plot_data_sideview_v2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a8147690-cf50-452d-a62e-aa53ecd389fc",
   "metadata": {},
   "source": [
    "# Plotting function test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "34d81763-e3d9-4b7f-ae74-adf6ae6e698f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "from matplotlib import rc_context\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "import numpy as np\n",
    "from osgeo import gdal\n",
    "from pathlib import Path\n",
    "import rasterio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "68b51b5c-8cff-4114-9969-61854dfb8cd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdal.UseExceptions()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e40ca61-3dc5-4b1f-a9ce-fa0ce9f37cec",
   "metadata": {},
   "source": [
    "### Plotting settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "40acdb7e-cc11-4152-b4d8-178c428c9ac4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the location as lon,lat\n",
    "# Bounding box: [-179.5,-50,5,85]\n",
    "lon = -110\n",
    "lat =   55"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "42a10b8d-5c03-49c3-8dc7-badcb6c6059c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_values = get_location_data_dict_from_geotiffs(file_paths,lon,lat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "f8fd6aab-7c8c-49ee-aefa-219cc05402b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "figure_path = Path('/Users/wmk934/data/perceptual_models/data/geotiff_same_resolution/res_0.01/figures')\n",
    "figure_name = f'res_0-01_lon-{lon}_lat-{lat}_X.png'\n",
    "figure_path.mkdir(exist_ok=True, parents=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "e47b746f-4da1-4a70-87ce-f89f873ff5ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data_for_location(figure_path,figure_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "ec86a76f-faf1-4828-ba0e-ab690a454c24",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_data_for_location(figure_path,figure_name):\n",
    "\n",
    "    # Make the 3 plots\n",
    "    plot_climate( figure_path / figure_name.replace('X.png','climate.png'))\n",
    "    plot_lai(     figure_path / figure_name.replace('X.png','lai.png'))\n",
    "    plot_overview(figure_path / figure_name.replace('X.png','overview.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d7be07b-5175-436c-ab44-697b1cf108a2",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Define variables of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "8d896839-26a4-4e93-b6fd-8b3184189a82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Forest height (GLCLU2020)\n",
    "vegetation_height    = data_values['veg_height'] # [m]\n",
    "\n",
    "# GLCLU2019\n",
    "land_type1           = data_values['land_glclu'] # Categorical\n",
    "land_type1_text      = glclu2019_dict[land_type1]\n",
    "\n",
    "# MODIS-IGBP\n",
    "land_type2           = data_values['land_igbp'] # Categorical\n",
    "land_type2_text      = igbp_dict[land_type2]\n",
    "\n",
    "# LGRIP30\n",
    "land_agriculture     = 1 # Categorical. 0: water, 1: non-cropland, 2: irrigated, 3: rainfed\n",
    "land_agriculture_txt = lgrip30_dict[land_agriculture]\n",
    "\n",
    "# Modis\n",
    "vegetation_lai       = np.array([data_values['lai_01'],data_values['lai_02'],data_values['lai_03'],\n",
    "                                 data_values['lai_04'],data_values['lai_05'],data_values['lai_06'],\n",
    "                                 data_values['lai_07'],data_values['lai_08'],data_values['lai_09'],\n",
    "                                 data_values['lai_10'],data_values['lai_11'],data_values['lai_12']])\n",
    "\n",
    "# Merit\n",
    "topography_elevation = data_values['elev'] # [m.a.s.l.]\n",
    "topography_slope     = math.tan(math.radians(data_values['slope'])) # [degrees] to [m m-1], derived\n",
    "topography_aspect    = data_values['aspect'] # [degrees], derived\n",
    "\n",
    "# Pelletier\n",
    "soil_depth           = data_values['soil_depth'] # [m]\n",
    "\n",
    "# Soilgrids\n",
    "soil_layer_top       = [0.00, 0.05, 0.15, 0.30, 0.60, 1.00] # Top of layer depths [m]\n",
    "soil_layer_bot       = [0.05, 0.15, 0.30, 0.60, 1.00, 2.00] # Bottom of layer depths in [m]\n",
    "soil_sand            = [data_values['sand1']/1000, data_values['sand2']/1000, data_values['sand3']/1000,\n",
    "                        data_values['sand4']/1000, data_values['sand5']/1000, data_values['sand6']/1000]\n",
    "soil_silt            = [data_values['silt1']/1000, data_values['silt2']/1000, data_values['silt3']/1000,\n",
    "                        data_values['silt4']/1000, data_values['silt5']/1000, data_values['silt6']/1000]\n",
