{
 "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": "2025-12-30T22:27:46.545579Z",
     "iopub.status.busy": "2025-12-30T22:27:46.545461Z",
     "iopub.status.idle": "2025-12-30T22:27:48.058745Z",
     "shell.execute_reply": "2025-12-30T22:27:48.057745Z"
    }
   },
   "outputs": [],
   "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": "2025-12-30T22:27:48.061577Z",
     "iopub.status.busy": "2025-12-30T22:27:48.061146Z",
     "iopub.status.idle": "2025-12-30T22:27:48.065297Z",
     "shell.execute_reply": "2025-12-30T22:27:48.064580Z"
    }
   },
   "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": "2025-12-30T22:27:48.067017Z",
     "iopub.status.busy": "2025-12-30T22:27:48.066909Z",
     "iopub.status.idle": "2025-12-30T22:27:48.263698Z",
     "shell.execute_reply": "2025-12-30T22:27:48.262785Z"
    }
   },
   "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 0.10s\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 0.15s\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": "2025-12-30T22:27:48.266127Z",
     "iopub.status.busy": "2025-12-30T22:27:48.265850Z",
     "iopub.status.idle": "2025-12-30T22:27:48.294228Z",
     "shell.execute_reply": "2025-12-30T22:27:48.293319Z"
    }
   },
   "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": "2025-12-30T22:27:48.296114Z",
     "iopub.status.busy": "2025-12-30T22:27:48.295993Z",
     "iopub.status.idle": "2025-12-30T22:27:48.299948Z",
     "shell.execute_reply": "2025-12-30T22:27:48.299370Z"
    }
   },
   "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": "2025-12-30T22:27:48.302048Z",
     "iopub.status.busy": "2025-12-30T22:27:48.301943Z",
     "iopub.status.idle": "2025-12-30T22:27:48.304593Z",
     "shell.execute_reply": "2025-12-30T22:27:48.303784Z"
    }
   },
   "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": "2025-12-30T22:27:48.306426Z",
     "iopub.status.busy": "2025-12-30T22:27:48.306326Z",
     "iopub.status.idle": "2025-12-30T22:27:48.438602Z",
     "shell.execute_reply": "2025-12-30T22:27:48.437906Z"
    }
   },
   "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",
      "\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/python3.14/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/python3.14/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/python3.14/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/python3.14/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/python3.14/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/python3.14/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/python3.14/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/python3.14/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/python3.14/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/python3.14/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/python3.14/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/python3.14/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/python3.14/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/python3.14/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/python3.14/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/python3.14/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/python3.14/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/python3.14/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/python3.14/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/python3.14/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/python3.14/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/python3.14/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/python3.14/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/python3.14/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/python3.14/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/python3.14/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/python3.14/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/python3.14/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": "2025-12-30T22:27:48.440289Z",
     "iopub.status.busy": "2025-12-30T22:27:48.440182Z",
     "iopub.status.idle": "2025-12-30T22:27:48.450088Z",
     "shell.execute_reply": "2025-12-30T22:27:48.448968Z"
    }
   },
   "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": "2025-12-30T22:27:48.451807Z",
     "iopub.status.busy": "2025-12-30T22:27:48.451703Z",
     "iopub.status.idle": "2025-12-30T22:27:48.598537Z",
     "shell.execute_reply": "2025-12-30T22:27:48.597590Z"
    }
   },
   "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": "2025-12-30T22:27:48.600530Z",
     "iopub.status.busy": "2025-12-30T22:27:48.600423Z",
     "iopub.status.idle": "2025-12-30T22:27:48.863953Z",
     "shell.execute_reply": "2025-12-30T22:27:48.863304Z"
    }
   },
   "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": "2025-12-30T22:27:48.866111Z",
     "iopub.status.busy": "2025-12-30T22:27:48.865967Z",
     "iopub.status.idle": "2025-12-30T22:27:48.882998Z",
     "shell.execute_reply": "2025-12-30T22:27:48.882340Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "</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",
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       "      <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": "2025-12-30T22:27:48.884701Z",
     "iopub.status.busy": "2025-12-30T22:27:48.884601Z",
     "iopub.status.idle": "2025-12-30T22:27:48.961858Z",
     "shell.execute_reply": "2025-12-30T22:27:48.960509Z"
    }
   },
   "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": "2025-12-30T22:27:48.963825Z",
     "iopub.status.busy": "2025-12-30T22:27:48.963712Z",
     "iopub.status.idle": "2025-12-30T22:27:49.003954Z",
     "shell.execute_reply": "2025-12-30T22:27:49.003454Z"
    }
   },
   "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/python3.14/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": "2025-12-30T22:27:49.005766Z",
     "iopub.status.busy": "2025-12-30T22:27:49.005643Z",
     "iopub.status.idle": "2025-12-30T22:27:49.055968Z",
     "shell.execute_reply": "2025-12-30T22:27:49.054935Z"
    }
   },
   "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": "3.14.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}