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   "cell_type": "code",
   "execution_count": 1,
   "id": "ec625f4b-c4e8-41fa-99c1-750889f9e452",
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   "outputs": [],
   "source": [
    "# Usar ambiente SMNAMonitoringApp\n",
    "\n",
    "import os\n",
    "import zarr\n",
    "import intake\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "363d2697-80fa-42b1-bce7-e165f2b65331",
   "metadata": {},
   "outputs": [],
   "source": [
    "config = {}\n",
    "variables = {}\n",
    "\n",
    "def parse_ctl_file(filein_ctl):\n",
    "    with open(filein_ctl, 'r') as f:\n",
    "        linhas = f.readlines()\n",
    "\n",
    "    ler_variaveis = False\n",
    "\n",
    "    for linha in linhas:\n",
    "        linha = linha.strip()\n",
    "\n",
    "        if linha.lower().startswith('dset'):\n",
    "            config['dset'] = linha.split('^')[-1].strip()\n",
    "\n",
    "        elif linha.lower().startswith('options'):\n",
    "            config['options'] = linha.split()[1:]\n",
    "\n",
    "        elif linha.lower().startswith('undef'):\n",
    "            config['undef'] = float(linha.split()[1])\n",
    "\n",
    "        elif linha.lower().startswith('xdef'):\n",
    "            partes = linha.split()\n",
    "            config['xdef'] = {\n",
    "                'num': int(partes[1]),\n",
    "                'type': partes[2],\n",
    "                'start': float(partes[3]),\n",
    "                'increment': float(partes[4])\n",
    "            }\n",
    "\n",
    "        elif linha.lower().startswith('ydef'):\n",
    "            partes = linha.split()\n",
    "            config['ydef'] = {\n",
    "                'num': int(partes[1]),\n",
    "                'type': partes[2],\n",
    "                'start': float(partes[3]),\n",
    "                'increment': float(partes[4])\n",
    "            }\n",
    "\n",
    "        elif linha.lower().startswith('zdef'):\n",
    "            partes = linha.split()\n",
    "            config['zdef'] = {\n",
    "                'num': int(partes[1]),\n",
    "                'type': partes[2],\n",
    "                'start': float(partes[3]),\n",
    "                'increment': float(partes[4])\n",
    "            }\n",
    "\n",
    "        elif linha.lower().startswith('tdef'):\n",
    "            partes = linha.split()\n",
    "            config['tdef'] = {\n",
    "                'num': int(partes[1]),\n",
    "                'type': partes[2],\n",
    "                'start_time': partes[3],\n",
    "                'increment': partes[4]\n",
    "            }\n",
    "\n",
    "        elif linha.lower().startswith('vars'):\n",
    "            ler_variaveis = True\n",
    "            num_vars = int(linha.split()[1])\n",
    "            config['nvars'] = num_vars\n",
    "\n",
    "        elif linha.lower().startswith('endvars'):\n",
    "            ler_variaveis = False\n",
    "\n",
    "        elif ler_variaveis and linha != '':\n",
    "            partes = linha.split(maxsplit=3)\n",
    "            varinfo = {\n",
    "                'name': partes[0],\n",
    "                'level': int(partes[1]),\n",
    "                'code': int(partes[2]),\n",
    "                'description': partes[3]\n",
    "            }\n",
    "            #varinfo2 = {str(partes[0]): str(partes[3])}\n",
    "            #variables.append(varinfo2)\n",
    "            variables[str(partes[0])] = str(partes[3])\n",
    "\n",
    "    return config, variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e49e8ab6-8860-466c-bc18-a6ecf75fb0d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "Regs = ['gl', 'hn', 'tr', 'hs', 'as']\n",
    "#Regs = ['hs']\n",
    "FStats = ['VIES', 'RMSE', 'MEAN']\n",
    "TStats = ['VIES', 'RMSE', 'ACOR']\n",
    "\n",
    "bpath = '/extra2/Lucas/SCANTEC'\n",
    "\n",
    "date_range = '20240715002024081500'\n",
    "\n",
    "dtype = np.float32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "13d6686a-09c9-4aba-babc-b35ea9869291",
   "metadata": {},
   "outputs": [],
   "source": [
    "config, variables = parse_ctl_file('/extra2/Lucas/SCANTEC/gl/RMSEEXP01_'+ str(date_range) + 'F.ctl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b46b0614-4ad4-422e-a04e-273ac9d5bab6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_scantec_binary(fnamein_bin, fnamein_ctl, reg):#, nx, ny, delta, npts):\n",
    "    with open(fnamein_bin, 'rb') as f:\n",
    "        \n",
    "        config, variables = parse_ctl_file(fnamein_ctl)\n",
