IO_era5-wave-dir.ipynb

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    "%matplotlib inline\n",
    "import boto3\n",
    "import botocore\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import os.path\n",
    "import os\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
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   "execution_count": 2,
   "id": "ae352a05-f601-412b-977b-b75bef90248f",
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     "text": [
      "There are 19 objects available for January, 2021\n",
      "--\n",
      "2021/01/data/air_pressure_at_mean_sea_level.nc\n",
      "2021/01/data/air_temperature_at_2_metres.nc\n",
      "2021/01/data/air_temperature_at_2_metres_1hour_Maximum.nc\n",
      "2021/01/data/air_temperature_at_2_metres_1hour_Minimum.nc\n",
      "2021/01/data/dew_point_temperature_at_2_metres.nc\n",
      "2021/01/data/eastward_wind_at_100_metres.nc\n",
      "2021/01/data/eastward_wind_at_10_metres.nc\n",
      "2021/01/data/integral_wrt_time_of_surface_direct_downwelling_shortwave_flux_in_air_1hour_Accumulation.nc\n",
      "2021/01/data/lwe_thickness_of_surface_snow_amount.nc\n",
      "2021/01/data/northward_wind_at_100_metres.nc\n",
      "2021/01/data/northward_wind_at_10_metres.nc\n",
      "2021/01/data/precipitation_amount_1hour_Accumulation.nc\n",
      "2021/01/data/sea_surface_temperature.nc\n",
      "2021/01/data/sea_surface_wave_from_direction.nc\n",
      "2021/01/data/sea_surface_wave_mean_period.nc\n",
      "2021/01/data/significant_height_of_wind_and_swell_waves.nc\n",
      "2021/01/data/snow_density.nc\n",
      "2021/01/data/surface_air_pressure.nc\n",
      "2021/01/main.nc\n"
     ]
    }
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   "source": [
    "era5_bucket = 'era5-pds'\n",
    "\n",
    "# AWS access / secret keys required\n",
    "# s3 = boto3.resource('s3')\n",
    "# bucket = s3.Bucket(era5_bucket)\n",
    "\n",
    "# No AWS keys required\n",
    "client = boto3.client('s3', config=botocore.client.Config(signature_version=botocore.UNSIGNED))\n",
    "\n",
    "keys = []\n",
    "date = datetime.date(2021,1,1) # update to desired date\n",
    "prefix = date.strftime('%Y/%m/')\n",
    "\n",
    "response = client.list_objects_v2(Bucket=era5_bucket, Prefix=prefix)\n",
    "response_meta = response.get('ResponseMetadata')\n",
    "\n",
    "data = xr.Dataset()\n",
    "if response_meta.get('HTTPStatusCode') == 200:\n",
    "    contents = response.get('Contents')\n",
    "    if contents == None:\n",
    "        print(\"No objects are available for %s\" % date.strftime('%B, %Y'))\n",
    "    else:\n",
    "        for obj in contents:\n",
    "            keys.append(obj.get('Key'))\n",
    "        print(\"There are %s objects available for %s\\n--\" % (len(keys), date.strftime('%B, %Y')))\n",
    "        for k in keys:\n",
    "            print(k)\n",
    "else:\n",
    "    print(\"There was an error with your request.\")"
   ]
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   "source": [
    "var = 'sea_surface_wave_from_direction'"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "id": "06f9fa09-6181-4351-baa3-80a0d506383f",
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   "source": [
    "s3_data_ptrn = '{year}/{month}/data/{var}.nc'\n",
    "data_file_ptrn = 'era5/{year}{month}_{var}.nc'\n",
    "\n"
   ]
  },
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     "text": [
      "Downloading 2017/08/data/sea_surface_wave_from_direction.nc from S3...\n"
     ]
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       "<bound method Dataset.info of <xarray.Dataset>\n",
       "Dimensions:                          (lon_ocean: 720, lat_ocean: 361, time0: 744)\n",
       "Coordinates:\n",
       "  * lon_ocean                        (lon_ocean) float32 0.0 0.5 ... 359.0 359.5\n",
       "  * lat_ocean                        (lat_ocean) float32 90.0 89.5 ... -90.0\n",
       "  * time0                            (time0) datetime64[ns] 2017-08-01 ... 20...\n",
       "Data variables:\n",
       "    sea_surface_wave_from_direction  (time0, lat_ocean, lon_ocean) float32 ...\n",
       "Attributes:\n",
       "    source:       Reanalysis\n",
       "    institution:  ECMWF\n",
       "    title:        ERA5 forecasts>"
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    "date= datetime.date(2017, 8, 1)\n",
    "\n",
    "year = date.strftime('%Y')\n",
    "month = date.strftime('%m')\n",