    "soil_clay            = [data_values['clay1']/1000, data_values['clay2']/1000, data_values['clay3']/1000,\n",
    "                        data_values['clay4']/1000, data_values['clay5']/1000, data_values['clay6']/1000]\n",
    "\n",
    "# WorldCLim\n",
    "climate_aridity      = data_values['climate_im']\n",
    "climate_seasonality  = data_values['climate_imr']\n",
    "climate_snow         = data_values['climate_fs']\n",
    "\n",
    "# Currently unused\n",
    "geology = 0 # Glhymps \n",
    "lake = 0 # # Hydrolakes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "660163a9-6c4e-45d4-babb-e42b4f3fee1d",
   "metadata": {},
   "source": [
    "### Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4e5eb5a7-700b-49f2-930e-f17b44dc6cd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_pm_plot_color(object):\n",
    "\n",
    "    '''Defines standard colors for objects in perceptual model plots'''\n",
    "\n",
    "    if object == 'bedrock boundary': color = [0.3, 0.3, 0.3] # Dark-ish grey\n",
    "    elif object == 'bedrock fracture': color = [0.7, 0.7, 0.7] # Light-ish grey\n",
    "    elif object == 'surface': color = 'saddlebrown' # Brown\n",
    "    elif object == 'soil':\n",
    "        print('WARNING: soil is defined based on sand/silt/clay percentages and requires get_pm_soil_color()')\n",
    "        color = None\n",
    "    \n",
    "\n",
    "    return color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "b57dc8ef-be09-4d03-984d-414742161b8e",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "expected an indented block after 'if' statement on line 8 (3432915117.py, line 11)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn[281], line 11\u001b[0;36m\u001b[0m\n\u001b[0;31m    elif (land_type == 2) or (land_type == 4):\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block after 'if' statement on line 8\n"
     ]
    }
   ],
   "source": [
    "def get_igbb_patch(land_type, veg_height):\n",
    "\n",
    "    # We know we have an x-dimension of size 10 to work with\n",
    "    # Y-dimension is dictated by veg_height\n",
    "    # We'll plot everything \n",
    "\n",
    "    # Needleleaf forest (>60% trees)\n",
    "    if (land_type == 1) or (land_type == 3):\n",
    "\n",
    "    # Broadleaf forest (>60% trees)\n",
    "    elif (land_type == 2) or (land_type == 4):\n",
    "\n",
    "    # Mixed forest (>60% trees)\n",
    "    elif land_type == 5:\n",
    "\n",
    "    # Closed shrubland (>60% shrubs)\n",
    "    elif land_type == 6:\n",
    "\n",
    "    # Open shrubland (10-60% shrubs)\n",
    "    elif land_type == 7:\n",
    "\n",
    "    # Woody savanna (30-60% trees)\n",
    "    elif land_type == 8:\n",
    "\n",
    "    # Savanna (10-30% trees)\n",
    "    elif land_type == 9:\n",
    "\n",
    "    # Grassland \n",
    "    elif land_type == 10:\n",
    "\n",
    "    # Permanent wetland\n",
    "    elif land_type == 11:\n",
    "\n",
    "    # Cropland\n",
    "    elif land_type == 12:\n",
    "        \n",
    "    # Urban\n",
    "    elif land_type == 13:\n",
    "\n",
    "    # Cropland/natural mosaic\n",
    "    elif land_type == 14:\n",
    "\n",
    "    # Snow/ice\n",
    "    elif land_type == 15:\n",
    "\n",
    "    # Barren\n",
    "    elif land_type == 16:\n",
    "        land_patch = None\n",
    "\n",
    "    # Water\n",
    "    elif land_type == 17:\n",
    "\n",
    "    return land_patch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "1604c571-b32e-46b0-8cc7-c8c6cc2880c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Determine the spacing\n",
    "bedrock_depth_factor = 1.05\n",
    "bedrock_depth = soil_depth * bedrock_depth_factor\n",
    "total_width  = 10 # [m]\n",
    "total_height = vegetation_height + soil_depth + bedrock_depth # [m]\n",
    "top = vegetation_height\n",
    "bottom = 0-bedrock_depth\n",
    "slope_l = 0.5*(topography_slope * total_width)\n",
    "slope_r = -0.5*(topography_slope * total_width)\n",
    "soil_depth_l = soil_depth + slope_l\n",
    "soil_depth_r = soil_depth + slope_r\n",
    "\n",
    "#with plt.xkcd():\n",
    "    \n",
    "# General figure components\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "ax = fig.add_axes((0.1, 0.1, 0.7, 0.7)) # left, bottom, width, height   \n",
    "\n",
    "# Specifics of the main figure\n",
    "ax.set_aspect('equal') \n",
    "ax.spines[['top', 'right', 'bottom']].set_visible(False)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([0-soil_depth,0,top])\n",
    "ax.minorticks_on()\n",
    "ax.tick_params(axis='y', which='minor', length=4)\n",
    "ax.set_yticks(np.arange(0-soil_depth,top), minor=True)\n",
    "plt.ylim([bottom*bedrock_shading_factor, top])\n",
