python/examples/kerchunk_hrrr_subhourly.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import fsspec\n",
    "\n",
    "fs_read = fsspec.filesystem('s3', anon=True, skip_instance_cache=True, use_ssl=False) # For now SSL false is solving my cert issues **shrug**\n",
    "fs_write = fsspec.filesystem('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read 19 HRRR files\n"
     ]
    }
   ],
   "source": [
    "hrr_subhourly_member_files = fs_read.glob('s3://noaa-hrrr-bdp-pds/hrrr.20230722/conus/hrrr.t14z.wrfsubhf*.grib2')\n",
    "\n",
    "files = sorted(['s3://'+f for f in hrr_subhourly_member_files])\n",
    "print(f'Read {len(files)} HRRR files')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ujson\n",
    "from gribberish.kerchunk import scan_gribberish\n",
    "\n",
    "so = {\"anon\": True, \"use_ssl\": False}\n",
    "json_dir = 'hrrr_subhourly/'\n",
    "\n",
    "def make_json_name(file_url, message_number): #create a unique name for each reference file\n",
    "    date = file_url.split('/')[3].split('.')[1]\n",
    "    name = file_url.split('/')[5].split('.')[0:3]\n",
    "    return f'{json_dir}{date}_{name[0]}_{name[1]}_{name[2]}_message{message_number}.json'\n",
    "\n",
    "def gen_json(file_url):\n",
    "    out = scan_gribberish(\n",
    "        file_url, \n",
    "        storage_options=so, \n",
    "        only_variables=['apcp', 'prate', 'ugrd', 'vgrd'], \n",
    "        filter_by_variable_attrs={\n",
    "            'ugrd': {\n",
    "                'statistical_process': '', 'fixed_surface_value': '10'\n",
    "            }, \n",
    "            'vgrd': {\n",
    "                'statistical_process': '', 'fixed_surface_value': '10'\n",
    "            },\n",
    "        }, \n",
    "        skip=16\n",
    "    )\n",
    "    for i, message in enumerate(out):\n",
    "        out_file_name = make_json_name(file_url, i)  # get name\n",
    "        with fs_write.open(out_file_name, \"w\") as f: \n",
    "            f.write(ujson.dumps(message)) # write to file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dask.distributed import Client, progress\n",
    "\n",
    "client = Client(processes=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "93bbdf906fdf49df96ab12d15f74e692",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "VBox()"
      ]
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     "output_type": "display_data"
    }
   ],
   "source": [
    "futures = client.map(gen_json, files[1:], retries=1)\n",
    "progress(futures)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "client.shutdown()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 288 reference files\n"
     ]
    }
   ],
   "source": [
    "from kerchunk.combine import MultiZarrToZarr\n",
    "\n",
    "reference_jsons = sorted(fs_write.ls(json_dir)) #get list of file names\n",
    "\n",
    "print(f'Found {len(reference_jsons)} reference files')\n",
    "\n",
    "# combine individual references into single consolidated reference\n",
    "mzz = MultiZarrToZarr(reference_jsons,\n",
    "                      concat_dims = ['time'],\n",
    "                      identical_dims=['x', 'y', 'latitude', 'longitude'])\n",
    "\n",
    "d = mzz.translate()\n",
    "\n",
    "with open(f'{json_dir}/hrrr_kerchunk.json', 'w') as f:\n",
    "    f.write(ujson.dumps(d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (time: 72, y: 1059, x: 1799)\n",
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       "    latitude   (y, x) float64 dask.array&lt;chunksize=(1059, 1799), meta=np.ndarray&gt;\n",
       "    longitude  (y, x) float64 dask.array&lt;chunksize=(1059, 1799), meta=np.ndarray&gt;\n",
