notebooks/quickstarter.ipynb

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    "# `naturf` Quickstarter\n",
    "\n",
    "The Neighborhood Adaptive Tissues for Urban Resilience Futures tool (`naturf`) is a Python workflow that generates files readable by the Weather Research and Forecasting (WRF) model. `naturf` uses *geopandas* and *hamilton* to calculate 132 building parameters from shapefiles with building footprint and height information. These parameters can be collected and used in many formats, and the primary output is a binary file configured for input to WRF. This workflow is a flexible adaptation of the National/World Urban Database and Access Portal Tool (NUDAPT/WUDAPT) that can be used with any study area at any spatial resolution. The climate modeling community and urban planners can identify the effects of building/neighborhood morphology on the microclimate using the urban parameters and WRF-readable files produced by `naturf`. The source code for `naturf` can be found on [GitHub](https://github.com/IMMM-SFA/naturf/tree/main), and more information on the urban parameters calculated can be found in the [documentation](https://immm-sfa.github.io/naturf/). Cases 1 and 2 below illustrate using *hamilton* to pull different outputs from `naturf` using example data. Case 3 shows how to use `naturf` with user data.\n",
    "\n",
    "> **_NOTE:_** [Run this quickstarter interactively in browser!](https://naturf.msdlive.org)"
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   "source": [
    "from hamilton import telemetry\n",
    "from importlib.resources import files\n",
    "from naturf import driver"
   ]
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    "## Case 1\n",
    "\n",
    "`naturf` requires two inputs: an input dictionary with the location of the input buildings shapefile, and a list of the desired outputs. For this first case, we can look at three outputs: `plan_area_fraction`, `area_weighted_mean_of_building_heights`, and `sky_view_factor`."
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    "input_shapefile_path = str(files(\"naturf\").joinpath(\"data\").joinpath(\"C-5.shp\"))\n",
    "\n",
    "inputs = {\n",
    "    \"input_shapefile\": input_shapefile_path,\n",
    "}\n",
    "\n",
    "outputs = [\n",
    "    \"plan_area_fraction\",\n",
    "    \"area_weighted_mean_of_building_heights\",\n",
    "    \"sky_view_factor\"\n",
    "    ]\n",
    "\n",
    "# Disable Hamilton anonymous user data tracking\n",
    "telemetry.disable_telemetry()\n",
    "\n",
    "model = driver.Model(inputs, outputs)"
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   "source": [
    "Render the Directed Acyclic Graph (DAG) to show which functions the model will call in order to create the desired output. This does not execute the model!\n",
    "\n",
    "Note: you will need to both install [graphviz](https://graphviz.org) following the instructions on their [download page](https://graphviz.org/download/), and `$ pip install graphviz` to visualize the Directed Acyclic Graph (DAG). "
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     },
     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "model.graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fac9404",
   "metadata": {},
   "source": [
    "Execute the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eca2c34b680305f8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-17T20:06:17.613667Z",
     "start_time": "2024-01-17T20:06:17.180922Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Naturf DAG -> building_area                         :  32%|███▏      | 6/19 [00:00<00:01,  9.13funcs/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Naturf DAG -> Execution Complete!                   : 100%|██████████| 19/19 [00:08<00:00,  2.13funcs/s]\n"
     ]
    }
   ],
   "source": [
    "df = model.execute()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6207db02",
   "metadata": {},
   "source": [
    "View the first three rows of the output dataframe. Note that `naturf` only outputs the desired columns, as specified above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "32c44419-f224-4e57-8958-91c3a068a9c1",
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>plan_area_fraction</th>\n",
       "      <th>area_weighted_mean_of_building_heights</th>\n",
       "      <th>sky_view_factor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.009073</td>\n",
       "      <td>2.660000</td>\n",
       "      <td>0.168611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.010528</td>\n",
       "      <td>2.660000</td>\n",
       "      <td>0.168611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.128917</td>\n",
       "      <td>20.193242</td>\n",
       "      <td>0.730937</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   plan_area_fraction  area_weighted_mean_of_building_heights  sky_view_factor\n",
       "0            0.009073                                2.660000         0.168611\n",
       "1            0.010528                                2.660000         0.168611\n",
       "2            0.128917                               20.193242         0.730937"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9b9c2a3-5036-4b73-bc35-bea844982c7a",
   "metadata": {},
   "source": [
    "## Case 2: Full Model Run\n",
    "\n",
    "Here we run the full model in order to get the required inputs to WRF. WRF requires two files: a binary file with the urban parameter data gridded and written row-by-row into binary and an index file that tells WRF where to put and how to display the data. See the [WRF User Guide](https://homepages.see.leeds.ac.uk/~lecrrb/wrf/aRWUsersGuide.pdf) for more information. The nodes `write_binary` and `write_index` will write the required files to the working directory.\n",
    "\n",
    "We include `merge_parameters` here to better demonstrate the output DataFrame, if you don't include it in the run, the output DataFrame shows only `write_binary` and `write_index`. This is great when you're running the model to create outputs for WRF, but not great for visualizing the output parameters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0227eb0f-fcc1-4bf3-a14f-af3e5eae38a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "outputs = [\n",
