notebooks/example_2_read_txt.ipynb

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    "<img src=\"../images/logo_VORTEX.png\" width=\"200\" height=\"auto\" alt=\"Company Logo\">\n",
    "\n",
    "| Project| Authors           | Company                                 | Year | Chapter |\n",
    "|--------|-------------------|-----------------------------------------|------|---------|\n",
    "| Pywind | Oriol L & Arnau T | [Vortex FdC](https://www.vortexfdc.com) | 2024 | 2       |\n",
    "\n",
    "# Chapter 2: Loading Txt\n",
    "\n",
    "_Overview:_\n",
    "\n",
    "This script reads meteorological **Text data (.txt)** and uses functions to load and show the file structure and a quick overview.\n",
    "\n",
    "- Measurements (txt) - Contains single height with limited variables.\n",
    "- Vortex (txt) - Contains single heights and variables.\n",
    "\n",
    "_Data Storage:_\n",
    "\n",
    "The acquired data is stored in two data structures for comparison and analysis:\n",
    "- Xarray Dataset\n",
    "- Pandas DataFrame\n",
    "\n",
    "_Objective:_\n",
    "\n",
    "- To understand the variance in data storage when using Xarray and Pandas.\n",
    "- Utilize the basic commands to make a quick overview of the loaded data; e.g. `describe()` and `head()`.\n",
    "- Define functions in external files."
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    "### Import Libraries"
   ]
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   "execution_count": 25,
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   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "\n",
    "sys.path.append(os.path.join(os.getcwd(), '../examples'))\n",
    "\n",
    "from typing import Dict\n",
    "from example_2_read_txt_functions import *\n",
    "#from examples import example_2_read_txt_functions import _get_coordinates_vortex_header"
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    "\n",
    "### Define Paths and Site"
   ]
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   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
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     "name": "stdout",
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     "text": [
      "\n",
      "Measurements txt:  /home/oriol/vortex/git/pywind_private/notebooks/../data/froya/measurements/obs.txt\n",
      "Vortex txt:  /home/oriol/vortex/git/pywind_private/notebooks/../data/froya/vortex/SERIE/vortex.serie.era5.utc0.100m.txt\n"
     ]
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   "source": [
    "SITE = 'froya'\n",
    "pwd = os.getcwd()\n",
    "base_path = str(os.path.join(pwd, '../data'))\n",
    "\n",
    "print()\n",
    "measurements_netcdf = os.path.join(base_path, f'{SITE}/measurements/obs.nc')\n",
    "vortex_netcdf = os.path.join(base_path, f'{SITE}/vortex/SERIE/vortex.serie.era5.utc0.nc')\n",
    "\n",
    "vortex_txt = os.path.join(base_path, f'{SITE}/vortex/SERIE/vortex.serie.era5.utc0.100m.txt')\n",
    "measurements_txt = os.path.join(base_path, f'{SITE}/measurements/obs.txt')\n",
    "\n",
    "# Print filenames\n",
    "print('Measurements txt: ', measurements_txt)\n",
    "print('Vortex txt: ', vortex_txt)"
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   "metadata": {},
   "source": [
    "### Read Vortex Text Series Functions"
   ]
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   "cell_type": "code",
   "execution_count": 27,
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      "/home/oriol/vortex/git/pywind_private/notebooks/../examples/example_2_read_txt_functions.py:149: FutureWarning: Support for nested sequences for 'parse_dates' in pd.read_csv is deprecated. Combine the desired columns with pd.to_datetime after parsing instead.\n",
      "  df: pd.DataFrame = pd.read_csv(infile, **readcsv_kwargs)\n"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 13MB\n",
       "Dimensions:  (time: 176424)\n",
       "Coordinates:\n",
       "    lat      float64 8B 63.67\n",
       "    lon      float64 8B 8.342\n",
       "    lev      float64 8B 100.0\n",
       "    utc      float64 8B 0.0\n",
       "  * time     (time) datetime64[ns] 1MB 2004-01-01 ... 2024-02-15T23:00:00\n",
       "Data variables:\n",
       "    M        (time) float64 1MB 4.6 4.3 4.9 4.7 4.7 5.2 ... 7.9 7.5 6.8 6.0 4.5\n",
       "    Dir      (time) int64 1MB 154 162 166 156 145 141 ... 93 100 104 108 113 122\n",
       "    T        (time) float64 1MB -1.8 -1.7 -1.7 -1.8 -1.9 ... 1.6 1.0 0.5 0.3 0.3\n",
       "    D        (time) float64 1MB 1.29 1.29 1.29 1.29 1.29 ... 1.27 1.27 1.27 1.27\n",
       "    P        (time) float64 1MB 1.006e+03 1.005e+03 1.005e+03 ... 994.4 993.9\n",