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       "    vgrd       (time, y, x) float64 dask.array&lt;chunksize=(1, 1059, 1799), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    meta:     Generated with gribberishpy</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-eaf50a8c-8654-499b-aa2c-ba04c4194a6a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-eaf50a8c-8654-499b-aa2c-ba04c4194a6a' 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>: 72</li><li><span class='xr-has-index'>y</span>: 1059</li><li><span class='xr-has-index'>x</span>: 1799</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b7aa0220-a223-4a8d-9bcd-37eb5767704d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b7aa0220-a223-4a8d-9bcd-37eb5767704d' 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>latitude</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1059, 1799), meta=np.ndarray&gt;</div><input id='attrs-b7e4be76-0d4b-41ba-8211-fc81c9b14596' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b7e4be76-0d4b-41ba-8211-fc81c9b14596' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24043595-ee5d-4e8a-ace1-09656db38a34' class='xr-var-data-in' type='checkbox'><label for='data-24043595-ee5d-4e8a-ace1-09656db38a34' 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>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>unit :</span></dt><dd>degrees_north</dd></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> 14.54 MiB </td>\n",
       "                        <td> 14.54 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1059, 1799) </td>\n",
       "                        <td> (1059, 1799) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"120\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,70.63924402445804 0.0,70.63924402445804\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"90.639244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1799</text>\n",
       "  <text x=\"140.000000\" y=\"35.319622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,35.319622)\">1059</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1059, 1799), meta=np.ndarray&gt;</div><input id='attrs-caaa3901-97c9-4af6-9416-35d59b03e2c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-caaa3901-97c9-4af6-9416-35d59b03e2c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0892c5d0-8656-4f85-b1de-39c3fb9a0db8' class='xr-var-data-in' type='checkbox'><label for='data-0892c5d0-8656-4f85-b1de-39c3fb9a0db8' 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>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>unit :</span></dt><dd>degrees_east</dd></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> 14.54 MiB </td>\n",
       "                        <td> 14.54 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1059, 1799) </td>\n",
       "                        <td> (1059, 1799) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"120\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
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       "\n",
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       "\n",
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       "\n",
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       "  <text x=\"60.000000\" y=\"90.639244\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1799</text>\n",
