calc_h3_cell_beach_slope.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_h3_cell_beach_slopes(h3_gdf, slope_gdf):\n",
    "    \"\"\"\n",
    "    Calculate the average beach slope for every row in hr5_cells using beach_slope stored in slope_gdf.\n",
    "\n",
    "    Parameters:\n",
    "    hr5_cells (GeoDataFrame): GeoDataFrame containing HR5 cells.\n",
    "    slope_gdf (GeoDataFrame): GeoDataFrame containing beach slopes.\n",
    "\n",
    "    Returns:\n",
    "    GeoDataFrame: GeoDataFrame with average beach slopes for each HR5 cell.\n",
    "    \"\"\"\n",
    "    # Perform spatial join\n",
    "    joined_gdf = gpd.sjoin(h3_gdf, slope_gdf, how=\"left\", predicate=\"intersects\")\n",
    "\n",
    "  # Group by HR5 cell and calculate the average beach slope\n",
    "    avg_slope_gdf = joined_gdf.groupby(joined_gdf.index).agg({'beach_slope': 'mean'}).reset_index()\n",
    "    avg_slope_gdf['beach_slope'] = avg_slope_gdf['beach_slope'].round(3)\n",
    "\n",
    "    # Merge the average slopes back to the original hr5_cells GeoDataFrame\n",
    "    h3_gdf = h3_gdf.merge(avg_slope_gdf, left_index=True, right_on='index')\n",
    "\n",
    "    h3_gdf.drop(columns=['index', 'index_y'], inplace=True)\n",
    "    h3_gdf.rename(columns={'index_x': 'cell_id'}, inplace=True)\n",
    "\n",
    "    return h3_gdf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "hr5_cells = gpd.read_file('global-inputs/HR6-change-cells-aoi.gpkg')\n",
    "hr5_cells.to_crs(crs='EPSG:4326', inplace=True)\n",
    "\n",
    "slope_gdf = gpd.read_file('global-inputs/transects_edit.geojson')\n",
    "slope_gdf = slope_gdf.clip(hr5_cells)\n",
    "slope_gdf = gpd.GeoDataFrame(slope_gdf[['id', 'beach_slope', 'geometry']])\n",
    "slope_gdf.dropna(inplace=True)\n",
    "\n",
    "hr5_cells.to_crs(\"EPSG:2193\", inplace=True)\n",
    "slope_gdf.to_crs(\"EPSG:2193\", inplace=True)\n",
    "\n",
    "hr5_cells = calc_h3_cell_beach_slopes(hr5_cells, slope_gdf)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "hr5_cells.to_file('global-inputs/HR6-cells-beach-slope.gpkg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
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   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n"
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  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
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     "text": [
      "/var/folders/39/4kh5mq6j58n_278vm1j6g170d4k_0p/T/ipykernel_78948/3308222097.py:26: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['total_area'] = landcover.sum(axis=1)\n",
      "/var/folders/39/4kh5mq6j58n_278vm1j6g170d4k_0p/T/ipykernel_78948/3308222097.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['sand_area_percentage'] = (df['sand_area (Ha)']/df['total_area'])*100\n",
      "/var/folders/39/4kh5mq6j58n_278vm1j6g170d4k_0p/T/ipykernel_78948/3308222097.py:28: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['water_area_percentage'] = (df['water_area (Ha)']/df['total_area'])*100\n",
      "/var/folders/39/4kh5mq6j58n_278vm1j6g170d4k_0p/T/ipykernel_78948/3308222097.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['vegetation_area_percentage'] = (df['vegetation_area (Ha)']/df['total_area'])*100\n",
      "/var/folders/39/4kh5mq6j58n_278vm1j6g170d4k_0p/T/ipykernel_78948/3308222097.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[['sand_area_percentage_change', 'water_area_percentage_change', 'vegetation_area_percentage_change']] = df[['sand_area_percentage', 'water_area_percentage', 'vegetation_area_percentage']].diff().round(2)\n",
      "/var/folders/39/4kh5mq6j58n_278vm1j6g170d4k_0p/T/ipykernel_78948/3308222097.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[['sand_area_percentage_change', 'water_area_percentage_change', 'vegetation_area_percentage_change']] = df[['sand_area_percentage', 'water_area_percentage', 'vegetation_area_percentage']].diff().round(2)\n",
      "/var/folders/39/4kh5mq6j58n_278vm1j6g170d4k_0p/T/ipykernel_78948/3308222097.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[['sand_area_percentage_change', 'water_area_percentage_change', 'vegetation_area_percentage_change']] = df[['sand_area_percentage', 'water_area_percentage', 'vegetation_area_percentage']].diff().round(2)\n"
     ]
    }
   ],
   "source": [
    "\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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