00_tef_core.ipynb

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   "execution_count": 1,
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   "source": [
    "# default_exp tef_core\n",
    "from nbdev import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TEF core framework\n",
    "\n",
    "> contains TEF framework"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "import math\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "\n",
    "class constructorTEF:\n",
    "    def __init__(self, inputFile, data_description, **kwargs):\n",
    "        \"\"\"Creates a constructor object that is passed to the pyTEF functions.\n",
    "\n",
    "        Args:\n",
    "            inputFile (str, xr.Dataset, xr.DataArray): If string is passed, function tries to read an netcdf file, otherwise xarray object can be passed as well.\n",
    "            data_description (dict): Describes the data that is read\n",
    "\n",
    "        Raises:\n",
    "            IOError: Could not read filename\n",
    "            IOError: No filename and invalid xarray object is passed.\n",
    "            \n",
    "        Example:\n",
    "            data_description = {\n",
    "                \"lon\" : None,\n",
    "                \"lat\" : \"yt_ocean\",\n",
    "                \"depth\" : \"st_ocean\",\n",
    "                \"time\" : \"time\"\n",
    "            }    \n",
    "        \"\"\"\n",
    "        if isinstance(inputFile, str):\n",
    "            try:\n",
    "                self._read(fileName=inputFile)\n",
    "            except (OSError, IOError, RunetimeError):\n",
    "                raise IOError(\"Could not read file.\")\n",
    "        elif isinstance(inputFile, xr.Dataset):\n",
    "            self.ds = inputFile\n",
    "        elif isinstance(inputFile, xr.DataArray):\n",
    "            self.ds = inputFile\n",
    "        else:\n",
    "            raise IOError(\"Please provide xarray Dataset/DataArray or netcdf-filepath\")\n",
    "        \n",
    "        self._setup(data=data_description)\n",
    "        \n",
    "        self.transport = None\n",
    "        self.tracer = None\n",
    "        \n",
    "    def _read(self, fileName, **kwargs):\n",
    "        self.ds = xr.open_dataset(filename, use_cftime=True, **kwargs)\n",
    "        \n",
    "    def _setup(self, data):\n",
    "        \"\"\"Iterates over keys in dictionary. Handles 4d-data, if one argument is left empty, dummy dimension will be created.\n",
    "\n",
    "        Args:\n",
    "            data (dict): Dictionary that describes geospatial dimensions of the dataset.\n",
    "        \"\"\"\n",
    "        for dimension in data.keys():\n",
    "            if data[dimension] is None:\n",
    "                self.ds = self.ds.expand_dims(dimension)\n",
    "            else:\n",
    "                self.ds = self.ds.rename({data[dimension]: dimension})\n",
    "                                                       \n",
    "        self.ds = self.ds.transpose(\"time\",\n",
    "                                    \"depth\",\n",
    "                                    \"lat\",\n",
    "                                    \"lon\")"
   ]
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   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<h4 id=\"constructorTEF.__init__\" class=\"doc_header\"><code>constructorTEF.__init__</code><a href=\"__main__.py#L4\" class=\"source_link\" style=\"float:right\">[source]</a></h4>\n",
       "\n",
       "> <code>constructorTEF.__init__</code>(**`inputFile`**, **`data_description`**, **\\*\\*`kwargs`**)\n",
       "\n",
       "Creates a constructor object that is passed to the pyTEF functions.\n",
       "\n",
       "Args:\n",
       "    inputFile (str, xr.Dataset, xr.DataArray): If string is passed, function tries to read an netcdf file, otherwise xarray object can be passed as well.\n",
       "    data_description (dict): Describes the data that is read\n",
       "\n",
       "Raises:\n",
       "    IOError: Could not read filename\n",
       "    IOError: No filename and invalid xarray object is passed.\n",
       "    \n",
       "Example:\n",
       "    data_description = {\n",
       "        \"lon\" : None,\n",
       "        \"lat\" : \"yt_ocean\",\n",
       "        \"depth\" : \"st_ocean\",\n",
       "        \"time\" : \"time\"\n",
       "    }    "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_doc(constructorTEF.__init__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 360, depth: 200, lon: 1, time: 1)\n",
       "Coordinates:\n",
       "  * lat      (lat) int64 -180 -179 -178 -177 -176 -175 ... 175 176 177 178 179\n",
       "  * depth    (depth) int64 0 1 2 3 4 5 6 7 8 ... 192 193 194 195 196 197 198 199\n",
       "Dimensions without coordinates: lon, time\n",
       "Data variables:\n",
