docs/source/data-structure/supy-io.ipynb

{
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  {
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   "metadata": {},
   "source": [
    "# Key IO Data Structures in SuPy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell below demonstrates a minimal case of SuPy simulation with all key IO data structures included:"
   ]
  },
  {
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   "execution_count": 1,
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     "start_time": "2019-02-25T08:37:09.217592Z"
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   "outputs": [],
   "source": [
    "import supy as sp\n",
    "df_state_init, df_forcing = sp.load_SampleData()\n",
    "df_output, df_state_final = sp.run_supy(df_forcing, df_state_init)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Input:\n",
    "    SuPy requires two `DataFrame`s to perform a simulation, which are:\n",
    "    * `df_state_init`: model initial states;\n",
    "    * `df_forcing`: forcing data.\n",
    "    \n",
    "    These input data can be loaded either through calling [load_SampleData()](../auto-gen/supy.load_forcing_grid.rst#supy.load_forcing_grid) as shown above or using [init_supy](../auto-gen/supy.init_supy.rst#supy.init_supy). Or, based on the loaded sample `DataFrame`s, you can modify the content to create new `DataFrame`s for your specific needs.\n",
    "\n",
    "\n",
    "* Output:\n",
    "    The output data by SuPy consists of two `DataFrame`s:\n",
    "    * `df_output`: model output results; this is usually the basis for scientific analysis.\n",
    "    * `df_state_final`: model final states; any of its entries can be used as a `df_state_init` to start another SuPy simulation.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `df_state_init`: model initial states"
   ]
  },
  {
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       "      <th colspan=\"2\" halign=\"left\">ah_slope_cooling</th>\n",
       "      <th colspan=\"2\" halign=\"left\">ah_slope_heating</th>\n",
       "      <th colspan=\"9\" halign=\"left\">ahprof_24hr</th>\n",
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       "var     ah_min       ah_slope_cooling      ah_slope_heating      ahprof_24hr  \\\n",
       "ind_dim   (0,)  (1,)             (0,) (1,)             (0,) (1,)      (0, 0)   \n",
       "grid                                                                           \n",
       "98        15.0  15.0              2.7  2.7              2.7  2.7        0.57   \n",
       "\n",
       "var                                                              ... tair24hr  \\\n",
       "ind_dim (0, 1) (1, 0) (1, 1) (2, 0) (2, 1) (3, 0) (3, 1) (4, 0)  ...   (275,)   \n",
       "grid                                                             ...            \n",
       "98        0.65   0.45   0.49   0.43   0.46    0.4   0.47    0.4  ...   273.15   \n",
       "\n",
       "var                                                                      \\\n",
       "ind_dim  (276,)  (277,)  (278,)  (279,)  (280,)  (281,)  (282,)  (283,)   \n",
       "grid                                                                      \n",
       "98       273.15  273.15  273.15  273.15  273.15  273.15  273.15  273.15   \n",
       "\n",
       "var                                     numcapita gridiv  \n",
       "ind_dim  (284,)  (285,)  (286,)  (287,)         0      0  \n",
       "grid                                                      \n",
       "98       273.15  273.15  273.15  273.15    204.58     98  \n",
       "\n",
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   "source": [
    "df_state_init.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`df_state_init` is organised with ***grids*** in __rows__ and ***their states*** in __columns__. The details of all state variables can be found in [the description page](../data-structure/df_state.rst#df-state-variables).\n",
    "\n",
    "Please note the properties are stored as *flattened values* to fit into the tabular format due to the nature of `DataFrame` though they may actually be of higher dimension (e.g. [ahprof_24hr](../data-structure/df_state.rst#cmdoption-arg-ahprof-24hr) with the dimension {24, 2}). To indicate the variable dimensionality of these properties, SuPy use the `ind_dim` level in columns for indices of values:\n",
    "\n",
    "* `0` for scalars;\n",
    "* `(ind_dim1, ind_dim2, ...)` for arrays (for a generic sense, vectors are 1D arrays)."
