{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1ff5e433",
   "metadata": {},
   "source": [
    "# Preprocessing hydrography\n",
    "Sometimes we might need to customize a pre-existing hydrography dataset in some way, for example by adding additional streams that weren't mapped or fixing errors in mapped streams. If we're working with a large area or with the High Resolution version of NHDPlus, we may also need to combine file sets from multiple areas, or prune intermittent headwater streams from the dataset. \n",
    "\n",
    "The preprocessing module of SFRmaker contains functions for working with both NHDPlus version 2 and NHDPlus High Resolution (HR) data. \n",
    "\n",
    "As of SFRmaker 0.11.3, the preprocessing module is still a work in progress, and liable to change as improvements are made to streamline the workflow. This page is based on the ``preprocessing_demo.ipynb`` notebook in the ``examples/`` folder, and therefore reflects the current state of the code."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab0a7ff2",
   "metadata": {},
   "source": [
    "## NHDPlus High Resolution\n",
    "The functions below accept NHDPlus High Resolution geodatabase files as input, and output single shapefiles of merged and culled flowlines with all of the information needed to build an SFR package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "619f9195",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-09T18:09:55.562688Z",
     "iopub.status.busy": "2025-05-09T18:09:55.562490Z",
     "iopub.status.idle": "2025-05-09T18:09:56.621131Z",
     "shell.execute_reply": "2025-05-09T18:09:56.620627Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/runner/micromamba/envs/sfrmaker_ci/lib/python3.13/site-packages/geopandas/_compat.py:7: DeprecationWarning: The 'shapely.geos' module is deprecated, and will be removed in a future version. All attributes of 'shapely.geos' are available directly from the top-level 'shapely' namespace (since shapely 2.0.0).\n",
      "  import shapely.geos\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "from sfrmaker.preprocessing import (\n",
    "    preprocess_nhdplus_hr_flowlines,\n",
    "    preprocess_nhdplus_hr_waterbodies\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c21d3393",
   "metadata": {},
   "source": [
    "In this example, we have two NHDPlus HR databases for adjacent watersheds, that we want to merge and cull to a model extent. Plots of these datasets are displayed in the [Using SFRmaker with NHDPlus High Resolution\n",
    "](https://doi-usgs.github.io/sfrmaker/notebooks/lines_from_NHDPlusHR_demo.html) example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bb1d52ad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-09T18:09:56.623518Z",
     "iopub.status.busy": "2025-05-09T18:09:56.623133Z",
     "iopub.status.idle": "2025-05-09T18:09:56.626569Z",
     "shell.execute_reply": "2025-05-09T18:09:56.626078Z"
    }
   },
   "outputs": [],
   "source": [
    "nhdplus_paths = [\n",
    "    '../neversink_rondout/NHDPLUS_HR_1.gdb', \n",
    "    '../neversink_rondout/NHDPLUS_HR_2.gdb'\n",
    "    ]\n",
    "\n",
    "model_outline = '../neversink_rondout/Model_Extent.shp'\n",
    "\n",
    "outfolder = Path('output')\n",
    "outfolder.mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8728f57",
   "metadata": {},
   "source": [
    "With `preprocess_nhdplus_hr_flowlines()` function, we can categorically exclude flowlines by FCODE (feature type), by selecting the FCODEs that we want to retain. We can also drop specific lines by their `NHDPlusID`, and optionally, all of the flowlines that route to them. With the `active_area` argument, flowlines can be clipped to a model area specified as a polygon shapefile or bounding box in the same coordinate reference system as the input flowlines.\n",
    "\n",
    "**Pending features (not implemented yet):**\n",
    "* `drop_ids_downstream` argument that removes a specified line and all lines downstream\n",
    "* adapting the other funtions (below) to work with NHDPlus HR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3235b2fb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-09T18:09:56.628397Z",
     "iopub.status.busy": "2025-05-09T18:09:56.628262Z",
     "iopub.status.idle": "2025-05-09T18:09:56.758397Z",
     "shell.execute_reply": "2025-05-09T18:09:56.757970Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reading ../neversink_rondout/NHDPLUS_HR_1.gdb...\n",
      "filtering flowlines...\n",
      "\n",
      "Getting routing information from NHDPlus HR Plusflow table...\n",
      "finished in 0.00s\n",
      "\n",
      "finished in 0.07s\n",
      "\n",
      "reading ../neversink_rondout/NHDPLUS_HR_2.gdb...\n",
      "filtering flowlines...\n",
      "\n",
      "Getting routing information from NHDPlus HR Plusflow table...\n",
      "finished in 0.00s\n",
      "\n",
      "finished in 0.10s\n",
      "\n",
      "wrote output/preprocessed_flowlines.shp\n"