       "    dummy    (time, depth, lat, lon) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</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-d28a3df7-cfb8-4493-9950-1871495c8d6f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d28a3df7-cfb8-4493-9950-1871495c8d6f' 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'>lat</span>: 360</li><li><span class='xr-has-index'>depth</span>: 200</li><li><span>lon</span>: 1</li><li><span>time</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-587bf57c-dc64-448c-a052-4d49e786ce8a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-587bf57c-dc64-448c-a052-4d49e786ce8a' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-180 -179 -178 -177 ... 177 178 179</div><input id='attrs-f0dd9dcd-944e-4962-9674-2adc8f1531fd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f0dd9dcd-944e-4962-9674-2adc8f1531fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a848d1d-2b66-42e2-9821-78625a8221c0' class='xr-var-data-in' type='checkbox'><label for='data-8a848d1d-2b66-42e2-9821-78625a8221c0' 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([-180, -179, -178, ...,  177,  178,  179])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth</span></div><div class='xr-var-dims'>(depth)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 195 196 197 198 199</div><input id='attrs-2a22e9c5-f9c5-4319-9a67-a69cf7c38145' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2a22e9c5-f9c5-4319-9a67-a69cf7c38145' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7ec54ce-5a32-4d10-82ee-e13a91fd2c6b' class='xr-var-data-in' type='checkbox'><label for='data-a7ec54ce-5a32-4d10-82ee-e13a91fd2c6b' 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,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
       "       168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
       "       182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n",
       "       196, 197, 198, 199])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f9ce5788-c2b3-4f4b-a0a1-73e432fcde13' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f9ce5788-c2b3-4f4b-a0a1-73e432fcde13' 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>dummy</span></div><div class='xr-var-dims'>(time, depth, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-663761fa-a22d-46a2-afd0-81deceb4957c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-663761fa-a22d-46a2-afd0-81deceb4957c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c7163fe-e36d-4e38-8a2e-11c198ccddd4' class='xr-var-data-in' type='checkbox'><label for='data-2c7163fe-e36d-4e38-8a2e-11c198ccddd4' 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.],\n",
       "         [0.],\n",
       "         [0.],\n",
       "         ...,\n",
       "         [0.],\n",
       "         [0.],\n",
       "         [0.]],\n",
       "\n",
       "        [[0.],\n",
       "         [0.],\n",
       "         [0.],\n",
       "         ...,\n",
       "         [0.],\n",
       "         [0.],\n",
       "         [0.]],\n",
       "\n",
       "        [[0.],\n",
       "         [0.],\n",
       "         [0.],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [0.],\n",
       "         [0.],\n",
       "         [0.]],\n",
       "\n",
       "        [[0.],\n",
       "         [0.],\n",
       "         [0.],\n",
       "         ...,\n",
       "         [0.],\n",
       "         [0.],\n",
       "         [0.]],\n",
       "\n",
       "        [[0.],\n",
       "         [0.],\n",
       "         [0.],\n",
       "         ...,\n",
       "         [0.],\n",
       "         [0.],\n",
       "         [0.]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-eaef2484-9540-49b0-a60c-35223734dfd6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-eaef2484-9540-49b0-a60c-35223734dfd6' 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>\n",
       "Dimensions:  (lat: 360, depth: 200, lon: 1, time: 1)\n",
       "Coordinates:\n",
       "  * lat      (lat) int64 -180 -179 -178 -177 -176 -175 ... 175 176 177 178 179\n",
       "  * depth    (depth) int64 0 1 2 3 4 5 6 7 8 ... 192 193 194 195 196 197 198 199\n",
       "Dimensions without coordinates: lon, time\n",
       "Data variables:\n",
       "    dummy    (time, depth, lat, lon) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Try using uncommon dimensions\n",
    "import xarray as xr\n",
    "import numpy as np \n",
    "\n",
    "\n",
    "# Create random data\n",
    "lat = np.arange(-180, 180)\n",
    "lon = 37\n",
    "depth = np.arange(0, 200)\n",
    "time = 2\n",
    "dummy_var = np.zeros((lat.size, depth.size))\n",
    "\n",
    "ds = xr.Dataset({'dummy': ([\"lAtiTude\", \"dePht\"], dummy_var)},\n",
    "                coords = {'lAtiTude': ([\"lAtiTude\"], lat),\n",
    "                          'dePht': ([\"dePht\"], depth)})\n",
    "\n",
    "# Create data description\n",
    "\n",
    "data_description = {\n",
    "    \"lon\" : None,\n",
    "    \"lat\" : \"lAtiTude\",\n",
    "    \"depth\" : \"dePht\",\n",
    "    \"time\" : None\n",
    "}    \n",
    "\n",
    "# Initialize TEF object\n",
    "\n",
    "tef = constructorTEF(inputFile=ds, \n",
    "                     data_description=data_description)\n",
    "tef.ds"
   ]
  }
 ],
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