   ]
  },
  {
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   "source": [
    "Take `ohm_coef` below for example, it has a dimension of {8, 4, 3} according to [the description](../data-structure/df_state.rst#cmdoption-arg-ohm-coef), which implies the actual values used by SuPy in simulations are passed in a layout as an array of the dimension {8, 4, 3}. As such, to get proper values passed in, users should follow the dimensionality requirement to prepare/modify `df_state_init`. "
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       "ind_dim  (0, 0, 0)  (0, 0, 1)  (0, 0, 2)  (0, 1, 0)  (0, 1, 1)  (0, 1, 2)  \\\n",
       "grid                                                                        \n",
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       "\n",
       "ind_dim  (0, 2, 0)  (0, 2, 1)  (0, 2, 2)  (0, 3, 0)  (0, 3, 1)  (0, 3, 2)  \\\n",
       "grid                                                                        \n",
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       "grid                                                                        \n",
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       "grid                                                                        \n",
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   "source": [
    "df_state_init.loc[:,'ohm_coef']"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "### `df_forcing`: forcing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`df_forcing` is organised with ***temporal records*** in __rows__ and ***forcing variables*** in __columns__. The details of all forcing variables can be found in [the description page](../data-structure/df_forcing.rst#df-forcing-variables). \n",
    "\n",
    "\n",
    "The missing values can be specified with `-999`s, which are the default NANs accepted by SuPy and its backend SUEWS."
   ]
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       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 00:15:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>4.515</td>\n",
       "      <td>85.463333</td>\n",
       "      <td>11.77375</td>\n",
       "      <td>1001.5125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.153333</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 00:20:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>4.515</td>\n",
       "      <td>85.463333</td>\n",
       "      <td>11.77375</td>\n",
       "      <td>1001.5125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.153333</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 00:25:00</th>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>4.515</td>\n",
       "      <td>85.463333</td>\n",
       "      <td>11.77375</td>\n",
       "      <td>1001.5125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.153333</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       iy  id  it  imin     qn     qh     qe     qs     qf  \\\n",
       "2012-01-01 00:05:00  2012   1   0     5 -999.0 -999.0 -999.0 -999.0 -999.0   \n",
       "2012-01-01 00:10:00  2012   1   0    10 -999.0 -999.0 -999.0 -999.0 -999.0   \n",
       "2012-01-01 00:15:00  2012   1   0    15 -999.0 -999.0 -999.0 -999.0 -999.0   \n",
       "2012-01-01 00:20:00  2012   1   0    20 -999.0 -999.0 -999.0 -999.0 -999.0   \n",
       "2012-01-01 00:25:00  2012   1   0    25 -999.0 -999.0 -999.0 -999.0 -999.0   \n",
       "\n",
       "                         U         RH      Tair       pres  rain     kdown  \\\n",
       "2012-01-01 00:05:00  4.515  85.463333  11.77375  1001.5125   0.0  0.153333   \n",
       "2012-01-01 00:10:00  4.515  85.463333  11.77375  1001.5125   0.0  0.153333   \n",
       "2012-01-01 00:15:00  4.515  85.463333  11.77375  1001.5125   0.0  0.153333   \n",
       "2012-01-01 00:20:00  4.515  85.463333  11.77375  1001.5125   0.0  0.153333   \n",
       "2012-01-01 00:25:00  4.515  85.463333  11.77375  1001.5125   0.0  0.153333   \n",
       "\n",
       "                      snow  ldown   fcld    Wuh   xsmd    lai  kdiff   kdir  \\\n",
       "2012-01-01 00:05:00 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0   \n",
       "2012-01-01 00:10:00 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0   \n",
       "2012-01-01 00:15:00 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0   \n",
       "2012-01-01 00:20:00 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0   \n",
       "2012-01-01 00:25:00 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0 -999.0   \n",
       "\n",
       "                      wdir  isec  \n",
       "2012-01-01 00:05:00 -999.0   0.0  \n",
       "2012-01-01 00:10:00 -999.0   0.0  \n",
       "2012-01-01 00:15:00 -999.0   0.0  \n",
       "2012-01-01 00:20:00 -999.0   0.0  \n",
       "2012-01-01 00:25:00 -999.0   0.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_forcing.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    " \n",
    "**Note:**\n",
    "\n",
    "The index of `df_forcing` **SHOULD BE** strictly of `DatetimeIndex` type if you want create a `df_forcing` for SuPy simulation.\n",
    "The SuPy runtime time-step size is instructed by the `df_forcing` with its index information.\n",
    "\n",
    "</div>\n",
    "\n",
    "\n",
    "The infomation below indicates SuPy will run at a 5 min (i.e. 300 s) time-step if driven by this specific `df_forcing`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T08:37:22.842376Z",
     "start_time": "2019-02-25T08:37:22.824978Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<300 * Seconds>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_forcing=df_forcing.index.freq\n",