    "        \n",
    "        #n_times = config['tdef']['num']\n",
    "        n_times = 10\n",
    "        n_vars = config['nvars']\n",
    "        ny = config['ydef']['num']\n",
    "        nx = config['xdef']['num']\n",
    "        delta = config['xdef']['increment']\n",
    "        npts = ny * nx\n",
    "        lat_start = config['xdef']['start']\n",
    "        lon_start = config['ydef']['start']\n",
    "        lons = np.linspace(lon_start, lon_start + (nx-1)*delta, nx)\n",
    "        lats = np.linspace(lat_start, lat_start + (ny-1)*delta, ny)\n",
    "        var_names = variables.keys()        \n",
    "        \n",
    "        #n_times, n_vars, ny, nx, delta, npts, variables = get_dims(filein_ctl)\n",
    "        data = np.empty((n_times, n_vars, ny, nx), dtype=dtype)\n",
    "        \n",
    "        start_date = pd.Timestamp('2024-09-15 00:00:00')\n",
    "        times = pd.date_range(start=start_date, periods=n_times, freq='D')\n",
    "        #lats, lons = get_lat_lon(reg, nx, ny, delta)\n",
    "        \n",
    "        for t in range(n_times):\n",
    "            for v in range(n_vars):\n",
    "                rec_start = np.fromfile(f, dtype=np.int32, count=1)\n",
    "                temp = np.fromfile(f, dtype=dtype, count=npts)\n",
    "                #print(f\"Tempo {t}, Var {v}: leu {temp.size} elementos\")\n",
    "                temp = temp.reshape((ny, nx))\n",
    "                data[t, v, :, :] = temp\n",
    "                rec_end = np.fromfile(f, dtype=np.int32, count=1)\n",
    "\n",
    "                if rec_start[0] != rec_end[0]:\n",
    "                    print(f\"Aviso: record markers diferentes no tempo {t}, variável {v}\")\n",
    "\n",
    "    ds = xr.Dataset({name: (('time', 'lat', 'lon'), data[:, i, :, :]) for i, name in enumerate(var_names)}, coords={'time': times, 'lat': lats, 'lon': lons})\n",
    "\n",
    "    return ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "83d786bf-f832-411a-9d03-20ff7249b29f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/extra2/Lucas/SCANTEC/gl/VIESEXP01_20240715002024081500F.scan /extra2/Lucas/SCANTEC/gl/VIESEXP01_20240715002024081500F.ctl /extra2/Lucas/SCANTEC/gl/VIESEXP01_20240715002024081500F.zarr VIES gl\n",
      "/extra2/Lucas/SCANTEC/gl/RMSEEXP01_20240715002024081500F.scan /extra2/Lucas/SCANTEC/gl/RMSEEXP01_20240715002024081500F.ctl /extra2/Lucas/SCANTEC/gl/RMSEEXP01_20240715002024081500F.zarr RMSE gl\n",
      "/extra2/Lucas/SCANTEC/gl/MEANEXP01_20240715002024081500F.scan /extra2/Lucas/SCANTEC/gl/MEANEXP01_20240715002024081500F.ctl /extra2/Lucas/SCANTEC/gl/MEANEXP01_20240715002024081500F.zarr MEAN gl\n",
      "/extra2/Lucas/SCANTEC/hn/VIESEXP01_20240715002024081500F.scan /extra2/Lucas/SCANTEC/hn/VIESEXP01_20240715002024081500F.ctl /extra2/Lucas/SCANTEC/hn/VIESEXP01_20240715002024081500F.zarr VIES hn\n",
      "/extra2/Lucas/SCANTEC/hn/RMSEEXP01_20240715002024081500F.scan /extra2/Lucas/SCANTEC/hn/RMSEEXP01_20240715002024081500F.ctl /extra2/Lucas/SCANTEC/hn/RMSEEXP01_20240715002024081500F.zarr RMSE hn\n",
      "/extra2/Lucas/SCANTEC/hn/MEANEXP01_20240715002024081500F.scan /extra2/Lucas/SCANTEC/hn/MEANEXP01_20240715002024081500F.ctl /extra2/Lucas/SCANTEC/hn/MEANEXP01_20240715002024081500F.zarr MEAN hn\n",
      "/extra2/Lucas/SCANTEC/tr/VIESEXP01_20240715002024081500F.scan /extra2/Lucas/SCANTEC/tr/VIESEXP01_20240715002024081500F.ctl /extra2/Lucas/SCANTEC/tr/VIESEXP01_20240715002024081500F.zarr VIES tr\n",
      "/extra2/Lucas/SCANTEC/tr/RMSEEXP01_20240715002024081500F.scan /extra2/Lucas/SCANTEC/tr/RMSEEXP01_20240715002024081500F.ctl /extra2/Lucas/SCANTEC/tr/RMSEEXP01_20240715002024081500F.zarr RMSE tr\n",
      "/extra2/Lucas/SCANTEC/tr/MEANEXP01_20240715002024081500F.scan /extra2/Lucas/SCANTEC/tr/MEANEXP01_20240715002024081500F.ctl /extra2/Lucas/SCANTEC/tr/MEANEXP01_20240715002024081500F.zarr MEAN tr\n",
      "/extra2/Lucas/SCANTEC/hs/VIESEXP01_20240715002024081500F.scan /extra2/Lucas/SCANTEC/hs/VIESEXP01_20240715002024081500F.ctl /extra2/Lucas/SCANTEC/hs/VIESEXP01_20240715002024081500F.zarr VIES hs\n",
      "/extra2/Lucas/SCANTEC/hs/RMSEEXP01_20240715002024081500F.scan /extra2/Lucas/SCANTEC/hs/RMSEEXP01_20240715002024081500F.ctl /extra2/Lucas/SCANTEC/hs/RMSEEXP01_20240715002024081500F.zarr RMSE hs\n",
      "/extra2/Lucas/SCANTEC/hs/MEANEXP01_20240715002024081500F.scan /extra2/Lucas/SCANTEC/hs/MEANEXP01_20240715002024081500F.ctl /extra2/Lucas/SCANTEC/hs/MEANEXP01_20240715002024081500F.zarr MEAN hs\n",
