Untitled.ipynb

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   "source": [
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
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   "execution_count": 2,
   "id": "bceb90c3",
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     "text": [
      "Collecting xarray\n",
      "  Using cached xarray-2022.3.0-py3-none-any.whl (870 kB)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\gustavi\\anaconda3\\lib\\site-packages (from xarray) (21.0)\n",
      "Requirement already satisfied: pandas>=1.1 in c:\\users\\gustavi\\anaconda3\\lib\\site-packages (from xarray) (1.3.4)\n",
      "Requirement already satisfied: numpy>=1.18 in c:\\users\\gustavi\\anaconda3\\lib\\site-packages (from xarray) (1.20.3)\n",
      "Requirement already satisfied: pyparsing>=2.0.2 in c:\\users\\gustavi\\anaconda3\\lib\\site-packages (from packaging>=20.0->xarray) (3.0.4)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\gustavi\\anaconda3\\lib\\site-packages (from pandas>=1.1->xarray) (2021.3)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\gustavi\\anaconda3\\lib\\site-packages (from pandas>=1.1->xarray) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\gustavi\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7.3->pandas>=1.1->xarray) (1.16.0)\n",
      "Installing collected packages: xarray\n",
      "Successfully installed xarray-2022.3.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install xarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "423b7c50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting netCDF4\n",
      "  Using cached netCDF4-1.5.8-cp39-cp39-win_amd64.whl (3.0 MB)\n",
      "Requirement already satisfied: numpy>=1.9 in c:\\users\\gustavi\\anaconda3\\lib\\site-packages (from netCDF4) (1.20.3)\n",
      "Collecting cftime\n",
      "  Using cached cftime-1.6.0-cp39-none-win_amd64.whl (161 kB)\n",
      "Installing collected packages: cftime, netCDF4\n",
      "Successfully installed cftime-1.6.0 netCDF4-1.5.8\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install netCDF4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b02eb024",
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "Collecting package metadata (current_repodata.json): ...working... done\n",
      "Solving environment: ...working... done\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: C:\\Users\\Gustavi\\anaconda3\n",
      "\n",
      "  added / updated specs:\n",
      "    - netcdf4\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    cftime-1.6.0               |   py39h5d4886f_1         161 KB  conda-forge\n",
      "    conda-4.12.0               |   py39hcbf5309_0         1.0 MB  conda-forge\n",
      "    hdf4-4.2.13                |       h712560f_2         1.3 MB\n",
      "    libnetcdf-4.6.1            |       hf59b723_4         501 KB\n",
      "    netcdf4-1.5.7              |   py39hb76ebac_0         329 KB\n",
      "    python_abi-3.9             |           2_cp39           4 KB  conda-forge\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:         3.3 MB\n",
      "\n",
      "The following NEW packages will be INSTALLED:\n",
      "\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "==> WARNING: A newer version of conda exists. <==\n",
      "  current version: 4.10.3\n",
      "  latest version: 4.12.0\n",
      "\n",
      "Please update conda by running\n",
      "\n",
      "    $ conda update -n base -c defaults conda\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  cftime             conda-forge/win-64::cftime-1.6.0-py39h5d4886f_1\n",
      "  hdf4               pkgs/main/win-64::hdf4-4.2.13-h712560f_2\n",
      "  libnetcdf          pkgs/main/win-64::libnetcdf-4.6.1-hf59b723_4\n",
      "  netcdf4            pkgs/main/win-64::netcdf4-1.5.7-py39hb76ebac_0\n",
      "  python_abi         conda-forge/win-64::python_abi-3.9-2_cp39\n",
      "\n",
      "The following packages will be UPDATED:\n",
      "\n",
      "  conda              pkgs/main::conda-4.10.3-py39haa95532_0 --> conda-forge::conda-4.12.0-py39hcbf5309_0\n",
      "\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "\n",
      "cftime-1.6.0         | 161 KB    |            |   0% \n",
      "cftime-1.6.0         | 161 KB    | 9          |  10% \n",
      "cftime-1.6.0         | 161 KB    | ########## | 100% \n",
      "cftime-1.6.0         | 161 KB    | ########## | 100% \n",
      "\n",
      "hdf4-4.2.13          | 1.3 MB    |            |   0% \n",
      "hdf4-4.2.13          | 1.3 MB    | 6          |   7% \n",
      "hdf4-4.2.13          | 1.3 MB    | ######6    |  66% \n",