    "freq_forcing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `df_output`: model output results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`df_output` is organised with ***temporal records of grids*** in __rows__ and ***output variables of different groups*** in __columns__. The details of all forcing variables can be found in [the description page](../data-structure/df_output.rst#df-output-variables). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T08:37:22.888148Z",
     "start_time": "2019-02-25T08:37:22.843866Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th colspan=\"15\" halign=\"left\">SUEWS</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"15\" halign=\"left\">DailyState</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>var</th>\n",
       "      <th>Kdown</th>\n",
       "      <th>Kup</th>\n",
       "      <th>Ldown</th>\n",
       "      <th>Lup</th>\n",
       "      <th>Tsurf</th>\n",
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       "      <td>40.574001</td>\n",
       "      <td>-46.53243</td>\n",
       "      <td>62.420064</td>\n",
       "      <td>3.576493</td>\n",
       "      <td>49.732605</td>\n",
       "      <td>9.832804</td>\n",
       "      <td>0.042327</td>\n",
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       "</table>\n",
       "<p>5 rows × 218 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "group                        SUEWS                                    \\\n",
       "var                          Kdown       Kup       Ldown         Lup   \n",
       "grid datetime                                                          \n",
       "98   2012-01-01 00:05:00  0.153333  0.018279  344.310184  371.986259   \n",
       "     2012-01-01 00:10:00  0.153333  0.018279  344.310184  371.986259   \n",
       "     2012-01-01 00:15:00  0.153333  0.018279  344.310184  371.986259   \n",
       "     2012-01-01 00:20:00  0.153333  0.018279  344.310184  371.986259   \n",
       "     2012-01-01 00:25:00  0.153333  0.018279  344.310184  371.986259   \n",
       "\n",
       "group                                                                \\\n",
       "var                           Tsurf         QN         QF        QS   \n",
       "grid datetime                                                         \n",
       "98   2012-01-01 00:05:00  11.775615 -27.541021  40.574001 -46.53243   \n",
       "     2012-01-01 00:10:00  11.775615 -27.541021  39.724283 -46.53243   \n",
       "     2012-01-01 00:15:00  11.775615 -27.541021  38.874566 -46.53243   \n",
       "     2012-01-01 00:20:00  11.775615 -27.541021  38.024849 -46.53243   \n",
       "     2012-01-01 00:25:00  11.775615 -27.541021  37.175131 -46.53243   \n",
       "\n",
       "group                                                                         \\\n",
       "var                              QH        QE    QHlumps   QElumps   QHresis   \n",
       "grid datetime                                                                  \n",
       "98   2012-01-01 00:05:00  62.420064  3.576493  49.732605  9.832804  0.042327   \n",
       "     2012-01-01 00:10:00  61.654096  3.492744  48.980360  9.735333  0.042294   \n",
       "     2012-01-01 00:15:00  60.885968  3.411154  48.228114  9.637861  0.042260   \n",
       "     2012-01-01 00:20:00  60.115745  3.331660  47.475869  9.540389  0.042226   \n",
       "     2012-01-01 00:25:00  59.343488  3.254200  46.723623  9.442917  0.042192   \n",
       "\n",
       "group                               ... DailyState                     \\\n",
       "var                      Rain  Irr  ...  WU_Grass2 WU_Grass3 deltaLAI   \n",
       "grid datetime                       ...                                 \n",
       "98   2012-01-01 00:05:00  0.0  0.0  ...        NaN       NaN      NaN   \n",
       "     2012-01-01 00:10:00  0.0  0.0  ...        NaN       NaN      NaN   \n",
       "     2012-01-01 00:15:00  0.0  0.0  ...        NaN       NaN      NaN   \n",
       "     2012-01-01 00:20:00  0.0  0.0  ...        NaN       NaN      NaN   \n",
       "     2012-01-01 00:25:00  0.0  0.0  ...        NaN       NaN      NaN   \n",
       "\n",
       "group                                                                    \\\n",
       "var                      LAIlumps AlbSnow DensSnow_Paved DensSnow_Bldgs   \n",
       "grid datetime                                                             \n",
       "98   2012-01-01 00:05:00      NaN     NaN            NaN            NaN   \n",
       "     2012-01-01 00:10:00      NaN     NaN            NaN            NaN   \n",
       "     2012-01-01 00:15:00      NaN     NaN            NaN            NaN   \n",
       "     2012-01-01 00:20:00      NaN     NaN            NaN            NaN   \n",
       "     2012-01-01 00:25:00      NaN     NaN            NaN            NaN   \n",
       "\n",
       "group                                                                  \\\n",
       "var                      DensSnow_EveTr DensSnow_DecTr DensSnow_Grass   \n",
       "grid datetime                                                           \n",
       "98   2012-01-01 00:05:00            NaN            NaN            NaN   \n",
       "     2012-01-01 00:10:00            NaN            NaN            NaN   \n",
       "     2012-01-01 00:15:00            NaN            NaN            NaN   \n",
       "     2012-01-01 00:20:00            NaN            NaN            NaN   \n",