       "  <text x=\"140.000000\" y=\"35.319622\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,35.319622)\">1059</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></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[s]</div><div class='xr-var-preview xr-preview'>2023-07-22T14:15:00 ... 2023-07-...</div><input id='attrs-a817994a-a8b1-4fa0-98f9-67cf14fa75c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a817994a-a8b1-4fa0-98f9-67cf14fa75c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a4dd914-14f6-4c16-a7ff-564daaa2dfdf' class='xr-var-data-in' type='checkbox'><label for='data-5a4dd914-14f6-4c16-a7ff-564daaa2dfdf' 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>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>unit :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd><dt><span>_FillValue :</span></dt><dd>1970-01-01T00:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2023-07-22T14:15:00&#x27;, &#x27;2023-07-22T14:30:00&#x27;, &#x27;2023-07-22T14:45:00&#x27;,\n",
       "       &#x27;2023-07-22T15:00:00&#x27;, &#x27;2023-07-22T15:15:00&#x27;, &#x27;2023-07-22T15:30:00&#x27;,\n",
       "       &#x27;2023-07-22T15:45:00&#x27;, &#x27;2023-07-22T16:00:00&#x27;, &#x27;2023-07-22T16:15:00&#x27;,\n",
       "       &#x27;2023-07-22T16:30:00&#x27;, &#x27;2023-07-22T16:45:00&#x27;, &#x27;2023-07-22T17:00:00&#x27;,\n",
       "       &#x27;2023-07-22T17:15:00&#x27;, &#x27;2023-07-22T17:30:00&#x27;, &#x27;2023-07-22T17:45:00&#x27;,\n",
       "       &#x27;2023-07-22T18:00:00&#x27;, &#x27;2023-07-22T18:15:00&#x27;, &#x27;2023-07-22T18:30:00&#x27;,\n",
       "       &#x27;2023-07-22T18:45:00&#x27;, &#x27;2023-07-22T19:00:00&#x27;, &#x27;2023-07-22T19:15:00&#x27;,\n",
       "       &#x27;2023-07-22T19:30:00&#x27;, &#x27;2023-07-22T19:45:00&#x27;, &#x27;2023-07-22T20:00:00&#x27;,\n",
       "       &#x27;2023-07-22T20:15:00&#x27;, &#x27;2023-07-22T20:30:00&#x27;, &#x27;2023-07-22T20:45:00&#x27;,\n",
       "       &#x27;2023-07-22T21:00:00&#x27;, &#x27;2023-07-22T21:15:00&#x27;, &#x27;2023-07-22T21:30:00&#x27;,\n",
       "       &#x27;2023-07-22T21:45:00&#x27;, &#x27;2023-07-22T22:00:00&#x27;, &#x27;2023-07-22T22:15:00&#x27;,\n",
       "       &#x27;2023-07-22T22:30:00&#x27;, &#x27;2023-07-22T22:45:00&#x27;, &#x27;2023-07-22T23:00:00&#x27;,\n",
       "       &#x27;2023-07-22T23:15:00&#x27;, &#x27;2023-07-22T23:30:00&#x27;, &#x27;2023-07-22T23:45:00&#x27;,\n",
       "       &#x27;2023-07-23T00:00:00&#x27;, &#x27;2023-07-23T00:15:00&#x27;, &#x27;2023-07-23T00:30:00&#x27;,\n",
       "       &#x27;2023-07-23T00:45:00&#x27;, &#x27;2023-07-23T01:00:00&#x27;, &#x27;2023-07-23T01:15:00&#x27;,\n",
       "       &#x27;2023-07-23T01:30:00&#x27;, &#x27;2023-07-23T01:45:00&#x27;, &#x27;2023-07-23T02:00:00&#x27;,\n",
       "       &#x27;2023-07-23T02:15:00&#x27;, &#x27;2023-07-23T02:30:00&#x27;, &#x27;2023-07-23T02:45:00&#x27;,\n",
       "       &#x27;2023-07-23T03:00:00&#x27;, &#x27;2023-07-23T03:15:00&#x27;, &#x27;2023-07-23T03:30:00&#x27;,\n",
       "       &#x27;2023-07-23T03:45:00&#x27;, &#x27;2023-07-23T04:00:00&#x27;, &#x27;2023-07-23T04:15:00&#x27;,\n",
       "       &#x27;2023-07-23T04:30:00&#x27;, &#x27;2023-07-23T04:45:00&#x27;, &#x27;2023-07-23T05:00:00&#x27;,\n",
       "       &#x27;2023-07-23T05:15:00&#x27;, &#x27;2023-07-23T05:30:00&#x27;, &#x27;2023-07-23T05:45:00&#x27;,\n",
       "       &#x27;2023-07-23T06:00:00&#x27;, &#x27;2023-07-23T06:15:00&#x27;, &#x27;2023-07-23T06:30:00&#x27;,\n",
       "       &#x27;2023-07-23T06:45:00&#x27;, &#x27;2023-07-23T07:00:00&#x27;, &#x27;2023-07-23T07:15:00&#x27;,\n",
       "       &#x27;2023-07-23T07:30:00&#x27;, &#x27;2023-07-23T07:45:00&#x27;, &#x27;2023-07-23T08:00:00&#x27;],\n",
       "      dtype=&#x27;datetime64[s]&#x27;)</pre></div></li><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'>float64</div><div class='xr-var-preview xr-preview'>-2.701e+06 -2.698e+06 ... 2.693e+06</div><input id='attrs-c1cdbe36-050b-4172-843f-7116d04f8ce2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c1cdbe36-050b-4172-843f-7116d04f8ce2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17e76382-8174-4a64-bcf0-055a85680b57' class='xr-var-data-in' type='checkbox'><label for='data-17e76382-8174-4a64-bcf0-055a85680b57' 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>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>unit :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([-2701000.130325, -2698000.130325, -2695000.130325, ...,  2686999.869675,\n",
