {},
   "outputs": [],
   "source": [
    "start=\"2021-12-31 09:00:00\"\n",
    "end=\"2022-01-13 09:00:00\"\n",
    "windage=0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "627116d6-f829-4a2e-937e-745768445c91",
   "metadata": {},
   "source": [
    "# Read files "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "37aa113b-b2c2-4525-bd3e-9c676c11929d",
   "metadata": {},
   "outputs": [],
   "source": [
    "files=sorted(glob.glob(f\"csv/latitude_from*_windage{windage:.1f}.csv\"))  # 同じwindageのファイルを列挙する"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "033b0ba1-8f85-433b-b181-ec710d8085b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "N=0\n",
    "for f in files:\n",
    "    lat=pd.read_csv(f,index_col=0).T                                          # 緯度データを読み込み、列と行を入れ替える\n",
    "    lon=pd.read_csv(f.replace(\"latitude\",\"longitude\"),index_col=0).T         # 同じく経度　　　　　　\n",
    "    \n",
    "    lat.columns=[f\"P{nn:05d}\" for nn in range(N+1,N+len(lat.columns)+1)]     # 粒子に通しで番号を振る　(列）\n",
    "    lon.columns=lat.columns                                                  #　同じく緯度\n",
    "    \n",
    "    lat.index=pd.to_datetime(lat.index)                                      #  indexをdatatime formatにする\n",
    "    lon.index=pd.to_datetime(lon.index)                                      #　同じく緯度　　\n",
    "    \n",
    "    if N==0:\n",
    "        latall=lat.loc[start:end]\n",
    "        lonall=lon.loc[start:end]\n",
    "    else:\n",
    "        latall=latall.join(lat.loc[start:end])                                # start から endまで選んで、indexを軸に、dataframeを合成する　　\n",
    "        lonall=lonall.join(lon.loc[start:end])\n",
    "        \n",
    "    N += len(lat.columns)                                                    # 粒子通し番号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8378d4f3-6a2f-45fe-87d6-a7f68d0fa6d5",
   "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>P00001</th>\n",
       "      <th>P00002</th>\n",
       "      <th>P00003</th>\n",
       "      <th>P00004</th>\n",
       "      <th>P00005</th>\n",
       "      <th>P00006</th>\n",
       "      <th>P00007</th>\n",
       "      <th>P00008</th>\n",
       "      <th>P00009</th>\n",
       "      <th>P00010</th>\n",
       "      <th>...</th>\n",
       "      <th>P23991</th>\n",
       "      <th>P23992</th>\n",
       "      <th>P23993</th>\n",
       "      <th>P23994</th>\n",
       "      <th>P23995</th>\n",
       "      <th>P23996</th>\n",
       "      <th>P23997</th>\n",
       "      <th>P23998</th>\n",
       "      <th>P23999</th>\n",
       "      <th>P24000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-12-31 09:00:00</th>\n",
       "      <td>123.896560</td>\n",
       "      <td>127.66187</td>\n",
       "      <td>123.704790</td>\n",
       "      <td>126.180305</td>\n",
       "      <td>124.029335</td>\n",
       "      <td>123.290360</td>\n",
       "      <td>125.401794</td>\n",
       "      <td>128.02122</td>\n",
       "      <td>125.44872</td>\n",
       "      <td>123.742170</td>\n",
       "      <td>...</td>\n",
       "      <td>128.00153</td>\n",
       "      <td>128.00153</td>\n",
       "      <td>128.00452</td>\n",
       "      <td>128.00186</td>\n",
       "      <td>128.0107</td>\n",
       "      <td>128.01974</td>\n",
       "      <td>128.00093</td>\n",
       "      <td>122.738075</td>\n",
       "      <td>122.726560</td>\n",
       "      <td>122.738400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31 10:00:00</th>\n",
       "      <td>123.895645</td>\n",
       "      <td>127.66187</td>\n",
       "      <td>123.708250</td>\n",
       "      <td>126.184425</td>\n",