       "     2012-01-01 00:25:00            NaN            NaN            NaN   \n",
       "\n",
       "group                                                               \n",
       "var                      DensSnow_BSoil DensSnow_Water  a1  a2  a3  \n",
       "grid datetime                                                       \n",
       "98   2012-01-01 00:05:00            NaN            NaN NaN NaN NaN  \n",
       "     2012-01-01 00:10:00            NaN            NaN NaN NaN NaN  \n",
       "     2012-01-01 00:15:00            NaN            NaN NaN NaN NaN  \n",
       "     2012-01-01 00:20:00            NaN            NaN NaN NaN NaN  \n",
       "     2012-01-01 00:25:00            NaN            NaN NaN NaN NaN  \n",
       "\n",
       "[5 rows x 218 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_output.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`df_output` are recorded at the same temporal resolution as `df_forcing`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T08:37:22.907369Z",
     "start_time": "2019-02-25T08:37:22.889603Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<300 * Seconds>, True)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_out = df_output.index.levels[1].freq\n",
    "(freq_out, freq_out == freq_forcing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `df_state_final`: model final states"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`df_state_final` has the identical data structure as `df_state_init` except for the extra level `datetime` in index, which stores the temporal information associated with model states. Such structure can facilitate the reuse of it as initial model states for other simulations (e.g., diagnostics of runtime model states with `save_state=True` set in `run_supy`; or simply using it as the initial conditions for future simulations starting at the ending times of previous runs).\n",
    "\n",
    "The meanings of state variables in `df_state_final` can be found in [the description page](../data-structure/df_state.rst#df-state-variables)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T08:37:22.953533Z",
     "start_time": "2019-02-25T08:37:22.909034Z"
    }
   },
   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>var</th>\n",
       "      <th>aerodynamicresistancemethod</th>\n",
       "      <th colspan=\"2\" halign=\"left\">ah_min</th>\n",
       "      <th colspan=\"2\" halign=\"left\">ah_slope_cooling</th>\n",
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       "      <th>2012-01-01 00:05:00</th>\n",
       "      <th>98</th>\n",
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       "      <td>2.7</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.46</td>\n",
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       "      <th>2013-01-01 00:05:00</th>\n",
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       "var                      aerodynamicresistancemethod ah_min        \\\n",
       "ind_dim                                            0   (0,)  (1,)   \n",
       "datetime            grid                                            \n",
       "2012-01-01 00:05:00 98                             2   15.0  15.0   \n",
       "2013-01-01 00:05:00 98                             2   15.0  15.0   \n",
       "\n",
       "var                      ah_slope_cooling      ah_slope_heating       \\\n",
       "ind_dim                              (0,) (1,)             (0,) (1,)   \n",
       "datetime            grid                                               \n",
       "2012-01-01 00:05:00 98                2.7  2.7              2.7  2.7   \n",
       "2013-01-01 00:05:00 98                2.7  2.7              2.7  2.7   \n",
       "\n",
       "var                      ahprof_24hr                                     \\\n",
       "ind_dim                       (0, 0) (0, 1) (1, 0) (1, 1) (2, 0) (2, 1)   \n",
       "datetime            grid                                                  \n",
       "2012-01-01 00:05:00 98          0.57   0.65   0.45   0.49   0.43   0.46   \n",
       "2013-01-01 00:05:00 98          0.57   0.65   0.45   0.49   0.43   0.46   \n",
       "\n",
       "var                                     ... wuprofm_24hr                  \\\n",
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       "datetime            grid                ...                                \n",
       "2012-01-01 00:05:00 98      0.4   0.47  ...       -999.0  -999.0  -999.0   \n",
       "2013-01-01 00:05:00 98      0.4   0.47  ...       -999.0  -999.0  -999.0   \n",
       "\n",
       "var                                                                       \\\n",
       "ind_dim                  (19, 1) (20, 0) (20, 1) (21, 0) (21, 1) (22, 0)   \n",
       "datetime            grid                                                   \n",
       "2012-01-01 00:05:00 98    -999.0  -999.0  -999.0  -999.0  -999.0  -999.0   \n",
       "2013-01-01 00:05:00 98    -999.0  -999.0  -999.0  -999.0  -999.0  -999.0   \n",
       "\n",
       "var                                                  z z0m_in zdm_in  \n",
       "ind_dim                  (22, 1) (23, 0) (23, 1)     0      0      0  \n",
       "datetime            grid                                              \n",
       "2012-01-01 00:05:00 98    -999.0  -999.0  -999.0  49.6    1.9   14.2  \n",
       "2013-01-01 00:05:00 98    -999.0  -999.0  -999.0  49.6    1.9   14.2  \n",
       "\n",
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