    "    \"merge_parameters\",\n",
    "    \"write_binary\", \n",
    "    \"write_index\",\n",
    "    ]\n",
    "\n",
    "model = driver.Model(inputs, outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1951f504-3e4a-4f71-9c9c-be598ca93b57",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "<!-- frontal_length&#45;&gt;frontal_area -->\n",
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       "<!-- aggregate_rasters&#45;&gt;raster_to_numpy -->\n",
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       "<!-- filter_height_range&#45;&gt;building_geometry -->\n",
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       "<title>filter_height_range&#45;&gt;building_height</title>\n",
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       "<!-- filter_height_range&#45;&gt;building_id -->\n",
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       "<title>filter_height_range&#45;&gt;building_id</title>\n",
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       "<title>plan_area_density&#45;&gt;merge_parameters</title>\n",
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       "<!-- rooftop_area_density -->\n",
       "<g id=\"node37\" class=\"node\">\n",
       "<title>rooftop_area_density</title>\n",
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       "<text text-anchor=\"start\" x=\"2398.67\" y=\"-754.5\" font-family=\"Helvetica,sans-Serif\" font-weight=\"bold\" font-size=\"14.00\">rooftop_area_density</text>\n",
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       "<title>plan_area_density&#45;&gt;rooftop_area_density</title>\n",
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       "<title>grimmond_oke_roughness_length</title>\n",
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       "<title>mean_building_height</title>\n",
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       "<text text-anchor=\"start\" x=\"2154.95\" y=\"-1059.5\" font-family=\"Helvetica,sans-Serif\" font-style=\"italic\" font-size=\"14.00\">Series</text>\n",
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       "<!-- mean_building_height&#45;&gt;height_to_width_ratio -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>mean_building_height&#45;&gt;height_to_width_ratio</title>\n",
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       "<title>mean_building_height&#45;&gt;merge_parameters</title>\n",
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       "<!-- wall_angle_direction_length -->\n",
       "<g id=\"node24\" class=\"node\">\n",
       "<title>wall_angle_direction_length</title>\n",
       "<path fill=\"#b4d8e4\" stroke=\"black\" d=\"M1128.6,-794.6C1128.6,-794.6 949.5,-794.6 949.5,-794.6 943.5,-794.6 937.5,-788.6 937.5,-782.6 937.5,-782.6 937.5,-743 937.5,-743 937.5,-737 943.5,-731 949.5,-731 949.5,-731 1128.6,-731 1128.6,-731 1134.6,-731 1140.6,-737 1140.6,-743 1140.6,-743 1140.6,-782.6 1140.6,-782.6 1140.6,-788.6 1134.6,-794.6 1128.6,-794.6\"/>\n",
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       "<!-- wall_length -->\n",
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       "<title>wall_length</title>\n",
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       "<!-- wall_angle_direction_length&#45;&gt;wall_length -->\n",
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       "<!-- rasterize_parameters&#45;&gt;aggregate_rasters -->\n",
       "<g id=\"edge21\" class=\"edge\">\n",
       "<title>rasterize_parameters&#45;&gt;aggregate_rasters</title>\n",
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       "<!-- standard_deviation_of_building_heights -->\n",
       "<g id=\"node26\" class=\"node\">\n",
       "<title>standard_deviation_of_building_heights</title>\n",
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       "<!-- standard_deviation_of_building_heights&#45;&gt;merge_parameters -->\n",
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       "<!-- vertical_distribution_of_building_heights -->\n",
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       "<!-- merge_parameters&#45;&gt;rasterize_parameters -->\n",
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       "<!-- distance_between_buildings -->\n",
       "<g id=\"node30\" class=\"node\">\n",
       "<title>distance_between_buildings</title>\n",
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       "<text text-anchor=\"start\" x=\"1758.97\" y=\"-885.5\" font-family=\"Helvetica,sans-Serif\" font-weight=\"bold\" font-size=\"14.00\">distance_between_buildings</text>\n",
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       "<!-- average_distance_between_buildings -->\n",
       "<g id=\"node36\" class=\"node\">\n",
       "<title>average_distance_between_buildings</title>\n",
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       "<!-- distance_between_buildings&#45;&gt;average_distance_between_buildings -->\n",
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       "<title>distance_between_buildings&#45;&gt;average_distance_between_buildings</title>\n",
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       "<g id=\"edge8\" class=\"edge\">\n",
       "<title>building_surface_area&#45;&gt;lot_area</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1605.23,-574.62C1627.98,-563.6 1652.81,-550.55 1674.55,-536.8 1688.45,-528.01 1688.89,-521.27 1703.55,-513.8 1734.98,-497.77 1773.5,-487.31 1803.18,-480.97\"/>\n",
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       "<g id=\"edge15\" class=\"edge\">\n",
       "<title>building_surface_area&#45;&gt;building_surface_area_to_plan_area_ratio</title>\n",
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       "<title>building_surface_area&#45;&gt;complete_aspect_ratio</title>\n",
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       "<g id=\"edge12\" class=\"edge\">\n",
       "<title>building_geometry&#45;&gt;building_area</title>\n",
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       "<title>building_geometry&#45;&gt;write_index</title>\n",