     ]
    }
   ],
   "source": [
    "# drop lines representing storm sewers and aquaducts, etc.\n",
    "keep_fcodes = {46003,  # intermittent streams\n",
    "               46006,  # perennial streams\n",
    "               55800,  # artificial path (thru lakes)\n",
    "               33600,  # canal/ditch (includes many wetlands, and the MS River)\n",
    "               33400  # connector (some wetlands)\n",
    "               }\n",
    "\n",
    "# drop these NHDPlusIDs and all IDs above them\n",
    "drop_ids_upstream_of = {'10000700059483'}\n",
    "\n",
    "# drop specific IDs\n",
    "drop_ids = None\n",
    "\n",
    "preprocess_nhdplus_hr_flowlines(nhdplus_paths, active_area=model_outline,\n",
    "                        keep_fcodes=keep_fcodes,\n",
    "                        drop_ids_upstream=drop_ids_upstream_of,\n",
    "                        dest_crs=26918, outfile=outfolder / 'preprocessed_flowlines.shp')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "768b73bb",
   "metadata": {},
   "source": [
    "A similar function merges and culls waterbody (polygon) features from one or more NHDPlus HR datasets. Waterbodies can be culled by their `NHDPlusID` or by specifying a minimum area in km<sup>2</sup>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "47229b35",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-09T18:09:56.760163Z",
     "iopub.status.busy": "2025-05-09T18:09:56.759972Z",
     "iopub.status.idle": "2025-05-09T18:09:56.776628Z",
     "shell.execute_reply": "2025-05-09T18:09:56.776281Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote output/preprocessed_flowlines.shp\n"
     ]
    }
   ],
   "source": [
    "nhdplus_path = '../../sfrmaker/test/data/nhdplus_hr_waterbodies.shp'\n",
    "\n",
    "# drop these waterbodies, regardless of size\n",
    "drop_waterbodies = set()\n",
    "\n",
    "preprocess_nhdplus_hr_waterbodies(nhdplus_path, \n",
    "                                    active_area=(-151.00350, 60.64855, -150.96778, 60.67559), \n",
    "                                    drop_waterbodies=drop_waterbodies,\n",
    "                                    min_areasqkm=0.05,\n",
    "                                    dest_crs=26905, outfile=outfolder / 'preprocessed_flowlines.shp')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d0a1394",
   "metadata": {},
   "source": [
    "## NHDPlus version 2\n",
    "The functions below accept NHDPlus version 2 data as input, and output either a culled and merged set of NHDPlus version 2 files (``cull_flowlines`` function) or a single shapefile with all of the information needed to build an SFR package (``preprocess_preprocess_nhdplus`` function).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "08783e35",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-09T18:09:56.778244Z",
     "iopub.status.busy": "2025-05-09T18:09:56.778112Z",
     "iopub.status.idle": "2025-05-09T18:09:56.780207Z",
     "shell.execute_reply": "2025-05-09T18:09:56.779869Z"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "from sfrmaker.preprocessing import (\n",
    "    clip_flowlines_to_polygon,\n",
    "    cull_flowlines,\n",
    "    preprocess_nhdplus\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6ee2799",
   "metadata": {},
   "source": [
    "We start by supplying one or more paths to NHDPlus data (in the same structure as downloaded). Multiple drainage basins can be combined by included multiple paths in the list (for example, the Great Lakes and Upper Mississippi basins as shown here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f61d69a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-09T18:09:56.781755Z",
     "iopub.status.busy": "2025-05-09T18:09:56.781624Z",
     "iopub.status.idle": "2025-05-09T18:09:56.787504Z",
     "shell.execute_reply": "2025-05-09T18:09:56.787116Z"
    }
   },
   "outputs": [],
   "source": [
    "NHDPlus_paths_list = ['/NHDPlusGL/NHDPlus04/',\n",
    "                      '/NHDPlusMS/NHDPlus07/']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11d2f94e",
   "metadata": {},
   "source": [
    "### Merging and culling flowlines\n",
    "The ``cull_flowlines()`` function culls NHDPlus version 2 data to a bounding box defined by an ``active_area`` coordinate tuple or polygon, and to flowlines with arbolate sums greater than specified thresholds. There are two arbolate sum thresholds:  \n",
    "\n",
    "* ``asum_thresh``: all non perennial streams (with FCodes other than 46006) with arbolate sums less than this amount (in km) will be culled.\n",
    "* ``intermittent_streams_asum_thresh``: streams coded as intermittent (FCode 46003) with arbolate sums less than this amount will be culled. This allows other features such as ditches or artifical paths to be culled more aggressively than intermittent streams.\n",
    "    \n",