    "\n",
    "# Bedrock\n",
    "n_bedrock_shading = 10\n",
    "for x in np.linspace(0,total_width,n_bedrock_shading, endpoint=False):\n",
    "    plt.plot([x,x+total_width/n_bedrock_shading],\n",
    "             [bottom,0-soil_depth], \n",
    "             color=get_pm_plot_color('bedrock fracture'))\n",
    "plt.plot([0,total_width],[0-soil_depth,0-soil_depth], color=get_pm_plot_color('bedrock boundary'))\n",
    "\n",
    "# Soil\n",
    "soil_text = 'Soil layer texture [%]\\ndepth [m]  sand silt clay'\n",
    "for ix,(soil_t,soil_b) in enumerate(zip(soil_layer_top,soil_layer_bot)): # SOILGRIDS layer loop\n",
    "    if soil_depth >= soil_t: # the top of this soil layer is within estimated soil depth\n",
    "\n",
    "        # Get the soil type in this layer\n",
    "        sand = soil_sand[ix]; silt = soil_silt[ix]; clay = soil_clay[ix]\n",
    "        _,soil_name = find_usda_soilclass(sand*100,silt*100,clay*100)\n",
    "        soil_color = get_pm_soil_color(soil_name)\n",
    "\n",
    "        # Check where the bottom of the plotting area is\n",
    "        if soil_depth < soil_b: # Estimated depth ends before this soilgrids layer does\n",
    "            soil_b = soil_depth # Therefore end the plotting layer at estimated soil depth\n",
    "\n",
    "        # Handle the special case where estimated soil depth is deeper than deepest soil grid layer\n",
    "        if soil_b == 2: \n",
    "            soil_b = soil_depth # extend this layer all the way to bedrock\n",
    "\n",
    "        # Calculate at which fraction of total soil depth the layer interfaces are\n",
    "        soil_t_frac = soil_t / soil_depth\n",
    "        soil_b_frac = soil_b / soil_depth\n",
    "\n",
    "        # Create a Polygon patch with the specified characteristics\n",
    "        x = [total_width, total_width, 0, 0, total_width]\n",
    "        y = [slope_r - soil_b_frac * soil_depth_r, slope_r - soil_t_frac * soil_depth_r,\n",
    "             slope_l - soil_t_frac * soil_depth_l, slope_l - soil_b_frac * soil_depth_l,\n",
    "             slope_r - soil_b_frac * soil_depth_r]\n",
    "        soil_patch = patches.Polygon(\n",
    "            xy=list(zip(x, y)),  # Combine x and y coordinates\n",
    "            closed=True,  # Connect the last point to the first to close the shape\n",
    "            facecolor=soil_color,\n",
    "            edgecolor='none')  \n",
    "        ax.add_patch(soil_patch)\n",
    "   \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "98a51e69-634c-4f93-9743-1f9f1c737f5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# General settings\n",
    "text_settings = {'font.family': 'Courier New', 'font.size': 12}\n",
    "\n",
    "# Determine the spacing\n",
    "bedrock_depth_factor = 1.05\n",
    "bedrock_depth = soil_depth * bedrock_depth_factor\n",
    "total_width  = 10 # [m]\n",
    "total_height = vegetation_height + soil_depth + bedrock_depth # [m]\n",
    "top = vegetation_height\n",
    "bottom = 0-bedrock_depth\n",
    "slope_l = soil_depth + 0.5*(topography_slope * total_width)\n",
    "slope_r = soil_depth - 0.5*(topography_slope * total_width)\n",
    "\n",
    "with plt.xkcd():\n",
    "    \n",
    "    # General figure components\n",
    "    fig = plt.figure(figsize=(8,6))\n",
    "    ax = fig.add_axes((0.1, 0.1, 0.7, 0.7)) # left, bottom, width, height   \n",
    "    \n",
    "    # Specifics of the main figure\n",
    "    ax.set_aspect('equal') \n",
    "    ax.spines[['top', 'right', 'bottom']].set_visible(False)\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([0-soil_depth,0,top])\n",
    "    ax.minorticks_on()\n",
    "    ax.tick_params(axis='y', which='minor', length=4)\n",
    "    ax.set_yticks(np.arange(0-soil_depth,top), minor=True)\n",
    "    plt.ylim([bottom*bedrock_shading_factor, top])\n",
    "\n",
    "    # Land use\n",
    "    # We'll primarily use IGBP categories here (land_type2)\n",
    "    #land_patch = get_igbb_patch(land_type2, vegetation_height)\n",
    "    #if land_patch: ax.add_patch(land_patch) # \"Barren\" returns a None type land_patch, which we don't plot\n",
    "    \n",
    "    # Bedrock\n",
    "    n_bedrock_shading = 10\n",
    "    for x in np.linspace(0,total_width,n_bedrock_shading, endpoint=False):\n",
    "        plt.plot([x,x+total_width/n_bedrock_shading],\n",
    "                 [bottom,0-soil_depth], \n",
    "                 color=get_pm_plot_color('bedrock fracture'))\n",
    "    plt.plot([0,total_width],[0-soil_depth,0-soil_depth], color=get_pm_plot_color('bedrock boundary'))\n",
    "    \n",
    "    # Surface\n",
    "    #plt.plot([0,total_width],[slope_l,slope_r], color=get_pm_plot_color('surface'))\n",