       "    RI       (time) float64 1MB -0.75 -1.48 -3.55 -2.01 ... -1.89 -1.7 0.18\n",
       "    RH       (time) float64 1MB 67.0 64.9 64.6 63.6 62.2 ... 77.6 78.5 79.7 78.9\n",
       "    RMOL     (time) float64 1MB -0.1985 -0.2098 -0.1699 ... -0.0406 -0.0795</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-ddd0eac5-359f-4822-94a5-03e792bc5b3e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ddd0eac5-359f-4822-94a5-03e792bc5b3e' 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>: 176424</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7b64b893-c5ac-4efd-91ef-ab759763802c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7b64b893-c5ac-4efd-91ef-ab759763802c' class='xr-section-summary' >Coordinates: <span>(5)</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>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>63.67</div><input id='attrs-2e409cd2-1f4c-4d4a-a5e6-e60f4d2ea63c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e409cd2-1f4c-4d4a-a5e6-e60f4d2ea63c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d80b28f-de91-4a09-a3aa-e46e3fedeb71' class='xr-var-data-in' type='checkbox'><label for='data-2d80b28f-de91-4a09-a3aa-e46e3fedeb71' 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'><dt><span>units :</span></dt><dd>degrees</dd><dt><span>long_name :</span></dt><dd>Latitude</dd></dl></div><div class='xr-var-data'><pre>array(63.66639)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.342</div><input id='attrs-e10c45d0-e33d-40f6-82a5-c7978d428b7b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e10c45d0-e33d-40f6-82a5-c7978d428b7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f479c0a-def8-47ce-a59e-24538ddc1982' class='xr-var-data-in' type='checkbox'><label for='data-6f479c0a-def8-47ce-a59e-24538ddc1982' 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'><dt><span>units :</span></dt><dd>degrees</dd><dt><span>long_name :</span></dt><dd>Longitude</dd></dl></div><div class='xr-var-data'><pre>array(8.342499)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lev</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>100.0</div><input id='attrs-ce1cd3d7-55ae-459f-9e4b-a0defea4a1e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce1cd3d7-55ae-459f-9e4b-a0defea4a1e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-175cafb8-2e8b-4911-b494-a0ca4a869515' class='xr-var-data-in' type='checkbox'><label for='data-175cafb8-2e8b-4911-b494-a0ca4a869515' 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'><dt><span>units :</span></dt><dd>metres</dd><dt><span>long_name :</span></dt><dd>Level</dd></dl></div><div class='xr-var-data'><pre>array(100.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>utc</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-5592bc25-f280-49b1-a619-2177fcb4722a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5592bc25-f280-49b1-a619-2177fcb4722a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-092c1ed0-fdb2-4a05-99a2-13d1cdf0b869' class='xr-var-data-in' type='checkbox'><label for='data-092c1ed0-fdb2-4a05-99a2-13d1cdf0b869' 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.)</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'>2004-01-01 ... 2024-02-15T23:00:00</div><input id='attrs-834c2a58-0fb0-485a-9d88-dfe44018625e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-834c2a58-0fb0-485a-9d88-dfe44018625e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76cac3b5-4cdd-411f-830a-d938acabaafa' class='xr-var-data-in' type='checkbox'><label for='data-76cac3b5-4cdd-411f-830a-d938acabaafa' 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;2004-01-01T00:00:00.000000000&#x27;, &#x27;2004-01-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;2004-01-01T02:00:00.000000000&#x27;, ..., &#x27;2024-02-15T21:00:00.000000000&#x27;,\n",
       "       &#x27;2024-02-15T22:00:00.000000000&#x27;, &#x27;2024-02-15T23:00:00.000000000&#x27;],\n",
       "      shape=(176424,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-372b5e05-9bed-457b-950a-60a0b3068ff8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-372b5e05-9bed-457b-950a-60a0b3068ff8' class='xr-section-summary' >Data variables: <span>(8)</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>M</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.6 4.3 4.9 4.7 ... 7.5 6.8 6.0 4.5</div><input id='attrs-bb21dad4-a302-470b-801a-3fdf3221bbb1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb21dad4-a302-470b-801a-3fdf3221bbb1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-027b34a1-2758-498b-ac8b-0499e88ec7d0' class='xr-var-data-in' type='checkbox'><label for='data-027b34a1-2758-498b-ac8b-0499e88ec7d0' 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'><dt><span>description :</span></dt><dd>Wind Speed (module velocity)</dd><dt><span>long_name :</span></dt><dd>Wind speed</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><pre>array([4.6, 4.3, 4.9, ..., 6.8, 6. , 4.5], shape=(176424,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Dir</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>154 162 166 156 ... 