       "      <td>124.022410</td>\n",
       "      <td>123.282050</td>\n",
       "      <td>125.401794</td>\n",
       "      <td>128.02122</td>\n",
       "      <td>125.44872</td>\n",
       "      <td>123.744540</td>\n",
       "      <td>...</td>\n",
       "      <td>128.00153</td>\n",
       "      <td>128.00153</td>\n",
       "      <td>128.00452</td>\n",
       "      <td>128.00186</td>\n",
       "      <td>128.0107</td>\n",
       "      <td>128.01974</td>\n",
       "      <td>128.00093</td>\n",
       "      <td>122.737110</td>\n",
       "      <td>122.725494</td>\n",
       "      <td>122.737434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31 11:00:00</th>\n",
       "      <td>123.894730</td>\n",
       "      <td>127.66187</td>\n",
       "      <td>123.712660</td>\n",
       "      <td>126.189220</td>\n",
       "      <td>124.017136</td>\n",
       "      <td>123.276200</td>\n",
       "      <td>125.401794</td>\n",
       "      <td>128.02122</td>\n",
       "      <td>125.44872</td>\n",
       "      <td>123.748350</td>\n",
       "      <td>...</td>\n",
       "      <td>128.00153</td>\n",
       "      <td>128.00153</td>\n",
       "      <td>128.00452</td>\n",
       "      <td>128.00186</td>\n",
       "      <td>128.0107</td>\n",
       "      <td>128.01974</td>\n",
       "      <td>128.00093</td>\n",
       "      <td>122.739790</td>\n",
       "      <td>122.728690</td>\n",
       "      <td>122.740160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31 12:00:00</th>\n",
       "      <td>123.893814</td>\n",
       "      <td>127.66187</td>\n",
       "      <td>123.717240</td>\n",
       "      <td>126.195435</td>\n",
       "      <td>124.013220</td>\n",
       "      <td>123.272250</td>\n",
       "      <td>125.401794</td>\n",
       "      <td>128.02122</td>\n",
       "      <td>125.44872</td>\n",
       "      <td>123.753105</td>\n",
       "      <td>...</td>\n",
       "      <td>128.00153</td>\n",
       "      <td>128.00153</td>\n",
       "      <td>128.00452</td>\n",
       "      <td>128.00186</td>\n",
       "      <td>128.0107</td>\n",
       "      <td>128.01974</td>\n",
       "      <td>128.00093</td>\n",
       "      <td>122.745220</td>\n",
       "      <td>122.735290</td>\n",
       "      <td>122.745605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31 13:00:00</th>\n",
       "      <td>123.892550</td>\n",
       "      <td>127.66187</td>\n",
       "      <td>123.721820</td>\n",
       "      <td>126.203260</td>\n",
       "      <td>124.010086</td>\n",
       "      <td>123.269104</td>\n",
       "      <td>125.401794</td>\n",
       "      <td>128.02122</td>\n",
       "      <td>125.44872</td>\n",
       "      <td>123.758130</td>\n",
       "      <td>...</td>\n",
       "      <td>128.00153</td>\n",
       "      <td>128.00153</td>\n",
       "      <td>128.00452</td>\n",
       "      <td>128.00186</td>\n",
       "      <td>128.0107</td>\n",
       "      <td>128.01974</td>\n",
       "      <td>128.00093</td>\n",
       "      <td>122.752490</td>\n",
       "      <td>122.743660</td>\n",
       "      <td>122.752880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-13 05:00:00</th>\n",
       "      <td>123.563030</td>\n",
       "      <td>127.66187</td>\n",
       "      <td>123.022540</td>\n",
       "      <td>133.489500</td>\n",
       "      <td>123.614210</td>\n",
       "      <td>122.686890</td>\n",
       "      <td>125.401794</td>\n",
       "      <td>128.02122</td>\n",
       "      <td>125.44872</td>\n",
       "      <td>122.957820</td>\n",
       "      <td>...</td>\n",
       "      <td>128.00153</td>\n",