    "    \n",
    "    # Soil\n",
    "    soil_text = 'Soil layer texture [%]\\ndepth [m]  sand silt clay'\n",
    "    for ix,(soil_t,soil_b) in enumerate(zip(soil_layer_top,soil_layer_bot)): # SOILGRIDS layer loop\n",
    "        if soil_depth >= soil_t: # the top of this soil layer is within estimated soil depth\n",
    "    \n",
    "            # Get the soil type in this layer\n",
    "            sand = soil_sand[ix]\n",
    "            silt = soil_silt[ix]\n",
    "            clay = soil_clay[ix]\n",
    "            _,soil_name = find_usda_soilclass(sand*100,silt*100,clay*100)\n",
    "            soil_color = get_pm_soil_color(soil_name)\n",
    "\n",
    "            # Check where the bottom of the plotting area is\n",
    "            if soil_depth < soil_b: # Estimated depth ends before this soilgrids layer does\n",
    "                soil_b = soil_depth # Therefore end the plotting layer at estimated soil depth\n",
    "    \n",
    "            # Handle the special case where estimated soil depth is deeper than deepest soil grid layer\n",
    "            if soil_b == 2: \n",
    "                soil_b = soil_depth # extend this layer all the way to bedrock\n",
    "    \n",
    "            # Calculate at which fraction of total soil depth the layer interfaces are\n",
    "            soil_t_frac = soil_t / soil_depth\n",
    "            soil_b_frac = soil_b / soil_depth\n",
    "\n",
    "            # Create a Polygon patch with the specified characteristics\n",
    "            x = [total_width, total_width, 0, 0, total_width]\n",
    "            y = [(1-soil_b_frac) * slope_r - soil_depth, (1-soil_t_frac) * slope_r - soil_depth, \n",
    "                 (1-soil_t_frac) * slope_l - soil_depth, (1-soil_b_frac) * slope_l - soil_depth,\n",
    "                 (1-soil_b_frac) * slope_r - soil_depth]\n",
    "            soil_patch = patches.Polygon(\n",
    "                xy=list(zip(x, y)),  # Combine x and y coordinates\n",
    "                closed=True,  # Connect the last point to the first to close the shape\n",
    "                facecolor=soil_color,\n",
    "                edgecolor='none'#,\n",
    "                #linestyle='dotted',\n",
    "                #linewidth=1.0  # Width of the outline\n",
    "            )  \n",
    "            ax.add_patch(soil_patch)\n",
    "            \n",
    "            # Append the annotation before we start to update soil_b\n",
    "            b_decimals = 3-len(str(int(soil_b)))\n",
    "            soil_text += f'\\n{soil_t:3.2f}-{soil_b:3.{b_decimals}f}  {sand:.2f} {silt:.2f} {clay:.2f}'\n",
    "       \n",
    "    # Text\n",
    "    clus_num = data_values['cluster_num']\n",
    "\n",
    "    clim_text = (f'Aridity      [-1,1]: {climate_aridity:3.2f}\\n'\n",
    "                 f'Seasonality   [0,2]: {climate_seasonality:3.2f}\\n'\n",
    "                 f'Fraction snow [0,1]: {climate_snow:3.2f}')\n",
    "    land_text = (f'Land class 1: {land_type2_text}\\n'\n",
    "                 f'Land class 2: {land_type1_text}\\n'\n",
    "                 f'Agriculture : {land_agriculture_txt}')\n",
    "    topo_text = (f'Elevation: {topography_elevation:4.1f} [m.a.s.l]\\n'\n",
    "                 f'Slope    : {topography_slope:4.3f} [m m-1]\\n'\n",
    "                 f'Aspect   : {topography_aspect:4.1f} [degrees]')\n",
    "    all_text = (f'Cluster {clus_num}\\n'\n",
    "                f'Lon/lat ({lon:5.2f},{lat:5.2f})\\n\\n'\n",
    "                f'{clim_text}\\n\\n'\n",
    "                f'{land_text}\\n\\n'\n",
    "                f'{topo_text}\\n\\n'\n",
    "                f'{soil_text}')\n",
    "    with rc_context(text_settings):\n",
    "        ax.text(total_width+1, 0-soil_depth, all_text, ha='left', va='bottom')\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34b6351c-8ae4-44b5-90bc-c5a74b4b1084",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### File specification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "f03045dc-3526-48c3-9461-d46153ebf1b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Files\n",
    "base_path = Path('/Users/wmk934/data/perceptual_models/data/geotiff_same_resolution/res_0.01/')\n",
    "file_paths = [['cluster_num', base_path / 'clustering/res_0-01_mask-MERIT_datasets-29_reruns-10/res_0-01_datasets-29_clusters-15_reruns-10.tif'],              ['climate_fs', base_path / 'worldclim/wc2.1_30s_climate_index_fs_resampled.tif'],\n",
    "              ['climate_im', base_path / 'worldclim/wc2.1_30s_climate_index_im_resampled.tif'],\n",
    "              ['climate_imr', base_path / 'worldclim/wc2.1_30s_climate_index_imr_resampled.tif'],\n",