    "Neither of the arbolate sum thresholds apply to streams classified as perennial, which are assumed to be important boundary conditions that should be included in the model. With the SFR package, streams are allowed to dry, in which case they have no effect on the groundwater solution (until there is water again). Therefore, the goal of culling of streams is mostly to achieve a stream network that is computationally managable and appropriate for the scale of the model, while retaining features that may affect groundwater flow.\n",
    "\n",
    "``cull_flowlines()`` can also remove lines that are isolated from the stream network (``cull_isolated=True``) or are missing attribute information (``cull_invalid=True``). The output data are merged in a single set of NHDPlus version 2 files that are saved to ``outfolder``:\n",
    "\n",
    "    elevslope.dbf\n",
    "    PlusFlow.dbf\n",
    "    PlusFlowlineVAA.dbf\n",
    "    NHDFlowline.shp\n",
    "\n",
    "\n",
    "The Tyler Forks example is contained in the Great Lakes basin, so we just need one path. We also need to specify a folder for writing the merged and culled files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "84909bed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-09T18:09:56.789140Z",
     "iopub.status.busy": "2025-05-09T18:09:56.789040Z",
     "iopub.status.idle": "2025-05-09T18:09:56.904540Z",
     "shell.execute_reply": "2025-05-09T18:09:56.904063Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for basins:\n",
      "../tylerforks/NHDPlus/\n",
      "\n",
      "reading ../tylerforks/active_area.shp...\n",
      "--> building dataframe... (may take a while for large shapefiles)\n",
      "\n",
      "reading ../tylerforks/NHDPlus/NHDSnapshot/Hydrography/NHDFlowline.shp...\n",
      "filtering on bounding box -90.59552642527598, 46.37324928457199, -90.45520721646749, 46.43603727904705...\n",
      "--> building dataframe... (may take a while for large shapefiles)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "reading ../tylerforks/NHDPlus/NHDPlusAttributes/PlusFlowlineVAA.dbf...\n",
      "--> building dataframe... (may take a while for large shapefiles)\n",
      "\n",
      "reading ../tylerforks/NHDPlus/NHDPlusAttributes/PlusFlow.dbf...\n",
      "--> building dataframe... (may take a while for large shapefiles)\n",
      "\n",
      "reading ../tylerforks/NHDPlus/NHDPlusAttributes/elevslope.dbf...\n",
      "--> building dataframe... (may take a while for large shapefiles)\n",
      "writing output/PlusFlowlineVAA_gt3km.dbf... Done\n",
      "writing output/PlusFlow_gt3km.dbf... Done\n",
      "writing output/elevslope_gt3km.dbf... Done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/runner/micromamba/envs/sfrmaker_ci/lib/python3.13/site-packages/gisutils/shapefile.py:278: FionaDeprecationWarning: instances of this class -- CRS, geometry, and feature objects -- will become immutable in fiona version 2.0\n",
      "  props['geometry'] = line.get('geometry', None)\n",
      "/Users/runner/micromamba/envs/sfrmaker_ci/lib/python3.13/site-packages/gisutils/shapefile.py:278: FionaDeprecationWarning: instances of this class -- CRS, geometry, and feature objects -- will become immutable in fiona version 2.0\n",
      "  props['geometry'] = line.get('geometry', None)\n",
      "/Users/runner/micromamba/envs/sfrmaker_ci/lib/python3.13/site-packages/gisutils/shapefile.py:278: FionaDeprecationWarning: instances of this class -- CRS, geometry, and feature objects -- will become immutable in fiona version 2.0\n",
      "  props['geometry'] = line.get('geometry', None)\n",
      "/Users/runner/micromamba/envs/sfrmaker_ci/lib/python3.13/site-packages/gisutils/shapefile.py:278: FionaDeprecationWarning: instances of this class -- CRS, geometry, and feature objects -- will become immutable in fiona version 2.0\n",
      "  props['geometry'] = line.get('geometry', None)\n",
      "/Users/runner/micromamba/envs/sfrmaker_ci/lib/python3.13/site-packages/gisutils/shapefile.py:278: FionaDeprecationWarning: instances of this class -- CRS, geometry, and feature objects -- will become immutable in fiona version 2.0\n",
      "  props['geometry'] = line.get('geometry', None)\n",
      "/Users/runner/micromamba/envs/sfrmaker_ci/lib/python3.13/site-packages/gisutils/shapefile.py:278: FionaDeprecationWarning: instances of this class -- CRS, geometry, and feature objects -- will become immutable in fiona version 2.0\n",
      "  props['geometry'] = line.get('geometry', None)\n",
      "/Users/runner/micromamba/envs/sfrmaker_ci/lib/python3.13/site-packages/gisutils/shapefile.py:278: FionaDeprecationWarning: instances of this class -- CRS, geometry, and feature objects -- will become immutable in fiona version 2.0\n",
      "  props['geometry'] = line.get('geometry', None)\n",
      "/Users/runner/micromamba/envs/sfrmaker_ci/lib/python3.13/site-packages/gisutils/shapefile.py:278: FionaDeprecationWarning: instances of this class -- CRS, geometry, and feature objects -- will become immutable in fiona version 2.0\n",
      "  props['geometry'] = line.get('geometry', None)\n",