      "hdf4-4.2.13          | 1.3 MB    | ########## | 100% \n",
      "\n",
      "libnetcdf-4.6.1      | 501 KB    |            |   0% \n",
      "libnetcdf-4.6.1      | 501 KB    | ###1       |  32% \n",
      "libnetcdf-4.6.1      | 501 KB    | ########## | 100% \n",
      "libnetcdf-4.6.1      | 501 KB    | ########## | 100% \n",
      "\n",
      "python_abi-3.9       | 4 KB      |            |   0% \n",
      "python_abi-3.9       | 4 KB      | ########## | 100% \n",
      "python_abi-3.9       | 4 KB      | ########## | 100% \n",
      "\n",
      "netcdf4-1.5.7        | 329 KB    |            |   0% \n",
      "netcdf4-1.5.7        | 329 KB    | 4          |   5% \n",
      "netcdf4-1.5.7        | 329 KB    | ####3      |  44% \n",
      "netcdf4-1.5.7        | 329 KB    | ########## | 100% \n",
      "netcdf4-1.5.7        | 329 KB    | ########## | 100% \n",
      "\n",
      "conda-4.12.0         | 1.0 MB    |            |   0% \n",
      "conda-4.12.0         | 1.0 MB    | 9          |   9% \n",
      "conda-4.12.0         | 1.0 MB    | ########## | 100% \n",
      "conda-4.12.0         | 1.0 MB    | ########## | 100% \n",
      "Preparing transaction: ...working... done\n",
      "Verifying transaction: ...working... done\n",
      "Executing transaction: ...working... done\n"
     ]
    }
   ],
   "source": [
    "conda install -c conda-forge netCDF4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d3002f32",
   "metadata": {},
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       "  * x                               (x) int32 549850 550350 ... 788850 789350\n",
       "  * y                               (y) int32 5271250 5270750 ... 5031750\n",
       "  * time                            (time) datetime64[ns] 2010-11-01 ... 2010...\n",
       "Data variables:\n",
       "    transverse_mercator             |S1 ...\n",
       "    equivalent_reflectivity_factor  (time, y, x) float32 dask.array&lt;chunksize=(286, 480, 480), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    Conventions:         CF-1.7\n",
       "    GDAL_AREA_OR_POINT:  Area\n",
       "    title:               MAX(Z) Reflectivity day 2010-11-01\n",
       "    institution:         Meteotrentino, Fondazione Bruno Kessler\n",
       "    source:              radar observation mt.Macaion\n",
       "    site_lat_lon_elev:   46.494002, 11.208826, 1876\n",
       "    references:          original dataset https://doi.org/10.5281/zenodo.3577451\n",
       "    comments:            NetCDF generation code https://github.com/MPBA/TAASR...\n",
       "    chunks:              164 122\n",
       "    tags:                rain snow</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-455e2c49-fed5-4573-bb32-1c4d1c20ee99' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-455e2c49-fed5-4573-bb32-1c4d1c20ee99' 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'>x</span>: 480</li><li><span class='xr-has-index'>y</span>: 480</li><li><span class='xr-has-index'>time</span>: 286</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-bf1cd9a8-263f-4ca6-a736-f9b35bd3c75e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bf1cd9a8-263f-4ca6-a736-f9b35bd3c75e' 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'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>549850 550350 ... 788850 789350</div><input id='attrs-10256b0c-fe16-43c3-b9d0-ee185e476539' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10256b0c-fe16-43c3-b9d0-ee185e476539' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8e4cf59-7d00-4f8a-8dd2-59784aed604f' class='xr-var-data-in' type='checkbox'><label for='data-b8e4cf59-7d00-4f8a-8dd2-59784aed604f' 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>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([549850, 550350, 550850, ..., 788350, 788850, 789350])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>5271250 5270750 ... 5032250 5031750</div><input id='attrs-787e40f2-dbc7-4f5f-bc5e-8c35b3d2f8a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-787e40f2-dbc7-4f5f-bc5e-8c35b3d2f8a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18c07109-dedb-4568-a6f4-2e82d0814da8' class='xr-var-data-in' type='checkbox'><label for='data-18c07109-dedb-4568-a6f4-2e82d0814da8' 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>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([5271250, 5270750, 5270250, ..., 5032750, 5032250, 5031750])</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'>2010-11-01 ... 2010-11-01T23:55:00</div><input id='attrs-257dc4bc-7eb5-4c67-8fab-ef6974d7e451' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-257dc4bc-7eb5-4c67-8fab-ef6974d7e451' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69468572-ac14-448b-bd04-5a1ab3e550ff' class='xr-var-data-in' type='checkbox'><label for='data-69468572-ac14-448b-bd04-5a1ab3e550ff' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-11-01T00:00:00.000000000&#x27;, &#x27;2010-11-01T00:05:00.000000000&#x27;,\n",