      "/extra2/Lucas/SCANTEC/as/VIESEXP01_20240715002024081500F.scan /extra2/Lucas/SCANTEC/as/VIESEXP01_20240715002024081500F.ctl /extra2/Lucas/SCANTEC/as/VIESEXP01_20240715002024081500F.zarr VIES as\n",
      "/extra2/Lucas/SCANTEC/as/RMSEEXP01_20240715002024081500F.scan /extra2/Lucas/SCANTEC/as/RMSEEXP01_20240715002024081500F.ctl /extra2/Lucas/SCANTEC/as/RMSEEXP01_20240715002024081500F.zarr RMSE as\n",
      "/extra2/Lucas/SCANTEC/as/MEANEXP01_20240715002024081500F.scan /extra2/Lucas/SCANTEC/as/MEANEXP01_20240715002024081500F.ctl /extra2/Lucas/SCANTEC/as/MEANEXP01_20240715002024081500F.zarr MEAN as\n",
      "CPU times: user 3.31 s, sys: 3.09 s, total: 6.39 s\n",
      "Wall time: 24.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Escrita convencional, formato zarr (1 arquivos por vez)\n",
    "\n",
    "for reg in Regs:\n",
    "    for stat in FStats:\n",
    "        filein_bin = str(stat) + 'EXP01_' + date_range + 'F.scan'\n",
    "        filein_ctl = str(stat) + 'EXP01_' + date_range + 'F.ctl'\n",
    "        \n",
    "        fnamein_bin = os.path.join(bpath, reg, filein_bin)\n",
    "        fnamein_ctl = os.path.join(bpath, reg, filein_ctl)\n",
    "               \n",
    "        fileout = str(stat) + 'EXP01_' + date_range + 'F.zarr'\n",
    "        fnameout_zarr = os.path.join(bpath, reg, fileout)\n",
    "\n",
    "        print(fnamein_bin, fnamein_ctl, fnameout_zarr, stat, reg)\n",
    "        \n",
    "        dset = read_scantec_binary(fnamein_bin, fnamein_ctl, reg)\n",
    "        \n",
    "        dset.to_zarr(fnameout_zarr, mode='w', consolidated=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0cfcfff6-e522-42b4-87be-bb4da82c5f35",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Teste de abertura do arquivo zarr escrito\n",
    "\n",
    "ds = xr.open_zarr('/extra2/Lucas/SCANTEC/gl/MEANEXP01_'+ str(date_range) + 'F.zarr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1cfd5468-23c7-4dba-bb42-cb94ca53f4fb",
   "metadata": {},
   "outputs": [
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       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 289MB\n",
       "Dimensions:  (time: 10, lat: 401, lon: 901)\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 3kB 0.0 0.4 0.8 1.2 1.6 ... 158.8 159.2 159.6 160.0\n",
       "  * lon      (lon) float64 7kB -80.0 -79.6 -79.2 -78.8 ... 279.2 279.6 280.0\n",
       "  * time     (time) datetime64[ns] 80B 2024-09-15 2024-09-16 ... 2024-09-24\n",
       "Data variables: (12/20)\n",
       "    agpl925  (time, lat, lon) float32 14MB dask.array&lt;chunksize=(3, 101, 451), meta=np.ndarray&gt;\n",
       "    psnm000  (time, lat, lon) float32 14MB dask.array&lt;chunksize=(3, 101, 451), meta=np.ndarray&gt;\n",
       "    temp250  (time, lat, lon) float32 14MB dask.array&lt;chunksize=(3, 101, 451), meta=np.ndarray&gt;\n",
       "    temp500  (time, lat, lon) float32 14MB dask.array&lt;chunksize=(3, 101, 451), meta=np.ndarray&gt;\n",
       "    temp850  (time, lat, lon) float32 14MB dask.array&lt;chunksize=(3, 101, 451), meta=np.ndarray&gt;\n",
       "    umes500  (time, lat, lon) float32 14MB dask.array&lt;chunksize=(3, 101, 451), meta=np.ndarray&gt;\n",
       "    ...       ...\n",
       "    vvel250  (time, lat, lon) float32 14MB dask.array&lt;chunksize=(3, 101, 451), meta=np.ndarray&gt;\n",
       "    vvel500  (time, lat, lon) float32 14MB dask.array&lt;chunksize=(3, 101, 451), meta=np.ndarray&gt;\n",
       "    vvel850  (time, lat, lon) float32 14MB dask.array&lt;chunksize=(3, 101, 451), meta=np.ndarray&gt;\n",
       "    zgeo250  (time, lat, lon) float32 14MB dask.array&lt;chunksize=(3, 101, 451), meta=np.ndarray&gt;\n",
       "    zgeo500  (time, lat, lon) float32 14MB dask.array&lt;chunksize=(3, 101, 451), meta=np.ndarray&gt;\n",
       "    zgeo850  (time, lat, lon) float32 14MB dask.array&lt;chunksize=(3, 101, 451), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-824e89e1-88ec-4956-a61c-66b7db400208' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-824e89e1-88ec-4956-a61c-66b7db400208' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 10</li><li><span class='xr-has-index'>lat</span>: 401</li><li><span class='xr-has-index'>lon</span>: 901</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-efc85e4a-8777-4383-b081-7f35af9b9086' class='xr-section-summary-in' type='checkbox'  checked><label for='section-efc85e4a-8777-4383-b081-7f35af9b9086' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.4 0.8 ... 