information_loss_bulk.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing and visualizing the information loss in scarps associated with landscape evolution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### load Python packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "# First, import a few Python packages and landlab tools\n",
    "import numpy as np\n",
    "from pylab import show, figure, plot\n",
    "import time\n",
    "from landlab import RasterModelGrid\n",
    "from landlab.plot.imshow import imshow_grid\n",
    "from landlab.io import read_esri_ascii\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import geopandas as gpd\n",
    "from scipy import stats\n",
    "from osgeo import gdal\n",
    "import glob\n",
    "from PIL import Image\n",
    "import matplotlib.cbook as cbook\n",
    "import scipy.optimize\n",
    "from scipy.optimize import curve_fit\n",
    "from matplotlib_scalebar.scalebar import ScaleBar\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['font.size'] = 8\n",
    "import utils\n",
    "import pandas as pd\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### User-defined features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_iter = np.array(\n",
    "    [0, 2, 20, 100, 200]\n",
    ")  # number of iterations (total_time = n_iter * dt) at which to produce output, default dt=50 years for linear problem\n",
    "\n",
    "# input file locations\n",
    "DEM_dir = \"DEMs/\"\n",
    "shapefile_dir = \"Shps/\"\n",
    "epsg_code = 32611  # Ridgecrest UTM 11N\n",
    "excel_filename = \"information_loss_analysis_outputs.csv\"\n",
    "linear_or_nonlinear = (\n",
    "    \"linear\"  # choice to use linear or nonlinear (Taylor) diffuser in landlab\n",
    ")\n",
    "\n",
    "save_fig_YN = \"No\"  # if Yes, all pdfs get saved to Figures directory\n",
    "save_data_excel_YN = \"Yes\"  # if Yes, info gets appended to csv file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot landscape evolution, line evolution, height evolution, and slope evolution over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E9\n",
      "E8\n",
      "R1\n",
      "R3\n",
      "R2\n",
      "R6\n",
      "R7\n",
      "R5\n",
      "R4\n",
      "R10\n",
      "E10\n",
      "R9\n",
      "E1\n",
      "R8\n",
      "E3\n",
      "E2\n",
      "E6\n",
      "E7\n",
      "E5\n",
      "E4\n"
     ]
    }
   ],
   "source": [
    "DEMs_shapefiles = utils.load_shapefiles_for_DEMs(DEM_dir, shapefile_dir)\n",
    "\n",
    "for dem_name, shapefiles in DEMs_shapefiles.items():\n",
    "    if linear_or_nonlinear == \"linear\":\n",
    "        line_length, coeff_t, years_t = utils.plot_evolution_time_linear(\n",
    "            n_iter, dem_name, shapefiles, epsg_code, save_fig_YN, D=0.001\n",
    "        )\n",
    "        diffusion_type = \"linear\"\n",
    "    elif linear_or_nonlinear == \"nonlinear\":\n",
    "        line_length, coeff_t, years_t = utils.plot_evolution_time_nonlinear(\n",
    "            n_iter, dem_name, shapefiles, epsg_code, save_fig_YN, D=0.001\n",
    "        )\n",
    "        diffusion_type = \"nonlinear\"\n",
    "    else:\n",
    "        NameError(\"User must choose linear or nonlinear diffusion\")\n",
    "    plt.close() # so that I can upload file to github without huge memory sink\n",
    "    print(dem_name)\n",
    "    first_char = dem_name[0]\n",
    "    numeric_chars = \"\".join(filter(str.isdigit, dem_name))\n",
    "    DEMID = first_char + numeric_chars\n",
    "\n",
    "    if first_char == \"R\":\n",
    "        location = \"Ridgecrest\"\n",
    "    elif first_char == \"E\":\n",
    "        location = \"EMC\"\n",
    "    else:\n",
    "        NameError(\"File name must start with R or E\")\n",
    "\n",
    "    repeated_locations = [location for _ in line_length]\n",
    "    dem_ID = [DEMID for _ in line_length]\n",
    "\n",
    "    combined_data = {\n",
    "        \"Length (m)\": line_length,\n",
    "        \"Degradation coefficient\": coeff_t,\n",
    "        \"Diffusion type\": diffusion_type,\n",
    "        \"Time (years)\": years_t,\n",
    "        \"DEM ID\": dem_ID,\n",
    "        \"Location\": repeated_locations,\n",
    "    }\n",
    "\n",
    "    df = pd.DataFrame(combined_data)\n",
    "\n",
    "    if save_data_excel_YN == \"Yes\":\n",
    "        dem_row = df\n",
    "\n",
    "        if not os.path.exists(excel_filename):\n",
    "            data_to_save = df\n",
    "        else:\n",
    "            data_to_save = pd.read_csv(excel_filename)\n",
    "            data_to_save = pd.concat([data_to_save, dem_row], ignore_index=True)\n",
    "\n",
    "        data_to_save.to_csv(excel_filename, index=False)"
   ]
  }
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