    "s3_data_key = s3_data_ptrn.format(year=year, month=month, var=var)\n",
    "data_file = data_file_ptrn.format(year=year, month=month, var=var)\n",
    "\n",
    "if not os.path.isfile(data_file): # check if file already exists\n",
    "    print(\"Downloading %s from S3...\" % s3_data_key)\n",
    "    client.download_file(era5_bucket, s3_data_key, data_file)\n",
    "\n",
    "ds = xr.open_dataset(data_file)\n",
    "ds.info"
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       "Dimensions:                          (lon_ocean: 720, lat_ocean: 361, time0: 744)\n",
       "Coordinates:\n",
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       "  * time0                            (time0) datetime64[ns] 2017-08-01 ... 20...\n",
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       "    sea_surface_wave_from_direction  (time0, lat_ocean, lon_ocean) float32 ...\n",
       "Attributes:\n",
       "    source:       Reanalysis\n",
       "    institution:  ECMWF\n",
       "    title:        ERA5 forecasts</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-899f7479-eab9-43cb-8e2e-e3217e005faa' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-899f7479-eab9-43cb-8e2e-e3217e005faa' 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'>lon_ocean</span>: 720</li><li><span class='xr-has-index'>lat_ocean</span>: 361</li><li><span class='xr-has-index'>time0</span>: 744</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d06a55cb-3b9b-42b8-a412-3c38184c387a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d06a55cb-3b9b-42b8-a412-3c38184c387a' 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'>lon_ocean</span></div><div class='xr-var-dims'>(lon_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.5 1.0 ... 358.5 359.0 359.5</div><input id='attrs-98817be6-2af3-418e-b7c1-8c952c6ed78c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98817be6-2af3-418e-b7c1-8c952c6ed78c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ff4ffc0-4424-4365-8a2e-414957efd2f9' class='xr-var-data-in' type='checkbox'><label for='data-7ff4ffc0-4424-4365-8a2e-414957efd2f9' 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>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0. ,   0.5,   1. , ..., 358.5, 359. , 359.5], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat_ocean</span></div><div class='xr-var-dims'>(lat_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>90.0 89.5 89.0 ... -89.5 -90.0</div><input id='attrs-82b2d2e8-b1dd-47e0-b1fa-3b10b9ecb030' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-82b2d2e8-b1dd-47e0-b1fa-3b10b9ecb030' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc282e63-99c6-4c76-baee-2c4c6fb3a1d7' class='xr-var-data-in' type='checkbox'><label for='data-cc282e63-99c6-4c76-baee-2c4c6fb3a1d7' 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>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 90. ,  89.5,  89. , ..., -89. , -89.5, -90. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time0</span></div><div class='xr-var-dims'>(time0)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-08-01 ... 2017-08-31T23:00:00</div><input id='attrs-6c98eff8-10a7-4c1c-b8ac-c86b20c62f92' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c98eff8-10a7-4c1c-b8ac-c86b20c62f92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d88e0e2-2b69-4c9c-b3c3-57e2669d834e' class='xr-var-data-in' type='checkbox'><label for='data-8d88e0e2-2b69-4c9c-b3c3-57e2669d834e' 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></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-08-01T00:00:00.000000000&#x27;, &#x27;2017-08-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;2017-08-01T02:00:00.000000000&#x27;, ..., &#x27;2017-08-31T21:00:00.000000000&#x27;,\n",
       "       &#x27;2017-08-31T22:00:00.000000000&#x27;, &#x27;2017-08-31T23:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-610eb6cf-27d3-4495-9b85-1be67506d32c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-610eb6cf-27d3-4495-9b85-1be67506d32c' class='xr-section-summary' >Data variables: <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-var-name'><span>sea_surface_wave_from_direction</span></div><div class='xr-var-dims'>(time0, lat_ocean, lon_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-47f130f0-f76d-48c3-8086-8b7b552a08d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47f130f0-f76d-48c3-8086-8b7b552a08d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05e87932-e1ee-405a-8d72-94697e427354' class='xr-var-data-in' type='checkbox'><label