104 108 113 122</div><input id='attrs-8134142e-f739-47ef-8099-18ba9ef9fee8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8134142e-f739-47ef-8099-18ba9ef9fee8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2423c31c-1265-4f47-a2b3-ed60afb4db38' class='xr-var-data-in' type='checkbox'><label for='data-2423c31c-1265-4f47-a2b3-ed60afb4db38' 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'><dt><span>description :</span></dt><dd>Wind Direction</dd><dt><span>long_name :</span></dt><dd>Wind direction</dd><dt><span>units :</span></dt><dd>degrees</dd></dl></div><div class='xr-var-data'><pre>array([154, 162, 166, ..., 108, 113, 122], shape=(176424,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>T</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.8 -1.7 -1.7 -1.8 ... 0.5 0.3 0.3</div><input id='attrs-dd13efb3-3ebe-46c9-84b1-22a823c79b98' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd13efb3-3ebe-46c9-84b1-22a823c79b98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89cb3999-7d08-4219-bb42-9ff4da01985f' class='xr-var-data-in' type='checkbox'><label for='data-89cb3999-7d08-4219-bb42-9ff4da01985f' 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'><dt><span>description :</span></dt><dd>Air Temperature</dd><dt><span>long_name :</span></dt><dd>Air Temperature</dd><dt><span>units :</span></dt><dd>Deg.Celsius</dd></dl></div><div class='xr-var-data'><pre>array([-1.8, -1.7, -1.7, ...,  0.5,  0.3,  0.3], shape=(176424,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>D</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.29 1.29 1.29 ... 1.27 1.27 1.27</div><input id='attrs-490aa02d-ac0a-4794-a46a-2ba7cd934f8e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-490aa02d-ac0a-4794-a46a-2ba7cd934f8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba6dfa53-0761-4a3d-b786-664d4f1a2b6d' class='xr-var-data-in' type='checkbox'><label for='data-ba6dfa53-0761-4a3d-b786-664d4f1a2b6d' 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'><dt><span>long_name :</span></dt><dd>Density</dd><dt><span>description :</span></dt><dd>Air Density</dd><dt><span>units :</span></dt><dd>kg/m^(-3)</dd></dl></div><div class='xr-var-data'><pre>array([1.29, 1.29, 1.29, ..., 1.27, 1.27, 1.27], shape=(176424,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>P</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.006e+03 1.005e+03 ... 994.4 993.9</div><input id='attrs-4bb153d9-c9fc-44f7-a985-6457dfa0ca97' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4bb153d9-c9fc-44f7-a985-6457dfa0ca97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2335c63-d0ce-49ce-a6f7-a21b1f714ee0' class='xr-var-data-in' type='checkbox'><label for='data-a2335c63-d0ce-49ce-a6f7-a21b1f714ee0' 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'><dt><span>description :</span></dt><dd>Pressure</dd><dt><span>long_name :</span></dt><dd>Pressure</dd><dt><span>units :</span></dt><dd>hPa</dd></dl></div><div class='xr-var-data'><pre>array([1005.5, 1005.2, 1005.2, ...,  995.5,  994.4,  993.9],\n",
       "      shape=(176424,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RI</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.75 -1.48 -3.55 ... -1.7 0.18</div><input id='attrs-2b6b925e-2ad1-4287-a3d8-d55ae1bca0e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b6b925e-2ad1-4287-a3d8-d55ae1bca0e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8625175a-f11c-40ed-9bd7-edef4c1e6f19' class='xr-var-data-in' type='checkbox'><label for='data-8625175a-f11c-40ed-9bd7-edef4c1e6f19' 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'><dt><span>long_name :</span></dt><dd>Richardson Number</dd><dt><span>description :</span></dt><dd>Richardson Number</dd><dt><span>units :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>array([-0.75, -1.48, -3.55, ..., -1.89, -1.7 ,  0.18], shape=(176424,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RH</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>67.0 64.9 64.6 ... 