159.2 159.6 160.0</div><input id='attrs-ad769763-26e4-41db-afa3-b7a8980488ae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad769763-26e4-41db-afa3-b7a8980488ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd525b6e-bec6-4ea3-9d32-5bae921679a6' class='xr-var-data-in' type='checkbox'><label for='data-dd525b6e-bec6-4ea3-9d32-5bae921679a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0. ,   0.4,   0.8, ..., 159.2, 159.6, 160. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-80.0 -79.6 -79.2 ... 279.6 280.0</div><input id='attrs-f3367608-2e98-4c70-8e9e-d8db21f0da49' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f3367608-2e98-4c70-8e9e-d8db21f0da49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-007f2518-c6fb-41c7-ae83-69efd0cb69c9' class='xr-var-data-in' type='checkbox'><label for='data-007f2518-c6fb-41c7-ae83-69efd0cb69c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-80. , -79.6, -79.2, ..., 279.2, 279.6, 280. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-09-15 ... 2024-09-24</div><input id='attrs-45f7ff31-cd0d-4f41-bd18-3f6da5842a82' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-45f7ff31-cd0d-4f41-bd18-3f6da5842a82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e057109b-5014-4319-8f5f-20061d87077d' class='xr-var-data-in' type='checkbox'><label for='data-e057109b-5014-4319-8f5f-20061d87077d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2024-09-15T00:00:00.000000000&#x27;, &#x27;2024-09-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-09-17T00:00:00.000000000&#x27;, &#x27;2024-09-18T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-09-19T00:00:00.000000000&#x27;, &#x27;2024-09-20T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-09-21T00:00:00.000000000&#x27;, &#x27;2024-09-22T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-09-23T00:00:00.000000000&#x27;, &#x27;2024-09-24T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9707b3de-438a-4b24-bac3-772f17c09bc3' class='xr-section-summary-in' type='checkbox'  ><label for='section-9707b3de-438a-4b24-bac3-772f17c09bc3' class='xr-section-summary' >Data variables: <span>(20)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>agpl925</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 101, 451), meta=np.ndarray&gt;</div><input id='attrs-0510b363-2330-4552-829f-4784a83ddf5b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0510b363-2330-4552-829f-4784a83ddf5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f242345-2164-4f92-ae1c-81eef4fb1676' class='xr-var-data-in' type='checkbox'><label for='data-7f242345-2164-4f92-ae1c-81eef4fb1676' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 13.78 MiB </td>\n",
       "                        <td> 533.80 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (10, 401, 901) </td>\n",
       "                        <td> (3, 101, 451) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "\n",
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       "\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>psnm000</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 101, 451), meta=np.ndarray&gt;</div><input id='attrs-3f9676c3-bf24-4fc3-b49b-fe21ae62442f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3f9676c3-bf24-4fc3-b49b-fe21ae62442f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2da25882-a368-47e6-890f-4fdbe8a34ce1' class='xr-var-data-in' type='checkbox'><label for='data-2da25882-a368-47e6-890f-4fdbe8a34ce1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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       "                <thead>\n",
       "                    <tr>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 13.78 MiB </td>\n",
       "                        <td> 533.80 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
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       "                    <tr>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 13.78 MiB </td>\n",
       "                        <td> 533.80 kiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (3, 101, 451) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zgeo850</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 101, 451), meta=np.ndarray&gt;</div><input id='attrs-1e2b72e5-5938-4ed0-9660-35d1550f3abb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e2b72e5-5938-4ed0-9660-35d1550f3abb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88f11dbd-00ec-4ac4-aa7e-c691cb97fbdd' class='xr-var-data-in' type='checkbox'><label for='data-88f11dbd-00ec-4ac4-aa7e-c691cb97fbdd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 13.78 MiB </td>\n",
       "                        <td> 533.80 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
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       "                        <th> Shape </th>\n",