      "/Users/runner/micromamba/envs/sfrmaker_ci/lib/python3.13/site-packages/gisutils/shapefile.py:278: FionaDeprecationWarning: instances of this class -- CRS, geometry, and feature objects -- will become immutable in fiona version 2.0\n",
      "  props['geometry'] = line.get('geometry', None)\n",
      "/Users/runner/micromamba/envs/sfrmaker_ci/lib/python3.13/site-packages/gisutils/shapefile.py:278: FionaDeprecationWarning: instances of this class -- CRS, geometry, and feature objects -- will become immutable in fiona version 2.0\n",
      "  props['geometry'] = line.get('geometry', None)\n",
      "/Users/runner/micromamba/envs/sfrmaker_ci/lib/python3.13/site-packages/gisutils/shapefile.py:278: FionaDeprecationWarning: instances of this class -- CRS, geometry, and feature objects -- will become immutable in fiona version 2.0\n",
      "  props['geometry'] = line.get('geometry', None)\n",
      "/Users/runner/micromamba/envs/sfrmaker_ci/lib/python3.13/site-packages/gisutils/shapefile.py:278: FionaDeprecationWarning: instances of this class -- CRS, geometry, and feature objects -- will become immutable in fiona version 2.0\n",
      "  props['geometry'] = line.get('geometry', None)\n",
      "/Users/runner/micromamba/envs/sfrmaker_ci/lib/python3.13/site-packages/gisutils/shapefile.py:278: FionaDeprecationWarning: instances of this class -- CRS, geometry, and feature objects -- will become immutable in fiona version 2.0\n",
      "  props['geometry'] = line.get('geometry', None)\n",
      "/Users/runner/micromamba/envs/sfrmaker_ci/lib/python3.13/site-packages/gisutils/shapefile.py:278: FionaDeprecationWarning: instances of this class -- CRS, geometry, and feature objects -- will become immutable in fiona version 2.0\n",
      "  props['geometry'] = line.get('geometry', None)\n",
      "/Users/runner/micromamba/envs/sfrmaker_ci/lib/python3.13/site-packages/gisutils/shapefile.py:278: FionaDeprecationWarning: instances of this class -- CRS, geometry, and feature objects -- will become immutable in fiona version 2.0\n",
      "  props['geometry'] = line.get('geometry', None)\n",
      "/Users/runner/micromamba/envs/sfrmaker_ci/lib/python3.13/site-packages/gisutils/shapefile.py:278: FionaDeprecationWarning: instances of this class -- CRS, geometry, and feature objects -- will become immutable in fiona version 2.0\n",
      "  props['geometry'] = line.get('geometry', None)\n",
      "/Users/runner/micromamba/envs/sfrmaker_ci/lib/python3.13/site-packages/gisutils/shapefile.py:278: FionaDeprecationWarning: instances of this class -- CRS, geometry, and feature objects -- will become immutable in fiona version 2.0\n",
      "  props['geometry'] = line.get('geometry', None)\n",
      "/Users/runner/micromamba/envs/sfrmaker_ci/lib/python3.13/site-packages/gisutils/shapefile.py:278: FionaDeprecationWarning: instances of this class -- CRS, geometry, and feature objects -- will become immutable in fiona version 2.0\n",
      "  props['geometry'] = line.get('geometry', None)\n",
      "/Users/runner/micromamba/envs/sfrmaker_ci/lib/python3.13/site-packages/gisutils/shapefile.py:278: FionaDeprecationWarning: instances of this class -- CRS, geometry, and feature objects -- will become immutable in fiona version 2.0\n",
      "  props['geometry'] = line.get('geometry', None)\n",
      "/Users/runner/micromamba/envs/sfrmaker_ci/lib/python3.13/site-packages/gisutils/shapefile.py:278: FionaDeprecationWarning: instances of this class -- CRS, geometry, and feature objects -- will become immutable in fiona version 2.0\n",
      "  props['geometry'] = line.get('geometry', None)\n",
      "/Users/runner/micromamba/envs/sfrmaker_ci/lib/python3.13/site-packages/gisutils/shapefile.py:278: FionaDeprecationWarning: instances of this class -- CRS, geometry, and feature objects -- will become immutable in fiona version 2.0\n",
      "  props['geometry'] = line.get('geometry', None)\n",
      "/Users/runner/micromamba/envs/sfrmaker_ci/lib/python3.13/site-packages/gisutils/shapefile.py:278: FionaDeprecationWarning: instances of this class -- CRS, geometry, and feature objects -- will become immutable in fiona version 2.0\n",
      "  props['geometry'] = line.get('geometry', None)\n",
      "/Users/runner/micromamba/envs/sfrmaker_ci/lib/python3.13/site-packages/gisutils/shapefile.py:278: FionaDeprecationWarning: instances of this class -- CRS, geometry, and feature objects -- will become immutable in fiona version 2.0\n",
      "  props['geometry'] = line.get('geometry', None)\n",
      "/Users/runner/micromamba/envs/sfrmaker_ci/lib/python3.13/site-packages/gisutils/shapefile.py:278: FionaDeprecationWarning: instances of this class -- CRS, geometry, and feature objects -- will become immutable in fiona version 2.0\n",
      "  props['geometry'] = line.get('geometry', None)\n",
      "/Users/runner/micromamba/envs/sfrmaker_ci/lib/python3.13/site-packages/gisutils/shapefile.py:278: FionaDeprecationWarning: instances of this class -- CRS, geometry, and feature objects -- will become immutable in fiona version 2.0\n",