       "      <td>128.00153</td>\n",
       "      <td>128.00452</td>\n",
       "      <td>128.00186</td>\n",
       "      <td>128.0107</td>\n",
       "      <td>128.01974</td>\n",
       "      <td>128.00093</td>\n",
       "      <td>124.752440</td>\n",
       "      <td>124.500084</td>\n",
       "      <td>124.724220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-13 06:00:00</th>\n",
       "      <td>123.565506</td>\n",
       "      <td>127.66187</td>\n",
       "      <td>123.018654</td>\n",
       "      <td>133.518370</td>\n",
       "      <td>123.621200</td>\n",
       "      <td>122.695340</td>\n",
       "      <td>125.401794</td>\n",
       "      <td>128.02122</td>\n",
       "      <td>125.44872</td>\n",
       "      <td>122.952740</td>\n",
       "      <td>...</td>\n",
       "      <td>128.00153</td>\n",
       "      <td>128.00153</td>\n",
       "      <td>128.00452</td>\n",
       "      <td>128.00186</td>\n",
       "      <td>128.0107</td>\n",
       "      <td>128.01974</td>\n",
       "      <td>128.00093</td>\n",
       "      <td>124.763460</td>\n",
       "      <td>124.506220</td>\n",
       "      <td>124.734490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-13 07:00:00</th>\n",
       "      <td>123.568214</td>\n",
       "      <td>127.66187</td>\n",
       "      <td>123.015495</td>\n",
       "      <td>133.546750</td>\n",
       "      <td>123.627556</td>\n",
       "      <td>122.703840</td>\n",
       "      <td>125.401794</td>\n",
       "      <td>128.02122</td>\n",
       "      <td>125.44872</td>\n",
       "      <td>122.949585</td>\n",
       "      <td>...</td>\n",
       "      <td>128.00153</td>\n",
       "      <td>128.00153</td>\n",
       "      <td>128.00452</td>\n",
       "      <td>128.00186</td>\n",
       "      <td>128.0107</td>\n",
       "      <td>128.01974</td>\n",
       "      <td>128.00093</td>\n",
       "      <td>124.773750</td>\n",
       "      <td>124.512910</td>\n",
       "      <td>124.744316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-13 08:00:00</th>\n",
       "      <td>123.571590</td>\n",
       "      <td>127.66187</td>\n",
       "      <td>123.013520</td>\n",
       "      <td>133.575130</td>\n",
       "      <td>123.633510</td>\n",
       "      <td>122.711900</td>\n",
       "      <td>125.401794</td>\n",
       "      <td>128.02122</td>\n",
       "      <td>125.44872</td>\n",
       "      <td>122.948440</td>\n",
       "      <td>...</td>\n",
       "      <td>128.00153</td>\n",
       "      <td>128.00153</td>\n",
       "      <td>128.00452</td>\n",
       "      <td>128.00186</td>\n",
       "      <td>128.0107</td>\n",
       "      <td>128.01974</td>\n",
       "      <td>128.00093</td>\n",
       "      <td>124.783646</td>\n",
       "      <td>124.520460</td>\n",
       "      <td>124.753930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-13 09:00:00</th>\n",
       "      <td>123.575870</td>\n",
       "      <td>127.66187</td>\n",
       "      <td>123.012794</td>\n",
       "      <td>133.603520</td>\n",
       "      <td>123.639460</td>\n",
       "      <td>122.719170</td>\n",
       "      <td>125.401794</td>\n",
       "      <td>128.02122</td>\n",
       "      <td>125.44872</td>\n",
       "      <td>122.948654</td>\n",
       "      <td>...</td>\n",
       "      <td>128.00153</td>\n",
       "      <td>128.00153</td>\n",
       "      <td>128.00452</td>\n",
       "      <td>128.00186</td>\n",
       "      <td>128.0107</td>\n",
       "      <td>128.01974</td>\n",
       "      <td>128.00093</td>\n",
       "      <td>124.793680</td>\n",
       "      <td>124.528910</td>\n",