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       "                        <td> (3, 101, 451) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-0a9e3fcd-3a36-4995-8108-652589ed4e38' class='xr-section-summary-in' type='checkbox'  ><label for='section-0a9e3fcd-3a36-4995-8108-652589ed4e38' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-3a218850-ef30-4b7f-8027-93ba0bcbfc7c' class='xr-index-data-in' type='checkbox'/><label for='index-3a218850-ef30-4b7f-8027-93ba0bcbfc7c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               0.0,                0.4,                0.8,\n",
       "       1.2000000000000002,                1.6,                2.0,\n",
       "       2.4000000000000004, 2.8000000000000003,                3.2,\n",
       "                      3.6,\n",
       "       ...\n",
       "                    156.4,              156.8, 157.20000000000002,\n",
       "       157.60000000000002,              158.0,              158.4,\n",
       "                    158.8, 159.20000000000002, 159.60000000000002,\n",
       "                    160.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=401))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-6ff423e5-c520-4cc0-b98c-628be837011a' class='xr-index-data-in' type='checkbox'/><label for='index-6ff423e5-c520-4cc0-b98c-628be837011a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             -80.0,              -79.6,              -79.2,\n",
       "                    -78.8,              -78.4,              -78.0,\n",
       "                    -77.6,              -77.2,              -76.8,\n",
       "                    -76.4,\n",
       "       ...\n",
       "       276.40000000000003,              276.8, 277.20000000000005,\n",
       "                    277.6,              278.0, 278.40000000000003,\n",
       "                    278.8, 279.20000000000005,              279.6,\n",
       "                    280.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=901))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-37960269-774b-4e49-8018-314eb3686361' class='xr-index-data-in' type='checkbox'/><label for='index-37960269-774b-4e49-8018-314eb3686361' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2024-09-15&#x27;, &#x27;2024-09-16&#x27;, &#x27;2024-09-17&#x27;, &#x27;2024-09-18&#x27;,\n",
       "               &#x27;2024-09-19&#x27;, &#x27;2024-09-20&#x27;, &#x27;2024-09-21&#x27;, &#x27;2024-09-22&#x27;,\n",
       "               &#x27;2024-09-23&#x27;, &#x27;2024-09-24&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2fdb27b5-a763-4a29-8574-c71b47b4c909' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2fdb27b5-a763-4a29-8574-c71b47b4c909' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 289MB\n",
       "Dimensions:  (time: 10, lat: 401, lon: 901)\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 3kB 0.0 0.4 0.8 1.2 1.6 ... 158.8 159.2 159.6 160.0\n",
       "  * lon      (lon) float64 7kB -80.0 -79.6 -79.2 -78.8 ... 279.2 279.6 280.0\n",
       "  * time     (time) datetime64[ns] 80B 2024-09-15 2024-09-16 ... 2024-09-24\n",
       "Data variables: (12/20)\n",
       "    agpl925  (time, lat, lon) float32 14MB dask.array<chunksize=(3, 101, 451), meta=np.ndarray>\n",
       "    psnm000  (time, lat, lon) float32 14MB dask.array<chunksize=(3, 101, 451), meta=np.ndarray>\n",
       "    temp250  (time, lat, lon) float32 14MB dask.array<chunksize=(3, 101, 451), meta=np.ndarray>\n",
       "    temp500  (time, lat, lon) float32 14MB dask.array<chunksize=(3, 101, 451), meta=np.ndarray>\n",
       "    temp850  (time, lat, lon) float32 14MB dask.array<chunksize=(3, 101, 451), meta=np.ndarray>\n",
       "    umes500  (time, lat, lon) float32 14MB dask.array<chunksize=(3, 101, 451), meta=np.ndarray>\n",
       "    ...       ...\n",
       "    vvel250  (time, lat, lon) float32 14MB dask.array<chunksize=(3, 101, 451), meta=np.ndarray>\n",
       "    vvel500  (time, lat, lon) float32 14MB dask.array<chunksize=(3, 101, 451), meta=np.ndarray>\n",
       "    vvel850  (time, lat, lon) float32 14MB dask.array<chunksize=(3, 101, 451), meta=np.ndarray>\n",
       "    zgeo250  (time, lat, lon) float32 14MB dask.array<chunksize=(3, 101, 451), meta=np.ndarray>\n",
       "    zgeo500  (time, lat, lon) float32 14MB dask.array<chunksize=(3, 101, 451), meta=np.ndarray>\n",