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       "<!-- building_geometry&#45;&gt;wall_angle_direction_length -->\n",
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       "<title>building_geometry&#45;&gt;wall_angle_direction_length</title>\n",
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       "<!-- building_geometry&#45;&gt;buildings_intersecting_plan_area -->\n",
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       "<title>building_geometry&#45;&gt;buildings_intersecting_plan_area</title>\n",
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       "<title>numpy_to_binary&#45;&gt;write_binary</title>\n",
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       "<title>building_plan_area</title>\n",
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       "<title>average_distance_between_buildings&#45;&gt;height_to_width_ratio</title>\n",
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     "execution_count": 7,
     "metadata": {},
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   "source": [
    "model.graph()"
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  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a07e303a-5d78-4b0d-8ae2-1e46e3ac8bfb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Naturf DAG -> Execution Complete!                      : 100%|██████████| 46/46 [00:13<00:00,  3.54funcs/s]\n",
      "WARNING: a single pandas index was found, but there are also 2 outputs without an index. Please check whether the dataframe created matches what what you expect to happen.\n"
     ]
    }
   ],
   "source": [
    "df = model.execute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6e055093",
   "metadata": {},
   "outputs": [
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       "   merge_parameters.frontal_area_north_0  \\\n",
       "0                               0.001903   \n",
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       "3                               0.023445   \n",
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       "\n",
       "   merge_parameters.frontal_area_north_1  \\\n",
       "0                               0.000000   \n",
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       "2                               0.019133   \n",
       "3                               0.023445   \n",
       "4                               0.036085   \n",
       "\n",
       "   merge_parameters.frontal_area_north_2  \\\n",
       "0                               0.000000   \n",
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       "2                               0.019133   \n",
       "3                               0.009331   \n",
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       "\n",
       "   merge_parameters.frontal_area_north_3  \\\n",
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       "2                               0.019133   \n",
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       "\n",
       "   merge_parameters.frontal_area_north_5  \\\n",
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       "\n",
       "   merge_parameters.frontal_area_north_6  \\\n",
       "0                                    0.0   \n",
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       "2                                    0.0   \n",
       "3                                    0.0   \n",
       "4                                    0.0   \n",
       "\n",
       "   merge_parameters.frontal_area_north_7  \\\n",
       "0                                    0.0   \n",
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       "2                                    0.0   \n",
       "3                                    0.0   \n",
       "4                                    0.0   \n",
       "\n",
       "   merge_parameters.frontal_area_north_8  \\\n",
       "0                                      0   \n",
       "1                                      0   \n",
       "2                                      0   \n",
       "3                                      0   \n",
       "4                                      0   \n",
       "\n",
       "   merge_parameters.frontal_area_north_9  ...  \\\n",
       "0                                      0  ...   \n",
       "1                                      0  ...   \n",
       "2                                      0  ...   \n",
       "3                                      0  ...   \n",
       "4                                      0  ...   \n",
       "\n",
       "   merge_parameters.vertical_distribution_of_building_heights_9  \\\n",
       "0                                                  0              \n",
       "1                                                  0              \n",
       "2                                                  0              \n",
       "3                                                  0              \n",
       "4                                                  0              \n",
       "\n",
       "   merge_parameters.vertical_distribution_of_building_heights_10  \\\n",
       "0                                                  0               \n",
       "1                                                  0               \n",
       "2                                                  0               \n",
       "3                                                  0               \n",
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       "\n",
       "   merge_parameters.vertical_distribution_of_building_heights_11  \\\n",
       "0                                                  0               \n",
       "1                                                  0               \n",
       "2                                                  0               \n",
       "3                                                  0               \n",
       "4                                                  0               \n",
       "\n",
       "   merge_parameters.vertical_distribution_of_building_heights_12  \\\n",
       "0                                                  0               \n",
       "1                                                  0               \n",