      "  props['geometry'] = line.get('geometry', None)\n",
      "/Users/runner/micromamba/envs/sfrmaker_ci/lib/python3.13/site-packages/gisutils/shapefile.py:278: FionaDeprecationWarning: instances of this class -- CRS, geometry, and feature objects -- will become immutable in fiona version 2.0\n",
      "  props['geometry'] = line.get('geometry', None)\n",
      "/Users/runner/micromamba/envs/sfrmaker_ci/lib/python3.13/site-packages/gisutils/shapefile.py:278: FionaDeprecationWarning: instances of this class -- CRS, geometry, and feature objects -- will become immutable in fiona version 2.0\n",
      "  props['geometry'] = line.get('geometry', None)\n",
      "/Users/runner/micromamba/envs/sfrmaker_ci/lib/python3.13/site-packages/gisutils/shapefile.py:278: FionaDeprecationWarning: instances of this class -- CRS, geometry, and feature objects -- will become immutable in fiona version 2.0\n",
      "  props['geometry'] = line.get('geometry', None)\n"
     ]
    }
   ],
   "source": [
    "outfolder = Path('output')\n",
    "outfolder.mkdir(exist_ok=True)\n",
    "\n",
    "results = cull_flowlines(NHDPlus_paths=['../tylerforks/NHDPlus/'],\n",
    "                         asum_thresh=3, intermittent_streams_asum_thresh=3,\n",
    "                         cull_invalid=True, cull_isolated=False,\n",
    "                         active_area='../tylerforks/active_area.shp',\n",
    "                         outfolder=outfolder)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "912e34b6",
   "metadata": {},
   "source": [
    "The output files are listed in a returned ``results`` dictionary, and are named based on ``asum_thresh``. This allows for easy experimentation with variuos arbolate sum thresholds, without a lot of extra file wrangling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ec5ad987",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-09T18:09:56.906175Z",
     "iopub.status.busy": "2025-05-09T18:09:56.906082Z",
     "iopub.status.idle": "2025-05-09T18:09:56.915709Z",
     "shell.execute_reply": "2025-05-09T18:09:56.915388Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'flowlines_file': 'output/flowlines_gt3km.shp',\n",
       " 'pfvaa_file': 'output/PlusFlowlineVAA_gt3km.dbf',\n",
       " 'pf_file': 'output/PlusFlow_gt3km.dbf',\n",
       " 'elevslope_file': 'output/elevslope_gt3km.dbf'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13a9da8d",
   "metadata": {},
   "source": [
    "#### Plot the culled flowlines\n",
    "We can use ``geopandas`` to quickly plot the results. As noted above, all perennial streams that intersect the active area bounding box are retained (blue lines). Within the active area, two intermittent streams with arbolate sums of less than 3km are culled (red lines)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "20e6d318",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-09T18:09:56.917307Z",
     "iopub.status.busy": "2025-05-09T18:09:56.917211Z",
     "iopub.status.idle": "2025-05-09T18:09:57.057398Z",
     "shell.execute_reply": "2025-05-09T18:09:57.056808Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1150x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "original_fl = gpd.read_file('../tylerforks/NHDPlus/NHDSnapshot/Hydrography/NHDFlowline.shp')\n",
    "fig, ax = plt.subplots(figsize=(11.5, 8))\n",
    "original_fl.plot(ax=ax, color='r')\n",
    "\n",
    "culled_fl = gpd.read_file(results['flowlines_file'])\n",
    "culled_fl.plot(ax=ax)\n",
    "\n",
    "active_area = gpd.read_file('../tylerforks/active_area.shp')\n",
    "active_area.plot(ax=ax, zorder=-1, fc='0.9')\n",
    "active_area.envelope.plot(ax=ax, color='1.0', ec='k', zorder=-2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75702939",
   "metadata": {},
   "source": [
    "### Preprocessing the flowlines\n",
    "The ``preprocess_nhdplus()`` function creates a single shapefile with the information needed to build an SFR package (that can be input to [``sfrmaker.Lines.from_shapefile()``](https://aleaf.github.io/sfrmaker/latest/api/sfrmaker.lines.html#sfrmaker.lines.Lines.from_shapefile)). The set of unified NHDPlus files from ``cull_flowlines()`` can be input or, if no culling is needed and the project area falls within a single drainage basin, an original set of NHDPlus version 2 files can be used. \n",
    "\n",
    "The shapefile output from ``preprocess_nhdplus()`` can be thought of as a grid-independent representation of the SFR package. Further editing can be done on the shapefile either manually in a GIS environment, or automatically with the [``preprocessing.edit_flowlines()``](https://aleaf.github.io/sfrmaker/latest/api/sfrmaker.preprocessing.html#sfrmaker.preprocessing.edit_flowlines) function. Abstracting the key details of the SFR package to this shapefile allows for the SFR package to be easily regenerated if other apsects of the model (such as the grid) change.\n",