       "        2689999.869675,  2692999.869675])</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'>float64</div><div class='xr-var-preview xr-preview'>-1.581e+06 -1.578e+06 ... 1.593e+06</div><input id='attrs-e9e0f860-3c86-4e87-aba3-5a9a93c20e11' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9e0f860-3c86-4e87-aba3-5a9a93c20e11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc5fa630-8fd2-43fe-addd-09f25680a4f7' class='xr-var-data-in' type='checkbox'><label for='data-bc5fa630-8fd2-43fe-addd-09f25680a4f7' 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>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>unit :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([-1580581.336877, -1577581.336877, -1574581.336877, ...,  1587418.663123,\n",
       "        1590418.663123,  1593418.663123])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9d9350dc-c377-4e7e-b00a-ecf3431bdba4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9d9350dc-c377-4e7e-b00a-ecf3431bdba4' class='xr-section-summary' >Data variables: <span>(4)</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>apcp</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1059, 1799), meta=np.ndarray&gt;</div><input id='attrs-8fd3a89c-73eb-4540-91e4-3dbb56da6acc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8fd3a89c-73eb-4540-91e4-3dbb56da6acc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73471b06-32bc-4ada-b597-f464098cfcb9' class='xr-var-data-in' type='checkbox'><label for='data-73471b06-32bc-4ada-b597-f464098cfcb9' 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>crs :</span></dt><dd>+proj=lcc lon_0=262.5 lat_0=38.5 lat_1=38.5 lat_2=38.5</dd><dt><span>fixed_surface_type :</span></dt><dd>ground or water surface</dd><dt><span>fixed_surface_value :</span></dt><dd>0</dd><dt><span>forecast_date :</span></dt><dd>2023-07-22T14:30:00+00:00</dd><dt><span>generating_process :</span></dt><dd>forecast</dd><dt><span>long_name :</span></dt><dd>totalprecipitation</dd><dt><span>proj_params :</span></dt><dd>{&#x27;lat_0&#x27;: 38.5, &#x27;lat_1&#x27;: 38.5, &#x27;lat_2&#x27;: 38.5, &#x27;lon_0&#x27;: 262.5, &#x27;proj&#x27;: &#x27;lcc&#x27;}</dd><dt><span>reference_date :</span></dt><dd>2023-07-22T14:00:00+00:00</dd><dt><span>standard_name :</span></dt><dd>totalprecipitation</dd><dt><span>statistical_process :</span></dt><dd>accumulation</dd><dt><span>time_interval_end :</span></dt><dd>2023-07-22T14:45:00+00:00</dd><dt><span>unit :</span></dt><dd>kgm-2</dd></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> 1.02 GiB </td>\n",
       "                        <td> 14.54 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (72, 1059, 1799) </td>\n",
       "                        <td> (1, 1059, 1799) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 72 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"199\" height=\"140\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"29\" y2=\"19\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"70\" x2=\"29\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"71\" />\n",