78.5 79.7 78.9</div><input id='attrs-10c326b9-9a6a-4619-be41-7a82dccb1204' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10c326b9-9a6a-4619-be41-7a82dccb1204' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d014d628-c61b-4daa-955a-914d8049feb7' class='xr-var-data-in' type='checkbox'><label for='data-d014d628-c61b-4daa-955a-914d8049feb7' 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'><dt><span>long_name :</span></dt><dd>Relative Humidity</dd><dt><span>description :</span></dt><dd>Relative Humidity</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><pre>array([67. , 64.9, 64.6, ..., 78.5, 79.7, 78.9], shape=(176424,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RMOL</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.1985 -0.2098 ... -0.0406 -0.0795</div><input id='attrs-84b2329b-73a4-449e-9188-c89248d2f7a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84b2329b-73a4-449e-9188-c89248d2f7a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b63b3d00-4d61-42e1-bd4a-f53b08ef720a' class='xr-var-data-in' type='checkbox'><label for='data-b63b3d00-4d61-42e1-bd4a-f53b08ef720a' 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'><dt><span>description :</span></dt><dd>Inverse Monin Obukhov Length</dd><dt><span>long_name :</span></dt><dd>Inverse Monin Obukhov Length</dd><dt><span>units :</span></dt><dd>m^-1</dd></dl></div><div class='xr-var-data'><pre>array([-0.1985, -0.2098, -0.1699, ..., -0.0304, -0.0406, -0.0795],\n",
       "      shape=(176424,))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a0e11424-782a-45a4-bb4a-228172f9befb' class='xr-section-summary-in' type='checkbox'  ><label for='section-a0e11424-782a-45a4-bb4a-228172f9befb' class='xr-section-summary' >Indexes: <span>(1)</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>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-6191d5bf-bdf0-4640-8fa9-e29affbd2d35' class='xr-index-data-in' type='checkbox'/><label for='index-6191d5bf-bdf0-4640-8fa9-e29affbd2d35' 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;2004-01-01 00:00:00&#x27;, &#x27;2004-01-01 01:00:00&#x27;,\n",
       "               &#x27;2004-01-01 02:00:00&#x27;, &#x27;2004-01-01 03:00:00&#x27;,\n",
       "               &#x27;2004-01-01 04:00:00&#x27;, &#x27;2004-01-01 05:00:00&#x27;,\n",
       "               &#x27;2004-01-01 06:00:00&#x27;, &#x27;2004-01-01 07:00:00&#x27;,\n",
       "               &#x27;2004-01-01 08:00:00&#x27;, &#x27;2004-01-01 09:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2024-02-15 14:00:00&#x27;, &#x27;2024-02-15 15:00:00&#x27;,\n",
       "               &#x27;2024-02-15 16:00:00&#x27;, &#x27;2024-02-15 17:00:00&#x27;,\n",
       "               &#x27;2024-02-15 18:00:00&#x27;, &#x27;2024-02-15 19:00:00&#x27;,\n",
       "               &#x27;2024-02-15 20:00:00&#x27;, &#x27;2024-02-15 21:00:00&#x27;,\n",
       "               &#x27;2024-02-15 22:00:00&#x27;, &#x27;2024-02-15 23:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=176424, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2fc6a98a-0e60-48ff-8d27-4ec8293a90cd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2fc6a98a-0e60-48ff-8d27-4ec8293a90cd' 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: 13MB\n",
       "Dimensions:  (time: 176424)\n",
       "Coordinates:\n",
       "    lat      float64 8B 63.67\n",
       "    lon      float64 8B 8.342\n",
       "    lev      float64 8B 100.0\n",
       "    utc      float64 8B 0.0\n",
       "  * time     (time) datetime64[ns] 1MB 2004-01-01 ... 2024-02-15T23:00:00\n",
       "Data variables:\n",
       "    M        (time) float64 1MB 4.6 4.3 4.9 4.7 4.7 5.2 ... 7.9 7.5 6.8 6.0 4.5\n",
       "    Dir      (time) int64 1MB 154 162 166 156 145 141 ... 93 100 104 108 113 122\n",
       "    T        (time) float64 1MB -1.8 -1.7 -1.7 -1.8 -1.9 ... 1.6 1.0 0.5 0.3 0.3\n",
       "    D        (time) float64 1MB 1.29 1.29 1.29 1.29 1.29 ... 1.27 1.27 1.27 1.27\n",
       "    P        (time) float64 1MB 1.006e+03 1.005e+03 1.005e+03 ... 994.4 993.9\n",
       "    RI       (time) float64 1MB -0.75 -1.48 -3.55 -2.01 ... -1.89 -1.7 0.18\n",
       "    RH       (time) float64 1MB 67.0 64.9 64.6 63.6 62.2 ... 77.6 78.5 79.7 78.9\n",
       "    RMOL     (time) float64 1MB -0.1985 -0.2098 -0.1699 ... -0.0406 -0.0795"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_vortex = read_vortex_serie(vortex_txt)\n",
    "ds_vortex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we convert *df_vortex* to Pandas DataFrame and we use `head()` Pandas function to display the first 5 rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>lev</th>\n",
       "      <th>utc</th>\n",
       "      <th>M</th>\n",
       "      <th>Dir</th>\n",
       "      <th>T</th>\n",
       "      <th>D</th>\n",
       "      <th>P</th>\n",
       "      <th>RI</th>\n",
       "      <th>RH</th>\n",
       "      <th>RMOL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-01-01 00:00:00</th>\n",
       "      <td>63.66639</td>\n",