       "    zgeo850  (time, lat, lon) float32 14MB dask.array<chunksize=(3, 101, 451), meta=np.ndarray>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a1c79cff-8dd3-4d14-bc8b-24b9a02dc583",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x70764320d2b0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds['umes925'].isel(time=0).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e2a12def-34e7-43e1-a92b-fd089f0177c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/extra2/Lucas/SCANTEC/gl/VIESEXP01_20240715002024081500T.scan /extra2/Lucas/SCANTEC/gl/VIESEXP01_20240715002024081500T.csv VIES gl\n",
      "/extra2/Lucas/SCANTEC/gl/RMSEEXP01_20240715002024081500T.scan /extra2/Lucas/SCANTEC/gl/RMSEEXP01_20240715002024081500T.csv RMSE gl\n",
      "/extra2/Lucas/SCANTEC/gl/ACOREXP01_20240715002024081500T.scan /extra2/Lucas/SCANTEC/gl/ACOREXP01_20240715002024081500T.csv ACOR gl\n",
      "/extra2/Lucas/SCANTEC/hn/VIESEXP01_20240715002024081500T.scan /extra2/Lucas/SCANTEC/hn/VIESEXP01_20240715002024081500T.csv VIES hn\n",
      "/extra2/Lucas/SCANTEC/hn/RMSEEXP01_20240715002024081500T.scan /extra2/Lucas/SCANTEC/hn/RMSEEXP01_20240715002024081500T.csv RMSE hn\n",
      "/extra2/Lucas/SCANTEC/hn/ACOREXP01_20240715002024081500T.scan /extra2/Lucas/SCANTEC/hn/ACOREXP01_20240715002024081500T.csv ACOR hn\n",
      "/extra2/Lucas/SCANTEC/tr/VIESEXP01_20240715002024081500T.scan /extra2/Lucas/SCANTEC/tr/VIESEXP01_20240715002024081500T.csv VIES tr\n",
      "/extra2/Lucas/SCANTEC/tr/RMSEEXP01_20240715002024081500T.scan /extra2/Lucas/SCANTEC/tr/RMSEEXP01_20240715002024081500T.csv RMSE tr\n",
      "/extra2/Lucas/SCANTEC/tr/ACOREXP01_20240715002024081500T.scan /extra2/Lucas/SCANTEC/tr/ACOREXP01_20240715002024081500T.csv ACOR tr\n",
      "/extra2/Lucas/SCANTEC/hs/VIESEXP01_20240715002024081500T.scan /extra2/Lucas/SCANTEC/hs/VIESEXP01_20240715002024081500T.csv VIES hs\n",
      "/extra2/Lucas/SCANTEC/hs/RMSEEXP01_20240715002024081500T.scan /extra2/Lucas/SCANTEC/hs/RMSEEXP01_20240715002024081500T.csv RMSE hs\n",
      "/extra2/Lucas/SCANTEC/hs/ACOREXP01_20240715002024081500T.scan /extra2/Lucas/SCANTEC/hs/ACOREXP01_20240715002024081500T.csv ACOR hs\n",
      "/extra2/Lucas/SCANTEC/as/VIESEXP01_20240715002024081500T.scan /extra2/Lucas/SCANTEC/as/VIESEXP01_20240715002024081500T.csv VIES as\n",
      "/extra2/Lucas/SCANTEC/as/RMSEEXP01_20240715002024081500T.scan /extra2/Lucas/SCANTEC/as/RMSEEXP01_20240715002024081500T.csv RMSE as\n",
      "/extra2/Lucas/SCANTEC/as/ACOREXP01_20240715002024081500T.scan /extra2/Lucas/SCANTEC/as/ACOREXP01_20240715002024081500T.csv ACOR as\n",
      "CPU times: user 30.5 ms, sys: 2.69 ms, total: 33.2 ms\n",
      "Wall time: 284 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "df_dic = {}\n",
    "\n",
    "for reg in Regs:\n",
    "    for stat in TStats:\n",
    "        filein = str(stat) + 'EXP01_' + date_range + 'T.scan'\n",
    "        fnamein = os.path.join(bpath, reg, filein)\n",
    "        fileout = str(stat) + 'EXP01_' + date_range + 'T.csv'\n",
    "        fnameout = os.path.join(bpath, reg, fileout)\n",
    "        print(fnamein, fnameout, stat, reg)\n",
    "        df = pd.read_csv(fnamein, sep=r\"\\s+\")\n",
    "        df.to_csv(fnameout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2012e85a-7b29-4988-968e-ea675d4b7a5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Teste de abertura do arquivo csv escrito\n",
    "\n",
    "dft = pd.read_csv('/extra2/Lucas/SCANTEC/gl/ACOREXP01_' + date_range + 'T.csv', index_col=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d018cf53-d84c-4dc2-8912-fd8b1ff3c29f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>%Previsao</th>\n",
       "      <th>vtmp:925</th>\n",
       "      <th>vtmp:850</th>\n",
       "      <th>vtmp:500</th>\n",
       "      <th>temp:850</th>\n",
       "      <th>temp:500</th>\n",
       "      <th>temp:250</th>\n",
       "      <th>psnm:000</th>\n",
       "      <th>umes:925</th>\n",
       "      <th>umes:850</th>\n",
       "      <th>...</th>\n",
       "      <th>agpl:925</th>\n",
       "      <th>zgeo:850</th>\n",
       "      <th>zgeo:500</th>\n",
       "      <th>zgeo:250</th>\n",
       "      <th>uvel:850</th>\n",
       "      <th>uvel:500</th>\n",
       "      <th>uvel:250</th>\n",
       "      <th>vvel:850</th>\n",
       "      <th>vvel:500</th>\n",
       "      <th>vvel:250</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.932</td>\n",
       "      <td>0.983</td>\n",
       "      <td>0.977</td>\n",
       "      <td>0.961</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.965</td>\n",
       "      <td>0.967</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.990</td>\n",
       "      <td>0.936</td>\n",
       "      <td>0.961</td>\n",