       "      <td>124.763890</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>313 rows × 24000 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         P00001     P00002      P00003      P00004  \\\n",
       "2021-12-31 09:00:00  123.896560  127.66187  123.704790  126.180305   \n",
       "2021-12-31 10:00:00  123.895645  127.66187  123.708250  126.184425   \n",
       "2021-12-31 11:00:00  123.894730  127.66187  123.712660  126.189220   \n",
       "2021-12-31 12:00:00  123.893814  127.66187  123.717240  126.195435   \n",
       "2021-12-31 13:00:00  123.892550  127.66187  123.721820  126.203260   \n",
       "...                         ...        ...         ...         ...   \n",
       "2022-01-13 05:00:00  123.563030  127.66187  123.022540  133.489500   \n",
       "2022-01-13 06:00:00  123.565506  127.66187  123.018654  133.518370   \n",
       "2022-01-13 07:00:00  123.568214  127.66187  123.015495  133.546750   \n",
       "2022-01-13 08:00:00  123.571590  127.66187  123.013520  133.575130   \n",
       "2022-01-13 09:00:00  123.575870  127.66187  123.012794  133.603520   \n",
       "\n",
       "                         P00005      P00006      P00007     P00008     P00009  \\\n",
       "2021-12-31 09:00:00  124.029335  123.290360  125.401794  128.02122  125.44872   \n",
       "2021-12-31 10:00:00  124.022410  123.282050  125.401794  128.02122  125.44872   \n",
       "2021-12-31 11:00:00  124.017136  123.276200  125.401794  128.02122  125.44872   \n",
       "2021-12-31 12:00:00  124.013220  123.272250  125.401794  128.02122  125.44872   \n",
       "2021-12-31 13:00:00  124.010086  123.269104  125.401794  128.02122  125.44872   \n",
       "...                         ...         ...         ...        ...        ...   \n",
       "2022-01-13 05:00:00  123.614210  122.686890  125.401794  128.02122  125.44872   \n",
       "2022-01-13 06:00:00  123.621200  122.695340  125.401794  128.02122  125.44872   \n",
       "2022-01-13 07:00:00  123.627556  122.703840  125.401794  128.02122  125.44872   \n",
       "2022-01-13 08:00:00  123.633510  122.711900  125.401794  128.02122  125.44872   \n",
       "2022-01-13 09:00:00  123.639460  122.719170  125.401794  128.02122  125.44872   \n",
       "\n",
       "                         P00010  ...     P23991     P23992     P23993  \\\n",
       "2021-12-31 09:00:00  123.742170  ...  128.00153  128.00153  128.00452   \n",
       "2021-12-31 10:00:00  123.744540  ...  128.00153  128.00153  128.00452   \n",
       "2021-12-31 11:00:00  123.748350  ...  128.00153  128.00153  128.00452   \n",
       "2021-12-31 12:00:00  123.753105  ...  128.00153  128.00153  128.00452   \n",
       "2021-12-31 13:00:00  123.758130  ...  128.00153  128.00153  128.00452   \n",
       "...                         ...  ...        ...        ...        ...   \n",
       "2022-01-13 05:00:00  122.957820  ...  128.00153  128.00153  128.00452   \n",
       "2022-01-13 06:00:00  122.952740  ...  128.00153  128.00153  128.00452   \n",
       "2022-01-13 07:00:00  122.949585  ...  128.00153  128.00153  128.00452   \n",
       "2022-01-13 08:00:00  122.948440  ...  128.00153  128.00153  128.00452   \n",
       "2022-01-13 09:00:00  122.948654  ...  128.00153  128.00153  128.00452   \n",
       "\n",
       "                        P23994    P23995     P23996     P23997      P23998  \\\n",
       "2021-12-31 09:00:00  128.00186  128.0107  128.01974  128.00093  122.738075   \n",
       "2021-12-31 10:00:00  128.00186  128.0107  128.01974  128.00093  122.737110   \n",
       "2021-12-31 11:00:00  128.00186  128.0107  128.01974  128.00093  122.739790   \n",