       "      <td>8.342499</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>154</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>1.29</td>\n",
       "      <td>1005.5</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>67.0</td>\n",
       "      <td>-0.1985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-01-01 01:00:00</th>\n",
       "      <td>63.66639</td>\n",
       "      <td>8.342499</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>162</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>1.29</td>\n",
       "      <td>1005.2</td>\n",
       "      <td>-1.48</td>\n",
       "      <td>64.9</td>\n",
       "      <td>-0.2098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-01-01 02:00:00</th>\n",
       "      <td>63.66639</td>\n",
       "      <td>8.342499</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>166</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>1.29</td>\n",
       "      <td>1005.2</td>\n",
       "      <td>-3.55</td>\n",
       "      <td>64.6</td>\n",
       "      <td>-0.1699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-01-01 03:00:00</th>\n",
       "      <td>63.66639</td>\n",
       "      <td>8.342499</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>156</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>1.29</td>\n",
       "      <td>1004.7</td>\n",
       "      <td>-2.01</td>\n",
       "      <td>63.6</td>\n",
       "      <td>-0.1740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-01-01 04:00:00</th>\n",
       "      <td>63.66639</td>\n",
       "      <td>8.342499</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>145</td>\n",
       "      <td>-1.9</td>\n",
       "      <td>1.29</td>\n",
       "      <td>1004.4</td>\n",
       "      <td>-1.03</td>\n",
       "      <td>62.2</td>\n",
       "      <td>-0.1747</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          lat       lon    lev  utc    M  Dir    T     D  \\\n",
       "time                                                                       \n",
       "2004-01-01 00:00:00  63.66639  8.342499  100.0  0.0  4.6  154 -1.8  1.29   \n",
       "2004-01-01 01:00:00  63.66639  8.342499  100.0  0.0  4.3  162 -1.7  1.29   \n",
       "2004-01-01 02:00:00  63.66639  8.342499  100.0  0.0  4.9  166 -1.7  1.29   \n",
       "2004-01-01 03:00:00  63.66639  8.342499  100.0  0.0  4.7  156 -1.8  1.29   \n",
       "2004-01-01 04:00:00  63.66639  8.342499  100.0  0.0  4.7  145 -1.9  1.29   \n",
       "\n",
       "                          P    RI    RH    RMOL  \n",
       "time                                             \n",
       "2004-01-01 00:00:00  1005.5 -0.75  67.0 -0.1985  \n",
       "2004-01-01 01:00:00  1005.2 -1.48  64.9 -0.2098  \n",
       "2004-01-01 02:00:00  1005.2 -3.55  64.6 -0.1699  \n",
       "2004-01-01 03:00:00  1004.7 -2.01  63.6 -0.1740  \n",
       "2004-01-01 04:00:00  1004.4 -1.03  62.2 -0.1747  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vortex = ds_vortex.to_dataframe() \n",
    "df_vortex.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And if we only want to print 'M' and 'Dir' columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>M</th>\n",
       "      <th>Dir</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-01-01 00:00:00</th>\n",
       "      <td>4.6</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-01-01 01:00:00</th>\n",
       "      <td>4.3</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-01-01 02:00:00</th>\n",
       "      <td>4.9</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-01-01 03:00:00</th>\n",
       "      <td>4.7</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-01-01 04:00:00</th>\n",
       "      <td>4.7</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       M  Dir\n",
       "time                         \n",
       "2004-01-01 00:00:00  4.6  154\n",
       "2004-01-01 01:00:00  4.3  162\n",
       "2004-01-01 02:00:00  4.9  166\n",
       "2004-01-01 03:00:00  4.7  156\n",
       "2004-01-01 04:00:00  4.7  145"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vortex[['M', 'Dir']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read Measurements Txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/oriol/vortex/git/pywind_private/notebooks/../examples/example_2_read_txt_functions.py:149: FutureWarning: Support for nested sequences for 'parse_dates' in pd.read_csv is deprecated. Combine the desired columns with pd.to_datetime after parsing instead.\n",
      "  df: pd.DataFrame = pd.read_csv(infile, **readcsv_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "    }\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>M</th>\n",
       "      <th>Dir</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-11-18 13:50:00</th>\n",