       "       &#x27;2010-11-01T00:10:00.000000000&#x27;, ..., &#x27;2010-11-01T23:45:00.000000000&#x27;,\n",
       "       &#x27;2010-11-01T23:50:00.000000000&#x27;, &#x27;2010-11-01T23:55:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-763be531-c230-47f4-a04e-e0a6feb55ae1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-763be531-c230-47f4-a04e-e0a6feb55ae1' class='xr-section-summary' >Data variables: <span>(2)</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>transverse_mercator</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>|S1</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-06c55588-e17a-4c7d-8eab-b9c79d3d12b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06c55588-e17a-4c7d-8eab-b9c79d3d12b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02720516-c7a5-488b-be3c-ceaa81b631ce' class='xr-var-data-in' type='checkbox'><label for='data-02720516-c7a5-488b-be3c-ceaa81b631ce' 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>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>9.0</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>long_name :</span></dt><dd>CRS definition</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 32N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,9],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32632&quot;]] </dd><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 32N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,9],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32632&quot;]] </dd></dl></div><div class='xr-var-data'><pre>array(b&#x27;&#x27;, dtype=&#x27;|S1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>equivalent_reflectivity_factor</span></div><div 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52.5]</dd><dt><span>grid_mapping :</span></dt><dd>transverse_mercator</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> (286, 480, 480) </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-49f17dde-12f1-4936-81a6-a87a9d711380' class='xr-section-summary-in' type='checkbox'  ><label for='section-49f17dde-12f1-4936-81a6-a87a9d711380' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>GDAL_AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>title :</span></dt><dd>MAX(Z) Reflectivity day 2010-11-01</dd><dt><span>institution :</span></dt><dd>Meteotrentino, Fondazione Bruno Kessler</dd><dt><span>source :</span></dt><dd>radar observation mt.Macaion</dd><dt><span>site_lat_lon_elev :</span></dt><dd>46.494002, 11.208826, 1876</dd><dt><span>references :</span></dt><dd>original dataset https://doi.org/10.5281/zenodo.3577451</dd><dt><span>comments :</span></dt><dd>NetCDF generation code https://github.com/MPBA/TAASRAD19</dd><dt><span>chunks :</span></dt><dd>164 122</dd><dt><span>tags :</span></dt><dd>rain snow</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                         (x: 480, y: 480, time: 286)\n",
       "Coordinates:\n",
       "  * x                               (x) int32 549850 550350 ... 788850 789350\n",
       "  * y                               (y) int32 5271250 5270750 ... 5031750\n",
       "  * time                            (time) datetime64[ns] 2010-11-01 ... 2010...\n",
       "Data variables:\n",
       "    transverse_mercator             |S1 ...\n",
       "    equivalent_reflectivity_factor  (time, y, x) float32 dask.array<chunksize=(286, 480, 480), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:         CF-1.7\n",
       "    GDAL_AREA_OR_POINT:  Area\n",
       "    title:               MAX(Z) Reflectivity day 2010-11-01\n",
       "    institution:         Meteotrentino, Fondazione Bruno Kessler\n",
       "    source:              radar observation mt.Macaion\n",
       "    site_lat_lon_elev:   46.494002, 11.208826, 1876\n",
       "    references:          original dataset https://doi.org/10.5281/zenodo.3577451\n",
       "    comments:            NetCDF generation code https://github.com/MPBA/TAASR...\n",
       "    chunks:              164 122\n",
       "    tags:                rain snow"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FILE = './training/20101101.nc'\n",
    "ds = xr.open_mfdataset(FILE)\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b0d739c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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