    "              ['lai_01', base_path / 'lai/2013_2023_01_MOD_Grid_MOD15A2H_Lai_500m_resampled.tif'],\n",
    "              ['lai_02', base_path / 'lai/2013_2023_02_MOD_Grid_MOD15A2H_Lai_500m_resampled.tif'],\n",
    "              ['lai_03', base_path / 'lai/2013_2023_03_MOD_Grid_MOD15A2H_Lai_500m_resampled.tif'],\n",
    "              ['lai_04', base_path / 'lai/2013_2023_04_MOD_Grid_MOD15A2H_Lai_500m_resampled.tif'],\n",
    "              ['lai_05', base_path / 'lai/2013_2023_05_MOD_Grid_MOD15A2H_Lai_500m_resampled.tif'],\n",
    "              ['lai_06', base_path / 'lai/2013_2023_06_MOD_Grid_MOD15A2H_Lai_500m_resampled.tif'],\n",
    "              ['lai_07', base_path / 'lai/2013_2023_07_MOD_Grid_MOD15A2H_Lai_500m_resampled.tif'],\n",
    "              ['lai_08', base_path / 'lai/2013_2023_08_MOD_Grid_MOD15A2H_Lai_500m_resampled.tif'],\n",
    "              ['lai_09', base_path / 'lai/2013_2023_09_MOD_Grid_MOD15A2H_Lai_500m_resampled.tif'],\n",
    "              ['lai_10', base_path / 'lai/2013_2023_10_MOD_Grid_MOD15A2H_Lai_500m_resampled.tif'],\n",
    "              ['lai_11', base_path / 'lai/2013_2023_11_MOD_Grid_MOD15A2H_Lai_500m_resampled.tif'],\n",
    "              ['lai_12', base_path / 'lai/2013_2023_12_MOD_Grid_MOD15A2H_Lai_500m_resampled.tif'],\n",
    "              ['veg_height', base_path / 'forest_height/forest_height_2020_resampled.tif'],\n",
    "              ['land_glclu', base_path / 'glclu2019/glclu2019_map_resampled.tif'],\n",
    "              ['land_igbp', base_path / 'modis_land/2013_2022_mode_MCD12Q1_LC_Type1_resampled.tif'],\n",
    "              ['agriculture', base_path / 'lgrip30/lgrip30_agriculture_resampled.tif'],\n",
    "              ['aspect', base_path / 'merit/merit_hydro_aspect_resampled.tif'],\n",
    "              ['slope', base_path / 'merit/merit_hydro_slope_resampled.tif'],\n",
    "              ['elev', base_path / 'merit/merit_hydro_elv_resampled.tif'],\n",
    "              ['clay1', base_path / 'soilgrids/clay_0-5cm_mean_resampled.tif'],\n",
    "              ['clay2', base_path / 'soilgrids/clay_5-15cm_mean_resampled.tif'],\n",
    "              ['clay3', base_path / 'soilgrids/clay_15-30cm_mean_resampled.tif'],\n",
    "              ['clay4', base_path / 'soilgrids/clay_30-60cm_mean_resampled.tif'],\n",
    "              ['clay5', base_path / 'soilgrids/clay_60-100cm_mean_resampled.tif'],\n",
    "              ['clay6', base_path / 'soilgrids/clay_100-200cm_mean_resampled.tif'],\n",
    "              ['sand1', base_path / 'soilgrids/sand_0-5cm_mean_resampled.tif'],\n",
    "              ['sand2', base_path / 'soilgrids/sand_5-15cm_mean_resampled.tif'],\n",
    "              ['sand3', base_path / 'soilgrids/sand_15-30cm_mean_resampled.tif'],\n",
    "              ['sand4', base_path / 'soilgrids/sand_30-60cm_mean_resampled.tif'],\n",
    "              ['sand5', base_path / 'soilgrids/sand_60-100cm_mean_resampled.tif'],\n",
    "              ['sand6', base_path / 'soilgrids/sand_100-200cm_mean_resampled.tif'],\n",
    "              ['silt1', base_path / 'soilgrids/silt_0-5cm_mean_resampled.tif'],\n",
    "              ['silt2', base_path / 'soilgrids/silt_5-15cm_mean_resampled.tif'],\n",
    "              ['silt3', base_path / 'soilgrids/silt_15-30cm_mean_resampled.tif'],\n",
    "              ['silt4', base_path / 'soilgrids/silt_30-60cm_mean_resampled.tif'],\n",
    "              ['silt5', base_path / 'soilgrids/silt_60-100cm_mean_resampled.tif'],\n",
    "              ['silt6', base_path / 'soilgrids/silt_100-200cm_mean_resampled.tif'],\n",
    "              ['soil_depth', base_path / 'pelletier/average_soil_and_sedimentary-deposit_thickness_resampled.tif']\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7557ba1a-69ee-45e9-8dfc-e01bc59fedc0",
   "metadata": {},
   "source": [
    "### Plotting functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "a765310f-cc0c-4784-b4c7-7b800ce6acec",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_xkcd_climate(save_file):\n",
    "    with plt.xkcd():\n",
    "    \n",
    "        c_im = data_values['climate_im']\n",
    "        c_imr = data_values['climate_imr']\n",
    "        c_fs = data_values['climate_fs']\n",
    "    \n",
    "        fig = plt.figure()\n",
    "        ax = fig.add_axes((0.4, 0.4, 0.2, 0.2))\n",
    "        ax.spines[['top', 'right', 'left', 'bottom']].set_visible(False)\n",
    "        ax.set_ylim([0.5, 4])\n",
    "        ax.set_xlim([0, 2])\n",
    "        ax.set_xticks([0,2])\n",
    "        ax.set_yticks([1,2,3])\n",
    "        ax.set_xticklabels(['less','more'])\n",
    "        ax.set_yticklabels(['fraction snow','seasonality','aridity'])\n",
    "        ax.tick_params(axis='both', length=0)\n",