       "      <td>0.964</td>\n",
       "      <td>0.927</td>\n",
       "      <td>0.956</td>\n",
       "      <td>0.958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>48</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.867</td>\n",
       "      <td>0.958</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.926</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.895</td>\n",
       "      <td>0.942</td>\n",
       "      <td>0.962</td>\n",
       "      <td>0.978</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.906</td>\n",
       "      <td>0.912</td>\n",
       "      <td>0.829</td>\n",
       "      <td>0.894</td>\n",
       "      <td>0.894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>72</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.801</td>\n",
       "      <td>0.924</td>\n",
       "      <td>0.919</td>\n",
       "      <td>0.880</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.822</td>\n",
       "      <td>0.904</td>\n",
       "      <td>0.932</td>\n",
       "      <td>0.957</td>\n",
       "      <td>0.761</td>\n",
       "      <td>0.836</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.726</td>\n",
       "      <td>0.811</td>\n",
       "      <td>0.815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>96</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.888</td>\n",
       "      <td>0.814</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.751</td>\n",
       "      <td>0.847</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.926</td>\n",
       "      <td>0.663</td>\n",
       "      <td>0.749</td>\n",
       "      <td>0.770</td>\n",
       "      <td>0.626</td>\n",
       "      <td>0.713</td>\n",
       "      <td>0.720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>120</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.669</td>\n",
       "      <td>0.835</td>\n",
       "      <td>0.852</td>\n",
       "      <td>0.733</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.691</td>\n",
       "      <td>0.771</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.565</td>\n",
       "      <td>0.659</td>\n",
       "      <td>0.690</td>\n",
       "      <td>0.522</td>\n",
       "      <td>0.606</td>\n",
       "      <td>0.613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>144</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.608</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.822</td>\n",
       "      <td>0.649</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.642</td>\n",
       "      <td>0.688</td>\n",
       "      <td>0.752</td>\n",
       "      <td>0.835</td>\n",
       "      <td>0.473</td>\n",
       "      <td>0.562</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.423</td>\n",
       "      <td>0.497</td>\n",
       "      <td>0.507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>168</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.542</td>\n",
       "      <td>0.736</td>\n",
       "      <td>0.795</td>\n",
       "      <td>0.555</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.593</td>\n",
       "      <td>0.597</td>\n",
       "      <td>0.668</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.386</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.507</td>\n",
       "      <td>0.332</td>\n",
       "      <td>0.391</td>\n",
       "      <td>0.403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>192</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.998</td>\n",
       "      <td>0.482</td>\n",
       "      <td>0.686</td>\n",
       "      <td>0.767</td>\n",
       "      <td>0.461</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.554</td>\n",
       "      <td>0.507</td>\n",
       "      <td>0.587</td>\n",
       "      <td>0.717</td>\n",
       "      <td>0.323</td>\n",
       "      <td>0.392</td>\n",
       "      <td>0.429</td>\n",
       "      <td>0.267</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>216</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.998</td>\n",
       "      <td>0.443</td>\n",
       "      <td>0.643</td>\n",
       "      <td>0.745</td>\n",
       "      <td>0.399</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.526</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.521</td>\n",
       "      <td>0.669</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.340</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.234</td>\n",
       "      <td>0.243</td>\n",
       "      <td>0.237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>240</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.998</td>\n",