       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"72\" />\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vgrd</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1059, 1799), meta=np.ndarray&gt;</div><input id='attrs-5ba4f8dd-affc-4b1b-b5cd-1004024e6709' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ba4f8dd-affc-4b1b-b5cd-1004024e6709' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37f819b2-c322-42f5-a2d1-2899c0dfdfd2' class='xr-var-data-in' type='checkbox'><label for='data-37f819b2-c322-42f5-a2d1-2899c0dfdfd2' 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>crs :</span></dt><dd>+proj=lcc lon_0=262.5 lat_0=38.5 lat_1=38.5 lat_2=38.5</dd><dt><span>fixed_surface_type :</span></dt><dd>specific height level above ground</dd><dt><span>fixed_surface_value :</span></dt><dd>10</dd><dt><span>forecast_date :</span></dt><dd>2023-07-22T14:15:00+00:00</dd><dt><span>generating_process :</span></dt><dd>forecast</dd><dt><span>long_name :</span></dt><dd>vcomponentwindspeed</dd><dt><span>proj_params :</span></dt><dd>{&#x27;lat_0&#x27;: 38.5, &#x27;lat_1&#x27;: 38.5, &#x27;lat_2&#x27;: 38.5, &#x27;lon_0&#x27;: 262.5, &#x27;proj&#x27;: &#x27;lcc&#x27;}</dd><dt><span>reference_date :</span></dt><dd>2023-07-22T14:00:00+00:00</dd><dt><span>standard_name :</span></dt><dd>vcomponentwindspeed</dd><dt><span>statistical_process :</span></dt><dd></dd><dt><span>time_interval_end :</span></dt><dd></dd><dt><span>unit :</span></dt><dd>ms-1</dd></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> 1.02 GiB </td>\n",
       "                        <td> 14.54 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (72, 1059, 1799) </td>\n",
       "                        <td> (1, 1059, 1799) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 72 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "                </tbody>\n",
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       "        </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-54dc6c8a-8f62-4907-a0dc-3e07bd287823' class='xr-section-summary-in' type='checkbox'  ><label for='section-54dc6c8a-8f62-4907-a0dc-3e07bd287823' 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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-10cedc91-ab26-49b7-8de2-f997932251bd' class='xr-index-data-in' type='checkbox'/><label for='index-10cedc91-ab26-49b7-8de2-f997932251bd' 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;2023-07-22 14:15:00&#x27;, &#x27;2023-07-22 14:30:00&#x27;,\n",
       "               &#x27;2023-07-22 14:45:00&#x27;, &#x27;2023-07-22 15:00:00&#x27;,\n",
       "               &#x27;2023-07-22 15:15:00&#x27;, &#x27;2023-07-22 15:30:00&#x27;,\n",
       "               &#x27;2023-07-22 15:45:00&#x27;, &#x27;2023-07-22 16:00:00&#x27;,\n",
       "               &#x27;2023-07-22 16:15:00&#x27;, &#x27;2023-07-22 16:30:00&#x27;,\n",
       "               &#x27;2023-07-22 16:45:00&#x27;, &#x27;2023-07-22 17:00:00&#x27;,\n",
       "               &#x27;2023-07-22 17:15:00&#x27;, &#x27;2023-07-22 17:30:00&#x27;,\n",
       "               &#x27;2023-07-22 17:45:00&#x27;, &#x27;2023-07-22 18:00:00&#x27;,\n",
       "               &#x27;2023-07-22 18:15:00&#x27;, &#x27;2023-07-22 18:30:00&#x27;,\n",
       "               &#x27;2023-07-22 18:45:00&#x27;, &#x27;2023-07-22 19:00:00&#x27;,\n",
       "               &#x27;2023-07-22 19:15:00&#x27;, &#x27;2023-07-22 19:30:00&#x27;,\n",
       "               &#x27;2023-07-22 19:45:00&#x27;, &#x27;2023-07-22 20:00:00&#x27;,\n",
       "               &#x27;2023-07-22 20:15:00&#x27;, &#x27;2023-07-22 20:30:00&#x27;,\n",
       "               &#x27;2023-07-22 20:45:00&#x27;, &#x27;2023-07-22 21:00:00&#x27;,\n",
       "               &#x27;2023-07-22 21:15:00&#x27;, &#x27;2023-07-22 21:30:00&#x27;,\n",
       "               &#x27;2023-07-22 21:45:00&#x27;, &#x27;2023-07-22 22:00:00&#x27;,\n",
       "               &#x27;2023-07-22 22:15:00&#x27;, &#x27;2023-07-22 22:30:00&#x27;,\n",