    "        \n",
    "        ax.plot([0,2],[1,1], color='blue', marker='D')\n",
    "        ax.plot([0,2],[2,2], color='green', marker='D')\n",
    "        ax.plot([0,2],[3,3], color='red', marker='D') \n",
    "        ax.plot([c_fs*2,c_fs*2],[1,1], color='black', marker='D') # scale [0,1] to [0,2]\n",
    "        ax.plot([c_imr,c_imr],[2,2],   color='black', marker='D') # no scaling, already on [0,2]\n",
    "        ax.plot([c_im+1,c_im+1],[3,3], color='black', marker='D') # scale [-1,1] to [0,2]\n",
    "        plt.savefig(save_file, dpi=300)\n",
    "        plt.show()        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "8c604d2a-e78f-4ff7-a47c-d368d2e99d56",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_xkcd_lai(save_file):\n",
    "    with plt.xkcd():\n",
    "    \n",
    "        fig = plt.figure()\n",
    "        ax = fig.add_axes((0.1, 0.1, 0.2, 0.2))\n",
    "        ax.spines[['top', 'right']].set_visible(False)\n",
    "        \n",
    "        ax.plot(vegetation_lai, color='green')\n",
    "        ax.set_ylabel('LAI [m2 m-2]')\n",
    "        ax.set_xticks([0,1,2,3,4,5,6,7,8,9,10,11])\n",
    "        ax.set_xticklabels(['J','','','','','','','','','','','D'])\n",
    "        plt.savefig(save_file, dpi=300)\n",
    "        plt.show()        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ce3007e-b514-49a9-abce-538bff0184af",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Plotting Support"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "da1474bf-0bc1-494c-aa5a-f65059e556ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "lgrip30_dict = dict([(0, 'water'),\n",
    "                     (1, 'non-cropland'),\n",
    "                     (2, 'irrigated cropland'),\n",
    "                     (3, 'rain-fed cropland')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "7dc09aca-9622-412f-80cb-f5baa47d53c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "glclu2019_dict = dict([(1,  'true desert'),\n",
    "                       (2,  'semi-arid'),\n",
    "                       (3,  'dense short vegetation'),\n",
    "                       (4,  'open tree cover'),\n",
    "                       (5,  'dense tree cover'),\n",
    "                       (6,  'tree cover gain'),\n",
    "                       (7,  'tree cover loss, not fire'),\n",
    "                       (8,  'salt pan'),\n",
    "                       (9,  'wetland sparse vegetation'),\n",
    "                       (10, 'wetland dense short vegetation'),\n",
    "                       (11, 'wetland open tree cover'),\n",
    "                       (12, 'wetland dense tree cover'),\n",
    "                       (13, 'wetland tree cover gain'),\n",
    "                       (14, 'wetland tree cover loss, not fire'),\n",
    "                       (15, 'ice'),\n",
    "                       (16, 'water'),\n",
    "                       (17, 'cropland'),\n",
    "                       (18, 'built-up'),\n",
    "                       (19, 'ocean'),\n",
    "                       (20, 'no data')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "6707ba6b-e465-45c7-b572-278ca979d5ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "igbp_dict = dict([(1,  'evergreen needleleaf forest'),\n",
    "                  (2,  'evergreen broadleaf forest'),\n",
    "                  (3,  'deciduous needleleaf forest'),\n",
    "                  (4,  'deciduous broadleaf forest'),\n",
    "                  (5,  'mixed forest'),\n",
    "                  (6,  'closed shrublands'),\n",
    "                  (7,  'open shrublands'),\n",
    "                  (8,  'woody savannas'),\n",
    "                  (9,  'savannas'),\n",
    "                  (10, 'grasslands'),\n",
    "                  (11, 'permanent wetlands'),\n",
    "                  (12, 'croplands'),\n",
    "                  (13, 'urban and built-up land'),\n",
    "                  (14, 'cropland/natural mosaic'),\n",
    "                  (15, 'permanent snow/ice'),\n",
    "                  (16, 'barren'),\n",
    "                  (17, 'water'),\n",
    "                  (255,'unclassified')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3c832568-717d-4fa0-a167-8fad4b892b0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_usda_soilclass(sand,silt,clay,no_data_value=255):\n",
    "\n",
    "    # Adapted from: Knoben (2021)\n",
    "    # Knoben, W. J. M. (2021). Global USDA-NRCS soil texture class map, \n",
    "    # HydroShare, https://doi.org/10.4211/hs.1361509511e44adfba814f6950c6e742\n",
    "    \n",
    "    # Based on Benham et al., 2009 and matching the following soil class table:\n",