    "\n",
    "To make a suitable SFR dataset, ``preprocess_nhdplus()`` does some additional processing that is described in more detail in the [code reference](https://aleaf.github.io/sfrmaker/latest/api/sfrmaker.preprocessing.html#sfrmaker.preprocessing.preprocess_nhdplus), including:  \n",
    "* selecting a single downstream segment at each divergence, using zonal statistics computed on buffered areas (customizable with ``buffersize_meters``) around the lines. \n",
    "    * for instances where the automated proceedure selects the wrong distributary, or where routing information is absent in NHDPlus, connections can be established manually via a dictionary of ``FROMCOMID:TOCOMID`` pairs\n",
    "* smoothed streambed elevations are also produced in the zonal statistics sampling; for large models with cell sizes of several hundred meters or more (in which zonal statistics may take a hour or longer to run), this may be preferable to sampling the elevations during the construction of the SFR package, which would greatly slow (re)building of the model.\n",
    "* after the divergences are pruned to single downstream segments, arbolate sums are recomputed, and the lines can optionally be culled again with the ``asum_thresh`` argument here. This can be used to remove minor distributies that are only active during high water events, and therefore potentially not relevant to the groundwater model.\n",
    "* the computed arbolate sums are also used to estimate channel widths, using the relationship $a\\cdot arbolate\\_sum ^b$ (Leaf, 2023; Feinstein et al, 2010). The $a$ and $b$ parameters in this relationship can be adjusted as needed to achieve the desired widths for a study area. A ``minimum_width`` can also be specified for headwater tributaries that may not be well represented by the above power-law relationship (and that may also have greater streambed conductance).\n",
    "\n",
    "#### Additional processing options not shown below\n",
    "* for large project areas, additional width information from the North American Width Dataset (NARWidth) can be incorporated via the ``narwidth_shapefile`` argument (see Leaf, 2023; Allen and Pavelsky, 2015 and the [code reference](https://aleaf.github.io/sfrmaker/latest/api/sfrmaker.preprocessing.html#sfrmaker.preprocessing.preprocess_nhdplus)\n",
    "* field measurements of streambed elevation can be incorporated into the smoothed streambed elevations produced for each line, via the ``update_up_elevations`` and ``update_dn_elevations`` arguments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7ce511f7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-09T18:09:57.060407Z",
     "iopub.status.busy": "2025-05-09T18:09:57.060265Z",
     "iopub.status.idle": "2025-05-09T18:09:57.315292Z",
     "shell.execute_reply": "2025-05-09T18:09:57.314877Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "reading output/PlusFlowlineVAA_gt3km.dbf...\n",
      "--> building dataframe... (may take a while for large shapefiles)\n",
      "\n",
      "reading output/PlusFlow_gt3km.dbf...\n",
      "--> building dataframe... (may take a while for large shapefiles)\n",
      "\n",
      "reading output/elevslope_gt3km.dbf...\n",
      "--> building dataframe... (may take a while for large shapefiles)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Smoothing elevations...\n",
      "finished in 0.00s\n"
     ]
    }
   ],
   "source": [
    "preprocessed_flowlines = preprocess_nhdplus(\n",
    "    flowlines_file=results['flowlines_file'],\n",
    "    pfvaa_file=results['pfvaa_file'],\n",
    "    pf_file=results['pf_file'],\n",
    "    elevslope_file=results['elevslope_file'],\n",
    "    demfile='../tylerforks/dem_26715.tif',\n",
    "    dem_length_units='meters',\n",
    "    buffersize_meters=50,\n",
    "    asum_thresh= 3.,\n",
    "    width_from_asum_a_param=0.0592,\n",
    "    width_from_asum_b_param=0.5127,\n",
    "    minimum_width=1.,\n",
    "    known_connections={1814967: 1814897},\n",
    "    output_length_units='meters',\n",
    "    outfolder=outfolder,\n",
    "    dest_crs=26915  # UTM zone 15 north   \n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f8f6e5f",
   "metadata": {},
   "source": [
    "``preprocess_nhdplus()`` writes a shapefile of the preprocessed flowlines, and returns a GeoDataFrame representation that is described in detail in the [code reference](https://aleaf.github.io/sfrmaker/latest/api/sfrmaker.preprocessing.html#sfrmaker.preprocessing.preprocess_nhdplus)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d659de74",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-09T18:09:57.316956Z",
     "iopub.status.busy": "2025-05-09T18:09:57.316865Z",
     "iopub.status.idle": "2025-05-09T18:09:57.327034Z",
     "shell.execute_reply": "2025-05-09T18:09:57.326680Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COMID</th>\n",
       "      <th>FDATE</th>\n",
       "      <th>RESOLUTION</th>\n",