       "      <td>0.411</td>\n",
       "      <td>0.615</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.345</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.509</td>\n",
       "      <td>0.381</td>\n",
       "      <td>0.464</td>\n",
       "      <td>0.632</td>\n",
       "      <td>0.247</td>\n",
       "      <td>0.290</td>\n",
       "      <td>0.325</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>264</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.998</td>\n",
       "      <td>0.998</td>\n",
       "      <td>0.376</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.297</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0.336</td>\n",
       "      <td>0.418</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0.261</td>\n",
       "      <td>0.287</td>\n",
       "      <td>0.191</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.159</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    %Previsao  vtmp:925  vtmp:850  vtmp:500  temp:850  temp:500  temp:250  \\\n",
       "0           0     1.000     1.000     1.000     1.000     1.000     1.000   \n",
       "1          24     1.000     1.000     1.000     0.932     0.983     0.977   \n",
       "2          48     1.000     1.000     1.000     0.867     0.958     0.950   \n",
       "3          72     1.000     1.000     1.000     0.801     0.924     0.919   \n",
       "4          96     0.999     0.999     0.999     0.735     0.883     0.888   \n",
       "5         120     0.999     0.999     0.999     0.669     0.835     0.852   \n",
       "6         144     0.999     0.999     0.999     0.608     0.785     0.822   \n",
       "7         168     0.999     0.999     0.999     0.542     0.736     0.795   \n",
       "8         192     0.999     0.999     0.998     0.482     0.686     0.767   \n",
       "9         216     0.999     0.999     0.998     0.443     0.643     0.745   \n",
       "10        240     0.999     0.999     0.998     0.411     0.615     0.730   \n",
       "11        264     0.999     0.998     0.998     0.376     0.590     0.714   \n",
       "\n",
       "    psnm:000  umes:925  umes:850  ...  agpl:925  zgeo:850  zgeo:500  zgeo:250  \\\n",
       "0      1.000       1.0       1.0  ...     1.000     1.000     1.000     1.000   \n",
       "1      0.961       1.0       1.0  ...     0.965     0.967     0.981     0.990   \n",
       "2      0.926       1.0       1.0  ...     0.895     0.942     0.962     0.978   \n",
       "3      0.880       1.0       1.0  ...     0.822     0.904     0.932     0.957   \n",
       "4      0.814       1.0       1.0  ...     0.751     0.847     0.886     0.926   \n",
       "5      0.733       1.0       1.0  ...     0.691     0.771     0.825     0.885   \n",
       "6      0.649       1.0       1.0  ...     0.642     0.688     0.752     0.835   \n",
       "7      0.555       1.0       1.0  ...     0.593     0.597     0.668     0.777   \n",
       "8      0.461       1.0       1.0  ...     0.554     0.507     0.587     0.717   \n",
       "9      0.399       1.0       1.0  ...     0.526     0.440     0.521     0.669   \n",
       "10     0.345       1.0       1.0  ...     0.509     0.381     0.464     0.632   \n",
       "11     0.297       1.0       1.0  ...     0.493     0.336     0.418     0.596   \n",
       "\n",
       "    uvel:850  uvel:500  uvel:250  vvel:850  vvel:500  vvel:250  \n",
       "0      1.000     1.000     1.000     1.000     1.000     1.000  \n",
       "1      0.936     0.961     0.964     0.927     0.956     0.958  \n",
       "2      0.850     0.906     0.912     0.829     0.894     0.894  \n",
       "3      0.761     0.836     0.848     0.726     0.811     0.815  \n",
       "4      0.663     0.749     0.770     0.626     0.713     0.720  \n",
       "5      0.565     0.659     0.690     0.522     0.606     0.613  \n",
       "6      0.473     0.562     0.603     0.423     0.497     0.507  \n",
       "7      0.386     0.465     0.507     0.332     0.391     0.403  \n",
       "8      0.323     0.392     0.429     0.267     0.300     0.304  \n",
       "9      0.286     0.340     0.364     0.234     0.243     0.237  \n",
       "10     0.247     0.290     0.325     0.204     0.202     0.195  \n",
       "11     0.233     0.261     0.287     0.191     0.178     0.159  \n",
       "\n",
       "[12 rows x 21 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68b059d8-c403-4b55-a113-692da1fc4bf4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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