       "2021-12-31 12:00:00  128.00186  128.0107  128.01974  128.00093  122.745220   \n",
       "2021-12-31 13:00:00  128.00186  128.0107  128.01974  128.00093  122.752490   \n",
       "...                        ...       ...        ...        ...         ...   \n",
       "2022-01-13 05:00:00  128.00186  128.0107  128.01974  128.00093  124.752440   \n",
       "2022-01-13 06:00:00  128.00186  128.0107  128.01974  128.00093  124.763460   \n",
       "2022-01-13 07:00:00  128.00186  128.0107  128.01974  128.00093  124.773750   \n",
       "2022-01-13 08:00:00  128.00186  128.0107  128.01974  128.00093  124.783646   \n",
       "2022-01-13 09:00:00  128.00186  128.0107  128.01974  128.00093  124.793680   \n",
       "\n",
       "                         P23999      P24000  \n",
       "2021-12-31 09:00:00  122.726560  122.738400  \n",
       "2021-12-31 10:00:00  122.725494  122.737434  \n",
       "2021-12-31 11:00:00  122.728690  122.740160  \n",
       "2021-12-31 12:00:00  122.735290  122.745605  \n",
       "2021-12-31 13:00:00  122.743660  122.752880  \n",
       "...                         ...         ...  \n",
       "2022-01-13 05:00:00  124.500084  124.724220  \n",
       "2022-01-13 06:00:00  124.506220  124.734490  \n",
       "2022-01-13 07:00:00  124.512910  124.744316  \n",
       "2022-01-13 08:00:00  124.520460  124.753930  \n",
       "2022-01-13 09:00:00  124.528910  124.763890  \n",
       "\n",
       "[313 rows x 24000 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lonall"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6fd6066-8238-4838-8466-ad9214a63db3",
   "metadata": {},
   "source": [
    "# make animation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cb171774-a744-47ab-b736-7ab4131d1d93",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import matplotlib.animation as animation\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "837413cb-3acb-434b-a6f2-16e65d5ba41c",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATES=pd.date_range(start=start,end=end,freq=\"H\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3f78113a-2f59-4e86-8553-c009c3195efe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def update(i,ax):\n",
    "    if i != 0:\n",
    "        plt.cla() #現在描写されているグラフを消去\n",
    "    \n",
    "    plon=lonall.iloc[i]\n",
    "    plat=latall.iloc[i]\n",
    "    cl=ax.plot(plon,plat,\"b.\")\n",
    "    ax.coastlines()\n",
    "    ax.add_feature(cfeature.LAND,color=\"gray\")\n",
    "    ax.set_title(DATES[i].strftime(\"%Y/%m/%d %H\"),fontsize=18)\n",
    "    gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,\n",
    "                  linewidth=2, color='gray', alpha=0.5, linestyle='--')\n",
    "    gl.top_lables = False\n",
    "    gl.right_labels = False\n",
    "    ax.set_extent([127,129,26,27.5])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "46492bdb-cf7c-46a9-905c-44b0e2fd133f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(12,8))\n",
    "proj=ccrs.PlateCarree()\n",
    "ax = fig.add_subplot(111, projection=proj)\n",
    "ani = animation.FuncAnimation(fig, update, fargs = (ax,),interval = 100, frames = len(latall))\n",
    "writergif = animation.PillowWriter(fps=30)\n",
    "ani.save(\"animation.gif\", writer = writergif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c88b19bd-27c5-48ef-b839-7d40fb24a9c3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "jupytext": {
   "text_representation": {
    "extension": ".py",
    "format_name": "light",
    "format_version": "1.5",
    "jupytext_version": "1.12.0"
   }
  },
  "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": "3.8.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}