    "    # SUMMA-ROSETTA-STAS-RUC soil parameter table\n",
    "    # 1  'CLAY' \n",
    "    # 2  'CLAY LOAM'\n",
    "    # 3  'LOAM' \n",
    "    # 4  'LOAMY SAND'\n",
    "    # 5  'SAND'\n",
    "    # 6  'SANDY CLAY'\n",
    "    # 7  'SANDY CLAY LOAM'\n",
    "    # 8  'SANDY LOAM'\n",
    "    # 9  'SILT'\n",
    "    # 10 'SILTY CLAY'\n",
    "    # 11 'SILTY CLAY LOAM'\n",
    "    # 12 'SILT LOAM'\n",
    "\n",
    "    # Check inputs\n",
    "    if (sand<=1) & (silt<=1) & (clay<=1):\n",
    "        print(f'WARNING: find_usda_soilclass(): sand, silt and clay all < 1. Please provide inputs in % [0,100].')\n",
    "        return\n",
    "    \n",
    "    # Legend\n",
    "    soiltype = ['clay','clay loam','loam','loamy sand','sand','sandy clay','sandy clay loam',\n",
    "                'sandy loam','silt','silty clay','silty clay loam','silt loam']\n",
    "    \n",
    "    # Classify\n",
    "    if (clay >= 40) & (sand <= 45) & (silt < 40):\n",
    "        soilclass = 1\n",
    "    elif (clay >= 27) & (clay < 40) & (sand > 20) & (sand <= 45):\n",
    "        soilclass = 2\n",
    "    elif (clay >= 7) & (clay < 27) & (silt >= 28) & (silt < 50) & (sand < 52):\n",
    "        soilclass = 3\n",
    "    elif ((silt + 1.5 * clay) >= 15) & ((silt + 2* clay) < 30):\n",
    "        soilclass = 4\n",
    "    elif ((silt + 1.5 * clay) < 15):\n",
    "        soilclass = 5\n",
    "    elif (clay >= 35 )& (sand > 45):\n",
    "        soilclass = 6\n",
    "    elif (clay >= 20) & (clay < 35) & (silt < 28) & (sand > 45):\n",
    "        soilclass = 7\n",
    "    elif ((clay >= 7) & (clay < 20) & (sand > 52) & ((silt+2*clay) >= 30)) | ((clay < 7) & (silt < 50) & ((silt+2*clay >= 30))):\n",
    "        soilclass = 8\n",
    "    elif (silt >= 80) & (clay < 12):\n",
    "        soilclass = 9\n",
    "    elif (clay >= 40) & (silt >= 40):\n",
    "        soilclass = 10\n",
    "    elif (clay >= 27) & (clay < 40) & (sand <= 20):\n",
    "        soilclass = 11\n",
    "    elif ((silt >= 50) & (clay >= 12) & (clay < 27)) | ((silt >= 50) & (silt < 80) & (clay < 12)):\n",
    "        soilclass = 12\n",
    "    elif (sand == no_data_value) | (silt == no_data_value) | (clay == no_data_value):\n",
    "        soilclass = 0\n",
    "\n",
    "    soilname = 'undefined'\n",
    "    if soilclass > 0:\n",
    "        soilname = soiltype[soilclass-1] # -1 to match 0-based indexing\n",
    "    \n",
    "    return soilclass, soilname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f6a41a12-759d-467c-a75e-78ed17e08486",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_pm_soil_color(soil_name):\n",
    "\n",
    "    '''Creates a soil color for a give sand/silt/clay percentage'''\n",
    "\n",
    "    # Color source: https://kremesti.com/water/silt_clay_mud.htm (accessed 2023-11-27)\n",
    "    if soil_name == 'clay':\n",
    "        color = [97,62,38]\n",
    "    elif soil_name == 'clay loam':\n",
    "        color = [68,45,30]\n",
    "    elif soil_name == 'loam':\n",
    "        color = [71,66,49]\n",
    "    elif soil_name == 'loamy sand':\n",
    "        color = [66,61,41]\n",
    "    elif soil_name == 'sand':\n",
    "        color = [91,87,67]\n",
    "    elif soil_name == 'sandy clay':\n",
    "        color = [85,82,71]\n",
    "    elif soil_name == 'sandy clay loam':\n",
    "        color = [78,64,40]\n",
    "    elif soil_name == 'sandy loam':\n",
    "        color = [74,71,42]\n",
    "    elif soil_name == 'silt':\n",
    "        color = [48,48,48]\n",
    "    elif soil_name == 'silty clay':\n",
    "        color = [73,52,39]\n",
    "    elif soil_name == 'silty clay loam':\n",
    "        color = [61,52,47]\n",
    "    elif soil_name == 'silt loam':\n",
    "        color = [88,83,77]\n",
    "    else:\n",
    "        print(f'WARNING: get_pm_soil_color(): soil {soil_name} not defined.')\n",
    "        color = None\n",
    "    return [C/255 for C in color]    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac5fdb35-fbf9-4be0-8e93-1002f6ca2c63",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Data extraction functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b2b4c049-27be-4952-b266-9396969b9c7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_geotiff_properties(file):\n",
    "\n",
    "    with rasterio.open(file) as src:\n",
    "        geoTransform = src.transform\n",
    "        pixelSize = [geoTransform[0],geoTransform[4]]\n",
    "        rasterSize = [src.width,src.height]\n",
    "        boundingBox = src.bounds\n",
    "    \n",
    "    return pixelSize, rasterSize, boundingBox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e23061aa-483e-473c-98e8-f14311e84bed",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_geotif_data_as_array(file, band=1):\n",