       "      <td>3.62</td>\n",
       "      <td>159.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-11-18 14:00:00</th>\n",
       "      <td>3.46</td>\n",
       "      <td>153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-11-18 14:10:00</th>\n",
       "      <td>2.99</td>\n",
       "      <td>153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-11-18 14:20:00</th>\n",
       "      <td>2.41</td>\n",
       "      <td>151.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-11-18 14:30:00</th>\n",
       "      <td>1.91</td>\n",
       "      <td>150.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        M    Dir\n",
       "time                            \n",
       "2009-11-18 13:50:00  3.62  159.0\n",
       "2009-11-18 14:00:00  3.46  153.0\n",
       "2009-11-18 14:10:00  2.99  153.0\n",
       "2009-11-18 14:20:00  2.41  151.5\n",
       "2009-11-18 14:30:00  1.91  150.5"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_obs = read_vortex_obs_to_dataframe(measurements_txt)\n",
    "ds_obs = convert_to_xarray(df_obs)\n",
    "df_obs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now we can compare statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>M</th>\n",
       "      <th>Dir</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>176424.00</td>\n",
       "      <td>176424.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.43</td>\n",
       "      <td>183.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.77</td>\n",
       "      <td>89.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.90</td>\n",
       "      <td>111.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.60</td>\n",
       "      <td>194.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11.00</td>\n",
       "      <td>248.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>34.70</td>\n",
       "      <td>360.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               M        Dir\n",
       "count  176424.00  176424.00\n",
       "mean        8.43     183.10\n",
       "std         4.77      89.74\n",
       "min         0.10       0.00\n",
       "25%         4.90     111.00\n",
       "50%         7.60     194.00\n",
       "75%        11.00     248.00\n",
       "max        34.70     360.00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>M</th>\n",
       "      <th>Dir</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>171895.00</td>\n",
       "      <td>171895.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.06</td>\n",
       "      <td>178.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.78</td>\n",
       "      <td>95.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.09</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.57</td>\n",
       "      <td>93.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.04</td>\n",
       "      <td>197.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.60</td>\n",
       "      <td>251.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>36.10</td>\n",
       "      <td>360.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               M        Dir\n",
       "count  171895.00  171895.00\n",
       "mean        8.06     178.48\n",
       "std         4.78      95.98\n",
       "min         0.09       0.00\n",
       "25%         4.57      93.00\n",
       "50%         7.04     197.00\n",
       "75%        10.60     251.00\n",
       "max        36.10     360.00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display\n",
    "\n",
    "display(df_vortex[['M', 'Dir']].describe().round(2))\n",
    "display(df_obs.describe().round(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thank you for completing this Notebook! \n",
    "### *Other references available upon request.*\n",
    "\n",
    "You now can:\n",
    "\n",
    "- Read Vortex SERIES txt files.\n",
    "- Convert from txt to NetCD.\n",
    "- Convert to **Pandas** DataFrames.\n",
    "- Have a quick overview of the data using `head()` and `describe()` Pandas functions.\n",
    "\n",
    "**Don't hesitate to [contact us](https://vortexfdc.com/contact/) for any questions and information.**\n",
    "\n",
    "## Change Log\n",
    "\n",
    "\n",
    "| Date (YYYY-MM-DD) | Version | Changed By | Change Description                         |\n",
    "|-------------------|---------|------------|--------------------------------------------|\n",
    "| 2024-06-25        | 0.0     | Arnau      | Notebook creation                          |\n",
    "\n",
    "<hr>\n",
    "\n",
    "## <h3 align=\"center\"> © Vortex F.d.C. 2024. All rights reserved. <h3/>"
   ]
  }
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