for='data-05e87932-e1ee-405a-8d72-94697e427354' 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>sea_surface_wave_from_direction</dd><dt><span>units :</span></dt><dd>Degree true</dd><dt><span>long_name :</span></dt><dd>Mean wave direction</dd><dt><span>nameECMWF :</span></dt><dd>Mean wave direction</dd><dt><span>shortNameECMWF :</span></dt><dd>mwd</dd><dt><span>nameCDM :</span></dt><dd>Mean_wave_direction</dd><dt><span>product_type :</span></dt><dd>analysis</dd></dl></div><div class='xr-var-data'><pre>[193380480 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a0ad611c-cd7b-4420-a798-9593e62e30b2' class='xr-section-summary-in' type='checkbox'  ><label for='section-a0ad611c-cd7b-4420-a798-9593e62e30b2' 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>lon_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bc5c6ded-e871-4bb9-8e7f-26691dda4c1e' class='xr-index-data-in' type='checkbox'/><label for='index-bc5c6ded-e871-4bb9-8e7f-26691dda4c1e' 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,   0.5,   1.0,   1.5,   2.0,   2.5,   3.0,   3.5,   4.0,   4.5,\n",
       "       ...\n",
       "       355.0, 355.5, 356.0, 356.5, 357.0, 357.5, 358.0, 358.5, 359.0, 359.5],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon_ocean&#x27;, length=720))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1bd9a755-73e7-447b-9b2d-335dff1a339d' class='xr-index-data-in' type='checkbox'/><label for='index-1bd9a755-73e7-447b-9b2d-335dff1a339d' 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([ 90.0,  89.5,  89.0,  88.5,  88.0,  87.5,  87.0,  86.5,  86.0,  85.5,\n",
       "       ...\n",
       "       -85.5, -86.0, -86.5, -87.0, -87.5, -88.0, -88.5, -89.0, -89.5, -90.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat_ocean&#x27;, length=361))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-aec3d96f-933a-494a-8630-b01a7769e318' class='xr-index-data-in' type='checkbox'/><label for='index-aec3d96f-933a-494a-8630-b01a7769e318' 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;2017-08-01 00:00:00&#x27;, &#x27;2017-08-01 01:00:00&#x27;,\n",
       "               &#x27;2017-08-01 02:00:00&#x27;, &#x27;2017-08-01 03:00:00&#x27;,\n",
       "               &#x27;2017-08-01 04:00:00&#x27;, &#x27;2017-08-01 05:00:00&#x27;,\n",
       "               &#x27;2017-08-01 06:00:00&#x27;, &#x27;2017-08-01 07:00:00&#x27;,\n",
       "               &#x27;2017-08-01 08:00:00&#x27;, &#x27;2017-08-01 09:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2017-08-31 14:00:00&#x27;, &#x27;2017-08-31 15:00:00&#x27;,\n",
       "               &#x27;2017-08-31 16:00:00&#x27;, &#x27;2017-08-31 17:00:00&#x27;,\n",
       "               &#x27;2017-08-31 18:00:00&#x27;, &#x27;2017-08-31 19:00:00&#x27;,\n",
       "               &#x27;2017-08-31 20:00:00&#x27;, &#x27;2017-08-31 21:00:00&#x27;,\n",
       "               &#x27;2017-08-31 22:00:00&#x27;, &#x27;2017-08-31 23:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time0&#x27;, length=744, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c234dddd-6e8c-472e-96d3-9a7cf86855d4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c234dddd-6e8c-472e-96d3-9a7cf86855d4' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>source :</span></dt><dd>Reanalysis</dd><dt><span>institution :</span></dt><dd>ECMWF</dd><dt><span>title :</span></dt><dd>ERA5 forecasts</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                          (lon_ocean: 720, lat_ocean: 361, time0: 744)\n",
       "Coordinates:\n",
       "  * lon_ocean                        (lon_ocean) float32 0.0 0.5 ... 359.0 359.5\n",
       "  * lat_ocean                        (lat_ocean) float32 90.0 89.5 ... -90.0\n",
       "  * time0                            (time0) datetime64[ns] 2017-08-01 ... 20...\n",
       "Data variables:\n",
       "    sea_surface_wave_from_direction  (time0, lat_ocean, lon_ocean) float32 ...\n",
       "Attributes:\n",
       "    source:       Reanalysis\n",
       "    institution:  ECMWF\n",
       "    title:        ERA5 forecasts"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cb0d81b8-10eb-4c54-af61-568157028b94",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fab6cdf8c10>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds['sea_surface_wave_from_direction'].isel(time0=0).sel(lon_ocean=slice(60, 80), lat_ocean=slice(25, 5)).plot.imshow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55239c42-2971-479f-8f4b-c909462ad3ca",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}