       "               &#x27;2023-07-22 22:45:00&#x27;, &#x27;2023-07-22 23:00:00&#x27;,\n",
       "               &#x27;2023-07-22 23:15:00&#x27;, &#x27;2023-07-22 23:30:00&#x27;,\n",
       "               &#x27;2023-07-22 23:45:00&#x27;, &#x27;2023-07-23 00:00:00&#x27;,\n",
       "               &#x27;2023-07-23 00:15:00&#x27;, &#x27;2023-07-23 00:30:00&#x27;,\n",
       "               &#x27;2023-07-23 00:45:00&#x27;, &#x27;2023-07-23 01:00:00&#x27;,\n",
       "               &#x27;2023-07-23 01:15:00&#x27;, &#x27;2023-07-23 01:30:00&#x27;,\n",
       "               &#x27;2023-07-23 01:45:00&#x27;, &#x27;2023-07-23 02:00:00&#x27;,\n",
       "               &#x27;2023-07-23 02:15:00&#x27;, &#x27;2023-07-23 02:30:00&#x27;,\n",
       "               &#x27;2023-07-23 02:45:00&#x27;, &#x27;2023-07-23 03:00:00&#x27;,\n",
       "               &#x27;2023-07-23 03:15:00&#x27;, &#x27;2023-07-23 03:30:00&#x27;,\n",
       "               &#x27;2023-07-23 03:45:00&#x27;, &#x27;2023-07-23 04:00:00&#x27;,\n",
       "               &#x27;2023-07-23 04:15:00&#x27;, &#x27;2023-07-23 04:30:00&#x27;,\n",
       "               &#x27;2023-07-23 04:45:00&#x27;, &#x27;2023-07-23 05:00:00&#x27;,\n",
       "               &#x27;2023-07-23 05:15:00&#x27;, &#x27;2023-07-23 05:30:00&#x27;,\n",
       "               &#x27;2023-07-23 05:45:00&#x27;, &#x27;2023-07-23 06:00:00&#x27;,\n",
       "               &#x27;2023-07-23 06:15:00&#x27;, &#x27;2023-07-23 06:30:00&#x27;,\n",
       "               &#x27;2023-07-23 06:45:00&#x27;, &#x27;2023-07-23 07:00:00&#x27;,\n",
       "               &#x27;2023-07-23 07:15:00&#x27;, &#x27;2023-07-23 07:30:00&#x27;,\n",
       "               &#x27;2023-07-23 07:45:00&#x27;, &#x27;2023-07-23 08:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-975e5cb6-51b2-4b02-9a3f-423bc08f2b04' class='xr-index-data-in' type='checkbox'/><label for='index-975e5cb6-51b2-4b02-9a3f-423bc08f2b04' 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(Float64Index([-2701000.130325057, -2698000.130325057, -2695000.130325057,\n",
       "              -2692000.130325057, -2689000.130325057, -2686000.130325057,\n",
       "              -2683000.130325057, -2680000.130325057, -2677000.130325057,\n",
       "              -2674000.130325057,\n",
       "              ...\n",
       "               2665999.869674943,  2668999.869674943,  2671999.869674943,\n",
       "               2674999.869674943,  2677999.869674943,  2680999.869674943,\n",
       "               2683999.869674943,  2686999.869674943,  2689999.869674943,\n",
       "               2692999.869674943],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=1799))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f3d8d7b8-eeca-4cf5-abcf-d6ad43d7dd6e' class='xr-index-data-in' type='checkbox'/><label for='index-f3d8d7b8-eeca-4cf5-abcf-d6ad43d7dd6e' 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(Float64Index([-1580581.3368766531, -1577581.3368766531, -1574581.3368766531,\n",
       "              -1571581.3368766531, -1568581.3368766531, -1565581.3368766531,\n",
       "              -1562581.3368766531, -1559581.3368766531, -1556581.3368766531,\n",
       "              -1553581.3368766531,\n",
       "              ...\n",
       "               1566418.6631233469,  1569418.6631233469,  1572418.6631233469,\n",
       "               1575418.6631233469,  1578418.6631233469,  1581418.6631233469,\n",
       "               1584418.6631233469,  1587418.6631233469,  1590418.6631233469,\n",