       "2                                                  0               \n",
       "3                                                  0               \n",
       "4                                                  0               \n",
       "\n",
       "   merge_parameters.vertical_distribution_of_building_heights_13  \\\n",
       "0                                                  0               \n",
       "1                                                  0               \n",
       "2                                                  0               \n",
       "3                                                  0               \n",
       "4                                                  0               \n",
       "\n",
       "   merge_parameters.vertical_distribution_of_building_heights_14  \\\n",
       "0                                                  0               \n",
       "1                                                  0               \n",
       "2                                                  0               \n",
       "3                                                  0               \n",
       "4                                                  0               \n",
       "\n",
       "                  merge_parameters.building_geometry  \\\n",
       "0  POLYGON ((-77.04892 38.87526, -77.04893 38.875...   \n",
       "1  POLYGON ((-77.04882 38.87532, -77.04884 38.875...   \n",
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       "4  POLYGON ((-77.02369 38.87912, -77.02309 38.878...   \n",
       "\n",
       "   merge_parameters.building_count  write_binary  write_index  \n",
       "0                                1          None         None  \n",
       "1                                1          None         None  \n",
       "2                                1          None         None  \n",
       "3                                1          None         None  \n",
       "4                                1          None         None  \n",
       "\n",
       "[5 rows x 136 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f8271e2",
   "metadata": {},
   "source": [
    "We have 136 columns here, which are the 132 output parameters plus 4 other parameters: `merge_parameters.building_geometry`, `merge_parameters.building_count`, `write_binary`, and `write_index`. The first two are used for calculations in the model but are not output parameters, and the last two are the nodes that are called to output the required WRF files. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db7c8f65",
   "metadata": {},
   "source": [
    "## Case 3\n",
    "\n",
    "If the user has their own shapefile to process, the config values and input path must be changed to reflect that data. The only config values that require changes are `DATA_ID_FIELD_NAME` and `DATA_HEIGHT_FIELD_NAME`. Config values can be changed by importing the `Settings` class from `naturf.config`. NOTE: Shapefiles input to `naturf` need to be in a projected coordinate system such as Albers Equal Area Conic. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e9f5fa66-7c36-4fec-bf04-e5a3a30913b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from naturf.config import Settings\n",
    "\n",
    "# Settings.DATA_ID_FIELD_NAME = \"YOUR_ID_FIELD_NAME_HERE\"\n",
    "# Settings.DATA_HEIGHT_FIELD_NAME = \"YOUR_HEIGHT_FIELD_NAME_HERE\"\n",
    "\n",
    "# input_shapefile_path = \"PATH_TO_YOUR_DATA\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dccaadaf",
   "metadata": {},
   "source": [
    "From here, the normal workflow can be continued."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8c3095b3-96da-4bd6-af50-a336ccbad43d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# inputs = {\"input_shapefile\": input_shapefile_path}\n",
    "# outputs = [\"DESIRED_PARAMETERS_HERE\"]\n",
    "# model = driver.Model(inputs, outputs)\n",
    "\n",
    "# df = model.execute()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fed10924",
   "metadata": {},
   "source": [
    "Currently, `naturf` does not have a function to convert the calculated parameters to any files other than the binary file to be given to WRF. However, this can be achieved using the `df` variable created when executing the model. For example, if we want to write all of the calculated parameters for each building with the building IDs to a CSV file, all we need to do is set the `outputs` to `[\"building_ids\", \"merge_parameters\"]` and write the `df` variable to a CSV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eb31af72-71e6-4441-8c3b-00b2ea895539",
   "metadata": {},
   "outputs": [],
   "source": [
    "# outputs = [\"building_id\", \"merge_parameters\"]\n",
    "# model = driver.Model(inputs, outputs)\n",
    "\n",
    "# df = model.execute()\n",
    "# df.to_csv(\"outfile.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45b95e28",
   "metadata": {},
   "source": [
    "## Optional: Hamilton UI's Interactive Dashboard\n",
    "\n",
    "Since we're using `hamilton` to run `naturf`, users can log each run to a [self-hostable Hamilton UI](https://github.com/dagworks-inc/hamilton/tree/main/ui), or via the hosted version by DAGWorks Inc. (researchers/academics have access to the free tier -- email support for details) by signing up at [dagworks.io](www.dagworks.io/hamilton). For both, one needs to have a project and username created. Then either set the environment variables `DAGWORKS_API_KEY` (if using the hosted version), `HAMILTON_UI_USERNAME`, and `HAMILTON_UI_PROJECT_ID` below or set it in the module directly. Then when the model is executed, the run will be logged to the Hamilton UI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f65c90521777512a",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# driver.DAGWORKS_API_KEY = \"your_api_key\"  # only required if using the hosted version of the Hamilton UI\n",
    "# driver.HAMILTON_UI_USERNAME = \"your_username\"\n",
    "# driver.HAMILTON_UI_PROJECT_ID = \"your_project_id\"  # some integer.\n",
    "\n",
    "# model = driver.Model(inputs, [\"input_shapefile_df\"])\n",
    "# model.execute()"
   ]
  }
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