    "    ds = gdal.Open(file) # open the file\n",
    "    band = ds.GetRasterBand(band) # get the data band\n",
    "    data = band.ReadAsArray() # convert to numpy array for further manipulation   \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c56fc5f0-d257-409f-a4c9-7fa686af37f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_geotif_noData(src_file, band=1):\n",
    "    src_ds = gdal.Open(src_file)\n",
    "    src_band = src_ds.GetRasterBand(band)\n",
    "    no_data_value = src_band.GetNoDataValue()\n",
    "    src_ds = None\n",
    "    return no_data_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1d7d6345-5dfd-4510-9bd4-c2850eefb01f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def confirm_dimensions_match(files):\n",
    "    pixel_sizes = []\n",
    "    raster_sizes = []\n",
    "    bounding_boxes = []\n",
    "    \n",
    "    for sub_list in files:\n",
    "        file = sub_list[1]\n",
    "        pixel, raster, bb = get_geotiff_properties(file)\n",
    "        pixel_sizes.append(pixel)\n",
    "        raster_sizes.append(raster)\n",
    "        bounding_boxes.append(bb)\n",
    "\n",
    "    assert all([sub_list == pixel_sizes[0] for sub_list in pixel_sizes]), 'pixel sizes don''t match'\n",
    "    assert all([sub_list == raster_sizes[0] for sub_list in raster_sizes]), 'raster sizes don''t match'\n",
    "    assert all([sub_list == bounding_boxes[0] for sub_list in bounding_boxes]), 'bounding boxes don''t match'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8c78434f-0fa3-4cc8-a727-4a0fc6ed203d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_pixel_value(tiff_path, pixel_x, pixel_y):\n",
    "\n",
    "    file = str(tiff_path)\n",
    "    \n",
    "    # Get data\n",
    "    with rasterio.open(file) as src:\n",
    "        pixel_value = src.read(1, window=((pixel_y-1, pixel_y), (pixel_x-1, pixel_x)))\n",
    "        pixel_value = pixel_value[0][0]\n",
    "\n",
    "    # Get scaling and offset\n",
    "    src = gdal.Open(file)\n",
    "    scale_factor = src.GetRasterBand(1).GetScale()\n",
    "    offset = src.GetRasterBand(1).GetOffset()\n",
    "    if scale_factor: pixel_value = pixel_value * scale_factor\n",
    "    if offset: pixel_value = pixel_value + offset\n",
    "    src = None\n",
    "    \n",
    "    return pixel_value # Extract the actual value from the array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d3f0d2e6-dc21-4360-8c58-5ca3e64b4d42",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Main: called from get_location_data_dict_from_geotiffs()\n",
    "def convert_lat_lon_to_raster_x_y(file,lat,lon):\n",
    "    pixel, raster, bb = get_geotiff_properties(file)\n",
    "    assert is_point_inside_bbox((lon,lat),bb), f'({lon},{lat}) not in {bb}' # sub 1\n",
    "    x,y = lon_lat_to_pixel(lon,lat, pixel[0], raster, bb) # sub 2\n",
    "    return x,y\n",
    "\n",
    "# Sub 1\n",
    "def is_point_inside_bbox(point, bbox):\n",
    "    x, y = point\n",
    "    min_x, min_y, max_x, max_y = bbox.left, bbox.bottom, bbox.right, bbox.top\n",
    "    return min_x <= x <= max_x and min_y <= y <= max_y\n",
    "\n",
    "# Sub 2\n",
    "def lon_lat_to_pixel(lon, lat, pixel_size, raster_size, bounding_box):\n",
    "    # Calculate pixel coordinates\n",
    "    x = int((lon - bounding_box.left) / pixel_size)\n",
    "    y = int((bounding_box.top - lat) / pixel_size)\n",
    "\n",
    "    # Ensure the coordinates are within the raster bounds\n",
    "    x = max(0, min(x, raster_size[0] - 1))\n",
    "    y = max(0, min(y, raster_size[1] - 1))\n",
    "\n",
    "    return x,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a1b1c1e2-9c0e-4b1a-bb1a-6b3ce6a7b3ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_location_data_dict_from_geotiffs(files,lon,lat):\n",
    "\n",
    "    # Confirm the all rasters have the same dimension (function will error out if not)\n",
    "    confirm_dimensions_match(files)\n",
    "    \n",
    "    # Get the raster indices for the provided lat/lon coordinates\n",
    "    x,y = convert_lat_lon_to_raster_x_y(files[0][1],lat,lon)\n",
    "\n",
    "    # Get the data values we want\n",
    "    data_dict = {}\n",
    "    for sublist in files:\n",
    "        data_name,data_file = sublist\n",
    "        data_value = extract_pixel_value(data_file,x,y)\n",
    "        data_dict[data_name] = data_value    \n",
    "    \n",
    "    return data_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7407e158-c313-41f3-b456-f986e9557716",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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