       "               1593418.6631233469],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=1059))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-37431636-3a31-49ef-bac6-3c0a67ece52c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-37431636-3a31-49ef-bac6-3c0a67ece52c' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>meta :</span></dt><dd>Generated with gribberishpy</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (time: 72, y: 1059, x: 1799)\n",
       "Coordinates:\n",
       "    latitude   (y, x) float64 dask.array<chunksize=(1059, 1799), meta=np.ndarray>\n",
       "    longitude  (y, x) float64 dask.array<chunksize=(1059, 1799), meta=np.ndarray>\n",
       "  * time       (time) datetime64[s] 2023-07-22T14:15:00 ... 2023-07-23T08:00:00\n",
       "  * x          (x) float64 -2.701e+06 -2.698e+06 ... 2.69e+06 2.693e+06\n",
       "  * y          (y) float64 -1.581e+06 -1.578e+06 ... 1.59e+06 1.593e+06\n",
       "Data variables:\n",
       "    apcp       (time, y, x) float64 dask.array<chunksize=(1, 1059, 1799), meta=np.ndarray>\n",
       "    prate      (time, y, x) float64 dask.array<chunksize=(1, 1059, 1799), meta=np.ndarray>\n",
       "    ugrd       (time, y, x) float64 dask.array<chunksize=(1, 1059, 1799), meta=np.ndarray>\n",
       "    vgrd       (time, y, x) float64 dask.array<chunksize=(1, 1059, 1799), meta=np.ndarray>\n",
       "Attributes:\n",
       "    meta:     Generated with gribberishpy"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xarray as xr\n",
    "\n",
    "# open dataset as zarr object using fsspec reference file system and xarray\n",
    "fs = fsspec.filesystem(\"reference\", fo=f'./hrrr_subhourly/hrrr_kerchunk.json', remote_protocol='s3', remote_options={'anon':True, 'use_ssl': False})\n",
    "m = fs.get_mapper(\"\")\n",
    "ds = xr.open_dataset(m, engine=\"zarr\", backend_kwargs=dict(consolidated=False), chunks={'time': 1})\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyproj\n",
    "to_xy = pyproj.Transformer.from_crs('epsg:4326', ds.prate.crs, always_xy=True).transform\n",
    "lat, lng = 41.42717976016072, -71.462122760827\n",
    "x_sel, y_sel = to_xy(lng, lat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2afe258b0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "f, ax = plt.subplots(figsize=(10, 4))\n",
    "\n",
    "selected_ds = ds[['prate', 'apcp']].interp(x=x_sel, y=y_sel, method='linear')\n",
    "\n",
    "selected_prate = (selected_ds.prate * 3600) / 25.4 # convert from kg/m^2/s to in/hr\n",
    "selected_apcp = (selected_ds.apcp) / 25.4 # convert from kg/m^2/s to in/hr\n",
    "\n",
    "ax.plot(selected_prate.time, selected_prate, label='prate')\n",
    "ax.plot(selected_apcp.time, selected_apcp, label='apcp')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time: 2023-07-22T14:45:00.000000000\n",
      "Wind speed: 3.52 m/s\n",
      "Wind direction: 347.11 degrees\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "ROTCON_P = 0.622515\n",
    "LON_XX_P = -97.5\n",
    "LAT_TAN_P = 38.5\n",
    "\n",
    "angle2 = ROTCON_P*(ds.longitude-LON_XX_P)*0.017453\n",
    "sinx2 = np.sin(angle2)\n",
    "cosx2 = np.cos(angle2)\n",
    "\n",
    "un = cosx2 * ds.ugrd + sinx2 * ds.vgrd\n",
    "vn = -sinx2 * ds.ugrd + cosx2 * ds.vgrd\n",
    "\n",
    "wind_speed = np.sqrt(un**2 + vn**2)\n",
    "wind_dir = (270 - np.arctan2(vn, un) * 180 / np.pi) % 360\n",
    "\n",
    "wind_speed_ll = wind_speed.interp(x=x_sel, y=y_sel, method='linear').isel(time=2)\n",
    "wind_dir_ll = wind_dir.interp(x=x_sel, y=y_sel, method='linear').isel(time=2)\n",
    "\n",
    "print(f'Time: {wind_speed_ll.time.values}')\n",
    "print(f'Wind speed: {wind_speed_ll.values:.2f} m/s')\n",
    "print(f'Wind direction: {wind_dir_ll.values:.2f} degrees')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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