       "      <th>GNIS_ID</th>\n",
       "      <th>GNIS_NAME</th>\n",
       "      <th>LENGTHKM</th>\n",
       "      <th>REACHCODE</th>\n",
       "      <th>FLOWDIR</th>\n",
       "      <th>WBAREACOMI</th>\n",
       "      <th>FTYPE</th>\n",
       "      <th>...</th>\n",
       "      <th>elevup</th>\n",
       "      <th>elevdn</th>\n",
       "      <th>elevupsmo</th>\n",
       "      <th>elevdnsmo</th>\n",
       "      <th>asum_calc</th>\n",
       "      <th>asum_diff</th>\n",
       "      <th>width1asum</th>\n",
       "      <th>width2asum</th>\n",
       "      <th>width1</th>\n",
       "      <th>width2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1814983</th>\n",
       "      <td>1814983</td>\n",
       "      <td>1999-07-05</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1580686</td>\n",
       "      <td>Tyler Forks</td>\n",
       "      <td>2.947</td>\n",
       "      <td>04010302000160</td>\n",
       "      <td>With Digitized</td>\n",
       "      <td>0</td>\n",
       "      <td>StreamRiver</td>\n",
       "      <td>...</td>\n",
       "      <td>343.306824</td>\n",
       "      <td>340.423096</td>\n",
       "      <td>343.306824</td>\n",
       "      <td>340.423096</td>\n",
       "      <td>133.052</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.558493</td>\n",
       "      <td>7.645791</td>\n",
       "      <td>7.558493</td>\n",
       "      <td>7.645791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1814907</th>\n",
       "      <td>1814907</td>\n",
       "      <td>1999-07-05</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1580686</td>\n",
       "      <td>Tyler Forks</td>\n",
       "      <td>1.483</td>\n",
       "      <td>04010302000161</td>\n",
       "      <td>With Digitized</td>\n",
       "      <td>0</td>\n",
       "      <td>StreamRiver</td>\n",
       "      <td>...</td>\n",
       "      <td>344.879608</td>\n",
       "      <td>343.306824</td>\n",
       "      <td>344.879608</td>\n",
       "      <td>343.306824</td>\n",
       "      <td>125.007</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.359996</td>\n",
       "      <td>7.405168</td>\n",
       "      <td>7.359996</td>\n",
       "      <td>7.405168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1814869</th>\n",
       "      <td>1814869</td>\n",
       "      <td>1999-07-05</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1580686</td>\n",
       "      <td>Tyler Forks</td>\n",
       "      <td>7.555</td>\n",
       "      <td>04010302000162</td>\n",
       "      <td>With Digitized</td>\n",
       "      <td>0</td>\n",
       "      <td>StreamRiver</td>\n",
       "      <td>...</td>\n",
       "      <td>353.597046</td>\n",
       "      <td>344.879608</td>\n",
       "      <td>353.597046</td>\n",
       "      <td>344.879608</td>\n",
       "      <td>119.314</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.991834</td>\n",
       "      <td>7.230301</td>\n",
       "      <td>6.991834</td>\n",
       "      <td>7.230301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1814859</th>\n",
       "      <td>1814859</td>\n",
       "      <td>1999-07-05</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1580686</td>\n",
       "      <td>Tyler Forks</td>\n",
       "      <td>2.558</td>\n",
       "      <td>04010302000163</td>\n",
       "      <td>With Digitized</td>\n",
       "      <td>0</td>\n",
       "      <td>StreamRiver</td>\n",
       "      <td>...</td>\n",
       "      <td>367.337708</td>\n",
       "      <td>353.597046</td>\n",
       "      <td>367.337708</td>\n",
       "      <td>353.597046</td>\n",
       "      <td>107.771</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.778782</td>\n",
       "      <td>6.862785</td>\n",
       "      <td>6.778782</td>\n",
       "      <td>6.862785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1814897</th>\n",
       "      <td>1814897</td>\n",
       "      <td>1999-07-05</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1580686</td>\n",
       "      <td>Tyler Forks</td>\n",
       "      <td>1.624</td>\n",
       "      <td>04010302000164</td>\n",
       "      <td>With Digitized</td>\n",
       "      <td>0</td>\n",
       "      <td>StreamRiver</td>\n",
       "      <td>...</td>\n",
       "      <td>389.698822</td>\n",
       "      <td>367.337708</td>\n",
       "      <td>389.698822</td>\n",
       "      <td>367.337708</td>\n",
       "      <td>102.417</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.631250</td>\n",
       "      <td>6.685816</td>\n",
       "      <td>6.631250</td>\n",
       "      <td>6.685816</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           COMID       FDATE RESOLUTION  GNIS_ID    GNIS_NAME  LENGTHKM  \\\n",
       "COMID                                                                     \n",
       "1814983  1814983  1999-07-05     Medium  1580686  Tyler Forks     2.947   \n",
       "1814907  1814907  1999-07-05     Medium  1580686  Tyler Forks     1.483   \n",
       "1814869  1814869  1999-07-05     Medium  1580686  Tyler Forks     7.555   \n",
       "1814859  1814859  1999-07-05     Medium  1580686  Tyler Forks     2.558   \n",
       "1814897  1814897  1999-07-05     Medium  1580686  Tyler Forks     1.624   \n",
       "\n",
       "              REACHCODE         FLOWDIR  WBAREACOMI        FTYPE  ...  \\\n",
       "COMID                                                             ...   \n",
       "1814983  04010302000160  With Digitized           0  StreamRiver  ...   \n",
       "1814907  04010302000161  With Digitized           0  StreamRiver  ...   \n",
       "1814869  04010302000162  With Digitized           0  StreamRiver  ...   \n",
       "1814859  04010302000163  With Digitized           0  StreamRiver  ...   \n",
       "1814897  04010302000164  With Digitized           0  StreamRiver  ...   \n",
       "\n",
       "             elevup      elevdn   elevupsmo   elevdnsmo  asum_calc asum_diff  \\\n",
       "COMID                                                                          \n",
       "1814983  343.306824  340.423096  343.306824  340.423096    133.052       0.0   \n",
       "1814907  344.879608  343.306824  344.879608  343.306824    125.007       0.0   \n",
       "1814869  353.597046  344.879608  353.597046  344.879608    119.314       0.0   \n",
       "1814859  367.337708  353.597046  367.337708  353.597046    107.771       0.0   \n",
       "1814897  389.698822  367.337708  389.698822  367.337708    102.417       0.0   \n",
       "\n",
       "         width1asum  width2asum    width1    width2  \n",
       "COMID                                                \n",
       "1814983    7.558493    7.645791  7.558493  7.645791  \n",
       "1814907    7.359996    7.405168  7.359996  7.405168  \n",
       "1814869    6.991834    7.230301  6.991834  7.230301  \n",
       "1814859    6.778782    6.862785  6.778782  6.862785  \n",
       "1814897    6.631250    6.685816  6.631250  6.685816  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessed_flowlines.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "50dba987",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-09T18:09:57.328677Z",
     "iopub.status.busy": "2025-05-09T18:09:57.328539Z",
     "iopub.status.idle": "2025-05-09T18:09:57.391061Z",
     "shell.execute_reply": "2025-05-09T18:09:57.390687Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = preprocessed_flowlines.plot()\n",
    "active_area.to_crs(26915).plot(ax=ax, zorder=-1, fc='0.9')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8decb94c",
   "metadata": {},
   "source": [
    "### Clipping the flowlines to a specific area\n",
    "As a final step, we may want to clip the flowlines to an irregular area where SFR will be included in the model. For large project areas, this can reduce the file size and make the preprocessed lines easier to work with. This step may be best done last, so that the information in the surrounding flowlines (routing, elevations, etc) can be used in the preprocessing above. For large study areas with an overly detailed active area boundary (for example, one generated from a raster), specifying a simplification tolerance (``simplify_tol``) for the active area boundary can greatly speed up the clipping. All lines that intersect the simplified active area will be retained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "39a289bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-09T18:09:57.392859Z",
     "iopub.status.busy": "2025-05-09T18:09:57.392734Z",
     "iopub.status.idle": "2025-05-09T18:09:57.439735Z",
     "shell.execute_reply": "2025-05-09T18:09:57.439356Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "reading ../tylerforks/active_area.shp...\n",
      "--> building dataframe... (may take a while for large shapefiles)\n",
      "starting lines: 19\n",
      "remaining lines: 11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/runner/micromamba/envs/sfrmaker_ci/lib/python3.13/site-packages/gisutils/shapefile.py:278: FionaDeprecationWarning: instances of this class -- CRS, geometry, and feature objects -- will become immutable in fiona version 2.0\n",
      "  props['geometry'] = line.get('geometry', None)\n"
     ]
    }
   ],
   "source": [
    "clipped_flowlines = clip_flowlines_to_polygon(\n",
    "    preprocessed_flowlines, '../tylerforks/active_area.shp',\n",
    "    simplify_tol=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a60e8b64",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-09T18:09:57.441418Z",
     "iopub.status.busy": "2025-05-09T18:09:57.441321Z",
     "iopub.status.idle": "2025-05-09T18:09:57.487396Z",
     "shell.execute_reply": "2025-05-09T18:09:57.487062Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clipped_flowlines.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "sfrmaker_ci",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}